diff --git a/.codespell-ignore-words b/.codespell-ignore-words
new file mode 100644
index 00000000..e69de29b
diff --git a/.codespellrc b/.codespellrc
new file mode 100644
index 00000000..029c71ef
--- /dev/null
+++ b/.codespellrc
@@ -0,0 +1,5 @@
+[codespell]
+skip = .git,*.bib,*.ps,*.js,*.pdf,_build,*.fodp,*.fods
+ignore-words = .codespell-ignore-words
+
+
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 00000000..5d808809
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,13 @@
+# Dependabot configuration
+# ref: https://docs.github.com/en/code-security/supply-chain-security/keeping-your-dependencies-updated-automatically/configuration-options-for-dependency-updates
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "weekly"
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 00000000..9f2c4e60
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,37 @@
+name: Publish JupyterBook to GitHub Pages
+
+on:
+  push: # trigger build only when push to main
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v5
+    - name: Set up Python
+      uses: actions/setup-python@v6
+      with:
+        python-version: "3.11"
+        cache: "pip"
+    - name: Install Python dependencies
+      run: |
+        sudo apt-get install python3-pip graphviz
+        pip install -r requirements.txt
+        pip install ghp-import
+        PATH="${PATH}:${HOME}/.local/bin"
+
+    - name: Build book HTML
+      run: |
+        jupyter-book build ./content
+
+    - name: Push _build/html to gh-pages
+      run: |
+        sudo chown -R $(whoami):$(whoami) .
+        git config --global user.email "$GITHUB_ACTOR@users.noreply.github.com"
+        git config --global user.name "$GITHUB_ACTOR"
+        git remote set-url origin "https://$GITHUB_ACTOR:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY"
+
+        ghp-import ./content/_build/html -f -p -n
diff --git a/.gitignore b/.gitignore
index d05a3f96..0045a3f3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,21 @@
 *.aux
 *.pyc
 *~
+
+.ipynb_checkpoints/
+Untitled*.ipynb
+
+\#*
+
+syllabus.pdf
+syllabus.out
+
+.cache
+
+*egg-info
+
+.~lock*
+
+*.fodp
+
+_build
diff --git a/CHANGES b/CHANGES
index 15e3e334..0c9edf6f 100644
--- a/CHANGES
+++ b/CHANGES
@@ -1,3 +1,79 @@
+-- python tutor: http://www.pythontutor.com/
+
+-- show pybind11
+   https://pybind11.readthedocs.io/en/stable/basics.html#compiling-the-test-cases
+
+-- use RISE:
+   https://damianavila.github.io/RISE/customize.html
+
+-- merge in the REU python stuff
+
+-- matplotlib example: mandelbrot
+
+-- matplotlib state vs OO interfaces
+
+   http://pbpython.com/effective-matplotlib.html
+
+-- git diff of jupyter notebooks:
+
+   https://nbdime.readthedocs.io/en/latest/
+
+
+-- interactive notebook online:
+
+   http://mybinder.org/
+   http://ivory.idyll.org/blog/2016-mybinder.html
+
+-- pandas:
+
+   https://github.com/jakevdp/PythonDataScienceHandbook
+
+   Star Wars API examples :)
+   http://nbviewer.jupyter.org/github/rasbt/pattern_classification/blob/master/data_viz/swapi_viz.ipynb
+
+-- snakify:
+
+    https://snakify.org/
+
+
+-- visualize python code execution line-by-line:
+
+    https://github.com/lgpage/nbtutor
+
+-- python 3 for scientists:
+  
+   http://python-3-for-scientists.readthedocs.io/en/latest/
+
+
+-- restructure it all:
+
+   after basic introduction class, have students run through ipython
+   notebooks before class, and then in class, have a workbook that we
+   will fill in together, interactively
+
+
+
+
+-- Machine learning: http://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
+
+
+-- post an "assignment" of tasks after each class to the discussion forum.
+
+-- try git:
+
+   https://try.github.io/levels/1/challenges/1
+
+-- python in HPC:
+
+    https://github.com/pyhpc/pyhpc-tutorial
+
+-- new-style classes:
+
+   http://python-history.blogspot.com/2010/06/new-style-classes.html
+   
+
+-- decorators: http://thecodeship.com/patterns/guide-to-python-function-decorators/
+
 -- use project Euler for in-class code projects
 
 -- piazza or freeforums.org  -- open discussion forum
@@ -21,6 +97,7 @@
 
   -- available in the Chrome store
 
+-- pythran?
 
 -- nbdiff
 
@@ -32,6 +109,19 @@
    requests
 
 
+-- debugger: from Nathan on yt:
+
+   "The easiest way to debug issues like this is to use a python
+   debugger. I prefer ipdb, which has nice integration with IPython,
+   including tab completions. Others like pudb. There's also pdb,
+   which is part of the python standard library.  All of these
+   debuggers have a gdb-like command-line interface, allowing printing
+   out backtraces, inspecting variables, and stepping through
+   code. Both ipdb and pudb can be installed via pip."
+
+
+
+ 
 
 references:
 
@@ -64,3 +154,5 @@ grading:
 
      B:   2 postings to the discussion board
 
+
+-- hadoop streaming API: http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/
diff --git a/README.md b/README.md
index 0c575916..143ae6cb 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,12 @@
 # Python in Scientific Computing
 *lecture notes and IPython notebooks*
 
+These are the lecture notes and IPython notebooks used to teach the
+*Python for Scientific Computing* class at Stony Brook University.
+
+Feel free to fork this repository and make any changes you wish.
+Contributions are welcomed.
 
-fork this repository and make any changes you wish
 
 
 
diff --git a/ad/python_science.fodp b/ad/python_science.fodp
new file mode 100644
index 00000000..89304f00
--- /dev/null
+++ b/ad/python_science.fodp
@@ -0,0 +1,3105 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
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+ <office:meta><dc:title>python for scientific computing</dc:title><meta:initial-creator>Presentation Magazine</meta:initial-creator><meta:creation-date>2007-05-29T09:52:18</meta:creation-date><dc:date>2016-10-18T09:58:40.402299682</dc:date><meta:editing-cycles>25</meta:editing-cycles><meta:editing-duration>PT1H51M48S</meta:editing-duration><meta:generator>LibreOffice/5.1.6.1.0$Linux_X86_64 LibreOffice_project/10$Build-1</meta:generator><meta:document-statistic meta:object-count="52"/></office:meta>
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+        <text:p text:style-name="P12"><text:span text:style-name="T5">Python for Scientific Computing</text:span><text:span text:style-name="T6"><text:line-break/></text:span><text:span text:style-name="T6"/></text:p>
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+      </text:list>
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+      <text:p text:style-name="P14"><text:span text:style-name="T7">Python has seen wide adoption in the scientific community for data analysis, simulation, prototyping, and visualization. It provides a simple, yet powerful means to build applications. <text:s/>This seminar introduces python and its use in scientific computing.</text:span></text:p>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p><text:span text:style-name="T7">Flipped course format:</text:span></text:p>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L5">
+       <text:list-item>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">We’ll work through interactive notebooks </text:span><text:span text:style-name="T9">outside of class</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">Class time will be used for exercises that we discuss together</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">Use </text:span><text:span text:style-name="T10">slack</text:span><text:span text:style-name="T8"> for out-of-class communication (and learn how to integrate </text:span><text:span text:style-name="T10">github</text:span><text:span text:style-name="T8"> + python + slack)</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">Grading is based on participation</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L5">
+       <text:list-item>
+        <text:list>
+         <text:list-item>
+          <text:list>
+           <text:list-item>
+            <text:p><text:span text:style-name="T8">Sharing examples and discussion</text:span></text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p><text:span text:style-name="T11">Advanced undergrads welcomed</text:span></text:p>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L5">
+       <text:list-item>
+        <text:list>
+         <text:list-header>
+          <text:p><text:span text:style-name="T8"/></text:p>
+         </text:list-header>
+        </text:list>
+       </text:list-item>
+      </text:list>
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+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p text:style-name="P16"><text:span text:style-name="T7">Topics include:</text:span></text:p>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L5">
+       <text:list-item>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T10">Python</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">Version control with </text:span><text:span text:style-name="T10">git</text:span><text:span text:style-name="T8">/</text:span><text:span text:style-name="T10">github</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T10">Jupyter</text:span><text:span text:style-name="T8"> notebooks /workflow management</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L5">
+       <text:list-item>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">The </text:span><text:span text:style-name="T10">NumPy</text:span><text:span text:style-name="T8"> array package</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">The </text:span><text:span text:style-name="T10">SciPy</text:span><text:span text:style-name="T8"> tools and basics of numerical methods</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T10">Matplotlib</text:span><text:span text:style-name="T8"> and </text:span><text:span text:style-name="T10">Plot.ly</text:span><text:span text:style-name="T8"> for visualization</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T10">SymPy</text:span><text:span text:style-name="T8"> for symbolic mathematics</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T10">Pandas</text:span><text:span text:style-name="T8"> and the dataframe</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">Building applications</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">Interfacing with Fortran/C/C++</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p><text:span text:style-name="T7">Details:</text:span></text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">PHY 546</text:span><text:span text:style-name="T12">, Spring 2017</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p text:style-name="P17"><text:span text:style-name="T8">Mondays, 3:00-3:53pm</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
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+      <text:list text:style-name="L3">
+       <text:list-header>
+        <text:p text:style-name="P19"><text:span text:style-name="T13">a weekly graduate seminar on techniques for scientific programming</text:span><text:span text:style-name="T11"><text:line-break/></text:span><text:span text:style-name="T8">instructor: Michael Zingale </text:span><text:span text:style-name="T14">(michael.zingale@stonybrook.edu)</text:span></text:p>
+       </text:list-header>
+      </text:list>
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+       RU5ErkJggg==
+      </office:binary-data>
+      <text:p/>
+     </draw:image>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr11" draw:text-style-name="P22" draw:layer="layout" svg:width="20.322cm" svg:height="1.268cm" svg:x="0.506cm" svg:y="17.726cm">
+     <draw:text-box>
+      <text:p><text:span text:style-name="T15">Flipped format this year—students need to bring laptops to class</text:span></text:p>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr9" draw:layer="layout" svg:width="12.7cm" svg:height="9.525cm" svg:x="3.175cm" svg:y="1.905cm" draw:page-number="1" presentation:class="page"/>
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+      <draw:text-box>
+       <text:p/>
+      </draw:text-box>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <presentation:settings presentation:mouse-visible="false"/>
+  </office:presentation>
+ </office:body>
+</office:document>
\ No newline at end of file
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diff --git a/lectures/01-python/NOTE b/content/01-python/NOTE
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diff --git a/lectures/01-python/VARDEN-tests.ini b/content/01-python/VARDEN-tests.ini
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diff --git a/content/01-python/basics.md b/content/01-python/basics.md
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+++ b/content/01-python/basics.md
@@ -0,0 +1,51 @@
+# Python Basics
+
+The following references give helpful introductions to python:
+
+* The [official python tutorial](http://docs.python.org/3/tutorial/)
+
+* The [software carpentry python lessons](https://swcarpentry.github.io/python-novice-inflammation/)
+
+
+## Practicing
+
+Some resources for practicing on your own:
+
+* [Code Academy python rack](http://www.codecademy.com/tracks/python): 
+  step-by-step tutorial through the basics of the language
+
+* [Project Euler](https://projecteuler.net/):
+  a set of increasingly complex programming tasks to try out with
+  python
+
+
+## Online books:
+
+* [Think python](https://greenteapress.com/wp/think-python-3rd-edition/)
+
+* [Dive into Python](https://diveintopython3.net/)
+
+* [SciPy Lecture Notes](http://scipy-lectures.github.io/)
+
+* [Google's python class](https://developers.google.com/edu/python/)
+
+* more resources can be found at: http://pythonbooks.revolunet.com/
+
+
+## Domain-specific libraries
+
+* Astronomy: [AstroPy](http://astropy.org)
+
+* Atmospheric sciences: [PyAOS](https://pyaos.github.io/)
+
+* Biology: [Biopython](http://biopython.org/)
+
+* Ocean and marine sciences: [OceanPython](http://oceanpython.org/)
+
+* Psychology resources: [PyschoPy](http://www.psychopy.org/)
+
+* Quantum physics: [QuTiP](http://qutip.org/)
+
+* Solar physics: [SunPy](http://sunpy.org/)
+
+
diff --git a/content/01-python/functions-classes.md b/content/01-python/functions-classes.md
new file mode 100644
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--- /dev/null
+++ b/content/01-python/functions-classes.md
@@ -0,0 +1,5 @@
+# Functions and Classes
+
+Functions and classes are the building blocks of complex programs.
+These allow you to organize your code into logical units that can
+reused.
diff --git a/content/01-python/installing.md b/content/01-python/installing.md
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--- /dev/null
+++ b/content/01-python/installing.md
@@ -0,0 +1,41 @@
+# Introduction
+
+This class will introduce the basics of the python programming
+language and the libraries used for scientific computing.
+
+```{tip}
+To get the most from this class, you should work on your own laptop.  That
+way you practice using python and the scientific libraries on the in the
+environment you are most comfortable with.
+```
+
+## Getting python
+
+You will want to install python and the associated libraries on your
+laptop that you can bring to the seminar.
+
+On Linux machines, you probably already have python and you can get
+the needed libraries through your system package manager.
+
+For Mac and Windows, I recommend the free Anaconda distribution:
+
+https://www.anaconda.com/products/individual
+
+This will install everything that you need.
+
+```{tip}
+If you have trouble getting a local install working, most of the class
+material will work automatically in the cloud, either on
+[binder](https://mybinder.org/) or [google
+colab](https://research.google.com/colaboratory/).
+```
+
+If you have python successfully installed, you should be able to start
+the python interpreter at the command line as: `python`.  A shell will
+come up, and you can try out your first program:
+
+```
+print("hello, world")
+```
+
+
diff --git a/content/01-python/misc.md b/content/01-python/misc.md
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+++ b/content/01-python/misc.md
@@ -0,0 +1,5 @@
+# Miscellaneous
+
+There are a lot of topics that we didn't cover, as well as a lot of
+the python standard library that we won't address.  Here we introduce
+a few more concepts.
diff --git a/content/01-python/myprofile.py b/content/01-python/myprofile.py
new file mode 100644
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--- /dev/null
+++ b/content/01-python/myprofile.py
@@ -0,0 +1,109 @@
+"""
+  A very simple profiling class.  Define some timers and methods
+  to start and stop them.  Nesting of timers is tracked so we can
+  pretty print the profiling information.
+
+  # define a timer object, labeled 'my timer'
+  a = timer('my timer')
+
+  This will add 'my timer' to the list of keys in the 'my timer'
+  dictionary.  Subsequent calls to the timer class constructor
+  will have no effect.
+
+  # start timing the 'my timer' block of code
+  a.begin()
+
+  ... do stuff here ...
+
+  # end the timing of the 'my timer' block of code
+  a.end()
+
+  for best results, the block of code timed should be large
+  enough to offset the overhead of the timer class method
+  calls.
+
+  Multiple timers can be instantiated and nested.  The stackCount
+  global parameter keeps count of the level of nesting, and the
+  timerNesting data structure stores the nesting level for each
+  defined timer.
+
+  timeReport() is called at the end to print out a summary of the
+  timing.
+
+  At present, no enforcement is done to ensure proper nesting.
+
+"""
+
+from __future__ import print_function
+
+import time
+
+timers = {}
+
+# keep basic count of how nested we are in the timers, so we can do some
+# pretty printing.
+stack_count = 0
+
+timer_nesting = {}
+timer_order = []
+
+class Timer(object):
+
+    def __init__ (self, name):
+        global timers, stack_count, timer_nesting, timer_order
+
+        self.name = name
+
+        keys = timers.keys()
+
+        if name not in keys:
+            timers[name] = 0.0
+            self.startTime = 0.0
+            timer_order.append(name)
+            timer_nesting[name] = stack_count
+
+
+    def begin(self):
+        global stack_count
+
+        self.startTime = time.time()
+        stack_count += 1
+
+
+    def end(self):
+        global timers, stack_count
+
+        elapsedTime = time.time() - self.startTime
+        timers[self.name] += elapsedTime
+
+        stack_count -= 1
+
+
+def time_report():
+    global timers, timer_order, timer_nesting
+
+    spacing = '   '
+    for key in timer_order:
+        print(timer_nesting[key]*spacing + key + ': ', timers[key])
+
+
+
+if __name__ == "__main__":
+    a = Timer('1')
+    a.begin()
+    time.sleep(10.)
+    a.end()
+
+    b = Timer('2')
+    b.begin()
+    time.sleep(5.)
+
+    c = Timer('3')
+    c.begin()
+
+    time.sleep(20.)
+
+    b.end()
+    c.end()
+
+    time_report()
diff --git a/lectures/01-python/paradigms.png b/content/01-python/paradigms.png
similarity index 100%
rename from lectures/01-python/paradigms.png
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diff --git a/content/01-python/python-io.ipynb b/content/01-python/python-io.ipynb
new file mode 100644
index 00000000..ffa434ff
--- /dev/null
+++ b/content/01-python/python-io.ipynb
@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the main things that we want to do in scientific computing is get data into and out of our programs.  In addition to plain text files, there are modules that can read lots of different data formats we might encounter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've already been using print quite a bit, but now we'll look at how to control how information is printed.  Note that there is an older and newer way to format print statements -- we'll focus only on the newer way (it's nicer).\n",
+    "\n",
+    "This is compatible with both python 2 and 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = 1\n",
+    "y = 0.0000354\n",
+    "z = 3.0\n",
+    "s = \"my string\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We write a string with `{}` embedded to indicate where variables are to be inserted.  Note that `{}` can take arguments.  We use the `format()` method on the string to match the variables to the `{}`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 1, y = 3.54e-05, z = 3.0, s = my string\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"x = {}, y = {}, z = {}, s = {}\".format(x, y, z, s))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before a semi-colon, we can give an optional index/position/descriptor of the value we want to print.\n",
+    "\n",
+    "After the semi-colon we give a format specifier.  It has a number field and a type, like `f` and `g` to describe how floating point numbers appear and how much precision to show.   Other bits are possible as well (like justification). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 1, y =   3.54e-05, z = 3.000, s = my string\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"x = {0}, y = {1:10.5g}, z = {2:.3f}, s = {3}\".format(x, y, z, s))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "there are other formatting things, like justification, etc.  See the tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                centered string                                 \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"{:^80}\".format(\"centered string\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# File I/O"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "as expected, a file is an object.  Here we'll use the `try`, `except` block to capture exceptions (like if the file cannot be opened). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<_io.TextIOWrapper name='./sample.txt' mode='w' encoding='UTF-8'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "try: f = open(\"./sample.txt\", \"w\")   # open for writing -- any file of the same name will be overwritten\n",
+    "except: \n",
+    "    print(\"cannot open the file\")\n",
+    "\n",
+    "print(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f.write(\"this is my first write\\n\")\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can easily loop over the lines in a file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Lorem', 'ipsum', 'dolor', 'sit', 'amet,', 'consectetur', 'adipisicing', 'elit,', 'sed', 'do']\n",
+      "['eiusmod', 'tempor', 'incididunt', 'ut', 'labore', 'et', 'dolore', 'magna', 'aliqua.', 'Ut', 'enim', 'ad']\n",
+      "['minim', 'veniam,', 'quis', 'nostrud', 'exercitation', 'ullamco', 'laboris', 'nisi', 'ut']\n",
+      "['aliquip', 'ex', 'ea', 'commodo', 'consequat.', 'Duis', 'aute', 'irure', 'dolor', 'in']\n",
+      "['reprehenderit', 'in', 'voluptate', 'velit', 'esse', 'cillum', 'dolore', 'eu', 'fugiat', 'nulla']\n",
+      "['pariatur.', 'Excepteur', 'sint', 'occaecat', 'cupidatat', 'non', 'proident,', 'sunt', 'in']\n",
+      "['culpa', 'qui', 'officia', 'deserunt', 'mollit', 'anim', 'id', 'est', 'laborum.']\n",
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "try: \n",
+    "    f = open(\"./test.txt\", \"r\")\n",
+    "except:\n",
+    "    print(\"error: cannot open the file\")\n",
+    "    \n",
+    "for line in f:\n",
+    "    print(line.split())\n",
+    "    \n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "as mentioned earlier, there are lots of string functions.  Above we used `strip()` to remove the trailing whitespace and returns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CSV Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "comma-separated values are an easy way to exchange data -- you can generate these from a spreadsheet program.  In the example below, we are assuming that the first line of the spreadsheet/csv file gives the headings that identify the columns.  \n",
+    "\n",
+    "Note that there is an amazing amount of variation in terms of what can be in a CSV file and what the format is -- the csv module does a good job sorting this all out for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "item: apples, quantity: 2, unit price: 0.33, total: 0.66\n",
+      "item: bananas, quantity: 5, unit price: 0.1, total: 0.5\n",
+      "item: milk, quantity: 1, unit price: 2.5, total: 2.5\n",
+      "item: soda, quantity: 3, unit price: 1, total: 3\n",
+      "item: rolls, quantity: 12, unit price: 0.33, total: 3.96\n",
+      "item: eggs, quantity: 1, unit price: 2.5, total: 2.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Item(object):\n",
+    "    def __init__(self):\n",
+    "        self.name = \"\"\n",
+    "        self.quantity = 0\n",
+    "        self.unitprice = 0.0\n",
+    "        self.total = 0.0\n",
+    "    \n",
+    "import csv\n",
+    "\n",
+    "reader = csv.reader(open(\"shopping.csv\", \"r\"))\n",
+    "\n",
+    "headings = None\n",
+    "\n",
+    "shopping_list = []\n",
+    "\n",
+    "for row in reader:\n",
+    "    if headings == None:\n",
+    "        # first row\n",
+    "        headings = row\n",
+    "    else:\n",
+    "        my_item = Item()\n",
+    "        my_item.name = row[headings.index(\"item\")]\n",
+    "        my_item.quantity = row[headings.index(\"quantity\")]\n",
+    "        my_item.unitprice = row[headings.index(\"unit price\")]\n",
+    "        my_item.total = row[headings.index(\"total\")]\n",
+    "        shopping_list.append(my_item)\n",
+    "    \n",
+    "\n",
+    "for i in shopping_list:\n",
+    "    print (\"item: {}, quantity: {}, unit price: {}, total: {}\".format(i.name, i.quantity, i.unitprice, i.total))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# INI Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "INI or Config files are a common way of specifying options to a program.  They can take the form (from the `ConfigParser` page):\n",
+    "\n",
+    "```\n",
+    "[My Section]\n",
+    "foodir: %(dir)s/whatever\n",
+    "dir=frob\n",
+    "long: this value continues\n",
+    "   in the next line\n",
+    "```\n",
+    "\n",
+    "Here we look at how to read in options and store them in a dictionary of the \n",
+    "form `dict[\"sec.option\"] = value`\n",
+    "\n",
+    "We'll use a sample .ini file from a regression test suite (`VARDEN-tests.ini`)\n",
+    "\n",
+    "(Note: the name of the module is `ConfigParser` in python 2 but `configparser` in python 3 -- the latter confirms to the python style guidelines.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bubble-2d.compileTest           : 0\n",
+      "bubble-3d.numprocs              : 3\n",
+      "bubble-3d.useOMP                : 1\n",
+      "bubble-2d.useMPI                : 1\n",
+      "bubble-2d.buildDir              : varden/test\n",
+      "bubble-restart.dim              : 3\n",
+      "bubble-restart.doVis            : 0\n",
+      "main.reportActiveTestsOnly      : 1\n",
+      "bubble-2d.useOMP                : 0\n",
+      "bubble-2d.inputFile             : inputs_2d-regt\n",
+      "main.MPIcommand                 : /usr/local/bin/mpiexec -n @nprocs@ @command@\n",
+      "bubble-3d.compileTest           : 0\n",
+      "main.testTopDir                 : /home/regtester/RegTesting/rt-VARDEN/\n",
+      "bubble-2d.restartTest           : 0\n",
+      "bubble-3d.inputFile             : inputs_3d-regt\n",
+      "bubble-restart.restartTest      : 1\n",
+      "bubble-3d.restartTest           : 0\n",
+      "bubble-3d.buildDir              : varden/test\n",
+      "bubble-restart.buildDir         : varden/test\n",
+      "bubble-3d.doVis                 : 0\n",
+      "main.numMakeJobs                : 8\n",
+      "bubble-restart.useOMP           : 1\n",
+      "bubble-restart.restartFileNum   : 4\n",
+      "main.webTopDir                  : /home/regtester/RegTesting/rt-VARDEN/web\n",
+      "main.sourceDir                  : /home/regtester/RegTesting/VARDEN/\n",
+      "bubble-restart.numthreads       : 2\n",
+      "main.suiteName                  : VARDEN\n",
+      "bubble-3d.useMPI                : 1\n",
+      "bubble-2d.dim                   : 2\n",
+      "main.FCOMP                      : gfortran\n",
+      "main.sourceTree                 : F_Src\n",
+      "main.boxLibDir                  : /home/regtester/RegTesting/BoxLib/\n",
+      "main.compareToolDir             : /home/regtester/RegTesting/AmrPostprocessing/F_Src\n",
+      "bubble-restart.compileTest      : 0\n",
+      "bubble-2d.numprocs              : 2\n",
+      "bubble-2d.doVis                 : 0\n",
+      "main.goUpLink                   : 1\n",
+      "main.MPIhost                    : \n",
+      "main.COMP                       : g++\n",
+      "bubble-restart.useMPI           : 1\n",
+      "bubble-restart.inputFile        : inputs-restart-regt\n",
+      "bubble-restart.numprocs         : 3\n",
+      "main.MAKE                       : make\n",
+      "bubble-2d.numthreads            : 2\n",
+      "bubble-3d.dim                   : 3\n",
+      "bubble-3d.numthreads            : 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import configparser\n",
+    "\n",
+    "options = {}\n",
+    "\n",
+    "\n",
+    "cp = configparser.ConfigParser()\n",
+    "cp.optionxform = str    # this makes options case-sensitive\n",
+    "cp.read(\"VARDEN-tests.ini\")\n",
+    "\n",
+    "for sec in cp.sections():\n",
+    "    for opt in cp.options(sec):\n",
+    "        key = str(sec) + \".\" + str(opt)\n",
+    "        value = cp.get(sec,opt)\n",
+    "        options[key] = value\n",
+    "    \n",
+    "for k, v in options.items():\n",
+    "    print(\"{:32s}: {}\".format(k, v))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/content/01-python/python.md b/content/01-python/python.md
new file mode 100644
index 00000000..60132f80
--- /dev/null
+++ b/content/01-python/python.md
@@ -0,0 +1,3 @@
+# python
+
+These notebooks introduce the core python language
diff --git a/lectures/01-python/python.png b/content/01-python/python.png
similarity index 100%
rename from lectures/01-python/python.png
rename to content/01-python/python.png
diff --git a/lectures/01-python/scipy.png b/content/01-python/scipy.png
similarity index 100%
rename from lectures/01-python/scipy.png
rename to content/01-python/scipy.png
diff --git a/lectures/01-python/shopping.csv b/content/01-python/shopping.csv
similarity index 100%
rename from lectures/01-python/shopping.csv
rename to content/01-python/shopping.csv
diff --git a/lectures/01-python/shopping.fods b/content/01-python/shopping.fods
similarity index 100%
rename from lectures/01-python/shopping.fods
rename to content/01-python/shopping.fods
diff --git a/content/01-python/shopping_cart.py b/content/01-python/shopping_cart.py
new file mode 100644
index 00000000..5a4ccebe
--- /dev/null
+++ b/content/01-python/shopping_cart.py
@@ -0,0 +1,57 @@
+class Item(object):
+    """ an item to buy """
+    
+    def __init__(self, name, quantity=1):
+        if name not in INVENTORY:
+            raise ValueError
+        self.name = name
+        self.quantity = quantity
+        
+    def __repr__(self):
+        return "Item({}, {})".format(self.name, self.quantity)
+        
+    def __eq__(self, other):
+        return self.name == other.name
+    
+    def __add__(self, other):
+        if isinstance(other, Item):
+            if self == other:
+                return(Item(self.name, self.quantity + other.quantity))
+            else:
+                raise ValueError
+        else:
+            raise NotImplementedError
+
+            
+
+class ShoppingCart(object):
+    
+    def __init__(self):
+        self.items = []
+        
+    def subtotal(self):
+        """ return a subtotal of our items """
+        sub = 0.0
+        for i in self.items:
+            sub += i.quantity * INVENTORY[i.name]
+        return sub
+
+    def add(self, name, quantity):
+        """ add an item to our cart """
+        item = Item(name, quantity)
+        if item in self.items:
+            # we already have this, so add to it
+            self.items[self.items.index(item)] += item
+        else:
+            self.items.append(item)
+        
+    def remove(self, name):
+        """ remove all of item name from the cart """
+        found = [q for q in self.items if q.name == name]
+        if len(found) == 1:
+            self.items.remove(found[0])
+        
+    def report(self):
+        for item in self.items:
+            print("{:20} : {:4}".format(item.name, item.quantity))
+        
diff --git a/lectures/01-python/test.txt b/content/01-python/test.txt
similarity index 100%
rename from lectures/01-python/test.txt
rename to content/01-python/test.txt
diff --git a/content/01-python/tic_tac_toe.py b/content/01-python/tic_tac_toe.py
new file mode 100644
index 00000000..390bc8e1
--- /dev/null
+++ b/content/01-python/tic_tac_toe.py
@@ -0,0 +1,82 @@
+import sys
+
+board = """
+ {s1:^3} | {s2:^3} | {s3:^3}
+-----+-----+-----
+ {s4:^3} | {s5:^3} | {s6:^3}
+-----+-----+-----      123
+ {s7:^3} | {s8:^3} | {s9:^3}       456
+                       789
+"""
+
+
+def initialize_board(play):
+    for n in range(9):
+        play["s{}".format(n+1)] = ""
+
+def show_board(play):
+    """ display the playing board.  We take a dictionary with the current state of the board
+    We rely on the board string to be a global variable"""
+    print(board.format(**play))
+
+
+def get_move(n, xo, play):
+    """ ask the current player, n, to make a move -- make sure the square was not
+        already played.  xo is a string of the character (x or o) we will place in
+        the desired square """
+    valid_move = False
+    while not valid_move:
+        idx = input("player {}, enter your move (1-9): ".format(n))
+        if play["s{}".format(idx)] == "":
+            valid_move = True
+        else:
+            print("invalid: {}".format(play["s{}".format(idx)]))
+
+    play["s{}".format(idx)] = xo
+
+
+def check_win(play):
+
+    # winning combinations
+    combos = [(1, 2, 3), (4, 5, 6), (7, 8, 9),
+              (1, 4, 7), (2, 5, 8), (3, 6, 9),
+              (1, 5, 9), (7, 5, 3)]
+
+    winner = None
+
+    for c in combos:
+        # if we are empty, then pass
+        if play["s{}".format(c[0])] == "":
+            continue
+        if play["s{}".format(c[0])] == play["s{}".format(c[1])] and \
+           play["s{}".format(c[0])] == play["s{}".format(c[2])]:
+            winner = play["s{}".format(c[0])]
+            break
+
+    return winner
+
+def play_game():
+    """ play a game of tic-tac-toe """
+
+    # initialize the board
+    play = {}
+    initialize_board(play)
+
+    symbols = ["x", "o"]
+
+    for n in range(9):
+        show_board(play)
+
+        player = n % 2
+
+        get_move(player, symbols[player], play)
+
+        winner = check_win(play)
+        if winner is not None:
+            show_board(play)
+            print("winner is {}!!!".format(winner))
+            sys.exit()
+
+if __name__ == "__main__":
+    play_game()
+
diff --git a/content/01-python/using.md b/content/01-python/using.md
new file mode 100644
index 00000000..c9e2738d
--- /dev/null
+++ b/content/01-python/using.md
@@ -0,0 +1,45 @@
+# Using These Notes
+
+These notes are built via [Jupyter book](https://jupyterbook.org/), as
+a collection of [Jupyter](https://jupyter.org/) notebooks and markdown
+pages.
+
+The course is on Github at:
+https://github.com/sbu-python-class/python-science, and the course
+website is built automatically via a Github action each time a change
+is pushed.
+
+If you find any problems or have suggestions for improving the notes,
+feel free to create an issue or pull request at the Github repo.
+
+## Interactive Usage
+
+For the Jupyter notebooks in this collection, there are a few ways to
+access them to run them on your own.
+
+* clicking on the {octicon}`download` icon in the upper right let's
+  you download the raw notebook so you can run it on your local
+  computer.
+
+* clicking on the {octicon}`rocket` icon in the upper right will allow
+  you to run the notebook directly in the cloud.  There are 2 different
+  compute clouds:
+
+  * [mybinder](https://mybinder.org/) : this is an open project with
+    ties to the Jupyter project.  It can take a few minutes for the
+    page to appear if it hasn't been accessed recently, but then it
+    will give you the standard Jupyter experience.
+
+  * [Google colab](https://colab.research.google.com/) : this is
+    Google's version of an online notebook, which runs directly in
+    Google's cloud.  This starts up almost instantly.
+
+````{note}
+Some notebooks use [MyST Markdown](https://jupyterbook.org/en/stable/content/myst.html) to 
+allow for more styling.  To see these styles, you need to install `jupyterlab-myst`, which
+can be done via:
+```
+pip install jupyterlab_myst
+```
+
+````
diff --git a/content/01-python/vector2d.py b/content/01-python/vector2d.py
new file mode 100644
index 00000000..07a71950
--- /dev/null
+++ b/content/01-python/vector2d.py
@@ -0,0 +1,93 @@
+import math
+
+class Vector(object):
+    """ a general two-dimensional vector """
+    
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        
+    def __str__(self):
+        return "({} î + {} ĵ)".format(self.x, self.y)
+    
+    def __repr__(self):
+        return "Vector({}, {})".format(self.x, self.y)
+
+    def __add__(self, other):
+        if isinstance(other, Vector):
+            return Vector(self.x + other.x, self.y + other.y)
+        else:
+            # it doesn't make sense to add anything but two vectors
+            print("we don't know how to add a {} to a Vector".format(type(other)))
+            raise NotImplementedError
+
+    def __sub__(self, other):
+        if isinstance(other, Vector):
+            return Vector(self.x - other.x, self.y - other.y)
+        else:
+            # it doesn't make sense to add anything but two vectors
+            print("we don't know how to add a {} to a Vector".format(type(other)))
+            raise NotImplementedError
+
+    def __mul__(self, other):
+        if isinstance(other, int) or isinstance(other, float):
+            # scalar multiplication changes the magnitude
+            return Vector(other*self.x, other*self.y)
+        else:
+            print("we don't know how to multiply two Vectors")
+            raise NotImplementedError
+
+    def __matmul__(self, other):
+        # a dot product
+        if isinstance(other, Vector):
+            return self.x*other.x + self.y*other.y
+        else:
+            print("matrix multiplication not defined")
+            raise NotImplementedError
+
+    def __rmul__(self, other):
+        return self.__mul__(other)
+
+    def __truediv__(self, other):
+        # we only know how to multiply by a scalar
+        if isinstance(other, int) or isinstance(other, float):
+            return Vector(self.x/other, self.y/other)
+
+    def __abs__(self):
+        return math.sqrt(self.x**2 + self.y**2)
+
+    def __neg__(self):
+        return Vector(-self.x, -self.y)
+
+    def cross(self, other):
+        # a vector cross product -- we return the magnitude, since it will
+        # be in the z-direction, but we are only 2-d 
+        return abs(self.x*other.y - self.y*other.x)
+
+        
+if __name__ == "__main__":
+
+    # test it out
+
+    u = Vector(1, 2)
+    v = Vector(3, 5)
+
+    print("{} + {} = {}".format(u, v, u + v))
+    print("{} - {} = {}".format(u, v, u - v))
+    print(" ")
+
+    print("{} * {} = {}".format(2.0, u, 2.0*u))
+    print("{} * {} = {}".format(u, 2.0, u*2.0))
+    print(" ")
+
+    print("{} / {} = {}".format(u, 5.0, u/5.0))
+    print(" ")
+
+    print("dot product: {} @ {} = {}".format(u, v, u@v))
+    print("cross product magnitude: {} x {} = {}".format(u, v, u.cross(v)))
+    print(" ")
+
+    print("magnitude: abs({}) = {}".format(u, abs(u)))
+
+
+
diff --git a/content/01-python/w1-jupyter.ipynb b/content/01-python/w1-jupyter.ipynb
new file mode 100644
index 00000000..5db52542
--- /dev/null
+++ b/content/01-python/w1-jupyter.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll be using Jupyter for all of our examples -- this allows us to run python in a web-based notebook, keeping a history of input and output, along with text and images.\n",
+    "\n",
+    "For Jupyter help, visit:\n",
+    "https://jupyter.readthedocs.io/en/latest/content-quickstart.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{note}\n",
+    "There are several interfaces to [Jupyter](https://jupyter.org/).\n",
+    "\n",
+    "We will use *JupyterLab*, which is traditionally started at the command line via:\n",
+    "```\n",
+    "jupyter lab\n",
+    "```\n",
+    "\n",
+    "The older (classic) interface is *Jupyter Notebook*, which can be started via:\n",
+    "```\n",
+    "jupyter notebook\n",
+    "```\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We interact with python by typing into _cells_ in the notebook.  \n",
+    "\n",
+    "By default, a cell is a _code_ cell, which means that you can enter any valid python code into it and run it.  \n",
+    "\n",
+    "Another important type of cell is a _markdown_ cell.  This lets you put text, with different formatting (italics, bold, etc) that describes what the notebook is doing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"markdown cell\" enables you to typeset LaTeX equations right in your notebook.  Just put them in `$` or `$$`:\n",
+    "\n",
+    "$$\\frac{\\partial \\rho}{\\partial t} + \\nabla \\cdot (\\rho U) = 0$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "You can change the cell type via the menu at the top, or using the shortcuts:\n",
+    "\n",
+    "  * ctrl-m m : mark down cell\n",
+    "  * ctrl-m y : code cell\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some useful short-cuts:\n",
+    "\n",
+    " * shift+enter = run cell and jump to the next (creating a new cell if there is no other new one)\n",
+    " * ctrl+enter = run cell-in place\n",
+    " * alt+enter = run cell and insert a new one below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{warning}\n",
+    "When you work through a notebook, everything you did in previous cells is still in memory and _known_ by python, so you can refer to functions and variables that were previously defined.  Even if you go up to the top of a notebook and insert a cell, all the information done earlier in your notebook session is still defined -- it doesn't matter where physically you are in the notebook.  If you want to reset things, you can use the options under the _Kernel_ menu.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "    \n",
+    "Create a new cell below this one.  Make sure that it is a _code_ cell, and enter the following code and run it:\n",
+    "    \n",
+    "```\n",
+    "\n",
+    " print(\"Hello, World\")\n",
+    " \n",
+    "```\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`print()` is a _function_ in python that takes arguments (in the `()`) and outputs to the screen.  You can print multiple quantities at once like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the default behavior in Jupyter is to print the return value from the last statement in a cell, so we don't need to `print` if we just want the value of something like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 10\n",
+    "a"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w1-python-datatypes.ipynb b/content/01-python/w1-python-datatypes.ipynb
new file mode 100644
index 00000000..a8608991
--- /dev/null
+++ b/content/01-python/w1-python-datatypes.ipynb
@@ -0,0 +1,1365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Python Datatypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python is a dynamically typed language -- this means that you don't\n",
+    "need to specify ahead of time what kind of data you are going to store\n",
+    "in a variable.  Nevertheless, there are some core datatypes that we\n",
+    "need to become familiar with as we use the language.\n",
+    "\n",
+    "The first set of datatypes are similar to those found in other\n",
+    "languages (like C/C++ and Fortran): floating point numbers, integers,\n",
+    "and strings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "Floating point is essential for computational science.  A great\n",
+    "introduction to floating point and its limitations is: [What every\n",
+    "computer scientist should know about floating-point\n",
+    "arithmetic](http://dl.acm.org/citation.cfm?id=103163) by\n",
+    "D. Goldberg.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next set of datatypes are containers.  In python, unlike some\n",
+    "languages, these are built into the language and make it very easy to\n",
+    "do complex operations.  We'll look at these later.\n",
+    "\n",
+    "\n",
+    "Some examples come from the python tutorial:\n",
+    "http://docs.python.org/3/tutorial/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## integers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Integers are numbers without a decimal point.  They can be positive or negative.  Most programming languages use a finite-amount of memory to store a single integer, but in python will expand the amount of memory as necessary to store large integers.\n",
+    "\n",
+    "The basic operators, `+`, `-`, `*`, and `/` work with integers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2+3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-8"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2*-4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "Integer division is one place where python and other programming languages differ.\n",
+    "In python, dividing two integers results in a float.  In C/C++/Fortran, dividing two integers results in an integer, so `1/2 = 0`.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an integer result, we can use the // operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1//2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python is a _dynamically-typed language_&mdash;this means that we do not need to declare the datatype of a variable before initializing it.  \n",
+    "\n",
+    "Here we'll create a variable (think of it as a descriptive label that can refer to some piece of data).  The `=` operator assigns a value to a variable.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 1\n",
+    "b = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Functions operate on variables and return a result.  Here, `print()` will output to the screen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a * b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that variable names are case sensitive, so a and A are different"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = 2048"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2048\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a, A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we initialize 3 variable all to `0`, but these are still distinct variables, so we can change one without affecting the others."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = y = z = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 0 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x, y, z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python has some built in help (and Jupyter/ipython has even more)\n",
+    "\n",
+    "try doing:\n",
+    "```\n",
+    "help(x)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "alternatively, try:\n",
+    "```\n",
+    "x?\n",
+    "```\n",
+    "\n",
+    "(this only works in Jupyter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another function, `type()` returns the data type of a variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "In languages like Fortran and C, you specify the amount of memory an integer can take (usually 2 or 4 bytes).  This puts a restriction on the largest size integer that can be represented.  Python will adapt the size of the integer so you don't *overflow*\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12345678901234567890123456789012345123456789012345678901234567890\n",
+      "213\n",
+      "<class 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 12345678901234567890123456789012345123456789012345678901234567890\n",
+    "print(a)\n",
+    "print(a.bit_length())\n",
+    "print(type(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## floating point"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "when operating with both floating point and integers, the result is promoted to a float."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1. + 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "but note the special integer division operator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.//2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{important}\n",
+    "Not every number can be represented in floating point.  Since there are infinitely many real numbers between any two bounds but we are using a finite amount of memory, on a computer we have to approximate numbers.  There is an IEEE standard for floating point that pretty much all languages and processors follow.  \n",
+    "\n",
+    "The means two things\n",
+    "\n",
+    "* not every real number will have an exact representation in floating point\n",
+    "* there is a finite precision to numbers -- below this we lose track of differences (this is usually called *roundoff* error)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider the following expression, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-4.440892098500626e-16"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.3/0.1 - 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's another example: The number 0.1 cannot be exactly represented on a computer.  In our print, we use a format specifier (the stuff inside of the `{}`) to ask for more precision to be shown:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        0.10000000000000000555\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 0.1\n",
+    "print(\"{:30.20}\".format(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can ask python to report the limits on floating point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.float_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this says that we can only store numbers between 2.2250738585072014e-308 and 1.7976931348623157e+308\n",
+    "\n",
+    "We also see that the precision is 2.220446049250313e-16 (this is commonly called _machine epsilon_).  To see this, consider adding a small number to 1.0.  We'll use the equality operator (`==`) to test if two numbers are equal:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Quick Exercise\n",
+    "    \n",
+    "Define two variables, $a = 1$, and $e = 10^{-16}$.\n",
+    "\n",
+    "Now define a third variable, `b = a + e`\n",
+    "\n",
+    "We can use the python `==` operator to test for equality.  What do you expect `b == a` to return? run it an see if it agrees with your guess.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## modules\n",
+    "\n",
+    "The core python language is extended by a standard library that provides additional functionality.  These added pieces are in the form of modules that we can _import_ into our python session (or program).\n",
+    "\n",
+    "The `math` module provides functions that do the basic mathematical operations as well as provide constants (note there is a separate `cmath` module for complex numbers).\n",
+    "\n",
+    "In python, you `import` a module.  The functions are then defined in a separate _namespace_&mdash;this is a separate region that defines names and variables, etc.  A variable in one namespace can have the same name as a variable in a different namespace, and they don't clash.  You use the \"`.`\" operator to access a member of a namespace.\n",
+    "\n",
+    "By default, when you type stuff into the python interpreter or here in the Jupyter notebook, or in a script, it is in its own default namespace, and you don't need to prefix any of the variables with a namespace indicator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`math` provides the value of pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.141592653589793"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is distinct from any variable `pi` we might define here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 3.141592653589793\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pi, math.pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note here that `pi` and `math.pi` are distinct from one another&mdash;they are in different namespaces."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### floating point operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same operators, `+`, `-`, `*`, `/` work are usual for floating point numbers.  To raise an number to a power, we use the `**` operator (this is the same as Fortran)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "R = 2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.566370614359172"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.pi * R**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "operator precedence follows that of most languages.  See\n",
+    "\n",
+    "https://docs.python.org/3/reference/expressions.html#operator-precedence\n",
+    "    \n",
+    "in order of precedence:\n",
+    "* quantites in `()`\n",
+    "* slicing, calls, subscripts\n",
+    "* exponentiation (`**`)\n",
+    "* `+x`, `-x`, `~x`\n",
+    "* `*`, `@`, `/`, `//`, `%`\n",
+    "* `+`, `-`\n",
+    "\n",
+    "(after this are bitwise operations and comparisons)\n",
+    "\n",
+    "Parentheses can be used to override the precedence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Quick Exercise\n",
+    "\n",
+    "Consider the following expressions.  Using the ideas of precedence, think about what value will result, then try it out in the cell below to see if you were right.\n",
+    "\n",
+    "  * `1 + 3*2**2`\n",
+    "  * `1 + (3*2)**2`\n",
+    "  * `2**3**2`\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The math module provides a lot of the standard math functions we might want to use.\n",
+    "\n",
+    "For the trig functions, the expectation is that the argument to the function is in radians&mdash;you can use `math.radians()` to convert from degrees to radians, ex:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7071067811865476"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.cos(math.radians(45))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that in that statement we are feeding the output of one function (`math.radians()`) into a second function, `math.cos()`\n",
+    "\n",
+    "When in doubt, as for help to discover all of the things a module provides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function sin in module math:\n",
+      "\n",
+      "sin(x, /)\n",
+      "    Return the sine of x (measured in radians).\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.sin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "python doesn't care if you use single or double quotes for strings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "a = \"this is my string\"\n",
+    "b = 'another string'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is my string\n",
+      "another string\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many of the usual mathematical operators are defined for strings as well.  For example to concatenate or duplicate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is my stringanother string'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is my string. another string'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + \". \" + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is my stringthis is my string'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a * 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are several escape codes that are interpreted in strings.  These start with a backwards-slash, `\\`.  E.g., you can use `\\n` for new line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is my string\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = a + \"\\n\"\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Quick Exercise\n",
+    "    \n",
+    "The `input()` function can be used to ask the user for input.\n",
+    "\n",
+    "  * Use `help(input)` to see how it works.  \n",
+    "  * Write code to ask for input and store the result in a variable.  `input()` will return a string.\n",
+    "\n",
+    "  * Use the `float()` function to convert a number entered as input to a floating point variable.  \n",
+    "  * Check to see if the conversion worked using the `type()` function.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"\"\" can enclose multiline strings.  This is useful for docstrings at the start of functions (more on that later...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "c = \"\"\"\n",
+    "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor \n",
+    "incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis \n",
+    "nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. \n",
+    "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore \n",
+    "eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt \n",
+    "in culpa qui officia deserunt mollit anim id est laborum.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor \n",
+      "incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis \n",
+      "nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. \n",
+      "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore \n",
+      "eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt \n",
+      "in culpa qui officia deserunt mollit anim id est laborum.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a raw string does not replace escape sequences (like \\n).  Just put a `r` before the first quote:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is a raw string\\\\n'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = r\"this is a raw string\\n\"\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "slicing is used to access a portion of a string.\n",
+    "\n",
+    "slicing a string can seem a bit counterintuitive if you are coming from Fortran.  The trick is to think of the index as representing the left edge of a character in the string.  When we do arrays later, the same will apply.\n",
+    "\n",
+    "Also note that python (like C) uses 0-based indexing\n",
+    "\n",
+    "Negative indices count from the right."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is my string\n",
+      "is\n",
+      "t\n",
+      "this is a raw string\\n\n",
+      "\\\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = \"this is my string\"\n",
+    "print(a)\n",
+    "print(a[5:7])\n",
+    "print(a[0])\n",
+    "print(d)\n",
+    "print(d[-2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Quick Exercise:\n",
+    "\n",
+    "Strings have a lot of _methods_ (functions that know how to work with a particular datatype, in this case strings).  A useful method is `.find()`.  For a string `a`,\n",
+    "`a.find(s)` will return the index of the first occurrence of `s`.\n",
+    "\n",
+    "For our string `c` above, find the first `.` (identifying the first full sentence), and print out just the first sentence in `c` using this result\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "there are also a number of methods and functions that work with strings.  Here are some examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "that is my string\n",
+      "17\n",
+      "this is my string\n",
+      "g\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a.replace(\"this\", \"that\"))\n",
+    "print(len(a))\n",
+    "print(a.strip())    # Also notice that strip removes the \\n\n",
+    "print(a.strip()[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that our original string, `a`, has not changed.  In python, strings are *immutable*.  Operations on strings return a new string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is my string'"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can format strings when we are printing to insert quantities in particular places in the string.  A `{}` serves as a placeholder for a quantity and is replaced using the `.format()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a = 1; b = 2.0; c = test\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "b = 2.0\n",
+    "c = \"test\"\n",
+    "print(\"a = {}; b = {}; c = {}\".format(a, b, c))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "But the more modern way to do this is to use *f-strings*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a = 1; b = 2.0; c = test\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"a = {a}; b = {b}; c = {c}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the `f` preceding the starting `\"`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w2-python-advanced-datatypes.ipynb b/content/01-python/w2-python-advanced-datatypes.ipynb
new file mode 100644
index 00000000..a34fd9ce
--- /dev/null
+++ b/content/01-python/w2-python-advanced-datatypes.ipynb
@@ -0,0 +1,916 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced Datatypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These notes follow the official python tutorial pretty closely: http://docs.python.org/3/tutorial/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lists group together data.  Many languages have arrays (we'll look at those in a bit in python).  But unlike arrays in most languages, lists can hold data of all different types -- they don't need to be homogeneos.  The data can be a mix of integers, floating point or complex #s, strings, or other objects (including other lists).\n",
+    "\n",
+    "A list is defined using square brackets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = [1, 2.0, \"my list\", 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2.0, 'my list', 4]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can index a list to get a single element -- remember that python starts counting at 0:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'my list'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Like with strings, mathematical operators are defined on lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2.0, 'my list', 4, 1, 2.0, 'my list', 4]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a*2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `len()` function returns the length of a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike strings, lists are _mutable_ -- you can change elements in a list easily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, -2.0, 'my list', 4]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[1] = -2.0\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like everything else in python, a list is an object that is the instance of a class.  Classes have methods (functions) that know how to operate on an object of that class.\n",
+    "\n",
+    "There are lots of methods that work on lists.  Two of the most useful are append, to add to the end of a list, and pop, to remove the last element:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -2.1, -2.0, 'my list', 4, 6]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.append(6)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -2.1, -2.0, 'my list', 4]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "An operation we'll see a lot is to begin with an empty list and add elements to it.  An empty list is created as:\n",
+    "```\n",
+    "\n",
+    " a = []\n",
+    " \n",
+    "```\n",
+    "\n",
+    "  * Create an empty list\n",
+    "  * Append the integers 1 through 5 to it.  \n",
+    "  * Now pop them out of the list one by one.\n",
+    "  \n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### copying lists\n",
+    "\n",
+    "copying may seem a little counterintuitive at first.  The best way to think about this is that your list lives in memory somewhere and when you do \n",
+    "\n",
+    "```\n",
+    "a = [1, 2, 3, 4]\n",
+    "```\n",
+    "\n",
+    "then the variable `a` is set to point to that location in memory, so it refers to the list.\n",
+    "\n",
+    "If we then do\n",
+    "```\n",
+    "b = a\n",
+    "```\n",
+    "then `b` will also point to that same location in memory -- the exact same list object.\n",
+    "\n",
+    "Since these are both pointing to the same location in memory, if we change the list through `a`, the change is reflected in `b` as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4]\n",
+      "['changed', 2, 3, 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = [1, 2, 3, 4]\n",
+    "b = a  # both a and b refer to the same list object in memory\n",
+    "print(a)\n",
+    "a[0] = \"changed\"\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if you want to create a new object in memory that is a copy of another, then you can either index the list, using `:` to get all the elements, or use the `list()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['changed', 'two', 3, 4]\n",
+      "['changed', 2, 3, 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = list(a)   # you can also do c = a[:], which basically slices the entire list\n",
+    "a[1] = \"two\"\n",
+    "print(a)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Things get a little complicated when a list contains another mutable object, like another list.  Then the copy we looked at above is only a _shallow copy_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When in doubt, use the `id()` function to figure out where in memory an object lies (you shouldn't worry about the what value of the numbers you get from `id` mean, but just whether they are the same as those for another object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "140643968865472 140643968865472 140643969109824\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(id(a), id(b), id(c))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or use the `is` operator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are lots of other methods that work on lists (remember, ask for `help`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -1, 2, 5, 9, 10, 24]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_list = [10, -1, 5, 24, 2, -1, 9]\n",
+    "my_list.sort()\n",
+    "my_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_list.count(-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also insert elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['changed', 'two', 3, 'my inserted element', 4]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.insert(3, \"my inserted element\")\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "joining two lists is simple.  Like with strings, the `+` operator concatenates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = [1, 2, 3]\n",
+    "c = [4, 5, 6]\n",
+    "d = b + c\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A dictionary stores data as a key:value pair.  Unlike a list where you have a particular order, the keys in a dictionary allow you to access information anywhere easily:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dict = {\"key1\":1, \"key2\":2, \"key3\":3}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict[\"key1\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can add a new key:pair easily, and it can be of any type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'key1': 1, 'key2': 2, 'key3': 3, 'newkey': 'new'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_dict[\"newkey\"] = \"new\"\n",
+    "my_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also easily get the list of keys that are defined in a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['key1', 'key2', 'key3', 'newkey']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "keys = list(my_dict.keys())\n",
+    "keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and check easily whether a key exists in the dictionary using the `in` operator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"key1\" in my_dict)\n",
+    "print(\"invalidKey\" in my_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "Create a dictionary where the keys are the string names of the numbers zero to nine and the values are their numeric representation (0, 1, ... , 5)\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tuples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "tuples are immutable -- they cannot be changed, but they are useful for organizing data in some situations.  We use () to indicate a tuple:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3, 4)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = (1, 2, 3, 4)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can unpack a tuple:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w, x, y, z = a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since a tuple is immutable, we cannot change an element:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "a[0] = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we can turn it into a list, and then we can change it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = list(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z[0] = \"new\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['new', 2, 3, 4]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is often not clear how tuples differ from lists.  The most obvious way is that they are immutable.  Often we'll see tuples used to store related data that should all be interpreted together.  A good example is a Cartesian point, (x, y).  Here is a list of points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 2), (2, 3), (3, 4)]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points = []\n",
+    "points.append((1,2))\n",
+    "points.append((2,3))\n",
+    "points.append((3,4))\n",
+    "points"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w2-python-control-flow.ipynb b/content/01-python/w2-python-control-flow.ipynb
new file mode 100644
index 00000000..d6652498
--- /dev/null
+++ b/content/01-python/w2-python-control-flow.ipynb
@@ -0,0 +1,485 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Control Flow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These notes follow the official python tutorial pretty closely: http://docs.python.org/3/tutorial/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To write a program, we need the ability to iterate and take action based on the values of a variable.  This includes if-tests and loops.\n",
+    "\n",
+    "Python uses whitespace to denote a block of code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## While loop\n",
+    "\n",
+    "A simple while loop&mdash;notice the indentation to denote the block that is part of the loop.\n",
+    "\n",
+    "Here we also use the compact `+=` operator: `n += 1` is the same as `n = n + 1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 0\n",
+    "while n < 10:\n",
+    "    print(n)\n",
+    "    n += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was a very simple example.  But often we'll use the `range()` function in this situation.  Note that `range()` can take a stride."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "4\n",
+      "6\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n in range(2, 10, 2):\n",
+    "    print(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`if` allows for branching.  python does not have a select/case statement like some other languages, but `if`, `elif`, and `else` can reproduce any branching functionality you might need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 0\n",
+    "\n",
+    "if x < 0:\n",
+    "    print(\"negative\")\n",
+    "elif x == 0:\n",
+    "    print(\"zero\")\n",
+    "else:\n",
+    "    print(\"positive\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Iterating over elements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "it's easy to loop over items in a list or any _iterable_ object.  The `in` operator is the key here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2.0\n",
+      "three\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "alist = [1, 2.0, \"three\", 4]\n",
+    "for a in alist:\n",
+    "    print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t\n",
+      "h\n",
+      "i\n",
+      "s\n",
+      " \n",
+      "i\n",
+      "s\n",
+      " \n",
+      "a\n",
+      " \n",
+      "s\n",
+      "t\n",
+      "r\n",
+      "i\n",
+      "n\n",
+      "g\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in \"this is a string\":\n",
+    "    print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can combine loops and if-tests to do more complex logic, like break out of the loop when you find what you're looking for"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 0\n",
+    "for a in alist:\n",
+    "    if a == \"three\":\n",
+    "        break\n",
+    "    else:\n",
+    "        n += 1\n",
+    "\n",
+    "print(n)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(for that example, however, there is a simpler way)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alist.index(\"three\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "for dictionaries, you can also loop over the elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "key = key1, value = 1\n",
+      "key = key2, value = 2\n",
+      "key = key3, value = 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_dict = {\"key1\":1, \"key2\":2, \"key3\":3}\n",
+    "\n",
+    "for k, v in my_dict.items():\n",
+    "    print(f\"key = {k}, value = {v}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "key1 1\n",
+      "key2 2\n",
+      "key3 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in sorted(my_dict):\n",
+    "    print(k, my_dict[k])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sometimes we want to loop over a list element and know its index -- `enumerate()` helps here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1\n",
+      "1 2.0\n",
+      "2 three\n",
+      "3 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for n, a in enumerate(alist):\n",
+    "    print(n, a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "    \n",
+    "`zip()` allows us to loop over two iterables at the same time.  Consider the following two\n",
+    "lists:\n",
+    "\n",
+    "```\n",
+    "\n",
+    " a = [1, 2, 3, 4, 5, 6, 7, 8]\n",
+    " b = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"]\n",
+    " \n",
+    "```\n",
+    "\n",
+    "`zip(a, b)` will act like a list with each element a tuple with one item from `a` and the corresponding element from `b`. \n",
+    "\n",
+    "Try looping over these lists together (using `zip()`) and print the corresponding elements from each list together on a single line.\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "    \n",
+    "\n",
+    "The `.split()` function on a string can split it into words (separating on spaces).  \n",
+    "\n",
+    "Using `.split()`, loop over the words in the string\n",
+    "\n",
+    "`a = \"The quick brown fox jumped over the lazy dog\"`\n",
+    "\n",
+    "and print one word per line\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "list comprehensions provide a compact way to initialize lists.  Some examples from the tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "squares = [x**2 for x in range(10)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "squares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "Use a list comprehension to create a new list from `squares` containing only the even numbers.  It might be helpful to use the modulus operator, `%`\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w2-python-exercises.ipynb b/content/01-python/w2-python-exercises.ipynb
new file mode 100644
index 00000000..1651a963
--- /dev/null
+++ b/content/01-python/w2-python-exercises.ipynb
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 1\n",
+    "\n",
+    "When talking about floating point, we discussed _machine epsilon_, $\\epsilon$&mdash;this is the smallest number that when added to 1 is still different from 1.\n",
+    "\n",
+    "We'll compute $\\epsilon$ here:\n",
+    "\n",
+    "  * Pick an initial guess for $\\epsilon$ of `eps = 1`.  \n",
+    "\n",
+    "  * Create a loop that checks whether `1 + eps` is different from `1`\n",
+    "  \n",
+    "  * Each loop iteration, cut the value of `eps` in half\n",
+    "  \n",
+    "What value of $\\epsilon$ do you find?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 2\n",
+    "\n",
+    "To iterate over the tuples, where the _i_-th tuple contains the _i_-th elements of certain sequences, we can use `zip(*sequences)` function.\n",
+    "\n",
+    "We will iterate over two lists, `names` and `age`, and print out the resulting tuples.\n",
+    "\n",
+    "  * Start by initializing lists `names = [\"Mary\", \"John\", \"Sarah\"]` and `age = [21, 56, 98]`.\n",
+    "  \n",
+    "  * Iterate over the tuples containing a name and an age, the `zip(list1, list2)` function might be useful here.\n",
+    "  \n",
+    "  * Print out formatted strings of the type \"*NAME is AGE years old*\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 3\n",
+    "\n",
+    "The function `enumerate(sequence)` returns tuples containing indices of objects in the sequence, and the objects. \n",
+    "\n",
+    "The `random` module provides tools for working with the random numbers. In particular, `random.randint(start, end)` generates a random number not smaller than `start`, and not bigger than `end`.\n",
+    "\n",
+    "  * Generate a list of 10 random numbers from 0 to 9.\n",
+    "  \n",
+    "  * Using the `enumerate(random_list)` function, iterate over the tuples of random numbers and their indices, and print out *\"Match: NUMBER and INDEX\"* if the random number and its index in the list match."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "random_number = random.randint(0,9)\n",
+    "print(random_number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 4\n",
+    "\n",
+    "The Fibbonacci sequence is a numerical sequence where each number is the sum of the 2 preceding numbers, e.g., 1, 1, 2, 3, 5, 8, 13, ...\n",
+    "\n",
+    "Create a list where the elements are the terms in the Fibbonacci sequence:\n",
+    "\n",
+    "  * Start with the list `fib = [1, 1]`\n",
+    "  \n",
+    "  * Loop 25 times, compute the next term as the sum of the previous 2 terms and append to the list\n",
+    "  \n",
+    "  * After the loop is complete, print out the terms \n",
+    "  \n",
+    "You may find it useful to use `fib[-1]` and `fib[-2]` to access the last to items in the list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 5\n",
+    "\n",
+    "We can use the `input()` function to ask for input from the prompt (note: in python 2 the function was called `raw_input()`).\n",
+    "\n",
+    "Create an empty list and use a while loop to ask the user for input and append their input to the list.  Keep looping until 10 items are added to the list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 6\n",
+    "\n",
+    "Here is a list of book titles (from http://thegreatestbooks.org).  Loop through the list and capitalize each word in each title.  You might find the `.capitalize()` method that works on strings useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "titles = [\"don quixote\", \n",
+    "          \"in search of lost time\", \n",
+    "          \"ulysses\", \n",
+    "          \"the odyssey\", \n",
+    "          \"war and piece\", \n",
+    "          \"moby dick\", \n",
+    "          \"the divine comedy\", \n",
+    "          \"hamlet\", \n",
+    "          \"the adventures of huckleberry finn\", \n",
+    "          \"the great gatsby\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span class=\"fa fa-star\"></span> Q 7\n",
+    "\n",
+    "Here's some text (the Gettysburg Address).  Our goal is to count how many times each word repeats.  We'll do a brute force method first, and then we'll look a ways to do it more efficiently (and compactly)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "gettysburg_address = \"\"\"\n",
+    "Four score and seven years ago our fathers brought forth on this continent, \n",
+    "a new nation, conceived in Liberty, and dedicated to the proposition that \n",
+    "all men are created equal.\n",
+    "\n",
+    "Now we are engaged in a great civil war, testing whether that nation, or \n",
+    "any nation so conceived and so dedicated, can long endure. We are met on\n",
+    "a great battle-field of that war. We have come to dedicate a portion of\n",
+    "that field, as a final resting place for those who here gave their lives\n",
+    "that that nation might live. It is altogether fitting and proper that we\n",
+    "should do this.\n",
+    "\n",
+    "But, in a larger sense, we can not dedicate -- we can not consecrate -- we\n",
+    "can not hallow -- this ground. The brave men, living and dead, who struggled\n",
+    "here, have consecrated it, far above our poor power to add or detract.  The\n",
+    "world will little note, nor long remember what we say here, but it can never\n",
+    "forget what they did here. It is for us the living, rather, to be dedicated\n",
+    "here to the unfinished work which they who fought here have thus far so nobly\n",
+    "advanced. It is rather for us to be here dedicated to the great task remaining\n",
+    "before us -- that from these honored dead we take increased devotion to that\n",
+    "cause for which they gave the last full measure of devotion -- that we here\n",
+    "highly resolve that these dead shall not have died in vain -- that this\n",
+    "nation, under God, shall have a new birth of freedom -- and that government\n",
+    "of the people, by the people, for the people, shall not perish from the earth.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've already seen the `.split()` method will, by default, split by spaces, so it will split this into words, producing a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ga = gettysburg_address.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ga"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, the next problem is that some of these still have punctuation.  In particular, we see \"`.`\", \"`,`\", and \"`--`\".\n",
+    "\n",
+    "When considering a word, we can get rid of these by using the `replace()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = \"end.,\"\n",
+    "b = a.replace(\".\", \"\").replace(\",\", \"\")\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another problem is case&mdash;we want to count \"but\" and \"But\" as the same.  Strings have a `lower()` method that can be used to convert a string:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = \"But\"\n",
+    "b = \"but\"\n",
+    "a == b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.lower() == b.lower()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall that strings are immutable, so `replace()` produces a new string on output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## your task\n",
+    "\n",
+    "Create a dictionary that uses the unique words as keys and has as a value the number of times that word appears.  \n",
+    "\n",
+    "Write a loop over the words in the string (using our split version) and do the following:\n",
+    "  * remove any punctuation\n",
+    "  * convert to lowercase\n",
+    "  * test if the word is already a key in the dictionary (using the `in` operator)\n",
+    "     - if the key exists, increment the word count for that key\n",
+    "     - otherwise, add it to the dictionary with the appropriate count of `1`.\n",
+    "\n",
+    "At the end, print out the words and a count of how many times they appear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# your code here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More compact way\n",
+    "\n",
+    "We can actually do this a lot more compactly by using another list comprehensions and another python datatype called a set.  A set is a group of items, where each item is unique (e.g., no repetitions)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a list comprehension that removes all the punctuation and converts to lower case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "words = [q.lower().replace(\".\", \"\").replace(\",\", \"\") for q in ga]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and by using the `set()` function, we turn the list into a set, removing any duplicates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "unique_words = set(words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "now we can loop over the unique words and use the `count` method of a list to find how many there are"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count = {}\n",
+    "for uw in unique_words:\n",
+    "    count[uw] = words.count(uw)\n",
+    "    \n",
+    "count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even shorter -- we can use a dictionary comprehension, like a list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "c = {uw: count[uw] for uw in unique_words}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w3-python-exceptions.ipynb b/content/01-python/w3-python-exceptions.ipynb
new file mode 100644
index 00000000..c5769516
--- /dev/null
+++ b/content/01-python/w3-python-exceptions.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python raises exceptions when it encounters an error.  The idea is that you can trap these exceptions and take an appropriate action instead of causing the code to crash.  The mechanism for this is `try` / `except`.  Here's an example that causes an exception, `ZeroDivisionError`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-6c16767f6731>\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;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "a = 1/0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and here we handle this  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "warning: you divided by zero\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    a = 1/0\n",
+    "except ZeroDivisionError:\n",
+    "    print(\"warning: you divided by zero\")\n",
+    "    a = 1\n",
+    "\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "another example&mdash;trying to access a key that doesn't exist in a dictionary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'d'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-0175b692bf59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"a\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"d\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'd'"
+     ]
+    }
+   ],
+   "source": [
+    "dict = {\"a\":1, \"b\":2, \"c\":3}\n",
+    "print(dict[\"d\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`KeyError` is the exception that was raised.  We can check for this and take the appropriate action instead"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    val = dict[\"d\"]\n",
+    "except KeyError:\n",
+    "    val = None\n",
+    "\n",
+    "print(val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We haven't talked about I/O yet, but as you might expect, when you open a file, you get an object which you can then interact with.  Here we try to open a file for reading that does not already exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    f = open(\"file.txt\", \"r\")\n",
+    "except IOError:\n",
+    "    print(\"error: that file does not exist\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a lot of different types of exceptions that you can catch, and you can catch multiple ones per except clause or have multiple except clauses.  You probably won't be able to anticipate every failure mode in advance.  In that case, when you run and your code crashes because of an exception, the python interpreter will print out the name of the exception and you can then modify your code to take the appropriate action."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w3-python-exercises.ipynb b/content/01-python/w3-python-exercises.ipynb
new file mode 100644
index 00000000..f84e4a67
--- /dev/null
+++ b/content/01-python/w3-python-exercises.ipynb
@@ -0,0 +1,500 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 1 (function practice)\n",
+    "\n",
+    "Let's practice functions.  Here's a simple function that takes a string and returns a list of all the 4 letter words:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def four_letter_words(message):\n",
+    "    words = message.split()\n",
+    "    four_letters = [w for w in words if len(w) == 4]\n",
+    "    return four_letters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['over', 'lazy']\n"
+     ]
+    }
+   ],
+   "source": [
+    "message = \"The quick brown fox jumps over the lazy dog\"\n",
+    "print(four_letter_words(message))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a version of this function that takes a second argument, n, that is the word length we want to search for"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 2 (primes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A prime number is divisible only by 1 and itself.  We want to write a function that takes a positive integer, n, and finds all of the primes up to that number.\n",
+    "\n",
+    "A simple (although not very fast) way to find the primes is to start at 1, and build a list of primes by checking if the current number is divisible by any of the previously found primes.  If it is not divisible by any earlier primes, then it is a prime.\n",
+    "\n",
+    "The modulus operator, `%` could be helpful here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 3 (exceptions for error handling)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to safely convert a string into a float, int, or leave it as a string, depending on its contents.  As we've already seen, python provides `float()` and `int()` functions for this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 <class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = \"2.0\"\n",
+    "b = float(a)\n",
+    "print(b, type(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But these throw exceptions if the conversion is not possible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: 'this is a string'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthis is a string\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'this is a string'"
+     ]
+    }
+   ],
+   "source": [
+    "a = \"this is a string\"\n",
+    "b = float(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: '1.2345'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1.2345\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(b, \u001b[38;5;28mtype\u001b[39m(b))\n",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '1.2345'"
+     ]
+    }
+   ],
+   "source": [
+    "a = \"1.2345\"\n",
+    "b = int(a)\n",
+    "print(b, type(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.2345 <class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = float(a)\n",
+    "print(b, type(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that an int can be converted to a float, but if you convert a float to an int, you rise losing significant digits.  A string cannot be converted to either.\n",
+    "\n",
+    "### your task\n",
+    "\n",
+    "Write a function, `convert_type(a)` that takes a string `a`, and converts it to a float if it is a number with a decimal point, an int if it is an integer, or leaves it as a string otherwise, and returns the result.  You'll want to use exceptions to prevent the code from aborting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q 4 (tic-tac-toe)\n",
+    "\n",
+    "Here we'll write a simple tic-tac-toe game that 2 players can play.  First we'll create a string that represents our game board:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "board = \"\"\"\n",
+    " {s1:^3} | {s2:^3} | {s3:^3}\n",
+    "-----+-----+-----\n",
+    " {s4:^3} | {s5:^3} | {s6:^3}\n",
+    "-----+-----+-----      123\n",
+    " {s7:^3} | {s8:^3} | {s9:^3}       456\n",
+    "                       789  \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This board will look a little funny if we just print it&mdash;the spacing is set to look right when we replace the `{}` with `x` or `o`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " {s1:^3} | {s2:^3} | {s3:^3}\n",
+      "-----+-----+-----\n",
+      " {s4:^3} | {s5:^3} | {s6:^3}\n",
+      "-----+-----+-----      123\n",
+      " {s7:^3} | {s8:^3} | {s9:^3}       456\n",
+      "                       789  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(board)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and well use a dictionary to denote the status of each square, \"x\", \"o\", or empty, \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'s1': '',\n",
+       " 's2': '',\n",
+       " 's3': '',\n",
+       " 's4': '',\n",
+       " 's5': '',\n",
+       " 's6': '',\n",
+       " 's7': '',\n",
+       " 's8': '',\n",
+       " 's9': ''}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "play = {}\n",
+    "\n",
+    "def initialize_board(play):\n",
+    "    for n in range(9):\n",
+    "        play[f\"s{n+1}\"] = \"\"\n",
+    "\n",
+    "initialize_board(play)\n",
+    "play"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that our `{}` placeholders in the `board` string have identifiers (the numbers in the `{}`).  We can use these to match the variables we want to print to the placeholder in the string, regardless of the order in the `format()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2 1'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = \"{s1:} {s2:}\".format(s2=1, s1=2)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an easy way to add the values of our dictionary to the appropriate squares in our game board.  First note that each of the {} is labeled with a number that matches the keys in our dictionary.  Python provides a way to unpack a dictionary into labeled arguments, using **\n",
+    "\n",
+    "This lets us to write a function to show the tic-tac-toe board."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     |     |    \n",
+      "-----+-----+-----\n",
+      "     |     |    \n",
+      "-----+-----+-----      123\n",
+      "     |     |           456\n",
+      "                       789  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def show_board(play):\n",
+    "    \"\"\" display the playing board.  We take a dictionary with the current state of the board\n",
+    "    We rely on the board string to be a global variable\"\"\"\n",
+    "    print(board.format(**play))\n",
+    "    \n",
+    "show_board(play)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need a function that asks a player for a move:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_move(n, xo, play):\n",
+    "    \"\"\" ask the current player, n, to make a move -- make sure the square was not \n",
+    "        already played.  xo is a string of the character (x or o) we will place in\n",
+    "        the desired square \"\"\"\n",
+    "    valid_move = False\n",
+    "    while not valid_move:\n",
+    "        idx = input(f\"player {n}, enter your move (1-9)\")\n",
+    "        if play[f\"s{idx}\"] == \"\":\n",
+    "            valid_move = True\n",
+    "        else:\n",
+    "            print(\"invalid move\")\n",
+    "            \n",
+    "    play[f\"s{idx}\"] = xo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function get_move in module __main__:\n",
+      "\n",
+      "get_move(n, xo, play)\n",
+      "    ask the current player, n, to make a move -- make sure the square was not \n",
+      "    already played.  xo is a string of the character (x or o) we will place in\n",
+      "    the desired square\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(get_move)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### your task\n",
+    "\n",
+    "Using the functions defined above,\n",
+    "  * `initialize_board()`\n",
+    "  * `show_board()`\n",
+    "  * `get_move()`\n",
+    "\n",
+    "fill in the function `play_game()` below to complete the game, asking for the moves one at a time, alternating between player 1 and 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def play_game():\n",
+    "    \"\"\" play a game of tic-tac-toe \"\"\"\n",
+    "    \n",
+    "    play ={}\n",
+    "    initialize_board(play)\n",
+    "    show_board(play)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     |     |    \n",
+      "-----+-----+-----\n",
+      "     |     |    \n",
+      "-----+-----+-----      123\n",
+      "     |     |           456\n",
+      "                       789  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "play_game()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w3-python-functions.ipynb b/content/01-python/w3-python-functions.ipynb
new file mode 100644
index 00000000..ee20eaf9
--- /dev/null
+++ b/content/01-python/w3-python-functions.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Functions are used to organize program flow, especially to allow us to easily do commonly needed tasks over and over again.  We've already used a lot of functions, such as those that work on lists (`append()` and `pop()`) or strings (like `replace()`).  Here we see how to write our own functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A function takes arguments, listed in the `()` and returns a value.  Even if you don't explicitly give a return value, one will be return (e.g., `None`). \n",
+    "\n",
+    "Here's a simple example of a function that takes a single argument, `i`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def my_fun(i):\n",
+    "    print(f\"in the function, i = {i}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in the function, i = 10\n",
+      "in the function, i = 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_fun(10)\n",
+    "my_fun(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "Functions are one place where _scope_ comes into play.  A function has its own _namespace_.  If a variable is not defined in that function, then it will look to the namespace from where it was called to see if that variable exists there.  \n",
+    "\n",
+    "However, you should avoid this as much as possible (variables that persist across namespaces are called global variables).\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Functions always return a value&mdash;if one is not explicitly given, then they return `None`, otherwise, they can return values (even multiple values) of any type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in the function, i = 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = my_fun(10)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a simple function that takes two numbers and returns their product."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def multiply(a, b):\n",
+    "    return a*b\n",
+    "\n",
+    "c = multiply(3, 4)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "    \n",
+    "\n",
+    "Write a simple function that takes a sentence (as a string) and returns an integer equal to the length of the longest word in the sentence.  The `len()` function and the `.split()` methods will be useful here.\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`None` is a special quantity in python (analogous to `null` in some other languages).  We can test on `None`&mdash;the preferred manner is to use `is`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def do_nothing():\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "we didn't do anything\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = do_nothing()\n",
+    "if a is None:\n",
+    "    print(\"we didn't do anything\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keyword arguments\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can have optional arguments which provide defaults.  Here's a simple function that computes $\\sin(\\theta)$ where $\\theta$ can optionally be in degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def my_sin(theta, in_degrees=False):\n",
+    "    if in_degrees:\n",
+    "        return math.sin(math.radians(theta))\n",
+    "    return math.sin(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7071067811865475, 0.7071067811865475)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_sin(math.pi/4), my_sin(45, in_degrees=True)                    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{important}\n",
+    "It is important to note that python evaluates the optional arguments once&mdash;when the function is defined.  This means that if you make the default an empty object, for instance, it will persist across all call.\n",
+    "\n",
+    "**This leads to one of the most common errors for beginners**\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example of trying to initialize to an empty list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(a, L=[]):\n",
+    "    L.append(a)\n",
+    "    return L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\n",
+      "[1, 2]\n",
+      "[1, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f(1))\n",
+    "print(f(2))\n",
+    "print(f(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that each call does not create its own separate list.  Instead a single empty list was created when the function was first processed, and this list persists in memory as the default value for the optional argument `L`.  \n",
+    "\n",
+    "If we want a unique list created each time (e.g., a separate place in memory), we instead initialize the argument's value to `None` and then check its actual value and create an empty list in the function body itself if the default value was unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fnew(a, L=None):\n",
+    "    if L is None:\n",
+    "        L = []\n",
+    "    L.append(a)\n",
+    "    return L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fnew(1))\n",
+    "print(fnew(2))\n",
+    "print(fnew(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "L = fnew(1)\n",
+    "print(fnew(2, L=L))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the same `None` that we saw previously comes into play here.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lambdas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lambdas are \"disposible\" functions.  These are small, nameless functions that are often used as arguments in other functions.\n",
+    "\n",
+    "Ex, from the official tutorial: we have a list of tuples.  We want to sort the list based on the second item in the tuple.  The `sort` method can take a `key` optional argument that tells us how to interpret the list item for sorting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]\n",
+    "pairs.sort(key=lambda p: p[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we use a lambda in an extract from a list (with the filter function)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0,\n",
+       " 36,\n",
+       " 144,\n",
+       " 324,\n",
+       " 576,\n",
+       " 900,\n",
+       " 1296,\n",
+       " 1764,\n",
+       " 2304,\n",
+       " 2916,\n",
+       " 3600,\n",
+       " 4356,\n",
+       " 5184,\n",
+       " 6084,\n",
+       " 7056,\n",
+       " 8100,\n",
+       " 9216]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "squares = [x**2 for x in range(100)]\n",
+    "sq = list(filter(lambda x : x%2 == 0 and x%3 == 0, squares))\n",
+    "sq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on class filter in module builtins:\n",
+      "\n",
+      "class filter(object)\n",
+      " |  filter(function or None, iterable) --> filter object\n",
+      " |  \n",
+      " |  Return an iterator yielding those items of iterable for which function(item)\n",
+      " |  is true. If function is None, return the items that are true.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __getattribute__(self, name, /)\n",
+      " |      Return getattr(self, name).\n",
+      " |  \n",
+      " |  __iter__(self, /)\n",
+      " |      Implement iter(self).\n",
+      " |  \n",
+      " |  __next__(self, /)\n",
+      " |      Implement next(self).\n",
+      " |  \n",
+      " |  __reduce__(...)\n",
+      " |      Return state information for pickling.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Static methods defined here:\n",
+      " |  \n",
+      " |  __new__(*args, **kwargs) from builtins.type\n",
+      " |      Create and return a new object.  See help(type) for accurate signature.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(filter)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w4-python-classes.ipynb b/content/01-python/w4-python-classes.ipynb
new file mode 100644
index 00000000..6e539844
--- /dev/null
+++ b/content/01-python/w4-python-classes.ipynb
@@ -0,0 +1,833 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Classes are the fundamental concept for object oriented programming.  A class defines a data type with both data and functions that can operate on the data.  An object is an instance of a class.  Each object will have its own namespace (separate from other instances of the class and other functions, etc. in your program).\n",
+    "\n",
+    "We use the dot operator, `.`, to access members of the class (data or functions).  We've already been doing this a lot, strings, ints, lists, ... are all objects in python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Naming conventions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The python community has some naming convections, defined in PEP-8:\n",
+    "\n",
+    "https://www.python.org/dev/peps/pep-0008/\n",
+    "\n",
+    "The widely adopted ones are:\n",
+    "\n",
+    "* class names start with an uppercase, and use \"camelcase\" for multiword names, e.g. `ShoppingCart`\n",
+    "\n",
+    "* variable names (including objects which are instances of a class) are lowercase and use underscores to separate words, e.g., `shopping_cart`\n",
+    "\n",
+    "* module names should be lowercase with underscores\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A simple class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a class that holds some student info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Student:\n",
+    "    def __init__(self, name, grade=None):\n",
+    "        self.name = name\n",
+    "        self.grade = grade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has a function, `__init__()` which is called automatically when we create an instance of the class.  \n",
+    "\n",
+    "The argument `self` refers to the object that we will create, and points to the memory that they object will use to store the class's contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mike\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = Student(\"Mike\")\n",
+    "print(a.name)\n",
+    "print(a.grade)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a bunch of them, stored in a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students = []\n",
+    "students.append(Student(\"fry\", \"F-\"))\n",
+    "students.append(Student(\"leela\", \"A\"))\n",
+    "students.append(Student(\"zoidberg\", \"F\"))\n",
+    "students.append(Student(\"hubert\", \"C+\"))\n",
+    "students.append(Student(\"bender\", \"B\"))\n",
+    "students.append(Student(\"calculon\", \"C\"))\n",
+    "students.append(Student(\"amy\", \"A\"))\n",
+    "students.append(Student(\"hermes\", \"A\"))\n",
+    "students.append(Student(\"scruffy\", \"D\"))\n",
+    "students.append(Student(\"flexo\", \"F\"))\n",
+    "students.append(Student(\"morbo\", \"D\"))\n",
+    "students.append(Student(\"hypnotoad\", \"A+\"))\n",
+    "students.append(Student(\"zapp\", \"Q\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "Loop over the students in the `students` list and print out the name and grade of each student, one per line.\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use list comprehensions with our list of objects.  For example, let's find all the students who have A's"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['leela', 'amy', 'hermes', 'hypnotoad']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "As = [q.name for q in students if q.grade.startswith(\"A\")]\n",
+    "As"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Playing Cards"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a more complicated class that represents a playing card.  Notice that we are using unicode to represent the suits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Card:\n",
+    "    \n",
+    "    def __init__(self, suit=1, rank=2):\n",
+    "        if suit < 1 or suit > 4:\n",
+    "            print(\"invalid suit, setting to 1\")\n",
+    "            suit = 1\n",
+    "            \n",
+    "        self.suit = suit\n",
+    "        self.rank = rank\n",
+    "        \n",
+    "    def value(self):\n",
+    "        \"\"\" we want things order primarily by rank then suit \"\"\"\n",
+    "        return self.suit + (self.rank-1)*14\n",
+    "    \n",
+    "    # we include this to allow for comparisons with < and > between cards \n",
+    "    def __lt__(self, other):\n",
+    "        return self.value() < other.value()\n",
+    "\n",
+    "    def __eq__(self, other):\n",
+    "        return self.rank == other.rank and self.suit == other.suit\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return self.__str__()\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        suits = [u\"\\u2660\",  # spade\n",
+    "                 u\"\\u2665\",  # heart\n",
+    "                 u\"\\u2666\",  # diamond\n",
+    "                 u\"\\u2663\"]  # club\n",
+    "        \n",
+    "        r = str(self.rank)\n",
+    "        if self.rank == 11:\n",
+    "            r = \"J\"\n",
+    "        elif self.rank == 12:\n",
+    "            r = \"Q\"\n",
+    "        elif self.rank == 13:\n",
+    "            r = \"K\"\n",
+    "        elif self.rank == 14:\n",
+    "            r = \"A\"\n",
+    "                \n",
+    "        return r +':'+suits[self.suit-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can create a card easily."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c1 = Card()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can pass arguments to `__init__` in when we setup the class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c2 = Card(suit=2, rank=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have our object, we can access any of the functions in the class using the `dot` operator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c2.value()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "invalid suit, setting to 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "c3 = Card(suit=0, rank=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `__str__` method converts the object into a string that can be printed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2:♠\n",
+      "2:♥\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(c1)\n",
+    "print(c2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the value method assigns a value to the object that can be used in comparisons, and the `__lt__` method is what does the actual comparing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(c1 > c2)\n",
+    "print(c1 < c2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that not every operator is defined for our class, so, for instance, we cannot add two cards together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'Card' and 'Card'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mc1\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mc2\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Card' and 'Card'"
+     ]
+    }
+   ],
+   "source": [
+    "c1 + c2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    " * Create a \"hand\" corresponding to a straight (5 cards of any suite, but in sequence of rank)\n",
+    " * Create another hand corresponding to a flush (5 cards all of the same suit, of any rank)\n",
+    " * Finally create a hand with one of the cards duplicated&mdash;this should not be allowed in a standard deck of cards.  How would you check for this?\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can define operations like `+` and `-` that work on our objects.  Here's a simple example of currency&mdash;we keep track of the country and the amount"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Currency:\n",
+    "    \"\"\" a simple class to hold foreign currency \"\"\"\n",
+    "    \n",
+    "    def __init__(self, amount, country=\"US\"):\n",
+    "        self.amount = amount\n",
+    "        self.country = country\n",
+    "        \n",
+    "    def __add__(self, other):\n",
+    "        return Currency(self.amount + other.amount, country=self.country)\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        return Currency(self.amount - other.amount, country=self.country)\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return f\"{self.amount} {self.country}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now create some monetary amounts for different countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d1 = Currency(10, \"US\")\n",
+    "d2 = Currency(15, \"US\")\n",
+    "print(d2 - d1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise   \n",
+    "\n",
+    "As written, our Currency class has a bug&mdash;it does not check whether the amounts are in the same country before adding.  Modify the `__add__` method to first check if the countries are the same.  If they are, return the new `Currency` object with the sum, otherwise, return `None`.\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span class=\"fa fa-star\"></span> Vectors Example\n",
+    "\n",
+    "Here we write a class to represent 2-d vectors.  Vectors have a direction and a magnitude.  We can represent them as a pair of numbers, representing the x and y lengths.  We'll use a tuple internally for this\n",
+    "\n",
+    "We want our class to do all the basic operations we do with vectors: add them, multiply by a scalar, cross product, dot product, return the magnitude, etc.\n",
+    "\n",
+    "We'll use the math module to provide some basic functions we might need (like sqrt)\n",
+    "\n",
+    "This example will show us how to overload the standard operations in python.  Here's a list of the builtin methods:\n",
+    "\n",
+    "https://docs.python.org/3/reference/datamodel.html\n",
+    "\n",
+    "To make it really clear what's being called when, I've added prints in each of the functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "    \"\"\" a general two-dimensional vector \"\"\"\n",
+    "    \n",
+    "    def __init__(self, x, y):\n",
+    "        print(\"in __init__\")\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "        \n",
+    "    def __str__(self):\n",
+    "        print(\"in __str__\")        \n",
+    "        return f\"({self.x} î + {self.y} ĵ)\"\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        print(\"in __repr__\")        \n",
+    "        return f\"Vector({self.x}, {self.y})\"\n",
+    "\n",
+    "    def __add__(self, other):\n",
+    "        print(\"in __add__\")        \n",
+    "        if isinstance(other, Vector):\n",
+    "            return Vector(self.x + other.x, self.y + other.y)\n",
+    "        else:\n",
+    "            # it doesn't make sense to add anything but two vectors\n",
+    "            print(f\"we don't know how to add a {type(other)} to a Vector\")\n",
+    "            raise NotImplementedError\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        print(\"in __sub__\")        \n",
+    "        if isinstance(other, Vector):\n",
+    "            return Vector(self.x - other.x, self.y - other.y)\n",
+    "        else:\n",
+    "            # it doesn't make sense to add anything but two vectors\n",
+    "            print(f\"we don't know how to add a {type(other)} to a Vector\")\n",
+    "            raise NotImplementedError\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        print(\"in __mul__\")        \n",
+    "        if isinstance(other, int) or isinstance(other, float):\n",
+    "            # scalar multiplication changes the magnitude\n",
+    "            return Vector(other*self.x, other*self.y)\n",
+    "        else:\n",
+    "            print(\"we don't know how to multiply two Vectors\")\n",
+    "            raise NotImplementedError\n",
+    "\n",
+    "    def __matmul__(self, other):\n",
+    "        print(\"in __matmul__\")\n",
+    "        # a dot product\n",
+    "        if isinstance(other, Vector):\n",
+    "            return self.x*other.x + self.y*other.y\n",
+    "        else:\n",
+    "            print(\"matrix multiplication not defined\")\n",
+    "            raise NotImplementedError\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        print(\"in __rmul__\")        \n",
+    "        return self.__mul__(other)\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        print(\"in __truediv__\")        \n",
+    "        # we only know how to multiply by a scalar\n",
+    "        if isinstance(other, int) or isinstance(other, float):\n",
+    "            return Vector(self.x/other, self.y/other)\n",
+    "\n",
+    "    def __abs__(self):\n",
+    "        print(\"in __abs__\")        \n",
+    "        return math.sqrt(self.x**2 + self.y**2)\n",
+    "\n",
+    "    def __neg__(self):\n",
+    "        print(\"in __neg__\")        \n",
+    "        return Vector(-self.x, -self.y)\n",
+    "\n",
+    "    def cross(self, other):\n",
+    "        # a vector cross product -- we return the magnitude, since it will\n",
+    "        # be in the z-direction, but we are only 2-d \n",
+    "        return abs(self.x*other.y - self.y*other.x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a basic class that provides two methods `__str__` and `__repr__` to show a representation of it.  There was some discussion of this on slack.  These two functions provide a readable version of our object.\n",
+    "\n",
+    "The convection is what `__str__` is human readable while `__repr__` should be a form that can be used to recreate the object (e.g., via `eval()`).  See:\n",
+    "\n",
+    "http://stackoverflow.com/questions/1436703/difference-between-str-and-repr-in-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = Vector(1,2)\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vectors have a length, and we'll use the `abs()` builtin to provide the magnitude.  For a vector:\n",
+    "\n",
+    "$$\\vec{v} = \\alpha \\hat{i} + \\beta \\hat{j}$$\n",
+    "\n",
+    "we have\n",
+    "\n",
+    "$$|\\vec{v}| = \\sqrt{\\alpha^2 + \\beta^2}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abs(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at mathematical operations on vectors now.  We want to be able to add and subtract two vectors as well as multiply and divide by a scalar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u = Vector(3,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = u + v\n",
+    "print(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u - v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It doesn't make sense to add a scalar to a vector, so we didn't implement this -- what happens?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u + 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now multiplication.  It makes sense to multiply by a scalar, but there are multiple ways to define multiplication of two vectors.  \n",
+    "\n",
+    "Note that python provides both a `__mul__` and a `__rmul__` function to define what happens when we multiply a vector by a quantity and what happens when we multiply something else by a vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u*2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2.0*u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and division: `__truediv__` is the python 3 way of division `/`, while `__floordiv__` is the old python 2 way, also enabled via `//`.\n",
+    "\n",
+    "Dividing a scalar by a vector doesn't make sense:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u/5.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "5.0/u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python 3.5 introduced a new matrix multiplication operator, `@` -- we'll use this to implement a dot product between two vectors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u @ v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a cross product, we don't have an obvious operator, so we'll use a function.  For 2-d vectors, this will result in a scalar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u.cross(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, negation is a separate operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "-u"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w4-python-exercises.ipynb b/content/01-python/w4-python-exercises.ipynb
new file mode 100644
index 00000000..9308beb6
--- /dev/null
+++ b/content/01-python/w4-python-exercises.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practicing Classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(w4-exercise-1)=\n",
+    "## Exercise 1 (shopping cart)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write a simple shopping cart class -- this will hold items that you intend to purchase as well as the amount, etc.  And allow you to add / remove items, get a subtotal, etc.\n",
+    "\n",
+    "We'll use two classes: `Item` will be a single item and `ShoppingCart` will be the collection of items you wish to purchase.\n",
+    "\n",
+    "First, our store needs an inventory -- here's what we have for sale:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INVENTORY_TEXT = \"\"\"\n",
+    "apple, 0.60\n",
+    "banana, 0.20\n",
+    "grapefruit, 0.75\n",
+    "grapes, 1.99\n",
+    "kiwi, 0.50\n",
+    "lemon, 0.20\n",
+    "lime, 0.25\n",
+    "mango, 1.50\n",
+    "papaya, 2.95\n",
+    "pineapple, 3.50\n",
+    "blueberries, 1.99\n",
+    "blackberries, 2.50\n",
+    "peach, 0.50\n",
+    "plum, 0.33\n",
+    "clementine, 0.25\n",
+    "cantaloupe, 3.25\n",
+    "pear, 1.25\n",
+    "quince, 0.45\n",
+    "orange, 0.60\n",
+    "\"\"\"\n",
+    "\n",
+    "# this will be a global -- convention is all caps\n",
+    "INVENTORY = {}\n",
+    "for line in INVENTORY_TEXT.splitlines():\n",
+    "    if line.strip() == \"\":\n",
+    "        continue\n",
+    "    item, price = line.split(\",\")\n",
+    "    INVENTORY[item] = float(price)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INVENTORY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `Item` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the start of an item class -- we want it to hold the name and quantity.  \n",
+    "\n",
+    "You should have the following features:\n",
+    "\n",
+    "* the name should be something in our inventory\n",
+    "\n",
+    "* Our shopping cart will include a list of all the items we want to buy, so we want to be able to check for duplicates.  Implement the equal test, `==`, using `__eq__`\n",
+    "\n",
+    "* we'll want to consolidate dupes, so implement the `+` operator, using `__add__` so we can add items together in our shopping cart.  Note, add should raise a ValueError if you try to add two `Items` that don't have the same name.\n",
+    "\n",
+    "Here's a start:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Item:\n",
+    "    \"\"\" an item to buy \"\"\"\n",
+    "    \n",
+    "    def __init__(self, name, quantity=1):\n",
+    "        \"\"\"keep track of an item that is in our inventory\"\"\"\n",
+    "        if name not in INVENTORY:\n",
+    "            raise ValueError(\"invalid item name\")\n",
+    "        self.name = name\n",
+    "        self.quantity = quantity\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        return f\"{self.name}: {self.quantity}\"\n",
+    "        \n",
+    "    def __eq__(self, other):\n",
+    "        \"\"\"check if the items have the same name\"\"\"\n",
+    "        return self.name == other.name\n",
+    "    \n",
+    "    def __add__(self, other):\n",
+    "        \"\"\"add two items together if they are the same type\"\"\"\n",
+    "        if self.name == other.name:\n",
+    "            return Item(self.name, self.quantity + other.quantity)\n",
+    "        else:\n",
+    "            raise ValueError(\"names don't match\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are some tests your code should pass:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = Item(\"apple\", 10)\n",
+    "b = Item(\"banana\", 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = Item(\"apple\", 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# won't work\n",
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# will work\n",
+    "a += c\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = Item(\"dog\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should be False\n",
+    "a == b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should be True -- they have the same name\n",
+    "a == c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do they behave in a list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items = []\n",
+    "items.append(a)\n",
+    "items.append(b)\n",
+    "items"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# should be True -- they have the same name\n",
+    "c in items"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `ShoppingCart`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to create a shopping cart.  The main thing it will do is hold a list of items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ShoppingCart:\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        # the list of items we control\n",
+    "        self.items = []\n",
+    "        \n",
+    "    def subtotal(self):\n",
+    "        \"\"\" return a subtotal of our items \"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def add(self, name, quantity):\n",
+    "        \"\"\" add an item to our cart -- the an item of the same name already\n",
+    "        exists, then increment the quantity.  Otherwise, add a new item\n",
+    "        to the cart with the desired quantity.\"\"\"\n",
+    "        pass\n",
+    "        \n",
+    "    def remove(self, name):\n",
+    "        \"\"\" remove all of item name from the cart \"\"\"\n",
+    "        pass\n",
+    "        \n",
+    "    def report(self):\n",
+    "        \"\"\" print a summary of the cart \"\"\"\n",
+    "        for item in self.items:\n",
+    "            print(f\"{item.name} : {item.quantity}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are some tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = ShoppingCart()\n",
+    "sc.add(\"orange\", 19)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.add(\"apple\", 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.report()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.add(\"apple\", 9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apple should only be listed once in the report, with a quantity of 11\n",
+    "sc.report()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.subtotal()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.remove(\"apple\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apple should no longer be listed\n",
+    "sc.report()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: Poker Odds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the deck of cards class from the notebook we worked through outside of class to write a _Monte Carlo_ code that plays a lot of hands of straight poker (like 100,000).  Count how many of these hands has a particular poker hand (like 3-of-a-kind).  The ratio of # of hands with 3-of-a-kind to total hands is an approximation to the odds of getting a 3-of-a-kind in poker.\n",
+    "\n",
+    "You'll want to copy-paste those classes into a `.py` file to allow you to import and reuse them here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: Tic-Tac-Toe\n",
+    "\n",
+    "Revisit the tic-tac-toe game you developed in the functions exercises but now write it as a class with methods to do each of the main steps.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w4-python-modules.ipynb b/content/01-python/w4-python-modules.ipynb
new file mode 100644
index 00000000..187eb7cc
--- /dev/null
+++ b/content/01-python/w4-python-modules.ipynb
@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we import our own module called `myprofile` (we have it in the same directory as this notebook)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import myprofile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a docstring at the top -- the comments there are what appear when we ask for help"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(myprofile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This module simply provides a way to time routines (python and ipython have built-in methods for this too)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = myprofile.Timer(\"main loop\")\n",
+    "t.begin()\n",
+    "\n",
+    "sum = 0.0\n",
+    "for n in range(1000):\n",
+    "    sum += n**2\n",
+    "\n",
+    "\n",
+    "t.end()\n",
+    "myprofile.time_report()\n",
+    "\n",
+    "print(sum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the file `myprofile.py`, you will see a block of code under\n",
+    "\n",
+    "```\n",
+    "if __name__ == \"__main__\":\n",
+    "```\n",
+    "\n",
+    "That code is executed if the file is run directly, either from the commandline as:\n",
+    "\n",
+    "`python myprofile.py`\n",
+    "\n",
+    "for through the `%run` magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run myprofile"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/01-python/w5-python-more-examples.ipynb b/content/01-python/w5-python-more-examples.ipynb
new file mode 100644
index 00000000..82a82920
--- /dev/null
+++ b/content/01-python/w5-python-more-examples.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: Rock-Paper-Scissors\n",
+    "\n",
+    "Implement a set of games of rock-paper-scissors against the computer.  \n",
+    "\n",
+    "  * Ask for input from the user (\"rock\", \"paper\", or \"scissors\") and the randomly select one of these for the computer's play.\n",
+    "  * Announce who won.\n",
+    "  * Keep playing until a player says that they no longer want to play.\n",
+    "  * When all games are done, print out how many games were won by the player and by the computer "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: Pascal's triangle\n",
+    "\n",
+    "Pascal's triangle is created such that each layer has 1 more element than the previous, with `1`s on the side and in the interior, the numbers are the sum of the two above it, e.g.,:\n",
+    "```\n",
+    "            1\n",
+    "          1   1\n",
+    "        1   2   1\n",
+    "      1   3   3   1\n",
+    "    1   4   6   4   1\n",
+    "  1   5   10  10  5   1\n",
+    "```\n",
+    "\n",
+    "Write a function to return the first `n` rows of Pascal's triangle.  The return should be a list of length `n`, with each element itself a list containing the numbers for that row.\n",
+    "\n",
+    "<span class=\"fa fa-star\" /> If you want to add complexity, write a function to print out Pascal's triangle with proper formatting, so the numbers in each row are centered between the ones in the row above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: Panagrams\n",
+    "\n",
+    "A _panagram_ is a sentence that includes all 26 letters of the alphabet, e.g., \"_The quick brown fox jumps over the lazy dog_.\"\n",
+    "\n",
+    "Write a function that takes as an argument a sentence and returns `True` or `False`, indicating whether the sentence is a panagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: Math practice\n",
+    "\n",
+    "We want to make a simple table of trigonometric functions for different angles.  Write a code that outputs in columns, the following data:\n",
+    "```\n",
+    "angle (degrees)    angle (radians)     sin(angle)     cos(angle)    sin(angle)**2 + cos(angle)**2\n",
+    "```\n",
+    "\n",
+    "For all angles spaced 30 degrees apart in the range 0 to 360 degrees.\n",
+    "\n",
+    "Keep in mind that the trig functions expect the input in radians."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5: Calendar events\n",
+    "\n",
+    "We want to keep a schedule of events.  We will do this by creating a class called `Day`.  It is sketched out below.  A `Day` holds a list of events and has methods that allow you to add an delete events.  Our events will be instances of a class `Event`, which holds the time, location, and description of the event.\n",
+    "\n",
+    "Finally, we can keep track of a list of all the `Day`s for which we have events to make our schedule.\n",
+    "\n",
+    "Fill in these classes and write some code to demonstrate their use:\n",
+    "\n",
+    "  * Create a full week of days in your calendar\n",
+    "  * Add an event every day at noon called \"lunch\"\n",
+    "  * Randomly add some other events to fill out your calendar\n",
+    "  * Write some code that tells you the start time of your first meeting and the end time of your last meeting (this is the length of your work day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Day:\n",
+    "    \"\"\"a single day keeping track of the events scheduled\"\"\"\n",
+    "    def __init__(month, day, year):\n",
+    "        # store the month, day, and year as data in the class\n",
+    "        \n",
+    "        # keep track of the events\n",
+    "        self.events = []\n",
+    "    \n",
+    "    def add_event(name, time=None, location=None):\n",
+    "        pass\n",
+    "    \n",
+    "    def delete_event(name):\n",
+    "        pass\n",
+    "    \n",
+    "    \n",
+    "class Event:\n",
+    "    \"\"\"a single event in our calendar\"\"\"\n",
+    "    def __init__(name, time=9, location=None, duration=1):\n",
+    "        self.name = name\n",
+    "        self.time = time\n",
+    "        self.location = location\n",
+    "        self.duration = duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/02-numpy/numpy-advanced.ipynb b/content/02-numpy/numpy-advanced.ipynb
new file mode 100644
index 00000000..bf09cdb1
--- /dev/null
+++ b/content/02-numpy/numpy-advanced.ipynb
@@ -0,0 +1,941 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NumPy Advanced Operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Copying Arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{important}\n",
+    "Simply using `=` does not make a copy, but much like with lists, you will just have multiple names pointing to the same ndarray object\n",
+    "\n",
+    "Therefore, we need to understand if two arrays, `A` and `B` point to:\n",
+    "* the same array, including shape and data/memory space\n",
+    "* the same data/memory space, but perhaps different shapes (a _view_)\n",
+    "* a separate copy of the data (i.e. stored completely separately in memory)\n",
+    "\n",
+    "All of these are possible.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All of these are possible:\n",
+    "\n",
+    "* `B = A`\n",
+    "  \n",
+    "  this is _assignment_.  No copy is made. `A` and `B` point to the same data in memory and share the same shape, etc.  They are just two different labels for the same object in memory\n",
+    "  \n",
+    "\n",
+    "* `B = A[:]`\n",
+    "\n",
+    "  this is a _view_ or _shallow copy_.  The shape info for A and B are stored independently, but both point to the same memory location for data\n",
+    "  \n",
+    "  \n",
+    "* `B = A.copy()`\n",
+    "\n",
+    "  this is a _deep_ copy.  A completely separate object will be created in memory, with a completely separate location in memory.\n",
+    "  \n",
+    "Let's look at examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(10)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is assignment&mdash;we can just use the `is` operator to test for equality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = a\n",
+    "b is a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since `b` and `a` are the same, changes to the shape of one are reflected in the other&mdash;no copy is made."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2 3 4]\n",
+      " [5 6 7 8 9]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(2, 5)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape = (2, 5)\n",
+    "print(b)\n",
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b is a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3, 4],\n",
+       "       [5, 6, 7, 8, 9]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a shallow copy creates a new *view* into the array&mdash;the _data_ is the same, but the array properties can be different"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]]\n",
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.arange(12)\n",
+    "c = a[:]\n",
+    "a.shape = (3,4)\n",
+    "\n",
+    "print(a)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "since the underlying data is the same memory, changing an element of one is reflected in the other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0, -1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[1] = -1\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even slices into an array are just views, still pointing to the same memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 4, 5, 6, 7])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = c[3:8]\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d[:] = 0 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0 -1  2  0]\n",
+      " [ 0  0  0  0]\n",
+      " [ 8  9 10 11]]\n",
+      "[ 0 -1  2  0  0  0  0  0  8  9 10 11]\n",
+      "[0 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)\n",
+    "print(c)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are lots of ways to inquire if two arrays are the same, views, own their own data, etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "True\n",
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(c is a)\n",
+    "print(c.base is a)\n",
+    "print(c.flags.owndata)\n",
+    "print(a.flags.owndata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "to make a copy of the data of the array that you can deal with independently of the original, you need a _deep copy_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0 -1  2  0]\n",
+      " [ 0  0  0  0]\n",
+      " [ 8  9 10 11]]\n",
+      "[[0 0 0 0]\n",
+      " [0 0 0 0]\n",
+      " [0 0 0 0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = a.copy()\n",
+    "d[:,:] = 0.0\n",
+    "\n",
+    "print(a)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boolean Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are lots of fun ways to index arrays to access only those elements that meet a certain condition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(12).reshape(3,4)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we set all the elements in the array that are > 4 to zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3],\n",
+       "       [4, 0, 0, 0],\n",
+       "       [0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[a > 4] = 0\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and now, all the zeros to -1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1,  1,  2,  3],\n",
+       "       [ 4, -1, -1, -1],\n",
+       "       [-1, -1, -1, -1]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[a == 0] = -1\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True, False, False, False],\n",
+       "       [False,  True,  True,  True],\n",
+       "       [ True,  True,  True,  True]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a == -1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if we have 2 tests, we need to use `logical_and()` or `logical_or()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(12).reshape(3,4)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[100,   1,   2,   3],\n",
+       "       [100, 100, 100, 100],\n",
+       "       [100, 100,  10,  11]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[np.logical_or(a == 0, np.logical_and(a > 3, a <= 9))] = 100.0\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our test that we index the array with returns a boolean array of the same shape:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True, False, False, False],\n",
+       "       [ True,  True,  True,  True],\n",
+       "       [ True,  True,  True,  True]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a > 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Avoiding Loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general, you want to avoid loops over elements on an array.\n",
+    "\n",
+    "Here, let's create 1-d x and y coordinates and then try to fill some larger array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(128,)\n",
+      "(256,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "M = 128\n",
+    "N = 256\n",
+    "xmin = ymin = 0.0\n",
+    "xmax = ymax = 1.0\n",
+    "\n",
+    "x = np.linspace(xmin, xmax, M, endpoint=False)\n",
+    "y = np.linspace(ymin, ymax, N, endpoint=False)\n",
+    "\n",
+    "print(x.shape)\n",
+    "print(y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we'll time out code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time elapsed: 0.05113625526428223 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "t0 = time.time()\n",
+    "\n",
+    "g = np.zeros((M, N))\n",
+    "\n",
+    "for j in range(N):\n",
+    "    for i in range(M):\n",
+    "\n",
+    "        g[i,j] = np.sin(2.0*np.pi*x[i]*y[j])\n",
+    "        \n",
+    "t1 = time.time()\n",
+    "print(f\"time elapsed: {t1-t0} s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's instead do this using all array syntax.  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "First will extend our 1-d coordinate arrays to be 2-d.  \n",
+    "NumPy has a function for this (`meshgrid()`)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at how `meshgrid()` works first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0],\n",
+       "       [1, 1, 1],\n",
+       "       [2, 2, 2],\n",
+       "       [3, 3, 3]])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2d, y2d = np.meshgrid([0, 1, 2, 3], [10, 20, 30], indexing=\"ij\")\n",
+    "x2d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 20, 30],\n",
+       "       [10, 20, 30],\n",
+       "       [10, 20, 30],\n",
+       "       [10, 20, 30]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y2d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that this creates 2 two-dimensional arrays, one with the x-values changing across rows and one with y-values changing across columns.  This means we can index all points\n",
+    "(`x[i]`, `y[j]`) through the pair `x2d`, `y2d`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's do our same example this method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "time elapsed: 0.0024487972259521484 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "t0 = time.time()\n",
+    "x2d, y2d = np.meshgrid(x, y, indexing=\"ij\")\n",
+    "g2 = np.sin(2.0*np.pi*x2d*y2d)\n",
+    "t1 = time.time()\n",
+    "print(f\"time elapsed: {t1-t0} s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A final check to make sure they give the same answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.max(np.abs(g2-g)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numerical differencing on NumPy arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to construct a derivative, \n",
+    "\n",
+    "$$\n",
+    "\\frac{d f}{dx}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 2*np.pi, 25)\n",
+    "f = np.sin(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to do this without loops&mdash;we'll use views into arrays offset from one another.  Recall from calculus that a derivative is approximately:\n",
+    "\n",
+    "$$\n",
+    "\\frac{df}{dx} = \\frac{f(x+h) - f(x)}{h}\n",
+    "$$\n",
+    "\n",
+    "Here, we'll take $h$ to be a single adjacent element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dx = x[1] - x[0]\n",
+    "dfdx = (f[1:] - f[:-1])/dx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.98861593,  0.92124339,  0.79108963,  0.60702442,  0.38159151,\n",
+       "        0.13015376, -0.13015376, -0.38159151, -0.60702442, -0.79108963,\n",
+       "       -0.92124339, -0.98861593, -0.98861593, -0.92124339, -0.79108963,\n",
+       "       -0.60702442, -0.38159151, -0.13015376,  0.13015376,  0.38159151,\n",
+       "        0.60702442,  0.79108963,  0.92124339,  0.98861593])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfdx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9886159294653692\n",
+      "0.9212433876373748\n",
+      "0.7910896313685742\n",
+      "0.6070244240594342\n",
+      "0.38159150540593506\n",
+      "0.13015375626880096\n",
+      "-0.13015375626880055\n",
+      "-0.38159150540593506\n",
+      "-0.6070244240594342\n",
+      "-0.7910896313685731\n",
+      "-0.9212433876373752\n",
+      "-0.9886159294653699\n",
+      "-0.9886159294653681\n",
+      "-0.9212433876373755\n",
+      "-0.7910896313685738\n",
+      "-0.6070244240594346\n",
+      "-0.38159150540593545\n",
+      "-0.1301537562688014\n",
+      "0.13015375626880013\n",
+      "0.3815915054059342\n",
+      "0.607024424059435\n",
+      "0.7910896313685731\n",
+      "0.9212433876373736\n",
+      "0.9886159294653716\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(x)-1):\n",
+    "    print((f[i+1] - f[i]) / dx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/02-numpy/numpy-basics.ipynb b/content/02-numpy/numpy-basics.ipynb
new file mode 100644
index 00000000..0658305f
--- /dev/null
+++ b/content/02-numpy/numpy-basics.ipynb
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NumPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is based on the SciPy NumPy tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{note}\n",
+    "    \n",
+    "Note that the traditional way to import numpy is to rename it `np`.  This saves on typing and makes your code a little more compact.\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy provides a multidimensional array class as well as a _large_ number of functions that operate on arrays.\n",
+    "\n",
+    "NumPy arrays allow you to write fast (optimized) code that works on arrays of data.  To do this, there are some restrictions on arrays:\n",
+    "\n",
+    "* all elements are of the same data type (e.g. float)\n",
+    "* the size of the array is fixed in memory, and specified when you create the array (e.g., you cannot grow the array like you do with lists)\n",
+    "\n",
+    "The nice part is that arithmetic operations work on entire arrays&mdash;this means that you can avoid writing loops in python (which tend to be slow).  Instead the \"looping\" is done in the underlying compiled code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Array Creation and Properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a lot of ways to create arrays.  Let's look at a few\n",
+    "\n",
+    "Here we create an array using `arange` and then change its shape to be 3 rows and 5 columns.  Note the _row-major ordering_&mdash;you'll see that the rows are together in the inner `[]` (more on this in a bit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(15).reshape(3,5)\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "an array is an object of the `ndarray` class, and it has methods that know how to work on the array data.  Here, `.reshape()` is one of the many methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A NumPy array has a lot of meta-data associated with it describing its shape, datatype, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(a.ndim)\n",
+    "print(a.shape)\n",
+    "print(a.size)\n",
+    "print(a.dtype)\n",
+    "print(a.itemsize)\n",
+    "print(type(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#help(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can also create an array from a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array( [1.0, 2.0, 3.0, 4.0] )\n",
+    "print(b)\n",
+    "print(b.dtype)\n",
+    "print(type(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can create a multi-dimensional array of a specified size initialized all to 0 easily, using the `zeros()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.zeros((10,8))\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also an analogous `ones()` and `empty()` array routine.  Note that here we can explicitly set the datatype for the array in this function if we wish. \n",
+    "\n",
+    "Unlike lists in python, all of the elements of a numpy array are of the same datatype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = np.eye(10, dtype=np.float64)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`linspace` creates an array with evenly space numbers.  The `endpoint` optional argument specifies whether the upper range is in the array or not"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = np.linspace(0, 1, 10, endpoint=False)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "Analogous to `linspace()`, there is a `logspace()` function that creates an array with elements equally spaced in log.  Use `help(np.logspace)` to see the arguments, and create an array with 10 elements from $10^{-6}$ to $10^3$.\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Array Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "most operations (`+`, `-`, `*`, `/`) will work on an entire array at once, element-by-element.\n",
+    "\n",
+    "Note that that the multiplication operator is not a matrix multiply, but `@` will do matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a simply array to start with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(12).reshape(3,4)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multiplication by a scalar multiplies every element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a*2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "adding two arrays adds element-by-element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a + a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "multiplying two arrays multiplies element-by-element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "a*a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "What do you think `1./a` will do?  Try it and see\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can think of our 2-d array as a 3 x 4 matrix (3 rows, 4 columns).  We can take the transpose to geta 4 x 3 matrix, and then we can do a matrix multiplication"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can do some basic linear algebra operations on arrays.  The operator `@` is a matrix multiplication / dot-product operator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = a.transpose()\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a @ b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can sum the elements of an array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "`sum()` takes an optional argument, `axis=N`, where `N` is the axis to sum over.  Sum the elements of `a` across rows to create an array with just the sum along each column of `a`.\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also easily get the extrema"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(a.min(), a.max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## universal functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "universal functions work element-by-element.  Let's create a new array scaled by `pi / 12`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = a*np.pi/12.0\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = np.cos(b)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = b + c "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "We will often want to write our own function that operates on an array and returns a new array.  We can do this just like we did with functions previously&mdash;the key is to use the methods from the `np` module to do any operations, since they work on, and return, arrays.\n",
+    "\n",
+    "Write a simple function that returns $\\sin(2\\pi x)$ for an input array `x`.  Then test it \n",
+    "by passing in an array `x` that you create via `linspace()`\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "slicing works very similarly to how we saw with strings.  Remember, python uses 0-based indexing\n",
+    "\n",
+    "Consider the following array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(9)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now look at accessing a single element vs. a range (using slicing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Giving a single (0-based) index just references a single value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we do a range, like `a[2:5]`, then we get the elements starting at index 2 and up to, *but not including* index 5.\n",
+    "\n",
+    "That is, slicing uses the interval: [begin, end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[2:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[2:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `:` can be used to specify all of the elements in that dimension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multidimensional Arrays\n",
+    "\n",
+    "Multidimensional arrays are stored in a contiguous space in memory -- this means that the columns / rows need to be unraveled (flattened) so that it can be thought of as a single one-dimensional array.  Different programming languages do this via different conventions:\n",
+    "\n",
+    "![](row_column_major.png)\n",
+    "\n",
+    "Storage order:\n",
+    "\n",
+    "* Python/C use *row-major* storage: rows are stored one after the other\n",
+    "* Fortran/matlab use *column-major* storage: columns are stored one after another\n",
+    "\n",
+    "The ordering matters when \n",
+    "\n",
+    "* passing arrays between languages (we'll talk about this later this semester)\n",
+    "* looping over arrays -- you want to access elements that are next to one-another in memory\n",
+    "  * e.g, in Fortran:\n",
+    "  \n",
+    "    ```\n",
+    "    double precision :: A(M,N)\n",
+    "    do j = 1, N\n",
+    "       do i = 1, M\n",
+    "          A(i,j) = …\n",
+    "       enddo\n",
+    "    enddo\n",
+    "    ```\n",
+    "  \n",
+    "  * in C\n",
+    "  \n",
+    "    ```\n",
+    "    double A[M][N];\n",
+    "    for (i = 0; i < M; i++) {\n",
+    "       for (j = 0; j < N; j++) {\n",
+    "          A[i][j] = …\n",
+    "       }\n",
+    "    }  \n",
+    "    ```\n",
+    "  \n",
+    "\n",
+    "In python, using NumPy, we'll try to avoid explicit loops over elements as much as possible\n",
+    "\n",
+    "Let's look at multidimensional arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(15).reshape(3,5)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the output of `a` shows the row-major storage.  The rows are grouped together in the inner `[...]`\n",
+    "\n",
+    "Giving a single index (0-based) for each dimension just references a single value in the array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[1,1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Doing slices will access a range of elements.  Think of the start and stop in the slice as referencing the left-edge of the slots in the array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[0:2,0:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Access a specific column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a[:,1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes we want a one-dimensional view into the array -- here we see the memory layout (row-major) more explicitly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.flatten()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can also iterate -- this is done over the first axis (rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for row in a:\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "or element by element"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for e in a.flat:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generally speaking, we want to avoid looping over the elements of an array in python&mdash;this is slow.  Instead we want to write and use functions that operate on the entire array at once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{admonition} Quick Exercise\n",
+    "\n",
+    "Consider the array defined as:\n",
+    "\n",
+    "```\n",
+    "\n",
+    " q = np.array([[1, 2, 3, 2, 1],\n",
+    "               [2, 4, 4, 4, 2],\n",
+    "               [3, 4, 4, 4, 3],\n",
+    "               [2, 4, 4, 4, 2],\n",
+    "               [1, 2, 3, 2, 1]])\n",
+    " \n",
+    "```\n",
+    "\n",
+    "  * using slice notation, create an array that consists of only the `4`'s in `q` (this will be called a _view_, as we'll see shortly)\n",
+    "  * zero out all of the elements in your view\n",
+    "  * how does `q` change?\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/02-numpy/numpy-exercises.ipynb b/content/02-numpy/numpy-exercises.ipynb
new file mode 100644
index 00000000..1a352e89
--- /dev/null
+++ b/content/02-numpy/numpy-exercises.ipynb
@@ -0,0 +1,233 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NumPy exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some  of these come from / are inspired from https://github.com/rougier/numpy-100 and http://www.scipy-lectures.org/intro/numpy/exercises.html\n",
+    "\n",
+    "You might want to look over these lists as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1\n",
+    "\n",
+    "We can use `np.random.random_sample()` to create an array with random values.  By default, these will be in the range `[0.0, 1.0)`.  You can\n",
+    "multiple the output and add a scalar to it to get it to be in a different range.\n",
+    "\n",
+    "Create a 10 x 10 array initialized with random numbers that lie between 0 and 10.\n",
+    "\n",
+    "Then compute the average of the array (there is a numpy function for this, `np.mean()`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2\n",
+    "\n",
+    "Create the array: \n",
+    "```\n",
+    "[[1,  6, 11],\n",
+    " [2,  7, 12],\n",
+    " [3,  8, 13],\n",
+    " [4,  9, 14],\n",
+    " [5, 10, 15]]\n",
+    "```\n",
+    "with out explicitly typing it in.\n",
+    "\n",
+    "Now create a new array containing only its 2nd and 4th rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q3\n",
+    "\n",
+    "Create a 2d array with `1` on the border and `0` on the inside, e.g., like:\n",
+    "```\n",
+    "1 1 1 1 1\n",
+    "1 0 0 0 1\n",
+    "1 0 0 0 1\n",
+    "1 1 1 1 1\n",
+    "```\n",
+    "\n",
+    "Do this using array slice notation to let it work for an arbitrary-sized array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q4\n",
+    "\n",
+    "  * Create an array with angles in degrees 0, 15, 30, ... 90 (i.e., every 15 degrees up to 90).\n",
+    "\n",
+    "  * Now create 3 new arrays with the sine, cosine, and tangent of the elements of the first array\n",
+    "  \n",
+    "  * Finally, calculate the inverse sine, inverse cosine, and inverse tangent the arrays above and compare to the original angles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q5\n",
+    "\n",
+    "Given the array:\n",
+    "```\n",
+    "x = np.array([1, -1, 2, 5, 8, 4, 10, 12, 3])\n",
+    "```\n",
+    "calculate the difference of each element with its neighbor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q6\n",
+    "\n",
+    "Here we will read in columns of numbers from a file and create a histogram, using NumPy routines.  Make sure you have the data file\n",
+    "\"`sample.txt`\" in the same directory as this notebook (you can download it from  https://raw.githubusercontent.com/sbu-python-summer/python-tutorial/master/day-3/sample.txt\n",
+    "\n",
+    "  * Use `np.loadtxt()` to read this file in.  \n",
+    "\n",
+    "  * Next, use `np.histogram()` to create a histogram array.  The output returns both the count and an array of edges.\n",
+    "  \n",
+    "  * Finally, loop over the bins and print out the bin center (averaging the left and right edges of the bin) and the count for that bin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q7\n",
+    "\n",
+    "NumPy has a standard deviation function, `np.std()`, but here we'll write our own that works on a 1-d array (vector).  The standard\n",
+    "deviation is a measure of the \"width\" of the distribution of numbers\n",
+    "in the vector.\n",
+    "\n",
+    "Given an array, $a$, and an average $\\bar{a}$, the standard deviation\n",
+    "is:\n",
+    "\n",
+    "$$\n",
+    "\\sigma = \\left [ \\frac{1}{N} \\sum_{i=1}^N (a_i - \\bar{a})^2 \\right ]^{1/2}\n",
+    "$$\n",
+    "\n",
+    "Write a function to calculate the standard deviation for an input array, `a`:\n",
+    "\n",
+    "  * First compute the average of the elements in `a` to define $\\bar{a}$\n",
+    "  * Next compute the sum over the squares of $a - \\bar{a}$\n",
+    "  * Then divide the sum by the number of elements in the array\n",
+    "  * Finally take the square root (you can use `np.sqrt()`)\n",
+    "  \n",
+    "Test your function on a random array, and compare to the built-in `np.std()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/02-numpy/numpy.md b/content/02-numpy/numpy.md
new file mode 100644
index 00000000..19ab63c9
--- /dev/null
+++ b/content/02-numpy/numpy.md
@@ -0,0 +1,3 @@
+# NumPy
+
+The NumPy library provides a class for n-dimensional arrays of data.
diff --git a/content/02-numpy/row_column_major.png b/content/02-numpy/row_column_major.png
new file mode 100644
index 00000000..a31c4ada
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diff --git a/content/02-numpy/sample.txt b/content/02-numpy/sample.txt
new file mode 100644
index 00000000..2c311fcd
--- /dev/null
+++ b/content/02-numpy/sample.txt
@@ -0,0 +1,100 @@
+0 -4.756772745910339
+1 6.889533541096673
+2 8.996896092374172
+3 -1.7461017125141964
+4 1.0304402925205614
+5 4.635768431188638
+6 5.177225916739488
+7 15.964690203916664
+8 -12.021098174294892
+9 -18.10087879725122
+10 -25.178886598285644
+11 4.518925348029733
+12 9.043916771177502
+13 -0.5221535906039717
+14 -3.1338886609447583
+15 -6.914714941474996
+16 -6.314263548047582
+17 6.706901891085466
+18 -25.971636440421232
+19 -4.435372425747847
+20 0.4889050161324564
+21 -0.036004810755168606
+22 -4.372545660650671
+23 0.4047031759550273
+24 3.090597990424291
+25 19.306526596830842
+26 6.48611581937894
+27 -10.781128554191731
+28 -12.85791882558559
+29 -1.0278401126239605
+30 -30.58842788782183
+31 -6.517109634143572
+32 -7.489210194855196
+33 -11.105721713012358
+34 4.120721916704712
+35 -3.153239578756935
+36 1.4135041696471733
+37 22.583374665055885
+38 -12.27556599640208
+39 -4.793102896606837
+40 -7.128537547009972
+41 -17.279019221677267
+42 19.655635821341445
+43 -1.2745066768475497
+44 3.738058165511088
+45 10.481959096601695
+46 -12.814187304346124
+47 -22.424530149878166
+48 4.137834007998959
+49 -0.04131996932026964
+50 7.375298054261892
+51 -4.098385522108854
+52 -5.599571617055626
+53 -13.253443154026934
+54 16.290743431455287
+55 10.424184766192798
+56 -4.27251751782754
+57 19.01684691299122
+58 3.15448738602801
+59 -9.704807918052413
+60 -0.0008101176931862717
+61 6.104424953162043
+62 24.47811290783359
+63 14.846618329919092
+64 -2.186378655814549
+65 9.86177633783792
+66 12.724976950604734
+67 4.037245731028271
+68 -0.656843975486256
+69 15.37262575716834
+70 -7.880872346846276
+71 2.54070374695715
+72 -1.022544832116583
+73 20.91034858050119
+74 9.222541985448974
+75 -11.056628565277206
+76 27.93950830400997
+77 -6.032857683782997
+78 18.098345716611153
+79 38.59487842850004
+80 -0.8720942826849941
+81 1.7229720788142675
+82 -4.174088180887814
+83 -8.318962830280652
+84 14.512558782294388
+85 5.630837289759344
+86 13.097667005419458
+87 9.517210836578036
+88 -3.2730322970019206
+89 14.22975371499751
+90 -12.903078745763068
+91 10.69546941555894
+92 -6.12535752853502
+93 -0.0538615350527278
+94 8.344238543768178
+95 12.569544805707704
+96 -1.5730683048208713
+97 9.588349649640545
+98 -17.791062662128468
+99 -4.613995471504272
diff --git a/content/02-numpy/slicing.png b/content/02-numpy/slicing.png
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diff --git a/content/03-practices/git-single.md b/content/03-practices/git-single.md
new file mode 100644
index 00000000..a82a2436
--- /dev/null
+++ b/content/03-practices/git-single.md
@@ -0,0 +1,162 @@
+A single-user interaction with git:
+
+* Make your project
+
+  We'll start by making our project directory and moving into it:
+
+  ```
+  $ mkdir myproject
+  $ cd myproject/
+  ```
+
+* Now have git track this
+
+  Now we do `git init .` -- this tells git to initialize this
+  directory under version control
+
+  ```
+  $ git init .
+  ```
+
+  If we do `ls` at this point, we see nothing.  However, there is a
+  "hidden" directory called `.git/` which we can see by passing `-a`
+  to `ls`:
+
+  ```
+  $ ls -al
+  $ ls -l .git
+  ```
+
+  In that directory are the files that git uses to keep track of
+  changes and the history.
+
+* Create a file
+
+  We'll create a file called `README` (use whatever editor you like,
+  I'll use emacs):
+
+  ```
+  $ emacs -nw README
+  ```
+
+  Add some descriptive text to the file and save it.  At the moment,
+  git doesn't know about this file -- you can see this via `git
+  status`:
+
+  ```
+  $ git status
+  ```
+
+* Tell git about the file
+
+  Now we need to tell git to start tracking the file.  We use
+  `git add` for this.  Then we need to tell git to store the current
+  state of the file -- we use `git commit` for this:
+
+  ```
+  $ git add README
+  $ git commit README
+  ```
+
+  Notice that an editor window pops up -- take the time to give a
+  descriptive message about the changes.
+
+  If you make more changes to the file, git won't store them until
+  you commit them.  So you'll commit the same file over and over as
+  it changes, but only add it once.
+
+* Create another file
+
+  Let's create a python program, `awesome.py` with the line:
+
+  ```
+  print("hello")
+  ```
+
+  Now add that file and commit it too
+
+  ```
+  $ git add awesome.py
+  $ git commit .
+  ```
+
+* Look at your log
+
+  `git log` will show you all the commits, the message you entered
+  when you made the commit (that helps you understand what was done).
+  It will also have a "hash" next to the commit (like
+  `dbc2916bb609759d54ca7668558bc639bab9b60b`)
+
+  ```
+  $ git log
+  ```
+
+* Add to you code
+
+  Edit `awesome.py` and make it a function and have your program call
+  the function.  Now we need to commit this again:
+
+  ```
+  $ git commit awesome.py 
+  ```
+
+  And `git log` will show this commit as separate from the one we made
+  when we created the file.
+
+* Go back in time
+
+  Suppose our code is not working anymore, and we know it was in the
+  past.  We can go back to any previous version of the code by using
+  `checkout` and the hash next to that commit (note: your hashes will
+  be different than mine).
+
+  ```
+  $ git log
+  $ git checkout dbc2916bb609759d54ca7668558bc639bab9b60b
+  ```
+
+  Look at the file, and you'll see it is different.  
+
+  If you want to go back to the latest version, you can checkout `master`
+  -- that's the name of the main "branch" git recognizes.
+
+  ```
+  git checkout master
+  ```
+
+* Working with branches
+
+  Suppose we want to do some development that might be invasive and we
+  don't want to break the working code on "master".  We can use a
+  branch for this -- thing of this as a parallel development that can
+  track the master branch and merge back and forth with it.  We can
+  work on this new branch until we are happy, and then incorporate our
+  changes back to `master`.
+ 
+  Here we'll create a new branch called `development`:
+
+  ```
+  $ git checkout -b development
+  ```
+
+  Now make some changes to `awesome.py` and commit them.
+
+  ```
+  $ emacs -nw awesome.py
+  $ git commit awesome.py
+  ```
+
+  If you go back to `master`, you won't see these changes:
+
+  ```
+  $ git checkout master
+  $ more awesome.py
+  ```
+
+  If you are happy with the changes, you can do a merge.  While on
+  `master`, merge `development` into `master` with:
+
+  ```
+  $ git merge development
+  ```
+
diff --git a/content/03-practices/git.png b/content/03-practices/git.png
new file mode 100644
index 00000000..3f35d2d7
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diff --git a/lectures/05-python-practices/git.txt b/content/03-practices/git.txt
similarity index 100%
rename from lectures/05-python-practices/git.txt
rename to content/03-practices/git.txt
diff --git a/content/03-practices/python-style.ipynb b/content/03-practices/python-style.ipynb
new file mode 100644
index 00000000..3eca3af9
--- /dev/null
+++ b/content/03-practices/python-style.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python Style (from the PEP-0008)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows the various code snippets from the PEP-0008 style guide: http://legacy.python.org/dev/peps/pep-0008/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## code lay-out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### indentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Indentation should use 4 spaces per indentation level (and NOT tabs).  Python 3 does not allow for a mixture of tabs and spaces.  Note that a lot of editors will map the tab key into a sequence of spaces\n",
+    "\n",
+    "Continuation lines should align wrapped elements either vertically inside parentheses, brackets, or braces, or using a hanging indent (with no arguments on the first line)\n",
+    "\n",
+    "note: to prevent ipython from throwing errors that some things are not defined, not all the code is in a code cell."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# Aligned with opening delimiter\n",
+    "foo = long_function_name(var_one, var_two,\n",
+    "                         var_three, var_four)\n",
+    "\n",
+    "# More indentation included to distinguish this from the rest.\n",
+    "def long_function_name(\n",
+    "        var_one, var_two, var_three,\n",
+    "        var_four):\n",
+    "    print(var_one)\n",
+    "    \n",
+    "# Note that the following is not allowed since you cannot distinguish \n",
+    "# between the function body and the arguments\n",
+    "def long_function_name(\n",
+    "    var_one, var_two, var_three,\n",
+    "    var_four):\n",
+    "    print(var_one)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### line length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lines should be a maximum of 79 characters.  \n",
+    "\n",
+    "The 80-column terminal goes back to the early terminals and page-widths for printers and is pretty much a good standard for any language.\n",
+    "\n",
+    "Comments and docstrings should be limited to 72-characters\n",
+    "\n",
+    "Implied line continuation is automatic inside parenthesis, brackets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/04-matplotlib/NOTES b/content/04-matplotlib/NOTES
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--- /dev/null
+++ b/content/04-matplotlib/NOTES
@@ -0,0 +1,6 @@
+anatomy of a figure:
+
+http://matplotlib.org/faq/usage_faq.html#parts-of-a-figure
+
+
+tutorial notebook exists
\ No newline at end of file
diff --git a/content/04-matplotlib/anatomy1.png b/content/04-matplotlib/anatomy1.png
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diff --git a/content/04-matplotlib/ipyvolume-example.ipynb b/content/04-matplotlib/ipyvolume-example.ipynb
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index 00000000..fd588bc9
--- /dev/null
+++ b/content/04-matplotlib/ipyvolume-example.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import ipyvolume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 64\n",
+    "x = np.linspace(0.0, 1.0, N)\n",
+    "y = np.linspace(0.0, 1.0, N)\n",
+    "z = np.linspace(0.0, 1.0, N)\n",
+    "\n",
+    "x3d, y3d, z3d = np.meshgrid(x, y, z, indexing=\"ij\")\n",
+    "\n",
+    "r = np.sqrt((x3d - 0.5)**2 + (y3d - 0.5)**2 + (z3d - 0.5)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = 1.0 + np.sin(x3d*np.pi*5)*np.sin(y3d*np.pi*7)*np.cos(z3d*np.pi*2) * np.exp(-r**2/0.25**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/zingale/.local/lib/python3.6/site-packages/ipyvolume/serialize.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  gradient = gradient / np.sqrt(gradient[0]**2 + gradient[1]**2 + gradient[2]**2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = ipyvolume.volshow(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71282540aea047f289ece9c4179a7715",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
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+    "a"
+   ]
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+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/zingale/.local/lib/python3.6/site-packages/ipyvolume/serialize.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  gradient = gradient / np.sqrt(gradient[0]**2 + gradient[1]**2 + gradient[2]**2)\n"
+     ]
+    },
+    {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import ipyvolume as ipv\n",
+    "V = np.zeros((128,128,128)) # our 3d array\n",
+    "# outer box\n",
+    "V[30:-30,30:-30,30:-30] = 0.75\n",
+    "V[35:-35,35:-35,35:-35] = 0.0\n",
+    "# inner box\n",
+    "V[50:-50,50:-50,50:-50] = 0.25\n",
+    "V[55:-55,55:-55,55:-55] = 0.0\n",
+    "ipv.quickvolshow(V, level=[0.25, 0.75], opacity=0.03, level_width=0.1, data_min=0, data_max=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+  "kernelspec": {
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+   "language": "python",
+   "name": "python3"
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+  "language_info": {
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+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/04-matplotlib/matplotlib-basics.ipynb b/content/04-matplotlib/matplotlib-basics.ipynb
new file mode 100644
index 00000000..20fae828
--- /dev/null
+++ b/content/04-matplotlib/matplotlib-basics.ipynb
@@ -0,0 +1,1242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib is the core plotting package in scientific python.  There are others to explore as well (which we'll chat about on slack)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "There are different interfaces for interacting with matplotlib, an interactive, function-driven (state machine) command-set and an object-oriented version.  We'll focus on the OO interface.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{tip}\n",
+    "To enable interactivity in the plots, install the [ipympl package](https://matplotlib.org/ipympl/) and then\n",
+    "in a cell, run:\n",
+    "\n",
+    "```\n",
+    "%matplotlib widget\n",
+    "```\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matplotlib concepts\n",
+    "\n",
+    "Matplotlib was designed with the following goals (from mpl docs):\n",
+    "\n",
+    "* Plots should look great---publication quality (e.g. antialiased)\n",
+    "* Postscript/PDF output for inclusion with TeX documents\n",
+    "* Embeddable in a graphical user interface for application development\n",
+    "* Code should be easy to understand it and extend\n",
+    "* Making plots should be easy\n",
+    "\n",
+    "Matplotlib is mostly for 2-d data, but there are some basic 3-d (surface) interfaces.\n",
+    "\n",
+    "Volumetric data requires a different approach"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gallery\n",
+    "\n",
+    "Matplotlib has a great gallery on their webpage -- find something there close to what you are trying to do and use it as a starting point:\n",
+    "\n",
+    "https://matplotlib.org/stable/gallery/index.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing\n",
+    "\n",
+    "There are several different interfaces for matplotlib (see https://matplotlib.org/3.1.1/faq/index.html)\n",
+    "\n",
+    "Basic ideas:\n",
+    "\n",
+    "* `matplotlib` is the entire package\n",
+    "* `matplotlib.pyplot` is a module within matplotlib that provides easy access to the core plotting routines\n",
+    "* `pylab` combines pyplot and numpy into a single namespace to give a MatLab like interface.  You should avoid this&mdash;it might be removed in the future.\n",
+    "\n",
+    "There are a number of modules that extend its behavior, e.g. `basemap` for plotting on a sphere, `mplot3d` for 3-d surfaces\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Anatomy of a figure\n",
+    "\n",
+    "Figures are the highest level object and can include multiple axes\n",
+    "![](anatomy1.png)\n",
+    "\n",
+    "(figure from: http://matplotlib.org/faq/usage_faq.html#parts-of-a-figure )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Backends\n",
+    "\n",
+    "Interactive backends: pygtk, wxpython, tkinter, ...\n",
+    "\n",
+    "Hardcopy backends: PNG, PDF, PS, SVG, ...\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0.0, 2.0*np.pi, 50)\n",
+    "y = np.cos(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot()` is the most basic command.\n",
+    "\n",
+    "We'll use `plt.subplots()` to create a `Figure` and `Axis` object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 6.283185307179586)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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nyPFqIjUR1hASQAgFwuw8fPhMCliRUuZRfh378mio5ERESCvffreVzKLoJLEyWWPBMCuZx3/DYWZlUAly9fDj8/P1hbWyMsLAyxsbF3PbZv376QyWS3bUOHDjUe8/zzz9/28UGDBjXQszE9rvZKfPxsRwDA18fSsOucWnQkIiISJOqsGt8cq7oufOmzIXC1V4qORHXIbArg999/j1mzZmHhwoWIj49HSEgIIiMjkZOTc8fjf/rpJ2RlZRm3s2fPQqFQ4Jlnnqlx3KBBg2oc99133zXQMzJNfVq5YmKvqiH+uT+eRlYB5wckIpKaa/mlmPvjaQDApN7+6N3KVXQkqmNmUwCXLl2KiRMnYty4cQgODsbKlStha2uLdevW3fF4JycneHh4GLc9e/bA1tb2tgKoVCprHNe0KdcyfC0yCO2bqZBfUoGZmxOg03NqGCIiqdDpDXjl+wQUlFagfTMVXh3YWnQkqgdmUQDLy8sRFxeHiIgI4z65XI6IiAjExMTU6hxr167FyJEjYWdnV2P//v374ebmhtatW2PKlCm4cePGXc+h1Wqh0WhqbI2RlYUcy57rBFsrBY6n5GHF/mTRkYiIqIF8sS8Zx1PyYGulwLLnOsHKwiyqAt0ns/ivmpubC51OB3d39xr73d3doVb//XVqsbGxOHv2LF544YUa+wcNGoSvvvoK0dHRWLx4MQ4cOIDBgwdDp7vzHbCLFi2CSqUybj4+Pg/5zExXCxc7vDOsHQDgkz+SEJd2U3QkIiKqZ3Fpefg0OgkA8O6wdmjhYve3jyHzZBYF8GGtXbsW7du3R7du3WrsHzlyJJ544gm0b98ew4cPx86dO3HixAns37//jueZP38+CgoKjFtGRkYDPQMxnurcDE+EeEGnN+Cl7/5EQSmXiiMiaqwKSivw0ndVl/0M6+iFJzs3Ex2J6pFZFEAXFxcoFApkZ2fX2J+dnQ0PD497Pra4uBibN2/GhAkT/vbz+Pv7w8XFBcnJd37LU6lUwsHBocbWmMlkMrz3j3bwcbLB1fxS/N+2M1wqjoioETIYDHh92xlczS+Fj5MN3hveDjIZp3xpzMyiAFpZWSE0NBTR0dHGfXq9HtHR0QgPD7/nY7ds2QKtVovRo0f/7efJzMzEjRs34OnpWSe5GwMHa0v8Z2QnKOQy7DydhS1xmaIjERFRHdtyMhO/ns6ChVyGZSM7wZ7r/DZ6ZlEAAWDWrFlYs2YNNm7ciMTEREyZMgXFxcUYN24cAGDMmDGYP3/+bY9bu3Ythg8fDmdn5xr7i4qK8Nprr+HYsWNITU1FdHQ0hg0bhsDAQERGRjbY8zIHnZs3xaxHWwEAFv58DpevF4mOREREdSQ5pwgLfzkHAJg1sBU6NedsGFJgITpAbY0YMQLXr1/HggULoFar0bFjR0RFRRlvDElPT4dcXrPPXrx4EYcPH8bu3btvO59CocDp06exceNG5Ofnw8vLCwMHDsS7774LpZKTXf6vyX0CcDgpFzFXbmDW9wn4aWoPKDgjPBGRWdPpDZj1QwJKK3ToHuCMyb0DREeiBiIz8KKuB6bRaKBSqVBQUNDorwcEAHVBGR795AAKyyqx4LFgjO/JNSGJiMzZ2sMpeHfnedhbW2DPK33gobIWHalBSO31+07M5i1gEs9DZY15g4MAAEt2X0TmzRLRkYiI6AFl3izBx7svAgDmD24jmfJHVVgA6b4817U5uvo1RUm5Dgt+Pse7gomIzJDBYMAb28+ipFyHbn5OGNm18c5rS3fGAkj3RS6XYdGT7WGpkGHvhRz8eiZLdCQiIrpPO09nYf/F67BSyPH+k+0g5zXdksMCSPct0M0eU/sGAgDe+uU8Cko4QTQRkbnILynH2zuq7vqd2i8AgW72oiORACyA9ECm9gtAgKsdcou0WPR7oug4RERUS4t+u4DconIEujXBlL6861eqWADpgSgtFPjgqQ4AgM0nMnDsyg3RkYiI6G/EXL6B709WLWO66Mn2UFooREciQVgA6YF19XPCP8OaAwBe/+kMyip0oiMREdFdlFXo8Pq2MwCAUWHN0dXPSXQkEogFkB7K3EFBcLVX4kpuMZbvu/MaykREJN7ne5ORklsMN3sl5gwKEh2HBGMBpIeisrHE20+0BQCs2H8ZF9WFoiMREdH/uKguxMoDlwEAbz/RFiobrvUrdSyA9NAGt/NARBt3VOoNmP/Taej1nBuQiMhU6PQGzPvpNCr1Bjwa7I5B7TxERyITwAJID00mk+GdYW1hZ6VAfHo+Nh1PEx2JiIiqbTqehj/T89FEaYF3hrWFTMY5/4gFkOqIl6ON8ZqSxVEXoS4oEx2JiEjysgpK8WFU1XJvcwa1hqfKRnQkMhEsgFRnRj/ii44+jijSVmLBz2dFxyEikjSDwYAFP59DkbYSnZo7YlSYr+hIZEJYAKnOKOQyfPBUe1jIZdh9PhtRZ9WiIxERSdauc2rsOZ8NC7kMHzzZAQou90Z/wQJIdSrIwwEv9vEHALz1yzmUlFeKjkREJDnF2kos/KVqubfJfQLQ2oPLvVFNLIBU52b0bwnvpjZQa8qw8sAV0XGIiCRn1YHLyNZo4eNkg+n9A0XHIRPEAkh1ztpSgdeHtAGqfwldzS8VHYmISDIyb5Zg1cGqP75fH9wG1pZc7o1uxwJI9WJwOw90a+EEbaUei3+/IDoOEZFkLI66CG2lHmEtnDjnH90VCyDVC5lMhgWPBUMmA345dQ1xaXmiIxERNXonU/Ow49Q1yGTAgseDOecf3RULINWbds1UeDbUBwDw9o7zXCGEiKge6fUGvL3jPABgRBcftPVSiY5EJowFkOrVq5Gt0URpgdOZBdj251XRcYiIGq2f/ryKM1cL0ERpgdkDW4uOQyaOBZDqlau9EtP6Vd2BtjjqAoq1nBaGiKiuFWsr8WFU1fXW0/sHwtVeKToSmTgWQKp343v6obmTLXIKtVh54LLoOEREjc6K/ZeRU6iFr7MtxvXwEx2HzAALINU7pcV/p4VZffAKMm+WiI5ERNRoZOSVYPWh6mlfhrSB0oLTvtDfYwGkBhHZ1h2P+FdNC/MBp4UhIqozH0RdQHmlHuH+zhgY7C46DpkJFkBqEFXTwrSFXAbsPJ2FE6mcFoaI6GHFpuTh19NZkHPaF7pPLIDUYIK9HDCia3MAwDucFoaI6KHo9Qa8s7Nqvd+R3ZqjjaeD6EhkRlgAqUHNHtgK9koLnLlagB/jM0XHISIyW1vjM3H2qgb2SgvMerSV6DhkZlgAqUG5NFFixoCqaWE+3HURRZwWhojovhVpK/HRrosAgJcGtIRLE077QveHBZAa3NjufvB1tsX1Qi1W7E8WHYeIyOx8sS8Z1wu18HO2xdjunPaF7h8LIDU4pYUC/1c9LcyaQynIyOO0MEREtZWRV4IvD6cAAP5vaDCsLPhSTvfPrL5rli9fDj8/P1hbWyMsLAyxsbF3PXbDhg2QyWQ1Nmtr6xrHGAwGLFiwAJ6enrCxsUFERASSkpIa4JnQo8Hu6BHojPJKPRb9nig6DhGR2Vj0eyLKK/XoGeiCiDZuouOQmTKbAvj9999j1qxZWLhwIeLj4xESEoLIyEjk5OTc9TEODg7IysoybmlpaTU+/uGHH2LZsmVYuXIljh8/Djs7O0RGRqKsrKwBnpG0yWQyvPlYMOQy4LczavyZflN0JCIikxeffhO/nVFDLgPeeKwNp32hB2Y2BXDp0qWYOHEixo0bh+DgYKxcuRK2trZYt27dXR8jk8ng4eFh3Nzd/ztBpsFgwKeffoo33ngDw4YNQ4cOHfDVV1/h2rVr2L59ewM9K2kL8nDAU529gep1gg0GTgtDRHQ3BoMBi6sn0n861BtBHpz2hR6cWRTA8vJyxMXFISIiwrhPLpcjIiICMTExd31cUVERfH194ePjg2HDhuHcuXPGj6WkpECtVtc4p0qlQlhY2F3PqdVqodFoamz0cGY+2gpWCjmOXcnDoaRc0XGIiEzWwaRcHE/Jg5WFHC9HcNoXejhmUQBzc3Oh0+lqjOABgLu7O9Rq9R0f07p1a6xbtw4///wzvvnmG+j1enTv3h2ZmVVzz9163P2cc9GiRVCpVMbNx8enjp6hdDVztMG/wn0BAB/tusjJoYmI7kCvN+CjXVWjf2Me8UUzRxvRkcjMmUUBfBDh4eEYM2YMOnbsiD59+uCnn36Cq6srVq1a9cDnnD9/PgoKCoxbRkZGnWaWqql9A2BnpcCZqwX4/eydyzcRkZT9djYLZ69q0ERpgan9AkXHoUbALAqgi4sLFAoFsrOza+zPzs6Gh4dHrc5haWmJTp06ITm5at65W4+7n3MqlUo4ODjU2OjhOTdRYmJvfwDAx7svolKnFx2JiMhkVOj0+Hj3JQDAxF7+cLKzEh2JGgGzKIBWVlYIDQ1FdHS0cZ9er0d0dDTCw8NrdQ6dToczZ87A09MTANCiRQt4eHjUOKdGo8Hx48drfU6qOy9U/1K7kluMrXFcIo6I6JatcZlIyS2Gs50VJvRqIToONRJmUQABYNasWVizZg02btyIxMRETJkyBcXFxRg3bhwAYMyYMZg/f77x+HfeeQe7d+/GlStXEB8fj9GjRyMtLQ0vvPACUH2H8MyZM/Hee+/hl19+wZkzZzBmzBh4eXlh+PDhwp6nVDVRWmBa9dsan/6RhLIKnehIRETClVXo8OkfVaN/0/oFoonSQnQkaiTM5jtpxIgRuH79OhYsWAC1Wo2OHTsiKirKeBNHeno65PL/9tmbN29i4sSJUKvVaNq0KUJDQ3H06FEEBwcbj5kzZw6Ki4sxadIk5Ofno2fPnoiKirptwmhqGKPCmmPd4RRczS/F1zFpxreFiYik6quYVGRrtGjmaINRjzQXHYcaEZmBk689MI1GA5VKhYKCAl4PWEd+OJmBOVtPw9HWEgfn9IODtaXoSEREQhSUVqD3h/tQUFqBj57ugGe6cOaJusLXbzN6C5ik4clOzRDo1gT5JRVYc/CK6DhERMKsOXgFBaUVaOnWBE9WT5pPVFdYAMmkWCjkeHVgawDA2sMpuF6oFR2JiKjBXS/UYu3hFADAq5GtoZBzyTeqWyyAZHIi27ojxMcRJeU6LN+XLDoOEVGD+3xvEkordOjo44iBwe61eATR/WEBJJMjk8kwN7JqFHDT8TRk5JWIjkRE1GAy8krwbWw6AGDOoNaQyTj6R3WPBZBMUvdAF/Rq6YIKnQGfVE+BQEQkBZ/suYQKnQG9Wrqge4CL6DjUSLEAksl6rXoUcNufV3FRXSg6DhFRvbug1mBbwlUAwJzIINFxqBFjASST1cHbEUPae8BgAJbsvig6DhFRvVuy6xIMBmBoe0+091aJjkONGAsgmbRZj7aGXAbsOZ+NuLSbouMQEdWbuLQ8/JGYDYVchlkDW4mOQ40cCyCZtEC3JngmtGry0w+jLoDzlhNRY2QwGLA4quqdjmdCvRHg2kR0JGrkWADJ5L0c0RJWFnIcT8nDkeQbouMQEdW5w8m5iE3Jg5WFHC9HtBQdhySABZBMnpejDUaFVa2B+ekflzgKSESNisFgwKd/JAEARof5wlNlIzoSSQALIJmFKX0CoLSQ42TaTY4CElGjcjg5F3FpN6G0kGNyH3/RcUgiWADJLLg5WOOfHAUkokbmr6N/o8J84eZgLToSSQQLIJkNjgISUWPD0T8ShQWQzAZHAYmoMeHoH4nEAkhm5a+jgIeTc0XHISJ6YIeSOPpH4rAAklmpOQqYxFFAIjJLVaN/Veucc/SPRGABJLNzaxQwjqOARGSmDiXlIj49n6N/JAwLIJkdjgISkTnj6B+ZAhZAMkscBSQic1Vj9K8vR/9IDBZAMktuDtYYFeYLcBSQiMzIX0f/Rj/iCzd7jv6RGCyAZLYm9/HnKCARmZW/jv69yGv/SCAWQDJbfx0F/GQP5wUkItNmMBjwCUf/yESwAJJZuzUKGJ+ej0NJHAUkItN1MCkXf3L0j0wECyCZtZrXAnIUkIhME6/9I1PDAkhmj6OARGTqOPpHpoYFkMweRwGJyJRx9I9MEQsgNQqT+3IUkIhM063RP2tLjv6R6WABpEbBzd4aox/hKCARmZYao39hHP0j08ECSI3Gi7wWkIhMzKG/jP5N4ugfmRAWQGo03Oz/u0bw5/uSRcchIsLne6t+F/2zG0f/yLSYVQFcvnw5/Pz8YG1tjbCwMMTGxt712DVr1qBXr15o2rQpmjZtioiIiNuOf/755yGTyWpsgwYNaoBnQvVlUm9/WCnkiE3JQ2xKnug4RCRhx6/cQGxqHqwUckzqzdE/Mi1mUwC///57zJo1CwsXLkR8fDxCQkIQGRmJnJycOx6/f/9+PPfcc9i3bx9iYmLg4+ODgQMH4urVqzWOGzRoELKysozbd99910DPiOqDp8oGT4V6AxwFJCLBbv0OerqLNzxUHP0j02I2BXDp0qWYOHEixo0bh+DgYKxcuRK2trZYt27dHY/ftGkTpk6dio4dOyIoKAhffvkl9Ho9oqOjaxynVCrh4eFh3Jo2bdpAz4jqy5Q+AVDIZTh46TpOZ+aLjkNEEnQqo+paZIVchil9AkTHIbqNWRTA8vJyxMXFISIiwrhPLpcjIiICMTExtTpHSUkJKioq4OTkVGP//v374ebmhtatW2PKlCm4ceNGneenhtXc2RbDQryAv1x/Q0TUkG6N/g3r6AUfJ1vRcYhuYxYFMDc3FzqdDu7u7jX2u7u7Q61W1+occ+fOhZeXV40SOWjQIHz11VeIjo7G4sWLceDAAQwePBg6ne6O59BqtdBoNDU2Mk1T+wVAJgN2n8/GRXWh6DhEJCEX1BrsOZ8NmQyY2jdQdByiOzKLAviwPvjgA2zevBnbtm2DtfV/r8MYOXIknnjiCbRv3x7Dhw/Hzp07ceLECezfv/+O51m0aBFUKpVx8/HxacBnQfcj0M0eg9p6AAC+2M9RQCJqOF/suwwAGNzOA4FuTUTHIbojsyiALi4uUCgUyM7OrrE/OzsbHh4e93zskiVL8MEHH2D37t3o0KHDPY/19/eHi4sLkpPvXBjmz5+PgoIC45aRkfEAz4YayrR+VX957zh1Dam5xaLjEJEEpOQWY+fpa8BffgcRmSKzKIBWVlYIDQ2tcQPHrRs6wsPD7/q4Dz/8EO+++y6ioqLQpUuXv/08mZmZuHHjBjw9Pe/4caVSCQcHhxobma52zVTo19oVegOwYv9l0XGISAJW7E+G3gD0D3JDWy+V6DhEd2UWBRAAZs2ahTVr1mDjxo1ITEzElClTUFxcjHHjxgEAxowZg/nz5xuPX7x4Md58802sW7cOfn5+UKvVUKvVKCoqAgAUFRXhtddew7Fjx5Camoro6GgMGzYMgYGBiIyMFPY8qW5N71/1F/hPf2bian6p6DhE1IhdzS/FT/FVU41x9I9MndkUwBEjRmDJkiVYsGABOnbsiISEBERFRRlvDElPT0dWVpbx+BUrVqC8vBxPP/00PD09jduSJUsAAAqFAqdPn8YTTzyBVq1aYcKECQgNDcWhQ4egVCqFPU+qW6G+Tgj3d0aFzoDVBzgKSET1Z9WBy6jUG9A9wBmhvpxSjEybzGAwGESHMFcajQYqlQoFBQV8O9iEHUnOxagvj0NpIcfhuf3has+CT0R1K6ewDD0X70N5pR7fvhCG7oEuoiPRPfD124xGAIkeVPcAZ3T0cYS2Uo8vD18RHYeIGqG1h1JQXqlHp+aOCA9wFh2H6G+xAFKjJ5PJMKP6WsBvYtKQX1IuOhIRNSI3i8vx9bE0AMCM/oGQyWSiIxH9LRZAkoT+QW5o4+mA4nId1h9JFR2HiBqR9UdTUVKuQ7CnA/q1dhMdh6hWWABJEmQyGaZX35W3/kgKCssqREciokagsKwCG46kANWzDnD0j8wFCyBJxqB2HvB3tYOmrBLfHEsXHYeIGoGvj6VBU1aJAFc74+pDROaABZAkQyGXGdflXHv4CkrL77zmMxFRbZSW67D2UNXo39S+gZDLOfpH5oMFkCRlWEcveDe1QW5ROTaf4CggET2472LTcaO4HD5ONniio5foOET3hQWQJMVSIcfkPgEAgNUHr6C8Ui86EhGZIW2lDqsPVk0rNblPACwVfDkl88LvWJKcp0O94WavRFZBGX6KzxQdh4jM0E/xV6HWlMHdQYmnQ71FxyG6byyAJDnWlgpM6u0PAFh18Ap0ei6GQ0S1p9MbsKp6acmJvfyhtFCIjkR031gASZKe69YcKhtLpOQWI+qsWnQcIjIjv5/NQuqNEjjaWuK5bs1FxyF6ICyAJEl2SguM7e4HAPhifzK4JDYR1YbBYMAX+6pG/8aG+8FOaSE6EtEDYQEkyRrX3Q82lgqcu6bBwaRc0XGIyAwcuHQd57M0sLVS4PnqPyKJzBELIElWUzsr49s3X+xLFh2HiMzAF/urRv+e69YcTe2sRMchemAsgCRpE3u3gKVChuMpeYhLuyk6DhGZsLi0PMSm5MFSIcMLvVqIjkP0UFgASdI8VTb4R6dmAIAV1X/ZExHdya3fEU928oanykZ0HKKHcl8FsKKiAhkZGbh48SLy8vLqLxVRA3qxTwBkMuCPxGxcVBeKjkNEJuiiuhB/JOZAJgNe7OMvOg7RQ/vbAlhYWIgVK1agT58+cHBwgJ+fH9q0aQNXV1f4+vpi4sSJOHHiRMOkJaoHAa5NjIu4rzzAUUAiut2K/VXXCQ9u5wF/1yai4xA9tHsWwKVLl8LPzw/r169HREQEtm/fjoSEBFy6dAkxMTFYuHAhKisrMXDgQAwaNAhJSUkNl5yoDk3tGwgA+OXUNWTklYiOQ0QmJCOvBDtOZwF/+V1BZO7uOYHRiRMncPDgQbRt2/aOH+/WrRvGjx+PlStXYv369Th06BBatmxZX1mJ6k17bxV6tXTBoaRcrDl0Be8Mayc6EhGZiNXVKwb1aumCds1UouMQ1QmZoZYz4BYWFsLe3r7+E5kRjUYDlUqFgoICODg4iI5DD+no5Vz8c81xKC3kODy3P1ztlaIjEZFg1wu16Ll4L7SVenw38RGEBziLjkR1gK/f93ETSK9evaBWc8ksarzC/Z3R0ccR2ko91h9JER2HiEzAuiMp0Fbq0am5Ix7xdxIdh6jO1LoAdurUCWFhYbhw4UKN/QkJCRgyZEh9ZCNqUDKZDFP6BgAAvo5Jg6asQnQkIhJIU1aBb2LSAABT+gRAJpOJjkRUZ2pdANevX4/nn38ePXv2xOHDh3Hp0iU8++yzCA0NhUKhqN+URA3k0TbuaOnWBIXaSmw6li46DhEJ9M2xNBRqK9HSrQki2riLjkNUp+5rHsC3334bs2bNwqOPPop27dqhsLAQMTEx2LFjR/0lJGpAcrkMk/tUjQKuPZyCsgqd6EhEJEBZhQ7rDlddCjKlbwDkco7+UeNS6wKYnZ2Nl19+Ge+99x6Cg4NhaWmJ559/Ht26davfhEQN7ImOXmjmaIPcIi22xGWKjkNEAmw5mYHconI0c7TB4yFeouMQ1blaF8AWLVrg4MGD2LJlC+Li4vDjjz9i0qRJ+Oijj+o3IVEDs1TIMal31Uz/qw9eRqVOLzoSETWgSp0eqw5eAVC16oelgqumUuNT6+/qdevW4c8//8TQoUMBAIMGDcK+ffvwySefYNq0afWZkajBPdvFB852VsjIK8XO6glgiUgadpy+hsybpXC2s8KzXXxExyGqF7UugCNHjrxtX+fOnXH06FHs3bu3rnMRCWVjpcC4Hn5A9QLwtZwuk4jMnF5vwIr9VUtCju/ZAtaWvMmRGqd7FsD09L+/C9LPzw9Hjx4FAFy9erXukhEJ9q9wPzRRWuBidiH2XsgRHYeIGsDeCzm4lF2EJkoLjH7EV3QconpzzwLYtWtXvPjiizhx4sRdjykoKMDWrVvRrl07/Pjjj/WRkUgIlY0lRj3SHKgeBSSixs1gMOCL/ckAgNGP+EJlYyk6ElG9uWcBPH/+POzs7PDoo4/Cw8MDQ4cOxcSJEzFjxgyMHj0anTt3hpubG9atW4cPP/wQL730Ur2GXb58Ofz8/GBtbY2wsDDExsbe8/gtW7YgKCgI1tbWaN++PX777bcaHzcYDFiwYAE8PT1hY2ODiIgIJCUl1etzIPMyoUcLWFnIcTLtJmJT8kTHIaJ6FJuSh/j0fFhZyDG+p5/oOET16p4F0NnZGUuXLkVWVhY+//xztGzZErm5ucaSNGrUKMTFxSEmJqbeVwP5/vvvMWvWLCxcuBDx8fEICQlBZGQkcnLu/Nbc0aNH8dxzz2HChAn4888/MXz4cAwfPhxnz541HvPhhx9i2bJlWLlyJY4fPw47OztERkairKysXp8LmQ83B2s8HeoNAFhRPTJARI3TigNVI/3PhHrDzd5adByieiUzmMnV7WFhYejatSs+//xzAIBer4ePjw9mzJiBefPm3Xb8iBEjUFxcjJ07dxr3PfLII+jYsSNWrlwJg8EALy8vzJ49G6+++ipQ/Xa2u7s7NmzYcMebXv4XF5OWhtTcYvT/eD/0BuD3l3uhjSf/WxM1NuevaTBk2SHIZcC+V/vC19lOdCSqR3z9vo+7gEtLS1FSUmL8d1paGj799FPs3r27vrIZlZeXIy4uDhEREcZ9crkcERERiImJueNjYmJiahwPAJGRkcbjU1JSoFaraxyjUqkQFhZ213OSNPm52GFIe0+A1wISNVq3Rv+GdvBi+SNJqHUBHDZsGL766isAQH5+Prp164aPP/4Yw4YNw4oVK+ozI3Jzc6HT6eDuXnMtRnd3d6jV6js+Rq1W3/P4W/97P+fUarXQaDQ1NpKGKX2rlofbefoa0m+U/O3xRGQ+0m4U49fT1wAAU6qXgiRq7GpdAOPj49GrVy8AwNatW+Hh4YG0tDR89dVXWLZsWX1mNBmLFi2CSqUybj4+nCBUKtp6qdCnlSv0BmD1IY4CEjUmqw9egd4A9G3timAvab4dSNJT6wJYUlICe3t7AMDu3bvx5JNPQi6X45FHHkFaWlp9ZoSLiwsUCgWys7Nr7M/OzoaHh8cdH+Ph4XHP42/97/2cc/78+SgoKDBuGRkZD/W8yLzcGgX84WQmcgp5oxBRY5BTWGZc85ujfyQltS6AgYGB2L59OzIyMrBr1y4MHDgQAJCTk1PvF1BaWVkhNDQU0dHRxn16vR7R0dEIDw+/42PCw8NrHA8Ae/bsMR7fokULeHh41DhGo9Hg+PHjdz2nUqmEg4NDjY2kI6yFEzo3d0R5pR7rj6SKjkNEdWDd4VSUV+oR6tsU3Vo4iY5D1GBqXQAXLFiAV199FX5+fggLCzOWpN27d6NTp071mREAMGvWLKxZswYbN25EYmIipkyZguLiYowbNw4AMGbMGMyfP994/Msvv4yoqCh8/PHHuHDhAt566y2cPHkS06dPBwDIZDLMnDkT7733Hn755RecOXMGY8aMgZeXF4YPH17vz4fMj0wmw5S+gQCAb2LSoCmrEB2JiB6CpqwCm45VvYM1pU8AZDKZ6EhEDcaitgc+/fTT6NmzJ7KyshASEmLcP2DAAPzjH/+or3xGI0aMwPXr17FgwQKo1Wp07NgRUVFRxps40tPTIZf/t892794d3377Ld544w28/vrraNmyJbZv34527doZj5kzZw6Ki4sxadIk5Ofno2fPnoiKioK1Ned/ojsbEOSGlm5NkJRThG+OpWFqdSEkIvPzdUwaCrWVaOXeBP2D3ETHIWpQZjMPoCniPELS9GNcJmZvOQWXJkocntuPi8UTmaGyCh16Lt6L3KJyLH02BE929hYdiRoQX7/vYwQQ1dO/rF27FomJiQCAtm3bYvz48VCpVPWVj8jkPNHRC0v3XMLV/FJsjcvkgvFEZmhLXCZyi8rRzNEGj4d4iY5D1OBqfQ3gyZMnERAQgE8++QR5eXnIy8vD0qVLERAQgPj4+PpNSWRCLBVyTOzVAgCw6uBlVOr0oiMR0X2o1Omx+mDVdE6TevvDUlHrl0KiRqPW3/WvvPIKnnjiCaSmpuKnn37CTz/9hJSUFDz22GOYOXNm/aYkMjEjujaHk50VMvJK8euZLNFxiOg+/HomCxl5pXCys8KzXTifK0nTfY0Azp07FxYW/33X2MLCAnPmzMHJkyfrKx+RSbKxUuD57n5A9fJwvJSWyDwYDAbjko7juvvBxorX8JI01boAOjg4ID09/bb9GRkZxgmiiaRkbLgf7KwUuKAuxP6L10XHIaJa2HcxBxfUhbCzUmBMuJ/oOETC1LoAjhgxAhMmTMD333+PjIwMZGRkYPPmzXjhhRfw3HPP1W9KIhOksrXEP8OaA9WjgERk+m79rI56xBcqW0vRcYiEqfVdwEuWLIFMJsOYMWNQWVkJALC0tMSUKVPwwQcf1GdGIpM1oac/NhxNRWxqHk6m5qGLH1cSIDJVJ1LzcCL1JqwUckzo2UJ0HCKhaj0CaGVlhf/85z+4efMmEhISkJCQgLy8PHzyySdQKpX1m5LIRHmorPFkp6r5wzgKSGTabv2MPtm5GdwdOOE/SVutC+CiRYuwbt062Nraon379mjfvj1sbW2xbt06LF68uH5TEpmwF/v4QyYDoi/k4IJaIzoOEd1BYpYGey/kQCYDXuwTIDoOkXC1LoCrVq1CUFDQbfvbtm2LlStX1nUuIrPh79oEg9t5ABwFJDJZKw9U/WwOaeeJFi52ouMQCVfrAqhWq+Hp6XnbfldXV2RlcR40krYpfarWBN5x6hrSb5SIjkNEf5F2oxg7Tl0DAEzpy9E/ItxPAfTx8cGRI0du23/kyBF4eXEZHZK29t4q9G7lCr2hanUQIjIdqw5egd4A9GnlinbNuHQpEe6nAE6cOBEzZ87E+vXrkZaWhrS0NKxbtw6vvPIKJk6cWL8piczAtOqRhS0nM5GjKRMdh4gAZGvKsPVkJgBgWr9A0XGITEatp4F57bXXcOPGDUydOhXl5eUAAGtra8ydOxfz58+vz4xEZqFbCyeE+jZFXNpNfHk4Ba8PaSM6EpHkfXnoCsp1enTxbYpuLThNE9EttR4BlMlkWLx4Ma5fv45jx47h1KlTyMvLw4IFC+o3IZGZkMlkmNavahTwm2NpyC8pFx2JSNJuFpdj0/GqFaw4+kdUU60L4C1NmjRB165d0a5dO87/R/Q/+rV2Q5CHPUrKddh4NE10HCJJ2xiTipJyHdp4OqBva1fRcYhMyn0XQCK6u6pRwKqRhvVHU1CsrRQdiUiSirSVWH8kFQAwrV8AZDKZ6EhEJoUFkKiODWnvCT9nW+SXVOC72HTRcYgk6bvj6SgorUALFzsMbnf7FGZEUscCSFTHFHKZca6x1QevQFupEx2JSFLKKnRYc+gKAGBKnwAo5Bz9I/pfLIBE9eAfnbzh4WCNnEItfoy7KjoOkaT8GJ+JnEItPFXWGN6pmeg4RCaJBZCoHlhZyDGxtz+AqomhK3V60ZGIJKFSp8eqA1WjfxN7+cPKgi9zRHfCnwyievJcNx80tbVE2o0S/HqGyyUSNYRfz2QhPa8ETnZWGNnNR3QcIpPFAkhUT2ytLDC+RwsAwBf7LkOvN4iORNSo6fUGfLGvainG8T38YGtV67UOiCSHBZCoHo0J90MTpQUuZhdi74Uc0XGIGrXoCzm4mF2IJkoL/CvcT3QcIpPGAkhUj1S2lhj9iC8AYPn+ZBgMHAUkqg8GgwHL9yUDAP4V7guVjaXoSEQmjQWQqJ5N6NkCSgs5/kzPx7EreaLjEDVKMVduICEjH0oLufHSCyK6OxZAonrmaq/Es12qLkb/Yn+y6DhEjdKta/9GdPWBqz2XKSX6OyyARA1gUm9/KOQyHErKxamMfNFxiBqVhIx8HE7OhYVchknV0y8R0b2xABI1AB8nWwzr6AVwFJCozn1Rfe3fsI7N4N3UVnQcIrPAAkjUQKb2DYBMBuw6l42k7ELRcYgahUvZhdh9PhsyGTClL0f/iGqLBZCogQS62SMy2AMAsGL/ZdFxiBqFWz9Lg9p6INDNXnQcIrNh8gUwLy8Po0aNgoODAxwdHTFhwgQUFRXd8/gZM2agdevWsLGxQfPmzfHSSy+hoKCgxnEymey2bfPmzQ3wjEjKpvULBAD8fOoa0m4Ui45DZNZSc4vxc0LVWttT+waKjkNkVky+AI4aNQrnzp3Dnj17sHPnThw8eBCTJk266/HXrl3DtWvXsGTJEpw9exYbNmxAVFQUJkyYcNux69evR1ZWlnEbPnx4PT8bkrr23ir0be0K3V9WLCCiB/PF/mToDUC/1q5o760SHYfIrMgMJjwzbWJiIoKDg3HixAl06dIFABAVFYUhQ4YgMzMTXl5etTrPli1bMHr0aBQXF8PComppIJlMhm3btj1U6dNoNFCpVCgoKICDg8MDn4ekJS7tJp5acRQWchn2vdoXPk68aJ3ofmXklaDfkv2o1Bvw09Tu6Ny8qehIZEb4+m3iI4AxMTFwdHQ0lj8AiIiIgFwux/Hjx2t9nlv/gW+Vv1umTZsGFxcXdOvWDevWreMqDdQgQn2bomegCyr1Bqw8wFFAogex4sBlVOoN6NXSheWP6AGY9ErZarUabm5uNfZZWFjAyckJarW6VufIzc3Fu+++e9vbxu+88w769+8PW1tb7N69G1OnTkVRURFeeumlu55Lq9VCq9Ua/63RaO77OREBwIz+gTicnIstJzMxvX8gPFU2oiMRmY1r+aXYcjIDADCjf0vRcYjMkpARwHnz5t3xJoy/bhcuXHjoz6PRaDB06FAEBwfjrbfeqvGxN998Ez169ECnTp0wd+5czJkzBx999NE9z7do0SKoVCrj5uPj89AZSZrC/J0R1sIJ5To9Vh24IjoOkVlZdeAyKnQGPOLvhG4tnETHITJLQgrg7NmzkZiYeM/N398fHh4eyMnJqfHYyspK5OXlwcPD456fo7CwEIMGDYK9vT22bdsGS8t7LwweFhaGzMzMGiN8/2v+/PkoKCgwbhkZGff5zIn+66UBVSMX38amI0dTJjoOkVnI0ZThuxNVv3tf4ugf0QMT8hawq6srXF1d//a48PBw5OfnIy4uDqGhoQCAvXv3Qq/XIyws7K6P02g0iIyMhFKpxC+//AJra+u//VwJCQlo2rQplMq7ryGpVCrv+XGi+9E9wBmhvk0Rl3YTqw9ewRuPBYuORGTyVh28gvJKPbr4NkV4gLPoOERmy6RvAmnTpg0GDRqEiRMnIjY2FkeOHMH06dMxcuRI4x3AV69eRVBQEGJjY4Hq8jdw4EAUFxdj7dq10Gg0UKvVUKvV0Ol0AIAdO3bgyy+/xNmzZ5GcnIwVK1bg/fffx4wZM4Q+X5IWmUyGGf2r5i7bdDwduUV3H30mIiC3SItNx9MAADMGtIRMJhMdichsmfRNIACwadMmTJ8+HQMGDIBcLsdTTz2FZcuWGT9eUVGBixcvoqSkBAAQHx9vvEM4MLDmxKApKSnw8/ODpaUlli9fjldeeQUGgwGBgYFYunQpJk6c2MDPjqSuTytXhHircCqzAF8eSsG8wUGiIxGZrDWHrqCsQo8QH0f0bukiOg6RWTPpeQBNHecRorrwx/lsvPDVSdhZKXB4bn80tbMSHYnI5OQVl6Pn4r0oKddh7dguGNDGXXQkMmN8/Tbxt4CJpGBAGzcEezqguFyHdUdSRMchMknrDqegpFyHtl4O6B/kVotHENG9sAASCSaTyfDSgKrLFTYcSUVBaYXoSEQmpaCkAhuPpgLV8/7x2j+ih8cCSGQCBgZ7oLW7PQq1ldhwJFV0HCKTsv5oCgq1lWjtbo+BwXzrl6gusAASmQC5XIbp1XcErzuSgsIyjgISAUBhWQXWHa66NGLGgEDI5Rz9I6oLLIBEJmJIe0/4u9qhoLQCX8WkiY5DZBK+ikmDpqwSAa52GNzOU3QcokaDBZDIRCjk/50XcO3hFBRrK0VHIhKqWFuJLw9VLZU4o39LKDj6R1RnWACJTMjjHbzg62yLvOJy44S3RFL1zbE03CypgJ+zLR7rwNE/orrEAkhkQiwUckzrWzUKuPpgCkrLdaIjEQlRWq7DmurRv6n9AmGh4MsVUV3iTxSRiflH52Zo5miD3CItvotNFx2HSIhvY9ORW1QO76Y2+EenZqLjEDU6LIBEJsZSIcfUfgEAgJUHLqOsgqOAJC1lFTqsOnAZADC1byAsOfpHVOf4U0Vkgp4O9UYzRxvkFGrxzTFeC0jS8s2xNOQUatHM0QZPh3qLjkPUKLEAEpkgpYXCuDrIiv2XeUcwSUaxthJf7K8a/Xt5QEtYWfBliqg+8CeLyEQ92dkbvs62uFFcjo0xXB2EpGHD0VTkFZfDz9kWT3bmtX9E9YUFkMhEWSrkmBnREgCw6sAVaLg6CDVymrIKrD5YdefvzIhWvPOXqB7xp4vIhD0R0gyBbk1QUPrf5bCIGqu1h1JQUFqBlm5N8HiIl+g4RI0aCyCRCVPIZXglohVQ/eKYX1IuOhJRvbhZXI611X/kvPJoK676QVTPWACJTNzgdh4I8rBHobbS+PYYUWOz+tAVFGkr0cbTAYPaeoiOQ9TosQASmTi5XIbZA1sDANYfSUVukVZ0JKI6db1Qiw1Hqm50mv1oK8g5+kdU71gAicxARBs3hHirUFqhw8rqKTKIGouVBy6jtEKHEB9HDGjjJjoOkSSwABKZAZlMhlnVo4BfH0tDtqZMdCSiOqEuKMPX1ZOdz360FWQyjv4RNQQWQCIz0bulC7r4NoW2Uo/l+5JFxyGqE8v3JaO8Uo+ufk3Rq6WL6DhEksECSGQmZLL/Xgv4XWw6Mm+WiI5E9FAyb5Zg84l0AMDsga05+kfUgFgAicxIeIAzugc4o0JnwOd7OQpI5u2z6GRU6AzoEeiMR/ydRcchkhQWQCIzM3tg1byAW+IykZpbLDoO0QNJzS3G1vhMAMCsR1uLjkMkOSyARGYm1NcJfVu7Qqc3YFl0kug4RA/kP9FJ0OkN6NfaFaG+TUXHIZIcFkAiMzS7esRkW8JVJOcUio5DdF+SsguxPeEqwNE/ImFYAInMUHtvFQYGu8NgAD75g6OAZF4+/SMJBgMQ2dYd7b1VouMQSRILIJGZmjWwFWQy4NfTWUjM0oiOQ1Qr569p8OuZLMhkVWv+EpEYLIBEZirIwwFD23sCAD7efUl0HKJaWbqn6nv1sQ5eCPJwEB2HSLJYAInM2MyIVpDLgD8SsxGXlic6DtE9nUzNwx+J2ZDLgJkRLUXHIZI0FkAiMxbo1gTPdvEBAPz710QYDAbRkYjuyGAw4P3fEgEAI7r6IMC1iehIRJJm8gUwLy8Po0aNgoODAxwdHTFhwgQUFRXd8zF9+/aFTCarsU2ePLnGMenp6Rg6dChsbW3h5uaG1157DZWVlfX8bIjq3iuPtoKNpQLx6fnYdU4tOg7RHUWdVSM+PR82lgq8EsFr/4hEM/kCOGrUKJw7dw579uzBzp07cfDgQUyaNOlvHzdx4kRkZWUZtw8//ND4MZ1Oh6FDh6K8vBxHjx7Fxo0bsWHDBixYsKCenw1R3XN3sMbEXi0AAIujLqJCpxcdiaiG8ko9FkddAABM7O0PNwdr0ZGIJM+kC2BiYiKioqLw5ZdfIiwsDD179sRnn32GzZs349q1a/d8rK2tLTw8PIybg8N/LzbevXs3zp8/j2+++QYdO3bE4MGD8e6772L58uUoLy9vgGdGVLcm9QmASxMrpOQW49vj6aLjENXw7fE0pN4ogUsTJV7s7S86DhGZegGMiYmBo6MjunTpYtwXEREBuVyO48eP3/OxmzZtgouLC9q1a4f58+ejpKSkxnnbt28Pd3d3477IyEhoNBqcO3eunp4NUf1porTAzOq31f4TnYTCsgrRkYgAAJqyCiyrXrf6lUdbwk5pIToSEQEw6Z9EtVoNNze3GvssLCzg5OQEtfru1zr985//hK+vL7y8vHD69GnMnTsXFy9exE8//WQ871/LHwDjv+91Xq1WC61Wa/y3RsO518h0jOjqg3VHUnDlejFWHriM1yKDREciwsr9l5FXXI4AVzuMqL5hiYjEEzICOG/evNtu0vjf7cKFCw98/kmTJiEyMhLt27fHqFGj8NVXX2Hbtm24fPnyQ+VetGgRVCqVcfPx4S8zMh2WCjnmDaoqfV8eSkFWQanoSCRx1/JLsfZwCgBg3uA2sFCY9JtORJIi5Kdx9uzZSExMvOfm7+8PDw8P5OTk1HhsZWUl8vLy4OHhUevPFxYWBgBITq56G8LDwwPZ2dk1jrn173udd/78+SgoKDBuGRkZ9/W8ierbo8Hu6OrXFNpKPZZycmgSbOmeS9BW6tHNzwkRbdxq8QgiaihC3gJ2dXWFq6vr3x4XHh6O/Px8xMXFITQ0FACwd+9e6PV6Y6mrjYSEBACAp6en8bz//ve/kZOTY3yLec+ePXBwcEBwcPBdz6NUKqFUKmv9eYkamkwmw+tD2uAfXxzF1vhMjO/ZAm08udoCNbzz1zT4MT4TAPD60DaQyWSiIxHRX5j0eHybNm0waNAgTJw4EbGxsThy5AimT5+OkSNHwsvLCwBw9epVBAUFITY2FgBw+fJlvPvuu4iLi0Nqaip++eUXjBkzBr1790aHDh0AAAMHDkRwcDD+9a9/4dSpU9i1axfeeOMNTJs2jQWPzF6n5k0xtIMnDAbgg98f/FIKoofxQdQFGAzAYx080dHHUXQcIvofJl0AUX03b1BQEAYMGIAhQ4agZ8+eWL16tfHjFRUVuHjxovEuXysrK/zxxx8YOHAggoKCMHv2bDz11FPYsWOH8TEKhQI7d+6EQqFAeHg4Ro8ejTFjxuCdd94R8hyJ6tqcyNawVMhw4NJ1HE7KFR2HJOZQ0nUcvHQdlgoZ5vBmJCKTJDNw7agHptFooFKpUFBQUGOeQSJT8PaOc1h/JBXBng7YOaMn5HK+BUf1T6c34LHPDiMxS4PxPVpgweN3v6yGSBS+fpvBCCARPZiX+reEvbUFzmdpsD3hqug4JBHb/7yKxCwN7K0tMKN/oOg4RHQXLIBEjVRTOytM7Vv1Arxk10WUVehER6JGrqxCh493XwQATOsXiKZ2VqIjEdFdsAASNWLjevjBS2WNawVl2HA0VXQcauTWH0nFtYIyNHO0wfPd/UTHIaJ7YAEkasSsLRV4NbI1AGD5vmTcLOZa11Q/8orL8cW+qrlWX41sBWtLhehIRHQPLIBEjdzwjs0Q7OmAwrJKLNubJDoONVLLopNQqK1EWy8HDAtpJjoOEf0NFkCiRk4ur5ocGgC+OZaGy9eLREeiRuby9SJsOp4GAHh9SBvecU5kBlgAiSSgZ0sX9A9yQ4XOgLd+OQfO/kR1xWCo+p6q0BnQP8gNPQJdREciolpgASSSiIWPB8PKQo5DSbmIOqsWHYcaid/PqnEoKRdWFnIs5Jx/RGaDBZBIInyd7TC5TwAA4N2d51FSXik6Epm5Ym0l3t15HgAwpU8AfJ3tREciolpiASSSkKl9A+Dd1AbXCsrw+d5k0XHIzH2+LxlZBWXwcbLBlL4BouMQ0X1gASSSEGtLBRY+3hYAsObQFd4QQg8sOacIXx66AgBY+FhbTvtCZGZYAIkkJqKNG28IoYfy1xs/BgS5ISLYXXQkIrpPLIBEEiOTyWrcEPI7bwih+/TbGTUOJ9+68aOt6DhE9ABYAIkkyNfZDlP+ckNIsZY3hFDt/PXGj6l9A9Dc2VZ0JCJ6ACyARBI1pW8AfJxskFVQhs/38YYQqp3P9iZDram68ePWXeVEZH5YAIkkytpSgYWPVb199+WhK0jO4Q0hdG/JOYXGGz/eepw3fhCZMxZAIgmLCHbHAN4QQrVgMBiw8JdzqNQbENHGDQPa8MYPInPGAkgkcQsfbwsrCzkOJ+fitzO8IYTu7NczWTiSfANK3vhB1CiwABJJXHNnW94QQvdU9NcVP/oGwMeJN34QmTsWQCIy3hCi1pRh2d4k0XHIxHwWnYRsjRbNnWx54wdRI8ECSESwtlTgreq39dYeSkFyTqHoSGQiknMKsfZwCgDgrSeCeeMHUSPBAkhEAIABbdwR0cYNlfqqi/15QwgZDAYs+PnWjR/u6B/EGz+IGgsWQCIyWvBY1Q0hR5Jv4Mf4q6LjkGBb4zJx9PKtGz+CRcchojrEAkhERs2dbfHygJYAgLd3nENWQanoSCRIVkEp3tlRdePHyxEteeMHUSPDAkhENbzY2x8hPo4oLKvE3B/P8K1gCTIYDJiz9TQKtZXo6OOISb38RUciojrGAkhENVgo5Pj4mQ6wspDj4KXr2HwiQ3QkamDfxWbgUFIulBZyLHkmBBYKvlQQNTb8qSai2wS62eO1ga0BAO/tPI+MvBLRkaiBZOSV4N+/Vr31+1pkawS6NREdiYjqAQsgEd3R+J4t0MW3KYrLdZiz9TT0er4V3Njp9Qa8tvUUist16OrXFON6tBAdiYjqCQsgEd2RQi7DkmdCYGOpQMyVG/j6WJroSFTPvopJxbErebCxVGDJMyFQyGWiIxFRPWEBJKK78nOxw7zBQQCAD36/gNTcYtGRqJ6k5hbjg6gLAID5Q4Lg62wnOhIR1SMWQCK6p3894otwf2eUVujw2tZT0PGt4EZHpzfg1S2nUFahR/cAZ4wO8xUdiYjqmckXwLy8PIwaNQoODg5wdHTEhAkTUFRUdNfjU1NTIZPJ7rht2bLFeNydPr558+YGelZE5kMul+HDpzvAzkqBE6k3sf5IiuhIVMfWHU7BybSbsLNS4MOnO0DOt36JGj2TL4CjRo3CuXPnsGfPHuzcuRMHDx7EpEmT7nq8j48PsrKyamxvv/02mjRpgsGDB9c4dv369TWOGz58eAM8IyLz4+Nki/8bWrUSxIe7LiI55+5/hJF5Sc4pxEe7LwIA3ngsGN5NOeEzkRRYiA5wL4mJiYiKisKJEyfQpUsXAMBnn32GIUOGYMmSJfDy8rrtMQqFAh4eHjX2bdu2Dc8++yyaNKk5nYGjo+NtxxLRnT3XzQdR59Q4eOk6Zm85hR8nh3N+ODNXqdNj9g+nUF6pR59WrhjZ1Ud0JCJqICb92zsmJgaOjo7G8gcAERERkMvlOH78eK3OERcXh4SEBEyYMOG2j02bNg0uLi7o1q0b1q1bxxUPiO5BJpNh8VPtYW9tgVMZ+Vh18IroSPSQVh28glOZBbC3tsAHT7WHTMa3fomkwqQLoFqthpubW419FhYWcHJyglqtrtU51q5dizZt2qB79+419r/zzjv44YcfsGfPHjz11FOYOnUqPvvss3ueS6vVQqPR1NiIpMRTZYOFj7cFAHz6xyVcUPNnwFwlZmnw6R+XAABvPd4Wniob0ZGIqAEJKYDz5s27640at7YLFy489OcpLS3Ft99+e8fRvzfffBM9evRAp06dMHfuXMyZMwcfffTRPc+3aNEiqFQq4+bjw7dLSHqe6twMEW3cUKEzYPYPp1Ch04uORPepovqt3wqdARFt3PFk52aiIxFRAxNSAGfPno3ExMR7bv7+/vDw8EBOTk6Nx1ZWViIvL69W1+5t3boVJSUlGDNmzN8eGxYWhszMTGi12rseM3/+fBQUFBi3jAyukUrSI5PJ8P6T7eFoa4lz1zRY/PvD/7FGDeuD3y/gfJYGjraWeP/Jdnzrl0iChNwE4urqCldX1789Ljw8HPn5+YiLi0NoaCgAYO/evdDr9QgLC/vbx69duxZPPPFErT5XQkICmjZtCqVSeddjlErlPT9OJBVu9tb44MkOmPxNHL48nIIQH0c8HnL7TVlken45dQ1rD1dN5bP4qQ5ws7cWHYmIBDDpawDbtGmDQYMGYeLEiYiNjcWRI0cwffp0jBw50ngH8NWrVxEUFITY2Ngaj01OTsbBgwfxwgsv3HbeHTt24Msvv8TZs2eRnJyMFStW4P3338eMGTMa7LkRmbtB7TwwuU8AAGDuj6dxKbtQdCT6GxfVhZi79TQAYErfAES25SwIRFJl0gUQADZt2oSgoCAMGDAAQ4YMQc+ePbF69WrjxysqKnDx4kWUlJTUeNy6devg7e2NgQMH3nZOS0tLLF++HOHh4ejYsSNWrVqFpUuXYuHChQ3ynIgai1cHtkKPQGeUlOvw4tdx0JRViI5Ed6Epq8Dkb+JQWqFDz0AXvDqwtehIRCSQzMC5Tx6YRqOBSqVCQUEBHBwcRMchEuJGkRaPf3YY1wrK8GiwO1aNDuVKEiZGrzdg0tdx+CMxG80cbbBjRk842VmJjkUkDF+/zWAEkIhMm3MTJVaMDoWVQo4957Pxxf5k0ZHofyzfl4w/ErNhZSHHitGdWf6IiAWQiB5eiI8j3hlWNT/gx3su4cCl66IjUbX9F3OwtHq+v3eHtUUHb0fRkYjIBLAAElGdGNmtOUZ29YHBALy8+U9k5JXU4lFUnzLySvDy5gQYDFVL+Y3o2lx0JCIyESyARFRn3nqiLTp4q5BfUnXDQVmFTnQkySqrqLoxp6C0AiHeKrz1RFvRkYjIhLAAElGdsbZUYMXoUDjZWeHcNQ3e2H6Wa2wLYDAY8H/bzuJ8lgZOdlZYMToUSguF6FhEZEJYAImoTjVztMFnz3WCXAZsjcvEpuPpoiNJzjfH0/FjfCbkMuDz5zrBy5Hr/BJRTSyARFTnegS64LXIIADA2zvOIT79puhIkhGXdhPv7DgHAJgzKAjdA11ERyIiE8QCSET1YnIffwxq64EKnQFTv4nH9cK7r7NNdSOnsAxTN8WhQmfA4HYeeLG3v+hIRGSiWACJqF7IZDJ89EwHBLjaQa0pw5h1scgvKRcdq9HKLynHmLWxyNZoEeBqh4+eCYFMxgm5iejOWACJqN7YW1viy7Fd4dJEicQsDcauP4FCLhdX5wrLKjB2XSwuqAvh0kSJL8d2RROlhehYRGTCWACJqF61cLHDNy90g6OtJU5l5GPChpMoLef0MHWlpLwSEzacxKnMAjjaWmLTC2Fo4WInOhYRmTgWQCKqd0EeDvhqfDfYKy0Qm5qHSV+fhLaSJfBh3ZrrLzY1D/ZKC3w9PgytPexFxyIiM8ACSEQNooO3I9aP6wobSwUOJeVi2qY/UaHTi45ltip0ekz/Nh6HknJha6XAhvFd0d5bJToWEZkJFkAiajBd/Jzw5dgusLKQ44/EbLzyfQJ0ek4Ufb90egNe+T4BfyTmwMpCji/HdEGor5PoWERkRlgAiahB9Qh0wcrRnWGpkGHn6SzM+/E09CyBtabXGzD3x9PYeToLlgoZVo0O5Vx/RHTfWACJqMH1D3LHf0ZWrRayJS4Tb+04xyXjasFgMGDhL+ewNa5qlY9lIzuhX5Cb6FhEZIZYAIlIiCHtPbHkmRDIZMBXMWn4IOoCS+A9GAwGfPD7BXx9LA0yGfDxsyEY3N5TdCwiMlMsgEQkzJOdvfHe8HYAgFUHrmBZdLLoSCbrP9FJWHXwCgDg38Pb4x+dvEVHIiIzxgJIREKNCvPFG0PbAAA++eMSPt59kdcE/oVeb8CSXRfx6R9JAIA3HwvGP8Oai45FRGaOBZCIhHuhlz9eHdgKAPDZ3mRM/OokNFwxBAWlFZj41Ul8vq9qZPS1yNaY0LOF6FhE1AiwABKRSZjevyWWPBMCKws5oi/kYPjnR5CcUyg6ljBJ2YUYvvwIoi9UTfXy8TMhmNYvUHQsImokWACJyGQ8HeqNrZPD4amyxpXcYgxffhS7zqlFx2pwu86pMXz5EaTkFsNLZY0fJ3fHU6G85o+I6g4LIBGZlA7ejtgxoyfCWjihSFuJF7+Ow1KJXBeo1xvw8e6LePHrOBSX6xDWwgm/zOjJFT6IqM6xABKRyXFposQ3L4Th+e5+AIBle5PxwlcnUVDaeK8LLCitwISNJ/DZ3qrr/cb18MM3L4TBpYlSdDQiaoRYAInIJFkq5Hjribb4+JkQKC3k2HshB8OXH0FSduO7LvDW9X77Ll6H0kKOpc+GYOHjbWGp4K9oIqof/O1CRCbtqVBvbJ3cHV4qa6TkFmP48iOIOtt4rguMOptlvN6vmaMNfpzSHU925vV+RFS/WACJyOS191Zhx4yeeMTfCcXlOkz+Jg5vbj+L3CKt6GgPLLdIize2n8Hkb+JRXK5DuL8zfpneA+2a8Xo/Iqp/MgPXXnpgGo0GKpUKBQUFcHBwEB2HqNGr1Onx/m8XsO5ICgDAzkqBSb0D8EKvFrBTWoiOVyvF2kp8eSgFqw9eRnG5DgAwvkcLvD4kCBZ8y5eoQfD1mwXwofAbiEiMo5dz8cHvF3A6swCovmnk5YiWGNnVx2Svm6vQ6bE5Nh3/iU5CblE5AKCDtwrzBgehe4CL6HhEksLXbxbAh8JvICJx9HoDfjubhY92XUTajRIAgJ+zLV6LDMKQ9h6QyWSiIwIADAYDfjujxke7LiC1Oqevsy1ei2yNoe09TSYnkZTw9ZsF8KHwG4hIvPJKPTafSMd//kjCjeKqkbUQbxXmDW6D8ABnodmOXs7F4t8v4JRxpNIKLw9oiZHdmpvsSCWRFPD12wxuAvn3v/+N7t27w9bWFo6OjrV6jMFgwIIFC+Dp6QkbGxtEREQgKSmpxjF5eXkYNWoUHBwc4OjoiAkTJqCoqKiengUR1RcrCznGhPvhwJx+eHlAS9haKXAqswDPrTmG59fH4mhyLsor9Q2WR1upw9HkXIxdF4t/rjmOU5kFsLVSYGZES+x/rR/+Fe7H8kdEwpn8CODChQvh6OiIzMxMrF27Fvn5+X/7mMWLF2PRokXYuHEjWrRogTfffBNnzpzB+fPnYW1tDQAYPHgwsrKysGrVKlRUVGDcuHHo2rUrvv3221pn418QRKbneqEWn+1NwrfH01FZvXqIrZUCYS2c0KulK3q3ckGAa5M6e+vVYDDg8vUiHLyUi0NJ13HsSh5KK6pu7rCQy/DPsOaY0b8lXO05oTORqeDrtxkUwFs2bNiAmTNn/m0BNBgM8PLywuzZs/Hqq68CAAoKCuDu7o4NGzZg5MiRSExMRHBwME6cOIEuXboAAKKiojBkyBBkZmbCy8urVpn4DURkulJzi/HF/mTsvXD9tuliPBys0aulC3q1ckWPAGc43+dqGzeKtDicnIvDSbk4lJQLtaasxsddmijRP8gVU/sGws/Frk6eDxHVHb5+A+Yxb8J9SElJgVqtRkREhHGfSqVCWFgYYmJiMHLkSMTExMDR0dFY/gAgIiICcrkcx48fxz/+8Q9B6Ymorvi52OHDp0NgMBhwQV2IQ0nXcSgpF8dT8qDWlGFLXCa2xGUCANo1c6gaFfybcxoAXL5ehLNXNTX2Ky3k6NbCqapUtnRFkIc9b+4gIpPW6AqgWl21QoC7u3uN/e7u7saPqdVquLm51fi4hYUFnJycjMfciVarhVb735EEjUZz12OJyDTIZDK08XRAG08HTOodgLIKHU6k5uFQUi4OXrqOC+pCnL2qua3U/Z0gD3v0buWKXi1d0NXPCdaWinp7DkREdU1IAZw3bx4WL158z2MSExMRFBTUYJlqY9GiRXj77bdFxyCih2BtqUCvlq7o1dIVrw9pg5zCMhxNvlHrVUVcmijRPdAZbvbW9Z6ViKi+CCmAs2fPxvPPP3/PY/z9/R/o3B4eHgCA7OxseHp6GvdnZ2ejY8eOxmNycnJqPK6yshJ5eXnGx9/J/PnzMWvWLOO/NRoNfHx8HignEZkGN3trDO/UTHQMIqIGJaQAurq6wtXVtV7O3aJFC3h4eCA6OtpY+DQaDY4fP44pU6YAAMLDw5Gfn4+4uDiEhoYCAPbu3Qu9Xo+wsLC7nlupVEKp5J18REREZN5MfjKq9PR0JCQkID09HTqdDgkJCUhISKgxZ19QUBC2bdsGVF/vM3PmTLz33nv45ZdfcObMGYwZMwZeXl4YPnw4AKBNmzYYNGgQJk6ciNjYWBw5cgTTp0/HyJEja30HMBEREZG5MvmbQBYsWICNGzca/92pUycAwL59+9C3b18AwMWLF1FQUGA8Zs6cOSguLsakSZOQn5+Pnj17IioqyjgHIABs2rQJ06dPx4ABAyCXy/HUU09h2bJlDfrciIiIiEQwm3kATRHnESIiIjI/fP02g7eAiYiIiKhusQASERERSQwLIBEREZHEsAASERERSQwLIBEREZHEsAASERERSQwLIBEREZHEsAASERERSQwLIBEREZHEmPxScKbs1iIqGo1GdBQiIiKqpVuv21JeDI0F8CHcuHEDAODj4yM6ChEREd2nwsJCqFQq0TGEYAF8CE5OTgCA9PR0yX4DPSiNRgMfHx9kZGRIdh3GB8Wv3YPj1+7B8Wv34Pi1e3D19bUzGAwoLCyEl5dXnZ3T3LAAPgS5vOoSSpVKxR/qB+Tg4MCv3QPi1+7B8Wv34Pi1e3D82j24+vjaSX3ghjeBEBEREUkMCyARERGRxLAAPgSlUomFCxdCqVSKjmJ2+LV7cPzaPTh+7R4cv3YPjl+7B8evXf2RGaR8DzQRERGRBHEEkIiIiEhiWACJiIiIJIYFkIiIiEhiWACJiIiIJIYF8AEtX74cfn5+sLa2RlhYGGJjY0VHMgsHDx7E448/Di8vL8hkMmzfvl10JLOxaNEidO3aFfb29nBzc8Pw4cNx8eJF0bHMwooVK9ChQwfjZLLh4eH4/fffRccyOx988AFkMhlmzpwpOorJe+uttyCTyWpsQUFBomOZjatXr2L06NFwdnaGjY0N2rdvj5MnT4qO1aiwAD6A77//HrNmzcLChQsRHx+PkJAQREZGIicnR3Q0k1dcXIyQkBAsX75cdBSzc+DAAUybNg3Hjh3Dnj17UFFRgYEDB6K4uFh0NJPn7e2NDz74AHFxcTh58iT69++PYcOG4dy5c6KjmY0TJ05g1apV6NChg+goZqNt27bIysoybocPHxYdySzcvHkTPXr0gKWlJX7//XecP38eH3/8MZo2bSo6WqPCaWAeQFhYGLp27YrPP/8cAKDX6+Hj44MZM2Zg3rx5ouOZDZlMhm3btmH48OGio5il69evw83NDQcOHEDv3r1FxzE7Tk5O+OijjzBhwgTRUUxeUVEROnfujC+++ALvvfceOnbsiE8//VR0LJP21ltvYfv27UhISBAdxezMmzcPR44cwaFDh0RHadQ4AnifysvLERcXh4iICOM+uVyOiIgIxMTECM1G0lJQUABUFxmqPZ1Oh82bN6O4uBjh4eGi45iFadOmYejQoTV+79HfS0pKgpeXF/z9/TFq1Cikp6eLjmQWfvnlF3Tp0gXPPPMM3Nzc0KlTJ6xZs0Z0rEaHBfA+5ebmQqfTwd3dvcZ+d3d3qNVqYblIWvR6PWbOnIkePXqgXbt2ouOYhTNnzqBJkyZQKpWYPHkytm3bhuDgYNGxTN7mzZsRHx+PRYsWiY5iVsLCwrBhwwZERUVhxYoVSElJQa9evVBYWCg6msm7cuUKVqxYgZYtW2LXrl2YMmUKXnrpJWzcuFF0tEbFQnQAIrp/06ZNw9mzZ3lN0X1o3bo1EhISUFBQgK1bt2Ls2LE4cOAAS+A9ZGRk4OWXX8aePXtgbW0tOo5ZGTx4sPH/d+jQAWFhYfD19cUPP/zAyw7+hl6vR5cuXfD+++8DADp16oSzZ89i5cqVGDt2rOh4jQZHAO+Ti4sLFAoFsrOza+zPzs6Gh4eHsFwkHdOnT8fOnTuxb98+eHt7i45jNqysrBAYGIjQ0FAsWrQIISEh+M9//iM6lkmLi4tDTk4OOnfuDAsLC1hYWODAgQNYtmwZLCwsoNPpREc0G46OjmjVqhWSk5NFRzF5np6et/1h1qZNG76FXsdYAO+TlZUVQkNDER0dbdyn1+sRHR3N64moXhkMBkyfPh3btm3D3r170aJFC9GRzJper4dWqxUdw6QNGDAAZ86cQUJCgnHr0qULRo0ahYSEBCgUCtERzUZRUREuX74MT09P0VFMXo8ePW6b4urSpUvw9fUVlqkx4lvAD2DWrFkYO3YsunTpgm7duuHTTz9FcXExxo0bJzqaySsqKqrxF3BKSgoSEhLg5OSE5s2bC81m6qZNm4Zvv/0WP//8M+zt7Y3XnKpUKtjY2IiOZ9Lmz5+PwYMHo3nz5igsLMS3336L/fv3Y9euXaKjmTR7e/vbrjG1s7ODs7Mzrz39G6+++ioef/xx+Pr64tq1a1i4cCEUCgWee+450dFM3iuvvILu3bvj/fffx7PPPovY2FisXr0aq1evFh2tcTHQA/nss88MzZs3N1hZWRm6detmOHbsmOhIZmHfvn0GALdtY8eOFR3N5N3p6wbAsH79etHRTN748eMNvr6+BisrK4Orq6thwIABht27d4uOZZb69OljePnll0XHMHkjRowweHp6GqysrAzNmjUzjBgxwpCcnCw6ltnYsWOHoV27dgalUmkICgoyrF69WnSkRofzABIRERFJDK8BJCIiIpIYFkAiIiIiiWEBJCIiIpIYFkAiIiIiiWEBJCIiIpIYFkAiIiIiiWEBJCIiIpIYFkAiIiIiiWEBJCIiIpIYFkAiIiIiiWEBJCL6i++++w42NjbIysoy7hs3bhw6dOiAgoICodmIiOoK1wImIvoLg8GAjh07onfv3vjss8+wcOFCrFu3DseOHUOzZs1ExyMiqhMWogMQEZkSmUyGf//733j66afh4eGBzz77DIcOHWL5I6JGhSOARER30LlzZ5w7dw67d+9Gnz59RMchIqpTvAaQiOh/REVF4cKFC9DpdHB3dxcdh4ioznEEkIjoL+Lj49G3b1+sWrUKGzZsgIODA7Zs2SI6FhFRneI1gERE1VJTUzF06FC8/vrreO655+Dv74/w8HDEx8ejc+fOouMREdUZjgASEQHIy8tD9+7d0bdvX6xcudK4f+jQodDpdIiKihKaj4ioLrEAEhEREUkMbwIhIiIikhgWQCIiIiKJYQEkIiIikhgWQCIiIiKJYQEkIiIikhgWQCIiIiKJYQEkIiIikhgWQCIiIiKJYQEkIiIikhgWQCIiIiKJYQEkIiIikhgWQCIiIiKJ+X/DO9JCGAPMFgAAAABJRU5ErkJggg==' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(x, y)\n",
+    "ax.set_xlabel(r\"$x$\")\n",
+    "ax.set_ylabel(r\"$\\cos(x)$\")\n",
+    "ax.set_xlim(0, 2*np.pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we also see that we can use LaTeX notation for the axes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition}  Quick Exercise\n",
+    "We can plot 2 lines on a plot simply by calling plot twice.  Make a plot with both `sin(x)` and `cos(x)` drawn\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can use symbols instead of lines pretty easily too&mdash;and label them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f5eb9b47770>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3f7f95a7f434092af09d81f07d17747",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(x, np.sin(x), marker=\"o\", label=\"sine\")\n",
+    "ax.plot(x, np.cos(x), marker=\"x\", label=\"cosine\")\n",
+    "ax.set_xlim(0.0, 2.0*np.pi)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "We can also specify basic style using a \"format string\" (see https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.plot.html)\n",
+    "\n",
+    "This has the form `'[marker][line][color]'`\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can change the linestyle and thickness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f5eb776b9d0>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad63aee9437e40dfbf9256dcff07dfe0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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O6wPizIl89HROHr5d5tCtrVrcLku9vjcd4xgU9+u4G/Dlw4K4qIuLGEBfC/kUbx1+Zu+NvbxnuAVFj668qlWa1w03vQ6najCPzfSDixz+xU2O12/m/2Rn7dlOSwvqq/N5+PjCIsuJJp2ZxJl8YhvHBjC4d1Ofk27psRx5H0bNNyiYgTObHX+AffXgCt74Cb3lXx5wb0F5bLs3rAze9DWDs4dQK+uQfyTpka6Z2FO5m1NtFhnb+3B9OHd6JR9cqm5XJG90TXZ3BUXYd1SenZjPo8zq0/JhcRjH351/dAdirUbm/8w68mz25BBaAUj6cP3PG5Mfn34Q3w8xNg0lRjy3ce452FOx3WeXta+WRoB1rVCTIlk7N74aZr6NywqsO6DYmpvL8owbRMImIyux1+fhyS/gK/ELh9hrGvF7egAlCKL6gO/GMqWKwQ/wXETSv3CEfSsxn79Z8OtafVAu/d2ZYujULKPY+r8PKw8sHg9oRXPfvR+PUtajK6ZyNTc4mIieI+g/gvjX36P6Ya+3hxGyoApWQa9oTezxvLvz4FB+LK7VcX2Ow8OiueY5mOs12M79+cvtdUzLl9S1NVf2+mDO1IZR9PxvRqzEd3t8ffR7NBirilA+vgl6eM5d4ToGEPsxNJOVMBKCXXdSxE/A0Kco3jAbOOlcuvfXfhTlbtdjzjt3dEDUZ2b1Auv78iaFozgEVP9OCJvs2wWtXeQcQtZR6Fb4aALQ+a3whdHzU7kZhABaCUnMUCAz+Eak0g/SB8NxwK8sv0V65MOMa7ixyP+wsL8uWN2yKxqE9VidQI8DU7goiYpSDf2GenH4SQpnDzB+r156ZUAMqV8Q2EO74AL3/YsxQWvVSmv+73LckOx/15WC28d1dbqvh7l+nvdUd2k07uEZFysOhF2LsMvCsb+3DfQLMTiUlUAMqVqxEBAycZyyvehi1zy+xXTbixBRNvaVU4x+0TfZrRvl7Vy15Pii/tVB6PfR3PtJV7zY4iImVhyw+w4h1j+eb3oXozsxOJiXQEuFyda/5uHEy86n2Y8yBUj4DqTUv911gsFu7sVJc24cF8Fbuf+69tWOq/w50t23mUJ7/9i6T0bH7ZeJjuTarTuIb6KYpUGEd3GPtogC4PG/tucWsaAZSrF/MvqNcNcjPg67shJ6PMflXzWoG8eHNLncBQinYfzWTI1FiSTs8MkpNv4/FvN5BfYDM7moiUhpwM+How5GZC/e7Q+wWzE4kTUAEoV8/DE277DALC4Nj2s60FxCU0rF6ZodH1HdZtSEzlw8W7TMskIqXol6fg2A5jH/2Pz4x9trg9FYBSOirXMIpALLBhJuycf8U3ZbPZOZaZU6rx5NKe7hdBwxB/h3XvLNzJpoNppmUSkVKw43djn4zF2EdXrm52InESKgCl9NTtDJ1PH2Py46OQfWXFw4dLdtHnv0tZsuNo6eaTIlXy9uDN2yM595P1fJudcd/Ea75gEVeVnWbsiwGiHzL20SKnqQCU0nXds1C1odFj6vfnSnz1bUnp/Hf+DlKychk6NZbX5m3TsWjlpG3dKjzUq7HDuh3Jmfx3/g7TMonIVfj9Wcg4ZOyTe/2f2WnEyagAlNLl7Qc3vW8sr58Ou/4o9lULbHae+d9G8m1n+9B9tGQXmw+ll0VSuYiHr2vCNWGOfcE+Xrab2D0ppmUSkSuwaxGsn2Es3zzJ2DeLnMOlCsBJkyZRv359fH19iYqKIjY2tshte/bsicViueByww03FG4zbNiwC37er1+/cro3FVj9rtBxlLE89xHIySzW1b5YvY/4xFSHdaN7NiIyPLgsUspFeHtaeev2Nnh7nN012O3w+LfxZOaU7WwvIlJKcjJg7umPfjvdB/W6mJ1InJDLFIBff/0148aNY8KECaxfv57IyEj69u3LkSNHLrr9999/z+HDhwsvmzZtwsPDg9tuu81hu379+jls99VXX5XTPargYl6A4LqQth8WXL7lwKHUU/xn3jaHdQ1D/Hn4uiZlGFIuplloAI/3cezlmJhyin//vNW0TCJSAgteMPa9wfWg9wSz04iTcpkC8K233mLUqFEMHz6cFi1aMHnyZPz8/Jg6depFt69atSqhoaGFl/nz5+Pn53dBAejj4+OwXZUqVcrpHlVwPpXhpveM5bWfwN7lRW5qt9t5/odNZOU6nmzwyi2t8PXyKOukchEjuzekY33H18JXsfv5Y/vF/+ESESexZxms/dRYvuk9Y18schEuUQDm5uYSFxdHTExM4Tqr1UpMTAyrVq0q1m1MmTKFQYMG4e/v2Opi8eLF1KhRg2bNmjF69GiOHz9e5G3k5OSQnp7ucJFLaNgT2g8zln8YA7knL7rZLxuTWLDVsbAY1DGczg2rlUdKuQgPq4U3bovEz9uxAH/6u79IPZlrWi4RuYTcLJg7xlhuPxwa9jA7kTgxlygAjx07RkFBATVr1nRYX7NmTZKSki57/djYWDZt2sTIkSMd1vfr148ZM2awcOFCXnvtNZYsWUL//v0pKLh424uJEycSFBRUeAkPD7/Ke+YGrn8RAmvDiT2w6OULfpx2Mo8Jczc7rAup7MP4/s3LMaRcTL1q/vzfDY5/hyMZOby/KMG0TCJyCYtehhN7IbCOse8VuQSXKACv1pQpU2jVqhWdOnVyWD9o0CBuuukmWrVqxcCBA/npp59Yu3YtixcvvujtjB8/nrS0tMJLYmJiOd0DF+YbBDe+ayyv/gD2r3H48avztl7Q9PmFm1oQ5OdVnimlCHd1qkuPpmcbxw7rUp/H+2gCeRGns381rP7QWL7pHfANvNw1xM25RAEYEhKCh4cHycnJDuuTk5MJDQ295HWzsrKYNWsWI0aMuOzvadiwISEhISQkXHyEw8fHh8DAQIeLFEOTGGgzGLDDDw9B3ikAVu8+zlexjkV074ga3NCqlklB5XwWi4X//KM1zWsF8uXIKF646Roqeeu4TBGnknfK2LdihzZ3Q+OYYlxJ3J1LFIDe3t60b9+ehQsXFq6z2WwsXLiQ6OjoS17322+/JScnh7vvvvuyv+fAgQMcP36cWrVUgJS6vv+GyqFwfCcsnkh2XgH//H6jwyb+3h68OLAlFoulyJuR8lcz0JdfHulG18YhZkcRkYv54xU4nmDsY/v+2+w04iJcogAEGDduHJ988gnTp09n69atjB49mqysLIYPHw7AkCFDGD9+/AXXmzJlCgMHDqRaNccTCjIzM3nyySdZvXo1e/fuZeHChdx88800btyYvn37ltv9chuVqsDf/mssr3yP7+f+wO5jWQ6bPNG3GbWDK5mTTy5JRbmIkzqwDladbr5/49tQSX1TpXg8zQ5QXHfccQdHjx7l+eefJykpiTZt2jBv3rzCE0P279+P1epYz27fvp3ly5fz+++/X3B7Hh4e/PXXX0yfPp3U1FTCwsLo06cPL730Ej4+PuV2v9xKxABodRts/Ja2f03AyivYTv8P0iY8mCHR9c1OKCLiOgryjbl+7TZodTs06292InEhFrvdbi/GdnIR6enpBAUFkZaWpuMBiyvrOLzXDrJTedXjfiZn9cDTauHHh7vRvJYeQ1dzPDOHo5k5RITqbydS7tZOgZ/HgW8wPLwe/NU6q7j0/u1CI4BSQfhXg17/hF+f4invb7G3GIhX5Woq/lxMfoGNmbH7eeO37dQI9OWXR7rj7ekyR5SIuL5TJ8621ur1fyr+pMS0x5by1+FeqB6B9VQK4/1/vGDaMXFuyenZ3Pj+Cp7/YTPp2fkkHMlk+sq9ZscScS+LX4NTKVC9ubFPFSkhFYBS/jy8oN9EYzn2YyzHdpidSEogpLIPHuftOd5esIMj6dlmRRJxL0e2QezHxnK/ieChD/Ok5FQAijkaXQdN+4MtH377p9lppAQ8rBZevLmlw7qs3AJe+WWraZlE3Ibdbuwz7QXQbAA06mV2InFRKgClzNntdrYcusi8yX3/DVYvSFgAOy48U1ucV7u6VbitfR2HdXPiD7Fmd9FzaYtIKdj5O+xaaOw7+1w4vaZIcakAlDL3x/YjDHh3GaO/iGPf8XN6/1VrBJ1HG8u/jYf8XNMySsk93T+CAF/Hj54mzN1MfoHNtEwiFVp+Lsw73e82+kFjHypyhVQASpnKK7Dx75+NjwZ/3ZREzFtL+GDxOVPtXfsk+Fc3utifOaZFXEJIZR8ev97xBJ5tSRl8sXqfaZlEKrTYjyBlF/jXgO5PmJ1GXJwKQClTX8XuZ9fRs6N+eQV2QgN9z27gGwi9JxjLS16DzKMmpJQrdXfnekSEBjise3P+Do5l5piWSaRCyjwCS/5jLMdMMPadIldBBaCUmbRTeby9YKfDula1gxjYprbjhm0GQ61IyEmHRS+Vb0i5Kp4e1gtOCMnIzuc/87aZlkmkQlr0krGPrNUGIu8yO41UACoApcx88EcCKVmOx/U9e0NzrNbz5pW1WqHfa8by+hlweEM5ppSr1alBVQa2CXNY9826A8QnppqWSaRCObwB1n9uLPd/zdhnilwlPYukTCSmnOSzFY7NgfteU5OohkV0q68XDS1vBezGQc6aodCl/HNAc/y9PRzWvfbrNjTTpMhVstvh12eMfWPLf0DdzmYnkgpCBaCUiVfnbSP3nLNBvTwsPNO/+aWvFPMv8KwE+1bAljllH1JKTY1AXx7p3cRh3ardx1m685hpmUQqhM2zYf9KY994/b/MTiMViApAKXVx+1L4+a/DDuuGRNenQYj/pa8YHA7dxhrLvz8HeafKMKWUtqFd6lMryNdhnUYBRa5C7kmY/7yx3O0xCKpzuWuIFJsKQClVdrudl35ynBEiqJIXD1/XuHg30OURCKwDaYmw8r2yCSllwtfLg8dizraFaVc3mAk3tsBisVzyeiJShJXvGfvCwDrQ5WGz00gFowJQStWPfx2+4OD/R3s3IdjPu3g34O139mOO5f+FjOQySCll5ZZ2tel3TSgf39Oe/43uUvQxnyJyaRnJsOJtY7nPi8a+UaQUqQCUUpOdV8Brvzq2/2gQ4s/dneuV7IZa3gq120PeSVj+VumGlDLl6WFl8j3t6XNNqEb+RK7GsjeNfWDtDnDNLWankQpIBaCUmqkr9nAw1fG4vWf6R+DtWcKnmcUC1z1nLK+bCqmJpZhSRMTJpe439n0AvZ8z9okipUwFoJSKY5k5fPDHLod1UQ2q0qdFzSu7wYY9oX53KMg1ZggREXEXS14DWx40uNbYF4qUARWAUiqsFgv/aF8Hbw/jKWWxwHN/u4oTACwW6H367Lf4mXAs4XLXECdns9nJziswO4aIczu209jnAVz3vNlppAJTASiloqq/Ny/cdA1LnurJ4Ki63NEhnJa1g67uRsM7QdN+YC+Axa+UVlQpZ3a7naU7jnLTpOW8NX+H2XFEnNsfr4DdBk37Q3hHs9NIBWaxq0nXFUtPTycoKIi0tDQCAzUx97nsdnvpnARw+C/4qLux/MByCG119bcp5SbhSCbPzdnEqt3HAfD2tLLkyZ7UCqpkdjQR55O0ESZ3M5a1vytTev/WCKCUkVI7A7RWa7jm78byon+Xzm1KufG0Wli7N6Xw+9x8G2/P32lqJhGntehl4+s1t6j4kzKnAlCcX6//A4sVdvwKiWvNTiMlUD/En0Gdwh3WfRuXSMKRTNMyiTilxLWwYx5YPIx9nkgZUwEozi+kCUTeZSwvesnsNFJCj1zXhEpeHoXf2+zwxm/bTc0k4nQWvWh8bXMnhBRz5iSRq6ACUK7Yiz9u4f1FO8nMyS/7X9bjKbB6wZ4lsHtJ2f8+KTU1An25t1t9h3XzNiddMGOMiNvavRj2LDX2cT2eNjuNuAkVgHJFdh/NZNrKPbzx+w66v7aIyUt2cSq3DFt8VKkHHYYby4teAp275FLu79GIYD8vh3Wv/boNnYMmbs9uh4WnP9nocC8E1zU7kbgJFYByRd5duBPb6ffuEyfzeHfhTk7mlvFIYPcnwLMSHDh9rIy4jEBfLx7s2chh3ardx1m685hpmUScwvZf4eA6Y9/W/XGz04gbUQEoJbYzOYMfNhxyWDesS32qVfYp218cUBOi7jeWF70MNlvZ/j4pVUOi61MryNdh3X/n79AooLgvmw3+ON3doPMDxj5OpJyoAJQSe3vBTodPYCv7eDKqe8Py+eVdHwWfQEjeBFtml8/vlFLh6+XB2JgmDuviE1NZnqBRQHFTm7839mU+gdDlEbPTiJtRASglsvVwOj9vPOyw7t5uDaji710+AfyqQvQYY/mPV6CgHE5AkVJzS7s61Kni2AT63YU7NQoo7qcg39iHAXR52Ni3iZQjFYBSIv89byqvQF9PRnRrUL4hoh8Ev2pwPAE2fFW+v1uuipeHlYd6Oba4WLv3BKt3pxR5HZEKacNMSNll7Ms6jzY7jbghlyoAJ02aRP369fH19SUqKorY2Ngit502bRoWi8Xh4uvrePyR3W7n+eefp1atWlSqVImYmBh27tQsBUXZeCCN37ckO6wb1b0hQZW8irxOmfAJgG6PGctLXoP8nPL9/XJVbm1Xh7DzjgV8b5Fed+JG8nNg8WvGcrdxxj5NpJy5TAH49ddfM27cOCZMmMD69euJjIykb9++HDlypMjrBAYGcvjw4cLLvn37HH7+n//8h3fffZfJkyezZs0a/P396du3L9nZ2eVwj1zPfxc4jv4F+3kxrGv9IrcvUx1HQkAtSEuE+JnmZJAr4u1pZfR5ZwRvPpTOkXS97sRNxH8J6QeMfVjHEWanETflMgXgW2+9xahRoxg+fDgtWrRg8uTJ+Pn5MXXq1CKvY7FYCA0NLbzUrHn2DCu73c7bb7/Ns88+y80330zr1q2ZMWMGhw4dYs6cOeV0r1zHn/tPsGibY7F9/7WNCPAt59G/M7wqGSeEACz/r44FdDG3dQinZqAPVf29eapfM5Y/3Ysagb7FuKaIiyvIM/ZZAF3HGvsyERO4RAGYm5tLXFwcMTExheusVisxMTGsWrWqyOtlZmZSr149wsPDufnmm9m8eXPhz/bs2UNSUpLDbQYFBREVFVXkbebk5JCenu5wcRdvnXfsXzV/b4ZE1zMtDwDthoJfCKTug03fmZtFSsTXy4MpQzuy7KlePNizsXn/SIiUt43fQep+Y9/VbojZacSNuUQBeOzYMQoKChxG8ABq1qxJUlLSRa/TrFkzpk6dyg8//MAXX3yBzWajS5cuHDhwAKDweiW5zYkTJxIUFFR4CQ8Pv+h2Fc3avSksO69h7+iejfD38TQtEwDefhD9kLG87E31BXQxLWsHmf8cEilPtgJjXwXQZYyxDxMxiUsUgFciOjqaIUOG0KZNG3r06MH3339P9erV+eijj674NsePH09aWlrhJTExsVQzO6t3FzoeoF89wIfBUSaP/p3RcST4BsGxHbB1rtlpRESKtnUuHN9p7LM66Ng/MZdLFIAhISF4eHiQnOx4BmpycjKhoaHFug0vLy/atm1LQkICQOH1SnKbPj4+BAYGOlwquq2H0y8Y/XuwZyMqeXuYlsmBbyBEPWAsL3tDcwSLiHOy22Hp6dG/qNHGvkvERC5RAHp7e9O+fXsWLlxYuM5ms7Fw4UKio6OLdRsFBQVs3LiRWrVqAdCgQQNCQ0MdbjM9PZ01a9YU+zbdQURoAFOHdaB9vSoAhFT24c5OTjZZedQD4OUPSRth5+9mp5GrlJyezYETJ82OIVK6dvwGyRvBu/LZKS1FTOQyB+CMGzeOoUOH0qFDBzp16sTbb79NVlYWw4cPB2DIkCHUrl2biRMnAvDiiy/SuXNnGjduTGpqKq+//jr79u1j5MiRcPoM4bFjx/Lyyy/TpEkTGjRowHPPPUdYWBgDBw409b46E4vFwnURNenVrAaxe1I4npWLr5eTjP6d4VcVOt4LK9+Dpa9Dkz5gsZidSkroUOopJi/Zxay1iVzfoiaT7mpndiSR0mG3G/smgA73atYPcQouUwDecccdHD16lOeff56kpCTatGnDvHnzCk/i2L9/P1br2QHNEydOMGrUKJKSkqhSpQrt27dn5cqVtGjRonCbp556iqysLO677z5SU1Pp1q0b8+bNu6BhtBiFYFTDambHKFr0w7DmYziwFvYshYY9zE4kJbBoWzL3fx5HXoHxEf4vGw+zMzmDJjXVIFcqgD1L4OA68PQ9O5WliMksdk3CecXS09MJCgoiLS3NLY4HdHo/PwFrP4EG18LQH81OIyWQdiqPbq8uIiPnbD/Hm9uE8c6gtqbmEikV0/4Ge5dBp/tgwOtmpxG9f4OrHAMoUixdHwWrpzECmFj0NIHifIIqeTH8vFllftxwiN1HM03LJFIq9q8xij+rF3R5xOw0IoVUAMoF8gtctJ9ecDhEDjKWl75hdhopoXu7NcD/nLPLbXaY9McuUzOJXLVlp/dFkYOMfZSIk1ABKBf474Id3PHRKpbsOIrLHSHQbRxYrLDzNzi8wew0UgLBft4M6eI4Cjgn/iD7j+uMYHFRhzcYnQksVuj2mNlpRByoABQH6dl5zFi5jzV7Uhg6NZYb319OfGKq2bGKr1ojuOYWY/lMx31xGSO7NaDSOWeZF9jsfLA4wdRMIlfszCcRLW819k0iTkQFoDj4YvU+hwPxNx9Kp7KrTdfV/XHj65a5cHS72WmkBKpV9uHuzo59Jr9ff5Aj6dmmZRK5Ike3w9bTJ6N1G2d2GpELqACUQtl5BUxdvsdhXb9rQmlco7Jpma5IzRYQ8TfADsv/a3YaKaFR1zbE2/Psrim3wMZnK/eamkmkxJa9ZeyDIv5m7JNEnIwKQCn0zbpEjmXmOqx7sGdj0/JclTOjgH99Ayl7Lre1OJEaAb7c2q6Ow7ovVu8jIzvPtEwiJZKyBzZ+ayxf+4TZaUQuSgWgAJBXYOOjJbsd1nVvEkKrOkGmZboqtdtBo95gL4AV75idRkpoVPcGDpO5ZGTnMys20cxIIsW34m1j39M4BsLUy1KckwpAAWBu/CEOpp5yWPdQLxcd/TvjzH/e8V9C+iGz00gJNKxemX7XhDqsm7J8D7n5LtqiSNxH2kH480tjubtG/8R5qQAUbDY7Hy5x7LfWrm4wUQ1cfL7Kel2gbhcoyIXVH5idRkrovmsbOnyflJ7N3A0q5MXJrf4AbHlQryvUizY7jUiRVAAKv29JJuGI44wLD/ZsjOXcz+BcVbexxtd10yA7zew0UgJt61a54J+QjQdcqCWRuJ/sNIibbix3HWt2GpFLUgHo5ux2Ox+e12ctIjSA6yJqmJapVDW+HqpHQG4GxE0zO42U0AM9GuFptfD3trX59dHu/OvmlmZHEinaus+MfU315tDkerPTiFySCkA3t3LXcTYccBwZG92zEVZrBRj9A7BaocvDxvLqyZCfe7lriBPp2aw6y57uxX/vaEPzWu45Ybu4iPxcWDPZWO7yMFSET1CkQlMB6ObOn2WhblU/bmhVy7Q8ZaLVbVA5FDIOwab/mZ1GSsBisVArqJLZMUQub9N3kHHY2Ne0+ofZaUQuSwWgG9t0MI0VCccd1t3foyGeHhXsaeHpA1H3G8sr3wNXm99YRJyb3W7sWwA6P2Dsc0ScXAV7p5eS+GyF4+wKIZV9LmjAW2F0GA5e/nBkM+xaaHYaEalIEhbCkS3gXRnaDzc7jUixqAB0Y8/e0Jwn+jQlpLLx3+rQ6Hr4enmYHatsVKoC7Ycay2f+UxeXVWCz85fOCBZnsfJd42u7oVAp2Ow0IsVisdv1ediVSk9PJygoiLS0NAIDXfcA9ey8AubGH+L6FjWp4u9tdpyyk7of3mljdOi/fynUijQ7kZRQdl4B38Yd4NNluzmcms3yp3tRI9DX7Fjizg7Fw8c9wOIBj26A4HCzE0kxVJT376uhEUDB18uD2zuGV+ziDyC4Llzzd2NZo4AuJzffxnVvLOa5OZvYd/wkuQU2Plu5txjXFClDZ/YlLW9R8ScuRQWguJczLWE2fQ+pmlvWlXh7Wrm2aXWHdV+s3kdGdp5pmcTNpe6HzbON5TP7FhEXoQJQ3EtYG2hwrfEx8OoPzU4jJTTq2oYO7dUysvOZFatCXkyy+kNjX9Kghw4pEZejAtDN6JBPoMujxtf10+GUTiRwJY2qV+b65jUd1k1ZvofcfJtpmcRNnTpxzrRvj5idRqTEVAC6EbvdzuBP1/CvHzeTmHLS7DjmadwbarSA3EyI+8zsNFJC9/do5PB9Uno2czccMi2PuKl1n0FeFtS4Bhr1NjuNSImpAHQja/eeYOWu43y2Yi89Xv+Dh2au50SWG06NZrFoejgX1r5eFTrWr+Kw7uOluzS6LeUnPwfWfGQsa9o3cVEqAN3Ix0t3Fy7b7PDXgVQCfD1NzWSalv+AgFqQmQQbvzU7jZTQ/dc6jgLuSM5k6c5jpuURN7PxW2PfERAGLW81O43IFVEB6CZ2H81k4bZkh3X3dm1Q8aZ9Ky5Pb4h6wFjW9HAu57qIGjSq7u+wbsryPablETdis5037VsFb58lFZabvvu7nynL9zjUOIG+ntzewc17VnUYDt4BcHQrJCwwO42UgNVq4d5uDRzWLd1xlJ3JGaZlEjeRsACObjP2He2HmZ1G5IqpAHQDxzNz+C7ugMO6wZ3r4e/jph//nuEbdHZ6uBXvmJ1GSuiWtnWo4uflsG7qCo0CShk7M+1b+6HGPkTERakAdANfrN5PzjltMrw8LAzrUt/UTE6j82iwesLeZXDoT7PTSAlU8vZgcFQ9h3Xfrz9Iijue2CTl4+B6Y19h9TT2HSIuTAVgBZedV8Dnqx2ny7oxMoyamj/VEFTn7EHcK983O42U0JDoenh5nD0DMyffxper95maSSqwVaf3ES3/Yew7RFyYCsAKbs6fBzmW6TgiMrJbQ9PyOKXoh4yvW+ZA2kGz00gJ1Aj05cbWYQ7rFmxNVksYKX1pB2HzHGP5zD5DxIW5VAE4adIk6tevj6+vL1FRUcTGxha57SeffEL37t2pUqUKVapUISYm5oLthw0bhsVicbj069evHO5J+bDZ7Hx63pmR3ZuE0CIs0LRMTqlWJNTrBrZ8WPup2WmkhM6cDBJZJ4h3BrXhu9FdsKgvm5S2tZ8Y077V7w61WpudRuSquUwB+PXXXzNu3DgmTJjA+vXriYyMpG/fvhw5cuSi2y9evJg777yTP/74g1WrVhEeHk6fPn04eNBxhKdfv34cPny48PLVV1+V0z0qe4t3HCHhSKbDupHdNfp3UWeO54n7DHLdeJYUF9SydhC/PNKdOQ915eY2tfFy19ZGUnZyTxozf4CO/ZMKw2X2lG+99RajRo1i+PDhtGjRgsmTJ+Pn58fUqVMvuv2XX37Jgw8+SJs2bYiIiODTTz/FZrOxcOFCh+18fHwIDQ0tvFSpUuWit+eKPlnqOPrXrGYA1zYJMS2PU2vWH4LrGfN7/vW12WmkhFqEBWrUT8rOX7MgOxWq1IemFedTInFvLlEA5ubmEhcXR0xMTOE6q9VKTEwMq1atKtZtnDx5kry8PKpWreqwfvHixdSoUYNmzZoxevRojh8/XuRt5OTkkJ6e7nBxVpsPpbFqt+N9GdG9gd4ki2L1ONsYevWHagwtIgabzdgngLGPsHqYnUikVLhEAXjs2DEKCgqoWbOmw/qaNWuSlJRUrNt4+umnCQsLcygi+/Xrx4wZM1i4cCGvvfYaS5YsoX///hQUFFz0NiZOnEhQUFDhJTzceRspf7bC8czf6gE+3NwmrMjtBWh7t9Hc9dh22LWwGFcQkQpv1yI4tsPYN7QZbHYakVLjEgXg1Xr11VeZNWsWs2fPxtf3bPuTQYMGcdNNN9GqVSsGDhzITz/9xNq1a1m8ePFFb2f8+PGkpaUVXhITE8vxXpTMXVF1+VvrWnhYjRG/ezrXw8dT/7lekm+gUQTC2f/4xaWlncwzO4K4utUfGF/b3WPsI0QqCJcoAENCQvDw8CA52XEu2+TkZEJDQy953TfeeINXX32V33//ndatL33mVsOGDQkJCSEhIeGiP/fx8SEwMNDh4qza1a3C+3e1Y/nTvXiwZyPuiqprdiTXEHU/YDk93dN2s9PIFbDb7azadZyR09fR9bVFKgLlyh3ZZnwaYLGe3jeIVBwuUQB6e3vTvn17hxM4zpzQER0dXeT1/vOf//DSSy8xb948OnTocNnfc+DAAY4fP06tWrVKLbvZagVV4ql+EYRU9jE7imuo2gAibjCWNQrocvIKbAyctII7P1nNgq3JZObk89Xa/WbHEle15vQ+oNkA4wQQkQrEJQpAgHHjxvHJJ58wffp0tm7dyujRo8nKymL48OEADBkyhPHjxxdu/9prr/Hcc88xdepU6tevT1JSEklJSWRmGm1RMjMzefLJJ1m9ejV79+5l4cKF3HzzzTRu3Ji+ffuadj/FCZxp87BhFpxMMTuNlICXh5Xwqn4O66av3Etega3I64hc1MkUYx8A0PlBs9OIlDqXKQDvuOMO3njjDZ5//nnatGlDfHw88+bNKzwxZP/+/Rw+fLhw+w8//JDc3Fz+8Y9/UKtWrcLLG2+8AYCHhwd//fUXN910E02bNmXEiBG0b9+eZcuW4eOj0TK3Vq8rhLaC/FMQN83sNFJCI043hj7jcFo2v24q3sliIoXiPoP8bAhtDfW6mJ1GpNRZ7Joz6Yqlp6cTFBREWlqaUx8PKFcgfibMGQ0BYTD2L/DwMjuRlMCtH64kbt+Jwu8jw4OZ86BmCJFiKsiDt1tBxmEYOBna3Gl2Iillev92oRFAubzX5m3jm7WJZOddvI2NlEDLW8G/BmQcgi0/mJ1GSuj8UcANiams33+iyO1FHGyeYxR/lWtCy1vMTiNSJlQAVhAHTpzkoyW7eOp/f9Hl1UW88dt20k7p7Mcr5ukDHUcay6smqTG0i+nToia1gys5rJty3rzYIhdlt8PqScZyx5HGvkCkAlIBWEF8vmofttM1SkpWLp+t2IM+7bpKHe4FD284tB4OrDU7jZSAp4eV4V0dz9qctymJxBTN8yyXkRgLh/4EDx9oP9zsNCJlRgVgBXAyN5+vYh1bXdzWIZxAXx23dlUqV4dWtxvLZ5rBisu4vWM4/t5nm5/b7PDF6n2mZhIXcOa13vo2Yx8gUkGpAKwA/rf+IOnZ+YXfWywwtIt6VpWKzqfnB94yF1Kdd+YXuVCgrxe3d3ScrvGr2P2czM0v8jri5lL3w9a5xnLUaLPTiJQpFYAuzmaz89kKx2ObrmtWgwYh/qZlqlBCW0H97mAvgNiPzU4jJTQ0ur7DoRDp2fnM/vOgmZHEmcV+DHYbNLgWQluanUakTKkAdHFLdx5l99Esh3XDuzYocnu5AtEPGV/XT4ecTLPTSAnUD/GnV7MaDuumrdiLul/JBXIyIW6Gsdz5IbPTiJQ5FYAu7rMVex2+b1qzMl0bVzMtT4XUpC9UaQDZabDhK7PTSAkNO+9wiJ1HMlmRcNy0POKkNnwFOWlQtSE06WN2GpEypwLQhSUcyWTJjqMO64Z3baBmt6XNaj07PdyayWDTtGKupHuTEBpVdzwkYtrKvUVuL27IZjs793fUaOM1L1LB6VnuwqatdDz2r4qfF39vW9u0PBVam7vAOwCOJ8CuRWankRKwWCwMO31YRBU/Lx7q1YiXBl5jdixxJrsWQsou8AnUrB/iNjzNDiBXJu1kHv+LczyY/c5OdfH18ijyOnIVfAKg7d2w5kNjFLBJjNmJpARuaVsbHw8rN7UJ02tELrRmsvG17d3Ga13EDWgE0EXNWrufU+dM+eZhtXBPdD1TM1V4nUYBFkiYD8d3mZ1GSsDfx5PbO4ar+JMLHUuAhAXGa/vM7D8ibkAFoAvKL7Ax/bxjmAa0qkWtoEpFXkdKQbVGZw8OV0sYkYrhzGu5aV/jNS7iJlQAuqD5W5I5lJbtsO78aa+kjETdb3z980vITjc7jYhcjex0iP/SWD7z2hZxEyoAXZC3p5WmNSsXfh8ZHky7ulVMzeQ2Gl0HIU0hN0MtYURcXfxMyM2EkGbQsJfZaUTKlQpAF9S7eU1+G3stM0dG0adFTUZ2U+PncmOxQKf7jOU1H6kljAtLSsvmzd+3M+6beLOjiBlsNoj9yFiOug/UPkvcjM4CdlEWi4UujUPo0jjE7CjuJ/JOWPii0TZi10Jocr3ZiaQEjqRn89LPW/l142HybcaMIGN6NaZh9cqXva5UIAkLIGU3+ARB60FmpxEpdxoBFCkpn8rQ9h5j+Uz7CHEZAb5eLN1xtLD4Ay44qUrcwJnXbrt7jNe0iJtRAShyJTqNPN0SZgEc22l2GimBSt4eDOoU7rDuu7gDpGfnmZZJytmxncbovVq/iBtTAShyJao2hKb9jOXYT8xOIyV0T+d6WM855Csrt4Dv1h0wM5KUpzOtX5r1h6o6hlrckwpAF/Hn/hPEJ6aaHUPOFXX6ZJB4tYRxNXWq+NH3mlCHddNX7aXgnI+FpYLKTjPO/oWzJ3SJuCEVgC5i4q/bGDhpBQMnrWDOnwfJzdfZp6Zr2MtoH5GbefYNRVzGsC6OvTP3HT/J4u1HTMsj5cSh9UtPs9OImEYFoAvYciid2D0pAMQnpjL263h+3XTY7FhisZwdBYxVSxhX06lBVZrXCnRYN00ng1RsNpvRvonTjZ/V+kXcmApAF3D+GYrVA3zo37KWaXnkHK0HGW0kUnafnk9UXIXFYrlgBp1lO4+xMznDtExSxhLmw4k9xms2Uq1fxL2pAHRyJ7JymRN/0GHd4Ki6eHvqT+cUfCobbSRQSxhXdFNkGFX9vR3WTV+lUcAK69zWL97+ZqcRMZWqCCc3a20iOecc7+flYeGuqLqmZpLzdDzdEmbXQji6w+w0UgK+Xh7ceV5LmP/FHSTtlFrCVDhHt8OuRcZrtdMos9OImE4FoBPLL7Dx+XmjETe0qkWNAF/TMslFVG1gtJPgnPYS4jLu7lwPj3N6wpzKK+DbdYmmZpIyUNj6ZQBUqX+5rUUqPBWATmz+lmQOpWU7rBvWVT2rnFLU/cbXDV8ZbSbEZdQKqkS/81rCzFi1Ty1hKpLsNIj/ylg+81oVcXMqAJ3Y+WckRoYH0yY82LQ8cgkNekD1CLWEcVFDz2sJsz9FLWEqlD+/hLwsqN4cGlxrdhoRp6AC0EltPZzOmtOtX84Y3kUfWzgti+XsyMIatYRxNR3rV6HF6ZYwFgvENK9B9QAfs2NJabAVnP34V61fRAp5mh1ALu5irV8GtFLrF6fW+g5Y8ILRZiJhPjTta3YiKSaLxcIDPRux8UAq93SuT91qfmZHktKy83TrF98gaH272WlEnIZLjQBOmjSJ+vXr4+vrS1RUFLGxsZfc/ttvvyUiIgJfX19atWrFL7/84vBzu93O888/T61atahUqRIxMTHs3LmzjO/F5Z3IymX2n46tX+7qpNYvTs/bH9qeaQnzkdlppIRuigzj/25ooeKvook9/VpsN0StX0TO4TIVxddff824ceOYMGEC69evJzIykr59+3LkyMWP01m5ciV33nknI0aM4M8//2TgwIEMHDiQTZs2FW7zn//8h3fffZfJkyezZs0a/P396du3L9nZ2Re9zfJysdYvg9X6xTV0GnW2Jcwx8/+ZEHFrR3cYrV8sVuio1i8i53KZAvCtt95i1KhRDB8+nBYtWjB58mT8/PyYOnXqRbd/55136NevH08++STNmzfnpZdeol27drz//vtwevTv7bff5tlnn+Xmm2+mdevWzJgxg0OHDjFnzpxyvndn5RfY+GL1Pod1A1rVokagWr+4hCr11RJGxFmceQ027Q9V6pmdRsSpuEQBmJubS1xcHDExMYXrrFYrMTExrFq16qLXWbVqlcP2AH379i3cfs+ePSQlJTlsExQURFRUVJG3mZOTQ3p6usOltO08knlBE9rzJ60XJ3fmZJD4mZBd+s8RESmG7LSzZ+SfmbNbRAq5RAF47NgxCgoKqFmzpsP6mjVrkpSUdNHrJCUlXXL7M19LcpsTJ04kKCio8BIeHn7R7a5G81qBrBp/HS/c2IKGIf5EhgfTtm6VUv89UobUEqbCyCuwMXfDIeL2pRRja3Eq8TNPt36JMF6TIuLAJQpAZzF+/HjS0tIKL4mJZTNbQICvF8O6NmDBuB58OqRDmfwOKUOWc6aailVLGFd0NCOHdxfupNtri3jkqz95d2GC2ZGkJGy2sx//drpPrV9ELsIlCsCQkBA8PDxITk52WJ+cnExoaOhFrxMaGnrJ7c98Lclt+vj4EBgY6HApS1arRb3IXFXrQeATBCm7jRNCxKUs3JrMW/N3kJyeA8CSHUfZdTTT7FhSXAkLjNeeTxBEDjI7jYhTcokC0Nvbm/bt27Nw4dk3UpvNxsKFC4mOjr7odaKjox22B5g/f37h9g0aNCA0NNRhm/T0dNasWVPkbYoUm09laHu3sbxmstlppIRublObYD8vh3Wfr9pX5PbiZM685trdo9YvIkVwiQIQYNy4cXzyySdMnz6drVu3Mnr0aLKyshg+fDgAQ4YMYfz48YXbP/roo8ybN48333yTbdu28cILL7Bu3TrGjBkDpxu/jh07lpdffpm5c+eyceNGhgwZQlhYGAMHDjTtfkoF0mmk0RImYQEc00eIrqSStwd3dHQ8xvfbdYlkZOcVeR1xEsd2nh51t0DHkWankRL6eu1+Fm1Lxqa5uMucy8wEcscdd3D06FGef/55kpKSaNOmDfPmzSs8iWP//v1YrWfr2S5dujBz5kyeffZZ/vnPf9KkSRPmzJlDy5YtC7d56qmnyMrK4r777iM1NZVu3boxb948fH3VckVKQdWGxmwgO+YZxyMN+I/ZiaQE7ulcj0+W7ubM+1BWbgH/izvAsK4NzI4ml1LY+qUfVNXfypVk5eTz8s9bycjOp141P4ZE1+f2DnUI8PUqxrWlpCx2u11l9hVKT08nKCiItLS0Mj8eUFzUrkXw+d/BOwDGbQFfPU9cyf2fr+O3zWePE24Q4s/CcT2wWnVSgVPKToe3mhtn4N8zBxr1MjuRlMAXq/fx7JyzkzVYLLD4iZ7Uq1b6H+Pr/duFPgIWcUkNe0FIU8jNgA1fmZ1GSmjoeT049xzLYunOo6blkcuIn2kUfyHNoGFPs9NICdjtdqat3OuwrndEjTIp/sSgAlCkLFksRhsKTn80pZYwLiW6YTWa1QxwWDf9vDcpcRLntn6JUusXV7Mi4TgJRxzPtD//HzApXSoARcpa5J3gEwjHE4yPhMVlWCyWC96E/th+lD3HskzLJEXYtRBSdhmtX1qr9YurOX/0r3GNynRrHGJaHnegAlCkrJ3bEib2I7PTSAkNbBtGUCXHg9A1CuiE1px+bbW923jNicvYf/wkC7c59uQd2qU+Fo3ilikVgCLloePpljA7f4fju8xOIyXg5+15QUuY7+IOkJmTb1omOc+xBEiYb7zGOqn1i6uZsWov556OGuDryS1ta5sZyS2oABQpD9UaQZM+xnLsJ2ankRK6p3M9zj3xNzMnn//FHTAzkpxr7enXVNO+RvslcRlZOfl8vc5xWtXbO4Tj7+MyXepclgpAkfISdfpkkD+/gJwMs9NICYRX9SOmeU2HddNW7lWzWmeQkwF/fmksnznhSlzG7D8PkpF9djTdYoEh0fVMzeQuVACKlJeG10G1xqdbwswyO42U0PCLNIBOSs82JYucI/4r4zVVrYnRdklcht1uv+B42uuaqfVLeVEBKFJerFbodL+xvOYjtYRxMZ0bViUiNIBrm1bns+EdWTiuB2HBlcyO5d5strMnVkXdb7zGxGWs3HWcnee1fhnWVa1fyos+ZBcpT23uhIUvwvGdsHsRNI4xO5EUk8ViYfaDXank7WF2FDlj1yKjvZJ3AESq9Yur+WyFWr+YSf8uiZQnnwBoO9hYXj3Z7DRSQir+nMyaD42vbe82XlviMhJTLtL6JbqeWr+UIxWAIuWt031Gu4qE+Ub7ChEpuWM7IWGB8VqK0skfrmbf8ZPUCPAp/D7Ax5Nb2tUxNZO7UQEoUt6qNTLaVaDG0CJX7Ezj56b91PrFBXVrEsLyp6/jvTvb0qFeFW7vqNYv5U0FoIgZoh4wvsbPhOw0s9PIVdqelEGBWsKUn1OpxmsHoPMDZqeRK+TlYeXGyDC+G92FZ/pHmB3H7agAFDFDw55QPQJyM42+gOJyCmx25m1KYtDHq+j79lL+2HbE7Eju488vIC8LqjeHBj3MTiOlwMtD5Uh50yMuYgaLxWhbwZmWMAVmJ5ISuv/zOB74Io7Vu1MA+GzlHrMjuQdbgWPrF500IHJFVACKmKX1IPANhtR9sOM3s9NICfW9xnFmkBUJx9mRrBleytyOeZC6HypVgdZ3mJ1GxGWpABQxi7cftB9qLJ9pZyEu48bIMKr5ezusm3berAZSBlaffq20G2q8hsRlHE47xd5jWWbHkNNUAIqYqeMosHjAnqWQvNnsNFICvl4e3BVV12Hd9+sPkHYyz7RMFV7SJti7zHjNdBpldhopoQ8X76LXm4sZOX0tKxKOYbfrxCkzqQAUMVNwODT/m7G8Ro2hXc3gqHp4Ws8eg5adZ2PW2v2mZqrQzrxGmt8IQeoZ50rSTuXxXdwB7HZYsPUIgz9dw8dLd5sdy62pABQx25mWMH99A1nHzU4jJRAa5Ev/VrUc1s1YtY/8As3zXOqyjsPGb43lKLV+cTXfrkvkZO7Zk908rBb+FhlmaiZ3pwJQxGx1oyG0NeRnw/ppZqeREhp+3uT1B1NPsWCrWsKUurjPjNdIrUio29nsNFICBTb7BcfH9rsmlNrBlUzLJCoARcxnsUDn0cby2ilQoGPIXEnb8GAi6wQ5rJumljClqyDPeG0ARI1W6xcXM39LMgdOnHJYd/4/TlL+VACKOIOWt4J/dUg/CFt/NDuNlIDFYmHYeW9mq3ensPVwummZKpytcyHjEPjXgJa3mJ1GSmjqCsd/iFrXCaJ9vSqm5RGDCkARZ+DpAx3uNZZ1MojLuaFVGNXPmdge4LMVGgUsNatPvyY63Gu8VsRlbD6URuyeFId1w7vWx6JRXNOpABRxFh3uBasXJK6Bg+vNTiMl4O1pZfB5LWHmxB/iWGaOaZkqjINxcCDWeG2c+SdJXMZnKxyP/ase4MMNrXTyhzNQASjiLAJC4Zq/G8saBXQ5g6Pq4X3OfKa5+Ta+XK2WMFftzOhfy1sgoOblthYncjQjh7nxhxzW3dO5Ht6eKj2cgf4KIs6k8+n2Fpu+h4wks9NICVQP8OHG89pafP/nAWw2Nbu9YhlJsHm2sazWLy5n5pr95J7TEsnbw3pB83QxjwpAEWdSuz3U6QS2PFj3mdlppIRGdGsAQFiQL/8cEMHcMd2wWnWs0xVbN9V4LYRHQe12ZqeREsjJL+Dz1fsc1t3cJoyQyjqG01l4mh1ARM7T+QH4LhbWTYHu43TQuwtpERbIFyOi6NywKp4e+v/6quTnGAUgGv1zRT//dfiCY2CHd21gWh65kPZQIs6m+U0QEAZZR2HT/8xOIyXUrUmIir/SsOl/xmsgsLYx9Zu4DLvdfkHrl84Nq9IiLNC0THIhp99LpaSkMHjwYAIDAwkODmbEiBFkZmZecvuHH36YZs2aUalSJerWrcsjjzxCWlqaw3YWi+WCy6xZs8rhHolchofX2YnuV30AmjBd3I3dDqsmGcsdRxqvCXEZe4+fZHtShsM6jf45H6cvAAcPHszmzZuZP38+P/30E0uXLuW+++4rcvtDhw5x6NAh3njjDTZt2sS0adOYN28eI0aMuGDbzz77jMOHDxdeBg4cWMb3RqSY2g8DLz9I3gh7lpqdRqR87VkCyZuM10CH4WankRJqEOLPiqev45HrGlPN35vwqpWIaa4zuJ2NxW533uGFrVu30qJFC9auXUuHDh0AmDdvHgMGDODAgQOEhRWvl9C3337L3XffTVZWFp6exmGPFouF2bNnX1XRl56eTlBQEGlpaQQGamhbStnPT8DaT6BJXxj8jdlp5Crl5Bfg4+lhdgzX8OVtsPN36HQfDHjd7DRyFbLzCkhMOUmTmgFmR3Gg928nHwFctWoVwcHBhcUfQExMDFarlTVr1hT7ds78gc8Uf2c89NBDhISE0KlTJ6ZOncrlauGcnBzS09MdLiJlpvNowAI7f4OjO8xOI1do19FMnp2zkS4TF5F6MtfsOM7v6Haj+MOikz8qAF8vD6cr/sTg1AVgUlISNWrUcFjn6elJ1apVSUoqXo+0Y8eO8dJLL13wsfGLL77IN998w/z587n11lt58MEHee+99y55WxMnTiQoKKjwEh4efgX3SqSYqjWCZgOM5dUfmJ1GSshms3PfjHX0fnMJX6zez/GsXGbGqjH0ZZ15rkfcYLwGRKRMmFIAPvPMMxc9CePcy7Zt267696Snp3PDDTfQokULXnjhBYefPffcc3Tt2pW2bdvy9NNP89RTT/H665f+qGH8+PGkpaUVXhITE686o8glRT9kfN3wFWQdMzuNlIDVasHXy/Ej3xkr95F3TmNcOU/WMdhw+mS8M899ESkTphSAjz/+OFu3br3kpWHDhoSGhnLkyBGH6+bn55OSkkJoaOglf0dGRgb9+vUjICCA2bNn4+V16bPIoqKiOHDgADk5Rc/d6ePjQ2BgoMNFpEzV6wK12kB+9tmeaOIyzjSGPiMpPZtfNh42LY/TWzvFeK6HtYW60WankRLIL7CRmZNvdgwpAVMaQVevXp3q1atfdrvo6GhSU1OJi4ujffv2ACxatAibzUZUVFSR10tPT6dv3774+Pgwd+5cfH19L/u74uPjqVKlCj4+arorTsRigS4Pw/9GQOzH0OUR8Lr881mcQ2R4MB3qVWHdvhOF66Ys38NNkWFYLJohxEFetnHSE0D0GOO5Ly7jt83JPPP9XwyOqsewLvUJDdJ+ytk59TGAzZs3p1+/fowaNYrY2FhWrFjBmDFjGDRoUOEZwAcPHiQiIoLY2Fg4Xfz16dOHrKwspkyZQnp6OklJSSQlJVFQUADAjz/+yKeffsqmTZtISEjgww8/5JVXXuHhhx829f6KXFSLm41muFlHYdN3ZqeREjp/FPCvA2kOBaGctvHb042f6xjPeXEZdrudT5btJiM7n8lLdtHttUW8v2in2bHkMpy6AAT48ssviYiIoHfv3gwYMIBu3brx8ccfF/48Ly+P7du3c/LkSQDWr1/PmjVr2LhxI40bN6ZWrVqFlzPH7Hl5eTFp0iSio6Np06YNH330EW+99RYTJkww7X6KFMnDC6LuN5ZXTVJjaBfT55pQwqtWclj36bLdpuVxSuc2fo66X42fXUzcvhPEJ6YWfp9vs1Ovmr+pmeTynLoPoLNTHyEpN6dS4a0WkJcF98yGRteZnUhKYMryPbz005bC7y0WWPJEL+pW8zM1l9NIWAhf3ALeleGxzVAp2OxEUgL3zVjH71uSC7+vHVyJJU/2dOopEfX+7QIjgCKC8YbY7h5j+cxIibiM2zvUobLP2UOu7Xb4bOWeS17HrZx5Tre9R8Wfi9lzLIv5W5Md1t3brYFTF39i0F9IxFVEPQAWKyQsgCNbzU4jJRDg68UdHR37hn6zNpH07DzTMjmN5C2wa6Hx3O6sxs+uZsry3Q5HpQT4el7wXBfnpAJQxFVUbQARfzOW1Rja5QzrUh/rOSe2ZuUW8M1a9RItfC43vxGq1Dc7jZTAiaxcvos74LDurk51HUa7xXmpABRxJdFjjK8bvobMI5fbWpxIeFU/+l7j2L/0sxV7yXfnxtCZR+Cvr43lM89tcRlfrN5Hdt7Z56+n1cKwririXYUKQBFXEt4JaneAghyjaa64lPNbwhxMPcXP7twYeu2nUJALdToaz21xGdl5BUxftddh3Y2RYdQKqlTkdcS5qAAUcSUWy9kpstZ+CnmnzE4kJdC+XhXahDue5PDx0t24ZTOGvFPGcxhN++aKfog/yLHMXId1I7s3KHJ7cT4qAEVcTfObICgcTh6Dv74xO42UgMVi4YEeDQHw9rByR4dw3hnU1j1nBfnrazh5HILqQsSNZqeRErDZ7HyyzPEs9q6Nq3FNWJBpmaTkdKSmiKvx8DTOCP79/4z2Ge2GaNosF3J9i1Ce7NuMf7SvQ81AN50uy2aDVadP/uj8gPGcFpexZMdREo5kOqwb2b2haXnkymgEUMQVtbsHvAPg2Hajia64DA+rhYd6NXbf4g+Mti/HtoNPoNH7T1zKJ+fNZNOkRmV6Nq1uWh65MioARVyRbxC0H2osr3jb7DQiJbPiHeNruyHg656zMLiqhCOZrNx13GHdyO4N3PMwBhenAlDEVUU9AFZP2LsMDsaZnUakeA7EGc9Zqyd0Hm12GimhxjUq87/RXejfMhSrBUIq+3Bzm9pmx5IroAMvRFxVcDi0uh02zITlb8Mdn5udSK6S3W6v+CMpK/5rfG19BwTVMTuNXIH29arQvl579h3PYu/xk/h6eZgdSa6ARgBFXFnXR42vW3+EYzvNTiNXKCsnn6nL9xDz1hJSsnKLcQ0XdWwnbP3JWD7z3BWXVa+aPz107J/LUgEo4spqRECzAYAdVr5rdhq5Au8u3EmXVxfx4k9b2HU0ixnnNdetUFa8YzxXm90A1ZuZnUbErakAFHF1XccaX+O/gvRDZqeREtp7PIu0U3mF309fuZdTuQWmZioT6YdgwyxjudtYs9OIuD0VgCKurm4U1O0CtjxY/YHZaaSE7rvWsX/aiZN5fBuXaFqeMrNqkvEcrddV0765mLwCGzabG85WU8GpABSpCLo9Znxd9xmcOmF2GimBiNBAejVzPI7qk2W7yS+wmZap1J06AXHTjOUzz1VxGbPWJnL9f5fw7bpE8irS89LNqQAUqQiaXA81roHcTFg7xew0UkL392jk8H1iyil+3ZRkWp5St/ZT47lZsyU0jjE7jZRAfoGNj5fuYtfRLJ787i96vr6Y+VuSzY4lpUAFoEhFYLGcPa5q9YeQd8rsRFICUQ2qEhke7LDuo6W7sNsrwMdueadg9WRjuetYTVvoYn766zCJKWf3JwdTT+HvrbYvFYEKQJGK4ppbILgunDwG8V+anUZKwGKx8MB5xwJuOph+wYwLLunPL4znZHBduObvZqeRErDZ7HywOMFhXWR4MNGNqpmWSUqPCkCRisLDE6IfNpZXvAsF+WYnkhLoc00o9av5OaybvGSXaXlKRUH+2fZEXR4xnqPiMhZuO8KO5EyHdQ/1bFTxm5W7CRWAIhVJ27vBrxqk7oMtc8xOIyXgYbUw6rxRwGU7j7H5UJppma7a5tmQuh/8QqDNYLPTSAnY7XYm/eE4+tekRmVimtc0LZOULhWAIhWJt58xRzAY08NVhGPI3Mit7eoQUtnbYd3HS3eblueq2O2w4m1jOeoB47kpLmPV7uPEJ6Y6rHuwVyOsVo3+VRQqAEUqmo4jwcsfkjdCwkKz00gJ+Hp5MKxLfYd1xkH4J03LdMUSFkDyJvCuDJ1Gmp1GSuiDPxwPP6hTpRI3tg4zLY+UPhWAIhWNX1XoMNxYPjMCIy7j7s718DvnLMsCm50PFrvgsYDLTz/32g+DSlXMTiMl8NeBVJYnHHNYd/+1DfH0UMlQkeivKVIRdX4QrF6wdxkkrjU7jZRAsJ83d3Wq67Duu7hEktOzTctUYomxsG+58RyMfsjsNFJC54/+hVT24bYO4ablkbKhAlCkIgqqDa3vMJY1Cuhy7ru2Id6exu65bd1gPh7SgRoBPmbHKr4zo3+Rd0CgPjZ0JQlHMpi32bEJ+YhuDfD1Uu+/ikbn5ItUVF0fMfoBbvsJjm6H6s3MTiTFVCPQl2dvaE7DkMp0bVzNtdpuHN0O238GLNDlUbPTSAl9uNjxpKMAX0/u7ly3yO3FdWkEUKSiqt4MIm4wlle8Y3YaKaEh0fXp1iTEtYo/znmuRdwA1ZuanUZK4MCJk/wQf9Bh3dDo+gT4epmWScqOCkCRiqzbY8bXv76GlD1mp5GKLmUPbJhlLHcbZ3YaKaGvYveTbzvbOsrXy8rwrvUveR1xXSoARSqyOh2gUW+w5cOyN81OIxXdsjfAXgCNY6BOe7PTSAmNjWnKm7dF0qi6PwCDOtalWmUXOvZUSsTpC8CUlBQGDx5MYGAgwcHBjBgxgszMzEtep2fPnlgsFofLAw884LDN/v37ueGGG/Dz86NGjRo8+eST5Odr6iypgHo+Y3zd8JVGASsAu91OXoHN7BgXStkD8V8Zyz2eMTuNXAEvDyu3tq/D/Md6MPnu9tx33sw0UrE4fQE4ePBgNm/ezPz58/npp59YunQp991332WvN2rUKA4fPlx4+c9//lP4s4KCAm644QZyc3NZuXIl06dPZ9q0aTz//PNlfG9ETBDeSaOAFYDdbmfZzqPc/tEq/jt/h9lxLnTu6F94R7PTyFWwWi30axlKWHAls6NIGbLY7c47V9TWrVtp0aIFa9eupUOHDgDMmzePAQMGcODAAcLCLt5eoGfPnrRp04a33754+4tff/2Vv/3tbxw6dIiaNY15DSdPnszTTz/N0aNH8fb2vuj1zpeenk5QUBBpaWkEBgZe8f0UKXOJsTDlerB6wph1ULWB2YmkBDYdTOPZOZsKp+aq7OPJ8qd7EexXvH1VmUvZA++1NwrAEQtUAIrT0/u3k48Arlq1iuDg4MLiDyAmJgar1cqaNWsued0vv/ySkJAQWrZsyfjx4zl58uxUSqtWraJVq1aFxR9A3759SU9PZ/PmzUXeZk5ODunp6Q4XEZcQ3gkaXadRQBdVyduDDQfOzsuamZPP1BV7Tc3k4MzoX6PeKv5EXIRTF4BJSUnUqFHDYZ2npydVq1YlKSmpyOvdddddfPHFF/zxxx+MHz+ezz//nLvvvtvhds8t/oDC7y91uxMnTiQoKKjwEh6uzujiQnqccyzgCScqHuSyGlWvzN/Om4f1sxV7SM/OMy1ToXPP/O2pY/9czZEMF5phRkqVKQXgM888c8FJGudftm3bdsW3f99999G3b19atWrF4MGDmTFjBrNnz2bXrqubT3P8+PGkpaUVXhITE6/q9kTKVd0ojQK6sIeva+zwfUZ2PtOdYRRw2ZvGc6pRb2OkWVzGiaxcrntjCfdOW8vGA2lmx5FyZspMII8//jjDhg275DYNGzYkNDSUI0eOOKzPz88nJSWF0NDQYv++qKgoABISEmjUqBGhoaHExsY6bJOcnAxwydv18fHBx0enxIsL6/EM7FoE8TOh++NQRT2+XEXTmgEMaBXKLxvPfkoxZcUehndrQGUfkyZ1StljjCij0T9X9PGy3WTm5LNo2xEWbTtCv2tCmTS4HR5WF2s+LlfElBHA6tWrExERccmLt7c30dHRpKamEhcXV3jdRYsWYbPZCou64oiPjwegVq1aAERHR7Nx40aH4nL+/PkEBgbSokWLUr2vIk5Fo4AubUyvJg7fp57M4/NV+0zLo9E/13UsM4dp540g+3pZVfy5Eac+BrB58+b069ePUaNGERsby4oVKxgzZgyDBg0qPAP44MGDREREFI7o7dq1i5deeom4uDj27t3L3LlzGTJkCNdeey2tW7cGoE+fPrRo0YJ77rmHDRs28Ntvv/Hss8/y0EMPaYRPKr4zxwLGz9SxgC6mRVgg17dwPH75k2W7OZlrQg9Tjf65tI+W7OJUXkHh9x5WC4/GaOo+d+LUBSCnz+aNiIigd+/eDBgwgG7duvHxxx8X/jwvL4/t27cXnuXr7e3NggUL6NOnDxERETz++OPceuut/Pjjj4XX8fDw4KeffsLDw4Po6GjuvvtuhgwZwosvvmjKfRQpVxoFdGmPXOc4CpiSlcvMNfvLP4hG/1zWkfRsZpw3cnxL29o0CPE3LZOUP6fuA+js1EdIXNb+NTC1j9EX8OE4HQvoYoZ/Fssf248Wfh9S2YflT/fC18ujfAKk7IH3OxgF4Ij5KgBdzAtzNzNt5dnRf0+rhT+e6El4VT9Tc5UnvX+7wAigiJQBjQK6tId7O44CHsvM4YvV5XgsoEb/XNbhtFMXjBjf1iHcrYo/MagAFHFXOhbQZbWrW4XuTUIc1k36I4GM8ugLqGP/XNr7ixLIPWcuaW8PK2POazEk7kEFoIi7qhsFDXtpFNBFPXa94wH7J07m8cnS3WX/iwtH/67T6J+LSUw5yTfrHPvXDuoUTm3N+euWVACKuLOeGgV0Ve3qVqHPeWcE/7TxMAW2Mjys+9zRvx4a/XM17y9KIK/g7PPD29PKgz01+ueuVACKuLO6nTUK6MKe7NsMqwWCKnkxvn8EvzzSvWz7uJ07+le3+L1YxXz7jmfx3foDDuvujqpHaJCvaZnEXCa1jxcRp9HzGdj9hzEK2HUsVGtkdiIppiY1A5h0Vzu6NA4hqJJX2f6yYwka/XNh7yzc6TA67Otl5YGeDU3NJObSCKCIu6vbGZr0MUZ2Fv7L7DRSQv1b1Sr74g+M54Yt33iuaPTPpew6msmcPw86rBsaXZ8aARr9c2cqAEUEYv4FFits+QES15qdRpxNYixsnWs8R2L0T4Kr2Xo43aFHpL+3B/f30Ei/u1MBKCJQswW0uctYnv8cqD+8nGG3w+/PGcttBhvPFXEpf2sdxtKnenHftQ3x9bIyrGt9qvp7mx1LTKaZQK6COolLhZJ2EN5rD/mnYNBMiLjB7ERyFfYcyyInv4CI0KvcN239Cb4eDJ6V4JH1EBhWWhHFBEcysvHx9CifwwacmN6/NQIoImcE1YboB43l+ROgIN/sRHIFktOz+efsjcS8tYR/fr+Rq/ofvyAPFkwwlqMfUvFXAdQI8HX74k8MKgBF5Kyuj4JfNTi+E/6cYXYaKaH1+0/Q4/U/mLlmPwU2O+v3p7Jg65GruMEZcDzBeE50fbQ0o4qIyVQAishZvkHQ42lj+Y+JkJNpdiIpgZZhQRec2fn6b9uurDl0TgYsnmgs93gGfN3zYzKRikoFoIg4aj8cqjSArCOw6n2z00gJeHtaebyP4xRxO5IzmX1eC5BiWfk+ZB2Fqg2h/bDSCyll7mRuPi/9tIUj6dlmRxEnpgJQRBx5ekPM6eO+VrwLGclmJ5ISuLF1GM1rOY7W/Xf+DrLzCop/IxlJsPI9Y7n3BOM5IS7joyW7mbJ8Dz3fWMx7C3dyKrcEf3txGyoAReRCLQZC7faQlwVLXjU7jZSA1WrhqX7NHNYdTD3Fl2v2F/9GFr9q/O1rd4AWN5d+SCkzSWnZfLx0NwAncwt4c/4O/m/2RrNjiRNSASgiF7JY4PqXjOW46XB0h9mJpAR6Nq1OVIOqDusm/ZFARnbe5a98dLtx8gdAn5eM54K4jDd+386pc0Z7rRbU9FkuSgWgiFxc/a7QbADYCzRFnIuxWCw83T/CYV1KVi6Tl+y6/JUX/Mv4mze7Aep1KbuQUuo2HUzjf+sPOKy7o2NdmoUGmJZJnJcKQBEpWswLxvRf236C/avNTiMl0K5uFfq0qOmw7pOle9h7LKvoK+1bBdt/BouH8bcXl2G32/n3z1sdJvHx9/Zg3PVNL3U1cWMqAEWkaNWbQbshxvLvmiLO1TzVrxme1rMf4eYW2Hjxpy0X39huN6YBBONvXl2FgytZsPUIq3Yfd1j3YK/GVA/wMS2TODcVgCJyaT3Hg5cfHIiFrT+anUZKoHGNAIZ1qe+wbtG2IyzYcpEzu7fOhQNrwcvf+JuLy8jNt/HKL1sd1tUOrsSIbg1MyyTOTwWgiFxaQCh0edhYXvCCMT2YuIxHY5pcMAr0r582O7aFKcgz/rZg/K0DaiKu48s1+9hz3kf7T/Vrhq+Xh2mZxPmpABSRy+vyMPhXh5RdsPoDs9NICQT4evHPAY4nhCSmnGLqij1nV6yaBCm7jb9xlzHlH1KuWNrJPN5ZuNNhXWR4MDe21rzNcmkqAEXk8nwCIOb0mcCLX4XURLMTSQkMbFObjvWrwOm2IMO61GdwVD3jh6n7YclrxvL1Lxp/a3EZ7y3aSepJx1H5525ojtWq9j1yaSoARaR42twFdbtA3kn49Wmz00gJWCwWXrjpGjo3rMrPj3TnhZuuIaiSl/HDX582/qb1ukLknWZHlRLYeyyL6av2Oqwb0CqUDvWrFnkdkTNUAIpI8Vgs8Le3wOpptArZ/qvZiaQErgkLYtZ90Y7TxG37Bbb/YvxNb3hTTZ9diN1u5+Wft5BXcPbMfG8PK0/3i7jk9UTOUAEoIsVXozlEnz5G7JenIPcSPeXEueVmwa9PGctdHjb+tuIy9qecZOUux7YvQ7vUo141f9MyiWtRASgiJdPjKQiqC2n7YenrZqeRK7XkP5CWaPwtr33K7DRSQvWq+fP7Y9fSs1l1AEIqezOmVxOzY4kLsdjt6ux6pdLT0wkKCiItLY3AwMBiXEOkgtj2C8y60/jo8IHlGj1yNclb4KPuYMuHO2eRFNoLqwVqBPqanUxKyG638+Nfh/H1tNLnmlCz47gMvX9rBFBErkTEAGOeYFs+/Py4ZghxJXa78Tez5WNrOoDJSU257s3FRc8QIk7NYrFwU2SYij8pMRWAInJl+r9mzBCybwVs+MrsNFJc8TNh/0psnpW46+AtvPrrNk7mFvDTX4dZueuY2elEpJw4fQGYkpLC4MGDCQwMJDg4mBEjRpCZmVnk9nv37sVisVz08u233xZud7Gfz5o1q5zulUgFEFwXepxuB/P7s3AyxexEcjknU4y/FZDd9Sm2ngxy+PELczeTV2AzKZyIlCenLwAHDx7M5s2bmT9/Pj/99BNLly7lvvvuK3L78PBwDh8+7HD517/+ReXKlenfv7/Dtp999pnDdgMHDiyHeyRSgUQ/BNWbw8njZ6cSE+e1YAKcSoEaLfC79mGe6NvM4cc7kjOZvnJvkVcX8+xIzuCFuZvJzMk3O4pUEE5dAG7dupV58+bx6aefEhUVRbdu3XjvvfeYNWsWhw4duuh1PDw8CA0NdbjMnj2b22+/ncqVKztsGxwc7LCdr68OgBYpEQ8vozcgwPrpkBhrdiIpyv41sH6GsXzDW+DhxV2d6nJNmOMB8G/8vp3dR4v+lEXKn81mZ/z3G5m2ci/Xv7WEBVuSzY4kFYBTF4CrVq0iODiYDh06FK6LiYnBarWyZs2aYt1GXFwc8fHxjBgx4oKfPfTQQ4SEhNCpUyemTp3K5U6IzsnJIT093eEi4vbqdYE2g43lnx6DAo1QOJ2CPONvA9DmbqgXDYCH1cKLN7d02DQ7z8bj324gXx8FO40v1+wjbt8JAA6nZTNyxjrm/HnQ7Fji4py6AExKSqJGjRoO6zw9PalatSpJSUnFuo0pU6bQvHlzunTp4rD+xRdf5JtvvmH+/PnceuutPPjgg7z33nuXvK2JEycSFBRUeAkPD7+CeyVSAV3/IlSqAsmbYM1ks9PI+dZMhiObjb/R9S86/Kh9vSoMia7nsO7P/al8tHR3OYeUi0lKy+a1edsd1tWr5ke/ljrrV66OKQXgM888U+SJGmcu27Ztu+rfc+rUKWbOnHnR0b/nnnuOrl270rZtW55++mmeeuopXn/90k1tx48fT1paWuElMTHxqjOKVAj+IRDzL2P5j1cg7YDZieSMtAPwx0Rj+foXwb/aBZs80z+CBiGOM0i8vWAHmw+llVdKKcLzP2y64Li/V/7eCl8vD9MyScVgSgH4+OOPs3Xr1kteGjZsSGhoKEeOHHG4bn5+PikpKYSGXv6/n++++46TJ08yZMiQy24bFRXFgQMHyMnJKXIbHx8fAgMDHS4iclrbeyA8CvKyYM6DYNNHiKaz2WDOaONvEh5lfPx7EX7enrx5eyTWc6YCziuwM+7rDeTkF5RfXnEwb9Nhfj/veL9/tK9D18YhpmWSisPTjF9avXp1qlevftntoqOjSU1NJS4ujvbt2wOwaNEibDYbUVFRl73+lClTuOmmm4r1u+Lj46lSpQo+Pj7FvBci4sBqhZsnwUfXwp4lsOo96Pqo2anc28p3Yc9So1/jzZOMv1ER2tWtwgM9GvHB4l2F67YnZ/Df+Tt5pn9EOQWWM9Kz83j+h80O66r5e/N/AzTrjpQOpz4GsHnz5vTr149Ro0YRGxvLihUrGDNmDIMGDSIsLAyAgwcPEhERQWys49mHCQkJLF26lJEjR15wuz/++COffvopmzZtIiEhgQ8//JBXXnmFhx9+uNzum0iFFNIE+p3+uHHhS3Ao3uxE7uvQn7DoZWO536vG3+YyxsY0pXktx082Plq6i3V71eOxvE38ZRtHMhw/kXr+xhZU8fc2LZNULE5dAAJ8+eWXRERE0Lt3bwYMGEC3bt34+OOPC3+el5fH9u3bOXnypMP1pk6dSp06dejTp88Ft+nl5cWkSZOIjo6mTZs2fPTRR7z11ltMmDChXO6TSIXWbihE/A1sefC/kZCbZXYi95ObZTz2tjxofiO0u/xhMADenlbeuj0SL4+znwXb7fD4txvIUv+5cvPrxsN8FbvfYV3PZtW5KTLMtExS8Vjsl+t9IkXSZNIiRTiZAh92hYxD0H4Y3PiO2Yncy9xHjL6MAWEwegX4VS3R1T9cvIvX5p09ES/Q15Pp93aibd0qZRBWzrX/+ElueG8ZGdlnC+5KXh78/ti1hFf1MzVbRaL3bxcYARQRF+RXFf4+GbBA3DTY+qPZidzH1h+N4g8L3PJRiYs/gPuubUj7ekax171JCL8/1kPFXznIyS9gzFfrHYo/gBduaqHiT0qdCkARKRsNe0CX08fVzn0Y0i8+e4+UovRDxmMN0PURaHDtFd2Mh9XCm7dF8vLAlsy4txOhQZolqTy8+us2/jrg2HpnYJswbu+gnrNS+lQAikjZue45qBUJp07A7AfUGqYs2Www+37jsa7VBno9e1U3Vz/En7s718NisRRja7laK3cd47MVjvMwN6zuz7//3kp/AykTKgBFpOx4esOtU4w2JGdaw0jZOLfly62fGo+9uIxO9asypldjztR6Pp5WJt3VDn8fU7q1iRtQASgiZUutYcreoT9h0UvGcjFbvlyNvAIbyenZZfo73I2nh5Un+jZj+vBOVPP3ZsKN11zQkkekNKkAFJGy59AaZoRaw5SmwpYv+SVq+XKlUk/mMnRqLHd+vJq0U3ll+rvc0bVNq7Pw8R7c2UnH/UnZUgEoImXPYoGb3jPakhxPgHnjzU5Uccx7xnhMA8LgxnehDI8X25mcwc2TVrBy13F2H8vi4a/+JL9Ax3WWtmA/bx33J2VOBaCIlI9zW8Osnw6b55idyPVtngPrZ1xVy5eSeHbOJvYdP9t0f+mOo0z8ddslryMizkkFoIiUn4Y9jPYkYJwVfDDO7ESu60Cc8RiCMefyFbZ8KYm37mhDSGXHk0umLN/DN2sTy/x3VyR5BTYe+zqeLYfSzY4ibkwFoIiUr+ueh8YxkH8KZt4BJ/YW40ri4MRe+OoO4zFsfL3Rbqcc1A6uxEf3tMfbw/Gt4//mbNR8wSXw+m/bmf3nQQZ+sIIv1+xDE3KJGVQAikj58vCE26ZBaCvIOgpf3mb0rpPiOZkCX/zDeOxCW8NtnxmPaTlpX68q//57S4d1eQV27v88jgMnThZ5PTF8vnofHy/dDUBuvo3/m71JH6OLKVQAikj58wmAu76FwNpwbAfMuhvyc8xO5fzyc+Dru+H4TgisA3d9YzyW5ey2DuGM7NbAYd3xrFxGzYgjKye/yOu5u7kbDvH8D5sc1nl7WLmxdZhpmcR9qQAUEXME1oLB34JPIOxbDj88pJlCLsVmgzkPwr4VxmM2+FvjMTTJ+AHN6dG0usO6rYfTefybDdhs+kjzfIu3H2Hc1/Gc/2nvhJta0KpOkFmxxI2pABQR89S8Bm6fAVZP2Pgt/PGy2Ymc16KXYNN3xmN1x+dQs4WpcTysFt67qy2Nqvs7rJ+3OYm3F+40LZczituXwugv1pN/XmE8NqYJg6PqmZZL3JsKQBExV6NeRv86gGVvQtx0sxM5n7hpsPwtY/mm96BhT7MTARDo68WnQzsSVMnLYf27C3fy81+HTcvlTLYlpTP8s7WcyitwWD+sS30e7V22M7aIXIoKQBExX9vB0ONpY/mnxyBhgdmJnMfOBfDTOGO5xzPQ5i6zEzloEOLPpLva4WF1bFz82DfxLNiSbFouZ7D/+EmGTIklPdvxuMiBbcJ4/m8t1OxZTKUCUEScQ8/xEHkn2Avgm6GQtNHsROY7/Bd8O9R4TCLvgp7PmJ3ooro1CeG5G5o7rMvNt/FV7H63bXFyJCObe6au4UiG48lN10XU4PXbIrFaVfyJuVQAiohzsFiMj4IbXAu5mfDl7ZB20OxU5kk7ADNvNx6LBtfCje+U6TRvV2tol/rcFVW38PtO9avy/l3t3HKUK+1UHkOnrnWYNQWgY/0qTLqrHV4eeusV8+lZKCLOw9Mbbv8cqjeHjEMw4yZI2WN2qvKXsgdm3AwZh43H4vbPjcfGiVksFl66uSW3ta9D6zpBTBnWgUreHmbHKnencgsYMW0tWw87zvIRERrAp0M7uuVjIs5JBaCIOJdKwUaLk6BwOJ4AU653rynjDsTBpzHGfQ8KNx6LSsFmpyoWD6uF125tzcxRnQnw9SrGNSqeH/86xLp9jo3N61XzY8aIThecLCNiJhWAIuJ8gsNhxPyzs4VM+xts/9XsVGVv2y8w7QY4ecyY5WPkAuOxcCFWq4XKPkXPTHIyN79CHxd4W/s6PN0vovD7GgE+fDEiihoBvqbmEjmfCkARcU6BtWD4r9CoN+SdhFl3wdpPzU5VdmI/ga8Hn57fNwaG/wIBoWanKlVZOfnc/ekanp2zqcI2i7ZYLIzu2YiJt7Siqr83n4+IIryqn9mxRC5gsVfkf8XKWHp6OkFBQaSlpREYGGh2HJGKqSDPaA3z5+fG913HQu8JYK0g/7/abLDwBVjxjvF9uyFww1vgUbE+LszOK2DE9LWsSDgOp1uhvHFbJJ4V+ISI9Ow8At30o3Bnp/dvjQCKiLPz8DKaH/f6P+P7FW/D96MqxtzB+Tnw/cizxV+vZ40zoStY8We32xk7K76w+AOYE3+IB76II/VkrqnZrlR2XgELt166z6GKP3FmKgBFxPlZLNDjKRj4oTEV2qbv4PNb4NSJYlzZSZ06YdyHTf8z7tPAydDjSadu9XKlLBYLA9uG4eXheN8WbD3C9f9dynwXaxidlJbNHR+vZsT0dW7f7FpclwpAEXEdbe6Cwd+BdwDsWw5T+0HqfrNTlVzqfpjS17gPPoFw9/+gzZ1mpypT/VrW4tOhHfH1cnzbOZqRw6gZ6xg760+XGA2M23eCG99fzobEVADGfh1PwpEMs2OJlJgKQBFxLY16wb3zICAMjm6Dj3oYc+XaCopxZZPZCoysH/WAY9uN+3DvPKeZ27es9WhanenDL94OZU78IWLeWsrvm5NMyXY5NpudmWv2c+fHqzl6zuwemTn5PDorvkKf2SwVk04CuQo6iFTERGkH4atBkPSX8X1YWxjwBtTpYHayizuwDn55Ag79aXwf2hrunAVBtc1OVu6S0rL55+yNLNp25KI/v7lNGC/ceA1V/M1vfm2321m84yhv/r6dTQfTL/h53ap+fDKkA81CA0zJJ1dG798qAK+KnkAiJivIM9qnLJ4IOaffnNveDb1fgMrVzU5nyDxqnOX75xfG9z6B0Ouf0HFkhTvZoyTsdjvfrz/Iv37cTHp2/gU/D6nsw7//3pK+15jXCmfN7uO8/tv2Cxo7n9G9SQjv3dmWYD/zC1UpGb1/qwC8KnoCiTiJjGRY8AJsmGl87xME1/0fdBgBHkU3JS5TBflG38I/XoGcNGNdm8EQ8wJUrmFOJieUnJ7N+O+LHg38z62tub1j+TbD3pCYyhu/b2fZzmNFbjOqewOe7hdRodvYVGR6/1YBeFX0BBJxMvvXGB+znvlYuMY1MOB1qN+1fHPsXQ6/PAVHNhvf14o0Pp4O71S+OVyE3W5n9p8HeWHuhaOBC8ZdS+Ma5fPx6vakDN78fTu/X+LM3kbV/Xmmf3Oub1GzXDJJ2dD7twucBPLvf/+bLl264OfnR3Bw8ebDtNvtPP/889SqVYtKlSoRExPDzp07HbZJSUlh8ODBBAYGEhwczIgRI8jMzCyjeyEi5aJuFNy32Gik7BtsFGDTBsA3Q2DrT5BThq/xnEzjd3wzxJjO7chmqFQF/vZfGPWHir9LsFgs3NKuDvPH9aB3xNnR0cjw4CKLv/TsPDKy80otQ1JaNgPeXVZk8VenSiXeuC2S38Zeq+JPKgSnHwGcMGECwcHBHDhwgClTppCamnrZ67z22mtMnDiR6dOn06BBA5577jk2btzIli1b8PU15mPs378/hw8f5qOPPiIvL4/hw4fTsWNHZs6cWexs+g9CxIllHYdFLxln3XJ6N+fhAw26Q9N+xuVq59lN3Q87foMd82DPMig4c3aoBToMh+ueA7+qV31X3Mm5o4FP9m3GPdH1L7rdpD8SeG/RTvq3rEWviBqEVPamip9xCfbzwtfL44LrnMjKZe/xLNrWrXLR23x01p/8EH/IYV2NAB8evq4xd3Ssi7en04+ZSDHp/dsFCsAzpk2bxtixYy9bANrtdsLCwnj88cd54oknAEhLS6NmzZpMmzaNQYMGsXXrVlq0aMHatWvp0ME4Y3DevHkMGDCAAwcOEBYWVqxMegKJuIDDGyB+Jmz/FVL3Of6sZkto2hea9ofa7cB6YdHgwFYAB9fDjl9h+7yzH/GeEVwPmvU3jvWr1br074sbScnKxdvTSmWfC4/htNvt9H5rCbuPZhV5/UpeHlT1N4rBKn7eHEo7xe6jWQT4eLJhQh+s1gsbbu89lkXvt5ZQYLMT7OfF6B6NGBJdn0rel3leiMvR+zeYdHR02dmzZw9JSUnExMQUrgsKCiIqKopVq1YxaNAgVq1aRXBwcGHxBxATE4PVamXNmjX8/e9/v+ht5+TkkJNztv9TevqFLQFExMnUijQu/V6Fo9uN4m3Hb5C4BpI3GZdlbxpn53pVuvRt5Z06e7YxgMUK4VFni8jqzSrkTB5mqHqJFjB/JqZesvgDOJVXwMHUUxxMPeWwPiMnn51HMi/atqV+iD/3dq2Pn7cnI7s3IEBTuUkFVuEKwKQko4lozZqOx2jUrFmz8GdJSUnUqOF4Fp6npydVq1Yt3OZiJk6cyL/+9a8yyS0iZcxigRoRxqXbY8ZHxAnzjY9vExYahV1OMf6p8wmExr2Ngq9xDPhXK4/0co7NB9PwtFrIt13ZB1hx+04U2bfv/25ocZXpRFyDKQXgM888w2uvvXbJbbZu3UpERES5ZSqO8ePHM27cuMLv09PTCQ8v3/YEIlJK/KtB5CDjkp8LxxPAfpnZRCweUK0xeKrvm5nuia5P/1a1+CH+EAu2JJOUns2Jk7mkncrjcgc1+XpZOeECU86JlDVTCsDHH3+cYcOGXXKbhg0bXtFth4YaTUOTk5OpVatW4frk5GTatGlTuM2RI449p/Lz80lJSSm8/sX4+Pjg4+NzRblExIl5ekNNjfy4kpDKPozo1oAR3RoUriuw2Uk/lceJk7nGJctYTj2Zh6+XlcjwYJrXCsRLvftEzCkAq1evTvXqZdOlv0GDBoSGhrJw4cLCgi89PZ01a9YwevRoAKKjo0lNTSUuLo727dsDsGjRImw2G1FRUWWSS0REypaH1UIVf2+nmEJOxNk5/b9B+/fvJz4+nv3791NQUEB8fDzx8fEOPfsiIiKYPXs2nO4nNXbsWF5++WXmzp3Lxo0bGTJkCGFhYQwcOBCA5s2b069fP0aNGkVsbCwrVqxgzJgxDBo0qNhnAIuIiIi4Kqc/CeT5559n+vTphd+3bdsWgD/++IOePXsCsH37dtLS0gq3eeqpp8jKyuK+++4jNTWVbt26MW/evMIegABffvklY8aMoXfv3litVm699Vbefffdcr1vIiIiImZwmT6Azkh9hERERFyP3r9d4CNgERERESldKgBFRERE3IwKQBERERE3owJQRERExM2oABQRERFxMyoARURERNyMCkARERERN6MCUERERMTNqAAUERERcTNOPxWcMzsziUp6errZUURERKSYzrxvu/NkaCoAr0JGRgYA4eHhZkcRERGREsrIyCAoKMjsGKbQXMBXwWazcejQIQICArBYLKV62+np6YSHh5OYmOi28xQWRY/NpenxuTQ9Ppemx6doemwuzZUeH7vdTkZGBmFhYVit7nk0nEYAr4LVaqVOnTpl+jsCAwOd/oVkFj02l6bH59L0+FyaHp+i6bG5NFd5fNx15O8M9yx7RURERNyYCkARERERN6MC0En5+PgwYcIEfHx8zI7idPTYXJoen0vT43NpenyKpsfm0vT4uBadBCIiIiLiZjQCKCIiIuJmVACKiIiIuBkVgCIiIiJuRgWgiIiIiJtRAeiEJk2aRP369fH19SUqKorY2FizIzmFpUuXcuONNxIWFobFYmHOnDlmR3IqEydOpGPHjgQEBFCjRg0GDhzI9u3bzY7lND788ENat25d2KQ2OjqaX3/91exYTunVV1/FYrEwduxYs6M4hRdeeAGLxeJwiYiIMDuWUzl48CB333031apVo1KlSrRq1Yp169aZHUsuQQWgk/n6668ZN24cEyZMYP369URGRtK3b1+OHDlidjTTZWVlERkZyaRJk8yO4pSWLFnCQw89xOrVq5k/fz55eXn06dOHrKwss6M5hTp16vDqq68SFxfHunXruO6667j55pvZvHmz2dGcytq1a/noo49o3bq12VGcyjXXXMPhw4cLL8uXLzc7ktM4ceIEXbt2xcvLi19//ZUtW7bw5ptvUqVKFbOjySWoDYyTiYqKomPHjrz//vtwer7h8PBwHn74YZ555hmz4zkNi8XC7NmzGThwoNlRnNbRo0epUaMGS5Ys4dprrzU7jlOqWrUqr7/+OiNGjDA7ilPIzMykXbt2fPDBB7z88su0adOGt99+2+xYpnvhhReYM2cO8fHxZkdxSs888wwrVqxg2bJlZkeREtAIoBPJzc0lLi6OmJiYwnVWq5WYmBhWrVplajZxPWlpaXC6yBFHBQUFzJo1i6ysLKKjo82O4zQeeughbrjhBod9kBh27txJWFgYDRs2ZPDgwezfv9/sSE5j7ty5dOjQgdtuu40aNWrQtm1bPvnkE7NjyWWoAHQix44do6CggJo1azqsr1mzJklJSablEtdjs9kYO3YsXbt2pWXLlmbHcRobN26kcuXK+Pj48MADDzB79mxatGhhdiynMGvWLNavX8/EiRPNjuJ0oqKimDZtGvPmzePDDz9kz549dO/enYyMDLOjOYXdu3fz4Ycf0qRJE3777TdGjx7NI488wvTp082OJpfgaXYAESl9Dz30EJs2bdJxSudp1qwZ8fHxpKWl8d133zF06FCWLFni9kVgYmIijz76KPPnz8fX19fsOE6nf//+hcutW7cmKiqKevXq8c033+jwgdP/cHbo0IFXXnkFgLZt27Jp0yYmT57M0KFDzY4nRdAIoBMJCQnBw8OD5ORkh/XJycmEhoaalktcy5gxY/jpp5/4448/qFOnjtlxnIq3tzeNGzemffv2TJw4kcjISN555x2zY5kuLi6OI0eO0K5dOzw9PfH09GTJkiW8++67eHp6UlBQYHZEpxIcHEzTpk1JSEgwO4pTqFWr1gX/RDVv3lwfkzs5FYBOxNvbm/bt27Nw4cLCdTabjYULF+o4Jbksu93OmDFjmD17NosWLaJBgwZmR3J6NpuNnJwcs2OYrnfv3mzcuJH4+PjCS4cOHRg8eDDx8fF4eHiYHdGpZGZmsmvXLmrVqmV2FKfQtWvXC1pO7dixg3r16pmWSS5PHwE7mXHjxjF06FA6dOhAp06dePvtt8nKymL48OFmRzNdZmamw3/ce/bsIT4+nqpVq1K3bl1TszmDhx56iJkzZ/LDDz8QEBBQeNxoUFAQlSpVMjue6caPH0///v2pW7cuGRkZzJw5k8WLF/Pbb7+ZHc10AQEBFxwr6u/vT7Vq1XQMKfDEE09w4403Uq9ePQ4dOsSECRPw8PDgzjvvNDuaU3jsscfo0qULr7zyCrfffjuxsbF8/PHHfPzxx2ZHk0uxi9N577337HXr1rV7e3vbO3XqZF+9erXZkZzCH3/8YQcuuAwdOtTsaE7hYo8NYP/ss8/MjuYU7r33Xnu9evXs3t7e9urVq9t79+5t//33382O5bR69Ohhf/TRR82O4RTuuOMOe61ateze3t722rVr2++44w57QkKC2bGcyo8//mhv2bKl3cfHxx4REWH/+OOPzY4kl6E+gCIiIiJuRscAioiIiLgZFYAiIiIibkYFoIiIiIibUQEoIiIi4mZUAIqIiIi4GRWAIiIiIm5GBaCIiIiIm1EBKCIiIuJmVACKiIiIuBkVgCIiIiJuRgWgiIiIiJtRASgiIiLiZlQAioiIiLgZFYAiIiIibkYFoIiIiIibUQEoIiIi4mZUAIqIiIi4GRWAIiIiIm5GBaCIiIiIm1EBKCIiIuJmVACKiIiIuBkVgCIiIiJuRgWgiIiIiJtRASgiIiLiZlQAioiIiLgZFYAiIiIibkYFoIiIiIibUQEoIiIi4mZUAIqIiIi4GRWAIiIiIm5GBaCIiIiIm/l/L9Cnb+B4qbcAAAAASUVORK5CYII=' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, np.sin(x), linestyle=\"--\", linewidth=3.0)\n",
+    "ax.plot(x, np.cos(x), linestyle=\"-\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are predefined styles that can be used too.  Generally you need to start from the figure creation for these to take effect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Solarize_Light2',\n",
+       " '_classic_test_patch',\n",
+       " '_mpl-gallery',\n",
+       " '_mpl-gallery-nogrid',\n",
+       " 'bmh',\n",
+       " 'classic',\n",
+       " 'dark_background',\n",
+       " 'fast',\n",
+       " 'fivethirtyeight',\n",
+       " 'ggplot',\n",
+       " 'grayscale',\n",
+       " 'petroff10',\n",
+       " 'seaborn-v0_8',\n",
+       " 'seaborn-v0_8-bright',\n",
+       " 'seaborn-v0_8-colorblind',\n",
+       " 'seaborn-v0_8-dark',\n",
+       " 'seaborn-v0_8-dark-palette',\n",
+       " 'seaborn-v0_8-darkgrid',\n",
+       " 'seaborn-v0_8-deep',\n",
+       " 'seaborn-v0_8-muted',\n",
+       " 'seaborn-v0_8-notebook',\n",
+       " 'seaborn-v0_8-paper',\n",
+       " 'seaborn-v0_8-pastel',\n",
+       " 'seaborn-v0_8-poster',\n",
+       " 'seaborn-v0_8-talk',\n",
+       " 'seaborn-v0_8-ticks',\n",
+       " 'seaborn-v0_8-white',\n",
+       " 'seaborn-v0_8-whitegrid',\n",
+       " 'tableau-colorblind10']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.style.available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 6.283185307179586)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e144370f89a6484b8f885b0651dc4b65",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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6TStgnPUuYHG8wcFXTWoUhDpltPDTMviyVQiWdQ5DOT/v/mGLMGoQEyD+Hu0CMDExB2MTsmFxVGCQEEJI6fA89Eu/h3HBF2B4x4dB8MGhKHpxMsyvfgI+LMqtISqFEsBS4qvVQdGUubAMeh7CXQo9ag/+A9PH48A4uG3sqwwaBvMfDsWu3pEYVdPfIzZ5PKgu0Ubs7B2JLhWl75dfThei75ZcXLd4/zgQQohbcBYYvvkA+vVLHF4iMAysHXuj8NOFsDd/2Kdq+1IC+CC0Wli7D0HhRz/BVq+Zw8s055Ng+uAFsJfOujU8Jdl4Aa/vzkZimuNFtg+F6FA1yLeWoQbrWSzpFIbX60tLCOzPsGH4QSP2pTs+eo4QQkgJ5GbD9Nl46Pb+7fASe8UqKHpnDiwjX/XJjZuUADqBEFke5tc+h/m5tyDo9LLXsBlpMH04Fppj+9wen7uZbQJGbsvEDycLMGhzBo5lUkJzJ5Zh8E6TIPz8SCj8teJPmxlWBv225GJhUoFi8RFCiCdjrlyE3wcvQHPmmMNruF7DUTRlnleXbrsXSgCdhWFga/Moit78EnygfA04pqgAxhlvQLtjvdvDc5dcjseATemI///JGDmcgP5/peN83v2dqegLelc24a/uEahUbF0gxwMvJ2Tj9d3ZsPK0LpAQQkqKPXkIflNfBHs9VbZf0Opgfv5tcP2f9vnDGygBdDK+Wh0UvfcN+HLRsv2M3Q7jj59Dv/wHwMt2fl4vsqPnhnTsusqJ2rM5Hsm5lADKqROqw7aeEXi4vHRd4A8nC9B7QzquF9FOckIIuRft7s0wTXvd4XGugn8giiZMg611Z7fHpkaUALqAEFkehe98DftDDRxeo1/7CwzffQhYOYfXeJKL+TY8tj4dhzLEt3uDdAz+eDQccRWoYr0joUYNlncOw+ia0jH6J43DsrO0i5wQQhwSBOhWL4Txuw/B2OSXHPER5VH47tfgazZ0e3hqRQmgqwQEoWjCNFhbdXJ4iW7PFpg+fx3Iz3FraM52MtuKrvHXcabYLF+EkcXax8LRpiwVOr4XLctgciN/TKlhgfGOO8K9KhnxQm3vLURKCCEPxGaD4cfPYVgx3+El9tjaKHzvGwjlYtwamtpRAuhKOj0sz78NrvcIh5dokg7D74MXwaRdcmtozrLvOofH1l9HaqG4vlJMgAYbukWgQZj8phgir1ukHas7BaOivwa1Q7T4pl0ZnyiRQwgh960wH8Yv3oBu558OL7E1bY+iN78Eghyfz+6rKAF0NYYB1+8pmJ95A4KDquJs2iWYPnoZzDX5Ratqte2yGb03pCPLIl7LWCtEi43dIxAb7NsLbEurQagW23pGYEmnMATo6EeUEEIkzIUwTZsA7bH/HF7CdRsM84uTgbvU6vVl9NfFTWztHoP5tc8h+MnfzmNzMm8sXvWQgtGrzhVh0OYMFNjEyV+zCB3Wd4vw+lM9XC3CpEGlQEqgCSFEwsrB+NV70Jw9IdstMCzMI18F9/hogKU0xxEaGTey12mCwne+Bh8uf8wMey0VxhkTgcJ8t8d2PxaeMWPU35mwFjtVJ66CAau6hKOMgd5WrpRn5fHc9kyk5NPOakKIj+HtMMz9BFoHNXUFownmVz+BrWNvt4fmaegvtZsJFSqj6N1vYK/ykGy/5mIyTDPfBjjHJ2goKT5Ng4l7C1C8gE2/KiYsiQuDP92ydKlsC49+G9Ox7GwRem9Ix5VCKhFDCPERggDDwlnQ/btNtpsPCUfRW1/B3qCF20PzRPTXWgFCSBiK3vjScRJ46hCM33wA2NU3w9MhzI4OZXWitmdq+mNe+zLQa2izgitlmu3ovTEde6/fKHNwNs9OdQIJIT5Dv/In6Latke3jg8ug6K1Z4CtVd3tcnooSQKWY/FA0/jOHBaO1BxJgmD8N4HnZfqUEaIFfOgRiWHU/AMDEhoGY1jIYGpaSP1fjAVjs4rnXpBwbem9MR6aZkkBCiPfS/fUH9KsXyvYJfv4wvz4NQlQFt8flySgBVFJQCIomTAcfGiHbrdu1Efql36nuxBAdy2BOmxDEPxaOtxoFUZkSNwk3arC6SziqBYk3hxzPsqHfXxnItqjrwwIhhDiD9p9NMCyeLdsn6PQoeuUT8DHV3B6Xp6MEUGFCWBSKJkyH4B8k26/fsAy69UvcHte9MAxDBZ4VEOWnwequ4ZLzgw9mWDFwUzryiu/MIYQQD6Y5tAeGHz6V7RNYFuaXJoN/qL7b4/IGlACqgFC+Eope+xSCQf64NMOyudD+vc7tcRVYecktR6K8Cv4arOkajor+4iRw73UrBm3KQAElgYQQL8AmHYFxzvtg7PJLXCxPvwF7w9Zuj8tbUAKoEnxsbZhf/hCCRr72m2HBF9Ds2+G2eGy8gFF/Z6LfX+l0a1GFKgVqsaZrOMqaxD/Cu9M4DNuaSYk7IcSjsSlnYfpyEhgHFTEsQ16ErW0Xt8flTSgBVBF73aawPP82BJk1dYzAw/jtVGhOHHB5HIIg4PXd2fjrkgUJVzl0ib+Oi1RzTnWqBmmxums4wo3iH+O/Uy14Y0+2YnERQsiDYK5fgXH6BDAOauJyPYbB2nWg2+PyNpQAqoytxSOwDH9Fto+xWWGc+TbYc6dcGsOXR/KxIKnw1tencmwYvCkDvMo2oxDgoRDd/4tviz80LEgqxE8nCxSLixBCSoPJyYTp89fAOjgVy9qhB7gBz7g9Lm9ECaAK2eJ6w9LvKdk+xlwI04yJYNIuu+S1lyUX4oP/ckVtBg3wResQsLTbV5Xqhuqw8tFwmIrVYZyYmI09aeosKE4IIRLmQhinTwR7LVW229a0PSxPvgrQ3yKnoARQpay9hoPr3E+2j8nLgXH2e4DF7NTX3HHFghd3ZYlfC8Dc9qFoGUU7ftWsYbgeX7cNEbVZeeCFnVmw8TRzSwhROUGAYf50aC6eke221W4M8+h3AJbOmXcWSgDVimHADX0J1ladZLs1KckwLPzSaTUCj2dZ8cTWDMn5vh82D0bvyianvAZxrX5V/TCubsCtr6sEavBLXBi0VKSbEKJyus0roUvcKttnr/IQzC9/COj0bo/Lm8lvOSXqwLKwPPMmmII8aA8nSrp1uzbCXr0ebA/3eKCXuVJox6BNGcjlxMnk6Nr+eLFOgMPHEfV5r0kQjmRawTDAjx1CEWKgz3iEEHVjzxyDfsnXsn18uWgUjf8MMPm5PS5vR38d1E6rhfmlybBXrCrbbVg0C+y5k6V++kIbj0GbMnCpQFxnqUeMER81Cy718xJlaFgGP3cMxbJOYZT8EUJUj8nNcljrT/APRNFrnwNBIbKPJQ/G4V+I/fv3Y+DAgYiJiUH58uXRqVMnrFy5ssRPXK9ePYSEhNz1f//884/oMXe7dsyYMQ/2nXoygwnmsR9AMPlLuhibFcY57wP5ubIPvZc39uTgSKZV1NYsQod5HULpfF8PFahj6d+OEKJ+vB2Gb6eCzUqXdAkMA/Pz70CIKKdIaL5A9hbwjh070L9/fxiNRvTr1w8BAQFYs2YNRo0ahUuXLmHs2LH3fOIxY8YgJydH0p6ZmYl58+YhJCQEjRs3lvRHR0dj6NChkvZ69eqV/LvyQkLZijA/+yZMX70r6WPT02D8/iOYX/0EYEs+67MsuRCLTheK2qoGarCkUxhMWkogvFWRTaB/X0KI4vQrfoL2+H7ZPmuvEbA3aOH2mHyJJAG02WwYN24cWJZFfHw86te/ccbexIkTERcXh6lTp6J3796IiYm56xO/8MILsu2zZ9840HnQoEEwGqVHn8XExGDSpEml/X68mr1JO3DdhkAvczaw9nAidGt/gbX3iBI91+kcK179R1ws2F/L4LdOYQg30i4rb2TjBUzel4sdVyzY0D0cflq6RUwIUYbmwD/Qr/1Fts9Wtxm4PiX7W0ZKT/IXYMeOHTh37hwGDBhwK/kDgODgYIwfPx4cx2HJEmkCUlK//HLjH3z48OGlfg5fxg14GvaaDWT79Ct/gubIv/d8DkEQ8HJCNgps4k0fX7YOQY0QndNiJeqRabZjwKYMzDmWj8OZVrySkA2BCnsTQhTApF2Gce5Hsn18aCTMo9+mci9uIEkAd+3aBQDo2LGj5OK4uDgAQEJCQqleLDExEadOnUKjRo0c3tLNycnBggULMGPGDMyfPx/Hjh0r1Wt5LY0W5jHvgQ8Jk3QxggDjtx+CSb9616dgGAaz24SgbujtZG94dT8MiqVdVt7q+R1Z+Dv1dlHoZWeL8PUx+WOWCCHEZTgLjHPeA1MoPalI0GhhfmkKEEibPtxBcgs4OTkZABAbGyu5OCoqCgEBATh79mypXmzRokUAgBEjHE/tHj16FK+8Ij4KrVOnTvj2228RERFRotcxm51bIFl1jP6wPjsJwTMmguHFhfuYglzoZ7+HnNen37VmUkUDsK5TIN7fX4DE6zZMaWi857hxHCf6L3EuV47vew1N2J3GIf+OWd/39uWiRoCA9mV9p7YWvYddj8bYtTx6fAUBAT9/Ac3FZNnugsdHw1yhCqDw33BXj7Hc8jclMNnZ2aL7QH379sW2bduwf/9+VK0qLT1Sq1YtFBQU4OLFi/f1Qvn5+ahZsyZ4nsfJkycRFBQkueadd95Br169UK1aNeh0Opw4cQLTpk3Dpk2b0LhxY2zatAkazb2nhc+ePQu7zJZybxORuAkVNy2T7bve5GFcemxYiZ6n0A740Wy719uRocFrJ8QnugRrBSxoaEZFI90OJoS4VtiBnYiJXyjbl1m3JS70fsrrj3nTaDSyuZUS3FYIesWKFcjPz8eQIUNkkz8A+PDDD0VfN2/eHEuXLkXPnj2RkJCA+Ph49OrV656vVb58eafFrWoVR8GSeQWG/3ZKuiL++xvG+k1haSl/kkhpcByHtLQ0REVFQa/3nVkjd3H1+A6LBtK0hfj8SNGtthwbgw/OBWJNpyDofKB0DL2HXY/G2LU8dXw1F04jZKP8/gFbhcrgn38T0Xp1zIx56hjfL0kCeDM5y82VryuXl5eHkJD7vz9/c/PH3W7/ymFZFiNHjkRCQgISExNLlACqZXrVHazPToIu9TzYKymSvoDFs6GJrQ0+2rmfNvR6vU+Nsbu5cnzfbGLA8RwB6y7evsVyIMOGOaesmNRI/oOZN6L3sOvRGLuWR41vfi785n4ExmaVdAkmf1he/hBGFRZ79qgxLgXJJpCba/9urgW8U1paGvLz8+97+vLkyZP4999/UaNGDbRq1eq+gwwLu7HhobCw8J7X+hyTH4rGToVgkL5JGc4C4+z38OfJdEzYnQ2zjW7z+TqWYfBt+zKoFiT+7Df9UB72XffANUWEEHXjeRjnfgzWweZE8zNvQihb0e1hEZkEsE2bNgCArVulhzJv2bJFdE1J3dz8UdrSL/v27QP+XyOQSAkVKsMyaoJsH5t2Cfr5n2PeiXw8Gn8dZ3Ntbo+PqEugjsXc9mWgueOOr10AntueiXwrf7eHEkLIfdGtXwLtoT2yfVy3wbA3bef2mMgNkgSwQ4cOqFy5MpYvX47Dhw/fas/JycEXX3wBvV6PwYMH32q/evUqkpKSZE/9AACr1YqlS5dCp9OJHlfcsWPHYLVKp4cTExMxa9Ys6HQ69OnTpzTfo0+wtYoD17mfbF/vtH8x+No/OJxpRYc113A+j5JAX9c4Qo+JDQNFbWfz7HjnX/mfY0IIuV/sxTPQr/hJts/+UANwA55xe0zkNskaQK1Wi6+++gr9+/dH9+7dRUfBpaSkYOrUqahUqdKt66dMmYIlS5bg66+/xrBh0l2n69evR3p6Onr27HnXMi5z5szBX3/9hZYtW6JChQrQ6XQ4efIktm7dCoZhMH36dFSpUsWZ37vX4QaPgebcKWjOSGsnzj69ANtDaqNt1fKoFEBbfgnwWv1AbL5kxt7rtz94LUgqRNcYI7pGmxSNjRDi4WxWGOZ+AsYunXDgg0NhfuE9QOO2fahEhuxZUO3bt8eGDRvQokULrFy5EvPnz0dkZCTmz59fonOA71TSzR/dunVDixYtcPToUSxevBhz587FqVOn0L9/f2zatAlPPfXUfb2uT9LqYH7xfQiBwZKuMrZCLEr+EV+0Cgbj5dvsScloWQbftw+Ff7Fzgcfuysb1Iu8vo0QIcR396oXQpEj3EggMC/OLkyHIHGZA3EtSB5B4vqwdWxH94weyfeanJsDWoXupntdsNiMlJQXR0dFevTNKKUqN78+nCjDujnOhg/QMFncMQ7tyhrs+zhPRe9j1aIxdyxPGlz17EqapL0gOKgAArscwcAOfVSSukvKEMXYGOg3ey/CCgBEF9bAkUn63teHXr+95VBzxLSNq+KFL9I1fcu3LGfBP70ivTP4IIW7AWWCc94ls8mevWBVcn5GKhEWkKAH0MgtOFWLnVQ4vV38SV/TSukqMuRCGHz8HZH44iW9iGAZz2oTg8xbBWNUlDBUDaF0OIaR09H/8CDb1gqRd0GhgeW7SXY8oJe5FCaAXScm34b29N3ZxZukCMLrG07LXaY/vh27rajdHR9QswqTBc7UDwNL6UEJIKbFJh6Hb+LtsH9d7JPhK1d0eE3GMEkAvIQgCxiVkI/+OYs/x4Y1xocmjstfrl34PJu2SGyMkhBDitSxFMM77FIwg3VZgr1wD1u5DFQmLOEYJoJf45XQhtqZaRG1PVPdD2DMvgw+NlFzPcGYY530K8LTbk9xbDkdLBgghjumXfg/2WqqkXdDpYHl2EqClpSVqQwmgF0gtsOPtveICvuX8WHzYLBjwC4DlmYmyj9OcPgrdBvnpekIAIJfj8cLOLHRadx2FNkoCCSFSmmP/Qb9llWwf1+9p8BWphq8aUQLoBd7bl4NcTjzt/mXrEIQYbvzz2us0BRcnf4qKfsWPYC6fd0ucxLPsTrOg7epr+PVMIU7n2PD+3lylQyKEqE1h/o2NhTLs1erC2nWg20MiJUMJoBeY0jQYj1a8XbZjUKxJcpIDN+g58BHlJY9lrFYY530CyFRrJ75LEAS8tzcHF/NvLxGYd7IAmy+ZFY2LEKIuhiXfgM1Ik7QLegPMz74JsHTylFpRAugFKvhrsLRTGL5rVwY1Q7T4rIW0/AuMfjA/+yYEmV2emnOnoFv3q3uCJR6BYRh8064MTBrx++WlXVnIttCtYEIIoDm4G7od62X7uEHPQyhb0e0xkZKjBNBLMAyDwdX88E+fSJQxyP+z8g/Vh7WL/HS8fvXPYC+cdnGUxJNUD9ZharMgUdvVIh5T99OtYEJ8Xn4uDPOnyXbZajWC1cGyI6IelAB6mXvVceP6Pw2+XIyknbHbYZj3KWDlXBgd8TRP1/RHpwriU0HmnyzAv9csDh9DCPF+hl++ApuTKWkXjCZYnp4IsJReqB39C/kavQHmZydBYKT/9JqUZOhXL1QkLKJODMNgRqsQ+Glvf7AQALzyTzasPB0jTogv0uzdDt3uzbJ9liEvQogo5/aYyP2jBNAD/Xyq4IHWYfGxtWDtIV+UU7fuV7DnTj1AdMTbVArUYlLDQFHb8Swbvj6ar1hMhBCF5OfA8POXsl22+i1g69Dd7SGR0qEE0MOsu1CEcf9ko/WqNGxMKf2OTK73CNijYyXtjMDf+OGmAtHkDqPrBKBuqE7U9tnBPJzPo93jhPgSw7J5YPOyJe2CXwAso14H6DhJj0EJoAfJsvB4bfeNH7zUQh6Pb87AxD3SH8QS0elhefZNCBrpFn3NuZPQ/r3uQcMlXkTHMpjVOgR3/movsgt4bXc2BJmjnwgh3oc9cwy67fJ/GyxPvAwhNMLtMZHSowTQg0xKzEZakfjWb+Nwfamfj69UHdaeT8j2GX6fB0ZmgS/xXU0i9Himlr+obctlC1aeK1IsJkKIm9htMPz8hWyXrWEr2Fp3dntI5MFQAughNqaY8Vuy+A/toxUNeDzW5PAxJcF1Hwo+qoKknSnMh/637x7ouYn3eadxEMr5iX9tvPlvDtUGJMTL6TavhOZisqRd0BtgeeJluvXrgSgB9AAF1tu3fm8K0jH4snUZMA/6Q6c3wDL8Fdku3T9/gT158MGen3iVYD2LT4sVGr9WxOPzQ1QbkBBvxWReh37FfNk+rvcI2vXroSgB9ADTD+XhUoF4U8aHzYNRwd85R+zY6zWDtfkjsn3Gn78EbFanvA7xDr0qGdEl2njr62HV/fBa/cC7PoYQ4rn0S74BY5Yu9eDLV4K16yBFYiIPjhJAlTudY8WcY+JyG+3K6jG8up9TX4cb8gIEo/R2Mpt6AbqNvzv1tYhnYxgG01sGo2GYDmu7huPrtmUQZqTzPgnxRpoje6H7d5tsn2XEK4BWJ9tH1I8SQBUTBAFv7MmB9Y7lVVoGmN4q5MFv/RZ/rdAIcP2eku3Tr1oIJv2qU1+PeLboAC229YxAu3KGElxNCPFInAWGRTNlu6ytH4W9ViO3h0SchxJAFVtzwYytqeIjt16oE4CHQlzzicvaqS/sMTK1ATkzDItnu+Q1iedy9ocQQoi66Nb/BjbtsqRd8AsAN3i0IjER56EEUKUKrDze/jdH1FbOj8WEhi5ca6XRwjJyvGyXdn8C9If2uO61CSGEqAaTdgn6db/I9lkGPAshONTtMRHnogRQpb44LLPxo1kwAnWu/Sfjq9WBtUMP2T7/pd+A5SyyfYTcdK3Ijt1p9D4hxGMJAgyLZoGxSjcA2qvUhO0R+b8RxLNQAqhCZ3KsmH1UuvGjX5UHq/lXUpZBz0IIDJa0azKuIWpXvFtiIJ6HFwT8fKoAzVakYcTWTGRRbUBCPJJm33Zoj+yVtAsMC8vIVwGWNn15A0oAVWjdBTO4Yhs/prlg44dDAcGwDJJf3xG55y9orlx0TxzEY+RbeXT/Mx3j/slGDifgupnH5H05JXgkIURVigphWDxHtssa1xt8lYfcHhJxDUoAVeiV+oFY1SUM1YO1AIDRtQNQ00UbPxyxte0Ce/W6knaWt8P/1zkAnf9K7uCvZRCiF/86+TmpEHvoVjAhHkW/agHYrHRJOx9cxmGlCOKZKAFUqYfLG5HQOxKfNA/GG40UKLLLsrCMHA+Blb5F9EmHof1nk/tjIqrFMAymtQyGv1Y8S/1mYg54+rBAiEdgLyZD99dy2T5uyIuAPxV89yaUAKqYXsNgTJ0Al2/8cISPrgprl4GyffrfvgUK8tweE1GvigFavNU4SNR2MMOKX88UKhYTIaSEeB6Gn78Ew0vX7tpqN4atZZwiYRHXcZhZ7N+/HwMHDkRMTAzKly+PTp06YeXKlSV+4sWLFyMkJMTh/3bu3OmS1yXOxfUZCT40QtLO5mZB/8ePisRE1Ou5Wv546P9LF2764L9c5HK0IYQQNdPu/BOaM0cl7YJGC8vwcQDV/fQ6WrnGHTt2oH///jAajejXrx8CAgKwZs0ajBo1CpcuXcLYsWNL/ALdunVDvXr1JO0xMTEufV3iJEY/WIaNhWn2e5Iu3dbVsLXtCr5qTUVCI+qjYxl83CIY/f/KuNV2rYjHF4fzMLmpdGc5IUQF8rJhWPq9bJe122AI5Su5PSTiepIE0GazYdy4cWBZFvHx8ahfvz4AYOLEiYiLi8PUqVPRu3dv2QROTvfu3TFs2LB7Xufs1/UkWy6bkW7mMaiqSZWnK9ibtIOtfgtoDyeK2hlBgOGXWSh69xv6dEhuiatgRJeKBmy8dHsDyDfH8jGyhj+qBMl+5iSEKMiw4icwBbmSdj68LLieTygSE3E9yS3gHTt24Ny5cxgwYMCtJAwAgoODMX78eHAchyVLljg9EKVeV2lFNgGv/pON53dkoduf6TiaKS28qTiGgWX4OAg6vaRLk3wC2j1bFQmLqNdHzYNx534Qjgfe2UtlYQhRG/bSOWi3rZXtswwfBxiMbo+JuIckAdy1axcAoGPHjpKL4+JuLAJNSEgo8QscPnwYs2fPxsyZM7FixQpkZmbKXufs1/UUXx7Jw8X8Gyd+7E7j0GHNNRxM55QOS0KILI/CxwbL9umXfQ/QCSHkDtWCdXi+doCoLf6iGdtTzYrFRAiR0v/2DRhBZuNH47awN2ylSEzEPST3Y5KTkwEAsbGxkoujoqIQEBCAs2fPlvgFvv9evK7AZDLhjTfewCuvvOLS1/UE53JtmHVEvJO2RaQeDcLcW/OvpIoeHQDt9ngYcjJE7WzmNeg2LIO113DFYiPqM6FBIH47U4iMO04EmZSYgx29DdCytGSAEKVpDiXKn/ih1cEy5AVFYiLuI0kAc3NvrAMICgqSux6BgYG3rrmbSpUq4fPPP0dcXBzKly+PrKws7NixAx988AEmT54Mk8mE559/3umvCwBms2fMMkzYnQvLHcf9ahjgo8YmWCzqnE3jBCC1Y39UWTlX0qdftxj5LTpCCA5TJDZvwHGc6L+ezgjgzfomTNhbcKvteLYNPxzLwZPVlbmt5G1jrEY0xq7ltPG12xCy5GvZrqKOfVAUFAp4yN9SZ3P1e9hoVMdtdZetyG7bti3atm1762uTyYTBgwejQYMGeOSRR/Dpp5/i6aefhlbr/BBSU1Nht9tLcKVyEjJZbE4VvwkGlbMiIO8KUtRcXq92U+Tv3YKAS8miZsZiBhZ/g5SeTyoWmrdIS0tTOgSnaacHqvsbcbrg9mqTTw/mo43uOowKHifqTWOsVjTGrvWg4xu+bxvCZY71tPoF4nSDduBTUh7o+b2BK97DGo0GVatWdfrzloYk+7o5A+doti0vLw8hISGlfsFatWqhZcuW+Pvvv3Hq1CnUqVPH6a9bvnz5UsfnDjZewNeHcwDcTlIjjAymtIpEkF69tbk5jkNaWhqKHh+DgBmvS/pDD/8DtudQ2KOlt/HJvd0c36ioKOj10g03nupTgxX9t974uW4cpsWHjf1QPTxckVi8dYzVhMbYtZwxvkxhPsrsXCfbZ+n7JCpUq/GAUXo2X3kPSxLAm2vwkpOT0bBhQ1FfWloa8vPz0bhx4wd60bCwG7cJCwtvnxDgzNdVy/SqIz+dLMDpXPEM5eSmwYgM8lMspvvB1KgLa6tO0O3eLG4XBAT+8QPMb3xBZWEegF6vV/17+H7EVTLi2Vp2NI3QY2BVE1gVvDe8bYzViMbYtR5kfPUr54OVKftir1gFiOsNo4bKNcEH3sOS6aY2bdoAALZulZb22LJli+ia0rDb7Thw4AAAIDo62m2vqxa5HI+PD4h/8OqH6jCkmmckfzdxA5+VLQujPXEAmgPet1ubPJhpLUPweKyfKpI/QnwZk3YJuk0rZPu4wS8AlPz5DEkC2KFDB1SuXBnLly/H4cOHb7Xn5OTgiy++gF6vx+DBt8uBXL16FUlJScjJEdf4OnjwoOTF7HY7Jk+ejLNnz6Jdu3YoW7ZsqV/XU311JB/XzeIt91ObBXvcH0YhLArWxx6X7TP89i1gU2E9Q0II8XGGpd+Dsdsk7bYGLWGv10yRmIgyJKm+VqvFV199hf79+6N79+6iI9lSUlIwdepUVKp0+1iYKVOmYMmSJfj6669FJ348/PDDqFOnDurUqXNrF3BCQgLOnDmDChUqYPbs2Q/0up7oUr4Nc46Jd3h0iTaiQ3mDYjE9CK77EGh3rAebXawsTNpl6DavgrXrQMViI4QQIqY5cQDa/3ZK2gWWhWXwGEViIsqR3XHQvn17bNiwAS1atMDKlSsxf/58REZGYv78+SU+j/ell15CYGAg/v77b3z99ddYvnw5jEYjXn/9dSQkJKBy5coueV01m7o/F+ZiZV8+aCpf9sYjGP3A9X9Gtku/+mcgn05+IHdn5wV1nn5DiLfh7dAv+Ua2y9qxN53364OY7OxsQekgfMHBdA4Pr70uanu6pj9mtCr9jmp3M5vNSElJQXR09O2FsTwP0+TnoblwWnI916kvuOHj3B+oh5IdXy+2PdWCSf9m41KBHfv7RyHcDXVhfG2MlUBj7FqlHV/tjj9h/PEzSbvgF4CCaYuBgGAnR+q5fOU9rN6aI17m+xMFoq8DdQzebBioWDxOw7KwDH1Rtku3dTWY1AtuD4mom40XMGJrBnpvTMfxLBtyOQEf7S9ZkXdCSCmYC6H/4wfZLq73SEr+fBQlgG4yq3UIPm0RjDKGG5s9xtcPRIRJwUq4TsTXbAhbk3aSdobnb2wIIeQOWpaBQSPe9PRzUiGO0K1gQlxCv/43yVptAOCjKsDaqY8iMRHlUQLoJnoNg9G1A3Cgf1m80TAQo2sHKB2SU1kefx6CTPkA7aE90Bzdp0hMRL0mNw2Gn/Z2EsgLwNv/5kAQaEUKIc7EZFyD7s+lsn2Wx0cDWnWePU9cjxJANwsxsJjUKAgmrWeVfbkXIaoirJ37yfbpl3wNyJQdIL6rgr8G4+qJPwTtuGLB1lR1noNNiKfS/z4XDCf9ubLVbAh747ayjyG+gRJA4jRcr+EQAqS7mjWXzkG7Y70iMRH1Gls3ABX8xMsg3t+XC55mAQlxCjb5hOTEJgAQGAbc0BfpxCYfRwkgcR7/QHB9R8l26f+YDxTmuz0kol5+WhaTGos3Qh3NtOL3s0WKxUSI1xAEGH79WrbL1u4x8JWquz0koi6UALrI5QK7T9Y3sz7SE7xMPSk2Lxv6dYsViYmo15BYP9QKEa8d/XB/Lsw2mgUk5EFo//0bmjNHJe2CwQiu/9OKxETUhRJAF5myLwftVl/D2F1ZuFJoL8EjvIRGC8vgF2S7dBuXg0m/6vaQiHppWAbvFyuGnpJvx4+nChw+hhByD1YO+t/nynZxPYZBCAlze0hEfSgBdIH91zksO1sEAcCi04Vo8kcaNl0yKx2W29gbtIBN5kxJxmaFfuUCRWIi6tWlohGto/SitumHcpFt4R0+hhDimO7vdWCvX5G082FRsHYdpEhMRH0oAXQyQRDwzl7xEWhaBmgc7ltb7bnBL0BgpG8vbcJfYC+dUyQmok4Mw2BKU3Eh2iyLgK+O5jl8DCHEgaJC6FYvlO3iBj4L6D3z7HnifJQAOln8RTP+SeNEba81CESYG465UhO+YhXY2nWVtDMCD/1y+Yr0xHc1i9SjVyXxkUvfHitAaoEPLZ8gxAl0G5aBzcuWtNsr1YCtRUdFYiLqRAmgE3F2Ae/vE8/+RQdo8Hwt7yr6XFJc3ych6PSSdu2BBLBJRxSJiajXu02CcOcBIUV2AZ8epCPiCCkpJjcL+g3yRZ+5Qc8BLP3JJ7fRu8GJfjpVgORc8YzF+02CYPSyos8lJYRGOiwObfh9LkD13sgdqgfrMLKG/62vo0wsmkZIP0AQQuTp1vwCxiwto2Sr3Rj2uk0ViYmoFyWATpJn5THtkHjNUpNwHfpXMSkWkxpw3YdC8POXtGuSjkBzaLciMRH1mtgwEGVNLN5pHIT9/aMwoob0vUMIkWKuX4Fu62rZPm7gc26Ph6gfJYBO8u2xfKSbxbsWpzYLBuPrldYDgsB1Gyrbpf99HsDTGi9yW1k/DQ4PLIvXGwTCX0e/nggpKf2K+WBkjty0NesAvmpNRWIi6ka/YZ0gw2zH7KPiUy66RBvRuizttgIA66P9wcvUndJcOgft7i2KxETUS6/x8Q9NhNwn9mIytHJHvrEsLAOeUSQmon6UADrBl4fzkWe9vZ6NAfBuY+mZuD7LYATXZ6Rsl37Fj4CVk+0jhBByb/rl88DIrKm2te8OoWy0IjER9aME8AFdLrBj3knx7N/AqibUDfWtun/3YmvXDXxURUk7m54G3bY1isRECCGejj11GNpDeyTtgt7g8IM3IaAE8MF9djAXljuWsWkZYFIjmv2T0God3orQr1kEFNHRX8Sx83k2PLM9Ez/TEXGE3CYIMCz7XrbL2rk/hDLhbg+JeA5KAB/AmRwrFp8uFLU9+ZA/qgRpHT7Gl9mbtoe9cg1JO5OXA/2fyxSJiahbutmON/Zko9mKNCw/W4SPD+SiwEpHxBECAJoDCdCcOSZpF/wCwHUfokhMxHNQAvgAAnQshlX3A/v/NesmDYPXGwQqHZZ6seyNYqQydBuWgsnJdHtIRN2OZdrw/YkC3Mz50op4fHucZgEJAW+H/nf5U5W4HsMAf/pbRO6OEsAHUNZPg6/alEFi30j0qWzCC3X8UdbPt458u1/2Ok1hq9NE0s5YzNCtWaRITES9OpQ3oFMF8W76WUfykGGm8kHEtxl2b4Em9byknS8T7rAAPyF3ogTQCaoH67DgkVC8Qzt/S8RRUVLdtrVgrqW6PR6ibu83DcadhWHyrIKk6DohvoSxWeG3Vv4DM9fnSUBPJcjIvVEC6EQ+X/S5hPgqD8Ha/BFJO2O3Qb9iviIxEfWqF6rDoFjxiTo/nizAhTxp0VtCfEH4vm3QZF2XtPPlomFr11WRmIjnoQSQKILr/xQEmYPJtXu2gL14RpGYiHq91SgI+jveLlYe+JxmAYkPYooKUDZhvWyfpf8zgIY2IZKSoQSQKEIoGw1bh+6SdkYQbhwRR8gdKgVq8XRN8bnAS84U4nSOVbGYCFGCaePv0MqUzbJXrQV70/aKxEQ8EyWA98HOC+i1IR1fH8uH2Satuk7uD9d7JASZtSraw4lgTx5UJCaiXuPrB8Jfe3uZBS8AnxygWUDiO5jsDJi2rJTt4wY9B9AyJHIfKAG8D8vPFWHHFQve/jcHTVekYWFSAew8JYKlJZQJh/XRAbJ9hmXfAzJHGxHfFWHSYHRt8SzginNFOJJJs4DEN+hXLwTDWSTttnrNYK/VSJGYiOeiBLCEOLuAj/fn3vr6UoEd3x3Ppw9cD4jrNhiCTL0qTfIJaPbvUiQmol5j6wYiSC/+ofvojp9LQrwVk3YZ2u3rZPscVVYg5G4cJoD79+/HwIEDERMTg/Lly6NTp05YuVJ+6rk4QRCwadMmjB8/Hq1bt0ZMTAzKlSuHNm3aYMaMGTCbzbKPCwkJcfi/MWPGlP67dIKfkwpwIV9ce+y9JkFgKQN8MP6BN4qWytCvmA/wdOoDuS3EwGJsnQBR24YUM/Zd5xSLiRB30K/6GYxdWv/S2jIOfKXqisREPJvsdqEdO3agf//+MBqN6NevHwICArBmzRqMGjUKly5dwtixY+/6pBaLBQMHDoTBYEDbtm0RFxcHs9mMrVu3YurUqYiPj8e6devg5+cneWx0dDSGDh0qaa9Xr96DfJ8PpMDKS+qOtYjUo0tFo2IxeRNrp77QbfoDbKa4rIHm0jlo/90GW8s4xWIj6jO6TgC+O16ADMvtDwcf7s/Fqi507inxTkzqBWh3b5K0CxoNuH6jFImJeD5JAmiz2TBu3DiwLIv4+HjUr18fADBx4kTExcVh6tSp6N27N2JiYhw+qUajwTvvvINnnnkGISEht9qtViuGDx+ODRs24IcffsDLL78seWxMTAwmTZrkvO/QCb4/UYBrReKZqPeaBFHdP2fRG8D1HgnjT9OlXasWwNasA5U2ILcE6li8Uj8A7+69fet31xULknNsiA2m9wnxPvqVC8DIrIm2te8GIaqiIjERzye5Bbxjxw6cO3cOAwYMuJX8AUBwcDDGjx8PjuOwZMmSuz6pTqfD66+/Lkr+braPHz8eAJCQkOC878KFsiw8Zh4Rz/51rmBAm7JUad2ZbG27go8oL2lnr6RAu3uzIjER9XqmZgDK+bFgAAyKNWFvvyhK/ohXYi+ege7fbZJ2QasD12u4IjER7yD5jblr142F9x07dpRcHBd341bcgyRvOp0O+P8soZycnBwsWLAAGRkZKFOmDFq0aIE6deqU+vUe1KwjecjlxJ+83mlCR745nVYLru+TMM79WNKlX/XzjdvAWp0ioRH1MWkZzGlbBuX9NKhVht4XxHvpV/wk227u0ANCaKTb4yHeQ5IAJicnAwBiY2MlF0dFRSEgIABnz54t9Qv+8ssvgIMEEwCOHj2KV155RdTWqVMnfPvtt4iIiCj165ZGWqEd3x8XF9zsV8WEBmF6t8bhK2yt4sCv/QXslYuidvb6FWh3/gnbI70Ui42oT1wFWoNLvBubfALaA9IJF7tOj8Kug0D3ociDkCSAubk31tUEBcnPcgUGBt665n5t2rQJP/30Ex566CEMHy6dun7ppZfQq1cvVKtWDTqdDidOnMC0adOwadMmPP7449i0aZPDmcM7OdplfL+mHShAkf327J+GAV6vY3Da83sajuNE/3UFvscTCJonnQXUrV6I/KYPAzrvTb7dMb6+jsbY9WiMnSdo+Q+y7enNOsJu9Ifgo3+LXM3V72GjUR0fXt22aGb//v146qmnEBQUhAULFsBgkH52+fDDD0VfN2/eHEuXLkXPnj2RkJCA+Ph49Op171mg1NRU2GW2y9+PNAuDn08bAdze6NEj0gZddipSsh/oqT1eWlqa6548shJqRlaE6dolUbMmKx3mNb/ienPv3xHs0vElAI2xW9AYPxj/i0kIP/6fpN2uNyKtZRfYaXxdzhXvYY1Gg6pVqzr9eUtDkgDenPlzNMuXl5cn2dxxLwcOHEDfvn3BMAxWrFiBWrVqlfixLMti5MiRSEhIQGJiYokSwPLlpZsJ7tfsvfmwCrcrrutY4J0W4Yj2v/cMpLfiOA5paWmIioqCXu+6mThr/6dh+naKpL38no0w9hgMGNTx6cnZ3DW+3q7IJuBigR0PyWwKoTF2PRpjJxAEBC/9SrarsGMf2P0CaHxdyFfew5LfkDfX/iUnJ6Nhw4aivrS0NOTn56Nx48YlfoEDBw6gT58+EAQBK1asuK/H3hQWFgYAKCwsLNH1Dzq9eiHPhiVnxcftjKzhj+ph/g4f40v0er1rp7BbPAz7ht+gOXdK1MzmZiFw15+wdh/iutdWAZePr5fi7AIWnS7A9EN5MGgY7O0XBR0rX6qJxtj1aIxLT3NsH3Snj0jaBf9AcF0GABlZNL5u4O1jLCkD06ZNGwDA1q1bJRdv2bJFdM293Ez+eJ7H8uXL0bRp01IFuW/fPuD/NQLdYcmZQljvKPtn0Nw4iJ64CcOA6/+0bJd+/RKgqEC2j/iuK4V2NF2Rhtd25+BKIY/zeXYsPl2yD4yEqIogQP/HfNku7rHBEPwCZPsIuV+SBLBDhw6oXLkyli9fjsOHD99qz8nJwRdffAG9Xo/Bgwffar969SqSkpKQk5Mjep6DBw+iT58+sNvt+P3339G8efO7BnLs2DFYrdJD3RMTEzFr1izodDr06dOntN/nfXmjYSAWdwxFnTI3Jkifesgf5X341q8S7HWbwV5DevoLk58L3V9/KBITUa+yJhaRJvGvs2kH82C2SYvnEqJmmkN7oEk+LmnnA0Ng7dxXkZiId5LcAtZqtfjqq6/Qv39/dO/eXXQUXEpKCqZOnYpKlSrdun7KlClYsmQJvv76awwbduNM16ysLPTp0wc5OTno1KkTtm3bhm3bxIUsg4OD8cILL9z6es6cOfjrr7/QsmVLVKhQATqdDidPnsTWrVvBMAymT5+OKlWquHY0/o9hGHSvZMJjMUasOW9GqyjvXQOgWgwDrt9TMH36qqRL/+dSWOP6AAFUj5HcwDAM3m0chN4bM261XS60Y/6pArxQh2ZMiIfg+RtnoMuw9hgKGP0A2vlLnER2F3D79u2xYcMGfPLJJ1i5ciWsVitq166NKVOmoF+/fvd80tzcXGRn39gqu3nzZmzeLD3JITo6WpQAduvWDTk5OTh69Cj+/vtvcByHqKgo9O/fH2PGjEGTJk0e7DstBZZh0KeKye2vS26w12oEW+3G0B7fL2pnigqg37AM3IBnFIuNqE+H8ka0K6vHzqu3Szd8eTgPI2r4IUAnudlBiOpo/tsJzYXTknY+JAzWjr0ViYl4LyY7O5vukZASMZvNSElJQXR0tNsWxrJnjsFv6ouSdsFgRMH034Cg+9uRrmZKjK+3SUyzoMv6dFHbe02Cbq3hpTF2PRrjUuLtML3zNDSXz0u6zCNegS3uxhIoGl/X85Uxpo/FRNX4anVga9BS0s5YzNDH/6pITES9WkQZ8GhFcY3RWUfykG3hHT6GEDXQJm6TTf74sCjY2ndTJCbi3SgB/L/jWdINKEQduL6jZNt1W1aByUqX7SO+6+3G4rWhOZyAOcfyFYuHkHuy26BfuUC2i+s9wqtPQCLKoQQQwKEMDq1XXUOvDenYk2YpwSOIO/FVHoKtaXtJO2PloFu3WJGYiHo1CNOjd2XxbZvvj+cji2YBiUppEzaBTbskaeejKsDWposiMRHvRwkggI8P5AEAdlyxoOv6dDy7PVPpkEgxXN8nITDSor66bWvBpF9VJCaiXpMaBeHOd0ueVcCco3kKRkSIAzYr9KsdzP71eRLQuu3EVuJjfD4B3Hedw8YU8bb62mV0isVD5PEVq8LWoqOknbHboF+zSJGYiHrVDNGhf1XxDv7vjxcgg2YBicpot68Hmy49c9ZevjJsLaW/8whxFp9PAD/eLz7zONzI4tladOSbGt2YBZS+ZbU7/wQjc/uE+LaJDQJx50lw+TYB354oUjIkQsQ4i8MPsFy/JwGWDiAgruPTCeDuNAu2porX/I2rF0A1w1RKKBsNW1vpehiG56Ff9bMiMRH1qhGiw8Bis4A/JpmRyTl8CCFupdu2Bmy2dCObPaYa7E2k654JcSafznSKz/5FmVg8XZNm/9SM6z0Cgkb6qVi7ewuYKxcViYmo18QGQdDcMQtYJVCDDKt0LSkhbmcxQ+eglBXX7ymA9ek/z8QNfPYdtuOKRXRiAACMrx8IP63PDolHECLKwda+u6SdEXjoVy9UJCaiXrHBWjwe64faIVr8/EgoNncNRnV/qn1PlKfbuhpsTpak3V61FuwNWykSE/EtPpntCIIgmf2r4KfByBo0++cJuF5PQNBKN+po92wBk3pBkZiIen3WMhi7+kSid2UTWJmd5IS4naUIuvglsl1cv1EAvU+JG/hkArg11YI918Szf681CIRRSz90nkAIjYS1g9wsoEBrAYlEoI6lxI+oim7LarB52ZJ2e7U6sNdtpkhMxPf4XAIoCAI+Kjb7FxOgwRPV/RSLidw/a49hEHQys4D/bgN76ZwiMRFCyD2ZC6FfT7N/RHk+lwBuvGTG/nTxsW8TGgRCr6EfOk8ihEbA+nAvSTsjCNDRWkBCiErptqwCk5cjabfXqAd77SaKxER8k08lgIIg4JMD4tMAqgRqMKQazf55ImuPoRBkzsjU7v0b7KWzisREPMeeNAt+PElnBBM3KiqEfv1vsl1cX5r9I+7lUwngnylmHMoQz/690TAIWpZ+6DyREBIG6yPys4C0FpA4ciSXxeBtuei6Ph2TEnNwKd+mdEjER+g2rwSTnytptz/UAPZajRSJifgun0oA0808gnS3k71qQVoMKFYolngWa/chEPQGSbt273awF5MViYmoV4aFx/NHDPj76o0PghwPfHGYZgGJGxQVQP/nUtkuru+TNPtH3M6nEsARNfxxeGBZTGgQiEAdg4kNA2n2z8MJIWGwduwt26dfTbOARCzMwKJPWfGM36LTBbhIs4DExXSbVoApkM7+2Wo2pNk/ogifSgABIMTA4u3GQTg8sCz6V6HZP29g7TZYfhZw3w6wF04rEhNRrycr2mC44zeflQe+OJR3t4cQ8mAK86HfsEy2i+s7yu3hEAJfTABvKmNgoaHZP68gBIfCGtdHto/WApLiIg0Chlczitp+OV2I83k0C0hc48bsn/RDhq12Y/A1GygSEyE+mwAS73JjFtAoadfu30WzgERibG0TjHccKW0TgBk0C0hcoSDvLrN/T7o9HEJuogSQeAUhqAysnfrK9ulXLnB7PETdokwsnqopPvrx1zOFOJdLs4DEuXR//QGmULrRyFanCfga9RWJiRB4ewLICwJmHcnD9SK70qEQN+C6PQ7BIDMLeCAB7LlTisRE1OuVeoEw3VEA3i4A02gWkDhTQR70f/0u20Vr/4jSvDoBXHvBjPf35aLB8jS8uzeHEkFvFxgCa6d+sl36VTQLSMQiTRo8U0s8C/hbciGSc2gWkDiHfuNyMIUFknZb3Wbgq9dVJCZCbvLaBJAXBHx24MaW+0KbgNlH8zF4c4bSYREX47o9DsEo3d2tPbgb7LmTisRE1GtcvQD4a2/PAvIC8NkhaakOQu5bQR50fy2X7aK1f0QNvDYBXHPejOPZ4k/yY+sGKhYPcZOAYFg795ftorWApLhwowbPFpsFXH62CKdzrA4fQ0hJ6Df+DqZIZvavXnPw1eooEhMhd/LKBJAXBHx2UPwpvnaIFr0qS9eHEe/DdR0EwSg931l7aA/Y5BOKxETUa2zdAAQUmwWktYDkgeTnQrfR0ewfrf0j6uCVCeCfF4twotjs3xuNgsDSUTu+ISAI1kcdzALSWkBSTJhRg+dr0ywgcR79hmVgzIWSdluDluBjaykSEyHFeWUCOPuoeMt97TJa9KxEs3++hOs6CILJX9KuPZwI9swxRWIi6vVinduzgIE6BuPrByLizkKBhJRUfg50m/6Q7eL60No/oh5emQCezhHv9n2jIc3++Rz/QFgfHSDbRbOApLhQowbjGwTi9fqBODQgCu80DkKIwSt/PRIX0/+5DIy5SNJua9gKfNWaisREiByv/w1Xh2b/fBbXZQAEP5lZwCN7wZ4+qkhMRL3G1w/EO02CEEozf6S08rKh27xCtotm/4ja3DUB3L9/PwYOHIiYmBiUL18enTp1wsqVK+/rBSwWCz777DM0btwYUVFRqFmzJsaNG4fr1687fMyyZcvQsWNHlC9fHpUqVcLjjz+OgwcP3tfr3kSzfz7MPxDcowNlu+iMYEKIszmc/WvUBnyVhxSJiRBHHCaAO3bsQJcuXbBnzx707dsXo0aNQlpaGkaNGoXZs2eX6Ml5nsfQoUPxySefICwsDGPGjEGzZs2wcOFCdO7cGenp6ZLHTJ8+Hc899xyuX7+OUaNGoU+fPvjnn39uxXI/6obq0INm/3yatcsACH4BknbtUZoFJIQ4Ue5dZv+o7h9RIdkE0GazYdy4cWBZFvHx8Zg1axY++ugj7Nq1C9WqVcPUqVNx8eLFez75r7/+ii1btmDAgAH466+/MHnyZCxatAgzZszA+fPn8eGHH4quT05Oxqeffopq1aph165d+OijjzBr1izEx8cDAMaNGwee50v8zb3ZMJBm/3ydXwC4roNku/Qrf3J7OIQQ76T/cykYi1nSbmvcFnyl6orERMjdyCaAO3bswLlz5zBgwADUr3/7sOrg4GCMHz8eHMdhyZIl93zyhQsXAgDee+89MHckYqNGjULlypXx+++/o6jo9nT54sWLYbPZ8NprryE4OPhWe/369dG/f3+cOnUKu3fvLtE3Vi9Uh+4xNPtHAGvnfhD8pUXAtcf+A5t0WJGYiGdIN9vx/t4cjN2VpXQoRM1ys6HbLL88iusz0u3hEFISsgngrl27AAAdO3aU9MXFxQEAEhIS7vrEZrMZ+/btQ/Xq1RETEyPqYxgGjzzyCAoKCnDgwAGnvu5NbzYMFCWdxIf5BYDr4mAtIJ0OQmRkW3hM3peDBr+nYdbRfPxyuhAnsqguIJGn//M3MJzM7F+TdjT7R1RLK9eYnJwMAIiNjZX0RUVFISAgAGfPnr3rE587dw48z6Nq1aqy/Tfbk5OT0bp161v/PyAgAFFRUZLrb8ZyM7a7qR3ComPkjSSUOA/HcaL/ehJL++7QbVgGtlBcI1J7fD9sR/bBpoKD2T15fD1FScfYzPH48WQBCmwCAEAA8On+bHzfho6TvBdfex8zudnwdzD7l/fYENid/HfI18ZXCa4c4+tmHtEh0pOqlCCbAObm3jhGLSgoSPZBgYGBt65x5Gb/nbdy73Tzue98ntzcXERERDh8zeLXO/J4ZBEuXaID3V0lLS1N6RBKxdK8E8r/vUrSrln+A8498ZoiMcnx1PH1JCUZ48fL6vBDiu7W12suWjAkLAex/oKLo/MOvvI+Lr/5dzCcRdKeXbMxzkMHpKS45HV9ZXyV5Iox/u6iATPiYkpwpevJJoCebkDdskqH4JU4jkNaWhqioqKg1+uVDue+MX2Gg9+7BWyB+JzXwPMnUaUoG7Ya9RSLDV4wvp7gfsb49SgeS69mI896cxaQwa8ZwZhbk2YB78aX3sdMbjZC/9su2ycMfAbRFaOd/pq+NL5KcdUYXy3k8ed/OZjhtGd8MLIJoNzs3J3y8vIQEhJy1ye++Rw5OTmy/XKzjEFBQXd9zeLXO2I00uYPV9Lr9Z45xkYjrI89DsPyHyRdAet/hbn+l4qEVZzHjq8HKckYlzUCY+rY8PnB2x8Y1l7kcLaxBrXL6O76WOIb72P9yhVgrNLZP1vT9tBVqw1Xvkt8YXyV5uwx/uZgNiwlL2TicrKbQO623i4tLQ35+fkO1/bdVLlyZbAs63Ct4M32O9cZxsbGIj8/X3ba9W7rEgkpKWunvhD8pR8itCcOgD1ZumLjxHu9UDsAQfrbm8kEQJQQEt/F5GRCt3W1bB/Xm3b+EqkgHQuTRj2bU2UTwDZt2gAAtm7dKunbsmWL6BpHTCYTmjRpgtOnT0tqBgqCgG3btsHf3x+NGjVy6usSclcmf3CPPS7bRTuCSXEhBhZjaosLia86X4TjtCPY5+nW/ya79s/WrAP4GJqoIFLvNAnCtl7y+xyUIJsAdujQAZUrV8by5ctx+PDtOmk5OTn44osvoNfrMXjw4FvtV69eRVJSkuR278iRNz4FffDBBxCE2wunf/rpJ5w/fx4DBw6EyWS61T5s2DBotVrMmDFD9FyHDx/GH3/8gYceegitWrVy1vdOfJS1U18IATKzgCcPQnPigOxjiO8aU2wWEDQL6POY7Aya/SOlEmFSz1njsgmgVqvFV199BZ7n0b17d4wbNw5vv/022rZtizNnzuDdd99FpUqVbl0/ZcoUNG/eHOvWrRM9z9ChQxEXF4fly5fj0UcfxeTJkzFixAi89tprqFSpEt555x3R9dWqVcObb76JM2fOoG3btnj77bcxbtw4dO/eHQAwa9YssOxdjy8m5N5Mfo5nAVfRLCARCzGweEFmFvBYJs0C+ipHs3/WZg+Dj7778ihC1MJhNtW+fXts2LABLVq0wMqVKzF//nxERkZi/vz5GDt2bMmenGXx66+/4s0330R6ejq++eYbJCYmYvjw4di0aRPCw8Mlj3n99dcxd+5chIeHY/78+Vi5ciVatWqFjRs3omXLlg/23RLyf9Y4+VlAzclDNAtIJEbXDkBw8VnAQ1Rqyhc5mv0TGAbWPiMUiYmQ0mCys7OpqBUpEbPZjJSUFERHR3vF7jNd/K8wLJsrabc/1ABFk2YCbj5JxtvGV40eZIw/O5iLTw6Ib/0m9I5EnVDaEXwnb38f6xfPgf6v5ZJ2a/NHYHnxfZe/vrePrxr4yhjT/VTis6xxfSAESguVa04dgoZ2BJNi5GYBPztIs4C+hMnOgG7bGkm7wDCw9qbZPyKWkm/Dxwdyka2m2i93oASQ+C6jH7jHBst26Vf8BAg0OU5uC9azeLGOeC3g2gtmnM+zKRYTcS9d/K9grNLjwWzNHwZfsYoiMRH1+vJwPj4/mIf6v19VZSJICSDxada43vKzgEmHoTm+X5GYiHo9f8csYJeKBmztGYHKgV55oBIphslKdzj7Rzt/SXEX821YdLoAAJBrFfD5wTxMO6Su6gGUABLfZvQD122IbJd+1QKaBSQiwXoWM1uHYEuPCCztHI5G4XQUl6/QxS8BY5Xu/LY1fwRChcqKxETU64tDebDeMeFn0AAv1Q2420PcjhJA4vOscb3BB0qPNtQkHYHm+H+KxETUq28VPzSJoMTPlzCZ16H729HsH639I2IX82345XShqO3JGv4o56eeGoCgBJAQAAYTrN0crAVcSbOAhPg63XoHs38tOtLsH5GYcSgPtjv+bBg1wCv1A5UMSRYlgITcbRbw9FFojtEsICG+6sbs31pJO83+ETnn82xYXHz27yH1zf6BEkBC/u+us4C0I5jcm50XkGG2Kx0GcTLdusWOZ//KV5J9DPFdsrN/9dQ3+wdKAAm5zRrXG3xQGUm75swxaI78q0hMRP3svIDlZwvRatU1PL8jS+lwiBMxGWnQbY+XtNPsH5FzPs+GJWfEs3+jHvJHWRXO/oESQELuYDDB2t3BjmCqC0hknM6xos3qa3hmexaScmzYfNmCf69Jz4glnkm/9hcwNpnZv5ZxNPtHJKYXm/0zaRjVzv6BEkBCxKyP9AIfHCpp15w7Cc2h3YrERNSrgr8GmcWKuxY/Lo54Jub6FWh3rJe0CwwLrg/V/SNi53Kls39P1fRHlEpn/0AJICHFGIyw9hgm20WzgKQ4Py0r+YS/LdWC3Wk0C+jp9GsWgbFL13Ta2nSGUDZakZiIek0/nAd7sdm/cfXUVfevOEoACSnG+nAP8CHhknbNhdPQ7N+lSExEvUY95I+yJvGvUpoF9GxM2mVod22QtAssC64Xrf0jYmdzbfit2Ozf0zX9EWlS7+wfKAEkRIbeAK7XE/JdKxcAvLrOcyTKMmkZvFqsxteOKxbsukqzgJ5Kv2YhGJmfc1vbrhCiKigSE1Gvzw/mSmb/Xlb57B8oASREnq19N/BhUZJ2TUoyNP/tUCQmol4ja/ijvJ/41+nH+3Mh0JIBj8NcTYE2YZOkXdBowfUarkhMRN2equmPh8sbbn39TC31z/6BEkBCHNDpHf6y169YAPBU743cZtQyGF9sFvCfNA47rnCKxURKR7/qZzCCzOxf+24QIsopEhNRt+aRBqzqEo51j4WjUwWD6tf+3UQJICEO2Np2BS/zC1+Teh7axL8ViYmo1/Aa/qjoL/7U/+lBmgX0JMzl89Du2SJpF7Q6cD3ll4UQclPbsgYsfzQc4Ub1z/6BEkBC7kKrdbjgW79qAWC3uT0kol4GDYPXis0C7k7j8HcqrQX0FDdm/6QJu/XhHhDCIhWJiRBXoQSQkLuwtekMPrK8pJ29mgLtnq2KxETUa1h1P0QHiD/9f3Igj2YBPQCbcha6f7dJ2gWdzmFpKEI8GSWAhNyNRguuz5OyXTQLSIrTaxhMaCCeBfz3Ooctl2kWUO30qxbItlsf6Q2hjLQsFPFt3vChjhJAQu7B1rIj+HLSwq/stVTZ3YLEtw2p5odKkllAWguoZuyF09Duk+7uF/QGh8dDEt/133UObVdfw9oLRR79c00JICH3otGC6+1gFnD1z4CNZgHJbTqWwYSG4lnA62YeVwqpfqRa6Vc6mP2L6wMhJMzt8RB1+/hALo5l2TB8ayY6rLmObZfNSodUKpQAElICthYPw16hsqSdTb8qe2IA8W2DY/1QJVCDiv4azGodgn39olDe3zN2Bvoa9txJaA8kSNoFgxFcN5r9I2J70iyiJR2HM604mmlVNKbSogSQkJJgNeD6OpgFXLMIsFK9N3KblmXwe+cw7O8fhZEP+UOvYZQOiTjgcPavc38gKMTt8RB1+7jYMY9RJhZP1/JXLJ4HQQkgISVkb9Ie9uhYSTubkQbtjj8ViYmoV7VgHSV+KseeOQbtoT2SdsHoB+6xQYrERNRr5xULdlwRb+h6tX4g/LSemUp5ZtSEKIFlHc8Crl0EcLTTkxBP4nD2r8sAICDY7fEQ9RIEAR8fyBW1lfdj8WQNz5z9AyWAhNwfe+O2sFeqLmlns9Kh2x6vSEyEkPvHJh2G9uheSbvg5w+uy0BFYiLqtS3Vgt1p4qU+rzUIhFHrubP8lAAScj8YBly/UbJdurW/0CwguacjmVZsT/XMXYPexNHsH9dlEOAfKNtHfJMgCPhov3j2LzpAg+HVPXf2D5QAEnL/7A1awV6lpqSdzcmEbusaRWIi6nc6x4qn/s5Eu9XXMDYhG5zdc+uHeTrNiQPQHt8vaRf8A2F9tL8iMRH12njJjP/SxTt9JzQI9Pg1vrIJYG5uLt566y3UrVsXkZGRqFevHt59913k5+eX+ImTk5MxY8YMPPbYY6hZsyYiIiJQp04dPP/880hKSpJ9zJgxYxASEuLwf4Sowt1mAdctBsyFbg+JqNuxTCtarLyGFeeKAAAX8+1YdLpA6bB8kyBAv2K+bBf32OOAX4DbQyLqJQgCPt4v3vlbJVCDIdX8FIvJWbTFGwoKCtC9e3ccOXIEHTt2xIABA3D48GHMnj0bCQkJWL9+PYxG4z2f+KOPPsKKFStQu3ZtdOvWDYGBgTh+/DiWLl2KNWvWYPny5WjTpo3sY0ePHo3gYFqAS9TLXq857NXqQHPmmKidzcuG7q8/YO01XLHYiPrULqNF8wg99ly7vYZo+qE8DK3mD5MHryHyRJrD/0KTdETSLgQEwdqpnyIxEfVad9GMw8Xq/E1sGAQd6/k/t5IEcNasWThy5AheeeUVTJ48+Vb75MmTMXPmTHzzzTcYP378PZ84Li4O48aNQ4MGDUTtf/zxB55++mm89tpr2LNHuv0e/58JrFSpUum+I0LcgWHA9XsKps9fk3Tp//wN1o69gYAgRUIj6sMwDN5uHISeG9JvtV0p5PHDyXyMrUvrzdyG56H/4wfZLq7bEMDk+bM6xHl4QcAnxdb+VQ/WYlBVk2IxOZPoFrAgCFi0aBECAgIwYcIE0YUTJkxAQEAAFi5cWKInHjZsmCT5A4D+/fujWrVqOHnyJDIyMh40fkIUY6/dGLZajSTtTGEB9Ot/UyQmol7tyhnwcHmDqG3m4XzkWemIOHfR7NsBzYXTknY+OBTWTn0ViYmo15qLHI5ni4/6fLNhIDReMPuH4glgcnIyrly5ghYtWsDfX7y7xd/fHy1atMD58+dx6dKlB3pRnU4HANBo5I9G2rhxI7744gvMmTMHmzZtAsfRKQtEhRgG3IBnZLt0m/4Ak00fcIjYu43Fs8IZFh7fHSv52mryAOw2GFb8KNvF9R4BGO69tIn4liVnxVUdaodo0beKd8z+ofgt4OTkZABA1apVZS+uWrUqtmzZguTkZFSsWLFUL/jff//hxIkTaNy4scONHRMnThR9XbZsWXz99deIi4sr0WuYzVRiwRVuJuKUkN+hYiw09VvAcDhR1MxwFrArf0bBkBdK/FQ0vq6n9BjXCQS6VNBh4+Xba4pmH83H8KpahOi9oyiD0mPsiCHhL7BXUiTt9rAo5LeIAzzk74Zax9eb3BzbH1oasCxFh6+OFyGtSMBrdY3gLA9e6qsk+yjcQZQA5ubeuNftaANGUFCQ6Lr7lZOTgzFjxoBlWUyZMkXS37p1a3Tp0gVNmzZFeHg4UlNTsXz5cnz55ZcYMmQINm7ciEaNpLfciktNTYXdbi9VjOTe0tLSlA5BVYwtuqJWsQQQAAw74nG2bitwIeH39Xw0vq6n5BiPiGCw8fLtWYRcq4BPE6/hxcqeeaC8I2p6HzM2K2qvkq/7d6lNN2Reuer2mB6UmsbXW2WnX8OjJqB9Q+CvdA3qCoVIkX6GuC8ajcbhJJu7aSdMmIDRo0cjNlZ6xqkzFRUV4YknnkBSUhLeffddtGvXTnLN8OHinZNVq1bFxIkTUa5cOYwdOxafffYZfvvt3murypcv79TYyQ0cxyEtLQ1RUVHQ6/VKh6Me0dEwH3wYxr1/i5pZ3o7Y/7Yi/0npRhE5NL6up4YxjgbQOzMPqy/ensFZekWH15pGIsLk+bOAahjj4oxbV0Ofmylpt5WLgf9jA+DPyi9HUiM1jq+3kRvj6pWVjsr5tPPmzUOvXr0QGxt7a4YvJydH9uKbM383rysps9mMoUOHYufOnRg/fjxee61kfxBvGjp0KCZMmIDEROksixy1TK96K71eT2NcjH3AMxD+2wGGFy/oN+zZAnuvJyCUL/mudhpf11N6jN9pqsHalGvg/18LusgOfJPE4ZMW3lPvVOkxvsVSBL8/5ScOrAOegdHPM09zUM34ejFvH2M2Ozv71mzczVnAs2fPyl58s/1+ZguLioowZMgQbNu2DePGjcN7771330FqNBoEBwejsJAK7BJ1EspWhK1dN0k7I/AwOCg6S3xX9WCdpJDs/FMFuFxAS1ecTffXCrC5WZJ2e+UasDeR3okixFeI7jfExsaiXLlySExMREGBuEp9QUEBEhMTUalSpRJvACkqKsLQoUOxbds2jB07VnbdX0mkpKQgLS0NMTExpXo8Ie7A9R4BQauTtGv3bgd77pQiMRH1mtggELo7fgNb7MC0g6VbX00cKMiDfv0S2S5uwDMA4x3lPIhzZFt4HM/yrrW4dyNKABmGwfDhw5Gfn49p06aJLpw2bRry8/MxcuRIUXthYSGSkpKQUmxl5M3bvtu2bcOLL76IqVOn3jWQtLQ0pKamStqzs7Pxwgs3dlIOGDDg/r9DQtxECIu8UQBahv4P+fITxHdVCtRiZA3x7cdfThfiXK7N4WPI/dH/uRRMobTMjv2hBrDXbaZITES9Zh3JQ5tV1/DS7jxcNnv/hwPJSSDjxo3D+vXrMXPmTBw+fBgNGjTAoUOHsHXrVjRu3BhjxowRXf/ff/+hZ8+eaNOmDeLj42+1v/rqq9i2bRuioqIQEBCATz75RPLiQ4cOvXXiR1JSEvr27YvmzZsjNjYW4eHhuHz5MjZv3ozMzEy0b98e48aNc80oEOIk1p7DoNu+DoxFXFJCe+RfsCcPga8pLY5OfNdrDQLxy+kCmO2AngVGPeSPQL33/+FxByYnE7qNy2X7LAOeptk/InK10I7vjhdAALD8PIdVF4yYrjHjydreuwZQkgD6+/sjPj4en376KdauXYudO3ciKioKL730Et544w2YTCUrgnjx4kXg/zN7n332mew1bdu2vZUAVqlSBUOHDsX+/fsRHx+P3Nxc+Pv7o06dOhg4cCBGjBjhsHA0IWohBJWBtctA6NcskvQZ/vgBRW99RX94yC3l/DQYUzsAmRYerzcIRHSA5FcyKSXd2sVgOGltP1uDluBr1FckJqJe0w/locgu3PpaEIBm4dIlPd6Eyc7OFkpwHSEwm81ISUlBdHS0V++MemAFefCfMBRMQZ6kq+i1z2Cv30L2YTS+rqfGMRYEAYwXfShQwxgzGWnwm/gEGJt0PVfhB/PAV6quSFzOoIbx9Tbncm1otiINtjuyoT5RNnzXMcqrx9jzi04Rojb+geC6D5Ht0i//AeDp7Fdymzclf2qhX/WzbPJnbf6IRyd/xDU+OZArSv4MLPBMjPdvBqEEkBAXsHbqCz44VNKuuXAamv92KBITIb6AuXIR2l0bJO0Cw4LrN0qRmIh6Hcu04vezRaK2UTWMiDJ4/81RSgAJcQWDCdZew+W7/pgP2GmnJ7k3QfD+P0LOpl/5k6QgOwDY2nWFUI5KiRGxqftzcedPWaCOwdjaJdvr4OkoASTERawP9wAfXlbSzl65CO0/mxSJiXiGPCuPjw/kou9fGZQE3gf2wmnoErdJ2gWtDlzvEYrERNRrT5oFG1LEG4XG1g1AmME3UiPf+C4JUYJWB67vk7Jd+lULACsn20d8l5UXMPd4PhotT8PnB/Pwd6oFay5Id7ISeY7qbVof6QVB5sMY8V2CIOCD/8SF18ONLMbUCVAsJnejBJAQF7K17gxe5hxgNj0Nur/XKRITUbfvjucj3Xz7FubU/3Jh5WkW8F7YpCPQHtojaRf0Rlh7DlMkJqJeWy5b8E+a+EP4a/UDEajznbTId75TQpTAamDp95Rsl27tIsBSJNtHfJOOZfBek2BR25lcGxYl0TnodyUIMCz/QbbL2mUABJkNWcR38YKAKcVm/yr6a/BUTX+Hj/FGlAAS4mL2pu1hr1xD0s7mZEG34XdFYiLq1buyEY2LFaD99GAu8q1UPsgRzaE90Jw6JGkX/ALAPfa4IjER9Vp1rghHMsVlXiY1CoRB41slmSgBJMTVGObGwfMy9OuXgMnJdHtIRL0YhsGUpuJZwGtFPL45Jj3TlgCw26Bf+r1sF9dtCOAf6PaQiHpZeQEfHRDP/j0UrMXgWD/FYlIKJYCEuIG9bjPYZc4BZsxF0K/6WZGYiHq1K2dA5woGUdtXR/JxvciuWExqpd25AZrU85J2PrgMrJ37KhITUS+zXUDH8kbcudTv7cZB0LC+NfsHSgAJcROGgWXQaNku7d9rwaRecHtIRN3ebxqMO/8k5dsETDskPV7Qp1mKoF/5k2wX13cUYPS9WR1yd4E6FtNahWBvvygMijWhaYQOPSt573Fvd0MJICFuwsfWgrXFI5J2hudh+H2eIjER9aobqsPgauIE5qdTBTiXS0XEb9Jt+B1sdoaknS8XA1v7borERDxD5UAt5rYPRfxjET57HCMlgIS4ETfgWQgaraRdu38XtGeOKhITUa+3GgXCoLn9tZW/cXIBAZicTOjXL5Htswx6HpD5OSOkOF/b+HEnSgAJcSMhsjyscX1k+/yX/wDQqQ/kDtEBWjxXS1yYdsW5IhxIpyLi+lU/gzFLyyjZH2oAe6PWisREiCehBJAQN+N6D4fgJ603pTt3EiEn9ysSE1Gv8fUDEawXz1K8vy/Xp4+IY65chPbvtbJ9lsdHAz56S4+Q+0EJICHuFhAMrscTsl3lt/4B2KyyfcQ3lTGwGF9fXMpkxxULtqZaFItJaYbf54HhpXURrS0eAR9bS5GYiHqN3ZWFTw/kooBqaYpQAkiIAqyd+4EPjZS0G7Kuw7hjvSIxEfV6rlYAKvhpRG3bLvtmAsgmHYb2v52SdkGjBddfvt4m8V17r3FYdLoQnx7MQ5M/0rAoqQB2OloRoASQEIXoDQ6LQ/utWwwUUtFfcptJy2BS4xuzgE0jdIh/LBwfNg++5+O8jiDA8Nt3sl3WuN4Qoiq4PSSiXoIg4J29Obe+vlrE4529Oci1UgIISgAJUY6tVSfYY6pJ2tmCXOjj5Xc3Et81JNYPSzuFYVP3CLQpayjBI7yPZt92aJKPS9oFP39wvUcoEhNRrzUXzEi8Jt4wNaFBIMoYKPUBJYCEKIhlwQ2WLw6t2/g7mIxrbg+JqJeGZdAl2uizNctgszqsl8n1GAYE+OCMKHGIswuYvC9H1FY5UINni+2q92WUABKiIHudprDVayZpZ6wc9CvmKxITIWqk27YWbNplSTsfGglr5/6KxETUa97JApzLEx+dOLlJsE/X/SuOEkBCFMYNGg1BZlZHm7AR7MUzisREiKoU5kO/Wv7MbK7/04DeN2+JE3lZFh7TDooLpjeP0KN3Zd888s0RSgAJURgfEwtbmy6SdkYQoF/2vSIxEc/BCwKWny3E5QJ7Ca72TPr1v4HJy5G022NiYWvdWZGYiHpNO5SLbE680ePD5kG+u3zCAUoACVEBrv9TEHR6Sbv2yF5oju5TJCaifn+nmvHI2ut4ZnsWpvwnTZC8AZN5DboNy2T7uMfHACz9GSO3ncu1Yd6JAlFb38omNI+kWeLi6CeHEBUQQiNRFNdXtk+/9FtApugt8W3fHc9Hn40ZOJRxo3D4suQi7L3mfUfE6Vf8BMYq/b5s9ZrBXrepIjER9Zr8Xw7urPesZ4H3mwYpGZJqUQJIiEoUdR0Eq590h5rmYjK0/2xSJCaiXv2rmBCkE9/SmvRvNngvOiKOvZgM7a4NknaBYcANel6RmIh6JaZZsPq8WdT2XK0AVA7UKhaTmlECSIhKCCZ/XG3XQ7ZP/8ePAOebJz8QeREmDSY0FB8Rt++6FcuSixSLydn0y74HI5PQ2to8Cl6mhibxXcWLPgNAGQOD1xsEOnyMr6MEkBAVyWjcAfbI8pJ2NvMadBuXKxITUa/nawUgNkh8RNyU/3KQ7wVnnmqO7oP2yL+SdkGnB9fvaUViIuq16nwR9l4Xn6M+sUEQQqjos0M0MoSoiKDRoqDPKNk+/dpFYLLS3R4TUS+9hsFHxY6Eu1LIY+ZhDz9K0GaDYfFs2S7rowMghEnP0Sa+7Z+r4nWiVQM1eLqmv2LxeALZBDA3NxdvvfUW6tati8jISNSrVw/vvvsu8vPv75dKSEiIw/+NGTNG9jHOem1CPBXXuC3ssbUl7YzFTGVhiESXikZ0LC/e4Tj7WB4u5NkUi+lB6basBJt6QdIuBASB6zFUkZiIuk1rFYLVXcJRP1QHAJjcNBh6Kvp8V5KVkQUFBejevTuOHDmCjh07YsCAATh8+DBmz56NhIQErF+/HkZjyYspRkdHY+hQ6Q9svXr1JG3Ofm1CPBLDwDL0RfhNfVHSpftnE6yP9AJfQ/rzQ3wTwzD4uEUw2qy6Bvv/l8tZ7MD7+3Kx4JFQpcO7b0xOJvQrF8j2cX1HATIbpQgBgA7lDfi7VwQ2ppjRNZpyhXuRJICzZs3CkSNH8Morr2Dy5Mm32idPnoyZM2fim2++wfjx40v8AjExMZg0aVKJrnX2axPiqfhqdWBt0wW6hI2SPsMvs1E0+VuA1cg+lviemiE6PF3TH3PvqH+26nwRdl21oG1Zz6p/pl/+A5iiAkm7PToW1kd6KhIT8Rwsw+CxGJPSYXgE0S1gQRCwaNEiBAQEYMKECaILJ0yYgICAACxcuNAlgSj52oSoETfoOQhG6S8yzYUkaLevVyQmol6TGgWhjKFYWZjEHNh5zykLwyafgG6H/Hvb8sRYQEPlPAhxFlECmJycjCtXrqBFixbw9xcvnvT390eLFi1w/vx5XLp0qcQvkJOTgwULFmDGjBmYP38+jh07JnudK16bEE8mhISB6z1Sts+wfB5QkOf2mIh6lTGweKuRuODtkUwrFp8pVCym+8LzMCz+SrbL2uIR8DUbuj0kQryZ6ONUcnIyAKBq1aqyF1etWhVbtmxBcnIyKlasWKIXOHr0KF555RVRW6dOnfDtt98iIiLCJa9tNpvv2k9Kh+M40X+Jc8mNr7l9d2j+Xgtt2mXRtUx+LjS/z0PB4BfcHqcn8/b38JBKGvxwQoNTObfPBZ6yLwddyzII0run6ENpx9iwexM0ySck7YLegLw+T4Gn3+uAD7yHS2pzKoerhTyGxhrAOvmMX1ePsVr2MogSwNzcXABAcHCw7MVBQUGi6+7lpZdeQq9evVCtWjXodDqcOHEC06ZNw6ZNm/D4449j06ZN0Gg0Tn/t1NRU2O3eezC60tLS0pQOwasVH9+cR/oj9jfpzIjx73U4X70RzJEl+zBGbvPm9/DYaBYv5dz+A5NhEfDt/jQMreDeXcH3M8aspQi1f58n23el9WNIKzADBSlOjM7zefN7+F7MdmDCfiOuWFjMP5GLibFW1Al0fu1LV4yxRqNxONHlbtoJEyZg9OjRiI2NdfqTf/jhh6KvmzdvjqVLl6Jnz55ISEhAfHw8evXq5fTXLV9eWkiXPDiO45CWloaoqCjo9Xqlw/E6Dsc3OhrcsUTojySKrmcEHrHbVyH31U8BJ38C9la+8B6OjgbWZuVi42UrokwM3qrvh4FVQp0+S+JIacbYb/k86AqkH+7t4WWhH/AUonXe+W9VGr7wHr6Xzw4X4orlxok3x/M1GHVIg/jOQWgcrnPK8/vKGGvnzZuHXr16ITY29tYsW05OjuzFN2ffbl5XGizLYuTIkUhISEBiYuKtBNCZr62W6VVvpdfraYxdSG58rcPHQvfWfjA2caV7/alD8D+aCHuzh90cpWfz9vfwJy21qHu6AK/WD0SATpl6/yUdYyb1AkxbV8n2ccNegjGw9H9vvJm3v4cdOZdrwzcnxccdti6rR6sKAWCc/CHH28eYzc7ORrt27QDg1izg2bNnZS++2f6gs4VhYWEAgMLC24uT3fXahHgiIaoirF0HyfYZlnwLWGh9FLmtapAW7zYJViz5KzFBgGHxHDAyS3ZsdZvB3qiNImER9XozMRuWO94uGgaY1jLE6cmfLxD9doiNjUW5cuWQmJiIggJxHaaCggIkJiaiUqVKJd4A4si+ffuA/9cIdPdrE+KpuJ7DwIeES9rZjDTo45coEhMhD0Jz4B9oj+6VtAsaDSzDXqKlDUTkz4tF2HjJImp7vrY/apdxzq1fXyNKABmGwfDhw5Gfn49p06aJLpw2bRry8/MxcqS4LEVhYSGSkpKQkiJeoHvs2DFYreLbVQCQmJiIWbNmQafToU+fPg/02oT4FKMfuMGjZbt0638Fc/2K20MipNQ4Cwy/fi3bZe3cH0L5Sm4PiahXkU3AG4niJWJRJhZvNqQlAqUlqao5btw4rF+/HjNnzsThw4fRoEEDHDp0CFu3bkXjxo0lZ/j+999/6NmzJ9q0aYP4+Phb7XPmzMFff/2Fli1bokKFCtDpdDh58iS2bt0KhmEwffp0VKlS5YFemxBfY2sZB/uWVdCcPipqZ6xWGH77FuaxHygWG/EMCVctqBWiRahR2ZNkdBuWgb2eKmnng8uA60Mf9onYzCN5uJgvXirwQbNgt5U38kaSkfP390d8fDzGjBmDpKQkzJkzB0lJSXjppZewevVqmEwlO2KlW7duaNGiBY4ePYrFixdj7ty5OHXqFPr3749NmzbhqaeekjzGWa9NiNdiGFiGj4Mgc2tMu28HNMf2KRIWUb8LeTaM3JaB7n+m4+MDyhYRZzKuQb92sWwfN/A5wOQv20d80/k8G2YeEb9nW0XpMagq5QQPgsnOzvacc4KIosxmM1JSUhAdHe3VO6OUcj/ja1jwBXTb1kja7eUro2jqD4CWjsyS46vv4ZXnCjF6Z9atxfMMgM09ItAkwvklLkoyxoZvPoAucauk3V61Fore/RpgaVbHEV98Dw/enIENKbc3umkYYHuvSNQNdc3aP18ZY/opI8QDWfo/BcE/UNKuST0PnYOSGsR3NYnQg8XtWWMBwLh/smFV4Jxg9uQh2eQPACxPvEzJHxHZkFIkSv4A4Nla/i5L/nwJ/aQR4okCQ8D1ky6jAAD9yp/A5Ga5PSSiXjEBWrzZSPyB4WimFd8dy3dvIHYbDL84OO+3fTfwsbXcGw9RNbNNwJvFNn5EmlhMakQbP5yBEkBCPJT1kZ6wV5QeKcQUFkDv4Fgt4rteqBOA2mXESwM+OZiHC3nuOyJO+/c6aFKSJe2CyR/cgGfcFgfxDLOO5uF8nnjjx5SmwQimjR9OQaNIiKfSaME9MVa2S7djPdiTB90eElEvHctgZusQ3Ll9qNAmYMKebAiC628FM5nXYXDwwYTr8ySE4FCXx0A8By8I2HlFXPOvZaQeg2Np44ezUAJIiAez12oEa/NHZPuM86cDnEW2j/im5pEGPFVTvMP2r0sWrD7v4pNkBAGGRTPBFBVIuvjylWDt1Ne1r088DsswWNM1HF+1CUGYgQXLANNa0YkfzkQJICEejhs8GoJeulONTbsE/ZpFisRE1Ou9JkGIMol/9b+RmI0cjnfZa2r27YB2f4Jsn2XYWNq1TmSxDIMRNfyxr38U5ncIRT3a+OFUlAAS4uGEsChwA56W7dOtXwL24hm3x0TUK1jP4rMWIaK2tCIeU//Ldc0LFuTBsGiWbJe19aOw123qmtclXqOMgUWfKnTr19koASTEC1g794O9Sk1JO2O3wzB/GmB330J/on69KxvxaEWDqO3HkwXYe41z+msZfvsWbE6mpF0IDIZl6AtOfz1CSMlQAkiIN2A1sDw1AYJGeryX5twp6DatUCQsok4Mw2BayxCYNMVrA2Y5tTag5vh+6Hasl+2zDBsLBIbI9hFCXI8SQEK8BB8TC2v3obJ9+j9+BHNNeu4q8V2VArWYVKw24PEsG75xVm1AzgLDT9Nlu2wNWsLWMs45r0O8gp0XMGJrBv5KcfGGJHILJYCEeBGu5xPgy0VL2hnOAsOCLwA3lPsgnmNMnQDJiQp/nC2C3QmzgH7rFoOV+dAhGIywjHwVoN2c5A7fHM/HmgtmDNqcgdE7MpFtcd2mJHIDJYCEeBO9AeZRE2S7tMf2QZuw0e0hEfW6szagngXebBiITT0ioGEfLDkzXbkA06blsn3cwOcghEU90PMT73Imx4qP9t/ehPRbchGGbc1QNCZfQAkgIV6Gf6g+rI/0ku0z/Po1GJkF+cR3NY3Q4/OWwdjVOxJvNgqCQfOAM3N2O2LiF4LhpTM49tjasMb1frDnJ16FFwSMTciG+Y4DPxgA7zam495cjRJAQryQZdBz4MuES9qZgjzoF89RJCaiXs/WCkCNEOfUWDNtXgG/qxcl7YJGC8tTEwBWulGJ+K55JwqwO028+/z52v5oGWVw+BjiHJQAEuKN/AJgGfGqbJcucSs0B/9xe0jE+zFpl+C3Vr74uLXnMPAVq7g9JqJe5/NsmFKs/mTlQA3N/rkJJYCEeCl74zawNntYts/w85eAzLFchJSaIMCw4AswVmktQb58JXA9hikSFlEnXhAwdlcWCm3iDUez25SBv45SE3egUSbEi3FPjIXgHyhpZzOvQ//7PEViIp7jRJYVL+7Kgtl2713B2p1/Qnt8v6RdYBiYn5oA6PQuipJ4ogWnCrHzqvjDwtM1/dGuHN36dRdKAAnxYkJIGCyD5U9b0G1dDTbpiNtjIupXZBMw9b8ctFt9DYtPF+KjA3c/Jo7JzoBhyTeyfda4PuCr13VRpMQTpeTb8N7eHFFbRX8NJjelW7/uRAkgIV7O1q4rbLUbS9oZQYDxp+mAzC074tue35GJGYfzcXPib87RfOy8YnF4vf6X2WAKpQWk+dAIcAOedWWoxMMIgoBxCdnILzar/FWbEATSrV+3otEmxNsxDCxPvgZB5hYcm3oB+rWLFQmLqNcbDYOgv+OvgwBgzM4s2eK8mv27oNv7t+zzWEa+Cpj8XBkq8TC/nC7E1lTxh4nh1f3QsYJRsZh8FSWAhPgAIaoCuH5Pyfbp1i0Ge+G022Mi6lUnVIf3mohvx10qsGPinmzxhfk5MPw8U/Y5LM06wN6wtSvDJB4mtcCOt4vd+i3nx2Jqs2DFYvJllAAS4iOsXQbAXqmGpJ2x22D8dipgKVIkLqJOL9QJQLuy4lnjZWeLsOJs4Y0vBAHGH6eBzU6XPNZm8kf+oDHuCpV4iPG7s5HLiW/9zmxdBiEGSkWUQKNOiK/QaGF5egIEVvpjz165CMPirxUJi6gTyzD4tl0ZBOnFJ4OM352N1AI7tNvWQLt/l+xjL3ceBCEoxE2REk/xQp0AVAq4XQj88VgTukTTrV+lUAJIiA/hK1WHtftQ2T7d9nXQOFjLRXxTxQAtZrQUJ3LZnIBP4w/D8Kv8BwauTlNk1mvlpgiJJ2lfzoB/+kTi+Vr+KGti8WkL+pCgJEoACfExXJ8nYa9SU7bPOH86mIxrbo+JqNfAWD/0r2K69bXBzuGVvx0UfA4qg7yR4wHmAc8TJl7LX8fis5Yh+LdfFMrQrV9F0egT4mu0WpjHvAvBaJJ0MYX5MH7/EcDbZR9KfNOMViGo4Hfj1t0nZ39D/YIU2essz7wJITjUzdERTxSkp/RDafQvQIgPEqIqODwrWHPqEHRUGobcIcTA4pt2IeiWcQAvX94oew336ADYG7Rwe2yEkNKhBJAQH2Vr3RnWVp1k+/SrFoA9c8ztMRH1etivAItPz5Xts8fEghv0nNtjIuqVb+Ux60gerPy9jxEkyqAEkBBfxTCwjHwVfEQ5aRfP3ygNI3O6A/FBPA/D3E8QaJYeCVfI6nFq2CQ665fcIggCXtudjff35aL7+nSk5NuUDonIkE0Ac3Nz8dZbb6Fu3bqIjIxEvXr18O677yI/v+R/DD755BOEhITc9X8vvfSS6DFjxoy56/WEECcz+cM8+h350jDpV2FY8AUg0Cd4X6fbsAzaY/tk+w53ex4xNau5PSaiXovPFGJp8o26ov9e59Bu9TVsT3V8lCBRhrZ4Q0FBAbp3744jR46gY8eOGDBgAA4fPozZs2cjISEB69evh9F477o9bdu2ddi3aNEipKamIi4uTrZ/9OjRCA6myuCEuANfrQ64vqNg+ONHSZ8ucSvs9ZvD1rarIrER5bHnTkG//AfZvryG7VB3QD+3x0TU60SWFRN2i0/74AUg5o76f0QdJAngrFmzcOTIEbzyyiuYPHnyrfbJkydj5syZ+OabbzB+/Ph7PnG7du3Qrl07Sfu1a9cwY8YMhIaGonv37rKPHTNmDCpVqnT/3w0hpFSsPYZCe2wfNCcPSfoMC2fCXq0uhLIVFYmNKMhcCOO3U8HYpbfw+NAIMM9OoJIv5JYCK49Rf2eiyC6+a/BVmzKoEiRJN4jCRPd9BEHAokWLEBAQgAkTJogunDBhAgICArBw4cIHesElS5bAZrPh8ccfh15Pa0YIUQVWA/Nzb0PwD5R0MRYzjN9NBWxWRUIjyjH8Mhts2iVJu8AwMD//NhAQJPs44psmJubgZLb4w8LTNf3Rp4q05BRRnigBTE5OxpUrV9CiRQv4+/uLLvT390eLFi1w/vx5XLok/YVQUosWLQIAjBgxwuE1GzduxBdffIE5c+Zg06ZN4DhpwVFCiHMJYZEwPzVRtk9z7hT0f8x3e0xEOdrErdDt/FO2z9rzCfA1Gzp8LC0b9T2/nSnE4tOForZ6oTp81IyWc6mVaE42OTkZAFC1alXZi6tWrYotW7YgOTkZFSve/+2gf/75B2fOnEGzZs1Qq1Yth9dNnCj+I1S2bFl8/fXXDtcMEkKcw960HayP9IRu21pJn379EtjrNoG9TlNFYiPuw1y/AsOCGbJ99tja4HqPlO0TBAFzTxbhwBUdvommLNBXJGVb8drubFFbgJbBgodDYdTSEgG1EiWAubk3tvg72oARFBQkuu5+3Zz9Gz58uGx/69at0aVLFzRt2hTh4eFITU3F8uXL8eWXX2LIkCHYuHEjGjVqdM/XMZvNpYqP3N3NmViakXUNtYyvud/TCDl5CNorFyV9hu8+QtZ730II9Mxd+WoZY1Wz2xH87VQwhQWSLt7oh5ynJoK32QCb+FZfkU3AhL35WH6eA6BDk5MFeKYW/fF3NrW9h4tsAkZuzUGBTZzwT2vmjwoGG8xmzysB4+oxLslGWnfQTpgwAaNHj0ZsbKxLXyg3NxerV69GQEAA+vWT3zVWPDGsWrUqJk6ciHLlymHs2LH47LPP8Ntvv93ztVJTU2G301FWrpKWlqZ0CF5NDeOb3uNJ1Jj/Mdhii//Z3CzoZ7+P5KGvQNB47qJuNYyxWlXY+Bt0ycdl+y52HYqsIiuQIj4KThCA0UcM2J97e6fn+4fMiLDnoHEw7/KYfZFa3sMfndbjRI74d0GfKBuaaq4Vf5t4HFeMsUajcXiX1d208+bNQ69evRAbG3trhi8nJ0f24pszfzevux8rVqxAYWEhhg8fjoCAgPt67NChQzFhwgQkJiaW6Pry5cvfd3zk3jiOQ1paGqKiomgDjwuoanyjo1GY+wwCln4n6Qq8cAo1d65B/hMve9wOUFWNsQoZt8cjYO8W2T5zy04IeGwAHP32HgMLnk24XSvWLjB4K8mEv7oEo4I/lQBxFjW9h1eet2BVmrg+cK1gDb5sHwqTB9/6VdMYu5I2O/v2ffubs4Bnz56Vvfhme2lmC2/uHr7b5g9HNBoNgoODcWesd6OW6VVvpdfraYxdSDXj+9jjsJ08CO2hPZIu464/wURXhbXrQEVCe1CqGWMV0RzbB+NvX8v28ZHlYXty/F3HbGANI07mATMO304IMiwCnk4owJ/dIjw6IVAjpd/DZ3KsmLBXvEzAX8vg545hKBOgUywuZ1J6jF1NtAs4NjYW5cqVQ2JiIgoKxP+wBQUFSExMRKVKle57A8ixY8ewf/9+1KpVC82aNbvvIFNSUpCWloaYmJj7fiwhpJQYBpZn3gAfGinbrf/tG2gO7nZ7WMT5mCsXYZwzGQwvvV0r6PQwv/AeYPK75/O81SgIceXFf/wPZlgx7p8sCLQ12KtMSsxBfrF1fzNahaBGiHckf75AlAAyDIPhw4cjPz8f06ZNE104bdo05OfnY+RI8e6vwsJCJCUlIeUuN/tvbv544oknHF6TlpaG1NRUSXt2djZeeOEFAMCAAQNK+n0RQpxACCoD86ufQDBIPwUzggDjtx+ATZG/Y0A8RH4uTF++BcbBuc+WZyeBr1KzRE+lYRl80yoAMUZxIrksuQjfHpduKiGe65t2ZdCxvOHW18Oq+2FwtXt/SCDqwWRnZ4tS+IKCAnTp0gVHjx5Fx44d0aBBAxw6dAhbt25F48aNER8fD5PpdlHHnTt3omfPnmjTpg3i4+MlL8BxHGrWrIn8/HycPHkSoaGhsoHs3LkTffv2RfPmzREbG4vw8HBcvnwZmzdvRmZmJtq3b49ly5Z59XSs2pnNZqSkpCA6Opr+HVxAzeOr2Z8A41fvgJGZxeHDo1D0/ncQgsooEtv9UPMYK8Jmg3H6BGhPHJDttvQdBWsf+ZIvjpjNZuw4dQlPHfYTzRBpGGDFo2HoUJ7G/UGo6T3MCwK+PJyPFecK8Vf3CPjrpGeKeyI1jbErSf61/P39ER8fjzFjxiApKQlz5sxBUlISXnrpJaxevVqU/JVEfHw8MjMz0b17d4fJHwBUqVIFQ4cORW5uLuLj4zF79mxs3LgRNWvWxJdffomVK1d69T8EIWpmb9wG3KDnZfvY9DQYZ70LcHTYu0cRBBgWznSY/FlbdIS19/2v2QaAKn4C5rQSbxexC8Cov7NwPs/zyoIQeSzD4LUGgdjWM9Jrkj9fIpkBJMQRX/lUpBTVj68gwPDj545Ph2jdGZbn3lL1zmDVj7Eb6Tb+DsOv8ps+7LG1UPTmTEBvkO2/mzvHeOYJDp8ezBP11w3VYWO3cEoYSonew67nK2NMP4GEkJJhGFieHA/7Qw1ku3X/bIJu3a9uD4vcP82hPdAv+Va2jw+NhPnlD0uV/BU3sWEgusWI/4AezbRibEI2bQrxICezrSi0UT1Hb0MJICGk5LQ6FI2dAj5CvtamYfk8aPbtcHtYpOTYS2dh/OYDMILMjl+DEeZXP4YQEuac12IYfNeuDB4KFhcKXnGuCF8dld90QtTlfJ4NPf9MR88/03G9iA5Y8CaUABJC7k9gCIpe/RiCyV+22/j9x2DPJ7k9LHJvTG4WjF9OAmMulPQJDAPz6HfAx1Rz6msG6VksjgtFkP720oAALYPYIM89ScZXZJrtGPBXBq6befyXbkXn+Os4k2NVOiziJJQAEkLum1ChMswvvg+Bkf4KYTgzjDPfApOVrkhsxAErB+Osd8Gmyx9vxQ18DvbGbV3y0tWCdfihfSgYAFUDNdjcMwI9Kt3fhkLiXkU2AUO3ZOJM7u1NO+fz7Pj4QN5dH0c8ByWAhJBSsddrDm7YS7J9bFY6jLPeBixmt8dFZAgCDPOnQ3PmqGy3td1jsHYb7NIQHo024qeHQ7G1ZyRqUrFgVeMFAaN3ZmLPNU7UXqeMFjNbhygWF3EuSgAJIaVm7dQX1o69Zfs0507B+O1UwEa3jBQlCND/8SN0//wl222vUR+Wka+6Zfd2nyomhBjoz47avbc3F6vPiz+8lfdjsaxzOIL09O/nLehfkhBSegwDy7CxsNVpItutPZAA4+z3ACsn209cTBCg/30e9Gt/ke3mI8qh6OUPAJ06DrynTQbK+/54PuYcE2/QCdIx+L1zOCr4axSLizgfJYCEkAej1cL84mTwZaPluw/uhnHWO1Qo2t0EAfrfvoU+Xr40j2DyR9GrnwCByt/SEwQBnxzIRcuV13Aii2aMlbLuQhHeTMwRtWkZYFHHUNQJpdv23oYSQELIg/MPRNGrn0DwD5Lt1h75F8YvJ9GaQHcRBOh/+Qr6DcvkuxkW5hfeg1ChsttDk8QiCJjyXy4+O5iHDAuP3hvTaaepAv69ZsEz2zNRvDrj7LZl6Pg+L0UJICHEKYSyFVH02qcOy8Noj++H6Ys3AJkSJMSJeB6Gn7+AfvNK2W6BYWF59k3Y67dwe2hyfjxZgJlHbt9yvFbEo9eGdDoyzo22p5rRd2MGzMXuwL/VKBBDqvkpFRZxMUoACSFOw8fWRtEbMyD4Bcj2a04egmn6RKCowO2x+QTeDsNP06Hbtla2W2BZWJ5/G7Y2j7o9NEcGV/NDi0jxGsTUwhtJYEo+JYGutvp8EQZuykCBTTz3N7y6HyY0CFQsLuJ6lAASQpyKr1ITRW9+CSFA/naw5vRRmKZNAAqonphT8XYY5n0G3Y71st2CRgPzC+/B1irO7aHdTYCOxbLOYWgULl5jdjHfjt4b0nGlkDaGuMrmS2aM+jsTXLFDYR6taMAXrUPAqPhcb/LgKAEkhDgdX6k6it6cCd7BBgNN8nGYpr0O5Oe6PTavZLfB8N1HDku9CBotzC9Ngb3Zw24PrSSC9SxWPBqOusU2GpzNu5EE0u5g12hT1oCWxWZf+1Y2YVHHMOhYSv68HSWAhBCX4KOromjSTPDBZWT7NedOwfTZeCAv2+2xeRWbDcZvp0KXuFW2W9DqYH55qstO+XCWMgYWq7qEoWaI+Ii4pBwbem9MR2bxBWrkgZm0DJZ0CkO9/yfeTz3kjx86lIFBQ8mfL6AEkBDiMkKFyiiaNAt8SLhsv+biGZg+HQ8mN8vtsXkFKwfj1+9Du3e7bLeg08P8ykewN2zl9tBKI9yowaou4YgNEtebO55lQ88N6bhAG0OcLljP4o9Hw/BR82DMaBUMDc38+QxKAAkhLiWUi0HRWzPBh0bK9msunYXxk1fBZGe4PTaPxllgnP0etPsTZLsFvRHm8Z/CXq+520N7EGX9NFjTNQKVAsRJ4LEsGx5Zex07rlA9SWeLNGnwYp0AWvPnYygBJIS4nBBVEUVvzQIfXla2X5N6HqYPx4K9cNrtsXkiJisdpukToT20R7ZfMBhR9NpnsNdu7PbYnKGCvwZruoajgp84Ccy08Oi7MR3fHc+HIBSvWEccyeF4TNyTjTwrX4Kria+gBJAQ4hZCRLkbSWBkedl+9noqTB+8AO2W1QD9cXdIc2QvTO8+A82pQ7L9gtEPRROmg6/ZwO2xOVOlQC3WPRaO6sHiNYF2AZh5OA85HL1HSuJ6kR09/0zH3BMFGL41ExY7jRu5gRJAQojbCGFRN9YEOjg2jrFZYVz4JQxfTwEK82Wv8Vl2G/TLf4BxxkSwDjbOCH7+KJo4HXz1um4PzxWqBGmxuUcEukbfPolCzwKLOoYhxEB/vu7lQp4NXddfx+HMGyer/J1qwXM7MmHnKQkklAASQtxMCI24sTu4fCWH1+j2/g2/958Dez7JrbGpFZN5HaZPx0O/9hcwDmZHBf9AFE38AnxsbbfH50rBeha/xoXi9f8XJZ7RKgTNipUuIVLrLhQhbt11JOeKd0/vTuNwqYB2VBNKAAkhChBCwlA0aSbs1RzPVLHXUmGa+iJ0m1b49C1hzeFE+L33DDRJhx1ew4eXvVF3scpDbo3NXViGwTuNg7CjVwSG15A/apDckMPxGLMzC09szUS6WbzmLyZAgw3dIlApUOvw8cR3UAJICFGEEFQGRZNmgus2xOE1jM0Kwy9fwTjnfd+7JWy3Qb9sLkwz3gCTl+PwMluTdij8YB74mFi3hqeE+mGOZ/4EQcDCpAJwPrzGbccVC9qsuoYlZ6TnbdcO0WJj9whUDaLkj9xACSAhRDlaLbjHn0fR+E8dHh0HANp9O+D33nNgz510a3hKYTKvwfTpq9DH/+rwGkGjhWXYWJjHfgD405mtXxzOx8sJ2ei9MR3XfOzkkCKbgLf+zUavDemyt3e7xRixvlsEyhXbVU18GyWAhBDF2Ru0ROEHP8B+l80L7PVUmKa+5PW3hDWHEuH37jPQJB1xeA0fUQ5F78yB9dH+ANVuw8YUMz7cf+NYwd1pHDqsuYbfkwt9olTMwXQOD6+5hm+OFUj6AnUMvm4bgsUdQ2nTDJGgdwQhRBWEsEgUvTkTXPehDq9h7LYbt4RnvQPmaopb43O53Gzof5kN0xdvgLnLGcm2pu1ROGUu+Ko13RqeWhXZBLyckIU7U70rhTye3ZGFzvHX8e817ywcbeMFfHYwF53WXcepHOkJKW3L6pHQJxLDqvtTgWciixYDEELUQ6sFN+g52Gs2gHHuxw7XvmkPJEBzcDdsrTuD6z0cQlRFt4fqNHnZ0P+5FLrNK8FYzA4vE7Q6cENegDWuD8363cGkZbC0Uxie2Jopuf2577oVj8ano38VE95vGoSYAO/5k7ftihWfHMiTtBs0wPtNgjG6tj9Yep+Qu6AZQEKI6tjrt7hxS7hGfYfXMAL/v/buPSrqOv/j+HMuDLdBOAKNkQwXFRFE/ZFWSnjv4mKmYK2/JXNN+xWaXexu53fytpmbYlteTmalpquWxmaH1kLR+IH3Wpc2M3CkZYwYN21QQJgZZn5/KCzsIIqC48y8H+dw1O/34/Dy4zi+v5/v5/v54FP0BQEvPYzvmsUoTlVc14zXrPosmq1rCHzuv9Hkbmqz+LOHR1y45Tt6ghR/rRgQpmH3feGMjPBt9fy2svPc9omJhV+fpdpDdsMYHeHDhGj/Fsf6h/rw1bibmJGoleJPXJYUgEKIG5KjazjnX8rGct9DbbZT2O34/N9fCXhpMr7v/RHFv36+bhmvSs05NJ+8T+Czky6s61d3vs3m1kHDqZ2/2mOXeOko4f4qtt0dysaRXYkNcn7Yoa4BlhSf49ZtJjaU1mB38/mBCoWCpYOD0fkrUSng+f5B7BwbTnyIj6ujCTfhOePhQgjPo1JjmTidht798HvnD20uh6JoaMCn4HPURV9gS/0NlnEP4QjVXde4baqtxueLrWi+/BhFrfOE/f/kUPtQ/7uZ2EbeL6N+V0ihUJAW5c9d3f1491gNfzxy1mnLONN5O08Umll9tIbXbg/mzm6tjxreCI7+amXVd9W8MCCIyFZuX3f1U7F6aFcCfRQMDJfFsUX7OI0AFhcXM3/+fNLT0+nRowchISGkpaVd9Tf45ptveOCBB9Dr9URERDB69GhycnIu2b6yspInnniC3r17o9PpGDhwIEuWLMFqtV51BiGEe2tIuo3aP3yAZdR4HOq2RzgUDQ347PmMgOcz8V23DMXpU9ctZ6tqq/H5dD2Bz07C9y9rr6j4sw0cSu38d7HJfL+rolEpmJmo5ZsMHY/2CUTVShcWn7Ey4YtfqKy9sZaMcTgc7DxZx4QvfmHIX07xYWkt735/6ffMsAhfKf7EVXG6pMjNzSU7OxuNRkPPnj05ffr0Vb94QUEBGRkZ+Pn5kZ6ejlarZfv27UydOpWTJ08ya9asFu1NJhOjR4/mp59+YuzYsfTo0YOioiIWLlzI119/zZ///Gd5mkkIL+UI7orl4aexpv0OzWcbUBd8jqLB+enHRooGGz75n+KT/ykNkT1o6DMAe8++qAK6dm5QSz2q49+h+v5vqL7/G8oTx9rM2ZwtOQXL+N9jj+rVuRm9RKifijfuCGFafCD/e7CKvJ9aPhH8WB8t3W6QtfHO2xx8ZKhl5XfVTk/1ri2p4YUBQWh9ZNaW6DgKs9ncYnz8+++/p76+nsTERM6cOUPv3r1JSUkhNze3XS9ss9kYNGgQFRUV5OXl0a/fhcncVVVVjBo1ivLycg4fPoxer2/6PY8//jibN28mOzubRx55BC5eDU2fPp1t27axZs0aJk6c2DF/ctFudXV1GI1GIiMj8fPzu4LfIdpD+rd9FL9Uotm+AXXhX1E0XPkojkOhwBbZE0diMg19kmmISwL/gKsPYrOiNHzfVPCpDN+haOcdC9uAwRcKPw+Y53cjv493/VTHKwerOGa2Eeqr5OsMXavr4zXYHfxPwa8Mi/BlTKQf4f6dUySaahsoPmNln6medT/Ucrr+0g+ovH57MI8naG/o/vUU3tLHTiOAffr06ZAXLigooKysjMzMzKbiDyA4OJjZs2czY8YMNm3axIsvvgjAuXPnyMnJITo6mqlTpza1VygUvPrqq2zbto1169ZJASiEAMAR1o36R57Dcl8mmu0foi7cgcJ++Sc8FQ4HPuWlUF4Kf92CQ6nEHhNPQ5//wq7viUN5JaMsDpSVP10o+Er/gcJy6Sd422Lrd/uFwq9Hx3zuiraNusWPYff7sq6khgC18pKLIx84ZWFb2Xm2lZ1HqYA7btIwNsqf3+j96B6oQq1s/52oOpuDL07WUXzaQvFpK8VnrJjOX/79GuqrZFqfQNJj/C/bVoj26LSHQAoLCwEYOXKk07lRo0YBUFRU1HTs0KFD1NfXM2LECKfbvHq9nl69enHgwAEaGhpQqW6MIXshhOs5wm+mftoLWMZmotm+HnVRHgrHlS/1obDbURmOojIc7dSczdn6DsIy4ffYeyZet+8pLlArFUyL17bZ5rN//vvJbLsD9pos7DVZmHPwwkNIWrWCEF8lXTQKQjRKgjUXislgjYI+IT5M6R3o9Jp2HEzdcwb7FT58HB+iZkailgdiA/BXy9Qn0fE6rQA0GAwA9OjhvEG5TqdDq9Vy4sQJp/axsbGtvl5sbCylpaUYjUaio6M7K7YQwk05dLdQ/+jLWO6bjObT9aj37WxXIXg92BKSsUyYij0uydVRxCU4HA4++2fbI7rVNgfVtgZo5dmM4RG+rRaAAWolccFqjpnbng86MsKXmX21jIzwlTnvolN1WgF49uyFrYy6dGl9g/egoKCmNs3bBwcHt9q+8XWqqi69DESjurqrux0j2maxWFr8KDqW9G8HCQnj/JTZKMdmovnHIXyOHcGn5FuU1Zf/7OhoDl8/rD37Yu3dH0vCrTREXrzA9eDPKHd/HzfYHczp58/nJy3sqrBwvp0PCQepHJf8P6hviJJj5pbHAtSQGKImOVTNpFhf+oRc+G+5vr71LezcvX/dQWf38Y0yr1D9/PPP8/jjj7c6UueuKioqaGjHpHDRPiaTydURPJr0bweK7X/ha4wdv39VEPTjMbQ//oC2vAR1XW2Hfzu72oea7j04Fx1PdVRvaiKiQdXsOtvoYfsXt8Gd38eDVDAoCl7sDgfMKnafVlF4RkWV7fIjcipLLUajudVzcWoVt4eo6R1op7fWTlygnUh/x7+XqTkHRufd3Vrlzv3rLjqjj1Uq1SXvdF5v6nfffZdx48Z1eAHYOGLXfJSvuXPnzhESEuLU/lIjfJcbIWwuIiLiqjKLtlksFkwmEzqdDo1G1p3qaNK/nUwfhSXpVspMJnThYfifOonm2N/x+eHvqEv/gbK+7R05WuNQqbHFxGON74+ld39sMfHgo0EDdL345W087X3cC3jo4sjgqTo7VVYHZy0Oqhq/rHbOWhyYLQ7OWh0MvklLZGTri0s/GQlPXmMeT+vfG5G39LHabG79SuVaNRaUBoOBAQMGtDhnMpmorq4mOTnZqX3zeYHNnThxAo1GQ/ful9/0/UYZXvVUGo1G+rgTSf92Po2fPz5xSTjikrDwEJYGG8qf/gl1l1+kuYmPBntENPhe+LtSy9ZKLXji+zjmGlYL6mie2L83Gk/v4077vEpJSSE7O5v8/HwyMjJanNu1a1dTm0YDBw5Eo9Gwe/duHA5Hi8mv5eXllJaWkpqailotH7FCiA6mUmPXe840GCGEuJxrXlbcarVSUlJCWVlZi+PDhg0jOjqarVu3Ulxc3HS8qqqqaaeRSZMmNR3v0qUL6enp/Pjjj3zwwQdNxx0OB/PnzwdgypQp1xpXCCGEEMLrOQ2nlZSUsGzZMmj2NG1paSlZWVlNbVatWtX084qKCm677TYiIyP59ttv//3CajVvvfUWGRkZpKWltdgKzmg0smDBAqKiolp877lz51JYWMizzz7Lnj17iI2NpaioiEOHDnHvvfc6jSQKIYQQQoj2cyoATSYTmzZtanHs1KlTLY41LwDbMnToUHbs2MGiRYvIycnBarWSkJDAvHnzSE9Pd2rfrVs3du7cycKFC/nyyy/ZsWMHkZGRvPLKKzz11FOyJpIQQgghRAdwKgBTU1Npz4MhUVFRbba/9dZb2bp16xW/Xrdu3Vi+fPkVtxdCCCGEEO1zzXMAhRBCCCGEe5ECUAghhBDCy0gBKIQQQgjhZaQAFEIIIYTwMlIACiGEEEJ4GSkAhRBCCCG8jBSAol1UKpWrI3g06d/OJ33c+aSPO5f0b+fzhj5WmM1mh6tDCCGEEEKI60dGAIUQQgghvIwUgEIIIYQQXkYKQCGEEEIILyMFoBBCCCGEl5ECUAghhBDCy0gBKIQQQgjhZaQAFEIIIYTwMm5fAH7zzTc88MAD6PV6IiIiGD16NDk5Oa6O5TG2bNnC008/zfDhw7npppsICQlh48aNro7lMSoqKli5ciUTJkygb9++hIeHExcXx+TJkzl8+LCr47m9uro65syZw5gxY4iPj0en0xEXF8c999zDhg0bsFqtro7okd58801CQkIICQnh0KFDro7jEZKSkpr69D+/0tLSXB3PY3z22WeMHz+emJgYdDod/fr1Y9q0aZw8edLV0Tqc2tUBrkVBQQEZGRn4+fmRnp6OVqtl+/btTJ06lZMnTzJr1ixXR3R7CxcuxGg0Ehoaik6nw2g0ujqSR1m9ejVvvvkmMTExjBgxgrCwMAwGA7m5ueTm5rJmzRrS09NdHdNt1dTU8P7775OcnMzdd99NWFgYZrOZvLw8nnjiCT755BO2bt2KUun218I3jKNHj7Jo0SICAwOpqalxdRyP0qVLF7KyspyO6/V6l+TxJA6Hg2eeeYa1a9cSExNDRkYGWq2Wn3/+maKiIoxGI927d3d1zA7ltjuB2Gw2Bg0aREVFBXl5efTr1w+AqqoqRo0aRXl5OYcPH5Z/GNdoz549xMbGotfrWbZsGfPmzWPFihVkZma6OppH2L59O127duXOO+9scXzv3r3cf//9BAYG8sMPP+Dr6+uyjO7Mbrdjs9nQaDQtjttsNsaPH09hYSFbtmzhnnvucVlGT2K1Whk9ejQ+Pj7Exsby0UcfkZeXx6BBg1wdze0lJSUB8O2337o6ikdatWoVL7/8MtOnT2fx4sVOW8HZbDbUarceM3Pitpe9BQUFlJWVMXHixKbiDyA4OJjZs2djsVjYtGmTSzN6guHDh0sR3YnGjRvnVPwBDBkyhNTUVMxmM0ePHnVJNk+gVCqdij8AtVrN2LFjAThx4oQLknmmJUuWcOzYMZYvX+4Ve6kKz3D+/HkWL15MdHQ0r7/+eqvvXU8r/nDnW8CFhYUAjBw50uncqFGjACgqKrruuYToKD4+PuAlm5Jfb3a7nV27dgGQkJDg6jge4ciRIyxdupQ5c+YQHx/v6jgeyWKxsHHjRiorKwkKCiI5OZmBAwe6Opbby8/Px2w2k5mZSUNDA59//jkGg4Hg4GCGDx9ObGysqyN2CrctAA0GAwA9evRwOqfT6dBqtXJlL9yW0Whkz549dOvWjcTERFfHcXsWi4WlS5ficDj49ddf+eqrrygpKSEzM5Nhw4a5Op7bq6+vJysri6SkJJ566ilXx/FYJpOJmTNntjiWnJzMe++9R0xMjMtyubsjR47AxYvtlJQUjh8/3nROqVQyY8YMFi5c6MKEncNtC8CzZ8/CxUmxrQkKCmpqI4Q7sVqtPPbYY9TX1zN37lwZAewAFouFxYsXN/1aoVAwa9YsXn31VZfm8hSvvfYaBoOBPXv2yPu1k2RmZjJ48GASEhIIDAzk+PHjrFixgi1btjBu3Dj27t1LUFCQq2O6pV9++QWAFStW0L9/f/Lz84mLi6O4uJinn36a5cuXExMTw7Rp01wdtUO57RxAITyR3W5nxowZ7N27lylTpjBp0iRXR/IIWq0Ws9nMmTNn+O6771iyZAnr169n7NixcqF4jQ4ePMjbb7/Nc889J7fTO9FLL73EsGHDCA8PJyAggH79+vHOO+/w29/+FqPRyLp161wd0W3Z7XYANBoNGzduJDk5Ga1Wy5AhQ1i7di1KpZLly5e7OmaHc9sCsHHk71If3ufOnbvk6KAQNyK73c7MmTP5+OOPefDBB1m2bJmrI3kcpVLJLbfcwrRp0/jTn/7E/v37Wbp0qatjuS2bzUZWVhaJiYk888wzro7jlaZOnQrAgQMHXB3FbTXWCgMGDODmm29ucS4hIYHo6GjKysowm80uStg53PYWcOPcP4PBwIABA1qcM5lMVFdXk5yc7KJ0QrRP48jf5s2bmThxIqtWrZK16TrZiBEjoNkDZaL9qqurm+Zjh4eHt9rmrrvuAmDDhg1NT16LjhMaGgpAbW2tq6O4rV69esHFVURa03i8rq7uuubqbG5bAKakpJCdnU1+fj4ZGRktzjU+3ZeSkuKidEJcuebFX3p6Ou+8847Mo7oOKisrodnT1qL9fH19mTx5cqvn9u7di8FgYMyYMYSFhclyUp2kcccg6d+rl5qaCkBJSYnTOavVyokTJwgMDCQsLMwF6TqP2xaAw4YNIzo6mq1bt/LYY4+1WAg6OzsbjUYj86fEDa/xtu/mzZsZP348q1evluKvAx07dgy9Xk9AQECL47W1tbzyyivQbIRKtJ+/vz9vv/12q+eysrIwGAzMnj1bFoK+RiUlJXTv3t3pfVxSUsLcuXMBmDhxoovSub+YmBhGjhxJfn4+69ev5+GHH246t2zZMqqqqnjwwQc9bi1At/3TqNVq3nrrLTIyMkhLS2uxFZzRaGTBggVERUW5OqbbW79+Pfv27YOLWzwBfPjhh023zQYPHtziH4ton8WLF7Np0ya0Wi09e/bkjTfecGqTlpbWYrFzceVycnJYuXIld9xxB3q9nqCgICoqKti5cydnzpxh8ODBzJgxw9UxhWjTtm3bWLlyJUOGDCEyMpKAgACOHz9OXl4eVquV2bNnyx2va7R06VLuvvtunnzySXJzc+nVqxfFxcUUFBQQGRnJggULXB2xw7ltAQgwdOhQduzYwaJFi8jJycFqtZKQkMC8efNk/9QOsm/fPqcdVfbv38/+/fubfi0F4NUrLy+Hi3OplixZ0mobvV4vBeBVuvfee6msrOTgwYMcPHiQmpoaunTpQmJiIhkZGTz00EMed1UvPE9qaiolJSUUFxezb98+amtrCQ0N5a677mL69Omtbogg2icmJobdu3fz2muvsWvXLvLz89HpdDz66KO88MILl5zj6s7cdi9gIYQQQghxdeQxQyGEEEIILyMFoBBCCCGEl5ECUAghhBDCy0gBKIQQQgjhZaQAFEIIIYTwMlIACiGEEEJ4GSkAhRBCCCG8jBSAQgghhBBeRgpAIYQQQggvIwWgEEIIIYSXkQJQCCGEEMLLSAEohBBCCOFlpAAUQgghhPAy/w86pg/A+TdgiQAAAABJRU5ErkJggg==",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.style.use(\"fivethirtyeight\")\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.plot(x, np.sin(x), linestyle=\"--\", linewidth=3.0)\n",
+    "ax.plot(x, np.cos(x), linestyle=\"-\")\n",
+    "ax.set_xlim(0.0, 2.0*np.pi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use(\"default\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multiple axes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a wide range of methods for putting multiple axes on a grid.  We'll look at the simplest method.\n",
+    "\n",
+    "The `add_subplot()` method we've been using can take 3 numbers: the number of rows, number of columns, and current index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7195c39fb524d3b876a020ad0442837",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "\n",
+    "ax1 = fig.add_subplot(211)\n",
+    "\n",
+    "x = np.linspace(0,5, 100)\n",
+    "ax1.plot(x, x**3 - 4*x)\n",
+    "ax1.set_xlabel(\"x\")\n",
+    "ax1.set_ylabel(r\"$x^3 - 4x$\", fontsize=\"large\")\n",
+    "\n",
+    "ax2 = fig.add_subplot(212)\n",
+    "\n",
+    "ax2.plot(x, np.exp(-x**2))\n",
+    "ax2.set_xlabel(\"x\")\n",
+    "ax2.set_ylabel(\"Gaussian\")\n",
+    "\n",
+    "# log scale\n",
+    "ax2.set_yscale(\"log\")\n",
+    "\n",
+    "# tight_layout() makes sure things don't overlap\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualizing 2-d array data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2-d datasets consist of (x, y) pairs and a value associated with that point.  Here we create a 2-d Gaussian, using the `meshgrid()` function to define a rectangular set of points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def g(x, y):\n",
+    "    return np.exp(-((x-0.5)**2)/0.1**2 - ((y-0.5)**2)/0.2**2)\n",
+    "\n",
+    "N = 100\n",
+    "\n",
+    "x = np.linspace(0.0, 1.0, N)\n",
+    "y = x.copy()\n",
+    "\n",
+    "xv, yv = np.meshgrid(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"heatmap\" style plot assigns colors to the data values.  A lot of work has gone into the latest matplotlib to define a colormap that works good for colorblindness and black-white printing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f5eb9bce3c0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3b68b5884bd4efa9bf1b5c83467a933",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "im = ax.imshow(g(xv, yv), origin=\"lower\")\n",
+    "fig.colorbar(im, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes we want to show just contour lines&mdash;like on a topographic map.  The `contour()` function does this for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.0), np.float64(99.0), np.float64(0.0), np.float64(99.0))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2eaad70173f4c69857d24aab2b89e81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "contours = ax.contour(g(xv, yv))\n",
+    "ax.axis(\"equal\")   # this adjusts the size of image to make x and y lengths equal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} Quick Exercise\n",
+    "Contour plots can label the contours, using the `ax.clabel()` function.\n",
+    "Try adding labels to this contour plot.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Error bars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For experiments, we often have errors associated with the $y$ values.  Here we create some data and add some noise to it, then plot it with errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def y_experiment(a1, a2, sigma, x):\n",
+    "    \"\"\" return the experimental data in a linear + random fashion a1\n",
+    "        is the intercept, a2 is the slope, and sigma is the error \"\"\"\n",
+    "\n",
+    "    N = len(x)\n",
+    "\n",
+    "    # standard_normal gives samples from the \"standard normal\" distribution\n",
+    "    rng = np.random.default_rng()\n",
+    "    r = rng.standard_normal(N)\n",
+    "    y = a1 + a2*x + sigma*r\n",
+    "    return y\n",
+    "\n",
+    "N = 40\n",
+    "x = np.linspace(0.0, 100.0, N)\n",
+    "sigma = 25.0*np.ones(N)\n",
+    "y = y_experiment(10.0, 3.0, sigma, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ErrorbarContainer object of 3 artists>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a0d4a5abcc143f6a6a6c847e2d8b1ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.errorbar(x, y, yerr=sigma, fmt=\"o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Annotations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "adding text and annotations is easy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx = np.linspace(0, 2.0*np.pi, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(1.5707963267948966, 1.0, 'maximum')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31ec1b1113bb49359a1197a1b1f96bf0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(xx, np.sin(xx))\n",
+    "ax.text(np.pi/2, np.sin(np.pi/2), r\"maximum\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can also turn off the top and right \"splines\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 704x528 with 1 Axes>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.xaxis.set_ticks_position('bottom')                                           \n",
+    "ax.yaxis.set_ticks_position('left') \n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Annotations with an arrow are also possible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.05, 0.05, 'a polar annotation')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b992ce606244997a6dae7cfde77bd52",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#example from http://matplotlib.org/examples/pylab_examples/annotation_demo.html\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='polar')\n",
+    "r = np.arange(0, 1, 0.001)\n",
+    "theta = 2*2*np.pi*r\n",
+    "line, = ax.plot(theta, r, color='#ee8d18', lw=3)\n",
+    "\n",
+    "ind = 800\n",
+    "thisr, thistheta = r[ind], theta[ind]\n",
+    "ax.plot([thistheta], [thisr], 'o')\n",
+    "ax.annotate('a polar annotation',\n",
+    "            xy=(thistheta, thisr),  # theta, radius\n",
+    "            xytext=(0.05, 0.05),    # fraction, fraction\n",
+    "            textcoords='figure fraction',\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
+    "            horizontalalignment='left',\n",
+    "            verticalalignment='bottom',\n",
+    "            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Surface plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "matplotlib can't deal with true 3-d data (i.e., x,y,z + a value), but it can plot 2-d surfaces and lines in 3-d."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<mpl_toolkits.mplot3d.art3d.Line3D at 0x7f5eb57bc7d0>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d13bb5530e0f448f92f662c4ac3a4163",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# parametric curves\n",
+    "N = 100\n",
+    "theta = np.linspace(-4*np.pi, 4*np.pi, N)\n",
+    "z = np.linspace(-2, 2, N)\n",
+    "r = z**2 + 1\n",
+    "\n",
+    "x = r*np.sin(theta)\n",
+    "y = r*np.cos(theta)\n",
+    "\n",
+    "ax.plot(x,y,z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "842b18179f8744a5a2e27d16c1bfcc49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img src='data:image/png;base64,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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "X = np.arange(-5,5, 0.25)\n",
+    "Y = np.arange(-5,5, 0.25)\n",
+    "X, Y = np.meshgrid(X, Y)\n",
+    "R = np.sqrt(X**2 + Y**2)\n",
+    "Z = np.sin(R)\n",
+    "\n",
+    "surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=\"coolwarm\")\n",
+    "\n",
+    "# and the view (note: most interactive backends will allow you to rotate this freely)\n",
+    "ax.azim = 90\n",
+    "ax.elev = 40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "here we generate a bunch of gaussian-normalized random numbers and make a histogram.  The probability distribution should match\n",
+    "$$y(x) = \\frac{1}{\\sigma \\sqrt{2\\pi}} e^{-x^2/(2\\sigma^2)}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'x')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = 10000\n",
+    "rng = np.random.default_rng()\n",
+    "r = rng.standard_normal(N)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.hist(r, density=True, bins=20)\n",
+    "\n",
+    "x = np.linspace(-5,5,200)\n",
+    "sigma = 1.0\n",
+    "ax.plot(x, np.exp(-x**2/(2*sigma**2)) / (sigma*np.sqrt(2.0*np.pi)),\n",
+    "        c=\"r\", lw=2)\n",
+    "ax.set_xlabel(\"x\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting data from a file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`numpy.loadtxt()` provides an easy way to read columns of data from an ASCII file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(128, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = np.loadtxt(\"test1.exact.128.out\")\n",
+    "print(data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f2d41246710>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(data[:,1], data[:,2]/np.max(data[:,2]), label=r\"$\\rho$\")\n",
+    "ax.plot(data[:,1], data[:,3]/np.max(data[:,3]), label=r\"$u$\")\n",
+    "ax.plot(data[:,1], data[:,4]/np.max(data[:,4]), label=r\"$p$\")\n",
+    "ax.plot(data[:,1], data[:,5]/np.max(data[:,5]), label=r\"$T$\")\n",
+    "ax.set_ylim(0,1.1)\n",
+    "ax.legend(frameon=False, fontsize=12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final fun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if you want to make things look hand-drawn in the style of xkcd, rerun these examples after doing\n",
+    "plt.xkcd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x7f2d4530b610>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.xkcd()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/04-matplotlib/matplotlib-exercises.ipynb b/content/04-matplotlib/matplotlib-exercises.ipynb
new file mode 100644
index 00000000..126e4c0a
--- /dev/null
+++ b/content/04-matplotlib/matplotlib-exercises.ipynb
@@ -0,0 +1,321 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# matplotlib exercises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1: planetary positions\n",
+    "\n",
+    "The distances of the planets from the Sun (technically, their semi-major axes) are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([0.39, 0.72, 1.00, 1.52, 5.20, 9.54, 19.22, 30.06, 39.48])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are in units where the Earth-Sun distance is 1 (astronomical units).\n",
+    "\n",
+    "The corresponding periods of their orbits (how long they take to go once around the Sun) are, in years"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P = np.array([0.24, 0.62, 1.00, 1.88, 11.86, 29.46, 84.01, 164.8, 248.09])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, the names of the planets corresponding to these are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = [\"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \n",
+    "         \"Uranus\", \"Neptune\", \"Pluto\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(technically, pluto isn't a planet anymore, but we still love it :)\n",
+    "\n",
+    "  * Plot as points, the periods vs. distances for each planet on a log-log plot.\n",
+    "\n",
+    "  * Write the name of the planet next to the point for that planet on the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2: drawing a circle\n",
+    "\n",
+    "For an angle $\\theta$ in the range $\\theta \\in [0, 2\\pi]$, the polar equations of a circle of radius $R$ are:\n",
+    "\n",
+    "$$x = R\\cos(\\theta)$$\n",
+    "\n",
+    "$$y = R\\sin(\\theta)$$\n",
+    "\n",
+    "We want to draw a circle.   \n",
+    "\n",
+    "  * Create an array to hold the theta values&mdash;the more we use, the smoother the circle will be\n",
+    "  * Create `x` and `y` arrays from `theta` for your choice of $R$\n",
+    "  * Plot `y` vs. `x`\n",
+    "  \n",
+    "Now, look up the matplotlib `fill()` function, and draw a circle filled in with a solid color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q3: Circles, circles, circles...\n",
+    "\n",
+    "Generalize your circle drawing commands to produce a function, \n",
+    "```\n",
+    "draw_circle(x0, y0, R, color)\n",
+    "```\n",
+    "that draws the circle.  Here, `(x0, y0)` is the center of the circle, `R` is the radius, and `color` is the color of the circle.  \n",
+    "\n",
+    "Now randomly draw 10 circles at different locations, with random radii, and random colors on the same plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q4: Climate\n",
+    "\n",
+    "Download the data file of global surface air temperature averages from here:\n",
+    "https://raw.githubusercontent.com/sbu-python-summer/python-tutorial/master/day-4/nasa-giss.txt\n",
+    "\n",
+    "(this data comes from: https://data.giss.nasa.gov/gistemp/graphs/)\n",
+    "\n",
+    "There are 3 columns here: the year, the temperature change, and a smoothed representation of the temperature change.  \n",
+    "\n",
+    "  * Read in this data using `np.loadtxt()`.  \n",
+    "  * Plot as a line the smoothed representation of the temperature changes.  \n",
+    "  * Plot as points the temperature change (no smoothing).  Color the points blue if they are < 0 and color them red if they are >= 0\n",
+    "  \n",
+    "You might find the NumPy `where()` function useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q5: subplots\n",
+    "\n",
+    "matplotlib has a number of ways to create multiple axes in a figure -- look at `plt.subplots()` (http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot)\n",
+    "\n",
+    "Create an `x` array using NumPy with a number of points, spanning from $[0, 2\\pi]$.  \n",
+    "\n",
+    "Create 3 axes vertically, and do the following:\n",
+    "\n",
+    "* Define a new numpy array `f` initialized to a function of your choice.\n",
+    "* Plot f in the top axes\n",
+    "* Compute a numerical derivative of `f`,\n",
+    "   $$ f' = \\frac{f_{i+1} - f_i}{\\Delta x}$$\n",
+    "  and plot this in the middle axes\n",
+    "* Do this again, this time on $f'$ to compute the second derivative and plot that in the bottom axes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q6: Mandelbrot set\n",
+    "\n",
+    "The [Mandelbrot set](https://en.wikipedia.org/wiki/Mandelbrot_set) is defined such that $z_{k+1} = z_k^2 + c$\n",
+    "remains bounded, which is usually taken as $|z_{k+1}| \\le 2$\n",
+    "where $c$ is a complex number and we start with $z_0 = 0$\n",
+    "\n",
+    "We want to consider a range of $c$, as complex numbers $c = x + iy$,\n",
+    "where $-2 < x < 2$ and $-2 < y < 2$.\n",
+    "\n",
+    "For each $c$, identify its position on a Cartesian grid as $(x,y)$ and \n",
+    "assign a value $N$ that is the number of iterations, $k$, required for $|z_{k+1}|$ to become greater than $2$.\n",
+    "\n",
+    "The plot of this function is called the Mandelbrot set.\n",
+    "\n",
+    "Here's a simple implementation that just does a fixed number of iterations and then colors points in Z depending on whether they satisfy $|z| \\le 2$.  \n",
+    "\n",
+    "Your task is to extend this to record the number of iterations it takes for each point in the Z-plane to violate that constraint,\n",
+    "and then plot that data -- it will show more structure\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 256\n",
+    "x = np.linspace(-2, 2, N)\n",
+    "y = np.linspace(-2, 2, N)\n",
+    "\n",
+    "xv, yv = np.meshgrid(x, y, indexing=\"ij\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = xv + 1j*y\n",
+    "\n",
+    "z = np.zeros((N, N), dtype=np.complex128)\n",
+    "\n",
+    "for i in range(10):\n",
+    "    z = z**2 + c\n",
+    "    \n",
+    "m = np.ones((N, N))\n",
+    "m[np.abs(z) <= 2] = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fd9934ba0e0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/04-matplotlib/matplotlib.md b/content/04-matplotlib/matplotlib.md
new file mode 100644
index 00000000..c089ad68
--- /dev/null
+++ b/content/04-matplotlib/matplotlib.md
@@ -0,0 +1,3 @@
+# matplotlib
+
+matplotlib is the core plotting library for python.
diff --git a/content/04-matplotlib/matplotlib.pdf b/content/04-matplotlib/matplotlib.pdf
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diff --git a/content/04-matplotlib/test.png b/content/04-matplotlib/test.png
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diff --git a/lectures/03-matplotlib/test1.exact.128.out b/content/04-matplotlib/test1.exact.128.out
similarity index 100%
rename from lectures/03-matplotlib/test1.exact.128.out
rename to content/04-matplotlib/test1.exact.128.out
diff --git a/content/05-scipy/bisection.avi b/content/05-scipy/bisection.avi
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diff --git a/content/05-scipy/condition-example.py b/content/05-scipy/condition-example.py
new file mode 100644
index 00000000..33b45584
--- /dev/null
+++ b/content/05-scipy/condition-example.py
@@ -0,0 +1,26 @@
+def hilbert(n):
+    """ return a Hilbert matrix, H_ij = (i + j - 1)^{-1} """
+
+    H = np.zeros((n,n), dtype=np.float64)
+
+    for i in range(1, n+1):
+        for j in range(1, n+1):
+            H[i-1,j-1] = 1.0/(i + j - 1.0)
+    return H
+
+import scipy.linalg as linalg
+import numpy as np
+
+n = 13
+H = hilbert(n)
+x = np.arange(n)
+print(x)
+b = np.dot(H,x)
+print(b)
+
+xnew = linalg.solve(H, b)
+print(xnew)
+print(np.linalg.cond(H))
+
+xs = linalg.lstsq(H, b)
+print(xs)
diff --git a/content/05-scipy/integrals.py b/content/05-scipy/integrals.py
new file mode 100644
index 00000000..3d6c886d
--- /dev/null
+++ b/content/05-scipy/integrals.py
@@ -0,0 +1,171 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Polygon
+
+
+def f(x):
+    """ the function we are integrating """
+    return 1.0 + x*0.25*np.sin(np.pi*x)
+
+
+plt.rcParams.update({'xtick.labelsize': 18,
+                     'ytick.labelsize': 18,
+                     'text.fontsize': 18})
+
+
+
+N_fine = 200
+N_bins = 3
+
+xmin = -0.5
+xmax = 2.5
+
+x = np.linspace(xmin, xmax, N_fine)
+
+xp = np.linspace(xmin, xmax, N_bins+1)
+fp = f(xp)
+
+# integral range
+a = 0.5
+b = 1.5
+c = 2.5
+
+#---------------------------------------------------------------------
+# rectangle method
+#---------------------------------------------------------------------
+plt.plot(x, f(x), "r", linewidth=2)
+plt.ylim(ymin = 0)
+
+ax = plt.gca()
+
+# shade region
+x1 = a
+x2 = b
+fl = f(x1)
+
+verts = [(x1, 0), (x1, fl), (x2, fl), (x2, 0)]
+ax.add_patch(Polygon(verts, facecolor="0.8"))
+
+
+fmax = fp.max()
+
+for xl in xp:
+    plt.plot([xl,xl], [0.0, 1.2*fmax], ls="--", color="0.5")
+
+plt.scatter(xp, fp, marker="o", color="r", zorder=100)
+
+plt.figtext(0.9, 0.05, '$x$', fontsize=20)
+plt.figtext(0.1, 0.9, '$y$', fontsize=20)
+
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+ax.xaxis.set_ticks_position('bottom')
+
+ax.set_xticks((a, b))
+ax.set_xticklabels(('$a$', '$b$'))
+ax.set_yticks([])
+
+plt.xlim(xmin, 1.05*xmax)
+
+plt.savefig("rectangle.png", bbox_inches="tight")
+
+
+#---------------------------------------------------------------------
+# trapezoid method
+#---------------------------------------------------------------------
+plt.clf()
+
+plt.plot(x, f(x), "r", linewidth=2)
+plt.ylim(ymin = 0)
+
+ax = plt.gca()
+
+x1 = a
+x2 = b
+
+# shade region
+f1 = f(x1)
+f2 = f(x2)
+
+verts = [(x1, 0), (x1, f1), (x2, f2), (x2, 0)]
+ax.add_patch(Polygon(verts, facecolor="0.8"))
+
+fmax = fp.max()
+
+for xl in xp:
+    plt.plot([xl,xl], [0.0, 1.2*fmax], ls="--", color="0.5")
+
+
+ax.add_patch(Polygon(verts, facecolor="0.8"))
+
+plt.scatter(xp, fp, marker="o", color="r", zorder=100)
+
+plt.figtext(0.9, 0.05, '$x$', fontsize=20)
+plt.figtext(0.1, 0.9, '$y$', fontsize=20)
+
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+ax.xaxis.set_ticks_position('bottom')
+
+ax.set_xticks((a, b))
+ax.set_xticklabels(('$a$', '$b$'))
+ax.set_yticks([])
+
+plt.xlim(xmin, 1.05*xmax)
+
+plt.savefig("trapezoid.png", bbox_inches="tight")
+
+
+#---------------------------------------------------------------------
+# simpsons method
+#---------------------------------------------------------------------
+plt.clf()
+
+plt.plot(x, f(x), "r", linewidth=2)
+plt.ylim(ymin = 0)
+
+ax = plt.gca()
+
+
+# shade region -- get the coefficients of the parabola
+f0 = f(a)
+f1 = f(b)
+f2 = f(c)
+
+delta = 0.5*(c-a)
+
+A = (f0 - 2*f1 + f2)/(2*delta**2)
+B = -(f2 - 4*f1 + 3*f0)/(2*delta)
+C = f0
+
+x1 = a
+x2 = c
+xsimp = np.linspace(x1,x2,100)
+fsimp = A*(xsimp-x1)**2  + B*(xsimp-x1) + C
+
+simpvert = list(zip(xsimp, fsimp))
+
+verts = [(x1, 0)] + simpvert + [(x2, 0)]
+ax.add_patch(Polygon(verts, facecolor="0.8"))
+
+fmax = fp.max()
+
+for xl in xp:
+    plt.plot([xl,xl], [0.0, 1.2*fmax], ls="--", color="0.5")
+
+plt.scatter(xp, fp, marker="o", color="r", zorder=100)
+
+plt.figtext(0.9, 0.05, '$x$', fontsize=20)
+plt.figtext(0.1, 0.9, '$y$', fontsize=20)
+
+ax.spines['right'].set_visible(False)
+ax.spines['top'].set_visible(False)
+ax.xaxis.set_ticks_position('bottom')
+
+ax.set_xticks((a, b, c))
+ax.set_xticklabels(('$a$', '$b$', '$c$'))
+ax.set_yticks([])
+
+plt.xlim(xmin, 1.05*xmax)
+
+plt.savefig("simpsons.png",bbox_inches="tight")
diff --git a/content/05-scipy/newton.avi b/content/05-scipy/newton.avi
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diff --git a/content/05-scipy/orbit_setup.png b/content/05-scipy/orbit_setup.png
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diff --git a/content/05-scipy/pendulum_answer.py b/content/05-scipy/pendulum_answer.py
new file mode 100644
index 00000000..49ce43cd
--- /dev/null
+++ b/content/05-scipy/pendulum_answer.py
@@ -0,0 +1,46 @@
+from scipy.integrate import ode
+import numpy as np
+import matplotlib.pyplot as plt
+
+def rhs(t, Y, q, omega_d, b):
+    """ damped driven pendulum system derivatives.  Here, Y = (theta, omega) are
+        the solution variables. """
+    f = np.zeros_like(Y)
+        
+    f[0] = Y[1]
+    f[1] = -q*Y[1] - np.sin(Y[0]) + b*np.cos(omega_d*t)
+
+    return f
+
+def restrict_theta(theta):
+    """ convert theta to be restricted to lie between -pi and pi"""
+    tnew = theta + np.pi
+    tnew += -2.0*np.pi*np.floor(tnew/(2.0*np.pi))
+    tnew -= np.pi
+    return tnew
+
+
+def int_pendulum(theta0, q, omega_d, b, tend):
+    r = ode(rhs)
+    r.set_integrator("dopri5", nsteps=150000)
+
+    sol = []
+    r.set_solout(lambda t, y: sol.append([t, *y]))
+
+    t0 = 0.0
+    omega0 = 0.0
+    r.set_initial_value((theta0, omega0), t0)
+
+    r.set_f_params(q, omega_d, b)
+
+    r.integrate(tend)
+    return np.array(sol)
+
+
+s = int_pendulum(np.radians(60), 0.5, 0.6666, 1.5, 200.0)
+q = int_pendulum(np.radians(60.001), 0.5, 0.6666, 1.5, 200.0)
+
+plt.plot(s[:,0], restrict_theta(s[:,1]))
+plt.plot(q[:,0], restrict_theta(q[:,1]))
+
+plt.show()
diff --git a/content/05-scipy/pendulum_fft.py b/content/05-scipy/pendulum_fft.py
new file mode 100644
index 00000000..0dcf848f
--- /dev/null
+++ b/content/05-scipy/pendulum_fft.py
@@ -0,0 +1,73 @@
+import numpy as np
+from scipy.integrate import ode
+import matplotlib.pyplot as plt
+
+
+def rhs(t, Y, q, omega_d, b):
+    """ damped driven pendulum system derivatives.  Here, Y = (theta, omega) are
+        the solution variables. """
+    f = np.zeros_like(Y)
+        
+    f[0] = Y[1]
+    f[1] = -q*Y[1] - np.sin(Y[0]) + b*np.cos(omega_d*t)
+
+    return f
+
+def restrict_theta(theta):
+    """ convert theta to be restricted to lie between -pi and pi"""
+    tnew = theta + np.pi
+    tnew += -2.0*np.pi*np.floor(tnew/(2.0*np.pi))
+    tnew -= np.pi
+    return tnew
+
+def int_pendulum(theta0, q, omega_d, b, tend, dt):
+    """ integrate the pendulum with a fixed timestep, dt"""
+    r = ode(rhs)
+    r.set_integrator("dopri5", nsteps=150000, first_step=dt, max_step=dt, dfactor=1.0)
+
+    sol = []
+    r.set_solout(lambda t, y: sol.append([t, *y]))
+
+    t0 = 0.0
+    omega0 = 0.0
+    r.set_initial_value((theta0, omega0), t0)
+
+    r.set_f_params(q, omega_d, b)
+
+    r.integrate(tend)
+    return np.array(sol)
+
+def power_spectrum(t, theta0):
+    """ return the power spectrum of theta.  For the frequency
+        component, return it in terms of omega """
+
+    theta = restrict_theta(theta0)
+    
+    # fill in the rest -- take the FFT of theta and return omega_k and 
+    # the transform of theta
+    N = len(t)    
+    F = (2.0/N)*np.fft.rfft(theta)
+
+    k = np.fft.rfftfreq(N)
+    kfreq = 2.0*np.pi*k*N/max(t)
+
+    return kfreq, F
+
+
+# normal (undamped, not driven pendulum)
+s = int_pendulum(np.radians(10), 0.0, 0.6666, 0.0, 200.0, 0.1)
+
+omega, F = power_spectrum(s[:,0], s[:,1])
+
+plt.plot(omega, np.abs(F)**2)
+plt.xlim(0.0, 2.0)
+plt.show()
+
+# chaotic pendulum
+s = int_pendulum(np.radians(10), 0.5, 0.6666, 1.15, 200.0, 0.1)
+
+omega, F = power_spectrum(s[:,0], s[:,1])
+
+plt.plot(omega, np.abs(F)**2)
+plt.xlim(0.0, 2.0)
+plt.show()
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diff --git a/content/05-scipy/rk4_k3.png b/content/05-scipy/rk4_k3.png
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diff --git a/content/05-scipy/rk4_k4.png b/content/05-scipy/rk4_k4.png
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diff --git a/content/05-scipy/rk4_plot.py b/content/05-scipy/rk4_plot.py
new file mode 100644
index 00000000..2af34852
--- /dev/null
+++ b/content/05-scipy/rk4_plot.py
@@ -0,0 +1,218 @@
+# make a plot of what the 4th-order Runge-Kutta is doing using the
+# example dy/dt = -y (this comes from Garcia).
+
+import numpy
+import pylab
+
+def rhs(y):
+    """ return dy/dt """
+    return -y
+
+def exact(t):
+    """ analytic solution """
+    return numpy.exp(-t)
+
+
+# frame 1 -- show the initial condition (y^n)
+
+y0 = 1.0
+t0 = 0.0
+
+dt = 0.75
+
+tt = numpy.linspace(t0, t0+2.0*dt, 100)
+
+
+def start():
+    """ default starting point """
+
+    pylab.plot(tt, exact(tt), label="analytic solution", color="k")
+
+    # draw the timeline
+    pylab.plot(tt, 0*tt, color="k")
+
+    # label the current point
+    pylab.scatter([t0], [y0], color="r")
+    pylab.text(t0, y0+0.03, r"$y^n$", 
+               horizontalalignment="left", color="r", fontsize=20) 
+
+    pylab.plot([t0,t0], [0, y0], ls=":", color="0.5")
+    pylab.text(t0, -0.05, r"$t^n$", 
+               verticalalignment="top", horizontalalignment="center",
+               fontsize=20)
+
+
+    # illustrate where t^n+1 is
+    pylab.plot([t0+dt,t0+dt], [0, y0], ls=":", color="0.5")
+    pylab.text(t0+dt, -0.05, r"$t^{n+1}$", 
+               verticalalignment="top", horizontalalignment="center",
+               fontsize=20)
+
+
+start()
+
+pylab.axis("off")
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.05*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_initial.png", bbox_inches='tight')
+
+
+# now draw the solution Euler would get
+slope = rhs(y0)
+tEuler = numpy.linspace(t0, t0+dt, 2)
+yEuler = y0 + slope*(tEuler-t0)
+
+pylab.plot(tEuler, yEuler, label="Euler step")
+
+pylab.scatter([tEuler[1]], [yEuler[1]], color="r")
+pylab.text(tEuler[1]+0.015, yEuler[1], r"$y^{n+1}$", 
+           horizontalalignment="left", verticalalignment="center",
+           color="r", fontsize=20) 
+
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.25*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_Euler.png", bbox_inches='tight')
+
+
+#----------------------------------------------------------------------------
+# show k1 slope 
+pylab.clf()
+start()
+
+pylab.axis("off")
+
+k1 = rhs(y0)
+tSlope = numpy.linspace(t0-0.2*dt, t0+0.2*dt, 2)
+ySlope = y0 + k1*(tSlope - t0)
+
+pylab.plot(tSlope, ySlope, label=r"slope", color="g", ls="--", lw=2, zorder=10)
+pylab.text(t0-0.1*dt, y0, r"$k_1$", color="g")
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.25*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_k1.png", bbox_inches='tight')
+
+
+#----------------------------------------------------------------------------
+# follow k1 to define k2
+
+ytmp = y0 + k1*0.5*dt
+k2 = rhs(ytmp)
+
+# draw the k1 half step
+pylab.plot([t0, t0+0.5*dt], [y0, ytmp], color="b", label="half-dt k1 step")
+pylab.scatter(t0+0.5*dt, ytmp, color="b")
+
+# draw slope there
+
+tSlope = numpy.linspace(t0+0.5*dt-0.2*dt, t0+0.5*dt+0.2*dt, 2)
+ySlope = ytmp + k2*(tSlope - (t0 + 0.5*dt))
+
+pylab.plot(tSlope, ySlope, color="g", ls="--", lw=2)
+pylab.text(t0+0.5*dt-0.1*dt, ytmp, r"$k_2$", color="g", verticalalignment="top")
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.25*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_k2.png", bbox_inches='tight')
+
+
+#----------------------------------------------------------------------------
+# follow k2 to define k3
+ytmp = y0 + k2*0.5*dt
+k3 = rhs(ytmp)
+
+# draw k2 half step
+pylab.plot([t0, t0+0.5*dt], [y0, ytmp], color="c", label="half-dt k2 step")
+pylab.scatter(t0+0.5*dt, ytmp, color="c")
+
+# draw slope there
+ySlope = ytmp + k3*(tSlope - (t0 + 0.5*dt))
+pylab.plot(tSlope, ySlope, color="g", ls="--", lw=2)
+pylab.text(t0+0.5*dt+0.05*dt, ytmp, r"$k_3$", color="g", verticalalignment="bottom")
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.25*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_k3.png", bbox_inches='tight')
+
+
+#----------------------------------------------------------------------------
+# follow k3 to define k4
+ytmp = y0 + k3*dt
+k4 = rhs(ytmp)
+
+# draw k3 full step
+pylab.plot([t0, t0+dt], [y0, ytmp], color="0.5", label="full-dt k3 step")
+pylab.scatter(t0+dt, ytmp, color="0.5")
+
+# draw slope there
+tSlope2 = numpy.linspace(t0+dt-0.2*dt, t0+dt+0.2*dt, 2)
+ySlope = ytmp + k4*(tSlope2 - (t0 + dt))
+pylab.plot(tSlope2, ySlope, color="g", ls="--", lw=2)
+pylab.text(t0+dt-0.1*dt, ytmp, r"$k_4$", color="g", verticalalignment="top")
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.25*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_k4.png", bbox_inches='tight')
+
+
+
+#----------------------------------------------------------------------------
+# final RK-4 step
+ynew = y0 + (dt/6.0)*(k1 + 2.0*k2 + 2.0*k3 + k4)
+
+
+# draw full RK-4 step
+pylab.plot([t0, t0+dt], [y0, ynew], color="r", label="full 4th-order RK step")
+pylab.scatter(t0+dt, ynew, color="r")
+pylab.text(t0+1.05*dt, ynew+0.015, r"$y^{n+1}$", color = "r", 
+           horizontalalignment="left", fontsize=20)
+
+
+leg = pylab.legend()
+ltext = leg.get_texts()
+pylab.setp(ltext, fontsize='large')
+leg.draw_frame(0)
+
+pylab.xlim(t0-0.25*dt, t0+2.1*dt)
+
+pylab.tight_layout()
+pylab.savefig("rk4_final.png", bbox_inches='tight')
+
diff --git a/content/05-scipy/scipy-basics-2.ipynb b/content/05-scipy/scipy-basics-2.ipynb
new file mode 100644
index 00000000..c40d60e5
--- /dev/null
+++ b/content/05-scipy/scipy-basics-2.ipynb
@@ -0,0 +1,1313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# More SciPy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting\n",
+    "\n",
+    "Fitting is used to match a model to experimental data.  E.g. we have N points of $(x_i, y_i)$ with associated errors, $\\sigma_i$, and we want to find a simply function that best represents the data.\n",
+    "\n",
+    "Usually this means that we will need to define a metric, often called the residual, for how well our function matches the data, and then minimize this residual.  [Least-squares fitting](https://en.wikipedia.org/wiki/Least_squares) is a popular formulation.\n",
+    "\n",
+    "We want to fit our data to a function $Y(x, \\{a_j\\})$, where $a_j$ are model parameters we can adjust.  We want to find the optimal $a_j$ to minimize the distance of $Y$ from our data, *measured parallel to the $y$-axis*:\n",
+    "\n",
+    "$$\\Delta_i = Y(x_i, \\{a_j\\}) - y_i$$\n",
+    "\n",
+    "[Least-squares](https://en.wikipedia.org/wiki/Ordinary_least_squares) minimizes the distance between the\n",
+    "data points and the model line parallel to the $y$-axis.  We write this as $\\chi^2$:\n",
+    "\n",
+    "$$\\chi^2(\\{a_j\\}) = \\sum_{i=1}^N \\left ( \\frac{\\Delta_i}{\\sigma_i} \\right )^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import optimize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### general linear least squares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we'll make some experimental data (a quadratic with random fashion).  We use the [standard_normal](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.standard_normal.html) function to provide Gaussian normalized errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def y_experiment2(a1, a2, a3, sigma, x):\n",
+    "    \"\"\" return the experimental data in a quadratic + random fashion,                              \n",
+    "        with a1, a2, a3 the coefficients of the quadratic and sigma is                             \n",
+    "        the error.  This will be poorly matched to a linear fit for                                \n",
+    "        a3 != 0 \"\"\"\n",
+    "\n",
+    "    N = len(x)\n",
+    "\n",
+    "    # standard_normal gives samples from the \"standard normal\" distribution\n",
+    "    rng = np.random.default_rng()\n",
+    "    r = rng.standard_normal(N)\n",
+    "\n",
+    "    y = a1 + a2*x + a3*x*x + sigma*r\n",
+    "\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 40\n",
+    "sigma = 5.0*np.ones(N)\n",
+    "\n",
+    "x = np.linspace(0, 100.0, N)\n",
+    "y = y_experiment2(2.0, 1.50, -0.02, sigma, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ErrorbarContainer object of 3 artists>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(x,y)\n",
+    "ax.errorbar(x, y, yerr=sigma, fmt=\"o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "\"linear\" in general linear least squares means that the fit parameters enter into the fitting function linearly,\n",
+    "the function itself can be nonlinear in $x$.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use the [leastsq()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html) function to minimize the square of the residual.  \n",
+    "\n",
+    "First our function to compute the residual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def resid(avec, x, y, sigma):\n",
+    "    \"\"\" the residual function -- this is what will be minimized by the\n",
+    "        scipy.optimize.leastsq() routine.  avec is the parameters we\n",
+    "        are optimizing -- they are packed in here, so we unpack to\n",
+    "        begin.  (x, y) are the data points \n",
+    "\n",
+    "        scipy.optimize.leastsq() minimizes:\n",
+    "\n",
+    "           x = arg min(sum(func(y)**2,axis=0))\n",
+    "                    y\n",
+    "\n",
+    "        so this should just be the distance from a point to the curve,\n",
+    "        and it will square it and sum over the points\n",
+    "        \"\"\"\n",
+    "\n",
+    "    a0, a1, a2 = avec\n",
+    "    \n",
+    "    return (y - (a0 + a1*x + a2*x**2))/sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.15667857  1.51771559 -0.01948624]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initial guesses\n",
+    "a0, a1, a2 = 1, 1, 1\n",
+    "\n",
+    "afit, flag = optimize.leastsq(resid, [a0, a1, a2], args=(x, y, sigma))\n",
+    "\n",
+    "print(afit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.plot(x, afit[0] + afit[1]*x + afit[2]*x*x )\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that we represent the data quite well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute the [reduced $\\chi^2$](https://en.wikipedia.org/wiki/Ordinary_least_squares#Reduced_chi-squared)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.2945742468418153\n"
+     ]
+    }
+   ],
+   "source": [
+    "chisq = sum(np.power(resid(afit, x, y, sigma),2))\n",
+    "normalization = len(x)-len(afit)\n",
+    "print(chisq/normalization)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In general a (reduced) $\\chi^2 < 1$ indicates a good fit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a nonlinear example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same interface works with nonlinear data.\n",
+    "\n",
+    "We'll create a new set of experimental data---an exponential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a0 = 2.5\n",
+    "a1 = 2./3.\n",
+    "sigma = 5.0\n",
+    "\n",
+    "a0_orig, a1_orig = a0, a1\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
+    "x = np.linspace(0.0, 4.0, 25)\n",
+    "y = a0*np.exp(a1*x) + sigma * rng.standard_normal(len(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ErrorbarContainer object of 3 artists>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(x,y)\n",
+    "ax.errorbar(x, y, yerr=sigma, fmt=\"o\", label=\"_nolegend_\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "our function to minimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def resid(avec, x, y):\n",
+    "    \"\"\" the residual function -- this is what will be minimized by the                             \n",
+    "        scipy.optimize.leastsq() routine.  avec is the parameters we                               \n",
+    "        are optimizing -- they are packed in here, so we unpack to                                 \n",
+    "        begin.  (x, y) are the data points                                                         \n",
+    "                                                                                                   \n",
+    "        scipy.optimize.leastsq() minimizes:                                                        \n",
+    "                                                                                                   \n",
+    "           x = arg min(sum(func(y)**2,axis=0))                                                     \n",
+    "                    y                                                                              \n",
+    "                                                                                                   \n",
+    "        so this should just be the distance from a point to the curve,                             \n",
+    "        and it will square it and sum over the points                                              \n",
+    "        \"\"\"\n",
+    "\n",
+    "    a0, a1 = avec\n",
+    "\n",
+    "    # note: if we wanted to deal with error bars, we would weight each                             \n",
+    "    # residual accordingly                                                                         \n",
+    "    return y - a0*np.exp(a1*x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "[3.22621618 0.58391382]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a0, a1 = 1, 1\n",
+    "afit, flag = optimize.leastsq(resid, [a0, a1], args=(x, y))\n",
+    "\n",
+    "print(flag)\n",
+    "print(afit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+8pe/MGrUqFLt89///hc/Pz+X1akwY8eOpU2bNixevJiQkJAKec8DBw6QmJjIli1bnILU66+/jouezSkiImWkoFIJGIaB1WolJCSk1B/6ERERLqtXUfbu3csjjzxCTEyMW97/UuHh4e6ugoiIFEK3fjxQdnY2f/7zn4mMjCQgIIDrrruODRs2OLavWLECk8nE4sWLadeuHf7+/nz33XdX3HLJy8vjz3/+M9WqVaNGjRqMGzeOIUOGON3muPzWT0JCAi+99BJDhw4lNDSUuLg43nvvPaf6jRs3jkaNGhEUFES9evV45plnyM3NLdG5HThwAJPJxO+//87QoUMxmUzMmDGDGTNmOD1FG2D+/PmYTCbH8oXzmzVrFgkJCYSHh3PPPfdw9uxZRxmbzcbLL79MgwYN8Pf3Jy4ujhdffBGAxMREAK655hpMJhPdunWDAm79lPTrv2zZMtq3b09QUBCdO3dmz549JfoaiIhIyVXJoJKZm0lmbqZTc3+uNZfM3ExyrDkFlrUZtotlbfay2dbsEpUtraeeeorPP/+cmTNnsnnzZho0aECvXr04deqUU7nx48czadIkdu/eTatWra44zuTJk/noo4+YPn06a9asIT09nfnz5xf7/q+++irt27dny5YtPProo4wYMcLpQzg0NJQZM2awa9cuXn/9daZNm8Zrr71WonOLjY3l2LFjhIWF8a9//Ytjx44xYMCAEu1LfkvM/PnzWbBgAQsWLGDlypVMmjTJsX3ChAlMmjSJZ555hl27djF79myioqLgkqcff/vttxw7doz//ve/Bb5HSb/+Tz/9NK+++iobN27E19eXoUOHlvg8RESkhAwvl5aWZgBGWlpaifdpMaOF0WJGC+P387871k39carRYkYLY+KaiU5lO3zYwWgxo4Xx29nfHOv+s/M/RosZLYynVj7lVLbrx12NFjNaGL+c+sWx7tM9n5bqfM6dO2f4+fkZH330kWNdTk6OUadOHePll182DMMwli9fbgDG/PnznfadOHGi0bp1a8dyVFSU8corrziW8/LyjLi4OOO2225zrLvhhhuM0aNHO5bj4+ONQYMGOZZtNpsRGRlpvPPOO4XW+ZVXXjHatWtXaD0KEh4ebkyfPt2xPH36dCM8PNypzLx584xLv0UnTpxoBAUFGenp6Y51Tz75pNGpUyfDMAwjPT3d8Pf3N6ZNm1bge+7fv98AjC1btjitHzJkiONrUpqv/7fffusos3DhQgMwzp8/X+R5i4i4WkZ2rhE/boERP26BkZGd6+7qFKqkn9/qo+Jh9u7dS25uLl26dHGs8/Pzo2PHjuzevdupbPv27Qs9TlpaGqmpqXTs2NGxzsfHh3bt2mGz2QrdD3BqnTGZTERHR3P8+HHHurlz5/LGG2+wd+9ezp07R15eHmFhYaU+16uRkJBAaGioY7l27dqOuu3evZvs7Gy6d+9+1ccvzdf/0q9T7dq1ATh+/DhxcXFX/f4iIp4iMyePZs8uAWDX870IsrgnMlTJoPLDfT8AEOgb6Fj3YPMHGdR0EL5m5y/JirvtT3wO8A1wrLunyT30b9gfH7OPU9mv+399RdnbGtzmorOA4OBglxz38lFAJpPJEW7Wrl3LwIEDee655+jVqxfh4eHMmTOHV199tUzvaTabrxh5U1C/l6LqFhgYeEV5V7q0Lhf60hQXAkVEpHSqZB+VIL8ggvyCnDpq+vn4EeQXhMXHUmBZs+nil8rPbC/r7+NforKlUb9+fSwWC2vWrHGsy83NZcOGDTRr1qzExwkPDycqKsqpE6jVamXz5s2lqs/lvv/+e+Lj43n66adp3749DRs25ODBg2U6JkCtWrU4e/YsGRkZjnVbt24t1TEaNmxIYGAgy5YtK3C7xWK/tlartdBjlNfXX0REykeVbFHxZMHBwYwYMYInn3ySiIgI4uLiePnll8nMzGTYsGGlOtaoUaNITk6mQYMGNGnShDfffJPTp087BbTSatiwIYcOHWLOnDl06NCBhQsXMm/evKs+3gWdOnUiKCiIv/71r/z5z3/mhx9+YMaMGaU6RkBAAOPGjeOpp57CYrHQpUsXTpw4wc6dOxk2bBiRkZEEBgby9ddfExMTQ0BAwBVDk8vz6y8i4g45eRdbdmeuOcCwrvWw+Hpvu4T31rwSmzRpEv3792fw4MG0bduWX3/9lSVLllC9evVSHWfcuHHce++93H///SQlJRESEkKvXr0ICAgowd4Fu/XWW3n88cd57LHHaNOmDd9//z3PPPPMVR/vgoiICD788EMWLVpEy5Yt+fjjj/n73/9e6uM888wzPPHEEzz77LM0bdqUAQMGOPqw+Pr68sYbbzB16lTq1KnDbbcVfFuuvL7+IiIVLXnRLtq+sNSxPHnJHpo8s5jkRbvcWq+yMBlePiVneno64eHhpKWlVViHTm9ls9lo2rQpd999Ny+88IK7qyMiIuUoedEupq7aX+j2h69PZEKfkt/CdnVn2pJ+fqtFpRI7ePAg06ZN4+eff2b79u2MGDGC/fv3c99997m7aiIiUo5y8mxMW114SAGYtnq/020hb6GgUomZzWZmzJhBhw4d6NKlC9u3b+fbb7+ladOm7q6aiIiUo1lrD2Ar5v6IzbCX8zbqTFuJxcbGOo1eERGRyungqcxyLedJ1KIiIiLi5eIjgsq1nCdRUBEREfFyg5MSMBcz84TZZC9XEj+d+om07DPlU7kyUlARERHxchZfMw91TSyyzENdE0s0n8qZrDOMXDaSQV/fg9mSWo61vDrqoyIiIlIJXBh6PG31fqeOtWaTPaSUdGjyqexT9kfMGCZsue6fP0rzqIiIiFQiZzJzaPO8fdK3cb0aX9XMtJm5mfyWnkrPV+wTxWkeFRERESkXl4aSIV0SShxSLm23CPILIiY01iX1Ky0FFRERkSouMzeTQYsHsexQwQ91dScFFRERkSpu5q6ZbDuxjeQfkjmfd97d1XGizrQiIiJV3J9a/Inzuee5PuZ6e0daD6KgIiIiUsX5+fgxtv1Yd1ejQLr1IyIiUgXlWnP5+sDXePrgXwUVERGRKuhfm//Fkyuf5IV1L7i7KkVSUBEREamCIoMi8TX70rVuV3dXpUjqoyIiIlIFDWk+hF4JvYgOjnZ3VYqkFhUREZEqwmqzYrVZHcueHlJQUBEREak6PtjxAQ8tfYjUDPc/bLCkFFRERESqgLTsNGbsmMGGlA38kPJDifapbzrCDL/JkHHC5fUrjIKKiIhIFRDuH85HfT9iROsR9KvXr9jyprPHmGmZTDefH7EsfbpC6lgQBRUREZEqIjE8kUfbPIrJZCq6YFYa/nMHEGM6yT5bNDk3v1RRVbyCgoqIiEgltvTgUvan7S/5DnnZMGcg5uM7OWGEc3/ueAiu6coqFklBRURE5Cpk5uSRMH4hCeMXkpmT5+7qFGjvmV+ZsHoCAxYM4JfTvxS/g80G8x6BA6vJ8wvhruyJ/GZEMnPNAXLybBVR5SsoqIiIiFRS4f7htKnVhnZR7ahfrX7xO3zzN9j5XxbZrqXFuSkcwD58efKSPTR5ZjHJi3a5vtKX0YRvIiIilVTNwFpMvXkq5/POYzYV0zbx/ZuwbgpfWzvwaO4owLkfi82Aqavst5Am9Gnmymo7UYuKiIhIZWPOdvzXx+xDiCWk6PLbPoFv/obVMPGXvEeuCCmXmrZ6f4XeBlJQERERqUSOnjtCcP2XsUSsxGaUIFDsXQ7zHwVgcczjnDMCiyxuM2DW2gPlVd1iKaiIiIhUIksOLsbsm4Fv6E5shrXowsd+hLmDwJYLze9kfa07SvQeB09llk9lS0BBRUREpBJ5oNkwso7dwfkjA/E1+xVe8PQB+PCPkHMOErrCHe8SX6OYW0T54iOCyq/CxVBQERERcbPyHOpsMpnIPdMJIy+88EIZJ2HWnZBxHKJawD0fga8/g5MSMBczF5zZBIOTEspUx9JQUBEREfFy3x78ln+s+we5ttziC+dkwOy74dReCI+FgZ9BgD3UWHzNPNQ1scjdH+qaiMW34uKDhieLiIh4sTNZZ/jbmr+RkZtBvfB63F7/7sILW/Pg0wfhyCYIrA6DPoew2k5FLgw9nrZ6Pzbj4nqzyR5SKnJoMgoqIiIi3q1aQDUmdZ3El3u/5O7Gd1PonSPDgAWj4Zcl4BsA986FWo0LLDqhTzNGdGtAm+eXAjCuV2OGda1XoS0pFyioiIiIeLlusd3oFtsNgBwKSSrLX4ItH4LJDH+cDnGdijzmpaFkSJcEt4QU1EdFRETEO839aS7pOeklK7zx37DqZfv/+/4TmvRxad3Kk1pUREREvMwnez7hHz/8g9k/zeaTfp/g7+Pv2BZk8eXApL4XC+9eAAufsP//hnHQ/kE31PjqqUVFRETEy7Ss2ZLo4Gj61e/nFFKucGgdfD4MDBu0vR+6TajIapYLtaiIiIh4maY1mvJZv88Is4QVXuj4TzB7AORlQaM/QN/XwFTMJCkeyKUtKu+88w6tWrUiLCyMsLAwkpKSWLx4sWN7VlYWI0eOpEaNGoSEhNC/f39SU1NdWSURERGvlJmbydFzRx3L4f7hmAoLHulH4cP+kHUGYjrYO8/6eGfbhEuDSkxMDJMmTWLTpk1s3LiRm266idtuu42dO3cC8Pjjj/PVV1/x6aefsnLlSo4ePcqdd97pyiqJiIh4HZth429r/saABQPYmLKx6MLnz9inxk//DWo0sA9DtlTclPflzaXxql+/fk7LL774Iu+88w7r1q0jJiaGDz74gNmzZ3PTTTcBMH36dJo2bcq6deu49tprXVk1ERERr3Eu9xxHzh0hIzcDX3MRH91ZafDhnXB8J4REwaD/QnCNiqxquauwdiCr1cqnn35KRkYGSUlJbNq0idzcXHr06OEo06RJE+Li4li7dm2hQSU7O5vs7GzHcnp6CYdmiYiIeKkwSxgz/zCTbSe20SayTcGFstLtt3scs87+F6rHV3RVy53LR/1s376dkJAQ/P39eeSRR5g3bx7NmjUjJSUFi8VCtWrVnMpHRUWRkpJS6PGSk5MJDw93vGJjY119CiIiIm5x6bN7AnwD6Fi7Y8EFs8/CR3+E3zZAQDW4/wuIblFxFXUhlweVxo0bs3XrVn744QdGjBjBkCFD2LVr11Ufb8KECaSlpTlehw8fLtf6ioiIeILTWae584s7+eLXL4oumH0OProbDv9gf7jg/fOhduuKqqbLufzWj8VioUGDBgC0a9eODRs28PrrrzNgwABycnI4c+aMU6tKamoq0dHRhR7P398ff/8ixoyLiIhUAnN+msOB9ANM3TaVXgm9CPANuLJQToZ9CPKh78E/HAbPgzrXuKO6LlPhY5VsNhvZ2dm0a9cOPz8/li1bRv/+/QHYs2cPhw4dIikpqaKrJSIi4lEebv0wJpOJHnE9CgkpmfDxPXDwO7CEwuD/Qt127qiqS7k0qEyYMIHevXsTFxfH2bNnmT17NitWrGDJkiWEh4czbNgwxo4dS0REBGFhYYwaNYqkpCSN+BERkSrPbDLzSOtHCt6Yex7m3Av7V4ElBAZ9DjHtK7qKFcKlQeX48ePcf//9HDt2jPDwcFq1asWSJUu4+eabAXjttdcwm83079+f7OxsevXqxdtvv+3KKomIiHgsn8D9TNv+LqPaPorZVEg30twsmDMQ9q0Av2AY+FmxT0L2Zi4NKh988EGR2wMCApgyZQpTpkxxZTVEREQ8n/k8ATEf8v6ODKoHhHF/8/uvLJOXDZ8Mhr3LwC8IBn4K8ZW7u4QeSigiIuIJbIFkH+9Dy5qtuKvxXVduz8uBT4bAL9+AbyDc9wkkdHFHTSuUgoqIiIiHyEtrx3s9phPoG3jZhhz49AH4eTH4BsB9cyCxq7uqWaEUVERERK5CTp7N8f+Zaw44LZeU1WZl+o7pnM8771h3Rd8Uay58PhT2LAQff7j3Y6jXrWyV9yIKKiIiIqWUvGgXbV9Y6lievGQPTZ5ZTPKi0k1o+uqmV/nnpn8yduWfAePKAtY8+PxPsPsr8LHAPbOh/k3lcQpeQ0FFRESkFJIX7WLqqv3YLssVNgOmrtpfqrDSM74n1fyr0b/hXYDJeaM1D+YNh13z7SFlwEfQsEdhh6q0FFRERERKKCfPxrTV+4ssM231/hLfBmoT2YZFdy6iR1xP5w02K8x/BHZ8DmY/uPs/0KhnYYep1BRURERESmjW2gNXtKRczmbYyxVm1W+rSMm4+PDdUEvoZQewwvxHYfunYPaFu2dC495lrru3UlAREREpoYOnMstU7odjPzB6+WgGLx5MakbqFdtN2LAsGg3b5oDJB/44HZr0LXO9vVmFP+tHRETEW8VHBJWpXFxoHDEhMTSq3oiagTWdtpmw8ZLvB/huW54fUj6AZreWS729mVpURERESmhwUgJmU9FlzCZ7uYLUDqnNrN6zSO6ajI/Zx7E+JzePoebFBJmyWGtrTu7t06D5HeVdfa+kFhUREZESsviaeahrIlNXFd6h9qGuiVh8L7YDpGakcuL8CVrUbAFAtYBqTuWTF25n7ne7OGP0hfw+uOY58NCRXUzo08xVp+I11KIiIiJSChP6NOPh6xOvaFkxm+Dh6xOdwkVadhqPfPsIQ5cMZf2x9Vcca9JXW5m6+iBnjGCn9Vcz1LmyUouKiIhIKU3o04wR3RrQ5nn7pG/jejVmWNd6Ti0pAH5mP2oF1iItO426oXWdtuWcPcWna3YBYVfOoZJv2ur9PNGzyRXHrQhBFl8OTHJ/R161qIiIiFyFS8PDkC4JBYaJIL8gpnSfwszeM6kbcklQST/G928/zO+EFxpSKMFQ56pAQUVERDxaZk4eCeMXkjB+IZk5ee6uTrFsho0tx7c4lv18/IgNjb1Y4Pe98O+epJ07W6LjlXRIdGWloCIiIlJODMPg1Y2vcv/i+/lo90dXFjj2I/y7F5w5RLUg/xIds6RDoisrBRUREZFyZDWsUNCMswe+gxm3QMYJiG5J0oipZRrqXFUoqIiIiJQTk8nEuA7j+LDPh9xa/5LJ2n5aBLPuhOx0iL8OHliIpVo0D3VNLPJ4lw91roqq9tmLiIiUg31pezEM+0OATCYTrWu1vrhxy0cwdxBYs6FxXxj0OQSEQymHOldVCioiIiJl4BO4nweWDOS5tc9htVmdN655Hb54FAwrtBlofwqyX4BTkQl9mrH5mZsdy+N6NeanF3orpOTTPCoiIiJlYLL8Tq4tl9/P/45B/qOVDQO+nWgPKgCdR8HNL4Cp4E4pJRnqXFUpqIiIiJRBXlp73hjYg0512uFr9gVrHiwYDVs+tBfo8RxcN8bd1fRaCioiIiKltO/MPsL8Lj79uEN0JwJ8fSE3Cz4fBj8tAJMZ+r0BbQe7ta7eTkFFRESqjMycPJo9uwSAXc/3IshS+o/B3b/v5qGlD1E/vAGYbgPDYt+QlQ5z7oMDq8HHH/74b2h6S3mfQpWjoCIiIlIKVsOK1WYl25oNJisY2OdGmXs3pGwDSyjcOxsSr3d3VSsFBRUREZFSaFGzBR/0+oCaAXXo+N0aYkwnCPhPHzi9D4Jq2ocf12nj7mpWGupWLCJSCt723BkpH1uPb+XIuSOO5WY1mhHiF0Ij02E+s/wd8+l9EB4HQ5copJQzBRUREZEibDm+heFLhzNsyTBSM1Id6837l/OZ5TmiTaex1WwMw5ZAzQZurWtlpKAiIiJShDrBdagZWJO40DjC/MPsK9dPw3/OAMJMmWy0NSJr8EIIq+PuqlZK6qMiIiJShKjgKGb8YQbh/uH4Y4YFY2HjB5iAz63X8dfcP7ElsLq7q1lpKaiIiIhcZtVvqwj2C6ZdVDsAIoMiIfMUfPoA7F8JmMi58RmeWNwYKOYRyFImuvUjIiJyic2pmxm9fDSPfvsov57+1b7y5C/wfg97SPELhns+Ii9ptEJKBVCLioiIyCWa1WhGu6h2hFvCiQ+Ph73L4dMhkJUG4bFw78cQ3RI06qtCKKiIiIhcIsA3gDdvehM/sx++G6fD4nH2px/HdIR7PoKQSHdXsUpRUBERkSrvq71fkWvL5c6GdwIQaPKDxeNhwzR7gVb3QL/XwS/AvRWtghRURESkStt2YhtPf/c0AInhiVwTmmDvNLtvhb0PSvdn4brHwaT+KO6goCIiIlVay5otGdB4AAYGrU3B9k6zv/9q7zR753t6sKCbKaiIiEiVZjKZmNBpAqZ9KzF90MPeaTYsBu6bY+80K26l4ckiIlLl+FX/jlc3vYxhGACYN07H9GF/e0iJ6QAP/U8hxUOoRUVERKoUsyUV/6iFfPKzwQ11k7h+xyJY/559Y6sB0O8NdZr1IAoqIiJSpdhyoshOuYNHu4XQdcXrsG+5fUP3Z+G6seo062EUVEREpNLLzM3EaljxIRCAummxjF7/DqZTv4JfUH6n2X6lOmaQxZcDk/q6qMZygYKKiIhUaifPn2TUslEE+QXxz+vforN5B2/7vY75VIa90+y9H0PtVu6uphRCnWlFRMSj5eTZHP+fueaA03JJ/H7+d/an7+fn0z+Tsup5ZvklU82UgbVOO3unWYUUj6agIiIiHit50S7avrDUsTx5yR6aPLOY5EW7SnyMxhGNeSPpBWblhNF47dv4mAzm5HUje9CXEBrloppLedGtHxER8UjJi3YxddX+K9bbDBzrJ/RpVuC+3x78lkbVGxEXFgcH19Lxv3+Gs0cx/IJ4IuN+/mu7nlt9NbLHG6hFRUREPE5Ono1pq68MKZeatnp/gbeBFuxbwNgVY3n02xGkrZwMM/rC2aNQszFZDyzlv7brXVhzKW8KKiIi4nFmrT2AzSi6jM2wl7vctbWvpXZQFJ0zzhKy/CX7k49b3g0P/Q+jVhPXVVpcQrd+RETE4xw8lVmqcoZhYMqf/6Tm7weYe+QY4WcOY/Lxhz4vQ9sh9vlRcvJcWu+rpaHOhVOLioiIeJz4iKASlzuVdYoHlzzIhmMbYN278O8/UO3MYUwR9eBP30K7BzSJmxdTUBEREY8zOCkBczHZwmyyl5u2bRqbUjcxcekj5H09Dmy50Ow2GL5CQ48rAQUVERHxOBZfMw91TSyyzENdE7H4mhld+0b65JiYcvgAvmY/6P0K3DUTAsIrrL7iOgoqIiLikSb0acbD1yde0bJiNsGAa0OZ0LspbPiAgBm3MPnIQRKD6sCwJdBpeKG3eso6eZxUPAUVERHxWBP6NGPzMzc7lsf1asyz951icdpj/HfObbBwLFizoVFveGQV1G1X6LHKY/I4qXgKKiIi4tEsvhc/qoZ0SSD9zC8YGOw5sQ1MPtDzH/bn9QRWL/QYFyaPu3zI84XJ4xRWPJeCiohIKejWgXv5bJvNqJXv8VbKccbnBsKDi6DzqCJH9ZRl8jhxPwUVEZES0q0D9zh67ghBkV8w2fdd/BeMwpR3nhvqXofpke8g7tpi9y/L5HHifi4NKsnJyXTo0IHQ0FAiIyO5/fbb2bNnj1OZrKwsRo4cSY0aNQgJCaF///6kpqa6sloiIqWmWwfukW3N5uElg/CpsZazNbZimMxw0zNw36cQXKNExyjt5HHiWVwaVFauXMnIkSNZt24dS5cuJTc3l549e5KRkeEo8/jjj/PVV1/x6aefsnLlSo4ePcqdd97pymqJiJSKK24dZObkkTB+IQnjF5LpobOlul1eDv4rXmb0kX20zMqmw1k/su/9L1z/FzCX/OOrNJPHiedx6RT6X3/9tdPyjBkziIyMZNOmTVx//fWkpaXxwQcfMHv2bG666SYApk+fTtOmTVm3bh3XXlt8k56IiKuV5tbBsK71KqpalVZ6TjqZx34kevEESNnOrUB2WmsG5w5hXULXUh9vcFICLy7aXeQ1vDB5nHieCu2jkpaWBkBERAQAmzZtIjc3lx49ejjKNGnShLi4ONauXVvgMbKzs0lPT3d6iYi4km4dVJx9p3/h3s968+dvHiIrdQcERpB9x3SezB1JOiFXdczSTB4nnqfCrorNZmPMmDF06dKFFi1aAJCSkoLFYqFatWpOZaOiokhJSSnwOMnJyYSHhztesbGxFVJ/Eam6dOuggpzaT9C8EZzNOk2ayURqg27w6DqsTW8t86GLmjzu4esTmdCnWZnfQ1yjwoLKyJEj2bFjB3PmzCnTcSZMmEBaWprjdfjw4XKro4hIQUrz3Bm5CoYBm2bAO12IPrSBt38/x9xWjxN/3zwIjSq3tylo8rifXuitkOLhKiSoPPbYYyxYsIDly5cTExPjWB8dHU1OTg5nzpxxKp+amkp0dHSBx/L39ycsLMzpJSLiSrp14DonT+xi+Kxr2bHkScjNgPgutPjTKqp1LHwa/LK4fPI4XTPP59IrZBgGjz32GPPmzeN///sfiYnOP+jt2rXDz8+PZcuWOdbt2bOHQ4cOkZSU5MqqiYgLVOaRLLp14AI7/stbn93BWiOTZ2rVwHbzP2DIAqiulim5yKWjfkaOHMns2bP54osvCA0NdfQ7CQ8PJzAwkPDwcIYNG8bYsWOJiIggLCyMUaNGkZSUpBE/IlVcZk4ezZ5dAsCu53sRZHHpr6sSmdCnGSO6NaDN8/ZJ38b1asywrvX0V3lpZZ6CRU/Cjs94wmTidGw9Hu/xOubEm9xdM/FALv3Jf+eddwDo1q2b0/rp06fzwAMPAPDaa69hNpvp378/2dnZ9OrVi7ffftuV1RIRuWq6dVA25/cs4rtvnuDm34+CyYfQ6//C69c/CT5+7q6aeCiXBhXDKGbiASAgIIApU6YwZcoUV1ZFRETcKSeDzCUTGHxsCT+HWXjTtx7d+r0PMYU/7VgEVwcVERERDv0A8x4m6PR+2kdU52RACCH93lBIkRJRUBEREdfIzcK2Ipm879/AYtggLIa/9HmDP9VpRa2gWu6unXgJBRURESl/+1ZwauHjPO17luo1qvNinZ6Y+kzGLyAcRRQpDQUVEREpP+dOwDdPw7a5/OZvYW3tKHzNfjzUfRyJAeHurp14IQUVEREpO5sNtsyCpc9C1hnARKvWD/DXuBa0rnMtieFFT5gnUhgFFRERKZvju2HB4+xM2cjLNarzqtGcmre8CTHtuNvddROvpwkARETk6uSeh2XPw7vXYRxaywu1arE5IIDXmnfTiB4pN2pRERHxcm6ZxffXb2HhE3D6AACmxn14ocujvH9gAU92eMr17y9VhoKKSAXyxGnhRUrlbCosmYCx43O+DAnGUqMuvXu8DE1voSEwOa6ru2solYx+S4qISPFsNtg0Hb59DrLTWBoczN9q1SDYN4hr4tpT8PPuRcpOQUVERIqWsgMWjIHfNtiXa7eh+y2v0WHHWyTVTqJWoGZGEddRUBERkYLlZMCKSbB2ClnY+KJ6De7q9BTmjg/hY/ZhWu1p+Jh93F1LqeQUVERE5Eo/L4GFf4G0Q9iAB+s1ZodxnrzwUAbmhxOFFKkICioiInLR6QPwzTOw+0v7cngs5j6vcKtxhmM/TiUuNM7dNZQqRkFFREQgKx1Wvwrr3gZrDsd8LVjbDiKm+wvgH8I9hkGfxD6E+1f8NPhBFl8OTOpb4e8rnkFBRUSkKrPmwZb/wP9ehMyTAHyf2IG/+J4lkePM9AvAFzCZTG4JKSIKKiIiVdWvy+Cbv8HxXfblGg2g5z9IqNMKvroLgPScdCICItxbT6nSFFRERKqaE3vsAeWXbwDIDajGto730+76Z8DXQh1g+h+mU79afXzN+pgQ99J3oIhIVZHxO6ycBBs+AMMKZl/OtnuAB3P3sfe3BXxy9n4aVm8IQOOIxu6urQjooYQiIu6Rk2dz/H/mmgNOy+UuLwe+fwvevAbWv2cPKY37wKPrCO37KnXCYgm2BHMs45jr6iByldSiIiJSwZIX7WLa6v2O5clL9vDKN3t4qGsiE/o0K783Mgz4aQEsfRZO7bOvi2rBqg730umah/D38Qfg2aRnMZvM6osiHklBRTyGHtgnVUHyol1MXbX/ivU2A8f6cgkrx36EJU/DgdX25eBI6P4ML2Qf4JOdUxnuYzDqmlEA1AysWfb3E3ER3foREakgOXk2p5aUgkxbvb9Mt4FMZ4/B/Edh6g32kOLjD12fgD9vhrb3k1SnM74mX3xN+kNAvIO+U0VEKsistQewGUWXsRn2csO61ivVsQPI5iGfhQS8+xDkZthXtvgju9oPJDe4Bq39QwHoHtedBXcuoG5I3as+D2+myeO8j4KKiEgFOXgqs1zLAZCXje/G6azwn0S06TTkAjEdoFcyy2zpjF35OLGhsXx+6+f4+/hjMpmqbEgR76SgIiLl5vKRLMO61sPi6/47zJ7S/yk+Iqj8yllzYetsWPUKlrTDRJvgN6MmtW5Pxr/NXWAy0THnLDUCatA0oilZeVmOzrMi3kRBRUTKRXmPZPHU0FOWWweDkxJ4cdHuIm//mE32coWyWWHbJ/b5UE4fsK8KiebZ0735xNSOZ/1tDDKZAAi1hPLZrZ9pNI94Nff/1Iu4QGZOHgnjF5IwfiGZOXnurk6ld2Eky+UfwBdGsiQv2lXq47V9YaljefKSPTR5ZnGpj+NpLL5mHuqaWGSZh7omFhzIbDbY8Tm8fS3Mf8QeUoJrQa+XyBqxkQ/pjKXeG0ze8CIbUjY4dlNIEW+nFhURKZOSjmR5omeTErWIVNjwXTe5UPdpq52DndlEwa1PhgE/LYTlL8HxnfZ1gdWhy2joOBwswZCTB9ZgctNb06b+OT08UCoVtaiISJmUZiRLcSpi+K4nmNCnGZufudmxPK5XY356obdzSDEM+PkbeK8bzB1oDyn+YdDtrzB6G3ua9WH0d3/lVNYpxy7ZqX15/+aZNKreqKJPScRl1KIiImVSniNZXDl819Nc2ro0pEvCxWXDgP0r4X8vwm/r7ev8guHaEdD5MQisjmEYTPx2ODt/30nk1kgebzsuf18LPmYfd5yOiMsoqIhImZTnSBaXDN/1Jge/tweUg9/Zl30DoeOfoMsYsvxDsPhYMAMmk4kn2j/Bxz99zP3N73d3rUVcSrd+RKRMBiclYDYVXabYkSz5ynX4rhcxH90Es+6A6b3tIcXHAh0fhtFboec/+ObEZvrN78eCfQsc+3SI7sA/u/2T2NBYt9ZdxNUUVESkTMo0kuUy5Rl6vEFL0z6m+f0fATN6wt7/gdkX2j0If94CfV6G0GgAfjv3GykZKXy651MMo5h7YyKVjG79iEiZlXokSyEuhJ6CRv1cUNLQ47EMA/b+D//Vr/GVv/2BgYbJjKn1fXDDk1A9gcPphzHSDxEXFgfAwKYDsZgt/LHRHzGZiklyIpWMgoqIlIsJfZoxolsD2jxvn/9kXK/GVzVJW3mFHo9jzYNd82HN65CyDR8g1/DhS1sSfR59lcDaTQD4au9XPPv9s3SI6sDUm6diMpnw9/FnULNB7j4DEbdQUBGRclPoSJZSKq/Q4xFyz8OWD2HtW46ZZPELIrfNYG5Y3Zyj1KR3jQaO4m1qtcGECbPJzPm88wT5Fd8fx1Nn8RUpDwoqIhVIHyglV16hx23On4b178MP70LmSfu6wAjo9Ah0fIhc3zCOrv4an5BdfPrzGYa0GAhAbFgs/731vySEl6wfTnk/uqA8ecozlsS76btGpIJ48geKlKO0I7Dubdg4HXIz7OuqxUHSKLhmEFjyW0hy8jAHHiIo9j+8udWfXondiQ62d54tTUipzLP4iqCgIlIx9IFSBZzYY+9/su0TsOXa10W1gC5joPkd4GP/dXs66zTVA6oDYDsfR15Gfe5vfz0hfiGlervyfnSBiKfSd6+Ii1WVaeGrrEM/wMf3wpSOsPUje0iJvw4GfgaPfAet7gIfX46eO8rQJUO5d+G95F4IMpg4f2gYI9v8mRBL6YJKeT66QMSTqUWlitK944pTlaaFrzJsNvjlG1jzLzi0Nn+lCZr0heseh5j2V+xSzb8ae8/s5WzOWXae3Emjai3yt1zd34uumsVXvxvE0+g7UMTFqvy08JVJTiZs/9TeQfb4Lvs6sx+0vsf+NOOaDR1FN6duZs3RNYy6ZhQAQX5BTL5+MglhCUQHR5OZk1emqlTVWXyl6lFQEXExfaBUAqf2w4b3YcssyEqzr7OEQvsH4dpHIay2U/HUjFSGLhmK1bByfcz1tK7VGoBra19bblUanJTAi4t2F9laV5lm8ZWqS0FFxMX0geKlbDb7tPbr37Pf5iH/AlaLhw5/grb3Q2A1R/HUjFSigqMAiAqO4o6Gd9j/HxTlkupViVl8RRRURFxPHyhe5vwZ2DobNkyDU/surm/QAzoOt/9r9nGsTs9J56mVT7H5+GYW37mYGoE1AHj22mddPt19pZ3FV+QSCioiFUAfKF4gdZc9nPw49+L8J/5h9rlP2g+Dmg0K3C3UL5Qz2WfIseawMXUjvRJ6AVTYM3kq1Sy+IgVQUBGpIPpA8UDWPNizENZPgwOrL66v1RQ6DYeWd4P/xWHDhmGw5fgWvtz7JX+79m/4mn0xmUxMTJpIiCWE2NBYt5yG18/iK1IEBRWRCqQPFA9x7gRsnmGfPTb9iH2dycc+vLjjcEi4DgpoEcm15fL4isc5lXWKtlFtubX+rQA0rdG0os9ApMpQUBGPoefgiKu1Mf2K5ct5sPsLsObYVwbVhHYP2EfwhMc4lc/IzWD1kdX8IeEPAFh8LDzQ/AEOph/kmlrXuOMURKocBRXxCHoOjvtU+gm+stLw3fopX1im0Nq8D3bkr6/b3t560vx28PW/YrfM3Ex6f96b09mniQuNo1kN+/fhgy0erOATEKnaKtlvJPFGeg6OlDubDQ5+B1s+hF1fYMnLorUZsg0/fFr1x/fa4VC33RW7ncg8Qa2gWpA/QVtSnSR2/b6Lszln3XASIoKCiribHqwm5erMYfjxY3tAOXPQsdpWszEvHmvPPGtXvut3F76XtRqdzTnL48sf58cTP/LNH79xPDTwb9f+jWC/YMwmfe+JuIuCiriVnoMjZZabZR+5s+VD2Lv84sRs/mHQoj9cM5isWq34YOI3TrsZhuEYQhziF8LZ3LPk2nLZmLqRm+NvBiDUElrx5yMiThRUxK30HBy5asd+tIeTbZ9A1pmL6xO6wjWDoWk/sNgfS5CTmePYPG31Hnyrr+b7Y6uZ2Xum0xDj6v7VqR1Su6B3ExE3UVARt9JzcKRUMk/ZHwq4ZRakbL+4PqwutBkIbe6DiESnXS7vqP3a0gOYfMPwj7Ky/PByR+vJhc6yIuJZFFTErVz1HBwNda48zNgw7/sfbJ8NPy28OKzYxwJNbrHPHFuvm9O09he8uHAH01YfyF+6OC+KkRdG1pFBrN9Rm5vjK+pMRORquPQ396pVq+jXrx916tTBZDIxf/58p+2GYfDss89Su3ZtAgMD6dGjB7/88osrqyQe5sJzcIpS2ufgJC/aRdsXljqWJy/ZQ5NnFpO8aFeZ6ioVy3TiJ57w/YTV/qMJmHMX7JxnDynRLaH3K/DEHrhrOjToXmBIycmz8cF3V4aUi8smPvjuoFOoFRHP49IWlYyMDFq3bs3QoUO58847r9j+8ssv88YbbzBz5kwSExN55pln6NWrF7t27SIgIMCVVRMPUp7PwXHFUOdKP8+IJzl9AHZ8Dts/J/D4Tkblf6mNgGqYWg2AawZC7dYF7ppny2Plbys5du4Yg5oNyu+oXfTzdtzZUTvI4suBSX0r/H1FvI1Lf+P27t2b3r17F7jNMAz+9a9/8be//Y3bbrsNgP/85z9ERUUxf/587rnnHldWTTxMeTwHR0OdvdTZVHtryY7P4LcNjtWG2Y9vc1vxhbUzLz81nqCgkCIPs+PkDsYsH0OATwC3NrhVHbVFKgm3/Wm4f/9+UlJS6NGjh2NdeHg4nTp1Yu3atYUGlezsbLKzsx3L6enpLqmf/oqueGV9Do6GOnuR86dh15f2cHLgOzDyb7+YzPZROy3/yPn6fXgo+QcAXvZ1bmG12qysPrKaXFuuozNs61qt6VS7E81rNMdms6mjtkgl4bZP35SUFACioqKc1kdFRTm2FSQ5OZnnnnvO5fUT76O/oD1cTgbsWQzbP4NfvwVb7sVtMR2gxR+h+R0Qmv87ISev0EN9feBrxq8eT92QunSP647ZZMZkMvF+z/cdZQYnhbmko7aIVCyvayaYMGECY8eOdSynp6cTG+ueR6uLZ9Ff0B4oLxt+XWZvOdmzGHIvCYmRzaFlf/ukbNWLCgs21hxdTc2galwTaX8Q4E1xNxEXGseNsTeSlZdFkN+V1/RCR+2C+ixdUNqO2iJS8dwWVKKjowFITU2ldu2LEyylpqbSpk2bQvfz9/fH3//KB4iJuGqos5SOGRvXmndhWbgI9nwFWWkXN1ZPsLectPwjRDYt0fEsNVYyduUSrq19LdN6TgMg0DeQBXcscMwsW5jy7KgtIu7htqCSmJhIdHQ0y5YtcwST9PR0fvjhB0aMGOGuaokX01/QbpSbBftWEPTTV+yLWAyZv8OP+dtCoqHFnfaAUrctFBEu0rLTWHJgCa1rtSY2pL790OmtiYz5gUbVG2EzbI7n7hQXUi4oj47aIuI+Lg0q586d49dff3Us79+/n61btxIREUFcXBxjxozhH//4Bw0bNnQMT65Tpw633367K6sllZj+gq5AWWnw8zfw0wJ7n5Occxe3BVSDZrfZW07iuxQ4z0lBJq+fzFf7vuKexvfw9LVPO4bv5truwc/sd9VVLWtHbRFxH5cGlY0bN3LjjTc6li/0LRkyZAgzZszgqaeeIiMjg+HDh3PmzBmuu+46vv76a82hImWiv6Bd6GyKfXbYnxbC/lXOHWJD60CTvtD0Fns48Sk6WGxM2ciCfQt47JrHqBlYE4C+9fry8+mfaRTRyKlsWUKKuI9miJby4NKg0q1bNwyj8A4DJpOJ559/nueff96V1ZAqSH9Bl6Pf98Lur+zh5LcNF59ODFCzkX0a+6a3QO1rwFzyr/OrG19lx+87aFi9IQObDgSgc53OdKnbxRVnIRXs8mcsTV6yh1e+2aOWTSk1rxv1IyIuZhhwbKs9mOxeACd2O2+v284eTprcArUaFXYUh6y8LD7Z8wlrjq5hSvcp+Jrtv3bubnw3DY83pHWtizPNlrTfiXg2V8wQLVWXgoqI4IOVDuY9+H2zAn5ZDGmHL240+0LCdfnhpC+E1Sn2eIZhOEKHj9mH97e/z+ns06w7to7r6l4HwB0N7+COhne47qTELTRDtJQ3BZUqSveOhfRj8Ou3WH7+hi3+3xJmyoSN+dv8guwP+2vSDxr1hMDqJTrk4bOHeXPzm5zOPu0YSuxn9mN4q+H4mn1pUaOFC09IykNZfzdohmgpbwoqVZDuHVdR1jz4bT38shR+XQop2yH/l0CYCU4ZIYS17odvs1uh/o3gF1jsIdOy08jMzaR2iH0upEDfQL4+8DUGBofPHiY21D4Z46Bmg1x8clIeyuN3g2aIlvKmoFLF6N5x5VGi51GdTbEPHf7lG9i7ArIvmXwNE9S5htx63blrWSjbjHrsuKU3viV8rtUnez4h+Ydk/pD4B5K7JgNQM7Amf+30V5rVaEZMSEz5nKhUiPL63aAZoqW8KahUIbp3XAVY8+wjc35dag8n+a0mDoHVoX53aNjTfmsnuCa5OXls/XZJkYdNyUhh2aFlXF/3emLD7K0kDao1IM/I48i5I059Uu5poiefe5vy/N2gGaKlvCmoVCG6d1w51eIMPttmw75lsG+585T1AHWuyQ8mN9tnhi3h5GuXem7tc3x35DsyrslgeKvhkP+04i9v/5LE8MTyOhVxk/L83aAZoqW8KahUIbp3XEnkZMDBtfjtXcECy1e0MB+ABZdsd7Sa3Gz/N6RWyQ9tzWHm1mmsPrKa93u+73jYX8/4nmTmZjr6nJA/mkchxTMEWXwds/hejfL+3aAZoqU8KahUIbp37H5X9YGSlw2/bbTPBLt/pf3/tlz8gBb5f5Raa7fBp2FPezip267ErSa51lwOn704FNnP7McXe7/gyLkjrDm6hpvjbwYNJa70XPG7QTNES3lRUHGxEnV4rCC6d+wlbFY49qM9lOxfBQfXQt555zLhceQldOUvG8L5ztaSVQ/eXervrR9P/Mjwb4ZTI7Am8CjkT7j2SOtHAOgY3bH8zkk8mqt+N2iGaCkPCipViO4deyjDgBN7LgaTA6uv7GcSXAsSr89/3QDVE8jJtTL/h6I7wV6w+/fdLDmwhJY1W9I9vjsA9cPrk2PLISM3A5NPBoY1GIDbG+ihoFWNfjeIJ1NQqWJ079hDnD5gDyX78sNJxnHn7f5h9tlgE2+wh5PIplCK6eVPnj9Jdf/q+OTfAlr12yo+2PEBN8Xe5AgqIZYQ5t06jzDfaNpuWQaa/K9K0+8G8VQKKlWQ7h1XsAstJofWwqF1cOh7OHPIuYxvIMRdaw8l9W6A6Nbgc3U/nsO/Gc7aY2uZ1XsWbSLbANAtthv70/fTPa67U9mP12QybfUyx7Im/6va9LtBPJGCShWle8culJdjf6ifI5isg/OnnMuYfaFue3soSbweYjqAr3+p3iYzNxPfsC2YLSeBXo711fyrAbDr912OoNI4ojGTuk5y2l+T/0lB9LtBPI2CikhZZaXD4fUXg8mRjZCX5VzGLwhi2kNckr3lJKYj+IeU6m3SstM4n3ee6OBoAM7lniOw7lwMw0RadhpBlhoAjLpmFE91fIqagTULPZYm/xMRb6GgIlJa6cfyQ0n+K3UnGDbnMkE18kNJ/qt2K/Dxu+q3nLlzJq9ufJU7Gt7Bc52fAyAyKJLc9ObYcmphNayOshdmji2KJv8TEW+hoCJSFGseppRt3OezjHbmPQS8PQHOHLyyXPXEi60l8Z2hRoNSdX691LRt01j12yqe6/wc9arZQ0K98HoYGKRmpjqVzToyGICIgIhSvYcm/xMRb6GgInKBYUDaYfuEakc22V9HtxKYd56XLjSGnAFMZohueTGYxCVBaHSp3y7PlsfO33fy29nf6Fvv4iRw61PWs/XEVtYdW+cIKh1rd2TZXcuIDIosl1PV5H8i4i0UVKTqOn8Gjm62B5Lf8oPJ5cOEAcM/jNWZ8Ww16jN80CACEjpBQFip3y7Xmst563nCLPZ996ftZ9CiQQT6BtIzvid++beG7mtyH70Te9OlThfHvv4+/uUWUtDkfyLiRRRUpGrIy4HjO51bS07+fGU5sy9EtbB3fK3bHuq2Iy0ojvtfsA/h9TuWyLAGIVhK+fYzd87kzS1vck/je/hLh79A/tOHE8MTqR9en7ScNEfn1xvjbiz7+RZDE3yJiLdQUBGPUdYHqznYrJhO/MRt5u9oZd6P/8zXIGUbWLOvLFs9wRFIiGkP0a3AL8CxOXnRrlLNM2IYBi/+8CIbUzby7s3vOkboRAREkG3N5pczvzjKmkwmvrjtC0xX2ZelrDTBl4h4AwWVQuTkXRzFodk6PVheNhzfbQ8ix36EY9sgdQeBuZm8fqHZ40j+vwHV8ltK2l0MJ8E1Cj10cfOMZOSl06j+z9gMGw+0eADyw8f2k9vZm7aXrce38ofEPwBwQ+wNfHH7FySGOT9t2F0h5QJN8CUink5BpQD2v6IvfkBptk4PkZNhHwp87Ef7hGrHttlDii33iqKGXzAbs+uyw5bIfXfejn98J4ioV+KROCWZZ2T2ulSCTv0fNQMjGNJ8iCN0PNzqYQwM2ke1d5QNs4Q5+qZ4Gk3wJSKeTEHlMpqt00OcPw0p2/NDSX5Lye+/XDlfCUBgdfstm9qtHa/zIXHc9fdvARjQoheU4snCmbmZvPa/9cXPM2LzJcG3H3c0q0eeLc/RGfamuJtKebIiIlIYBZVLaLZON7Dmwam99paS47sgdRek7ih4rhKAkOhLAkl+OAmPvbKlJCevRG+fY80hMzeTagH2aefTstO4fu715JxpBdxT7P5tQ+9lWMsWJXovEREpPQWVS2i2ThcyDDh7zB5Eju+8+O+Jnwvu5ApQLd6plYToVhAaddVVsNqsmEwmzCZ7yPz4p495ZcMr3FLvFp7v8jwA4f7hRAVFcTrbxPkSHFPzjHiHcuuoLSIVTkHlEpqts5xkpdv7jjgCyS57i0nWmYLL+wVDZFOIagaRze3/Rre039IpJyOXjWRDygZm9Z5F44jGcGEKelsuB9OdW28+v/Vz/M3BNHlmseYZERFxMwWVS2i2zlLKPgsnf7HPR3Jiz8VbN2mHCi5v8rFPLX9pIIlsZm85MZfPrbRtJ7YxecNkQv1COTDpXcf683nnOZ93np2/73QElaTaSSy6cxExITFOxwi1hEL+PCKaZ0RExL0UVC6h2ToLYBiQccIeRE7usQeTE3vs4ST9SOH7hda5GESimtv/rdnIaY6Ssnpj8xssObCEMe3GcHP8zQAE+Aaw7cQ2gv2CsRk2x22eJ9o/QYBPAAlhF69dkF8QQX6Fh07NMyIi4n4KKpeo0rN12qxw5tDF1pGTP1/8f2G3bACCI+0BpFYjexiJbGa/jRNUuofkFeXYuWM8veZpTmedZt5t8xzrT2Wd4tDZQ+z+fbcjqCSGJ/LK9a/QKKIRJi52sG1eo/lVvbfmGRERcS8FlctU+r+iz5+G3/fhc/xnxvh+Qz3TMQLefwlO/Qp5WYXsZILq8fZAUrMR1GoMNRtDzYZlDiR5tjx8zRe/Def+NJe5P8/l1nq3OiZRC/cPZ2PKRgwMTp4/6Zhq/p4m99AroRdNI5o69vcz+zkmWSsvmmdERMR9FFQK4PV/RWefhd/32of9/r4v/9/85czfAfAHxly4+heew+djsfchcYSR/H9rNAC/wDJVKdeWS2ZuJuH+4fYqWrO5d+G9HEw7yMoBKwmxhABwLvccv5z+hZ2/73TsG+QXxP/d8H/EhsZSzb+aY32TiCZlqpOIiHg+BZVCePxf0TkZcGpfwYGkgCcAOwmJwlq9Hp/t92efUZux992Cf3RT+3NvzD5lqlau1T6KJj4s3jEB2pyf5jB5w2T6JPbhxetehPynAadlpZFjy2Fv2l5a12oNQI/4HtSvVt+plQSgZ0LPMtVLRES8k4KKpzIMOJcKpw/C6QNw5iB5v+9j09YtxJmOU9t0quj9g2pCjfoQUR9q1Mv/t759Gnn/ULJz8hj37BIARjcs3cytABm5Gfxy+hcMDK6JvMaxvufnPTl5/iSf3PIJTWvYw0aNwBrk2fL47exvTsf4543/pEZADeqE1HGsiw+LJz4svlR1qar0PCoRqQoUVNwp+5x9BtbTB/JfF0MJpw9CnvOUY75Ap0s/hwKrXxJALgkiNepDQHi5VXPF4RXsObWHW+rfQt2QugCs/m01T656kraRbZnZe6ajbFxoHOfzznPy/EnHus51OvNN/28cTxK+4EIripSenkclIlWFgoor5WZh+v0AXczbiTGdxG/FBkg/dDGQZJ4sen+TGcJi7B1Zq8eTExbHX5amc8iIZPb4+wgKr1Wu1d13Zh+zds8iwCeAcR3HOdZP2z6NbSe2kRCe4Agq8WHxRAdHUyvIuQ5vdX+LEL8Qp6cCB/sFE+wXXK51rcr0PCoRqUoUVK6WYdhH0Jw5BGm/Qdph+7+XLmecIBD4yJK/z/cFHCewur1vSPUE+8Rn1RPyg0mC/Rk2+f08APJy8vhyyZKL+5VQWnYap7JOkRie6Fj3z02vEFx/CVmpfYFekH8757OfP6NWYC2noHJDzA0khCUQGRTpWNe0RlOW/nHpFe91YbI0cQ09j0pEqhoFlcJYc4kxnaAOJ/HZfgYyjsKZ/DByIYjkFj+VvuEXzC/Z1Thq1KRLh3b41aznHEjK8RbN9hPb2X1qN20i29CoeiPHuvsW3UdUUBTf3vWto+yZ7NOYLb/j43+x421ieCLDWw0nISwBwzAcrSLDWw0vtzpK2eh5VOJN9IwlKQ8KKoXwWzqB7/yn2xe+KqJgcCRUi4XwGHsLSHis03KaEUzPF+wBYVxoY4Z1LH2HR6vNSp6RB9hH5Jh803lp/fNkW8/zyg2vOMrN2jWLxQcW80S7JxxBpW6o/VaNzbCRa8vFz2xvoRnY5H7mrYzFlnPxIX8hlhBGXTOqVHWTiqXnUYlIVaOgUggjLIZsw5djRg1iExvhUz0uP4jE5AeRWAirW+SU8KXp8JiVl8XG1I2cyjrFrfVvdayftH4Sc3+ay+i2o7m70aD8ypn5Yu88TJh48boXsfjY7y21jmxNRl4G0SEXO61W96/OD/f9cMVU8Y0jmmA97/wwPvF8eh6ViFQ1CiqFyOs4guZfN8bAzK6BvQgq5fDdix0eDfvMrvnsHR73sTxlLqNuaE+/+v0gv3/IiG9HYMJE74TejjlIAnwCyDPyOJZxzHEMwxrM8JYjSAiPxeDifYCBTQcysOlAp3qYTKYin2cj3kXPoxKRqkZBpTC+/hiU/BbN1uNb2ZS6iaY1mtI+8tr8lhTnkHKpX/Y14semOxxBJSIggmY1mlErsBaZeZmE+9j7rgxqNoh7m9xLzcCaZOdd+HQyMazF8FKHp0vp3rF3qtLPo5IKod8N4mkUVIpg8juF2T+FPafiuSa6BeS3fAxaNIjUzFRW3L3CcdtlxeEVfLDjA+5tci+790bn/8VbcEgBE0ZeNUzpdS+uMZmYe8vcK0peeK6NXV75nqB4pUr/PCoRkUvoz64i+FVbT1Dsf/hq33zHuiDfIA6lH+JszllOnD/hWN+qViv61etHq1qtStyRMTurZglKiVxpQp9mbH7mZsfyuF6N+emF3gopIlLpqEWlCLacWljP16Vm4MVJzUwmE+/1fI9q/tWc5hW5Ke4mboq7CYDUI/tKdHx1eJSy8PjnUYmIlAP9ZitCXlo7Mg+M4oHmw5zWt4tqR/1q9R1DfS83OCkBc2F3ffKpw6OIiEjxFFRc4EKHx6Kow6OIiEjxdOvHRdThUUREpOwUVFxoQp9mjOjWgDbP25+JM65XY4Z1Lf3MtCIiIlWVPjFdTB0eRURErp5aVESqOE3wJSKeTH/eS5ll5uSRMH4hCeMXkpmjSelERKT8KKiIiIiIx1JQEREREY+loCIiIiIeS0FFREREPJaCioiIiHgsjwgqU6ZMISEhgYCAADp16sT69evdXSURERHxAG4PKnPnzmXs2LFMnDiRzZs307p1a3r16sXx48fdXTURuGSekQOT+hJk0dRDIiIVye1B5Z///CcPPfQQDz74IM2aNePdd98lKCiIf//73+6umoiIiLiZW/88zMnJYdOmTUyYMMGxzmw206NHD9auXVvgPtnZ2WRnZzuW09PTXVI3zdYpIiLifm5tUTl58iRWq5WoqCin9VFRUaSkpBS4T3JyMuHh4Y5XbGxsBdVWREREKprbb/2U1oQJE0hLS3O8Dh8+7O4qVZicPJvj/zPXHHBaFhERqYzceuunZs2a+Pj4kJqa6rQ+NTWV6OjoAvfx9/fH39+/gmroOZIX7WLa6v2O5clL9vDKN3t4qGsiE/o0c2vdREREXMWtLSoWi4V27dqxbNkyxzqbzcayZctISkpyZ9U8SvKiXUxdtR+b4bzeZsDUVftJXrTLXVUTERFxKbff+hk7dizTpk1j5syZ7N69mxEjRpCRkcGDDz7o7qp5hJw8m1NLSkGmrd6v20AiIlIpuX1SiAEDBnDixAmeffZZUlJSaNOmDV9//fUVHWyrqllrD1zRknI5m2EvN6xrvYqqloiISIVwe1ABeOyxx3jsscfcXQ2PdPBUZrmWExER8SZuv/UjRYuPCCrXciIiIt5EQcXDDU5KwGwquozZZC8nIiJS2SioeDiLr5mHuiYWWeahrolYfHUpRUSk8vGIPipStAvzpExb7TxE2WxC86iIiEilpqDiJSb0acaIbg1o8/xSAMb1asywrvXUklKF6XlUIlIV6FPOi1waSoZ0SVBIERGRSk+fdCIiIuKxFFRERETEYymoiIiIiMdSZ1oXU4dHERGRq6cWFREREfFYCioiIiLisRRUpMxy8myO/89cc8BpWUREpCwUVKRMkhftou0LSx3Lk5fsockzi0letMut9RIRkcpBnWnlqiUv2sXUVfuvWG8zcKzX9P4iIlIWalGRq5KTZ2Pa6itDyqWmrd6v20AiIlImCipyVWatPeD0gMSC2Ax7ORERkauloCJX5eCpzHItJyIiUhAFFbkq8RFB5VpORESkIAoqclUGJyVgNhVdxmyylxMREblaCipyVSy+Zh7qmlhkmYe6JmLx1beYiIhcPQ1Plqt2YejxtNX7nTrWmk32kKKhySIiUlYKKlImE/o0Y0S3BrR53j7p27hejRnWtZ5aUkREpFzo00TK7NJQMqRLgkKKiIiUG32iiIiIiMdSUBERERGPpaAiIiIiHktBRURERDyWgoqIiIh4LAUVERER8VgKKiIiIuKxFFRERETEYymoiIiIiMfSFPpeJMjiy4FJfd1dDRERkQqjFhURERHxWAoqIiIi4rEUVERERMRjKaiIiIiIx1JQEREREY+loCIiIiIeS0FFREREPJaCioiIiHgsBRURERHxWAoqIiIi4rEUVERERMRjKaiIiIiIx1JQEREREY+loCIiIiIeS0FFREREPJaCioiIiHgsX3dXQLxfkMWXA5P6ursaIiJSCalFRURERDyWgoqIiIh4LAUVERER8VgKKiIiIuKxFFRERETEYymoiIiIiMdyWVB58cUX6dy5M0FBQVSrVq3AMocOHaJv374EBQURGRnJk08+SV5enquqJCIiIl7GZfOo5OTkcNddd5GUlMQHH3xwxXar1Urfvn2Jjo7m+++/59ixY9x///34+fnx0ksvuapaIiIi4kVMhmEYrnyDGTNmMGbMGM6cOeO0fvHixdxyyy0cPXqUqKgoAN59913GjRvHiRMnsFgsJTp+eno64eHhpKWlERYW5pJzEBERkfJV0s9vt/VRWbt2LS1btnSEFIBevXqRnp7Ozp07C90vOzub9PR0p5eIiIhUTm4LKikpKU4hBXAsp6SkFLpfcnIy4eHhjldsbKzL6yoiIiLuUaqgMn78eEwmU5Gvn376yXW1BSZMmEBaWprjdfjwYZe+n4iIiLhPqTrTPvHEEzzwwANFlqlXr16JjhUdHc369eud1qWmpjq2Fcbf3x9/f/8SvYeIiIh4t1IFlVq1alGrVq1yeeOkpCRefPFFjh8/TmRkJABLly4lLCyMZs2alct7iIiIiHdz2fDkQ4cOcerUKQ4dOoTVamXr1q0ANGjQgJCQEHr27EmzZs0YPHgwL7/8MikpKfztb39j5MiRajERERERcOXw5AceeICZM2desX758uV069YNgIMHDzJixAhWrFhBcHAwQ4YMYdKkSfj6ljw/paWlUa1aNQ4fPqzhySIiIl4iPT2d2NhYzpw5Q3h4eKHlXD6Piqv99ttvGvkjIiLipQ4fPkxMTEyh270+qNhsNo4ePUpoaCgmk6lcj30h7VXW1hqdn/er7Oeo8/N+lf0cdX5XzzAMzp49S506dTCbCx+E7LI+KhXFbDYXmcTKQ1hYWKX8BrxA5+f9Kvs56vy8X2U/R53f1Snqls8FenqyiIiIeCwFFREREfFYCipF8Pf3Z+LEiZV2uLTOz/tV9nPU+Xm/yn6OOj/X8/rOtCIiIlJ5qUVFREREPJaCioiIiHgsBRURERHxWAoqIiIi4rGqdFCZMmUKCQkJBAQE0KlTJ9avX19k+U8//ZQmTZoQEBBAy5YtWbRoUYXV9WqV5hxnzJiByWRyegUEBFRofUtj1apV9OvXjzp16mAymZg/f36x+6xYsYK2bdvi7+9PgwYNmDFjRoXU9WqU9vxWrFhxxfUzmUykpKRUWJ1LIzk5mQ4dOhAaGkpkZCS33347e/bsKXY/b/k5vJrz87afwXfeeYdWrVo5JgNLSkpi8eLFRe7jLdePqzg/b7t+l5s0aRImk4kxY8YUWa6ir2GVDSpz585l7NixTJw4kc2bN9O6dWt69erF8ePHCyz//fffc++99zJs2DC2bNnC7bffzu23386OHTsqvO4lVdpzJH/2wWPHjjleBw8erNA6l0ZGRgatW7dmypQpJSq/f/9++vbty4033sjWrVsZM2YMf/rTn1iyZInL63o1Snt+F+zZs8fpGkZGRrqsjmWxcuVKRo4cybp161i6dCm5ubn07NmTjIyMQvfxpp/Dqzk/vOxnMCYmhkmTJrFp0yY2btzITTfdxG233cbOnTsLLO9N14+rOD+87PpdasOGDUydOpVWrVoVWc4t19Coojp27GiMHDnSsWy1Wo06deoYycnJBZa/++67jb59+zqt69Spk/Hwww+7vK5Xq7TnOH36dCM8PLwCa1h+AGPevHlFlnnqqaeM5s2bO60bMGCA0atXLxfXruxKcn7Lly83AOP06dMVVq/ydPz4cQMwVq5cWWgZb/w5vKAk5+fNP4MXVK9e3Xj//fcL3ObN1++Cos7PW6/f2bNnjYYNGxpLly41brjhBmP06NGFlnXHNaySLSo5OTls2rSJHj16ONaZzWZ69OjB2rVrC9xn7dq1TuUBevXqVWh5d7uacwQ4d+4c8fHxxMbGFvuXg7fxtmt4tdq0aUPt2rW5+eabWbNmjburU2JpaWkAREREFFrGm69hSc4PL/4ZtFqtzJkzh4yMDJKSkgos483XryTnh5dev5EjR9K3b98rrk1B3HENq2RQOXnyJFarlaioKKf1UVFRhd7PT0lJKVV5d7uac2zcuDH//ve/+eKLL/jwww+x2Wx07tyZ3377rYJq7VqFXcP09HTOnz/vtnqVl9q1a/Puu+/y+eef8/nnnxMbG0u3bt3YvHmzu6tWLJvNxpgxY+jSpQstWrQotJy3/RxeUNLz88afwe3btxMSEoK/vz+PPPII8+bNo1mzZgWW9cbrV5rz88brN2fOHDZv3kxycnKJyrvjGnr905Ol/CQlJTn9pdC5c2eaNm3K1KlTeeGFF9xaNyle48aNady4sWO5c+fO7N27l9dee41Zs2a5tW7FGTlyJDt27OC7775zd1VcoqTn540/g40bN2br1q2kpaXx2WefMWTIEFauXFnoh7m3Kc35edv1O3z4MKNHj2bp0qUe3em3SgaVmjVr4uPjQ2pqqtP61NRUoqOjC9wnOjq6VOXd7WrO8XJ+fn5cc801/Prrry6qZcUq7BqGhYURGBjotnq5UseOHT3+w/+xxx5jwYIFrFq1ipiYmCLLetvPIaU8v8t5w8+gxWKhQYMGALRr144NGzbw+uuvM3Xq1CvKeuP1K835Xc7Tr9+mTZs4fvw4bdu2dayzWq2sWrWKt956i+zsbHx8fJz2ccc1rJK3fiwWC+3atWPZsmWOdTabjWXLlhV67zEpKcmpPMDSpUuLvFfpTldzjpezWq1s376d2rVru7CmFcfbrmF52Lp1q8deP8MweOyxx5g3bx7/+9//SExMLHYfb7qGV3N+l/PGn0GbzUZ2dnaB27zp+hWmqPO7nKdfv+7du7N9+3a2bt3qeLVv356BAweydevWK0IK7rqGLuum6+HmzJlj+Pv7GzNmzDB27dplDB8+3KhWrZqRkpJiGIZhDB482Bg/fryj/Jo1awxfX1/j//7v/4zdu3cbEydONPz8/Izt27e78SyKVtpzfO6554wlS5YYe/fuNTZt2mTcc889RkBAgLFz5043nkXhzp49a2zZssXYsmWLARj//Oc/jS1bthgHDx40DMMwxo8fbwwePNhRft++fUZQUJDx5JNPGrt37zamTJli+Pj4GF9//bUbz6JwpT2/1157zZg/f77xyy+/GNu3bzdGjx5tmM1m49tvv3XjWRRuxIgRRnh4uLFixQrj2LFjjldmZqajjDf/HF7N+Xnbz+D48eONlStXGvv37ze2bdtmjB8/3jCZTMY333xjGF5+/YyrOD9vu34FuXzUjydcwyobVAzDMN58800jLi7OsFgsRseOHY1169Y5tt1www3GkCFDnMp/8sknRqNGjQyLxWI0b97cWLhwoRtqXTqlOccxY8Y4ykZFRRl9+vQxNm/e7KaaF+/CcNzLXxfOaciQIcYNN9xwxT5t2rQxLBaLUa9ePWP69Oluqn3xSnt+kydPNurXr28EBAQYERERRrdu3Yz//e9/bjyDohV0boDTNfHmn8OrOT9v+xkcOnSoER8fb1gsFqNWrVpG9+7dHR/ihpdfP+Mqzs/brl9BLg8qnnANTYb9B0pERETE41TJPioiIiLiHRRURERExGMpqIiIiIjHUlARERERj6WgIiIiIh5LQUVEREQ8loKKiIiIeCwFFREREfFYCioiIiLisRRURERExGMpqIiIiIjHUlARERERj/X/XRQBX8TkzYQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.plot(x, afit[0]*np.exp(afit[1]*x),\n",
+    "           label=r\"$a_0 = $ %f; $a_1 = $ %f\" % (afit[0], afit[1]))\n",
+    "ax.plot(x, a0_orig*np.exp(a1_orig*x), \":\", label=\"original function\")\n",
+    "ax.legend(numpoints=1, frameon=False)\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "What about uncertainties in both $x$ and $y$?  SciPy has an\n",
+    "[orthogonal distance regression](https://docs.scipy.org/doc/scipy/reference/odr.html) implementation based on ODRPACK for this.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## FFTs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Fourier transforms](https://en.wikipedia.org/wiki/Fourier_transform) convert a physical-space (or time series) representation of a function into frequency space.  This provides an equivalent representation of the data with a new view.\n",
+    "\n",
+    "The FFT and its inverse in NumPy use:\n",
+    "\n",
+    "$$F_k = \\sum_{n=0}^{N-1} f_n e^{-2\\pi i nk/N}$$\n",
+    "\n",
+    "$$f_n = \\frac{1}{N} \\sum_{k=0}^{N-1} F_k \n",
+    "   e^{2\\pi i n k/N}$$\n",
+    "   \n",
+    "\n",
+    "Both NumPy and SciPy have FFT routines that are similar.  However, the NumPy version returns the data in a more convenient form."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "It's always best to start with something you understand---let's do a simple sine wave.  Since our function is real, we can use the rfft routines in NumPy---the understand that we are working with real data and they don't return the negative frequency components.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{important}\n",
+    "FFTs assume that you are periodic.  If you include both endpoints of the domain in the points that comprise your sample then you will not match this assumption.  Here we use `endpoint=False` with `linspace()`\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "For real-valued data, we'll use `np.fft.rfft()`, which will return N/2 complex values given N real samples.\n",
+    "\n",
+    "To get the frequencies, we can use `np.fft.rfftfreq()`, which will return dimensionless frequencies of the form\n",
+    "$0, 1/N, 2/N, 3/N, ...$.  We know that the shortest lowest frequency corresponds to a single wavelength in the domain, so physically, $1/N$ corresponds to a frequency $1/L$, where $L$ is the domain size.  This means that\n",
+    "we can convert the frequencies by dividing by $\\Delta x =  L/N$.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make our life easier, we'll define a function that plots all the stages of the FFT process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_FFT(xx, f):\n",
+    "\n",
+    "    npts = len(xx)\n",
+    "    dx = xx[1] - xx[0]\n",
+    "\n",
+    "    # Forward transform: f(x) -> F(k)\n",
+    "    fk = np.fft.rfft(f)\n",
+    "\n",
+    "    # Normalization -- the '2' here comes from the fact that we are\n",
+    "    # neglecting the negative portion of the frequency space, since\n",
+    "    # the FFT of a real function contains redundant information, so\n",
+    "    # we are only dealing with 1/2 of the frequency space.\n",
+    "    #\n",
+    "    # technically, we should only scale the 0 bin by N, since k=0 is\n",
+    "    # not duplicated -- we won't worry about that for these plots\n",
+    "    norm = 2.0 / npts\n",
+    "\n",
+    "    fk = fk * norm\n",
+    "\n",
+    "    fk_r = fk.real\n",
+    "    fk_i = fk.imag\n",
+    "\n",
+    "    # rfftfreq returns the frequencies as 0, 1/N, 2/N, 3/N, ...\n",
+    "    k = np.fft.rfftfreq(npts)\n",
+    "\n",
+    "    # to make these dimensional, we need to divide by dx.\n",
+    "    kfreq = k / dx\n",
+    "\n",
+    "    # Inverse transform: F(k) -> f(x) -- without the normalization\n",
+    "    fkinv = np.fft.irfft(fk/norm)\n",
+    "\n",
+    "    # plots\n",
+    "    fig, ax = plt.subplots(nrows=4, ncols=1)\n",
+    "    \n",
+    "    ax[0].plot(xx, f)\n",
+    "    ax[0].set_xlabel(\"x\")\n",
+    "    ax[0].set_ylabel(\"f(x)\")\n",
+    "\n",
+    "    ax[1].plot(kfreq, fk_r, label=r\"Re($\\mathcal{F}$)\")\n",
+    "    ax[1].plot(kfreq, fk_i, ls=\":\", label=r\"Im($\\mathcal{F}$)\")\n",
+    "    ax[1].set_xlabel(r\"$\\nu_k$\")\n",
+    "    ax[1].set_ylabel(\"F(k)\")\n",
+    "\n",
+    "    ax[1].legend(fontsize=\"small\", frameon=False)\n",
+    "\n",
+    "    ax[2].plot(kfreq, np.abs(fk))\n",
+    "    ax[2].set_xlabel(r\"$\\nu_k$\")\n",
+    "    ax[2].set_ylabel(r\"|F(k)|\")\n",
+    "\n",
+    "    ax[3].plot(xx, fkinv.real)\n",
+    "    ax[3].set_xlabel(r\"$\\nu_k$\")\n",
+    "    ax[3].set_ylabel(r\"inverse F(k)\")\n",
+    "\n",
+    "    f = plt.gcf()\n",
+    "    \n",
+    "    f.set_size_inches(10,8)\n",
+    "    plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll test it on $f(x) = \\sin(2\\pi \\nu_0 x)$, where $\\nu_0$ is a frequency we set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def single_freq_sine(npts):\n",
+    "\n",
+    "    # a pure sine with no phase shift will result in pure imaginary\n",
+    "    # signal\n",
+    "    f_0 = 0.2\n",
+    "\n",
+    "    xmax = 10.0/f_0\n",
+    "    \n",
+    "    xx = np.linspace(0.0, xmax, npts, endpoint=False)\n",
+    "\n",
+    "    f = np.sin(2.0*np.pi*f_0*xx)\n",
+    "\n",
+    "    return xx, f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "npts = 128\n",
+    "xx, f = single_freq_sine(npts)\n",
+    "plot_FFT(xx, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that all of the power is at our single frequency, *and* it is all in the imaginary components, which is expected since $e^{ix} = \\cos(x) + i\\sin(x)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can try $f(x) = \\cos(2\\pi\\nu_0 x)$, and we know that a cosine is just a sine shifted in phase by $\\pi/2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def single_freq_cosine(npts):\n",
+    "\n",
+    "    # a pure cosine with no phase shift will result in pure real\n",
+    "    # signal\n",
+    "    f_0 = 0.2\n",
+    "\n",
+    "    xmax = 10.0/f_0\n",
+    "\n",
+    "    xx = np.linspace(0.0, xmax, npts, endpoint=False)\n",
+    "\n",
+    "    f = np.cos(2.0*np.pi*f_0*xx)\n",
+    "\n",
+    "    return xx, f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xx, f = single_freq_cosine(npts)\n",
+    "plot_FFT(xx, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, as expected, all of the power is in the real component."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at a sine with a $\\pi/4$ phase shift"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def single_freq_sine_plus_shift(npts):\n",
+    "\n",
+    "    # a pure sine with no phase shift will result in pure imaginary\n",
+    "    # signal\n",
+    "    f_0 = 0.2\n",
+    "\n",
+    "    xmax = 10.0/f_0\n",
+    "\n",
+    "    xx = np.linspace(0.0, xmax, npts, endpoint=False)\n",
+    "\n",
+    "    f = np.sin(2.0*np.pi*f_0*xx + np.pi/4)\n",
+    "\n",
+    "    return xx, f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xx, f = single_freq_sine_plus_shift(npts)\n",
+    "plot_FFT(xx, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No surprise---the power is now equally in the real and imaginary parts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A frequency filter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we'll setup a simple two-frequency sine wave and filter a component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def two_freq_sine(npts):\n",
+    "\n",
+    "    # a pure sine with no phase shift will result in pure imaginary             \n",
+    "    # signal                                                                    \n",
+    "    f_0 = 0.2\n",
+    "    f_1 = 0.5\n",
+    "\n",
+    "    xmax = 10.0/f_0\n",
+    "\n",
+    "    # we call with endpoint=False -- if we include the endpoint, then for       \n",
+    "    # a periodic function, the first and last point are identical -- this       \n",
+    "    # shows up as a signal in the FFT.                                          \n",
+    "    xx = np.linspace(0.0, xmax, npts, endpoint=False)\n",
+    "\n",
+    "    f = 0.5*(np.sin(2.0*np.pi*f_0*xx) + np.sin(2.0*np.pi*f_1*xx))\n",
+    "\n",
+    "    return xx, f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0157f2350>]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "npts = 256\n",
+    "\n",
+    "xx, f = two_freq_sine(npts)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(xx, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we'll take the transform: $f(x) \\rightarrow F(k)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# normalization factor: the 2 here comes from the fact that we neglect          \n",
+    "# the negative portion of frequency space because our input function            \n",
+    "# is real                                                                       \n",
+    "norm = 2.0/npts\n",
+    "fk = norm*np.fft.rfft(f)\n",
+    "\n",
+    "ofk_r = fk.real.copy()\n",
+    "ofk_i = fk.imag.copy()\n",
+    "\n",
+    "# get the frequencies\n",
+    "k = np.fft.rfftfreq(len(xx))\n",
+    "\n",
+    "# since we don't include the endpoint in xx, to normalize things, we need       \n",
+    "# max(xx) + dx to get the true length of the domain\n",
+    "#\n",
+    "# This makes the frequencies essentially multiples of 1/dx\n",
+    "kfreq = k*npts/(max(xx) + xx[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fe01566d6d0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(kfreq, fk.real, label=\"real\")\n",
+    "ax.plot(kfreq, fk.imag, \":\", label=\"imaginary\")\n",
+    "ax.legend(frameon=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can filter out the higher frequencies---this is done in Fourier space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fk[kfreq > 0.4] = 0.0\n",
+    "\n",
+    "# element 0 of fk is the DC component                                           \n",
+    "fk_r = fk.real\n",
+    "fk_i = fk.imag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, transform back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inverse transform: F(k) -> f(x)                                               \n",
+    "fkinv = np.fft.irfft(fk/norm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0156f0cd0>]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(xx, fkinv.real)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Algebra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### general manipulations of matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use regular NumPy arrays or you can use a special matrix class that offers some short cuts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "Since the introduction of the matrix multiplication operator, `@`, using the numpy matrix class is no longer recommended.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a simply 2x2 matrix, ${\\bf A}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([[1.0, 2.0],\n",
+    "              [3.0, 4.0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 2.]\n",
+      " [3. 4.]]\n",
+      "[[1. 3.]\n",
+      " [2. 4.]]\n",
+      "[[1. 3.]\n",
+      " [2. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a)\n",
+    "print(a.transpose())\n",
+    "print(a.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can solve for the inverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2.   1. ]\n",
+      " [ 1.5 -0.5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ainv = np.linalg.inv(a)\n",
+    "print(ainv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And multiply ${\\bf A}$ by its inverse, ${\\bf A}^{-1}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.0000000e+00, 0.0000000e+00],\n",
+       "       [8.8817842e-16, 1.0000000e+00]])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a @ ainv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the eye() function will generate an identity matrix (as will the identity())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 0.]\n",
+      " [0. 1.]]\n",
+      "[[1. 0.]\n",
+      " [0. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.eye(2))\n",
+    "print(np.identity(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear systems\n",
+    "we can solve ${\\bf A}{\\bf x} = {\\bf b}$ easily.\n",
+    "\n",
+    "```{note}\n",
+    "Linear system solvers don't usually compute the inverse first and then multiply\n",
+    "by it.  It is much cheaper to solve the system directly (e.g., via Gaussian elimination)\n",
+    "than to first compute the inverse.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.  4.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.array([5, 7])\n",
+    "x = np.linalg.solve(a, b)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### tridiagonal matrix solve"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we'll solve the Poisson problem:\n",
+    "\n",
+    "$$f^{\\prime\\prime} = g(x)$$\n",
+    "\n",
+    "with $g(x) = \\sin(x)$, and the domain $x \\in [0, 2\\pi]$, with boundary conditions $f(0) = f(2\\pi) = 0$.\n",
+    "\n",
+    "The solution is simply $f(x) = -\\sin(x)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use a grid of $N$ points with $x_0$ on the left boundary and $x_{N-1}$ on the right boundary.\n",
+    "\n",
+    "We difference our equation as:\n",
+    "\n",
+    "$$f_{i+1} - 2 f_i + f_{i-1} = \\Delta x^2 g_i$$\n",
+    "\n",
+    "We keep the boundary points fixed, so we only need to solve for the $N-2$ interior points.  Near the boundaries, our difference is:\n",
+    "\n",
+    "$$f_2 - 2 f_1 = \\Delta x^2 g_1$$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$$-2f_{N-1} + f_{N-2} = \\Delta x^2 g_{N-1}$$\n",
+    "\n",
+    "We can write the system of equations for solving for the $N-2$ interior points as:\n",
+    "\n",
+    "$${\\bf A} = \\left (\n",
+    "\\begin{array}{ccccccc}\n",
+    "-2 &  1 &    &        &         &           &          \\newline\n",
+    "1  & -2 &  1 &        &         &           &          \\newline\n",
+    "   &  1 & -2 & 1      &         &           &          \\newline\n",
+    "   &         & \\ddots & \\ddots & \\ddots  &           &          \\newline\n",
+    "   &         &        & \\ddots & \\ddots  & \\ddots  &          \\newline\n",
+    "   &         &        &        & 1       & -2 & 1  \\newline\n",
+    "   &         &        &        &         & 1  & -2 \\newline\n",
+    "\\end{array}\n",
+    "\\right )\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "{\\bf x} = \\left (\n",
+    "\\begin{array}{c}\n",
+    "f_\\mathrm{1} \\\\\\\n",
+    "f_\\mathrm{2} \\\\\\\n",
+    "f_\\mathrm{3} \\\\\\\n",
+    "\\vdots \\\\\\\n",
+    "\\vdots \\\\\\\n",
+    "f_\\mathrm{N-2} \\\\\\\n",
+    "f_\\mathrm{N-1} \\\\\\\n",
+    "\\end{array}\n",
+    "\\right )\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "{\\bf b} = \\Delta x^2 \\left (\n",
+    "\\begin{array}{c}\n",
+    "g_\\mathrm{1} \\\\\\\n",
+    "g_\\mathrm{2} \\\\\\\n",
+    "g_\\mathrm{3} \\\\\\\n",
+    "\\vdots \\\\\\\n",
+    "\\vdots \\\\\\\n",
+    "g_\\mathrm{N-2} \\\\\\\n",
+    "g_\\mathrm{N-1}\\\\\\\n",
+    "\\end{array}\n",
+    "\\right )\n",
+    "$$\n",
+    "\n",
+    "Then we just solve ${\\bf A}{\\bf x} = {\\bf b}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "SciPy has banded solvers that work with banded matrices like we have above.  They\n",
+    "will be much more efficient than using a solver based on a dense matrix\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.linalg as linalg\n",
+    "\n",
+    "# our grid -- including endpoints\n",
+    "N = 100\n",
+    "x = np.linspace(0.0, 2.0*np.pi, N, endpoint=True)\n",
+    "dx = x[1] - x[0]\n",
+    "\n",
+    "# our source\n",
+    "g = np.sin(x)\n",
+    "\n",
+    "# our matrix will be tridiagonal, with [1, -2, 1] on the diagonals\n",
+    "# we only solve for the N-2 interior points\n",
+    "\n",
+    "# diagonal\n",
+    "d = -2 * np.ones(N-2)\n",
+    "\n",
+    "# upper -- note that the upper diagonal has 1 less element than the\n",
+    "# main diagonal.  The SciPy banded solver wants the matrix in the \n",
+    "# form:\n",
+    "#\n",
+    "# *    a01  a12  a23  a34  a45    <- upper diagonal\n",
+    "# a00  a11  a22  a33  a44  a55    <- diagonal\n",
+    "# a10  a21  a32  a43  a54   *     <- lower diagonal\n",
+    "#\n",
+    "\n",
+    "u = np.ones(N-2)\n",
+    "u[0] = 0.0\n",
+    "\n",
+    "# lower\n",
+    "l = np.ones(N-2)\n",
+    "l[N-3] = 0.0\n",
+    "\n",
+    "# put the upper, diagonal, and lower parts together as a banded matrix\n",
+    "A = np.matrix([u, d, l])\n",
+    "\n",
+    "# solve A sol = dx**2 g for the inner N-2 points\n",
+    "sol = linalg.solve_banded((1,1), A, dx**2*g[1:N-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe00f75e850>]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x[1:N-1], sol)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks like $-\\sin(x)$, as expected."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/05-scipy/scipy-basics.ipynb b/content/05-scipy/scipy-basics.ipynb
new file mode 100644
index 00000000..0bd91e9f
--- /dev/null
+++ b/content/05-scipy/scipy-basics.ipynb
@@ -0,0 +1,1381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SciPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[SciPy](https://scipy.org/) is a collection of numerical algorithms with python interfaces.  In many cases, these interfaces are wrappers around standard numerical libraries that have been developed in the community and are used with other languages.  Usually detailed references are available to explain the implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{note}\n",
+    "There are many subpackages generally, you load the subpackages separately, e.g.\n",
+    "\n",
+    "```\n",
+    "from scipy import linalg, optimize\n",
+    "```\n",
+    "then you have access to the methods in those namespaces\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{important}\n",
+    "One thing to keep in mind---all numerical methods have strengths and weaknesses, and make assumptions.  You should always do some research into the method to understand what it is doing.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "It is also always a good idea to run a new method on some test where you know the answer, to make sure it is behaving as expected.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we'll do some integrals of the form\n",
+    "\n",
+    "$$I = \\int_a^b f(x) dx$$\n",
+    "\n",
+    "We can imagine two situations:\n",
+    "* our function $f(x)$ is given by an analytic expression.  This gives us the freedom to pick our integration points, and in general can allow us to optimize our result and get high accuracy\n",
+    "* our function $f(x)$ is defined on at a set of (possibly regular spaced) points.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "In numerical analysis, the term [quadrature](https://en.wikipedia.org/wiki/Numerical_integration) is used to describe any integration method that represents the integral as the weighted sum of a discrete number of points.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import integrate\n",
+    "#help(integrate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's consider integrating\n",
+    "\n",
+    "$$I = \\int_0^{2\\pi} \\sin^2(x) dx$$\n",
+    "\n",
+    "[quad()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.quad.html) is the basic integrator for a general (not sampled) function.  It uses a general-interface from the Fortran package QUADPACK (QAGS or QAGI).  It will return the integral in an interval and an estimate of the error in the approximation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "    return np.sin(x)**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#help(integrate.quad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`quad` will return the integral and an estimate of the error.  We can seek more accuracy by setting `epsabs` and `epsrel`,\n",
+    "but remember that we can't do better than roundoff error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653589793\n",
+      "3.4878684980086318e-15\n"
+     ]
+    }
+   ],
+   "source": [
+    "I, err = integrate.quad(f, 0.0, 2.0*np.pi, epsabs=1.e-14, epsrel=1.e-14)\n",
+    "print(I)\n",
+    "print(err)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#help(integrate.quad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Additional arguments\n",
+    "\n",
+    "Sometimes our integrand function takes optional arguments.  Let's consider integrating\n",
+    "\n",
+    "$$g(x) = A e^{-(x/\\sigma)^2}$$\n",
+    "\n",
+    "now we want to be able to define the amplitude, $A$, and width, $\\sigma$ as part of the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def g(x, A, sigma):\n",
+    "    return A*np.exp(-x**2/sigma**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.8451240256511698 2.0484991765669867e-14\n"
+     ]
+    }
+   ],
+   "source": [
+    "I, err = integrate.quad(g, -1.0, 1.0, args=(1.0, 2.0))\n",
+    "print(I, err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integrating to infinity\n",
+    "\n",
+    "numpy defines the `inf` quantity which can be used in the integration limits.  We can integrate a Gaussian over $[-\\infty, \\infty]$ (we know the answer\n",
+    "is $\\sqrt{\\pi}$)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "Behind the scenes, what the integration function does is do a variable transform like: $t = x/(c +x)$.  This works when one limit is $\\infty$, giving, e.g.,\n",
+    "\n",
+    "$$\\int_a^\\infty f(x) dx = c \\int_{a/(c + a)}^1 f\\left (c\\frac{t}{1-t}\\right) (1 - t)^{-2} dt$$\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.7724538509055159 1.4202636780944923e-08\n"
+     ]
+    }
+   ],
+   "source": [
+    "I, err = integrate.quad(g, -np.inf, np.inf, args=(1.0, 1.0))\n",
+    "print(I, err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multidimensional integrals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Multidimensional integration can be done with successive calls to quad(), but there are wrappers that help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compute \n",
+    "\n",
+    "$$I = \\int_{y=0}^{1/2} \\int_{x=0}^{1-2y} xy dxdy = \\frac{1}{96}$$\n",
+    "\n",
+    "(this example comes from the SciPy tutorial)\n",
+    "\n",
+    "Notice that the limits of integration in $x$ depend on $y$.  This means that we need to do the $x$\n",
+    "integration first, which gives:\n",
+    "\n",
+    "$$I = \\int_{y=0}^{1/2} \\int_{x=0}^{1-2y} xy \\,dxdy = \\frac{1}{2} \\int_{y=0}^{1/2} y \\left [ x^2 \\right |_0^{1-2y} dy = \\frac{1}{2} \\int_0^{1/2} (1-2y)^2 y \\, dy = \\frac{1}{96}$$\n",
+    "\n",
+    "Note the form of the function:\n",
+    "\n",
+    "```\n",
+    "dblquad(f, a, b, xlo, xhi)\n",
+    "```\n",
+    "where `f` = `f(y, x)` -- the y argument is first to indicate that the $y$ integration is done first and\n",
+    "then the $x$ and $[a, b]$ are the limits of the $x$ integration.  We want the opposite in this example,\n",
+    "so we'll switch the meaning of $x$ and $y$ in our example below.\n",
+    "\n",
+    "The integral will be from: $y = [0, 1/2]$, and $x$ = `xlo(y)`, $x$ = `xhi(y)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.010416666666666668 95.99999999999999\n"
+     ]
+    }
+   ],
+   "source": [
+    "def integrand(x, y):\n",
+    "    return x*y\n",
+    "\n",
+    "def x_lower_lim(y):\n",
+    "    return 0\n",
+    "    \n",
+    "def x_upper_lim(y):\n",
+    "    return 1-2*y\n",
+    "\n",
+    "# we change the definitions of x and y in this call\n",
+    "I, err = integrate.dblquad(integrand, 0.0, 0.5, x_lower_lim, x_upper_lim)\n",
+    "print(I, 1.0/I)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you remember the python lambda functions (one expression functions), you can do this more compactly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.010416666666666668\n"
+     ]
+    }
+   ],
+   "source": [
+    "I, err = integrate.dblquad(lambda x, y: x*y, 0.0, 0.5, lambda y: 0, lambda y: 1-2*y)\n",
+    "print(I)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integration of a sampled function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we integrate a function that is defined only at a sequence of points.  A popular method\n",
+    "is [Simpson's rule](https://en.wikipedia.org/wiki/Simpson%27s_rule) which fits a parabola to 3 consecutive points\n",
+    "and integrates under the parabola."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compute\n",
+    "\n",
+    "$$I = \\int_0^{2\\pi} f(x_i) dx$$\n",
+    "\n",
+    "with $x_i = 0, \\ldots, 2\\pi$ defined at $N$ points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653589793\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = 17\n",
+    "x = np.linspace(0.0, 2.0*np.pi, N, endpoint=True)\n",
+    "y = np.sin(x)**2\n",
+    "\n",
+    "I = integrate.simpson(y, x=x)\n",
+    "print(I)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Romberg integration](https://en.wikipedia.org/wiki/Romberg%27s_method) is specific to equally-spaced samples, where $N = 2^k + 1$  and can be more converge faster (it uses extrapolation of coarser integration results to achieve higher accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.1430658353300385\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = 17\n",
+    "x = np.linspace(0.0, 2.0*np.pi, N, endpoint=True)\n",
+    "y = np.sin(x)**2\n",
+    "\n",
+    "I = integrate.romb(y, dx=x[1]-x[0])\n",
+    "print(I)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpolation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interpolation fills in the gaps between a discrete number of points by making an assumption about the behavior of the functional form of the data.\n",
+    "\n",
+    "Many different types of interpolation exist\n",
+    "* some ensure no new extrema are introduced\n",
+    "* some conserve the quantity being interpolated\n",
+    "* some match derivative at end points"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{caution}\n",
+    "Pathologies exist---it is not always best to use a high-order polynomial to pass through all of the points in your dataset.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [interp1d()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) function allows for a variety of 1-d interpolation methods.  It returns an object that acts as a function, which can be evaluated at any point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.interpolate as interpolate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#help(interpolate.interp1d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's sample \n",
+    "\n",
+    "$$f(x) = x \\sin(x)$$\n",
+    "\n",
+    "and try to interpolate it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f_exact(x):\n",
+    "    return np.sin(x)*x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 10\n",
+    "x = np.linspace(0, 20, N)\n",
+    "\n",
+    "y = f_exact(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f04d3dfe850>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UDnFrotLY2IisrCwsWrSozcdffvllvP7663jzzTexbds2BAcHY+LEiTCZTG1uT0REylBuMOGfa47ixe8OSR0KncdfrYLGT1kJrFu7fiZNmoRJkya1+ZgoivjHP/6Bp556ClOmTAEA/Oc//0FcXBy++OIL3Hbbbe4MjYjI64iiiJM1TegRFeTx8vkXajBb8bdVRxDor8ZjE3qzvgp1mmTvnMLCQpSXl2P8+PEt9+l0OgwbNgz5+fntPs9sNsNoNLb6IyIi4HhVA8b8dT3G/HW95DM7UqNDcOPgRDw2MR02hc0y8UYbj1Thwfd24L2tp6QOpcMkS1TKy8sBAHFxca3uj4uLa3msLQsXLoROp2v5S0pKcnusRERKcKyyAQFqFRLCA6FSSduiolIJ+NstWbh3VArHysjAvtMGrNxfjt1FdVKH0mGKm/Uzf/58zJ07t+W20WhkskJEBCA3Mx57n4lFTaNF6lBIZsakxyIoQI1UBZXOP0eyREWv/2XUcUVFBeLj41vur6iowMCBA9t9nkajgUaj8UiMRERKYHeI2F5Yi8p6E2JDtchOiZQ6pBZGkxVHKxowRKKp0vSLjIQwZCQoc5kZyRKVlJQU6PV6rFmzpiUxMRqN2LZtG2bOnClVWEREirKyoAwLVhxAmeHX2ZLxOi3yJmcgNzP+ks91t+oGM674y2qoBKBgwUQEBSiuEZ9kwK1jVBoaGrB7927s3r0bODuAdvfu3SgqKoIgCJgzZw7+8pe/4KuvvsK+fftw9913IyEhAVOnTnVnWEREXmFlQRlmLt/ZKknB2anBM5fvxMqCMsliA4DoEA30YVp0iwhEaR3LTkjFandge2Etyg0mKHEdYreunrx+/XqMGTPmovunT5+OZcuWQRRF5OXl4e2330ZdXR1GjRqFN954A71793b6GFw9mYh8kd0hYtRLay9KUs4RAOh1Wvz4+FioJRxY22i2IVjDlhQpFVY3Ysxf1yPQX40Dz06UfOr6Oc5ev9367hk9evQlszdBEPDss8/i2WefdWcYREReZ3thbbtJCgCIAMoMJmwvrEVOzyiPxnY+JinSMzZbkRQZiFCNv2ySlI7gO4iISIEq653rSnF2O/JeWUnh2PSnsZLX1ukslgokIlIgZxf9k3pxQIdDxJ8+3YNrXt2AqnqzpLH4Oqlr63QWExUiIgXKTolEvE6L9i49wtnZP1JPVVapBOwqqsPRygbsLVFesTGSHrt+iIgUSK0SkDc5AzOX77zosXPJS97kDEkH0p4zb0Jv+KtVuKKHfOq7+JKHP9gFu8OBeRPS0VOBBd/YokJEpFC5mfF4485BCPRv/VWu12mxeNpgyeuonJObGY9xfeOgC/SXOhSfI4oi1h6swLf72l+aRu7YokJEpGCT+idgQr/4iyrTyqElhaQnisBrtw1CUW0TEiMCpQ6nU5ioEBEpnFolSDoF2RkHy4zYVVSHcX1jERcm7QBfX6JSCRifEefElvLFrh8iIgUz2+xSh+CUJz/fhyc/34etJ2qkDoUUhi0qREQKNv7VDdD4qfHmtCFIi5XvQMlRadEIDvBDCAvAedShciMaTDakxYYgPChA6nA6he8YIiKFqm4wo7i2GYLwywBaOZs3IV3qEHzSO5sK8cmOEsy9pjceGddL6nA6hYkKEZFCRYdo8PNT43GkvJ4tFdSmsEB/JEYEIiU6WOpQOs2tixJ6AhclJGqfKIqt1vb4cvdpGE02jO4dg6TIIEljI9/kcIiwOUQE+HGIpK9z9vrNdwqRF2qy2PDMV/vx5Of7Wt3/5oYT+PMXBfhqT6lksZHv+vMXBeiX9z3ff9QhTFSIvNDBMiPezT+JD7YX43B5fcv9V/WOxg2DuuHBq3tKGh91nd0hYv5ne/HfbacUM/NHrRLQbLXjcLlR6lBIQdj1Q+Sl/m/tUWR202F0emy724iiiD99uhfXD0zAlb1iPBofdc3BMiMmvbYJIRo/7MmboIgCb8W1TTDbHOgRFQQ/NX8nu9uWY9VY+N0hjEyLxhOT+kgdzkWcvX5z9BWRFxBFEe9tPYUbBie2DKqcPfbyI/w/3VGCT3aU4NOdJVj5h6uQrg/1QLTkCqFaP8wekwar3aGIJAUAx0V52NHKBuw7bUBCuLxnhF0OExUiL7Bsy0ksWHEA728rwoqHR8HfyV+rvxmQgL0lBlzdO4ZJisIkRgThsYmc8kvtm9hPj4TwQIRplX2pV3b0RAQAGJAYjrgwDW4akuh0kgIAgQFqPDc1062xEZ1v7aEKbC88g98MiEdmN53U4Xg1vU4r+/o6zmCiQuQFhnSPwA9zrkZYYNc+0g1mG/YW12FEWrTLYiPXa7bYUVjdiN5xIYob6/HpjhJ8u68ckcH+TFTIKcp6hxNRC4vNgcp6U8ttXZB/q5opHVVhNOG61zfhnmU/4WhFvRPPIKnsLDqDa1/fhEmvbZI6lA4b1ycO04YnIzOBSYo7iaKIL3adxo5TtbDZHVKH0yVsUSFSqBe+PYiv95bhjTsHIzslssv7iwnRIDkyCDa7iAazzSUxkntUGE0I0fgpclzRjUMSceOQRKnD8HrVDRbM+Wg3BAE49Fyuoi/2So6dyGc1mG1Ye6gS1Q1mGJutLtmnSiXg77cOhL9aBV2gv0v2Se5xw+BETB3YDY0WJpTUtmaLHTmpUbDYHdD4qaUOp0tYR4VIocw2O7Ycq8GYPu3XSSGSI1EUUVlvRlCAGqFaJsW+iiX0ibycxk/ttiRFFEWsPVSB97aecsv+ybfds+wnDHthDdYcrJQ6FFIAJipECmKxOfDj0Wq4uyF064la/G7Zz3jhm4M402hx67GoY7adqMGtb+XjzQ3HpQ6l0xIjAqESgHKjyYmtyddxjAqRgizfegrPfn0Avx3UDX+/daDbjjM8NRLDUiIxMCkcXZhIRG6ws6gO2wprER2qkTqUTpt3TTqeui4DWn9lj52Qs/ve/RmV9SbkTc7AkO5dH2wvJSYqRArSaLYhQK1yySyfSxEEAR/+fniXpjuTe/xmQDyiQgIQr+BCXhHBAVKH4PUKThtQbjRB5QWfYQ6mJVKY4tomxOu0iiv0RUSec7DMiOLaJoxIi25Z/0tuOJiWyEslRXp25dnC6kY889V+mKx2jx2TvN97+Scxc/kO7Co6I3UoXqlvfBgm9NPLNknpCCYqRArw1Z5SlNY1e/y4DoeI6Uu2Y9mWk/jop2KPH59aO1XTiJUFZZK8F1xt09FqfFdQjl1FdVKHQjKn/FSLyMtVGE2Y9/FuiCKwZt7V6B4V7LFjq1QC7r8qFesPVWJgUrjHjktt+35/OV749hAmZeqxeNoQqcPpkhsGJyI7JRKjenFdKVcrOG3A8aoGZMSHoVec8qoXX4iJCpHM1ZusGNojEhabw6NJyjnThiXjruHdPX5culio1h/9EsK8ImnMzdRLHYLX+mZfGRavP47pOd2xYIryV0dnokIkc2mxoXj//uFokqhcOmf+yMft2cm4PTtZ6jBI5rqFB2J4aiT6ecnCj5z1Q0ROqWuyYMWeUozPiEO8LlDqcMgLGJqsOFRuRO+4UE5Z9kGc9UOkcHaHiP/tKJHNbJtZ7+/En7/cj892npY6FJ/kcCj6N2Wbpi/djlvf3orNx6ulDoVkjIkKkUx9v78c8z7Zg8n//NHtJfOdMSWrGzLiw5AQrtxCY0q2dMtJDH9hDRatOyZ1KC7TNz4U3cIDYbE5pA6FZEzyMSrPPPMMFixY0Oq+9PR0HDp0SLKYiOTA7hCRoNNiUv94WYwTuWlIIm4ZmiR1GD7rXKVRuxe1rPxlan+oVdK/t73Jscp63PrWVvSND8Py+4ZJHY5LSJ6oAEC/fv2wevXqltt+frIIi0hSk7MSkJuph9Uuj1+bKl5QJPXc1Ezcnp0MfZj3tGgxSXG94tpm1DRaUONFi4nKIiPw8/ODXs+pakQX8ler4C+zUvl2h4itJ2pwRY8IaPy4qJynhGj83L7GEynf8NQofPPIKJi9qDtNFonK0aNHkZCQAK1Wi5ycHCxcuBDJyW1PwTObzTCbzS23jUajByMlcr9ygwmlhmYMSgqXRZfPhW56cwt2FdXh0fG90SM6CLGhWmSnRPLXMXXKn78owM6iM3j1loFI1yu/OJnUAgPUXjMt+RzJE5Vhw4Zh2bJlSE9PR1lZGRYsWIArr7wSBQUFCA29+E27cOHCi8a0EHmT97aexKJ1x3HHsGS88Nv+UodzkZgQDQQB+PvqIy33xeu0yJucgdzMeElj81YbjlThUJkRo3pFe91F6GCZEftLjThcUc9EhdokeZvypEmTcPPNN2PAgAGYOHEivv32W9TV1eHjjz9uc/v58+fDYDC0/BUXc/0R8i42uwiNnwqj0uRXWnxlQRl+OFCBCychlRtMmLl8J1YWlEkVmlf7ancpFn53CD/sr5A6FJebNTYN/7r7CozoGSV1KF7ho5+K8OXu06hr4hgVtwkPD0fv3r1x7FjbU/A0Gg00Go3H4yLylPnX9sVDo9MQpJHX+A+7Q8SCFQfafEwEIABYsOIArsnQsxvIxYanRsJktWOYF45RGZMeK3UIXuWFbw/B0GzFyjlXIjzIO4roSd6icqGGhgYcP34c8fFsQibfpQvyl90g2u2FtSgzmNp9XARQZjBhe2GtR+PyBTdfkYRFdw7GCBm2spF82OwOjO8bh2EpkUiMCJI6HJeRvEXlsccew+TJk9G9e3eUlpYiLy8ParUat99+u9ShEXnUmUYLrA4HYkPlOf20sr79JKUz2xHhbMXdPSV1OFbZgKmDuskuQVcSP7UKf7slS+owXE7yRKWkpAS33347ampqEBMTg1GjRmHr1q2IiYmROjQij1q65STeWHcMD4/thT+M7yV1OBdxNoGSa6KlVLWNFgT6qxEYIK+uQFcRBGDav7eh0WLHoOQIpMWGSB0SyYzkicqHH34odQhEsnCssh42h4iUmGCpQ2lTdkok4nValBtMaKs2qgBAr9Oy1oeLvbrqMN7fVoQ/5fbBg1f3lDoclxMEAcNSo2Cy2llKv4scDtErCzNKnqgQ0S/euHMIDpfXo0e0PPuW1SoBeZMzMHP5Tghnx6Scc+6rMW9yBgfSutjpM81wiL9MAfdWS2YMlToEr/DcNwfw1e5SzB6bhntGpkgdjsswUSGSEbnXkcjNjMfiaYOxYMWBVgNr9ayj4jZL78lGZb0JQQH8uqZLKznzS/l8Py8b5yOIcliWtQuMRiN0Oh0MBgPCwsKkDoeow5osNlmWyr8Uu0PE9sJa7C2pQ/7xGiSEB+KFG+RXnI6URRRFWVZjVgpDsxUlZ5oQE6pRxFgxZ6/fyvlmJPJS72wqRM7CNfhwe5HUoThNrRKQ0zMKw1OjsP5IFb7cfRomq13qsEihahrMmLpoM4Y+v8arVof2NF2gP/ol6BSRpHQE2xKJJLb2cCWqGywI8FPe74b+3XSYObonRqVFw49jU1zuva2ncKDUiKkDEzAs1Xsrt4YHBeBAmREWmwOnzzQjOUqe47RIGkxUiCT28QM52HC4CiPSlHchUqkEPJ7bR+owvNYP+8ux6Wg1+nfTeXWiolYJePuuIUgID0RCuHe1BnhKcW0TvtpTil6xIZjQTy91OC7FRIVIYv5qFcZnxEkdBsnQPSN7oH83nU9M+R7NUvpdsu+0Aa98fxiDksOZqBCRa3jTwMGT1Y344UA5ru4dK/uZS0oytk8cxvZhEkuXFxuqwQ2Du6FHlDzrMHUFExUiiazYW4almwvxu5EpmJyVIHU4XfLKD4fxzd4y1DVZ8Sd2BVEnGJqsWH+kEkaTDXcN7y51OIpzRY9IXNHDO1vemKgQSeSznSXYVVSHo2n1UofSZZMy9TA0WdE3niUCXOVIxS/vi9ToYK+ri9GWinoT/vDhboRo/DBtWLLXtDZS1zFRIZLIyzcOwJe7SzHRC/qTfzMgAb8ZoOxWIbl5bfVRfLOvDPMn9cEDXlg6/0I9ooKRnRKJnjEhMNsc0Pp759pG7mK22aHx887/MyYqRB50rlBaZb0JsaFa/G5UCkvOU5v81AKCA9TISPCNVqoAPxU+fiBH6jAUSRRFDFywCsEaNVY8PArxukCpQ3IpJipEHrKyoOyi0vPxXlZ63mS142hFA/on6qQORfFeu20QHA6xzQUgic5X3WBBs9UOk82OqGCN1OG4HEvoE3nAyoIyzFy+86KLzrm2lMXTBis+WSmta8aYv66HCGBv3gQ23VOHnWtxLDnThMSIIGSnRLLF0UmGJitKDc2KGifm7PWbLSpEbmZ3iFiw4kCbv4zFs8nKghUHcE2GXtFfyvE6LSKDA+AQRZyqaeI0ZeqQlQVlePKzfahtsrbc520tju6kC/KHLshf6jDcwvuHkhNJbHthbavunguJAMoMJmwvrPVoXK4mCAK+nDUSW+ePY5LSRS98exB3/Gsr1h2ulDoUjzjX4nh+kgIA5QYTZi7fiZUFZZLFRtJjokLkZpX17ScpndlOzmLDtJxW6gL5x2uw5XgNTBbvX+jxci2OONviyMUK2/f5rhIsWncMh8qNUofiFuz6IXIzZ1cy9bYVT72p8q6nvXhjf+w/bcSQHhFSh+J2HWlxzOnpvesddcVnO09j09FqxIZq0EevnDEqzmKi4uUunA7LwWmel50SiXidtt0vYwGAXqf1mvVc/pN/Eu9vK8L9V6bixiGJUoejSP0SdOiX4Bszp3ypxdFdxveNQ0yoRlEDaTuCiYoX84XpsEqgVgnIm5yBB5fvvOixcylj3uQMr0kgK41mHCqvx4YjVUxU6LJ8tcXRlaaP6CF1CG7FMSpe6tzgtAt/xXNwmjRyM+Px8NieCLigFLpep/WKqcnnmzooAa/dNhB5kzOkDkWRNhypwg/7y1HTYJY6FI841+LYXpounP2B5S0tjtRxrKPiheqaLJj4j42oMLb9RXeuq+HHx8d6za94pbA7RGw7UYOqBjO74qhNt7+9FfknavDyTQNwyxVJUofjEed+WOG8AbTwsjpD7mKy/jLgWol1i5y9frNFRcHKDM2YuXwHpvzfj7DZHS33v/TdoXaTFHjRdFglUqsEjEiLxpSB3ZDTM4pJCl0kXR+KvvFhyPSRMSo42+K4eNpg6HWtu3e8scXR1VYWlKPPn1figfd+ljoUt+EYFQU5VtmABrMNA5PCAQCRwQHYeKQKjRY7KurN6Bb+y/oOFRycJiuiKGLNwUqM6hWtyF89nWE0WbFqfwXKDM2YPbaX1OEoyjPX95M6BEnkZsbjmgw9B/93UKmhGQAQpvXOYm9goqIc728rwpOf78OInlF4//7hAACNnxp/uyULfioVwgN/fZPef2VPrD1Uddl9cnCaZ+wpMeC+//yMuDANNj8+Fn5q72/IrGmwYN4ne+CvFvC7USkICuBXDV2eWiW0TEE2mqxoMNugC/TeC7ArzLy6J+7IToblvFZ1b+P935gKZbU7YGj+tUrj1ekxCFCrEKLxg+O8wke5mfEYnxGHYM2vFwJnB6cN7RGBZ77aj70ldW59Lb6uut6MBJ0Ww1OjfCJJAYAeUUEY3zcW91+ZCrPVe79AXc3qxRebjnjqi30Y8MwPWL71lNShyJ4gCAgPCvDqH578mSNDm49V46kvCjAyLQp/mdofANAtPBBbnxyHyOCAyz7/3HTYmct3QmhncFre5Ays2FuKZVtO4uOfi5H/xDivXSdCauMz4jC2TyzqzTapQ/EYQRDw7+lDpQ5DcWb9dyf2lxrxzPX9cE1GnNThSCbu7EW30sjuaWKiIluF1Y2w2h2w2R0tv8KdSVLOOTc47cI6Kvrz6qhU1ZsxZWACeseFMklxM5VKYBM2Xdb+UiNO1zUjVOvbX813j+iBGSN7INSLx124gt0hIu+rAnQLD8I9I3t47Rg4Tk+WgRNVDaisN2N46q/loT/dUYLcTD1CNF37wnKmMu35pc6rG8woN5iQ2c13Zhy407nl6n2VwyHiYLkRieFBTIadYGi24lCZEf0TdRzXQ5dVWteMES+uhZ9KwOG/TFLcwGNnr9/8JEjsx6PVuGfZdkSHaLB23mgEBvySEd/kooqe5w9Oa8+5JKXZYse97/6MI+X1eOuuIbiqd4xLYvBVhiYrxv51A1JjgvHh74cjPMj5FjFvcfeS7fjxWDVevSULNwxmldrL0QX6Y1gq17Mh5/irVXhkbBoaLXbFJSkdwURFYlf0iEBsqBa940JQb7K2JCpSsIsiwrR+0PirkBgRKFkc3mLPeYOUfbXbp1+3MOwsOoPaRovUoZDCvL+tCDtOncHvRvXwmXWPOiomVIO5E9KlDsPt2PXjYVtP1GDNwQr8v+t+LS9e02BGVIhG0rjOsdodOFXThLTYEKlD8QqGJitO1zUjI0H+7013MJqsCPRXw99HZjt1xX+3nUKj2YaJ/fToHhUsdTiSu3vJdmw8UoUXb+iP27KTpQ6H3IBdPzJUaTThrne2wWoXcWWvmJauFbkkKTjblHh+knKyuhHHKhsw3odnIHSFLsjfp8dmeHMRKld7L/8UDpXXIzU6hIkKgN8OSsDQ7hEYkBgudSiyVVVvRqjWz2sH0Z7DRMXNTFZ7y5soNkyLe0amoMliU8Qv7OLaJty4eAvqTTa8d282+847wGp3sBWBOmTKwG5IPV3Hgexn/XYQxzRdziMf7EL+iRosumMwrhvgvcsMMFFxE6vdgZdXHsLHP5dg5ZwrEa/7ZczH/El9Wgavyl1CeCCG9ohE8ZkmpMTwF56zHA4R17y6AX3jw7Dg+n6IDfPeQkzO2HqiBq+vOYqkiCC8dNMAqcORrZmje0odAinMubFfcWHyaZV3ByYq7XBmWu+l+KtV2FtigKHZii92lbZ8CSklScHZGUP/uG0grHYH6xl0wK7iMzhZ04TaRgvCfHQQ7fkcDhFbjtcgNrSh1VR4ostpNNtQWN2IdH0oWyjbsHLOlahrsraqTO6NZDGYdtGiRXjllVdQXl6OrKws/POf/0R2drZTz3XHYNqVBWUXFUqLP69Q2oUcDhE/HKjAl7tP42+3ZLXUP9hTXIczTRZc3TvGa76c95ca0CMq2Os/GF11oNSIkzWNuLa/9zbHOstkteOTHSXISY1Cz5hgr/ksuFJpXTMigwO8fqxBR4iiiP7P/IAGsw2r517NAf5eyNnrt+Qp6kcffYS5c+ciLy8PO3fuRFZWFiZOnIjKykpJ4llZUIaZy3e2SlIAoNxgwszlO7GyoAy4YE0OQQAWfncQ3xWU49t95S33ZyWFY3R6rNd8MX++qwS/XbQF8z/bBxnkt7KWkRDGJOUsrb8adw3vjrTYEK/5LLjaHz7chYynV+L7/eVObO0bBEFASnQwooIDUNNgljockpDkP4tfffVV3H///bjnnnsAAG+++Sa++eYbLFmyBE888YRHY7E7RCxYcQBtXYLFs+vkPP3lfry14QTKDCbkzx8LQRAgCALuuzIVJWeakN0j0qMxe1JSRBDsoogmix1mm4O//ohcQBRFVNab4RCBHpzt08onD+bwe6Ydm45WYWVBOUb0jPbqgbSQOlGxWCzYsWMH5s+f33KfSqXC+PHjkZ+f3+ZzzGYzzOZfs2uj0eiyeLYX1l7UknI+EUBlvRlnmiyw2kWcqG5Ez5hfmiPvGt7dZXHI1RU9IvHZzBEYkKjjL+N2PPLBLnSLCMQ9I3t49WqmHWW1O7DxSBV2nDqDxyakQ+XFVTQ7ShAErH9sNCrrzYiWUakCOWCS0r6fTp7Bf7cVwSGCiYo7VVdXw263Iy6udY2OuLg4HDp0qM3nLFy4EAsWLHBLPJX1zq3Uee+oFNw5rDuSIn1vDZespNY1DewO0atLN3dEUU0TvtpTCkEA7s7x/sS1I0QRmP3+LjRb7ZgysBvS9aFShyQrgiAgzsdnh1HHjOwZBVEUkeUDdWYk7/rpqPnz52Pu3Lktt41GI5KSklyyb2d/AV/dO9Ynk5Tz2R0i/r7qCA6WGfGvu6/gL2QA8eFaLL5zMApKDS3T0ekXAX4qTB2UAECAv5rvFXJObaMFL313CBX1Jiy7x7kJFr5iWGqUz9S2kjRRiY6OhlqtRkVFRav7KyoqoNfr23yORqOBRuOe5tHslEjE67QoN5jaHKciANDrfpmq7OtO1jTiX5tOwGxzYN3hSozry8q1/moVJvWPxyQOom3TwhtYQ6UtL3x7EI1mG6aP6IHecWxpOl+gvxof/VwMnE1aIoN9b2FPknjWT0BAAIYMGYI1a9a03OdwOLBmzRrk5OR4PB61SkDe5F/W4LnwN9+523mTM9jVAaBnTAgW3tAf/3fHICYpRF3w5e7T+O+2ItSbrFKHIjuBAWr8v2v74rXbBkLjJ/kkVdmw2R04XtUAk9UudSgeIXnXz9y5czF9+nRcccUVyM7Oxj/+8Q80Nja2zALytNzMeCyeNviiOir6S9RR8VU3DGaJ63MWfncQaTEh+M2ABElXwFaC0rpmaP3V/HV8dsbPU9dlYH+pEX308l9WQwr3X5UqdQiyc7quGeP+tgGB/moceHai109ukDxRufXWW1FVVYWnn34a5eXlGDhwIFauXHnRAFtPys2MxzUZ+i5VpvU1JqsdW45XY2wf32tdOV3XjLc3noAo/jIzKiWaU0zb8/ine/HRz8X4f9f25QXo7CDayVkJmJyVIHUopCDVDRYEB6gRHx7o9UkK5JCoAMDs2bMxe/ZsqcNoRa0SkNPTNwYqdZXRZMVvF21GYXUjPnkwB0O6+9YYnuAANf44MR2FVY1MUi4jLTYEKgGXLANAdD6zzY7C6kY0mGy4wovrVHXEkO4RKFgwEU0Wdv0QOSVM64+sxHDUm2wwWx1OPMO7hAcF4KHRaVKHoQi3Zifhtuwkrh111rYTNYgKCUBKdAhbbNvxU+EZTHtnG1JjgrF23mipw5ENQRB8ZikT33iV5HbPTOkHu11EBMcd0CWEMUFp5bFP96C4thn/vW8YRqZFSx2OLKXGBCNM64eo4AAuaumjmKiQS/jqBejfm05gYFI4hnSP4BcodYjF5kBcqBY1DRZkJuikDke24nVa7MmbwM/XeZ7/5gCarXbcMzKlpTq6N2OiQi6Xf7wGi9Ydw1t3DfHqpsnSuma88O1BOERgwx9HozvXaXFKwWkD3lh/DEEBfvjrzVlShyOZAD8VPp05gtWdL4MJysW+2lOKCqMZNw9xTbFTuePEdHIpq92BP366Bz8eq8bi9celDsetHKKIm4YkYlyfWCYpHWB3iPh2Xzm+318Ou4OrcDNJoY6aNyEds8ek+cwiloIoior+pjAajdDpdDAYDAgLYx0COdh0tArf7ivHk9f28YlBk+w37xib3YG3N51Ado9IDE6O4PILdFmrDlTgnR9PYHByBP6U20fqcMhFnL1+e2+7PEnmyl4xuLJXjNRheAyTlI7xU6s4SwrAb9/YjOAAPzw3NZPT2i+jwWzF1hO1UPbPauosJirkdkU1TUiO8p5FHEVRxJLNJzF5QDxiueItdUK9yYpdRXUAAF2g97c6dtWwlCi8eksW10I6OzbOZLWjW0QgNH6+UQWbY1TIbWx2B574316M+dt67Cmukzocl8k/UYPnvj6A8a9u8Jm1NlzN7hDx88lavLnhOBw+OE4l0F+NFbNH4dVbsriUgBMSwgNxw+BEZHbj7KhlW05i7N824KXvDksdisewRYXcxk+tQpPFDrtDxLbCGmQlhUsdkkto/FS4onsE+saHQevvG79oXM0hipi+ZDsaLXZc1SsGGQm+Nb7MT61C/0Qd+ifywksdY7OLCApQIzkyUOpQPIaDacmtahstOFbZgOwU7yt9bbE5EMAVXTvt0Y92w2S1Y/bYNPRjHRG6jOLaJhwoMyIlOtjnu4BEUYTdIcJPrezvHw6mJVmIDA7wyiQFZ+tgUOf9/daBUocgmWWbC5EYEYSRadFcbdtJb6w/hg+2F+ORcb0w9xrfTlQEQYCf2ncG8fObljymrsmCv686AptdmesB1TSY8b8dJbDYlBk/yUOTxYYFXx/Aff/5GfUmq9ThKEZGfBj6d9MhMoiDj30NW1TII+wOETe9mY9jlQ0IDFDjwat7Sh1Sh7239RT+sfoovt1XhndmDJU6HK9x7mLtCzV3AKDBbMNvB3VDaV0zZ411wF05PXBXTg+pw5DU4fJ6LPzuIPp302HehHSpw/EYtqiQR6hVAn5/VSrSYkMwVKFLtUeFaBAbqsGUQd2kDsVrPPPVfgx8dhX+t6NE6lA8JjZUi1dvGYgPf58jdSikMMcqG7D+cBU2H6uWOhSPYosKeczNQxIxZWCCYuf+3zW8O269IgkspOo6MaEa2B0ijlY2SB0KkewNSNThxRv6I0TrW5duzvohyZhtdsUmLeQa1Q1mWGwOJIT7zlTLepPVZ7q5XO3xT/fix2PVeOWmARiRFi11ONRFzl6/2fVDkvh2XxmuenkdDpfXSx3KZX21pxQ7TtVKHYZXig7R+FSSYrLaMejZVRj10loYmjiQtqNqGs04XdeM49WNUodCHsREhTxOFEX8b0cJKoxmvLVB3issG5qseOrzfbhxcb7P9QuT6x2vaoDNIaLZYkdYoG8137vCw2N74dMHczBlYILUoUhie2EtCqsbFTtzsrP4SSGPEwQBC2/sjw+2FWPmaHnP/rE5HJiUGY99pw0YnholdThe6UhFPZZuPolAfzWenpwhdThu1S9Bhz15E3D6TDMXs+wEb6lu3Rl2h4g7/70VVruIzU+MRTcfaolkokKSiA3V4g/je0kdxmVFhWjw0k0DYLE5oOYoWreoN9nwwfYihAf546nr+kLl5f/PukB/LkRIHWZstiIlOhhlBhP0PjatnYkKSU4URXy1pxTj+8YhWCPPtySr0LrPgEQdfn9VKrJ7RMIhilDBuxMV6jyHQ8S6w5U4UdWIu0d096nB+BHBAfjh0ashiqLPtcbx25ck9/SX+/GHD3djwYr9UofS4nhVA575aj+qG8xSh+L1/NUqPHltX4zPiFP82iWX0myx4w8f7sKSHwt9boyBqwgCMOfD3Xj+24M4VdMkdTiS8LUkBUxUSA4m9dcjwE+FdH0Y5DJb/tVVR7Bsy0nkfSWf5ImUbd9pA77cXYq3Nh5nN2InCYKA8RlxuG5APNvdfIg829nJp4zoGY1NfxqDOBn1u95/ZSoOl9fjkbHyH0fjDURRxInqRmwvrP2lqJ4XXsjjdVrMu6Y3VCrBJ38Vu4qvLmb50spDOFRmxO9GpeDKXjFSh+NRTFRIFs5PUmx2B5qtdkmLYg1MCsf3c67iL18PsTtEXP/PH9FosWNAog79EnRSh+RySZFBeHgcE1/qnO2Ftdhx6gxuHJIodSgex0SFZKWy3oTZ7+9CUIAaS6YP9egva1EUYWy2QXd2dVYmKZ7jp1ZhVK9oGJttMHN1anKCze7w6jFNF3piUh8cq2zAoOQIqUPxOCYqJCvV9RbsKa6Dv1qFY1UN6B0X6rFjf7H7NJ5dcQALb+iP3Mx4jx2XfvHmtCFe2yViNFlxrLIB/RLCfGqmijsU1TRh2jvb0GSx4eenrpE6HI8Z2iNSsQu6dpXvpKOkCBkJYXj99kH4cvZIjyYpoijio5+KcabJiqMVXCBPCt6apADA1uM1uOGNLfjtoi1Sh6J40aEBKKptQnWDBWcaLVKHQx7AFhWSnYn99K1unyu2tr2wFpX1JsSGapGdEunSrhlBEPCf3w3Dhz8V4Y7sZJftlzrOanfAZhcRGOA9LQ9Gkw2RwQHI7MaFU7sqKMAP/5uZg6TIIIQH+UbhvNN1zThR1YDUmBCfqkh7DldPJlk7UdWAW97Kh90h4sx5i7jF67TIm5zBLhov8/dVR/DvTSfw6DW9cd+VqVKH41KiKMJkdXhVAkae8e7ZUgkT+8XhrbuukDocl+HqyeQVFqzY/0sT7wUrzZYbTJi5fCdWFpR1af9vbzyOReuOdTFKcpVgjRqNFjt2F9dJHYrLCYLAJIU6ReOnQq/YEPSK9Vx3uJyw64dky+4Qcbi8vs3HRAACgAUrDuCaDH2nuoF2FZ3BC98eAs4OVMtO8c2BanIydWA3jEqLQR+9b34hk3NKzjRh1YEKBPipcOew7lKH43a3ZSfjNh/ukmaLCsnW9sJalBvbL2EvAigzmLC9sLZT+x+UHIE/TkzHnPG9mKTIRGyYFhkJYV5V8O2LXacxZdFmLNtcKHUoXqOwuhELVhzAkh/5f+oL2KJCslVZb3Lpdji7qJmh2YqI4AAAwKwxaZ2Oj8gZ20/WYk9xHYYxGXaZ3nGhmNgvDul6jkv0BUxUSLZiQ50rqe/sdgdKjfjjp3sQqvXDB/cP9+rpsEpWXNuED7YXweYQ8eS1faUOp8seGt0Tw1Ii0TMmROpQvEZcmNarBpVeyplGC255Kx/do4Lw9l1XeFVro7Mk7frp0aMHBEFo9ffiiy9KGRLJSHZKJOJ12nYXHxPOzv4J1frh9TVHcbK68ZL70wX542hlAwpOG1FyptktMVPX1ZtseGP9cSzfegoWL6hSmxgRhCkDuyGzm/ctC0DuV1TbhKOVDdh32uCTSQrk0KLy7LPP4v7772+5HRrKQXT0C7VKQN7kDMxcvhPC2TEp55z7uOZNzsBnO09jyeZCHK9qwGu3DQIAHC6vx0P/3YGgAD+seHgUAKBbeCAW3TEYQ7pHIPJs1w/JTx99KO4YlowhyRFwKLt6ArmZ1e5Ak8UOXaD31lNJjQnGe/dmo8lilzoUyUieqISGhkKv1zuxJfmi3Mx4LJ42GAtWHECZ4dexKPoL6qgcrazH1IHdWh4P8FPheFUjgi+YDnpNRpwHo6fOUKkEvPDb/lKH4RJbjlej0mjGsNRIxOt8r1CXOy3bXIi/fHMQNwzuhpdvypI6HLcJ1fr73GrJF5K04FuPHj1gMplgtVqRnJyMO+64A48++ij8/NrPn8xmM8zmX2eCGI1GJCUlseCbl7M7xA5VpjXb7Nhx6gyigjVI51RXksis/+7EN/vK8KfcdDw0mgO3XWnFnlI8/MEu5KRG4YPfD5c6HOoEZwu+Sdqi8sgjj2Dw4MGIjIzEli1bMH/+fJSVleHVV19t9zkLFy7EggULPBonSU+tEpDTM8rp7TV+aozoGe3WmMi9ahrM2Hy8BmPSYxCqVWbTfh99KE7XNWOwD654625j+sQif/5Y6MOcG0yvVKsOVCAoQI2spHCEaCTvBJGEy1tUnnjiCbz00kuX3ObgwYPo06fPRfcvWbIEDzzwABoaGqDRaNp8LltUiHzD2L+tx4mqRrx91xBM6MfuYfJNw19Yg3KjCZ8/NAKDvCzhlaxFZd68eZgxY8Ylt0lNbXsNj2HDhsFms+HkyZNIT09vcxuNRtNuEkNE3mNUWjQC1CoOqCWfZXeI6JcQhiCNGj2igqUORzIuT1RiYmIQE9O5gT+7d++GSqVCbGysq8MiIoXJm9zPpStke1ptowURQf6s1+NGaw9VYOORaoxOj8HodO+7bqhVAt6ZMVTqMCQnWYdXfn4+tm3bhjFjxiA0NBT5+fl49NFHMW3aNEREeFfzFhF1nJKTFAC4651tKDeY8OZdQzC0B6vSusPGI9VYtuUkAvxUXpmo0C8kS1Q0Gg0+/PBDPPPMMzCbzUhJScGjjz6KuXPnShUSEcmQKIpostgRrKCBhM0WOwqrG9FksaN7ZJDU4Xitq9NjEOCnwqg0Dpz3ZpJOT3YFZwfjEJHyrCwowzNfHUB2SiRev32Q1OF0iNlmx6GyemQlhUsdCinUghX7sflYNWaNScOU8+pEeQtnr99cPZmIZCsiKADlRhN2nDoDpf2m0vipmaRQlxwur8eRigbY7Mp677saW1SISLYsNge2F9biih4R0PqrnXgG+Rq7Q8TpM82ICPZXbL2d9hTXNuFEdSP66EMR54X1YtiiQkSKF+Cnwqhe0YpKUhrNNkxfsh2L1h2Dza78RRXl7pa38nHVK+uw+Vi11KG4XFJkEK7uHeOVSUpHMFEhInKhHafOYMORKry/rQh+an7FultyZBAC/FSobbRKHQq5Cbt+iEjWLDYH3t1yEtsKa7DozsHQ+Mm7daXcYMLKgjIIgoDpI3pIHY7XqzdZERTgp/jp7Bc6UlGPHafOIDNBh/6JOqnDcQt2/RCRV/BXC3h70wmsPliJHSfPSB3OZel1WswYmcIkxUNCtf5el6QAwMYjVZj/2T68ufG41KFITjmFCYjIJwmCgPuvTIFKEJAWGyJ1OEQeEa8LxOj0GC5oya4fIiLXKaxuxMmaRlzRPcLrZqDIlSiKePG7Qzha2YCXbhyAmFCuBacU7PohIvKwL3adxj1Lf8JTXxRIHYrPEAQBK/eXY+2hShyrbJA6HHIDdv0QkSKYbXZsO1ELtUrASJmWTA/WqJEcGYTsFK7t40kPXNUTDlFEj2jvWK7gXEcHF7T8Bbt+iEgR/pN/Ek9/uR/ZKZH4+IEcqcO5JIdDhMoLB3iSZ5yoasB1r/+IvvGh+OyhkVKH4zbs+iEirzImPRaxoRr0ig2RfTl9JinUFSdrGtFstaPZyoKBYIsKESmJKIqybQ432+yyr/HirRwOEWVGE4prmzA8NUrqcLrManeg5EwzGs02ZHbzzhoq6MD1m2NUiEgx5JqkAMB97/6M03XN+MvUTIzoKc8xNN7qTJMFI19cC0EA9i+YiKAAZV/a/NUqpEQHSx2GbCj7bBKRTyqta0ZMqAb+MilRb7bZsfPUGTRa7Ijl9FiPiwrRIC5MgzCtP2oaLAiK5KXNm7Drh4gU5a53tmHT0Wp8cP9w5PSUTzO/0WTF1uM1uCYjTtYtP97K7hC9pkLtWxuOIyIoABMz9dAFem89Hnb9EJFXig3VQhCAg2VGWSUqYVp/TOinlzoMn+UtSYrV7sDL3x+G3SHiqt4xXp2oOIuJChEpyqPX9MKT1/ZBVAi7WMj7mKx23DksGSVnmtmNeBYTFSJSlMQIeRX12ldiwP+tO4rrBiTg+qwEqcPxWcW1TXju6wNotNjw3/uGSx1Op4Vq/fHslEypw5AVJipERF3ww4FyfL+/AmqVwERFQkEBavxwoAKCADRb7AgM4FRxb8FEhYgU50RVA15bcxTNFjvevvsKSWOZnJUAlSBgYFK4pHH4uqgQDZ6bmomUqGBFj1dpstgQ6K/mgOzzMFEhIsXR+Kvx5e5SCAJQZmhGvC5Qslh6x4Wi9zWhkh2ffnXX8O5Sh9Bls/67E9sLa/HyTVm4bkC81OHIAhMVIlKcbuGBeOq6vhjcPQL6MK3U4RC5zKnaJjRa7IgI4myfc1hHhYiokz7dUYKeMcHISgzn+j4y0GC2YW9JHZotdozrGyd1OJ1ittlRXNuMhHCt4ivsXg7rqBARuVGTxYYnP98Hi82BHx69Cr3j2P0jtX0lBtzxr21IigxUbKKi8VMjLTZE6jBkhYkKESlWaV0zPvm5BMEaNe67MtWjxzY0W3FN3zicqG5EL15YZKF3XAi6RwWhV2yoV1Wq9XXs+iEixfp+fzkeeG8HYkM1yJ8/TpILk5xXdCZl+X5/OfaW1GF0eiyG9oiUOhy3Y9cPEXm9MemxmNgvDhP76eEQRajh+YSBSQq5yqoDFfh0Rwk0fmqfSFScxUSFiBQrwE+Ft+7yfB2V2kYL1IIAHWdmyJYSW7pGp8fAX61CdgqTlPPJY410IiIFeW31EQz+yyq8tvqo1KHQBTYeqcLEv2/ErPd3Sh1Kh/1mQAIW3tAfw1Pls9imHLBFhYgUr9lix6qDFYgN1bj9S14URZyqbYLdIWJwd1ajlRt/tQqHK+rRbLVLHQq5CBMVIlK8Nzccx2trjmJMeozbExVBELDsnmwcLq/nbB8Z6p+ow9IZQxU3xdfQZIXV4UBUcIDiuqzcjV0/RKR41w9MQGJEILKSwuGpiYzp+lAWeZOhEI0fxvSJRVKkvFbZvpzPd5Xgir+sxpyPdksdiuywRYWIFK9nTAg2/WmM23+JGpqsCAxQI8CPv/HItaobLBAEICFcunWr5Ip1VIiInPTUF/uwsqAcT0/uh+uzEqQOh9pxsroR+SdqEBuqUVSFWpPVDrPNAV2gb8wmc/b6zZ8FRORVdhadwZqDFS7fr8MhYsvxGlQ3WBAVHODy/ZPrbDxahfmf7cMH24ukDqVDtP5qn0lSOoJdP0TkNX7YX47fv7cD+jAtRvWKhsZP7bJ9q1QCvp9zFTYeqUIOp4/KWmY3Ha7sFY3B3SOkDoVcwG0tKs8//zxGjBiBoKAghIe3PYWvqKgI1113HYKCghAbG4s//vGPsNls7gqJiLzc1ekxSIwIxFW9o9Fscf30VH+1CuP6xnEQrcwNTo7Ae/cOw0Oj06QOxSn7Sw2Y/f5OLNtcKHUosuS2FhWLxYKbb74ZOTk5eOeddy563G6347rrroNer8eWLVtQVlaGu+++G/7+/njhhRfcFRYReTGNnxqr514Nrb/rWlIAwGJzcAAtuc2+EgO+3lsGQ7MVM0amSB2O7Lh9MO2yZcswZ84c1NXVtbr/u+++w29+8xuUlpYiLu6XwU5vvvkmHn/8cVRVVSEgwLk+YA6mJSJ3e/LzfThQasTjuX2Q05PdPkrhcIiw2B0uT1xd7UhFPdYdqoRep8WUgd2kDsdjZD+YNj8/H/37929JUgBg4sSJMBqN2L9/f7vPM5vNMBqNrf6IiC5UWW/CG+uPwe7o2m8xk9WOdYcqsbu4zomtSS4Wrz+OzGe+x99XHZE6lMvqHReKB67u6VNJSkdINpi2vLy8VZICoOV2eXl5u89buHAhFixY4Pb4iEi5bHYHfvP6j6isN6NHVDCu7R/f6X1p/dX4+uFR+HRHCYancrE4pQgP8keTxY4jFfVSh0Jd1KEWlSeeeAKCIFzy79ChQ+6LFsD8+fNhMBha/oqLi916PCJSHj+1CrdlJ2NgUjgiXTCVOCpEgweu7snS5goysZ8eq+dehX9PHyp1KJdkttmx41QtDE1WqUORrQ61qMybNw8zZsy45DapqalO7Uuv12P79u2t7quoqGh5rD0ajQYajcapYxCR73p4bBoeHd+r08lFhdGE0rpmDErmFFcligwOcEmS6m5HKxpw4+J8RAT5Y9fTE6QOR5Y6lKjExMQgJibGJQfOycnB888/j8rKSsTGxgIAVq1ahbCwMGRkZLjkGETku/zVrRuMDU1W6IKcL6aV9+V+fH+gHHm/yeBMDHIbY7MV8TotkiKUtTaRJ7ltjEpRURFqa2tRVFQEu92O3bt/WWgpLS0NISEhmDBhAjIyMnDXXXfh5ZdfRnl5OZ566inMmjWLLSZE5FKf7SzB01/ux5IZQ5Gd8us4E7tDxPbCWlTWmxAbqkV2SiTUKgEWmwNBGjXUgoBhLO6mWDtOncEPB8rRL0En2yUPRqRFI3/+OFjtDqlDkS23JSpPP/003n333ZbbgwYNAgCsW7cOo0ePhlqtxtdff42ZM2ciJycHwcHBmD59Op599ll3hUREPkgURaw7XIUGsw0f/1zckqisLCjDghUHUGYwtWwbr9Mib3IGcjPj8eotAzFnXG8kR/GXrlLtOFWLtzacwLX99bJNVM65sAWQfsVFCYnI6zVZbPj4p2LcndMDKpWAlQVlmLl8J9r78ntz2mDkZnZ+phDJw96SOnz8czFyUqNx3QCeT7lx9vrNRIWIfIrdIWL4wtWoqre0u028TosfHx8LNUvlkxtZ7Q7c8lY+UqKC8dzUTARrfGv5PdkXfCMiksL2wtpLJikAUGYwYXthrcdiIt90qqYJu4rqsHJ/OQJlXj1XSr6VvhGRz6usNzmxlfPbkbyJoogKoxk2hwOJMptZExemwZvThqCuycKFLi+BLSpE5FNiQ7Uu3Y7k7Y31xzF84Rq8tvqo1KFcJFTrj9xMPW7LTpY6FFljokJEPiU7JRLxOi3a+/0qnB2jcv40ZlKunjEhUKsENFpsUodCncREhYh8ilolIG/yL0UlL0xWzt3Om5zBgbReYkyfGOxfMBFv3DlE6lAu8s3eMhScNsDGGiqXxESFiHxObmY8Fk8bDL2udfeOXqfFYk5N9ioaPzW0Mhyo2mSxYfYHO/Gbf/6IM1zn55I4mJaIfFJuZjyuydC3WZmWyN3ONFmRkxqF6gYzYkJZjf1SWEeFiIi82sYjVXh/WxH6J+owa0ya1OHQWayjQkREdHYl7JX7y7HpaJXUoVAnsOuHiIi82vDUKDx1XV9kJYVLHUoLURQhCOxmdAYTFSIi8mpJkUG478pUqcNoYbbZMeyFNegVG4J3ZgxFmNZf6pBkjV0/REREHnS0ogF1TVYcrWxAqI+t79MZ/B8iIiKvV2+yYn+pEQF+KgxOjpA0lj76UPzw6FWoMJrY/eMEtqgQEZHX+3RHCW57eyveWHdc6lDgp1ahd1woruwVI3UoisBEhYiIvF6/BB26hQciNow1S5SGXT9EROT1slMisfmJsVKHAZvdgb+vPoKMeB0m9IuDv5rtBZfDRIWIiMhDTlQ3YtG64wgOUGNS5kSpw1EEJipERORTbHYH/CRqyfBXq3B7djIAESou1+AUJipEROQTdhWdwfzP9iFU64dPHhwhSQwp0cFYeEN/SY6tVExUiIjIJ0QEBeBQeT0C1CqYbXZo/OS3qjJdjIkKERH5hO5RQXhz2hAMTg6XJElxOERUN5oRG6r1+LGVjMONiYjIJwiCgNxMPWLDpEkUimqbkP38Gox6aS0cDlGSGJSIiQoREZEHFNY0QiUAUcEBHEjbAez6ISIin2Gy2vHVnlLsP23AM9f382gJ+zHpsdi/IBfVDWaPHdMbsEWFiIh8hkoQ8NQXBXg3/xROVDd6/PiBAWokRQZ5/LhKxhYVIiLyGQF+Ktw+NAmBAX7Q+nPWjxIwUSEiIp+yYEqmx49ZbjDh2a/3IysxHA9c3dPjx1cydv0QERG5Wf6Jany7rxxf7y2TOhTFYYsKERH5HIdDxJHKeiRFBCFY4/5L4eDkCMyf1AcRwQFuP5a3YaJCREQ+54bFW7C7uA7/vvsKjM+Ic/vxukcFs8unk9j1Q0REPic9LhRBAWpU1nOqsNwJoigqujye0WiETqeDwWBAWFiY1OEQEZECGJqsCNaoPbKK8r4SA+rNVgxOjuBMo/M4e/1miwoREfkcXZC/R5IUAPj3jydwx7+24Y31xz1yPG/DRIWIiMiNooI1iA3VYETPKKlDUSR2/RARkU/6cvdpfPRTMSZl6nFXTg+3HksURYgiuMbPeSTv+nn++ecxYsQIBAUFITw8vM1tBEG46O/DDz90V0hEREQtKo1mbDlegx8OVLj9WIIgMEnpJLdNT7ZYLLj55puRk5ODd955p93tli5ditzc3Jbb7SU1RERErpSbqYefWsA1bpye3GyxIzCAA2i7wm2JyoIFCwAAy5Ytu+R24eHh0Ov17gqDiIioTUmRQbhnZIrb9u9wiBjx4hrE6wLx7+lXICE80G3H8maSD6adNWsWoqOjkZ2djSVLluByQ2bMZjOMRmOrPyIiIrk5VtWAM01WFNU2ITZUI3U4iiVpZdpnn30WY8eORVBQEH744Qc89NBDaGhowCOPPNLucxYuXNjSWkNERNQVDoeIjUersPZQJf7fdX2h8XNdN03vuFD89P/G43hVg8emQnujDs36eeKJJ/DSSy9dcpuDBw+iT58+LbeXLVuGOXPmoK6u7rL7f/rpp7F06VIUFxe3u43ZbIbZ/GslQaPRiKSkJM76ISKiDnM4ROS8uAYVRjOWzhiKMX1iu7xPu0PE9sJaVNabEBuqRXZKJNQcSHsRZ2f9dKhFZd68eZgxY8Ylt0lNTe3ILlsZNmwYnnvuOZjNZmg0bTeTaTSadh8jIiLqCJVKwK1Dk1HTYEZ8uLbL+1tZUIYFKw6gzGBquS9ep0Xe5AzkZsZ3ef++qEOJSkxMDGJiYtwWzO7duxEREcFEhIiIPGbuNb1dsp+VBWWYuXwnLuymKDeYMHP5TiyeNpjJSie4bYxKUVERamtrUVRUBLvdjt27dwMA0tLSEBISghUrVqCiogLDhw+HVqvFqlWr8MILL+Cxxx5zV0hERERuYXeIWLDiwEVJCgCIAAQAC1YcwDUZenYDdZDbEpWnn34a7777bsvtQYMGAQDWrVuH0aNHw9/fH4sWLcKjjz4KURSRlpaGV199Fffff7+7QiIiImpXcW0TygwmZKdEdvi52wtrW3X3XEgEUGYwYXthLXJYSr9D3JaoLFu27JI1VHJzc1sVeiMiIpLKusOVuGfpT0iJDsbaeVdDEDrW6lFZ336S0pnt6FecL0VERD5vaI9IBPqrERuqQYPZ1uHnx4Y6NxDX2e3oV5LWUSEiIpKDEI0ffn5qPII1nbssZqdEIl6nbbf7RwCg12k71a3k69iiQkREBHQ6SQEAtUpA3uSMNh8714mUNzmDA2k7gYkKERHReWobLagwdnwsSW5mPB6b0Bsav9aXVr1Oy6nJXdChyrRy5GxlOyIiosv5em8pnvjfPoxOj8H/3TG4U/tgZVrnuKUyLRERkTfrGROCRosNJ2saYbLaofXv+No/apXAKcguxESFiIjorL7xYfj0wREYlBQOVQdaQepNVny1pxRTBnZDSBfGutDFOEaFiIjoPEO6R3QoSQGAL3eX4v99XoA7/rXVbXH5KiYqREREbbA7RKw6UAFnhnKGBfojNSYYUwZ280hsvoTtU0RERBdwOETc9OYW7Cqqw1t3DcHEfvpLbn99VgImD4iHzaHo+SmyxBYVIiKiC6hUAkb2jEaY1g/1pvYr1VY3mFv+LQgC/NW8rLoa/0eJiIja8ODontj0p7G4aUhim49/s7cMV760DmsOVng8Nl/CRIWIiKgNIRo/6IL8W24vWncMb244DqPJCgBYub8czVY7Vh+slDBK78cxKkRERJdhaLJi0bpjaLLY0UcfitHpsXj1liwMS4nE7dnJUofn1ZioEBERXUZggBrPTsnEusOVuLp3DADAX63CtOHdpQ7N6zFRISIiuowAPxVuGpLY7ngVch+OUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbTFSIiIhItpioEBERkWwxUSEiIiLZYqJCREREssVEhYiIiGSLiQoRERHJFhMVIiIiki0mKkRERCRbflIH0FWiKAIAjEaj1KEQERGRk85dt89dx9uj+ESlvr4eAJCUlCR1KERERNRB9fX10Ol07T4uiJdLZWTO4XCgtLQUoaGhEATBpfs2Go1ISkpCcXExwsLCXLpvOeDrUz5vf418fcrn7a+Rr6/zRFFEfX09EhISoFK1PxJF8S0qKpUKiYmJbj1GWFiYV74Bz+HrUz5vf418fcrn7a+Rr69zLtWScg4H0xIREZFsMVEhIiIi2WKicgkajQZ5eXnQaDRSh+IWfH3K5+2vka9P+bz9NfL1uZ/iB9MSERGR92KLChEREckWExUiIiKSLSYqREREJFtMVIiIiEi2fDpRWbRoEXr06AGtVothw4Zh+/btl9z+k08+QZ8+faDVatG/f398++23Hou1oxYuXIihQ4ciNDQUsbGxmDp1Kg4fPnzJ5yxbtgyCILT602q1Hou5I5555pmLYu3Tp88ln6Ok8wcAPXr0uOg1CoKAWbNmtbm93M/fxo0bMXnyZCQkJEAQBHzxxRetHhdFEU8//TTi4+MRGBiI8ePH4+jRo5fdb0c/x+50qddotVrx+OOPo3///ggODkZCQgLuvvtulJaWXnKfnXmvu8vlzuGMGTMuijU3N/ey+5XLObzc62vr8ygIAl555ZV29ymn8+fMdcFkMmHWrFmIiopCSEgIbrzxRlRUVFxyv5397DrLZxOVjz76CHPnzkVeXh527tyJrKwsTJw4EZWVlW1uv2XLFtx+++249957sWvXLkydOhVTp05FQUGBx2N3xoYNGzBr1ixs3boVq1atgtVqxYQJE9DY2HjJ54WFhaGsrKzl79SpUx6LuaP69evXKtYff/yx3W2Vdv4A4Keffmr1+latWgUAuPnmm9t9jpzPX2NjI7KysrBo0aI2H3/55Zfx+uuv480338S2bdsQHByMiRMnwmQytbvPjn6O3e1Sr7GpqQk7d+7En//8Z+zcuROfffYZDh8+jOuvv/6y++3Ie92dLncOASA3N7dVrB988MEl9ymnc3i513f+6yorK8OSJUsgCAJuvPHGS+5XLufPmevCo48+ihUrVuCTTz7Bhg0bUFpaihtuuOGS++3MZ7dDRB+VnZ0tzpo1q+W23W4XExISxIULF7a5/S233CJed911re4bNmyY+MADD7g9VleorKwUAYgbNmxod5ulS5eKOp3Oo3F1Vl5enpiVleX09ko/f6Ioin/4wx/Enj17ig6Ho83HlXT+AIiff/55y22HwyHq9XrxlVdeabmvrq5O1Gg04gcffNDufjr6OfakC19jW7Zv3y4CEE+dOtXuNh19r3tKW69v+vTp4pQpUzq0H7meQ2fO35QpU8SxY8dechu5nj+xjetCXV2d6O/vL37yySct2xw8eFAEIObn57e5j85+djvCJ1tULBYLduzYgfHjx7fcp1KpMH78eOTn57f5nPz8/FbbA8DEiRPb3V5uDAYDACAyMvKS2zU0NKB79+5ISkrClClTsH//fg9F2HFHjx5FQkICUlNTceedd6KoqKjdbZV+/iwWC5YvX47f/e53l1x8U0nn73yFhYUoLy9vdY50Oh2GDRvW7jnqzOdYbgwGAwRBQHh4+CW368h7XWrr169HbGws0tPTMXPmTNTU1LS7rZLPYUVFBb755hvce++9l91WrufvwuvCjh07YLVaW52PPn36IDk5ud3z0ZnPbkf5ZKJSXV0Nu92OuLi4VvfHxcWhvLy8zeeUl5d3aHs5cTgcmDNnDkaOHInMzMx2t0tPT8eSJUvw5ZdfYvny5XA4HBgxYgRKSko8Gq8zhg0bhmXLlmHlypVYvHgxCgsLceWVV6K+vr7N7ZV8/gDgiy++QF1dHWbMmNHuNko6fxc6dx46co468zmWE5PJhMcffxy33377JRd76+h7XUq5ubn4z3/+gzVr1uCll17Chg0bMGnSJNjt9ja3V/I5fPfddxEaGnrZbhG5nr+2rgvl5eUICAi4KHG+3LURHfzsdpTiV0+my5s1axYKCgou2y+ak5ODnJycltsjRoxA37598dZbb+G5557zQKTOmzRpUsu/BwwYgGHDhqF79+74+OOPnfqFozTvvPMOJk2ahISEhHa3UdL583VWqxW33HILRFHE4sWLL7mtkt7rt912W8u/+/fvjwEDBqBnz55Yv349xo0bJ2lsrrZkyRLceeedlx2wLtfz5+x1QQ58skUlOjoaarX6opHMFRUV0Ov1bT5Hr9d3aHu5mD17Nr7++musW7cOiYmJHXquv78/Bg0ahGPHjrktPlcJDw9H7969241VqecPAE6dOoXVq1fjvvvu69DzlHT+zp2HjpyjznyO5eBcknLq1CmsWrXqkq0pbbnce11OUlNTER0d3W6sSj2HmzZtwuHDhzv8mYRMzl971wW9Xg+LxYK6urpW21/u2ogOfnY7yicTlYCAAAwZMgRr1qxpuc/hcGDNmjWtfpGeLycnp9X2ALBq1ap2t5eaKIqYPXs2Pv/8c6xduxYpKSkd3ofdbse+ffsQHx/vlhhdqaGhAcePH283VqWdv/MtXboUsbGxuO666zr0PCWdv5SUFOj1+lbnyGg0Ytu2be2eo858jqV2Lkk5evQoVq9ejaioqA7v43LvdTkpKSlBTU1Nu7Eq8RzibAvnkCFDkJWV1eHnSnn+LnddGDJkCPz9/Vudj8OHD6OoqKjd89GZz25nAvdJH374oajRaMRly5aJBw4cEH//+9+L4eHhYnl5uSiKonjXXXeJTzzxRMv2mzdvFv38/MS//vWv4sGDB8W8vDzR399f3Ldvn4Svon0zZ84UdTqduH79erGsrKzlr6mpqWWbC1/jggULxO+//148fvy4uGPHDvG2224TtVqtuH//foleRfvmzZsnrl+/XiwsLBQ3b94sjh8/XoyOjhYrKytF0QvO3zl2u11MTk4WH3/88YseU9r5q6+vF3ft2iXu2rVLBCC++uqr4q5du1pmvLz44otieHi4+OWXX4p79+4Vp0yZIqakpIjNzc0t+xg7dqz4z3/+s+X25T7HcnqNFotFvP7668XExERx9+7drT6XZrO53dd4ufe6XF5ffX29+Nhjj4n5+fliYWGhuHr1anHw4MFir169RJPJ1O7rk9M5vNx7VBRF0WAwiEFBQeLixYvb3Iecz58z14UHH3xQTE5OFteuXSv+/PPPYk5OjpiTk9NqP+np6eJnn33WctuZz25X+GyiIoqi+M9//lNMTk4WAwICxOzsbHHr1q0tj1199dXi9OnTW23/8ccfi7179xYDAgLEfv36id98840EUTsHQJt/S5cubdnmwtc4Z86clv+PuLg48dprrxV37twp0Su4tFtvvVWMj48XAwICxG7duom33nqreOzYsZbHlX7+zvn+++9FAOLhw4cvekxp52/dunVtvifPvQaHwyH++c9/FuPi4kSNRiOOGzfuotfdvXt3MS8vr9V9l/oce9qlXmNhYWG7n8t169a17OPC13i597pcXl9TU5M4YcIEMSYmRvT39xe7d+8u3n///RclHHI+h5d7j4qiKL711ltiYGCgWFdX1+Y+5Hz+nLkuNDc3iw899JAYEREhBgUFib/97W/FsrKyi/Zz/nOc+ex2hXD2oERERESy45NjVIiIiEgZmKgQERGRbDFRISIiItliokJERESyxUSFiIiIZIuJChEREckWExUiIiKSLSYqREREJFtMVIiIiEi2mKgQERGRbDFRISIiItliokJERESy9f8BwFACIeB2ZDQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x_fine = np.linspace(0, 20, 10*N)\n",
+    "\n",
+    "ax.scatter(x, y)\n",
+    "ax.plot(x_fine, f_exact(x_fine), ls=\":\", label=\"original function\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we create an interpolant via `interp1d`, it creates a function object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_interp = interpolate.interp1d(x, y, kind=\"cubic\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.plot(x_fine, f_interp(x_fine), label=\"interpolant\")\n",
+    "\n",
+    "ax.legend(frameon=False, loc=\"best\")\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-d interpolation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example of mult-d interpolation from the official tutorial.\n",
+    "\n",
+    "First we define the \"answer\"---this is the true function that we will sample at a number of points and then try to use interpolation to recover"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func(x, y):\n",
+    "    return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use `np.meshgrid()` to create the two-dimensional rectangular grid of points were we define our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nx = 100\n",
+    "ny = 200\n",
+    "\n",
+    "x = np.linspace(0, 1, nx)\n",
+    "y = np.linspace(0, 1, ny)\n",
+    "\n",
+    "x, y = np.meshgrid(x, y, indexing=\"ij\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "here's what the exact function looks like---note that our function is defined in x,y, but imshow is meant for plotting an array, so the first index is the row.  We take the transpose when plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f04d3aaf380>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "data = func(x, y)\n",
+    "im = ax.imshow(data.T, extent=(0, 1, 0, 1), origin=\"lower\")\n",
+    "fig.colorbar(im, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll coarsen it by taking only every 4th point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coarse = data[::4, ::4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f04cdf09090>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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S/Idib04eyNkO5YL6DTh79ix8Ph8SExMD1icmJuKTTz7pMObee+/F2bNnMXHiROi6jra2Nvz0pz/tssugqKgIK1euDObUiIiISEGPt4tUVFRg9erVePHFF7Fv3z784Q9/wI4dO7Bq1apOYxYvXoz6+nr/UlNT09OnSUREBuD7avpj1cWMgnrVgwcPhs1mQ21tbcD62tpaJCUldRizbNkyzJ49Gz/+8Y9x3XXX4Qc/+AFWr16NoqIiaFrHzWwOhwMulytgISIiupSLXQaqS7CCqb776KOPcPfddyMtLQ0WiwXFxcXttlmxYgUsFkvAMmrUqKDPKxhBJQR2ux0ZGRkoLy/3r9M0DeXl5cjKyuowprm5GVZr4GFstn8+b1PXw9B5RkREFELBVt81NzdjxIgRWLNmTad/TAPAtddei1OnTvmXd999twdfheDBRAUFBZg7dy7Gjx+PCRMmoLi4GE1NTcjNzQUAzJkzB8OGDUNRUREAYOrUqVi/fj3GjRuHzMxMHDp0CMuWLcPUqVP9iQEREVEoaLBCU2zyDzb+X6vvAKC0tBQ7duzApk2bsGjRonbb33DDDbjhhhsAoMOfXxQREdFlwhBqQScE06dPx5kzZ7B8+XK43W6kp6ejrKzMP9Dw+PHjAS0CS5cuhcViwdKlS3HixAkMGTIEU6dOxVNPPRXaV0JERKbn0y3wKVYJXIz/ZoWbw+GAw+EIWCepvuuuzz77DCkpKXA6ncjKykJRUREuu+wypX12RVRnk5+fj/z8/A5/VlFREXiAiAgUFhaisLBQdoYhYG2V3xy6QjWRVJusMg0A4B0yQBzraFW40YSz8bUMlZ+vFikOxYUmuzj2L/XyfjwfZLMdNmmObmzVsffrFN5Xj/wvLZ98Ej+0JseL4iIi5K2OepT8nvAkyN8fzS7/fLIIu10trfL31domOF9JTB+Qmpoa8O/CwkKsWLEiYJ2k+q47MjMzsXnzZlx99dU4deoUVq5ciVtuuQUffvghYmNjxfvtCucyICIiwwjlcwhqamoCBrV/s3WgJ333u9/1//+YMWOQmZmJyy+/HL/73e8wb968HjkmEwIiIjIMPQSzHepfxXenyk1SfScRHx+Pq666CocOHQrZPr/JnMWWRERkSD5YQrJ0l6T6TqKxsRGHDx9GcnJyyPb5TWwhICIiUhBs9Z3X68XHH3/s//8TJ06guroaMTExGDlyJADgkUcewdSpU3H55Zfj5MmTKCwshM1mw8yZM3vsdTAhICIiw9B09bkIgh0jHWz13cmTJzFu3Dj/v9etW4d169Zh0qRJ/oH5n3/+OWbOnIkvvvgCQ4YMwcSJE/Hee+9hyJAhSq+tK0wIiIjIMLQQjCGQxAdTfZeWlnbJB/Nt27Yt6HNQxTEEREREZI4WAqtXoflIGqrQZNWqUGLacLm8ljpy8CBxrHQ2VW+M/Dr5FCqA9C8VnkNw+Cpx7Psxqd3Yqj1Nk+fuDQ3yB1vYGuV1/V6X/L2tHxktiosYJn/4gRYhP19vrDxW5bkjFuHs4bYLCs8+EDybRXqeEhos0BTnrVeN769MkRAQEZE5hPJJhWbDLgMiIiJiCwERERlHuAYVGgETAiIiMgwNIXh0sUnHEJgzDSIiIqIAbCEgIiLD0ENQZaCbtIXAFAmBUtmhkLQMD4plSD6VqVS13r9OKl11KtMfRzbID6w1y0riAKDeJotVuZ9sClN4W73yWJX7uHGY7F60aPIySZXvAJX7WFc4ZatPep3kx5TQFaagD1YoZzs0G1MkBEREZA4cVChnzldNREREAdhCQEREhsEuAzkmBEREZBh8dLEcuwyIiIiILQRERGQc7DKQY0JARESGwYRAzhQJQW/X3KpSqUtWiTUTa5v8F97ai1O5hp3C8w94H/cC4fuj8lwL0fH62WewWZkiISAiInNgC4EcEwIiIjIMJgRyrDIgIiIithAQEZFx6CF4jkAvD7HoM5gQEBGRYbDLQI4JARERGQYTAjlTJARaRO83AEmnJYXilLNKsa3yWCmVKYx9TnlsW4z8nvA55bGaXVZ/ZVH4gLJ45LERFxRim+SxthZZnEpJaLim4vbZFY4rjNWt8ntYUrKo28zaCN+/mCIhICIic2ALgRwTAiIiMgwmBHIsOyQiIiK2EBARkXHougW64l/4qvH9FRMCIiIyDA0W5ecQqMb3V+wyICIiInO0EKiUBEkTRYuwbAoAIprlsVFn5dOKOeoVpiQTVhV54uU5aeMweWyryyeOdaWcF8cmxcpi2xRq4k5+GSeO9dYMEMc6T8v/yhrglt2L9kb5+6pFys+3JV4+PeOFIfLj+qJkv3jSckUAsAgusS5/W4LGQYVypkgIiIjIHDiGQI5dBkRERIpKSkqQlpYGp9OJzMxM7N27t9NtP/roI9x9991IS0uDxWJBcXGx8j5DgQkBEREZxsUuA9UlGNu3b0dBQQEKCwuxb98+jB07Fjk5OTh9+nSH2zc3N2PEiBFYs2YNkpKSQrLPUGBCQEREhnGxy0B1AYCGhoaAxePxdHjM9evXIy8vD7m5ubjmmmtQWlqK6OhobNq0qcPtb7jhBjzzzDOYMWMGHA5HSPYZCkwIiIjIMPQQtA5cTAhSU1MRFxfnX4qKitodz+v1oqqqCtnZ2f51VqsV2dnZqKysFL2Gnthnd3BQIRERUQdqamrgcrn8/+7or/mzZ8/C5/MhMTExYH1iYiI++eQT0XF7Yp/dwYSAiIgMQwegK06ueDHc5XIFJARGZ4qEQLMr3B3CThVbx11N3RLZJI+NOSGf/9j+eZ38wMLfwMjUgeJDXhgin//YFief6/m21M/Esbe7PhbFNWsd9zN2x+tR48Sxe74YKY6NVJj+OPYfsodxRLjl97AeLb+frGkKz3qIlT8oRU+Qxfkc8s9Eqzf491XrxW8aDRZYevFJhYMHD4bNZkNtbW3A+tra2k4HDIZjn93BMQRERERCdrsdGRkZKC8v96/TNA3l5eXIysrqM/vsDlO0EBARkTmE48FEBQUFmDt3LsaPH48JEyaguLgYTU1NyM3NBQDMmTMHw4YN8w9K9Hq9+Pjjj/3/f+LECVRXVyMmJgYjR47s1j57AhMCIiIyDE23wNLLjy6ePn06zpw5g+XLl8PtdiM9PR1lZWX+QYHHjx+H1fp1g/zJkycxbtzXXXnr1q3DunXrMGnSJFRUVHRrnz2BCQEREZGi/Px85Ofnd/izi1/yF6WlpUHvxrirrvbZE5gQEBGRYeh6CKoMFOP7KyYERERkGJzcSM4UCYEeoZDuiWc1ld9QSiWL5y6IY/Wak/JYadnhAHmpl6VNHuuMkpdn3hBzVBybE10vimvW5GWSh2LlfY577CPEsTaPfErgiNMNori24yfEx7S5YsSx9oHR4lhrq/xjWPq9pUfKpzrXfcEXp+k2k/7J3c+YIiEgIiJzYAuBHBMCIiIyjHBUGRgFEwIiIjIMDiqU45MKiYiIiC0ERERkHP9sIVAdQxCy0+lXmBAQEZFhcFChHLsMiIiIiC0EhqPQ1iV9lgAAQDNPG5sN8hpuqzAHt1rkf7FY0Q/fG+m9qMvfGyVmbWPug/SvFtV9mBETAiIiMgx2Gcixy4CIiIjYQkBERAbCPgMxUQtBSUkJ0tLS4HQ6kZmZib1793a5fV1dHRYsWIDk5GQ4HA5cddVV2Llzp/SciYiIOvZVl4HKIp4kop8LuoVg+/btKCgoQGlpKTIzM1FcXIycnBwcPHgQQ4cObbe91+vFv/3bv2Ho0KH4/e9/j2HDhuEf//gH4uPjQ/UaiIiIAD6pUEnQCcH69euRl5eH3NxcAEBpaSl27NiBTZs2YdGiRe2237RpE86dO4fdu3cjMjISAJCWlhaKcyciIqIQCSoh8Hq9qKqqwuLFi/3rrFYrsrOzUVlZ2WHMG2+8gaysLCxYsAD/8z//gyFDhuDee+/FY489Bput4+lRPR4PPJ6v5wBuaJBNhXqRxSdv/glHpuiLlMe2xUWJY+3J8mlypRfK65JPYazLZ9eFp0V+kf+vOVUcmxQhm/64SR8gPuZnze1b7rqtVf6747PLD+sbFCuKi2hNER9Tj3KIY1td8herR8ivsUXY2W1pUzimoLJTEiPFKgO5oMYQnD17Fj6fD4mJgV8ciYmJcLvdHcYcOXIEv//97+Hz+bBz504sW7YMzz77LJ588slOj1NUVIS4uDj/kpoq/wAmIiITuTgGQHUxoR4vO9Q0DUOHDsWGDRuQkZGB6dOn4/HHH0dpaWmnMYsXL0Z9fb1/qamp6enTJCIiMrWgugwGDx4Mm82G2tragPW1tbVISkrqMCY5ORmRkZEB3QOjR4+G2+2G1+uF3d6+qc3hcMDhkDffERGROXFQoVxQLQR2ux0ZGRkoLy/3r9M0DeXl5cjKyuow5uabb8ahQ4egaV93In366adITk7uMBkgIiIS00O0mFDQXQYFBQXYuHEjfvvb3+LAgQP42c9+hqamJn/VwZw5cwIGHf7sZz/DuXPn8OCDD+LTTz/Fjh07sHr1aixYsCC0r4SIiIjEgi47nD59Os6cOYPly5fD7XYjPT0dZWVl/oGGx48fh9X6dZ6RmpqKt956CwsXLsSYMWMwbNgwPPjgg3jsscdC+0qIiMj0WGUgJ3p0cX5+PvLz8zv8WUVFRbt1WVlZeO+99ySHCgmrV6HsUDrsUuGGaosWh6I5Sd4No9mHyA8sbGJrGSR/eram0OPUdl5edvhO7Uhx7ClPnCjO45Nfp0++kJcdWlrktZ2+KPnvQPMw2S+BPUbl/pePsb4wSOE6KQyXsvhkcSqfiVZJyaJCmaOISZv8VXFyIyIiIuLkRkREZBzsMpBjQkBERMbB2Q7FmBAQEZGBWL5aVPdhPhxDQERERGwhICIiA2GXgRhbCIiIyDjC9KTCkpISpKWlwel0IjMzE3v37u1y+1dffRWjRo2C0+nEddddh507dwb8/L777oPFYglYJk+eHPyJBcEULQTWNnmsdLCpRSHD9MlnMEbzEHk9tDdWIT8Uvt42hVp1TeHutTXKr9OJEwniWPc5lyxQYdSzr1F+oSIuhOd5Gk1JsvfnQoL8fRU/cwRAW3R4pokOx3MIRNMfK3wG9wfbt29HQUEBSktLkZmZieLiYuTk5ODgwYMYOrT9c0B2796NmTNnoqioCP/+7/+OrVu3Ytq0adi3bx++/e1v+7ebPHkyfvOb3/j/3dNz/LCFgIiIjCMM0x+vX78eeXl5yM3NxTXXXIPS0lJER0dj06ZNHW7/q1/9CpMnT8YvfvELjB49GqtWrcL111+PF154IWA7h8OBpKQk/zJw4EClS3MpTAiIiMgwLs52qLoAQENDQ8Di8XjaHc/r9aKqqgrZ2dn+dVarFdnZ2aisrOzwHCsrKwO2B4CcnJx221dUVGDo0KG4+uqr8bOf/QxffPFFaC5SJ5gQEBERdSA1NRVxcXH+paioqN02Z8+ehc/n88/nc1FiYiLcbneH+3W73ZfcfvLkyXjllVdQXl6Op59+Gn/961/x3e9+Fz6fsJ+oG0wxhoCIiEwihFUGNTU1cLm+HvfT0334/2rGjBn+/7/uuuswZswYXHHFFaioqMAdd9zRI8dkCwERERlHCMcQuFyugKWjhGDw4MGw2Wyora0NWF9bW4ukpKQOTzEpKSmo7QFgxIgRGDx4MA4dOiS8MJfGhICIiEjIbrcjIyMD5eXl/nWapqG8vBxZWVkdxmRlZQVsDwC7du3qdHsA+Pzzz/HFF18gOTk5hGcfyBRdBhaFqTfD8QBLlTIkTTa7LgCgNbb3X61KqZdS2aFCOZ2lVT51MqwKsUKRCl2O1lZ5rMp93JIge38kJXFfB8tDVe5jXV4pCYsmO2mV91XE13ufLRZdrewbgrLxgoICzJ07F+PHj8eECRNQXFyMpqYm5ObmAgDmzJmDYcOG+ccgPPjgg5g0aRKeffZZ3HXXXdi2bRvef/99bNiwAQDQ2NiIlStX4u6770ZSUhIOHz6MRx99FCNHjkROTo7ai+uCKRICIiIyiTA8qXD69Ok4c+YMli9fDrfbjfT0dJSVlfkHDh4/fhxW69dZ40033YStW7di6dKlWLJkCa688kq8/vrr/mcQ2Gw2/N///R9++9vfoq6uDikpKbjzzjuxatWqHh3HwISAiIiMQ/AcgQ73EaT8/Hzk5+d3+LOKiop26+655x7cc889HW4fFRWFt956K+hzUMUxBERERMQWAiIiMhBObiTGhICIiIyDCYEYuwyIiIiILQRERGQgbCEQM0VCoFSbHAYqdckqsWZibZWPQu71Gu5+ivdxLxB+canW6Qd9vN78DA5TlYERsMuAiIiIzNFCQERE5hCOJxUaBRMCIiIyDo4hEGOXARERETEhICIiInYZEBGRgVhCMAbAnDUGZkkIwvHuqtyQCrEq5T3hKM9UmjZWJTZCfpGVyunCcC8q3RMK09ZaFKZdlsaqfBEovTdhmjq537Tx9uZ9z7JDsf5yOxEREVEPMkcLARERmQOrDMSYEBARkXEwIRBjlwERERGxhYCIiIyDTyqUY0JARETGwS4DMVMkBJpNpY5PFmZtU5hNzysORUSLPFbluFKaXR7bOkAe26ZwXF+MvI5PjxTGKpRBWTzynsGIJnEoIlvk5xzRLIuztokPqVQa53PIY9uc8lhNWAKrK3wmSspYdatJv2H7GVMkBEREZBJsIRBjQkBERIbBMQRyrDIgIiIithAQEZGB8NHFYkwIiIjIODiGQIwJARERGQbHEMhxDAERERGZo4VAV3iV0q4klalfbR55rL1OntraG+X19dKM2hMrz0nbnPJ+Pt8A+WuNHHJBHBsXI4v1afLXWt8gf2CD1iovsLd55OfsPCe7oSKb5e+rFiE/X69LHqvbFO5jp+w6aQqfiaLnEPTmNw27DMRMkRAQEZFJhKDLwKwJAbsMiIiIiC0ERERkIOwyEGNCQERExsGEQIxdBkRERIpKSkqQlpYGp9OJzMxM7N27t8vtX331VYwaNQpOpxPXXXcddu7cGfBzXdexfPlyJCcnIyoqCtnZ2fjss8969DUwISAiIsO4+BwC1SUY27dvR0FBAQoLC7Fv3z6MHTsWOTk5OH36dIfb7969GzNnzsS8efOwf/9+TJs2DdOmTcOHH37o32bt2rV47rnnUFpaij179mDAgAHIyclBS4vClLaXYNF1vc83jjQ0NCAuLg5XLFoNmzP4uUJbYxReonDazohGea4VVSsORexJ+fyvzjMK8x8Lb6OWofKyti9Hynu8GkfLX+vVw0+JY6+LPymK8yjUiVWdTRXHnvpsiDg27oBwbl4A8UdaRXH2OnnNruaQn29zovw+bhwm/6y4MERYduhQmP64NfgySa2lBccKH0d9fT1cLpf42F3xf08skX1P/CtfSwsOr17S7fPNzMzEDTfcgBdeeAEAoGkaUlNT8cADD2DRokXttp8+fTqamprw5ptv+tfdeOONSE9PR2lpKXRdR0pKCh5++GE88sgjAID6+nokJiZi8+bNmDFjhtLr6wxbCIiIiDrQ0NAQsHg87RNOr9eLqqoqZGdn+9dZrVZkZ2ejsrKyw/1WVlYGbA8AOTk5/u2PHj0Kt9sdsE1cXBwyMzM73WcoMCEgIiLj0EO0AEhNTUVcXJx/KSoqane4s2fPwufzITExMWB9YmIi3G53h6fodru73P7if4PZZyiwyoCIiAwjlHMZ1NTUBHQZOBzyrqH+gC0ERERkLCFoHQAAl8sVsHSUEAwePBg2mw21tYGDv2pra5GUlNTh6SUlJXW5/cX/BrPPUGBCQEREJGS325GRkYHy8nL/Ok3TUF5ejqysrA5jsrKyArYHgF27dvm3Hz58OJKSkgK2aWhowJ49ezrdZyiwy4CIiIwjDA8mKigowNy5czF+/HhMmDABxcXFaGpqQm5uLgBgzpw5GDZsmH8MwoMPPohJkybh2WefxV133YVt27bh/fffx4YNGwAAFosFDz30EJ588klceeWVGD58OJYtW4aUlBRMmzZN8cV1jgkBEREZRijHEHTX9OnTcebMGSxfvhxutxvp6ekoKyvzDwo8fvw4rNavG+RvuukmbN26FUuXLsWSJUtw5ZVX4vXXX8e3v/1t/zaPPvoompqaMH/+fNTV1WHixIkoKyuDU7GksiumSAh0m/zu0KWlyQp3pE3hcQCOL2X12wAQeapOfmBNNu2sHjlIfEirT3772qLkz2uQPksAAP7N9WE3tmqvWZcPZjrnlU9/fDJysDjW5pH/Dji+kD18xeb+UnxMPVr+QRsZLb8Xra0KPbfCmZO1SPl7YxVMxS3+HO1H8vPzkZ+f3+HPKioq2q275557cM8993S6P4vFgieeeAJPPPFESM+zK6ZICIiIyCQ4l4EYEwIiIjKMcHQZGAWrDIiIiEiWEAQ7q9NF27Ztg8Vi6dFRkkREZGIhfFKh2QSdEAQ7q9NFx44dwyOPPIJbbrlF5XyJiIg6x4RALOiEYP369cjLy0Nubi6uueYalJaWIjo6Gps2beo0xufzYdasWVi5ciVGjBhxyWN4PJ52k0oQERFRzwlqUOHFWZ0WL17sX3epWZ0A4IknnsDQoUMxb948/O///u8lj1NUVISVK1cGc2pdUip5EZcsCuuBAFjllYOwNcprFvVzCiVbPlnZoW1QrPiYFl+UONZul5cdXqkwP/X1Dllp53lN/ifL3qhz4ti/RcjeVwCwtsl/8az1zaI47cxZ8TEtsfJ7MTJJ5T6Wj+3WpR8zKp+JkinhhdPIS3BQoVxQLQSSWZ3effddvPzyy9i4cWO3j7N48WLU19f7l5qammBOk4iIzIpdBmI9WnZ4/vx5zJ49Gxs3bsTgwd1/wInD4TD8rFJERNQD+BwCsaASgmBndTp8+DCOHTuGqVOn+tdpXz3RLiIiAgcPHsQVV1whP3siIiIKiaC6DIKd1WnUqFH44IMPUF1d7V++973v4bbbbkN1dTVSU1ND8yqIiIj+ZQyB6mJGQXcZBDOrk9PpDJisAQDi4+MBoN16IiIiZewyEAs6IQh2ViciIiLq+0SDCoOd1elfbd68WXJINSrtP/LqQfkh5ZVesHh84lit6YI4VvfJjmtVOF+VLN5mk1/kQbZGcexgm2zmwWiLvJw0LkL+vqo83FzpPvbKam99LbJZEgHAGhkpjrV4FV5sGP4a1RXKAHWLYLbDXvwcZdmhHCc3IiIi42CXgRjb9omIiIgtBEREZCBsIRBjQkBERIZhCcHQrzAMHesT2GVAREREbCEgIiIDYZeBGBMCIiIyDJYdyjEhMJowdQJZrMJet37YaeULQw+jBoU6d+oWi6C+/uvgUJ4JKWELgVg//DgmIiKiUGMLARERGYtJ/8JXxYSAiIgMg2MI5NhlQERERGwhICIiA+GgQjEmBEREZBjsMpAzR0KgMvem3vt3hq7QkaPb5W+pdUCU/Lg+WVmcyvmqlHq1tdnEsefaYsSxp30nRXHnNfl9WN8mf19Vqh2V7mOHXRRnjY6WHzTKKQ7VI1XmiZaHig+pyQ8q+bI06xdsf2OOhICIiMyBXQZiTAiIiMgw2GUgxyoDIiIiYkJAREQGoodo6SHnzp3DrFmz4HK5EB8fj3nz5qGxsbHLmJaWFixYsACDBg1CTEwM7r77btTW1gZsY7FY2i3btm0L6tyYEBARkXH08YRg1qxZ+Oijj7Br1y68+eabeOeddzB//vwuYxYuXIg//vGPePXVV/HXv/4VJ0+exH/8x3+02+43v/kNTp065V+mTZsW1LlxDAERERlGKMcQNDQ0BKx3OBxwOBzi/R44cABlZWX4+9//jvHjxwMAnn/+eUyZMgXr1q1DSkpKu5j6+nq8/PLL2Lp1K26//Xbgqy/+0aNH47333sONN97o3zY+Ph5JSUni82MLARERUQdSU1MRFxfnX4qKipT2V1lZifj4eH8yAADZ2dmwWq3Ys2dPhzFVVVVobW1Fdna2f92oUaNw2WWXobKyMmDbBQsWYPDgwZgwYQI2bdoEPciyeVO0EFhUaqnDUCOsKbwrPqc82BbnEsdahM8h8EZFio+pUufe1ip/DsHn3gRx7CetA0RxzZr8r5Jaj/x9hU/+C6ByH2sxstercg/rCs/haHPK7yfdpvBMAGnbtsps2pJD9uao/RCWHdbU1MDl+vqeUmkdAAC3242hQ4cGrIuIiEBCQgLcbnenMXa7HfHx8QHrExMTA2KeeOIJ3H777YiOjsaf/vQn3H///WhsbMTPf/7zbp+fKRICIiIyB4uuw6L4QLmL8S6XKyAh6MyiRYvw9NNPd7nNgQMHlM7pUpYtW+b//3HjxqGpqQnPPPMMEwIiIqLe8vDDD+O+++7rcpsRI0YgKSkJp0+fDljf1taGc+fOddr3n5SUBK/Xi7q6uoBWgtra2i7HC2RmZmLVqlXweDzdbtlgQkBERMYRhicVDhkyBEOGDLnkdllZWairq0NVVRUyMjIAAG+//TY0TUNmZmaHMRkZGYiMjER5eTnuvvtuAMDBgwdx/PhxZGVldXqs6upqDBw4MKhuDiYERERkGH35SYWjR4/G5MmTkZeXh9LSUrS2tiI/Px8zZszwVxicOHECd9xxB1555RVMmDABcXFxmDdvHgoKCpCQkACXy4UHHngAWVlZ/gqDP/7xj6itrcWNN94Ip9OJXbt2YfXq1XjkkUeCOj8mBERERL1ky5YtyM/Pxx133AGr1Yq7774bzz33nP/nra2tOHjwIJqbm/3rfvnLX/q39Xg8yMnJwYsvvuj/eWRkJEpKSrBw4ULouo6RI0di/fr1yMvLC+rcmBAQEZFx9PHJjRISErB169ZOf56WltauXNDpdKKkpAQlJSUdxkyePBmTJ09WPjdTJAQWn1K0LEyhXlGPkN+NbTHyMr6IePm0vtJpotsGyG9BXV7pBV+rvGbxWPMgcez/F3m5KK5Fl1+nUxfCU3aocMpoi5WVd1kT4sTH1BRKYH1R8vtJU7iPpeWDFoX3VTJ1ssp0y0Efqw93GfR1fDARERERmaOFgIiITKKPdxn0ZUwIiIjIMNhlIMeEgIiIjIMtBGIcQ0BERERsISAiImMxa5O/KlMkBGolL71/Z2mR8vNtHSBv9LElyGd7s2iy69QaIz9fldkOdY/CbIeN8d3YqmNRtm+J4jwKtWlnmuTlpBaF8kyl+zhG9tFk8UWLj+lzyF9rq0LZoUr5rPSLz9ImP6ao1FFldsVg6bq4DDpgHybELgMiIiIyRwsBERGZA6sM5JgQEBGRcbDKQIxdBkRERMQWAiIiMg6L9s9FdR9mxISAiIiMg10GYuwyICIiInO0ECg1/yhMYyylyWdhRWu0PMez+hRuB2FGHbb6bYX6+rONA+THFQ5f9mny861vdIpjrQr16prC7eSNFb5ei8IUxnb573pblMI00SrTH0ufQ6Ay/bHgmL05ap9VBnKmSAiIiMgk+GAiMSYERERkGGwhkOMYAiIiImILARERGQirDMSYEBARkWGwy0COXQZERERkkhYChbJDi7A6RyXDVCnXapPPYAyLynzCQirnq1IRavHKg5sbHeLY08L7QlMoO2xttotjI9rk10nlPm6Nlh1Xt6qUsSqUHTr7WdmhymeiILZXn/zHKgMxcyQERERkCuwykGOXAREREbGFgIiIDIRVBmJMCIiIyDDYZSDHLgMiIiJiCwERERmIpv9zUd2HCTEhICIi4+AYAjFTJARKNbC9P/uxUl2yT6EeWum1Cn+BVM5X5bEJ1laFmvNm+a/NBU1YXy+MAwC0yC+Uyu+OrvDpIr0vVH53VO4nTf6oB7XnaUi/uFQ+EyWxvfgcAksIxgCE4WO/T+AYAiIiol5y7tw5zJo1Cy6XC/Hx8Zg3bx4aGxu7jNmwYQNuvfVWuFwuWCwW1NXVhWS/38SEgIiIjOPikwpVlx4ya9YsfPTRR9i1axfefPNNvPPOO5g/f36XMc3NzZg8eTKWLFkS0v1+kym6DIiIyBz6ctnhgQMHUFZWhr///e8YP348AOD555/HlClTsG7dOqSkpHQY99BDDwEAKioqQrrfbxK1EJSUlCAtLQ1OpxOZmZnYu3dvp9tu3LgRt9xyCwYOHIiBAwciOzu7y+2JiIj6goaGhoDF4/Eo7a+yshLx8fH+L20AyM7OhtVqxZ49e8K+36ATgu3bt6OgoACFhYXYt28fxo4di5ycHJw+fbrD7SsqKjBz5kz85S9/QWVlJVJTU3HnnXfixIkTwR6aiIioa3qIFgCpqamIi4vzL0VFRUqn5na7MXTo0IB1ERERSEhIgNvtDvt+g04I1q9fj7y8POTm5uKaa65BaWkpoqOjsWnTpg6337JlC+6//36kp6dj1KhReOmll6BpGsrLyzs9hsfjaZeZERERXYpF10OyAEBNTQ3q6+v9y+LFizs85qJFi2CxWLpcPvnkk16+EsELagyB1+tFVVVVwEWxWq3Izs5GZWVlt/bR3NyM1tZWJCQkdLpNUVERVq5cGcypdU2lPygM9agq5U8+pfKn3i+20SIVglXKtXzyWKtCGZ8mLL+yKNSmWTwKsSplhyr3sXCGaZUpjJXKDhXuY6VZx6X3hcKDd0T96/20rt/lcsHlcl1yu4cffhj33Xdfl9uMGDECSUlJ7VrT29racO7cOSQlJYnPM1T7DSohOHv2LHw+HxITEwPWJyYmdjv7eeyxx5CSkoLs7OxOt1m8eDEKCgr8/25oaEBqamowp0pERGakheC5B0HGDxkyBEOGDLnkdllZWairq0NVVRUyMjIAAG+//TY0TUNmZqb0bEO2314tO1yzZg22bduG1157DU6ns9PtHA6HPzPrboZGREQUyi6DUBs9ejQmT56MvLw87N27F3/729+Qn5+PGTNm+CsBTpw4gVGjRgUMvne73aiursahQ4cAAB988AGqq6tx7ty5bu+3O4JKCAYPHgybzYba2tqA9bW1tZdslli3bh3WrFmDP/3pTxgzZkwwhyUiIjKELVu2YNSoUbjjjjswZcoUTJw4ERs2bPD/vLW1FQcPHkRzc7N/XWlpKcaNG4e8vDwAwHe+8x2MGzcOb7zxRrf32x1BdRnY7XZkZGSgvLwc06ZNAwD/AMH8/PxO49auXYunnnoKb731VkBZBBERUUj18bkMEhISsHXr1k5/npaWBv0bLRQrVqzAihUrlPbbHUE/mKigoABz587F+PHjMWHCBBQXF6OpqQm5ubkAgDlz5mDYsGH+8oynn34ay5cvx9atW5GWluYvgYiJiUFMTIzSyRMREQUIxZMGe/BJhX1Z0AnB9OnTcebMGSxfvhxutxvp6ekoKyvzDzQ8fvw4rNaveyJ+/etfw+v14oc//GHAfgoLCy+Z8RAREQWjLz+psK8TPbo4Pz+/0y6Cbz5a8dixY7IzCyGLykxxYbgzVMqQVGaYU7lMUiqz0ymVHSqMQrZ6Fcr4fL0/fYhFYWZHldHa4SjjC9f9pKkcV+E6iT+elEqxBRdKZUpH6jWcy4CIiIyDXQZiTAiIiMgwLJpaCyAUWxD7M05/TERERGwhICIiA2GXgRgTAiIiMo4+/hyCvoxdBkRERMQWAiIiMo5QzEXQU3MZ9HVMCC4lHPWzYaqHDsPsx2rPXAjT9Mcq10npmRjSY6q8VqVneMhDpc8TUJpKWEG47uNwHFTy7INefZwLxxCIscuAiIiI2EJAREQGoqs9YdO/DxNiQkBERIbBMQRyTAiIiMg49BCMATBnPsAxBERERMQWAiIiMhJWGYiZIiEIy9zWCsdUKqfrb+VPKsdUiVUpu/IpvLnhmDRF4XRVfneU7qf+1nYZrvtYekgjf99pIbimnNyIiIiIzMoULQRERGQOrDKQY0JARETGwTEEYuwyICIiIrYQEBGRgbCFQIwJARERGQcTAjF2GRARERFbCPoklWljw3Tc/kapDltpSuAw/OURlgdMKN7H/FOFpPgcAjEmBEREZBgsO5RjQkBERMbBMQRibJgjIiIithAQEZGBaLr6WB3NnC0ETAiIiMg42GUgxi4DIiIiMklCoCss/Y3FREu4LrGusGiW3l8Uzjd8F9lEi4pwfK71+c9S/etWAunSgyd87tw5zJo1Cy6XC/Hx8Zg3bx4aGxu7jNmwYQNuvfVWuFwuWCwW1NXVtdsmLS0NFoslYFmzZk1Q52aOhICIiMxBNRkIRZdDF2bNmoWPPvoIu3btwptvvol33nkH8+fP7zKmubkZkydPxpIlS7rc7oknnsCpU6f8ywMPPBDUuXEMARERUS84cOAAysrK8Pe//x3jx48HADz//POYMmUK1q1bh5SUlA7jHnroIQBARUVFl/uPjY1FUlKS+PzYQkBERMah6aFZADQ0NAQsHo9H6dQqKysRHx/vTwYAIDs7G1arFXv27FF+6WvWrMGgQYMwbtw4PPPMM2hrawsqni0ERERkHLr2z0V1HwBSU1MDVhcWFmLFihXi3brdbgwdOjRgXUREBBISEuB2u8X7BYCf//znuP7665GQkIDdu3dj8eLFOHXqFNavX9/tfTAhICIi6kBNTQ1cLpf/3w6Ho8PtFi1ahKeffrrLfR04cCDk5/evCgoK/P8/ZswY2O12/OQnP0FRUVGn5/1NTAiIiMg4QvgcApfLFZAQdObhhx/Gfffd1+U2I0aMQFJSEk6fPh2wvq2tDefOnVPq++9IZmYm2tracOzYMVx99dXdimFCcCn9sfSQLk3lfTXRrJC8/w3KyO+rFoKywSCfVDhkyBAMGTLkkttlZWWhrq4OVVVVyMjIAAC8/fbb0DQNmZmZ4tPtSHV1NaxWa7suiq4wISAiIuPow08qHD16NCZPnoy8vDyUlpaitbUV+fn5mDFjhr/C4MSJE7jjjjvwyiuvYMKECcBXYw/cbjcOHToEAPjggw8QGxuLyy67DAkJCaisrMSePXtw2223ITY2FpWVlVi4cCH+8z//EwMHDuz2+bHKgIiIqJds2bIFo0aNwh133IEpU6Zg4sSJ2LBhg//nra2tOHjwIJqbm/3rSktLMW7cOOTl5QEAvvOd72DcuHF44403gK/GNmzbtg2TJk3Ctddei6eeegoLFy4M2G93WHS97z+0uaGhAXFxcbhi0WrYnM5wnw6ZHbsMiILia2nB4TVLUF9f360+eYmL3xPZyT9BhNWutK82zYs/n/p/PXq+fRG7DIiIyDj6cJdBX8cuAyIiImILARERGYimAVB8MJGmGN9PMSEgIiLjYJeBGBMComCZ87OCiAyOCQERERkHWwjEmBAQEZFxhOFJhUbBKgMiIiJiCwERERmHrmvQFac/Vo3vr5gQEBGRcei6epM/xxAQERH1c3oIxhCYNCHgGAIiIiJiCwERERmIpgEWxTEAHENARETUz7HLQIxdBkRERMQWAiIiMg5d06Ardhmw7JCIiKi/Y5eBGLsMiIiIiC0ERERkIJoOWNhCIMGEgIiIjEPXAaiWHZozIWCXAREREbGFgIiIjEPXdOiKXQa6SVsImBAQEZFx6FoIugzMWXYo6jIoKSlBWloanE4nMjMzsXfv3i63f/XVVzFq1Cg4nU5cd9112Llzp/R8iYiIOqVrekgWMwo6Idi+fTsKCgpQWFiIffv2YezYscjJycHp06c73H737t2YOXMm5s2bh/3792PatGmYNm0aPvzww1CcPxEREYWARQ+ysyQzMxM33HADXnjhBQCApmlITU3FAw88gEWLFrXbfvr06WhqasKbb77pX3fjjTciPT0dpaWlHR7D4/HA4/H4/11fX4/LLrsMwxcuh9XhDOZ0iYgozDRPC47+8gnU1dUhLi6uR47R0NCAuLg4TMQURCBSaV9taMW72In6+nq4XK6QnWOfpwfB4/HoNptNf+211wLWz5kzR//e977XYUxqaqr+y1/+MmDd8uXL9TFjxnR6nMLCwouPmuLChQsXLgZZDh8+HMxXTlAuXLigJyUlhexck5KS9AsXLvTY+fZFQQ0qPHv2LHw+HxITEwPWJyYm4pNPPukwxu12d7i92+3u9DiLFy9GQUGB/991dXW4/PLLcfz48R7LLo2goaEBqampqKmpMVdWGyRep0vjNeoeXqfuudjKm5CQ0GPHcDqdOHr0KLxeb0j2Z7fb4XSaq0W6T1YZOBwOOByOduvj4uL4S9cNLpeL16kbeJ0ujdeoe3idusdq7dlH3zidTtN9iYdSUO/O4MGDYbPZUFtbG7C+trYWSUlJHcYkJSUFtT0RERH1vqASArvdjoyMDJSXl/vXaZqG8vJyZGVldRiTlZUVsD0A7Nq1q9PtiYiIqPcF3WVQUFCAuXPnYvz48ZgwYQKKi4vR1NSE3NxcAMCcOXMwbNgwFBUVAQAefPBBTJo0Cc8++yzuuusubNu2De+//z42bNjQ7WM6HA4UFhZ22I1AX+N16h5ep0vjNeoeXqfu4XXqH4IuOwSAF154Ac888wzcbjfS09Px3HPPITMzEwBw6623Ii0tDZs3b/Zv/+qrr2Lp0qU4duwYrrzySqxduxZTpkwJ7SshIiIiMVFCQERERMbC2Q6JiIiICQERERExISAiIiImBERERIS+lBBwSuXuCeY6bdy4EbfccgsGDhyIgQMHIjs7+5LX1QiCvZcu2rZtGywWC6ZNm9bj59gXBHud6urqsGDBAiQnJ8PhcOCqq64yxe9dsNepuLgYV199NaKiopCamoqFCxeipaWl1843HN555x1MnToVKSkpsFgseP311y8ZU1FRgeuvvx4OhwMjR44MqEyjMAn3ZAq6ruvbtm3T7Xa7vmnTJv2jjz7S8/Ly9Pj4eL22trbD7f/2t7/pNptNX7t2rf7xxx/rS5cu1SMjI/UPPvig18+9NwV7ne699169pKRE379/v37gwAH9vvvu0+Pi4vTPP/+818+9twR7jS46evSoPmzYMP2WW27Rv//97/fa+YZLsNfJ4/Ho48eP16dMmaK/++67+tGjR/WKigq9urq618+9NwV7nbZs2aI7HA59y5Yt+tGjR/W33npLT05O1hcuXNjr596bdu7cqT/++OP6H/7wBx1AuwnwvunIkSN6dHS0XlBQoH/88cf6888/r9tsNr2srKzXzpna6xMJwYQJE/QFCxb4/+3z+fSUlBS9qKiow+1/9KMf6XfddVfAuszMTP0nP/lJj59rOAV7nb6pra1Nj42N1X/729/24FmGl+QatbW16TfddJP+0ksv6XPnzjVFQhDsdfr1r3+tjxgxQvd6vb14luEX7HVasGCBfvvttwesKygo0G+++eYeP9e+ojsJwaOPPqpfe+21AeumT5+u5+Tk9PDZUVfC3mXg9XpRVVWF7Oxs/zqr1Yrs7GxUVlZ2GFNZWRmwPQDk5OR0ur0RSK7TNzU3N6O1tbVHZxwLJ+k1euKJJzB06FDMmzevl840vCTX6Y033kBWVhYWLFiAxMREfPvb38bq1avh8/l68cx7l+Q63XTTTaiqqvJ3Kxw5cgQ7d+7kg9i+wYyf4f1B2Gc77K0plfs7yXX6psceewwpKSntfhGNQnKN3n33Xbz88suorq7upbMMP8l1OnLkCN5++23MmjULO3fuxKFDh3D//fejtbUVhYWFvXTmvUtyne69916cPXsWEydOhK7raGtrw09/+lMsWbKkl866f+jsM7yhoQEXLlxAVFRU2M7NzMLeQkC9Y82aNdi2bRtee+01Tg/6lfPnz2P27NnYuHEjBg8eHO7T6dM0TcPQoUOxYcMGZGRkYPr06Xj88cdRWloa7lPrUyoqKrB69Wq8+OKL2LdvH/7whz9gx44dWLVqVbhPjeiSwt5CwCmVu0dynS5at24d1qxZgz//+c8YM2ZMD59p+AR7jQ4fPoxjx45h6tSp/nWapgEAIiIicPDgQVxxxRW9cOa9S3IvJScnIzIyEjabzb9u9OjRcLvd8Hq9sNvtPX7evU1ynZYtW4bZs2fjxz/+MQDguuuuQ1NTE+bPn4/HH38cViv/BkMXn+Eul4utA2EU9ruTUyp3j+Q6AcDatWuxatUqlJWVYfz48b10tuER7DUaNWoUPvjgA1RXV/uX733ve7jttttQXV2N1NTUXn4FvUNyL9188804dOiQP2ECgE8//RTJycmGTAYgvE7Nzc3tvvQvJlGcNuZrZvwM7xfCPapR/6q0x+Fw6Js3b9Y//vhjff78+Xp8fLzudrt1Xdf12bNn64sWLfJv/7e//U2PiIjQ161bpx84cEAvLCw0TdlhMNdpzZo1ut1u13//+9/rp06d8i/nz58P46voWcFeo28yS5VBsNfp+PHjemxsrJ6fn68fPHhQf/PNN/WhQ4fqTz75ZBhfRc8L9joVFhbqsbGx+n//93/rR44c0f/0pz/pV1xxhf6jH/0ojK+i550/f17fv3+/vn//fh2Avn79en3//v36P/7xD13XdX3RokX67Nmz/dtfLDv8xS9+oR84cEAvKSlh2WEf0CcSAl3X9eeff16/7LLLdLvdrk+YMEF/7733/D+bNGmSPnfu3IDtf/e73+lXXXWVbrfb9WuvvVbfsWNHGM669wVznS6//HIdQLulsLAwTGffO4K9l/6VWRICXXCddu/erWdmZuoOh0MfMWKE/tRTT+ltbW1hOPPeFcx1am1t1VesWKFfccUVutPp1FNTU/X7779f//LLL8N09r3jL3/5S4efNRevzdy5c/VJkya1i0lPT9ftdrs+YsQI/Te/+U2Yzp4u4vTHREREFP4xBERERBR+TAiIiIiICQERERExISAiIiImBERERAQmBERERAQmBERERAQmBERERAQmBERERAQmBERERAQmBERERAQA/z+kH3yLx1tYewAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(coarse.T, extent=(0, 1, 0, 1), origin=\"lower\")\n",
+    "fig.colorbar(im, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now use interpolation to try to recover the look of the original data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "This is considered structured grid interpolation, and SciPy has the [RegularGridInterpolator()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.RegularGridInterpolator.html) class for this type of data.\n",
+    "\n",
+    "If the data were unstructured, then you should explore [griddata()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html) instead.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import interpolate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_coarse = np.linspace(0, 1, nx//4)\n",
+    "y_coarse = np.linspace(0, 1, ny//4)\n",
+    "\n",
+    "interp = interpolate.RegularGridInterpolator((x_coarse, y_coarse), coarse, method=\"cubic\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `interp()` is a function that we can use to sample the coarsened data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now interpolate it onto the original grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 200)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data = interp((x, y))\n",
+    "new_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f04cdfe8e10>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(new_data.T, extent=(0, 1, 0, 1), origin=\"lower\")\n",
+    "fig.colorbar(im, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and let's plot the difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f04cdea8910>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff = new_data - data\n",
+    "fig, ax = plt.subplots()\n",
+    "im = ax.imshow(diff.T, origin=\"lower\", extent=(0, 1, 0, 1))\n",
+    "fig.colorbar(im, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Root Finding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often we need to find a value of a variable that zeros a function -- this is _root finding_.  Sometimes, this is a multidimensional problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [brentq()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brentq.html) method offers a very robust method for find roots from a scalar function.  You do need to provide an interval that bounds the root."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "It's a good idea to plot the function, if you can, so you can learn how the function behaves\n",
+    "in the vicinity of a root (and how many roots there might be)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's consider:\n",
+    "\n",
+    "$f(x) = \\frac{x e^x}{e^x - 1} - 5$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.optimize as optimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "    return (x*np.exp(x)/(np.exp(x) - 1.0) - 5.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.965114231744287\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "root, r = optimize.brentq(f, 0.1, 10.0, full_output=True)\n",
+    "\n",
+    "print(root)\n",
+    "print(r.converged)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0.1, 10.0, 1000)\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, f(x))\n",
+    "ax.scatter(np.array([root]), np.array([f(root)]))\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ODEs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many methods exist for integrating ordinary differential equations.  Most will want you to write your ODEs as a system of first order equations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [Lorenz system](https://en.wikipedia.org/wiki/Lorenz_system) is a very simple\n",
+    "model of convection in our atmosphere, but demonstrates the idea of chaos well.\n",
+    "\n",
+    "This system of ODEs for the Lorenz system is:\n",
+    "\n",
+    "$$\\frac{dx}{dt} = \\sigma (y - x)$$\n",
+    "$$\\frac{dy}{dt} = rx - y - xz$$\n",
+    "$$\\frac{dz}{dt} = xy - bz$$\n",
+    "\n",
+    "the steady states of this system correspond to:\n",
+    "\n",
+    "$${\\bf f}({\\bf x}) = \n",
+    "\\left (\n",
+    "\\begin{array}{c}\n",
+    "\\sigma (y -x) \\\\\n",
+    "rx - y -xz \\\\\n",
+    "xy - bz\n",
+    "\\end{array}\n",
+    "\\right )\n",
+    "= 0$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# system parameters\n",
+    "sigma = 10.0\n",
+    "b = 8./3.\n",
+    "r = 28.0\n",
+    "\n",
+    "def rhs(t, x):\n",
+    "    xdot = sigma*(x[1] - x[0])\n",
+    "    ydot = r*x[0] - x[1] - x[0]*x[2]\n",
+    "    zdot = x[0]*x[1] - b*x[2]\n",
+    "\n",
+    "    return np.array([xdot, ydot, zdot])\n",
+    "\n",
+    "def jac(t, x):\n",
+    "\n",
+    "    return np.array(\n",
+    "        [ [-sigma, sigma, 0.0], \n",
+    "          [r - x[2], -1.0, -x[0]],\n",
+    "          [x[1], x[0], -b] ])\n",
+    "\n",
+    "def f(x):\n",
+    "    return rhs(0.,x), jac(0.,x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SciPy has a uniform interface to the different ODE solvers, [solve_ivp()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html)---we use that here.\n",
+    "\n",
+    "These integrators will do error estimation along the way and adapt the stepsize to ensure that the accuracy\n",
+    "you request is met."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ode_integrate(X0, dt, tmax):\n",
+    "    \"\"\" integrate using the VODE method, storing the solution each dt \"\"\"\n",
+    "\n",
+    "    r = integrate.solve_ivp(rhs, (0.0, tmax), X0,\n",
+    "                            method=\"RK45\", dense_output=True)\n",
+    "\n",
+    "    # get the solution at intermediate times\n",
+    "    ts = np.arange(0.0, tmax, dt)\n",
+    "    \n",
+    "    Xs = r.sol(ts)\n",
+    "    return ts, Xs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{tip}\n",
+    "Execute\n",
+    "```\n",
+    "%matplotlib widget\n",
+    "```\n",
+    "in a cell before making this 3D plot and you will be able to interactively\n",
+    "rotate it in the notebook.\n",
+    "\n",
+    "You may need to install the [ipympl](https://matplotlib.org/ipympl/) package first.\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lpaWo1WqKi4unTJNPFQXPRdTkcjkxMTHExMR4ncqGh4f9zE3Cw8P9Ktk/bcymz1vcIIlDVkZHR9Hr9eh0Opqbm70OceLrGhERMW6N2VTXTybmVqvVextJzCXmC0m8JWbEVL3bviInCAJdXV3U1NT4VXWHiomifN91J7JWnei5TUSoL7gKhYL4+Hji4+MhoC2tpaUFk8lEWFgYTqeTwcHBeW9LWyrM5XWWyWRER0cTHR1NTk6ON9uh1+sZGBigsbERpVLpJ+bh4eFeq965Pu7piHlgj7kk5hKz5cS/GkiEDDHaFiPrYBc8MfJ2Op1UVVUxNDQ0b+nqYNHwfKbJ5zOdHGhuYrfbaW9vp7u7268tLT4+nri4OKKjo+e1LW2xCtZCiW+2Izc3F7fbzfDwMAaDwe/oQnTxs1gsIRuyMpGYu91ur5jL5XJcLhcqlYqwsDBJzCVmhCTeElPimyafqndbJpNht9v5+OOPiYiIYM+ePfN2lhso3mKaXKPRhHzdhZ4qJp6HDw0NsXPnTiwWCwaDAZ1ON29V1yKLeeY9n8LlWzDoe3TR2tqKyWTiwIEDaDQav6LCUL2HJhLzpqYmlEolubm53poRKTKXmA6SeEtMykzmbguCwMDAACaTiVWrVpGXlzevFx5RUAVBoLOzk9raWvLy8igoKAj5uot9AQ0PDyc8PNzbQuVbdd3e3o4gCH6iExkZueiPeTYs5GMWjy5GRkYICwtj1apV3jqEjo4OqquriYiI8EuzT3fIylQEirlSqfR2brhcrnEFcL6+7Mvx9yoReiTxlpgQX4vTqSIAu91OeXk5IyMjhIeHk5+fP++PTyaT4XK5KC8vR6fTTTqBLBQsFcOUiaquAwu1ZtKWthRY7IhfqVT6DVlxOBxeMReHrERGRvoVFc7Vl913NG6wiWmiW6FvH3qgL/tS/71KzA+SeEuMI7B3eyrhHhoaory8nNjYWNasWUNDQ8OCPE6TyQRjG4fi4uJ5bbVa6LS5yHTWDNaWJs7cXuptab7Md9p8Itxud9B1VSrVuDoEsaiwubkZk8nkHbIyW6978RgqkInE3Ol04nA4/MTc15ddGn/66UESbwk/ZpImd7vdNDU10draSlFREZmZmej1+pAOl5joMXZ0dFBbWwvA1q1b5/2itZyiG7E9KjY2NmhbWk1NzZRjOhfr+S5Wodx01lWr1X72uDabzSvmvkWFvn3mUxUVTndtScwlApHEW8KLGG1PZ6CIxWKhvLwch8PBrl27iIqKggWIUJ1OJ5WVlej1ejZs2EBpaWnI7ns6Zh4LSaiELLAtbaIxnWIl+3xvviZisdPmM0Wj0fgNWRG97g0GA3V1ddhsNqKjo71CHmzIykSR91RMJeZM4P4mifmJgyTeEuN6t6cS7r6+PiorK0lJSWH16tV+F6RgJi2hYnh4mLKyMm8Vu+/jn2+WU+Q9FRON6dTr9dTV1WG1WmlsbGRkZCSkw0CmYrHS5qFa19frnrENbqAvuyjmYrvfRCn7mTKRmPuOPxXP9SUxPzGQxPtTTmDv9mQFMC6Xi7q6Orq7u1m7dq33IuXLRMYpc0EQBNrb26mvryc/P5/8/HxkMpk3wlioiG2pnnnPlcAxnfv37yc+Ph6LxTKuLS0+Ph6tVjtvF/3FEu/5eD5ih0B6evq4ISudnZ3eQlCVSkVkZGRI2/2Cibn4WRc/N6Koi54BYjW7xPJAEu9PKTPp3WbM4rSsrAy5XD6pxel0/MZngsPhoLKyEoPBwLZt24iLi/NbixAK3Hzaoy4n5HI58fHxJCYmBm1Lw8c/PJRtacstbT4Tgg1ZMZlMlJeXY7FYKCsrQxCEeZtCJ/OZmMbYc+7r68NoNFJUVDQuMvetZpdYmkji/SlkJkVpAF1dXVRXV0/L4jSUafPh4WFKS0uJjIxkz54943psQy3ek7EY4r2YF05x7Yna0nQ6HUNDQzQ1NaFQKPwq2cPCwmb12Bczbb7Q6WOZTIZWq0WlUpGbm0tiYuK4KXQymWxeNkn4vJ+VSiUqlcpvMy9G5HK5fFwBnCTmSwdJvD9lzKR329dqdNOmTd4z0snwNU6Z7QddEATa2tpoaGigoKBgQrMX8Wuh2CyIRi9Wq5WEhIRxoyU/TRetqTzexbY00T88sC1tLi5ly/nMezaIZ97B2v1EMQ/cJImCHmzIykzXFjctwSLzYGIujT9dOkji/SnB1/BhOmlysTgsLCxsWhO5RHzP2GbzwXY4HFRUVDAyMjIuTR5IqCJv39R8VFQUHR0dMJYaFiuwxQ3JQrPUU/XB2tLESnZfl7LJ2tJETuS0+WRrTzQjIHDIirhJ8h2y4ttjPlMjnskq3X3FXPy9uN1u7Ha7n/ubJOaLhyTenwJmanEqRr2+xWHTZS6CajAYKCsrQ6vVUlxcPKUVpRixzOWiPzIyQklJCZGRkezcudN7URJnb4sXSoVCgcvloqenZ8manCwFFArFOJeywLa0qKgoP9Hxjfg+jZH3dFL2vpskxopHRTHv6emhrq7Oz4gnLi5uyg232+2elkOcr40rkpgvGSTxPsFxOp1YLJZpDTmw2+1UVFQwOjo6ZdQ7EeKFyO12T3vqlSAItLa20tjYyIoVK8jNzZ32h3624i0avdTV1Xk3KWKfu8xntGRubi4ul4ve3l4aGhr8TE7EqDwUNplLjVBddAPb0mw2m19bmm8vtJiqXWgWW7xns7ZvnQFjYh44Hz4sLMzvzDxwwzmXHnMCxFz8Y7PZsNvtMEGfuSTmoUMS7xMUsXd7aGiIkpISTjvttEk/ODqdjrKyMmJjY6cV9U6Eb9p8OtjtdiorKxkZGWH79u3eyGK6zEa8fceVTscPXaFQEB0djVwuZ9u2bd5oUqfT0dTUhNls9gpQfHx8yMZ1nogXOo1GQ2pqKqmpqRDQCw1w+PBhP8EJrD2YD0LVaz0bQlUsN9l8+GDHF7GxsbMW70Bkk8wyt9lsEw5ZkSamzQ1JvE9AxHnaYlHaZBcncSxhS0sLhYWFZGVlzekDNZMiMr1eT1lZGdHR0bPeMMxUvI1GIyUlJajV6hmd5fu+JoHRpOispdfrqaqq8vZFi5H5XAToRD9nF3uhU1NT6evrY+PGjZhMJnQ6HW1tbTBPbWm+LMfIeyoC58MHHl+YTCYUCgV2u907OS1U2aPJxFycZS6TyTAajcTGxqLRaKTxp7NAEu8TCN8KUd9JSRMJqdVqpaysDLvdzs6dO4mOjp7zYxA/uJOJt2+afOXKleTk5Mz6QzsT8e7u7qaqqoqcnBxWrFgx46hjonV8nbV8+6J9Bci3lWo5TPhaLMSpXWJb2ujoqF/FtW+Rljgtba4sxYK1UBPMVU+0FhazR2Itgni2PtMhKxMxkZiXlJSwZcsWb9V84MQ0ScwnRxLvE4RAi1NfhyW32z3uAtXf309FRQXJycls3bo1ZB9UphBU8VzdaDSyY8cOYmJi5rTWdBzdXC4XNTU19PX1TbvlLZDpbhIC+6LdbrdXgPr7+2loaECtVnuj8pm2Up2oBHttfWsPfCuudTqdt0hrLm1pvmufaJH3VKjVapRKpXfT6VuL4DtkRXxdg/myzxbfolaNRuOdZe52u72RuVwuH3dmLom5P5J4nwD49m77WiISpHXL7XZTV1dHZ2cna9euJT09PeSPZyKjFjFNHhMTQ3FxcUjSdFNF+SaTidLSUq8z3GSR2lQOa7NBLpcTExNDTEyMt/hteHgYnU7nPYsUo834+Hi/iGcpmLQspXWDVVwHnuvOZt72YprD4PMZXWh8z7wDaxF8j4Jqamqw2+3jfNnnIubiZzZwJrlvZO52u7HZbFitVknMgyCJ9zJmOr3bvtXfog0jQHFxMZGRkfPyuAIFVRAEWlpaaGpqYtWqVWRnZ4fcKSoYvb29VFZWkpGRQWFh4ZwvkqE4Cw424StYxBMfH49arT7hz7wD15zJ+2KytjRx3vZkbWm+ay+GgIqfkaUg3oEEHgVZLBbva+vrdy/WI4gFndPFt201EFnAfAVRzF0uFy6Xa8ICuMCfO9GRxHuZMt3ebfHD0d3dTV1dHZmZmSERssnwTWXb7XbKy8sxmUwhSZMHEky8xexCV1cX69at80YToV4nFKhUKr8Z0VarFZ1O5x1e4XQ6KS0t9abZQ+l3vZQIxWs7k7Y0sStAfK8uZuS91HvMZT6+7OKQFV+/+87OTtxut3d4jfg+ney+JxPvYOvLgkxMEwMXmc8s80Bf9hPxsyIiifcyRDRHmI5Tmri7r6+vZ+PGjV6RmE/EyDuw/Ww+eqEDRdVisVBaWorb7Wb37t0zyi7MR9p8poSFhZGenk56ejoGg4Hy8nISEhLQ6XS0tLQgl8vHFb+dSITydZ6sLa2yshKn00lsbCw2mw2LxbLgIr6UI+/JCKzrEIesiK9tW1vblENWpnPtmmz9YGIuzjKfSMxPtPGnkngvI8TdplhNPtWbf2RkxJsm37p166xMV2aDTCajq6uL3t7ekLSfTbWWKN4DAwOUl5cHnTMeKhbyAi+e82VlZZGVleUtftPpdON8xMXIfLb9+YvNQqTqA0d0ioIzPDxMc3Mzra2tfsVvc/UOn4rlEnlPhWxsyIpWqyUrK8s7vCZwyEpgj3koC+CmK+a+Q1aWu5hL4r1MmKnFqTj/Oi8vj+bm5gVzABMdlgYHB0PWfjYZYpRfX19PW1vbnIvwJnpNfStkF+ti61v8Fugj3tbWRlVVFVqt1u8iOZsugsV4jgstZL6C09XVxcqVK1GpVBN6h4eqLc0X36Eki0GoxDsQWZAhK2LHhfjaikWtXV1d3tc2lHUwk4m5eBuHw0FUVNSyjcwl8V7iBOvdnuxNLjqWDQ8Ps3XrVuLj42lra1sQ28mhoSHKy8uRyWQUFRXNu3Az9vrU19cDsHv3brRa7byssxTPzgILtux2uzfaqa+v957xilH5TIuKFoPFeJ1FEQtsSxPtRoO1pYkFhXNddzHfV/Ml3oEEdly43W46Oztpa2vzZpDUarVfmj2UG6VgYm6329m/fz/FxcUoFAruvfdeFAoF3//+90O27nwjifcSZqLe7YkQW7GioqL85l/L5XJvxD5fj9PXpa2zs3NBLkpDQ0OYTCZiYmLYtm1bSHvVJ2KhK7Fnsp5arSYlJYWUlBTwOePV6XTeoiLxAhkfHz8vbmWzZTGnpwXLNPjWFjBmNyqK+Vza0gLXXazNlBgULMb6crmc8PBwNBoNW7Zs8bZPGgyGcRsl8f06XSfE6eB71Cb6AjQ3N09pk7zUkMR7iSJG29MpahEEgebmZpqbm4M6lk3Udx0KbDYb5eXlWCwWb5q8u7t7XiN93+cbHh5OVlbWvAu3b9p8uRB4xht4DimXy/3MYhaz+G0xz3+nI6JKpXJcW5r4WgY6lMXHx0/L1GSxPdVZxGI5l8vlfX2C+bKLG6XOzk5qamoIDw/3O8KYa9ZDXF98/U0mEzk5OSF4ZguHJN5LDN80+XQqMq1WK+Xl5Vit1glbseZLvIeGhigrKyMhIYHNmzd7BXQ6rmezRWw9M5vN7Ny5k9ra2nlZJ5DFuMiGcs1g55CBbmVhYWHExcV5+2kXmsUUspmuHdji59uWJpqa+LZOBTuyWMzIeylXugdulHyHrIi1HZGRkX5p9plmPXw3DwBms3nefC/mC0m8lxAzKUpjrLq6oqKCxMREtmzZMmH0GWoxFQSBxsZGWltbKSoqIjMz0+9xTuV6Nlt8Hdp2796NSqWat/7riVhOkfdkBLqV+V4gXS6Xd66679jT+ajeF1lsf/G5ru3bliaamkzUBx0fH49Wq13UyHspiPd0309TDVmprKyccaGmOLRJxGQyzVu9zHwhifcSwdfidDq92/X19XR0dLBmzRoyMjImve9QRt5ipG+z2di1axdRUVFB1wv1ZmGiQSYLJd6Lnd6cb3wvkH19fRQVFeF0OtHr9dTW1vpFkvHx8URFRYX0wr/Uzrzngq+pSUZGxrg+6NbWVm+vtMvlwmQyzXtbWiCLLd6B4jkTgg1ZEV/bxsZGvyMM0Zc9UMxdLpff10wmkxR5S8yMmfZum81mysrKvCYk09kthqpgbXBwkPLy8mlF+qHaLDgcDioqKiac9y1F3vODSqUiMTFxXCQperILguBXeR0K8VnOkfdkBPZBu91ujEYjnZ2dGI1GDh8+PO9taYGcSG1qgYWawZz1AsVcirwl5sRM0+Q9PT1UVVWRnp5OYWHhtNNOcxVTt9tNY2MjbW1trF69moyMjCndyEIhcsPDw5SWlhIZGTnhvO9QpujtdjsGgyFosdFiFKwtlcEkwSJJo9GITqcbN6pTTLPPtDr4RIq8p0JsS0tMTMRkMrF169ZxbWli/UGoCrQCWaxKc5HAM+dQMpGznsFg8NYjhIWF4Xa7eeutt9izZ4/XB38+uOeee/j+97/PHXfcwR/+8AcATj31VD744AO/291yyy387W9/m/b9SuK9SIjR9nSK0nxHWq5fv967w5wucxFv35nfE6XJQ7keYxfTjo4O6urqyM/PJz8/f1LzlFBc+PV6PaWlpbhcLu/5pFgBe6L6ic8W3+K3wJ7orq4uamtrx1UHT1VQtJj+4ou5tuiiF6wtTZwHLxZo+b6ec+2uWGzxDqXD2lQEdl1YLBaam5tpa2vjK1/5CmazGUEQeP7559FqtWzdujVkplaHDx/m73//Oxs2bBj3vZtuuomf/vSn3v9HRETM6L4l8V5gAnu3pxLu0dFRysrKUKlUU460nIjZiqloNzrTmd9ziYadTidVVVXodDq2bNkyZe/lXM/XBUGgra2NhoYGVqxYQWpqqnccok6no7W11XtxZWwXf6LP357p6+krPvn5+d6zct+CInFS2mSzoT9tw0EmKlibqi3NYrHMedb2Yot34JnzQiFmkbRaLbm5ubS2tlJaWspZZ51FY2MjF1xwAXa7nZNOOonbb7+d8847b9ZrGY1GrrnmGh544AF+/vOfj/t+RETEnIYmSeK9gLjdbgwGAyqVasoRdr7RZ25uLgUFBbP+sM1UvN1uNw0NDbS3t09YECcIAtU9o5jtLpKiNCRq1USqFV4no9kI6ujoKKWlpWg0GoqLi6clknOJvJ1OJ5WVlej1erZt20ZMTAx2u91v6IKvnzhASUkJYWFh3qh8NuYcM2E5nrErlcqg0710Oh01NTU4HI5xxW+L9TyXen85E7SliZPnptuWFshCRr4Trb8UNg9yuZyNGzficDh44IEHyMnJoby8nPfee2/Om/TbbruNCy64gDPPPDOoeD/xxBM8/vjjpKamctFFF/HDH/5wRtG3JN4LgG/vdklJCatWrZp0upfD4aCyshKDwTCt6HMqZiLeYprc4XCMK4hzuQVKOgy8Ud3PWzX99Azb/H42XCUnKUpDpNxFbLiCggY3iVoNSVFqkrQegU+K0hAfoUKp8P/gdnV1UV1dTW5uLitWrJj2xXS24m00GikpKfHbKAR7jXytHVtbW9m+fbt3bGdzc3NQc46lbkG60AS2UfmOk2xvbwfwtk4tdOX1Ykfes3mvaDSacbO2A9vSJpvoNZe1Q8V8nnlPd33x+ZtMJgBvB8WmTZvYtGnTnO7/6aef5tixYxw+fDjo96+++mpycnJIT0+nvLycu+66i7q6Ol588cVpryGJ9zwTWJSmUCgmFVKDwUBpaSlardbP4nQuTFe8g03lcrrcHGrV80Z1P2/XDjBotHtvH6FWkKRVM2C0Y7a7sDjctOss3u/v7+gMuo5MBvERapKi1CRGqlE6zajdVopyM3DYojG0GbxCL0bzEzEb8e7p6aGyspKcnBxWrlw5o42CXC736zkVoyCdTkdVVRVOp9OvCnsuFqRLpWAt1PcbOE5ydHSUnp4ehoeHOXz4MCqVyu81nM9jisWOvENRoT9ZW1rgRC9xWtpii/dir+9yubzvK7PZDBCyVrGOjg7uuOMO3nrrrQkLN2+++Wbvv9evX09aWhpnnHEGTU1NFBQUTGsdSbznkWC92xO1bQmCQEtLC01NTaxYsYLc3NyQXVCmEm/fNPnatWtJTE5lX5OON6r7eaduAIPZ4b1tdJiS0wuTOHtNMnsL4tGoPLtnk83JkMlO/6idioZWBox2VNp4Box2Bow2Bo12BkZtDJnsuAUYMtkZMtn9HsebHd1At9/XwlXycdF7cpSGk1cmsCYtekbi7Xa7qauro6ura9azzQPXCoyCTCaTN6XZ3NzsV4U930K0HJHJZERHR+N2uxkcHGTXrl3jrDEjIiL8bFxDeVa6mDah8yFgwdrSAid6KZVKwsPDcTgcWK3WkPqGT5elEHmL65tMJlQqVcg+m0ePHqW/v58tW7b4rffhhx/yl7/8BZvNNu6579y5E4DGxkZJvBcTsXfb6XSOqyYPFnmL/uBms3lCi9O5IJfLvQVygVgsFsrKyjDbHFgSCvndfh3v1dVhtB3fYMRFqDizKIlz1qawMzcOtXL8BSdSoyRSoyQ7PoIYeyQ2m5J161aMu53LLaA326lp7eZYdRMKbRzKyDgGTQ4/kfeN5jv0Fjr0Fr/7+f07TazPiObkdBmn5E3dn2m1WiktLcXtdlNcXDzjyk6mEZ35XjhFC1Kxarirq4uamhpv1bB4Xr4YRTtTsRjnz2IU6utzXVBQ4HXT0ul044q1QnFMsdhp8/leN3Cil8vlYmRkhPb2doxGI/v375/3trRgLPaZu694G43GkA7pOeOMM6ioqPD72g033EBRURF33XVX0OddWloKQFpa2rTXWXpXjmXOVL3bCoXCL/IWjU/i4+PZs2fPvFzMJ4q8Wzt7eOajKmqMYZT2ObE46r3fS4pSc/bqZM5ek8y27NhxZ9SzWQ9AhkB/exO23m6uOmXytjeTzekRc6OdQaONgVGPuDcPmnivfpCKrhEquuCfJSNc0iLjqm0ZFKWOb2UbGhqitLTU7zhgtsxE2AJbgHyrhhsaGrBard6RnRO5li3HgrXZEuziGeimJXYC6PV67zGF2NYXFxdHVFTUjC7Ci91fvtARv0KhIC4uDpPJhEwmY82aNfMyE34q5uKwFqr1fSPvULqrRUVFsW7dOr+vRUZGkpCQwLp162hqauLJJ5/k/PPPJyEhgfLycu68805OPvnkoC1lEyGJdwiZTu+2KGy+qerpGJ/MBV8xHbE4eLu2n38dbuFYtwWnIAOsAKTHhHH2Go9gb86MQS6f3eOZSLwtFgulpaUIgjCt6FeM5nMSxt9OZ7LzYkk3j+9vpcfo5KnDnTx1uJONmdFcuS2T89amEK6Se6ePrV69mszMzFk9H5G59pQHVg0HG9npa3TyaRLu6T7XsLAwv2MKs9nsPaYQbUd9X8Pw8PBJP1dixH+iRt6TrS2Xy8f5hotGRaLVaCja0iZbf7EIJt4L9btQq9W8/fbb/OEPf8BkMpGVlcVll13G3XffPaP7kcQ7BPj2bk9lcSqXy7FarRw8eBCXyzVti9O5MGIXeLfZzJ/LS9jfrMPpFi+UMnLiwzl7TTLnrElhXfrMopaJCCZy/f39VFRUkJqaSlFR0ZwvAPGRam7cm8veJBslXSYODCp5u2aAss4Ryjqr+eVrdexKU7I72cVFJ3lGlc6VUH+4g43s1Ol0fmeTbreb3t5e4uPjFySdKbLQojKb4i3f4rfA892+vj7q6+tRq9V+5+WB55qLPRBlqU31UqvVfhtM30yH2OYXHR09Z4/7pXbmPd++5u+//77331lZWePc1WaDJN5zxO1243Q6p21xKlYnZ2RkhETEJqJ/1MZbNf28Ud3P4VY9Hr32tERkRSu5cFMm565NoTAl9O5hvpF3YDFcenp6SNdSyOWsT1bz+dPXMWi08WJJD08f7qBr2MZbrS7eaoX/9NRx5bYMzlubQphqbq/3fEXDga5lLpeL/v5+ampqaG9vp7q6ekGnfC0Gc30fBjvfFaPIjo4OqquriYyM9HsNFzv6Xazf4XQj38BMx2za0uay/nwReOa93HzNkcR79vj2bk8n9eZyuaitrUWn0xEfH8/atWtD/pi6DBbequ7njZp+SjqG8dWZLC1sSoDLd61g15rckK/tixh5T9YzHuq1ABK1Gs7PU5JjsTIakcYHnS7erRukpGOYko5h/u+1ei7emMZV2zJYkTzzx7KQF3mFQkFMTAwymYwdO3Z4JyfpdDq/KV++5+WhenyLVbAWahQKxYROZWLNQWRkJG63G71ev+A9+ksx8p6MiTzufdvSxFGzkx1biAW9SyXyNpvNsypgXWwk8Z4FgRanUwm30WiktLQUpVLpde0KJUfbDfzy9Xoqukb8vr4xM5rTV8SRaOsiRuGguLh4QcbeyeVybDYbn3zyCYmJiTOyVp0poni7XC6qq6sZGBhg21aPsc3lYxmIF0u6efZoF10GK48d7OCxgx1szY7lc9syOHdNsl80vhADV2aD7+QkMQISz3pFoxPflrT5nko1H8z35ihYzUFPTw8mk4nKykpcLpef8My3p/1iR/1z3Tj4ZovE7grx2KK/v5+Ghga/nn1xYI34GVoq4i1F3p8SfHu3xb7tiRAEwdsilJOTw4oVK2hpacFoNIbs8exrHOK2p8uwOtzIZLAtO5az1yRz1upkZBYDlZWVxCbGem0/5xtBEOjv72d0dJS1a9eSmZk57+MW7XY7Bw4cQKFQUFxc7Ne3mhyl4daT87h5by77moZ45kgX79UPcrTdwNF2A//3Wh2XbEzjym2Z5CWET9k6tBSmivlGQIEWruJZr0aj8Qr5dAaDLDaLcfYcHh5OQkIC3d3d3slSYnZDjCJ9zWJCvSFazMh7PrzFgx1bBA6sCQsL87bCOp3OBa3jEHG73QiC4HfmLYn3CcxkvdvBcDgc3gEbmzdv9lZzTuWwNhPeqR3gjmfLcbgETlmZwC8uXkNSlMfms7a2lu7ubtatW4dcLqehoSEka06G3W6nrKyM0dFRr0nEfGM0GhkaGiI7O5vCwsIJL4ZyuYyTVyZy8spE+kZsPH+si+ePddM9bOWRAx08cqCDbTmxXLYxmbOKkrzmM74sZuQ9Gb4Xzby8PJxOp/esN9hgkNjY2ClFYzEK1hYD3yOvQHOTkZER9Ho9vb293g2RbyX7XIVnMc99F2Lj4Nuzz5hYGwwGBgcHAThw4MCCtKUF4ut2yQIVrM0HknhPg5nO3RbnUEdERLBnzx6/CteJHNZmyquVvXznhSqcboGzVyfx28vXo1bKMZvN3oZ/sR1rcHAw5Kn6QMSRmrGxsaxevZrm5uZ5XU8shOvt7SUqKorVq1dP+2dTojXcdmo+t56cx77GIZ4+0sn79YMcaTNwpM3Ar95q5uINqVy2OZW8IG1qS53A9h/fwSDV1dU4nc4FTQ9Pl6VkUSqe3cbGxno3RIFjOkXhEc1iZio8i1npvhgbB/F9GRERQU9PD3v27BlXgzAfbWmBiNdC3zNv0UdgOSGJ9xS43W7sdvu0om1BEGhtbfVa3OXl5Y27fSgi73+VdvODl6pxC3DRhlTuuWQNSoWc3t5eKisrycjI8ItC5zpfezJ8n/PKlSvJyclhcHBwXiMpm83mnTGel5fHyMjINH5qPAq5jFNWJXLKqkR6h608d7SL54510Tdq55GDnTxysJOdubF88/Q81qRFLUrkHYr1gg0GEf3YxfSwKOTx8fEnTMHadNedjogFjukUCwj1ej11dXXYbDav4c5MJnstp4K1UK/tW8dBQFuauMn0nZY227a0QJxOp9+13Gg0kp+fP+f7XWgk8Z4AMU0uVpNPJdx2u53y8nJMJhPbt28nNjY26O0CHdZmylOHO/nxf2sBuGJLOj+5aDUIbqqrq+nu7mb9+vGuZfMl3g6Hg4qKCkZGRvye83xuFsQIPz4+ni1btngHWsyV1Jgwbjs1jxt2pfNxs57nS3rZ16TjYKuBzz9UwrU7Mtgetvwdz4L1Ro+MjKDT6eju7qaurs67IUtOTg65l/hELFYUOtt1A4XHt4DQt4VKFPNgJiCLWbC2mA5nE1WaB2tLE1/Tjo6OWbelBRLYomc2m6W0+YnCTNPkQ0NDlJeXExsbS3Fx8aTFQXMRtoc+aeOeNzxn11/YmcUPzl2FxeJJk8vl8gldy+ZDTMWjAa1WS3Fxsd/533xEqIIg0NbWRkNDA6tWrSI7O9t7VhmqtWQyGUq5jFNXJnDaqkS6h6384d0WXqse4NGDXbwSBt+JGOWCOY5oncnjmW9808P5+fk4nU4++ugjAD8vcfHscjoR5WxYbuIdSHh4OBkZGeNaqERPdnFAjfgnPDx82bWKLeTagUWZE7WlBb6m0/ldOp1OP/GWCtZOEHyj7alawNxuN01NTbS2tlJUVDStyurZnHkLgsB9H7bwx3c958g3783lm2cW0NvbS1VVFZmZmaxatWqSYq3QibcgCHR0dFBXVzfh0UCoNwtOp5OKigqGh4fHZTVCvVHwfS7pMWH8+rOruWh9Cj9/vYHuYRvfe62d99ss3HVWAYnaha+UnW+USiUymYy8vDwiIiK8s8v1ej0VFRV+EWV8fPyCzt6eD+Zj0xCshUrMbvT09FBXV0dYWBhOp5ORkRHi4+MXvBtgMcV7Nj3eE7Wl+XZYqNXqaY2SDYy8JfFe5sy0d1ucxuV0Otm1axdRUeMHYgRjpmfegiDwu7ebuH9fKwB3nJ7PzXuyqa6upre3lw0bNkw52jJUYup0OqmsrESv17N161ZvFWkgMpksZOI9OjpKaWkpYWFh4yJ8Qize4mzpiIgIv1TxSSvi+dfN2/jhswd4u93F69UDfNys55un53HpplTky1i8guH7eoaFhZGenu5n4arX6xkaGvJGlL4tabMdq7jcI+/J8M1u4FN1XVNTw8DAAB0dHX6FWgvhnrfco/7ADotgbWnh4eF+r6l47QjcPEjV5ssYsXdbFJyp3lh9fX1UVlbOakrVTCJvQRD4xWv1PHawA4DvnbOSKzYkcPDgQW+afDq9p3K5HEEQ5nShEkVUo9FQXFw86UVaXG8mWB0u1Aq53zCU7u5uqqqqyM3NZcWKFZMOepkrDoeD8vJyBgcHkclkftFlZGQkEWoFV65ScenWLP70ySDVvUZ+8moD/6no43/PX0V+4vxVpS9mVbIvgdGP7wUz0H5UHHk6k8/Gp2U4iFh1rVKpWLlyJVqt1psODnTPC2Whli/LLfKeiona0sQUuxhdixP+xOcuCAImk2nawddS4lMt3r4Wp9OpJne5XNTV1dHd3c3atWtnNHtVZLqRt8st8OP/1vLs0S4A/veCIk7NlLN//36ys7NZuXLltD984u1m66Xc2dlJTU3NpCLqi9MtYLC6qe8zojfb0Zsd6M0OdKbj/z7+dc/fVoeb2HAV23Nj2Z4TS5JgQGUeYOPGjZNmFkIReY+OjnLs2DG0Wi179uzxsyJtbm72RpdOp5O8GCVP3LCZJw938ZcPWjnWMcJlDxzlxuIsbtyTjSbIrPPZshQEezKCzd4WXzexAlusFhbPyyczvzlRI++JEK85gd0AvtPm2tvbEQTBLx0ciqOKxSxYW4iNQ7BpaeIGaWBggJGREYqLi9m1axcGg2HeijLvuecevv/973PHHXfwhz/8Acaq6r/1rW/x9NNPY7PZOOecc7j33nsnHY8c9DnOyyNeBsy0KM1oNFJWVjZpYdh0ECPvyS4aTpeb771UzX/Ke5HL4OefKaJQbaCmpm9KMZtoTWYh3qLlaH9/Pxs2bqTfEcZ79YMe0R0TYt2Y+Bp8xHnE6gTkcPjAjB6nweLgrZoB3qoZACA2XMmOwR525FrZkRfPyqTIcWNK5yreYnSfl5dHfn4+DocDlUrlZ9Yh9vfa7XZqa2vp7OykOCGebVeu4C/7B/ioSc/f9rXzes0APzpvJdtzgncaLCdmIw7B7EfFlrSODk/2KNCxTFxnsU1aFmvtQBGbyD9cp9P5HVX4msX4OgpOlxMt8p4K3+6A8PBwent7ueaaa/jggw8YGRnh7LPPZu/evZx++umcfvrpbNu2bc6CfvjwYf7+97+Pm9F955138sorr/Dcc88RExPD1772NS699FI+/vjjGd3/p1K83W43/f396HQ68vPzp+zd7u7uprq6esYRbzDEN+1EFw270823X6jkjep+lHIZP7tgBcnmVox25bTT5IGIj9flck27MMZkMlFSUoJCocCaWMiNzzVR32+a9poyBGIj1MRFqIgb+zs+Uvz/2J/I49+PCVNyrKmbVw830GZRU6dzYbA4ebNmgDdFMY9QsSMnlh25cV4xn614iy50PT09bNq0iaSkpKD341vRqtfrSUtLQ6lUeoq4+pq4IsXBxphIHq+20Tpk4UuPl3PpxlS+eUYeMeGhKUJaKmnzmRJYgS0WGIm+176OZaLd8EKz2Ge/Uz3nwGlzvpvJwLNd8ahiOp/xpV5tPp+4XC5iYmK47bbbuOWWW4iPj+eVV16hrq6Od955h9/85jf86Ec/4hvf+Mas1zAajVxzzTU88MAD/PznP/d+fXh4mAcffJAnn3yS008/HYCHHnqI1atXc+DAAXbt2jXtNT5V4u1rcWoymRgYGKCgoGDC2zudTqqrqxkcHPRe4OeKKN7B0lZWh4uvP1POBw1DqBQyfnRGBlH6epLmuGkQLxDTPRvu6emhoqKSQVUSL1TZqOz29JVHahTkJ0Z6xTfeV5wjj4t0pFLg2IF9nHvOSdN6zIIg0NTUhKOnha+ftZrMzEwcLjdV3aMcbNFxqFXP0XYDBrNjnJhvTIsgXeEkaaUxaGQeDKvVSmlpqXee+nSzKDKZDIVC4TcgxGw2s0KnY33SEI+WDfNxr4wXy3p5t26Ab5+Ry2c2pi874Z2PKFgmkxEdHU10dLTfuE7RscxoNKJQKGhsbPQ6li1EdLbUIu+p8N1MMlarIZ7tNjU1YTab/axwJ3odP22R90Trm0yegGTz5s2cccYZfPWrX/Uac82F2267jQsuuIAzzzzTT7yPHj2Kw+HgzDPP9H6tqKiI7Oxs9u/fL4l3MALT5EqlclIxGx4epqyszFvlPJvUVDB8U9i+mGxOvvpUGQda9IQp5Xx7VzQJ1i42hGDTIA5QmUq8xWj0w5pu3hmIpLzH40EcoVZw/a5svlScTfQ0okmHw4FcNvUFwuZw0WMw0ddSh81qYefOnURHRwOgUsjZlBXDpqwYbjk5D4fLTWX3CIda9H5i/kGTx6DlqfoDQSPzQDHX6XSUlZWRkJDA2rVr51RQFWh4smuLm49qu7nnnXY6R5zc/UoTTx9o4Wu7EinKSloWBVwLReC4zubmZoaGhnA4HNTU1OBwOPxGns6Xhetin3nPdW2VSkVSUpL3GmGz2bytfb6voxiZi4VZi51xWOzIW6w8F8Xbt1VMLpfP6Xr/9NNPc+zYMQ4fPjzue729vajV6nEmXikpKfT29s5onU+FeIu9275FaUqlMmjVt68ZSH5+/pRp9Zkiru+79qjVyc1PlHCsfZgIlZyvbVSyKtrFxo17QrppmCyiMpvN/OvDY7xQb6dqCMCMWinnmu2Z3HxSLvGR0+9pFl8vh9NFv9FJp8FCp95Ch97zd6fB8++BUc/uVq2ANWnRrDf2sD7DxPqMaHLjI/yEV6WQszkrls1ZsX5i/n51Nx/W9NBilI2LzOMiVOzIjWNPQTyf2ZBKX3cnDQ0NFBYWkpWVNavf62SvoVwu55Q1mexelc4/93dw/8ftVA65+frr/VyYM8DJqS7iY2NISEhYUp7iSwGlUkl4eDirV6/2ZjTEoq3W1lZvxOk7JzoULLfIeyo0Go2fS5n4OvqOjhWFw2q1otFoFvz5z+T4br7W9428NRpNyArWOjo6uOOOO3jrrbdCdu2eiBNavAN7t32L0oLZlNrtdioqKhgdHWXbtm3e1FSo8Y2CDWYHX36shMruEbRqObeudnFSYSYrVqwI6Qd7ssh7f00bv3+rgbIhz/+VchmXb0nnq6fkkRI98RtQEAQMFseYKFs9wqy30KE3U9+t4FsH9+F0T56ClcvA7oLSzhFKO497lGs1CtamR7M+PZp1GZ6/M2LDvL8/UcxztQKb1H3sOekUT2Te6onMj7Ub0JsdvFHdzxvV/fz+rXrOyhS47dwtpCbNziFtuufraqWcW0/K4Zw1Sfz01QaOtA/zYrNA5WgkX90ehXysfUWhUPjN4J5tj/R8sJhTxXwzGoEjT31NTnxb0mYrBotlUSq2bs5nBBr4Oop1B4ODgwwODlJSUuI1Nplrn/5MWAqRt+8s72DWtbPl6NGj9Pf3s2XLFr/1PvzwQ/7yl7/wxhtvYLfbMRgMftF3X18fqampM1rrhBXvwN7tQNOVQPEW06mixel8zpkVK84HjTZueLSE+j4jUWoZt68XuKD4+PjQ+VjTl+YBI7/8TzkftZkQkCGXwcUb07jt1Hyy4vwjm8ruEUraDXQarH4RtMk2Uc+6DBBQKWSkx4SRGRdOZmw46TEaMA2ito9yyrZ15KUn0zJkprJ7hIquESq7R6juGcVoc3GwRc/BFr33HuMiVKxLj2Z9RrT3b81YRkGtlLMlO5Yt2bHcenIedqcnMv+oro+nj3Sgs7p5thHef6SaW07K5Yot6UHHfk7GTD/geQkR/PPaDbxU1sdv3mmmfsDCN1+zcPX2DG47aQ0Oi9FbeFRTUzOuR1pkufupT4fJIuCJRp6K1qO+Fq7iOe90xWExW9RY4E2SWHeg0WhobW1l7969jI6OjuvT97UcnY8WqqV25h1Kg5YzzjiDiooKv6/dcMMNFBUVcdddd5GVlYVKpeKdd97hsssuA6Curo729nZ27949o7VOOPGebu+2KN5isVRLS8uc0qkzQaFQ0Dts5Y6Xq2gZNBOjhu/viuC8PZvnLdXiG3l3GSz86Z1G/l3RhycwlnHe2hRuPy2fgiT/N3LPsJVfv9nAq5V9E953cpSGzLgwMmPDyYoLJzMunO6GCs47eQe5ybEoxtLfJpOJ0tJSlAlKvyOBgqRICpIiuXijp2/e6XLTOGCiomuEiu4RKrtGqOszojc7+KhxiI8ah46vrVWRqnFTp2hhfUY0a9OjiItQo1bKyQyzs1bWwf2fyaDCpOXvH7XSO2LjZ6/Wcf++1jERz0Dt05s9VXQ9UyGVyWR8dlMqJ6+M59dvNfFq1QCPH+ri7dpBfnLBKorHCiZ9e6Rra2txOBze83+j0Thpj3QoWcyNwnSfX7CRp2JLWnd3N06n068lbbLIarHEe7qGUPO5tq87ntinH2xT5Dv2NBSPdylF3qJ4h+o9EBUVxbp16/y+FhkZSUJCgvfrX/7yl/nmN7/p9T64/fbb2b1794yK1TjRxHsmvduiWcqhQ4ew2+1+xVLzzZBNxo+fq6NnxE6cRuBX52Zw8ubV83oRkcvl9I/auO9gLU8f6fKms09dmcA3zljB6jR/hyGrw8WDH7dx/75WrA43chmcsjKRvMRIj1CPRdKZsWFBI9i3BqtI0aq8wt3X10dFRcWUPuwASoWcotQoilKjuGJrBowVt9X1Gb1iXtE9QtOAiX6jg34jlL/b5P35rLhwijNUbIoYZveWdaSlpbEeuHxLBs8d6+LvH7XSN2Ljp6/U8cC+Vm45KY/LNqejUkzdtjNbcUuIVPOrS1bzmfUp/Oz1RroMVr7ydAXfOC2PL+7K9OuRFo06BgYGMBgM3sEz4oV2qaXYQ8FcNg2B57wmk8lbtCWa7Ex0PPFpirxFJgpqAovfxBGdOp2Oqqoq7xx48bWcbc3GiRx5T4ff//73yOVyLrvsMj+Tlplywoi3GG2LLVhTvan0ek86NiwsjK1bty7I2EOAlkET/++oA70NksLgb1euZV1++ryuqTfbeb7Bybvv1WJzei4aW9IjuOv8tWzKivG7rSAIvFUzwD1v1NNlsAKwPSeW/zmvcJzAT4YodG63m4aGBjo6Oli3bt2Mz3VENCoFGzJj2JB5/PGabE6OtfTzyoEqHNpUKrpGaNN5iuGe0Vt4SSnnMmGYLxXHkBUf4SnA25HF5ZvTee5YN3//qJWeYRs//m8t93/kicQvWpeIcoJ2s1BcaPcUxPOvm7fyyzca+VdZH797t4XaPiM/uWAVYWObINGoIz09naamJoqLi72CNFGKPdQXw8U48w7FmjKZDK1Wi1ar9Q6w8O2LFl+7xe4vX+zIezrrBo7oFOfA6/V6Wltbkclk44oIp/NaLoXIW1zfaDTO+1CS999/3+//YWFh/PWvf+Wvf/3rnO532Yu3b+/2dCxO3W43dXV1dHZ2wliP3UIJd32fkS8+fBS9DdIiZTz2pe1kJc5ftD9qdfLQJ208fKDdezZdECPjO+cUctrazKCP7xev1XFg7Jw5NVrDXees5Ly1KTO+wMnlcqxWK9XV1TgcDnbv3h3yHW6kRsmWrBgs7QJnn72ekZER9h06RqsljHe6ZVR2j/Lk4U6ePtLJeWtTuGlvLqvTotCoFFy7M4srtqTzzNEu7v+ole5hK//731ru/0jDjXuyuXhDCirF+AtMKNLK4SoFP7lgFUUpWm8qvXXIwh+vWENqkALBwLGdwWxIfb3Yl2sV+3w85mB90WL1dUNDAxaLBbVajVqt9rZSLYSw+NbiLDRTiafTLVDZPcInzXoOtQ2jkHnqN/ISI8hL0JKfm8TatSqv85vvVC9RyOPj4yesG1oKkbd4zTebzbN2y1xslrV4z9Ti1GQyUVZWBkBxcTH79u2b8XjO2VLZNcwNjxxjxOYiUwt/vKRg3oTbbHfx+MEOHvy4DYPFAUBmpMDlhWHceP6ucZW5wxYHf36vmScPd+Jye4q/btyTw017c4lQz+5DJggC5eXlJCUleTMbJpuTNp2Z1iELbUNmWofMtOnMaJRyClO0FKZGUZiiZWVS5LSLycQIv6uri+rqagrz8zkvP5+vAAdb9Tywr419jUO8UtnHK5V97F2RwE17c9iZG4dGpeC6Xdl8bmsGTx/p4oGPWukatvGTVxv4xycd3LInmwvXJ3tFPJQXWplMxtXbM1iZHMm3XqyhutfIlf8s4feXrWFLkGyIL8FS7OKZr9hWNdsU+2LalC4EgRau1dXV2O12TCYTnZ2duN1uv2hyvkaeLrU+6+5hKx836dnfoudAq4FRq9Pv+4fahv3+H66Sk5sQQV5COHkJCeQmZ6BVOxHc5nFDasSpXqJgLqXIe7mOA2U5i3ew3u3JED2sMzMzKSwsRC6XB20Xmw/qe4f5wkNHMDsE1qRE8I3NKqI18/PmHbU6ueofh2kc8JgPZMWoODvNzs4MDbk52X7C7XILPHe0i9+/24TB7BH5s1cn8d1zVo2rNp8uFruTA5WNHOl14gyPwzyqpO1IKa06s7evOxgHfKrKFXIZuQkRFKZoKfIR9dTo8T2pYstNbW0tmzf7V+rvyotnV1481T0j/GNfG69V9bGvcYh9jUNsyIjmpr25nFmURJhKwRd3Z/O5Lek8caidf37SQZfByo9eqeeBT9q5eU82F65PCfnscMaOJJ66YTN3PFdFXb+JLz9ezvfPKeBzW6Z3lOLrhS22VYmzo0X7zIiIiHlNsYeCxTp7VigUREVFUVBQ4OcjPjAwQGNjIyqValrR5ExZrBY1cW2HIOPDhiE+adHzcbOe1iGL322iw5TszoujOD8WpVxOy5B57I+Fdp0Fi8NNTa+Rml6j38/JZZARG0ZOXCJpkXISdEbSVQPEKmxER0cTFxeHw+FY1N56t9vt3UgsRNp8vlh24u3buy3uXid7IzidTmpqaujv7x831GMhxHtkZIR7XjqK2SGwMSOKf163lYaaipDNu/bF7Rb4zouVNA6YSIxUc8UqJRtibGzdsoOmpiY/4TnSpufnr9Z5P3wrkyP5n/MK2Z0ffEa3Lw6Xmw69J3puGzLTqrPQOmSmddBE74gNzyoKYGTsz3HiIlTkJkSQmxBBTnwEOQkRWB0uantHqeszUttnxGB20DRgomnA5FflHhOu9EToKR4xz4tTM9pVD8CuXbsmTMuvSYvmd1es584zCvjnJ+28UNJNedcItz9TTm5CBDfuyeHijWmEqxVcvzOTyzel8lxJDw/t76RDb+WH/63ngY/buSBXwbmrQ/97y4gN49HrN/Gj/9bzRs0AP3utkbo+E98+PWfG9zWdFLvoXJaQkLCkUuyLPVUs0EdcHHkqTveqrq5Gq9X6RZOz3QgtdOQtCAJ1fSY+adHzfm0fFT0OnEKV9/sKGWzIiKY4P47i/DjWpkV5i00DcbjcdBqstAx6xLxlyDz2bzOjNhcdeisdeqv39kq5jL9evpa0SCd6vR6n00lZWZlfS9pCvQ99s7SMpc2X4yxvlpt4u91unE7ntNPkIyMjlJWVoVar2bNnvFvZfIq3IAh0dHRQUlXHkX7PG+WucwvRhinnbd2/ftDMe3WDqBUybipysT4jkvXrt6FWq72tYj3DVv7fmw28MiaK0WFKvn5aPp/fnokyyBmvLw6XmycOdvDXD1rGJocFJ0qjIEHjZmVqDIXpceSMiXVufMSU9qqCIDBgtHvFvK7PSF3vKM2DZoYtTg61GjjUavDeXgYkhSnYZGhkdVoUxfkJbMwM3laVFR/B/15YxNdOzefRg+08eaiT1iEzd/+7hj+918z1u7K4dGMykWoFX9yVxee2pPP00W4ePtBJu97KfXr4d6ONb54Vydmr5+5z70uEWsH/+2wRRSmR/On9Vp491kNDv5HLZlff5yUwTSwWHYme4r7OZb6R5WKatCz0upN1pPjOiPYdFRs4d1s8L5/u67YQkfeQyc4nzZ5U+CfNeoZMDr/vZ8RoKM6Ppzg/jh25sUSHTU8OVAq55ww8wf+sWBAEhkwOr5g3D5k50jZMXb+Jv+zr4okvbiI9PZ3+/n7WrVvndX9raWmZNwe9QMTrrm+1+WxGOy8FloV4+/Zuix+2qSaBtbe3U19fT15eHgUFBZP2eocah8NBVVUVer2ebnUWNlcnq1K03rPM6fiMz5S3a/v5y/stAFyZ7+aU9fnk5uYetyoVZDxZquP56lYsDjcyGXxuawbfOL1gWtannzQN8fPX6mkaS8eHq+ReUU5QuZCZBtmYn8qeDauIj1Szf/9+CgqyZjyjViaTkRylITlKw8krj6fA7U43zYMmantHOVTfRVWXgT6bEr3FRb9V5rVF/eO7zWzIiOaLu7M5e01y0KKzBK2aO89YwU17c3n2SBcP7W+nf9TG/3urkb992MLntqZz7fYMErVqvrQ7i6u2pvPkkS4e3NdG16iTb71Yw4Xrhvifc1eg1YTuIySTybhxTzarkiO56+VaSjpHaelXkLdmlA1ZoXH7C0yxj46OMjQ0RHd3N3V1dd6Lpk6nIykpaUFT7ItVvDXddX3HSvrWGvhaj/puhCYToPk493W43JR0jPBxs579zXpq+vxT2uEqOdtzYlmXqCBHbeK8vVtDXseRqFWTqFV7x+IOGu2cf+8hKrpHebtukDMLExEEAa1WS2JiorcjYGRkBL1e73XQ02g0XiGPi4sL2XGF2F0gnXkvAIEWp1MJt91up7KykuHhYbZu3erdNQdjPsR7eHiY0tJSIiMjKS4u5tf3HwXgqm0Zk1qzzoWmARPffcGTAjstQ8ZXz9/ifd6CIPB27QA/fVNHv9mz5tbsWO4+fxVr0qYumOvQW/jVG/XeGdtxESq+deYKLt2cjgyBmpoaensH2LDXf4BKqM+H1Uo5KxLDsfY2EZts4rtnbyc2NpZeg5mnX/+IyIyVVPeaeLt2gPKuEb75fCWp0Rqu2ZHF57ZmEBsxPuLXapR8aU8O1+7M4t/lPfxjXxstQ2Ye/KSDxw52csnGVL64K5OsuHBuLM5mc+QIr7Y4eb56hP9W9lPSOcI9FxexKTO0hYcnr0zgiS9u5uvPVdKms/KlJyv56YWFnL92ZnPcp8LXuUxMsYs9vc3NzdTU1Mw6spwpi1XANduz9mC1BqKFq1h9LQqQKEK+9Saher56s4PXqvrHKsMNWBz+QUFRSiTF+fHsyY9jU2Y0aqWcrq4uBgZskxrXmO0uRm1Ohi1ORm1ORixOtBol23NiZvR6JWrVfHFXJvd91M6f3mtlb54ngPHdFPoe9YgOeuJxRVtbG1VVVWi1Wr+xp7PdVAZWuptMJu+wluXGkhZv395t393SROj1esrKyoiKimLPnj1T7tZCKaK+0X5BQQF5eXkcbNXTPGgmQq3g4g3HUzPBrEpny6jVyVeeLMFkd1EUr+A31+5CG+HZ8Tf0G/nFa/Xsb9YBkBCu4AcXrOaCdVO3flnsLh7Y18o/Pm7D5nSjkMu4ensmXz8tn+hwFRaLhZKSEhir3A+MMkKdXTAajZSUlKDRaPzsa5OiNBTFCpy+MxO1Ws2Q0c5TRzp56nAnvSM2fvt2I/d+0Mwlm9L5ws6scQ5yjG0MLt+SwWc3pvFmdS8PftJBRfcozx7r4fmSHq7ZnsHXT/VU3l+7MZILt+bxvZdr6TJY+eKjpdxyUg437cmesD98NuQnRvDYdRu59eH9VBvk3PVSLbW9Ru44LW/Cs8i5olKpvAV/27Ztw+l0etPE7e3tfn298fHxIXUDXIpp85kwkYWrmBaurKz0WrjGx8fjdDrntG6H3sKjB7t4qawXq/P45yw+UkVxnufcendeHAmRKur7TQwa7bxbP8iIxUlHn47BERsvd9czYnUyanWO+9s1wa9ja3YMd5+7ghVBPkcTcf3OTJ452kOrzsJLZX2kTNHfrlQq/SbO+R5X+NZt+E5Km+5GKJh4S2feIWSmvduCINDc3ExzczMrV64kJydnWh8MhULhjejngsPhoLKyEoPB4DfQ5OnDXQBctCEVrc95klwun/O8WMYK1G5/4ghtOiuJEQoe/FIx2ghPW9BLZT384KVqb+vXxYWRXFoUyZb1kx+iCoLAG9X9/OqNBrqHPUUnO/PiuPu8QlaleNJLAwMDlJeXk5qayurVq4N+cEIp3qI7W1ZW1ri55uLvWbz4J2jVfO3UfG7em8srlb08vL+d2l4jTx32CPrJKxP44u5sivPjg4z4hE2JMu67NJ96vZt/HuhkX5Oexw51sa9Jx03rNaxN07A5K4bnb9zKz19v4NWqAe79sI39zXp+eXERGbGhE7ToMCU3FbkpJ4uHDnTx0IFO6vtN/OqSImKmMZp1LvhGlhkZGUGHg4SHh/tVsc/VL2GxC9ZCSTALV1+3MofDgVwup729fUYFW5Xdozx0oIO3awcRZ/6sTtFyzpokivPjKEyJRAbU9Zl47FAXr1f30z1sm+Depj+CsjA5kna9haPtw1zxj2N8YUcGt56UM61W0kiNkpv3ZnPPm038/ZNO7lo7M3Ma3+MKwO+4QmzvE30O4uLiJrU7DSbeUto8RMy0d9tqtVJeXo7VamXHjh3ExMRMeNtARIvUuSCmybVarV+0PzBq462afgCu3u5viBKKiN/lcvHj5w+xv92EWiHjb9duJTHKI9y1vaP86N81uNwCpxUm8j/nrsI61IXNNtGH2ENDv5Gfv3rcpCU9Joy7zlnJOWuSvWnwxsZGWltbWbNmDRkZGRPeVyjS5oIgUF9fT3t7O+vXrw/qzhYo3iJqpZzPbkrnko1pHGrV8/D+dt6rH+TDhiE+bBhiZXIk1+/K5qINqYSpPL+PyspKBgcHvSYOt2+I59yCdP7w8QAtQxbu/sDCZatdfD/HTVSYkl9dspq9BfH84vVGSjpHuPwfR7n73JVcsC506W25DG4/KZs1aVH88D/1fNys5+qHSvjTFWuDZhHmi2CRpShGDQ0NWK3WOaXYF9OmdCHW1Wg0pKamkpqa6s3SdXZ2+hVs+bak+WY13ILAvkYdDx3o5Ej78X7rPflx3LA7kx05schkMtp0Fv6+r53XqvppCWj9mg1qhQz7WAiuMzu4ZGMqzYNmDrYaeOhAJ69W9XPX2QWcWZg45Wv4uS1pPHaoiy6DlQ975Zw3h9c8PDycjIwMMjIyvO19er2eoaEhmpqavHa4wV5LKfKeJ9xuN3a7fdq92wMDA1RUVJCYmMiWLVtmvPOfi4j6zv1esWKFX3EYwPPHunG6BTZlxlCU6n+mMteo1Gg08vCbR3m22lM9+pOLVrM+w3PuarI5+cZzFdicbk5emcC9V21ELpfRpJ94zRGLgz8FmLTcNGbSEj62s7bb7ZSVlWGxWNi1a9eU50ROQcah9lEqhnuxOd3YHG7P3043dqf4b1fA/4//sTqcDI+aUMkETlmdSbRFTbzT7TdEBJ8aiImem0wmY2dePDvz4mkbMvPYwQ5eKOmmod/E3f+u4bdvN3LZxhTWqAdJiFCyY8cOFAqFN8pMN+n45moLL3dqONjr4rnqUcoHS/jFZwopTNFy0foUNmdF872X6ijrGuF7L9eyr0kX0mI2QRA4d00yufER3PF8Fe16K9c8XMo9Fxdx6qrZjTedbK3poFQq/XywfY1ixOItX6OYqVLsyz1tPhNkMhkajYbw8HA2btzo15vvWzgYFRPH0SE5L1QaaB4TY6Vcxnlrk7h+ZyaFKVp6R2w8erCLV6v6qQ7ouZ4t4So5yVEabE43vSOeDf+A0c5TR7qJClNSlBJJu95K36idb75Qw578OH5wzgqy4ycu0FMp5Hz91FzueqmWt7vkfNfsIC5IHcpM8W3v87XD9S1+E8fHxsXFYbPZvOIt+uBLZ94hYLqmK263m/r6ejo6OqaMACdjtmlzh8NBRUUFIyMjQed+u9wCzx71pMw/v338Y5tLxN/T08Pbhyr5R5VHxK7dkcmlmz2GHoIg8OP/1tIyaCYlWsOvPrsW+dj5aLANg8st8GJJN799uxH9JCYt4nCMmJgYdu/ePens5JZBE88e7eLZwwaMDj3QPavn6UvjgS4ePNBFmErOtuxYdufHszs/ntWpUcjlsmlH+TkJEdx9fiFfPy2fF0q6eeygx4zlH/s7Ucrh/HUphKXZKUo5PhaxoKAAu93Odp2O5w428UStk7p+E1c+eIzrNsdz40l5ZMZG8vB1G3lgXzt/29fmU8xWyKbM6WeCpqIoVctTN2zmWy/WcKR9mK8/V8Vtp+Rw857sRe/V9o2GZpNiX8zIe7H8xcXnG9ibrzdZefyTVp7/eACd1fOZDVPAuSu1XLsjk8S4GN6pG+KXbzZxtH14ipVmjsXhpk0XPHIftTqpDWgT/bhZz2fvP8KXi7P40u4sr0d/IOeuSeIf+1ppGLRy/8ft3HVWQcgfe6Adru/42JaWFkwmE263mzvuuINTTz0Vk8kUMnvU++67j/vuu4/W1lYA1q5dy49+9CPOO+88AE499VQ++OADv5+55ZZb+Nvf/jar9ZaUeMvHZjNP9iE2m82UlZXhdrvZvXv3nM4rFAoFDodjGrc8jihkUVFRE879/rBhkO5hK7HhKs5dO75VajYRv9vtpra2lqb2bh5tDsPitLE9J5bvnbvKe5vnj3Xz7/JeFHIZv7t8vV8LWGB0WtoxzE9fraWqexTGxnL+z3mr2FNwPJITe9Xr6uqCZhdE7E4379QN8PThTj+ntIQIBStSotEo5d4/aqUcjVIR8P/jf0wjwwz0dpOVkUZWeioDRjv7m3Xsb9YxYLSzr0nHviZPAV5shIpdeXHEWmVk6SwUTdPKMjpcxRd3Z3NSiosXDzVxUB9BZZ+Ff5f38e/yPnbmxvDD81aRMxZJqNVqUlNTOWf1CDvzXDxUaWdfyzAPHdPxXv0QX1qrZE1WIpevjmd79jru/m/DWDFbGbfszeGmvbMrZgv2XOIj1dx/9Xp+/VYzTx/t5i8ftNE8aObnFxUGbYtbDKabYo+LiyMhIWFRI5+ltGnoHbHx+KEuni/pwWT3XB+StWqu3JzCjlQ5+5uG+NF/aqnVC7iZ5vhUuYztOTGsSYtCrZChlMsx6AZRKeSkpiSjUshQymUoFTJUCjlKue/fnj8N/WbuebPRm0KPDVdisPgLuN0lcN9H7fynop8fnLOCk1aM7/KRy2TcsjOFb7/SxjNHu7l2e0ZIa0SCPv+A2oOmpiZaW1sZHBzkO9/5Dnq9nttvv53PfOYznHHGGWzbtm3WtRuZmZncc889rFy5EkEQeOSRR7j44ospKSlh7dq1ANx000389Kc/9f7MXDYOS068J4tIe3p6qKqqIj09ncLCwjn3oCoUCqxW6zRu6fmwtba20tjYOKmQATx12DP05LOb0oLuQmeaNjebzZSWluIWBP4zEEe7QU9qtIY/fG6994Jd12fkZ6/WAfCN0wvYNtZnGbjmwKinAvtfpT0AaDUKbj81n2t2Zvld/J1OJ1VVVeh0uglb7jr1Fp472sXzJd0MGj0FeLKx0aG7Eh1sz4ml3RnNf8p7ONY+jHuK6Njlco1ZF6qQdw4Bx+d2J0SqKUqNQjV2DlfSYcBgdvB6VT8g4+mmUjJiw9idH09xvscWNUEbvNvA6XR6CwxvOX8H342JobxrmEf2t/N6VT8HW4e57IGj3H5KLtfuyPBWd8tkMuLCldz7+SL+U9HPL99spNXo4hdH3VxjM7MzXofNauV/tkXxbFME77eaufejNj5p0XNPCIvZVAo5/3PuCopSIvn56428WjWA2e7iN5euQaMMjYCHUtAmS7F3dHSATxbMYrHMm0FHMBZznrco3nV9Rh452MlrVQPeUb0rkiL4/NZ0tGFK3q4d5P79OmzeqvLJH69KIaM4L44zixI5bVXCuOLGmhoTGo2G/GlOM9yUGcPqVC13PFdFv9GOW4Cb9mRR1WPkk2a93207DVa++kwlNxVn8fXT8sbd15aMCNbEy6nWufnrh63832eKpvUYQoVMJiM9PZ0nnngCm81GUlISl1xyCceOHeN3v/sdTqeTBx98kMsvv3zG933RRRf5/f8Xv/gF9913HwcOHPCKd0RExKwnK457LsJiHTYFQawwD/b1mpoa+vr6WLdu3YyNPyaitbUVvV7P5s2bJ72d3W6noqKC0dFRNm3aRGxs7IS37dRbOPOPHyMI8MbXi8lNGL+zGhgYoK6ujr179075GPv7+ykvLyc9PZ13ejX85YMW1Eo5T3xpKxsyPClZk83J5fcfonnQzEkrErj/mk3edLlIV1cXb5a18edSm3fC2KWb0/jWmStI1PoPrjAajZSWlqJSqdi0aZPfYAuny80HDUM8faSTjxqHEN89SVo1l2/J4LIt6XTozDz6YS0HuqxYHKF/e0WoFezIjSM+QoVSIedYYxetozLvhU+kMEXLySsT+MLObFKiPc/BbDZz7NixoM8NoKV/hB+/Us+BMRe3DRlR/OzCQvITI2hoaEAQBFat8mQ7ekc81qkHWjy33ZETw/+clYPG6Rnf+UadnmcaweqCCJWc75+VxyWbp3/E43a7ef/999m7d++EbY8fNur45gvV2JxudubG8qcr1s56mAxjG5sPP/yQk08+eUGm7QmCwMjICLW1tbhcLmw2G+Hh4X5uW/P5OI4cOUJ2drafbfJC0NbWxv5mHe/3KvnYRwA3Z0azOlXLqM3Ju3VD3gh8KjQKGesSZKyNcbA9I5yM5IQJe6KrqqqIjIwkNzd3Ro95YNTGN56vprx7FIUMvnNWAZszo7n3wzY+aNSRHqPh9FWJPH64iwi1ggPfLh63Mert7eXj6nZ+vN+KDHjuxi0UpixctbfvZ1iv15OTk4Neryc2Nha3201paSkpKSmzPooVcblcPPfcc1x//fWUlJSwZs0aTj31VKqqqhAEgdTUVC666CJ++MMfzjr6XlKRdzBGR0cpLS1FrVYH7SeeC9NJX4u949HR0ezZs2fS816AZ492IQhQnB8fVLiZZuTte66/bt06qoeV/OUDz0S0n1xY5BVuQRD4ySu1NA+aSY7S8OtL144TboAhs4u/HjNjcsD6jGh+dH6h32xskd7eXiorK8e1ZfWNWHnuaDfPHevyFrGA53leuS2DtJgwXqvs4/MPHvYbQJISrWFtWhQZseG068zU9BrpH5286j2Q5CgNdqfbOyHNbHfxfv2g9/vpkfDZ9UnERUVgcbg43KanttfotVd99EAHV23L4NLVUXQ11XgzN8HOOjPjwvnbVWt5qbyf37zTTHnXKFf84yhfOTmHkxIBn71uanQY939+Pc8e6+G37zRzqG2Yqx+t4rtn5fPZjetYu1bggq5BfvRqM7WDdn74ahP/PdbC13YlkZ2WRExMzLSyR5Ptr09eEc99V63ja89WcbDVwE1PlnPvletm3Uq20Ht5mUxGTEwMkZGRREdHk56e7j2jbGpqwmKxEB0d7T0vj44Obn07WxY68na6Bd6sGeD+D7tp0nnez3IZrEqORCmX06qzUNI5MuX9AESqFZyyMp6zipLYUxBHuEoxoZe9uBmKjo7G7XbPKmuZFKXhn1/YyE9fa+Df5X3c82YTl25M5Ufnr+SMPx2ke9jG9bsyeOJwF2a7C53ZQUKAe6Pb7SY/TsV5a6J4rXqA+z5q4w+Xr53xY5ktvkNJTCaPW6RYbS6Xy9myZcuc7r+iooLdu3djtVrRarX861//Ys2aNQBcffXV5OTkkJ6eTnl5OXfddRd1dXW8+OKLs1prSYm374fI97w1NzeXgoKCkBeWTCbevmny6faO251unj/mKdC6Kkih2nTWZaz9rbS0FKfTye7du+m3yvjui4cgoEAN4MXSHl4u60Uug99fsS6o1anT5eZn73Qy6oDVqVqeuGHruJGb4mahs7OT9evXk5KSgtst8FHjEE8f7uS9+kFcY5FtbISKyzans6cgnrLOYf74bhPNg+agz0VnsvNu3eC4r8tlkBmtIlltZ1NuMlHR0XTqLXQarHTqLfQMW72R9FRi322C58o8bXlajYI9BQlcsC4VtULGGzX9HGsf5pEDHTx5SOCSdQl8Y2f+pO8lmUzGZZvT2FMQx09ebWBfk54/vtfKK/Eqvro1msKA2165NZ3deXH88L91HOsY4X9faeCduiF+fP5KVmcl89RNSd5itoO9bprfHOCm1QOkhTm9/akJCQmzHj+5PSeWf1y9nlufrqS8a5QvP17O369eP+7CuZQRNw2BZ5QWi8UrRmKKfboWpNNhoaZ7me0u/lXWy6MHO71912oFbMuJw2J3UdY1gnsa+6aoMCVnrErgrKJEduXFebsvBkZtvFbV7xkMJIBAGIKQjsPlwNJlwdw4gNXSgTB2tKXWWIiJMaNQKtEo5Fy0IWWcV3kwNEo5P79wFYXJkfz2nWZeLOulechMQqSKIZOD0s5RUqI19I7Y6NBbg4q3QqHg3DVJvFY94DfAZCFwOp3ebJvJZCI8PDykFsCFhYWUlpYyPDzM888/z/XXX88HH3zAmjVruPnmm723W79+PWlpaZxxxhk0NTVRUDDz4r0lJd4ivqYnW7Zs8TrthJqJRFRMkxuNxhn1jr9V08+QyU5SlJrTCyceXDFZ5D04OEhZWRnJycmsWbMGi0PgtqcOYbS52BZQoFbfZ+Snr9QCcMfpBWzLCe5//ef3minvMROmgD98bsM44bZarZSVleFwONi9ezeRkZEMjNq45YlSqnpGvbfblhPLOWuScbsFXq/u58GP26Z8TRwugUiNgqKUKIpStaxOjWJlUji2/lYsxhE2bw7++jpdbvpGbX6C7vm3hU69dUJBN9pcvFHdzxvVHjEvTIlkZbynwGbAIuO5ch3/rfmYq3dkceOenEl93VOjw7j3ynW8XN7Hr99uplHn4DtvD3GrrY0vF/vXCGTHh/PPazfy+KEu/vR+Cx826rjk/qPcfe4KzlubzFdOzmF3fizfe6mWrmEbf6xQcM+FhSRonQwNDdHc3OwdP5mQkDDjdPH6jGge+sJGbn6ynLp+E198tIwHrllPavT8FgSFkmAiGh4eTnh4OOnp6QiCMM6CVGwDmm2Kfb4j70GjnSePdPHM0R7vMJ+4CBUbkpSM2FwcaNFPKdpqhYxLNqZyZlEi27JjUCk8hb0NA2berx/i/YYhKrpHJ7+TcVjH/nh49GAnd56Ww9U7sqZ8PWQyGdftzKQgMYLvvFRLqU+m4HCbgey4sDHxtoyzDhZnaXcYPGvnJixcfQM+mwfGjgcnM3SZDWq1mhUrVgCwdetWDh8+zB//+Ef+/ve/j7vtzp07AWhsbDwxxFus5g40PZkPgom3Xq+ntLSU2NhYiouLp0yT+/L0EU+h2ue2ZExa+RvMHtXXAGX16tVjfskC3/1XOc1jrV9/9ClQM9tdfOO5CqwON3sL4rl5b/Dzqw8bBvnbR57WhetWK8al8oeGhigrKyMxMZFt27ahUCjoNlj54iNHadNZ0GoUfHZTOuvSo3i9yuO8Fni27EtajIai1CiSVXayo+WcvX0NmbHhDBjtHGzVYRg18/K+BmQKJUkpWVSVDOF0D+JwuXG4hIC/Pf8GWJMWxee3Z7I+IxqVQo7N4aJr2ErrkJlH363g2KCAzTn+cdX1HR+ksiFDi9HmpHnQzIMft/HU4U6u3ZHFl/ZkExcR/H0mk3kunMX5cXzvhTIOd1n564dtvFM3yM8uLKQo9fh5nUIu4/pdmewpiON//l1Hda+R775Uy+G2YX5w7go2Zcbw7I1bufP5Kg61DXPnS43832cKOW/TJr/xky0tLVRVVXmrsEdGRkhISJgy87QqOZJHrtvETU+U06qzcN0jZfzjmg2T9t9OxGJMFZuOaERHRxMdHU1ubq5fG1CwFPt0bDPnS7zbdBYe2t/Bfyr6vFXa2XFhnLwygd5hG28HyUYFcu32DM4sSmRTZjQKuQyHy82xjmHer9fxXsMQXQb/qHV9ehRFKVrkMtH/wFPa5vnb8x8Z0N/fR5Q2ioiIcOw2G5Xdo1QO2Lnn7VZeLW3nzj1J5KdPfayzpyCeJ7+4ia8+U+mNoA+1GdiaFcOhtmE69OPbzUSTlOYBT6YufxrRfihxOp1+E8VC1SY2EW63e0JzrNLSUoBZTzVbUuKt0+k4fPjwlNXcocJXvAVBoKWlhaamJlatWkV29sx6Zxv7jRxqNSAfm9Y11bput9t74bDZbJSXl48zQLn3wxbeqR1ArZTzlys3+BWW/fSVWpoGTGPn3OuCnnP3Dlv5zouegSWXbUhia8Lxwhjf51tUVERmZqbHpWnIzBcfOUb3sJWM2DAeuX4LVT2jfPfFKm+1a1GqFovd5dcL+rmtGXz9tHySxlzeGhsbGTaaqewe4aev1PFx01BAhOGE6pZpv75iJB2ukrM1O5YduXHsyIvjpBUJqPpV/Dh/FQe6Hd5Z3YFYHG7Ku0aIDVexIzcWnclB44CJ+/e18vihDq7blcUNu3PQqoP/zpOjNNx9ciLvNA7zSKWF2j4Tn3+ohC8XZ3HL3my/zdqKpEge/+Im/vFxB3/b18ZzJT30jtj4zaWriQ5Tct9V6/nBv+t4o2aA775Uy6DJzhd2ZHpFZ8WKFdhsNoaGhhgZGaGmpgammS7OiQ/n0es3ctMTFbTqLFz/aBn3X72elclL20VqNmftgSl2q9U6rop9qtcs1OI9aLTzt4/aeL6kx+sPviEjilNXJtDQb+LxQ12T/vzGjGhu2ZvN3oI4ZDIZo1Ynb9UO8F79EB816Rn16bFWK2TsyovjtFUJnLIi3vvZm4rDh3Xk5qZ6q//dgsCTh7v5/bvNlA+6ueO1fq5ZNcCaGKefa14wC9fchAhOW5XAowc9z6t1yMKefE8GMFhKXKyybx7yiHdB0sKKt2/kLVqjhur3//3vf5/zzjuP7OxsRkdHefLJJ3n//fd54403aGpq4sknn+T8888nISGB8vJy7rzzTk4++WQ2bNgwq/WWlHjHx8dPy70rVIjibbfbKS8vx2QyzdhiVeTpI54372mFSaTGTJ6qFN88ohuQOJh+8+bN3rTfu3UD/Pm9ZgB+fGGRX3HZiyXd/Ku0B7kMfnf5uqAtUU6Xmzufr8BgdrAmLYpvnJpNZZmn9crXZMb3+Tb0G7nhkWMMGO3kJkTwyPVb+Hd5L799uxGAU1clcu7aZB472OEV7jVpUfz4wiI2Zh4voCvrHOHhg0N82GLE5Oj3PqZsLWQlRqGNCEelkI/1kU72t+ffdqebko5hDrbqMZgdfr3eEWoF+VECe0wDnL42iye+tJWPSut57mgnx3RKhq3+GQ6DxcGhVgNJWjUnrUigd8RKQ7+Jv33YyuMHO7h2RybXbEsjNkgkLpPJ2JOp4bPFa/m/Nxp5q3aQv+9r5526QX5+YSFr04+/b1UKOV85OYfClEjueqmWj5p03PBYGX+9ch2JWjW//mwRCZEqnjzSza/fambAaOfO0/K8FxKNRkN6ejq1tbVs377dO/FLTBf7mp7ExcX5RUip0WE89IWN3PpUBXX9Jm54vIy/XbWedelTf64Ws/lkrhfRsLAw0tPTZ5RiD5VJi9nu4pGDnTx8oBPzWIX43oI4zihM5FCbgT+93zrpz5+6MoEvF2exKTOa7mErTx3p5r2GIY60DftluuIjVJy8Ip5TVyWwOy9uVp0FgeNI5TIZ1+7IYGduLN97uZb6fhP3VQpcsi6Z63O0DA8baG1tnXDmduAccLGgdVLxHquRmc45eyjxtUcNtTVqf38/1113HT09PcTExLBhwwbeeOMNzjrrLDo6Onj77bf5wx/+gMlkIisri8suu4y777571ustKfEWre4WCrG39OOPPyYuLm7GaXIRs93FS2WevunPB/iYB0P84DQ3N9Pa2kphYSFZWcfPmpoHTXznhUoArtmRyWU+BWoN/UZ+MnbO/fXTCtieG/yc+w/vNnGsfRitRsEfP7eecJXLa8NYWlpKRESEn8lMVfcIX3qsBIPZQWGKlgeu3cSf32vmubECvIs2pKJRyvn+S9UIgqdw5s7TC7hqeyYKuYzeYSsvl/Xwr7IeWnyK11KjNexIFtiZDOft3TqtD4vd6UYuA6VPNHvD2CCWhgETh1p0HGr1jEA0mB1UDkHl/l7+vr8XjQJWxMg4dU0Wt56fRt+olReOdfNRo3/kP2C0M9A4REZsGGetTqJ1yOwR8Y/aeOJQJ1/Ykcl1OzOI9LE4FZ3cErVqfnfZGt6oGeAXrzfSOGDmmodLuGF3FreelOPXa316YSIPXruBrz1bRXWvkWsfLuHeq9aTnxjB984uIEmr5o/vt/LQ/k6GjHZ+fMGqcUcuwdLFYhFXfX09NpvNW/gmRkiJWjUPXruBrz4zVsT2RDl/+dxa75zlpUaoNw3TTbE7nU6vReZsRNzpFniprJe/ftDGoMnTZbEuLYrPbkzhSPswP3m1YdKfv2h9MjfsysLhcvNu/RA/f62Bun6T323yEsI5dVUCp61MYENGtNd3wOUWqOoepWnQTJhKjlajIFKjJFKt8PxbrSRCrRg3hW6iWeIrkyN56obN/PmDVh450MlLlYOUdJu45+IiTlq3bpxrnrgZkgd09/aMFeR1GIKnzc1uJSNWJ3IZE3bkzBe+4m00GkM6lOTBBx+c8HtZWVnj3NXmypIS74VEEAQ6OzsRBIH8/PwZp8l9eaWil1Grk6y4cPbkTzw/XERM1Xd1dY2L9N1ugTueKcdoc7E1O5bvnXO8QM1sd/GNZz3n3HsK4rnlpODn3B/UD/LAPk8x2S8uXkN2fAQmkwmXy8XBgwfJy8ujoKDA+3yPtRu46fESjDYX6zOi+f0V6/nev6r5ZGyUaGGKlo8ahzCMWajuXZFAfISKizem8WplH/8q7eaTZp23iypMJWdPdiTbEpzkRtiJj4tj3bp13qzCiMVB97CVLoOVnrG/u4ctdI/9f8BoR6WQkZcYycqkSFYkR7IyWcuKpEhWJEVSmKLlC7uyvWL+zLtHabOoKek2YXJAlU6gal8nf93XyY7cOG47NY+fXrSal8p6eKGkm3afdH+XwbN+QVIkF65PpbZ3hMYBj7nKyxV9k87rPmd1EtuzY/jlm028Xj3APz7p4OMmPX+5ci3JPinMDRnRPH79Jr7ydAXteitfeKSUP39uLVuyYrhxTzaJWjU/fqWef1f0M2Ry8LvL1kwaUQWanpjNZm+6uLW1FYVC4RXyv15exLdfbuBgq4GvPF3J7y9bE9T9arGZ78KxiVLsdXV1NDY20tjY6BdVTnUWKggCHzTo+P17Ld4oMjM2jAvXJdMyZOFnrzdO+vNn52m4fH08w4pYfvTfeip9CkPlMtiUGc3pqxI5ZeXxllNBEGgaGwxysNXAkTYDo7ap+8DDVXK0Y6IeHa5ke7STNWuCb1TUSjnfOiOfvWN1G206C9c+XML/u3Q1ZxUljRt5qtPpMBsG/O6ja0y0dSYHJpvTbwPsdrvpGnV5X69QmQpNF1/xNpvN837mPZ8sKfFeqCIZ8YzZbPZ86DIyMua09lNjhWpXbssIevbsi1iQB7B58+ZxKfqyrmHq+01EahT86cr1foM4fvZKLY0DJpKi1Py/Cc65e4atfHfsnPvaHZmcuzYFl8tFY2MjgiCwadMm70UfYH+zjq8+VYbZ7mJ7Tiw/vKCIW58opXHg+O6/rs8z8GBVciSf357ljfz/Xe4/UnBbTiyf3ZTGOauTaa6vprO7h3Yhg486ldxXVUm3wUL3sBXjNC44DpdAfZ+R+j7/YQtqpZz8xAhWJmvHhF3LmjjYET/Kt4uzkcWkc6jNwMEWPR80DHoi9If1bMuJ5bZT8rh5by5H2g08eaiT16r6vPfbNGCiacDEmjQtn92YwsFWg3de961j87qDeajHR6r5f59dzbmrk/jpaw3U9Bm55uFS7r1ynd85c3Z8OI9dv4nbn6uivGuUm54o5/8uLuKc1UlcsjGV+Eg1336xmo+b9Xz58XL+euVabyX8VFGpOLrTU+To9ha+dXR0MDpazQ35kbjsYRzptvL156q45xLPupOxGK5jC7mmmGJvbGxk8+bNCILgl2LXaDTeqv+4uDi/jFxF1wi/fbfF6yseG67kjMJE9GYHf9vXPuGaMuDGPVlcuimVpz+s4ofvDtBn9GTsNEo5JxXEc9qqBE5aEU9chMoTYBisPF/Sw6FWAwfbDOhM/nbOURoFa9KicLjcmGwujHYXZruLUavTm263ONxYHHZEiS3vApe2lzvPikY+wWu+MzeOF27ayo/+W8+79UM8ebibs4qOv2d8N0N98kSoqiQtSkWf0eF3VHW4ppVtK1K9Vd0ul4uusX1KfuLCC2dg2ny5jgNlqYn3QjA0NER5eTlxcXHs3r2bd9991zsCcjZUdI1Q1T2KSiHzS28HIo4BrK+vZ8WKFTQ2NgZNXb1V4/mInbYq0a9A7V+l3bzoPedeH/Sc2+Fyc+dzFRgsDtamR3HXOau81qoivm1379cPcvsz5didYxXrJ+VywyPHGDL5zxqPUCv4+mn55CVGcssTpX7fy4gN45KNaVyyKY3s+AhcLhdllVX8t7yft7pV9Jv7CEZchAq3IJCo1ZAVF378T3w4mbHh2JxuWoZMNA+YaR400TJoolVnwe50U9trpDZggpJaoWDPkJVrdji4dkcW1+3KpmfYyv0ftfLcsS6OtBm44dESNmfFcNup+fz+inV8fnsGP/5vrV+PenWPkeoeI6tTtKgVHtOMv37Yxv4WPbdvi2aiaoYzihJZlRLJV5+ppHXIwnWPlvL7y9awK+/4sUZ8pJp/XLOB771Uy7v1Q3z7xRp6z7Rx3Y4MTl4Rzz+u2cBtz1RS2TPKdY+Wcd9V6yZYbWJ8BzOIQ1V0Oh13Rg3x+0+sHBkQ+M6LNXQVD3LVzhzCw8MXfagJizxVTC6Xe01icnNzcblc6PV677hOsfLfporm2VoL7zZ6RFujlLMrNxaHW+CF0olnY2uUcm47OYfTCxN5ubyXKx8s8baNxYYr+fy2dK7cmk5CpJr+URsfN+s42GLgUJthklncHkZtLg6OuQHGhCvZnRdHcpSauHAVkRolaoUMtVJOfIQKjUrOBw06Hj7QycOH++g1ufnZhasmHCQSE67i5j3ZvFs/RMtQcB8HcV0AmVzO2rQov7a1xl4DGDq9YzrNZjNtBs91LW+BxVsQBEm855NQzIEOhiAINDU10dLS4nfGLO4GZ4voY37u2pQJe4ZFL229Xu+dQtbS0jKu11sQBN4emwF+RtFxu8bGfiM/+a8n2r39tHx2THDO/ft3mijpGCYqTMkfr1jPsN6zUUlLS6OgoID33nvPe971WlUf336+Eqdb4IyiJM5fl8JNj5f6+Cd7OH9dCt87ZyXv1g36CXdOfDg/+8xqtufEeTMAhhEjf3n1KK+2OBmyygCB2AgVl25KIzchgpToMHqGrVT3jPJxk6fVZdjipGnANO65TEZKtIZkrRqly8qAyUmvWcDuEnivbpD36gbJTYjg6u2ZfHZTGv97YRG3npzLA/vaePZoFyUdw9z4WAkbMqK57dR8Xv7KLv75SRv3ftDi99xr+owoZFCQGEH3sJVjHSN8rdfIlzaEs24CTc2KC+fx6zfx9eeqONYxwleeruTHF6zk4g3HvYzDVQp+d9kafv1WE08e6eY3bzfTM2zlO2cWsCEjmkev38StT1XQprPwhUdK+VLB3IRVHKqSmprKA6vd/O9/avh31RC//2SAjp4+zspV+/WWf5pGc060rkKh8Eux9+pG+ev7zfyntheXADIE8mKUqNVKPmjUTXjf0WFKvnFaHhsyonjqSDeX3n/E2zaWHCHjcxsSuO7kQpoHzPx9XzsHWvRzmsM9bHHyevVA0O8p5TJ+dN5KvnVGPvaBdp5tUfB69QDdw1b+dMXaCQ19xD7sIZODYYsjqHOfWLA2YnVy7ppkP/FWxKRw8s4t3mzQ4OAg9b1WQE6kc5TBwcGgU+bmA/F6K4n3MsJms1FWVobVamXnzp1ERx8/w5zLTO9hi4NXKj077qu2BS9UEwvEwsPD/frWg63bNGCiTWdBpZBx8kpPhGy2u7jj2Qos3nPu8Wb/AO/VDXhNU/7v4tVYh7qobW1l7dq1pKene9dyu938q7SbH7xUjVuAC9ensDJZy7eer/S7v9yECP73gkJ25sXz6zcbeHj/8XTgKSsT+OPnNnhnfVvsLh76sJ6HD3YxPBa0x4UrOSdHzh2f2cWxdgNv1w7wXl2T1+J0LvSN2OgbsXnPBren2chLjaPfpuSlsh5ah8z83+v1/P6dRj6zMY1rdmRx9/mF3HxSLg9+3MbTRzop7xrhlidKWZsexW2n5PPKbbv42at1fNBwfBiKS4CmQTNZcWG43QJdwzb+fNREu6OO759d4HeWJxITruL+qzfww//U8Vr1AHf/p54ug5WvnHTcoU8hl/G9swtIjwnjN+8088ThbnpHbNxzcRF5CRE8dv0mbn26koZ+E3+qkpO7aoSTC+duZqFUyPn5xWuI1Tbz6MEunm+Rk5uVxG654C3i8u0tj4mJWVBBXawBIROta3W4ePxwFw9+0uE96kmOUhOmkNFssAHBrxvJUWq+fUY+CZFqHjvUyU9fO160tiEjiht2ZaE1ttNgUfDlx8tnYbAyc5xugR+9Us+gycaOJDcnb13Ld//dQHnXKNc8VMJfrlzHiqTxxaSRGiUpUWr6Ru20DFnYlDmxeBttLgpT/O+jQ2/1ywbpdDqGnHbARUaUwjtlbqb9+bNBvAb6inewgUvLhRNevEUTkoSEBLZs2TJuhzcX8X65rAerw82q5Ei2ZvufXQuCQFdXFzU1NeMKxJjAqEVMmRfnx6MdE4Zfv9ngd84dWDkK0G2wcte/xs65t2cQZ2qn12r1a7sTPwxPHu7kl296WtAu3ZSGXC7j9+80+d1fRmwYT9+4DbVCzm1PlfGej4/4NTsy+Z/zClHIZRhtTp461Mk/9jVjGJs7nBqt4YotGajcVt6v7eO03+/D6pjd7PKpcAtwrGOEYwANvcSEK9mS7enhbh0yYbS5eOZIF88c6WJHbizX7Mji22et4Ka9Ofzzk3aePNRBVfcoX32qjNWpWr56Sj6XbEzlnjca6PPxZ+/QW4kJV1KYqKF+0MbL5X0c6xjmVxcXsT5jfDGbRinnnkuKSI8N48FPOrjvo3a6hm38+PyV3kpymcxj6JISreEH/67lnbohvvxEOX++wlPs9vAXNnLHc1UcaR/mjhfr+eXFMs5dM/fhGTKZjG+fkY9CLuOh/Z389sMe7rmkiPN3rcJqtdLX18fIyAjl5eUw1r4pRuaBQ1xCyWJE3mKWIXBdl1vgPxV9/OWDVu/7ICpMiVIuo3/UHvS+AFIjZFyc7USugQc+aqJhyLNRlQGnrkrghrHf97PHenj2sJFRh3HC+5qK5Cg1W7JiSIvRkBqlITVaQ0q0hv3Nev44SVvan95v45Q0Ob89KYYnvriJ256p9BZR/vbS1RQHKbjNS4wYE29z0OLNKJ9WscNtBr/vBVacj1qd6Mye696pW4rQapTeKXN6vZ7Ozk7cbve44sFQvDdcLpc324oUeYeeUKXNfR3LfE1IApmteAuC4O3tvmq7/307nU6qq6sZHByc0N5VNGrx5e1aj3iftdpzkbY73bxU6mnVuueStUHPue1Ozzn3sMXJmpQIdkf0olL594wz9rq+2y3n5bYx4d6cRu+wjU+adchknpGbg0Y7aqWcP125AbvTzTX/POqX0v7u2Sv5UnE2o1Ynjx3s4JED7QyPzfVNj1aTFhuBWxC498MWrw/6QjJscfKhT+Tsy6FWA4daDSRHabhqWwY37M7mxj05PPRJO48f6qCm18jtz5SzLTuG+65ax0vlfTxxqMtrtDFscTJscbIyToHBoaBDb+W6R8u47ZQcbtiVNW5TJZfJ+MZpeWTEhPGL1z2DHPpGbPz+sjV+F7tz1ySRpFXz9bFCti88Usp9V60nOz6cv31+PTf+4yNKh+A7/6pFZ3Jw9SSe+dNFJpNx52l5mG0unjnWww9eriVcpeC0VQmkpqbS1NTEnj17MBqNDA0N0d3dTV1dHREREV4xDzatai4sRro+ULwFQeDjZj2/e7eFhrF2rSiNgnCVAp3ZMaGz4No0LXeclkfLoJlHD3bSNXZOrZTBzhSBS4qicahU/OPjNvY1G6blYS5y1dZ0duTGkBYdRmq0hvhI1YRFZp7HEsWNe7IZtjh4s2aQ5471UBNQ9PlBj5wfvtLILz5TxBNf3Mw3XqjmaPswd75Qwztf3+kNHETyEiI40GKY8NxbpZAToVZgtrt4u9az0d+UGU1p5wj2gGO4LqPn/ylRau864eHhZGRkkJGRgSAIGI1GdDodAwMDNDY2em2DRUGfreumeN7tK96LOUt+riw58Q4FVquV8vJybDbblKYvsxXvQ616mgZMRKgVXLzhuL2d7zjN4uJiwsKClzgFRt49w1Yqu0eQyeC0Qs9Z2+E2PRaHm6QoNXsKgqd3fvd2I6Wdw2jVcj6XZWJFfvAhKk8f7uTlNk/Ud+OeHD5p1lHdM0q4Ss7lWzJ44pDHkepH5xeikMk4+0+f+EXMv7t8HResT+Wtmn6+/1K11+kpLVLOF/fkcaxz1OuCNhUyGVy0PpXkKA3xkWrUYzO6bU4XdqeAzelm0Ghj0GgnJVpDdJiCnr5+rDYn/a5wavtmdkbOWDSskMvoH7Xxp/eaue/DFi7fksG3zlzBl/Zk88j+Dh7Z386R9mG+8HAJ3z4lgye/uIlfvNlEedfxtGaD3kVegprUaE9hzh/fa+WTZj3/95kiUqPHR6ZXbEkjNVrDt16s5mCrgese9VSip/kY+WzNjuExn1ayax8p5b4r17E2PYobCgU+MiXzfNkAv3yziTCVnEs3zc5O0ReZTMYPzl2B2eHiPxX9fPvFav565To2p3uKiORyOTExMcTExJCfn++dVjU0NERtbS0Oh8OvtzwUHtGLFXnL5XKqe0b53bst3uKvCLWCuHAleouTfmPwaHtdWhR3nJbL4bZhvvOvGu9GNjZcyVVb07lwXTLv1PTyx2O9dI5MLzW+Nk3LpRtTOb0wkcQJ5tFPh5hwFVdsSeOKLWl0D1t5ubyPez88PovgtepBhq1V/P6yNdz/+fWc9eeD6MwO2nUW1qT5Xy9FC9OWwYnP46PDlJjtLgxjr4HR5vl7b8B1q9voueZNVGkuen1ERUWRk5PjZxvc3t5OdXU1Wq3WK+Yz2UT6FqshtYotPQYHBykvLycxMTFomjyQ2Yq3GHVftD4V7Vgk1d3dTVVVFdnZ2X7jNCda1zfyfmcs6t6cFeOtMhejyJNXJAa9sL1d289DY2fR166Ec/d6iuECsTpc/Pl9T8R94650UqM1VPeMEhuh4hcXr+bul2twC3D5lnQStWou+dtBv59/7Iat7MiN48OGQe58rgKHSyAtQuDazUnsXZ/PN56v9DNmmYjvn7MSlwCtQ2Zv4VrPiNU7X3w6RKitrM+IJiVKg8Plpstg9Wtrmwib001MuJINGdE4XG5qeo08dbiTd+sG+N8LivjqSVnky/q5t8RMy4jAT97uZFdyB7dtjKIyK4FHSgyMjD3OliELUWFKTlkRz6E2A4fbhrnsgaP89MJVnDG28fLlpBXxPHKdJ0XpMXQp5a9XrmO1jy96fmIET3xxM199ppKaXiM3P1XBQ1/YiFwG3z4tG224hocPdPKTVxuI1CinbPWaDnKZjJ9eWIjZ7uKduiG+/lwVf7m8MOhtVSoVycnJJCcnIwiCX2+571AV8c9MzY4WK/IessLd/23g1WpPxKhSyMiICWPE5vRG0IGkRKn55hn5HG4zcNszld4itMzYMK7fmcnaNC0vlfdxxYPHsEzjyCg9Ssm121I4b0MmiVoNgiDQN2rn7dpBKrpHqekdRRgTSM8f1fF/h3v+jo9QsSo5+AYqPSaMr5yUQ3qMhrv/U+/9+ifNer76TCUPXbuB9JgwdGYHvSO2ceKdl+ipt5i04jxM6XVWi4tQeR0YT1/ln3XsNgmAbNptYr6eBYwNjRINimpra7Hb7cTGxnqj8qioqAk3gYHibTQapcg7lMx29+12u2lsbKStrY3Vq1dPu3d7NuI9aLTx1lhV+Oe3Z+JyuaitraW3t5eNGzeSnDz12WRg5O1NmftUmX/Y4LmgiMVrgY/he2P93OfmqfnqxTsnPJN8oaSbQaOdeA1csTGJqx6rBuC2U/L4+4et6M0O1qZFkRMfwa1Plnl/LlKj4Jkbt7MyWcuRNj23P12OwyWwNRF+e8UGDva6uPLBI5Oeae/Mi+O6nVl80qzj9+82zfn822x3UdE1QkXA1yNUcnbmxzNicXK03RD0Z4ctTsq7RkiJ1nDOmmQqukboHrby1afK2Jos4+atcbzw1c3c92Eb//ikgwP9MtoPWLl1o8BdGxz8s05O05ht+qjVyQeNOnbnxTJgtNM4YOYbz1fz/bMLgqa2V6dqeWJsiEPjgJnrHy3lN5eu4WQfw5RErZqHv7CRm5+soKxrhJufLOerhZ7PxDdPz2PU6uSF0l6+91ItkWrFuKhmNijlMn59yWq+9mwl+1sMfOPFem4tnPxzKJPJiIyMJDIykqysLL/oqK2tjaqqqnEzuKdTgLSQkfewxcHfP2rlyVIFLsHzOStIjMDmdNOqCx5hqhQy7j53BU2DZn74nzqvaG9Ij+LaHZ7f+bPHevjFG5MbtABoFPC14lR2Z0UwbNBT1tHF717qpNuqomVEQGeZeUCxMzeWX19SNGHXy2fWp/iJN8DR9mG6h22kRKup7DlubeqLaGHaqfe0aqqDGKtofY6CbE7PQKHc+HA/kRYEgd4ZincgarWalJQUUlJSEATB77y8vd0TyEzkZx8o3qG2R11olpx4zwbfkZYz9UYXLVJnwvPHunG4BDZlxpATo+DgwYPIZDJ279497TSMb+RtMDs41OoZGnLmWETVobfQPGhGIZcFLSJ55pMGRm0ucmNV/OqaYjSq4L9Kh8vtrUI/J0fOk8d60Zsd5CVGUN9npLxrhJhwJRmxYV4Pc8aGa/zzui2kRGuo7B7h5sdLsTrdbEiU88drt/Onjzp59ujEQxZu3p2O2thLk1vN7c+Uz+icbzaYHW7eG5vUtC49mrgIFR81Bj//7hux8UZ1P/GRKqI1CkZsLo72C3z3/RG+px7itpOz2Z0fx/dfrqV71M5PP3Fx28l5/PxMG3/+pI9Peo8/mf0tBnLjNOzMjeVgq4FfvtnEiNXJLXvHO/alxYTx6HWb+OaL1RxoMfD1Zyu5+7yVXL75eBo8Qq3g3qvW8aXHyqjrN/GXKhnbNtvIj4jgh+et9Iw8rRngzuer+fvV69mSNXMf/kDUSjl/uHwttz5VQUnnCPdVKzip2Dxt3+nA6Mhms3mj8oqKCtxut19UvhADQibC5nTz9JFu7v+4fazXWkZegsdrv75/4gzOd87Mx2x38f/ebvZWnm/PieFLu7Oo6zPxm7ebJ0yv+/K/569kQ0Y0z753jIo+Ky9UDftsFmSegT0zIFKtIC1GQ4feysFWA5978Bi/u2wNG4IUUspkMn53cQHffNm/QHXQZPce+fQFKchL0qqJVCsw2V106C0UBFSl251uPzMl0dv99EL/jKEgCPRZPP8PhUGLTCYbZ1AU6Gev0Wi87zuHw+HdRIrZo+UceS+sN908MDAwwMcff0xERMSshpoEKxybDJdb4JmxlPl5q6L45JNPiI2NZefOnTM6P/Gd6f1+wyAut8CqFC3Z8Z77EKPuzVkxRPv0VrrdbmpqanizytOiduWOXMImEG6A/1b00mWwkqhVsz5RydMlnozBiqRInjvWjUwGl2/J4M2a4/2hewrieebG7aREa2jsN/KlR45isrtYm6jiZ5dv5aanq4IKt1Iu41eXruXez2/kWOcof6lw81pV37wLdyCV3SN81DhEolbNzrzgPfGM2TeO+KTshy1Ovv9yDV95por0mDBeuGkrZxUl4nQL/PH9Nn61f5grVofz7TPy8ZWZVr2NknYD2dGeXf1fP2zj12834w6SCo4KU3Lvleu4eEMKLgF+8moD/yrzN/iIDlPy96vXk5sQjt4m4/YX6xk02lHIZfzy4kL2FsRhdbq5bSzFHgoi1Ar+euU6ipIjMDpl3PRE+biRk9NFo9GQlpbG2rVr2bt3L5s3byYqKoq+vj4OHDjAgQMHqK+vZ3Bw0Ltxnu+0uSAIvFU7wMV/P8Jv3mlmxOokI0ZDjlagZ9g2oXDfujeb756VzyMHOvnrh22edqjkSP54+RpOX5XI3f+p4w/vtUwq3J/fls5bt+/g5xet4vXqAS5/4CjPNAq8WW+YMMqfLia7i8YBM+esTiRMKadv1M71j5bx9JHuoK/p9sxIsgMKrAeNx8W7d2T871wmOx4pNwdJnX/crA9q03pGoX/G0Gi1oxsL7OdjFKhYp5GXl8eWLVs46aSTWLVqFQqFgpaWFurq6jAYDHzzm9/klVdewWg0huzM+7777mPDhg1eP/3du3fz2muveb9vtVq57bbbSEhIQKvVctlll9HXF9zAarosuch7urtvt9tNQ0MD7e3trFmzhoyM2VXhzjRt/lHjEN3DVrRqGcnWTtZtXEdqauo0fnLidUVjljN97Ac/GjvvPmXl8TNUq9VKaWkpRpuTxhGPCcppQc5YRVxugb+PzfL+4u5sXq9uweYUiFArvOfpXzk5j//42JxesjGNn1+8GpVCTofOzHUPHWbY6qIwUcMX9hZw7cMlfmMJRe757BpkMhkPftw2ztJ0sRg02r2itzYtKuio0GDsbzHw2fuP8PVT8/h/n13Ny+W93PNmE6U9FhoG4RcXh/GXz63luy/VYhqLMuxu6Bx1kRMto21E4PFDXXQP6PnhuQXEx8b4pY1VCjk/u3AVMeFKHj3YxY9fqUerUfjZTyZEqnng6vV87v6DtOtt3PJUBf+8dgMx4Sp+d9kabn2qgmMdI9z6VAUPX7cxJNOZosKU/PHSVVz3SAl9o3ZuerKcR76wcdqjJoMx2VAVscc3JiYGh8OBxWKZlwi8ptfIr95q8tqZJkaqyY4Po3XIjM4sA8Zv3q/cksaq5EgeO9xF65hxSkaMhltPysHucvPLN5uCpph9+ckFK0nUani9up/P/O3ItM6/RQqTI8mKC0erURAVpkSOG6PJwqjZgsFko2RQwOE+/jr9u+J4sajTLfCLNxop6xrhR+evJNzHQc3tdnNhnoJ7K45f84b8xDv4cxIr7e3O8RuC16rGF6oma9Xjpti1DJoRkBEXrpwwtR9KAv3sm5ubaW5upru7m6997WsMDw9z5513cvHFF3PWWWexfv36WfeXZ2Zmcs8997By5UoEQeCRRx7h4osvpqSkhLVr13LnnXfyyiuv8NxzzxETE8PXvvY1Lr30Uj7++OPZP79Z/+QiYrFYKCsrw+l0snv37jn16s00bf74AU8KujhNwSl7t8/6zEQ887bYXd707lljKXObw8WBFo9zk3jeLfarJyUlodcm4nRXkZsQQV7ixOu/VdNPy6CZ6DAl23Pi+O1bnrS4mNY6eWUCWXHhdOg9F6e4sQI2pUJOj8HMNf84wJDZTU6chrWZ8Xzvpeqg6/zu8nX86b1mWicpaAlGbkIE56xJ5qzVycitBurr6sjPyyE/Lw+lXI5cBnK5DIVMhlzuGQ1a1TPC4VYDR9r0HG03TMsn3eUWvMKdEaNBZ7Qx1ZGixeHmV2818VpVPz+5cBXPfHkL33qugoYhG994vporNqfx4DUb+Na/arwRqluAthGB9WlaqnqNvNtiZujZCr5YKJCSeLxfOiwszNtvbbS6eLGsl+/+q5a/XqnwOyJJjQ7j6+vgLzVK6vtNfPWZSh64egMRagV/+dw6vvxEuae47ckKHr1uo18F+2yJi1Dx1dUu7m/W0qG3ctOTnsK5uIiZT9sLxkRDVcThIC0tLd7XaS5tQYxt3v70fgsvlfUhAGFKOatTtQyZ7BzrCL6R21sQx1lFibzw/9k77/A4yuv7f7ZLWvXee+/ulguYYkxvpndCIBBIIEACpJFACCmEQAgl9N7B9I67JVm2epfVe6+7krbO74/ZHe1qV7ZcSMj3l/M8fkDS7syWmfe+995zzynv560yUXfc31PJD9fG4uuh5Jk9nXS6sbq0Q62Q8dMTEhjVm3h0e7uLFrk7+HooyYzwRiWXE+mvQaOUo7bZ4aodrHG1YT6ckBqMj0aBXq+nvW+Ih3f2sa/fef26dFk4b5f180nNIDMmC49ckCX9zWq1siRU4SQwM6w3kmoTVxlwE7wHpwzU9+uQ2frqjpg2WtjuZjzzhLQgl5E2+z3475ZFtUMmkxEZGcmbb77JzMwMYWFhbNy4ke3bt/O73/0OrVbL888/zxlnnHHYxz7rrLOcfn7ggQd48sknKS4uJjo6mueee47XX3+dE088EYAXXniBjIwMiouLWb169RG9n/+64D04OEh1dTVhYWFkZGQc9aypQqHAZFqc6ld1aw+7W8Te9G1nLs7e8lDn3dM6wqzJSqSfBxnh4k51f+c4MyYroT4aUkO1tLS00NraSkZGBtHR0bxmE2Q5IXXhrFsQ5rLuK1bF8NTONhz3zFH+Htx/VgbHP7xb+t0D52SiVMjpGZ7giuf3M6AXiPLX4KlR8X5Fn9vzPH5pLr//pJHBqYNnIXYkh2jZlBnKpqwwUkO1WKxWvtlXR2HTAKOKAB5rHGRsugetWrQ39FIrpP/XqhXizxolK+MDuPXEJAA+2dtA3bCJhlGz5Hy2EOwM4mWxfvSMzy6YaWiUcgRBoKp3igufLePXpybz2DnxPLq1hc/bzbxT3kfLsJ6nL83ht582SVkdQHWfjqUxvtT0TlE5IvBqpw+/iNbS399PU1OT07z0rzYlojOa+ap+mNverePpy3LId/BuD/GER89P5cfvNlHVM8VP36nl8Yuz8fFQ8tQl2VzzSiVtIzNc/3o1L16Zd1TjRXYEeMh45rIcrnm5kpbhaW58o5rnrsh1mf89FrD3LDs7O0lPT0cmk0mmKo5jQUFBQfj5+S0qMzKYrbyyt5tnCrukjWpGmDdmQaC8233Qjvb34IdrYvi2cYR7PxUV0TxVcq5cGU1soAcvFnfTPLTw5jRYq2ZtUgAtQ9M89E3rIV9jiIdATLAvDYPTTM6aKW5zT7Kcj0CvNm49IZ5z88LJTvHm+ZQEyjrHuO+zA7SMiJuKN0r7uW2JmkfKjexqHsVotqBWiuuk1Wp1WTOHdSbCfOZ63lZBcAq8O20SsDmRPi7X187mEbcVhflTFxarwOulA27/9u+CI2FtZkZMWG677TZ8fX0xGo3s3buXxMTEY3Ked955B71eT0FBAaWlpZhMJk4++WTpMenp6cTGxlJUVPR/J3gvVDJzLJPbJT+PBRQKBbOzB+/t2QVfvtzfjoBYgk0KPTqigz3z/rZJ7DWflB4ivfcdNkWztYkBlJeXo9PpJOtQi1Vgu+3vByuZ72oeoa5vCi+1goxwH57Y0Sb9Ta2U89jFuWxxCMi5Ub6cmBZMe08/179eQ68ewnw0bMoM4/lC905JT1yax28/rmf4EESdtDBvMWBnhhId4El1zyTbGof4y5eNlHWMMS0lDnML2LTRwtAiCEARfhryQpScnOjFU1fn0j0+w5e1g7xS0nXQQF7aOYFWo2BjRoikbOcIg9lKkFYcyWkbmeF3nx3g0rxANqcoObcggzvfr6Osa5Lb36vjnxdn8eTOTt536F2XdU2yNMaXhgE9pd067t8l48lLctGqcBp1MZlMXJbgz8iEJ6W9M/z4zRpeuDKPtLC5alJSkAdPXZLND1+vZm/7OHe+X8/DmzMI1Kp5+rJcrn65go7RGSnIutOfPlzEBHjy9GU5XPtqFXX9Om55u5Z/XZrznVk42g1C7GM/jqYqo6Oj1NbWYrFYpNnyoKAgF1MVQRD4qmGYv3/bKm3Sovw98NUoXYRKHPGrTclU9U7y+88OINi4G5vzw8mxaZI/vWfh5wZqVUT4amgc0PNh1eJ7mEOzMoa6D18WdXTaxL2fHuCdsn5+uSmJnChflsYG8O4NK/jZu3VSFpwUE4amqguDReCDr3eTFil+bu7kYIf1RkJ8NMhlYnl8VG9yCtI7pBae69TL57Wu946vh5Ll8xQnv24YonvCgFaJE0Hz34n5Xt7YNo/YWOzr168/quNXV1dTUFDA7Ows3t7ebNmyhczMTCoqKlCr1fj7O1ctwsLC6O9f2NDmUPjeBW93sJfJLRbLUZfJ5+NQPW+7LrrBYCA6IQkaWo9JCVGhUGAyW9jaJGbyGx3mdnfayuihliHAlzVr1khzs1U9E4xNm/DxEKVAF8JTO8Ws+6JlUTy921ku8benpxHqo+GRrXOs01+flkpt4wHu+rSDTp2MAC8Vfzgng1vfnj+UJeKJS/P45Yd1LgFSpZBhso3QaJUCj1ycz3GpIQzrDPz1q2Y+remX/m6Hp0rOkhh/lsX6szTWjyh/T2ZMFnQGC3qjGb3BwrTtv/afW4b1FLWO0jdhoG/CwBfNev5WtIOCxEBOSA3hrR+uYHvTMM/vaXfLoAXQGyx8XT9EZoQPQzoDQ/MeN6I3MWOyEh/oSfvoDG9UjnIgTM6TV/vywpV5/OiNahoH9Vz3ahVPX5ZLcqgXD33TKhH07AG8ZXiamr4prnmlkn9dmkPYvHnpkZERbswZ5kGdQOukheteqeAf58STmzDHpciJ8uWfF2Vx05s1bD8wwq8/buTBc0RhGHsAbxzUc/NbtTx9Wc5B/cAPBkeSU1KIlqcuEcvzpZ0T/OqjRv5yXvpBFb6OBvODiqOpiiAI6PV6ydyipaUFtVotVTD6jWr+trVdKocHeKkI0qronTAsSLy7eW0ktW29/OWbFuma3JQRwtrEAD6omiubu0NyiBceSgU1fVOLKo0fCt4aBdmRPiSHaIn00zCmN1HZM0l175TbzLamb4rLXqzg3Nww7tmUjJdawcObM1j6J7GSdvcXvaSE+lDTN4VHeCIBAQIjIyOMjY3ZjjB3fYzojMgAD5WokjY2PRe8Z00WqSpwfIrz1MvkrJldLa7GLMenBEoywNiuqWf2iCJQJ8XIj/jaPFpYLBapDWMfEzuWGuppaWlUVFQwMTHBu+++y9VXX82OHTuO2fHn43sfvO1l8vDwcNLT04+pJCOHCN6jo6NUVlYSEBDA0qVLebtM3CV5HoOLTy6XUzdkYHzahL+XimW2QNw1Ok3b8DRymcDJ2dHkpCc7LWr2cajjkoOcbhBH2PvBKoWMCD8Pqh2IWicmenPhsih++3G99LtNmSGYB1v4+74Jmidl+Hgoee7KJTz8TbNUdrQjUKviD2dncteWWrfENfsimB/ty3mho6yK9+f1km7+/m2zZIMY5KUk1svEqsRgTs5PJCPcB6XDe5k2WjCYLSSoFKiV8oMaR+xtG2NLSTP7eqYZnraytXGYrbbPKCfSmwsTBWasHnzdzYK9yro+0dJ1TWIgha3Oi9G00UL76AxKuQyzVWD/gJXrXq3iHxdl8fJV+dzwehWdY7Nc9XIFT1+awz8vyubOLfXS52YP4F1js7QMT3PVyxU8c1kusYGeTvPSsbGxvJRh4AevVnJgeJaffdjKrdkHCNCI4j8REREsj/Xj4fMzuPXdOj6rHUKrVvKb05KJC/TkX5eKWXJlzyQ/e6+Oxy7McjuPe7jIjPDhkQsyufGNGr6sHyLSX8PtJx59aXE+DsU2l8lkeHt74+3tTWxsLBaLhfHxcZp7hnjkkyYK+ywIyFArZET6qpkwWBcsc/+gIAaFHF7c14PofiuwMs6PTRkhbG0a4befNrl9HsDyWD8MZutRmYl4axRYzGZmLHPXtc4gBsmKrkmCtCqCvNUEadXcsDaWVfH+1Pbp2N0yKrmYeSjlzJqtfFA1QLC3mltPSEClkJMR5k39gA690UK3TVe8a9LC2UviiY2NpbOz08ZynmO594zq+LxEvGZ9NArJSQxgb/s4s2YrEb5iC88RWxuHXTbiACfNa+ftah6laVCPp0rOSbH/uZAzP/M+FqqAjlCr1SQnJwOwbNky9u3bx6OPPsrFF1+M0WhkfHzcKfseGBg4IrKzHd+74G3/MK1WK01NTXR1dZGdnU1ExHdTanEXvAVBkJiJjvah00Yx+ByLnaNCoWBfn5jpnZgajFIhltHf2CFmurkRPuRmpLg8b1ujze87bWGFLXvWfVZuOK/u7XL629npvhwY1EnjbgDH+Y0xNO3F3n5xh//EpXk0DoiLhSNSQ7XcemISd75X4xLUHfGDNbHcekICr378LZe/UCYtdJkRPlyd5YG3YYi8vHyJtKSbNVPaMsre9jFK2seo7Z2UsleZTLTR9FDJ8VQpbP8vzrauTw7i+JRgbl4VxOysFlVIPNsah9neNExl9wTVvTqqe2FFnJY/nZ/MwOQs/9rV7uIFjm3TUdg6SkKQF93jMy6LkqOudVWv6MT0+MXZvHRVPj96o5qW4WmufqWSpy7J4fGLs7jpjRpmbbrOZV2TLIn2Ra2Q0TNh4KqXK3j5qnxiA53nnf21Gp65Ip9rXq6kfXSG51u1/Ch5Gp1OR1lZmWhVGRjIXceH88dtfbxT3oe3RsHtJyWSFubNk5dkc/1rVRS2jvH7z5r4w1lpx2RxWhUfwO/PSOVXHzfyQlE3UX4eXLzs2LStHHE4r9Vkhfcb9DyzZ9iWmcqI9VczYzTTPuaex3BGdiipoVpeLemRWjIx3nDx6kTKuya4/4uFxVWWx/qhN1rY78BtOFKIJMu59+rrocRsFZg2Wpg1W+mZMEhl/21NI2SGe/Onc9O5eFkE931+gHfL+zFbBU7JCOar+mG+qBvipxvikclkXLAkXHofdqnS+ZsYg0ztFLwtyCnsFEfl0nzM1NVUSxWN7U02lceUQJfvx539qEYppyBxbjxTEASetmXdZ2f446M5utG4o4Fj8P53SKNarVYMBgPLli1DpVLx7bffsnnzZgAaGxvp7OykoKDgiI//vQve2D7YyspKrFYra9as+U5VcOYHb6PRSHV1tVOf2Y4Zk/g4L/XRf2wymYz9fWK57aSMEKanpykvL2d/j7ionJTpuiPrHpuhaVCPQi5jfbJr/wmHGWe5DHw0SolJDhDiJScpQMmfvpyzKDwhUiA/OZq3D1iwCpOsSwokPsiLm9+odDn2T22Be6GRF2+Ngj+dl0VBQiAPfdPMq1UKBKbw1ii45bg40pWDWMxTpC9fRc2ggb1lByhpH6Oub3LBWXBBELNfcbMwV56s6Z1zYUsIUJEfquKCtWZ+uDaW0+JklNaOUzEbxAd14+zrGOey5/ZzcnoID23Opnd8loe+aXY70tY2Mi1ZjVYsQG7CRny78qUKHt6cyYtX5vHjt2qo7p3iuteq+MeFmTxyQSY/eadW2gSUd0+SF+WLSimnfWSGH79Vw6tX5+M/rwUTpFXzzOU5XPVSJR1jszxRJ+e5KxLID/RjYmKCkZEREpWjXJxo4c1WBS8UdxPmBZetTiAvypdHLsjkx2/W8FH1IOnh3ly50r1V7cHgLoienRtG78Qsj+/s4I9fNhPuq3HbAz1SLHZETBAEvqwf5u9bW+m1BbiYAA88VArJTGQ+Qj0ETk9UsbNnlE9rxLGmKH8Pzs8OoLyl/6AEs8RgLyxW4ZgEbZnttaoUMrwsek7KT2RJjB8x/h5SNW9s2sSI3siIzkT3+CxP7+mkrl/Hxc+VcefJifz61BT0Bguf1w3xVb1YYeoen6VhQE9GuDdrkuYCp49GwZTBwgEH+WCr1crwjPPNVpAUSKVtg33uyrkSe3NzC1/XidWbvGA5RqNRKjuP6o0Ut40xH2sSA5ySm/2dE1T2TKJWyDgv0w/DxKF5LN8VvsvM+5577uG0004jNjaWqakpXn/9dbZv386XX36Jn58f1113HbfffrukOPiTn/yEgoKCIyar8X0M3nq9nsLCQiIjI0lLSzvmZfL5cAze4+PjVFRU4Ovr3Ge2w55tHovMu3XMxMisgKdKTpqvlcLCQkLCI2kcE0vz7iRR7US1pTF+Lou+HU/bGObHpwbzUZUzGWJNjCflfTPsdlAeu/O0bMJCQ3j37V0AXLMmjvs/bZBK3HY8elEOt79b4+ISZEdmhA+PXpRDbd8Up/6z0NY/lnFqRjA/XR9F14FaPLR+NCujuOOpMhdf79hAT1bGB0j/gr3VGExWZkwW2z8rs/b/N1qo759i54ERKronaBsz0TZmYktjKVqVjHR/OHdFEvcuj+fHJxt5bFsrWyp6+aZhiK2NQ5ybH8FjF+fy5r5uSRveEVYBKronKUjwp+ggLOApg4Wb3qzht6el8Ozlufz0nVr2to9z05s1/PW8DB46L4Pb36uTnMkqeyZJDdUSrFXTMTrDre/W8vRluS4ksHBfD56+LIerX6mkW2/iFx+38swV+ZInMkBengH11gO8XDbKX7d1YxrpZnl8IPGBgdy2IY6/bevgoW9aSQ7RUnAQoZrDwY/WxdI7McuWygF+vqWeF67MIyvi2ChULUakpbZ3ij9/3SIxxoO0KsJ9NbSPzKA3um+J3HdGKntaRnixTrzmPRSwMdqKRmPl2eJ+ZtzMLQPIZRDopaZ1EZr9B4OnSk5sgCcalRyj2Ur7yIytIiOnepurdefyWD9uOzGBk9LF0vOpmSH86uNG9raPc//nzVR0T/LA2WnojRaJBY6NEJYR7k20/1w1x74h7hydYcZkwVMlClKNzDq/54QgLz6vHUKlkHFyZgRajZLY2FhqeiaYKKzEQykj0DTM7t0d+Pj4EBAQwK4+cFMxdxFmebZQzLrPyw/H30POsO4/0+9mXvA+1nagg4ODXHXVVfT19eHn50dubi5ffvklGzduBODvf/87crmczZs3YzAY2LRpE0888cRRnfN7F7y9vLxYunTpv80k3T7n3d7ezoEDB0hOTiY+Pt7tjkx/DIP3nnZxp5sfrqGhtprs7Gxa9Gpmzb2E+Wqc2MZ2HKpk3jKk5yub4ItcJmNs2kSUvwcjeiOzJiuroz15bO/cDX/bhjiSYyN4fk8HeoOF5BAtOoPZSW0Nm5DLPR/ULRi4L1oWxa9PS+Wl4i5JYjUu0JMzI/ScudyXlroK1MGxPFQ+xf6ORrBlPmsSA1mZEMDKuAB8PJTsODBM99gsLxd3MT5jZHzaxPiMibFpE+PTJjRKOSmh3qSGeZMaquW3Z6QRpFXzSUkTe9rGqRwwojMJlA5B6WetPF/Sz09PSOQPZ2dw3do4Hvm2ma/qh3i/vI9Pqge4fEU0/7wkl/s+dT/qVtQ2TnqYlvEZs9uRMk+VnBmTld9+2kTn2AyPX5zN3R808E3jMLe/V8fvzkjlj+ekc/cHDdKYXtOgmCHNmi2UdU3ym48b+dO5riSw+CAvnr40hytfKKOyV88vtjTw6IWZ0uM0Gg13nJpFr76ebxqHefGAkuwEDb29vcTMTLI2UsOeXgt3vFfHG9fmExe0uOrVwYKoTCbjN6el0D9poKhtnFvequW1a/OJPAbz5RykbD44ZeDR7e18ZGNze6rkpIRqGZwyUtvnngn+sxMT0Bss/PHLZmbNVmTA+fnhZEV483xhF90LmI7g0E8e1h99ljhjstLopiLgpxZQKNXMmCzMmqzS9bG/c4IrXqxgY3owt56QQFygyPp/paSHv3/bysfVg9ywNpbr1sTMC97D/OR453XLsbVlt+i1Wq0MTTt/xybbfb0q3h+twzjgrhZx47o2KZC1BVlO7P+PK4dd3pNCBhscko7a3ikKW8dQyODa1TFYJgePKUHscDE/eB/Liu5zzz130L97eHjw+OOP8/jjjx+zc37vgrdcLv+3BW5si5XRaKStrY3ly927ctlhvxm0xyB472gRy3CZviaJQf/852JgW58c5LKQ6Qxm9tr0zzcsMN/99K52BAHyo/3Y0yJmGqsSAni/vI9wXw0dY7OSJZ+XWsEP1iVitlh5ea+YfZ6XH8H9nza6HHfGZFmwx31qVij3n53B1sYhHv5WDNw3rIvnx8fHsXPbVhqbW6kwhvPax92YLGKl4ScnJHHV6hiUchmV3ZP8c3srn9YMHLSPbkf/pMFJs1whlxGulROmMXPTcn/y0pPY3TzKm/t7aBue5mfv1PCv8HZuPTGJf1ycS1XPJA993UxJ+xgvFHWyrWmYv12QzSvFnS6bFoCGAT3hvhoywr1dpEgNZitxgZ50jM7wbGEXBrOVv56fwX2fNbGlcoDfftLEb09L4XdnpEizw9gUv1bE+VHeNcnndUNE+ntw2wkJLudOC/Pmx9nwWI2M7QdGeHp3Jzeuj5P+LpfJeODsNNpfnKZ5aJqHS3S8cOVS5IKF1MFhfvphG81jZq5/eT/3H+9PVGiQNGJ1pFAp5Dy8OZOrX66kaVDPTW/W8PJVeUc9nuaubD5rsvDS3m6eK+ySWjW5kT4YzFYnm1ZHnJcXRnaED88UdkkbrqUxvpyfH85HVQO8V7HwaI5KLvbSZxfYpB5L6EyQ7K8mJVRLSqiW5GAv4oK8eK6wkw8qB/i6YZhtTSPcc0oSFy2L5OpV0exqHmVv+ziFrWMc58D8VilktI/M0DYy41YzPDPCW5rRt1qtHBibq3pF+XtQbLNBPXHeurJ93oiYnf2PVwDNE67BO9kPetoOMGPrlz9jGzE9IzuUKH8P2set/2eD938C37vgjW0H/u+wCJycnKSqqgpBEFi7du0h1ZyOVdm8uq2f1tFZ5Aj84LRVeHuLi6lkAeqmZF7YMorJIhA3z6nHju6xGT6uFhem/Bg/KronSAvzxmyrbS0J1/BM+YRElPn9WeloVAo+q+mnb8JAkFZNbd8UI/Oyjd+ekcZfvjrgcj5szPN7z0ineVDHne/VIAiiy9otx8VQUV5Gwzh81KOme0LMmI5PCeK3Z6TjpVbw2t4u3i3vdepTxgV6sjTWnwAvFf6eKvy9xH8BXmr8PVXoDGaaBnQ0Deo4MKijaUDP+IyJnikLPVMyyoYnCKut5YIlkbz6g2V8WTvI80UdNPTruOn1SvKiffnZScm8fM1SdjWP8JuP6mkfmeb6V8u594x0jk8N5oHPmpg2OW8i+icNTM2ayYvUUtnr0D8UxJLkkmhfyrsneaWkhyCtmt+fkYqPhyh9+sAXB/jnxdncfUoSf/pqbjRvX8cE65IC2N0yxnOFXUT5eXDhUldSZpKfjDtPiOGP33TyxM4OMiN8nJzIvNQK/nFhFpc8X0517xR/+PwA952ZSkxUBP+6MpBLni+nT2fk2RojP8oa4MCBA3h4eEgKZgEBAYfdmvLWKHn84mwuf7Gc1uFpfvZeHU9dknNU7HbH+13saw/x8Ldt9NkCcHKIF74eSsq7JnG3MiQEefKjdXG8XdbLlkrxeovw1XDN6miaBvUublqO8PdUMj5j5igN7w4LFkFG46DeKSvfkBLEn89N54qV0fx9ayu7W8Z4eGsbZ2SHotUoKUgIYG/7OEVtY2x2mJVWyGSYEJicNTPkUEGSy8TStmPbxGq1UtIz95jz88J5bEc7MuAEB/vOwSkDdTZVtfXz3Ou+qh9y+x2cmhOBRiOnq6uLrfvr+LZRiQzYnOWLxWJxcfX6d+O7LJv/J/C9DN7fNQRBoLu7m4aGBmJiYmhvb1+U//Bc8D6yj00QBDo6Onht+wFARrIfBPuIgbtrdJr2kWmUchlrEl2Dt2PJ3F158c393VisAmuTAlEpxL9nR/ryZZ24kHUOT2IRxN+nhGo5M1ucnbULsMQEePJZjavIRMfI9II2nr87Uyz3/viNSvQGCyvj/bmlIJQ9e/bwcY+Gz5oVgJEQHzW/Pi2NE1KD+eMXTbxX3isRuTxUcjZlhnLB0ihWxPkfkkBin223Wq3U1dXR2NmPwSOI/d06CvusDEwaeHxHG0/ubOP4lGB+c3oaTQN6Xt/XRWX3JNe8VMaqhAB+eWoqW25cxc/fr2V38wj3fFDH5iWRvHHdMn79Ub3LKJDeaKGmT0+KHxxw4C4JNjJaTIAHXWOzPLKtjUAvFXeelMj4jJmPqga48/16Xr46n1tPiOdRhz5nSfs4axMD2NM6xgNfHCDCT+PW5vOMjECaR028XdbH3R828NYPlhATMJc9xwR48tD5Gdz4RjUfVA2QHu7N5SuixFn+CzK55pVKirtmWBIfyw3r8ySRmKamJgwGA/7+/lIwZ5Gs73BfDU9cnM3VL1eyr2OCez9t4o9nHx27XSaTUWPra9vJgqE+ahKDvWga0C84+vWHs1Ip75rkng8bJBnUK1dFoVLI+cf2dqndNR9BXkpGps0SK/vfhRCtilmjidQIP5ZG+9I5Nsu2pmG2Hxjh6pcreOyibB6/OJtz/rWf9pEZPqwa4LIVUaxJDOCRbW2UdEwgl4Gfp5KJGbNUKUgJ8aKkY+7itPekHYP32Lz3KtjCcE6UzzxhloVV1dyxzAHOzI8l3FdDUlIS722pA4ZZEalmpr+NXV1NqNVqNBrNdzKmdSgIguCkLqfT6f7rg/d/vavY4cJsNlNdXc2BAwdYunQpSUmixOZizEnso2Ke6sP/2MxmM5WVlbS1tdFqFEk+uYFz+1d71r001g8fD+fNgcUqsN3mMrZQydzOnD41M4xG2/8PTs6gM1gI0MhonZp7zb86LQ25XEZZ14Q0A94w4FqGfPDcTN7c79728/TsME5KC+Fn71TTMTpDlL8Hv1gbRHlZKWXTAXzWPIMMuCAvhM9vWcPSWH+ufLGUN/f3YLIIZEX6cO8Z6ey6Yz1/OT+blfEBi76ZDQYD+/btY2JigtM2rGF9SjCXZXqy8471PHyBeCyrANuahrl7Sx2f1/Zz6wlJXLkqBpVCxt62MS58Zh+f1w7w9OX53HpiInKZ6Hv+8/dr+f0ZKVywxJXtbxHEwJ0T6UrS6hqbxbZn4nefNbH9wCi/Oz2FFXHiiNHNb9VwTk4Y16+NkZ5jtAjU9+tYFuuHRYA73q+Xvrv5uGtjErlRPkzNmrn13TqXFkNBQgC3nyTOX//16xZKbKXQ3ChffnOaOHL45K5OdjSPExISQlpaGgUFBaxatYrg4GDGxsbYv38/FRUVWK1WBgcHDykbnBbmzcObM1DI4JOaQZ7Y2XHoL28BjM0K/P7LNi59oZyK7kk8VXLWJgbgo1FS3DbOqBu1vNtOSOC2ExL401ctvFfRjwCclhnCbScm8EXdEE/s7HAbuIO04kZ9ZPrfG7TtGNKbmDKJKn/vV/aTF+3Dk5fkEKhV0TCg57IXyhmcMnKpbRzvjdJerIJAWpiWQK2KaaOFyp5JQhyCarS/B1qNkpp5m05PlZz86Dl70CdK5/6eEe5NuU3UZn7J3C4UNX+ioGtsxu2M+/JYP8ncpHt8hi9sTPjbN2WxZs0aVqxYgVqtxmg0sn//fvbs2UNdXR39/f0Yjd89A91qtSIIgtOo2P+C93eA72pHptPpKC4uZmZmhjVr1hAUFCR9mYsJ3jNSz/vwMm+dTkdRUREmk4m0vOVU2Ug22QFWqVy4wxac1ye7BueqnglG9aKq2vI496pq9pGwmEBPKQA09oo98hMzw6WeYZCnjAKb+cWLheJiG+arccmuQ3zU1PVNYXDT/wvSqvnN6WI5vbB1FE+VnNtXejPY1cpsYBLP7hdv/ItSFNx+fDTtI9Ns/lcJld2id/izVy7h/R+t4rKV0U52p4vBxMQERUVFaDQaVq9ejZeXl9RmUSvlnJETzivXLuPznxRwbUEs/p4q+iYM/PmrA7QN63n+qqVsSA3GaLZy36eN3PZ2FZevjOH5q5YSpFXTNKjnypcqWRUfwLWr3Y9ZVfdOkRvlGsDtmY5VgJ9vqaeqZ4q/b84kPtCT/kkDt7xdyw/XxHLZ8rkZ6dFpE8M6I5nh3kwbLfz4rRoncpz9XlAr5Tx8fiZBWhUHBvX8/rMml9bSVSujODM71LYRqJOUxc7LC+eKFaLr3j0fNUj2l3Y/5JiYGPLy8li/fj0JCWLvva2tjd27d1NaWkpbWxuTk5NuW1lrEgO59/RUAJ7a3blgVrYQZkwWntrVwR/K5XxmY4SvivcnK8KHPa1jtLhhe5+WFcIDZ6XxQWU/j2xrQ2ewkBHuza82JTOiN/Knr1roWkCQx0ejYOQYKKIdDeQySA+QEeajZkRv4i9ft/LItjZevTqf+CBPhnRGPqoa4JzcMLRqBe0jM5S0jyOXyQi0TZnoZi0o5XPrpF1A5bN5Dl9LY/ykdobBbKViYO69n5QWxF57v9uBJe6otT5fVW2h7/eOk+aEe14s6sYiiGNjWZE+khiRl5cX4eHhHHfccWRlZaHRaOjq6mL37t2UlJTQ3NzM6OjoYbk8LhZ2++X/Sz3v72Xw/i7Q29tLUVERoaGhrFixAg8PkSErl4sKXou5YI6EbW4/b1hYGMuXL2dP2ySCAFkR3gRoxE2DwWSRyGhuR8Qa7YHdvaqa1SrQbVuotWqF5Aw0bFu/HJ3HloWJN3/n6DRfN4g3ojtm+883pvCWG89ubP3y7U3DvFQsjoFcl60mUDZNaEo+937ZgSCIDPSTYxV81TjK5c/vZ3DKQFKIlneuX7ngjPqh0NvbS0lJCbGxseTl5Uk3ojuORGKwlrtPTWXnHeu47cQk1Eo5u1tGuf7VcpbF+vPzjcmoFDK+qh/i3Cf3olHK2XLjKpbZxDh+vqUelULOT46Pd/taqnqm2Ji+sLa8wWzlJ2+LgfiJS7IJ8FJR16/jng8buPPkRFbEzekHdNgU3OIDPRmcMnLzWzXoDa5ZYZivhr+dn4lSLuOz2iFe29fr9HeZTMa9p6eQGe7N+IyZW9+plbQJ7jg5kVXx/syYrPz0nVq3uu8KhUIy/1i1ahUFBQVERESg0+koLy9n9+7d1NbWumRL5+WHc/UqcaPzm48bqes7tPqYIAh8VitaZT6+swOjVUZ6qBcbUgKp6J50O1cd5qPm4c0ZTM2a+dXHjbSPzhCoVXH7iQlkhnvzwJfNTmVjR/h7ihtud77T/25YBWgYEzBbBS5ZFom3RkF17xT7Oye4xJZtl3VNoNUoWRIjZs2dozPMmiy02TYzwd5qGgbm+uWpoVqxHThPCtZRMOW9cme5V7VCjtkqkBDk6WQp+/TuTmbNVpJDvJxU1Yxmq4v3PMA5uWGS/efQlEF6zA/XxDg9zt5zlsvlkn79ihUrWLduHXFxcZhMJurr69m1axcVFRV0dnai0+mOCf/J7hxpJ8z9L3j/F8BisVBTU0N9fT15eXmkpqa6MB4X6+l9OIQ1e0+2rq5OOq9MJpPmRpc79G5LOsaZNVkXHBHb2nTwkvmQzoDRbEUhg9LqOdtOqyDe5I5+vjE+4nt/ubgLQRA3C/PHpHKjfKnqnnA7GnZmThjxQV6SvOoZcTKOT/QlNWcJt73fIPW+f3N6Gh+0Wvndlx0YzFY2pAbz9g9XEHcEvtOCINDQ0EBdXR35+fkkJiY6VWcORnDUqBTcdHwCH920ilUJAcyarPztm2Y+ru7nl6emERvoSe/ELFe8UMo7+zr4YdI0J0eK7/vpPZ2MTExx18muTHBs4zmnZbkf21MpZNIcuEwGj16QiUohY2vTCI9ua+cv52Y4lT2reqeICfAkSKuiaVDPHxZQ+1oW68edJ4tZzkPftLCvw3kO3UOl4NELMwnUqmgc1PPbT8QMXSmX8dB5GUT5e9AzPssvPqjHusBnZv9sPTw8iIyMJCcnh/Xr15OTk4OHh4eULe3bt4+WlhbGx8e5dUMcaxMDmDWLm4ODmdVU9Uxy5UuV3PVBA/2TBiJ8NeQHWhmfMbP9wKjbas9fzk3njOwwfrGlgd0tYyjlMq5eFc11BTE8X9S1IIvcPkP/7+5rLwYjehN728e4xlbheXxHuzQ3X9E9icUqMKITN1nhvhoaB/VYBJEoOn/yIS3MW6qoOMLe7xYEgQcdCJMZ4d7U2Y7h6E7YNTbDa/vETfsdJznfZy/v7XapaHipFdy6YW6D+0pJD0aLQH60r4s5idXqnm2uVqsll0h7iT0oKIixsTFKS0udSuwGw+LcC+fD3u+2v5//C4S1/9PBe3p6muLiYqamplizZg2hoaFuH3esg/fMzAx79+5lfHzc5bz2Spf9IrZYLOw8MKdXPr9l0DM+Q9OADrnMfVaOQ8k8QAM9OucF+ZSMUBr65zKhGG+YmDHxXrmYtZ2TF+EiF3rtmli3WXewt5pfn57Gy8WdmCwCmQECPzkxibSMLH72bi3dYzPEBHjy6EW5PL69lU/bxAXzhnXxPHFpHt4eh0/0M5lM7N+/n6GhIQoKCiRJVUcsZjohIVjLS1cv5cFzM/H3VNHQr+O+zxpYlxTE6dlhWKwCj+3s5F/1cn59wUp+ulbseb9eMcL+uhauz/VA7qab83ntkNsMXCGT4eepZEhn5EevVxMb6MkfzkoD4KW93WxtHOZv52c4lT53tYyyIs5f6iF/XC0SCOe/t8uWR3KGrTx+55Z6Fw/mcF8PHrZl6F/UDfFmqc2X2kvFPy7MwlMlp6htnJeKuw/6mTnC7vjlmC3FxMQwOztLdXU1hXt2c02alWhfFQNTRm5711UXoH/SwD0fNnD5ixVU9oh97U0ZIQR7q6kYldPvxkDmpxvi+cu56Ty8tY3ni7owWwWOSw7kz+emU9E9yV+/aT1oYHa3Efg+QKMQ8FLJaBuZYWBilghfDQNTRmr7REVCvdFC44COvkkxWIb5aqiztdsyw71dPLRTQ7Xc97nzVEiwVk1KiLhZLpqnhpYS4sU3DeK6c7JDyfzhb9swWwXWJgY4kSf7JmZ5areroNH1a2MIsdmJTsyYJDOX69fEuKxli2Gb20vsjq0cxxL7nj17pBL7yMjIokvs8889PT39v8z7u8Cx6Hn39/dTWFhIYGAgq1atOuhs62KCt9lilRaCgwXv4eFhCgsL8fHxYdWqVS76uQrbYm21WSBarVZpbvm4FNcgYC+ZiyNU7kfZqlrFQBwd4IFe6bzb3ZQVyramuZnMCC94p7SHaaOF1DBv3H3S+9rH3RoO3LUpBQUCH1WKN+jNJ6aSEB/PA583UdI+jlaj4MnL8uifnOWZPWI//RcbIrljY7L0vg8HU1NTFBUVIZfLWb169YI322JHC2UyGecvieTznxRwdm44ggCv7+tmSqfnoiQBjUJGRb+BW95p4OLVidxzikhm/LZXzqhZw4/zNMjdDMlsaxph2bwsY9ZsRaOUE6RV0Tk2y+3v1XFKRgi3HC/Oaf/xy2b0RotTrxDgm4Zhqa3wwBfNDM24ns9eHk8L1TKqN3H7+66Bclmsn3Tsv33bKlV8UkO13LVRfF//2N5O7REabNhnfrOysli3bh1LliwhPNCPW/KUeCoEKnsmufOtUoaHh9HNGnlyZwdnP7WPT2zypMcnB7I6IYAv64fcEqDWJgbw4pV5lHZO8Atbhh7lp+HBc9KIDfDkjvfrqexxL19rJ6R9n2GwyIjxFTez71QMEO0lbkC6h6fwtW1yh/UmaWMS4edBvW0TnhDk5SRNqlUrCPfVuMy+r06Ym954YF4lp6J7UtoI5USJpfn9neN80ziMXIZU3bHjr9+0umyEov09nOR3X9/fy7TRQlqolvXJrlMTC2XeB8P8Evv69euJj4/HZDLR0NDArl27KC8vp6Ojg6mpqQXXAbPZ7BS8/5d5fw9htVqpr6+npqaG7OxsMjIyDnnBLCZ4O+p5a90Q1gRBoKWlhfLyctLS0sjOzna7y7TvS6zCnKf3qG22OinENTjtOAjL3Gq1UltbS3WrGEzTIoNoHJzLogO1KvKj/aQSpkYpQyETpBv/4mVRLvrE162N450y16xbpZCxLt6Xxz/Zy6xZIDbAg5NyYqntneSt0h7kMvj7BTkkBHnx6w/rsVgFVkYoOTXVz+VYi8HAwADFxcVERESwdOnSg47yyeXyw+qLBWrV/HVzNn85LxOlDHa162ic8eFfVyzB31NFTZ+OH75WxRnZofxyk+gS9F7dBCNyP27MdD2P2SpQ2ztJiLfzaxycMhLmo8FLraCsa5Knd3dww9pYzs4NwyLAXR80sDE9mE0OdrBmq0DbyDSZ4d7ojRZeqBekWX1HeKoU/P2CTHw9lFT1TPGP7a5Sm5etiGRNYgAGs5W7P2zAZBGv4fPzw9mYHozZKnDXhw1OzPUj6S/KZDJ8fX2Jj4/njONW8PDmLOQy2NY+w1Wv1XHaY0U8sauDGZOV7AgtZ+WEUtY9ybamEbfHe+ayHHIifbjh9Sr2tI6hUsi4YW0sP1oXx8PftvHqPvdcjBRbf/Y/RUhLDPYiL8qXlXF+rE0MINr/4MpznZNzpLNxk7im9PQPSprtJp3YEtGqFfhoFFLmPTFjwuhwTZyXH85f3eizX2IjRh4Y1Ds56gV4Kukcm0Upl0lB2ioI/PVr8RgXLIkg2WEtKmob4+sGV1GWO09OlNoS00aLVG7/4dpYtwnYsZjzVqlUhIaGOpXYQ0JCmJiYoKysjD179lBbW0tfX59Tid1xTMxuL/vfHrz/T815z8zMSKMuBQUFiy6LLCZ42xc4pVwmzVHb4WhmsmrVKnx9fRc4ilhOxTb+pVCIWsN2Epp9cXWEXaIxJdT5QpudnaW8vFxUpvIJAYaI8PNgS+UcKWVjRijtI3Ns3Q1J/lgsU/ROiDdyUoiWF+dpe4d4q91m3fmR3lTs38vuXvEGuGSFWBazj5Kdnh3G8anBPLO7ndq+Kfw8lVyd7XnYwcC+CWprayMnJ2dRlnkymUxiky4WBoOBsNlObl+m4clqM5W9On7/aQMPnJPOrz9qoL5fx3WvVvHM5bnIZfCHL5p5o2yQDREyfj9PMQ1g1iygmDU6uUUB1PXrSA7xonlomn/t7mRFnD+/Oz2F5kE9df067v20iYfOz6BpUEfbiNj+6Bid4YTUILrGZmibsvDCvn7u3OQ6ZRAT4MkDZ6Xxk3dqeXlvNyenB5EfPbdZkstk3H9mKuc/U0p9v44ndnZw6wkJUuZe1TNJx+gMf/qqmfvOTDusz+9gWJcSzKbMED6vHaJLByAjVKsgM1BOw7COmj73BiK/3JhAuL8Xv/vsgMSUX5MYwGXLI3mlpIen97iWbbEJuPRNGBY0JvmusDYxgKUxfuRF+5Ad4eMkLYrtWj4wNM3jO9rZ6rBRkctEKVHHhEBvq/wbPQKAMSK8ldR1iYRSf7WVospGyWDE0Z9eKZdx+YpITnt8n9O5T04LJs+WUb+417k9YrLdK5csj5SIap/WDFLXr0OrVvDj4+ZU/EwWK3/80pV/sSrenxMdRF3eKe9jYsZMXKDngkTOI8m8DwZHS93o6GisViuTk5OMjo7S09NDfX09Wq2WwEDREc3x3Hq9Hh+fY6PL/5/C9zLzPpKy+dDQkFSuPliJ1R0WF7ztM94Kp9dnH12SyWSsWbPmoIEbQG7baVtsZXOLxYLaFrzdEcTswd6RXDQyMuJUmu+dNErHdDzGqZmhTszf1FAtVquVHpvPryAITq5jgFsWMkCMchIComkeM6FSyDgvPwLdrJlPbKpuFy8XR8Ie2ybu3u/elEqAp/KwgqrZbKa8vJyenh5Wr169aK/bw1Xkm5ycpKioCE9PT645fQ2vX7eCCD8NbcPT3PtxI3ecGE+QjfB13WtVnJwezG9OEzPw7X0y0b97VZTLcfVmGUFervvhlqFpfDQKrALc86FI6nvwnHQ0Sjl7Wsf4pHqQRy4Qe9F2bGsa4RRbRv7y/kEXYpodG1KDOCc3DAH49cdNErvcjlAfDb+zjXI9V9hFqY3F7eep4sFz0pEBWyoHnEaAjqZt1T85y90fNvB5rfNIkUKhZHuXif4Z12OvjVRyZ46Znc0j/PSdWnrGZwn1UfPAWWlkhXtLhi/ukBnuTfPQ9IJCLMcaOZE+/ObUZHb9rICnLs3hhnWxrIoPcAnc2D7H1FAtfz0vg6Uxc+uCVcBFzW3Mdt+12Tbb+XEBFI+IbbKNaUFsaZjCKkCCj0Bd7xyj/pzcML5tdK5gKGTw0xNEEtmwbezMDq0SdAYr/p5KblwXC7ZxvUe2tQFw/dpYgrRz7blXSnpoH3FeI+Qy+MXGJOk6MZqtEn/iuoKYBVtk37XCmp2XkZiYyPLly6XRR4vFQm9vL0NDQ5x88snce++9TE5OHpVMsCMefPBBVqxYgY+PD6GhoZx77rk0NjrLTG/YsAGZTOb078Ybbzyq834vg/fhwO77XVFRQXp6+oLl6oPBbk5yMMwnqwmCQFdXFyUlJcTExLBkyZJFqbRJwdgh87bPYRrdZN72G8FiFRAEgba2NsrKykhJSZHea7ctAOvnjcGkhfk4Be+0MC2TBiuzJisy2dxC4QjHsrsjLlqfza4e8fWdnB5KoFbNx9X9TBstJAZ7sTzWn998VI/BbGVNYiDn5UccVkas1+spLi7GYrFQUFBwWLviwwne/f397N27l9jYWHJzc1EoFKSGefPWD1eQHu7NsN7IH75s4acb4gnxVnNgUM91r1ZxYmowvzxZXBCfK+wiJ9LHRTYSm/BHQYJzliw4jCgNTBm56/1qYvxU/OxEkcX+t29bkcngvjNTnZ63q3mUvCDx+fd82MDEjPuN1S82JhHqIzqVuSufn5wezLm2AP/LjxqYsjnGrYjz5/q14gJ+32dNUkXmSDBttPD4jnbOenI/n9YMusht9rkxdgnxVvPcJRkkhnjzaK2C3W2TyGVwVoonP1wexD93tPNMYZdbu9hV8eJnXOfGm/27wA/XxPDZj1fw+rVLuGhZ5IKufu6gVso5KW3hsUIc2nL2II4gjiOqFDLOXBLL1nbxuwkN9GPKOPeBZKsGXCxNz8+PkDLqR21B2Q77puGW4+MlPfoXi7sZnDIS5afhipVzm9L+SQNP7XIV3rlwaYTTCNmHVQMM6YyE+ag5M8c9KZjvIPM+FOwl9vT0dOLj4/H19WXjxo3s27cPvV7Ppk2buPzyy3nppZfo7e1dxBHdY8eOHdx8880UFxfz9ddfYzKZOOWUU9DrnStB119/PX19fdK/v/zlL0f1/v6rg/fs7Cz79+9nYGCA1atXExXlmg0tBvYgejA4Bm+LxeKk0jZ/dOmg55IIa3M9b3sZ3l252u4iZTSJCm0dHR2sWLGCmBhxhnLGaGHI1tO2VwfsUCvl7OuY62mnh3lLBKhQHw2l87K51FCtW4/rKH8NaTGhfFQlluQvXh6FIAi8ub/b9nM0Wyr7KGkfw1Ml576z0qUy1WKC6vDwMMXFxQQFBbFs2bJDaszPx2KCtyAIHDhwgJqaGvLy8ly+szBfD167djkFieI42cNb2/jd6SmE+ahpGZ7mhjeq2ZQRyIm2MbJ7Pz3ATetjSXKjM1/UNs4Z2a6LmEYpQy6Dwg4dD76zhzR5P3nhHsyarfzyo0ZOTg/hQgfN6kGdESsyov3UDEwZ+f1nB9y+T18PJb8/Qwz8r5X0sL/TNUu9+5Qkov096J0w8OBXc2XQG9fHisptBgt3f9DgtnVzMFisAu+V93HGE/t4yjYfvCTal8tWRFK9gHkIwB/PTuMPZ6Vy/1ftvFI5jskqY1mMLw+dmcDAtMAft/W5DfjJIV5E+XssmIkfa9x2QgKFd6zh1hMSnCRpDxeLea7MYQ2olxQTQ/i4ehCjRSAmwMOp5XB6Zgg6L2c9fLUcTosVGB8fR28w8YFD1h3qrcZohaQgD0kbfXDKwAtFol7DbScmOtnTPvRNq1NpH9u1dstxc6NhZqvA87bnX1sQ41aHwo7/pLa51WolICCAu+66i/feew+AZ599lsTERJ588kliYmJ49913j+jYX3zxBddccw1ZWVnk5eXx4osv0tnZSWlpqdPj7CI19n+HqtIeCt/L4L2YQGgvHWs0msPO1ObjcHreHgoZxcXFTE9PSypthwO5RFhzyLwPVja3fUMNjU0YjUbWrFmDv/9cZtdtK4H7eihdbCuVcpnTGFiQt4bhGfEckX4eLmS1UzJDpV6jIzakhvBF7QA6g4XYQE9WxQdQ1TNJQ78OtVLOuXkRvGMbLbvpuARiAsWAdqjM215JKC8vJz09fVHkQnc4VPC2l+P7+vpYvXr1giOD3h5KHr8kl+wIbyZmzDz4VQt/2zynavbgNx2cGWtlRawf00YLv/6kiT+fmy4JgDhie9MIGfNm9g1mgQibfeaHnQpmNQFcl63GUyFQ0zvFgx+UckWut9P8d/WIwJp4X5RyGV83DPP+AvPM65IC2ZwfjgD85uMmF/lUrUbJg+ekIZfBx9WDfFEnsr5VCjl/OicdrVpBefckL+931bd3B0EQ2N0yygXPlvK7zw4wrDcS7e/BNaujMVsFXtvXu6Cl5ic3rWBn8yg/eqOG9tEZgrxUXJls4aycUO79qpOSHvcVgGUh0Dw07fYaPdb41aZk9v1iLdetiXGRKz4SdM5rT7mDYAuGcYGedNsIZqdnhfJ2mZgVmiyCU3vgmoIY/rbDOWPcnBOAl8xEdXU1Z/6z0OlvdtOhn22IlYhyj21vZ8ZkJS/Kl00Zc9WB4rYxvnTjsvfj4+Kcqg5v7u+le3yWAC8V5+cv3OYSBLFy+J9yFXPcOExPixXHk046ifvvv5/i4mKGh4c59dRTj8m5JibEtsZ8d8zXXnuN4OBgsrOzueeee6TXcaT4Xgbvg8FOaLKXjnNzc1Eqj+7mWkzZ3H7TmGf1BAYGsnLlSkml7XAgdyCsST3vg5TNLWaxjObj68vy5ctdslJJFjXA02meG3Aqg65LDkIulzM6O/d+5juIOYq5OOL4lGDetgXnC5dGIZfLpKz79KwwBAQqu8UL9uy8uUzgYJm3vXrR3t7OihUrjrhqcqjz2Gf9zWYzq1evPiTD1FOl4B8XZhLt70H3+Cx//qqFP5yVhkIGX9SPUDgg4y/nphPqraZ1eJpn9nTx8OZM5nEYF+zBjuiMJAR5YrQI/HXPKKuX5vDbM0Sy2LsN05TUtXN2lPNC/1n9qOQ49tC3rQsKoNx5ciLhvhq6x2f5+9Y2l7/nR/vNlck/b5Y2ezEBnvza1tN/oaSf1smDVzEaB3T86I1qbnqzhuahaXw9lFy7OprsSB9eLO52O/rliDOf3McXdUPIZXD5ikgevzCdfUMyfvdZs1sFtNMyQ0gP01J6eMqrR4Qb1say9+druWR5JB4L3A9Hgn2HUSkI0qowWwXyonwp65xgxmRFKZc5zfOfkBrE5/OkUP09lfx0YwZZWVkIEZkMz85dlH5qAYsgSjKn+gmYzWbq+3V8aMvMf75xrhJlslidBF3sSAr24iIH57uWIb3UK7/5uLgF1w8c5Kf/U5m3Y/DW6XSSNLAdAQEBx4R9brVaue2221i7di3Z2dnS7y+77DJeffVVtm3bxj333MMrr7zCFVdccVTn+q8K3kajkdLSUrq7u1m5ciUxMa5CAEeCQ5XNrVYrrZ1isAr29zniDBEHwppj5u2ObS4IAk1NTUzrxDJZaHiE23N2jYoLvc+8zFshlzkF8+wIHzF4G8Tzd4667vp6+/pcfqdRysmO9KW8SwzO5+ZHMDljkhzILl4exZ6WUayCWHa3Z5YcJPO2i9jYqxeOlYQjwULnGR0dpaioiMDAQLcbn4UQpFXzxCXZ+Hkqqe6d4u2yPm61+W1vaZfTNTbD3zaLAitf1g9R36/jZycmuhynfkDHhnnCOrNmKwq5jCCtitbhaR7f0cEZ2WGcmhmCVYDXWpVcfcoK1sTO9RR1RitV7UOkhXigM1h4eKvrWBA2q0573/zN0l5JctcRP1oXS3aEaHBy76dz+uhnZodxZnYoVgFeaXQvbjIwaeA3Hzdy4bNlFLWNo1LIuGx5JBctjeCtsr4Fda//vjmTz368wul3aoWM169dQmKQFz94vZaGCff307Wro/m8bshJCvS7wMb0YL64eSU/2RB/1Ja/8zFrsriVe3WHxGAv2m339Fk5oby+X8yszVbBSWHgmtXRvDBPZOdH6+Lw1iiZNlq4+a1a6feBXiomjDKUcjgnzkp7ezs7d+7i3i2VCMDGVH9yHcx2XtvXI+kCOOIXGxOd1qq7P2zAYLayNjHAKai7g/3+/D5k3vYxse/CQ+Pmm2+mpqaGN9980+n3N9xwA5s2bSInJ4fLL7+cl19+mS1bttDS4rpJWiy+l8Hb3Yc6NjZGYWEhCoWCNWvW4Od3ZPPD7nCwsrnBYGD//v2MjIuBMMDn8OU9nc5le2sWKw5s8znWJrZNir2XHxhgl1F1nw3ZM297mdT+0akUMieyWroUvMWf3dl8DrpRuFoZH8DkrJj9azUKQn007GkdZdZkJSlEy5IYP0khbv08kRl3GbE9oPr6+rJy5Uo0Gs1iPraDwl3Z3N5zSk1NJTMz87AXjYQgL/5xYRZqhYxtTSMMThk5OS0QiyDjji0NRPt78nPbjOzD37aSGeHt1m1sV/MI6WHOkw/NQ9Pk2sZ4XtvXQ9vIDL8+NZlQbzXtIzM8v2+Q+87JRusQRGqHTcR7mZAh8HH1IJ+WNLgVpShImFtIf/tJk4tGukoh58Fz0lArZBS2jvG5Q8C9+5QkgryUDMzgRFTSG8z8c0c7Zz65jw+qBhBsvdi7NyZR2DbGs4VdLmV6gGsLoim8Yw1dYzOc/7Rz/89oEbj3kybu/6KZaTfX4o3rYlka4+sSoI41/DRynr8il4c3ZxJ1iLnsI8VbpX2LZsPrZs2M6k3EBoiVH3fPW5cUwE/ernX6XZSfRvreb3yj2ulv9kmVS5ZGEOoJq1atwhiSRv2oBZUc1vuOSbr1NS1dPOnGHW5DSiBrEufKwI/v7KBhQI+/p5L7z0w9ZCC0r6/fh+D9XVmS3nLLLXzyySds27aN6Gj3pkZ2rFq1CoDmZvcyyIvB9zJ4O0IQBNrb29m/fz/x8fHk5+cvitV9OFgoeNs3DBqNhtBIkSB2tLtyt5m3vWxuFpiYmKCwsBClUklBQQFqldgSsCwQvLvnBe8kmwmJSiGnundOgSo9zNsp854/qx7jq2RC5jpe56h9HuItBto+W88xI9wHQUBSiJtvH2hXkLPDHlCTk5PJyso6ZjeyY/C2a8ofOHCAZcuWScS+I8HSGD/+eE46AK/u62FjWjBhngJDOiO/+KCei5ZFcnpWCBYB7v20iV+dmuwkd4rNaWxy1rUlU9I+TmqoFrNV4C9ft+DroeTe00XrzldLejBarNx2grOmetWYklNtfckn9o6wr7RM0n0eGBiQLDxvPzGBKD8NvRMG/rnDdSGOD/LiBtuY0J+/bpFY7H6eKm4/Xlx0XijqoqZ3infK+jjjyX38y4GM9pdz0zGYrdz/RbPLGBE23ewPbljGaZmhXPdaFQ9vbWPWbGVVvD+vXJ0nPa7RzVy2Ui7jFxsTeWlvN2Vd7hXUjhXOSvHi6bPCWLGAU9+xwNSsmWcL3c+nu8OgrSWyeUkEL+913bj4aBQsi/VzuaZ+siEBtVJOcdsY5d1zn1ukn4bxGTOBXiquWy0Gd7NV4J+7xTbYNatjOGejqFvv6enJI9s7XTZTophLkvRzaecEzxeKJLXfnZ4qyaMeDHam+b/Tw9sR83ve85UvjwaCIHDLLbewZcsWtm7dKjnzHQwVFRUAREQcvGJxMHyvg7fJZKKiooL29naWL19OfHz8d/Llzw/ediLV/v37SUpKIjc3l1mzGBwO1tdZ1Lkc5rbnz3kPjoxSUlJCXFwc+fn5KJXKubnwQ2Tedha73VPX7lttR2ygaJ1pz7xj57FfT8qKdGtsEBfkJbHZQ203ab9NbzncV0NN3ySjehNajYKlsc6LoD2o2pXgmpubWbZsGbGx7hWYjhT289grFmNjY6xZs8aFMHIk2JQRItlp/m1rO5clWfBSy9nXMcEre7v57WkphHir6RqbZXfLKDfYesqO6J0wcOY89rneaGHWJCps7WkdY2fzKMelBLEmMQCzVeDvW9u4aFkEib5zn1PfpIEgHw98PZR0Tlro9UoiKysLtVpNe3u7ZOE52NvFHRvE1/xmaa/bEui1q2NIDPZiVG+S+pYAxyf5sTRE3HRc+kI5931+gBFbJvjHs9NYGe/Prz9uXFAd7U/npPPSVXl8WD3Apc+XUd+vw9dDLOf/clMyD33j2ou34xcbE1mfHMhfvnZlOR9L+KrhvuP9uSBZjuo7XgFf2tt92KYoZ+eE8nZZn9sRuZ9uSODRbc7jgBnh3pyWFYLeYOb6152zbvvo2S3Hx+OlkiOXy3m7rJ8OmyPbdWtipPnoEUUge/tdk5jjI6yMdzXR1dXF4Ogkv/yoAQE4NzeMkw7irOeI/yTT3H5+e7JwrDPvm2++mVdffZXXX38dHx8f+vv76e/vZ2ZGXJtbWlq4//77KS0tpb29nY8++oirrrqK4447jtzc3CM+7/cyeMtkMiYnJyksLMRisbBmzRoCAgIW8cwjg2PwNpvNVFRUSCNZ9kCjl7y8jzLzdqOwZs/WevsHWbp0KQkJCdKFJZXZ3RCyHEVWsiPFMqyd4Tt/ZEMhlzE2bcJoFQ/op3SeGQ7wUrkVaJEBg7Ze+lzwFn8O99Ww07aIr00MdDmnXC7HZDKxb98+JiYmKCgoOCYB1eU12nreRUVFqFSqQ2rZHy5+ekI8sQEeDOqM7BmQ84uTxJ31U7s70RstksTkM3u6OC0rhOQQ1139t43DLvPfnWOzRPiJn+lfvm7BaLZyx0mJyGWiY1ll9yRXpitw/FjfK++TbCOf2NmBVa0lOTnZxcJTPdJCbpCYZd3/SZ2ThSe2MUK78My75f1U2AiHB4ZmGJgX6+/amMQtx8fz2PZ2/rW700ma047LlkdSeMcaQrzVbH6mlBdsns6bMkL44IZlTM6aufDZ0gX1yH+caeX1fb0LbgqOFc7PC+OdqzLJDvNEp9PR1dV1SM/yI0XToJ4XD7PsH+WnYcZkdcuoPy0zhAfmqZ15qsRpAblMxt0fNjj9LcJXPFZqqJbz88NFBTKTnCdtLZGfHB8vicsspKQW6KXinrPyCQgIYGRkhHveK6d3wkCol5zrlvhK1Z5D4d894z0fFotFIjYfa2nUJ598komJCTZs2EBERIT076233gKbD8A333zDKaecQnp6OnfccQebN2/m448/Pqrzfi/lUY1GIyUlJSQkJBzWDPWRwh68p6amKC8vx9PTkzVr1jgRnA7HDvRgsF+/9jnv6elpJsbEDDkyJtZl9Exhe4K7nveQzojBbEUuE2083yvvpdd207ubt7Szz/3UoJY5H2+hNUsuk0ll81Af8fOws17D/Tz4rFZcCNyZqhgMBgYGBggLCzsi8ZzFYmREXPAjIyNJTk4+5teLp0rBH85K4+qXKykZknOpp5LcKB+qeqZ4eGsbD56dxrvlfezrmODhrW3cd0YqV7xU4ZQ5zZis+Hu6tnuMZiuBXqJ5yav7evhBQQzn5YXzXkU/D33Tyi3pMi7JC+a18mHpOD0Ts2SEe1Pfr+ORrW3cb3Mrs1t4RkZGYrVaCYwb5Ko3mtjfM82/Pt5DQZwPQUFBBAUF4ePjw/JYf87LC2NL5QA/fquW9UkBfF475GK98kJRl1TOnY/EYC/+dE460f4ePPRNK+/bvJxDfdT8+tQUciJ9+PXHjexpdSXPOeKJOjnw3Y6A3X9mKufm2caZwoIxGo1oNBq8vLwYGRmhq6sLmUxGYGAggYGBBAUFHbbmgB16g5k73q87bFezgsQA3i13HQcM8Va77cn/5rQUEoO9KGwdY/uBUae/2efk79qYhEIuw2iy8HyD2MbJCPPmvLy50a7X9/fSPORaobn1hHjCgvwgyI/GaU9KBkUhnVtXBzDQ00nbgQZ8fHykz8vHx8dtkP4+ZN6OhLVj6Sh2qA1fTEwMO3bsOGbns+N7mXmr1WrWr19PUlLSv6VHolAoMBgMkhGGO2bysQrejoppBoOBwcFBtB62npHM9dj2GOwm2WHIFlQDvNRE2G5sey9MrZA5qSBZrAL1HSJDPFSrJDzUOdgupN4ll4l+4bgpm4f5ahi1mUAkBDvfDL29vfT09KDVaiUls2MN+9hgQ4OYcRyrwO3uGEti/LhypZjxPvBlKzcfF4/Mpgld3j3JLzeJ/e5tTSOMzZidlKrs+LJ+iLPnKVANTBlJtRHa/rW7k6EpA7ccH4+nSk5V7xT7BqxctiQEP4dZ8k9rBjknNwyAD6oGqOh2zWblcjk58eFctUrs+X/e70VImJiVV1RUSASl89PEKsHUrJnPbIF7RaiMd3+4VDrWQoH73tNT2HLDMvRGMxc8WyoF7ouXRfDhj5ajVsjY/GzpgoH79Wvy3f7+WCNYq+bdHy6dC9w2CIKASqWSPMvXrVsn9X67u7tdPMsXqxYoCAK///yAWz7AwbA2MYAv610NQABuWh/Hs7Y+sx3n54VzVk4YU7NmfjSPpGbHtQXRrLSp0T2xp4eWSfDWKPjr+RnSWjQ0ZXBLUssI9+acXPEzG5wy8PvPRD3/6wpiOLsgS6r2REZGMj09TWVlJbt376a6upre3l5mZ+c2Y9+HzPu7Ct7/KXwvgze2LOLfAavVSmdnJwaDgfz8fFJSUtwu3nbNaC83GsaHA3vZXKefpre3F29vb8JCxGzb3Zy33EFOdT7CbP3t0Wkjfh7OWZ1KISfZwcyksr6Z0gaxVxYf4uOSmS8UvGUOmXeIjwazxSr1wMN8PKTxNjsBzmq10tDQQF1dHVFRUXh5eX0nGzCLxUJlZSVdXV0sXSoGmmNV8rQLSszHzcfFEeohMKw3sbVxmM1LxIXtj180kxDkxeW23vifvmrm2tUx+GicNyxWwb3jVfeYmElPG0WN6WBvNdetEYPu+y2iAt+1q52Jd5/XDnG2LYD/+auWBd/7DWtjCNaq6Ro3sK3HKgWpjKxstndbuPkD5x703ceFkhPEgsEA4Ly8MHb+rICzc8J4ZFsbP3ilit4JA9H+Hrx4ZR53bUziX7s7ufHNGmlz54gl0b58dONy7v/8yJm2i8XSGF8+vHE5aWGuZVJBEJyuTUdtbHee5bt27aK6upqenh6pn+kOb5f1uWi7Hwoh3mpGp02SdK0jLlkW6eLVnRzixd2bRBLZLz9qdHkOwMo4P366QWzxfFU/xFuV4sbgD2emERc411b629Y2t6z2u08RM3ZBEPjtJ01MzJjJCPfmJgfjEnu1Jzs7m/Xr15Ofn4+3tzd9fX0UFRVRXFxMU1MT4+Pj36vg/d/uKMb3OXj/O2CfN9br9SiVSkJCQhZ8rF169KhnQAUx2E3PzBIfH49Go5ECqXuFtTkjk/kI0qrRahQIAi6GFCqFXMqUAerauvEPEzNHrVqOcl7wXmiURS6bGyEL9dEwojeK/Xq5jGBvtUSkU8hl0hz+8PCwpHp3LHuIdti/N4PBQEFBgSQzeLjOYocLD5WCixLFc7xX0c9FSyPx9VDSOKjnnbI+blofS6iNvPZJzYAUzB2xp3WMzfOUqLrHZ8kMFxeTj6sHaR+Z5qpV0YT6qBmZhXerhrl0eaSTT3VlzyTpYVo8VXJq+qbY1Tzqci5symq32kwqntrVybDOyK6WMX7wdjNP7R9n0ggx/nPXyZ92DvJ8vbCgreaLV+Zx35lpjOiNXPZCOS8UdSPYAvq7P1xKsLeaK16sWLDX+9iFWVy3JobLX6yQJEC/K5yQGsTTl+VK/tjzMT94z8d8z/KlS5dKhKTi4mKKi4s5cOAAIyMjEmfmi7pBt73jg0Epl5EWpqXejU57UrAXTfP8BjxVcv52fiaeKgXfNg6z/YArTyDUW82fzxW1CNpHpvntJ00AnBqncCKZfVE3yKc1gy7P/0FBDEtjxHHcN/b3sqd1DI1S7K8vJIFqt4ZNSEhg2bJlUvXUarXS39/P5OSk5L2t0+m+k7VhIcwnrP0veH+H+K7L5XYXMl9fX/Lz8w+58OuPQdlcp9PR0izuoH18fdFqtU5sc3e60o4Et/mQyWTEB86VPR2nlFQKGT4OyXh0ciZJYWL5rHfS4DLSNH8e2PH8c6NiaomsFuqjRiGXSa/LMDNNUVERCoVCcnWbPyp2LDA2NibNia9YsQKNRiNdK9/1YiCTyUjxE1ge44PZpul9y/FiYHxyVwdKhVz6+ZWSHi5ZHom3xvV6cWemsad1jIIEfwQbQ9lTpeBWW9b0WvkwVkGQ1NHseLu0T1Jee3JX54Lv/+zcMLIiRH/wEx4t5pa3a2kbmcHfU8k9pyTx7vXLXdjw7rA+wZf8aB9eKenm4ufKaBzUE+Cl4pELMvn9Gal82zjChc+WLmgWsu3W1XSMzXDL27VuM8xjifPywnh4c6aTVvd8HI5cp0wmw8fHh/j4eCkwJSYmYrFYaGhoYNeuXTz7xT7u+qDBLUv8YDgpLZjiNlcFNqVcxrqkQJeROXufe2LGxG3v1rl93t82ZxDsrWbaaOFn79WhN1rIDffkgtS5dmDjgI67P2hwef6qeH9+skG8jluHp3nYptZ3+4kJJLrR8l8I9oQoPT2duLg4AgICCAkJYXx8nNLSUmnMsb+/34VQeSxhtVoRBEEirE1PT/+vbP7fCEEQaG5ullzIsrKyUKlU0he8EKaPkm3e399PUVERAXY1MZnMwVXMJtLiprGtOMSoWJzNPahrbIZABys/rGZG++bmS8cNVuKlxxpc5rwXyrwNZqv03oO9NZKkaqCXeC6z7XXVVlcTGRnJkiVLpJvkcK06D4Wenh5pfM9xTvxYBu+uri6Ki4ulRcWRTWs/zw8LxIz6vYp+1icHEOGrYXTaxGe1g5yRHUqwVs3glJGitjEuW+6afdf36yQ7Rjv6Jw2ShvZHVQMM64ycmRNKhFaG3mjl/Yp+LlgSQZjP3HfcPjpDjL8nHkox+97d4r63PDhlcAmWPyiI4dMfryQ5RMvFz5fxiZvsCyA9zIs714chA3a1TZL/4G7+8nUrRovA2kR/3r9+GQUJAdzzUSO/+rjR7YjX0hhfSu9ax4vF3S4OWAC+Hu7vKaVc5rLJXAzOywvj92ekHvK5h8q8DwalUik5Vq1ZswZTcCqPl88cduBeneDPzuYR6T5yxMXLInhp3qy3vc9tsQqse7jI7THvPDmR/Gg/BEHg/s8P0Dw0TbBWzS9PiEClFD/riRkTt75b58KlCfNR8+dz01HKZS4qapcujzy8N+cAO78gOjqavLw81q9fT1ZWFhqNhs7OTid+wdjY2DHd9M8XiJmenv5f5v3fBntZt7e318mFzN4LOZg5iT2AHe6ct9VqpbGxkZqaGnJzc4mOEm8A+6iY6Cp26LK5dYHAFGfLvDtGpwl2MLSY0evITJzrk47ojFLwHtCZXNjl4zMmtz68HiqF5DM9rDNI5xjSGcXZapMYFDLSU134Ascq87b30RsaGliyZAlxcXFO5zkWwdsu7tLc3Ex8fDweHh7SorJ//37a2tqYmhIV65ZG+7Aizg+zVeDF4m4usS1qr5X0oFLIpEXu5b09XLUqyu2Gz91MfWnnBKmhWowWgTf29yKXydgYKz73tX09KOQyfrQuzuk5H1T1c9EyMft+aleH02egM5h5dFsbZz65n86xOfJQepiWq1dF8eevmrnutaoFiVV3npTIGz9YxtXHpRHoULKXy+DKDBUXhg5TWl7Opc+WuC29YsvWnr08l99+2uQSiABiAjyYnHV/35mtgtugdjCsSwrgN6e5563Mx9EEb0d83TDM3Z+0cJjEcmliwN2G56ycUF7b52w64tjn3vxMqctzAE7LCuEy2/X3Tlkfn9QMopDBX89PJ8BDnPO2WAV+8UGDyziaUi7j4c2Zkp/3kzs7qO/X4ecpzukfzWc1n20ul8sJCAggKSmJlStXSvwCg8FAbW0tu3btoqqqiu7u7qM28Jivq26f8/5vx/c2eB/rsrlduUyhULi4kB1O8D6csrldWnVoaIiCggLCwsLmCGi2kp2jq5i7srmjnKo7xAeJxJOOEXF3bUdwYAApsXO91WGdkWBvNR4KkTjVN+l843aPzRDoxqNYKZdJQb9tZFqyNhycMrB3f5m0uIaHuvIFjkXwNplMlJWVMTQ0xOrVqwkOdh1JO9rgbTKZKC0tZXR0lIKCAsLDw0lMTGTlypWsWbOGyMhIdDodZWVlYJM0vCRb7LO/X9HPuqRAPJRyGgf17O+c4KKlEXgo5dT362ga1EuLqSO2HxiRetF2iGIo4uf7Vlkv00YLq8Pl+Hko6J0w8G3jMOfmhRHtMDJU26cjI9wbjVJkpxe2jmGyWHlzfy9nPLGPZwu7MJitLIv1449niyNlDQN6jn+kmI+q3QdcO87NC5OyN8ce+BnZofzi/AL8k/J4YJ+Z1jH3/fFXrs7noqUR3PJ2rdvgHuWnoWvs2I2HZUV487fzMw9qS+kIq9V6VOuMIAg8uauDO96vd1s1OxhCPGX0T8ww4UbAZUNKEB/P+24c+9x/+OIALW6Ed5KCvfjd6WKQremd4k9fi7rZt52YwPJYf4nx/Y/t7RS6mQC455QkSba3vGuC52xWn/eenuLEnzkSHIptbucXZGZmsnbtWpYuXYqfnx+Dg4Ps3buXwsJCGhsbGRoaOqSJ1HzY+9327/p/mfd/CQRBoLOz00m5bL68qv2LXSh4C4LgELwXxzYfHx+nqKgItVot9YBxKoM7aJsfxFVMfqjM2x5Yh/VYp+f6Zp4alVMZfVhnQCaTEe4tbj665y2aJovgXHa3n182NwbWNjyNv6cKb9sGpn/KhIBNTMbNjXm0ZXOdTkdRUREymczpM3R3niM9l16vp6ioCLlczurVq/H09HRa0DUajTRKtH79emQyGUqlEj/DIEm+AiaLwIf7Wjg1XRSfebWkB38vlTTK9fLebq5cFe1SwrUKMOlm4R6dNhIT4MHEjJktlf2oFTLOyQyQjqVSyF1U3D6oHJB63ze+WcP5z5TywJfNjE6biA/05B8XZvHCFbnkRR3aP/jO46OIsLU1//BFM9e8UsnbZX3IQGIof1U/zFO7OrjhzTrGZtzfMw8sNzPS3sDlz+1zCRQRvhrCfNT0TLj6dYd6q92K3BwKGqWchzdnHtbm+mgy7xmThZ9vaeAJNyNWh4K3CkxW3H52S6J93RLQ7H3uDyr7eavU1UDIS62Q3v/4tInb36vDZBE4OS2Yq1eJkrdWq5W9fSbJf9sR5+SGSdeQzmDmno8asQoiX2Jj+sJE3sXCarUuelzUzi+Ii4tj6dKlHHfccaSmipuSlpYWdu3aRVlZGe3t7YsS1pmf9f9vVOy/AHbbSbssp6Ny2XwczJzEZBGknvOhet72zcK+ffuIj48nLy/PybLUrZ+38iBl84MQ1nAomw9MGQnynVv02kempfIXDjKqEbbg3TPuWioNchO8J2fNJNg2CK3DeoaHh/FXiZ+TT2Qi3rY+7bDOdSE+msx7aGiI4uJiwsLCWLp06SH17A/lHe4Ow8PDTuc4lLWsfZMXGxvLqlWruHS1SCjb1jJJpkrMlLY1jVDV0sOly8KRAdsPjDI5a+a4ZFdluQ+rBzgvL8zpd2Vdk5IT2St7u7FYBc7JCkSlkFHVM0VF9ySnZTnPfe9tH5c2VADtIzMEeKn45aZk3r9hGcenBPJmaR8XPOu+1Iqt3Lz1p6s4OyuQi1LEY31RN0RVzxQ+Hkr+eXE2H9+4nJVxfhjMVh7f2eE224zy01B+z3qWLl/JX8ottMzLyjNCNFisVgbcmOD4eig5OT3YrVjIofCrTclE+h3eeOmRBu/O0RmufaXSrd/1oeCpkiOXwbjB9bOL9ILWQdeZ/UuWRXJWThgV3RP8xsYan4/7z0wlMdgLqyBw94cN9E0aiA3wcCp3t4zM8K9y13ZNepiWX586p5Hw569a6BmfJcpPwz2nJLk8/kjgyPY+XCgUCoKDg0lNTWX16tWsXr2asLAwpqamnDQL+vr6MBhc1yF3wft/mff3GPaMamZmZlE61wcL3nrjXIbkeZDgbbFYqKmpkTYL7rTYHQlo9szbTh4zuVkMD6ZtLggCI70deCpsuuvec05rBwb1zDqMjzXaxnIibcF7zI0UqmNf047yrnEp867vHqWiooKEULHl0DM+S26UeE67vKYjjiQbtuvKV1RUkJmZSVpa2qIW2MM9V2dnJ+Xl5aSlpS36HPPPc2p2BEq5jK4pK0uXLmN5tBYBeGtvO521+8kJET/rTyp73LK5R/UmqSXhCKsgEOClomfCQNWIQKCXUnr+y3u78VApnNSxsEm1OuLTm1Zw6fJIhnVGbni9mj9+2bygXvg9pyTxxMXZhPhosFqtDEw7f45v/WAJxyUHMmOyMrzACBnASWlBfH7zSvQGMz95r4HeSefHro/1ZNpgZFDnegy5DK5YGcXbZWJWeTgZ9JrEAM6dtwlaDA43eFsFgVdLetj8TCm1fYc/5ubvqSRIq2bSDbE63FdDVIgfE/P+tjpczoVJAjXt/Vz5UqXb4169KppTMsTs+F+7O9nTOoaHrRJhJ0FOzJh4YMcw8+3SfT2UPLw5U/Iu/6ZhmA+qBpABD5ydjvdR6lrYcSwV1jw9PYmKinIR1unp6WHPnj3s3buX5uZmRkdHsVgsTucWBAG9Xu/UNv1vxfc2eB9NL8rO7A4ODmbFihWLEnw5WPBWOuwY3el/Y+uj2GfGD7ZZcOx5S5m3YuGyuZR5zwtM9l5wX18fCcHiLnL+uFdp51wZ3V72j/RdOINVutkZ7+8YJz5Q/Pw6x2ZZuXIlqZHie+samyEvWizFVroJ3oebedsrJe3t7axcuZLIyMWzWxcbvB2JacuXLz+kdZ+789jh56liXZJY0v6qYYQzcsXX227wYtWqVWxIEicLPq/qQT3chFblek3vODCCv6fzAvlF3RBnZInB2m4UceVK8XV+2zhM9/gMZ+W4BqsQB8LiiN7Ilop+znu6lL3trmNIdvzu9BQuWxGFTCZjxmThj1t7ePOA83c2pDMyNm3impcr3ZqcYMsO/745kxmTlZvfqnXJns/NDUPjqaVj0v13dGqMwOsl3ZitAskhXm6rUAvhpvVHZnRzOMG7fWSaa16u5M9ftzB7uMw0m8pbTIAn3W40y309lKyI82Nfh/M9dEJKIL8/PZmJaSOXvuY+414W68dtJ4oVoD0to5JS2m9OS5GEaSxWgbs+aGBA77y+yWxGMnYey9CUgd9/Jp7nB2tiWBZ77GyXvyuFNUdhneXLl7N+/Xri4+MxmUzU19eza9cuWltbMZvN1NTUYLVaj2nm/eCDD7JixQp8fHwIDQ3l3HPPpbHRWTRndnaWm2++maCgILy9vdm8eTMDAwNHfe7vbfA+EthZyTU1NWRnZ5Oenr7oC+ZgwdvHQ0mGTUSjsNVVDGN4eJiioiL8/f1ZuXLlQTcLc2XzOb9rldzZz9sRSltW7uiXbO8FC4JAQUEBiTYltfnjXvvax5z0kM0WK1G24O1uisZdX72ia4L+FtE7eMoEqL2km71rbIb8aHvm7VruO5xseHZ2lpKSEvR6PQUFBYft176YczkS01avXn3EZjeO5zk1UwyyX9QNsj5J3NRU904xKyg5b1UKMqBDJyMsNpGCaNfroqxrkl9sdC5NjuhNeKrF67ZqyIreYCYlVMuqeH+sAtz9QSM/fbvG5Vjn5oZJ5fmzntrPbz9tOqSPtL302z9p4KqXKviqaRy5DH5+ciIX2BTk7v2kieteq1pQVOXGdbH86tRkzFaBO96vczEfWZ3gT7C3mm8a56Q/PR3svM7L9KVbL2N81kKEp8D09MyiWeb50b7SNXi4WEzwnpo18/iOdi54tszJavNwEOqtxs9TSXXvlMvfNEo5J6S6EtTWJAbw0PmZBIeEcv2n7s1afNVwU66a8dERukenuftD0e3rwiURkvoewD93tLuVqL1pfRzrbdeLIAj89tMmxmdE3fObj4tzefzR4N+lba5SqQgLCyMjI4M1a9awYsUKvLy8GB8fZ8OGDaSmpjIyMsKePXsYGzu43v5isGPHDm6++WaKi4v5+uuvMZlMnHLKKej1c+2Jn/3sZ3z88ce888477Nixg97eXs4///yjPvf/meA9OzvLvn37JHWv8PDwRTxrDgcL3gBrksQ+ZGHL3I1k19YuLy8nPT2dzMzMQ24WHMvm9ovZTo51xzbPjBDLO3vbxhAEgYGBAYqKiggPD2fZsmWoVCqp7z0/897fMU5SyBwxY2zaRJSvmJ1ZBYgNdHbeche8Z81WpmRehNnYpm3Dc4zz7rEZcqP8kMnE/5/f915s5j0xMUFRURFarfaQm5+FcKjgPZ+YdjA/X6vVuuDrnr/Yn5AahIdSTufYLDqDmfQwsXS+u2WMYG81OVHi91c1LHD5uhS3x2xoclXkaujXExfoidEKhR1i0FwRJwapyp5Jt2SvD6sHXLL4+QjWqnnqkmy+uHklSrmMorZxvqgb5KqXKmgY0OPvoeBn+UquWhXNTevFBbx9dIYDbkbb7Dg1MwSrIPCbT5pcZs39PZWclR3mpMutVSukyYZV8f6siPOnakScbFidEkavfvHtj42LtKR0h4MF72mjhWf3dHLq4yU8tbvzsA1G7Aj1VqNUyNyyw2XAialBfFjlnIUtjfHlkQsyUSpk/PC1Kre+BgoZ3H9qHP4ecmobGrnxlX2Mz5hJDlRz85ow6V74qn7IRRMdYH1SID9aP0d8fKu0j90toorag+ekLZqxv1j8J7TNZTIZWq0WPz8/YmJiaG9v56GHHsJsNvPss88SEhJCQUEB9957L62trvoDi8EXX3zBNddcQ1ZWFnl5ebz44ot0dnZSWirySyYmJnjuued4+OGHOfHEE1m2bBkvvPAChYWFFBcXH9X7+94G78Mpg42OjlJYWIinp+dBWckHw6GC9zpbVrWndRRBEDCZTJSXl9Pd3c2qVaukmfFDwX5T6Axm6WJW2hy+3C0QBYkiWalrbIbtpfVUVVWRk5MjsS9xYJzPz7Lq+6ec5tJH9Eb8PJX42LK6+Z7eDQsoY43KA0gIniOtOWbeHio5KbYNwvy+92KCd19fHyUlJcTHx5OTk3PEu/ODBW97ZeRQxDRBELBYLFitViwWC0ajEbPZLP3O3Xm81AqyIsUAXdunkzKZXS1iheaEVHHTt7VphKUxfk4iK3a0G7xICXR+TcVto6xLEIP1N80TvLavh3/umGM2r00MoOjONU6VlcEp40HHv9YnBfLe9UtZmxRIlL8HmzLFPunPt4gEp7hAT/51QRLpgeL1IV/kPfiHL5p5ZFub23Gw169dwh++cNblzo70oWfCgKdKzi83JfNEoWhoctHSCHa1iCX+KL/FjSbV9E253fQuBu4U1oamDDxb2Mlpj5fw6PZ2yejnSBCkVWG2CvS62Wgp5DJOywrh8zpn0ltmuDf/vChbHAn7vHnBbP+eTcmcmBNHWloau3ShdOhkeKvl3JyvobqijMLCQr4sruZXH7kqqEX5e/DgOWnS99s2Ms3fvhWD189OTHDa8B8rHA7b/FjDnvV7e3uzceNGAHbt2kVHRwc/+tGPaGpqoq/PlcF/JJiYENdAe9u0tLQUk8nEySefLD0mPT2d2NhYiorci+wsFt/b4L0Y2MlNpaWlJCcnk5OTc0jG8EJQKBQHnR9cFuuPRilnYNJAVfsgRUVFWK1WJ23txSA11BsPlZxhnVES6gjxFrOQrrEZFyUsb42SZTaN4W8bBtxWFeykp2GdEQ+VfeEVs+v2kbkd/+CUAblcLjHOo/ydg3fzkN6tnOf+znGncbEofw8CtSpmTVZ2t4ySZytbVs6TcTxYQBUEgaamJmpra8nPzz/oJMBisNC57MS0jIyMgxLTBEGQMm6VSoVSqZTaGhaLBbPZLKmtzd+Q2Fsq9f06qWy9p3UMs1XgxFQxMyzpGGfGaGFFnL/Luct6p7lmrXPp3GQF3bAo0lHSpedPX7U4/T0zwhtvjVIqbR8K16+N4Z8XZzmNAzqOjvl5KnnpqjzCbNfisM7Ida9VLXi8O05K4IubV+KhlLO/c4IXilwFWArvWMMDXzgT5QoS/CX51JuPj2f7gRG6J4z4qUV70WG9kRBvNSrbBMahVNI+rx3irCf28kF5t1NraTGwZ95Gs5Wv6oe4+a0aNj62l0e3tTO6ALdlsfD3VDJjsro9jlousCk9iM/mmZckBXvx1KU5+HgoeWZPJ++Uuw8od56UyMU2T/dn93RKo2N/Pi+TkwuWsH79eqITU3lo7ySzZud7Qi2Hv2/OwM9mTztjsnDXBw3Mmq0UJPgflYrawXA0bPNjcW77xsEu+KLVaomKiuKaa67hjTfeYO3atUd9HqvVym233cbatWvJzs4GG/9KrVbj7+9834eFhdHf72r9ejj4rw3eJpOJiooKOjo6WLlyJbGxR0ZascNOHlsIGpWC5baF960dVURERLBs2bLD9vv1VCtYayvBf9s4jEKhIMpXTVKIFpNFYMcBZ0vAqakpYlVir6zL5OOWaGEvmw9OGSR2qD2gOxqW7G0bcwreQd5qKdjbkeoaWyjrHCfV1lff0zKCUiHnrBwxaLxf3isF78Vm3mazmfLycvr7+1m9evVBDWEWi/nnmk9MO1hlxB6g7QuMQqFApVKh0WhQq9Wo1WoUCgWjo6NSALdn5VarVTIVqeufIifSFw+lnKlZMz3jsyQEeRLuq8FkEWgY0LMk2nWjpzNY3I4gftjh/N1ckgy3LBM3UZ/VDCIIgpMwz0L463np/HRDglMmvbtllIe/nSsVnpgaJI0K6kwC174qktPcHf/8vHCuWR1DlL8HFyyJcHvO16/JZ/uBEadea7S/B15qBVOzZrIivLlkWSQv21TXLkhRscVmKbo81o/2kRmUcpmLjK879Eya+M1nraz/2x5ueKmEd0va6ByZXjAjnzFZ2NcxzmftVn7xSRsbHi3mjvfr2dk8ikXgoHroi0F8oCfTRovbzYSfh5JMf4HP6pzv85gAD565LIcALxUfVvXzj+3tbo/90w3xXL1aJC++vLebR22Pu+OkhLlxRJmcP+3op1/nev6LkqwMH6igtraW7p5efv5+naSidv+ZaYuuthwu/pN+3o7n1ul0KBSK78S18uabb6ampoY333zzmB/bHY7NHMB3gIMF4qmpKcrLy/Hy8mLNmjWHHUDd4VBlc6vVSoqPmT1Aj9WXlBT3/cvF4MS0YL5tGGJr4xAZyWLQOTk9hJYhPV/XD3KmLTD29/dTXV3NSRlRvH2gl9KuCXQGs8v4hr+XCn9PFeMzJmk8LMBLDUw7jdw8u6eD85MTCdOKi1PfxCwr4gLY1TzXx5+1yAHnz2FixkxSiBcqhYzavilqeyc5f0kkLxV3sbVxiGsKxN5Zedc4A5OzhPmKN4Y9G3bsLU5PT1NWVoZGo2H16tXH5LtjXuZt39gZDIZF97ftPbn51509++7p6aG5uZnMzEx8fX0lLXyz2UxykFjire/XIQhWYgM9aRrU0z4yTVygJ6mhWvonDRwY1C/I4D0UGSoj3JufnJZMz8AQT1fo6Zkw8IPnCikbPHi2mR3hI5Hq7Piqfoi7PmjAbBWkCs2X9cPceXISZovAU9Vm2sdMhPtqSA/Tsv2AM0nzd2eI175VEDgw5NoLv2BJOJH+Htz0ljOp7oa1sZK15a82JVPYOsaI3oS/p4I4PwXP1epQKWRE2Oa1Vyf4Sz30EG+1ZEXriKtWReGtUfJRVT/d4waKumcp6u6Cr7uQASFaJWG+GkxWkXw2abCgmzUzl4+Kn3uoj5qMMG96bd/TkUCtkLEy3p+S9vEFZ+Cj/D0o6XCuroX5qHnmslxCfDS8Xda7oFXqjetiJXOat0p7+atNJ/7Hx8VxjYNl7OM7293q3J8Yq+KKtbFotVpGRkb40zdt7Oi2oJLD3Wv8UVumsVpV30mG/J/087ZYLGg04j1qF2g51q/llltu4ZNPPmHnzp1OEyzh4eEYjUbGx8edsu+BgYHD5mXNx39d5t3T00NxcTGRkZFHlPkuhIOVzQ0Ggyi64in2rip79Yc1yjIfJ6SGIJNBbe8UE0ZR2e1k25zmzgMjzBrNkh56Xl4exy/NIDbQE5NFoMgN2x0gziaTau9/99vkT5sH9f2Ss9gAAQAASURBVGxInSP1NAwZCdeKAaq2d5K1Sc4jbXXDJicBEDuah/RszBCDwNulPaSH+5AR7o3JIlDfN8WKOH9MFoGXiufIMfYbxB5UR0ZGKCoqIigo6Jh+dzgEbzsxze5utlDgtpfJLRaL1Pt0t2G0TzC0tbWxdOlSIiMjnbJylUpFUog3Hko5MyYrbUM6YgPEhaJ1WAwCKaFitnxgSE9SiJc0e+uIfR3jbhm+y0LFz7BzdAattw8ZKUlS5Wb/gOWQRhg9E7NOGWhh65gUuE/LDGH/XetIDvFi2mjh3fI+/lnYR+OYgJdawelZoS6B20utQGcbFn5tX4/bMbTfnJbCY9vbnaQ/l8X60TEmssiXxfqRE+UrZdobk31pnxRfY06kz9xYo4Poit3W097j97Blxx9WDXBqZgif/Xglb/1gCdetiSEp2Au1QoYADOrNVPfpaRgQNzxTtsAd6q1mSbDATWsiuWx5JOG+GnY0jx5x4A7xVpMT5cvuljG3gTs1VIuXRknJvHGwQK2KZy7PJcrfg+cKuxYM3D8oiOHHtutjS0U/f/hCfNwP18Q4mdx80zDMM3tcCWq5kT5cmq5CoVDg7+/PN31KdnRbkAG/PCGCRF+oqalZtGf54eL70PPmOxBoEQSBW265hS1btrB161YSEhKc/m4nFX/77bfS7xobG+ns7KSgoOCozv1fE7wtFgu1tbU0NDSQn59PcnLyUZXJ52OhsvnY2BiFhYV4eHhwwckFBHurmTFZKe9aeHb2UAjyVkvjLTVjojJYTqQvYb4apo0WXv6qhMHBQVavXk1oaCgymYzjU8QAvL1p2O0x7aXzPBv72246YLYKTjZ+37RMkRWkQK2U0zSoRz/mqhK1Is51hKq0Y5yLloml50+q+5k2WjgvX+yPvV/Rx3VrxYXlzf3dUt/e/v1YrVY6OzspKysjLS2NjIyMY77zlclkEms9LCzMyd1sPhzL5DjIq86HnZQ4Pj7OypUrXfpWcrkcpVKJp4dGMmyZMgqSTWvb8DRGo5HEQDGYNw3qkctk5LuRKa3r1/FGaa/L79MCFWjVcvRGC40DOqZmzWxrcj865Ig/rvPAVyNnbNokBdj6fh0/e69OCtwP2ryZr7GVYf++tY0PaseQAdesinaR0QyxWUy+UNxF9/gMf7dZRTpCq1YwrDPyUbUzg/qGtTG8a+vhXrkyiokZEzttMqCnpvrROiHee7lRvpLtbIDX3Fhjto0UaGepz5qteKkVTMyYuf61Kna3jJER7s1tJyTwwY+Ws++udWz96SpeuTqPv56bxp9OjeYPG/y5d4WM3y8XuDpThbdS4NXSAV7f30tVj+sY12IRH+iJh0pOaaer1gE2ydMhndFlYxDuq+HZy3JJCPLi71vbeGSb6+cJonjNbSeIgk+f1gxy76dN0u9/umFOCKplSM+vPm50eX6gl4q/bc5EgbhJ3VLZL5Ef79mUzAWrU8jMzFy0Z/nhwn6//acyb8eNg16vP2gl7nBx88038+qrr/L6669Ln1t/f7+08fHz8+O6667j9ttvZ9u2bZSWlnLttddSUFDA6tWrj+rc39vg7biY2gVQJicnWbNmzTHpkc7H/LK5IAh0dHSwf/9+EhMTyc3NRaVUUpAoZqqFLe4z4MXixDTxPVQOixe2TCbjuEQxoJcNWCgoKHDaIdqz5x0Hht0Ss+wZ96zZwuoE52zacezjs8YJVDIrp9hGbMq7pwj1ds6A3c3YFrWNkh/tR0yAJzqDhS9qBzgrNxylXEZN7yRR/p4khWjRGyy8tV/sY9pv1vr6+iMWRVksTCYTra2th0VMk8lkC2bc09PT7Nu3D7lczooVK/D09HR7PDu0tlaGwQLJYWKg6RqfRaFQSFrdBwb1GAwGsiPcs3lH3aiXNYxayQ4Tz/1F3RDXvOJeZWs+ei0+rIwQA93L22vYsb+GG1+vZNpoYWWcH384K00aWzw9y7msfkqcQlI6s+OFK3P51anJYNNv/92nBzBZBBeCo95o4YJny5zUAtcmBjCiNzExYyba34MNKUE0DuixCGIfPNZfTact886K8JEUDeMCPfHzVGIVkKoVPROzEm/Afh8MTBn58Vs1XPlSJU/u6mBv+xhtw9PMmq2E+mgI8/XAotLSbfbliyE//lAu568lenb1y5maLzl2mEgK9mJQZ1zQYGVtYgDl3ZMuioYZYd68dk0+KaFafvdpk1u9cYCLl0bwi5MTkclkfN0wxK8+Eme5HX+PbQz01nfrXPrschn85bx0wn1F9bx93Xp+bwv+162JcSKoufMsT0pKcvIsr6iooKurC71ev2gNB/vj/lOZt9lsdgreWq32mCV+Tz75JBMTE2zYsIGIiAjp31tvvSU95u9//ztnnnkmmzdv5rjjjiM8PJz333//qM/9ve152zE0NERVlUgQOxzRlcOFY/C2Z/kjIyMsX77cScxjbVIgH1f1s6d1lJ8dxflOSg/hb9800zBqRTdrwtLXR7h5AJBROWxFJne+0FfE+eOpkjM0ZaS+f4rMCOfsLdHGBi/tGOem4xOcyuvlXWLW/HZpDwAV/QYy1DN8ApQOCaxP9uMrB53m7U3DBGpVTsFkVG/ivfJeLlwaycPftvBOWQ/nL4nk+FSxf/9BpZh9//KDOl4q7uKq1bFYbW2IiYkJCgoKDhkAjwT2srbBYCA5OXlRxLSF+tt2jI2NUVlZSUREhNNI3sFgJ5zpjRZplK5v0oharSY13A+lXIbeaGFQZyLCd/Htgm6dlXPivdjbpeeFYnFT5KVWHJJZXT5g5rp1aXzTXkVRP7ROTTA6YyHSS+DaFDN9PV0EBQWh1WpdAoveJDCsn+svn5sbxvJYfwRBIC/Kl8qeSSmb1zkEv1s3xPPo9naX412+IoqvG8Tr65SMEBRyGc22XnlyiFbkRdge66WSE+Pvyb6OCVEEKMqXHc2jqOQykoK9aBmeJsbfkyh/DxdLy8qeSReBmO8KgV4qtBqF2/ltOzamB/N1g2ulLDNA4IWr8vBUybnt3Vq+bXRfSTk/L5xf2jTHtzeN8IstDVgE8fv4pYMW+fi0iR++VkXHqGuZ+7YTElgVL65freNm/l7Rg0UQLUdv3RDv8nhHKJVKQkJCCAkJEc2ZpqcZGRlhZGSElpYW1Go1gYGBBAUFERAQsGCla76f9r8bjpm3Tqc75mXzQ8HDw4PHH3+cxx9//Jidl+9z5i0IAgcOHKCiooKMjIxFCaAcDezBe3p6muLiYmZmZigoKHBR4VqTKPYba3onGZt2I1K8SCQGexEf5IVZgM8rOqitreX8dbn4eSoZnTa5lOU1KoWU9bsrnR+fEoS3RkHH6AzeGqUTUa2ie5JTHbKrHZ0Gjs+MJjlEu+A4y5IYV9r507vbOSMnHIVcRlnnBM2DOs7PF9nGH1X2cXpWGCE+aganDLy7r529e/eKx1qy5DsJ3I6KaT4+Pockpi0mcPf29lJWVkZycvJhaZ5rNXPB2y53a9ej16iUEo/AICiICnCfeV+5IoLcSGfN5aFpAUfz9UCtysmExI75xiclHeOkh3lLoi0DegvhvhqevjyfhOhwJiYm2L9/P3v27OGud8qdnru717l99OvTRIKaTCbjthPnFnxHhvw5uWFcWxDDfPhoFKxO8KfcNka4LEbcdNqDnr0qYR8JM1oEEoLFa6W+X8+ZNhnY1/f3cv3aWOQyMUh7quTEBBx7xvChILNl094axYLZtqdKzpUro9wG7vNygrkxS4ZGKecHr1YtGLjPygnlt6enIJfJKGwd5fb3be2OrBB+d0aqxAqfmDFx/etVbv3hN+eHSy2RrrEZHqu0MGMbCfv9GYfnz20XPImNjSU/P5/169eTlpaGXC53cfqamppyCmr2duT3ZVTs/4KjGN/34D0zM8Pq1asPS+P6SKFQKJidnaWoqIjAwMAFNdHDfDWkhGoRBChuO3J5PZlMxvG2BXdvt7hRiIwIk8rjX7txLLL/bacby0CtRjnXgy7v5ZSMuWBtNFud5mUrR+VExiZwyXIxS+0cnWH+fezuth6YNLCreUR6He+U9XJcSjABXiqGdEa+qh/k6tUieebpnS1E2L63Y+3NjoNErJ2YplAo3O6CF0tMs28WGxsbWbJkyWGX9+1iODNGi/RZOr4cx7+rlO7Lh/2TRjYkO28WBeCZfXNBYFRvYnAe6/rm4+J47KIsqUeMzeSmqG2McQfS2FOXZBMX6kdUVBS5ubmsX7+eXlUk+/uMKGQQ6uH6+b1z3VKn0SnH9+QoCnT3KUko5DLSw5wXxuNTgpicNdM+OoMMJC18O5ktUKtCEATs1fcRvZHVtkxxZ/MI6WFaycns2cJOfrExCS+1guahaXrHZyUi278DoR4CqyJVlHWO07lA4E4M9uLUzBBeKelx+dvtJyZwx/FRyIALnytj/wI98k0ZIdx3ptjW2Ncxzq3vzNl7/vHsdKndMTFj4vrXqmkYcA3c5+SG8dvTU5DJZIzqjdz4RjVTJkgN8eTvmxfveb4QFAoFQUFBLk5fk5OTlJWVsWfPHurq6ujv72d2dlZqUf0nMH9U7H/B+zuGXC4nLy/v3+L+IggCg4OD6HQ6MjIyDkmosrN99zQfmji0ECYmJggxin3FujEZGg8x27Azur9pGHIJRsfZSGsV3ROM6l2z/ktXiAFne9OwNJNuxyfFdZwSO/eetjYOc05eBB4qOYNTBubHva2NQ05GF3Y8vaud82zZ9geV4uu3B+w/fdFEpvcsHgqBvmkZzQafo7IFXQiOVp52Ypq78yyWmGaxWKiqqmJgYICVK1ce0oHOHSZmxOqFn6fKyXzGDrsbXcOAjp+9W+v2GJ3js+REuxm0PwQ254cjl8kk+VQ7fv+Zs7JZ3DwHs4lZC/8sFIllP1oXR1K48/OXh8qQT/Y6kZXe2O9KqssIEwVjLFbBpZQd5e8hmZnEBHhI4iA+HrbF1CBuqtJtCnM7m0dJD/fmuORArAI8sq2N35yWQrBWTfPQNM/u6eLqVVHkRfliETgqBbTFQimXsSYxAK2XJ8W9JmbM7kulp2eFMDZtYkulM1lPrZDx0PkZXFsQg8Fs5bf7WJDVflJaEA+ek4ZSLqOie4Kb36ph1mzluORA/nJeurQJn5gxccPr1W715s/IDuX3tux82mjhlrdr6RybJUgj8PA5yRI/41jC7vRl3xRmZWWh0WgkqVBBEGhtbWViYuKYrweHwnfJNv9P4nsbvPmOMrb5sLtzjY2N4enpuagsf41tvKrQJpV6uOjt7aWkpIQN2bH4qGVMGa2UdYm78LVJQWiUcrrHZiQbTzsi/DxIC/NGEHCazbYjKUTLmkRx0WsbmSbcd05ism7YzHUb86Wf3y7txtdTxelZ7q0UrQLkuGFF907MMqwzEuarYXzaxGe1A1y3No6kYC9Gp008V9zDFbZNxL0fNzBulB/RZ7QQOjo63CqmzVdYc+xvH4yYZtfEN5lMrFy58oh35QNTIkM61Eft5Nluhz3zvu+zJkbcbLwApmYtLgF2Mai0MaVX2jZs9nc5P7DV9Dr3g//0dQtj0yZSQrWckR3K3g7nv//y1BQEQaCxsZFdu3axZ18529y0bH5zmkhkq+2bYspgcSKxPV/UJb1frXouaPh6qJxe47Jw8efitjHGp038+Lg4FDL4tnGEV0t6eP1akdw1rDfy5K5OhvVGNqYHE+XvQZBWdUgltiNFcogXWRE+FLaO0TbqPttWyWFTtMBntUMu/X5/TyXPXZHLpowQdAYzJz1VzZirWirYWh9/PS8DlUJObe8UN71Zw4xJLHM/7JAtT86a+dEb1ZJSnSM2ZYRIZESzVeDnW+qp7p3Cz1PJjZlWQnwWJzt7NJDL5QQEBJCUlMTKlSvJzc1FoVAwMzNDVVUVu3fvpqamht7eXrf+28ca/wve/wcxNTVFYWEhANnZ2YsOMiviAlApZPSMz7oliSwEq9VKfX099fX15Ofnk5KcxLII8WbaaiP0eKkVrEsWM/uv6121oudK5+5Hxi63WUe+V97LcQlzVYvGMQthfnOBoaR9nFG9kYuXz5WH56utzd882PHM7nYuWiqW3P/y5QFGJnVclChmZnv6BNamhpIT5cvkrJkXG8FoPjpGL7bPrra2lpaWFreKaY7Be7HEtMnJSUpKSvDx8WHp0qVHNXc+OCUGqFAfjcTWdzxvlQORKjPC20k5zB54dAazU+n7YFgV58emDPE6aRqYwmKxsDzWxsJ2eJxWreBEm776zuY5EmNJ+zif1w4hl8F9Z6Ty6r4el5nxhKgw0tLSKCgoYMWKFVRPqDBbIdLL+YF1/eLmwV4GTnTYgJgsAj/fIuprq5Rz7znSpl1e2yv2R2N8FaSGajFaBP70dQtZET48cHY6MuCtsj7u+/wA95+Zyk3rY/H3VNIzPsvXDcP0jM8yoje5dck7GuRE+nBqZgjDOuNBSXDxQZ5cuDSSL7tdX0C4t4LHzokjJ8KbUb2RgocKFzzO8cmBUoBuHNBxwxvV6AwWlsX68eiFWVLrYmrWzI9er3brJ+6YtQuCwB8+P8DO5lE0Sjn/vDCLUA9XLfd/BxQKBWq1mqysLNatW0d+fj5arZbe3l4KCwud/LePdVZub5vNZ5v/X8D/t8G7t7eX4uJioqOjWbp0KRqNZtFzjF5qBUtjxSxnsaVzu9DLyMgIBQUF0rjbymixXP5t41yZfKNNsOWbBte+t710vqt5BLMb+ccNqcFE2LLisZG555ssAk/ubOP6grmg90XtIHnRvpLvb2ygc9bXMz7LqgTXme+e8VmCvNUkh2gZ0Rv5xRslLI8LkPSW7/uskT+dmymycSfhuSLX/t/hwGg0Ulpayvj4uFsSIQ7Be7HEtIGBAfbv309sbOxRkyF1BrPE/g7xUdM1Jm7oom2CIu/N06h+6aolUvkYh+CtN1ho63TVCJ+PtDAtj16UTZZt4qB5aBqLxUK0r4ogrXPwv3hZJCelideMXXVLEAQe2yHKal64NIIIPw3vlLnqaG+3zZPbyUq7u8Us+eKVzizlR75ppmh/BS194uZA6bAx8XDolztKb25IETcU5d2TjEybkclk/O70FOQy+LRmkI+rB6Tyr1IuY3fLGFe8WEHfhIG7T0nmzpMTOS0zhDibM547YZSDQS4DD4XgkrFnhHlz/doYZDJxNM+RMzAfG9ODUchkvO6mlZAb7sUfTwhkeqCDFz7ZxfGPLOwgdcXKKClAtwzpuf71aiZnzeRF+fL4RVlS1WZq1syNb1RT0+c6k368Q9YO8NTuTt6r6BdHxc5NJydSvMf/E8HbccZbJpPh6+tLQkICy5cvZ926dSQkJGAymairq2Pnzp1UVlbS3d0t6ZAf7blxGFP7X+b9b8J3UTa3a17bs9+kpCRkMtkh5VHnY23inMvYoWAXD7FLgjqyopdFeqGSi6SxFtv4zIbUYBRyGQ39OikQ2JEf7Yufp5KJGbNbD20EK8dHixdqj8mb7Mi50vd7Zb3kOLCZXy3pQiaTcbGNuGY0W10chap7Jt3qSz9f2MFPC4KQIVAyCAOKUO7cmEKIt5q24Wk+qxngvrMyAHihpI+S9iMj9+l0OoqLi1EoFKxatWpB1rpMJls0Ma2trY3a2lqys7OJj48/6uvM7sYW6KVCq1ZK1ZjYQE+e3NnObz6ec3Z67oo8tBqlk+qZPdhZBIHbP3Iv1OGIpy7Nw9dTTVq4+F22jsygVqtRKpVE+DqXRYemZlkdL14D9f06hnVG9rSOUdE9iUYp54a1sbxa0uN2tv/1/XObLnsGKgPS540p6swy3jpgYWBM/BwM0+J/U4I9eODsVOlxFd2T0gY1zFdDvo289sWBSWQyGTlRvhJj/ZcfNfL07k7OzQtjyw3LOD45ELNV4IOqAe7+sIFHtrZxYEhPmI+atYkBrIr3Jz1MS7BWjcxmmekO0f4eRPlp8PVQMmuRSe87JVTLpcsj8fZQ8MyeroOKtoR4q/nRuli+bhh2GRWTy+Cm9bG8dO0ylueks88QwaM17pdZuQx+tSmRuzaKZL+mQT0/fK2asWkTmeHePHFJttSf1hnM3PhmNVVuPMHXJgY4ldXfr+jjiZ2iCMuvTk3mxLTg/yjj+2DqaiqVitDQUDIyMli7di3Lly/H39+foaEh9u7dS1FREU1NTQwPDx+RSIy74P2/zPu/ELOzs5SUlDA2NuaU/eKgsLbY0vlaW2m7uG30oJaE3d3dlJSUEBcXR15enssspFajJCfUVjpvFEvhAV5qiXD27bzSuVIhZ73t3PNL53Yxm3URoqFD3YDeSV3NbBX4qmEEb5X4HluG9PSMz3B2bgSeKjntI9NOUqrYfI3n/w4bQ720sZPLlok983s/FcUjfnV6GtjGyjLCfVgbIccqwJ3v1Rz2aJ07Ypo72L+z0dFRJifFDc1CUqe1tbV0dXWxYsUKQkNDXR5zJCi0beAKEsWKQKcteH9UNcBj252DsV232zwvU7S/2u5FqHPaS6h22dWO0Rksggy1Wk3NvHJqWdck/h4KMmws8J0Hhvmnzczi4mUReKoUvDlP2e1XKzUoZLCvY0IaQbKXjpNCvGgZnnuRfz1PLG1/26pnb7+4UCaHiUF5dGoWj6EGNqfNTW085mC4cdFSkfj4VtU4PVPic39yfDxXrBA3k4/taOe616ronTDw2EVZvHBlLpeviCQmwAOzVaB5aJqSjgn2tI6xt32chgE9w3ojAmARQKWQkRfly7qkAFbG+ZEb6cPAlIGeCQPjM2ZUcoGzckL5QUEMnio5b+zvZV+Hewa4/Tu6bHkkS2P8+NfuTpe/R/hqeOHKPH58XDxmi5VN/yxx66UN4KWS8ZNcOeG6ZsrLy3m/qIErXypnWG8kJVTLvy7NkZj0eoOZG9+ocbuhWJ3gzyMXZKK2XRM7D4xwn42oeMPaWC5aKlbD/pPBe7HqajKZDG9vb+Li4liyRHRHS0lJkRwId+7cSXl5OZ2dneh0ukWt1fZzO3or/F/JvL/3Ii3HCqOjo1RWVhIUFERWVpbLTtD+s8ViWZStaGa4D/5eKsanTVT1TLIs1pklbBcP6evrY8mSJQQHuwZAbDfTsnAVZf0GtjYOccN6sSS5MT2EvW1jfN0wxDVrnDWvj0sJ5pPqAbY3DXP7ySJZaGRkhIqKCiIiIihIT+f04To+rOxnYsaEUj6XYXxaO8SKEIGSIfFi/rR6gBvWx3NGTjjvlvUyMGlgWaw/pZ1zc+Zf1w8RHeBJ97wqwLcDGt45J52iDh2tw9M8+EUTD56byYbUYLY3DfObj+q5Nl1F1wx0jhu454M6nrgkD/kiGpQdHR00NTWRmZm5KOGVqKgouru7KSsrQ6lUEhwcTHBwMIGBgSiVSoxGI5WVlVitVlatWiUZFRwL7LGp7a2xVWOaBucCqAz42UmJkoNXiM3P21GBTImVwyn6jk4b8fdSEeajwUejZMpgpn1kmq5x5+9HLoOeCQOjs1YKEgOoH9Dz8NZ2JmbNeKrkXL0igk+q+52EVhIDNST5yzkxTRQYeWN/D/eenkq1LePLi/KlxEHP/JSMECq6J3lt39wGID7UH+rGMaIgPz+H6OFh3msU2wHPFHYxrZvgxvXxnJEVwme1g+xuGeOJMj1rl5rx8VBy1ylJJAR58uBXLezrmGBfRzXpYVrWJwWyOj6A69fGMm200DU2y9i0ifEZE5OzZlQKGSazwKzZit5oZnBKrBbMV67LDPfm9IxAejrbqRic5uODeKA7PmfzkvAFtcdPyQjmt6el4OeponN0hjOe3LfgsUI8ZTxz5VISg72YmZnhxT1tPFUyiGBz9btntRcm/ThmdQAGC9z0Zo3bvvuKOD/+cWEWHrayenXPJHduqcciiKNitxw/t258XzPvg8HxPsYWeEdHRxkZGaG1tfX/sXfW8XHV2ft/jycTd3dt0ni9FFpaSlso7m672KKLLbDIYsvCwuLubqUUl9KWusTTuLtNfGYyen9/zMzNTKxpaYHd3/d5vfJqk5E7M/fO53zOOc95HhQKhYtIjEIxkS8y3s3sfynz/kMH78NRNnfInNbU1JCSkkJUVNSkz3uwwVsqlbAwzp9v93exo07jErwNBgNFRUWYzWYWLlw4rXiITCYjJ8R20RW1DtI7YiDQU8Xy1GAe/Laa/OYBNCNGApzGtpYkBiCR2AhlHQN6jIPdVFdXk5qaSlSUrex4/rwovijuZGdDPxkR3hTa2ewC0KUfe////qmW8+dFcvacCD4taOf78i6eOTuT/PddRWKiJgnerQMG7vuqkodPSePc1/bxeVEHq9JDuOeEFPY09pPfPICHRcZ9K+O58rNaNlX18rf15Tx08izkU8yZOkh9XV1dE9TtxsOZmOb4ElutVgYGBujp6aGmpga9Xo+3tzc6nQ5vb29yc3MPq0zjgM5EmT2wLU7wp2NwVPwd4D9nziYp2IMnNtajVsrwUMoxmC0Ynao1A4ax0H3fCSnc9/VEfeq5Mb609OvpHDKgNYyNvsUFqilpG6Kya4SnNo1ZfEolNlvY+l4dFV06FsT58/rOVgbt7O6zc0PxUysmeEpfkBMA9HPunHB+rOzl67JublwWJxLuMiO8XLzFpRIJ1y+NY3O1hrZBG3PYYs+Ihg0WJEo18fHxXHO0TCzlvlcyRHtvCacnwLmJvhQ2S2gYtHDR20W8cM5sQr3dOCsvnCWJ/ry9u43Pijqo7NK6zDOr5FJUcilucikqhc2Cdar+tJtcSm60D3NjfAhQK6nu1vLa7nb6dVJgclKmAx5KGVcviWFdceekgdtdIeVvxydySmYIEomE78q7RYLeZEgPduOaTDkJQR6YrQJPbW3n/X02bsHJGcFcOz+A4cF+amtrGdSO8mq1kqr+ieXinEhvnj1rttgPb+7Tc+3H+9GbrCyO9+Ne+4y3A7+3q9fhOLZarUatVhMZGYnFYmFwcJC+vj6xDebt7S2uA15eXkgkkkmD9/9K5v0/XTY3m82UlJTQ0NDA3Llzp/X8dpRWDqrvbR8Z2+6kcz4wMCAamRzIjtJxXB+lQEaEN4IAm+yl83BfN9LDvRAE28y1M/w9xoxNPthSIjKwHYEbu8FDergXRrN1Qj+zaUTiMkb25MY6MsK9SQ/zwmQR2FarYeUsV/34nfV9JEycHOP78m4Kmge5xD7rfc+XFXip5DxyShpSCfzSZuGH6n4ePTUdmVTC+uIObvykdFJXtpkQ0xyYilEulUrx9/cnJSWFxYsXM2vWLIaHh0U/7t27d1NdXX3YmK1fl3XZMqZgD0wWK8uf2ine9vbFORw3K0hk7TsUwZqnmVCYbH4f4PaViaIV7IhhLEgF2zP5f/9UR8eggXAfNzyUMqzCGEGsomOYnCjXGe4LFsTQq7NSNK4UmxtiE03JDvcgKUiN3mRlfUmX2NefHe6N3uT6uamVMu49Yay3vaGkSySS7W2ybQLPyQt3Ia9taoeXatUIMiU3ZIC3wlYGP+uVfXywqxGTxUqYjxu3r0zg+7/M5741SZyaFSK2gQxmK0OjZlFX3BG4PZQ2LfklCf5csySGNy/M4pMrcsmL8mFDSRf3fF3Nu3vb6NcdeD78+FlBXL4oisc31ouz6s6YFerJx5fncmpWKBYB7viictrAfeLsYB5eGYaPmwytwcz1H+8XyW43LIvlgbUphIUEkZycTFbePN5v9Zk0cM8KduPpM1JFFUWN1shVH471yp+YRITl9wzeR8JRTCaT4e/vT2JiIvPnz2fhwoWEhYUxMjJCUVER27ZtY//+/fT09Ihrg8N18LfQDvkt8D8bvLVaLbt27WJ0dJRFixZNcISaDAdNWrOLtZS0DTGkN9Ha2srevXuJi4sTZxtnesxj7Yxg50C9InVq1vmiONtivKdVx6JFiyYEOolEwgXzbMG8c3BUnL11CK/4q8eqC+/sbqG0bYibVjiMJ1pZEOcvKjk5MGiWu6htOfD4jzUsjPcnNkBN15CBR76vZlV6iEhY+7Col9Z+Pc+cnYlSLuXHih6uer/IRZ97psS0g2GUt7S0UFlZSVpaGkuWLGHp0qUkJCRgMpkoLS1ly5YtlJSU0NHRgdF48FK3RouV13bYep8ZEd6c+3qBeNuieD+Rt+AIYI7f82umZt+P75E74KtWiOfQWdnM4Wbm8Lq+aXm8GKgdH0tl18gEYY5wHze+KXe9rhZGe9LS1CBqHZyVY+MzvLWrRTTwcN70uTuNFi50mkpo7h8VWe877CqEfmoFp2S5+heXdui46fsemi3e3H+0DzG+SvpHrTy8sZlVT23j2W8KKG9ow0sp4fScMP5xYgpfXDmHnbcs4rtr5/HFlXP46LIc3r4oi0+vyGXHXxex85ZFPH1mOqvTg+geMfDgdzWsfXEfz2xppEEzs7HOhXG+PHxSCt9X9PC0U4/eGZcuiOS9S7KJDVDTO2Jk7qPb+Lps6vL7X46J4eGTUlBIoW8ULny7mK11tjGuf582iysWjSUWNmGVMgrbJlYFEvzkXJlipnDPTgoLC6mqa+Tq90to6R8lwteN586e7SKN7MD/QuY9Hdzc3AgPDycjI4OjjjqKjIwM3N3d6e3tZWhoiIULF3LjjTfS399/2Fpmv/zyC2vXriU8PByJRML69etdbr/kkktEYSjHz6pVqw7LsflfDd7d3d3s3LmTwMBA5s6dO+OTdbDBO9zXjdgANRarwCe/lFBVVUVubu5BMZgdRDmHy9iO+j709sXZoba2vU7jkm0NDAzgo7X1ECv7BXSWyU/jmtkh+LrbpEvVdoEMX7UCKQLlnVrOyB0TpLn4rQIWxPlxnl1g5cWtjRyX6pp99+rMor66M6wC3LF+P9cvi0cigXWFHTy/pZ4z8yI4b5bts39iYx2dQ6O8fH42aqWM7XV9XPZ2AUN600ER0w7Gg7uuro68vDzCwmzEKLlcTkhICOnp6Rx99NHk5ubi4eFBU1MTv/zyC3v27KGhoWGCNvNU+Kq0S7Sv/Kywg14n2dI/LR7rNzp6xHOifamrq6OgduJokfgep/i7UiYdMz9x6lEHeoy1U2aFerI6PZg4e3bq0ECv6BzB4DRr79jAfbvfNdgkqIbJyMggJiYGpVLJSVlheKlk9IzYesZyqQSVZOx5TONId8+fPVv8f4Fdy3xjpYZRk+0x1x4dI853JwapWRzvh8ki8G7xII/v0XJGXgRXHRWNn7ucbj28VDjC2e/Xserp7dzwzg6e+KaE93c1sqO+n5Z+Pc19eso7R9jdOMAnhR3c9WUVy5/ezZrn93Lnhio+Leyktmfm40ZHJ/rz+gWZ9IwYuXPDxNYFdlLaS+dmcPPyeBQyKbsa+ln21K5J2frYy/uPnTqLK4+KQSKRUNmt5+9bh6jp1hLooeTNC7Ncqlx6k4XrPi6blDiXEuzBW5fOZfnRi5g/fz5+AYE8sLGdim4dngq4fZ4aQT+I2TyxqvC/lnlPB6lUiq+vL/Hx8cTGxuLl5cXll19OZ2cng4ODrF69mtNOO42XX36ZpqamQz6OVqslKytrWsORVatW0dHRIf588MEHh3y88fif6nkLgkBtbS2NjY3Mnj1bXLRnioMN3gALYn1o1OjY2zLMeecvOmgDDoesZ0qIp+iUtKO+j+WpQSQGeRAboKZRo2NrrYbV6SG0trZSUVHB0RmJrGvtorJrhP/8XCdmuc5wU8g4PTec17Y3ISAgk0qo6dailoPObCvfJod4Ut01gs5o4dVtTdy6Mokd9X00anQ0dvailIGzgdXm6t5JyWt9WhNv7WrmxmMTeHJjHU/9XI8ECSckuiN18+TdQg3/+LqKf52WzhsX5fKndwspbBnk7Jd38qdEA/OzZ0ZMcwTVqRYiR1Y9Ojp6wNEyHx8ffHx8SEhIwGAw0NvbS09PDw0NDaKjkoP0Nn7xMVutvLR18i++t5ucPLtUaZ/WKDpoeek7adcMolP6AWOjc1F+7uJIYHygetISrUouFYlJetPkAfSmY+ORSiTifLkjA+scMrCusFO8n1opQ6M1ThDhOXPJbJGBL5VK8XJXcXpOOG/usjGm/dQKl8/BbBUwmUziJmp+7MTqVq/WyAf72rl0YRS+agX/OSOdC98qorZHR1yAmrtXJfLUz/W0DVv498YG3BVSliYFoDNaaB0Ypb5XR5deQlezGZoHgIEJx/i1ODY5gEsXRvLsliYue7dk0vuo5FIuXxjFJQsjcVfIMFms3PhpuYvwDXYjFpPFRpoL8FDw9JnpZNqVCn+s7OGOH7swWgSSgj147qx0cfoAe8Z9/Sf72TNJ4E4MUvPK+ZmiPoBc6cbTe4cp7jHjJpfyrxNjCFWMUldXh16vx8fHh4CAANE57rcOoM6wWCyTksl+q2N7enpyxRVXcOmll+Ln58eHH35IaWkp7733Htdeey0fffQRp5122kE/9+rVq1m9evW091GpVISGhk57n0PF/0zm7eiXdnR0sGDBgoMO3BxC8O7v78fPYNMxrtMqDsk5y3FMiUQiZt+O0rlEImG5Pfv9sbybiooKMbuPi4sTx7I+zm9jf/vkKlDnzo1EIoGeYaMo/qIz2zZFexr7OX/umMLaf362Zcf3roxBaveSDvOZ+J4m0zwHKG4domvIwF/t5ff//FzHFzWjXJTjz4XzbSX8v60vR6M18vYlufi6SanvM/JEuZIa7dTVkcmkTieDXq9n714by3cmHtzOUKlUREREkJ2dzdKlS0lPT0cikVBVVcXmzZspLCykpaUFvd4WZB/4ptplBv+KRdEk20e3zpkTgdz+GnfU24J0pKcUmVnP3Llz+bnGdebd+XmcjTac/6+USzDYeQLOSngfF4xl8Q4ORqSv7X33602iPenTm8fIbM19ejZO4niVFDlxdM6h2Ie9NK9SqSaY1lgsFlu2ZzWzPHliZebJnxtE7fdZoZ48uNamAvZjZS/v7m3jrqMDuCzLk4RAW4/92/IettTaNpBBXko8lLLDrqAmk0JesIT3L8lmxGDmwreKRZvT8TguNZANV83h6qNjcFfI2Ns0QO4/t00I3MGeSoYNFkbNVmaFevL+pTlkRngjCAKv7mjm5s8qMFoEckKVvHNRlkvg7hgc5aK3iiZ9DXEB7rxyXqaovqczWrj24zJ+rOxFLrXppi9JiyIpKUk0CQkODmZgYIB9+/axY8cOmpubx87Tb4w/Ssleq7VtohcsWMDf/vY3tmzZgkajOayl7PHYvHkzwcHBpKSkcPXVV6PRHLofxnj8TwTvoaEhdu7ciVQqZeHChYdMSJhp8BYEgebmZvbt28fK7FjkUgnNffoJgiozgbOhhiNQb6rqFe0kHQH358ouunp6WbhwIQEBtl77vFg/TpgdgiDAA99UTVrqjfJzZ6ldlc3XXUHiOBGWdUXt3LQ8Qfz9qncLGG4u59wsWw+zdWCinnNhyyC50T4T/g7w/t5Wgr2U3Gx/zo8rR/mwuI87VyVzSlYYFqvA9R+V8NGWYm7LUxDt50bPiIkr3yvils/KJhC2BEHAbDZPWybH3krYvXs3/v7+ZGdn/6qdvlQqJSAggNTUVBYvXsyCBQvw8/Ojq6uL7du385c3fnFRJHv8tDQyIryp7tbioZRxiZM15od7bVnr3HAlc+bM4d5v6yc9pgPOwjsOERMAhUwqZtzODGNnLW3HZxNpJ8a19o+KBK9BJya2ALy23bVqcFJmCM9taWDFUzt5Z/fYbHKEr5uowOdAntO5l8oVKJVKZDIZUqmU1WkTRyIF4PJ3S0Q9hFVpQbxxYRbBXkoaNXru3NhDdZ+ZR05O5Y0LM1mTHoS/hwKLYJOd1RotE6RbDxXpYZ7csTKBd89NZtgI571ZNGmmiz3bffX8DJ44PY1wHze0BjPnv1E4ITt3k0vxUsnoHjEiAa5YFMV7l2QT7uOGyWLlnq+qeWqTrXd+QpIHdyzydeEgFLUOcc4bhVRNYlYS4+/Oa+dnityGfrt3966GAdwVUp4/ZzbH2NXqHHB3dycyMpKsrCyWLFkiGi0ZjcZprTuPFMYzvn9LjLcDBVxGxby9vQ9IKj5UrFq1irfffpuNGzfy6KOPsmXLFlavXn1IYjOT4b++bN7W1kZ5eTkJCQnExcX9qvGymQRvh0Jbd3c3eXl5+Pv7kxXZR37zADvqNC5a4Qd7zDkxvni5ydFojZS0DZIT5UuctwRvJQwZgZCUCRfabSuT+Lmqh8KWQb4o6eSUrIkVh/PmRbKpupevSjt56qxMrninUOytFrcO8aejYvFUyRgxWGjqN9CsiOJvJydT3LOPsvYhPFUyjBbBhSFe0DxITpSPOILmjHu+rOSDy+fYXKF+ruP1fA1+fk08dPIsRo1GvqvQ8EGFgZYEf968ZBbv7m7lzZ1NfFnSyfY6DX9fk8qqtCDx8z5Q4O7o6KCiooKkpCQXxv3hgEMa1MPDA//QCG75dD/bWsYy57vyIMLaxV2bbSXoC+dH4msvbebXdVDQOoxUAn9Znc33Fb18Wdo15bHGw8FTCPFSIZVIGDU5Mm/bYvTStjEyVWzAWJUhwp55D426aqVH+rqRFubFDxU9tIzblM0K9aJ3xEj74OiEDduNx8Zz9Qe2gFXcOsiylEBRx7xjyECMv1pcII9PD+XN3e2UdYwwJ8qbffbed1W3ltNfzufzP9tG9bIjvfn48lzu+7qazTV9bGsZZdtrBeRF+zA/xpdTskIJ9VaJfe2abi06owWrIGAVbKYvZouA2SrgppAyMmqhV2sQ+/POCFDLODE9iFNzIjBbBc57o3BaOVUvNznXHh3D2Xnhonzqj5U93PxZxYT7Rvio6BgyMGq26bU/fFKquLkZ1Ju46bNy9jYNIpXAHSsTmePjGqC/LO3i3q+rJ/AHAGL93Xnl/EzRTKRzaJQ/v19Kg0aPr7uc58+ePal5kDMcrGyDwYDFYmHWrFnirHRTU5No7TndrPSvxe9dsndWV1MoFIdV42E6nHPOOeL/MzIyyMzMJCEhgc2bN7N8+fJf/fz/tZm3Qy2rsrKSnJwc4uPjf/VcuEwmm7asNDo6yu7duxkeHmbRokWideSiSUbGZgrnzFshk4q76J8re+no6GDvnj0sjrVVEiZb+EN93Lj66DgAHv+hhpFJLBKPSgggPlDNiMHCusJ2jo9x/SI9s6mOW+eObQoe39xKn9bIY6en2xZGg8UlC3SgsGWQtLCJVQ6D2crFbxWQE+3DOWm2Xe6/f6rluZ8qODFAwzVz/VDJpWyr6+PsV/ZydFIAH10xl+RgD/q0Jm76pJS/fFhCx4DugFKntbW1VFZWkpWVddgDtwNGi5X397ay5N/b2VY/Frh33rqYExbn8F2jmfo+A24ygUxVL42NjTQ1NfHyRttivzwliOouLXesn7j4TwcHs98xYubIvNUKKc19ejY4WU86K7aplTKx5O7MPF45K4jkwMkXrpQQT0LsTPLOQcO428Yylee2NIo9XIB9Ta5lXplMJrZNitqGuXdNknhbQ5+e7H9up2tAi9lsxtdNxlNnpPHEyiCWRLshk0B+8yDPb23iz++XcspL+3jk+1p+rOyle9iIVRBQyWXoTTaBltL2YUrbh9nbNEhF14gLsS470puL5oTy8IoQHl7szv7Gdk55OZ8zXi2YMnBL7PaqX101h/PnRiCXSugdMbLm+T0TArdKLiXAQ0HboAGrAGszgvn0ijwxcNf36rjgzSL2Ng3ioZTx3NmzOXdOuNj6sQoCT29q4M4NVZMG7nkxPrx7SbbI7q/v1XHhW8U0aPSEeCl566LsAwZuZzgC6HjrzrS0NORyOQ0NDWzbto38/PzDnpX/Fmzz6Y493sv7t3CrnAzx8fEEBgZSWzu50M/B4r8yeDuC6ODgIIsWLZpSvexg4WB+T4a+vj527NiBp6cn8+bNw81trF91VMKYVKrlIOt7UqnUhYjl6Ht/W9LK/v37ycrK4rKlqUgkNnbz7oaJG4RLF8UQ4+9Oz4iR53+ZOGoklUp45BTbnPXXZV0EqaVE+Y71rau6tPTozJw3Z4x9/rf15cQFqLntONviW9w6xGnZE7P68o5hFwlWB4ZHzVzxTiFBHnLOz7QtaM/v6GCvPpC/rMnl0z/PIzHIg54RI5e+XcDGyh4+/tM8rj0mDrlUwsaqXlY9u5u7v6yiYhLrQ4vFQmlpKZ2dncydO1dsJRxOWAWBb/d3sfb5PTz4bY3LIrv+yrn4uCupG4J3i23Z5a0rEkiICqOtrY3C/dWiil24p4QbP90/JSPZgUBPpUsJVGl34XIYxjhY5u5KGS9ta8QiCPjbM+vxAcDfPqrl4RS888KUyAYmNz5JCfEQA0XnkGvm7Zy9b6vrEx3UAF6fRP5zfpwfJ2aEYLYKPPlzAx9fkedy+4rn8nlqcyOjRhNms5lobyk3zPPhq6vncNfxiaxJDyLcR4VVsCnE1XRrKW4bYmfDAJtrNJS0DdM5ZMBsFZDYGffzYny4ZkkMr52fyY5bFvHWRVlEBXhy509dXPnjCPt6pl7qlDIJZ+WG8dXVc7nvhGT8PZQIgsBH+e0se2oXLf2un0esfYZdozXh5SbnsVNTefikVLzc5OLjzn6tgMY+PWHeKt65OJuj7Bt8QRAwWODmz8p5ZQrp1FOzQnjx3AyRnLa/fZhL3immc8hAbIA771ycPel3bjpM1nd2aCIkJSUxf/58FixYQGhoKENDQxQUFLB9+3bKy8vp6urCZJpY0TiYY/9RMu/fU12ttbUVjUZzSHysyfBfVzbXaDQUFxeLYvaH86KYrGzu6G9XV1dPqdA2O9wLLzebWcj+9iEyIyfvB091TMdxJBIJC2N9kEmgZciMNSSV4OBggoFz5kTywd5W7vmyki+vWSBqGQMo5VLuXJ3Cle8V8fauZs7IDSc+0PUizY7y4YZl8TyxsY6Pq03cuCScf21uF8vn6+oE3r40ThSN2F7Xxw8V3Zw3L5Kfq3rYVtdHVfcIp2SFsb7Y1YGqvlcnMuWdYbIIPLN3kFMS5JwQI+HrJoG38nvZ35vPP09N59M/z+OR76r5KL+NF7c2squhnwdPSuHYZH8e/K6GwpYh1hd3sr64k7xoHy6YF8ny1EAsJhNFRUVIpVLmzZv3q6w8J8PQqImvS7v5uKB9AiPbx13OWxflkBxis3m85bNyLILAiRkhnDUnkqqqKiwWC7v0IRgsNnLKh4XdGGbQ5nr0lFlc/m6x+LuDoBbl54bRbKXb7hlusghi1n3DsfHc+1UV5nGbTn+1rZ/sHHiMHTWsyE3hqdKaCcf2UysJ87ZtSB3jbw6o5DIXFvzTm+oJ8FCg0Zpo0OgYHrXJmjrjHyem0NKnp7htiFvWlfPNtfNZX9zJy9tsvfY3d7fz5u52FsR4cWrYMOmJMQR7yDktM5AzsoORSqX0jJjoHLYpyumNFnQmC3qTBT+1gmAvFSFeSgI8lKIgSVn7MC9sbZpAJJsK7nJYHq1ggf8oYX79mPqlDEgD6DXKOfWVggn3l0lsc+6NdpGd+bG+PLg2Rdz0aLRG7v2qmi324y+O9+PBtSlivxqge8TE43v6aeifPBjeuCyOyxZGimvM7sZ+rv+kHJ3RQnqYJ8+fPRt/j4O/3mdCGnNk5REREVitVgYHB9FoNDQ2NlJeXo63t7dYYvf09JxxBvtHybwdwftwZd4jIyMuWXRDQwNFRUX4+/vj7+/P/fffz+mnn05oaCh1dXXcdtttJCYmcvzxxx+W4/+hg7czBEGgsbGR2tpaFxnQw4nxZXOLxUJ5eTm9vb3TSnXKZVIWxvnxQ0UP2+o0hxS8LRYLOp2OssICVsap+LbewKMbm1iSEoa7UsbNyxP4qaKbRo2Ol7c28pdl8S7PszQ5UNQUf+jbal69IHvCRXrFUbFsr+9jd0M/Hxd2c1SowNZO233aB0e5fd1+njk7k+s+svU3r/+olD13HMPDp6Sx9vld7G8f5qiEABbE+bGrwZUxbbIIBHoqXWadHVhfZ2bt7GDuP9GfR3+oYV/TACc9v4u7Vidz/9pUFib48/cvKihqHWTt83s4Pi2Ie9akMGqy8O6eVr4v7yG/eZD85kFCvJTMCzAxL8aHExdloFQcnkvYbLWyr2mQzwo7+KmyRwyczgj1VvH6hTZhDp3Rwl8/20/XsIG4ADV3rYynuLgYg8GAIjyVDZv3i48zWGBWiJraHh3O4mQB7jI0evtMf2oQMU4+2KmhnjRqxtzJWgf0CPYy+CcF7VgEgSWJ/swKtZHJxitqObLlvU1j5yk+dTYJUSGAa/B2CKqE+9qCUM+IEZ3R4lJynxXqKQbvpj49KSGeaOya4R/lt3HFYlf9fTeFjGfOzuDc1/Np7tNz1qv7eOikWXz7l/msfna3eL9dTcPsagJ2NREX0M15c8JYEONDmLcSPzcJ/u7uYttEKpUiCAKDejONfXq+3d/D9xU9k3pbT4cQLyWnpvmQ6TnCkgVzMZlM9PX1UdHczSXr2hiYRK9nfqwvFZ0jtA0aUMgk3LA0jgvnR4gqdltr+7j7qyr6tCaUMgk3L4/n3DnhLjaoxW1D3Ll5gEHDxAqMSi7l4ZNSXGa+f6rs5bb1FZgsAvNjfXnqjLQJYjszxcEyvqVSKX5+fvj5+ZGYmMjo6CgajYa+vj6xV+6QIvX395+2V/57s82PVOa9b98+li1bJv5+8803A3DxxRfzwgsvUFJSwltvvcXAwADh4eGsXLmSBx544LD13P/wwVsikWAymSgrK2NgYIC5c+fOSC3tUCCTycTykF6vp7CwUGSwO5fJJ8OihAB+qOhhR10f1xwTP+19neG4qLu6uqisrCQqKooH58VT+NwuWvv1vPBLAzevSMTbXcGdq1O46ZNSXtzawJqMkAnZ9Z2rktlep2FbrYaNVT2sSHUd/ZFJJTx6ShonPLOdhkEz2dFBRBm0Iks+v3mAKD93lqcGsdGu6vbP76p55NR07jsxlZs+KePV7U08dVYG3cMGl3nk7mEDaWE2OdahSfruX5Z106M18d5leTz4jU2z/a4vKvi5qpf7T0zhsz/n8cj3tWyq1vBdeQ/flfewLDmQK5fEcOtxiXy0r40P9rbSNWzky2H4snGAh7ZvJzvKhznRPsyN8SUjwlskcx0IIwYzxa1DFLQMUtgySHHrkMv8tK+7ggH9WHaUEuLBs2dnEuHrxoDexDUflFDUOoS7QsojaxMpLylEpVKRnZvHWa8XuhwrL9qHOdG+VHS5MrwdgRvgslSBbwrGWh7LkgN5xZ6lZkZ4U+vERP6yxJZ1X3N0rFhKH6+q5Siba5xMOQyyyUfnkoJtGwA/tS2T1dhn051726khni4KYjVO5itPbKzn7LyICdl3oKeS9y7N5a+f7Se/eZAbPinj9JwwfrhuAU3t3VzzeR0m61hwa9Doeej76dn4vwapIR5cMC+CNenBaHq6aW+3Xff9o1Zu/a6DwkksdhO9BSRSmTjClRCo5tFTUkUG/qjJwhM/N/CBvWKVGKTm0VNmiWODDnxd1s09X1VN2m8P8FDwzJnpLj3sTws7eODbGqwCrEgJ5NFTUl2qbQeLXxtA3dzcDpiVj9cVd+CPwjYfGRk5rLrmS5cunZYX8P333x+2Y02GP3zwHhkZobCwEKVSyaJFiw57idQZMplM3GEWFxcTEhIijlkcCI4Z28KWQUYMZlGH+kBwXOTl5eVkZGSI/ZC/r0nh2g9LeG17E2szQ0kKtqlnfV4UwC81Gu79spK3L8l1+ZLEBKi5bFEML21t5JHvajgqIcAlmBmNRpqrSrgwGV4sh89Le/nTUTFikABYX9whzmRj9wbOjfblzLwIfq7q5cuSTv76aRm3H5/Es5vrXRybyjuGmR/nR1HLAAbzxIt6V0M/t362nxfPy+K78m6e2ljHxsoeCpsHuP/EZJ49O4Oabi0vb2vi2/3dbKruZVN1L4vi/TgpUcU9OWa6VBHkdxrZ1zTIgN7EroZ+lyqAl0qOr1qBn1qBr7scP7USicTG/O3Xjf1MtsHwdpMzJ8YXi1UQXdXkUglXHx3L5YujUcqkdA8b+NN7xdR0a/F2k/PvkxLobygTZzn/83MDdU6qXsuSA3nopFQWPb5tymtgbZo/anc3fiwfm9k2aYcwWwUifN2I8HUX7WIdkrJLEv3JivQR/z7+enOWLnWgc8jA7PAJfybEaywTSA72YGeDkZruEZfgvSQxgH9vrEchk5Aa4im6jDnw4tZGbj0uccJzB3upeOOibP6zsZ7Xd7bwWWEHnxd1kBco8MLpiSRFhfDWzhbe2tUqGpocKjLCvajr1bnI7sb6u7M6PYhVacEufWJBEBg2Cvx1XTk/VEyceY/xdycuwJ3t9f2YLFakElgaDifH6TD3NtEpBKCxuHHXV7Wip/cFcyO48dg4FwlhqyDw7JZGXtk+eX97vFiLIAi8trNFHC07PTuUv69OmiBVfLA4nNnvZFm5g8He3Nwsjlo6mwX9EYK3Tqc7YmNhvwf+0MFbEAQKCgoIDg4mKSnpiJdepFIpw8PDFBQUHHRpPtpfLSpl7W3sZ1lK0AEfYzabKS0tBfsogTORYcWsYDEDvvfLSt69NA+pVMI9J6Ry4nM72dPYz/riDk7Ndl2Nr1wSy/riDlr79by2vYlrl9qqAA4Siq+vL0uTlbRbTWyoHGJdYQcrZwXxQ8WYzvU7u1s4NiVQDAx3b6hAIoEHT5rF8KiZzdW9PPJdNRfMi+K9va0uI2S7G/pJ8xOoHpQwSeWZ2h4t57y2j3vWpPDh5Xnc8UUFNd1arvt4P6dkhXL10bE8fno61x4TxyvbbeNjO+r72VEPAWoFRydbOWF2CA+elErXkJF9TQPstf9otEaGDWaGDeYZzdxH+rqRE+VDTpQPVkFgT+MAP1f1isSyzAhvHlibKvpm5zcPcMf6CtoGRgnyVPLomih07ZXEx8cTExPD+3vbeHXHmM/zqdmh3H9iCpkPbpn2dVy33EaSKtWMkck6+m2Zbay7gcrKSva3uPahrz3GNmGgNY4ZcjhgMBjo65k4mdA1ZBDv7wxPt7HH2oJ3P1VdriNNScEexAa406jRszo9mLaBUfqcZszf2NnCsuRAUb/dGXKplFuOS2RpcgBPfl9JYecoe3sk7P2kjkDPFubF+HL3miSyImznoaVfT/ewEY3WSJ/WSO+Ika5hA3KphAhfNwI9bFa8DRodVd068Xw5NhThPipWpQWzOi2IlJCJPc6hUTPP7uhiQ6UWcH2fUgmsSA2ksGWIzTV2n/Y4X24/zmZTOjw8THdPD69sreezGhMWQYKfm5S/Hx/HivRwl2PpjBbu2lDFT1UTNwcARyX48dips8SNl1UQ+PfGet7ebdO+v2JRFNcvnbnU8nSwWq0zcks8FDh0xcPDw8WsvK+vj+bmZioqKhAEgfb2dkJDQydk5UcaRzLz/r3xhw7eEomEhQsXHrGLzhkWi4XOzk60Wi3z5s07pNL8ogR/PtrXxqbq3gMGb51OR2FhIXK5HIVCMemO8O9rUthZb5sh/6ywnTPzIojyc+e6pfE89mMt//y+hmOSAl0ILB4qObevTOLmT8t4eVsjp2SHIRsdpLS0lPj4eOLj4yktLeWyPE8q+61Ud43QM2IkzEdFh9OI0I76PlalB/OdXf/6ri8qkEokPHtOJret2883ZV28tauZxQkBbK11VQ0q75ewIM6PRo1uAvkJoHfEyPUfl7IgzpdHTk7lm7Ju3tjZwvriTjaUdHJ8WjCXLYzmvtUJLPEb5svaUfZ0CWh0Zj4v6uTzok7kUgm5UT4cnRTAhfMjuW1lAgqZlCG9mX6dkX69iQGd7f9WwdYDtmXjCrzc5PSOGGnU6ChuG+K1Hc0uZLvcKB/OmxvB8WnByKQShkfNPLGxjo/ybZlxlJ87DxwbxEh7Lenp6YSEhPD+3lYe+m6sl3z5omhuXh7PXz4qnfY6OD0njEg/dz5xUko7Ky+c8o5hwMjStAisVhN7GsdIWPMiPUixj3yNjFrs532sr1dQUICnmxJwDdQ9I0ba+ieK7rg7VWdm2Uf/iltd5/clEgnHzwrmpW1N7Gzo59FT0/jTe8Uu97norUI237SIYK+JPT2r1Yr7SDtXzTLjviKdd/O72VytoXfEyDf7u/lmnM66Si7Fx12OxSowoDOLWfn4jB97fz8z3JPZYZ7MifImI9wTmUyGRCIRiaDYWztPbWpgwxT+3cfPCqK5Ty9m4lF+bty6IoGlSf7ic4xK3PjXHh27GsyAhIXRHlw8S45ZU8v27Q0iocsk9+CmdVVUdE3ejz8nL5zbVyaIc+Rmq8C9X1ezwd4SuWV5PBcvODjNiOnwW/WdnbPyhIQERkdH2bFjB3q9nqKiIiQSiUuv/EhWUpmk5/1/wfs3hFKpPCzWjdNBp9OJ/tteXl6H3FM/Pi2Yj/a1sa6wnYsXRJMQNDk5QqPRUFRURFhYGKmpqfzyyy+TvscwHzduWBbPI9/X8NiPNRybEkSAp5KLF0azoaSTqq4R/vVDDf88Nd3lcWtmh/DB3lb2Ng3w988KOTdaS2ZmJiEhNpcoqVSKXAJPnjGb01/eQ2HLIEcl+IvBW62UoTNayG8e4Ny5NpY79vExqQQeP302Hnbi1NZaDcFeSpfxIewlcrVSxpwId/a1TZ4F72oY4JzXCrhgXiQvnZfJ27tb2VbXx7f7u/l2fzepfhJOSfXg6YsWYUVKfvMAv9Ro2FKroVGjZ0/TAHuc5ozlUpvVabiPG2E+bni7y9EbLWiNFnT2nxGDmYZeHaPjygLuChlrM0M4d06E2M8UBIEfK3p46Ltq8f2dnh3KybEC2t5W8vLy8PHx4cmNdbyyfSzj/vfp6aycFcSD39awqXpqOUS5VMKVR8UgCALPOrmJzY/15WP7RuG4zGjkUgmdurFMelWkha1bt+Lp6UlFq623HeKlYmBggKKiIiIiIoiWq6C8Tryta9jAoN5E1/DEzZTaKXjPtWfO+zuGJ7R/TskO5ZXtTWyt7eO6pXFctSSGF8fpu69+djff/WW+KCyCfQEtKSlhdHSUuXPn4ubmxpz4YAxmCyWtQ7bz2DhAbY+WIb0tUBvM1gnXlLebHH8PBQEeSlJDPcmK8CEr0ptIXzfb7LSTaY3VarX9bhXY1TjI7RuqJ1iZOnBscgAmi8D39uqTWinjyqOiuWBuhEuf+afKXu77pppBvRl3hZRbVyRwRk6oeGxHH3hjUR3PFhkZMk3MMCXANQuDuerYsRbDqMnCrZ9XsrlGg0wC95+YzMmZh1cP+/cijTkSr7S0NGQyGUNDQ2J5fXyv3Nvb+7Bn5ePL5r/nqNjhxh8+eB9pOAdSPz8/Ghomt2ScCRbF+4uM7398XcmbF7v2pAVBoKmpiZqaGmbNmkVkpG1nPZ2y2wXzo1hf3EFF5wj//L6ax06fjUIm5R9rZ3HOa3v5vKiDU7LDWBA3piktkUi4Y2UiZ7yyj+3NOi5YMEsM3DiboQR7cueqZO75spLdjf3MCvWkonMEd4UMLzc5XUMGiloGuHxxjCinefvn5UgkEh44aRYeKjlv7myme9hIlLeMXr3gskDqjBb2telZEatia6tpUga32Srw5q4Wvirr4pYVCdy0PJ6XNtfyU80Alf0C/9w5wuf1BVy6MJoVqUEsjPfn9uOTaOrT8UtNH1trNTbzCvvcb+vARIWwyeDtJiczwpvsSG+yIn3IjvQWmbwjBjPrizv5cF+bSMqL8XfnntWJuA21oB/RM2/ePEYsUtL+scnleb++Zj5BXkr+8lEpW2qm1zE+JSuUSD93CpoHREvP2AB3kZy2MM6PYC+ViyXs0Yn+nLMyC6PRSG9vL237bdervreNffuaCQ8PJy4ujh2asXG+5amBvL+3jQG9yYWj4IBzgArzcRPbP/lNAxyTPKahEOOvZm1GKF+UdPLclkaePyeDQb2ZD/aNWZzqTRaOeXIHX1w1j6RgD4xGo5hxzZkzx4WVrJLLmBvrx9xYP649xvY3QRDQGi0M6k0M6s1IJRICPBT4qhUTGPXjIZVKxQDVOajnvb1tvDbFLDXAqjgVgYGBrCvuFPvkJ2eGcMPSWJfNR5/WyBM/N/CFPStOC/Xkn6ekEuc0HSCVSvH28eWz8mGeyzdjtk4MQm5yCVekSTlp1tg0yvComes+sRH6lDIJj5+WxrLkI6BZ8DsFb0dS4jg3vr6++Pr6imZAjl55a2vrYc/KHZs458w7KOjA7cz/Fvzhg/eR6o84j545Amlvb++v0p2VSCTcvTqZnfV97Gro5+uyLk7MsO2gLRYL+/fvR6PRTBg7c1ZZGw+5TMo/TprFWa/sZUNJJ6flhLMw3p/sKB/OnRPJ+3tbuffLSjZcPR+Vw3VKr2ewaT/LYxT81GTiic0tLEkNFxc/5+OdlRfB9ro+vi/vpmvYIMqz+qoVyKQSKjpHCPd156olsby41UaiuW3dfqQSCVfk+dHd1sw3zdAyZOGYpACa+vQ0alxdsX5qNLA8NZDGXp1I7hmP3hEjd6yvIC3YjRPDdFx8diI/NBj5uKCDKrs6mUJWSW6UDwvj/Vkc78/58yK4cL5tA2S22jK1jsFR2gdHaR8YRWsfd/JQylA7fhQyIv3ciQtUu4zxmK1WStqG+Lyogw0lXWNqZkoZF86L5OK5IVTuL8Uil5OVk8cbe9p5dour1/POW49iQG/inNfyJ3UGc4ZKLuXPR9nGq5wDzNVHx/LkRhvj+vQcGwfirV1jt19j73UrlUrCw8MZEdoBI/4qK4GBgQwMDLB582Y2N4wFn8wIb97f28ag3oxGO3EOajxNbH6sLy39erbX97sEb4Crjo7hq9IuttRo2FbXx12rk7AIglgpcODkF/dw58p4YsyteHt5Mnv27BmRliQSCZ4qOZ4qOREHWQAzmq3srO/joe9qpt3AXbk4ko7uXjY3GRhqsG08MsO9+NvxicwOH1MMNFttgivP/dLE8KgZCXDZoiiuPTpmwkaidUDPXRuqRCvU8QjykHPHIh8U2m4qKyvp7OxE5uHH/Zt7qO7R46mS8fSZ6WLl43Dj9wrejvV0smOrVCrCwsIICwvDarWKWXlLSwvl5eV4eXmJbYhDycodx3buecfHz3wS6I+OP3zwPhIwm83i6Nm8efPw8bHthA/FEnQ8ovzV/HlJLM9squef31ezNCkQOWYKC23jQ5ONnTlU1qZCZoQP58+N5N09rdz31VigvnlFIj9W2me/tzVy3bIE+vv7KSwsJCQkhAfOTmTfs7uo6dby/p5WLl4YLR7PMRLnyKJL2gbpGLSNe3UOjbpkaBsre4gNUPPno2J52a6nfctnZVyaInDr6nQyG0388/sattRoODEjhLhANZvGkXQ2VvailEk4LjWQHydxtHKgvHuU8m4p8wf7OC0njG8XRbO+uJOP89tpHxxld+MAuxsH+M/P9fi6K1gQ58eieD+Sgj0I9lKRGeFNXvSBF0Cd0UJx6yAFLYMUNA9SNG5ULD5QzXlzIzgpMxTBqKeoqABvXz9aCeSix3a4PNey5ACeOTuDL4o7efSH2kmZ7ACJQR609OsxmK38dUUCkX7u7GsaYFP12OfhqZTTOWTA201uE6SxCuTbdcTd5FIXBrggCDRpbFn6ktw0shNtwV6n03FPwT7xfu0Ntl68ZliPRjuxbG62uG4clyYH8mlhB9+Xd3P7ykQXpnOMv5pz50bw7p5W7vyignVXzuWeNckoZBLe29Pm8jwP/1CPu1zC02dHHjG2cVOfjs3VGp76uX5CK8QZCUFqzsgJp6Vfz1u72xi1T0OE+6i4anEkq9MCkUokmM1mpFIp+S1DPPJDHTX2KkhqiAd3rUoke5yGgyAIfFHSxT9/qENrnPw7nBbqybNnpRPkpWLXrmGio6NpGxjl1q9b6dJZ8VLAPUt8ife0HLGxqt8z83bwD6bDgbJyQMzKAwICZpSVOxKU/yub/4/AmSi2cOFCl4H5wxG8Af60OIYNxR009el57LtylvpoCAwMFPs+4zGT4964PJHv7SItL21t5PpjE/Byk3P36hRu+LiUl7Y2khsooOtsICUlhehoW6C+eXkC93xZyTOb6zkxI5QAT+WEzYKPu4LHT5/NhW/kU94xzFVHx/JZQbtYysXuQvXQybO4bGE0r++09XffqJKQmSnj0kXheCjl3PNVBV+V2qoN1y2L57nN9S5uUEaLwI+VvZwzJ5xvyrqnDHKAGKQ9VTLWzA7h36en4eOuYEd9Hzvq+9nd0M+A3sR35d18V+5KQPJXKwjyUhHkqcRPrUBntDA0arb/mBjSmyddaL1UchYl+HFOXgTzYn2RSCT09vbyy75S9ut9+PSXXkyWHpfHvHhuJvGBav78XrFo/zkZ4gPVuCmkGMxWFsX7cd7cCCxWwUXv/JYVCXyYbwuAazNDUMll3P91lXj7+qvmif+3WCzsKChlyGBFAqRFjZUD1Wo1A6Nj729WfBQUN6AZHqWmYaI8alOfKyfhqER/vN3k9IzY2Pzz41zFif66Ip69TTZG+h2fV/DieZnctSqZuTG+3L2hkhEnOTm9WeBP75WglEm5fHE0q9KCRfb+wUIQBLqHjVR0DvNdebdI7poOx6YEclSCPwUtgzz+Yy2OMevkIDV/WhLLcamByKUS0XK2c2iU/2xq4vtKW8vDx03OX46J4czc8AnjWv06E//4pmZKNjnA8pQAHj4pVZzDt1qtVPRZ+MdPXfTprIT7qHh0dSTu5hGqqqowGo34+fmJQepQbIYnw++ZeR/Kccdn5cPDw2Igr6iomFFWbjabXSyE/4+w9hvjcJbNe3t7KS4uJjw8nJSUlAkX1eEK3iqFjL+fkMoV7xTycWEXx5wczezZyVO+l+k01R1wDtQvb2vkhIxQEoI8OD4tmGOSAthSo+HhHxt566IcF633M3Ij+GhfG/s7hnn0h2oePTV90jL9nBg/rjkmjmc3N/DO7haePDODv2+ooMuJLX7vl5XcNs+d46Jl/Nhs+5xu+qQUqQTOmhOBh0rGbev281VpJ0sSA3hgbSqP/VjLgN41SH+4r520ICUhkb5sqRuY1u5xxGDh4/x2Ps5vJyFIzalZYdx7QjK+7gpK24bYUd/PnsZ+2gZH6Rk2YrYK9OlM9OlMVB1gbQ/zUZEb5UtetA+5UT4kBnuIpXStwcy3+bV8VthOsUYCuLKvc6N8ePCkVH6p0XDTp2VTkqGw+58vjPfjvT1teLvJeeikWUglEj7Y10b7oK28669WkBriyeM/1SGXSjh/biRao1lkuGNXWgMw2eVhK7ps1ZG4QLWL8pbRKZNOCfEkNiIYaGBUkOHl5wOdrvKhO2t7GBqKEMd4lDIpK2cF8WlhB1+Wdk0I3iq5jMdPS+fMV/axs6GfGz/ZzxNnpLFyVjDhKhN3bqiidsj1WjdarLzwSyMv/GKr3ByV4E9ysAcxAWpi/N3xUMox2wlmVsFWsh7Qm2jo1bG7od+FmHggzIvx5aSsULzd5HxS0M4/vqkWb0v1l3L9ilSOSQl2+T6arfDW3nZe2tqI3mSb6z49K4SrjrK5xFnMJgR731YqtZnq/P3LanonaUNg10u/eXk8580ZGx+zCgLfNlrY0NiAVYCUYA+eP2e2yM4XBAGdTodGo6Gnp4eamhrc3d3FIOXr63vIAfj3zLx/7XGlUik+Pj74+PgQHx+P0WgU1d6Ki20TD869ckdCNn6+/P+C938hBEGgoaGBuro60tLSiIiImPR+jiDqPF5yKLBarQQYu8gJhMJeCa8UDrE0G6Z6ygOVzR1wDtT3fVXB25fkYTKZOCXKyM46qB2ELS1GTndqU8qkEv5+QgrnvLqPL4o7ifJzZ22cbFJloKuPjmNnfT/5zQPc92Ul95+Yyj++rhIDjNkq8FS+nifPzCCkto9399iyuBs+LuWZsyWckBGKWinj+o9K2VqrobBlgHPyIthe3zfBXKS8x0h5j5GFwVYEqZy93VYsBxgqqOvR8fhPdTz+Ux2xAe6kh3mRHubF9cvimRXqibtSxqDeRPewke5hA93DRgb0JjzsLlvebnK83RV4u8nxdbeRoBwYNVnY0zjA7oZ+djf2U9I2ZN9UuJ60hCA1tyxPoHvEyJXvlxxwnjw3yod5sb4iK/v+E1MI8VbRNWTggW/Hgsrjp6fzn59tve4zcsOIDVBz94ZK8faXz8sEJ+U/tVqNwcMfaCYj3NVdar/TONXscC/RolRntDBqsb0fN7lULDN3aS3s27cPuVxOYGAggYGBNpeswg6+Ku3ihmPjCPJ0Hf9KCPLgP2fO5oZPythU3cs1H5Ryx1H+dDfW8sq56VQMynhlW9OkqmXYDU62HYIL31Q4KsGf1enBJAd7srVOw5s7W6jtsZW8pRKYEyJndZyc05bOmSDluaW6l0d+qKXZXoHIjfLhzlVJpIZ4uDDXBUFgWG/g6S3NfFw49c5wvAob9tnyuzZUsbnB9r07KSOYu1cnuYzpOdvPRkdHYzab6e/vp7e3l/LyciwWi0tWfiDVR2f8npn34W4DKJVKMSsXBEHslTuyck9PT/HzcX7Pv7cxyeHG/3zwdgihDA4OuvS3J4OzzvihzpYbjUYKCwsxm808fGYep79WREHzIOuLOzgtZxJ5qxlm3ti/3PeckMoJz+1kT+MAH+5uJMLYQqiXJ9cfG8/jP9Xzr+9rWJbsOvudE+XL3auTefDbap7d3IBBG8iysInHk8ukPHHGbC5+q4BGjY7b1u3n7yek8OSPNbQP2TIMrUngqg9KuPHYBM6eY8vqAa77qIQbj43n8sXRfHBZDvd+XU1Z+zCv7mhmdrgXOZHeky7kO7ulgJX5IbZxm0LN5OIu49Go0dOo0YuSnRJ7Bpoe5kWErxvuChnuShm+7grcFVLcFTLkMim9I0b2tw/TNWyge9hA15CBrmEDrf2jLhnreIR6q/jzUTGMmizc93X1pCNX43HOnHAifd15/CfbyNZdq5I4Ps02InXl+2Mz0qvSghkeNVPSZpNbvfroWHY19LOuaIwxvijBn6GhIZHPkJKSwot2j21nkhXjbDo9lLbJAYmdmOaYu/fzULjM9S9dulQMFDU1Nej1oyT6KqgdsPD6tkZuX5Uy4f0dnRTAC+dmcu2HJexs6Oey9n7uWpVASEgIISG23nl+8wCv72hx6esfDgR5Kjk6KYC8aB9mh3uzo66PD/e1ucyBK2VSTs4MJtejnyhfN7KyslwCSVOfjn9+XytOBQR5KrnluAROnB3isnl3PKa0bZDbP68QjUkmwzl54fx1eZyLsmFl5wg3ryunpX8UuQT+uiya8xfEHDBBkMvlBAUFERQUhCAIjIyMoNFo6OzspLq6Gg8PD5fS8XTB+fcyBznSmwaJRDIhK3fulZvNZi666CLmzp3L4OAgXl4TLYwPBb/88guPPfYY+fn5dHR08Pnnn3PKKaeItwuCwL333ssrr7zCwMAAixcv5oUXXiApKWna5z0Y/OGD96/JgLVa7UFJq/7a4O2sYpaXl4dcLueaY+J4/MdaHvuxhuWpQaLNnzNmmnkDRPq585el8fbnrOOlkyPIyUgl0yrwVVk3lZ0jPPp9DY+e5jr7feGCaHRGC09srOOVvb0YZ7uRlzfx+UN93Pjg8jlc80ExhS2D3Lm+nJOireyRqGgZtAVwqwBPbKxjRWoQa2aH8E2ZLQv5z8/1fJzfxovnZvL+pbl8sK+N//zcQFn7sE1WM9QTrcE8wWIRYHeXAEhYFK0Gs5G9nSYX3esDQbC7mx2I5X0wUMqkHJXoT5iPCqPZyjObG+jXzcwa8a/L41Er5WJ2feOx8Zw/LxJBEHjgmxqqnbTK/7I0lqvetwXiixdEoVbIuO3zcvH2e9Yk06fRUFJSIqq5ma0ChXZmc+Y4X+e944K3VCLBTSFFb7KKr99frXQJ3jqTVQwEKSkpaLVaNKom7t/UxYf5bWS79RIXbgskPj4+4oI8P9aXe5b48tj2PjQGuPmLerY06LjmmFii/NzJi/YlL9oXg9lCceuQvbIxQEGLaxtiOjjaGxnhXswO92JWmBdag4XN1b18UdzJXV9Uiox5qQTmx/pxQkYIR8V4UlNegre3N+np6eJrbu7T89qOJj4v6sRsFZBLJVw0P5Krj46d1PjDbLXy6vZmnt/SOKWtq6+7nHtXJ3B0gh9SiYDFYkEikbChtJsHv6vFYLb1t8+P0XNqZtBBr2sSiQQvLy+8vLyIjY0VzVQ0Gg2lpaUIgjAtoet/KfOeDkqlktDQUEJDQ+nutjH7w8LCePfdd2lvb+fqq6/mtNNOY/Xq1SxcuHBaQ5XpoNVqbXbNl13GaaedNuH2f/3rXzz99NO89dZbxMXF8fe//53jjz+e8vLyg6qYTIc/fPA+VPT09FBcXExkZCTJyckzunAd7kWH0vfu6OigrKxMVDFzfDkvXhDN50Ud1PVoeXJjHfedmDrhsTPNvLHv6I4JtfC+WqBdJ2FdvZW5mRLkMgkPrJ3FWa/uZX2xbfZ7Yby/y2OvPDoOrdHCS1sbeatslLSkdk7JnlgN8PdQ8tqF2Vz55i72thtY1yDhz0vC2VjZI5YiAX6ys9CdbULbBw2c9OJebjsugQvnR3FsShAPfFPFL7V9VHaOEOYp46gwCXVaBR1DE/uFO5ptwXdxvB9uEgsFbSP0j/46zeuDgcSukR3gqUIqgX3NAwzqpybWTYYH1qZS1j4k9quvWBwtjoV9uK/dJaN+48JsXtzaROvAKBG+bly2KJqHvqsR3dm83eTMD7ZSXFxMWlqaKKFb2DLAsMGMv1pBWpjzeJPVJTCq7cHITSFDb7IyaDdbcTiJOVDQPMjRTl7iHh4enHnULD6q0FLZOcI3He78OchESUmJrS1kDxLd3d0ESfV8cdVcXtrZwXt7WvmipJMvSjpZFO/HmbnhLEsJRCWXMS/Wj3mxflxnP8aoycKAk+a83mTBx02Bj7sCX7UcH3cFSpkUqyBQ16OjsGWQTwo6KGipnNCuyI70Zs3sEI5PCyLIU8XIyAgFBQUEBQWRmpqKRCKhplvLK9ub+KasS+RZHJXgz9+OTyJuCn/sln49d3xePmX53/EcD65Nwc99rO2mN5p5bGMjn5fYKkNHxfvxz1NSKdi17bAEUYVCYa9whNjK+XZCV1tb26SErv/mnvevObanpyePPvoogiAQEhLC5ZdfTmlpKWeccQZGo5H333+fNWvWHPRzr169mtWrV096myAI/Oc//+Huu+/m5JNPBuDtt98mJCSE9evXc8455/zq98b/YvAWBIH6+nrq6+tJT08nPHzyUvVUOFjSmiAIVFdX09LSQlZWFsHBrk5eSrmUe09I4aI3C/hwXyun54S7uAdxEJm3xWKhrKyM/v5+/nFSGld8WMG6Qpu++bxYPzIjx0bK7v2ygi+vWSDOfjtw0/IEegaGWFfax9/Wl+OulHF8WojLfYxGI/uLi7gyXUJsSCifFHby0tZGTs0OQyrBJWts1OjoHjZw9ZJoPszvEDO7f/1Yx2dFHTx3diYvnJvJl8XtPPxdNR0jFjpG4LTsACJ93Xhvb6uL85UD2+tt2aNUAmflhiFYTBS1DFLbZ5owl3yokEkkqBRSZBLAakEpl6FQyCnvHMFsHSu/+ror8HGXozNaXBj44zE/1pe/LI3jXz/UiuXba46O5dpjYgH4uqzLpc999+okuoYNfFXahUwi4bFT0/jC7l/uwHmzvWisqyUnJwd//7HNmEO57eikABcWdEXHiIsxh7+9r+8wy3AQ6xxGGA5sq+tzCd4AUomE+05I4bzX8/mxZpAz5mZy9NFpDA0N0dXVJXqXe3t7M9TXw3WLQ1mTHsRzWxrtevS2H3eFlFmhXqTbM+fEIA88lHLcFFLUSpmtxSNAz4itjVHbo6Vn2NbOqO+1Be3xkwkSICXUk1VpwaxJDybSb4yVPTg4SGFhIVFRUcTHx7O/Y5iXtzXxk9OI4pJEf/58VMyUY4VGi5X39rTy3JZGl8/TGQqZhL8uT+CC+ZEumgHNGi03fVZORecIEvtc+eULI5BgFbkmhzOoSSQSvL298fb2Ji4uTiR0OYufWCwW+vr6cHNzO+Rs81DwR3EUs1gsjI6OcvbZZ3PbbbdhtVopKCgQp3IOJxoaGujs7GTFihXi33x8fJg/fz47d+78v+A9GRz97aGhIebPn4+3t/cMHuWKgwneJpOJ4uJi9Ho9CxYsmJLJOD/On7WZoXxZ0sl9X1Xy8Z/muiy4MpkMo3HqoAAwOjpKYWGhqPeuUqk4e84QH+1r494vK/ji6gUo5VJuWp7IDxU9NPXpeXFrIzccm+DyPBKJhBuWRNClGWR7u4W/flqG27kyjkmysdwcGYunpye5ubkcJZMRHeDJv3+q5fOiDo5K8EcAcf7VSyVj2GDhha3NXDw/EqVcKkqF1vXoWPXsLm5ZFk2C0MaTx/nzVauCz4u7WFfUQZCnkr8sjaNn2Mjbu1tcRowcsArwccFYpnp0oj+Bahkd/VoK23WM/orhAIsguC7MJgvYbTpDvVVE+rphMFup6BxxsQcdD5lEwu3HJ9p05z8qY0BvwttNzqOnpnGMPSB+uK/NhfV8Zm4YixP8Of1l2zz2tcfEYrYKPPpDrXgff3cps9VDzJ0zd8K1tdneQ146To1rX7MrKzvQ01Y+dRvnMjZef3x9cQd3rprYj8uM8ObCeZG8tbuVuzdU8uHlefipVGg0Gvz8/EhJSaGvr4/e3l4aGhpQKBTcnBfIdQuT+alBz+fFXWi0Rts8/UGUysfDMePuMJLJjvLG221iEHI4AiYmJtItePPn94rZbh/hkwDHzQriz0fFuFQrxuOXGg3//KFG9FOfDPGBah47LY1Zoa7Ps6VGw+2flzM0asbXXcHjp6WxIM4Xs9lMQ0OD2I5zHmNyHmc6HHAmdDnGrPLz8+ns7KS+vl7MygMDA/H09DyiRiF/JC9vQOx5S6VS5syZc0SO29lp23w7q1o6fnfcdjjwhw/eM72wHNahbm5uLFy48JCl9WYavB1BzsPDgwULFhxwN3v7yiQ2VfVQ1j7Ex/ltnDt3zHTgQJn3wMAAhYWFBAYGuvTu/roikY2VPdT36nh1eyPXHBOPp5ucv69J4bqPSnhlWyOL4v2ZG+s66iOXybgoVYqPfyDflHVx3YclvHJBNvGeFoqLi4mOjiYpKUn87P+8JJZgbxV3rS9nW10faaFexAWoadDosAgQF+BOg0bPW7tbWRDnx4vnZnLDJ2WiHOrjm5pJClDy/HnJPJTnzkmZYdz3dRVNfXru/7qaAA8lJ2aEMKQ3s7Gqd1IZVQd+qR1jKAd7KVke7YOH1MyITkfv8Ch9Bgl9Rin9euuMMnSlFBIC3YkO9EQQwGQRMFmsbKvrm9RUZTyOSw3ilKxQPi3s4OFqmxhKepgX/zkznQhfWzb4yrYmnvx5zKf66ER//rQ4hsvfLUJntDA3xpe1mSGc/Wq+S0/13GQZixbMmdAj298+TFOfHoVMwqJxrRFne1Rcgrdr9uOndr1eRwwW2uyl+/G4blk82+v7qe3R8qd3i7hulpHIkADRLletVhMZGYnFYhFJb0MdDWRJDByzzI9RRSidBgXVGoP9tevQm6yMmiwuY4JKmZRgLyXBXipCvFUEeyoJ93UnO9Kb1FDPA8qjdnV1UVJahtYrmjs39lLQYiMKyiQSTswI4YrFU/sNYK8iPfpD7QFlbc+ZE86txyW6MMUtVoHntjSIUwUZ4V48eeZswn3cxGmXzs5O8vLycHNzG9Ned/reS51G0Q4XpFKpGLCys7PBvsFx6IvLZDKxvO7v73/YTaD+KJm3I3j/36jYHwzd3d2UlJQQFRVFcvLU89QzwUyCt+N444PcdAjyUnHDsQk89G01T26s5fi0YJERPl3Pu729nf3795OUlERMjCtD1cddwd9WJfPXT8t44ZdG1swOJTZAzXGzgjg+LZjvy7v507uFvHh+tov2uVQqBUHgX6elozdZ2FTVy5XvFnJtupUTFkzeajglK4wgTyXXfVhCeecw4T4qInzcaBscpUGjF9nMuxr6aerT8cr5mXy+t4HPy23ZVo3GyHHP7OKBtSmclh3G51fO5c2dLby/r43eESMf7mtHKoFZoV64K6SMGC1Udk7uyCSeh2EjX+93Fk1xfDZWEn3ATyngpVbh46lGqXJDkEixWgXMFiua/gFGRo30mVVUdOup6D6whagDcqktGJyeE8a3+7u5/uMyLIKATCLh3LkR/HVFPCq5DIPZwmM/1vH+3jHlsZMyQ7h5eQKXvVNES/+ozbnquAQuf7fYxWJzQbicK09YMOli+pFdyGXlrGAX4xCd0cLuhikyb/n0mTd2DsPFCyba4KqVMl48L5NzXt1HvUbPK1UqXlkw0aJXJpOJo2aCIKDVaunt7aW3txefgQGWentwRnwgQUHx4tSHySqIQdzHTX7I393tpXV8tKeJogElvVrbCKNCJuG07DAuXxTtUlYfD63BzItbm3hrV8uUhDTs7ZMHT0rl2BRXydg+rZFb15Wz075xOm9uBLcdl4hSLkUQBCoqKtBoNMydO9fFPdDRH3ceRTObbe2Bw5mVO+uLKxSKCfadGo2GhoYG9u/fj4+PjxjMPTwm2qkeyrH/KJm3SqX6TRwqQ0NtkthdXV0uNs9dXV3iBupw4L86eAuCQF1dHQ0NDcyePdvlgzpUTBe8nY+XkZEhnqSZ4ry5kawrbKeic4THfqzhkVNsjPDJMm/nXnp2dvaUgvonzA7h88J2ttX1cf9Xlbx+UQ4SiYR/nZaOzmhha62GK98r4oVzs1iUECAez2q1opBJefKMdC58ZSel3UZeqpSzZJEXU7EEFsX789bF2Vz1QSntgwZ83RUsSfRne12fSwbVMWjg8neKOCdJwvNnJHHNp2NWmX//sopPCjq4YVkcVy6J4fLF0Wys6uWjfW3sbhxgf4etVxzp68aZueF4qmTsaxqY1ApyOtQOYg/mRvvPVDiwiYkD7gopp2aHcVxqELsb+7n6gxKx1L8sOYC/rkggPtCW2ZV3DPOXj0pdsveL50dy5ZJYLn2nkPpeHaHeKv51ahq3fV4uzhgDeKskPHbOvEkXmhGDWRyPOzvP9Uztauh3GXdzk0sJtG8QVeOCt1ImEd3GHPimrGvS4A2gMA5zRZKRp/dLKe8xcM5r+TxzVgbJIZNnMhKJBE9PTzw9PUV2tEajobe3l6KiIgACAgIICgoiICDgkPqwfVojX5d188neJmr7jPbzbcbbTc6p2WFcujBq0k2KA1ZBYENJJ09srBcJglNhYZwfj5wya8LzFbcOctOn++kcMuCukHL/iamcmGErl1qtVsrKyhgZGRHd1JzhCGqOAOMI4oc7K3cO3uOP77DvTExMRK/Xiwx2RwvEEcj9/PwOKfD93pm34zWPjIwcls3ITBAXF0doaCgbN24Ug/XQ0BC7d+/m6quvPmzH+cMH76k+bJPJxnzVarUsWLDgsM3vTRW8D0c/XS6Tcu+JqZzz6j7WFXZwRm4EedG+EzJvx3vT6XQsXLhwWmEBiUTCvSemcuJzu9hR38cn+e2cNScCN4WM587J5PqPS9lc3ctV7xfz3LlZLEkMEIO30WiktKiIq2dLeanKi+K2YS59u4B3L50zobwoCLbxl5RgDz64NJcrPyihvldHUcsQt6xI4IuSLqqcvItNVninSqDW2MvrF2bz7f4uPrH3rkvahrj83WL81ApuPS6BNekhrEoLpr5Xy0f57XxR3EnrwCifFLSjkElYOSuYtZmh9Awb2NM4QHHb1Mzfw41gLyXHJAUyP9aXQb2Zb/d3uWTSaWGe3LoiUVQhM1utvLa9mac2ubrT3XhsPGvSg7n0nSKqurQEeip57LQ07tpQOaG3ev/aNAI8Jw86X9pNU+ID1eRFu2oWOPrg0f7uNPfpifZ3F78/40vOFsHmV+0cvEvbh8XHOaO9vZ2KigpWzktn/nxPrvuolJb+Uc59PZ8blsVzzhxX68zJoFAoxBEeQRAYHBwU++RlZWX4+PgQFBREYGDgtIvsqMnC1to+1hd3srVWI2bKMqmEY5ICODkzlGOSAg74ekrahnj4uxpKDnAt+boruHl5PKflhLmQ0gRB4P29bTz6Qy1mq0BsgDtPnZkhSr86bFANBgNz5syZURvPOTg7MnGHxemvycqnCt7j4e7uTkREBBEREVgsFgYGBtBoNNTW1jI6Ooqvry+BgYGibOtMAqHVav1NCXLOsFgsotqaVqt1qXr8WoyMjFBbO8ZPaWhooKioCH9/f6Kjo7nxxht58MEHSUpKEkfFwsPDXWbBfy0kwmRSW38wGI1GF0UwR79ZrVaTlZV1WC+OgoIC/P39iY2NFf+m0+koKChAqVSSnZ39q63q7lxfzmeF7aSEeLLuynn09nRTX1/PokWL0Gq1FBQU4O7uflDv7aVfGnhiYx1SCTx8Shqn2kfAjGYrN35SysbKHhQyCc+dk0VeuBvbt2/H3d0dLy8vMjIy0JkELn6rgPKOYUK8Vbx32Ryi7KVGxyLiKIFJJBIG9Cb+8mEpBS2DKGQSblmRwIB2lFd2tE4qsrI8JZC8aB/+/VM9lkkuueuXxXF2Xjh+aiV6k4Vvyrr5KL+NsnEZd3ygmnAfN9tYkbsCk8VKQ6+OtoHRw8JC91criA1QMz/Oj4RANUaLlV0N/fxY0SMytSXA4gR/TssJY+WsIKQSCYIgsLtxgEd/qKGqa4yNr1bKeP6cDEwWgVvW7WdQbxvvuntNMv/5ud4l48buKnbd0rhJX5vBbGH1s7vpHDJw56okLpg3xpswWqwc88R2BvVmliUHsqm6lxWpgTx9VgYAV71f7MIXeP6cDLbUaFzkVwEuWxjFLcfZvKYdznuNjY1kZWWJbPcBnYm/frZfLBNH+Lpx/bI4VqcHIz+EzHB0dJSenh56e3vp6+tDqVSKgdzT24eyDi27G/vZ0zhAUesgJsvYmY7xgjPnRHNKbpSLMNFU6Bkx8OTGehdG/2SQAGfPCef6ZfGiQp0D/TojD3xTI2rqr5wVxIMnpYotDLPZTHFxMRaLhZycnMOyPo33KndeDw+Ulet0Onbv3s2yZcsO+fgO2VaNRsPAwAAqlcpFtnWq7LqsrAwvLy9iYmIO+diHitLSUnx9fYmKiuL777/n73//OxUVFTN45IGxefPmST/Piy++mDfffFMUaXn55ZcZGBjgqKOO4vnnnyc5OfmwHJ//xuDd1dVFaWnpQfWbDwbFxcV4eXmJ1nEH0kM/FPRpjax+ZicDehN3rkpmTaI7VVVVpKamUlxcTEREBCkpKQf13qxWgXu/quTj/DYkEvjHibM4a45NBtZotvLXT0v5ocIWwO9ZHo73cBPx8fEun2Gf1siFb+RT26Ml0s+d9y7NJdhLJZbxHIHbAYPZwu2fV/BDha3vHOkBp6T7srtLYG/TRGaxBFgzO4SMcC/WFXW4jJw5cGZuOBcviBTLz2XtQ6wr6mBP48CkAiyBnkpyo3xIC7P1ygfsuuZ9WhNaoxlBsF0/IyMjuKvVLkY0Id4qov3c8VcBRi2agUEaNKO0jipoGBQYNrruQuIC1JySFcrazBBCvW0lUEfQ/s/P9ROyuDNywrj1uETe39vK05saEOxEphMzQnlqU/2EEaS1GSH885RZU573N3c2868f6wj1VvHtX+ajko8tmFuqe7n6w1ICPJQsTQ7gs8IO/rQ4mpuW26YNrv2wRBwvA3j6rNkM6Ezc81WVyzG83eRsvHEhaoWM6upqOjs7yc3NnVDZslgFPivs4LktDeL4nJ9awfKUQFakBrEg3g/lAQhm4yEIAh0Desqautjb0EtBq5a6QSvGcWI9od4q5gVLyAu0sPboiYS+yaDRGnlrVwsf7G2b0v3LgcwIb/6+Opn0ccp1giDwXXkPD31bTZ/OhFxq27ReOD9SPGcmk4nCwkJkMhlZWVlHpMfqnJVPFsglEolLVj4yMkJ+fj7HHHPMYTm+g5joCObTmamUlJTg5+dHVNTk7ZgjiaKiIoKDgwkPD2fdunU888wz7Nu3bwaP/O/Af03wtlqt1NbW0tjYeEj95pmirKwMlUpFYmIiTU1N1NTUiH7fhxMf7Wvlni8r8VDJeP/8VNpqy7FardNqrx8IVqvAQ99WiZrj96xJ4fz5ti+NyWLllk/L+K68G6lE4JIkK7eee9yEzUjXkIEL3thHc5+e+EA1b1yYRYCHUlwQxsNiFXhx435e39ON3q6bvSY9mLhANW/vamXYMFHgRC6VcEZuGEsSAviipFMM/s6YH+vLlUtimW9398Ke8RS2DFHQbFPoKmsfnpZg5O0mx1MBbpgIC/AmwFuNzmhhUG9mUG8S/53KSlIuEYjxkpAe6sHqjFCOSo0Y608KAjvr+3l6U/2k/fgXzskgLtCDh76rZqs94z09JwxfdwWv7Wi2fw6IVYo50T68ekH2lOXeEYOZlU/vYkBv4oG1qaLXtwO3rivn67IuLpgXSVGr7bN58ox0jk+z6Q5c91EpG53crx45eRZJwR6c8crExez2lQnkqAcYHh4mNzd3WmcrvcnCO7tbeXNni8s4nUouJdrfnRh/d6L93In0c0cptxEGLYKAYDcf6R4y0NSnp6lPR3OfftJz4aOSkuovJdbdSFaoG/5KWy9zJuXojsFR3tjZwqcF7dNahmLffNy8PIFTs0NdSuQAPcMG/vFNtfgZJgV78ODaVBfNBqPRSEFBASqViszMzN+s1+vIyh2B3LGkOwL4yMgIJSUlLFmy5LAf29lMxZGVO5upNDY2EhoaetB6G4cD+fn5REREEBoayttvv80nn3zC5s2bf/PXcaTwXxG8dTodRUVFaLVacnNzjyjdv6KiQvwyaDQacnJy8PU9sD/0wcJqFTj71b2UtA2xKELB2dG2WfFfeyxBEPjXDzW8bg8QdxyfxKWLYrBarZSW7edfW7vY1w1SBB4/PZ0TMid+qVr6dFzwRj6dQwaSgz14+qyMCX1Qx7Fqampoa2sjOjmdtwv7+bSgHQFwV8g4LSeUlj69S7nW2RDDTS7l/HmRnDA7hC9LO3ljZ8uk72nlrCAWJ/gzP9aPKD83MZiPmiyUtQ/bPbkHaOrT068zTWs1OhWkEgj3cSMr0pusSB+yIrxJCnJneHBALOkaTWY0Em+K+2SsL5/c5eqOlYmckBHCW7taeGtXCyaLgEIm4eL5UZR1DIujXIHuUvpHrVgEW6b34nmZE8qzznj0+xre2t1KfKCa9VfNdSlPD+hNHPvkDkbNVt65JIfL3inCZBH47i8LxPN24ydlLpuku1YlcVZeOAsf2zahAuCpkPDPJW4cNS93xi0ik8XKvqYBfqzs4afK3gMSwKaCTCIh3NeNpGAP5sf6sSDOl8QgWw/cIXfsCFQSiYTAwECCgoLw9/d3KU839el4dXszXxR3TrvBw37uz5kTwXVL4ybIFwuCwHonn3a5VMKfj4rhz0tiXCoLo6Ojoj7C7Nmzf1dVsfFZeX9/PzU1NSxYsOCwj6KNh7OZiiMr9/LyIjw8/KDNVH4t9uzZQ3x8PIGBgbz44ots2rSJb7755jc7/pHGH56wJggCe/fuRaFQ/Cot2oM5XmdnJ2q1moULFx6xi00qlXDnynjOfaOIHW0m8vxkrDoMmwSJRMJtK5NQyqS8uLWRf35fg85gYq5HHyaTiZcuWcT939axobSLW9eVg0TKCRljVQxBEAjzVvLKeRlc8k4x1d1aTn1pL7cel8DZeWP2hmazmbKyMrRaLfPmzcPDw4P7I4I5Oy+ch76tprB1iPf2tBHl586p2aFsre2jd8Tokv2Mmq28tqOZj/LbuGxhND/fuJAfynv4p5NQCcAPFT0ugefU7FAWxPmxINaPOTG+zInxBcZ6anqDkZ35xfRrjYTGJqEzS+jXmRgeNeGhkuPjLhdlOH3cbTKcHirZhGwLwOLtx/CInF+0cjtRbWKWLZXAHccncnpOOD9W9HD6y3vpHrYFr7kxvgR7KXl7dytGixWVXEqEBzQM2ubQlyUH8vjpaS4zw+Oxt7Gft3fbqim3Hpc4oa/8eVEHo2YrKSGeqORSTBYBL5WcKL+xa3f8exs2mFHIpMyL8WXzuLnmEZPAnhF/jj0IbodCJmVhvD8L4/25e3UyzX16Wvr1NPXpae7T0zagx2IVkEokSKUSZBIJUikEeCiJ8VeLWXqEr9uk89yjo6MUFxfj7e3N7Nmzwa6k1tvbS11dndjfHJH78EW1jh+r+qa1mnUgO9Kbu1cnTyra0jYwyv1fV4nuZ+lhXjx4kqtbGPbkIj8/n4AA2+z7b8FongrjGezDw8PU1NSIRMEjMYrmjPFmKrt27UKtVh+SmcqvhbMZy/+aHSj/DcFbIpGQlZWFm5vbEf9S9Pf309bWhkqlYv78+Uf0whoaGmKwsYzj4tz4oWGUj+vgz2brAVmyM4FEIuGmFbY506c31fP05kZOT3Hn/jPnoVAoePiUNDo7O9jTI+WWz8owWwVOzgpzYbfGB3ny4eV53PVFJXuaBvjHN9X8VNnDA2tT8VPZ+klyuZx58+a5bKjSwrx499Jcvizt4t8/1dHSb1vEcyK9SQhUs69pcAJhbcRg4enNDbyw1SYqc98JKVgFgU3VvWLJ2RmfF3XyeZGNcOSukHFqdigx/u4EeanwVgj0NtcS7O3G0cvmz7jnKAgCbQN6KrtGqOocoapLy9ZazbSl1uRAFUvCpSS4aSmpr+eUHY20DNkWxyg/N45NDuSX2j7RKGRWiAejeh31g7b3f1ZeOHevTpqW5KU1mLlzg8144/ScMFGxzQGrIPCh3dnt/LkR7G60HWtOjI/L98Uh6CeTSLAIAlr7iNvCeP8JwRvg3b1tLE8NmiDwMxNIJRJiA9TEBqg5HIVaB4lzfHB0jDklJSWxt76H57c0sKuldUbP6a9W8NcVCZycNbFEbhUEPtrXzr831qEzWlDKpFy3NJaLF0ZNOFeOfnJoaOiv1pg43HAoq0VGRpKQkOAyU/5bCMQ4Wm2hoaEEBAQctJnKr8X4UbH/C96/Azw8PGZs3HGoaGlpobKyUhSYOJKBu7Ozk9LSUuLj43lgXhR7n9lJl87Emzub+POSyZnGh4Kz0r1oaoAvGuGzKj0Bm5u4eUUCCrmM8xIFIsKC+bykm9s/34/FYuWkzBAXYlqErzuvX5TNe3vaeGJjHTvq+zn5hT2cHmflxNlBosLWeEgkEk7KDGV5SiAv/NLI27tbKWwdQi6VsGa2TZzm67KuCaVVk0VgS42GLTUapBJb1vq34xMJ83GjvlfHpqreCWNiepPFZXRrDEb4ditqpYy0UE9CfdywWgX0JgsjBgt9OiP9WpshxsH0jbIivDkxI4QVqUH06Yx8uK+NBwoNdia6FZXUxoCWSs28Zc+WAzyU5EZ4sLuhnyGTLZDesCyeKxZHT7vYC4LAQ9/V0DYwSriPG7evTJxwn42VvbT0j+LtJueEjBBu+LgMsDlrOUNqj95qpYxhg5kROxdhRWogj3xvm8NXySQY7ExuqwB/XVfOuj/PFYVefg84nPoiIiJITEx0+bxGDGa+3d/N50UdFE1jHOIMhUzCOXMiuPaY2EnlVRs1Ou75spJ9zTbCZW6UDw+sTZ3UuMTx2hwa6n+kwO14bdHR0SL51jnL/q0EYpznvA/WTOXXfp7OmbdOp/uf8vLmvyV4H2nt3YqKCpFVq9Pp6OrqOiLHEgRBJN05m5jcsjyeu76s4vktDZyYEUb4JBKVB3scB9nuxuPTSWsy88h31by8rRGD2cLfViUjl0m5c2UcKqWCD/e1cecXFRjNFs7IDR+XsUm4cH4kRyX4c8unJVR063mrCloEC/fFmKdd2D1Ucm45zlZOfuT7GrbV9fFlaRfBXkquWhKDSi7li+JOcaF0hlWA3Y0DYiaZFeHNillB/GNtKhqtkV0N/Xxf3k3TNN7K2FXHbM9/aLra/mqF3Q3Ll2XJgXQMjrKroZ8bPilzYZfHBajxUyvoGTFQ3T8KmJFKIMvfilVi5sdqW687PlDNIyfPmmBOMxle2d7M+uJOpBJ4yGkUyQGLVeCZzbZ58nPnRCAB8u3a5o65cwdk9nPqobIFb4eBjNKsJclHoGZQQqS/O3U9Nka/m9zmfX7l+8W8ekEWfurfPoD39fVRXFxMXFycOLppFQT2Ng7weXEHP5T3HJCE5oBcCsdEyjkmyECUbx89bVKEwEAxSFisAm/tauGZzQ0YzFbcFTJuXh7PuXMjJm2nOCSLnV/bHwWDg4MUFBRM+9omE4hxHkU7nAIxU23wD2Sm4pyVH2y71FFFdFZYOxwiXn8k/FcE7yMFg8FAUVERZrOZRYsW4e7ujsFgOCRL0APBWeRlvKjMyVmhvL65krphK498V8Uz52Qd8nGsVivl5eV0d3czd+5cfH19uSTcpht9/9eVvLWrBaNFYLGbBMFq5e5ViUgReH9fO/d+XY0VODvPle0uCALCUCdXJekoiwvljb3d/FytoeCFPdx7QrLIaJ4KcYFqXjovk83VNsOHlv5RHvy2Bje5lBWzgrjtuEAaNDq+Ku1Gb5r8sy9uG6K4bYh//1RHSogHGeHenJ0XTpCXirbOHnp7ulH7h6ATlHQPG6jr1YqBaKbIjfIhI9yL5BBPUkI8iQ9U09KvZ1dDP1trNfz7pzqXMSO5VEJCkJogTxUNGp1ovOF4XxJgY2U3OpMVCbA8QuDURCPKkQ76+sz4+vpOuSB+VdrFf+x66H87PmlCMAb4dn83tT1avN3kXLooim11fehNVkK9VaJYiAOO+BPspaJzyObe1dHRQXl5Oadkh/PYlk6G9GZyo3woaBnEV61g1GQzZbn4rSJeuyCLoGnUyg43uru7KSsrIyUlhYiICNoGRllf3CEK+MwUCpmEM3PD+dPiGEK8VRiNRlGytbm5GalUil7pyyvFYxK5C+P8+MfaFFGbfjwc5ifJycmHfQrl16K/v5+ioiISEhIOyjFrOoEYR3bOIQrEzIR1P5mZiuMcOWflMzVTcbxe5+D9f2Xz/xE4bAN9fX2ZM2eOeJIP1hJ0JtDpdBQWFoqku/G9HblczhlxFh4vlfNDRQ+/1PRydFLglM83FYxGI0VFRZhMJhYuXOgy4nPevEiUcgl3b6jgg72tNIVCdrYZd0HgzlVJyGRS3tndyv1fV2OxCpxnN05xbAb6+/tZMG8ux3l5sSY7mjvWV1DVNcJNn+7nhNk93LU6eVq2tEQiYVlKIIsS/Hh/bxufFLTTqNHzVWkXX9mz8ZMybbKSexoHaNBMHXirurQuQijYTS3ihrTEB0JikAfHpwUR7adGpZAyMmpmQG9CKZfiqZLjoZThqZLjrrSRu9oHRmkbHKV9YJT2wVF+qOjhzV0ttA2MTmBie7vJUStljBjMSJC4vBY/tYI1s4MxmgW+LusSH5sY4MY/TkojI9wTjUZDT08PpaWloi+2Q5DEkV3saujnrg02MYmLF0Rx/ryJAWLUZOGZzbbgfsnCKLzdFHxfbiP1HZ8WPCFbtNjZWxG+bpS0DdHar6WiooKsrCyW+PnzTmE/nUMGTswIobxjmM4hA4vi/ajt0VLbo+WUl/by9zXJrDrARu1wwKboVolfVBKb2gR+/rmI3Q39B9XeGB+0HVAqlaK2t9Zg4vmfa3hnaxdmK7jLBM5Lc+eMXD/8lJMfrbu7m9LSUhdv9T8K+vr6KCoq+tWbiplm5c7z5JMFcscG4GAzdqlUio+PDz4+PiQkJGAwGA7aTMXxGv8veP/OONxlc4fZR0JCAnFxcS7Pf7iDd19fH4WFhYSFhZGamjplCSnCU8K5eWG8u6+DB76p4qtr/CZ4cU8Hh+qcl5cXubm5k17QZ+RGoJBJuePz/ezohDvWlXHPqnhCgoO4Y2UicqmEN3a28OC3NVisAmfnhFBcXIzVamXevHmiwElKiCcfXZHHi7808sq2Zr4u62ZP4wD/ODGFY5Kn33So5DIuXRjNJQuiKGkb4ouSTr4p66Z72CiqfaWFeXJsSiB6o4Xqbi0a7YHHjowWYdKgfjggl0rw91Dgp1ag0Zpc9MoVMgnzY/0I87F9NusKO8XqQaQnXLcsgROyo8RgGhwcTHBwMIIgMDQ0RE9PD42Njezfvx9fX18qte48vq0Hk0Vg5awgbj0uYdLX9PwvjbT0jxLipeLCeZFojWY22eVRV6VN1MG32kmC4fbX2aezkJWTS4CfbcLh0oVRPPJ9LRtKOvnbqiTu/aqKHfX9nJIVSkXnMFVdWm7+dD9fp3Txp6NiyJxB2f9goTWY+WpPNT9XdlGrVdGxvXba+0slIJdKkUgQneimCtrOsFht41/PbK4XpwKWJQdw27HRSEZt56SmpgZ3d3dxFM3X15euri4qKirIyMgQW15/FPT29lJSUkJqauphn6meKit3Jr8xiUDM+Oz3UKFSqQ7aTMWxhjuzzf+v5/1fDEEQqKqqorW1dUqzj8MZvB0kuJSUlAOWsGQyGX9aFMEPVX009+l5bksDN6+YSFCaDD09PZNaeU6GtRkhyBC47fNytrWZuOurGs6LKycwwI+zkgMRrOG8ubudR76v5afCOi7P9WXxnIwJX0ClTMr1y+JZmhzInV9UUN+r4+oPSzk9J4zbVyZO6M+Oh0Qisc1TR/pwx8okttRoRL3q8o4RyjtGkEslLEkMICfKG5VcRkXnMPnNA7T0z7xsOh4OtvVkcJNL8XKTM2qyuojLmK0C3cNGcaH3UMo4OimACB83Roxmttb2ieNEADHeUk6Kl3PJcVMLnEgkEjG7cJhCvL61lud3dSMA2UESLk9XMNDfP6G8Xtk5whs7bDPxd69JwkMl54O9beiMFmL83ScNrA6/EstIHzKJTdvcIB3jVpydF8FH+e3U9+oobBnklhUJPP5THeuLO1mVFszieH/e2tXKxqpeNlb1khXhzRm54SyI8yXc5+AnQUwWK40aPbU9I1R3aSloGaSwZcBJWnf6DZtMIkFAEI1YZhK0AbbVanj8pzpR3S/C142blyewKi3I/h58iY6Oxmw209fXJ1ZJzGYzgiAQHR19RHQffg16enooKSn5TaoBU2XlzgQ47Nf3+AB6uI4/EzMVDw8PF0VIrVZ72Pwv/ij4/yZ4m0wmioqKGB0dndbs43AEb6vVSmVlJR0dHeTl5Yma0NNBKpXiJoO/rUrmpk9KeWlrI2qljKuOnpp97kxMS0+f3MrT+b6OHfPxaUEoZOnc/Fk5e7useHgFcnOSH729veRI++mPU7ChwcLebqjaPMytii5Oyw6bdIHOjPDm0z/N4amf63l7dyufFXawo76PB9emsjD+wO8bQCmXctysII6bFSQ6RW0o6WR/xzCbqnvZVN2LxN47Tw9Rk+JhQKp0Q+XhTXW3bfGfaUlVmOaeo2Yro5OIi0T5uZEU7ImHUoZcJkGtlLG1to9v93eL93FXyDgm0ZdUt2Hywt0OShrTbLXywvZ2Xt1ly5zPzAnlqrn+9Gl6xfK6IwP08Pblzg0VWARbZr48xTZP+95eG7P9vLmRk54nk/2atpiMhHqraBs00G5nsWM/Bw+uTeX8NwpYX9zJvSck8/fVyTz8nU3DOzbAnWuPiaWxT8c3Zd0iBwG7XGletA/hPm74qRX4qZX4qRUIgsCI0cLwqI3dPjxqpn1wlJpuLfW9ugOKpzhDKZNisox5tDs2YJG+bpyVF86p2WEETKNtXtk5wuM/1bKj3kYc9HaTc9WSGM6bGznpeKZcLherJA0NDTQ0NBAaGkp/fz/Nzc14e3uL9qdeXl6/G9O8q6uLsrIyZs+eTUhIyG9+/Omy8uFhmyaCcxA/3FM8U5mpNDc3Y7VaOeuss8jKyqKvr++wGpM447777uP+++93+VtKSgqVlZVH5HgO/FcorFmtVkwm0wzuOTmGh4cpLCzEw8PjgIuqVqtl+/btrFy58pCO5dx3zsnJmfEFs2XLFjIyMvDz8+PZzfU8a2cRX7UklhuXJ0xYHBy96J6engOqwDmTT3DqVW2p0XDDx2UYLVaOTvTnqbNm097STF1dHRqrB6+X6mnR2o6bHe7BAyenkRA0dd9ob2M/d22oFAlFedE+XDQ/imNTApFJD35xq+nWsqGkk2/2d9ExaJj0PtH+7kTZ2flBXipCvFSYrQIarVH86dOa6B0xTggW7gopaqVs7Edh+zfAUylaaY6arTRqdJS1D09QblPJpRyTFMDq9GDywlRUlJXg7+8/5QjdZGjU6LhjfYXIXL9+qc0q1XG+nR24uru7eblYz54eKd4qKR9fmkV0sK+oa65Wyth806IJVQ+j0cilr++isNvCPasT2VTTx9baPu5Zk8w5c1zJiS/80sgzmxuQSuDJM2bj5Sbn1nX70Wht37/cKB+WpQTSpzVS0DzI/o7pJWp/DQI8FMT4qxkx2IK+w35VJpGwNDmAs/PCWZTgPykb3IHOoVGe3tTAF8WdCPYM/by5kVy5JGZajgb2z76+vp6WlhZyc3NFJ0GDwSCS3jQaDXK5XAzkAQEBv5ksamdnJ/v37/9DlvEdbPzY2FjCw8MnlW093AIxzujr66OsrIwdO3bw008/sWfPHsLDwznttNNYs2YNS5cunVb292Bw33338emnn/LTTz+Jf3NcE0cS//PBu6uri5KSEmJjYyfMiU6G0dFRNm/ezPHHH3/Qu+nh4WEKCgrw9vYmIyPjoEwJtm7dSmpqqljKf3VbI4/9aOv5XbQgijtXjQlAGI1GCgsLMZvNB9SediaZjDcWAdhR18dfPipl1GwlK0TJZUkm5uZm4+vri9Fs4ZXN1by6uwuDRUAuETg1Rc3li6IJCwmadHxDazDzxMZ6PiloFxf1SF83LpgXyWk5YQcsp0+FkupGNhfXYVAH0zwC5Z3DUwZ0mUSCh8pGSvO0/6tWyrAKAnKphGAvFV5ucobtRLYBnYkBvW18alBvmjIYyaUSUkI8SQ/3Yk60L8uSA/BQycWRJsdM7UyuG0EQ+KSgg0d/qEFvsuKlknPvCcmsmT119vThvjb+8U01UgncPt+DMOkQbu7uPJxvpXHAzMULIrl9ZZLLY/R6PQUFBbxQJlDQZeKBtSk09Op4fWcL582N4O7Vri5HgiBw95eVfF7UiQS44dh4zp0Twavbm3h7d6vYW1bKpCxO8CM+0AOt0YzWYEEhkzJiMDM0atOMl0okmK1WdEYLWoMFrdGC1UnXfDJE+LoR5KlEAmh0JhfXtRAvFWfkhnF6TphoDDMVRgxmXtvezFu7WsRxstXpwdx4bLzolneg81NdXU1XV9e0ksxWq1WUA+3p6cFgMODn5ydWSg5XgBiP9vZ2KisryczMPOJB4mDhGFVzZryP9yo/GFe0Q0FPTw8NDQ3MmzcPQRCIjY3l7rvvpq6ujq+//pquri4++OCDw2LRed9997F+/XrRp/63wv9s8BYEgbq6OhoaGg7KyMRkMrFx40ZWrFhxUMG3u7ub4uLiGW8SxmPHjh0kJCS4lL7e293CP76xuT6dnRfBfSemotNpyc/Pn9EGYTIrz8mws66Xaz4sxWCBlGA1D5w0i9nhY33T1n49//imWuzrhntIOCvOTF6MnyiFOH6R6hoy8P7eVj4uaGdQb8tYPZQyzsgJ4/x5kUTOYAHFST+9vb2drKws/PzGRqb6dUYqOkbY3zFMeecw5R0jtPRPP/c9U6jkUuID1aSHeZEe7sXsMG+Sgj0mlFgd2U9qauqMDWVqe7Q89mOtqB43P9aXh06eJZawJ8Pm6l6u/9imhvfXFQlcvsjWl/1wZx0Pb2rHXQb3z4PYsCAxA3TobYeEhPBEvoFtdX08fHIqSpmUW9aVkxHuxUdXzJlwLJPFykPf1fCxnUC4IM6PW1Yk4KdWsK6og2/3d0/q8OYMqQQkTM0vcNwnyktKdmwQepOV3hEjTX06McvHvmFaFO/HmXnhHJMUcEDLUZPFymeFHTy7uYE+3Vi14NbjEsiK9Jn2sQ4IgkBFRQUajYa8vLyDKrdqtVoxK+/v70etVovTBD4+PoclQLW2tlJdXU1WVhYBAQEzeMRvB0fGPd2o2mQCMYc7K+/s7KStrY28vDwEQSA4OJjCwkJmzZqFIAiiINdkvKeDxX333cdjjz2Gj48Pbm5uLFy4kEceeeSgRvUOBf8VwVsQBIzGmRsdmM1mSkpKRFekgyEqWK1WfvjhB5YtW+ZiHznda6uvr6e+vv5XuZ3t2rWL6OjoCX3rzwrbufuLcqwCHJ/ixwlB/cTHxhxwg+C8y50ucDtMX5p1cp4qNDCot4mLnDc3kuuXxYmZsiAIfLO/m0e+qxEXxRNSvFkbI2AcGcDDw0MM5M7qSHqThQ0lnbyzu1Vc8KUSWJ4SxEULIsmN8pnytVksFsrKyhgeHiYnJ2dGbFGdU49Va7TY/jVYJvxusljxcVfg667AVy23/6sQ/51OaxwnvkF9ff2Ms5+2gVGe29LAhpJOrIKthHvTsfFctCBq2tLvLzUarvu4FJNF4ITZIfzrVJtE6KDexMkv7qF72MiNy+I4K8OXnp4eenp60Olsn3VAQADJycnc8HkNO+r7+ecps5gT48uKp3Yil0rYffuSKd/rR/ltPPxdjeiffXxaECdlhLI4wZ8GjY7tdX3U9mip69FS16ubMFbngL9aQbC3ilAvFcHeKqSClZ6eHqQyOX1WN8o7hkWvdAAvlZyjkwI4NiWQJYn+M6rWGM1WNpR08tqOZlG4JzbAnZuXJ7A8JXDGm2mr1UpZWRkjIyPk5ub+Km8Dhxyow9hGEASX8vqhyIG2tLRQU1NDTk6Oy0b2j4CZBO7J8Gu8yqdCW1sbPT09ZGdnYzKZCAgIoLm5+YhYk3777beMjIyQkpJCR0cH999/P21tbaKX+ZHC/1zwdjgPqVQqsrKyDukL8v3337NkyZID7rgdwaW/v9+lJ3Yo2Lt3L2FhYZPOZ35V0sGt6/ZjFeDoOC+eu2DulBrozsQ0nPrbk6G/v5/i4mLCwsJISkqiX2/mXz/U8mWpTWEuxEvFXauTWJE6tjsd0Jt4/Mc61hV1ABDkqeSO4+LJChDEHqBMJhMDub+/v21sRBDYVtvHO7tb2G4nDQHMDvfiwvmRHJ8W7OLS5OAOOLTtD7fu8a+BY2qhq6uLnJycA553jdbIK9ua+GBfmxgIV6QGcuOx8aJv+VTYXtfHtR+WYrRYWTkriMdPTxOzzzvWl7OhpIsYf3fWXTlXDMIOElNQUBAmk4n+/n6er5BT2S9w3/ExnDE3luOe2UXHoIEXzs2coJfujJZ+PU9vqufrsjFynqdKxsI4fxKD1MQHeRAXoEatlInvDWBQb6Jr2IDRbGVAb6J72EijRkdJ6yD9+omub6HeKo5NCeTYlEDmxPjO2At8eNTMR/ltvL27VZTb9VMruPaYWM7MDZ/U5GQqWCwWSkpKMBgM5ObO3FFtJnAeDezt7WVkZAQfHx8xmM9EeMSxWTxSToe/BocauMfjYL3Kp0JzczODg4NkZGTQ399PTEwM/fbJjSONgYEBYmJieOKJJ7j88suP2HH+p4J3b28vxcXFhIeHk5KScshll59++on58+dPu2tylCSlUik5OTkzytKnQ35+PoGBgcTExLj83UFM+7Gim9cqBEwWgWOSAnj67EzcxmVMUxHTJoNNCKOC5OTkCbvRHfV93P91tViCXpYcyF2rk1zKursb+rnv6yoxy1mWHMjdq5MI8VLS398vZoCOXa8jmCsUCmq6tbyzu4UNJV3iqE+wl5Lz5kZySlYoaoltMsDb25v09PTfjAA0Ezg2bI7MbKqepsPv+7PCDjZW9YiBbX6sLzceG3/AEq4gCHyU387D39VgtgqsSA3k36eni8Hop8oerv+4DKkE3r0kl+wo2/M5MrOMjAyxJGgymbjwzQJKOnRcPgvygiR83qLix4ZRzswJ5f61sw74vss7hllf3MkPFd3iyNyhQiaB1FBPMiK8yQj3Zna4N4lB6oNqNfUMG3h7dysf5beJRLYQLxUXL4jkzNxwPA6SW2E2mykuLsZisZCTk3PE3QtHR0ddSG9KpVIM5P7+/hOu+YaGBpqamn51knAk4AjciYmJhz2zPZBX+VSBvKGhAb1eT1paGq2traSlpWE0Go/4eXVg7ty5rFixgkceeeSIHeN/IngLgkBjYyO1tbWkpaXNuPc4FTZt2jTt7tZxsQYGBpKenn5Y+lhFRUX4+PgQFzc2GuYgpjkWlH1tOv7yYTGjJisL4vx4/twscZGaaX/bwQVoaWkhMzNzyp7ZqMnCS1ubeG1HM2argLtCxvXL4jh/XoSY+RnMtvu8ut12H7VSxo12gpNMKrGNCo2M0N3dTU9PDyMjI/j6+oqBfFSQ83F+O+/va3MxKQlxh9xID47LjGFerB/+04wA/ZaYSTWgbWCUz4s6+Ly4w4VQlxHuxQ3L4lkY73fAIGU0W3nou2o+KbBVN9akB/PwKbPEbLS6a4Tz3yhAa7RwxaJobl6RIJ5Xh4bB+Gv3rFf3UdY+zHNnzyY7WM4PJa3cv6UXD7nAMyu8CQ8NnhHByioIFLUMUtI2RH2vjvpeHU19OkZNVsxWAYtVsF8vUoK9VPYfJd4KAfNQDwuSQjk2NxmV/NA2ZDayXTMbSjrFDVFCkI1EuWZ2yIwzdmeYTCYKCwuRyWQHNeJ3uGCxWFxIb0ajEX9/fzGYt7e309LSQl5e3h9uVvlIBu7xOFBW7liHpVIptbW1WCwWUlJSqKqq4uijj2ZkZOQ38VkfGRkhOjqa++67j+uvv/6IHee/InhjH8+YDI5MqK+vj9zcXHx8ZkZKmQ6//PIL6enpkwa2trY2ysvLSUpKIiYm5qCJaVOhpKQEtVpNYqJNmMVhNTiemLa3sZ8/v1eEzmghJ8qHVy7IwVMlm1HgPpQeck23lvu/rhJ1u9PCPLn/hFTSw72c7jPCvV9Vic5OGeFe3LkqicwIV2eg0dFRMSN3zF0GBwfj4xfAjtZR3t7ZSGW3HgHX158c7MH8OJs5yNwY30ndoI40HBK3np6ezJ49W8yMRk0WClsG2d04wJ7Gfopbh8RZZG83OSdmhHBadtikftGToblPzx3ryylqHUIC3LQ8nssXjbmP9YwYOOe1fDoGDcyL8eXlC7KQS6CyshKNRkNOTs6kzOi1L+ymrkfHGxdmMz/OD7PVynFP7aJr2MBtS4KZ7aVnYMDGXXAQeXx8puYjTAfn7Ag787e0tPSQZTsFQaC4dYjXdjTzc1Wv+PnmRvlw+aJojkkOmJYzMB2MRiMFBQWoVCoyMzN/9yqPIAgi6a2np4eBgQEkEomoMHao5+RI4LcM3JNhuqy8rq4OmUxGUlIS+fn5nHnmmXR3dx+Rz+6WW25h7dq1xMTE0N7ezr333ktRURHl5eWHhRA3Ff5rgrfRaGT8S9Xr9RQWFh620rUD27dvJykpyWV20tHnbGtrIysr67CPZ+zfvx+FQkFycrKomBYTMzkxrbh1kCveKWRo1Ex6mBcvn5eJt5ts2sA9OjpKUVGRmF0cTD/PKgisK+zg8Z/qGBq1EdrOnxfJ9UvjxMzfKgh8nN/OExvrxDJmaqgnZ+WGszYjZEIZ02QyiTrfDjKP1WolOCKGLsGbvc1D7GnsF5WwHJBKYFaoF/NjfZkf50dulM9Bl0gPFoODgxQVFREaGkp4TDxVXVp2N/azu6Gf4rYhl14v9tL46TnhrEgNnNDamAomi5W3drXw3JZGDGYr3m5yHjstjSWJYxvIAZ2JK94rorxjhNgAd96/LA8vpZTS0lJ0Ot20BKsVT+2kfXCUDy/PExXYntvSwHNbGkkIUrP+ynlYLWaXcyKRSESmdEBAwCFlpA7zk0MREenXGfmqtIt1RR0usrfLkgO5fFEUudG/rn/paH05NmS/RVY2UzhG1To7O4mNjRWNOgAX0ttvVQYej4GBAQoKCkhKSvpdAvd4OBN0TSYTJSUlBAYGEhkZyY4dO7jmmmtobm4+IsH7nHPO4ZdffkGj0RAUFMRRRx3FQw89RELC5NLGhwv/tcHbIcIfHBxMWlraYf3i7dq1i5iYGFFq0GQyUVxcjF6vJzc394ho5FZUVCAIAmq1ekaKaeXtQ1z2TiH9OhNJQR68ckEmwV6TL9xDQ0MUFRUREBBwUAIi49E7YuRfP9by1TSEtq4hA09tquebsm6xn+2ukHFiRjBn5Ua4ZOw4sXv7+voICAhgYGDApU8udfemsF3HnsZ+djf206iZOArm664gxFtJiJeKEG+V/V83QryUtn+9lXip5Ad2IhIEzBaBfp2Jln49Lf16qto07G/uZkRwo1tnFa00nRHipWJ+nC/zYv1YEOc37cjXZChrH+LvX1ZR1TUC9tGs+09McZlH7h42cPm7RdT16PBTK3jv0lwivBUUFhYikUjIzs6ediFf/Pg2+nUmvrhqLknBtsx8aNTEyqd3MTRqniDYYrVaGRgYEDNAvV6Pv7+/GMxnMr/c3NxMbW3tQY00WawC2+v6WFfUwc9VveI8uFImZc3sYC5bFE1i0K///ul0OvLz88XvxB8lm8UeuCsrK+nt7XUZVXMI9jg2V1qtFl9fXzGYO3S9jzT6+/spLCz8wwRuZ1gsFoqKirBYLGRmZiKRSLjqqqvYsGEDOp3uD3Wefy3+K4N3c3MzVVVVM9IMPxQ4M7+1Wi0FBQWo1WoyMzOP2E63qqqK7u5uzGbzjBTTrFYrNd3DXPFuCT0jRmID3Hn9wuwJ4hUOa8X4+PjDVubfUdfH/d+MEdqOTQnkrlVJhDkFrQGdiS9KOvk4v93FISw9zIuz8sJZMzsYpUSgqKgIq9VKdnY2KpVK7JM7yuvDw8P4+PgQFBREcHAww2YZuxv72dM4wO7GftpmaA/prpAR6KnAKoDZImC2WjFZbP1Zx+8zFQoL8lQyJ8aX+Xaf7xh/90P6XGu6tby8rZFvymx65j7ucm47LpFTskJdnq+pT8ef3i2mdWCUYC8lr16QTaSXTLwuMzImas+PR+7DWxg1W/nhugUuM/bv7G7hke9rcVdI+fiKOSRMERi1Wq0YNBzl9clGAxmnTJaTkzOjVlZTn47Pizr5oriTruGxFll6mBenZYexZnYwPgdQRJspHC2p0NBQkpOT/1ALuiAIootfXl7etJskvV4vkt76+vpQqVRiIPfz8zsiLQBH4P4j2qFarVbR4jk3NxeZTMaTTz7Jv//9bz799FOWL1/+e7/Ew4r/muBtMpkwm82iV3V2dvaMNMMPBQUFBfj7++Ph4UFxcTFRUVFH9EtuNBrZsWMHFouFxYsXTztb6kzYkEgktAyMctk7RXQMGojwdeP1C7OJ8nMXSXwNDQ3Mnj37sMsnjposvLi1idd3jJHVrl8ax3lOhDbH693XNMDHBe38UDHGuvZQSpkTBMcnerD2qJwpF5rJ+uSOoOHj48PgqJnuIQOdQwa6hyf/d7ys6YEgk0CgWoafwkJKZACJob5E+7kT6edGlJ/7IavEOVDVNcKLWxv5obxH7N+eMDuEO45PnKDP/V15N/d8WcmIwUKUnzuvXZCFr8JCQUEBgYGBM8oarYLA7Ac2A7D1r4tdjmEVBP70bjE7G/oJ9Vbx5kU5RPtPn1WbTCYxI9doNEilUrFP7u/vT21tLd3d3dMqkwmCQHW3lk1VNu360vZh8TYfdzknZYZyalYYqaGH18ZxaGiIgoICoqKiZqyG91vBMVkyODhIXl7eQc2YWywW+vr6xGBuNBpF/+vAwMBfNa/uwB89cBcXF2M0GkVXxWeffZZ//vOffP/998ybN+/3fomHHf81wduxW7ZareTk5Bwx2UHszG/Hl+FA5etfC8f7kslkqNVqcnNzp7zvVIzy9v/X3pmHNXWm7/8O+74TFkVFERBZQgIudam2VkRFsNt01daO3dT+bO23tWOn68x0cabtdNdOq53pMrYK7tpaxWrVtrKDbIIge0iAAAlkPef3h75nEmQnIQd9P9fFNdMQ5ZUk5z7v8z7PfbdfEfCa1m4EuDvis/tjoZFdRktLC0QikUVHS3o2tE0LdMNDs0NwW6T/Nee9bV1aZOQ1YVdWHWoVpp3Yd0vGIXm6EC4Ofe8W9Pr/ncnKZDLY2NiYzJP3dQPQrTNA2qFBW5cONgLA3tYGdjYC2NkKYGdjc/V/r3zZCIDqijJ0tiv6bP4aDjoDg18qWrE7t5GL7gSA2yL98di8idc0tKl1Brz5YwXnciYa74F/3hUNe8OVPo+hWLG2d+swe+svAIDcP82/ptO7RaXF6i9zcUneBV9Xe/wlJXLAaFcCKa+T16S7uxs2NjYIDQ1FcHCwiWho9QzOX1ZwYTPG3fgCAHOm+OD2+CDcEu7Xp4/BSCANVqGhoZg0aZLZ//6RYGwOI5FIRtS/Q6pXRMjb29vh5ubG3WD1rJQMBr4Ld2FhIbq7uyGRSGBnZ4ft27fjlVdewZEjR3DTTTdZe4kWYcyI9+nTp+Ho6GjS6WsJGIbBL7/8Ao1Gg8TERIsO9Rs3ptnb23N2jL0x0CiYrFODNVfPRN0dBHhW4oCUeSNziBosDMtiT24j/nG1oQ1XO61TYgNwZ3wwIgL+J4BX7ESL0eU2Dpm1evxUKuPONd0dr/yZeWG+iBvv0W9wRE/R0Gg0JvPkwzHYII0uJFRmpA2QLMuiqKET+wqacORCM3deLgCQFCXE4/MmIjzg2puDc5da8dejF3FJ3gUBgD/OmYD1C0LR1iJHUVHRkM8aq1u6sPSj3+DiYIuszfN7fY5MqcGjX+dzjWG3Rvjhj3MmDNpSlJw1qtVqBAYGorW1FQ3ydkj1zmjSO6GqAyhqVEFl5MLmZGeD2ZO9sTDcDzdP9YW/u3kaTnujpaUF+fn5vBWfgoICTnzMbUik1Wq5m15SKSGflcE0IvJduIuKiqBSqSCRSGBvb4+dO3fihRdewMGDBzF/fu/v9+uBMSPeKpUKtra2Fi1zaTQa5ObmoqurC/7+/oiJibHIz+ktyrOurg6NjY1ITEy85rnGPsD9dZTXyxRY859c1CqviOdn98chppdsZ0vRotLiu+wG7MltREP7/86iY4LdcUd8EKa7qyGtu2xiINKi0mJvXiO+y2m8xpd8sp8L4kM8IRrvCdF4D4T6ufQ6EkTGa8g8ufE5ub+//6AaDNVqNefMFxsbO+xZX43egIK6DvxarcDRC80m5/2+rg5YHi3EneLgXs+W6xXdePvHShwrlV19vj3eTIvCnCk+nJ/19OnTh9y1nVvbjvt35GC8lxN+fGp2v2t/9/gl/Oe3Oq6cP8nXGTdN9oFkghfGeTkh0MMRPq72YJgr4SJ6hkFLhxo/ZxehpZuFracQ9e1aFDV0cAY+xng52WBOqCeWRAfjpjDfAW1ozUFzczMKCwtHJe96qBBXN1LutXT3OMMwXEodsdElQSqk6c0YPgs3y7K4cOECOjo6kJCQAHt7e3z99dfYtGkT9u3bh1tuucXaS7QoY0a89Xr9iHO2+6O9vR25ubnw9vaGo6MjDAYDpk+fbvaf01eUZ0NDA2pqajBr1izuucaijQEc01paWlBQUADvgHF461wn8us74Opgi0/vi4VkhCM1Q8XAsPi1qg3f5zSYdAw72gBJUX64d8aEa2bAGfbKnzlUJEVubXuvXeUeTnYQjfeAKMQT8eM9ET3OHa4O14osca8i5+ROTk4QCoV9zi4rlUrk5OQMqxu/W2dAfl0Hzl9W4Hx1GwrqO7kue1zdXd4a6YcVsYGYPdm712CNFpUW//mtDl/+WguNnoGtQIB7EoOx/uZQeDjZce5aIpFoWH7Wx8tk2LCrqM8gkp5UylT44lwNDhRIRxz3OcnXGbHjPBAb7I5JHoA3VGiRy6HRaEy61y1VIWpsbERJSYlF+j5GinFn9Gi4uvVGd3c314hIPivkNWFZFgUFBbwV7uLiYigUCiQkJMDBwQHff/891q9fj927d2PJkiXWXqLFoeJ99QNeVFSEKVOmIDQ0FJcuXYJKpUJsbKxZf46xY1rPmVypVIrKykrufKan1Wl/glJbW4vy8nJMmzYNwcHBUGn0WPffQvx+WQFnext88IcY3DTZMs19A9GkUGH7j/k4WaNFU9f/3mpTha64Mz4IKbGBvZbH27q0yK/rQG5tO/LqOlBY38FFOxJsBEBEgBviQzwx2c8Fvq4O//tys4e7ox0MBoPJ7DIAbkfu6+uL9vb2AeM8GZaFtENzdXxMjdq2btS1daOmrRulTcprBM7fzQGJE70wZ4oPbpvm32eDW3VLF3b+Wot9+U1c1ObMSV54IWkqwgPcuJEhcqM3XHet//xWhzd+uIjbIv3xz7ujB/3nOtV6/FbdhrOXWlHapOSaAHvqub0NMN7bBeO8nLiviIAr9qe9vbakUkJeE3ImS14Xd3d3s1TY+Jy+pdfrkZeXB5ZlER8fP+qubr1BPiskO16n08Hd3R0hISHw8/Mzm4/GSCGpb62trUhISICTkxMyMjLw6KOPYteuXVi+fLm1lzgq3NDiTeIma2pqEBsby92ZV1dXo62tDfHx8Wb7WX05phFkMhlKS0sxb968IVmdlpeXo7Gx8Zq4zG6dAf/vuyL8UtkKB1sbvHfXdCwYZBOSuSAmOk5OToiJiUF+gwp7chtxtLjZJBf6tml+uCM+GDMmefXplKUzMCiTKpFb24G8unbk1rajqaN31z2Cva2AE3MfV3v4utjD1Y6FnUGNTqUSaq0eegZwdHGFq4cnDKwNdAYGWj0DrYFBt9aAhnYN6hTd1xixGBPg7ojEq+5viRP7Hx1jWBbZlxX46vd6/FT6v27zmGB3rJ07kUvAGqyH+mB484eL+PdvdXh4dgj+77awYf89AKBnGCjVBnR3KVFYkI/gwEBERYaPyGdBq9WadK/b2dmZdK8Pp8eFTFrwMcTD2I5VJBJZ3dWtJ62trcjNzcXEiRNhY2MDuVyOjo4OuLu7c6+LuW6whgoxy5LL5ZxwHzx4EA8//DC++uorrFy5ctTXZC3GjHgbDAbo9UMb+ekPEhtKLo7GXcW1tbWQSqVISBi4xDgYBnJMw9UPTGFhIebPn8/9O/srk+v1eq7DUiQS9ZqAptUzeDb9An4qlcNWIMCqWePx5PxJFnckg5ErmVAovCYkpkOtw6HCZnyf24DSJiX3eIi3M5KnCxEf4jlgwxoANLarkVfXgfy6djS0q9Gq0kGu0qJFqTVpjDIHdjYCjPO6MioWYjQ2Fi50Q4i3U78XMpZlUdjQiSNFUhwtlpnMMd881RdrZocgYaIX93cQUyAy/z7SBqZ1/y1EZrn8GiOW4UK6tidOnIjQ0FCzXsQZhjEJtjEeefL39x9w92fs8c7HEA+dToecnBzY29sjLi6Ol8Kdl5eHiIgIk4wIcoNFglR6jgeORuWAbFaam5uRkJAAZ2dnHD16FA8++CC++OIL/OEPf7D4GvjEDSneXV1dnJ9xb1ahJAhg5syZI/o5xo1p0dHR/TbLkMaQ+fPnD7jj7u7uRl5eHhwdHRETE9PvWZnOwODlg2XYm98EXE3veu62MCRPF1rszpkYw5B4wP5+zoWGTuzObcDBQuk1gjvZz4VrVhNdLY0P1sNarTOgRaVDq0qLFpUWLSodWlRayJUa1DS1QKNRI8jfD64ujnCwtYEtWGg1XdB2d0Gn7oKTgz18vdwRFuSLaSH+CPR0gq3N4H9fWj2D4sZOZJbLceRCM+qMzGTcHe2QFOWPB2eGYKrQtEGINM45OTmZzWt78fvnUKdQ4/MH4jB7hMcncrkcBQUFo+KuZTzyJJPJuN0fKa/3jNEkF3epVNrvjLm1ID7q5LXlkx0rjIQ7MjKy3/FY40kPuVyO7u5ueHt7c2flA0UpDweWZVFRUYHGxkYkJCTAxcUFJ06cwD333INPP/0U999/P69m9keDG068W1pakJeXh6CgIERGRvb6Aep5/jwc+mpM6w2WZdHZ2YmzZ89CKBRCKBTCz8+vV1FWKBTIz89HQEAAwsMHX678uVyOv/1wEbVtV0Rk5iQvbEkON4vVpPG/o6amBpWVlUPuiu7SGnCsRIbfqtuQW9vea6eyh5MdYsd5XBHz8Z6IHe8xJMMUg8HA+YD35xVgfE4uk13p/Pbz84NQKISvr2+vgtqh1iGvtgM5te3IqVGgsKGTOxrAVYe3WyJ8kTw9AHOn+PQ6x0zc/Hx8fEZkY2tMq0qLuf84AwGAX5+bB3en4e+Qroz5XcD06dMRGBg44rUNFa1WywlGS0sL7O3tud2fl5cXysvLuXFLSwjISNBoNMjOzualjzqGINy90dXVxd1gtbW1wcXFhete9/LyMsu/taKiAvX19UhISICrqytOnTqFu+66C//85z/x8MMP33DCjbEk3sRwfiQQW9XIyMh+dw1yuRwlJSWYN2/esH5Of41pPTFuTCPhA83NzVCpVFw3rr+/P5ycnLiQh6lTpw7LFlajN+CLs7XY/stlaPQM7GwEeHCmeUrpDMNwux6RSDTidLdW1ZWGtby6duTXdaCwoQPdOtOGNcHVxjdRiCcihG7wc3PgvnxdHUwMX4zjPAfyATeGZVlulyFtboasQwOtowfUtq7oZBzQpNShuLET5VIVen6QvF3skTjRC0umC3Hz1P7Hosi0w7hx4/o8WhkOJ8vlePK/hZjs54KDTw6/kkRywmNjY80eyjMcSIymcXndxsYGkydPRlBQEG+aq3C1mkL6XcwVIWxOyAz8cIS7J8RIiZTYGYYxcXobzhEQsdqVSCRwc3PD2bNncfvtt+Ott97C448/fkMKN24U8WYYBiUlJZywDGSr2tbWhvz8fCxYsGDIP6uzsxM5OTl9NqYZQ0bBDAbDNWXyrq4uyGQyNDc3o729HQ4ODtDpdIiIiBjx2EZdWzfe+KGCc/oaaSnd+PzdUu53eoZBuVTFdZ/n1bUP6Gvu4mALPzcH+DjbwUarhJ+bA8InBELo7gRXR1voDOzVrytZ1DoDA/3V/ya+5yqtHvUKNfel6dHxbswEH2eIQzwhmeAJcYgXJvkOzvOclKLDwsLM7tVPmtVuFwXhLysih/znWZblRtX42PxlMBi40CChUIjW1lZ0dnbCw8OD25X3LK+PJt3d3cjOzoa3tzeioqJ4JzTmFO6esCyLjo4OTsiH87qQ915CQgLc3Nxw/vx5rFixAq+//jo2bNjAu9/naHLdizfZBROz+sEIS0dHB86fPz9kI/vBNKYRBttRTkq9bW1tcHd3R3t7Oze3LBQKh2V1SDBHKZ1Ejdrb21s0uKU3ZEoNN05W09oNufLK+bZMqe1XZEeCjQAI8HDEeC9nBLnbw8uBgbeNGkJBJ/zdHblKyWDLhQ0NDSgpKbFIKZphWdz2/jk0tmvw/t3RJulvg8E4llIsFg97VM1S6PV65OfnXzMnrdFoTLrXHRwcuPNYHx+fUdv5dnd3IysrC35+foiMjOSd0FhSuHuDvC7k2GOgqYLLly/j0qVLSEhIgLu7O3Jzc7F8+XJs2bIFmzZt4t3vc7QZM+LNsiy0Wu2Q/sxQdsHGqFQqnDlzBosXLx702gbbmIYe2bP9CbdGozEp9To4OFzj721ra8slbnl7ew/5wqTRG/D5mRp8dqZmyKX0zs5O5Obmjjhq1NywLAuV1oCyy43IvnARLr6BgJMHJ+5ypRZdWgPsbQWcz7m9rQ3sba/4nXP/38YGzvY2CPK8Mrs83ssZQZ6OsLe99t/Z1zl5XxaU5D1TVVWF2NhYi8whn6lsxdqv8+HuaIeTz9w0JDcz0rOhUCggFot5d4ZsPG4VFxfX52fbOLBDJpNBr9dz1qDDLeMOBpVKhezsbK43hW9CM9rC3RMyVUBeF41GY9L0JpPJUFlZCYlEAg8PDxQWFmLp0qXYtGkTXnjhBd79Pq3BdSveUqkUBQUFCA0NxZQpU4b0YqvVapw8eRJJSUkDJzYNsTGN7LgxwChYZ2cn8vLy4O3tjWnTpvXaJGU8VtPc3AyDwWDSWDWU8Y2hltLlcjkKCwstMi5kDsgZ7XDsREeKce4ysaD08fHhGhEdHR25HW18fLzFxpke/ncufqtW4P4Z47BlSfig/xyx7FSr1RCLxbw6P4ZR1zaxsh1sRz7pKyFNb6SMa2yja473MfF0CA4ONmv/grkgwj1t2jRe2MWyLMsdE8rlcrS1tYFhGBw6dAhLly5FUFAQVqxYgXXr1uHll1/m3e/TWlx34k2yhC9duoSYmJhhlSJ1Oh2OHz+ORYsW9SuAw21MwwCOaTKZDIWFhZg0adKghZGcLxEh7+rqMgnqGOwFeDCl9Lq6OpSVlfHSK5qMlNTX10MkEvHijJa4iclkMrS3t8POzg4syyI6Ohp+fn4WuRiRRjU7GwGObpiFYM/B2Y/qdDrk5eUBwJAa+0YLczZ/9bTRJXnY/v7+w6pi4epNd3Z2NsaPHz/kTcNowDfh7kl9fT1KS0vh5+eHv/71rzh16hSUSiWmTZuGF154AcnJybxzy7MWY0a8cbWM3B/kfJiU+oa7o2EYBj/++CMWLlzYp+gZl+QHuvvvrzGt5/OGO2rVEyIYzc3N6Ojo4II6hELhgCXQvkrpT8ybiIaaKjQ0NFzj6MYHGIbBhQsXuNd/MIEko4ler0dubi7UajVcXV3R1tYGR8ehn5MPRFuXFndsz0JThwZrZofg2UG6qpFgnqHuaEeLrq4uZGdnc8c05hRGUl4nu7/hlNdJVjix2uUbfBdu4kNPmoorKyuRlJSE+fPnIywsDIcPH0ZBQQFmzZqF999/v9/45BuB60a8iRUnsRwcaanvhx9+wLx583oVOks0pjEMw/lYm2PUyhiNRsMJeWtrK1xdXTkh78/msGcp3dtJgDum2ODRJP4ZYJg7ztPcEGEkzlp2dnYmgiGTycAwzLCPPQhaA4PHvynAr1VtmOjjjPTHEgd11k26oj09PXk5zkRK0YGBgRY/QzYur8tkMiiVSu7mlyRv9fz57e3tyMnJ4WVWOIwmGvgq3E1NTSguLuZ86Kurq5GcnIyUlBS8//773Puxvr4ehw8fxtKlS00c4G5ExpR4a7Va9LZc4k4mFAoRFRVllgvPTz/9hJkzZ5p02LIsi+rqalRUVJi1Mc1YeEQikUUzuPV6PTdLLpfLYW9vzwl5Xzu/n4ob8fqhMsi6r/zuLWHwMhLMFedpKYijX3/C2Nc5ufGc/0DoDAye2X0Bx8vkcLa3wX8fkWCqcOCbLJKqRqxs+VbqJTvakJCQPsNjLElv5XXjagmxAiaOgnxDLpcjPz+fl8dcuNqfdOHCBc5DoK6uDklJSVi8eDE++eQT3t1I8oUxL951dXUoKSlBeHj4gFacQyEzM9OkAY2UZOVyOcRicb8746E0pqlUKuTl5cHV1RXR0dGjKjwMw6C1tZXLwWYYhhNy4iSmVCqRm5sLF3dPnFO44fOztVwpfUVsINLiAiGe4Dlo21JzQzreyTgO3z7oHR0dyM3NHfKOseecP0ndEgqFvc7HKrp0eHp3EX6rVsDB1gYf3RODOVMGtkIl5jDWEsaBaGtrQ15eHm92tMbJWzKZjKuqBQUFITw8nHc9AnwXbpK1HhsbC39/fzQ2NmLJkiWYN28ePvvsM94d3fCJMSveDMOgrKwMDQ0NEIlEZm9iOHXqFKZPnw5fX98RNab1J9ytra3Iz8/H+PHjrd6Varzza25uhlqthru7Ozo7OzF+/HhOeHqW0gFgvJcTUmIDsSI2ABN9Rm+kiPz++NrxTjLWRyo8PVO3SLWENFbl1HbgT/tKUKdQw8XBFu/eOR3zwgb+PJAzUEuYw5gDsj4+5knj6vFZQUEBfH19oVarTcrrpHvdmhDhtpad7UCQ319MTAyEQiGkUimSk5ORkJCAL7/8kgr3AIwp8dbpdJxZS15eHtRqtcV8jM+cOYOpU6fC2dmZK3nGxMQM2Jg2mPNtGHVsR0ZG8u7shhwPVFZWwtHRERqNBl5eXtzOz9nZGVmXFdib34QfiptNAkVE4z2wIjYQS6YLB0wFGwnEKpZkmPMN4gNu7vUZn5NXNzTjYDWLnxsAFsA4L0d8fE/soErlUqkURUVFvN+R8XV9RHiM16dWq7ljj7a2Njg5OZl4r49mVYjvwk1uzKKiohAYGAi5XI6lS5ciKioK33zzDe+OvvjImBNvcv7l6urarznDSPn111/h7e2N2tpaszamkQzxhoYGxMbGDmjVOtqQSMXa2lrOPIRclJqbm9HW1mZSwrV1dEZmeQv25Tfh7KVWMFffTfa2AiwM98OK2EDMDfOBQy/GJsNdX3V1Naqrqy1mbjJSampqUFFRYTEfcK2ewbdZ9fj0dDXau6+E9cwbZ4vl47QI9vfmdn59uQnW1dWhvLwcMTEx8PcfmuvaaEC6jqOjoyEUCq29nGsgNz7R0dF9ToTo9XqT7nXSjEi+LFleJzcWfBVuEoJCmudaW1uxfPlyhIaGYteuXRYzzrneGFPiXV9fj7y8PISEhFi045RlWZw+fRpqtRoxMTFma0wjHuBdXV0QiURWL6v1xHjUKj4+vteOcp1OxzW8GVtPCoVC6GydcehCM/blN6G8WcX9GW8XeyyNFiI1NhDTg/rubh/M+srKytDc3MxLu07jGfP4+HizTgzg6rn2f7Pr8c35esiVVzwPwvxd8fziMMyZ4sOdk8tkMigUCu4my9/fn/tdkRsfkUjEu1E/GN1YkK5jvkEqKuSMdjAYezDIZDKoVCqukkW6180FEe7+biysCWkuJs5uCoUCKSkpCAwMRHp6Ou+mRPjMmBLvvLw8eHp6WrRMSgSssbERISEhmDZtWp/PHUpjGumIdnBwGHUP8MGg1WqRn58PhmEGPWpHSrik4Q0AJ+QynQMOFMlwsFCKFtX/zHUm+7kgNTYQy2MCEDRI4xAYuX5ZMvxkJBCnvba2NrPOmDMsi5yaduwraMKhQinUVz3bA9wdse7mSUgTBcKul3IsuckiOz87Ozs4ODigu7t7wIZLa1FdXY2qqipeBqDgqg99aWnpiCsq3d3dJt3rzs7O3E2Wp6fnsMvrfBduhUKBnJwcroeho6MDaWlp8PDwwP79+y06ZXM9MqbEW6/Xc0JpCYwb05ycnODl5dWn2YKx8QoR7b6Em4yS+Pv787IjuqurC7m5uVzW8HAaRUh0JhFyjUYDPz8/+Pr542KnLQ5dkON4mZwLDBEAmDHJC6lxgbgt0r9fH3XyuhAfa77d+JjbTpRlWVTIVDh6oRn7C6Um6WnTAt3w0OwQJEUJB30UQQI82tvbYWtry8U0kp2ftX+f5Kimrq5uROZKloRUBAaTSjgUjLMK5PIrTaDktfH19R30a8N34SZz8GFhYQgJCYFKpcLtt98OOzs7HDp0iHfe+WMBKt5XIY5ppDGtpKQEjo6OmDp16jXPNT7fFggE/YoxMR8gM6B864hWKBTIy8tDcHAwpk6dapb1sSwLpVLJnZMrlUp4e3vDxdMH+a02OFLSivOXFdzzne1tIA7xRHSwB6LHuSMm2ANC9ysCqFKpkJubCw8PD0RHR/PuxofkhNvY2IzoxkJrYJBVrUDmRTl+Lm9BnZFguzrYIilKiLS4QEgmeA7pNSKug11dXdyNRc8SLgmE6O+c3FKQ5DKpVAqxmH/mP7jaw1BZWWnxo4beZv29vb25pre+BI7vwt3R0YHs7GzuGtjV1YW77roLer0eR44c4eVrPhag4n21s7WgoMCkMa2kpAQArimbD6UxraqqCtXV1bxtDCLnd+Hh4QgJCbHYz+nu7uaEXKFQwN3dHYyzN35vBo6WtuJya/c1f0bo7oAIf2d4Me0QT/TF4sRIeLvwq5Glu7sbOTk5w6pYaPUMiho6kFPbjuyadmRdVph07TvY2uCmyd5YHhOAhRF+Q0oEI+j1euTl5YFhGJPIzJ7/BuMO6cG675kDlmVRUlKC1tZWXiaXwcql/J6vjYuLC1ct8fLygkAg4Lry+SrcxOt90qRJmDRpEtRqNf7whz9AqVTi6NGjvDy+GSuMKfE2GAzQ6/Vm+/v6c0wrLy+HTqfD9OnTTZ4/2AxuEqcoEol42VhFLkqjfWNBZpZJw5ujoyOU9l5o1Dqhsk2HooZOVMhUXNe6MSHeTld258FXdufTgtzg6mCdkRJSqREKhQNmNesZBrWtalTIVLjQ2InsGgUK6zuhNZhmjvu6OmBBuC8WhPtidqgPXByGP+dKkrccHBwQFxc3qBsL43PylpYWLm6W5C2bs+rBMAyKioqgVCoH9E6wFlVVVbh8+TIvSvm9ldfd3NygUCgQFRXFy3FJpVKJrKwszodBo9Hg/vvvR3NzM44dO8bLhsmxxA0r3gM5plVWVkKlUiE2NnZIjWmkjAoAcXFxvOueZBgGJSUlaGlpQXx8vFVvLIhbFbFqFQgE8Pf3h8bA4nxFE3RuQajuZFHY0ImaXnbnNgJgsp8rooPdERHgBj83B/i6Xvnyc7OHp7O9RZzfejOHYVkWHWo9ZEotquRdqJCpUClToUKmQlVLF3SGaz9mPi72EE/whGSCFyQTPBEV5G6W9ZKKgLu7+7CPGnrGzer1eq58O9JzctIjoNFoIBaLeTcaRJIJa2trIZFIeHnzXVVVxfkwaLVaqx599IZKpUJWVhaXrqbT6bBq1SpcvnwZx48f5+UkwVjjhhTvwTimVVdXo62tDSKRiBsDwwDCTaxEiYc13xyCiIe6VqtFfHw8r3Y7RCwuXryIzs5O2NjYmFi1dumBCw2dKGzowIXGThQ1dKKpo/+UOVuBAN6u9lfE3NUevibi7gAfF3s4O9iCZa90dTNX/9f4v1mjx7V6BtVNLSivaYKNiye6WXu0qLSQK7VoUWl7FWiCs70Npvi7YqrQFeKQK4I90cfZ7GVplUqF7Oxs+Pn5mS15q7egjp6mPYOFNM8ZDIY+S/nWhIz7NTQ0QCKR8PI8lpTKiTNZzyxsUl4n3euj3WfT1dWFrKwsBAUFISwsDAaDAWvWrEFpaSkyMzN5eYQ4FhlT4k3c1UZCz8a0vgS2trYWTU1NiI+PH1QGt1wuR2FhIRcHyLfGNJK65uzsjJiYGN45GJFKSHt7O3fDRDrXVSoVF9IhFAq5aoasU4Oihk4UNXSgqqUbLSot90XMS0Ybd0c7TPR1Rpi/K6b4uSDM3xVhQlcEeTpZ3P+d+JRbOku6r3Nyf39/eHh49BvAYzw1wLf3oHHznEQi4Z0PA3oR7p7odDqT8rpAIOAqJsNNqhsK3d3dyMrKQkBAAKZOnQqGYfDYY48hNzcXmZmZvDSNGavcUOJNGtMmTZo04MWtvr4eVVVVXFmvv8a02tpaXLx4kbeORmRUjaRG8a1jW6fTcbux3mbMe4Z0eHh4cELe1wVWa2DQptJxO+PWLi1alFrIrz7WcnW3rNEzsBEIIBAANgIBbARXqis2Jv8NaDUa6LVahAi9EOTtCj9XB/i6OXD/S0r1jnbWqbYQ16opU6Zg4sSJo/Zze4qFra0tJxY+Pj7czbFWq0V2djZ388i3qhTLsigtLYVcLreY5fJIGUi4e8IwjEn3end3N3cT7OfnZ/byOhFuf39/REREgGEYbNiwAWfOnMHJkyd5ZwM91rkhxHuoUZ6kTEj80319fSEUCuHv729S5jN2/IqLi+OlsURzczOKiop4O6qmVquRk5MDZ2dnxMbGDnhR12q1JtnkxOBCKBT2u+sbLiRnnfRG8LWMWlRUxLlWWQvjc3KZTAadTgdfX194eXmhtraWt1nhLMtyBjsSiYQXZ8Y9IZasgxXu3lCpVFxDokKhGHTFZDCo1WpkZWXB19cXkZGRYFkWGzduxIkTJ5CZmTmqN5Q3CmNKvFmWhVarHcQz/8dwojyNrU5VKhVkMhmkUik3rywUCuHt7Y2ysjJotVqIRCLefeBZluXmU/nqET3SOE/SgUsa3kh3NHl9RioSPWek+dQjQKivr0dZWRnvXmMy619fX4+6ujqwLAsvLy/uJpgvO1tyfejo6IBEIuHla2wO4e4JmSwgXzY2Nibl9aFURjQaDbKysuDl5YWoqCiwLIvnn38eBw4cwMmTJ/s0uqKMjOtavIca5TlQY1p3dzeam5vR1NSEjo4O2NnZYeLEiQgKCuKVeDMMw53diUQiXs5SkrhMMv850h0z2fWRc3KDwQA/Pz+u4W2oZ30kuQ4ARCIR7xqrYDSDHBcXx7uAG1xt4MzOzkZgYCAmTJhgYglq7aYq9BhXk0gkvJsMgZFwD8VLfagwDAOFQsEdfajVapPu9f6um1qtFllZWfDw8MD06dPBsixefPFFfP/99zh58mSvJleW4o033kB6ejpKS0vh7OyMm266CW+99RYiIiK456jVamzatAn//e9/odFokJSUhI8//piXM/IDcd2K92Ab02A0vz2YxrS2tjbk5+dzJhbkYuTm5gahUMidw1qrPE3CT/jqAY6rHtElJSUWi/MkQRBEyI3P+oRC4YCjSaSU7+LiwtvzWRKAwocZ5N4g6X8hISHXNHDq9XoT33UyWdDznNySMAxjYmnLt3E1jJJw9wapNspkMrS3t8PNzY3blRuX10kfAzEpAoDXXnsNX375JU6ePInIyMhRWzMALFmyBPfccw8SExOh1+vxpz/9CUVFRSguLuZ6Y5544gkcOnQIO3fuhKenJ9avXw8bGxucOXNmVNdqDsaUeONqiWYghtKYNpQMbiI6ERERGD9+PPe4cdKWXC6Hk5MTJ+SWOIftC7Vajby8PNjb2/My/ITMp16+fHlU4zzJxai5uRkdHR3w9PTkhLxn+ZaM+5GzOz6ez5I5fXMGoJiTtrY25OXlYfLkyQOedRrv+pqbm6HVarkeEz8/P4uIqsFgQH5+PnQ6HcRiMe8+J7CicPekp3EPudHy9vZGdXU1d4MrEAjw5ptv4tNPP8WJEycQExNjtTUTZDIZhEIhfv75Z8yfPx/t7e3w9/fHN998gzvvvBMAUFpaimnTpuHcuXOYNWuWtZc8JK4r8TZuTIuJiRmw89t4x93f/DbZ6dTV1Q0oOgaDwUTI7ezsOCEnloaWgJwf+/r6Ytq0abwTHePGL2uaw2g0GpOGN2M7UHJRt/So1XBhGAaFhYVQqVS8PYNvaWlBfn4+lxw1FIw98WUyGTo7O7kbLX9/f7PcqBgMBuTl5fF2zhw8Eu6ekBstqVTKTePs2rULSUlJaG9vx5dffonjx49DJBJZe6kAgIqKCkydOpWzjz1x4gRuvfVWtLW1mTQXT5w4ERs3bsTTTz9t1fUOFX4NWg4C4mbVE+PGtBkzZgx4zjvYDG6DwYCioiJ0dnZixowZA15AbG1tERAQgICAADAMw0Vm5ufnAwAn5Oa0m5TL5SgoKEBoaKhZzo/NjXGc54wZM6wqOo6Ojhg/fjzGjx/PjTk1Nzfj/PnzYBgG3t7e8PHx4W7o+AIxN9Hr9UhISOBlmZd0vU+bNm3AiY7eEAgEcHd3h7u7OyZPngy1Ws0JeUVFxYjPyfV6PXJzcwEAYrGYd3PmGGZe+GhhY2MDDw8PVFRUwMfHB6GhoaipqcG2bdvQ2NiIyMhI7N27l+sxsubnh2EYbNy4EXPmzOFK+k1NTXBwcLhmKiggIABNTU1WWunw4d+7dxgYN6bNnj17SI1p/Qk3KUPb2dlhxowZQ75gkg5O4nZFIjNLSko4u0lSHhzuOV9tbS3Ky8sRFRU1rAumpdFoNMjLy4OtrS0SExN5tdOxt7dHYGAgDAYDmpubMWnSJOh0OhQWFoJlWZOGN2uee5P3t52dHSQSCS9Fp7GxESUlJWbtendyckJISAhCQkJMvL3z8vKuMR8Z6PUxNogRiUS862MAz4UbV2/CyfswLi4ONjY2GD9+PLq6unD48GHI5XIcOHAA77zzDtzd3XH69GmrdZqvW7cORUVF+OWXX6zy80cD/l0FhoilGtM6OjqQl5dntjK0QCCAt7c3vL29ER4ezjVUVVRUoKioyETIByNwLMvi4sWLaGhogFgs5qXJP4nz5PN8r3H4BOnYJtGMzc3NKC8vh0ajsfg5bF+Q5jlXV1fExMTw7neIqzeQFy9eRFxcnMX6GOzs7EwqWuSc3Pj1Ibvynq+PTqfjQloG4yVgDYhwx8XFwc/Pz9rLuQYi3CT61sbGBjt37sTLL7+MgwcPYv78+QCABx98EFqtFqdOnbLabPf69etx8OBBnDp1yuToJjAwEFqtFgqFwmT3LZVKeWmuNRBj7sxbp9Nxu2ZSjg4NDR1UYxrDMDAYDAM2pkmlUly4cIFruLF0LKJKpUJzczOXfe3j48PNw/Y2vkJK+UqlEiKRiJdNSyQnfNy4cVzMKp9gWRZlZWVcjnRfZ/DGrw85hzWeV7ZkN79KpUJOTg58fHwQFRXFu98hrByZCaPXh5TXOzo64OHhYWKqlJOTAycnJ8TGxvLy5mcsCDeJlhWLxbCxscFXX32FZ599Fvv378fChQutvUTg6nthw4YNyMjI6HVMjTSsffvtt7jjjjsAAGVlZYiMjKQNa6OBTqeDwWAYVmPaYDK4ycXIWqYXZJacWIF6enpy5+TOzs5cGZrcAfP57HPq1KkWzQkfLqTxi8RRDkWAyTlsc3Mz2tra4ObmxjW8ubm5mU1gyagVn29+KisrUVdXx6txNdKQSLqjAcDZ2RnTpk2Dt7c3736PTU1NKC4uRmxsLC+Fm2EY5OXlQa/XQywWw9bWFt999x02bNiAPXv2ICkpydpL5HjyySfxzTffYN++fSaz3Z6entxn/IknnsDhw4exc+dOeHh4YMOGDQCAs2fPWm3dw2XMibdarUZRUdGgHNMwhMY0hmFQXFyM1tZWiEQiXlyMNBoNt+MjxhYajQZeXl68Lf/V1NRwNrR8cvwiGPuox8fHj+jmp+eIoKOjIyfkI5ksIKNWpAGRbxgHePDVMpbYdTo5OcHJyYnLwCaldWv3McCoT4DPwp2fnw+tVsuN1KWnp+Oxxx7Drl27sHz5cmsv0YS+Pm87duzAQw89BBiZtHz77bcmJi20bD4KnD9/ntsxDdSYNpQM7vz8fDAMg7i4OF6O4JBQAicnJ6jVajg7O3M7cnd3d6vvKIzP4EUiES993jUaDXJycuDo6Ii4uDizXrwNBgM3WSCTyYCrQkEmCwb7s2QyGQoLCxEREcHLIAdjH3CxWMwbm1Njuru7kZ2dDW9vb+64wTiko7m5GRqNhjPu6et4ypKMBeEmZk8SiQT29vY4ePAgHn74YXz11VdYuXKltZd4wzPmxLu9vR329vZDakwbKIM7Ly8P7u7uiI6OtvrdeG8Qc5jIyEiMGzfOZJZcJpPB3t5+VGbJ+4LYTHZ0dCA+Pp6XZ/Dk/Jhc0C159smyLDdZ0NzczAV0DNSQSF7n6OhoXto1GtuJ8nXOvKuri8szj4yM7PWz0Nc5ufE8uSU/Q0S4LdngNxLI66xSqSCRSODg4ICjR49i1apV+OKLL3D33Xdbe4mUsSjeer2e2033xlAa04i/dkhICC9NOci5Ym1tbZ/+1caz5M3NzRAIBCY7Pks36BAPcIZhRlyGthQk59oa58fEeITcaBmH2xj7Rl++fBmVlZW8vaCTWX2NRsNbO1GVSoXs7GwEBAQgPDx80K+zRqPhboZbW1vh6OjIvT6enp5m/QzxXbhZluWCWoifwIkTJ3DPPfdg27ZtuO+++3h3nbxRGXPibTAYoNfre/3eUKxO+T4fTUxnFAoF4uPjB3WuSEZoiJAbDAZOyC1xxtfd3Y3c3NxBx3laA2JgM9o5133R3d3NlW4VCgXc3d1ha2uLzs5Oq3VsDwQxiOGzKxkJQQkODh7RDZrBYODmycnxh/E8+Uhm7MeCcBcXF0OhUCAhIQGOjo44deoU7rrrLrz//vt46KGHqHDziOtGvIfSmFZeXo6mpibens0an8GLRKJhnccZh3M0NzdDrVYPeZa8P8h8vVAoREREBC9HcBobG1FcXMzbGzSNRoPCwkK0t7eDZVmuj8GaSVs9GQvmJp2dncjOzu41BGUkGM/7y2QyqNXqYZ+TjwXhLikpQWtrKxISEuDk5ISzZ8/i9ttvx9atW/Hoo4/y4v1I+R9jXryH0phG3LPUajVvE7e6urqQm5vLJfWY42LZ3yz5YFK2emLuOE9LUF1djUuXLvH2YknOFTs7O7kyNLFqlclkXADEaB1/9AZJjXJ2duZluhqMRuomTpyI0NBQi/6snmlb7u7u3M1Wf+fkDQ0NKC0t5e17kXgeyOVyTrh///13pKWl4fXXX8f69et5+Rm/0Rlz4s0wDHQ6HTDExjRS4nVyckJMTAwvS38kbjQ4OBhTp0612Aemq6uLE3KSshUQEDAo0xHSVMXX3SwZY2psbOTV/LExJACFjOD0vHkyPv6QyWTQ6XQmVZPRsEdVq9XIzs7mcpr5WFlRKBTIzc0dVHqZudFqtSbz5GRM0N/fH15eXtzvaywId3l5OZqbm5GQkABnZ2fk5OQgJSUFL774Ip555hkq3DxlzIr3UM63idtXYGAgwsPDeXkhIi5L4eHho2ps0tN0hOwmSC45wdhKtK/mOWtD+gTa29t5O8ZEytA2NjYQiUQDCjHLsujs7OSEXKVSDejAN1JIxzaxBubjxbutrQ25ubm8MAIyHhOUy+VgGAb+/v6wtbVFQ0MD4uPjefl5IWmJjY2NSEhIgIuLCwoKCrBs2TI8++yz2Lx5My9fe8oVxqR4a7XaQQs3OWviw4e8N4xd3aw986nT6Tghb2lpMTmDraurQ0tLi1XjPPtDr9ejoKAAWq0W8fHxoz63OxjInPlIytCkakJKt8ZWoOYY0SONX0FBQRat/oyE1tZW5OXlDSt21NKQc/KqqirI5XIIBAKTc3I+jddVVFSgvr4eCQkJcHV1RXFxMZKTk7F+/Xq89NJLvHztKf9jzIn3uXPn4ObmhokTJ8LW1rZfq1MyZhUTE8NbI4SSkhJeiiJJcWpqauJG0IKDgxEcHMybZiqCcepWXFwcL1O3urq6kJOTAy8vL7PNmfe0AnVxceGE3MPDY8ivETk/Nnfjlzkh0wORkZEIDg629nJ6pb6+HmVlZVyzac9zciLk5rTTHSqXLl1CbW0tJBIJ3NzcUFZWhuTkZKxZswZ//etfefnaU0wZc+L99NNP46OPPsL06dORmpqK1NTUa2Y6u7q6UF5ezgV38NG+UafToaCgADqdDiKRiFd35ASNRsOJ4vjx47kRGoFAwJXWvb29rXoMQUTRw8MD0dHRvDwSIZ355NjGEhdGcrNFSre2trackA/mNSKWrNY4Px4sMpkMBQUFmD59Om/tLI2Fu2epXKvVQi6XQyaTQS6Xw8HBwcROd7Teu+T4KyEhAW5ubqioqEBycjLuvfdevP3227z8DFGuZcyJN8uyaG1txb59+7Bnzx789NNPmDp1KlJTU7Fy5Uq4uLjg9ttvx1133YVNmzbx0kzCeD46JiaGlztF4kjm5eVl0rA02rPk/dHR0YHc3FyLiuJIIU1Vo9mZzzAM2trauNeIYRiTbPKe77eWlhbk5+fzsgxNkEqlKCoq4q37HAYQ7p6Qc3KyKyevkb+/v0WbEi9fvoxLly4hISEB7u7uqK6uxpIlS5Camop//vOfVLjHEGNOvI0h50v79+9Heno6jh49Cr1ej0mTJmH79u2YMWMG796M7e3tyMvL41yg+LY+DCHO03iWXCqVQqPRmHWWvD9aW1uRn5/P63E14lNuTVE0fo1kMhm6u7tNGt4UCgWKioowbdo0Xk4PwGhePzY2Fv7+/tZeTq8MRbh7Ql4j0m/S1dVlkXPympoaVFZWQiKRwMPDA3V1dUhKSuLCOfh4LaL0zZgWb2P279+PBx54AMnJyTAYDDh69CiEQiFWrFiBlStXQiKRWP3NSaIyp0yZggkTJvBScEiW+VAb/IxtQJubm026ooczS94fpDN/2rRpvD33JILDtxKvcTZ5e3s7ACA4OBihoaG87M4no1bWbubsj5EId290dXVxO3KFQmGW2Nm6ujpcvHiRS2JsbGxEUlIS5s+fj88++4yXM/yU/rkuxPudd97BSy+9hC+//JILWVepVDh69Cj27NmDQ4cOwdPTEytWrEBaWhpmzpw5qm9WlmW5u16+RmXCKM4zJiZmxDucnrPkXl5enJCPZCdhzjVaitraWly8eJHXgkPsgceNG4euri60trbC1dWV25HzIamurq4O5eXlZhNFS0DWGB8fD29vb7P//cbn5C0tLbC3t+d25IPtNyE3F2KxGF5eXpBKpUhOTkZiYiJ27tw5qtfCU6dOYevWrcjOzkZjYyMyMjKQlpbGfZ9lWbz88sv47LPPoFAoMGfOHHzyySeYOnXqqK1xrHBdiPf27dshFouRkJDQ6/e7u7tx7NgxpKenY//+/XByckJKSgpWrlyJm266yaJnzsSOVSqVQiQSDZg/bg2MjU3i4+PNvsbBzpIPtEYy2sJXW1uWZXHp0iXU1NTw1qccV93nqqqqTNZIsslJMxVJqutpOjJakJtdkUhkEVE0B5YW7p4YDAa0tbVxu3KDwcCl1fn6+vZ6TEVGZckNkFwux9KlSzF9+nR8/fXXo95vc+TIEZw5cwYSiQS33377NeL91ltv4Y033sCXX36J0NBQ/PnPf0ZhYSGKi4t52dRrTa4L8R4KWq0WJ06cwJ49e7B3714IBAIsX74cK1euxLx588xa3tXr9VwmLl/tWA0GA5ciNBrGJsSZiiQ4kVnygICAPkuCZKSutbUVYrGYl5GjxGJSKpVy4zd8g4xP1tXV9es+17OZimVZbrc3Gk2J5OaClHj5yGgLd0+Mz8mJeY+3tzf3Ojk7O6OpqQnFxcWcu1trayuWLVuGyZMn47vvvrO6y6RAIDARb5ZlERwcjE2bNuHZZ58FrvYIBQQEYOfOnbjnnnusul6+ccOJtzF6vR6nTp3C999/j71790Kj0WD58uVITU3FLbfcMiKjD7Vajby8PNjb2yM2NtbqH5TeIHGeLMtCJBKNeme+Xq/nohjJ6AzZkZNZchJFSfzo+Xj3bezsJpFIeHmTRqorUqkUYrF40DcXxuEczc3N0Gg0Jtnk5n7PkMoFX61twQPh7o2uri7us6RQKODo6AilUgkASEpKQkdHB1JSUhAUFIT09HReTOH0FO9Lly5hypQpyM3NhUgk4p538803QyQS4Z///KcVV8s/bmjxNsZgMODMmTPYvXs3MjIy0NnZieTkZKSmpmLRokVD2pF2dnYiNzeXs5e0dqNcb5BxNRcXF16EThhbTJJZcj8/P7S3t8POzo63UZTGNxdisZiXzm4k6rGtrW1E1ZXeAm5IL8NgfPEH+rtJVUAikfDKsMgY0s/AJ+HuSUNDA4qLiyGXy7F+/Xq4uLhAIBBgwoQJyMzM5M1NUU/xPnv2LObMmYOGhgaTyYe7774bAoEAu3btsuJq+QcV715gGAa//fYbJ+QymQyLFy9GWloakpKS+t21EAeo0NBQ3o4wkflooVCIyMhI3q2RYRhIpVKUlpZyFrjWmiXvD1K5AACRSMTLmwuSXqZUKiEWi81auSDZ5DKZDG1tbXBzc+OEfChd0aSfoaGhgbdHDjASbtL4xUeIkU1MTAyEQiFkMhm387axsUFHRweSk5OxYsUKLF++3KrHElS8Rwb/3EF4gI2NDWbPno3Zs2dj69atyMnJwe7du/H666/jsccew6JFi5CWlobk5GQTG8r9+/fDzc0NUVFRvBoPMobcXBAnLb4JN65OCly8eBGBgYGIiIjg5pTLy8uh1WpHPWGrN4hPuZOTE2JjY3lzQ2EMqQpoNBokJCSYvVTq7OyMCRMmYMKECZwvvkwmQ1VVFRwdHU0a3vrzCiDlfOKxzUdqa2tRUVHBa+FuaWlBYWEhpk+fDqFQiK6uLqxevRpCoRBZWVlwdXVFbm4u9u3bh61bt2LChAmYN2+etZfNQa6ZUqnURLxJsy/FFLrzHgJkF7N7926kp6ejoqICt9xyC1JSUnDy5EkcPXoUp06d4u1YQ319PUpLS3kb5wkjm06Sz2x80e9tlpycv/r7+4/aOV53dzeys7Ph6enJ27hMvV6PvLw8MAwz6kcOBoOBs9IlRyCkkcrHx4e70WFZFqWlpVyONB97BWAk3HyeICBhLcRsR61W4w9/+AM3MsuXUrkxfTWsPfvss9i0aRNwtUooFAppw1ovUPEeJuTC88033+CDDz5Ae3s7Zs6ciQceeAApKSnw8/Pjza7WeISJr3GeMDKxGawjmUql4jrXzTlL3h9KpRI5OTkQCoWIiIjgzWtsDIkdtbW1hUgksmpVgNjpktdJp9PB19cX/v7+aGlp4XWTH8aIcJN4VBLWotFocP/990Mmk+HYsWO8WrdSqURFRQUAID4+Hu+88w4WLlwIHx8fTJgwAW+99RbefPNNk1GxgoICOirWC1S8R4BUKsWKFSvg6OiIv//97zhx4gTS09ORm5uLOXPmIDU1FStWrEBgYKDVLvLGY1bx8fG8PU8kHbzDNbFRq9XcjlyhUJg9KhNGtrF8Tt3SarXIzs4eUeyopSCVE6lUitraWuj1enh5eSEwMJB3cZkYI8KtUCiQk5PD3fBqtVqsWrUKtbW1+Omnn+Dr62vtJZpw8uRJLFy48JrHV69ejZ07d3ImLdu3b4dCocDcuXPx8ccfIzw83Crr5TNUvIeJUqlEbGwsZs+ejS+++ILrMmZZFpcvX8aePXuQnp6O33//HTNnzsSKFSuQmpqK8ePHj9pFn2RcazQa3o5ZGVcFzGXIYTxL3tLSwjmHjcRekoR3hIWFYcKECSNeoyVQq9XIzs6Gh4cHb8v55OhJpVJh2rRp3Bgaics0bnizJsQkhs/C3d7ejpycHISFhSEkJAR6vR5r1qxBWVkZTpw4wVsHQop5oOI9An7++WfMnz+/32ac+vp6pKenIz09HWfOnEF8fDzS0tKQmppq0W50Eudpb2/P24xrlmVRUlICuVw+pNnjoTCYWfKBIF7qfO4V6OrqQnZ2NjeeyMeqAMMwJmN1xj0KPc17nJycOCEf7fx4Itx8Nonp6OhAdnY2l5Og1+vx2GOPIT8/HydOnOBtwyzFfFDxHiVYloVUKkVGRgbS09Px888/Y/r06ZyQT5061WwXKKVSidzcXHh7eyMqKoqXOzCDwYDCwkJ0dXWNmvscaaQis+Q2NjYD5pKTcj6fvdSVSiWys7MRFBRk1veROTEYDMjPz4dOp4NYLO63gY5kk5OGN/I6kYY3S76fx4Jwd3Z2Ijs7m0vUMxgMWL9+Pc6dO4eTJ0/yNqyHYl6oeFsBlmXR0tLCZZIfP34c4eHhSE1NRVpa2oh2TqRbOyQkBFOmTOHlhdzazm7oJfOaWIAKhUJOIKqrq1FdXc1rf+2Ojg7k5OTw+hzeYDAgLy8PBoNhyJ3v5HUiu3JjP29zjwqOBeFWKpXIysripjEYhsHGjRuRmZmJzMxM3h7pUMwPFW8rw7IsFAoFDhw4gD179uDHH3/ExIkTuSjTmJiYQe80pFIpioqKEBERYbX86IFQq9XIzc3l1Xx0TwtQrVYLR0dHaDQaXs/1khs1MrPPR/R6PXJzcyEQCCASiUYktn3lXpNd+Ujc7caCcKtUKmRlZWH8+PGYMmUKGIbBc889h0OHDuHkyZMIDQ219hIpowgVb57R0dGBQ4cOYc+ePTh69CgCAgI4IReLxX0K+eXLl1FZWcn78m5ubi58fHx4axtLjE0UCgUcHBygVqtNBIIPntAwaqAb7FidNbD0yFrPUcHhThhcvnwZly5d4rVwd3V1ISsrC0FBQQgLCwPLsnjxxRexe/duZGZm8tZbgmI5qHjzGJVKhSNHjmDPnj04fPgwvLy8uEzyGTNmwNbWFgaDAW+88QZmzZqFxMRE3l58yJgV2TXwtbxrnALn5ORk4uXd2dkJb29vTiCs1b3f3NyMwsJCXjfQ6XQ65OTkwMHBYVQqLBqNxqThzcXFhXudjF0QezIWhLu7uxtZWVkICAjgRPq1117Dv//9b2RmZiIyMtLaS6RYASreY4Tu7m78+OOPSE9Px4EDB+Ds7IwlS5YgJycHMpkMR44cwZQpU6y9zF6RyWQoLCzk9ZjVYBzJiJd3z1lyoVBo8ShVAslnHu48/GhgPGseGxs76hWWnhMGdnZ2nJAbNyaOJeH29/dHREQEAODNN9/Etm3bcOLECURHR1t7iRQrQcV7DKLVapGeno6NGzeipaUFnp6eXLPb/PnzeRWQQSxZp0+fztvxFa1Wy+0S4+LiBrVLtMQs+UCQYAySz8xHNBoNsrOz4ebmhujoaKsfjTAMY5JWxzAMd6zU3NyMhIQEXlqH4mp/SFZWFnx9fbnd9TvvvIP33nsPJ06cQFxcnLWXSLEiVLzHIFVVVUhOTkZUVBR27tyJ8+fPc5nkOp2OyyRfuHCh1SIqWZblurX5bMna3d2NnJwcuLu7D1tsdDqdyU7P0dERAQEBEAqF/ZZsh0J1dTWqqqp4bRpCTGI8PT15OaJIGhMrKirQ1tYGgUBgFW/8waDRaJCVlQUvLy9ERUUBAD744AO8/fbb+OGHH5CYmGjtJVKsDBXvMYZer0dUVBSWLFmCd99912SXaDAY8Msvv2D37t3Yu3cvl0melpaGRYsWjZp/NMuyKCsrg1QqhVgs5m02M/Ep9/f3N1s0as9ZcltbW25H7uXlNWRBM865FovFvN0lkrAW4i3Ax54GGN0ESSQS2NjYcNWTzs5OeHp6cq+VNb3WtVotsrKyOKc8ANi2bRtee+01HDlyBLNnz7ba2ij8gYr3GOTy5cuYMGFCvxdIhmHw66+/ckJOcn3T0tKwePFii9lPEvvLzs5OiMVi3gZOtLe3Izc316INdP3Nkvv6+g4o5MZxmZZyoDMHxN3Nz8+Pl/nwBGPh7nkTpFarOSFva2sblWOQ3iD9AuTYAQB27NiBP/3pTzh06BCvIjwp1oXX4l1dXY3XX38dJ06cQFNTE4KDg/HAAw9gy5YtJiWugoICrFu3DufPn4e/vz82bNiA5557zqpr5xMMwyA7Oxu7d+9GRkYG6urqcNttt5lkkpsDvV6P/Px86PV6xMfH86oMaQwZs5oyZcqozUeTkq1UKuXStfz8/BAQEABfX99r5p9ZlkVxcTHa2tp4nbqlUqmQnZ2NgIAAhIeH81q4q6urB1W9MD4GaWlpgb29vUk2uaWOA3Q6nUmojEAgwFdffYVnn30W+/fv7zXQg3LjwmvxPnr0KHbt2oV7770XYWFhKCoqwtq1a/Hggw/i73//O3B1Ljo8PByLFi3CCy+8gMLCQqxZswbvvfceHn30UWv/E3gH8ZcmwSmVlZW49dZbkZqaimXLlsHLy2tYF2DipU5Gg/jopY6rRjYXLlzg4hOtAcuy6Ozs5Hbk3d3dJmevtra2KCoqglKphFgs5mWgDIxsWYODgxEWFsZb4a6qqsLly5eHdexgMBhMGt4AcNnkvr6+ZhuBI6N1jo6OiI2NhUAgwHfffYcNGzYgPT0dixcvNsvPoVw/8Fq8e2Pr1q345JNPcOnSJQDAJ598gi1btqCpqYnb6W3evBl79+5FaWmplVfLb0gwyO7du5Geno7i4mIsWLAAqampWL58+aAzyVUqFXJzc7nmGr41KhHq6+tRVlbGuzGrnrPkdnZ2sLGxQXx8PG/7BYi/Np9tWWEk3BKJZMS/S+KGSMrrGo0Gfn5+nJgPd8pDr9cjJyeHCxGysbFBeno6Hn/8cezatQvLli0b0bop1ydjTrxffPFFHD16FFlZWQCAVatWoaOjA3v37uWek5mZiVtuuQWtra289aTmGyzLoqKighPyvLw8zJ07l8skDwgI6PUCTc6Og4ODeRuKAaPzTj53vuv1emRnZ0On08HBwcHENWw0Z8kHgrzmxF+br5hTuHtCssmJkCuVSnh7e3M9DYOtlhD7WFtbW25M8cCBA1izZg2+/vprpKWlmXXdw+Wjjz7C1q1b0dTUhLi4OHzwwQeYMWOGtZd1QzOmxLuiogISiQR///vfsXbtWgDA4sWLERoaim3btnHPKy4uxvTp01FcXIxp06ZZccVjEzLmRUrr58+fx8yZM5GamorU1FSMGzcOAoEAGRkZqKysxJ133olJkyZZe9m9Qm5K6uvred2t3ZuVaE/XMNJEFRAQAFdXV6vcKCkUCuTm5vLaTx0WFu7e6Gng4+bmxt109fVaGQwGE993W1tbHD16FKtWrcKOHTtw1113WXzdg2HXrl1YtWoVPv30U8ycORPvvfcevv/+e5SVlfGqgnWjYRXx3rx5M956661+n1NSUmJi+1dfX4+bb74ZCxYswL/+9S/ucSreloVlWdTV1XGZ5GfPnoVYLEZQUBCOHDmCN998E4899pi1l9kr5FigpaUFYrF4SH7Xo4mxI1lMTEyv56g9Z8lJ3rU5Z8kHggShhIWFISQkxOI/b7iMtnD3RKvVmjS8OTo6XpMhT5LWGIaBWCyGra0tjh8/jnvvvRfbt2/Hvffey5sq1syZM5GYmIgPP/wQuNo3ExISgg0bNmDz5s3WXt4Ni1XEWyaToaWlpd/nTJ48mTvDbmhowIIFCzBr1izs3LnT5EyVls1HD5Zl0dTUhA0bNmDv3r1gWRZxcXGcuxufmpYYhkFhYSFUKhWvm76IsQmZ6R1Mv4C5Z8kHw1gIQgGAS5cuoaamxmrC3RPj10oul0MgEMDPzw+dnZ0AgISEBNjZ2eHUqVO466678MEHH2D16tW8+RxptVq4uLhg9+7dJiX81atXQ6FQYN++fVZd342MVVqCSYPHYKivr8fChQshkUiwY8eOay5Ms2fPxpYtW6DT6biGkWPHjiEiIoIKt5lhWRbvvvsuTp06hXPnziE0NBT79u3D7t278be//Q0RERFccMpIMslHivHIWkJCAm9H1sh8tK+v75B+X8ZibWz/WVhYOORZ8sEgl8tRUFBg1Q79wcA34UYvr1VbWxuKi4uhVqvx1FNPYerUqZg+fTq++OIL/OMf/+CVcOPqa28wGBAQEGDyeEBAAG0ItjL8bAu+Sn19PRYsWIAJEybg73//O2QyGZqamtDU1MQ957777oODgwMeeeQRXLhwAbt27cI///lPPPPMM1Zd+/XI559/jt27d+PMmTNITEyEn58fHnnkERw+fBhNTU3YtGkTCgoKMG/ePCQkJODVV19FQUEBGIYZtTWSEjQASCQS3gq3UqnE+fPnERAQMKIbHRsbG/j5+SEqKgrz589HXFwc7OzsUFpaipMnT6KwsBBSqRR6vX5Yf39zczPy8/MRFRVFhdsM1NXVwd7eHvPnz8fnn38ODw8PfPjhh+ju7saePXuwbds2NDQ0WHuZlDEArxvWdu7ciYcffrjX7xkv29ikxc/PDxs2bMDzzz8/iiu9MdDpdGhraxuwSaWjowMHDx7kMsmDgoK4TPL4+HiLjZKp1Wrk5OTA1dUVMTExvB1Z6+joQE5OjkXHrAaaJR/MWJNUKkVRURFiYmJ43ZhUWVmJ2tpaXgs3cR5UqVTcTWVOTg5SUlLw0ksvISUlBfv378fevXtx9uxZbNiwAe+++661l03L5jyG1+JtTf7617/i0KFDyMvLg4ODAxQKxTXP6e2i++233+Kee+4ZpVXyH6VSaZJJ7uPjg5SUFJNMcnOgUqmQk5Mz5BL0aEOavka7W1upVHJCTsaaSDm3t/AaEj0aExMz6CMua0CEOyEhgbf2sSzL4sKFC+jo6OCOcQoKCrB06VI899xzeP75503er2S6gC853TNnzsSMGTPwwQcfAFdvRCZMmID169fThjUrQsW7D15++WV4eXmhrq4On3/+eZ/ivWPHDixZsoR7zMvLi7fNUdamu7sbP/zwA5dJ7uLighUrViA1NRU33XTTsF3ZyE523LhxvGqa6wlfmr66u7s5IW9vb78mkKOhoQGlpaW8jh7FGBLu4uJiKBQKJCQkwNHREcXFxUhOTsaGDRvw5z//mbfvV8KuXbuwevVqbNu2DTNmzMB7772H7777DqWlpdechVNGDyreA7Bz505s3LixT/HOyMjgjZHCWEKtVuP48eNIT0/Hvn37YGtri+XLl2PlypWYN2/eoN2qWltbkZ+fj9DQUN7OmuPqbqqoqAhRUVG8yjXvOUvu4OAArVaLadOmITg4mLfCMlaEu6SkBK2trUhISICTkxPKysqQnJyMRx55BH/5y194+/vtyYcffsiZtIhEIrz//vuYOXOmtZd1Q0PFewAGEu/g4GBoNBpMnjwZjz/+OB5++OEx84HkCzqdDj///DOXgEYyydPS0rBgwYI+M8mJIEZERGDcuHGjvu7BMlZK0FVVVbh06RI8PDzQ0dFhlVnywUAiUiUSCa+Fu6ysDHK5nBPuiooKJCcn47777sNbb73F254MytiAn+kRY4TXXnsNt9xyC1xcXPDjjz/iySefhFKpxFNPPWXtpY0p7O3tsWjRIixatAgffvghl0m+YcMGKJVKLF26FGlpabj11lu5dK3t27fD3d0dt956K6+bqWpra3Hx4kXel6BJ6lZCQgI8PT1hMBg4o5GcnBzY2dmZzJJbQ8hZlsWlS5fGhHCXl5dDJpNxwl1dXY3ly5fjjjvuoMJNMQs31M57OM5u/e28e/LSSy9hx44dqK2tNct6b3QMBoNJJnlLSwuSkpKg1+vxww8/4N///jeWLl1q7WX2CfFTj4+Ph5eXl7WX0ydkzKov+1jjWfLm5mYA4ITcx8dnVISIZVlUVlaivr6e98JdUVGBxsZGJCQkwMXFBbW1tUhKSsKSJUvw8ccfU+GmmIUbSryH6uyGIYr3oUOHsHz5cqjV6j5LvZThwTAMzp8/j40bN+L333+Hvb09lixZgtTUVLNmkpsDIjRkh8jX8aXhrJMkaxEh1+v18PPzg1AohJ+fn9mmB3pbJ9+FG1fzF+rr65GQkABXV1c0NjYiKSkJN998M7Zv326R3w/lxuSGKpsPxdltOOTl5cHb25sKtwVgWRY7d+5EXV0dCgsLodFosGfPHmzduhVPPPEEFi1ahBUrVowok9xc6ywvL4dUKkViYiJv/dRZlsXFixe5HeJgBVEgEMDb2xve3t4IDw9HR0cHmpubUVFRgaKioiHPkg9mnWNFuC9dusSt09XVFVKpFMuWLcPs2bOpcFPMzg218x4KNTU1aG1txf79+7F161acPn0aABAWFgY3NzccOHAAUqkUs2bNgpOTE44dO4Znn30Wzz77LF599VVrL/+6Y8uWLdizZw9+/PFHTJgwgXucjOKQKNOSkhIsWLAAaWlpWL58OXx9fUdNyMla2traIJFIuPN5vkGaqZqbmzmhMcffaZxLrlQq4ePjwwn5cG5ojYWb7GT5CglDITdCMpkMy5YtQ3R0NL766qthj0FSKH1BxbsPHnroIXz55ZfXPJ6ZmYkFCxbg6NGjeOGFF1BRUQGWZREWFoYnnngCa9eupWdaFqCpqQm2trb9Vk7IbpIIeX5+PubOnYu0tDSkpKT0mUluDoiDllKp5HUQinHSWkJCgsVuMAaaJR/MOseKcF++fBmXLl1CQkIC3N3d0draiqVLlyIsLAy7du0ySwWCQukJFW+eMRhnt5qaGjzxxBPIzMyEm5sbVq9ejTfeeIPe3RvBsiyqqqpMMslnz56N1NRUrFixgsskNwcGgwEFBQXQaDQQi8W89VO3VmVAo9FwQt7W1maSdd1bGZw0fTU0NPBeuGtqalBZWQmJRAIPDw8oFAqkpKQgODgYe/bs4e17gTL2oeLNMwZydjMYDBCJRAgMDMTWrVvR2NiIVatWYe3atfjb3/5mtXXzGZZlUVtbi/T0dGRkZODMmTNISEhAamoqUlNTMXHixGELuV6vR15eHliWhUgk4u0ui2EYXLhwAZ2dnVatDOh0Os4UpqWlBc7OzpyQk4a5sSLcdXV1uHjxIsRiMTw9PdHR0YHU1FR4e3tj7969vK2+UK4PqHjzlL663I8cOYLly5ejoaGBsyb89NNP8fzzz0Mmk9E7/QFgWRaNjY3IyMhAeno6Tp06hdjYWE7Ih2KvqtPpkJubC1tbW4hEIt42JJFs866uLojFYt40VOr1epNccnt7ezg4OKCrqwuJiYm8bk6rr69HWVkZxGIxvLy8oFQqcfvtt8PR0REHDx7kbb8D5fqBHs6OMc6dO4eYmBgTT+GkpCR0dHTgwoULVl3bWIC44q1btw4//fQTGhoa8Pjjj+Ps2bNITEzE7Nmz8cYbb6CkpAT93ddqNBpkZWXBwcGB98JdUFCA7u5uSCQS3gg3ANjZ2SEgIAAxMTG4+eab4eHhAaVSCQDIzs5GcXEx5HL5qEbKDobGxkaUlZVBJBLBy8sLXV1duPvuu2Fra4t9+/ZR4aaMCvSQdIzR1NR0TRgA+W/jnHPKwAgEAvj7+2Pt2rX44x//iLa2Nuzfv58bQQsNDUVqaipWrlyJ6dOnc42IlZWVyM7OxrRp00we5xsGgwH5+fnQ6XSQSCS8LemT5rT29nbMmjULzs7O3Cx5cXExDAYD/P39IRQK4evra9UbpaamJpSUlCAuLg4+Pj5Qq9W49957odVqcfToUV5XCyjXF/y86lxnbN68GQKBoN+v0tJSay/zhkYgEMDHxwcPPfQQNwb44osvory8HLfccgtEIhH+/Oc/47vvvsOiRYtw+vRpREdH81q48/LyYDAYeC/cFy9eRFNTEze2ZmNjAx8fH0RGRmLevHlcqb+8vBwnT55Efn4+GhsbodPpRnWtUqkUxcXFiI2Nha+vLzQaDR544AG0t7fj8OHDvDIKolz/0J33KLBp0yY89NBD/T5n8uTJg/q7AgMD8fvvv5s8JpVKue9RzIOnpyfuv/9+3H///VAqlTh8+DC++OILvPvuu3BxcYGLiwt+++03JCYm8q5krtfrkZubC4FAgPj4eN5OIRgLN7ES7YlAIICnpyc8PT0RFhYGlUoFqVSK6upqXLhwgZslFwqFFu33ICE4sbGx8PPzg1arxerVq9HY2Ijjx4/z2v6Wcn3Cz0/1dYY5nd1mz56Nv/71r2hubuYCOY4dOwYPDw9ERUWZ5WdQTHFzc8PkyZORlZWFLVu2ID4+HhkZGbjjjjvg6uqKlJQUpKWlYfbs2VYXyrHSRDcY4e6JQCCAm5sb3NzcMGXKFHR1daG5uZnLH/f09ERAQAD8/f3Neu4sk8lQWFjIpcLpdDr88Y9/RFVVFTIzM+Hj42O2n0WhDBbabc4zBnJ2I6NiwcHBePvtt9HU1IQHH3wQf/zjH+momIX4/fffcdttt+GVV17B008/zT2uVqvx008/cZnk9vb2XCb53LlzR71UrdPpkJ2dDUdHR8TGxvJauImF7GCFeyDUajU3gtbW1gZ3d3duRz6ScbOWlhbk5+dzOex6vR6PPfYY8vPzkZmZeU3/iTWg3hA3JlS8ecZAzm646uj0xBNP4OTJk3B1dcXq1avx5ptv0g+ihZDL5Th+/Dj+8Ic/9PkcnU6HkydPcgloBoPBJJPc0iN8Wq0W2dnZcHFxQUxMDG/P4i0h3D3RarVcnGlvs+SDHQVsbW1FXl4epk2bhqCgIBgMBqxfvx7nzp3DyZMnERwcbPa1DwfqDXFjQsWb0ieTJk3C5cuXTR574403sHnzZqutaSyg1+vxyy+/4Pvvv8fevXuhUqmwbNkypKamYtGiRWY379BoNMjOzoabmxuvm+iIcBNPdUsId0/ILLlUKoVcLoe9vT2EQiECAgLg6enZp5C3tbUhNzcXkZGRCA4OBsMw2LhxIzIzM5GZmWnir88XqDfEjQUVb0qfTJo0CY888gjWrl3LPebu7s5r1yu+YTAYcO7cOW5H3traykWZLl68eMS/S7VajezsbHh6emL69OlWS1MbCGsId08MBgOXSy6TybhRwYCAAHh7e3M3PQqFAjk5OQgPD8f48ePBMAyee+45HD58GJmZmQgNDR31tQ+GvsT7pZdewv79+5GXl8c9VlVVhcmTJyMnJwfx8fFWWC1lpNA6K6Vf3N3daRf7CLC1tcXcuXMxd+5cvPPOOzh//jx2796Nl19+GY8++igWL17MZZIPNfe7u7sb2dnZ8PHxwbRp06hwDwAJtvH39wfDMNws+YULF7hZcjc3N1y6dAlTp07lhHvLli3Yv38/Tp48yVvh7g/qDXF9ws/6GoU3vPnmm/D19UV8fDy2bt0KvV5v7SWNWWxsbDBz5kxs3boV5eXlOH36NKKiovDWW29h0qRJuPvuu/H1119DoVD06+4GAF1dXcjKyoKvry/vhZvEj1rqjHs49DZLDgAXL17E6dOn8cQTT2D79u144YUX8N133+H48eMICwsbtfVRbwjKQNCdN6VPnnrqKYjFYvj4+ODs2bN44YUX0NjYiHfeecfaSxvz2NjYQCwWQywW4y9/+QsuXLiA3bt344MPPsD69etNMsl9fHxMxFkul6O4uBgBAQEIDw/nvXDLZDKLxo+OFIFAABsbG8jlcoSFhSEwMBCdnZ147bXX0NbWhnnz5uH06dPw8fEx28jnQFBvCMpA0J33DcZQ7uifeeYZLFiwALGxsXj88cfxj3/8Ax988AE0Go21/xnXFQKBANHR0XjllVeQn5+PgoIC3HzzzfjXv/6FyZMnIyUlBf/6178glUrx+++/QywWQ6VSUeE2E0qlEtnZ2Zg4cSJCQ0MRHR2NoKAg2NjYYN++fUhJScHnn3+OoKAgLFiwABcvXrT4mvz9/REZGdnv12AbzWbPno3CwkI0Nzdzj1FviLEPbVi7wZDJZGhpaen3OZMnT+71wnDhwgVER0ejtLQUERERFlwlBVcF8NKlSyaZ5AKBADfddBM+++wzjB8/npfiPZaEW6VSISsrC+PHj8eUKVPAsiw++OADvP322/jxxx+RkJDAPbe+vh4ZGRlYvXr1kPsTLAn1hrgxoeJNGTRff/01Vq1aBblcDm9vb2sv54bi/PnzuO222zBnzhx0dnZyKWgrVqxAWloaJkyYwAshH0vCTfoGgoKCuPPsbdu24bXXXsPRo0cxa9Ysay9xUFBviBsTKt6UXjl37hx+++03LFy4EO7u7jh37hyefvppJCcn93qhoFiOc+fOITk5GS+//DKefvppsCyLhoYGLpP89OnTiI2NRVpaGlJTUzFlyhSrCDnLsigtLYVcLue9cHd3dyMrKwsBAQGYOnUqAGDHjh3YsmULDh06hLlz51p7iRRKv1DxpvRKTk4OnnzySZSWlkKj0SA0NBQPPvggnnnmGV5lQt8IbNq0CVOmTMGTTz55zfdYloVMJsPevXuxZ88eZGZmIjIykhPyyMjIURFyItwtLS2QSCRjQrj9/f2545///Oc/+L//+z8cOHCA261SKHyGijeFwnNYlh2UALMsi7a2Nuzbtw979uzBTz/9hMmTJyM1NRVpaWkWyx4fS8KtVqu5EbvIyEgAwK5du/DUU08hPT0dixcvtvYSKZRBQbvNKbzgo48+wqRJk+Dk5ISZM2deM9pyIzPYnTPJJH/44Ydx8OBBSKVS/OlPf0JpaSkWLlyI+Ph4vPTSS8jJyQHDMGZZ21gSbmIj6+3tzVUkMjIy8NRTT+G7776jwk0ZU9CdN8Xq7Nq1C6tWrcKnn36KmTNn4r333sP333+PsrIyLvaUMjI6Oztx+PBh7NmzB0eOHIGfnx/X7JaYmDisHTnLsigpKUFrayvvhVur1SIrKwseHh6cjeyBAwewZs0afPPNN0hNTbX2EimUIUHFm2J1Zs6cicTERHz44YcAAIZ4FMagAAANgElEQVRhEBISgg0bNtAQFAvQ1dWFo0ePIj09HQcPHoS7u7tJJvlgokSNhTshIcHsYSvmhCSukeAWgUCAI0eOYPXq1di5cyfuvPNOay+RQhkyVLwpVkWr1cLFxQW7d+9GWloa9/jq1auhUCiwb98+q67veketVuPYsWNIT0/H/v37YW9vj5SUFKxcuRJz5szpNZN8LAk3yTh3dnbmolKPHz+Oe++9F5999hnuueceXozYUShDhZ55U6yKXC6HwWDoNTiBhiZYHicnJ6SkpGDHjh1oamrCv//9bwgEAjz88MNch/uPP/4IrVYLXE3meu6551BdXT0mhDsnJwdOTk6ccJ86dQr33XcfPvroIyrclDENFW8KhQIAsLe3x+LFi7F9+3bU19fj+++/h4uLC9atW4fQ0FCsXbsWt912GzIyMhAZGclr4dbr9cjNzYWDgwNiY2NhY2ODM2fO4O6778Y777yDVatWUeGmjGmoeFOsip+fH2xtbbmgBIJUKqWhCVbEzs4OCxcuxMcff4yamhrs3bsXeXl5yMrKQkdHB1577TXs3bsXXV1d1l7qNRDhtrOz44T7999/x5133ok33ngDf/zjH6lwU8Y8VLwpVsXBwQESiQTHjx/nHmMYBsePH8fs2bOtujbKFQQCAf7zn/9Ap9OhqqoKP/30EyZMmICXXnoJkyZNwv3334/vv/8enZ2d1l4qDAYD8vLyYGNjg7i4ONja2iInJwcrV67Eq6++iieffJIKN+W6gDasUazOrl27sHr1amzbtg0zZszAe++9h++++w6lpaXXnIVTRp/nn38eGRkZyMzMxLhx47jHGYZBbm4uF5xy+fJlLFq0CKmpqVi6dCk8PT1HVSiJcDMMA7FYDFtbWxQUFGDp0qXYvHkz/u///o8KN+W6gYo3hRd8+OGH2Lp1K5qamiASifD+++9j5syZ1l4WBcDFixfh6uqK4ODgPp/DsiyKioqwe/dupKeno7y8HAsXLkRaWhqWLVt2TSa5uWEYBnl5edDr9RCLxbCzs0NxcTGWLFmC//f//h9efPFFKtyU6woq3pQblldeeQWvvvqqyWMRERFcnjlleJBksT179mDPnj0oLCzE/PnzkZqaipSUFAiFQrMKKcMwyM/Ph1arhVgshr29PUpLS5GcnIy1a9fi9ddfp8JNue6g4k25YXnllVewe/du/PTTT9xjdnZ28PPzs+q6ridYlkVlZSVXWs/OzsZNN92E1NRUrFixAsHBwSMSVoZhUFhYiO7ubkgkEtjb26OiogJLlizBAw88gDfffNMifu4UirWh4k25YXnllVe4LmqK5WFZFjU1NdizZw8yMjJw7tw5JCYmIjU1FampqUPOJGcYBkVFRVCpVJBIJHBwcEB1dTWWLFmClStX4t1336XCTbluoe9syg3NxYsXERwcjMmTJ+P+++9HTU2NtZd03SIQCDBx4kQ888wzOHXqFC5fvoz77rsPP/zwA2JjY3HzzTfjnXfeQWVlJQbaU7Asi+LiYiiVSk64a2trsXTpUixbtowKN+W6h767KTcsM2fOxM6dO3H06FF88sknqKqqwrx583gx8nS9IxAIMG7cOGzYsAEnTpxAXV0d1q5di1OnTkEikeCmm27CW2+9hdLS0muE3GAwID8/H+3t7ZxwNzY2YtmyZVi0aBE++ugjXgh3dXU1HnnkEYSGhsLZ2RlTpkzByy+/zLnVEQoKCjBv3jw4OTkhJCQEb7/9ttXWTBlDsBQKhWVZlm1ra2M9PDzYf/3rX9Zeyg0LwzCsXC5nP//8c3bp0qWso6MjGxUVxb7wwgvsb7/9xioUCjYtLY1NSUlhW1paWJVKxVZWVrLh4eHsgw8+yOr1emv/EziOHDnCPvTQQ+wPP/zAVlZWsvv27WOFQiG7adMm7jnt7e1sQEAAe//997NFRUXst99+yzo7O7Pbtm2z6top/IeeeVMoRiQmJmLRokV44403rL2UGx6WZdHe3o4DBw5gz549+OGHH+Dg4ACDwYB//etfWL58OVpaWrB06VLExsbiP//5D+zs7Ky97H7ZunUrPvnkE1y6dAkA8Mknn2DLli1oamqCg4MDAGDz5s3Yu3cvnXqg9Iv1a0sUCk9QKpWorKxEUFCQtZdCuVpa9/LywoMPPoiMjAysWbMGDg4OmDt3LtauXYuoqCgkJCQgPDwc//73v3kv3ADQ3t4OHx8f7r/PnTuH+fPnc8INAElJSSgrK0NbW5uVVkkZC1DxptywPPvss/j5559RXV2Ns2fPYuXKlbC1tcW9995r7aVRjGBZFps3b8a+ffvw66+/4ujRo2hqasIrr7yCSZMmYdeuXb1Gl/KNiooKfPDBB3jssce4x5qamnpN1CPfo1D6goo35Yalrq4O9957LyIiInD33XfD19cXv/76K/z9/a29NIoRly9fRkZGBk6cOIEpU6YAAFxdXbFmzRqcP3/eZNc6GmzevBkCgaDfr54l7/r6eixZsgR33XUX1q5dO6rrpVyf0DNvCmWUOHXqFLZu3Yrs7Gw0NjYiIyMDaWlp3PdZlsXLL7+Mzz77DAqFAnPmzMEnn3yCqVOnWnXdfECv1/OmLC6TydDS0tLvcyZPnszdVDQ0NGDBggWYNWsWdu7cadIJv2rVKnR0dGDv3r3cY5mZmbjlllvQ2toKb29vC/5LKGMZfnwaKJQbAJVKhbi4OKxZswa33377Nd9/++238f777+PLL79EaGgo/vznPyMpKQnFxcW8zs4eDfgi3ADg7+8/6OpMfX09Fi5cCIlEgh07dlwzwjZ79mxs2bIFOp2OK/0fO3YMERERVLgp/UJ33hSKFRAIBCY7b5ZlERwcjE2bNuHZZ58FrjY3BQQEYOfOnbjnnnusvGLKUKmvr8eCBQswceJEfPnll7C1teW+R7Lq29vbERERgcWLF+P5559HUVER1qxZg3fffRePPvqoFVdP4Tv8uZ2lUG5gqqqq0NTUhEWLFnGPeXp6YubMmTh37hwV7zHIsWPHUFFRgYqKCowfP97ke2TP5OnpiR9//BHr1q2DRCKBn58fXnrpJSrclAGh4k2h8ADSWdxb5zHtOh6bPPTQQ3jooYcGfF5sbCxOnz49KmuiXD/QbnMKhUKhUMYYVLwpFB5AzkClUqnJ41KplPsehUKhEKh4Uyg8IDQ0FIGBgTh+/Dj3WEdHB3777TfMnj3bqmujUCj8g555UyijhFKpREVFBfffVVVVyMvLg4+PDyZMmICNGzfiL3/5C6ZOncqNigUHB5vMglMoFAroqBiFMnqcPHkSCxcuvObx1atXY+fOnZxJy/bt26FQKDB37lx8/PHHCA8Pt8p6KRQKf6HiTaFQKBTKGIOeeVMoNwinTp1CSkoKgoODIRAITCw5cXW0qadH95IlS6y2XgqF0jdUvCmUGwRiz/rRRx/1+ZwlS5agsbGR+/r2229HdY0UCmVw0IY1CuUGITk5GcnJyf0+x9HRkY6mUShjALrzplAoHCdPnoRQKERERASeeOKJAdOzKBSKdaA7bwqFAlwtmd9+++0IDQ1FZWUl/vSnPyE5ORnnzp0zCdWgUCjWh4o3hUIBAJPwk5iYGMTGxmLKlCk4efIkbr31VquujUKhmELL5hQKpVcmT54MPz8/E2MZCoXCD6h4UyiUXqmrq0NLSwuCgoKsvRQKhdIDWjanUG4Q+rNn9fHxwauvvoo77rgDgYGBqKysxHPPPYewsDAkJSVZdd0UCuVaqMMahXKD0J896yeffIK0tDTk5uZCoVAgODgYixcvxuuvv35NxjiFQrE+VLwpFAqFQhlj0DNvCoUyKrzxxhtITEyEu7s7hEIh0tLSUFZWZvIctVqNdevWwdfXF25ubrjjjjuuyTinUChUvCkUyijx888/Y926dfj1119x7Ngx6HQ6LF68GCqVinvO008/jQMHDuD777/Hzz//jIaGBtx+++1WXTeFwkdo2ZxCoVgFmUwGoVCIn3/+GfPnz0d7ezv8/f3xzTff4M477wQAlJaWYtq0aTh37hxmzZpl7SVTKLyB7rwpFIpVaG9vBwD4+PgAALKzs6HT6bBo0SLuOZGRkZgwYQLOnTtntXVSKHyEijeFQhl1GIbBxo0bMWfOHERHRwMAmpqa4ODgAC8vL5PnBgQEoKmpyUorpVD4CZ3zplAoo866detQVFSEX375xdpLoVDGJHTnTaFQRpX169fj4MGDyMzMxPjx47nHAwMDodVqoVAoTJ4vlUppTCmF0gMq3hQKZVRgWRbr169HRkYGTpw4gdDQUJPvSyQS2Nvb4/jx49xjZWVlqKmpwezZs62wYgqFv9BucwqFMio8+eST+Oabb7Bv3z5ERERwj3t6esLZ2RkA8MQTT+Dw4cPYuXMnPDw8sGHDBgDA2bNnrbZuCoWPUPGmUCijgkAg6PXxHTt24KGHHgKumrRs2rQJ3377LTQaDZKSkvDxxx/TsjmF0gMq3hQKhUKhjDHomTeFQqFQKGMMKt4UCoVCoYwxqHhTKBQKhTLGoOJNoVAoFMoYg4o3hUKhUChjDCreFAqFQqGMMah4UygUCoUyxqDiTaFQKBTKGIOKN4VCoVAoYwwq3hQKhUKhjDGoeFMoFAqFMsb4/4KOS5uRjOZlAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t, X = ode_integrate([1.0, 1.0, 20.0], 0.02, 30)\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection='3d')\n",
+    "ax.plot(X[0,:], X[1,:], X[2,:])\n",
+    "fig.set_size_inches(8.0,6.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} try it\n",
+    "Rerun the integration, but change the initial conditions by 1 part in $10^6$ for one of the components.\n",
+    "The make a plot of $x$ vs. $t$ comparing the solutions.  You'll see that the 2 solutions track well\n",
+    "for some time but then greatly diverged.  This is the sensitivity to initial conditions that is the\n",
+    "hallmark of chaos.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-variate root find"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can find the steady points in this system by doing a multi-variate root find on the RHS vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0. 0. 0.]\n",
+      "[ 8.48528137  8.48528137 27.        ]\n",
+      "[-8.48528137 -8.48528137 27.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sol1 = optimize.root(f, [1., 1., 1.], jac=True)\n",
+    "print(sol1.x)\n",
+    "\n",
+    "sol2 = optimize.root(f, [10., 10., 10.], jac=True)\n",
+    "print(sol2.x)\n",
+    "\n",
+    "sol3 = optimize.root(f, [-10., -10., -10.], jac=True)\n",
+    "print(sol3.x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'z')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "fig.set_size_inches(8, 8)\n",
+    "ax = plt.axes(projection='3d')\n",
+    "\n",
+    "ax.plot(X[0,:], X[1,:], X[2,:])\n",
+    "\n",
+    "ax.scatter(sol1.x[0], sol1.x[1], sol1.x[2], marker=\"x\", color=\"r\")\n",
+    "ax.scatter(sol2.x[0], sol2.x[1], sol2.x[2], marker=\"x\", color=\"r\")\n",
+    "ax.scatter(sol3.x[0], sol3.x[1], sol3.x[2], marker=\"x\", color=\"r\")\n",
+    "\n",
+    "ax.set_xlabel(\"x\")\n",
+    "ax.set_ylabel(\"y\")\n",
+    "ax.set_zlabel(\"z\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stiff system of ODEs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A stiff system of ODEs is one where there are multiple disparate timescales for change and we need to respect all of them to get an accurate solution\n",
+    "\n",
+    "Here is an example from Chemical Kinetics (see, ex. Byrne & Hindmarsh 1986, or the VODE source code)\n",
+    "\n",
+    "$$\n",
+    "\\frac{d}{dt} \\left (\n",
+    "   \\begin{array}{c} y_1 \\newline y_2 \\newline y_3 \\end{array}\n",
+    "  \\right ) =\n",
+    "%\n",
+    "\\left (\n",
+    " \\begin{array}{rrr}\n",
+    "  -0.04 y_1 & + 10^4 y_2 y_3  & \\newline\n",
+    "   0.04 y_1 & - 10^4 y_2 y_3 & -3\\times 10^7 y_2^2 \\newline\n",
+    "   &  & 3\\times 10^7 y_2^2 \n",
+    "\\end{array}\n",
+    "\\right )\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "{\\bf J} = \\left (\n",
+    "\\begin{array}{ccc}\n",
+    "  -0.04 & 10^4 y_3 & 10^4 y_2 \\newline\n",
+    "   0.04 & -10^4 y_3 - 6\\times 10^7 y_2 & -10^4 y_2 \\newline\n",
+    "   0 & 6\\times 10^7 y_2 & 0 \n",
+    "\\end{array}\n",
+    "\\right )\n",
+    "$$\n",
+    "\n",
+    "start with $y_1(0) = 1, y_2(0) = y_3(0) = 0$.  Long term behavior is $y_1, y_2 \\rightarrow 0; y_3 \\rightarrow 1$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rhs(t, Y):\n",
+    "    \"\"\" RHS of the system -- using 0-based indexing \"\"\"\n",
+    "    y1 = Y[0]\n",
+    "    y2 = Y[1]\n",
+    "    y3 = Y[2]\n",
+    "\n",
+    "    dy1dt = -0.04*y1 + 1.e4*y2*y3\n",
+    "    dy2dt =  0.04*y1 - 1.e4*y2*y3 - 3.e7*y2**2\n",
+    "    dy3dt =                         3.e7*y2**2\n",
+    "\n",
+    "    return np.array([dy1dt, dy2dt, dy3dt])\n",
+    "\n",
+    "def jac(t, Y):\n",
+    "    \"\"\" J_{i,j} = df_i/dy_j \"\"\"\n",
+    "\n",
+    "    y1 = Y[0]\n",
+    "    y2 = Y[1]\n",
+    "    y3 = Y[2]\n",
+    "\n",
+    "    df1dy1 = -0.04\n",
+    "    df1dy2 = 1.e4*y3\n",
+    "    df1dy3 = 1.e4*y2\n",
+    "\n",
+    "    df2dy1 = 0.04\n",
+    "    df2dy2 = -1.e4*y3 - 6.e7*y2\n",
+    "    df2dy3 = -1.e4*y2\n",
+    "\n",
+    "    df3dy1 = 0.0\n",
+    "    df3dy2 = 6.e7*y2\n",
+    "    df3dy3 = 0.0\n",
+    "\n",
+    "    return np.array([ [ df1dy1, df1dy2, df1dy3 ],\n",
+    "                      [ df2dy1, df2dy2, df2dy3 ],\n",
+    "                      [ df3dy1, df3dy2, df3dy3 ] ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vode_integrate(Y0, tmax):\n",
+    "    \"\"\" integrate using the NDF method \"\"\"\n",
+    "\n",
+    "    r = integrate.solve_ivp(rhs, (0.0, tmax), Y0,\n",
+    "                            method=\"BDF\", jac=jac, rtol=1.e-7, atol=1.e-10)\n",
+    "\n",
+    "    # Note: this solver does not have a dens_output method, instead we \n",
+    "    # access the solution data where it was evaluated internally via\n",
+    "    # the return object\n",
+    "    \n",
+    "    return r.t, r.y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'time')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Y0 = np.array([1.0, 0.0, 0.0])\n",
+    "tmax = 4.e7\n",
+    "\n",
+    "ts, Ys = vode_integrate(Y0, tmax)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.loglog(ts, Ys[0,:], label=r\"$y_1$\")\n",
+    "ax.loglog(ts, Ys[1,:], label=r\"$y_2$\")\n",
+    "ax.loglog(ts, Ys[2,:], label=r\"$y_3$\")\n",
+    "\n",
+    "ax.legend(loc=\"best\", frameon=False)\n",
+    "ax.set_xlabel(\"time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{admonition} try it\n",
+    "Redo this integration, but now use the `RK45` solver instead of `BDF`.  Does it work?\n",
+    "\n",
+    "You may need to use the `kernel` menu in Jupyter to interrupt the kernel if you get impatient.\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/05-scipy/scipy-exercises-2.ipynb b/content/05-scipy/scipy-exercises-2.ipynb
new file mode 100644
index 00000000..75750590
--- /dev/null
+++ b/content/05-scipy/scipy-exercises-2.ipynb
@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# More SciPy Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Algebra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1: Condition number\n",
+    "\n",
+    "For a linear system, ${\\bf A x} = {\\bf b}$, we can only solve for $x$ if the determinant of the matrix ${\\bf A}$ is non-zero.  If the determinant is zero, then we call the matrix _singular_.  The _condition number_ of a matrix is a measure of how close we are to being singular.  The formal definition is:\n",
+    "\n",
+    "\\begin{equation}\n",
+    "\\mathrm{cond}({\\bf A}) = \\| {\\bf A}\\| \\| {\\bf A}^{-1} \\|\n",
+    "\\end{equation}\n",
+    "\n",
+    "But we can think of it as a measure of how much ${\\bf x}$ would change due to a small change in ${\\bf b}$.  A large condition number means that our solution for ${\\bf x}$ could be inaccurate.\n",
+    "\n",
+    "A _Hilbert matrix_ has $H_{ij} = (i + j + 1)^{-1}$, and is known to have a large condition number.  Here's a routine to generate a Hilbert matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hilbert(n):\n",
+    "    \"\"\" return a Hilbert matrix, H_ij = (i + j - 1)^{-1} \"\"\"\n",
+    "\n",
+    "    H = np.zeros((n,n), dtype=np.float64)\n",
+    "\n",
+    "    for i in range(1, n+1):\n",
+    "        for j in range(1, n+1):\n",
+    "            H[i-1,j-1] = 1.0/(i + j - 1.0)\n",
+    "    return H"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's solve ${\\bf Hx} ={\\bf b}$.  Create a linear system by picking an ${\\bf x}$ and generating a ${\\bf b}$ by multiplying by the matrix ${\\bf H}$.  Then use the `scipy.linalg.solve()` function to recover ${\\bf x}$.  Compute the error in ${\\bf x}$ as a function of the size of the matrix.\n",
+    "\n",
+    "You won't need a large matrix, $n \\sim 13$ or so, will start showing big errors.\n",
+    "\n",
+    "You can compute the condition number with `numpy.linalg.cond()`\n",
+    "\n",
+    "There are methods that can do a better job with nearly-singular matrices.  Take a look at `scipy.linalg.lstsq()` for example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FFTs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2: Noisy signal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A convolution is defined as:  \n",
+    "\n",
+    "  \\begin{equation}                                                                           \n",
+    "    (f \\star g)(t) \\equiv \\int_{-\\infty}^{\\infty} f(\\tau) g(t - \\tau) d\\tau                   \n",
+    "  \\end{equation}                                                                             \n",
+    "\n",
+    "  It is easy to compute this with FFTs, via the [convolution theorem](https://en.wikipedia.org/wiki/Convolution_theorem),\n",
+    "  \n",
+    "  \\begin{equation}                                         \n",
+    "    \\mathcal{F}\\{f \\star g\\} = \\mathcal{F}\\{f\\} \\, \\mathcal{F}\\{g\\}                                          \n",
+    "  \\end{equation}                                         \n",
+    "  That is the Fourier transform of the convolution of $f$ and $g$ is simply\n",
+    "  the product of the individual transforms of $f$ and $g$.  This allows us\n",
+    "  to compute the convolution via multiplication in Fourier space and then take\n",
+    "  the inverse transform, $\\mathcal{F}^{-1}\\{\\}$, to recover the convolution in real space:\n",
+    "  \n",
+    "  \\begin{equation}\n",
+    "  f \\star g = \\mathcal{F}^{-1}\\{ \\mathcal{F}\\{f\\} \\, \\mathcal{F}\\{g\\}\\}\n",
+    "  \\end{equation}\n",
+    "  \n",
+    "A common use of a convolution is to smooth noisy data, for example by convolving noisy data with a Gaussian.  We'll do that here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's some noisy data we'll work with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fdata(x, L):\n",
+    "    A = L/10.0\n",
+    "    return 2*np.sin(2*np.pi*x/L) + x*(L-x)**2/L**3 * np.cos(x) + \\\n",
+    "           5*x*(L-x)/L**2 + A/2 + 0.1*A*np.sin(13*np.pi*x/L)\n",
+    "\n",
+    "N = 2048\n",
+    "L = 50.0\n",
+    "x = np.linspace(0, L, N, endpoint=False)\n",
+    "orig = fdata(x, L)\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "noisy = orig + 0.5 * rng.standard_normal(N)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbd696d4fd0>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, noisy)\n",
+    "plt.plot(x, orig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SciPy provides a convolution function `scipy.signal.convolve()` that can do the convolution for us directly.  To smooth the data, we want to use a Gaussian, which can be produced by `scipy.signal.gaussian()`.\n",
+    "\n",
+    "Convolve the noisy data with a Gaussian and plot the result together with the original data `orig`.  You'll need to play with the width of the Gaussian to get a nice smoothing.  You also will need to normalize the Gaussian so that it sums to 1, otherwise, your convolved data will be shifted verfically from the original function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span class=\"fa fa-star\"></span> Q3: FFT of chaotic pendulum\n",
+    "\n",
+    "Last time we looked at ODEs and the chaotic pendulum, and were interested in writing a method to integrate the pendulum in time.\n",
+    "\n",
+    "Here we want to examine its behavior in frequency space.  The code below will integrate the chaotic pendulum, while requesting that the solution be stored at points spaced with a fixed dt, which makes it suitable for taking the FFT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "def rhs(t, Y, q, omega_d, b):\n",
+    "    \"\"\" damped driven pendulum system derivatives.  Here, Y = (theta, omega) are\n",
+    "        the solution variables. \"\"\"\n",
+    "    f = np.zeros_like(Y)\n",
+    "        \n",
+    "    f[0] = Y[1]\n",
+    "    f[1] = -q*Y[1] - np.sin(Y[0]) + b*np.cos(omega_d*t)\n",
+    "\n",
+    "    return f\n",
+    "\n",
+    "def restrict_theta(theta):\n",
+    "    \"\"\" convert theta to be restricted to lie between -pi and pi\"\"\"\n",
+    "    tnew = theta + np.pi\n",
+    "    tnew += -2.0*np.pi*np.floor(tnew/(2.0*np.pi))\n",
+    "    tnew -= np.pi\n",
+    "    return tnew\n",
+    "\n",
+    "def int_pendulum(theta0, q, omega_d, b, tend, dt):\n",
+    "    \"\"\" integrate the pendulum and return solution with dt\"\"\"\n",
+    "\n",
+    "    # points in time where we'll request the solution\n",
+    "    tpoints = np.arange(0.0, tend, dt)\n",
+    "    \n",
+    "    r = solve_ivp(partial(rhs, q=q, omega_d=omega_d, b=b),\n",
+    "                  [0.0, tend], [theta0, 0.0],\n",
+    "                  method='RK45', t_eval=tpoints)\n",
+    "\n",
+    "    return r.t, r.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The call below will give an undamped pendulum.  For a small amplitude, since we have $L = g$ in our pendulum, the period is simply $T = 2\\pi$, and the frequency is $\\nu_k = 1/(2\\pi)$.  We plot things in terms of angular frequency, $\\omega_k = 2\\pi \\nu_k$, so all the power will be at $\\omega_k = 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t, y = int_pendulum(np.radians(10), 0.0, 0.6666, 0.0, 200.0, 0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your task is to complete the power spectrum routine below to calculate the FFT of theta and plot it.  Experiment with the damping and driving parameters to see the complexity of the pendulum in frequency space when it becomes chaotic.  For reference, here's a plot of the solution theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\theta$')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t, restrict_theta(y[0,:]))\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(r\"$\\theta$\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def power_spectrum(t, theta0):\n",
+    "    \"\"\" return the power spectrum of theta.  For the frequency\n",
+    "        component, return it in terms of omega \"\"\"\n",
+    "\n",
+    "    theta = restrict_theta(theta0)\n",
+    "    \n",
+    "    # fill in the rest -- take the FFT of theta and return omega_k and \n",
+    "    # the transform of theta\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q4: Let's find the errors on our fit\n",
+    "\n",
+    "We looked at fits, but not what the errors are on the fit.  Look at `scipy.optimize.curve_fit()`.  This is a simplified wrapper on the least squares fitting.  It can return the convariance matrix, the diagonals of which can give the error of the fit for the parameters.  \n",
+    "\n",
+    "Make up some data that models a non-linear function (by introducing some random noise) and perform a fit and find the errors on the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/05-scipy/scipy-exercises.ipynb b/content/05-scipy/scipy-exercises.ipynb
new file mode 100644
index 00000000..0741ef0e
--- /dev/null
+++ b/content/05-scipy/scipy-exercises.ipynb
@@ -0,0 +1,398 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SciPy exercises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import integrate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q1: integrating an analytic function\n",
+    "\n",
+    "Numerical integration methods work differently depending on whether you have the analytic function available (in which case you can evaluate it freely at any point you please) or if it is sampled for you.\n",
+    "\n",
+    "Consider the function $f(x) = e^{-x^2}$.  We want to integrate this from $[-5, 5]$.  The\n",
+    "analytic integral is not easily obtained.  Use `integrate.quad` to do the integration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q2: integrating a sampled function\n",
+    "\n",
+    "Consider now that you have data that represents a function sampled a `N` points, but you don't know the analytic form of the function.  Here, we create the sampling here for a Gaussian and we will do the same integral as in Q1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.38879439e-11, 3.15061953e-10, 5.80457065e-09, 8.68481106e-08,\n",
+       "       1.05527775e-06, 1.04133225e-05, 8.34503173e-05, 5.43103745e-04,\n",
+       "       2.87047478e-03, 1.23208538e-02, 4.29481052e-02, 1.21580337e-01,\n",
+       "       2.79510942e-01, 5.21855680e-01, 7.91258065e-01, 9.74320895e-01,\n",
+       "       9.74320895e-01, 7.91258065e-01, 5.21855680e-01, 2.79510942e-01,\n",
+       "       1.21580337e-01, 4.29481052e-02, 1.23208538e-02, 2.87047478e-03,\n",
+       "       5.43103745e-04, 8.34503173e-05, 1.04133225e-05, 1.05527775e-06,\n",
+       "       8.68481106e-08, 5.80457065e-09, 3.15061953e-10, 1.38879439e-11])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "N = 32\n",
+    "x = np.linspace(-5, 5, N)\n",
+    "f = np.exp(-x**2)\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the integral of this sampled function using Simpson's method (`integrate.simps`).  Now, vary the number of sample points (try 64, 128, ...) and see how the answer changes.  Simpson's method is 4-th order accurate, which means that the error should decrease by $2^4$ when we double the number of sample points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optional: Make a plot of the error (compared to the analytic integral from Q1) vs. N"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpolation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import interpolate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q3: interpolation error\n",
+    "\n",
+    "There are a large number of different interpolation schemes available through scipy.  Let's test them out.\n",
+    "\n",
+    "Create a python function, $f(x)$, that is your true function.  Now create $N$ samples of it (either regularly spaced or irregularly spaced).\n",
+    "\n",
+    "Try some of the different interolation routines.  `interpolate.interp1d` takes a `kind` argument that let's you choose the order of the interpolation.  Measure the error in the method, by comparing the interpolated result with the actual function value.  Also, try using cubic splines (look at `CubicSpline`)\n",
+    "\n",
+    "Try plotting the resulting interpolant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Root Finding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q4: scalar function roots\n",
+    "\n",
+    "Consider the function\n",
+    "\n",
+    "$$q(x) = x^3 - 2x^2 - 11x + 12$$\n",
+    "\n",
+    "This has 3 roots, but is known to cause problems for some root-finding methods (it exhibits basis of attraction: https://en.wikipedia.org/wiki/Newton%27s_method#Basins_of_attraction -- very closely spaced initial guesses leave to very different roots)\n",
+    "\n",
+    "Use the SciPy `optimize.brentq` method to find the roots.  You might need to play around with the intervals to find all 3 roots (try plotting the function to help)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import optimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ODEs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q5: orbits\n",
+    "\n",
+    "We want to consider planetary orbits.  To do this, we need to solve Newton's second law together with Newton's law of gravity.  If we restrict ourselves to the x-y plane, then there are 4 quantities we need to solve for: $x$, $y$, $v_x$, and $v_y$.  These evolve according to:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "\\frac{dx}{dt} &= v_x \\\\\n",
+    "\\frac{dy}{dt} &= v_y \\\\\n",
+    "\\frac{dv_x}{dt} &= a_x = -\\frac{GM_\\star x}{r^3} \\\\\n",
+    "\\frac{dv_y}{dt} &= a_y = -\\frac{GM_\\star y}{r^3}\n",
+    "\\end{align*}\n",
+    "\n",
+    "To integrate these forward in time, we need an initial condition for each quantity.  We'll setup our system such that the Sun is at the origin (that will be one focus), and the planet begins at perihelion and orbits counterclockwise. \n",
+    "\n",
+    "![geometry](orbit_setup.png)\n",
+    "\n",
+    "The distance of perihelion from the focus is:\n",
+    "\n",
+    "$$r_p = a (1 - e)$$\n",
+    "\n",
+    "where $a$ is the semi-major axis and $e$ is the eccentricity.  The perihelion velocity is all in the $y$ direction and is:\n",
+    "\n",
+    "$$v_y = v_p = \\sqrt{\\frac{GM_\\star}{a} \\frac{1+e}{1-e}}$$\n",
+    "\n",
+    "We'll work in units of AU, years, and solar masses, in which case, $GM_\\star = 4\\pi^2$ (for the Sun).  \n",
+    "\n",
+    "Your initial conditions should be:\n",
+    "\n",
+    "  * $x(t=0) = r_p$\n",
+    "  * $y(t=0) = 0$\n",
+    "  * $v_x(t=0) = 0$\n",
+    "  * $v_y(t=0) = v_p$\n",
+    "\n",
+    "Here's a righthand side function for the ODEs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rhs(t, Y, GM=4*np.pi**2):\n",
+    "    \"\"\"RHS for orbits, Y is the solution vector, containing\n",
+    "    x, y, v_x, and v_y\"\"\"\n",
+    "\n",
+    "    x, y, vx, vy = Y\n",
+    "    f = np.zeros_like(Y)\n",
+    "\n",
+    "    # dx/dt = vx\n",
+    "    f[0] = vx\n",
+    "\n",
+    "    # dy/dt = vy\n",
+    "    f[1] = vy\n",
+    "\n",
+    "    # d(vx)/dt = -GMx/r**3\n",
+    "    r = np.sqrt(x**2 + y**2)\n",
+    "    f[2] = -GM*x/r**3\n",
+    "\n",
+    "    # d(vy)/dt = -GMy/r**3\n",
+    "    f[3] = -GM*y/r**3\n",
+    "\n",
+    "    return f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the SciPy ODE integration methods to integrate an orbit and plot it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <span class=\"fa fa-star\"></span> Q6: damped driven pendulum and chaos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a large class of ODE integration methods available through the `scipy.integrate.ode()` function.  Not all of them provide _dense output_ -- most will just give you the value at the end of the integration.  \n",
+    "\n",
+    "The explicit Runge-Kutta integrator will give you access to the solution at intermediate points and provides methods to interpolate to any value.  You enable this via `dense_output=True` (see the example in our out-of-class notebook).\n",
+    "\n",
+    "The damped driven pendulum obeys the following equations:\n",
+    "\n",
+    "$$\\dot{\\theta} = \\omega$$\n",
+    "\n",
+    "$$\\dot{\\omega} = -q \\omega - \\sin \\theta + b \\cos \\omega_d t$$\n",
+    "\n",
+    "here, $\\theta$ is the angle of the pendulum from vertical and $\\omega$ is the angular velocity.  $q$ is a damping coefficient, $b$ is a forcing amplitude, and $\\omega_d$ is a driving frequency.\n",
+    "\n",
+    "Choose $q = 0.5$ and $\\omega_d = 2/3$.\n",
+    "\n",
+    "Integrate the system for different values of $b$ (start with $b = 0.9$ and increase by $0.05$, and plot the results ($\\theta$ vs. $t$).  Here's a RHS function to get you started:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rhs(t, Y, q, omega_d, b):\n",
+    "        \"\"\" damped driven pendulum system derivatives.  Here, Y = (theta, omega) are\n",
+    "        the solution variables. \"\"\"\n",
+    "        f = np.zeros_like(Y)\n",
+    "        \n",
+    "        f[0] = Y[1]\n",
+    "        f[1] = -q*Y[1] - np.sin(Y[0]) + b*np.cos(omega_d*t)\n",
+    "\n",
+    "        return f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the pendulum can flip over, giving values of $\\theta$ outside of $[-\\pi, \\pi]$.  The following function can be used to restrict it back to $[-\\pi, \\pi]$ for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def restrict_theta(theta):\n",
+    "    \"\"\" convert theta to be restricted to lie between -pi and pi\"\"\"\n",
+    "    tnew = theta + np.pi\n",
+    "    tnew += -2.0*np.pi*np.floor(tnew/(2.0*np.pi))\n",
+    "    tnew -= np.pi\n",
+    "    return tnew"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function that takes an initial angle, $\\theta_0$, and integrates the system and returns the solution.\n",
+    "\n",
+    "Note, the righthand side function, `rhs`, takes additional arguments that you need to pass through the integrator.  The preferred method to do this with the `solve_ivp()` interface appears to be to use `functools.partial()`, as:\n",
+    "```\n",
+    "from functools import partial\n",
+    "\n",
+    "r = solve_ivp(partial(rhs, q=q, omega_d=omega_d, b=b), ...)\n",
+    "```\n",
+    "\n",
+    "Some values of $b$ will show very non-periodic behavior.  To see chaos, integrate two different pendula that are the same except for $\\theta_0$, with only a small difference between then (like 60 degrees and 60.0001 degrees.  You'll see the solutions track for a while, but then diverge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/05-scipy/scipy.md b/content/05-scipy/scipy.md
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+# SciPy
+
+SciPy provides implementations of many common numerical methods.
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new file mode 100644
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@@ -0,0 +1,2420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SymPy examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "sources:\n",
+    "http://docs.sympy.org/latest/tutorial/\n",
+    "http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/notebooks/SymPy%20Examples.ipynb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SymPy provides support for symbolic math to python, similar to what you would do with Mathematica or Maple.  The major difference is that it acts just like any other python module, so you can use the symbolic math together in your own python projects with the rest of python functionality.\n",
+    "\n",
+    "The following import and function (`init_session()`) sets up a nice environment for us when working in Jupyter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython console for SymPy 1.13.3 (Python 3.13.2-64-bit) (ground types: gmpy)\n",
+      "\n",
+      "These commands were executed:\n",
+      ">>> from sympy import *\n",
+      ">>> x, y, z, t = symbols('x y z t')\n",
+      ">>> k, m, n = symbols('k m n', integer=True)\n",
+      ">>> f, g, h = symbols('f g h', cls=Function)\n",
+      ">>> init_printing()\n",
+      "\n",
+      "Documentation can be found at https://docs.sympy.org/1.13.3/\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sympy import init_session\n",
+    "init_session(use_latex=\"mathjax\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SymPy types and basic symbolic manipulation\n",
+    "\n",
+    "Sympy defines its own types, you can convert them to python types, but you don't always want to (and will probably lose accuracy when you do).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.4142135623730951\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(math.sqrt(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sqrt(2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sqrt(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2*sqrt(2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sqrt(8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#help(sqrt(8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can do symbolic math not just on numbers, but we can tell SymPy what to treat as a symbol, using `symbols()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy import symbols\n",
+    "x, y, z = symbols(\"x y z\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x + 2 y$"
+      ],
+      "text/plain": [
+       "x + 2⋅y"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = x + 2*y\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x + 2 y - 1$"
+      ],
+      "text/plain": [
+       "x + 2⋅y - 1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x + y$"
+      ],
+      "text/plain": [
+       "x + y"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr - y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x \\left(x + 2 y\\right)$"
+      ],
+      "text/plain": [
+       "x⋅(x + 2⋅y)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = x*expr\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{2} + 2 x y$"
+      ],
+      "text/plain": [
+       " 2        \n",
+       "x  + 2⋅x⋅y"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = expand(f)\n",
+    "g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x \\left(x + 2 y\\right)$"
+      ],
+      "text/plain": [
+       "x⋅(x + 2⋅y)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factor(g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## substitution\n",
+    "\n",
+    "SymPy provides methods to substitute values for symbols in symbolic expressions.  Note, the follow likely does not do what you expect:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sin{\\left(2 \\pi z \\right)}$"
+      ],
+      "text/plain": [
+       "sin(2⋅π⋅z)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = sin(z*2*pi)\n",
+    "z = 0\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've now redefined `z` to be a python type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "to do substitution, we use the `subs()` method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sin{\\left(2 \\pi x \\right)}$"
+      ],
+      "text/plain": [
+       "sin(2⋅π⋅x)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = sin(x*2*pi)\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\sqrt{2}}{2}$"
+      ],
+      "text/plain": [
+       "√2\n",
+       "──\n",
+       "2 "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = expr.subs(x, 0.125)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this is not a floating point number -- it is still a SymPy object.  To make it floating point, we can use evalf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.707106781186548 <class 'sympy.core.numbers.Float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = a.evalf()\n",
+    "print(b, type(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is still a SymPy object, because SymPy can do arbitrary precision "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 0.70710678118654752440084436210484903928483593768847$"
+      ],
+      "text/plain": [
+       "0.70710678118654752440084436210484903928483593768847"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.evalf(50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "want regular python types?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7071067811865476 <class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = float(b)\n",
+    "print(c, type(c))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python and SymPy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y, z, t = symbols('x y z t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SymPy symbols are just objects and when you do operations on two sympy objects the result is a sympy object.  \n",
+    "\n",
+    "When you combine a sympy and python object, the result is also a sympy object.  \n",
+    "\n",
+    "But we need to be careful when doing fractions.  For instance doing `x + 1/3` will first compute `1/3` in python (giving `0.333...`) and then add it to the sympy `x` symbol.  The `Rational()` function makes this all happen in sympy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sin{\\left(2 \\pi x \\right)} + \\frac{1}{3}$"
+      ],
+      "text/plain": [
+       "sin(2⋅π⋅x) + 1/3"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = expr + Rational(1,3)\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sin{\\left(2 \\pi x \\right)} + 0.333333333333333$"
+      ],
+      "text/plain": [
+       "sin(2⋅π⋅x) + 0.333333333333333"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr + 1/3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## equality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`=` is still the assignment operator of python (it does not mean symbolic equality), and `==` is still the logical test (exact structural equality).  There is a separate object, `Eq()` to specify symbolic equality.\n",
+    "\n",
+    "And testing for _algebraic_ equality is not always accomplished using `==`, since that tests for _structural equality_."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x + 1 == 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x + 1 = 4$"
+      ],
+      "text/plain": [
+       "x + 1 = 4"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eq(x + 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = (x + 1)**2\n",
+    "b = x**2 + 2*x + 1    # these are algebraically equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a == b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `simplify()` to test for algebraic equality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 0$"
+      ],
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(a - b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle i \\sin{\\left(x \\right)} + \\cos{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "ⅈ⋅sin(x) + cos(x)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = cos(x) + I*sin(x)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle e^{i x}$"
+      ],
+      "text/plain": [
+       " ⅈ⋅x\n",
+       "ℯ   "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More substitution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "note that substitution returns a new expression: SymPy expressions are immutable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1$"
+      ],
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = cos(x)\n",
+    "expr.subs(x, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\cos{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "cos(x)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x$"
+      ],
+      "text/plain": [
+       "x"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "multiple substitutions, pass a list of tuples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{3} + 4 x y - z$"
+      ],
+      "text/plain": [
+       " 3            \n",
+       "x  + 4⋅x⋅y - z"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = x**3 + 4*x*y - z\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 40$"
+      ],
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr.subs([(x, 2), (y, 4), (z, 0)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## simplifying"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is not unique definition of what the simplest form of an expression is.\n",
+    "\n",
+    "`simplify()` tries lots of methods for simplification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1$"
+      ],
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(sin(x)**2 + cos(x)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x - 1$"
+      ],
+      "text/plain": [
+       "x - 1"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify( (x**3 + x**2 - x - 1)/(x**2 + 2*x + 1) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left(x - 2\\right) \\left(x - 1\\right)$"
+      ],
+      "text/plain": [
+       "(x - 2)⋅(x - 1)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(gamma(x)/gamma(x - 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "but sometimes it doesn't have your idea of what the simplest form is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{2} + 2 x + 1$"
+      ],
+      "text/plain": [
+       " 2          \n",
+       "x  + 2⋅x + 1"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(x**2 + 2*x + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "instead factor may be what you want"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left(x + 1\\right)^{2}$"
+      ],
+      "text/plain": [
+       "       2\n",
+       "(x + 1) "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factor(x**2 + 2*x + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### polynomial simplification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{2} + 2 x + 1$"
+      ],
+      "text/plain": [
+       " 2          \n",
+       "x  + 2⋅x + 1"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expand((x + 1)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{2} - x - 6$"
+      ],
+      "text/plain": [
+       " 2        \n",
+       "x  - x - 6"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expand((x + 2)*(x - 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle -2$"
+      ],
+      "text/plain": [
+       "-2"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expand( (x + 1)*(x - 2) - (x - 1)*x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle z \\left(x + 2 y\\right)^{2}$"
+      ],
+      "text/plain": [
+       "           2\n",
+       "z⋅(x + 2⋅y) "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factor(x**2*z + 4*x*y*z + 4*y**2*z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left( 1, \\  \\left[ \\left( z, \\  1\\right), \\  \\left( x + 2 y, \\  2\\right)\\right]\\right)$"
+      ],
+      "text/plain": [
+       "(1, [(z, 1), (x + 2⋅y, 2)])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factor_list(x**2*z + 4*x*y*z + 4*y**2*z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "collect collects common powers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{3} - x^{2} z + 2 x^{2} + x y + x - 3$"
+      ],
+      "text/plain": [
+       " 3    2        2              \n",
+       "x  - x ⋅z + 2⋅x  + x⋅y + x - 3"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = x*y + x - 3 + 2*x**2 - z*x**2 + x**3\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle x^{3} + x^{2} \\left(2 - z\\right) + x \\left(y + 1\\right) - 3$"
+      ],
+      "text/plain": [
+       " 3    2                        \n",
+       "x  + x ⋅(2 - z) + x⋅(y + 1) - 3"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "collected_expr = collect(expr, x)\n",
+    "collected_expr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "cancel cancels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{x^{2} + 2 x + 1}{x^{2} + x}$"
+      ],
+      "text/plain": [
+       " 2          \n",
+       "x  + 2⋅x + 1\n",
+       "────────────\n",
+       "    2       \n",
+       "   x  + x   "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = (x**2 + 2*x + 1)/(x**2 + x)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{x + 1}{x}$"
+      ],
+      "text/plain": [
+       "x + 1\n",
+       "─────\n",
+       "  x  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancel(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "trigsimp simplifies trigonometric identities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\cos{\\left(4 x \\right)}}{2} + \\frac{1}{2}$"
+      ],
+      "text/plain": [
+       "cos(4⋅x)   1\n",
+       "──────── + ─\n",
+       "   2       2"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trigsimp(sin(x)**4 - 2*cos(x)**2*sin(x)**2 + cos(x)**4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sin^{2}{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "   2   \n",
+       "sin (x)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trigsimp(sin(x)*tan(x)/sec(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the tutorial discusses some of the nuances of simplification of powers and special functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculus operations are simple in SymPy\n",
+    "\n",
+    "### derivatives"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle - \\sin{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "-sin(x)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(cos(x), x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 2 x e^{x^{2}}$"
+      ],
+      "text/plain": [
+       "     ⎛ 2⎞\n",
+       "     ⎝x ⎠\n",
+       "2⋅x⋅ℯ    "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(exp(x**2), x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "third derivative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 24 x$"
+      ],
+      "text/plain": [
+       "24⋅x"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(x**4, x, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "differentiate different variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left(x^{2} y^{2} z^{2} + 3 x y z + 1\\right) e^{x y z}$"
+      ],
+      "text/plain": [
+       "⎛ 2  2  2              ⎞  x⋅y⋅z\n",
+       "⎝x ⋅y ⋅z  + 3⋅x⋅y⋅z + 1⎠⋅ℯ     "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = exp(x*y*z)\n",
+    "diff(expr, x, y, z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "unevaluated derivatives can be useful for building up ODEs and PDEs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\partial^{3}}{\\partial z\\partial y\\partial x} e^{x y z}$"
+      ],
+      "text/plain": [
+       "    3           \n",
+       "   ∂    ⎛ x⋅y⋅z⎞\n",
+       "────────⎝ℯ     ⎠\n",
+       "∂z ∂y ∂x        "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deriv = Derivative(expr, x, y, z)\n",
+    "deriv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left(x^{2} y^{2} z^{2} + 3 x y z + 1\\right) e^{x y z}$"
+      ],
+      "text/plain": [
+       "⎛ 2  2  2              ⎞  x⋅y⋅z\n",
+       "⎝x ⋅y ⋅z  + 3⋅x⋅y⋅z + 1⎠⋅ℯ     "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deriv.doit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### integrals\n",
+    "\n",
+    "definite and indefinite integrals are supported"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sin{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "sin(x)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(cos(x), x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "definite integral -- note the construction of the infinity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1$"
+      ],
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(exp(-x), (x, 0, oo))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "double integral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\pi$"
+      ],
+      "text/plain": [
+       "π"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(exp(-x**2 - y**2), (x, -oo, oo), (y, -oo, oo))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if it is unable to do the integral, it returns an Integral object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Integral(x**x, x)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\int x^{x}\\, dx$"
+      ],
+      "text/plain": [
+       "⌠      \n",
+       "⎮  x   \n",
+       "⎮ x  dx\n",
+       "⌡      "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = integrate(x**x, x)\n",
+    "print(expr)\n",
+    "expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{x}{\\sqrt{x^{4} + 10 x^{2} - 96 x - 71}}$"
+      ],
+      "text/plain": [
+       "             x             \n",
+       "───────────────────────────\n",
+       "   ________________________\n",
+       "  ╱  4       2             \n",
+       "╲╱  x  + 10⋅x  - 96⋅x - 71 "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = x / sqrt(x**4 + 10*x**2 - 96*x - 71)   # example from Wikipedia Risch algorithm page)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\int \\frac{x}{\\sqrt{x^{4} + 10 x^{2} - 96 x - 71}}\\, dx$"
+      ],
+      "text/plain": [
+       "⌠                               \n",
+       "⎮              x                \n",
+       "⎮ ─────────────────────────── dx\n",
+       "⎮    ________________________   \n",
+       "⎮   ╱  4       2                \n",
+       "⎮ ╲╱  x  + 10⋅x  - 96⋅x - 71    \n",
+       "⌡                               "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(a, x)     # this has a known solution, but SymPy fails to find it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### limits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1$"
+      ],
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "limit(sin(x)/x, x, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### series expansions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1 + x + \\frac{x^{2}}{2} - \\frac{x^{4}}{8} - \\frac{x^{5}}{15} - \\frac{x^{6}}{240} + \\frac{x^{7}}{90} + \\frac{31 x^{8}}{5760} + \\frac{x^{9}}{5670} + O\\left(x^{10}\\right)$"
+      ],
+      "text/plain": [
+       "         2    4    5    6     7       8     9          \n",
+       "        x    x    x    x     x    31⋅x     x      ⎛ 10⎞\n",
+       "1 + x + ── - ── - ── - ─── + ── + ───── + ──── + O⎝x  ⎠\n",
+       "        2    8    15   240   90   5760    5670         "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr = exp(sin(x))\n",
+    "a = expr.series(x, 0, 10)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle -1 - \\frac{\\left(x - 1\\right)^{2}}{2} + \\frac{\\left(x - 1\\right)^{3}}{3} - \\frac{\\left(x - 1\\right)^{4}}{4} + \\frac{\\left(x - 1\\right)^{5}}{5} + x + O\\left(\\left(x - 1\\right)^{6}; x\\rightarrow 1\\right)$"
+      ],
+      "text/plain": [
+       "            2          3          4          5                         \n",
+       "     (x - 1)    (x - 1)    (x - 1)    (x - 1)         ⎛       6       ⎞\n",
+       "-1 - ──────── + ──────── - ──────── + ──────── + x + O⎝(x - 1) ; x → 1⎠\n",
+       "        2          3          4          5                             "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c = log(x).series(x, x0=1, n=6)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{x^{5}}{5} - \\frac{5 x^{4}}{4} + \\frac{10 x^{3}}{3} - 5 x^{2} + 5 x - \\frac{137}{60}$"
+      ],
+      "text/plain": [
+       " 5      4       3                   \n",
+       "x    5⋅x    10⋅x       2         137\n",
+       "── - ──── + ───── - 5⋅x  + 5⋅x - ───\n",
+       "5     4       3                  60 "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(c.removeO())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## solvers\n",
+    "\n",
+    "`solveset()` is the main interface to solvers in SymPy.  Note that it used to be `solve()`, but this has been replaced (see http://docs.sympy.org/latest/modules/solvers/solveset.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If no Eq() is done, then it is assumed to be equal to 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left\\{0, 1\\right\\}$"
+      ],
+      "text/plain": [
+       "{0, 1}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solveset(x**2 - x, x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can restrict the domain of the solution (e.g. to reals).  Recall that Z is the set of integers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left\\{2 n \\pi + \\frac{\\pi}{2}\\; \\middle|\\; n \\in \\mathbb{Z}\\right\\}$"
+      ],
+      "text/plain": [
+       "⎧        π │      ⎫\n",
+       "⎨2⋅n⋅π + ─ │ n ∊ ℤ⎬\n",
+       "⎩        2 │      ⎭"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solveset(sin(x) - 1, x, domain=S.Reals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### linear systems\n",
+    "\n",
+    "`linsolve()` is the interface to linear systems"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left\\{\\left( \\frac{1}{2}, \\  \\frac{5}{2}\\right)\\right\\}$"
+      ],
+      "text/plain": [
+       "{(1/2, 5/2)}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linsolve([x - y + 2, x + y - 3], [x, y])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left\\{\\left( - y - 1, \\  y, \\  2\\right)\\right\\}$"
+      ],
+      "text/plain": [
+       "{(-y - 1, y, 2)}"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linsolve([x + y + z - 1, x + y + 2*z - 3 ], (x, y, z))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "roots will report if a solution is multiple by listing it multiple times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left\\{ 0 : 1, \\  3 : 2\\right\\}$"
+      ],
+      "text/plain": [
+       "{0: 1, 3: 2}"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roots(x**3 - 6*x**2 + 9*x, x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "0 is 1 root, and 3 is 2 more roots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Differential equations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you need an undefined function (f and g already are by our init_session() above, but we've probably reset these"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f, g = symbols('f g', cls=Function)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "f(x)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{d}{d x} f{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "d       \n",
+       "──(f(x))\n",
+       "dx      "
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(x).diff(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diffeq = Eq(f(x).diff(x, 2) - 2*f(x).diff(x) + f(x), sin(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(x \\right)} - 2 \\frac{d}{d x} f{\\left(x \\right)} + \\frac{d^{2}}{d x^{2}} f{\\left(x \\right)} = \\sin{\\left(x \\right)}$"
+      ],
+      "text/plain": [
+       "                     2                \n",
+       "         d          d                 \n",
+       "f(x) - 2⋅──(f(x)) + ───(f(x)) = sin(x)\n",
+       "         dx           2               \n",
+       "                    dx                "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diffeq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f{\\left(x \\right)} = \\left(C_{1} + C_{2} x\\right) e^{x} + \\frac{\\cos{\\left(x \\right)}}{2}$"
+      ],
+      "text/plain": [
+       "                    x   cos(x)\n",
+       "f(x) = (C₁ + C₂⋅x)⋅ℯ  + ──────\n",
+       "                          2   "
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dsolve(diffeq, f(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "consider the Euler equations:\n",
+    "\n",
+    "$$q_t + A(q) q_x = 0$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$q = \\left ( \\begin{array}{c} \\rho \\\\ u \\\\ p \\end{array} \\right )\n",
+    "\\qquad\n",
+    "A(q) = \\left ( \\begin{array}{ccc} u  & \\rho     & 0 \\\\                          \n",
+    "                                  0  &  u       & 1/\\rho \\\\                     \n",
+    "                                  0  & c^2 \\rho & u \\end{array} \\right ) $$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}u & \\rho & 0\\\\0 & u & \\frac{1}{\\rho}\\\\0 & c^{2} \\rho & u\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "⎡u   ρ    0⎤\n",
+       "⎢          ⎥\n",
+       "⎢         1⎥\n",
+       "⎢0   u    ─⎥\n",
+       "⎢         ρ⎥\n",
+       "⎢          ⎥\n",
+       "⎢    2     ⎥\n",
+       "⎣0  c ⋅ρ  u⎦"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy.abc import rho\n",
+    "rho, u, c = symbols('rho u c')\n",
+    "A = Matrix([[u, rho, 0], [0, u, rho**-1], [0, c**2 * rho, u]])\n",
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}u & \\rho & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "[u  ρ  0]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.row(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The eigenvalues of the system are the speeds at which information propagates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left\\{ u : 1, \\  - c + u : 1, \\  c + u : 1\\right\\}$"
+      ],
+      "text/plain": [
+       "{u: 1, -c + u: 1, c + u: 1}"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.eigenvals()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can diagonalize it, such that\n",
+    "$$ A = PDP^{-1}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P, D = A.diagonalize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$D$ will be a matrix of the eigenvalues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}u & 0 & 0\\\\0 & - c + u & 0\\\\0 & 0 & c + u\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "⎡u    0       0  ⎤\n",
+       "⎢                ⎥\n",
+       "⎢0  -c + u    0  ⎥\n",
+       "⎢                ⎥\n",
+       "⎣0    0     c + u⎦"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$P$ will be the matrix of right eigenvectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}1 & \\frac{1}{c^{2}} & \\frac{1}{c^{2}}\\\\0 & - \\frac{1}{c \\rho} & \\frac{1}{c \\rho}\\\\0 & 1 & 1\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "⎡   1    1  ⎤\n",
+       "⎢1  ──   ── ⎥\n",
+       "⎢    2    2 ⎥\n",
+       "⎢   c    c  ⎥\n",
+       "⎢           ⎥\n",
+       "⎢   -1    1 ⎥\n",
+       "⎢0  ───  ───⎥\n",
+       "⎢   c⋅ρ  c⋅ρ⎥\n",
+       "⎢           ⎥\n",
+       "⎣0   1    1 ⎦"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "P"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}\\frac{1}{u} & - \\frac{\\rho}{- c^{2} + u^{2}} & \\frac{1}{- c^{2} u + u^{3}}\\\\0 & \\frac{u}{- c^{2} + u^{2}} & - \\frac{1}{- c^{2} \\rho + \\rho u^{2}}\\\\0 & - \\frac{c^{2} \\rho}{- c^{2} + u^{2}} & \\frac{u}{- c^{2} + u^{2}}\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "⎡1     -ρ            1      ⎤\n",
+       "⎢─  ─────────   ─────────── ⎥\n",
+       "⎢u     2    2      2      3 ⎥\n",
+       "⎢   - c  + u    - c ⋅u + u  ⎥\n",
+       "⎢                           ⎥\n",
+       "⎢       u           -1      ⎥\n",
+       "⎢0  ─────────  ─────────────⎥\n",
+       "⎢      2    2     2        2⎥\n",
+       "⎢   - c  + u   - c ⋅ρ + ρ⋅u ⎥\n",
+       "⎢                           ⎥\n",
+       "⎢      2                    ⎥\n",
+       "⎢    -c ⋅ρ           u      ⎥\n",
+       "⎢0  ─────────    ─────────  ⎥\n",
+       "⎢      2    2       2    2  ⎥\n",
+       "⎣   - c  + u     - c  + u   ⎦"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A**-1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Units\n",
+    "\n",
+    "Sympy can attach units to numbers and propagate them through"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy.physics.units import newton, kilogram, meter, second, convert_to"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 9.81 \\text{N}$"
+      ],
+      "text/plain": [
+       "9.81⋅newton"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "F = 1 * kilogram * 9.81 * meter / second**2\n",
+    "convert_to(F, newton)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/06-sympy/sympy-exercises.ipynb b/content/06-sympy/sympy-exercises.ipynb
new file mode 100644
index 00000000..72b50736
--- /dev/null
+++ b/content/06-sympy/sympy-exercises.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SymPy Exercises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython console for SymPy 1.9 (Python 3.10.2-64-bit) (ground types: gmpy)\n",
+      "\n",
+      "These commands were executed:\n",
+      ">>> from __future__ import division\n",
+      ">>> from sympy import *\n",
+      ">>> x, y, z, t = symbols('x y z t')\n",
+      ">>> k, m, n = symbols('k m n', integer=True)\n",
+      ">>> f, g, h = symbols('f g h', cls=Function)\n",
+      ">>> init_printing()\n",
+      "\n",
+      "Documentation can be found at https://docs.sympy.org/1.9/\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sympy as sym\n",
+    "from sympy import init_session\n",
+    "init_session()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1: Creating an expression\n",
+    "\n",
+    "Create the expression:\n",
+    "\n",
+    "$$f = x e^{-x} + x (1-x)$$\n",
+    "\n",
+    "Then evaluate it for \n",
+    "\n",
+    "$$x = 0, 0.1, 0.2, 0.4, 0.8$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2: Factoring a polynomial, and finding its roots\n",
+    "\n",
+    "Factor\n",
+    "\n",
+    "$$x^{4} - 6 x^{3} + x^{2} + 24 x + 16$$\n",
+    "\n",
+    "Then find its zeros."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q3: Integratation and differentiation\n",
+    "\n",
+    "Integrate the function:\n",
+    "\n",
+    "$$f = \\sin(x) e^{-x}$$\n",
+    "\n",
+    "Then differentiate the result to see if you get back the original function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q4: Parsing an expression\n",
+    "\n",
+    "Write a program that reads in a mathematical expression as a string (e.g., `\"sin(2*pi*x)\"`), converts it to a SymPy expression, and then evaluates it as needed.  \n",
+    "\n",
+    "Have your program either make a plot of the entered function, or use the input function as the function to fit a dataset to using curvefit.\n",
+    "\n",
+    "The following will be helpful:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`parse_expr()` will convert a string into a SymPy expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sympy.parsing.sympy_parser import parse_expr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$\\displaystyle \\sin{\\left(2 \\pi x \\right)}$"
+      ],
+      "text/plain": [
+       "sin(2⋅π⋅x)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = \"sin(2*pi*x)\"\n",
+    "a = parse_expr(s)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sympy.lambdify()` will convert a SymPy expression into a function that is callable by python.  You can make it a numpy-compatible function too (this means, e.g., that any `sin()` in your SymPy expression will be evaluate using `np.sin()`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = sym.lambdify(x, a, \"numpy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$\\displaystyle -2.44929359829471 \\cdot 10^{-16}$"
+      ],
+      "text/plain": [
+       "-2.4492935982947064e-16"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#help(lambdify)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q5: Units\n",
+    "\n",
+    "SymPy can deal with physical units.  See:\n",
+    "\n",
+    "http://docs.sympy.org/latest/modules/physics/units/quantities.html\n",
+    "\n",
+    "Let's try this out.  Newton's 2nd law is\n",
+    "\n",
+    "$$F = ma$$\n",
+    "\n",
+    "Create a mass of 1 kg and an acceleration of 10 m/s$^2$, and compute the force, $F$, and express the result in Newtons.\n",
+    "\n",
+    "Note: the `convert_to` function was added in SymPy 1.1, so if you are using an earlier version, you will need to divide by the target unit to do the conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/06-sympy/sympy.md b/content/06-sympy/sympy.md
new file mode 100644
index 00000000..0a3160ad
--- /dev/null
+++ b/content/06-sympy/sympy.md
@@ -0,0 +1,5 @@
+# SymPy
+
+SymPy is the symbolic math library for python.  A key design feature
+of it is that it can interoperate with all of the libraries we've
+already seen.
diff --git a/content/07-pandas/exercises.txt b/content/07-pandas/exercises.txt
new file mode 100644
index 00000000..c4b054ba
--- /dev/null
+++ b/content/07-pandas/exercises.txt
@@ -0,0 +1,2 @@
+-- work with the World Bank data
+-- curve fit from pandas
diff --git a/content/07-pandas/ideas.txt b/content/07-pandas/ideas.txt
new file mode 100644
index 00000000..7236428d
--- /dev/null
+++ b/content/07-pandas/ideas.txt
@@ -0,0 +1 @@
+https://gist.github.com/jakevdp/9ffd72a81c1af80a10314cba0569af84
diff --git a/content/07-pandas/pandas-babynames.ipynb b/content/07-pandas/pandas-babynames.ipynb
new file mode 100644
index 00000000..1f1176ee
--- /dev/null
+++ b/content/07-pandas/pandas-babynames.ipynb
@@ -0,0 +1,6652 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# pandas exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use the sample dataset from the Social Secury Administration on baby names:\n",
+    "https://www.ssa.gov/oact/babynames/limits.html\n",
+    "\n",
+    "Download the \"National\" version and unzip it.  There will be one file for each year.\n",
+    "\n",
+    "This example follows the _Python for Data Analysis_ book closely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by reading in just a single dataset, for the first year available (1880).  We give the names of the columns here.  The index will just be the line / record number in the file (not really important for us)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>F</td>\n",
+       "      <td>7065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Anna</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Emma</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Elizabeth</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Minnie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Margaret</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ida</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bertha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Annie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Clara</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Ella</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Florence</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cora</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Martha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Laura</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Nellie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Grace</td>\n",
+       "      <td>F</td>\n",
+       "      <td>982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Carrie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Maude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Mabel</td>\n",
+       "      <td>F</td>\n",
+       "      <td>808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bessie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Jennie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Gertrude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Julia</td>\n",
+       "      <td>F</td>\n",
+       "      <td>783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Hattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Edith</td>\n",
+       "      <td>F</td>\n",
+       "      <td>768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Rose</td>\n",
+       "      <td>F</td>\n",
+       "      <td>700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970</th>\n",
+       "      <td>Philo</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1971</th>\n",
+       "      <td>Phineas</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>Presley</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1973</th>\n",
+       "      <td>Ransom</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1974</th>\n",
+       "      <td>Reece</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1975</th>\n",
+       "      <td>Rene</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1976</th>\n",
+       "      <td>Roswell</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>Rowland</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1978</th>\n",
+       "      <td>Sampson</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979</th>\n",
+       "      <td>Samual</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980</th>\n",
+       "      <td>Santos</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1981</th>\n",
+       "      <td>Schuyler</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>Sheppard</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983</th>\n",
+       "      <td>Spurgeon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1984</th>\n",
+       "      <td>Starling</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985</th>\n",
+       "      <td>Sylvanus</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1986</th>\n",
+       "      <td>Theadore</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>Theophile</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>Tilmon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1989</th>\n",
+       "      <td>Tommy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991</th>\n",
+       "      <td>Vann</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>Wes</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1993</th>\n",
+       "      <td>Winston</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994</th>\n",
+       "      <td>Wood</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>Woodie</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>Worthy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>Wright</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>York</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>Zachariah</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           name sex  births\n",
+       "0          Mary   F    7065\n",
+       "1          Anna   F    2604\n",
+       "2          Emma   F    2003\n",
+       "3     Elizabeth   F    1939\n",
+       "4        Minnie   F    1746\n",
+       "5      Margaret   F    1578\n",
+       "6           Ida   F    1472\n",
+       "7         Alice   F    1414\n",
+       "8        Bertha   F    1320\n",
+       "9         Sarah   F    1288\n",
+       "10        Annie   F    1258\n",
+       "11        Clara   F    1226\n",
+       "12         Ella   F    1156\n",
+       "13     Florence   F    1063\n",
+       "14         Cora   F    1045\n",
+       "15       Martha   F    1040\n",
+       "16        Laura   F    1012\n",
+       "17       Nellie   F     995\n",
+       "18        Grace   F     982\n",
+       "19       Carrie   F     949\n",
+       "20        Maude   F     858\n",
+       "21        Mabel   F     808\n",
+       "22       Bessie   F     796\n",
+       "23       Jennie   F     793\n",
+       "24     Gertrude   F     787\n",
+       "25        Julia   F     783\n",
+       "26       Hattie   F     769\n",
+       "27        Edith   F     768\n",
+       "28       Mattie   F     704\n",
+       "29         Rose   F     700\n",
+       "...         ...  ..     ...\n",
+       "1970      Philo   M       5\n",
+       "1971    Phineas   M       5\n",
+       "1972    Presley   M       5\n",
+       "1973     Ransom   M       5\n",
+       "1974      Reece   M       5\n",
+       "1975       Rene   M       5\n",
+       "1976    Roswell   M       5\n",
+       "1977    Rowland   M       5\n",
+       "1978    Sampson   M       5\n",
+       "1979     Samual   M       5\n",
+       "1980     Santos   M       5\n",
+       "1981   Schuyler   M       5\n",
+       "1982   Sheppard   M       5\n",
+       "1983   Spurgeon   M       5\n",
+       "1984   Starling   M       5\n",
+       "1985   Sylvanus   M       5\n",
+       "1986   Theadore   M       5\n",
+       "1987  Theophile   M       5\n",
+       "1988     Tilmon   M       5\n",
+       "1989      Tommy   M       5\n",
+       "1990    Unknown   M       5\n",
+       "1991       Vann   M       5\n",
+       "1992        Wes   M       5\n",
+       "1993    Winston   M       5\n",
+       "1994       Wood   M       5\n",
+       "1995     Woodie   M       5\n",
+       "1996     Worthy   M       5\n",
+       "1997     Wright   M       5\n",
+       "1998       York   M       5\n",
+       "1999  Zachariah   M       5\n",
+       "\n",
+       "[2000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_1880 = pd.read_csv('babynames/yob1880.txt', names=[\"name\", \"sex\", \"births\"])\n",
+    "names_1880"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## number of births\n",
+    "\n",
+    "Now we want to start doing some analysis.  `.groupby()` creates a new type of container that groups items.  Here, we'll group by sex.\n",
+    "\n",
+    "See this:\n",
+    "http://pandas.pydata.org/pandas-docs/stable/groupby.html\n",
+    "https://community.modeanalytics.com/python/tutorial/pandas-groupby-and-python-lambda-functions/\n",
+    "\n",
+    "for a nice explanation of this method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sex\n",
+       "F     90992\n",
+       "M    110490\n",
+       "Name: births, dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_1880.groupby(\"sex\")[\"births\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we can use a column name as a parameter of the object, so we can equivalently do this as:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sex\n",
+       "F     90992\n",
+       "M    110490\n",
+       "Name: births, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_1880.groupby(\"sex\").births.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On its own, you can't print a `GroupBy` object.  You need to do a transformation or filter on it to get back a `Series` or `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>births</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>942</td>\n",
+       "      <td>942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>1058</td>\n",
+       "      <td>1058</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     name  births\n",
+       "sex              \n",
+       "F     942     942\n",
+       "M    1058    1058"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gb = names_1880.groupby(\"sex\")\n",
+    "gb.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we saw in the intro, we can propagate a scalar to all indices in the `DataFrame`.  We'll use this to add a new column that represents the year:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>births</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>F</td>\n",
+       "      <td>7065</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Anna</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2604</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Emma</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Elizabeth</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Minnie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Margaret</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ida</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1414</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bertha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Annie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1258</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Clara</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1226</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Ella</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Florence</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1063</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cora</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1045</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Martha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Laura</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1012</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Nellie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Grace</td>\n",
+       "      <td>F</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Carrie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>949</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Maude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>858</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Mabel</td>\n",
+       "      <td>F</td>\n",
+       "      <td>808</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bessie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>796</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Jennie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>793</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Gertrude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>787</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Julia</td>\n",
+       "      <td>F</td>\n",
+       "      <td>783</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Hattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>769</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Edith</td>\n",
+       "      <td>F</td>\n",
+       "      <td>768</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>704</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Rose</td>\n",
+       "      <td>F</td>\n",
+       "      <td>700</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970</th>\n",
+       "      <td>Philo</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1971</th>\n",
+       "      <td>Phineas</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>Presley</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1973</th>\n",
+       "      <td>Ransom</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1974</th>\n",
+       "      <td>Reece</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1975</th>\n",
+       "      <td>Rene</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1976</th>\n",
+       "      <td>Roswell</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>Rowland</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1978</th>\n",
+       "      <td>Sampson</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979</th>\n",
+       "      <td>Samual</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1980</th>\n",
+       "      <td>Santos</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1981</th>\n",
+       "      <td>Schuyler</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>Sheppard</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983</th>\n",
+       "      <td>Spurgeon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1984</th>\n",
+       "      <td>Starling</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1985</th>\n",
+       "      <td>Sylvanus</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1986</th>\n",
+       "      <td>Theadore</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>Theophile</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>Tilmon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1989</th>\n",
+       "      <td>Tommy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991</th>\n",
+       "      <td>Vann</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>Wes</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1993</th>\n",
+       "      <td>Winston</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994</th>\n",
+       "      <td>Wood</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>Woodie</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>Worthy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>Wright</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>York</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>Zachariah</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2000 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           name sex  births  year\n",
+       "0          Mary   F    7065  1880\n",
+       "1          Anna   F    2604  1880\n",
+       "2          Emma   F    2003  1880\n",
+       "3     Elizabeth   F    1939  1880\n",
+       "4        Minnie   F    1746  1880\n",
+       "5      Margaret   F    1578  1880\n",
+       "6           Ida   F    1472  1880\n",
+       "7         Alice   F    1414  1880\n",
+       "8        Bertha   F    1320  1880\n",
+       "9         Sarah   F    1288  1880\n",
+       "10        Annie   F    1258  1880\n",
+       "11        Clara   F    1226  1880\n",
+       "12         Ella   F    1156  1880\n",
+       "13     Florence   F    1063  1880\n",
+       "14         Cora   F    1045  1880\n",
+       "15       Martha   F    1040  1880\n",
+       "16        Laura   F    1012  1880\n",
+       "17       Nellie   F     995  1880\n",
+       "18        Grace   F     982  1880\n",
+       "19       Carrie   F     949  1880\n",
+       "20        Maude   F     858  1880\n",
+       "21        Mabel   F     808  1880\n",
+       "22       Bessie   F     796  1880\n",
+       "23       Jennie   F     793  1880\n",
+       "24     Gertrude   F     787  1880\n",
+       "25        Julia   F     783  1880\n",
+       "26       Hattie   F     769  1880\n",
+       "27        Edith   F     768  1880\n",
+       "28       Mattie   F     704  1880\n",
+       "29         Rose   F     700  1880\n",
+       "...         ...  ..     ...   ...\n",
+       "1970      Philo   M       5  1880\n",
+       "1971    Phineas   M       5  1880\n",
+       "1972    Presley   M       5  1880\n",
+       "1973     Ransom   M       5  1880\n",
+       "1974      Reece   M       5  1880\n",
+       "1975       Rene   M       5  1880\n",
+       "1976    Roswell   M       5  1880\n",
+       "1977    Rowland   M       5  1880\n",
+       "1978    Sampson   M       5  1880\n",
+       "1979     Samual   M       5  1880\n",
+       "1980     Santos   M       5  1880\n",
+       "1981   Schuyler   M       5  1880\n",
+       "1982   Sheppard   M       5  1880\n",
+       "1983   Spurgeon   M       5  1880\n",
+       "1984   Starling   M       5  1880\n",
+       "1985   Sylvanus   M       5  1880\n",
+       "1986   Theadore   M       5  1880\n",
+       "1987  Theophile   M       5  1880\n",
+       "1988     Tilmon   M       5  1880\n",
+       "1989      Tommy   M       5  1880\n",
+       "1990    Unknown   M       5  1880\n",
+       "1991       Vann   M       5  1880\n",
+       "1992        Wes   M       5  1880\n",
+       "1993    Winston   M       5  1880\n",
+       "1994       Wood   M       5  1880\n",
+       "1995     Woodie   M       5  1880\n",
+       "1996     Worthy   M       5  1880\n",
+       "1997     Wright   M       5  1880\n",
+       "1998       York   M       5  1880\n",
+       "1999  Zachariah   M       5  1880\n",
+       "\n",
+       "[2000 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_1880[\"year\"] = 1880\n",
+    "names_1880"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## all data sets\n",
+    "\n",
+    "Now we can read in all of the datasets.  We will read them in one-by-one and add the \"year\" column to them.  We'll keep a list of all the `DataFrame`s and then we'll use a pandas method to transform this list of `DataFrame`s into a single `DataFrame`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "years = range(1880, 2016)\n",
+    "\n",
+    "pieces = []\n",
+    "columns = [\"name\", \"sex\", \"births\"]\n",
+    "\n",
+    "for yr in years:\n",
+    "    frame = pd.read_csv(\"babynames/yob{}.txt\".format(yr), names=columns)\n",
+    "    \n",
+    "    # this creates a new column and gives all rows the same value -- the year\n",
+    "    frame[\"year\"] = yr\n",
+    "    pieces.append(frame)\n",
+    "\n",
+    "# create a single dataframe through concatenation -- ignore_index means that\n",
+    "# we don't care about the original line numbers\n",
+    "names = pd.concat(pieces, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>births</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>F</td>\n",
+       "      <td>7065</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Anna</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2604</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Emma</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Elizabeth</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Minnie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Margaret</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ida</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1414</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bertha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Annie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1258</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Clara</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1226</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Ella</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Florence</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1063</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cora</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1045</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Martha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Laura</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1012</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Nellie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Grace</td>\n",
+       "      <td>F</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Carrie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>949</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Maude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>858</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Mabel</td>\n",
+       "      <td>F</td>\n",
+       "      <td>808</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bessie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>796</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Jennie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>793</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Gertrude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>787</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Julia</td>\n",
+       "      <td>F</td>\n",
+       "      <td>783</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Hattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>769</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Edith</td>\n",
+       "      <td>F</td>\n",
+       "      <td>768</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>704</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Rose</td>\n",
+       "      <td>F</td>\n",
+       "      <td>700</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858659</th>\n",
+       "      <td>Zavon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858660</th>\n",
+       "      <td>Zay</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858661</th>\n",
+       "      <td>Zaylor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858662</th>\n",
+       "      <td>Zayquan</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858663</th>\n",
+       "      <td>Zecharia</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858664</th>\n",
+       "      <td>Zecharya</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858665</th>\n",
+       "      <td>Zee</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858666</th>\n",
+       "      <td>Zeev</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858667</th>\n",
+       "      <td>Zeik</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858668</th>\n",
+       "      <td>Zekariah</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858669</th>\n",
+       "      <td>Zepplin</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858670</th>\n",
+       "      <td>Zerek</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858671</th>\n",
+       "      <td>Zeyd</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858672</th>\n",
+       "      <td>Zhi</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858673</th>\n",
+       "      <td>Ziel</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858674</th>\n",
+       "      <td>Zihan</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858675</th>\n",
+       "      <td>Zihao</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858676</th>\n",
+       "      <td>Ziheng</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858677</th>\n",
+       "      <td>Zimere</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858678</th>\n",
+       "      <td>Ziyi</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858679</th>\n",
+       "      <td>Ziyu</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858680</th>\n",
+       "      <td>Zoel</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858681</th>\n",
+       "      <td>Zohar</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858682</th>\n",
+       "      <td>Zolton</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858683</th>\n",
+       "      <td>Zyah</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858684</th>\n",
+       "      <td>Zykell</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858685</th>\n",
+       "      <td>Zyking</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858686</th>\n",
+       "      <td>Zykir</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858687</th>\n",
+       "      <td>Zyrus</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858688</th>\n",
+       "      <td>Zyus</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1858689 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              name sex  births  year\n",
+       "0             Mary   F    7065  1880\n",
+       "1             Anna   F    2604  1880\n",
+       "2             Emma   F    2003  1880\n",
+       "3        Elizabeth   F    1939  1880\n",
+       "4           Minnie   F    1746  1880\n",
+       "5         Margaret   F    1578  1880\n",
+       "6              Ida   F    1472  1880\n",
+       "7            Alice   F    1414  1880\n",
+       "8           Bertha   F    1320  1880\n",
+       "9            Sarah   F    1288  1880\n",
+       "10           Annie   F    1258  1880\n",
+       "11           Clara   F    1226  1880\n",
+       "12            Ella   F    1156  1880\n",
+       "13        Florence   F    1063  1880\n",
+       "14            Cora   F    1045  1880\n",
+       "15          Martha   F    1040  1880\n",
+       "16           Laura   F    1012  1880\n",
+       "17          Nellie   F     995  1880\n",
+       "18           Grace   F     982  1880\n",
+       "19          Carrie   F     949  1880\n",
+       "20           Maude   F     858  1880\n",
+       "21           Mabel   F     808  1880\n",
+       "22          Bessie   F     796  1880\n",
+       "23          Jennie   F     793  1880\n",
+       "24        Gertrude   F     787  1880\n",
+       "25           Julia   F     783  1880\n",
+       "26          Hattie   F     769  1880\n",
+       "27           Edith   F     768  1880\n",
+       "28          Mattie   F     704  1880\n",
+       "29            Rose   F     700  1880\n",
+       "...            ...  ..     ...   ...\n",
+       "1858659      Zavon   M       5  2015\n",
+       "1858660        Zay   M       5  2015\n",
+       "1858661     Zaylor   M       5  2015\n",
+       "1858662    Zayquan   M       5  2015\n",
+       "1858663   Zecharia   M       5  2015\n",
+       "1858664   Zecharya   M       5  2015\n",
+       "1858665        Zee   M       5  2015\n",
+       "1858666       Zeev   M       5  2015\n",
+       "1858667       Zeik   M       5  2015\n",
+       "1858668   Zekariah   M       5  2015\n",
+       "1858669    Zepplin   M       5  2015\n",
+       "1858670      Zerek   M       5  2015\n",
+       "1858671       Zeyd   M       5  2015\n",
+       "1858672        Zhi   M       5  2015\n",
+       "1858673       Ziel   M       5  2015\n",
+       "1858674      Zihan   M       5  2015\n",
+       "1858675      Zihao   M       5  2015\n",
+       "1858676     Ziheng   M       5  2015\n",
+       "1858677     Zimere   M       5  2015\n",
+       "1858678       Ziyi   M       5  2015\n",
+       "1858679       Ziyu   M       5  2015\n",
+       "1858680       Zoel   M       5  2015\n",
+       "1858681      Zohar   M       5  2015\n",
+       "1858682     Zolton   M       5  2015\n",
+       "1858683       Zyah   M       5  2015\n",
+       "1858684     Zykell   M       5  2015\n",
+       "1858685     Zyking   M       5  2015\n",
+       "1858686      Zykir   M       5  2015\n",
+       "1858687      Zyrus   M       5  2015\n",
+       "1858688       Zyus   M       5  2015\n",
+       "\n",
+       "[1858689 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## pivot tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a _pivot table_ creates a new dataframe from our original one, usually summarizing the data in a new way.  In particular, with a pivot table, we can create a new index and columns, with the data in the `DataFrame` reduced via some operation across another column.\n",
+    "\n",
+    "Here, the column that we are going to aggregate is \"births\", and the function will will use for the aggregating is `sum` (to sum over the names)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>births</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>F</td>\n",
+       "      <td>7065</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Anna</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2604</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Emma</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Elizabeth</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Minnie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>1880</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        name sex  births  year\n",
+       "0       Mary   F    7065  1880\n",
+       "1       Anna   F    2604  1880\n",
+       "2       Emma   F    2003  1880\n",
+       "3  Elizabeth   F    1939  1880\n",
+       "4     Minnie   F    1746  1880"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_births = names.pivot_table(\"births\", index=\"year\", columns=\"sex\", aggfunc=sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>sex</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</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>1880</th>\n",
+       "      <td>90992</td>\n",
+       "      <td>110490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1881</th>\n",
+       "      <td>91953</td>\n",
+       "      <td>100743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1882</th>\n",
+       "      <td>107848</td>\n",
+       "      <td>113686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1883</th>\n",
+       "      <td>112318</td>\n",
+       "      <td>104627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1884</th>\n",
+       "      <td>129020</td>\n",
+       "      <td>114443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1885</th>\n",
+       "      <td>133055</td>\n",
+       "      <td>107799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1886</th>\n",
+       "      <td>144534</td>\n",
+       "      <td>110785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1887</th>\n",
+       "      <td>145981</td>\n",
+       "      <td>101415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1888</th>\n",
+       "      <td>178624</td>\n",
+       "      <td>120850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1889</th>\n",
+       "      <td>178366</td>\n",
+       "      <td>110582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1890</th>\n",
+       "      <td>190376</td>\n",
+       "      <td>111025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1891</th>\n",
+       "      <td>185482</td>\n",
+       "      <td>101193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1892</th>\n",
+       "      <td>212341</td>\n",
+       "      <td>122036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1893</th>\n",
+       "      <td>212905</td>\n",
+       "      <td>112317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1894</th>\n",
+       "      <td>222922</td>\n",
+       "      <td>115770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1895</th>\n",
+       "      <td>233628</td>\n",
+       "      <td>117396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1896</th>\n",
+       "      <td>237918</td>\n",
+       "      <td>119568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1897</th>\n",
+       "      <td>234200</td>\n",
+       "      <td>112758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1898</th>\n",
+       "      <td>258770</td>\n",
+       "      <td>122691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1899</th>\n",
+       "      <td>233023</td>\n",
+       "      <td>106212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1900</th>\n",
+       "      <td>299822</td>\n",
+       "      <td>150490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1901</th>\n",
+       "      <td>239347</td>\n",
+       "      <td>106469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1902</th>\n",
+       "      <td>264075</td>\n",
+       "      <td>122659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1903</th>\n",
+       "      <td>261972</td>\n",
+       "      <td>119233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1904</th>\n",
+       "      <td>275371</td>\n",
+       "      <td>128125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1905</th>\n",
+       "      <td>291623</td>\n",
+       "      <td>132318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1906</th>\n",
+       "      <td>295304</td>\n",
+       "      <td>133157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1907</th>\n",
+       "      <td>318565</td>\n",
+       "      <td>146830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1908</th>\n",
+       "      <td>334315</td>\n",
+       "      <td>154343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1909</th>\n",
+       "      <td>347232</td>\n",
+       "      <td>163999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1986</th>\n",
+       "      <td>1714784</td>\n",
+       "      <td>1840347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>1737981</td>\n",
+       "      <td>1865804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>1779975</td>\n",
+       "      <td>1912764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1989</th>\n",
+       "      <td>1843175</td>\n",
+       "      <td>2000082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <td>1897807</td>\n",
+       "      <td>2052662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991</th>\n",
+       "      <td>1874489</td>\n",
+       "      <td>2019523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>1843383</td>\n",
+       "      <td>1996492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1993</th>\n",
+       "      <td>1808508</td>\n",
+       "      <td>1960486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994</th>\n",
+       "      <td>1785184</td>\n",
+       "      <td>1931313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>1758010</td>\n",
+       "      <td>1902971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>1752364</td>\n",
+       "      <td>1893548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>1739963</td>\n",
+       "      <td>1884324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>1766075</td>\n",
+       "      <td>1910424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>1772788</td>\n",
+       "      <td>1918984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000</th>\n",
+       "      <td>1814738</td>\n",
+       "      <td>1962556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001</th>\n",
+       "      <td>1799199</td>\n",
+       "      <td>1941401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>1795367</td>\n",
+       "      <td>1939950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003</th>\n",
+       "      <td>1825509</td>\n",
+       "      <td>1973612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004</th>\n",
+       "      <td>1834379</td>\n",
+       "      <td>1983010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005</th>\n",
+       "      <td>1845640</td>\n",
+       "      <td>1995115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006</th>\n",
+       "      <td>1898776</td>\n",
+       "      <td>2052652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>1919775</td>\n",
+       "      <td>2072505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008</th>\n",
+       "      <td>1887660</td>\n",
+       "      <td>2036750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009</th>\n",
+       "      <td>1833461</td>\n",
+       "      <td>1979895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>1773440</td>\n",
+       "      <td>1914629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011</th>\n",
+       "      <td>1754424</td>\n",
+       "      <td>1894262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012</th>\n",
+       "      <td>1755254</td>\n",
+       "      <td>1890889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>1747544</td>\n",
+       "      <td>1883945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>1777242</td>\n",
+       "      <td>1910876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015</th>\n",
+       "      <td>1769325</td>\n",
+       "      <td>1898858</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>136 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "sex         F        M\n",
+       "year                  \n",
+       "1880    90992   110490\n",
+       "1881    91953   100743\n",
+       "1882   107848   113686\n",
+       "1883   112318   104627\n",
+       "1884   129020   114443\n",
+       "1885   133055   107799\n",
+       "1886   144534   110785\n",
+       "1887   145981   101415\n",
+       "1888   178624   120850\n",
+       "1889   178366   110582\n",
+       "1890   190376   111025\n",
+       "1891   185482   101193\n",
+       "1892   212341   122036\n",
+       "1893   212905   112317\n",
+       "1894   222922   115770\n",
+       "1895   233628   117396\n",
+       "1896   237918   119568\n",
+       "1897   234200   112758\n",
+       "1898   258770   122691\n",
+       "1899   233023   106212\n",
+       "1900   299822   150490\n",
+       "1901   239347   106469\n",
+       "1902   264075   122659\n",
+       "1903   261972   119233\n",
+       "1904   275371   128125\n",
+       "1905   291623   132318\n",
+       "1906   295304   133157\n",
+       "1907   318565   146830\n",
+       "1908   334315   154343\n",
+       "1909   347232   163999\n",
+       "...       ...      ...\n",
+       "1986  1714784  1840347\n",
+       "1987  1737981  1865804\n",
+       "1988  1779975  1912764\n",
+       "1989  1843175  2000082\n",
+       "1990  1897807  2052662\n",
+       "1991  1874489  2019523\n",
+       "1992  1843383  1996492\n",
+       "1993  1808508  1960486\n",
+       "1994  1785184  1931313\n",
+       "1995  1758010  1902971\n",
+       "1996  1752364  1893548\n",
+       "1997  1739963  1884324\n",
+       "1998  1766075  1910424\n",
+       "1999  1772788  1918984\n",
+       "2000  1814738  1962556\n",
+       "2001  1799199  1941401\n",
+       "2002  1795367  1939950\n",
+       "2003  1825509  1973612\n",
+       "2004  1834379  1983010\n",
+       "2005  1845640  1995115\n",
+       "2006  1898776  2052652\n",
+       "2007  1919775  2072505\n",
+       "2008  1887660  2036750\n",
+       "2009  1833461  1979895\n",
+       "2010  1773440  1914629\n",
+       "2011  1754424  1894262\n",
+       "2012  1755254  1890889\n",
+       "2013  1747544  1883945\n",
+       "2014  1777242  1910876\n",
+       "2015  1769325  1898858\n",
+       "\n",
+       "[136 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_births"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f781807bd30>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f781e6a24a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "total_births.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `.apply()` and new columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll use `.groupby()` again to allow use to derive a new column -- the fraction of the births with the name specified by the index.  \n",
+    "\n",
+    "`apply()` uses what Pandas calls the _split-apply-combine_ rules, e.g., it splits the data into groups, applies your function, and then intelligently concatenates it back together.  Here, the result will be a `DataFrame` with the original information and this new column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_prop(group):\n",
+    "    group[\"prop\"] = group[\"births\"]/group[\"births\"].sum()\n",
+    "    return group\n",
+    "\n",
+    "names = names.groupby([\"year\", \"sex\"]).apply(add_prop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>births</th>\n",
+       "      <th>year</th>\n",
+       "      <th>prop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>F</td>\n",
+       "      <td>7065</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.077644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Anna</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2604</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.028618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Emma</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.022013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Elizabeth</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.021310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Minnie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.019189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Margaret</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.017342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ida</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.016177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1414</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.015540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bertha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.014507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.014155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Annie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1258</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.013825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Clara</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1226</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.013474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Ella</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.012704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Florence</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1063</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cora</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1045</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Martha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Laura</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1012</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Nellie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.010935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Grace</td>\n",
+       "      <td>F</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.010792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Carrie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>949</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.010429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Maude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>858</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.009429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Mabel</td>\n",
+       "      <td>F</td>\n",
+       "      <td>808</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bessie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>796</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Jennie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>793</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Gertrude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>787</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Julia</td>\n",
+       "      <td>F</td>\n",
+       "      <td>783</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Hattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>769</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Edith</td>\n",
+       "      <td>F</td>\n",
+       "      <td>768</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>704</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.007737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Rose</td>\n",
+       "      <td>F</td>\n",
+       "      <td>700</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.007693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858659</th>\n",
+       "      <td>Zavon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858660</th>\n",
+       "      <td>Zay</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858661</th>\n",
+       "      <td>Zaylor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858662</th>\n",
+       "      <td>Zayquan</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858663</th>\n",
+       "      <td>Zecharia</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858664</th>\n",
+       "      <td>Zecharya</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858665</th>\n",
+       "      <td>Zee</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858666</th>\n",
+       "      <td>Zeev</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858667</th>\n",
+       "      <td>Zeik</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858668</th>\n",
+       "      <td>Zekariah</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858669</th>\n",
+       "      <td>Zepplin</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858670</th>\n",
+       "      <td>Zerek</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858671</th>\n",
+       "      <td>Zeyd</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858672</th>\n",
+       "      <td>Zhi</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858673</th>\n",
+       "      <td>Ziel</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858674</th>\n",
+       "      <td>Zihan</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858675</th>\n",
+       "      <td>Zihao</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858676</th>\n",
+       "      <td>Ziheng</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858677</th>\n",
+       "      <td>Zimere</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858678</th>\n",
+       "      <td>Ziyi</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858679</th>\n",
+       "      <td>Ziyu</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858680</th>\n",
+       "      <td>Zoel</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858681</th>\n",
+       "      <td>Zohar</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858682</th>\n",
+       "      <td>Zolton</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858683</th>\n",
+       "      <td>Zyah</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858684</th>\n",
+       "      <td>Zykell</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858685</th>\n",
+       "      <td>Zyking</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858686</th>\n",
+       "      <td>Zykir</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858687</th>\n",
+       "      <td>Zyrus</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1858688</th>\n",
+       "      <td>Zyus</td>\n",
+       "      <td>M</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1858689 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              name sex  births  year      prop\n",
+       "0             Mary   F    7065  1880  0.077644\n",
+       "1             Anna   F    2604  1880  0.028618\n",
+       "2             Emma   F    2003  1880  0.022013\n",
+       "3        Elizabeth   F    1939  1880  0.021310\n",
+       "4           Minnie   F    1746  1880  0.019189\n",
+       "5         Margaret   F    1578  1880  0.017342\n",
+       "6              Ida   F    1472  1880  0.016177\n",
+       "7            Alice   F    1414  1880  0.015540\n",
+       "8           Bertha   F    1320  1880  0.014507\n",
+       "9            Sarah   F    1288  1880  0.014155\n",
+       "10           Annie   F    1258  1880  0.013825\n",
+       "11           Clara   F    1226  1880  0.013474\n",
+       "12            Ella   F    1156  1880  0.012704\n",
+       "13        Florence   F    1063  1880  0.011682\n",
+       "14            Cora   F    1045  1880  0.011485\n",
+       "15          Martha   F    1040  1880  0.011430\n",
+       "16           Laura   F    1012  1880  0.011122\n",
+       "17          Nellie   F     995  1880  0.010935\n",
+       "18           Grace   F     982  1880  0.010792\n",
+       "19          Carrie   F     949  1880  0.010429\n",
+       "20           Maude   F     858  1880  0.009429\n",
+       "21           Mabel   F     808  1880  0.008880\n",
+       "22          Bessie   F     796  1880  0.008748\n",
+       "23          Jennie   F     793  1880  0.008715\n",
+       "24        Gertrude   F     787  1880  0.008649\n",
+       "25           Julia   F     783  1880  0.008605\n",
+       "26          Hattie   F     769  1880  0.008451\n",
+       "27           Edith   F     768  1880  0.008440\n",
+       "28          Mattie   F     704  1880  0.007737\n",
+       "29            Rose   F     700  1880  0.007693\n",
+       "...            ...  ..     ...   ...       ...\n",
+       "1858659      Zavon   M       5  2015  0.000003\n",
+       "1858660        Zay   M       5  2015  0.000003\n",
+       "1858661     Zaylor   M       5  2015  0.000003\n",
+       "1858662    Zayquan   M       5  2015  0.000003\n",
+       "1858663   Zecharia   M       5  2015  0.000003\n",
+       "1858664   Zecharya   M       5  2015  0.000003\n",
+       "1858665        Zee   M       5  2015  0.000003\n",
+       "1858666       Zeev   M       5  2015  0.000003\n",
+       "1858667       Zeik   M       5  2015  0.000003\n",
+       "1858668   Zekariah   M       5  2015  0.000003\n",
+       "1858669    Zepplin   M       5  2015  0.000003\n",
+       "1858670      Zerek   M       5  2015  0.000003\n",
+       "1858671       Zeyd   M       5  2015  0.000003\n",
+       "1858672        Zhi   M       5  2015  0.000003\n",
+       "1858673       Ziel   M       5  2015  0.000003\n",
+       "1858674      Zihan   M       5  2015  0.000003\n",
+       "1858675      Zihao   M       5  2015  0.000003\n",
+       "1858676     Ziheng   M       5  2015  0.000003\n",
+       "1858677     Zimere   M       5  2015  0.000003\n",
+       "1858678       Ziyi   M       5  2015  0.000003\n",
+       "1858679       Ziyu   M       5  2015  0.000003\n",
+       "1858680       Zoel   M       5  2015  0.000003\n",
+       "1858681      Zohar   M       5  2015  0.000003\n",
+       "1858682     Zolton   M       5  2015  0.000003\n",
+       "1858683       Zyah   M       5  2015  0.000003\n",
+       "1858684     Zykell   M       5  2015  0.000003\n",
+       "1858685     Zyking   M       5  2015  0.000003\n",
+       "1858686      Zykir   M       5  2015  0.000003\n",
+       "1858687      Zyrus   M       5  2015  0.000003\n",
+       "1858688       Zyus   M       5  2015  0.000003\n",
+       "\n",
+       "[1858689 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1: Sanity check\n",
+    "\n",
+    "Verify that within each of the groups we just used above that the \"prop\" column sums to 1 (it should be close, to roundoff).  The `np.allclose()` function might be useful here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will group by \"year\" and \"sex\" and then sort by births to get just the top N most popular names.\n",
+    "\n",
+    "Notice that we simply append any additional arguments to our apply function after the function name (this is an example of the `**kwargs` behavior in python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_top(group, N=1000):\n",
+    "    return group.sort_values(by=\"births\", ascending=False)[:N]\n",
+    "\n",
+    "grouped = names.groupby([\"year\", \"sex\"])\n",
+    "top = grouped.apply(get_top, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>births</th>\n",
+       "      <th>year</th>\n",
+       "      <th>prop</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\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 rowspan=\"30\" valign=\"top\">1880</th>\n",
+       "      <th rowspan=\"30\" valign=\"top\">F</th>\n",
+       "      <th>0</th>\n",
+       "      <td>Mary</td>\n",
+       "      <td>F</td>\n",
+       "      <td>7065</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.077644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Anna</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2604</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.028618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Emma</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.022013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Elizabeth</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.021310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Minnie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.019189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Margaret</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.017342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ida</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.016177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1414</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.015540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bertha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.014507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sarah</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.014155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Annie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1258</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.013825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Clara</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1226</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.013474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Ella</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.012704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Florence</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1063</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cora</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1045</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Martha</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Laura</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1012</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.011122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Nellie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.010935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Grace</td>\n",
+       "      <td>F</td>\n",
+       "      <td>982</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.010792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Carrie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>949</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.010429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Maude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>858</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.009429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Mabel</td>\n",
+       "      <td>F</td>\n",
+       "      <td>808</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bessie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>796</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Jennie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>793</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Gertrude</td>\n",
+       "      <td>F</td>\n",
+       "      <td>787</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Julia</td>\n",
+       "      <td>F</td>\n",
+       "      <td>783</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Hattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>769</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Edith</td>\n",
+       "      <td>F</td>\n",
+       "      <td>768</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.008440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Mattie</td>\n",
+       "      <td>F</td>\n",
+       "      <td>704</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.007737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Rose</td>\n",
+       "      <td>F</td>\n",
+       "      <td>700</td>\n",
+       "      <td>1880</td>\n",
+       "      <td>0.007693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"30\" valign=\"top\">2015</th>\n",
+       "      <th rowspan=\"30\" valign=\"top\">M</th>\n",
+       "      <th>1845703</th>\n",
+       "      <td>Yadiel</td>\n",
+       "      <td>M</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845704</th>\n",
+       "      <td>Yahya</td>\n",
+       "      <td>M</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845700</th>\n",
+       "      <td>Aarush</td>\n",
+       "      <td>M</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845702</th>\n",
+       "      <td>Robin</td>\n",
+       "      <td>M</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845701</th>\n",
+       "      <td>Deangelo</td>\n",
+       "      <td>M</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845705</th>\n",
+       "      <td>Boden</td>\n",
+       "      <td>M</td>\n",
+       "      <td>209</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845707</th>\n",
+       "      <td>Kye</td>\n",
+       "      <td>M</td>\n",
+       "      <td>209</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845708</th>\n",
+       "      <td>Kylen</td>\n",
+       "      <td>M</td>\n",
+       "      <td>209</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845709</th>\n",
+       "      <td>Todd</td>\n",
+       "      <td>M</td>\n",
+       "      <td>209</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845710</th>\n",
+       "      <td>Truman</td>\n",
+       "      <td>M</td>\n",
+       "      <td>209</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845706</th>\n",
+       "      <td>Ean</td>\n",
+       "      <td>M</td>\n",
+       "      <td>209</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845712</th>\n",
+       "      <td>Gilbert</td>\n",
+       "      <td>M</td>\n",
+       "      <td>208</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845713</th>\n",
+       "      <td>Haiden</td>\n",
+       "      <td>M</td>\n",
+       "      <td>208</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845711</th>\n",
+       "      <td>Chevy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>208</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845715</th>\n",
+       "      <td>Dangelo</td>\n",
+       "      <td>M</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845716</th>\n",
+       "      <td>Juelz</td>\n",
+       "      <td>M</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845717</th>\n",
+       "      <td>Osvaldo</td>\n",
+       "      <td>M</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845714</th>\n",
+       "      <td>Brixton</td>\n",
+       "      <td>M</td>\n",
+       "      <td>207</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845718</th>\n",
+       "      <td>Bishop</td>\n",
+       "      <td>M</td>\n",
+       "      <td>206</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845719</th>\n",
+       "      <td>Freddy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>206</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845720</th>\n",
+       "      <td>Reagan</td>\n",
+       "      <td>M</td>\n",
+       "      <td>206</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845721</th>\n",
+       "      <td>Frankie</td>\n",
+       "      <td>M</td>\n",
+       "      <td>205</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845722</th>\n",
+       "      <td>Malaki</td>\n",
+       "      <td>M</td>\n",
+       "      <td>205</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845725</th>\n",
+       "      <td>Jayvion</td>\n",
+       "      <td>M</td>\n",
+       "      <td>204</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845726</th>\n",
+       "      <td>Leroy</td>\n",
+       "      <td>M</td>\n",
+       "      <td>204</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845724</th>\n",
+       "      <td>Deshawn</td>\n",
+       "      <td>M</td>\n",
+       "      <td>204</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845723</th>\n",
+       "      <td>Camren</td>\n",
+       "      <td>M</td>\n",
+       "      <td>204</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845728</th>\n",
+       "      <td>Jaydon</td>\n",
+       "      <td>M</td>\n",
+       "      <td>203</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845727</th>\n",
+       "      <td>Briar</td>\n",
+       "      <td>M</td>\n",
+       "      <td>203</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1845730</th>\n",
+       "      <td>Ayan</td>\n",
+       "      <td>M</td>\n",
+       "      <td>202</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>271877 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name sex  births  year      prop\n",
+       "year sex                                               \n",
+       "1880 F   0             Mary   F    7065  1880  0.077644\n",
+       "         1             Anna   F    2604  1880  0.028618\n",
+       "         2             Emma   F    2003  1880  0.022013\n",
+       "         3        Elizabeth   F    1939  1880  0.021310\n",
+       "         4           Minnie   F    1746  1880  0.019189\n",
+       "         5         Margaret   F    1578  1880  0.017342\n",
+       "         6              Ida   F    1472  1880  0.016177\n",
+       "         7            Alice   F    1414  1880  0.015540\n",
+       "         8           Bertha   F    1320  1880  0.014507\n",
+       "         9            Sarah   F    1288  1880  0.014155\n",
+       "         10           Annie   F    1258  1880  0.013825\n",
+       "         11           Clara   F    1226  1880  0.013474\n",
+       "         12            Ella   F    1156  1880  0.012704\n",
+       "         13        Florence   F    1063  1880  0.011682\n",
+       "         14            Cora   F    1045  1880  0.011485\n",
+       "         15          Martha   F    1040  1880  0.011430\n",
+       "         16           Laura   F    1012  1880  0.011122\n",
+       "         17          Nellie   F     995  1880  0.010935\n",
+       "         18           Grace   F     982  1880  0.010792\n",
+       "         19          Carrie   F     949  1880  0.010429\n",
+       "         20           Maude   F     858  1880  0.009429\n",
+       "         21           Mabel   F     808  1880  0.008880\n",
+       "         22          Bessie   F     796  1880  0.008748\n",
+       "         23          Jennie   F     793  1880  0.008715\n",
+       "         24        Gertrude   F     787  1880  0.008649\n",
+       "         25           Julia   F     783  1880  0.008605\n",
+       "         26          Hattie   F     769  1880  0.008451\n",
+       "         27           Edith   F     768  1880  0.008440\n",
+       "         28          Mattie   F     704  1880  0.007737\n",
+       "         29            Rose   F     700  1880  0.007693\n",
+       "...                     ...  ..     ...   ...       ...\n",
+       "2015 M   1845703     Yadiel   M     211  2015  0.000111\n",
+       "         1845704      Yahya   M     211  2015  0.000111\n",
+       "         1845700     Aarush   M     211  2015  0.000111\n",
+       "         1845702      Robin   M     211  2015  0.000111\n",
+       "         1845701   Deangelo   M     211  2015  0.000111\n",
+       "         1845705      Boden   M     209  2015  0.000110\n",
+       "         1845707        Kye   M     209  2015  0.000110\n",
+       "         1845708      Kylen   M     209  2015  0.000110\n",
+       "         1845709       Todd   M     209  2015  0.000110\n",
+       "         1845710     Truman   M     209  2015  0.000110\n",
+       "         1845706        Ean   M     209  2015  0.000110\n",
+       "         1845712    Gilbert   M     208  2015  0.000110\n",
+       "         1845713     Haiden   M     208  2015  0.000110\n",
+       "         1845711      Chevy   M     208  2015  0.000110\n",
+       "         1845715    Dangelo   M     207  2015  0.000109\n",
+       "         1845716      Juelz   M     207  2015  0.000109\n",
+       "         1845717    Osvaldo   M     207  2015  0.000109\n",
+       "         1845714    Brixton   M     207  2015  0.000109\n",
+       "         1845718     Bishop   M     206  2015  0.000108\n",
+       "         1845719     Freddy   M     206  2015  0.000108\n",
+       "         1845720     Reagan   M     206  2015  0.000108\n",
+       "         1845721    Frankie   M     205  2015  0.000108\n",
+       "         1845722     Malaki   M     205  2015  0.000108\n",
+       "         1845725    Jayvion   M     204  2015  0.000107\n",
+       "         1845726      Leroy   M     204  2015  0.000107\n",
+       "         1845724    Deshawn   M     204  2015  0.000107\n",
+       "         1845723     Camren   M     204  2015  0.000107\n",
+       "         1845728     Jaydon   M     203  2015  0.000107\n",
+       "         1845727      Briar   M     203  2015  0.000107\n",
+       "         1845730       Ayan   M     202  2015  0.000106\n",
+       "\n",
+       "[271877 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2: split by sex\n",
+    "\n",
+    "create two new dataframes, one `boys` with just those in `top` that are \"M\" and one `girls` with those in `top` who are \"F\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'boys' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-82107729bbc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'boys' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "boys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many times does each name appear, by year?  This dataframe is the total number of births by year and name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib64/python3.6/site-packages/pandas/core/reshape/pivot.py:135: FutureWarning: 'year' is both a column name and an index level.\n",
+      "Defaulting to column but this will raise an ambiguity error in a future version\n",
+      "  grouped = data.groupby(keys)\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_births = top.pivot_table(\"births\", index=\"year\", columns=\"name\", aggfunc=sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>name</th>\n",
+       "      <th>Aaden</th>\n",
+       "      <th>Aaliyah</th>\n",
+       "      <th>Aanya</th>\n",
+       "      <th>Aarav</th>\n",
+       "      <th>Aaron</th>\n",
+       "      <th>Aarush</th>\n",
+       "      <th>Ab</th>\n",
+       "      <th>Abagail</th>\n",
+       "      <th>Abb</th>\n",
+       "      <th>Abbey</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Zoa</th>\n",
+       "      <th>Zoe</th>\n",
+       "      <th>Zoey</th>\n",
+       "      <th>Zoie</th>\n",
+       "      <th>Zola</th>\n",
+       "      <th>Zollie</th>\n",
+       "      <th>Zona</th>\n",
+       "      <th>Zora</th>\n",
+       "      <th>Zula</th>\n",
+       "      <th>Zuri</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",
+       "      <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",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1880</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1881</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1882</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1883</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1884</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1885</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1886</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1887</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1888</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1889</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>53.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>65.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>45.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>53.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>70.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>64.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>72.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>79.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>83.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>60.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>57.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12678.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>247.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14397.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>241.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1989</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15314.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>376.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>507.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>478.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14240.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>451.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>722.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14506.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>981.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1993</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13825.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>503.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1193.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1451.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14380.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>597.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1333.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1255.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13286.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1726.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>831.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11969.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>552.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2064.0</td>\n",
+       "      <td>339.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1738.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11166.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>645.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2362.0</td>\n",
+       "      <td>407.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1399.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10544.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>661.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2692.0</td>\n",
+       "      <td>478.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1088.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9852.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>211.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>710.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3237.0</td>\n",
+       "      <td>563.0</td>\n",
+       "      <td>257.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1495.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9551.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3785.0</td>\n",
+       "      <td>691.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3352.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9534.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>244.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>687.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4644.0</td>\n",
+       "      <td>822.0</td>\n",
+       "      <td>439.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4778.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8999.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4884.0</td>\n",
+       "      <td>1182.0</td>\n",
+       "      <td>438.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3671.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8859.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>276.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>625.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5085.0</td>\n",
+       "      <td>1469.0</td>\n",
+       "      <td>449.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3486.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8386.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>504.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5362.0</td>\n",
+       "      <td>1622.0</td>\n",
+       "      <td>515.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3456.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7799.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>287.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>452.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4957.0</td>\n",
+       "      <td>2270.0</td>\n",
+       "      <td>502.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3742.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8291.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>298.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>404.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5152.0</td>\n",
+       "      <td>2848.0</td>\n",
+       "      <td>531.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3952.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8933.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>313.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4931.0</td>\n",
+       "      <td>3031.0</td>\n",
+       "      <td>527.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008</th>\n",
+       "      <td>956.0</td>\n",
+       "      <td>4038.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>8536.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>344.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4779.0</td>\n",
+       "      <td>3445.0</td>\n",
+       "      <td>493.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009</th>\n",
+       "      <td>1267.0</td>\n",
+       "      <td>4366.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>7966.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>297.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5144.0</td>\n",
+       "      <td>3991.0</td>\n",
+       "      <td>499.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>450.0</td>\n",
+       "      <td>4659.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>438.0</td>\n",
+       "      <td>7458.0</td>\n",
+       "      <td>227.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>281.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>299.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6263.0</td>\n",
+       "      <td>5200.0</td>\n",
+       "      <td>508.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>259.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011</th>\n",
+       "      <td>274.0</td>\n",
+       "      <td>5105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>436.0</td>\n",
+       "      <td>7610.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>287.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6294.0</td>\n",
+       "      <td>6394.0</td>\n",
+       "      <td>523.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>313.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012</th>\n",
+       "      <td>223.0</td>\n",
+       "      <td>5499.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>435.0</td>\n",
+       "      <td>7519.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6445.0</td>\n",
+       "      <td>7457.0</td>\n",
+       "      <td>516.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>434.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>203.0</td>\n",
+       "      <td>5217.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>495.0</td>\n",
+       "      <td>7289.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5967.0</td>\n",
+       "      <td>7224.0</td>\n",
+       "      <td>431.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>566.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>237.0</td>\n",
+       "      <td>4871.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>531.0</td>\n",
+       "      <td>7368.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5857.0</td>\n",
+       "      <td>7384.0</td>\n",
+       "      <td>365.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>664.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015</th>\n",
+       "      <td>296.0</td>\n",
+       "      <td>4836.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>539.0</td>\n",
+       "      <td>7113.0</td>\n",
+       "      <td>211.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5995.0</td>\n",
+       "      <td>6900.0</td>\n",
+       "      <td>367.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>709.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>136 rows × 7062 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "name   Aaden  Aaliyah  Aanya  Aarav    Aaron  Aarush   Ab  Abagail  Abb  \\\n",
+       "year                                                                      \n",
+       "1880     NaN      NaN    NaN    NaN    102.0     NaN  NaN      NaN  NaN   \n",
+       "1881     NaN      NaN    NaN    NaN     94.0     NaN  NaN      NaN  NaN   \n",
+       "1882     NaN      NaN    NaN    NaN     85.0     NaN  NaN      NaN  NaN   \n",
+       "1883     NaN      NaN    NaN    NaN    105.0     NaN  NaN      NaN  NaN   \n",
+       "1884     NaN      NaN    NaN    NaN     97.0     NaN  NaN      NaN  NaN   \n",
+       "1885     NaN      NaN    NaN    NaN     88.0     NaN  6.0      NaN  NaN   \n",
+       "1886     NaN      NaN    NaN    NaN     86.0     NaN  NaN      NaN  NaN   \n",
+       "1887     NaN      NaN    NaN    NaN     78.0     NaN  NaN      NaN  NaN   \n",
+       "1888     NaN      NaN    NaN    NaN     90.0     NaN  NaN      NaN  NaN   \n",
+       "1889     NaN      NaN    NaN    NaN     85.0     NaN  NaN      NaN  NaN   \n",
+       "1890     NaN      NaN    NaN    NaN     96.0     NaN  NaN      NaN  6.0   \n",
+       "1891     NaN      NaN    NaN    NaN     69.0     NaN  NaN      NaN  NaN   \n",
+       "1892     NaN      NaN    NaN    NaN     95.0     NaN  NaN      NaN  NaN   \n",
+       "1893     NaN      NaN    NaN    NaN     81.0     NaN  NaN      NaN  NaN   \n",
+       "1894     NaN      NaN    NaN    NaN     79.0     NaN  NaN      NaN  NaN   \n",
+       "1895     NaN      NaN    NaN    NaN     94.0     NaN  NaN      NaN  NaN   \n",
+       "1896     NaN      NaN    NaN    NaN     69.0     NaN  NaN      NaN  NaN   \n",
+       "1897     NaN      NaN    NaN    NaN     87.0     NaN  NaN      NaN  NaN   \n",
+       "1898     NaN      NaN    NaN    NaN     89.0     NaN  NaN      NaN  NaN   \n",
+       "1899     NaN      NaN    NaN    NaN     71.0     NaN  NaN      NaN  NaN   \n",
+       "1900     NaN      NaN    NaN    NaN    103.0     NaN  NaN      NaN  NaN   \n",
+       "1901     NaN      NaN    NaN    NaN     80.0     NaN  NaN      NaN  NaN   \n",
+       "1902     NaN      NaN    NaN    NaN     78.0     NaN  NaN      NaN  NaN   \n",
+       "1903     NaN      NaN    NaN    NaN     93.0     NaN  NaN      NaN  NaN   \n",
+       "1904     NaN      NaN    NaN    NaN    117.0     NaN  NaN      NaN  NaN   \n",
+       "1905     NaN      NaN    NaN    NaN     96.0     NaN  NaN      NaN  NaN   \n",
+       "1906     NaN      NaN    NaN    NaN     96.0     NaN  NaN      NaN  NaN   \n",
+       "1907     NaN      NaN    NaN    NaN    130.0     NaN  NaN      NaN  NaN   \n",
+       "1908     NaN      NaN    NaN    NaN    114.0     NaN  NaN      NaN  NaN   \n",
+       "1909     NaN      NaN    NaN    NaN    142.0     NaN  NaN      NaN  NaN   \n",
+       "...      ...      ...    ...    ...      ...     ...  ...      ...  ...   \n",
+       "1986     NaN      NaN    NaN    NaN  12690.0     NaN  NaN      NaN  NaN   \n",
+       "1987     NaN      NaN    NaN    NaN  12678.0     NaN  NaN      NaN  NaN   \n",
+       "1988     NaN      NaN    NaN    NaN  14397.0     NaN  NaN      NaN  NaN   \n",
+       "1989     NaN      NaN    NaN    NaN  15314.0     NaN  NaN      NaN  NaN   \n",
+       "1990     NaN      NaN    NaN    NaN  14550.0     NaN  NaN      NaN  NaN   \n",
+       "1991     NaN      NaN    NaN    NaN  14240.0     NaN  NaN      NaN  NaN   \n",
+       "1992     NaN      NaN    NaN    NaN  14506.0     NaN  NaN      NaN  NaN   \n",
+       "1993     NaN      NaN    NaN    NaN  13825.0     NaN  NaN      NaN  NaN   \n",
+       "1994     NaN   1451.0    NaN    NaN  14380.0     NaN  NaN      NaN  NaN   \n",
+       "1995     NaN   1255.0    NaN    NaN  13286.0     NaN  NaN      NaN  NaN   \n",
+       "1996     NaN    831.0    NaN    NaN  11969.0     NaN  NaN      NaN  NaN   \n",
+       "1997     NaN   1738.0    NaN    NaN  11166.0     NaN  NaN      NaN  NaN   \n",
+       "1998     NaN   1399.0    NaN    NaN  10544.0     NaN  NaN      NaN  NaN   \n",
+       "1999     NaN   1088.0    NaN    NaN   9852.0     NaN  NaN    211.0  NaN   \n",
+       "2000     NaN   1495.0    NaN    NaN   9551.0     NaN  NaN    222.0  NaN   \n",
+       "2001     NaN   3352.0    NaN    NaN   9534.0     NaN  NaN    244.0  NaN   \n",
+       "2002     NaN   4778.0    NaN    NaN   8999.0     NaN  NaN    256.0  NaN   \n",
+       "2003     NaN   3671.0    NaN    NaN   8859.0     NaN  NaN    276.0  NaN   \n",
+       "2004     NaN   3486.0    NaN    NaN   8386.0     NaN  NaN    258.0  NaN   \n",
+       "2005     NaN   3456.0    NaN    NaN   7799.0     NaN  NaN    287.0  NaN   \n",
+       "2006     NaN   3742.0    NaN    NaN   8291.0     NaN  NaN    298.0  NaN   \n",
+       "2007     NaN   3952.0    NaN    NaN   8933.0     NaN  NaN    313.0  NaN   \n",
+       "2008   956.0   4038.0    NaN  219.0   8536.0     NaN  NaN    320.0  NaN   \n",
+       "2009  1267.0   4366.0    NaN  270.0   7966.0     NaN  NaN    297.0  NaN   \n",
+       "2010   450.0   4659.0    NaN  438.0   7458.0   227.0  NaN    281.0  NaN   \n",
+       "2011   274.0   5105.0    NaN  436.0   7610.0     NaN  NaN      NaN  NaN   \n",
+       "2012   223.0   5499.0    NaN  435.0   7519.0     NaN  NaN      NaN  NaN   \n",
+       "2013   203.0   5217.0    NaN  495.0   7289.0     NaN  NaN      NaN  NaN   \n",
+       "2014   237.0   4871.0  266.0  531.0   7368.0     NaN  NaN      NaN  NaN   \n",
+       "2015   296.0   4836.0    NaN  539.0   7113.0   211.0  NaN      NaN  NaN   \n",
+       "\n",
+       "name  Abbey  ...     Zoa     Zoe    Zoey   Zoie  Zola  Zollie  Zona  Zora  \\\n",
+       "year         ...                                                            \n",
+       "1880    NaN  ...     8.0    23.0     NaN    NaN   7.0     NaN   8.0  28.0   \n",
+       "1881    NaN  ...     NaN    22.0     NaN    NaN  10.0     NaN   9.0  21.0   \n",
+       "1882    NaN  ...     8.0    25.0     NaN    NaN   9.0     NaN  17.0  32.0   \n",
+       "1883    NaN  ...     NaN    23.0     NaN    NaN  10.0     NaN  11.0  35.0   \n",
+       "1884    NaN  ...    13.0    31.0     NaN    NaN  14.0     6.0   8.0  58.0   \n",
+       "1885    NaN  ...     6.0    27.0     NaN    NaN  12.0     6.0  14.0  48.0   \n",
+       "1886    NaN  ...    13.0    25.0     NaN    NaN   8.0     NaN  20.0  52.0   \n",
+       "1887    NaN  ...     9.0    34.0     NaN    NaN  23.0     NaN  28.0  46.0   \n",
+       "1888    NaN  ...    11.0    42.0     NaN    NaN  23.0     7.0  30.0  42.0   \n",
+       "1889    NaN  ...    14.0    29.0     NaN    NaN  22.0     NaN  29.0  53.0   \n",
+       "1890    NaN  ...     NaN    42.0     NaN    NaN  32.0     7.0  27.0  60.0   \n",
+       "1891    NaN  ...     NaN    34.0     NaN    NaN  29.0     6.0  14.0  52.0   \n",
+       "1892    NaN  ...     NaN    34.0     NaN    NaN  27.0     NaN  25.0  66.0   \n",
+       "1893    NaN  ...     NaN    23.0     NaN    NaN  34.0     6.0  15.0  67.0   \n",
+       "1894    NaN  ...     NaN    28.0     NaN    NaN  51.0     NaN  23.0  66.0   \n",
+       "1895    NaN  ...     NaN    34.0     NaN    NaN  60.0    11.0  38.0  55.0   \n",
+       "1896    NaN  ...     NaN    36.0     NaN    NaN  47.0     NaN  38.0  72.0   \n",
+       "1897    NaN  ...     NaN    35.0     NaN    NaN  51.0     NaN  28.0  67.0   \n",
+       "1898    NaN  ...     NaN    30.0     NaN    NaN  62.0     NaN  28.0  65.0   \n",
+       "1899    NaN  ...     NaN    27.0     NaN    NaN  49.0     6.0  31.0  56.0   \n",
+       "1900    NaN  ...     NaN    26.0     NaN    NaN  48.0     9.0  44.0  99.0   \n",
+       "1901    NaN  ...     NaN    26.0     NaN    NaN  56.0     NaN  31.0  58.0   \n",
+       "1902    NaN  ...     NaN    34.0     NaN    NaN  58.0     NaN  23.0  58.0   \n",
+       "1903    NaN  ...     NaN    19.0     NaN    NaN  64.0     NaN  41.0  83.0   \n",
+       "1904    NaN  ...     NaN    27.0     NaN    NaN  46.0     NaN  35.0  54.0   \n",
+       "1905    NaN  ...     NaN    24.0     NaN    NaN  66.0     8.0  24.0  55.0   \n",
+       "1906    NaN  ...     NaN    19.0     NaN    NaN  59.0     NaN  37.0  64.0   \n",
+       "1907    NaN  ...     NaN    19.0     NaN    NaN  53.0    11.0  39.0  92.0   \n",
+       "1908    NaN  ...     NaN    23.0     NaN    NaN  70.0     NaN  31.0  59.0   \n",
+       "1909    NaN  ...     NaN    22.0     NaN    NaN  59.0     NaN  39.0  57.0   \n",
+       "...     ...  ...     ...     ...     ...    ...   ...     ...   ...   ...   \n",
+       "1986  370.0  ...     NaN   213.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1987  327.0  ...     NaN   247.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1988  404.0  ...     NaN   241.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1989  470.0  ...     NaN   376.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1990  507.0  ...     NaN   478.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1991  451.0  ...     NaN   722.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1992  430.0  ...     NaN   981.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1993  503.0  ...     NaN  1193.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1994  597.0  ...     NaN  1333.0     NaN    NaN   NaN     NaN   NaN   NaN   \n",
+       "1995  549.0  ...     NaN  1726.0   219.0    NaN   NaN     NaN   NaN   NaN   \n",
+       "1996  552.0  ...     NaN  2064.0   339.0    NaN   NaN     NaN   NaN   NaN   \n",
+       "1997  645.0  ...     NaN  2362.0   407.0    NaN   NaN     NaN   NaN   NaN   \n",
+       "1998  661.0  ...     NaN  2692.0   478.0  225.0   NaN     NaN   NaN   NaN   \n",
+       "1999  710.0  ...     NaN  3237.0   563.0  257.0   NaN     NaN   NaN   NaN   \n",
+       "2000  660.0  ...     NaN  3785.0   691.0  320.0   NaN     NaN   NaN   NaN   \n",
+       "2001  687.0  ...     NaN  4644.0   822.0  439.0   NaN     NaN   NaN   NaN   \n",
+       "2002  600.0  ...     NaN  4884.0  1182.0  438.0   NaN     NaN   NaN   NaN   \n",
+       "2003  625.0  ...     NaN  5085.0  1469.0  449.0   NaN     NaN   NaN   NaN   \n",
+       "2004  504.0  ...     NaN  5362.0  1622.0  515.0   NaN     NaN   NaN   NaN   \n",
+       "2005  452.0  ...     NaN  4957.0  2270.0  502.0   NaN     NaN   NaN   NaN   \n",
+       "2006  404.0  ...     NaN  5152.0  2848.0  531.0   NaN     NaN   NaN   NaN   \n",
+       "2007  349.0  ...     NaN  4931.0  3031.0  527.0   NaN     NaN   NaN   NaN   \n",
+       "2008  344.0  ...     NaN  4779.0  3445.0  493.0   NaN     NaN   NaN   NaN   \n",
+       "2009  307.0  ...     NaN  5144.0  3991.0  499.0   NaN     NaN   NaN   NaN   \n",
+       "2010  299.0  ...     NaN  6263.0  5200.0  508.0   NaN     NaN   NaN   NaN   \n",
+       "2011  287.0  ...     NaN  6294.0  6394.0  523.0   NaN     NaN   NaN   NaN   \n",
+       "2012    NaN  ...     NaN  6445.0  7457.0  516.0   NaN     NaN   NaN   NaN   \n",
+       "2013    NaN  ...     NaN  5967.0  7224.0  431.0   NaN     NaN   NaN   NaN   \n",
+       "2014    NaN  ...     NaN  5857.0  7384.0  365.0   NaN     NaN   NaN   NaN   \n",
+       "2015    NaN  ...     NaN  5995.0  6900.0  367.0   NaN     NaN   NaN   NaN   \n",
+       "\n",
+       "name  Zula   Zuri  \n",
+       "year               \n",
+       "1880  27.0    NaN  \n",
+       "1881  27.0    NaN  \n",
+       "1882  21.0    NaN  \n",
+       "1883  25.0    NaN  \n",
+       "1884  27.0    NaN  \n",
+       "1885  38.0    NaN  \n",
+       "1886  43.0    NaN  \n",
+       "1887  33.0    NaN  \n",
+       "1888  45.0    NaN  \n",
+       "1889  55.0    NaN  \n",
+       "1890  65.0    NaN  \n",
+       "1891  45.0    NaN  \n",
+       "1892  53.0    NaN  \n",
+       "1893  70.0    NaN  \n",
+       "1894  64.0    NaN  \n",
+       "1895  55.0    NaN  \n",
+       "1896  65.0    NaN  \n",
+       "1897  79.0    NaN  \n",
+       "1898  83.0    NaN  \n",
+       "1899  60.0    NaN  \n",
+       "1900  71.0    NaN  \n",
+       "1901  57.0    NaN  \n",
+       "1902  66.0    NaN  \n",
+       "1903  74.0    NaN  \n",
+       "1904  74.0    NaN  \n",
+       "1905  61.0    NaN  \n",
+       "1906  58.0    NaN  \n",
+       "1907  72.0    NaN  \n",
+       "1908  53.0    NaN  \n",
+       "1909  76.0    NaN  \n",
+       "...    ...    ...  \n",
+       "1986   NaN    NaN  \n",
+       "1987   NaN    NaN  \n",
+       "1988   NaN    NaN  \n",
+       "1989   NaN    NaN  \n",
+       "1990   NaN    NaN  \n",
+       "1991   NaN    NaN  \n",
+       "1992   NaN    NaN  \n",
+       "1993   NaN    NaN  \n",
+       "1994   NaN    NaN  \n",
+       "1995   NaN    NaN  \n",
+       "1996   NaN    NaN  \n",
+       "1997   NaN    NaN  \n",
+       "1998   NaN    NaN  \n",
+       "1999   NaN    NaN  \n",
+       "2000   NaN    NaN  \n",
+       "2001   NaN    NaN  \n",
+       "2002   NaN    NaN  \n",
+       "2003   NaN    NaN  \n",
+       "2004   NaN    NaN  \n",
+       "2005   NaN    NaN  \n",
+       "2006   NaN    NaN  \n",
+       "2007   NaN    NaN  \n",
+       "2008   NaN    NaN  \n",
+       "2009   NaN    NaN  \n",
+       "2010   NaN  259.0  \n",
+       "2011   NaN  313.0  \n",
+       "2012   NaN  434.0  \n",
+       "2013   NaN  566.0  \n",
+       "2014   NaN  664.0  \n",
+       "2015   NaN  709.0  \n",
+       "\n",
+       "[136 rows x 7062 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_births"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "year\n",
+       "1880     9701.0\n",
+       "1881     8795.0\n",
+       "1882     9597.0\n",
+       "1883     8934.0\n",
+       "1884     9428.0\n",
+       "1885     8801.0\n",
+       "1886     9074.0\n",
+       "1887     8166.0\n",
+       "1888     9299.0\n",
+       "1889     8600.0\n",
+       "1890     8558.0\n",
+       "1891     7724.0\n",
+       "1892     9093.0\n",
+       "1893     8110.0\n",
+       "1894     8298.0\n",
+       "1895     8381.0\n",
+       "1896     8189.0\n",
+       "1897     7596.0\n",
+       "1898     8214.0\n",
+       "1899     7030.0\n",
+       "1900     9875.0\n",
+       "1901     6938.0\n",
+       "1902     7945.0\n",
+       "1903     7649.0\n",
+       "1904     8148.0\n",
+       "1905     8103.0\n",
+       "1906     8307.0\n",
+       "1907     9048.0\n",
+       "1908     9400.0\n",
+       "1909     9658.0\n",
+       "         ...   \n",
+       "1986    30410.0\n",
+       "1987    29732.0\n",
+       "1988    29561.0\n",
+       "1989    29838.0\n",
+       "1990    29066.0\n",
+       "1991    27798.0\n",
+       "1992    26241.0\n",
+       "1993    24989.0\n",
+       "1994    24175.0\n",
+       "1995    23232.0\n",
+       "1996    22169.0\n",
+       "1997    21372.0\n",
+       "1998    20615.0\n",
+       "1999    20341.0\n",
+       "2000    20084.0\n",
+       "2001    18879.0\n",
+       "2002    17442.0\n",
+       "2003    17222.0\n",
+       "2004    16439.0\n",
+       "2005    15760.0\n",
+       "2006    15153.0\n",
+       "2007    14424.0\n",
+       "2008    13310.0\n",
+       "2009    12103.0\n",
+       "2010    11543.0\n",
+       "2011    11027.0\n",
+       "2012    10607.0\n",
+       "2013    10687.0\n",
+       "2014    10659.0\n",
+       "2015    10303.0\n",
+       "Name: John, Length: 136, dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_births[\"John\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that this matches what we had in our previous data frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
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+       "\n",
+       "    .dataframe thead th {\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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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
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+       "      <th>year</th>\n",
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+       "      <th></th>\n",
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+       "      <th rowspan=\"2\" valign=\"top\">1880</th>\n",
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+       "      <td>0.087383</td>\n",
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+       "      <th rowspan=\"2\" valign=\"top\">1881</th>\n",
+       "      <th>F</th>\n",
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+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1881</td>\n",
+       "      <td>0.000283</td>\n",
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+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>2938</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8769</td>\n",
+       "      <td>1881</td>\n",
+       "      <td>0.087043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1882</th>\n",
+       "      <th>F</th>\n",
+       "      <th>4212</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1882</td>\n",
+       "      <td>0.000371</td>\n",
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+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>4963</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>9557</td>\n",
+       "      <td>1882</td>\n",
+       "      <td>0.084065</td>\n",
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+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1883</th>\n",
+       "      <th>F</th>\n",
+       "      <th>6340</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1883</td>\n",
+       "      <td>0.000356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>7116</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8894</td>\n",
+       "      <td>1883</td>\n",
+       "      <td>0.085007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1884</th>\n",
+       "      <th>F</th>\n",
+       "      <th>8454</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1884</td>\n",
+       "      <td>0.000310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>9318</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>9388</td>\n",
+       "      <td>1884</td>\n",
+       "      <td>0.082032</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1885</th>\n",
+       "      <th>F</th>\n",
+       "      <th>10739</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1885</td>\n",
+       "      <td>0.000338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>11640</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8756</td>\n",
+       "      <td>1885</td>\n",
+       "      <td>0.081225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1886</th>\n",
+       "      <th>F</th>\n",
+       "      <th>13036</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>48</td>\n",
+       "      <td>1886</td>\n",
+       "      <td>0.000332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>14019</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>9026</td>\n",
+       "      <td>1886</td>\n",
+       "      <td>0.081473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1887</th>\n",
+       "      <th>F</th>\n",
+       "      <th>15414</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1887</td>\n",
+       "      <td>0.000384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>16435</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8110</td>\n",
+       "      <td>1887</td>\n",
+       "      <td>0.079968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1888</th>\n",
+       "      <th>F</th>\n",
+       "      <th>17834</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1888</td>\n",
+       "      <td>0.000291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>18976</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>9247</td>\n",
+       "      <td>1888</td>\n",
+       "      <td>0.076516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1889</th>\n",
+       "      <th>F</th>\n",
+       "      <th>20498</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>52</td>\n",
+       "      <td>1889</td>\n",
+       "      <td>0.000292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>21632</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8548</td>\n",
+       "      <td>1889</td>\n",
+       "      <td>0.077300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1890</th>\n",
+       "      <th>F</th>\n",
+       "      <th>23086</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1890</td>\n",
+       "      <td>0.000294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>24277</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8502</td>\n",
+       "      <td>1890</td>\n",
+       "      <td>0.076577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1891</th>\n",
+       "      <th>F</th>\n",
+       "      <th>25816</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1891</td>\n",
+       "      <td>0.000237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>26971</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>7680</td>\n",
+       "      <td>1891</td>\n",
+       "      <td>0.075895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1892</th>\n",
+       "      <th>F</th>\n",
+       "      <th>28460</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>54</td>\n",
+       "      <td>1892</td>\n",
+       "      <td>0.000254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>29759</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>9039</td>\n",
+       "      <td>1892</td>\n",
+       "      <td>0.074068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1893</th>\n",
+       "      <th>F</th>\n",
+       "      <th>31361</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1893</td>\n",
+       "      <td>0.000287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>32671</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8049</td>\n",
+       "      <td>1893</td>\n",
+       "      <td>0.071663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1894</th>\n",
+       "      <th>F</th>\n",
+       "      <th>34211</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1894</td>\n",
+       "      <td>0.000269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>35552</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>8238</td>\n",
+       "      <td>1894</td>\n",
+       "      <td>0.071158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1015237</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>29535</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.015830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1988</th>\n",
+       "      <th>F</th>\n",
+       "      <th>1024361</th>\n",
+       "      <td>John</td>\n",
+       "      <td>F</td>\n",
+       "      <td>167</td>\n",
+       "      <td>1988</td>\n",
+       "      <td>0.000094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <th>1037259</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>29394</td>\n",
+       "      <td>1988</td>\n",
+       "      <td>0.015367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1989</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1060288</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>29838</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>0.014918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1084747</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>29066</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>0.014160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1109686</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>27798</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>0.013765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1134943</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>26241</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>0.013144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1993</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1160554</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>24989</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>0.012746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1186469</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>24175</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>0.012517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1212466</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>23232</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>0.012208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1238683</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22169</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>0.011708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1265371</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21372</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>0.011342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1292776</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20615</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>0.010791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1321012</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20341</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>0.010600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1350272</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20084</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.010234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1380350</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>18879</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0.009724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1410730</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17442</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>0.008991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1441635</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17222</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>0.008726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1473212</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16439</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.008290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1505607</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>15760</td>\n",
+       "      <td>2005</td>\n",
+       "      <td>0.007899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1539014</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>15153</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>0.007382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1573596</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>14424</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>0.006960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1608427</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>13310</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.006535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1643211</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>12103</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.006113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1677534</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11543</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>0.006029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1711330</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11027</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>0.005821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1745139</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>10607</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>0.005610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1778561</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>10687</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>0.005673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1811736</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>10659</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.005578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015</th>\n",
+       "      <th>M</th>\n",
+       "      <th>1844755</th>\n",
+       "      <td>John</td>\n",
+       "      <td>M</td>\n",
+       "      <td>10303</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.005426</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>245 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  name sex  births  year      prop\n",
+       "year sex                                          \n",
+       "1880 F   228      John   F      46  1880  0.000506\n",
+       "     M   942      John   M    9655  1880  0.087383\n",
+       "1881 F   2312     John   F      26  1881  0.000283\n",
+       "     M   2938     John   M    8769  1881  0.087043\n",
+       "1882 F   4212     John   F      40  1882  0.000371\n",
+       "     M   4963     John   M    9557  1882  0.084065\n",
+       "1883 F   6340     John   F      40  1883  0.000356\n",
+       "     M   7116     John   M    8894  1883  0.085007\n",
+       "1884 F   8454     John   F      40  1884  0.000310\n",
+       "     M   9318     John   M    9388  1884  0.082032\n",
+       "1885 F   10739    John   F      45  1885  0.000338\n",
+       "     M   11640    John   M    8756  1885  0.081225\n",
+       "1886 F   13036    John   F      48  1886  0.000332\n",
+       "     M   14019    John   M    9026  1886  0.081473\n",
+       "1887 F   15414    John   F      56  1887  0.000384\n",
+       "     M   16435    John   M    8110  1887  0.079968\n",
+       "1888 F   17834    John   F      52  1888  0.000291\n",
+       "     M   18976    John   M    9247  1888  0.076516\n",
+       "1889 F   20498    John   F      52  1889  0.000292\n",
+       "     M   21632    John   M    8548  1889  0.077300\n",
+       "1890 F   23086    John   F      56  1890  0.000294\n",
+       "     M   24277    John   M    8502  1890  0.076577\n",
+       "1891 F   25816    John   F      44  1891  0.000237\n",
+       "     M   26971    John   M    7680  1891  0.075895\n",
+       "1892 F   28460    John   F      54  1892  0.000254\n",
+       "     M   29759    John   M    9039  1892  0.074068\n",
+       "1893 F   31361    John   F      61  1893  0.000287\n",
+       "     M   32671    John   M    8049  1893  0.071663\n",
+       "1894 F   34211    John   F      60  1894  0.000269\n",
+       "     M   35552    John   M    8238  1894  0.071158\n",
+       "...                ...  ..     ...   ...       ...\n",
+       "1987 M   1015237  John   M   29535  1987  0.015830\n",
+       "1988 F   1024361  John   F     167  1988  0.000094\n",
+       "     M   1037259  John   M   29394  1988  0.015367\n",
+       "1989 M   1060288  John   M   29838  1989  0.014918\n",
+       "1990 M   1084747  John   M   29066  1990  0.014160\n",
+       "1991 M   1109686  John   M   27798  1991  0.013765\n",
+       "1992 M   1134943  John   M   26241  1992  0.013144\n",
+       "1993 M   1160554  John   M   24989  1993  0.012746\n",
+       "1994 M   1186469  John   M   24175  1994  0.012517\n",
+       "1995 M   1212466  John   M   23232  1995  0.012208\n",
+       "1996 M   1238683  John   M   22169  1996  0.011708\n",
+       "1997 M   1265371  John   M   21372  1997  0.011342\n",
+       "1998 M   1292776  John   M   20615  1998  0.010791\n",
+       "1999 M   1321012  John   M   20341  1999  0.010600\n",
+       "2000 M   1350272  John   M   20084  2000  0.010234\n",
+       "2001 M   1380350  John   M   18879  2001  0.009724\n",
+       "2002 M   1410730  John   M   17442  2002  0.008991\n",
+       "2003 M   1441635  John   M   17222  2003  0.008726\n",
+       "2004 M   1473212  John   M   16439  2004  0.008290\n",
+       "2005 M   1505607  John   M   15760  2005  0.007899\n",
+       "2006 M   1539014  John   M   15153  2006  0.007382\n",
+       "2007 M   1573596  John   M   14424  2007  0.006960\n",
+       "2008 M   1608427  John   M   13310  2008  0.006535\n",
+       "2009 M   1643211  John   M   12103  2009  0.006113\n",
+       "2010 M   1677534  John   M   11543  2010  0.006029\n",
+       "2011 M   1711330  John   M   11027  2011  0.005821\n",
+       "2012 M   1745139  John   M   10607  2012  0.005610\n",
+       "2013 M   1778561  John   M   10687  2013  0.005673\n",
+       "2014 M   1811736  John   M   10659  2014  0.005578\n",
+       "2015 M   1844755  John   M   10303  2015  0.005426\n",
+       "\n",
+       "[245 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top[top.name==\"John\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which names are the most popular of all time?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name\n",
+       "James          5142912.0\n",
+       "John           5116313.0\n",
+       "Robert         4822265.0\n",
+       "Michael        4343106.0\n",
+       "Mary           4131873.0\n",
+       "William        4086383.0\n",
+       "David          3600738.0\n",
+       "Joseph         2589732.0\n",
+       "Richard        2565300.0\n",
+       "Charles        2381863.0\n",
+       "Thomas         2295502.0\n",
+       "Christopher    2011702.0\n",
+       "Daniel         1886299.0\n",
+       "Elizabeth      1613326.0\n",
+       "Patricia       1574798.0\n",
+       "Matthew        1569338.0\n",
+       "Jennifer       1467548.0\n",
+       "George         1465334.0\n",
+       "Linda          1454380.0\n",
+       "Barbara        1436646.0\n",
+       "Anthony        1417510.0\n",
+       "Donald         1412138.0\n",
+       "Paul           1385235.0\n",
+       "Mark           1347586.0\n",
+       "Edward         1286089.0\n",
+       "Steven         1276499.0\n",
+       "Kenneth        1269967.0\n",
+       "Andrew         1267471.0\n",
+       "Margaret       1244662.0\n",
+       "Joshua         1187636.0\n",
+       "                 ...    \n",
+       "Amado                5.0\n",
+       "Pryor                5.0\n",
+       "Laban                5.0\n",
+       "Sheppard             5.0\n",
+       "Vinia                5.0\n",
+       "Tella                5.0\n",
+       "Tilford              5.0\n",
+       "Estie                5.0\n",
+       "Jep                  5.0\n",
+       "Ollis                5.0\n",
+       "Clide                5.0\n",
+       "Tilmon               5.0\n",
+       "Affie                5.0\n",
+       "Lovisa               5.0\n",
+       "Alys                 5.0\n",
+       "Etter                5.0\n",
+       "Lenzy                5.0\n",
+       "Maryland             5.0\n",
+       "Orlie                5.0\n",
+       "Orr                  5.0\n",
+       "Lovey                5.0\n",
+       "Mathis               5.0\n",
+       "Orva                 5.0\n",
+       "Matthews             5.0\n",
+       "Mattye               5.0\n",
+       "Voyle                5.0\n",
+       "Fronnie              5.0\n",
+       "Cordella             5.0\n",
+       "Bertina              5.0\n",
+       "Manervia             5.0\n",
+       "Length: 7062, dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = total_births.sum()\n",
+    "a.sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot naming trends, for a few of the most popular names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f781bf63978>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f781bf96780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subset = total_births[[\"Michael\", \"Jennifer\"]]\n",
+    "subset.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q3: unique names\n",
+    "\n",
+    "how many unique names appear in our top 1000 list?  Use the `.unique()` method on the \"name\" `Series` to get a an array (it will actually be a NumPy `ndarray` of objects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q4: gender neutral names\n",
+    "\n",
+    "What are all the names that appear for both boys and girls?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q5: name diversity\n",
+    "\n",
+    "We want to make a plot of how many names it takes to reach 50% of the births in a given year.  Let's start with the boys names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boys = top[top.sex == \"M\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, to understand the process, let's work just on a particular year, 2015"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b15 = boys[boys.year == 2015]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.info of                        name sex  births  year      prop\n",
+       "year sex                                               \n",
+       "2015 M   1844730       Noah   M   19511  2015  0.010275\n",
+       "         1844731       Liam   M   18281  2015  0.009627\n",
+       "         1844732      Mason   M   16535  2015  0.008708\n",
+       "         1844733      Jacob   M   15816  2015  0.008329\n",
+       "         1844734    William   M   15809  2015  0.008326\n",
+       "         1844735      Ethan   M   14991  2015  0.007895\n",
+       "         1844736      James   M   14705  2015  0.007744\n",
+       "         1844737  Alexander   M   14460  2015  0.007615\n",
+       "         1844738    Michael   M   14321  2015  0.007542\n",
+       "         1844739   Benjamin   M   13608  2015  0.007166\n",
+       "         1844740     Elijah   M   13511  2015  0.007115\n",
+       "         1844741     Daniel   M   13408  2015  0.007061\n",
+       "         1844742      Aiden   M   13378  2015  0.007045\n",
+       "         1844743      Logan   M   12862  2015  0.006774\n",
+       "         1844744    Matthew   M   12648  2015  0.006661\n",
+       "         1844745      Lucas   M   12246  2015  0.006449\n",
+       "         1844746    Jackson   M   12182  2015  0.006415\n",
+       "         1844747      David   M   11691  2015  0.006157\n",
+       "         1844748     Oliver   M   11592  2015  0.006105\n",
+       "         1844749     Jayden   M   11475  2015  0.006043\n",
+       "         1844750     Joseph   M   11375  2015  0.005990\n",
+       "         1844751    Gabriel   M   10782  2015  0.005678\n",
+       "         1844752     Samuel   M   10733  2015  0.005652\n",
+       "         1844753     Carter   M   10727  2015  0.005649\n",
+       "         1844754    Anthony   M   10564  2015  0.005563\n",
+       "         1844755       John   M   10303  2015  0.005426\n",
+       "         1844756      Dylan   M   10232  2015  0.005389\n",
+       "         1844757       Luke   M   10219  2015  0.005382\n",
+       "         1844758      Henry   M   10112  2015  0.005325\n",
+       "         1844759     Andrew   M   10027  2015  0.005281\n",
+       "...                     ...  ..     ...   ...       ...\n",
+       "         1845703     Yadiel   M     211  2015  0.000111\n",
+       "         1845704      Yahya   M     211  2015  0.000111\n",
+       "         1845700     Aarush   M     211  2015  0.000111\n",
+       "         1845702      Robin   M     211  2015  0.000111\n",
+       "         1845701   Deangelo   M     211  2015  0.000111\n",
+       "         1845705      Boden   M     209  2015  0.000110\n",
+       "         1845707        Kye   M     209  2015  0.000110\n",
+       "         1845708      Kylen   M     209  2015  0.000110\n",
+       "         1845709       Todd   M     209  2015  0.000110\n",
+       "         1845710     Truman   M     209  2015  0.000110\n",
+       "         1845706        Ean   M     209  2015  0.000110\n",
+       "         1845712    Gilbert   M     208  2015  0.000110\n",
+       "         1845713     Haiden   M     208  2015  0.000110\n",
+       "         1845711      Chevy   M     208  2015  0.000110\n",
+       "         1845715    Dangelo   M     207  2015  0.000109\n",
+       "         1845716      Juelz   M     207  2015  0.000109\n",
+       "         1845717    Osvaldo   M     207  2015  0.000109\n",
+       "         1845714    Brixton   M     207  2015  0.000109\n",
+       "         1845718     Bishop   M     206  2015  0.000108\n",
+       "         1845719     Freddy   M     206  2015  0.000108\n",
+       "         1845720     Reagan   M     206  2015  0.000108\n",
+       "         1845721    Frankie   M     205  2015  0.000108\n",
+       "         1845722     Malaki   M     205  2015  0.000108\n",
+       "         1845725    Jayvion   M     204  2015  0.000107\n",
+       "         1845726      Leroy   M     204  2015  0.000107\n",
+       "         1845724    Deshawn   M     204  2015  0.000107\n",
+       "         1845723     Camren   M     204  2015  0.000107\n",
+       "         1845728     Jaydon   M     203  2015  0.000107\n",
+       "         1845727      Briar   M     203  2015  0.000107\n",
+       "         1845730       Ayan   M     202  2015  0.000106\n",
+       "\n",
+       "[1000 rows x 5 columns]>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b15.info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use the `cumsum()` property to do a cumulative sum over the `prop` column we added previously"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prop_cumsum = b15.sort_values(by=\"prop\", ascending=False)[\"prop\"].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and now we can use `searchsorted` to return the position where \"if the corresponding elements in v were inserted before the indices, the order of self would be preserved.\" (see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.searchsorted.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([134])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prop_cumsum.searchsorted(0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we start counting at 0, we add 1 to this to get the number of names needed in this year to reach 50% of births."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now generalize this to a function that you can use `apply()` on and make a plot of the number of names to reach 50% by year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/content/07-pandas/pandas-experiments.ipynb b/content/07-pandas/pandas-experiments.ipynb
new file mode 100644
index 00000000..21660d13
--- /dev/null
+++ b/content/07-pandas/pandas-experiments.ipynb
@@ -0,0 +1,719 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sort by Two Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(np.random.randint(1, 5, (10,2)), columns=['a','b'])\n",
+    "df.sort_values([\"a\", \"b\"], inplace=True)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b\n",
+       "4  1  2\n",
+       "2  1  3\n",
+       "7  1  4\n",
+       "8  2  2\n",
+       "6  2  3\n",
+       "0  2  4\n",
+       "1  3  2\n",
+       "5  3  2\n",
+       "9  3  3\n",
+       "3  3  4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Duplicated Labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(np.random.randint(1, 5, (15,3)), columns=['a','b','b'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a  b  b\n",
+       "0   1  3  1\n",
+       "1   1  4  4\n",
+       "2   2  2  1\n",
+       "3   4  1  4\n",
+       "4   3  2  2\n",
+       "5   1  2  2\n",
+       "6   3  3  1\n",
+       "7   4  1  2\n",
+       "8   1  1  3\n",
+       "9   1  3  4\n",
+       "10  3  1  4\n",
+       "11  4  2  3\n",
+       "12  1  3  2\n",
+       "13  4  1  2\n",
+       "14  2  3  4"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>b</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    b  b\n",
+       "0   3  1\n",
+       "1   4  4\n",
+       "2   2  1\n",
+       "3   1  4\n",
+       "4   2  2\n",
+       "5   2  2\n",
+       "6   3  1\n",
+       "7   1  2\n",
+       "8   1  3\n",
+       "9   3  4\n",
+       "10  1  4\n",
+       "11  2  3\n",
+       "12  3  2\n",
+       "13  1  2\n",
+       "14  3  4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     3\n",
+       "1     4\n",
+       "2     2\n",
+       "3     1\n",
+       "4     2\n",
+       "5     2\n",
+       "6     3\n",
+       "7     1\n",
+       "8     1\n",
+       "9     3\n",
+       "10    1\n",
+       "11    2\n",
+       "12    3\n",
+       "13    1\n",
+       "14    3\n",
+       "Name: b, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[:,1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `pivot_table` vs. `groupby`\n",
+    "\n",
+    "see https://stackoverflow.com/questions/34702815/pandas-group-by-and-pivot-table-difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.122273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.785140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.003996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.669116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.599575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.095478</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   a  b         c\n",
+       "0  1  1  0.122273\n",
+       "1  2  1  0.785140\n",
+       "2  3  1  0.003996\n",
+       "3  1  2  0.669116\n",
+       "4  2  2  0.599575\n",
+       "5  3  2  0.095478"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\"a\": [1,2,3,1,2,3], \"b\":[1,1,1,2,2,2], \"c\":np.random.rand(6)})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A pivot table creates a new dataframe, with the specified column in the original treated as the index, and any columns given as columns in the dataframe, with the values taken to be \"c\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>b</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.122273</td>\n",
+       "      <td>0.669116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.785140</td>\n",
+       "      <td>0.599575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.003996</td>\n",
+       "      <td>0.095478</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "b         1         2\n",
+       "a                    \n",
+       "1  0.122273  0.669116\n",
+       "2  0.785140  0.599575\n",
+       "3  0.003996  0.095478"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.pivot_table(\"c\", index=[\"a\"], columns=[\"b\"]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a  b\n",
+       "1  1    0.122273\n",
+       "   2    0.669116\n",
+       "2  1    0.785140\n",
+       "   2    0.599575\n",
+       "3  1    0.003996\n",
+       "   2    0.095478\n",
+       "Name: c, dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(['a','b'])['c'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/content/07-pandas/pandas-intro.ipynb b/content/07-pandas/pandas-intro.ipynb
new file mode 100644
index 00000000..17a8e536
--- /dev/null
+++ b/content/07-pandas/pandas-intro.ipynb
@@ -0,0 +1,5108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "pandas extends the numpy ndarray to allow for a data-structure that labels the columns (called a data frame).  You can kind of think about this functionality as operating how a spreadsheet might work.\n",
+    "\n",
+    "In this manner, it provides much of the same functionality of R -- the data frame provides the basis for data analysis.\n",
+    "\n",
+    "Nice documentation is here:\n",
+    "\n",
+    "http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## series\n",
+    "\n",
+    "A series is a labeled array.  It looks superficially like a dictionary, but is fixed size, and can handle missing values.  It also can also be operated on with any numpy operation or the standard operators (a dictionary cannot).  The labels are referred to as the _index_.\n",
+    "\n",
+    "Some examples from: http://pandas.pydata.org/pandas-docs/stable/dsintro.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.272166\n",
+       "b   -0.594024\n",
+       "c   -0.533180\n",
+       "d   -0.666801\n",
+       "e   -0.749968\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't specify an index, one will be made up for you"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   -0.098297\n",
+       "1   -0.840109\n",
+       "2    1.282849\n",
+       "3    0.475101\n",
+       "4   -0.283392\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(np.random.randn(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can initialize from a dictionary.  By default it will use the dictionary keys (sorted) as the index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.0\n",
+       "b    1.0\n",
+       "c    2.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
+    "pd.Series(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b    1.0\n",
+       "c    2.0\n",
+       "d    NaN\n",
+       "a    0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(d, index=['b', 'c', 'd', 'a'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that NaN indicates a missing value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can operate on a series as you would any ndarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.272166\n",
+       "b   -0.594024\n",
+       "c   -0.533180\n",
+       "d   -0.666801\n",
+       "e   -0.749968\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.27216618170818768"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.272166\n",
+       "b   -0.594024\n",
+       "c   -0.533180\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.272166\n",
+       "c   -0.533180\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[s > s.median()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    0.761728\n",
+       "b    0.552101\n",
+       "c    0.586736\n",
+       "d    0.513348\n",
+       "e    0.472382\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can also index by label -- this mimics the behavior of a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.27216618170818768"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s['a']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.74996754801095999"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s['e']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'e' in s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `get()` method can be used to safely access an element if it is possible it does not exist -- you can specify a default to return in that case.  The alternative is to use a `try` / `except` block."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.get('f', np.nan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Operations, like those you use with an ndarray work fine on a Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.544332\n",
+       "b   -1.188048\n",
+       "c   -1.066360\n",
+       "d   -1.333602\n",
+       "e   -1.499935\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s + s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a   -0.544332\n",
+       "b   -1.188048\n",
+       "c   -1.066360\n",
+       "d   -1.333602\n",
+       "e   -1.499935\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s * 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "note that operations are always done on like labels, so the following is not exactly the same as numpy arrays.  In this sense, pandas results respect the union of indices "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a         NaN\n",
+       "b   -1.188048\n",
+       "c   -1.066360\n",
+       "d   -1.333602\n",
+       "e         NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[1:] + s[:-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a series can have a name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1.026684\n",
+       "1    0.306580\n",
+       "2    0.135363\n",
+       "3   -0.352485\n",
+       "4   -0.188115\n",
+       "Name: something, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(np.random.randn(5), name='something')\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataFrame\n",
+    "\n",
+    "The dataframe is like a spreadsheet -- the columns and rows have labels.  It is 2-d.  This is what you will usually use with pandas.\n",
+    "\n",
+    "you can initialize from:\n",
+    "  * Dict of 1D ndarrays, lists, dicts, or Series\n",
+    "  * 2-D numpy.ndarray\n",
+    "  * Structured or record ndarray\n",
+    "  * A Series\n",
+    "  * Another DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = {'one' : pd.Series([1., 2., 3.], index=['b', 'a', 'c']),\n",
+    "     'two' : pd.Series([2, 1., 3., 4.], index=['b', 'a', 'c', 'd'])}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "a  2.0  1.0\n",
+       "b  1.0  2.0\n",
+       "c  3.0  3.0\n",
+       "d  NaN  4.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(d)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "one    2.0\n",
+       "two    2.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can exclude some labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "d  NaN  4.0\n",
+       "b  1.0  2.0\n",
+       "a  2.0  1.0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d, index=['d', 'b', 'a'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's initialization from lists / ndarrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = {'one' : [1., 2., 3., 4.],\n",
+    "     'two' : [4., 3., 2., 1.]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "0  1.0  4.0\n",
+       "1  2.0  3.0\n",
+       "2  3.0  2.0\n",
+       "3  4.0  1.0"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "a  1.0  4.0\n",
+       "b  2.0  3.0\n",
+       "c  3.0  2.0\n",
+       "d  4.0  1.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(d, index=['a', 'b', 'c', 'd'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "there are lots of other initialization methods, e.g, list of dicts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>first</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>second</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        a   b     c\n",
+       "first   1   2   NaN\n",
+       "second  5  10  20.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]\n",
+    "pd.DataFrame(data2, index=['first', 'second'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### working with the dataframe\n",
+    "\n",
+    "you can index it as it it were Series objects.  Other access is as follows:\n",
+    "\n",
+    "  * Select column: `df[col]` (returns Series)\n",
+    "  * Select row by label: `df.loc[label]` (returns Series)\n",
+    "  * Select row by integer location: `df.iloc[loc]` (returns Series)\n",
+    "  * Slice rows: `df[5:10]` (returns DataFrame)\n",
+    "  * Select rows by boolean vector: `df[bool_vec]` (return DataFrame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    2.0\n",
+       "b    1.0\n",
+       "c    3.0\n",
+       "d    NaN\n",
+       "Name: one, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['one']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two\n",
+       "a  2.0  1.0\n",
+       "b  1.0  2.0\n",
+       "c  3.0  3.0\n",
+       "d  NaN  4.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df['one'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>two</th>\n",
+       "      <th>three</th>\n",
+       "      <th>flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one  two  three   flag\n",
+       "a  2.0  1.0    2.0  False\n",
+       "b  1.0  2.0    2.0  False\n",
+       "c  3.0  3.0    9.0   True\n",
+       "d  NaN  4.0    NaN  False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['three'] = df['one'] * df['two']\n",
+    "df['flag'] = df['one'] > 2\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can also treat any index name as if it were a property"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    2.0\n",
+       "b    2.0\n",
+       "c    9.0\n",
+       "d    NaN\n",
+       "Name: three, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.three"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can delete or pop columns---popping returns a `Series`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del df['two']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "three = df.pop('three')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one   flag\n",
+       "a  2.0  False\n",
+       "b  1.0  False\n",
+       "c  3.0   True\n",
+       "d  NaN  False"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    2.0\n",
+       "b    2.0\n",
+       "c    9.0\n",
+       "d    NaN\n",
+       "Name: three, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "three"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(three)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "initializing with a scalar propagates that scalar to all the rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['foo'] = 'bar'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>one</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>foo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>bar</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   one   flag  foo\n",
+       "a  2.0  False  bar\n",
+       "b  1.0  False  bar\n",
+       "c  3.0   True  bar\n",
+       "d  NaN  False  bar"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CSV\n",
+    "\n",
+    "you can also read from CSV\n",
+    "\n",
+    "Note, if there is stray whitespace in your strings in the CSV, pandas will keep it.  This is a bit annoying, and you might need to investigate converters to get things properly formatted.\n",
+    "\n",
+    "There are similar methods for HDF5 and excel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grades = pd.read_csv('sample.csv', index_col=\"student\", skipinitialspace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam\n",
+       "student                              \n",
+       "A        10.0   9.0    10     7    97\n",
+       "B         8.0   7.0     9     9    82\n",
+       "C         NaN   9.0     6     5    75\n",
+       "D         8.0   9.0     9     9    90\n",
+       "E         NaN  10.0    10    10    95\n",
+       "F         8.0   2.0     6     7    66\n",
+       "G         6.0   NaN     4     5    60\n",
+       "H         8.0   8.0     9     8    84\n",
+       "I        10.0   7.0    10    10    92\n",
+       "J        10.0   6.0     9     9    91\n",
+       "K         8.0   7.0     6     8    87\n",
+       "L         3.0   8.0     5     7    71\n",
+       "M         9.0   9.0     8     9    94\n",
+       "N         8.0  10.0     9     9    90\n",
+       "O        10.0  10.0    10     9    99\n",
+       "P         8.0   9.0     8    10    94\n",
+       "Q         5.0   7.0     6     5    78"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',\n",
+       "       'O', 'P', 'Q'],\n",
+       "      dtype='object', name='student')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['hw 1', 'hw 2', 'hw 3', 'hw 4', 'exam'], dtype='object')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A single student's grades"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hw 1    10.0\n",
+       "hw 2     9.0\n",
+       "hw 3    10.0\n",
+       "hw 4     7.0\n",
+       "exam    97.0\n",
+       "Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades.loc[\"A\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the grades for the first homework"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "student\n",
+       "A    10.0\n",
+       "B     8.0\n",
+       "C     NaN\n",
+       "D     8.0\n",
+       "E     NaN\n",
+       "F     8.0\n",
+       "G     6.0\n",
+       "H     8.0\n",
+       "I    10.0\n",
+       "J    10.0\n",
+       "K     8.0\n",
+       "L     3.0\n",
+       "M     9.0\n",
+       "N     8.0\n",
+       "O    10.0\n",
+       "P     8.0\n",
+       "Q     5.0\n",
+       "Name: hw 1, dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades['hw 1']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating a new column based on the existing ones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grades['hw average'] = (grades['hw 1'] + grades['hw 2'] + grades['hw 3'] + grades['hw 4'])/4.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>71</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "A        10.0   9.0    10     7    97        9.00\n",
+       "B         8.0   7.0     9     9    82        8.25\n",
+       "C         NaN   9.0     6     5    75         NaN\n",
+       "D         8.0   9.0     9     9    90        8.75\n",
+       "E         NaN  10.0    10    10    95         NaN\n",
+       "F         8.0   2.0     6     7    66        5.75\n",
+       "G         6.0   NaN     4     5    60         NaN\n",
+       "H         8.0   8.0     9     8    84        8.25\n",
+       "I        10.0   7.0    10    10    92        9.25\n",
+       "J        10.0   6.0     9     9    91        8.50\n",
+       "K         8.0   7.0     6     8    87        7.25\n",
+       "L         3.0   8.0     5     7    71        5.75\n",
+       "M         9.0   9.0     8     9    94        8.75\n",
+       "N         8.0  10.0     9     9    90        9.00\n",
+       "O        10.0  10.0    10     9    99        9.75\n",
+       "P         8.0   9.0     8    10    94        8.75\n",
+       "Q         5.0   7.0     6     5    78        5.75"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grades"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "this didn't handle the missing data properly -- let's replace the NaNs with 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2 = grades.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2['hw average'] = (g2['hw 1'] + g2['hw 2'] + g2['hw 3'] + g2['hw 4'])/4.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>71</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "A        10.0   9.0    10     7    97        9.00\n",
+       "B         8.0   7.0     9     9    82        8.25\n",
+       "C         0.0   9.0     6     5    75        5.00\n",
+       "D         8.0   9.0     9     9    90        8.75\n",
+       "E         0.0  10.0    10    10    95        7.50\n",
+       "F         8.0   2.0     6     7    66        5.75\n",
+       "G         6.0   0.0     4     5    60        3.75\n",
+       "H         8.0   8.0     9     8    84        8.25\n",
+       "I        10.0   7.0    10    10    92        9.25\n",
+       "J        10.0   6.0     9     9    91        8.50\n",
+       "K         8.0   7.0     6     8    87        7.25\n",
+       "L         3.0   8.0     5     7    71        5.75\n",
+       "M         9.0   9.0     8     9    94        8.75\n",
+       "N         8.0  10.0     9     9    90        9.00\n",
+       "O        10.0  10.0    10     9    99        9.75\n",
+       "P         8.0   9.0     8    10    94        8.75\n",
+       "Q         5.0   7.0     6     5    78        5.75"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For big dataframes, we can view just pieces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "A        10.0   9.0    10     7    97        9.00\n",
+       "B         8.0   7.0     9     9    82        8.25\n",
+       "C         0.0   9.0     6     5    75        5.00\n",
+       "D         8.0   9.0     9     9    90        8.75\n",
+       "E         0.0  10.0    10    10    95        7.50"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "P         8.0   9.0     8    10    94        8.75\n",
+       "Q         5.0   7.0     6     5    78        5.75"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.tail(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we can get lots of statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>7.470588</td>\n",
+       "      <td>7.882353</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>85.000000</td>\n",
+       "      <td>7.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.221025</td>\n",
+       "      <td>2.741296</td>\n",
+       "      <td>1.964763</td>\n",
+       "      <td>1.732051</td>\n",
+       "      <td>11.407235</td>\n",
+       "      <td>1.747635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>3.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>78.000000</td>\n",
+       "      <td>5.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>8.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>8.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>9.750000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            hw 1       hw 2       hw 3       hw 4       exam  hw average\n",
+       "count  17.000000  17.000000  17.000000  17.000000  17.000000   17.000000\n",
+       "mean    7.000000   7.470588   7.882353   8.000000  85.000000    7.588235\n",
+       "std     3.221025   2.741296   1.964763   1.732051  11.407235    1.747635\n",
+       "min     0.000000   0.000000   4.000000   5.000000  60.000000    3.750000\n",
+       "25%     6.000000   7.000000   6.000000   7.000000  78.000000    5.750000\n",
+       "50%     8.000000   8.000000   9.000000   9.000000  90.000000    8.250000\n",
+       "75%     9.000000   9.000000   9.000000   9.000000  94.000000    8.750000\n",
+       "max    10.000000  10.000000  10.000000  10.000000  99.000000    9.750000"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "want to sort by values?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>71</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "G         6.0   0.0     4     5    60        3.75\n",
+       "F         8.0   2.0     6     7    66        5.75\n",
+       "L         3.0   8.0     5     7    71        5.75\n",
+       "C         0.0   9.0     6     5    75        5.00\n",
+       "Q         5.0   7.0     6     5    78        5.75\n",
+       "B         8.0   7.0     9     9    82        8.25\n",
+       "H         8.0   8.0     9     8    84        8.25\n",
+       "K         8.0   7.0     6     8    87        7.25\n",
+       "D         8.0   9.0     9     9    90        8.75\n",
+       "N         8.0  10.0     9     9    90        9.00\n",
+       "J        10.0   6.0     9     9    91        8.50\n",
+       "I        10.0   7.0    10    10    92        9.25\n",
+       "M         9.0   9.0     8     9    94        8.75\n",
+       "P         8.0   9.0     8    10    94        8.75\n",
+       "E         0.0  10.0    10    10    95        7.50\n",
+       "A        10.0   9.0    10     7    97        9.00\n",
+       "O        10.0  10.0    10     9    99        9.75"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.sort_values(by=\"exam\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hw 1           7.000000\n",
+       "hw 2           7.470588\n",
+       "hw 3           7.882353\n",
+       "hw 4           8.000000\n",
+       "exam          85.000000\n",
+       "hw average     7.588235\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hw 1           8.00\n",
+       "hw 2           8.00\n",
+       "hw 3           9.00\n",
+       "hw 4           9.00\n",
+       "exam          90.00\n",
+       "hw average     8.25\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hw 1          10.00\n",
+       "hw 2          10.00\n",
+       "hw 3          10.00\n",
+       "hw 4          10.00\n",
+       "exam          99.00\n",
+       "hw average     9.75\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>5.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>71</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "A        10.0   9.0    10     7    97        9.00\n",
+       "B         8.0   7.0     9     9    82        8.25\n",
+       "C         0.0   9.0     6     5    75        5.00\n",
+       "D         8.0   9.0     9     9    90        8.75\n",
+       "E         0.0  10.0    10    10    95        7.50\n",
+       "F         8.0   2.0     6     7    66        5.75\n",
+       "G         6.0   0.0     4     5    60        3.75\n",
+       "H         8.0   8.0     9     8    84        8.25\n",
+       "I        10.0   7.0    10    10    92        9.25\n",
+       "J        10.0   6.0     9     9    91        8.50\n",
+       "K         8.0   7.0     6     8    87        7.25\n",
+       "L         3.0   8.0     5     7    71        5.75\n",
+       "M         9.0   9.0     8     9    94        8.75\n",
+       "N         8.0  10.0     9     9    90        9.00\n",
+       "O        10.0  10.0    10     9    99        9.75\n",
+       "P         8.0   9.0     8    10    94        8.75\n",
+       "Q         5.0   7.0     6     5    78        5.75"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`.apply()` let's you apply a function to the `DataFrame`.  By default, it will work over indices (e.g., applying your function on a column), treating the inputs as a `Series`.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "hw 1          10.0\n",
+       "hw 2          10.0\n",
+       "hw 3           6.0\n",
+       "hw 4           5.0\n",
+       "exam          39.0\n",
+       "hw average     6.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.apply(lambda x: x.max() - x.min())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### access"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas provides optimizes methods for accessing data: .at, .iat, .loc, .iloc, and .ix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The standard slice notation works for rows, but note *when using labels, both endpoints are included*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>5.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "E         0.0  10.0    10    10    95        7.50\n",
+       "F         8.0   2.0     6     7    66        5.75\n",
+       "G         6.0   0.0     4     5    60        3.75\n",
+       "H         8.0   8.0     9     8    84        8.25\n",
+       "I        10.0   7.0    10    10    92        9.25"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2[\"E\":\"I\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>exam</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  exam\n",
+       "student            \n",
+       "A        10.0    97\n",
+       "B         8.0    82\n",
+       "C         0.0    75\n",
+       "D         8.0    90\n",
+       "E         0.0    95\n",
+       "F         8.0    66\n",
+       "G         6.0    60\n",
+       "H         8.0    84\n",
+       "I        10.0    92\n",
+       "J        10.0    91\n",
+       "K         8.0    87\n",
+       "L         3.0    71\n",
+       "M         9.0    94\n",
+       "N         8.0    90\n",
+       "O        10.0    99\n",
+       "P         8.0    94\n",
+       "Q         5.0    78"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.loc[:,[\"hw 1\", \"exam\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`at` is a faster access method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "97"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.at[\"A\",\"exam\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `i` routines work in index space, similar to how numpy does"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2\n",
+       "student            \n",
+       "D         8.0   9.0\n",
+       "E         0.0  10.0"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.iloc[3:5,0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 2  hw 3  hw 4  exam\n",
+       "student                        \n",
+       "B         7.0     9     9    82\n",
+       "D         9.0     9     9    90\n",
+       "F         2.0     6     7    66"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.iloc[[1,3,5], [1,2,3,4]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.iat[2,2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### boolean indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average\n",
+       "student                                          \n",
+       "A        10.0   9.0    10     7    97        9.00\n",
+       "E         0.0  10.0    10    10    95        7.50\n",
+       "I        10.0   7.0    10    10    92        9.25\n",
+       "J        10.0   6.0     9     9    91        8.50\n",
+       "M         9.0   9.0     8     9    94        8.75\n",
+       "O        10.0  10.0    10     9    99        9.75\n",
+       "P         8.0   9.0     8    10    94        8.75"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2[g2.exam > 90]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### np arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2.loc[:, \"new\"] = np.random.random(len(g2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "      <th>new</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>A</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.848599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.394722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>0.957668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.953680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.000388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.898409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3.75</td>\n",
+       "      <td>0.346747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.716042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "      <td>0.965628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>0.124690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>0.694847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>71</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.930668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.606070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.212891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "      <td>0.905785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.415708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.145941</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average       new\n",
+       "student                                                    \n",
+       "A        10.0   9.0    10     7    97        9.00  0.848599\n",
+       "B         8.0   7.0     9     9    82        8.25  0.394722\n",
+       "C         0.0   9.0     6     5    75        5.00  0.957668\n",
+       "D         8.0   9.0     9     9    90        8.75  0.953680\n",
+       "E         0.0  10.0    10    10    95        7.50  0.000388\n",
+       "F         8.0   2.0     6     7    66        5.75  0.898409\n",
+       "G         6.0   0.0     4     5    60        3.75  0.346747\n",
+       "H         8.0   8.0     9     8    84        8.25  0.716042\n",
+       "I        10.0   7.0    10    10    92        9.25  0.965628\n",
+       "J        10.0   6.0     9     9    91        8.50  0.124690\n",
+       "K         8.0   7.0     6     8    87        7.25  0.694847\n",
+       "L         3.0   8.0     5     7    71        5.75  0.930668\n",
+       "M         9.0   9.0     8     9    94        8.75  0.606070\n",
+       "N         8.0  10.0     9     9    90        9.00  0.212891\n",
+       "O        10.0  10.0    10     9    99        9.75  0.905785\n",
+       "P         8.0   9.0     8    10    94        8.75  0.415708\n",
+       "Q         5.0   7.0     6     5    78        5.75  0.145941"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "resetting values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = g2[g2.exam < 80].index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['C', 'F', 'G', 'L', 'Q'], dtype='object', name='student')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2.loc[a, \"exam\"] = 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "      <th>new</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>A</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.848599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.394722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>0.957668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.953680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.000388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.898409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.75</td>\n",
+       "      <td>0.346747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.716042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "      <td>0.965628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>0.124690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>0.694847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.930668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.606070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.212891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "      <td>0.905785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.415708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.145941</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average       new\n",
+       "student                                                    \n",
+       "A        10.0   9.0    10     7    97        9.00  0.848599\n",
+       "B         8.0   7.0     9     9    82        8.25  0.394722\n",
+       "C         0.0   9.0     6     5    80        5.00  0.957668\n",
+       "D         8.0   9.0     9     9    90        8.75  0.953680\n",
+       "E         0.0  10.0    10    10    95        7.50  0.000388\n",
+       "F         8.0   2.0     6     7    80        5.75  0.898409\n",
+       "G         6.0   0.0     4     5    80        3.75  0.346747\n",
+       "H         8.0   8.0     9     8    84        8.25  0.716042\n",
+       "I        10.0   7.0    10    10    92        9.25  0.965628\n",
+       "J        10.0   6.0     9     9    91        8.50  0.124690\n",
+       "K         8.0   7.0     6     8    87        7.25  0.694847\n",
+       "L         3.0   8.0     5     7    80        5.75  0.930668\n",
+       "M         9.0   9.0     8     9    94        8.75  0.606070\n",
+       "N         8.0  10.0     9     9    90        9.00  0.212891\n",
+       "O        10.0  10.0    10     9    99        9.75  0.905785\n",
+       "P         8.0   9.0     8    10    94        8.75  0.415708\n",
+       "Q         5.0   7.0     6     5    80        5.75  0.145941"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## histogramming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80    5\n",
+       "94    2\n",
+       "90    2\n",
+       "95    1\n",
+       "92    1\n",
+       "91    1\n",
+       "87    1\n",
+       "84    1\n",
+       "82    1\n",
+       "99    1\n",
+       "97    1\n",
+       "Name: exam, dtype: int64"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2[\"exam\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fca05ef80f0>"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fca05ed4dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g2.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fca03c7d9b0>"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fca03c90ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g2.plot.scatter(x=\"hw average\", y=\"exam\", marker=\"o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A lot more examples at: http://pandas.pydata.org/pandas-docs/stable/visualization.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "      <th>new</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>A</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>97</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.848599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.394722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>0.957668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.953680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>95</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.000388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.898409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3.75</td>\n",
+       "      <td>0.346747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>84</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.716042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.25</td>\n",
+       "      <td>0.965628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>0.124690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>0.694847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.930668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.606070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.212891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.75</td>\n",
+       "      <td>0.905785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.415708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.145941</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average       new\n",
+       "student                                                    \n",
+       "A        10.0   9.0    10     7    97        9.00  0.848599\n",
+       "B         8.0   7.0     9     9    82        8.25  0.394722\n",
+       "C         0.0   9.0     6     5    80        5.00  0.957668\n",
+       "D         8.0   9.0     9     9    90        8.75  0.953680\n",
+       "E         0.0  10.0    10    10    95        7.50  0.000388\n",
+       "F         8.0   2.0     6     7    80        5.75  0.898409\n",
+       "G         6.0   0.0     4     5    80        3.75  0.346747\n",
+       "H         8.0   8.0     9     8    84        8.25  0.716042\n",
+       "I        10.0   7.0    10    10    92        9.25  0.965628\n",
+       "J        10.0   6.0     9     9    91        8.50  0.124690\n",
+       "K         8.0   7.0     6     8    87        7.25  0.694847\n",
+       "L         3.0   8.0     5     7    80        5.75  0.930668\n",
+       "M         9.0   9.0     8     9    94        8.75  0.606070\n",
+       "N         8.0  10.0     9     9    90        9.00  0.212891\n",
+       "O        10.0  10.0    10     9    99        9.75  0.905785\n",
+       "P         8.0   9.0     8    10    94        8.75  0.415708\n",
+       "Q         5.0   7.0     6     5    80        5.75  0.145941"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2.loc[\"R\", :] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hw 1</th>\n",
+       "      <th>hw 2</th>\n",
+       "      <th>hw 3</th>\n",
+       "      <th>hw 4</th>\n",
+       "      <th>exam</th>\n",
+       "      <th>hw average</th>\n",
+       "      <th>new</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>student</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>A</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.848599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.394722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>0.957668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.953680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.000388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.898409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>3.75</td>\n",
+       "      <td>0.346747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>0.716042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>9.25</td>\n",
+       "      <td>0.965628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>J</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>0.124690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>0.694847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.930668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.606070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>N</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>0.212891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>9.75</td>\n",
+       "      <td>0.905785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>0.415708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.145941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         hw 1  hw 2  hw 3  hw 4  exam  hw average       new\n",
+       "student                                                    \n",
+       "A        10.0   9.0  10.0   7.0  97.0        9.00  0.848599\n",
+       "B         8.0   7.0   9.0   9.0  82.0        8.25  0.394722\n",
+       "C         0.0   9.0   6.0   5.0  80.0        5.00  0.957668\n",
+       "D         8.0   9.0   9.0   9.0  90.0        8.75  0.953680\n",
+       "E         0.0  10.0  10.0  10.0  95.0        7.50  0.000388\n",
+       "F         8.0   2.0   6.0   7.0  80.0        5.75  0.898409\n",
+       "G         6.0   0.0   4.0   5.0  80.0        3.75  0.346747\n",
+       "H         8.0   8.0   9.0   8.0  84.0        8.25  0.716042\n",
+       "I        10.0   7.0  10.0  10.0  92.0        9.25  0.965628\n",
+       "J        10.0   6.0   9.0   9.0  91.0        8.50  0.124690\n",
+       "K         8.0   7.0   6.0   8.0  87.0        7.25  0.694847\n",
+       "L         3.0   8.0   5.0   7.0  80.0        5.75  0.930668\n",
+       "M         9.0   9.0   8.0   9.0  94.0        8.75  0.606070\n",
+       "N         8.0  10.0   9.0   9.0  90.0        9.00  0.212891\n",
+       "O        10.0  10.0  10.0   9.0  99.0        9.75  0.905785\n",
+       "P         8.0   9.0   8.0  10.0  94.0        8.75  0.415708\n",
+       "Q         5.0   7.0   6.0   5.0  80.0        5.75  0.145941\n",
+       "R         1.0   1.0   1.0   1.0   1.0        1.00  1.000000"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\\\begin{tabular}{lrrrrrrr}\\n\\\\toprule\\n{} &  hw 1 &  hw 2 &  hw 3 &  hw 4 &  exam &  hw average &       new \\\\\\\\\\nstudent &       &       &       &       &       &             &           \\\\\\\\\\n\\\\midrule\\nA       &  10.0 &   9.0 &  10.0 &   7.0 &  97.0 &        9.00 &  0.848599 \\\\\\\\\\nB       &   8.0 &   7.0 &   9.0 &   9.0 &  82.0 &        8.25 &  0.394722 \\\\\\\\\\nC       &   0.0 &   9.0 &   6.0 &   5.0 &  80.0 &        5.00 &  0.957668 \\\\\\\\\\nD       &   8.0 &   9.0 &   9.0 &   9.0 &  90.0 &        8.75 &  0.953680 \\\\\\\\\\nE       &   0.0 &  10.0 &  10.0 &  10.0 &  95.0 &        7.50 &  0.000388 \\\\\\\\\\nF       &   8.0 &   2.0 &   6.0 &   7.0 &  80.0 &        5.75 &  0.898409 \\\\\\\\\\nG       &   6.0 &   0.0 &   4.0 &   5.0 &  80.0 &        3.75 &  0.346747 \\\\\\\\\\nH       &   8.0 &   8.0 &   9.0 &   8.0 &  84.0 &        8.25 &  0.716042 \\\\\\\\\\nI       &  10.0 &   7.0 &  10.0 &  10.0 &  92.0 &        9.25 &  0.965628 \\\\\\\\\\nJ       &  10.0 &   6.0 &   9.0 &   9.0 &  91.0 &        8.50 &  0.124690 \\\\\\\\\\nK       &   8.0 &   7.0 &   6.0 &   8.0 &  87.0 &        7.25 &  0.694847 \\\\\\\\\\nL       &   3.0 &   8.0 &   5.0 &   7.0 &  80.0 &        5.75 &  0.930668 \\\\\\\\\\nM       &   9.0 &   9.0 &   8.0 &   9.0 &  94.0 &        8.75 &  0.606070 \\\\\\\\\\nN       &   8.0 &  10.0 &   9.0 &   9.0 &  90.0 &        9.00 &  0.212891 \\\\\\\\\\nO       &  10.0 &  10.0 &  10.0 &   9.0 &  99.0 &        9.75 &  0.905785 \\\\\\\\\\nP       &   8.0 &   9.0 &   8.0 &  10.0 &  94.0 &        8.75 &  0.415708 \\\\\\\\\\nQ       &   5.0 &   7.0 &   6.0 &   5.0 &  80.0 &        5.75 &  0.145941 \\\\\\\\\\nR       &   1.0 &   1.0 &   1.0 &   1.0 &   1.0 &        1.00 &  1.000000 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.to_latex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/content/07-pandas/pandas-worldbank.ipynb b/content/07-pandas/pandas-worldbank.ipynb
new file mode 100644
index 00000000..be238897
--- /dev/null
+++ b/content/07-pandas/pandas-worldbank.ipynb
@@ -0,0 +1,4013 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dataset from: http://data.worldbank.org/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb = pd.read_csv(\"WDI_Data.csv\", index_col=\"Country Name\")"
+   ]
+  },
+  {
+   "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></th>\n",
+       "      <th>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th>Indicator Code</th>\n",
+       "      <th>1960</th>\n",
+       "      <th>1961</th>\n",
+       "      <th>1962</th>\n",
+       "      <th>1963</th>\n",
+       "      <th>1964</th>\n",
+       "      <th>1965</th>\n",
+       "      <th>1966</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2008</th>\n",
+       "      <th>2009</th>\n",
+       "      <th>2010</th>\n",
+       "      <th>2011</th>\n",
+       "      <th>2012</th>\n",
+       "      <th>2013</th>\n",
+       "      <th>2014</th>\n",
+       "      <th>2015</th>\n",
+       "      <th>2016</th>\n",
+       "      <th>Unnamed: 61</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Name</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",
+       "      <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>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>2005 PPP conversion factor, GDP (LCU per inter...</td>\n",
+       "      <td>PA.NUS.PPP.05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>2005 PPP conversion factor, private consumptio...</td>\n",
+       "      <td>PA.NUS.PRVT.PP.05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>EG.ELC.ACCS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.436235e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.628035e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>EG.ELC.ACCS.RU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.198423e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.391781e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Access to electricity, urban (% of urban popul...</td>\n",
+       "      <td>EG.ELC.ACCS.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.383966e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.515673e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Access to non-solid fuel (% of population)</td>\n",
+       "      <td>EG.NSF.ACCS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.602252e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.658057e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Access to non-solid fuel, rural (% of rural po...</td>\n",
+       "      <td>EG.NSF.ACCS.RU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.456626e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.494069e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Access to non-solid fuel, urban (% of urban po...</td>\n",
+       "      <td>EG.NSF.ACCS.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.455237e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.497705e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Account at a financial institution (% age 15+)...</td>\n",
+       "      <td>WP_time_01.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Account at a financial institution, female (% ...</td>\n",
+       "      <td>WP_time_01.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Account at a financial institution, income, po...</td>\n",
+       "      <td>WP_time_01.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Account at a financial institution, income, ri...</td>\n",
+       "      <td>WP_time_01.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Account at a financial institution, male (% ag...</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adequacy of social insurance programs (% of to...</td>\n",
+       "      <td>per_si_allsi.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adequacy of social protection and labor progra...</td>\n",
+       "      <td>per_allsp.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adequacy of social safety net programs (% of t...</td>\n",
+       "      <td>per_sa_allsa.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adequacy of unemployment benefits and ALMP (% ...</td>\n",
+       "      <td>per_lm_alllm.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net enrollment rate, primary, female ...</td>\n",
+       "      <td>SE.PRM.TENR.FE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.147380e+01</td>\n",
+       "      <td>8.169569e+01</td>\n",
+       "      <td>8.287165e+01</td>\n",
+       "      <td>8.286139e+01</td>\n",
+       "      <td>8.440141e+01</td>\n",
+       "      <td>8.391403e+01</td>\n",
+       "      <td>8.382083e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net enrollment rate, primary, male (%...</td>\n",
+       "      <td>SE.PRM.TENR.MA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.644414e+01</td>\n",
+       "      <td>8.659069e+01</td>\n",
+       "      <td>8.745258e+01</td>\n",
+       "      <td>8.752652e+01</td>\n",
+       "      <td>8.772817e+01</td>\n",
+       "      <td>8.703988e+01</td>\n",
+       "      <td>8.675339e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net enrolment rate, primary, both sex...</td>\n",
+       "      <td>SE.PRM.TENR</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.401187e+01</td>\n",
+       "      <td>8.419596e+01</td>\n",
+       "      <td>8.521200e+01</td>\n",
+       "      <td>8.524514e+01</td>\n",
+       "      <td>8.610167e+01</td>\n",
+       "      <td>8.551194e+01</td>\n",
+       "      <td>8.532015e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net national income (annual % growth)</td>\n",
+       "      <td>NY.ADJ.NNTY.KD.ZG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.524779e+00</td>\n",
+       "      <td>6.706404e+00</td>\n",
+       "      <td>2.499334e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net national income (constant 2010 US$)</td>\n",
+       "      <td>NY.ADJ.NNTY.KD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.529192e+12</td>\n",
+       "      <td>1.674845e+12</td>\n",
+       "      <td>1.787166e+12</td>\n",
+       "      <td>1.831834e+12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net national income (current US$)</td>\n",
+       "      <td>NY.ADJ.NNTY.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.424482e+12</td>\n",
+       "      <td>1.354331e+12</td>\n",
+       "      <td>1.529192e+12</td>\n",
+       "      <td>1.773875e+12</td>\n",
+       "      <td>2.012007e+12</td>\n",
+       "      <td>2.088333e+12</td>\n",
+       "      <td>2.156246e+12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net national income per capita (annua...</td>\n",
+       "      <td>NY.ADJ.NNTY.PC.KD.ZG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.122247e+00</td>\n",
+       "      <td>4.458066e+00</td>\n",
+       "      <td>4.026424e-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net national income per capita (const...</td>\n",
+       "      <td>NY.ADJ.NNTY.PC.KD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.330613e+03</td>\n",
+       "      <td>4.639050e+03</td>\n",
+       "      <td>4.845862e+03</td>\n",
+       "      <td>4.865373e+03</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net national income per capita (curre...</td>\n",
+       "      <td>NY.ADJ.NNTY.PC.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.228375e+03</td>\n",
+       "      <td>3.924980e+03</td>\n",
+       "      <td>4.330613e+03</td>\n",
+       "      <td>4.913349e+03</td>\n",
+       "      <td>5.455513e+03</td>\n",
+       "      <td>5.546640e+03</td>\n",
+       "      <td>5.611972e+03</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net savings, excluding particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNX.GN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.725361e+01</td>\n",
+       "      <td>9.157418e+00</td>\n",
+       "      <td>1.220151e+01</td>\n",
+       "      <td>1.690138e+01</td>\n",
+       "      <td>1.645197e+01</td>\n",
+       "      <td>1.592986e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net savings, excluding particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNX.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net savings, including particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNG.GN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.704717e+01</td>\n",
+       "      <td>8.936666e+00</td>\n",
+       "      <td>1.198311e+01</td>\n",
+       "      <td>1.668852e+01</td>\n",
+       "      <td>1.624965e+01</td>\n",
+       "      <td>1.573042e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arab World</th>\n",
+       "      <td>ARB</td>\n",
+       "      <td>Adjusted net savings, including particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNG.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban land area where elevation is below 5 met...</td>\n",
+       "      <td>AG.LND.EL5M.UR.K2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban population</td>\n",
+       "      <td>SP.URB.TOTL</td>\n",
+       "      <td>473101.000000</td>\n",
+       "      <td>497024.000000</td>\n",
+       "      <td>524099.000000</td>\n",
+       "      <td>562238.000000</td>\n",
+       "      <td>603076.000000</td>\n",
+       "      <td>646516.000000</td>\n",
+       "      <td>692786.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.529077e+06</td>\n",
+       "      <td>4.579794e+06</td>\n",
+       "      <td>4.638775e+06</td>\n",
+       "      <td>4.706484e+06</td>\n",
+       "      <td>4.782430e+06</td>\n",
+       "      <td>4.864823e+06</td>\n",
+       "      <td>4.955055e+06</td>\n",
+       "      <td>5.051547e+06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban population (% of total)</td>\n",
+       "      <td>SP.URB.TOTL.IN.ZS</td>\n",
+       "      <td>12.608000</td>\n",
+       "      <td>12.821000</td>\n",
+       "      <td>13.082000</td>\n",
+       "      <td>13.578000</td>\n",
+       "      <td>14.092000</td>\n",
+       "      <td>14.620000</td>\n",
+       "      <td>15.16500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.356000e+01</td>\n",
+       "      <td>3.337800e+01</td>\n",
+       "      <td>3.319600e+01</td>\n",
+       "      <td>3.301500e+01</td>\n",
+       "      <td>3.283400e+01</td>\n",
+       "      <td>3.265400e+01</td>\n",
+       "      <td>3.250100e+01</td>\n",
+       "      <td>3.237600e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban population growth (annual %)</td>\n",
+       "      <td>SP.URB.GROW</td>\n",
+       "      <td>4.897746</td>\n",
+       "      <td>4.932942</td>\n",
+       "      <td>5.304228</td>\n",
+       "      <td>7.024465</td>\n",
+       "      <td>7.011798</td>\n",
+       "      <td>6.955472</td>\n",
+       "      <td>6.91232</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.316901e-01</td>\n",
+       "      <td>1.113585e+00</td>\n",
+       "      <td>1.279630e+00</td>\n",
+       "      <td>1.449081e+00</td>\n",
+       "      <td>1.600765e+00</td>\n",
+       "      <td>1.708155e+00</td>\n",
+       "      <td>1.837793e+00</td>\n",
+       "      <td>1.928627e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban population living in areas where elevati...</td>\n",
+       "      <td>EN.POP.EL5M.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban poverty gap at national poverty lines (%)</td>\n",
+       "      <td>SI.POV.URGP</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.550000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Urban poverty headcount ratio at national pove...</td>\n",
+       "      <td>SI.POV.URHC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.650000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Use of IMF credit (DOD, current US$)</td>\n",
+       "      <td>DT.DOD.DIMF.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.294260e+08</td>\n",
+       "      <td>5.423170e+08</td>\n",
+       "      <td>5.287290e+08</td>\n",
+       "      <td>5.270950e+08</td>\n",
+       "      <td>5.201240e+08</td>\n",
+       "      <td>5.193420e+08</td>\n",
+       "      <td>4.867300e+08</td>\n",
+       "      <td>4.637530e+08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Use of insecticide-treated bed nets (% of unde...</td>\n",
+       "      <td>SH.MLR.NETS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.730000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.700000e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.680000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Value lost due to electrical outages (% of sales)</td>\n",
+       "      <td>IC.FRM.OUTG.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.800000e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Vitamin A supplementation coverage rate (% of ...</td>\n",
+       "      <td>SN.ITK.VITA.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>7.700000e+01</td>\n",
+       "      <td>4.900000e+01</td>\n",
+       "      <td>4.700000e+01</td>\n",
+       "      <td>6.100000e+01</td>\n",
+       "      <td>3.400000e+01</td>\n",
+       "      <td>3.200000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Vulnerable employment, female (% of female emp...</td>\n",
+       "      <td>SL.EMP.VULN.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.310000e+01</td>\n",
+       "      <td>7.560000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Vulnerable employment, male (% of male employm...</td>\n",
+       "      <td>SL.EMP.VULN.MA.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.590000e+01</td>\n",
+       "      <td>5.660000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Vulnerable employment, total (% of total emplo...</td>\n",
+       "      <td>SL.EMP.VULN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.450000e+01</td>\n",
+       "      <td>6.550000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Wage and salaried workers, female (% of female...</td>\n",
+       "      <td>SL.EMP.WORK.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.650000e+01</td>\n",
+       "      <td>2.410000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Wage and salaried workers, male (% of male emp...</td>\n",
+       "      <td>SL.EMP.WORK.MA.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.340000e+01</td>\n",
+       "      <td>4.270000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Wage and salaried workers, total (% of total e...</td>\n",
+       "      <td>SL.EMP.WORK.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.490000e+01</td>\n",
+       "      <td>3.400000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Wanted fertility rate (births per woman)</td>\n",
+       "      <td>SP.DYN.WFRT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.500000e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Water productivity, total (constant 2010 US$ G...</td>\n",
+       "      <td>ER.GDP.FWTL.M3.KD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.543247e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Wholesale price index (2010 = 100)</td>\n",
+       "      <td>FP.WPI.TOTL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women participating in the three decisions (ow...</td>\n",
+       "      <td>SG.DMK.ALLD.FN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.450000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.REAS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.960000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.740000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.ARGU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.560000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.BURN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.500000e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.GOES.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.230000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.NEGL.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.140000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.REFU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.690000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women who were first married by age 18 (% of w...</td>\n",
+       "      <td>SP.M18.2024.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.010000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.050000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.350000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Women's share of population ages 15+ living wi...</td>\n",
+       "      <td>SH.DYN.AIDS.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.876080e+01</td>\n",
+       "      <td>5.881242e+01</td>\n",
+       "      <td>5.882594e+01</td>\n",
+       "      <td>5.889931e+01</td>\n",
+       "      <td>5.893908e+01</td>\n",
+       "      <td>5.890013e+01</td>\n",
+       "      <td>5.882233e+01</td>\n",
+       "      <td>5.885555e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Zimbabwe</th>\n",
+       "      <td>ZWE</td>\n",
+       "      <td>Youth literacy rate, population 15-24 years, b...</td>\n",
+       "      <td>SE.ADT.1524.LT.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.093070e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.175005e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>383328 rows × 61 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Country Code                                     Indicator Name  \\\n",
+       "Country Name                                                                   \n",
+       "Arab World            ARB  2005 PPP conversion factor, GDP (LCU per inter...   \n",
+       "Arab World            ARB  2005 PPP conversion factor, private consumptio...   \n",
+       "Arab World            ARB            Access to electricity (% of population)   \n",
+       "Arab World            ARB  Access to electricity, rural (% of rural popul...   \n",
+       "Arab World            ARB  Access to electricity, urban (% of urban popul...   \n",
+       "Arab World            ARB         Access to non-solid fuel (% of population)   \n",
+       "Arab World            ARB  Access to non-solid fuel, rural (% of rural po...   \n",
+       "Arab World            ARB  Access to non-solid fuel, urban (% of urban po...   \n",
+       "Arab World            ARB  Account at a financial institution (% age 15+)...   \n",
+       "Arab World            ARB  Account at a financial institution, female (% ...   \n",
+       "Arab World            ARB  Account at a financial institution, income, po...   \n",
+       "Arab World            ARB  Account at a financial institution, income, ri...   \n",
+       "Arab World            ARB  Account at a financial institution, male (% ag...   \n",
+       "Arab World            ARB  Adequacy of social insurance programs (% of to...   \n",
+       "Arab World            ARB  Adequacy of social protection and labor progra...   \n",
+       "Arab World            ARB  Adequacy of social safety net programs (% of t...   \n",
+       "Arab World            ARB  Adequacy of unemployment benefits and ALMP (% ...   \n",
+       "Arab World            ARB  Adjusted net enrollment rate, primary, female ...   \n",
+       "Arab World            ARB  Adjusted net enrollment rate, primary, male (%...   \n",
+       "Arab World            ARB  Adjusted net enrolment rate, primary, both sex...   \n",
+       "Arab World            ARB     Adjusted net national income (annual % growth)   \n",
+       "Arab World            ARB   Adjusted net national income (constant 2010 US$)   \n",
+       "Arab World            ARB         Adjusted net national income (current US$)   \n",
+       "Arab World            ARB  Adjusted net national income per capita (annua...   \n",
+       "Arab World            ARB  Adjusted net national income per capita (const...   \n",
+       "Arab World            ARB  Adjusted net national income per capita (curre...   \n",
+       "Arab World            ARB  Adjusted net savings, excluding particulate em...   \n",
+       "Arab World            ARB  Adjusted net savings, excluding particulate em...   \n",
+       "Arab World            ARB  Adjusted net savings, including particulate em...   \n",
+       "Arab World            ARB  Adjusted net savings, including particulate em...   \n",
+       "...                   ...                                                ...   \n",
+       "Zimbabwe              ZWE  Urban land area where elevation is below 5 met...   \n",
+       "Zimbabwe              ZWE                                   Urban population   \n",
+       "Zimbabwe              ZWE                      Urban population (% of total)   \n",
+       "Zimbabwe              ZWE                 Urban population growth (annual %)   \n",
+       "Zimbabwe              ZWE  Urban population living in areas where elevati...   \n",
+       "Zimbabwe              ZWE    Urban poverty gap at national poverty lines (%)   \n",
+       "Zimbabwe              ZWE  Urban poverty headcount ratio at national pove...   \n",
+       "Zimbabwe              ZWE               Use of IMF credit (DOD, current US$)   \n",
+       "Zimbabwe              ZWE  Use of insecticide-treated bed nets (% of unde...   \n",
+       "Zimbabwe              ZWE  Value lost due to electrical outages (% of sales)   \n",
+       "Zimbabwe              ZWE  Vitamin A supplementation coverage rate (% of ...   \n",
+       "Zimbabwe              ZWE  Vulnerable employment, female (% of female emp...   \n",
+       "Zimbabwe              ZWE  Vulnerable employment, male (% of male employm...   \n",
+       "Zimbabwe              ZWE  Vulnerable employment, total (% of total emplo...   \n",
+       "Zimbabwe              ZWE  Wage and salaried workers, female (% of female...   \n",
+       "Zimbabwe              ZWE  Wage and salaried workers, male (% of male emp...   \n",
+       "Zimbabwe              ZWE  Wage and salaried workers, total (% of total e...   \n",
+       "Zimbabwe              ZWE           Wanted fertility rate (births per woman)   \n",
+       "Zimbabwe              ZWE  Water productivity, total (constant 2010 US$ G...   \n",
+       "Zimbabwe              ZWE                 Wholesale price index (2010 = 100)   \n",
+       "Zimbabwe              ZWE  Women participating in the three decisions (ow...   \n",
+       "Zimbabwe              ZWE  Women who believe a husband is justified in be...   \n",
+       "Zimbabwe              ZWE  Women who believe a husband is justified in be...   \n",
+       "Zimbabwe              ZWE  Women who believe a husband is justified in be...   \n",
+       "Zimbabwe              ZWE  Women who believe a husband is justified in be...   \n",
+       "Zimbabwe              ZWE  Women who believe a husband is justified in be...   \n",
+       "Zimbabwe              ZWE  Women who believe a husband is justified in be...   \n",
+       "Zimbabwe              ZWE  Women who were first married by age 18 (% of w...   \n",
+       "Zimbabwe              ZWE  Women's share of population ages 15+ living wi...   \n",
+       "Zimbabwe              ZWE  Youth literacy rate, population 15-24 years, b...   \n",
+       "\n",
+       "                        Indicator Code           1960           1961  \\\n",
+       "Country Name                                                           \n",
+       "Arab World               PA.NUS.PPP.05            NaN            NaN   \n",
+       "Arab World           PA.NUS.PRVT.PP.05            NaN            NaN   \n",
+       "Arab World              EG.ELC.ACCS.ZS            NaN            NaN   \n",
+       "Arab World           EG.ELC.ACCS.RU.ZS            NaN            NaN   \n",
+       "Arab World           EG.ELC.ACCS.UR.ZS            NaN            NaN   \n",
+       "Arab World              EG.NSF.ACCS.ZS            NaN            NaN   \n",
+       "Arab World           EG.NSF.ACCS.RU.ZS            NaN            NaN   \n",
+       "Arab World           EG.NSF.ACCS.UR.ZS            NaN            NaN   \n",
+       "Arab World                WP_time_01.1            NaN            NaN   \n",
+       "Arab World                WP_time_01.3            NaN            NaN   \n",
+       "Arab World                WP_time_01.8            NaN            NaN   \n",
+       "Arab World                WP_time_01.9            NaN            NaN   \n",
+       "Arab World                WP_time_01.2            NaN            NaN   \n",
+       "Arab World    per_si_allsi.adq_pop_tot            NaN            NaN   \n",
+       "Arab World       per_allsp.adq_pop_tot            NaN            NaN   \n",
+       "Arab World    per_sa_allsa.adq_pop_tot            NaN            NaN   \n",
+       "Arab World    per_lm_alllm.adq_pop_tot            NaN            NaN   \n",
+       "Arab World              SE.PRM.TENR.FE            NaN            NaN   \n",
+       "Arab World              SE.PRM.TENR.MA            NaN            NaN   \n",
+       "Arab World                 SE.PRM.TENR            NaN            NaN   \n",
+       "Arab World           NY.ADJ.NNTY.KD.ZG            NaN            NaN   \n",
+       "Arab World              NY.ADJ.NNTY.KD            NaN            NaN   \n",
+       "Arab World              NY.ADJ.NNTY.CD            NaN            NaN   \n",
+       "Arab World        NY.ADJ.NNTY.PC.KD.ZG            NaN            NaN   \n",
+       "Arab World           NY.ADJ.NNTY.PC.KD            NaN            NaN   \n",
+       "Arab World           NY.ADJ.NNTY.PC.CD            NaN            NaN   \n",
+       "Arab World           NY.ADJ.SVNX.GN.ZS            NaN            NaN   \n",
+       "Arab World              NY.ADJ.SVNX.CD            NaN            NaN   \n",
+       "Arab World           NY.ADJ.SVNG.GN.ZS            NaN            NaN   \n",
+       "Arab World              NY.ADJ.SVNG.CD            NaN            NaN   \n",
+       "...                                ...            ...            ...   \n",
+       "Zimbabwe             AG.LND.EL5M.UR.K2            NaN            NaN   \n",
+       "Zimbabwe                   SP.URB.TOTL  473101.000000  497024.000000   \n",
+       "Zimbabwe             SP.URB.TOTL.IN.ZS      12.608000      12.821000   \n",
+       "Zimbabwe                   SP.URB.GROW       4.897746       4.932942   \n",
+       "Zimbabwe             EN.POP.EL5M.UR.ZS            NaN            NaN   \n",
+       "Zimbabwe                   SI.POV.URGP            NaN            NaN   \n",
+       "Zimbabwe                   SI.POV.URHC            NaN            NaN   \n",
+       "Zimbabwe                DT.DOD.DIMF.CD            NaN            NaN   \n",
+       "Zimbabwe                SH.MLR.NETS.ZS            NaN            NaN   \n",
+       "Zimbabwe                IC.FRM.OUTG.ZS            NaN            NaN   \n",
+       "Zimbabwe                SN.ITK.VITA.ZS            NaN            NaN   \n",
+       "Zimbabwe             SL.EMP.VULN.FE.ZS            NaN            NaN   \n",
+       "Zimbabwe             SL.EMP.VULN.MA.ZS            NaN            NaN   \n",
+       "Zimbabwe                SL.EMP.VULN.ZS            NaN            NaN   \n",
+       "Zimbabwe             SL.EMP.WORK.FE.ZS            NaN            NaN   \n",
+       "Zimbabwe             SL.EMP.WORK.MA.ZS            NaN            NaN   \n",
+       "Zimbabwe                SL.EMP.WORK.ZS            NaN            NaN   \n",
+       "Zimbabwe                   SP.DYN.WFRT            NaN            NaN   \n",
+       "Zimbabwe             ER.GDP.FWTL.M3.KD            NaN            NaN   \n",
+       "Zimbabwe                   FP.WPI.TOTL            NaN            NaN   \n",
+       "Zimbabwe             SG.DMK.ALLD.FN.ZS            NaN            NaN   \n",
+       "Zimbabwe                SG.VAW.REAS.ZS            NaN            NaN   \n",
+       "Zimbabwe                SG.VAW.ARGU.ZS            NaN            NaN   \n",
+       "Zimbabwe                SG.VAW.BURN.ZS            NaN            NaN   \n",
+       "Zimbabwe                SG.VAW.GOES.ZS            NaN            NaN   \n",
+       "Zimbabwe                SG.VAW.NEGL.ZS            NaN            NaN   \n",
+       "Zimbabwe                SG.VAW.REFU.ZS            NaN            NaN   \n",
+       "Zimbabwe             SP.M18.2024.FE.ZS            NaN            NaN   \n",
+       "Zimbabwe             SH.DYN.AIDS.FE.ZS            NaN            NaN   \n",
+       "Zimbabwe             SE.ADT.1524.LT.ZS            NaN            NaN   \n",
+       "\n",
+       "                       1962           1963           1964           1965  \\\n",
+       "Country Name                                                               \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "Arab World              NaN            NaN            NaN            NaN   \n",
+       "...                     ...            ...            ...            ...   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe      524099.000000  562238.000000  603076.000000  646516.000000   \n",
+       "Zimbabwe          13.082000      13.578000      14.092000      14.620000   \n",
+       "Zimbabwe           5.304228       7.024465       7.011798       6.955472   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "Zimbabwe                NaN            NaN            NaN            NaN   \n",
+       "\n",
+       "                      1966     ...               2008          2009  \\\n",
+       "Country Name                   ...                                    \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...       8.147380e+01  8.169569e+01   \n",
+       "Arab World             NaN     ...       8.644414e+01  8.659069e+01   \n",
+       "Arab World             NaN     ...       8.401187e+01  8.419596e+01   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...       1.424482e+12  1.354331e+12   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...       4.228375e+03  3.924980e+03   \n",
+       "Arab World             NaN     ...       1.725361e+01  9.157418e+00   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "Arab World             NaN     ...       1.704717e+01  8.936666e+00   \n",
+       "Arab World             NaN     ...                NaN           NaN   \n",
+       "...                    ...     ...                ...           ...   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe      692786.00000     ...       4.529077e+06  4.579794e+06   \n",
+       "Zimbabwe          15.16500     ...       3.356000e+01  3.337800e+01   \n",
+       "Zimbabwe           6.91232     ...       9.316901e-01  1.113585e+00   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...       1.294260e+08  5.423170e+08   \n",
+       "Zimbabwe               NaN     ...                NaN  1.730000e+01   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...       0.000000e+00  7.700000e+01   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "Zimbabwe               NaN     ...                NaN  3.010000e+01   \n",
+       "Zimbabwe               NaN     ...       5.876080e+01  5.881242e+01   \n",
+       "Zimbabwe               NaN     ...                NaN           NaN   \n",
+       "\n",
+       "                      2010          2011          2012          2013  \\\n",
+       "Country Name                                                           \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World    8.436235e+01           NaN  8.628035e+01           NaN   \n",
+       "Arab World    7.198423e+01           NaN  7.391781e+01           NaN   \n",
+       "Arab World    9.383966e+01           NaN  9.515673e+01           NaN   \n",
+       "Arab World    8.602252e+01           NaN  8.658057e+01           NaN   \n",
+       "Arab World    7.456626e+01           NaN  7.494069e+01           NaN   \n",
+       "Arab World    9.455237e+01           NaN  9.497705e+01           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World    8.287165e+01  8.286139e+01  8.440141e+01  8.391403e+01   \n",
+       "Arab World    8.745258e+01  8.752652e+01  8.772817e+01  8.703988e+01   \n",
+       "Arab World    8.521200e+01  8.524514e+01  8.610167e+01  8.551194e+01   \n",
+       "Arab World             NaN  9.524779e+00  6.706404e+00  2.499334e+00   \n",
+       "Arab World    1.529192e+12  1.674845e+12  1.787166e+12  1.831834e+12   \n",
+       "Arab World    1.529192e+12  1.773875e+12  2.012007e+12  2.088333e+12   \n",
+       "Arab World             NaN  7.122247e+00  4.458066e+00  4.026424e-01   \n",
+       "Arab World    4.330613e+03  4.639050e+03  4.845862e+03  4.865373e+03   \n",
+       "Arab World    4.330613e+03  4.913349e+03  5.455513e+03  5.546640e+03   \n",
+       "Arab World    1.220151e+01  1.690138e+01  1.645197e+01  1.592986e+01   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "Arab World    1.198311e+01  1.668852e+01  1.624965e+01  1.573042e+01   \n",
+       "Arab World             NaN           NaN           NaN           NaN   \n",
+       "...                    ...           ...           ...           ...   \n",
+       "Zimbabwe               NaN           NaN           NaN           NaN   \n",
+       "Zimbabwe      4.638775e+06  4.706484e+06  4.782430e+06  4.864823e+06   \n",
+       "Zimbabwe      3.319600e+01  3.301500e+01  3.283400e+01  3.265400e+01   \n",
+       "Zimbabwe      1.279630e+00  1.449081e+00  1.600765e+00  1.708155e+00   \n",
+       "Zimbabwe               NaN           NaN           NaN           NaN   \n",
+       "Zimbabwe               NaN  1.550000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  4.650000e+01           NaN           NaN   \n",
+       "Zimbabwe      5.287290e+08  5.270950e+08  5.201240e+08  5.193420e+08   \n",
+       "Zimbabwe               NaN  9.700000e+00           NaN           NaN   \n",
+       "Zimbabwe               NaN  8.800000e+00           NaN           NaN   \n",
+       "Zimbabwe      4.900000e+01  4.700000e+01  6.100000e+01  3.400000e+01   \n",
+       "Zimbabwe               NaN  8.310000e+01  7.560000e+01           NaN   \n",
+       "Zimbabwe               NaN  6.590000e+01  5.660000e+01           NaN   \n",
+       "Zimbabwe               NaN  7.450000e+01  6.550000e+01           NaN   \n",
+       "Zimbabwe               NaN  1.650000e+01  2.410000e+01           NaN   \n",
+       "Zimbabwe               NaN  3.340000e+01  4.270000e+01           NaN   \n",
+       "Zimbabwe               NaN  2.490000e+01  3.400000e+01           NaN   \n",
+       "Zimbabwe               NaN  3.500000e+00           NaN           NaN   \n",
+       "Zimbabwe               NaN           NaN           NaN           NaN   \n",
+       "Zimbabwe               NaN           NaN           NaN           NaN   \n",
+       "Zimbabwe               NaN  7.450000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  3.960000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  1.560000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  7.500000e+00           NaN           NaN   \n",
+       "Zimbabwe               NaN  2.230000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  2.140000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  1.690000e+01           NaN           NaN   \n",
+       "Zimbabwe               NaN  3.050000e+01           NaN           NaN   \n",
+       "Zimbabwe      5.882594e+01  5.889931e+01  5.893908e+01  5.890013e+01   \n",
+       "Zimbabwe               NaN  9.093070e+01           NaN           NaN   \n",
+       "\n",
+       "                      2014          2015  2016  Unnamed: 61  \n",
+       "Country Name                                                 \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World    8.382083e+01           NaN   NaN          NaN  \n",
+       "Arab World    8.675339e+01           NaN   NaN          NaN  \n",
+       "Arab World    8.532015e+01           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World    2.156246e+12           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World    5.611972e+03           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "Arab World             NaN           NaN   NaN          NaN  \n",
+       "...                    ...           ...   ...          ...  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe      4.955055e+06  5.051547e+06   NaN          NaN  \n",
+       "Zimbabwe      3.250100e+01  3.237600e+01   NaN          NaN  \n",
+       "Zimbabwe      1.837793e+00  1.928627e+00   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe      4.867300e+08  4.637530e+08   NaN          NaN  \n",
+       "Zimbabwe      2.680000e+01           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe      3.200000e+01           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe      3.543247e+00           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe      3.740000e+01           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe               NaN           NaN   NaN          NaN  \n",
+       "Zimbabwe      3.350000e+01           NaN   NaN          NaN  \n",
+       "Zimbabwe      5.882233e+01  5.885555e+01   NaN          NaN  \n",
+       "Zimbabwe               NaN  9.175005e+01   NaN          NaN  \n",
+       "\n",
+       "[383328 rows x 61 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World',\n",
+       "       'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World',\n",
+       "       ...\n",
+       "       'Zimbabwe', 'Zimbabwe', 'Zimbabwe', 'Zimbabwe', 'Zimbabwe', 'Zimbabwe',\n",
+       "       'Zimbabwe', 'Zimbabwe', 'Zimbabwe', 'Zimbabwe'],\n",
+       "      dtype='object', name='Country Name', length=383328)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wb.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "us = set([n for n in wb.index if n.startswith(\"U\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"United States\" in wb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Uganda',\n",
+       " 'Ukraine',\n",
+       " 'United Arab Emirates',\n",
+       " 'United Kingdom',\n",
+       " 'United States',\n",
+       " 'Upper middle income',\n",
+       " 'Uruguay',\n",
+       " 'Uzbekistan'}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "us"
+   ]
+  },
+  {
+   "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>Country Code</th>\n",
+       "      <th>Indicator Name</th>\n",
+       "      <th>Indicator Code</th>\n",
+       "      <th>1960</th>\n",
+       "      <th>1961</th>\n",
+       "      <th>1962</th>\n",
+       "      <th>1963</th>\n",
+       "      <th>1964</th>\n",
+       "      <th>1965</th>\n",
+       "      <th>1966</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2008</th>\n",
+       "      <th>2009</th>\n",
+       "      <th>2010</th>\n",
+       "      <th>2011</th>\n",
+       "      <th>2012</th>\n",
+       "      <th>2013</th>\n",
+       "      <th>2014</th>\n",
+       "      <th>2015</th>\n",
+       "      <th>2016</th>\n",
+       "      <th>Unnamed: 61</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country Name</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",
+       "      <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>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>2005 PPP conversion factor, GDP (LCU per inter...</td>\n",
+       "      <td>PA.NUS.PPP.05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>2005 PPP conversion factor, private consumptio...</td>\n",
+       "      <td>PA.NUS.PRVT.PP.05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Access to electricity (% of population)</td>\n",
+       "      <td>EG.ELC.ACCS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Access to electricity, rural (% of rural popul...</td>\n",
+       "      <td>EG.ELC.ACCS.RU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Access to electricity, urban (% of urban popul...</td>\n",
+       "      <td>EG.ELC.ACCS.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Access to non-solid fuel (% of population)</td>\n",
+       "      <td>EG.NSF.ACCS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Access to non-solid fuel, rural (% of rural po...</td>\n",
+       "      <td>EG.NSF.ACCS.RU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Access to non-solid fuel, urban (% of urban po...</td>\n",
+       "      <td>EG.NSF.ACCS.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Account at a financial institution (% age 15+)...</td>\n",
+       "      <td>WP_time_01.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.795786e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.358380e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Account at a financial institution, female (% ...</td>\n",
+       "      <td>WP_time_01.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.406905e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.479771e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Account at a financial institution, income, po...</td>\n",
+       "      <td>WP_time_01.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.099529e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.711619e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Account at a financial institution, income, ri...</td>\n",
+       "      <td>WP_time_01.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.281008e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.790414e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Account at a financial institution, male (% ag...</td>\n",
+       "      <td>WP_time_01.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.198194e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.236126e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adequacy of social insurance programs (% of to...</td>\n",
+       "      <td>per_si_allsi.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adequacy of social protection and labor progra...</td>\n",
+       "      <td>per_allsp.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adequacy of social safety net programs (% of t...</td>\n",
+       "      <td>per_sa_allsa.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adequacy of unemployment benefits and ALMP (% ...</td>\n",
+       "      <td>per_lm_alllm.adq_pop_tot</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net enrollment rate, primary, female ...</td>\n",
+       "      <td>SE.PRM.TENR.FE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.794343e+01</td>\n",
+       "      <td>9.709736e+01</td>\n",
+       "      <td>9.513818e+01</td>\n",
+       "      <td>9.467457e+01</td>\n",
+       "      <td>9.410391e+01</td>\n",
+       "      <td>9.262706e+01</td>\n",
+       "      <td>9.443206e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net enrollment rate, primary, male (%...</td>\n",
+       "      <td>SE.PRM.TENR.MA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.704943e+01</td>\n",
+       "      <td>9.501399e+01</td>\n",
+       "      <td>9.446875e+01</td>\n",
+       "      <td>9.324649e+01</td>\n",
+       "      <td>9.411461e+01</td>\n",
+       "      <td>9.292106e+01</td>\n",
+       "      <td>9.365279e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net enrolment rate, primary, both sex...</td>\n",
+       "      <td>SE.PRM.TENR</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.748575e+01</td>\n",
+       "      <td>9.603130e+01</td>\n",
+       "      <td>9.479582e+01</td>\n",
+       "      <td>9.394464e+01</td>\n",
+       "      <td>9.410937e+01</td>\n",
+       "      <td>9.277709e+01</td>\n",
+       "      <td>9.403453e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net national income (annual % growth)</td>\n",
+       "      <td>NY.ADJ.NNTY.KD.ZG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.788922e+00</td>\n",
+       "      <td>-8.504354e-01</td>\n",
+       "      <td>3.342398e+00</td>\n",
+       "      <td>2.076668e+00</td>\n",
+       "      <td>3.645016e+00</td>\n",
+       "      <td>1.333261e+00</td>\n",
+       "      <td>2.769931e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net national income (constant 2010 US$)</td>\n",
+       "      <td>NY.ADJ.NNTY.KD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.231703e+13</td>\n",
+       "      <td>1.221228e+13</td>\n",
+       "      <td>1.262046e+13</td>\n",
+       "      <td>1.288254e+13</td>\n",
+       "      <td>1.335212e+13</td>\n",
+       "      <td>1.353013e+13</td>\n",
+       "      <td>1.390491e+13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net national income (current US$)</td>\n",
+       "      <td>NY.ADJ.NNTY.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.216984e+13</td>\n",
+       "      <td>1.203082e+13</td>\n",
+       "      <td>1.262046e+13</td>\n",
+       "      <td>1.319294e+13</td>\n",
+       "      <td>1.391432e+13</td>\n",
+       "      <td>1.429645e+13</td>\n",
+       "      <td>1.494105e+13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net national income per capita (annua...</td>\n",
+       "      <td>NY.ADJ.NNTY.PC.KD.ZG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-3.704072e+00</td>\n",
+       "      <td>-1.715833e+00</td>\n",
+       "      <td>2.482066e+00</td>\n",
+       "      <td>1.299926e+00</td>\n",
+       "      <td>2.858440e+00</td>\n",
+       "      <td>5.887723e-01</td>\n",
+       "      <td>1.970734e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net national income per capita (const...</td>\n",
+       "      <td>NY.ADJ.NNTY.PC.KD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.050401e+04</td>\n",
+       "      <td>3.980903e+04</td>\n",
+       "      <td>4.079712e+04</td>\n",
+       "      <td>4.132745e+04</td>\n",
+       "      <td>4.250877e+04</td>\n",
+       "      <td>4.275905e+04</td>\n",
+       "      <td>4.360171e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net national income per capita (curre...</td>\n",
+       "      <td>NY.ADJ.NNTY.PC.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.001999e+04</td>\n",
+       "      <td>3.921754e+04</td>\n",
+       "      <td>4.079712e+04</td>\n",
+       "      <td>4.232319e+04</td>\n",
+       "      <td>4.429865e+04</td>\n",
+       "      <td>4.518081e+04</td>\n",
+       "      <td>4.685075e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net savings, excluding particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNX.GN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.075956e+00</td>\n",
+       "      <td>1.735874e+00</td>\n",
+       "      <td>2.973072e+00</td>\n",
+       "      <td>3.712649e+00</td>\n",
+       "      <td>5.847932e+00</td>\n",
+       "      <td>5.621072e+00</td>\n",
+       "      <td>6.560417e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net savings, excluding particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNX.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.070588e+11</td>\n",
+       "      <td>2.516063e+11</td>\n",
+       "      <td>4.495612e+11</td>\n",
+       "      <td>5.867062e+11</td>\n",
+       "      <td>9.705286e+11</td>\n",
+       "      <td>9.607086e+11</td>\n",
+       "      <td>1.169276e+12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net savings, including particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNG.GN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.889611e+00</td>\n",
+       "      <td>1.548930e+00</td>\n",
+       "      <td>2.797694e+00</td>\n",
+       "      <td>3.545417e+00</td>\n",
+       "      <td>5.690151e+00</td>\n",
+       "      <td>5.470228e+00</td>\n",
+       "      <td>6.418385e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Adjusted net savings, including particulate em...</td>\n",
+       "      <td>NY.ADJ.SVNG.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.794962e+11</td>\n",
+       "      <td>2.245096e+11</td>\n",
+       "      <td>4.230421e+11</td>\n",
+       "      <td>5.602786e+11</td>\n",
+       "      <td>9.443431e+11</td>\n",
+       "      <td>9.349277e+11</td>\n",
+       "      <td>1.143962e+12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban land area where elevation is below 5 met...</td>\n",
+       "      <td>AG.LND.EL5M.UR.K2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.752022e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban population</td>\n",
+       "      <td>SP.URB.TOTL</td>\n",
+       "      <td>1.264625e+08</td>\n",
+       "      <td>1.292762e+08</td>\n",
+       "      <td>1.319887e+08</td>\n",
+       "      <td>1.346154e+08</td>\n",
+       "      <td>1.372160e+08</td>\n",
+       "      <td>1.396631e+08</td>\n",
+       "      <td>1.420087e+08</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.446071e+08</td>\n",
+       "      <td>2.472763e+08</td>\n",
+       "      <td>2.498656e+08</td>\n",
+       "      <td>2.523052e+08</td>\n",
+       "      <td>2.547624e+08</td>\n",
+       "      <td>2.571827e+08</td>\n",
+       "      <td>2.597405e+08</td>\n",
+       "      <td>2.623324e+08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban population (% of total)</td>\n",
+       "      <td>SP.URB.TOTL.IN.ZS</td>\n",
+       "      <td>6.999600e+01</td>\n",
+       "      <td>7.037700e+01</td>\n",
+       "      <td>7.075700e+01</td>\n",
+       "      <td>7.113400e+01</td>\n",
+       "      <td>7.150800e+01</td>\n",
+       "      <td>7.187900e+01</td>\n",
+       "      <td>7.224700e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.043800e+01</td>\n",
+       "      <td>8.060600e+01</td>\n",
+       "      <td>8.077200e+01</td>\n",
+       "      <td>8.094000e+01</td>\n",
+       "      <td>8.110800e+01</td>\n",
+       "      <td>8.127700e+01</td>\n",
+       "      <td>8.144700e+01</td>\n",
+       "      <td>8.161700e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban population growth (annual %)</td>\n",
+       "      <td>SP.URB.GROW</td>\n",
+       "      <td>2.449104e+00</td>\n",
+       "      <td>2.200571e+00</td>\n",
+       "      <td>2.076494e+00</td>\n",
+       "      <td>1.970559e+00</td>\n",
+       "      <td>1.913437e+00</td>\n",
+       "      <td>1.767653e+00</td>\n",
+       "      <td>1.665559e+00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.156186e+00</td>\n",
+       "      <td>1.085290e+00</td>\n",
+       "      <td>1.041720e+00</td>\n",
+       "      <td>9.716271e-01</td>\n",
+       "      <td>9.691533e-01</td>\n",
+       "      <td>9.455534e-01</td>\n",
+       "      <td>9.896394e-01</td>\n",
+       "      <td>9.929296e-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban population living in areas where elevati...</td>\n",
+       "      <td>EN.POP.EL5M.UR.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.263942e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban poverty gap at national poverty lines (%)</td>\n",
+       "      <td>SI.POV.URGP</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Urban poverty headcount ratio at national pove...</td>\n",
+       "      <td>SI.POV.URHC</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Use of IMF credit (DOD, current US$)</td>\n",
+       "      <td>DT.DOD.DIMF.CD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Use of insecticide-treated bed nets (% of unde...</td>\n",
+       "      <td>SH.MLR.NETS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Value lost due to electrical outages (% of sales)</td>\n",
+       "      <td>IC.FRM.OUTG.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Vitamin A supplementation coverage rate (% of ...</td>\n",
+       "      <td>SN.ITK.VITA.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Vulnerable employment, female (% of female emp...</td>\n",
+       "      <td>SL.EMP.VULN.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Vulnerable employment, male (% of male employm...</td>\n",
+       "      <td>SL.EMP.VULN.MA.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Vulnerable employment, total (% of total emplo...</td>\n",
+       "      <td>SL.EMP.VULN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Wage and salaried workers, female (% of female...</td>\n",
+       "      <td>SL.EMP.WORK.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.440000e+01</td>\n",
+       "      <td>9.430000e+01</td>\n",
+       "      <td>9.440000e+01</td>\n",
+       "      <td>9.450000e+01</td>\n",
+       "      <td>9.440000e+01</td>\n",
+       "      <td>9.440000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Wage and salaried workers, male (% of male emp...</td>\n",
+       "      <td>SL.EMP.WORK.MA.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.170000e+01</td>\n",
+       "      <td>9.160000e+01</td>\n",
+       "      <td>9.170000e+01</td>\n",
+       "      <td>9.200000e+01</td>\n",
+       "      <td>9.220000e+01</td>\n",
+       "      <td>9.250000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Wage and salaried workers, total (% of total e...</td>\n",
+       "      <td>SL.EMP.WORK.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.300000e+01</td>\n",
+       "      <td>9.290000e+01</td>\n",
+       "      <td>9.300000e+01</td>\n",
+       "      <td>9.320000e+01</td>\n",
+       "      <td>9.320000e+01</td>\n",
+       "      <td>9.340000e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Wanted fertility rate (births per woman)</td>\n",
+       "      <td>SP.DYN.WFRT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Water productivity, total (constant 2010 US$ G...</td>\n",
+       "      <td>ER.GDP.FWTL.M3.KD</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.200610e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.331436e+01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Wholesale price index (2010 = 100)</td>\n",
+       "      <td>FP.WPI.TOTL</td>\n",
+       "      <td>1.715426e+01</td>\n",
+       "      <td>1.709399e+01</td>\n",
+       "      <td>1.713467e+01</td>\n",
+       "      <td>1.708947e+01</td>\n",
+       "      <td>1.712412e+01</td>\n",
+       "      <td>1.746468e+01</td>\n",
+       "      <td>1.803880e+01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.026299e+02</td>\n",
+       "      <td>9.359888e+01</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "      <td>1.088326e+02</td>\n",
+       "      <td>1.094325e+02</td>\n",
+       "      <td>1.101092e+02</td>\n",
+       "      <td>1.111422e+02</td>\n",
+       "      <td>1.030900e+02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women participating in the three decisions (ow...</td>\n",
+       "      <td>SG.DMK.ALLD.FN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.REAS.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.ARGU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.BURN.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.GOES.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.NEGL.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who believe a husband is justified in be...</td>\n",
+       "      <td>SG.VAW.REFU.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women who were first married by age 18 (% of w...</td>\n",
+       "      <td>SP.M18.2024.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Women's share of population ages 15+ living wi...</td>\n",
+       "      <td>SH.DYN.AIDS.FE.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United States</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>Youth literacy rate, population 15-24 years, b...</td>\n",
+       "      <td>SE.ADT.1524.LT.ZS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1452 rows × 61 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Country Code                                     Indicator Name  \\\n",
+       "Country Name                                                                    \n",
+       "United States          USA  2005 PPP conversion factor, GDP (LCU per inter...   \n",
+       "United States          USA  2005 PPP conversion factor, private consumptio...   \n",
+       "United States          USA            Access to electricity (% of population)   \n",
+       "United States          USA  Access to electricity, rural (% of rural popul...   \n",
+       "United States          USA  Access to electricity, urban (% of urban popul...   \n",
+       "United States          USA         Access to non-solid fuel (% of population)   \n",
+       "United States          USA  Access to non-solid fuel, rural (% of rural po...   \n",
+       "United States          USA  Access to non-solid fuel, urban (% of urban po...   \n",
+       "United States          USA  Account at a financial institution (% age 15+)...   \n",
+       "United States          USA  Account at a financial institution, female (% ...   \n",
+       "United States          USA  Account at a financial institution, income, po...   \n",
+       "United States          USA  Account at a financial institution, income, ri...   \n",
+       "United States          USA  Account at a financial institution, male (% ag...   \n",
+       "United States          USA  Adequacy of social insurance programs (% of to...   \n",
+       "United States          USA  Adequacy of social protection and labor progra...   \n",
+       "United States          USA  Adequacy of social safety net programs (% of t...   \n",
+       "United States          USA  Adequacy of unemployment benefits and ALMP (% ...   \n",
+       "United States          USA  Adjusted net enrollment rate, primary, female ...   \n",
+       "United States          USA  Adjusted net enrollment rate, primary, male (%...   \n",
+       "United States          USA  Adjusted net enrolment rate, primary, both sex...   \n",
+       "United States          USA     Adjusted net national income (annual % growth)   \n",
+       "United States          USA   Adjusted net national income (constant 2010 US$)   \n",
+       "United States          USA         Adjusted net national income (current US$)   \n",
+       "United States          USA  Adjusted net national income per capita (annua...   \n",
+       "United States          USA  Adjusted net national income per capita (const...   \n",
+       "United States          USA  Adjusted net national income per capita (curre...   \n",
+       "United States          USA  Adjusted net savings, excluding particulate em...   \n",
+       "United States          USA  Adjusted net savings, excluding particulate em...   \n",
+       "United States          USA  Adjusted net savings, including particulate em...   \n",
+       "United States          USA  Adjusted net savings, including particulate em...   \n",
+       "...                    ...                                                ...   \n",
+       "United States          USA  Urban land area where elevation is below 5 met...   \n",
+       "United States          USA                                   Urban population   \n",
+       "United States          USA                      Urban population (% of total)   \n",
+       "United States          USA                 Urban population growth (annual %)   \n",
+       "United States          USA  Urban population living in areas where elevati...   \n",
+       "United States          USA    Urban poverty gap at national poverty lines (%)   \n",
+       "United States          USA  Urban poverty headcount ratio at national pove...   \n",
+       "United States          USA               Use of IMF credit (DOD, current US$)   \n",
+       "United States          USA  Use of insecticide-treated bed nets (% of unde...   \n",
+       "United States          USA  Value lost due to electrical outages (% of sales)   \n",
+       "United States          USA  Vitamin A supplementation coverage rate (% of ...   \n",
+       "United States          USA  Vulnerable employment, female (% of female emp...   \n",
+       "United States          USA  Vulnerable employment, male (% of male employm...   \n",
+       "United States          USA  Vulnerable employment, total (% of total emplo...   \n",
+       "United States          USA  Wage and salaried workers, female (% of female...   \n",
+       "United States          USA  Wage and salaried workers, male (% of male emp...   \n",
+       "United States          USA  Wage and salaried workers, total (% of total e...   \n",
+       "United States          USA           Wanted fertility rate (births per woman)   \n",
+       "United States          USA  Water productivity, total (constant 2010 US$ G...   \n",
+       "United States          USA                 Wholesale price index (2010 = 100)   \n",
+       "United States          USA  Women participating in the three decisions (ow...   \n",
+       "United States          USA  Women who believe a husband is justified in be...   \n",
+       "United States          USA  Women who believe a husband is justified in be...   \n",
+       "United States          USA  Women who believe a husband is justified in be...   \n",
+       "United States          USA  Women who believe a husband is justified in be...   \n",
+       "United States          USA  Women who believe a husband is justified in be...   \n",
+       "United States          USA  Women who believe a husband is justified in be...   \n",
+       "United States          USA  Women who were first married by age 18 (% of w...   \n",
+       "United States          USA  Women's share of population ages 15+ living wi...   \n",
+       "United States          USA  Youth literacy rate, population 15-24 years, b...   \n",
+       "\n",
+       "                         Indicator Code          1960          1961  \\\n",
+       "Country Name                                                          \n",
+       "United States             PA.NUS.PPP.05           NaN           NaN   \n",
+       "United States         PA.NUS.PRVT.PP.05           NaN           NaN   \n",
+       "United States            EG.ELC.ACCS.ZS           NaN           NaN   \n",
+       "United States         EG.ELC.ACCS.RU.ZS           NaN           NaN   \n",
+       "United States         EG.ELC.ACCS.UR.ZS           NaN           NaN   \n",
+       "United States            EG.NSF.ACCS.ZS           NaN           NaN   \n",
+       "United States         EG.NSF.ACCS.RU.ZS           NaN           NaN   \n",
+       "United States         EG.NSF.ACCS.UR.ZS           NaN           NaN   \n",
+       "United States              WP_time_01.1           NaN           NaN   \n",
+       "United States              WP_time_01.3           NaN           NaN   \n",
+       "United States              WP_time_01.8           NaN           NaN   \n",
+       "United States              WP_time_01.9           NaN           NaN   \n",
+       "United States              WP_time_01.2           NaN           NaN   \n",
+       "United States  per_si_allsi.adq_pop_tot           NaN           NaN   \n",
+       "United States     per_allsp.adq_pop_tot           NaN           NaN   \n",
+       "United States  per_sa_allsa.adq_pop_tot           NaN           NaN   \n",
+       "United States  per_lm_alllm.adq_pop_tot           NaN           NaN   \n",
+       "United States            SE.PRM.TENR.FE           NaN           NaN   \n",
+       "United States            SE.PRM.TENR.MA           NaN           NaN   \n",
+       "United States               SE.PRM.TENR           NaN           NaN   \n",
+       "United States         NY.ADJ.NNTY.KD.ZG           NaN           NaN   \n",
+       "United States            NY.ADJ.NNTY.KD           NaN           NaN   \n",
+       "United States            NY.ADJ.NNTY.CD           NaN           NaN   \n",
+       "United States      NY.ADJ.NNTY.PC.KD.ZG           NaN           NaN   \n",
+       "United States         NY.ADJ.NNTY.PC.KD           NaN           NaN   \n",
+       "United States         NY.ADJ.NNTY.PC.CD           NaN           NaN   \n",
+       "United States         NY.ADJ.SVNX.GN.ZS           NaN           NaN   \n",
+       "United States            NY.ADJ.SVNX.CD           NaN           NaN   \n",
+       "United States         NY.ADJ.SVNG.GN.ZS           NaN           NaN   \n",
+       "United States            NY.ADJ.SVNG.CD           NaN           NaN   \n",
+       "...                                 ...           ...           ...   \n",
+       "United States         AG.LND.EL5M.UR.K2           NaN           NaN   \n",
+       "United States               SP.URB.TOTL  1.264625e+08  1.292762e+08   \n",
+       "United States         SP.URB.TOTL.IN.ZS  6.999600e+01  7.037700e+01   \n",
+       "United States               SP.URB.GROW  2.449104e+00  2.200571e+00   \n",
+       "United States         EN.POP.EL5M.UR.ZS           NaN           NaN   \n",
+       "United States               SI.POV.URGP           NaN           NaN   \n",
+       "United States               SI.POV.URHC           NaN           NaN   \n",
+       "United States            DT.DOD.DIMF.CD           NaN           NaN   \n",
+       "United States            SH.MLR.NETS.ZS           NaN           NaN   \n",
+       "United States            IC.FRM.OUTG.ZS           NaN           NaN   \n",
+       "United States            SN.ITK.VITA.ZS           NaN           NaN   \n",
+       "United States         SL.EMP.VULN.FE.ZS           NaN           NaN   \n",
+       "United States         SL.EMP.VULN.MA.ZS           NaN           NaN   \n",
+       "United States            SL.EMP.VULN.ZS           NaN           NaN   \n",
+       "United States         SL.EMP.WORK.FE.ZS           NaN           NaN   \n",
+       "United States         SL.EMP.WORK.MA.ZS           NaN           NaN   \n",
+       "United States            SL.EMP.WORK.ZS           NaN           NaN   \n",
+       "United States               SP.DYN.WFRT           NaN           NaN   \n",
+       "United States         ER.GDP.FWTL.M3.KD           NaN           NaN   \n",
+       "United States               FP.WPI.TOTL  1.715426e+01  1.709399e+01   \n",
+       "United States         SG.DMK.ALLD.FN.ZS           NaN           NaN   \n",
+       "United States            SG.VAW.REAS.ZS           NaN           NaN   \n",
+       "United States            SG.VAW.ARGU.ZS           NaN           NaN   \n",
+       "United States            SG.VAW.BURN.ZS           NaN           NaN   \n",
+       "United States            SG.VAW.GOES.ZS           NaN           NaN   \n",
+       "United States            SG.VAW.NEGL.ZS           NaN           NaN   \n",
+       "United States            SG.VAW.REFU.ZS           NaN           NaN   \n",
+       "United States         SP.M18.2024.FE.ZS           NaN           NaN   \n",
+       "United States         SH.DYN.AIDS.FE.ZS           NaN           NaN   \n",
+       "United States         SE.ADT.1524.LT.ZS           NaN           NaN   \n",
+       "\n",
+       "                       1962          1963          1964          1965  \\\n",
+       "Country Name                                                            \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "...                     ...           ...           ...           ...   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States  1.319887e+08  1.346154e+08  1.372160e+08  1.396631e+08   \n",
+       "United States  7.075700e+01  7.113400e+01  7.150800e+01  7.187900e+01   \n",
+       "United States  2.076494e+00  1.970559e+00  1.913437e+00  1.767653e+00   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States  1.713467e+01  1.708947e+01  1.712412e+01  1.746468e+01   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "\n",
+       "                       1966     ...               2008          2009  \\\n",
+       "Country Name                    ...                                    \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...       9.794343e+01  9.709736e+01   \n",
+       "United States           NaN     ...       9.704943e+01  9.501399e+01   \n",
+       "United States           NaN     ...       9.748575e+01  9.603130e+01   \n",
+       "United States           NaN     ...      -2.788922e+00 -8.504354e-01   \n",
+       "United States           NaN     ...       1.231703e+13  1.221228e+13   \n",
+       "United States           NaN     ...       1.216984e+13  1.203082e+13   \n",
+       "United States           NaN     ...      -3.704072e+00 -1.715833e+00   \n",
+       "United States           NaN     ...       4.050401e+04  3.980903e+04   \n",
+       "United States           NaN     ...       4.001999e+04  3.921754e+04   \n",
+       "United States           NaN     ...       2.075956e+00  1.735874e+00   \n",
+       "United States           NaN     ...       3.070588e+11  2.516063e+11   \n",
+       "United States           NaN     ...       1.889611e+00  1.548930e+00   \n",
+       "United States           NaN     ...       2.794962e+11  2.245096e+11   \n",
+       "...                     ...     ...                ...           ...   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States  1.420087e+08     ...       2.446071e+08  2.472763e+08   \n",
+       "United States  7.224700e+01     ...       8.043800e+01  8.060600e+01   \n",
+       "United States  1.665559e+00     ...       1.156186e+00  1.085290e+00   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...       9.440000e+01  9.430000e+01   \n",
+       "United States           NaN     ...       9.170000e+01  9.160000e+01   \n",
+       "United States           NaN     ...       9.300000e+01  9.290000e+01   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States  1.803880e+01     ...       1.026299e+02  9.359888e+01   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "United States           NaN     ...                NaN           NaN   \n",
+       "\n",
+       "                       2010          2011          2012          2013  \\\n",
+       "Country Name                                                            \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States  1.000000e+02           NaN  1.000000e+02           NaN   \n",
+       "United States  1.000000e+02           NaN  1.000000e+02           NaN   \n",
+       "United States  1.000000e+02           NaN  1.000000e+02           NaN   \n",
+       "United States  1.000000e+02           NaN  1.000000e+02           NaN   \n",
+       "United States  1.000000e+02           NaN  1.000000e+02           NaN   \n",
+       "United States  1.000000e+02           NaN  1.000000e+02           NaN   \n",
+       "United States           NaN  8.795786e+01           NaN           NaN   \n",
+       "United States           NaN  8.406905e+01           NaN           NaN   \n",
+       "United States           NaN  8.099529e+01           NaN           NaN   \n",
+       "United States           NaN  9.281008e+01           NaN           NaN   \n",
+       "United States           NaN  9.198194e+01           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States  9.513818e+01  9.467457e+01  9.410391e+01  9.262706e+01   \n",
+       "United States  9.446875e+01  9.324649e+01  9.411461e+01  9.292106e+01   \n",
+       "United States  9.479582e+01  9.394464e+01  9.410937e+01  9.277709e+01   \n",
+       "United States  3.342398e+00  2.076668e+00  3.645016e+00  1.333261e+00   \n",
+       "United States  1.262046e+13  1.288254e+13  1.335212e+13  1.353013e+13   \n",
+       "United States  1.262046e+13  1.319294e+13  1.391432e+13  1.429645e+13   \n",
+       "United States  2.482066e+00  1.299926e+00  2.858440e+00  5.887723e-01   \n",
+       "United States  4.079712e+04  4.132745e+04  4.250877e+04  4.275905e+04   \n",
+       "United States  4.079712e+04  4.232319e+04  4.429865e+04  4.518081e+04   \n",
+       "United States  2.973072e+00  3.712649e+00  5.847932e+00  5.621072e+00   \n",
+       "United States  4.495612e+11  5.867062e+11  9.705286e+11  9.607086e+11   \n",
+       "United States  2.797694e+00  3.545417e+00  5.690151e+00  5.470228e+00   \n",
+       "United States  4.230421e+11  5.602786e+11  9.443431e+11  9.349277e+11   \n",
+       "...                     ...           ...           ...           ...   \n",
+       "United States  1.752022e+04           NaN           NaN           NaN   \n",
+       "United States  2.498656e+08  2.523052e+08  2.547624e+08  2.571827e+08   \n",
+       "United States  8.077200e+01  8.094000e+01  8.110800e+01  8.127700e+01   \n",
+       "United States  1.041720e+00  9.716271e-01  9.691533e-01  9.455534e-01   \n",
+       "United States  2.263942e+00           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States  9.440000e+01  9.450000e+01  9.440000e+01  9.440000e+01   \n",
+       "United States  9.170000e+01  9.200000e+01  9.220000e+01  9.250000e+01   \n",
+       "United States  9.300000e+01  9.320000e+01  9.320000e+01  9.340000e+01   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN  3.200610e+01           NaN   \n",
+       "United States  1.000000e+02  1.088326e+02  1.094325e+02  1.101092e+02   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "United States           NaN           NaN           NaN           NaN   \n",
+       "\n",
+       "                       2014          2015  2016  Unnamed: 61  \n",
+       "Country Name                                                  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States  9.358380e+01           NaN   NaN          NaN  \n",
+       "United States  9.479771e+01           NaN   NaN          NaN  \n",
+       "United States  8.711619e+01           NaN   NaN          NaN  \n",
+       "United States  9.790414e+01           NaN   NaN          NaN  \n",
+       "United States  9.236126e+01           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States  9.443206e+01           NaN   NaN          NaN  \n",
+       "United States  9.365279e+01           NaN   NaN          NaN  \n",
+       "United States  9.403453e+01           NaN   NaN          NaN  \n",
+       "United States  2.769931e+00           NaN   NaN          NaN  \n",
+       "United States  1.390491e+13           NaN   NaN          NaN  \n",
+       "United States  1.494105e+13           NaN   NaN          NaN  \n",
+       "United States  1.970734e+00           NaN   NaN          NaN  \n",
+       "United States  4.360171e+04           NaN   NaN          NaN  \n",
+       "United States  4.685075e+04           NaN   NaN          NaN  \n",
+       "United States  6.560417e+00           NaN   NaN          NaN  \n",
+       "United States  1.169276e+12           NaN   NaN          NaN  \n",
+       "United States  6.418385e+00           NaN   NaN          NaN  \n",
+       "United States  1.143962e+12           NaN   NaN          NaN  \n",
+       "...                     ...           ...   ...          ...  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States  2.597405e+08  2.623324e+08   NaN          NaN  \n",
+       "United States  8.144700e+01  8.161700e+01   NaN          NaN  \n",
+       "United States  9.896394e-01  9.929296e-01   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States  3.331436e+01           NaN   NaN          NaN  \n",
+       "United States  1.111422e+02  1.030900e+02   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "United States           NaN           NaN   NaN          NaN  \n",
+       "\n",
+       "[1452 rows x 61 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wb.loc[\"United States\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/content/07-pandas/pandas_solutions.txt b/content/07-pandas/pandas_solutions.txt
new file mode 100644
index 00000000..1db8c26b
--- /dev/null
+++ b/content/07-pandas/pandas_solutions.txt
@@ -0,0 +1,37 @@
+Q1:
+
+np.allclose(names.groupby(["year", "sex"]).prop.sum(), 1.0)
+
+
+Q2:
+
+boys = top[top.sex == "M"]
+girls = top[top.sex == "F"]
+
+Q3:
+
+all_names = top["name"].unique()
+all_names.dtype
+
+len(all_names)
+
+
+Q4:
+
+what are all the names that appear for both boys and girls?
+
+boy_names = top[top["sex"] == "M"]["name"].unique()
+girl_names = top[top["sex"] == "F"]["name"].unique()
+joint = np.intersect1d(boy_names, girl_names)
+
+
+Q5:
+
+def get_count(group, q=0.5):
+    group = group.sort_values(by="prop", ascending=False)
+    return group["prop"].cumsum().searchsorted(0.5)[0] + 1
+
+diversity = top.groupby(["year", "sex"]).apply(get_count)
+diversity = diversity.unstack("sex")
+diversity.plot()
+
diff --git a/content/07-pandas/sample.csv b/content/07-pandas/sample.csv
new file mode 100644
index 00000000..65ee2acc
--- /dev/null
+++ b/content/07-pandas/sample.csv
@@ -0,0 +1,20 @@
+"student",  "hw 1", "hw 2", "hw 3", "hw 4", "exam"
+"A",  10  , 9   , 10  , 7   , 97
+"B",  8   , 7   , 9   , 9   , 82
+"C",      , 9   , 6   , 5   , 75
+"D",  8   , 9   , 9   , 9   , 90
+"E",      , 10  , 10  , 10  , 95
+"F",  8   , 2   , 6   , 7   , 66
+"G",  6   ,     , 4   , 5   , 60
+"H",  8   , 8   , 9   , 8   , 84
+"I",  10  , 7   , 10  , 10  , 92
+"J",  10  , 6   , 9   , 9   , 91
+"K",  8   , 7   , 6   , 8   , 87
+"L",  3   , 8   , 5   , 7   , 71
+"M",  9   , 9   , 8   , 9   , 94
+"N",  8   , 10  , 9   , 9   , 90
+"O",  10  , 10  , 10  , 9   , 99
+"P",  8   , 9   , 8   , 10  , 94
+"Q",  5   , 7   , 6   , 5   , 78
+
+
diff --git a/lectures/08-packages/NOTES b/content/09-packages/NOTES
similarity index 100%
rename from lectures/08-packages/NOTES
rename to content/09-packages/NOTES
diff --git a/content/09-packages/argparse_example.py b/content/09-packages/argparse_example.py
new file mode 100644
index 00000000..003893a2
--- /dev/null
+++ b/content/09-packages/argparse_example.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+
+# to get usage: use -h
+import argparse
+
+
+def setup_args():
+
+    # simple example of argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-a", help="the -a option", action="store_true")
+    parser.add_argument("-b", help="-b takes a number", type=int, default=0)
+    parser.add_argument("-c", help="-c takes a string", type=str, default=None)
+    parser.add_argument("--darg", help="the --darg option", action="store_true")
+    parser.add_argument("--earg", help="--earg takes a string", type=str, metavar="test",
+                        default="example string")
+
+    # extra arguments (positional)
+    parser.add_argument("extras", metavar="extra", type=str, nargs="*",
+                        help="optional positional arguments")
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+
+    args = setup_args()
+
+
+    if args.a:
+        print("-a set")
+    print(f"-b = {args.b}")
+    print(f"-c = {args.c}")
+    if args.darg:
+        print("--dargs set")
+    print(f"--earg value = {args.earg}")
+
+    print(" ")
+    print("extra positional arguments: ")
+    if len(args.extras) > 0:
+        for e in args.extras:
+            print(e)
diff --git a/content/09-packages/packaging.pdf b/content/09-packages/packaging.pdf
new file mode 100644
index 00000000..9b324497
Binary files /dev/null and b/content/09-packages/packaging.pdf differ
diff --git a/content/09-packages/python-arguments.md b/content/09-packages/python-arguments.md
new file mode 100644
index 00000000..0cbb2683
--- /dev/null
+++ b/content/09-packages/python-arguments.md
@@ -0,0 +1,21 @@
+# Command line arguments
+
+For standalone programs, we often want to have our program take
+command line arguments that affect the runtime behavior of our
+program.  There are a variety of mechanisms to do this in python, but
+the best option is the [argparse
+module](https://docs.python.org/3/library/argparse.html).
+
+Here's an example of using `argparse` to take a variety of options:
+
+```{literalinclude} argparse_example.py
+---
+language: python
+```
+
+A nice feature of `argparse` is that it automatically generates help for us.  If
+we place the above code in `argparse_example.py` then we can do:
+
+```python
+python argparse_example.py --help
+```
diff --git a/content/09-packages/python-modules.md b/content/09-packages/python-modules.md
new file mode 100644
index 00000000..9aa264c0
--- /dev/null
+++ b/content/09-packages/python-modules.md
@@ -0,0 +1,91 @@
+# Python Modules
+
+So far, we've been writing our code all in Jupyter.  But when it comes
+time to write code that we want to reuse, we want to put it into a
+standalone `*.py` file.
+
+Then we can load it on in python (or Jupyter) and use the capabilities
+it provides or make it a standalone program that can be run from the
+command line.
+
+```{tip}
+Jupyter is great for interactive explorations and sharing your workflow with others
+in a self-contained way.  But if there is an operation that you do over and over,
+you should put it into a separate module that you import.  That way you only need to
+maintain and debug a single instance of the function, and all your workflows can reuse it.
+```
+
+
+## Editors
+
+There are a number of popular editors for writing python source.  Some
+popular ones include:
+
+* spyder: https://www.spyder-ide.org/
+
+* VS Code: https://code.visualstudio.com/
+
+* emacs / vi
+
+
+## Standalone module
+
+Here's a very simply module (lets call it `hello.py`):
+
+```python
+def hello():
+    print("hello")
+
+if __name__ == "__main__":
+    hello()
+```
+
+There are two ways we can use this.
+
+* Inside of python (or IPython), we can do:
+
+  ```python
+  import hello
+  hello.hello()
+  ```
+
+* From the command line, we can do:
+
+  ```python
+  python hello.py
+  ```
+
+Additionally, on a Unix system, we can add:
+
+```python
+#!/usr/bin/env python3
+```
+
+to the top and then mark the file as executable, via:
+
+```bash
+chmod a+x hello.py
+```
+
+allowing us to execute it simply as:
+
+```bash
+./hello.py
+```
+
+```{hint}
+Here we see how the `__name__` variable is treated by python:
+
+* If we import our module into python, then `__name__` is set to the module name
+
+* If we run the module from the command line, then `__name__` is set to `__main__`
+```
+
+## Changing module contents
+
+If we make changes to our module file, then we need to re-import it.  This can be done as:
+
+```python
+import importlib
+example = importlib.reload(example)
+```
diff --git a/content/09-packages/python-more-modules.md b/content/09-packages/python-more-modules.md
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+# Module Paths
+
+How does python find modules?  It has a [search order](https://docs.python.org/3/tutorial/modules.html#the-module-search-path):
+
+* current directory
+
+* `PYTHONPATH` environment variable (this follows the same format as
+  the shell `PATH` environment variable)
+
+* System-wide python installation default path (usually has a
+  `site-packages` directory)
+
+We can look at the path via ``sys.path``.  On my machine I get:
+
+```
+['/home/zingale/.local/bin',
+ '/home/zingale/classes/python-science/content/09-packages',
+ '/home/zingale/classes/numerical_exercises',
+ '/home/zingale/classes/astro_animations',
+ '/usr/lib64/python312.zip',
+ '/usr/lib64/python3.12',
+ '/usr/lib64/python3.12/lib-dynload',
+ '',
+ '/home/zingale/.local/lib/python3.12/site-packages',
+ '/usr/lib64/python3.12/site-packages',
+ '/usr/lib/python3.12/site-packages']
+
+```
+
+```{note}
+You can explicitly add paths to the ``sys.path`` by setting the `PYTHONPATH`
+environment variable.
+```
+
+
+Notice that the general places that it looks are in `~/.local` and in
+`/usr`.  The first is the user-specific path&mdash;you can install things
+here without admin privileges.  The second is a system-wide path.
+
+You can find your user-specific path via:
+
+```bash
+python3 -m site --user-site
+```
+
+on my machine, this gives:
+
+```
+/home/zingale/.local/lib/python3.12/site-packages
+```
+
+```{tip}
+Using `PYTHONPATH` to quickly add a module to your search path is an easy hack,
+but if you are developing a library that will be used by others, it is better
+to make the modules installable to the system search paths.  This is where
+_packaging_ comes into play.
+```
diff --git a/content/09-packages/python-packages.md b/content/09-packages/python-packages.md
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+# Packaging
+
+
+
+Let's look at the structure of creating an installable python package.
+
+```{note}
+The python packaging system is constantly evolving, and the current recommendations
+of tools is list here: https://packaging.python.org/en/latest/guides/tool-recommendations/
+```
+
+![xkcd](python_environment.png)
+
+(from https://xkcd.com)
+
+
+## Our example
+
+We'll work on an example that builds on the Mandelbrot set exercise
+from our matplotlib discussion.  Our example is hosted here:
+
+https://github.com/sbu-python-class/mymodule
+
+On your local computer, if you have `git` installed, you can clone this via:
+
+```
+git clone https://github.com/sbu-python-class/mymodule.git
+```
+
+The directory structure appears as:
+
+```
+mymodule/
+├── mymodule
+│   ├── __init__.py
+│   └── mandel.py
+├── pyproject.toml
+└── README.md
+```
+
+This is a rather common way of structuring a project:
+
+* The top-level `mymodule` directory is not part of the python
+  package, but instead is where the source control (e.g. git) begins,
+  and also hosts setup files that are used for installation
+
+* `mymodule/mymodule` is the actual python module that we will load.
+
+   ```{important}
+   To make python recognize this as a module, we need an `__init__.py`
+   file there&mdash;it can be completely empty.
+   ```
+
+* The actual `*.py` files that make up our module are in `mymodule/mymodule`
+
+Right now, this package does not appear in our python search path, so
+the only way to load it is to work in the top-level `mymodule/`
+directory, and then we can do:
+
+```python
+import mymodule.mandel
+```
+
+we could also do:
+
+```python
+from mymodule.mandel import mandelbrot
+```
+
+
+## setuptools
+
+A popular set of packages are:
+
+* Installation:
+
+  * `pip` to install packages from PyPI
+  * `conda` for disctribution cross-platform software stacks
+
+* Packaging tools:
+
+  * `setuptools` to create source distributions
+  * `build` for binary distributions
+  * `twine` to upload to PyPI
+
+We'll look at how to use [`setuptools`](https://setuptools.pypa.io/en/latest/build_meta.html) to package our library.
+
+```{note}
+A lot of setuptools documentation is out-of-date and
+inconsistent with the packaging guidelines.
+
+Packages used to create a `setup.py` file that had all of the project information,
+but this is deprecated.  Instead we should create a
+[pyproject.toml](https://packaging.python.org/en/latest/guides/writing-pyproject-toml/) file---this
+is consistent with [PEP 517](https://peps.python.org/pep-0517/).
+```
+
+Here's a first `pyproject.toml`:
+
+```toml
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "mymodule"
+description = "test module for PHY 546"
+readme = "README.md"
+license.text = "BSD"
+version="0.1.0"
+authors = [
+  {name="Michael Zingale"},
+  {email="michael.zingale@stonybrook.edu"},
+]
+
+dependencies = [
+  "numpy",
+  "matplotlib",
+]
+
+```
+
+Some notes:
+
+* We have a `[build-system]` table that specifies the build tool.
+  Here we choose `setuptools`.
+
+* We have a lot of metadata for our project defined in the `[project]`
+  table.
+
+* We also list the dependencies of our project in the `[project]` table.
+  This will allow the installer to install any missing packages that are
+  required.
+
+There are many additional options to specify how to find files that
+are part of the project as well as data files, etc.
+
+
+```{tip}
+`pyproject.toml` also allows you to specify defaults for tools, like
+`pylint`, `flake8`, and others with a `[tool.X]` subtable.
+```
+
+```{note}
+Some projects also contain a `setup.cfg`
+file when using `setuptools`.  This is
+usually not needed, since we can put everything
+in the `[project]` table.
+```
+
+
+## Installing
+
+We can now install simply as:
+
+```bash
+pip install .
+```
+
+```{tip}
+Look in your `.local/lib/python3.12/site-packages` directory, and you'll
+see the module there.
+```
+
+If instead, we want to install in a way that still allows us to edit the source,
+we can install as "editable" via:
+
+```bash
+pip install -e .
+```
+
+To uninstall, we can do:
+
+```bash
+pip uninstall mymodule
+```
+
+in a directory outside of our project (otherwise, `pip` may get confused).
+
+## Using our module
+
+Once the module is installed, we can use it from any directory.  For example, if we do:
+
+```python
+import mymodule
+print(mymodule.__file__)
+```
+
+it shows us where the module is installed on our system.  In my case, it is:
+
+```
+/home/zingale/.local/lib/python3.12/site-packages/mymodule-0.1.0-py3.12.egg/mymodule/__init__.py
+```
+
+Let's generate a plot:
+
+```python
+from mymodule.mandel import mandelbrot
+fig = mandelbrot(128)
+fig.savefig("test.png")
+```
+
+This produces the plot shown below:
+
+![sample Mandelbrot set image using 128x128 points](test.png)
diff --git a/content/09-packages/python-tools.md b/content/09-packages/python-tools.md
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+# Tools to Make Your Life Easier
+
+## Version control
+
+Generally, you should put your project into version control.  The most widely used
+package today is [git](https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control).
+git will track the changes you make to your code, allow you to revert changes, collaboratively
+develop with others, work on several different features independently from one another while
+keeping the main codebase clean and more.  
+
+git is often used together with [github](https://github.com), which provides a web-based view
+of your source code and provides additional mechanisms for collaboration.
+
+A nice introduction to git/github is provided by the [Software
+Carpentry _Version Control with Git_
+lesson](https://swcarpentry.github.io/git-novice/).
+
+
+## Code checkers
+
+There are a number of tools that help check code for formatting and
+syntax errors that are quite useful for developers.  Many projects
+automatically enforce these tools on changes submitted to github.
+
+```{tip}
+Many editors have plugins that can automatically run these tools
+as your write your code.
+```
+
+* [flake8](https://flake8.pycqa.org/en/latest/)
+
+  `flake8` is a checker for [PEP 8](https://peps.python.org/pep-0008/)
+  style conformance.  You can turn off checks that you don't like
+  via a [`.flake8`
+  file](https://flake8.pycqa.org/en/latest/user/configuration.html#configuration-locations).
+
+* [pylint](https://pypi.org/project/pylint/)
+
+  `pylint` is a static code analyzer.  It can find errors and also suggest improvements
+  to your code.  You can [generate a configuration file](https://pylint.readthedocs.io/en/latest/user_guide/configuration/index.html)
+  to customize its behavior (or add a section to `pyproject.toml`).
+
+* [black](https://pypi.org/project/black/)
+
+  `black` is an _uncompromising code formatted_.  It will automatically rewrite your code
+  based on PEP-8 style.
+
+* [pyupgrade](https://github.com/asottile/pyupgrade)
+
+  `pyupgrade` will upgrade source to a later python standard, making
+  use of new features where available.  For instance, you can run as:
+
+  ```
+  pyupgrade --py39-plus file.py
+  ```
+
+  to update to python 3.9 support.
+
+* [isort](https://pycqa.github.io/isort/)
+
+  `isort` simply sorts the module imports at the top of your modules,
+  grouping the standard python ones together followed by
+  package-specific ones.
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+# More pytest
+
+Unit tests sometimes require some setup to be done before the test is run.
+Fixtures provide this capability, allowing tests to run with a consistent
+environment and data.
+
+Standard pytest fixtures are written as functions with the `@pytest.fixture`
+decorator:
+```python
+@pytest.fixture
+def message():
+    return "Hello world!"
+```
+
+A fixture may return an object, which will be passed to any function
+that requests it, or it may just do some setup tasks (like creating a file or
+connecting to a database).
+
+Test functions can request a fixture by specifying a parameter with the same
+name as the fixture:
+```python
+def test_split(message):
+    assert len(message.split()) == 2
+```
+
+An alternate method for initializing test state is with explicit setup/teardown
+functions, which we'll look at a bit later. This is a style that's available in
+many other languages as well: see https://en.wikipedia.org/wiki/XUnit.
+
+## Fixtures examples
+
+Fixtures are reusable across different tests. This lets us avoid repeating the
+same setup code in multiple places, especially as we add more tests or need
+more complicated inputs.
+
+Here are some tests for the `Item` class that use fixtures, adapted from the
+[shopping cart exercise](w4-exercise-1). The full code is available
+[here](https://github.com/sbu-python-class/python-science/blob/main/examples/testing/pytest/fixtures/test_item.py)
+on the github repository for this site. You can download this file and run
+the tests with `pytest -v test_item.py`.
+
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_item.py
+:lines: 58-68
+```
+
+All the fixtures that a test depends on will run once for each test.
+This gives each test a fresh copy of the data, so any changes made to the
+fixture results inside a test won't impact other tests.
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_item.py
+:lines: 70-83
+```
+
+We can also test that a function raises specific exceptions with `pytest.raises`:
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_item.py
+:lines: 85-91
+```
+
+### Fixtures can request other fixtures
+
+This is useful to split up complex initialization into smaller parts.
+A fixture can also modify the results of the fixtures it requests, which *will*
+be visible to anything that includes the fixture.
+
+Here is a set of tests that show how this can be used ([test_list.py](https://github.com/sbu-python-class/python-science/blob/main/examples/testing/pytest/fixtures/test_list.py)):
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_list.py
+:lines: 1-13
+```
+
+Note that `append_1()` and `append_2()` only modify `numbers`, and don't return
+anything. `append_2()` requires `append_1`, to make sure they are run in the
+right order.
+
+This test only requires `numbers`, so it will receive an empty list:
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_list.py
+:lines: 15-16
+```
+
+This test requires `append_1`, but not `append_2`:
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_list.py
+:lines: 18-19
+```
+
+This test requires `append_2`, which itself pulls in `append_1`:
+```{literalinclude} ../../examples/testing/pytest/fixtures/test_list.py
+:lines: 21-22
+```
+
+
+## Example class
+
+It is common to use a class to organize a set of related unit tests.  This is
+not a full-fledged class -- it simply helps to organize tests and data.  In particular,
+there is no constructor, `__init__()`.  See https://stackoverflow.com/questions/21430900/py-test-skips-test-class-if-constructor-is-defined
+
+We'll look at an example with a NumPy array
+
+* We'll use xunit-style setup/teardown methods to store the array as a class
+  member
+
+  * This way we don't have to ask for it in each of the tests
+
+* We'll use NumPy's own assertion functions: https://numpy.org/doc/stable/reference/routines.testing.html
+
+
+Here's an example:
+
+```{include} ../../examples/testing/pytest/class/test_class.py
+:code: python
+```
+
+```{note}
+Here we see the [`@classmethod` decorator](https://docs.python.org/3/library/functions.html#classmethod).
+This means that the function receives the class itself as the first argument rather than an instance,
+e.g., `self`.
+```
+
+Put this into a file called `test_class.py` and then we can run as:
+
+```bash
+pytest -v
+```
+
+```{admonition} Quick Exercise
+Try adding a new test that modifies `self.a`, above `test_max()`.
+Does this behave as you expect? What happens if you move the array creation
+into `setup_class()` instead?
+```
+
+% By default, pytest will capture stdout and only show it on failures. To make it
+% always show stdout, we add the `-s` flag.
diff --git a/content/10-testing/notes.txt b/content/10-testing/notes.txt
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+-- define testing: unit, integration, regression testing
+ 
+   (also verification and validation)
+
+-- nose and unit tests
+
+   very nice:
+   http://ivory.idyll.org/articles/nose-intro.html
+
+
+   http://pythontesting.net/framework/nose/nose-introduction/
+
+-- regression tests
+
+  * write a comparison tool
+  * self-documenting file helps
+
+-- reproducibility
+
+-- coverage
+
+   http://blog.jameskyle.org/2010/10/nose-unit-testing-quick-start/
+
+   nosetests-3 -sv --with-coverage --cover-erase --cover-inclusive --cover-package=.
diff --git a/content/10-testing/pytest b/content/10-testing/pytest
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+pip3 install -U pytest --user
+
+note: it might install it in ~/.local/bin/ but you might have a
+different version of pytest elsewhere in your path.
+
+the older version was `py.test` now, it is `pytest`
+
+to make sure that you get the version consistent with your install, you can do:
+
+python3 -m pytest
+
diff --git a/content/10-testing/pytest.md b/content/10-testing/pytest.md
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+# pytest
+
+`pytest` is a unit testing framework for python code.
+
+Basic elements:
+
+* Discoverability: it will find the tests
+
+* Automation
+
+* Fixtures (setup and teardown)
+
+## Installing
+
+You can install `pytest` for a single user as:
+
+```
+pip install pytest
+```
+
+This should put `pytest` in your search path, likely in `~/.local/bin`.
+
+If you want to generate coverage reports, you should also install `pytest-cov`:
+
+```
+pip install pytest-cov
+```
+
+## Test discovery
+
+Adhering to these naming conventions will ensure that your tests are automatically found:
+
+* File names should start or end with "test":
+
+  * `test_example.py`
+  * `example_test.py`
+
+* For tests in a class, the class name should begin with `Test`
+
+  * e.g., `TestExample`
+  * There should be no `__init__()`
+
+* Test method / function names should start with `test_`
+
+  * e.g., `test_example()`
+
+## Assertions
+
+Tests use assertions (via python’s `assert` statement) to check behavior at runtime
+
+* https://docs.python.org/3/reference/simple_stmts.html#assert 
+
+* Basic usage: `assert expression`
+
+  * Raises `AssertionError` if expression is not true
+
+  * e.g., `assert 1 == 0` will fail with an exception
+
+* pytest does some magic under the hood to add more details about what
+  exactly went wrong, which we will see below
+
+## Simple pytest example
+
+Create a file named `test_simple.py` with the following content:
+
+```python
+def multiply(a, b):
+    return a*b
+
+def test_multiply():
+    assert multiply(4, 6) == 24
+
+def test_multiply2():
+    assert multiply(5, 6) == 2
+```
+
+then we can run the tests as:
+
+```
+pytest -v
+```
+
+and we get the output:
+
+```
+============================= test session starts ==============================
+platform linux -- Python 3.11.3, pytest-7.2.2, pluggy-1.0.0 -- /usr/bin/python3
+cachedir: .pytest_cache
+rootdir: /home/zingale/temp/pytest
+plugins: anyio-3.6.2
+collected 2 items                                                              
+
+test_simple.py::test_multiply PASSED                                     [ 50%]
+test_simple.py::test_multiply2 FAILED                                    [100%]
+
+=================================== FAILURES ===================================
+________________________________ test_multiply2 ________________________________
+
+    def test_multiply2():
+>       assert multiply(5, 6) == 2
+E       assert 30 == 2
+E        +  where 30 = multiply(5, 6)
+
+test_simple.py:8: AssertionError
+=========================== short test summary info ============================
+FAILED test_simple.py::test_multiply2 - assert 30 == 2
+========================= 1 failed, 1 passed in 0.04s ==========================
+```
+
+this is telling us that one of our tests has failed.
diff --git a/content/10-testing/real-world-example.md b/content/10-testing/real-world-example.md
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+# Real World Example
+
+Let's look at the testing in a larger python package.  We'll use our
+group's python hydrodynamics code, pyro, as a test:
+
+https://github.com/python-hydro/pyro2
+
+## Installing
+
+We need to install the package first, via the `setup.py`:
+
+```bash
+python setup.py install --user
+```
+
+or alternately as
+
+```bash
+pip install .
+```
+
+## Running the tests
+
+We can run the tests via:
+
+```bash
+pytest -v pyro
+```
+
+## Using notebooks as tests
+
+Sometimes we want to use Jupyter notebooks as tests themselves&mdash;this
+is enabled via the [nbval plugin](https://nbval.readthedocs.io/en/latest/).  In
+this way, pytest will execute the cells in the notebook and compare
+the result to the result stored in the notebook.  If they agree, then
+the test passes.
+
+Sometimes there's a particular cell that we don't want to be part of the
+testing&mdash;we can disable these on a cell-by-cell basis by [adding
+tags to a cell](https://nbval.readthedocs.io/en/latest/#Using-tags-instead-of-comments).
+
+We can test notebooks as:
+
+```bash
+pytest -v --nbval pyro
+```
+
+## Coverage report
+
+The [pytest-cov](https://pytest-cov.readthedocs.io/en/latest/) plugin enables the generation
+of a coverage report.  This will tell you what fraction of each python file was tested.
+We run this as:
+
+```bash
+pytest -v --cov=pyro --nbval pyro
+```
+
+We can also generate a more detailed interactive report with
+
+```bash
+coverage html
+```
+
+## Other types of tests
+
+Unit tests are only one form of testing&mdash;they test a function in
+isolation of others.  Sometimes we need to test everything working together.
+For scientific codes, regression testing is often used.  The basic workflow
+is:
+
+* Start with the project working in a way you are happy with
+
+* Store the output of one (or more) runs as a _benchmark_.
+
+* Each time you make changes, run the code and compare the new output
+  to the stored benchmark.
+
+  * If there are no differences, then your changes are likely good
+    (but there is always the case of some feature not being tested).
+
+  * If there are differences, then either you introduced a bug, in which
+    case you should fix it, or you fixed a bug, in which case you should
+    update the benchmarks.
+
+For our example code, pyro, the regression test runs simulations using
+all the different solvers and compares against the stored output, zone-by-zone
+for any differences.  The comparison itself is built into the main driver
+of the code and can be invoked as:
+
+```bash
+./pyro/test.py
+```
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+++ b/content/10-testing/testing.fodp
@@ -0,0 +1,177709 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<office:document xmlns:office="urn:oasis:names:tc:opendocument:xmlns:office:1.0" xmlns:style="urn:oasis:names:tc:opendocument:xmlns:style:1.0" xmlns:text="urn:oasis:names:tc:opendocument:xmlns:text:1.0" xmlns:table="urn:oasis:names:tc:opendocument:xmlns:table:1.0" xmlns:draw="urn:oasis:names:tc:opendocument:xmlns:drawing:1.0" xmlns:fo="urn:oasis:names:tc:opendocument:xmlns:xsl-fo-compatible:1.0" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:meta="urn:oasis:names:tc:opendocument:xmlns:meta:1.0" xmlns:number="urn:oasis:names:tc:opendocument:xmlns:datastyle:1.0" xmlns:presentation="urn:oasis:names:tc:opendocument:xmlns:presentation:1.0" xmlns:svg="urn:oasis:names:tc:opendocument:xmlns:svg-compatible:1.0" xmlns:chart="urn:oasis:names:tc:opendocument:xmlns:chart:1.0" xmlns:dr3d="urn:oasis:names:tc:opendocument:xmlns:dr3d:1.0" xmlns:math="http://www.w3.org/1998/Math/MathML" xmlns:form="urn:oasis:names:tc:opendocument:xmlns:form:1.0" xmlns:script="urn:oasis:names:tc:opendocument:xmlns:script:1.0" xmlns:config="urn:oasis:names:tc:opendocument:xmlns:config:1.0" xmlns:ooo="http://openoffice.org/2004/office" xmlns:ooow="http://openoffice.org/2004/writer" xmlns:oooc="http://openoffice.org/2004/calc" xmlns:dom="http://www.w3.org/2001/xml-events" xmlns:xforms="http://www.w3.org/2002/xforms" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:smil="urn:oasis:names:tc:opendocument:xmlns:smil-compatible:1.0" xmlns:anim="urn:oasis:names:tc:opendocument:xmlns:animation:1.0" xmlns:rpt="http://openoffice.org/2005/report" xmlns:of="urn:oasis:names:tc:opendocument:xmlns:of:1.2" xmlns:xhtml="http://www.w3.org/1999/xhtml" xmlns:grddl="http://www.w3.org/2003/g/data-view#" xmlns:officeooo="http://openoffice.org/2009/office" xmlns:tableooo="http://openoffice.org/2009/table" xmlns:drawooo="http://openoffice.org/2010/draw" xmlns:calcext="urn:org:documentfoundation:names:experimental:calc:xmlns:calcext:1.0" xmlns:loext="urn:org:documentfoundation:names:experimental:office:xmlns:loext:1.0" xmlns:field="urn:openoffice:names:experimental:ooo-ms-interop:xmlns:field:1.0" xmlns:formx="urn:openoffice:names:experimental:ooxml-odf-interop:xmlns:form:1.0" xmlns:css3t="http://www.w3.org/TR/css3-text/" office:version="1.2" office:mimetype="application/vnd.oasis.opendocument.presentation">
+ <office:meta><meta:initial-creator>Michael Zingale</meta:initial-creator><meta:creation-date>2013-01-02T12:36:14</meta:creation-date><dc:date>2019-07-23T20:09:25.108117001</dc:date><meta:editing-duration>P1DT13H52M28S</meta:editing-duration><meta:editing-cycles>192</meta:editing-cycles><meta:generator>LibreOffice/6.2.5.2$Linux_X86_64 LibreOffice_project/20$Build-2</meta:generator><meta:document-statistic meta:object-count="199"/></office:meta>
+ <office:settings>
+  <config:config-item-set config:name="ooo:view-settings">
+   <config:config-item config:name="VisibleAreaTop" config:type="int">-301</config:config-item>
+   <config:config-item config:name="VisibleAreaLeft" config:type="int">-4555</config:config-item>
+   <config:config-item config:name="VisibleAreaWidth" config:type="int">37578</config:config-item>
+   <config:config-item config:name="VisibleAreaHeight" config:type="int">21870</config:config-item>
+   <config:config-item-map-indexed config:name="Views">
+    <config:config-item-map-entry>
+     <config:config-item config:name="ViewId" config:type="string">view1</config:config-item>
+     <config:config-item config:name="GridIsVisible" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="GridIsFront" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="IsSnapToGrid" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="IsSnapToPageMargins" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="IsSnapToSnapLines" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="IsSnapToObjectFrame" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="IsSnapToObjectPoints" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="IsPlusHandlesAlwaysVisible" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="IsFrameDragSingles" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="EliminatePolyPointLimitAngle" config:type="int">1500</config:config-item>
+     <config:config-item config:name="IsEliminatePolyPoints" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="VisibleLayers" config:type="base64Binary">Hw==</config:config-item>
+     <config:config-item config:name="PrintableLayers" config:type="base64Binary">Hw==</config:config-item>
+     <config:config-item config:name="LockedLayers" config:type="base64Binary"/>
+     <config:config-item config:name="NoAttribs" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="NoColors" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="RulerIsVisible" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="PageKind" config:type="short">0</config:config-item>
+     <config:config-item config:name="SelectedPage" config:type="short">19</config:config-item>
+     <config:config-item config:name="IsLayerMode" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="IsDoubleClickTextEdit" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="IsClickChangeRotation" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="SlidesPerRow" config:type="short">4</config:config-item>
+     <config:config-item config:name="EditMode" config:type="int">0</config:config-item>
+     <config:config-item config:name="VisibleAreaTop" config:type="int">-331</config:config-item>
+     <config:config-item config:name="VisibleAreaLeft" config:type="int">-9360</config:config-item>
+     <config:config-item config:name="VisibleAreaWidth" config:type="int">47262</config:config-item>
+     <config:config-item config:name="VisibleAreaHeight" config:type="int">21862</config:config-item>
+     <config:config-item config:name="GridCoarseWidth" config:type="int">2540</config:config-item>
+     <config:config-item config:name="GridCoarseHeight" config:type="int">2540</config:config-item>
+     <config:config-item config:name="GridFineWidth" config:type="int">254</config:config-item>
+     <config:config-item config:name="GridFineHeight" config:type="int">254</config:config-item>
+     <config:config-item config:name="GridSnapWidthXNumerator" config:type="int">254</config:config-item>
+     <config:config-item config:name="GridSnapWidthXDenominator" config:type="int">1</config:config-item>
+     <config:config-item config:name="GridSnapWidthYNumerator" config:type="int">254</config:config-item>
+     <config:config-item config:name="GridSnapWidthYDenominator" config:type="int">1</config:config-item>
+     <config:config-item config:name="IsAngleSnapEnabled" config:type="boolean">false</config:config-item>
+     <config:config-item config:name="SnapAngle" config:type="int">1500</config:config-item>
+     <config:config-item config:name="ZoomOnPage" config:type="boolean">true</config:config-item>
+     <config:config-item config:name="AnchoredTextOverflowLegacy" config:type="boolean">false</config:config-item>
+    </config:config-item-map-entry>
+   </config:config-item-map-indexed>
+  </config:config-item-set>
+  <config:config-item-set config:name="ooo:configuration-settings">
+   <config:config-item config:name="ApplyUserData" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="BitmapTableURL" config:type="string">$(brandbaseurl)/share/palette%3B$(user)/config/standard.sob</config:config-item>
+   <config:config-item config:name="CharacterCompressionType" config:type="short">0</config:config-item>
+   <config:config-item config:name="ColorTableURL" config:type="string">$(brandbaseurl)/share/palette%3B$(user)/config/standard.soc</config:config-item>
+   <config:config-item config:name="DashTableURL" config:type="string">$(brandbaseurl)/share/palette%3B$(user)/config/standard.sod</config:config-item>
+   <config:config-item config:name="DefaultTabStop" config:type="int">1270</config:config-item>
+   <config:config-item config:name="EmbedAsianScriptFonts" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="EmbedComplexScriptFonts" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="EmbedFonts" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="EmbedLatinScriptFonts" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="EmbedOnlyUsedFonts" config:type="boolean">false</config:config-item>
+   <config:config-item-map-indexed config:name="ForbiddenCharacters">
+    <config:config-item-map-entry>
+     <config:config-item config:name="Language" config:type="string">en</config:config-item>
+     <config:config-item config:name="Country" config:type="string">US</config:config-item>
+     <config:config-item config:name="Variant" config:type="string"/>
+     <config:config-item config:name="BeginLine" config:type="string"/>
+     <config:config-item config:name="EndLine" config:type="string"/>
+    </config:config-item-map-entry>
+   </config:config-item-map-indexed>
+   <config:config-item config:name="GradientTableURL" config:type="string">$(brandbaseurl)/share/palette%3B$(user)/config/standard.sog</config:config-item>
+   <config:config-item config:name="HandoutsHorizontal" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="HatchTableURL" config:type="string">$(brandbaseurl)/share/palette%3B$(user)/config/standard.soh</config:config-item>
+   <config:config-item config:name="IsKernAsianPunctuation" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintBooklet" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintBookletBack" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="IsPrintBookletFront" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="IsPrintDate" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintDrawing" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="IsPrintFitPage" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintHandout" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintHiddenPages" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="IsPrintNotes" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintOutline" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintPageName" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintTilePage" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="IsPrintTime" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="LineEndTableURL" config:type="string">$(brandbaseurl)/share/palette%3B$(user)/config/standard.soe</config:config-item>
+   <config:config-item config:name="LoadReadonly" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="PageNumberFormat" config:type="int">4</config:config-item>
+   <config:config-item config:name="ParagraphSummation" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="PrintQuality" config:type="int">0</config:config-item>
+   <config:config-item config:name="PrinterIndependentLayout" config:type="string">low-resolution</config:config-item>
+   <config:config-item config:name="PrinterName" config:type="string">LaserJet-P2055dn</config:config-item>
+   <config:config-item config:name="PrinterPaperFromSetup" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="PrinterSetup" config:type="base64Binary">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</config:config-item>
+   <config:config-item config:name="SaveThumbnail" config:type="boolean">true</config:config-item>
+   <config:config-item config:name="SaveVersionOnClose" config:type="boolean">false</config:config-item>
+   <config:config-item config:name="SlidesPerHandout" config:type="short">6</config:config-item>
+   <config:config-item config:name="UpdateFromTemplate" config:type="boolean">true</config:config-item>
+  </config:config-item-set>
+ </office:settings>
+ <office:scripts>
+  <office:script script:language="ooo:Basic">
+   <ooo:libraries xmlns:ooo="http://openoffice.org/2004/office" xmlns:xlink="http://www.w3.org/1999/xlink">
+    <ooo:library-embedded ooo:name="Standard"/>
+   </ooo:libraries>
+  </office:script>
+ </office:scripts>
+ <office:font-face-decls>
+  <style:font-face style:name="Inconsolata1" svg:font-family="Inconsolata" style:font-pitch="fixed"/>
+  <style:font-face style:name="Arial2" svg:font-family="Arial" style:font-pitch="variable"/>
+  <style:font-face style:name="Carlito2" svg:font-family="Carlito" style:font-pitch="variable"/>
+  <style:font-face style:name="Inconsolata" svg:font-family="Inconsolata" style:font-pitch="variable"/>
+  <style:font-face style:name="Arial" svg:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable"/>
+  <style:font-face style:name="Carlito" svg:font-family="Carlito" style:font-family-generic="roman" style:font-pitch="variable"/>
+  <style:font-face style:name="Liberation Sans" svg:font-family="&apos;Liberation Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
+  <style:font-face style:name="Noto Sans" svg:font-family="&apos;Noto Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
+  <style:font-face style:name="Tahoma" svg:font-family="Tahoma" style:font-family-generic="roman" style:font-pitch="variable"/>
+  <style:font-face style:name="Times New Roman" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
+  <style:font-face style:name="Arial1" svg:font-family="Arial" style:font-family-generic="swiss" style:font-pitch="variable"/>
+  <style:font-face style:name="Carlito1" svg:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable"/>
+  <style:font-face style:name="Tahoma1" svg:font-family="Tahoma" style:font-family-generic="swiss" style:font-pitch="variable"/>
+  <style:font-face style:name="DejaVu Sans" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
+  <style:font-face style:name="Lohit Devanagari" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
+  <style:font-face style:name="Source Han Sans CN" svg:font-family="&apos;Source Han Sans CN&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
+  <style:font-face style:name="Tahoma2" svg:font-family="Tahoma" style:font-family-generic="system" style:font-pitch="variable"/>
+  <style:font-face style:name="WenQuanYi Zen Hei Sharp" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
+ </office:font-face-decls>
+ <office:styles>
+  <draw:gradient draw:name="Filled" draw:style="linear" draw:start-color="#ffffff" draw:end-color="#cccccc" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="300" draw:border="0%"/>
+  <draw:gradient draw:name="Filled_20_Blue" draw:display-name="Filled Blue" draw:style="linear" draw:start-color="#729fcf" draw:end-color="#355269" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="300" draw:border="0%"/>
+  <draw:gradient draw:name="Filled_20_Green" draw:display-name="Filled Green" draw:style="linear" draw:start-color="#77bc65" draw:end-color="#127622" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="300" draw:border="0%"/>
+  <draw:gradient draw:name="Filled_20_Red" draw:display-name="Filled Red" draw:style="linear" draw:start-color="#ff6d6d" draw:end-color="#c9211e" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="300" draw:border="0%"/>
+  <draw:gradient draw:name="Filled_20_Yellow" draw:display-name="Filled Yellow" draw:style="linear" draw:start-color="#ffde59" draw:end-color="#b47804" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="300" draw:border="0%"/>
+  <draw:gradient draw:name="Shapes" draw:style="rectangular" draw:cx="50%" draw:cy="50%" draw:start-color="#cccccc" draw:end-color="#ffffff" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="0" draw:border="0%"/>
+  <draw:gradient draw:name="new" draw:style="axial" draw:start-color="#808080" draw:end-color="#ffffff" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="0" draw:border="0%"/>
+  <draw:gradient draw:name="redpurple" draw:style="linear" draw:start-color="#ff3366" draw:end-color="#b80047" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="900" draw:border="0%"/>
+  <draw:marker draw:name="Arrow" svg:viewBox="0 0 20 30" svg:d="M10 0l-10 30h20z"/>
+  <draw:marker draw:name="Dimension_20_Lines" draw:display-name="Dimension Lines" svg:viewBox="0 0 836 110" svg:d="M0 0h278 278 280v36 36 38h-278-278-280v-36-36z"/>
+  <draw:marker draw:name="Short_20_line_20_Arrow" draw:display-name="Short line Arrow" svg:viewBox="0 0 3000 3000" svg:d="M1500 0l1500 2789v211h-114l-1286-2392v2392h-200v-2392l-1286 2392h-114v-211z"/>
+  <style:default-style style:family="graphic">
+   <style:graphic-properties svg:stroke-color="#808080" draw:fill-color="#cfe7f5" fo:wrap-option="no-wrap"/>
+   <style:paragraph-properties style:text-autospace="ideograph-alpha" style:punctuation-wrap="simple" style:line-break="strict" style:font-independent-line-spacing="false">
+    <style:tab-stops/>
+   </style:paragraph-properties>
+   <style:text-properties style:use-window-font-color="true" style:font-name="Times New Roman" fo:font-size="24pt" fo:language="en" fo:country="US" style:font-name-asian="DejaVu Sans" style:font-size-asian="24pt" style:language-asian="zh" style:country-asian="CN" style:font-name-complex="Tahoma2" style:font-size-complex="24pt" style:language-complex="hi" style:country-complex="IN"/>
+  </style:default-style>
+  <style:style style:name="standard" style:family="graphic">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0cm" svg:stroke-color="#808080" draw:marker-start-width="0.2cm" draw:marker-start-center="false" draw:marker-end-width="0.2cm" draw:marker-end-center="false" draw:fill="solid" draw:fill-color="#cfe7f5" draw:textarea-horizontal-align="justify" fo:padding-top="0.125cm" fo:padding-bottom="0.125cm" fo:padding-left="0.25cm" fo:padding-right="0.25cm" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080">
+    <text:list-style style:name="standard">
+     <text:list-level-style-bullet text:level="1" text:bullet-char="●">
+      <style:list-level-properties text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="2" text:bullet-char="●">
+      <style:list-level-properties text:space-before="0.6cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="3" text:bullet-char="●">
+      <style:list-level-properties text:space-before="1.2cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="4" text:bullet-char="●">
+      <style:list-level-properties text:space-before="1.8cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="5" text:bullet-char="●">
+      <style:list-level-properties text:space-before="2.4cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="6" text:bullet-char="●">
+      <style:list-level-properties text:space-before="3cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="7" text:bullet-char="●">
+      <style:list-level-properties text:space-before="3.6cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="8" text:bullet-char="●">
+      <style:list-level-properties text:space-before="4.2cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="9" text:bullet-char="●">
+      <style:list-level-properties text:space-before="4.8cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="10" text:bullet-char="●">
+      <style:list-level-properties text:space-before="5.4cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+    </text:list-style>
+   </style:graphic-properties>
+   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:margin-top="0cm" fo:margin-bottom="0cm" fo:line-height="100%" fo:text-indent="0cm"/>
+   <style:text-properties style:use-window-font-color="true" style:text-outline="false" style:text-line-through-style="none" style:text-line-through-type="none" style:font-name="Arial" fo:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable" fo:font-size="18pt" fo:font-style="normal" fo:text-shadow="none" style:text-underline-style="none" fo:font-weight="normal" style:letter-kerning="true" style:font-name-asian="WenQuanYi Zen Hei Sharp" style:font-family-asian="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic-asian="system" style:font-pitch-asian="variable" style:font-size-asian="18pt" style:font-style-asian="normal" style:font-weight-asian="normal" style:font-name-complex="Lohit Devanagari" style:font-family-complex="&apos;Lohit Devanagari&apos;" style:font-family-generic-complex="system" style:font-pitch-complex="variable" style:font-size-complex="18pt" style:font-style-complex="normal" style:font-weight-complex="normal" style:text-emphasize="none" style:font-relief="none" style:text-overline-style="none" style:text-overline-color="font-color"/>
+  </style:style>
+  <style:style style:name="objectwithoutfill" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties svg:stroke-color="#000000" draw:fill="none"/>
+  </style:style>
+  <style:style style:name="Object_20_with_20_no_20_fill_20_and_20_no_20_line" style:display-name="Object with no fill and no line" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+  </style:style>
+  <style:style style:name="Text" style:family="graphic">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-color="#cccccc" draw:fill="solid" draw:fill-color="#eeeeee"/>
+   <style:text-properties style:font-name="Noto Sans" fo:font-family="&apos;Noto Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
+  </style:style>
+  <style:style style:name="A4" style:family="graphic" style:parent-style-name="Text">
+   <style:graphic-properties draw:fill="none"/>
+   <style:text-properties fo:font-size="18pt"/>
+  </style:style>
+  <style:style style:name="Title_20_A4" style:display-name="Title A4" style:family="graphic" style:parent-style-name="A4">
+   <style:graphic-properties draw:stroke="none"/>
+   <style:text-properties fo:font-size="44pt"/>
+  </style:style>
+  <style:style style:name="Heading_20_A4" style:display-name="Heading A4" style:family="graphic" style:parent-style-name="A4">
+   <style:graphic-properties draw:stroke="none"/>
+   <style:text-properties fo:font-size="24pt"/>
+  </style:style>
+  <style:style style:name="Text_20_A4" style:display-name="Text A4" style:family="graphic" style:parent-style-name="A4">
+   <style:graphic-properties draw:stroke="none"/>
+  </style:style>
+  <style:style style:name="A4" style:family="graphic" style:parent-style-name="Text">
+   <style:graphic-properties draw:fill="none"/>
+   <style:text-properties fo:font-size="18pt"/>
+  </style:style>
+  <style:style style:name="Title_20_A0" style:display-name="Title A0" style:family="graphic" style:parent-style-name="A4">
+   <style:graphic-properties draw:stroke="none"/>
+   <style:text-properties fo:font-size="96pt"/>
+  </style:style>
+  <style:style style:name="Heading_20_A0" style:display-name="Heading A0" style:family="graphic" style:parent-style-name="A4">
+   <style:graphic-properties draw:stroke="none"/>
+   <style:text-properties fo:font-size="72pt"/>
+  </style:style>
+  <style:style style:name="Text_20_A0" style:display-name="Text A0" style:family="graphic" style:parent-style-name="A4">
+   <style:graphic-properties draw:stroke="none"/>
+  </style:style>
+  <style:style style:name="Graphic" style:family="graphic">
+   <style:graphic-properties draw:fill="solid" draw:fill-color="#ffffff"/>
+   <style:text-properties style:font-name="Liberation Sans" fo:font-family="&apos;Liberation Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable" fo:font-size="18pt"/>
+  </style:style>
+  <style:style style:name="Shapes" style:family="graphic" style:parent-style-name="Graphic">
+   <style:graphic-properties draw:stroke="none" draw:fill="gradient" draw:fill-gradient-name="Shapes"/>
+   <style:text-properties fo:font-size="14pt" fo:font-weight="bold"/>
+  </style:style>
+  <style:style style:name="Filled" style:family="graphic" style:parent-style-name="Shapes">
+   <style:graphic-properties draw:fill="gradient" draw:fill-gradient-name="Filled"/>
+  </style:style>
+  <style:style style:name="Filled_20_Blue" style:display-name="Filled Blue" style:family="graphic" style:parent-style-name="Filled">
+   <style:graphic-properties draw:fill-gradient-name="Filled_20_Blue"/>
+   <style:text-properties fo:color="#ffffff"/>
+  </style:style>
+  <style:style style:name="Filled_20_Green" style:display-name="Filled Green" style:family="graphic" style:parent-style-name="Filled">
+   <style:graphic-properties draw:fill-gradient-name="Filled_20_Green"/>
+   <style:text-properties fo:color="#ffffff" style:font-name="Liberation Sans" fo:font-family="&apos;Liberation Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
+  </style:style>
+  <style:style style:name="Filled_20_Red" style:display-name="Filled Red" style:family="graphic" style:parent-style-name="Filled">
+   <style:graphic-properties draw:fill-gradient-name="Filled_20_Red"/>
+   <style:text-properties fo:color="#ffffff"/>
+  </style:style>
+  <style:style style:name="Filled_20_Yellow" style:display-name="Filled Yellow" style:family="graphic" style:parent-style-name="Filled">
+   <style:graphic-properties draw:fill-gradient-name="Filled_20_Yellow"/>
+   <style:text-properties fo:color="#ffffff"/>
+  </style:style>
+  <style:style style:name="Outlined" style:family="graphic" style:parent-style-name="Shapes">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0.081cm" svg:stroke-color="#000000" draw:fill="none"/>
+  </style:style>
+  <style:style style:name="Outlined_20_Blue" style:display-name="Outlined Blue" style:family="graphic" style:parent-style-name="Outlined">
+   <style:graphic-properties svg:stroke-color="#355269"/>
+   <style:text-properties fo:color="#355269"/>
+  </style:style>
+  <style:style style:name="Outlined_20_Green" style:display-name="Outlined Green" style:family="graphic" style:parent-style-name="Outlined">
+   <style:graphic-properties svg:stroke-color="#127622"/>
+   <style:text-properties fo:color="#127622"/>
+  </style:style>
+  <style:style style:name="Outlined_20_Red" style:display-name="Outlined Red" style:family="graphic" style:parent-style-name="Outlined">
+   <style:graphic-properties svg:stroke-color="#c9211e"/>
+   <style:text-properties fo:color="#c9211e"/>
+  </style:style>
+  <style:style style:name="Outlined_20_Yellow" style:display-name="Outlined Yellow" style:family="graphic" style:parent-style-name="Outlined">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-color="#b47804"/>
+   <style:text-properties fo:color="#b47804"/>
+  </style:style>
+  <style:style style:name="Lines" style:family="graphic" style:parent-style-name="Graphic">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-color="#000000" draw:fill="none"/>
+  </style:style>
+  <style:style style:name="Arrow_20_Line" style:display-name="Arrow Line" style:family="graphic" style:parent-style-name="Lines">
+   <style:graphic-properties draw:marker-start="Arrow" draw:marker-start-width="0.2cm" draw:marker-end="Arrow" draw:marker-end-width="0.2cm" draw:show-unit="true"/>
+  </style:style>
+  <style:style style:name="Arrow_20_Dashed" style:display-name="Arrow Dashed" style:family="graphic" style:parent-style-name="Lines">
+   <style:graphic-properties draw:stroke="dash"/>
+  </style:style>
+  <style:style style:name="objectwitharrow" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0.15cm" svg:stroke-color="#000000" draw:marker-start="Arrow" draw:marker-start-width="0.7cm" draw:marker-start-center="true" draw:marker-end-width="0.3cm"/>
+  </style:style>
+  <style:style style:name="objectwithshadow" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:shadow="visible" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
+  </style:style>
+  <style:style style:name="text" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+  </style:style>
+  <style:style style:name="textbody" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:text-properties fo:font-size="16pt"/>
+  </style:style>
+  <style:style style:name="textbodyjustfied" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:paragraph-properties fo:text-align="justify"/>
+  </style:style>
+  <style:style style:name="textbodyindent" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:text-indent="0.6cm"/>
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+  <style:style style:name="title" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:text-properties fo:font-size="44pt"/>
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+  <style:style style:name="title1" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="solid" draw:fill-color="#008080" draw:shadow="visible" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
+   <style:paragraph-properties fo:text-align="center"/>
+   <style:text-properties fo:font-size="24pt"/>
+  </style:style>
+  <style:style style:name="title2" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties svg:stroke-width="0.05cm" draw:fill-color="#ffcc99" draw:shadow="visible" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
+   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0.2cm" fo:margin-top="0.1cm" fo:margin-bottom="0.1cm" fo:text-align="center" fo:text-indent="0cm"/>
+   <style:text-properties fo:font-size="36pt"/>
+  </style:style>
+  <style:style style:name="headline" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
+   <style:text-properties fo:font-size="24pt"/>
+  </style:style>
+  <style:style style:name="headline1" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
+   <style:text-properties fo:font-size="18pt" fo:font-weight="bold"/>
+  </style:style>
+  <style:style style:name="headline2" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
+   <style:text-properties fo:font-size="14pt" fo:font-style="italic" fo:font-weight="bold"/>
+  </style:style>
+  <style:style style:name="measure" style:family="graphic" style:parent-style-name="standard">
+   <style:graphic-properties draw:stroke="solid" svg:stroke-color="#000000" draw:marker-start="Arrow" draw:marker-start-width="0.2cm" draw:marker-end="Arrow" draw:marker-end-width="0.2cm" draw:fill="none" draw:show-unit="true"/>
+   <style:text-properties fo:font-size="12pt"/>
+  </style:style>
+  <style:style style:name="Default-background" style:family="presentation">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:text-properties style:letter-kerning="true"/>
+  </style:style>
+  <style:style style:name="Default-backgroundobjects" style:family="presentation">
+   <style:graphic-properties draw:textarea-horizontal-align="justify" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
+   <style:text-properties style:letter-kerning="true"/>
+  </style:style>
+  <style:style style:name="Default-notes" style:family="presentation">
+   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
+   <style:paragraph-properties fo:margin-left="0.6cm" fo:margin-right="0cm" fo:text-indent="0cm"/>
+   <style:text-properties style:use-window-font-color="true" style:text-outline="false" style:text-line-through-style="none" style:text-line-through-type="none" style:font-name="Arial" fo:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable" fo:font-size="20pt" fo:font-style="normal" fo:text-shadow="none" style:text-underline-style="none" fo:font-weight="normal" style:letter-kerning="true" style:font-name-asian="WenQuanYi Zen Hei Sharp" style:font-family-asian="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic-asian="system" style:font-pitch-asian="variable" style:font-size-asian="20pt" style:font-style-asian="normal" style:font-weight-asian="normal" style:font-name-complex="Lohit Devanagari" style:font-family-complex="&apos;Lohit Devanagari&apos;" style:font-family-generic-complex="system" style:font-pitch-complex="variable" style:font-size-complex="20pt" style:font-style-complex="normal" style:font-weight-complex="normal" style:text-emphasize="none" style:font-relief="none" style:text-overline-style="none" style:text-overline-color="font-color"/>
+  </style:style>
+  <style:style style:name="Default-outline1" style:family="presentation">
+   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:auto-grow-height="false" draw:fit-to-size="shrink-to-fit" style:shrink-to-fit="true">
+    <text:list-style style:name="Default-outline1">
+     <text:list-level-style-bullet text:level="1" text:bullet-char="●">
+      <style:list-level-properties text:space-before="0.3cm" text:min-label-width="0.9cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="2" text:bullet-char="–">
+      <style:list-level-properties text:space-before="1.5cm" text:min-label-width="0.9cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="75%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="3" text:bullet-char="●">
+      <style:list-level-properties text:space-before="2.8cm" text:min-label-width="0.8cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="4" text:bullet-char="–">
+      <style:list-level-properties text:space-before="4.2cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="75%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="5" text:bullet-char="●">
+      <style:list-level-properties text:space-before="5.4cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="6" text:bullet-char="●">
+      <style:list-level-properties text:space-before="6.6cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
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+      <style:list-level-properties text:space-before="7.8cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="8" text:bullet-char="●">
+      <style:list-level-properties text:space-before="9cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+     <text:list-level-style-bullet text:level="9" text:bullet-char="●">
+      <style:list-level-properties text:space-before="10.2cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
+     <text:list-level-style-bullet text:level="10" text:bullet-char="●">
+      <style:list-level-properties text:space-before="11.4cm" text:min-label-width="0.6cm"/>
+      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+     </text:list-level-style-bullet>
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+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.5cm"/>
+   <style:text-properties style:use-window-font-color="true" style:text-outline="false" style:text-line-through-style="none" style:text-line-through-type="none" style:font-name="Carlito1" fo:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable" fo:font-size="26pt" fo:font-style="normal" fo:text-shadow="none" style:text-underline-style="none" fo:font-weight="normal" style:letter-kerning="true" style:font-name-asian="WenQuanYi Zen Hei Sharp" style:font-family-asian="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic-asian="system" style:font-pitch-asian="variable" style:font-size-asian="26pt" style:font-style-asian="normal" style:font-weight-asian="normal" style:font-name-complex="Lohit Devanagari" style:font-family-complex="&apos;Lohit Devanagari&apos;" style:font-family-generic-complex="system" style:font-pitch-complex="variable" style:font-size-complex="26pt" style:font-style-complex="normal" style:font-weight-complex="normal" style:text-emphasize="none" style:font-relief="none" style:text-overline-style="none" style:text-overline-color="font-color"/>
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+  <style:style style:name="Default-outline3" style:family="presentation" style:parent-style-name="Default-outline2">
+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.3cm"/>
+   <style:text-properties style:font-name="Carlito1" fo:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable" fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
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+  <style:style style:name="Default-outline4" style:family="presentation" style:parent-style-name="Default-outline3">
+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.2cm"/>
+   <style:text-properties style:font-name="Carlito1" fo:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable" fo:font-size="20pt" style:font-size-asian="20pt" style:font-size-complex="20pt"/>
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+  <style:style style:name="Default-outline5" style:family="presentation" style:parent-style-name="Default-outline4">
+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.1cm"/>
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+  <style:style style:name="Default-outline6" style:family="presentation" style:parent-style-name="Default-outline5">
+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.1cm"/>
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+  <style:style style:name="Default-outline7" style:family="presentation" style:parent-style-name="Default-outline6">
+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.1cm"/>
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+  <style:style style:name="Default-outline8" style:family="presentation" style:parent-style-name="Default-outline7">
+   <style:paragraph-properties fo:margin-top="0cm" fo:margin-bottom="0.1cm"/>
+   <style:text-properties fo:font-size="20pt" style:font-size-asian="20pt" style:font-size-complex="20pt"/>
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+  </style:style>
+  <style:style style:name="T9" style:family="text">
+   <style:text-properties fo:color="#ff3333" style:font-name="Inconsolata1" fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
+  </style:style>
+  <style:style style:name="T10" style:family="text">
+   <style:text-properties fo:color="#ff3333"/>
+  </style:style>
+  <style:style style:name="T11" style:family="text">
+   <style:text-properties style:font-name="Carlito2" fo:font-size="24pt" style:font-size-asian="24pt" style:font-size-complex="24pt"/>
+  </style:style>
+  <style:style style:name="T12" style:family="text">
+   <style:text-properties style:font-name="Carlito2" fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
+  </style:style>
+  <style:style style:name="T13" style:family="text">
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+  </style:style>
+  <style:style style:name="T14" style:family="text">
+   <style:text-properties style:font-name="Inconsolata1"/>
+  </style:style>
+  <style:style style:name="T15" style:family="text">
+   <style:text-properties fo:font-size="16pt" style:font-size-asian="16pt" style:font-size-complex="16pt"/>
+  </style:style>
+  <style:style style:name="T16" style:family="text">
+   <style:text-properties fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
+  </style:style>
+  <style:style style:name="T17" style:family="text">
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+  </style:style>
+  <style:style style:name="T18" style:family="text">
+   <style:text-properties fo:color="#0093d9" style:font-name="Inconsolata1" fo:font-size="13pt" style:font-size-asian="13pt" style:font-size-complex="13pt"/>
+  </style:style>
+  <text:list-style style:name="L1">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="4" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="5" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="6" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="7" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="8" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="9" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="10" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+   <text:list-level-style-bullet text:level="1" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="2" text:bullet-char="●">
+    <style:list-level-properties text:space-before="0.6cm" text:min-label-width="0.6cm"/>
+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="3" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
+   <text:list-level-style-bullet text:level="4" text:bullet-char="●">
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
+   </text:list-level-style-bullet>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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+   <draw:layer draw:name="background"/>
+   <draw:layer draw:name="backgroundobjects"/>
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+     <draw:text-box>
+      <text:p><text:span text:style-name="T2">Testing</text:span></text:p>
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+     <draw:text-box>
+      <text:p>What Kinds of Testing Exist?</text:p>
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+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>This is a big topic:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:a xlink:href="https://en.wikipedia.org/wiki/Software_testing" xlink:type="simple">https://en.wikipedia.org/wiki/Software_testing</text:a><text:s/></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>We most commonly hear about:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Unit testing</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Tests that a single function does what it was designed to do</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Integration testing</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Tests whether the individual pieces work together as intended</text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p>Sometimes done one piece at a time (iteratively)</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Regression testing</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Checks whether changes have changed answers</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Verification &amp; Validation (from the science perspective)</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Verification: are we solving the equations correctly?</text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p>Validation: are we using the right equations in the first place?</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
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+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page3" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
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+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Automation</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="13.005cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>The best testing is automated—you don’t need to remember to run it</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Developers can forget to manually run tests</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Github / bitbucket have <text:span text:style-name="T3">continuous integration</text:span><text:span text:style-name="T4"> service that can be run on pull requests</text:span></text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T4">This is commonly used on large development projects</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="3" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page4" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>We want to test the smallest piece of functionality alone</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>E.g., a simple test that ensures that a function does what it is designed to do</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Unit testing alone is not enough</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Although it gives you confidence that each piece works alone as designed, it doesn’t test what happens when you put it all together</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T5">Integration testing</text:span><text:span text:style-name="T6"> is a much harder problem</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="4" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page5" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Unit vs. Integration Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
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+      </office:binary-data>
+      <text:p/>
+     </draw:image>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
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+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
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+     <draw:text-box>
+      <text:p>Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>When to write tests?</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Some people advocate writing a unit test for a specification before you write the functions they will test</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>This is called Test-driven development (TDD): <text:a xlink:href="https://en.wikipedia.org/wiki/Test-driven_development" xlink:type="simple">https://en.wikipedia.org/wiki/Test-driven_development</text:a></text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p>This helps you understand the interface, return values, side-effects, etc. of what you intend to write </text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Often we already have code, so we can start by writing tests to cover some core functionality</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Add new tests when you encounter a bug, precisely to ensure that this bug doesn’t arise again</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Tests should be short</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>You want to be able to run them frequently</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="6" presentation:class="page"/>
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+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page7" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
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+     <draw:text-box>
+      <text:p>Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>There are several frameworks for unit testing in python—we’ll focus on <text:span text:style-name="T7">pytest</text:span></text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Note: like packaging, unit testing frameworks in python appear in a state of flux</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>For a long time, <text:span text:style-name="T8">nose</text:span> was one of the most popular frameworks, but its development has ceased</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p><text:span text:style-name="T8">nose2</text:span> is a successor to nose that may become standard soon</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">unittest</text:span> is built into python, and may interact with nose2 in the future</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T9">pytest</text:span><text:span text:style-name="T10"> (sometimes </text:span><text:span text:style-name="T9">py.test</text:span><text:span text:style-name="T10">) seems to be the most popular now—we’ll explore that</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Basic elements:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Discoverability: it will find the tests</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Automation</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Fixtures (setup and teardown)</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
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+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page8" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Install (single-user) via: <text:span text:style-name="T7">pip3 install -U pytest –user</text:span></text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">pytest</text:span><text:span text:style-name="T11"> should now be in your path</text:span></text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Note: older name was<text:span text:style-name="T12"> </text:span><text:span text:style-name="T13">py.test</text:span></text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>For user-install, it may be that <text:span text:style-name="T8">~/.local/bin/</text:span> comes later in your <text:span text:style-name="T8">PATH</text:span> than a system-wide directory with an older <text:span text:style-name="T8">pytest</text:span></text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>You may need to explicitly alias it </text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>For coverage reports: <text:span text:style-name="T7">pip3 install -U pytest-cov --user</text:span></text:p>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="8" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page9" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Test discovery makes unit testing easy—adhere to these conventions and your tests will be found:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>File names should start or end with “<text:span text:style-name="T8">test</text:span>”:</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p><text:span text:style-name="T13">test_example.py</text:span></text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p><text:span text:style-name="T13">example_test.py</text:span></text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>For tests in a class, the class name should begin with “<text:span text:style-name="T8">Test</text:span>”</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>e.g., <text:span text:style-name="T13">TestExample</text:span></text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p>There should be no <text:span text:style-name="T13">__init__()</text:span></text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Test method / function names should start with “<text:span text:style-name="T8">test_</text:span>”</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>e.g., <text:span text:style-name="T13">test_example()</text:span></text:p>
+           </text:list-item>
+          </text:list>
+          <text:p/>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame draw:style-name="gr6" draw:text-style-name="P14" draw:layer="layout" svg:width="25.686cm" svg:height="0.963cm" svg:x="0.73cm" svg:y="19.611cm">
+     <draw:text-box>
+      <text:p><text:a xlink:href="https://pytest.readthedocs.io/en/reorganize-docs/new-docs/user/naming_conventions.html" xlink:type="simple">https://pytest.readthedocs.io/en/reorganize-docs/new-docs/user/naming_conventions.html</text:a><text:s/></text:p>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="9" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page10" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Tests use assertions (via python’s <text:span text:style-name="T7">assert</text:span> statement) to check behavior at runtime</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:a xlink:href="https://docs.python.org/3/reference/simple_stmts.html#assert" xlink:type="simple">https://docs.python.org/3/reference/simple_stmts.html#assert</text:a><text:s/></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Basic usage: <text:span text:style-name="T8">assert expression</text:span></text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Raises <text:span text:style-name="T13">AssertionError</text:span> if <text:span text:style-name="T13">expression</text:span> is not true</text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p>e.g., <text:span text:style-name="T13">assert 1 == 0</text:span> will fail with an exception</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+        <text:p/>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="10" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page11" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Simple pytest Example</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Here’s a simple example (<text:span text:style-name="T7">examples/testing/pytest/simple/</text:span> in our git repo):</text:p>
+        <text:p/>
+        <text:p/>
+        <text:p/>
+        <text:p/>
+        <text:p/>
+        <text:list>
+         <text:list-item>
+          <text:p>There are 2 tests here</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>First will pass, second will fail</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Run the tests as: <text:span text:style-name="T8">pytest -v .</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame draw:style-name="gr7" draw:text-style-name="P15" draw:layer="layout" svg:width="15.788cm" svg:height="7.701cm" svg:x="7.072cm" svg:y="6.35cm">
+     <draw:text-box>
+      <text:p><text:span text:style-name="T14">def multiply(a, b):</text:span></text:p>
+      <text:p><text:span text:style-name="T14"><text:s text:c="4"/></text:span><text:span text:style-name="T14">return a*b</text:span></text:p>
+      <text:p><text:span text:style-name="T14"/></text:p>
+      <text:p><text:span text:style-name="T14">def test_multiply():</text:span></text:p>
+      <text:p><text:span text:style-name="T14"><text:s text:c="4"/></text:span><text:span text:style-name="T14">assert multiply(4, 6) == 24</text:span></text:p>
+      <text:p><text:span text:style-name="T14"/></text:p>
+      <text:p><text:span text:style-name="T14">def test_multiply2():</text:span></text:p>
+      <text:p><text:span text:style-name="T14"><text:s text:c="4"/></text:span><text:span text:style-name="T14">assert multiply(5, 6) == 24</text:span></text:p>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="11" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page12" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Fixtures</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Unit tests sometimes require some setup to be done before the test</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Fixtures provide this capability</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>We’ll look at setup and teardown functions/methods for tests</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>These are sometimes referred to the standard xUnit fixtures</text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p>pytest supports a more flexible system for fixtures in addition to these, but we won’t look at it here</text:p>
+           </text:list-item>
+           <text:list-item>
+            <text:p><text:a xlink:href="http://pytest.org/dev/fixture.html" xlink:type="simple">http://pytest.org/dev/fixture.html</text:a><text:s/></text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>By default, <text:span text:style-name="T7">pytest</text:span> will capture stdout, and only show it on failures</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>See, e.g., <text:a xlink:href="https://docs.pytest.org/en/latest/capture.html" xlink:type="simple">https://docs.pytest.org/en/latest/capture.html</text:a><text:s/></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>This can be changed with the <text:span text:style-name="T7">-s</text:span> flag</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Example of function-level setup/teardown in <text:span text:style-name="T7">examples/testing/pytest/function_setup/</text:span></text:p>
+        <text:p/>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="12" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page13" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Unit Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="17.018cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Class example</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Note, a class used for testing is not a full-fledged class—it simply helps to organize data used for a bunch of tests with common needs</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>In particular, it does not have a constructor (<text:span text:style-name="T8">__init__()</text:span>)</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>See, e.g., <text:span text:style-name="T15"><text:a xlink:href="https://stackoverflow.com/questions/21430900/py-test-skips-test-class-if-constructor-is-defined" xlink:type="simple">https://stackoverflow.com/questions/21430900/py-test-skips-test-class-if-constructor-is-defined</text:a></text:span></text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>We’ll look at an example with a NumPy array</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">examples/testing/pytest/class/</text:span> in our class git</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>We always want the array to exist for our tests, so we’ll use fixtures (in particular <text:span text:style-name="T8">setup_method()</text:span>) to create the array </text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Using a class means that we can access the array created in setup from our class</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>NumPy has its own assertion functions</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:a xlink:href="https://docs.scipy.org/doc/numpy/reference/routines.testing.html" xlink:type="simple">https://docs.scipy.org/doc/numpy/reference/routines.testing.html</text:a></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Note in particular, the approximately equal tests</text:p>
+         </text:list-item>
+        </text:list>
+        <text:p/>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="13" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page14" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Coverage</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>We can determine the coverage of our testing</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Note just because a line / function is covered in our suite, doesn’t mean we’ll capture every error—there can always be corner-cases, things you don’t anticipate</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Adding unit tests is an ongoing process</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Basic running: </text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T7">pytest --cov .</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Here, ‘<text:span text:style-name="T14">.</text:span>’ is the path we are testing</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Detailed report (lines missed): </text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T8">pytest --cov-report term-missing --cov .</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="14" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page15" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>pytest Coverage</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="13.462cm" svg:height="16.51cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Let’s look at a larger example:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>pyro is my tutorial hydrodynamics code: <text:span text:style-name="T16"><text:a xlink:href="https://github.com/zingale/pyro2" xlink:type="simple">https://github.com/zingale/pyro2</text:a></text:span><text:s/></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>pyro has both unit tests (via pytest) and regression testing (more on that later)</text:p>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Tests are stored in the various <text:span text:style-name="T7">tests/</text:span> subdirectories</text:p>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame draw:style-name="gr8" draw:text-style-name="P15" draw:layer="layout" svg:width="12.783cm" svg:height="18.373cm" svg:x="14.941cm" svg:y="2.394cm">
+     <draw:text-box>
+      <text:p><text:span text:style-name="T17">.</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">advection</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">advection_rk</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems -&gt; ../advection/problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">analysis</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">compressible</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">compressible_react</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">compressible_rk</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems -&gt; ../compressible/problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">diffusion</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">incompressible</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">lm_atm</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">problems</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">lm_combustion</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">mesh</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   ├── </text:span><text:span text:style-name="T17">experiments</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">multigrid</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">radhydro</text:span></text:p>
+      <text:p><text:span text:style-name="T17">├── </text:span><text:span text:style-name="T17">util</text:span></text:p>
+      <text:p><text:span text:style-name="T17">│   └── </text:span><text:span text:style-name="T18">tests</text:span></text:p>
+      <text:p><text:span text:style-name="T17">└── </text:span><text:span text:style-name="T17">www</text:span></text:p>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="15" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page16" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Regression Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="13.005cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Basic idea:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Pick some problems / workflows that exercise your codebase</text:p>
+          <text:list>
+           <text:list-item>
+            <text:p>Might need many tests to get good coverage / explore all options</text:p>
+           </text:list-item>
+          </text:list>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Store a benchmark containing the “right” answer</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Each time you change your code, run the regression tests</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Compare the new answer to the stored benchmark</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>If a regression test fails, then either:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>You’ve introduced a bug—look at what changed and fix it</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>You fixed a bug—update the benchmarks</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="16" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page17" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Regression Testing</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="13.005cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>Automating the testing</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>You need a tool to do the comparisons </text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Store benchmarks in a separate directory (so they are not overwritten when you run)</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Run your code, at the end of the run, compare the new output to the stored benchmark and report</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Our example, pyro, has regression testing built in with the <text:span text:style-name="T7">--compare_benchmark</text:span> option</text:p>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Here’s another example from my research codes: <text:a xlink:href="http://bender.astro.sunysb.edu/Castro/test-suite/test-suite-gfortran/" xlink:type="simple">http://bender.astro.sunysb.edu/Castro/test-suite/test-suite-gfortran/</text:a><text:s/></text:p>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="17" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page18" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Verification</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="15.494cm" svg:height="17.018cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>For science codes, <text:span text:style-name="T3">verification</text:span><text:span text:style-name="T4"> means test our algorithm against a known solution to the system of equations we are solving</text:span></text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T4">This doesn’t help us understand if we picked the right equations (e.g., correct model of reality) to begin with</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p><text:span text:style-name="T4">For the equations of hydrodynamics, the Sedov blast wave problem is a common verification problem</text:span></text:p>
+        <text:list>
+         <text:list-item>
+          <text:p><text:span text:style-name="T4">A lot of energy is put into a small volume in a uniform domain</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T4">Spherical blast wave develops</text:span></text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p><text:span text:style-name="T4">Analytic solution found in the 1940s</text:span></text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
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+       GxERyc7/DxWGW4fsM6fXAAAAAElFTkSuQmCC
+      </office:binary-data>
+      <text:p/>
+     </draw:image>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="18" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page19" draw:style-name="dp6" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Our Class Slack</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="17.018cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>After the semester, I’ll keep on our class Slack for as long as people want to continue to chat</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>Feel free to continue to ask questions that might help with your research, etc</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>I’ll share links to new developments / conferences / summer schools, etc. that might be of interest </text:p>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="19" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <draw:page draw:name="page20" draw:style-name="dp6" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
+    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
+    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:p>Next Semester: Numerical Methods</text:p>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="17.018cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
+     <draw:text-box>
+      <text:list text:style-name="L4">
+       <text:list-item>
+        <text:p>I’m offering<text:span text:style-name="T3"> PHY 604: Computational Methods in Physics and Astronomy II</text:span> next semester</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>All the “II” means is that you know how to program </text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+       <text:list-item>
+        <text:p>Topics:</text:p>
+        <text:list>
+         <text:list-item>
+          <text:p>basic of computing</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>software engineering (git, testing, …)</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>differentiation and integration</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>interpolation</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>root finding</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>ODEs (explicit/implicit methods, two point boundary problems, eigenvalue problems)</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>linear algebra</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>fast Fourier transforms</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>fitting</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>Monte Carlo (integration, Markov chain Monte Carlo, simulated annealing)</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>PDEs (hyperbolic, elliptic/multigrid, &amp; parabolic)</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>genetic algorithms</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>machine learning / neural nets</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>intro to parallel computing</text:p>
+         </text:list-item>
+         <text:list-item>
+          <text:p>computational fluid dynamics</text:p>
+         </text:list-item>
+        </text:list>
+       </text:list-item>
+      </text:list>
+     </draw:text-box>
+    </draw:frame>
+    <draw:frame draw:style-name="gr6" draw:text-style-name="P14" draw:layer="layout" svg:width="16.441cm" svg:height="0.963cm" svg:x="11.43cm" svg:y="20.037cm">
+     <draw:text-box>
+      <text:p><text:span text:style-name="T15"><text:a xlink:href="http://bender.astro.sunysb.edu/classes/numerical_methods/" xlink:type="simple">http://bender.astro.sunysb.edu/classes/numerical_methods/</text:a></text:span><text:span text:style-name="T15"><text:s/></text:span></text:p>
+     </draw:text-box>
+    </draw:frame>
+    <presentation:notes draw:style-name="dp4">
+     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="20" presentation:class="page"/>
+     <draw:frame presentation:style-name="pr21" draw:text-style-name="P13" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
+      <draw:text-box/>
+     </draw:frame>
+    </presentation:notes>
+   </draw:page>
+   <presentation:settings presentation:mouse-visible="false"/>
+  </office:presentation>
+ </office:body>
+</office:document>
\ No newline at end of file
diff --git a/content/10-testing/testing.md b/content/10-testing/testing.md
new file mode 100644
index 00000000..c7f3be34
--- /dev/null
+++ b/content/10-testing/testing.md
@@ -0,0 +1,55 @@
+# Testing
+
+Testing is an integral part of the software development process.  We want to catch
+mistakes early, before they go on to affect our results.
+
+## Types of testing
+
+There are a lot of different types of software testing that exist.
+Most commonly, for scientific codes, we hear about:
+
+* Unit testing : Tests that a single function does what it was designed to do
+
+* Integration testing : Tests whether the individual pieces work together as intended.
+  Sometimes done one piece at a time (iteratively)
+
+* Regression testing : Checks whether code changes have changed answers
+
+* Verification & Validation (from the science perspective)
+
+  * Verification: are we solving the equations correctly?
+
+  * Validation: are we solving the correct equations?
+
+## Automating testing
+
+The best testing is automated.  Github provides a *continuous integration* service that can
+be run on pull requests.  You write a short definition (a Github workflow) that tells Github
+how to run your tests and then any time there is a change, the tests are run.
+
+## Unit testing
+
+* When to write tests?
+
+  * Some people advocate writing a unit test for a specification
+    before you write the functions they will test
+
+    * This is called Test-driven development (TDD):
+      https://en.wikipedia.org/wiki/Test-driven_development
+
+  * This helps you understand the interface, return values,
+    side-effects, etc. of what you intend to write
+
+* Often we already have code, so we can start by writing tests to
+  cover some core functionality
+
+  * Add new tests when you encounter a bug, precisely to ensure that
+    this bug doesn’t arise again
+
+* Tests should be short and simple
+
+  * You want to be able to run them frequently
+
+  * The more granular your tests are, the easier it will be to track down bugs
+
+
diff --git a/content/10-testing/unit_integration.gif b/content/10-testing/unit_integration.gif
new file mode 100644
index 00000000..07c463bf
Binary files /dev/null and b/content/10-testing/unit_integration.gif differ
diff --git a/content/11-machine-learning/README b/content/11-machine-learning/README
new file mode 100644
index 00000000..56cc6456
--- /dev/null
+++ b/content/11-machine-learning/README
@@ -0,0 +1,15 @@
+Note: on older machines, tensorflow generates an illegal instruction
+and crashes python on import.  The issue is the CPU instructions it
+was compiled with.  The solution seems to be to drop down to
+tensorflow 1.5:
+
+https://github.com/tensorflow/tensorflow/issues/17411
+
+On my system, I need to make sure I got numpy from pip (instead of the
+Fedora package manager).
+
+
+
+clustering examples:
+
+https://laxmikants.github.io/blog/neural-network-using-make-moons-dataset/
diff --git a/content/11-machine-learning/gradient-descent.ipynb b/content/11-machine-learning/gradient-descent.ipynb
new file mode 100644
index 00000000..489d03cb
--- /dev/null
+++ b/content/11-machine-learning/gradient-descent.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "50dc4c55-a121-4353-8271-aec309b81a74",
+   "metadata": {},
+   "source": [
+    "# Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1eea374-2243-44dd-ba94-98ab1bda1fbb",
+   "metadata": {},
+   "source": [
+    "[Gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) is a simple algorithm for finding the minimum of a function of multiple variables.  It works on the principle of looking at the local gradient of a function then then moving in the direction where it decreases the fastest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13513127-4294-4910-bcdb-6ae9803b7e58",
+   "metadata": {},
+   "source": [
+    "```{warning}\n",
+    "There is no guarantee that you arrive at the global minimum instead of a local minimum.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b29e298-853d-4941-9411-72ddd1f0edf2",
+   "metadata": {},
+   "source": [
+    "Given a function $f({\\bf x})$, where ${\\bf x} = (x_0, x_1, \\ldots, x_{N-1})$,\n",
+    "the idea is to first compute the derivative:\n",
+    "\n",
+    "$$\\partial f / \\partial {\\bf x} = (\\partial f/\\partial x_0, \\partial f/\\partial x_1, \\ldots, \\partial f/\\partial x_{N-1})$$\n",
+    "\n",
+    "and then move in the opposite direction by some fraction, $\\eta$:\n",
+    "\n",
+    "$${\\bf x} \\leftarrow {\\bf x} - \\eta \\frac{\\partial f}{\\partial {\\bf x}}$$\n",
+    "\n",
+    "There are different ways to define what $\\eta$ should be, but we'll use a fixed value.  We'll call $\\eta$ the _learning rate_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bdcd77d-f54b-4cf3-9e13-7754d4d66906",
+   "metadata": {},
+   "source": [
+    "Let's demonstrate this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "62e0fa3e-74c3-474e-82f7-a7b8276a18b7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22754331-6821-4de0-b85f-8468a174be4d",
+   "metadata": {},
+   "source": [
+    "## Test function\n",
+    "\n",
+    "The [Rosenbrock function](https://en.wikipedia.org/wiki/Rosenbrock_function)\n",
+    "or the _banana function_ is a very difficult problem for minimization.  It\n",
+    "has the form:\n",
+    "\n",
+    "$$f(x, y) = (a - x)^2 + b (y - x^2)^2$$\n",
+    "\n",
+    "and for $a = 1$ and $b = 100$, the minimimum is at a point $(a, a^2)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1a017bb2-fa5c-40af-9bf9-c27e0f905179",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def rosenbrock(x0, x1, a, b):\n",
+    "    return (a - x0)**2 + b*(x1 - x0**2)**2\n",
+    "\n",
+    "def drosdx(x, a, b):\n",
+    "    x0 = x[0]\n",
+    "    x1 = x[1]\n",
+    "    return np.array([-2.0*(a - x0) - 4.0*b*(x1 - x0**2)*x0,\n",
+    "                     2.0*b*(x1 - x0**2)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ffe26a7-8bcf-4e87-b293-0d6ef27519ca",
+   "metadata": {},
+   "source": [
+    "Let's plot the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "189e89c6-3328-4bc2-b8e3-4f218fb0ed85",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xmin = -2.0\n",
+    "xmax = 2.0\n",
+    "ymin = -1.0\n",
+    "ymax = 3.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bb125ea6-baab-446a-adef-1095f5006021",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "a = 1.0\n",
+    "b = 100.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "997582fc-e0d6-4368-9c58-2575ac567fb5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "N = 256\n",
+    "x = np.linspace(xmin, xmax, N)\n",
+    "y = np.linspace(ymin, ymax, N)\n",
+    "\n",
+    "x2d, y2d = np.meshgrid(x, y, indexing=\"ij\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f3f72877-be76-4a9c-b052-e793cbeb09b3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f19905bf790>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "im = ax.imshow(np.log10(np.transpose(rosenbrock(x2d, y2d, a, b))),\n",
+    "               origin=\"lower\", extent=[xmin, xmax, ymin, ymax])\n",
+    "\n",
+    "fig.colorbar(im, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "516c5508-6ebc-4b66-ba57-1a164ac78a6b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Implementing gradient descent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7bf4946-7f78-4744-8ba9-d17345a8fff4",
+   "metadata": {},
+   "source": [
+    "Let's start with an initial guess.  We'll keep guessing until the change in the solution is small.\n",
+    "\n",
+    "Note: our success is very sensitive to our choice of $\\eta$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c41e0313-4a3f-4d8d-ac23-f63292cf0552",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x0 = np.array([-1.0, 1.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44e78938-96d9-46ad-9841-07d5b02cbec4",
+   "metadata": {},
+   "source": [
+    "We'll set a tolerance and keep iterating until the change in the solution, `dx` is small"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ff9fbe69-a68c-4a52-a1ec-a8d96b9116ac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def do_descent(dfdx, x0, eps=1.e-5, eta=2.e-3, args=None, ax=None):\n",
+    "\n",
+    "    # dx will be the change in the solution -- we'll iterate until this\n",
+    "    # is small\n",
+    "    dx = 1.e30\n",
+    "    xp_old = x0.copy()\n",
+    "\n",
+    "    if args:\n",
+    "        grad = dfdx(xp_old, *args)\n",
+    "    else:\n",
+    "        grad = dfdx(xp_old)\n",
+    "\n",
+    "    while dx > eps:\n",
+    "\n",
+    "        xp = xp_old - eta * grad\n",
+    "        \n",
+    "        if ax:\n",
+    "            ax.plot([xp_old[0], xp[0]], [xp_old[1], xp[1]], color=\"C1\")\n",
+    "            \n",
+    "        dx = np.linalg.norm(xp - xp_old)\n",
+    "        \n",
+    "        if args:\n",
+    "            grad_new = dfdx(xp, *args)\n",
+    "        else:\n",
+    "            grad_new = dfdx(xp)\n",
+    "            \n",
+    "        #eta_new = np.abs(np.transpose(xp) @ (grad_new - grad)) / np.linalg.norm(grad_new - grad)**2\n",
+    "        #eta = min(10*eta, eta_new)\n",
+    "        \n",
+    "        grad = grad_new\n",
+    "        \n",
+    "        xp_old[:] = xp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dc9db9da-a87b-480a-9c45-fab0ddd8368d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "do_descent(drosdx, x0, args=(a, b), ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "989577fb-6a21-4bd1-a5de-fc2da3eff011",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab5fb353-278d-422d-b190-a4f08d38f6b5",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## momentum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d785f3a-cf4c-4485-a2fd-cfce97872809",
+   "metadata": {},
+   "source": [
+    "A variation on gradient descent is to add \"momentum\"\n",
+    "to the update.  This means that the correct depends\n",
+    "on the past gradients as well as the current one,\n",
+    "via some combination.  This has the effect of reducing\n",
+    "the zig-zag effect that we see in our attempt above."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/content/11-machine-learning/ideas.txt b/content/11-machine-learning/ideas.txt
new file mode 100644
index 00000000..09e3df03
--- /dev/null
+++ b/content/11-machine-learning/ideas.txt
@@ -0,0 +1,18 @@
+Packages to try:
+
+  -- keras
+  -- tensorflow
+  -- scikit-learn
+
+Some nice tutorials:
+
+  -- https://github.com/zotroneneis/machine_learning_basics
+
+
+https://elitedatascience.com/keras-tutorial-deep-learning-in-python
+
+
+types of machine learning:
+
+https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464
+
diff --git a/content/11-machine-learning/keras-clustering.ipynb b/content/11-machine-learning/keras-clustering.ipynb
new file mode 100644
index 00000000..86900705
--- /dev/null
+++ b/content/11-machine-learning/keras-clustering.ipynb
@@ -0,0 +1,807 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d8726fab-28bc-4600-82b1-83eb9f7abb37",
+   "metadata": {},
+   "source": [
+    "# Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "935a9130-4606-44d1-a670-b96e997118b1",
+   "metadata": {},
+   "source": [
+    "[Clustering](https://en.wikipedia.org/wiki/Cluster_analysis) seeks to group data into clusters based on their properties and then allow us to predict which cluster a new member belongs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5aef60e6-70d3-4bd5-b0e6-7ee4d58b4028",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f841dc4-8484-44af-9ac9-c5bac1caf163",
+   "metadata": {},
+   "source": [
+    "We'll use a dataset generator that is part of [scikit-learn](https://scikit-learn.org/stable/index.html) called [`make_moons`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html).  This generates data that falls into 2 different sets with a shape that looks like half-moons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "360de666-828d-4fe3-a8cb-ce7243373a64",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dcea3bcf-3f35-4133-8a52-064985382b4a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def generate_data():\n",
+    "    xvec, val = datasets.make_moons(200, noise=0.2)\n",
+    "\n",
+    "    # encode the output to be 2 elements\n",
+    "    x = []\n",
+    "    v = []\n",
+    "    for xv, vv in zip(xvec, val):\n",
+    "        x.append(np.array(xv))\n",
+    "        v.append(vv)\n",
+    "\n",
+    "    return np.array(x), np.array(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0f675629-596c-4e5c-9975-4e57a60bd4d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x, v = generate_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "980e1976-2da3-46a9-89cd-7b61042bc758",
+   "metadata": {},
+   "source": [
+    "Let's look at a point and it's value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68b11f44-9f9e-40b8-a6f2-e0cf93d35717",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = [0.69873964 0.40237326], value = 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"x = {x[0]}, value = {v[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0a3a621-f8c8-44ae-84ed-a50425b9766f",
+   "metadata": {},
+   "source": [
+    "Now let's plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "56cee5e7-415d-4f5f-9aa0-16dee740d754",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_data(x, v):\n",
+    "    xpt = [q[0] for q in x]\n",
+    "    ypt = [q[1] for q in x]\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.scatter(xpt, ypt, s=40, c=v, cmap=\"viridis\")\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e5789e05-4b8b-45c3-bb38-5f29f0ae6f36",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plot_data(x, v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26822875-799a-4ceb-8cb2-3e1e0abcaa0c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We want to partition this domain into 2 regions, such that when we come in with a new point, we know which group it belongs to."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b2779ae-6435-4112-a95b-ddd72e75f731",
+   "metadata": {},
+   "source": [
+    "First we setup and train our network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "83d69d84-10a3-47e4-a91c-b9963f60ea86",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input\n",
+    "from keras.optimizers import RMSprop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ecb97643-ec86-48cd-a510-f782a2adacd5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(Input(shape=(2,)))\n",
+    "model.add(Dense(50, activation=\"relu\"))\n",
+    "model.add(Dense(20, activation=\"relu\"))\n",
+    "model.add(Dense(1, activation=\"sigmoid\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "097b8c96-7342-4ab6-8e5a-54da12af7aec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rms = RMSprop()\n",
+    "model.compile(loss='binary_crossentropy',\n",
+    "              optimizer=rms, metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fb0c9a41-844f-4352-ad83-cff6de8a1afd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,020</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │           \u001b[38;5;34m150\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)             │         \u001b[38;5;34m1,020\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m21\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,191</span> (4.65 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,191\u001b[0m (4.65 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,191</span> (4.65 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,191\u001b[0m (4.65 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "005375e3-bc94-4eae-99c8-129021c575ca",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We seem to need a lot of epochs here to get a good result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "56e83baf-a853-4191-b135-a9781ee5f74f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "4/4 - 0s - 6ms/step - accuracy: 0.7750 - loss: 0.6451\n",
+      "Epoch 2/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.7900 - loss: 0.6110\n",
+      "Epoch 3/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.7900 - loss: 0.5877\n",
+      "Epoch 4/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.7950 - loss: 0.5679\n",
+      "Epoch 5/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.7900 - loss: 0.5496\n",
+      "Epoch 6/100\n",
+      "4/4 - 0s - 4ms/step - accuracy: 0.7950 - loss: 0.5323\n",
+      "Epoch 7/100\n",
+      "4/4 - 0s - 5ms/step - accuracy: 0.8000 - loss: 0.5164\n",
+      "Epoch 8/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8050 - loss: 0.5016\n",
+      "Epoch 9/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8050 - loss: 0.4872\n",
+      "Epoch 10/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8100 - loss: 0.4733\n",
+      "Epoch 11/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8200 - loss: 0.4604\n",
+      "Epoch 12/100\n",
+      "4/4 - 0s - 4ms/step - accuracy: 0.8200 - loss: 0.4478\n",
+      "Epoch 13/100\n",
+      "4/4 - 0s - 4ms/step - accuracy: 0.8300 - loss: 0.4367\n",
+      "Epoch 14/100\n",
+      "4/4 - 0s - 4ms/step - accuracy: 0.8300 - loss: 0.4263\n",
+      "Epoch 15/100\n",
+      "4/4 - 0s - 4ms/step - accuracy: 0.8300 - loss: 0.4153\n",
+      "Epoch 16/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8350 - loss: 0.4062\n",
+      "Epoch 17/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8300 - loss: 0.3967\n",
+      "Epoch 18/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8350 - loss: 0.3888\n",
+      "Epoch 19/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8400 - loss: 0.3811\n",
+      "Epoch 20/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8400 - loss: 0.3741\n",
+      "Epoch 21/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8400 - loss: 0.3670\n",
+      "Epoch 22/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8450 - loss: 0.3602\n",
+      "Epoch 23/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8400 - loss: 0.3547\n",
+      "Epoch 24/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8500 - loss: 0.3496\n",
+      "Epoch 25/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8500 - loss: 0.3456\n",
+      "Epoch 26/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8550 - loss: 0.3396\n",
+      "Epoch 27/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8550 - loss: 0.3355\n",
+      "Epoch 28/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8600 - loss: 0.3315\n",
+      "Epoch 29/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8650 - loss: 0.3297\n",
+      "Epoch 30/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8600 - loss: 0.3257\n",
+      "Epoch 31/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8650 - loss: 0.3215\n",
+      "Epoch 32/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8650 - loss: 0.3209\n",
+      "Epoch 33/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8650 - loss: 0.3167\n",
+      "Epoch 34/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8650 - loss: 0.3140\n",
+      "Epoch 35/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8700 - loss: 0.3129\n",
+      "Epoch 36/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8700 - loss: 0.3095\n",
+      "Epoch 37/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8750 - loss: 0.3078\n",
+      "Epoch 38/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8700 - loss: 0.3058\n",
+      "Epoch 39/100\n",
+      "4/4 - 0s - 2ms/step - accuracy: 0.8700 - loss: 0.3042\n",
+      "Epoch 40/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8700 - loss: 0.3027\n",
+      "Epoch 41/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.3009\n",
+      "Epoch 42/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2991\n",
+      "Epoch 43/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2978\n",
+      "Epoch 44/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2954\n",
+      "Epoch 45/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2946\n",
+      "Epoch 46/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2936\n",
+      "Epoch 47/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2917\n",
+      "Epoch 48/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2914\n",
+      "Epoch 49/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2895\n",
+      "Epoch 50/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8750 - loss: 0.2887\n",
+      "Epoch 51/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2871\n",
+      "Epoch 52/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2864\n",
+      "Epoch 53/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8900 - loss: 0.2861\n",
+      "Epoch 54/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2871\n",
+      "Epoch 55/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2835\n",
+      "Epoch 56/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2820\n",
+      "Epoch 57/100\n",
+      "4/4 - 0s - 4ms/step - accuracy: 0.8850 - loss: 0.2822\n",
+      "Epoch 58/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2801\n",
+      "Epoch 59/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8800 - loss: 0.2803\n",
+      "Epoch 60/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8900 - loss: 0.2785\n",
+      "Epoch 61/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2786\n",
+      "Epoch 62/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2766\n",
+      "Epoch 63/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2765\n",
+      "Epoch 64/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8900 - loss: 0.2750\n",
+      "Epoch 65/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8900 - loss: 0.2739\n",
+      "Epoch 66/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8900 - loss: 0.2729\n",
+      "Epoch 67/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2730\n",
+      "Epoch 68/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8900 - loss: 0.2716\n",
+      "Epoch 69/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2711\n",
+      "Epoch 70/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8850 - loss: 0.2700\n",
+      "Epoch 71/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2697\n",
+      "Epoch 72/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2679\n",
+      "Epoch 73/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2679\n",
+      "Epoch 74/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2666\n",
+      "Epoch 75/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2657\n",
+      "Epoch 76/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2651\n",
+      "Epoch 77/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2633\n",
+      "Epoch 78/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2623\n",
+      "Epoch 79/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2614\n",
+      "Epoch 80/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2623\n",
+      "Epoch 81/100\n",
+      "4/4 - 0s - 2ms/step - accuracy: 0.8950 - loss: 0.2604\n",
+      "Epoch 82/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2585\n",
+      "Epoch 83/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2589\n",
+      "Epoch 84/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2577\n",
+      "Epoch 85/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2556\n",
+      "Epoch 86/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2560\n",
+      "Epoch 87/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2549\n",
+      "Epoch 88/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2528\n",
+      "Epoch 89/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2524\n",
+      "Epoch 90/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2512\n",
+      "Epoch 91/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2498\n",
+      "Epoch 92/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2506\n",
+      "Epoch 93/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2489\n",
+      "Epoch 94/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2471\n",
+      "Epoch 95/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2475\n",
+      "Epoch 96/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2467\n",
+      "Epoch 97/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2433\n",
+      "Epoch 98/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2435\n",
+      "Epoch 99/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.9000 - loss: 0.2413\n",
+      "Epoch 100/100\n",
+      "4/4 - 0s - 3ms/step - accuracy: 0.8950 - loss: 0.2408\n"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 100\n",
+    "results = model.fit(x, v, batch_size=50, epochs=epochs, verbose=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ade4dc3f-8e67-4d03-8d35-8d6e6922390a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "score = 0.2388213872909546\n",
+      "accuracy = 0.8999999761581421\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(x, v, verbose=0)\n",
+    "print(f\"score = {score[0]}\")\n",
+    "print(f\"accuracy = {score[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4f23a95-64b2-4839-8ff8-fbae663328b7",
+   "metadata": {},
+   "source": [
+    "Let's look at a prediction.  We need to feed in a single point as an array of shape `(N, 2)`, where `N` is the number of points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8f45aba4-4253-4c0c-8f35-21a3c55e8427",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.043571e-07]], dtype=float32)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = model.predict(np.array([[-2, 2]]))\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "814a2748-ff76-4dd0-9587-40384001463a",
+   "metadata": {},
+   "source": [
+    "We see that we get a floating point number.  We will need to convert this to 0 or 1 by rounding."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75417e5d-3dd3-4710-be34-caa03a5e1e5b",
+   "metadata": {},
+   "source": [
+    "Let's plot the partitioning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a7518506-ba8b-4dac-a680-8bb229e8877f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "M = 128\n",
+    "N = 128\n",
+    "\n",
+    "xmin = -1.75\n",
+    "xmax = 2.5\n",
+    "ymin = -1.25\n",
+    "ymax = 1.75\n",
+    "\n",
+    "xpt = np.linspace(xmin, xmax, M)\n",
+    "ypt = np.linspace(ymin, ymax, N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27a9a3c8-806d-441f-9246-dd7af6757b8e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "To make the prediction go faster, we want to feed in a vector of these points, of the form:\n",
+    "```\n",
+    "[[xpt[0], ypt[0]],\n",
+    " [xpt[1], ypt[1]],\n",
+    " ...\n",
+    "]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2de12ce3-8d36-4868-a37f-c3e311f5f1f9",
+   "metadata": {},
+   "source": [
+    "We can see that this packs them into the vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "64d168f1-e962-45a1-884a-f6942bf4eafb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.75, -1.25])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairs = np.array(np.meshgrid(xpt, ypt)).T.reshape(-1, 2)\n",
+    "pairs[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "928f9bef-f23c-4f48-a063-57a137ad59bf",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Now we do the prediction.  We will get a vector out, which we reshape to match the original domain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "729b336f-b3ef-49ef-9c87-25a1615a5b9c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "res = model.predict(pairs, verbose=0)\n",
+    "res.shape = (M, N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04fc2d82-ec13-491b-a903-4aabe372d2a1",
+   "metadata": {},
+   "source": [
+    "Finally, round to 0 or 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "cf8c78ed-02db-480b-b8c6-09aa27ceeae8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "domain = np.where(res > 0.5, 1, 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1915a8cc-60ac-417d-92c9-f36d9d4025ac",
+   "metadata": {},
+   "source": [
+    "and we can plot the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bbc4e2c1-4682-46ea-9e2a-6a1bfb4bcb53",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f6f61c5d950>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(domain.T, origin=\"lower\",\n",
+    "          extent=[xmin, xmax, ymin, ymax], alpha=0.25)\n",
+    "xpt = [q[0] for q in x]\n",
+    "ypt = [q[1] for q in x]\n",
+    "\n",
+    "ax.scatter(xpt, ypt, s=40, c=v, cmap=\"viridis\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/content/11-machine-learning/keras-mnist.ipynb b/content/11-machine-learning/keras-mnist.ipynb
new file mode 100644
index 00000000..049d965c
--- /dev/null
+++ b/content/11-machine-learning/keras-mnist.ipynb
@@ -0,0 +1,1129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KERAS and MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We’ll apply the ideas we just learned to a neural network that does character recognition using the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database). This is a set of handwritten digits (0–9) represented as a 28×28 pixel grayscale image.\n",
+    "\n",
+    "There are 2 datasets, the training set with 60,000 images and the test set with 10,000 images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{important}\n",
+    "Keras requires a backend, which can be [tensorflow](https://www.tensorflow.org/), [pytorch](https://pytorch.org/), or [jax](https://docs.jax.dev/en/latest/index.html).\n",
+    "\n",
+    "By default, it will assume tensorflow.\n",
+    "\n",
+    "This notebook has been tested with both pytorch and tensorflow.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{tip}\n",
+    "To have keras use pytorch, set the environment variable `KERAS_BACKEND` as:\n",
+    "```\n",
+    "export KERAS_BACKEND=\"torch\"\n",
+    "```\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We follow the example for setting up the network:\n",
+    "https://github.com/Vict0rSch/deep_learning/tree/master/keras/feedforward"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````{note}\n",
+    "For visualization of the network, you need to have `pydot` installed.\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The MNIST data\n",
+    "\n",
+    "The keras library can download the MNIST data directly and provides a function to give us both the training and test images and the corresponding digits.  This is already in a format that Keras wants, so we don't use the classes that we defined earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from keras.datasets import mnist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, the training set consists of 60000 digits represented as a 28x28 array (there are no color channels, so this is grayscale data).  They are also integer data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60000, 28, 28)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('uint8')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the first digit and the \"y\" value (target) associated with it&mdash;that's the correct answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X_train[0], cmap=\"gray_r\")\n",
+    "print(y_train[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing the Data\n",
+    "\n",
+    "The neural network takes a 1-d vector of input and will return a 1-d vector of output.  We need to convert our data to this form."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll scale the image data to fall in [0, 1) and the numerical output to be categorized as an array.  Finally, we need the input data to be one-dimensional, so we fill flatten the 28x28 images into a single 784 vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train = X_train.astype('float32')/255\n",
+    "X_test = X_test.astype('float32')/255\n",
+    "\n",
+    "X_train = np.reshape(X_train, (60000, 784))\n",
+    "X_test = np.reshape(X_test, (10000, 784))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.01176471, 0.07058824, 0.07058824,\n",
+       "       0.07058824, 0.49411765, 0.53333336, 0.6862745 , 0.10196079,\n",
+       "       0.6509804 , 1.        , 0.96862745, 0.49803922, 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.11764706, 0.14117648, 0.36862746, 0.6039216 ,\n",
+       "       0.6666667 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
+       "       0.99215686, 0.88235295, 0.6745098 , 0.99215686, 0.9490196 ,\n",
+       "       0.7647059 , 0.2509804 , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.19215687, 0.93333334,\n",
+       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
+       "       0.99215686, 0.99215686, 0.99215686, 0.9843137 , 0.3647059 ,\n",
+       "       0.32156864, 0.32156864, 0.21960784, 0.15294118, 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.07058824, 0.85882354, 0.99215686, 0.99215686,\n",
+       "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.7137255 ,\n",
+       "       0.96862745, 0.94509804, 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.3137255 , 0.6117647 , 0.41960785, 0.99215686, 0.99215686,\n",
+       "       0.8039216 , 0.04313726, 0.        , 0.16862746, 0.6039216 ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.05490196,\n",
+       "       0.00392157, 0.6039216 , 0.99215686, 0.3529412 , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.54509807,\n",
+       "       0.99215686, 0.74509805, 0.00784314, 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.04313726, 0.74509805, 0.99215686,\n",
+       "       0.27450982, 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.13725491, 0.94509804, 0.88235295, 0.627451  ,\n",
+       "       0.42352942, 0.00392157, 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.31764707, 0.9411765 , 0.99215686, 0.99215686, 0.46666667,\n",
+       "       0.09803922, 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.1764706 ,\n",
+       "       0.7294118 , 0.99215686, 0.99215686, 0.5882353 , 0.10588235,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.0627451 , 0.3647059 ,\n",
+       "       0.9882353 , 0.99215686, 0.73333335, 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.9764706 , 0.99215686,\n",
+       "       0.9764706 , 0.2509804 , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.18039216, 0.50980395,\n",
+       "       0.7176471 , 0.99215686, 0.99215686, 0.8117647 , 0.00784314,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
+       "       0.5803922 , 0.8980392 , 0.99215686, 0.99215686, 0.99215686,\n",
+       "       0.98039216, 0.7137255 , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.09411765, 0.44705883, 0.8666667 , 0.99215686, 0.99215686,\n",
+       "       0.99215686, 0.99215686, 0.7882353 , 0.30588236, 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.09019608, 0.25882354, 0.8352941 , 0.99215686,\n",
+       "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.31764707,\n",
+       "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.07058824, 0.67058825, 0.85882354,\n",
+       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059 ,\n",
+       "       0.3137255 , 0.03529412, 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.21568628, 0.6745098 ,\n",
+       "       0.8862745 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
+       "       0.95686275, 0.52156866, 0.04313726, 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.53333336, 0.99215686, 0.99215686, 0.99215686,\n",
+       "       0.83137256, 0.5294118 , 0.5176471 , 0.0627451 , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "       0.        , 0.        , 0.        , 0.        ], dtype=float32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use categorical data.  Keras includes routines to categorize data.  In our case, since there are 10 possible digits, we want to put the output into 10 categories (represented by 10 neurons)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from keras.utils import to_categorical\n",
+    "\n",
+    "y_train = to_categorical(y_train, 10)\n",
+    "y_test = to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at the target for the first training digit.  We know from above that it was '5'.  Here we see that there is a `1` in the index corresponding to `5` (remember we start counting at `0` in python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build the Neural Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll build the neural network.  We will have 2 hidden layers, and the number of neurons will look like:\n",
+    "\n",
+    "784 &rarr; 500 &rarr; 300 &rarr; 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Layers\n",
+    "\n",
+    "Let's start by setting up the layers.  For each layer, we tell keras the number of output neurons.  It infers the number of inputs from the previous layer (with the exception of the input layer, where we need to tell it what to expect as input).\n",
+    "\n",
+    "Properties on the layers:\n",
+    "\n",
+    "  * Dense layers: We will use a _dense_ network.  This means that all neurons in one\n",
+    "    layer are connected to all neurons in the next layer (sometimes the\n",
+    "    term \"fully-connected\" is used here).\n",
+    "\n",
+    "  * Activation function: We previously used the _sigmoid_ function.  Now we'll\n",
+    "    use [_rectified linear unit_](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) (see  also http://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#relu) for all but the last layer.  \n",
+    "\n",
+    "    For the very last layer (the output layer), we use a [softmax activation](https://en.wikipedia.org/wiki/Softmax_function).  This is commonly used with categorical data (like we have) and has the nice property that all of entries add to 1 (so we can interpret them as probabilities).\n",
+    "\n",
+    "    See https://keras.io/api/layers/activations/ for a list of activation functions supported.\n",
+    "\n",
+    "  * Dropout: for some of the layers, we will specify a _dropout_.  This means that\n",
+    "    we will ignore some of the neurons in a layer during training (randomly selected\n",
+    "    at the specified probability).  This can help present overfitting of the network.\n",
+    "    \n",
+    "    Here's a nice discussion: https://medium.com/@amarbudhiraja/https-medium-com-amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Input\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Input(shape=(784,)))\n",
+    "model.add(Dense(500, activation=\"relu\"))\n",
+    "model.add(Dropout(0.4))\n",
+    "model.add(Dense(300, activation=\"relu\"))\n",
+    "model.add(Dropout(0.4))\n",
+    "model.add(Dense(10, activation=\"softmax\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loss function\n",
+    "\n",
+    "We need to specify what we want to optimize and how we are going to do it.  \n",
+    "\n",
+    "Recall: the loss (or cost) function measures how well our predictions match the expected target.\n",
+    "Previously we were using the sum of the squares of the error.\n",
+    "\n",
+    "For categorical data, like we have, the \"cross-entropy\" metric is often used.  See here for an explanation: https://jamesmccaffrey.wordpress.com/2013/11/05/why-you-should-use-cross-entropy-error-instead-of-classification-error-or-mean-squared-error-for-neural-network-classifier-training/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimizer\n",
+    "\n",
+    "We also need to specify an optimizer.  This could be gradient descent, as we used before.  Here's a list of the optimizers supoprted by keras: https://keras.io/api/optimizers/  We'll use `RMPprop`, which builds off of gradient descent and includes some momentum.\n",
+    "\n",
+    "Finally, we need to specify a metric that is evaluated during training and testing.  We'll use `\"accuracy\"` here.  This means that we'll see the accuracy of our model reported as we are training and testing.\n",
+    "\n",
+    "More details on these options is here: https://keras.io/api/models/model/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from keras.optimizers import RMSprop\n",
+    "\n",
+    "rms = RMSprop()\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer=rms, metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Network summary\n",
+    "\n",
+    "Let's take a look at the network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">392,500</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">150,300</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,010</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m)            │       \u001b[38;5;34m392,500\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │       \u001b[38;5;34m150,300\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m3,010\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">545,810</span> (2.08 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m545,810\u001b[0m (2.08 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">545,810</span> (2.08 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m545,810\u001b[0m (2.08 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that there are > 500k parameters that we will be training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train\n",
+    "\n",
+    "For training, we pass in the inputs and target and the number of epochs to run and it will optimize the network by adjusting the weights between the nodes in the layers.\n",
+    "\n",
+    "The number of epochs is the number of times the entire data set is passed forward and backward through the network.  The batch size is the number of training pairs you pass through the network at a given time.  You update the parameter in your model (the weights) once for each batch.  This makes things more efficient and less noisy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.8868 - loss: 0.3697 - val_accuracy: 0.9540 - val_loss: 0.1459\n",
+      "Epoch 2/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9529 - loss: 0.1573 - val_accuracy: 0.9682 - val_loss: 0.1009\n",
+      "Epoch 3/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9642 - loss: 0.1172 - val_accuracy: 0.9735 - val_loss: 0.0821\n",
+      "Epoch 4/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9702 - loss: 0.0959 - val_accuracy: 0.9793 - val_loss: 0.0693\n",
+      "Epoch 5/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9748 - loss: 0.0826 - val_accuracy: 0.9788 - val_loss: 0.0682\n",
+      "Epoch 6/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9772 - loss: 0.0732 - val_accuracy: 0.9815 - val_loss: 0.0638\n",
+      "Epoch 7/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9801 - loss: 0.0646 - val_accuracy: 0.9813 - val_loss: 0.0620\n",
+      "Epoch 8/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9814 - loss: 0.0592 - val_accuracy: 0.9808 - val_loss: 0.0636\n",
+      "Epoch 9/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9832 - loss: 0.0540 - val_accuracy: 0.9803 - val_loss: 0.0641\n",
+      "Epoch 10/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9847 - loss: 0.0486 - val_accuracy: 0.9837 - val_loss: 0.0622\n",
+      "Epoch 11/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9858 - loss: 0.0449 - val_accuracy: 0.9828 - val_loss: 0.0608\n",
+      "Epoch 12/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9872 - loss: 0.0412 - val_accuracy: 0.9840 - val_loss: 0.0599\n",
+      "Epoch 13/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9875 - loss: 0.0391 - val_accuracy: 0.9836 - val_loss: 0.0604\n",
+      "Epoch 14/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9876 - loss: 0.0374 - val_accuracy: 0.9845 - val_loss: 0.0595\n",
+      "Epoch 15/20\n",
+      "235/235 - 2s - 7ms/step - accuracy: 0.9887 - loss: 0.0351 - val_accuracy: 0.9847 - val_loss: 0.0579\n",
+      "Epoch 16/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9895 - loss: 0.0331 - val_accuracy: 0.9849 - val_loss: 0.0611\n",
+      "Epoch 17/20\n",
+      "235/235 - 2s - 6ms/step - accuracy: 0.9898 - loss: 0.0321 - val_accuracy: 0.9834 - val_loss: 0.0638\n",
+      "Epoch 18/20\n",
+      "235/235 - 2s - 6ms/step - accuracy: 0.9901 - loss: 0.0303 - val_accuracy: 0.9856 - val_loss: 0.0590\n",
+      "Epoch 19/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9904 - loss: 0.0275 - val_accuracy: 0.9846 - val_loss: 0.0702\n",
+      "Epoch 20/20\n",
+      "235/235 - 1s - 6ms/step - accuracy: 0.9906 - loss: 0.0291 - val_accuracy: 0.9842 - val_loss: 0.0664\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x7f830e692900>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "epochs = 20\n",
+    "batch_size = 256\n",
+    "model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size,\n",
+    "          validation_data=(X_test, y_test), verbose=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test\n",
+    "\n",
+    "keras has a routine, `evaluate()` that can take the inputs and targets of a test data set and return the loss value and accuracy (or other defined metrics) on this data.\n",
+    "\n",
+    "Here we see we are > 98% accurate on the test data&mdash;this is the data that the model has never seen before (and was not trained with)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9806 - loss: 0.0810\n",
+      "0.9842000007629395\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_value, accuracy = model.evaluate(X_test, y_test, batch_size=16)\n",
+    "print(accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predicting\n",
+    "\n",
+    "Suppose we simply want to ask our neural network to predict the target for an input.  We can use the `predict()` method to return the category array with the predictions.  We can then use `np.argmax()` to select the most probable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(7)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(model.predict(np.array([X_test[0]])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's loop over the test set and print out what we predict vs. the true answer for those we get wrong.  We can also plot the image of the digit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 8: prediction = 6, truth is 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 247: prediction = 2, truth is 4\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 321: prediction = 7, truth is 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 340: prediction = 3, truth is 5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 445: prediction = 0, truth is 6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 449: prediction = 5, truth is 3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 495: prediction = 0, truth is 8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 582: prediction = 2, truth is 8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 619: prediction = 8, truth is 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 659: prediction = 7, truth is 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wrong = 0\n",
+    "max_wrong = 10\n",
+    "\n",
+    "for n, (x, y) in enumerate(zip(X_test, y_test)):\n",
+    "    try:\n",
+    "        res = model.predict(np.array([x]), verbose=0)\n",
+    "        if np.argmax(res) != np.argmax(y):\n",
+    "            print(f\"test {n}: prediction = {np.argmax(res)}, truth is {np.argmax(y)}\")\n",
+    "            plt.imshow(x.reshape(28, 28), cmap=\"gray_r\")\n",
+    "            plt.show()\n",
+    "            wrong += 1\n",
+    "            if (wrong > max_wrong-1):\n",
+    "                break\n",
+    "    except KeyboardInterrupt:\n",
+    "        print(\"stopping\")\n",
+    "        break\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experimenting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a number of things we can play with to see how the network performance\n",
+    "changes:\n",
+    "\n",
+    "* batch size\n",
+    "\n",
+    "* adding or removing hidden layers\n",
+    "\n",
+    "* changing the dropout\n",
+    "\n",
+    "* changing the activation function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Callbacks\n",
+    "\n",
+    "Keras allows for callbacks each epoch to store some information.  These can allow you to,\n",
+    "for example, plot of the accuracy vs. epoch by adding a callback.  Take a look here for some inspiration:\n",
+    "\n",
+    "https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/History\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Going Further\n",
+    "\n",
+    "Convolutional neural networks are often used for image recognition, especially with larger images.  They use filter to try to recognize patterns in portions of images (A tile).  See this for a keras example: \n",
+    "\n",
+    "https://www.tensorflow.org/tutorials/images/cnn\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/content/11-machine-learning/machine-learning-basics.ipynb b/content/11-machine-learning/machine-learning-basics.ipynb
new file mode 100644
index 00000000..dcc7a97e
--- /dev/null
+++ b/content/11-machine-learning/machine-learning-basics.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f324283b-1ab5-4c9c-8b41-0d96e76ff84d",
+   "metadata": {},
+   "source": [
+    "# Machine Learning Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1346fda-937c-4521-80fd-879674bf8d58",
+   "metadata": {},
+   "source": [
+    "## Neural networks\n",
+    "\n",
+    "When we talk about machine learning, we usually mean an [_artifical neural network_](https://en.wikipedia.org/wiki/Artificial_neural_network).\n",
+    "A neural network mimics the action of neurons in your brain.  \n",
+    "\n",
+    "Basic idea:\n",
+    "\n",
+    "* Create a nonlinear fitting routine with free parameters\n",
+    "* Train the network on data with known inputs and outputs to set the parameters\n",
+    "* Use the trained network on new data to predict the outcome\n",
+    "\n",
+    "We can think of a neural network as a map that takes a set of n parameters and returns a set of m parameters, $\\mathbb{R}^n \\rightarrow \\mathbb{R}^m$ and we can express this as:\n",
+    "\n",
+    "$${\\bf z} = {\\bf A} {\\bf x}$$\n",
+    "\n",
+    "where ${\\bf x} \\in \\mathbb{R}^n$ are the inputs, ${\\bf z} \\in \\mathbb{R}^m$ are the outputs, and ${\\bf A}$ is an $m \\times n$ matrix.\n",
+    "\n",
+    "Our goal is to determine the matrix elements of ${\\bf A}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0eddb35-abe2-4026-a064-ee05f232fbe8",
+   "metadata": {},
+   "source": [
+    "### Some nomeclature\n",
+    "\n",
+    "We can visualize a neural network as:\n",
+    "\n",
+    "![NN diagram](nn_fig2.png)\n",
+    "\n",
+    "* Neural networks are divided into _layers_\n",
+    "\n",
+    "  * There is always an _input layer_&mdash;it doesn't do any processing\n",
+    "  \n",
+    "  * There is always an _output layer_\n",
+    "  \n",
+    "* Within a layer there are neurons or _nodes_.\n",
+    "\n",
+    "  * For input, there will be one node for each input variable.\n",
+    "  \n",
+    "* Every node in the first layer connects to every node in the next layer\n",
+    "\n",
+    "  * The _weight_ associated with the _connection_ can vary&mdash;these are the matrix elements.\n",
+    "  \n",
+    "* In this example, the processing is done in layer 2 (the output)\n",
+    "\n",
+    "* When you train the neural network, you are adjusting the weights connecting to the nodes\n",
+    "\n",
+    "  * Some connections might have zero weight\n",
+    "  \n",
+    "  * This mimics nature&mdash;a single neuron can connect to several (or lots) of other neurons."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c678e3c3-4b2f-4d4a-abf0-3716c59d2f0d",
+   "metadata": {},
+   "source": [
+    "## Universal approximation theorem and non-linearity\n",
+    "\n",
+    "A neural network can be designed to approximate any function, $f(x)$.  For this to work, there must be a source of non-linearity in the network.  This is applied on a layer.  This is a result of the [universal approximation theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem).\n",
+    "\n",
+    "We call this an [_activation function_](https://en.wikipedia.org/wiki/Activation_function) and it has the form:\n",
+    "\n",
+    "\n",
+    "$$g({\\bf x}) = \\left ( \\begin{array}{c} g(x_0) \\\\ g(x_1) \\\\ \\vdots \\\\ g(x_{n-1}) \\end{array} \\right )$$\n",
+    "\n",
+    "Then our neural network has the form: ${\\bf z} = g({\\bf A x})$\n",
+    "\n",
+    "We want to choose a $g(x)$ that is differentiable.  A common choice is the _sigmoid function_:\n",
+    "\n",
+    "$$g(p) = \\frac{1}{1 + e^{-p}}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2a029ea0-33bd-4058-bc58-26dde63ddd14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9d672b0-594f-4a03-9065-496e24abe89b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(p):\n",
+    "    return 1 / (1 + np.exp(-p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "69eebe24-d010-4c40-905a-eb014bee84ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f4b5fe48c20>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = np.linspace(-10, 10, 200)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(p, sigmoid(p))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eaf8794-cfa8-475b-a255-711560c5f0c2",
+   "metadata": {},
+   "source": [
+    "Notice that the sigmoid scales all output to be in $z_i \\in (0, 1)$\n",
+    "\n",
+    "This means that we need to ensure that our training set set is likewise mapped to $(0, 1)$, and if not, we need to transform it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c193973c-d51c-4dfd-a30e-8667fd330158",
+   "metadata": {},
+   "source": [
+    "## Basic algorithm\n",
+    "\n",
+    "* Training\n",
+    "\n",
+    "  * We have $T$ pairs $(x^k, y^k)$ for $k = 1, \\ldots, T$\n",
+    "  \n",
+    "  * We require that $g({\\bf A x}^k) = {\\bf y}^k$ for all $k$\n",
+    "  \n",
+    "    Recall that $g(p)$ is a scalar function that works element-by-element:\n",
+    "    \n",
+    "    $$z_i = g([{\\bf A x}]_i) = g \\left ( \\sum_j A_{ij} x_j \\right )$$\n",
+    "    \n",
+    "  * Find the elements of ${\\bf A}$\n",
+    "  \n",
+    "    This is a minimization problem, where we are minimizing:\n",
+    "    \n",
+    "    $$f(A_{ij}) = \\| g({\\bf A x}^k) - {\\bf y}^k \\|^2$$\n",
+    "    \n",
+    "    We call this function the _cost function_.\n",
+    "    \n",
+    "    A common minimization technique is [_gradient descent_](https://en.wikipedia.org/wiki/Gradient_descent).\n",
+    "    \n",
+    "    Some caveats:\n",
+    "    \n",
+    "    * When you minimize with one set of training data, there is no guarantee that your are still minimimzed with respect to the others.  We do multiple _epochs_ or passes through the training data to fix this.\n",
+    "    \n",
+    "    * We often don't apply the full correction from gradient descent, but instead scale it by some $\\eta < 1$ called the _learning rate_.\n",
+    "    \n",
+    "* Using the network\n",
+    "\n",
+    "  With the trained ${\\bf A}$, we can now use the network on data we haven't seen before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9801589-7251-48b2-9099-e8b22f9c9250",
+   "metadata": {},
+   "source": [
+    "## Hidden layers\n",
+    "\n",
+    "We can get better performance from a neural network by adding a hidden layer:\n",
+    "\n",
+    "![hidden layers](nn_fig_hidden.png)\n",
+    "\n",
+    "The side of the hidden layer is independent of the size of the input and output layers.  Now we have an additional matrix ${\\bf B}$ to train.  This can all be done together using the same algorithm described above.  Where we now minimize:\n",
+    "\n",
+    "$$f(A_{ls}, B_{ij}) = \\sum_{l=1}^m (z_l - y_l)^2$$\n",
+    "\n",
+    "$$\\tilde{z}_i = g \\left ( \\sum_{j=1}^n B_{ij} x_j \\right )$$\n",
+    "\n",
+    "$$z_l = g \\left ( \\sum_{s=1}^k A_{ls} \\tilde{z}_s \\right )$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7409a682-1905-4bde-a8a9-1d0c4d4d8347",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/content/11-machine-learning/machine-learning-libraries.md b/content/11-machine-learning/machine-learning-libraries.md
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+# Diving Deeper into Machine Learning
+
+We've focused on neural networks, using labeled data that we
+can use to learn the trends in our data.  This is an example
+of _supervised learning_.  
+
+Broadly speaking there are
+3 main [approaches to machine learning](https://en.wikipedia.org/wiki/Machine_learning#Approaches)
+
+* [Supervised learning](https://en.wikipedia.org/wiki/Supervised_learning) 
+
+  This uses labeled pairs (input and output) to train the model
+  to learn how to predict the outputs from the inputs.
+
+* [Unsupervised learning](https://en.wikipedia.org/wiki/Unsupervised_learning)
+
+  No labeled data is provided.  Instead the machine learning
+  algorithm seeks to find the structure on its own.  The goal
+  is to learn patterns and features to be able to produce
+  new data.
+  
+* [Reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning)
+
+  As with unsupervised learning, no labeled data is used,
+  but the model is "rewarded"  when it does something right,
+  and the model tries to maximize rewards (think: self-driving
+  cars).
+  
+## Libraries
+
+There are a number of popular libraries that implement machine learning algorithms.
+Their features and performance vary quite a bit.  An comparison of their
+features is provided by Wikipedia: [Comparison of deep learning software](https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software).
+
+Some additional comparisons are provided here: https://ritza.co/articles/scikit-learn-vs-tensorflow-vs-pytorch-vs-keras/
+
+* [TensorFlow](https://www.tensorflow.org/)
+
+  This is an open source machine learning library released by Google.  It has support
+  for CPUs, GPUs, and [TPUs](https://en.wikipedia.org/wiki/Tensor_Processing_Unit),
+  and provides all the features you need to build deep learning workflows:
+  [TensorFlow feactures](https://en.wikipedia.org/wiki/TensorFlow#Features).
+
+  You can install tensorflow via:
+
+  ```
+  pip install tensorflow
+  ```
+  
+  ```{note}
+  At the moment, tensorflow only supports python <= 3.12.  So I'll be using
+  pytorch instead (since I am running python 3.13).
+  ```
+  
+* [PyTorch](https://pytorch.org/)
+
+  This is a machine learning library build off of the Torch library, originally
+  developed by Facebook.
+
+  You can install pytorch via:
+
+  ```
+  pip install torch
+  ```
+  
+* [scikit-learn](https://scikit-learn.org/stable/)
+
+  This is a python library developed for machine learning.  It has a lot of
+  sample datasets that provide a nice means to learn how different methods work.
+  It is designed to work with NumPy and SciPy.
+  
+  General recommendations on the web seem to be to use Scikit-learn to get
+  started with machine learning and to explore ideas, but to switch to
+  one of the other packages for computationally-intensive work.
+
+  You can install scikit-learn via:
+
+  ```
+  pip install scikit-learn
+  ```
+  
+  Scikit-learn provides some nice sample datasets: 
+
+  https://scikit-learn.org/stable/datasets/toy_dataset.html
+  
+  as well as generators for
+  datasets:
+  
+  https://scikit-learn.org/stable/datasets/sample_generators.html
+  
+There are also tools that provide higher-level interfaces to these
+
+* [Keras](https://keras.io/)
+
+  Keras provides a common python interface to several different machine learning
+  libraries, including tensorflow and torch.  This hides a lot of the implementation
+  details and makes it easy to get started using these libraries.
+
+## Keras 
+
+We'll focus on Keras.
+
+There are a large number of examples provided by Keras:
+
+https://keras.io/examples/
+
diff --git a/content/11-machine-learning/machine-learning.md b/content/11-machine-learning/machine-learning.md
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+# Machine Learning
+
+We'll look at a popular library for machine learning.
diff --git a/content/11-machine-learning/model.png b/content/11-machine-learning/model.png
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diff --git a/content/11-machine-learning/neural-net-basics.md b/content/11-machine-learning/neural-net-basics.md
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+# Artificial Neural Network Basics
+
+## Neural networks
+
+An [_artifical
+neural
+network_](https://en.wikipedia.org/wiki/Artificial_neural_network)
+mimics the action of neurons in your brain to form connections
+between nodes (neurons) that link the input to the output.
+
+```{note}
+We'll loosely 
+follow the notation from _Computational Methods for Physics_ by
+Franklin.
+```
+
+Basic idea:
+
+* Create a nonlinear fitting routine with free parameters
+* Train the network on data with known inputs and outputs to set the parameters
+* Use the trained network on new data to predict the outcome
+
+We can think of a neural network as a map that takes a set of
+$N_\mathrm{in}$ parameters and returns a set of $N_\mathrm{out}$
+parameters, which we can express this as:
+
+$${\bf z} = {\bf A} {\bf x}$$
+
+where
+
+$${\bf x} = (x_1, x_2, \ldots, x_{N_\mathrm{in}})$$
+
+are the inputs,
+
+$${\bf z} = (z_1, z_2, \ldots, z_{N_\mathrm{out}})$$
+
+are the outputs, and
+${\bf A}$ is an $N_\mathrm{out} \times N_\mathrm{in}$ matrix.
+
+Our goal is to determine the matrix elements of ${\bf A}$.
+
+## Nomenclature
+
+We can visualize a neural network as:
+
+![NN diagram](nn_fig.png)
+
+* Neural networks are divided into _layers_
+
+  * There is always an _input layer_&mdash;it doesn't do any processing.
+
+  * There is always an _output layer_.
+
+* Within a layer there are neurons or _nodes_.
+
+  * For input, there will be one node for each input variable.  In this figure,
+    there are 3 nodes on the input layer.
+
+  * The output layer will have as many nodes are needed to convey the answer
+    we are seeking from the network.  In this case, there are 2 nodes on the
+    output layer.
+
+* Every node in the first layer connects to every node in the next layer
+
+  * The _weight_ associated with the _connection_ can vary&mdash;these are the matrix elements.
+
+    ```{note}
+    This is called a _dense layer_.  There are alternate types of layers
+    we can explore where the nodes are connected differently.
+    ```
+
+* In this example, the processing is done in layer 2 (the output)
+
+* When you train the neural network, you are adjusting the weights connecting to the nodes
+
+  * Some connections might have zero weight
+
+  * This mimics nature&mdash;a single neuron can connect to several (or lots) of other neurons.
+
+## Universal approximation theorem
+
+A neural network can be designed to approximate any function, $f(x)$.  For this to work, there must be a source of non-linearity in the network&mdash;this is a result of the [universal approximation theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem).
+
+We use a nonlinear [_activation function_](https://en.wikipedia.org/wiki/Activation_function) that is applied in a layer.  It has
+the form:
+
+$$g({\bf v}) = \left ( \begin{array}{c} g(v_0) \\ g(v_1) \\ \vdots \\ g(v_{n-1}) \end{array} \right )$$
+
+```{note}
+The activation function, $g({\bf v})$ works element-by-element on the vector ${\bf v}$.
+```
+
+Then our neural network has the form: ${\bf z} = g({\bf A x})$
+
+We want to choose a function $g(\xi)$ that is differentiable.  A common choice is the _sigmoid function_:
+
+$$g(\xi) = \frac{1}{1 + e^{-\xi}}$$
+
+```{figure} sigmoid.png
+---
+align: center
+---
+The sigmoid function
+```
+
+```{note}
+There are [many choices for the activation function](https://en.wikipedia.org/wiki/Activation_function) which have
+different properties.  Often the choice of activation function will be empirical, by experimenting with the 
+performance of the network.
+```
+
+## Basic algorithm
+
+We'll consider the case where we have training data---a set of inputs, ${\bf x}^k$,
+together with the expected output (answer), ${\bf y}^k$.  These training pairs
+allow us to constrain the output of the network and train the weights.
+
+* Training
+
+  * Loop over the $T$ pairs $({\bf x}^k, {\bf y}^k)$ for $k = 1, \ldots, T$
+
+    * Predict the output for ${\bf x}^k$ as:
+
+      $$z_i = g([{\bf A x}^k]_i) = g \left ( \sum_{j=1}^{N_\mathrm{in}} A_{ij} x^k_j \right )$$
+
+    * Constrain that ${\bf z} = {\bf y}^k$.
+
+      This is a minimization problem, where we are minimizing:
+
+      \begin{align*}
+      \mathcal{L}(A_{ij}) &= \| g({\bf A x}^k) - {\bf y}^k \|^2 \\
+                &= \sum_{i=1}^{N_\mathrm{out}} \left [ g\left (\sum_{j=1}^{N_\mathrm{in}} A_{ij} x^k_j \right ) - y^k_i \right ]^2
+      \end{align*}
+
+      We call this function, $\mathcal{L}$, the _cost function_ or [loss function](https://en.wikipedia.org/wiki/Loss_function).
+
+      ```{note}
+      This is called the _mean square error_ loss function, and is one possible choice for $\mathcal{L}(A_{ij})$, but [many others exist](https://en.wikipedia.org/wiki/Loss_function).
+      ```
+
+    * Update the matrix ${\bf A}$ based on the training pair $({\bf x}^k, {\bf y^{k}})$.
+
+* Using the network
+
+  With the trained ${\bf A}$, we can now use the network on data we haven't seen before, $\boldsymbol \chi$:
+
+  $$z_i = g([{\bf A {\boldsymbol \chi}}^k]_i) = g \left ( \sum_{j=1}^{N_\mathrm{in}} A_{ij} \chi^k_j \right )$$
+
+There are a lot of details that we still need to figure out involving the training and minimization.
+We'll start with minimization: a common minimization technique used with
+neural networks is [_gradient descent_](https://en.wikipedia.org/wiki/Gradient_descent).
diff --git a/content/11-machine-learning/neural-net-derivation.md b/content/11-machine-learning/neural-net-derivation.md
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+# Deriving the Learning Correction
+
+For gradient descent, we need to derive the update to the matrix
+${\bf A}$ based on training on a set of our data, $({\bf x}^k, {\bf y}^k)$.
+
+```{important}
+The derivation we do here is specific to our choice of loss function, $\mathcal{L}(A_{ij})$
+and activation function, $g(\xi)$.
+```
+
+Let's start with our cost function:
+
+$$\mathcal{L}(A_{ij}) = \sum_{i=1}^{N_\mathrm{out}} (z_i - y_i^k)^2 = \sum_{i=1}^{N_\mathrm{out}}
+  \Biggl [ g\biggl (\underbrace{\sum_{j=1}^{N_\mathrm{in}} A_{ij} x^k_j}_{\equiv \alpha_i} \biggr ) - y^k_i \Biggr ]^2$$
+
+where we'll refer to the product ${\boldsymbol \alpha} \equiv {\bf
+Ax}$ to help simplify notation.  This means that ${\bf z} = g({\boldsymbol \alpha})$.
+
+We can compute the derivative with respect to a single matrix
+element, $A_{pq}$ by applying the chain rule:
+
+$$\frac{\partial \mathcal{L}}{\partial A_{pq}} =
+  2 \sum_{i=1}^{N_\mathrm{out}} (z_i - y^k_i) \left . \frac{\partial g}{\partial \xi} \right |_{\xi=\alpha_i} \frac{\partial \alpha_i}{\partial A_{pq}}$$
+
+
+with
+
+$$\frac{\partial \alpha_i}{\partial A_{pq}} = \sum_{j=1}^{N_\mathrm{in}} \frac{\partial A_{ij}}{\partial A_{pq}} x^k_j = \sum_{j=1}^{N_\mathrm{in}} \delta_{ip} \delta_{jq} x^k_j = \delta_{ip} x^k_q$$
+
+and for $g(\xi)$, we will assume the sigmoid function,so
+
+$$\frac{\partial g}{\partial \xi}
+  = \frac{\partial}{\partial \xi} \frac{1}{1 + e^{-\xi}}
+  =- (1 + e^{-\xi})^{-2} (- e^{-\xi})
+  = g(\xi) \frac{e^{-\xi}}{1+ e^{-\xi}} = g(\xi) (1 - g(\xi))$$
+
+which gives us:
+
+\begin{align*}
+\frac{\partial \mathcal{L}}{\partial A_{pq}} &= 2 \sum_{i=1}^{N_\mathrm{out}}
+   (z_i - y^k_i) z_i (1 - z_i) \delta_{ip} x^k_q \\
+   &= 2 (z_p - y^k_p) z_p (1- z_p) x^k_q
+\end{align*}
+
+where we used the fact that the $\delta_{ip}$ means that only a single term contributes to the sum.
+
+```{note}
+Observe that:
+
+* $e_p^k \equiv (z_p - y_p^k)$ is the error on the output layer,
+  and the correction is proportional to the error (as we would
+  expect).
+
+* The $k$ superscripts here remind us that this is the result of
+  only a single pair of data from the training set.
+```
+
+Now ${\bf z}$ and ${\bf y}^k$ are all vectors of size $N_\mathrm{out} \times 1$ and ${\bf x}^k$ is a vector of size $N_\mathrm{in} \times 1$, so we can write this expression for the matrix as a whole as:
+
+$$\frac{\partial \mathcal{L}}{\partial {\bf A}} = 2 ({\bf z} - {\bf y}^k) \circ {\bf z} \circ (1 - {\bf z}) \cdot ({\bf x}^k)^\intercal$$
+
+where the operator $\circ$ represents _element-by-element_ multiplication (the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_(matrices))).
+
+## Performing the update
+
+We could do the update like we just saw with our gradient descent
+example: take a single data point, $({\bf x}^k, {\bf y}^k)$ and
+do the full minimization, continually estimating the correction,
+$\partial \mathcal{L}/\partial {\bf A}$ and updating ${\bf A}$ until we
+reach a minimum.  The problem with this is that $({\bf x}^k, {\bf y}^k)$ is only one point in our training data, and there is no
+guarantee that if we minimize completely with point $k$ that we will
+also be a minimum with point $k+1$.
+
+Instead we take multiple passes through the training data (called _epochs_) and apply only a single push in the direction that gradient
+descent suggests, scaled by a _learning rate_ $\eta$.
+
+The overall minimization appears as:
+
+```{card} Minimization
+* Loop over epochs
+
+  * Loop over the training data, $\{ ({\bf x}^0, {\bf y}^0), ({\bf x}^1, {\bf y}^1), \ldots \}$.  We'll refer to the current training
+    pair as $({\bf x}^k, {\bf y}^k)$
+
+    * Propagate ${\bf x}^k$ through the network, getting the output
+      ${\bf z} = g({\bf A x}^k)$
+
+    * Compute the error on the output layer, ${\bf e}^k = {\bf z} - {\bf y}^k$
+
+    * Update the matrix ${\bf A}$ according to:
+
+      $${\bf A} \leftarrow {\bf A} - 2 \,\eta\, {\bf e}^k \circ {\bf z} \circ (1 - {\bf z}) \cdot ({\bf x}^k)^\intercal$$
+```
diff --git a/content/11-machine-learning/neural-net-hidden.md b/content/11-machine-learning/neural-net-hidden.md
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+# Hidden Layers
+
+
+ We can get better performance from a neural network by adding a hidden layer:
+
+![hidden layers](nn_fig_hidden.png)
+
+The size of the hidden layer is independent of the size of the input and output
+layers.  In this case, we have a hidden layer that is larger
+than either the input or output layers.
+
+Now we have an additional matrix ${\bf B}$ to train.  The matrix sizes are:
+
+* ${\bf A}$ : $N_\mathrm{out} \times N_\mathrm{hidden}$
+* ${\bf B}$ : $N_\mathrm{hidden} \times N_\mathrm{in}$
+
+
+```{note}
+Neglecting the activation functions, the action of the network
+is to do ${\bf z} = {\bf A B x}$ which has size $N_\mathrm{out}$.
+```
+
+The derivation of the corrections to matrices ${\bf A}$ and ${\bf B}$ can be done
+via the chain rule.
+
+```{note}
+We'll consider the case of a single hidden layer, but the derivation we
+do here generalizes to multiple hidden layers.
+```
+
+\begin{equation}
+\mathcal{L}(A_{lm}, B_{ij}) = \sum_{l=1}^{N_\mathrm{out}} (z_l - y^k_l)^2
+\end{equation}                  
+
+$$\tilde{z}_i = g \biggl ( \underbrace{\sum_{j=1}^{N_\mathrm{in}} B_{ij} x^k_j}_{\equiv \beta_i} \biggr )$$
+
+$$z_l = g \biggl ( \underbrace{\sum_{m=1}^{N_\mathrm{hidden}} A_{lm} \tilde{z}_m}_{\equiv \alpha_l} \biggr )$$
+
+Note that we are assuming here that the same activation function, $g(\xi)$
+is used on each layer.
+
+## Updates to ${\bf A}$
+
+Matrix ${\bf A}$ is trained based on the output layer, we know the error there
+directly, ${\bf e}^k = {\bf z} - {\bf y}^k$.  As a result, we can just use
+the result that we got for a single layer, but now the input is $\tilde{\bf z}$
+instead of ${\bf x}$:
+
+$$\frac{\partial \mathcal{L}}{\partial {\bf A}} = 2 {\bf e}^k \circ {\bf z} \circ (1 - {\bf z}) \cdot \tilde{\bf z}^\intercal$$
+
+## Updates to ${\bf B}$
+
+To find the corrections to matrix ${\bf B}$, we essentially need to know what the
+error is on the hidden layer.  But we only know the error on the output layer, so
+by applying the chainrule on our cost function, we will work out this correction,
+and in the process see how the error on the output layer informs the error on the
+hidden layer&mdash;a process called _backpropagation_.
+
+Let's start with our cost function:
+
+\begin{align*}
+\mathcal{L}(A_{lm}, B_{ij}) &= \sum_{l=1}^{N_\mathrm{out}} (z_l - y^k_l)^2 \\
+                  &= \sum_{l=1}^{N_\mathrm{out}} \Biggl [ g \biggl ( \sum_{m=1}^{N_\mathrm{hidden}} A_{lm} \tilde{z}_m \biggr ) - y_l^k \Biggr ]^2 \\
+                  &= \sum_{l=1}^{N_\mathrm{out}} \Biggl [ g \biggl ( \sum_{m=1}^{N_\mathrm{hidden}} A_{lm} \,g \biggl ( \sum_{j=1}^{N_\mathrm{in}} B_{mj} x_j^k \biggr ) \biggr ) - y_l^k \Biggr ]^2
+\end{align*}                  
+
+Differentiating with respect to an element in matrix ${\bf B}$, we apply the chain rule over and over,
+giving:
+
+$$\frac{\partial \mathcal{L}}{\partial B_{pq}} = 2 \sum_{l=1}^{N_\mathrm{out}} (z_l - y_l^k)
+    \left .\frac{\partial g}{\partial \xi} \right |_{\xi = \alpha_l}
+    \sum_{m=1}^{N_\mathrm{hidden}} A_{lm}\, \left . \frac{\partial g}{\partial \xi} \right |_{\xi = \beta_m}
+    \sum_{j=1}^{N_\mathrm{in}} \frac{\partial B_{mj}}{\partial B_{pq}} x_j^k $$
+    
+     
+Now we have 3 derivatives left, which are straightforward:
+
+$$\left .\frac{\partial g}{\partial \xi} \right |_{\xi = \alpha_l} = g(\alpha_l)\left [ 1 - g(\alpha_l)\right ]
+   = z_l (1 - z_l)$$
+
+$$\left .\frac{\partial g}{\partial \xi} \right |_{\xi = \beta_m} = g(\beta_m)\left [ 1 - g(\beta_m)\right ]
+   = \tilde{z}_m (1 - \tilde{z}_m)$$
+
+$$\frac{\partial B_{mj}}{\partial B_{pq}} = \delta_{mp} \delta_{jq}$$
+
+Inserting these dervatives and using the $\delta$'s, we are left with:
+
+$$\frac{\partial \mathcal{L}}{\partial B_{pq}} = 2 \sum_{l=1}^{N_\mathrm{out}}
+   \underbrace{(z_l - y_l^k)}_{ = e_l^k} z_l (1 - z_l) A_{lp} \tilde{z}_p (1 - \tilde{z}_p) x^k_q$$
+   
+Now, that remaining sum is contracting on the first of the indices of
+the matrix ${\bf A}$, indicating a matrix vector product involving
+${\bf A}^\intercal$.  This allows us to define the error _backpropagated_ to the hidden layer:
+
+$$\tilde{e}_p^k = \sum_{l=1}^{N_\mathrm{out}} e_l^k z_l (1 - z_l) A_{lp} 
+   = \left [ {\bf A}^\intercal  \cdot ({\bf e}^k \circ {\bf z} \circ (1 - {\bf z})) \right ]_p$$
+
+and we can write
+
+$$\frac{\partial \mathcal{L}}{\partial {\bf B}} = 2 \tilde{\bf e}^k \circ \tilde{\bf z} \circ (1 - \tilde{\bf z}) \cdot ({\bf x}^k)^\intercal$$
+
+
+Notice the symmetry in the update of each matrix:
+
+\begin{align*}
+\frac{\partial \mathcal{L}}{\partial {\bf A}} &= 2 {\bf e}^k \circ {\bf z} \circ (1 - {\bf z}) \cdot \tilde{\bf z}^\intercal \\
+\frac{\partial \mathcal{L}}{\partial {\bf B}} &= 2 \tilde{\bf e}^k \circ \tilde{\bf z} \circ (1 - \tilde{\bf z}) \cdot ({\bf x}^k)^\intercal
+\end{align*}
+
+Adding additional hidden layers would continue the trend, with each hidden layer's matrix update depending
+on the error backpropagated to that layer.
diff --git a/content/11-machine-learning/neural-net-improvements.md b/content/11-machine-learning/neural-net-improvements.md
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--- /dev/null
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@@ -0,0 +1,103 @@
+# Improvements
+
+There are many ways we could improve the performance of our network, but these will add
+a lot of complexity to the simple class that we wrote.  Fortunately there are a lot of
+machine learning libraries that provide these features, and work efficiently, so for
+real applications we would want to use one of those libraries (we'll explore these next).
+
+## Batching
+
+Right now, we did our training as:
+
+* Loop over the $T$ pairs $({\bf x}^k, {\bf y}^k)$ for $k = 1, \ldots, T$
+
+  * Propagate $({\bf x}^k, {\bf y}^k)$ through the network
+  * Compute the corrections $\partial \mathcal{L}/\partial {\bf A}$, $\partial \mathcal{L}/\partial {\bf B}$
+  * Update the matrices:
+  
+    $${\bf A} \leftarrow {\bf A} - \eta \frac{\partial \mathcal{L}}{\partial {\bf A}}$$
+
+    $${\bf B} \leftarrow {\bf B} - \eta \frac{\partial \mathcal{L}}{\partial {\bf B}}$$
+
+In this manner, each training pair sees slightly different
+matrices ${\bf A}$ and ${\bf B}$, as each previous pair
+updates it immediately.
+
+We could instead divide our training set into $N$ batches,
+each with $\tau = T/N$ training pairs and do our update as:
+
+* Loop over $N$ batches
+
+  * Loop over the $\tau$ pairs $({\bf x}^k, {\bf y}^k)$ for $k = 1, \ldots, \tau$ in the current batch 
+
+    * Propagate $({\bf x}^k, {\bf y}^k)$ through the network
+    * Compute the gradients $\partial \mathcal{L}/\partial {\bf A}^k$, $\partial \mathcal{L}/\partial {\bf B}^k$ from the current pair
+    
+    * Accumulate the gradients:
+  
+      $$\frac{\partial \mathcal{L}}{\partial {\bf A}} = \frac{\partial \mathcal{L}}{\partial {\bf A}} + \frac{\partial \mathcal{L}}{\partial {\bf A}^k}$$
+      
+      $$\frac{\partial \mathcal{L}}{\partial {\bf B}} = \frac{\partial \mathcal{L}}{\partial {\bf B}} + \frac{\partial \mathcal{L}}{\partial {\bf B}^k}$$
+      
+  * Apply a single update to the matrices for this batch:
+
+    $${\bf A} \leftarrow {\bf A} - \frac{\eta}{\tau} \frac{\partial \mathcal{L}}{\partial {\bf A}}$$
+
+    $${\bf B} \leftarrow {\bf B} - \frac{\eta}{\tau} \frac{\partial \mathcal{L}}{\partial {\bf B}}$$
+
+```{note}
+We normalize the accumulated gradients by the batch size, $\tau$, which means that
+we are applying the average gradient over the batch.
+```
+
+The advantage of this is that the $\tau$ trainings in a batch
+can all be done in parallel now, spread across many CPU cores
+or GPU cores.  This greatly accelerates the training time.
+  
+
+## Different activation or cost functions
+
+We used a simple cost function: the sum of the square of the errors.  This is analogous to the $L_2$ norm we discussed previously.  But there are a lot of other cost functions
+we could explore.  Changing the cost function will require
+us to recompute our derivatives.
+
+Likewise, there are a wide number of activation functions,
+some of which are not differentiable.  The choice of activation
+function can depend on what type of data you are using.  You
+might also want to use a different activation function
+on each layer.  Again, this would require us to redo
+our derivatives.
+
+
+## Use a different minimization technique
+
+We only explored gradient descent.  But there are improvements
+to this (like momentum that we mentioned previously) as well
+as alternate minimization techniques we could use (some of 
+which don't need the gradient at all).
+
+
+## Different types of layers / connections
+
+We only considered a dense network: every node on one
+layer was connected to every node on the adjacent layer.
+But there are alternatives.
+
+For example, a [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) performs a convolution on a layer with some kernel.  This
+helps identifying features.
+
+## More hidden layers
+
+There is no restriction on the number of hidden layers we
+can use.  Each additional hidden layer means an additional
+matrix is added to our network.  For our code, we'd simply need to backpropagate
+the error to each hidden layer and compute the update to
+the new matrix.
+
+## Auto-differentiation libraries
+
+At some point, with all of these options, doing all of the
+differentiation / chain-rule by hand becomes burdensome and
+prone to errors.  For this reason, libraries often use
+automatic differentiation libraries, like [JAX](https://jax.readthedocs.io/en/latest/) which can take
+the derivatives of our python functions themselves.
\ No newline at end of file
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diff --git a/content/11-machine-learning/nn_fig.py b/content/11-machine-learning/nn_fig.py
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+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class Neuron(object):
+    def __init__(self, x, y, R=0.35):
+        self.x = x
+        self.y = y
+        self.R = R
+
+    def draw(self, color="C0", label=None):
+        theta = np.linspace(0, 2*np.pi, 180)
+        xc = self.x + self.R*np.cos(theta)
+        yc = self.y + self.R*np.sin(theta)
+
+        plt.fill(xc, yc, color=color, alpha=0.75)
+
+        if label is not None:
+            plt.text(self.x, self.y, label, color="k", 
+                     horizontalalignment="center", verticalalignment="center")
+
+
+class Layer(object):
+    def __init__(self, x, num_neurons=3, dy=1.0):
+        self.x = x
+        self.dy = dy
+        self.num_neurons = num_neurons
+        self.neurons = []
+
+        for i in range(self.num_neurons):
+            y = -i*self.dy
+            self.neurons.append(Neuron(self.x, y))
+
+    def draw(self, color="C1", label=None, top_label=None):
+        # compute the bounding box
+        ys = [q.y for q in self.neurons]
+
+        ymin = min(ys) - self.neurons[0].R
+        ymax = max(ys) + self.neurons[0].R
+
+        xmin = self.neurons[0].x - self.neurons[0].R
+        xmax = self.neurons[0].x + self.neurons[0].R
+
+        dx = xmax - xmin
+        xmin -= 0.25*dx
+        xmax += 0.25*dx
+        ymin -= 0.25*dx
+        ymax += 0.25*dx
+
+        plt.fill([xmin, xmin, xmax, xmax, xmin],
+                 [ymin, ymax, ymax, ymin, ymin], 
+                 color=color, zorder=-100, alpha=0.5, edgecolor="none")
+
+        for i, n in enumerate(self.neurons):
+            n.draw(label="{}".format(i))
+
+        if label is not None:
+            plt.text(self.x, ymin-0.5*dx, label,
+                     horizontalalignment="center", color="k")
+
+        if top_label is not None:
+            plt.text(self.x, ymax+0.25*dx, top_label,
+                     horizontalalignment="center", color="k")
+
+class NeuralNet(object):
+
+    def __init__(self, dx=2.0, nlayers=2, neurons_in=3, neurons_out=2):
+        self.nlayers = nlayers
+
+        self.layers = []
+        for i in range(self.nlayers):
+            if i == 0:
+                neurons = neurons_in
+            elif i == nlayers-1:
+                neurons = neurons_out
+            else:
+                neurons = int(1.5*neurons_in)
+
+            self.layers.append(Layer(i*dx, num_neurons=neurons, dy=1.5))
+
+    def draw(self):
+        for i, l in enumerate(self.layers):
+            if i == 0:
+                label = "input\nlayer {}".format(i+1)
+                top_label = r"${\bf x}$"
+            elif i == len(self.layers)-1:
+                label = "output\nlayer {}".format(i+1)
+                if len(self.layers) == 3:
+                    top_label = r"${\bf z} = g({\bf A}\tilde{\bf z})$"
+                else:
+                    top_label = r"${\bf z} = g({\bf A}{\bf x})$"
+            else:
+                label = "hidden\nlayer {}".format(i+1)
+                top_label = r"$\tilde{\bf z} = g({\bf B x})$"
+
+            l.draw(label=label, top_label=top_label)
+
+        colors = ["0.5", "C1", "C2", "C3", "C4", "C5", "C6"]
+
+        # now connect
+        for i in range(0, len(self.layers)-1):
+            for j, n in enumerate(self.layers[i].neurons):
+                x0 = n.x
+                y0 = n.y
+                R0 = n.R
+
+                c = random.choice(colors)
+
+                for q in self.layers[i+1].neurons:
+                    xt = q.x
+                    yt = q.y
+                    Rt = q.R
+
+                    # figure out the angle
+                    theta = np.arctan2(yt-y0, xt-x0)
+                    L = np.sqrt((xt - x0)**2 + (yt - y0)**2) - Rt - R0
+
+                    plt.arrow(x0+R0*np.cos(theta), y0+R0*np.sin(theta), 
+                              L*np.cos(theta), L*np.sin(theta), 
+                              head_width=0.075, zorder=-50, 
+                              color=c,
+                              length_includes_head=True)
+
+            xc = 0.5*(self.layers[i].x + self.layers[i+1].x)
+            yc = self.layers[i].neurons[0].y + 0.25*(self.layers[i].neurons[0].y - self.layers[i].neurons[1].y)
+            if i == 0 and self.nlayers > 2:
+                label = r"${\bf B}$"
+            else:
+                label = r"${\bf A}$"
+
+            plt.text(xc, yc, label, color="r")
+
+        # draw inputs and outputs
+        ls = self.layers[0]
+        xf = ls.neurons[0].x - 1.5*ls.neurons[0].R
+        L = 2.0*ls.neurons[0].R
+
+        for n in ls.neurons:
+            plt.arrow(xf - L, n.y, L, 0, head_width=0.075, color="k")
+
+        ls = self.layers[-1]
+        xf = ls.neurons[0].x + 1.25*ls.neurons[0].R
+        L = 2.0*ls.neurons[0].R
+
+        for n in ls.neurons:
+            plt.arrow(xf, n.y, L, 0, head_width=0.075, color="k")
+
+
+# simple version
+nn = NeuralNet()
+nn.draw()
+
+plt.axis("off")
+ax = plt.gca()
+ax.set_aspect("equal", "datalim")
+
+f = plt.gcf()
+f.set_size_inches(5, 5)
+
+plt.tight_layout()
+plt.savefig("nn_fig.png", dpi=150, bbox_inches="tight")
+
+# hidden layer
+plt.clf()
+
+nn = NeuralNet(nlayers=3)
+nn.draw()
+
+plt.axis("off")
+ax = plt.gca()
+ax.set_aspect("equal", "datalim")
+
+f = plt.gcf()
+f.set_size_inches(7, 5.5)
+plt.tight_layout()
+plt.savefig("nn_fig_hidden.png", dpi=150)
+
+# add some labels
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diff --git a/content/11-machine-learning/sigmoid.py b/content/11-machine-learning/sigmoid.py
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+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def sigmoid(p):
+    return 1 / (1 + np.exp(-p))
+
+p = np.linspace(-10, 10, 200)
+
+fig, ax = plt.subplots()
+
+ax.plot(p, sigmoid(p))
+
+fig.savefig("sigmoid.png")
diff --git a/content/12-extensions/extensions-example.md b/content/12-extensions/extensions-example.md
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+# Example Extension
+
+Let's rewrite our Mandelbrot generator using different languages
+to see how the performance differs.
+
+Recall the Mandelbrot set is defined as the set such that $z_{k+1} = z_k^2 + c$
+remains bounded, defined as $|z_{k+1}| \le 2$, where $c$ is a complex number,
+$c = x + iy$, in the complex plane, and $z_0 = 0$ is the starting condition.
+
+We'll do a fixed number of iterations, and store the iteration for which $|z_{k+1}|$
+first becomes larger than 2.
+
+
+
+## NumPy array syntax
+
+Here's an example of a python implementation using NumPy array operations:
+
+
+```{literalinclude} ../../examples/extensions/python/mandel.py
+:language: python
+```
+
+We can test this as:
+
+```{literalinclude} ../../examples/extensions/python/test_mandel.py
+:language: python
+```
+
+Here's the resulting image
+
+```{image} test.png
+:align: center
+```
+
+## Python with explicit loops
+
+Here's a version where the loops are explicitly written out in python:
+
+```{literalinclude} ../../examples/extensions/python-slow/mandel.py
+:language: python
+```
+
+This can be run using the same driver as the numpy vectorized version.
+
+
+## Numba version
+
+We can install Numba simply by doing:
+
+```bash
+pip install numba
+```
+
+To get a Numba optimized version of the python with explicit loops we just add:
+
+```python
+from numba import njit
+```
+
+and then right before the function definition:
+
+```python
+@njit()
+```
+
+Here's the full code:
+
+```{literalinclude} ../../examples/extensions/numba/mandel.py
+:language: python
+```
+
+Again, this uses the same driver.
+
+
+```{note}
+We didn't need to do anything special to *compile* the numba code.
+This is done for us when we first encounter it.
+```
+
+```{tip}
+We run it twice in our driver, since the first call will have the overhead
+of the JIT compilation.  
+```
+
+```{literalinclude} ../../examples/extensions/numba/test_mandel.py
+:language: python
+```
+
+````{tip}
+We can get even better performance if we let numba do things in parallel, with
+
+```
+@njit(parallel=True)
+```
+````
+
+## Cython version
+
+We can install Cython by doing
+
+```bash
+pip install Cython
+```
+
+For Cython, we mainly need to specify the datatypes of the different
+variables.  We use the extension `.pyx` for a cython file.
+
+Here's the full code:
+
+```{literalinclude} ../../examples/extensions/cython/mandel.pyx
+:language: python
+```
+
+To build it, we can use a `setup.py` file:
+
+```{literalinclude} ../../examples/extensions/cython/setup.py
+:language: python
+```
+
+and make the extension as:
+
+```bash
+python setup.py build_ext --inplace
+```
+
+```{note}
+This build process will likely change in the near future, as
+the community is transitioning away from `setup.py`, but the 
+docs don't seem to be fully up to date on the new way to build.
+```
+
+````{tip}
+To help understand where the slow parts of your Cython code are, you
+can do
+```
+cythonize -a mandel.pyx
+```
+This will produce an HTML file with the parts of the code that interact
+with python highlighted.  (Make sure there are no `.c` files hanging around).
+These highlighted lines are places you should try to optimize.
+
+For our example, if we do
+```
+np.abs(z[i,j])
+```
+instead of
+```
+abs(z[i,j])
+```
+we get a dramatic slowdown!
+
+Thanks to Eric Johnson for pointing this out.
+
+````
+
+
+## Fortran implementation
+
+If we want to write the code in Fortran, we need to [compile it into a shared
+object library](https://numpy.org/doc/stable/f2py/usage.html) that python can import.  
+This is where `f2py` comes in---it is part of the numpy project, so you probably
+already have it installed.
+
+```{note}
+Support for this is in transition at the moment.  The old official way to do this
+was to use `distutils`, but this is removed in python 3.12.  
+
+Instead, we will use the [meson build system](https://mesonbuild.com/).
+```
+
+We need to install `meson` and `ninja`:
+
+```bash
+pip install meson ninja
+```
+
+Here's our Fortran implementation for the Mandelbrot generator:
+
+```{literalinclude} ../../examples/extensions/f2py/mandel.f90
+:language: fortran
+```
+
+To build the extension, we can do:
+
+```bash
+f2py -c mandel.f90 -m mandel_f2py
+```
+
+````{note}
+If the `f2py` command-line tool is not available, you can try running it as a module instead:
+```bash
+python -m numpy.f2py -c mandel.f90 -m mandel_f2py
+```
+````
+
+````{tip}
+The build doesn't show you the compilation commands used to make the library.  But if you look
+at the output, it will say something like:
+```
+The Meson build system
+Version: 1.4.0
+Source dir: /tmp/tmp0sbl86zt
+Build dir: /tmp/tmp0sbl86zt/bbdir
+Build type: native build
+Project name: mandel_f2py
+```
+If you then look in the build directory, there will be a file `compile_commands.json` that
+lists the commands that meson + f2py use to compile the extension.  In our case,
+it is using the optimization flag `-O3`.
+````
+
+This will create a library (on my machine, it is called `mandel_f2py.cpython-312-x86_64-linux-gnu.so`)
+which we can import as `import mandel_f2py`.
+
+Here's a driver:
+
+```{literalinclude} ../../examples/extensions/f2py/test_mandel.py
+:language: python
+```
+
+```{note}
+   Even though our Fortran subroutine takes the array `m` as an
+   argument, since it is marked as `intent(out)`, the python module
+   will use this as the return value.
+```
+
+```{note}
+The numpy array returned to python will have Fortran ordering (column-major) instead
+of the usual row-major ordering (take a look at the ``.flags`` attributes).
+```
+
+## C++ / pybind11 implementation
+
+pybind11 allows you to construct a numpy-compatible array in C++
+and return it.  There are different constructors for this---here
+we use on that allows us to specify the shape and stride.
+
+We can install pybind11 via pip:
+
+```bash
+pip install pybind11
+```
+
+Inside of the `mandelbrot()` function, we need temporary
+two-dimensional arrays to store $z$ and $c$.  With C++23
+we could use `std::mdspan` to give us nice multidimensional
+indexing.  For now, we need to do something different.
+Our first attempt will use `std::vector<std::vector<std::complex<double>>>`.
+
+Here's the implementation of our Mandelbrot generator:
+
+
+```{literalinclude} ../../examples/extensions/pybind11/mandel.cpp
+:language: c++
+```
+
+We build the shared library as:
+
+```bash
+g++ -DNDEBUG -O3  -Wall -Wextra -shared -std=c++17 -fPIC $(python3 -m pybind11 --includes) mandel.cpp -o mandel$(python3-config --extension-suffix)
+```
+
+Our driver is essentially the same as the Fortran one.
+
+
+```{literalinclude} ../../examples/extensions/pybind11/test_mandel.py
+:language: python
+```
+
+A slightly more complicated version that creates a contiguous `Array` class
+that can be indexed with `()` runs faster.  That code is here:
+
+```{literalinclude} ../../examples/extensions/pybind11/contiguous/mandel.cpp
+:language: C++
+```
+
+It uses the same driver.
+
+
+## Timings
+
+On my machine, (python 3.13, numpy 2.2.5, Cython 3.0.12, GCC 15, numba
+0.61.2, pybind11 2.13.6) here are some timings (average of 3 runs):
+
+
+|   technique                                  |   timings (s)  |
+| -------------------------------------------- | -------------- |
+| python / numpy                               |      0.218     |
+| python w/ explicit loops                     |     17.4       |
+| Numba(*)                                     |      0.0922    |
+| Cython                                       |      0.0866    |
+| Fortran + f2py                               |      0.0860    |
+| C++ + pybind11 (vector of vector)            |      0.120     |
+| C++ + pybind11 (contiguous `Array`)          |      0.108     |
+
+
+(*) timing for the second invocation, which excludes JIT overhead.
+
+
+We see that Numba, Cython, and Fortran are all quite close in
+performance, with C++ contiguous only slightly slower, and all of
+these much faster than the other implementations.  It may be possible
+to further optimize the numpy version, but it is so much easier to
+just use Numba in this situation.
diff --git a/content/12-extensions/extensions-overview.md b/content/12-extensions/extensions-overview.md
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@@ -0,0 +1,77 @@
+# Extensions
+
+Python code can be slow, so we sometimes turn to _extension modules_ to
+get performance in critical parts of our algorithms There are a number
+of ways to write extension modules in python -- these can be in
+another language like C or Fortran or use a library that converts
+python into compiled code (with some restrictions) like Cython or
+Numba.
+
+We'll look at some examples of these and talk about their strengths
+and weaknesses.
+
+
+## Methods
+
+* C++
+
+  * [pybind11](https://github.com/pybind/pybind11) : this is a
+    header-only library that allows you to call C++ functions directly
+    from python.
+
+    A related library is [nanobind](https://github.com/wjakob/nanobind),
+    which has similar syntax but may be more efficient.
+
+* C
+
+  * [C-API](https://docs.python.org/3/c-api/index.html) : the
+    standard python interpreter (cpython) is written in C, so it is
+    natural that we can write C code to interact with our python code.
+    This is the python C-API.  Since NumPy is also written in C, we
+    can work with NumPy arrays in C code as well.
+
+    This will give us the performance of C compiled code, but the
+    downside is that we lose a lot of what makes python great.  We
+    need to pass data into C as pointers and cast them into types that
+    represent the arrays we use.  This means writing a lot of
+    boilerplate code just to deal with some simple operations.
+
+    This underlies many of the techniques that we'll see here.
+
+    ```{note}
+    These days, there are better methods for most applications,
+    and you should probably not use the C-API directly.
+    ```
+    
+  * [ctypes](https://docs.python.org/3/library/ctypes.html) : this
+    is a module that allows you to call functions in shared libraries.
+    This is part of standard python.
+
+    With ctypes, you don't need to modify your C code -- you just need to
+    define an interface to the C function in python.  However, the calling
+    mechanism can be slow.
+
+    There is support for NumPy through numpy.ctypeslib.
+
+* Fortran
+
+  * [f2py](https://numpy.org/doc/stable/f2py/) : this is part of
+    NumPy.  It allows for easy calling of Fortran from python.
+
+    You essentially just need to add some comments to your Fortran
+    code to allow f2py to build an interface.  f2py understands the
+    different orderings of indices between C and Fortran arrays.
+
+* python
+
+  * [Cython](https://cython.org/) : this is a superset of python that can convert python into
+    compiled C code.
+
+    The advantage here is that the code looks like python, with some
+    declarations of the variable types with `cdef`.  Performance can be
+    really great when you need to explicitly write out loops over
+    NumPy array indices.
+
+  * [Numba](https://numba.pydata.org/) : this is a just-in-time
+    compiler.  It just requires a simple decorator and then it will
+    compile a python function the first time it is encountered.
diff --git a/content/12-extensions/test.png b/content/12-extensions/test.png
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diff --git a/content/CHANGES b/content/CHANGES
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+++ b/content/CHANGES
@@ -0,0 +1,8 @@
+-- make this S/U grading
+
+-- perhaps have BB "grade" participation on the forum?
+
+
+debugging:
+
+memray: https://github.com/bloomberg/memray
diff --git a/content/Introduction.md b/content/Introduction.md
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--- /dev/null
+++ b/content/Introduction.md
@@ -0,0 +1,55 @@
+# PHY 546: Python for Scientific Computing
+
+![xkcd](01-python/python.png)
+
+(from https://xkcd.com)
+
+## Why python?
+
+* Python is a very high-level language
+
+  * it provides many complex data-structures (lists, dictionaries, ...)
+
+  * your code is shorter than a comparable algorithm in a compiled language
+
+* Many powerful libraries to perform complex tasks
+
+  * Parse structured inputs files
+
+  * send e-mail
+
+  * interact with the operating system
+
+  * make plots
+
+  * make GUIs
+
+  * do scientific computations
+
+  * ...
+
+* Python makes it easy to prototype new tools
+
+* Python is cross-platform and Free
+
+## Language Features
+
+Some of the language features are:
+
+* Dynamical typing
+
+* Object-oriented foundation
+
+* Extensible (easy to call Fortran, C/C++, ...)
+
+* Automatic memory management (garbage collection)
+
+* Ease of readability (whitespace matters)
+
+
+## Scientific python
+
+Perhaps most importantly, and why we are here:
+
+> Python has been widely adopted in the scientific community.
+
diff --git a/lectures/NOTES b/content/NOTES
similarity index 100%
rename from lectures/NOTES
rename to content/NOTES
diff --git a/content/_config.yml b/content/_config.yml
new file mode 100644
index 00000000..2c0567e0
--- /dev/null
+++ b/content/_config.yml
@@ -0,0 +1,57 @@
+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: "PHY 546: Python for Scientific Computing"
+author: <a href="https://zingale.github.io">Michael Zingale</a>
+#logo: logo.png
+copyright: "2022"
+
+# Force re-execution of notebooks on each build.
+# See https://jupyterbook.org/content/execute.html
+execute:
+  execute_notebooks: force
+  allow_errors: true
+
+# Define the name of the latex output file for PDF builds
+latex:
+  latex_documents:
+    targetname: book.tex
+
+# Information about where the book exists on the web
+repository:
+  url: https://github.com/sbu-python-class/python-science
+  path_to_book: content  # Optional path to your book, relative to the repository root
+  branch: main  # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+html:
+  use_issues_button: true
+  use_repository_button: true
+  extra_footer: |
+    <p>
+    &copy; 2023-2025; CC-BY-NC-SA 4.0
+    <p>
+
+sphinx:
+  config:
+    html_show_copyright: false
+    nbsphinx_timeout: 300
+    nb_execution_timeout: 300
+
+launch_buttons:
+   binderhub_url: "https://mybinder.org"
+   colab_url: "https://colab.research.google.com"
+
+parse:
+  extensions:
+    - myst_parser
+    - sphinx_design
+
+  myst_enable_extensions:
+    # don't forget to list any other extensions you want enabled,
+    # including those that are enabled by default!
+    - amsmath
+    - dollarmath
+    - linkify
+    - colon_fence
diff --git a/content/_static/myfile.css b/content/_static/myfile.css
new file mode 100644
index 00000000..aeeec947
--- /dev/null
+++ b/content/_static/myfile.css
@@ -0,0 +1,10 @@
+@import url('https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,100;0,300;0,400;0,700;0,900;1,100;1,300;1,400;1,700;1,900&display=swap');
+@import url('https://fonts.googleapis.com/css2?family=Open+Sans:ital,wght@0,300;0,400;0,500;0,600;0,700;1,300;1,400;1,500;1,600;1,700&display=swap');
+
+body {
+    font-family: "Open Sans", sans-serif;
+}
+
+.heading-style, h1, h2, h3, h4, h5, h6 {
+    font-family: "Open Sans", sans-serif;
+}
diff --git a/content/_toc.yml b/content/_toc.yml
new file mode 100644
index 00000000..6a8039a8
--- /dev/null
+++ b/content/_toc.yml
@@ -0,0 +1,97 @@
+format: jb-book
+root: Introduction
+parts:
+  - caption: Getting Started
+    chapters:
+    - file: 01-python/installing
+    - file: 01-python/using
+    - file: 01-python/w1-jupyter.ipynb
+
+  - caption: Python Basics
+    chapters:
+    - file: 01-python/basics
+      sections:
+      - file: 01-python/w1-python-datatypes.ipynb
+      - file: 01-python/w2-python-advanced-datatypes.ipynb
+      - file: 01-python/w2-python-control-flow.ipynb
+      - file: 01-python/w2-python-exercises.ipynb
+    - file: 01-python/functions-classes
+      sections:
+      - file: 01-python/w3-python-functions.ipynb
+      - file: 01-python/w3-python-exercises.ipynb
+      - file: 01-python/w4-python-classes.ipynb
+      - file: 01-python/w4-python-modules.ipynb
+      - file: 01-python/w4-python-exercises.ipynb
+    - file: 01-python/misc
+      sections:
+      - file: 01-python/w3-python-exceptions.ipynb
+      - file: 01-python/python-io.ipynb
+      - file: 01-python/w5-python-more-examples.ipynb
+
+  - caption: Arrays
+    chapters:
+    - file: 02-numpy/numpy
+      sections:
+      - file: 02-numpy/numpy-basics.ipynb
+      - file: 02-numpy/numpy-advanced.ipynb
+      - file: 02-numpy/numpy-exercises.ipynb
+
+  - caption: Scientific Python
+    chapters:
+    - file: 04-matplotlib/matplotlib
+      sections:
+      - file: 04-matplotlib/matplotlib-basics.ipynb
+      - file: 04-matplotlib/matplotlib-exercises.ipynb
+    - file: 05-scipy/scipy
+      sections:
+      - file: 05-scipy/scipy-basics.ipynb
+      - file: 05-scipy/scipy-exercises.ipynb
+      - file: 05-scipy/scipy-basics-2.ipynb
+      - file: 05-scipy/scipy-exercises-2.ipynb
+    - file: 06-sympy/sympy
+      sections:
+      - file: 06-sympy/sympy-examples.ipynb
+      - file: 06-sympy/sympy-exercises.ipynb
+
+  - caption: Machine Learning
+    chapters:
+    - file: 11-machine-learning/neural-net-basics
+      sections:
+      - file: 11-machine-learning/gradient-descent
+      - file: 11-machine-learning/neural-net-derivation
+    - file: 11-machine-learning/neural-net-hidden
+      sections:
+      - file: 11-machine-learning/neural-net-improvements
+    - file: 11-machine-learning/machine-learning-libraries
+      sections:
+      - file: 11-machine-learning/keras-mnist
+      - file: 11-machine-learning/keras-clustering
+
+  - caption: Python packaging and applications
+    chapters:
+    - file: 09-packages/python-modules
+    - file: 09-packages/python-arguments
+    - file: 09-packages/python-more-modules
+    - file: 09-packages/python-packages
+    - file: 09-packages/python-tools
+
+  - caption: Git and Github
+    chapters:
+    - file: git/version-control
+    - file: git/git
+    - file: git/github
+    - file: git/pull-requests
+
+  - caption: Unit tests
+    chapters:
+    - file: 10-testing/testing
+    - file: 10-testing/pytest
+      sections:
+      - file: 10-testing/more-pytest
+    - file: 10-testing/real-world-example
+
+  - caption: Extensions
+    chapters:
+    - file: 12-extensions/extensions-overview
+    - file: 12-extensions/extensions-example
+
diff --git a/content/git/distributed_version_control.png b/content/git/distributed_version_control.png
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diff --git a/content/git/git-branches.md b/content/git/git-branches.md
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+++ b/content/git/git-branches.md
@@ -0,0 +1,374 @@
+# Git Branches
+
+When we develop as a team, we often use *branches* to organize our
+changes.  Here we walk through an example of branches.
+
+To get more practice, we'll start a new project and initialize it.
+
+```{figure} https://imgs.xkcd.com/comics/git.png
+   :width: 75%
+   :align: center
+   :alt: xkcd comic on git
+
+   from XKCD
+```
+
+1. Let's repeat the setup we did before...
+
+   ```bash
+   mkdir project2
+   cd project2
+   echo "a second git project" > README
+   git init
+   ```
+
+2. Now let's add our `README` to git and commit:
+
+   ```bash
+   git add README
+   git commit
+   ```
+
+   (Remember to enter a log and save...)
+
+3. Let's create and add another file.
+
+   We write a simple shell script.  Open a new file, called `myscript`, e.g., with nano:
+
+   ```bash
+   nano myscript
+   ```
+
+   and copy-paste the following content into it:
+
+   ```bash
+   ls -l > script.out
+   ```
+
+   be sure to end with a new line.
+
+   Now, this script is not that fancy and it needs to be run as:
+
+   ```bash
+   bash ./myscript
+   ```
+
+   when you do this, you should see the output `script.out` created.
+
+   Now let's tell git that we want it to track this:
+
+   ```bash
+   git add myscript
+   git commit
+   ```
+
+   Be sure to add a useful message.
+
+4. Ignoring things.
+
+   Let's look at the status of our project:
+
+   ```bash
+   git status
+   ```
+
+   You'll see something like:
+
+   ```
+   On branch main
+   Untracked files:
+     (use "git add <file>..." to include in what will be committed)
+
+        script.out
+
+   nothing added to commit but untracked files present (use "git add" to track)
+   ```
+
+   It is telling us that it is not keeping track of `script.out`.
+   But we don't want it to&mdash;that is the output from running out
+   script, and generally we don't keep the output of our codes in
+   version control.
+
+   So we'd like to tell git to ignore that file.  The way to do this is to
+   create a `.gitignore` file:
+
+   ```bash
+   nano .gitignore
+   ```
+
+   and add the following:
+
+   ```
+   *.out
+   ```
+
+   now if you do `git status`, that file will not appear, but `.gitignore` does!
+
+   Be sure to add `.gitignore` to git by doing `git add` followed
+   by `git commit`.
+
+
+
+## A Feature Branch
+
+Now let's imagine that our project is mature and we don't want to break it as
+we test out some new ideas.  This is where *branches* come into play.
+
+Let's create a new branch called `feature` that we can work on without
+disturbing our code in ``main``.
+
+```bash
+git checkout -b feature
+```
+
+This creates a new branch called `feature` that is initially identical to `main`.
+
+You can tell what branch you are on by doing:
+
+```bash
+git branch
+```
+
+and we see:
+
+```
+* feature
+  main
+```
+
+The `*` indicates which branch we are currently on.
+
+What about the log?
+
+```bash
+git log
+```
+
+we see:
+
+```
+commit 69eb3bf482bd78c3bf63e890f52b9aac33d5ee2a (HEAD -> feature, main)
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:21:19 2022 -0500
+
+    add an ignore file
+
+commit 9b0ae624393bd28f26f37d633d9692be3c2929f0
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:18:53 2022 -0500
+
+    add my first script
+
+commit 9625926dd4bc26e04d37988ffceaa7eba64a76da
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:18:02 2022 -0500
+
+    start of our new project
+```
+
+Notice that the most recent commit line shows that both `feature` and `main`
+are at the same hash, and it also calls that commit `HEAD`.
+`HEAD` is the most recent change on the branch.
+
+
+Now let's make a change.
+
+Let's put a "Hello, World" code in our repo!  Create a file called
+`hello.cpp` and add the following:
+
+```c++
+#include <iostream>
+
+int main() {
+
+    std::cout << "Hello, World!" << std::endl;
+
+}
+```
+
+Let's add it to git control:
+
+```bash
+git add hello.cpp
+git commit
+```
+
+Now look at the log:
+
+```
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:23:51 2022 -0500
+
+    add hello world
+
+commit 69eb3bf482bd78c3bf63e890f52b9aac33d5ee2a (main)
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:21:19 2022 -0500
+
+    add an ignore file
+
+commit 9b0ae624393bd28f26f37d633d9692be3c2929f0
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:18:53 2022 -0500
+
+    add my first script
+
+commit 9625926dd4bc26e04d37988ffceaa7eba64a76da
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:18:02 2022 -0500
+
+    start of our new project
+```
+
+Now it is clear that `main` is still on the last commit but
+`feature` is on the latest (`HEAD`) commit.
+
+
+Recall that we can compile our `hello.cpp` via:
+
+```bash
+g++ -o hello hello.cpp
+```
+
+```{tip}
+We don't want the executable `hello` to be under git control, so
+add it to your `.gitignore` and commit that change.
+```
+
+
+## Switching Branches
+
+Let's go back to `main`.  The `checkout` command does this for us:
+
+```bash
+git checkout main
+```
+
+Now notice that if you do `ls`, you don't see `hello.cpp`!  That
+file is in your `feature` branch, and under git control, and git
+knows it is not on `main` so when you switch to main, it does not
+appear.
+
+Let's add an `authors.txt` file to our project, just containing your name.
+
+```{admonition} Try it...
+create an `authors.txt` and add it to git control.
+```
+
+Note that this is on `main`.  If you switch to `feature` you won't see it:
+
+```bash
+git checkout feature
+```
+
+````{tip}
+Just like we can use ``cd -`` to switch to the previous directory we were on,
+we can use
+
+```bash
+git checkout -
+```
+to switch back to the previous branch we were on -- in this case, `main`
+````
+
+Switch back to ``main``.
+
+
+## Diff
+
+Let's look at the differences between our branches.  Since we're on
+`main`, we can ask git what the difference between our current code
+and the code in `feature` is via:
+
+```bash
+git diff feature
+```
+
+As you use git more and more, you'll see that `diff` is very handy.
+
+
+## Merging
+
+Now we're happy with the changes we made on `feature` and we want to
+incorporate them into `main`&mdash;this is called *merging*, we
+accomplish this by doing
+
+```bash
+git merge feature
+```
+
+This is a special type of commit, and your editor will pop up with a
+merge commit already entered.  Just save this, and it will be logged.
+
+
+## Going back in time...
+
+If we look at our project history so far:
+
+```bash
+git log
+```
+
+We see something like this (again, your hashes will be different)
+
+```
+commit 42596acdd432e1dbdc4f8abd668dffa30c707473 (HEAD -> main)
+Merge: c8904ec bb38a3d
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:54:51 2022 -0500
+
+    Merge branch 'feature' into main
+
+commit c8904ec0bd8ac1bc3449ec79ade971ee9902c14e
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:31:03 2022 -0500
+
+    add authors
+
+commit bb38a3d1f3f4f2971ced93a1f203c52c276f37a5 (feature)
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:27:09 2022 -0500
+
+    don't track executable
+
+commit 22e1d58cee38021da961516b24dde689d3b8a66e
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:23:51 2022 -0500
+
+    add hello world
+
+commit 69eb3bf482bd78c3bf63e890f52b9aac33d5ee2a
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:21:19 2022 -0500
+
+    add an ignore file
+
+commit 9b0ae624393bd28f26f37d633d9692be3c2929f0
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:18:53 2022 -0500
+
+    add my first script
+
+commit 9625926dd4bc26e04d37988ffceaa7eba64a76da
+Author: Michael Zingale <michael.zingale@stonybrook.edu>
+Date:   Tue Feb 1 10:18:02 2022 -0500
+
+    start of our new project
+```
+
+Imagine that our current code is not working, but we remember that it
+was before we did our branching and added the `hello.cpp`.  Looking
+at the log or the graph shows that that change came in with the commit
+`22e1d58cee38021da961516b24dde689d3b8a66e`.  We can checkout the
+state of the code before that commit by using the hash from the
+previous commit:
+
+```bash
+git checkout 69eb3bf482bd78c3bf63e890f52b9aac33d5ee2a
+```
+
+Note that you don't need to type out the entire hash&mdash;you only need the starting bits,
+as long as it is unique.
+
+This command puts you in a detached branch, but you could make it a named branch by using
+`git checkout -b name`.
diff --git a/content/git/git-remotes.md b/content/git/git-remotes.md
new file mode 100644
index 00000000..6b7971a2
--- /dev/null
+++ b/content/git/git-remotes.md
@@ -0,0 +1,110 @@
+# Git Remotes
+
+## Bare Repository
+
+So far, we've just been using our git repo for ourselves.
+
+Let's look back at the figure illustrating ways we can share with distributed version control:
+
+```{image} distributed_version_control.png
+:align: center
+```
+
+When multiple developers are working on the same code together, it is
+convenient to have a central repository that everyone can communicate
+with.
+
+We use a special `bare` repository for this purpose.  A bare repo
+has all of the metadata for the project, but we don't work directly in
+it.  This way we avoid the risk of having unsaved changed in the repo
+that other people are using to synchronize with.
+
+Let's create a bare repo from our `project2` project.  From your home directory
+(assuming that project2 is `~/project2/`, we do:
+
+```bash
+git clone --bare project2
+```
+
+Now we see a new directory `project2.git`.
+
+
+
+## A First Example of Collaboration
+
+Let's pretend we are a different user.  Let's make a directory for our
+pretend user and clone our project:
+
+```bash
+cd ~
+mkdir newuser
+cd newuser
+git clone ~/project2.git
+```
+
+The `clone` command make a new git repo for our user called `project2/`
+
+If we do a `git log` in it, we'll see the whole history we had from
+our earlier work.
+
+Now, this repo knows where it was cloned from, through a concept
+called *remotes*.  A remote is a repo (usually a bare repo) that we
+communicate with the share our changes (a *push*) of get changes from
+other users (a *pull*).  We can see our remote by doing:
+
+```bash
+git remote -v
+```
+
+We'll see something like:
+
+```
+origin	/home/campus.stonybrook.edu/mzingale/project2.git (fetch)
+origin	/home/campus.stonybrook.edu/mzingale/project2.git (push)
+```
+
+Now's let's make a change
+
+```{admonition} try it...
+add the new user's name to `authors.txt` and commit the change.
+```
+
+Now we can share our changes with our remote&mdash;the bare repo by doing a *push*.
+
+```bash
+git push
+```
+
+This pushes our changes back to the bare repo.  Now go back to our original repo:
+
+```bash
+cd
+cd project2
+```
+
+We need to add a remote to this original repo (if you do `git
+remote` it will show nothing).  We'll add a remote called `origin`.
+
+```bash
+git remote add origin ~/project2.git
+```
+
+Now, we can communicate with the bare repo and get the changes that
+the other user made by doing a *pull*:
+
+```bash
+git pull origin main
+```
+
+To make our life easier, we can tell git what remote branch to track:
+
+```bash
+git branch --set-upstream-to=origin/main main
+```
+
+then we can do just
+
+```bash
+git pull
+```
+
diff --git a/content/git/git.md b/content/git/git.md
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+# A Git Walkthrough
+
+We'll do a walkthrough on git.
+
+```{note}
+An alternate walkthrough is provided by the [Software
+Carpentry](https://software-carpentry.org/) lesson [_Version Control
+with Git_](https://swcarpentry.github.io/git-novice/index.html) and it
+is highly suggested that you work through that on your own.
+```
+
+There are a few ways in which we can use git.  We'll start by assuming
+that we are the only developer on a project and learn the basics and
+then we'll see how to share what we've done locally and remotely using
+GitHub.
+
+```{important}
+You should create a [GitHub account](https://github.com).  Pick a
+username that is professional and meaningful.
+```
+
+Let's start by setting up our git environment:
+
+```bash
+git config --global user.name "name"
+git config --global user.email "email"
+git config --global core.editor "nano -w"
+```
+
+Replace `name` with your name and `email` with your email.  This sets `nano`
+as our default editor, but you can choose something else that you are comfortable
+with (see https://swcarpentry.github.io/git-novice/02-setup.html).
+
+This information will be stored in a file called ``.gitconfig`` in your home directory.
+
+
+Our goal here is to create a project (we'll call the directory
+`project/`) and have git keep track of the files and changes to our
+project.
+
+1. First create a project directory with some basic content:
+
+   ```bash
+   mkdir project
+   cd project
+   echo "this is the start of my awesome new project" > README
+   ```
+
+2. Now let's tell git that we want to track this directory.
+
+   ```bash
+   git init
+   ```
+
+   If you do `ls` it will look like nothing has changed, but this
+   command created a `.git/` sub-directory in our project, which you
+   can see by doing:
+
+   ```bash
+   ls -a
+   ```
+
+3. At this point, we haven't told git about any of our files.  To tell git
+   to track the file `README` we do:
+
+   ```bash
+   git add README
+   git commit README
+   ```
+
+   ```{note}
+   When you `commit`, and editor window will pop up asking you to
+   make a commit message.  This is where you describe the change so
+   "future you" or someone else can understand why the change was
+   made.
+   ```
+
+   The editor will be whatever your default is.
+   We can now ask git the state of our project with
+
+   ```bash
+   git status
+   ```
+
+   You should see something like:
+
+   ```
+   On branch main
+   nothing to commit, working tree clean
+   ```
+
+   We can also see our log message:
+
+   ```bash
+   git log
+   ```
+
+   The output will look like:
+
+   ```
+   commit 2001a0e996110926a576dcb5fc13fc8022864d0b (HEAD -> main)
+   Author: Michael Zingale <michael.zingale@stonybrook.edu>
+   Date:   Sun Jan 30 13:11:24 2022 -0500
+
+       my first change
+   ```
+
+   But should show your name, and the long string of numbers of
+   letters after `commit` on the first line will be different.  We'll call
+   the string the `hash`.  More on that later...
+
+4. Now let's modify the file
+
+   Open the file with nano and add a second line of text:
+
+   ```bash
+   nano README
+   ```
+   
+   Here's my file:
+
+   ```bash
+   cat README
+   ```
+   
+   ```
+   this is the start of my awesome new project
+   this is now under version control!
+   ```
+  
+   What does git think about our changes?
+
+   ```bash
+   git status
+   ```
+   
+   you should see something like:
+
+   ```
+   On branch main
+   Changes not staged for commit:
+     (use "git add <file>..." to update what will be committed)
+     (use "git checkout -- <file>..." to discard changes in working directory)
+
+           modified:   README
+
+   no changes added to commit (use "git add" and/or "git commit -a")
+   ```
+   
+   This is telling you that you have local changes but you haven't yet told git to care about them.
+
+   Let's `add` the changes:
+
+   ```bash
+   git add README
+   ```
+
+   and now `git status` will show something like:
+
+   ```
+   On branch main
+   Changes to be committed:
+     (use "git reset HEAD <file>..." to unstage)
+
+           modified:   README
+   ```
+
+   ```{admonition} What is `add` really doing?
+
+   Git has a concept call the *staging area*.  When we ``add`` a
+   file, git puts the changes into the staging area.  We can add
+   multiple changes via separate ``git add`` invocations, and git
+   will accumulate these in the staging area.
+
+   Once we do `git commit`, git will record the all of the
+   changes that are staged into a "commit".
+   ```
+   
+   To have git track these changes, we can now just do:
+
+   ```bash
+   git commit
+   ```
+   
+   Notice that we didn't specify the file here -- all the changes that
+   were staged were part of that commit.
+
+   If we now do `git log`, we'll see that there is a second commit
+   in our project, and it has a different unique hash:
+
+   ```
+   commit 78b6925752e8388dddb3d65b6355bfeeb87b87a7 (HEAD -> main)
+   Author: Michael Zingale <michael.zingake@stonybrook.edu>
+   Date:   Sun Jan 30 14:23:09 2022 -0500
+
+       make some modifications
+
+   commit 2001a0e996110926a576dcb5fc13fc8022864d0b
+   Author: Michael Zingale <michael.zingake@stonybrook.edu>
+   Date:   Sun Jan 30 13:11:24 2022 -0500
+
+       my first change
+   ```
+
+This repo now captures the state of our project.  Next we'll see how to use branches to
+help our development process.
+
+
+Summary
+=======
+
+We learned:
+
+* `git init`
+
+* `git add`
+
+* `git commit`
+
+* `git status`
+
+* `git log`
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+# github
+
+Github provides a web-based way to interact with git repositories.  At
+its heart it hosts a bare repo that we can push-pull to/from, but it
+also provides features to make it easier for users to request their
+changes be incorporated.
+
+
+## Creating a repository on github
+
+Let's start by creating a new git repository using github's web interface.  Start
+on your github home page and click on the "+" icon and select "New repository":
+
+```{image} github-new.png
+:align: center
+```
+
+Now we give the repository a name.  Let's use our initials, followed
+by `_class_repo`, so for me, it will be `mz_class_repo`.
+
+Make sure that it defaults the repo to be public, which means anyone on the internet
+can see the contents&mdash;that's what we want.
+
+Finally, check the box to add a `README` file&mdash;this means that our repository will
+not be empty initially.
+
+```{image} github-create.png
+:align: center
+```
+
+Our project is now found at: ``https://github.com/ [username]/ [reponame]``,
+where *username* is your Github username and *reponame* is the name of
+the repository you just created.
+
+
+## SSH interlude
+
+Github works best is we communicate via [secure
+shell](https://en.wikipedia.org/wiki/Secure_Shell) or *SSH*.
+
+There is some nice documentation describing key pairs in the Software
+Carpentry [Create an SSH key pair](https://swcarpentry.github.io/git-novice/07-github.html#ssh-background-and-setup)
+section.
+
+Here's how we will set things up:
+
+1. A the bash prompt generate a new key pair:
+
+   ```bash
+   ssh-keygen -t ed25519
+   ```
+
+   The `-t` option picks a secure encryption method.
+
+   It will ask you for a passpharse&mdash;just hit "Enter" to keep it
+   empty (if other people had access to your account, the you would
+   want to pick a passphrase).
+
+   If you do
+
+   ```bash
+   ls -l ~/.ssh
+   ```
+
+   you'll see 2 files: `id_ed25519` and `id_ed25519.pub` this is
+   the private and public key for encryption.
+
+   ```{caution}
+   Never share your private key (`id_ed25519`) with anyone.
+
+   Our public key (`id_ed25519.pub`) is meant to be public, and
+   we can give it to places we want to communicate with, like github
+   ```
+
+2. Go to you Github profile SSH keys settings: https://github.com/settings/keys
+
+   Click on the *New SSH key* button and:
+
+   * give a *title* which is descriptive of the machine you are using, like
+     ``MathLab``
+
+   * copy and paste the contents of `id_ed25519.pub` into the *key*
+     text box.  You can see the contents by doing:
+
+     ```bash
+     cat ~/.ssh/id_ed25519.pub
+     ```
+     
+   * Click on ``Add SSH key``
+
+3. Test things out:
+
+   ```bash
+   ssh -T git@github.com
+   ```
+
+   It will ask you if we want to save the *fingerprint*&mdash;say "yes", and then
+   it should report:
+
+   ```
+   Hi zingale! You've successfully authenticated, but GitHub does
+   not provide shell access.
+   ```
+
+That means everything is working.
+
+
+## Working remotely
+
+Now we can git clone this repo.  From the github project page, click on the
+*code* button.
+
+```{image} github-clone.png
+:align: center
+```
+
+Copy the string in the text box there and then on your command line clone
+the repo as:
+
+```bash
+git clone git@github.com:zingale/mz_class_repo.git
+```
+
+(replacing my repo and username with your own).
+
+Now we can go into our repo and look around.
+
+```bash
+cd mz_class_repo
+ls -al
+```
+
+Notice that there is a
+`.git/` directory.  Also look at the remotes:
+
+```bash
+git remote -v
+```
+
+```
+origin	git@github.com:zingale/mz_class_repo.git (fetch)
+origin	git@github.com:zingale/mz_class_repo.git (push)
+```
+
+This is just like the example or remotes we did previously, except now
+github is acting as our remote.
+
+This means that we call push to github and pull from there.
+
+Let's add a `hello.py`:
+
+```python
+#!/usr/bin/env python
+
+def main():
+    print("hello, world")
+
+if __name__ == "__main__":
+    main()
+```
+
+```bash
+git add hello.py
+git commit
+git push
+```
+
+Notice that the `git push` pushes to our remote: github.  If you refresh
+your browser page, you'll see that our file now appears on github.
+
+As a single user, this will allow you to develop from any computer
+and keep the code base in sync across all of them.
+
+If the project has multiple developers, this can be where all of the
+developers sync up their projects.
+
+
+### `README.md` is special
+
+The web interface that github provides to our repo has a number of features.
+
+First, the `README.md` file is always displayed on the main project
+page.  This is where you can put descriptions of what your project is,
+how people can contribute, even share the status of testing and
+documentation builds (we'll talk about those later in class).
+
+This file is in github-flavored [Markdown
+format](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)
+(that's what the `.md` extension signifies).  Markdown allows you to
+do basic formatting.
+
+Here's an example of what you can do in a `README.md` from one of my
+projects: https://github.com/pynucastro/pynucastro
+
diff --git a/content/git/pull-requests.md b/content/git/pull-requests.md
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+# Pull requests
+
+Github allows you to give permissions to users to contribute to a
+repository (read, write, or admin access).  But the best type of workflow
+is one where users don't push directly to the git repo.  Instead it is based
+around pulls.
+
+How do we contribute to a project that we don't own?
+
+Here's a github *organization* for our class: https://github.com/sbu-python-class
+
+and here's a simple repo in this organization: https://github.com/sbu-python-class/test_repo
+
+```{note}
+An organization is meant to be used by a collection of developers who
+can all have different access permissions.  It provides tools for
+managing who can do different things to the repos under its control.
+```
+
+Let's clone this repo:
+
+```bash
+git clone git@github.com:sbu-python-class/test_repo.git
+cd test_repo
+```
+
+Now, let's each try to add a file of the form *username.txt* containing
+your full name.  Ex:
+
+```bash
+echo Michael Zingale > zingale.txt
+git add zingale.txt
+git commit
+```
+
+Now try to push it to the repo we clone:
+
+```bash
+git push
+```
+
+what happened?
+
+The issue is that you don't have *write* permission to that repo,
+since I own it.  So you are denied access.
+
+This is okay.  The workflow that github emphasizes is one based around
+*pulls* not *pushes*, so let's see how we do that.
+
+First, we need to *fork* the repo -- this creates a clone under our
+control that we can do with as we please.  Click on the "fork" button.
+
+```{image} github-fork.png
+:align: center
+```
+
+It may ask you where you want the fork to live&mdash;you want it to live
+under your profile / username.
+
+This will bring you to a new github page, displaying the fork, with a
+URL that should look something like: ``https://github.com/zingale/test_repo``
+
+Now click on the *code* button and copy the SSH location.
+
+```{image} github-copy-ssh.png
+:align: center
+```
+
+We want to add this fork as a new remote:
+
+```bash
+git remote add myfork git@github.com:zingale/test_repo.git
+```
+
+(again, make sure you replace that with the link to your repo).
+
+Now you can do:
+
+```bash
+git push myfork
+```
+
+If you reload your github page, you should see your change there.
+
+Now we can do all *pull-request*.  Select "pull requests"
+
+```{image} github-pr.png
+:align: center
+```
+
+Then click on the "New pull request" button, and you'll see something like:
+
+```{image} github-pr2.png
+:align: center
+```
+
+This is showing that you are asking to merge the changes in your fork into the
+class ``test_repo`` repository.
+
+Click on *create pull request*, type in a comment about what this does, and then click
+on the *create pull request* button again.
+
+Now it is out of your hands.
+
+The owner of the class repo (me) will get a notification that you want
+to incorporate your changes into the class repo, and I can merge them
+via the github web tools.
+
+
+The overall workflow that we did: fork, push to our fork, issue a PR, looks like:
+
+```{image} github-workflow.png
+:align: center
+:width: 80%
+```
+
+
+
+## Our class notes github
+
+Let's take a tour of our class notes on github: https://github.com/sbu-python-class/python-science
+
+There are a lot of other features that github provides that we can use to make our life easier, including:
+
+* github actions : automating some workflows (like testing) on our code
+
+* github pages : building and hosting web pages for our project
+
+
+
diff --git a/content/git/version-control.md b/content/git/version-control.md
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+# Version Control
+
+````{note}
+To follow along, you will need to install `git` on your computer.
+You can follow the instructions from the [Software Carpentry lessons](https://carpentries.github.io/workshop-template/install_instructions/#git).  In particular, for:
+
+* Windows: use [git for Windows](https://gitforwindows.org/)
+
+* Mac: in a teminal, do:
+
+  ```
+  git --version
+  ```
+  
+  If it is not already installed, it will prompt you for installation.
+
+* Linux: it is probably already installed, but otherwise, use your
+  package manager.
+````
+
+## Why use version control?
+
+When we develop code, we are going to be making lots of changes over
+time.  And you will find yourself in the following situations:
+
+* *You swear that the code worked perfectly 6 months ago* but today it doesn't and
+  you can't figure out what changed.
+
+* *The code looks different than you remembered and you don't know why it changed*.
+
+* *Your research group is all working on the same code* and you need to sync up with
+  everyone's changes and make sure that no one breaks the code.
+
+This is what [version control](https://en.wikipedia.org/wiki/Version_control) does for us.
+
+
+```{note}
+Version control systems keep track of the history of changes to
+source code and allow multiple developers to all work on the same code
+base.
+```
+
+In particular:
+
+* Logs tell you what changes have been made to each file over time.
+
+* You can request the source as it was at any time in the past.
+
+* Multiple developers can all work on the same source code and share and synchronize changes.
+
+  * Changes by different developers to the same file are merged.
+
+  * If two developers changed the same part of a file, version control
+    provides mechanisms to resolve conflicts.
+
+* You can make a branch and work on new features without breaking the
+  current working code, and when you are ready, merge those changes
+  into the main version.
+
+
+```{note}
+Even for a single developer, version control is a great asset.
+
+Common task: you notice your code is giving different answers than you've
+seen in the past.
+
+With version control, you can checkout an old copy when you know it
+was working, and ask for the difference with the current code.
+```
+
+```{tip}
+Version control is not just for source code.  You can use it for
+writing papers in LaTeX, course notes, etc.
+
+These course notes are in git, hosted on github here:
+https://github.com/zingale/computational_astrophysics
+```
+
+## Centralized vs. distributed version control
+
+Broadly speaking, there are two different types of version control:
+centralized and distributed.  We'll call the collection of code under
+version control the *repository*.
+
+### Centralized version control
+
+Examples: [CVS](https://en.wikipedia.org/wiki/Concurrent_Versions_System) , [subversion](https://en.wikipedia.org/wiki/Apache_Subversion)
+
+* A server holds the master copy of the source, stores the history, changes
+
+* Each user communicates with the server:
+
+  * "checkout" source
+  * "commit" changes back to the source
+  * request the log (history) of a file from the server
+  * "diff" your local version with the version on the server
+
+This is the older style of version control, and not widely used for new projects.
+
+
+### Distributed version control
+
+Examples: [git](https://en.wikipedia.org/wiki/Git), [mercurial](https://en.wikipedia.org/wiki/Mercurial)
+
+* Everyone has a full-fledged repository
+
+* Commits, history, diff, logs, etc. are all local operations
+
+* You can clone another person's repo and they can pull your changes
+  back to their version
+
+* Each copy is a backup of the whole history of the project
+
+* Easy to "fork" -- just clone and go.
+
+```{tip}
+Any version control system is better than no version control.
+
+Git is the most popular right now, so we'll focus on that.
+```
+
+Consider the figure below:
+
+```{image} distributed_version_control.png
+:align: center
+```
+
+We see:
+
+*  You in the upper left box interacting with your local computer.
+   You can add changes to your repo and query the log, etc. all just
+   using your own machine.
+
+*  Your colleague in the upper right.  They can also interact with
+   their own computer, using their own version of the repo.
+
+   Now, imagine that they make a change that you want.  You can *pull*
+   their version of the code into your repo, getting all of their
+   changes.
+
+*  Suppose you both want to efficiently share work as a group.  So you
+   setup a group server and you can both synchronize your repo with
+   that server by doing *pull* and *push*.
+
+   This server provides a mechanism for everyone in the group to stay
+   in sync.
+
+*  Imagine now that you have an external collaborator who doesn't have
+   access to your server.  You can let them *pull* from your copy of
+   the repo, without giving them permission to push changes back.
+
+   They can then interact with their copy locally.
+
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@@ -0,0 +1,63 @@
+Creative Commons Attribution 3.0 Unported
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+License
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+THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.
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+1. Definitions
+
+   1. "Adaptation" means a work based upon the Work, or upon the Work and other pre-existing works, such as a translation, adaptation, derivative work, arrangement of music or other alterations of a literary or artistic work, or phonogram or performance and includes cinematographic adaptations or any other form in which the Work may be recast, transformed, or adapted including in any form recognizably derived from the original, except that a work that constitutes a Collection will not be considered an Adaptation for the purpose of this License. For the avoidance of doubt, where the Work is a musical work, performance or phonogram, the synchronization of the Work in timed-relation with a moving image ("synching") will be considered an Adaptation for the purpose of this License.
+   2. "Collection" means a collection of literary or artistic works, such as encyclopedias and anthologies, or performances, phonograms or broadcasts, or other works or subject matter other than works listed in Section 1(f) below, which, by reason of the selection and arrangement of their contents, constitute intellectual creations, in which the Work is included in its entirety in unmodified form along with one or more other contributions, each constituting separate and independent works in themselves, which together are assembled into a collective whole. A work that constitutes a Collection will not be considered an Adaptation (as defined above) for the purposes of this License.
+   3. "Distribute" means to make available to the public the original and copies of the Work or Adaptation, as appropriate, through sale or other transfer of ownership.
+   4. "Licensor" means the individual, individuals, entity or entities that offer(s) the Work under the terms of this License.
+   5. "Original Author" means, in the case of a literary or artistic work, the individual, individuals, entity or entities who created the Work or if no individual or entity can be identified, the publisher; and in addition (i) in the case of a performance the actors, singers, musicians, dancers, and other persons who act, sing, deliver, declaim, play in, interpret or otherwise perform literary or artistic works or expressions of folklore; (ii) in the case of a phonogram the producer being the person or legal entity who first fixes the sounds of a performance or other sounds; and, (iii) in the case of broadcasts, the organization that transmits the broadcast.
+   6. "Work" means the literary and/or artistic work offered under the terms of this License including without limitation any production in the literary, scientific and artistic domain, whatever may be the mode or form of its expression including digital form, such as a book, pamphlet and other writing; a lecture, address, sermon or other work of the same nature; a dramatic or dramatico-musical work; a choreographic work or entertainment in dumb show; a musical composition with or without words; a cinematographic work to which are assimilated works expressed by a process analogous to cinematography; a work of drawing, painting, architecture, sculpture, engraving or lithography; a photographic work to which are assimilated works expressed by a process analogous to photography; a work of applied art; an illustration, map, plan, sketch or three-dimensional work relative to geography, topography, architecture or science; a performance; a broadcast; a phonogram; a compilation of data to the extent it is protected as a copyrightable work; or a work performed by a variety or circus performer to the extent it is not otherwise considered a literary or artistic work.
+   7. "You" means an individual or entity exercising rights under this License who has not previously violated the terms of this License with respect to the Work, or who has received express permission from the Licensor to exercise rights under this License despite a previous violation.
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+   9. "Reproduce" means to make copies of the Work by any means including without limitation by sound or visual recordings and the right of fixation and reproducing fixations of the Work, including storage of a protected performance or phonogram in digital form or other electronic medium.
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+2. Fair Dealing Rights. Nothing in this License is intended to reduce, limit, or restrict any uses free from copyright or rights arising from limitations or exceptions that are provided for in connection with the copyright protection under copyright law or other applicable laws.
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+3. License Grant. Subject to the terms and conditions of this License, Licensor hereby grants You a worldwide, royalty-free, non-exclusive, perpetual (for the duration of the applicable copyright) license to exercise the rights in the Work as stated below:
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+   1. to Reproduce the Work, to incorporate the Work into one or more Collections, and to Reproduce the Work as incorporated in the Collections;
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+
+      For the avoidance of doubt:
+         1. Non-waivable Compulsory License Schemes. In those jurisdictions in which the right to collect royalties through any statutory or compulsory licensing scheme cannot be waived, the Licensor reserves the exclusive right to collect such royalties for any exercise by You of the rights granted under this License;
+         2. Waivable Compulsory License Schemes. In those jurisdictions in which the right to collect royalties through any statutory or compulsory licensing scheme can be waived, the Licensor waives the exclusive right to collect such royalties for any exercise by You of the rights granted under this License; and,
+         3. Voluntary License Schemes. The Licensor waives the right to collect royalties, whether individually or, in the event that the Licensor is a member of a collecting society that administers voluntary licensing schemes, via that society, from any exercise by You of the rights granted under this License.
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+The above rights may be exercised in all media and formats whether now known or hereafter devised. The above rights include the right to make such modifications as are technically necessary to exercise the rights in other media and formats. Subject to Section 8(f), all rights not expressly granted by Licensor are hereby reserved.
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+4. Restrictions. The license granted in Section 3 above is expressly made subject to and limited by the following restrictions:
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+   1. You may Distribute or Publicly Perform the Work only under the terms of this License. You must include a copy of, or the Uniform Resource Identifier (URI) for, this License with every copy of the Work You Distribute or Publicly Perform. You may not offer or impose any terms on the Work that restrict the terms of this License or the ability of the recipient of the Work to exercise the rights granted to that recipient under the terms of the License. You may not sublicense the Work. You must keep intact all notices that refer to this License and to the disclaimer of warranties with every copy of the Work You Distribute or Publicly Perform. When You Distribute or Publicly Perform the Work, You may not impose any effective technological measures on the Work that restrict the ability of a recipient of the Work from You to exercise the rights granted to that recipient under the terms of the License. This Section 4(a) applies to the Work as incorporated in a Collection, but this does not require the Collection apart from the Work itself to be made subject to the terms of this License. If You create a Collection, upon notice from any Licensor You must, to the extent practicable, remove from the Collection any credit as required by Section 4(b), as requested. If You create an Adaptation, upon notice from any Licensor You must, to the extent practicable, remove from the Adaptation any credit as required by Section 4(b), as requested.
+   2. If You Distribute, or Publicly Perform the Work or any Adaptations or Collections, You must, unless a request has been made pursuant to Section 4(a), keep intact all copyright notices for the Work and provide, reasonable to the medium or means You are utilizing: (i) the name of the Original Author (or pseudonym, if applicable) if supplied, and/or if the Original Author and/or Licensor designate another party or parties (e.g., a sponsor institute, publishing entity, journal) for attribution ("Attribution Parties") in Licensor's copyright notice, terms of service or by other reasonable means, the name of such party or parties; (ii) the title of the Work if supplied; (iii) to the extent reasonably practicable, the URI, if any, that Licensor specifies to be associated with the Work, unless such URI does not refer to the copyright notice or licensing information for the Work; and (iv) , consistent with Section 3(b), in the case of an Adaptation, a credit identifying the use of the Work in the Adaptation (e.g., "French translation of the Work by Original Author," or "Screenplay based on original Work by Original Author"). The credit required by this Section 4 (b) may be implemented in any reasonable manner; provided, however, that in the case of a Adaptation or Collection, at a minimum such credit will appear, if a credit for all contributing authors of the Adaptation or Collection appears, then as part of these credits and in a manner at least as prominent as the credits for the other contributing authors. For the avoidance of doubt, You may only use the credit required by this Section for the purpose of attribution in the manner set out above and, by exercising Your rights under this License, You may not implicitly or explicitly assert or imply any connection with, sponsorship or endorsement by the Original Author, Licensor and/or Attribution Parties, as appropriate, of You or Your use of the Work, without the separate, express prior written permission of the Original Author, Licensor and/or Attribution Parties.
+   3. Except as otherwise agreed in writing by the Licensor or as may be otherwise permitted by applicable law, if You Reproduce, Distribute or Publicly Perform the Work either by itself or as part of any Adaptations or Collections, You must not distort, mutilate, modify or take other derogatory action in relation to the Work which would be prejudicial to the Original Author's honor or reputation. Licensor agrees that in those jurisdictions (e.g. Japan), in which any exercise of the right granted in Section 3(b) of this License (the right to make Adaptations) would be deemed to be a distortion, mutilation, modification or other derogatory action prejudicial to the Original Author's honor and reputation, the Licensor will waive or not assert, as appropriate, this Section, to the fullest extent permitted by the applicable national law, to enable You to reasonably exercise Your right under Section 3(b) of this License (right to make Adaptations) but not otherwise.
+
+5. Representations, Warranties and Disclaimer
+
+UNLESS OTHERWISE MUTUALLY AGREED TO BY THE PARTIES IN WRITING, LICENSOR OFFERS THE WORK AS-IS AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE WORK, EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, INCLUDING, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTIBILITY, FITNESS FOR A PARTICULAR PURPOSE, NONINFRINGEMENT, OR THE ABSENCE OF LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OF ABSENCE OF ERRORS, WHETHER OR NOT DISCOVERABLE. SOME JURISDICTIONS DO NOT ALLOW THE EXCLUSION OF IMPLIED WARRANTIES, SO SUCH EXCLUSION MAY NOT APPLY TO YOU.
+
+6. Limitation on Liability. EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES ARISING OUT OF THIS LICENSE OR THE USE OF THE WORK, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+7. Termination
+
+   1. This License and the rights granted hereunder will terminate automatically upon any breach by You of the terms of this License. Individuals or entities who have received Adaptations or Collections from You under this License, however, will not have their licenses terminated provided such individuals or entities remain in full compliance with those licenses. Sections 1, 2, 5, 6, 7, and 8 will survive any termination of this License.
+   2. Subject to the above terms and conditions, the license granted here is perpetual (for the duration of the applicable copyright in the Work). Notwithstanding the above, Licensor reserves the right to release the Work under different license terms or to stop distributing the Work at any time; provided, however that any such election will not serve to withdraw this License (or any other license that has been, or is required to be, granted under the terms of this License), and this License will continue in full force and effect unless terminated as stated above.
+
+8. Miscellaneous
+
+   1. Each time You Distribute or Publicly Perform the Work or a Collection, the Licensor offers to the recipient a license to the Work on the same terms and conditions as the license granted to You under this License.
+   2. Each time You Distribute or Publicly Perform an Adaptation, Licensor offers to the recipient a license to the original Work on the same terms and conditions as the license granted to You under this License.
+   3. If any provision of this License is invalid or unenforceable under applicable law, it shall not affect the validity or enforceability of the remainder of the terms of this License, and without further action by the parties to this agreement, such provision shall be reformed to the minimum extent necessary to make such provision valid and enforceable.
+   4. No term or provision of this License shall be deemed waived and no breach consented to unless such waiver or consent shall be in writing and signed by the party to be charged with such waiver or consent.
+   5. This License constitutes the entire agreement between the parties with respect to the Work licensed here. There are no understandings, agreements or representations with respect to the Work not specified here. Licensor shall not be bound by any additional provisions that may appear in any communication from You. This License may not be modified without the mutual written agreement of the Licensor and You.
+   6. The rights granted under, and the subject matter referenced, in this License were drafted utilizing the terminology of the Berne Convention for the Protection of Literary and Artistic Works (as amended on September 28, 1979), the Rome Convention of 1961, the WIPO Copyright Treaty of 1996, the WIPO Performances and Phonograms Treaty of 1996 and the Universal Copyright Convention (as revised on July 24, 1971). These rights and subject matter take effect in the relevant jurisdiction in which the License terms are sought to be enforced according to the corresponding provisions of the implementation of those treaty provisions in the applicable national law. If the standard suite of rights granted under applicable copyright law includes additional rights not granted under this License, such additional rights are deemed to be included in the License; this License is not intended to restrict the license of any rights under applicable law.
diff --git a/docs/assets/css/font-awesome.min.css b/docs/assets/css/font-awesome.min.css
new file mode 100644
index 00000000..24fcc04c
--- /dev/null
+++ b/docs/assets/css/font-awesome.min.css
@@ -0,0 +1,4 @@
+/*!
+ *  Font Awesome 4.3.0 by @davegandy - http://fontawesome.io - @fontawesome
+ *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ */@font-face{font-family:'FontAwesome';src:url('../fonts/fontawesome-webfont.eot?v=4.3.0');src:url('../fonts/fontawesome-webfont.eot?#iefix&v=4.3.0') format('embedded-opentype'),url('../fonts/fontawesome-webfont.woff2?v=4.3.0') format('woff2'),url('../fonts/fontawesome-webfont.woff?v=4.3.0') format('woff'),url('../fonts/fontawesome-webfont.ttf?v=4.3.0') format('truetype'),url('../fonts/fontawesome-webfont.svg?v=4.3.0#fontawesomeregular') format('svg');font-weight:normal;font-style:normal}.fa{display:inline-block;font:normal normal normal 14px/1 FontAwesome;font-size:inherit;text-rendering:auto;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;transform:translate(0, 0)}.fa-lg{font-size:1.33333333em;line-height:.75em;vertical-align:-15%}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-fw{width:1.28571429em;text-align:center}.fa-ul{padding-left:0;margin-left:2.14285714em;list-style-type:none}.fa-ul>li{position:relative}.fa-li{position:absolute;left:-2.14285714em;width:2.14285714em;top:.14285714em;text-align:center}.fa-li.fa-lg{left:-1.85714286em}.fa-border{padding:.2em .25em .15em;border:solid .08em #eee;border-radius:.1em}.pull-right{float:right}.pull-left{float:left}.fa.pull-left{margin-right:.3em}.fa.pull-right{margin-left:.3em}.fa-spin{-webkit-animation:fa-spin 2s infinite linear;animation:fa-spin 2s infinite linear}.fa-pulse{-webkit-animation:fa-spin 1s infinite steps(8);animation:fa-spin 1s infinite steps(8)}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.fa-rotate-90{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1);-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2);-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3);-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);-webkit-transform:scale(-1, 1);-ms-transform:scale(-1, 1);transform:scale(-1, 1)}.fa-flip-vertical{filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);-webkit-transform:scale(1, -1);-ms-transform:scale(1, -1);transform:scale(1, -1)}:root .fa-rotate-90,:root .fa-rotate-180,:root .fa-rotate-270,:root .fa-flip-horizontal,:root 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+	padding: 1em 0 0 1em;
+    }
+
+    .row.uniform.\35 0\25 {
+	margin: -1em 0 -1px -1em;
+    }
+
+    .row.\32 5\25 > * {
+	padding: 0 0 0 0.5em;
+    }
+
+    .row.\32 5\25 {
+	margin: 0 0 -1px -0.5em;
+    }
+
+    .row.uniform.\32 5\25 > * {
+	padding: 0.5em 0 0 0.5em;
+    }
+
+    .row.uniform.\32 5\25 {
+	margin: -0.5em 0 -1px -0.5em;
+    }
+
+    .\31 2u\28xlarge\29, .\31 2u\24\28xlarge\29 {
+	width: 100%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 1u\28xlarge\29, .\31 1u\24\28xlarge\29 {
+	width: 91.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 0u\28xlarge\29, .\31 0u\24\28xlarge\29 {
+	width: 83.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\39 u\28xlarge\29, .\39 u\24\28xlarge\29 {
+	width: 75%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\38 u\28xlarge\29, .\38 u\24\28xlarge\29 {
+	width: 66.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\37 u\28xlarge\29, .\37 u\24\28xlarge\29 {
+	width: 58.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\36 u\28xlarge\29, .\36 u\24\28xlarge\29 {
+	width: 50%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\35 u\28xlarge\29, .\35 u\24\28xlarge\29 {
+	width: 41.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\34 u\28xlarge\29, .\34 u\24\28xlarge\29 {
+	width: 33.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\33 u\28xlarge\29, .\33 u\24\28xlarge\29 {
+	width: 25%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\32 u\28xlarge\29, .\32 u\24\28xlarge\29 {
+	width: 16.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 u\28xlarge\29, .\31 u\24\28xlarge\29 {
+	width: 8.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 2u\24\28xlarge\29 + *,
+    .\31 1u\24\28xlarge\29 + *,
+    .\31 0u\24\28xlarge\29 + *,
+    .\39 u\24\28xlarge\29 + *,
+    .\38 u\24\28xlarge\29 + *,
+    .\37 u\24\28xlarge\29 + *,
+    .\36 u\24\28xlarge\29 + *,
+    .\35 u\24\28xlarge\29 + *,
+    .\34 u\24\28xlarge\29 + *,
+    .\33 u\24\28xlarge\29 + *,
+    .\32 u\24\28xlarge\29 + *,
+    .\31 u\24\28xlarge\29 + * {
+	clear: left;
+    }
+
+    .\-11u\28xlarge\29 {
+	margin-left: 91.66667%;
+    }
+
+    .\-10u\28xlarge\29 {
+	margin-left: 83.33333%;
+    }
+
+    .\-9u\28xlarge\29 {
+	margin-left: 75%;
+    }
+
+    .\-8u\28xlarge\29 {
+	margin-left: 66.66667%;
+    }
+
+    .\-7u\28xlarge\29 {
+	margin-left: 58.33333%;
+    }
+
+    .\-6u\28xlarge\29 {
+	margin-left: 50%;
+    }
+
+    .\-5u\28xlarge\29 {
+	margin-left: 41.66667%;
+    }
+
+    .\-4u\28xlarge\29 {
+	margin-left: 33.33333%;
+    }
+
+    .\-3u\28xlarge\29 {
+	margin-left: 25%;
+    }
+
+    .\-2u\28xlarge\29 {
+	margin-left: 16.66667%;
+    }
+
+    .\-1u\28xlarge\29 {
+	margin-left: 8.33333%;
+    }
+
+}
+
+@media screen and (max-width: 1366px) {
+
+    .row > * {
+	padding: 0 0 0 1.5em;
+    }
+
+    .row {
+	margin: 0 0 -1px -1.5em;
+    }
+
+    .row.uniform > * {
+	padding: 1.5em 0 0 1.5em;
+    }
+
+    .row.uniform {
+	margin: -1.5em 0 -1px -1.5em;
+    }
+
+    .row.\32 00\25 > * {
+	padding: 0 0 0 3em;
+    }
+
+    .row.\32 00\25 {
+	margin: 0 0 -1px -3em;
+    }
+
+    .row.uniform.\32 00\25 > * {
+	padding: 3em 0 0 3em;
+    }
+
+    .row.uniform.\32 00\25 {
+	margin: -3em 0 -1px -3em;
+    }
+
+    .row.\31 50\25 > * {
+	padding: 0 0 0 2.25em;
+    }
+
+    .row.\31 50\25 {
+	margin: 0 0 -1px -2.25em;
+    }
+
+    .row.uniform.\31 50\25 > * {
+	padding: 2.25em 0 0 2.25em;
+    }
+
+    .row.uniform.\31 50\25 {
+	margin: -2.25em 0 -1px -2.25em;
+    }
+
+    .row.\35 0\25 > * {
+	padding: 0 0 0 0.75em;
+    }
+
+    .row.\35 0\25 {
+	margin: 0 0 -1px -0.75em;
+    }
+
+    .row.uniform.\35 0\25 > * {
+	padding: 0.75em 0 0 0.75em;
+    }
+
+    .row.uniform.\35 0\25 {
+	margin: -0.75em 0 -1px -0.75em;
+    }
+
+    .row.\32 5\25 > * {
+	padding: 0 0 0 0.375em;
+    }
+
+    .row.\32 5\25 {
+	margin: 0 0 -1px -0.375em;
+    }
+
+    .row.uniform.\32 5\25 > * {
+	padding: 0.375em 0 0 0.375em;
+    }
+
+    .row.uniform.\32 5\25 {
+	margin: -0.375em 0 -1px -0.375em;
+    }
+
+    .\31 2u\28large\29, .\31 2u\24\28large\29 {
+	width: 100%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 1u\28large\29, .\31 1u\24\28large\29 {
+	width: 91.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 0u\28large\29, .\31 0u\24\28large\29 {
+	width: 83.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\39 u\28large\29, .\39 u\24\28large\29 {
+	width: 75%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\38 u\28large\29, .\38 u\24\28large\29 {
+	width: 66.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\37 u\28large\29, .\37 u\24\28large\29 {
+	width: 58.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\36 u\28large\29, .\36 u\24\28large\29 {
+	width: 50%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\35 u\28large\29, .\35 u\24\28large\29 {
+	width: 41.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\34 u\28large\29, .\34 u\24\28large\29 {
+	width: 33.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\33 u\28large\29, .\33 u\24\28large\29 {
+	width: 25%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\32 u\28large\29, .\32 u\24\28large\29 {
+	width: 16.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 u\28large\29, .\31 u\24\28large\29 {
+	width: 8.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 2u\24\28large\29 + *,
+    .\31 1u\24\28large\29 + *,
+    .\31 0u\24\28large\29 + *,
+    .\39 u\24\28large\29 + *,
+    .\38 u\24\28large\29 + *,
+    .\37 u\24\28large\29 + *,
+    .\36 u\24\28large\29 + *,
+    .\35 u\24\28large\29 + *,
+    .\34 u\24\28large\29 + *,
+    .\33 u\24\28large\29 + *,
+    .\32 u\24\28large\29 + *,
+    .\31 u\24\28large\29 + * {
+	clear: left;
+    }
+
+    .\-11u\28large\29 {
+	margin-left: 91.66667%;
+    }
+
+    .\-10u\28large\29 {
+	margin-left: 83.33333%;
+    }
+
+    .\-9u\28large\29 {
+	margin-left: 75%;
+    }
+
+    .\-8u\28large\29 {
+	margin-left: 66.66667%;
+    }
+
+    .\-7u\28large\29 {
+	margin-left: 58.33333%;
+    }
+
+    .\-6u\28large\29 {
+	margin-left: 50%;
+    }
+
+    .\-5u\28large\29 {
+	margin-left: 41.66667%;
+    }
+
+    .\-4u\28large\29 {
+	margin-left: 33.33333%;
+    }
+
+    .\-3u\28large\29 {
+	margin-left: 25%;
+    }
+
+    .\-2u\28large\29 {
+	margin-left: 16.66667%;
+    }
+
+    .\-1u\28large\29 {
+	margin-left: 8.33333%;
+    }
+
+}
+
+@media screen and (max-width: 980px) {
+
+    .row > * {
+	padding: 0 0 0 1.5em;
+    }
+
+    .row {
+	margin: 0 0 -1px -1.5em;
+    }
+
+    .row.uniform > * {
+	padding: 1.5em 0 0 1.5em;
+    }
+
+    .row.uniform {
+	margin: -1.5em 0 -1px -1.5em;
+    }
+
+    .row.\32 00\25 > * {
+	padding: 0 0 0 3em;
+    }
+
+    .row.\32 00\25 {
+	margin: 0 0 -1px -3em;
+    }
+
+    .row.uniform.\32 00\25 > * {
+	padding: 3em 0 0 3em;
+    }
+
+    .row.uniform.\32 00\25 {
+	margin: -3em 0 -1px -3em;
+    }
+
+    .row.\31 50\25 > * {
+	padding: 0 0 0 2.25em;
+    }
+
+    .row.\31 50\25 {
+	margin: 0 0 -1px -2.25em;
+    }
+
+    .row.uniform.\31 50\25 > * {
+	padding: 2.25em 0 0 2.25em;
+    }
+
+    .row.uniform.\31 50\25 {
+	margin: -2.25em 0 -1px -2.25em;
+    }
+
+    .row.\35 0\25 > * {
+	padding: 0 0 0 0.75em;
+    }
+
+    .row.\35 0\25 {
+	margin: 0 0 -1px -0.75em;
+    }
+
+    .row.uniform.\35 0\25 > * {
+	padding: 0.75em 0 0 0.75em;
+    }
+
+    .row.uniform.\35 0\25 {
+	margin: -0.75em 0 -1px -0.75em;
+    }
+
+    .row.\32 5\25 > * {
+	padding: 0 0 0 0.375em;
+    }
+
+    .row.\32 5\25 {
+	margin: 0 0 -1px -0.375em;
+    }
+
+    .row.uniform.\32 5\25 > * {
+	padding: 0.375em 0 0 0.375em;
+    }
+
+    .row.uniform.\32 5\25 {
+	margin: -0.375em 0 -1px -0.375em;
+    }
+
+    .\31 2u\28medium\29, .\31 2u\24\28medium\29 {
+	width: 100%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 1u\28medium\29, .\31 1u\24\28medium\29 {
+	width: 91.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 0u\28medium\29, .\31 0u\24\28medium\29 {
+	width: 83.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\39 u\28medium\29, .\39 u\24\28medium\29 {
+	width: 75%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\38 u\28medium\29, .\38 u\24\28medium\29 {
+	width: 66.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\37 u\28medium\29, .\37 u\24\28medium\29 {
+	width: 58.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\36 u\28medium\29, .\36 u\24\28medium\29 {
+	width: 50%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\35 u\28medium\29, .\35 u\24\28medium\29 {
+	width: 41.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\34 u\28medium\29, .\34 u\24\28medium\29 {
+	width: 33.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\33 u\28medium\29, .\33 u\24\28medium\29 {
+	width: 25%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\32 u\28medium\29, .\32 u\24\28medium\29 {
+	width: 16.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 u\28medium\29, .\31 u\24\28medium\29 {
+	width: 8.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 2u\24\28medium\29 + *,
+    .\31 1u\24\28medium\29 + *,
+    .\31 0u\24\28medium\29 + *,
+    .\39 u\24\28medium\29 + *,
+    .\38 u\24\28medium\29 + *,
+    .\37 u\24\28medium\29 + *,
+    .\36 u\24\28medium\29 + *,
+    .\35 u\24\28medium\29 + *,
+    .\34 u\24\28medium\29 + *,
+    .\33 u\24\28medium\29 + *,
+    .\32 u\24\28medium\29 + *,
+    .\31 u\24\28medium\29 + * {
+	clear: left;
+    }
+
+    .\-11u\28medium\29 {
+	margin-left: 91.66667%;
+    }
+
+    .\-10u\28medium\29 {
+	margin-left: 83.33333%;
+    }
+
+    .\-9u\28medium\29 {
+	margin-left: 75%;
+    }
+
+    .\-8u\28medium\29 {
+	margin-left: 66.66667%;
+    }
+
+    .\-7u\28medium\29 {
+	margin-left: 58.33333%;
+    }
+
+    .\-6u\28medium\29 {
+	margin-left: 50%;
+    }
+
+    .\-5u\28medium\29 {
+	margin-left: 41.66667%;
+    }
+
+    .\-4u\28medium\29 {
+	margin-left: 33.33333%;
+    }
+
+    .\-3u\28medium\29 {
+	margin-left: 25%;
+    }
+
+    .\-2u\28medium\29 {
+	margin-left: 16.66667%;
+    }
+
+    .\-1u\28medium\29 {
+	margin-left: 8.33333%;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    .row > * {
+	padding: 0 0 0 1.25em;
+    }
+
+    .row {
+	margin: 0 0 -1px -1.25em;
+    }
+
+    .row.uniform > * {
+	padding: 1.25em 0 0 1.25em;
+    }
+
+    .row.uniform {
+	margin: -1.25em 0 -1px -1.25em;
+    }
+
+    .row.\32 00\25 > * {
+	padding: 0 0 0 2.5em;
+    }
+
+    .row.\32 00\25 {
+	margin: 0 0 -1px -2.5em;
+    }
+
+    .row.uniform.\32 00\25 > * {
+	padding: 2.5em 0 0 2.5em;
+    }
+
+    .row.uniform.\32 00\25 {
+	margin: -2.5em 0 -1px -2.5em;
+    }
+
+    .row.\31 50\25 > * {
+	padding: 0 0 0 1.875em;
+    }
+
+    .row.\31 50\25 {
+	margin: 0 0 -1px -1.875em;
+    }
+
+    .row.uniform.\31 50\25 > * {
+	padding: 1.875em 0 0 1.875em;
+    }
+
+    .row.uniform.\31 50\25 {
+	margin: -1.875em 0 -1px -1.875em;
+    }
+
+    .row.\35 0\25 > * {
+	padding: 0 0 0 0.625em;
+    }
+
+    .row.\35 0\25 {
+	margin: 0 0 -1px -0.625em;
+    }
+
+    .row.uniform.\35 0\25 > * {
+	padding: 0.625em 0 0 0.625em;
+    }
+
+    .row.uniform.\35 0\25 {
+	margin: -0.625em 0 -1px -0.625em;
+    }
+
+    .row.\32 5\25 > * {
+	padding: 0 0 0 0.3125em;
+    }
+
+    .row.\32 5\25 {
+	margin: 0 0 -1px -0.3125em;
+    }
+
+    .row.uniform.\32 5\25 > * {
+	padding: 0.3125em 0 0 0.3125em;
+    }
+
+    .row.uniform.\32 5\25 {
+	margin: -0.3125em 0 -1px -0.3125em;
+    }
+
+    .\31 2u\28small\29, .\31 2u\24\28small\29 {
+	width: 100%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 1u\28small\29, .\31 1u\24\28small\29 {
+	width: 91.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 0u\28small\29, .\31 0u\24\28small\29 {
+	width: 83.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\39 u\28small\29, .\39 u\24\28small\29 {
+	width: 75%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\38 u\28small\29, .\38 u\24\28small\29 {
+	width: 66.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\37 u\28small\29, .\37 u\24\28small\29 {
+	width: 58.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\36 u\28small\29, .\36 u\24\28small\29 {
+	width: 50%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\35 u\28small\29, .\35 u\24\28small\29 {
+	width: 41.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\34 u\28small\29, .\34 u\24\28small\29 {
+	width: 33.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\33 u\28small\29, .\33 u\24\28small\29 {
+	width: 25%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\32 u\28small\29, .\32 u\24\28small\29 {
+	width: 16.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 u\28small\29, .\31 u\24\28small\29 {
+	width: 8.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 2u\24\28small\29 + *,
+    .\31 1u\24\28small\29 + *,
+    .\31 0u\24\28small\29 + *,
+    .\39 u\24\28small\29 + *,
+    .\38 u\24\28small\29 + *,
+    .\37 u\24\28small\29 + *,
+    .\36 u\24\28small\29 + *,
+    .\35 u\24\28small\29 + *,
+    .\34 u\24\28small\29 + *,
+    .\33 u\24\28small\29 + *,
+    .\32 u\24\28small\29 + *,
+    .\31 u\24\28small\29 + * {
+	clear: left;
+    }
+
+    .\-11u\28small\29 {
+	margin-left: 91.66667%;
+    }
+
+    .\-10u\28small\29 {
+	margin-left: 83.33333%;
+    }
+
+    .\-9u\28small\29 {
+	margin-left: 75%;
+    }
+
+    .\-8u\28small\29 {
+	margin-left: 66.66667%;
+    }
+
+    .\-7u\28small\29 {
+	margin-left: 58.33333%;
+    }
+
+    .\-6u\28small\29 {
+	margin-left: 50%;
+    }
+
+    .\-5u\28small\29 {
+	margin-left: 41.66667%;
+    }
+
+    .\-4u\28small\29 {
+	margin-left: 33.33333%;
+    }
+
+    .\-3u\28small\29 {
+	margin-left: 25%;
+    }
+
+    .\-2u\28small\29 {
+	margin-left: 16.66667%;
+    }
+
+    .\-1u\28small\29 {
+	margin-left: 8.33333%;
+    }
+
+}
+
+@media screen and (max-width: 480px) {
+
+    .row > * {
+	padding: 0 0 0 1.25em;
+    }
+
+    .row {
+	margin: 0 0 -1px -1.25em;
+    }
+
+    .row.uniform > * {
+	padding: 1.25em 0 0 1.25em;
+    }
+
+    .row.uniform {
+	margin: -1.25em 0 -1px -1.25em;
+    }
+
+    .row.\32 00\25 > * {
+	padding: 0 0 0 2.5em;
+    }
+
+    .row.\32 00\25 {
+	margin: 0 0 -1px -2.5em;
+    }
+
+    .row.uniform.\32 00\25 > * {
+	padding: 2.5em 0 0 2.5em;
+    }
+
+    .row.uniform.\32 00\25 {
+	margin: -2.5em 0 -1px -2.5em;
+    }
+
+    .row.\31 50\25 > * {
+	padding: 0 0 0 1.875em;
+    }
+
+    .row.\31 50\25 {
+	margin: 0 0 -1px -1.875em;
+    }
+
+    .row.uniform.\31 50\25 > * {
+	padding: 1.875em 0 0 1.875em;
+    }
+
+    .row.uniform.\31 50\25 {
+	margin: -1.875em 0 -1px -1.875em;
+    }
+
+    .row.\35 0\25 > * {
+	padding: 0 0 0 0.625em;
+    }
+
+    .row.\35 0\25 {
+	margin: 0 0 -1px -0.625em;
+    }
+
+    .row.uniform.\35 0\25 > * {
+	padding: 0.625em 0 0 0.625em;
+    }
+
+    .row.uniform.\35 0\25 {
+	margin: -0.625em 0 -1px -0.625em;
+    }
+
+    .row.\32 5\25 > * {
+	padding: 0 0 0 0.3125em;
+    }
+
+    .row.\32 5\25 {
+	margin: 0 0 -1px -0.3125em;
+    }
+
+    .row.uniform.\32 5\25 > * {
+	padding: 0.3125em 0 0 0.3125em;
+    }
+
+    .row.uniform.\32 5\25 {
+	margin: -0.3125em 0 -1px -0.3125em;
+    }
+
+    .\31 2u\28xsmall\29, .\31 2u\24\28xsmall\29 {
+	width: 100%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 1u\28xsmall\29, .\31 1u\24\28xsmall\29 {
+	width: 91.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 0u\28xsmall\29, .\31 0u\24\28xsmall\29 {
+	width: 83.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\39 u\28xsmall\29, .\39 u\24\28xsmall\29 {
+	width: 75%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\38 u\28xsmall\29, .\38 u\24\28xsmall\29 {
+	width: 66.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\37 u\28xsmall\29, .\37 u\24\28xsmall\29 {
+	width: 58.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\36 u\28xsmall\29, .\36 u\24\28xsmall\29 {
+	width: 50%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\35 u\28xsmall\29, .\35 u\24\28xsmall\29 {
+	width: 41.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\34 u\28xsmall\29, .\34 u\24\28xsmall\29 {
+	width: 33.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\33 u\28xsmall\29, .\33 u\24\28xsmall\29 {
+	width: 25%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\32 u\28xsmall\29, .\32 u\24\28xsmall\29 {
+	width: 16.6666666667%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 u\28xsmall\29, .\31 u\24\28xsmall\29 {
+	width: 8.3333333333%;
+	clear: none;
+	margin-left: 0;
+    }
+
+    .\31 2u\24\28xsmall\29 + *,
+    .\31 1u\24\28xsmall\29 + *,
+    .\31 0u\24\28xsmall\29 + *,
+    .\39 u\24\28xsmall\29 + *,
+    .\38 u\24\28xsmall\29 + *,
+    .\37 u\24\28xsmall\29 + *,
+    .\36 u\24\28xsmall\29 + *,
+    .\35 u\24\28xsmall\29 + *,
+    .\34 u\24\28xsmall\29 + *,
+    .\33 u\24\28xsmall\29 + *,
+    .\32 u\24\28xsmall\29 + *,
+    .\31 u\24\28xsmall\29 + * {
+	clear: left;
+    }
+
+    .\-11u\28xsmall\29 {
+	margin-left: 91.66667%;
+    }
+
+    .\-10u\28xsmall\29 {
+	margin-left: 83.33333%;
+    }
+
+    .\-9u\28xsmall\29 {
+	margin-left: 75%;
+    }
+
+    .\-8u\28xsmall\29 {
+	margin-left: 66.66667%;
+    }
+
+    .\-7u\28xsmall\29 {
+	margin-left: 58.33333%;
+    }
+
+    .\-6u\28xsmall\29 {
+	margin-left: 50%;
+    }
+
+    .\-5u\28xsmall\29 {
+	margin-left: 41.66667%;
+    }
+
+    .\-4u\28xsmall\29 {
+	margin-left: 33.33333%;
+    }
+
+    .\-3u\28xsmall\29 {
+	margin-left: 25%;
+    }
+
+    .\-2u\28xsmall\29 {
+	margin-left: 16.66667%;
+    }
+
+    .\-1u\28xsmall\29 {
+	margin-left: 8.33333%;
+    }
+
+}
+
+/* Basic */
+
+@-ms-viewport {
+    width: device-width;
+}
+
+body {
+    -ms-overflow-style: scrollbar;
+}
+
+@media screen and (max-width: 480px) {
+
+    html, body {
+	min-width: 320px;
+    }
+
+}
+
+body {
+    background: #fff;
+}
+
+body.is-loading *, body.is-loading *:before, body.is-loading *:after {
+    -moz-animation: none !important;
+    -webkit-animation: none !important;
+    -ms-animation: none !important;
+    animation: none !important;
+    -moz-transition: none !important;
+    -webkit-transition: none !important;
+    -ms-transition: none !important;
+    transition: none !important;
+}
+
+/* Type */
+
+body {
+    background-color: #fff;
+    color: #777;
+}
+
+body, input, select, textarea {
+    font-family: Arial, Helvetica, sans-serif;
+    font-size: 13pt;
+    font-weight: normal;
+    line-height: 2;
+}
+
+@media screen and (max-width: 1680px) {
+
+    body, input, select, textarea {
+	font-size: 13.5pt;
+    }
+
+}
+
+@media screen and (max-width: 1366px) {
+
+    body, input, select, textarea {
+	font-size: 12.5pt;
+    }
+
+}
+
+@media screen and (max-width: 980px) {
+
+    body, input, select, textarea {
+	font-size: 12pt;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    body, input, select, textarea {
+	font-size: 11.5pt;
+    }
+
+}
+
+@media screen and (max-width: 480px) {
+
+    body, input, select, textarea {
+	font-size: 11.5pt;
+    }
+
+}
+
+a {
+    -moz-transition: color 0.2s ease;
+    -webkit-transition: color 0.2s ease;
+    -ms-transition: color 0.2s ease;
+    transition: color 0.2s ease;
+    text-decoration: underline;
+}
+
+a:hover {
+    color: #00BC8A !important;
+    text-decoration: none;
+}
+
+strong, b {
+    font-weight: bold;
+}
+
+em, i {
+    font-style: italic;
+}
+
+p {
+    margin: 0 0 2em 0;
+}
+
+h1, h2, h3, h4, h5, h6 {
+    font-weight: bold;
+    line-height: 1.5;
+    margin: 0 0 1em 0;
+}
+
+h1 a, h2 a, h3 a, h4 a, h5 a, h6 a {
+    color: inherit;
+    text-decoration: none;
+}
+
+h2 {
+    font-size: 1.75em;
+}
+
+
+h3 {
+    font-size: 1.35em;
+}
+
+h4 {
+    font-size: 1.1em;
+}
+
+h5 {
+    font-size: 0.9em;
+}
+
+h6 {
+    font-size: 0.7em;
+}
+
+sub {
+    font-size: 0.8em;
+    position: relative;
+    top: 0.5em;
+}
+
+sup {
+    font-size: 0.8em;
+    position: relative;
+    top: -0.5em;
+}
+
+blockquote {
+    border-left: solid 4px;
+    font-style: italic;
+    margin: 0 0 2em 0;
+    padding: 0.5em 0 0.5em 2em;
+}
+
+code {
+    border-radius: 0;
+    border: solid 1px;
+    font-family: "Courier New", monospace;
+    font-size: 0.9em;
+    margin: 0 0.25em;
+    padding: 0.25em 0.65em;
+}
+
+pre {
+    -webkit-overflow-scrolling: touch;
+    font-family: "Courier New", monospace;
+    font-size: 0.9em;
+    margin: 0 0 2em 0;
+}
+
+pre code {
+    display: block;
+    line-height: 1.75;
+    padding: 1em 1.5em;
+    overflow-x: auto;
+}
+
+hr {
+    border: 0;
+    border-bottom: solid 1px;
+    margin: 2em 0;
+}
+
+hr.major {
+    margin: 3em 0;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+input, select, textarea {
+    color: #666;
+}
+
+a {
+    color: #0089FF;
+}
+
+strong, b {
+    color: #666;
+}
+
+h1, h2, h3, h4, h5, h6 {
+    color: #666;
+}
+
+blockquote {
+    border-left-color: rgba(144, 144, 144, 0.25);
+}
+
+code {
+    background: rgba(144, 144, 144, 0.075);
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+hr {
+    border-bottom-color: rgba(144, 144, 144, 0.25);
+}
+
+/* Box */
+
+.box {
+    border-radius: 0;
+    border: solid 1px;
+    margin-bottom: 2em;
+    padding: 1.5em;
+}
+
+.box > :last-child,
+.box > :last-child > :last-child,
+.box > :last-child > :last-child > :last-child {
+    margin-bottom: 0;
+}
+
+.box.alt {
+    border: 0;
+    border-radius: 0;
+    padding: 0;
+}
+
+.box {
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+/* Button */
+
+input[type="submit"],
+input[type="reset"],
+input[type="button"],
+button,
+.button {
+    -moz-appearance: none;
+    -webkit-appearance: none;
+    -ms-appearance: none;
+    appearance: none;
+    -moz-transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out;
+    -webkit-transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out;
+    -ms-transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out;
+    transition: background-color 0.2s ease-in-out, color 0.2s ease-in-out;
+    border-radius: 2em;
+    border: 0;
+    cursor: pointer;
+    display: inline-block;
+    font-size: 0.8em;
+    font-weight: bold;
+    height: 3.5em;
+    letter-spacing: 0.1em;
+    line-height: 3.65em;
+    padding: 0 3em;
+    text-align: center;
+    text-decoration: none;
+    text-transform: uppercase;
+    white-space: nowrap;
+}
+
+input[type="submit"].icon,
+input[type="reset"].icon,
+input[type="button"].icon,
+button.icon,
+.button.icon {
+    padding-left: 1.35em;
+}
+
+input[type="submit"].icon:before,
+input[type="reset"].icon:before,
+input[type="button"].icon:before,
+button.icon:before,
+.button.icon:before {
+    margin-right: 0.5em;
+}
+
+input[type="submit"].fit,
+input[type="reset"].fit,
+input[type="button"].fit,
+button.fit,
+.button.fit {
+    display: block;
+    margin: 0 0 1em 0;
+    width: 100%;
+}
+
+input[type="submit"].small,
+input[type="reset"].small,
+input[type="button"].small,
+button.small,
+.button.small {
+    font-size: 0.8em;
+}
+
+input[type="submit"].big,
+input[type="reset"].big,
+input[type="button"].big,
+button.big,
+.button.big {
+    font-size: 1.35em;
+}
+
+input[type="submit"].disabled, input[type="submit"]:disabled,
+input[type="reset"].disabled,
+input[type="reset"]:disabled,
+input[type="button"].disabled,
+input[type="button"]:disabled,
+button.disabled,
+button:disabled,
+.button.disabled,
+.button:disabled {
+    -moz-pointer-events: none;
+    -webkit-pointer-events: none;
+    -ms-pointer-events: none;
+    pointer-events: none;
+    opacity: 0.25;
+}
+
+@media screen and (max-width: 480px) {
+
+    input[type="submit"],
+    input[type="reset"],
+    input[type="button"],
+    button,
+    .button {
+	padding: 0;
+    }
+
+}
+
+input[type="submit"],
+input[type="reset"],
+input[type="button"],
+button,
+.button {
+    background-color: #fafafa;
+    color: #666 !important;
+}
+
+input[type="submit"]:hover,
+input[type="reset"]:hover,
+input[type="button"]:hover,
+button:hover,
+.button:hover {
+    background-color: white;
+}
+
+input[type="submit"]:active,
+input[type="reset"]:active,
+input[type="button"]:active,
+button:active,
+.button:active {
+    background-color: #ededed;
+}
+
+input[type="submit"].alt,
+input[type="reset"].alt,
+input[type="button"].alt,
+button.alt,
+.button.alt {
+    background-color: transparent;
+    box-shadow: inset 0 0 0 2px rgba(144, 144, 144, 0.25);
+    color: #666 !important;
+}
+
+input[type="submit"].alt:hover,
+input[type="reset"].alt:hover,
+input[type="button"].alt:hover,
+button.alt:hover,
+.button.alt:hover {
+    background-color: rgba(144, 144, 144, 0.075);
+}
+
+input[type="submit"].alt:active,
+input[type="reset"].alt:active,
+input[type="button"].alt:active,
+button.alt:active,
+.button.alt:active {
+    background-color: rgba(144, 144, 144, 0.2);
+}
+
+input[type="submit"].alt.icon:before,
+input[type="reset"].alt.icon:before,
+input[type="button"].alt.icon:before,
+button.alt.icon:before,
+.button.alt.icon:before {
+    color: #bbb;
+}
+
+input[type="submit"].special,
+input[type="reset"].special,
+input[type="button"].special,
+button.special,
+.button.special {
+    background-color: #00BC8A;
+    color: #ffffff !important;
+}
+
+input[type="submit"].special:hover,
+input[type="reset"].special:hover,
+input[type="button"].special:hover,
+button.special:hover,
+.button.special:hover {
+    background-color: #00d69d;
+}
+
+input[type="submit"].special:active,
+input[type="reset"].special:active,
+input[type="button"].special:active,
+button.special:active,
+.button.special:active {
+    background-color: #00a377;
+}
+
+/* Form */
+
+form {
+    margin: 0 0 2em 0;
+}
+
+label {
+    display: block;
+    font-size: 0.9em;
+    font-weight: bold;
+    margin: 0 0 1em 0;
+}
+
+input[type="text"],
+input[type="password"],
+input[type="email"],
+select,
+textarea {
+    -moz-appearance: none;
+    -webkit-appearance: none;
+    -ms-appearance: none;
+    appearance: none;
+    border-radius: 0;
+    border: none;
+    border: solid 1px;
+    color: inherit;
+    display: block;
+    outline: 0;
+    padding: 0 1em;
+    text-decoration: none;
+    width: 100%;
+}
+
+input[type="text"]:invalid,
+input[type="password"]:invalid,
+input[type="email"]:invalid,
+select:invalid,
+textarea:invalid {
+    box-shadow: none;
+}
+
+.select-wrapper {
+    text-decoration: none;
+    display: block;
+    position: relative;
+}
+
+.select-wrapper:before {
+    -moz-osx-font-smoothing: grayscale;
+    -webkit-font-smoothing: antialiased;
+    font-family: FontAwesome;
+    font-style: normal;
+    font-weight: normal;
+    text-transform: none !important;
+}
+
+.select-wrapper:before {
+    content: '\f078';
+    display: block;
+    height: 2.75em;
+    line-height: 2.75em;
+    pointer-events: none;
+    position: absolute;
+    right: 0;
+    text-align: center;
+    top: 0;
+    width: 2.75em;
+}
+
+.select-wrapper select::-ms-expand {
+    display: none;
+}
+
+input[type="text"],
+input[type="password"],
+input[type="email"],
+select {
+    height: 2.75em;
+}
+
+textarea {
+    padding: 0.75em 1em;
+}
+
+input[type="checkbox"],
+input[type="radio"] {
+    -moz-appearance: none;
+    -webkit-appearance: none;
+    -ms-appearance: none;
+    appearance: none;
+    display: block;
+    float: left;
+    margin-right: -2em;
+    opacity: 0;
+    width: 1em;
+    z-index: -1;
+}
+
+input[type="checkbox"] + label,
+input[type="radio"] + label {
+    text-decoration: none;
+    cursor: pointer;
+    display: inline-block;
+    font-size: 1em;
+    font-weight: normal;
+    padding-left: 2.4em;
+    padding-right: 0.75em;
+    position: relative;
+}
+
+input[type="checkbox"] + label:before,
+input[type="radio"] + label:before {
+    -moz-osx-font-smoothing: grayscale;
+    -webkit-font-smoothing: antialiased;
+    font-family: FontAwesome;
+    font-style: normal;
+    font-weight: normal;
+    text-transform: none !important;
+}
+
+input[type="checkbox"] + label:before,
+input[type="radio"] + label:before {
+    border-radius: 0;
+    border: solid 1px;
+    content: '';
+    display: inline-block;
+    height: 1.65em;
+    left: 0;
+    line-height: 1.58125em;
+    position: absolute;
+    text-align: center;
+    top: 0;
+    width: 1.65em;
+}
+
+input[type="checkbox"]:checked + label:before,
+input[type="radio"]:checked + label:before {
+    content: '\f00c';
+}
+
+input[type="checkbox"] + label:before {
+    border-radius: 0;
+}
+
+input[type="radio"] + label:before {
+    border-radius: 100%;
+}
+
+::-webkit-input-placeholder {
+    opacity: 1.0;
+}
+
+:-moz-placeholder {
+    opacity: 1.0;
+}
+
+::-moz-placeholder {
+    opacity: 1.0;
+}
+
+:-ms-input-placeholder {
+    opacity: 1.0;
+}
+
+.formerize-placeholder {
+    opacity: 1.0;
+}
+
+label {
+    color: #666;
+}
+
+input[type="text"],
+input[type="password"],
+input[type="email"],
+select,
+textarea {
+    background: rgba(144, 144, 144, 0.075);
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+input[type="text"]:focus,
+input[type="password"]:focus,
+input[type="email"]:focus,
+select:focus,
+textarea:focus {
+    border-color: #00BC8A;
+    box-shadow: 0 0 0 1px #00BC8A;
+}
+
+.select-wrapper:before {
+    color: rgba(144, 144, 144, 0.25);
+}
+
+input[type="checkbox"] + label,
+input[type="radio"] + label {
+    color: #777;
+}
+
+input[type="checkbox"] + label:before,
+input[type="radio"] + label:before {
+    background: rgba(144, 144, 144, 0.075);
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+input[type="checkbox"]:checked + label:before,
+input[type="radio"]:checked + label:before {
+    background-color: #00BC8A;
+    border-color: #00BC8A;
+    color: #ffffff;
+}
+
+input[type="checkbox"]:focus + label:before,
+input[type="radio"]:focus + label:before {
+    border-color: #00BC8A;
+    box-shadow: 0 0 0 1px #00BC8A;
+}
+
+::-webkit-input-placeholder {
+    color: #bbb !important;
+}
+
+:-moz-placeholder {
+    color: #bbb !important;
+}
+
+::-moz-placeholder {
+    color: #bbb !important;
+}
+
+:-ms-input-placeholder {
+    color: #bbb !important;
+}
+
+.formerize-placeholder {
+    color: #bbb !important;
+}
+
+form.alt input[type="text"],
+form.alt input[type="password"],
+form.alt input[type="email"],
+form.alt select,
+form.alt textarea {
+    background-color: transparent;
+}
+
+/* Icon */
+
+.icon {
+    text-decoration: none;
+    border-bottom: none;
+    position: relative;
+}
+
+.icon:before {
+    -moz-osx-font-smoothing: grayscale;
+    -webkit-font-smoothing: antialiased;
+    font-family: FontAwesome;
+    font-style: normal;
+    font-weight: normal;
+    text-transform: none !important;
+}
+
+.icon > .label {
+    display: none;
+}
+
+/* Image */
+
+.image {
+    border-radius: 0;
+    border: 0;
+    display: inline-block;
+    position: relative;
+}
+
+.image img {
+    border-radius: 0;
+    display: block;
+}
+
+.image.left, .image.right {
+    max-width: 40%;
+}
+
+.image.left img, .image.right img {
+    width: 100%;
+}
+
+.image.left {
+    float: left;
+    margin: 0 1.5em 1em 0;
+    top: 0.25em;
+}
+
+.image.right {
+    float: right;
+    margin: 0 0 1em 1.5em;
+    top: 0.25em;
+}
+
+.image.fit {
+    display: block;
+    margin: 0 0 2em 0;
+    width: 100%;
+}
+
+.image.fit img {
+    width: 100%;
+}
+
+.image.main {
+    display: block;
+    margin: 0 0 3em 0;
+    width: 100%;
+}
+
+.image.main img {
+    width: 100%;
+}
+
+/* List */
+
+ol {
+    list-style: decimal;
+    margin: 0 0 2em 0;
+    padding-left: 1.25em;
+}
+
+ol li {
+    padding-left: 0.25em;
+}
+
+ul {
+    list-style: disc;
+    margin: 0 0 2em 0;
+    padding-left: 1em;
+}
+
+ul li {
+    padding-left: 0.5em;
+    padding-top: 0.5em;
+    line-height: 1.6;
+}
+
+ul li ul li {
+    list-style: square;
+    padding-top: 0.0em;    
+}
+
+ul li ul {
+    margin-top: 0em;
+    margin-bottom: 0em;
+}
+
+ul.special {
+    text-align: center;
+}
+
+ul.alt {
+    list-style: none;
+    padding-left: 0;
+}
+
+ul.alt li {
+    border-top: solid 1px;
+    padding: 0.5em 0;
+}
+
+ul.alt li:first-child {
+    border-top: 0;
+    padding-top: 0;
+}
+
+ul.checklist {
+    list-style: none;
+    padding-left: 0;
+}
+
+ul.checklist li {
+    text-decoration: none;
+    margin-bottom: 1.5em;
+    padding-left: 0;
+}
+
+ul.checklist li:before {
+    -moz-osx-font-smoothing: grayscale;
+    -webkit-font-smoothing: antialiased;
+    font-family: FontAwesome;
+    font-style: normal;
+    font-weight: normal;
+    text-transform: none !important;
+}
+
+ul.checklist li:before {
+    content: '\f00c';
+    margin-right: 0.75em;
+}
+
+ul.icons {
+    cursor: default;
+    list-style: none;
+    padding-left: 0;
+}
+
+ul.icons li {
+    display: inline-block;
+    padding: 0 1em 0 0;
+}
+
+ul.icons li:last-child {
+    padding-right: 0;
+}
+
+ul.icons li .icon:before {
+    font-size: 2em;
+}
+
+ul.contact {
+    list-style: none;
+    padding: 0;
+}
+
+ul.contact li {
+    text-decoration: none;
+    padding: 0.65em 0 0 2em;
+}
+
+ul.contact li:before {
+    -moz-osx-font-smoothing: grayscale;
+    -webkit-font-smoothing: antialiased;
+    font-family: FontAwesome;
+    font-style: normal;
+    font-weight: normal;
+    text-transform: none !important;
+}
+
+ul.contact li:before {
+    float: left;
+    margin-left: -2em;
+}
+
+ul.contact li:first-child {
+    padding-top: 0;
+}
+
+ul.contact li a {
+    color: inherit;
+    text-decoration: none;
+}
+
+ul.actions {
+    cursor: default;
+    list-style: none;
+    padding-left: 0;
+}
+
+form ul.actions {
+    margin-top: 2em;
+}
+
+ul.actions li {
+    display: inline-block;
+    padding: 0 1em 0 0;
+    vertical-align: middle;
+}
+
+ul.actions li:last-child {
+    padding-right: 0;
+}
+
+
+ul.actions.small li {
+    padding: 0 0.5em 0 0;
+}
+
+ul.actions.vertical li {
+    display: block;
+    padding: 1em 0 0 0;
+}
+
+ul.actions.vertical li:first-child {
+    padding-top: 0;
+}
+
+ul.actions.vertical li > * {
+    margin-bottom: 0;
+}
+
+ul.actions.vertical.small li {
+    padding: 0.5em 0 0 0;
+}
+
+ul.actions.vertical.small li:first-child {
+    padding-top: 0;
+}
+
+ul.actions.fit {
+    display: table;
+    margin-left: -1em;
+    padding: 0;
+    table-layout: fixed;
+    width: calc(100% + 1em);
+}
+
+ul.actions.fit li {
+    display: table-cell;
+    padding: 0 0 0 1em;
+}
+
+ul.actions.fit li > * {
+    margin-bottom: 0;
+}
+
+ul.actions.fit.small {
+    margin-left: -0.5em;
+    width: calc(100% + 0.5em);
+}
+
+ul.actions.fit.small li {
+    padding: 0 0 0 0.5em;
+}
+
+
+
+ul.instructors {
+    cursor: default;
+    margin: 0;
+    margin-top: -1em;
+    padding: 0;
+    padding-left: 2em;
+}
+
+ul.instructors li {
+    margin: 0;
+    padding: 0;
+}
+
+ul.instructors li:last-child {
+    padding-right: 0;
+}
+
+
+@media screen and (max-width: 480px) {
+
+    ul.actions {
+	margin: 0 0 2em 0;
+    }
+
+    ul.actions li {
+	padding: 1em 0 0 0;
+	display: block;
+	text-align: center;
+	width: 100%;
+    }
+
+    ul.actions li:first-child {
+	padding-top: 0;
+    }
+
+    ul.actions li > * {
+	width: 100%;
+	margin: 0 !important;
+    }
+
+    ul.actions li > *.icon:before {
+	margin-left: -2em;
+    }
+
+    ul.actions.small li {
+	padding: 0.5em 0 0 0;
+    }
+
+    ul.actions.small li:first-child {
+	padding-top: 0;
+    }
+
+}
+
+dl {
+    margin: 0 0 2em 0;
+}
+
+dl dt {
+    display: block;
+    font-weight: bold;
+    margin: 0 0 1em 0;
+}
+
+dl dd {
+    margin-left: 2em;
+}
+
+ul.alt li {
+    border-top-color: rgba(144, 144, 144, 0.25);
+}
+
+ul.checklist li:before {
+    color: #00BC8A;
+}
+
+ul.contact li:before {
+    color: #00BC8A;
+}
+
+/* Section/Article */
+
+section.special, article.special {
+    text-align: center;
+}
+
+header p {
+    position: relative;
+    margin: 0 0 1.5em 0;
+}
+
+header h2 + p {
+    font-size: 1.25em;
+    margin-top: -1em;
+}
+
+header h3 + p {
+    font-size: 1.1em;
+    margin-top: -0.8em;
+}
+
+header h4 + p,
+header h5 + p,
+header h6 + p {
+    font-size: 0.9em;
+    margin-top: -0.6em;
+}
+
+header p {
+    color: #bbb;
+}
+
+/* Table */
+
+.table-wrapper {
+    -webkit-overflow-scrolling: touch;
+    overflow-x: auto;
+}
+
+table {
+    margin: 0 0 2em 0;
+    width: 100%;
+}
+
+table tbody tr {
+    border: solid 1px;
+    border-left: 0;
+    border-right: 0;
+}
+
+table td {
+    padding: 0.75em 0.75em;
+}
+
+table th {
+    font-size: 0.9em;
+    font-weight: bold;
+    padding: 0 0.75em 0.75em 0.75em;
+    text-align: left;
+}
+
+table thead {
+    border-bottom: solid 2px;
+}
+
+table tfoot {
+    border-top: solid 2px;
+}
+
+table.alt {
+    border-collapse: separate;
+}
+
+table.alt tbody tr td {
+    border: solid 1px;
+    border-left-width: 0;
+    border-top-width: 0;
+}
+
+table.alt tbody tr td:first-child {
+    border-left-width: 1px;
+}
+
+table.alt tbody tr:first-child td {
+    border-top-width: 1px;
+}
+
+table.alt thead {
+    border-bottom: 0;
+}
+
+table.alt tfoot {
+    border-top: 0;
+}
+
+table tbody tr {
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+table tbody tr:nth-child(2n + 1) {
+    background-color: rgba(144, 144, 144, 0.075);
+}
+
+table th {
+    color: #666;
+}
+
+table thead {
+    border-bottom-color: rgba(144, 144, 144, 0.25);
+}
+
+table tfoot {
+    border-top-color: rgba(144, 144, 144, 0.25);
+}
+
+table.alt tbody tr td {
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+/* Split */
+
+.split {
+    display: -moz-flex;
+    display: -webkit-flex;
+    display: -ms-flex;
+    display: flex;
+    -moz-flex-wrap: wrap;
+    -webkit-flex-wrap: wrap;
+    -ms-flex-wrap: wrap;
+    flex-wrap: wrap;
+    padding-bottom: 2em;
+}
+
+.split > * {
+    width: 50%;
+}
+
+.split > *:nth-child(odd) {
+    padding-right: 3em;
+}
+
+.split > *:nth-child(even) {
+    padding-left: 3em;
+    border-left: solid 2px;
+}
+
+.split > * > :last-child,
+.split > * > :last-child > :last-child,
+.split > * > :last-child > :last-child > :last-child {
+    margin-bottom: 0;
+}
+
+@media screen and (max-width: 980px) {
+
+    .split > :nth-child(odd) {
+	padding-right: 2em;
+    }
+
+    .split > :nth-child(even) {
+	padding-left: 2em;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    .split {
+	padding-bottom: 0;
+    }
+
+    .split > * {
+	width: 100%;
+	margin-bottom: 2em;
+    }
+
+    .split > *:nth-child(odd) {
+	padding-right: 0;
+    }
+
+    .split > *:nth-child(even) {
+	border-left: none;
+	padding-left: 0;
+    }
+
+}
+
+.split > :nth-child(even) {
+    border-left-color: rgba(144, 144, 144, 0.25);
+}
+
+/* Spotlight */
+
+.spotlight {
+    display: -moz-flex;
+    display: -webkit-flex;
+    display: -ms-flex;
+    display: flex;
+    flex-wrap: wrap;
+    border-bottom: solid 2px;
+    margin-bottom: 3em;
+}
+
+
+.spotlight .content {
+    padding: 0.5em 3em 0.1em 3em;
+    padding-top: 0em;
+    margin: 0;
+    width: 100%;
+}
+
+.spotlight .contentw {
+    padding: 2em 3em 0.1em 3em;
+    width: 100%;
+}
+
+.spotlight .contentw h2 {
+    margin-bottom: 0.0em;
+}
+
+
+
+@media screen and (max-width: 980px) {
+
+    .spotlight .content {
+	padding: 0.5em 2em 0.1em 2em;
+	width: 95%;
+    }
+
+    .spotlight .contentw {
+	padding: 1.5em 2em 0.1em 2em;
+	width: 100%;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    .spotlight {
+	display: block;
+	margin-bottom: 2em;
+    }
+
+
+    .spotlight .content {
+	padding: 0.5em 2em 0.1em 2em;
+	width: 100%;
+    }
+
+    .spotlight .contentw {
+	padding: 1em 2em 0.1em 2em;
+	width: 100%;
+    }
+
+}
+
+@media screen and (max-width: 980px) {
+
+    .spotlight .content {
+	padding: 0.5em 1.5em 0.1em 1.5em;
+    }
+
+    .spotlight .contentw {
+	padding: 1em 1.5em 0.1em 1.5em;
+    }
+
+}
+
+.spotlight {
+    background-color: white;
+    border-bottom-color: rgba(144, 144, 144, 0.25);
+}
+
+.spotlight .contentw {
+    background-color: #ebebeb;
+    margin-bottom: 1em; 
+}
+
+/* Wrapper */
+
+.wrapper {
+    padding: 5em 0 3em 0;
+    position: relative;
+}
+
+.wrapper > .inner {
+    margin-left: auto;
+    margin-right: auto;
+    width: 70em;
+    max-width: 100%;
+}
+
+.wrapper.alt {
+    border-top: solid 2px;
+    border-top-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style1 {
+    background-color: #00BC8A;
+    color: #bfeee1;
+}
+
+.wrapper.style1 input, .wrapper.style1 select, .wrapper.style1 textarea {
+    color: #ffffff;
+}
+
+.wrapper.style1 a {
+    color: #ffffff;
+}
+
+.wrapper.style1 strong, .wrapper.style1 b {
+    color: #ffffff;
+}
+
+.wrapper.style1 h1, .wrapper.style1 h2, .wrapper.style1 h3, .wrapper.style1 h4, .wrapper.style1 h5, .wrapper.style1 h6 {
+    color: #ffffff;
+}
+
+.wrapper.style1 blockquote {
+    border-left-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 code {
+    background: rgba(255, 255, 255, 0.075);
+    border-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 hr {
+    border-bottom-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 .box {
+    border-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 input[type="submit"],
+.wrapper.style1 input[type="reset"],
+.wrapper.style1 input[type="button"],
+.wrapper.style1 button,
+.wrapper.style1 .button {
+    background-color: #fafafa;
+    color: #666 !important;
+}
+
+.wrapper.style1 input[type="submit"]:hover,
+.wrapper.style1 input[type="reset"]:hover,
+.wrapper.style1 input[type="button"]:hover,
+.wrapper.style1 button:hover,
+.wrapper.style1 .button:hover {
+    background-color: white;
+}
+
+.wrapper.style1 input[type="submit"]:active,
+.wrapper.style1 input[type="reset"]:active,
+.wrapper.style1 input[type="button"]:active,
+.wrapper.style1 button:active,
+.wrapper.style1 .button:active {
+    background-color: #ededed;
+}
+
+.wrapper.style1 input[type="submit"].alt,
+.wrapper.style1 input[type="reset"].alt,
+.wrapper.style1 input[type="button"].alt,
+.wrapper.style1 button.alt,
+.wrapper.style1 .button.alt {
+    background-color: transparent;
+    box-shadow: inset 0 0 0 2px rgba(255, 255, 255, 0.25);
+    color: #ffffff !important;
+}
+
+.wrapper.style1 input[type="submit"].alt:hover,
+.wrapper.style1 input[type="reset"].alt:hover,
+.wrapper.style1 input[type="button"].alt:hover,
+.wrapper.style1 button.alt:hover,
+.wrapper.style1 .button.alt:hover {
+    background-color: rgba(255, 255, 255, 0.075);
+}
+
+.wrapper.style1 input[type="submit"].alt:active,
+.wrapper.style1 input[type="reset"].alt:active,
+.wrapper.style1 input[type="button"].alt:active,
+.wrapper.style1 button.alt:active,
+.wrapper.style1 .button.alt:active {
+    background-color: rgba(255, 255, 255, 0.2);
+}
+
+.wrapper.style1 input[type="submit"].alt.icon:before,
+.wrapper.style1 input[type="reset"].alt.icon:before,
+.wrapper.style1 input[type="button"].alt.icon:before,
+.wrapper.style1 button.alt.icon:before,
+.wrapper.style1 .button.alt.icon:before {
+    color: #99e4d0;
+}
+
+.wrapper.style1 input[type="submit"].special,
+.wrapper.style1 input[type="reset"].special,
+.wrapper.style1 input[type="button"].special,
+.wrapper.style1 button.special,
+.wrapper.style1 .button.special {
+    background-color: #ffffff;
+    color: #00BC8A !important;
+}
+
+.wrapper.style1 label {
+    color: #ffffff;
+}
+
+.wrapper.style1 input[type="text"],
+.wrapper.style1 input[type="password"],
+.wrapper.style1 input[type="email"],
+.wrapper.style1 select,
+.wrapper.style1 textarea {
+    background: rgba(255, 255, 255, 0.075);
+    border-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 input[type="text"]:focus,
+.wrapper.style1 input[type="password"]:focus,
+.wrapper.style1 input[type="email"]:focus,
+.wrapper.style1 select:focus,
+.wrapper.style1 textarea:focus {
+    border-color: #ffffff;
+    box-shadow: 0 0 0 1px #ffffff;
+}
+
+.wrapper.style1 .select-wrapper:before {
+    color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 input[type="checkbox"] + label,
+.wrapper.style1 input[type="radio"] + label {
+    color: #bfeee1;
+}
+
+.wrapper.style1 input[type="checkbox"] + label:before,
+.wrapper.style1 input[type="radio"] + label:before {
+    background: rgba(255, 255, 255, 0.075);
+    border-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 input[type="checkbox"]:checked + label:before,
+.wrapper.style1 input[type="radio"]:checked + label:before {
+    background-color: #ffffff;
+    border-color: #ffffff;
+    color: #00BC8A;
+}
+
+.wrapper.style1 input[type="checkbox"]:focus + label:before,
+.wrapper.style1 input[type="radio"]:focus + label:before {
+    border-color: #ffffff;
+    box-shadow: 0 0 0 1px #ffffff;
+}
+
+.wrapper.style1 ::-webkit-input-placeholder {
+    color: #99e4d0 !important;
+}
+
+.wrapper.style1 :-moz-placeholder {
+    color: #99e4d0 !important;
+}
+
+.wrapper.style1 ::-moz-placeholder {
+    color: #99e4d0 !important;
+}
+
+.wrapper.style1 :-ms-input-placeholder {
+    color: #99e4d0 !important;
+}
+
+.wrapper.style1 .formerize-placeholder {
+    color: #99e4d0 !important;
+}
+
+.wrapper.style1 form.alt input[type="text"],
+.wrapper.style1 form.alt input[type="password"],
+.wrapper.style1 form.alt input[type="email"],
+.wrapper.style1 form.alt select,
+.wrapper.style1 form.alt textarea {
+    background-color: transparent;
+}
+
+.wrapper.style1 ul.alt li {
+    border-top-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 ul.checklist li:before {
+    color: #00BC8A;
+}
+
+.wrapper.style1 ul.contact li:before {
+    color: #00BC8A;
+}
+
+.wrapper.style1 header p {
+    color: #99e4d0;
+}
+
+.wrapper.style1 table tbody tr {
+    border-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 table tbody tr:nth-child(2n + 1) {
+    background-color: rgba(255, 255, 255, 0.075);
+}
+
+.wrapper.style1 table th {
+    color: #ffffff;
+}
+
+.wrapper.style1 table thead {
+    border-bottom-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 table tfoot {
+    border-top-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 table.alt tbody tr td {
+    border-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 .split > :nth-child(even) {
+    border-left-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1 .spotlight {
+    background-color: #0affbe;
+    border-bottom-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style1.alt {
+    border-top-color: rgba(255, 255, 255, 0.25);
+}
+
+.wrapper.style2 {
+    background-color: #fafafa;
+    color: #777;
+}
+
+.wrapper.style2 input, .wrapper.style2 select, .wrapper.style2 textarea {
+    color: #666;
+}
+
+.wrapper.style2 a {
+    color: #00BC8A;
+}
+
+.wrapper.style2 strong, .wrapper.style2 b {
+    color: #666;
+}
+
+.wrapper.style2 h1, .wrapper.style2 h2, .wrapper.style2 h3, .wrapper.style2 h4, .wrapper.style2 h5, .wrapper.style2 h6 {
+    color: #666;
+}
+
+.wrapper.style2 blockquote {
+    border-left-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 code {
+    background: rgba(144, 144, 144, 0.075);
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 hr {
+    border-bottom-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 .box {
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 input[type="submit"],
+.wrapper.style2 input[type="reset"],
+.wrapper.style2 input[type="button"],
+.wrapper.style2 button,
+.wrapper.style2 .button {
+    background-color: #666;
+    color: #fafafa !important;
+}
+
+.wrapper.style2 input[type="submit"].alt,
+.wrapper.style2 input[type="reset"].alt,
+.wrapper.style2 input[type="button"].alt,
+.wrapper.style2 button.alt,
+.wrapper.style2 .button.alt {
+    background-color: transparent;
+    box-shadow: inset 0 0 0 2px rgba(144, 144, 144, 0.25);
+    color: #666 !important;
+}
+
+.wrapper.style2 input[type="submit"].alt:hover,
+.wrapper.style2 input[type="reset"].alt:hover,
+.wrapper.style2 input[type="button"].alt:hover,
+.wrapper.style2 button.alt:hover,
+.wrapper.style2 .button.alt:hover {
+    background-color: rgba(144, 144, 144, 0.075);
+}
+
+.wrapper.style2 input[type="submit"].alt:active,
+.wrapper.style2 input[type="reset"].alt:active,
+.wrapper.style2 input[type="button"].alt:active,
+.wrapper.style2 button.alt:active,
+.wrapper.style2 .button.alt:active {
+    background-color: rgba(144, 144, 144, 0.2);
+}
+
+.wrapper.style2 input[type="submit"].alt.icon:before,
+.wrapper.style2 input[type="reset"].alt.icon:before,
+.wrapper.style2 input[type="button"].alt.icon:before,
+.wrapper.style2 button.alt.icon:before,
+.wrapper.style2 .button.alt.icon:before {
+    color: #bbb;
+}
+
+.wrapper.style2 input[type="submit"].special,
+.wrapper.style2 input[type="reset"].special,
+.wrapper.style2 input[type="button"].special,
+.wrapper.style2 button.special,
+.wrapper.style2 .button.special {
+    background-color: #00BC8A;
+    color: #ffffff !important;
+}
+
+.wrapper.style2 input[type="submit"].special:hover,
+.wrapper.style2 input[type="reset"].special:hover,
+.wrapper.style2 input[type="button"].special:hover,
+.wrapper.style2 button.special:hover,
+.wrapper.style2 .button.special:hover {
+    background-color: #00d69d;
+}
+
+.wrapper.style2 input[type="submit"].special:active,
+.wrapper.style2 input[type="reset"].special:active,
+.wrapper.style2 input[type="button"].special:active,
+.wrapper.style2 button.special:active,
+.wrapper.style2 .button.special:active {
+    background-color: #00a377;
+}
+
+.wrapper.style2 label {
+    color: #666;
+}
+
+.wrapper.style2 input[type="text"],
+.wrapper.style2 input[type="password"],
+.wrapper.style2 input[type="email"],
+.wrapper.style2 select,
+.wrapper.style2 textarea {
+    background: rgba(144, 144, 144, 0.075);
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 input[type="text"]:focus,
+.wrapper.style2 input[type="password"]:focus,
+.wrapper.style2 input[type="email"]:focus,
+.wrapper.style2 select:focus,
+.wrapper.style2 textarea:focus {
+    border-color: #00BC8A;
+    box-shadow: 0 0 0 1px #00BC8A;
+}
+
+.wrapper.style2 .select-wrapper:before {
+    color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 input[type="checkbox"] + label,
+.wrapper.style2 input[type="radio"] + label {
+    color: #777;
+}
+
+.wrapper.style2 input[type="checkbox"] + label:before,
+.wrapper.style2 input[type="radio"] + label:before {
+    background: rgba(144, 144, 144, 0.075);
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 input[type="checkbox"]:checked + label:before,
+.wrapper.style2 input[type="radio"]:checked + label:before {
+    background-color: #00BC8A;
+    border-color: #00BC8A;
+    color: #ffffff;
+}
+
+.wrapper.style2 input[type="checkbox"]:focus + label:before,
+.wrapper.style2 input[type="radio"]:focus + label:before {
+    border-color: #00BC8A;
+    box-shadow: 0 0 0 1px #00BC8A;
+}
+
+.wrapper.style2 ::-webkit-input-placeholder {
+    color: #bbb !important;
+}
+
+.wrapper.style2 :-moz-placeholder {
+    color: #bbb !important;
+}
+
+.wrapper.style2 ::-moz-placeholder {
+    color: #bbb !important;
+}
+
+.wrapper.style2 :-ms-input-placeholder {
+    color: #bbb !important;
+}
+
+.wrapper.style2 .formerize-placeholder {
+    color: #bbb !important;
+}
+
+.wrapper.style2 form.alt input[type="text"],
+.wrapper.style2 form.alt input[type="password"],
+.wrapper.style2 form.alt input[type="email"],
+.wrapper.style2 form.alt select,
+.wrapper.style2 form.alt textarea {
+    background-color: transparent;
+}
+
+.wrapper.style2 ul.alt li {
+    border-top-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 ul.checklist li:before {
+    color: #00BC8A;
+}
+
+.wrapper.style2 ul.contact li:before {
+    color: #00BC8A;
+}
+
+.wrapper.style2 header p {
+    color: #bbb;
+}
+
+.wrapper.style2 table tbody tr {
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 table tbody tr:nth-child(2n + 1) {
+    background-color: rgba(144, 144, 144, 0.075);
+}
+
+.wrapper.style2 table th {
+    color: #666;
+}
+
+.wrapper.style2 table thead {
+    border-bottom-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 table tfoot {
+    border-top-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 table.alt tbody tr td {
+    border-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 .split > :nth-child(even) {
+    border-left-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2 .spotlight {
+    background-color: white;
+    border-bottom-color: rgba(144, 144, 144, 0.25);
+}
+
+.wrapper.style2.alt {
+    border-top-color: rgba(144, 144, 144, 0.25);
+}
+
+@media screen and (max-width: 1366px) {
+
+    .wrapper {
+	padding: 4em 2.5em 2em 2.5em;
+    }
+
+}
+
+@media screen and (max-width: 980px) {
+
+    .wrapper {
+	padding: 4em 2em 2em 2em;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    .wrapper {
+	padding: 3em 2em 1em 2em;
+    }
+
+}
+
+@media screen and (max-width: 480px) {
+
+    .wrapper {
+	padding: 3em 2em 1em 2em;
+    }
+
+}
+
+/* Banner */
+
+#banner {
+    background-image: url("images/0101_banner.png"), -moz-linear-gradient(45deg, #4A4771 0%, #7C3875 100%);
+    background-image: url("images/0101_banner.png"), -webkit-linear-gradient(45deg, #4A4771 0%, #7C3875 100%);
+    background-image: url("images/0101_banner.png"), -ms-linear-gradient(45deg, #4A4771 0%, #7C3875 100%);
+    background-image: url("images/0101_banner.png"), linear-gradient(45deg, #4A4771 0%, #7C3875 100%);
+    background-size: cover;
+    background-position: center; 
+    color: rgba(255, 255, 255, 0.7);
+    padding: 14em 0 4em;
+}
+
+#banner h2 {
+    font-size: 2.5em;
+    line-height: 1.3;
+    margin-bottom: 0;
+    color: #fff;
+}
+
+#banner h3 {
+    font-size: 1.5em;
+    line-height: 1.2;
+    margin-bottom: 0;
+    color: #eee;
+}
+
+#banner p {
+    font-size: 1.15em;
+    font-weight: bold;
+    line-height: 1.5em;
+    margin-bottom: 1.5em;
+}
+
+#banner > .inner {
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 70em;
+    width: 100%;
+}
+
+
+#banner a {
+    color: #ffffff;
+}
+
+@media screen and (max-width: 1366px) {
+
+    #banner {
+	padding: 10em 2.5em 3em;
+    }
+
+    #banner h2 {
+	font-size: 2em;
+    }
+
+}
+
+@media screen and (max-width: 980px) {
+
+    #banner {
+	padding: 10em 2em 2em;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    #banner {
+	padding: 8em 2em 2em;
+    }
+
+    #banner h2 {
+	font-size: 1.75em;
+    }
+
+    #banner p {
+	font-size: 1.1em;
+    }
+
+}
+
+@media screen and (max-width: 480px) {
+
+    #banner {
+	padding: 8em 2em 2em;
+    }
+
+}
+
+/* Main */
+
+#main {
+    padding: 4em 0 2em 0;
+}
+
+@media screen and (max-width: 736px) {
+
+    #main {
+	padding: 3em 0 1em 0;
+    }
+
+}
+
+/* Footer */
+
+#footer {
+    background-color: #fafafa;
+    border-top: solid 2px rgba(144, 144, 144, 0.25);
+    color: #777;
+}
+
+#footer .copyright {
+    color: #bbb;
+    font-size: 0.9em;
+    padding: 4em 2em;
+    text-align: right;
+}
+
+#footer .image {
+    color: #bbb;
+    font-size: 0.9em;
+    padding: 1em 1em;
+    text-align: left;
+}
+
+#footer .image.left {
+    float: left;
+    margin: 0 1.5em 1em 0;
+    top: 0.25em;
+}
+
+#footer .copyright li {
+    border-left: solid 1px rgba(144, 144, 144, 0.25);
+    display: inline-block;
+    list-style: none;
+    margin-left: 1.5em;
+    padding-left: 1.5em;
+}
+
+#footer .copyright li:first-child {
+    border-left: 0;
+    margin-left: 0;
+    padding-left: 0;
+}
+
+#footer a {
+    color: inherit;
+    text-decoration: none;
+}
+
+@media screen and (max-width: 980px) {
+
+    #footer .copyright {
+	padding: 3em 2em;
+    }
+
+}
+
+@media screen and (max-width: 736px) {
+
+    #footer .copyright li {
+	display: block;
+	border-left: 0;
+	margin-left: 0;
+	padding-left: 0;
+    }
+
+}
+
+@media screen and (max-width: 480px) {
+
+    #footer .copyright {
+	padding: 2em 1em;
+    }
+
+}
+
+
+
+label {
+        text-align: right;
+        float: right;
+}
+
+label:hover { text-decoration:underline }
+input {
+	position: absolute;
+	left: -999em
+}
+.hide {
+	width: 100%;
+        min-height: 0em;
+	max-height: 120em;
+	opacity: 1;
+	height: auto;
+	overflow: hidden;
+	transition: opacity 0.25s linear, max-height 0.25s linear;
+}
+
+input[type=checkbox]:checked + div {
+	opacity:0;
+	max-height:0;
+	border:none;
+}
+
+.follow{border-top: 1px solid blue; margin: 0}
+
+.classnote  {
+    line-height: 1.75em;
+    border: 1px solid black;
+    padding: 1em;
+    margin-bottom: 1em;
+    padding-bottom: 0.5em;
+    background: #ffcccc;
+    color: black;
+}
+
+.getpython p {
+    margin-bottom: 1em;
+}
+
+.getpython img {
+    margin: auto;
+    display: block;
+    max-width: 100%;
+}
+
+p.center {
+    text-align: center;
+}
+
+
+.getgit {
+    padding: 1em;
+    background: #ffff66;
+    color: black;
+    border: 1px solid black;
+}
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index 00000000..f7936cc1
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diff --git a/docs/assets/fonts/fontawesome-webfont.eot b/docs/assets/fonts/fontawesome-webfont.eot
new file mode 100644
index 00000000..33b2bb80
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diff --git a/docs/assets/fonts/fontawesome-webfont.svg b/docs/assets/fonts/fontawesome-webfont.svg
new file mode 100644
index 00000000..1ee89d43
--- /dev/null
+++ b/docs/assets/fonts/fontawesome-webfont.svg
@@ -0,0 +1,565 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1">
+<metadata></metadata>
+<defs>
+<font id="fontawesomeregular" horiz-adv-x="1536" >
+<font-face units-per-em="1792" ascent="1536" descent="-256" />
+<missing-glyph horiz-adv-x="448" />
+<glyph unicode=" "  horiz-adv-x="448" />
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diff --git a/docs/assets/js/main.js b/docs/assets/js/main.js
new file mode 100644
index 00000000..29dd0097
--- /dev/null
+++ b/docs/assets/js/main.js
@@ -0,0 +1,44 @@
+/*
+	Prism by TEMPLATED
+	templated.co @templatedco
+	Released for free under the Creative Commons Attribution 3.0 license (templated.co/license)
+*/
+
+(function($) {
+
+	skel.breakpoints({
+		xlarge:	'(max-width: 1680px)',
+		large:	'(max-width: 1280px)',
+		medium:	'(max-width: 980px)',
+		small:	'(max-width: 736px)',
+		xsmall:	'(max-width: 480px)'
+	});
+
+	$(function() {
+
+		var	$window = $(window),
+			$body = $('body');
+
+		// Disable animations/transitions until the page has loaded.
+			$body.addClass('is-loading');
+
+			$window.on('load', function() {
+				window.setTimeout(function() {
+					$body.removeClass('is-loading');
+				}, 100);
+			});
+
+		// Fix: Placeholder polyfill.
+			$('form').placeholder();
+
+		// Prioritize "important" elements on medium.
+			skel.on('+medium -medium', function() {
+				$.prioritize(
+					'.important\\28 medium\\29',
+					skel.breakpoint('medium').active
+				);
+			});
+
+	});
+
+})(jQuery);
\ No newline at end of file
diff --git a/docs/assets/js/skel.min.js b/docs/assets/js/skel.min.js
new file mode 100644
index 00000000..4f6a6749
--- /dev/null
+++ b/docs/assets/js/skel.min.js
@@ -0,0 +1,2 @@
+/* skel.js v3.0.0 | (c) n33 | skel.io | MIT licensed */
+var skel=function(){"use strict";var t={breakpointIds:null,events:{},isInit:!1,obj:{attachments:{},breakpoints:{},head:null,states:{}},sd:"/",state:null,stateHandlers:{},stateId:"",vars:{},DOMReady:null,indexOf:null,isArray:null,iterate:null,matchesMedia:null,extend:function(e,n){t.iterate(n,function(i){t.isArray(n[i])?(t.isArray(e[i])||(e[i]=[]),t.extend(e[i],n[i])):"object"==typeof n[i]?("object"!=typeof e[i]&&(e[i]={}),t.extend(e[i],n[i])):e[i]=n[i]})},newStyle:function(t){var e=document.createElement("style");return e.type="text/css",e.innerHTML=t,e},_canUse:null,canUse:function(e){t._canUse||(t._canUse=document.createElement("div"));var n=t._canUse.style,i=e.charAt(0).toUpperCase()+e.slice(1);return e in n||"Moz"+i in n||"Webkit"+i in n||"O"+i in n||"ms"+i in n},on:function(e,n){var i=e.split(/[\s]+/);return t.iterate(i,function(e){var a=i[e];if(t.isInit){if("init"==a)return void n();if("change"==a)n();else{var r=a.charAt(0);if("+"==r||"!"==r){var o=a.substring(1);if(o in 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diff --git a/docs/assets/js/util.js b/docs/assets/js/util.js
new file mode 100644
index 00000000..ecf7b371
--- /dev/null
+++ b/docs/assets/js/util.js
@@ -0,0 +1,587 @@
+(function($) {
+
+	/**
+	 * Generate an indented list of links from a nav. Meant for use with panel().
+	 * @return {jQuery} jQuery object.
+	 */
+	$.fn.navList = function() {
+
+		var	$this = $(this);
+			$a = $this.find('a'),
+			b = [];
+
+		$a.each(function() {
+
+			var	$this = $(this),
+				indent = Math.max(0, $this.parents('li').length - 1),
+				href = $this.attr('href'),
+				target = $this.attr('target');
+
+			b.push(
+				'<a ' +
+					'class="link depth-' + indent + '"' +
+					( (typeof target !== 'undefined' && target != '') ? ' target="' + target + '"' : '') +
+					( (typeof href !== 'undefined' && href != '') ? ' href="' + href + '"' : '') +
+				'>' +
+					'<span class="indent-' + indent + '"></span>' +
+					$this.text() +
+				'</a>'
+			);
+
+		});
+
+		return b.join('');
+
+	};
+
+	/**
+	 * Panel-ify an element.
+	 * @param {object} userConfig User config.
+	 * @return {jQuery} jQuery object.
+	 */
+	$.fn.panel = function(userConfig) {
+
+		// No elements?
+			if (this.length == 0)
+				return $this;
+
+		// Multiple elements?
+			if (this.length > 1) {
+
+				for (var i=0; i < this.length; i++)
+					$(this[i]).panel(userConfig);
+
+				return $this;
+
+			}
+
+		// Vars.
+			var	$this = $(this),
+				$body = $('body'),
+				$window = $(window),
+				id = $this.attr('id'),
+				config;
+
+		// Config.
+			config = $.extend({
+
+				// Delay.
+					delay: 0,
+
+				// Hide panel on link click.
+					hideOnClick: false,
+
+				// Hide panel on escape keypress.
+					hideOnEscape: false,
+
+				// Hide panel on swipe.
+					hideOnSwipe: false,
+
+				// Reset scroll position on hide.
+					resetScroll: false,
+
+				// Reset forms on hide.
+					resetForms: false,
+
+				// Side of viewport the panel will appear.
+					side: null,
+
+				// Target element for "class".
+					target: $this,
+
+				// Class to toggle.
+					visibleClass: 'visible'
+
+			}, userConfig);
+
+			// Expand "target" if it's not a jQuery object already.
+				if (typeof config.target != 'jQuery')
+					config.target = $(config.target);
+
+		// Panel.
+
+			// Methods.
+				$this._hide = function(event) {
+
+					// Already hidden? Bail.
+						if (!config.target.hasClass(config.visibleClass))
+							return;
+
+					// If an event was provided, cancel it.
+						if (event) {
+
+							event.preventDefault();
+							event.stopPropagation();
+
+						}
+
+					// Hide.
+						config.target.removeClass(config.visibleClass);
+
+					// Post-hide stuff.
+						window.setTimeout(function() {
+
+							// Reset scroll position.
+								if (config.resetScroll)
+									$this.scrollTop(0);
+
+							// Reset forms.
+								if (config.resetForms)
+									$this.find('form').each(function() {
+										this.reset();
+									});
+
+						}, config.delay);
+
+				};
+
+			// Vendor fixes.
+				$this
+					.css('-ms-overflow-style', '-ms-autohiding-scrollbar')
+					.css('-webkit-overflow-scrolling', 'touch');
+
+			// Hide on click.
+				if (config.hideOnClick) {
+
+					$this.find('a')
+						.css('-webkit-tap-highlight-color', 'rgba(0,0,0,0)');
+
+					$this
+						.on('click', 'a', function(event) {
+
+							var $a = $(this),
+								href = $a.attr('href'),
+								target = $a.attr('target');
+
+							if (!href || href == '#' || href == '' || href == '#' + id)
+								return;
+
+							// Cancel original event.
+								event.preventDefault();
+								event.stopPropagation();
+
+							// Hide panel.
+								$this._hide();
+
+							// Redirect to href.
+								window.setTimeout(function() {
+
+									if (target == '_blank')
+										window.open(href);
+									else
+										window.location.href = href;
+
+								}, config.delay + 10);
+
+						});
+
+				}
+
+			// Event: Touch stuff.
+				$this.on('touchstart', function(event) {
+
+					$this.touchPosX = event.originalEvent.touches[0].pageX;
+					$this.touchPosY = event.originalEvent.touches[0].pageY;
+
+				})
+
+				$this.on('touchmove', function(event) {
+
+					if ($this.touchPosX === null
+					||	$this.touchPosY === null)
+						return;
+
+					var	diffX = $this.touchPosX - event.originalEvent.touches[0].pageX,
+						diffY = $this.touchPosY - event.originalEvent.touches[0].pageY,
+						th = $this.outerHeight(),
+						ts = ($this.get(0).scrollHeight - $this.scrollTop());
+
+					// Hide on swipe?
+						if (config.hideOnSwipe) {
+
+							var result = false,
+								boundary = 20,
+								delta = 50;
+
+							switch (config.side) {
+
+								case 'left':
+									result = (diffY < boundary && diffY > (-1 * boundary)) && (diffX > delta);
+									break;
+
+								case 'right':
+									result = (diffY < boundary && diffY > (-1 * boundary)) && (diffX < (-1 * delta));
+									break;
+
+								case 'top':
+									result = (diffX < boundary && diffX > (-1 * boundary)) && (diffY > delta);
+									break;
+
+								case 'bottom':
+									result = (diffX < boundary && diffX > (-1 * boundary)) && (diffY < (-1 * delta));
+									break;
+
+								default:
+									break;
+
+							}
+
+							if (result) {
+
+								$this.touchPosX = null;
+								$this.touchPosY = null;
+								$this._hide();
+
+								return false;
+
+							}
+
+						}
+
+					// Prevent vertical scrolling past the top or bottom.
+						if (($this.scrollTop() < 0 && diffY < 0)
+						|| (ts > (th - 2) && ts < (th + 2) && diffY > 0)) {
+
+							event.preventDefault();
+							event.stopPropagation();
+
+						}
+
+				});
+
+			// Event: Prevent certain events inside the panel from bubbling.
+				$this.on('click touchend touchstart touchmove', function(event) {
+					event.stopPropagation();
+				});
+
+			// Event: Hide panel if a child anchor tag pointing to its ID is clicked.
+				$this.on('click', 'a[href="#' + id + '"]', function(event) {
+
+					event.preventDefault();
+					event.stopPropagation();
+
+					config.target.removeClass(config.visibleClass);
+
+				});
+
+		// Body.
+
+			// Event: Hide panel on body click/tap.
+				$body.on('click touchend', function(event) {
+					$this._hide(event);
+				});
+
+			// Event: Toggle.
+				$body.on('click', 'a[href="#' + id + '"]', function(event) {
+
+					event.preventDefault();
+					event.stopPropagation();
+
+					config.target.toggleClass(config.visibleClass);
+
+				});
+
+		// Window.
+
+			// Event: Hide on ESC.
+				if (config.hideOnEscape)
+					$window.on('keydown', function(event) {
+
+						if (event.keyCode == 27)
+							$this._hide(event);
+
+					});
+
+		return $this;
+
+	};
+
+	/**
+	 * Apply "placeholder" attribute polyfill to one or more forms.
+	 * @return {jQuery} jQuery object.
+	 */
+	$.fn.placeholder = function() {
+
+		// Browser natively supports placeholders? Bail.
+			if (typeof (document.createElement('input')).placeholder != 'undefined')
+				return $(this);
+
+		// No elements?
+			if (this.length == 0)
+				return $this;
+
+		// Multiple elements?
+			if (this.length > 1) {
+
+				for (var i=0; i < this.length; i++)
+					$(this[i]).placeholder();
+
+				return $this;
+
+			}
+
+		// Vars.
+			var $this = $(this);
+
+		// Text, TextArea.
+			$this.find('input[type=text],textarea')
+				.each(function() {
+
+					var i = $(this);
+
+					if (i.val() == ''
+					||  i.val() == i.attr('placeholder'))
+						i
+							.addClass('polyfill-placeholder')
+							.val(i.attr('placeholder'));
+
+				})
+				.on('blur', function() {
+
+					var i = $(this);
+
+					if (i.attr('name').match(/-polyfill-field$/))
+						return;
+
+					if (i.val() == '')
+						i
+							.addClass('polyfill-placeholder')
+							.val(i.attr('placeholder'));
+
+				})
+				.on('focus', function() {
+
+					var i = $(this);
+
+					if (i.attr('name').match(/-polyfill-field$/))
+						return;
+
+					if (i.val() == i.attr('placeholder'))
+						i
+							.removeClass('polyfill-placeholder')
+							.val('');
+
+				});
+
+		// Password.
+			$this.find('input[type=password]')
+				.each(function() {
+
+					var i = $(this);
+					var x = $(
+								$('<div>')
+									.append(i.clone())
+									.remove()
+									.html()
+									.replace(/type="password"/i, 'type="text"')
+									.replace(/type=password/i, 'type=text')
+					);
+
+					if (i.attr('id') != '')
+						x.attr('id', i.attr('id') + '-polyfill-field');
+
+					if (i.attr('name') != '')
+						x.attr('name', i.attr('name') + '-polyfill-field');
+
+					x.addClass('polyfill-placeholder')
+						.val(x.attr('placeholder')).insertAfter(i);
+
+					if (i.val() == '')
+						i.hide();
+					else
+						x.hide();
+
+					i
+						.on('blur', function(event) {
+
+							event.preventDefault();
+
+							var x = i.parent().find('input[name=' + i.attr('name') + '-polyfill-field]');
+
+							if (i.val() == '') {
+
+								i.hide();
+								x.show();
+
+							}
+
+						});
+
+					x
+						.on('focus', function(event) {
+
+							event.preventDefault();
+
+							var i = x.parent().find('input[name=' + x.attr('name').replace('-polyfill-field', '') + ']');
+
+							x.hide();
+
+							i
+								.show()
+								.focus();
+
+						})
+						.on('keypress', function(event) {
+
+							event.preventDefault();
+							x.val('');
+
+						});
+
+				});
+
+		// Events.
+			$this
+				.on('submit', function() {
+
+					$this.find('input[type=text],input[type=password],textarea')
+						.each(function(event) {
+
+							var i = $(this);
+
+							if (i.attr('name').match(/-polyfill-field$/))
+								i.attr('name', '');
+
+							if (i.val() == i.attr('placeholder')) {
+
+								i.removeClass('polyfill-placeholder');
+								i.val('');
+
+							}
+
+						});
+
+				})
+				.on('reset', function(event) {
+
+					event.preventDefault();
+
+					$this.find('select')
+						.val($('option:first').val());
+
+					$this.find('input,textarea')
+						.each(function() {
+
+							var i = $(this),
+								x;
+
+							i.removeClass('polyfill-placeholder');
+
+							switch (this.type) {
+
+								case 'submit':
+								case 'reset':
+									break;
+
+								case 'password':
+									i.val(i.attr('defaultValue'));
+
+									x = i.parent().find('input[name=' + i.attr('name') + '-polyfill-field]');
+
+									if (i.val() == '') {
+										i.hide();
+										x.show();
+									}
+									else {
+										i.show();
+										x.hide();
+									}
+
+									break;
+
+								case 'checkbox':
+								case 'radio':
+									i.attr('checked', i.attr('defaultValue'));
+									break;
+
+								case 'text':
+								case 'textarea':
+									i.val(i.attr('defaultValue'));
+
+									if (i.val() == '') {
+										i.addClass('polyfill-placeholder');
+										i.val(i.attr('placeholder'));
+									}
+
+									break;
+
+								default:
+									i.val(i.attr('defaultValue'));
+									break;
+
+							}
+						});
+
+				});
+
+		return $this;
+
+	};
+
+	/**
+	 * Moves elements to/from the first positions of their respective parents.
+	 * @param {jQuery} $elements Elements (or selector) to move.
+	 * @param {bool} condition If true, moves elements to the top. Otherwise, moves elements back to their original locations.
+	 */
+	$.prioritize = function($elements, condition) {
+
+		var key = '__prioritize';
+
+		// Expand $elements if it's not already a jQuery object.
+			if (typeof $elements != 'jQuery')
+				$elements = $($elements);
+
+		// Step through elements.
+			$elements.each(function() {
+
+				var	$e = $(this), $p,
+					$parent = $e.parent();
+
+				// No parent? Bail.
+					if ($parent.length == 0)
+						return;
+
+				// Not moved? Move it.
+					if (!$e.data(key)) {
+
+						// Condition is false? Bail.
+							if (!condition)
+								return;
+
+						// Get placeholder (which will serve as our point of reference for when this element needs to move back).
+							$p = $e.prev();
+
+							// Couldn't find anything? Means this element's already at the top, so bail.
+								if ($p.length == 0)
+									return;
+
+						// Move element to top of parent.
+							$e.prependTo($parent);
+
+						// Mark element as moved.
+							$e.data(key, $p);
+
+					}
+
+				// Moved already?
+					else {
+
+						// Condition is true? Bail.
+							if (condition)
+								return;
+
+						$p = $e.data(key);
+
+						// Move element back to its original location (using our placeholder).
+							$e.insertAfter($p);
+
+						// Unmark element as moved.
+							$e.removeData(key);
+
+					}
+
+			});
+
+	};
+
+})(jQuery);
\ No newline at end of file
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new file mode 100644
index 00000000..f1ab84b5
--- /dev/null
+++ b/docs/index.html
@@ -0,0 +1,749 @@
+<!DOCTYPE HTML>
+<!--
+    Prism by TEMPLATED
+    templated.co @templatedco
+    Released for free under the Creative Commons Attribution 3.0 license (templated.co/license)
+  -->
+<html lang="en">
+  <head>
+    <title>PHY 546: Python for Scientific Computing</title>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <!--[if lte IE 8]><script src="assets/js/ie/html5shiv.js"></script><![endif]-->
+    <link rel="stylesheet" href="assets/css/main.css" />
+    <!--[if lte IE 9]><link rel="stylesheet" href="assets/css/ie9.css" /><![endif]-->
+  </head>
+  <body>
+
+    <!-- Banner -->
+    <section id="banner">
+      <div class="inner split">
+	<section>
+	  <h2>PHY 546: Python for Scientific Computing</h2>
+	  <h3>Spring 2018</h3>
+
+	</section>
+	<section>
+	  <p>a weekly graduate seminar on techniques for scientific programming</p>
+	    <p>Instructor: <a href="http://www.astro.sunysb.edu/mzingale/">Michael Zingale</a></p>
+	  <p><ul class="actions">
+	    <li><a href="syllabus.pdf" class="button special">syllabus</a></li>
+	  </ul>
+
+	</section>
+      </div>
+    </section>
+
+
+    <!-- One -->
+    <section id="one" class="wrapper">
+      <div class="inner">
+        <section>
+
+	  <div class="getpython">
+
+            <img src="images/python.png" alt="xkcd">
+            <p class="center">xkcd</p>
+          </div>
+
+          <div class="classnote">
+
+	    <p>Some basic programming background, be it C/C++,
+	      Fortran, matlab, mathematica, ..., (enough to understand
+	      the logic of programming, control statements, basic data
+	      structures, etc.) is assumed.
+
+	    <p>This is a 1-credit class.  The primary
+	      method of evaluation is class participation.
+
+	    <p>To make the most of this class, you should have python 3.x
+	      installed on a laptop that you can bring to the seminar.
+	      On Linux machines, you can get python and the needed
+	      libraries through your package manager.  For Mac and
+	      Windows, you might want to consider the free
+	      distributions provided
+	      by <a href="https://www.enthought.com/products/canopy/">Enthought
+	      Canopy</a>
+	      or <a href="https://store.continuum.io/cshop/anaconda/">Anaconda</a>.
+	      These both install everything you need.
+
+          </div>
+
+	  <div class="getgit">
+	    All of the course material (Jupyter notebooks, examples,
+	    etc.) are available on the course github
+	    page: <a href="https://github.com/sbu-python-class/python-science">https://github.com/sbu-python-class/python-science</a>
+	  </div>
+	</section>
+      </div>
+    </section>
+
+    <!-- Two -->
+    <section id="two" class="wrapper style2 alt">
+
+      <div class="inner">
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Introduction to Python</h2>
+	    <p>The core language, data structures, control flow, functions, classes...
+              <label for="item-1">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="one" id="item-1" checked>
+
+	  <div class="content hide">
+
+            Lecture slides:
+            <ul>
+              <li><a href="./lectures/python.pdf">python.pdf</a></li>
+            </ul>
+
+	    Jupyter notebooks:
+	    <ul>
+              <li>Week 1:
+                <ul>
+                  <li>(in class) datatypes: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w1-python-datatypes.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w1-python-datatypes.ipynb">w1-python-datatypes.ipynb</a></li>
+                </ul>
+              </li>
+              <li>Week 2:
+                <ul>
+                  <li>(outside class) advanced datatypes: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w2-python-advanced-datatypes.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w2-python-advanced-datatypes.ipynb">w2-python-advanced-datatypes.ipynb</a></li>
+
+                  <li> (outside class) control
+		    flow: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w2-python-control-flow.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w2-python-control-flow.ipynb">w2-python-control-flow.ipynb</a></li>
+
+	          <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w2-python-exercises.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w2-python-exercises.ipynb">w2-python-exercises.ipynb</a></li>
+                </ul>
+              </li>
+              <li>Week 3:
+                <ul>
+                  <li>(outside class) functions: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w3-python-functions.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w3-python-functions.ipynb">w3-python-functions.ipynb</a></li>
+
+                  <li>(outside class) exceptions: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w3-python-exceptions.ipynb">github link</a>,
+	            raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w3-python-exceptions.ipynb">w3-python-exceptions.ipynb</a></li>
+
+	          <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w3-python-exercises.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w3-python-exercises.ipynb">w3-python-exercises.ipynb</a></li>
+                </ul>
+              </li>
+
+              <li>Week 4:
+                <ul>
+                  <li>(outside class) classes: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w4-python-classes.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w4-python-classes.ipynb">w4-python-classes.ipynb</a></li>
+
+                  <li>(outside class) modules: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w4-python-modules.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w4-python-modules.ipynb">w4-python-modules.ipynb</a>;<br>
+		    <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/myprofile.py">[myprofile.py]</a>
+		    (the module example file) </li>
+
+	          <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w4-python-exercises.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w4-python-exercises.ipynb">w4-python-exercises.ipynb</a></li>
+                </ul>
+              </li>
+
+              <li>Week 5:
+                <ul>
+	          <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/w5-python-more-examples.ipynb">github link</a>,
+		    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/w5-python-more-examples.ipynb">w5-python-more-examples.ipynb</a></li>
+                </ul>
+              </li>
+
+              <li>Extras:
+                <ul>
+                  <li>I/O: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-io.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-io.ipynb">python-io.ipynb</a>;<br>
+		<a href="./lectures/test.txt">[plain text sample]</a>
+		<a href="./lectures/shopping.csv">[CSV sample]</a>
+		<a href="./lectures/VARDEN-tests.ini">[Config/INI sample]</a></li>
+                </ul>
+              </li>
+	    </ul>
+
+            Readings
+            <ul>
+              <li>The official <a href="http://docs.python.org/3/tutorial/">
+		  <em>python tutorial</em></a></li>
+
+              <li><a href="http://ipython.readthedocs.org/en/stable/"><em>IPython tutorial</em></a></li>
+	      <li><a href="http://maxburstein.com/blog/python-shortcuts-for-the-python-beginner/"><em>Python Shortcuts for the Python Beginner</em></a></li>
+
+              <li><a href="http://dl.acm.org/citation.cfm?id=103163">
+		  <em>What every computer scientist should know about
+		  floating-point arithmetic</em></a> by
+		  D. Goldberg</li>
+
+	      <li><a href="https://github.com/asmeurer/python3-presentation/blob/gh-pages/python3-presentation.pdf">
+		  <em>10 awesome features of Python that you can't use because you refuse to upgrade to Python 3</em></a> by A. Meurer</li>
+	    </ul>
+
+            Practice on your own:
+	    <ul>
+	      <li><a href="http://www.codecademy.com/tracks/python">
+		  Code Academy python track</a>: a step-by-step tutorial
+		through the basics of the language</li>
+
+	      <li><a href="https://projecteuler.net/">Project Euler</a>:
+		a set of increasingly complex programming tasks to try
+		out with python</li>
+	    </ul>
+
+            Online books:
+	    <ul>
+	      <li><a href="http://www.greenteapress.com/thinkpython/">Think
+	          Python</a></li>
+
+	      <li><a href="http://www.diveintopython.net/">Dive Into
+	          Python</a></li>
+
+	      <li><a href="http://scipy-lectures.github.io/">Python
+	          Scientific Lecture Notes</a></li>
+
+	      <li><a href="https://hplgit.github.io/primer.html/doc/pub/half/book.pdf">A Primer on Scientific Programming with Python</a> by Hans Petter Langtangen</li>
+
+	      <li><a href="https://developers.google.com/edu/python/">Google's
+	          Python Class</a></li>
+
+	      <li>and a whole lot of other texts that are free can be
+	        found
+	        here: <a href="http://pythonbooks.revolunet.com/">python
+	        books</a></li>
+
+	    </ul>
+
+            Some python resources by disicipline
+	    <ul>
+	      <li>Astronomy: <a href="http://astropy.org">AstroPy</a></li>
+
+	      <li>Atmospheric
+		    sciences: <a href="http://pyaos.johnny-lin.com/">PyAOS</a></li>
+
+	      <li>Biology resources: <a href="http://biopython.org/">Biopython</a></li>
+
+	      <li>Ocean and marine
+		    sciences: <a href="http://oceanpython.org/">OceanPython.org</a></li>
+
+	      <li>Physics resources: <a href="http://qutip.org/">QuTiP</a>: the Quantum
+		    Toolbok in Python</li>
+
+	      <li>Social sciences resources: <a href="http://networkx.github.io/">NetworkX</a>: a
+		    library for exploring the structure and complexity of social
+		    networks</li>
+
+	      <li>Solar physics: <a href="http://sunpy.org/"> SunPy</a></li>
+
+	      <li>Psychology resources: <a href="http://www.psychopy.org/">PyschoPy</a></li>
+	    </ul>
+
+	  </div>
+	</div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>NumPy</h2>
+	    <p>The core array library for scientific computing in python
+              <label for="item-2">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="two" id="item-2" checked>
+	  <div class="content hide">
+	    Jupyter notebooks:
+	    <ul>
+	      <li>(outside class) <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/02-numpy/numpy-basics.ipynb">github link</a>,
+	    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/02-numpy/numpy-basics.ipynb">numpy-basics.ipynb</a>;
+              images: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/02-numpy/row_column_major.png">row_column_major.png</a>,
+                      <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/02-numpy/slicing.png">slicing.png</a></li>
+	      <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/02-numpy/numpy-exercises.ipynb">github link</a>,
+	    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/02-numpy/numpy-exercises.ipynb">numpy-exercises.ipynb</a></li>
+	    </ul>
+
+            Readings
+	    <ul>
+              <li>The
+              official <a href="http://www.scipy.org/Tentative_NumPy_Tutorial">
+		  <em>NumPy Tutorial</em></a></li>
+
+              <li><a href="http://mathesaurus.sourceforge.net/idl-numpy.html">
+		  <em>NumPy for IDL users</em></a></li>
+	    </ul>
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Software Development Practices</h2>
+	    <p>How to write, test, and debug code effectively...
+              <label for="item-3">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="three" id="item-3" checked>
+	  <div class="content hide">
+            Before class materials:
+            <ul>
+              <li>Install git (and bash, if you are on Windows) following
+                the instructions on the Software Carpentry page:
+                <a href="https://swcarpentry.github.io/workshop-template/#git">setting up git</a></li>
+              <li>Create an account on <a href="http://github.com">github</a></li>
+              <li>Read through the Software Carpentry <a href="https://swcarpentry.github.io/git-novice/">Version Control with Git</a> pages</li>
+            </ul>
+
+	    In-class materials:
+	    <ul>
+	      <li>Lecture notes: <a href="lectures/python-practices.pdf">python-practices.pdf</a></li>
+	    </ul>
+
+            Readings:
+	    <ul>
+              <li><a href="http://legacy.python.org/dev/peps/pep-0008/"><em>PEP-0008:
+		    Style Guide for Python Code</em></a></li>
+
+	    </ul>
+
+            Readings on software engineering:
+	    <ul>
+              <li><a href="https://arxiv.org/abs/1810.08055"><em>Ten Simple
+                  Rules for Reproducible Research in Jupyter Notebooks</em></a>
+                by Rule et al.</li>
+
+              <li><a href="https://faculty.washington.edu/rjl/pubs/topten/topten.pdf"><em>Top
+                    Ten Reasons to Not Share Your Code (and why you should
+                    anyway)</em></a> by R. J. LeVeque</li>
+
+	      <li><a href="http://arxiv.org/pdf/1401.2134v1.pdf"><em>10
+		    Simple Rules for the Care and Feeding of Scientic
+		    Data</em></a> by Goodman et al.</li>
+
+              <li><a href="https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1007296"><em>Ten simple rules for helping newcomers become contributors to open projects
+
+</em></a> by Sholler et al.</li>
+
+              <li><a href="http://arxiv.org/abs/1301.7064"><em>How to
+		    Scale a Code in the Human Dimension</em></a> by Matt
+                Turk</li>
+
+              <li><a href="http://arxiv.org/abs/1304.6780"><em>Practices
+		    in source code sharing in astrophysics</em></a> by L. Shamir
+                et al.</li>
+
+              <li><a href="http://arxiv.org/abs/1210.0530">
+                  <em>Best Practices for Scientific Computing</em></a> by
+                G. Wilson et al.</li>
+
+	      <li><a href="http://arxiv.org/abs/1609.00037">
+	          <em>Good Enough Practices in Scientific Computing</em></a> by
+	        G. Wilson et al.</li>
+
+
+	      <li><a href="http://openresearchsoftware.metajnl.com/article/view/jors.ay/63"><em>Best
+		    Practices for Computational Science: Software
+		    Infrastructure and Environments for Reproducible
+		    and Extensible Research</em></a> by V. Stodden and
+		    Sheila Miguez</li>
+
+              <li><a href="http://arxiv.org/abs/astro-ph/0210161">
+                  <em>Reliability in the Face of Complexity; The
+		    Challenge of High-End Scientific
+		    Computing</em></a> by G. Ferland</li>
+
+              <li><a href="http://arxiv.org/abs/1404.6248">
+                  <em>The Nature of Scientific Proof in the Age of
+		    Simulations</em></a> by K. Heng</li>
+	    </ul>
+
+
+	  </div>
+	</div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Matplotlib and plotting</h2>
+	    <p>The core array library for scientific computing in python
+              <label for="item-4">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="four" id="item-4" checked>
+
+	  <div class="content hide">
+	    Jupyter notebooks:
+            <ul>
+	      <li>(outside class) basics:
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/matplotlib-basics.ipynb">github link</a>, raw file:   <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/04-matplotlib/matplotlib-basics.ipynb">matplotlib-basics.ipynb</a>
+		<a href="./lectures/test1.exact.128.out">[sample data]</a></li>
+	      <li>interactive backend: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/matplotlib-interactive-backend.ipynb">github line</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/04-matplotlib/matplotlib-interactive-backend.ipynb">matplotlib-interactive-backend.ipynb</a></li>
+              <li><b>in-class exercises: </b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/matplotlib-exercises.ipynb">github line</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/04-matplotlib/matplotlib-exercises.ipynb">matplotlib-exercises.ipynb</a>; accompanying files: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/test1.exact.128.out">test1.exact.128.out</a> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/shore_leave.txt">shore_leave.txt</a></li>
+            </ul>
+
+	    Readings:
+	    <ul>
+              <li><a href="https://github.com/matplotlib/cheatsheets">Matplotlib cheatsheats</a></li>
+
+              <li><a href="https://serialmentor.com/dataviz/">Fundamentals of Data Visualization</a>
+                  by C. O. Wilke</li>
+
+              <li>The
+              official <a href="http://matplotlib.org/users/pyplot_tutorial.html"><em>matplotlib
+		    tutorial</em></a></li>
+              <li>The <a href="http://matplotlib.org/faq/index.html"><em>matplotlib
+		    FAQ</em></a></li>
+              <li>The <a href="https://python-graph-gallery.com/">python graph gallery</a></li>
+
+              <li>The <a href="http://wiki.scipy.org/Cookbook/Matplotlib"><em>matplotlib
+		    cookbook</em></a></li>
+              <li>The <a href="http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html"><em>mplot3d
+		    tutorial</em></a>: basic 3-d plotting (surfaces, lines,
+		scatter plots)</li>
+
+              <li><a href="http://www.loria.fr/~rougier/teaching/matplotlib/"><em>Matplotlib
+		    Tutorial</em></a> by Nicolas P. Rougier</li>
+	    </ul>
+
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>SciPy and numerical methods</h2>
+	    <p>The core array library for scientific computing in python
+              <label for="item-5">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="five" id="item-5" checked>
+	  <div class="content hide">
+
+	    Jupyter notebooks:
+	    <ul>
+	      <li>overview: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/05-scipy/scipy-basics.ipynb">github link</a>, raw file:
+	    <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/05-scipy/scipy-basics.ipynb">scipy-basics.ipynb</a></li>
+	      <li><b>in-class esxercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/05-scipy/scipy-exercises.ipynb">github link</a>, raw file:
+		<a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/05-scipy/scipy-exercises.ipynb">scipy-exercises.ipynb</a></li>
+	      <li><b>in-class exercises 2:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/05-scipy/scipy-exercises-2.ipynb">github link</a>, raw file:
+		<a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/05-scipy/scipy-exercises-2.ipynb">scipy-exercises-2.ipynb</a></li>
+	    </ul>
+
+            Lecture slides:
+            <ul>
+              <li><a href="lectures/scipy.pdf">scipy.pdf</a></li>
+            </ul>
+
+
+            Other examples:
+	    <ul>
+	      <li>Gaussian elimination with pivoting:
+		<a href="examples/gauss.py">gauss.py</a> (main module),
+		<a href="examples/gauss-test.py">gauss-test.py</a> (test routine)
+		<a href="examples/matmul.py">matmul.py</a> (auxiliary routine)</li>
+	    </ul>
+
+            Readings
+	    <ul>
+	      <li>The official <a href="http://docs.scipy.org/doc/scipy/reference/tutorial/index.html"><em>SciPy Tutorial</em></a></li>
+
+              <li>The <a href="http://wiki.scipy.org/Cookbook"><em>SciPy cookbook</em></a></li>
+
+              <li><a href="http://scikits.appspot.com/scikits"><em>SciKits</em></a> are additional toolkits for SciPy which provide extra functionality</li>
+
+              <li><a href="http://scipy-central.org/"><em>SciPy Central</em></a> user-submitted SciPy snippets</li>
+
+              <li><a href="http://www.scipy.org/NumPy_for_Matlab_Users"><em>NumPy for Matlab Users</em></a></li>
+
+              <li><a href="http://adsabs.harvard.edu/abs/1963JAtS...20..130L"><em>Deterministic Nonperiodic Flow</em></a> by E. N. Lorenz&mdash;this is the system we integrated when discussing ODEs</li>
+
+              <li><a href="http://dl.acm.org/citation.cfm?id=1052672"><em>A simple example of an ill-conditioned matrix</em> by G. J. Tee</a></li>
+
+	    </ul>
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>SymPy</h2>
+	    <p>Symbolic math in python
+              <label for="item-6">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="six" id="item-6" checked>
+
+	  <div class="content hide">
+	    Jupyter notebooks:
+            <ul>
+	      <li><a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/06-sympy/sympy-examples.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/06-sympy/sympy-examples.ipynb">sympy-examples.ipynb</a></li>
+	      <li><b>in-class esxercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/06-sympy/sympy-exercises.ipynb">github link</a>, raw file:
+		<a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/06-sympy/sympy-exercises.ipynb">sympy-exercises.ipynb</a></li>
+            </ul>
+
+            Readings:
+	    <ul>
+              <li>The official <a href="http://docs.sympy.org/latest/tutorial/"><em>SymPy tutorial</em></a></li>
+	    </ul>
+
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Pandas and the DataFrame</h2>
+	    <p>a library for data analysis
+              <label for="item-7">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="seven" id="item-7" checked>
+
+	  <div class="content hide">
+	    Jupyter notebooks:
+	    <ul>
+	      <li>intro: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/07-pandas/pandas-intro.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/07-pandas/pandas-intro.ipynb">pandas-intro.ipynb</a> (sample data: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/07-pandas/sample.csv">sample.csv</a>)</li>
+	      <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/07-pandas/pandas-babynames.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/07-pandas/pandas-babynames.ipynb">pandas-babynames.ipynb</a></li>
+	    </ul>
+
+	    Sample datasets:
+            <ul>
+              <li><a href="https://github.com/wesm/pydata-book">pydata-book examples</a></li>
+            </ul>
+
+            Readings:
+	    <ul>
+	      <li>Readings: <em><a href="http://pandas.pydata.org/pandas-docs/stable/10min.html">10 Minutes to Pandas</a></em></li>
+            </ul>
+
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Extending python with C/Fortran</h2>
+	    <p>interacting with external code and the OS
+              <label for="item-8">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="eight" id="item-8" checked>
+
+	  <div class="content hide">
+	    Lecture slides:
+            <ul>
+              <li>
+                <a href="lectures/extensions.pdf">extensions.pdf</a>
+              </li>
+            </ul>
+
+
+            Example codes:
+	    <ul>
+              <li>C-API: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/C-API/test-C-API.py">test-C-API.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/C-API/numpy-ex.c">numpy-ex.c</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/C-API/setup.py">setup.py</a></li>
+
+              <li>ctypes: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/ctypes/simple/numpy-example.py">numpy-example.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/ctypes/simple/cfunc.c">cfunc.c</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/ctypes/simple/Makefile">Makefile</a></li>
+
+              <li>f2py: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/f2py/test_f2py.py">test_f2py.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/f2py/numpy_in_f.f90">numpy_in_f.f90</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/f2py/Makefile">Makefile</a></li>
+
+              <li>Cython: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/Cython/square/test_cy.py">test_cy.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/Cython/square/square.pyx">square.pyx</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/Cython/square/setup.py">setup.py</a></li>
+
+              <li>Timing comparison for Laplace smoothing (this extends the comparison from the blog entry by T. Oliphant listed above):
+		<a href="https://github.com/sbu-python-class/python-science/tree/master/examples/extensions/timing">code directory</a>
+
+              <li>Calling an external command and capturing both stdout and stderr: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/external/Popen/githash.py">githash.py</a></li>
+
+	    </ul>
+
+            Readings:
+	    <ul>
+              <li><a href="http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html"><em>Interfacing with C</em></a> by Valentin Haenel as part of his SciPy lecture notes.  This is a very nice comparison of different methods</li>
+
+              <li><a href="http://technicaldiscovery.blogspot.com/2011/06/speeding-up-python-numpy-cython-and.html"><em>Speeding up Python (NumPy, Cython, and Weave)</em></a> by T. Oliphant</li>
+
+	      <li>C-API:
+		<ul>
+		  <li><a href="https://docs.python.org/3/extending/extending.html">
+		      <em>Extending Python with C or C++</em></a>: this is the "hard" way to do things.</li>
+		</ul>
+	      </li>
+
+              <li>ctypes:
+		<ul>
+		  <li><a href="https://docs.python.org/3/library/ctypes.html"><em>ctypes — A foreign function library for Python</em></a>:
+		    ctypes makes it easy to call existing C code.</li>
+		  <li><a href="http://scipy-cookbook.readthedocs.io/items/Ctypes.html"><em>SciPy ctypes cookbook</em></a>
+		</ul>
+              </li>
+
+              <li>f2py:
+		<ul>
+		  <li><a href="https://docs.scipy.org/doc/numpy-dev/f2py/">
+		      <em>f2py Users Guide</em></a></li>
+
+		  <li><a href="http://cens.ioc.ee/~pearu/papers/IJCSE4.4_Paper_8.pdf">
+		      <em>F2PY: a tool for connecting Fortran and Python programs</em></a></li>
+		</ul>
+              </li>
+
+	      <li>Cython:
+		<ul>
+		  <li><a href="http://cython.org/"><em>Cython, C-Extensions for Python</em></a>
+		    the official project page</li>
+
+		  <li><a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5582062">
+		      <em>Cython: The Best of Both Worlds</em></a>
+		    by S. Behnet et al. (alternate links:
+		    <a href="http://folk.uio.no/dagss/cython_cise.pdf">[here]</a>)</li>
+		</ul>
+	      </li>
+
+	    </ul>
+
+
+          </div>
+        </div>
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Python applications and packaging</h2>
+	    <p>wrapping your code up for others to use
+              <label for="item-9">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="nine" id="item-9" checked>
+
+	  <div class="content hide">
+
+	    Lecture slides:
+            <ul>
+              <li><a href="lectures/packaging.pdf">packaging.pdf</a></li>
+            </ul>
+
+            Readings:
+            <ul>
+              <li><a href="https://docs.python.org/3/tutorial/modules.html"><em>Python modules</em></a></li>
+
+	      <li><a href="https://packaging.python.org/current/"><em>Python packaging recommendations</em></a></li>
+
+	      <li><a href="https://packaging.python.org/glossary/">A glossary that explains the evolving terminology</a>&mdash;
+		this is part of the <a href="https://python-packaging.readthedocs.io/en/latest/"><em>Installation &amp; Packaging Tutorial</em></a></li>
+
+	      <li><a href="http://mrtopf.de/blog/en/a-small-introduction-to-python-eggs/"><em>A Small Introduction to Python Eggs</em></a></li>
+
+	      <li><a href="http://stackoverflow.com/questions/6344076/differences-between-distribute-distutils-setuptools-and-distutils2"><em>Differences between distribute, distutils, setuptools and distutils2?</em></a> on stackoverflow</li>
+
+	    </ul>
+
+
+	    Examples:
+	    <ul>
+	      <li>argparse example: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/commandline/argparse_example.py">argparse_example.py</a></li>
+	    </ul>
+
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Testing</h2>
+	    <p>testing to ensure that your code performs well
+              <label for="item-10">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="ten" id="item-10" checked>
+
+	  <div class="content hide">
+
+            Lecture slides:
+            <ul>
+              <li><a href="lectures/testing.pdf">testing.pdf</a></li>
+            </ul>
+
+
+            Examples:
+	    <ul>
+	      <li>simple example: <a href="https://github.com/sbu-python-class/python-science/tree/master/examples/testing/pytest/simple/test_simple.py">test_simple.py</a></li>
+	      <li>function setup example: <a href="https://github.com/sbu-python-class/python-science/tree/master/examples/testing/pytest/function_setup/test_function_setup.py">test_function_setup.py</a></li>
+	      <li>class-based example: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/testing/pytest/class/test_class.py">test_class.py</a>
+            </ul>
+
+          </div>
+        </div>
+
+
+	<!-- .............................................................. -->
+	<div class="spotlight">
+
+	  <div class="contentw">
+	    <h2>Machine Learning</h2>
+	    <p>a quick example of machine learning using python libraries
+              <label for="item-11">[+/- content]</label></p>
+	  </div>
+          <input type="checkbox" name="eleven" id="item-11" checked>
+
+	  <div class="content hide">
+
+            Lecture slides:
+            <ul>
+              <li><a href="lectures/machine-learning.pdf">machine-learning.pdf</a></li>
+            </ul>
+
+	    Jupyter notebooks:
+            <ul>
+	      <li><a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/11-machine-learning/keras-mnist.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/11-machine-learning/keras-mnist.ipynb">keras-mnist.ipynb</a></li>
+            </ul>
+
+	    Examples:
+	    <ul>
+              <li>steepest (gradient) descent: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/machine-learning/steepest_descent/steepest_descent.py">steepest_descent.py</a></li>
+              <li>character recognition: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/machine-learning/character_recognition/char_recognition.py">char_recognition.py</a></li>
+            </ul>
+
+          </div>
+        </div>
+
+
+      </div>  <!-- inner -->
+    </section>
+
+
+    <!-- Footer -->
+    <footer id="footer">
+      <!-- <div class="image left"> -->
+      <!-- 	<img src="images/iacs-logo.png" alt="IACS logo"> -->
+      <!-- </div> -->
+      <div class="copyright">
+	based on a design from <a href="http://templated.co">TEMPLATED</a>.
+      </div>
+    </footer>
+
+    <!-- Scripts -->
+    <script src="assets/js/jquery.min.js"></script>
+    <script src="assets/js/skel.min.js"></script>
+    <script src="assets/js/util.js"></script>
+    <!--[if lte IE 8]><script src="assets/js/ie/respond.min.js"></script><![endif]-->
+    <script src="assets/js/main.js"></script>
+
+  </body>
+</html>
diff --git a/docs/old/class.css b/docs/old/class.css
new file mode 100644
index 00000000..d1d75542
--- /dev/null
+++ b/docs/old/class.css
@@ -0,0 +1,138 @@
+body {
+   background: #dddddd;
+   font-size: 100%;
+}
+
+body, h1, h2, h3 {
+   font-family: Carlito, Calibri, Arial, Helvetica, sans-serif;
+   color: #333333;
+}
+
+h4
+{
+  font-family: "Palatino Linotype", Palatino, "Book Antiqua", serif;
+  font-style: italic;
+  font-size: medium;
+  color: #0000cc;
+  font-weight: normal;
+  line-height: 100%;
+  position: relative;
+  top: -0.25em;
+  display: inline;
+}
+
+#main {
+  width: 800px;
+  margin: 0 auto;
+  border: 2px solid black;
+  padding: 1em;
+  background: #ffffff;
+}
+
+h1
+{
+  color: #770077;
+  display: inline;
+}
+
+h2
+{
+  margin-top: 1.5em;
+  margin-bottom: 0.5em;
+  font-size: 135%;
+}
+
+
+h3
+{
+  color: #990099;
+  font-weight: normal;
+  font-size: 120%;
+  margin: 0;
+  padding: 0;
+}
+
+
+
+li 
+{
+   margin: 0;
+   padding-bottom: 5px;
+   border: none;
+   color: #000088;
+}
+
+li li
+{
+   margin: 0;
+   padding-bottom: 5px;
+   border: none;
+   color: #555555;
+
+}
+
+li li li
+{
+   margin: 0;
+   padding-bottom: 5px;
+   border: none;
+   color: #005577;
+}
+
+li li li a
+{
+   color: #0088aa;
+}
+
+li li li a:hover
+{
+   color: #00aabb;
+}
+
+
+td {
+  text-align: center;
+  padding-left: 1em;
+  padding-right: 1em;
+}
+
+td.gray {
+  background-color: #eeeeee;
+}
+
+th {
+  background-color: #000000;
+  color: #ffffff;
+}
+
+table {
+  border-collapse: collapse;
+  border: 1px solid black;
+  margin: 0 auto;
+}
+
+p.center {
+  margin: 0 auto;
+  padding-top: 1em;
+  text-align: center;
+}
+
+div.getpython {
+  margin: 2em;
+  padding: 1em;
+  border: 1px solid blue;
+  background-color: lightblue;
+  font-size: 90%;
+}
+
+div.getgit {
+  margin: 2em;
+  padding: 1em;
+  border: 1px solid black;
+  background-color: yellow;
+  font-size: 90%;
+}
+
+#heading {
+    text-align: center;
+}
diff --git a/examples/extensions/C-API/numpy-ex.c b/docs/old/examples/C-API/numpy-ex.c
similarity index 100%
rename from examples/extensions/C-API/numpy-ex.c
rename to docs/old/examples/C-API/numpy-ex.c
diff --git a/examples/extensions/C-API/setup.py b/docs/old/examples/C-API/setup.py
similarity index 100%
rename from examples/extensions/C-API/setup.py
rename to docs/old/examples/C-API/setup.py
diff --git a/examples/extensions/C-API/test-C-API.py b/docs/old/examples/C-API/test-C-API.py
similarity index 100%
rename from examples/extensions/C-API/test-C-API.py
rename to docs/old/examples/C-API/test-C-API.py
diff --git a/examples/extensions/Cython/square/setup.py b/docs/old/examples/Cython/setup.py
similarity index 100%
rename from examples/extensions/Cython/square/setup.py
rename to docs/old/examples/Cython/setup.py
diff --git a/examples/extensions/Cython/square/square.pyx b/docs/old/examples/Cython/square.pyx
similarity index 100%
rename from examples/extensions/Cython/square/square.pyx
rename to docs/old/examples/Cython/square.pyx
diff --git a/examples/extensions/Cython/square/test_cy.py b/docs/old/examples/Cython/test_cy.py
similarity index 100%
rename from examples/extensions/Cython/square/test_cy.py
rename to docs/old/examples/Cython/test_cy.py
diff --git a/docs/old/examples/argparse_example.py b/docs/old/examples/argparse_example.py
new file mode 100755
index 00000000..fa046465
--- /dev/null
+++ b/docs/old/examples/argparse_example.py
@@ -0,0 +1,37 @@
+#!/usr/bin/env python
+
+# to get usage: use -h
+import sys
+import argparse
+
+
+# simple example of argparse
+#
+# ./argparse_example.py -a -b # -c string --darg --earg fextra
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-a", help="the -a option", action="store_true")
+parser.add_argument("-b", help="-b takes a number", type=int, default=0)
+parser.add_argument("-c", help="-c takes a string", type=str, default=None)
+parser.add_argument("--darg", help="the --darg option", action="store_true")
+parser.add_argument("--earg", help="--earg takes a string", type=str,
+                    default="example string")
+
+# extra arguments (positional)
+parser.add_argument("extras", metavar="extra", type=str, nargs="*",
+                    help="optional positional arguments")
+
+args = parser.parse_args()
+
+
+if args.a: print "-a set"
+print "-b = {}".format(args.b)
+print "-c = {}".format(args.c)
+if args.darg: print "--dargs set"
+print "--earg value = {}".format(args.earg)
+
+print " "
+print "extra positional arguments: "
+if len(args.extras) > 0:
+    for e in args.extras:
+        print e
diff --git a/examples/extensions/ctypes/multi-d/Makefile b/docs/old/examples/ctypes/Makefile
similarity index 100%
rename from examples/extensions/ctypes/multi-d/Makefile
rename to docs/old/examples/ctypes/Makefile
diff --git a/examples/extensions/ctypes/multi-d/cfunc_multid.c b/docs/old/examples/ctypes/cfunc_multid.c
similarity index 100%
rename from examples/extensions/ctypes/multi-d/cfunc_multid.c
rename to docs/old/examples/ctypes/cfunc_multid.c
diff --git a/examples/extensions/ctypes/multi-d/test-ctypes.py b/docs/old/examples/ctypes/test-ctypes.py
similarity index 100%
rename from examples/extensions/ctypes/multi-d/test-ctypes.py
rename to docs/old/examples/ctypes/test-ctypes.py
diff --git a/examples/extensions/f2py/Makefile b/docs/old/examples/f2py/Makefile
similarity index 100%
rename from examples/extensions/f2py/Makefile
rename to docs/old/examples/f2py/Makefile
diff --git a/examples/extensions/f2py/numpy_in_f.f90 b/docs/old/examples/f2py/numpy_in_f.f90
similarity index 100%
rename from examples/extensions/f2py/numpy_in_f.f90
rename to docs/old/examples/f2py/numpy_in_f.f90
diff --git a/examples/extensions/f2py/test_f2py.py b/docs/old/examples/f2py/test_f2py.py
similarity index 100%
rename from examples/extensions/f2py/test_f2py.py
rename to docs/old/examples/f2py/test_f2py.py
diff --git a/docs/old/examples/gauss-test.py b/docs/old/examples/gauss-test.py
new file mode 100644
index 00000000..9d283a43
--- /dev/null
+++ b/docs/old/examples/gauss-test.py
@@ -0,0 +1,78 @@
+from __future__ import print_function
+
+import numpy
+from gauss import *
+from matmul import *
+
+
+# tests of gaussian elimination
+
+A = numpy.array([ [1, 1, 1], [-1, 2, 0], [2, 0, 1] ], dtype=numpy.float64)
+b = numpy.array([6, 3, 5], dtype=numpy.float64)
+
+# gaussElim changes A in place -- send a copy
+x, d = gauss_elim(A.copy(), b.copy(), return_det=1)  
+
+# test it by multiplying A x
+bout = mult_Ax(A, x)
+
+print("matrix A:\n", A)
+print("RHS (b): ", b)
+print("solved x: ", x)
+print("det{A}: ", d)
+print("A.x: ", bout)
+print(" ")
+
+A = numpy.array([ [0, 1, 1], [1, 1, 0], [1, 0, 1] ], dtype=numpy.float64)
+b = numpy.array([5, 3, 4], dtype=numpy.float64)
+x = gauss_elim(A.copy(), b.copy())
+
+# test it by multiplying A x
+bout = mult_Ax(A, x)
+
+print("matrix A:\n", A)
+print("RHS (b): ", b)
+print("solved x: ", x)
+print("A.x: ", bout)
+print(" ")
+
+
+A = numpy.array([ [0, 0, 0, 4], 
+                  [0, 0, 3, 0], 
+                  [5, 6, 7, 8],
+                  [0, 4, 3, 2] ], dtype=numpy.float64)
+b = numpy.array([5, 4, 9, 1], dtype=numpy.float64)
+x = gauss_elim(A.copy(), b.copy())
+
+# test it by multiplying A x
+bout = mult_Ax(A, x)
+
+print("matrix A:\n", A)
+print("RHS (b): ", b)
+print("solved x: ", x)
+print("A.x: ", bout)
+print(" ")
+
+
+A = numpy.array([ [ 4,  3, 4, 10], 
+                  [ 2, -7, 3,  0], 
+                  [-2, 11, 1,  3],
+                  [ 3, -4, 0,  2] ], dtype=numpy.float64)
+b = numpy.array([2, 6, 3, 1], dtype=numpy.float64)
+x = gauss_elim(A.copy(), b.copy())
+
+# test it by multiplying A x
+bout = mult_Ax(A, x)
+
+print("matrix A:\n", A)
+print("RHS (b): ", b)
+print("solved x: ", x)
+print("A.x: ", bout)
+print(" ")
+
+
+
+
+
+
+
diff --git a/docs/old/examples/gauss.py b/docs/old/examples/gauss.py
new file mode 100644
index 00000000..9082a333
--- /dev/null
+++ b/docs/old/examples/gauss.py
@@ -0,0 +1,117 @@
+# gaussian elimination with scaled pivoting
+#
+# M. Zingale (2013-02-25)
+
+from __future__ import print_function
+import numpy as np
+
+def gauss_elim(A, b, return_det=0, quiet=0):
+    """ perform gaussian elimination with pivoting, solving A x = b.
+
+        A is an NxN matrix, x and b are an N-element vectors.  Note: A
+        and b are changed upon exit to be in upper triangular (row
+        echelon) form """
+
+    # b is a vector
+    if not b.ndim == 1:
+        print("ERROR: b should be a vector")
+        return None
+
+    N = len(b)
+
+    # A is square, with each dimension of length N
+    if not A.shape == (N, N):
+        print("ERROR: A should be square with each dim of same length as b")
+        return None
+
+    # allocation the solution array
+    x = np.zeros((N), dtype=A.dtype)
+
+    # find the scale factors for each row -- this is used when pivoting
+    scales = np.max(np.abs(A), 1)
+
+    # keep track of the number of times we swapped rows
+    num_row_swap = 0
+
+    if not quiet: print_Ab(A, b)
+
+    # main loop over rows
+    for k in range(N):
+
+        # find the pivot row based on the size of column k -- only consider
+        # the rows beyond the current row
+        row_max = np.argmax(A[k:, k]/scales[k:])
+        if k > 0: row_max += k  # we sliced A from k:, correct for total rows
+
+        # swap the row with the largest scaled element in the current column
+        # with the current row (pivot) -- do this with b too!
+        if not row_max == k:
+            A[[k, row_max],:] = A[[row_max, k],:]
+            b[[k, row_max]] = b[[row_max, k]]
+            if not quiet: print("pivoted")
+            num_row_swap += 1
+
+        # do the forward-elimination for all rows below the current
+        for i in range(k+1, N):
+            coeff = A[i,k]/A[k,k]
+
+            for j in range(k+1, N):
+                A[i,j] += -A[k,j]*coeff
+
+            A[i,k] = 0.0
+            b[i] += -coeff*b[k]
+
+        if not quiet: print_Ab(A, b)
+
+    # back-substitution
+
+    # last solution is easy
+    x[N-1] = b[N-1]/A[N-1,N-1]
+
+    for i in reversed(range(N-1)):
+        sum = b[i]
+        for j in range(i+1,N):
+            sum += -A[i,j]*x[j]
+        x[i] = sum/A[i,i]
+
+
+    # determinant
+    det = np.prod(np.diagonal(A))*(-1.0)**num_row_swap
+
+    if not return_det:
+        return x
+    else:
+        return x, det
+
+
+
+# for debugging:
+# output: np.savetxt("test.out", a, fmt="%5.2f", delimiter="  ")
+# convert -font Courier-New-Regular -pointsize 20 text:test.out test.png
+
+def print_Ab(A, b):
+    """ printout the matrix A and vector b in a pretty fashion.  We
+        don't use the numpy print here, because we want to make them
+        side by side"""
+
+    N = len(b)
+
+    open_t = "/"
+    close_t = "\\"
+
+    open_b = "\\"
+    close_b = "/"
+
+    # numbers take 6 positions + 2 spaces
+    a_fmt = " {:6.3f} "
+    space = 8*" "
+
+    line = "|" + N*a_fmt + "|" + space + "|" + a_fmt + "|"
+    top = open_t + N*space + close_t  + space + open_t + space + close_t
+    bottom = open_b + N*space + close_b + space + open_b + space + close_b + "\n"
+
+    print(top)
+    for i in range(N):
+        out = tuple(A[i,:]) + (b[i],)
+        print(line.format(*out))
+    print(bottom)
diff --git a/examples/commandline/getopt_example.py b/docs/old/examples/getopt_example.py
similarity index 100%
rename from examples/commandline/getopt_example.py
rename to docs/old/examples/getopt_example.py
diff --git a/docs/old/examples/githash.py b/docs/old/examples/githash.py
new file mode 100644
index 00000000..1d583d91
--- /dev/null
+++ b/docs/old/examples/githash.py
@@ -0,0 +1,13 @@
+import subprocess
+import string
+
+# issue the command 'git rev-parse HEAD'
+prog = ["git", "rev-parse", "HEAD"]
+p0 = subprocess.Popen(prog, stdout=subprocess.PIPE,
+                      stderr=subprocess.PIPE)
+stdout0, stderr0 = p0.communicate()
+
+print "stdout: {}".format(stdout0)
+print "stderr: {}".format(stderr0)
+
+
diff --git a/docs/old/examples/matmul.f90 b/docs/old/examples/matmul.f90
new file mode 100644
index 00000000..87cdb3e4
--- /dev/null
+++ b/docs/old/examples/matmul.f90
@@ -0,0 +1,39 @@
+program matmul
+
+  ! matrix multiply with OpenMP
+  !
+  ! M. Zingale (2013-04-14)
+
+  integer, parameter :: N = 1000
+  
+  double precision a(N,N)
+  double precision x(N)
+  double precision b(N)
+
+  integer :: i, j
+
+  ! initialize the matrix and vector
+  !$omp parallel do private(i, j)
+  do j = 1, N
+     do i = 1, N
+        a(i,j) = dble(i + j)
+     enddo
+  enddo
+  !$omp end parallel do
+
+  do i = 1, N
+     x(i) = i
+  enddo
+
+  ! multiply
+  !$omp parallel do private(i, j)
+  do i = 1, N
+     b(i) = 0.0
+     do j = 1, N
+        b(i) = b(i) + a(i,j)*x(j)
+     enddo
+  enddo  
+  !$end parallel do
+
+end program matmul
+
diff --git a/docs/old/examples/matmul.py b/docs/old/examples/matmul.py
new file mode 100644
index 00000000..d584f381
--- /dev/null
+++ b/docs/old/examples/matmul.py
@@ -0,0 +1,31 @@
+# routines for multiplying matrices
+
+from __future__ import print_function
+
+import numpy
+
+def mult_Ax(A, x):
+    """ return the product of matrix A and vector x: Ax = b """
+
+    # x is a vector
+    if not x.ndim == 1:
+        print("ERROR: x should be a vector")
+        return None
+
+    N = len(x)
+
+    # A is square, with each dimension of length N
+    if not A.shape == (N, N):
+        print("ERROR: A should be square with each dim of same length as x")
+        return None
+
+    # allocation the product array
+    b = numpy.zeros((N), dtype=A.dtype)
+
+    # each row of b is the product of the like row of A dotted with
+    # the vector x
+    for i in range(N):
+        b[i] = numpy.dot(A[i,:], x)
+
+    return b
+
diff --git a/docs/old/examples/mayavi-basic.py b/docs/old/examples/mayavi-basic.py
new file mode 100644
index 00000000..8b5b1341
--- /dev/null
+++ b/docs/old/examples/mayavi-basic.py
@@ -0,0 +1,42 @@
+# http://docs.enthought.com/mayavi/mayavi/mlab_case_studies.html
+
+import numpy as np
+
+from mayavi import mlab
+
+x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j]
+s = np.sin(x*y*z)/(x*y*z)
+
+mlab.contour3d(s)
+mlab.show()
+
+# volume rendering
+mlab.pipeline.volume(mlab.pipeline.scalar_field(s))
+mlab.show()
+
+
+# change the data limits
+mlab.pipeline.volume(mlab.pipeline.scalar_field(s), vmin=0, vmax=0.8)
+mlab.show()
+
+
+# cut planes
+mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
+                                 plane_orientation='x_axes',
+                                 slice_index=10)
+mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
+                                 plane_orientation='y_axes',
+                                 slice_index=10)
+mlab.outline()
+mlab.show()
+
+
+# combination
+src = mlab.pipeline.scalar_field(s)
+mlab.pipeline.iso_surface(src, contours=[s.min()+0.1*s.ptp(), ], opacity=0.1)  # ptp is the range 
+mlab.pipeline.iso_surface(src, contours=[s.max()-0.1*s.ptp(), ],)
+mlab.pipeline.image_plane_widget(src,
+                                 plane_orientation='z_axes',
+                                 slice_index=10)
+
+mlab.show()
diff --git a/docs/old/examples/mayavi-vector.py b/docs/old/examples/mayavi-vector.py
new file mode 100644
index 00000000..2b55dde1
--- /dev/null
+++ b/docs/old/examples/mayavi-vector.py
@@ -0,0 +1,64 @@
+# http://docs.enthought.com/mayavi/mayavi/mlab_case_studies.html
+
+import numpy as np
+
+from mayavi import mlab
+
+x, y, z = np.mgrid[0:1:20j, 0:1:20j, 0:1:20j]
+
+u =    np.sin(np.pi*x) * np.cos(np.pi*z)
+v = -2*np.sin(np.pi*y) * np.cos(2*np.pi*z)
+w = np.cos(np.pi*x)*np.sin(np.pi*z) + np.cos(np.pi*y)*np.sin(2*np.pi*z)
+
+
+# a quiver field
+mlab.quiver3d(u, v, w)
+mlab.outline()
+mlab.show()
+
+
+# thin things out -- this doesn't seem to work?
+src = mlab.pipeline.vector_field(u, v, w)
+#mlab.pipeline.vectors(src, mask_points=20, scale_factor=1.)  # keep only one point out of 20
+#mlab.outline()
+#mlab.show()
+
+
+mlab.pipeline.vector_cut_plane(src, mask_points=2, scale_factor=3)
+mlab.outline()
+mlab.show()
+
+
+# isosurfaces of the magnitude -- doesn't work
+#magnitude = mlab.pipeline.extract_vector_norm(src)
+#mlab.pipeline.iso_surface(magnitude, contours=[1.9, 0.5])
+#mlab.outline()
+#mlab.show()
+
+
+mlab.figure(fgcolor=(0., 0., 0.), bgcolor=(1, 1, 1))
+src = mlab.pipeline.vector_field(u, v, w)
+magnitude = mlab.pipeline.extract_vector_norm(src)
+
+# We apply the following modules on the magnitude object, in order to
+# be able to display the norm of the vectors, eg as the color.
+iso = mlab.pipeline.iso_surface(magnitude, contours=[1.9, ], opacity=0.3)
+
+#vec = mlab.pipeline.vectors(magnitude, mask_points=40,
+#                                    line_width=1,
+#                                    color=(.8, .8, .8),
+#                                    scale_factor=4.)
+
+flow = mlab.pipeline.streamline(magnitude, seedtype='plane',
+                                        seed_visible=False,
+                                        seed_scale=0.5,
+                                        seed_resolution=1,
+                                        linetype='ribbon',)
+
+vcp = mlab.pipeline.vector_cut_plane(magnitude, mask_points=2,
+                                        scale_factor=4,
+                                        colormap='jet',
+                                        plane_orientation='x_axes')
+mlab.show()
+
+
diff --git a/examples/extensions/timing/Makefile b/docs/old/examples/timing/Makefile
similarity index 100%
rename from examples/extensions/timing/Makefile
rename to docs/old/examples/timing/Makefile
diff --git a/examples/extensions/timing/README b/docs/old/examples/timing/README
similarity index 100%
rename from examples/extensions/timing/README
rename to docs/old/examples/timing/README
diff --git a/examples/extensions/timing/laplace.py b/docs/old/examples/timing/laplace.py
similarity index 100%
rename from examples/extensions/timing/laplace.py
rename to docs/old/examples/timing/laplace.py
diff --git a/examples/extensions/timing/laplace_C.c b/docs/old/examples/timing/laplace_C.c
similarity index 100%
rename from examples/extensions/timing/laplace_C.c
rename to docs/old/examples/timing/laplace_C.c
diff --git a/examples/extensions/timing/laplace_CAPI.c b/docs/old/examples/timing/laplace_CAPI.c
similarity index 100%
rename from examples/extensions/timing/laplace_CAPI.c
rename to docs/old/examples/timing/laplace_CAPI.c
diff --git a/examples/extensions/timing/laplace_cython.pyx b/docs/old/examples/timing/laplace_cython.pyx
similarity index 100%
rename from examples/extensions/timing/laplace_cython.pyx
rename to docs/old/examples/timing/laplace_cython.pyx
diff --git a/examples/extensions/timing/laplace_fortran.f90 b/docs/old/examples/timing/laplace_fortran.f90
similarity index 100%
rename from examples/extensions/timing/laplace_fortran.f90
rename to docs/old/examples/timing/laplace_fortran.f90
diff --git a/examples/extensions/timing/setup.py b/docs/old/examples/timing/setup.py
similarity index 100%
rename from examples/extensions/timing/setup.py
rename to docs/old/examples/timing/setup.py
diff --git a/docs/old/index.html b/docs/old/index.html
new file mode 100755
index 00000000..4e7e8ec3
--- /dev/null
+++ b/docs/old/index.html
@@ -0,0 +1,637 @@
+<!DOCTYPE html>
+
+<html lang="en">
+  <head>
+    <title>PHY 546: Python for Scientific Computing</title>
+    <link rel="stylesheet" type="text/css" href="./class.css">
+    <meta HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=utf-8">
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css">
+  </head>
+
+  <body>
+
+    <div id="main">
+
+      <div id="heading">
+	<h1>PHY 546: Python for Scientific Computing</h1><br>
+	<h4>a weekly graduate seminar on techniques for scientific programming</h4>
+
+	<h3>Instructor: <a href="http://www.astro.sunysb.edu/mzingale/">Michael Zingale</a></h3>(Spring 2017)
+      </div>
+
+      <p class="center"><img src="python.png" alt="xkcd"><br>(xkcd)
+
+	<div class="getpython">
+
+	  Some basic programming background, be
+	  it C/C++, Fortran, matlab, mathematica, ..., (enough to
+	  understand the logic of programming, control statements,
+	  basic data structures, etc.) would be useful.
+
+	  <p>This is intended to be a 1-credit class.  The primary
+	    method of evaluation is class participation.
+
+	  <p>To make the most of this class, you should have python
+	    installed on a laptop that you can bring to the seminar.
+	    On Linux machines, you can get python and the needed
+	    libraries through your package manager.  For Mac and
+	    Windows, you might want to consider the free distributions
+	    provided
+	    by <a href="https://www.enthought.com/products/canopy/">Enthought
+	    Canopy</a>
+	    or <a href="https://store.continuum.io/cshop/anaconda/">Anaconda</a>.
+	    These both install everything you need.
+
+<!--	  <p>Alternately, if you run the Chrome web browser, you can
+	    install
+	    the <a href="https://chrome.google.com/webstore/detail/colaboratory-notebook/pianggobfjcgeihlmfhfgkfalopndooo?hl=en-US">coLaboratory
+	    Notebook</a> app&mdash;it will provide an IPython
+	    environment that runs in your browser with no other
+	    dependencies needed (not all the libraries we need will be
+	    installed, but it is a good way to get started).
+-->
+	</div>
+
+	<div class="getgit">
+	  All of the course slides (in LibreOffice flat XML format),
+	  scripts, and IPython notebooks are availble on the course
+	  github page: <a href="https://github.com/sbu-python-class/python-science">https://github.com/sbu-python-class/python-science</a>
+	</div>
+	
+      <p><h2>Course Information:</h2>
+      <p>syllabus: <a href="./syllabus.pdf">syllabus.pdf</a>
+
+
+      <p><h2>Online Resources:</h2>
+
+      <p>The following free online books might be helpful:
+	<ul>
+	  <li><a href="http://www.greenteapress.com/thinkpython/">Think
+	      Python</a></li>
+
+	  <li><a href="http://www.diveintopython.net/">Dive Into
+	      Python</a></li>
+
+	  <li><a href="http://scipy-lectures.github.io/">Python
+	      Scientific Lecture Notes</a></li>
+
+	  <li><a href="https://hplgit.github.io/primer.html/doc/pub/half/book.pdf">A Primer on Scientific Programming with Python</a> by Hans Petter Langtangen</li>
+	    
+	  <li><a href="https://developers.google.com/edu/python/">Google's
+	      Python Class</a></li>
+
+	  <li>and a whole lot of other texts that are free can be
+	    found
+	    here: <a href="http://pythonbooks.revolunet.com/">python
+	      books</a></li>
+
+	</ul>
+
+
+      <p><h2>Other Readings (dealing with Open science and managing
+	  code projects):</h2>
+
+      <p>
+	<ul>
+	  <li><a href="http://arxiv.org/pdf/1401.2134v1.pdf"><em>10
+		Simple Rules for the Care and Feeding of Scientic
+		Data</em></a> by Goodman et al.</li>
+
+          <li><a href="http://arxiv.org/abs/1301.7064"><em>How to
+		Scale a Code in the Human Dimension</em></a> by Matt
+            Turk</li>
+
+          <li><a href="http://arxiv.org/abs/1304.6780"><em>Practices
+		in source code sharing in astrophysics</em></a> by L. Shamir
+            et al.</li>
+
+          <li><a href="http://arxiv.org/abs/1210.0530">
+              <em>Best Practices for Scientific Computing</em></a> by
+            G. Wilson et al.</li>
+
+	  <li><a href="http://arxiv.org/abs/1609.00037">
+	      <em>Good Enough Practices in Scientific Computing</em></a> by
+	    G. Wilson et al.</li>
+
+
+	  <li><a href="http://openresearchsoftware.metajnl.com/article/view/jors.ay/63"><em>Best
+		Practices for Computational Science: Software Infrastructure
+		and Environments for Reproducible and Extensible
+		Research</em></a> by V. Stodden and Sheila Miguez</li>
+
+          <li><a href="http://arxiv.org/abs/astro-ph/0210161">
+              <em>Reliability in the Face of Complexity; The Challenge
+		of High-End Scientific Computing</em></a> by
+            G. Ferland</li>
+
+          <li><a href="http://arxiv.org/abs/1404.6248">
+              <em>The Nature of Scientific Proof in the Age of
+		Simulations</em></a> by K. Heng</li>
+	</ul>
+
+
+      <p><h2>Python Resources by Disicipline:</h2>
+      <p>The following list provides links to discipline-specific python
+	software:
+
+	<ul>
+	  <li>Astronomy resources:
+	    <ul>
+	      <li><a href="http://adsabs.harvard.edu/abs/2013A%26A...558A..33A">
+		  <em>Astropy: A community Python package for
+		    astronomy</em></a>: an article describing a
+		community Astronomy package for python</li>
+
+	      <li><a href="http://www.astropython.org/">AstroPython</a></li>
+	    </ul>
+	  </li>
+
+	  <li>Atmospheric Sciences resources:
+	    <ul>
+	      <li><a href="http://pyaos.johnny-lin.com/">PyAOS</a>: a
+		list of python resources for Atmospheric Sciences</li>
+	    </ul>
+	  </li>
+
+	  <li>Biology resources:
+	    <ul>
+	      <li><a href="http://arxiv.org/pdf/0803.1838v1.pdf">
+		  <em>Python&mdash;All a Scientist Needs</em></a>: an
+		article describing how python is used in
+		bioinformatics</li>
+
+	      <li><a href="http://biopython.org/wiki/Main_Page">
+		  Biopython</a>: a set of tools for computational
+		  biology</li>
+	    </ul>
+	  </li>
+
+	  <!-- <li>Cognitive Science resources: -->
+	  <!--   <ul> -->
+	  <!--     <li><a href="http://www.pycogsci.info/"> pycogsci</a>:a -->
+	  <!-- 	  blog providing information about how python is used -->
+	  <!-- 	  in Cognitive Science</li> -->
+	  <!--   </ul> -->
+	  <!-- </li> -->
+
+	  <li>Ocean and marine sciences resources:
+	    <ul>
+	      <li><a href="http://oceanpython.org/">OceanPython.org</a>:
+		a blog for the ocean and marine sciences
+		communities</li>
+	    </ul>
+	  </li>
+
+	  <li>Physics resources:
+	    <ul>
+	      <li><a href="http://qutip.org/">QuTiP</a>: the Quantum
+		Toolbok in Python</li>
+	    </ul>
+	  </li>
+
+	  <li>Social sciences resources:
+	    <ul>
+	      <li><a href="http://networkx.github.io/">NetworkX</a>: a
+		library for exploring the structure and complexity of social
+		networks</li>
+	    </ul>
+	  </li>
+
+	  <li>Solar physics resources:
+	    <ul>
+	      <li><a href="http://sunpy.org/"> SunPy</a>: a library
+		providing routines to analyze solar data</li>
+	    </ul>
+	  </li>
+
+	  <li>Psychology resources:
+	    <ul>
+	      <li><a href="http://www.psychopy.org/">PyschoPy</a>:
+		psychology software to "allow the presentation of
+		stimuli and collection of data for a wide range of
+		neuroscience, psychology and psychophysics
+		experiments."</li>
+	    </ul>
+	  </li>
+
+	</ul>
+
+      <p><h2>Course Topics:</h2><br>
+	Note: this information will be updated continuously throughout the
+	semester, so it is best to look at the relevant topics just before the
+	class meeting.
+
+      <p><h3>Introduction to python</h3>  (lectures 1&ndash;4)
+
+	<ul>
+	  <li>Readings:
+            <ul>
+              <li>The official <a href="http://docs.python.org/3/tutorial/">
+		  <em>python tutorial</em></a></li>
+
+              <li><a href="http://ipython.readthedocs.org/en/stable/"><em>IPython tutorial</em></a></li>
+	      <li><a href="http://maxburstein.com/blog/python-shortcuts-for-the-python-beginner/"><em>Python Shortcuts for the Python Beginner</em></a></li>
+
+              <li><a href="http://dl.acm.org/citation.cfm?id=103163">
+		  <em>What every computer scientist should know about
+		  floating-point arithmetic</em></a> by
+		  D. Goldberg</li>
+
+	      <li><a href="https://github.com/asmeurer/python3-presentation/blob/gh-pages/python3-presentation.pdf">
+		  <em>10 awesome features of Python that you can't use because you refuse to upgrade to Python 3</em></a> by A. Meurer</li>
+	    </ul>
+	  </li>
+
+	  <li>Practice on your own:
+	    <ul>
+	      <li><a href="http://www.codecademy.com/tracks/python">
+		  Code Academy python track</a>: a step-by-step tutorial
+		through the basics of the language</li>
+
+	      <li><a href="https://projecteuler.net/">Project Euler</a>:
+		a set of increasingly complex programming tasks to try
+		out with python</li>
+	    </ul>
+	  </li>
+		
+	  <li>Lecture
+	    slides: <a href="./lectures/python.pdf">python.pdf</a></li>
+
+	  <li>Lecture IPython notebooks (based on the python tutorial
+	    above):
+	    <ul>
+              <li>datatypes: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-datatypes.ipynb">github link</a>, 
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-datatypes.ipynb">python-datatypes.ipynb</a></li>
+
+              <li>more
+		datatypes: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-advanced-datatypes.ipynb">github link</a>,
+		raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-advanced-datatypes.ipynb">python-advanced-datatypes.ipynb</a></li>
+
+              <li>control
+		flow: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-control-flow.ipynb">github link</a>,
+		raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-control-flow.ipynb">python-control-flow.ipynb</a></li>
+
+              <li>functions: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-functions.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-functions.ipynb">python-functions.ipynb</a></li>
+
+	      <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-inclass-2.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-inclass-2.ipynb">python-inclass-2.ipynb</a></li>
+		
+              <li>classes: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-classes.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-classes.ipynb">python-classes.ipynb</a></li>
+
+	      <li><b>in-class exercises:</b> <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-inclass-3.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-inclass-3.ipynb">python-inclass-3.ipynb</a></li>
+	      
+              <li>modules: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-modules.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-modules.ipynb">python-modules.ipynb</a>;<br>
+		<a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/myprofile.py">[myprofile.py]</a>
+		(the module example file) </li>
+
+              <li>exceptions: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-exceptions.ipynb">github link</a>, 
+	          raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-exceptions.ipynb">python-exceptions.ipynb</a></li>
+
+              <li>I/O: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/01-python/python-io.ipynb">github link</a>,
+		  raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/01-python/python-io.ipynb">python-io.ipynb</a>;<br>
+		<a href="./lectures/test.txt">[plain text sample]</a>
+		<a href="./lectures/shopping.csv">[CSV sample]</a>
+		<a href="./lectures/VARDEN-tests.ini">[Config/INI sample]</a></li>
+
+	    </ul>
+	  </li>
+
+	</ul>
+
+
+      <p><h3>The NumPy library</h3> (lecture 5)
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li>The
+              official <a href="http://www.scipy.org/Tentative_NumPy_Tutorial">
+		  <em>NumPy Tutorial</em></a></li>
+
+              <li><a href="http://mathesaurus.sourceforge.net/idl-numpy.html">
+		  <em>NumPy for IDL users</em></a></li>
+	    </ul>
+	  </li>
+
+	  <!-- <li>Lecture slides: <a href="./lectures/numpy.pdf">numpy.pdf</a></li> -->
+	  <li>NumPy notebook:
+	    <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/02-numpy/numpy-basics.ipynb">github link</a>,
+	    raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/02-numpy/numpy-basics.ipynb">numpy-basics.ipynb</a>
+	  </li>
+	</ul>
+
+
+      <p><h3>Python Practices</h3> (lecture 6)
+	
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li><a href="http://legacy.python.org/dev/peps/pep-0008/"><em>PEP-0008:
+		    Style Guide for Python Code</em></a></li>
+
+	    </ul>
+	  </li>
+
+	  <li>Lecture
+	    slides: <a href="lectures/python-practices.pdf">python-practices.pdf</a></li>
+
+	</ul>
+
+
+      <p><h3>Matplotlib and others</h3> (lecture 7)
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li>The
+              official <a href="http://matplotlib.org/users/pyplot_tutorial.html"><em>matplotlib
+		    tutorial</em></a></li>
+              <li>The <a href="http://matplotlib.org/faq/index.html"><em>matplotlib
+		    FAQ</em></a></li>
+              <li>The <a href="http://wiki.scipy.org/Cookbook/Matplotlib"><em>matplotlib
+		    cookbook</em></a></li>
+              <li>The <a href="http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html"><em>mplot3d
+		    tutorial</em></a>: basic 3-d plotting (surfaces, lines,
+		scatter plots)</li>
+
+              <li><a href="http://www.loria.fr/~rougier/teaching/matplotlib/"><em>Matplotlib
+		    Tutorial</em></a> by Nicolas P. Rougier</li>
+	    </ul>
+	  </li>
+	  <!-- <li>Lecture slides: <a href="./lectures/matplotlib.pdf">matplotlib.pdf</a></li> -->
+
+	  <li>Lecture notebooks:
+	    <ul>
+	      <li>basics: 
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/matplotlib-basics.ipynb">github link</a>, raw file:   <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/04-matplotlib/matplotlib-basics.ipynb">matplotlib-basics.ipynb</a>
+		<a href="./lectures/test1.exact.128.out">[sample data]</a></li>
+	      <li>interactive backend: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/matplotlib-interactive-backend.ipynb">github line</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/04-matplotlib/matplotlib-interactive-backend.ipynb">matplotlib-interactive-backend.ipynb</a></li>
+	      <li>ipyvolume example: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/04-matplotlib/ipyvolume-example.ipynb">github line</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/04-matplotlib/ipyvolume-example.ipynb">ipyvolume-example.ipynb</a></li>	      
+	    </ul>
+	  </li>
+	</ul>
+
+      <p><h3>SciPy and numerical methods</h3> (lectures 8&ndash;9)
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+	      <li>The official <a href="http://docs.scipy.org/doc/scipy/reference/tutorial/index.html"><em>SciPy Tutorial</em></a></li>
+
+              <li>The <a href="http://wiki.scipy.org/Cookbook"><em>SciPy cookbook</em></a></li>
+
+              <li><a href="http://scikits.appspot.com/scikits"><em>SciKits</em></a> are additional toolkits for SciPy which provide extra functionality</li>
+
+              <li><a href="http://scipy-central.org/"><em>SciPy Central</em></a> user-submitted SciPy snippets</li>
+
+              <li><a href="http://www.scipy.org/NumPy_for_Matlab_Users"><em>NumPy for Matlab Users</em></a></li>
+
+              <li><a href="http://adsabs.harvard.edu/abs/1963JAtS...20..130L"><em>Deterministic Nonperiodic Flow</em></a> by E. N. Lorenz&mdash;this is the system we integrated when discussing ODEs</li>
+
+              <li><a href="http://dl.acm.org/citation.cfm?id=1052672"><em>A simple example of an ill-conditioned matrix</em> by G. J. Tee</a></li>
+
+	    </ul>
+	  </li>
+
+	  <!-- <li>Lecture slides: <a href="./lectures/scipy.pdf">scipy.pdf</a></li> -->
+	  <li>Lecture IPython notebooks:
+	    <ul>
+	      <li>overview: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/05-scipy/scipy-basics.ipynb">github link</a>, raw file:
+	    <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/05-scipy/scipy-basics.ipynb">scipy-basics.ipynb</a></li>
+	      <li>in-class exercises: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/05-scipy/scipy-exercises.ipynb">github link</a>, raw file:
+		<a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/05-scipy/scipy-exercises.ipynb">scipy-exercises.ipynb</a></li>
+	      <li>in-class exercises 2: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/05-scipy/scipy-exercises-2.ipynb">github link</a>, raw file:
+		<a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/05-scipy/scipy-exercises-2.ipynb">scipy-exercises-2.ipynb</a></li>
+	    </ul>
+	  </li>
+	  <li>Other examples:
+	    <ul>
+	      <li>Gaussian elimination with pivoting:
+		<a href="examples/gauss.py">gauss.py</a> (main module),
+		<a href="examples/gauss-test.py">gauss-test.py</a> (test routine)
+		<a href="examples/matmul.py">matmul.py</a> (auxillary routine)</li>
+	    </ul>
+	  </li>
+
+	</ul>
+
+
+      <p><h3>SymPy</h3> (lecture 10)
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li>The official <a href="http://docs.sympy.org/latest/tutorial/"><em>SymPy tutorial</em></a></li>
+	    </ul>
+	  </li>
+
+	  <li>Lecture IPython notebook: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/06-sympy/sympy-examples.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/06-sympy/sympy-examples.ipynb">sympy-examples.ipynb</a></li>
+
+	</ul>
+
+
+      <p><h3>Pandas and the data frame</h3> (lecture 11)
+
+	<ul>
+	  <li>Readings: <em><a href="http://pandas.pydata.org/pandas-docs/stable/10min.html">10 Minutes to Pandas</a></em>
+	  </li>
+
+	  <li>Sample datasets: <a href="https://github.com/wesm/pydata-book">pydata-book examples</a></li>
+	  
+	  <li>Lecture IPython notebooks:
+	    <ul>
+	      <li>intro: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/07-pandas/pandas-intro.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/07-pandas/pandas-intro.ipynb">pandas-intro.ipynb</a> (sample data: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/07-pandas/sample.csv">sample.csv</a>)</li>
+	      <li>in-class exercise: <a href="https://github.com/sbu-python-class/python-science/blob/master/lectures/07-pandas/pandas-babynames.ipynb">github link</a>, raw file: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/07-pandas/pandas-babynames.ipynb">pandas-babynames.ipynb</a></li>
+	    </ul>
+	  </li>
+	</ul>
+		
+
+      <p><h3>Extending python with C/Fortran & System Operations</h3> (lecture 12)
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li><a href="http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html"><em>Interfacing with C</em></a> by Valentin Haenel as part of his SciPy lecture notes.  This is a very nice comparison of different methods</li>
+
+              <li><a href="http://technicaldiscovery.blogspot.com/2011/06/speeding-up-python-numpy-cython-and.html"><em>Speeding up Python (NumPy, Cython, and Weave)</em></a> by T. Oliphant</li>
+
+	      <li>C-API:
+		<ul>
+		  <li><a href="https://docs.python.org/3/extending/extending.html">
+		      <em>Extending Python with C or C++</em></a>: this is the "hard" way to do things.</li>
+		</ul>
+	      </li>
+
+              <li>ctypes:
+		<ul>
+		  <li><a href="https://docs.python.org/3/library/ctypes.html"><em>ctypes — A foreign function library for Python</em></a>:
+		    ctypes makes it easy to call existing C code.</li>
+		  <li><a href="http://scipy-cookbook.readthedocs.io/items/Ctypes.html"><em>SciPy ctypes cookbook</em></a>
+		</ul>
+              </li>
+
+              <li>f2py:
+		<ul>
+		  <li><a href="https://docs.scipy.org/doc/numpy-dev/f2py/">
+		      <em>f2py Users Guide</em></a></li>
+
+		  <li><a href="http://cens.ioc.ee/~pearu/papers/IJCSE4.4_Paper_8.pdf">
+		      <em>F2PY: a tool for connecting Fortran and Python programs</em></a></li>
+		</ul>
+              </li>
+
+	      <li>Cython:
+		<ul>
+		  <li><a href="http://cython.org/"><em>Cython, C-Extensions for Python</em></a>
+		    the official project page</li>
+
+		  <li><a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5582062">
+		      <em>Cython: The Best of Both Worlds</em></a>
+		    by S. Behnet et al. (alternate links:
+		    <a href="http://folk.uio.no/dagss/cython_cise.pdf">[here]</a>)</li>
+		</ul>
+	      </li>
+
+	    </ul>
+	  </li>
+
+	  <li>Lecture slides: <a href="lectures/extensions.pdf">extensions.pdf</a></li>
+	  <li>Example codes:
+	    <ul>
+              <li>C-API: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/C-API/test-C-API.py">test-C-API.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/C-API/numpy-ex.c">numpy-ex.c</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/C-API/setup.py">setup.py</a></li>
+
+              <li>ctypes: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/ctypes/simple/numpy-example.py">numpy-example.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/ctypes/simple/cfunc.c">cfunc.c</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/ctypes/simple/Makefile">Makefile</a></li>
+
+              <li>f2py: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/f2py/test_f2py.py">test_f2py.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/f2py/numpy_in_f.f90">numpy_in_f.f90</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/f2py/Makefile">Makefile</a></li>
+
+              <li>Cython: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/Cython/square/test_cy.py">test_cy.py</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/Cython/square/square.pyx">square.pyx</a>
+		<a href="https://github.com/sbu-python-class/python-science/blob/master/examples/extensions/Cython/square/setup.py">setup.py</a></li>
+
+              <li>Timing comparison for Laplace smoothing (this extends the comparison from the blog entry by T. Oliphant listed above):
+		<a href="https://github.com/sbu-python-class/python-science/tree/master/examples/extensions/timing">code directory</a>
+
+              <li>Calling an external command and capturing both stdout and stderr: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/external/Popen/githash.py">githash.py</a></li>
+
+	    </ul>
+	  </li>
+	</ul>
+
+
+      <p><h3>Building python applications / Packaging</h3> (lecture 13)
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li><a href="https://docs.python.org/3/tutorial/modules.html"><em>Python modules</em></a></li>
+
+	      <li><a href="https://packaging.python.org/current/"><em>Python packaging recommendations</em></a></li>
+	      
+	      <li><a href="https://packaging.python.org/glossary/">A glossary that explains the evolving terminology</a>&mdash;
+		this is part of the <a href="https://python-packaging.readthedocs.io/en/latest/"><em>Installation &amp; Packaging Tutorial</em></a></li>
+
+	      <li><a href="http://mrtopf.de/blog/en/a-small-introduction-to-python-eggs/"><em>A Small Introduction to Python Eggs</em></a></li>
+
+	      <li><a href="http://stackoverflow.com/questions/6344076/differences-between-distribute-distutils-setuptools-and-distutils2"><em>Differences between distribute, distutils, setuptools and distutils2?</em></a> on stackoverflow</li>
+
+	    </ul>
+
+	  <li>Lecture slides: <a href="lectures/packaging.pdf">packaging.pdf</a></li>
+
+	  <li>Examples:
+	    <ul>
+	      <li>argparse example: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/commandline/argparse_example.py">argparse_example.py</a></li>
+	    </ul>
+	  </li>
+
+	</ul>
+	
+      <p><h3>Testing</h3> (lecture 14)
+
+	<ul>
+	  <li>Readings:
+	  </li>
+
+	  <li>Lecture slides: <a href="lectures/testing.pdf">testing.pdf</a></li>
+
+	  <li>Examples:
+	    <ul>
+	      <li>simple example: <a href="https://github.com/sbu-python-class/python-science/tree/master/examples/testing/pytest/simple/test_simple.py">test_simple.py</a></li>
+	      <li>function setup example: <a href="https://github.com/sbu-python-class/python-science/tree/master/examples/testing/pytest/function_setup/test_function_setup.py">test_function_setup.py</a></li>
+	      <li>class-based example: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/testing/pytest/class/test_class.py">test_class.py</a></li>
+
+	    </ul>
+	  </li>
+	</ul>	
+	
+	
+
+      <p><h2>Other topics (if time)</h2>
+
+      <p><h3>GUIs</h3>
+
+	<ul>
+	  <li>Readings:
+	  </li>
+
+	  <li>Lecture slides: <a href="lectures/gui.pdf">gui.pdf</a></li>
+
+	  <li>Lecture IPython notebooks: <a href="https://raw.githubusercontent.com/sbu-python-class/python-science/master/lectures/10-GUI/gui-notebook.ipynb">gui-notebook.ipynb</a>
+	    <a href="http://nbviewer.ipython.org/github/sbu-python-class/python-science/blob/master/lectures/10-GUI/gui-notebook.ipynb">[render online]</a></li>
+	  
+	  <li>Examples:
+	    <ul>
+	      <li>A function plotter: <a href="https://github.com/sbu-python-class/python-science/blob/master/examples/GUI/plotter.py">plotter.py</a></li>
+	    </ul>
+	  </li>
+	</ul>	
+	
+      <p><h3>MayaVi</h3> 
+
+	<ul>
+	  <li>Readings:
+	    <ul>
+              <li><a href="http://wiki.scipy.org/Cookbook/MayaVi"><em>MayaVi Cookbook</em></a></li>
+              <li><a href="http://docs.enthought.com/mayavi/mayavi/mlab_case_studies.html"><em>MayaVi case studies</em></a></li>
+
+              <li><a href="http://docs.enthought.com/mayavi/mayavi/example_using_with_scipy.html"><em>Using MayaVi with SciPy</em></a></li>
+
+	    </ul>
+	  </li>
+
+	  <li>Lecture slides: <a href="lectures/mayavi.pdf">mayavi.pdf</a></li>
+
+	  <li>Examples:
+	    <ul>
+	      <li>Scalar data: <a href="examples/mayavi-basic.py">mayavi-basic.py</a></li>
+	      <li>Vector data: <a href="examples/mayavi-vector.py">mayavi-vector.py</a></li>
+	    </ul>
+	  </li>
+	</ul>	
+
+      <p><h3>NetworkX</h3>
+
+
+      <p><h3>Interfacing with Arduino Microcontrollers</h3>
+
+
+      <p><h3>h5py and HDF5</h3>
+
+      <p>&nbsp;
+      <p>Last Modified: <!--#echo var="LAST_MODIFIED" -->
+
+    </div>
+
+  </body>
+</html>
diff --git a/docs/old/lectures/VARDEN-tests.ini b/docs/old/lectures/VARDEN-tests.ini
new file mode 100644
index 00000000..15cd4b45
--- /dev/null
+++ b/docs/old/lectures/VARDEN-tests.ini
@@ -0,0 +1,74 @@
+[main]
+boxLibDir      = /home/regtester/RegTesting/BoxLib/
+sourceDir      = /home/regtester/RegTesting/VARDEN/
+testTopDir     = /home/regtester/RegTesting/rt-VARDEN/
+webTopDir      = /home/regtester/RegTesting/rt-VARDEN/web
+compareToolDir = /home/regtester/RegTesting/AmrPostprocessing/F_Src
+
+MAKE = make
+sourceTree = F_Src
+numMakeJobs = 8
+
+COMP = g++
+FCOMP = gfortran
+
+# suiteName is the name prepended to all output directories
+suiteName = VARDEN
+
+reportActiveTestsOnly = 1
+
+# Add "GO UP" link at the top of the web page?
+goUpLink = 1
+
+
+# MPIcommand should use the placeholders:
+#   @host@ to indicate where to put the hostname to run on
+#   @nprocs@ to indicate where to put the number of processors
+#   @command@ to indicate where to put the command to run
+#
+# only tests with useMPI = 1 will run in parallel
+# nprocs is problem dependent and specified in the individual problem
+# sections.
+
+#MPIcommand = mpiexec -host @host@ -n @nprocs@ @command@
+MPIcommand = /usr/local/bin/mpiexec -n @nprocs@ @command@
+MPIhost = 
+
+# individual problems follow
+
+[bubble-2d] 
+buildDir = varden/test
+inputFile = inputs_2d-regt
+dim = 2
+restartTest = 0
+useMPI = 1
+numprocs = 2
+useOMP = 0
+numthreads = 2
+compileTest = 0
+doVis = 0
+
+[bubble-3d] 
+buildDir = varden/test
+inputFile = inputs_3d-regt
+dim = 3
+restartTest = 0
+useMPI = 1
+numprocs = 3
+useOMP = 1
+numthreads = 2
+compileTest = 0
+doVis = 0
+
+[bubble-restart] 
+buildDir = varden/test
+inputFile = inputs-restart-regt
+dim = 3
+restartTest = 1
+restartFileNum = 4
+useMPI = 1
+numprocs = 3
+useOMP = 1
+numthreads = 2
+compileTest = 0
+doVis = 0
diff --git a/docs/old/lectures/extensions.pdf b/docs/old/lectures/extensions.pdf
new file mode 100644
index 00000000..7ed13f91
Binary files /dev/null and b/docs/old/lectures/extensions.pdf differ
diff --git a/docs/old/lectures/gui.pdf b/docs/old/lectures/gui.pdf
new file mode 100644
index 00000000..63fce9a4
Binary files /dev/null and b/docs/old/lectures/gui.pdf differ
diff --git a/docs/old/lectures/matplotlib.pdf b/docs/old/lectures/matplotlib.pdf
new file mode 100644
index 00000000..4265cc15
Binary files /dev/null and b/docs/old/lectures/matplotlib.pdf differ
diff --git a/docs/old/lectures/mayavi.pdf b/docs/old/lectures/mayavi.pdf
new file mode 100644
index 00000000..e0c76650
Binary files /dev/null and b/docs/old/lectures/mayavi.pdf differ
diff --git a/docs/old/lectures/numpy.pdf b/docs/old/lectures/numpy.pdf
new file mode 100644
index 00000000..7c47e436
Binary files /dev/null and b/docs/old/lectures/numpy.pdf differ
diff --git a/docs/old/lectures/packaging.pdf b/docs/old/lectures/packaging.pdf
new file mode 100644
index 00000000..399f0db7
Binary files /dev/null and b/docs/old/lectures/packaging.pdf differ
diff --git a/lectures/01-python/profile.py b/docs/old/lectures/profile.py
similarity index 100%
rename from lectures/01-python/profile.py
rename to docs/old/lectures/profile.py
diff --git a/docs/old/lectures/python-practices.pdf b/docs/old/lectures/python-practices.pdf
new file mode 100644
index 00000000..455d8fcf
Binary files /dev/null and b/docs/old/lectures/python-practices.pdf differ
diff --git a/docs/old/lectures/python.pdf b/docs/old/lectures/python.pdf
new file mode 100644
index 00000000..08cac446
Binary files /dev/null and b/docs/old/lectures/python.pdf differ
diff --git a/docs/old/lectures/scipy.pdf b/docs/old/lectures/scipy.pdf
new file mode 100644
index 00000000..54195ff2
Binary files /dev/null and b/docs/old/lectures/scipy.pdf differ
diff --git a/docs/old/lectures/shopping.csv b/docs/old/lectures/shopping.csv
new file mode 100644
index 00000000..e3fbe462
--- /dev/null
+++ b/docs/old/lectures/shopping.csv
@@ -0,0 +1,7 @@
+item,quantity,unit price,total
+apples,2,0.33,0.66
+bananas,5,0.1,0.5
+milk,1,2.5,2.5
+soda,3,1,3
+rolls,12,0.33,3.96
+eggs,1,2.5,2.5
diff --git a/docs/old/lectures/test.png b/docs/old/lectures/test.png
new file mode 100644
index 00000000..ac5f8f2d
Binary files /dev/null and b/docs/old/lectures/test.png differ
diff --git a/docs/old/lectures/test.txt b/docs/old/lectures/test.txt
new file mode 100644
index 00000000..3c6f52fc
--- /dev/null
+++ b/docs/old/lectures/test.txt
@@ -0,0 +1,8 @@
+Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do
+eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad
+minim veniam, quis nostrud exercitation ullamco laboris nisi ut
+aliquip ex ea commodo consequat. Duis aute irure dolor in
+reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla
+pariatur. Excepteur sint occaecat cupidatat non proident, sunt in
+culpa qui officia deserunt mollit anim id est laborum.
+
diff --git a/docs/old/lectures/test1.exact.128.out b/docs/old/lectures/test1.exact.128.out
new file mode 100644
index 00000000..1f67c6a1
--- /dev/null
+++ b/docs/old/lectures/test1.exact.128.out
@@ -0,0 +1,129 @@
+#  i                         x                       rho                         u                         p                         T                         e                   gamma_1
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+  98      761718.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+  99      769531.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 100      777343.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 101      785156.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 102      792968.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 103      800781.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 104      808593.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 105      816406.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 106      824218.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 107      832031.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 108      839843.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 109      847656.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 110      855468.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 111      863281.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 112      871093.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 113      878906.250000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 114      886718.750000000          2952874.14623905          322580638.298037         0.182888671613425E+24      758652699.898829         0.109749790986499E+18      1.49555196135596    
+ 115      894531.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 116      902343.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 117      910156.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 118      917968.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 119      925781.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 120      933593.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 121      941406.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 122      949218.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 123      957031.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 124      964843.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 125      972656.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 126      980468.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 127      988281.250000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
+ 128      996093.750000000          1000000.00000000          0.00000000000000         0.255457263570597E+23      999999.999998600         0.408260700921391E+17      1.57201407471898    
diff --git a/docs/old/lectures/testing.pdf b/docs/old/lectures/testing.pdf
new file mode 100644
index 00000000..74fd92b5
Binary files /dev/null and b/docs/old/lectures/testing.pdf differ
diff --git a/docs/old/python.png b/docs/old/python.png
new file mode 100644
index 00000000..23a4c6e3
Binary files /dev/null and b/docs/old/python.png differ
diff --git a/examples/GUI/NOTES b/examples/GUI/NOTES
new file mode 100644
index 00000000..47d1a0e0
--- /dev/null
+++ b/examples/GUI/NOTES
@@ -0,0 +1,22 @@
+Basic intro:
+http://sebsauvage.net/python/gui/
+
+Nice example of a phonelist app:
+http://www.openbookproject.net/py4fun/gui/tkPhone.html
+
+Layouts:
+http://zetcode.com/gui/tkinter/layout/
+
+
+sympy evaluate:
+
+from sympy.parsing.sympy_parser import parse_expr
+a = sympy.parse_expr("1+x", evaluate=0)
+from sympy.abc import x
+print a.evalf(subs={x:1})
+
+
+
+-- doing everything as a class:
+
+   http://programmers.stackexchange.com/questions/213935/why-use-classes-when-programming-a-tkinter-gui-in-python
\ No newline at end of file
diff --git a/examples/GUI/event-example.py b/examples/GUI/event-example.py
new file mode 100644
index 00000000..10d317e9
--- /dev/null
+++ b/examples/GUI/event-example.py
@@ -0,0 +1,54 @@
+# a simple example of an event loop that implements a CLI inventory
+# system
+#
+# requires python 3
+
+import sys
+
+help_str = """
+{h, help}                 : show this help
+{a, add} item quantity    : add a quantity of item to inventory
+{r, remove} item quantity : remove a quantity of item from inventory
+{l, list}                 : list the inventory
+{q, quit}                 : quit
+"""
+
+inventory = {}
+
+while True:
+
+    a = input("\nCommand (or Enter 'h' for help)\n>> ")
+
+    parts = a.split()
+    cmd = parts.pop(0)
+    
+    if cmd in ["h", "help"]:
+        print(help_str)
+
+    elif cmd in ["a", "add"]:
+        if not len(parts) == 2:
+            print("invalid add")
+        else:
+            if not parts[0] in inventory.keys():
+                inventory[parts[0]] = int(parts[1])
+            else:
+                inventory[parts[0]] += int(parts[1])
+            
+    elif cmd in ["r", "remove"]:
+        if not len(parts) == 2:
+            print("invalid remove")
+        else:
+            if not parts[0] in inventory.keys():
+                print("unable to remove -- no items in inventory")
+            else:
+                inventory[parts[0]] -= int(parts[1])
+
+    elif cmd in ["l", "list"]:
+        for k, v in inventory.items():
+            print("{:30} {:10}".format(k, v))
+
+    elif cmd in ["q", "quit"]:
+        sys.exit()
+
+
+
diff --git a/examples/GUI/fitter.py b/examples/GUI/fitter.py
new file mode 100644
index 00000000..95f990d6
--- /dev/null
+++ b/examples/GUI/fitter.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+
+import matplotlib
+matplotlib.use('TkAgg')
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+import sympy
+from sympy.abc import x
+from sympy.parsing.sympy_parser import parse_expr
+
+import sys
+if sys.version_info[0] < 3:
+    import Tkinter as Tk
+    import tkFont as font
+else:
+    import tkinter as Tk
+    from tkinter import font
+
+
+def str_to_f(str):
+    a = parse_expr(str, evaluate=0)
+    f = sympy.lambdify(x, a, "numpy")
+    return f
+
+
+class FitterGUI:
+    def __init__(self, parent):
+
+        self.parent = parent
+        
+        myfont = font.Font() #size=14)
+
+        # data file
+        
+        self.frame_data = Tk.Frame(self.parent, relief=Tk.SUNKEN, borderwidth=1)
+        self.frame_data.pack(fill=Tk.BOTH, expand=1, ipadx=5, ipady=5)
+        
+        self.dlabel = Tk.Label(self.frame_data, text="data", font=myfont, fg="red")
+        self.dlabel.grid(row=0, column=0, sticky=Tk.W, padx=5)
+
+        self.file_label = Tk.Label(self.frame_data, text="file: ", font=myfont)
+        self.file_label.grid(row=1, column=0, sticky=Tk.W, padx=5, ipadx=15)
+
+
+        # fitting function
+        
+        self.frame_func = Tk.Frame(self.parent, relief=Tk.SUNKEN, borderwidth=1)
+        self.frame_func.pack(fill=Tk.BOTH, expand=1, ipadx=5, ipady=5)
+        
+        self.flabel = Tk.Label(self.frame_func, text="fitting function", font=myfont, fg="red")
+        self.flabel.grid(row=0, column=0, sticky=Tk.W, padx=5)
+
+
+        # # a tk.DrawingArea
+        
+        self.f = Figure(figsize=(7,7), dpi=100)
+        self.a = self.f.add_subplot(111)
+        self.canvas = FigureCanvasTkAgg(self.f, master=self.parent)
+        self.canvas.show()
+        self.canvas.get_tk_widget().pack()
+        
+
+        # button bar
+        
+        self.frame_buttons = Tk.Frame(root)
+        self.frame_buttons.pack(fill=Tk.BOTH, expand=1)
+
+        self.plot_button = Tk.Button(self.frame_buttons, text='Fit',
+                                     command=self._plot, font=myfont)
+        self.plot_button.pack(side=Tk.BOTTOM)
+
+        self.clear_button = Tk.Button(self.frame_buttons, text='Clear',
+                                      command=self._clear, font=myfont)
+        self.clear_button.pack(side=Tk.BOTTOM)
+
+        self.quit_button = Tk.Button(self.frame_buttons, text='Quit',
+                                     command=self._quit, font=myfont)
+        self.quit_button.pack(side=Tk.BOTTOM)
+
+        self.plot_button.pack(side=Tk.LEFT, padx=5, pady=5)
+        self.clear_button.pack(side=Tk.LEFT, padx=5, pady=5)
+        self.quit_button.pack(side=Tk.RIGHT)
+        
+        
+    def _plot(self):
+        f_str = self.func.get()
+        f = str_to_f(f_str)
+
+        xm = float(self.xmin.get())
+        xM = float(self.xmax.get())
+    
+        xv = np.linspace(xm, xM, 1000)
+        fv = f(xv)
+
+        self.a.plot(xv, fv)
+        self.a.set_xlim([xm, xM])
+        self.canvas.show()
+
+
+    def _clear(self):
+        self.a.clear()
+        self.canvas.draw()
+    
+    
+    def _quit(self):
+        self.parent.quit()     # stops mainloop
+
+        # this is necessary on Windows to prevent Fatal Python Error:
+        # PyEval_RestoreThread: NULL tstate
+        self.parent.destroy()  
+
+    
+root = Tk.Tk()
+root.wm_title("plotter")
+
+plotter_gui = FitterGUI(root)
+
+root.mainloop()
+# If you put root.destroy() here, it will cause an error if
+# the window is closed with the window manager.
+
+
diff --git a/examples/GUI/fitter_theme.py b/examples/GUI/fitter_theme.py
new file mode 100644
index 00000000..ee392f84
--- /dev/null
+++ b/examples/GUI/fitter_theme.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+
+import matplotlib
+matplotlib.use('TkAgg')
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+import sympy
+from sympy.abc import x
+from sympy.parsing.sympy_parser import parse_expr
+
+import sys
+if sys.version_info[0] < 3:
+    import Tkinter as Tk
+    import ttk
+    import tkFont as font
+else:
+    import tkinter as Tk
+    from tkinter import ttk
+    from tkinter import font
+
+
+def str_to_f(str):
+    a = parse_expr(str, evaluate=0)
+    f = sympy.lambdify(x, a, "numpy")
+    return f
+
+
+class FitterGUI:
+    def __init__(self, parent):
+
+        self.parent = parent
+
+        self.style = ttk.Style()
+        self.style.theme_use("default")
+        
+        #myfont = font.Font(size=14)
+
+        # data file
+        
+        self.frame_data = ttk.Frame(self.parent, relief=Tk.SUNKEN, borderwidth=1)
+        self.frame_data.pack(fill=Tk.BOTH, expand=1, ipadx=5, ipady=5)
+        
+        self.dlabel = ttk.Label(self.frame_data, text="data")
+        self.dlabel.grid(row=0, column=0, sticky=Tk.W, padx=5)
+
+        self.file_label = ttk.Label(self.frame_data, text="file: ")
+        self.file_label.grid(row=1, column=0, sticky=Tk.W, padx=5, ipadx=15)
+
+
+        # fitting function
+        
+        self.frame_func = ttk.Frame(self.parent, relief=Tk.SUNKEN, borderwidth=1)
+        self.frame_func.pack(fill=Tk.BOTH, expand=1, ipadx=5, ipady=5)
+        
+        self.flabel = ttk.Label(self.frame_func, text="fitting function")
+        self.flabel.grid(row=0, column=0, sticky=Tk.W, padx=5)
+
+
+        # # a tk.DrawingArea
+        
+        self.f = Figure(figsize=(7,7), dpi=100)
+        self.a = self.f.add_subplot(111)
+        self.canvas = FigureCanvasTkAgg(self.f, master=self.parent)
+        self.canvas.show()
+        self.canvas.get_tk_widget().pack()
+        
+
+        # button bar
+        
+        self.frame_buttons = ttk.Frame(root)
+        self.frame_buttons.pack(fill=Tk.BOTH, expand=1)
+
+        self.plot_button = ttk.Button(self.frame_buttons, text='Fit',
+                                     command=self._plot)
+        self.plot_button.pack(side=Tk.BOTTOM)
+
+        self.clear_button = ttk.Button(self.frame_buttons, text='Clear',
+                                      command=self._clear)
+        self.clear_button.pack(side=Tk.BOTTOM)
+
+        self.quit_button = ttk.Button(self.frame_buttons, text='Quit',
+                                     command=self._quit)
+        self.quit_button.pack(side=Tk.BOTTOM)
+
+        self.plot_button.pack(side=Tk.LEFT, padx=5, pady=5)
+        self.clear_button.pack(side=Tk.LEFT, padx=5, pady=5)
+        self.quit_button.pack(side=Tk.RIGHT)
+        
+        
+    def _plot(self):
+        f_str = self.func.get()
+        f = str_to_f(f_str)
+
+        xm = float(self.xmin.get())
+        xM = float(self.xmax.get())
+    
+        xv = np.linspace(xm, xM, 1000)
+        fv = f(xv)
+
+        self.a.plot(xv, fv)
+        self.a.set_xlim([xm, xM])
+        self.canvas.show()
+
+
+    def _clear(self):
+        self.a.clear()
+        self.canvas.draw()
+    
+    
+    def _quit(self):
+        self.parent.quit()     # stops mainloop
+
+        # this is necessary on Windows to prevent Fatal Python Error:
+        # PyEval_RestoreThread: NULL tstate
+        self.parent.destroy()  
+
+    
+root = Tk.Tk()
+root.wm_title("plotter")
+
+plotter_gui = FitterGUI(root)
+
+root.mainloop()
+# If you put root.destroy() here, it will cause an error if
+# the window is closed with the window manager.
+
+
diff --git a/examples/GUI/parse.py b/examples/GUI/parse.py
new file mode 100644
index 00000000..759120c6
--- /dev/null
+++ b/examples/GUI/parse.py
@@ -0,0 +1,14 @@
+import numpy as np
+import sympy
+from sympy.abc import x
+from sympy.parsing.sympy_parser import parse_expr
+
+fstr = input("f(x) = ")
+
+a = parse_expr(fstr, evaluate=0)
+
+f = sympy.lambdify(x, a, "numpy")
+
+x = np.linspace(0,1,100)
+
+print(f(x))
diff --git a/examples/GUI/plotter.py b/examples/GUI/plotter.py
new file mode 100644
index 00000000..58c70a9e
--- /dev/null
+++ b/examples/GUI/plotter.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+
+# based on http://matplotlib.org/1.4.2/examples/user_interfaces/embedding_in_tk.html
+
+import matplotlib
+
+matplotlib.use('TkAgg')
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import sympy
+from matplotlib.backends.backend_tkagg import (FigureCanvasTkAgg,
+                                               NavigationToolbar2Tk)
+from matplotlib.figure import Figure
+from sympy.abc import x
+from sympy.parsing.sympy_parser import parse_expr
+
+if sys.version_info[0] < 3:
+    import tkFont
+    import Tkinter as Tk
+else:
+    import tkinter as Tk
+    import tkinter.font as tkFont
+
+
+def str_to_f(str):
+    a = parse_expr(str, evaluate=0)
+    f = sympy.lambdify(x, a, "numpy")
+    return f
+
+
+
+class PlotterGUI:
+    def __init__(self, parent):
+
+        self.parent = parent
+        
+        myfont = tkFont.Font(size=14)
+
+        self.frame_function = Tk.Frame(self.parent)
+        self.frame_function.pack(fill=Tk.BOTH, expand=1)
+
+        self.label = Tk.Label(self.frame_function, text="f(x) = ", font=myfont)
+        self.label.grid(row=0, column=0, sticky=Tk.W)
+        self.v = Tk.StringVar()
+        self.func = Tk.Entry(self.frame_function, textvariable=self.v,
+                             font=myfont)
+        self.func.grid(row=0, column=1, sticky=Tk.W)
+
+        self.frame_extrema = Tk.Frame(self.parent)
+        self.frame_extrema.pack(fill=Tk.BOTH, expand=1)
+
+        self.xmin_label = Tk.Label(self.frame_extrema, text="xmin: ",
+                                   font=myfont)
+        self.xmin_label.grid(row=0, column=0, sticky=Tk.W)
+        self.xmin_v = Tk.StringVar()
+        self.xmin = Tk.Entry(self.frame_extrema, textvariable=self.xmin_v,
+                             font=myfont)
+        self.xmin_v.set("0.0")
+        self.xmin.grid(row=0, column=1, sticky=Tk.W)
+
+        self.xmax_label = Tk.Label(self.frame_extrema, text="xmax: ",
+                                   font=myfont)
+        self.xmax_label.grid(row=0, column=2, sticky=Tk.W)
+        self.xmax_v = Tk.StringVar()
+        self.xmax = Tk.Entry(self.frame_extrema, textvariable=self.xmax_v,
+                             font=myfont)
+        self.xmax_v.set("1.0")
+        self.xmax.grid(row=0, column=3, sticky=Tk.W)
+
+
+        # a tk.DrawingArea
+        self.f = Figure(figsize=(6,5), dpi=100)
+        self.a = self.f.add_subplot(111)
+        self.canvas = FigureCanvasTkAgg(self.f, master=self.parent)
+        self.canvas.draw()
+        self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+
+        #toolbar = NavigationToolbar2TkAgg( canvas, root )
+        #toolbar.update()
+        #canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+
+        self.frame_buttons = Tk.Frame(root)
+        self.frame_buttons.pack(fill=Tk.BOTH, expand=1)
+
+        self.plot_button = Tk.Button(self.frame_buttons, text='Plot',
+                                     command=self._plot, font=myfont) 
+        self.plot_button.pack(side=Tk.BOTTOM)
+
+        self.clear_button = Tk.Button(self.frame_buttons, text='Clear',
+                                      command=self._clear, font=myfont)
+        self.clear_button.pack(side=Tk.BOTTOM)
+
+        self.quit_button = Tk.Button(self.frame_buttons, text='Quit',
+                                     command=self._quit, font=myfont)
+        self.quit_button.pack(side=Tk.BOTTOM)
+
+        self.plot_button.pack(side=Tk.LEFT, padx=5, pady=5)
+        self.clear_button.pack(side=Tk.LEFT, padx=5, pady=5)
+        self.quit_button.pack(side=Tk.RIGHT)
+
+
+        self.func.bind('<Return>', self._plot)
+        self.xmin.bind('<Return>', self._plot)
+        self.xmax.bind('<Return>', self._plot)
+        # this allows the use of the enter key to plot the function as well 
+        # while the cursor is in any one of the three text-entry fields
+
+        
+    def _plot(self, event=None):
+        f_str = self.func.get()
+        f = str_to_f(f_str)
+
+        xm = float(self.xmin.get())
+        xM = float(self.xmax.get())
+    
+        xv = np.linspace(xm, xM, 1000)
+        fv = f(xv)
+
+        self.a.plot(xv, fv)
+        self.a.set_xlim([xm, xM])
+        self.canvas.draw()
+
+
+    def _clear(self):
+        self.a.clear()
+        self.canvas.draw()
+    
+    
+    def _quit(self):
+        self.parent.quit()     # stops mainloop
+
+        # this is necessary on Windows to prevent Fatal Python Error:
+        # PyEval_RestoreThread: NULL tstate
+        self.parent.destroy()  
+
+    
+root = Tk.Tk()
+root.wm_title("plotter")
+
+plotter_gui = PlotterGUI(root)
+
+root.mainloop()
+# If you put root.destroy() here, it will cause an error if
+# the window is closed with the window manager.
+
+
diff --git a/examples/commandline/argparse_example.py b/examples/commandline/argparse_example.py
index fa046465..c4a93063 100755
--- a/examples/commandline/argparse_example.py
+++ b/examples/commandline/argparse_example.py
@@ -1,20 +1,16 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 # to get usage: use -h
-import sys
 import argparse
 
-
 # simple example of argparse
-#
-# ./argparse_example.py -a -b # -c string --darg --earg fextra
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-a", help="the -a option", action="store_true")
 parser.add_argument("-b", help="-b takes a number", type=int, default=0)
 parser.add_argument("-c", help="-c takes a string", type=str, default=None)
 parser.add_argument("--darg", help="the --darg option", action="store_true")
-parser.add_argument("--earg", help="--earg takes a string", type=str,
+parser.add_argument("--earg", help="--earg takes a string", type=str, metavar="test",
                     default="example string")
 
 # extra arguments (positional)
@@ -23,15 +19,16 @@
 
 args = parser.parse_args()
 
+if args.a:
+    print("-a set")
+print(f"-b = {args.b}")
+print(f"-c = {args.c}")
+if args.darg:
+    print("--dargs set")
+print(f"--earg value = {args.earg}")
 
-if args.a: print "-a set"
-print "-b = {}".format(args.b)
-print "-c = {}".format(args.c)
-if args.darg: print "--dargs set"
-print "--earg value = {}".format(args.earg)
-
-print " "
-print "extra positional arguments: "
+print(" ")
+print("extra positional arguments: ")
 if len(args.extras) > 0:
     for e in args.extras:
-        print e
+        print(e)
diff --git a/examples/extensions/C-API/README b/examples/extensions-old/C-API/README
similarity index 75%
rename from examples/extensions/C-API/README
rename to examples/extensions-old/C-API/README
index 2c52ea3a..adbb9be1 100644
--- a/examples/extensions/C-API/README
+++ b/examples/extensions-old/C-API/README
@@ -1,9 +1,13 @@
+This example for using the C-API with NumPy is for python 3.x only --
+the interface for python 2.x is different.  Note that this was written
+to use the latest API.
+
 reference: http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html
            http://wiki.scipy.org/Cookbook/C_Extensions/NumPy_arrays
 
 to build:
 
-python setup.py build_ext --inplace
+python3 setup.py build_ext --inplace
 
 to run:
 
@@ -28,7 +32,3 @@ to run:
  [ 225.  256.  289.  324.  361.]]
 
 
-
-
-Note that directly accessing the fields in the PyArrayObject struct is
-deprecated, and there will be a warning issued.
diff --git a/examples/extensions-old/C-API/numpy-ex.c b/examples/extensions-old/C-API/numpy-ex.c
new file mode 100644
index 00000000..95425267
--- /dev/null
+++ b/examples/extensions-old/C-API/numpy-ex.c
@@ -0,0 +1,131 @@
+/* see 
+   http://wiki.scipy.org/Cookbook/C_Extensions/NumPy_arrays
+   http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html 
+   http://stackoverflow.com/questions/24189002/seg-fault-while-using-numpy-with-python3
+   https://docs.python.org/3/extending/extending.html
+*/
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <Python.h>
+#include <numpy/arrayobject.h>
+#include <math.h>
+
+
+/* a static function in C limits its scope to this file -- the linker
+   won't complain about clashes */
+static PyObject* ex_function(PyObject* self, PyObject* args)
+{
+
+  PyArrayObject *iarray, *oarray;
+  double **iA, **oA;
+  int i, j, m, n, dims[2];
+
+  /* parse the inputs -- we need to know what the arguments of our
+     call were.  We'll assume:
+
+     ex_function(array)
+
+     and that we return a new array of the same dimensions
+
+     In the parsing, you can do O or O! here (from the docs):
+
+     O (object) [PyObject *]
+
+       Store a Python object (without any conversion) in a C object
+       pointer. The C program thus receives the actual object that was
+       passed. The object’s reference count is not increased. The
+       pointer stored is not NULL.
+
+     O! (object) [typeobject, PyObject *]
+
+       Store a Python object in a C object pointer. This is similar to
+       O, but takes two C arguments: the first is the address of a
+       Python type object, the second is the address of the C variable
+       (of type PyObject*) into which the object pointer is stored. If
+       the Python object does not have the required type, TypeError is
+       raised.
+    
+     O! seems safer and preferred
+
+  */
+
+  if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &iarray)) return NULL;
+  if (NULL == iarray) return NULL;
+
+  /* check to make sure we are a double type */
+  if (PyArray_DTYPE(iarray)->type_num != NPY_DOUBLE ||
+      PyArray_NDIM(iarray) != 2) {
+    PyErr_SetString(PyExc_ValueError, "wrong input array type");
+    return NULL;
+  }
+
+  /* get the dimensions */
+  n = dims[0] = PyArray_DIM(iarray, 0);
+  m = dims[1] = PyArray_DIM(iarray, 1);
+  
+
+  /* the new C interface can create iteration "object" using NpyIter, but we
+     are not going to do that here, we want to explicitly see the different
+     dimensions 
+  */
+
+  /* make a NumPy double matrix with the same dimensions -- this will
+     be contiguous, and will be our output (note, there is also a
+     PyArray_NewLikeArray function) */
+  oarray = (PyArrayObject *) PyArray_FromDims(2, dims, NPY_DOUBLE);
+
+  /* change contigous arrays into C ** arrays -- we need to have a
+     vector of pointers that point to the correct location in the
+     contiguous block of memory that stores the multi-dimensional
+     array data */
+  iA = (double **) malloc( (size_t) (n*sizeof(double)));
+  for (i = 0; i < n; i++) {
+    iA[i] = (double *) PyArray_DATA(iarray) + i*m;
+  }
+
+  oA = (double **) malloc( (size_t) (n*sizeof(double)));
+  for (i = 0; i < n; i++) {
+    oA[i] = (double *) PyArray_DATA(oarray) + i*m;
+  }
+  
+  /* now we can do our manipulation */
+  for (i = 0; i < n; i ++) {
+    for (j = 0; j < m; j++) {
+      oA[i][j] = iA[i][j]*iA[i][j];
+    }
+  }
+
+  /* free up the memory we allocated for the array indexing */
+  free (iA);
+  free (oA);
+
+  /* return our python array */
+  return PyArray_Return(oarray);
+
+}
+
+     
+/* this is the table for function names that Python will see */ 
+static PyMethodDef numpy_in_cMethods[] = {
+  {"example", ex_function, METH_VARARGS, 
+   "a simple example: square the elements of an array"},
+  {NULL, NULL}
+};
+
+static struct PyModuleDef moduledef = {
+  PyModuleDef_HEAD_INIT,
+  "numpy_in_c",  // name
+   "a simple example: square the elements of an array",  // documentation
+  -1,  // size
+  numpy_in_cMethods,  // methods
+};
+
+/* this tells python what to do when it first imports this module --
+   the name follows directly from the table name above */
+PyMODINIT_FUNC PyInit_numpy_in_c(void) {
+  PyObject *m;
+  m = PyModule_Create(&moduledef);
+  import_array();
+  return m;
+}
diff --git a/examples/extensions-old/C-API/setup.py b/examples/extensions-old/C-API/setup.py
new file mode 100644
index 00000000..adef569d
--- /dev/null
+++ b/examples/extensions-old/C-API/setup.py
@@ -0,0 +1,10 @@
+from distutils.core import setup, Extension
+import numpy
+
+# define the extension module
+numpy_in_c = Extension('numpy_in_c', sources=['numpy-ex.c'],
+                       include_dirs=[numpy.get_include()]) #,
+#                       define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")])
+
+# run the setup
+setup(ext_modules=[numpy_in_c])
diff --git a/examples/extensions-old/C-API/test-C-API.py b/examples/extensions-old/C-API/test-C-API.py
new file mode 100644
index 00000000..fa2df0b9
--- /dev/null
+++ b/examples/extensions-old/C-API/test-C-API.py
@@ -0,0 +1,12 @@
+import numpy_in_c
+import numpy as np
+
+a = np.arange(20, dtype=np.float64)
+a.shape = (4,5)
+print(a)
+
+b = numpy_in_c.example(a)
+print(b)
+
+
+
diff --git a/examples/extensions/Cython/laplace/README b/examples/extensions-old/Cython/laplace/README
similarity index 100%
rename from examples/extensions/Cython/laplace/README
rename to examples/extensions-old/Cython/laplace/README
diff --git a/examples/extensions/Cython/laplace/laplace.pyx b/examples/extensions-old/Cython/laplace/laplace.pyx
similarity index 100%
rename from examples/extensions/Cython/laplace/laplace.pyx
rename to examples/extensions-old/Cython/laplace/laplace.pyx
diff --git a/examples/extensions/Cython/laplace/setup.py b/examples/extensions-old/Cython/laplace/setup.py
similarity index 100%
rename from examples/extensions/Cython/laplace/setup.py
rename to examples/extensions-old/Cython/laplace/setup.py
diff --git a/examples/extensions/Cython/laplace/test_cy.py b/examples/extensions-old/Cython/laplace/test_cy.py
similarity index 84%
rename from examples/extensions/Cython/laplace/test_cy.py
rename to examples/extensions-old/Cython/laplace/test_cy.py
index ca44260a..6c7607a3 100644
--- a/examples/extensions/Cython/laplace/test_cy.py
+++ b/examples/extensions-old/Cython/laplace/test_cy.py
@@ -13,8 +13,8 @@
 # pure python version
 def py_update(u):
     nx, ny = u.shape
-    for i in xrange(1,nx-1):
-        for j in xrange(1, ny-1):
+    for i in range(1,nx-1):
+        for j in range(1, ny-1):
             u[i,j] = ((u[i+1, j] + u[i-1, j]) * dy2 +
                       (u[i, j+1] + u[i, j-1]) * dx2) / (2*(dx2+dy2))
 
@@ -32,14 +32,14 @@ def calc(N, Niter=100, func=py_update, args=()):
 
 res_py = calc(64)
 
-print "pure python: ", time.time() - start
+print("pure python: ", time.time() - start)
 
 
 start = time.time()
 
 res_cy = calc(64, func=laplace.cy_update, args=(dx2, dy2) )
 
-print "cython: ", time.time() - start
+print("cython: ", time.time() - start)
 
 
 
diff --git a/examples/extensions-old/Cython/square/setup.py b/examples/extensions-old/Cython/square/setup.py
new file mode 100644
index 00000000..35d7937a
--- /dev/null
+++ b/examples/extensions-old/Cython/square/setup.py
@@ -0,0 +1,11 @@
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+import numpy
+
+ext = Extension("square", ["square.pyx"],
+    include_dirs = [numpy.get_include()])
+                
+setup(ext_modules=[ext],
+      cmdclass = {'build_ext': build_ext})
diff --git a/examples/extensions-old/Cython/square/square.pyx b/examples/extensions-old/Cython/square/square.pyx
new file mode 100644
index 00000000..aaeb97f5
--- /dev/null
+++ b/examples/extensions-old/Cython/square/square.pyx
@@ -0,0 +1,25 @@
+cimport numpy as np
+import numpy as np
+
+#cython: boundscheck=False
+#cython: wraparound=False
+
+def cy_square(np.ndarray[double, ndim=2] A):
+    
+    # in Cython, we should use cdef to declare the types of the variables
+    # this helps with generating the C code
+    cdef int nx = A.shape[0]
+    cdef int ny = A.shape[1]
+
+    # return array -- we allocate it much like in straight NumPy
+    cdef np.ndarray B = np.zeros( (nx, ny), dtype=np.float64)
+
+    cdef int i, j
+    for i in range(nx):
+        for j in range(ny):
+            B[i,j] = A[i,j]**2
+
+    return B
+
+    
+
diff --git a/examples/extensions-old/Cython/square/test_cy.py b/examples/extensions-old/Cython/square/test_cy.py
new file mode 100644
index 00000000..ddd8bf83
--- /dev/null
+++ b/examples/extensions-old/Cython/square/test_cy.py
@@ -0,0 +1,41 @@
+import numpy as np
+import square
+
+import time
+
+N = 400
+M = 600
+
+A = np.arange(M*N, dtype=np.float64)
+A.shape = (N,M)
+
+# python version (note: this is a simple example, so doing B = A**2
+# will be faster than any of these methods)
+start = time.time()
+
+B = np.zeros_like(A)
+
+for i in range(A.shape[0]):
+    for j in range(A.shape[1]):
+        B[i,j] = A[i,j]**2
+
+
+print("pure python: ", time.time() - start)
+
+start = time.time()
+B = A**2
+print("numpy: ", time.time() - start)
+
+
+# using our Cython routine
+start = time.time()
+
+B_cy = square.cy_square(A)
+
+print("cython: ", time.time() - start)
+
+
+
+
+
+
diff --git a/examples/extensions-old/ctypes/multi-d/Makefile b/examples/extensions-old/ctypes/multi-d/Makefile
new file mode 100644
index 00000000..ee5ce8ab
--- /dev/null
+++ b/examples/extensions-old/ctypes/multi-d/Makefile
@@ -0,0 +1,12 @@
+ALL: cfunc_multid.so
+
+SOURCES := cfunc_multid.c
+OBJECTS := $(SOURCES:.c=.o)
+
+%.o: %.c
+	gcc -c -fPIC $< -o $@
+
+
+cfunc_multid.so: $(OBJECTS)
+	gcc -shared -o cfunc_multid.so $(OBJECTS)
+
diff --git a/examples/extensions/ctypes/multi-d/README b/examples/extensions-old/ctypes/multi-d/README
similarity index 100%
rename from examples/extensions/ctypes/multi-d/README
rename to examples/extensions-old/ctypes/multi-d/README
diff --git a/examples/extensions-old/ctypes/multi-d/cfunc_multid.c b/examples/extensions-old/ctypes/multi-d/cfunc_multid.c
new file mode 100644
index 00000000..6bb6b1d0
--- /dev/null
+++ b/examples/extensions-old/ctypes/multi-d/cfunc_multid.c
@@ -0,0 +1,27 @@
+#include <stdio.h>
+
+int my_subroutine(double *iarray, int ilen, int jlen,
+		  double *oarray) {
+
+  /* here iarray and oarray are pointers to a contiguous space in
+     memory.  We wish to access them as 2-d arrays. */
+
+  /* note: there is no error or bounds checking done here! */
+  double *iA[ilen], *oA[ilen];
+
+  int i;
+  for (i = 0; i < ilen; i++) {
+    iA[i] = iarray + i*jlen;
+    oA[i] = oarray + i*jlen;
+  }
+
+
+  int j;
+  for (i = 0; i < ilen; i++) {
+    for (j = 0; j < jlen; j++) {
+      oA[i][j] = iA[i][j]*iA[i][j];
+    }
+  }
+
+  return 0;
+}
diff --git a/examples/extensions-old/ctypes/multi-d/test-ctypes.py b/examples/extensions-old/ctypes/multi-d/test-ctypes.py
new file mode 100644
index 00000000..ec08ec9f
--- /dev/null
+++ b/examples/extensions-old/ctypes/multi-d/test-ctypes.py
@@ -0,0 +1,28 @@
+import numpy as np
+import ctypes as C
+from numpy.ctypeslib import ndpointer
+
+# the NumPy way to call this function
+
+_cfunc = np.ctypeslib.load_library('cfunc_multid', '.')
+_cfunc.my_subroutine.restype = C.c_int
+_cfunc.my_subroutine.argtypes = [ndpointer(C.c_double, flags="C_CONTIGUOUS"),
+                                 C.c_int, C.c_int, 
+                                 ndpointer(C.c_double, flags="C_CONTIGUOUS")]
+
+def my_subroutine(A, B):
+    return _cfunc.my_subroutine(A, C.c_int(A.shape[0]), C.c_int(A.shape[1]), B)
+
+A = np.arange(20, dtype=np.float64)
+A.shape = (4,5)
+
+
+# here we pre-allocate the return array
+B = np.zeros_like(A)
+
+n = my_subroutine(A, B)
+
+print(B)
+
+
+
diff --git a/examples/extensions/ctypes/multi-d/test_multid.py b/examples/extensions-old/ctypes/multi-d/test_multid.py
similarity index 98%
rename from examples/extensions/ctypes/multi-d/test_multid.py
rename to examples/extensions-old/ctypes/multi-d/test_multid.py
index 4f917e9f..1a6824ab 100644
--- a/examples/extensions/ctypes/multi-d/test_multid.py
+++ b/examples/extensions-old/ctypes/multi-d/test_multid.py
@@ -19,7 +19,7 @@ def my_subroutine(A, B):
 
 n = my_subroutine(A, B)
 
-print B
+print(B)
 
 
 
diff --git a/examples/extensions/ctypes/simple/Makefile b/examples/extensions-old/ctypes/simple/Makefile
similarity index 100%
rename from examples/extensions/ctypes/simple/Makefile
rename to examples/extensions-old/ctypes/simple/Makefile
diff --git a/examples/extensions/ctypes/simple/README b/examples/extensions-old/ctypes/simple/README
similarity index 100%
rename from examples/extensions/ctypes/simple/README
rename to examples/extensions-old/ctypes/simple/README
diff --git a/examples/extensions/ctypes/simple/cfunc.c b/examples/extensions-old/ctypes/simple/cfunc.c
similarity index 100%
rename from examples/extensions/ctypes/simple/cfunc.c
rename to examples/extensions-old/ctypes/simple/cfunc.c
diff --git a/examples/extensions-old/ctypes/simple/numpy-example.py b/examples/extensions-old/ctypes/simple/numpy-example.py
new file mode 100644
index 00000000..014bce37
--- /dev/null
+++ b/examples/extensions-old/ctypes/simple/numpy-example.py
@@ -0,0 +1,23 @@
+# see https://docs.scipy.org/doc/numpy/reference/routines.ctypeslib.html
+
+import numpy as np
+import numpy.ctypeslib as npc
+import ctypes as C
+
+# load our extension -- this returns a ctypes library object
+cfunc = npc.load_library('cfunc', '.')
+
+# set the return type
+cfunc.my_subroutine.restype = C.c_int
+
+# set the argument types
+cfunc.my_subroutine.argtypes = [npc.ndpointer(C.c_double, flags="C_CONTIGUOUS"),
+                                C.c_int]
+
+def my_subroutine(A):
+    return cfunc.my_subroutine(A, len(A))
+
+A = np.arange(10, dtype=np.float64)
+n = my_subroutine(A)
+
+
diff --git a/examples/extensions-old/ctypes/simple/pointer-example.py b/examples/extensions-old/ctypes/simple/pointer-example.py
new file mode 100644
index 00000000..7eefda4d
--- /dev/null
+++ b/examples/extensions-old/ctypes/simple/pointer-example.py
@@ -0,0 +1,24 @@
+# this version of the driver uses the normal ctypes POINTER(), so you
+# need to have your numpy array make itself appear as a C pointer
+# explicitly.
+
+import numpy as np
+import ctypes as C
+
+# load our extension -- this returns a ctypes library object
+cfunc = np.ctypeslib.load_library('cfunc', '.')
+
+# set the return type
+cfunc.my_subroutine.restype = C.c_int
+
+# set the argument types
+cfunc.my_subroutine.argtypes = [C.POINTER(C.c_double),
+                                C.c_int]
+
+def my_subroutine(A):
+    return cfunc.my_subroutine(A.ctypes.data_as(C.POINTER(C.c_double)), len(A))
+
+A = np.arange(10, dtype=np.float64)
+n = my_subroutine(A)
+
+
diff --git a/examples/extensions-old/f2py/Makefile b/examples/extensions-old/f2py/Makefile
new file mode 100644
index 00000000..96bee698
--- /dev/null
+++ b/examples/extensions-old/f2py/Makefile
@@ -0,0 +1,4 @@
+ALL: numpy_in_f.so
+
+numpy_in_f.so: numpy_in_f.f90
+	f2py3 --fcompiler=gnu95 -c numpy_in_f.f90 -m numpy_in_f
diff --git a/examples/extensions-old/f2py/numpy_in_f.f90 b/examples/extensions-old/f2py/numpy_in_f.f90
new file mode 100644
index 00000000..2ffc848b
--- /dev/null
+++ b/examples/extensions-old/f2py/numpy_in_f.f90
@@ -0,0 +1,23 @@
+subroutine square(a, b, nx, ny)
+
+  implicit none
+
+  integer, intent(in) :: nx, ny
+  double precision, intent(in) :: a(nx, ny)
+  double precision, intent(out) :: b(nx, ny)
+
+!f2py depend(nx, ny) :: a, b
+!f2py intent(in) :: a
+!f2py intent(out) :: b
+
+! call this as b = numpy_in_f.square(a, nx, ny)
+
+  integer :: i, j
+
+  do j = 1, ny
+     do i = 1, nx
+        b(i,j) = a(i,j)**2
+     enddo
+  enddo
+
+end subroutine square
diff --git a/examples/extensions-old/f2py/test_f2py.py b/examples/extensions-old/f2py/test_f2py.py
new file mode 100644
index 00000000..e384ccca
--- /dev/null
+++ b/examples/extensions-old/f2py/test_f2py.py
@@ -0,0 +1,14 @@
+import numpy as np
+import numpy_in_f
+
+a = np.arange(24, dtype=np.float64)
+a.shape = (4,6)
+
+print(a.flags)
+
+b = numpy_in_f.square(a, a.shape[0], a.shape[1])
+
+print(b)
+
+print(b.flags)
+
diff --git a/examples/extensions-old/timing/Makefile b/examples/extensions-old/timing/Makefile
new file mode 100644
index 00000000..59d1a22e
--- /dev/null
+++ b/examples/extensions-old/timing/Makefile
@@ -0,0 +1,18 @@
+ALL: laplace_fortran.so laplace_C.so laplace_CAPI.so
+
+laplace_fortran.so: laplace_fortran.f90
+	f2py3 --fcompiler=gnu95 -c laplace_fortran.f90 -m laplace_fortran
+
+laplace_C.so: laplace_C.c
+	gcc -c -fPIC laplace_C.c -o laplace_C.o
+	gcc -shared -o laplace_C.so laplace_C.o
+
+laplace_CAPI.so: laplace_CAPI.c
+	python3 setup.py build_ext --inplace
+
+
+.PHONY: clean
+
+clean: 
+	rm -f laplace_fortran.so laplace_C.so laplace_CAPI.so
+	rm -rf build
diff --git a/examples/extensions-old/timing/README b/examples/extensions-old/timing/README
new file mode 100644
index 00000000..09c92aca
--- /dev/null
+++ b/examples/extensions-old/timing/README
@@ -0,0 +1 @@
+comparison on Laplacian
\ No newline at end of file
diff --git a/examples/extensions-old/timing/laplace.py b/examples/extensions-old/timing/laplace.py
new file mode 100644
index 00000000..a10001b1
--- /dev/null
+++ b/examples/extensions-old/timing/laplace.py
@@ -0,0 +1,119 @@
+# based on http://technicaldiscovery.blogspot.com/2011/06/speeding-up-python-numpy-cython-and.html
+
+import numpy as np
+import time
+
+# here's an alternate way to do the building of the cython version
+import pyximport
+pyximport.install(setup_args={'include_dirs':[np.get_include()]})
+from laplace_cython import cy_update
+
+# f2py version
+from laplace_fortran import f90_update
+
+# ctypes version
+import ctypes as C
+from numpy.ctypeslib import ndpointer
+_cfunc = np.ctypeslib.load_library('laplace_C', '.')
+_cfunc.C_update.restype = C.c_int
+_cfunc.C_update.argtypes = [ndpointer(C.c_double, flags="C_CONTIGUOUS"),
+                            C.c_int, C.c_int,
+                            C.c_double, C.c_double]
+
+# C-API version
+from laplace_CAPI import CAPI_update
+
+
+dx = 0.1
+dy = 0.1
+dx2 = dx*dx
+dy2 = dy*dy
+
+# pure python (the wrong way) method
+def py_update(u):
+    nx, ny = u.shape
+    for i in range(1,nx-1):
+        for j in range(1, ny-1):
+            u[i,j] = ((u[i+1, j] + u[i-1, j]) * dy2 +
+                      (u[i, j+1] + u[i, j-1]) * dx2) / (2*(dx2+dy2))
+
+# numpy method -- note that this is actually a Jacobi iteration, not G-S.
+# the operation count should be the same, but the convergence will be worse.
+def num_update(u):
+    u[1:-1,1:-1] = ((u[2:,1:-1]+u[:-2,1:-1])*dy2 + 
+                    (u[1:-1,2:] + u[1:-1,:-2])*dx2) / (2*(dx2+dy2))
+
+
+
+# ctypes wrapper 
+def ctypes_update(u):
+    return _cfunc.C_update(u, C.c_int(u.shape[0]), C.c_int(u.shape[1]),
+                           dx2, dy2)
+
+
+# our driver that will run things with a given implementation of the 
+# smoothing
+def calc(N, Niter=100, func=py_update, args=(), order="C"):
+    u = np.zeros([N, N], dtype=np.float64, order=order)
+    u[0] = 1   # boundary condition
+    for i in range(Niter):
+        func(u,*args)
+    return u
+
+
+
+N = 64
+iters = 1000
+
+# pure python
+start = time.time()
+res_py = calc(N, Niter=iters)
+print("pure python: {} s".format(time.time()-start))
+
+
+# NumPy
+start = time.time()
+res_np = calc(N, Niter=iters, func=num_update)
+print("NumPy: {} s".format(time.time()-start))
+
+
+# Cython
+start = time.time()
+res_cy = calc(N, Niter=iters, func=cy_update, args=(dx2, dy2))
+print("Cython: {} s".format(time.time()-start))
+
+
+# f2py -- here we need to initialize the array in Fortran order so
+# we can use the "inout" argument type in Fortran
+start = time.time()
+res_f90 = calc(N, Niter=iters, func=f90_update, 
+              args=(N, N, dx2, dy2), order="F")
+print("f2py: {} s".format(time.time()-start))
+
+
+# ctypes
+start = time.time()
+res_ctypes = calc(N, Niter=iters, func=ctypes_update)
+print("ctypes: {} s".format(time.time()-start))
+
+
+# C-API
+start = time.time()
+res_CAPI = calc(N, Niter=iters, func=CAPI_update, args=(dx2, dy2))
+print("C-API: {} s".format(time.time()-start))
+
+
+
+
+
+
+
+# check the answers
+print(" ")
+print("max diff NumPy: {}".format(np.max(abs(res_py-res_np)[1:-1,1:-1])))
+print("max diff Cython: {}".format(np.max(abs(res_py-res_cy)[1:-1,1:-1])))
+print("max diff F90: {}".format(np.max(abs(res_py-res_f90)[1:-1,1:-1])))
+print("max diff ctypes: {}".format(np.max(abs(res_py-res_ctypes)[1:-1,1:-1])))
+print("max diff C-API: {}".format(np.max(abs(res_py-res_CAPI)[1:-1,1:-1])))
+
+
diff --git a/examples/extensions-old/timing/laplace_C.c b/examples/extensions-old/timing/laplace_C.c
new file mode 100644
index 00000000..bba75bec
--- /dev/null
+++ b/examples/extensions-old/timing/laplace_C.c
@@ -0,0 +1,26 @@
+int C_update(double *iarray, int ilen, int jlen, double dx2, double dy2) {
+		  
+
+  /* here iarray is a pointer to a contiguous space in
+     memory.  We wish to access it as a 2-d array. */
+
+  /* note: there is no error or bounds checking done here! */
+  double *iA[ilen];
+
+  int i;
+  for (i = 0; i < ilen; i++) {
+    iA[i] = iarray + i*jlen;
+  }
+
+
+  int j;
+  for (i = 1; i < ilen-1; i++) {
+    for (j = 1; j < jlen-1; j++) {
+      iA[i][j] = ( (iA[i+1][j] + iA[i-1][j])*dy2 +
+		   (iA[i][j+1] + iA[i][j-1])*dx2 ) /
+	(2.0*(dx2 + dy2));
+    }
+  }
+
+  return 0;
+}
diff --git a/examples/extensions-old/timing/laplace_CAPI.c b/examples/extensions-old/timing/laplace_CAPI.c
new file mode 100644
index 00000000..621210f8
--- /dev/null
+++ b/examples/extensions-old/timing/laplace_CAPI.c
@@ -0,0 +1,94 @@
+/* see 
+   http://wiki.scipy.org/Cookbook/C_Extensions/NumPy_arrays  and
+   http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html 
+*/
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <Python.h>
+#include <numpy/arrayobject.h>
+
+
+/* a static function in C limits its scope to this file -- the linker
+   won't complain about clashes */
+static PyObject* CAPI_update(PyObject* self, PyObject* args)
+{
+
+  PyArrayObject *iarray;
+  double **iA;
+  int i, j, m, n;
+  double dx2, dy2;
+
+  /* parse the inputs -- we need to know what the arguments of our
+     call were.  We'll assume:
+
+     ex_function(array, dx2, dy2)
+
+     and array is updated in place
+  */
+
+  if (!PyArg_ParseTuple(args, "O!dd", 
+			&PyArray_Type, &iarray, &dx2, &dy2)) return NULL;
+  if (NULL == iarray) return NULL;
+
+  /* check to make sure we are a double type */
+  if (PyArray_DTYPE(iarray)->type_num != NPY_DOUBLE ||
+      PyArray_NDIM(iarray) != 2) {
+    PyErr_SetString(PyExc_ValueError, "wrong input array type");
+    return NULL;
+  }
+
+  /* get the dimensions */
+  n = PyArray_DIM(iarray, 0);
+  m = PyArray_DIM(iarray, 1);
+
+  /* change contigous arrays into C ** arrays -- we need to have a
+     vector of pointers that point to the correct location in the
+     contiguous block of memory that stores the multi-dimensional
+     array data */
+  iA = (double **) malloc( (size_t) (n*sizeof(double)));
+  for (i = 0; i < n; i++) {
+    iA[i] = (double *) PyArray_DATA(iarray) + i*m;
+  }
+
+  /* now we can do our manipulation */
+  for (i = 1; i < n-1; i++) {
+    for (j = 1; j < m-1; j++) {
+      iA[i][j] = ( (iA[i+1][j] + iA[i-1][j])*dy2 +
+		   (iA[i][j+1] + iA[i][j-1])*dx2 ) /
+	(2.0*(dx2 + dy2));
+    }
+  }
+
+
+  /* free up the memory we allocated for the array indexing */
+  free (iA);
+
+  /* even though we are returning an integer, it is a Python object */
+  return Py_BuildValue("d", 0);
+
+}
+
+     
+/* this is the table for function names that Python will see */ 
+static PyMethodDef laplace_CAPIMethods[] = {
+  {"CAPI_update", CAPI_update, METH_VARARGS, "Laplace G-S update in pure C"},
+  {NULL, NULL, 0, NULL}
+};
+
+static struct PyModuleDef moduledef = {
+  PyModuleDef_HEAD_INIT,
+  "laplace_CAPI",  // name
+   "a simple example: square the elements of an array",  // documentation
+  -1,  // size
+  laplace_CAPIMethods,  // methods
+};
+
+/* this tells python what to do when it first imports this module --
+   the name follows directly from the table name above */
+PyMODINIT_FUNC PyInit_laplace_CAPI(void) {
+  PyObject *m;
+  m = PyModule_Create(&moduledef);
+  import_array();
+  return m;
+}
diff --git a/examples/extensions-old/timing/laplace_cython.pyx b/examples/extensions-old/timing/laplace_cython.pyx
new file mode 100644
index 00000000..e72da590
--- /dev/null
+++ b/examples/extensions-old/timing/laplace_cython.pyx
@@ -0,0 +1,11 @@
+#cython: boundscheck=False
+#cython: wraparound=False
+
+cimport numpy as np
+
+def cy_update(np.ndarray[double, ndim=2] u, double dx2, double dy2):
+    cdef unsigned int i, j
+    for i in xrange(1,u.shape[0]-1):
+        for j in xrange(1, u.shape[1]-1):
+            u[i,j] = ((u[i+1, j] + u[i-1, j]) * dy2 +
+                      (u[i, j+1] + u[i, j-1]) * dx2) / (2*(dx2+dy2))
diff --git a/examples/extensions-old/timing/laplace_fortran.f90 b/examples/extensions-old/timing/laplace_fortran.f90
new file mode 100644
index 00000000..d15806b4
--- /dev/null
+++ b/examples/extensions-old/timing/laplace_fortran.f90
@@ -0,0 +1,22 @@
+subroutine f90_update(u, nx, ny, dx2, dy2)
+
+  implicit none
+
+  integer, intent(in) :: nx, ny
+  double precision, intent(inout) :: u(nx, ny)
+  double precision, intent(in) :: dx2, dy2
+
+!f2py depend(nx, ny) :: u
+!f2py intent(inout) :: u
+!f2py intent(in) :: dx2, dy2
+
+  integer :: i, j
+
+  do j = 2, ny-1
+     do i = 2, nx-1
+        u(i,j) = ( (u(i+1,j) + u(i-1,j))*dy2 + (u(i,j+1) + u(i,j-1))*dx2 ) / &
+             (2.0d0*(dx2 + dy2))
+     enddo
+  enddo
+
+end subroutine f90_update
diff --git a/examples/extensions-old/timing/setup.py b/examples/extensions-old/timing/setup.py
new file mode 100644
index 00000000..2887b0f4
--- /dev/null
+++ b/examples/extensions-old/timing/setup.py
@@ -0,0 +1,11 @@
+from distutils.core import setup, Extension
+import numpy
+
+# define the extension module
+laplace_CAPI = Extension('laplace_CAPI', sources=['laplace_CAPI.c'],
+                       include_dirs=[numpy.get_include()]) #,
+#                       define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")])
+
+# run the setup
+setup(ext_modules=[laplace_CAPI])
+
diff --git a/examples/extensions/ctypes/simple/test.py b/examples/extensions/ctypes/simple/test.py
deleted file mode 100644
index 9890e6f3..00000000
--- a/examples/extensions/ctypes/simple/test.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import numpy as np
-import ctypes as C
-
-_cfunc = np.ctypeslib.load_library('cfunc', '.')
-_cfunc.my_subroutine.restype = C.c_int
-_cfunc.my_subroutine.argtypes = [C.POINTER(C.c_double), C.c_int]
-
-def my_subroutine(A):
-    return _cfunc.my_subroutine(A.ctypes.data_as(C.POINTER(C.c_double)), len(A))
-
-A = np.arange(10, dtype=np.float64)
-n = my_subroutine(A)
-
-
diff --git a/examples/extensions/cython/README b/examples/extensions/cython/README
new file mode 100644
index 00000000..7cc6fcf3
--- /dev/null
+++ b/examples/extensions/cython/README
@@ -0,0 +1,3 @@
+build via:
+
+python setup.py build_ext --inplace
diff --git a/examples/extensions/cython/mandel.pyx b/examples/extensions/cython/mandel.pyx
new file mode 100644
index 00000000..3dd93373
--- /dev/null
+++ b/examples/extensions/cython/mandel.pyx
@@ -0,0 +1,38 @@
+import cython
+import numpy as np
+cimport numpy as np
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+@cython.returns(np.ndarray)
+def mandelbrot(int N,
+               double xmin=-2.0, double xmax=2.0,
+               double ymin=-2.0, double ymax=2.0,
+               int max_iter=10):
+
+    cdef np.ndarray[np.float64_t, ndim=1] x = np.linspace(xmin, xmax, N, dtype=np.float64)
+    cdef np.ndarray[np.float64_t, ndim=1] y = np.linspace(ymin, ymax, N, dtype=np.float64)
+
+    cdef np.ndarray[np.complex128_t, ndim=2] c = np.zeros((N, N), dtype=np.complex128)
+
+    cdef unsigned int i, j
+    for i in range(N):
+        for j in range(N):
+            c[i, j] = x[i] + 1j * y[j]
+
+    cdef np.ndarray[np.complex128_t, ndim=2] z = np.zeros((N, N), dtype=np.complex128)
+
+    cdef np.ndarray[np.int32_t, ndim=2] m = np.zeros((N, N), dtype=np.int32)
+
+    cdef unsigned int n
+    for n in range(1, max_iter+1):
+
+        for i in range(N):
+            for j in range(N):
+                if m[i, j] == 0:
+                    z[i, j] = z[i, j] * z[i, j] + c[i, j]
+
+                    if abs(z[i,j]) > 2:
+                        m[i, j] = n
+
+    return m
diff --git a/examples/extensions/cython/setup.py b/examples/extensions/cython/setup.py
new file mode 100644
index 00000000..be66c4d4
--- /dev/null
+++ b/examples/extensions/cython/setup.py
@@ -0,0 +1,6 @@
+from setuptools import setup
+from Cython.Build import cythonize
+
+setup(name="mandel",
+      ext_modules=cythonize("mandel.pyx"),
+      zip_safe=False)
diff --git a/examples/extensions/cython/test_mandel.py b/examples/extensions/cython/test_mandel.py
new file mode 100644
index 00000000..733a08cc
--- /dev/null
+++ b/examples/extensions/cython/test_mandel.py
@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter=50)
+
+print(f"execution time = {time.time() - start}\n")
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m % 16), origin="lower",
+          extent=[xmin, xmax, ymin, ymax], cmap="viridis")
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/extensions/f2py/README b/examples/extensions/f2py/README
new file mode 100644
index 00000000..8fe88f61
--- /dev/null
+++ b/examples/extensions/f2py/README
@@ -0,0 +1,7 @@
+Note: python >= 3.12 needs ninja and meson to build.
+
+Build this as:
+
+f2py -c mandel.f90 -m mandel_f2py
+
+then you can `import mandel_f2py` in python.
diff --git a/examples/extensions/f2py/mandel.f90 b/examples/extensions/f2py/mandel.f90
new file mode 100644
index 00000000..87dfb630
--- /dev/null
+++ b/examples/extensions/f2py/mandel.f90
@@ -0,0 +1,62 @@
+subroutine mandelbrot(N, xmin, xmax, ymin, ymax, max_iter, m)
+
+  implicit none
+
+  integer, intent(in) :: N
+  double precision, intent(in) :: xmin, xmax, ymin, ymax
+  integer, intent(in) :: max_iter
+
+  integer, intent(out) :: m(N, N)
+
+  double complex, parameter :: i_unit = (0, 1)
+
+!f2py depend(N) :: m
+!f2py intent(out) :: m
+
+  integer :: i, j, niter
+  double precision :: x(N), y(N)
+  double precision :: dx, dy
+  double complex, allocatable :: c(:, :)
+  double complex, allocatable :: z(:, :)
+
+  ! compute coordinates
+  dx = (xmax - xmin) / (N - 1)
+  dy = (ymax - ymin) / (N - 1)
+
+  do i = 1, N
+     x(i) = xmin + (i-1) * dx
+     y(i) = ymin + (i-1) * dy
+  enddo
+
+  allocate(c(N, N))
+
+  do j = 1, N
+     do i = 1, N
+        c(i, j) = x(i) + i_unit * y(j)
+     enddo
+  enddo
+
+  m(:, :) = 0
+
+  allocate(z(N, N))
+  z(:, :) = 0.0
+
+  do niter = 1, max_iter
+
+     do j = 1, N
+        do i = 1, N
+
+           if (m(i, j) == 0) then
+              z(i, j) = z(i, j) * z(i, j) + c(i, j)
+
+              if (abs(z(i,j)) > 2) then
+                 m(i, j) = niter
+              endif
+           endif
+
+        enddo
+     enddo
+
+  enddo
+
+end subroutine mandelbrot
diff --git a/examples/extensions/f2py/test_mandel.py b/examples/extensions/f2py/test_mandel.py
new file mode 100644
index 00000000..172f2eeb
--- /dev/null
+++ b/examples/extensions/f2py/test_mandel.py
@@ -0,0 +1,26 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel_f2py
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+max_iter = 50
+
+m = mandel_f2py.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter)
+
+print(f"execution time = {time.time() - start}\n")
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m), origin="lower",
+          extent=[xmin, xmax, ymin, ymax])
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/extensions/numba/mandel.py b/examples/extensions/numba/mandel.py
new file mode 100644
index 00000000..36fef202
--- /dev/null
+++ b/examples/extensions/numba/mandel.py
@@ -0,0 +1,36 @@
+import numpy as np
+
+from numba import njit
+
+
+@njit(nopython=True)
+def mandelbrot(N,
+               xmin=-2.0, xmax=2.0,
+               ymin=-2.0, ymax=2.0,
+               max_iter=10):
+
+    x = np.linspace(xmin, xmax, N)
+    y = np.linspace(ymin, ymax, N)
+
+    c = np.zeros((N, N), dtype=np.complex128)
+
+    for i in range(N):
+        for j in range(N):
+            c[i, j] = x[i] + 1j * y[j]
+
+    z = np.zeros((N, N), dtype=np.complex128)
+
+    # note: we need to use a numba type here
+    m = np.zeros((N, N), dtype=np.int32)
+
+    for n in range(1, max_iter+1):
+
+        for i in range(N):
+            for j in range(N):
+                if m[i, j] == 0:
+                    z[i, j] = z[i, j] * z[i, j] + c[i, j]
+
+                    if np.abs(z[i, j]) > 2:
+                        m[i, j] = n
+
+    return m
diff --git a/examples/extensions/numba/test_mandel.py b/examples/extensions/numba/test_mandel.py
new file mode 100644
index 00000000..70a3f7a3
--- /dev/null
+++ b/examples/extensions/numba/test_mandel.py
@@ -0,0 +1,30 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter=50)
+
+print(f"execution time (including jit) = {time.time() - start}\n")
+
+start = time.time()
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter=50)
+
+print(f"second run time = {time.time() - start}\n")
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m % 16), origin="lower",
+          extent=[xmin, xmax, ymin, ymax], cmap="viridis")
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/extensions/pybind11/README b/examples/extensions/pybind11/README
new file mode 100644
index 00000000..7203287f
--- /dev/null
+++ b/examples/extensions/pybind11/README
@@ -0,0 +1,9 @@
+
+pip install pybind11
+
+g++ -O3 -DNDEBUG -Wall -Wextra -shared -std=c++17 -fPIC $(python3 -m pybind11 --includes) mandel.cpp -o mandel$(python3-config --extension-suffix)
+
+
+see https://stackoverflow.com/questions/44659924/returning-numpy-arrays-via-pybind11
+for how to construct the array we are going to return in the subroutine itself.
+
diff --git a/examples/extensions/pybind11/contiguous/README b/examples/extensions/pybind11/contiguous/README
new file mode 100644
index 00000000..32f3c304
--- /dev/null
+++ b/examples/extensions/pybind11/contiguous/README
@@ -0,0 +1,10 @@
+This version uses a simple Array class to give us nice indexing into a contiguous array
+
+pip install pybind11
+
+g++ -DNDEBUG -O3 -Wall -Wextra -shared -std=c++17 -fPIC $(python3 -m pybind11 --includes) mandel.cpp -o mandel$(python3-config --extension-suffix)
+
+
+see https://stackoverflow.com/questions/44659924/returning-numpy-arrays-via-pybind11
+for how to construct the array we are going to return in the subroutine itself.
+
diff --git a/examples/extensions/pybind11/contiguous/mandel.cpp b/examples/extensions/pybind11/contiguous/mandel.cpp
new file mode 100644
index 00000000..1eb43136
--- /dev/null
+++ b/examples/extensions/pybind11/contiguous/mandel.cpp
@@ -0,0 +1,97 @@
+#include <cstddef>
+#include <cmath>
+#include <complex>
+
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+
+
+namespace py = pybind11;
+
+using namespace std::complex_literals;
+
+class Array {
+
+    int N;
+    std::vector<std::complex<double>> _data;
+
+public:
+
+    Array(int N_in)
+        : N(N_in), _data(N_in * N_in, 0.0) {}
+
+    inline std::complex<double>& operator() (int row, int col) {
+        return _data[row * N + col];
+    }
+};
+
+py::array_t<int> mandelbrot(int N,
+                            double xmin, double xmax,
+                            double ymin, double ymax, int max_iter) {
+
+    // construct the numpy array we will return
+    // we need to specify the strides manually
+
+    constexpr std::size_t elsize = sizeof(int);
+    std::size_t shape[2]{N, N};
+    std::size_t strides[2]{N * elsize, elsize};
+    auto m = py::array_t<int>(shape, strides);
+    auto m_view = m.mutable_unchecked<2>();
+
+    // we'll use a simple contiguous array here.  When
+    // C++23 mdspan is available, that will be preferred.
+
+    std::vector<double> x(N, 0.0);
+    std::vector<double> y(N, 0.0);
+
+    double dx = (xmax - xmin) / static_cast<double>(N - 1);
+    double dy = (ymax - ymin) / static_cast<double>(N - 1);
+
+    for (int i = 0; i < N; ++i) {
+        x[i] = xmin + static_cast<double>(i) * dx;
+        y[i] = ymin + static_cast<double>(i) * dy;
+    }
+
+    Array c(N);
+    Array z(N);
+
+    // initialize c;
+
+    for (int i = 0; i < N; ++i) {
+        for (int j = 0; j < N; ++j) {
+            c(i, j) = x[i] + 1i * y[j];
+        }
+    }
+
+    // zero out the output array
+
+    for (int i = 0; i < m.shape(0); ++i) {
+        for (int j = 0; j < m.shape(1); ++j) {
+            m_view(i, j) = 0;
+        }
+    }
+
+    for (int niter = 1; niter <= max_iter; ++niter) {
+
+        for (int i = 0; i < m.shape(0); ++i) {
+            for (int j = 0; j < m.shape(1); ++j) {
+
+                if (m_view(i, j) == 0) {
+                    z(i, j) = z(i, j) * z(i, j) + c(i, j);
+
+                    if (std::abs(z(i, j)) > 2) {
+                        m_view(i, j) = niter;
+                    }
+                }
+            }
+        }
+    }
+
+    return m;
+}
+
+
+PYBIND11_MODULE(mandel, m) {
+    m.doc() = "C++ Mandelbrot example";
+    m.def("mandelbrot", &mandelbrot, "generate the Mandelbrot set of size N");
+}
diff --git a/examples/extensions/pybind11/contiguous/test_mandel.py b/examples/extensions/pybind11/contiguous/test_mandel.py
new file mode 100644
index 00000000..8599fa88
--- /dev/null
+++ b/examples/extensions/pybind11/contiguous/test_mandel.py
@@ -0,0 +1,27 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+max_iter = 50
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter)
+
+print(f"execution time = {time.time() - start}\n")
+
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m), origin="lower",
+          extent=[xmin, xmax, ymin, ymax])
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/extensions/pybind11/mandel.cpp b/examples/extensions/pybind11/mandel.cpp
new file mode 100644
index 00000000..babf7e6d
--- /dev/null
+++ b/examples/extensions/pybind11/mandel.cpp
@@ -0,0 +1,87 @@
+#include <iostream>
+#include <cmath>
+#include <complex>
+#include <vector>
+
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+
+
+namespace py = pybind11;
+
+using cmplx_arr = std::vector<std::vector<std::complex<double>>>;
+
+using namespace std::complex_literals;
+
+
+py::array_t<int> mandelbrot(int N,
+                            double xmin, double xmax,
+                            double ymin, double ymax, int max_iter) {
+
+    // construct the numpy array we will return
+    // we need to specify the strides manually
+
+    constexpr std::size_t elsize = sizeof(int);
+    std::size_t shape[2]{N, N};
+    std::size_t strides[2]{N * elsize, elsize};
+    auto m = py::array_t<int>(shape, strides);
+    auto m_view = m.mutable_unchecked<2>();
+
+    // for the other arrays used only here, we can
+    // do whatever we want.  Since we can't yet rely
+    // on C++23 mdspan, we'll just do a vector of vectors
+
+    std::vector<double> x(N, 0.0);
+    std::vector<double> y(N, 0.0);
+
+    double dx = (xmax - xmin) / static_cast<double>(N - 1);
+    double dy = (ymax - ymin) / static_cast<double>(N - 1);
+
+    for (int i = 0; i < N; ++i) {
+        x[i] = xmin + static_cast<double>(i) * dx;
+        y[i] = ymin + static_cast<double>(i) * dy;
+    }
+
+    cmplx_arr c(N, std::vector<std::complex<double>>(N, 0.0));
+    cmplx_arr z(N, std::vector<std::complex<double>>(N, 0.0));
+
+    // initialize c;
+
+    for (int i = 0; i < N; ++i) {
+        for (int j = 0; j < N; ++j) {
+            c[i][j] = x[i] + 1i * y[j];
+        }
+    }
+
+    // zero out the output array
+
+    for (int i = 0; i < m.shape(0); ++i) {
+        for (int j = 0; j < m.shape(1); ++j) {
+            m_view(i, j) = 0;
+        }
+    }
+
+    for (int niter = 1; niter <= max_iter; ++niter) {
+
+        for (int i = 0; i < m.shape(0); ++i) {
+            for (int j = 0; j < m.shape(1); ++j) {
+
+                if (m_view(i, j) == 0) {
+                    z[i][j] = z[i][j] * z[i][j] + c[i][j];
+
+                    if (std::abs(z[i][j]) > 2) {
+                        m_view(i, j) = niter;
+                    }
+                }
+            }
+        }
+    }
+
+    return m;
+}
+
+
+PYBIND11_MODULE(mandel, m) {
+    m.doc() = "C++ Mandelbrot example";
+    m.def("mandelbrot", &mandelbrot, "generate the Mandelbrot set of size N");
+}
diff --git a/examples/extensions/pybind11/test_mandel.py b/examples/extensions/pybind11/test_mandel.py
new file mode 100644
index 00000000..8599fa88
--- /dev/null
+++ b/examples/extensions/pybind11/test_mandel.py
@@ -0,0 +1,27 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+max_iter = 50
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter)
+
+print(f"execution time = {time.time() - start}\n")
+
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m), origin="lower",
+          extent=[xmin, xmax, ymin, ymax])
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/extensions/python-slow/mandel.py b/examples/extensions/python-slow/mandel.py
new file mode 100644
index 00000000..8ff3b61a
--- /dev/null
+++ b/examples/extensions/python-slow/mandel.py
@@ -0,0 +1,32 @@
+import numpy as np
+
+def mandelbrot(N,
+               xmin=-2.0, xmax=2.0,
+               ymin=-2.0, ymax=2.0,
+               max_iter=10):
+
+    x = np.linspace(xmin, xmax, N)
+    y = np.linspace(ymin, ymax, N)
+
+    c = np.zeros((N, N), dtype=np.complex128)
+
+    for i in range(N):
+        for j in range(N):
+            c[i, j] = x[i] + 1j * y[j]
+
+    z = np.zeros((N, N), dtype=np.complex128)
+
+    # note: we need to use a numba type here
+    m = np.zeros((N, N), dtype=np.int32)
+
+    for n in range(1, max_iter+1):
+
+        for i in range(N):
+            for j in range(N):
+                if m[i, j] == 0:
+                    z[i, j] = z[i, j] * z[i, j] + c[i, j]
+
+                    if np.abs(z[i,j]) > 2:
+                        m[i, j] = n
+
+    return m
diff --git a/examples/extensions/python-slow/test_mandel.py b/examples/extensions/python-slow/test_mandel.py
new file mode 100644
index 00000000..733a08cc
--- /dev/null
+++ b/examples/extensions/python-slow/test_mandel.py
@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter=50)
+
+print(f"execution time = {time.time() - start}\n")
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m % 16), origin="lower",
+          extent=[xmin, xmax, ymin, ymax], cmap="viridis")
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/extensions/python/mandel.py b/examples/extensions/python/mandel.py
new file mode 100644
index 00000000..ba149a06
--- /dev/null
+++ b/examples/extensions/python/mandel.py
@@ -0,0 +1,24 @@
+import numpy as np
+
+def mandelbrot(N,
+               xmin=-2.0, xmax=2.0,
+               ymin=-2.0, ymax=2.0,
+               max_iter=10):
+
+    x = np.linspace(xmin, xmax, N)
+    y = np.linspace(ymin, ymax, N)
+
+    xv, yv = np.meshgrid(x, y, indexing="ij")
+
+    c = xv + 1j * yv
+
+    z = np.zeros((N, N), dtype=np.complex128)
+
+    m = np.zeros((N, N), dtype=np.int32)
+
+    for i in range(1, max_iter+1):
+        z[m == 0] = z[m == 0]**2 + c[m == 0]
+
+        m[np.logical_and(np.abs(z) > 2, m == 0)] = i
+
+    return m
diff --git a/examples/extensions/python/test_mandel.py b/examples/extensions/python/test_mandel.py
new file mode 100644
index 00000000..733a08cc
--- /dev/null
+++ b/examples/extensions/python/test_mandel.py
@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import mandel
+
+import time
+
+start = time.time()
+
+xmin = -2.5
+xmax = 1.5
+ymin = -2.0
+ymax = 2.0
+
+m = mandel.mandelbrot(1024, xmin, xmax, ymin, ymax, max_iter=50)
+
+print(f"execution time = {time.time() - start}\n")
+
+fig, ax = plt.subplots()
+ax.imshow(np.transpose(m % 16), origin="lower",
+          extent=[xmin, xmax, ymin, ymax], cmap="viridis")
+
+fig.tight_layout()
+fig.savefig("test.png")
diff --git a/examples/external/Popen/githash.py b/examples/external/Popen/githash.py
index 1d583d91..f201b145 100644
--- a/examples/external/Popen/githash.py
+++ b/examples/external/Popen/githash.py
@@ -2,7 +2,7 @@
 import string
 
 # issue the command 'git rev-parse HEAD'
-prog = ["git", "rev-parse", "HEAD"]
+prog = ["git", "rev-parser", "HEAD"]
 p0 = subprocess.Popen(prog, stdout=subprocess.PIPE,
                       stderr=subprocess.PIPE)
 stdout0, stderr0 = p0.communicate()
diff --git a/examples/h5py/h5_example.py b/examples/h5py/h5_example.py
new file mode 100644
index 00000000..3cc6d3dc
--- /dev/null
+++ b/examples/h5py/h5_example.py
@@ -0,0 +1,36 @@
+import numpy as np
+import h5py
+
+
+a = np.arange(1000, dtype=np.float64).reshape(25, 40)
+
+with h5py.File("test.h5", "w") as f:
+    
+    # attributes are meta-data that can help describe the data
+    # in h5py, they are dictionary keys
+    f.attrs["about"] = "a test HDF5 file with h5py"
+
+    # groups can be thought of as subdirectories in the output file,
+    # and can help you organize data together logically
+    grp = f.create_group("data")
+    
+    # a numpy array is a dataset in HDF5 -- it will figure out the
+    # dimensions and type from the array itself
+    grp.create_dataset("a", data=a)
+
+    # there can be attributes in the group too
+    grp.attrs["array info"] = "a simple array"
+
+
+# now we'll try to read it in
+with h5py.File("test.h5", "r") as f:
+
+    # access the group we stored our data in
+    g = f["data"]
+
+    aread = np.array(g["a"])
+
+
+# are they the same?
+da = a - aread
+print(np.max(abs(da)))
diff --git a/examples/machine-learning/character_recognition/README b/examples/machine-learning/character_recognition/README
new file mode 100644
index 00000000..9d134bd9
--- /dev/null
+++ b/examples/machine-learning/character_recognition/README
@@ -0,0 +1,16 @@
+This example is inspired from "Make your own Neural Network" by Tariq
+Rashid, using the notation from Franklin.
+
+Some diffs w/ Franklin:
+
+ -- we use back-propagation to get the errors at on the hidden layer
+
+ -- we set alpha = 1 -- the intent is that you come in with x in [0, 1]
+
+ -- we initialize the matricies with Gaussian normal random numbers
+    with a width mu = 1/sqrt(n), where n is the size of the input
+    vector.
+
+ -- we don't offset the output from the hidden layer to shift it into
+    [-0.5, 0.5]
+
diff --git a/examples/machine-learning/character_recognition/char_recognition.py b/examples/machine-learning/character_recognition/char_recognition.py
new file mode 100644
index 00000000..8d066d14
--- /dev/null
+++ b/examples/machine-learning/character_recognition/char_recognition.py
@@ -0,0 +1,231 @@
+"""A neural network for recognizing the handdrawn digits in the MNIST
+database.  This follows the ideas from Franklin Ch 14 and the book by
+Rashid.
+
+"""
+
+# Note: this uses progressbar2 to show status during training.  Do
+# pip3 install progressbar2 --user
+
+import matplotlib.pyplot as plt
+import numpy as np
+import progressbar
+
+
+class TrainingDigit(object):
+    """a handwritten digit from the MNIST training set"""
+
+    def __init__(self, raw_string):
+        """we feed this a single line from the MNIST data set"""
+        self.raw_string = raw_string
+
+        # make the data range from 0.01 to 1.00
+        _tmp = raw_string.split(",")
+        self.scaled = np.asfarray(_tmp[1:])/255.0 * 0.99 + 0.01
+
+        # the correct answer
+        self.num = int(_tmp[0])
+
+        # the output for the NN as a bit array -- make this lie in [0.01, 0.99]
+        self.out = np.zeros((10)) + 0.01
+        self.out[self.num] = 0.99
+
+    def plot(self, outfile=None, output=None):
+        """plot the digit"""
+        plt.clf()
+        plt.imshow(self.scaled.reshape((28, 28)),
+                   cmap="Greys", interpolation="nearest")
+        if output is not None:
+            dstr = ["{}: {:6.4f}".format(n, v) for n, v in enumerate(output)]
+            ostr = "correct digit: {}\n".format(self.num)
+            ostr += "  ".join(dstr[0:5]) + "\n" + "  ".join(dstr[5:])
+            plt.title("{}".format(ostr), fontsize="x-small")
+        if outfile is not None:
+            plt.savefig(outfile, dpi=150)
+
+class TrainingSet(object):
+    """Manage reading digits from the MNIST training set csv and provide
+    methods for returning the next digit, reading only what is needed
+    each time.
+
+    """
+
+    def __init__(self):
+        self.f = open("mnist_train.csv", "r")
+
+    def get_next(self):
+        """return the next digit from the training database"""
+        return TrainingDigit(self.f.readline())
+
+    def reset(self):
+        """reset the training set to the first digit in the database"""
+        self.f.seek(0)
+
+    def close(self):
+        """close the training set file"""
+        # should implement a context manager
+        self.f.close()
+
+class UnknownDigit(TrainingDigit):
+    """A digit from the MNIST test database.  This provides a method to
+    compare a NN result to the correct answer
+
+    """
+    def __init__(self, raw_string):
+        super().__init__(raw_string)
+        self.out = None
+
+    def check_output(self, out):
+        """given the output array from the NN, return True if it is
+        correct for this digit"""
+        guess = np.argmax(out)
+        return guess == self.num
+
+class TestSet(object):
+    """read the next digit from the test set csv file and return a
+    UnknownDigit object"""
+
+    def __init__(self):
+        self.f = open("mnist_test.csv", "r")
+
+    def get_next(self):
+        """return the next digit from the test database"""
+        return UnknownDigit(self.f.readline())
+
+    def close(self):
+        """close the test set file"""
+        # should implement a context manager
+        self.f.close()
+
+
+class NeuralNetwork(object):
+    """A neural network class with a single hidden layer."""
+
+    def __init__(self, num_training_unique=100, n_epochs=10,
+                 learning_rate=0.1,
+                 hidden_layer_size=100):
+
+        self.num_training_unique = num_training_unique
+        self.n_epochs = n_epochs
+
+        self.train_set = TrainingSet()
+
+        # learning rate
+        self.eta = learning_rate
+
+        # we get the size of the layers from the length of the input
+        # and output
+        d = self.train_set.get_next()
+
+        # the number of nodes/neurons on the output layer
+        self.m = len(d.out)
+
+        # the number of nodes/neurons on the input layer
+        self.n = len(d.scaled)
+
+        # the number of nodes/neurons on the hidden layer
+        self.k = hidden_layer_size
+
+        # we will initialize the weights with Gaussian normal random
+        # numbers centered on 0 with a width of 1/sqrt(n), where n is
+        # the length of the input state
+
+        # A is the set of weights between the hidden layer and output layer
+        self.A = np.random.normal(0.0, 1.0/np.sqrt(self.k), (self.m, self.k))
+
+        # B is the set of weights between the input layer and hidden layer
+        self.B = np.random.normal(0.0, 1.0/np.sqrt(self.n), (self.k, self.n))
+
+    def g(self, p):
+        """our sigmoid function that operates on the hidden layer"""
+        return 1.0/(1.0 + np.exp(-p))
+
+    def train(self):
+        """Train the neural network by doing gradient descent with back
+        propagation to set the matrix elements in B (the weights
+        between the input and hidden layer) and A (the weights between
+        the hidden layer and output layer)
+
+        """
+
+        for i in range(self.n_epochs):
+            self.train_set.reset()
+            print("epoch {} of {}".format(i+1, self.n_epochs))
+            bar = progressbar.ProgressBar()
+            for q in bar(range(self.num_training_unique)):
+
+                model = self.train_set.get_next()
+
+                x = model.scaled.reshape(self.n, 1)
+                y = model.out.reshape(self.m, 1)
+
+                z_tilde = self.g(self.B @ x)
+                z = self.g(self.A @ z_tilde)
+
+                e = z - y
+                e_tilde = self.A.T @ e
+
+                dA = -2*self.eta * e * z*(1-z) @ z_tilde.T
+                dB = -2*self.eta * e_tilde * z_tilde*(1-z_tilde) @ x.T
+
+                self.A[:, :] += dA
+                self.B[:, :] += dB
+
+
+        self.train_set.close()
+
+    def predict(self, model):
+        """ predict the outcome using our trained matrix A """
+        y = self.g(self.A @ (self.g(self.B @ model.scaled)))
+        return y
+
+
+def main(n_epochs=5, hidden_layer_size=100,
+         num_training_unique=60000,
+         learning_rate=0.1,
+         do_plots=True):
+    """a driver for the NN"""
+
+    nn = NeuralNetwork(num_training_unique=num_training_unique,
+                       learning_rate=learning_rate,
+                       hidden_layer_size=hidden_layer_size,
+                       n_epochs=n_epochs)
+
+    # train
+    nn.train()
+
+    # histogram of the matrix elements
+    if do_plots:
+        plt.clf()
+        plt.hist(nn.A.flatten(), bins=20)
+        plt.title(r"${\bf A}$")
+        plt.savefig("A_hist.png", dpi=150, bbox_inches="tight")
+
+        plt.clf()
+        plt.hist(nn.B.flatten(), bins=20)
+        plt.title(r"${\bf B}$")
+        plt.savefig("B_hist.png", dpi=150, bbox_inches="tight")
+
+    # now try it out on the unseen data from the test database
+    unk = TestSet()
+    n_test = 10000
+    n_correct = 0
+    for t in range(n_test):
+        d = unk.get_next()
+        num_guess = nn.predict(d)
+        if d.check_output(num_guess):
+            n_correct += 1
+        else:
+            if do_plots:
+                d.plot(outfile="incorrect_digit_{:04d}.png".format(t), output=num_guess)
+
+    unk.close()
+
+    return n_correct/n_test
+
+
+if __name__ == "__main__":
+    f_right = main(do_plots=False)
+
+    print("correct fraction: {}".format(f_right))
+ 
diff --git a/examples/machine-learning/character_recognition/character_recognition.ipynb b/examples/machine-learning/character_recognition/character_recognition.ipynb
new file mode 100644
index 00000000..df23ad96
--- /dev/null
+++ b/examples/machine-learning/character_recognition/character_recognition.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TrainingDigit(object):\n",
+    "    \"\"\"a handwritten digit from the MNIST training set\"\"\"\n",
+    "\n",
+    "    def __init__(self, raw_string):\n",
+    "        \"\"\"we feed this a single line from the MNIST data set\"\"\"\n",
+    "        self.raw_string = raw_string\n",
+    "\n",
+    "        # make the data range from 0.01 to 1.00\n",
+    "        _tmp = raw_string.split(\",\")\n",
+    "        self.scaled = np.asfarray(_tmp[1:])/255.0 * 0.99 + 0.01\n",
+    "        \n",
+    "        # the correct answer\n",
+    "        self.num = int(_tmp[0])\n",
+    "        \n",
+    "        # the output for the NN as a bit array\n",
+    "        self.out = np.zeros((10))\n",
+    "        self.out[self.num] = 1\n",
+    "        \n",
+    "    def plot(self):\n",
+    "        plt.imshow(self.scaled.reshape((28,28)), cmap=\"Greys\", interpolation=\"nearest\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TrainingSet(object):\n",
+    "    \"\"\"read the next digit from the training set csv file and return a\n",
+    "    TrainingDigit object\"\"\"\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        self.f = open(\"mnist_train.csv\", \"r\")\n",
+    "        \n",
+    "    def get_next(self):\n",
+    "        return TrainingDigit(self.f.readline())\n",
+    "    \n",
+    "    def close(self):\n",
+    "        # show implement a context manager\n",
+    "        self.f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tset = TrainingSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'5'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = tset.get_next()\n",
+    "d.num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2qKRuDV31tVfSLyU9IemPki6Q9HdJP3T3lr/xVqG3bg09df3Xys3HX2O3uLcrJf2vpDck\nHcs292ro9XVhj12ir8Uq4HHjE35AUHzCDwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8PRZ8V\nlgh2BcUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f126bdf6978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = tset.get_next()\n",
+    "d.num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3SFqQXV8g6ZUCe/kb7bJyc6WVpVXwfdduK14X8iafbCrjWUnDJK1z96da3sQgzOzb6j/b\nS/2LmP6syN7MbKOk6er/1NchST+S9F+SfinpMkl7JM1x95a/8Faht+nqf+j615WbTz3HbnFvN0h6\nW9IOSSezzcvU//y6sPsu0dc8FXC/8Q4/ICje4QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/\nBxmeJtv9WSKzAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f126bd8e908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tset.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class UnknownDigit(TrainingDigit):\n",
+    "    \"\"\"this is like the TrainingDigit, but we will have a method that\n",
+    "    takes the output and compares to the right answer\"\"\"\n",
+    "    def __init__(self, raw_string):\n",
+    "        super().__init__(raw_string)\n",
+    "        self.out = None\n",
+    "        \n",
+    "    def check_output(out):\n",
+    "        guess = np.argmax(out)\n",
+    "        return guess == self.num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TestSet(object):\n",
+    "    \"\"\"read the next digit from the test set csv file and return a\n",
+    "    UnknownDigit object\"\"\"\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        self.f = open(\"mnist_test.csv\", \"r\")\n",
+    "        \n",
+    "    def get_next(self):\n",
+    "        return UnknownDigit(self.f.readline())\n",
+    "    \n",
+    "    def close(self):\n",
+    "        # show implement a context manager\n",
+    "        self.f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = TestSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJ/R8EiLFW9B5y7gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f126bc51f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test.get_next().plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/machine-learning/character_recognition/nn_epochs.py b/examples/machine-learning/character_recognition/nn_epochs.py
new file mode 100644
index 00000000..53f2b647
--- /dev/null
+++ b/examples/machine-learning/character_recognition/nn_epochs.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import char_recognition as cr
+
+epoch = []
+fraction = []
+
+for e in range(1, 10):
+    f = cr.main(do_plots=False, n_epochs=e)
+    epoch.append(e)
+    fraction.append(f)
+
+plt.plot(epoch, fraction)
+plt.xlabel("number of training epochs")
+plt.ylabel("success fraction")
+plt.tight_layout()
+plt.savefig("epoch_trends.png", dpi=150)
+
+for e, f in zip(epoch, fraction):
+    print(e, f)
diff --git a/examples/machine-learning/character_recognition/nn_hidden_size.py b/examples/machine-learning/character_recognition/nn_hidden_size.py
new file mode 100644
index 00000000..74b5027d
--- /dev/null
+++ b/examples/machine-learning/character_recognition/nn_hidden_size.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import char_recognition as cr
+
+fraction = []
+
+hidden_sizes = [25, 50, 100, 200, 400]
+for h in hidden_sizes:
+    f = cr.main(do_plots=False, hidden_layer_size=h)
+    fraction.append(f)
+
+
+plt.plot(hidden_sizes, fraction)
+plt.xlabel("hidden layer size")
+plt.ylabel("success fraction")
+plt.tight_layout()
+plt.savefig("hidden_layer_trends.png", dpi=150)
+
+for h, e in zip(hidden_sizes, fraction):
+    print(h, e)
diff --git a/examples/machine-learning/character_recognition/nn_learning_rate.py b/examples/machine-learning/character_recognition/nn_learning_rate.py
new file mode 100644
index 00000000..91362248
--- /dev/null
+++ b/examples/machine-learning/character_recognition/nn_learning_rate.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import char_recognition as cr
+
+fraction = []
+
+learning_rate = [0.05, 0.1, 0.2, 0.3, 0.4]
+for l in learning_rate:
+    f = cr.main(do_plots=False, learning_rate=l)
+    fraction.append(f)
+
+
+plt.plot(learning_rate, fraction)
+plt.xlabel("learning rate")
+plt.ylabel("success fraction")
+plt.tight_layout()
+plt.savefig("learning_rate_trends.png", dpi=150)
+
+for l, e in zip(learning_rate, fraction):
+    print(l, e)
diff --git a/examples/machine-learning/character_recognition/nn_training_size.py b/examples/machine-learning/character_recognition/nn_training_size.py
new file mode 100644
index 00000000..dcca36de
--- /dev/null
+++ b/examples/machine-learning/character_recognition/nn_training_size.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+import char_recognition as cr
+
+fraction = []
+
+training_size = [1875, 3750, 7500, 15000, 30000, 60000]
+for t in training_size:
+    f = cr.main(do_plots=False, num_training_unique=t)
+    fraction.append(f)
+
+
+plt.plot(training_size, fraction)
+plt.xlabel("training sample size")
+plt.ylabel("success fraction")
+plt.tight_layout()
+plt.savefig("training_size_trends.png", dpi=150)
+
+for h, e in zip(training_size, fraction):
+    print(h, e)
diff --git a/examples/machine-learning/character_recognition/show_character.py b/examples/machine-learning/character_recognition/show_character.py
new file mode 100644
index 00000000..46527184
--- /dev/null
+++ b/examples/machine-learning/character_recognition/show_character.py
@@ -0,0 +1,9 @@
+import matplotlib.pyplot as plt
+import char_recognition as cr
+
+train_set = cr.TrainingSet()
+
+d = train_set.get_next()
+d.plot()
+plt.tight_layout()
+plt.savefig("example_digit.png", bbox_inches="tight", dpi=150)
diff --git a/examples/machine-learning/keras/feed-forward/README.md b/examples/machine-learning/keras/feed-forward/README.md
new file mode 100644
index 00000000..890ff472
--- /dev/null
+++ b/examples/machine-learning/keras/feed-forward/README.md
@@ -0,0 +1,3 @@
+These examples come from:
+
+https://github.com/Vict0rSch/deep_learning/tree/master/keras/feedforward
diff --git a/examples/machine-learning/keras/feed-forward/feedforward_keras_mnist.py b/examples/machine-learning/keras/feed-forward/feedforward_keras_mnist.py
new file mode 100644
index 00000000..9623e24f
--- /dev/null
+++ b/examples/machine-learning/keras/feed-forward/feedforward_keras_mnist.py
@@ -0,0 +1,93 @@
+import time
+import numpy as np
+from matplotlib import pyplot as plt
+from keras.utils import np_utils
+import keras.callbacks as cb
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.optimizers import RMSprop
+from keras.datasets import mnist
+
+
+class LossHistory(cb.Callback):
+    def on_train_begin(self, logs={}):
+        self.losses = []
+
+    def on_batch_end(self, batch, logs={}):
+        batch_loss = logs.get('loss')
+        self.losses.append(batch_loss)
+
+
+def load_data():
+    print 'Loading data...'
+    (X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+    X_train = X_train.astype('float32')
+    X_test = X_test.astype('float32')
+
+    X_train /= 255
+    X_test /= 255
+
+    y_train = np_utils.to_categorical(y_train, 10)
+    y_test = np_utils.to_categorical(y_test, 10)
+
+    X_train = np.reshape(X_train, (60000, 784))
+    X_test = np.reshape(X_test, (10000, 784))
+
+    print 'Data loaded.'
+    return [X_train, X_test, y_train, y_test]
+
+
+def init_model():
+    start_time = time.time()
+    print 'Compiling Model ... '
+    model = Sequential()
+    model.add(Dense(500, input_dim=784))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.4))
+    model.add(Dense(300))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.4))
+    model.add(Dense(10))
+    model.add(Activation('softmax'))
+
+    rms = RMSprop()
+    model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
+    print 'Model compield in {0} seconds'.format(time.time() - start_time)
+    return model
+
+
+def run_network(data=None, model=None, epochs=20, batch=256):
+    try:
+        start_time = time.time()
+        if data is None:
+            X_train, X_test, y_train, y_test = load_data()
+        else:
+            X_train, X_test, y_train, y_test = data
+
+        if model is None:
+            model = init_model()
+
+        history = LossHistory()
+
+        print 'Training model...'
+        model.fit(X_train, y_train, nb_epoch=epochs, batch_size=batch,
+                  callbacks=[history],
+                  validation_data=(X_test, y_test), verbose=2)
+
+        print "Training duration : {0}".format(time.time() - start_time)
+        score = model.evaluate(X_test, y_test, batch_size=16)
+
+        print "Network's test score [loss, accuracy]: {0}".format(score)
+        return model, history.losses
+    except KeyboardInterrupt:
+        print ' KeyboardInterrupt'
+        return model, history.losses
+
+
+def plot_losses(losses):
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.plot(losses)
+    ax.set_title('Loss per batch')
+    fig.show()
diff --git a/examples/machine-learning/steepest_descent/min_2d_descent.png b/examples/machine-learning/steepest_descent/min_2d_descent.png
new file mode 100644
index 00000000..d38476dc
Binary files /dev/null and b/examples/machine-learning/steepest_descent/min_2d_descent.png differ
diff --git a/examples/machine-learning/steepest_descent/min_2d_start.png b/examples/machine-learning/steepest_descent/min_2d_start.png
new file mode 100644
index 00000000..a9f51bec
Binary files /dev/null and b/examples/machine-learning/steepest_descent/min_2d_start.png differ
diff --git a/examples/machine-learning/steepest_descent/steepest_descent.py b/examples/machine-learning/steepest_descent/steepest_descent.py
new file mode 100644
index 00000000..71988d95
--- /dev/null
+++ b/examples/machine-learning/steepest_descent/steepest_descent.py
@@ -0,0 +1,66 @@
+# an example of steepest (gradient) descent minimization
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def rosenbrock(x0, x1, a, b):
+    return (a - x0)**2 + b*(x1 - x0**2)**2
+
+def drosdx(x, a, b):
+    x0 = x[0]
+    x1 = x[1]
+    return np.array([-2.0*(a - x0) - 4.0*b*(x1 - x0**2)*x0,
+                     2.0*b*(x1 - x0**2)])
+
+def main():
+
+    xmin = -2.0
+    xmax = 2.0
+    ymin = -1.0
+    ymax = 3.0
+
+    a = 1.0
+    b = 100.0
+
+    N = 256
+    x = np.linspace(xmin, xmax, N)
+    y = np.linspace(ymin, ymax, N)
+
+    x2d, y2d = np.meshgrid(x, y, indexing="ij")
+
+    plt.imshow(np.log10(np.transpose(rosenbrock(x2d, y2d, a, b))), 
+               origin="lower",
+               extent=[xmin, xmax, ymin, ymax])
+
+    plt.colorbar()
+    plt.tight_layout()
+
+    plt.savefig("min_2d_start.png", dpi=150)
+
+
+    # do descent
+    xp = np.array([-1.0, 1.5])
+    xp_old = 1000*xp
+
+    eps = 1.e-5
+
+    eta = 0.002
+
+    while np.linalg.norm(xp - xp_old) > eps:
+        xp_old[:] = xp[:]
+        grad = drosdx(xp, a, b)
+
+        # eta_pred = rosenbrock(xp[0], xp[1], a, b)/np.linalg.norm(grad)
+        # print("eta predict = ", eta_pred)
+        xp[:] += -eta * grad[:]
+
+        plt.plot([xp_old[0], xp[0]], [xp_old[1], xp[1]], color="C1")
+
+    plt.scatter([xp[0]], [xp[1]], marker="o", color="C1")
+
+    print(xp)
+
+    plt.savefig("min_2d_descent.png", dpi=150)
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/matplotlib/fractal/mandelbrot.py b/examples/matplotlib/fractal/mandelbrot.py
new file mode 100644
index 00000000..5c826216
--- /dev/null
+++ b/examples/matplotlib/fractal/mandelbrot.py
@@ -0,0 +1,74 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+# the mandelbrot set is defined such that z_{n+1} = z_n^2 + c
+# remains bounded, which is sufficient to say that |z_{n+1}| <= 2
+# where c is a complex # and we start with z_0 = 0
+
+# we want to consider a range of c, as complex numbers c = x + iy,
+# were -2 < x < 2 and -2 < y < 2
+
+N = 1024
+
+c = np.zeros((N, N), dtype=np.complex)
+
+region = 2
+
+if region == 0:
+    xmin = -2
+    xmax = 1
+    ymin = -1.5
+    ymax = 1.5
+
+elif region == 1:
+    xmin = 0
+    xmax = 0.5
+    ymin = -0.75
+    ymax = -0.25
+
+elif region == 2:
+    # nice spot
+    xc = -0.7435669
+    yc = 0.1314023
+    dx = 0.004 #0.0022878
+
+    xmin = xc-0.5*dx
+    xmax = xc+0.5*dx
+    ymin = yc-0.5*dx
+    ymax = yc+0.5*dx
+
+elif region == 3:
+    # dagger
+    xmin = -0.8
+    xmax = -0.7
+    ymin = 0.0
+    ymax = 0.1
+
+x = np.linspace(xmin, xmax, N, dtype=np.float)
+y = np.linspace(ymin, ymax, N, dtype=np.float)
+
+c[:,:].real = x[:,np.newaxis]
+c[:,:].imag = y[np.newaxis,:]
+
+z = np.zeros((N,N), dtype=np.complex)
+
+niter = np.zeros((N,N), dtype=np.int)
+niter[:,:] = -1
+
+MAX_ITER = 1024
+
+for n in range(MAX_ITER):
+    idx = niter == -1
+    z[idx] = z[idx]**2 + c[idx]
+    idx = np.logical_and(abs(z) > 2, niter == -1)
+    niter[idx] = n
+
+# some things may still have not passed
+niter[niter == -1] = MAX_ITER-1
+
+plt.imshow(np.log10(niter.T), extent=[xmin, xmax, ymin, ymax],
+           origin="lower", cmap="magma_r")
+
+f = plt.gcf()
+f.set_size_inches(8.0, 8.0)
+plt.savefig("mandel.png")
diff --git a/examples/packaging/distutils/foo/foo.py b/examples/packaging/distutils/foo/foo.py
new file mode 100644
index 00000000..93689fa2
--- /dev/null
+++ b/examples/packaging/distutils/foo/foo.py
@@ -0,0 +1,4 @@
+def foo():
+    print("you made it here!")
+
+
diff --git a/examples/packaging/distutils/foo/setup.py b/examples/packaging/distutils/foo/setup.py
new file mode 100644
index 00000000..cdf3d282
--- /dev/null
+++ b/examples/packaging/distutils/foo/setup.py
@@ -0,0 +1,5 @@
+from distutils.core import setup
+setup(name='foo',
+      version='1.0',
+      py_modules=['foo'],
+      )
diff --git a/examples/packaging/example/mytest/__init__.py b/examples/packaging/example/mytest/__init__.py
new file mode 100644
index 00000000..64f28cc4
--- /dev/null
+++ b/examples/packaging/example/mytest/__init__.py
@@ -0,0 +1,7 @@
+"""
+This is my simple test module for demonstrating packaging
+"""
+
+#from mytest.demo import demo
+#from mytest.util import msg
+
diff --git a/examples/packaging/example/mytest/demo/__init__.py b/examples/packaging/example/mytest/demo/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/packaging/example/mytest/demo/demo.py b/examples/packaging/example/mytest/demo/demo.py
new file mode 100644
index 00000000..c67c9a26
--- /dev/null
+++ b/examples/packaging/example/mytest/demo/demo.py
@@ -0,0 +1,5 @@
+def show():
+    print("this is my demo")
+
+def test(x):
+    print("x = {}".format(x))
diff --git a/examples/packaging/example/mytest/util/__init__.py b/examples/packaging/example/mytest/util/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/packaging/example/mytest/util/msg.py b/examples/packaging/example/mytest/util/msg.py
new file mode 100644
index 00000000..fa4d2525
--- /dev/null
+++ b/examples/packaging/example/mytest/util/msg.py
@@ -0,0 +1,5 @@
+def fail(s):
+    print("ERROR: {}".format(s))
+
+def warn(s):
+    print("WARNING: {}".format(s))
diff --git a/examples/packaging/setuptools/foo2/foo2/__init__.py b/examples/packaging/setuptools/foo2/foo2/__init__.py
new file mode 100644
index 00000000..577cc446
--- /dev/null
+++ b/examples/packaging/setuptools/foo2/foo2/__init__.py
@@ -0,0 +1,7 @@
+"""
+this is my foo2 test package
+"""
+
+# make these available into the parent namespace
+from foo2.foo import *
+
diff --git a/examples/packaging/setuptools/foo2/foo2/foo.py b/examples/packaging/setuptools/foo2/foo2/foo.py
new file mode 100644
index 00000000..93689fa2
--- /dev/null
+++ b/examples/packaging/setuptools/foo2/foo2/foo.py
@@ -0,0 +1,4 @@
+def foo():
+    print("you made it here!")
+
+
diff --git a/examples/packaging/setuptools/foo2/setup.py b/examples/packaging/setuptools/foo2/setup.py
new file mode 100644
index 00000000..f85d7a23
--- /dev/null
+++ b/examples/packaging/setuptools/foo2/setup.py
@@ -0,0 +1,9 @@
+from setuptools import setup
+setup(name="foo2",
+      version="1.0",
+      description="this is a setuptools test example",
+      packages=["foo2"],
+      author="Michael Zingale",
+      author_email="michael.zingale@stonybrook.edu",
+      license="BSD"
+      )
diff --git a/examples/packaging/single_file/example.py b/examples/packaging/single_file/example.py
new file mode 100644
index 00000000..91e2d437
--- /dev/null
+++ b/examples/packaging/single_file/example.py
@@ -0,0 +1,10 @@
+_hidden = "you may not see this"
+
+def demo():
+    print("this is a single file demo")
+
+
+if __name__ == "__main__":
+    print("in the main block -- you are running from the commandline")
+    demo()
+
diff --git a/examples/practices/git/working-with-github.txt b/examples/practices/git/working-with-github.txt
new file mode 100644
index 00000000..3ad57446
--- /dev/null
+++ b/examples/practices/git/working-with-github.txt
@@ -0,0 +1,89 @@
+I've created a git repository on our course github page for us to experiment
+with.  It is at:
+
+https://github.com/sbu-python-class/test-repo
+
+Here I'll describe some ways to interact with it.
+
+By default, anyone in the world has "read-access" to this repo, but I
+am the only one with "write-access".  This means that if you want to
+contribute changes to the repo, you need to ask me to pull your
+changes into the class repo, github calls this "pull requests".
+
+1. To operate with github, you should first create a github account --
+   it's free.  You can do this by going to
+
+   https://github.com/
+
+2. Next, you want to "fork" our project.  A fork is a clone of the git
+   repository (essentially what you would get if you did 'git clone'
+   on the commandline).  When you fork the project, it will appear
+   under your account's profile.  The important thing about your fork
+   is that you have write access to it, since you own it.
+
+   Go to the test repo page:
+
+   https://github.com/sbu-python-class/test-repo
+
+   and click on the "fork" button in the upper right.  Follow the
+   prompts and it will create a copy in your profile.
+
+3. Now you are ready to work with it.  At a command prompt on your
+   computer (assuming you are running linux or OSX), do:
+
+   git clone https://github.com/your-username/test-repo.git
+
+   where "your-username" is your github account name.  Note that this
+   URL is what appears on the page of your forked repo.
+
+   This will create a directory on your machine called test-repo/
+
+4. Make any additions you wish.
+
+   If you add a new file, like test.py, then to make git aware of it,
+   you'll do:
+
+   git add test.py
+   git commit test.py
+
+   (you can also do 'git commit .' -- the '.' means the present directory)
+
+   If you just edit the README, then you do:
+
+   git commit README
+
+   You'll be prompted to enter a log message when you commit.
+
+5. Push your changes back to your forked repo by typing:
+
+   git push
+
+   Note that your forked repo is not kept in sync with the main
+   class repo, so if you want to get changes that others in the class
+   may have made, you'll need to do:
+
+   git pull https://github.com/sbu-python-class/test-repo
+
+   That will tell git to pull from the original repo you forked.  Any
+   changes made there will be merged with your local changes.  You can
+   then share these changes with your fork by doing
+
+   git push
+
+6. Share your changes with the rest of the class:
+
+   Go to your github page and click on your fork of test-repo.  Now,
+   click on the green "New pull request" button.  This will identify
+   all of the differences between your version of the code and the
+   class version at sbu-python-class.  You can then click on "Create
+   pull request" and notification will be sent to the main project
+   that you wish for your changes to be merged.
+
+   That's where I come in -- I'll look over your changes, potentially
+   make suggestions via the commenting facilities on the page, and
+   then merge your changes in.  At that point, everyone in the class
+   can see them.
+
+
+Feel free to experiment with this repo -- you can't hurt anything, and
+by playing around with it, you'll get a better idea of how git works.
diff --git a/examples/python-snippets/README b/examples/python-snippets/README
new file mode 100644
index 00000000..70714f30
--- /dev/null
+++ b/examples/python-snippets/README
@@ -0,0 +1,2 @@
+These are basic examples that are posted to the discussion forum
+throughout the semester.
\ No newline at end of file
diff --git a/examples/python-snippets/advanced/inheritance.ipynb b/examples/python-snippets/advanced/inheritance.ipynb
new file mode 100644
index 00000000..5df460ea
--- /dev/null
+++ b/examples/python-snippets/advanced/inheritance.ipynb
@@ -0,0 +1,171 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class Sequence(object):\n",
+    "    def __init__(self, start, end):\n",
+    "        self.start = start\n",
+    "        self.end = end\n",
+    "        self.numbers = list(range(start, end+1))\n",
+    "        \n",
+    "    def sum(self):\n",
+    "        s = 0\n",
+    "        for n in range(len(self.numbers)):\n",
+    "            s += self.numbers[n]\n",
+    "        return s\n",
+    "    \n",
+    "    def max(self):\n",
+    "        return max(self.numbers)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "s = Sequence(0, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55\n"
+     ]
+    }
+   ],
+   "source": [
+    "print s.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class OddSequence(Sequence):\n",
+    "    def __init__(self, start, end):\n",
+    "        self.start = start\n",
+    "        self.end = end\n",
+    "        self.numbers = list(range(start, end+1, 2))\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "o = OddSequence(0, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    }
+   ],
+   "source": [
+    "print o.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 2, 4, 6, 8, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print o.numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 2, 4, 6, 8]\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/python-snippets/advanced/looplistdelete.post b/examples/python-snippets/advanced/looplistdelete.post
new file mode 100644
index 00000000..08309def
--- /dev/null
+++ b/examples/python-snippets/advanced/looplistdelete.post
@@ -0,0 +1,6 @@
+Be careful when looping over items in a list and deleting them.  You
+need to make a copy first:
+
+a_tmp = a[:]
+for a in a_tmp:
+   ...
diff --git a/examples/python-snippets/advanced/mail.post b/examples/python-snippets/advanced/mail.post
new file mode 100644
index 00000000..b16068a4
--- /dev/null
+++ b/examples/python-snippets/advanced/mail.post
@@ -0,0 +1,26 @@
+ The following lines will send an e-mail:
+
+
+import smtplib
+from email.mime.text import MIMEText
+
+sender = "your e-mail address here"
+receiver = "your e-mail address here"
+
+subject = "python e-mail"
+
+body = """
+your e-mail body goes here
+"""
+
+msg = MIMEText(body)
+msg['Subject'] = subject
+msg['From'] = sender
+msg['To'] = receiver
+
+try:
+    smtpObj = smtplib.SMTP('localhost')
+    smtpObj.sendmail(sender, receiver, msg.as_string())
+except SMTPException:
+    sys.exit("ERROR sending mail")
+
diff --git a/examples/python-snippets/regex.post b/examples/python-snippets/advanced/regex.post
similarity index 100%
rename from examples/python-snippets/regex.post
rename to examples/python-snippets/advanced/regex.post
diff --git a/examples/python-snippets/regex.py b/examples/python-snippets/advanced/regex.py
similarity index 82%
rename from examples/python-snippets/regex.py
rename to examples/python-snippets/advanced/regex.py
index 9ce5745b..5fc99780 100644
--- a/examples/python-snippets/regex.py
+++ b/examples/python-snippets/advanced/regex.py
@@ -14,7 +14,8 @@
     if not a == None:
         if a.group(1) == a.group(3):
             # we found a match
-            print "string in '{}' tags is: {}".format(a.group(1), a.group(2))
+            print("string in '{}' tags is: {}".format(a.group(1), a.group(2)))
+
 
 
 
diff --git a/examples/python-snippets/zip.post b/examples/python-snippets/advanced/zip.post
similarity index 100%
rename from examples/python-snippets/zip.post
rename to examples/python-snippets/advanced/zip.post
diff --git a/examples/python-snippets/basics/class-example.py b/examples/python-snippets/basics/class-example.py
new file mode 100644
index 00000000..a041f46f
--- /dev/null
+++ b/examples/python-snippets/basics/class-example.py
@@ -0,0 +1,20 @@
+class Box(object):
+    def __init__(self, width, height):
+        self.width = width
+        self.height = height
+
+    def area(self):
+        return self.width * self.height
+
+
+boxes = []
+for n in range(1, 10):
+    boxes.append(Box(2*n, 4*n))
+
+
+for b in boxes:
+    print(b.area())
+
+
+big_boxes = [b for b in boxes if b.area() > 100.]
+print big_boxes
diff --git a/examples/python-snippets/basics/escapesequences.post b/examples/python-snippets/basics/escapesequences.post
new file mode 100644
index 00000000..1ca67140
--- /dev/null
+++ b/examples/python-snippets/basics/escapesequences.post
@@ -0,0 +1,10 @@
+In class, we saw the escape sequence "\n" that generated a new line
+when printed.  There are many other escape sequences.  You can find
+a list here:
+
+https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals
+
+Here's a fun one -- to get your code to beep, do:
+
+print("\a")
+
diff --git a/examples/python-snippets/functionfunction.post b/examples/python-snippets/basics/functionfunction.post
similarity index 100%
rename from examples/python-snippets/functionfunction.post
rename to examples/python-snippets/basics/functionfunction.post
diff --git a/examples/python-snippets/in.post b/examples/python-snippets/basics/in.post
similarity index 100%
rename from examples/python-snippets/in.post
rename to examples/python-snippets/basics/in.post
diff --git a/examples/python-snippets/basics/shallowcopy.post b/examples/python-snippets/basics/shallowcopy.post
new file mode 100644
index 00000000..ac665b67
--- /dev/null
+++ b/examples/python-snippets/basics/shallowcopy.post
@@ -0,0 +1,25 @@
+We briefly discussed shallow copies in class:
+
+>>> a = [1, 2, [3,4], 5]
+>>> b = a[:]
+>>> print id(a)
+140022054383056
+>>> print id(b)
+140022054485312
+>>> 
+>>> print a[2]
+[3, 4]
+>>> print id(a[2])
+140022054381760
+>>> print id(b[2])
+140022054381760
+>>> 
+>>> 
+>>> a[2][0] = "changed"
+>>> print a
+[1, 2, ['changed', 4], 5]
+>>> print b
+[1, 2, ['changed', 4], 5]
+
+notice that the list in the list is not a copy, but they both point to
+the same list, so this is shallow in that sense.
diff --git a/examples/python-snippets/basics/stringsmutable.post b/examples/python-snippets/basics/stringsmutable.post
new file mode 100644
index 00000000..61c7420f
--- /dev/null
+++ b/examples/python-snippets/basics/stringsmutable.post
@@ -0,0 +1,24 @@
+Strings in python are immutable -- that means that they cannot be
+changed.  You can perform an operation on a string and produce a new
+string as a result.
+
+Remember, that the id() function can tell you about where a quantity
+exists in memory.
+
+Consider the following:
+
+In [1]: a = "this is my string"
+
+In [2]: print(id(a))
+139676110774200
+
+In [3]: a = a.replace("this", "that")
+
+In [4]: print(id(a))
+139676095173496
+
+Here we see that even though we stored the result of a.replace() back
+into a, id() shows us that it is pointing to a different location in
+memory.  That means that our new string "that is my string" is in a
+different place in memory from our old string.
+
diff --git a/examples/python-snippets/basics/zip-ex.py b/examples/python-snippets/basics/zip-ex.py
new file mode 100644
index 00000000..dc870af3
--- /dev/null
+++ b/examples/python-snippets/basics/zip-ex.py
@@ -0,0 +1,6 @@
+a = ["one", "two", "three", "four", "five"]
+b = [0, 9, 10, 11, 12]
+
+for x in zip(a, b):
+    print x
+
diff --git a/examples/python-snippets/numpy/functionfunction.py b/examples/python-snippets/numpy/functionfunction.py
new file mode 100644
index 00000000..50cc0277
--- /dev/null
+++ b/examples/python-snippets/numpy/functionfunction.py
@@ -0,0 +1,15 @@
+import sys
+
+def execute(x, function=None):
+    if function == None:
+        sys.exit("ERROR: no function supplied")
+
+    return function(x)
+
+
+def test_function(x):
+    a = "you passed in {}".format(x)
+    return a
+
+
+print execute("test", function=test_function)
diff --git a/examples/python-snippets/numpy/simple-exercise.txt b/examples/python-snippets/numpy/simple-exercise.txt
new file mode 100644
index 00000000..f770eda8
--- /dev/null
+++ b/examples/python-snippets/numpy/simple-exercise.txt
@@ -0,0 +1,21 @@
+Here's a simple exercise to play around with some numpy concepts.
+
+1. create a function that takes a 1-d array of floating point values,
+x, and returns some mathematical operation on them (e.g. sin(x), or
+something like that)
+
+2. in your main program, create x as evenly spaced numbers between
+some xmin and xmax
+
+3. call your function on your vector x
+
+4. compute the derivative of your function by differencing.  Remember
+from calculus that f' = (f(x+h) - f(x))/h -- you can difference
+adjacent function values
+
+5. compare the numerical derivative you computed with the analytic
+derivative (so you'll need to create an fprime(x) function too that
+has the analytic derivative of your original function).
+
+
+If you do this, post your code here.
diff --git a/examples/python-snippets/resources.txt b/examples/python-snippets/resources.txt
new file mode 100644
index 00000000..6c2cdfc2
--- /dev/null
+++ b/examples/python-snippets/resources.txt
@@ -0,0 +1,4 @@
+Common Python Mistakes:
+
+http://www.toptal.com/python/top-10-mistakes-that-python-programmers-make
+
diff --git a/examples/scipy/FFT/cleaned.png b/examples/scipy/FFT/cleaned.png
new file mode 100644
index 00000000..3853b58a
Binary files /dev/null and b/examples/scipy/FFT/cleaned.png differ
diff --git a/examples/scipy/FFT/convolve.py b/examples/scipy/FFT/convolve.py
new file mode 100644
index 00000000..e7980b3c
--- /dev/null
+++ b/examples/scipy/FFT/convolve.py
@@ -0,0 +1,26 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import signal
+
+def main():
+
+    data = np.loadtxt("signal.txt")
+
+    x = data[:,0]
+    sig = data[:,2]
+    orig = data[:,1]
+
+    N = len(x)
+
+    window = signal.gaussian(N, 20.0)
+    window /= np.sum(window)
+    clean = signal.convolve(sig, window, mode="same")
+
+    plt.plot(x, clean)
+    plt.plot(x, orig)
+    plt.savefig("cleaned.png")
+    
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/scipy/FFT/gaussian.png b/examples/scipy/FFT/gaussian.png
new file mode 100644
index 00000000..cfc04010
Binary files /dev/null and b/examples/scipy/FFT/gaussian.png differ
diff --git a/examples/scipy/FFT/make_signal.py b/examples/scipy/FFT/make_signal.py
new file mode 100644
index 00000000..4aa11ebe
--- /dev/null
+++ b/examples/scipy/FFT/make_signal.py
@@ -0,0 +1,27 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# make a signal that we will convolve
+
+def f(x, L):
+    A = L/10.0
+    return 2*np.sin(2*np.pi*x/L) + x*(L-x)**2/L**3 * np.cos(x) + 5*x*(L-x)/L**2 + A/2 + 0.1*A*np.sin(13*np.pi*x/L)
+
+
+N = 2048
+L = 50.0
+
+x = np.linspace(0, L, N, endpoint=False)
+f = f(x, L)
+fnew = f + 0.5*np.random.randn(N)
+
+with open("signal.txt", "w") as fo:
+    for n in range(N):
+        fo.write("{:20} {:20} {:20}\n".format(x[n], f[n], fnew[n]))
+
+plt.plot(x, f)
+plt.plot(x, fnew, zorder=-100)
+
+
+
+plt.savefig("test.png")
diff --git a/examples/scipy/FFT/signal.txt b/examples/scipy/FFT/signal.txt
new file mode 100644
index 00000000..ff6af131
--- /dev/null
+++ b/examples/scipy/FFT/signal.txt
@@ -0,0 +1,2048 @@
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diff --git a/examples/scipy/FFT/test.png b/examples/scipy/FFT/test.png
new file mode 100644
index 00000000..ce9c3a5c
Binary files /dev/null and b/examples/scipy/FFT/test.png differ
diff --git a/examples/scipy/ODEs/chaotic_pendulum.py b/examples/scipy/ODEs/chaotic_pendulum.py
new file mode 100644
index 00000000..9253c6fa
--- /dev/null
+++ b/examples/scipy/ODEs/chaotic_pendulum.py
@@ -0,0 +1,51 @@
+"""Solve the chaotic damped driven pendulum using the newer SciPy IVP
+ODE interfaces.  Passing optional arguments is now done using the
+functools partial.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.integrate import solve_ivp
+
+from functools import partial
+
+
+def rhs(t, Y, q, omega_d, b):
+    """ damped driven pendulum system derivatives.  Here, Y = (theta, omega) are
+    the solution variables. """
+    f = np.zeros_like(Y)
+
+    f[0] = Y[1]
+    f[1] = -q*Y[1] - np.sin(Y[0]) + b*np.cos(omega_d*t)
+
+    return f
+
+def restrict_theta(theta):
+    """ convert theta to be restricted to lie between -pi and pi"""
+    tnew = theta + np.pi
+    tnew += -2.0*np.pi*np.floor(tnew/(2.0*np.pi))
+    tnew -= np.pi
+    return tnew
+
+def integrate(Y0, tmax, q, omega_d, b, dt=0.05):
+
+    r = solve_ivp(partial(rhs, q=q, omega_d=omega_d, b=b),
+                  (0.0, tmax),
+                  Y0,
+                  dense_output=True)
+
+    t = np.arange(0.0, tmax, dt)
+    return t, r.sol(t)
+
+
+if __name__ == "__main__":
+    q = 0.5
+    omega_d = 2./3.
+    b = 1.5
+
+    T_d = 2.0*np.pi/omega_d
+
+    t, Y = integrate([np.radians(60), 0.0], 50*T_d, q, omega_d, b, dt=T_d/200.0)
+
+    plt.plot(t, restrict_theta(Y[0,:]))
+    plt.savefig("damped_pendulum.png")
diff --git a/examples/testing/pytest/class/test_class.py b/examples/testing/pytest/class/test_class.py
new file mode 100644
index 00000000..02e23c67
--- /dev/null
+++ b/examples/testing/pytest/class/test_class.py
@@ -0,0 +1,30 @@
+# a test class is useful to hold data that we might want set up
+# for every test.
+
+import numpy as np
+from numpy.testing import assert_array_equal
+
+class TestClassExample:
+    @classmethod
+    def setup_class(cls):
+        """ this is run once for each class, before any tests """
+        pass
+
+    @classmethod
+    def teardown_class(cls):
+        """ this is run once for each class, after all tests """
+        pass
+
+    def setup_method(self):
+        """ this is run before each of the test methods """
+        self.a = np.arange(24).reshape(6, 4)
+
+    def teardown_method(self):
+        """ this is run after each of the test methods """
+        pass
+
+    def test_max(self):
+        assert self.a.max() == 23
+
+    def test_flat(self):
+        assert_array_equal(self.a.flat, np.arange(24))
diff --git a/examples/testing/pytest/fixtures/shopping_cart.py b/examples/testing/pytest/fixtures/shopping_cart.py
new file mode 100644
index 00000000..8d7868a0
--- /dev/null
+++ b/examples/testing/pytest/fixtures/shopping_cart.py
@@ -0,0 +1,53 @@
+INVENTORY_TEXT = """
+apple, 0.60
+banana, 0.20
+grapefruit, 0.75
+grapes, 1.99
+kiwi, 0.50
+lemon, 0.20
+lime, 0.25
+mango, 1.50
+papaya, 2.95
+pineapple, 3.50
+blueberries, 1.99
+blackberries, 2.50
+peach, 0.50
+plum, 0.33
+clementine, 0.25
+cantaloupe, 3.25
+pear, 1.25
+quince, 0.45
+orange, 0.60
+"""
+
+# this will be a global -- convention is all caps
+INVENTORY = {}
+for line in INVENTORY_TEXT.splitlines():
+    if line.strip() == "":
+        continue
+    item, price = line.split(",")
+    INVENTORY[item] = float(price)
+
+
+class Item:
+    """ an item to buy """
+
+    def __init__(self, name, quantity=1):
+        """keep track of an item that is in our inventory"""
+        if name not in INVENTORY:
+            raise ValueError("invalid item name")
+        self.name = name
+        self.quantity = quantity
+
+    def __repr__(self):
+        return f"{self.name}: {self.quantity}"
+
+    def __eq__(self, other):
+        """check if the items have the same name"""
+        return self.name == other.name
+
+    def __add__(self, other):
+        """add two items together if they are the same type"""
+        if self.name == other.name:
+            return Item(self.name, self.quantity + other.quantity)
+        raise ValueError("names don't match")
diff --git a/examples/testing/pytest/fixtures/test_item.py b/examples/testing/pytest/fixtures/test_item.py
new file mode 100644
index 00000000..bacbad6b
--- /dev/null
+++ b/examples/testing/pytest/fixtures/test_item.py
@@ -0,0 +1,38 @@
+import pytest
+import shopping_cart
+
+
+@pytest.fixture
+def a():
+    return shopping_cart.Item("apple", 10)
+
+@pytest.fixture
+def b():
+    return shopping_cart.Item("banana", 20)
+
+@pytest.fixture
+def c():
+    return shopping_cart.Item("apple", 20)
+
+def test_add(a, c):
+    # modifies a
+    a += c
+    assert a.quantity == 30
+
+def test_repr(a, b, c):
+    # receives unmodified a
+    assert repr(a) == "apple: 10"
+    assert repr(b) == "banana: 20"
+    assert repr(c) == "apple: 20"
+
+def test_equality(a, b, c):
+    assert a != b
+    assert a == c
+
+def test_invalid_add(a, b):
+    with pytest.raises(ValueError, match="names don't match"):
+        a + b
+
+def test_invalid_name():
+    with pytest.raises(ValueError, match="invalid item name"):
+        d = shopping_cart.Item("dog")
diff --git a/examples/testing/pytest/fixtures/test_list.py b/examples/testing/pytest/fixtures/test_list.py
new file mode 100644
index 00000000..421cd41e
--- /dev/null
+++ b/examples/testing/pytest/fixtures/test_list.py
@@ -0,0 +1,22 @@
+import pytest
+
+@pytest.fixture
+def numbers():
+    return []
+
+@pytest.fixture
+def append_1(numbers):
+    numbers.append(1)
+
+@pytest.fixture
+def append_2(numbers, append_1):
+    numbers.append(2)
+
+def test_initial(numbers):
+    assert numbers == []
+
+def test_append_1(numbers, append_1):
+    assert numbers == [1]
+
+def test_append_2(numbers, append_2):
+    assert numbers == [1, 2]
diff --git a/examples/testing/pytest/function_setup/test_function_setup.py b/examples/testing/pytest/function_setup/test_function_setup.py
new file mode 100644
index 00000000..d6395307
--- /dev/null
+++ b/examples/testing/pytest/function_setup/test_function_setup.py
@@ -0,0 +1,21 @@
+# to run this, in this directory, simply do
+#
+#   pytest -vs .
+
+def multiply(a, b):
+    return a*b
+
+def setup_function(function):
+    # this is called before every function
+    print("setting up {}".format(function))
+
+def teardown_function(function):
+    # this is called after every function
+    print("done")
+
+def test_multiply():
+    assert multiply(4, 6) == 24
+
+def test_multiply2():
+    assert multiply(5, 6) == 24
+
diff --git a/examples/testing/pytest/simple/test_simple.py b/examples/testing/pytest/simple/test_simple.py
new file mode 100644
index 00000000..b61fa6ba
--- /dev/null
+++ b/examples/testing/pytest/simple/test_simple.py
@@ -0,0 +1,13 @@
+# to run this, in this directory, simply do
+#
+#   pytest .
+
+def multiply(a, b):
+    return a*b
+
+def test_multiply():
+    assert multiply(4, 6) == 24
+
+def test_multiply2():
+    assert multiply(5, 6) == 24
+
diff --git a/lectures/01-python/projectile-motion.ipynb b/lectures/01-python/projectile-motion.ipynb
deleted file mode 100644
index 8b1637e6..00000000
--- a/lectures/01-python/projectile-motion.ipynb
+++ /dev/null
@@ -1,190 +0,0 @@
-{
- "metadata": {
-  "name": "projectile-motion"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Projectile Motion"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "This notebook shows off some basic IPython notebook features.  We create a simple class to describe a projectile, given an angle and initial velocity and then instantiate some objects and make plots."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class projectile:\n",
-      "\n",
-      "    # this is called every time a new object is created\n",
-      "    def __init__ (self, v=1.0, theta=45, grav=9.81):\n",
-      "\n",
-      "        self.v = v           # velocity m/s\n",
-      "        self.theta = theta   # angle (degrees)\n",
-      "        \n",
-      "        self.thetaRad = theta*math.pi/180.0\n",
-      "        self.vx = v*math.cos(self.thetaRad)\n",
-      "        self.vy = v*math.sin(self.thetaRad)\n",
-      "\n",
-      "        self.g = grav\n",
-      "\n",
-      "        self.Npts = 1000\n",
-      "\n",
-      "    def height(self):\n",
-      "\n",
-      "        # how high does this projectile go?\n",
-      "        # vf_y^2 = 0 = vi_y^2 - 2 g h\n",
-      "        h = self.vy**2/(2.0*self.g)\n",
-      "\n",
-      "        return h\n",
-      "\n",
-      "    def time(self):\n",
-      "        \n",
-      "        # time of flight up\n",
-      "        # vf_y = 0 = vi_y - g t\n",
-      "        t = self.vy/self.g\n",
-      "\n",
-      "        # total time = up + down\n",
-      "        t = 2.0*t\n",
-      "\n",
-      "        return t\n",
-      "        \n",
-      "    def distance(self):\n",
-      "        \n",
-      "        d = self.vx*self.time()\n",
-      "\n",
-      "        return d\n",
-      "\n",
-      "    def __str__(self):\n",
-      "        # a string representation for this class -- so we can print it\n",
-      "        str = \" v: %s m/s\\n theta: %s (degrees)\\n height: %s m\\n distance: %s m\\n\" % \\\n",
-      "            (`self.v`, `self.theta`, `self.height()`, `self.distance()`)\n",
-      "        \n",
-      "        return str\n",
-      "    \n",
-      "    def t(self):\n",
-      "        return numpy.linspace(0.0, self.time(), num=self.Npts)\n",
-      "    \n",
-      "    def x(self):\n",
-      "        return self.vx*self.t()\n",
-      "    \n",
-      "    def y(self):\n",
-      "        return self.vy*self.t() - 0.5*self.g*self.t()**2\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p1 = projectile(theta=80, v=10)\n",
-      "p2 = projectile(theta=45, v=10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print p1\n",
-      "print p2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " v: 10 m/s\n",
-        " theta: 80 (degrees)\n",
-        " height: 4.943151429118012 m\n",
-        " distance: 3.4864438667244526 m\n",
-        "\n",
-        " v: 10 m/s\n",
-        " theta: 45 (degrees)\n",
-        " height: 2.54841997961264 m\n",
-        " distance: 10.19367991845056 m\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.plot(p1.x(), p1.y())\n",
-      "pylab.plot(p2.x(), p2.y())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x296cb10>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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NpzZy9fZVujXuRp+mfXim4TMyMxd2p8vynjkT0tLg44+tmUT/hg6FDh1g1CjrHbPQVMiW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\nISlvIYTQISlvIYTQof8HaP9yjz6griAAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-advanced-datatypes.ipynb b/lectures/01-python/python-advanced-datatypes.ipynb
deleted file mode 100644
index 3f5c3c99..00000000
--- a/lectures/01-python/python-advanced-datatypes.ipynb
+++ /dev/null
@@ -1,805 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:21e36a92b0263e22d0d204f87a2279f389393d27d194f8c115a3659762313582"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "These notes follow the official python tutorial pretty closely: http://docs.python.org/2/tutorial/"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Lists"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Lists group together data -- the data can be a mix of integers, floating point or complex #s, strings, or other objects (including other lists)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = [1, 2.0, \"my list\", 4]\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 2.0, 'my list', 4]\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a[2]     # remember, 0-based indexing"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my list\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a*2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 2.0, 'my list', 4, 1, 2.0, 'my list', 4]\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print len(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can change elements in a list easily"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a[1] = -2.0\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "[1, -2.0, 'my list', 4]"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a[0:1] = [-1, -2.1]   # this will put two items in the spot where 1 existed before\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "[-1, -2.1, -2.0, 'my list', 4]"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there are lots of methods that work on lists"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.append(6)\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "[-1, -2.1, -2.0, 'my list', 4, 6]"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.pop()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "6"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "[-1, -2.1, -2.0, 'my list', 4]"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "copying may seem a little counterintuitive at first"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = a  # this is just a pointer, both a and b refer to the same list object in memory\n",
-      "print a\n",
-      "a[0] = \"changed\"\n",
-      "print b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[-1, -2.1, -2.0, 'my list', 4]\n",
-        "['changed', -2.1, -2.0, 'my list', 4]\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "if you want to create a new object in memory that is a copy of another, then you can index the list"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = list(a)   # you can also do c = a[:], which basically slices the entire list\n",
-      "a[1] = \"two\"\n",
-      "print a\n",
-      "print b\n",
-      "print c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['changed', 'two', -2.0, 'my list', 4]\n",
-        "['changed', 'two', -2.0, 'my list', 4]\n",
-        "['changed', -2.1, -2.0, 'my list', 4]\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Note: this is what is referred to as a shallow copy.  If the original list had any special objects in it (like another list), then the new copy and the old copy will still point to that same object.  There is a deep copy method when you really want everything to be unique in memory.\n",
-      "\n",
-      "When in doubt, use the id() function to figure out where in memory an object lies"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print id(a), id(b), id(c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "139840246254624 139840246254624 139840246283008\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "We can also start with an empty list an build from there"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "myList = []\n",
-      "myList.append(200.0)\n",
-      "myList.append(-1.0)\n",
-      "myList.append(-1.0)\n",
-      "myList.append(3.14)\n",
-      "myList.append(27)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "myList"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "[200.0, -1.0, -1.0, 3.14, 27]"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "myList.sort()\n",
-      "print myList.count(-1.0)\n",
-      "myList"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "[-1.0, -1.0, 3.14, 27, 200.0]"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "joining two lists is simple"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = [1,2,3]\n",
-      "c = [4,5,6]\n",
-      "d = b+c\n",
-      "print d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 2, 3, 4, 5, 6]\n"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(a.insert)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on built-in function insert:\n",
-        "\n",
-        "insert(...)\n",
-        "    L.insert(index, object) -- insert object before index\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.insert(3, \"my inserted element\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "['changed',\n",
-        " 'two',\n",
-        " -2.0,\n",
-        " 'my inserted element',\n",
-        " 'my inserted element',\n",
-        " 'my list',\n",
-        " 4]"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Dictionaries"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "A dictionary stores data as a key:value pair.  Unlike a list where you have a particular order, the keys in a dictionary allow you to access information anywhere easily"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_dict = {\"key1\":1, \"key2\":2, \"key3\":3}"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print my_dict[\"key1\"]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can add a new key:pair easily, and it can be of any type"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_dict[\"newkey\"] = \"new\"\n",
-      "print my_dict"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "{'key3': 3, 'key2': 2, 'key1': 1, 'newkey': 'new'}\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can also easily get the list of keys that are defined in a dictionary"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "keys = my_dict.keys()\n",
-      "print keys"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['key3', 'key2', 'key1', 'newkey']\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "and check easily whether a key exists in the dictionary"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"key1\" in keys\n",
-      "print \"invalidKey\" in keys"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "True\n",
-        "False\n"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "List Comprehensions"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "list comprehensions provide a compact way to initialize lists.  Some examples from the tutorial"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "squares = [x**2 for x in range(10)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "squares"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "here we use another python type, the tuple, to combine numbers from two lists into a pair"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 31,
-       "text": [
-        "[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Tuples"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "tuples are immutable -- they cannot be changed, but they are useful for organizing data in some situations"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = (1, 2, 3, 4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1, 2, 3, 4)\n"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w, x, y, z = a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print w, x, y, z"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 2 3 4\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a[0] = 2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "'tuple' object does not support item assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-36-48f8c1dbf98d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
-       ]
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-classes.ipynb b/lectures/01-python/python-classes.ipynb
deleted file mode 100644
index 83360d9a..00000000
--- a/lectures/01-python/python-classes.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:39cbd3ae9242d743ed8aa24f464f3652e56ace32d5a6121427ce6fa763dead4a"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Classes"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Classes are the fundamental concept for object oriented programming.  A class defines a data type with both data and functions that can operate on the data.  An object is an instance of a class.  Each object will have its own namespace (separate from other instances of the class and other functions, etc. in your program)."
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "simplest example: just a container (like a struct in C)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class Container:\n",
-      "    pass\n",
-      "        \n",
-      "a = Container()\n",
-      "a.x = 1\n",
-      "a.y = 2\n",
-      "a.z = 3\n",
-      "\n",
-      "b = Container()\n",
-      "b.xyz = 1\n",
-      "b.uvw = 2\n",
-      "\n",
-      "print a.x, a.y, a.z\n",
-      "print b.xyz, b.uvw"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 2 3\n",
-        "1 2\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "notice that you don't have to declare what variables are members of the class ahead of time (although, usually that's good practice)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Playing Cards"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "here's a simple class that represents a playing card.  Notice that we are using unicode to represent the suits."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class Card:\n",
-      "    \n",
-      "    def __init__(self, suit=1, rank=2):\n",
-      "        if suit < 1 or suit > 4:\n",
-      "            print \"invalid suit, setting to 1\"\n",
-      "            \n",
-      "        self.suit = suit\n",
-      "        self.rank = rank\n",
-      "        \n",
-      "\n",
-      "\n",
-      "    def value(self):\n",
-      "        \"\"\" we want things order primarily by rank then suit \"\"\"\n",
-      "        return self.suit + (self.rank-1)*14\n",
-      "\n",
-      "\n",
-      "    def __cmp__(self, other):\n",
-      "        return cmp(self.value(), other.value())\n",
-      "    \n",
-      "\n",
-      "    def __unicode__(self):\n",
-      "        suits = [u\"\\u2660\",  # spade\n",
-      "                 u\"\\u2665\",  # heart\n",
-      "                 u\"\\u2666\",  # diamond\n",
-      "                 u\"\\u2663\"]  # club\n",
-      "        \n",
-      "        r = `self.rank`\n",
-      "        if self.rank == 11:\n",
-      "            r = \"J\"\n",
-      "        elif self.rank == 12:\n",
-      "            r = \"Q\"\n",
-      "        elif self.rank == 13:\n",
-      "            r = \"K\"\n",
-      "        elif self.rank == 14:\n",
-      "            r = \"A\"\n",
-      "                \n",
-      "        return r +':'+suits[self.suit-1]\n",
-      "    \n",
-      "    def __str__(self):\n",
-      "        return unicode(self).encode('utf-8')\n",
-      "        "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can create a card easily:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c1 = Card()\n",
-      "c2 = Card(suit=1, rank=13)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c3 = Card(suit=0, rank=4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "invalid suit, setting to 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "The __str__ method converts the object into a string that can be printed.  The __unicode__ method is a new python feature."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print c1\n",
-      "print c2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2:\u2660\n",
-        "K:\u2660\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "the value method assigns a value to the object that can be used in comparisons, and the __cmp__ method is what does the actual comparing"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print c1 > c2\n",
-      "print c1 < c2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "False\n",
-        "True\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "classes can use other include other classes as data objects -- here's a deck of cards.  Note that we are using the python random module here."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "\n",
-      "class Deck:\n",
-      "    \"\"\" the deck is a collection of cards \"\"\"\n",
-      "\n",
-      "    def __init__(self):\n",
-      "\n",
-      "        self.nsuits = 4\n",
-      "        self.nranks = 13\n",
-      "        self.minrank = 2\n",
-      "        self.maxrank = self.minrank + self.nranks - 1\n",
-      "\n",
-      "        self.cards = []\n",
-      "\n",
-      "        for rank in range(self.minrank,self.maxrank+1):\n",
-      "            for suit in range(1,self.nsuits+1):\n",
-      "                current_card = Card(rank=rank, suit=suit)\n",
-      "                self.cards.append(current_card)\n",
-      "\n",
-      "\n",
-      "    def shuffle(self):\n",
-      "        random.shuffle(self.cards)\n",
-      "\n",
-      "\n",
-      "    def get_cards(self, num=1):\n",
-      "        hand = []\n",
-      "\n",
-      "        for n in range(num):\n",
-      "            hand.append(self.cards.pop())\n",
-      "\n",
-      "        return hand\n",
-      "\n",
-      "    \n",
-      "    def __str__(self):\n",
-      "        string = \"\"\n",
-      "        for c in self.cards:\n",
-      "            string += str(c) + \" \"\n",
-      "        return string"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "let's create a deck, shuffle, and deal a hand (for a poker game)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mydeck = Deck()\n",
-      "print mydeck\n",
-      "print len(mydeck.cards)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2:\u2660 2:\u2665 2:\u2666 2:\u2663 3:\u2660 3:\u2665 3:\u2666 3:\u2663 4:\u2660 4:\u2665 4:\u2666 4:\u2663 5:\u2660 5:\u2665 5:\u2666 5:\u2663 6:\u2660 6:\u2665 6:\u2666 6:\u2663 7:\u2660 7:\u2665 7:\u2666 7:\u2663 8:\u2660 8:\u2665 8:\u2666 8:\u2663 9:\u2660 9:\u2665 9:\u2666 9:\u2663 10:\u2660 10:\u2665 10:\u2666 10:\u2663 J:\u2660 J:\u2665 J:\u2666 J:\u2663 Q:\u2660 Q:\u2665 Q:\u2666 Q:\u2663 K:\u2660 K:\u2665 K:\u2666 K:\u2663 A:\u2660 A:\u2665 A:\u2666 A:\u2663 \n",
-        "52\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "notice that there is no error handling in this class.  The get_cards() will deal cards from the deck, removing them in the process.  Eventually we'll run out of cards."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mydeck.shuffle()\n",
-      "\n",
-      "hand = mydeck.get_cards(5)\n",
-      "for c in sorted(hand): print c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "IndexError",
-       "evalue": "pop from empty list",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-19-df42d5fdf4ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmydeck\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mhand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmydeck\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_cards\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m<ipython-input-7-fc4bd84ad704>\u001b[0m in \u001b[0;36mget_cards\u001b[1;34m(self, num)\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m             \u001b[0mhand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcards\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mhand\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mIndexError\u001b[0m: pop from empty list"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Projectiles"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Here we have a class that represents a projectile.  It has member functions for finding the distance and height of the projectile"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import math\n",
-      "\n",
-      "class Projectile:\n",
-      "\n",
-      "    # this is called every time a new object is created\n",
-      "    def __init__ (self, v=1.0, theta=45, grav=9.81):\n",
-      "\n",
-      "        self.v = v           # velocity m/s\n",
-      "        self.theta = theta   # angle (degrees)\n",
-      "        \n",
-      "        self.theta_rad = math.radians(theta)\n",
-      "        self.vx = v*math.cos(self.theta_rad)\n",
-      "        self.vy = v*math.sin(self.theta_rad)\n",
-      "\n",
-      "        self.g = grav\n",
-      "\n",
-      "\n",
-      "    def height(self):\n",
-      "\n",
-      "        # how high does this projectile go?\n",
-      "        # vf_y^2 = 0 = vi_y^2 - 2 g h\n",
-      "        h = self.vy**2/(2.0*self.g)\n",
-      "\n",
-      "        return h\n",
-      "\n",
-      "    def distance(self):\n",
-      "        \n",
-      "        # time of flight up\n",
-      "        # vf_y = 0 = vi_y - g t\n",
-      "        t = self.vy/self.g\n",
-      "\n",
-      "        # total time = up + down\n",
-      "        t = 2.0*t\n",
-      "\n",
-      "        d = self.vx*t\n",
-      "\n",
-      "        return d\n",
-      "\n",
-      "    def __str__(self):\n",
-      "        # a string representation for this class -- so we can print it\n",
-      "        str = \" v: %s m/s\\n theta: %s (degrees)\\n height: %s m\\n distance: %s m\\n\" % \\\n",
-      "            (`self.v`, `self.theta`, `self.height()`, `self.distance()`)\n",
-      "        \n",
-      "        return str\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there is a lot going on there.\n",
-      "\n",
-      "When you instantiate a class, the __init__ method is called.  Note that all method in a class always have \"self\" as the first argument -- this refers to the object that is invoking the method.\n",
-      "\n",
-      "Here we create a projectile"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p1 = Projectile()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "The class has a special __str__ function that tells python what it should do if you print an object"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print p1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "We can put objects into a list"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "projectiles = []\n",
-      "projectiles.append(p1)\n",
-      "\n",
-      "projectiles.append(Projectile(v = 100, theta = 70))\n",
-      "projectiles.append(Projectile(v = 1000, theta = 30))\n",
-      "\n",
-      "print projectiles"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Then we can loop over these and use them, e.g., call the height method"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"heights:\"\n",
-      "for p in projectiles:\n",
-      "    print p.height()\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "There are advanced features like inheritance -- a class can be built upon other classes and inherit from them.  We'll look at this as needed."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-control-flow.ipynb b/lectures/01-python/python-control-flow.ipynb
deleted file mode 100644
index 60383143..00000000
--- a/lectures/01-python/python-control-flow.ipynb
+++ /dev/null
@@ -1,432 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:b892460be93b31ea450ca2fbba43538c2bafe3a6de19ab3e1606112ad44e1811"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "A simple while loop -- notice the indentation to denote the block that is part of the loop."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "n = 0\n",
-      "while n < 10:\n",
-      "    print n\n",
-      "    n += 1\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n",
-        "5\n",
-        "6\n",
-        "7\n",
-        "8\n",
-        "9\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "often we'll use range() in this situation"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "n++"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "SyntaxError",
-       "evalue": "invalid syntax (<ipython-input-2-8b5cd7780211>, line 1)",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-2-8b5cd7780211>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    n++\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for n in range(10):\n",
-      "    print n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n",
-        "5\n",
-        "6\n",
-        "7\n",
-        "8\n",
-        "9\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "if allows for branching.  python does not have a select/case statement"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 10\n",
-      "if x < 0:\n",
-      "    print \"negative\"\n",
-      "elif x == 0:\n",
-      "    print \"zero\"\n",
-      "else:\n",
-      "    print \"positive\"\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "positive\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "it's easy to loop over items in a list"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "alist = [1, 2.0, \"three\", 4]\n",
-      "for a in alist:\n",
-      "    print a\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n",
-        "2.0\n",
-        "three\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "and break out of the loop when you find what you're looking for"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "n = 0\n",
-      "for a in alist:\n",
-      "    if a == \"three\":\n",
-      "        break\n",
-      "    else:\n",
-      "        n += 1\n",
-      "\n",
-      "print n\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "(for that example, however, there is a simpler way)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print alist.index(\"three\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "for dictionaries, you can also loop over the elements"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_dict = {\"key1\":1, \"key2\":2, \"key3\":3}\n",
-      "\n",
-      "for k, v in my_dict.iteritems():\n",
-      "    print \"key = {}, value = {}\".format(k, v)    # notice how we do the formatting here\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "key = key3, value = 3\n",
-        "key = key2, value = 2\n",
-        "key = key1, value = 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "sometimes we want to loop over a list element and know its index.  enumerate() helps here:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for n, a in enumerate(alist):\n",
-      "    print n, a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 1\n",
-        "1 2.0\n",
-        "2 three\n",
-        "3 4\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(enumerate)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on class enumerate in module __builtin__:\n",
-        "\n",
-        "class enumerate(object)\n",
-        " |  enumerate(iterable[, start]) -> iterator for index, value of iterable\n",
-        " |  \n",
-        " |  Return an enumerate object.  iterable must be another object that supports\n",
-        " |  iteration.  The enumerate object yields pairs containing a count (from\n",
-        " |  start, which defaults to zero) and a value yielded by the iterable argument.\n",
-        " |  enumerate is useful for obtaining an indexed list:\n",
-        " |      (0, seq[0]), (1, seq[1]), (2, seq[2]), ...\n",
-        " |  \n",
-        " |  Methods defined here:\n",
-        " |  \n",
-        " |  __getattribute__(...)\n",
-        " |      x.__getattribute__('name') <==> x.name\n",
-        " |  \n",
-        " |  __iter__(...)\n",
-        " |      x.__iter__() <==> iter(x)\n",
-        " |  \n",
-        " |  next(...)\n",
-        " |      x.next() -> the next value, or raise StopIteration\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Data and other attributes defined here:\n",
-        " |  \n",
-        " |  __new__ = <built-in method __new__ of type object>\n",
-        " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(range)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on built-in function range in module __builtin__:\n",
-        "\n",
-        "range(...)\n",
-        "    range(stop) -> list of integers\n",
-        "    range(start, stop[, step]) -> list of integers\n",
-        "    \n",
-        "    Return a list containing an arithmetic progression of integers.\n",
-        "    range(i, j) returns [i, i+1, i+2, ..., j-1]; start (!) defaults to 0.\n",
-        "    When step is given, it specifies the increment (or decrement).\n",
-        "    For example, range(4) returns [0, 1, 2, 3].  The end point is omitted!\n",
-        "    These are exactly the valid indices for a list of 4 elements.\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(xrange)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on class xrange in module __builtin__:\n",
-        "\n",
-        "class xrange(object)\n",
-        " |  xrange(stop) -> xrange object\n",
-        " |  xrange(start, stop[, step]) -> xrange object\n",
-        " |  \n",
-        " |  Like range(), but instead of returning a list, returns an object that\n",
-        " |  generates the numbers in the range on demand.  For looping, this is \n",
-        " |  slightly faster than range() and more memory efficient.\n",
-        " |  \n",
-        " |  Methods defined here:\n",
-        " |  \n",
-        " |  __getattribute__(...)\n",
-        " |      x.__getattribute__('name') <==> x.name\n",
-        " |  \n",
-        " |  __getitem__(...)\n",
-        " |      x.__getitem__(y) <==> x[y]\n",
-        " |  \n",
-        " |  __iter__(...)\n",
-        " |      x.__iter__() <==> iter(x)\n",
-        " |  \n",
-        " |  __len__(...)\n",
-        " |      x.__len__() <==> len(x)\n",
-        " |  \n",
-        " |  __reduce__(...)\n",
-        " |  \n",
-        " |  __repr__(...)\n",
-        " |      x.__repr__() <==> repr(x)\n",
-        " |  \n",
-        " |  __reversed__(...)\n",
-        " |      Returns a reverse iterator.\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Data and other attributes defined here:\n",
-        " |  \n",
-        " |  __new__ = <built-in method __new__ of type object>\n",
-        " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-datatypes.ipynb b/lectures/01-python/python-datatypes.ipynb
deleted file mode 100644
index 15a5ded6..00000000
--- a/lectures/01-python/python-datatypes.ipynb
+++ /dev/null
@@ -1,1766 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:5c503a373abb0dbc6abad82241c7ace2cf9bc9c8904e8db2dc475ec848028872"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Basic Python Datatypes"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "This notebook explores the basic python datatypes.\n",
-      "\n",
-      "Some examples come from the python tutorial:\n",
-      "http://docs.python.org/2/tutorial/\n",
-      "\n",
-      "For IPython help, visit:\n",
-      "http://ipython.org/ipython-doc/dev/interactive/notebook.html\n",
-      "\n",
-      "Some useful short-cuts:\n",
-      " shift+enter = run cell and jump to the next (creating a new cell if there is no other new one)\n",
-      " ctrl+enter = run cell-in place\n",
-      " alt+enter = run cell and insert a new one below\n",
-      "\n",
-      "ctrl+m h lists other commands"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A \"markdown cell\" enables you to typeset LaTeX equations right in your notebook.  Just put them in <span>$</span> or <span>$$</span>:\n",
-      "\n",
-      "$$\\frac{\\partial \\rho}{\\partial t} + \\nabla \\cdot (\\rho U) = 0$$"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "integers"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Note: integer division is one place where python 2 and python 3 different"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "2+2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "4"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "1/2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "0"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "1//2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "0"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Python is a dynamic language--this means that we do not need to declare the datatype of a variable before initializing it"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = 2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a+b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a*b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Note that variable names are case sensitive, so a and A are different"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "A = 2048"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a, A"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 2048\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = y = z = 0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print x, y, z"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 0 0\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "z = 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print x, y, z"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 0 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python has some built in help (and IPython has even more)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on int object:\n",
-        "\n",
-        "class int(object)\n",
-        " |  int(x=0) -> int or long\n",
-        " |  int(x, base=10) -> int or long\n",
-        " |  \n",
-        " |  Convert a number or string to an integer, or return 0 if no arguments\n",
-        " |  are given.  If x is floating point, the conversion truncates towards zero.\n",
-        " |  If x is outside the integer range, the function returns a long instead.\n",
-        " |  \n",
-        " |  If x is not a number or if base is given, then x must be a string or\n",
-        " |  Unicode object representing an integer literal in the given base.  The\n",
-        " |  literal can be preceded by '+' or '-' and be surrounded by whitespace.\n",
-        " |  The base defaults to 10.  Valid bases are 0 and 2-36.  Base 0 means to\n",
-        " |  interpret the base from the string as an integer literal.\n",
-        " |  >>> int('0b100', base=0)\n",
-        " |  4\n",
-        " |  \n",
-        " |  Methods defined here:\n",
-        " |  \n",
-        " |  __abs__(...)\n",
-        " |      x.__abs__() <==> abs(x)\n",
-        " |  \n",
-        " |  __add__(...)\n",
-        " |      x.__add__(y) <==> x+y\n",
-        " |  \n",
-        " |  __and__(...)\n",
-        " |      x.__and__(y) <==> x&y\n",
-        " |  \n",
-        " |  __cmp__(...)\n",
-        " |      x.__cmp__(y) <==> cmp(x,y)\n",
-        " |  \n",
-        " |  __coerce__(...)\n",
-        " |      x.__coerce__(y) <==> coerce(x, y)\n",
-        " |  \n",
-        " |  __div__(...)\n",
-        " |      x.__div__(y) <==> x/y\n",
-        " |  \n",
-        " |  __divmod__(...)\n",
-        " |      x.__divmod__(y) <==> divmod(x, y)\n",
-        " |  \n",
-        " |  __float__(...)\n",
-        " |      x.__float__() <==> float(x)\n",
-        " |  \n",
-        " |  __floordiv__(...)\n",
-        " |      x.__floordiv__(y) <==> x//y\n",
-        " |  \n",
-        " |  __format__(...)\n",
-        " |  \n",
-        " |  __getattribute__(...)\n",
-        " |      x.__getattribute__('name') <==> x.name\n",
-        " |  \n",
-        " |  __getnewargs__(...)\n",
-        " |  \n",
-        " |  __hash__(...)\n",
-        " |      x.__hash__() <==> hash(x)\n",
-        " |  \n",
-        " |  __hex__(...)\n",
-        " |      x.__hex__() <==> hex(x)\n",
-        " |  \n",
-        " |  __index__(...)\n",
-        " |      x[y:z] <==> x[y.__index__():z.__index__()]\n",
-        " |  \n",
-        " |  __int__(...)\n",
-        " |      x.__int__() <==> int(x)\n",
-        " |  \n",
-        " |  __invert__(...)\n",
-        " |      x.__invert__() <==> ~x\n",
-        " |  \n",
-        " |  __long__(...)\n",
-        " |      x.__long__() <==> long(x)\n",
-        " |  \n",
-        " |  __lshift__(...)\n",
-        " |      x.__lshift__(y) <==> x<<y\n",
-        " |  \n",
-        " |  __mod__(...)\n",
-        " |      x.__mod__(y) <==> x%y\n",
-        " |  \n",
-        " |  __mul__(...)\n",
-        " |      x.__mul__(y) <==> x*y\n",
-        " |  \n",
-        " |  __neg__(...)\n",
-        " |      x.__neg__() <==> -x\n",
-        " |  \n",
-        " |  __nonzero__(...)\n",
-        " |      x.__nonzero__() <==> x != 0\n",
-        " |  \n",
-        " |  __oct__(...)\n",
-        " |      x.__oct__() <==> oct(x)\n",
-        " |  \n",
-        " |  __or__(...)\n",
-        " |      x.__or__(y) <==> x|y\n",
-        " |  \n",
-        " |  __pos__(...)\n",
-        " |      x.__pos__() <==> +x\n",
-        " |  \n",
-        " |  __pow__(...)\n",
-        " |      x.__pow__(y[, z]) <==> pow(x, y[, z])\n",
-        " |  \n",
-        " |  __radd__(...)\n",
-        " |      x.__radd__(y) <==> y+x\n",
-        " |  \n",
-        " |  __rand__(...)\n",
-        " |      x.__rand__(y) <==> y&x\n",
-        " |  \n",
-        " |  __rdiv__(...)\n",
-        " |      x.__rdiv__(y) <==> y/x\n",
-        " |  \n",
-        " |  __rdivmod__(...)\n",
-        " |      x.__rdivmod__(y) <==> divmod(y, x)\n",
-        " |  \n",
-        " |  __repr__(...)\n",
-        " |      x.__repr__() <==> repr(x)\n",
-        " |  \n",
-        " |  __rfloordiv__(...)\n",
-        " |      x.__rfloordiv__(y) <==> y//x\n",
-        " |  \n",
-        " |  __rlshift__(...)\n",
-        " |      x.__rlshift__(y) <==> y<<x\n",
-        " |  \n",
-        " |  __rmod__(...)\n",
-        " |      x.__rmod__(y) <==> y%x\n",
-        " |  \n",
-        " |  __rmul__(...)\n",
-        " |      x.__rmul__(y) <==> y*x\n",
-        " |  \n",
-        " |  __ror__(...)\n",
-        " |      x.__ror__(y) <==> y|x\n",
-        " |  \n",
-        " |  __rpow__(...)\n",
-        " |      y.__rpow__(x[, z]) <==> pow(x, y[, z])\n",
-        " |  \n",
-        " |  __rrshift__(...)\n",
-        " |      x.__rrshift__(y) <==> y>>x\n",
-        " |  \n",
-        " |  __rshift__(...)\n",
-        " |      x.__rshift__(y) <==> x>>y\n",
-        " |  \n",
-        " |  __rsub__(...)\n",
-        " |      x.__rsub__(y) <==> y-x\n",
-        " |  \n",
-        " |  __rtruediv__(...)\n",
-        " |      x.__rtruediv__(y) <==> y/x\n",
-        " |  \n",
-        " |  __rxor__(...)\n",
-        " |      x.__rxor__(y) <==> y^x\n",
-        " |  \n",
-        " |  __str__(...)\n",
-        " |      x.__str__() <==> str(x)\n",
-        " |  \n",
-        " |  __sub__(...)\n",
-        " |      x.__sub__(y) <==> x-y\n",
-        " |  \n",
-        " |  __truediv__(...)\n",
-        " |      x.__truediv__(y) <==> x/y\n",
-        " |  \n",
-        " |  __trunc__(...)\n",
-        " |      Truncating an Integral returns itself.\n",
-        " |  \n",
-        " |  __xor__(...)\n",
-        " |      x.__xor__(y) <==> x^y\n",
-        " |  \n",
-        " |  bit_length(...)\n",
-        " |      int.bit_length() -> int\n",
-        " |      \n",
-        " |      Number of bits necessary to represent self in binary.\n",
-        " |      >>> bin(37)\n",
-        " |      '0b100101'\n",
-        " |      >>> (37).bit_length()\n",
-        " |      6\n",
-        " |  \n",
-        " |  conjugate(...)\n",
-        " |      Returns self, the complex conjugate of any int.\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Data descriptors defined here:\n",
-        " |  \n",
-        " |  denominator\n",
-        " |      the denominator of a rational number in lowest terms\n",
-        " |  \n",
-        " |  imag\n",
-        " |      the imaginary part of a complex number\n",
-        " |  \n",
-        " |  numerator\n",
-        " |      the numerator of a rational number in lowest terms\n",
-        " |  \n",
-        " |  real\n",
-        " |      the real part of a complex number\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Data and other attributes defined here:\n",
-        " |  \n",
-        " |  __new__ = <built-in method __new__ of type object>\n",
-        " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print type(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<type 'int'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "floating point and complex numbers"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "when operating with both floating point and integers, the result is promoted to a float"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "1./2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "0.5"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "but note the special integer division operator"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "1.//2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "0.0"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "The math module provides functions that do the basic mathematical operations as well as provide constants (note there is a separate cmath module for complex numbers).\n",
-      "\n",
-      "In python, you 'import' a module.  The functions are then defined in a separate namespace -- this is a separate region that defines names and variables, etc.  A variable in one namespace can have the same name as a variable in a different namespace, and they don't clash.  You use the \".\" operator to access a member of a namespace.\n",
-      "\n",
-      "By default, when you type stuff into the python interpreter or here in IPython, or in a script, it is in its own default namespace, and you don't need to prefix any of the variables with a namespace indicator."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import math"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print math.pi"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.14159265359\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pi = 3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print pi, math.pi"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3 3.14159265359\n"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "let's look at some floating point operations"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "R = 2.0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print math.pi*R**2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "12.5663706144\n"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print math.cos(math.radians(45))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.707106781187\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(math)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on module math:\n",
-        "\n",
-        "NAME\n",
-        "    math\n",
-        "\n",
-        "FILE\n",
-        "    /usr/lib64/python2.7/lib-dynload/math.so\n",
-        "\n",
-        "DESCRIPTION\n",
-        "    This module is always available.  It provides access to the\n",
-        "    mathematical functions defined by the C standard.\n",
-        "\n",
-        "FUNCTIONS\n",
-        "    acos(...)\n",
-        "        acos(x)\n",
-        "        \n",
-        "        Return the arc cosine (measured in radians) of x.\n",
-        "    \n",
-        "    acosh(...)\n",
-        "        acosh(x)\n",
-        "        \n",
-        "        Return the hyperbolic arc cosine (measured in radians) of x.\n",
-        "    \n",
-        "    asin(...)\n",
-        "        asin(x)\n",
-        "        \n",
-        "        Return the arc sine (measured in radians) of x.\n",
-        "    \n",
-        "    asinh(...)\n",
-        "        asinh(x)\n",
-        "        \n",
-        "        Return the hyperbolic arc sine (measured in radians) of x.\n",
-        "    \n",
-        "    atan(...)\n",
-        "        atan(x)\n",
-        "        \n",
-        "        Return the arc tangent (measured in radians) of x.\n",
-        "    \n",
-        "    atan2(...)\n",
-        "        atan2(y, x)\n",
-        "        \n",
-        "        Return the arc tangent (measured in radians) of y/x.\n",
-        "        Unlike atan(y/x), the signs of both x and y are considered.\n",
-        "    \n",
-        "    atanh(...)\n",
-        "        atanh(x)\n",
-        "        \n",
-        "        Return the hyperbolic arc tangent (measured in radians) of x.\n",
-        "    \n",
-        "    ceil(...)\n",
-        "        ceil(x)\n",
-        "        \n",
-        "        Return the ceiling of x as a float.\n",
-        "        This is the smallest integral value >= x.\n",
-        "    \n",
-        "    copysign(...)\n",
-        "        copysign(x, y)\n",
-        "        \n",
-        "        Return x with the sign of y.\n",
-        "    \n",
-        "    cos(...)\n",
-        "        cos(x)\n",
-        "        \n",
-        "        Return the cosine of x (measured in radians).\n",
-        "    \n",
-        "    cosh(...)\n",
-        "        cosh(x)\n",
-        "        \n",
-        "        Return the hyperbolic cosine of x.\n",
-        "    \n",
-        "    degrees(...)\n",
-        "        degrees(x)\n",
-        "        \n",
-        "        Convert angle x from radians to degrees.\n",
-        "    \n",
-        "    erf(...)\n",
-        "        erf(x)\n",
-        "        \n",
-        "        Error function at x.\n",
-        "    \n",
-        "    erfc(...)\n",
-        "        erfc(x)\n",
-        "        \n",
-        "        Complementary error function at x.\n",
-        "    \n",
-        "    exp(...)\n",
-        "        exp(x)\n",
-        "        \n",
-        "        Return e raised to the power of x.\n",
-        "    \n",
-        "    expm1(...)\n",
-        "        expm1(x)\n",
-        "        \n",
-        "        Return exp(x)-1.\n",
-        "        This function avoids the loss of precision involved in the direct evaluation of exp(x)-1 for small x.\n",
-        "    \n",
-        "    fabs(...)\n",
-        "        fabs(x)\n",
-        "        \n",
-        "        Return the absolute value of the float x.\n",
-        "    \n",
-        "    factorial(...)\n",
-        "        factorial(x) -> Integral\n",
-        "        \n",
-        "        Find x!. Raise a ValueError if x is negative or non-integral.\n",
-        "    \n",
-        "    floor(...)\n",
-        "        floor(x)\n",
-        "        \n",
-        "        Return the floor of x as a float.\n",
-        "        This is the largest integral value <= x.\n",
-        "    \n",
-        "    fmod(...)\n",
-        "        fmod(x, y)\n",
-        "        \n",
-        "        Return fmod(x, y), according to platform C.  x % y may differ.\n",
-        "    \n",
-        "    frexp(...)\n",
-        "        frexp(x)\n",
-        "        \n",
-        "        Return the mantissa and exponent of x, as pair (m, e).\n",
-        "        m is a float and e is an int, such that x = m * 2.**e.\n",
-        "        If x is 0, m and e are both 0.  Else 0.5 <= abs(m) < 1.0.\n",
-        "    \n",
-        "    fsum(...)\n",
-        "        fsum(iterable)\n",
-        "        \n",
-        "        Return an accurate floating point sum of values in the iterable.\n",
-        "        Assumes IEEE-754 floating point arithmetic.\n",
-        "    \n",
-        "    gamma(...)\n",
-        "        gamma(x)\n",
-        "        \n",
-        "        Gamma function at x.\n",
-        "    \n",
-        "    hypot(...)\n",
-        "        hypot(x, y)\n",
-        "        \n",
-        "        Return the Euclidean distance, sqrt(x*x + y*y).\n",
-        "    \n",
-        "    isinf(...)\n",
-        "        isinf(x) -> bool\n",
-        "        \n",
-        "        Check if float x is infinite (positive or negative).\n",
-        "    \n",
-        "    isnan(...)\n",
-        "        isnan(x) -> bool\n",
-        "        \n",
-        "        Check if float x is not a number (NaN).\n",
-        "    \n",
-        "    ldexp(...)\n",
-        "        ldexp(x, i)\n",
-        "        \n",
-        "        Return x * (2**i).\n",
-        "    \n",
-        "    lgamma(...)\n",
-        "        lgamma(x)\n",
-        "        \n",
-        "        Natural logarithm of absolute value of Gamma function at x.\n",
-        "    \n",
-        "    log(...)\n",
-        "        log(x[, base])\n",
-        "        \n",
-        "        Return the logarithm of x to the given base.\n",
-        "        If the base not specified, returns the natural logarithm (base e) of x.\n",
-        "    \n",
-        "    log10(...)\n",
-        "        log10(x)\n",
-        "        \n",
-        "        Return the base 10 logarithm of x.\n",
-        "    \n",
-        "    log1p(...)\n",
-        "        log1p(x)\n",
-        "        \n",
-        "        Return the natural logarithm of 1+x (base e).\n",
-        "        The result is computed in a way which is accurate for x near zero.\n",
-        "    \n",
-        "    modf(...)\n",
-        "        modf(x)\n",
-        "        \n",
-        "        Return the fractional and integer parts of x.  Both results carry the sign\n",
-        "        of x and are floats.\n",
-        "    \n",
-        "    pow(...)\n",
-        "        pow(x, y)\n",
-        "        \n",
-        "        Return x**y (x to the power of y).\n",
-        "    \n",
-        "    radians(...)\n",
-        "        radians(x)\n",
-        "        \n",
-        "        Convert angle x from degrees to radians.\n",
-        "    \n",
-        "    sin(...)\n",
-        "        sin(x)\n",
-        "        \n",
-        "        Return the sine of x (measured in radians).\n",
-        "    \n",
-        "    sinh(...)\n",
-        "        sinh(x)\n",
-        "        \n",
-        "        Return the hyperbolic sine of x.\n",
-        "    \n",
-        "    sqrt(...)\n",
-        "        sqrt(x)\n",
-        "        \n",
-        "        Return the square root of x.\n",
-        "    \n",
-        "    tan(...)\n",
-        "        tan(x)\n",
-        "        \n",
-        "        Return the tangent of x (measured in radians).\n",
-        "    \n",
-        "    tanh(...)\n",
-        "        tanh(x)\n",
-        "        \n",
-        "        Return the hyperbolic tangent of x.\n",
-        "    \n",
-        "    trunc(...)\n",
-        "        trunc(x:Real) -> Integral\n",
-        "        \n",
-        "        Truncates x to the nearest Integral toward 0. Uses the __trunc__ magic method.\n",
-        "\n",
-        "DATA\n",
-        "    e = 2.718281828459045\n",
-        "    pi = 3.141592653589793\n",
-        "\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "python uses 'j' to denote the imaginary unit"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 1.0 + 2j"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1+2j)\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1j\n",
-      "b = 3.0 + 2.0j\n",
-      "print a + b\n",
-      "print a*b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(3+3j)\n",
-        "(-2+3j)\n"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can use abs() to get the magnitude and separately get the real or imaginary parts "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print abs(b)\n",
-      "print a.real\n",
-      "print a.imag"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.60555127546\n",
-        "0.0\n",
-        "1.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "strings"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can use single or double quotes for strings"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = \"this is my string\"\n",
-      "b = \"another string\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a\n",
-      "print b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my string\n",
-        "another string\n"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a+b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my stringanother string\n"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a + \". \" + b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my string. another string\n"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a*2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my stringthis is my string\n"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can use \\n for new line"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = a + \"\\n\"\n",
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my string\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "\"\"\" can enclose multiline strings.  This is useful for docstrings at the start of functions (more on that later...)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = \"\"\"\n",
-      "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor \n",
-      "incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis \n",
-      "nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. \n",
-      "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore \n",
-      "eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt \n",
-      "in culpa qui officia deserunt mollit anim id est laborum.\"\"\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor \n",
-        "incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis \n",
-        "nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. \n",
-        "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore \n",
-        "eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt \n",
-        "in culpa qui officia deserunt mollit anim id est laborum.\n"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "a raw string does not replace escape sequences (like \\n)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = r\"this is a raw string\\n\"\n",
-      "print d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is a raw string\\n\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "slicing a string can seem a bit counterintuitive if you are coming from Fortran.  The trick is to think of the index as representing the left edge of a character in the string.  When we do arrays later, the same will apply.\n",
-      "\n",
-      "Also note that python (like C) uses 0-based indexing"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a\n",
-      "print a[0:4]\n",
-      "print a[0]\n",
-      "print a[-2]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my string\n",
-        "\n",
-        "this\n",
-        "t\n",
-        "g\n"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there are also a number of methods and functions that work with strings (and a string module)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a.replace(\"this\", \"that\")\n",
-      "print len(a)\n",
-      "print a.strip()    # hey! this is a comment!  Also notice that strip removes the \\n\n",
-      "print a.strip()[-1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "that is my string\n",
-        "\n",
-        "18\n",
-        "this is my string\n",
-        "g\n"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "this is my string\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print type(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<type 'str'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(str)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on class str in module __builtin__:\n",
-        "\n",
-        "class str(basestring)\n",
-        " |  str(object='') -> string\n",
-        " |  \n",
-        " |  Return a nice string representation of the object.\n",
-        " |  If the argument is a string, the return value is the same object.\n",
-        " |  \n",
-        " |  Method resolution order:\n",
-        " |      str\n",
-        " |      basestring\n",
-        " |      object\n",
-        " |  \n",
-        " |  Methods defined here:\n",
-        " |  \n",
-        " |  __add__(...)\n",
-        " |      x.__add__(y) <==> x+y\n",
-        " |  \n",
-        " |  __contains__(...)\n",
-        " |      x.__contains__(y) <==> y in x\n",
-        " |  \n",
-        " |  __eq__(...)\n",
-        " |      x.__eq__(y) <==> x==y\n",
-        " |  \n",
-        " |  __format__(...)\n",
-        " |      S.__format__(format_spec) -> string\n",
-        " |      \n",
-        " |      Return a formatted version of S as described by format_spec.\n",
-        " |  \n",
-        " |  __ge__(...)\n",
-        " |      x.__ge__(y) <==> x>=y\n",
-        " |  \n",
-        " |  __getattribute__(...)\n",
-        " |      x.__getattribute__('name') <==> x.name\n",
-        " |  \n",
-        " |  __getitem__(...)\n",
-        " |      x.__getitem__(y) <==> x[y]\n",
-        " |  \n",
-        " |  __getnewargs__(...)\n",
-        " |  \n",
-        " |  __getslice__(...)\n",
-        " |      x.__getslice__(i, j) <==> x[i:j]\n",
-        " |      \n",
-        " |      Use of negative indices is not supported.\n",
-        " |  \n",
-        " |  __gt__(...)\n",
-        " |      x.__gt__(y) <==> x>y\n",
-        " |  \n",
-        " |  __hash__(...)\n",
-        " |      x.__hash__() <==> hash(x)\n",
-        " |  \n",
-        " |  __le__(...)\n",
-        " |      x.__le__(y) <==> x<=y\n",
-        " |  \n",
-        " |  __len__(...)\n",
-        " |      x.__len__() <==> len(x)\n",
-        " |  \n",
-        " |  __lt__(...)\n",
-        " |      x.__lt__(y) <==> x<y\n",
-        " |  \n",
-        " |  __mod__(...)\n",
-        " |      x.__mod__(y) <==> x%y\n",
-        " |  \n",
-        " |  __mul__(...)\n",
-        " |      x.__mul__(n) <==> x*n\n",
-        " |  \n",
-        " |  __ne__(...)\n",
-        " |      x.__ne__(y) <==> x!=y\n",
-        " |  \n",
-        " |  __repr__(...)\n",
-        " |      x.__repr__() <==> repr(x)\n",
-        " |  \n",
-        " |  __rmod__(...)\n",
-        " |      x.__rmod__(y) <==> y%x\n",
-        " |  \n",
-        " |  __rmul__(...)\n",
-        " |      x.__rmul__(n) <==> n*x\n",
-        " |  \n",
-        " |  __sizeof__(...)\n",
-        " |      S.__sizeof__() -> size of S in memory, in bytes\n",
-        " |  \n",
-        " |  __str__(...)\n",
-        " |      x.__str__() <==> str(x)\n",
-        " |  \n",
-        " |  capitalize(...)\n",
-        " |      S.capitalize() -> string\n",
-        " |      \n",
-        " |      Return a copy of the string S with only its first character\n",
-        " |      capitalized.\n",
-        " |  \n",
-        " |  center(...)\n",
-        " |      S.center(width[, fillchar]) -> string\n",
-        " |      \n",
-        " |      Return S centered in a string of length width. Padding is\n",
-        " |      done using the specified fill character (default is a space)\n",
-        " |  \n",
-        " |  count(...)\n",
-        " |      S.count(sub[, start[, end]]) -> int\n",
-        " |      \n",
-        " |      Return the number of non-overlapping occurrences of substring sub in\n",
-        " |      string S[start:end].  Optional arguments start and end are interpreted\n",
-        " |      as in slice notation.\n",
-        " |  \n",
-        " |  decode(...)\n",
-        " |      S.decode([encoding[,errors]]) -> object\n",
-        " |      \n",
-        " |      Decodes S using the codec registered for encoding. encoding defaults\n",
-        " |      to the default encoding. errors may be given to set a different error\n",
-        " |      handling scheme. Default is 'strict' meaning that encoding errors raise\n",
-        " |      a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n",
-        " |      as well as any other name registered with codecs.register_error that is\n",
-        " |      able to handle UnicodeDecodeErrors.\n",
-        " |  \n",
-        " |  encode(...)\n",
-        " |      S.encode([encoding[,errors]]) -> object\n",
-        " |      \n",
-        " |      Encodes S using the codec registered for encoding. encoding defaults\n",
-        " |      to the default encoding. errors may be given to set a different error\n",
-        " |      handling scheme. Default is 'strict' meaning that encoding errors raise\n",
-        " |      a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n",
-        " |      'xmlcharrefreplace' as well as any other name registered with\n",
-        " |      codecs.register_error that is able to handle UnicodeEncodeErrors.\n",
-        " |  \n",
-        " |  endswith(...)\n",
-        " |      S.endswith(suffix[, start[, end]]) -> bool\n",
-        " |      \n",
-        " |      Return True if S ends with the specified suffix, False otherwise.\n",
-        " |      With optional start, test S beginning at that position.\n",
-        " |      With optional end, stop comparing S at that position.\n",
-        " |      suffix can also be a tuple of strings to try.\n",
-        " |  \n",
-        " |  expandtabs(...)\n",
-        " |      S.expandtabs([tabsize]) -> string\n",
-        " |      \n",
-        " |      Return a copy of S where all tab characters are expanded using spaces.\n",
-        " |      If tabsize is not given, a tab size of 8 characters is assumed.\n",
-        " |  \n",
-        " |  find(...)\n",
-        " |      S.find(sub [,start [,end]]) -> int\n",
-        " |      \n",
-        " |      Return the lowest index in S where substring sub is found,\n",
-        " |      such that sub is contained within S[start:end].  Optional\n",
-        " |      arguments start and end are interpreted as in slice notation.\n",
-        " |      \n",
-        " |      Return -1 on failure.\n",
-        " |  \n",
-        " |  format(...)\n",
-        " |      S.format(*args, **kwargs) -> string\n",
-        " |      \n",
-        " |      Return a formatted version of S, using substitutions from args and kwargs.\n",
-        " |      The substitutions are identified by braces ('{' and '}').\n",
-        " |  \n",
-        " |  index(...)\n",
-        " |      S.index(sub [,start [,end]]) -> int\n",
-        " |      \n",
-        " |      Like S.find() but raise ValueError when the substring is not found.\n",
-        " |  \n",
-        " |  isalnum(...)\n",
-        " |      S.isalnum() -> bool\n",
-        " |      \n",
-        " |      Return True if all characters in S are alphanumeric\n",
-        " |      and there is at least one character in S, False otherwise.\n",
-        " |  \n",
-        " |  isalpha(...)\n",
-        " |      S.isalpha() -> bool\n",
-        " |      \n",
-        " |      Return True if all characters in S are alphabetic\n",
-        " |      and there is at least one character in S, False otherwise.\n",
-        " |  \n",
-        " |  isdigit(...)\n",
-        " |      S.isdigit() -> bool\n",
-        " |      \n",
-        " |      Return True if all characters in S are digits\n",
-        " |      and there is at least one character in S, False otherwise.\n",
-        " |  \n",
-        " |  islower(...)\n",
-        " |      S.islower() -> bool\n",
-        " |      \n",
-        " |      Return True if all cased characters in S are lowercase and there is\n",
-        " |      at least one cased character in S, False otherwise.\n",
-        " |  \n",
-        " |  isspace(...)\n",
-        " |      S.isspace() -> bool\n",
-        " |      \n",
-        " |      Return True if all characters in S are whitespace\n",
-        " |      and there is at least one character in S, False otherwise.\n",
-        " |  \n",
-        " |  istitle(...)\n",
-        " |      S.istitle() -> bool\n",
-        " |      \n",
-        " |      Return True if S is a titlecased string and there is at least one\n",
-        " |      character in S, i.e. uppercase characters may only follow uncased\n",
-        " |      characters and lowercase characters only cased ones. Return False\n",
-        " |      otherwise.\n",
-        " |  \n",
-        " |  isupper(...)\n",
-        " |      S.isupper() -> bool\n",
-        " |      \n",
-        " |      Return True if all cased characters in S are uppercase and there is\n",
-        " |      at least one cased character in S, False otherwise.\n",
-        " |  \n",
-        " |  join(...)\n",
-        " |      S.join(iterable) -> string\n",
-        " |      \n",
-        " |      Return a string which is the concatenation of the strings in the\n",
-        " |      iterable.  The separator between elements is S.\n",
-        " |  \n",
-        " |  ljust(...)\n",
-        " |      S.ljust(width[, fillchar]) -> string\n",
-        " |      \n",
-        " |      Return S left-justified in a string of length width. Padding is\n",
-        " |      done using the specified fill character (default is a space).\n",
-        " |  \n",
-        " |  lower(...)\n",
-        " |      S.lower() -> string\n",
-        " |      \n",
-        " |      Return a copy of the string S converted to lowercase.\n",
-        " |  \n",
-        " |  lstrip(...)\n",
-        " |      S.lstrip([chars]) -> string or unicode\n",
-        " |      \n",
-        " |      Return a copy of the string S with leading whitespace removed.\n",
-        " |      If chars is given and not None, remove characters in chars instead.\n",
-        " |      If chars is unicode, S will be converted to unicode before stripping\n",
-        " |  \n",
-        " |  partition(...)\n",
-        " |      S.partition(sep) -> (head, sep, tail)\n",
-        " |      \n",
-        " |      Search for the separator sep in S, and return the part before it,\n",
-        " |      the separator itself, and the part after it.  If the separator is not\n",
-        " |      found, return S and two empty strings.\n",
-        " |  \n",
-        " |  replace(...)\n",
-        " |      S.replace(old, new[, count]) -> string\n",
-        " |      \n",
-        " |      Return a copy of string S with all occurrences of substring\n",
-        " |      old replaced by new.  If the optional argument count is\n",
-        " |      given, only the first count occurrences are replaced.\n",
-        " |  \n",
-        " |  rfind(...)\n",
-        " |      S.rfind(sub [,start [,end]]) -> int\n",
-        " |      \n",
-        " |      Return the highest index in S where substring sub is found,\n",
-        " |      such that sub is contained within S[start:end].  Optional\n",
-        " |      arguments start and end are interpreted as in slice notation.\n",
-        " |      \n",
-        " |      Return -1 on failure.\n",
-        " |  \n",
-        " |  rindex(...)\n",
-        " |      S.rindex(sub [,start [,end]]) -> int\n",
-        " |      \n",
-        " |      Like S.rfind() but raise ValueError when the substring is not found.\n",
-        " |  \n",
-        " |  rjust(...)\n",
-        " |      S.rjust(width[, fillchar]) -> string\n",
-        " |      \n",
-        " |      Return S right-justified in a string of length width. Padding is\n",
-        " |      done using the specified fill character (default is a space)\n",
-        " |  \n",
-        " |  rpartition(...)\n",
-        " |      S.rpartition(sep) -> (head, sep, tail)\n",
-        " |      \n",
-        " |      Search for the separator sep in S, starting at the end of S, and return\n",
-        " |      the part before it, the separator itself, and the part after it.  If the\n",
-        " |      separator is not found, return two empty strings and S.\n",
-        " |  \n",
-        " |  rsplit(...)\n",
-        " |      S.rsplit([sep [,maxsplit]]) -> list of strings\n",
-        " |      \n",
-        " |      Return a list of the words in the string S, using sep as the\n",
-        " |      delimiter string, starting at the end of the string and working\n",
-        " |      to the front.  If maxsplit is given, at most maxsplit splits are\n",
-        " |      done. If sep is not specified or is None, any whitespace string\n",
-        " |      is a separator.\n",
-        " |  \n",
-        " |  rstrip(...)\n",
-        " |      S.rstrip([chars]) -> string or unicode\n",
-        " |      \n",
-        " |      Return a copy of the string S with trailing whitespace removed.\n",
-        " |      If chars is given and not None, remove characters in chars instead.\n",
-        " |      If chars is unicode, S will be converted to unicode before stripping\n",
-        " |  \n",
-        " |  split(...)\n",
-        " |      S.split([sep [,maxsplit]]) -> list of strings\n",
-        " |      \n",
-        " |      Return a list of the words in the string S, using sep as the\n",
-        " |      delimiter string.  If maxsplit is given, at most maxsplit\n",
-        " |      splits are done. If sep is not specified or is None, any\n",
-        " |      whitespace string is a separator and empty strings are removed\n",
-        " |      from the result.\n",
-        " |  \n",
-        " |  splitlines(...)\n",
-        " |      S.splitlines(keepends=False) -> list of strings\n",
-        " |      \n",
-        " |      Return a list of the lines in S, breaking at line boundaries.\n",
-        " |      Line breaks are not included in the resulting list unless keepends\n",
-        " |      is given and true.\n",
-        " |  \n",
-        " |  startswith(...)\n",
-        " |      S.startswith(prefix[, start[, end]]) -> bool\n",
-        " |      \n",
-        " |      Return True if S starts with the specified prefix, False otherwise.\n",
-        " |      With optional start, test S beginning at that position.\n",
-        " |      With optional end, stop comparing S at that position.\n",
-        " |      prefix can also be a tuple of strings to try.\n",
-        " |  \n",
-        " |  strip(...)\n",
-        " |      S.strip([chars]) -> string or unicode\n",
-        " |      \n",
-        " |      Return a copy of the string S with leading and trailing\n",
-        " |      whitespace removed.\n",
-        " |      If chars is given and not None, remove characters in chars instead.\n",
-        " |      If chars is unicode, S will be converted to unicode before stripping\n",
-        " |  \n",
-        " |  swapcase(...)\n",
-        " |      S.swapcase() -> string\n",
-        " |      \n",
-        " |      Return a copy of the string S with uppercase characters\n",
-        " |      converted to lowercase and vice versa.\n",
-        " |  \n",
-        " |  title(...)\n",
-        " |      S.title() -> string\n",
-        " |      \n",
-        " |      Return a titlecased version of S, i.e. words start with uppercase\n",
-        " |      characters, all remaining cased characters have lowercase.\n",
-        " |  \n",
-        " |  translate(...)\n",
-        " |      S.translate(table [,deletechars]) -> string\n",
-        " |      \n",
-        " |      Return a copy of the string S, where all characters occurring\n",
-        " |      in the optional argument deletechars are removed, and the\n",
-        " |      remaining characters have been mapped through the given\n",
-        " |      translation table, which must be a string of length 256 or None.\n",
-        " |      If the table argument is None, no translation is applied and\n",
-        " |      the operation simply removes the characters in deletechars.\n",
-        " |  \n",
-        " |  upper(...)\n",
-        " |      S.upper() -> string\n",
-        " |      \n",
-        " |      Return a copy of the string S converted to uppercase.\n",
-        " |  \n",
-        " |  zfill(...)\n",
-        " |      S.zfill(width) -> string\n",
-        " |      \n",
-        " |      Pad a numeric string S with zeros on the left, to fill a field\n",
-        " |      of the specified width.  The string S is never truncated.\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Data and other attributes defined here:\n",
-        " |  \n",
-        " |  __new__ = <built-in method __new__ of type object>\n",
-        " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-exceptions.ipynb b/lectures/01-python/python-exceptions.ipynb
deleted file mode 100644
index 959b4270..00000000
--- a/lectures/01-python/python-exceptions.ipynb
+++ /dev/null
@@ -1,152 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:c39b03ca77b5d326d2f28516396be56561e362a185e62f918e11bbb7352c9905"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Exceptions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Exceptions are how python handles errors.  A simple example:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dict = {\"a\":1, \"b\":2, \"c\":3}\n",
-      "print dict[\"d\"]\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "KeyError",
-       "evalue": "'d'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-1-9b892cc8c2e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"a\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"b\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"d\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;31mKeyError\u001b[0m: 'd'"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try: val = dict[\"d\"]\n",
-      "except KeyError:\n",
-      "    val = \"error that key is not found\"\n",
-      "\n",
-      "print val"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "error that key is not found\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We haven't talked about I/O yet, but as you might expect, when you open a file, you get an object which you can then interact with.  Here we try to open a file for reading that does not already exist."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try: f = open(\"file.txt\", \"r\")\n",
-      "except IOError:\n",
-      "    print \"error: that file does not exist\"\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "error: that file does not exist\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are a lot of different types of exceptions that you can catch, and you can catch multiple ones per except clause or have multiple except clauses.  You probably won't be able to anticipate every failure mode in advance.  In that case, when you run and your code crashes because of an exception, the python interpreter will print out the name of the exception and you can then modify your code to take the appropriate action."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# example from tutorial                                                         \n",
-      "def this_fails():\n",
-      "    x = 1/0\n",
-      "\n",
-      "try:\n",
-      "    this_fails()\n",
-      "except ZeroDivisionError as detail:\n",
-      "    print 'Handling run-time error:', detail\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Handling run-time error: integer division or modulo by zero\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "you can have your own functions raise exceptions when things go wrong and you can build your own exception classes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-functions.ipynb b/lectures/01-python/python-functions.ipynb
deleted file mode 100644
index dff987fb..00000000
--- a/lectures/01-python/python-functions.ipynb
+++ /dev/null
@@ -1,314 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:f5eafc5d42e1d14ee4675af979fce95c94092543ee6d83ebccd8eef135083c52"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "functions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_fun(i):\n",
-      "    print \"in the function, here's i:\" , i\n",
-      "\n",
-      "my_fun(10)\n",
-      "my_fun(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in the function, here's i: 10\n",
-        "in the function, here's i: 5\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "functions are one place where scope comes into play.  A function has its own namespace.  If a variable is not defined in that function, then it will look to the namespace from where it was called to see if that variable exists there.  "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "global_var = 10\n",
-      "\n",
-      "def print_fun(string, n):\n",
-      "    if n < global_var:\n",
-      "        print string*n\n",
-      "    else:\n",
-      "        print string*global_var\n",
-      "\n",
-      "print_fun(\"-\", 5)\n",
-      "print_fun(\"-\", 20)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "-----\n",
-        "----------\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "global_var = 100"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print_fun(\"-\",50)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--------------------------------------------------\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "functions always return a value -- if one is not explicitly given, then they return None, otherwise, they can return values (even multiple values) of any type"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = my_fun(10)\n",
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in the function, here's i: 10\n",
-        "None\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def fib2(n): # return Fibonacci series up to n (from the python tutorial)\n",
-      "    \"\"\"Return a list containing the Fibonacci series up to n.\"\"\"\n",
-      "    result = []\n",
-      "    a, b = 0, 1\n",
-      "    while a < n:\n",
-      "        result.append(a)    # see below\n",
-      "        a, b = b, a+b\n",
-      "    return result, len(result)\n",
-      "\n",
-      "list, n = fib2(250)\n",
-      "print n\n",
-      "print list"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "14\n",
-        "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233]\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Note that this function includes a docstring (just after the function definition).  This is used by the help system"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(fib2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on function fib2 in module __main__:\n",
-        "\n",
-        "fib2(n)\n",
-        "    Return a list containing the Fibonacci series up to n.\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can have optional arguments which provide defaults"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def check_answer(val, correct_answer=\"a\"):\n",
-      "    if val == correct_answer:\n",
-      "        return True\n",
-      "    else:\n",
-      "        return False\n",
-      "\n",
-      "print check_answer(\"a\")\n",
-      "print check_answer(\"a\", correct_answer=\"b\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "True\n",
-        "False\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "it is important to note that python evaluates the optional arguments once--when the function is defined.  THis means that if you make the default an empty object, for instance, it will persist across all calls."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def f(a, L=[]):\n",
-      "    L.append(a)\n",
-      "    return L\n",
-      "\n",
-      "print f(1)\n",
-      "print f(2)\n",
-      "print f(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1]\n",
-        "[1, 2]\n",
-        "[1, 2, 3]\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "instead do"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def fnew(a, L=None):\n",
-      "    if L is None:\n",
-      "        L = []\n",
-      "    L.append(a)\n",
-      "    return L\n",
-      "\n",
-      "print fnew(1)\n",
-      "print fnew(2)\n",
-      "print fnew(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1]\n",
-        "[2]\n",
-        "[3]\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-io.ipynb b/lectures/01-python/python-io.ipynb
deleted file mode 100644
index ec3910bf..00000000
--- a/lectures/01-python/python-io.ipynb
+++ /dev/null
@@ -1,474 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:cf529b5fbe0b780881ac272b72d2d7b62f38d08083ef46cacd1d2e0852c34873"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "One of the main things that we want to do in scientific computing is get data into and out of our programs"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Print"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 1\n",
-      "y = 0.0000354\n",
-      "z = 3.0\n",
-      "s = \"my string\"\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "old print style"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"x = %d, y = %6.2g, z = %f, s = %s\" % (x, y, z, s)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "x = 1, y = 3.5e-05, z = 3.000000, s = my string\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "newer print style"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "{} takes can take arguments.  The first is the index/position/name of the value we want to print"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"x = {}, y = {}, z = {}, s = {}\".format(x, y, z, s)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "x = 1, y = 3.54e-05, z = 3.0, s = my string\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"x = {0}, y = {1:6.2g}, z = {2:.3f}, s = {3}\".format(x, y, z, s)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "x = 1, y = 3.5e-05, z = 3.000, s = my string\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there are other formatting things, like justification, etc.  See the tutorial"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"{:^80}\".format(\"centered string\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "                                centered string                                 \n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "File I/O"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "as expected, a file is an object"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try: f = open(\"./sample.txt\", \"w\")   # open for writing -- any file of the same name will be overwritten\n",
-      "except: \n",
-      "    print \"cannot open the file\"\n",
-      "\n",
-      "print f\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<open file './sample.txt', mode 'w' at 0x7f1f461c39c0>\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f.write(\"this is my first write\\n\")\n",
-      "f.close()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "reading from a file, we go line by line"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try: f = open(\"./test.txt\", \"r\")\n",
-      "except:\n",
-      "    print \"error: cannot open the file\"\n",
-      "    \n",
-      "for line in f.readlines():\n",
-      "    print line\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do\n",
-        "\n",
-        "eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad\n",
-        "\n",
-        "minim veniam, quis nostrud exercitation ullamco laboris nisi ut\n",
-        "\n",
-        "aliquip ex ea commodo consequat. Duis aute irure dolor in\n",
-        "\n",
-        "reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla\n",
-        "\n",
-        "pariatur. Excepteur sint occaecat cupidatat non proident, sunt in\n",
-        "\n",
-        "culpa qui officia deserunt mollit anim id est laborum.\n",
-        "\n",
-        "\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "CSV Files"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "comma-separated values are an easy way to exchange data -- you can generate these from a spreadsheet program.  In the example below, we are assuming that the first line of the spreadsheet/csv file gives the headings that identify the columns.  \n",
-      "\n",
-      "Note that there is an amazing amount of variation in terms of what can be in a CSV file and what the format is -- the csv module does a good job sorting this all out for you."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class item:\n",
-      "    def __init__(self):\n",
-      "        self.name = \"\"\n",
-      "        self.quantity = 0\n",
-      "        self.unitprice = 0.0\n",
-      "        self.total = 0.0\n",
-      "    \n",
-      "import csv\n",
-      "\n",
-      "reader = csv.reader(open(\"shopping.csv\", \"r\"))\n",
-      "\n",
-      "headings = None\n",
-      "\n",
-      "shoppingList = []\n",
-      "\n",
-      "for row in reader:\n",
-      "    if headings == None:\n",
-      "        # first row\n",
-      "        headings = row\n",
-      "    else:\n",
-      "        myItem = item()\n",
-      "        myItem.name = row[headings.index(\"item\")]\n",
-      "        myItem.quantity = row[headings.index(\"quantity\")]\n",
-      "        myItem.unitprice = row[headings.index(\"unit price\")]\n",
-      "        myItem.total = row[headings.index(\"total\")]\n",
-      "        shoppingList.append(myItem)\n",
-      "    \n",
-      "\n",
-      "for i in shoppingList:\n",
-      "    print \"item: {}, quantity: {}, unit price: {}, total: {}\".format(i.name, i.quantity, i.unitprice, i.total)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "item: apples, quantity: 2, unit price: 0.33, total: 0.66\n",
-        "item: bananas, quantity: 5, unit price: 0.1, total: 0.5\n",
-        "item: milk, quantity: 1, unit price: 2.5, total: 2.5\n",
-        "item: soda, quantity: 3, unit price: 1, total: 3\n",
-        "item: rolls, quantity: 12, unit price: 0.33, total: 3.96\n",
-        "item: eggs, quantity: 1, unit price: 2.5, total: 2.5\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "INI Files"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "INI or Config files are a common way of specifying options to a program.  They can take the form (from the ConfigParser page):\n",
-      "\n",
-      "[My Section]\n",
-      "foodir: %(dir)s/whatever\n",
-      "dir=frob\n",
-      "long: this value continues\n",
-      "   in the next line\n",
-      "   \n",
-      "Here we look at how to read in options and store them in a dictionary of the \n",
-      "form dict[\"sec.option\"] = value\n",
-      "\n",
-      "We'll use a sample .ini file from a regression test suite (VARDEN-tests.ini)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import ConfigParser\n",
-      "\n",
-      "options = {}\n",
-      "\n",
-      "\n",
-      "cp = ConfigParser.ConfigParser()\n",
-      "cp.optionxform = str    # this makes options case-sensitive\n",
-      "cp.read(\"VARDEN-tests.ini\")\n",
-      "\n",
-      "for sec in cp.sections():\n",
-      "    for opt in cp.options(sec):\n",
-      "        key = str(sec) + \".\" + str(opt)\n",
-      "        value = cp.get(sec,opt)\n",
-      "        options[key] = value\n",
-      "    \n",
-      "for k, v in options.items():\n",
-      "    print \"{:32s}: {}\".format(k, v)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "bubble-3d.compileTest           : 0\n",
-        "bubble-restart.dim              : 3\n",
-        "bubble-restart.compileTest      : 0\n",
-        "bubble-restart.numthreads       : 2\n",
-        "bubble-2d.useOMP                : 0\n",
-        "bubble-restart.doVis            : 0\n",
-        "main.testTopDir                 : /home/regtester/RegTesting/rt-VARDEN/\n",
-        "main.COMP                       : g++\n",
-        "bubble-restart.inputFile        : inputs-restart-regt\n",
-        "main.suiteName                  : VARDEN\n",
-        "bubble-2d.buildDir              : varden/test\n",
-        "bubble-restart.numprocs         : 3\n",
-        "bubble-3d.doVis                 : 0\n",
-        "main.sourceTree                 : F_Src\n",
-        "bubble-2d.dim                   : 2\n",
-        "bubble-3d.useMPI                : 1\n",
-        "main.reportActiveTestsOnly      : 1\n",
-        "bubble-2d.restartTest           : 0\n",
-        "main.numMakeJobs                : 8\n",
-        "bubble-2d.numthreads            : 2\n",
-        "main.MPIcommand                 : /usr/local/bin/mpiexec -n @nprocs@ @command@\n",
-        "bubble-restart.useOMP           : 1\n",
-        "bubble-3d.numprocs              : 3\n",
-        "bubble-restart.useMPI           : 1\n",
-        "bubble-2d.useMPI                : 1\n",
-        "bubble-3d.dim                   : 3\n",
-        "bubble-3d.numthreads            : 2\n",
-        "bubble-restart.buildDir         : varden/test\n",
-        "bubble-restart.restartTest      : 1\n",
-        "main.MPIhost                    : \n",
-        "bubble-2d.compileTest           : 0\n",
-        "bubble-3d.useOMP                : 1\n",
-        "bubble-2d.doVis                 : 0\n",
-        "bubble-2d.numprocs              : 2\n",
-        "bubble-3d.inputFile             : inputs_3d-regt\n",
-        "main.webTopDir                  : /home/regtester/RegTesting/rt-VARDEN/web\n",
-        "main.MAKE                       : make\n",
-        "bubble-3d.restartTest           : 0\n",
-        "main.sourceDir                  : /home/regtester/RegTesting/VARDEN/\n",
-        "main.FCOMP                      : gfortran\n",
-        "bubble-2d.inputFile             : inputs_2d-regt\n",
-        "bubble-3d.buildDir              : varden/test\n",
-        "main.compareToolDir             : /home/regtester/RegTesting/AmrPostprocessing/F_Src\n",
-        "main.boxLibDir                  : /home/regtester/RegTesting/BoxLib/\n",
-        "bubble-restart.restartFileNum   : 4\n",
-        "main.goUpLink                   : 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python-modules.ipynb b/lectures/01-python/python-modules.ipynb
deleted file mode 100644
index 17c8655d..00000000
--- a/lectures/01-python/python-modules.ipynb
+++ /dev/null
@@ -1,188 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:cc9fbf3e5cec529cadee08855ddbcb754839ac1eafb7d3e87e42b9cfa21a73c6"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import profile"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(profile)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on module profile:\n",
-        "\n",
-        "NAME\n",
-        "    profile\n",
-        "\n",
-        "FILE\n",
-        "    /home/zingale/classes/python_science/lectures/01-python/profile.py\n",
-        "\n",
-        "DESCRIPTION\n",
-        "    A very simple profiling class.  Define some timers and methods\n",
-        "    to start and stop them.  Nesting of timers is tracked so we can\n",
-        "    pretty print the profiling information.\n",
-        "    \n",
-        "    # define a timer object, labeled 'my timer'\n",
-        "    a = timer('my timer')\n",
-        "    \n",
-        "    This will add 'my timer' to the list of keys in the 'my timer'\n",
-        "    dictionary.  Subsequent calls to the timer class constructor\n",
-        "    will have no effect.\n",
-        "    \n",
-        "    # start timing the 'my timer' block of code\n",
-        "    a.begin()\n",
-        "    \n",
-        "    ... do stuff here ...\n",
-        "    \n",
-        "    # end the timing of the 'my timer' block of code\n",
-        "    a.end()\n",
-        "    \n",
-        "    for best results, the block of code timed should be large\n",
-        "    enough to offset the overhead of the timer class method\n",
-        "    calls.\n",
-        "    \n",
-        "    Multiple timers can be instanciated and nested.  The stackCount\n",
-        "    global parameter keeps count of the level of nesting, and the\n",
-        "    timerNesting data structure stores the nesting level for each\n",
-        "    defined timer.\n",
-        "    \n",
-        "    timeReport() is called at the end to print out a summary of the\n",
-        "    timing.\n",
-        "    \n",
-        "    At present, no enforcement is done to ensure proper nesting.\n",
-        "\n",
-        "CLASSES\n",
-        "    timer\n",
-        "    \n",
-        "    class timer\n",
-        "     |  Methods defined here:\n",
-        "     |  \n",
-        "     |  __init__(self, name)\n",
-        "     |  \n",
-        "     |  begin(self)\n",
-        "     |  \n",
-        "     |  end(self)\n",
-        "\n",
-        "FUNCTIONS\n",
-        "    timeReport()\n",
-        "\n",
-        "DATA\n",
-        "    stackCount = 0\n",
-        "    timerNesting = {}\n",
-        "    timerOrder = []\n",
-        "    timers = {}\n",
-        "\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "t = profile.timer(\"main loop\")\n",
-      "t.begin()\n",
-      "\n",
-      "n = 0\n",
-      "sum = 0.0\n",
-      "while (n < 1000):\n",
-      "    sum += n**2\n",
-      "    n += 1\n",
-      "\n",
-      "\n",
-      "t.end()\n",
-      "profile.timeReport()\n",
-      "\n",
-      "print sum"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "main loop:  0.00133609771729\n",
-        "332833500.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run profile"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1:  10.01009202\n",
-        "2:  25.0252840519\n",
-        "   3:  20.0201430321\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/01-python/python.fodp b/lectures/01-python/python.fodp
deleted file mode 100644
index 8b70fdf7..00000000
--- a/lectures/01-python/python.fodp
+++ /dev/null
@@ -1,14438 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-
-<office:document xmlns:office="urn:oasis:names:tc:opendocument:xmlns:office:1.0" xmlns:style="urn:oasis:names:tc:opendocument:xmlns:style:1.0" xmlns:text="urn:oasis:names:tc:opendocument:xmlns:text:1.0" xmlns:table="urn:oasis:names:tc:opendocument:xmlns:table:1.0" xmlns:draw="urn:oasis:names:tc:opendocument:xmlns:drawing:1.0" xmlns:fo="urn:oasis:names:tc:opendocument:xmlns:xsl-fo-compatible:1.0" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:meta="urn:oasis:names:tc:opendocument:xmlns:meta:1.0" xmlns:number="urn:oasis:names:tc:opendocument:xmlns:datastyle:1.0" xmlns:presentation="urn:oasis:names:tc:opendocument:xmlns:presentation:1.0" xmlns:svg="urn:oasis:names:tc:opendocument:xmlns:svg-compatible:1.0" xmlns:chart="urn:oasis:names:tc:opendocument:xmlns:chart:1.0" xmlns:dr3d="urn:oasis:names:tc:opendocument:xmlns:dr3d:1.0" xmlns:math="http://www.w3.org/1998/Math/MathML" xmlns:form="urn:oasis:names:tc:opendocument:xmlns:form:1.0" xmlns:script="urn:oasis:names:tc:opendocument:xmlns:script:1.0" xmlns:config="urn:oasis:names:tc:opendocument:xmlns:config:1.0" xmlns:ooo="http://openoffice.org/2004/office" xmlns:ooow="http://openoffice.org/2004/writer" xmlns:oooc="http://openoffice.org/2004/calc" xmlns:dom="http://www.w3.org/2001/xml-events" xmlns:xforms="http://www.w3.org/2002/xforms" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:smil="urn:oasis:names:tc:opendocument:xmlns:smil-compatible:1.0" xmlns:anim="urn:oasis:names:tc:opendocument:xmlns:animation:1.0" xmlns:rpt="http://openoffice.org/2005/report" xmlns:of="urn:oasis:names:tc:opendocument:xmlns:of:1.2" xmlns:xhtml="http://www.w3.org/1999/xhtml" xmlns:grddl="http://www.w3.org/2003/g/data-view#" xmlns:officeooo="http://openoffice.org/2009/office" xmlns:tableooo="http://openoffice.org/2009/table" xmlns:drawooo="http://openoffice.org/2010/draw" xmlns:calcext="urn:org:documentfoundation:names:experimental:calc:xmlns:calcext:1.0" xmlns:loext="urn:org:documentfoundation:names:experimental:office:xmlns:loext:1.0" xmlns:field="urn:openoffice:names:experimental:ooo-ms-interop:xmlns:field:1.0" xmlns:formx="urn:openoffice:names:experimental:ooxml-odf-interop:xmlns:form:1.0" xmlns:css3t="http://www.w3.org/TR/css3-text/" office:version="1.2" office:mimetype="application/vnd.oasis.opendocument.presentation">
- <office:meta><meta:initial-creator>Michael Zingale</meta:initial-creator><meta:creation-date>2013-01-02T12:36:14</meta:creation-date><dc:date>2015-02-16T17:18:12.317077782</dc:date><dc:creator>Michael Zingale</dc:creator><meta:editing-duration>P2DT6H27M4S</meta:editing-duration><meta:editing-cycles>108</meta:editing-cycles><meta:generator>LibreOffice/4.3.5.2.0$Linux_X86_64 LibreOffice_project/430$Build-2</meta:generator><meta:document-statistic meta:object-count="355"/></office:meta>
- <office:settings>
-  <config:config-item-set config:name="ooo:view-settings">
-   <config:config-item config:name="VisibleAreaTop" config:type="int">-288</config:config-item>
-   <config:config-item config:name="VisibleAreaLeft" config:type="int">-5062</config:config-item>
-   <config:config-item config:name="VisibleAreaWidth" config:type="int">38250</config:config-item>
-   <config:config-item config:name="VisibleAreaHeight" config:type="int">21684</config:config-item>
-   <config:config-item-map-indexed config:name="Views">
-    <config:config-item-map-entry>
-     <config:config-item config:name="ViewId" config:type="string">view1</config:config-item>
-     <config:config-item config:name="GridIsVisible" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="GridIsFront" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsSnapToGrid" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToPageMargins" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToSnapLines" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectFrame" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsPlusHandlesAlwaysVisible" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsFrameDragSingles" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="EliminatePolyPointLimitAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="IsEliminatePolyPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="VisibleLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="PrintableLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="LockedLayers" config:type="base64Binary"/>
-     <config:config-item config:name="NoAttribs" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="NoColors" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="RulerIsVisible" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="PageKind" config:type="short">0</config:config-item>
-     <config:config-item config:name="SelectedPage" config:type="short">54</config:config-item>
-     <config:config-item config:name="IsLayerMode" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsDoubleClickTextEdit" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsClickChangeRotation" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="SlidesPerRow" config:type="short">4</config:config-item>
-     <config:config-item config:name="EditModeStandard" config:type="int">0</config:config-item>
-     <config:config-item config:name="EditModeNotes" config:type="int">0</config:config-item>
-     <config:config-item config:name="EditModeHandout" config:type="int">1</config:config-item>
-     <config:config-item config:name="VisibleAreaTop" config:type="int">-2763</config:config-item>
-     <config:config-item config:name="VisibleAreaLeft" config:type="int">-428</config:config-item>
-     <config:config-item config:name="VisibleAreaWidth" config:type="int">29183</config:config-item>
-     <config:config-item config:name="VisibleAreaHeight" config:type="int">26810</config:config-item>
-     <config:config-item config:name="GridCoarseWidth" config:type="int">2540</config:config-item>
-     <config:config-item config:name="GridCoarseHeight" config:type="int">2540</config:config-item>
-     <config:config-item config:name="GridFineWidth" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridFineHeight" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthXNumerator" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthXDenominator" config:type="int">1</config:config-item>
-     <config:config-item config:name="GridSnapWidthYNumerator" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthYDenominator" config:type="int">1</config:config-item>
-     <config:config-item config:name="IsAngleSnapEnabled" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="SnapAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="ZoomOnPage" config:type="boolean">true</config:config-item>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-  </config:config-item-set>
-  <config:config-item-set config:name="ooo:configuration-settings">
-   <config:config-item config:name="ApplyUserData" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="BitmapTableURL" config:type="string">$(user)/config/standard.sob</config:config-item>
-   <config:config-item config:name="CharacterCompressionType" config:type="short">0</config:config-item>
-   <config:config-item config:name="ColorTableURL" config:type="string">$(user)/config/standard.soc</config:config-item>
-   <config:config-item config:name="DashTableURL" config:type="string">$(user)/config/standard.sod</config:config-item>
-   <config:config-item config:name="DefaultTabStop" config:type="int">1270</config:config-item>
-   <config:config-item config:name="EmbedFonts" config:type="boolean">false</config:config-item>
-   <config:config-item-map-indexed config:name="ForbiddenCharacters">
-    <config:config-item-map-entry>
-     <config:config-item config:name="Language" config:type="string">en</config:config-item>
-     <config:config-item config:name="Country" config:type="string">US</config:config-item>
-     <config:config-item config:name="Variant" config:type="string"/>
-     <config:config-item config:name="BeginLine" config:type="string"/>
-     <config:config-item config:name="EndLine" config:type="string"/>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-   <config:config-item config:name="GradientTableURL" config:type="string">$(user)/config/standard.sog</config:config-item>
-   <config:config-item config:name="HandoutsHorizontal" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="HatchTableURL" config:type="string">$(user)/config/standard.soh</config:config-item>
-   <config:config-item config:name="IsKernAsianPunctuation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBooklet" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBookletBack" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintBookletFront" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintDate" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintDrawing" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintFitPage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHandout" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHiddenPages" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintNotes" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintOutline" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintPageName" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTilePage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTime" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="LineEndTableURL" config:type="string">$(user)/config/standard.soe</config:config-item>
-   <config:config-item config:name="LoadReadonly" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PageNumberFormat" config:type="int">4</config:config-item>
-   <config:config-item config:name="ParagraphSummation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PrintQuality" config:type="int">0</config:config-item>
-   <config:config-item config:name="PrinterIndependentLayout" config:type="string">low-resolution</config:config-item>
-   <config:config-item config:name="PrinterName" config:type="string">hp</config:config-item>
-   <config:config-item config:name="PrinterSetup" config:type="base64Binary">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</config:config-item>
-   <config:config-item config:name="SaveVersionOnClose" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="SlidesPerHandout" config:type="short">6</config:config-item>
-   <config:config-item config:name="UpdateFromTemplate" config:type="boolean">true</config:config-item>
-  </config:config-item-set>
- </office:settings>
- <office:scripts>
-  <office:script script:language="ooo:Basic">
-   <ooo:libraries xmlns:ooo="http://openoffice.org/2004/office" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <ooo:library-embedded ooo:name="Standard"/>
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-  <style:font-face style:name="Cantarell" svg:font-family="Cantarell" style:font-pitch="variable"/>
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-  <style:font-face style:name="WenQuanYi Zen Hei Sharp1" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial3" svg:font-family="Arial" style:font-family-generic="modern" style:font-pitch="variable"/>
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-  <style:font-face style:name="Times New Roman" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial1" svg:font-family="Arial" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Carlito1" svg:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari2" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Times New Roman1" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp2" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="DejaVu Sans" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Tahoma" svg:font-family="Tahoma" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
- </office:font-face-decls>
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-   <style:paragraph-properties style:text-autospace="ideograph-alpha" style:punctuation-wrap="simple" style:line-break="strict" style:writing-mode="lr-tb" style:font-independent-line-spacing="false">
-    <style:tab-stops/>
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-   <style:text-properties style:use-window-font-color="true" style:font-name="Times New Roman" fo:font-size="24pt" fo:language="en" fo:country="US" style:font-name-asian="DejaVu Sans" style:font-size-asian="24pt" style:language-asian="zh" style:country-asian="CN" style:font-name-complex="Tahoma" style:font-size-complex="24pt" style:language-complex="hi" style:country-complex="IN"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:list-level-properties text:space-before="3cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:list-level-properties text:space-before="3.6cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-    </text:list-style>
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-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:margin-top="0cm" fo:margin-bottom="0cm" fo:line-height="100%" fo:text-indent="0cm"/>
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-  <style:style style:name="text" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
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-  <style:style style:name="textbody" style:family="graphic" style:parent-style-name="standard">
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-   <style:text-properties fo:font-size="16pt"/>
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-  <style:style style:name="textbodyjustfied" style:family="graphic" style:parent-style-name="standard">
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-   <style:paragraph-properties fo:text-align="justify"/>
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-   <style:text-properties fo:font-size="44pt"/>
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-  <style:style style:name="gr8" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr13" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr14" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr15" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\sqrt{-1}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr16" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="8.196cm"/>
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-  <style:style style:name="gr17" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\sqrt{x+1} - \sqrt{x}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr18" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x = 1984" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr19" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\sqrt{x+1} - \sqrt{x} = 44.55 - 44.54 = 0.01" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr20" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§\sqrt{x+1} - \sqrt{x} = (\sqrt{x+1} - \sqrt{x}) \left ( \frac{\sqrt{x+1} + \sqrt{x}}{\sqrt{x+1} + \sqrt{x}} \right ) = \frac{1}{\sqrt{x+1} + \sqrt{x}}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr21" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§e^x \sim S(x) = 1 + \frac{x}{1!} + \frac{x^2}{2!} + \cdots + \frac{x^n}{n!}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
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-  <style:style style:name="gr22" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\frac{|x|^{n+1}}{(n+1)!} \max\{1,e^x\}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr23" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§e^{-24} = \left (e^{-1} \right )^{24} \Longrightarrow S(-24) = S(-1)^{24}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr24" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr25" style:family="graphic" style:parent-style-name="standard">
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-  </style:style>
-  <style:style style:name="gr26" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§|x - y| &lt; \epsilon" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
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-  <style:style style:name="gr27" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x = y" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
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-  <style:style style:name="gr28" style:family="graphic" style:parent-style-name="Default_5f_1">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.746cm"/>
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-  <style:style style:name="gr29" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr30" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr33" style:family="graphic" style:parent-style-name="standard">
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-  <style:style style:name="gr36" style:family="graphic" style:parent-style-name="standard">
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-          <text:p>Symbol mathematics</text:p>
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-          <text:p>send e-mail</text:p>
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-          <text:p>make plots</text:p>
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-          <text:p>do scientific computations</text:p>
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-        <text:p><text:span text:style-name="T4">Ease of readability</text:span> (whitespace matters)</text:p>
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-      <text:p>Hello, World!</text:p>
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-        <text:p>If you have python installed properly, you can start python simply by typing <text:span text:style-name="T3">python</text:span> at your command line prompt</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>The python shell will come up</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Our hello world program is simply:</text:p>
-          <text:p><text:span text:style-name="T3">print “Hello, World!”</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">Or, in python 3.x (more on this later...):</text:span></text:p>
-          <text:p><text:span text:style-name="T3">print(“Hello World”)</text:span></text:p>
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-      <text:p>Communities</text:p>
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-       <text:list-item>
-        <text:p>Many scientific disciplines have their own python communities that</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Provide tutorials</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Cookbooks</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Libraries to do standard analysis in the field</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>For the most part, these uild on the Open nature of python</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>I&apos;ve put links on our course page to some information on the python communities for:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Astronomy</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Atmospheric Science</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Biology</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Cognitive Science</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Psychology</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T5">Let me know of any others!</text:span></text:p>
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-      <text:p>Python in Astro</text:p>
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-        <text:p>Python is seeing rapidly increasing adoption in Astrophysics</text:p>
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-         <text:list-item>
-          <text:p>Open-source alternative to IDL</text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Python for IDL users: <text:span text:style-name="T8"><text:a xlink:href="https://www.cfa.harvard.edu/~jbattat/computer/python/science/idl-numpy.html" xlink:type="simple">https://www.cfa.harvard.edu/~jbattat/computer/python/science/idl-numpy.html</text:a></text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Astropython: <text:a xlink:href="http://www.astropython.org/" xlink:type="simple">http://www.astropython.org/</text:a></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Lots of tutorials, links to resources, forum, ...</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Astropy mailing list: <text:a xlink:href="http://mail.scipy.org/mailman/listinfo/astropy" xlink:type="simple">http://mail.scipy.org/mailman/listinfo/astropy</text:a></text:p>
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-      <text:p>Python in Biology</text:p>
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-        <text:p>Biopython (<text:a xlink:href="http://biopython.org/wiki/Main_Page" xlink:type="simple">http://biopython.org/wiki/Main_Page</text:a>) provides tools for using python for computational molecular biology and bioinformatics projects</text:p>
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-          <text:p>Able to parse many different bioinformatics file formats</text:p>
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-         <text:list-item>
-          <text:p>Access online databases</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Do analysis, operate on sequences, ...</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>See <text:span text:style-name="T9">Python—All a Scientist Needs</text:span> by J. B. Lucks (arxiv:0803.1838)</text:p>
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-      <text:p>Scientific Python Stack</text:p>
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-        <text:p>Most scientific libraries in python build on the Scientific Python stack:</text:p>
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-      <text:p><text:span text:style-name="T6">(scipy.org)</text:span></text:p>
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-      <text:p>Starters...</text:p>
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-        <text:p>Before we get into python, we&apos;ll review some of the core ideas about numerical computing</text:p>
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-      <text:p>Basics of Computation</text:p>
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-        <text:p><text:span text:style-name="T5">Computers store information and allow us to operate on it.</text:span></text:p>
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-         <text:list-item>
-          <text:p>That&apos;s basically it.</text:p>
-         </text:list-item>
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-          <text:p>Computers have finite memory, so it is not possible to store the infinite range of numbers that exist in the real world, so approximations are made.</text:p>
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-        <text:p>Great floating point reference</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T9">What Every Computer Scientist Should Know About Floating-Point Arithmetic</text:span> by D. Goldberg</text:p>
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-      <text:p>Integers</text:p>
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-        <text:p>Basic unit of information in a computer is a <text:span text:style-name="T4">bit</text:span>: 0 or 1</text:p>
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-        <text:p>8 bits = 1 byte</text:p>
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-       <text:list-item>
-        <text:p>Different types of information require more bits/bytes to store.</text:p>
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-         <text:list-item>
-          <text:p>A <text:span text:style-name="T4">logical</text:span> (T or F) can be stored in a single bit.</text:p>
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-           <text:list-item>
-            <text:p>C/C++: <text:span text:style-name="T3">bool</text:span> datatype</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Fortran: <text:span text:style-name="T3">logical</text:span> datatype</text:p>
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-            <text:p>Standard in many languages is 4-bytes. <text:s/>This allows for 2<text:span text:style-name="T10">32</text:span>-1 distinct values.</text:p>
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-              <text:p>This can store: -2,147,483,648 to 2,147,483,647 (signed)</text:p>
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-               <text:list-item>
-                <text:p>C/C++: <text:span text:style-name="T3">int</text:span> (usually) or <text:span text:style-name="T3">int32_t</text:span></text:p>
-               </text:list-item>
-               <text:list-item>
-                <text:p>Fortran: <text:span text:style-name="T3">integer</text:span> or <text:span text:style-name="T3">integer*4</text:span></text:p>
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-              <text:p>Or it can store: 0 to 4,294,967,295 (unsigned)</text:p>
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-               <text:list-item>
-                <text:p>C/C++: <text:span text:style-name="T3">uint</text:span> or <text:span text:style-name="T3">uint32_t</text:span></text:p>
-               </text:list-item>
-               <text:list-item>
-                <text:p>Fortran (as of 95): <text:span text:style-name="T3">unsigned</text:span></text:p>
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-      <text:p>Integers</text:p>
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-          <text:p>Integers (continued): </text:p>
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-           <text:list-item>
-            <text:p>Sometimes 2-bytes. <text:s/>This allows for 2<text:span text:style-name="T10">16</text:span>-1 distinct values.</text:p>
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-             <text:list-item>
-              <text:p>This can store: -32,768 to 32,767 (signed)</text:p>
-              <text:list>
-               <text:list-item>
-                <text:p>C/C++: <text:span text:style-name="T3">short</text:span> (usually) or <text:span text:style-name="T3">int16_t</text:span></text:p>
-               </text:list-item>
-               <text:list-item>
-                <text:p>Fortran: <text:span text:style-name="T3">integer*2</text:span></text:p>
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-             <text:list-item>
-              <text:p>Or it can store: 0 to 65,535 (unsigned)</text:p>
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-               <text:list-item>
-                <text:p>C/C++: <text:span text:style-name="T3">uint16_t</text:span></text:p>
-               </text:list-item>
-               <text:list-item>
-                <text:p>Fortran (as of 95): <text:span text:style-name="T3">unsigned*2</text:span></text:p>
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-            <text:p>Note for IDL users: the standard integer in IDL is a 2-byte integer. <text:s/>If you do </text:p>
-            <text:p><text:span text:style-name="T3">i = 2</text:span></text:p>
-            <text:p>that&apos;s 2-bytes. <text:s/>To get a 4-byte integer, do:</text:p>
-            <text:p><text:span text:style-name="T3">i = 2l</text:span></text:p>
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-      </office:binary-data>
-      <text:p/>
-     </draw:image>
-    </draw:frame>
-    <draw:frame draw:style-name="gr6" draw:text-style-name="P8" draw:layer="layout" svg:width="3.302cm" svg:height="0.725cm" svg:x="24.26cm" svg:y="14.686cm">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T6">(xkcd)</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame draw:style-name="gr8" draw:text-style-name="P9" draw:layer="layout" svg:width="16.51cm" svg:height="1.119cm" svg:x="2.45cm" svg:y="16.51cm">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T11">Overflow example...</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="15" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr14" draw:text-style-name="P7" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page16" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-    <draw:frame presentation:style-name="pr15" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Overflow</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr16" draw:layer="layout" svg:width="26.162cm" svg:height="16.51cm" svg:x="1.27cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T3">iold = -1</text:span></text:p>
-      <text:p><text:span text:style-name="T3">i = 1</text:span></text:p>
-      <text:p><text:span text:style-name="T3"/></text:p>
-      <text:p><text:span text:style-name="T3">type_init = type(i)</text:span></text:p>
-      <text:p><text:span text:style-name="T3">print &quot;type currently: &quot;, type_init</text:span></text:p>
-      <text:p><text:span text:style-name="T3"/></text:p>
-      <text:p><text:span text:style-name="T3">while (i &gt; iold):</text:span></text:p>
-      <text:p><text:span text:style-name="T3"><text:s text:c="4"/></text:span><text:span text:style-name="T3">print i</text:span></text:p>
-      <text:p><text:span text:style-name="T3"><text:s text:c="4"/></text:span><text:span text:style-name="T3">iold = i</text:span></text:p>
-      <text:p><text:span text:style-name="T3"><text:s text:c="4"/></text:span><text:span text:style-name="T3">i *= 2</text:span></text:p>
-      <text:p><text:span text:style-name="T3"/></text:p>
-      <text:p><text:span text:style-name="T3"><text:s text:c="4"/></text:span><text:span text:style-name="T3">if (not type(i) == type_init):</text:span></text:p>
-      <text:p><text:span text:style-name="T3"><text:s text:c="8"/></text:span><text:span text:style-name="T3">print &quot;type changed, now: &quot;, type(i)</text:span></text:p>
-      <text:p><text:span text:style-name="T3"><text:s text:c="8"/></text:span><text:span text:style-name="T3">break</text:span></text:p>
-      <text:p><text:span text:style-name="T3"/></text:p>
-      <text:p><text:span text:style-name="T3">print i</text:span></text:p>
-      <text:p/>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="16" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr14" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page17" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-    <draw:frame presentation:style-name="pr15" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Integers</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr16" draw:layer="layout" svg:width="26.924cm" svg:height="16.764cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Python allows for the date size of the integer to scale with the size of the number: https://www.python.org/dev/peps/pep-0237/</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T12">Initially, it is the largest value supported in hardware on a machine (64-bits on a 64-bit machine—see </text:span><text:span text:style-name="T13">sys.maxint</text:span></text:p>
-         </text:list-item>
-        </text:list>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">This prints:</text:span></text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame draw:style-name="gr9" draw:text-style-name="P10" draw:layer="layout" svg:width="21.336cm" svg:height="7.701cm" svg:x="2.54cm" svg:y="7.958cm">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T14">def fac(x):</text:span></text:p>
-      <text:p><text:span text:style-name="T14"><text:s text:c="4"/></text:span><text:span text:style-name="T14">if (x == 0):</text:span></text:p>
-      <text:p><text:span text:style-name="T14"><text:s text:c="8"/></text:span><text:span text:style-name="T14">return 1</text:span></text:p>
-      <text:p><text:span text:style-name="T14"><text:s text:c="4"/></text:span><text:span text:style-name="T14">else:</text:span></text:p>
-      <text:p><text:span text:style-name="T14"><text:s text:c="8"/></text:span><text:span text:style-name="T14">return x*fac(x-1)</text:span></text:p>
-      <text:p><text:span text:style-name="T14"/></text:p>
-      <text:p><text:span text:style-name="T14"/></text:p>
-      <text:p><text:span text:style-name="T14">a = fac(52)</text:span></text:p>
-      <text:p><text:span text:style-name="T14">print a</text:span></text:p>
-      <text:p><text:span text:style-name="T14">print a.bit_length()</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame draw:style-name="gr10" draw:text-style-name="P11" draw:layer="layout" svg:width="26.416cm" svg:height="2.617cm" svg:x="1.27cm" svg:y="17.259cm">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T14">80658175170943878571660636856403766975289505440883277824000000000000</text:span></text:p>
-      <text:p><text:span text:style-name="T15">226</text:span></text:p>
-      <text:p><text:span text:style-name="T16"/></text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame draw:style-name="gr11" draw:text-style-name="P12" draw:layer="layout" svg:width="22.036cm" svg:height="1.673cm" svg:x="5.842cm" svg:y="19.004cm">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T17">Note: python 3.x does away with the distinction between int and long altogether.</text:span></text:p>
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-      <text:p>Real Numbers</text:p>
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-        <text:p><text:span text:style-name="T4">Floating point</text:span> format</text:p>
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-         <text:list-item>
-          <text:p>Infinite real numbers on the number line need to be represented by a finite number of bits</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T4">Approximations made</text:span></text:p>
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-            <text:p>Finite number of decimal places stored, maximum range of exponents.</text:p>
-           </text:list-item>
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-         </text:list-item>
-         <text:list-item>
-          <text:p>Not every number can be represented. </text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>In base-2 (what a computer uses), <text:span text:style-name="T3">0.1</text:span> does not have an exact representation (see S 2.1 of Goldberg)</text:p>
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-      <text:p>Exact Representations</text:p>
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-        <text:p><text:span text:style-name="T3">0.1</text:span> as represented by a computer:</text:p>
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-      <text:p><text:span text:style-name="T18">&gt;&gt;&gt; b = 0.1</text:span></text:p>
-      <text:p><text:span text:style-name="T18">&gt;&gt;&gt; print type(b)</text:span></text:p>
-      <text:p><text:span text:style-name="T18">&lt;type &apos;float&apos;&gt;</text:span></text:p>
-      <text:p><text:span text:style-name="T18">&gt;&gt;&gt; print &quot;{:30.20}”.format(b)</text:span></text:p>
-      <text:p><text:span text:style-name="T18"><text:s text:c="8"/></text:span><text:span text:style-name="T18">0.10000000000000000555</text:span></text:p>
-      <text:p><text:span text:style-name="T18"/></text:p>
-      <text:p><text:span text:style-name="T18">&gt;&gt;&gt; import sys</text:span></text:p>
-      <text:p><text:span text:style-name="T18">&gt;&gt;&gt; print sys.float_info</text:span></text:p>
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-      <text:p>Floating Point</text:p>
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-        <text:p>In the floating point format, a number is represented by a <text:span text:style-name="T4">significand</text:span> (or mantissa), and <text:span text:style-name="T4">exponent</text:span>, and a <text:span text:style-name="T4">sign</text:span>.</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Base 2 is used (since we are using bits)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">0.1 ~ (1 + 1</text:span><text:span text:style-name="T19">·2</text:span><text:span text:style-name="T20">-1</text:span><text:span text:style-name="T19"> + 0·2</text:span><text:span text:style-name="T21">-2</text:span><text:span text:style-name="T19"> + 0·2</text:span><text:span text:style-name="T21">-3</text:span><text:span text:style-name="T19"> + 1·2</text:span><text:span text:style-name="T21">-4</text:span><text:span text:style-name="T19"> + 1·2</text:span><text:span text:style-name="T21">-5</text:span><text:span text:style-name="T19"> + ...) · 2</text:span><text:span text:style-name="T21">-4</text:span></text:p>
-          <text:p><text:span text:style-name="T22"/></text:p>
-          <text:p><text:span text:style-name="T22"/></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T23">All significand&apos;s will start with 1, so it is not stored</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Single precision:</text:span></text:p>
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-           <text:list-item>
-            <text:p><text:span text:style-name="T23">Sign: 1 bit; exponent: 8 bits; significand: 24 bits (23 stored) = 32 bits</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T23">Range: 2</text:span><text:span text:style-name="T25">7</text:span><text:span text:style-name="T23">-1 in exponent (because of sign) = 2</text:span><text:span text:style-name="T25">127 </text:span><text:span text:style-name="T23">multiplier ~ 10</text:span><text:span text:style-name="T25">38</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T23">Decimal precision: ~6 significant digits</text:span></text:p>
-           </text:list-item>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Double precision:</text:span></text:p>
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-           <text:list-item>
-            <text:p><text:span text:style-name="T23">Sign: 1 bit; exponent: 11 bits; significand: 53 bits (52 stored) = 64 bits</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T23">Range: 2</text:span><text:span text:style-name="T25">10</text:span><text:span text:style-name="T23">-1 in exponent = 2</text:span><text:span text:style-name="T25">1023</text:span><text:span text:style-name="T23"> multiplier ~ 10</text:span><text:span text:style-name="T25">308</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T23">Decimal precision: ~15 significant digits</text:span></text:p>
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-      <text:p>Floating Point</text:p>
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-        <text:p>Overflows and underflows can still occur when you go outside the representable range. <text:s/></text:p>
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-          <text:p>The floating-point standard will signal these (and compilers can catch them)</text:p>
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-        <text:p>Some special numbers:</text:p>
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-          <text:p><text:span text:style-name="T4">NaN</text:span> = <text:span text:style-name="T3">0/0</text:span> or</text:p>
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-          <text:p><text:span text:style-name="T4">Inf</text:span> is for overflows, like <text:span text:style-name="T3">1/0</text:span></text:p>
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-          <text:p>Both of these allow the program to continue, and both can be trapped (and dealt with)</text:p>
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-          <text:p><text:span text:style-name="T3">-0</text:span> is a valid number, and <text:span text:style-name="T3">-0 = 0</text:span> in comparison </text:p>
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-        <text:p>Floating point is governed by an IEEE standard</text:p>
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-          <text:p>Ensures all machines do the same thing</text:p>
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-          <text:p>Aggressive compiler optimizations can break the standard</text:p>
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-      <text:p>Numerical Precision</text:p>
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-        <text:p>Finite number of bits used to represent #s means there is a finite precision, the <text:span text:style-name="T4">machine epsilon</text:span>, <text:span text:style-name="T27">ε</text:span></text:p>
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-          <text:p><text:span text:style-name="T28">One way to find this is to ask when</text:span><text:span text:style-name="T27"> 1 + ε = 1</text:span></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
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-          <text:p><text:span text:style-name="T28">This gives</text:span><text:span text:style-name="T27"> </text:span><text:span text:style-name="T29">2.22044604925e-16</text:span></text:p>
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-      <text:p><text:span text:style-name="T30">x = 1.0</text:span></text:p>
-      <text:p><text:span text:style-name="T30"/></text:p>
-      <text:p><text:span text:style-name="T30">eps = 1.0</text:span></text:p>
-      <text:p><text:span text:style-name="T30"/></text:p>
-      <text:p><text:span text:style-name="T30">while (not x + eps == x):</text:span></text:p>
-      <text:p><text:span text:style-name="T30"><text:s text:c="4"/></text:span><text:span text:style-name="T30">eps = eps/2.0</text:span></text:p>
-      <text:p><text:span text:style-name="T30"/></text:p>
-      <text:p><text:span text:style-name="T30"># machine precision is 2*eps, since that was the last </text:span></text:p>
-      <text:p><text:span text:style-name="T30"># value for which 1 + eps was not 1</text:span></text:p>
-      <text:p><text:span text:style-name="T30">print 2*eps</text:span></text:p>
-      <text:p><text:span text:style-name="T30"/></text:p>
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-     <draw:text-box>
-      <text:p>Numerical Precision</text:p>
-     </draw:text-box>
-    </draw:frame>
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-        <text:p text:style-name="P16"><text:span text:style-name="T31">This means that </text:span><text:span text:style-name="T32">most real numbers do not have an exact representation on a computer</text:span><text:span text:style-name="T31">.</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T28">Spacing between numbers varies with the size of numbers</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T28">Relative spacing is constant</text:span></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
-          <text:p><text:span text:style-name="T27"/></text:p>
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-     <svg:desc>24§display§\mathrm{relative~roundoff~error}~ = \frac{|\mathrm{true~number} - \mathrm{computer~number}|}{|\mathrm{true~number}|} \le \epsilon§png§600§FALSE§</svg:desc>
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-      <text:p>Round-off Error</text:p>
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-        <text:p><text:span text:style-name="T4">Round-off error</text:span> is the error arising from the fact that no every number is exactly representable in the finite precision floating point format.</text:p>
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-          <text:p>Can accumulate in a program over many arithmetic operations</text:p>
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-      <text:p>Round-off Example 1</text:p>
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-        <text:p>Imagine that we can only keep track of 4 significant digits</text:p>
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-        <text:p>Compute</text:p>
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-          <text:p>Take</text:p>
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-          <text:p>Keeping only 4 digits each step of the way, <text:s/></text:p>
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-          <text:p><text:span text:style-name="T4">We&apos;ve lost a lot of precision</text:span></text:p>
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-       <text:list-item>
-        <text:p>Instead, consider:</text:p>
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-       <text:list-item>
-        <text:p>Then</text:p>
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-      <text:p/>
-     </draw:image>
-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§\sqrt{1985} - \sqrt{1984} =  \frac{1}{\sqrt{1985} + \sqrt{1984}} = \frac{1}{44.55 + 44.54} = 0.01122</svg:desc>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="25" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr14" draw:text-style-name="P7" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page26" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-    <draw:frame presentation:style-name="pr15" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Round-off Example 2</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr16" draw:layer="layout" svg:width="26.924cm" svg:height="16.256cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Consider computing <text:span text:style-name="T3">exp(-24)</text:span> via a truncated Taylor series</text:p>
-        <text:p><text:span text:style-name="T33"/></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>The error in this approximation is less than</text:p>
-        <text:p/>
-        <text:p/>
-       </text:list-item>
-       <text:list-item>
-        <text:p>We can compute S(-24) by adding terms until the term is less than machine precision</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>We find <text:span text:style-name="T3">S(-24) = 3.44305354288101977E-007</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>But <text:span text:style-name="T3">exp(-24) = 3.77513454427909773E-011</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T4">The error is far bigger than the true answer!</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T6">(Yakowitz &amp; Szidarovszky)</text:span></text:p>
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-      <text:p>Round-off Example 2</text:p>
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-      <text:p><text:span text:style-name="T34">Look at the terms—we are relying on cancellation to get our result</text:span></text:p>
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-      <text:p>Round-off Example 2</text:p>
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-        <text:p>Instead recognize:</text:p>
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-       </text:list-item>
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-        <text:p><text:span text:style-name="T3">S(-1)</text:span> is well behaved, since each term is smaller in absolute magnitude than the previous.</text:p>
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-          <text:p><text:span text:style-name="T35">S(-1)</text:span><text:span text:style-name="T36">24</text:span><text:span text:style-name="T35"> = 3.77513454427912681E-011</text:span></text:p>
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-          <text:p><text:span text:style-name="T35">exp(-24) = 3.77513454427909773E-011</text:span></text:p>
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-      <text:p><text:span text:style-name="T6">(Yakowitz &amp; Szidarovszky)</text:span></text:p>
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-      <text:p>Round-off Example 3</text:p>
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-        <text:p>In spherically-symmetric codes (like a stellar evolution code), it is common to compute the volume of a spherical shell as</text:p>
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-        <text:p/>
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-         <text:list-item>
-          <text:p>This <text:span text:style-name="T4">relies on the cancellation of two very big numbers</text:span><text:span text:style-name="T37">.</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Rewriting reduces round-off errors:</text:p>
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-      <text:p>Comparing Floating Point #s</text:p>
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-        <text:p>When comparing two floating point #s, we need to be careful</text:p>
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-        <text:p>If you want to check whether 2 computed values are equal, instead of asking if</text:p>
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-       </text:list-item>
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-        <text:p>it is safer to ask whether they agree to within some tolerance</text:p>
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-      <text:p>Associative Property</text:p>
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-        <text:p>You learned in grade school that: (a + b) + c = a + (b + c)</text:p>
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-        <text:p>Adding lots of numbers together can compound round-off error</text:p>
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-        <text:p>Kahan summation (see reading list)</text:p>
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-      <text:p>Python for Scientific Computing</text:p>
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-      <text:p><text:span text:style-name="T30">http://bender.astro.sunysb.edu/classes/python-science</text:span></text:p>
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-      <text:p>Computer Languages</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T38">You can write any algorithm in any programming language—they all provide the necessary logical constructs</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T12">However, some languages make things much easier than others</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T39">C </text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Excellent low-level machine access (operating systems are written in C)</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Multidimensional arrays are “kludgy”</text:span></text:p>
-           </text:list-item>
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-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T39">Fortran</text:span><text:span text:style-name="T12"> (Formula Translate)</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">One of the earliest complied languages</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Large code base of legacy code</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Modern Fortran offers many more conveniences than old Fortran</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Great support for arrays</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T39">Python</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Offers many high-level data-structures (lists, dictionaries, arrays)</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Great for quick prototyping, analysis, experimentation</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Increasingly popular in scientific computing</text:span></text:p>
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-      <text:p>Computer Languages</text:p>
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-          <text:p><text:span text:style-name="T39">IDL</text:span><text:span text:style-name="T12"> </text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Proprietary</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Great array language</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Modern (like object-oriented programming) features break the “clean-ness” of the language</text:span></text:p>
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-          <text:p><text:span text:style-name="T8">C++</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Others</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T8">Ruby, Perl, shell scripts, ...</text:span></text:p>
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-      <text:p>Computer Languages</text:p>
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-        <text:p><text:span text:style-name="T4">Compiled languages</text:span> (Fortran, C, C++, ...)</text:p>
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-         <text:list-item>
-          <text:p>Compiled into machine code—machine specific</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Produce faster code (no interpretation is needed)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Can offer lower level system access (especially C)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T4">Interpreted languages</text:span> (python, IDL, perl, Java (kind-of) ...)</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Not converted into machine code, but instead interpreted by the interpreter</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Great for prototyping</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Can modify itself while running</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Platform independent</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Often has dynamic typing and scoping</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Many offer garbage collection</text:p>
-         </text:list-item>
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-      <text:p><text:span text:style-name="T6">http://en.wikipedia.org/wiki/Interpreted_language</text:span></text:p>
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-      <text:p>Computer Languages</text:p>
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-        <text:p><text:span text:style-name="T4">Vector languages</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Some languages are designed to operate on entire arrays at once (python + NumPy, many Fortran routines, IDL, ...)</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>For interpreted languages, getting reasonable performance requires operating on arrays instead of explicitly writing loops</text:p>
-            <text:list>
-             <text:list-item>
-              <text:p>Low level routines are written in compiled language and do the loop behind the scenes</text:p>
-             </text:list-item>
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-           </text:list-item>
-           <text:list-item>
-            <text:p>We&apos;ll see this in some detail when we discuss python</text:p>
-           </text:list-item>
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-         </text:list-item>
-         <text:list-item>
-          <text:p>Next-generation computing = GPUs ?</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Hardware is designed to do the same operations on many pieces of data at the same time</text:p>
-           </text:list-item>
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-      <text:p>Object-Oriented Languages</text:p>
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-        <text:p><text:span text:style-name="T4">Python is an object-oriented language</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Think of an object as a container that holds both data and functions (methods) that know how to operate on that date.</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Objects are build from a datatype called a class—you can create as many instances (objects) from a class as memory allows</text:p>
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-            <text:p>Each object will have its own memory allocated</text:p>
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-        <text:p>Objects provide a convenient way to package up data </text:p>
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-         <text:list-item>
-          <text:p>In Fortran, think of a derived type that also has it&apos;s own functions</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>In C, think of a struct that, again, also has it&apos;s own functions</text:p>
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-        <text:p>Everything, even integers, etc. is an object</text:p>
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-      <text:p>Programming Paradigms</text:p>
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-      <text:p>Arrays and Memory Layout</text:p>
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-        <text:p>Building block of many numerical methods</text:p>
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-        <text:p>Row vs. Column major: A(m,n)</text:p>
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-          <text:p>Second index is called the column</text:p>
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-          <text:p><text:span text:style-name="T4">Multi-dimensional arrays are flattened into a one-dimensional sequence for storage</text:span></text:p>
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-          <text:p>Column-major (Fortran, matlab): columns are stored one after the other</text:p>
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-        <text:p>Ordering matters for:</text:p>
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-          <text:p>Passing arrays between languages</text:p>
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-          <text:p>Deciding which index to loop over first</text:p>
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-      <text:p>Roundoff vs. Truncation Error</text:p>
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-        <text:p>Roundoff error is just one of the errors we deal with</text:p>
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-        <text:p>Translating continuous mathematical expressions into discrete forms introduces <text:span text:style-name="T4">truncation error</text:span></text:p>
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-        <text:p>Consider the Taylor series expansion for sine:</text:p>
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-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§\sin(x) = x - \frac{x^3}{3!} + \frac{x^5}{5!} + \ldots</svg:desc>
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-      <text:p>Roundoff vs. Truncation Error</text:p>
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-        <text:p>Roundoff error is just one of the errors we deal with</text:p>
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-        <text:p>Translating continuous mathematical expressions into discrete forms introduces <text:span text:style-name="T4">truncation error</text:span></text:p>
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-       <text:list-item>
-        <text:p>Consider the definition of a derivative:</text:p>
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-        <text:p>We can&apos;t take the limit to 0. <text:s/>Our discrete version will be:</text:p>
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-        <text:p>If we choose h small enough, this should be a good approximation.</text:p>
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-      <text:p><text:span text:style-name="T6">(Yakowitz &amp; Szidarovszky)</text:span></text:p>
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-      <text:p>Potential Topics</text:p>
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-        <text:p><text:span text:style-name="T12">General python</text:span></text:p>
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-        <text:p><text:span text:style-name="T12">Advanced python / special modules</text:span></text:p>
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-          <text:p><text:span text:style-name="T8">Regular expressions</text:span></text:p>
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-          <text:p><text:span text:style-name="T8">Command line/input file parsing</text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T39">NumPy</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T39">SciPy</text:span><text:span text:style-name="T12"> and numerical methods</text:span></text:p>
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-          <text:p><text:span text:style-name="T8">Integration</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">ODEs</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Curve fitting/optimization</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Interpolation</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Signal processing (FFT)</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Linear algebra</text:span></text:p>
-         </text:list-item>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T41">matplotlib</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T41">MayaVi</text:span></text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Workflow management with </text:span><text:span text:style-name="T39">IPython</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Extending python w/ Fortran and C/C++</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Symbolic math with </text:span><text:span text:style-name="T39">SymPy</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Writing python applications</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">GUI programming</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Unit testing</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Git/github</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T42">Others? </text:span></text:p>
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-      <text:p>Introduction to Python</text:p>
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-      <text:p>Class Participation</text:p>
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-        <text:p>We&apos;ll learn by interactively trying out some ideas and looking at code</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>I want to learn from all of you—share you experiences and expertise</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T5">We&apos;ll use the Discussion Forum on Blackboard to interact with one-another outside of class.</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Try out ideas and report to the class what you&apos;ve learned</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>I can set up an account for you on one of my Linux servers if you want a place to try things out (you would log in remotely)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p>Who Are We?</text:p>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Physics</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Ecology &amp; Evolution</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Sociology</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Biology / Applied Ecology / Evolution</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Cognitive Science</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Integrative Neuroscience</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Applied Math &amp; Statistics</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Mechanical Engineering</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Psychology</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Marine and Atmospheric Science</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Earth &amp; Space Science—Geoscience </text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p>What Do You Want To Learn?</text:p>
-     </draw:text-box>
-    </draw:frame>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>From your posts (in some order by popularity):</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Replace R / matlab (most popular)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>General (scientific) programming</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Data analysis</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Plotting &amp; Visualization (including 3-d visualization)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Interacting with experiments (a la PsychoPy)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>High performance computing</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Curve / parameter fitting</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>General scripting / glue for C + Fortran programs</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p>Python Shell</text:p>
-     </draw:text-box>
-    </draw:frame>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>This is the standard (and most basic) interactive way to use python</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Runs in a terminal window</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Provides basic interactivity with the python language</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Simply type <text:span text:style-name="T3">python</text:span> at your command prompt</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You can scroll back through the history using the arrows</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>IPython Shell</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Type <text:span text:style-name="T3">ipython</text:span> at your prompt</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Like the standard shell, this runs in a terminal, but provides many conveniences (type <text:span text:style-name="T3">%quickref</text:span> to see an overview)</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Scrollback history preserved between sessions</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Build-in help with <text:span text:style-name="T3">?</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T3">function-name?</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T3">object?</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Magics (</text:span><text:span text:style-name="T3">%lsmagic</text:span><text:span text:style-name="T43"> lists all the magic functions)</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T3">%run script</text:span><text:span text:style-name="T43">: runs a script </text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T3">%timeit</text:span><text:span text:style-name="T43">: times a command</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T43">Lots of magics to deal with command history (including </text:span><text:span text:style-name="T3">%history</text:span><text:span text:style-name="T43">)</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Tab completion</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Run system commands (prefix with a </text:span><text:span text:style-name="T3">!</text:span><text:span text:style-name="T43">)</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Last 3 </text:span><text:span text:style-name="T44">output</text:span><text:span text:style-name="T43"> objects are referred to via </text:span><text:span text:style-name="T3">_</text:span><text:span text:style-name="T43">, </text:span><text:span text:style-name="T3">__</text:span><text:span text:style-name="T43">, </text:span><text:span text:style-name="T3">___</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>IPython Notebooks</text:p>
-     </draw:text-box>
-    </draw:frame>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>A web-based environment that combines code and output, plots, plain text/stylized headings, LaTeX (later versions), ...</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Notebooks can be saved and shared</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Viewable on the web via: </text:span><text:span text:style-name="T3">http://nbviewer.ipython.org/</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T43">Provides a complete view of your entire workflow</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Start with <text:span text:style-name="T3">ipython notebook</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T45">I&apos;ll provide notebooks for a lot of the lectures to follow so you can play along on your own</text:span></text:p>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>Python Scripts</text:p>
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-       <text:list-item>
-        <text:p>Scripts are a non-interactive means of writing python code</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">filename.py</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Can be executable by adding:</text:p>
-          <text:p><text:span text:style-name="T3">#!/usr/bin/env python</text:span></text:p>
-          <text:p><text:span text:style-name="T43">as the first line and setting the executable bit for the file</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T7">This is also the way that you write python modules that can be </text:span><text:span text:style-name="T3">import</text:span><text:span text:style-name="T7">-ed into other python code (more on that later...)</text:span></text:p>
-       </text:list-item>
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-     <draw:text-box>
-      <text:p>Python 2.x vs. Python 3</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>See <text:a xlink:href="https://wiki.python.org/moin/Python2orPython3" xlink:type="simple">https://wiki.python.org/moin/Python2orPython3</text:a></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Mostly about cleaning up the language and making things consistent</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>e.g. <text:span text:style-name="T3">print</text:span> is a statement in python 2.x but a function in 3.x</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Some trivial differences</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">.pyc</text:span> are now stored in a <text:span text:style-name="T3">__pycache__</text:span> directory</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Some gotyas:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">1/2</text:span> will give different results between python 2 and 3</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>It&apos;s possible to write code that works with both python 2 and 3—often we will do so by <text:span text:style-name="T3">import</text:span>ing from <text:span text:style-name="T3">__future__</text:span></text:p>
-       </text:list-item>
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-     <draw:text-box>
-      <text:p>Python 2.x vs. Python 3</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Write for both</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>In python 2.6+, do </text:p>
-          <text:p><text:span text:style-name="T3">from __future__ import print_function</text:span> </text:p>
-          <text:p>and then use the new <text:span text:style-name="T3">print()</text:span> style</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">exec cmd</text:span> becomes <text:span text:style-name="T3">exec(cmd)</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Some small changes to how <text:span text:style-name="T3">__init__.py</text:span> are done (more on this later)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>One some systems, you can have python 2.x and 3.x installed side by side</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>May need to install packages twice, e.g. <text:span text:style-name="T3">python-numpy</text:span> and <text:span text:style-name="T3">python3-numpy</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-   <draw:page draw:name="page55" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
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-     <draw:text-box>
-      <text:p>Intro to Python: Data Types</text:p>
-     </draw:text-box>
-    </draw:frame>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Python has the same basic data types as most languages: integer, floating point, complex, strings</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T5">data type IPython notebook</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>And it has more complex data structures, including lists and dictionaries built-in</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T5">advanced data type IPython notebook</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p>Lists vs. Arrays</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Lists (note that python lists are not implemented as a simple linked-list: <text:s/>see <text:span text:style-name="T8"><text:a xlink:href="http://docs.python.org/2/faq/design.html#how-are-lists-implemented" xlink:type="simple">http://docs.python.org/2/faq/design.html#how-are-lists-implemented</text:a></text:span> )</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Implemented as a variable-length array</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Can store multiple different datatypes in a single list</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Easy to add items to the end (increasing list length)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Accessing <text:span text:style-name="T3">a[i]</text:span> is independent of list length (unlike a traditional linked list)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Arrays:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Generally used for fixed-length, homogeneous data</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Less overhead than a list</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>We&apos;ll use a special array library (NumPy) for performance later</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-   <draw:page draw:name="page57" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
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-     <draw:text-box>
-      <text:p>Intro to Python: Control Flow</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Python uses indentation to denote blocks of code (no explicit endif, enddo, ...)</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Control flow via. while, if, for , ...</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Can iterate over items in a list—very useful</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T5">control flow IPython notebook</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>Intro to Python: Functions</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>No distinction between functions and procedures/subroutines</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Always return something. <text:s/>If not explicitly set, then None</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Can have default values for arguments, optional arguments, <text:s/>variable number of arguments</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Can return multiple values, objects</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T5">Function examples...</text:span></text:p>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>Intro to Python: Classes</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T4">Classes</text:span> are a fundamental concept of object-oriented programming</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>An <text:span text:style-name="T4">object</text:span> is an instance of a class</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Carries data (state) and has methods that can act on that data</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Objects are easy to pass around </text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Simplest use is just as a container to carry associated data together (similar to a struct in C)</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Inheritance, operator overloading, ... all supported. <text:s/>See the tutorial.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T5">Classes example...</text:span></text:p>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>Intro to Python: Modules</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T4">Modules</text:span> are a collection of python statements, functions, variables, grouped together in a file ending with .py</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Can be <text:span text:style-name="T4">import</text:span>ed to be used by any routine</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Python includes a host of useful modules </text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>You can define your own. <text:s/>Python will look in the current directory and then in the PYTHONPATH</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Modules have their own <text:span text:style-name="T4">namespace</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Variables (even global variables) don&apos;t clash with those with the same name in the importing program</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>You can change the name of a module on import or import it into the current namespace (*). <text:s/></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>General practice: put all the imports at the top of your code</text:p>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>Intro to Python: Modules</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Example: <text:span text:style-name="T3">profile.py</text:span>—a simple timing module for measuring code performance</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>In the <text:span text:style-name="T5">python</text:span> shell:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">import profile</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">help(profile)</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Note that this module can also be run on its own</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">if __name__ == &quot;__main__&quot;: </text:span><text:span text:style-name="T43">clause at the end</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T45">Let&apos;s look at an example of using this module in a notebook</text:span></text:p>
-       </text:list-item>
-      </text:list>
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-      <text:p>Intro to Python: Exceptions</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T4">Exceptions</text:span> are raised if an action you tried (e.g. opening a file) fails. <text:s/></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Left alone, the code will abort, printing the exception</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>You can catch the exception, and take appropriate action to fix the situation </text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Built-in exceptions have particular names that let you check for specific failure modes</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Allows you to write robust code for other users</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T5">Exceptions example...</text:span></text:p>
-       </text:list-item>
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-     <draw:text-box>
-      <text:p>Intro to Python: I/O</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr16" draw:layer="layout" svg:width="26.924cm" svg:height="12.18cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Basic I/O</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>File I/O</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>CSV reading</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>INI file reading</text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
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-     <draw:text-box>
-      <text:p>Caveats</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr13" draw:layer="layout" svg:width="26.924cm" svg:height="16.256cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Slicing (we&apos;ll look at this with arrays soon)</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Function defaults: default values are only evaluated once. <text:s/></text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">From python tutorial:</text:span></text:p>
-          <text:p><text:span text:style-name="T46"/></text:p>
-          <text:p/>
-          <text:p/>
-          <text:p/>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Prints:</text:span></text:p>
-          <text:p><text:span text:style-name="T47"/></text:p>
-          <text:p><text:span text:style-name="T46"/></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T8">Correct way:</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">def f(a, L=[]):</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"><text:s text:c="4"/></text:span><text:span text:style-name="T48">L.append(a)</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"><text:s text:c="4"/></text:span><text:span text:style-name="T48">return L</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"/></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">print f(1)</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">print f(2)</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">print f(3)</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">[1]</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">[1, 2]</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">[1, 2, 3]</text:span></text:p>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p text:style-name="P19"><text:span text:style-name="T48">def f(a, L=None):</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"><text:s text:c="4"/></text:span><text:span text:style-name="T48">if L is None:</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"><text:s text:c="8"/></text:span><text:span text:style-name="T48">L = []</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"><text:s text:c="4"/></text:span><text:span text:style-name="T48">L.append(a)</text:span></text:p>
-      <text:p text:style-name="P19"><text:span text:style-name="T48"><text:s text:c="4"/></text:span><text:span text:style-name="T48">return L</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
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\ No newline at end of file
diff --git a/lectures/02-numpy/numpy-basics.ipynb b/lectures/02-numpy/numpy-basics.ipynb
deleted file mode 100644
index ec479fb1..00000000
--- a/lectures/02-numpy/numpy-basics.ipynb
+++ /dev/null
@@ -1,1264 +0,0 @@
-{
- "metadata": {
-  "name": "numpy-basics"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Array Creation and Properties"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "this notebook is based on the SciPy NumPy tutorial"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.arange(15).reshape(3,5)\n",
-      "\n",
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0  1  2  3  4]\n",
-        " [ 5  6  7  8  9]\n",
-        " [10 11 12 13 14]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a.ndim\n",
-      "print a.shape\n",
-      "print a.size\n",
-      "print a.dtype\n",
-      "print a.itemsize\n",
-      "print type(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2\n",
-        "(3, 5)\n",
-        "15\n",
-        "int64\n",
-        "8\n",
-        "<type 'numpy.ndarray'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can create an array from a list"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = np.array( [1.0, 2.0, 3.0, 4.0] )\n",
-      "print b\n",
-      "print b.dtype"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ 1.  2.  3.  4.]\n",
-        "float64\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can create a multi-dimensional array of a specified size initialized all to 0 easily.  There is also an analogous ones() and empty() array routine."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = np.zeros( (10,7), dtype=np.float64)\n",
-      "print c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]\n",
-        " [ 0.  0.  0.  0.  0.  0.  0.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "linspace (and logspace) create arrays with evenly space (in log) numbers.  For logspace, you specify the start and ending powers (base**start to base**stop)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = np.linspace(0, 1, 10, endpoint=False)\n",
-      "print d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ 0.   0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9]\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e = np.logspace(-1, 2, 15, endpoint=False, base=10)\n",
-      "print e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[  0.1          0.15848932   0.25118864   0.39810717   0.63095734   1.\n",
-        "   1.58489319   2.51188643   3.98107171   6.30957344  10.          15.84893192\n",
-        "  25.11886432  39.81071706  63.09573445]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can also initialize an array based on a function"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f = np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)\n",
-      "f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "array([[0, 1, 2],\n",
-        "       [1, 2, 3],\n",
-        "       [2, 3, 4]])"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def myFun(x,y): \n",
-      "    return 10*x+y\n",
-      "\n",
-      "g = np.fromfunction(myFun, (5,4), dtype=int)\n",
-      "g"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "array([[ 0,  1,  2,  3],\n",
-        "       [10, 11, 12, 13],\n",
-        "       [20, 21, 22, 23],\n",
-        "       [30, 31, 32, 33],\n",
-        "       [40, 41, 42, 43]])"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Array Operations"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "most operations will work on an entire array at once"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.arange(12).reshape(3,4)\n",
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0  1  2  3]\n",
-        " [ 4  5  6  7]\n",
-        " [ 8  9 10 11]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a.sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[12 15 18 21]\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a.sum()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "66\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a.min(), a.max()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 11\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "universal functions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = a*np.pi/12.0\n",
-      "print b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0.          0.26179939  0.52359878  0.78539816]\n",
-        " [ 1.04719755  1.30899694  1.57079633  1.83259571]\n",
-        " [ 2.0943951   2.35619449  2.61799388  2.87979327]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = np.cos(b)\n",
-      "print c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[  1.00000000e+00   9.65925826e-01   8.66025404e-01   7.07106781e-01]\n",
-        " [  5.00000000e-01   2.58819045e-01   6.12323400e-17  -2.58819045e-01]\n",
-        " [ -5.00000000e-01  -7.07106781e-01  -8.66025404e-01  -9.65925826e-01]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = b + c "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 1.          1.22772521  1.38962418  1.49250494]\n",
-        " [ 1.54719755  1.56781598  1.57079633  1.57377667]\n",
-        " [ 1.5943951   1.64908771  1.75196847  1.91386744]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Slicing"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "slicing works very similarly to how we saw with strings"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.fromfunction(myFun, (5,4),dtype=int)\n",
-      "print a\n",
-      "print \" \"\n",
-      "\n",
-      "print a[1,1]\n",
-      "print \" \"\n",
-      "\n",
-      "print a[0:2,0:2]\n",
-      "print \" \"\n",
-      "\n",
-      "print a[:,1]\n",
-      "print \" \"\n",
-      "\n",
-      "print a.flatten()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0  1  2  3]\n",
-        " [10 11 12 13]\n",
-        " [20 21 22 23]\n",
-        " [30 31 32 33]\n",
-        " [40 41 42 43]]\n",
-        " \n",
-        "11\n",
-        " \n",
-        "[[ 0  1]\n",
-        " [10 11]]\n",
-        " \n",
-        "[ 1 11 21 31 41]\n",
-        " \n",
-        "[ 0  1  2  3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43]\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can also iterate -- this is done over the first axis"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for row in a:\n",
-      "    print row\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0 1 2 3]\n",
-        "[10 11 12 13]\n",
-        "[20 21 22 23]\n",
-        "[30 31 32 33]\n",
-        "[40 41 42 43]\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "or element by element"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for e in a.flat:\n",
-      "    print e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "10\n",
-        "11\n",
-        "12\n",
-        "13\n",
-        "20\n",
-        "21\n",
-        "22\n",
-        "23\n",
-        "30\n",
-        "31\n",
-        "32\n",
-        "33\n",
-        "40\n",
-        "41\n",
-        "42\n",
-        "43\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Copying Arrays"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "simply using \"=\" does not make a copy, but much like with lists, you will just have multiple names pointing to the same ndarray object"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.arange(10)\n",
-      "print a\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0 1 2 3 4 5 6 7 8 9]\n"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = a\n",
-      "b is a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "True"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b.shape = (2,5)\n",
-      "print b\n",
-      "a.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[0 1 2 3 4]\n",
-        " [5 6 7 8 9]]\n"
-       ]
-      },
-      {
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "(2, 5)"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "a shallow copy creates a new view into the array -- the data is the same, but the array properties can be different"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.arange(12)\n",
-      "c = a[:]\n",
-      "a.shape = (3,4)\n",
-      "\n",
-      "print a\n",
-      "print c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0  1  2  3]\n",
-        " [ 4  5  6  7]\n",
-        " [ 8  9 10 11]]\n",
-        "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c[1] = -1\n",
-      "print a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0 -1  2  3]\n",
-        " [ 4  5  6  7]\n",
-        " [ 8  9 10 11]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print c is a\n",
-      "print c.base is a\n",
-      "print c.flags.owndata"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "False\n",
-        "True\n",
-        "False\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "to make a copy of the data of the array that you can deal with independently of the original, you need a deep copy"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = a.copy()\n",
-      "d[:,:] = 0.0\n",
-      "\n",
-      "print a\n",
-      "print d\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0 -1  2  3]\n",
-        " [ 4  5  6  7]\n",
-        " [ 8  9 10 11]]\n",
-        "[[0 0 0 0]\n",
-        " [0 0 0 0]\n",
-        " [0 0 0 0]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Boolean Indexing"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.arange(12).reshape(3,4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = a > 4\n",
-      "b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 29,
-       "text": [
-        "array([[False, False, False, False],\n",
-        "       [False,  True,  True,  True],\n",
-        "       [ True,  True,  True,  True]], dtype=bool)"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a[b] = 0\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "array([[0, 1, 2, 3],\n",
-        "       [4, 0, 0, 0],\n",
-        "       [0, 0, 0, 0]])"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a[a == 0] = -1\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 31,
-       "text": [
-        "array([[-1,  1,  2,  3],\n",
-        "       [ 4, -1, -1, -1],\n",
-        "       [-1, -1, -1, -1]])"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Avoiding Loops"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "start by initialize coordinate arrays and a Gaussian function"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# create 1-d x and y arrays -- we define the coordinate values such that\n",
-      "# they are centered in the bin\n",
-      "N = 32\n",
-      "xmin = ymin = 0.0\n",
-      "xmax = ymax = 1.0\n",
-      "\n",
-      "x = (np.arange(N)+0.5)*(xmax - xmin)/N + xmin\n",
-      "y = x.copy()\n",
-      "\n",
-      "x2d = np.repeat(x, N)\n",
-      "x2d.shape = (N, N)\n",
-      "\n",
-      "y2d = np.repeat(y, N)\n",
-      "y2d.shape = (N, N)\n",
-      "y2d = np.transpose(y2d)\n",
-      "\n",
-      "print x2d\n",
-      "print y2d\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0.015625  0.015625  0.015625 ...,  0.015625  0.015625  0.015625]\n",
-        " [ 0.046875  0.046875  0.046875 ...,  0.046875  0.046875  0.046875]\n",
-        " [ 0.078125  0.078125  0.078125 ...,  0.078125  0.078125  0.078125]\n",
-        " ..., \n",
-        " [ 0.921875  0.921875  0.921875 ...,  0.921875  0.921875  0.921875]\n",
-        " [ 0.953125  0.953125  0.953125 ...,  0.953125  0.953125  0.953125]\n",
-        " [ 0.984375  0.984375  0.984375 ...,  0.984375  0.984375  0.984375]]\n",
-        "[[ 0.015625  0.046875  0.078125 ...,  0.921875  0.953125  0.984375]\n",
-        " [ 0.015625  0.046875  0.078125 ...,  0.921875  0.953125  0.984375]\n",
-        " [ 0.015625  0.046875  0.078125 ...,  0.921875  0.953125  0.984375]\n",
-        " ..., \n",
-        " [ 0.015625  0.046875  0.078125 ...,  0.921875  0.953125  0.984375]\n",
-        " [ 0.015625  0.046875  0.078125 ...,  0.921875  0.953125  0.984375]\n",
-        " [ 0.015625  0.046875  0.078125 ...,  0.921875  0.953125  0.984375]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 55
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Here we initialize an array with a Gaussian"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "g = np.exp(-((x2d-0.5)**2 + (y2d-0.5)**2)/0.2**2)\n",
-      "print g\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[  8.04100059e-06   1.67261841e-05   3.31343050e-05 ...,   3.31343050e-05\n",
-        "    1.67261841e-05   8.04100059e-06]\n",
-        " [  1.67261841e-05   3.47923410e-05   6.89230749e-05 ...,   6.89230749e-05\n",
-        "    3.47923410e-05   1.67261841e-05]\n",
-        " [  3.31343050e-05   6.89230749e-05   1.36535517e-04 ...,   1.36535517e-04\n",
-        "    6.89230749e-05   3.31343050e-05]\n",
-        " ..., \n",
-        " [  3.31343050e-05   6.89230749e-05   1.36535517e-04 ...,   1.36535517e-04\n",
-        "    6.89230749e-05   3.31343050e-05]\n",
-        " [  1.67261841e-05   3.47923410e-05   6.89230749e-05 ...,   6.89230749e-05\n",
-        "    3.47923410e-05   1.67261841e-05]\n",
-        " [  8.04100059e-06   1.67261841e-05   3.31343050e-05 ...,   3.31343050e-05\n",
-        "    1.67261841e-05   8.04100059e-06]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 56
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
-     "prompt_number": 57
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "imshow(g, interpolation=\"nearest\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 58,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x3c375d0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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8kUVY8EQWEQu+ra0N8+fPx/Tp01FcXIw9e/YAAHbs2IFQKIRIJIJIJILGxsaMJEtEqRHb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\n"
-      }
-     ],
-     "prompt_number": 58
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Numerical differencing on NumPy arrays"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we want to construct\n",
-      "\n",
-      "$$\\nabla^2 g \\cdot \\Delta x^2$$\n",
-      "\n",
-      "using differences between adjacent cells (think about the Calculus definition of a derivative)\n",
-      "\n",
-      "$$\\nabla^2 g \\cdot \\Delta x^2 \\sim {g_{i-1,j} + g_{i+1,j} - 2 g_{i,j}} + {g_{i,j-1} + g_{i,j+1} - 2 g_{i,j}}$$\n",
-      "\n",
-      "We'll do this two different ways---the first is with explicit loops and the second is using NumPy slice notation"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d2g = np.zeros((N,N), dtype=np.float64)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 59
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 1\n",
-      "while i < N-1:\n",
-      "    j = 1\n",
-      "    while j < N-1:\n",
-      "        d2g[i,j] = g[i+1,j] + g[i-1,j] + g[i,j+1] + g[i,j-1] - 4*g[i,j]\n",
-      "        j += 1\n",
-      "    i += 1\n",
-      "\n",
-      "imshow(d2g, interpolation=\"nearest\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 72,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x4201950>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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8mSqd4UUsooQXsYgSXsQiSngRiyjhRSyihBexSFrKcgzrGcQG8xslNbQcUppiWC8sP5iW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\nJbyIRZTwIhZRwotYRAkvYpH/BwckDS1g/cbRAAAAAElFTkSuQmCC\n"
-      }
-     ],
-     "prompt_number": 72
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d2gb = np.zeros((N,N), dtype=np.float64)\n",
-      "print d2gb.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(32, 32)\n"
-       ]
-      }
-     ],
-     "prompt_number": 61
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d2gb[1:N-1,1:N-1] = g[2:N,1:N-1] + g[0:N-2,1:N-1] + g[1:N-1,2:N] + g[1:N-1,0:N-2] - 4*g[1:N-1,1:N-1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 62
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print np.max(d2g - d2gb)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 63
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      " let's take this further, and try to radially bin the data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "rmin = 0\n",
-      "rmax = np.sqrt(xmax**2 + ymax**2)\n",
-      "\n",
-      "nbins = np.int(np.sqrt(N**2 + N**2))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 64
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# bins will hold the edges, so there is one more value than actual bin\n",
-      "# bin_centers holds the center value of the bin\n",
-      "bins = np.linspace(rmin, rmax, nbins+1)\n",
-      "bin_centers = 0.5*(bins[1:] + bins[:-1])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 65
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# radius of each zone\n",
-      "xc = 0.5*(xmin + xmax)\n",
-      "yc = 0.5*(ymin + ymax)\n",
-      "\n",
-      "r = np.sqrt( (x2d-xc)**2 + (y2d-yc)**2 )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 66
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# bin the radii -- digitize returns an array with the same shape \n",
-      "# as the input array but with elements of the array specifying\n",
-      "# which bin that location belongs to (it gives i such that\n",
-      "# bins[i-1] <= x < bins[i]).  The value of whichbin will be 1 \n",
-      "# if we are located in the bin defined by bins[0] to bins[1].  \n",
-      "# This means that whichbin[0] will be 0.\n",
-      "whichbin = np.digitize(r.flat, bins)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 67
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# bincount counts the number of occurrences of each non-negative\n",
-      "# integer value in whichbin.\n",
-      "ncount = np.bincount(whichbin)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 68
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# now bin our function\n",
-      "g_bin = np.zeros(len(ncount)-1, dtype=np.float64)\n",
-      "\n",
-      "n = 1\n",
-      "while (n < len(ncount)):\n",
-      "    # there is no whichbin == 0 -- that corresponds to the left edge\n",
-      "    g_bin[n-1] = np.sum(g.flat[whichbin==n])/np.sum(ncount[n])\n",
-      "    n += 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 69
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "bin_centers = bin_centers[0:len(ncount)-1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 70
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "scatter(bin_centers, g_bin)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 71,
-       "text": [
-        "<matplotlib.collections.PathCollection at 0x3fcfd90>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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-      }
-     ],
-     "prompt_number": 71
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/02-numpy/numpy.fodp b/lectures/02-numpy/numpy.fodp
deleted file mode 100644
index 60f289f9..00000000
--- a/lectures/02-numpy/numpy.fodp
+++ /dev/null
@@ -1,2281 +0,0 @@
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-
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-   <config:config-item config:name="VisibleAreaHeight" config:type="int">21662</config:config-item>
-   <config:config-item-map-indexed config:name="Views">
-    <config:config-item-map-entry>
-     <config:config-item config:name="ViewId" config:type="string">view1</config:config-item>
-     <config:config-item config:name="GridIsVisible" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="GridIsFront" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsSnapToGrid" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToPageMargins" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToSnapLines" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectFrame" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsPlusHandlesAlwaysVisible" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsFrameDragSingles" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="EliminatePolyPointLimitAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="IsEliminatePolyPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="VisibleLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="PrintableLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
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-     <config:config-item config:name="RulerIsVisible" config:type="boolean">false</config:config-item>
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-     <config:config-item config:name="SnapAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="ZoomOnPage" config:type="boolean">true</config:config-item>
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-  </config:config-item-set>
-  <config:config-item-set config:name="ooo:configuration-settings">
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-   <config:config-item config:name="BitmapTableURL" config:type="string">$(user)/config/standard.sob</config:config-item>
-   <config:config-item config:name="CharacterCompressionType" config:type="short">0</config:config-item>
-   <config:config-item config:name="ColorTableURL" config:type="string">$(user)/config/standard.soc</config:config-item>
-   <config:config-item config:name="DashTableURL" config:type="string">$(user)/config/standard.sod</config:config-item>
-   <config:config-item config:name="DefaultTabStop" config:type="int">1270</config:config-item>
-   <config:config-item config:name="EmbedFonts" config:type="boolean">false</config:config-item>
-   <config:config-item-map-indexed config:name="ForbiddenCharacters">
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-     <config:config-item config:name="Language" config:type="string">en</config:config-item>
-     <config:config-item config:name="Country" config:type="string">US</config:config-item>
-     <config:config-item config:name="Variant" config:type="string"/>
-     <config:config-item config:name="BeginLine" config:type="string"/>
-     <config:config-item config:name="EndLine" config:type="string"/>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-   <config:config-item config:name="GradientTableURL" config:type="string">$(user)/config/standard.sog</config:config-item>
-   <config:config-item config:name="HandoutsHorizontal" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="HatchTableURL" config:type="string">$(user)/config/standard.soh</config:config-item>
-   <config:config-item config:name="IsKernAsianPunctuation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBooklet" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBookletBack" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintBookletFront" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintDate" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintDrawing" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintFitPage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHandout" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHiddenPages" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintNotes" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintOutline" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintPageName" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTilePage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTime" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="LineEndTableURL" config:type="string">$(user)/config/standard.soe</config:config-item>
-   <config:config-item config:name="LoadReadonly" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PageNumberFormat" config:type="int">4</config:config-item>
-   <config:config-item config:name="ParagraphSummation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PrintQuality" config:type="int">0</config:config-item>
-   <config:config-item config:name="PrinterIndependentLayout" config:type="string">low-resolution</config:config-item>
-   <config:config-item config:name="PrinterName" config:type="string">hp</config:config-item>
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-   <config:config-item config:name="SaveVersionOnClose" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="SlidesPerHandout" config:type="short">6</config:config-item>
-   <config:config-item config:name="UpdateFromTemplate" config:type="boolean">true</config:config-item>
-  </config:config-item-set>
- </office:settings>
- <office:scripts>
-  <office:script script:language="ooo:Basic">
-   <ooo:libraries xmlns:ooo="http://openoffice.org/2004/office" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <ooo:library-embedded ooo:name="Standard"/>
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- </office:scripts>
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-  <style:font-face style:name="Courier New1" svg:font-family="&apos;Courier New&apos;" style:font-family-generic="modern" style:font-pitch="fixed"/>
-  <style:font-face style:name="Arial2" svg:font-family="Arial" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="Courier New" svg:font-family="&apos;Courier New&apos;" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial" svg:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Liberation Sans" svg:font-family="&apos;Liberation Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Times New Roman" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial1" svg:font-family="Arial" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Carlito" svg:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Times New Roman1" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="DejaVu Sans" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Tahoma" svg:font-family="Tahoma" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
- </office:font-face-decls>
- <office:styles>
-  <draw:marker draw:name="Arrow" svg:viewBox="0 0 20 30" svg:d="M10 0l-10 30h20z"/>
-  <draw:marker draw:name="Short_20_line_20_Arrow" draw:display-name="Short line Arrow" svg:viewBox="0 0 3000 3000" svg:d="M1500 0l1500 2789v211h-114l-1286-2392v2392h-200v-2392l-1286 2392h-114v-211z"/>
-  <style:default-style style:family="graphic">
-   <style:graphic-properties svg:stroke-color="#808080" draw:fill-color="#cfe7f5" fo:wrap-option="no-wrap"/>
-   <style:paragraph-properties style:text-autospace="ideograph-alpha" style:punctuation-wrap="simple" style:line-break="strict" style:writing-mode="lr-tb" style:font-independent-line-spacing="false">
-    <style:tab-stops/>
-   </style:paragraph-properties>
-   <style:text-properties style:use-window-font-color="true" style:font-name="Times New Roman" fo:font-size="24pt" fo:language="en" fo:country="US" style:font-name-asian="DejaVu Sans" style:font-size-asian="24pt" style:language-asian="zh" style:country-asian="CN" style:font-name-complex="Tahoma" style:font-size-complex="24pt" style:language-complex="hi" style:country-complex="IN"/>
-  </style:default-style>
-  <style:style style:name="standard" style:family="graphic">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0cm" svg:stroke-color="#808080" draw:marker-start-width="0.2cm" draw:marker-start-center="false" draw:marker-end-width="0.2cm" draw:marker-end-center="false" draw:fill="solid" draw:fill-color="#cfe7f5" draw:textarea-horizontal-align="justify" fo:padding-top="0.125cm" fo:padding-bottom="0.125cm" fo:padding-left="0.25cm" fo:padding-right="0.25cm" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080">
-    <text:list-style style:name="standard">
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-      <style:list-level-properties text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="2" text:bullet-char="●">
-      <style:list-level-properties text:space-before="0.6cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="3" text:bullet-char="●">
-      <style:list-level-properties text:space-before="1.2cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="4" text:bullet-char="●">
-      <style:list-level-properties text:space-before="1.8cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="5" text:bullet-char="●">
-      <style:list-level-properties text:space-before="2.4cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="6" text:bullet-char="●">
-      <style:list-level-properties text:space-before="3cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="7" text:bullet-char="●">
-      <style:list-level-properties text:space-before="3.6cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="8" text:bullet-char="●">
-      <style:list-level-properties text:space-before="4.2cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="9" text:bullet-char="●">
-      <style:list-level-properties text:space-before="4.8cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="10" text:bullet-char="●">
-      <style:list-level-properties text:space-before="5.4cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-    </text:list-style>
-   </style:graphic-properties>
-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:margin-top="0cm" fo:margin-bottom="0cm" fo:line-height="100%" fo:text-indent="0cm"/>
-   <style:text-properties style:use-window-font-color="true" style:text-outline="false" style:text-line-through-style="none" style:text-line-through-type="none" style:font-name="Arial" fo:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable" fo:font-size="18pt" fo:font-style="normal" fo:text-shadow="none" style:text-underline-style="none" fo:font-weight="normal" style:letter-kerning="true" style:font-name-asian="WenQuanYi Zen Hei Sharp" style:font-family-asian="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic-asian="system" style:font-pitch-asian="variable" style:font-size-asian="18pt" style:font-style-asian="normal" style:font-weight-asian="normal" style:font-name-complex="Lohit Devanagari" style:font-family-complex="&apos;Lohit Devanagari&apos;" style:font-family-generic-complex="system" style:font-pitch-complex="variable" style:font-size-complex="18pt" style:font-style-complex="normal" style:font-weight-complex="normal" style:text-emphasize="none" style:font-relief="none" style:text-overline-style="none" style:text-overline-color="font-color"/>
-  </style:style>
-  <style:style style:name="objectwitharrow" style:family="graphic" style:parent-style-name="standard">
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-  </style:style>
-  <style:style style:name="objectwithshadow" style:family="graphic" style:parent-style-name="standard">
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-  </style:style>
-  <style:style style:name="objectwithoutfill" style:family="graphic" style:parent-style-name="standard">
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-  </style:style>
-  <style:style style:name="Object_20_with_20_no_20_fill_20_and_20_no_20_line" style:display-name="Object with no fill and no line" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-  </style:style>
-  <style:style style:name="text" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-  </style:style>
-  <style:style style:name="textbody" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:text-properties fo:font-size="16pt"/>
-  </style:style>
-  <style:style style:name="textbodyjustfied" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:text-align="justify"/>
-  </style:style>
-  <style:style style:name="textbodyindent" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:text-indent="0.6cm"/>
-  </style:style>
-  <style:style style:name="title" style:family="graphic" style:parent-style-name="standard">
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-   <style:text-properties fo:font-size="44pt"/>
-  </style:style>
-  <style:style style:name="title1" style:family="graphic" style:parent-style-name="standard">
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-   <style:paragraph-properties fo:text-align="center"/>
-   <style:text-properties fo:font-size="24pt"/>
-  </style:style>
-  <style:style style:name="title2" style:family="graphic" style:parent-style-name="standard">
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-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0.2cm" fo:margin-top="0.1cm" fo:margin-bottom="0.1cm" fo:text-align="center" fo:text-indent="0cm"/>
-   <style:text-properties fo:font-size="36pt"/>
-  </style:style>
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-   <style:text-properties fo:font-size="24pt"/>
-  </style:style>
-  <style:style style:name="headline1" style:family="graphic" style:parent-style-name="standard">
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-   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
-   <style:text-properties fo:font-size="18pt" fo:font-weight="bold"/>
-  </style:style>
-  <style:style style:name="headline2" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
-   <style:text-properties fo:font-size="14pt" fo:font-style="italic" fo:font-weight="bold"/>
-  </style:style>
-  <style:style style:name="measure" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-color="#000000" draw:marker-start="Arrow" draw:marker-start-width="0.2cm" draw:marker-end="Arrow" draw:marker-end-width="0.2cm" draw:fill="none" draw:show-unit="true"/>
-   <style:text-properties fo:font-size="12pt"/>
-  </style:style>
-  <style:style style:name="Default-background" style:family="presentation">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:text-properties style:letter-kerning="true"/>
-  </style:style>
-  <style:style style:name="Default-backgroundobjects" style:family="presentation">
-   <style:graphic-properties draw:textarea-horizontal-align="justify" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
-   <style:text-properties style:letter-kerning="true"/>
-  </style:style>
-  <style:style style:name="Default-notes" style:family="presentation">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-left="0.6cm" fo:margin-right="0cm" fo:text-indent="0cm"/>
-   <style:text-properties style:use-window-font-color="true" style:text-outline="false" style:text-line-through-style="none" style:text-line-through-type="none" style:font-name="Arial" fo:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable" fo:font-size="20pt" fo:font-style="normal" fo:text-shadow="none" style:text-underline-style="none" fo:font-weight="normal" style:letter-kerning="true" style:font-name-asian="WenQuanYi Zen Hei Sharp" style:font-family-asian="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic-asian="system" style:font-pitch-asian="variable" style:font-size-asian="20pt" style:font-style-asian="normal" style:font-weight-asian="normal" style:font-name-complex="Lohit Devanagari" style:font-family-complex="&apos;Lohit Devanagari&apos;" style:font-family-generic-complex="system" style:font-pitch-complex="variable" style:font-size-complex="20pt" style:font-style-complex="normal" style:font-weight-complex="normal" style:text-emphasize="none" style:font-relief="none" style:text-overline-style="none" style:text-overline-color="font-color"/>
-  </style:style>
-  <style:style style:name="Default-outline1" style:family="presentation">
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-    <text:list-style style:name="Default-outline1">
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="75%"/>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="4" text:bullet-char="–">
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="75%"/>
-     </text:list-level-style-bullet>
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-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
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-        <text:p>NumPy provides a multidimensional array </text:p>
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-          <text:p>All elements must be the same data type</text:p>
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-        <text:p>Arithmetic operations work on arrays</text:p>
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-        <text:p>Provides MANY functions that operate on whole arrays</text:p>
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-        <text:p>Lots of ways to create arrays</text:p>
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-          <text:p><text:span text:style-name="T6">A * B</text:span> is not a matrix product, but instead multiples the corresponding elements in each array together</text:p>
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-          <text:p><text:span text:style-name="T6">dot(A,B)</text:span> does a dot product</text:p>
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-        <text:p>Universal functions (<text:span text:style-name="T7">sin</text:span>, <text:span text:style-name="T7">cos</text:span>, <text:span text:style-name="T7">exp</text:span>, ...) work elementwise</text:p>
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-      <text:p><text:span text:style-name="T8">Intro to NumPy: Array Indexing/Slicing</text:span></text:p>
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-        <text:p><text:span text:style-name="T9">Biggest source of confusion: selecting a range is best thought of as referring to the “edges” of the array locations</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T10">Differs from Fortran, IDL</text:span></text:p>
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-          <text:p><text:span text:style-name="T10"/></text:p>
-          <text:p><text:span text:style-name="T10"/></text:p>
-          <text:p><text:span text:style-name="T10"/></text:p>
-          <text:p><text:span text:style-name="T10"/></text:p>
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-          <text:p><text:span text:style-name="T10">For the array above:</text:span></text:p>
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-            <text:p><text:span text:style-name="T11">A[2] = 2</text:span></text:p>
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-            <text:p><text:span text:style-name="T11">A[2:3] = [2]</text:span></text:p>
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-           <text:list-item>
-            <text:p><text:span text:style-name="T11">A[2:4] = [2 3]</text:span></text:p>
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-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T12">Note also:</text:span><text:span text:style-name="T13"> zero-based indexing</text:span></text:p>
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-           <text:list-header>
-            <text:p><text:span text:style-name="T14"/></text:p>
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-     <text:p/>
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-      <text:p>0</text:p>
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-      <text:p>1</text:p>
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-      <text:p>2</text:p>
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-      <text:p>3</text:p>
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-      <text:p>4</text:p>
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-      <text:p>5</text:p>
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-      <text:p>6</text:p>
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-      <text:p>7</text:p>
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-      <text:p>8</text:p>
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-      <text:p>9</text:p>
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-      <text:p><text:span text:style-name="T15">0</text:span></text:p>
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-      <text:p>Arrays</text:p>
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-        <text:p>Building block of many numerical methods</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Row vs. Column major: A(m,n)</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>First index is called the row</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Second index is called the column</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T9">Multi-dimensional arrays are flattened into a one-dimensional sequence for storage</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Row-major (C, python): rows are stored one after the other</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Column-major (Fortran, matlab): columns are stored one after the other</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Ordering matters for:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Passing arrays between languages</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Deciding which index to loop over first</text:p>
-         </text:list-item>
-        </text:list>
-        <text:p/>
-       </text:list-item>
-      </text:list>
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-      <text:p><text:span text:style-name="T8">Intro to NumPy: Array Indexing/Slicing</text:span></text:p>
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-        <text:p><text:span text:style-name="T10">Remember, multi-dimensional arrays are stored in row-major fashion</text:span></text:p>
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-          <text:p><text:span text:style-name="T12">Rows are stored one after the other, within a row, the column data is closest to one another</text:span></text:p>
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-          <text:p><text:span text:style-name="T12">You see this when you print an array:</text:span></text:p>
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-            <text:p><text:span text:style-name="T11">a = numpy.arange(15).reshape(3,5)</text:span></text:p>
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-            <text:p><text:span text:style-name="T11">print a</text:span></text:p>
-            <text:p><text:span text:style-name="T11">[[ 0 <text:s/>1 <text:s/>2 <text:s/>3 <text:s/>4]</text:span></text:p>
-            <text:p><text:span text:style-name="T11"><text:s/></text:span><text:span text:style-name="T11">[ 5 <text:s/>6 <text:s/>7 <text:s/>8 <text:s/>9]</text:span></text:p>
-            <text:p><text:span text:style-name="T11"><text:s/></text:span><text:span text:style-name="T11">[10 11 12 13 14]]</text:span></text:p>
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-        <text:p><text:span text:style-name="T16">Some slicing examples...</text:span></text:p>
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-    <draw:frame presentation:style-name="pr10" draw:text-style-name="P7" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p><text:span text:style-name="T8">More NumPy</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr11" draw:text-style-name="P8" draw:layer="layout" svg:width="26.924cm" svg:height="16.256cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T10">See the tutorial for some other features:</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T10">Shape manipulation</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T10">Merging/splitting arrays</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T10">Fancier indexing</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T10">Other numpy functions/methods</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">NumPy functions page...</text:span></text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <draw:page-thumbnail draw:style-name="gr3" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="10" presentation:class="page"/>
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-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <presentation:settings presentation:mouse-visible="false"/>
-  </office:presentation>
- </office:body>
-</office:document>
\ No newline at end of file
diff --git a/lectures/03-matplotlib/NOTES b/lectures/03-matplotlib/NOTES
deleted file mode 100644
index ac2b1c36..00000000
--- a/lectures/03-matplotlib/NOTES
+++ /dev/null
@@ -1 +0,0 @@
-tutorial notebook exists
\ No newline at end of file
diff --git a/lectures/03-matplotlib/matplotlib-basics.ipynb b/lectures/03-matplotlib/matplotlib-basics.ipynb
deleted file mode 100644
index f15b6489..00000000
--- a/lectures/03-matplotlib/matplotlib-basics.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "metadata": {
-  "name": "matplotlib-basics"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Basic plotting"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "plot() is the most basic command.  Here we also see that we can use LaTeX notation for the axes"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = numpy.linspace(0,2.0*numpy.pi, num=100)\n",
-      "y = numpy.cos(x)\n",
-      "\n",
-      "pylab.plot(x,y)\n",
-      "pylab.xlabel(r\"$x$\")\n",
-      "pylab.ylabel(r\"$\\cos(x)$\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "<matplotlib.text.Text at 0x4274b50>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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nnrKdRApKxSMEtGkDBw/C2rW2k4gYX30FubnQvLntJFJQKh4hoGhR6NtX3Yc4x9ix5ggd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\nTcVDRES8puIhIiJe+/+7rgB0J91a6wAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can plot multiple lines on a plot and set their colors or linestyle -- the latter come through the third argument.  Here we also tighten up the x range of the plot"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.plot(x, numpy.sin(x), \"r--\")\n",
-      "pylab.plot(x, numpy.cos(x), \"b-\")\n",
-      "pylab.xlim(0.0, 2.0*numpy.pi)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "(0.0, 6.283185307179586)"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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uMH9/2gr8yy+iI5GNq3aF2doCwcGyErspXLmz53D1rrAdO6hL26lTmq3euWpX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\nAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can use symbols instead of lines pretty easily too -- and label them"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.plot(x, numpy.sin(x), \"ro\", label=\"sine\")\n",
-      "pylab.plot(x, numpy.cos(x), \"b^\", label=\"cosine\")\n",
-      "pylab.xlim(0.0, 2.0*numpy.pi)\n",
-      "pylab.legend(frameon=False, loc=\"best\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "<matplotlib.legend.Legend at 0x4880fd0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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KMRTwPRdDoFcsK9lODqgyPcLJotSjt6RifM/FEaqYC/vcmR7hZFHi0Issku+5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\nAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "most functions take a number of optional named argumets too"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.plot(x, numpy.sin(x), \"r--\", linewidth=3.0)\n",
-      "pylab.plot(x, numpy.cos(x), \"b-\")\n",
-      "pylab.xlim(0.0, 2.0*numpy.pi)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "(0.0, 6.283185307179586)"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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eudsVIWjAOXEi0LKl7Gh07tgxoFUr2nr55ZdA8eKyI9KEK1eAmjWBc+eA0qVl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\nRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there is a command setp() that can also set the properties.  We can get the list of settable properties as"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "line = pylab.plot(x, numpy.sin(x))\n",
-      "pylab.setp(line)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  agg_filter: unknown\n",
-        "  alpha: float (0.0 transparent through 1.0 opaque)         \n",
-        "  animated: [True | False]         \n",
-        "  antialiased or aa: [True | False]         \n",
-        "  axes: an :class:`~matplotlib.axes.Axes` instance         \n",
-        "  clip_box: a :class:`matplotlib.transforms.Bbox` instance         \n",
-        "  clip_on: [True | False]         \n",
-        "  clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         \n",
-        "  color or c: any matplotlib color         \n",
-        "  contains: a callable function         \n",
-        "  dash_capstyle: ['butt' | 'round' | 'projecting']         \n",
-        "  dash_joinstyle: ['miter' | 'round' | 'bevel']         \n",
-        "  dashes: sequence of on/off ink in points         \n",
-        "  drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         \n",
-        "  figure: a :class:`matplotlib.figure.Figure` instance         \n",
-        "  fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         \n",
-        "  gid: an id string         \n",
-        "  label: string or anything printable with '%s' conversion.         \n",
-        "  linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]         and any drawstyle in combination with a linestyle, e.g., ``'steps--'``.         \n",
-        "  linewidth or lw: float value in points         \n",
-        "  lod: [True | False]         \n",
-        "  marker: unknown\n",
-        "  markeredgecolor or mec: any matplotlib color         \n",
-        "  markeredgewidth or mew: float value in points         \n",
-        "  markerfacecolor or mfc: any matplotlib color         \n",
-        "  markerfacecoloralt or mfcalt: any matplotlib color         \n",
-        "  markersize or ms: float         \n",
-        "  markevery: None | integer | (startind, stride)\n",
-        "  path_effects: unknown\n",
-        "  picker: float distance in points or callable pick function         ``fn(artist, event)``         \n",
-        "  pickradius: float distance in points         \n",
-        "  rasterized: [True | False | None]         \n",
-        "  sketch_params: unknown\n",
-        "  snap: unknown\n",
-        "  solid_capstyle: ['butt' | 'round' |  'projecting']         \n",
-        "  solid_joinstyle: ['miter' | 'round' | 'bevel']         \n",
-        "  transform: a :class:`matplotlib.transforms.Transform` instance         \n",
-        "  url: a url string         \n",
-        "  visible: [True | False]         \n",
-        "  xdata: 1D array         \n",
-        "  ydata: 1D array         \n",
-        "  zorder: any number         \n"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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T0KMHDB+uIh8u6uhFHGrsWHjvPTPz5cILbacJnpEjYccOWLgQyqnVDJiWbkRc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\n9CIiEU6FXkQkwv0/zNTaVDOmiPoAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Multiple axes"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there are a wide range of methods for putting multiple axes on a grid.  We'll look at the simplest method.  All plotting commands apply to the current set of axes"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.subplot(211)\n",
-      "\n",
-      "x = numpy.linspace(0,5,100)\n",
-      "pylab.plot(x, x**3 - 4*x)\n",
-      "pylab.xlabel(\"x\")\n",
-      "pylab.ylabel(r\"$x^3 - 4x$\")\n",
-      "\n",
-      "pylab.subplot(212)\n",
-      "\n",
-      "pylab.plot(x, numpy.exp(-x**2))\n",
-      "pylab.xlabel(\"x\")\n",
-      "pylab.ylabel(\"Gaussian\")\n",
-      "\n",
-      "# log scale\n",
-      "ax = pylab.gca()\n",
-      "ax.set_yscale(\"log\")\n",
-      "\n",
-      "# get the figure and set its size\n",
-      "f = pylab.gcf()\n",
-      "f.set_size_inches(6,8)\n",
-      "\n",
-      "# tight_layout() makes sure things don't overlap\n",
-      "pylab.tight_layout()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "png": 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mGaKWLTUkXCQchFSLqiLsNjy9pMS0wn77zYTWgdstW+D33w/eHji2boWiInO7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\nERFL+z9+HWQPXdWRbAAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Visualizing 2-d array data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def g(x, y):\n",
-      "    return numpy.exp(-((x-0.5)**2)/0.1**2 - ((y-0.5)**2)/0.2**2)\n",
-      "\n",
-      "N = 100\n",
-      "\n",
-      "x = numpy.linspace(0.0,1.0,N)\n",
-      "y = x.copy()\n",
-      "\n",
-      "xv, yv = numpy.meshgrid(x, y)\n",
-      "\n",
-      "pylab.imshow(g(xv,yv))\n",
-      "pylab.colorbar()\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "<matplotlib.colorbar.Colorbar instance at 0x4ddd0e0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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9fvjDH3Lw4EEuvfRSLrjgAj7+8Y8XjmViGPiWt7yF97znPRw+fHjw2YEDB9iw\nYQMAGzZs4MCBA5Mu4wCUX3pjuLXNyTFzL6sqeNWYSrVHLewOyKqe5Y7UNjAN2WImmaXWaxPMxfAE\nQ1V1mDQM7JD6+9QuwcrXVWNY+y8bauDH1CptmmHInNekLtoDX1d9oKy6VMMulUqPbjUk1nNWJlGN\nPBLzOdlISPVxs4FLhbww8Ju7W3xrdyv3vDLVbfr9Pnv27OHf//3fabVaPOc5z+FXf/VXeeYzn2nt\nX0hWn/3sZznppJPYunUru3fvzh3UfMruOBQpgBwyM+1X+l5WGWH51YRKpUc17FAVqZFAqSqV8G7K\nOZrJHPVeG3/WCP9UzkrtJKznrBQpql1As/H4lYj6TEJU95kTczRES7tfO93JwU8Jq1LtEVXr9M1d\nF2zbrw9uXCb0c//vLSXyyOr8S9Zx/iXrBn//P+88OHK8THWbU089lSc/+cnU63Xq9Tq//uu/zne/\n+92FkdW9997LXXfdxd13302n0+Hw4cNcd911bNiwgccee4yNGzfy6KOPctJJJxVcZbf2+6asrXUU\nzXJpPiuhLbMxicqwLnhhTMXvUxVdLQzraDmrFrWoS7UTEc4meEdkSk5PZO0IqbpSZKUrK7UdvNre\nPBuLV5eEswnVSp9arUujomYBlYEhC0dFh4rfpxPGdp/VmLrKnou0bRdjKi0X+qXYn7XFxUJ9VmWq\n27z0pS/ljW98I3Ec0+12ue+++/ijP/qj3GsW5qxuvPFGHnroIR588EFuv/12XvCCF/Dxj3+cq666\nip07dwKwc+dOrr766oKrXKK1TSW+5lpCwQygWjCnPtKJSm/Zi++HCRWvN5KrMpPs9X6XylyMf0Qi\nFDkdZkhY6vdDRnvCaFnIKI6Af0RSmYup97uZqmoPSHJIWF0qoocfJqP5KpuDfRD9epYP9efjVNUo\nNjH6ri0OFpqz0qvbbN68mVe+8pWD6jaqws2ZZ57Ji1/8Yp71rGdx0UUXccMNN7B58+bcsczL8aXC\nvXe84x284hWv4JZbbhlYFxxsKBPy5amszLKgZgJNZTV42SVUwQ9iQtGjypCwapm6UqFZtd8laCV4\nKuTTCUvlrFRRGV1ZqQIzajOD7N6iDqIJfiOhWu8OclX1AWF1houfvT5eEKfjrQChGFVVqillJbxs\n2U2eB63Mc3Um0KPF0Sy3ufzyy7n88stHPlMluBTe9ra38ba3va3U9UqT1cUXX8zFF18MwAknnMCu\nXbvKnrpB2kZ9AAAgAElEQVTGMR9fkEFYQlijw8EuKiEQSgglnh8Nav+lO6V3DYXVohL1EK0EZhmS\nlUlabcbDQFUFS+WxVXK/CcyCtz6hEvUGuTFFlIqoKvQIRR/PT2sNEgqoiPF8lR7xyUmTEXnPz4WF\ni4mexZawUpgeL/0xiUkOamHpq/XX31VzNnCQu5KIMMYLIoKMrKqDZcUpYdVkh0bSodLr4SmyMglL\nhYAdkMZsoFBkpcajk9U68FoJlV6PeuxR9zrUxShRVegRiB5+ECHCGBn64/4qMzWVgF1N2Z6d/pma\nHXQm0MWAWxt4zGNSmJK3X5MY72qJDnU1IoIEL4zxDWWlk1at36PWiwjnEjxVS1QnLBUCHoGkA3EP\nZEZWwge/C15PG4+yMsym1/JmIWwl1NoRtUqfaqUzQlRKWfl+hBfGJKFA+r7dQpUroswHoT9HWzVm\nnbhcOLhQTNPawOkZyTGFSSZQXTEYniqzr/5umkb3AEQg8SoRQdAnFGlV5RBNYcku1X6fajsibCVD\nojIJS5FVF6IuJJkw8TwQVfD0ilhqD63sGmJOEs5JaEdURS/1VQmNqBRZBX28MEIGvr1ivJW3TTVl\ndsqzNejP3pHVQuG2iHHIgT7T5TEou6WrKSPJ7lVigjAi8CMCkVZTNtVVEEUIVcOvZbQ5kLMQzUF/\nDrp96EYQZ++3L6CaQDWC0E/bYKM/S6X3oBqN5qqyGoOBiAj9iKASIcOQxLQsGFt44eU5+h2WE46s\nHCywKC1FVraXOnOxe2FCEEaEQZ9QRIPqNMMwsEMQR9CVKaGYhJWFhf1ZaM1CO4GWTL2fZLdp9CHu\nQsOHwAcxxyhRZT9FVxJEUUZUOln1CUWfIIgIwoi4koxaFvTvN7JGUI99HVGtBFzOak2iKCGjhzSG\nkhBiyGNGCEgAXpDgBxGBp5TVkKxU86MYoYhKqaCMZGQbkjZ0ezCXpK3FKFnFMvOC9qHaBq8Noj1+\nPdEBP4pH7j0oSS8iQq+PH0SphWFMSWmPQQiDrMyW92xduLfY6E1R/XhHVsuColnBPCe71tc2K5i9\n6J6fpCGgl6kXIwysyD5+FEMnU1YGUcl2qpq6EczKVGiZZJUAUkI1U1giO1/oZNUBOhI/irPSEcP8\nWUifQGSE6kd4fjKupEaW2phf2vw9Lw/oZgAXGy4MXJMoa2Q0/jYdDfqL7YPnSXwvJhAxAdGg4ENA\nf9D8OE7tB93RJjsQdaDbhXY8iAjHyErplUac9qULQSdVUiPX7IEXx8PQbzCerIkIX8R4nsxRVNrv\ntmdR6tm6cHEx4cJAh3KwiS7jMyEkPglpLb9oQFQpUaTNixNQ2xVrpCW7afjX6kIry1WpVJROVgCe\nhFaU9qOb2hlGiKoPIpL4cUIo07BPlaIPs5/pnpIJQsh8MTkfD63DksNZFxyYrASEnaiMF1sIiUeC\nT0JAnBFWutmsIgsvSVL26WmtC0kP+r2MhBjNvY+RFRmhJRD20nN9da3+sHlJYhCUaumYPJGRVd5m\noFZ1lffcHKMtNVwY6MAEB+SwS15KKwujhJeSlchaSlwZUcmIIInw4wShEYoiLNmDfjKaylK/62Tl\nZT/VsUaSudwNohIReHGS3lPEaSPBQ+IhEch0nF4BWZVyKJR4dg6LAkdWDownjfXPNeSpqsEuMjJT\nV3pLCcuXMaHMwsAo8yNohCX70I+1/LjWlCdceUEVWXVJz5Ea6Q0JKw0DA5kRpUgVlZeRqJcRF7ac\nlS2Pbn1mEzs5LCKmiaxs88AOK4aSamvwXy1dSiIGRBAPyEFIiScTRJKk3oOItMXDJmNItENlWyIZ\nbsQXY1xb4iVJem+ZqimdrFJ1pX2XidGcU1Erie7IStP8ZsOk6ja7d+/muOOOY+vWrWzdupW/+qu/\nKhyLU1ZTgxKKwZKuSX8d5q1GiEFKRCIRsRwlFY204hyyUspK2o5JkAkjxKeuLxKJSBghKpWvEoNw\nVY5+5VJk5f5dXQksZXUbSHdzueuuu0pd0/0fsFpgEpVyNohhPohBXihriqzUJgTqZ9akTFvCeFP8\nY/tcSsuB7PoDopJyoKQUPCSekCDkKElNJC1h/HRYLiy0bmCZ6jbAoAhNGTiyWg2wvcyDz+RArQxf\n6UzVSImXpISFTlga2SQ5ZFXUpFJWlmuKJL2nJw3i1Jv+fUoTlsNKQJ/PLWomylS3EUJw7733smXL\nFq644goeeOCBwrG4MHA1weIbzUv7iAGDML5LivGPmX5YFvxtHhvrZLm+MG6mCMthdWChPqsyRWTO\nP/98HnroIRqNBp/73Oe4+uqr2bdvX25/R1bHKhwfOCwC8nJW+3f/hJ/s/knueWWq26xbN6yOc/nl\nl/P617+egwcPcsIJJ1iv6chqNcEiecbUjjosNL1lSi/jH72chT5jf+dO3hVcXxq9VTDosDqQR1an\nXvI0Tr3kaYO/v/zO/zNyvEx1mwMHDnDSSSchhOAb3/gGUspcogJHVqsDNkYa5IvEwHI5PJTZL4Ug\n8QQyy23Zlut4onhvA6sFSpBWCbMsAZKeIPEgEbkZq2GC3iTfBKcIpwy20vBloFe3ieOY1772tYPq\nNpAWjviXf/kX/u7v/o4gCGg0Gtx+++3F11zQSByWH/rLrTUph+n1sSYE0hNInahGluowUpPC9Jxi\n/K1+Cp3wDMeF9FJVJ4XILKBDEk0QJFKkxSB0YppIVNL46bBcOJq1gZOq27zhDW/gDW94Q+nrObKa\nGuh+gJyX0pLl1m2hY15xRVY+4EtrNRlfjG9CGjCM5GyblPoChG13zyBVVilBisG4lF116LEXo1+5\nkIP05+Kw3JgmB7sjq6lCibd3rHCL7hFX+xqkRJUID+l5oO8fpTXhp2HgfMnKE4wS1QgBChLPS++d\nEZYak1JaI7OJE+s5uPhwJeHIyoF8JWW8lHqXMdemQCYCKQdLhAfLmWN8YuETiYDAjyBIshqDDIuU\nhume6jWGTRlB9YXM+vEq6TlCL7Q6qGEoSHyPSATpvbP9HwxfPSTC7jidKC5lmU4Oiwi3n5UDpVWU\n+X7qZBWBTIYEJbWQKyIgEgF9LyD0E2RoEEwFRAUCb0hEdSaTVQ0IvfTclLkYKCsZQOx7RF56b22l\nopZX85CKrGyEVYqDnNpaLrj9rByYHAPJYSI6T10lKsGeksJwFyt/SFgEJF6kFUVlUKTUq0AlhEYA\n9SRt6jZqbWBAWsymDjS8tIWV9FzMFkLieQy33QuIBy0LUaWHlDnKynTFW5+N1Z7qsERwYaBDOdhU\nlfGZlCIjqoDxvTnTDYUT38tKtsthNeUqiCpUst8b/bSppVoj1W2AhkhJrRFCpZKei95CkIEg9j36\nIhhsrjxKXCmpSins38v8jg4rDlc+fk2iSA3kLXIhVVc6QenhUwxJIogTn0im+SlFVPou6LHvIzWS\nGpBVDcJaGtLVE2j201sKxsmqCdR9qFYhqKXnDpJYqlUg8X3t3sHI75EMiKVPkhcGjjyG3EU+E56f\nY7nFhMtZrTnobDPmNWdcZqimzEvaR8b+UUnsEccBURLS90L6IhgthCVC4sCHmhhNPDVA1NPf/RrU\nImiKoXd0jKwEVAPwq+l5ok4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AwxAfDukertOenWG2t47Dyfq0sZ7DHMdhjuMJjuMQx/OE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\nVQFHVg4ODqsC/z8ZlMRENGN+cgAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.contour(g(xv,yv))\n",
-      "pylab.axis(\"scaled\")   # this adjusts the size of image to make x and y lengths equal\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "(0.0, 100.0, 0.0, 100.0)"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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ldgo7e+jcE7JP17zHzsGBrjfcQMru3TbXJ+XbbwkZbbb2YHaicTDZZZVk4Ujt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\n"
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Surface plots"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "matplotlib can't deal with true 3-d data (i.e., x,y,z + a value), but it can plot 2-d surfaces and lines in 3-d."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from mpl_toolkits.mplot3d import Axes3D\n",
-      "fig = pylab.figure()\n",
-      "ax = fig.gca(projection=\"3d\")\n",
-      "\n",
-      "# parametric curves\n",
-      "N = 100\n",
-      "theta = numpy.linspace(-4*numpy.pi, 4*numpy.pi, N)\n",
-      "z = numpy.linspace(-2, 2, N)\n",
-      "r = z**2 + 1\n",
-      "\n",
-      "x = r*numpy.sin(theta)\n",
-      "y = r*numpy.cos(theta)\n",
-      "\n",
-      "ax.plot(x,y,z)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "[<mpl_toolkits.mplot3d.art3d.Line3D at 0x530e110>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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FXAtAlZMuUGi7nM/nbX2PXmzD9WRLhXbcEJQT0iUdbC6XgyAIqK+vR9euHFav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\n"
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from matplotlib import cm\n",
-      "\n",
-      "fig = pylab.figure()\n",
-      "ax = fig.gca(projection=\"3d\")\n",
-      "X = numpy.arange(-5,5, 0.25)\n",
-      "Y = numpy.arange(-5,5, 0.25)\n",
-      "X, Y = numpy.meshgrid(X, Y)\n",
-      "R = numpy.sqrt(X**2 + Y**2)\n",
-      "Z = numpy.sin(R)\n",
-      "\n",
-      "surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1) #, cmap=cm.coolwarm)\n",
-      "\n",
-      "# we can use setp to investigate and set options here too\n",
-      "pylab.setp(surf)\n",
-      "pylab.setp(surf,lw=1)\n",
-      "pylab.setp(surf, facecolor=\"red\")\n",
-      "\n",
-      "# and the view (note: most interactive backends will allow you to rotate this freely)\n",
-      "ax = pylab.gca()\n",
-      "ax.azim = 90\n",
-      "ax.elev = 20"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  agg_filter: unknown\n",
-        "  alpha: float or None         \n",
-        "  animated: [True | False]         \n",
-        "  antialiased or antialiaseds: Boolean or sequence of booleans         \n",
-        "  array: unknown\n",
-        "  axes: an :class:`~matplotlib.axes.Axes` instance         \n",
-        "  clim: a length 2 sequence of floats         \n",
-        "  clip_box: a :class:`matplotlib.transforms.Bbox` instance         \n",
-        "  clip_on: [True | False]         \n",
-        "  clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         \n",
-        "  cmap: a colormap or registered colormap name         \n",
-        "  color: matplotlib color arg or sequence of rgba tuples\n",
-        "  contains: a callable function         \n",
-        "  edgecolor: unknown\n",
-        "  edgecolors: unknown\n",
-        "  facecolor: unknown\n",
-        "  facecolors: unknown\n",
-        "  figure: a :class:`matplotlib.figure.Figure` instance         \n",
-        "  gid: an id string         \n",
-        "  hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ]         \n",
-        "  label: string or anything printable with '%s' conversion.         \n",
-        "  linestyle or linestyles or dashes: ['solid' | 'dashed', 'dashdot', 'dotted' |         (offset, on-off-dash-seq) ]         \n",
-        "  linewidth or lw or linewidths: float or sequence of floats         \n",
-        "  lod: [True | False]         \n",
-        "  norm: unknown\n",
-        "  offset_position: unknown\n",
-        "  offsets: float or sequence of floats         \n",
-        "  path_effects: unknown\n",
-        "  paths: unknown\n",
-        "  picker: [None|float|boolean|callable]         \n",
-        "  pickradius: unknown\n",
-        "  rasterized: [True | False | None]         \n",
-        "  sketch_params: unknown\n",
-        "  snap: unknown\n",
-        "  sort_zpos: unknown\n",
-        "  transform: :class:`~matplotlib.transforms.Transform` instance         \n",
-        "  url: a url string         \n",
-        "  urls: unknown\n",
-        "  verts: unknown\n",
-        "  visible: [True | False]         \n",
-        "  zorder: any number         \n",
-        "  zsort: unknown\n"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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P43keiUSCuro6SkpKWtQnDxg/ejSnnHoq+fkNP8K6ujqSySTFxcUNtltbfPvtt1QaZ/tO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\nL+xIZICzBkLILul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05LHxAdKvxkN3LTIlXTvQPGvvQ0b7fOReFveuWA9J8TZc+AKkPvgW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\nQmCC\n"
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Plotting on a sphere"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "the map funcationality expects stuff in longitude and latitude, so if you want to plot x,y,z on the surface of a sphere using the idea of spherical coordinates, remember that the spherical angle from z (theta) is co-latitude\n",
-      "\n",
-      "note: you need the python-basemap package installed for this to work\n",
-      "\n",
-      "This also illustrates getting access to a matplotlib toolkit"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def to_lonlat(x,y,z):\n",
-      "    SMALL = 1.e-100\n",
-      "    rho = numpy.sqrt((x + SMALL)**2 + (y + SMALL)**2)\n",
-      "    R = numpy.sqrt(rho**2 + (z + SMALL)**2)\n",
-      "    \n",
-      "    theta = numpy.degrees(numpy.arctan2(rho, z + SMALL))\n",
-      "    phi = numpy.degrees(numpy.arctan2(y + SMALL, x + SMALL))\n",
-      "    \n",
-      "    # latitude is 90 - the spherical theta\n",
-      "    return (phi, 90-theta)\n",
-      "\n",
-      "\n",
-      "from mpl_toolkits.basemap import Basemap\n",
-      "\n",
-      "# other projections are allowed, e.g. \"moll\"\n",
-      "map = Basemap(projection='ortho', lat_0 = 45, lon_0 = 45,\n",
-      "              resolution = 'l', area_thresh = 1000.)\n",
-      "\n",
-      "map.drawmapboundary()\n",
-      "\n",
-      "map.drawmeridians(numpy.arange(0, 360, 15), color=\"0.5\", latmax=90)\n",
-      "map.drawparallels(numpy.arange(-90, 90, 15), color=\"0.5\", latmax=90) #, labels=[1,0,0,1])\n",
-      "\n",
-      "# unit vectors (+x, +y, +z)\n",
-      "points = [(1,0,0), (0,1,0), (0,0,1)]\n",
-      "labels = [\"+x\", \"+y\", \"+z\"]\n",
-      "\n",
-      "i = 0\n",
-      "while i < len(points):\n",
-      "    p = points[i]\n",
-      "    print p\n",
-      "    lon, lat = to_lonlat(p[0], p[1], p[2])\n",
-      "    xp, yp = map(lon, lat)\n",
-      "    s = pylab.text(xp, yp, labels[i], color=\"b\", zorder=10)\n",
-      "    i += 1\n",
-      "\n",
-      "# draw a great circle arc between two points\n",
-      "lats = [0, 0]\n",
-      "lons = [0, 90]\n",
-      "\n",
-      "map.drawgreatcircle(lons[0], lats[0], lons[1], lats[1], linewidth=2, color=\"r\")\n",
-      "\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1, 0, 0)\n",
-        "(0, 1, 0)\n",
-        "(0, 0, 1)\n"
-       ]
-      },
-      {
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x5d12490>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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GvPnmm2BZNqj5bzW+lpJWLBbjlVdewVNPPYWHH354SuuInp4ecByHVatW+T2n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Bc0NjbiS1/6EvLy8m58sMsFDA6Ov4aGALMZZDKBNRoRPToKv9kMqdcLzmZDlM8HjI1BNMsauhQT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\nRAgzIqKNECHMiIg2QoQw4/8A9nRR62Rx5KoAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "also, if you really are interested in earth..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "map = Basemap(projection='ortho', lat_0 = 45, lon_0 = 45,\n",
-      "              resolution = 'l', area_thresh = 1000.)\n",
-      "\n",
-      "map.drawcoastlines()\n",
-      "map.drawmapboundary()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "<matplotlib.patches.Ellipse at 0x554b450>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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5ZMGCBVAqlSYFr5gCmwjtu3fvYGdnh6VLl9qi+QyFeFOegWWFJyKUK1cuE0dn\nGpgTPcsuLyfkj7kS9ujRI9MSMycmJkqCEKy5V8+MfOJ16+zZs1GrVi2uJsfExKBatWrw9/eXbdMo\nVqwYGjRoYLVximEToW3fvj18fX1t0XSmgIjw7bffSlwd2Vef5cDNDmBjZkXTESIpKQnu7u6YNm0a\nXrx4gVGjRnEHf3Ex5ltsbTA2zebNm9vEuUbzo6xSqeDu7o5mzZrxY8nJyVAqlbyuMSvx2bNnoVAo\ncPv2beuP19oNhoeHQ6FQZLs8qoYgTsilmW0+O0E8bl3GJ2aZViqV+PzzzyX1Fy9eLCGE15e9T6VS\nWT3M8NSpUyAimwgAoC20LDRTV59My3JxcTEayvf555+jYsWK1h+vtRusV68eSpQoYe1mMx3JycmS\n1JnWNohkBMRj18XsyMLbnj17JmG5ZCU8PBxVqlTh/5ctW1aSwY45gQwYMMAmY7eFpxmgVr+J1Ck5\nWcKwQ4cOYebMmRg0aJBW/SlTpqBcuXIoWLAg9u/fr7fd27dvQ6FQmO06qg9WFdrQ0FAoFIoswaOT\nAykYrYohDYE5L7x9+xY9evQAEUly/167dk0SKMBKoUKFJEEDutwHLUXz5s1tqtlEREQgOjqaf3gK\nFCjAY6Lfvn0rqZueno5SpUph6tSpICKMHj1ab7sNGzZEsWLFrDpWq96FChUqoHLlytZsMgdWAkuC\nZUhombvi06dPce/ePZ6WRdc+uyAISE9Px4YNG3ibFy9e1Jvy0lKoVCrs2rXLJm1rQhAECILAP1y6\n1tGnT59GoUKF+JJBn0X5xYsXUCqVVrV9WE1oz5w5A4VCYbX0DzlQR7zExsZKgrmHDx9uVlssxpTN\nWLr8d1n4HzM0bdmyBUTqtCppaWl49eqVzpBIFj3j5+dn1tiyKtg917XFxK6Z0R7VqFFDbzudOnWy\niLBBa1zWaqhIkSJZOulxVkdCQgK2bdumpXrqK6ZCc3+WiLRSdjJn+gIFCgDQTTxARDh16pRW+6tX\nr+a/h4SEYO3atRJ2i+wItt01depUrd9iY2NlP5PY2FjY29vLIjCUA6sI7datW6FUKjOdWjK7Qhf3\nlWbRTFNijtP8b7/9prPtrVu34vjx47h16xanm+nduzfS0tIkzgViEnVdXlRMnRSXoUOH4sKFCyYz\nOmYVGBLI48ePI1euXLJyC33zzTdwdXW1Sl4gqwht/vz50alTJ2s09clBV5KvZ8+eafnXPn/+XKue\nObl9BEHayZe2AAAgAElEQVTgTvHitjS9vVgRBAG//vorV38XLFjAf9P1AqpUKm5lF9O6TJgwwex7\nlJkICQlBvnz59N7rDh068Gs0RCGUkpKCXLlyYfbs2RaPyWKhvXTpEhQKRc4sawY0038UK1ZM54MX\ne2Vt3bpVco6dnZ3Br/f79+8xcuRICTsiqx8VFYXSpUuDSO3OGBMTg2HDhqFnz56SGSYlJQVEarfN\nkydPIjY21qiF2Fw13hQw45etMXr0aOTNm1dnvmFdLB760Lt3b6s4HVl8V+vVq2eTDeSPHZozmqHo\nGVY/KCgIwH+zM4s86t69u8RAJHaxEyfyEpOjjx071uA6NTo6GsePHwfwX3LugIAAHD16VNb1scgg\nTY5ia0Ksmdga/fr10+lM8eHDBy3NRB+ioqKgUChw7tw5i8ZikdBGR0dDoVAYdTrPgRTih8wc080F\ncwwg+o8/mf1fu3ZtLFq0CEQkIcYjIjRp0gRz587Fn3/+iU2bNulsWxAEDBw4UHKeKVs6zP1QqVRq\nxcZaA2fPngURwd/fX1ZGw+vXr4OI8Ndff5nc17lz51CiRAmdWo34/hij96lUqRJq165tcv+S/iw6\nOQNUoI8FYuGS81U21I7mUoS9jETqxM1s059IvfHP/p43bx7/29hLHhwcrDVeU3PxqFQqfP311/z8\nJ0+emHy9xsBcTL29vY3WZbNimzZtTO5HpVLBy8tLy61R0zZg7INw+PBhKBQKi6LfLJI4Z2dndOnS\nxZImsi1u3rxp1Hn97Nmz2LRpk9bLb4gU4PLlyyAi7Nu3T+fvI0aMAJG2qyDz0lm7di3PyVOmTBk0\na9YMRISrV6+iS5cuINL28NEFNtYLFy7wvD6WIDEx0aI22H0MDw+XHBdnctC15tTE8uXLsXfvXrPG\nwO5lUFCQxPdanDzt559/NtqOu7s7hg4datYYAAuEdufOnVAqlRlKu5LRYKb8KlWqSL6wYnIwTeza\ntUunFZYVQ07vAwYMkNTVFUgtTtLcp08f/hILgoBmzZqhX79+fNz29vZwdXXF2bNnAagTJDOhDQ0N\nRcGCBfWuB7OaFiW+LyzFCAMLbjhy5IhNx8DobsqWLQs3Nzc0btwYkZGROHnypGxjFKBOjZInTx6z\nx2H2UylXrhwaNmxodsfZAZoC16dPHwiCwJksNAm/dG24E6kjYrZv367lzCAGU7MKFCigM4YXgFbi\nrgoVKsDDwwMtW7bkmQvEJSwsTEuVJiIt1VcXWKKzrALNaxOTlrMk4hs2bLD5GFiQSGpqKiZPngwi\nkpDXExkP0k9KSoJSqTQ7Ptmsp/L8+XMoFApcuXLFrE6zA9g2h2YxpFlo1pWzkZ6ens7z5RARTpw4\ngZIlS4JIbRUWCx1TBZkDxKlTp/Dq1Svs2rULvr6+qFChAi5fvowyZcrgp59+MjpG1q8usFmlefPm\nRq8hoxAaGsrz8IjtAoxaVXMGtibEfbJMCyqVCnnz5kWBAgUkv8vhk2rcuLHZNLhmCW23bt3g7+9v\nVodZGeJE1IzUWlwMsfKJ1VY5apIupwpWatWqJdlKENOWpKamcgqYoUOHcuFWKpVYsWKF0WtkoXX6\n1sxiMEL0rAZxTtnr16/zjASWrBP1YfHixdyOoO8jTkQSg5uc4IBr165BoVBordHlwKwn4uzsjPnz\n55tzapaDOH0GK8wYRETYtGkT4uPjUaBAAfTs2VNvO+Lze/fubbRfcX3x/un8+fMRHx+P58+f81Qb\nN2/elJz78OFDnS+Qvb29xTmDxGDePlkVBw4cQHR0NJ4+fQoiwsCBA63eh/j+Tpkyhc+ymkVTsOUk\nWPP395ed7EsyJlNPOH/+PBQKhWRWyq5gjAhySsuWLfVuk7CIDyLC/fv3jfbLQt6Y9VlTrRL3W6pU\nKRCRJMsgEfE1J5E6lcXBgwdlzfCmgKW3nDZtmt4cPQsXLpSMN3/+/Jg0aVKGJslmW1y2iOMWq755\n8uSBSqVCYmIi3rx5Y/B9kSOMo0ePlrVVpQmTn3CXLl0+CmYK8Wy6bds2JCYm4q+//pLceOZnK+YK\n0oVOnTpJBEZMbK4JsVrMQKSd/1WziH272bFHjx5hw4YN/KtubaEVBAE1atTg7f70008QBAE7d+6U\n9aFTKpUYOnSorK0YMUxN4vXhwwercSFr4siRIyhRooTeeyu+XkEQJJFaxsD8yU11ATb5CXt4eGRa\nBmxrQWz4ISLExMRww4u4zJw5E127djXKKjlmzBgQqa3JzIik78GxLzTbhD9z5gyICHPnzjUoAMxS\nvXfvXr1tM9b8OXPmWOEu/QeVSiX5MLEyevRonWogU1d1FWOZ1tm5cqhp7927Z3UqF02EhIQgICCA\nZw1kdDTM7ZTtGPTs2ZMbZq9fv46oqChZ7ZsjTyYJLVvwZ/fgAPYCdejQARcvXoRKpYKLiwuqV68O\nANyL6Pr168ibNy9PMqUPYWFhOl9QMTmarv7Ff+vKqSouzHAkPqZrRhJncpfjRGEKmNsgEWHYsGGy\nzklPT8esWbNQqFAhfm7x4sX11mdBFHLUa5b+VE6uHXORnJwMPz8//Pnnn1rGRjEKFy5slgZqjuZq\nktCOGjXKLB08q+HPP//UEoq6detqGROYo4QcpKWlIT4+Hvv27UPfvn212mcufFeuXJE8dOa4zwgE\nDAluaGgo3xskIr1s9+wDoG/bx1JYooZ37twZRPqTdIm5hY0lDmeuoV5eXmaNRS4qV66MxYsX48CB\nA3xsvXr1ktR5/vy5Wcm6zbERmXTnixYtih49epg8sKwKtsfn5+eHpUuXSgTk33//5ceMQczaIC41\natTg2ybt27eX/Cb2UTUkqO3atUOlSpUwe/ZsfPHFF1w9IzLsAWRLQ5AlQsvOP3bsmM7fBEFA7969\nQSTPqPfPP//gxIkTZo/FGJgNQtN5xVqJxgDAyckJS5YskV1f9p1PSkqCQqHgSYU/JrAHUblyZSQm\nJiIxMdGgq6IYuihZ2FYNy3Z37Ngxye9svZOWloYxY8YgMDBQ8vvFixc5o763t7fkN+b0UbduXbx5\n88YqTAimwlKhlcPW+PLlywy1QBuCPiOhtVC/fn2+NJMD2T0HBwfD2dnZrEFlZSQnJ/OHsHLlSr6t\nwyx748aNM3g+U2+/++473s6QIUPQsWNHXoelnhg1ahSKFy/Onc0nTZokeQkCAgJQpUoVzmTftGlT\nFCtWDF5eXujcubNk2ycyMhJEamIxSzFp0iT0799fdn1rvLRZRSDl4u3bt0hKSkJERARPq2ItrFu3\nDo6OjrLry+65atWqaNSokVmDyorQ5yfMYoOZ0c1QPhZ9xGdEhJEjRwLQ7fnEggbEx1q2bAkAPELH\nGAeUIAjYs2ePVTiGTRXCunXrom3btmb3xyzmtkpQZWuwjPGRkZFWCZhJS0uDUqmUTTAg+0k5ODjg\njz/+MHtgWQ2aSaKJ1ImaU1JSkJKSgjlz5sDV1RUVKlTg5wiCgFevXvFZgkXN6CqMV4kVFha3evVq\nAP/FgbZt2xaVK1fmXlQszMuY5Xfz5s1WmfHE7pIZBXP6e/jwIRo0aMAjljIb4mdrbE09ZswYXL16\n1WCd0qVLy47zlXXnbt68CYVCkSnrJ1vjwYMHCAoK4lm/NQvbV1y/fr1Bg5Gjo6PWsVGjRsHT05NT\nrjBh/+mnn1CiRAlcuHABuXPnhre3N1d9GdewsX0+cT8vX740+/pZKhAi45nezQHjmxKnUCEynb+Z\nGQVtmffVFNy+fRtbtmyR7BToo9aR85EaNWqUbN5oWUI7bdo0eHh4yGowOyAyMhJ+fn4IDAzE27dv\ncfHiRaxcuRJPnz6V5Ko5dOgQjh49ijJlyvBj3bt358mNWRGfQ6R2R9Rn+RQEAYUKFUJoaChUKhWm\nTp2KVatWSerIMf+L03OI17rm3Atx6g9rg7Urvh/Tp0+X7XzAkJKSgnXr1kn20DMbf/31F1atWsWT\ndRGRTk2gefPmRuXn1KlTsLOzk9WvrKfUuHFj1KxZU1aD2QGaM2L58uVBpI6LPHv2LDp27IgCBQog\nICCA11EoFDh58iT3D05PT8erV68wduxYPH78WPLinzhxArGxsbh37x62bduG3bt3c88hlnXNGoaY\niIgIvj4WW7vnzZsnu43FixeDSB3uZ6q7oRwY22s1FYcOHULfvn2tmljaXGi+R25ubujVq5fOxGzG\nnrdKpYJCoZCV1E2W0Pr6+hpMMpTd8NVXX4GIJM7u4tQbYmNRREQENmzYIPsl8fDw4G24u7vztaxm\nsTbE3kpy2xdbzm2Vg8faYNtoc+fOzeyhIG/evHqXS+YsJd3d3Y3mvQVkCq1SqcwyBgBrQbxm3bt3\nL1JTU3H69GmD7BJy8Pz5c84BxR6c2PlCqVTalKuX9RMfH4/09HQsXbrU4Fc+u6XuePv2LYhs5+1l\nCtgWEFtri4V28ODBJrdXo0YNNGnSxGg9o0L7sRqhMtpieu3aNZukgNTVj76vP/NCCg4OxpAhQ2w+\nFlvh5MmTWUozEGtnYuoZUyHXGGW05exkhBIEgaekqF69OhITE/XWbdOmTbZNVWEMHz58wMyZM7F/\n/37ExsZi1qxZGaKiWwoWLZWVBFIOypYtK7mfzOHGVJw5c0aWMcpoy9nJCMUI11hp166d3rr169c3\nyETxsUGcaaBx48ZGMxpkBlhUlJubW2YPxSR8//33EiFlBHymfnzkGqOMCq2fn1+2MUIxloGkpCQc\nP36c75XqAhGhTp06GTzCzAMTWHPY9TMKzOGlQIECFtsWMhLbt2/X2hFgFnlTIccYZVRolUqlzQON\nrYHIyEi4u7vj77//5sdYYLtmnhoWh8mSJ38KYEKb1VlHsrL6rgtpaWncq0zMIc2u4cyZM7IIzBnk\nGKMM3pm7d+9mGyNUt27d0K9fP63jR44cAREhd+7cEAQBN2/e5DfU2nuIWRni0MOsjLNnz2LlypWy\n2CKzAvRt9bD/GUuj3D3wUaNGIX/+/Ib7NPTj+vXrs0VkD0vXIE7VIMbRo0dBRAgMDERAQECW2OPL\naDDGR1uFVrLACLlePR8LZsyYgT59+mhNBOfPnwfRf3HUy5Ytk9XeunXrjMqcQaGdMmUKfHx8ZA4/\n45GcnIzq1auDSJtmVBM3b95Eo0aNrM6flAM1MiPwIKtDPAPXqFFD1jlXrlyBUqk03K6hH7/66it8\n9tln8keZwahZsyaItJMy5UAN5p+szxhnbVy6dMks90IxOVydOnV4+pXsDrHQuri4yFKRGRG7If9z\nJRnAixcvyNfX11CVTMU///xDRESFChXK5JFkTbi6uhIRUb58+TKkv5o1a5KTk5Ps+iqVisqXL0+u\nrq60Y8cOIiJycHCg9evXU2hoqK2GyREbG0vJyck27yd//vyUkJBAV65cMVrXycmJlEol3b17V28d\ng0IbFRVF/v7+po8yA5CYmEgFChSgAQMGkEKhMKuN1NRUmjJlCgUHB9PLly+tPELzkZ6eToIgWNyO\n+mNPNH36dIvbshaSkpJo586dpFAoyN7enu7cuUNERNWqVSMiotOnT1NUVBRVrFjR5mNxc3MjZ2dn\nCgkJsWk/c+bMISKi1atXy6qfK1cuun37tv4KhqZqHx8fTJ482WS1ICPAeJUsUaNIw/KnyRahi9M3\nLS1NVoIlS9CjRw8QEQ4fPmx2G+Igf1PJv20JzXu+dOlSvek2bQ1vb2+UKlXKZpTAJFrjx8XFyWbq\n8Pb2xtSpU/W3a+hkZ2dnrFu3zoRhZhzu37/Pb4rc/da3b9/ir7/+QlxcHI8WWbZsmSShU6dOnfDF\nF1/wBFzff/89P58lviIiODg42OrSJGTfpu6RC4IAlUrFsyNY+mGzJsREAsa4pA2B0dXIYWvMTIiF\n1hSUKlXKcN4oQycrlcoszb548eJFEBnPm6JSqfD777/zm6hQKODp6YkaNWpwwwnjjLKzs8OCBQuw\nZs0aTsjGAq4ZoRcrp0+fttm1vX79mvfTv39/2bN7v379sqyDAhtTlSpVLGqHCb8t4n+tCX3PYN++\nfQZ3MerVq2eQj03vU2VUnVk90zv9//6rPqhUKjRv3hy1atVCcHAwkpOTZTFDqFQqvHjxAvXq1ePc\nWJUrV+bEa0SEVq1aWe069EGc1c8YxPGx5szStgZzpHdxccnsoWQI2HakGOKE4fryD3Xv3h2lS5fW\n267eNyEkJAQKhcLM4WYciNTcwLt379aZ1e7hw4coWLCgxPvJmLoo5rktUaIEZ8kbOnQoRo8ejZSU\nFAwePNjmjieCIGD37t2yhDY1NZUH3Ds5ORn8kFkCSwMNxowZk2V4nmwNXc8tNDSUH+/atavO8yZN\nmgRfX1/97er74Y8//oCTk5OZw5UHQRAsciU8dOgQvwGVKlVCgQIFcO3aNQiCgAcPHmDnzp2cz4mB\nEbBt2LBB75hYmxMmTEC+fPmwePFiAEB0dDQ8PDwQERHBM5JbE4Ig4NixY1i4cCHS09Pxww8/6FUp\nBUGQfHzEDBm2cDsVE6rbKnB+7969Vs8/lFlgNhfN5G2JiYlwdXXFvHnzYGdnh5SUFMTExEjqrFq1\nyqA2ovetW7FiBVxdXS0cumEw5kE5eUXT09Px119/4fr160hMTERQUBB8fX3Rpk0bnkx469atIFIn\nV2bCuXXrVsl6ME+ePCAinDt3TmcfT548kagvkyZNkhid/P39ERERwcnMrcXosXz5ci3LKiv37t3D\n3bt3sXjxYq2sfAUKFACgzk9UvHhx7N27V6vttLQ0ntENUFO2+Pj4oGLFiqhQoQK+++47g4IuTkVC\nJC9hsimIioriWfl++eUXq7adGYiKijL4AW3UqBE8PT1BRDy37p49e/jvO3bsMDhh6hXahQsXIm/e\nvBYO3zDu3LkDHx8f7Ny502C9hw8fwtvbG+XKlcNnn32GXLlyoW7duggPD8fVq1fh4+PDv2j37t1D\ncHAwkpKSdJry4+Pj9RowgoKCJDM3oPYqcnFxQUREBAD1NhhLw8joQfUlk5ILFnVERBJqGk9PTwQH\nB+sV5mHDhsmynDM2BU2e5oIFC/KXR9/syT5OzLJORLLIx+RCrNm4uLhkqe0pc6BJTq8LmzZtgrOz\nM08fwwqbXPbt22cw44BeoV2wYEGGBCMTEerVq4dNmzbhn3/+0fp969at8PT0NJigSO66zxg6duyI\nNm3awMfHB7Vq1eLHO3TogN9++w2AWlDF+UQt7Vc8izHVkGkg4nLt2jWkpaVxAZTry8og5nXu1q2b\n5Dd3d3ed9x4AlixZgvLly3MmSuYAb81dhfXr12Pbtm3ZjrFCE2JGTCLSy/iiLyk3s0McPXrU4Jai\n3rdt7ty5GUIzc+/ePcycORNEhPXr10t+i42NhZ+fHy5dumS0HUap0rdvX1SrVg3NmzfHyJEjMXny\nZM6saAjM8jpw4EAQSbmEmWAFBwfj4cOH/CbHxcXh4MGDICKj4VS6ILYkijfTV6xYASJtsjBWChcu\nbNLszu6v5gdm1apVPHRMHIcsxpAhQyTxoD4+PgYtn58y2P1dsmQJiEhvmg9dqVaJiE8MZ86cgb29\nvf5+9P0wbdq0DMtFm5qaiqdPn+LRo0eS4wcOHECpUqVktxMWFoa5c+di9erVaNOmDaZNm4bJkyfD\nz88PtWrVwsKFC7UW/QyzZ8/mN0/XxjYzer1584avnZmDwKhRo0BkenwuY9fQRdxdpEgREKnZNTQf\nrqlrShZYIc6uzuKMhwwZgmbNmulVs4cPHy5hLrGGRvOxIm/evNi6dSu+/fZbEBl2atGVkYJInTz8\nwoULBkMc9d79qVOnZojQPn36FOfPn0fhwoWRK1cujB49GsOHD0efPn3g6elpdL0rB/Hx8Vi8eDHq\n1avHt4c0wdZ7vr6+Ot3qWLKtsmXLIi0tDZ07d0aXLl34g3FycjKJoeDGjRtwd3fHDz/8oPP3lStX\nIl++fPjll1/4eF69egUiQv369WVHNjHNQLxnywSWiCQGKl04ffo0r8tm2azOfpHZkPNhYwmxdZXL\nly8bDM/T2/KsWbPg6elp/shlYNu2bciXLx/y58+PDRs24OnTp5g6dSqCgoKwcOFCm/iEMlW3bdu2\nmDZtGmJjY2WfyzKBP3v2jBuMfvvtN258mDJliqx24uLi4Ofnh549e+oN3NeHpKQkjBs3DgULFpSV\nw+fOnTsgIskHhd2DKlWqGA2lS0pK4txbTGBNTenxKeHZs2cgIqxYscJoXc3cw6ycO3fOvJk2KCgI\n7u7u5o3cCN69e4fjx4/D09PTaPC6LXD+/HmsXLkSDg4OIFLzASclJRm1XKpUKlSuXFkrKzhLwiSX\n15ipT5bsSc6ePRs1atTQ6VCiicGDB2uNLzQ0FK6urnjy5InZY8iBFGLLvxybgyAImDFjhpbQbtmy\nxbw1bXBwMPLly2fe6A2AOXsT6XdwyCikpaVh3LhxcHBw4Ll42IwkCAJ+/vlnjB8/HvHx8fycsWPH\nwsvLC+XKleMGHnt7ey116M2bN/jw4QPevHmDiIgIyXo3Li4ORGRRPhpBEDBo0CB4enpi69atBusy\nDUFzjVWvXj0QqXMZ6Vvr50AedKVO1Wfc0wSbPFhZunSpeVs+S5cuRZ48eUwfvR6Ivyo9e/bMMpEn\ngFoFFCeZ3r9/v2R/smLFiggKCpJ4KNnZ2SEpKQnnzp3jVCss9IplaRcXBwcH7Nq1C4A6TaKzszN6\n9uwpmSnj4uKwefNmDBw4EL1798bChQsxduxYvTM486Cyt7dHSEiI3utLTk5G4cKFcfDgQa3f2B5x\nz549sXfv3iz1XLIT1q1bByKSpA5dvny5wXPS09MRGRnJU52wMn36dOTKlUvveXqF9vfff0fu3LnN\nvwod7RUsWNBoAt7MBEv0vGrVKgiCgLFjx6Jq1apYuHAhSpQowW9qzZo1tfiD3d3dsWDBAgD/RR+x\ncvHiRUkSaM1SsWJFDBo0CC4uLmjdujV+++03rFy5Ev379wcRoUWLFlqWdTFGjBiBoKAgg9d27Ngx\n+Pn56VwHs3F4eHjgzz//NOPO5YDdw9u3b/O/y5QpY/CcX375hWtAmu+EWR5Rp06dMqhXi5GWlmY0\nIRJ7+bIyatWqJWFqTE9PR7169TB48GC4uLigf//+CAoK0hkm17RpU76WYX6nGzduxMqVK5Gamoq1\na9fqFdqff/4ZEydO1OmVdPPmTdSvXx8KhQKzZs3iVmM2IyYkJKBLly6ysq3NmDEDAQEBWk7/4pmB\niLBt2zaT7ltWBdOW5syZg3///Rf169eXbHtZE+wenjp1iv9tLLM72zoUa3UshNSQPUmv0LJ4TjnO\n50wlMGTIkWMGz2wEBgZixIgRkmNi1eXu3bt6z923bx+ICA0aNOD1WbZzlUrFE1Nv3rwZUVFRuHHj\nBjZv3ixrXImJifjjjz942ozSpUuDSJ32xMvLC+3bt5e9d9u5c2ctVoQ9e/ZIPiK2ihDKaDAa02PH\njnE2EGvvSIiXQh06dMCFCxf4GvXChQsGz2X2HbadOHPmTC53xYoV03ueQSkiUjurG4MgCEb3+1q0\naGFW+r+MQHR0NBYtWgR3d3fs2LFD6/f79+8bjU1lL4hmuXTpksRIwQTZXDx79gw3btzAgwcPsH79\ner1q8/z58zFixAitNWp4eDgfC3MOYWOvWrUq9u/f/1Gua6Oiooy+o+ZATB07adIkCIKAHj16oH37\n9kbP/fHHH/lEplQq0bdvX/58DLmpGhVa5lplCdhLwRggshp69uyJJk2a8FSQ5oAl/2JsF6x06tQJ\nwH+ahhw6U7G12lyMHz8eRISrV69KjicnJ0OpVKJWrVp8u0dMb5NjRTYPbD1rClhSc0Ad2fP48WP+\nHnXo0EF/X8YGMnToUJMGogt///03ypcvb3E7tgKzCj9+/NjsNpiKU79+fS4AnTt3xq1btwD8lxzM\nGNP8b7/9BiLLw99UKpVOVfDcuXM6lylszHXr1rWo38zGzz//nCnLMG9vbxw4cMCkc9hSSgymHhv6\nuBukUM2dOzeFhYUZqiILd+7cyRBKTHMxefJkIiIqUaKE2W04ODhQuXLl6Ny5c0SkpmfduXMnVahQ\ngYjUHNJEZJT71tvbm4iInj59alL/8fHxNHr0aHJwcKD379+TUqkkPz8/rXr169fXeT6jkH3w4IFJ\n/WY1sPuf0ahVqxa1bduWKlWqRFFRUQZ5ixmuXr2qdYw9f4PvoqEvgY+Pj0GuGjlISUlBlSpVdK4V\nsxLYF1ozw54pSEhIwIQJE1C/fn2dv9P/z2arVq0y2o6pGDRoEG8/ISEBsbGxOik78+fPrzf65OjR\no2jWrJnJfedAbddZtmwZ3N3d+XPIly+fQUOun58fGjduLGmDnXvy5Em95xkU2nLlysHLy8uMS/gP\n7du3R+3atbOFcePUqVPw9vZGrVq1DDorGMOgQYN0qsHM64qI0KVLFwDqtcyRI0csvj83b95Eq1at\neLa5woULg4i01sdt27bVa7W+ffs2ihcvbtE4PjUkJyejTp06nH1FEAS8ffuWJ30zZBNKTExEamoq\nrl69isTERAnjhSH/boNC27JlS9l7tbouRkxiZWx2ySpISEjAqlWr4OXlZdasK7YUN2nSBOfOneNf\nW0YtwvZ0Wexu+fLl0bp1ay5ggiDgyJEjePjwIf7++2+TgwoA4Ndff9W5Rj106BBcXFzw2WefaTmI\nMCPIgwcPTO4vO+H8+fPw8PBAYmKixW2xZ82epxjMF1mfRxtzriAiHDlyhNszjCjAhoV29OjRZi/q\nWUp7pnYSUbZyTj927Bi8vLxMNi4A6rC7smXLSh5oUlISbt++jXXr1kEQBGzatAm9e/fGjBkzkJKS\ngk6dOqFHjx5YsGAB7O3tUahQIX7uuHHjTB4D27DX5UqXkpKCzZs3w8vLC4GBgfjhhx8kBjRj+4vZ\nHew6rZGf+JtvvsHOnTv5HromAR8R4csvvzQ4DqYRif83OH5DP7JNd3PY/R4/fsxJx9gAy5UrZ3I7\nmWNegjcAACAASURBVIm5c+dKAsBNQVJSEg4dOoSJEyeif//+RtXfmJgYDB8+HJ07d0bt2rVx8OBB\nHuNrKyb9yMhILFu2DBMnTsTBgwctCmDITiAiq2fOEKu2kyZNkvSlTwiJ1LxYRMRDKC0WWpYuQxdz\noTH89ddfkq+Zs7NzppjiLcGKFSvQqlUrvV/kmJgY7Nu3D3v27JHQ05iD7777TvLQ7OzscOLECRBl\nnbQeHwuIyGIDqy6IBZeBcXPpGweR2uebsYQSqemEDMGoFCkUCm40MQVt27YFEfEAATZrZKcX8MOH\nD2jevDlKlSql9eFasWIF3N3d0aJFCzRq1AgeHh74/vvvzVa5ypUrJxFaQRDw8OFDg6TVOTANd+7c\ngSAIGDNmDIgM54A1F+xDy3JATZ8+HfXq1dOqpyuUT9/aWBNGhdbOzs6sNA5sABUqVADwn/OBJVbZ\nzIAgCNi3bx98fHwk68NChQpJGAmZD6oh1khDSEhIwLFjxyReY9u3b892S4qsDLFg5M2bF+np6bh/\n/z569eplVXZJIkK1atUAAK1bt9YZNy4OEtDUsHRxV0vaNzaAFi1amJVpQDxjMBw9etQmX7eMwOPH\nj+Hn54eTJ08iJSUFBQsW1EqNOXz4cL6msdQCm5CQgAIFCvA8QnIwd+5cPHnyJIcORg+eP3+OwoUL\nw8vLC5GRkZyMgBVLU54w2NnZceOSvb29TuZKxrTJWEzExZiMGBVaRudpKnbt2mV0kzi74fTp0/Dw\n8ECJEiXQpUsXnQa6R48eYfTo0ciTJw9u3bplNvn2smXL8MUXX5i0nGAb+kSEsWPHmtXvpwTGiMi2\n4lgJCgoyy/jKfImJiEd1NWjQQGfd7t27Y9GiRRIaXSIyGPzOYFQaExISQEQmczmJvTs+Jty7dw8X\nL140Kkzdu3dH8eLFUaxYMSxatMikRNRxcXHw9fU1OevdpUuXMGfOHBCRrCiTTxWCIEiySaSlpUny\nHet6b0eOHGmUAbN27dr8XLYHqy85dNGiRREWFoaIiAiD/eqCLIkiMhx1YOi8j01oTcWFCxdQu3Zt\nFCtWDFu3boUgCEhNTcWQIUNApCa01pyNN27ciObNm5vcF4vPbNOmDb/3zZs3x/Hjx611OdkeYgsv\nEUnYONPT0zmfFpGasI+BHRsyZIjB9jWFX5dqHBcXBxcXFyQnJ+P06dPInz8/ry9nKSpbaH18fORU\n1TrvUxdaQO2uNn36dFSoUAGNGjXiLobicuTIEaSkpODNmzcICAgwaozQhdevX+Ozzz7D+fPnJU4t\nRKSTH+pTxIULF/g90ac9vn37Fn369AGROlSROQoREfz8/Hi99+/fo27dujynU+/evREeHi6hnNWl\nZp87dw41a9YEAK33oEePHkavQZZE1atXzyyStwULFmS7bR5bIiUlBRs2bJCw9InJwIkIefLkkc2f\nLBft27fXm6snsyAIglEWSWsjOjoafn5+WL16tSxbg7u7u2T//N27dyAiboAcOnSo3m0bQwI4Z84c\nHvLKUqayIocOR5bQbt++3WBCIH1gDIfmuAJ+SlAqlfjyyy9x586dTyYI3dBMZCucPXtWSyU2hE6d\nOnGtiO2dsnGnpqaiR48eqFmzJs9YwQxa/fr1kwi3GElJSfD39+dbe6auZwGZQss8owxxJOntgNQp\nFU1FaGgodu7cKZsAPDuDiFC0aFGb9pGamorx48dzq35ISEim3dt27dqBSB1QkZGIjo6WPZsBUoFi\nGf3YfryzszOCg4MxYMAArfMYVbAuDbN3795cOJlcsXSjVhVaQJ1MuX///nKrc7i5uaFIkSI6f7t7\n9y6ePHmixcauyRtcoECBbLu/KwcFCxZEmTJl+ENevny5WR9IQ9BF0+nr62tW7K6lyCxbx+LFi03q\nl3k3aRqT2Iw9dOhQk5ldxNeu+Z6XLFlSXhtyOxs4cKAkAMCcQTKsWbMGtWrVAhFJgoaJ1FFBtWvX\nRmpqKuceZr9lxguWERg3bhzc3d1x+vRpvgVw5MgRq/fD7uPo0aNx6NAhBAYGmmWltsY4GHF7RoFx\nd8llwDQE8QdQ35aOGLdu3cKgQYMAqK+dGaHmzp0LIuJpZuTSHckWWmbKfvfundxT+CA1hTYwMBBE\nxKNfDhw4wKMdiNSJrMSqBWO8a926tUl9ZxeEhobC2dkZgwYNwoABA/iayZpgCY/FqUNZOFhGU6ay\n5ywngZg1+2zZsqVV2yMizJs3z2jdSpUqgYg44T1z5RXv65qiAZiko+TNmxczZsww5RS+72XpLDly\n5MiPmgqF5ZBlxdT7LAfR0dFwdHSU+Dcz762MsPC/fPkSgiBw98GMotQ9efIkiAzzcpsK9py+/vpr\nox/YhIQEPgbGq82SubH8w6YkJTdJaFu1aoWyZcuacgpXJSy1IDNK0I8VO3bs4Krr6dOnkTt3bm78\nsCaYlsOEVKVSoWbNmihUqBC+/PJLnbxSFy9eRJ8+fTBs2DCz+rx79y4+++wzrgoyLztLOaDlonbt\n2hIuJjlIT0/HwoUL9f7Okn4T6Q9yF4NxeDGwc5nvsZwse/xc2TUBHD58GEql0mQz/R9//GE0r4kx\nCIKgxf7/MUEQBPj5+XGuoe7du2P8+PFW70eca4ZpPykpKbhx4wb/6k+dOhU3btzAmzdvUL16dW4M\nJCK8efPGaB8qlQpLly6VaA7dunXDV199JTn2/Plzq3A8G8KbN29ARDoTiRvCrVu3QER6XRd79erF\nt4KI1Bnc582bh40bN+oMFmnRogUX2o0bN5q11cNg8tTl4OCATZs2mXQOc4o+dOiQqd1J0LhxY1n5\nWLMrevXqhTlz5gBQR6R4eXnZhKJHnAleHBEkCAJu3LiBRo0acdc6T09P+Pn58frffPMNTp48icOH\nD2Pt2rU4e/Ys+vbti82bN/MXmZWyZcti+PDhXGMQBIGriawsWLAA6enpCAwM5NkS3r17B3t7e2zc\nuNHiax0yZAhGjhxp8nkvXrwAkf4MG4zPqU2bNvzDxgLeiUiLf4p5TT19+pRTCTHHjUKFCpk0NpOF\ntlq1amjYsKGpp2Hs2LHo3bu3yeeJQfTxJIfShdu3b8PHx4c/8L59+9rMuss+pAMGDMDEiRNx+PBh\nrToJCQn48OEDfH19QURYuHAhDztzcnKSCF+xYsVARHBzc8Pp06cN9s34lDTLli1bAKiXYdbYFnr0\n6BE8PT3x7Nkzk8+NiYkBkX7fBM00MOJsg4ULF9YS9p9++klS/9WrV/xvUyPhTL4rixYtgrOzs6mn\nISoqCh4eHoiIiDD53E8JTZs2xfbt2wEAT548gbu7u83CG42paIIgoHPnznzWZKq7tcAEg0hNlsAM\nOizHjSVCKwgCFAqF2Wlt2I6FIaql9+/f49ixYyCSuiwSkVYctHibaNiwYZyp1M7OzuSxmXxXkpKS\noFAozHqAAwcOxOTJk00+71PC2rVr0a5dO/7/1KlTJf9bEyyTPZs5xRAEASVLloS3tzfevn2LiRMn\nokOHDiaFGMoBIw4ICgpCWloaXr58ieLFi/PZ2Fwnk8TERBCRxdxdcrBhwwYQESfGY1s8mlbl/fv3\no2fPntixYweUSiWIDCfa0gezPmUVKlTQG9xrCGfOnEGuXLkkpndG6vypBhUcPHgQLi4u/PpjY2Ph\n4uKC9evXA1DzFBNpJ9KyFlq2bMkFl80c4tSXe/bsAaBWlTt37oySJUviyy+/xIABA8yKRNKFR48e\noWnTpvD29oa3tzdmzZoFAHBycsK///5rcntsBvf397cKTaoxsFmZ5ZS9efMmiAgBAQG8DhuHpmfa\nzp07Te7PLKHdu3cvlEqlWXuvDRs2lBCXs8FbmnAqu4Jdv/irrKkaMiOGqUHxciAIAoKDg3mffn5+\nqFOnDnLnzo2JEydq1f3nn3+wefNm/Pzzz8ifPz82btxolf1PQRAQEREhoYs1R0VmKv3nn39uky0z\nfWBjFRujmCGtY8eO/DpYHDWRmqfKrL7MHWS+fPnM4gS+cuUKfH19+dbBypUrJarFpwLmOM6KWNOY\nMGGCVmb3gwcPolixYlZ1EBAjJiYGJ06cMEnjYesySw2M+mCO0DKrLrPC2wKCIGDdunWoX78+Hjx4\ngBs3buDZs2fw9vaGi4sLcuXKha5du2LevHmSZ3zlyhX+t6urK7799luz+jdbaEeNGgU3Nzezzh0x\nYgQ6derEjVK61knGHti1a9eyrUot3vYoXbq01vbAtWvX4OXlpZVKhYgyxVfYEEaNGmUzOwV7yU3R\nMBo2bGhTpo7Q0FCMGDFCkgHCWOnQoQMSEhL4On3gwIFQKpVm71GbLbTx8fFQKpU84ZMp+PDhA/r2\n7csvSlfOHPbb6tWrdbZBZBun+oxAkSJF+PpHH0JDQ+Hr64u1a9fyY1mN8J3lxDHGnWQJiExjTSlb\ntiw6duxok7FcvXoVTk5O6NatG6KiohAREQFfX1/MnTsXI0eOxMqVK3Hv3j0toX3y5Ilki6hixYoW\n5QG26A2oX78+5zU2B+/fv+dfn6VLl2rNnOwidblOsptw8eJFs/vP6li1ahXatGnD/7fG3qW1wDyN\nzPlomwJGkCZ3WTBp0iSb+ajfunULnp6e6N27N7p27Ypt27aBSDvJ2dOnT/mzqlmzpsSZZdu2bVAo\nFBblS7LoDbh8+TIUCoXOjOOm4MGDBzrXdob2ENnmdMWKFS3qOyuDeeUwC+6PP/5oFge1taHvedkC\nzNraqVMnWfXj4uKQN29eWe6W5uDatWsICgrCyJEj+T0IDg7WWdfFxQWurq683vTp0xEYGCjhmTIH\nFn+28+fPj6+++srSZriv56hRo/iLYEhow8LCzPImAdQsDrYy6FgT7IW9ceMGAPWHysfHB5cuXcrU\ncU2cOBG9evXKMFL0vXv3mrQc8vLyMis9qByEh4eDiLB//35s27YN06dP1+lMAai3Ru3t7fk7rFKp\n4OzsjLlz51o0BouFNigoCE5OTlbh+nn79i2qVKmCSZMmcfVL09uEQRAElC5dmr/QpoA5b2cHPiYi\nqZM+20MdN25chvIriTFx4kRJZriMAHOfNEYV8/TpU3h6etrso8z2zRs2bAgiwvbt29GkSRMQSXMw\ns6B7InVkU2JiIpYvXw4HBweLx2ax0KalpcHBwcFgGJMpePr0KerWrQt3d3d06tSJr1t1bZKzm2Kq\n8DFH7YoVK2b5GZeItGbWp0+fwt/fH/369cvQsSQkJCAwMBB58uThCaYyCuKEVYawZs0avfRG1sSt\nW7fQtGlT7NixA3fv3kVgYCCcnJzQt29fdO/eXaIlsv3iIkWKWMW7zSpWjd69e8PNzc2qX/4XL15g\nw4YNPL2Cl5cXZs2aJdkeYcRYpjpnaHLzZGXHjs6dO2Pw4MFaHxfGJMIiYzICa9asQePGjTPUaUEM\nxu754sULvXVOnTqVocY6seq7bds21KlTB7Vr10bdunVBRHj79i0AYN++fVAoFGYFL2j1aXELUAuP\no6MjZs+ebY3mtBATE4N79+6ha9eu/CYxsq24uDgULlzYZIrXL7/8EkRq+hVbbllYijt37uCzzz6D\nu7u71seldevWJhOLmYOLFy+iSZMmcHJy4sEMmYVhw4bpZEBkOHfuHIgIt2/fzpDxnD59Gvv37zda\nL3/+/FajS7LaJ2ncuHFwdna2ubrJCND9/f35S8zc8Jo0afJRUq6Gh4fD0dFRizni5cuXcHd3x7Vr\n12zWtyAIaNCgAcaOHWsyP5gtEBcXhxIlSqB169ZaDJ2CIMDf359/2Js1a2bxzoY1sGLFCtjZ2Vnt\n/llNaFUqFVxcXDKEQiQ1NRXdunXjbmCMPYAVXZQp2R2vX79Gvnz5tLYyGI/x2bNnrdZXYmIidu/e\njRo1asDOzg7FihWzmTXWHKSlpaF48eI8BywDozYNDAxErly5eKJupqJmBlQqFfLlyyfJC2QprKr8\nL1iwAPb29janEAHULzERoVGjRvDy8uIb8JreKB8T00W7du14oLgYzFPK0j1TQRAwZ84ceHl5oUGD\nBtixYwdiY2OzpLGOuTiyLb/Xr1+jcOHCnGXj22+/RWpqKlxdXc3i67YWZsyYAUdHR6v61lt9xe7p\n6Ylu3bpZu1md0BRQhrCwMJw4cQI//PBDhowjozB16lRMmDBB63hKSgp8fX1l8+bqw5kzZ1CiRAmT\nSMYyEwULFuRGIBY9w/a2W7VqBQDYvXs3vvjii0wZX1paGpycnHQ+M0tgdaHdtGkTlEplhqwl5s+f\nDyI10/uVK1ds3l9m4+rVqyBSp1sUz6qCIMDe3h6hoaFmtRsXF4dt27bh888/5zlrsgOYQ06PHj1Q\nuHBhTkX0448/YsyYMYiNjUXVqlWxYsWKTBnfkCFD4OLiYvX9dJvYxgsVKpQhHMWpqamSmTYsLMzq\nzApZDVWqVAGROrseY3V4+fKlXq8cfUhKSsKWLVvQvXt3uLq6olmzZti9e3emOWyYi1evXmHmzJnY\nsWMHP3bx4kU4OjqiQoUKGDp0aKZEg8XHx8Pe3h6//vqr1du2idCePHkSCoUiQ6g+UlJStNTkPn36\n2LzfzEJ6ejpCQkLw+eefw8Xl/9o796CoyjeOP+fssix3iYsLyR1FAc1LDoKjKFjhpKMYGJqZRppj\nkppgNjqh/sbUTM0cNccymBjDG2opCt4VFTEvkJAIISKIhOQFV2GX3e/vDzsbiMDusmeXhf3M7MCw\ny/s+B873PO/leZ/HSpXhkuh5bR51KC8vVz1Y169f3yFWhXXNvn37sGPHDoMd34yKioKjoyMvbfO2\nCx0QEICAgAC+mm+GUqnE5cuXO9RJGL7JysqCg4MDJk+ejKSkJIjF4hZv0qqqKixZsgRWVlYQi8VY\nvXq1nq3tOty4cQMsy2o08tEE3u7u0tJSCAQCrF27lq8umvHi6rGxHpLXBC75OFcblUsBJJPJsHHj\nRgQFBcHb2xt2dnaYOnUqCgsL1ZpC7N27F5988gmGDRvWobZ7jAF3d3cMHDiQt/Z5dUmJiYkQCoV6\n2+BWKBSIiIjA2LFjVXNcY2bVqlWYPHlymyK7evUqiAiWlpawt7dXVRcXi8U4ceIEioqKNJ7rc8Wi\niKhDBCgYCwkJCTAzM2tWHlOX8D6O9PHxQWBgIN/dNIOo5awXxoJIJFI7WISbGiQkJGDz5s3YsmWL\nWon3CgsLERISgpCQkCbhnAqFosvl7Wov3LB48+bNvPbDu2i5YfI333zDd1cquMUpQ0bC6AKZTKZW\niCKXhpbovzKKL9LQ0IDLly/j4sWLWLp0KRYuXAhvb284Ojri888/b3YE0ITm8D0s5mAAgHhm6dKl\ntGLFCrpz5w5JJBK+u6Po6Gjas2cPKZVKYhiG9/4MTV1dHV25coV27dpFx44do4yMDHJ2dqadO3fS\n0aNHydvbm1JTU0kul5OVlRWFhYWRg4MDhYWFUXBwMEmlUrKxsaFff/2Vxo4da+jLMUoWLlxI3377\nLd29e5ccHR357Yz3x8K/6HOYTF1oBbkxSqUSX331FaytrWFjY4MRI0Zg06ZNiI+PR0ZGRosLc9wh\njIMHD+rZ4s6BvobFHHrxtEREt2/fJh8fH1q9ejUtWLCAt3769+9Pubm5RERUUlJCXl5evPXVUZFK\npVRfX0+vvPKKWp+fNWsWubi4UGJiIs+WdU48PDzIycmJfv/9d/10qJdHw7/oYzWZ/vWyrR2UNvEf\nZ86cgaOjo6kwmpboY7X4RfQ+hvTx8YGvry9v4XK1tbVaJ3wDYDTB8rqgpqYGr732mk7qwHZFcnJy\n9Dos5tC7aCsrKyEWi9VOiakNI0eO1Cq4gluFNbb4W22ZO3cuxo0b12WuV5dUV1fD0tISEREReu/b\nIKs1p0+fBsuyWLlyJS/tc97W29tbo9/jMuzNnDkTYrEYEokE06ZNM+hQu6ioiJcjhsePH4erq6ve\n0qB2JhQKBdzd3eHh4WGQB57BlljXr18PhmF4K5jMJT7TJG8VEbX5aikxNV9wVdh1TXBwcJOTMSbU\n580334SlpaXBUvAadF8kJiYGIpGIt/QwXO6othZZFAqF6mwmETVLB1taWoq9e/eqMkP6+PjwYu/L\nuH79us6DRLZt24Y+ffropXZrZ2PJkiVgWdag57cNvpnp7+8PZ2fnZkm6dMWaNWvg7+/fYtrP8vLy\nJp70ww8/bDVF6MmTJ0FEiIiI4OVAQkFBAYj4KyJ97tw5ODk5NakDa0I99u/fD4ZhDB4ea3DR1tbW\nws7ODkOGDOGlfaVSiejoaCQmJr70/V69eqnC//7++28QUZtP0e3bt6tEruvQvx9//BFExMuKbnZ2\nNiQSiSmIQgtu3rwJoVDYITJ7GFy0wHPvIhQKecvhW1paCiJCeXl5s/eWL1+OYcOGAfgvGbY6c8iq\nqioEBASoPcSUy+XYvXs3iAh3795t8XOZmZkgIoSEhOjUkyclJcHJyYn3KnedkWfPnsHe3h6DBg0y\ntCkAOohoAWD37t1gGAZJSUm8tK+uGImncL6goCCVDXV1dcjOzkZkZORLU2tyScry8vLa3a9cLse8\nefPg6+uL/Pz8drfX1VAoFOjXrx8cHR15m8JpSocRLQB88cUXYFkWaWlpOm+bqyWq7wPdSqUSdXV1\nWLlyJRYvXqxKR9p4Hv2iR1UoFNi3b1+7Forkcjn27t2LIUOG4I033jCKYmMdkZEjR8Lc3Fwn5Tx0\nRYcSLQDMnj0bLMsiPT1d523HxsZi8eLFOm+3Nby8vEBETc6qrlmzpolodTlkVSgUSE5Ohru7O0JC\nQvDLL790yLzFxsDo0aMhEom0znLJFx1OtAAwffp0CAQCHD9+XKftlpWVwd7evt35gdXl8ePH2LBh\nA9atW6fahOfm1y97tXc4fO3aNQQHB2Pw4ME4d+6cLi6hyzJhwgQIhcIOmZq3Q4oWACZNmgSBQICs\nrCydtSmVSkFEevO2nBgbL4AdPHiwydyaS65NRNi+fbtW/SiVSmzZsgWOjo7Ytm2bKSyxnUyePFnn\n954uEergoBAv7Nixg+rr6yk0NJQyMzMpLCys3W1aWFiQnZ0dubm56cBC9bG1tVV9n52dTUREhYWF\nRETUu3dvIiIqLi4mHx8fjduurq6muLg4+vPPPykrK4v8/Px0YHHXZeLEiZSWlkYZGRk0dOhQQ5vz\ncgz91GgLzuPqao576dIlCAQCvdR1ra6uhq2tbZOIr48//rjZsNjS0lKr9u/duwciwvjx45vU7TWh\nHWPGjIFQKNRpMTM+6PCiBZ7PcXW5qjxlyhRVrRc+OXXqFIgICxYsUP1MoVDgp59+QmJiIogI8fHx\nqggspVKp1t4sFwRC/xaa6gqpYvlm1KhRMDMzQ3Z2tqFNaROjEC3w36pye/dxU1JS0Lt3b0yZMkVH\nlrVMfn4+iAhCobBNT5iWlqbWfLtx2GVycrIuze2SyOVyBAcHw9zcvMWkeB0NoxEt8Hwfl2EYrSOn\n6urqQEQYM2YMbt26pVvjXkJDQ4NKYFz5jpa4ffs25syZ0+rhAG4hzQhmNUZBZWUlXF1dYW1traqL\nZAwY3X9/9+7dEAqFCAoK0jhCJSMjQ++VB8LCwkBEGDBgQLvyCDdeZW7tQIMJ9Th79izEYjF8fX3x\n6NEjQ5ujEaw+F710QVRUFOXl5dGNGzfIzc2NysvL1f7d8+fPExHpNa1qamoqicViunr1Ko0ePVrr\ndnbu3ElERJcuXSI7Oztdmdcl2bhxI4WGhlJERAQVFhY2Wd03Cgz91NAWqVQKf39/iEQitQ/SFxQU\nwNPTUy+etqGhAVu3bsWiRYtw4MABlZfMz8/H7NmzsXLlSo3siIyMNA2NdcB7770HhmHw1VdfGdoU\nrTH6OyAmJgYMw6hVBU6hUGDQoEG8pblpzPHjx5ts65iZmam+5w4PpKamqt3exYsXQUSQSCQ8Wt15\nkUqlCAwMhEgkQmZmpqHNaRdGL1rgeYYKlmUxYcKENqOBysrK4Ofnh2XLlvFqk1QqxcyZM1FRUYF7\n9+5BqVSiqKhItW1VX1+vscc3eVrtuHHjBrp16wZnZ+cmMeDGSqe5A06fPq1aWGgrEdu9e/fg6+ur\nt9SXXCGtlkQ6btw4zJgxo83AfpNoNSc5OVnrhcuOSqe6AyorK+Hr6wuhUNhmwa/i4mL06NEDO3fu\n5N0uTmytiZaI2tyDjouLAxHpZbvK2Hn06BFCQkLAMAzmzZtnaHN0SqcSLUdiYiJYlkVAQECr1Qyu\nXLkCZ2dnjeaWfHDlyhUQEXr16oXa2tpWP8t5bZPHbZnk5GSYm5tDIpEgNzfX0ObonE77ny8pKVF5\n3daq0efm5kIikWDDhg16tK4pmzZtUglRnZusJdEePHhQ4+LRnYnG3nX27Nmd9rRTpxUtB+d1AwMD\nW/S63BlXQyU8q6mpgVAoBBHhwYMHbX5+2bJlzUTLRXupM9znVqKJqNOkUe3s3rUxnV60gHpeNy4u\nDoMHD8bjx4910mdBQQFGjRqFxYsXNxHioUOHkJmZ2WwYzOWFam3uyzF27NiXfq6ioqJVT6tUKpGS\nktJkKyomJkaLq+s4dBXv2pguIVqOxnPdF/P+yuVyREVFYdy4ce0Ovrhz506z43ccL/6cy4xQX1+P\n1atXIzY2tk3vx50e0vS44rvvvqvq98KFCygpKYFAIDDaVdVNmzZ1Ge/amC4lWuD5UDgwMBAMwyA8\nPLzJkLm2thbW1tbw9fVtl3Dv3r2L5cuX48mTJ2hoaHipCGtqapCbm6t6r7S0FKmpqWr1y5U8UXcx\niou55l4KhQJKpRLff/89iAhbt27V7AINzJ49e+Ds7AyBQIC4uLgu4V0b0+VEy3Hs2DG4u7uDZVlE\nR0erhqu3b9+Gq6srIiMj9VqcKjY2ViWqFzMn1tbW4vr160hKSmoivpaO+6WlpcHb27uZV+/Zs6fq\nodD4AMLJkyf5vjydcPbsWXh7e4NhGIwfP97oAv11RZcVLUdKSgocHBwgFAoxZ84cyOVyPHv2ODLn\nEgAABV5JREFUDAkJCXBycsKWLVv09iS/f/++SkjV1dUtJoBrrbzihQsXwLIsevbsiaFDh2Lfvn3I\nyMho5sF37dql1RDbEBQUFKB///5gGAbDhw/nrfaTsdDlRcuxdu1aWFlZwcLCAv/73/+gUChw/fp1\nvP7663p7qn/55ZeQSCRwc3NTCXTVqlWQyWRqtzFnzhyEhoa2+hm5XA5/f3989tlnGttYVFSk1vlg\nXVBRUYERI0aAYRj069evw6UyNRQm0TZCoVAgISEBIpEIdnZ2+O677/D06VPMmjULnp6eOHLkCK/9\nc0KdPn06pFKpVm3k5OQ0WeB6GdHR0SAiPHnypM32pFIpwsPDVadiiouL8dFHH+m8hlFjKisrERkZ\nCZZl4enpaTTDd31hEu1LePbsGaZOnQqRSASxWIxJkybh559/VntLRlsaZ7rQJqO9XC7HtGnTWk2A\nfuLECRARzpw5o1abjx8/xoABA/RyMurAgQPo27cvGIaBi4uLXkJMjRGTaFtBoVDg66+/hqurKxiG\ngaenp0pUR44c4UW8MpkMRM+r0WsKZ1tL89SbN2/qbWirLlKpFPPnz0e3bt3AsiyGDRtmFMnVDIlJ\ntGqSk5OD4cOHg2EYlThsbW2xcOFC7Nq1S+vFqr/++kvVXnh4OEaMGAEiwvvvv69xW62tKtfX18PK\nygrx8fFa2alruL8ny7Kws7PDp59+2mbctYnnmESrIZxnsLOzA8MwsLGxaSLiRYsWaVSdjisiHRQU\nhKNHj+LEiRPIzc3VyotzeZCjo6Nx7do1PHjwABcuXMCKFStUNhqyrg8XQMKNXAIDA3kpttbZMYm2\nHezfv181BzM3N4efnx88PDzAMIxqy+Wtt95CVlYWysrKUFNTg6qqKuTn5+PQoUOIjo6Gj48Punfv\n3mrNWk04evSo6gFibW2NgQMHwszMDJMmTWpXYjltqaioQHx8vGp/1cLCAjExMW2eeTbRMgwAvDR5\nlAm1qaurox9++IGSk5MpLy+P5HI5eXh4kEQioeLiYvL09KTKykqSSqXEsiw5OztT9+7daeLEiRQe\nHk49evQgKysrQ1+Gzrh06RKtW7eOjh07Rvfv3ycHBwcKCwujuXPndtxSG8aEoZ8anZH09HS8/fbb\nsLW1BRHBxcUFsbGxRpVbVxMUCgVSUlIQGhoKCwsLMAwDLy8vzJ8/v8sHQvCBydPyTGFhIa1du5bS\n09OpoqKCzMzMyMXFhfr27UthYWEUFRVF7u7uhjZTbZRKJZ05c4YOHDhA58+fp6KiInr48CEJhULq\n168fffDBBzRjxgwSi8WGNrXTYhKtHqmrq6PDhw9Teno65eTk0K1bt6i2trbDClmpVNKpU6fot99+\nayJQhmHI2dmZ/P39afjw4fTOO+9QYGCgoc3tMphEa2BkMhkdPnyYDh06RDk5OVRSUkK1tbUkEAjI\n0tKSbG1tycHBgVxcXMjNzY18fHzIz8+PAgMDycfHh1hWu3zzT58+pT/++IMKCgro5s2bVFpaShUV\nFVRVVUUPHjygJ0+eUF1dHbEsS05OTiaBdiBMou2AyGQyysrKooKCAioqKqKysjKqqKig+/fv08OH\nD0kqlZJcLicAJBAIyNzcnFiWVb0EAgGxLEsASKlUklKpJIVCofpaX19PAIhlWRKLxWRjY0P29vYk\nkUjo1VdfJS8vL/Lz86NBgwZRnz59DP3nMPECJtEaMf/88w/l5eXRrVu3SCaTkUwmo7q6OpLJZFRf\nX68StEgkIpFIpBJonz59qHfv3iQSiQx9CSa0wCRaEyaMDKMrwGXCRFfHJFoTJowMk2hNmDAyTKI1\nYcLIMInWhAkj4//VDXyqcC35sAAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Histograms"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "here we generate a bunch of gaussian-normalized random numbers and make a histogram.  The probability distribution should match\n",
-      "$$y(x) = \\frac{1}{\\sigma \\sqrt{2\\pi}} e^{-x^2/(2\\sigma^2)}$$"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "N = 10000\n",
-      "r = numpy.random.randn(N)\n",
-      "pylab.hist(r, normed=True, bins=20)\n",
-      "\n",
-      "x = numpy.linspace(-5,5,200)\n",
-      "sigma = 1.0\n",
-      "pylab.plot(x, numpy.exp(-x**2/(2*sigma**2))/(sigma*math.sqrt(2.0*math.pi)),\n",
-      "           c=\"r\", lw=2)\n",
-      "pylab.xlabel(\"x\")\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "<matplotlib.text.Text at 0x6408dd0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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AgT71FaK9+nKKZHbjxEgxo/QOx6vzXEERNxKG8vxREqIjeR3Bh4eHk5+fT3Z2Ni6Xi7y8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\n"
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Plotting data from a file"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "numpy.loadtxt() provides an easy way to read columns of data from an ASCII file"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "data = numpy.loadtxt(\"test1.exact.128.out\")\n",
-      "print data.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(128, 8)\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.plot(data[:,1], data[:,2]/numpy.max(data[:,2]), label=r\"$\\rho$\")\n",
-      "pylab.plot(data[:,1], data[:,3]/numpy.max(data[:,3]), label=r\"$u$\")\n",
-      "pylab.plot(data[:,1], data[:,4]/numpy.max(data[:,4]), label=r\"$p$\")\n",
-      "pylab.plot(data[:,1], data[:,5]/numpy.max(data[:,5]), label=r\"$T$\")\n",
-      "pylab.ylim(0,1.1)\n",
-      "pylab.legend(frameon=False, loc=\"best\", fontsize=12)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "<matplotlib.legend.Legend at 0x913cfd0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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OpbCAR/uTvC1IkgIgSaFEcXFxrFu3zugwTOfVza/SJbILVza58u8nGzbUE8Og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\n"
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/03-matplotlib/matplotlib.fodp b/lectures/03-matplotlib/matplotlib.fodp
deleted file mode 100644
index 4a3ae4d8..00000000
--- a/lectures/03-matplotlib/matplotlib.fodp
+++ /dev/null
@@ -1,1349 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-
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-   </draw:page>
-   <draw:page draw:name="page3" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
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-     <draw:text-box>
-      <text:p>Importing</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr14" draw:layer="layout" svg:width="26.924cm" svg:height="16.002cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>There are several interfaces to matplotlib that provide varying amounts of access to its underlying functionality</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>See <text:a xlink:href="http://matplotlib.org/faq/usage_faq.html" xlink:type="simple">http://matplotlib.org/faq/usage_faq.html</text:a></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">matplotlib</text:span> is the entire package</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">matplotlib.pyplot</text:span> is a module within <text:span text:style-name="T6">matplotlib</text:span> that provides easy access to the core plotting routines</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">pylab</text:span> combines <text:span text:style-name="T6">pyplot</text:span> and <text:span text:style-name="T6">numpy</text:span> into a single namespace to give a MatLab like interface</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>This is best for interactive work</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>A number of toolkits extend the functionality</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">basemap</text:span> and <text:span text:style-name="T6">cartopy</text:span>: mapping (e.g. projecting onto a globe, geographical boundaries)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">mplot3d</text:span>: basic 3-d plotting</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">AxesGrid</text:span>: high-level methods for arranging multiple plots together in a figure</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-     <draw:page-thumbnail draw:style-name="gr3" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="3" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page4" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-    <draw:frame presentation:style-name="pr13" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title">
-     <draw:text-box>
-      <text:p>Figures vs. Axes</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr14" draw:layer="layout" svg:width="26.924cm" svg:height="13.564cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Figures are the highest level object and can include multiple axes (see <text:a xlink:href="http://matplotlib.org/users/pyplot_tutorial.html" xlink:type="simple">http://matplotlib.org/users/pyplot_tutorial.html</text:a>)</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>There are many matplotlib routines to subdivide a figure into multiple subplots</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-     <draw:page-thumbnail draw:style-name="gr3" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="4" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page5" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-    <draw:frame presentation:style-name="pr13" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title">
-     <draw:text-box>
-      <text:p>Backends</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr14" draw:layer="layout" svg:width="26.924cm" svg:height="15.748cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>matplotlib can output to a number of different devices—the backends provide this functionality</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Interactive backends:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>pygtk, wxpython, tkinter, q3, macosx</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>These allow for plotting to the screen, and updates with each command (if desired)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Hardcopy backends:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>PNG, SVG, PDF, PS</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>To select a backend:</text:p>
-        <text:list>
-         <text:list-header>
-          <text:p><text:span text:style-name="T6">import matplotlib</text:span></text:p>
-          <text:p><text:span text:style-name="T6">matplotlib.use(&apos;PS&apos;)</text:span></text:p>
-          <text:p><text:span text:style-name="T6">import matplotlib.pyplot</text:span></text:p>
-         </text:list-header>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-     <draw:page-thumbnail draw:style-name="gr3" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="5" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page6" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-    <draw:frame presentation:style-name="pr13" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title">
-     <draw:text-box>
-      <text:p>IPython and matplotlib</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr14" draw:layer="layout" svg:width="26.924cm" svg:height="13.564cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>IPython supports matplotlib:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">Ipython --pylab</text:span> will pop up a window in interactive mode</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">%pylab inline</text:span> magic in notebooks</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
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-     <draw:frame presentation:style-name="pr12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page7" draw:style-name="dp4" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
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-     <draw:text-box>
-      <text:p>Some Examples</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr14" draw:layer="layout" svg:width="26.924cm" svg:height="13.564cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>There are far more examples than we can cover—we&apos;ll see more as the class goes on.</text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
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diff --git a/lectures/04-scipy/scipy-basics.ipynb b/lectures/04-scipy/scipy-basics.ipynb
deleted file mode 100644
index b754d6b4..00000000
--- a/lectures/04-scipy/scipy-basics.ipynb
+++ /dev/null
@@ -1,3529 +0,0 @@
-{
- "metadata": {
-  "name": "scipy-basics"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import math\n",
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Integration"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "we'll do some integrals of the form\n",
-      "\n",
-      "$$I = \\int_a^b f(x) dx$$"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from scipy import integrate\n",
-      "help(integrate)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on package scipy.integrate in scipy:\n",
-        "\n",
-        "NAME\n",
-        "    scipy.integrate\n",
-        "\n",
-        "FILE\n",
-        "    /usr/lib64/python2.7/site-packages/scipy/integrate/__init__.py\n",
-        "\n",
-        "DESCRIPTION\n",
-        "    =============================================\n",
-        "    Integration and ODEs (:mod:`scipy.integrate`)\n",
-        "    =============================================\n",
-        "    \n",
-        "    .. currentmodule:: scipy.integrate\n",
-        "    \n",
-        "    Integrating functions, given function object\n",
-        "    ============================================\n",
-        "    \n",
-        "    .. autosummary::\n",
-        "       :toctree: generated/\n",
-        "    \n",
-        "       quad          -- General purpose integration\n",
-        "       dblquad       -- General purpose double integration\n",
-        "       tplquad       -- General purpose triple integration\n",
-        "       fixed_quad    -- Integrate func(x) using Gaussian quadrature of order n\n",
-        "       quadrature    -- Integrate with given tolerance using Gaussian quadrature\n",
-        "       romberg       -- Integrate func using Romberg integration\n",
-        "    \n",
-        "    Integrating functions, given fixed samples\n",
-        "    ==========================================\n",
-        "    \n",
-        "    .. autosummary::\n",
-        "       :toctree: generated/\n",
-        "    \n",
-        "       cumtrapz      -- Use trapezoidal rule to cumulatively compute integral.\n",
-        "       simps         -- Use Simpson's rule to compute integral from samples.\n",
-        "       romb          -- Use Romberg Integration to compute integral from\n",
-        "                     -- (2**k + 1) evenly-spaced samples.\n",
-        "    \n",
-        "    .. seealso::\n",
-        "    \n",
-        "       :mod:`scipy.special` for orthogonal polynomials (special) for Gaussian\n",
-        "       quadrature roots and weights for other weighting factors and regions.\n",
-        "    \n",
-        "    Integrators of ODE systems\n",
-        "    ==========================\n",
-        "    \n",
-        "    .. autosummary::\n",
-        "       :toctree: generated/\n",
-        "    \n",
-        "       odeint        -- General integration of ordinary differential equations.\n",
-        "       ode           -- Integrate ODE using VODE and ZVODE routines.\n",
-        "       complex_ode   -- Convert a complex-valued ODE to real-valued and integrate.\n",
-        "\n",
-        "PACKAGE CONTENTS\n",
-        "    _dop\n",
-        "    _ode\n",
-        "    _odepack\n",
-        "    _quadpack\n",
-        "    lsoda\n",
-        "    odepack\n",
-        "    quadpack\n",
-        "    quadrature\n",
-        "    setup\n",
-        "    setupscons\n",
-        "    vode\n",
-        "\n",
-        "CLASSES\n",
-        "    __builtin__.object\n",
-        "        scipy.integrate._ode.ode\n",
-        "            scipy.integrate._ode.complex_ode\n",
-        "    \n",
-        "    class complex_ode(ode)\n",
-        "     |  A wrapper of ode for complex systems.\n",
-        "     |  \n",
-        "     |  This functions similarly as `ode`, but re-maps a complex-valued\n",
-        "     |  equation system to a real-valued one before using the integrators.\n",
-        "     |  \n",
-        "     |  Parameters\n",
-        "     |  ----------\n",
-        "     |  f : callable ``f(t, y, *f_args)``\n",
-        "     |      Rhs of the equation. t is a scalar, ``y.shape == (n,)``.\n",
-        "     |      ``f_args`` is set by calling ``set_f_params(*args)``.\n",
-        "     |  jac : callable ``jac(t, y, *jac_args)``\n",
-        "     |      Jacobian of the rhs, ``jac[i,j] = d f[i] / d y[j]``.\n",
-        "     |      ``jac_args`` is set by calling ``set_f_params(*args)``.\n",
-        "     |  \n",
-        "     |  Attributes\n",
-        "     |  ----------\n",
-        "     |  t : float\n",
-        "     |      Current time.\n",
-        "     |  y : ndarray\n",
-        "     |      Current variable values.\n",
-        "     |  \n",
-        "     |  Examples\n",
-        "     |  --------\n",
-        "     |  For usage examples, see `ode`.\n",
-        "     |  \n",
-        "     |  Method resolution order:\n",
-        "     |      complex_ode\n",
-        "     |      ode\n",
-        "     |      __builtin__.object\n",
-        "     |  \n",
-        "     |  Methods defined here:\n",
-        "     |  \n",
-        "     |  __init__(self, f, jac=None)\n",
-        "     |  \n",
-        "     |  integrate(self, t, step=0, relax=0)\n",
-        "     |      Find y=y(t), set y as an initial condition, and return y.\n",
-        "     |  \n",
-        "     |  set_initial_value(self, y, t=0.0)\n",
-        "     |      Set initial conditions y(t) = y.\n",
-        "     |  \n",
-        "     |  set_integrator(self, name, **integrator_params)\n",
-        "     |      Set integrator by name.\n",
-        "     |      \n",
-        "     |      Parameters\n",
-        "     |      ----------\n",
-        "     |      name : str\n",
-        "     |          Name of the integrator\n",
-        "     |      integrator_params :\n",
-        "     |          Additional parameters for the integrator.\n",
-        "     |  \n",
-        "     |  ----------------------------------------------------------------------\n",
-        "     |  Data descriptors defined here:\n",
-        "     |  \n",
-        "     |  y\n",
-        "     |  \n",
-        "     |  ----------------------------------------------------------------------\n",
-        "     |  Methods inherited from ode:\n",
-        "     |  \n",
-        "     |  set_f_params(self, *args)\n",
-        "     |      Set extra parameters for user-supplied function f.\n",
-        "     |  \n",
-        "     |  set_jac_params(self, *args)\n",
-        "     |      Set extra parameters for user-supplied function jac.\n",
-        "     |  \n",
-        "     |  successful(self)\n",
-        "     |      Check if integration was successful.\n",
-        "     |  \n",
-        "     |  ----------------------------------------------------------------------\n",
-        "     |  Data descriptors inherited from ode:\n",
-        "     |  \n",
-        "     |  __dict__\n",
-        "     |      dictionary for instance variables (if defined)\n",
-        "     |  \n",
-        "     |  __weakref__\n",
-        "     |      list of weak references to the object (if defined)\n",
-        "    \n",
-        "    class ode(__builtin__.object)\n",
-        "     |  A generic interface class to numeric integrators.\n",
-        "     |  \n",
-        "     |  Solve an equation system :math:`y'(t) = f(t,y)` with (optional) ``jac = df/dy``.\n",
-        "     |  \n",
-        "     |  Parameters\n",
-        "     |  ----------\n",
-        "     |  f : callable ``f(t, y, *f_args)``\n",
-        "     |      Rhs of the equation. t is a scalar, ``y.shape == (n,)``.\n",
-        "     |      ``f_args`` is set by calling ``set_f_params(*args)``.\n",
-        "     |      `f` should return a scalar, array or list (not a tuple).\n",
-        "     |  jac : callable ``jac(t, y, *jac_args)``\n",
-        "     |      Jacobian of the rhs, ``jac[i,j] = d f[i] / d y[j]``.\n",
-        "     |      ``jac_args`` is set by calling ``set_f_params(*args)``.\n",
-        "     |  \n",
-        "     |  Attributes\n",
-        "     |  ----------\n",
-        "     |  t : float\n",
-        "     |      Current time.\n",
-        "     |  y : ndarray\n",
-        "     |      Current variable values.\n",
-        "     |  \n",
-        "     |  See also\n",
-        "     |  --------\n",
-        "     |  odeint : an integrator with a simpler interface based on lsoda from ODEPACK\n",
-        "     |  quad : for finding the area under a curve\n",
-        "     |  \n",
-        "     |  Notes\n",
-        "     |  -----\n",
-        "     |  Available integrators are listed below. They can be selected using\n",
-        "     |  the `set_integrator` method.\n",
-        "     |  \n",
-        "     |  \"vode\"\n",
-        "     |  \n",
-        "     |      Real-valued Variable-coefficient Ordinary Differential Equation\n",
-        "     |      solver, with fixed-leading-coefficient implementation. It provides\n",
-        "     |      implicit Adams method (for non-stiff problems) and a method based on\n",
-        "     |      backward differentiation formulas (BDF) (for stiff problems).\n",
-        "     |  \n",
-        "     |      Source: http://www.netlib.org/ode/vode.f\n",
-        "     |  \n",
-        "     |      .. warning::\n",
-        "     |  \n",
-        "     |         This integrator is not re-entrant. You cannot have two `ode`\n",
-        "     |         instances using the \"vode\" integrator at the same time.\n",
-        "     |  \n",
-        "     |      This integrator accepts the following parameters in `set_integrator`\n",
-        "     |      method of the `ode` class:\n",
-        "     |  \n",
-        "     |      - atol : float or sequence\n",
-        "     |        absolute tolerance for solution\n",
-        "     |      - rtol : float or sequence\n",
-        "     |        relative tolerance for solution\n",
-        "     |      - lband : None or int\n",
-        "     |      - rband : None or int\n",
-        "     |        Jacobian band width, jac[i,j] != 0 for i-lband <= j <= i+rband.\n",
-        "     |        Setting these requires your jac routine to return the jacobian\n",
-        "     |        in packed format, jac_packed[i-j+lband, j] = jac[i,j].\n",
-        "     |      - method: 'adams' or 'bdf'\n",
-        "     |        Which solver to use, Adams (non-stiff) or BDF (stiff)\n",
-        "     |      - with_jacobian : bool\n",
-        "     |        Whether to use the jacobian\n",
-        "     |      - nsteps : int\n",
-        "     |        Maximum number of (internally defined) steps allowed during one\n",
-        "     |        call to the solver.\n",
-        "     |      - first_step : float\n",
-        "     |      - min_step : float\n",
-        "     |      - max_step : float\n",
-        "     |        Limits for the step sizes used by the integrator.\n",
-        "     |      - order : int\n",
-        "     |        Maximum order used by the integrator,\n",
-        "     |        order <= 12 for Adams, <= 5 for BDF.\n",
-        "     |  \n",
-        "     |  \"zvode\"\n",
-        "     |  \n",
-        "     |      Complex-valued Variable-coefficient Ordinary Differential Equation\n",
-        "     |      solver, with fixed-leading-coefficient implementation.  It provides\n",
-        "     |      implicit Adams method (for non-stiff problems) and a method based on\n",
-        "     |      backward differentiation formulas (BDF) (for stiff problems).\n",
-        "     |  \n",
-        "     |      Source: http://www.netlib.org/ode/zvode.f\n",
-        "     |  \n",
-        "     |      .. warning::\n",
-        "     |  \n",
-        "     |         This integrator is not re-entrant. You cannot have two `ode`\n",
-        "     |         instances using the \"zvode\" integrator at the same time.\n",
-        "     |  \n",
-        "     |      This integrator accepts the same parameters in `set_integrator`\n",
-        "     |      as the \"vode\" solver.\n",
-        "     |  \n",
-        "     |      .. note::\n",
-        "     |  \n",
-        "     |          When using ZVODE for a stiff system, it should only be used for\n",
-        "     |          the case in which the function f is analytic, that is, when each f(i)\n",
-        "     |          is an analytic function of each y(j).  Analyticity means that the\n",
-        "     |          partial derivative df(i)/dy(j) is a unique complex number, and this\n",
-        "     |          fact is critical in the way ZVODE solves the dense or banded linear\n",
-        "     |          systems that arise in the stiff case.  For a complex stiff ODE system\n",
-        "     |          in which f is not analytic, ZVODE is likely to have convergence\n",
-        "     |          failures, and for this problem one should instead use DVODE on the\n",
-        "     |          equivalent real system (in the real and imaginary parts of y).\n",
-        "     |  \n",
-        "     |  \"lsoda\"\n",
-        "     |  \n",
-        "     |      Real-valued Variable-coefficient Ordinary Differential Equation\n",
-        "     |      solver, with fixed-leading-coefficient implementation. It provides\n",
-        "     |      automatic method switching between implicit Adams method (for non-stiff\n",
-        "     |      problems) and a method based on backward differentiation formulas (BDF)\n",
-        "     |      (for stiff problems).\n",
-        "     |  \n",
-        "     |      Source: http://www.netlib.org/odepack\n",
-        "     |  \n",
-        "     |      .. warning::\n",
-        "     |  \n",
-        "     |         This integrator is not re-entrant. You cannot have two `ode`\n",
-        "     |         instances using the \"lsoda\" integrator at the same time.\n",
-        "     |  \n",
-        "     |      This integrator accepts the following parameters in `set_integrator`\n",
-        "     |      method of the `ode` class:\n",
-        "     |  \n",
-        "     |      - atol : float or sequence\n",
-        "     |        absolute tolerance for solution\n",
-        "     |      - rtol : float or sequence\n",
-        "     |        relative tolerance for solution\n",
-        "     |      - lband : None or int\n",
-        "     |      - rband : None or int\n",
-        "     |        Jacobian band width, jac[i,j] != 0 for i-lband <= j <= i+rband.\n",
-        "     |        Setting these requires your jac routine to return the jacobian\n",
-        "     |        in packed format, jac_packed[i-j+lband, j] = jac[i,j].\n",
-        "     |      - with_jacobian : bool\n",
-        "     |        Whether to use the jacobian\n",
-        "     |      - nsteps : int\n",
-        "     |        Maximum number of (internally defined) steps allowed during one\n",
-        "     |        call to the solver.\n",
-        "     |      - first_step : float\n",
-        "     |      - min_step : float\n",
-        "     |      - max_step : float\n",
-        "     |        Limits for the step sizes used by the integrator.\n",
-        "     |      - max_order_ns : int\n",
-        "     |        Maximum order used in the nonstiff case (default 12).\n",
-        "     |      - max_order_s : int\n",
-        "     |        Maximum order used in the stiff case (default 5).\n",
-        "     |      - max_hnil : int\n",
-        "     |        Maximum number of messages reporting too small step size (t + h = t)\n",
-        "     |        (default 0)\n",
-        "     |      - ixpr : int\n",
-        "     |        Whether to generate extra printing at method switches (default False).\n",
-        "     |  \n",
-        "     |  \"dopri5\"\n",
-        "     |  \n",
-        "     |      This is an explicit runge-kutta method of order (4)5 due to Dormand &\n",
-        "     |      Prince (with stepsize control and dense output).\n",
-        "     |  \n",
-        "     |      Authors:\n",
-        "     |  \n",
-        "     |          E. Hairer and G. Wanner\n",
-        "     |          Universite de Geneve, Dept. de Mathematiques\n",
-        "     |          CH-1211 Geneve 24, Switzerland\n",
-        "     |          e-mail:  ernst.hairer@math.unige.ch, gerhard.wanner@math.unige.ch\n",
-        "     |  \n",
-        "     |      This code is described in [HNW93]_.\n",
-        "     |  \n",
-        "     |      This integrator accepts the following parameters in set_integrator()\n",
-        "     |      method of the ode class:\n",
-        "     |  \n",
-        "     |      - atol : float or sequence\n",
-        "     |        absolute tolerance for solution\n",
-        "     |      - rtol : float or sequence\n",
-        "     |        relative tolerance for solution\n",
-        "     |      - nsteps : int\n",
-        "     |        Maximum number of (internally defined) steps allowed during one\n",
-        "     |        call to the solver.\n",
-        "     |      - first_step : float\n",
-        "     |      - max_step : float\n",
-        "     |      - safety : float\n",
-        "     |        Safety factor on new step selection (default 0.9)\n",
-        "     |      - ifactor : float\n",
-        "     |      - dfactor : float\n",
-        "     |        Maximum factor to increase/decrease step size by in one step\n",
-        "     |      - beta : float\n",
-        "     |        Beta parameter for stabilised step size control.\n",
-        "     |  \n",
-        "     |  \"dop853\"\n",
-        "     |  \n",
-        "     |      This is an explicit runge-kutta method of order 8(5,3) due to Dormand\n",
-        "     |      & Prince (with stepsize control and dense output).\n",
-        "     |  \n",
-        "     |      Options and references the same as \"dopri5\".\n",
-        "     |  \n",
-        "     |  Examples\n",
-        "     |  --------\n",
-        "     |  \n",
-        "     |  A problem to integrate and the corresponding jacobian:\n",
-        "     |  \n",
-        "     |  >>> from scipy.integrate import ode\n",
-        "     |  >>>\n",
-        "     |  >>> y0, t0 = [1.0j, 2.0], 0\n",
-        "     |  >>>\n",
-        "     |  >>> def f(t, y, arg1):\n",
-        "     |  >>>     return [1j*arg1*y[0] + y[1], -arg1*y[1]**2]\n",
-        "     |  >>> def jac(t, y, arg1):\n",
-        "     |  >>>     return [[1j*arg1, 1], [0, -arg1*2*y[1]]]\n",
-        "     |  \n",
-        "     |  The integration:\n",
-        "     |  \n",
-        "     |  >>> r = ode(f, jac).set_integrator('zvode', method='bdf', with_jacobian=True)\n",
-        "     |  >>> r.set_initial_value(y0, t0).set_f_params(2.0).set_jac_params(2.0)\n",
-        "     |  >>> t1 = 10\n",
-        "     |  >>> dt = 1\n",
-        "     |  >>> while r.successful() and r.t < t1:\n",
-        "     |  >>>     r.integrate(r.t+dt)\n",
-        "     |  >>>     print r.t, r.y\n",
-        "     |  \n",
-        "     |  References\n",
-        "     |  ----------\n",
-        "     |  .. [HNW93] E. Hairer, S.P. Norsett and G. Wanner, Solving Ordinary\n",
-        "     |      Differential Equations i. Nonstiff Problems. 2nd edition.\n",
-        "     |      Springer Series in Computational Mathematics,\n",
-        "     |      Springer-Verlag (1993)\n",
-        "     |  \n",
-        "     |  Methods defined here:\n",
-        "     |  \n",
-        "     |  __init__(self, f, jac=None)\n",
-        "     |  \n",
-        "     |  integrate(self, t, step=0, relax=0)\n",
-        "     |      Find y=y(t), set y as an initial condition, and return y.\n",
-        "     |  \n",
-        "     |  set_f_params(self, *args)\n",
-        "     |      Set extra parameters for user-supplied function f.\n",
-        "     |  \n",
-        "     |  set_initial_value(self, y, t=0.0)\n",
-        "     |      Set initial conditions y(t) = y.\n",
-        "     |  \n",
-        "     |  set_integrator(self, name, **integrator_params)\n",
-        "     |      Set integrator by name.\n",
-        "     |      \n",
-        "     |      Parameters\n",
-        "     |      ----------\n",
-        "     |      name : str\n",
-        "     |          Name of the integrator.\n",
-        "     |      integrator_params :\n",
-        "     |          Additional parameters for the integrator.\n",
-        "     |  \n",
-        "     |  set_jac_params(self, *args)\n",
-        "     |      Set extra parameters for user-supplied function jac.\n",
-        "     |  \n",
-        "     |  successful(self)\n",
-        "     |      Check if integration was successful.\n",
-        "     |  \n",
-        "     |  ----------------------------------------------------------------------\n",
-        "     |  Data descriptors defined here:\n",
-        "     |  \n",
-        "     |  __dict__\n",
-        "     |      dictionary for instance variables (if defined)\n",
-        "     |  \n",
-        "     |  __weakref__\n",
-        "     |      list of weak references to the object (if defined)\n",
-        "     |  \n",
-        "     |  y\n",
-        "\n",
-        "FUNCTIONS\n",
-        "    cumtrapz(y, x=None, dx=1.0, axis=-1, initial=None)\n",
-        "        Cumulatively integrate y(x) using the composite trapezoidal rule.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        y : array_like\n",
-        "            Values to integrate.\n",
-        "        x : array_like, optional\n",
-        "            The coordinate to integrate along.  If None (default), use spacing `dx`\n",
-        "            between consecutive elements in `y`.\n",
-        "        dx : int, optional\n",
-        "            Spacing between elements of `y`.  Only used if `x` is None.\n",
-        "        axis : int, optional\n",
-        "            Specifies the axis to cumulate.  Default is -1 (last axis).\n",
-        "        initial : scalar, optional\n",
-        "            If given, uses this value as the first value in the returned result.\n",
-        "            Typically this value should be 0.  Default is None, which means no\n",
-        "            value at ``x[0]`` is returned and `res` has one element less than `y`\n",
-        "            along the axis of integration.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        res : ndarray\n",
-        "            The result of cumulative integration of `y` along `axis`.\n",
-        "            If `initial` is None, the shape is such that the axis of integration\n",
-        "            has one less value than `y`.  If `initial` is given, the shape is equal\n",
-        "            to that of `y`.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        numpy.cumsum, numpy.cumprod\n",
-        "        quad: adaptive quadrature using QUADPACK\n",
-        "        romberg: adaptive Romberg quadrature\n",
-        "        quadrature: adaptive Gaussian quadrature\n",
-        "        fixed_quad: fixed-order Gaussian quadrature\n",
-        "        dblquad: double integrals\n",
-        "        tplquad: triple integrals\n",
-        "        romb: integrators for sampled data\n",
-        "        ode: ODE integrators\n",
-        "        odeint: ODE integrators\n",
-        "        \n",
-        "        Examples\n",
-        "        --------\n",
-        "        >>> from scipy import integrate\n",
-        "        >>> x = np.linspace(-2, 2, num=20)\n",
-        "        >>> y = x\n",
-        "        >>> y_int = integrate.cumtrapz(y, x, initial=0)\n",
-        "        >>> plt.plot(x, y_int, 'ro', x, y[0] + 0.5 * x**2, 'b-')\n",
-        "        >>> plt.show()\n",
-        "    \n",
-        "    dblquad(func, a, b, gfun, hfun, args=(), epsabs=1.49e-08, epsrel=1.49e-08)\n",
-        "        Compute a double integral.\n",
-        "        \n",
-        "        Return the double (definite) integral of func(y,x) from x=a..b and\n",
-        "        y=gfun(x)..hfun(x).\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        func : callable\n",
-        "            A Python function or method of at least two variables: y must be the\n",
-        "            first argument and x the second argument.\n",
-        "        (a,b) : tuple\n",
-        "            The limits of integration in x: a < b\n",
-        "        gfun : callable\n",
-        "            The lower boundary curve in y which is a function taking a single\n",
-        "            floating point argument (x) and returning a floating point result: a\n",
-        "            lambda function can be useful here.\n",
-        "        hfun : callable\n",
-        "            The upper boundary curve in y (same requirements as `gfun`).\n",
-        "        args : sequence, optional\n",
-        "            Extra arguments to pass to `func2d`.\n",
-        "        epsabs : float, optional\n",
-        "            Absolute tolerance passed directly to the inner 1-D quadrature\n",
-        "            integration. Default is 1.49e-8.\n",
-        "        epsrel : float\n",
-        "            Relative tolerance of the inner 1-D integrals. Default is 1.49e-8.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        y : float\n",
-        "            The resultant integral.\n",
-        "        abserr : float\n",
-        "            An estimate of the error.\n",
-        "        \n",
-        "        See also\n",
-        "        --------\n",
-        "        quad : single integral\n",
-        "        tplquad : triple integral\n",
-        "        fixed_quad : fixed-order Gaussian quadrature\n",
-        "        quadrature : adaptive Gaussian quadrature\n",
-        "        odeint : ODE integrator\n",
-        "        ode : ODE integrator\n",
-        "        simps : integrator for sampled data\n",
-        "        romb : integrator for sampled data\n",
-        "        scipy.special : for coefficients and roots of orthogonal polynomials\n",
-        "    \n",
-        "    fixed_quad(func, a, b, args=(), n=5)\n",
-        "        Compute a definite integral using fixed-order Gaussian quadrature.\n",
-        "        \n",
-        "        Integrate `func` from a to b using Gaussian quadrature of order n.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        func : callable\n",
-        "            A Python function or method to integrate (must accept vector inputs).\n",
-        "        a : float\n",
-        "            Lower limit of integration.\n",
-        "        b : float\n",
-        "            Upper limit of integration.\n",
-        "        args : tuple, optional\n",
-        "            Extra arguments to pass to function, if any.\n",
-        "        n : int, optional\n",
-        "            Order of quadrature integration. Default is 5.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        val : float\n",
-        "            Gaussian quadrature approximation to the integral\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        quad : adaptive quadrature using QUADPACK\n",
-        "        dblquad, tplquad : double and triple integrals\n",
-        "        romberg : adaptive Romberg quadrature\n",
-        "        quadrature : adaptive Gaussian quadrature\n",
-        "        romb, simps, trapz : integrators for sampled data\n",
-        "        cumtrapz : cumulative integration for sampled data\n",
-        "        ode, odeint - ODE integrators\n",
-        "    \n",
-        "    newton_cotes(rn, equal=0)\n",
-        "        Return weights and error coefficient for Newton-Cotes integration.\n",
-        "        \n",
-        "        Suppose we have (N+1) samples of f at the positions\n",
-        "        x_0, x_1, ..., x_N.  Then an N-point Newton-Cotes formula for the\n",
-        "        integral between x_0 and x_N is:\n",
-        "        \n",
-        "        :math:`\\int_{x_0}^{x_N} f(x)dx = \\Delta x \\sum_{i=0}^{N} a_i f(x_i)\n",
-        "        + B_N (\\Delta x)^{N+2} f^{N+1} (\\xi)`\n",
-        "        \n",
-        "        where :math:`\\xi \\in [x_0,x_N]` and :math:`\\Delta x = \\frac{x_N-x_0}{N}`\n",
-        "        is the averages samples spacing.\n",
-        "        \n",
-        "        If the samples are equally-spaced and N is even, then the error\n",
-        "        term is :math:`B_N (\\Delta x)^{N+3} f^{N+2}(\\xi)`.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        rn : int\n",
-        "            The integer order for equally-spaced data or the relative positions of\n",
-        "            the samples with the first sample at 0 and the last at N, where N+1 is\n",
-        "            the length of `rn`.  N is the order of the Newton-Cotes integration.\n",
-        "        equal : int, optional\n",
-        "            Set to 1 to enforce equally spaced data.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        an : ndarray\n",
-        "            1-D array of weights to apply to the function at the provided sample\n",
-        "            positions.\n",
-        "        B : float\n",
-        "            Error coefficient.\n",
-        "        \n",
-        "        Notes\n",
-        "        -----\n",
-        "        Normally, the Newton-Cotes rules are used on smaller integration\n",
-        "        regions and a composite rule is used to return the total integral.\n",
-        "    \n",
-        "    odeint(func, y0, t, args=(), Dfun=None, col_deriv=0, full_output=0, ml=None, mu=None, rtol=None, atol=None, tcrit=None, h0=0.0, hmax=0.0, hmin=0.0, ixpr=0, mxstep=0, mxhnil=0, mxordn=12, mxords=5, printmessg=0)\n",
-        "        Integrate a system of ordinary differential equations.\n",
-        "        \n",
-        "        Solve a system of ordinary differential equations using lsoda from the\n",
-        "        FORTRAN library odepack.\n",
-        "        \n",
-        "        Solves the initial value problem for stiff or non-stiff systems\n",
-        "        of first order ode-s::\n",
-        "        \n",
-        "            dy/dt = func(y,t0,...)\n",
-        "        \n",
-        "        where y can be a vector.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        func : callable(y, t0, ...)\n",
-        "            Computes the derivative of y at t0.\n",
-        "        y0 : array\n",
-        "            Initial condition on y (can be a vector).\n",
-        "        t : array\n",
-        "            A sequence of time points for which to solve for y.  The initial\n",
-        "            value point should be the first element of this sequence.\n",
-        "        args : tuple, optional\n",
-        "            Extra arguments to pass to function.\n",
-        "        Dfun : callable(y, t0, ...)\n",
-        "            Gradient (Jacobian) of `func`.\n",
-        "        col_deriv : bool, optional\n",
-        "            True if `Dfun` defines derivatives down columns (faster),\n",
-        "            otherwise `Dfun` should define derivatives across rows.\n",
-        "        full_output : bool, optional\n",
-        "            True if to return a dictionary of optional outputs as the second output\n",
-        "        printmessg : bool, optional\n",
-        "            Whether to print the convergence message\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        y : array, shape (len(t), len(y0))\n",
-        "            Array containing the value of y for each desired time in t,\n",
-        "            with the initial value `y0` in the first row.\n",
-        "        infodict : dict, only returned if full_output == True\n",
-        "            Dictionary containing additional output information\n",
-        "        \n",
-        "            =======  ============================================================\n",
-        "            key      meaning\n",
-        "            =======  ============================================================\n",
-        "            'hu'     vector of step sizes successfully used for each time step.\n",
-        "            'tcur'   vector with the value of t reached for each time step.\n",
-        "                     (will always be at least as large as the input times).\n",
-        "            'tolsf'  vector of tolerance scale factors, greater than 1.0,\n",
-        "                     computed when a request for too much accuracy was detected.\n",
-        "            'tsw'    value of t at the time of the last method switch\n",
-        "                     (given for each time step)\n",
-        "            'nst'    cumulative number of time steps\n",
-        "            'nfe'    cumulative number of function evaluations for each time step\n",
-        "            'nje'    cumulative number of jacobian evaluations for each time step\n",
-        "            'nqu'    a vector of method orders for each successful step.\n",
-        "            'imxer'  index of the component of largest magnitude in the\n",
-        "                     weighted local error vector (e / ewt) on an error return, -1\n",
-        "                     otherwise.\n",
-        "            'lenrw'  the length of the double work array required.\n",
-        "            'leniw'  the length of integer work array required.\n",
-        "            'mused'  a vector of method indicators for each successful time step:\n",
-        "                     1: adams (nonstiff), 2: bdf (stiff)\n",
-        "            =======  ============================================================\n",
-        "        \n",
-        "        Other Parameters\n",
-        "        ----------------\n",
-        "        ml, mu : int, optional\n",
-        "            If either of these are not None or non-negative, then the\n",
-        "            Jacobian is assumed to be banded.  These give the number of\n",
-        "            lower and upper non-zero diagonals in this banded matrix.\n",
-        "            For the banded case, `Dfun` should return a matrix whose\n",
-        "            columns contain the non-zero bands (starting with the\n",
-        "            lowest diagonal).  Thus, the return matrix from `Dfun` should\n",
-        "            have shape ``len(y0) * (ml + mu + 1)`` when ``ml >=0`` or ``mu >=0``.\n",
-        "        rtol, atol : float, optional\n",
-        "            The input parameters `rtol` and `atol` determine the error\n",
-        "            control performed by the solver.  The solver will control the\n",
-        "            vector, e, of estimated local errors in y, according to an\n",
-        "            inequality of the form ``max-norm of (e / ewt) <= 1``,\n",
-        "            where ewt is a vector of positive error weights computed as\n",
-        "            ``ewt = rtol * abs(y) + atol``.\n",
-        "            rtol and atol can be either vectors the same length as y or scalars.\n",
-        "            Defaults to 1.49012e-8.\n",
-        "        tcrit : ndarray, optional\n",
-        "            Vector of critical points (e.g. singularities) where integration\n",
-        "            care should be taken.\n",
-        "        h0 : float, (0: solver-determined), optional\n",
-        "            The step size to be attempted on the first step.\n",
-        "        hmax : float, (0: solver-determined), optional\n",
-        "            The maximum absolute step size allowed.\n",
-        "        hmin : float, (0: solver-determined), optional\n",
-        "            The minimum absolute step size allowed.\n",
-        "        ixpr : bool, optional\n",
-        "            Whether to generate extra printing at method switches.\n",
-        "        mxstep : int, (0: solver-determined), optional\n",
-        "            Maximum number of (internally defined) steps allowed for each\n",
-        "            integration point in t.\n",
-        "        mxhnil : int, (0: solver-determined), optional\n",
-        "            Maximum number of messages printed.\n",
-        "        mxordn : int, (0: solver-determined), optional\n",
-        "            Maximum order to be allowed for the non-stiff (Adams) method.\n",
-        "        mxords : int, (0: solver-determined), optional\n",
-        "            Maximum order to be allowed for the stiff (BDF) method.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        ode : a more object-oriented integrator based on VODE.\n",
-        "        quad : for finding the area under a curve.\n",
-        "    \n",
-        "    quad(func, a, b, args=(), full_output=0, epsabs=1.49e-08, epsrel=1.49e-08, limit=50, points=None, weight=None, wvar=None, wopts=None, maxp1=50, limlst=50)\n",
-        "        Compute a definite integral.\n",
-        "        \n",
-        "        Integrate func from a to b (possibly infinite interval) using a technique\n",
-        "        from the Fortran library QUADPACK.\n",
-        "        \n",
-        "        If func takes many arguments, it is integrated along the axis corresponding\n",
-        "        to the first argument. Use the keyword argument `args` to pass the other\n",
-        "        arguments.\n",
-        "        \n",
-        "        Run scipy.integrate.quad_explain() for more information on the\n",
-        "        more esoteric inputs and outputs.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        \n",
-        "        func : function\n",
-        "            A Python function or method to integrate.\n",
-        "        a : float\n",
-        "            Lower limit of integration (use -numpy.inf for -infinity).\n",
-        "        b : float\n",
-        "            Upper limit of integration (use numpy.inf for +infinity).\n",
-        "        args : tuple, optional\n",
-        "            extra arguments to pass to func\n",
-        "        full_output : int\n",
-        "            Non-zero to return a dictionary of integration information.\n",
-        "            If non-zero, warning messages are also suppressed and the\n",
-        "            message is appended to the output tuple.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        \n",
-        "        y : float\n",
-        "            The integral of func from a to b.\n",
-        "        abserr : float\n",
-        "            an estimate of the absolute error in the result.\n",
-        "        \n",
-        "        infodict : dict\n",
-        "            a dictionary containing additional information.\n",
-        "            Run scipy.integrate.quad_explain() for more information.\n",
-        "        message :\n",
-        "            a convergence message.\n",
-        "        explain :\n",
-        "            appended only with 'cos' or 'sin' weighting and infinite\n",
-        "            integration limits, it contains an explanation of the codes in\n",
-        "            infodict['ierlst']\n",
-        "        \n",
-        "        Other Parameters\n",
-        "        ----------------\n",
-        "        epsabs :\n",
-        "            absolute error tolerance.\n",
-        "        epsrel :\n",
-        "            relative error tolerance.\n",
-        "        limit :\n",
-        "            an upper bound on the number of subintervals used in the adaptive\n",
-        "            algorithm.\n",
-        "        points :\n",
-        "            a sequence of break points in the bounded integration interval\n",
-        "            where local difficulties of the integrand may occur (e.g.,\n",
-        "            singularities, discontinuities). The sequence does not have\n",
-        "            to be sorted.\n",
-        "        weight :\n",
-        "            string indicating weighting function.\n",
-        "        wvar :\n",
-        "            variables for use with weighting functions.\n",
-        "        limlst :\n",
-        "            Upper bound on the number of cylces (>=3) for use with a sinusoidal\n",
-        "            weighting and an infinite end-point.\n",
-        "        wopts :\n",
-        "            Optional input for reusing Chebyshev moments.\n",
-        "        maxp1 :\n",
-        "            An upper bound on the number of Chebyshev moments.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        dblquad, tplquad : double and triple integrals\n",
-        "        fixed_quad : fixed-order Gaussian quadrature\n",
-        "        quadrature : adaptive Gaussian quadrature\n",
-        "        odeint, ode : ODE integrators\n",
-        "        simps, trapz, romb : integrators for sampled data\n",
-        "        scipy.special : for coefficients and roots of orthogonal polynomials\n",
-        "        \n",
-        "        Examples\n",
-        "        --------\n",
-        "        Calculate :math:`\\int^4_0 x^2 dx` and compare with an analytic result\n",
-        "        \n",
-        "        >>> from scipy import integrate\n",
-        "        >>> x2 = lambda x: x**2\n",
-        "        >>> integrate.quad(x2,0.,4.)\n",
-        "        (21.333333333333332, 2.3684757858670003e-13)\n",
-        "        >> print 4.**3/3\n",
-        "        21.3333333333\n",
-        "        \n",
-        "        Calculate :math:`\\int^\\infty_0 e^{-x} dx`\n",
-        "        \n",
-        "        >>> invexp = lambda x: exp(-x)\n",
-        "        >>> integrate.quad(invexp,0,inf)\n",
-        "        (0.99999999999999989, 5.8426061711142159e-11)\n",
-        "        \n",
-        "        >>> f = lambda x,a : a*x\n",
-        "        >>> y, err = integrate.quad(f, 0, 1, args=(1,))\n",
-        "        >>> y\n",
-        "        0.5\n",
-        "        >>> y, err = integrate.quad(f, 0, 1, args=(3,))\n",
-        "        >>> y\n",
-        "        1.5\n",
-        "    \n",
-        "    quad_explain(output=<IPython.zmq.iostream.OutStream object>)\n",
-        "        Print extra information about integrate.quad() parameters and returns.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        output : instance with \"write\" method\n",
-        "            Information about `quad` is passed to ``output.write()``.\n",
-        "            Default is ``sys.stdout``.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        None\n",
-        "    \n",
-        "    quadrature(func, a, b, args=(), tol=1.49e-08, rtol=1.49e-08, maxiter=50, vec_func=True)\n",
-        "        Compute a definite integral using fixed-tolerance Gaussian quadrature.\n",
-        "        \n",
-        "        Integrate `func` from `a` to `b` using Gaussian quadrature\n",
-        "        with absolute tolerance `tol`.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        func : function\n",
-        "            A Python function or method to integrate.\n",
-        "        a : float\n",
-        "            Lower limit of integration.\n",
-        "        b : float\n",
-        "            Upper limit of integration.\n",
-        "        args : tuple, optional\n",
-        "            Extra arguments to pass to function.\n",
-        "        tol, rol : float, optional\n",
-        "            Iteration stops when error between last two iterates is less than\n",
-        "            `tol` OR the relative change is less than `rtol`.\n",
-        "        maxiter : int, optional\n",
-        "            Maximum number of iterations.\n",
-        "        vec_func : bool, optional\n",
-        "            True or False if func handles arrays as arguments (is\n",
-        "            a \"vector\" function). Default is True.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        val : float\n",
-        "            Gaussian quadrature approximation (within tolerance) to integral.\n",
-        "        err : float\n",
-        "            Difference between last two estimates of the integral.\n",
-        "        \n",
-        "        See also\n",
-        "        --------\n",
-        "        romberg: adaptive Romberg quadrature\n",
-        "        fixed_quad: fixed-order Gaussian quadrature\n",
-        "        quad: adaptive quadrature using QUADPACK\n",
-        "        dblquad: double integrals\n",
-        "        tplquad: triple integrals\n",
-        "        romb: integrator for sampled data\n",
-        "        simps: integrator for sampled data\n",
-        "        cumtrapz: cumulative integration for sampled data\n",
-        "        ode: ODE integrator\n",
-        "        odeint: ODE integrator\n",
-        "    \n",
-        "    romb(y, dx=1.0, axis=-1, show=False)\n",
-        "        Romberg integration using samples of a function.\n",
-        "        \n",
-        "        Parameters\n",
-        "        -----------\n",
-        "        y : array_like\n",
-        "            A vector of ``2**k + 1`` equally-spaced samples of a function.\n",
-        "        dx : array_like, optional\n",
-        "            The sample spacing. Default is 1.\n",
-        "        axis : array_like?, optional\n",
-        "            The axis along which to integrate. Default is -1 (last axis).\n",
-        "        show : bool, optional\n",
-        "               When y is a single 1-D array, then if this argument is True\n",
-        "               print the table showing Richardson extrapolation from the\n",
-        "               samples. Default is False.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        ret : array_like?\n",
-        "            The integrated result for each axis.\n",
-        "        \n",
-        "        See also\n",
-        "        --------\n",
-        "        quad - adaptive quadrature using QUADPACK\n",
-        "        romberg - adaptive Romberg quadrature\n",
-        "        quadrature - adaptive Gaussian quadrature\n",
-        "        fixed_quad - fixed-order Gaussian quadrature\n",
-        "        dblquad, tplquad - double and triple integrals\n",
-        "        simps, trapz - integrators for sampled data\n",
-        "        cumtrapz - cumulative integration for sampled data\n",
-        "        ode, odeint - ODE integrators\n",
-        "    \n",
-        "    romberg(function, a, b, args=(), tol=1.48e-08, rtol=1.48e-08, show=False, divmax=10, vec_func=False)\n",
-        "        Romberg integration of a callable function or method.\n",
-        "        \n",
-        "        Returns the integral of `function` (a function of one variable)\n",
-        "        over the interval (`a`, `b`).\n",
-        "        \n",
-        "        If `show` is 1, the triangular array of the intermediate results\n",
-        "        will be printed.  If `vec_func` is True (default is False), then `function` is\n",
-        "        assumed to support vector arguments.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        function : callable\n",
-        "            Function to be integrated.\n",
-        "        a : float\n",
-        "            Lower limit of integration.\n",
-        "        b : float\n",
-        "            Upper limit of integration.\n",
-        "        \n",
-        "        Returns\n",
-        "        --------\n",
-        "        results  : float\n",
-        "            Result of the integration.\n",
-        "        \n",
-        "        Other Parameters\n",
-        "        ----------------\n",
-        "        args : tuple, optional\n",
-        "            Extra arguments to pass to function. Each element of `args` will\n",
-        "            be passed as a single argument to `func`. Default is to pass no\n",
-        "            extra arguments.\n",
-        "        tol, rtol : float, optional\n",
-        "            The desired absolute and relative tolerances. Defaults are 1.48e-8.\n",
-        "        show : bool, optional\n",
-        "            Whether to print the results. Default is False.\n",
-        "        divmax : int, optional\n",
-        "            Maximum order of extrapolation. Default is 10.\n",
-        "        vec_func : bool, optional\n",
-        "            Whether `func` handles arrays as arguments (i.e whether it is a\n",
-        "            \"vector\" function). Default is False.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        fixed_quad : Fixed-order Gaussian quadrature.\n",
-        "        quad : Adaptive quadrature using QUADPACK.\n",
-        "        dblquad, tplquad : Double and triple integrals.\n",
-        "        romb, simps, trapz : Integrators for sampled data.\n",
-        "        cumtrapz : Cumulative integration for sampled data.\n",
-        "        ode, odeint : ODE integrators.\n",
-        "        \n",
-        "        References\n",
-        "        ----------\n",
-        "        .. [1] 'Romberg's method' http://en.wikipedia.org/wiki/Romberg%27s_method\n",
-        "        \n",
-        "        Examples\n",
-        "        --------\n",
-        "        Integrate a gaussian from 0 to 1 and compare to the error function.\n",
-        "        \n",
-        "        >>> from scipy.special import erf\n",
-        "        >>> gaussian = lambda x: 1/np.sqrt(np.pi) * np.exp(-x**2)\n",
-        "        >>> result = romberg(gaussian, 0, 1, show=True)\n",
-        "        Romberg integration of <function vfunc at 0x101eceaa0> from [0, 1]\n",
-        "        \n",
-        "        ::\n",
-        "        \n",
-        "           Steps  StepSize  Results\n",
-        "               1  1.000000  0.385872\n",
-        "               2  0.500000  0.412631  0.421551\n",
-        "               4  0.250000  0.419184  0.421368  0.421356\n",
-        "               8  0.125000  0.420810  0.421352  0.421350  0.421350\n",
-        "              16  0.062500  0.421215  0.421350  0.421350  0.421350  0.421350\n",
-        "              32  0.031250  0.421317  0.421350  0.421350  0.421350  0.421350  0.421350\n",
-        "        \n",
-        "        The final result is 0.421350396475 after 33 function evaluations.\n",
-        "        \n",
-        "        >>> print 2*result,erf(1)\n",
-        "        0.84270079295 0.84270079295\n",
-        "    \n",
-        "    simps(y, x=None, dx=1, axis=-1, even='avg')\n",
-        "        Integrate y(x) using samples along the given axis and the composite\n",
-        "        Simpson's rule.  If x is None, spacing of dx is assumed.\n",
-        "        \n",
-        "        If there are an even number of samples, N, then there are an odd\n",
-        "        number of intervals (N-1), but Simpson's rule requires an even number\n",
-        "        of intervals.  The parameter 'even' controls how this is handled.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        y : array_like\n",
-        "            Array to be integrated.\n",
-        "        x : array_like, optional\n",
-        "            If given, the points at which `y` is sampled.\n",
-        "        dx : int, optional\n",
-        "            Spacing of integration points along axis of `y`. Only used when\n",
-        "            `x` is None. Default is 1.\n",
-        "        axis : int, optional\n",
-        "            Axis along which to integrate. Default is the last axis.\n",
-        "        even : {'avg', 'first', 'str'}, optional\n",
-        "            'avg' : Average two results:1) use the first N-2 intervals with\n",
-        "                      a trapezoidal rule on the last interval and 2) use the last\n",
-        "                      N-2 intervals with a trapezoidal rule on the first interval.\n",
-        "        \n",
-        "            'first' : Use Simpson's rule for the first N-2 intervals with\n",
-        "                    a trapezoidal rule on the last interval.\n",
-        "        \n",
-        "            'last' : Use Simpson's rule for the last N-2 intervals with a\n",
-        "                   trapezoidal rule on the first interval.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        quad: adaptive quadrature using QUADPACK\n",
-        "        romberg: adaptive Romberg quadrature\n",
-        "        quadrature: adaptive Gaussian quadrature\n",
-        "        fixed_quad: fixed-order Gaussian quadrature\n",
-        "        dblquad: double integrals\n",
-        "        tplquad: triple integrals\n",
-        "        romb: integrators for sampled data\n",
-        "        cumtrapz: cumulative integration for sampled data\n",
-        "        ode: ODE integrators\n",
-        "        odeint: ODE integrators\n",
-        "        \n",
-        "        Notes\n",
-        "        -----\n",
-        "        For an odd number of samples that are equally spaced the result is\n",
-        "        exact if the function is a polynomial of order 3 or less.  If\n",
-        "        the samples are not equally spaced, then the result is exact only\n",
-        "        if the function is a polynomial of order 2 or less.\n",
-        "    \n",
-        "    tplquad(func, a, b, gfun, hfun, qfun, rfun, args=(), epsabs=1.49e-08, epsrel=1.49e-08)\n",
-        "        Compute a triple (definite) integral.\n",
-        "        \n",
-        "        Return the triple integral of func(z, y, x) from\n",
-        "        x=a..b, y=gfun(x)..hfun(x), and z=qfun(x,y)..rfun(x,y)\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        func : function\n",
-        "            A Python function or method of at least three variables in the\n",
-        "            order (z, y, x).\n",
-        "        (a,b) : tuple\n",
-        "            The limits of integration in x: a < b\n",
-        "        gfun : function\n",
-        "            The lower boundary curve in y which is a function taking a single\n",
-        "            floating point argument (x) and returning a floating point result:\n",
-        "            a lambda function can be useful here.\n",
-        "        hfun : function\n",
-        "            The upper boundary curve in y (same requirements as gfun).\n",
-        "        qfun : function\n",
-        "            The lower boundary surface in z.  It must be a function that takes\n",
-        "            two floats in the order (x, y) and returns a float.\n",
-        "        rfun : function\n",
-        "            The upper boundary surface in z. (Same requirements as qfun.)\n",
-        "        args : Arguments\n",
-        "            Extra arguments to pass to func3d.\n",
-        "        epsabs : float, optional\n",
-        "            Absolute tolerance passed directly to the innermost 1-D quadrature\n",
-        "            integration. Default is 1.49e-8.\n",
-        "        epsrel : float, optional\n",
-        "            Relative tolerance of the innermost 1-D integrals. Default is 1.49e-8.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        y : float\n",
-        "            The resultant integral.\n",
-        "        abserr : float\n",
-        "            An estimate of the error.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        quad: Adaptive quadrature using QUADPACK\n",
-        "        quadrature: Adaptive Gaussian quadrature\n",
-        "        fixed_quad: Fixed-order Gaussian quadrature\n",
-        "        dblquad: Double integrals\n",
-        "        romb: Integrators for sampled data\n",
-        "        simps: Integrators for sampled data\n",
-        "        ode: ODE integrators\n",
-        "        odeint: ODE integrators\n",
-        "        scipy.special: For coefficients and roots of orthogonal polynomials\n",
-        "    \n",
-        "    trapz(y, x=None, dx=1.0, axis=-1)\n",
-        "        Integrate along the given axis using the composite trapezoidal rule.\n",
-        "        \n",
-        "        Integrate `y` (`x`) along given axis.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        y : array_like\n",
-        "            Input array to integrate.\n",
-        "        x : array_like, optional\n",
-        "            If `x` is None, then spacing between all `y` elements is `dx`.\n",
-        "        dx : scalar, optional\n",
-        "            If `x` is None, spacing given by `dx` is assumed. Default is 1.\n",
-        "        axis : int, optional\n",
-        "            Specify the axis.\n",
-        "        \n",
-        "        Returns\n",
-        "        -------\n",
-        "        trapz : float\n",
-        "            Definite integral as approximated by trapezoidal rule.\n",
-        "        \n",
-        "        See Also\n",
-        "        --------\n",
-        "        sum, cumsum\n",
-        "        \n",
-        "        Notes\n",
-        "        -----\n",
-        "        Image [2]_ illustrates trapezoidal rule -- y-axis locations of points will\n",
-        "        be taken from `y` array, by default x-axis distances between points will be\n",
-        "        1.0, alternatively they can be provided with `x` array or with `dx` scalar.\n",
-        "        Return value will be equal to combined area under the red lines.\n",
-        "        \n",
-        "        \n",
-        "        References\n",
-        "        ----------\n",
-        "        .. [1] Wikipedia page: http://en.wikipedia.org/wiki/Trapezoidal_rule\n",
-        "        \n",
-        "        .. [2] Illustration image:\n",
-        "               http://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png\n",
-        "        \n",
-        "        Examples\n",
-        "        --------\n",
-        "        >>> np.trapz([1,2,3])\n",
-        "        4.0\n",
-        "        >>> np.trapz([1,2,3], x=[4,6,8])\n",
-        "        8.0\n",
-        "        >>> np.trapz([1,2,3], dx=2)\n",
-        "        8.0\n",
-        "        >>> a = np.arange(6).reshape(2, 3)\n",
-        "        >>> a\n",
-        "        array([[0, 1, 2],\n",
-        "               [3, 4, 5]])\n",
-        "        >>> np.trapz(a, axis=0)\n",
-        "        array([ 1.5,  2.5,  3.5])\n",
-        "        >>> np.trapz(a, axis=1)\n",
-        "        array([ 2.,  8.])\n",
-        "\n",
-        "DATA\n",
-        "    __all__ = ['absolute_import', 'complex_ode', 'cumtrapz', 'dblquad', 'd...\n",
-        "    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n",
-        "    division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...\n",
-        "    print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n",
-        "\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "quad is the basic integrator for a general (not sampled) function.  It uses a general-interface from the Fortran package QUADPACK (QAGS or QAGI).  It will return the integral in an interval and an estimate of the error in the approximation"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def f(x):\n",
-      "    return math.sin(x)**2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "I, err = integrate.quad(f, 0.0, 2.0*math.pi, epsabs=1.e-14)\n",
-      "print I\n",
-      "print err"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.14159265359\n",
-        "2.30587916716e-09\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(integrate.quad)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on function quad in module scipy.integrate.quadpack:\n",
-        "\n",
-        "quad(func, a, b, args=(), full_output=0, epsabs=1.49e-08, epsrel=1.49e-08, limit=50, points=None, weight=None, wvar=None, wopts=None, maxp1=50, limlst=50)\n",
-        "    Compute a definite integral.\n",
-        "    \n",
-        "    Integrate func from a to b (possibly infinite interval) using a technique\n",
-        "    from the Fortran library QUADPACK.\n",
-        "    \n",
-        "    If func takes many arguments, it is integrated along the axis corresponding\n",
-        "    to the first argument. Use the keyword argument `args` to pass the other\n",
-        "    arguments.\n",
-        "    \n",
-        "    Run scipy.integrate.quad_explain() for more information on the\n",
-        "    more esoteric inputs and outputs.\n",
-        "    \n",
-        "    Parameters\n",
-        "    ----------\n",
-        "    \n",
-        "    func : function\n",
-        "        A Python function or method to integrate.\n",
-        "    a : float\n",
-        "        Lower limit of integration (use -numpy.inf for -infinity).\n",
-        "    b : float\n",
-        "        Upper limit of integration (use numpy.inf for +infinity).\n",
-        "    args : tuple, optional\n",
-        "        extra arguments to pass to func\n",
-        "    full_output : int\n",
-        "        Non-zero to return a dictionary of integration information.\n",
-        "        If non-zero, warning messages are also suppressed and the\n",
-        "        message is appended to the output tuple.\n",
-        "    \n",
-        "    Returns\n",
-        "    -------\n",
-        "    \n",
-        "    y : float\n",
-        "        The integral of func from a to b.\n",
-        "    abserr : float\n",
-        "        an estimate of the absolute error in the result.\n",
-        "    \n",
-        "    infodict : dict\n",
-        "        a dictionary containing additional information.\n",
-        "        Run scipy.integrate.quad_explain() for more information.\n",
-        "    message :\n",
-        "        a convergence message.\n",
-        "    explain :\n",
-        "        appended only with 'cos' or 'sin' weighting and infinite\n",
-        "        integration limits, it contains an explanation of the codes in\n",
-        "        infodict['ierlst']\n",
-        "    \n",
-        "    Other Parameters\n",
-        "    ----------------\n",
-        "    epsabs :\n",
-        "        absolute error tolerance.\n",
-        "    epsrel :\n",
-        "        relative error tolerance.\n",
-        "    limit :\n",
-        "        an upper bound on the number of subintervals used in the adaptive\n",
-        "        algorithm.\n",
-        "    points :\n",
-        "        a sequence of break points in the bounded integration interval\n",
-        "        where local difficulties of the integrand may occur (e.g.,\n",
-        "        singularities, discontinuities). The sequence does not have\n",
-        "        to be sorted.\n",
-        "    weight :\n",
-        "        string indicating weighting function.\n",
-        "    wvar :\n",
-        "        variables for use with weighting functions.\n",
-        "    limlst :\n",
-        "        Upper bound on the number of cylces (>=3) for use with a sinusoidal\n",
-        "        weighting and an infinite end-point.\n",
-        "    wopts :\n",
-        "        Optional input for reusing Chebyshev moments.\n",
-        "    maxp1 :\n",
-        "        An upper bound on the number of Chebyshev moments.\n",
-        "    \n",
-        "    See Also\n",
-        "    --------\n",
-        "    dblquad, tplquad : double and triple integrals\n",
-        "    fixed_quad : fixed-order Gaussian quadrature\n",
-        "    quadrature : adaptive Gaussian quadrature\n",
-        "    odeint, ode : ODE integrators\n",
-        "    simps, trapz, romb : integrators for sampled data\n",
-        "    scipy.special : for coefficients and roots of orthogonal polynomials\n",
-        "    \n",
-        "    Examples\n",
-        "    --------\n",
-        "    Calculate :math:`\\int^4_0 x^2 dx` and compare with an analytic result\n",
-        "    \n",
-        "    >>> from scipy import integrate\n",
-        "    >>> x2 = lambda x: x**2\n",
-        "    >>> integrate.quad(x2,0.,4.)\n",
-        "    (21.333333333333332, 2.3684757858670003e-13)\n",
-        "    >> print 4.**3/3\n",
-        "    21.3333333333\n",
-        "    \n",
-        "    Calculate :math:`\\int^\\infty_0 e^{-x} dx`\n",
-        "    \n",
-        "    >>> invexp = lambda x: exp(-x)\n",
-        "    >>> integrate.quad(invexp,0,inf)\n",
-        "    (0.99999999999999989, 5.8426061711142159e-11)\n",
-        "    \n",
-        "    >>> f = lambda x,a : a*x\n",
-        "    >>> y, err = integrate.quad(f, 0, 1, args=(1,))\n",
-        "    >>> y\n",
-        "    0.5\n",
-        "    >>> y, err = integrate.quad(f, 0, 1, args=(3,))\n",
-        "    >>> y\n",
-        "    1.5\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "sometimes our integrand function takes optional arguments"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def g(x, A, sigma):\n",
-      "    return A*math.exp(-x**2/sigma**2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "I, err = integrate.quad(g, -1.0, 1.0, args=(1.0, 2.0))\n",
-      "print I, err"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1.84512402565 2.04849917657e-14\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "numpy defines the inf quantity which can be used in the integration limits.  We can integrate a Gaussian (we know the answer is sqrt(pi)\n",
-      "\n",
-      "Note: behind the scenes, what the integration function does is do a variable transform like: $t = 1/x$.  This works when one limit is $\\infty$, giving\n",
-      "\n",
-      "$$\\int_a^b f(x) dx = \\int_{1/b}^{1/a} \\frac{1}{t^2} f\\left (\\frac{1}{t}\\right) dt$$"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "I, err = integrate.quad(g, -np.inf, np.inf, args=(1.0, 1.0))\n",
-      "print I, err"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1.77245385091 1.42026367809e-08\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Multidimensional integrals"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "multidimensional integration can be done with successive calls to quad(), but there are wrappers that help"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's compute \n",
-      "\n",
-      "$$I = \\int_{y=0}^{1/2} \\int_{x=0}^{1-2y} xy dxdy = \\frac{1}{96}$$\n",
-      "\n",
-      "(this example comes from the SciPy tutorial)\n",
-      "\n",
-      "Notice that the limits of integration in x depend on y.\n",
-      "\n",
-      "Note the form of the function:\n",
-      "\n",
-      "dblquad(f, a, b, ylo, yhi)\n",
-      "\n",
-      "where f = f(y, x) -- the y argument is first\n",
-      "\n",
-      "The integral will be from: x = [a,b], and y = ylo(x), y = yhi(x)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def integrand(x,y):\n",
-      "    return x*y\n",
-      "\n",
-      "def x_lower_lim(y):\n",
-      "    return 0\n",
-      "    \n",
-      "def x_upper_lim(y):\n",
-      "    return 1-2*y\n",
-      "\n",
-      "# we change the definitions of x and y in this call\n",
-      "I, err = integrate.dblquad(integrand, 0.0, 0.5, x_lower_lim, x_upper_lim)\n",
-      "print I, 1.0/I\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.0104166666667 96.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "If you remember the python lambda functions (one expression functions), you can do this more compactly:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "I, err = integrate.dblquad(lambda x, y: x*y, 0.0, 0.5, lambda y: 0, lambda y: 1-2*y)\n",
-      "print I"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.0104166666667\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "integration of a sampled function"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "here we integrate a function that is defined only at a sequece of points.  Recall that Simpson's rule will use piecewise parabola data.  Let's compute\n",
-      "\n",
-      "$$I = \\int_0^{2\\pi} f(x_i) dx$$\n",
-      "\n",
-      "with $x_i = 0, \\ldots, 2\\pi$ defined at $N$ points"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "N = 5\n",
-      "x = np.linspace(0.0, 2.0*math.pi, N, endpoint=True)\n",
-      "y = np.sin(x)**2\n",
-      "\n",
-      "I = integrate.simps(y, x)\n",
-      "print I"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4.18879020479\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Romberg integration is specific to equally-spaced samples, where $N = 2^k + 1$  and can be more converge faster (it uses extrapolation of coarser integration results to achieve higher accuracy)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "N = 17\n",
-      "x = np.linspace(0.0, 2.0*math.pi, N, endpoint=True)\n",
-      "y = np.sin(x)**2\n",
-      "\n",
-      "I = integrate.romb(y, dx=x[1]-x[0])\n",
-      "print I"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.04961657237\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Interpolation"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "the interp1d function allows for a variety of 1-d interpolation methods.  It returns an object that acts as a function, which can be evaluated at any point."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import scipy.interpolate as interpolate"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help (interpolate.interp1d)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on class interp1d in module scipy.interpolate.interpolate:\n",
-        "\n",
-        "class interp1d(scipy.interpolate.polyint._Interpolator1D)\n",
-        " |  interp1d(x, y, kind='linear', axis=-1, copy=True, bounds_error=True,\n",
-        " |           fill_value=np.nan)\n",
-        " |  \n",
-        " |  Interpolate a 1-D function.\n",
-        " |  \n",
-        " |  `x` and `y` are arrays of values used to approximate some function f:\n",
-        " |  ``y = f(x)``.  This class returns a function whose call method uses\n",
-        " |  interpolation to find the value of new points.\n",
-        " |  \n",
-        " |  Parameters\n",
-        " |  ----------\n",
-        " |  x : (N,) array_like\n",
-        " |      A 1-D array of monotonically increasing real values.\n",
-        " |  y : (...,N,...) array_like\n",
-        " |      A N-D array of real values. The length of `y` along the interpolation\n",
-        " |      axis must be equal to the length of `x`.\n",
-        " |  kind : str or int, optional\n",
-        " |      Specifies the kind of interpolation as a string\n",
-        " |      ('linear','nearest', 'zero', 'slinear', 'quadratic, 'cubic')\n",
-        " |      or as an integer specifying the order of the spline interpolator\n",
-        " |      to use. Default is 'linear'.\n",
-        " |  axis : int, optional\n",
-        " |      Specifies the axis of `y` along which to interpolate.\n",
-        " |      Interpolation defaults to the last axis of `y`.\n",
-        " |  copy : bool, optional\n",
-        " |      If True, the class makes internal copies of x and y.\n",
-        " |      If False, references to `x` and `y` are used. The default is to copy.\n",
-        " |  bounds_error : bool, optional\n",
-        " |      If True, a ValueError is raised any time interpolation is attempted on\n",
-        " |      a value outside of the range of x (where extrapolation is\n",
-        " |      necessary). If False, out of bounds values are assigned `fill_value`.\n",
-        " |      By default, an error is raised.\n",
-        " |  fill_value : float, optional\n",
-        " |      If provided, then this value will be used to fill in for requested\n",
-        " |      points outside of the data range. If not provided, then the default\n",
-        " |      is NaN.\n",
-        " |  \n",
-        " |  See Also\n",
-        " |  --------\n",
-        " |  UnivariateSpline : A more recent wrapper of the FITPACK routines.\n",
-        " |  splrep, splev\n",
-        " |      Spline interpolation based on FITPACK.\n",
-        " |  interp2d\n",
-        " |  \n",
-        " |  Examples\n",
-        " |  --------\n",
-        " |  >>> from scipy import interpolate\n",
-        " |  >>> x = np.arange(0, 10)\n",
-        " |  >>> y = np.exp(-x/3.0)\n",
-        " |  >>> f = interpolate.interp1d(x, y)\n",
-        " |  \n",
-        " |  >>> xnew = np.arange(0,9, 0.1)\n",
-        " |  >>> ynew = f(xnew)   # use interpolation function returned by `interp1d`\n",
-        " |  >>> plt.plot(x, y, 'o', xnew, ynew, '-')\n",
-        " |  >>> plt.show()\n",
-        " |  \n",
-        " |  Method resolution order:\n",
-        " |      interp1d\n",
-        " |      scipy.interpolate.polyint._Interpolator1D\n",
-        " |      __builtin__.object\n",
-        " |  \n",
-        " |  Methods defined here:\n",
-        " |  \n",
-        " |  __init__(self, x, y, kind='linear', axis=-1, copy=True, bounds_error=True, fill_value=nan)\n",
-        " |      Initialize a 1D linear interpolation class.\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Methods inherited from scipy.interpolate.polyint._Interpolator1D:\n",
-        " |  \n",
-        " |  __call__(self, x)\n",
-        " |      Evaluate the interpolant\n",
-        " |      \n",
-        " |      Parameters\n",
-        " |      ----------\n",
-        " |      x : array-like\n",
-        " |          Points to evaluate the interpolant at.\n",
-        " |      \n",
-        " |      Returns\n",
-        " |      -------\n",
-        " |      y : array-like\n",
-        " |          Interpolated values. Shape is determined by replacing\n",
-        " |          the interpolation axis in the original array with the shape of x.\n",
-        " |  \n",
-        " |  ----------------------------------------------------------------------\n",
-        " |  Data descriptors inherited from scipy.interpolate.polyint._Interpolator1D:\n",
-        " |  \n",
-        " |  __dict__\n",
-        " |      dictionary for instance variables (if defined)\n",
-        " |  \n",
-        " |  __weakref__\n",
-        " |      list of weak references to the object (if defined)\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def fExact(x):\n",
-      "    return sin(x)*x\n",
-      "\n",
-      "N = 10\n",
-      "x = np.linspace(0, 20, N)\n",
-      "\n",
-      "y = fExact(x)\n",
-      "\n",
-      "fInterp = interpolate.interp1d(x, y, kind=3)\n",
-      "\n",
-      "# use finer points when we plot\n",
-      "xplot = np.linspace(0, 20, 10*N)\n",
-      "\n",
-      "pylab.plot(x, y, \"ro\", label=\"known points\")\n",
-      "pylab.plot(xplot, fExact(xplot), \"b:\", label=\"exact function\")\n",
-      "pylab.plot(xplot, fInterp(xplot), \"r-\", label=\"interpolant\")\n",
-      "\n",
-      "pylab.legend(frameon=False, loc=\"best\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "<matplotlib.legend.Legend at 0x2f5c3d0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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sMGEF5eQAO3Ygt++r0sYxbpzYsjAjA5UqcZJXS2Kizso2ACd6ZgCHDgFvvqni\nyZgYMf1m0CDg6lXx3xMngK1bgcGDxV6mw4aJHrd372LdOiDfDoysOMePA05OaPZafVy/LmEcbm5A\ns2bAH39IGITxuXYNUNk4VIf1eYATPTOArl2BX34p9ODDh6InilwO9O0rCvfLlwNDhgB16wJNmoik\nP2eOeDOoUQNo2hTlN62R4P/ARG3bBrz6Kv78U+RaSf3vf4qFEmFhPJ8eEPPnL15U8aSOEz3X6Jnh\nXbsmdlro0EFMIlZ3c+roaOD114HJk/GdbBJatBDTMJkSRKLPzB9/AH5+UkcjFkU4OwPHjuFJrQa6\n6r5rviZNAjw9gcmTizzFNXpmVL78Evj990IPnjolEvyUKWJTaXWTPAD4+wN79wLz56PTgx1wctJp\nuObl/HmACKkuvqq3qTMka2tg5Ehg1SpUrsxJvkRcumGmYtAgsV5GISxMrJj5/nvR4UoT9eoBO3bA\ne8l41L19UidxmqW8sk2fvjJlEzekMWoUsGGDuDuPYnq8lAEpKUB8fDEHmFKid3Nzg4+PD/z9/dGq\nVSt9XooZIVdXvJy7HREhZl+EhQF9+mh34pYtxV8DffviwT/XkZOjdajmZ/duoHdvHD0q2ksYBW9v\n0dnyr7+weDEQGip1QNI5flz0fFPJlGr09erVw5kzZ1CtWrWiF+Yavdm6ckX8PitKK6dOiZH8jh1A\nu3a6u9CCBTj59VFYRuxG8+a6O63Ju3cPqF9f/NfaWupoCpo/H0hMxPOvv4O1NbctVio3F6hQQaw9\nULLNo1HW6DmZlz2HD4sBPAAxZbJvX7EMXpdJHgCmTUPLanFonhKu2/OaugMHgIAAXL5urXr6nlQG\nDwa2bEF5iyxO8qqkpIj9d3W1ly/0nOhlMhm6du2KFi1a4Kf8PWiZWZswARg9GsCdO2IJfGioSPa6\nZm0N2eLFYprm8+e6P7+piogAunXDp58C//0ndTCF1K8vZgMdPIhnzxSNLcsUIvFerHIMfPOmmGKs\nQ3rdSvDYsWOoVasW7t69i8DAQHh5eaFjx46K50PzFenkcjnkcrk+w2GGlJ0tRm+DBonMry/du+NO\nFQeEub6Gm40eI7t8eQRNnqzbfVBNCRGwbx8waxZ+NdZGcEOGABs2YOqObpDLxYzZsuTpU2DpUqBL\nFxUH3LghbnDliYyMRGRkpFbXNNg8+jlz5qBy5cqYltdzg2v05mfTJjGInzwZwMyZojYfEaHXxjRH\nwsNxYuLu+Z4NAAAgAElEQVQUvJ2QjAp4BksQPnJ3R7CuN702FefPizYScXFSR6JaSgrQqBEoMQky\nm4pSR2N8vvgCSE4usBVjfkZVo3/69CkeP34MAEhPT8e+ffvg7e2tr8sxI9ClCxAcDGDnTmD9ejGV\nTs/dx/YtXYoPEuJQCU9hCfHDPy8uDvuXLdPrdY3W3r1AcDD++kvcizVKTk5AixaQhYdJHYlx0kPp\nRm+J/vbt2+jYsSP8/PzQunVr9OrVC0H63n2eSapGDaBhuTixvv2330q3GEpD5VTU5i2N7i6kgezd\nC3Trhl27xKDQaA0ZAmzciGvXyt7tlRMniulDDxQp3eiC3mr09erVw9mzZ/V1emZEcnLEIKRenUxR\nk589u9BKKf3JLl9e8e8YNMZzlEczRCNHhzMWTEZ6OvD330Dnzlig5VIFvevfH3jnHXz49iPM+aYK\nvLykDshwoqKAjh2BOnVUHHDzps4TPa+MZVqLjQXefRdidk3t2qJPh4EETZ6Mj9zdAQB2eAA/nMXH\n9eohMCTEYDEYjchIoHlzsYjB2NnZAR07YvOo3WUqyQPA9OklzDTWQ+nGNJua3b8PrF0rasEPHgBP\nnojRjJcX0Lmz+Gjd2vgWi5gxOnIUstcHAefOATVrGvTaR8LDsX/ZMlhmZGBoTAxo6FA0XLLEoDEY\nhcmTgVq1sLfZh/DwELMYjdrKlaLU9NtvUkdiPB4/BhwdRT5TsdBAk9xpWon+339Ft8PwcKB3b1Hn\nc3ISIxgbGzHj4PBh0QA9ORn46CNRL+aEr18PHwK+vsC33wK9ekkby969yJn2ASz/PVv2ll02bAhs\n2oQfT/mjZUvRA86o3b0L8vDA39tT0KZz2Zh9c+uW6MitcmplTIzYyP3SJZXnMKpZNzq3fr3YcaJZ\nM9Hmdt06say+WTOgQQNR8OreHVi0CDh9Woz2d+0SP/xr1hSzOoFpikiU4zMnTBLfC6mTPIDUFkGI\n/y8TOYfK2DZUN28CqamAry8mTDCBJA8ANWqA/PwR9s4BZGdLHYxh3LkjxqMq6aFsA+h5wZROZGWJ\n/S737BGj9aZN1Xtd8+biNX/+KQrIv/8udr9wcNBruGVJdjbQPvl3WJ09KXrFG4FqDjJU/XoyLL9d\nAnSRSx2O4Rw+LAZCFqYzdgMAiwH98Vn0H0C53lKHYhDNmokPlfQw4wYw9hF9RgYQFCRG8KdOqZ/k\n8+vQATh2TIzs/f2Bo0d1H2cZZfXgDrqFh0C2Zo0onRkJyzEjxff5xg2pQzGcgweBV17B9u1i+p7J\nePVV8Zd3We5ZnJ8eZtwAxpzoiYA33hBzsXfuFHfpNWVtLXbBWLFC7KxeFm/U6ciR8HDMCg7Gx51e\nwQVvbyS0b2+wqZRqq1QJd+UD8fD7DVJHYhhE4r5Uly4oX97Ebkm5uCDbtT7OLj0idSQGsWqVaIGg\nkp5KN8ab6BcvFsWs1at19+doz55invHy5eJGLdftS+VIeDj2TpmCz/btg/NRd7jeeYaf//kHR8KN\nr3vkfsfhKLdxXdn4Hv/3n/gdcXdH9+4llAaM0AU3X8SFLkKoXI5ZwcFG+fOkC0QipZUrrmCup9IN\nSCLFXnrvXiInJ6IbN/Rz8Tt3iFq0IPrf/4iys/VzDTP0UVAQkfh5pVyAnsGaCKBZwcFSh1ZUbi6R\nmxvRmTNSR6J/y5cTjRoldRQaiQoLo69cXBQ/VwTQTHd3igoLkzo0abi6EsXFFXuIJmnb+Eb0168D\nI0aIubX6eGcDRDno0CFxrcGDuT6opvztBmQAKiATgJG2G5DJgOHDxWwtc5dXttmyReztYkr2LV2K\nqQkJBR4rs72KsrPFtHDFtmy6Y3yJfsoU0V88XztjvbC1FdvapacDo0fjyM6dmBUcbPZ/Pmoju3x5\nPEJlPEXB9gLG2m7gUvNheLZ6I8x67l5urphx07kzGjQQW+qakheDhzuojlt42RPAKAcPWjpwQMwp\nUSkpSQxC9XCTxbimV+7ZA1y8CGzZYpjrlS8PbN2KtNatUXHXLnyW120TAD7Ka/NaJlvdqtBzxAjQ\ngSisyx2JNyA2kpnp7o5uRtpuQNbICxkOzqh46JCYvWWOzp8XU4adneGn+4Gg3r3oVXQTrvDAy9bK\nxjp40MazZyVsGqWnGTeAMY3oMzPFSH7xYpGA9eTx44Kz7m7cqYhplq+gZb4kD5ThPx9VIULbP/7A\nwwF9cCv4JkIDAjA7OBjdjLjve8OGgH2ImZdv8so2pupFr6IW+Ad2eAhADB7MsVdR795itrdKeppx\nAxjTiH7pUsDDQ8yM0aGMDLGq+MXm0WfOAJs3i5mWgOjZnfS4vuL4i2iIneiL6Vhkln8+auy334DL\nl+H6zz+Yq8c3Yp0bPBj45BNRoqtUSepodO/gQWDMGPz2m/hZfustqQMqnReDhNnLlqH/uXNIsrND\nty+/NNrBg16Z/Yg+OVn0sPnmG52cLv+MuqSkgqeVy18meUC8ATR3f1mP98QVDIMYAeZUqICTJ4G7\nd3USlum6fRs0ZQo+rf8LHmeaUJIH8KSSI05bt0XuH9ulDkX3srLEym+5HO3bm+7AvlPPnpgbEYGM\n4d9BXs3ZLJN8SooYyxZLX1MrYSyJfvZsYNw4wNNT61Pl5IjGlamp4vP69Uv+yz1/q1tL5MIZSfip\ndm0EhoTgwAHRhKjMIgImTgSNGQff8S1RubLUAZVO5cpAlQlDINtihh0ST50SP+DVq6NOHVGqMmUJ\njYJQ4ewJ4NEjqUPRudxclPy7o8fSjfTdK5OSgCZNxB6X1appdK74eHGTw8lJfH7tmph9UJrmhflb\n3bqmp2P41auwPnYMaNxYcczz5+JesZ+fRmGapg0bgAULRKM4UyrZ5PfggfgFSkpS47fNhMybJ1p2\nq9hb1CR16yY6zr72mtSRGJ63txiV+voWe5hpdq/89ltg2DCNkzwgvjYnT778vH790neoffHnY2hk\nJMaeOgXrJUtEN8Z8dZuLF8Wi2jIjORl4912kL18DsjbRJA8A9vagtu2QG7Zb6kh0K29a5datYjML\ns9C7t+h9U9YQ6bV0I+2I/skTwM1NdGEqxS4JV66In4WpU/UXHwBg5kzRHOvAAdMdzWqKCOjbF/D3\nx/jEOejRQ+z+Zqq+afQjRjkfRLX9m6UORTeePweqVwcSEvDU2g6pqXpZZ2NwX0y6gfc2t4AsJUXv\nG8sb0sKFYjyr8nuUliaS/MOHJY5SjWpEHxERAS8vLzRo0AALFy5UftDq1UCnTmol+fyNgBwcXpZp\n9Oqzz8Qv08SJRXqmJCaKAX9OjgHikMIvv4ia4Ucf4YcfgD7GvgdpCcaH9UO1U3vFNCxzcPKkKMrb\n2cHGxjySPABU8a6LXMdaoieVGalVq4RJXy9m3Ohrs5xSN01QQ3Z2Nrm7u9P169cpMzOTfH19KTY2\ntsAxAOh+xYoU/cUXJZ4vJ4fI05Po9m19RFuCx4+JfH2JCsWZm0sUHS1BPIZw/TpR9epE589LHYlu\nBQQQ7dghdRS6MWcO0XvvUU6O+Fk0KzNnEs2YIXUUhrVzJ1GPHmodqkna1suI/uTJk/Dw8ICbmxus\nrKwwePBg7FDShKPas2fY8v33StsNLFjwsq+2hYUkW5EKlSuLNslffw3sflnjlckK3pRNS5MgNn3I\nyQFGjgSmT0e8rTf27pU6IN3J6NEfGRv/kDoM3YiMBDp3xv79ovO2WendW7QnKUvi4/U24wbQU+km\nMTERLi4uis+dnZ2RmJhY9DjUVqxAXb68YOcDubzg/7ekK6JdXcUOVaNHK93L8cIF065fF/D11+Jd\n7N13cfeuee3dsTylv3lscpGRIUo3HTogKEhUQM1FYiIwa0dLMfG8ULMzUxURAWzaVMJBV6+KBaN6\nopeVsTI160zTUBleAI5euoSxQyPRtq1c8VzbtvqITAvt2olFXX36iPqhvb3iqaZNCwz2Tde//4o9\nd0+dAiwt0bIl0LKl1EHpztSvnYG/GojRcGCg1OFo7u+/xZTkKlUgg+jPZy6qVgV8/C2B4GDR+2rC\nBKlD0pqzsxqTCq9eVbniLTIyEpGRkVrFoJdEX6dOHSTkezdOSEiAs5K7RZvwHwBgtpcXRo6U6yMU\n3Ro7VjSRGjJE/GmZbweBF39xPH0qPqpXlyhGTT17BgwdCnzxhZgJZa769we2bjXtRJ83rTIrS0y+\nMaelAZUrA4MGAcjqLv7EN4NEr9YOqMWM6OVyOeRyueLzOXPmlDoGvZRuWrRogStXriA+Ph6ZmZnY\nvHkz+qiYtmFyDYy+/FLUsT/4QOnTa9cCP/5o4Jh04b33xE/kqFG4e1eUzsxxRlFK+wHI3LJdLFU0\nVYcPA3I5YmLEzC+zFBws/j/z7YFgtnJyRI20fv2Sj9VUqW/fqmn37t3k6elJ7u7uNH/+/CLPI29n\nIpPcSSY1lahBA6KVK4s8lZtrgrMgtm0jqlePKC2NiET8MTESx6Qnf/1FdMepqfiHKXr6lKhSJTEb\njEzwZ00NERFEy5YRUZs2RAcOSB2OVtLTiUaMKOGg69eJXFzUPqcmaVv6Fgim6tIlsQbgjz9U9h5N\nSgJq1zZwXKWVkAC0aCG2JjK2Tb715aOPxIh+wQKpIym9Q4dE/MePSx2J3sTHi3VDvjvniulsX30l\ndUgae/ZMrLfs3buYgw4cAObPF99bNRjVgimz5+UFrFsn5rbFxxd5mkiU8q9cMXxoasvOFtvtvfOO\nIsn/8495lmwK6NvX9PbceyFffV7JBDCz4OaW1+6le3eTn+VQsWIJSR7Q+4wbgBO9doKDgRkzxHey\nUMc9mUy8QTdoIFFs6pg5U9xFzrvfQCRat9+5I3Fcena3bgs8SUwz8ndhFSIjAbkcN2+qvE1kPpo1\nE21or12TOhL94kRvAiZPFiWcgQOLzM/O36rD6Dqvbt0qNhPZsEERqEwmppnXqiVxbHpWydYCcY17\ni4VwpiQ9HYiOBjp0gLu76YVfGnPmAPsOWIhR/Z49UoejsVmzStgnFhCJvhS9vjTBiV5bMhmwZImY\navn220V64gBAVJSYmWk0Ll4E3nxTLAJzcJA6GoOzsQF8Z5tg+ebPP8Uo18ZG6kj0btAg8b+KHj1M\nOtH366fGbGUDjOj5ZqyuPH4MdOwo5qIr+Zv62TNRr5Pc48dAq1ZiOuW4cYqHJ00S29Dla79v3jIy\nAEdHsQ+CqSx6mD4dqFgRWR+F4tAhUTk0ey/2Erh920h+gXTsxY4kd+6ovSCCb8ZKydZWLKL69lul\nW1q9+BnNzDRwXPllZ4s7xJ06FUjygHhYn9N4jU2udQWcsO2KzG1F+ywZrUOHgFdewb17oupWJtjb\nizuzUVFSR6IfSUliObCeV71xotclZ2fR2OK99wAljdoAUcp/0azNoIjE/YTMTPFmVEj79hL3EzIw\nCwvAdmgfWO42kUJ3WpqYZtO6NWrVAlaulDog/evQQWygZap1+ogI4NNPSzjIAGUbgBO97jVuLO6S\njRkjNi0pZNUqsaetwX39tajxbtkCWFkpHr5woWwsPlSmyQc9YXnogGn0qD9yRDSAKkMb4KxaJQa7\npproW7QAXn+9hIM40ZuwVq3EbJYBA8QsiXwcHF7uLWCwVfi//w588434K6Nq1QJPLVkiepmVSdWr\nA35+oAMHpY6kZHllm+xs0WLDnG5vqeLpmddOytdXTFuLi5M6pFKpXl2NDds50Zu4rl2BH34Qmx3n\n39A2z+XL4im9/8KGhYnZQLt2AflaR7/w009i5FFWrU3rg5SfTKB8c+gQ0LkzHj0Crl/X30ZERsnC\nQvyyRERIHYnuxcVxojd5r74qiqm9eomyST6ensDPP+v5FzYsTNx0DQsD/P0LPGXKPb10Kfi7PnA6\ntcu4vyB37oimV82bo1o10+zcoImrV0V3cAAmV77JzgaaNxf/LRaP6M1Er17Ar7+KpH/wZYlAJnu5\n4TuRHkb2+ZN8oabyWVliFJ+aquNrmiDHDg0gs7MDTp+WOhTVIiPFTKlyeukqbrTc3PItCgsMFPcp\nTOF+CsQfIWvXlvAtIzLIYimAE71hBAaKOvmQIUqnS3z2GbBmjQ6vt2aNyiQPiHuxu3apsRlCGZHT\nqy+ebzHi8k1efT4rS6y0LAv1eUAkScUSh2rVAG9vkexNgIWF2BumWHfuiKludnb6j0fvV2BCQID4\nIV20SExzzNcu4X//A157TQfXyMkBpk0D5s4Vza+K2R6qTh0dXM9M/HS7D55sNP5En5EhuqGWqfo8\n8jXZM7HyTYkMVLYBONEblpeX2Abu6lVxcyklBQDg5PRyvcSzZxqeOy1NlInOnRM3f5UscT11Cpg9\nW8Pzm7EJP7eCQ9ZtcZfT2CQkiBqbtzdsbcXq5bJk5UrRXBWASSX6N99UY39zA5VtAE70hmdnJ+om\nHToAPj5iZk6+G4F9+gBnz5bifERibnzTpuIO7549KmsyDRqI3xVWkIWVpehAaoxdwvbvFzO4LMrm\nr+rw4WIKMAAxoeDBA+N8Qy5k/nzxR3yxeERv5iwtRXu+gwdFPb1jR9EIHqKU7+cnDjsSHo5ZwcEI\nlcsxKzgYRwqvto2LE02f5swR28wvWVJgMVRhdnb5ZjGwAh7J++DJr0bY5GzfPiAoCA8fAqNGlZ36\n/Avly+d7j3sxzdIERvXVqqmxabsBE73ethIsiYSXNi45OUTLlxPVqUPUti3R2rVEz55RVFgYDa01\njJ7A5sWkHJrp7k5Htm8XW//17k1kZ0e0aBFRZqbK02dkEI0aRXTvnuH+l0zR4fB0emZtK7aJNBbZ\n2UQODkQ3b9KTJ0R79kgdkDQyM8XPMRERbdpE1KuXpPGURO3tHZs1IzpxotTn1yR3cvdKY5GdLYp6\nK1YAJ08iHkBymhc8cBU1cE9x2BNra1Ru3Vr0PX7tNbWaIe3bV6b/+ldf376iP+6wYVJHIpw6JYbx\nsbFSRyKpYcPEt6VvX4j7FW5uL2esGKFdu4DNm5X2NnwpJweoUkXcpytx6F+Q0XSvDA0NhbOzM/z9\n/eHv748Ic1zRpmvlyonm1Xv3ApcvI7JuXbTFCUWSf4qKeIby+NnPT8zeGT1aZZInEi3nXwgK4iSv\nlj59jKtH/b59ZaQXcfHWrctL8oCoifj4GPU0y549ge++K+Gg69eBGjVKneQ1pZdff5lMhqlTpyI6\nOhrR0dHo1q2bPi5jvmrWxFVHxwIPbcBQfI4Pcd/evsSXp6aKu/4lrspjBaR16oPnYfuMZ1FOXn3+\n5k2RPMqqIoOUHj2Mei9ZC4siLaWKiolRY6K97uhtnMdlGe0ETZ6Mj/JNvRqPlcisvx6BISEAgKlT\nFfdvAYjOyC8mIzg4iPbdZWwhpdas69RAYg1/0N59UociNog5cwbo1AlOTsCXX0odkLQSE/NNTjPy\nRK+Y91+cCxfETDkD0VuiX7ZsGXx9fTFu3DikpaXp6zJmq1PPnghesgSzg4MRGhCA2cHB6L50MTrl\nDe2GDy+4UUjfvua5AY8h2dgA9d/rD9m2P6QORbQ9aN0aqFQJ1tZAo0ZSByStvn3zbVrv6ws8eSJm\nrRiZ3FyxLubJkxIONPCIXuObsYGBgUjJW/CT37x589CmTRvUqFEDADB79mwkJydjZaGl/zKZDJ98\n8onic7lcDrlcrkkojOlOQoKY35qSUuxUVb0LCQGcnUEfTAcR32MpYtw48X3K+wvXmGRkqHGf2McH\n+OWXvI1xixcZGYnIyEjF53PmzCl1xUTvs27i4+PRu3dv/Fuo6TnPumHGKt6xNap+Ow/2A7tKF4Sn\nJ/Dbbzhv4Ye331a6h03ZtnWraP9qAnPqi8jKEkX8+/c1+jPcaGbdJCcnK/69bds2eHt76+MyjOnF\n8579YROxVboArl8HHj4EfHzg46NyV8oyJTOz0CzTrl2BY8eAp08li0mZJ0/UWNR29apoNmXAWqte\nEv306dPh4+MDX19fREVF4ZtvvtHHZRjTi4Yf9kf53dvVvKumB/v3i46nefWaKlWkCcOY3Lsn+vUp\nVK0qGr4fPixZTMpMniw6khTLwDdiAQOUblRemEs3zJj5+IjFa+3bG/7affoAr7+Oh72GgcggXWxN\n06JFwM2bSje7lxJRCR1GQ0PF3OfPPtPo/EZTumHM1B2t0R/nPpFg9k16uphx06MHDh8W/eeZCj16\niLqWkQ0YS2wjLcGInhM9Y0o0ntUfPle2Gj6J7Nsn9hGwt0e/fkY3WJVUSorIkQpNmojvj5G0iHjy\nRNxaKZGBp1YCnOgZU8pB7g1ZeWulG7vr1Y4d+db7s/zOnSvU410mE3sw7NolWUz5HTgAzJhRwkHP\nnwPx8UDDhoYISYFr9IypQKFz8OzWfdj8vNQwF8zOBmrVAk6fxoXHdVGpElCvnmEubbL27hU7qv35\np9SRqOf8eWDwYK3+CuEaPWM6FOM3DM/XbTZc06C//hLT7urWxcmTBVtcMBUCAoB//xXTckyBBPV5\ngBM9Yyo16esBOz838Te5IeQr24wdCwwYYJjLmpLTp8V+OwoVKgBdukje++bFrYISB9oS1OcBTvSM\nqSSTAbJhw4Bff9X/xYi4Pq+GM2eU7CTYu7caG7Tq16NH4s25RBKN6LlGz1hxbt9GlntDZF5LRKWa\nlfR3nZgYsaHvjRtY/YsMr7wC1K2rv8uZldu3AS8v8V9ra6mjKZ6Hh3hT8vLS+BRco2dM1xwdcb1m\nGzzZoOeNw3fsEAulZDI8eyb2SmVqcnQUs1iMeDMSAKJdQ2Ki4faJzYcTPWMl8Px0OBz3F7cvnA7k\nK9u89ZZodcuU27RJyb3XXr0kLd/8+6/YQqDEgxo1kmSjCE70jJWkXz/RQOvuXf2c/8oVMbea23Sr\n5dYtJUm1d28xn16icvDy5Wq0xz95EmjVyiDxFMaJnrGSVK6MOy174Nikjfo5/5o1wLBhuJZgZTT7\nkhuz995Tsr7Ax0e0/82/WbIBrVgB+PuXcNDff3OiZ8yY5Yx7A/4nVuh+xJiTA6xdC4wejVq1xBaR\nTAMymbjHsX271JGoxiN6xoxbrdc7waaqle7n1B8+DFSvDvj4oGJF0XmXFS8nB5gzJ98esi/07w/8\nYfhGdFeuAJcvl3BQaqpo1iPRnpCc6BlTh0wGhIQgd8ky3bap/+UXYMwYPH1qdE0YjZalpdiz4/nz\nQk906iTuddy8adB4oqOB48dLOOjUKfEubmlpkJgK43n0jKnr6VM8sndFzKqTaDusfsnHl+ThQzFZ\n/upVvDuvOry91Vx0w1QbO1ZsHj5litSRFDR3rmhvuXCh1qfiefSM6ZONDSq8OQZto5fr5ny//SaW\n71evjq++AoYP181pyzSJyjclkrA+D3CiZ6xUrN95S5Rb0tO1P9kvvwCjRwMQuwYa+6JOY5KcLDZq\nKqJrV9HP+PZtg8UREVHCQURixk3r1gaJSRlO9IyVRr16yGnbHuc+0LL/zeXLQFwcEr278ebfGqhS\nBaivrHpWoQLQrRuwU88rmfPcvy/WQRXrxg2xSKpOHYPEpIzGiX7Lli1o0qQJLC0t8U+hfqoLFixA\ngwYN4OXlhX379mkdJGPGJHviZNT89Ws8T9eiffG8ecDEibibZlW0SRcrUaVKwMiRKp7s3x/Yts0g\ncTRtCrz/fgkHvSjblLjHoP5onOi9vb2xbds2dOrUqcDjsbGx2Lx5M2JjYxEREYG33noLuUXmQTFm\nusp3fwW1Wjij/OrvNTtBTIzYMGPqVPj5AZMm6Ta+Mq97d7GSWa19/QxA4vo8oEWi9/LygqenZ5HH\nd+zYgSFDhsDKygpubm7w8PDASUNvx8aYPslkwOLFwKefIivlfulfP3s28MEHoMq2uo+tDDl+HFi0\nSMkTtrZiQxI918RycoCvv1ZjWqzE9XlADzX6pKQkODs7Kz53dnZGYmKiri/DmLSaNkXOgEHY0vgT\npKWV4nWnTgEnT+LuwLcglytZ9MPU5uoqps4r1b8/8Pvver3+06eiD32xFZnsbDHRvkULvcZSkmLb\nqAUGBiIlJaXI4/Pnz0fv3r3VvohMxVciNN9tc7lcDjk3dWImxPKzORj8eyNY3HoTsFNzM4lZs4DZ\ns1HDtSJWrBCzbZhm6tQp5v5mv35iLn1aGmBnp5fr29qqmPmTX0wM4OICVK2q8XUiIyMRGRmp8euB\nEhL9/v37S33COnXqICEhQfH5rVu3UEfFdyO0xK8SY0bMwQEWn3wMvPMOsH9/yTfboqJEi8O8VVGN\nGxsgxrLKzk5MtfzjD2lXoemgbFN4EDxnzpxSn0Mn44n8q7T69OmDTZs2ITMzE9evX8eVK1fQSuIb\nEYzpzZtvAnfv4s/u87BuXTHHJSYC48cjotN87NxjZbDwzN2WLcAXX6h4Us/bQH71lRrT9Y8cAdq2\n1VsM6tK4A/62bdswefJk3Lt3Dz179oS/vz/27NmDxo0bY9CgQWjcuDHKlSuH5cuXqyzdMGbyypUD\n9u5F64CuaPbPU2D4PEAmw5HwcOxbuhTlnj9HRSKEXL0KmylT4NrrdVSuLHXQ5qN9+2IGzD16AOPH\nizdZPcxhr1hRfKiUmyv+0ps7V+fXLi3udcOYLty7BwQHAx06YLVHfxxZuBirE0XL3KeoiAN2DrBb\n/z069ewpcaBlzLhxQJMm0vR/jo4GXn8d+O8/nZ6We90wJpXq1YGDB4FTpxD0/gB8kXhU8dTfaI06\naTWxf9kyCQMso4YO1Wv5plh794o3fyPAiZ4xXbGzAw4fRliTuqiOl/PrOyMSzfEPLDMyJAzOfH3z\njYr59IDYnjE5Gbh0SafX/PFH4MSJEg7at48TPWNmqXx5JFSvrvSpnAoVDBxM2TBqFBASouJJS0tg\n8GBgwwadXtPTE6hRo5gDnjwRayaMZMo4J3rGdCxo8mR85O5e4LGZ7u4IVJmNmDaqVSvhpuiL2Tc6\nvCcolwOFvsUFRUWJRVJGcudd41k3jDHlXtxwnb1sGSwzMpBToQK6hYTwjVg9IhJ7gytt9dysmehq\nGRo1mzoAAAp/SURBVBVluBG2EdXnAZ51wxgzA+++K7ZjnTBBxQHLlgF//gls3qz1tRYvBjw8gF69\nijmoYUNg0ybA31/r6xWmSe7kRM8YM3nPnwPlyxdzwMOHgJsbcPEi4OSk1bUuXQJsbESvHaXi48Xk\n/uRkvfS44OmVjLEyqdgkD4heM6+9BqxcqfW1vLyKSfKAmG0TGGhUjYyMJxLGGNPC48cl7PA4caKY\nF5mTo99AjKw+D3CiZ4yZiWnTxJo1lZo1E2Wb3bs1vsaCBcCqVcUc8PQpcOiQGNEbEa7RM8bMApEa\nu/X98gvw228aJ/vUVNFivmZNFQesXy/m7GvxZlISvhnLGGPFefZM9Ic/dQqoV0/35+/cGXj7bXE/\nQE840TPGyrRr18Sovtgc/v77YprO0qWlOndubgn3V+PiREviW7dUTOjXDZ51wxgr0/buFXtxF+u9\n90SJ5datUp174UJRo1dp9WqxClePSV5TPKJnjJU9H3wg+tEsX672S3Jzxb1WpV0NcnKAunWBPXsA\nb2/dxakEj+gZY0wdH3wgVsneuKH2Sywsimlds38/ULu23pO8pjjRM8bMyr//ih38ilW9utgGct48\ntc55/77opaPSypXS7k1bAk70jDGzkpoqug+UaNo0sXn4tWslHrpihfhQ6t49MaIfMqRUcRqSxjX6\nLVu2IDQ0FJcuXcKpU6fQrFkzAEB8fDwaNWoELy8vAEDbtm2xXEkdjGv0jDHJffKJ6E2zZk2Jh6qc\npz9tGvDoEfDTTzoPTxlNcqfGbYq9vb2xbds2vPHGG0We8/DwQHR0tKanZowxw5g6FfDxEQucevQo\n9lClSf7ff4G1a4GYGP3EpyMal268vLzg6empy1gYY0wnzp0rptSSX9WqwLp1wPjxwO3bSg85dkw0\nvyyCCHjrLeDTT4tZKmsc9FKjv379Ovz9/SGXy/Hnn3/q4xKMMaaSgwNQp46aB3fqBIwZI26mKimJ\nbNqk4j1g7VogI6OYJvjGo9gafWBgIFJSUoo8Pn/+fPTu3RsA0LlzZ3z11VeKGn1mZibS09Nhb2+P\nf/75B/369UNMTAxsbW0LXphr9IwxY5GVBbRvD4wcCUyaVPLxDx4AjRsDO3cCLVvqP758dF6j379/\nf6mDsLa2hnXeyrBmzZrB3d0dV65cUbwR5BcaGqr4t1wuh9xINtJljJkHtRqdAYCVldhXtl07wN0d\nR3JzsW/pUpR7/hzZ5csjaPLkl1tBPn8upmb27WuQJB8ZGYnIyEjtTkJaksvldPr0acXnd+/epezs\nbCIiiouLozp16tCDBw+KvE4Hl2aMsWK98grRlSuleMHRo5Th4ECH7ewoF6AFmE7PYUUz3d0pKiyM\nKD6eqGVLoldfJXr0SG9xF0eT3KlxjX7btm1wcXHBiRMn0LNnT3Tv3h0AEBUVBV9fX/j7+2PgwIH4\n4YcfYGdnp927EWOMaWDVKsDdvRQv6NABXzdtCnlaGnJhAUekwApZmBcXh5uzZ4stAgcNArZuBQqV\no40Z97phjLF8QuVyhEZFFXn8XsWKqL5nDxAQIEFUL3GvG8YYKyQrC0hKUv/47PLloSyNLunUSfIk\nrylO9Iwxs7Z1K7B4sfrHB02ejFZVV2MXeikem+nujsCQED1EZxhcumGMmTW1Z97ks3PDfhxduQKV\nclKRU6ECAkNCXs66kRjvMMUYY2aOa/SMMabC4cPA3LnFH3PzplrNLE0OJ3rGWJnQtCnQp0/xx5w5\nA+zYYZh4DIlLN4wxZkK4dMMYY2oYOhS4fl38+8oVNTtdmjBO9IyxMmfKFMDFRfzb1takFrlqhEs3\njDFmQrh0wxhjrAhO9IwxZuY40TPGmJnjRM8YY2aOEz1jjJk5TvSMMWbmONEzxpiZ40TPGGNmjhM9\nY4yZOY0T/fvvv49GjRrB19cX/fv3x8OHDxXPLViwAA0aNICXlxf27dunk0AZY4xpRuNEHxQUhJiY\nGJw7dw6enp5YsGABACA2NhabN29GbGwsIiIi8NZbbyE3N1dnATPlIiMjpQ7BrPDXU7f46yktjRN9\nYGAgLCzEy1u3bo1bt24BAHbs2IEhQ4bAysoKbm5u8PDwwMmTJ3UTLVOJf5F0i7+eusVfT2nppEa/\natUq9OjRAwCQlJQEZ2dnxXPOzs5ITEzUxWUYY4xpoFxxTwYGBiIlJaXI4/Pnz0fv3r0BAPPmzYO1\ntTWGDh2q8jyy0u7MyxhjTHdIC6tXr6Z27drRs2fPFI8tWLCAFixYoPg8ODiYTpw4UeS17u7uBIA/\n+IM/+IM/SvHh7u5e6lytcT/6iIgITJs2DVFRUahevbri8djYWAwdOhQnT55EYmIiunbtiqtXr/Ko\nnjHGJFJs6aY4ISEhyMzMRGBgIACgbdu2WL58ORo3boxBgwahcePGKFeuHJYvX85JnjHGJCTZDlOM\nMcYMQ5KVsREREfDy8kKDBg2wcOFCKUIwK25ubvDx8YG/vz9atWoldTgmZezYsXB0dIS3t7fisdTU\nVAQGBsLT0xNBQUFIS0uTMELTouzrGRoaCmdnZ/j7+8Pf3x8RERESRmhaEhIS0LlzZzRp0gRNmzbF\n0qVLAZT+Z9TgiT4nJweTJk1CREQEYmNjsXHjRly8eNHQYZgVmUyGyMhIREdH85qFUhozZkyRxPP5\n558jMDAQ//33H7p06YLPP/9couhMj7Kvp0wmw9SpUxEdHY3o6Gh069ZNouhMj5WVFb755hvExMTg\nxIkT+O6773Dx4sVS/4waPNGfPHkSHh4ecHNzg5WVFQYPHowdO3YYOgyzwxU4zXTs2BH29vYFHtu5\ncydGjRoFABg1ahS2b98uRWgmSdnXE+CfT005OTnBz88PAFC5cmU0atQIiYmJpf4ZNXiiT0xMhIuL\ni+JzXlClPZlMhq5du6JFixb46aefpA7H5N2+fRuOjo4AAEdHR9y+fVviiEzfsmXL4Ovri3HjxnEp\nTEPx8fGIjo5G69atS/0zavBEzzNwdO/YsWOIjo7Gnj178N133+Ho0aNSh2Q2ZDIZ/8xqaeLEibh+\n/TrOnj2LWrVqYdq0aVKHZHKePHmCAQMGYMmSJbC1tS3wnDo/owZP9HXq1EFCQoLi84SEhAItE1jp\n1apVCwBQo0YNvPrqq1yn15Kjo6NiRXhycjJq1qwpcUSmrWbNmopkNH78eP75LKWsrCwMGDAAI0aM\nQL9+/QCU/mfU4Im+RYsWuHLlCuLj45GZmYnNmzejT58+hg7DbDx9+hSPHz8GAKSnp2Pfvn0FZjyw\n0uvTpw/WrFkDAFizZo3il4tpJjk5WfHvbdu28c9nKRARxo0bh8aNG+Odd95RPF7qn9FSr6XVgd27\nd5Onpye5u7vT/PnzpQjBbFy7do18fX3J19eXmjRpwl/PUho8eDDVqlWLrKysyNnZmVatWkX379+n\nLl26UIMGDSgwMJAePHggdZgmo/DXc+XKlTRixAjy9vYmHx8f6tu3L6WkpEgdpsk4evQoyWQy8vX1\nJT8/P/Lz86M9e/aU+meUF0wxxpiZ460EGWPMzHGiZ4wxM8eJnjHGzBwnesYYM3Oc6BljzMxxomeM\nMTPHiZ4xxswcJ3rGGDNz/we+CxfrmTh2jwAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Multi-d interpolation"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Here's an example of mult-d interpolation from the official tutorial.\n",
-      "\n",
-      "First we define the \"answer\" -- this is the true function that we will sample at a number of points and then try to use interpolation to recover"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def func(x, y):\n",
-      "    return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "here will use mgrid to create the grid of (x,y) where we know func exactly -- this will be for plotting.  Note the fun trick here, this is not really a function, but rather something that can magically look like an array, and we index it with the start:stop:stride.  If we set stride to an imaginary number, then it is interpreted as the number of points to put between the start and stop"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "grid_x, grid_y = np.mgrid[0:1:100j, 0:1:200j]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print grid_x.shape\n",
-      "print grid_y.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(100, 200)\n",
-        "(100, 200)\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "here's what the exact function looks like -- note that our function is defined in x,y, but imshow is meant for plotting an array, so the first index is the row.  We take the transpose when plotting"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin=\"lower\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 20,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x32eda10>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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0/vwX7vi9F6MX5Yj0vTWJ5EeF/psFByGBhfT36TKq6en76e/95CZd5vkz/9r7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\n"
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "now we'll define 1000 random points where we'll sample the function"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "points = np.random.rand(1000, 2)\n",
-      "values = func(points[:,0], points[:,1])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Here's what those points look like:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.scatter(points[:,0], points[:,1], s=1)\n",
-      "pylab.xlim(0,1)\n",
-      "pylab.ylim(0,1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "(0, 1)"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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Ep0+fFjnW19cX+/btE8soa936ZxCRVvQP9vb2/toZaiKAvEUeEYGI8Ndff8ll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\n"
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "The interpolate.griddata() function provides many ways to interpolate a collection of points into a uniform grid.  There are many different interpolation methods within this function"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "grid_z0 = interpolate.griddata(points, values, (grid_x, grid_y), method='nearest')\n",
-      "pylab.imshow(grid_z0.T, extent=(0,1,0,1), origin=\"lower\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x3568e10>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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5zyZzD1+nfHwbM3trc4Olq9glQsMl2xduDnKQpW3XZRb5Ir/IV/lo7HbzaJGj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\ngg==\n"
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "grid_z0 = interpolate.griddata(points, values, (grid_x, grid_y), method='linear')\n",
-      "pylab.imshow(grid_z0.T, extent=(0,1,0,1), origin=\"lower\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x353ea50>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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WE1cgc1LgJrl1CeBJYF4ampjFznS+oUr5Lfqm4bDq6M5WDNsV49WKuDHFF2KT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\nFSU5lChRwor/B3i9NQGMTRG4AAAAAElFTkSuQmCC\n"
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "grid_z0 = interpolate.griddata(points, values, (grid_x, grid_y), method='cubic')\n",
-      "pylab.imshow(grid_z0.T, extent=(0,1,0,1), origin=\"lower\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 25,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x3879a10>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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Xc7wcOU4D3juCd4S5I8490VvwpCS5fcD0Htcv2G7BdclvxCgvRmMDNq+heKQa\nW6sDS0RlrQrU6vDW2lBzxEUpmUSKEishf+t2jgbPKR1bc/+bcg+PyjkUN57znIJM2odttbdE/duy\nGKDNq0W5qZ/LvawbJZ8qmi18qB3jmPowcug8SzcQbU/UU58Tldpxoh9nhmGmd2kT9yxrwnbhgJIS\nq0PEKVMRYuDIyL06pfRWNWeIVubgIc6raM+Lo3O7Ni1h+nV72FXEmFdgKDqnOtSu8DKAun66crmb\n2u8lWMs0Dsxjv9KVDyl/h9QZsSblVuicrslRhNO2aUvBKV2dS1ZY5rudWxmL4TwttyBx7v2HrA9a\nOydj+Si4BviPwH1aBvaq1lot2vdkQs5BQdFItNerCbs1i8pGEH5GbxT51ENNs4udWbAmgAPfOULX\nEYaILv5lx4AdZ7phxvVL8uRUXpDnf6NwS/V4Y/Vc37M+1V7V6gzHoEHhfJ4B1l/SbK3cv3AO57xZ\n5XkNxnUdB/FwLb9zqrUfOay8k8fibYe3Ls9DQKJYE/f1wQ8dvQnl5D+NIg7VXdWWttoNPNGIWelz\nzXz1gEjRfv/8PZZkLx9Fe3RwkFaQsT25whlyLhqG93Pl1h1ElG76N/UpVU6qsvm0TFhvkNP70OAg\n5kZnPDa7b0cROyzJNNDqj8zptaQn4pOT93VzICFrBRBPWdz6t4qYVUzNmpWuOQl98moFsXwv/Zbo\nHrTJuW0yvod0HnpS5HccBQzPnd3auKv1Abq1B0rxITEnvfY8YL3uOXA4d8ZruCbPWFrF12d9LC5c\n9syM1J/5KJSR8CjWCrId9iGGzVSfNasMFtSnXt/KEsSTbSDCRPmt9EuapLRsWO7stJ/zQ9PAozdh\nqtBkid6cBgfm90OwSWZ2PsnWGRBKYI7PIwtIURMhl5YRFq118dEvsrC2GEU1YuDEHCbv1yJWOX0f\nYmXlM7UfysPycgu09Wf0t+pn7ZxrbtSeQEZ9fbvSR7mWBgitRixQKIdLbX0TmtMzUQPVw+39ejIW\nujx3mLaU/ib+kR8zOBT0O09IcqY//M0P9luFEFuWrmZ/61OhJgbNObQyeisjakI9sT8Hh597/LFL\nmvSJZDbDpCxP1uFtwNrAYgLGdtX1JJmoxeGQRB+g6zDKa/pu9b1p3xHd9Rgh4pr5KgKD3ljpHZmL\n82tQ5rXMlWyr+i4eajWHd/5EP/et9lNwqrSuOZn0jms2mKYP0bbIyA2nWRprkNPg+8HbuUNKACHd\nseYfygyzsWbeAAAgAElEQVR9VO1jBQeZUKtQV78nJp4P29KJqMGgQI4WEWqnkdq7Uv+txZm0QdPW\nbEGhHmMhiIDlEJOtf/a5oO6xS2nhJFgopu2YzKUGgkkchhCxIReyEdnWEZXPxjnbuPy+5gnOAZkW\nLzQ80rxSPqd1DLaZp1OgkHlP/3sFMeUztW7/9P36eu0Jfiqi6HtuwbxdY8lSCrEyE2tw0FxZq6BO\nNhabITtBd+FSHtYXFf6pXovTzxV9RBpD+XSZn/aO26t/+PZIOSRDs2yvt0y8brDllG2lvrI9hHBq\n81kJ2tKnRCHE+r7lhJZ7Wp/H0/szJnLwG/bLluN+i7/v4b6r854GUk5LbwjepMpOwabsR5FUxNWW\nUN5zxN/a0rV6ULPUDylWXbbLy0rId4qeIyKFe/W8pLmsU6Pq9RIDanF3r1lhWYtkwRDH4TqArj25\ni+LSVJ9t10NoolrPYAnR4qNLnFy+pjPJKc5ZjxTeNQYwORGsSY4S5d7TL3V0+Fc4OrWAXUBAWzRO\nudLTz7d0HtWzU0DVoPwm7ZHTxJ0qyM5NiCxw+5p+PNdOWcL0KyUJflPJmUQ80pcotuK8WHldfHT4\n6NJJD0mPIHkpM/2YbB+fjz3zscff9sSXXQkK0lHeMzki0BJDRwiGJZKIdBOxnWchpYfTm0aAoS2G\nUjztT4lWnJ/6ZhYMcfVwlMSvMm+OlOHaElajqAaHZIU4Pf3F6Uk8JFtlpHxGgGjJ5lm9tnoExS08\nux1FxxIdIbh1zUABdcwOWtEVXc5i1w6kbFgu4jqPMSF/1yRfFRtxbsG6BWdDsiCpvpgS7lesFqVw\nrlWzUg4/fRjW9F4reut9UANGUZZrIC2fFQHuzVSTjwYO7WlWJkarrlITNlKeF9XLeVY0beZCrHXU\npqs8+JbYM8d+3fDiROMXu3pHWmIChcWuGaBDPu0J+dHb5EG5ekhGMKG8vzcpavCeAgqSN0F8+BcD\n3hEXy+LTSWct2C6kcu/W4U1JhNKCQykZPHMu+Wx5b1mdqgQegZxPoV8dnIX4ema27OlW1+z+ZE51\n+vVSZCD9qhTSnekrQhZ23JJ8M0rMjcls/+lIpjgwxYE59CxLl2p5zN26NjGYqq/JhL1Nbu+zLYFl\nEj+hSUd2VAe4iB0XzGbG9R7beVxXHns707k8WlPWwRf4qoA50b3Qp/xc2QOtubnlJk73RC0ay+sf\nhUgBj8w5aPOPzuP/kIihRYOW1QzVhkm4WU6c4tYrLrszqcr2cRmZlpF56ljmbt38LC53U2pcSJSf\nxEa0BXUlf4gp2pU82Lokm3CGOifCBhXAZSA6ojH4zjO7hW5Y8HZGgpJ0Ey4hlcQrkQybNaTpsHpa\nShcAEMeoiFk3oLwnBDnGIxfxjoF5FRUm0zObUsStbOTCkcmv3XLFlvsMPkM2WSaZX9ZbcwnyXCex\nOTKmquhh4LjfcLjb4Pc9HLqU41Nnx5XQc13oWCel1bkg2mQ2VZYpQxh7GHv8BhgjZgzYzYQdZ/p+\noRvm4lxlk1t+8cRNM6Fpvv6pWrQTbqIVTc6JB9pZT2sk9D550/ZoFa8eYnvLufiQp1itCBNZXDqw\nPurvCK5PpJPnGEcO04bjfmQ+DMRjTzz254uu6iCeczUXhRDPqUx0qHPbdHrnIynGYo0gNOAMobcs\no8MHi492TcwqM9GR6j9sc9yjVNqWvsuxkfJeCXtKICLgIPMWcEr0SE5D43Fhc7PQT8mZK3YwbRzT\nxjLZnsnW0KPL2+3Z8JQXa5aqG65WESOFm5ccmAJOmmuQ6+3Zcjhs2N9vCbcD4aaHO1vKh7xubTQ4\nPNR0UiiJwNQRsztD3Dn8dkPYjoRxYh4nun6hHzp6NxPdRLQSb1IOMQEJoUbUHtCRtgIIWizR3EWr\nN5PXNdcVs/K36Cc+XHt0zqEAw6krqjbRadt1wKyErCcIznMXLXt6XEb204bpsGG52xDuB5XAhbpg\nShsCrGswnEtTLi4ZZ3IerBF/Oq+p7rO6hoXYGULvmIeBxc34oVtPhBocjhUoXPGSJ7zMRe5vVpDY\ncs827tnGA+NhYdh73BLLPUeIQfkOTBEOEXMP5iamGhgOTAfjZmHYGtjOxM0+JZUZDfPgOAwj+74U\n6BOouuWSDQfu2a0AImPR6yTK0YWOYxjZz1vupgvmlxv8yw3xxqZsT7qMiAZwXfymXaN2raDmGFpQ\nkMQ/VUIeQwzglz7pL4YFP+cI39HhO0dvLNFYpV/QokOshLC6dmzrcVlblOQvm60siVRqPU4Cijc3\naX7s4KADePQkSAm8rpqs87oHwdYFh8lD0GAg3xG2a8lqoikOHOKGw7zhuN8y3w1w29X5ANre5gnQ\nmaDOEZw0Ibg2F4CIEPrvLXViW5F5BwhjEnGit+t6C5ENTGw4rLUxr3jJNS95wgue8oJrXnDNS67D\nDVfTnotpj7sP2H3A3kfMfUzJXtr6FsLtHNQc3JJAMYcym0swl3HNtuw20G1g2Hm2u5nri3uOu45p\n13FrL7gziWu45LbKBiVOcZrrEDFD1ux4HDncbYkvOuJzCy9NLn9HKe3Xgro8F8BtxQptNdc5Htoc\nlZIGTtfHCSQ9xmhT2buNIWxscYmPibvCgbhut2y+5p5FJ3R6WJ4HhzQ32phbcq9GzMpNf1daK1oN\nrVaUae1v7WlQg0PiGGpgKKjZvpZZVJ8qbc+HEb8fiXd9qlOpq2nrIrq6a/AQkNAZoPRJBIVTkM2/\no84gtKXmGNqs1wIocwaHIM4vhWPomRk5suWeC2655iXPeI9nPOcp7/E0vMfT+SWXxzuGF57hpS/j\n0xX92opNwvXkok/rmCWbe0/JFSlj25Ay7W8j9irSXQW6J57t04nNxczF7o5dd8/WpDQvQy4Ym8DB\nIRW3ZZMgr8eB5TgSbkZ4YeG5KfU+n5PKdL3gFMA1OLRd59OQ+dYl7ST13BV11UPxvJbsUaKLCh0x\nOHw0GGXSNgasiSnKMwO6VhaK4VaUynXN2FOHcCgHnlZXJjGz+IFoy9+btNeCwy//8i/zl/7SX8J7\nz8/8zM/wV/7KX6nef/fdd/mpn/opvvnNb7IsC3/5L/9l/uyf/bPv68etmpiSHGNBKyetOo61zkGK\nmhfbc5oOPZlyKq0+Db5nOqYkIfG+SzKrAIKuI/meeq0FijtUkeaQal1K1W0iK+VYC86Vk0hSt+vE\nMBpUIkU3IXVWBgMbC1tD9I4QDWLh1vqGDUd27LnkjituMkg858l8w9XNPdsXU6q0LfXuXlBXD5eN\n1SpcNWDIppIs0JJEVgOepLzLOTDd04h7FrHPJsanM/1loNt4un5ZT0XRNRxJmaN0jswQbYqsPHRw\n68oafSf3b+f+nLoC3v7MffsAIVdFj9lOLM3k9bIu1TQZXBrbPadlwLSouK6lOK8luIsmpnBvG3Ip\ngXQ8af+bc3q3wj2XosI67yTwwOaXa6a4G+1j8SbtleDgvednf/Zn+ZVf+RU+/elP80f+yB/hS1/6\nEp///OfXz3zlK1/hD/2hP8Tf/Jt/k3fffZcf+qEf4qd+6qfouocuXTvuvFqskKItAgRQ0moVcNCW\nixppa3PP4jvmqccfOuK9KQQlRXOFTX1OAQqdkHWPqrZ9Ll9cpCQfzGzB3CWNujd1erI6TLRmbXW+\nypywJPpkag2UXAcCDlJId5szNF6GW67CDReHxDHYd0kVtn+PsrFkfMJJZMeseExDWybwSxpqzJvA\nGug6cAO469SrYi6Sol4S5GZwdXcRt4/s3jli3wlY44nWsNiUHPeebU5Ek7gGQyRGg/cOP3XEg1qr\ncwDxHTWOtfC5h2VJgIDYjUXeaFiHaMF34IdUOPe4hdDB1FU6oEp31KtLZHCPxhBdlyJwXWBxCRSi\n1eZFfcS1GSC98uKQfVAOOnHxs0pE0aZLv9KGeLP+B+QcfvM3f5PPfe5zfN/3fR8AP/mTP8k/+kf/\nqAKHT33qU/zLf/kvAXj58iWf+MQnXgEMVBPTZu9vU2vpjS5yqHAH0sI6XQ+7q2pw8FOf0r9JenkN\nEEJ4AhLvUcDhPiZi8zN1XncppCs8py6ddQHLZepDV2R70awLwc0UbmFpPpNZ4eizM0+2WAg4iJpV\nQGLLnp3fc3E8sL2dsFJRWzaSgER+Lb6AcAP+DuZj6ocF9kuqoTnFcmh2JuXh3TrY3sD2ObgLcJdg\nhHvQmbt1EXIPXfTsnAcD8zhyGDZsuFT5KhTXECx+ccyTIxxsupZUP5d10uKFgMPiMzfXapN1lmBd\n9FhYNZEpriFew/4S5sssG3AqJm6pTaEGcBY6m/Jc2p7ZegZbV55q3dk1SNT0X3PQxVSfdkJRb2in\nsdMiOG/SXgkO3/jGN/jsZz+7/v2Zz3yG3/iN36g+8xf+wl/gj/2xP8b3fu/3cnNzwy/+4i++rx8u\nbJXOmVubNqVL0/bbonxJ1nJHt07k6S9l7UVwyRlmtrWJUgjvpeparLiPcFwogrrmybWmKlIUCVcU\nBNgmDVVb81HAXdvm256BIi7pJA3OpnRmphCYgITUvBqXmeEQ6ORWBfD0hsos+fIC9i/h/h5uPNyE\nImWIYUYOzj7CzsOFh+sZrm7gYgu7HfQXWUl5RV3MWpbPgu3BjjD0gcHOjMNROWIt6pQ0yc3ZO/zi\nCLPKDSlrdaIPSpaVoiRpNcxa3hAE1sAgeehniEvWJ2zhPme61pmnZYznclB2QJdM0MEnN+3i+NU6\n6ul90O6BcHJAJnCwChqEtrVAcd4t8MO0V4KDMa+//N/4G3+DH/3RH+VXf/VX+drXvsYf/+N/nN/6\nrd/i6urq5LN/53/82/zft9/g3/2vju/94uf4fV/8fWgHqBYghLOQJuy0RsbEFRQupDZrqh5NZh8d\nLDYt/kPFszVHsQ+wBNI2kfr1bR50LawLOIjRvK3AzSkQKC13BRyqxWAIPrlpRykCTLFciM/nwEzn\nPeYY63HJeGSf5Nfne7g5wHtzkaY0P1SBA6Vsw12EmwhP9vBsgt0EmwU6ff/aPChKvjxVNtRr3vq1\nREr4epTEvmKiFEZt9UOJiVvA50G+RwFyvagatXStTAGHo1qMzP0tI+yzT8WuuWQLDtnaEb1JJk5v\nz8bcpHWj4ZyLS58Wq1sPSTH7ilVOnP4CAZ8/++9/9f/lG7/6VZ7/f5FvXf3m2d9/P+2V4PDpT3+a\nr3/96+vfX//61/nMZz5TfebXf/3X+Wt/7a8B8AM/8AP8/t//+/nt3/5tfuzHfuzkev/9z/0P/C/f\n/A3+xY93bNnjOFB7RupzvlgqdLRgINU4fJ0HWFSYLL730Wdvx9kUzqF1mGkJ0AdSfTjt+HBPESt0\nDnQt5zX1M+WQ6h7oqqAzUDwwW5BY9SunEYk2g4QLC2aOdZlIzSmpcUefinXJsDX8aWFJzlfN6Cyw\n1tV8us86iXIzBRSaWh9mjtgQzoKCGmgCh8Wq7N8UbkqPZ4pJQVLZMjUiCirKTcgItL1ZClsIuOfv\nz8A+x8RcULNVuiJ6r+6ttVypVsQJf3Kyn8JIXFdaDJfls0IFBkn+I+2TX/whnn7xD/IH/2ngv/nU\nf87f/dt/5/zNvKa9cof92I/9GL/zO7/Dv/k3/4ZpmviH//Af8qUvfan6zA//8A/zK7/yKwB861vf\n4rd/+7f5/u///g91M7oJSGjgeOMmJ3arqxGKP+vlKLvaqQ/JsaUftQ81lKNEuVBrTXfbNVC8D5BI\nb5UTpCq5pjmQBzgRPTr9s7rJZcSQci5lqdzenFUyy5wA4+Ntcqd6LbQn1DlnDj2p8v0WYPbJunEM\n9SUmdVkNWOccrB5o4j6t4y80lyAitxgrRS/Rej1KpFGr4P8o2is5h67r+MpXvsKf/JN/Eu89f/7P\n/3k+//nP8wu/8AsAfPnLX+av/tW/yk//9E/zhS98gRACf+tv/S3eeeedN76xcvILSn4ESha9z3VJ\ntBOfetUXC6HPRV52FCqRmOt25+p6al35EeHLpZqSBoPWZVdfoqeuytS0hGsl/m8dp1XfewiMerBd\nqusr4rSo62YKcGi3i3O3OZKsrp0D15HSuws2yhikqyl5ZTNgbcC6QDBZytbcl65960xapwri9CaS\nLwU1Aa0JQqOyEkjF5KlFv/aQaTwtjYtYl+5dZ7bWLV3GoFXvhRMsznwln0RRzbeT99GEWZ221/o5\n/MRP/AQ/8RM/Ub325S9/eX3+Pd/zPfyTf/JP3ugmajcPAQWZFEgMc6QSFc5OaJvHJ78jKcZdrGlB\nbxbZuLJL1mIoJpkhly6/oTUZQkyOouCqnPGT3a+zRaElvyF+AQ/ttsbVWgjO5JoYRRGlDWHZ1GlM\nPS4p3LsWz2HlsHsPlzHtrziDWZT/Ve6Caxo3ZZSXNvXdCP0uKSVXU6YUnRVFRb6HOKQKWWLRDxrY\n2naO41KW4hWZliG/uc93pv2noUCcDq7Q4CCj1h5qLRtHzWQ8dF4V15vXWA5ONQqJhvUOqPdG/V1t\n77Bn9sSbtUeuW6GDU0uItVHAYFclpJa366j5elKKJ+E6fSYrvaxPGjNd+VrTxCW1hUsUTYbkIDPv\nUsXmyvS1pajvIgVdspdQv02OTLJBxOSngUIDR1vINXMOpos4F7C2nEYCEDpo6cjA7HrCxtb1Nxuz\nouCbzX5WzoHZw2YPVwFuQwEHQ+29sSH5CY0ONkPq3S6ZNNfq2rrniuHxGuIVLDvL1CXHpxRo5Spw\nsKQEK84lh6ll6AgD9XxpIN+bVE17b/MLumjpQO2PootmygJr1k3WNJc3N0OaJM2FVXTRdKU7Omey\nLCb5wgtrr1/5vOwDUcJLq/dN8eFpwxc/ivboRW10FgftDy42W8FPmajzaUzOcRK1crMjZX82zic+\nenB1mXVdMq0NkOpJ5qy7nMnJ22Tm4pKi7BLnGjmmn5HAoS8FemWjas7hXKzFGZAwLqbs1aauIS4B\nSxM9h+xleOjvmXaOfjbYQ8SKbNxy0i7RPtt06l/dwfYumSmnCWafDDWGxPx0NtWw7Ry4HrpsmrSb\n7OcgZemfkUDhE6o/A//EsFxZDruOvRvX+pjJy6H2BZTqUs55fBdPq6CLYlDrfb2BeZscz6KsgyiM\ntZ+73tWavToz+a6DwdZinqYN7RSl3jM2roVx2s0dT2DDVcCQnkv+hzqosI4vKmrJcwAReRXX8vr2\niKnpCxu14E4m0GJWnkK/rrmHNslIy1qJIm2dfuux3UIYUsAMW5MIWh80Wk5uQ3ZvSdGA+xEOYzJz\nLbtszRAKzbKv3aVdszHFT1/YbM05tAFY8t76foQ+Ylw9ShBdQ7d6OEgE5K3bs9ncY4JnMy0M0Rcn\nKy1eCL7dgbmD7h66PWyyTi7M2eeLzFn0GUz0ZtD3LuDwhMQtCOeQAWJ56ri/GLkdJJB8qwAiFdHV\nuTliOn5rRu2CYv3Qij8Jmrod4G5IHN6yS4OolMV6wyjliMnddolLtCa5VIsYdpnHqDk6DQ5KzWRc\nTAl6chk9KDqG4rBk1vXzKyBoISIS160f1TVa38qyB+T184UHPnh7NHAIKzCUbSwTprW3rwOHUuCt\n5iJqH/acMamb6TcTzBY/dwRNL3KQyAYSu7xyA149KVcfmzEBxRJzfEXMtGeSHDyaslmeUJ6vvDmn\n0Zpb9ThG6D2mT/UunDktRadjE/Y5BOulPdLZObkzvnOHGw6YEewm1sR+TQnCEjcAZXa0E1jhqFrX\nYdkcGhy067QAxFMIzwz+Hctht+Flf8kLrrnhinsuuGd3UmV7dQM2EeMiZoh1CPW5CFYBjtUxqk/+\nCRNJmax1xquaytQ6jVZBrCVHidBswb1lOvqI6UPKFuX8GnRVnKdrOtaRw5pDDisPYVcuorz+MOeM\nuv6btkcUK2oZK73WihinUZmyIVpwmBV6ysTooiOWQN/NbOwBvCX4PqVwiyQiEWXXhtofQBRsrWOk\ndi6aTMoapY0XQkByemYpg2v1nuZQzvUN2I3HjDOumyuRQuZBRArhHG65QEq9xc4SryBsIuO4MF54\nzHXEiKuxBgZh0XW+irZCtdZMtuCgWf7MJcUnhnAN+6uB+8uRl90VL3jCywwOt1yu4CBVtmcBB2Ow\nXaAbF5ZtSBXIZ3NqKR7y72oJT/J0rh6wpvZFO/FopFg+RGRo10YAQkBCi4mibB0DZuMx44LrfarF\naWsvR03nQsPldbsejlIQeY01USLFeV3DqX9lUcZ+uPYo4KDlppSs1NCtPEQx4whUlO+JnOXOTI54\npRdfyQISuQa0m+mdw2+PLNESjSeaPgXLCIG0WaBko+wprtX7pmvOVcBBwEZO0Gf5UcKcN9Qn1BmQ\nMJuI23rcONF1dVXsSM53wJC5hiP9GtWYwcM4ZtOzH7dc9rdcPL1juAuMdx5zFzHaifBceLP2kRAd\nnrYYtHq8DBDxAsKl4XDZcbjsuDFX3HLFDZcrKNxxyV3mHEpOz2HdLM74rJRcsNslKUJwidXvTB0K\nL4pQPR6d+EWkPjFDtuDQPmoOqeXshEMRN+qL0u2Fx15M9NtUDLnvUyoCnYbAKoAQgNccsnZyKwKy\njiWytMllPTqXZwnnftP2sYOD6BkMdW4UeS9NSp2NWr+v0dNncJEchsksVqIWdUDLsE6qJXYGtjAR\nmY1lcQ767D/f+svozELPKL79ErqtLQCykXTMhGQQepb7NbW5UoODPB8i9AHGBTfODGMu9W5KYJLM\nhQDEgS2pejYZdB0TPXtJz2av2Nl7ttcHNlcHNvORzXGiP3rskZT0pfIMVY9wqqFv7j0OEAaYR8dh\nHNj3I3cmJXkRENA9ZXXYrRmiJMGsbAdLSOnvbM4yZSLebvDdSBxMLcbImugwinYdtb+T7Bvt89Ga\nuF8HElWejgAXAXcxMVwcGMYjg5P0NdmlnboQskQEmQfoXAKoXiVaF4VkXR6gFTM+bHsUcCgJWZLu\nwSrVi1NIKsPX3z1FTfvKiUjegwvDKu2FJL+7VLDWuACdJ2z6VHBGXHXP5R8UTqJNHSfE1wKD5D/o\nSUT8ND9qcKgcoiJ0IekY+gU3LLhhThmPswlTs6TFfDlSEqQUYDiw5Y4DNzmce8uewUwM5sg4HNn0\nR4aLmT7OdHGmi54uemzwuBAwIWCCkpcNKSzZWry1qUqXsQRjWUzHbHomM3AwIweTLCd3XKzp4lKq\n25ICV14XsWLJ5FjCl2cGM+EHR+wsk4kc+0DY9sRDBxKtqRPSrCHb1Ouolc7nTJBtb8WLc4856azb\nzrjdkX4zMYwTQzcxmgNjDipr4ySEliVHVG2mbw/H9nta4a5rsbRRSa+u/v5+2qOCg/wdM5papC6S\nnhCNnqf5IbU5R/4u4bFixhS/t+SoZEye4j4l5OiGJUf/dXhvS9DMYolLjuRss1DreAHtKSfAAOWU\nEpd9YUdXzXZIYCCgYAMme9a5zuO6hWGY6N1EZ5b1PNBKq5mOIwMphVg5SWYGDhyrbNM6AjKBREkL\nW9KUJbHExvxbUXxM8pBMnlWT7kTkW1EKz/Qc1gwTAgYl6exRmS+nrEg95PeSQrJbOQe5z+iSh6nb\nely/sGx6lqknTI4wOeLRJaA4mlOXBs3Ntcpn6fAwaJyYK2MC8T5ge48dPP32yLg5MHQTvU2AVpcK\n0CJhm27AKYnHYCk5UVsXqPSdU2DQ+0CDRxnYh2uPAg4yIIBSyrR4RoK4M9VslRYryt81kYpUVxSd\n56I68zTaJNv2bsYPqRJSqorkiMESgklRgTldfQwmRdxNDj+5nMLNQq5OVXVpQnRdIirTB8zoV422\ndX71fDQ2PdpcM9NaT2eXFRjOOcNICjxxsfU4Jgb2bGhrVgihduvjgi5uoxVn1mQCNafzf0qYpS8Z\nrAQAZpWVuuT7Kpmlp+b9krCk1OQIpBwWtk+gufQzfuPwvsPPLpUTmFNod5SCNT57tkrdikB6rvUn\nBrAZwds1s/k9l4E7d9sFXL/kOhYLrvNJhHDTCgqdmtOSuEUSzOr6amRaFiuDuD/Zlcpbg2ThJcoO\naWu46H3wJubMR3WC8hklEzGcAoM8b793Dj5qD8nU5BMip0v6b1mkjoXezHjTZuuzKYqTlFcghNQ9\n+dF3eN8Rvc2JX4W4zqF0XIkwmeZCBoSkyRZttjHxxO1WOJw2rl/moSij+ny6p82ZzvFxtYCX/ITF\nviNO6CWxyGkadPktbYYra3B6cp3TA4n1oTU3lzoiHbpQjjb6ybnaC1iYsl6+y1WuxrwmUvUqr1WM\nCbRjsLm6uUHqkKa5BfKcx1jC4WXAxsQE2C5grMdkvwXnEmBL+jdnSwr/XoGu5hZKBGZxe5IRomlO\nwa9WxDfUpOheP6/No9qn4sO2RwGHxCekRRLLhG7t0KXFdYpPB145zlQAcVpqT1hWj6WrcFwRusnX\nM0bVIchWkmjXrExSgq3ca31n6/YSgjN5CU0xs9Ybvh6DLHnNmsZ1zKLtTspJt256nea8Bpi2dO0J\nLJ6sx0OtBghTXVFAQpcabO3yWnFW6pam+ZT07em5gET+nsnXMHZdG7lzmbe1GHFMPWSzdcSs6yAr\nlXJVugQoMjK1XtYkTs7ZDAjKPC66Eem13ex8oldN2bIP9GfOKSIfmnv9d00/b94+VnAoIrlEnJ3j\nDUo7p8Q5Bwzyng7sFhgp72rBJV3DkRLFaAZOKz0fAqBgLMEUGa8VV7Tg0xKtHoVsk1ZulJ5kbzlB\nH+YelrwBDZ26h1Of/poJjSefPU/IryY0vSY1SNSinh5T/cupF8hinRvN5flVG1Vr5NvfXX1jsjgk\nRYBwD9FMvl6vryc0FKt7EDBouwhKp366WpkuV9SP5+e45Y1fNef6+vpwrHm+D9ceWax4PeHV4PBB\nvL/KJ6MiOnmnAFSsts7rrqa3lv68LKjedDYDz6klRStMS9GectYXMtObXHM+7Vjaezk9jWqgbcGh\n7XrM9bxo9eT5E0zfqQaDdi5r4j6dfwGJtCm1Zt5lPb89uZ6GnrQ5Hz6B9Wq1W74+g4uXbcv/FHAo\n0XtWlwQAACAASURBVCE1vNTzJLMCopB8mObOzX57CJ6Oqcz9m7ZHAIf3j2b1ifsmvxhOJlhY8bD+\nZV856bq1xP6Q6CK/cboRCljpz3h81sOkHpRV51wiD1FxlTP3g8xJfa8yrsStpN/SG7wFoHPtHEjJ\nfZ4C5MN3pu9PX9dQxFDNbZ27r7b2agsQcg25vlRbl22vP9faAlqxQv/dgiu0a//6Q1F/9v0dhDXH\n+1G1RzJlduuEf9hrtBPRelO+qslWLCBhFUi8npBbtrc+kesT+hxhnrp/pSZijiesflXlvZoAX9X0\n6f1Q0xyD3gzp7lyV9eD9XO/cmnyQNT4Vv17f0jqk2EP5vvYRkM+0LL7QinzSZL3NcmatWluA1t9o\nz8dWd/T+ONEPTsNyJx9He8SozA/fZHPXhPQmRhvtNiev1At8DghqVrbWMLdM+ymbf471L9douZgP\noij8ICM+J0Y83IqBTWD0g7TCUtdSfRnrqZjxpiehBvvzHEQNIOfm+PwdPCxa6RbUqOWzcb2f9luv\nX19Ri2ut2kfNMUh7BHBIBq+PhtBfrc0Vtu51nzmnKGsXVZqWptvXzsnQmk2Vk1S+b9WJo0+6AhL+\ntSf2uXsDSYR2usFasWdV4FXEbk/Gcl6saJ0DTuV//Zvn1jx9zmaor38nNtesLVL1yLUK0Jx841S3\nIvel70NfuzwXxzwpZ6fHna4stpeHxlffaVLJt7//wVtROSbh+KMHiY8dHB4ikrKVP9yEnd9Ep/Le\nub81+dRacH3HxdrQ5pk4d8+t+S5SKwHFnLp6JCqCkZnQW+y8CPOwsrJwA+UebfXdAky1gjN9GvUo\nFqZXA+35+Sy/Xz6jr1PrEPT1zm3xh2C5BuFyBaor6LU6R4d6/eswKZkvq1ShxXrhmzU4dw/tHdO8\n9kF1RjXHJXRyygG/SXs0seKUpX71wp1rGuWTWbQo99rPPdS1806bm7L9jNybfpTrh+b9VU0VC9Ea\nE9Zf0Sawc6bHcu1Xj6L1VShg0Z6a9eOpmfPUI68e43nyPb+JS8LbUzXtqUlTujYz1+9pCKvNl62B\nUdPFQ1xCbeY+T1Ml3Vr6W+sayq+2Ss/z1h8N/MULRM/ew/f6UEu6Ev1rcp2grvpm7dHAQZ+WcB4Y\nHmZF9bvFRCSfFgMW6nOyJNo3Iailqt2vNYGbkyUk5mXNHnfiDBWUp+R6jVyYBVhdozu70LklPdpS\nE7HWhGuiE+CrwU/u7Jzt/TTSP9A6RtlqBvT5fAqC5wEXtU3qK52+dpqcRP+tz9NYrZkuRN/etVt9\nP0MGYVmPVx0wcV2nMy07P7UbzJqU1zK5lb8OHFrvjhr0E49UA0naE+9fryScggbOAhEfjXjxsYKD\nMJRa3i7v6fOg+A2cn6z29C/TX34nZuI9tWO3LH9x7+0IarN7khtuzGBQ4i5Mir3wLvnyS9yF6nLo\nSPUjSERnXcANHjfkyMt+wZkUIdpld9zOpHgKXUxVA4LMmzwXX35tcy+xFPJYPPdeBR5aK39u1ssm\nTgRZAq/a+S1/i5v0qzrraif+AYoHaA0Gygk8diyhY4ludWkPwSSX9le01Z36DIGKV2SabsVt2LR2\nKY9nWMHC2bD+bU8oq4iOYiRNI0zeNeW0r82lp1Dc3L+ifSmPV8zx8ok3r13xKJyDPq/L+X7qufe6\npuFEfNUSF5Gm3J8BBu3jr4OAltAxh36tzxhDipsImZCCRGvmKM242NNoTZ12TnIFqNciuZhzzg1p\n+iWFZ3c+BfQMCSz6fqazC73Lm9sk332ZO00KEqUgvv11qFNKBVOeC1gsrwSKVmOgT7cWkMtc6pR9\ndYBVgSkdkNVXdyhbImBXsGgBYaZnjqkvoWP2PX7qWeYUgOWnHO+y2DczXhnAxByUlf9WsTGuCyk+\nJj+mCFqPMwudzTNiivdkz0zMxuFa3CgzWeb9YXCW+deetGaFBlHGinrS8maT8EjgIBNTTqyahW0V\nbLoJpmqmzKyyoeQ0qO32Ah4S4LOoM3WJicim48B87PFzh5874uxSXU2J7JMq7m3WJ+lttqF6wMIy\nqRIJhjj0xL5Padd7MKNPCV42M/04MYwzQ3fEdxNg1pOogEMiQNlqJVuCDpje56DpOmxbR2kWrqOI\nM+cVbKXJiV6Hc3XVxtf5Jo4qLFueH9iQbAEaGByObv3tgF2vMYWBox+Zp575OLAce8JhSCHbUuJQ\nZ+Nq88m+rskaaZ2pqocZLUQHwcUcyu3B+RXUu2Gh6xdcl0HCLPRmye7tBg2tDtFlnAPnWk9R03JR\nnNe5TIquS773poLFI+ocarlMg0H9/DTZS63h1oFbIkq0rrolenFSp9e0DEzHTGj7DX4/5sxPpk4X\np+sztqnHvHpNiLK18OksSq+ocBU3qRjEsulZtiPH7czmosdf7FOqditZraYsUgR6FkYmtjmhS8qv\nlFK3yt/bnGply37dpkMTMN2x4GIm0Ohx4byjlozJG4c3OQmaqaMwC0cwrrkt92vCly33bHFrgaB0\nwbR1bF6jkgZPgGHPlv1+y+F+gz8MxGNfJ3uRfJE6g5UuPPN+QEKvVZs+Ttet6E1O0tNB1+HHHj8G\n5s5DF+j6mX6cGcaJ4FI+iqLTSRvXZ24hKbq1eNdGx9Z6CBHj0vob9a1yIFq1L94EID52cHhYh62x\n8Hwg0Kmeoci80kqlrHOsbz7ZQs/kB6Zp4LgfWfYD8a6De1NneJIUcC0ozJyChC6geo4j1CnJJL25\nThGvs07vLBwtcbbMMakh7eDphoXBTivnoCMCR45sc+K1lJ3xjktuucjPdZK2LXvGZWJYJrop0B0i\nbiJl0c5gaDyspeCkrRyQIboF3BE6iL0h9LCMhrl3HLuBQzeuvyiwdZ/vRuIvZW2FA9HKvZWTiB3H\neeCwbJhvt/jbLfHOprWS7E9S31jyeer0cBocHlqbFsB1jswWJNrSXyMwWhgtsXfQwTL0xHlKuqiN\nSakvFdf3ULN5PXVSn3YPiIt9S/etn+VH0R5NrCgyc6vCak1GxSbcBsqUSaitFrrpz68JWZeR43Fk\n3o/4u4F419fFmHW1dl2LVZfJlASmGizOiRVCeA+VctNZmyVlfL52DBZvO0I3MtqJZahrdMjIRTTY\ncOCCO655yTUvMzjccpn7Vbxls5/Z7GfcPmL2AbMHI+PVdYHbMg9VxqSIabIjuRG6DYzbwG634HcH\n9ps995vbDE0XK8eSzLd+XVOvOA6tUlvomOLAcdqw328JNz3xPQs35jQLuOYgZE3anJ5wHiT0Ggkg\ntAVszoICdaLg0aRyf9s0pmhTlTLXe7x1OdS8WBLqPSAKzHOxGiUPf3onoONwXKbtMsgiWryJ3uGR\nPCTLhOjEGFqK0sqZ8s3kRyc28cRKFUVZawrSCq6ZnikMiWs4jsz3G5a7nnjbpUI1UpNCg4TOMK3B\noC3erDmIh8SKc+KEcAs6m3GVEt4QXI/pHUs/MYcji3UERVzCPWwyw57yOt9UAHEVbrk83nN53GNf\nRNyLeH5z6RRrOpW7Hosej6Oqt2G2YC4i9jLirgLdZWB3ObEdJ+7GA4M90udEM5oKBLQdC2KnXzm+\n0OGnAX+3Jd5YeGngPeAFdakAnddTcxBtCj8BhtCMpy1+1YKBTlXfljDUJRXzukXbEZ0h9EtSZmOI\n1qxp9lqAODX2FgO79v+QzxWQCCzrHtDc9XdhVGbLKslgtdmutruf15RrWay8VhcG0aLFQsfsO6Zp\nYDn0+Ps+iRI3ptRwuKUuYNOChICCrrCmN5MueS/tnDihy/BJavVLaj2GWDxGS9xawq5k1xYrdwKG\nSYkUiXG/4pZrXvIkvOA63HBxvGO88fQvAzwnbS5Js6/rVkiX8ZwDOj0eKSajC87kOh/mCtx1wD0J\n2KvI5mpiGD2uj2CpxLwjIz1TVojWZsw5JotEvOvK+rxHGseL/LcAhNSrkPXRVbFC8yjOhBrsBMCl\nHkerFxJRUNatrVQmgNMZGAxxiYQlWb+CcQRbU3WZ1qgOynnlHrQrWS1SG5wC1np/JDNpUPvmw7ZH\nDbyq3WVqTwTNRUjTG94iGZAEHBKSavARP4c1Ct93LEuXTF4HB/e2FKu5IRHaCwrRtUVfWpDQCstI\nrqwUUheqsyZ3Tqt5y2Zqs1hDIdCc/jxOBu8dwVmi0eCwZOtEAYfLXCXiyt9yub9nezdhnwPfIW0q\nAYj3aCp4UZ+6AnRFb1jAoS23rYCB69yzuNIdPN3sCdcHlkvHMrhVYXlgky0op3U5fHQsoSPMNs2R\ngMMLNQ5ZL1lDDQ4yp5prOE+Ipynp21oVwuXt1G/I2OUwkANg1XkY4pLWzTtx9irh5+e4BlHtasuF\n3uTFo1eU9Q/p5Mx3Jzi0VglhtMRHsWar6gSx2lqRiChNrW0AxeSjIah3fHAsc0eYbKpSJSyobA59\nMmmik/dXpVdMyjufgSCKcKtV5VrRoNTd1qaKtIOp06nrEm8Nu84ewpT9LDpLtCnVmYgUiXM4sGOf\ndQs3XMUbLqZ7xtuF7j3g2yRw+Hbusrn0yZtFjDhDnNKwQlNq3lgwuQCvFe6nBQZdJEdxQp1Z2A33\nLJ1NqevNbs093aZwjzFvqtkRj6asU8s9tJzQmhk8pCrAQSsdHpCTjHQLLq+RBj/hFC44NWcLh7ea\nqKkU1n52MHcszqUExqaw+7WfZy1SdFkx2VortDek0LgATpcFDDkQv+v8HAT3jBr4OQS16yTV4BAy\nU5X+pppQn0GibauTlE8+DOHoUhpzrXDUAHFDAgYhuvVUjXCMnJbE0l2Of82jKpki9DANMPfJeWpv\nCzDIyayJMhN7nJKnZQh29eQTcBga0WIX7rmY92z3R9xNLBvp28C7uWtwuIF4A/Eewj3MEywzLD71\noDgHqS879Km7i9TNFXXB8VZEArpuYbfxBGe57+/ZdvdsSAAxMOFQRXty0lc/OcLB1uUH9RrJOITz\nm33qsa3pp/PUQ8UGxS45MCAeagPMOd19a9LWX9fcYE9NT8e8ZrMlzh2+79akxcAJvbdcc9kHLeeQ\nslYUL+DCKdj13weJ5X24PWrg1akTcw0McpLU37ProJMziM+KLK2tlfeLPiLgCNGlmpaLrUvdaQ5C\nE6GuKXmI+RidKQoJbcbQhBgoR09bijmzBHEDh02q1m1NrQwTNl1O4AninE7S6JNiq9V0i//DhgOb\nZWJz8PR3EXPupNVixUuILyHcwOE+9Xuf+rk9Iftht8961Hu4uIN+D+YIRu9DLY5YcCO4bWTTL+zM\nkV13zx2HKo17e1ICxZdE1kpzecLh3Ua4i5yyedrWLDend7Yu36XYNZ97MMnvRcpO6poW7dfbM2K1\n+NT6BQ0M3RlAKCChTZpJDV12TGqioZNoUTls21IGH6Y9qs4hLdFpqq1OPW/FilpBCV3+VIdnaWRW\n2T6r5jaY5PW4ZLdnbX3QVoi2LzHXotdaynPaL1Hxpzure6vm3ubPDemrN6YovP5/9t4tVpLjPBP8\n4pJZVed0i+JYa4/YzVmuRMJq2UvBWBISF2MvjX2gKIxpjOwBuPtiGJRMCOIKgiFAr2M/WX60KBt8\nsP3gC0FjXugHmQYom7BgDC8re+0ZD2aXgkG7yV1pV7YosbvPqcqMiH2I+DP++DMyq86l+3SL/RPJ\n6lOVt4j444v/Hvvitj3gewXXx0Su7F0kBqMtYlJ5dOegD0N0UdIkIoDgxzsRGNz3gPUV4PubeAz7\nA6OUheIzEzik4z2HwHoN7G+AVQ80BAo84It09aRC6S7A+r7YJo5PlBFTk00gF4fOgHGIKNH1BGUk\nQvBNQGlsSMUg4i4kPjbMWNE38YAeb7xLEoPEH3aEPobhUy6HBHWpDpeqBdWl5O/MN4QCDFSaAw7R\nqblb6sEudIPBIU5ZXq1PNmQcA9GLqzUDB5U6xWGnRBOv8mYn3LMgQ6H59nedBzxxIcmyRwEHYj5a\n9mj2OwxirWujqrHWpUeE+euDi3kd3pnRSmThUsxnqg/R+6j+cDflNXGk7/okLVw9AL7n662TdlKa\nD8Pu9iH+0VwFGm4vkc8mV+MG0G6wBA1qJrVnGHsd8xeUDeN9LQuZ2aeOOkBEw7dRbmg6BQ4kzdH4\nSOmvRzSmGMDrMsZlnS6VfCP5KrE3T5OXwMA9dzlHuF4SUKMM0ggJGGrnnpS2OkNfeOEFfOhDH8J9\n992HL33pS9VzXnrpJfzET/wEfvzHfxwPP/zwqbwYD+AgMYm7OaUVd47IuBVoUnHb4RQoDBMzAMGL\nH4kJuQ+UW/VoZ1f52wG7lr47BFwPbMI4foK/l895JVP9ZRJI2NBDUbRjTSJi3/kNsOmAQxc1J3pz\n3jrZSvnbFQDXPLDuALcGAo85IMMkizFQPqTt9tywUIwK6KgAZVIm5JDLgC0gAfagDtmCKd+et4IH\nTdDBxzHtdxhCyTNc1wrsb2H3VNoP2ZxliLRngOhBMR40lhwksqHeF/OCzuWfp0mzkoNzDk899RRe\nfPFFXLhwAQ8++CAee+wxXLp0aTjn7bffxmc/+1n86Z/+KS5evIjvfOc7x3wVHhQtpQn6Pf977j45\nHSXdj8KACRQkMHCkHxm3ZfQMT6aQhknuN+PJYBUFHE1+YMwJHzMXb+aOY5+ZBZlpBcgMz/ER91wQ\nHlmU+MkdKXSpFa3vQjJg9oDtATPVt6x99Qi+NH60eYyJu00VIr0MUGpU3JpwMAJQ40N6CULEmlph\nWctp3LinKUU2hSQ9ODWf0MVfwyACnHURHBRJSeXCp9nkLyMjcy5Gvn2OIFXb1/YT0ezdX331Vdx7\n772455570DQNHn/8cTz//PPFOX/4h3+In/u5n8PFixcBAO973/uO9ALZQCOzJo4XxJFTwMMwCLRr\nkdKhzLiTc57Lz0PkHA+h0+wHupEED45AXOKomfaIk45mV84lRSQMip0TpH9/4jG8RZz45bIXZG8g\nJIxLR/FMfoyeoYqpQOCv4WNNC91DG19GKvLgpBYx6EjzyCVuoOAN52Mkx2YjPiWShulYCZmbQcBg\nU/0O2vqwMDQGxqs8OHBeCijPOqk/Yp5mweGtt97C3XffPfx98eJFvPXWW8U5r7/+Ov75n/8ZP/3T\nP40HHngAv/d7v3fklygbWQiXo/OoNGv53XRHKWDY97CYBXwwhxNRDG7kMRUd+9XgepJtpfm6NoVq\n4XjpUCYHSk0dqmyvhFMZcBvIA8IXQf7Y9J3ScTMo3hJphF/OHBQbtNRAawHbpHnKn8P/zdojl4Mc\nnZLtDhYJHGTo+ZL9e6EAw0NQebyzfJmJjh0BBypjynhF3kb2b3qctmnHdLaVXiHVziyCBJwc/MtF\nQQmYOV2aVSuU2v7QruvwV3/1V/ja176Ga9eu4aGHHsLHPvYx3HfffTu9QMnsscHRU+uLYcOIkcrO\nog7Kq12ly2l+irikYXCbymEVoGVoI6kRPFZao4w5lkkVIj+bB+ZrE59TPBfjdOFRn2VgyLUqLJxm\nlvWGfXJ8S//WDdA0wLIDFj4etJbS2/OpQrcavBUA7lDAvgUWS8DuISLGAuX8ZIlnwai4pSADMz6e\ntAQYpLJsJpSYzJGJnndVBonQj9x9QnoO2Hcytl0CC0PRCq5XeYYdyoYkOYwzLWvAwBdAKhgb2Dnj\nfOb5xfEkNAsOFy5cwOXLl4e/L1++PKgPRHfffTfe9773YbVaYbVa4ad+6qfwN3/zN1Vw+MpvPI3/\ncuVNfPtrChcevhf/6uF7it95bPhY2ILolLxajrs7Z85zqFDaA9bHKDhaLvmEkfN3AeAwRf4M7khi\nOIeMNmTt5qIo/cZD54hhKQUzzSKrh4y+YVJxV5mNuitJPzx2I1dgynWgnLHwrR4nCFWShswyAoPy\nMfjJbTJWcuEaqIPDeQu8xwCrFdBQpOQ55GQyDhTpCK2CM2aoq9GjAU8/Bht5rZK3YhGylCC7kfIc\nGsQgM0ehjBuQwy8Xe+CqH60MHLz3UCZP2DiOGlmM4nkXvG/5uLUpirSJkoPW0xXCiUgtpNSA2A/l\nDiZzRXppnnz7pf8Tb730TXznHwK+df41HJdmweGBBx7A66+/jjfeeAN33XUXnnvuOTz77LPFOT/7\nsz+Lp556Cs45rNdrvPLKK/jlX/7l6v0++7mn8B++9Qr+009qLJK5nE3dEQJKkYl+r2dhxH/XavcP\nUZkqVu6Jq7SJhiwpN8tJRAFSRgN+mcQYmj7SDcbTMmuSAy135wHsA2oV79OozI980WP8qWzITKZy\nvQOqDEkpOxs06IyFXyqEPUDxSXQNpcdiA2gHaA+oEE+Bi9LDKowztwtw0MCeAvaWwP4eYM4h51TQ\ncT4dBBYrICwB3yh0OpaNG6SdCfFYqQBYByw6YGGi/rIUAHGO2qaAg0Ucr0BGJJ06kSQ9sjUQOPDx\nIaljmV9craLU0IhxqUlIXOu0AAzzuFCtycIgmikqNZQjlIFDowyD5gulG3ieW9iAH374R3HHwx/B\n/V93+Hfv/xh+88tfqT5zG82Cg7UWTz/9NB555BE45/DEE0/g0qVLeOaZZwAATz75JD70oQ/h4x//\nOO6//35orfHpT38aH/7wh2cfSh0Rt4yPgc+ElhTOwVGWdwwv4EKCWkkVBqPO1QHKuAgMbYirUE3a\n55oDzXetgcMVsF6yE68hcsk2cOCZPCkNU+1FicGqkslpUq3K91JNFE+1ieAA5Gi5fgiBioHUG2Ox\nXmrYfQV7R4Dhbkxee4JJ3EYD77HAuQY4XMfAJhfGWrgF0GhgtQRWi9gMRZmllFtxBzsYULjzQL+v\nsF5qbCy9b5PSrhrIrFPKp9W2g150CCsg7OkSRM+hLNATFsB6Eb0K0OkkChqR7ig+RiJCkhC1UTEP\nZh/jg8aIqzeDShgA6weD5BQo8HHMMi8P9Cu9d7xMnAwflHHGYeaZu9DWIKhHH30Ujz76aPHdk08+\nWfz9hS98AV/4whd2eJxiDYtKdBBomV2ZOVWViPTs0viWY+sovo6elKMt06dyUMZDNR6hNSUorDCW\no2vkFOBX8Rg4UwYl8BBdqbsMymhO1+arLWc+Jjlo6+OhsyuLF7Hp0A6VIw/sCgd2gcb10Ac9zKEv\nvXk0L5impPYBcxiPZg2cX6fFtxI7rUhokpoSAcQdAN6bjjsxgIQ7p7HeszhYLnCQ3rVDCweLqfoD\nSkWJqV2s0S8U3MLG6krU/TwDk+b/GsBmBfQWCOexGzjwnOxl9lTRc/gYncNYOywMpB5YRGlH2S4G\npqWRqrnqSQLUCMh7asXxlSkE42QDHl8auf00ajkAZxg+ne3RGS1JaoiSha5KDhFccpwZFTHlVaUH\ni30CCtLSrO3RLjboNhr9wiJQWC+3LdaqIJE9khaVQ5VCBxcxunHw3XGLN9jFOhu0TFqJlhinOL8X\ncSIR80nJwWbJgfrIQQ8ByFQ05RCxRFtjPez+ARq3yW0iA32LDE6yFkIKklJcugBK+52UwnldCmrP\nnalNPxT/7c5ZHDYrXMM+DrEo6nl2A2OrYtyM8rFYq/XwiwDHJyWvq8ErcQHAOxa4aoBukU6ujY1w\nW2mVVE6V28crdJGaxEGCAzkDCdX4WJlaZRlXEoF7zIrI0kM2xI+rm3HJIVc/4fx+eobJGw4OWVKI\nQJDViIiTQAAVuaIcifLacZCp3DOBM1hOREnuJOPgGg+1CBEcSGLgwACU0ia3jFOm5AFiQo5TImou\nlL5wg8R0KBOrCBz2kfVyAgeSIJheq1qmu6ZWRiaxoMK5VB8hVpJco7EO9lwPbToYD1iEPLEpEJBX\ng5K1ELhOQW0iA75Uw3haM02kJDn0dyq4O4GDVYOrdoWr2McB9oZa2Ru0A7iTRMnHThsPE+K4FaX1\nKPqSx6iQOWiJaBRZq5RLk8aGF+JRaWxqORMkCXD1hQPEefY3B4l9QK0CzLJH025gTbkTN8/9kWmH\nZF6kOZBjP6bAYezC5vcsG3t0OhPJQfrno7IQXyW7KXkNyXxduUmNHXTVHmOJgWjwmeseTdvBdQ36\nRR+NWzTBa1Y3roJy5udJBzICMbB7UeY2d5PWxHFub7gDJcMtASwClI0huJSHMFYrmgEcrmIvirA6\nAK1H0MCe38DaLk8sqrsgazDyorpcCiei9vBJVBTHRSlB3AF05yyu7bd4x57DO+o83sF5XME5XMMe\n1liACgX2bPwKv5SOertaBKhVQNhHaeLhqg9NdG6AHWLQVM6uBLLQUNP8pHGa2sXHi8CB/t4PwF6A\n3uthlz3atoO1uYgNr3IlvQ09s7NFWORRDNKVyRdHOwKI05Igzkhy0IPUIF062RXpZsCBOqMsuOqH\n36XfNz1VJ+mh7aEXfcy1dwbwyY1GYnMlg7cABkqM4uDAo6S5hsH8+wMTEuNxqze3PxA47AFYRfeB\nthR+m02wOcYhSw4HWIIqGCsVIlY1Bu78VYRlQLPnYc97KEr1kMBA4EAhG1ytqAVU1ZJN94Gwr9Cd\n0+jOGVxp9nC12cM76hzeSeVur2EvlapfDqoFZ/TBdqQSQBgP3fTAsk82YAJ2lFGtJJnx1AhZ0gHs\nfB7jwiUH6drmFa+4hFTYiDzUnoNedrDLDZq2Q6Pz/iA1FZmk4FjiMI+rLJWI4TcCh+yl60d2t1tw\nO7xIUmrI1tk8t7I4NbVvBVctMhLLvS3JMJmCapRHgw7OGriFBjzgwiLWeZDOBVrlydhNuvgGGSBk\nVidfYWnFNey+3HYhg3mkYe8cgHMBer+HXnUwzabQX4lh4qpDtoaoUhgIo6Wy6Np4LJsNlvsb2EMH\nc+BjavcaUNRGAj0ZPQyU4CBih8IiuSmXCm6l0S8tDtoWB4vFUByfiuZzcKC9LaJh0hRTIj4y5Vmo\nHtr20ItN9FqsU7EcDgyDOoFyawFuaJ5Lo5DgwF2T3LYijxVJDA5mtUGzXKNtNmjNRtSqkBM9xzX4\nxP9Dm0FFW8aVoLLdjWdllNLIaQRGnYHkENGPSsRyPSmGq8RqT7ykZr42N7q0O/CIdWKsDCV8t3SA\nrwAAIABJREFUV2sPjdZsEBYKCgqbVJ0naB39eQQOlNYv06d5pqNMpeCrEk9K4FGZ3KU+CxIB6pyD\n3e9gV4domg2sKkupAVl6IHCgUmHUN1FcJ5VjhWV7iGV7iMVijcV+h6bvYTsP3XnodYBaBygX4x9G\n4MDa4amAUqPgrYJvNfpGo2ss1raNe1ckj0TeMSMetIsGbbcTwSEalXP9jbw4DONno1qolhr9no3J\nskC5+pMtQlbS5kZmHqcmAa8WSSrHiqtRK0AtHbBysMsN2tUabbvGwlAZ/jEwSJWapjQtjDK0ujYH\nuHpRbvBQ5qqchM5ErQjJpkAA4WDYb2RxmMpywSBW+QIcpAAWKTNYtlJ4reGbFHodgKA8vGngTZMs\n1Wqc3sy3WpN5VFLvzS+aVT8pPcgEIpJSlgBWHmrpoFc97HKDxSKuQi2iW0watCj4KW4v58FhMf4W\nd4y6hj3QRnQLu8HC0vZ4GzShg+0dmr6H8YBxAaqSuBXS4YyKJQ6sRW8NOpV3utogb39HdpC8Od8q\nHREkSLXgAMHHM85fDwuPxnTwCw30Br53cEFnoyKt+nKPEZlqygGCwEGCBLdB8OjHQt2I9g8sPcyi\ng15s0C42WCzWaG3sU77tQjZEAlTjMS6RVD06QA/zIu+dWacyMpJHC9/iakUGCJdCU1Va7TSy+3IO\nNaVBk+/wGIbRjlcAzJWZnkMMjiZbwzvr0DUeWFuEjYkFaKe2xZN2htoYclc695hJBixAIoYJ64WD\nXmwSMBxi2R6wVYgzGQ+CWgy/kYGLB0YdYgm+VybfBs+ig1U9mibu9SgL+wJlIDqX0Cgyswzhzntl\n8m3xamDBvRU0jWoArxE9TY3u4JyB9hpeBQRjy/BzueNVDRQkEctIlYnHRjXp+yaOkVpkULBp8+PG\ndkKVSMV3qgoTcXpAD4sANUhI3IW5TXrmUkSZ+G1wUsPkDQUHWoR6GPGtAe1+SSvfWAbgV0hwKP3j\nAM3H6MKMJWm5qBUPrQP0IkA3kfG0dXCLWG3JdxboGoRNMnxJlxlfjWqvKmoXFIxHQDHERHmgddCN\ng2562LTPYtN0aHWcZhQNwCsHUe8REAABDpqxZnZzEiDwje/5RrpkMOPxdnIMuM0nx5xkVxyt/Hkf\nbzvYQ2p7aPLNdckgWYJ7maFplINTBk3bIQRAaQ9nW7jWAksDrPS4IAuXHKaIgzdPtxjCoAGYmNei\nbARus+hg27QjuulhTQLYUd9ScPhU2PQQ3A/SS7m3ZookOJyWtMDpzIKgiCjMibSuaI/Y1jF03W56\nlUYspcbDcmPQtokDqC26ZY92YWM5dGfgegOXSrO53iA4k+oBGsArwOsEDjPP516LQXQNiHK7h7I+\nMpuN4GSaHk3bR2bTPRrNV3i+GsX1Ncc5UNyDLlYOqktNAcqcaSlsSmapkDjLtyEsKQvBJThwn33e\neTtO+gwePPApGyNr6kR22GWHbTJSpkhR2zh0bYd+2cBtYmVx9CbWCaWxmQPw3CSmXoSkAjogBZ1p\n62JFJx2gjcu7aes+GolTKjaHxFqNyBztWG74XC6GIakctIyNJQc//BJfPksOCuEUQeLMwMEnOS5L\nCqRJ6zSNs3wgqbaCZRQlsbSMrgQAk0LoFAOHASRUj15ZeKURrIJrDVwwcD6ChXcawWt4rxB8LBga\nAPB6jgDib+K7YWwVoDTlSKRQ7vS3MTEOw6p40ORt0oSmv2tBNP0QIyJ10Dg5LRYMBMqCrqXVpjSE\n8ReXkllerfhVpZGYnsIlvGEzY3AbRYt+EIPzEyn8PaTgBJIGVUrj1ioFtbV9lvi8igVdQxyrqEPO\n82Ks9xHHQukArelvn/5On8pDKz/sdq5VrWo0B9gcGTm12HFgiGe3VcWBeoX6mV/PQaFUNbY2fZbO\nwCCpkz4FAI7pVsTyuTlU3WF8j7xycYaqISYxGf83YXieXmZYf51Kq2+qN+C1jrtMQQ37DvgQ/z2m\nNCjV39IZKjOZgodWIcYvqOxRIebKq3kZ4kVEb0//rk1Cbi3Pq1cZUUA2bpn0U/Z52bc1hiyNZNSC\nDOJc1ZCqB4enPP6kFubAHg5XRht41cMbDd+k8QkaPsQxCEEVQK1UGJLWJI+oNCYRfFIfqMQzKgGm\nylBYqmC8D7ODkW9nN6cmUN/RORkw4tJZnhvfuDY2+TPf8yR0RgZJarRm63ypZ5FhbfoeuUN26YQs\nawBxG1cuhvWj4R0kE6WKzU+5KsOfXTMaTRmD+GTMb+GLQxUMKH8rAYKe6ZE3GOZb2pcCJ09wL3/n\nQLprn4+BIT9pbEDO0ERGTC9azscrvxNFxFD/Ecg7eJVWUlW+V4hflOOjplbwUijnfSBbUY5fyS38\nd+6hyP2b+5nkAZKZ6dopXubXTZ0z5bE7Lp2hzUGNOggoxSwpZsrvJLNOISYHHPrMLj/OvHXGljod\nf372VpZPHTEqo3KlroWu5O8lKJTMFXtOAoSHLmwHvHfo7xowSLbiz5Jt4u2UVvOxlCGt6mNDsvTR\n83GKsYNc+slr8+zioMrx3zbxxmI8qtKUVL3kG3DJIt8/qxi877hDi/9/m5RRH63pNh6HbgKDJO+g\ncbKVRNUacwIomEqS7MrafeQUlefm+2+XVOSzpATEV5Ia80kQm2IHkgOozzjoTYWnlytyuaKN21FO\nrKk1V7ZkfE6GplIdyX3KW5n7Rr6PT7KmQkAv7jF+d+IOmpRyYvFzJEDI72tjM0W8b3ch2W+5V8pz\n5DXjWianS2cODkSBdTtRVjkicMjzgWlQyNrXmCQATX2X36t8hnQb1SafvJ7T3ISvveu2cyIYlMCq\nENUy+S75vmOJ5TjmqxJmNJt042fydZauye+T7xZhZNxqnVolJZkpn8q4cEoJ9HLyj0Hz+AJ6vnY8\n0bfRrDQ0cf71oDOs5wCQa3GKyGWT2S6bo3ZxY04xPa0nUxLKtAsvB6fQtXM6+tw71aPfjjrIdH5m\nfjf0Fzdeja3c0l1ZWyX5O0/R2BajhjVtanWjKTju//FqS5N1fP3UosAnumc9lMdvLJFMgcJUu3cb\np/mxPgmNJdv6XLgFDZIAiaNjJq2fq1KQyC73HTMeabNEKt2xzqRj3TD/klfnKePZNpoT42vvM9/O\nDFAyQa3srxo4RJPe3DvPeS+IuMSQn183JM+1L/7Gaxrwtw+D/p7PrU+IEqpInciqD0lU2xeWOh/s\nMkZzY3xSymb1609nBg7Tk/DWoaOAwu73q4PWrUw1t7MkuUgcte3bdHzqU7fjxDrKuTeSyEG8S5+e\nlM4QHG4cxRWj1H3lW1BHc6G85n3gqF03JE3bHLLua9i/wa7P12yjo9kKpFqWn1yuweXbSF08t0uJ\ns/jOCmWfRmnLQ9qMau+4jeYkhhwvEwbpjkKxSVKgAK38XnmK5WC8o4P9UYyPuxCXxaZtavU+zXad\nkwPbTWOQPC2Kg+TZMNfdifm7cYqrnBR07q5ILcFB/nYUBpw/t5zwYwF/l/uMWyTPzXr/bqqQ7Pdd\n+i2MxmF8jzl1gI8Tf/dx1KgZRpe3lRtoyziH/AZz7577/viSJB8/ugc5qesgMeVhAnJ1lOPTGYBD\nFNgk1VevXe427jQ1rGR1RqLza+HX+X75fTKKT0+Co71pOQUkcXDaxbMhV8AaQ/PAcrpm7n78nHk7\nyVi8lX1Ziw/hvRDYE8u3G4/NlL491RYPPYrAlM/jsZxjcKAWlG9fA89Q3FmBw934feke45bX2hVl\n2SlDeU3OPTndcHAohx2Q1vKjUuyY+cixfG4JCmWMWy0KMoIEsVMpyPOpuKsakCcYtVfaXegJFKSU\nV6UxSND3MkSXAnDKvo7P58+RAVLjdwYgzqOjLh2Uxj8ODLm/1eiceJ8Yu8qjDMZvnzcK5svJ3HhP\ngwOfxh4ytyT3QT1IjYfkKzFOAVSwBanP6/w+NYn5+M61LY9EHsHTgYVIZ2hzmBeBj3KfUHTOuIOm\nJIUsauaYNsnQUkyV95HrQm1wuJRQkwrKPqHa3Lp4M858ZdjuOPSa50nWwEM+Xwb+1N5fXl9b/cfJ\nWNv7uwybpvUxP0PeI6oF42hV3v/l+5QZox5myLfg40u7UeU4zXhGffkY9x0fJ/qN3iHWKcnxn7X+\nHfPLGCDk6HDeH8+Dk4PEmRokp0CgJorJvzlz0u98feSdIxmUMxrPSRznJoqVRjCVTOwBxgakQohN\nCT30SQk9OfKirFjFs/xqqxpQhuraIuNy+nopZSjR2lq/18aE+oJLCTxPUUpnZV6oBW1PwCuB1e4t\nrx9P1QmACunakK71MdM2BAXvmVs37cJOCXFa+ZiIRX2iSGYpIU+CMo1DlgajckFcwXl1qm+ngKIG\nDAUvinngK/c6Kp0JOEjRjL7bdv64Q/TwF+8gTnKFk6DAsxgH5gtJfA1p6oS0RnuVszED4J2Gd2kl\nmhsHhTItWIUhDZhnA+pUG8DCiboL8e9pYOgHQOD1mIYqT6x1HBzKlXI+6WiKONzwSTxOZo5v0sFC\nDzW5UkdiHDvCBW+qBVEml5drNo2bDxouffpg4L2Oh4uHcyam29fAQTtom8rgD+nbGTCodgOX8HiB\n+Bg5olkfxkR03jb6V02SK/u65Oa5xVEeAHDyOlA3GBzoZfkOQBIk8rlcDhj/Slg8Zip+Ton1tGp5\nBgg8fbiDhfMG3ueCL76o4cD+HVRiMpWLv9TmkA5xj06VPlPREMVqBhgTd2Kmug5GO/TKDtWbAJ6s\npUcTmMCkSdOPl4HjFaRktacpiaIcg7EZb0qNqFc2mKrhEO/Sp1KBIalSGuWuZ1wlkMVjHKIk4KEH\nEOg7i76LtR2CV7HGg9MITrMiPQoIabwUABXibsLaxxobNgP3MC42AUfa3kCnNHuNAIsOrlheevCd\nsjH02niy1gB418DAMABOaTCfnhNHozO1OfCot8xuuUPkSkYUQPEIVKY2DEm9NdGUgIFWNFn3sIdF\n5xv0waLbWPSdTZWg7FD9KfQ6VhjqE1N5cdB3klLZsZAqDQUd+RM6AJYqQjko42AaF2sRtl2s6GwV\nY5E8UQ3G9geSLqhOJC/zSuXheFk4Dgx1kbhc3ej7mh1AqhOjvmWfVCbOoh8qQRGwyHEjYKACusM9\ng0UXmqJqV7dp0Hc2j1VvAKdj+Xpe67NWYFYr0HaFgcbKBsAAvY07s2sbC/RwsLCp5mYGSOqBDJgN\nuoLrY3apG/qXS298LmxLvqbFcSoT9jToDNWKyBJq9F2JpFN6LpVqIfkh/wZAMFq0WGfBW9Y67HyD\nbtNgs2nQd7HkWOgsQmfyVmoOmdH4fpqS4fjOWQPzicMg7sloDGA1QmMRmoDQOPjWou9trGwUDKAV\nlKHCJz6Vu8v9ZYppF6feCmsscVBUapSFZcdmulLlIPCm76lvx0pJWfmY6ldFGaaFLDSbASgb7KRN\nIauJGm6Asxab0KILDbq+QdclEO8s3MYCXRyzYesAXvdzCshr40PFZY1KnxawBt4EeAs466CaHjqV\nimuauKtV39hUQzIqTtwbVVOjazQ2ds6p2hrZUxFBIi6X3MB7Mrrh4JAxlVsO+Dq0Xc8FckQcYODA\nnV/kgMylzLLEQEdciTrfousbbLoW3bpBt24zKKx1qkCNXLC0VoVaMh8nepnaZjBUfdqqVGhWISwU\nXKsRlrFOZQgA2hBLosGzapGkV/pBpVgkSWGZ6jvTntsrXMMKhwNIEEBQfeiyylT8lFaEPBYZsvl+\nCdzIKNUHXivSpkrM0fVHHUT3zRxAZe/kd5uuwWG3RLdp4dYt3NoAa5OLANN4DQBRGaeatM7BQW5f\naNMYWQU0QGgUQmMQ2hZh0aFvLZpFhzZsEIyCMlGFjPXFXAI78lnoEUBkkPcjIKmpHDT+BKDj8oq3\nuCtzrMvWXETz+hahJNfUsrmHMzNnvFTkNDToQouNa7HZtOg2DdxhC79u48arhzrvfcAPWfZ8rkS9\nRgkOHCDkxilDaXoFLAxCB/QuIAQFpQBtA6zq4ZQswEpeimy0bLEZdojYY7tELFlB+AgemwQSZUFU\nAwftHYz3UD5Ah3I2BaUQtIpl9JRGrwx6RVWuS2sH39y3Qd6KPo5xHh9uQzAMQAAUtoZNv8D62gru\nsEE4sAhrkzawUeP9Kjg4yJ3TpQootwyQ2xfyEvWtAhYKoQVcr6B6CyS7hl/ooWK1lH7JzVkT/3WS\n0mqShrS7abG0ZglizPNcKT0OnakrU/roJVJOgUTZeJIUsppROz+LrRa9b9C5Bt2mRXfYoj9sEdYW\nWJu8byTf0IZvcDO1j4WUHBTGkgOtTENJepQb26Tt1kKvEUILQMMZj77t0WuLXsfalpy4vYEDw/6w\nZcz4WOIAi5DUDdejcQ62d9AuQHkP1Yf0bwAhcKwFlIp6uVGABrzVcEajsxZra7ExWWo4TBvW8NL3\nWe4ppYJxWbsc+twHiz5YuK6NIH7VAlc1cKDGO4PLcaqBgxwrLjFIyaG2+RCNVa8Reg3nQixmCwx1\nKsk1mkHcwMMVvDsV/TGeC5ynfXFlliC2x08clc4kfJo3XBrFyojJsd8dyLpvabzMLjkZSCpXp95b\nbDZRjXCHC/iDBjhMjCY3lZ1iPi5FyL0RFPvcth8jZ7i9dD+v4iQ0Fr416BcWfWPgdGYhLj1Y9IMB\ncolD7OEA+7iWACJuQreiDejCAVb+GhZ9h0XXw24czDrAbEKeVFJd4jSUbg9pZfVAA7hFh+VSo2sN\nNo3FxrY4wBoLtWLqROnxIGDgqkg2fKYuDdH92LtoWwgbCxwY4CqAK5Xx4ntk1rbD43YHYp+aulcb\nHwKFBYodtYKP7mynAjoVvRzK+mjEhC84PHNnlvxIpeDbDnB1jvM5V7XIPiNtDHML5VHoTMKnp6zk\n4/DiMTjkzvFp7qmkcXGfvSzyUQY+9d6g7xv0awu/NlGN4DtO852nifHoc0qCkHlJ0iBZW5Ekw9GG\nOQrDNvf92iCsG7SqgbMWPqkW1JdkZiWTX7QzHGAP19J+1lciSIQIEKvNGqvNGvaah70W8ga6vF18\ntyg+iRR7NyFum0WAWTq0K4/FXo9u1aFtOrRNNn5yC/xgR0Cbto5bMD5gABJ08ka0cIcmSgvXEIHh\nCiJIXGNjNgfi0jBJbeKGSC4t8J3W5Z6mRf9EF4dXAX1yT2vtB5tQCQr8sRwgyDI2di1LcMgTn0zz\ncZduDN/kUIGT0JlnZZJNmmNr+VtZxCRPDOkjzjDAcyG4kOaho1/cR1el7wzCOkkMxGBX0yFBgv7N\nVycusk6pd1xUnVqV9hAZLqDYbDesFfzaIGxa9M168OkTo2WbA21fk12YpELs4yrOhyvYd1ex319D\nc8WjueqhqJ3UbrlZcIfxhjB8IlE7luWh9wLavYBmL8Ce82jOdTCGog8zMGQPRosWLexgk/DF2DkY\ndC5Kev7QIBA4XAXwDjJAXGXt4Bsg12xENFbSJsTBge9fukr/5numVoDTG41gGsDEDXdc6OM+KIxT\nJZVygCsAkseeEhEwxLnClZK8F0y2U9xikgNRqV9lV92czaG00tLeF/GsWADDo1bdyENFgc0buLQS\nhU2DcGiBAz0GBn5cY5/cHsFXJs5wuYHTjMcZkPZ3dOw8tiKHpUboY1APCaDUamKNBn3aMi/bHMhT\nsYdr2PdXsX9tjf2rfbni8pWXS0gkitfsKFL8ruw6jXOAOhew2Di0vYNaaWAFBBPHgrbsy56MvOdV\nXgxS7ESwcM7C9wahU3nS07teQQQJDupSgpAqoCTZ7zVwoIPGih807lojGIvQeLi2g0uqYK8NrFAt\nuASdvWzcY5SBQRWvSlyuR/MjXhG3f6zt93JU2goOL7zwAj7/+c/DOYdPfepT+OIXv1g977XXXsND\nDz2EP/qjP8InP/nJnV+AROMSNafSg3OX1lB4igbhLaTIuc5kAyQx2wHqqxEHCPq3BIcptYIDRM0Q\nSQxHk5BPvgbRMr5M7rqlTZJDuQJxw1bDJIjlYA48wMKvYQ/72LbvpUOCBK26XF+XbaL3k5IDtWMP\nwH55H+WAhd9A2R5KeQSl0CuLNRY4wN4oapPWWQL0LjRwfQN/2EQgv6ryWBAwfI+NmwQHrjLRZJbE\nwYFUCC7VSfUkiOsGwFdAY+DbBq5p0NkYB+F1TBarEUm7ZaB5GV/CiRZGAgnADgtGTfo+Cc2Cg3MO\nTz31FF588UVcuHABDz74IB577DFcunRpdN4Xv/hFfPzjH0cI8y9GYCDFqKyXTjeQBM6xuJRzK/qZ\nJrlex0CnjUXoVRzoDbJawYGAJg8HiwNxHAcc+KpEDId0Htd3l0gMHtUevx+DoihzNFvBczB4k/bT\nHjwT7gD7m0PsXdtAvxNiW+j4PvKkoslGIEleGik6c8MqbUvPJxEBwxqFNNXAw2oPv7dBt1hjbQ9x\niEMc4ACLtPs3RcuSy5kbkENvgXVTTniuWnxfjJOU8naVHBqMJQYCSrLFyAA3boMhu1EPOKfjYcpk\nscz/eQHkhkm5E5mk0kOhAeRdubk0IXdHOw7NgsOrr76Ke++9F/fccw8A4PHHH8fzzz8/Aocvf/nL\n+Pmf/3m89tprOz42r328s6iD5o0xCiZBS4xfj6PqwfPrxwacmJgTQ6J9pxE2qlxZ6CD7whVEpqtJ\nEcR43HA3RdRQLhGQOkGMS0QAskIpnfRAcCrlfehBV85uTFIreDDUIZZujXbtouGRgO77AN5GliBo\nUtWMenyllV4XacGXahYz+CkTjwYOC7vG0h6yqM1+NFnowkHa63U5VjRGUoKoqRcDeCekCpXB8iqG\nWpPaYlCoSIXaSLYKDij0bskuERYKodOR11gmb6lElBGU9Dd9cimarg8MGMhTweNdPMg8KfM6jkez\n4PDWW2/h7rvvHv6+ePEiXnnlldE5zz//PP7sz/4Mr732GpQ6XoQWXwW5LsaJbLO0cwHFNwREy3fN\n1sD9Is6nZKo+xdzTqsDBgbsvOVC8g8yQdE1wgJ/y+/FlxcSEij5F8dH1pK/SxGOxDlUPArt9GQTV\nD1GPlF+xxCEWbgN74CMwcBGcwIGvuBwEa4Y36nBhExmMkdRn9K4OIzVJLzwa1xdJYVyt4MbIIcvS\naQSnsh1ESg50UNuoLQNIOSDULJI8ftpEg4izgLdAb+PPNeOjVP3IUMntG72C73U8vIG0LND4Tbnz\nuQmT7A/xLD+cTZSN9HxRvAFqxS4T/fOf/zx+7dd+DUophBC2qhXDvUFeijL4oxYpRkQJPmXHymiy\nMpCKDGBkkPQuBq4EmnQ1YJBuTWI4Wl19DzgeTy1lVmmJtECwQGiSVS7lbHD8IH1X6vCJ4YKLNQiC\nVzEAKREHiCE+MWyw9IdYdBvoA5eB4fvI6gRXK+hIABE2gF/HeeVdmhMK0BrQBtAWUKRW0PuS1MBd\nhMJwqfc8mm6DhVuj0R2MygHt3M4UoGLNBUqx7lQ5VnKcuARB4zaAAl0odUABDoR4YRGPLoGEjHTl\n6iEdBBKJp8IGQB/f3QXKBB4L+ZJfucrN/RARMLk5PsrP1FdTi+lJaRYcLly4gMuXLw9/X758GRcv\nXizO+cY3voHHH38cAPCd73wHf/Inf4KmafDYY4+N7veV3/gy/u7KW/jW1zTuevhe3P3wfzcY1Hil\nHB4QVQY6xVJisUPi3kecahZgDHdRcedlZxA6isXHWHrggVDcDkEGuy6kC7hfswYOUvZMBoWwAEKy\neq1RTiKSGrhdg8ChS6tRE0ElFozJshGpFw06LNwGi65Dc9jDHITxBOK6OkkP7wDhCuCvAZs1sNkA\nvQMcm+xGAVanYxEPvQIUl2x4s7n+fgDoQ492HbBou5h9anumY4dimvgQJT3f62gfkuMkx2gwFoeo\nQgR+EjeE8AAFOUaEdj3gVwBMtPkI/BjaNRU526uczevn3Zha8Dz3QkVg8EPfcF8d0ujzeUPX/r8v\n/Vf83y99E9/5h4Bvnd9V1R/TLDg88MADeP311/HGG2/grrvuwnPPPYdnn322OOfv//7vh3//4i/+\nIn7mZ36mCgwA8NnPPYX/8K1X8X/8pMFi6NUxZcAYGx6pA0jFkOLZLAWknH5VuqK4yDoFEuuQZkqf\nvnwHpSxNq1JsQcl4nKtSQEPQwCalgRNvShccY77QKbhex7oFQbrDREkV79B0HvYwQNH7y0Po6/4d\noLsKbK4C1/p49CFrPsU0UsCyA1aHQLMBmi6WQwBQrq4kCaX5qdcANgG292hMLk5jhdTgoeFTtSbX\n6yw5EDhQt8s2HQDwHRA6ZJGPG1I4OHiUQRs0PhzkTZQerukoOkl1SiZ39fxIY+unPRVEtcWRAqLG\nknCUSGNcZN45VLPjXz78o7jz4ftx/9c9fv79H8Nvfvkrs8+follwsNbi6aefxiOPPALnHJ544glc\nunQJzzzzDADgySefPMYjsyVVhofIwCZZzyHW98WAoVzvpgy4KYAIUEBIh0cJCiR91tSMYWWg1egQ\nWc+Q4YV9eloNHIijHL0Q0C9j3QFmzCoMpIzxSK1wvtRKAYxBwjuYPkCTJ0YGcgmACFcAdxW4dhW4\ncpBPkwb6IcQhAKsOOOyAfQ/su/idVoDiwHANg9SAA0CtAd0BxgWYUNa6lAFAILWiN/CdHo/PoWjb\nGkyqu4KxSMGj1ggcpPGAR0ylcXTLeFg1CdzFZT0AlxafBOTE7VNEfF+rBzpleyv5vx7fc1LaGufw\n6KOP4tFHHy2+mwKF3/3d3935wRIhZWUiM9ExvFJDDCjUg3glieImhzsGE9Hc6TiABA58gLkEURgD\nffqRB0XImOqePV3YHAozPgePRXwXzvg1hpsJeCOBlQLJtA9lmjkHCZr5SWrwVwB3DTg8AK700QxB\nraLmSzucTb8vAfRd7JqVAhYGsBwY9thN0qHWgOoC9MKDZ2kSuFFkX4BK5d1MNEg6lKrFRvzb04jT\nGMkoLwkOXB6il+ZjRKCR4ti9HkuXMp2/lh6+hfg8kDY2HsNClJ0m8/vMngadefh0jabrMrGdAAAg\nAElEQVQana23QA8FAwUHXm6utHhTt49QmyQHHvbcYzzgPDR6YDyZcEHmfe5+qBgkiwgammZJxXDI\nacd0cEaT6eBFnwxOvwywKbOyyJMggCBxPGGaWwMHB8DVA+BaEJoUssEeKLVzek3lU0GrTVQxCnuA\nNMmkya27AOv6VE+CFoAZRidtTjodhj4KCbwdSiTk7oxDdgHvPS06muQkHsrqox2jm5AeaqDuVDy8\nEs+jfwUGiOTCrIdRS8qShkuqtRndN9orTkY3HThwq620I0hRGoirpYVDv4tY5dmAET8S4xHDSfG1\nC0xqkL5OmbLJVyUCBxJZ6d3SSoQ2Xx+a6EbjurWMF6hQdv1m/7iGhw6+bI9caZlNxR0CBx1wxY/T\nFPhiSPIOqdwEFDQVWw+s6HnFxGXvwW6oQ5l0lwOA/JipOZjLGho9IjgELiJxnUMmWPAxIpAmkJDi\nI4WA9snVmdzRNRuDDKlO3wenotdCxVDqo0T35sWQl7mPTEsh0vHNS9X6tCSKMweH0s/rmGg5LUGg\nQM2ciDVFIcQAoqGMfMCY0WgSjlapEOMZRgwmDRa0RMo6cVLGJHAg31cfRVZnStASTOadhupNrJos\nukUNfUf6OuoTiqsZ62hCcRtg48fTiU8pmYnObRCDKZa6iU8UmmMCMGIRmaxWUMJVOWgYqkYH3i+8\nf/jE9LUxGlkJWYt4Vhm3D9Wu72L8A682JQGBS5lHnJvZhTktMaukSER54PqqE0RnDg4Ab/z4e751\nHo9s8CiNlrNoST/RhOFBLVyCcBgzoCeul4w2BRbchw7xN4mrXXkEO14RJZOF7P+XPZUNuVlvV1Sk\npbbS9jEEwPdA56OttZaUScIGTVvDXmlUiS2k0AIHqKl5ySaTCryGBx8kQXxsPMbj5NPDB9VPImEN\nHOSNe5SSQ8WQEFyUHiTfHAMM6o0k9bBcJMc2iGy834n3T0BnWn16Kn48Epldau5McuNwI86OHVSZ\ndMUiwhmQ9NmR5EDcKUWPGsfIIgJ+/JDggRDGq33FqCV94pIUApRsU0WS8A4xjsHFJm5bY4k0+54L\nOC7EJgwX8JP4s3tAufiOBO475QFMjdPQRtl5rnYSxuOjxQ3lGLPDh/K2pzwnpbducnzT+FNRWUk5\nanKLa38LnSk4zBtbpjWz8pwTjFAN+auMl59aKjDy76M82Jef4s+5VWmq7yYfUTlCOjxNatSbzk2s\nYLeQ0y+A3ctjGqDSv1XIRWy5W+5IbRvxvnzothvURHT+ojs8v+CRG0Gl+3/bWSehm0KtKGkeOW8Y\nkRagAKhY6adMtSTS4qjdaIpC8TGcLmfidVildqHaI2sQCWSg8SFWkht+nwE4vgCcfMy5h0gW0xgr\nrGVc9NRvgqZu9wNKNyE4XGfi/CA/6d/FfCd3lAyyl5+kkcslzUyczziNf8VPB0Yr7pHbOXGodOh0\n8Mr5vKQiPZ53DU+ZGF433UepiKWTKMLo2Ctb9d58HOgN6e3k2NVuJNNOZ6Iaazh0wwBjXpE+DYmB\n6MzAYb4BKp1zSn1O0iOffDzpiU/G0YKjACUrkPKa5fRvelvu+lCV66x4uI33V6p8hx0m12x7qa0T\nvK8tYC3QGGDRl9nXNZMqdV+t9cMePTomZyk+x3j/ssOrbDkBMj9U+aIGcMW9VR6rob/pRz52dI7s\nqNrYiAEgNOV9OwcM1wkwuBlePmBcxPZkdKbgwMue8e8pkzKGdkwbVXb2F6sQN0dNNQyrEmdNujSI\n2UaeV1SVANEgTyWDbMyim/Pw6UZcl35TerzgySV8R3G26BM+iUSksGoB0wJNA9iufKOWtYhChOhv\n2Qt0nVExY1NJQYnPVQEWpamn3NIICnETW+vhTUCoLeb8GU52ILmLudFYEt201qLKGGlVnir7VoLx\nFgFkmjJT8g0ea9LCaQFBjW5itUIlALED2xBlp11eeY75iMwbtQXeInJ9L8VVzkgLdjMgW7+JeFIP\n5WJTRlLaAUXr+JyagCKYi4OqZIwhEFdpBD1xPz4f2ihBtCrnEfFUOOoeLgexDGzeAhg6mZfBo7kl\n3iGYuCkOr/Y03lSgQlIwGP6tYh+O5Bp6GRqjDUqELWryoSxQQUcCdWWQt8hDOfFrIDgIHHFhmot6\njA57U4CjTzeJiQGhOJcvoKcpKUg6c3AgZqdN7badK0Un6lgZIi39/uLH8UpNn7xU2LA/Aed8Po0C\nuyFxiLQe1gRxOlJMv9FxhtKEqhkAKiBBFFuZ93j2JInIla2yD4NugdYCKw30ZFBM96X1l0sOEhxW\nCjinYqlLKys31+oeGOQds2CK984F7AXJxV3O6VYBPcuDKICb+1Vo4PmNeVD4lEjQAMqOTRr0iCoI\nBsAk6ywbtTkKacLXts0jyqVns6/OD3PgdEHizMGBiNDSp/+Xv8UkbW5sCcNv5SenkdquacBQin98\noIm3OJ8U+iyv83YUcKjtkJJEA6WmJReSaq2HNg5a55XHD5Mp6+seGkHpWBCGt6t2tIBZAMsVoDzg\nungAZUgQt6LQLYeC0wZYWqBdAFoCAgcJ3g0t4HVeLeWqOVlEuGZvHMaIDybfBIQbiLv0O31Xkxyk\n9NDE8dUoJaK58TIBMB4wDlqXUb9zlHk5b8FAZQnyOSd24O9MZ2pzqItEedMO7toqgSGnlxzL6qND\nMtOjNNTJLdC4aKwVYiUnyt7rMAYHznhEXLTlpZOI8VCmW0hxPB1KA0oHKOWHbdYAvjcHE9O1Kvld\nfq4wxEjrDmjS7N8LgO8y3/PwaTK30ivR1NkzwGoJtCtAca1JgBBvVzAKTptUJI4yKqh47gwo1FZt\nuvcgEFjk1HgO1AR3NG4BWSSbUitWGMCBi01VQBCHBmKwx/RUpspmshYaFYMDxjYFPt6nbYCUdAbg\nkAtt5qNsLBVzqRkr5ZE7aKqj8plKeUD7KMZbIJrYMWY+aZNqADQ6iq4OmK5RzmOEa2KJ2CWFvBRc\nsJgAJ2UCtA7QahwPQKrZoFYYDdco+DYaHhUHBs77e7EpqgNsByzTq9t0yNhPro0tTTqWQLMC9F66\nHx0EEFJgagFvFZyKG+RSCbVSchBGVW4Xqql+JJkMAE47BHETHpeFuP+FG0oIEKiU9l40ymg95ofB\n2IK6FEEqhaoDAwVBM+5kUkHmbTnWuZ94wQNdzKlb1FtBOlIpBZR1cCj7bMwovCga3/JjrIeNAUJz\ncNAhG5eYqFvsKyF3puoVYmaeQnb4cc6V2TgcdUjUJebbB9QSMLY0rNfegTGg1h6KRRhxwywX0Z3S\ncEbBJY+EqtnZeN3HHlAuLcAKsBug7QDnU2AT4icQMdLqaKdYNIDZqwDDin2yZ4Yl4BeAaxR6o9Gj\nGWwOtVVQqbhTVlBhbCeSmgAdfRMNyCH2UCQaBw535FHiqgiBN6Fbm+xBrB0ckOhzJOkFKOOhjItb\n421RBLJ7km9lM70pdHneeJE9LZXjprA5lJ0TMy09SCRT7DyZv2lwZB1MJUSfkhJquvlQG5CMFUuU\nUgFP5qfVSvry5AxdRMlFGsYlw5HNQYfkao+t5f5uEk2p6FqvDXobpQfdAmjDuHCtSKFWIc4DYwCz\nARablEgVgOARs9aR8MwCpolHsdDyYw/FAowV4JcK3QLYNBqdtoPkkIvTsz24VYDSHsokMOddKcGb\n2rYGcGjSOBFH8Isob4KHT0+pFUmdsOz+cgilGkgAoQGYEF2xinPtWOLjNhYODJQ7IT2h2UtBkvV1\nCqjAmYND2TFR5/Tp4I2PVKoU8rpp3WvIZCNms0m14NLDlEeL79TcIabs+lRFumA8XlQN7L0rpm1l\nowjMd72qGAsLcKDVSIipWXqIk6yDxUY32DQNFq2FWTiYZSgXRllCnusNOkobhv/mI0BApTgGPkEJ\nHM4BOJ8+OTgwG4RfKPQLg65p0CubQMEye8MEk3ObUE1a4HbEHrFvvU3qBY1TizKdjEsOFTcLxTU0\n4v7SjiJBQVGsVEimipCaMM4dqdnduJsyW5W4WiHVES4t5/iI06AzNEjqwYUpAcAP3yoEZtyrWbJr\nBhtOOfPPpaAaF7e2nwIFzgh7qCf3bXQ8Avdg8BLMHqXIyh6iTMmPS8wzH3NtKhKvk/dElpalo1cN\nOtWgbxo0ywDs+TxZeekJnnzIBSGZYu2jNwPAePXmEgIHBSaZU1/6hUZnG2w07VrRMoPkhIGNA9Ic\niO+xNm0AdAbwOuXE1MCBj5HQ45TJ/c/3AeXt4mMl4qVgA5SN8Q1a5e0WthG1P9Y4i40P8AIcsrRY\n2t3KfbpPw+5w5moFX/lK40uAmehQfk05QfLqw002A3orD208vPVQNiBMeRhpByeKc+CrLK2wHjFy\n0hsg0AyQrjMgc0ya1Zo9g08mvs17JYBImQBtPXNljg2SPeLmtLSDdafWWNq+BDu5oQ5QrsxcjaLg\nQi4MyWAnDg4kPdBxDll6WAC+0eiVTdvmllveSeMzFGCMh7UOvgljDwg34ZBWd5j+7tO/1wpw1Ink\n3uzZQ4S7SqncFyRlcdCrjRGXaLiR8ghzk3hYww/zYK5Qco3vHQMLipfwE/fYlc4cHKZJJelirFYc\nFRmHNUk7GNPDmwbBJHDg/nK+KvHt6mTxFeInKrkWyM81tTqosVjMPZo0cfnfXILYEoYrXZkRJCx6\nbeEXKq96vLxTQBkbxCc8gUMtLZkvxHLlJpWCJtM+gPfkv32r0Bub9uaizfDsEPyUN39JZfd1BHNl\nQ91LwScr7WtJ5gQaUwpic4o1sDI+SG2Twaw1+wkfIx4mylRAneJSlD76BOWeOjLNE0lg+AFzZUbi\n1tWIlnmnP6KQTJP879IgmaWHbX7fQqwjg6Q0G5CkwMshybGlicRVA15UVJKMUOSTSurlNRWDuzJN\nlByoL7ILUyfDnh2khg0W6PQBukbDLWIMgibRmwDPiXek9ki1QtpYazY8AgVpc9gD/AoIC6BvNDaq\nGdQJXnOZe6EAMkjSJAtjtYL30yHGKiAH/KLo68RYyjFasUOCwZR9iD1TGw9jPLTeLv3GfS7zPKBF\nhvhf2iikxDCeA7e4zYEo+3tJb8q5mBTvwM/NkkO2/U7FPgDZdKPhYVQcMGdCzNPhDM53v5I10Mh+\nJQ3go4rDKF3o3P0mXaM1PXbKW2ESOKAsHcbby6UGAom1brBuLZpg0Gw89DqMi1Zxs8gCY3uETBfn\nQCLDNzhAMJBwewrdnsa6TcZSRIDokiuTjyeNmVYeRruYMNf4euQlPZcbjXmcNwcHLgkREWvVAJzb\nGvg4TQV5CZCI3iW5A1uObJV2M8nL9L1Ut2Q8CHdnj1SzE9INBwcyOIY0itl9SQIUrypY7mhVA4Ap\naYF38gAQ2kcjUbI5VA1cXHKQk4N0bn4dn0y1c7eBA48J4IxXWMPDwGw1YCBrP6kTG7RYY4G1XuJQ\nReFdr3o0XZeBjjqZ2xt4hX1hkEwdOQ5flgZcbndINge3tFi3Fod2gY2i/cCzG7OQGNgyYVQMP1Ym\nAE2IORRSrZDVumXsGUl2BA5ScuBhnzXprjZG/ODSRBqv6FmK/KZE0JqMw6nxM231SItjnfd5bEOe\nR6X34mR0JpJDYI3Rw2euigd26OKaMtaBbxomt9EdP9EPOqy2Dmj6GNHT6Mx0g6Ubk26+YqUlBuRi\nes14V2M8qdfuYwwODYDWx8PwjD4uZnJXpkXaShdrLHCIBRZqgVZvYBce7X4MdlJ8sssgziGmA2OJ\niSaSnHxcDGeSQ9gHwh6waQ0O9RKHaoVDLLHGIqkWOQgq3j63iIOEMn2M8W41sNBjcODvKyW2FnW3\nbS0Upea5qkkO0v08BK4FwAbAupgHY8iAXN+1KogWU+i0Y+cQQHBQyDu05L3jSmmaFsuTAcQZujJz\ngwkYImbSf8QeBBhgLCPDSsYuTk7Dr4okBwfV9FCtRlhYoDdlweFaEVE+2WnV5G6/GphMTaYa41UZ\nLgBNfFdt+riSshYGxigU5yDtDmu0aPUCTdujUR2sCzA+3UNKNtzWUqsuC4wTGQkcRORx2I/qRL+n\nsGks1jpJM+nokjGSXJmDEXI4mPRge6i2Q2htBHIuQaxQSm38/aQdhYOdBIepHCwC7Jp0J6W8JsSx\nsj20dYMrc6oYsIxToL9LG4OUHMp0AY8SGDzrwZPSmdocJEDk4CdyywSUEZL1MNNaAE0pR6QjJS1p\nHVULbz3QhOiJpEnBPZI1YOCSw9x2CDV9VhrTpNGrEtijmwBlXdS/VW1Hr+zjpq1pu0K9WKJRPUwT\nPTWL3kH7VPiGTwwODg5jNUn2A02+mtdiHwj7Ct2+wXppcKgXWKsIVJtkayBwIFDjtTlGSwDFp1gD\nL1WzHnVwoHbx9nAjLD+3lnzH2zRlnBQAoVoP1TrotmcZtFna47zIKTvdsyqR1SzFBmBsX5PAwm0X\nJ6UzUytywMe4Q3SBlyWSSmCY6wQe65DLn0f93WgHbz28ZS5NYrxaeXi+0kj7hNTP+bl8MrXsGRRd\nKJmOMZ5qAdX4gdFosuR+lBGS3E3YYo0WDZbDCmyMg1quYZVP8f/IG9/yzTGnjJFzqhLlT6yiKuH2\nFDZtgwOzwAGWOACpFG16RzJMWjZClTgVFYFRGw/feKg2ej6G/uc2Ef6OMrWUt0eOUc1zxV2aNYAY\nqRSAsiwWRZMSHBj/ze8xwSUHrk7w3+nI3rqx3U1+HpfOwCBZZl5y5g6gzNsYAEW/S7SNLnpyY3J/\n7zxR7KVRSSe0DqENcdMFcnVJ375cLYnhNijtDfK6/NAxONRE16olPEC1Hsa6wYXJSbq1yOZAxwYL\nHILvKuWANiBYj4Xp0RoPY8PYcMeDn2pqBRllKyutXyls9qLEcKAjMBxiiQMsscYyuljZO/LEKxpd\nqSgZ3cPYHsHaqNM3KgOStPVwVyvZUHjwam1sapJDi1LKq0l25FFqwiA5kDphVJSHdqnjQOMIZCym\nDHS5MHLbQ82mIBfQk9AZSg4R/aggVvl7dNqVInRpwKl5J8jnL3/jgrdJq5E1Dt46eNsjphnqMTjU\ncpUpsEaGGddqi0BcJ5lP6rUVV6Y2AXZQK/rq6pPbHicZSRANGli0aNBjjWU03SoARiE0a6jlBtAO\n2kYpZbC58GjQGlW8ML4F+hXQtRrrtsHaLHCIJQ6xGkAhGkkjWHRoCnDjEa/F+ClA6xDHqwlwXGrj\n78iBjMCBwG6BUrUAO49Ld6Qm1cZIBq0VrswA2JiFaQZw4Pt/jsvDhYH/DZrKePr0YjU1hF5e9hdP\nyrpl1QpO1EmZFGI8eXbjjKlweDEknSfKsSARW9uoHyqvAN/EfSj55OaqAWcgYrwaOMRG8YeOLeJ8\nVeJ67RB9F4CFB1oH3fQxSlDnm3NG45OqT3aGGEXQYsOqJZTuNCA0Ct4qtIsOTetgNx66A7QMkBp3\nYrHiehvz0PpWo1sYbJoGhwMQrAZ1god186NPcQ5ZciCbk04YFOMdtPXQ1ifJIQALNTYcc7ckT3cR\nSWQDEcvI8akZW2ckCLXwwKKLtoYBHBx2z6mQ8Qzcec9lKu6hUuz6H9gycRQEVXaDHjpgyspbBotM\nuW1KG28y3+nkCLIOzjkYa7LtoUU9KpKv+iSq8tWor1xH1065y4S+XkoOHsq6aIy0DkYn/ZsBAxch\nc+pz6bHglRmJ3RxijkOvDLpmg9Z0aBY9bOdgeg/lo8uzVqvEp3yzkHKanNXoG4PeWHTaYqOa5JGI\ndgYCB/okgyQ3RvbDW44lB6VikRtjHRR5LbrkYeKSHh+nKelOqhVE3MDKx4iPU8VYTCCvEnDpNFba\npAAuxnPzIFHyMvFztGvLLAvFQEJ66G5xcMgNM8OUHgc5aeQQKX5tCSS8g7LXIiMwXzFLr0UYou+M\niapFaHTJ+ZxqwTLEeNx4yY1d+SXq7jKKSKyCQ4BKXooYhpurHZR9WYagewYOBAw1cOCh672y6EwH\nq3s0tof1fdyqLgT2mfshIHaRVxpOazgdt5fvVCkZkMsy/5tLDGSMzCDBp9Ko62su6J6pgVNBZzJU\nvCY58LiImmdJRmYWmd0+jVV0txI/GU3bINS9E3n8NJsH+dxok4tnyJRtPkfGqvbp0hlFSGoBBqVY\nRT4K+iWTNMrIWId8UFdl5BYxlckKbm0fd64OJKnYsSQiDZIN6lGEBA78lWvgwOu/yFWpBVQboJse\ntulhTQ+tqEjpmFE4MJLEQGVULFuTOTgQOMdrmjhNVYeN6mG1G+CGNrulZ/LrY8UpaQxtBslgg3YI\neCKQyIDAYxy4t6IW3RdSrED2WoTGA4sQa0zUgtS4+ifHqDY2tXgH6Y3hnqZkLEZLcShka+jjO6rs\nR+B9V6p2dVck8Sy/JhRjXnPl8+iQMhT9JHTmakWtkwAMAKIqjaydn3/Lagf/VuYtGmh43SPYdB+v\n4L1G8ArBm3FUIF9RpMQgdd+aRbzmLpNxD21IgTQOpnWwTQdjyooHtZWIuzJzboWFRTNID6Sg8YhK\nB5OmazxGO0moEhio7znM1nI6uPRA9oe1UCn6itRQA/jCumR6NE0HOA3nNIJTiFsVqtzHNWCYUisk\noEiA4OqFVDNaQLVRkjGk+hk/2Bp4gPM40HlKkhjzdc3wPsX/UoGeU7d3oZsAHJAQM7ND+fv2zimP\nHGIqr428kNcprzyCSRlxTsM4B+cMYn00jbyhJErG4TYGYjpe92D80LFhkidWDRJEAJYOqu1hmh7W\neFhdWrv5JKVPbpTMQUURIAwajENwdQEoJG3wjVfImCY1XnkfDrf0TCrkQlJDBojFABwyEGrKZ0/f\nWDg0podregSv4Z2OwOBT8QQl+ngKwCU4yHgUAhhewKXwMiWDaOOhGwfTdrBtB2MdbIpi5VBXszlI\neJBgQFdPAckc/58m7QQOL7zwAj7/+c/DOYdPfepT+OIXv1j8/gd/8Af49V//dYQQcP78efzWb/0W\n7r///q33jR1AghOPCiubLK/hqwx1fVmDcAwKpNdR11KabFAKXmsYo+GNQ2i6+D7KImibmEaVIcNS\npZgzdtVWp5rRqwkpwq4frN6ahd/WmMyDp67zidpgg1oGJ7fbZHBo0DGJoQSiuTGopQ33Azg0DAj4\nkQHBs/fm0X01uKeAKGt7eK9hfAppCwph2BEYZRxGTarbZjSuqRYDOASg9VBNBHDdxPgLa6NEY3VZ\nwsYO6lzmyKxWU8oASbkxRhis/RKY8zhK9ZoWw9OlreDgnMNTTz2FF198ERcuXMCDDz6Ixx57DJcu\nXRrO+cAHPoC/+Iu/wB133IEXXngBv/RLv4SXX3559r4ZDAKM+F7qZSclWgk9NCi1Jf7bwUPBKA2j\nNbx1cRHSAU4HeB0QtAG0jgcVlXbIzCdtDTWDpFzVRpmaftBdNemvdKhyws7FvTlobNAwUTZONQ4i\n3GgZhftu+DffQWGbeasmPZTqRU4C44ZIXlS2L4AlHuTbL83M6Szj0Jgu8kWgNis4FRA36tS5n8lA\nPOdJItpFvbB+AAbdRMmuabvBLhRjUErJi7//eKJTK6en9Xb+l7+drvSwFRxeffVV3HvvvbjnnnsA\nAI8//jief/75Ahweeuih4d8f/ehH8eabb+78AoSPlGRFIjBXEPK5Y32M+3h37Zi8bqZrlEKwCsoA\n3cZimN0qAgSUjTtI9Sr2GIGADH46iuQwMF4AUtFb3fRo2k1kOtsXu1vRO1Nv5bZbuOE/vmcYifn5\nNXjLHQws+gEYSuPl9nhTvk0AlzlyfkdTlSDokwyRR0kQGkZbuSjpWYcQugiZGnF/UIsYQVkLTNtm\nc5gCCRugrAfamFQVgTvmTxCAW0WxnhEktoHrHEmbS80IPb4mj8lpAcRWcHjrrbdw9913D39fvHgR\nr7zyyuT5v/3bv41PfOITp/JytU4AuN57NKQkJKfy95xTtPbQIRaDCb5M/PdIE9gYYNB1MWa6nSWH\nkADC5fRe66MBsiF1QkYBTC99UszvYQtg1fDoUhweV8u43aGmVszRWLXIHpOhTF3KnYip2aX6Uf49\nriHJFR/HpLwABa8cjDHwwUUDslXwKiDoNEY2ZdkaNR4TMhjz9O5JgCDgdjFl3jqYxkUVoomuS53i\nT3jei7TubOvH2NII+bF8wdi2VO9/aWPbHWh3oa3goNTuk+/P//zP8Tu/8zv4y7/8y+rvX/mNL+M/\nXfl/8NbXGvzQwz+G9z38Y6NzsqdhvCMFx9EsPWx5f6a/kQckpwR7Jq1EXqLCKrqPvnVnTDycQTAG\n3pkEDio5/CmqUpUMmF8gf9JenYnhCBRM+relVYkZInP0AumwY2mKJpuBG4n4dC43kdE1EhROQ63g\nB9kX+Kc0RJLKw+9Db8BVjFgjLL2/7qOUoDGUk/O9hzMe3rg4RsbEsHjyaMjmkPuTj40GoD2GvU10\niHUgB28EU/fSPpik9uUwLm79Oor0oJI8COSqJrW8IjX0TXaMlXP0n176z/inl/4Oh//Q4Z/O/8cj\nvENJW8HhwoULuHz58vD35cuXcfHixdF5f/u3f4tPf/rTeOGFF3DnnXdW7/XZz/1vePZb38DLP3k+\nYSXPMIyQzbfLJRzmlA1qXHrgPl+Oub7oYDUAARX/dmMxLK0eCgFOmRihZ3z0ZBBIJJcngkoh1wws\nJKkwMJsajrh/htI+MlgCCGOi/moHpvOQq7k0bEnjLNWTVGiK86S0NQUKOSZku1ohawpMg0N2XfIa\nDk4AAh8HDv18xA0cvIrjF0ySMLRHry3QBSitob1HsC6O0+DynFlVGRiotCMabapDgECFWygozRgH\nq6iCRvbXyA3qONiWkxwFdxI0+KTs5gCo+hzIs2QsPf/Qwz+OOx++Hx/9+lX8L+9/AL/55adnx3KK\ntoLDAw88gNdffx1vvPEG7rrrLjz33HN49tlni3P+8R//EZ/85Cfx+7//+7j33nsn75UlOtrVCsWK\nHilKDTxEKl/P2QQg4VN2FtidyrPBAIOkCAe+S0B0TgQ4E4NvnDbwXsNrPYCE9xzJkYIAABH0SURB\nVDoGTQ1xEToBxRgc1LCFXYju+FQen/IltAoD81kdDVtWlavQ3ApUruAxkUeznuLELdslKPC6QuPA\nHfm8sc0nm944SHCVogQFMwMK3EPBQ3vinQNQ2IridqPpfVWAT4DtXToIIGbAgcaHxorKuw2Vw9in\n0VmVyO7fHqXMQzA8H9eQjZL5r8j7WWqYipCEGAe+QJwWbQUHay2efvppPPLII3DO4YknnsClS5fw\nzDPPAACefPJJ/Oqv/iq++93v4jOf+QwAoGkavPrqq7P35fov18uycKWQ4aS85riBHZnxonaXJYge\nCmF4Fw8dgcGYKLJ6DW90CpRSCEHHT69z8FSKsqy1dNjGjioqp9h7kiSMdkMxlzLk2bHpvEsATRbA\nNWgPBA8FM5wTP0twUMXULqPzuNYrY/0AFDKHDDWToFCqEmWVAy7d8PEqvRbxd3pXwA19qpq4iYxP\nwOCMGQAi2KmxSc9JY0N1GKJEF2JxoDRGsUiQr6oRMoRLi7/niLSbLCMEZJjIhuRynMd/SwmxpuEe\nlVQI4STX7/4gpfCf/6//ime/9Q38x598T9GB21arGk2hZsZsueYYscrK+v+l9b1cEdO9QgIBr+K/\nE0DE1WrOyYghgUgrP4CCVmmFUg5GlW+Tw4rcIIzLeAQy95XeBs+0fC72Sqb2yCBUjkNWK6ZXPKnO\n5X4fGx5rUoN8wx6l4XKsghjWytgz0rDqEKU8500amzhOEdBnxgYh7X4nxoYqSKsy/N4wTuHeHRlE\nxlUKqYDV+F/KxLvMASnFcf5/6Ovv4H99//+AH7vvR3GcaX7mEZJAOalI/+KdM1Ytxh0zNzHpHhyd\no82Dsz+Vl4nedj3IEXkotdLwKgZO6eDhtUbwUSkSN6OH5n+S5KAyKJSR8SVsyepBNWbhoNgjR4vk\nEv+ynFw8TJKPfKGG5PfgUsN0b3IrTmbxUrUoE8LK77jiMC0N8klD0kM81w1jytdJpTW09lHtC1mi\nC6FilJRjBCZB0FhVJKtSdaBqTxwoyjwY3oZtKmLZnjpIlGr01FknpzMHB94hdNTES2q+/G1kUBTE\nGYuewwXluEJmtSZWsuSejQgSxWRQKkZWQiOY8vnBp/Yk5uLvwZ/JhUCaNnmCypW9XlFI6pg+3Ssg\nJkZFqhdmIDbn/VP+e5sLbmwMzas4SWp6eBd5UJ/xaSfHjcaMYGps/YgSH58aNH5BaSiVi3kGREmC\nSxCkkvBnypjDcb+UoE7jJ1WJmjFyTiWUCyD/jv8m+X0MFKdHZw4Ou9BpNZ7bNnj9KcUGOQ6KhgQJ\nYok48GQrSYzI323CHlRjNiIpnpZi6e5rQgkK8Rs5rWhVrzG5nAhzVAeGDG/8+7EVRY9A4igUxyo7\nO/3M6sqn59TY8PtO9cW2/iqlm6MlUFMfyX6/XpN+V7ppwKEmQVwvIuaaA4BdQILeG5gO2OIiOg3z\nWCwdi/Qls5V67BRFJpPvQT4ajxhMxJOq6AnZwcnjS+ZWOnp7sNZNgUIpD5WBU9uYP9/VDd8ohKG/\nqRUUmSjfI7dz/jncEJufux0U5Dm78m1sEc88PRq/X0+JgejMwCF29bhj5kCi1hn0t2TWbcTVC3Ke\nAuUmImOQoAg2slfkaWQmxPfac0shfDdm2z5RNfJUGbu0FIM5B41yGpfvsFtLxs8v4a40CmeVo7Tt\nl9OuHFuasPRb5JMeVKU6PymTT6NG6sZxJRPeqprHqOTN409Rrj7wcZtaBOZAwc/8dhw6830raGLK\n5ki9i39fv9f2IaIB5h1IYinBAoWgEmOVIBEnH1FUKY6G+bSOc6aj5/F/j41f89IUh1mqosUnbba5\ncFlIp8+8Eu8quUkmlcAwZZOQ024XZuaTkEdNYvgrjxV5WfL3u1MtiqZ8VqgcJdDXQOQotE0SneP/\nWkDUSeimUSum6DQaS4Mk70UDF9dcCtoOA3PTORIk8vXHY8AaKPBjeoXKxCUlxd6tlCQggCGMQIJf\nWd59Pghq/Pb57ylQGE+jMbhIg1s5QaO1xw8AEEb3I4pnHgccSjtPbQx08aZZXp07jkqnOcmPS2cO\nDnx6HrcTt0kN/L6S8fi/Q2K9mJRFE4dCVMbMHKefO9JATjNbFsZ3xf886SI8AChAgZJ4xsBQggSf\nbDWljYu9OdYU6XkZSMt3qhssUWkhlzq2xRJGeSc+OQcKyTrNXHY42tjQ5/jfARw8JCjI6/g9d5HC\nyK9y/DlwnCvn6SYABwWKBItpDbsbdKZYelfig0r3iO+R9+vkURd8knCj5HGey4ezBIVyQm5bffgE\n5BOIpqxnKy6QXZehaBnYexxdDJaTsgYKHFT576XNoeyjuX8TFEbJyQ3tkc84Lsm+GLeqDgrHmaZ0\nt3Ev7Hbt7svJ0ejMwYETde9cB50WKHBAKOWCOI3yc/JT5PknJQ4KmjEXf8cpFWQXGsNMbkMW1TH8\nfZx21SbiPCiUKsU2iU9OePq3QlYAMnDTbpPHB+787LoxWBXnBPHbydZwOT5T5/C3uH4+vZsIHPLU\nLAemds5JUHIXUbPGFHHgNJtA7lgDI8NX5vRbyXjbYg/orvSphv/nT266kmBwHMlhvOpnuMvnjEGB\nv+txqTYRadfVHPx0nPvKZ/jRm+7ST8cBWnJvknI4vmPtba4P3TTgQEST8HoQlxgkQns28UtQyIK+\njBo8ikjHp0f9t3IVP4l4WrueAsZJmC8ZMZ47ZSGfI2kr4NIBf68p1eM4JMeR30fmhJzmGE2NyWmN\nH39nd4MAYI5uOnA4K6LBnbKW1xhRIW74e1zaVRo4LtXAIoJEnqgRFI4uMdSfNQ8KJ6XY364Yh7Hk\nMB6/k47RrqDwg0bvSnCYX3ly4I0Xk6hkkFLv21V/ntMWa4y4yypEujdp4RIUqD11iSJOH1I6ytaF\ndL+yfeOYFP7t9Epdt09slyJ4bApvx9z40bscbWzC8I1snXyfo0oXtyK9K8FhF6qtCiWTjY1U25hi\n6nfOUPKcXZktgoOCFKnpU4LEtvegt/HsOv4s/gw58Wp/T9sZdhf55fW8z+oAkL852thMT/xd7vWD\nQu9qcNjGXFOrw67nzj23BgLXg6SYv+05x7EDTF0zBQon8TaNI1zrLs851WOO5sD4OOB+K9O7GhyA\nMXPNMe0UI570uVO/H/UZ0Zmnq9dKkKg957gGQn7fOSkiu+oU+/fuNNemKSA/Du163VFAoVRwbg0A\nedeDA1FtwHYBievxHsddhejKHFQ9noK1yXsaDLubJLDdvrArbQO06z1G9Ln7c2494+VtcBDEjV2S\nToOpa8+Tzz4pZZDIADH39qc1YaffB8i2hdMDBvr3lFR0WnRcO9CtTjccHLjF+WamKdH8NJnvejMZ\nF+Ona0JeH1iQE/Z6BO7UJKxbbYxuZjoDySHWGdylFNnNRrcyo1CakKTolTm9dhEQXE9JZI5u5TG6\n2egHTq0IjN1v03YKuDmi8W7TzUc/cOBwm27TzUgUmcqzb292ug0Ot+k23UAKqXZJbUe303zGaRh/\nr0+G0wRd/OF/iX/7Lz6MX/o68JGX34ZZH6Va4W26TT8YdFpem+3POBndUHC444478MCP3Y9H//uP\n4l8dLqD707OVZ1vDbXvDbbr56XrwazQGn979zkytyA3RJxKxcsTd2VnIz4Ly6qOA6ySe3qbrS6Vb\nf5yxctT77JqnsiudITjwCDcFrocR1cp1lI1/94ECJ6qjoFPtqtt0a5LkYR64tsu1pw0KRDeNQZLH\n3BONQ2fe3WAwRRQKdPzaR7fpZqKbhcfPDBzUDo0/+j5A704KrJ7DGGJvNTr7SXGbIt00ksNtOjll\nkMhxircKUWHY23Tz0G1w+AGkKJaa6m+nHbY+FZGaFcBbK0T+NmW6DQ7vMor1Hk5PdN9W6CXv/DG1\n9+NuW+LdphtPt8HhXUg3ciLm9HHD3NU8i/LGvs9t2p3O3JV5m94NlCGhrHN5GxRuZtpqAXrhhRfw\noQ99CPfddx++9KUvVc/53Oc+h/vuuw8f+chH8Nd//den/pK36QeFFLNR3FYnTpNW313jw//7d/Fv\nXro6HB9r3o/33XHnse85Kzk45/DUU0/hxRdfxIULF/Dggw/isccew6VLl4ZzvvrVr+Kb3/wmXn/9\ndbzyyiv4zGc+g5dffnnrg09T771e9PZLf4v3Pnz/Wb/GznSrvS9w+513ocU7HX7kH67iwtvT59zZ\nW/yP770X//rhB0/tubPg8Oqrr+Lee+/FPffcAwB4/PHH8fzzzxfg8Md//Mf4hV/4BQDARz/6Ubz9\n9tv49re/jR/5kR85tZc8K7rVGPdWe1/g3fvOy+9t8C++fYjzV7YnH773AHjYXsSj//onT/TMo9Is\nOLz11lu4++67h78vXryIV155Zes5b7755iw4GG1wh2rx31y+iq69eX3b/9931/jhv3/nrF9jZ7rV\n3hd4977zf/utHh9v78H//MBDp/RWp0+z4KDUbqJ/CKIA55br3vOe9+CJ/+nf4Imd7n529O/v/Dv8\n+w/8u7N+jZ3pVntf4F38zh84nXe5njQLDhcuXMDly5eHvy9fvoyLFy/OnvPmm2/iwoULo3t98IMf\n3Blsbib6lV/5lbN+hSPRrfa+wO13vt70wQ9+8FjXzYLDAw88gNdffx1vvPEG7rrrLjz33HN49tln\ni3Mee+wxPP3003j88cfx8ssv473vfW9VpfjmN795rBe8TbfpNp0NzYKDtRZPP/00HnnkETjn8MQT\nT+DSpUt45plnAABPPvkkPvGJT+CrX/0q7v3/27mbUGjXOI7jP2UW0pOeMZK3BfOwmG4ztxoxiUjK\nWzYsZimykA07ZcFKymqymc2gNFYoC8ZGpLyUqJkakjQJpbymKGbqdxZ65uEYZ65xxn2fU9dn6y7f\n7rr+TZf/+PUL6enpmJqa0iRckqTvlcK/XxhIkiThG/5N3P9taSper9frhc1mg9VqRVVVFQKBgA6V\n74m8YwDY3d1FamoqFhYWNKz7SKR3fX0dZWVlUBQFtbW12gbGEK/5+voajY2NUFUViqJgenpa+8g3\nurq6kJ2djdLS0k+fSfjcMYkikQjNZjNDoRBfXl5os9l4cHDw7pmlpSU2NTWRJHd2dlhRUZHMhISI\n9G5tbfH+/p4k6fP5dO0lxZp/P1dXV8eWlhbOzc3pUPqnI17v3d0dLRYLz87OSJJXV1d6pEaJNA8P\nD3NwcJDka6/RaGQ4HNYjlyS5sbHB/f19KooS8+dfOXdJ/eTwdmnKYDBEl6be+mxpSg8ivQ6HAxkZ\nGQBee8/Pz/VIjRJpBoCJiQl0dHQgKytLh8o/RHpnZ2fR3t4e/UuYyWTSIzVKpDknJwcPDw8AgIeH\nB2RmZiI1Vb/vMVZXV+Pnz89Xpb9y7pI6HGItRF1cXMR9Rq8DJ9L7lsfjQXNzsxZpnxJ9x4uLi+jt\n7QUgvq/yHUR6j4+PcXt7i7q6OtjtdszMzGid+Y5Ic09PD4LBIHJzc2Gz2eByubTOTMhXzl1SR913\nLU19l0R+79raGiYnJ7G5ufmNRfGJNPf392NsbAwpKSkg+eF9a0mkNxwOY39/H6urq3h6eoLD4UBl\nZSWKi4s1KPxIpHl0dBSqqmJ9fR0nJydoaGiA3+/Hjx8/NCj8mkTPXVKHQzKXprQg0gsAgUAAPT09\nWFlZ+cePbloQad7b24PT6QTwenHm8/lgMBjQ1tamaSsg1ltQUACTyYS0tDSkpaWhpqYGfr9ft+Eg\n0ry1tYWhoSEAr0tGhYWFODo6gt1u17RV1JfOXdJuREiGw2EWFRUxFArx+fk57oXk9va2rhd8Ir2n\np6c0m83c3t7WqfI9kea3Ojs7OT8/r2HheyK9h4eHrK+vZyQS4ePjIxVFYTAY1KlYrHlgYIAjIyMk\nycvLS+bl5fHm5kaP3KhQKCR0ISl67pI6HEhyeXmZJSUlNJvNHB0dJUm63W663e7oM319fTSbzbRa\nrdzb20t2QkLi9XZ3d9NoNFJVVaqqyvLycj1zSYq949/0Hg6kWO/4+DgtFgsVRaHL5dIrNSpe89XV\nFVtbW2m1WqkoCr1er565dDqdzMnJocFgYH5+Pj0ez78+d3IJSpKkmP6735eWJElXcjhIkhSTHA6S\nJMUkh4MkSTHJ4SBJUkxyOEiSFJMcDpIkxSSHgyRJMf0FVAPfhqfxtegAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Root Finding"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "The brentq() routine offers a very robust method for find roots from a scalar function.  You do need to provide an interval that bounds the root."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import scipy.optimize as optimize\n",
-      "\n",
-      "def f(x):\n",
-      "    # this is the non-linear equation that comes up in deriving Wien's law for radiation\n",
-      "    return (x*np.exp(x)/(np.exp(x) - 1.0) - 5.0)\n",
-      "\n",
-      "root, r = optimize.brentq(f, 0.1, 10.0, full_output=True)\n",
-      "\n",
-      "print root\n",
-      "print r.converged"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4.96511423174\n",
-        "True\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = np.linspace(0.1, 10.0, 1000)\n",
-      "pylab.plot(x, f(x))\n",
-      "pylab.plot(np.array([root]), np.array([f(root)]), 'ro')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 27,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x3882a10>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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Kz6/7zEUCSW4uDBsGhw7Bxx9Dx462JxLxH11oS4JeaSm8+aY5czM+HjIyFORS+aiZS1A7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\nKV8afyMAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "ODEs"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This system of ODEs is the Lorenz system:\n",
-      "\n",
-      "$$\\frac{dx}{dt} = \\sigma (y - x)$$\n",
-      "$$\\frac{dy}{dt} = rx - y - xz$$\n",
-      "$$\\frac{dz}{dt} = xy - bz$$\n",
-      "\n",
-      "the steady states of this system correspond to:\n",
-      "\n",
-      "$${\\bf f}({\\bf x}) = \n",
-      "\\left (\n",
-      "\\sigma (y -x), \n",
-      "rx - y -xz, \n",
-      "xy - bz\n",
-      "\\right )^\\intercal\n",
-      "= 0$$\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# system parameters\n",
-      "sigma = 10.0\n",
-      "b = 8./3.\n",
-      "r = 28.0\n",
-      "\n",
-      "def rhs(t, x):\n",
-      "    xdot = sigma*(x[1] - x[0])\n",
-      "    ydot = r*x[0] - x[1] - x[0]*x[2]\n",
-      "    zdot = x[0]*x[1] - b*x[2]\n",
-      "\n",
-      "    return np.array([xdot, ydot, zdot])\n",
-      "\n",
-      "\n",
-      "def jac(t, x):\n",
-      "\n",
-      "    return np.array(\n",
-      "        [ [-sigma, sigma, 0.0], \n",
-      "          [r - x[2], -1.0, -x[0]],\n",
-      "          [x[1], x[0], -b] ])\n",
-      "\n",
-      "\n",
-      "def f(x):\n",
-      "    return rhs(0.,x), jac(0.,x)\n",
-      "\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "This class stores the integrated solution in a simple datatype"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class intHistory:\n",
-      "    \"\"\" a simple container to store the integrated history \"\"\"\n",
-      "    \n",
-      "    def __init__(self, t=None, x=None, y=None, z=None):\n",
-      "        self.t = numpy.array(t)\n",
-      "        self.x = numpy.array(x)\n",
-      "        self.y = numpy.array(y)\n",
-      "        self.z = numpy.array(z)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def Integrate(X0, dt, tmax):\n",
-      "    \"\"\" integrate using the VODE method, storing the solution each dt \"\"\"\n",
-      "\n",
-      "    r = integrate.ode(rhs, jac).set_integrator(\"vode\", method=\"adams\",\n",
-      "                                               with_jacobian=True,\n",
-      "                                               atol=1.e-10, rtol=1.e-10,\n",
-      "                                               nsteps = 15000, order=12)\n",
-      "\n",
-      "    t = 0.0\n",
-      "    r.set_initial_value(X0, t)\n",
-      "\n",
-      "    tout = [t]\n",
-      "    x1out = [X0[0]]\n",
-      "    x2out = [X0[1]]\n",
-      "    x3out = [X0[2]]\n",
-      "\n",
-      "    while r.successful() and r.t < tmax:\n",
-      "        r.integrate(r.t+dt)\n",
-      "\n",
-      "        tout.append(r.t)\n",
-      "        x1out.append(r.y[0])\n",
-      "        x2out.append(r.y[1])\n",
-      "        x3out.append(r.y[2])\n",
-      "\n",
-      "\n",
-      "    return intHistory(np.array(tout), np.array(x1out), np.array(x2out), np.array(x3out))\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "H1 = Integrate([1.0, 1.0, 20.0], 0.02, 30)\n",
-      "\n",
-      "from mpl_toolkits.mplot3d import Axes3D\n",
-      "\n",
-      "fig = pylab.figure()\n",
-      "ax = fig.gca(projection='3d')\n",
-      "ax.plot(H1.x, H1.y, H1.z)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 31,
-       "text": [
-        "[<mpl_toolkits.mplot3d.art3d.Line3D at 0x3e04290>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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EvSDvEsBIZByakp2cnByVRvevxt+edLNyI3g8nuAysbD6j4WOIbsAWX7JJfTByC3Z5uWh\nCs2IA4iLi8vwmembNqPe5rLyepNrReP/DJ2fJhHl52V1LZJ3OLIaX04vr0jBO/PzzZs3c/78eeLj\n4wt17AWBvy3pZke2Zv+xuLg4fD5fvgg3O8IMzVbLzkecW9L1+WD3boFffhH4+WeRbdsSOXZMpGJF\njUqVFKpVc1KjRgLVqkG1ajpVq+pEoRfPEaFka6o5TII1ScO8zqakztwup0BLfq3iq4n8viDDl9y6\nrmfoQRaNlVeQLqKs8FfL2Uxkp8c25+K2bdv48ssv2bdvHz/88AP16tXjxRdfpFWrVlnuN7zC2KVL\nl+jevTvHjx+natWqLF26tMBTgOFvSLq6HrlwuGn9hfYfMwk5P4hEmOFkmxsfcThUFQ4eFPj5Z4Ng\nf/lFYN8+gerVdW6/XeeOO1Qee8xFuXI+Ll1ycOGCnePHJY4dE/jpJ4GjR40fWU4n4NDfTZumX6Os\nHgjTYg21bEOlczldw5y0oFkRTfo1yH+t2YJ2UxQUGYW6unJTXyE8EPV3XzVEgnnOsiwzYsQIbr31\nVnbu3En//v3Zs2cPlSpVynb78ApjkyZNol27dowcOZI33niDSZMmMWnSpAIf99+GdE2yDc0QCyXb\nSEvtgvAZhkuI8kK2GfcBK1aIbNhgkOyvvwqUKQO3367RuLFOt24qt92mc+qUzubNKitXijz9dGQz\ntm1bjR49NG67TSMhARQF/vc/g4R37BD59FOB3bsF7rsvkYEDRVq0gNDn1rymZinH0KSQSMiLMD87\nuZZJwpFqzf6dit5kh9xKtICgrjy/Kb3XiqUb7T7NFOCqVatStWrVbLeLVGHsq6++Yt26dQD069eP\n1q1bx0g3EiKRraZpwZY42fk1CzJQY/qI82LZmmNOSYEhQ2T27xd46CGNF15QaNRIR1Fg82aRJUtE\nRo3KuiBzOFavFlm9OvIytGZNjXvv1RgyRGf/fpXhw+14vQK9e6v07q1yww1ZVxcLH7v5u6CupWm9\nmCUzbTZbBqLJKeIdrhAIH2tRR1YrB3Pem9fuWqvHezVINz8Vxs6ePUu5cuUAKFeuHGfPni3QsZoo\nsqSbE9na7fZg1aWskF+iMC1bMGoX5MeNsHu3xMCBFtq00dm0KcCXX4r07GkhNTXrSdq2rUbx4jrn\nzumcOCGQkiKSkAAlSugkJkIgAKdOCZw4kXkfBw+KHDwo8u67ABbatFEZOFBl/Xpo2tRCnTrQp4/E\nQw8J2Gx/PVlFG/HOavl9veiRBUHIlMiT1TVKSxP47TcbPp9IICCiKCKBgIDfb/x4PCJerwVNs+D3\n8+ePQNWqGgMGBCgkSXnUCL+fKSkpVKlSJcftQiuMrV27NuJ3CvOFVORI1yTbtLQ0JEnCarVmaomT\n24pfuX0Dh7sRwPBz5iW4oevw4YdWXn/dxtSpKp07a/TpI7NsWfpysn37AElJLkqVEnnnnTg2bTI+\ni2TFXr4Mf/wR3bmULq0TFwfHjwusWSOxZo2x35EjvVStKvHFFzKjRok88IBGnz4qd9+tE81lupoS\nqGiX32ZSw7Vg8YUi3H9dGAi9RqdOCXz7rcw338j89JNEzZoqcXEaFouO1ar/+RtsNh2bTcBiAadT\nx2oViI8Hm01j2TKZRYsszJrl5dZbo88wK6w5Ye4zJSUlzxXG+vTpQ7ly5Thz5gzly5fn9OnTlC1b\ntsDHCiDoRez1rygKgUAAt9sdtG5NsrXb7bm+qZcvX446YSCcbB0OB7Isc+XKFRISEnKtgkhLg6ee\nktm9G+bNS0GW47jzznT3Qd26Ku3be9i928r330fvVsgL6tQJcOqURHJy+nWQZZ1p0xQuXRJYvFhE\nUeCxxzQGD1YxlTnJycnExcUFl7GCIOByuXA4HPkmE9N9YLPZ8rUfINhWyJwz4VlSuZGzud1ubDZb\ngcgMTRlhQVSPM6P5GSV8sGuXyDffyHz7rczx4yLt2yt06qRwzz0KiYkZ96GqOqqqI4pa8Pob+wlN\neRZZssTO2LEO+vQJMHq0H4cj5/GZUq+CSmAIP98RI0YwePDgbNvzhMOsMLZs2TJGjhxJqVKlGDVq\nFJMmTeLKlSsxny4YEctQKVh+yxBG42IwraSsfLZ5cVPs3SvQs6fMXXfprF7tZv58mX/+M51Ye/d2\ns2iRk717M+oOK1TQEUV4+GGVEiUgMVHH4VCw2wOULWsnMRESEyE+3nAxxMUZboYdOwS2bBHZskXg\nhx9EUlIyXq99+zI/9IoiMGyY8fcOHVS6dNFYt07k1lutvPqqQu/eWr5dNFcTuQ1KZSVnu1a1sOa4\nfD7YuFFi+XKZFStkLBbo3FlhwgQfd92lIsvGnDh6VGTdOpHffhM5cMD4OXRIRJLgzjtVmjXz0bx5\ngMaNJSQpo4uie3cXrVq5GDMmgaZNHUyblkbz5lq2L6zCDszlxqcbCnMfo0ePplu3brz//vtByVhh\noMhZumbnBPNBCH2r5wWmpRbJFxtOtqZlG46UlJQMnQ1ywoIFImPGyEycqNC1q0bLljK//WYQQVKS\nlzNnJLZty7ivkiV1ypfXee45lW7dNEIbnJpyroSERM6ehd9+Ezh4UMBqhZtv1rn5Zp1y5QxlguH3\n9vD77yo7djj45Rc7GzfC3r3Rv39nzgywaJGE3w+vvppC27ZykLyuZUs3N9ZpuB80NJkBCLZoyq9c\ny1w15dfSvXTJUL0sXy6xfr2N2rU1OnVSaNtWQRThwAGDXA8eNH4fOyZSoYJO7doatWqp1KqlUbOm\nRsWKOmfOCOzbJ/LLL7Bpk4XTpyWaNlVp2VKhRQuV+vU1zFuraRrLlkmMHOmgXTs/L72USny8GlHS\nFggEEEUx2+68uYHZFcNMK+7WrRuLFy+OysXwV6LIka5piZiFYvJLupEIM1qyNZGamorNZstxMrlc\nMHy4zM8/CyxerODxQLNm6dv07+/hgw8yrtPi43UaNNAZMULlvvuMya6qcOwYf1ooAvv26ezfD0eO\nyEgS1KqlU6uWjs8HR44IHDliWD/VqqlUqRLg5puhdm2JGjWgenUduz0FVXWwc6eVtWtFJk+W0PXs\nSSQxUefll1WmTxdo1Ehn7Fgv1appfxvSzQpmxp3FYslAzHmVs3k8HiwWS54CsGlpsGSJhS++kNm1\nS6JFiwD33uuhVSuJH3+U+eQTmZ9/lqhUScfrNe5ZqVI6pUvrlCihk5oqcOGCwMWL6b9l2fD1JycL\ndO7sY9AgLzfeKLNhg8T69RLr18tcvCjQqpXCtGk+SpUy6OPKFXj5ZRurVsnMmOHhnnuUDKuHUPdT\nQRUGMl2Njj99G506dWLNmjWFVjSqoFDkSNfMijL7POU37S+UMEPJ1mKxYLfbo7qBqampWK3WbMnh\n1ClISrLQoIHOjBkKr7wi8c47xr779XOza5fMr78aBGyx6MgytG+v8dxzKk2aGLfo0CGB55+XWLdO\npGxZqF1bp1YtjRo1VKpUcdOwoZMLFwROn4YSJQwVQ4kSOlarl7NnfZw4YePkSQe//y5x+LAQJGRJ\n0unWTaFfP7j1VoOs//1vkSlTJA4dyp44O3f2Ubu2zvz5Vnr29PL88z5sNi8OhyPfBHctki6Q6aUS\nLmcLtYpzkrPlhXRPnRKYM8fCggUWmjdX6dUrQPPmKuvXCyxZIrNunY0771QpV84g29WrJSpW1Lnx\nRo3SpQ3SNck3/P+mb/b8eYF58wQWLLBTpYrOE08EuP9+BYvF0HpPmGDF4xF4/31vhrGtWCExYoSd\nvXtdmcYd2kg1fPUQen2iXT2E+4g7duzIhg0brknXTyiKLOmaxJuQkJCv/aWmpgatltySrYm0tLRg\nc8Cs8OSTMomJOmPGKNSsaSM52ZgY27a5+Mc/7GzfbpBBXJxOt24azz6rUrOmcWu8Xpg8WWL2bInR\no1UGDFCJizOCJL//DqtXww8/6GzebMPhgCpVdJKT4eJFQ83g8QgUL26QcMmSJhnDHXfodOmicvmy\ni88/j2PJEiulSun07q3RvbtKmTKwZo3AjBkSK1ZkT1bPPOPh3DmBVausjBiRSs+ebiyW/KX4FhXS\nzQrhLgqTkEOtYkVRsFgs2SadmNixQ+Sdd6x8951Mjx4BBg/2c+mSwCefWPjPf2RKltQpXlzFZhPZ\nsUOiQQOVLl0UunRRqFRJ//M6wP79Inv3SuzZI7J3r8jJkyK33qrSpIlKhw4KN99szjsvui6xcqWD\nOXMsHDok0r9/gP79AyQk6DRtGsfkyV46dAitgwBVqsTz888uypbNSC1ZBQ3NZJhwV04kF0UoGYeS\nrq7rdOrUKUa6hQXTyvV4PCSGh19zAV3XSUlJQVVVrFZrrsnWhFl/IKuo7O+/Q4sWVn75JY1nn7Xw\nxRfG97Zv95GUZOXUKWOSlCql88knAVq0SL8lq1YJDB9uoUEDjcmTFXQd1q41AiBr14poGrRsqdKk\niYuOHQ2rJLS4jsPhQNdlrlyBy5cFLl2CK1cEzp+H9etFvv1WpHx5laQkla5djc8XLZJYvlykZUuN\n3r01OnbUOHpUYNYsiblzsyetCRO8fP21yKVLEhMnemnZ0hfRqommw0BRJ91ICA/c+f3+YHAuEsHo\nusDKlTKzZln5/XeRJ5/0c889KitWGO6DEydEEhJ0nE64cEGgeXM/XboYK5wvv5RZujRvvuLXXvMx\nYEAKTmc6Se7bJzJnjoX//MfCSy/5qFFDY+hQO1u2uAi1fbp2dfDkk346dlQz7DM3Vn34dQqV/oVq\nr83v2mw2unTpwsaNG/N0vlcTRZJ0zXbbuelxH4pQN4IpJs+Pb9iUrzki6GZ0XefxxyXKlvXz4INe\nmjcvBUD//ipLloh4vSbhqowfr/HYY4bu8X//g1GjZLZtE5k+XeHOOzUGDZLZulWkdWuNVq00Wrc2\ngmQnTujs35/G4cNxrF4tsHKljUAg8tu+YkWdpCSNLl1UWrTQkSRYvdrLd9/ZWb7ciqbB/fertG6t\nc/YsfPKJxP79At26qfTrp1CypIf335eZMiUeVY18DFHUefVVL/Pn22jb1oia/xnryNGqCSVjs5XQ\n34l0sxpX+JI7NVVj6VIbc+bEER+v06ePD0EQ+OILG5s2pZNW8eI67dsrdO6scPasxqhRzkz++OLF\ndRwOQ3/r9xNUriQmGoG0mjU1atfWqF5d4+xZgeeftwdXYpKkM326j759A0GN9v79IklJDvbudfHs\ns3YcDp2pU33B440da0WSYMwYf6ZztVqt+fK5hhJxIBBA13Xeffddpk2bRkJCAklJSdSvX59WrVpF\nbCrp9Xpp1aoVPp8Pv99P165dmThxIq+++irz5s2jTJkyAEycOJEOHTrkeZzZociSrqqqUbdbNhHu\ns3U4HMGMMjMCmheYzftC92EWidm/30/HjiX49VcX/fo5WbtWCsq5/vc/YxbfcINOUpKL6dNlNE1g\nzhyR8eNlBgxQGT1aZf9+gZ49LTzwgMq4cSoWC2gaPPeczHvvFUxJyoYNVSZNUilZEpYtE1m2TOT4\ncYGuXTUeekhhwwaNDz6w0qaNwrhxGmXLikyfLvHKK1k/QL16BfD7YfdukfnzvdSrF1lIn5NSwGKx\n5LuwS0GSblpaWq4TcKId1+nTAnPnWvjgAwtNm6q0aaOwdavI119bcbmM45UooZGU5OXee/38+quV\nadNyN3eLFTNI+MIFAUUJb3WjU7KkTqtWKnXr+nj9dSNm0qlTgEWLvMEstK5dHXTvHqBjR4W77opj\nwQIvd91lWLbLl8u8/76F//zHk+255hc+n/EislqtHD16lGHDhvHII4+wZ88eGjduzKBBgyJu53a7\ncTqdKIpC8+bNefPNN/nhhx9ISEjgueeeK5CxZYdrO8yXDXKjDw0n29D6uQVV9MYsNhJekWvGjBI8\n+aTGuXM21q41jtmrl8bs2ca/K1fWqVdP46WXUklOLkGXLhYcDvj++wC1aunMnSvy+usyM2cqPPCA\nhq7DjBkSI0dmfeseekilQweNSpV0PB4jsLZnj8jOnQI7dwrBhzcUv/4qcd99xpg6d1b56qsAbrfO\n++9Dnz5WOnf2s2yZn88/t9CkiZXBg1Wee06lU6dURoxIYP36zOP5+GNjWTp+vJekJAejR/t54ol0\niyn0+kUnOZgxAAAgAElEQVQqfGOuaARByKSfvRZa4xQkdu0y/LXffivzyCMBJkzw8d//Wnj5ZTvx\n8UZg9Y47VAYM8LNxo8TChU4WLsyboZCcLJCcLFC8uE58vEZ8vE58vCEpPHVK4IUX/KxfL7Fli4Uz\nZ64wY4aDCRNsjBqVbtEOHuxn4kQbjz6qMHmyj6FDbWzc6MZuh9tvVxkyxI6uZy6gVNA6XXO1IYoi\nN9xwA0OHDs1xO9M4Mo0303C7WvZn4RflLASE3rjsLpRZZezKlSuoqkpiYiLx8fEZHu6CEvebZJua\nmorb7cZut3P+fDFWrJB5+mmVRx81SOmRR9Qg4ZYurZOQoLNggYIsC7z4okzdujorVwaoXFmnf3+Z\nuXMl1qwJ0LWrxtKlIg6HLRPh9uypsGnTOTweL16vj48/VujTR6NNG51OnTQGDtSYNk1h9eoAFy/6\n8Xp9wZ/Dh3089JAvw/6WL5eoXNlGrVp2EhIUdu70UrGiTMeOdi5fFvjsswCHDwvcequVLVss/Pe/\nbubPzxjFDsWYMXaeecbP4sUWevRwcPFidA+e6XIw/e1Op5O4uDjsdnuGIukejweXyxWslWwGWIrC\nIm7jRgtdu8bzyCMOatbUeOstL/v3iwwebGfdOgldh6ZNVbp1C7Btm8TgwQ4+/rhgdK5XrqRLxCpV\n0jh8WKRlS5VZs6z4/XDpksCgQU5GjPDTpUuAuXOt/OnxoX17leRkga1bRbp2VahVS2PqVGNc5cvr\nOJ06x44V7kswr8VuNE2jQYMGlCtXjjZt2lC3bl0AZs6cyW233cbAgQO5cuVKoY27SJIupBekiPRg\nRUO2ofvJ78Np+pdcLlewqpnNZmPSJJnBg1UOHRI4fNi41EePpk9ESYLPPw+QmAjr1ln5/nuRKVMU\nDh4UaN7cgtUK69cHOH0aHA4bffumB0Xuvz/A5csGyc6fr1KtmpppXNGgUiWYM8fF5ctXSE31MmNG\nxiXh//1fPDfe6GTpUomVKwMkJuo8/LCFUqV03npLYeFCG61bx1GunM7x4ykMHOiPeJz/+z87FgtU\nqaLRrJmTdevytsQ0rVxTLeJwOIiLi8PpdAYVAKZo3uVy4Xa7/4zCp/uSrwX89JPhF33++US6dfMz\ndqyP//5XZsAABxs3ysTHG+6Z225T+e9/LcydmzeiveMOhaef9vHWWy4+/TSFRYuuMGxYGnXrGux5\n7JjIxo0y//2vhcuXBT79VOa221QqV9Y5dEjm66+tDBpkD0rDevc24haiCP/4h59//csY16BBgWBN\nEIBGjVR++SVzyc7CykjLTaseURTZsWMHJ0+eZP369axdu5bBgwdz9OhRduzYQYUKFRgxYkSBjTMc\nRdKnaxYpD695EMlnm5P/KD/SM9PKMotsJyYmBifB4cMCrVpZ2LvXT/nyRiDoySfVoA82IUHniy8C\nNG+uk5oKDRvKzJoVwOuVGDpUZtw4hcce05g/X2TIkIwR6IMHvdx4Y8bJe+nSJUqUKIGuC3z1leGT\nPXJE4PffBc6dyzzRH3xQpXdvo7xjIOAKkhIYyy9Zlpk1S+aFFzJa1TffrPHllwHmzJFYuFCie3cf\n1avrzJplo149lXHjfJw65WfIkGL88Ufkaz9lipepU6307h3gxRf9ZJWMld9c/VBfsc/nCwarcpIi\n5YT8+HR37BAZN87G/v0izzzjJxDw8d578Rw7ZlyrG2/USEpSWLTIEgxmRYMHHwzQvLkXt1vkzBkL\nO3aIbNqUOcnl8cf93HOPws03a1SooLBnj8BPP0msWWNhzRobdrtO48YBLlwQefrpVKZMSeToUYnu\n3QPccIPGtGk29u1Lo1IlnZQUqFcvns2bXfz6q8SiRTKffGKQ81tvWTl/XmDiRGMVZSaVFJQvHDL6\niL/++muOHTvGiy++mKt9vP766zgcDp5//vng344dO0ZSUhK7d+8ukHGGo0iTrpnCK0lSrsnWRF6k\nZ4qiBMnWbrcjiiJerzfDPgYMkKleXadZM40OHTJaKU6nzvTpCn37GlbXsGEyLpef11/XueMOB19/\nHaBRI521a3U6dEgnnNq1NbZvDxApaL58uYuHHioZ9TlEQlJSgHHjNGrVyvj31asFOnXKeA6PP67y\n4osK48frfP21lTfe8HPsGMycaeXVV9Po1k1l9mwnL78cWXnw9NN+9u0TuXJFYMECD5UrZ56GBVkg\nJVQlkJMUKae6vHkh3X37RCZMsLJ1q8TgwQF03SAmU0nQpInKLbeofPhh9BbtDTdonDuXORiWGzz7\nrI9XXvEjCDBkiI2PP7Zy880qPXt6mTXLTocOPk6elPD7Be67z89rr8VTsaLG3r1GM9JRowyfc/Xq\nGuvWycyZY5Du2rUSEyda+e47Y+Vkkm5B9jALVZEsXrwYTdMYPHhwtttcuHABWZYpXrw4Ho+H++67\nj1deeYW6detSvnx5AKZNm8a2bdtYvHhxgY01FEWedCVJIhAI5JpsQ/flcrmiWpqEk63NZkMQhEz7\nOHRIoE0bw8otV84gnY0b/TRvbjxQDz2k8vHHRh3gNWsEBg2ysHbtBSZPLo7TKTBhgp+zZ73cdFM6\niX7wQYBHH824NE5NhTfekHjzzXAfr1GfoV49DYvFcGPIsvEjScbP4cMq69erfPaZlY0bMxPjsGEK\n48erGWo8bNgg0K5dRlKYPTuNG2+EIUOcNG2q0K+fwrBhVho0UJk2zc+BAyIdOjjx+zMTQ6lSGk88\nEWDBAguffOKhQYOM51cYpJvV/IgmkcH8MStbRUO6hw8LTJxoBFH79QugKDBnjhW329g2KcmD223h\nhx+ii2lXqKBx+nTevIIJCTqaRqZAqtOp88cfRkfqBx90sH69TKNGKp06udi61cGWLTKjR3s5eFAg\nMVFjxgwn//vfGUDnxx9tTJqUwIMP+jl2TGbKFENRcPiwQFKSk/37jcy0SBXQ8ovQl997771HpUqV\nePTRR7PdZvfu3fTr1y94n/v06cMLL7xA37592bFjB4IgUK1aNWbPnh0saF7QKJKka/pPzWIhprWb\nF6iqmqPe1yRbRVFwOBxBss1qH/37y9SooVO7tk7PnhY6d1bxeo3qXjfcoDNxokL37hppadC4sZXp\n0xXKl0+lU6fi/PRTMk6nj4oVywf3v3u3nxo1Mt6mDz8UefLJ9HV506Z+hg/X6dABQjnK5zNeAvv2\nCTidULy4itPpJT7eT/nyVhIS7Hi9Xi5cgF274li8WOTzz0MDjTobNgRo3Dj9+JHId8MGDx98ILFq\nlcyECSmsXOngxx8tzJ/voVIlnT597Pz4Y2RimT7dy+uvW3n3XW8GQf3VJN1IiFSBLNr03j/+EJg8\n2cry5TKPPRZA02DuXCtpacZ3HnggwOnTIj/9lPN4ihfXuXKlYINSzZsr/PabyBNPBJgwwUbFihpb\ntrjQNGjXzsnBgxIVKqjY7QJlyujceqvK6dMC48b5aNgwnlOnUomL0/jkE5nvvpO5+WYFRdF54QXD\nAl661Mnq1TY++MAdXF14vd4CI91wd8Ubb7xBs2bN6NixY4HsvzBRJEnX5XIFgyNm2m5eoWlalnpf\nszi62Ykiq3q9oaR78KBA27YZrdzff/dx003Gv+PjdQ4c8FOqFDzzjIzLBbNn++nRQ6BOHT8vvKDQ\ntWsxNm40HsY9e/zBtEwTffrIfPqp8fmQIQqvv66iKIarxeuVWblSZOxYid9+i94i6t7dx2uvQdWq\nRkGd5ctFunXL6GxduDBAt27p1uikSRKvvppOpP/v//np3TvA0KF2kpICNGig8/LLNkaMMKRiY8fa\nePvtdLJ2OvWgxfevf3l49VUbI0f6efxxI8jzV5NuJJgPu8PhiGgVnzsn8/bbcXzxhY0+fYzA47vv\nWrl0ybgX992ncPSowMGDBaNVNdG2rcK993q4/XaNOnVEfD6jY8ipUyJ//CGwZYvEV19lvJ8PPxxg\n7VqJhg01Vq2SqVZNY8UKN14v3HOPkwsXjDH/4x/+YJr655+7ufHGBFascHP33SrjxlkRBEhLE7jh\nBo2hQ40swkGD7Nx9t59evTyZqrOF92/LC8LdFWPGjKFHjx7cfffdeb2EVw1FUr1g1tA1ZUP5gale\nCN2PqqqkpaWRkpKCJEkUL14ch8OR5QQJVUD8978iPXqobN2a/t05c9IfsHr1dEqVgnXrjIDX2LFp\nrFvnYutWC8OG6Xz0UUKQcO+5R8tEuAMHphPu3LkB3nrLqMNw5YpA7doOSpe20bOnhd9+E2nfXmPd\nOi8XL17m9OkzXLx4Cbfbg9frw+32ceCAj6lTFcqW1fj3v23Urm3DbrcRF2e8IDweH9u2+Wnf3iDa\nvn0t2O029u0z64+qHDt2MTi2L76w8tBDcXz22UUuXNCZOtXKpEkePvvMQs+eDoYP97NwYbo6wiRc\ngMGDHYwZ4+e99yyMGWPjGhEZZIlQBYXT6cTjiWf8+BK0alUSqxWefdbF55/LjBtn59IlkZYt/dSr\np/Ddd3K+CbdWLZXp073Mm+dhwgQvgwYZPtk5cxw8+GAi7do5GT7cFlQkNGqksXChl5SUVObPT7/+\nxn1R2LnTVNaIzJtnoVo1nXvvNdxflSsbLaF27hQ5d07A5zPu2Y03GjfoyBGR6tU1kpMFEhNNVZHI\n+vUW7r1XCKpLTIPFDGaa6pJwqV8oQWeHcCVESkpKobRLLwwUSdI1UVCJDSZyS7aRxnHhAlSsaGRh\nAZQrpzNpkmENJibqdOhgTKrJkwVefDGZ+HiFSZNK8MILHq5cEXnmmXRrZNmyQIbjPP64zMcfGw/s\nd9/56dPHmPgffyxSq1YZTp40JGdXrvjweLx88kkKPl8a//mPzL//XZKZMxN58UULjz8uM3iwzJdf\nStSqpbFhQxoXLlzi3DkfY8YYD1u3bhYcDhvz5kl88UWAkyd99OplLP0bNbJy001WLlxwYbX6OXv2\nHJMnp2t9mzUry/33+/nnP9MYPdrOI4+kUb26l2bNHFSo4GPz5mTKlcvMqk8/befRRxW2bxfp29fO\nn02Ir2m43fDmm1buuCMOr1fgtdf8rFtn5eWXEzh1SuLuuxVuv11h/Xore/bkPRepYUOVoUP9PPBA\ngDNnRGbNsvLvf1s4flykRg2NJ5/08/HHyezZk8z8+V66d1coXlznp58khg+38fDDhj764YcV9uxJ\no0IF4/rPmGFl+PB0md+UKcYLt0QJYz5Xq6bh84HDAcePi0GN9Q03GJ8fOSJy881GU9VixYy/7d8v\n4nRC1aoZn81ImuvQDtOmGy9U6mcmyIRL/cKf+6JEukU2Iw0yZoLlFy6XK+qGlllB143Uynr1NHbv\nNiZnxYo6Z88Kf34OrVt7uHzZxc6dZXnvPRtbtkj8738CvXv76dgxXf0wd25GlcI//iHz0UcG4e7c\n6adWLZ20NKhb18rZswItWvhZvtyPxSJz7pyPxYsFFi2Kx+USadxYD5Z6rFAB6tTR8PuNiPqyZTJ7\n9sQjCFC/PnTponHihA+Hw3BjzJ5tVDdr21bjs88C/N//KdSubeN//xOoVKkkI0Z4GDPGy5AhCo8+\n6qVyZSOY+NhjxWjVSuG77zw8/HACDzzgZ8IENz16JPLuuyls2HCep54qxqpVGV0HY8fa6NvXj9cr\n8MADiSxceIXKlXN9KyLem4KEqsKSJTLjx9u44w6V8eN9fPihlXnzDPdJo0YqdruepR87GtSurXLH\nHSp//CGyfbtEhQoanTsrTJ3qo0yZzOfj8ahYLCKlSmkZUq4DAXj9dSstWjj54AMPTZpo7N3rolUr\nJ7t3S4wZY6dGDZWyZfVgXYcSJYztq1bV2LZNonZtjZ9+ktiyxZiDomjM54yWrjGmNWsk2rRRMowt\nkkY3q67G4UHNSM1Gw/eb164RfwWKpKVr3rz8WrqapuFyuYL7KlasWJ4KmYQmaly8CKVLG3pMIMPD\n4XBo3HKLj5SURCRJoGJFkZdekhk71mihsn17+gPau3f6Q7Nli8CCBcZkP3HCR61aRunG0qVtnD0r\n8M03fj7/PJmDB1Uee0ynfv0Etm51Mnmyxr59Ad58U6F3b5VGjYzmg4cOCRw6JFCrlsbIkQpr1rhY\nuzaZZ55R2b5doF49KwMHygwapJGS4mP4cIXVq0VKlrTx9NM6Z85cZMkSw7KdOtVByZIlOHtWICEB\nUlJSeeIJw0Rdt07m9tvj+fxzN5s3y3z1lSGyHzo0kXXrElmyxEePHhmz4QAWLrRy4AA0a+ajc+fi\nnDiRq9uR7X3KL3Qd1qyx0by5k4ULLbzwgh+PR2DwYAc//SRxyy0q996rsH27lGfCbdXKT4MGgT+X\n8wr9+qWyc+d5Fi5M4dFHvZQqFTnbLqvkA4sFxo71M3Wql549HXz2mYwsQ//+6SupxETj3MDoQmFa\nrWXL6uzcKbF3r0HmZvNSgHPnBKxW44WekiIEt1m7VqZ167wl60DmBJhIVrEp8/vhhx9o3Lgx58+f\nZ/r06SxfvpxTp05F3K/X66VJkyY0aNCAOnXqBDW9ly5dol27dtSsWZP27dsXajYaFFHSNZFX0jXJ\nNjk5Ofj2NDWc+YGu61y6JFCqlM7PPxuT32ZLJ8/27TWKFUtg926jVOMPPwgIAjzwgMaBA+mTec0a\nf4ac9datDetp2rQAZcoYD4cZpDt92kuLFn5++UWgU6d4atQQ2b3bz8cfq/j9AklJFho1sjJihMzi\nxUYfrIQEKF/eqMcwdapMUlIcd9xRnPHjJW67TWft2gDt22tMmSJRs6YVp1PlyJGzPP+8i5Ur7ZQv\nX4r4eIHz533cf7+xNK1e3cmyZQbJjBvn4ttvU4Ljb9QongkTfMgyjBtnY/58L6+9ZmP2bBvvvefn\n8cczZ7Ht3Glh0SIHvXu76dLFwaFDnqDvz1xuXu0Y8M6dIg884ODllxPp2zfAjTfqPPOMne++kylb\nVqNbtwD790t8/33eyLZdO4XSpTUsFpExYwIcPuxi7lyFBx8UKVZMzpT27Ha7c5X23LGjyty5XmbN\nsv75/3RrND5e548/jPm/Z48UtHQ3bZIQBIN8S5bUgwE1MK1c45gXLxqWbiAAmzdLtGyZkXTzm41m\nPqeyLGO1WoMlIlu0aMG8efNwOp1cunSJGTNmZEh0CIXdbmfNmjXs2LGDXbt2sWbNGjZu3MikSZNo\n164dBw8e5J577imUZpShKJLuhbxauqZW0O/3B9N1zULSBeUbvnBBoGRJo9AMwP79xm9Z1unUyfju\n9u0CDRvqHDwoctddRlvz115LL17StGn6WHbsSJ+o/fsbD0K/fsZt++MPF5Lk5ptvZJ56qiQzZni4\n/36ZefMkZs2SSEiAoUNV/vMfjeyqI/r9fpKTfezalcjSpRJvvGGhXj2dRx/1U6OGm7lznbRsWYbX\nXlO4dMlH/fpWkpKslCuns2tXCkOHemjfvhh9+sTRqVOADz/0cuedCkePplGtmhFdbts2jmXL3Gzc\nKDF0qJ0ZM7y8+KKN06dFpkzxkZioM3VqxkFeuiTywQdxPPFEgEceKcVXX6VSrpySIeASbW3e/ODE\nCYHXX7exZo3EU0/5OHdO5cUX49A0w9Lr1s3IIFu6NPcv7cREjfr1NfbulaheXWPSJG+YPDDrJbip\nmjCLAZmyrOwKx7doofL770ZQrGLF9OPExemcPGkcY9cukZtvNkhz82aZ0qWNYNrJk0KGeXT4sOFa\n+OUXY7ubbtLZulXippu0YBufwobNZuO2225DlmWmTJmS472PVOzmq6++Yt26dQD069eP1q1bFyrx\nFllLN7vaC+EwmjG6SU5OBqBYsWIZ/LYFWWns4kWwWtOtvEOH5D8/h7ZtDdLcscMg3ePHjS4PAN99\nZwTQXn45oy+sZUvj7zNnBrDbDc3t0qUSL7/swmpNY/nyOIYNK84HH6TSrp1Knz4yn30mMXu2wo8/\nBujVK3vCNWGz6dxzj87s2QqHD7sZNCiNVaugb9/i3HGHxPvvK8yZI9OunYWFCwMsW+bn7FmBcuWK\nceGCyNGjhkvhm28slC1bhtRUowXMxYupwWMkJTmpXVvjxRd9PPGEnXHjfPz8s8igQXZefNHPq69m\ndjWcOiXx8ccW+vZV6No1kUuX7MGIeFxcXLCteqR6C2YQJq/31uj7ZaV58zgqVdJ49lk/M2bYmDUr\nHk0T6N49gNUKixblvlB45cpGQ8gyZTQeeEBh7940Jk/2ZdJjZwVBEIJWnxmHAIOEZDlrqxgCtG4d\n4LvvDIt16FBjlRGqpDMSadLHYbcbZUg7dVI4cUKkTh2DkI8cEbj5Zo3337cycGAASTL8ua1bZ5zD\nUPidgM1rkhMiFbs5e/ZsMBGiXLlynD17tsDGGQlFlnQh50BaKNnquk6xYsWIi4vLZDUUVKWx5GQ3\nbrfhaw1HkyY6pp//119FGjTQ+OMPgRtv1HGFtJMaODB9WZaaSjCTq18/Db9fpX59Y2n47LMBjhwp\nzqhRDpYvD9CokUrv3g4CAVi+PECzZnqmEopZwZyspnrD70+la1edTz+F779X2LJFZNAgCwMGqAwZ\notKvn4VPP5W4cMHHLbeo9OyZwIgRVs6fT6ZTJ8NPWLVqKY4cEbBYIDk5FavVuL79+zu4dEngvfe8\nDBliZ+xYPy6XwMCBdoYPN/yO4Th4UOLLL2V69w7QubOT06fTVzrhxBMXFxfU45qtnVwuV9AKjMY9\nEQjAu+9aaNQojsuXBd5808vKlTKjR9u5cEGkZUsfTZsq/PvflmCyQ7QoUUKnTh2V4sV1XnnFy/r1\nF/jHPwLks+tU8HqIopipGFD4y6lNGw/Ll5t6d4MgV61KX/Q2b66yZk36i8RqhTNnxKA7wnw5/vab\nRMmSOl9/LdOnj3Hf166VIvpzC5N0/X5/1B2Gw4vdrFmzJsPnV6NEaJEm3dCWHaEIJ9vExMSIZGsi\nP6RrdrBQVZWUFAslS0JamkG6xYun77NlS+PlcPq08VDfeCMcPy5QpYrOf/6TPq7QzMNnnzUehJkz\n/QQCLj74wLBK9u41Gj9OnCgzerTR06p37wQcDp0lS5SoLNtQmEvVlJQURFGkWLFiQV1l7do6S5cq\nfPRRgIULJd56S2LhQiOdtWVLCwsWeJg+3ZClVaiQyOLFXt56y7BuGzaMZ+dOEUGACxfSuPtu46H9\n5z/tbNggM326j9697Ywa5SM5WWD4cBuDBgV45x1PpjH++qvEypUSPXsG6NLFwZkzWWumIwVhgGDH\ngnAL0Ov1EggEUBSV5cslmjSJ4/vvZWbO9HHxosDAgQ527ZKoVUulRw8/69fb2Lw5d545UdSpX1+l\ndGmNkSP9bNjgplMnJWIdjbwiK2ILfzlVrWohJcVIKvr2W2OymB1MAKpVczFzplFNrGpVwx0BcP68\nmeChcvKkwObNEqdOCdx3n0Lp0kYBnN27jXbtVxO5qTBmolixYnTu3JlffvmFcuXKcebMGQBOnz5N\n2bJlC2OYQRRZ0o1UU9cs6ZicnIymaUGyzSkLKS+ka2ahmY0tLRYLyckWSpbUg1W97r033Qo3DfId\nO0QaNDCsUJN0x4yJ/AAvWmSMu2vXSwAMH26YytWrC+zYIbB9u8iAARpTp0rY7Tpz57qzrNgVCeb1\nMhUc4W6XUDRtqvPDDwGef16le3cLjRrpDB2q0rGjE5tNZ9MmD4oiULx4AklJfr791nDltGgRx08/\nGftbscITlBK9/baV7dtFJk3y0aOHg9de87F3r8Qrr1jp00fhtdcyuxo2b5bZuFGiRw+FpCQHly9H\nd56m9SLLciYL0Ayg7twJ99/v4JVXLIwYkcLNN/t59FEHX39tITFRZ/hwPwcOSHzySe5LLJqlEocM\n8fPTT24efFD5U3JVsNZftNi/X+SWWzQkSeLUqczPxpEjBhHLsk7x4sbE7dLFy9ChhrxPVRVmzrTQ\nq5dR1H7QIMMY2LRJonFjlQhdqwq9rGM0BasuXLgQVCZ4PB5WrVpFw4YNuf/++1mwYAEACxYs4IEH\nHiiwcUZCkSVdSH+YzABZNPVzs9pPtKQbmkBhVisyrcKLFwVKlYI/X5pBHy5Ayp9uXiOIppGaanT5\nLV2aTKUXdV0nLS3d2itVKjForb3zjrGMmzBB4tlnVVQVZs+WeO01L5IUfScNn89HcnIyiqIEiTYn\n9YYgwKOPaqxd6+eTT4zykfPne5gyxcmHH8ocPmycZI0apShbVmP1aoPM27WLC9bP/fJLD/XqGZbQ\n1Kk2PB545RUfPXs6ePNNLytWyEybZuWZZ/w88URmV8Pq1TLnzgnce69Kr14OfJm5OWoIgsCFCzIj\nRiTQvXtxunbVeeIJhZdeSuS99wwfaf/+bipUUDKkL0eLNm2MbL++fQP88ouLXr2UYLubvxL794vU\nqaNFLPk5apSPr782yLVECR2Hw/hOgwbG3Fqx4gpnzyosWWKhenUvcXEqt91mpMp/9VVmfW5hIS8F\nzE+fPk3btm1p0KABTZo0ISkpiXvuuYfRo0ezatUqatasyerVqxk9enShjr3Ikm4oUZodfXNLtpH2\nlRVMmVnoEjw0W80gXaMK//79xmUNTeE1a6OaQTTTnxv68q9SRQ2S4e7dxrb33mtYJMeOGd/p1ctI\nvNi6VWTQIJX58yWaN9eoUSPnJBHTx5mSkoLP5yM+Pp6EhIRg4CVaVK8Oq1cHqFNHZ8gQBzNnpnL4\nsMjIkXbOnzdcC7ffXoLSpXV++MEg3qQkJ+vXG/flxx/dwYynp55yULmyzogRfh57zMG//uVl/nwL\nCxZYGD/ew/33Zybe2bOt1K+vUrKkzlNP2cmLZ8jrhalTrTRp4iQxEebM8bJ4sYV//tNoyti0qcKI\nET4++MDJgQO5C5TdfbdCuXIaN92ksW2bi8cfDxClyzHPyM3927dPok4djX37Mj/+SUkK771nDFYQ\nCBYinzXLIOK775ZYuDCRpCSFVaucDBzoRxQF9uyBFStkunW7EjGQWRiWrolo3Qv169dn+/btQcnY\nCwDnJVQAACAASURBVC+8AEDJkiX5/vvvOXjwICtXriz0JIsiS7o+n48rV64EG0LmhWxNZEe6of5h\nM4Ei0hI81NLdts0MTKV//qdwApdLICFBD7oWQtGzpwufz0dcXBwXLxpSKzOVctYs49zsdvjuO5FH\nHlGRJJg+XeL559UcXxxmKyGPx4PD4QiSbV5htcL48Sqvv+6jX79Ehg9PJSVFo29fK8ePnwPg1lvj\nqVxZ4/vvDeLt0sUZLMJz/HhacF9dujhp1UqhT58AI0faWbrUw8SJVlatsjBzZgrNm6dbT2XLGi+X\nJ5908OSTAX7/XWTcuOgZTdfh889lGjeOY/t2kaVLPSQnQ7duDvbtEylRQue113xs3ixnkrDlhGLF\nNOrVC+B2ayxceIU33kglIeHqaopzIjZdh99+E6ldWw2+BBMSQhN4jIpmxYppnDsn4vcLNGumcOWK\nwOuve3G5YO5cCw89pLBli0yPHhpWq5Vx4xJ54YUAFSs6IwYyzXoLBdlOyTzXopQCDEVUpwtGEC0h\nIQG3253vpIZIhKVpWjDabbVag5re7PZhkK4e9GFu3Jj+fdPSrVLFIFxFMf5ttBk3Hu477lBISEhA\nEATMpBqTmGfNSr9Vx44Zqcbr1glUrqxz++063ixalKmqitvtRlVVHA5HMIpdEFBVlS5dXMiyh4ED\ni/Puuy4++shG//7FOHLkLNWrl6NmzQQOHjzPN98E6NSpOHfeGceJEykUKyaQnJxKsWJG2P722+M5\ndiyV3btF3nnHwqJFHrp3d7JsmY/Fiz20a+fkwAGJc+dEmjVT2LRJplMnJ+vXu+jb10HVqhp9+mS/\ntN21S2TUKAdut8A773g5cEDkkUeceL1GWuuAAQHOnBEYMyb3Vc3atzdqRgwcqNC3r+Hj1DQ9g6Y4\nXE9sprZeTfz6q0ipUkYjyjffNOad6aJp21Zh+XLDqk9ONuau3Z6eGvz00wH+9S8LzZqpf/rWA8TF\nwerVEkeOiCxZEggGMkMNILMimPmSD1WPRFs4Phx5bdVzLaDIkq7Vag12ii2oSmOQfefgnPZx8aJA\n1apQqpTxtzVrQknX+G2SrqpqlCnj+zOIZVi1pUqlP4QnTpjVnNLPrXNnw3Q+dkygSxfj9y236JnO\nATImgtjtduLj46OqkhYNQvdtsVjo0EGlXLkAvXrF8c47Xj75RGLYsOKcP59KmTIJ1KxZhjNnLjJj\nRgpPP51I5cqJnD59FkkSOXUqQMWKRrH2qlUTOHkylXbtnDRooDF6tIcBA4qxerWHTz/10LhxHH6/\nwKZNMq1aKaxbJ9OyZRzr1rl4+GEHlSrptGmTOXJ+8aLAK68ksmKFnZde8lOrlsYLL9j44w8Rnw8a\nNFDp1y/Ak09GiADlgI4dFY4fF7DbdbZudVO6tI6xgMz4gg4vkO73+4PX3ExoyA3phCPa5fvbb1t5\n4gk/S5emP/qmLHHcOB/33JOepCMIRoNJr1egRQuFQABmzbLywQceevVy8O23blQVxoyxMXasL0sX\nijkuM4U3dMw51VgIvSbh24ZaujfccEP0F+svRpF1L5goyMQGr9ebr2BcfLzGhQvQpo2xBD5wIH2S\nmJZu5coqR46oJCZ6OX/ekuENnZqa/v2TJ9MtYxOmj/jYMcPtcOKEkKnNTSR3SDSV0qJBqDoECDbg\nBGjRQuPjj9089ZSdYcM8XLwoMmWKlZMnDR9v+fKleOwxgR49jEBgnTpl/tSUqmzadCF4jEcftbFg\nQQoTJ1qpXVulfv0ATz9tp0oVPUNZyHXrZG66ybjO/+//OfjwQy+PP27njz/Sz9Po0mDhjjucWCw6\nK1emsW2bSM+edlJTBSRJZ/Jko7hPdoRr+p/D0b+/n61bRYYN8/PRR94/CTcyQlNY7XZ7BuVE+FI8\ntNxhQaY8HzoksH69RN++AQYPzni+oqizZ48YzKQE0HWBunWNa/z++14+/VSmRg2NP/4wAnE1augs\nXiyTmKiTlJT1KiOrsUdTYyGrymOh+y1q7oUiS7qhAaz8VBozM3fMjr4JCQl59g83baqwaZPIXXcZ\n4wltNZ6cbLQXKV06jZMnJe68087+/Rnf/Js2pZsKp04Zfw+dSx6PgKYZVnCVKplJV1GUDIkg0VZL\ny+nFFa52yEr33KSJysSJPh57LIEpU9JYssTC99/LbNxo+HRfesnGu+96iYvTuXxZ5K234rHb7dSv\nb2P8eMM/smGDlQMHJN5+O5mBA+N45plU9u2Dd98VaN/exzPPpPtRTP3opUsiGzZIPP20n379DEXD\nhg0SLVo4+eorma+/9nDnnQE6dIhn1y4JqxXq11eZN8/LsGF2Vq+OvOCLizOuyeXLGV9Yd93lp2lT\nhQMHRNauddOrlxJ1IkooTOstK9KBzJrirHyi0ZDy9OlWHn88EPF8169388QTGYm4Th2FDRuM75Yt\nqzNtmpVnn/Uzd66FQYMCpKUZtTTGj/dFdf7RvPjDayyEy/skSQo+7xcuXKB+/fqsXbuWxYsX89ln\nn3Ho0KGI1+LEiRPBDLR69eoxY8YMAF599VUqVapEw4YNadiwIStWrMj5RPKJIku6JrJKkMgJoURi\n+FXJV3BJEAQaNfKza5dA/fqZx2O0WxGpW9fJiRMS9evr7NkjZIi8f/ZZui/RbJZw+HD6RD14UODy\nZeMzh8OwhitVMs7D7XZHlQiSWwQCAVJSUoKtVkK7L0dC9+4KDz/s4/nn4/nwQw8jRhiW8KhRPmbM\nsLJzp8iePUYQbcIEW/DlMmxYIBjQ6dMnkebNZXr39vPKK8VYuNDNW2852bpVZ8SIK9x1V7pOrHlz\n495NmmSjWTPDtVCmTAKDB9sZPdrPe+8ZxXXGjk2gXDlDQz15su//s/feYVIU3fv3p6d74u6SJSsg\nKBkEeRAlByUHyUoGQURQMiKCgIhkJAgoKDlJUgQUJUtGQeRBggpIEJCwu+zuxA7vH2XPzEZ2YfH5\n4vs718UF7E53V9dU3XXq1H3ugyzDiy+GttJJ7YUX1GS1xABGjXLz++8y9etrbN7sSXYYmhFLLZXV\nBJ20ssqSpjybYzi1uXDlisRXX1l59VU/nTsnBtenntJSTGUuX16A288/x7Nli0JEhFiAbt0SKcGz\nZtmoWlWjUqUHrzgf7hWb/ZAzZ042bNhA/vz5cTqdLFu2jDZt2qR4vdVqZfr06Zw8eZKDBw/y0Ucf\ncerUKSRJYuDAgRw7doxjx47RoEGDB/4uD21M91493fCcdEmSiIiIQFEUotPLtE+jPRERBiVLGkGR\nm6Sm6y7y54foaIiKEgyAK1fEibHHI/Hrr1YMw4skiW3dd99ZOHVKxG9BgG727CKjLTbWwO02UNUE\nfD4Vh8NBIBC4r3I04SAQXoQzrQO4lLzk4cO9dOggs3WrwrhxPnr3drB7t5tp02zUrh1BdHQcX33l\npmlTFyVLRnLnjghBXLwYT/bs4mDtsceiuHYtmjp1IjlyxMaECT4GDMjKnj1uFi3y8eyzVqKjLezd\na+WVVxJYsCCC2rVD9bcGDvT+rTPsonhxnUBAeLe9eweoVi3tOl0VK2p8+23yqfHuuz7mznUwd24s\nL7zwz04dM7kj3MJjoiY1KyEhIcWY6KxZDjp0CAQpYOE2YYKPBg0SL0CNG3tZudJBiRIahQoZdOtm\no1OnAEOG2FmxwsONGxLz5tnYsych2f2S2oOii8myTLFixfD7/YwePZpcuXKlek3evHmD1X4jIyMp\nWbJkUALyn1are+g93fTGdMM5quG0KWtYCtf9dL7Zjuee09m1S6NmTeGNmQr9IEIMFgs8+qgIDZQp\nY3DihCWR2pNpTZuK606eFIPV6TT4808JiwWKFtU5ftxDkSJ+Ll1yBN/jXtsfPiFMPrKZaWfGbdO7\nNQTxjlOmxPPZZ1bKl9fJk8dg1iwbf/4pPNwGDVzUrKlRp46IAw4bJrxhWSZILwNYsMDOzJlxvPOO\nnWrVNIoU0ZkyxUb+/BJz53rDPpcYRBcsiKV/fxejR9spVy7AhQsSM2fGULJk2oBbsaLwlMPlCwGa\nNQvQqZOfNWsUvvkmjurVAyld/o9buPdntVqRZTnFmOhPPwVYudJK5853aN06Mbj26OEP7kbC7exZ\nAfA7drjZuFEhLk6wFF5+OUDlyjrjxtno0sWfLk//QWTehd8vPj4+Q+yFCxcucOzYMapUqQLArFmz\nKF++PD169HjgWrrw/xPQNTmqbrcbp9NJlixZEnlumTUgdF2nQoV49u+X6dhR/MzhSH6YVqgQ/PEH\nlCsnEh1q104em3vuOfH3qlUCAEyWQlxcHEWK+LlwwU7Zsgq//WbNtPabh2TmAVxqhTjTY3nz6owe\n7ef11x1MmeJl5kwrly5JzJzp5dAhmQsXJBYuFAdjc+fauHlTPKdyZT0ojvPOOy6eeEKlRw9xmDZ9\nuo9PP7Vy4oSFRo00GjRI+fDmlVeyIkkG8fEWHA4Lu3ffYciQrIwYkTrgDhvmSwa2AFOmeLl5UyIm\nRmLbNjeFC2feVvpBgVF4TFSSnLz+enbGjvUxdWryVNmnnnJz8mTi965fP8CvvyrMnOkmNlZi4EA7\n9epp/Pqrhbff9vPf/1rYulVh0KDkOsj/hCXtN03T0h0WjI+Pp3Xr1syYMYPIyEhee+01zp8/z08/\n/US+fPkYNGjQg2p20B5a0A0HzNRAV1VV7ty5Q0JCQlA/NyPb5PSY6UHHx8djGAa1ain88IOVKlXE\nMy5eDH32xx9DjATBtTU4cUKiW7cQzenmTZMCFrouENCDmVmKolC6tJXz520ULw6nT99fFQ2TIgcE\nWRv3Wq4oqZnVcHftUhg82M+QIQ66dhVeYrlykWTPDosXC+AtVSoEiFevhhInGjXKzpAhfq5ckdi/\nX+a993z06eNgyxaZXbtSD6UYhuiXnDnhiSeyc+lS6pPygw9imTgxube3ZUsCK1daKVlSZ+lSL38X\nnn2obPRoO8WK6eTLZ/D554k5XR984KVfv+Sn/lu3WlEUg7Zt3fTsaadFiwBr1ijMm+fFbhcUsaFD\n/aTXuXyQugsZGfOBQIBWrVrRsWPHoL5C7ty5gwear7zyCocPH860dqZmDy3oQuqauqbyV1xcXDCx\n4W5b5HsBLfOQyQxXAOTLZ6VAAYO4v2VkNS30zM8/FyBhcnXNw7QKFULPPXQoBCQFC4qf//e/8XTo\nILyKv/5y8uSTBr/9JhTAwmlpGTFzsYiNjQ1ScNIjDpTUknKcE/8ORo70MX26je7dA5w9a+GHHyz8\n+KMA1e++k2nRQnirXq8UPDS0WglKPJ48aeXcOQsTJ/oYM8ZO/foax4/LtG/vYs0aT9BbTskmT/YG\nD4gcjpS/26lTvQwfnhw9tmy5xbBhNipW9DJ+fCyq6s+ULKoHZSkB23ffyWzcqDBqlC9ZWKFwYT24\n80rJzpy5xuzZTjTN4MIFg5declOyZDxff61x6ZJE587e/1N9cfdMPIMePXpQqlQp+vfvH/z51atX\ng//esGEDZcuWfWBtNO2hBl1IPOlNMZq4uLhkYjQZuc/dLNyDdjgcwXCFadWqGezbF+pas3bU9u0W\nrl2DokUNTp6UKF7c4Px5CZ8PrFbxmV69bEHvs1cv84ApC48+KkC9Tx8rTz1lsH+/hcKFDW7dkrh8\nOePtN1OCXS4XUVFR98wCCbeU+rlyZZ2iRXU2bFB44w0/U6fagkLdrVq5kCRYu1YIoFesGHIle/YM\nhN0jgurVNW7elHj88Uj+8x9R9LFMGZ0ffkh9kRgyJMQGCZcuBChQQGf6dC+DBiXPPtu3L4G33spG\n7do6EycGUBQ5URqrpmnB5JD/Vemgu9mNGxKvv+5g3jxvinHs6dO9TJiQ2LuXJPEOS5a4OXPGyrx5\nTl54weDKFSvvvKMjy3ZGj45k1Kh4dD1xCfW0+uJBe7rpufe+fftYtmwZO3fuDNLDvv76a4YNG0a5\ncuUoX748u3fvZvr06ZnWztTsoQbdcJUxU4wmI6XTk97rbhMnXM4xqQcdPgiqVtXZt0+iSxcRNhg4\nMBQ+WLdOpn59nYMHLdy8KQD41CmJ5cuFxxcdLQVpbO3aiS3x++8Lb61+fY3t2y2ULGlQoIDB9u0W\n2rXTWbgwfd5p+KJkt9uTxbbv1Uz6nZk2bZ6om/05eLCfadPE6fcPP4gih4cOiQOz06ctPP98qH/O\nng0NyeXLQ2GGhg2dQcHwNWvc1KmjUqRIJGfOWBg6NGNSY5Ura3+zIZID7smT8bz7rp1nntF47z0/\niiIOqkyRdJvNhizLiaozuN1u3G53MKEhPPX3bv32INKADQNef93Byy8H2L1bTpTwALB6tTsZXc5q\nNTAMiXz5dOrUUXn99WwMGeLno4+szJvnxeGQmDzZSb580Ly5JRGnOKVKFeF9oWmZq68b3m/x8fFB\nBb60rFq1aui6zk8//RSkhzVs2JAlS5bw888/c/z4cb744otgBYkHaQ816JrpqJC4mu+9DOS0QDct\nOceU7iFA10KHDmKwOcLm9urVojBkmzY6ixbJVKpksGuXhQYNQsBx+bJQ/ypUSIDpzz8LD3n2bAHM\nv/0m0aOHxoIFFnr21Fi4UEZV0xbtSboopZeRkJaZE80wDFRVRVGUII/UfKbb7eY//3GjqnDmDHTt\nGmD5chEnBahWTXi7q1cLb7devRAYNGoUOijbt0/hwoU4unb106mTM0jpKlhQZ+lSK5UqaYlE41Oz\nihU1Ro3y0alT8gy0H3+MZ948G6oKEyemTPhPmtBg8mjvlkWVWSIvqZkJRIYBb79tJzpacGknT07s\nzY4b56Vdu+T85EBAvOzx4wkMHeqgShU/O3Yo9OgR4KmndDZtUlixwsr8+d5gv5iHdkn7wuVyJara\nq2kaqqpmWhmlcEuvlu7/JXuoQTchrM6Nw+G4r8Of1ERv0pJzTO0ejz0mgNbMJps+PeSJHj5s4fff\nCcoyduvmZ/ZsidjY0LtUqhRauVu0EMC9YIHMo4+Kn736qkKbNjqHDlmIijIoVMhgyxbx7kmzlJKm\n7abW/ozGtM14tu9vtZTIyMjgibnpDZqEfotFonFjH+vXS9Svf4cNG2Q8Hi8zZsTj90sEAgb164v3\njIkRYkAAW7YkPvy6eVMUQvz+e4UZM0TMd9EiGzt3ulmzxv13AkraNn26lyZNkoPOF1+4OXpUZvNm\nhUWLPBnSvb1bFpXFYkkxocFcnDITiCdMsLFnj8z48V7q1UvsAdasqbJ5c/IXq1RJ9P3q1W42b1Y4\ndEimbFmVP/+UGDLEz5kzFt54w87SpR5y5757W5OmPCuKEgTmzEh5Tqq78DClAMNDDrpRUVHB7KvM\nFr1Jq5BleqxqVT1Yhv3aNSmoxwDiQK10aZX8+VXOn/fy+OMG332XnU8/DRVx/PtsiwULBAKNG6fg\n98NLL2ns22fB6SQYWujZU2PBgtBkSm/a7r2YGWIx49l3E9IxJ1+rVgZff+2kYkU7LhccP26jXTux\nS1m4UMfrddO3r/B2x45VGDXKxrBhLjZvvh28X6VKEcybZ6NkSY3XX3dSsqSGLBsYBhQpknaRsUcf\nVSleXKNmzeRb0fHjvZQurTN8uACWHDnutXeSv78sy6nWcAPRn+mNjaZlhmEwb56DNWuszJzppW7d\n5O/51FN6sjJDdeuq/PCDzAsvqJQuLYSAxo71MHVqJPPmefF44OWXHYwd6+Ppp++dLnc3nQXIWMrz\nw6owBg856KaHNpaRe4WL3oSX+0kvWIW3wwwxNGokvAiT2gWwcqVEbOwdunf3s3JlFgYPNpg+XaFh\nwxDvsU8fMTkiI6FUKTHY16+3MG+eAOH33pN55RWNxYtlGjXSOXVKYu9eW4bTdlNqe0pmCumYIZaM\nJE2AmPCXL1uIj5do3Fhlxw4HLpeIuwwdKu710kvi8OzDD50cOgRbt96gQgUf9euH+mXdOk8wtrt1\nq5vXXgtQsqQ4gBs/PhV9S6BXr4SgVkO4FS6s8/rrAYYNs9O5c4CyZR9sSms4+FgsFmw2WyLwuVts\nNLXvaOlSO5984mT+fE+izDzTJk70Jqt+Ubq0xvbtYpytXu2hZ08H/foF+OwzOz16uCldWufVVx3U\nrKnRseO9V4RILXZ9rynPplJbXFxcuqtG/F+y/we6hAozmrG3exW9CW9H9eoG339v4Z13BOiaFWxB\nVLe9eDEb7dsrHD1qoVgxHV2H/futQZGVZcvkoC7Djh0CjLp2tWK3C4nH8eMVnnhCZMBNmiQzY4aP\nwYOzcuOGJ8ioCM+2u1czmRThQjpJK2akp+9lGYoX1zl9WlRCPnFCDL3Bg33ougCinDlD/b1unY+8\nea2ARJYsISCsUiWCcuX8PPNMgC1bLKxdK0Bj6lQvkyalLjo+cmTWYNwy3LZscbNjh8yxYzLDhv1v\nyP6pgU/S2KjX600xTrx2rcykSS4WLIhPEXBnz/YybFhKh4aiv69fj2PKFBs2myg7Hxsr0a9fApMm\n2bh9W2LChPuoiUTGDwzDd0ipVXnWNI3mzZvTr18/VqxYwfDhw1m9ejVxcXHJ7pea2M3t27d5/vnn\nefLJJ3nhhRf+kWw0eMhB17T7TWyIjY1F0zSsVut9i96Y7She3CBbNoOLF6FECQG8pUuHvIU1axSc\nTujQQRyE9e+vMWuWk+3bQ4PGjNNmy0YwVfjIEYm1a8V9nnnGytSpfpYts+B0JlC5coDJk7Pf8yFZ\n0i2c2TfmQpRerz+176NECZ3Tp2XKltWCE753b7Gg/PCDheefD8Vav/zSiiRZGDkyC3/8kXjxWLDA\nQ8+eHnr3juDaNQuKYjBokAO/X1SvDbc8efRU/z9lipeCBQ2mTbMxYoQvxYKK/0tLSQ4yaZx42TKJ\noUPtzJsXTf36ybfZK1d6ggUlU7JffhEZlJ98YqVCBY1NmxRWroxn1y47ixZZWbrU+8BLDaXHwncI\nkiThcDjYtm0bffv2pX79+jidTlavXs0dsxhhmKUmdjNhwgSef/55zp49S926dZkwYcI/8i7/vwXd\n8MQGl8t1XymvKbfDYNQoLyNHWpgzR8SGT54MgfnSpTK6Dj166CxZIvPiizpnz8qJKkC0ahUCm717\nhRdWvboNXYeFC/2cOmXh3LkE3n/fzeDB2Rk5Mp4vv5T5/vt7Y2+YlhKP935K+5iWLZtIGnn8caEj\n4fUSPJipUyeCESN8LFok4rzvvmtn1CgHP/xgZd06d6L7+P0KOXKI9rRtGyBvXnGP3Ll17PbE4+D6\n9cRD3OcLvWeLFqLaw/nzFl58MX3b5wdF80qvmeCjKDYmTcrC1KlRrFzppUWL5GIvc+fG8NJLqa8k\n+/YlcOmShVdecdCypcqaNVY2bPAQHS3Rv38WlizxkCfP/R/yPSiersViQVVVateuzahRo1i/fj0F\nChRI9vm8efPy1FNPAYnFbjZu3EiXLl0A6NKlC1988UWmtTEte6hB917CC6klNmTGYRyEaFR37tyh\nWrV48uSROHMmKhgrND3Wv/6S2L9f4sknDUqVEuyD3r19zJrl4MKF0HZu2zbxjvnyQf784toJEwwa\nNBAHTHXq5KJjRytPPGHw2Wcupk/306uXlZs3ybDpuh7k8dpstmRJH3d777uZLIu6cbIMLhe43bBz\np/B4s2Y1ePllleeeE57qX39Z2LHDyqpV0WzcqCTxUO20bCm84mPHLMFyM4Yh4fOlPaRNhoMsG4wZ\nY2XBAiF3mAmRmAzbvYKR2w1dujjYt0/ms888PP988pDCggVuXnst9VjnmjWxeL0qHTo46NQpwLp1\nCmvXesie3aBjx0jefjuBypUfvGRjRi3pOMtoTNcUu3nmmWe4fv16kJebJ08erl+/nqltTc0eatA1\nLT3yjmklNpj3uF/QNeOfJqBnzZqF99/XGDdOYeFC4UmZ+rEA48crGAb07Kkxf75M166CG+nzQbt2\nAnyaNLFhGOLeR4+KZIH33nPw229ZOXlSoE2/fgozZ6osW+bkkUcM2rTRaNLESnpDVGaMzKw3l9FM\nvvSapoU0JRwOg9WrrfToIba+JqiaXivA2rXx/Pqrwrvv2tm8OZTuO2uWWAjmzPHw669ysGLDH39Y\nyJEjdH3Dhql7rydO3GLbNoVly2zUqxeX6fzRB2VXr0o0bOjC5YJhw/zUqZMccOfN8/DKK6lrBc+a\nlUC+fAbt20fRubObpUsV5s+PoUgRN7162ahSxU/HjqmnV2fUHqTKWEYoY/Hx8bRq1YoZM2YQFZWY\n7RKe4PSg7aEG3ZCUYOpeakYTG+7FzGeYerbhgP7MMwaVKul8+62FihUTLww7dlhYtsxCs2Y6Fy5I\nHD2q8PLLPmbNklm0KAQYJUtaiYuLQ5I8zJ0rJkP16iI7aMwYlQULZE6elJg5M4727e20bq1TtapO\nixZW4uNJ1cxFwjxAcDqduFyuBzb4zp8XqcsA165ZGDbMwYYN4n3M6gw7doQO0/x+iZ49szJ3rpcn\nn9RxucI1KhJYt064p3/8YaF6ddFfR4/KOBwGO3cm8PXXqYdEChSw07WruKZwYWuq/NH/S6m+x45Z\nqFPHRfPmKhUqaLRokRxYZ83yplh6yEwcGTbMR7VqBm3bZqFXrwArVrj48EMftWpZmDVLxMjHjo0N\nUtnCF6N7rdCSmf2WFMDTC7qm2E2nTp2CYjd58uTh2rVrgNBgyJ07d6a1My17qEHXtMxMbMiIJX2G\nw+FIsbDgmDEa06bJQbpXuA0YoHD7NsybF6BvXyft2/tYtUrm558l1q0TAd4LFywcOuQiS5YsdOsW\nKgfUs6fCsGEapUrpNG5so3BhjSlT/DRvbqVnT50SJQxatbLiScFpMUMgfr8/qMf7oFf6M2csFC+u\n88EHwlP94YeEYHWChASJn38W8UWzgkTVqlF07eoJJk4MHRpiF3z3ncyFC2L45s5tBEu7A4wYcXdO\n6YYNCgULis9Mm+ZMkT+aUqqvyRj4J4FYVWHyZButWjn54AMfx45ZGDo0+eHYxIlu+vVL/vMKFi0G\nbQAAIABJREFUFTRiYiRatw7QpUuAFi1cdO8eYMUKK2+95ad5c43t22189pmDFSt8REZaURQFp9MZ\nXIzMPkhKY0tvHzyosZWe8EJqYjfNmjVj8eLFACxevDgIxg/aJON/vXzfh5meid/vx+fzERUVFfTe\nvF5vMDsoPSfuuq4TGxtL9uzZ0/XclJ5hZhmllAveu7dCrlwGO3da+PFHC+3ba0Gt3DZtNJYuVRkw\nQOLPPw1efFHivfdkvv76Jk8+GcoFj431YbeL3HqnU9CjvvwyQP36Og6H+P+5c/Fs325nzBiFrVv9\njBmj8OefEkuXBsiXL3FFiHBKUkJCArIs43BkvPz47du3cblc+Hw+LBYLkiTh9/uJiIgITrbz5yVq\n13bRtq3KokVWDANu3BBueJYsYquXP7/OhAk+vvhCYf164cX++ed1IiNdiT5nWsuWgeDnunTxs3ix\nAPOrV+MwDMifP+2EiTff9BMTA5s2iTpqZhHGlMwwjOB4MyvWplZa3eyDu5nb7U6UKJGSnT5t4dVX\nHWTPbvD++z6qVXMlUq4z7cMPY+nfPzl7oUoVlYMHFapUUVm2zEuDBi7atAmwZYtCo0Yqw4f7+eUX\nC02bOlm+3EuVKoIXK0lSsni+2QfhVY3DS6kn7QfTkUlISEg0Fu7HTN6uyyXGRMOGDdm9e3eafbh3\n715q1KhBuXLlgm344IMPqFy5Mm3btuXixYsULlyYzz///B/h/D605Xogecker9eLx+PJUOn08Hul\nJ/3Q5/Ol+oy07vHOOyqVK9vYtCnAc8/ZgoALsGaNTPv2OqNHe6le3Um9evFUr+7g7bdz8scfPgoV\nEoCaNasdr1doAly+7KNgQTvNm1s5ftxPfLyPyEg7jz8eya1bPtxulcaNbXz9tZ/ly2WefdbG3Llx\nVKkihNzTyiTLiKmqKc3oDTIcTIETE8gtFgvLl0dw+7aFo0ctvPeeL0Ut3Dfe8NOihcorrwjgHz/e\ng7lebtyYfKju3y8zbpyXd95xJOLgWq3JU4hNs9uNIINhxgwbH37opUoVH126ONi5001UKjhtxvxM\n9oBZBdmMhyctrR5eKicjQGyaponY9YwZVkaN8pM3r86zzyZfzLNlM5g2zUv37skBt1o1lb17FerW\nVfn0Uw9Nmrho3jzAgQMyTz+t8dZbfo4ft9CqlZOJE31UqZK2MI35/kmdmKR9YHrA5nsDwf9nht5H\n0nvczakyxW5Ssm3btt1Xe+7F/hXhBXPLd7/VfCHl+FNS7dl7eUbBgtCtm8aCBTIVKogBMH58KNzw\n6qsyMTFuZs+OYcyYrAwcaOHMGQsbN1p4++3Q52bNEs/MlQvWrRMc1/Llbfz6q8TvvwtGQ86cdjp1\n0nnjDZVatWyULetlxoxoeveOZPbsnFit9x/TDmc6AIk8GTPEYmZZ/fWXhUmTnGTPrrNixU2+/x6e\nf16Q+xMSxERXFIM+fQKcOyfh90t/31O0R1WhY0cnRYoknjhly+r8+qsYwitWCI83WzaDPXtkvvgi\nZdD1+aSgVi8IT7JDB8Ga6NPHQXq6ILzvkhL506O54Penrc17/LiF+vVdfPedzFdfeVi1SklRpOa1\n1/y0bx+ge/fkMdxChXT27lVo0SLAkiUeWrd2UaOGxvnzFrJkMZg61cePP1po2dLJtGk+WrcOjbHM\nTGYwAdHsg8xIdw5X9HsY7aEGXcMwuHPnDoFAAEmS7juxISXgScrnTesZdwOuwYM1Nm60MG2aGOBv\nv62QPbv4/M2bFiZNykb58jpvvqnx+usKy5apjBmj0Ly5HjwIGTJE4fffxf0aN9aDgFyhgo2rV2XO\nnRPk8Bw57NSq5eWTT6IZONDJoUNRfP99gD17ZOrWtbJv372Ln5siOmasXJIkvN6QqLVZmVbTNA4f\ntlC6tBAzOHMmDkVxsHu3jRdeCOD3azRrJsDk8OGbxMT46NQpFN4oVEhMyDZtBKgcPJi4CGLnzgG+\n/DLE9apYUWPwYB8bN4rwRFSUQeXKyb23Dh0COJ2irfPm2dA0mDzZx5UrFkaOtKcLeNOyu2kumNq8\n4WEqv9/P2bM63bo5aN3ayUsvBXjzTT/PPReRTC8B4OOPPWzYoDBvXsqUvj/+sDBihI+PP/bSvr2T\nUqU0vF64fl1iwQIvR47ItG3rZPZsL82a3XuK7936wFSey4gUZEaB+H/Jm74Xe6hB1wTayEyqoxIO\nmmaCQFI+b1pf8N1AN3t26N9fY+ZMmbp1zW156PeffmrnwAGFgQM1dF1kpE2dqtKhg8LZs6FDpNKl\n7ZiJN6NGabz2mgCWatWycuWKFIyVVqwYxY0bLg4dUvnvf2W6dLGyYEGAnj01unWz0rKlEix8ebe2\nh3v7qqoSFRWF3W4PVgs2JQ3N7fWdOzLvvOOiQQOx7f355xhkWWPuXBsNGgTInRvefTeKI0cEaBQp\nYuXttyMpWlSlQAHxPoUKeTh/3sL27QoTJrjxeBK3748/pETyjy++GKBJE5VFi8Q9W7cOcPiwnMxD\nXrTIyubNoYSLiRNt2O1CTH3nTpn33rPdN/AmtfCMKhOIAex2OzdvKgwZ4uKFF6IoWtTL5s03Wb5c\nTlbtwbS5cz28+qqTa9dSn74rVnjo3j1As2YucuQwuHlT4rffLKxa5eHwYZmXXnIwf76Xhg2TL0qZ\nSfEKv1dKUpAulyvR2cLdZDHD76eq6n1Vv/5f2UMNukAwZmZ+IfdjZo67uW2+l2q4d2tD794BDh6E\nnj0Fano8UiKRlTfeyEIgAJ9+GmDqVJlSpQxq1jR4/XWFuLhQ0kTu3Hb+FkFj+nSViRMF+FSvHsWx\nYz5iY+9Qr57GK6846NdP4YsvAjRsqPPcczYuXpQ4cMBP7doGDRta6dRJ4bvvlKCkYlJLmqHmcrmC\nByogQg2BQABFUdC0KJYsyUG1ajn5738VJMlg6dJocuXy8OefGvPnOxgyxMPUqU727BFe6iOP6Cxf\nrnDwoJXJk71cuSImUoECMs89J2g8XbvGMW1a4uE6Z46Npk1DFSaaNlV5/PFQ/z/xhGjfq68m1lR4\n6y0HxYvrPP20AJwJE+xs3SqTIwd8+aWHb75R6NPHgTtxIlym27lzCiNHuqhaNRsRETJ797rRNBuV\nKuXmhx+SZ2s0beqlb183r72WPJzgcoXG0IEDCRQpolOnjovSpTUuXxYazuvXezhyRKZLFwdLlnip\nWzdzxcVTsrsBeEZlMc3QzKFDh/jmm28eOoUx+BeALpApMR7zRDY+Pj64bc5oanBaoGvKRQYCsQwd\n6uWjj7KydasAgxMnLMF02IsXFT74QKZwYRHz7dpV4f33VU6flli0yMKNGyHgzZPHTnS0aPsrr9zh\n44+jAXjhhZxs3+5k0yaVWbMCbNgg43LZ6dhRY88eP6dOSTz9tA2r1eDoUT/PPqszaZKD0qWzMnCg\nzN69Em538gw1s6yPCbYml/POHZXDh7MwaFBWypaN4MgRmQEDVH79VWHuXD8tW9pxubLQp092unXz\nMXeunS+/VBg1SvCDBwxwM3q0i0WL4vnll5Dn8sknwmP98cdY7txxsHRpYs/P7YbLl/3kzGlm+/kS\nVSkwQzcpfSUjRtj56qsQqrZp4+LoUQu5chl8+60bv18Iqv/8c+ZOEb8f1q1TaNLESYsWObBaDXbt\nSqBAAZ1SpSKZOjVl0Z4VKxI4e9bK7NnJvd9SpVTcbtHO33+P49IliSZNRIhi506FOnVUPvnEy/bt\nMr16OVi50kv16qkD7v861RlSD9GY4YqzZ88yY8YMtm7dymOPPUazZs3Yu3dvivfq3r07efLkSVT/\nbPTo0RQsWDBYuuebb775p17t4aaMgfDCNE0jJiYm3TKG4RYeVzOFNO6FNgUp086SMh6cTieGIVOr\nlpWGDXVy5TJ4883kXs3WrX5q1DB4+WUFlwuGDNGoV8/KwoUBSpc2ePzx0OQ8ceI6+fOL7dmBAwqN\nG4sj+A4dNGbNUvnrLyhRQny+Zk2dTZsCnDwpMXKkwm+/SfTurdGsmZu4OJ2NGyPZssXCqVMSjz2m\nUrGiTokSFiIjDSIidCIiIDYWLl/WuHpV4swZGz//LFOunE7DhhpNmmgsWaLw+ecyn33mp0YNHcOA\nIUOsHD5sIXduSEiAVat85M8vAOSRR3TmzYujalUv778fwaxZoXBRnTp+1qyJZ+RIJ36/xIIFoe+m\ne3cf589bcLslDh1SOHbsFvnyqZQtm4sbN2QWL46lS5esDBjgYfr05N7hnj0JTJhgY8uWUP/v3JnA\n00+LNi9dauW992zUqKHx1ls+nnjCSJVOlZbFx8P338ts26bw5ZcKJUrodOsWoEaNO2zdGkWfPqln\njwE89ZTGTz+lPK6fey7A/v2i/RcuXGPRIhdz50bw2mse5sxxMWKEl65dVTZutDJwoJ3Vqz1UqpQ2\nhzk9VLb0mhkauNc5ldS8Xm8wTHPs2DFWr17NgAED+OmnnyhXrhxPPvlksmu+//57IiMj6dy5MydO\nnABgzJgxREVFMXDgwExpV0bsX+HpQsZP31MS+jZX0cywtBgPigJr1wZYvFgmMlLwdJNa48ZWDhyQ\nmD9f5fJliQ8+kFmxIkCPHlZ275Y4fDh0qFS2bB6iowV74Nln1WB68PLlMjly2Ll1S8Lr9TFlisru\n3Raioux8842FjRsDLFggALhKlSgGD3aRN2+A5ctv8ttvt/j0U5WqVSEmxuD0adi9W2b1aonvv9fx\n+SyULSvz9tsqFy54mD3bT0yMxPPPO7hzR2LfPi81auj4/aLY5ty5Vk6fFpUfvvjCx1dfhSb0pEkB\nGjSwEhERyddfJwbHJUuiuXAhwIoVdl54IbFebo0afo4cUbDZxPd+9qwNu91OoUJmGXsxvK9fDxX+\nBChWTPv7+ghGjkwceqhdO4Lly8XBVefOAY4dS6BYMZ0GDVzUqOHiww8dHDqkcOtWyuPE5xOMiM2b\nFSZPttG4sZMnn4zko49sFCxo8PXXbubO9XLokEzRonnSBNwcOXRy5dJTBdzChXX277fy9NMa589f\nZfToHKxZE0Hfvn5mzXIxc+Yd2rWLY9kyjYEDbaxaFUu5ct67lg36v+DppmZJBcyzZ89OsWLFaN26\ndYqAC1C9evUU+ff/K3/zofd0zeygO3fuJFKhT83CT03NU1WTjXA/CQLmvaOjo4mKisLj8WAYBk6n\nM1Wv6JdfJOrXt7JkSYC2ba3ExUlkzaoTGyvAwmo12LYtQLlyBu3aWXG5DAYP9tO2rY3XXnNTpYqc\nSA5x9+44ypQJEBERgaZBpUpWTp0S9xoyRGX0aA3DgNatFb75Rkzk/v1V3ntP484dN5s3w/r1Lvbu\ntfHIIwbly+uUL69RvLiOy6VitfrJmlVGVW38+afM5csSp09LbNsmFNOaNtV4802VRx8VQ+rUKYmW\nLe1cvChoSosW+ahfX+fGDShcWLR78WIfrVsLut/WrTrt2oUmx8qVPpo103jzTfHuN27AypWhvhww\nIJ7jx20cOaJQv36AsmU1+vb1UrlyVn7/XWbgQA/Tpjlp3NjPtm3WID939uw4+vYVu4GqVX1UqaIy\nd64LtzsENC++GGDcOF/wXTQN9u2TWbdO4vhxK7//LmOxQM6cBrouQhh+P9y8KfHoowZFi+o88YRO\n9eoq1atr2GwixblXL2e6ygqZHNuUrEQJjdOnxfc3fbqXFi38dOhgJ0sWmYoVNRYtsrJ6tYfy5UWs\nfMwYO+vWxVOihJooqcGMpyZNakhISEh3UtHdzDxYNXnN92tutxubzYaiKGzcuJE///yTIUOG3PW6\nCxcu0LRp00Se7sKFC8maNSuVKlVi6tSp/5gY+r8GdM0Kt2lt+0ywNcEwaeprQkICFosF5z0Kq2qa\nRmxsbBDM01Npd8cOia5draxfH6BaNdH2atU09u4Vk8pmM9i5M0DJkiodOyr4fDoTJgR4+eUIWrTQ\n6dpVo3Tp0IAePz6BgQNDk3XfPom6dUN98uWXAV54QSc2Flq0sHLwoJhYFovB55/H0LChA1XV+fVX\nOH5c5uhRibNnwe2W8Hhk3G4Jh0MUhCxY0KBIEYM6dTRKljSCYjZXr8Lrr4vDKYBBgwIMHhwgSxa4\ncwfy5ROAu2aNqAphVgPo2DEH27aJRfPRR3VOn/Zy7pxEzZoONm/20rChIxFgVaigUaaMyo8/yrRr\n5+GPPyyMHx9PiRKPEBNjoUaNAHv2WClSRMUwQFUlLl8WbfrgAzfDh4t2tG3r49o1CUkSwt43b4Y8\ny65dffTo4adsWQOLRQpubxXFyo0bUvAw02IBRRFKcOa6f/GixHffKcyaZUuxakVKVreuGqzmkJIV\nLCgqcACcOBGP3w9t2jipV8+DYdjYu1dm7VoPjz5q8NlnViZPtvHllx6efDJxSME8eDYB2ARjc8do\ntVqDYHw/YjD3Eo5Jy8JDH0uWLMFqtdKrV6+7XpcUdP/66y8eeeQRAEaOHMnVq1f59NNPM6WNd7N/\nDejGx8cHqSgpfcbtdgfpTamBofvv42qTzpNeM6sSm6t61qxZMxQPW7LEwvjxCsuWhYC3fn0tCFp2\nu8GmTbcoV06mb98s3LwpMW9egA4drJQpYzB9ukr27Inf20wZFu2Dpk2tbN8emvhr1/qpXTsBn8/H\nmjVRvPlm4mynXr38dOsWT+HCAZxOx11DL9euwbx5ViZPDu00OnVSGTUqEJSkPHNGomJFsaB9/72H\nUqVEn9lsNrZvd9G2bWiHERvrRlGgVSs7zz2ncfOmRCAAc+cm3sn07RvA74caNXRWrZJZscJDgQKR\nwV3DnTsSEREGpUurxMZauHxZJj5e4uuv7zBnjoOvvhL93aCBn0uXZAoW1HnsMZ3585OPoy5d3BQv\nrlKkiMHjj1uw20HTRDJHQgL89puF06cFxe3EiYzFQ2vXVomOllINJeTJowe1gRs1CrBqlZcdO2R6\n9nQwZIiP7dslPB6FZcs8uFyiIvC2bQrr1rkpWjR9U9wEYrfbjdVqDYJx0lRnM/klPUCc2aAb7oV/\n9NFHFC1alDZt2tz1uqSgm97fPQh7qNOAIW1N3XAwvFsRRfMeGVFSSqrBkDVr1hSV6+9mnTvrnDun\nMXCgwvLl0XTokJ2tW2UqVFA5dkzB55No2jQne/cGWLRIpXdvhW7drHz+eYCePa107qwQHe1j5EiY\nPTuUMrx2bYAmTXQsFti8OcCvv0qULWtyWG2AjeXLvXTvrtO16x1u3gwwc6adGTMi+eQTG598krxC\nY86cBooiSPYpmaIYfPqpnxYttGBFXU2DunXtHDkiAOXs2ViiojzoukJkZCQJCRYGDQpNyp9/FtV4\nN2+WOXdOYto0japVHXz/vTcR6JYuLdTZWrfWKFjQ4MoVceJtrnePPqrx3/9aKVJER5LEYaAsG4BE\nw4ZZOHHiFnfuRLF7t41vvrGRJYtOjhywfbvCmDFuzp+3sGhRaCFYvDhji7FpWbMaxMam3F8NGqjc\nvCmxc2fqU7FqVZV9+8Tvv/3WTfnyGsOG2fniC4WhQ/0sXGijVCk/y5d7uHFDonVrJ488orNrVwIZ\n2TGHz41wx8QEX13XE3GxU9NbCDfzc5ll4THdmJiYe6aMXb16lXz58gGwYcOGRMyGB20PvadrckTd\nbncw9dTUYfD5fNjt9nSXZ/d6vaiqetdkC/OQzOPxIMsyLpcr6NnGxMQEy5FnxAwDunZVSEgI8J//\nGLz7rvAIFcVAVcUgczoN9u8PULy4Qf/+CkePSqxbF2DwYFPUxs2pUyqNG4dmmiwbREf7sdkI9tP5\n8zJ16+YgISE0QTp1CvDii14qVUrA6RQls2NjDbZvl/nuO4VTp2TOnlWC1CQQW91SpQwaNtRo3VpN\nVkU3OhpGjbLy2WcCKPPm1fnpp5tIEsHS3KoKbduGQhFz5vjo0kXjxg2oUsXBwoV+1q6ViYiAevU0\nmjULgeDw4QE++UThwAEvFotBlSpOTp++TdGi2YmNtTB4sJ8pU2y0aqVy9qyFS5ckKlTQ0TTYs0em\nRAmN9etjeestFxs3hu77xBMqmiZhtUKzZn5sNoPbty0cOKDw00+pf68Oh0H+/OKQTlUlLl5M3dtt\n397PL7/Azz+n7gFWqaJy8qRMXJxEnjw6v/ySwE8/Wejd20HZsjrFiul8+qmVsWO9tGwZx48/RvHK\nKw5efTXAgAF+7gXrTIGa9MyB8NCE+SccgGVZxu/3B0uw368lFc9566236NKlC5UrV07zupdeeond\nu3dz8+ZN8uTJw5gxY9i1axc//fQTkiRRpEgRPv7446Cg+YO2hx50wwFQ0zRkWcbr9QbpWRnZ5ptp\niGkNuKQAn3QwxcbGBvmEGbWEBJVGjRQqVlQ5edLOnj3iHhaLga6H9AgOHgxQrJjBW2/Jf2vyqixb\nZmHRIgujR8fTurWdPHkSb48//DCedu3ig+EVXdeJiTFo394ZjB+bVru2RteuKmXK6BQtKmKUZjKE\nGc4xJ5w5uYTHI3PxosLu3TJvvGHDMEKgvnVrLOXKiaKZZizdMGDAACvz54s+rFBBY+9eH4YB7dvb\nKFbMoHVrlZYtHfz4o4fixZ243RJ58+pcu2ZhwQIf771n5eRJDz5fgHz5svL777coUyYnigILF/po\n1sxBmzYqu3fLFC2q8/jjBt98I/Pss6Ie2DPPaKxb52PxYpkRI5KHFPLk0YiOtuBwGBQqpFGwoEbO\nnELr1+cTyS2xsRK//SZz61baKFezpp/ChXV++MEarBGXmjVpEmDTJtEv8+d7aNFCZeJEG0uWWBk0\nyM/WrQpxcRLz53t47DGV6dNlPv44gvnzvdSufe9JD+buMCWlvLtZSgpk5oFdSuGJe7l/+ILQp08f\n3nnnHYoXL57he/0v7V8BuuHZKibY3gvohUtEJrXUJBGTWnpZFOEWHgaJjrbQrFku+vXTmDxZ4fJl\n8YzcuQ3++kv8OyrK4LvvApQvbzBvnoVx4xSGD9eoWNFP3742Cha0MGtWgK++sjB4cOJ2fPKJj5de\nCiRSZtN1nV27Ihk82Bk8pElq//mPxnPPCZ6u3W7gcIAkGVy7JpI7vvsu5ffdtCmWihXdQZK72Wce\nD3TtamPTptD3lJAgYurjxyts3Srz9dc+GjSw88orKp06aUGJR9NmzvSzf7/EjBnRGIZB5cqPsGmT\nj0aN7FitMGOGnz59bFy+bOGdd/xs3y7z668WWrZUOXLEgtUKhw/LPPqozqJFfvLlM3j3XStr1mRe\n1K1yZZXKlVVu35ZYseLuJ/g1a6rs3i2eX6KExr597qC842OP6bzwgsb48Ta6dw8wdKgftxtee83O\npUsSy5eH2Bb3avcDuilZQkJCcK6khzmRkbZ16NCBBQsW/GPi45llDz3oaprGrVu3grGe+0kLNNkN\nWbJkCf5M1/WgeLXT6bxrSnB6WBSmhceEzTBIQkICV644aNAgglmzVD791BKkdz3+uMG5c+HlflT6\n99c4fx5eecWKxWIwaVIM27ZlYcYMmf794+nVK8DAgVlZsSIxkIwenUCvXnE4HPZkB4tXr8LEiSEP\nNCPmchlMmeKlVSs3mhbiwIZ7xOfPW3n66ciw3xncueNBkmDlSpmxY63s3Oll/nwrhw5Z2LjRx9Kl\nMn36JAattm19/Oc/Xnr00LHZbNSv72DEiAAjRwp6WLduKoUK6bRu7WDtWi+vvWanQgUdm03QvBQF\nbtyQOHBA9G/HjipDhwaQJFi7VmbpUplz5zJ2IFakiEabNn7sdpFEMn++A4/n7l5d3bo+tm8Pvd+R\nIzEULgwzZjiYN8/G8OF+TpywsGuXwvz5Hp55RufUKQsdOjipVi3A2LGxZMt2/+WMzVLvmQW6KSVa\npMWcSCqJGX5glxR0mzZtytatWzONjvZP2UMPuiBqH5kAFg6YGTVVVUlISCBr1qxBNa2MxoXTYlGY\nFh4SCVfoD7/+2DEHrVtb+egjlXPnJIYPF6BZoICRqM5arVo6CxYIgfIZM2DKFCtDhyZQrZqfYcOy\n4ffD7Nk+ihTRadLEyeHDiUGkXj2NVavuXn7c74fff5e4cEEiEJCQZVHvLE8egzJl9DCmhB7cEYTv\nOISmhcbgwU5WrgzFT+vV8/P554IfvWKFnTFjrGzc6OP6dYmePW3s2+clZ07Ilk14uWacdvr0WCZM\niGTrVi9PPCH6o2tXGy+8oLFhg4zPJ1GwoMFHH/mJjHQRGWkwebKfmTOtWK1Qs6bG2bMW/H4oXlxn\n4UIFRYFAAGrV0nj+eS+1a7vJlcvO2bM2fvlF5q+/JPx+kQChaWL3kT+/gd0ObrfB1asiVrxrV8qL\nld1u/M3nDX1/rVv7+O03ORgrXr48ngYN/Jw8CW++GUVkpE63bh7GjYukcmWViRO9ZMtmYf16K4MH\n2xk3zke7dt6gU3C/llQk/H4tvZzfcIH0cDAOZ06AmKNmSamGDRuyZ8+eTD2o+yfsXwG6fr//b23W\nhPvydDVNC4YHzLRdl8uVoS/1bgkWZkwYCIYpwi0ctI8ckXj5ZSvt22vUqaPTsKGpyGVw/nxo4joc\nBgsW+KlfP45fftEZODAnWbIIsN22zcLYsXbatfPTrdsdHA5o1Cgnly4lf6emTVVmz/aTK3k177ua\nGeYxKWDhO4IrVyTefNPG118nBvw1a9zUretDVTVmzrSzcKGT1atj8Ptl2rbNysKFHmrXhsmTrYwZ\nI979kUc0btyQ+e67BPr1c/Ljj6EstREjrGTPbgTL+Ozfb+HoUS+ffy7TrZudPXu8fPihiIWeOyfR\ntKmGzwfr1yu0by8y/776SkZRhNC5pklkzSq4yEWK6Nhs4sBT14U63LlzFn75Je2xoSgGEREGqgoJ\nCaHPtm3r5vp1hd27xXu98YaHUaM8/PWXxMSJTjZvtjFwoI+4OFiwwM7EiQk0aeLF69V5770Ivv3W\nwWef3aF8+ZAqXmYApclO+KdBNzUL94hN/d09e/YwadIkNE2jT58+VKxYkTJlyqQ457qDPdrNAAAg\nAElEQVR3787mzZvJnTt3kBJ2+/Zt2rVrxx9//PGPVoww7V8DumaCxL12ngkabrcbRVESZaplxFID\n3fRyhZNe/9df0KmTFUWB999XqVIl9bDFyy/7GDMmhty5o5g+XWbWLBtvv+2jZs0EFixwsHq1k9q1\ndfr1U3nqKZ1x46xMnZp6CGHGDD9NmqjkyROq4pvUzAw/s3KEaLeF48clPvzQytq1yfvw2Wc1Nm70\n4XKJ9+vd286tW7B0qY+4OJ3mzV2MHRtP06Yezp6VqFFDxOwaN/awebPw5oYODaBpMHZsSGVszhyh\nJVGwoMGff0qsXq1w8KCXAgUMIiIEiFy75qZJEzt58gi5w6go6NBBZd48kWFXvbofp1Pm6lWZAwcs\nqVK9TLNaDaKihPdrtQowjowUXq2miQw1k30SGWnQqJGH3bvtXL8uFqCWLT18+GEMqioOwj75xMlL\nL/kpWVJj2jQHxYtrTJrkpkABnSNHFPr3j6BIEZ05c9xkyRICIzO8dr8HVqqqZprXDMKJyKxSPYFA\nIHiIe+zYMd555x3KlSvHsWPHqFatGh999FGya1LSXRg6dCi5cuVi6NChTJw4kejoaCZMmHDf7Uuv\n/StA1/wy0lvjLKXrzUw1TdPInj37PQ+ScGYDJOcK3025LOn1IConjBols26dzGefBWjTxsrt2+Ie\ntWv72LkzFMrIl0/j009jqFRJ55dfLIwaFcmvvyq88YZKmzY669fLzJmjkCePQd++Ks2aady6Bf36\n2di8OX2LTFSU2Fbny6cjyxqXLgk62d3sqad0tmzxkjWreKelS2XGjbPSoYPGyJEBDhyw0Lmznfff\n99Ohg8adOwYVKoR0YwcOTGDatAiWLr3N++9HMW1aAlWqGMFY8ZdfKqxcKdOjh8r06VYee0yEPvr1\nU7l2DYoWdfH44zoHDnjp3NlOTAw8+aTO5s0yzZp5qFhR4sQJK7t2yVy/LvHUUzqRkQYJCYKpAIKt\n4PGI8IKqCr5yoUIGTqcA3EBAlEkPTymuXz9AxYpeJk6MDLJQBg0KMGZMAF2HJUtk3n/fyrPPqrRs\n6eXjj+389ZeF996Lp3ZtjYQEC++/72LjRhvjxrl58UV/cBE05UhNUZnwsjn3cmCVmaCb2fXRwsVz\ndF2ncePGQWWxtPQikiY/lChRgt27dwerAdeqVYvTp0/fd/vSaw99coRpZnJERsQ6UvI+o6OjM60d\nSRMn7nWLpSgwfrzG008LDYZx43zs22ewfLmTnTvtidKGr16VadQoJ6+9Fk/fvm4+/zyGn36yMGdO\nFFOm2OnWzce33/o4csTK7NlWRoyw0rWryvDhAVat8nPtmsTcuQoff6wk4vGGW1ycxJkzEmfOWLjb\nEMqSxWDxYh/PP68jSQKU1q8XIPPIIwarV/upUEFn9myFadOsLFrko3ZtHY9HpVs3exBw33wzwLRp\n4gClWDEncXEylSsbwbpkuq6TPbuNy5ez8NRTPo4etTNokI+33rLx+usqefPCxo1emjVz0KCBnZ07\nvXz0kYUpU+zUrRvA7bbz4YcWZBmefVbn0Ud1PB7pbyA18HoF2AYCBoGAKPvj80GuXAZnz1qIi0vc\nV2XL6rz4osr58zpLlzrYulXsKEytCcOAb7+18M47NrJnF7Hnb76R6d8/irffDtC9ux9Jktm82cqQ\nIU5q1PCzY8df5MplwTAsSJIlCLiqqgZ1Z80YqLlLMz9j8tnDD6yS1m8zx25mi908CEF0r9ebaGHI\nyDOuX78e5OTmyZOH69evZ0r70mv/KtBNr6WVqZYZA8/0umVZztQCmS1aBChUKJ4uXaKoWVPn44/9\nvPqqLQi4RYtq/P67+PfcuZEsWBBB374qb7zhZ+lSP2fP+pg1y0blyhG8+KKHmTPjiY62smGDnW7d\nbMTGStSrJ2hJp097yJFDgOSRIxY2b5Y5ftzC2bMSV64kXzwkyeDRRw2qVtXp2FGlShUdM8JiGHDi\nhMSKFQqrVik8+aTO++/7qV9f58wZieeft2Ozwc6dXgoX1rhzx0evXi6++SbkwZuqXpMm+Vm4UKFL\nFy1YEVk8w+Dxx3UuX5aJjAxQuLCKpiWgKDIbNqg0bapTq5bM+vXQsqWDbNki+O9/b9CypcYnn9hZ\nulTmiSd0oqLg9m24ckXm9m2R5JCQAPHxEg6HoMolJIiwgUjyAFkWB4p162rUqKFx5ozE9Ok2TpwQ\noaD8+XU2bvRRsqSI7W7YIDN7tsLt2xLvvhvgyhWJXr3stG6tcvSoh5w5hbc8dKiL48ctzJ/vp2ZN\nA8OICnq1JtiaYzV8/AKJMitTAuLwIpLhQGx+3vSS72ceZDaAZ1Y2Wrjd7zve0zP/DeEFM84THR2d\nJsgl9T5TCvDfqy6v2Q6TSREZGXlPWTgplXEPz7BzOBz4fHZ69rRy9arE6NF+mjQJrfgFCugpguIb\nbwTo3z9AnjyCEjZnjsLChQrly2s0aOCjXj0PgYDB7t1Oduywc+CAleLFdcqU0SlWzODxx1UKFnRT\nuLBOtmypJ534/XDtmsTlyxI//mhh/34L+/fLREYatGmj0bGjSrFiBqdOSUyaZGXHDpnhwwP07BlA\nVf1cv+6nX7/sbNsWil2vXeuldWuB4L/95uY//3Fy5IiXfPkSD13DgLx5nZw542HOHIXoaIlatQK8\n9ZadXbuisdmEx3funEy1aiJOXLGixhdfeImMlNi1y8LBgzIHD1o4d07i5k0JiwWcTqGJGwhARAQU\nLGhQooROiRLib12HTZtk1q0L+TAWi8Hy5R6aNRP/j42FxYsV5s5VKFDAoHdvFV0X1Lz8+Q0mTvRT\nqpSgsi1eLDN6tI2uXVXeeiuQjFlixtDNqsRmWMz8kzQzLLX6f0nBWtf1YIqv6M8Qc+BexG8ym/Mb\nruNw+vRp5s2bly6RmpTCC7t27SJv3rxcvXqV2rVr/6PhhX8V6KaWDZZW2m5Su5eMMk3T8Hg8BAIB\nbDYbmqbdM3UtHHRTEkAHMxcepk2zMm+elRkzYoiLs9GzZ4j7WrWqxr59yd/x9dcDDBggKGbx8bB9\nu8ymTTJbt8oUKKDTqJFK/fo+ihXzc/iwhbNnZc6dUzh3TvBrr1yxkDOnoElZrSL0ITLWBNhGR4eo\nVOXL6zz3nE7VqkKRLCYGvvpKZu1ahePHLfTtG6BXLxWXS8Xr9XL8uEKvXtmC7AOAL77w0qKFANyt\nW7189ZWMqsLUqYFk7wZQrZqd6dMD5MhhUKeOg9OnPbz6qo3ISI0PPohGUeRgRt6kSTYmTAgBQvXq\nAXr18lOtmsEjj0iARHx86HDM44FDhyzs2iX6y6xEbFqePDrjxsXy4ovgcIiD0vPnJebMUVi5Uvm7\nfJLK6dMWZsxQeOQRg6FDA9SvL0IvZ85IvPGGDY8HPvpIKJuFm7n4qqqaZgJOSiCsaVq6gFhV1UQC\nNUkzzDIifpPZ9LNwAfODBw/yzTffMHXq1LtelxR0hw4dSs6cORk2bBgTJkwgJibm/x2kZdTS0tS9\nW9puUstIRllSLq/T6SQQCKSa1ZYeM1OR7XZ7onaHl8kBMTm8Xi979zoZNCiKMmV0RowIsG6diI2a\n1ratyuefJ19AunZVadVKpUYNHUURh0IHD4owwubNMh4PPPWUyhNP+ChVyqBkSYMiRfwYhsa1axK6\nrqDrMrouYxhi4uXNKwDXXM+uX4djxywcPWrh8GGZQ4cs1Kyp0aqVRqNGGk6nAJGbNzWmTs3GypW2\nRHHkdeu8tGolALdbN5XevQM0buzg0CEPefOm3H/du9uoVUujc2eN1q3t1KoVoFWrOJo2zUaLFhoj\nRujJmBh790oMG2ZLVeErNStTRmfAgADNm/tQVU9QFjQmxhJcXH7+2ULnzirt26ts2SIzd66VChV0\nBg0K8Nxz4vu8dEnigw+sbNokM2xYgN69VcJ9gnCGiFnYMqNb4nAObPifcCA2f2a1WoN1B83nhAN1\n0sO61MRvzKrHmQW6pvOhKArffvstJ0+eZNSoUWlek1R3YezYsTRv3py2bdty8eLF/0cZu1dLSVNX\nVVXcbje6rqeZtpvU0pNRltQDDefyppTVlhHzer1BgXUT/M2BbQ548/emaIzXC598ojB1qpUmTTR6\n9w7Qt6+NH34QM9flMqhYUU+msQCQLZtB8+YaLVqo1KqlY7UaBAIqJ0/6OX3azu+/Ozh9WvBRL1wQ\nJ/W5chm4XDpOp4HDoeN0CvCIjpa5fdvC7dsWbtwQVKkKFXSeflqnYkWd2rU1smQJ7TwuXQqwdGkk\nixc70TSCjAyAr78W+rkgkhf27vXSoIGdzp2Ft5iaffSRwunTEjNn+jl2TKVFi0j274/DYrHRqpWD\nMmV0pkzxk1Y4UFXhzBm4dMlAknSsVo0sWVQKFFCJigpl1kmSFCyU6Pc72brVwbp1CgcOWKhbV6Nl\nS42yZUXixdKlCvXrawwYIMotgViUJk+2smqVQo8eKv37B0hKvjG36Lqu33N6e2pmArHf78fvT5w9\naIIopF6DMCUgNsE4HLD/v/auPS6Ken0/u7O7XAQRUMQLBiqKqIDc6XgJC7NM00NHkz5i3jJ/x7ym\npKViHYWTGamZeSzTjqadLmYnTcVUyo5AIIpHPCioqAQo4oU77O78/pi+w+ywu+xlZsV1ns+Hj7nG\nfmd2Z955v8/7vM+rUqkMupCZA66B+VdffYXq6mrMnz/f4vd7ULCroFtTUwOKotgCgSltu3wY6ygj\nGUddXR3kcrleLS+3q80ckK1jQ0MD5HI5OnbsqFPQIFk1kczoe4jcucN0pH3+uQKzZqkxerQGsbG6\neuEnntDgxAn9GZ2zM42xYxvw7LONGDaMQteuuv9fYyNQVCRDVRVTXKqvZ/6sq5OBprVwd9eiUyc1\n3N3V6NRJDS8vLRQKig1SFEXh3j0tjhzR4ocfHJGe7gBHR6YVlyAmRoM332zGc88xx+3hQaOoqB5L\nlihRWSnD7t1NrTJVLs6ckWH6dBVOnLgFhUKB1NSOyMujsG9fIxobgaVLmSaNpUubMXmy2mjw5YNs\n25ldlRq//UYhM1OFrCwH5OcrMGyYBvHxajzxBOPO9sUXDI3y4otqnYkad+4AH3ygxPbtCkyapMaS\nJQzXzl+LeIGQJEDogg+pcTQ3N7PXFAC9GTFXA8ylE7jhg1AP5HoltB/JerlcM185YQq4LcWffPIJ\nPDw8MHXqVEE/E1vALoIu2cZUV1ezQcnJycmii9RQc4OpmbNGozGrSYObNatUKtYljbQdk2xKX6eX\nIVy/LsPbbytx9CizXfXxoTFxou5DJDBQiw4daNbjVh969NAiKIiM7WF+evWijQY97nmp1TQuX6bx\n3/8yo4n+8x8lcnOVcHQEGhpkrSRp337bgE8+UeDgQeZBNno006K8dq0SBw5QOHq0AcY2EMz04noE\nBHgiO7sWPXtSrHWkkxON7dsZT4TcXDnS0hQ4dozCE09oEB2tRXg4wzt36cJobsn4nfp6hqsuLpaj\nuFiGoiLg7FkZCgooDBpEY+hQDWJi1AgLa0JurgxffqnCkSMOiIpqxosvNuHZZzXo0IH6YwwOw+9+\n+KESY8dq8MYbzXoNaoi5EqErxGhzJWuQa93YGoaoibYCMVFXkGuWvM63hOTbQRoKxNzutrS0NAQF\nBWH8+PGCfzZiwy6CblNTE6qqqiCXy9ntvqUw1txgSuasbyKwIfD5ZrlcDrVajcbGRp3hgXK5HCqV\nig30pj5M8vNlWLFChStXGGnSwIFavPuuEl9+qZudT5xYjwsXlDh3ru2tK0XR6NyZGW/eqRONTp0Y\nigJgCnM1NTL2z2vXZFAoSOssrdMGS+DsrMVHH91Hbq4Kmza1fG8bNjTh5ZfVWL5ciePHKRw82IA/\npqu0Ar8Fee5cV4SG0vi//2NoiMZGYOZMFS5elGPr1kaEhDDHe+sWcPQohexsOXJz5SgvZxQLABNw\nFQpGueDlxcw88/VVo1evJgwaJENMDKPNPX6cwk8/MT89emiRkKBBfHwzPD1bgtPVqzR27nTCnj3O\nGDGiGcuXN6JfP1mr4KIv8xQzuzXXDY//PvxATGoOhA/WarUstcDPiEmQJa/zPRf0BWJuo0VycjLG\njRuHESNGWP+h2Bh2EXRJYGxubgZN01YFXe4MNb4DmCkZhylBl9+UoY+3bWhoYGdVkd/RaBifVO52\nnWsGYgjHjsnxt78pUVIiw6RJGkyapEZJCY3Jk1t/TlFRDO+and12C6w1eOedJvTuTWPHDsYknSA4\nuBk7dlRBJpNj7lw3qFQyfPppPbp00d/Sqi8r/PlnOebPVyE3t4E18qZppgNu9WolQkO1SEzUYORI\nDfhqJppm1AoqFdhiVnNzM6qrG3D1qgpFRU7Iy6Nw7BjTNjxihAZPPqnFyJEa+PnROu+TkSHHxx8r\n8OuvFCZPVmPGjEb4+jbrBCnyHQJgDb/Fym5JvcHSYlxbIJQIoci4k1j4GbG+sEO0wtxAzA3G5P/Z\ntm0biouLMX36dAwfPlzQc7AF7CLocr9svsbVXJDgTaq45hqh0zQzEVhfKzG/KYNoLLm8LZEEGcp0\n+NkFkfiQm1ehUBjsuS8slGH3bgp79lDw8NAiIUGNp56isWuXEhs36s94nn1WDS8v4O5dZrT4//5n\nfjAYOJDpzurenUZdnQy7dilw5ozu+wwbpsGGDU3o04fGli0U3n1XiVdeacTChXUAWirt3AcNKWLx\nMzaaZsYDvfyyGomJuobeDQ3A7t0KfPMNhdOn5fD316JfPxq+vjQcHRkpnEzGFPUqK4GKCi1KSuQo\nLmb0tYMGaREUpEVsLFMc5Ne1qquBPXuYjj65HHj1VTUmTVJDny8+qRE0NjbqXAP6HqrWBEhSL+A7\nvwkJQ2twbRy5PwB0eF0CQ1pi7r3xzjvvICMjA6WlpfDy8kJcXBy2bt3a5jH6+vqyOn6lUons7GwB\nPwHTYVdB15gJuSkgTmU0TcPV1dXii7Oqqkon6PJ5W8IXcyVg5vK2BMakQPwbl6xBUSpkZTlhzx6G\nK338cSYAx8RokJfH+Mh+/73xc/fxYVQJHTsyY4QcHBi3M5WKsXC8fl2JgoKWEfD6IJPReP/9ZkyY\noIaTE/DPfyrw4YcK9O5NY/36JvTrp7slJeYu5KEItPj0kocNCVB5eTJMmOCIY8ca0Lu3/kv8/n3g\nwgWmy+7aNTkaGhgqQqsF3NzUcHNrQteucvTurUBgIN0qKyaorwfS0xlvjKNHKcTGajB7thpDh7aW\np3HPh8jAuN+5vgDFzYjNCcRCSM1MgbkZtCmBmM8RkwSDfE4TJ07E3r17UVlZidLSUowcObLN4/Tz\n80Nubi48+HOlbAy7CLpAi77VErkWd7uvVCqtam4AgDt37rBeC01NTTpqB+6WC2jR2xKHLiG2ldxA\nrFar2YIGAFbn2MKTybB/P4XduxXIyZEjOFiL6GgtYmK0GDxYi6Iixq3rxx8plu+0FAoFjVmz1Hjq\nKQ0iIrRwcQGOH5dj/34FDhygMGyYBvPmqREVpX84KKESuBy4sQC1c6cTNm1yxHffNcDf37RjJJI8\nAEZ3OdxAm55OYcgQLf78Zw3GjmV2BkKsQWBJIBZTakYgdAatL3kglBtN08jJyYGXlxfy8/OxatUq\nFBUVmUUl+vn5IScnB56enlYdp7Wwm6BriaeuPgcwcmNbE3Tv3r0LZ2dntiBGpGX6eFugZUij0CBr\naLVaNgMxduPW1lI4fVqBzExGa5qTI0e3bjSio7WIjtagb18aPXvS8PCgceuWDNevy/D770B9fRM0\nGhoKhRJyOQWtFujYEejWjelM8/ZmMmHSLJGbSyE3V47sbDkCA7UYN06D8eM1BkfNcDux2iow8QPU\nzp0qrF3bAYsW1WHq1Ca4uOjPFNsqYjU2Mh4Uv/wix88/M9REWJgW8fGmBVqyBin4WSsDMxaIAbAJ\nhFAPcj7E5oeBFvkl2cm8/vrrOHz4MG7duoWIiAhERkZi5cqVJjsL9u7dG25ubqAoCrNnz8asWbME\nP2ZTYFdB11RPXWMeDJbqbAlIIQ0A61xmLm9rLbgBxNjNzb9xSUbcUtxRoLBQicxMCtnZFK5elaG0\nVIaKChnc3Wl0766FtzcTcJhAxvChZKmqKhnKylp+nJ0Ze8ewMOYnMlLTSp/KPz4htscFBcxU4pwc\nCs8804jhwxvRr18TfH0BZ+eWIhZFKaDROOHOHQrFxbI/OGwZLlyQIz9fjoAALYYN02LYMA1iYrRm\naXxtKQMj3yEpRAnJEdsig+YrLBQKBQ4cOIB169ZhzZo1CAsLQ15eHnJzc7FgwQKTx/WQseu3bt1C\nXFwcNm3ahGHDhgl+/G3BboKuKZ66pngwmKuz5b434W0BoEOHDuyFT2Apb2vOMXCDlIODg9k3t74t\nHqCrmFCraVy/3ozycgUqKx1x5w4FmobOD8A0NnTrxvx4exvmRPXB3C24KSguluHAAQonTlC4dIkx\n5VEqaahUZE4bM6zSw0MLX18agYHE1IbGkCFaoxphQ7CVDMxQI0VbD1ZTA7EhDlpocPXDTk5OuH//\nPpYuXQq5XI4PPvjAIr9sfVi9ejVcXFywePFiQd7PHNhV0NVoNAaVA6S5gUjKDOkTzdHZArpdauRC\nqa2tZTXDhHcUmrflg893ChGkAN2iByliEYqEW8CydKy2vvVsFaRqaxuhVqsgkzlAo5GhY0em5dfY\nA8cUiR4B2YKL+b2TeoQ5GTTJgEnHGOFODQXiB5XdnjhxAsnJyVi+fDnGjx9vdQuxRqOBq6sramtr\nMWrUKKxatQqjRo0S8CxMg9346QL6PXVJxtTc3AxnZ+c2eTRjfrZ86OtS02g0rPcD2eoBgEKhgEKh\nYIOYUIHEHL7TEpD3Iq3VKpWKpUzIDUseeHzFhDmBmJtJKRQKuLi4iP5wcnPrwHs4yQHI2Qcyv8pO\nJGqA8UDMDVKWjn1qC1x+2Nzvnci0uMfF3eGQ74E0MWi12lYDVIUEl3pxcXFBfX09kpKScPv2bRw8\neBBdDHXFmIGKigpMmDCBXe+ll156IAEXsKNMl+upS/hYrgetqXygMZ0tAb9LzRhvS7b4XDUBTdM6\nGaI52RP3OA0NghQKpH+eu90zdJx86RoZImhI0sUFeTCSphSxMylrHk78QEyuO5L5A8y1SKSBYm7B\nxeSHtVotm1CQZKGtjNhc8B8cCoUCWVlZWLZsGebPn4+EhARRPr8HDbvKdAlIsLVkTA5XjM3/wvkF\nOKJwINkPAJ1A6Orqqvei4QYnfvbU1nadBELS9SNWRshVPpiSrXEbNLjHyg1OpAmAe7OSzMrR0VE0\nUxcSpITIoMn1Qegj/hoAWKmgWq22OPM3dC5CtPC2tYYx7tZQRmxuIObSIi4uLmhqasKqVatw8eJF\n7Nu3Dz169BD83NoL7CrTra+vZ53GzDUi54I/gUIfbyuk3tbUBgcAbODiukIJCSFlTYben3DDzc0t\nRuT6HjjWrmsrLlLf52Xqd2pqILYFP8ynRUylEgzpaw1RMHxa5OzZs1i8eDGmTZuGmTNninJu7Ql2\nE3RrampQW1sL4jNrqoxEH+7evQsXFxcoFAqdAhzJLriibZIRCr01NtTgIJPJoFQqBWsR5a7HzQht\ncWOTz8sUxYQ5FIwtqBfA/G2+JYHY1GkR1kAMZYKhQAwwu4Vjx46hf//+2LdvH7KysrB161b07t1b\niNNp97CboEu2O7W1tQb9cE3FvXv32EYJU3hbMavs3OISfx6WUPwwl0qwRUbY1o1tjDfln6s+lYrY\nfKe+Srs1TQ58JQHXZUutVotqgmNpdmsOiFqESBk1Gg0SExORl5eHmpoaREdHIyoqCmvXrjX7c2xo\naMCIESPYa+v5559HSkoKqqqqMGnSJJSUlMD3AUyHMAa74XTJdtQc9YE+kBu+trYWjo6OFvO21oLQ\nFUCL5pfAUNXZEn7YlCYKa0EeHKZy0IZ4Uz6fyM0SSYDidgCKyQ8rlUpBvnt9XDgpLGo0GnYnUF1d\nLWiTAz+7JS3qQoMU5ADA5Q/Xn82bN6OhoQEZGRnw9PREbm4uLl++bNH6jo6OOH78OJydnaFWqzF0\n6FCcPHkS33//PeLi4rB06VL8/e9/R2pqqk3noBmD3WS6Wq22lT+tOeDytjRNs4UdfbwtRTHGz2Jk\nBdZKwEzdwmo0zNBAsakEMbfGJBsmLeDkgUvkUEJSMLbihw1t8y3xXzDlXMTMbsm5kAf6lStXMG/e\nPIwcORJJSUmCXw91dXUYMWIEduzYgfj4eGRkZKBr164oLy/HE088YdOJv8ZgV5ku+dPc5wi/cYJU\nnkkWQtM0K2kSU3dJuoqsyaANqQhIICY3AtAy44pU2YXkh4U4l7ZAgjpN02zhtC3FhLmBmH8uYmaE\nJBDydzYAWGqFe+3xz1WfkoBk/OS+sFV2S+4XYjr+6aefYu/evdi8eTOGDBki+HqhoaEoLi7GnDlz\nMHDgQFRUVKDrHz3mXbt2RUVFhaBrWgO7CboEfFWBMZCtD+HmCG9LURRbLeY2NxAXfKHB3X7ru+Gs\nBdmqE/qBZNDcQh25Ya3lh7nNB2KcC2BcYWEsOBHVhKFz5SsmuK3IYp6LpbPQ9J2rPkkXAFYrTnaB\nYtEv/Ez9999/x7x58xASEoLjx49bVWsxBLlcjjNnzuDevXt4+umncfz4cZ1/F0IJIyTsJuiak+ly\n9bYODg5wc3Njb0wAOhV1YoVI6AsSUNoq6JgCbgFLTAmYoU4vY11J5vLDYnfGEfC7l0x5KLQVnPR1\nmpHvWyz9MCBOUCcFOC4X3tjYiMbGRva7I2taqg7RB36mLpPJsGfPHmzbtg1paWmIiYkRPfC5ublh\nzJgxyM3NZWkFb29vlJWVwcsUGzgbwW6CLoGxTJdkFaTDimx7uUUyciMQrS//RjBW0DGVR7RVAYt7\nU5tCi+i7YY0Vr/j8sFDFJX0Qmh/Wd67crJOcA7HnFKrBgawlphaagF/E4urOLSvZ3BwAABqsSURB\nVGlvNgSSjBDK4tatW1i0aBF69uzJFrnEQmVlJRQKBTp16oT6+nqkp6dj1apVGDduHHbu3ImkpCTs\n3LmzXQ2wtJtCGmDcU5evt+X723J5KHMLJcaKHPwMkZt1ilXAEqrl1dB7c/lhtZoZ/ihG8YqsZ4vp\nB/qCutANDoDtJG3mUhbcQMyVr5Eagb6dDt/EnKIofP/993j//feRmpqKkSNHip7dnjt3DlOnTmVN\nfKZMmYIlS5agqqoKEydOxLVr19qdZMzugi7fU5fL2xJTGmM+CUJlHfpmmQFgmxtIg4PQ3V7WWjua\nug43qPMbHITUDwtt76jvXMwJ6oZ0tW0V6sR8EHLBzW6t/cyMPXTIDvHevXtwc3ODVqvFkiVL4Ojo\niLS0NIv9qB8F2BW9wK3StsXbAmCzATG2xWT7SlEUW8wgEiBuhiBUhmhM1ysUbMUP23L7ba4bmCUe\nEzKZ7A+TdEo0rwxrCnKGoO9cyWdGmja+/vprrF27Fo6OjggJCcHzzz+PmzdvWhR0r1+/jsTERNy8\neRMymQyvvPIK5s2b164bHSyBXWW6ZLt79+5dNgiQzIXL85ICllwuF01vy70JDGVQptISxm5SrVbL\nzocTM4MiWael7c6mbtXJA0ls+oUrAxPToY1wwuT9hSxeEdhiRwC0UHSkQ66mpgZvvvkmamtrMWvW\nLFy+fBk5OTkYPXq0RRxqeXk5ysvLERISgpqaGoSFheG7777DZ599hs6dO7ONDnfu3Gk3jQ6WwK6C\nbn19Paqrq6HRaFjvBEO8rVhqAUBXAmZuUDfmQ8ANxADaDOpCQMysk+8vQQxwxOKHAdsFKH0zxMTw\nmBA6uzW0Dr/t+ddff8Vbb72FRYsWYdKkSaKsO378eMydOxdz585tt40OlsCugm51dTXbwkvGsBO6\ngWSDYqsFhJaAGcoQAebcVCoVO6FCaH7YFgY4/KyTdAEKzQ9zHx5ic6qmTsglNBiXHza1UGfL7Jbr\np9zQ0IC3334bJSUl2LJlC7p16ybKulevXsWIESPw3//+F7169cKdO3cAMJ+Zh4cH+/eHEXbF6To4\nOLBcU3V1tQ7hr1QqH4hY31pweTXuDU1E5lyzaXNoCWOwxeQDwLBOVUh+GNANHGJyqtyCnIuLS5vX\nAKlBqFQqnfdpS6an1WrZh4etslulUonc3FwsWbIEr7zyCtLS0kSzYKypqUF8fDw2bNjAJk8E7a3R\nwRLYVdB99dVXUVZWhtDQULi4uODcuXNISUlhzTCam5utzpi4MFZYEhJ8frhjx46tLjxrAxNZxxYF\nLHMr+Zbqh2UyGRobG0W1RQSEfUgZK9QRjphsTpubm1uZwgvxffEbUNRqNd555x2cPn0ae/fuha+v\nr9VrGEJzczPi4+MxZcoUlhduz40OlsCu6AWapvGf//wHr732Gm7cuIHhw4ejtLQU/v7+iIiIQHR0\nNPr06QOgZbwP90ZVKBQmX7hctQCRTYkBSwdOtlW44o/P4fLQYulHAfHMuPn8sNj+w2RNsQty/HXI\nwxCAwSIs93o2JzPUR8EUFBRg4cKFmDRpEv7617+KajBO0zSmTp0KT09PpKWlsa8vXboUnp6eSEpK\nQmpqKu7evSsV0toTDh8+jMLCQsyZM4f17iwsLMSpU6eQmZmJgoICODg4IDQ0FBEREYiMjESnTp30\nXrjcwERgq3ZXMdYxpJYgvDcJHLYyLxcD3HVIAUtofhiwHafK7ypsS8liaaFOo9GdKqzVarFp0yYc\nPXoUH3/8Mfr37y/K+XFx8uRJDB8+HEFBQey1npKSgsjIyHbb6GAJ7C7otgWaplFTU4OcnBycOnUK\nWVlZqKioQK9evRAeHo6oqCgMHDiQnd/FvXABJvNUKpXsyB4xjs+W2RPhIPWdrxDj1R9kNmjII8IU\ndYixFm5bKQaEWMeUQEyoN/JwLyoqwoIFC/D000/j9ddfF+0B+ajikQu6+qDValFSUsJmw2fPngVN\n0wgKCkJ4eDgcHR1RUlKCxMRE1q1JDFkTl0sTSz/c1jrm0hLt4Xy4WZq565jT6kuyaEL1iLXVNie7\nNReGZHonT57E3r174ezsjLNnz2Lbtm2IioqyeJ3p06fjwIED8PLywrlz5wDA7pocLIUUdPWAcFs/\n/PADVq9ejRs3buBPf/oTaJpGZGQkoqKiEBISwsqbSIuvPj7NFPD7/sWw3bNmHWNNHPpoGFu1vIq1\nDj8wkfMFmO+YOM+JIdOzhayNn0UrlUqcOXMG69evR2VlJerr61FQUIA5c+Zg/fr1Fq3xyy+/wMXF\nBYmJiWzQXbp0qV01OVgKKegawbp166BWq7Fw4UI4ODigoqICmZmZyMzMRE5ODurr6xEQEMDSEn5+\nfjq6y7aKdA9i6y1UI4Ux7bBWqxV1rhdgm+m4gG4hk/gtC80PAy3ZrdhZNN+bQSaTYffu3dixYwc+\n+OADNrttbGzEvXv3rFIKXL16FWPHjmWDbkBAgF01OVgKKehaAbVajfPnz7O0xMWLF9GhQweEhYUh\nMjIS4eHhcHV11ZsdAkzgoCjxRv8AthXRk8YQiqLY4GQJLWEMYo8AItCnU9V3LNbww2QdW2W3/PE5\nFRUVWLhwIXr37o21a9eaPeKqLfCDrru7u101OVgKKegKCJqmce/ePWRnZ7NFuqqqKvj5+bGSNXd3\ndxQUFODxxx8H0GKwbaz7yNJjsdUW35C211xaoq11bGHxCFieRZtrBclXDIiZ3XJ9M+RyOfbt24eN\nGzfi3XffxYgRI0T5LI0FXQDw8PBAVVWV4Ou2d0hlSQEhk8nQqVMnjBo1CqNGjQLAXPDFxcXIyMjA\nwoULkZ+fj9jYWPzyyy8sLdG5c2d2UoFGo2l1g5qTHerrjBJ7621oioO+iQ1c+sXUJg5DnWtCw9os\n2lBjA5cfbmpq0pHpKZVKURUQ/PE5d+7cweLFi+Hm5oajR4+y065tAXtrcrAUUtAVGXK5HP7+/vj2\n22/RvXt37N27F15eXsjNzUVmZiaWLVuG0tJSeHt7s7pholPkzrgypUhHghNNizdAE7Cu8Mf1EwZa\n+w/wu8vIvzk4OIjGeQO6BjVC2nxyA7FKpYJGo0FtbS2rfiEcq5D8MNB6fI5cLsfhw4eRkpKC1atX\n45lnnrF5O217nuZgSzzU9MKSJUvwww8/QKVSoU+fPvjss89YH8+UlBRs374dFEVh48aNbOb5oEAy\nWH2gaRo3btxgi3SnT59GU1MTBg0ahPDwcERHR6Nnz546W1cuV0qGTgptxK7vOG2xxecW/oCW4hyf\nlhCiD99WTRttcbdC8MME3PE5Dg4OqK6uxrJly9Dc3IyNGzfCw8NDlHPkYvLkycjIyEBlZSW6du2K\nt99+G88//7xdNTlYioc66Kanp+PJJ5+EXC7HG2+8AQBITU1FQUEBEhIS8Ntvv6G0tBRPPfUULl68\nKGoLo9BoampCfn4+G4iLi4vRqVMnhIWFISoqCmFhYXB0dMS1a9fQpUuXVgUrazMlPmxVkDPERetz\n4wKgNxCbug5/6y1W5mcJd2suPwzoH5/zyy+/YMWKFVi6dCleeOGFh94sxh7wUAddLvbt24dvvvkG\nu3btQkpKCuRyOZKSkgAAo0ePRnJyMqKjox/wUVoOmqZx+/ZtZGVl4dSpU8jIyMClS5fg7OyM+fPn\nIyYmBn379gUAnaBkbZHOViY4gHkFrLZsEY2pJfgGNWI+QIRUJhjSD5PvlXDG7u7uaGpqQnJyMn7/\n/Xds2bIFXbt2FeScDh06hAULFkCj0WDmzJnsPSbBdNgNp7t9+3ZMnjwZAPD777/rBNiePXuitLT0\nQR2aIJDJZOjcuTPGjBkDHx8ffPzxx3j99dcxevRo5ObmYtu2bQZ9JYgVoLmGKFwTHDELctwMzZyx\nOYZsEYnhDX9sDukqa2xsFNUWEbBsVHxb4PPDQIvuVq1Wg6IorF+/Hp9//jkrXZw2bZpg35tGo8Hc\nuXNx9OhR9OjRAxERERg3bhwGDBggyPs/Kmj3QTcuLg7l5eWtXl+7di3Gjh0LAFizZg1UKhUSEhIM\nvk9bN9dXX32F5ORk/O9//8Nvv/2G0NBQAIzsZcCAAQgICAAAxMTE4KOPPrL0dATB4MGDkZ+fj+7d\nuwMAQkJCMGPGjFa+El988QUqKirg4+PDBuFBgwYZLNKR7JBs8W2hheVu8a31szCkHiBBmNAjJCvk\nFq+EdB6zhe4W0B2f06FDB3bNESNGYPz48bh69Sr+8Y9/oKqqCjNmzLB6vezsbPTt25e1dnzxxRex\nf/9+KeiaiXYfdNPT043++44dO3Dw4EH89NNP7Gs9evTA9evX2b/fuHEDPXr0MPo+gwcPxr59+zB7\n9uxW/9a3b1/k5eWZeeTiQSaTsQGX/7qrqytiY2MRGxsLQNdX4ttvv8WqVatYX4mwsDBER0fD29ub\n3XJrNMw8L7lczjqOEYtEIWErGRhZi8sRAy3WntwBoca6B02BGNmtPuhr3MjPz8eiRYvw0ksvITU1\nVZS1S0tL4ePjw/69Z8+eyMrKEnwde0e7D7rGcOjQIaxbtw4ZGRlwdHRkXx83bhwSEhKwaNEilJaW\n4tKlS4iMjDT6XiSTtTfI5XL4+fnBz88PCQkJbCaWl5eHzMxMJCcno6SkBCqVCrdv30ZQUBDef/99\nVt7EpSWEKNLZkiM2FgQN0RL8czZFLWGrRhTuOVEUBVdXV6jVaqxbtw4///wzdu7cCX9/f1HWBdre\nLUowDQ910H3ttdfQ1NSEuLg4AC1b/8DAQEycOBGBgYFQKBT46KOPrLpgrly5giFDhsDNzQ1/+9vf\nMHToUKFOweaQyWRwdHRETEwMYmJiAACrV6/Gpk2bMHnyZDg7O2PKlCmoq6tDQEAAIiIidHwliDTN\nkk66B5EJmhIE25rWwG/i4AZikrFTlHijgPjnRLLbwsJCLFiwAM899xyOHDki6m4BaL2DvH79Onr2\n7CnqmvYIu1EvmAJT+OHY2FisX7+e5XSbmppQW1sLd3d3nD59GuPHj8f58+dbzW7iwhA/DLQ//TDA\nUDhBQUE6FW5jvhIRERGIiIiAq6srmyEaK9LxA4ZYLmrkuEkQFHoyhTHZmljOY4Duw8rJyQk0TWPr\n1q3Yv38/tmzZgkGDBgm6nrHj6N+/P3766Sd0794dkZGR2LNnj8TpmomHOtM1F23xw/pAJtQCQGho\nKPr06YNLly7pBFI+DPHDBQUF+PLLL1FQUNCu9MNkp8CFQqFAcHAwgoOD8eqrr7bylfj00091fCWi\noqIQEBAAuVyuU6TjNjbYOhMUEly1BCnMKRQKnenFlihE2jonflGupKQE8+bNw9ChQ3Hs2DHRipz6\noFAo8OGHH+Lpp5+GRqPBjBkzpIBrAR6poGsquMl/ZWUl3N3dQVEULl++jEuXLqF3795Gf98QP7x/\n/35MnjwZSqUSvr6+6Nu3L7Kzsx8K/bAxX4lTp05h9+7dOHfuHCiKQnBwMPz9/XHq1CkkJiYiNDQU\nNE2zE5qFdB0DxGvh5cOUwM6lJYwpRNo6Rm5DhYuLCwBg586d2LVrFzZs2ICIiAjhT9AEPPPMM3jm\nmWceyNr2Aino/oF9+/Zh3rx5qKysxJgxYzBkyBD8+OOPyMjIwKpVq9iRNlu3brW4ddHe9MPEV8Lf\n3x+JiYmsZG3lypV44403EBMTg7fffhteXl4IDw9HZGQkgoODQVGUjo7WUs8BS/S9lsLUMe58kx9C\nS5BA3JZaQl92W15ejvnz52PAgAE4duyYTtFYwsMHKej+gQkTJmDChAmtXo+Pj0d8fHyr103hh02B\nPVWEScC5efMmsrOzERgYqOMrcejQIaxdu1bHVyIyMhKPPfYY27TQVqsroN9J7UFmt8ZAqAV9I+T5\nagkiz9NoNKiqqoKvry++/vprfPTRR3jvvfcwdOhQQc7zYas52BukoGshLOGHLdEPc5GcnIxPPvkE\nXbp0AcDcIKNHjzb7OMSEk5MTdu/ezf5dJpPBx8cHPj4++Mtf/gKAKU6ePXsWWVlZWLduHYqLi+Hm\n5sYG4fDwcL2SNZIFNzc3sy287SG7NRd8tQTJbhsbG6FQKFBWVobRo0ejubkZHTt2RGJiIktTCIGH\nreZgb5CCrsjg8sOW6Ie5kMlkWLRoERYtWiTGodoMKpWKVUDMnTu3la/E5s2bcf/+ffj7+7Oew336\n9EFOTg769+/PmtOQzFhI83dA/KIcF9zxOSSwX7p0CT4+Pli0aBGUSiWys7Px8ccf6y14WgJ7rDk8\nTJCCrggwxA8LoR+2R4Uf11dizJgxAJhCUmFhIU6dOoX169fjp59+QpcuXfDcc8+xLc3u7u6tPHit\nndDMba0VuyjHH59z//591kAmPT0d7u7uAMDuEMSGvdUc2iukoCsCDPHDALB8+XIsX77c4vfetGkT\nPv/8c4SHh2P9+vV260dKURQCAwPh5eWF5cuX46233sLLL7/MdtJ98cUXKC8vR69evVr5SpCClTnG\n4LbObonZPDEYP3HiBJKTk7Fs2TJMmDDB6mAv1RzaL6Sg285g6GZZs2YN5syZg5UrVwIAVqxYgcWL\nF+PTTz816/0fNmu+zp07o7CwkH24jBw5EiNHjgRg2Fdi8ODBLC3RvXt3g0U6ohog8ixbZrfEw7eu\nrg4rVqzA7du3cfDgQZavtxYPouYgwTQ8Uh1p9gT+0D9ToNFo0L9/fx1rPnvqKOL7SmRmZqKkpASd\nO3dmOeTQ0FA4ODiwtAQx8yGNDkKbvxPwPXzlcjk7rmn+/PlISEiweVYZGxuL9957D2FhYQDAmv9n\nZ2ezhbSioiIp2xUYUqb7EKGsrAzdunUDwPDGgwcPNuv37d2aT5+vBE3TKC8vR2ZmJn7++We8//77\nqKurg5ubG/Lz8/HWW2/hxRdfZH0lrBmTYwjc8TnOzs5obGzEmjVrcPHiRezbt8/m2aSYNQcJbUPK\ndB8iJCYm4syZM5DJZPDz88PWrVvNmgjw9ddf4/Dhw9i2bRsAYNeuXcjKysKmTZvEOuR2B7Vajdmz\nZ+PHH3/ESy+9hFu3buHixYtwdnZGWFgYIiMjERERgY4dO7aaSGFukY4/PkehUODMmTNYvHgxpk2b\nhpkzZ0pyrEcQUqb7EOHzzz+36vfFyFp8fX3RsWNH1vQlOztb8DWEhEKhQHR0NNLS0tjx4235SkRG\nRmLAgAGQy+VskQ5AqwYObgDltia7uLhArVYjJSUFmZmZ2LVrF/r06fNAzl/Cg4cUdB8hiGHNJ5PJ\ncOLECZtMmBUKs2bN0vm7IV+JoqIidgJHfn4+KIpCSEgIa/DTpUsXvUU6MruMTMO4cOECFixYgD//\n+c84dOiQIBaMD9MkbAm6kOiFRwhiWPP5+fkhJycHnp6eAh5p+wNN06irq0Nubi4yMzORlZWF0tJS\neHt7s0U6jUaDiooKjB49Gnfv3kV4eDj8/f1RWVmJJUuW4IUXXtA78cMS2PMkbHuHlOk+QhDDmk8m\nk+Gpp54CRVGYPXt2qyzSXiCTydChQwcMHz4cw4cPBwDWV+LEiRNISkpCcXExhg8fjlOnTuGxxx5D\nZGQkAgMD0aVLFxw5cgQpKSm4fPkynJycrD4ebndaVFQUvvnmGwBSV9nDACnoPmIQ2prv119/Rbdu\n3XDr1i3ExcUhICAAw4YNE+z92zOIr0RRUREGDx6MY8eOoUOHDjh79iz++c9/YuHChTqNCGLMmgPs\nfxK2vUEKuhKsApGwdenSBRMmTEB2drZZQXf69Ok4cOAAvLy8WM1xVVUVJk2ahJKSEvj6+uJf//pX\nu+68W7lypQ5PS+gGPswNuLaahC3BtpCIHgkWo66uDtXV1QCA2tpaHDlyxGzt8LRp03Do0CGd11JT\nUxEXF4eLFy/iySefRGpqqmDHLAbEmk2Wnp6Oc+fOtfohAZdMwua6ukldZQ8BaAkSLMTly5fp4OBg\nOjg4mB44cCC9du1ai97nypUr9KBBg9i/9+/fny4vL6dpmqbLysro/v37C3K89oQff/yRDgwMpG/d\nuqXz+vnz5+ng4GC6sbGRvnz5Mt27d29aq9U+oKOUoA8SvSDBYvj5+eHMmTOCv29FRQXb9NG1a1dU\nVFQIvsbDDltNwpYgPCTJmIQHDr6PhLu7O+7cucP+u4eHB6qqqkx6L30c8cNg/i7h0YHE6Upod+ja\ntStbQCorK4OXl5fJv6uPIybm73l5ecjLy5MCroQHCinoSmh3GDduHHbu3AmAmYA7fvx4k3932LBh\nrPk3F9KGTkJ7gRR0JTxQTJ48GY8//jgKCwvh4+ODzz77DG+88QbS09PRr18/HDt2jO24sgabNm1C\ncHAwZsyYgbt37wpw5MJgxYoVCA4ORkhICJ588kkd5UFKSgr8/f0REBCAI0eOPMCjlCAkJE5Xgt2B\nzxHfvHmT5XNXrFiBsrIys83fxUJ1dTVcXV0BMA+Gs2fP4pNPPpHaee0Y0jcowe7h5eXFjkKfOXOm\nWU5o169fR2xsLAYOHIhBgwZh48aNAJgGjri4OPTr1w+jRo2yOHsmARcAampq0LlzZwCG23klPPyQ\ngq4Eu0dZWRn73+aavyuVSqSlpeH8+fPIzMzE5s2bceHCBUEbON5880306tULO3bswLJlywAw7bxc\nBzipndd+IAVdCXYFPke8fft2JCUlISgoCMHBwcjIyEBaWprJ7+ft7Y2QkBAAzIj0AQMGoLS0FN9/\n/z2mTp0KAJg6dSq+++47g+8RFxeHwYMHt/r597//DYBp5b127RqmTZuGBQsWGHwfSW9rH5CaIyTY\nFfbs2dPqtenTpwvy3levXkVeXh6ioqLMauAwdUhkQkICnn32WQBSO689Q8p0JUgwATU1NYiPj8eG\nDRt0eFgALF9sCS5dusT+9/79+zFkyBAAjGxu7969aGpqwpUrV3Dp0iVERkZafgIS2g2kTFeChDbQ\n3NyM+Ph4TJkyhdUMkwYOb29vsxs4uFi2bBkKCwtBURT69OmDLVu2AIDUzmvHkCRjEiQYAU3TmDp1\nKjw9PXW44KVLl8LT0xNJSUlITU3F3bt3270bmoT2ASnoSpBgBCdPnsTw4cMRFBTEZpopKSmIjIzE\nxIkTce3atYfC81dC+4EUdCVIkCDBhpAKaRIkSJBgQ0hBV4IECRJsiP8H4B0xW+4u8owAAAAASUVO\nRK5CYII=\n"
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Multi-variate root find"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can find the steady points in this system by doing a multi-variate root find on the RHS vector"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sol1 = optimize.root(f, [1., 1., 1.], jac=True)\n",
-      "print sol1.x\n",
-      "\n",
-      "sol2 = optimize.root(f, [10., 10., 10.], jac=True)\n",
-      "print sol2.x\n",
-      "\n",
-      "sol3 = optimize.root(f, [-10., -10., -10.], jac=True)\n",
-      "print sol3.x\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ 0.  0.  0.]\n",
-        "[  8.48528137   8.48528137  27.        ]\n",
-        "[ -8.48528137  -8.48528137  27.        ]\n"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig = pylab.figure()\n",
-      "ax = fig.gca(projection='3d')\n",
-      "ax.plot(H1.x, H1.y, H1.z)\n",
-      "\n",
-      "ax.scatter(sol1.x[0], sol1.x[1], sol1.x[2], marker=\"x\", color=\"r\")\n",
-      "ax.scatter(sol2.x[0], sol2.x[1], sol2.x[2], marker=\"x\", color=\"r\")\n",
-      "ax.scatter(sol3.x[0], sol3.x[1], sol3.x[2], marker=\"x\", color=\"r\")\n",
-      "\n",
-      "ax.set_xlabel(\"x\")\n",
-      "ax.set_ylabel(\"y\")\n",
-      "ax.set_zlabel(\"z\")\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 33,
-       "text": [
-        "<matplotlib.text.Text at 0x4070ed0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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88glYLPq2UFVh3jyR++/PlA5Wr7awerX+80svKRw9KvPRR3Zat1aoW1fhtdds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\nrohbkgAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Still system of ODEs"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is an example from Chemical Kinetics (see, ex. Byrne & Hindmarsh 1986, or the VODE source code)\n",
-      "\n",
-      "\\begin{equation}\n",
-      "\\frac{d}{dt} \\left (\n",
-      "   \\begin{array}{c} y_1 \\newline y_2 \\newline y_3 \\end{array}\n",
-      "  \\right ) =\n",
-      "%\n",
-      "\\left (\n",
-      " \\begin{array}{rrr}\n",
-      "  -0.04 y_1 & + 10^4 y_2 y_3  & \\newline\n",
-      "   0.04 y_1 & - 10^4 y_2 y_3 & -3\\times 10^7 y_2^2 \\newline\n",
-      "   &  & 3\\times 10^7 y_2^2 \n",
-      "\\end{array}\n",
-      "\\right )\n",
-      "\\end{equation}\n",
-      "\n",
-      "\\begin{equation}\n",
-      "{\\bf J} = \\left (\n",
-      "\\begin{array}{ccc}\n",
-      "  -0.04 & 10^4 y_3 & 10^4 y_2 \\newline\n",
-      "   0.04 & -10^4 y_3 - 6\\times 10^7 y_2 & -10^4 y_2 \\newline\n",
-      "   0 & 6\\times 10^7 y_2 & 0 \n",
-      "\\end{array}\n",
-      "\\right )\n",
-      "\\end{equation}\n",
-      "\n",
-      "start with $y_1(0) = 1, y_2(0) = y_3(0) = 0$.  Long term behavior is $y_1, y_2 \\rightarrow 0; y_3 \\rightarrow 1$\n",
-      "\n",
-      "Note, SciPy 0.13 has a set_solout() method that can store the intermediate solution for some (but not all) integrators.  We'll work around that here for older version compatibility"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def rhs(t, Y):\n",
-      "    \"\"\" RHS of the system -- using 0-based indexing \"\"\"\n",
-      "    y1 = Y[0]\n",
-      "    y2 = Y[1]\n",
-      "    y3 = Y[2]\n",
-      "\n",
-      "    dy1dt = -0.04*y1 + 1.e4*y2*y3\n",
-      "    dy2dt =  0.04*y1 - 1.e4*y2*y3 - 3.e7*y2**2\n",
-      "    dy3dt =                         3.e7*y2**2\n",
-      "\n",
-      "    return np.array([dy1dt, dy2dt, dy3dt])\n",
-      "\n",
-      "\n",
-      "def jac(t, Y):\n",
-      "    \"\"\" J_{i,j} = df_i/dy_j \"\"\"\n",
-      "\n",
-      "    y1 = Y[0]\n",
-      "    y2 = Y[1]\n",
-      "    y3 = Y[2]\n",
-      "\n",
-      "    df1dy1 = -0.04\n",
-      "    df1dy2 = 1.e4*y3\n",
-      "    df1dy3 = 1.e4*y2\n",
-      "\n",
-      "    df2dy1 = 0.04\n",
-      "    df2dy2 = -1.e4*y3 - 6.e7*y2\n",
-      "    df2dy3 = -1.e4*y2\n",
-      "\n",
-      "    df3dy1 = 0.0\n",
-      "    df3dy2 = 6.e7*y2\n",
-      "    df3dy3 = 0.0\n",
-      "\n",
-      "    return np.array([ [ df1dy1, df1dy2, df1dy3 ],\n",
-      "                      [ df2dy1, df2dy2, df2dy3 ],\n",
-      "                      [ df3dy1, df3dy2, df3dy3 ] ])\n",
-      "\n",
-      "\n",
-      "    \n",
-      "\n",
-      "def VODEIntegrate(Y0, dt, tmax):\n",
-      "    \"\"\" integrate using the VODE method, start with a timestep of dt and        \n",
-      "        increase by 10x after each call until we reach tmax.  This is           \n",
-      "        the behavior used in the DVODE Fortran source. \"\"\"\n",
-      "\n",
-      "    r = integrate.ode(rhs, jac).set_integrator(\"vode\", method=\"bdf\",\n",
-      "                                               with_jacobian=True,\n",
-      "                                               atol=1.e-10, rtol=1.e-10,\n",
-      "                                               nsteps = 15000, order=12) #, min_step=dt)   \n",
-      "\n",
-      "   \n",
-      "    t = 0.0\n",
-      "    r.set_initial_value(Y0, t)\n",
-      "\n",
-      "    tout = [t]\n",
-      "    y1out = [Y0[0]]\n",
-      "    y2out = [Y0[1]]\n",
-      "    y3out = [Y0[2]]\n",
-      "\n",
-      "    while r.successful() and r.t < tmax:\n",
-      "        r.integrate(r.t+dt)\n",
-      "\n",
-      "        tout.append(r.t)\n",
-      "        y1out.append(r.y[0])\n",
-      "        y2out.append(r.y[1])\n",
-      "        y3out.append(r.y[2])\n",
-      "\n",
-      "        dt = 10.0*dt\n",
-      "\n",
-      "    return np.array(tout), np.array(y1out), np.array(y2out), np.array(y3out)\n",
-      "\n",
-      "\n",
-      "\n",
-      "Y0 = np.array([1.0, 0.0, 0.0])\n",
-      "tmax = 4.e7\n",
-      "\n",
-      "# first call -- give an initial small timestep and have our integrate\n",
-      "# routine increase it by 10x each call to VODE to get to tmax\n",
-      "dt_init = 4.e-6\n",
-      "\n",
-      "t, y1, y2, y3 = VODEIntegrate(Y0, dt_init, tmax)\n",
-      "\n",
-      "ax = pylab.gca()\n",
-      "ax.set_xscale('log')\n",
-      "ax.set_yscale('log')\n",
-      "\n",
-      "pylab.plot(t, y1, label=r\"$y_1$\", color=\"r\")\n",
-      "pylab.plot(t, y2, label=r\"$y_2$\", color=\"g\")\n",
-      "pylab.plot(t, y3, label=r\"$y_3$\", color=\"b\")\n",
-      "\n",
-      "pylab.legend(loc=\"best\", frameon=False)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 34,
-       "text": [
-        "<matplotlib.legend.Legend at 0x439bed0>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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1LTdX72wsxthzp8HrEIKCgtizZw9ZWVlUrlyZadOmMWjQILZu3Xr3ttPBgwfz1ltv5Tu2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\nEAKQgiCEEOIvUhCEEEIA8P9yoVbtg171ngAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Fitting"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "general linear least squares"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "First we'll make some experimental data (a quadratic with random fashion).  We use the randn() function to provide Gaussian normalized errors."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def yExperiment2(a1, a2, a3, sigma, x):\n",
-      "    \"\"\" return the experimental data in a quadratic + random fashion,                              \n",
-      "        with a1, a2, a3 the coefficients of the quadratic and sigma is                             \n",
-      "        the error.  This will be poorly matched to a linear fit for                                \n",
-      "        a3 != 0 \"\"\"\n",
-      "\n",
-      "    N = len(x)\n",
-      "\n",
-      "    # randn gives samples from the \"standard normal\" distribution                                  \n",
-      "    r = numpy.random.randn(N)\n",
-      "\n",
-      "    y = a1 + a2*x + a3*x*x + sigma*r\n",
-      "\n",
-      "    return y\n",
-      "\n",
-      "N = 40\n",
-      "sigma = 5.0*np.ones(N)\n",
-      "\n",
-      "x = numpy.linspace(0, 100.0, N)\n",
-      "y = yExperiment2(2.0, 1.50, -0.02, sigma, x)\n",
-      "\n",
-      "pylab.scatter(x,y)\n",
-      "pylab.errorbar(x, y, yerr=sigma, fmt=None)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 35,
-       "text": [
-        "<Container object of 3 artists>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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XQlg1madvXqZmZ62dkSuEqP/khEXbI3v6QgizMLZHL3v65iVTNoUQQlSZ7OkL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\n13OzB6mMczIAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def resid(avec, x, y, sigma):\n",
-      "    \"\"\" the residual function -- this is what will be minimized by the\n",
-      "        scipy.optimize.leastsq() routine.  avec is the parameters we\n",
-      "        are optimizing -- they are packed in here, so we unpack to\n",
-      "        begin.  (x, y) are the data points \n",
-      "\n",
-      "        scipy.optimize.leastsq() minimizes:\n",
-      "\n",
-      "           x = arg min(sum(func(y)**2,axis=0))\n",
-      "                    y\n",
-      "\n",
-      "        so this should just be the distance from a point to the curve,\n",
-      "        and it will square it and sum over the points\n",
-      "        \"\"\"\n",
-      "\n",
-      "    a0, a1, a2 = avec\n",
-      "    \n",
-      "    return (y - (a0 + a1*x + a2*x**2))/sigma\n",
-      "\n",
-      "\n",
-      "# initial guesses\n",
-      "a0, a1, a2 = 1, 1, 1\n",
-      "\n",
-      "afit, flag = optimize.leastsq(resid, [a0, a1, a2], args=(x, y, sigma))\n",
-      "\n",
-      "print afit\n",
-      "\n",
-      "pylab.plot(x, afit[0] + afit[1]*x + afit[2]*x*x )\n",
-      "pylab.scatter(x,y)\n",
-      "pylab.errorbar(x, y, yerr=sigma, fmt=None)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ 1.87648037  1.5198079  -0.02022884]\n"
-       ]
-      },
-      {
-       "output_type": "pyout",
-       "prompt_number": 36,
-       "text": [
-        "<Container object of 3 artists>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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eMmXB5/lbvDy2DO7u0nbvKAot9MPCwpgwYQIGg4ExY8YQHBxcWIcqMtu2bSMj\n4zGUmggbK2MwdOPateqULn0ZcEKr1cJeaHm7n6aHhwcZGdeAs4APAOnpp6hRo4Z1XoCwW0opkpMT\ngDqmZQZDHRISEnLdZsWKb0lKmge0ByA5+W1WrvyG8Kc3mHr1OHUOIXzGNHbtgkqVICoqimnT3oYm\n8N57i5kw4RX2/bNPLvAWI4US+gaDgVdeeYWdO3fi5eVF69atCQoKomHDhoVxuCLj4uKCRnP763R6\nGeAqzs4puTbNVKhQgfnz5zF9egecnLpwCxg3bpTd/z2IoqfRaOjWrRd6/Sukp78DgJPTdzz22IRc\ntylb1h24mG3JxdvL7uwT6DadxxKn0aMHrFp1gfbtO5OY+Bo0gbfe+i9xcZdYuHCuXOAtRgqlTf/Q\noUPUrVsXHx8fXF1dGTJkCJs2bSqMQxWpoKAgypb9HReX12HIANzdn+TVVyfk2R7/2msv88sv21iy\npCcA7747t6jKFcXM+vUrePzxNMqVM36T3LhxTZ4nEHPmvEHp0jOBmQCULj2XmTNfv3sljXF2znbt\noGdPDamp/cjKmgpAcvI3LF36SaG8FmE9hRL6sbGx1KxZ0/Szt7c3sbGxhXGoIlWxYkWOHv2VMWMM\nALz33ossWPDOA7d79NFHGTFixF3L9Gf16PQ6dHqd6XZ5Or3O9DVaiHuVL1+eTZvWkJBgPHsPCAjI\nc/3WrVuzf/9uJk1KA+Dw4X08+uij962n0Rjv4FajxnXS01/N9hsFSAeD4qZQmnfy2xNFp9OZnmu1\nWmObuI2rVq0aS5d+wCezP+SFF8YWeD/SHiqKQpMmTVi0aD7vzV5w17eClJQUfvjhB8DYQaF69eqs\nW1cRX9+fSE8vCRmlcHcfyMsvv2St0sVter0evV5vsf0VSuh7eXkRE/Pv3XtiYmLw9va+b73soW/P\nHvam2EJY082bN2nVqgtxcRVhMjRo0JyfftpB06ZNOX68JYGBF/ln3QbmzDnLpEnG0D9//jwTJ86A\nxjBhQjAhITrc3NxyPYaM7rWce0+IZ8+ebd4OVSHIyMhQderUUdHR0SotLU01a9ZMRUZG3rVOIR26\nyKB7+PoLso0QOXnY91L29WfNmqNKlHhaQZZCh4JPVfv2gabfZ2Qohd8mNXy4UgaDUgkJCapatTrK\n2fkthQ5VqlR/1aNHv0KrVeTN3OwslDZ9FxcXPv74Y3r06IG/vz+DBw+WHitC2Ih//okjPb0N/7bX\ntyY29oIT9qXJAAAWY0lEQVTp9y4uwMAhnDsHr78Oe/fuIynJB4NhDgCpqWvYvTuceBnSa5cKrZ/+\nE088wRNPPFFYu7cb0vQjLMVS76Xu3Tuyfn0oSUnGQYOlSi3kscc63b1SiRQ2b4bOnSE+vh6QhvHC\nLkAmkIWTTNBvl2RE7kMoyH86CXdhKQ/7Xsrt/dqlfRfGj+/Hu+/6kAl07pzKxx8vvG/7ihXhxx+h\nQ4d6lCzZnbS0caQD7u5BBAUNply5cma/JlH05CYqQjgog8GAyzsuOd6MJftNWk6fhs6ds2jZciXb\n2oxiXokFTJkyEReXvM8Zz5w5Q0REBIP/HEzWzCyZX8pC5M5ZQogCyx7uefW4KXNVS8+ecKVXZ9SK\nfQ/c74YN3zFixAu4unbi5qTvee7cOJYt+1iC3wIk9IUQBZaf2y7esWsXdA+6zInDVfH3z309g8FA\nuXJVbk8O1wJ0GkovrMv27cvp1KlT7huKfDE3O+VKjBAiX7p1AwIn06sX3J4dPEeJiYlkZGQCLUzL\nnJxa3DV2R1iPhL4QIv8eXc2IEdCnD+Q2lX/58uXx9KwBfG5alpm5hxYtWuS8gShS0rwjhIMxZ7Ss\nZraGrJmKp5+G9HRYtw5y6rn5559/EhDQh6tXr5A2LZ6v6q5h+PChln0hDkra9IUQhSqnD4nMTNi0\nWEsvfy2hoTlvp5Ti2rVrePzHI9/XDcSDSegLIazi6lVo3x7eeAPG5jH34MNcLBYPZm52yuAsIUSB\nVKkCM5freXG+nn1OEOMsE6vZAznTF0KYZd8+GDgQrgxqgPr45H2/lzN9y5Ium0IIq+rcGUJCgLWb\nkDnYbJ+c6QshLELT5mN6erzC5s3wU4zMp19Y5EKuEMImaGa60mVfBh06wLx51q6m+JILuUIIqzl1\n6hRPPfUcUVGRMDWTt98+yYgRDWjWDAYPvn99uaOW9cmZvhCiQFJSUqhduxGXL7+OUkNAV4XKH9Vk\n8+aT9OnjTng45HAfdhO5wFswciFXCGEVp06dIjnZHaVeBioDkJHhgbPz//j4Y+jXD65csW6N4n4S\n+kKIAqlYsSIZGReBBNOyjIwLVKxYkcGDYehQGDQIMjKsV6O4n4S+EKJAatWqxciRIyhduiPOzsEA\nDB7cDz8/PwDefhvc3GD6dGtWKe4lbfpCiAJTSrF582ZOnDjBjIwZ990h69o1aNkS3n/f2NwDsHXr\nVr77bjsrai3h4osX8fT0tFL19km6bAohbEJuF2YPH4ZeveDXXyE8/DMmTw4hOXkC6CZQdUktTpw4\nQpUqVaxQsX2SC7lCCJvWujXodMapGt58812Sk78HXgMgIaETa9assWp9jkb66QshCt1LL8HPP0Nk\npI47PX0ADIYqpKSkSP/9IlTg5p0pU6awdetWSpQoga+vLytWrKB8+fIAhISEsHz5cpydnVm8eDGB\ngYH3H1iad4QoVh7U7/7WLahZ8yJJSV+RkdEVdK1wX1CFw4f34p/tprvSfz9vVmveCQwM5MSJExw7\ndgw/Pz9CQkIAiIyMZN26dURGRhIWFsa4cePIysoqcIFCiOKhTBnYu7cyLi4vUqPGAgB+/PH7uwJf\nFL4CN+8EBASYnrdt25YNGzYAsGnTJoYOHYqrqys+Pj7UrVuXQ4cO0a5dO/OrFULYlOzNMl1qdUGn\n1wG5N8s0berKsmWuvPXWOkgNo2PHjkVXrAAs1Ka/fPlyhg413v/ywoULdwW8t7c3sbGxljiMEMLG\nFKTNfehQ+OknWLrlU9Q8yNbDk+TkZMsWKO6TZ+gHBARw8eLF+5bPmzeP3r17AzB37lxKlCjBsGHD\nct1P9n672el0OtNzrVaLVqvNR8lCCHu3aBEs9fFn+XIYPRqSkpLo338Eu3dvhzdh/PjJLF68MNfs\ncCR6vR69Xm+x/eUZ+uHh4Xlu/OWXX7Jt2zZ27dplWubl5UVMTIzp5/Pnz+Pl5ZXj9tlDXwjhONzc\ngIFDmDo1kg4d4L33prFvXwkyMxOAkixfvpcmTZbx/PNjrF2q1d17Qjx79myz9lfg5p2wsDAWLlzI\n3r17KVWqlGl5UFAQw4YNY9KkScTGxhIVFUWbNm3MKlIIUQxV/ZN584zNPYmJR0hNfR8oAUBy8lh2\n796PX2Bd6cppYQUO/fHjx5Oenm66oNu+fXuWLFmCv78/gwYNwt/fHxcXF5YsWSJf0YQQwP0Xfs/X\n0pHxf3DzcG800ftRqi0AJUrsp06dmneFu2a2Bv1IvXUKL0ZkGgYhhFXFx0OjRhkkJIzFyek6ia9v\nod5/m3H48F7T2B+Q/vt3yDQMQgi7VqECfPONK+7uy1mwYCwAx47tvyvwheVI6AshrK5DB3jtNSfW\nresNWU64ublZu6RiS0JfCGETpk693Wf/5+C7lq9cuRo/v9YAfPjhx9IsbCYJfSGETXB2hpUrgQMT\nOHrUuOz77zcybtybREWFAjB9+hI+/fQL6xVZDEjoCyFsRs2awOMTePppSE2FZcvWkZw8G+gGQHLy\nQpYvX2/VGu2dhL4QwrY0+ZrGjY23WSxTxg2NJvvd1a9QurS095tD5tMXQljdXf33fbrg01bH0qXw\nfGB33Le+RlJSIgDu7lOYM+c7K1Zq/6SfvhDCJv34I4wdC+vXn+Trr79gcaVFHHnyCC1btrR2aVYl\n98gVQhRbr7wCCQmwerUMzrrD3OyU5h0hhM1asACaN4dvvrF2JcWHXMgVQtgsd3fjWf4rrwA3q1u7\nnGJBQl8IYdPatDHeWJ0tnyMtwuaT0BdC2LwZM4CbXqxebe1K7J+06QshbFb2rpwtphzhpXWtOFYR\nanpUID413rSOzLOff9J7RwhhN2bOhN9/h02b/r23rqP16pGplYUQDuPNNyE6GtassXYl9ktCXwhh\nN0qUgBUrYNIkuHjR2tXYJwl9IYRdadUKRo+GceOQ3jwFIKEvhLA7M2fCiRMGRo7cBkBERISVK7If\nEvpCCLuTnHydGzcGs2pVB7jlQceOj7Nt2zZrl2UXJPSFEHbns88+JyGhDFABtn9ESsoqxo+fbu2y\n7IL00xdC2J3r1+NJT69j/OFCS8CdmzcTrFqTvZAzfSGE3XnyySdwd/8U+BV6vwC48MQT/axdll0w\nO/QXLVqEk5MT169fNy0LCQmhXr16NGjQgB07dph7CCGEuEvnzp354otFVKv2LNTZTZ06f1G+/AJr\nl2UXzAr9mJgYwsPDqVWrlmlZZGQk69atIzIykrCwMMaNG0dWVpbZhQohRHZDhw4hLi4KgIMH2/PN\nNy4cOWLlouyAWW36kyZNYsGCBfTp08e0bNOmTQwdOhRXV1d8fHyoW7cuhw4dol27dmYXK4QQOalS\nBRYuhCHT9AybpsfJSebkyU2BQ3/Tpk14e3vTtGnTu5ZfuHDhroD39vYmNja24BUKIUQ+PP00rFql\npVyElsmTjXPy6EfqrV2Wzckz9AMCAriYw1jnuXPnEhIScld7fV4TAGnuzIx0D51OZ3qu1WrRarUP\nKFcIIXKm0cAnn0DbtjBggLWrsRy9Xo9er7fY/go0y+Yff/xBt27dcHd3B+D8+fN4eXlx8OBBVqxY\nAcDUqVMBePzxx5k9ezZt27a9+8Ayy6YQwgLunWVz/nzYswd+bKdB6YpfxtjEjdFr167Nb7/9RqVK\nlYiMjGTYsGEcOnSI2NhYunfvzpkzZ+4725fQF0IUVPZ59u9tu/8/Ly2tWimO1x2O2lD8puO0iRuj\nZw90f39/Bg0ahL+/Py4uLixZsiTX5h0hhCiIvC7Mfv31Ok6d+hz+Xo2/fyA//riMmjVrFm2BNkxu\noiKEKDaOHz9Ou3YBpKTsgFa/ovmtFU2aTODYsV+sXZrF2MSZvhBC2IIDBw6g0TwJNIPHuqKOXOP4\n8QwyMzNxcZG4A5mGQQhRjFSrVg0np2NABrjfAM7i5PQJGo0E/h0S+kKIYuPJJ5+kY8dalClj7C3o\n5taOevVq8cUXVi7MhkibvhCiWMnKymL79u08eeRJIgdFkpHRkO7d4cQJ8PCwdnXms4kumwU6sIS+\nEKIQZe+/P3EiJCTA8uVWLsoC5EKuEEI8QPcxeoZM08Mq+DvLsefkkTN9IUSxdO9I3bVrISQEjvdx\nQc3JtGJl5jE3O+VCrhDCIQwebJyNk8MvW7sUq5LQF0I4BI0G/vMf4NDLZGRYuxrrkeYdIUSxkdec\nPHeea94shXon1ToFWoD03hFCiHzIysriu+++46kTTxHWLowePXpYu6QCkTZ9IYR4AKUUQ4eOYuTI\nUAAGDBjPjBmzrVyVdciZvhCi2Dt69CidOvUnKSkSdO6gu0yJEr7ExZ2lUqVK1i7vociZvhBCPMD1\n69dxdn4EcLu9xANX14rcuHHDmmVZhYS+EKLYa968OXAKWAuAk9MiKlZ0o1atWlatyxok9IUQxV6l\nSpXYtWsrdeqEANCkyffo9T845HTL0qYvhHAo947UtTfSpi+EECLfJPSFEMKBSOgLIYQDkdAXQggH\nIhdyhRDFXn7m5LEXMveOEEI4EKv23vnoo49o2LAhjRs3Jjg42LQ8JCSEevXq0aBBA3bs2GHOIYQQ\nQlhQgUcm7Nmzh82bN3P8+HFcXV25cuUKAJGRkaxbt47IyEhiY2Pp3r07p0+fxslJLh8IIYS1FTiJ\nly5dyrRp03B1dQXA4/Zt5jdt2sTQoUNxdXXFx8eHunXrcujQIctUK4QQwiwFDv2oqCj27dtHu3bt\n0Gq1HDlyBIALFy7g7e1tWs/b25vY2FjzKxVCCGG2PJt3AgICuHjx4n3L586dS2ZmJjdu3ODAgQMc\nPnyYQYMG8ffff+e4H41Gk+NynU5neq7VatFqtfmvXAghHIBer0ev11tsf3mGfnh4eK6/W7p0Kf37\n9wegdevWODk5cfXqVby8vIiJiTGtd/78eby8vHLcR/bQF0IIcb97T4hnzzbv5i8Fbt7p27cvu3fv\nBuD06dOkp6dTpUoVgoKCWLt2Lenp6URHRxMVFUWbNm3MKlIIIYRlFLj3zqhRoxg1ahRNmjShRIkS\nrFq1CgB/f38GDRqEv78/Li4uLFmyJNfmHSGEEEVLBmcJIYQdkamVhRBC5JuEvhBCOBAJfSGEcCAS\n+kII4UAk9IUQwoFI6AshhAOR0BdCCAcioS+EEA5EQl8IIRyIhL4QQjgQCX0hhHAgEvpCCOFAJPSF\nEMKBSOgLIYQDkdAXQggHIqEvhBAOREJfCCEciIS+EEI4EAl9IYRwIBL6QgjhQCT0hRDCgUjoCyGE\nAylw6B86dIg2bdrQvHlzWrduzeHDh02/CwkJoV69ejRo0IAdO3ZYpFAhhBDmK3Dov/HGG7z99tsc\nPXqUOXPm8MYbbwAQGRnJunXriIyMJCwsjHHjxpGVlWWxgm2FXq+3dglmkfqtS+q3Hnuu3RIKHPrV\nq1cnISEBgPj4eLy8vADYtGkTQ4cOxdXVFR8fH+rWrcuhQ4csU60Nsfc3jtRvXVK/9dhz7ZbgUtAN\nQ0ND6dixI5MnTyYrK4v9+/cDcOHCBdq1a2daz9vbm9jYWPMrFUIIYbY8Qz8gIICLFy/et3zu3Lks\nXryYxYsX069fP7755htGjRpFeHh4jvvRaDSWqVYIIYR5VAGVLVvW9DwrK0uVK1dOKaVUSEiICgkJ\nMf2uR48e6sCBA/dt7+vrqwB5yEMe8pDHQzx8fX0LGttKKaUK3LxTt25d9u7dS5cuXdi9ezd+fn4A\nBAUFMWzYMCZNmkRsbCxRUVG0adPmvu3PnDlT0EMLIYQooAKH/meffcbLL79MWloabm5ufPbZZwD4\n+/szaNAg/P39cXFxYcmSJdK8I4QQNkKjlFLWLkIIIUTRKPIRuVOmTKFhw4Y0a9aM/v37m7p9gv0M\n6goLC6NBgwbUq1eP+fPnW7ucPMXExNC1a1caNWpE48aNWbx4MQDXr18nICAAPz8/AgMDiY+Pt3Kl\neTMYDDRv3pzevXsD9lV/fHw8AwcOpGHDhvj7+3Pw4EG7qj8kJIRGjRrRpEkThg0bRlpamk3XP2rU\nKDw9PWnSpIlpWV712lru5FS/RXPTrCsCBbBjxw5lMBiUUkoFBwer4OBgpZRSJ06cUM2aNVPp6ekq\nOjpa+fr6mtazJZmZmcrX11dFR0er9PR01axZMxUZGWntsnIVFxenjh49qpRSKjExUfn5+anIyEg1\nZcoUNX/+fKWUUqGhoaZ/B1u1aNEiNWzYMNW7d2+llLKr+p955hm1bNkypZRSGRkZKj4+3m7qj46O\nVrVr11apqalKKaUGDRqkvvzyS5uuf9++fSoiIkI1btzYtCy3em0xd3Kq35K5WeShn913332nhg8f\nrpRSat68eSo0NNT0ux49eqj9+/dbq7Rc/frrr6pHjx6mn+/trWTr+vTpo8LDw1X9+vXVxYsXlVLG\nD4b69etbubLcxcTEqG7duqndu3erJ598Uiml7Kb++Ph4Vbt27fuW20v9165dU35+fur69esqIyND\nPfnkk2rHjh02X390dPRdoZlbvbaaO/fWn525uWnVCdeWL19Oz549AeOgLm9vb9PvbHVQV2xsLDVr\n1jT9bKt15uTs2bMcPXqUtm3bcunSJTw9PQHw9PTk0qVLVq4udxMnTmThwoU4Of37drWX+qOjo/Hw\n8OC5556jRYsWjB07lqSkJLupv1KlSrz++us88sgj1KhRgwoVKhAQEGA39d+RW732kjvZmZubhRL6\nAQEBNGnS5L7Hli1bTOvMnTuXEiVKMGzYsFz3Y4u9fmyxpvy4desWAwYM4MMPP6Rs2bJ3/U6j0djs\n69q6dStVq1alefPmqFz6HNhy/ZmZmURERDBu3DgiIiIoXbo0oaGhd61jy/X/9ddffPDBB5w9e5YL\nFy5w69Ytvvrqq7vWseX6c/Kgem35tVgiNwvcZTMvuY3MvePLL79k27Zt7Nq1y7TMy8uLmJgY08/n\nz583zedjS+6tMyYm5q5PWluUkZHBgAEDGDFiBH379gWMZzsXL16kWrVqxMXFUbVqVStXmbNff/2V\nzZs3s23bNlJTU7l58yYjRoywm/q9vb3x9vamdevWAAwcOJCQkBCqVatmF/UfOXKEDh06ULlyZQD6\n9+/P/v377ab+O3J7v9hL7oDlcrPIm3fCwsJYuHAhmzZtolSpUqblQUFBrF27lvT0dKKjo3Md1GVt\nrVq1IioqirNnz5Kens66desICgqydlm5UkoxevRo/P39mTBhgml5UFAQK1euBGDlypWmDwNbM2/e\nPGJiYoiOjmbt2rU89thjrF692m7qr1atGjVr1uT06dMA7Ny5k0aNGtG7d2+7qL9BgwYcOHCAlJQU\nlFLs3LkTf39/u6n/jtzeL/aSOxbNTQtdd8i3unXrqkceeUQ9+uij6tFHH1UvvfSS6Xdz585Vvr6+\nqn79+iosLKyoS8u3bdu2KT8/P+Xr66vmzZtn7XLy9NNPPymNRqOaNWtm+jvfvn27unbtmurWrZuq\nV6+eCggIUDdu3LB2qQ+k1+tNvXfsqf7ff/9dtWrVSjVt2lT169dPxcfH21X98+fPV/7+/qpx48bq\nmWeeUenp6TZd/5AhQ1T16tWVq6ur8vb2VsuXL8+zXlvLnXvrX7ZsmUVzUwZnCSGEA5HbJQohhAOR\n0BdCCAcioS+EEA5EQl8IIRyIhL4QQjgQCX0hhHAgEvpCCOFAJPSFEMKB/D+0IpGyoSSfZAAAAABJ\nRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "a nonlinear example"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "our experiemental data -- an exponential"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a0 = 2.5\n",
-      "a1 = 2./3.\n",
-      "sigma = 5.0\n",
-      "\n",
-      "a0_orig, a1_orig = a0, a1\n",
-      "\n",
-      "x = np.linspace(0.0, 4.0, 25)\n",
-      "y = a0*np.exp(a1*x) + sigma*np.random.randn(len(x))\n",
-      "\n",
-      "pylab.scatter(x,y)\n",
-      "pylab.errorbar(x, y, yerr=sigma, fmt=None, label=\"_nolegend_\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 37,
-       "text": [
-        "<Container object of 3 artists>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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/DkREgqHFaZuUlERiYuIZzy9fvpypU6cSFRVFfHw8/fr1Iz8/36ciRUTEe37f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\nzfwfrgL+tQj7Wo8AAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "our function to minimize"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def resid(avec, x, y):\n",
-      "    \"\"\" the residual function -- this is what will be minimized by the                             \n",
-      "        scipy.optimize.leastsq() routine.  avec is the parameters we                               \n",
-      "        are optimizing -- they are packed in here, so we unpack to                                 \n",
-      "        begin.  (x, y) are the data points                                                         \n",
-      "                                                                                                   \n",
-      "        scipy.optimize.leastsq() minimizes:                                                        \n",
-      "                                                                                                   \n",
-      "           x = arg min(sum(func(y)**2,axis=0))                                                     \n",
-      "                    y                                                                              \n",
-      "                                                                                                   \n",
-      "        so this should just be the distance from a point to the curve,                             \n",
-      "        and it will square it and sum over the points                                              \n",
-      "        \"\"\"\n",
-      "\n",
-      "    a0, a1 = avec\n",
-      "\n",
-      "    # note: if we wanted to deal with error bars, we would weight each                             \n",
-      "    # residual accordingly                                                                         \n",
-      "    return y - a0*numpy.exp(a1*x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a0, a1 = 1, 1\n",
-      "afit, flag = optimize.leastsq(resid, [a0, a1], args=(x, y))\n",
-      "\n",
-      "print flag\n",
-      "print afit"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n",
-        "[ 3.16084631  0.58383599]\n"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pylab.plot(x, afit[0]*numpy.exp(afit[1]*x),\n",
-      "           label=r\"$a_0 = $ %f; $a_1 = $ %f\" % (afit[0], afit[1]))\n",
-      "pylab.plot(x, a0_orig*np.exp(a1_orig*x), \":\", label=\"original function\")\n",
-      "pylab.legend(numpoints=1, frameon=False)\n",
-      "pylab.scatter(x,y, c=\"k\")\n",
-      "pylab.errorbar(x, y, yerr=sigma, fmt=None, label=\"_nolegend_\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 40,
-       "text": [
-        "<Container object of 3 artists>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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asVZXzPHt37/f8D0D1bt3bxUXF1eqfen1Ss398oKqU1evpk1TKi3N/JjWrFmj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\nhLAxktiFEMLGSGIXQggbI4ldCCFszP8H6uTY7KBMWu0AAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "FFTs"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Both NumPy and SciPy have FFT routines that are similar.  However, the NumPy version returns the data in a more convenient form.\n",
-      "\n",
-      "It's always best to start with something you understand -- let's do a simple sine wave.  Since our function is real, we can use the rfft routines in NumPy -- the understand that we are working with real data and they don't return the negative frequency components.\n",
-      "\n",
-      "One important caveat -- FFTs assume that you are periodic.  If you include both endpoints of the domain in the points that comprise your sample then you will not match this assumption.  Here we use endpoint=False with linspace()"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def singleFreqSine(npts):\n",
-      "\n",
-      "    # a pure sine with no phase shift will result in pure imaginary                                         \n",
-      "    # signal                                                                                                \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    xmax = 10.0/f_0\n",
-      "    \n",
-      "    xx = numpy.linspace(0.0, xmax, npts, endpoint=False)\n",
-      "\n",
-      "    # input frequency                                                                                       \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    f = numpy.sin(2.0*math.pi*f_0*xx)\n",
-      "\n",
-      "    return xx, f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "To make our life easier, we'll define a function that plots all the stages of the FFT process"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def plotFFT(xx, f):\n",
-      "\n",
-      "    npts = len(xx)\n",
-      "\n",
-      "    # Forward transform: f(x) -> F(k)                                                                       \n",
-      "    fk = numpy.fft.rfft(f)\n",
-      "\n",
-      "    # Normalization -- the '2' here comes from the fact that we are                                         \n",
-      "    # neglecting the negative portion of the frequency space, since                                         \n",
-      "    # the FFT of a real function contains redundant information, so                                         \n",
-      "    # we are only dealing with 1/2 of the frequency space.                                                  \n",
-      "    #                                                                                                       \n",
-      "    # technically, we should only scale the 0 bin by N, since k=0 is                                        \n",
-      "    # not duplicated -- we won't worry about that for these plots                                           \n",
-      "    norm = 2.0/npts\n",
-      "\n",
-      "    fk = fk*norm\n",
-      "\n",
-      "    fk_r = fk.real\n",
-      "    fk_i = fk.imag\n",
-      "\n",
-      "    # the fftfreq returns the postive and negative (and 0) frequencies                                      \n",
-      "    # the newer versions of numpy (>=1.8) have an rfftfreq() function                                       \n",
-      "    # that really does what we want -- we'll use that here.                                                 \n",
-      "    k = numpy.fft.rfftfreq(npts)\n",
-      "\n",
-      "    # to make these dimensional, we need to divide by dx.  Note that                                        \n",
-      "    # max(xx) is not the true length, since we didn't have a point                                          \n",
-      "    # at the endpoint of the domain.                                                                        \n",
-      "    kfreq = k*npts/(max(xx) + xx[1])\n",
-      "\n",
-      "\n",
-      "    # Inverse transform: F(k) -> f(x) -- without the normalization                                          \n",
-      "    fkinv = numpy.fft.irfft(fk/norm)\n",
-      "\n",
-      "    # plots\n",
-      "    pylab.subplot(411)\n",
-      "\n",
-      "    pylab.plot(xx, f)\n",
-      "    pylab.xlabel(\"x\")\n",
-      "    pylab.ylabel(\"f(x)\")\n",
-      "\n",
-      "    pylab.subplot(412)\n",
-      "\n",
-      "    pylab.plot(kfreq, fk_r, label=r\"Re($\\mathcal{F}$)\")\n",
-      "    pylab.plot(kfreq, fk_i, ls=\":\", label=r\"Im($\\mathcal{F}$)\")\n",
-      "    pylab.xlabel(r\"$\\nu_k$\")\n",
-      "    pylab.ylabel(\"F(k)\")\n",
-      "\n",
-      "    pylab.legend(fontsize=\"small\", frameon=False)\n",
-      "\n",
-      "    pylab.subplot(413)\n",
-      "\n",
-      "    pylab.plot(kfreq, numpy.abs(fk))\n",
-      "    pylab.xlabel(r\"$\\nu_k$\")\n",
-      "    pylab.ylabel(r\"|F(k)|\")\n",
-      "\n",
-      "    pylab.subplot(414)\n",
-      "\n",
-      "    pylab.plot(xx, fkinv.real)\n",
-      "    pylab.xlabel(r\"$\\nu_k$\")\n",
-      "    pylab.ylabel(r\"inverse F(k)\")\n",
-      "\n",
-      "    f = pylab.gcf()\n",
-      "    \n",
-      "    f.set_size_inches(10,8)\n",
-      "    pylab.tight_layout()\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "npts = 128\n",
-      "xx, f = singleFreqSine(npts)\n",
-      "plotFFT(xx, f)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "png": 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RgRUrViA8PByTJk3CdgON0pDdYgIY6wKQ3a2oMNJAFT1aCwBjzXChR+shYKxW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05U2b9f/+fWm961d5q2bYGBA+1Rm6137Z2mRw8gLg7YvFn/59abEbcVAbk+bO/ewK5d+j+33jZt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\n7CQzDMMwDMMwjBXsJDMMwzAMwzCMFf8PIPz21cpHHlUAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "A cosine is shifted in phase by pi/2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def singleFreqCosine(npts):\n",
-      "\n",
-      "    # a pure cosine with no phase shift will result in pure real                                            \n",
-      "    # signal                                                                                                \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    xmax = 10.0/f_0\n",
-      "\n",
-      "    xx = numpy.linspace(0.0, xmax, npts, endpoint=False)\n",
-      "\n",
-      "    # input frequency                                                                                       \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    f = numpy.cos(2.0*math.pi*f_0*xx)\n",
-      "\n",
-      "    return xx, f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 44
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xx, f = singleFreqCosine(npts)\n",
-      "plotFFT(xx, f)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "png": 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gedWqVXj77beRkZGBJUuW4B//+AfmzZuHJUuWICMjA2+99RZWrlzp1uBJSUkI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\n"
-      }
-     ],
-     "prompt_number": 45
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Now let's look at a sine with a pi/4 phase shift"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def singleFreqSinePlusShift(npts):\n",
-      "\n",
-      "    # a pure sine with no phase shift will result in pure imaginary                                         \n",
-      "    # signal                                                                                                \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    xmax = 10.0/f_0\n",
-      "\n",
-      "    xx = numpy.linspace(0.0, xmax, npts, endpoint=False)\n",
-      "\n",
-      "    # input frequency                                                                                       \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    f = numpy.sin(2.0*math.pi*f_0*xx + math.pi/4)\n",
-      "\n",
-      "    return xx, f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 46
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xx, f = singleFreqSinePlusShift(npts)\n",
-      "plotFFT(xx, f)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "png": 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yaTxHMVI2t+xYXAUjKUO9jWQjJOvoNQfK4TJGiNHX84GQnm6MdaCnkbxnjzGS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GklNlYuq1dWpt059VRaKscPe7LTYFO6d5fLgFll9z2nUyb1WibJgwbJMf+NjAAzLa3GI7sMGybf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\nJ1kQBEEQBEEQnPh/BX2j1XtlLWkAAAAASUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 47
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "A frequency filter"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we'll setup a simple two-frequency sine wave and filter a component"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def twoFreqSine(npts):\n",
-      "\n",
-      "    # a pure sine with no phase shift will result in pure imaginary             \n",
-      "    # signal                                                                    \n",
-      "    f_0 = 0.2\n",
-      "    f_1 = 0.5\n",
-      "\n",
-      "    xmax = 10.0/f_0\n",
-      "\n",
-      "    # we call with endpoint=False -- if we include the endpoint, then for       \n",
-      "    # a periodic function, the first and last point are identical -- this       \n",
-      "    # shows up as a signal in the FFT.                                          \n",
-      "    xx = numpy.linspace(0.0, xmax, npts, endpoint=False)\n",
-      "\n",
-      "    # input frequency                                                           \n",
-      "    f_0 = 0.2\n",
-      "\n",
-      "    f = 0.5*(numpy.sin(2.0*math.pi*f_0*xx) + numpy.sin(2.0*math.pi*f_1*xx))\n",
-      "\n",
-      "    return xx, f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "npts = 256\n",
-      "\n",
-      "xx, f = twoFreqSine(npts)\n",
-      "\n",
-      "pylab.plot(xx, f)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 49,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x54cc650>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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G19MXNegwU2r3wFFVFV3p605eepW+yN4Js+GaV+mL7J3u7tFlsm4kvSLX3THd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\nThRfxyP97m76ftxIivTLy4Gf/pRUTF2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/fz6uvvpqdHZ2JviE+tDY2IhPfepTWLx4MZYs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\nLCwscgj/H/3RT0h0Be6IAAAAAElFTkSuQmCC\n"
-      }
-     ],
-     "prompt_number": 49
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we'll take the transform: f(x) -> F(k)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# normalization factor: the 2 here comes from the fact that we neglect          \n",
-      "# the negative portion of frequency space because our input function            \n",
-      "# is real                                                                       \n",
-      "norm = 2.0/npts\n",
-      "fk = norm*numpy.fft.rfft(f)\n",
-      "\n",
-      "ofk_r = fk.real.copy()\n",
-      "ofk_i = fk.imag.copy()\n",
-      "\n",
-      "# get the frequencies\n",
-      "k = numpy.fft.rfftfreq(len(xx))\n",
-      "\n",
-      "# since we don't include the endpoint in xx, to normalize things, we need       \n",
-      "# max(xx) + dx to get the true length of the domain\n",
-      "#\n",
-      "# This makes the frequencies essentially multiples of 1/dx\n",
-      "kfreq = k*npts/(max(xx) + xx[1])\n",
-      "\n",
-      "\n",
-      "pylab.plot(kfreq, fk.real, label=\"real\")\n",
-      "pylab.plot(kfreq, fk.imag, \":\", label=\"imaginary\")\n",
-      "pylab.legend(frameon=False)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 50,
-       "text": [
-        "<matplotlib.legend.Legend at 0x51cdb10>"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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FZiAianbN4q/X61FVVdXm+wsXLmy1LUmS1R9mZmZmGr/W6XTQ6XQW9fNb/W94ZecrWKxf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\nrkJggg==\n"
-      }
-     ],
-     "prompt_number": 50
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Filter out the higher frequencies"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fk[kfreq > 0.4] = 0.0\n",
-      "\n",
-      "# element 0 of fk is the DC component                                           \n",
-      "fk_r = fk.real\n",
-      "fk_i = fk.imag\n",
-      "\n",
-      "\n",
-      "# Inverse transform: F(k) -> f(x)                                               \n",
-      "fkinv = numpy.fft.irfft(fk/norm)\n",
-      "\n",
-      "pylab.plot(xx, fkinv.real)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 51,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x54de1d0>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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peOmwE30A1E7fJTg0oldxcCG0W5HxYvBJBqc/ZUrw9h6qYgB49BGzPdzOAL3g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\n"
-      }
-     ],
-     "prompt_number": 51
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Linear Algebra"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "general manipulations of matrices"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can use regular NumPy arrays or you can use a special matrix class that offers some short cuts"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.array([[1.0, 2.0], [3.0, 4.0]])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 52
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print a\n",
-      "print a.transpose()\n",
-      "print a.T"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 1.  2.]\n",
-        " [ 3.  4.]]\n",
-        "[[ 1.  3.]\n",
-        " [ 2.  4.]]\n",
-        "[[ 1.  3.]\n",
-        " [ 2.  4.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 53
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ainv = np.linalg.inv(a)\n",
-      "print ainv"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[-2.   1. ]\n",
-        " [ 1.5 -0.5]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 54
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print numpy.dot(a, ainv)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[  1.00000000e+00   0.00000000e+00]\n",
-        " [  8.88178420e-16   1.00000000e+00]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 55
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "the eye() function will generate an identity matrix (as will the identity())"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print np.eye(2)\n",
-      "print np.identity(2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 1.  0.]\n",
-        " [ 0.  1.]]\n",
-        "[[ 1.  0.]\n",
-        " [ 0.  1.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 56
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "we can solve Ax = b"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = np.array([5, 7])\n",
-      "x = numpy.linalg.solve(a, b)\n",
-      "print x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[-3.  4.]\n"
-       ]
-      }
-     ],
-     "prompt_number": 57
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "The matrix class"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "A = np.matrix('1.0 2.0; 3.0 4.0')\n",
-      "print A\n",
-      "print A.T"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 1.  2.]\n",
-        " [ 3.  4.]]\n",
-        "[[ 1.  3.]\n",
-        " [ 2.  4.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 58
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "X = np.matrix('5.0 7.0')\n",
-      "Y = X.T\n",
-      "\n",
-      "print A*Y"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 19.]\n",
-        " [ 43.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 59
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print A.I*Y"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[-3.]\n",
-        " [ 4.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 60
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "tridiagonal matrix solve"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we'll solve the equation:\n",
-      "\n",
-      "$$f^{\\prime\\prime} = g(x)$$\n",
-      "\n",
-      "with $g(x) = sin(x)$, and the domain $x \\in [0, 2\\pi]$, with boundary conditions $f(0) = f(2\\pi) = 0$.  The solution is simply $f(x) = -sin(x)$.\n",
-      "\n",
-      "We'll use a grid of $N$ points with $x_0$ on the left boundary and $x_{N-1}$ on the right boundary.\n",
-      "\n",
-      "We difference our equation as:\n",
-      "\n",
-      "$$f_{i+1} - 2 f_i + f_{i-1} = \\Delta x^2 g_i$$\n",
-      "\n",
-      "We keep the boundary points fixed, so we only need to solve for the $N-2$ interior points.  Near the boundaries, our difference is:\n",
-      "$$f_2 - 2 f_1 = \\Delta x^2 g_1$$\n",
-      "\n",
-      "and\n",
-      "\n",
-      "$$-2f_{N-1} + f_{N-2} = \\Delta x^2 g_{N-1}$$.\n",
-      "\n",
-      "We can write the system of equations for solving for the $N-2$ interior points as:\n",
-      "\n",
-      "\\begin{equation}\n",
-      "A = \\left (\n",
-      "\\begin{array}{ccccccc}\n",
-      "-2 &  1 &    &        &         &           &          \\newline\n",
-      "1  & -2 &  1 &        &         &           &          \\newline\n",
-      "   &  1 & -2 & 1      &         &           &          \\newline\n",
-      "   &         & \\ddots & \\ddots & \\ddots  &           &          \\newline\n",
-      "   &         &        & \\ddots & \\ddots  & \\ddots  &          \\newline\n",
-      "   &         &        &        & 1       & -2 & 1  \\newline\n",
-      "   &         &        &        &         & 1  & -2 \\newline\n",
-      "\\end{array}\n",
-      "\\right )\n",
-      "\\end{equation}\n",
-      "\n",
-      "\\begin{equation}\n",
-      "x = \\left (\n",
-      "\\begin{array}{c}\n",
-      "f_\\mathrm{1} \\\\\\\n",
-      "f_\\mathrm{2} \\\\\\\n",
-      "f_\\mathrm{3} \\\\\\\n",
-      "\\vdots \\\\\\\n",
-      "\\vdots \\\\\\\n",
-      "f_\\mathrm{N-2} \\\\\\\n",
-      "f_\\mathrm{N-1} \\\\\\\n",
-      "\\end{array}\n",
-      "\\right )\n",
-      "\\end{equation}\n",
-      "\n",
-      "\\begin{equation}\n",
-      "b = \\Delta x^2 \\left (\n",
-      "\\begin{array}{c}\n",
-      "g_\\mathrm{1} \\\\\\\n",
-      "g_\\mathrm{2} \\\\\\\n",
-      "g_\\mathrm{3} \\\\\\\n",
-      "\\vdots \\\\\\\n",
-      "\\vdots \\\\\\\n",
-      "g_\\mathrm{N-2} \\\\\\\n",
-      "g_\\mathrm{N-1}\\\\\\\n",
-      "\\end{array}\n",
-      "\\right )\n",
-      "\\end{equation}\n",
-      "\n",
-      "Then we just solve $A x = b$"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import scipy.linalg as linalg\n",
-      "\n",
-      "# our grid -- including endpoints\n",
-      "N = 100\n",
-      "x = np.linspace(0.0, 2.0*np.pi, N, endpoint=True)\n",
-      "dx = x[1]-x[0]\n",
-      "\n",
-      "# our source\n",
-      "g = np.sin(x)\n",
-      "\n",
-      "# our matrix will be tridiagonal, with [1, -2, 1] on the diagonals\n",
-      "# we only solve for the N-2 interior points\n",
-      "\n",
-      "# diagonal\n",
-      "d = -2*np.ones(N-2)\n",
-      "\n",
-      "# upper -- note that the upper diagonal has 1 less element than the\n",
-      "# main diagonal.  The SciPy banded solver wants the matrix in the \n",
-      "# form:\n",
-      "#\n",
-      "# *    a01  a12  a23  a34  a45    <- upper diagonal\n",
-      "# a00  a11  a22  a33  a44  a55    <- diagonal\n",
-      "# a10  a21  a32  a43  a54   *     <- lower diagonal\n",
-      "#\n",
-      "\n",
-      "u = np.ones(N-2)\n",
-      "u[0] = 0.0\n",
-      "\n",
-      "# lower\n",
-      "l = np.ones(N-2)\n",
-      "l[N-3] = 0.0\n",
-      "\n",
-      "# put the upper, diagonal, and lower parts together as a banded matrix\n",
-      "A = np.matrix([u,d,l])\n",
-      "\n",
-      "# solve A sol = dx**2 g for the inner N-2 points\n",
-      "sol = linalg.solve_banded((1,1), A, dx**2*g[1:N-1])\n",
-      "\n",
-      "pylab.plot(x[1:N-1], sol)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 64,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x57bbc50>]"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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nQi8i4nIq9CIiLqdCLyLicir0IiIu53WhnzFjBnXq1CE0NJQ1a9bkeV10dDRX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\n"
-      }
-     ],
-     "prompt_number": 64
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/04-scipy/scipy.fodp b/lectures/04-scipy/scipy.fodp
deleted file mode 100644
index 1f0571f6..00000000
--- a/lectures/04-scipy/scipy.fodp
+++ /dev/null
@@ -1,40411 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-
-<office:document xmlns:office="urn:oasis:names:tc:opendocument:xmlns:office:1.0" xmlns:style="urn:oasis:names:tc:opendocument:xmlns:style:1.0" xmlns:text="urn:oasis:names:tc:opendocument:xmlns:text:1.0" xmlns:table="urn:oasis:names:tc:opendocument:xmlns:table:1.0" xmlns:draw="urn:oasis:names:tc:opendocument:xmlns:drawing:1.0" xmlns:fo="urn:oasis:names:tc:opendocument:xmlns:xsl-fo-compatible:1.0" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:meta="urn:oasis:names:tc:opendocument:xmlns:meta:1.0" xmlns:number="urn:oasis:names:tc:opendocument:xmlns:datastyle:1.0" xmlns:presentation="urn:oasis:names:tc:opendocument:xmlns:presentation:1.0" xmlns:svg="urn:oasis:names:tc:opendocument:xmlns:svg-compatible:1.0" xmlns:chart="urn:oasis:names:tc:opendocument:xmlns:chart:1.0" xmlns:dr3d="urn:oasis:names:tc:opendocument:xmlns:dr3d:1.0" xmlns:math="http://www.w3.org/1998/Math/MathML" xmlns:form="urn:oasis:names:tc:opendocument:xmlns:form:1.0" xmlns:script="urn:oasis:names:tc:opendocument:xmlns:script:1.0" xmlns:config="urn:oasis:names:tc:opendocument:xmlns:config:1.0" xmlns:ooo="http://openoffice.org/2004/office" xmlns:ooow="http://openoffice.org/2004/writer" xmlns:oooc="http://openoffice.org/2004/calc" xmlns:dom="http://www.w3.org/2001/xml-events" xmlns:xforms="http://www.w3.org/2002/xforms" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:smil="urn:oasis:names:tc:opendocument:xmlns:smil-compatible:1.0" xmlns:anim="urn:oasis:names:tc:opendocument:xmlns:animation:1.0" xmlns:rpt="http://openoffice.org/2005/report" xmlns:of="urn:oasis:names:tc:opendocument:xmlns:of:1.2" xmlns:xhtml="http://www.w3.org/1999/xhtml" xmlns:grddl="http://www.w3.org/2003/g/data-view#" xmlns:officeooo="http://openoffice.org/2009/office" xmlns:tableooo="http://openoffice.org/2009/table" xmlns:drawooo="http://openoffice.org/2010/draw" xmlns:calcext="urn:org:documentfoundation:names:experimental:calc:xmlns:calcext:1.0" xmlns:loext="urn:org:documentfoundation:names:experimental:office:xmlns:loext:1.0" xmlns:field="urn:openoffice:names:experimental:ooo-ms-interop:xmlns:field:1.0" xmlns:formx="urn:openoffice:names:experimental:ooxml-odf-interop:xmlns:form:1.0" xmlns:css3t="http://www.w3.org/TR/css3-text/" office:version="1.2" office:mimetype="application/vnd.oasis.opendocument.presentation">
- <office:meta><meta:initial-creator>Michael Zingale</meta:initial-creator><meta:creation-date>2013-01-02T12:36:14</meta:creation-date><dc:date>2014-01-24T15:13:37.372298707</dc:date><dc:creator>Michael Zingale</dc:creator><meta:editing-duration>PT15H1M57S</meta:editing-duration><meta:editing-cycles>50</meta:editing-cycles><meta:generator>LibreOffice/4.2.3.2$Linux_X86_64 LibreOffice_project/5b49ae607df7cb5a837397b40c7dba9b437980e9</meta:generator><meta:document-statistic meta:object-count="687"/></office:meta>
- <office:settings>
-  <config:config-item-set config:name="ooo:view-settings">
-   <config:config-item config:name="VisibleAreaTop" config:type="int">-305</config:config-item>
-   <config:config-item config:name="VisibleAreaLeft" config:type="int">-543</config:config-item>
-   <config:config-item config:name="VisibleAreaWidth" config:type="int">29104</config:config-item>
-   <config:config-item config:name="VisibleAreaHeight" config:type="int">21642</config:config-item>
-   <config:config-item-map-indexed config:name="Views">
-    <config:config-item-map-entry>
-     <config:config-item config:name="ViewId" config:type="string">view1</config:config-item>
-     <config:config-item config:name="GridIsVisible" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="GridIsFront" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsSnapToGrid" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToPageMargins" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToSnapLines" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectFrame" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsPlusHandlesAlwaysVisible" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsFrameDragSingles" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="EliminatePolyPointLimitAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="IsEliminatePolyPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="VisibleLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="PrintableLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="LockedLayers" config:type="base64Binary"/>
-     <config:config-item config:name="NoAttribs" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="NoColors" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="RulerIsVisible" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="PageKind" config:type="short">0</config:config-item>
-     <config:config-item config:name="SelectedPage" config:type="short">83</config:config-item>
-     <config:config-item config:name="IsLayerMode" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsDoubleClickTextEdit" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsClickChangeRotation" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="SlidesPerRow" config:type="short">4</config:config-item>
-     <config:config-item config:name="EditModeStandard" config:type="int">0</config:config-item>
-     <config:config-item config:name="EditModeNotes" config:type="int">0</config:config-item>
-     <config:config-item config:name="EditModeHandout" config:type="int">1</config:config-item>
-     <config:config-item config:name="VisibleAreaTop" config:type="int">-305</config:config-item>
-     <config:config-item config:name="VisibleAreaLeft" config:type="int">-543</config:config-item>
-     <config:config-item config:name="VisibleAreaWidth" config:type="int">29105</config:config-item>
-     <config:config-item config:name="VisibleAreaHeight" config:type="int">21643</config:config-item>
-     <config:config-item config:name="GridCoarseWidth" config:type="int">2540</config:config-item>
-     <config:config-item config:name="GridCoarseHeight" config:type="int">2540</config:config-item>
-     <config:config-item config:name="GridFineWidth" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridFineHeight" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthXNumerator" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthXDenominator" config:type="int">1</config:config-item>
-     <config:config-item config:name="GridSnapWidthYNumerator" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthYDenominator" config:type="int">1</config:config-item>
-     <config:config-item config:name="IsAngleSnapEnabled" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="SnapAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="ZoomOnPage" config:type="boolean">true</config:config-item>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-  </config:config-item-set>
-  <config:config-item-set config:name="ooo:configuration-settings">
-   <config:config-item config:name="ApplyUserData" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="BitmapTableURL" config:type="string">$(user)/config/standard.sob</config:config-item>
-   <config:config-item config:name="CharacterCompressionType" config:type="short">0</config:config-item>
-   <config:config-item config:name="ColorTableURL" config:type="string">$(user)/config/standard.soc</config:config-item>
-   <config:config-item config:name="DashTableURL" config:type="string">$(user)/config/standard.sod</config:config-item>
-   <config:config-item config:name="DefaultTabStop" config:type="int">1270</config:config-item>
-   <config:config-item config:name="EmbedFonts" config:type="boolean">false</config:config-item>
-   <config:config-item-map-indexed config:name="ForbiddenCharacters">
-    <config:config-item-map-entry>
-     <config:config-item config:name="Language" config:type="string">en</config:config-item>
-     <config:config-item config:name="Country" config:type="string">US</config:config-item>
-     <config:config-item config:name="Variant" config:type="string"/>
-     <config:config-item config:name="BeginLine" config:type="string"/>
-     <config:config-item config:name="EndLine" config:type="string"/>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-   <config:config-item config:name="GradientTableURL" config:type="string">$(user)/config/standard.sog</config:config-item>
-   <config:config-item config:name="HandoutsHorizontal" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="HatchTableURL" config:type="string">$(user)/config/standard.soh</config:config-item>
-   <config:config-item config:name="IsKernAsianPunctuation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBooklet" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBookletBack" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintBookletFront" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintDate" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintDrawing" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintFitPage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHandout" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHiddenPages" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintNotes" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintOutline" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintPageName" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTilePage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTime" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="LineEndTableURL" config:type="string">$(user)/config/standard.soe</config:config-item>
-   <config:config-item config:name="LoadReadonly" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PageNumberFormat" config:type="int">4</config:config-item>
-   <config:config-item config:name="ParagraphSummation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PrintQuality" config:type="int">0</config:config-item>
-   <config:config-item config:name="PrinterIndependentLayout" config:type="string">low-resolution</config:config-item>
-   <config:config-item config:name="PrinterName" config:type="string">hp</config:config-item>
-   <config:config-item config:name="PrinterSetup" config:type="base64Binary">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</config:config-item>
-   <config:config-item config:name="SaveVersionOnClose" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="SlidesPerHandout" config:type="short">6</config:config-item>
-   <config:config-item config:name="UpdateFromTemplate" config:type="boolean">true</config:config-item>
-  </config:config-item-set>
- </office:settings>
- <office:scripts>
-  <office:script script:language="ooo:Basic">
-   <ooo:libraries xmlns:ooo="http://openoffice.org/2004/office" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <ooo:library-embedded ooo:name="Standard"/>
-   </ooo:libraries>
-  </office:script>
- </office:scripts>
- <office:font-face-decls>
-  <style:font-face style:name="OpenSymbol1" svg:font-family="OpenSymbol" style:font-charset="x-symbol"/>
-  <style:font-face style:name="OpenSymbol2" svg:font-family="OpenSymbol" style:font-adornments="Regular" style:font-charset="x-symbol"/>
-  <style:font-face style:name="Arial7" svg:font-family="Arial" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="Lohit Devanagari3" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="OpenSymbol" svg:font-family="OpenSymbol" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp3" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="Arial6" svg:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="Lohit Devanagari2" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="system" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp2" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="system" style:font-pitch="variable" style:font-charset="x-symbol"/>
-  <style:font-face style:name="Arial3" svg:font-family="Arial" style:font-family-generic="swiss"/>
-  <style:font-face style:name="Arial4" svg:font-family="Arial" style:font-adornments="Regular" style:font-family-generic="swiss"/>
-  <style:font-face style:name="DejaVu Sans2" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="swiss"/>
-  <style:font-face style:name="Inconsolata1" svg:font-family="Inconsolata" style:font-pitch="fixed"/>
-  <style:font-face style:name="Courier New1" svg:font-family="&apos;Courier New&apos;" style:font-family-generic="modern" style:font-pitch="fixed"/>
-  <style:font-face style:name="Arial2" svg:font-family="Arial" style:font-pitch="variable"/>
-  <style:font-face style:name="Inconsolata" svg:font-family="Inconsolata" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial5" svg:font-family="Arial" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="Courier New" svg:font-family="&apos;Courier New&apos;" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial" svg:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Liberation Sans" svg:font-family="&apos;Liberation Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="STIXGeneral" svg:font-family="STIXGeneral" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Times New Roman" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="cmmi10" svg:font-family="cmmi10" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="cmr10" svg:font-family="cmr10" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial1" svg:font-family="Arial" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="DejaVu Sans1" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari1" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Times New Roman1" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp1" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="DejaVu Sans" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Tahoma" svg:font-family="Tahoma" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
- </office:font-face-decls>
- <office:styles>
-  <draw:marker draw:name="Arrow" svg:viewBox="0 0 20 30" svg:d="M10 0l-10 30h20z"/>
-  <draw:marker draw:name="Dimension_20_Lines" draw:display-name="Dimension Lines" svg:viewBox="0 0 836 110" svg:d="M0 0h278 278 280v36 36 38h-278-278-280v-36-36z"/>
-  <draw:stroke-dash draw:name="Fine_20_Dashed" draw:display-name="Fine Dashed" draw:style="rect" draw:dots1="1" draw:dots1-length="0.508cm" draw:dots2="1" draw:dots2-length="0.508cm" draw:distance="0.508cm"/>
-  <draw:stroke-dash draw:name="Ultrafine_20_Dashed" draw:display-name="Ultrafine Dashed" draw:style="rect" draw:dots1="1" draw:dots1-length="0.051cm" draw:dots2="1" draw:dots2-length="0.051cm" draw:distance="0.051cm"/>
-  <style:default-style style:family="graphic">
-   <style:graphic-properties svg:stroke-color="#808080" draw:fill-color="#cfe7f5" fo:wrap-option="no-wrap"/>
-   <style:paragraph-properties style:text-autospace="ideograph-alpha" style:punctuation-wrap="simple" style:line-break="strict" style:writing-mode="lr-tb" style:font-independent-line-spacing="false">
-    <style:tab-stops/>
-   </style:paragraph-properties>
-   <style:text-properties style:use-window-font-color="true" style:font-name="Times New Roman" fo:font-size="24pt" fo:language="en" fo:country="US" style:font-name-asian="DejaVu Sans" style:font-size-asian="24pt" style:language-asian="zh" style:country-asian="CN" style:font-name-complex="Tahoma" style:font-size-complex="24pt" style:language-complex="hi" style:country-complex="IN"/>
-  </style:default-style>
-  <style:style style:name="standard" style:family="graphic">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0cm" svg:stroke-color="#808080" draw:marker-start-width="0.2cm" draw:marker-start-center="false" draw:marker-end-width="0.2cm" draw:marker-end-center="false" draw:fill="solid" draw:fill-color="#cfe7f5" draw:textarea-horizontal-align="justify" fo:padding-top="0.125cm" fo:padding-bottom="0.125cm" fo:padding-left="0.25cm" fo:padding-right="0.25cm" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080">
-    <text:list-style style:name="standard">
-     <text:list-level-style-bullet text:level="1" text:bullet-char="●">
-      <style:list-level-properties text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="2" text:bullet-char="●">
-      <style:list-level-properties text:space-before="0.6cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="3" text:bullet-char="●">
-      <style:list-level-properties text:space-before="1.2cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="4" text:bullet-char="●">
-      <style:list-level-properties text:space-before="1.8cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="5" text:bullet-char="●">
-      <style:list-level-properties text:space-before="2.4cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="6" text:bullet-char="●">
-      <style:list-level-properties text:space-before="3cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="7" text:bullet-char="●">
-      <style:list-level-properties text:space-before="3.6cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="8" text:bullet-char="●">
-      <style:list-level-properties text:space-before="4.2cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="9" text:bullet-char="●">
-      <style:list-level-properties text:space-before="4.8cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-     <text:list-level-style-bullet text:level="10" text:bullet-char="●">
-      <style:list-level-properties text:space-before="5.4cm" text:min-label-width="0.6cm"/>
-      <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-     </text:list-level-style-bullet>
-    </text:list-style>
-   </style:graphic-properties>
-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:margin-top="0cm" fo:margin-bottom="0cm" fo:line-height="100%" fo:text-indent="0cm"/>
-   <style:text-properties style:use-window-font-color="true" style:text-outline="false" style:text-line-through-style="none" style:text-line-through-type="none" style:font-name="Arial" fo:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable" fo:font-size="18pt" fo:font-style="normal" fo:text-shadow="none" style:text-underline-style="none" fo:font-weight="normal" style:letter-kerning="true" style:font-name-asian="WenQuanYi Zen Hei Sharp" style:font-family-asian="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic-asian="system" style:font-pitch-asian="variable" style:font-size-asian="18pt" style:font-style-asian="normal" style:font-weight-asian="normal" style:font-name-complex="Lohit Devanagari" style:font-family-complex="&apos;Lohit Devanagari&apos;" style:font-family-generic-complex="system" style:font-pitch-complex="variable" style:font-size-complex="18pt" style:font-style-complex="normal" style:font-weight-complex="normal" style:text-emphasize="none" style:font-relief="none" style:text-overline-style="none" style:text-overline-color="font-color"/>
-  </style:style>
-  <style:style style:name="objectwitharrow" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0.15cm" svg:stroke-color="#000000" draw:marker-start="Arrow" draw:marker-start-width="0.7cm" draw:marker-start-center="true" draw:marker-end-width="0.3cm"/>
-  </style:style>
-  <style:style style:name="objectwithshadow" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:shadow="visible" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
-  </style:style>
-  <style:style style:name="objectwithoutfill" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties svg:stroke-color="#000000" draw:fill="none"/>
-  </style:style>
-  <style:style style:name="Object_20_with_20_no_20_fill_20_and_20_no_20_line" style:display-name="Object with no fill and no line" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-  </style:style>
-  <style:style style:name="text" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-  </style:style>
-  <style:style style:name="textbody" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:text-properties fo:font-size="16pt"/>
-  </style:style>
-  <style:style style:name="textbodyjustfied" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:text-align="justify"/>
-  </style:style>
-  <style:style style:name="textbodyindent" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0cm" fo:text-indent="0.6cm"/>
-  </style:style>
-  <style:style style:name="title" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:text-properties fo:font-size="44pt"/>
-  </style:style>
-  <style:style style:name="title1" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="solid" draw:fill-color="#008080" draw:shadow="visible" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
-   <style:paragraph-properties fo:text-align="center"/>
-   <style:text-properties fo:font-size="24pt"/>
-  </style:style>
-  <style:style style:name="title2" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties svg:stroke-width="0.05cm" draw:fill-color="#ffcc99" draw:shadow="visible" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
-   <style:paragraph-properties fo:margin-left="0cm" fo:margin-right="0.2cm" fo:margin-top="0.1cm" fo:margin-bottom="0.1cm" fo:text-align="center" fo:text-indent="0cm"/>
-   <style:text-properties fo:font-size="36pt"/>
-  </style:style>
-  <style:style style:name="headline" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
-   <style:text-properties fo:font-size="24pt"/>
-  </style:style>
-  <style:style style:name="headline1" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
-   <style:text-properties fo:font-size="18pt" fo:font-weight="bold"/>
-  </style:style>
-  <style:style style:name="headline2" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:paragraph-properties fo:margin-top="0.42cm" fo:margin-bottom="0.21cm"/>
-   <style:text-properties fo:font-size="14pt" fo:font-style="italic" fo:font-weight="bold"/>
-  </style:style>
-  <style:style style:name="measure" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-color="#000000" draw:marker-start="Arrow" draw:marker-start-width="0.2cm" draw:marker-end="Arrow" draw:marker-end-width="0.2cm" draw:fill="none" draw:show-unit="true"/>
-   <style:text-properties fo:font-size="12pt"/>
-  </style:style>
-  <style:style style:name="Default-background" style:family="presentation">
-   <style:graphic-properties draw:stroke="none" draw:fill="none"/>
-   <style:text-properties style:letter-kerning="true"/>
-  </style:style>
-  <style:style style:name="Default-backgroundobjects" style:family="presentation">
-   <style:graphic-properties draw:textarea-horizontal-align="justify" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080"/>
-   <style:text-properties style:letter-kerning="true"/>
-  </style:style>
-  <style:style style:name="Default-notes" style:family="presentation">
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-  </style:style>
-  <style:style style:name="gr55" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§\int_a^b f(x) dx = \frac{\Delta x}{2} \sum_{i=0}^{N-1} (f_i + f_{i+1}) + \mathcal{O}(\Delta x^2)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr56" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.869cm"/>
-  </style:style>
-  <style:style style:name="gr57" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="28§display§\int_0^1 e^{-x} dx" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr58" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\int_a^b f(x) dx \approx w_1 f(x_1) + \ldots w_N f(x_N)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr59" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\int_a^b q(x) \rho(x) x^k dx = 0" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr60" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\int_a^b f(x) \rho(x) dx \approx w_1 f(x_1) + \ldots w_N f(x_N)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr61" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_0^x e^{-y^2} dy" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr62" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="2.881cm"/>
-  </style:style>
-  <style:style style:name="gr63" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.945cm"/>
-  </style:style>
-  <style:style style:name="gr64" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties draw:stroke="dash" draw:stroke-dash="Ultrafine_20_Dashed" svg:stroke-width="0.051cm" svg:stroke-color="#0000ff" draw:marker-start-width="0.279cm" draw:marker-end-width="0.279cm" draw:fill="none" draw:textarea-vertical-align="middle" fo:padding-top="0.15cm" fo:padding-bottom="0.15cm" fo:padding-left="0.275cm" fo:padding-right="0.275cm"/>
-  </style:style>
-  <style:style style:name="gr65" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties draw:stroke="dash" draw:stroke-dash="Ultrafine_20_Dashed" svg:stroke-color="#808080" draw:fill="none" draw:textarea-vertical-align="middle"/>
-  </style:style>
-  <style:style style:name="gr66" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_1" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr67" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_2&#x0a;" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr68" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\color{red} f_2}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr69" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\color{red} f_1}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr70" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§f(x) = \frac{f_2 - f_1}{x_2 - x_1} (x - x_1) + f_1" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr71" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§l_i(x) = \prod_{j=0, i\ne j}^{n-1} \frac{x - x_j}{x_i - x_j}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr72" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§f(x) = \sum_{i=0}^{n-1} l_i f_i" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr73" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="22§display§f(x) = \frac{(x-x_1)(x-x_2)}{2 \Delta x^2} f_0 -&#x0a;          \frac{(x-x_0)(x-x_2)}{\Delta x^2} f_1 +&#x0a;          \frac{(x-x_0)(x-x_1)}{2 \Delta x^2} f_2" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr74" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="1.737cm"/>
-  </style:style>
-  <style:style style:name="gr75" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.712cm"/>
-  </style:style>
-  <style:style style:name="gr76" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties svg:stroke-color="#000000" draw:marker-end="Arrow" draw:marker-end-width="0.3cm" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle"/>
-  </style:style>
-  <style:style style:name="gr77" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle"/>
-  </style:style>
-  <style:style style:name="gr78" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr79" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_\star" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr80" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties svg:stroke-width="0.102cm" svg:stroke-color="#ff0000" draw:marker-start-width="0.457cm" draw:marker-end-width="0.457cm" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" fo:padding-top="0.05cm" fo:padding-bottom="0.05cm" fo:padding-left="0.05cm" fo:padding-right="0.05cm"/>
-  </style:style>
-  <style:style style:name="gr81" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_m" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr82" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_2" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr83" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§f(x_0 + \delta) \approx f(x_0) + f&apos;(x_0) \delta + ..." draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr84" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§f(x_0 + \delta) \approx 0 \longrightarrow \delta = - \frac{f(x_0)}{f&apos;(x_0)}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr85" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="28§display§x_1 = x_0 + \delta" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr86" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.736cm"/>
-  </style:style>
-  <style:style style:name="gr87" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.433cm"/>
-  </style:style>
-  <style:style style:name="gr88" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_1 = x_0 - \frac{f(x_0)}{[f(x_0) - f(x_{-1})]/(x_0 - x_{-1})}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr89" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf f}({\bf x})" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr90" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf f}({\bf x}) = &#x0a;( f_1({\bf x}) \,\,&#x0a;f_2({\bf x}) \,\,&#x0a;f_3({\bf x}) \,&#x0a;\ldots \,&#x0a;f_N({\bf x}) )^T" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr91" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf x} = ( x_1 \,\, x_2 \,\, x_3 \, \ldots \, x_N )^T" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr92" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf x}^{(0)}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr93" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties svg:stroke-width="0.051cm" svg:stroke-color="#0000ff" draw:marker-start-width="0.279cm" draw:marker-end-width="0.279cm" draw:fill="none" draw:textarea-vertical-align="middle" draw:auto-grow-height="false" fo:min-height="0.749cm" fo:min-width="0.499cm" fo:padding-top="0.15cm" fo:padding-bottom="0.15cm" fo:padding-left="0.275cm" fo:padding-right="0.275cm"/>
-  </style:style>
-  <style:style style:name="gr94" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\delta {\bf x} = - {\bf J}^{-1} {\bf f}({\bf x}^{(0)})" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr95" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§f_i ({\bf x}^{(0)} + \delta {\bf x}) \approx 0 =&#x0a;f_i({\bf x}^{(0)}) + \sum_{j=1}^N \frac{\partial f_i}{\partial x_j} \delta x_j + \ldots" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr96" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf J} \delta {\bf x}^{(k)} = -{\bf f}({\bf x}^{(k)}) \; ,&#x0a;\quad&#x0a;{\bf x}^{(k+1)} = {\bf x}^{(k)} + \delta {\bf x}^{(k)}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr97" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties svg:stroke-color="#0000ff" draw:marker-end="Arrow" draw:marker-end-width="0.3cm" draw:fill="none" draw:textarea-vertical-align="middle"/>
-  </style:style>
-  <style:style style:name="gr98" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:fill="solid" draw:fill-color="#0000ff" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle"/>
-  </style:style>
-  <style:style style:name="gr99" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:fill="solid" draw:fill-color="#ffff00" draw:textarea-vertical-align="middle" draw:auto-grow-height="false" fo:min-height="0.749cm" fo:min-width="0.499cm"/>
-  </style:style>
-  <style:style style:name="gr100" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:marker-start="Arrow" draw:marker-end="" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle"/>
-  </style:style>
-  <style:style style:name="gr101" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\dot{\bf x} = {\bf v} \qquad \dot{\bf v} = - \frac{G M {\bf r}}{r^3}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr102" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§4\pi^2 a^3 = GM_\star P^2" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr103" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="1.575cm"/>
-  </style:style>
-  <style:style style:name="gr104" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="2.605cm"/>
-  </style:style>
-  <style:style style:name="gr105" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf r}^\star = {\bf r}^n + (\tau / 2) {\bf v}^n" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr106" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf v}^\star = {\bf v}^n + (\tau/2) {\bf a}^n" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr107" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf r}^{n+1} = {\bf r}^n + \tau {\bf v}^\star " draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr108" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf v}^{n+1} = {\bf v}^n + \tau {\bf a}({\bf r}^\star)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr109" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\dot{\bf y} = {\bf g}(t, {\bf y})" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr110" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf k}_1 = {\bf g}(t, {\bf y})" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr111" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf k}_2 = {\bf g}(t + \tfrac{\tau}{2}, {\bf y} + \tfrac{\tau}{2} {\bf k}_1 )" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr112" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf k}_4 = {\bf g}(t + \tau, {\bf y} + \tau {\bf k}_3)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr113" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf k}_3 = {\bf g}(t + \tfrac{\tau}{2}, {\bf y} + \tfrac{\tau}{2} {\bf k}_2)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr114" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§{\bf y}^{n+1} = {\bf y}^n + \frac{\tau}{6} &#x0a; ({\bf k}_1 + 2{\bf k}_2 + 2{\bf k}_3 + {\bf k}_4) + \mathcal{O}(\tau^5)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr115" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="6.107cm"/>
-  </style:style>
-  <style:style style:name="gr116" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\dot{y} = -10^3 (y - e^{-t}) - e^{-t}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr117" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§y(0) = 0" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr118" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§y(t) = e^{-t} - e^{-10^3 t}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr119" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§Y(x,\{a_j\})" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr120" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\Delta_i = Y(x_i,\{a_j\}) - y_i" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr121" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\chi^2(\{a_j\}) = \sum_{i=1}^N \left (\frac{\Delta_i}{\sigma_i} \right )^2" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr122" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="1.889cm"/>
-  </style:style>
-  <style:style style:name="gr123" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties svg:stroke-width="0.051cm" svg:stroke-color="#0000ff" draw:marker-start-width="0.279cm" draw:marker-end="Arrow" draw:marker-end-width="0.381cm" draw:fill="none" draw:textarea-vertical-align="middle" fo:padding-top="0.15cm" fo:padding-bottom="0.15cm" fo:padding-left="0.275cm" fo:padding-right="0.275cm"/>
-  </style:style>
-  <style:style style:name="gr124" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§| y_i - Y(x_i) | \sim \sigma_i" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr125" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\frac{\chi^2}{N - M}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr126" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties svg:stroke-color="#ff0000" draw:fill="none" draw:textarea-vertical-align="middle" draw:auto-grow-height="false" fo:min-height="0.749cm" fo:min-width="0.499cm"/>
-  </style:style>
-  <style:style style:name="gr127" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§= a_2^2 \mathrm{Var}(x_i) + \mathrm{Var}(y_i)&#x0a;= a_2^2 \sigma_{x,i}^2 + \sigma_{y,i}^2" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr128" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§\mathrm{Var}(a_1 + a_2 x_i - y_i) = &#x0a;\mathrm{Var}(a_2 x_i - y_i)" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr129" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\chi^2(a_1, a_2) = \sum_{i=1}^N&#x0a; \frac{(a_1 + a_2 x_i - y_i)^2}{\sigma_{y,i}^2 + a_2^2 \sigma_{x,i}^2}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr130" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§f(a_0,a_1) = a_0 e^{a_1 x}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr131" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§Q \equiv \sum_{i=1}^N (y_i - a_0 e^{a_1 x_i})^2" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr132" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§f_0 \equiv \frac{\partial Q}{\partial a_0} =&#x0a;\sum_{i=1}^N e^{a_1 x_i} \left (a_0 e^{a_1 x_i} - y_i \right ) = 0" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr133" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§f_1 \equiv \frac{\partial Q}{\partial a_1} =&#x0a;\sum_{i=1}^N x_i e^{a_1 x_i} \left (a_0 e^{a_1 x_i} - y_i \right ) = 0" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr134" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§f_n = \frac{1}{N} \sum_{n=0}^{N-1} F_k e^{2\pi i n k /N} &#x0a;= \sum_{n=0}^{N-1} \left (\frac{F_k}{N} \right )&#x0a;e^{2\pi i n k /N} " draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr135" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties svg:stroke-width="0.051cm" svg:stroke-color="#ff0000" draw:marker-start-width="0.279cm" draw:marker-end-width="0.279cm" draw:fill="none" draw:textarea-vertical-align="middle" draw:auto-grow-height="false" fo:min-height="0.749cm" fo:min-width="0.499cm" fo:padding-top="0.15cm" fo:padding-bottom="0.15cm" fo:padding-left="0.275cm" fo:padding-right="0.275cm"/>
-  </style:style>
-  <style:style style:name="gr136" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="1.583cm"/>
-  </style:style>
-  <style:style style:name="gr137" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\epsilon x_1 + x_2 + x_3 = 5" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr138" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_1 + x_2 \phantom{+} \phantom{x_3 } \,\,= 3" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr139" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§x_1 + \phantom{x_2} \phantom{+} \,\,x_3 = 4" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr140" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties svg:stroke-width="0.254cm" svg:stroke-color="#ff3333" draw:marker-start-width="0.965cm" draw:marker-end="Arrow" draw:marker-end-width="1.067cm" draw:fill="none" draw:textarea-vertical-align="middle" fo:padding-top="0.252cm" fo:padding-bottom="0.252cm" fo:padding-left="0.377cm" fo:padding-right="0.377cm"/>
-  </style:style>
-  <style:style style:name="gr141" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0cm"/>
-  </style:style>
-  <style:style style:name="gr142" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="16.514cm"/>
-  </style:style>
-  <style:style style:name="gr143" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="0.512cm"/>
-  </style:style>
-  <style:style style:name="gr144" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="26§display§\mathrm{cond}(A) = \| A\|  \|A^{-1} \|" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr145" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="24§display§\frac{\| x^\star - x \|}{\| x^\star \|} \approx \mathrm{cond}(A) \cdot \epsilon_\mathrm{machine}" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
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-      <text:p>Conventions</text:p>
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-        <text:p>The SciPy docs recommend loading the various modules as:</text:p>
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-         <text:list-header>
-          <text:p><text:span text:style-name="T4">import numpy as np</text:span></text:p>
-          <text:p><text:span text:style-name="T4">import scipy as sp</text:span></text:p>
-          <text:p><text:span text:style-name="T4">import matplotlib as mpl</text:span></text:p>
-          <text:p><text:span text:style-name="T4">import matplotlib.pyplot as plt</text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T5">Each subpackage must be imported separately:</text:span></text:p>
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-         <text:list-header>
-          <text:p><text:span text:style-name="T4">from scipy import linalg, optimize</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T5">Note that some popular functions are available in the main scipy namespace</text:span></text:p>
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-      <text:p>Getting Help</text:p>
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-        <text:p>In addition to the <text:span text:style-name="T4">help()</text:span>, there is a <text:span text:style-name="T4">sp.info()</text:span> function that outputs things without a pager</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Python also has a <text:span text:style-name="T4">dir()</text:span> function that lists the names a module defines</text:p>
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-      <text:p>Caveats</text:p>
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-        <text:p>Every numerical method has its own strengths, weaknesses, and assumptions</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You should research a bit to understand what these methods are doing under the hood</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Many of the SciPy methods are wrappers to well-tested implementations of algorithms that were developed many years (decades even) ago.</text:p>
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-      <text:p>Integration</text:p>
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-      <text:p>Numerical Integration</text:p>
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-        <text:p>We want to compute:</text:p>
-        <text:p/>
-        <text:p/>
-       </text:list-item>
-       <text:list-item>
-        <text:p>We can imagine 2 situations</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">Our function f(x) is defined only at a set of (possibly regularly spaced) points</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Generally speaking, asking for greater accuracy involves using more of the discrete points in the approximation for I</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">We have an analytic expression for f(x)</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>We have the freedom to pick our integration points, and this can allow us to optimize the calculation of I</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Any numerical integration method that represents the integral as a (weighted) sum at a discrete number of points is called a <text:span text:style-name="T6">quadrature rule</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Fixed spacing between points: <text:span text:style-name="T6">Newton-Cotes quadrature</text:span></text:p>
-        <text:p/>
-        <text:p/>
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-      <text:p>Gaussian Quadrature</text:p>
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-        <text:p>Instead of fixed spacing, what if we strategically pick the spacings?</text:p>
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-          <text:p>We want to express</text:p>
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-          <text:p>w&apos;s are weights. <text:s/>We will choose the location of points x<text:span text:style-name="T12">i</text:span></text:p>
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-          <text:p>q(x) is a polynomial of degree N, such that</text:p>
-          <text:p/>
-          <text:p/>
-         </text:list-item>
-         <text:list-item>
-          <text:p>k = 0, ..., N-1 and <text:span text:style-name="T14">ρ</text:span>(x) is a specified weight function.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Choose x<text:span text:style-name="T15">1</text:span>, x<text:span text:style-name="T15">2</text:span>, ... x<text:span text:style-name="T15">N</text:span> as the roots of the polynomial q(x)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>We can write</text:p>
-          <text:p/>
-          <text:p/>
-          <text:p>and there will be a set of w&apos;s for which the integral is exact if f(x) is a polynomial of degree &lt; <text:span text:style-name="T9">2N</text:span>!</text:p>
-         </text:list-item>
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-        <text:p/>
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-      <text:p>Gaussian Quadrature</text:p>
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-        <text:p>Many quadratures exist:</text:p>
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-        <text:p>In practice, the roots and weights are tabulated for these out to many numbers of points, so there is no need to compute them.</text:p>
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-      <text:p>Gaussian Quadrature</text:p>
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-      <text:p><text:span text:style-name="T16">erf(1) (exact): <text:s text:c="9"/>0.84270079295</text:span></text:p>
-      <text:p><text:span text:style-name="T16">3-point trapezoidal: <text:s text:c="4"/>0.825262955597 -0.017437837353</text:span></text:p>
-      <text:p><text:span text:style-name="T16">3-point Simpson&apos;s: <text:s text:c="6"/>0.843102830043 0.000402037093266</text:span></text:p>
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-      <text:p>Interpolation</text:p>
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-      <text:p>Interpolation</text:p>
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-        <text:p>We frequently have data only at a discrete number of points</text:p>
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-          <text:p><text:span text:style-name="T6">Interpolation fills in the gaps by making an assumption about the behavior of the functional form of the data</text:span></text:p>
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-        <text:p>Many different types of interpolation exist</text:p>
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-          <text:p>Some ensure no new extrema are introduced</text:p>
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-          <text:p>Some match derivatives at end points</text:p>
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-      <text:p>Linear Interpolation</text:p>
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-        <text:p>Simplest idea—draw a line between two points</text:p>
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-      <text:p>Lagrange Interpolation</text:p>
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-        <text:p>General method for building a single polynomial that goes through all the points (alternate formulations exist)</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Given <text:span text:style-name="T18">n</text:span><text:span text:style-name="T19"> points: </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T20">0</text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T20">1</text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">...</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T23">n</text:span><text:span text:style-name="T20">{1</text:span><text:span text:style-name="T19"> , with associated function values: </text:span><text:span text:style-name="T18">f</text:span><text:span text:style-name="T20">0</text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">f</text:span><text:span text:style-name="T20">1</text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">...</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">f</text:span><text:span text:style-name="T23">n</text:span><text:span text:style-name="T20">{1</text:span><text:span text:style-name="T19"> </text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T19">construct basis functions:</text:span></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T19">Basis function </text:span><text:span text:style-name="T18">l</text:span><text:span text:style-name="T23">i</text:span><text:span text:style-name="T19"> is 0 at all </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T23">j</text:span><text:span text:style-name="T19"> except for </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T23">i</text:span><text:span text:style-name="T19"> (where it is 1)</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T19">Function value at </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T19"> is:</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-      <text:p>Lagrange Interpolation</text:p>
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-        <text:p>Quadratic Lagrange polynomial:</text:p>
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-      <text:p><text:span text:style-name="T24">High-order is not always better: Interpolation through 15 points sampled uniformly from tanh(). <text:s/>The error is shown below.</text:span></text:p>
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-      <text:p>Splines</text:p>
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-        <text:p>So far, we&apos;ve only worried about going through the specified points</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Large number of points <text:span text:style-name="T14">→</text:span><text:span text:style-name="T25"> two distinct options:</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Use a single high-order polynomial that passes through them all</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T6">Fit a (somewhat) high order polynomial to </text:span><text:span text:style-name="T26">each interval</text:span><text:span text:style-name="T6"> and match all derivatives at each point—this is a spline</text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Splines match the derivatives at end points of intervals</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Piecewise splines can give a high-degree of accuracy</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Cubic spline is the most popular</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Matches first and second derivative at each data point</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Results in a smooth appearance</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Avoids severe oscillations of higher-order polynomial</text:p>
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-       </text:list-item>
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-      <text:p>Cubic Splines</text:p>
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-          <text:p>Note the sign of <text:span text:style-name="T18">f</text:span><text:span text:style-name="T21">(</text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T21">)</text:span><text:span text:style-name="T19"> </text:span>changes at the root</text:p>
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-      <text:p>Bisection</text:p>
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-        <text:p>Simplest method: <text:span text:style-name="T6">bisection</text:span></text:p>
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-          <text:p><text:span text:style-name="T19">The root lies in one of the two intervals—repeat the process</text:span></text:p>
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-      <text:p>Bisection</text:p>
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-        <text:p>Need two initial points (guesses) that you believe bound the root</text:p>
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-          <text:p>If there are two roots in-between, then you are in trouble</text:p>
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-          <text:p>Some pathological cases: e.g. <text:span text:style-name="T18">f</text:span><text:span text:style-name="T21">(</text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T21">)</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">=</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T30">2</text:span><text:span text:style-name="T19"> </text:span></text:p>
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-        <text:p><text:span text:style-name="T19">Convergence can be slow—each iteration reduces the error by a factor of 2</text:span></text:p>
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-      <text:p>Newton-Raphson</text:p>
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-        <text:p>If we know <text:span text:style-name="T31">df</text:span><text:span text:style-name="T32">/</text:span><text:span text:style-name="T31">dx <text:s text:c="3"/></text:span><text:span text:style-name="T33"><text:s/>we can do better </text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T34">Start with an initial guess, </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T28">0</text:span><text:span text:style-name="T19">,</text:span><text:span text:style-name="T34"> that is “close” to the root</text:span></text:p>
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-          <text:p><text:span text:style-name="T34">Taylor expansion:</text:span></text:p>
-          <text:p><text:span text:style-name="T34"/></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T34">If we are close, then</text:span></text:p>
-          <text:p><text:span text:style-name="T34"/></text:p>
-          <text:p><text:span text:style-name="T34"/></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T34">Update </text:span></text:p>
-          <text:p><text:span text:style-name="T34"/></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T34">We can continue, iterating again and again, until the change in the root &lt; </text:span><text:span text:style-name="T35">ε</text:span></text:p>
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-        <text:p><text:span text:style-name="T35">Converges fast: usually only a few iterations are needed</text:span></text:p>
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-        <text:p>Requirements for good convergence:</text:p>
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-        <text:p><text:span text:style-name="T36">Can be used with systems (we&apos;ll see this later)</text:span></text:p>
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-        <text:p><text:span text:style-name="T36">Multiple roots?</text:span></text:p>
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-          <text:p><text:span text:style-name="T36">Generally: try to start with a good estimate</text:span><text:span text:style-name="T37">*</text:span></text:p>
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-      <text:p>Newton-Raphson</text:p>
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-        <text:p><text:span text:style-name="T34">Consider </text:span><text:span text:style-name="T18">f</text:span><text:span text:style-name="T21">(</text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T21">)</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">=</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T39">3</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">{</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">2</text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">+2</text:span><text:span text:style-name="T19"> </text:span></text:p>
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-          <text:p><text:span text:style-name="T19">Start with 0 </text:span><text:span text:style-name="T40">→</text:span><text:span text:style-name="T41"> 1 </text:span><text:span text:style-name="T42">→ 0 → 1 → 0 ...</text:span></text:p>
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-      <text:p><text:span text:style-name="T44">(wikipedia)</text:span></text:p>
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-      <text:p>Secant Method</text:p>
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-       <text:list-item>
-        <text:p>If we don&apos;t know <text:span text:style-name="T31">df</text:span><text:span text:style-name="T32">/</text:span><text:span text:style-name="T31">dx <text:s text:c="3"/></text:span><text:span text:style-name="T33">, we can still use the same ideas</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T36">We need to initial guesses: </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T28">{1</text:span><text:span text:style-name="T19"> and </text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T20">0</text:span><text:span text:style-name="T19"> </text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T19">Use approximate derivative</text:span></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T19">Used when an analytic derivative is unavailable, or too expensive to compute (e.g. EOS)</text:span></text:p>
-           </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T45">Brent&apos;s method </text:span><text:span text:style-name="T19">combines bisection, secant, and other methods to provide a very reliable method</text:span></text:p>
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-      <text:p>Multivariate Newton&apos;s Method</text:p>
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-        <text:p>Imagine a vector function: </text:p>
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-          <text:p>Column vector of unknowns:</text:p>
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-        <text:p><text:span text:style-name="T6">We want to find the zeros:</text:span></text:p>
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-          <text:p>Initial guess: </text:p>
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-          <text:p><text:span text:style-name="T34">Taylor expansion:</text:span></text:p>
-          <text:p><text:span text:style-name="T46"/></text:p>
-          <text:p><text:span text:style-name="T34"/></text:p>
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-          <text:p><text:span text:style-name="T34">Update to initial guess is:</text:span></text:p>
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-            <text:p><text:span text:style-name="T47">Cheaper to solve the linear system than to invert the Jacobian</text:span></text:p>
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-          <text:p><text:span text:style-name="T34">Iterate:</text:span></text:p>
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-        <text:p>Consider orbits around the Sun</text:p>
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-      <text:p>Orbits: Euler&apos;s Method</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T51">Simplest case: Euler&apos;s method</text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T51">Need to specify a semi-major axis and eccentricity</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T51">Initial conditions:</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T36"/></text:p>
-          <text:p><text:span text:style-name="T52"/></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T47">This is counter-clockwise orbiting</text:span></text:p>
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-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§u = - \sqrt{ \frac{GM}{a} \frac{1+e}{1-e} } , \, v = 0</svg:desc>
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-      <text:p><text:span text:style-name="T53">Our planet escapes—clearly energy is not conserved here!</text:span></text:p>
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-      <text:p>Orbits: Euler&apos;s Method</text:p>
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-      <text:p><text:span text:style-name="T11">Things get better with a smaller timestep, but this is still first-order</text:span></text:p>
-      <text:p><text:span text:style-name="T11"/></text:p>
-      <text:p><text:span text:style-name="T54">Let&apos;s look at the code and see how small we need to get a closed circle</text:span></text:p>
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-      <text:p>Higher-order Methods</text:p>
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-        <text:p><text:span text:style-name="T17">Midpoint</text:span><text:span text:style-name="T51"> or </text:span><text:span text:style-name="T17">2</text:span><text:span text:style-name="T55">nd</text:span><text:span text:style-name="T17"> order Runge-Kutta</text:span><text:span text:style-name="T51">:</text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T51">The updates are then:</text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T51">This is third-order accurate (locally), to start things off, we do:</text:span></text:p>
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-     </draw:image>
-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§\frac{{\bf r}^{n+1} - {\bf r}^n}{\tau} = {\bf v}^{n+1/2} + \mathcal{O}(\tau^2)
-\qquad
-\frac{{\bf v}^{n+1} - {\bf v}^n}{\tau} = {\bf a}^{n+1/2} + \mathcal{O}(\tau^2)</svg:desc>
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-      <text:p>4<text:span text:style-name="T57">th</text:span>-order Runge-Kutta</text:p>
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-       <text:list-item>
-        <text:p>One of the most popular methods is 4<text:span text:style-name="T57">th</text:span>-order Runge-Kutta</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Consider system:</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Update through <text:span text:style-name="T18">¿</text:span><text:span text:style-name="T19"> :</text:span></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
-          <text:p><text:span text:style-name="T19"/></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T19">Notice the similarity to Simpson&apos;s integration</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T19">Derivation found in many analysis texts</text:span></text:p>
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-      <text:p>4<text:span text:style-name="T57">th</text:span>-order Runge-Kutta</text:p>
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-      <text:p><text:span text:style-name="T24">Even with a coarse timestep, RK4 does very well.</text:span></text:p>
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-      <text:p>Adaptive Stepping</text:p>
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-        <text:p><text:span text:style-name="T6">We need a smaller timestep where the solution changes most rapidly</text:span></text:p>
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-         <text:list-item>
-          <text:p>We can get away with large timesteps in regions of slow evolution</text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Monitoring the error can allow us to estimate the optimal timestep to reach some desired accuracy</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Lot&apos;s of different techniques for this in the literature</text:p>
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-         <text:list-item>
-          <text:p>Take two half steps and compare to one full state</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Compare higher and lower order methods</text:p>
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-        <text:p>Example from Garcia...</text:p>
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-      <text:p>Ex: Highly Elliptical Orbit</text:p>
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-        <text:p><text:span text:style-name="T13">Consider a highly elliptical orbit: a = 1.0, e = 0.95 </text:span></text:p>
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-      <text:p><text:span text:style-name="T11">Just to get a reasonable-looking solution, we needed to use </text:span><text:span text:style-name="T58">¿</text:span><text:span text:style-name="T59"> = 0.0005</text:span><text:span text:style-name="T60"> </text:span></text:p>
-      <text:p><text:span text:style-name="T60"/></text:p>
-      <text:p><text:span text:style-name="T60">This takes 2001 steps</text:span></text:p>
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-      <text:p>Ex: Highly-Elliptical Orbit</text:p>
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-        <text:p><text:span text:style-name="T13">Solutions with various uniform timesteps</text:span></text:p>
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-      <text:p>Ex: Highly-Elliptical Orbit</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T61">Look at the total energy</text:span></text:p>
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-          <text:p text:style-name="P18"><text:span text:style-name="T62">At perihelion, conservation is the worse</text:span></text:p>
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-          <text:p text:style-name="P18"><text:span text:style-name="T62">Perihelion is where the velocity is greatest, and therefore the solution changes the fastest</text:span></text:p>
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-        <text:p text:style-name="P18"><text:span text:style-name="T61">We can take a larger timestep at aphelion than perihelion</text:span></text:p>
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-      <text:p>Adaptive Stepping</text:p>
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-      <text:p>Adaptive Stepping/Error Estimation</text:p>
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-        <text:p>You should always perform some sort of error estimation</text:p>
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-        <text:p>Specifying absolute and relative errors in the state variables ensures you know about the quality of the solution</text:p>
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-        <text:p>The SciPy ODE packages can control all of this for you</text:p>
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-      <text:p>Stiff Equations / Implicit Methods</text:p>
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-        <text:p>Consider the ODE <text:span text:style-name="T65">(example from Byrne &amp; Hindmarsh 1986)</text:span>:</text:p>
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-        <text:p>This has the exact solution:</text:p>
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-          <text:p>Looking at this, we see that there are two characteristic timescales for change, <text:span text:style-name="T18">¿</text:span><text:span text:style-name="T28">1</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">=</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">1</text:span><text:span text:style-name="T34"> and </text:span><text:span text:style-name="T18">¿</text:span><text:span text:style-name="T28">2</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">=</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">10</text:span><text:span text:style-name="T39">{3</text:span><text:span text:style-name="T34"> </text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T34">A problem with dramatically different timescales for change is called </text:span><text:span text:style-name="T66">stiff</text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T33">Stiff ODEs can be hard for the methods we discussed so far</text:span></text:p>
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-          <text:p><text:span text:style-name="T34">Stability requires that we evolve on the shortest timescale</text:span></text:p>
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-      <text:p>VODE</text:p>
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-        <text:p>For stiff systems, robust integration packages exist. <text:s/></text:p>
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-       <text:list-item>
-        <text:p>VODE is a popular one (F77, but also in SciPy)</text:p>
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-         <text:list-item>
-          <text:p>Uses a 5<text:span text:style-name="T57">th</text:span>-order implicit method, with error estimation (alternately, a 15<text:span text:style-name="T57">th</text:span> order explicit method)</text:p>
-         </text:list-item>
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-          <text:p>Can either use a user-supplied Jacobian or compute one numerically</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Two types of tolerances: absolute and relative—these have a big influence on your solution</text:p>
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-         <text:list-item>
-          <text:p>Will do adaptive timesteps to reach your specified stop time</text:p>
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-      <text:p>Fitting</text:p>
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-      <text:p>Fitting Data<text:line-break/><text:span text:style-name="T3">(discussion following Garcia)</text:span></text:p>
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-        <text:p>We get experimental/observational data as a sequence of times (or positions) and associate values</text:p>
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-          <text:p><text:span text:style-name="T67">N</text:span><text:span text:style-name="T34"> points: </text:span><text:span text:style-name="T21">(</text:span><text:span text:style-name="T18">x</text:span><text:span text:style-name="T29">i</text:span><text:span text:style-name="T21">,</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">y</text:span><text:span text:style-name="T29">i</text:span><text:span text:style-name="T21">)</text:span><text:span text:style-name="T19"> </text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Often we have errors in our measurements at each of these values: <text:span text:style-name="T18">¾</text:span><text:span text:style-name="T29">i</text:span><text:span text:style-name="T19"> for each </text:span><text:span text:style-name="T18">y</text:span><text:span text:style-name="T23">i</text:span><text:span text:style-name="T19"> </text:span></text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T6">To understand the trends represented in our data, we want to find a simple functional form that best represents the data—this is the fitting problem</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>The <text:span text:style-name="T4">scipy.optimize</text:span> module offers routines to do fitting</text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>We&apos;ll look at least squares fitting</text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T6">Two cases: general linear and nonlinear</text:span></text:p>
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-      <text:p>Fitting Data</text:p>
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-        <text:p>We want to fit our data to a function: <text:span text:style-name="T33"><text:s/></text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T36">Here, the </text:span><text:span text:style-name="T18">a</text:span><text:span text:style-name="T29">j</text:span><text:span text:style-name="T34"> are a set of parameters that we can adjust</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>We want to find the optimal set of <text:span text:style-name="T68">a</text:span><text:span text:style-name="T69">j</text:span><text:span text:style-name="T70"> </text:span><text:span text:style-name="T71">that make </text:span><text:span text:style-name="T72">Y</text:span><text:span text:style-name="T73"> </text:span><text:span text:style-name="T71">best represent our data</text:span></text:p>
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-        <text:p><text:span text:style-name="T13">The distance between a point and the representative curve is</text:span></text:p>
-        <text:p><text:span text:style-name="T13"/></text:p>
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-          <text:p><text:span text:style-name="T6">Least squares fit minimizes the sum of the squares of all these errors</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T13">With error bars, we weight each distance error by the uncertainty in that measurement, giving:</text:span></text:p>
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-        <text:p><text:span text:style-name="T51">Minimization: derivative of </text:span><text:span text:style-name="T67">Â</text:span><text:span text:style-name="T74">2</text:span><text:span text:style-name="T34"> with respect to all parameters is zero:</text:span></text:p>
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-          <text:p><text:span text:style-name="T34">Define:</text:span></text:p>
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-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§\frac{\partial \chi^2}{\partial a_2} = 
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-     <svg:title>TexMaths</svg:title>
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-\xi_1 = \sum_{i=1}^N \frac{x_i}{\sigma_i^2} \qquad
-\xi_2 = \sum_{i=1}^N \frac{x_i^2}{\sigma_i^2}</svg:desc>
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-      <text:p>Goodness of the Fit</text:p>
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-        <text:p>Typically, if <text:span text:style-name="T31">M</text:span><text:span text:style-name="T33"> is the number of parameters (2 for linear), then </text:span><text:span text:style-name="T31">N</text:span><text:span text:style-name="T75"> </text:span><text:span text:style-name="T76">≫</text:span><text:span text:style-name="T75"> </text:span><text:span text:style-name="T31">M</text:span><text:span text:style-name="T33"> </text:span><text:s/></text:p>
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-          <text:p>Average pointwise error should be </text:p>
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-          <text:p><text:span text:style-name="T51">Number of degrees of freedom is </text:span><text:span text:style-name="T67">N</text:span><text:span text:style-name="T77"> { </text:span><text:span text:style-name="T67">M</text:span><text:span text:style-name="T34"> </text:span></text:p>
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-            <text:p><text:span text:style-name="T47">i.e. larger </text:span><text:span text:style-name="T48">M</text:span><text:span text:style-name="T47"> makes it easier to fit all the points</text:span></text:p>
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-            <text:p><text:span text:style-name="T47">See discussion in Numerical Recipes for more details and limitations</text:span></text:p>
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-          <text:p><text:span text:style-name="T34">Putting these ideas into the </text:span><text:span text:style-name="T18">Â</text:span><text:span text:style-name="T39">2</text:span><text:span text:style-name="T19"> </text:span><text:span text:style-name="T34">expression suggests that we consider</text:span></text:p>
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-            <text:p><text:span text:style-name="T34">If this is &lt; 1, then the fit is good</text:span></text:p>
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-            <text:p><text:span text:style-name="T34">But watch out, </text:span><text:span text:style-name="T78">≪</text:span><text:span text:style-name="T34"> 1 may also mean our errors were too large to begin with, we used too many parameters, ...</text:span></text:p>
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-      <text:p>General Linear Least Squares</text:p>
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-        <text:p><text:span text:style-name="T51">Garcia and Numerical Recipes provide a good discussion here</text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T51">We want to fit to</text:span></text:p>
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-        <text:p><text:span text:style-name="T79"/></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T11">Note that the </text:span><text:span text:style-name="T58">Y <text:s text:c="8"/></text:span><text:span text:style-name="T60">s may be nonlinear but we are still linear in the </text:span><text:span text:style-name="T58">a </text:span><text:span text:style-name="T60">s</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T80">Here, </text:span><text:span text:style-name="T81">Y</text:span><text:span text:style-name="T82">j</text:span><text:span text:style-name="T80"> are our basis set—they can be </text:span><text:span text:style-name="T81">x</text:span><text:span text:style-name="T83">j</text:span><text:span text:style-name="T80"> in which case we fit to a general polynomial</text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T84">Minimize:</text:span></text:p>
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-     </draw:image>
-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§latex§\begin{eqnarray}
-\frac{\partial \chi^2}{\partial a_j} =
-  \frac{\partial}{\partial a_j} \sum_{i=1}^N 
-  \frac{1}{\sigma_i^2} \left \{
-  \sum_{k=1}^M a_k Y_k(x_i) - y_i
-  \right \}^2 = 0 
-\longrightarrow \nonumber \\
-\qquad \sum_{i=1}^N \sum_{k=1}^M  \frac{Y_j(x_i)}{\sigma_i}
- \frac{Y_k(x_i)}{\sigma_i} a_k = \sum_{i=1}^N \frac{Y_j(x_i)}{\sigma_i} \frac{y_i}{\sigma_i}
-\nonumber
-\end{eqnarray}</svg:desc>
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-      <text:p>Errors in Both x and y</text:p>
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-        <text:p>Depending on the experiment, you may have errors in the dependent variable</text:p>
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-          <text:p>For linear regression, our function to minimize becomes:</text:p>
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-            <text:p>Denominator is the total variance of the linear combination we are minimizing:</text:p>
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-        <text:p>We cannot solve analytically for the parameters, but we can use our root finding techniques on this.</text:p>
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-         <text:list-item>
-          <text:p>See <text:s/>NR and references therein for more details</text:p>
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-      <text:p>General Non-linear Fitting</text:p>
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-        <text:p><text:span text:style-name="T51">Consider fitting directly to a function where the parameters enter nonlinearly: </text:span></text:p>
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-          <text:p><text:span text:style-name="T24">We want to minimize</text:span></text:p>
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-          <text:p><text:span text:style-name="T24">Set the derivatives to zero:</text:span></text:p>
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-          <text:p><text:span text:style-name="T85"/></text:p>
-          <text:p><text:span text:style-name="T24"/></text:p>
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-        <text:p><text:span text:style-name="T51">This is a nonlinear system—we use something like the multivariate root find</text:span></text:p>
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-      <text:p><text:span text:style-name="T3">(Yakowitz and Szidarovszky)</text:span></text:p>
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-      <text:p>Fourier Transform</text:p>
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-        <text:p><text:span text:style-name="T51">Fourier transform converts a physical-space (or time series) representation of a function into frequency-space</text:span></text:p>
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-          <text:p><text:span text:style-name="T24">Equivalent representation of the function, new view into its behavior</text:span></text:p>
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-          <text:p><text:span text:style-name="T24">Inverse operation exists</text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T17">You can think of </text:span><text:span text:style-name="T86">F</text:span><text:span text:style-name="T87">(</text:span><text:span text:style-name="T86">k</text:span><text:span text:style-name="T87">)</text:span><text:span text:style-name="T66"> as being the amount of the function </text:span><text:span text:style-name="T86">f</text:span><text:span text:style-name="T66"> represented by a frequency </text:span><text:span text:style-name="T86">k</text:span><text:span text:style-name="T66"> </text:span></text:p>
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-        <text:p><text:span text:style-name="T84">For discrete data, the discrete analog of the Fourier transform gives:</text:span></text:p>
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-          <text:p><text:span text:style-name="T24">Amplitude and phase at discrete frequencies (wavenumbers)</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Allows for an investigation into the periodicity of the discrete data</text:span></text:p>
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-          <text:p><text:span text:style-name="T24">Allows for filtering in frequency-space</text:span></text:p>
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-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§f(x) = \int_{-\infty}^{\infty} F(k) e^{2\pi i x k} dk</svg:desc>
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-      <text:p>Discrete Fourier Transform</text:p>
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-        <text:p><text:span text:style-name="T51">The discrete Fourier transform (DFT) operates on discrete data</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Usually we have evenly-spaced, real data </text:span></text:p>
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-           <text:list-item>
-            <text:p>E.g. a time-series from an experiment</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Simulation data for a velocity field</text:p>
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-          </text:list>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T17">DFT transforms the N spatial/temporal points into N frequency points</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Transform:</text:span></text:p>
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-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Inverse:</text:span></text:p>
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-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">This is the form used in NumPy, Garcia&apos;s text, and others</text:span></text:p>
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-      <text:p>Discrete Fourier Transform</text:p>
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-        <text:p><text:span text:style-name="T17">What are the significance of the Re and Im parts?</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Recall that we are integrating with</text:span> <text:span text:style-name="T48">e</text:span><text:span text:style-name="T39">{2</text:span><text:span text:style-name="T88">¼</text:span><text:span text:style-name="T89"> </text:span><text:span text:style-name="T88">i</text:span><text:span text:style-name="T89"> </text:span><text:span text:style-name="T88">k</text:span><text:span text:style-name="T89"> </text:span><text:span text:style-name="T88">x</text:span><text:span text:style-name="T19"> </text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T47">Euler&apos;s formula:</text:span><text:span text:style-name="T34"> </text:span><text:span text:style-name="T48">e</text:span><text:span text:style-name="T90">i</text:span><text:span text:style-name="T91"> </text:span><text:span text:style-name="T90">x</text:span><text:span text:style-name="T77"> </text:span><text:span text:style-name="T49">= cos(</text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T49">) + </text:span><text:span text:style-name="T48">i</text:span><text:span text:style-name="T49"> sin(</text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T49">)</text:span><text:span text:style-name="T47"> </text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T47">Real part represents the cosine terms, symmetric functions</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T47">Imaginary part represents the sine terms, antisymmetric functions</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T47">Can also think in terms of an amplitude and a phase</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T47">If </text:span><text:span text:style-name="T48">f</text:span><text:span text:style-name="T29">n</text:span><text:span text:style-name="T19"> </text:span><text:span text:style-name="T47">is Real, then:</text:span></text:p>
-          <text:p><text:span text:style-name="T47"/></text:p>
-          <text:p><text:span text:style-name="T47"/></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T34">An FFT is simply an efficient way to implement the sums in the DFT</text:span></text:p>
-       </text:list-item>
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-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§\mathrm{Im}(F_k) = \sum_{n=0}^{N-1} f_n \sin \left (\frac{2\pi n k}{N} \right )</svg:desc>
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-      <text:p>Python/NumPy&apos;s FFT</text:p>
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-        <text:p><text:span text:style-name="T92">numpy.fft</text:span>: <text:a xlink:href="http://docs.scipy.org/doc/numpy/reference/routines.fft.html" xlink:type="simple">http://docs.scipy.org/doc/numpy/reference/routines.fft.html</text:a></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T92">fft</text:span>/<text:span text:style-name="T92">ifft</text:span>: 1-d data</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>By design, the k=0, ... N/2 data is first, followed by the negative frequencies. <text:s/>These later are not relevant for a real-valued f(x)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>k&apos;s can be obtained from <text:span text:style-name="T92">fftfreq(n)</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T92">fftshift(x)</text:span><text:span text:style-name="T5"> shifts the k=0 to the center of the spectrum</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T92">rfft</text:span><text:span text:style-name="T5">/</text:span><text:span text:style-name="T92">irfft</text:span><text:span text:style-name="T5">: for 1-d real-valued functions. <text:s/>Basically the same as </text:span><text:span text:style-name="T92">fft</text:span><text:span text:style-name="T5">/</text:span><text:span text:style-name="T92">ifft</text:span><text:span text:style-name="T5">, but doesn&apos;t return the negative frequencies</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T5">2-d and n-d routines analogously defined</text:span></text:p>
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-      <text:p>Real Space vs. Frequency Space</text:p>
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-        <text:p>Imagine transforming a real-valued function, <text:span text:style-name="T31">f</text:span><text:span text:style-name="T33"> , with </text:span><text:span text:style-name="T31">N</text:span><text:span text:style-name="T33"> data points</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T51">The FFT of </text:span><text:span text:style-name="T67">f</text:span><text:span text:style-name="T34"> will have </text:span><text:span text:style-name="T67">N</text:span><text:span text:style-name="T77">/2</text:span><text:span text:style-name="T34"> <text:s/>complex data points</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Same amount of information in each case, but each complex point corresponds to 2 real numbers.</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T24">This is a consequence of the analytic Fourier transform satisfying </text:span><text:span text:style-name="T48">F</text:span><text:span text:style-name="T49">({</text:span><text:span text:style-name="T48">k</text:span><text:span text:style-name="T49">) = </text:span><text:span text:style-name="T48">F</text:span><text:span text:style-name="T93">⋆</text:span><text:span text:style-name="T49">(</text:span><text:span text:style-name="T48">k</text:span><text:span text:style-name="T49">)</text:span><text:span text:style-name="T47"> if </text:span><text:span text:style-name="T48">f</text:span><text:span text:style-name="T49">(</text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T49">)</text:span><text:span text:style-name="T47"> is real</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T17">Most FFT routines will return </text:span><text:span text:style-name="T94">N</text:span><text:span text:style-name="T66"> complex points—half of them are duplicate, i.e. they don&apos;t add to the information content</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T80">Often, there are specific implementations optimized for the case where the function is real (e.g. rfft)</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T84">This affects normalization (note k=0 different)</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T95">This is also referred to as aliasing. <text:s/>The maximum frequency, </text:span><text:span text:style-name="T96">1/ (2 ¢</text:span><text:span text:style-name="T97">x</text:span><text:span text:style-name="T96">)</text:span><text:span text:style-name="T95"> is called the </text:span><text:span text:style-name="T98">Nyquist frequency</text:span></text:p>
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-      <text:p>Normalization</text:p>
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-        <text:p><text:span text:style-name="T51">When plotting, we want to put the physical scale back in, as well as make the correspondence to the continuous representation</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Wavenumber to frequency:</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T24">In index space, the smallest wavelength is from one cell to the next, and the smallest frequency is 1/N</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T24">Adding points to our data means we open up higher and higher frequencies</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T24">Lowest frequency: 1/L; <text:s/>highest frequency: 1/</text:span><text:span text:style-name="T99">Δ</text:span><text:span text:style-name="T100">x</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Amplitudes: rewrite inverse expression:</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Another interpretation: </text:span><text:span text:style-name="T48">F</text:span><text:span text:style-name="T29">k</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T49">/ </text:span><text:span text:style-name="T48">N</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T101">⇔</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">F</text:span><text:span text:style-name="T29">k</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">dk</text:span><text:span text:style-name="T47"> </text:span></text:p>
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-      <text:p><text:span text:style-name="T7">This is what we plot</text:span></text:p>
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-      <text:p>FFT Tips</text:p>
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-        <text:p>Different FFT codes make different assumptions about the normalization, etc.</text:p>
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-        <text:p>Make sure the FFT does what you expect it should be working on a single-frequency sine first.</text:p>
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-      <text:p>Linear Algebra</text:p>
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-      <text:p>Numerical Linear Algebra</text:p>
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-        <text:p>The basic problem we wish to solve is: <text:span text:style-name="T31">A</text:span><text:span text:style-name="T75"> </text:span><text:span text:style-name="T31">x</text:span><text:span text:style-name="T75"> </text:span><text:span text:style-name="T32">=</text:span><text:span text:style-name="T75"> </text:span><text:span text:style-name="T31">b</text:span><text:span text:style-name="T33"> </text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T34">It is generally simpler to solve this system directly than to first find the inverse and then multiply A</text:span><text:span text:style-name="T103">-1</text:span><text:span text:style-name="T34"> b</text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T33">Gaussian elimination works with a general matrix</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T33">Faster methods exist for banded, sparse, symmetric, ... matrices</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T33">Explicit code for doing Gaussian elimination posted on our course page. <text:s/>See any numerical text for a discussion of the algorithm.</text:span></text:p>
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-      <text:p>Gaussian Elimination</text:p>
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-        <text:p>The main, general technique for solving a linear system <text:span text:style-name="T31">Ax</text:span><text:span text:style-name="T32">=</text:span><text:span text:style-name="T31">b</text:span><text:span text:style-name="T33"> is Gaussian-Elimination</text:span></text:p>
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-         <text:list-item>
-          <text:p>Doesn&apos;t require computing an inverse</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>For special matrices, faster techniques may apply</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T13">Forward-elimination + Back-substitution steps</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T13">Round-off can cause issues and lead to systems that are unsolvable</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T13">Example from Garcia</text:span></text:p>
-          <text:p><text:span text:style-name="T13"/></text:p>
-          <text:p><text:span text:style-name="T13"/></text:p>
-          <text:p><text:span text:style-name="T13"/></text:p>
-          <text:p><text:span text:style-name="T13">in limit </text:span><text:span text:style-name="T68">²</text:span><text:span text:style-name="T104"> </text:span><text:span text:style-name="T105">→</text:span><text:span text:style-name="T104"> </text:span><text:span text:style-name="T106">0</text:span><text:span text:style-name="T107"> </text:span></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p text:style-name="P15"><text:span text:style-name="T24">Eliminate x</text:span><text:span text:style-name="T108">1</text:span><text:span text:style-name="T24"> from the second and third eqs</text:span></text:p>
-        <text:p text:style-name="P15"><text:span text:style-name="T24"/></text:p>
-        <text:p text:style-name="P15"><text:span text:style-name="T24"/></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p text:style-name="P15"><text:span text:style-name="T24">Eliminate x</text:span><text:span text:style-name="T109">2</text:span><text:span text:style-name="T24"> from the last eq.</text:span></text:p>
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-     <svg:desc>24§latex§\begin{alignat}{4}
-\phantom{+}x_1   &amp;+ &amp;   x_2 &amp;+&amp;           x_3 &amp;=&amp; 6 \nonumber \\
--x_1 &amp;+&amp;  2x_2                                &amp;\phantom{+}&amp;        &amp;=&amp; 3 \nonumber \\
-2x_1 &amp;\phantom{+}&amp;  \phantom{x}  &amp;+&amp; x_3  &amp;=&amp; 5 \nonumber
-\end{alignat}
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-     <svg:desc>24§latex§\begin{alignat}{4}
-\phantom{+}x_1   &amp;+&amp;   x_2   &amp;+&amp;   x_3  &amp;=&amp; 6 \nonumber \\
-                               &amp;\phantom{+}&amp;  3x_2 &amp;+ &amp;  x_3  &amp;=&amp; 9 \nonumber \\
-                               &amp;-&amp;  2x_2  &amp;-&amp; x_3  &amp;=&amp; -7 \nonumber
-\end{alignat}
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-     <svg:desc>24§latex§\begin{alignat}{4}
-\phantom{+}x_1   &amp;+&amp;   x_2   &amp;+&amp;   x_3  &amp;=&amp; 6 \nonumber \\
-                               &amp;\phantom{+}&amp;  3x_2 &amp;+ &amp;  x_3  &amp;=&amp; 9 \nonumber \\
-                               &amp;\phantom{+}&amp;            &amp;-&amp; \tfrac{1}{3}x_3  &amp;=&amp; -1 \nonumber
-\end{alignat}
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-      <text:p>Gaussian Elimination</text:p>
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-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>4.000 <text:s text:c="2"/>3.000 <text:s text:c="2"/>4.000 <text:s/>10.000 | <text:s text:c="7"/>| <text:s/>2.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>2.000 <text:s/>-7.000 <text:s text:c="2"/>3.000 <text:s text:c="2"/>0.000 | <text:s text:c="7"/>| <text:s/>6.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| -2.000 <text:s/>11.000 <text:s text:c="2"/>1.000 <text:s text:c="2"/>3.000 | <text:s text:c="7"/>| <text:s/>3.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>3.000 <text:s/>-4.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>2.000 | <text:s text:c="7"/>| <text:s/>1.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">\ <text:s text:c="31"/>/ <text:s text:c="7"/>\ <text:s text:c="7"/>/</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T112"/></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">/ <text:s text:c="31"/>\ <text:s text:c="7"/>/ <text:s text:c="7"/>\</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>3.000 <text:s/>-4.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>2.000 | <text:s text:c="7"/>| <text:s/>1.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s/>-4.333 <text:s text:c="2"/>3.000 <text:s/>-1.333 | <text:s text:c="7"/>| <text:s/>5.333 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>8.333 <text:s text:c="2"/>1.000 <text:s text:c="2"/>4.333 | <text:s text:c="7"/>| <text:s/>3.667 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>8.333 <text:s text:c="2"/>4.000 <text:s text:c="2"/>7.333 | <text:s text:c="7"/>| <text:s/>0.667 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">\ <text:s text:c="31"/>/ <text:s text:c="7"/>\ <text:s text:c="7"/>/</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T112"/></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">/ <text:s text:c="31"/>\ <text:s text:c="7"/>/ <text:s text:c="7"/>\</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>3.000 <text:s/>-4.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>2.000 | <text:s text:c="7"/>| <text:s/>1.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>8.333 <text:s text:c="2"/>4.000 <text:s text:c="2"/>7.333 | <text:s text:c="7"/>| <text:s/>0.667 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>0.000 <text:s/>-3.000 <text:s/>-3.000 | <text:s text:c="7"/>| <text:s/>3.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>5.080 <text:s text:c="2"/>2.480 | <text:s text:c="7"/>| <text:s/>5.680 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">\ <text:s text:c="31"/>/ <text:s text:c="7"/>\ <text:s text:c="7"/>/</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T112"/></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">/ <text:s text:c="31"/>\ <text:s text:c="7"/>/ <text:s text:c="7"/>\</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>3.000 <text:s/>-4.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>2.000 | <text:s text:c="7"/>| <text:s/>1.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>8.333 <text:s text:c="2"/>4.000 <text:s text:c="2"/>7.333 | <text:s text:c="7"/>| <text:s/>0.667 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>5.080 <text:s text:c="2"/>2.480 | <text:s text:c="7"/>| <text:s/>5.680 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>0.000 <text:s/>-1.535 | <text:s text:c="7"/>| <text:s/>6.354 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">\ <text:s text:c="31"/>/ <text:s text:c="7"/>\ <text:s text:c="7"/>/</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T112"/></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">/ <text:s text:c="31"/>\ <text:s text:c="7"/>/ <text:s text:c="7"/>\</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>3.000 <text:s/>-4.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>2.000 | <text:s text:c="7"/>| <text:s/>1.000 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>8.333 <text:s text:c="2"/>4.000 <text:s text:c="2"/>7.333 | <text:s text:c="7"/>| <text:s/>0.667 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>5.080 <text:s text:c="2"/>2.480 | <text:s text:c="7"/>| <text:s/>5.680 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">| <text:s/>0.000 <text:s text:c="2"/>0.000 <text:s text:c="2"/>0.000 <text:s/>-1.535 | <text:s text:c="7"/>| <text:s/>6.354 |</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T111">\ <text:s text:c="31"/>/ <text:s text:c="7"/>\ <text:s text:c="7"/>/</text:span></text:p>
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-      <text:p text:style-name="P22"><text:span text:style-name="T113">solved x: <text:s/>[ 6.04615385 <text:s/>2.21538462 <text:s/>3.13846154 -4.13846154]</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T113">A.x: <text:s/>[ 2. <text:s/>6. <text:s/>3. <text:s/>1.]</text:span></text:p>
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-      <text:p text:style-name="P25"><text:span text:style-name="T54">Sometimes we do partial pivoting: swap rows to put the one with the largest (scaled) element on top.</text:span></text:p>
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-      <text:p>Caveats</text:p>
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-        <text:p><text:span text:style-name="T6">Singular matrix</text:span></text:p>
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-         <text:list-item>
-          <text:p>If the matrix has no determinant, then we cannot solve the system</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Common way for this to enter: one equation in our system is a linear combination of some others</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Not always easy to detect from the start</text:p>
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-      <text:p>Gaussian Elimination</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T6">Condition number</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Measure of how close to singular we are</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T24">Think of it as a measure of how much </text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T47"> </text:span><text:span text:style-name="T24">would change due to a small change in </text:span><text:span text:style-name="T48">b</text:span><text:span text:style-name="T47"> </text:span><text:span text:style-name="T24">(large condition number means G-E inaccurate)</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Formal definition:</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Further elucidation requires defining the norm </text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Rule of thumb <text:span text:style-name="T65">(Yakowitz &amp; Szidarovszky, Eq. 2.31)</text:span>:</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T24">If </text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T93">⋆</text:span><text:span text:style-name="T47"> is the exact solution and </text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T47"> is the computed solution, then</text:span><text:span text:style-name="T114"> </text:span></text:p>
-            <text:p><text:span text:style-name="T114"/></text:p>
-            <text:p><text:span text:style-name="T114"/></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T115">Simple ill-conditioned example: see </text:span><text:span text:style-name="T116">G. J. Tee, ACM SIGNUM Newsletter, v. 7, issue 3, Oct. 1972, p. 19 (posted) </text:span></text:p>
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-      <text:p>Tridiagonal Solve</text:p>
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-        <text:p>Tridiagonal systems come up very often in physical systems</text:p>
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-        <text:p>Efficient, O(N), algorithm derived from looking at the Gaussian elimination sequence <text:span text:style-name="T65">(see e.g. Wikipedia)</text:span>:</text:p>
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-          <text:p>Standard data layout. </text:p>
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-      <text:p><text:span text:style-name="T11">Note that </text:span><text:span text:style-name="T58">a</text:span><text:span text:style-name="T117">0</text:span><text:span text:style-name="T118"> </text:span><text:span text:style-name="T59">=</text:span><text:span text:style-name="T118"> </text:span><text:span text:style-name="T58">c</text:span><text:span text:style-name="T119">N</text:span><text:span text:style-name="T117">{1</text:span><text:span text:style-name="T118"> </text:span><text:span text:style-name="T59">=</text:span><text:span text:style-name="T118"> </text:span><text:span text:style-name="T59">0</text:span><text:span text:style-name="T60"> </text:span></text:p>
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-      <text:p>LU Decomposition</text:p>
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-        <text:p>LU Decomposition is an alternate to Gaussian elimination</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>See Pang/Wikipedia for details</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Basic idea:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Find upper and lower triangular matrices such that <text:span text:style-name="T18">A</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T21">=</text:span><text:span text:style-name="T22"> </text:span><text:span text:style-name="T18">LU</text:span><text:span text:style-name="T19"> </text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T51">Express </text:span><text:span text:style-name="T67">Ax</text:span><text:span text:style-name="T77"> =</text:span><text:span text:style-name="T67">b</text:span><text:span text:style-name="T34"> as </text:span><text:span text:style-name="T67">Ly</text:span><text:span text:style-name="T77"> = </text:span><text:span text:style-name="T67">b</text:span><text:span text:style-name="T34"> and </text:span><text:span text:style-name="T67">Ux</text:span><text:span text:style-name="T77"> = </text:span><text:span text:style-name="T67">y</text:span><text:span text:style-name="T34"> </text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T66">Most of the work in the solve is in doing the decomposition, so if you need to solve for many different </text:span><text:span text:style-name="T86">b</text:span><text:span text:style-name="T66">&apos;s, this is more efficient</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T33">This is often used in stiff integration packages</text:span></text:p>
-       </text:list-item>
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-      <text:p>BLAS/LINPACK/LAPACK</text:p>
-     </draw:text-box>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T6">BLAS (basic linear algebra subroutines)</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>These are the standard building blocks (API) of linear algebra on a computer (Fortran and C)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Most linear algebra packages formulate their operations in terms of BLAS operations</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Three levels of functionality:</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T24">Level 1: vector operations (</text:span><text:span text:style-name="T48">®</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T49"> + </text:span><text:span text:style-name="T48">y</text:span><text:span text:style-name="T47"> )</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T47">Level 2: matrix-vector operations (</text:span><text:span text:style-name="T48">®</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">A</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">x</text:span><text:span text:style-name="T49"> + </text:span><text:span text:style-name="T48">¯</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">y</text:span><text:span text:style-name="T47"> )</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T47">Level 3: matrix-matrix operations (</text:span><text:span text:style-name="T48">®</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">A</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">B</text:span><text:span text:style-name="T49"> + </text:span><text:span text:style-name="T48">¯</text:span><text:span text:style-name="T49"> </text:span><text:span text:style-name="T48">C</text:span><text:span text:style-name="T47"> )</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Available on pretty much every platform</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Some compilers provide specially optimized BLAS libraries (-lblas) that take great advantage of the underlying processor instructions</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>ATLAS: automatically tuned linear algebra software</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
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-      <text:p>BLAS/LINPACK/LAPACK</text:p>
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-        <text:p><text:span text:style-name="T6">LINPACK</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Older standard linear algebra package from 1970s designed for architectures available then</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Superseded by LAPACK</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T6">LAPACK</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>The standard for linear algebra</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Built upon BLAS</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Routines named in the form <text:span text:style-name="T92">xyyzzz</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T92">x</text:span> refers to the data type (s/d are single/double precision floating, c/z are single/double complex)</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T92">yy</text:span> refers to the matrix type</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T92">zzz</text:span> refers to the algorithm (e.g. <text:span text:style-name="T92">sgebrd</text:span> = single precision bi-diagonal reduction of a general matrix)</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Ex: single precision routines: <text:a xlink:href="http://www.netlib.org/lapack/single/" xlink:type="simple">http://www.netlib.org/lapack/single/</text:a></text:p>
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-     <draw:text-box>
-      <text:p xml:id="id8" text:id="id8"><text:span text:style-name="T54">See Top 500 list</text:span></text:p>
-     </draw:text-box>
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-      <text:p>Python Support for Linear Algebra</text:p>
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-        <text:p>Basic methods in numpy.linalg</text:p>
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-          <text:p><text:a xlink:href="http://docs.scipy.org/doc/numpy/reference/routines.linalg.html" xlink:type="simple">http://docs.scipy.org/doc/numpy/reference/routines.linalg.html</text:a></text:p>
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-        <text:p>More general stuff in SciPy (scipy.linalg)</text:p>
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-         <text:list-item>
-          <text:p><text:a xlink:href="http://docs.scipy.org/doc/scipy/reference/linalg.html" xlink:type="simple">http://docs.scipy.org/doc/scipy/reference/linalg.html</text:a></text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>SciPy calls the LAPACK routines under the hood (in the SciPy docs, you can click on the “source” link to see the explicit calls).</text:p>
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-      <text:p>Python Support for Linear Algebra</text:p>
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-        <text:p>Numpy has a matrix type built from the array class</text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T92">*</text:span> operator works element by element for arrays but does matrix product for matrices</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Vectors are automatically converted into 1<text:span text:style-name="T14">×</text:span><text:span text:style-name="T25">N or N</text:span><text:span text:style-name="T14">×1 matrices</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T14">Matrix has </text:span><text:span text:style-name="T120">.H</text:span><text:span text:style-name="T14">, </text:span><text:span text:style-name="T120">.I</text:span><text:span text:style-name="T14">, and </text:span><text:span text:style-name="T120">.A</text:span><text:span text:style-name="T14"> attributes (transpose, inverse, as array)</text:span></text:p>
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-          <text:p><text:span text:style-name="T14">See </text:span><text:span text:style-name="T14"><text:a xlink:href="http://www.scipy.org/NumPy_for_Matlab_Users" xlink:type="simple">http://www.scipy.org/NumPy_for_Matlab_Users</text:a></text:span><text:span text:style-name="T14"> for more details</text:span></text:p>
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-          <text:p><text:span text:style-name="T122"><text:a xlink:href="http://www.scipy.org/Tentative_NumPy_Tutorial" xlink:type="simple">http://www.scipy.org/Tentative_NumPy_Tutorial</text:a></text:span></text:p>
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diff --git a/lectures/05-python-practices/python-practices.fodp b/lectures/05-python-practices/python-practices.fodp
deleted file mode 100644
index 7bf50cfc..00000000
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-        <text:p><text:span text:style-name="T3">We&apos;ll look at some basic python style guidelines and some tools that help with the development process</text:span></text:p>
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-      <text:p>Software Engineering Practices</text:p>
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-        <text:p><text:span text:style-name="T4">Some basic practices that can </text:span><text:span text:style-name="T5">greatly </text:span><text:span text:style-name="T6">enhance your ability to write maintainable code</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Version control</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Documentation</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Testing procedures</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>For compiled languages, I would add Makefiles, profilers, code analysis tools (like valgrind)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">There is an official python coding standard: PEP 8</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">Helps you interact with a distributed group of developers—everyone writes code with the same convention</text:span></text:p>
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-      <text:p>Python Style</text:p>
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-        <text:p>The recommended python style is described in a “Python Enhancement Proposal”, PEP-8</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:a xlink:href="http://legacy.python.org/dev/peps/pep-0008/" xlink:type="simple">http://legacy.python.org/dev/peps/pep-0008/</text:a></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Based on the idea that “code is read much more often than it is written”</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Some highlights:</text:p>
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-         <text:list-item>
-          <text:p>Indentation should use 4 spaces (no tabs)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Lines should be less than 79 characters</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Continuation via &apos;<text:span text:style-name="T8">\</text:span>&apos; or <text:span text:style-name="T8">()</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Classes should be capitalized of form <text:span text:style-name="T8">MyClass</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Function names, objects, variables, should be lower case, with <text:span text:style-name="T8">_</text:span> separating words in the name</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Constants in <text:span text:style-name="T8">ALL_CAPS</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>We&apos;ll look at the basic programming styles at the official PEP-8 page</text:p>
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-      <text:p>Python Bits We Glossed Over</text:p>
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-        <text:p>With</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Try/except</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Pickle</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>...</text:p>
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-      <text:p><text:span text:style-name="T8">with</text:span></text:p>
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-        <text:p><text:span text:style-name="T8">with</text:span> uses a context manager that has a special <text:span text:style-name="T8">__enter__</text:span> and <text:span text:style-name="T8">__exit__</text:span> function.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Simplifies some common constructions</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Eg.</text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P9"><text:span text:style-name="T9">with open(&quot;x.txt&quot;) as f:</text:span></text:p>
-      <text:p text:style-name="P9"><text:span text:style-name="T9"><text:s text:c="4"/></text:span><text:span text:style-name="T9">data = f.read()</text:span></text:p>
-      <text:p text:style-name="P9"><text:span text:style-name="T9"><text:s text:c="4"/></text:span><text:span text:style-name="T9">do something with data</text:span></text:p>
-      <text:p text:style-name="P9"><text:span text:style-name="T8"/></text:p>
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-          <text:p text:style-name="P10"><text:span text:style-name="T10">This will open the file, return the file object </text:span><text:span text:style-name="T8">f</text:span><text:span text:style-name="T10">, allow you to work with it, and close the file automatically when this block is over</text:span></text:p>
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-      <text:p><text:span text:style-name="T11">http://effbot.org/zone/python-with-statement.htm</text:span></text:p>
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-      <text:p><text:span text:style-name="T8">try</text:span>/<text:span text:style-name="T8">except</text:span></text:p>
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-       <text:list-item>
-        <text:p>We skipped over this originally.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T8">try</text:span>/<text:span text:style-name="T8">except</text:span> provide a good way to catch errors</text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P12"><text:span text:style-name="T9">try: f = open(&quot;existing-file&quot;, &quot;r&quot;)</text:span></text:p>
-      <text:p text:style-name="P12"><text:span text:style-name="T9">except:</text:span></text:p>
-      <text:p text:style-name="P12"><text:span text:style-name="T9"><text:s text:c="4"/></text:span><text:span text:style-name="T9">print &quot;Error openning the file&quot;</text:span></text:p>
-      <text:p text:style-name="P12"><text:span text:style-name="T9">else:</text:span></text:p>
-      <text:p text:style-name="P12"><text:span text:style-name="T9"><text:s text:c="4"/></text:span><text:span text:style-name="T9"># work with the file</text:span></text:p>
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-        <text:p/>
-       </text:list-header>
-       <text:list-item>
-        <text:p>It is good practice to use these when interacting with files, the user, etc., to allow you to recover from errors.</text:p>
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-      <text:p><text:span text:style-name="T8">pickle</text:span></text:p>
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-        <text:p><text:span text:style-name="T8">pickle</text:span> takes an object and “serializes” it so it can be written to disk</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>You can then read in a pickled object and it is restored as the original object.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Ex:</text:p>
-       </text:list-item>
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-      <text:p text:style-name="P13"><text:span text:style-name="T9">pF = open(filename + &quot;.pyro&quot;, &quot;wb&quot;)</text:span></text:p>
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-      <text:p>Coding Style</text:p>
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-        <text:p><text:span text:style-name="T10">Don&apos;t make assumptions</text:span></text:p>
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-          <text:p><text:span text:style-name="T10">For </text:span><text:span text:style-name="T8">if</text:span><text:span text:style-name="T10"> clauses, have a default block (else) to catch conditions outside of what you may have expected</text:span></text:p>
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-          <text:p><text:span text:style-name="T10">Use </text:span><text:span text:style-name="T8">try</text:span><text:span text:style-name="T10">/</text:span><text:span text:style-name="T8">except</text:span><text:span text:style-name="T10"> to catch errors</text:span></text:p>
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-        <text:p><text:span text:style-name="T10">Use functions/subroutines for repetitive tasks</text:span></text:p>
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-          <text:p><text:span text:style-name="T10">Check return values for errors</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T10">Use well-tested libraries instead of rolling your own when possible</text:span></text:p>
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-      <text:p>Version Control</text:p>
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-        <text:p><text:span text:style-name="T7">Old days: create a tar file with the current source, mail it around, manually merge different people&apos;s changes...</text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Version control systems keep track of the history of changes to source code</text:span></text:p>
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-         <text:list-item>
-          <text:p>Logs describe the changes to each file over time</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Allow you to request the source as it was at any time in the past</text:p>
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-          <text:p>Multiple developers can share and synchronize changes</text:p>
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-            <text:p>Merges changes by different developers to the same file</text:p>
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-           <text:list-item>
-            <text:p>Provide mechanisms to resolve conflicting changes to files</text:p>
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-          <text:p>Provide mechanisms to create a branch to develop new features and then merge it back into the main source.</text:p>
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-      <text:p>Version Control</text:p>
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-        <text:p><text:span text:style-name="T3">Even for a single developer version control is a great asset</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">Common task: you notice that your code is giving different answers/behavior than you&apos;ve seen in the past</text:span></text:p>
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-           <text:list-item>
-            <text:p><text:span text:style-name="T7">Check out an old copy where you know it was working</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T7">Bisect the history between the working and broken dates to pin down the change</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T12">Can also use it for papers and proposals—all the authors can work on the same LaTeX source and share chages</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>All of these slides are stored in version control—let&apos;s me work on them from anywhere easily</text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T3">Fun trick:</text:span> LibreOffice files are zipped XML, but you can have it store the output as uncompressed “flat” XML files (.fodp instead of .odp)</text:p>
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-      <text:p><text:span text:style-name="T13">Centralized vs. Distributed Version Control</text:span></text:p>
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-        <text:p><text:span text:style-name="T14">Centralized</text:span><text:span text:style-name="T15"> (e.g. CVS, subversion)</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T16">Server holds the master copy of the source, stores history, changes</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T16">User communicates with server</text:span></text:p>
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-            <text:p><text:span text:style-name="T16">Checkout source</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T16">Commit changes back to the source</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T16">Request the log (history) of a file from the server</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T16">Diff your local version with the version in the server</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T16">Doesn&apos;t scale well for very large projects</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T16">“</text:span><text:span text:style-name="T16">Older” style of version control</text:span></text:p>
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-        <text:p><text:span text:style-name="T14">Distributed</text:span><text:span text:style-name="T15"> (e.g. git, mercurial)</text:span></text:p>
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-          <text:p><text:span text:style-name="T16">Everyone has a full-fledged repository</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T16">You clone another person&apos;s repo</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T16">Commits, history, diff, logs are all local operations (these operations are faster)</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T16">You push your changes back to others.</text:span></text:p>
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-          <text:p><text:span text:style-name="T16">Each copy is a backup of the whole history of the project</text:span></text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T16">Easier to fork—just clone and go</text:span></text:p>
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-      <text:p><text:span text:style-name="T17">Any version control system is better than none!</text:span></text:p>
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-      <text:p>Distributed Version Control</text:p>
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-      <text:p>Distributed Version Control</text:p>
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-       <text:list-item>
-        <text:p>In an ideal world, people only pull from others, never push.</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>See, e.g. <text:a xlink:href="http://bitflop.com/document/111" xlink:type="simple">http://bitflop.com/document/111</text:a> </text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Github/bitbucket provide a centralized repo built around <text:span text:style-name="T19">pull requests</text:span></text:p>
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-      <text:p>Version Control</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Note that with git, <text:span text:style-name="T3">every change generates a new “hash” that identifies the entire collection of source.</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You cannot update just a single sub-directory—it&apos;s all or nothing. <text:s/></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Branches in a repo allow you to work on changes in a separate are from the main source. <text:s/></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You can perfect them, then merge back to the main branch, and then push back to the remote.</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>LOTS of resources on the web.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Best way to learn is to practice.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>There is more than one way to do most things</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Free (for open source), online, web-based hosting sites exist (e.g. Github, BitBucket, ...)</text:span></text:p>
-       </text:list-item>
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-     <draw:text-box>
-      <text:p>Quick git Example</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Start your project out</text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">mkdir myproject</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">cd my project</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">git init</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T21">Now create a file (or two), add it to the repository (so git knows about it) and commit your changes</text:span></text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">emacs -nw README</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">&lt;</text:span><text:span text:style-name="T22">enter something in the file and save</text:span><text:span text:style-name="T20">&gt;</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">git add README</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">git commit README</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T21">Notice that when you commit, an editor will pop up asking for a log message—this is important. <text:s/>Take the time to write an explanation of the change.</text:span></text:p>
-       </text:list-item>
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-      <text:p text:style-name="P22"><text:span text:style-name="T23"/></text:p>
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-     <draw:text-box>
-      <text:p>Quick git Example</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>We&apos;ll look at the example of having people work with a shared remote repository—this is common with groups.</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Each developer will have their own clone that they interact with, develop in, branch for experimentation, etc.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>You can push and pull to/from the remote repo to stay in sync with others</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>You probably want to put everyone in the same UNIX group on the server</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Creating a master bare repo:</text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">git init --bare --shared myproject.git</text:span></text:p>
-      <text:p text:style-name="P13"><text:span text:style-name="T20">chgrp -R groupname myrepo.git</text:span></text:p>
-      <text:p><text:span text:style-name="T23"/></text:p>
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-      <text:p><text:span text:style-name="T8">Note the permissions set the sticky bit for the group</text:span></text:p>
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-     <draw:text-box>
-      <text:p>Quick git Example</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>This repo is empty, and bare—it will only contain the git files, not the actual source files you want to work on</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Each user should clone it</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>In some other directory. <text:s/>User A does:</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P22"><text:span text:style-name="T20">git clone </text:span><text:span text:style-name="T22">/path/to/</text:span><text:span text:style-name="T20">myrepo.git</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:list>
-         <text:list-item>
-          <text:p>Now you can operate on it</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P22"><text:span text:style-name="T15">Create a file (</text:span><text:span text:style-name="T24">README</text:span><text:span text:style-name="T15">)</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T15">Add it to your repo: </text:span><text:span text:style-name="T25">git add README</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T15">Commit it to your repo: </text:span><text:span text:style-name="T25">git commit README</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T15">Push it back to the bare repo: </text:span><text:span text:style-name="T25">git push</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:list>
-         <text:list-item>
-          <text:p>Note that for each commit you will be prompted to add a log message detailing the change</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-      <text:p><text:span text:style-name="T26">* older versions of git won&apos;t know where push to. <text:s/>Instead of this, you can tell git to use the proposed new (git 2.0) behavior by doing:</text:span></text:p>
-      <text:p text:style-name="P23"><text:span text:style-name="T25">git config --global push.default simple</text:span></text:p>
-      <text:p text:style-name="P23"><text:span text:style-name="T25">git push</text:span></text:p>
-      <text:p text:style-name="P12"><text:span text:style-name="T9"/></text:p>
-      <text:p><text:span text:style-name="T9"/></text:p>
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-     <draw:text-box>
-      <text:p>Quick git Example</text:p>
-     </draw:text-box>
-    </draw:frame>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Now user B comes along and wants to play too:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>In some other directory. <text:s/>User B does:</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P22"><text:span text:style-name="T20">git clone </text:span><text:span text:style-name="T22">/path/to/</text:span><text:span text:style-name="T20">myrepo.git</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:list>
-         <text:list-item>
-          <text:p>Note that they already have the README file</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P22"><text:span text:style-name="T27">Edit </text:span><text:span text:style-name="T24">README</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T15">Commit you changes locally: </text:span><text:span text:style-name="T28">git commit README</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T15">Push it back to the bare repo: </text:span><text:span text:style-name="T28">git push</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:p>Now user A can get this changes by doing: <text:span text:style-name="T23">git pull</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T21">Note that I did this on my laptop for demonstration, but the different users can be on completely different machines (and in different countries), as long as they have access to the same server</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T21">In general, you can push to a </text:span><text:span text:style-name="T29">bare repo</text:span><text:span text:style-name="T21">, but you can pull from anyone</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
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-     <draw:text-box>
-      <text:p>Quick git Example</text:p>
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-       <text:list-item>
-        <text:p>You can easily look at the history by doing: <text:span text:style-name="T23">git log</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>You can checkout an old version using the hash:</text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P21"><text:span text:style-name="T20">git checkout </text:span><text:span text:style-name="T22">hash</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T30">Make changes, use this older version</text:span></text:p>
-      <text:p text:style-name="P21"><text:span text:style-name="T30">Look at the list of branches: </text:span><text:span text:style-name="T20">git branch</text:span></text:p>
-      <text:p text:style-name="P24"><text:span text:style-name="T30">Switch back to the tip: </text:span><text:span text:style-name="T20">git checkout master</text:span></text:p>
-      <text:list text:continue-numbering="true" text:style-name="L4">
-       <text:list-item>
-        <text:p>Other useful commands:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T20">git diff</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T20">git status</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T21">Branching</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T20">git branch experiment</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T20">git checkout experiment</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T20">git blame</text:span></text:p>
-         </text:list-item>
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-      <text:p text:style-name="P25"><text:span text:style-name="T28">git checkout -b experiment</text:span></text:p>
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-      <text:p>Quick git Example</text:p>
-     </draw:text-box>
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-       <text:list-item>
-        <text:p>You can also put a link to your bare repo on the web and people can clone it remotely</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Note you need to do <text:span text:style-name="T20">git update-server-info -f</text:span> after each change to the repo</text:p>
-         </text:list-item>
-        </text:list>
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-      <text:p>Github example</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Don&apos;t want to use your own server, use github or bitbucket</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Free for public (open source) projects</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Pay for private projects</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>These class notes are on github:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Create a github account (free)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T25">git clone </text:span><text:span text:style-name="T25"><text:a xlink:href="https://github.com/sbu-python-class/python-science" xlink:type="simple">https://github.com/sbu-python-class/python-science</text:a></text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Note: since you are not a member of that project, you cannot push back to it</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You don&apos;t have write access</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Instead:</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Fork the project into your own account</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Push back to your fork</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Issue a <text:span text:style-name="T19">pull-request</text:span> asking for your changes to be incorporated</text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Feel free to try this out with the class repo!</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-        <text:p><text:span text:style-name="T25"/></text:p>
-       </text:list-item>
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-      <text:p>Version Control</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>There&apos;s no reason not to use version control</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Pick one (git or hg) and use it</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Even if you are working alone</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Each clone has all the history of the project</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Cloning on different machines means you have backups</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Allows you to sync up your work between home and the office</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p/>
-         </text:list-item>
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-      <text:p>Unit Testing</text:p>
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-        <text:p>When writing a complex program (e.g. a simulation code), there can be many separate steps / solvers involved in getting your answer.</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Finding out the source of errors in such a complicated code can be tough.</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Unit testing is the practice in which each smallest, self-contained unit of the code is tested independently of the others.</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">You could, for example, start by writing tests for each of the major physics units, and then worry about lower levels</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Implementation:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Either write your own simple driver for each routine to be tested</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Unit testing frameworks automate some tasks </text:p>
-         </text:list-item>
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-      <text:p>Unit Testing</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Simple example: matrix inversion</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Your code have a matrix inversion routine that computes <text:span text:style-name="T31">A</text:span><text:span text:style-name="T32">-1</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>A unit test for this routine can be:</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Pick a vector <text:span text:style-name="T31">x</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Compute <text:span text:style-name="T31">b = A x</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Compute <text:span text:style-name="T31">x = A</text:span><text:span text:style-name="T33">-1</text:span><text:span text:style-name="T31"> b</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p>Does the <text:span text:style-name="T31">x</text:span> you get match (to machine tol) the original <text:span text:style-name="T31">x</text:span>?</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">More complicated example: a hydro program may consist of</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Advection routines, EOS calls (and inverting the EOS), Particles, Diffusion, Reactions</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Each of these can be tested alone</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>There is a python unit testing framework called <text:span text:style-name="T3">nose</text:span>—we can explore that in the discussion forum if there is interest.</text:p>
-        <text:p/>
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-      <text:p>Test of diffusion in our low Mach hydro code, Maestro</text:p>
-      <text:p><text:span text:style-name="T11">(Malone et al. 2011)</text:span></text:p>
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-      <text:p>Unit Testing</text:p>
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-        <text:p>Whenever you important some legacy code into your project, write a unit test</text:p>
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-         <text:list-item>
-          <text:p>Verifies that it performs as the authors intend</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Allows for tests to interface changes you make to the code</text:p>
-         </text:list-item>
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-      <text:p>Regression Testing</text:p>
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-        <text:p><text:span text:style-name="T3">Imagine you&apos;ve “perfected” your program</text:span></text:p>
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-         <text:list-item>
-          <text:p>You are confident that the answer it gives is “right”</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>You want to make sure that any changes you do in the future do not change the output</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">Regression testing tests whether changes to the code change the solution</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Regression testing:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Store a copy of the current output (a benchmark)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Make some changes to the code</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Compare the new solution to the previous solution</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>If the answers differ, either:</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>You&apos;ve introduced a bug <text:span text:style-name="T34"></text:span><text:span text:style-name="T35"> fix it</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T35">You&apos;ve fixed a bug </text:span><text:span text:style-name="T36"></text:span><text:span text:style-name="T37"> update your benchmark</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-        <text:p><text:span text:style-name="T38"/></text:p>
-        <text:list>
-         <text:list-header>
-          <text:p><text:span text:style-name="T39"/></text:p>
-         </text:list-header>
-        </text:list>
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-      <text:p>Regression Testing</text:p>
-     </draw:text-box>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T40">Simplest requirements:</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T41">You just need a tool to compare the current output to benchmark</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T42">You can build up a more complex system from here with simple scripting</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T43">Big codes need a bunch of tests to exercise all possible options for the code</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T44">If you spend a lot of time hunting down a bug, once you fix it, put a test case in your suite to check that case</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T44">You&apos;ll never have complete coverage, but your number of tests will grow with time, experience, and code complexity</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T43">Simple example with my python hydro code</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T45">./pyro.py --compare_benchmark advection smooth inputs.smooth</text:span></text:p>
-          <text:p><text:span text:style-name="T39"/></text:p>
-         </text:list-item>
-        </text:list>
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-      <text:p>Regression Testing</text:p>
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-    </draw:frame>
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-        <text:p>Maestro (automated regression testing)</text:p>
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-      <text:p>General Rules</text:p>
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-        <text:p>When you write code, think to yourself: “<text:span text:style-name="T3">if I come back to this 6 months from now, while I understand what I&apos;ve done?</text:span>”</text:p>
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-          <text:p>If not, take the time now to make things clearer, document (even a simple README) what you&apos;ve done, where the equations come from, etc.</text:p>
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-          <text:p>You&apos;ll be surprised and how long your code lives on!</text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Some languages let you do cute tricks. <text:s/></text:p>
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-         <text:list-item>
-          <text:p>Even if they might offer a small speed bump, if they complicate the code a lot to the point that it is hard to follow, then they&apos;re probably not worth it.</text:p>
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-      <text:p>Automating Reproducibility</text:p>
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-        <text:p><text:span text:style-name="T3">Store meta-data in your output files that tell you where, when, what, and how the data was produced.</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Already saw the example of the git hash in the makefile examples</text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T12">Maestro example...</text:span></text:p>
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-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s/></text:span><text:span text:style-name="T46">Job Information</text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46">job name: <text:s text:c="3"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">inputs file: inputs_2d</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">number of MPI processes <text:s text:c="5"/>1</text:span></text:p>
-      <text:p><text:span text:style-name="T46">number of threads <text:s text:c="11"/>1</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
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-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s/></text:span><text:span text:style-name="T46">Plotfile Information</text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46">output date: <text:s text:c="13"/>2013-05-08</text:span></text:p>
-      <text:p><text:span text:style-name="T46">output time: <text:s text:c="13"/>11:39:43</text:span></text:p>
-      <text:p><text:span text:style-name="T46">output dir: <text:s text:c="14"/>/home/zingale/development/MAESTRO/Exec/TEST_PROBLEMS/test2</text:span></text:p>
-      <text:p><text:span text:style-name="T46">time to write plotfile (s) <text:s text:c="3"/>0.5483140945 <text:s text:c="3"/></text:span></text:p>
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-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s/></text:span><text:span text:style-name="T46">Build Information</text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46">build date: <text:s text:c="3"/>2013-05-08 11:38:04.553714</text:span></text:p>
-      <text:p><text:span text:style-name="T46">build machine: Linux nan.astro.sunysb.edu 3.8.9-200.fc18.x86_64 #1 SMP Fri Apr 26 12:50:07 UTC 2013 x8</text:span></text:p>
-      <text:p><text:span text:style-name="T46">6_64 x86_64 x86_64 GNU/Linux</text:span></text:p>
-      <text:p><text:span text:style-name="T46">build dir: <text:s text:c="4"/>/home/zingale/development/MAESTRO/Exec/TEST_PROBLEMS/test2</text:span></text:p>
-      <text:p><text:span text:style-name="T46">BoxLib dir: <text:s text:c="3"/>/home/zingale/development/BoxLib/</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">MAESTRO <text:s text:c="3"/>git hash: bad8ea8d66871a2172dcb276643edce53f739695</text:span></text:p>
-      <text:p><text:span text:style-name="T46">BoxLib <text:s text:c="4"/>git hash: febf34dba7cc701a78c73e16a543f062cf36d587</text:span></text:p>
-      <text:p><text:span text:style-name="T46">AstroDev <text:s text:c="2"/>git hash: 5316edb829577f80977fd2db8a113ccc4da42e02</text:span></text:p>
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-      <text:p><text:span text:style-name="T46">modules used: <text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span><text:span text:style-name="T46">Util/model_parser</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span><text:span text:style-name="T46">Util/random</text:span></text:p>
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-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span><text:span text:style-name="T46">../../../Microphysics/EOS</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span><text:span text:style-name="T46">../../../Microphysics/EOS/helmeos</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span><text:span text:style-name="T46">../../../Microphysics/networks/ignition_simple</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span><text:span text:style-name="T46">../../../Microphysics/conductivity/timmes_stellar</text:span></text:p>
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-      <text:p><text:span text:style-name="T46">FCOMP version: <text:s text:c="3"/>gcc version 4.7.2 20121109 (Red Hat 4.7.2-8) (GCC)</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">F90 compile line: mpif90 <text:s/>-Jt/Linux.gfortran.debug.mpi/m -It/Linux.gfortran.debug.mpi/m -g -fno-range-</text:span></text:p>
-      <text:p><text:span text:style-name="T46">check -O1 -fbounds-check -fbacktrace -Wuninitialized -Wunused -ffpe-trap=invalid -finit-real=nan <text:s/>-I..</text:span></text:p>
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-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">F77 compile line: gfortran <text:s text:c="2"/>-Jt/Linux.gfortran.debug.mpi/m -It/Linux.gfortran.debug.mpi/m -g -fno-ran</text:span></text:p>
-      <text:p><text:span text:style-name="T46">ge-check -O1 -fbounds-check -fbacktrace -Wuninitialized -Wunused -ffpe-trap=invalid -finit-real=nan <text:s/>-</text:span></text:p>
-      <text:p><text:span text:style-name="T46">I../../../Microphysics/EOS/helmeos <text:s/>-c</text:span></text:p>
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-      <text:p><text:span text:style-name="T46">C compile line: <text:s text:c="2"/>gcc <text:s/>-std=c99 -Wall -g -O1 -DBL_Linux -DBL_FORT_USE_UNDERSCORE <text:s text:c="2"/>-c</text:span></text:p>
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-      <text:p><text:span text:style-name="T46">linker line: <text:s text:c="5"/>mpif90 <text:s/>-Jt/Linux.gfortran.debug.mpi/m -It/Linux.gfortran.debug.mpi/m -g -fno-range-</text:span></text:p>
-      <text:p><text:span text:style-name="T46">check -O1 -fbounds-check -fbacktrace -Wuninitialized -Wunused -ffpe-trap=invalid -finit-real=nan <text:s/>-I..</text:span></text:p>
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-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s/></text:span><text:span text:style-name="T46">Grid Information</text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s/></text:span><text:span text:style-name="T46">level: <text:s text:c="11"/>1</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="4"/></text:span><text:span text:style-name="T46">number of boxes = <text:s text:c="10"/>60</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="4"/></text:span><text:span text:style-name="T46">maximum zones <text:s text:c="2"/>= <text:s text:c="9"/>384 <text:s text:c="8"/>640</text:span></text:p>
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-      <text:p><text:span text:style-name="T46"><text:s text:c="3"/></text:span><text:span text:style-name="T46">+y: outlet <text:s text:c="13"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="2"/></text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s/></text:span><text:span text:style-name="T46">Species Information</text:span></text:p>
-      <text:p><text:span text:style-name="T46">===============================================================================</text:span></text:p>
-      <text:p><text:span text:style-name="T46">index <text:s text:c="16"/>name <text:s text:c="10"/>short name <text:s text:c="7"/>A <text:s text:c="7"/>Z</text:span></text:p>
-      <text:p><text:span text:style-name="T46">-------------------------------------------------------------------------------</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="4"/></text:span><text:span text:style-name="T46">1 <text:s text:c="4"/>carbon-12 <text:s text:c="22"/>C12 <text:s text:c="5"/>12.00 <text:s text:c="4"/>6.00</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="4"/></text:span><text:span text:style-name="T46">2 <text:s text:c="4"/>oxygen-16 <text:s text:c="22"/>O16 <text:s text:c="5"/>16.00 <text:s text:c="4"/>8.00</text:span></text:p>
-      <text:p><text:span text:style-name="T46"><text:s text:c="4"/></text:span><text:span text:style-name="T46">3 <text:s text:c="4"/>magnesium-24 <text:s text:c="19"/>Mg24 <text:s text:c="4"/>24.00 <text:s text:c="3"/>12.00</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:p>+ values of all runtime parameters...</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp4">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="36" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr30" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page37" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Debuggers</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr22" draw:layer="layout" svg:width="26.924cm" svg:height="12.18cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Simplest debugging: lots of prints!</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Interactive debuggers let you step through your code line-by-line, inspect the values of variables as they are set, etc.</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T8">pdb</text:span> is the python debugger</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>If you just want to know how the code gets to a certain function:</text:p>
-       </text:list-item>
-      </text:list>
-      <text:p text:style-name="P22"><text:span text:style-name="T23">import traceback</text:span></text:p>
-      <text:p text:style-name="P22"><text:span text:style-name="T23">traceback.print_stack()</text:span></text:p>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp4">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="37" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr21" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page38" draw:style-name="dp6" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Profiling</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr22" draw:layer="layout" svg:width="26.924cm" svg:height="12.18cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Profililers examine your code when it runs and determine where you spend most of your time</text:span></text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T7">gprof is the standard GNU profiler</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Just add -pg to the compile lines</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Run as normal</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>&apos;gprof executable&apos; to get information on the subroutine/function level</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>&apos;gprof -l executable&apos; to get information on the line-by-line level</text:p>
-         </text:list-item>
-        </text:list>
-        <text:p><text:span text:style-name="T12"/></text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp4">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="38" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr21" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page39" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Commenting and Documentation</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr22" draw:layer="layout" svg:width="26.924cm" svg:height="12.18cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>The only thing worse than no comments are wrong comments</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Comments can easily get out of date as code evolves</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Comments should convey to the reader the basic idea of what the next set of lines will accomplish. <text:s/></text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Avoid commenting obvious steps if you&apos;ve already described the basic idea</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Many packages allow for automatic documentation of routines/interfaces using pragmas put into the code as comments.</text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp4">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="39" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr21" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page40" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Source Code Libraries</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr22" draw:layer="layout" svg:width="26.924cm" svg:height="12.18cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">There are many sources for open, well-tested, published codes that may already do what you want.</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>This makes it easier to get going, may offer better algorithms than you were prepared to code.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Benefits from a community of developers and maturity</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Still need to test, examine return codes, etc.</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Many of these mature codes are already wrapped for you in SciPy.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>For other codes, we&apos;ll look next at how to extend python in Fortran and C</text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp4">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="40" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr21" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <draw:page draw:name="page41" draw:style-name="dp5" draw:master-page-name="Default" presentation:presentation-page-layout-name="AL1T1">
-    <draw:frame presentation:style-name="pr19" draw:layer="layout" svg:width="25.199cm" svg:height="1.957cm" svg:x="1.4cm" svg:y="0.537cm" presentation:class="title" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:p>Summary</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="12.18cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T3">Some basic coding practices can greatly improve the reliability of your code</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Frees you to do science</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Small learning curve is greatly offset by the improved productivity and stability</text:p>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp4">
-     <draw:page-thumbnail draw:style-name="gr4" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="41" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr21" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true" presentation:user-transformed="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <presentation:settings presentation:mouse-visible="false"/>
-  </office:presentation>
- </office:body>
-</office:document>
\ No newline at end of file
diff --git a/lectures/05-python-practices/python-style.ipynb b/lectures/05-python-practices/python-style.ipynb
deleted file mode 100644
index cd90b941..00000000
--- a/lectures/05-python-practices/python-style.ipynb
+++ /dev/null
@@ -1,107 +0,0 @@
-{
- "metadata": {
-  "name": "python-style"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Python Style (from the PEP-0008)"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "This notebook shows the various code snippets from the PEP-0008 style guide: http://legacy.python.org/dev/peps/pep-0008/"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "code lay-out"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "indentation"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Intentation should use 4 spaces per indentation level (and NOT tabs).  Python 3 does not allow for a mixture of tabs and spaces.  Note that a lot of editors will map the tab key into a sequence of spaces\n",
-      "\n",
-      "Continuation lines should align wrapped elements either vertically inside parentheses, brackets, or braces, or using a hanging indent (with no arguments on the first line)\n",
-      "\n",
-      "note: to prevent ipython from throwing errors that some things are not defined, not all the code is in a code cell."
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "# Aligned with opening delimiter\n",
-      "foo = long_function_name(var_one, var_two,\n",
-      "                         var_three, var_four)\n",
-      "\n",
-      "# More indentation included to distinguish this from the rest.\n",
-      "def long_function_name(\n",
-      "        var_one, var_two, var_three,\n",
-      "        var_four):\n",
-      "    print(var_one)\n",
-      "    \n",
-      "# Note that the following is not allowed since you cannot distinguish \n",
-      "# between the function body and the arguments\n",
-      "def long_function_name(\n",
-      "    var_one, var_two, var_three,\n",
-      "    var_four):\n",
-      "    print(var_one)\n"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "line length"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Lines should be a maximum of 79 characters.  \n",
-      "\n",
-      "The 80-column terminal goes back to the early terminals and page-widths for printers and is pretty much a good standard for any language.\n",
-      "\n",
-      "Comments and docstrings should be limited to 72-characters\n",
-      "\n",
-      "Implied line continuation is automatic inside paranthesis, brackets"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/06-sympy/sympy-examples.ipynb b/lectures/06-sympy/sympy-examples.ipynb
deleted file mode 100644
index fc60880d..00000000
--- a/lectures/06-sympy/sympy-examples.ipynb
+++ /dev/null
@@ -1,2461 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:91b94cda1617e34752d65a06428bc7ae63d88874cb13214f85292f7421701ffb"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "SymPy examples"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "sources:\n",
-      "http://docs.sympy.org/latest/tutorial/\n",
-      "http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/notebooks/SymPy%20Examples.ipynb"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import division\n",
-      "import sympy as sym\n",
-      "\n",
-      "# make things look pretty in the notebook\n",
-      "from sympy.interactive import printing\n",
-      "printing.init_printing()\n",
-      "\n",
-      "import math"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "basic symbolic manipulation"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print math.sqrt(2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1.41421356237\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print sym.sqrt(2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "sqrt(2)\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print sym.sqrt(8)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2*sqrt(2)\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "We can do symbolic math not just on numbers, but we can tell SymPy what to treat as a variable"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy import symbols\n",
-      "x, y, z = sym.symbols(\"x y z\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = x + 2*y\n",
-      "expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x + 2 y$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAADsAAAASBAMAAAAXqLt+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n75nQ6/gxAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVQoFWNgwAL8Xu/BIgoTYjRgyA+AcZBo\nEQibWYCB9wKSMIy5CMLgbWBg/gMTQ6Kh0pwfkaSFlF3VFCBqoNJADsdHxjwFBl8BBsYE9iaOCejS\n8QlsXBcY9BkY2ATYPnI5oEt3M3jxTWDYzcDAyMC5ASLJwAA3nG8DgwCvAsMnkAS/Alg6c+bM3pkz\n54HZykAyXoAH7PzzBxgEwIJw3VwPGFwYXjIwAT3PJaDPwIQmfY6BQYVhMwO3AdCMBfsZ3CGaYbpZ\n2tIeNzC8ZogvYGAQVhJKB1JgAAuW////NzAIJ713gIpDKbjLIfyXqLIM4Uh85gmMX5G46EwOBW4F\ndDEkPqvSGyAPAEZYN5OZu5VXAAAAAElFTkSuQmCC\n",
-       "prompt_number": 6,
-       "text": [
-        "x + 2\u22c5y"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr - 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x + 2 y - 1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAFwAAAASBAMAAAAkk6R3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n75nQ6/gxAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABL0lEQVQoFWNgwAQs73YfwBQFikzEKirMwPYV\nm4T3RxRRRgcI15CB4S6KBIQj/RhVOZcCRPwGA4P9AQgThWTCrny+AB7lQsquagpgU2CmAzn9AmKP\nGFguohjOADKdMYG9iWMCmnLWbwwJaxmYPmAqZxNg+8jlgKacYwPLgpcM3BswlTMycMJEEY55zcDI\ncImBXwGoXLIDBOaANEK8ChZlYJs5c8bNmTMfgMUNGBhYPzKcPwDiIABEOVBUACwGN30dA4sD3wQG\nXYRKMAuknEtAn4EJVTnPAwYmB24Dhs1YlMcv2M/gDhGHme6RlmfBwLeB9TsW5cJKQukFqMrn////\ng4HRTO8LqnI+2x/myCIw06FiQNfjBaxQW8CKLBnkA/CqRpVsYtiEKoCfF6R3AIsCAJFDR5d7C5YY\nAAAAAElFTkSuQmCC\n",
-       "prompt_number": 7,
-       "text": [
-        "x + 2\u22c5y - 1"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr - y"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x + y$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAADAAAAAQBAMAAACigOGCAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n75nQ6/gxAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVQYGWNgQABGBwQbhcWlgMJFcNAkhJRd\n1SBKYRJijxhYLjIwJrA3cUwAa4NJJKxlYPrAwCbA9pHLAVmCZcFLBu4NDIwMnBvAwgwMUB2MDJcY\n+BWAYmCCgW3mzBk3Z858ABRg/chw/gCQBhICQAqug4FvAoMukCegz8CEKsFtwLCZgSF+wX4Gd7AG\nmB0MfBtYvzMwCCsJpRegSjCa6X2BiEBJmD8YQLYgA1aoTksG+QBkcTi7iWETnI3CCNI7wMAAAJK5\nIwDwxyZ7AAAAAElFTkSuQmCC\n",
-       "prompt_number": 8,
-       "text": [
-        "x + y"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f = x*expr\n",
-      "f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x \\left(x + 2 y\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAFgAAAAVBAMAAAAwfTS1AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ndM4vDT6TLog4+AHWhVNcMpnZGwAAAABJRU5ErkJggg==\n",
-       "prompt_number": 9,
-       "text": [
-        "x\u22c5(x + 2\u22c5y)"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "g = sym.expand(f)\n",
-      "g"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{2} + 2 x y$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAE8AAAAYBAMAAABJkb36AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
-       "prompt_number": 10,
-       "text": [
-        " 2        \n",
-        "x  + 2\u22c5x\u22c5y"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sym.factor(g)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x \\left(x + 2 y\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAFgAAAAVBAMAAAAwfTS1AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ndM4vDT6TLog4+AHWhVNcMpnZGwAAAABJRU5ErkJggg==\n",
-       "prompt_number": 11,
-       "text": [
-        "x\u22c5(x + 2\u22c5y)"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Using SymPy interactively"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can tell SymPy to setup some sane defaults that make an interactive session nicer"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy import init_session\n",
-      "init_session()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "IPython console for SymPy 0.7.5 (Python 2.7.8-64-bit) (ground types: gmpy)\n",
-        "\n",
-        "These commands were executed:\n",
-        ">>> from __future__ import division\n",
-        ">>> from sympy import *\n",
-        ">>> x, y, z, t = symbols('x y z t')\n",
-        ">>> k, m, n = symbols('k m n', integer=True)\n",
-        ">>> f, g, h = symbols('f g h', cls=Function)\n",
-        "\n",
-        "Documentation can be found at http://www.sympy.org\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "WARNING: Hook shutdown_hook is deprecated. Use the atexit module instead.\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "substitution"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = sin(x*2*pi)\n",
-      "expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sin{\\left (2 \\pi x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAE4AAAAVBAMAAAAazcUaAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-       "prompt_number": 13,
-       "text": [
-        "sin(2\u22c5\u03c0\u22c5x)"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr.subs(x,1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$0$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJdjLNVN0iZu+7\nq0QgoRR7AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVklEQVQIHWNgEDJRZWBgSGeQmMDAtYGBOYGB\n5wID+0cG/gsMfN8Z5BUY+L4wzDdgYP0MJeUNQCL8Cgzs3xk4DjBwfWRg2cDAlMDA0M4gHcDAIOxy\nlQEA9FISlFfRJtkAAAAASUVORK5CYII=\n",
-       "prompt_number": 14,
-       "text": [
-        "0"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = expr.subs(x,0.125)\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{\\sqrt{2}}{2}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAvBAMAAAACzbekAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAInarRM2ZVBDdiWbv\nuzJCz3LGAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVQoFbWRsQ4BQRCG/ztH7khEq6KnUCio\neASlQuIqKqHSulDplDoqhUrtDSQKLyDxEAokcmb2ds5ezxT/zHeZ3dvkA3SVQi4h1OOJB6uTwMwk\ngcsEYQhrcL3JN3eFBbJ3wewBDeAsSEdPQEu2K0A/UOi0aWWl1jYBUKUH2GVG50XPmdN9HhO8C4X7\nBo4KRyrHBax5sGsKS35uy8MUVoFa6uEF1PI+bMb8a0qJ4n7X5I5el7Mfhk/umPmqSaT5yN9LuYjj\n578z/dDlph9C0w+h6YdQ+6FJiv18i/0Ypfx8OfIjrP0Iaj8axY/G2E/EsZ8P68JG4E3ZBukAAAAA\nSUVORK5CYII=\n",
-       "prompt_number": 15,
-       "text": [
-        "  ___\n",
-        "\u2572\u2571 2 \n",
-        "\u2500\u2500\u2500\u2500\u2500\n",
-        "  2  "
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "type(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "sympy.core.mul.Mul"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Note that this is not a floating point number -- it is still a SymPy object.  To make it floating point, we can use evalf()"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = a.evalf()\n",
-      "print b, type(b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.707106781186548 <class 'sympy.core.numbers.Float'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "This is still a SymPy object, because SymPy can do arbitrary precision "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pi.evalf(100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAABBIAAAAPBAMAAACrcfGjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIom7VJlmdt1E7xDN\nMqsI8sYEAAAACXBIWXMAAA7EAAAOxAGVKw4bAAALDElEQVRYCe2Y32+kZRXHv512pttO2x01GIMm\nNCXLGhG2iiYq0W1M9IZo65K9QGUZjUICxhYiIUGEKnpnoBcaEzWhBGIERCcYAkFiG1SI2QgNCYkX\nxp1EWQzqLgsrv7rueM7n+7zzjv8Dk/Tp9DnnfL+fc95n3pmp9PaFD4nHTC9/PRc/+eyp3nMbenj/\n56Sxw5fsSI39F2nmm7NXyAWE2zft21D70I0d/fn5hYV5p6N3zuUfj8KF85dETtFduGFHRFCTZleK\nGjWP3BDi1OA4ImmzQzfumMWSpNe8IxjwK83MD0ZSkgP/7B2HFhZKPzqqjESP+KALJZvUOB0zJI2e\n/B4JmyM9p5r5TcloE4hCmwFIjXNywB71w/uvlNhEFwL3nDmeIGGEmJiB2ESSTkYibHouyQKbMZCk\ncGxFy90g1NjBSJFuL89+NXhN7Z/p3I5+q8Ybav9RX9T0YLAmF2RYTenveqw7drdWB4NBn3TC7b4O\nbOgcTbzqHOs+vtHcdgQ1ac+i1Sy5orkl1+BYSwqzmaX2t82CrUiveWsMd4IZ/GAQZsGsFbxn3Y/e\nsRtnP8Xtk7qmZJMap6eZRwI6CyNhc6RnCuE3JaNNoJhD9Gyzehp0wkhoXL/Xox0KbQYBPZNjH2oQ\noj0D1ehMrI64BgwW2Kj2lUBt8rT2rgdh+8Mn8yRM3lOePfFCR3ObmlvUP6X7NTOvSzRz8fWRQUGG\n9WnpPXpR+pvi1jHndMJzPU1v6rYlve4cdPWExhcdQU26edFq1Ex0NbbtGhxrSWF2rfR+s2Dr9Jq3\nxnAnmMEPBmEWzJpB33c/vz4SJwFxfNA1JZvUOD3NPBLQWRgJmyM9Uwg/ajVQDDh6LmbVNNwJI6Hx\n1qYa28aAHwJ6Jgcf1yBEe+i6htEzsTpCDRhmgY1qJK02dZ+2toNQui1Pwjt/V5714/febTV29XPp\n5M5qjC4udjxc0M+n50uP6ox0oDcvXeR0wtPrmvuvfrzRPuMcdFunosQR1DT2tUWrUdNYVHvdNTjW\nkupHoT4hLfeIYOv0mrfGoBPMzA8GYRabSY0d96PJOAmI44OuKdmkpkF6mlkSdBZGwuZIzxTCXyhj\ntADF/TZ6JlxPwzmMhPbG+5rYZdNmENAzOfi4BiHaQ5dNYzCxOuJNX+G8zLBRjaTVYsbL3VjKSZgf\nPQlb8Zp9Q8/04iR8LDPmcolHFPTz912X6oL2K1G5EQdkTU7P8MxungTlu0PmxLPQHd+M346gpsbM\nYuzYfrk79Uqv0Y0/owbHEcl+bOts+C0RQbKkV7wjGHSCWQU08SrhnxRUO/6o6idPAuI0ha4pc/Oy\nqibSs4kiCXoujITNkZ6tliMplDF9gOJARQ3hehrOYSS0t7evqdNsouvO6JkcfFxTfGJi6LJpDCbG\nLIkUNl7rLMlGNZJWiwb/ED/xyJRWpzoJx6+5UtPbaoSLdGfvzLM3bmju6vxcRUGGNTf4cle/iHtC\nVxqP13tJR29PzFczK85Bd++f9sdHwnhH2xVq+pJPAumxnHz1ggxHTTzuDJihZJq1/xPXZY0Itk6v\neWsMOsGsAgpJwiXHDS8GP/3ESSjiNFUw9ux6s6qJ9DQrkqDngkTZzM6Sv0IN/tJUjLZ0HzUOj0wD\nR48kG99a0dRrFFoXAnqucsInayqfnBhAQ3TRiWdJhKnX94Scrast6cLZ94ZQPvIkPKLqJFyq8zYa\nL2kmPhhq6uX2ma7u1Xhv7I34lJoFGZauOtPTAemZNeUL3+lF76556dnvRXHmoLv1pKaXYuOueau1\n1/IkkM7SHByNKDXhGE+Hkmk2FjvH5pOlSJJe89YYdIJZ4U9Jws4xYN5/3E+chCJOU8YISm8W3UxP\nM0uCzoJENafSc4WaB9tqMVp3nzUO19MgxyOhvfPmNfsym9Y1QYyxygk2wsUn2/Msh+jl8tSRnLqv\nsI9DzJbqC7muhVJ6304gO29+eBJiTut6Sp/Me8Kexfagp8/HFdVHMpOCCLeufOY+zaxNHOiq/VLs\nl3TCt2di40HnKHW3dtV8IDbjAzhqDfmeQHosX/3Lmxuu8SfhUcnx9Wq88dkbW6fXvCMY2azNClBg\nEHaO+beSj37qk0DPBeP2cjxKTaZjhiToFX+MpPiUngsq/FYbAmWNw/U06KQacLTnk0AhuhBkz1VO\nfr/LcDWSaM8sQ/SAjcvD/CvKZAuMsgQb1RdyJarRazqvDykTOyMnoXlKrZuePR2RyxVfArSa1+mq\nTiwURPjrar3Z0zXfOrCkye3YL+kZbi7G3/Glo0cOunvj6MQtJSOofaacBNSmH2j1tQrI/T0c/0+y\neaod94J8dwgWJEkf5a0xsmObFaDAMCU55r8Fvuwn3x2KuJqnCkZQlk3XRLrNkAS94g8J+1Q9/8Zq\nOZKiNgTKGuvW03BOGXC0tzUf7w7etG4S0HPJCR/CQ+r7e7AUM2PE5WH+hZLN+iRMbpf2kHThWEfj\nebU5CQ+pOglz8Z4fl01z8dY32Ve+z652/xV32Q0KHI4Ld2xJ8WbYy6/J+ZjbLXq3xh9vkw4ukYPu\n9LZacYfJSKpdN58ngXSW6Z5mT7tGk/1IGkraLD4nLMeJiwiSpNe8kV9jRMfFLPnBcDhzCmC+RuiH\n7w6I42MMKO2Ibr4Mi1lIthOdpUh4TsOeXZj8Ra0Ccg3hehrOyZF0NdmPb2wrmjhdCpl/EtCzc5LN\nYYSyvcuYZamJsCdWRzz1kXtCslGNpAv37mqcT4WZ99kTJ958gTMxva0mn38C7VNxveIz5upGlF3V\no4Bw3v7mupKejoMcefGY6VtvYiUuwKCng9eRg+74Zt4TiKTaD06cOPnLNdJZ8v57nJoujrWkWeJa\nrPYy8m4kSa9503yIEZ3YLIGKJOFcDNg+GwX0w0lAHB9jQGlHCjO9mIXkRKJ/A35L4DPsuevCHInV\ncrQAUbhGuJ6GcxgwjcdVau2WwuSPx9MetXPSx2GEcspfgcWbGfbE6oinPnISko1qJF04vag9p9LN\nebrHz5rxmtxu3a1jHc2uaLKzHJ8TtCJ9XxQQ1t3xdtS5tjMb9cciRrr1npOuT6k7O+SE5j3xJVnN\nzfxv9vXxj4FQi5O9aDVqpjvSB1yDYy1ps5sV/+UkgqTTU9fkNQZ/Y2Z+MAizGHA2XhHuh5OAOD7W\nhZJN65KeZpYEnYWReLPumUL4C2WcBHdPDeF6Gs5ZzpHQ3uSmmttsWhcCeiaHCboGIdrzLJlgYnhi\ndcRTHzkJebmoRtJqra6W1+I/QNVJiO/PWTG2oms7k0fbT0rvWtj3D43Pt++NfwUHGwWE9dN4+9Ut\nvce7ceMOadId/s7CkXVdrYnXnBOaoftBPbY0RgS1uA/G6U97lqmjaq25Bsda0mZ7ltoPmgVbp6NL\nhzWGO0kz84NBmAUzTeRJoB9OAuL4oGtKNq1LeppZEnQWJNh0DWYUwl8oY6B0H57Rs8PDaTiHkbjx\n7+qxHTZtBgE9k4OPaxDCEV02CXtidcRsIychLxfVSFpNPzz8UekO6civ7ogrev7guJ+dc/EXpH2X\nb8R3xMEgPlfsO7KjsUPPR0sUEJ59ft+GGlmvc5diIT3DM/Fv/XVNHY5ycqzbOPzvEkFNjYNnu1ZD\n8q+HrohvUVljx1oSs/b+W3ccsSTpNe8IBp2kmYGQJOwczMbuDV76ab74+nEhLnxS1/xsuoZ0zOjR\n6MnvkeTmSM9Wgx/KGohCh+tpuJMcsBt/6KZAZxMzCNxz5tiHMEK0Z6DcdJhO6og3wfBlTjZjpKTN\nYiBvPd6agPQ/ALgCyvGUUdcAAAAASUVORK5CYII=\n",
-       "prompt_number": 18,
-       "text": [
-        "3.1415926535897932384626433832795028841971693993751058209749445923078164062862\n",
-        "08998628034825342117068"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "want regular python types?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = float(b)\n",
-      "print c, type(c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.707106781187 <type 'float'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Python and SymPy"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "sympy symbols are just objects and when you do operations on two sympy objects the result is a sympy object.  When you combine a sympy and python object, the result is also a sympy object.  But now we need to be careful when doing fractions.  For instance doing x + 1/2 will first compute 1/3 in python (giving 0 or 0.333... depending on the division operator) and then add it to the sympy x symbol.  The Rational() function makes this all happen in sympy"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f = expr + Rational(1,3)\n",
-      "f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sin{\\left (2 \\pi x \\right )} + \\frac{1}{3}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAHcAAAAqBAMAAAB7B9mQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nfCwnm240zgB/AVQTj2XVq4BIAAAAAElFTkSuQmCC\n",
-       "prompt_number": 20,
-       "text": [
-        "sin(2\u22c5\u03c0\u22c5x) + 1/3"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr + 1/3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sin{\\left (2 \\pi x \\right )} + 0.333333333333333$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAARQAAAAVBAMAAAB1dQxbAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nVKNvAAAAAElFTkSuQmCC\n",
-       "prompt_number": 21,
-       "text": [
-        "sin(2\u22c5\u03c0\u22c5x) + 0.333333333333333"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "equality"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "= is still the assignment operator of python (it does not mean symbolic equality), and == is still the logical test (exact structural equality).  There is a separate object, Eq() to specify symbolic equality"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x + 1 == 4"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "False"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Eq(x + 1, 4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x + 1 = 4$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAFUAAAAQBAMAAACVTi62AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\n75nQ6/gxAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVQoFWNgwAYmYgryPMAUA4l4f8QU59iA\nIsboAOFKP8aiVhdVLZcCVCsTplqWSuLVsnGC1Aopu6pBDMRnbglILWMCexPHBLDl6GozZ4LAPJAc\nowJILZsA20cuBxCfAV0tWBBCsDGAzQWTYBE8atdAVfErgFSyzZw54+bMmQ9AbIxwYEyAqj1/gEEA\npALDDRkdINAMkjE2tr+swMAloM/AhF0t2AAYwQv0W/yC/QzuEAE87mVg4AeqFVYSSi9AVctn+8Mc\nZhyMZtv/GaoKVS1MHh/NiqITUyUAJRA2IktDjQEAAAAASUVORK5CYII=\n",
-       "prompt_number": 23,
-       "text": [
-        "x + 1 = 4"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = (x + 1)**2\n",
-      "b = x**2 + 2*x + 1    # these are algebraically equal"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a == b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 25,
-       "text": [
-        "False"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify(a - b)   # this will test equality algebraically"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$0$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJdjLNVN0iZu+7\nq0QgoRR7AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVklEQVQIHWNgEDJRZWBgSGeQmMDAtYGBOYGB\n5wID+0cG/gsMfN8Z5BUY+L4wzDdgYP0MJeUNQCL8Cgzs3xk4DjBwfWRg2cDAlMDA0M4gHcDAIOxy\nlQEA9FISlFfRJtkAAAAASUVORK5CYII=\n",
-       "prompt_number": 26,
-       "text": [
-        "0"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = cos(x) + I*sin(x)\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$i \\sin{\\left (x \\right )} + \\cos{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAJYAAAAVBAMAAABWCwqnAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n8/Ab1VqiTq7Lqa4AAAAASUVORK5CYII=\n",
-       "prompt_number": 27,
-       "text": [
-        "\u2148\u22c5sin(x) + cos(x)"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$e^{i x}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAABkAAAAUBAMAAACKWYuOAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEIl2mSJE3e9UMqtm\nzbsXyEShAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAiklEQVQYGWNgAANmBwgNIbkCkHl42GzWDAzK\nXtYLIEqY5AWYBfayJ0B4gdMZuBjuQNhA8jEQf4XxGD8uYOD6wAXlcl5QiGcqYILyGDcFSJspGwJ5\njMomSA5JD2BVgCphYGDtZBBZAOdx9xgfhHMY+Dcg2AwM/ArIPA4gzwAuwHyBQQTJBhObA3ApACQE\nFT0dxmoHAAAAAElFTkSuQmCC\n",
-       "prompt_number": 28,
-       "text": [
-        " \u2148\u22c5x\n",
-        "\u212f   "
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "More substitution"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "note that substitution returns a new expression: SymPy objects are immutable"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = cos(x)\n",
-      "expr.subs(x, 0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAJcEhZcwAA\nDsQAAA7EAZUrDhsAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCH\ncAAAAABJRU5ErkJggg==\n",
-       "prompt_number": 29,
-       "text": [
-        "1"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\cos{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAADkAAAAVBAMAAAAOWFv7AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJVCLvMs1Edmar\n3bti/yyrAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABP0lEQVQoFZWRL0gDURzHP+ft3J1uczgwCQ7E\nYpqoYVNwBovFSxbTRJNlaaAiHtj1gsmyjRXRIoIYRLRYTAMRbF5eGjIQTf52vL0NXfEX3vu83/f3\n770H/7R9FX/bL88KlDdW7CPbyY7T60DPXtPcJe1iW+OYJg1OXaOdh9HZOYxczWdv9VwEMw3GxdNZ\nLkRri5J74NIwAuKiJq4gxX3lDYY9bI+14gsc7hYwRY3nYYal5ApE6kxIpWgLRjLvDVfUQR+SHAsR\nbVKuSNCXqEHq5EOp8K1UnTvl8iB57cpEPzFAxpe+5DehfJohJhO1p1oealINp7J2MN1SkaNIgYQr\n13jEaSWalgQNpGF8eh5rctF3Fi6zUlTKGc/V2p2g7cvyy171eV1TF641ynP9sXbz0AxPQe+mf1+H\n9apsqNMN/AAQ00PuNbV4pAAAAABJRU5ErkJggg==\n",
-       "prompt_number": 30,
-       "text": [
-        "cos(x)"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAAJBAMAAAAWSsseAAAAKlBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADmU0mKAAAADXRSTlMAEHarIkSJZt3NVLsyNZ302gAA\nAAlwSFlzAAAOxAAADsQBlSsOGwAAAEJJREFUCB1jYBBSdlVTYGBMYG/imMDAJsB2kcuBgZGBcwMD\nCPAqgKmzBxgEGLgEdBmYBBhiF+xlcGdgEFYSSi9gAAAZ4Ql2LUcmxgAAAABJRU5ErkJggg==\n",
-       "prompt_number": 31,
-       "text": [
-        "x"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "multiple substitutions, pass a list of tuples"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = x**3 + 4*x*y - z\n",
-      "expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{3} + 4 x y - z$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAHMAAAAYBAMAAAA7Xt9MAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRK5CYII=\n",
-       "prompt_number": 32,
-       "text": [
-        " 3            \n",
-        "x  + 4\u22c5x\u22c5y - z"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr.subs([(x, 2), (y, 4), (z, 0)])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$40$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAPBAMAAAAWtvJmAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBU\nq0Qb3U6NAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkUlEQVQIHT3NMQ6CQBCF4R+VZGM2QDwB3sHS\nwkvYU3AASjuvYLGlxRZyAYwWNJzABAsTG24hFECCs4VO9eXN5A3ACop8LYANnLlmIj9FJSxPwoNh\nUaMHoTWENcEIKjNUMUELNwxHi9fL/k8dCSvrDkqEYYweeTRN95rvUK6MFD9h5l7wgS33SHSZ3pT7\npwt/8wVNsydniKnjSgAAAABJRU5ErkJggg==\n",
-       "prompt_number": 33,
-       "text": [
-        "40"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "simplifying"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "There is not unique definition of what the simplest form of an expression is.\n",
-      "\n",
-      "simplify() tries lots of methods for simplification"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify(sin(x)**2 + cos(x)**2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAJcEhZcwAA\nDsQAAA7EAZUrDhsAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCH\ncAAAAABJRU5ErkJggg==\n",
-       "prompt_number": 34,
-       "text": [
-        "1"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify( (x**3 + x**2 - x - 1)/(x**2 + 2*x + 1) )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x - 1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAC4AAAAPBAMAAABpZ+CXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\nme8Q6PJIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAdklEQVQYGWNgwAYmYhNkYPD+iFVc+jJ2cQYm\nfOJCyq5qCqjmgdQzJrA3cUzAFGcTYPvI5YApzsjAuQEkyjijAwg6D4DYEHv5FUBsZAARP3+AQQBZ\nFKKeS0CfgQmLePyC/QzuqMrB5gsrCaUXoIrz2v0wRxVB8ADsXhxGPMeYxwAAAABJRU5ErkJggg==\n",
-       "prompt_number": 35,
-       "text": [
-        "x - 1"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify(gamma(x)/gamma(x - 2))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left(x - 2\\right) \\left(x - 1\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAH8AAAAVBAMAAACd/CwcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nqKFOdtAPhRCVHbYULNy0wdS+E/KQc8Af5MxoqwJlZNgAAAAASUVORK5CYII=\n",
-       "prompt_number": 36,
-       "text": [
-        "(x - 2)\u22c5(x - 1)"
-       ]
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "but sometimes it doesn't have your idea of what the simplest form is"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify(x**2 + 2*x + 1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{2} + 2 x + 1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAGgAAAAWBAMAAADeGsQjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n8S2aAAAAAElFTkSuQmCC\n",
-       "prompt_number": 37,
-       "text": [
-        " 2          \n",
-        "x  + 2\u22c5x + 1"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "instead factor may be what you want"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "factor(x**2 + 2*x + 1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left(x + 1\\right)^{2}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAAAbBAMAAAAzEOSeAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2Z\nVKvu110NAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABSklEQVQoFWNgIAB6fBIIqGDgMOALIKSG24Dx\nMyE1nAuYfxFSw8DA+42wGu4Cwmp6gEoYFYAEVqAIEmU6ACRMQCxMEMbAwCcAFF7BsJCBAcjBAkxA\nLgWGDGPsXTcG9gYUFckQXso5kJoKBgbO//8/MHAaoKi5DuWxg9RkQjmrUZQwoKhhvQCRBHktvc6q\nogDCRVHDcQAi6MTAwNbAtJ0b6D4QQFHDBBXcy8DAkcDxjdcAixreDRDBWUBzGFgdIBw0cxg/wNUw\nMPAXgDmtoaE7Q0NjQGywv2BqgHYxMMgLMCSAaDRzuKB2Ad3Mm1DPwJ4AVoLdzX0MDOsv+DMYQZSg\nquE5ABEFhmFeeXqTAqoanpe/HzEwsC6AiHIbQGgoCQsfMNcSKghOHwhlixBMBoZ2GMcZxsCg2QJg\nQjjSGFCaBe4MZgWYanRaGSoAABjwSSfir+drAAAAAElFTkSuQmCC\n",
-       "prompt_number": 38,
-       "text": [
-        "       2\n",
-        "(x + 1) "
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "polynomial simplification"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expand((x + 1)**2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{2} + 2 x + 1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAGgAAAAWBAMAAADeGsQjAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n8S2aAAAAAElFTkSuQmCC\n",
-       "prompt_number": 39,
-       "text": [
-        " 2          \n",
-        "x  + 2\u22c5x + 1"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expand((x + 2)*(x - 3))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{2} - x - 6$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAF0AAAAUBAMAAAAdCCxUAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\nme8Q6PJIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVQoFWNggIDcvQJQFlEUmwPzBKIUQhVx\nOPB8IUU9XwDLV1LUMzBwfSRNPYcCaepzCStnS8qBK2K/AGfiYjBOZbgJlwtjiIGzcTA4Exh2w6R4\nZq3aCGPjousdEDJ8//9/QPCws/ZBhIWUXdUUMFVgCv88phvAwMCYwN7EgZkWMIUZfxYwvGJgYBNg\n+8jlgGE+pjDjfwGGewIMjAycG+CqJTtAYA6QDxdGCP1gYKgHOoiBXwGuHpmBIfwSqL4AqOL8AQYB\nZIVQNoYwMHyA5nMJ6DMwYarHIrwe6H4GhvgF+xncMY3HIsydwAgMH2EloXSQq9AANuHcywcYAJ9M\nPFKc+f7RAAAAAElFTkSuQmCC\n",
-       "prompt_number": 40,
-       "text": [
-        " 2        \n",
-        "x  - x - 6"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expand( (x + 1)*(x - 2) - (x - 1)*x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$-2$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAABkAAAAOBAMAAAAoFKpzAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAEM3dMiKZu6uJRO92VKoV\n2MsAAAAJcEhZcwAADsQAAA7EAZUrDhsAAABeSURBVAgdY2CAAta0cgcYm4FBgoHzFYLXyMCwG8Hb\nzMDQh1CqJ4DMA6qyE0AoZeB6icRhYC5gYBAyBgEVoHA6shRPAzLvGgNrAJzPksDAg+AFz5zWDJdi\n0Hv37jEDAGKfELAuwdx+AAAAAElFTkSuQmCC\n",
-       "prompt_number": 41,
-       "text": [
-        "-2"
-       ]
-      }
-     ],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "factor(x**2*z + 4*x*y*z + 4*y**2*z)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$z \\left(x + 2 y\\right)^{2}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAGAAAAAbBAMAAABxU5cJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ndArxjuiZ8kX2Bu17bFGacPuAAAAAAElFTkSuQmCC\n",
-       "prompt_number": 42,
-       "text": [
-        "           2\n",
-        "z\u22c5(x + 2\u22c5y) "
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "factor_list(x**2*z + 4*x*y*z + 4*y**2*z)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{pmatrix}1, & \\begin{bmatrix}\\begin{pmatrix}z, & 1\\end{pmatrix}, & \\begin{pmatrix}x + 2 y, & 2\\end{pmatrix}\\end{bmatrix}\\end{pmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAARYAAAAaBAMAAACA1m6zAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nbEpOYmvTiE7Qf/D/Arj77Gv4jyAFAAAAAElFTkSuQmCC\n",
-       "prompt_number": 43,
-       "text": [
-        "(1, [(z, 1), (x + 2\u22c5y, 2)])"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "collect collects common powers"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = x*y + x - 3 + 2*x**2 - z*x**2 + x**3\n",
-      "expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{3} - x^{2} z + 2 x^{2} + x y + x - 3$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAAYBAMAAADHZzbDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-       "prompt_number": 44,
-       "text": [
-        " 3    2        2              \n",
-        "x  - x \u22c5z + 2\u22c5x  + x\u22c5y + x - 3"
-       ]
-      }
-     ],
-     "prompt_number": 44
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "collected_expr = collect(expr, x)\n",
-      "collected_expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{3} + x^{2} \\left(- z + 2\\right) + x \\left(y + 1\\right) - 3$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAASEAAAAZBAMAAACMbYVcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-       "prompt_number": 45,
-       "text": [
-        " 3    2                         \n",
-        "x  + x \u22c5(-z + 2) + x\u22c5(y + 1) - 3"
-       ]
-      }
-     ],
-     "prompt_number": 45
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "cancel cancels"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = (x**2 + 2*x + 1)/(x**2 + x)\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{x^{2} + 2 x + 1}{x^{2} + x}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAGkAAAAxBAMAAAArcZqTAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\nme8Q6PJIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAB/UlEQVRIDe2Vv0vDUBDHLzFNY60arLi4pBVc\n/YGgk2YRBRdx0EXwx6A4FN10dnPxx2YUqYqTIOgsYv8DC47iroukolJEiOn7kbx3xh8R3MyQd/e9\n+7x7eS13APTJX5jMirHods1mjHSWatjpp/hUw6j2HJ8CSJV/QxmWTGm350VZ4d4WN/w1L9hVMwN6\n9JlHhEMlS4xSbGp0ATwwiSxcb70WqHGYpDkpi653AANFapI310ENqfTO8RlN4dGC+SXV1DbYbjV4\nnitTvrdhKrmesVmkk1rKdHLVEP4XvBZA4gVaYMVai6J0Uy+nbBqpvkPKOIUZWDSPaCzQaS2oPQ0Z\nkboBMGGXx2QKoNHiEdAdZ/veccivoHZW5UcSk3R6h1dFf8/gCfY8Ac2G9BsoNBTo5IQpswPUCCpd\nAtU+UF3IRlFT+5cwFFQKv2t4Lt+XeK1ztaUoKpNrmmcBEuYnKXheRenOti+zHble31/pFYqgKI5w\nCuvUT4iFhZTPdCHl34x1A94vHjdWhT9I/p8v0qXy+dIsqd85fL4cfpcoxoP58kOK9HwI5gunqCxu\nLNq054fzhVF4FIiIb9OeH84XRuFRgCgF9XxeC8mIknr+guOsO84eSRFGwQfEF+Sez28DyQjEPZ9R\nWEYU7vmMwjKicM9nFJYRhd3gu3DgS3/is+g7Pq2xmEJMqxYAAAAASUVORK5CYII=\n",
-       "prompt_number": 46,
-       "text": [
-        " 2          \n",
-        "x  + 2\u22c5x + 1\n",
-        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
-        "    2       \n",
-        "   x  + x   "
-       ]
-      }
-     ],
-     "prompt_number": 46
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cancel(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{1}{x} \\left(x + 1\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAE8AAAAqBAMAAAAAIxtcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nUfNUcswAAAAASUVORK5CYII=\n",
-       "prompt_number": 47,
-       "text": [
-        "x + 1\n",
-        "\u2500\u2500\u2500\u2500\u2500\n",
-        "  x  "
-       ]
-      }
-     ],
-     "prompt_number": 47
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "trigsimp simplifies trigonometric identities"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "trigsimp(sin(x)**4 - 2*cos(x)**2*sin(x)**2 + cos(x)**4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{1}{2} \\cos{\\left (4 x \\right )} + \\frac{1}{2}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAHwAAAAqBAMAAACD5yJnAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nOOlVK3gAAAAASUVORK5CYII=\n",
-       "prompt_number": 48,
-       "text": [
-        "cos(4\u22c5x)   1\n",
-        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
-        "   2       2"
-       ]
-      }
-     ],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "trigsimp(sin(x)*tan(x)/sec(x))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sin^{2}{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAAAZBAMAAABzxjXSAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nOiuW8hEAAAAASUVORK5CYII=\n",
-       "prompt_number": 49,
-       "text": [
-        "   2   \n",
-        "sin (x)"
-       ]
-      }
-     ],
-     "prompt_number": 49
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "the tutorial discusses some of the nuances of simplification of powers and special functions"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Calculus"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "derivatives"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diff(cos(x), x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$- \\sin{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEoAAAAVBAMAAAATJmVgAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMnZUu0TviSKZ\nZquXRLrEAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABSklEQVQoFaWSPUvDYBSFTz6a1ITYUOziIBVE\ndHIQXc2i4NQidBH8AHHP0kkkgj/AdHOSjA62vIOgi+jg6BD8A2bQubhL8b4mNwmazQvJeXI4OfcN\nBPjn6HFa4Hi/i7a7hXPH+M7A2omYgHlGlaFCaxGbBbFT6JRgVkKmTG+93DjPCYsFYvN4xw4Sffm5\n9yHdG7pa+/e79D0Ss1F8aDATqH08ymWngBI5a1obeKXHbOoDWDATmCEaMXnrgOVanwa9cYTmqpwF\nsjf64ifVRoO24JK6oPpE6MhbNrMrXzCpy89TSKmcsgXezkop2kgtHlyg9LlGhOm4lKLTG+4BapQq\nnd4YwBS0jjeOgIvDAA/U+CJr06kP96718WRmPNkKrsiiNXO91jAmXMoiFaIJNu2Q6a86T+xZXaYK\nPWGvyVCl+V+Yx6tStjw4jSPwDWX2RQ0oD4CmAAAAAElFTkSuQmCC\n",
-       "prompt_number": 50,
-       "text": [
-        "-sin(x)"
-       ]
-      }
-     ],
-     "prompt_number": 50
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diff(exp(x**2), x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$2 x e^{x^{2}}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAC8AAAAXBAMAAABpIAtfAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVQoFWNgwAJYyg2xiAKFmBm6sEtwM8QH\nYJdhuIVDnHUjDokwHCaxKoZj6mA/5JPC9f8XpsRRtg0rwKKMSsYOyNIO3A8g3DAG9h/IEgz8AhBu\nKgPDTBQJfwYHMH8WA0M+hAXmMn6oYigAs/YHoEiw5rSfgXtifQDrmcSeGgaG8DOKYMVQguM7QzjD\nqwOrGTgWMogjS3AZMJQy1AdcZJC8wKqCLKHMwBDAsBkokvnmHNx4IJcnAaTqDxB/AzEQ4BIDowAD\nyxcGVobvCEEgi0WBgUfgKs8HhqMM04HaEXJS5UVZHH+ZPzA+YDjNwIrk3P3///9izTl6/B1QuQ6Q\ngAEANjc7jUOBAFwAAAAASUVORK5CYII=\n",
-       "prompt_number": 51,
-       "text": [
-        "     \u239b 2\u239e\n",
-        "     \u239dx \u23a0\n",
-        "2\u22c5x\u22c5\u212f    "
-       ]
-      }
-     ],
-     "prompt_number": 51
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "third derivative"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diff(x**4, x, 3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$24 x$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAB4AAAAPBAMAAAABlGKvAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVQIHWNgVDJ2YGBgYFEAEiAQxsD+A0hx\nGYB5DAypDAwzgcyTMP4sBoZ8BwbG1zD+/gAQn50bxgeqXR/A8BDIZz2T2FMD5HJ8Z2A9AOSHM7w6\nsBrIBxrNzgDklzLUB1wE8pUZGK6A+AEMm4E8Bp4EBtYCEJ+B4Q+If4mBUTYtLX/aAQaWLwysIIfy\nCDAwcBowXOX5wHCUQaq8KAuoit+A4y/zB8YHDPv////FwMBu/+1pztHj70AGoAAAzuItVAGSqRAA\nAAAASUVORK5CYII=\n",
-       "prompt_number": 52,
-       "text": [
-        "24\u22c5x"
-       ]
-      }
-     ],
-     "prompt_number": 52
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "differentiate different variables"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = exp(x*y*z)\n",
-      "diff(expr, x, y, z)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left(x^{2} y^{2} z^{2} + 3 x y z + 1\\right) e^{x y z}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAN0AAAAcBAMAAAAXXqc4AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMkS7zRCZdiKJ71Rm\nq90icBAQAAAACXBIWXMAAA7EAAAOxAGVKw4bAAADf0lEQVRIDa1VS2gTURQ9mXaSSdOmwY0oQkcQ\nRfFTEOtKGjTqQpQBBXUhRlpaN0KgtqGidCgoKooBf4j1h4roxmz8oC4CLsRSJKJQqwuxFCulYhRL\nFT/1znvz5s3UmaClbzHv3HvOvWfu5M0EcNaZBsPGfsiRTReImjVJ3ssPTZcL9ZnBe8XN0Ldg9J9+\nkWB9qJ5zVbr2JRgF1/syStE3bSUrxa+G2JgQ+SHB/dveI2XRjpnPlQuIG9rQNh1odZh4RkA/JLh/\n2+POFGivSQ5Ga43ZCD86C2jOUDjjtPJDnNTOncw7sgBgPbFI2iETlQUcXIo2QKXSqENUl0V2+SHE\nfjqd/IHWSH5aSZK1JjCAfqCbclXkyddCzA1Gqsm5rjx+26rJ2zaeUBvekh9GJV2HBAZxBZoeMlBn\nkdYKbXi4IhjFMpxbpKtfOfrrek9kuqyWTSLSSheRwzulhOPnHwO9Il81MVHi2A8JPwQ/T6/fPtFX\n7V+YNbCkLYnM7LVAM+W3Huju5Pe/vRXada5URvQ5ooZc7Pno/UlDzb5acBZcK2Vev2XWkNie3c16\nqGZFgQEsp2OTqx6OJ1mYe4CIPeXmbEuRS6yr4zfrPXXBs8wQuFbKvH51CapSPuAI61GTm8d2YCMd\nUSM6FjOtWMs3o6KeMwaOc8Cujh+iV3Aal4z7tlbKvH4ndKo7mld38fKnObYDdLsqKm0PFTdQm7GZ\nUNEG1ib9cMswsMaq4lpHNskvT1Wvn+w3XE0sSPNBeihjYA/CSs63LmxFU6lN11KpNAWHgUbqNE7Q\n1nLZqVRqJJVax+TsfLL5frHYc2F+5MHvI5yEOFixtFsn5psw0FhA6DONx7VS5p2vzqTy7+4WHL+g\nZ2V0IcL9KopYZWvuAkUpF35XgU/mHTpT7eBaKfP6sfN5kz5ssgdDNM6yfCOO8XS4XrE/V6HRnr26\n1Aq/PsTGlR8VJa0ApnXJvH7svd4Llb8PshHdx47s1p4Cz6gDHfafbhi9q6XKOS9KS5Ou9rd39tGB\nsbQumfDbc/sjdWPfl0gr6byrSvfG9Kv4LTGfh/NqhR+XrPcoZRDOSQy8xE7dHTtYKThQgMnaxYKw\ndlX+zbnT9Jm+7I6HsdIdlsVltdWus+bt8sYdLuhIuMOyuKw2HthnS9mmUyWfBhbKFzZQ8v+EVh9c\n8zCYmjITNqZcOr2FfwBusOrBenqrQQAAAABJRU5ErkJggg==\n",
-       "prompt_number": 53,
-       "text": [
-        "\u239b 2  2  2              \u239e  x\u22c5y\u22c5z\n",
-        "\u239dx \u22c5y \u22c5z  + 3\u22c5x\u22c5y\u22c5z + 1\u23a0\u22c5\u212f     "
-       ]
-      }
-     ],
-     "prompt_number": 53
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "unevaluated derivatied"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "deriv = Derivative(expr, x, y, z)\n",
-      "deriv"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{\\partial^{3}}{\\partial x\\partial y\\partial z}  e^{x y z}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAGwAAAAzBAMAAACAkPDcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nmJ5PegAAAABJRU5ErkJggg==\n",
-       "prompt_number": 54,
-       "text": [
-        "    3           \n",
-        "   \u2202    \u239b x\u22c5y\u22c5z\u239e\n",
-        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239d\u212f     \u23a0\n",
-        "\u2202z \u2202y \u2202x        "
-       ]
-      }
-     ],
-     "prompt_number": 54
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "deriv.doit()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left(x^{2} y^{2} z^{2} + 3 x y z + 1\\right) e^{x y z}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAN0AAAAcBAMAAAAXXqc4AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n8zCYmjITNqZcOr2FfwBusOrBenqrQQAAAABJRU5ErkJggg==\n",
-       "prompt_number": 55,
-       "text": [
-        "\u239b 2  2  2              \u239e  x\u22c5y\u22c5z\n",
-        "\u239dx \u22c5y \u22c5z  + 3\u22c5x\u22c5y\u22c5z + 1\u23a0\u22c5\u212f     "
-       ]
-      }
-     ],
-     "prompt_number": 55
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "integrals"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "integrate(cos(x), x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sin{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAADcAAAAVBAMAAAAQkWtIAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZUzRC73UTviSKZ\nZqszMyTAAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABLElEQVQoFZWRvUvDUBRHTzRpa2NFxdUSKl0q\nSBcFdcnUOZOgk4MIIkgGN5fgpqOrDiI66yQUEfInqIvgB1TBSYSoIH6g8b0kfYndvEPeeffc/Lgk\n8K8aTqbPk1PbSl835xPu649B+0yl7rTZbkN61hSmpFrbimZjMgdUp+Aq1OtoIxX7+IehncExS/SL\nPhiVx/W9CGfAZQUalvklZO4M5ri0VqFks+GwK+USfAuZr8MBh840dLvk30aRcgFehOzxwKEhCC3A\n2A99KRdTGZOUJ5TeM1LGoj1jgFh8Cp4yUi403hVQjRaadFiWmUlssUXhNReYYqbXZ/PiyiuHD+Xw\nbuKjFWUZ99XarQjXPfH4W2vq2lSk4EaR+EidlTtKOobdqUD9bDWVnTlNLtfwC1XgR6gJ3g8BAAAA\nAElFTkSuQmCC\n",
-       "prompt_number": 56,
-       "text": [
-        "sin(x)"
-       ]
-      }
-     ],
-     "prompt_number": 56
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "definite integral"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "integrate(exp(-x), (x, 0, oo))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAJcEhZcwAA\nDsQAAA7EAZUrDhsAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCH\ncAAAAABJRU5ErkJggg==\n",
-       "prompt_number": 57,
-       "text": [
-        "1"
-       ]
-      }
-     ],
-     "prompt_number": 57
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "double integral"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "integrate(exp(-x**2 - y**2), (x, -oo, oo), (y, -oo, oo))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\pi$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAwAAAAJBAMAAAD0ltBnAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAdpmJMlQiZrurEN1E71u8\n6TcAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAA+SURBVAgdY2CQe/fu3SMGZpPAdLEEBlcGM0YHBiBo\n4FwAJFkmcIM4TAV8IN66BXwHgJQ0A68BkGpn4DRgAADO5AwIf9stDwAAAABJRU5ErkJggg==\n",
-       "prompt_number": 58,
-       "text": [
-        "\u03c0"
-       ]
-      }
-     ],
-     "prompt_number": 58
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "if it is unable to do the integral, it returns an Integral object"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = integrate(x**x, x)\n",
-      "print expr\n",
-      "expr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Integral(x**x, x)\n"
-       ]
-      },
-      {
-       "latex": [
-        "$$\\int x^{x}\\, dx$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEcAAAAvBAMAAABDOcobAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n",
-       "prompt_number": 59,
-       "text": [
-        "\u2320      \n",
-        "\u23ae  x   \n",
-        "\u23ae x  dx\n",
-        "\u2321      "
-       ]
-      }
-     ],
-     "prompt_number": 59
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = x / sqrt(x**4 + 10*x**2 - 96*x - 71)   # example from Wikipedia Risch algorithm page)\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{x}{\\sqrt{x^{4} + 10 x^{2} - 96 x - 71}}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAANIAAAAqBAMAAAA0dhgFAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n",
-       "prompt_number": 60,
-       "text": [
-        "             x             \n",
-        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
-        "   ________________________\n",
-        "  \u2571  4       2             \n",
-        "\u2572\u2571  x  + 10\u22c5x  - 96\u22c5x - 71 "
-       ]
-      }
-     ],
-     "prompt_number": 60
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "integrate(a, x)     # this has a known solution, but SymPy fails to find it"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\int \\frac{x}{\\sqrt{x^{4} + 10 x^{2} - 96 x - 71}}\\, dx$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAQgAAAAwBAMAAAAfhvm4AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-       "prompt_number": 61,
-       "text": [
-        "\u2320                               \n",
-        "\u23ae              x                \n",
-        "\u23ae \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 dx\n",
-        "\u23ae    ________________________   \n",
-        "\u23ae   \u2571  4       2                \n",
-        "\u23ae \u2572\u2571  x  + 10\u22c5x  - 96\u22c5x - 71    \n",
-        "\u2321                               "
-       ]
-      }
-     ],
-     "prompt_number": 61
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "limits"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "limit(sin(x)/x, x, 0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAJcEhZcwAA\nDsQAAA7EAZUrDhsAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCH\ncAAAAABJRU5ErkJggg==\n",
-       "prompt_number": 62,
-       "text": [
-        "1"
-       ]
-      }
-     ],
-     "prompt_number": 62
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "series expansions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expr = exp(sin(x))\n",
-      "expr.series(x, 0, 6)    # expansion about x=0 up to 6th order"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1 + x + \\frac{x^{2}}{2} - \\frac{x^{4}}{8} - \\frac{x^{5}}{15} + \\mathcal{O}\\left(x^{6}\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAARwAAAAwBAMAAAAxw9gqAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n0CabzTVaNrWHXEL1ViH8wB5BZ/0v0H8ABrlD6uSJz+8AAAAASUVORK5CYII=\n",
-       "prompt_number": 63,
-       "text": [
-        "         2    4    5        \n",
-        "        x    x    x     \u239b 6\u239e\n",
-        "1 + x + \u2500\u2500 - \u2500\u2500 - \u2500\u2500 + O\u239dx \u23a0\n",
-        "        2    8    15        "
-       ]
-      }
-     ],
-     "prompt_number": 63
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "note: the order term O() is only supported for expansions about 0 or oo.  So when you compute the series about a point other than 0 or oo, the result will be shifted to 0.  You need to shift back by first removing the O() term."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = log(x).series(x, 1, 6)\n",
-      "b = a.removeO().subs(x, x-1)\n",
-      "b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x + \\frac{1}{5} \\left(x - 1\\right)^{5} - \\frac{1}{4} \\left(x - 1\\right)^{4} + \\frac{1}{3} \\left(x - 1\\right)^{3} - \\frac{1}{2} \\left(x - 1\\right)^{2} - 1$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAArBAMAAAD1SY1vAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
-       "prompt_number": 64,
-       "text": [
-        "           5          4          3          2    \n",
-        "    (x - 1)    (x - 1)    (x - 1)    (x - 1)     \n",
-        "x + \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - 1\n",
-        "       5          4          3          2        "
-       ]
-      }
-     ],
-     "prompt_number": 64
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "solvers"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "If no Eq() is done, then it is assumed to be equal to 0"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "solve(Eq(x**2, 1), x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{bmatrix}-1, & 1\\end{bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEkAAAAZBAMAAACP0x4YAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAu90iEM0ymat272ZEieIx\nuvsAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAB6SURBVCgVYxBSMmDADw4pXWBQwK8EJMvcQI6qEAyD\nWR0wzDJ8gq6KWQ5DlWMFuioW8T4MVQyc6KoYGOYNelWuSiAQxjCoXe8XgBJLmKHK3vWom4EJJfwr\n1mlswJpyDqOYBeJgS18biFHFQpQqbgyjsNpIc1VKROTHBgCD+TIQNJw1owAAAABJRU5ErkJggg==\n",
-       "prompt_number": 65,
-       "text": [
-        "[-1, 1]"
-       ]
-      }
-     ],
-     "prompt_number": 65
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "solve(x**2 - 1, x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{bmatrix}-1, & 1\\end{bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEkAAAAZBAMAAACP0x4YAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAu90iEM0ymat272ZEieIx\nuvsAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAB6SURBVCgVYxBSMmDADw4pXWBQwK8EJMvcQI6qEAyD\nWR0wzDJ8gq6KWQ5DlWMFuioW8T4MVQyc6KoYGOYNelWuSiAQxjCoXe8XgBJLmKHK3vWom4EJJfwr\n1mlswJpyDqOYBeJgS18biFHFQpQqbgyjsNpIc1VKROTHBgCD+TIQNJw1owAAAABJRU5ErkJggg==\n",
-       "prompt_number": 66,
-       "text": [
-        "[-1, 1]"
-       ]
-      }
-     ],
-     "prompt_number": 66
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "solve([x - y + 2, x + y - 3], [x, y])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{Bmatrix}x : \\frac{1}{2}, & y : \\frac{5}{2}\\end{Bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAIMAAAAcBAMAAACwgxHDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngg==\n",
-       "prompt_number": 67,
-       "text": [
-        "{x: 1/2, y: 5/2}"
-       ]
-      }
-     ],
-     "prompt_number": 67
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "roots will report if a solution is multiple by listing it multiple times"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "roots(x**3 - 6*x**2 + 9*x, x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{Bmatrix}0 : 1, & 3 : 2\\end{Bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAHsAAAAaBAMAAABlQT6zAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n2tVwzqvu7e8AAAAASUVORK5CYII=\n",
-       "prompt_number": 68,
-       "text": [
-        "{0: 1, 3: 2}"
-       ]
-      }
-     ],
-     "prompt_number": 68
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "0 is 1 root, and 3 is 2 more roots"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "Differential equations"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you need an undefined function (f and g already are by our init_session() above, but we've probably reset these"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f, g = symbols('f g', cls=Function)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 69
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$f{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAACUAAAAVBAMAAAAzyjqdAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJl2IquJVETdZu8y\nu83OyatpAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVQYGX2QrU4DURCFv6VlW6C7bBANBLOC\nB2ghqa4GAQkEg2qqUJQEdJvwAgiQkAYwEBw8QDWqBr8vQLIEQQWCM3cvK5lkZs45c+4v/BNvfvZc\n9JXNLtWp1+YnDhwOB8SJ18gMBLNV6PxJBarl4q+ltm4ovIS60kc8gMWj65Qw1R5bBxd9WQRZbkE0\nhiaPrVt5Mmm7E1jQgh4nyRnUbJuhcmkECR9CNHKVY6Vp8GXFae8CtpbGD4G/wouonXFayWkXZwQz\naeEe9e8or2o0l8r7KU13DvbbnSfBeCTLVIArKy524DweG7xz3IqecrPmWNR1TS/M4H7DkfJPy6Hk\nbe97gF/iMDFQFwguCwAAAABJRU5ErkJggg==\n",
-       "prompt_number": 70,
-       "text": [
-        "f(x)"
-       ]
-      }
-     ],
-     "prompt_number": 70
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f(x).diff(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{d}{d x} f{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAAArBAMAAAA6dJN0AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAAAElFTkSuQmCC\n",
-       "prompt_number": 71,
-       "text": [
-        "d       \n",
-        "\u2500\u2500(f(x))\n",
-        "dx      "
-       ]
-      }
-     ],
-     "prompt_number": 71
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diffeq = Eq(f(x).diff(x, 2) - 2*f(x).diff(x) + f(x), sin(x))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 72
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diffeq"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$f{\\left (x \\right )} - 2 \\frac{d}{d x} f{\\left (x \\right )} + \\frac{d^{2}}{d x^{2}}  f{\\left (x \\right )} = \\sin{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAAvBAMAAACcfiVoAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nzwAAAABJRU5ErkJggg==\n",
-       "prompt_number": 73,
-       "text": [
-        "                      2               \n",
-        "         d           d                \n",
-        "f(x) - 2\u22c5\u2500\u2500(f(x)) + \u2500\u2500\u2500(f(x)) = sin(x)\n",
-        "         dx           2               \n",
-        "                    dx                "
-       ]
-      }
-     ],
-     "prompt_number": 73
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dsolve(diffeq, f(x))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$f{\\left (x \\right )} = \\left(C_{1} + C_{2} x\\right) e^{x} + \\frac{1}{2} \\cos{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAASYAAAAqBAMAAADsX+smAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nKvIvlt0Zysr3vQUAAAAASUVORK5CYII=\n",
-       "prompt_number": 74,
-       "text": [
-        "                    x   cos(x)\n",
-        "f(x) = (C\u2081 + C\u2082\u22c5x)\u22c5\u212f  + \u2500\u2500\u2500\u2500\u2500\u2500\n",
-        "                          2   "
-       ]
-      }
-     ],
-     "prompt_number": 74
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Matrices"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "consider the Euler equations:\n",
-      "\n",
-      "$$q_t + A(q) q_x = 0$$\n",
-      "\n",
-      "where\n",
-      "\n",
-      "$$q = \\left ( \\begin{array}{c} \\rho \\\\ u \\\\ p \\end{array} \\right )\n",
-      "\\qquad\n",
-      "A(q) = \\left ( \\begin{array}{ccc} u  & \\rho     & 0 \\\\                          \n",
-      "                                  0  &  u       & 1/\\rho \\\\                     \n",
-      "                                  0  & c^2 \\rho & u \\end{array} \\right ) $$\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy.abc import rho\n",
-      "rho, u, c = symbols('rho u c')\n",
-      "A = Matrix([[u, rho, 0], [0, u, rho**-1], [0, c**2 * rho, u]])\n",
-      "A"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left[\\begin{matrix}u & \\rho & 0\\\\0 & u & \\frac{1}{\\rho}\\\\0 & c^{2} \\rho & u\\end{matrix}\\right]$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAHAAAABLCAMAAACfg/MXAAAAPFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo1xBWAAAAE3RSTlMA\nMquZdlQQQOkwRIki72bNu91spdw0jAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA5xJREFUaAXlmtuC\noyAMhkGQmfVYl/d/102wYACRg7U3y8VIMfAZQPonHca1KR17uGw7hzGuhYTSP8xjC1I6jUD+IEuJ\nUYoDsDwOXGHm1DZZlx4HLjOixvVrwJfZi71Wb+LjHmoDHLTdkh5QjULCg0yzfRw7D2fXQcju2Atn\nFtimtMDLoHFgLB5QsB5nXMJGypYRBhrM+lyaTnrE+3y/QI0Cp4UJXNzXC22uy7LB/UkP11ZocgGE\niZzRc/MnM9K8gMGxMklrtQMTUwojALTkHDCGMPfu9UoS9zXszzcNEzhRJUtoDNmK5pmymfVZEq+F\neWlgCZfcNt1w75VMBZNmY5m9YR6NbhrGBDzOMAtmFvri0ZUGQ7XhOmbLhkfb7F4gH6i6Ufa8k+52\nYrheT5KeyAkz06xG+Io4BvSBVx3pvWOGaGtRvQ1Y8uIk8E3A6cYXaBNwKHgdEg56R1vK5qPtTR7e\neYJvADn9MngeyEVHl/x5IGP9/wwMROud3Uj6XkxpIFpJpzvVNDAUrXcopG8aGIpW0ulONQ0MResd\nCulrtKf9TN/DSLRao+hao5gnsenOyuBAl4YaMgLZhhrFbPu8r9TDSLQGtu5jjWJ2nU6AkWgNje3n\nGsW8h9nw9wTIQtFqAfG1WDFHXXFKf37/7O2haI2sXUOxYnY9bOXvLxG0oWi1RvG1WDFHXemmYSwQ\nrZG1ayhVzK6Dq/jAQLQ6q6hSqpijjl8RUeDGIb19D+MH+kTLyiGKtwN9ASghraAS4Zp9jI9ecScO\n9r3/whoa54TLHD4/pT04xzcX4T4PFBBzCsf7wpTiEpLyuIfH/typjcBRvMg0EQeiKnd5xDvAEfIz\nXTBShDpvaPMQ8628ICt0wmwDzpBjK0i0nfBu7NIj53o2brLN97AmttgO6Zcc/eSGD6yILTBf2lI8\nYEVssTTy/DUsjy0GyMYNRz6rwlXPw+LYgq9937+OA7IRWBJbjJCq42w28tZirtLtURRCPcxL/QlP\n4jU4Yi7T7VEUUgecMZHaBcc/5ihSp0AchVBgNrYQTpnY2YTrZbo9jkIoMBtbzDSl9Gbm0u1hFOIB\nM7HFewaId1DNpdvDKMQD5mILvSe5vTXMpdvDKMQD5mILo70U+fURBWcm3R5GIT4wF1uM47L4Z3Y2\n3R5GIT7QX56ST9Xp9rvA6nT7TWB9uv0msD7dvgPNWezkf8natdi4fxxQ+IO+lNkfH1sgtI/5xwEp\n2T/6TyqDaMS6EAAAAABJRU5ErkJggg==\n",
-       "prompt_number": 83,
-       "text": [
-        "\u23a1u   \u03c1    0\u23a4\n",
-        "\u23a2          \u23a5\n",
-        "\u23a2         1\u23a5\n",
-        "\u23a20   u    \u2500\u23a5\n",
-        "\u23a2         \u03c1\u23a5\n",
-        "\u23a2          \u23a5\n",
-        "\u23a2    2     \u23a5\n",
-        "\u23a30  c \u22c5\u03c1  u\u23a6"
-       ]
-      }
-     ],
-     "prompt_number": 83
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "The eigenvalues of the system are the speeds at which information propagates"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "A.eigenvals()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{Bmatrix}u : 1, & - c + u : 1, & c + u : 1\\end{Bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAARUAAAAaBAMAAABr4dWwAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngfhhz5L+FvwF3ZSsJKuLJVEAAAAASUVORK5CYII=\n",
-       "prompt_number": 84,
-       "text": [
-        "{u: 1, -c + u: 1, c + u: 1}"
-       ]
-      }
-     ],
-     "prompt_number": 84
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "We can also find the right and left eigenvectors (it seems we need to take the transpose of A and find it's eigenvectors to get the left eigenvectors).  Note that these are not normalized."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "A.eigenvects()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{bmatrix}\\begin{pmatrix}u, & 1, & \\begin{bmatrix}\\left[\\begin{matrix}1\\\\0\\\\0\\end{matrix}\\right]\\end{bmatrix}\\end{pmatrix}, & \\begin{pmatrix}- c + u, & 1, & \\begin{bmatrix}\\left[\\begin{matrix}\\frac{1}{c^{2}}\\\\- \\frac{1}{c \\rho}\\\\1\\end{matrix}\\right]\\end{bmatrix}\\end{pmatrix}, & \\begin{pmatrix}c + u, & 1, & \\begin{bmatrix}\\left[\\begin{matrix}\\frac{1}{c^{2}}\\\\\\frac{1}{c \\rho}\\\\1\\end{matrix}\\right]\\end{bmatrix}\\end{pmatrix}\\end{bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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-       "prompt_number": 85,
-       "text": [
-        "\u23a1               \u239b           \u23a1\u23a11  \u23a4\u23a4\u239e  \u239b          \u23a1\u23a11  \u23a4\u23a4\u239e\u23a4\n",
-        "\u23a2\u239bu, 1, \u23a1\u23a11\u23a4\u23a4\u239e, \u239c-c + u, 1, \u23a2\u23a2\u2500\u2500 \u23a5\u23a5\u239f, \u239cc + u, 1, \u23a2\u23a2\u2500\u2500 \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2\u239c      \u23a2\u23a2 \u23a5\u23a5\u239f  \u239c           \u23a2\u23a2 2 \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2 2 \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2\u239c      \u23a2\u23a20\u23a5\u23a5\u239f  \u239c           \u23a2\u23a2c  \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2c  \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2\u239c      \u23a2\u23a2 \u23a5\u23a5\u239f  \u239c           \u23a2\u23a2   \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2   \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2\u239d      \u23a3\u23a30\u23a6\u23a6\u23a0  \u239c           \u23a2\u23a2-1 \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2 1 \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2               \u239c           \u23a2\u23a2\u2500\u2500\u2500\u23a5\u23a5\u239f  \u239c          \u23a2\u23a2\u2500\u2500\u2500\u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2               \u239c           \u23a2\u23a2c\u22c5\u03c1\u23a5\u23a5\u239f  \u239c          \u23a2\u23a2c\u22c5\u03c1\u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2               \u239c           \u23a2\u23a2   \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2   \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a3               \u239d           \u23a3\u23a3 1 \u23a6\u23a6\u23a0  \u239d          \u23a3\u23a3 1 \u23a6\u23a6\u23a0\u23a6"
-       ]
-      }
-     ],
-     "prompt_number": 85
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "A.T.eigenvects()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{bmatrix}\\begin{pmatrix}u, & 1, & \\begin{bmatrix}\\left[\\begin{matrix}- c^{2}\\\\0\\\\1\\end{matrix}\\right]\\end{bmatrix}\\end{pmatrix}, & \\begin{pmatrix}- c + u, & 1, & \\begin{bmatrix}\\left[\\begin{matrix}0\\\\- c \\rho\\\\1\\end{matrix}\\right]\\end{bmatrix}\\end{pmatrix}, & \\begin{pmatrix}c + u, & 1, & \\begin{bmatrix}\\left[\\begin{matrix}0\\\\c \\rho\\\\1\\end{matrix}\\right]\\end{bmatrix}\\end{pmatrix}\\end{bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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-       "prompt_number": 87,
-       "text": [
-        "\u23a1\u239b      \u23a1\u23a1  2\u23a4\u23a4\u239e                                            \u23a4\n",
-        "\u23a2\u239cu, 1, \u23a2\u23a2-c \u23a5\u23a5\u239f, \u239b-c + u, 1, \u23a1\u23a1 0  \u23a4\u23a4\u239e, \u239bc + u, 1, \u23a1\u23a1 0 \u23a4\u23a4\u239e\u23a5\n",
-        "\u23a2\u239c      \u23a2\u23a2   \u23a5\u23a5\u239f  \u239c           \u23a2\u23a2    \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2   \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2\u239c      \u23a2\u23a2 0 \u23a5\u23a5\u239f  \u239c           \u23a2\u23a2-c\u22c5\u03c1\u23a5\u23a5\u239f  \u239c          \u23a2\u23a2c\u22c5\u03c1\u23a5\u23a5\u239f\u23a5\n",
-        "\u23a2\u239c      \u23a2\u23a2   \u23a5\u23a5\u239f  \u239c           \u23a2\u23a2    \u23a5\u23a5\u239f  \u239c          \u23a2\u23a2   \u23a5\u23a5\u239f\u23a5\n",
-        "\u23a3\u239d      \u23a3\u23a3 1 \u23a6\u23a6\u23a0  \u239d           \u23a3\u23a3 1  \u23a6\u23a6\u23a0  \u239d          \u23a3\u23a3 1 \u23a6\u23a6\u23a0\u23a6"
-       ]
-      }
-     ],
-     "prompt_number": 87
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Units"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sympy.physics.units as u\n",
-      "\n",
-      "M = 20*u.kilogram\n",
-      "g = 9.81 * u.m / u.s**2\n",
-      "W = M*g\n",
-      "print W, W/ (1*u.N)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "196.2*kg*m/s**2 196.200000000000\n"
-       ]
-      }
-     ],
-     "prompt_number": 75
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "there are methods to help you find units"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print u.find_unit('force')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['force']\n"
-       ]
-      }
-     ],
-     "prompt_number": 76
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print u.find_unit(u.force)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['N', 'force', 'pound', 'newton', 'pounds', 'newtons']\n"
-       ]
-      }
-     ],
-     "prompt_number": 77
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "if you want to do conversions, you divide by the units you want to express the result in, but you need to evaluate it."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mph = u.miles/u.hours\n",
-      "print mph\n",
-      "print mph.n(4)\n",
-      "print (u.c/mph).n(5)\n",
-      "# or\n",
-      "cmph = u.c/mph\n",
-      "cmph.evalf()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1397*m/(3125*s)\n",
-        "0.447*m/s\n",
-        "6.7062e+8\n"
-       ]
-      },
-      {
-       "latex": [
-        "$$670616629.384395$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAPBAMAAACRq9klAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nTkSuQmCC\n",
-       "prompt_number": 78,
-       "text": [
-        "670616629.384395"
-       ]
-      }
-     ],
-     "prompt_number": 78
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "you can define new units (like CGS!)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "erg = u.g*u.cm**2/u.s**2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 79
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "L = 4.e33*erg\n",
-      "(L/u.joule).evalf()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$4.0 \\cdot 10^{26}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAE0AAAAVBAMAAADx+n4ZAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nT/9dVJZH8gPs02wPhHnuPgAAAABJRU5ErkJggg==\n",
-       "prompt_number": 81,
-       "text": [
-        "4.00000000000000e+26"
-       ]
-      }
-     ],
-     "prompt_number": 81
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Vectors"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/lectures/07-extensions/extensions.fodp b/lectures/07-extensions/extensions.fodp
deleted file mode 100644
index f54952aa..00000000
--- a/lectures/07-extensions/extensions.fodp
+++ /dev/null
@@ -1,4087 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-
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- <office:meta><meta:initial-creator>Michael Zingale</meta:initial-creator><meta:creation-date>2013-01-02T12:36:14</meta:creation-date><dc:date>2013-09-18T17:29:34.092726914</dc:date><dc:creator>Michael Zingale</dc:creator><meta:editing-duration>PT23H10M59S</meta:editing-duration><meta:editing-cycles>96</meta:editing-cycles><meta:generator>LibreOffice/4.2.3.3$Linux_X86_64 LibreOffice_project/420$Build-3</meta:generator><meta:document-statistic meta:object-count="179"/></office:meta>
- <office:settings>
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-   <config:config-item config:name="VisibleAreaLeft" config:type="int">0</config:config-item>
-   <config:config-item config:name="VisibleAreaWidth" config:type="int">1153</config:config-item>
-   <config:config-item config:name="VisibleAreaHeight" config:type="int">755</config:config-item>
-   <config:config-item-map-indexed config:name="Views">
-    <config:config-item-map-entry>
-     <config:config-item config:name="ViewId" config:type="string">view2</config:config-item>
-     <config:config-item config:name="GridIsVisible" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="GridIsFront" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsSnapToGrid" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToPageMargins" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToSnapLines" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectFrame" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsSnapToObjectPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsPlusHandlesAlwaysVisible" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="IsFrameDragSingles" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="EliminatePolyPointLimitAngle" config:type="int">1500</config:config-item>
-     <config:config-item config:name="IsEliminatePolyPoints" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="VisibleLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="PrintableLayers" config:type="base64Binary">//////////////////////////////////////////8=</config:config-item>
-     <config:config-item config:name="LockedLayers" config:type="base64Binary"/>
-     <config:config-item config:name="NoAttribs" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="NoColors" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="RulerIsVisible" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="PageKind" config:type="short">0</config:config-item>
-     <config:config-item config:name="SelectedPage" config:type="short">18</config:config-item>
-     <config:config-item config:name="IsLayerMode" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsDoubleClickTextEdit" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="IsClickChangeRotation" config:type="boolean">true</config:config-item>
-     <config:config-item config:name="SlidesPerRow" config:type="short">4</config:config-item>
-     <config:config-item config:name="EditModeStandard" config:type="int">0</config:config-item>
-     <config:config-item config:name="EditModeNotes" config:type="int">0</config:config-item>
-     <config:config-item config:name="EditModeHandout" config:type="int">1</config:config-item>
-     <config:config-item config:name="VisibleAreaTop" config:type="int">-322</config:config-item>
-     <config:config-item config:name="VisibleAreaLeft" config:type="int">-715</config:config-item>
-     <config:config-item config:name="VisibleAreaWidth" config:type="int">29677</config:config-item>
-     <config:config-item config:name="VisibleAreaHeight" config:type="int">21811</config:config-item>
-     <config:config-item config:name="GridCoarseWidth" config:type="int">2540</config:config-item>
-     <config:config-item config:name="GridCoarseHeight" config:type="int">2540</config:config-item>
-     <config:config-item config:name="GridFineWidth" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridFineHeight" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthXNumerator" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthXDenominator" config:type="int">1</config:config-item>
-     <config:config-item config:name="GridSnapWidthYNumerator" config:type="int">254</config:config-item>
-     <config:config-item config:name="GridSnapWidthYDenominator" config:type="int">1</config:config-item>
-     <config:config-item config:name="IsAngleSnapEnabled" config:type="boolean">false</config:config-item>
-     <config:config-item config:name="SnapAngle" config:type="int">1500</config:config-item>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-  </config:config-item-set>
-  <config:config-item-set config:name="ooo:configuration-settings">
-   <config:config-item config:name="ApplyUserData" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="BitmapTableURL" config:type="string">$(user)/config/standard.sob</config:config-item>
-   <config:config-item config:name="CharacterCompressionType" config:type="short">0</config:config-item>
-   <config:config-item config:name="ColorTableURL" config:type="string">$(user)/config/standard.soc</config:config-item>
-   <config:config-item config:name="DashTableURL" config:type="string">$(user)/config/standard.sod</config:config-item>
-   <config:config-item config:name="DefaultTabStop" config:type="int">1270</config:config-item>
-   <config:config-item config:name="EmbedFonts" config:type="boolean">false</config:config-item>
-   <config:config-item-map-indexed config:name="ForbiddenCharacters">
-    <config:config-item-map-entry>
-     <config:config-item config:name="Language" config:type="string">en</config:config-item>
-     <config:config-item config:name="Country" config:type="string">US</config:config-item>
-     <config:config-item config:name="Variant" config:type="string"/>
-     <config:config-item config:name="BeginLine" config:type="string"/>
-     <config:config-item config:name="EndLine" config:type="string"/>
-    </config:config-item-map-entry>
-   </config:config-item-map-indexed>
-   <config:config-item config:name="GradientTableURL" config:type="string">$(user)/config/standard.sog</config:config-item>
-   <config:config-item config:name="HandoutsHorizontal" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="HatchTableURL" config:type="string">$(user)/config/standard.soh</config:config-item>
-   <config:config-item config:name="IsKernAsianPunctuation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBooklet" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintBookletBack" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintBookletFront" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintDate" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintDrawing" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintFitPage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHandout" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintHiddenPages" config:type="boolean">true</config:config-item>
-   <config:config-item config:name="IsPrintNotes" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintOutline" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintPageName" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTilePage" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="IsPrintTime" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="LineEndTableURL" config:type="string">$(user)/config/standard.soe</config:config-item>
-   <config:config-item config:name="LoadReadonly" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PageNumberFormat" config:type="int">4</config:config-item>
-   <config:config-item config:name="ParagraphSummation" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="PrintQuality" config:type="int">0</config:config-item>
-   <config:config-item config:name="PrinterIndependentLayout" config:type="string">low-resolution</config:config-item>
-   <config:config-item config:name="PrinterName" config:type="string">hp</config:config-item>
-   <config:config-item config:name="PrinterSetup" config:type="base64Binary">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</config:config-item>
-   <config:config-item config:name="SaveVersionOnClose" config:type="boolean">false</config:config-item>
-   <config:config-item config:name="SlidesPerHandout" config:type="short">6</config:config-item>
-   <config:config-item config:name="UpdateFromTemplate" config:type="boolean">true</config:config-item>
-  </config:config-item-set>
- </office:settings>
- <office:scripts>
-  <office:script script:language="ooo:Basic">
-   <ooo:libraries xmlns:ooo="http://openoffice.org/2004/office" xmlns:xlink="http://www.w3.org/1999/xlink">
-    <ooo:library-embedded ooo:name="Standard"/>
-   </ooo:libraries>
-  </office:script>
- </office:scripts>
- <office:font-face-decls>
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-  <style:font-face style:name="Arial1" svg:font-family="Arial" style:font-pitch="variable"/>
-  <style:font-face style:name="Carlito2" svg:font-family="Carlito" style:font-pitch="variable"/>
-  <style:font-face style:name="Inconsolata" svg:font-family="Inconsolata" style:font-pitch="variable"/>
-  <style:font-face style:name="Liberation Mono" svg:font-family="&apos;Liberation Mono&apos;, &apos;Cumberland AMT&apos;, Cumberland, &apos;Courier New&apos;, Cousine, &apos;Nimbus Mono L&apos;, &apos;DejaVu Sans Mono&apos;, Courier, &apos;Lucida Sans Typewriter&apos;, &apos;Lucida Typewriter&apos;, Monaco, Monospaced" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari1" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp1" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="modern" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial" svg:font-family="Arial" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Carlito" svg:font-family="Carlito" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Liberation Sans" svg:font-family="&apos;Liberation Sans&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Tahoma" svg:font-family="Tahoma" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Times New Roman" svg:font-family="&apos;Times New Roman&apos;" style:font-family-generic="roman" style:font-pitch="variable"/>
-  <style:font-face style:name="Arial2" svg:font-family="Arial" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Carlito1" svg:font-family="Carlito" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="Tahoma1" svg:font-family="Tahoma" style:font-family-generic="swiss" style:font-pitch="variable"/>
-  <style:font-face style:name="DejaVu Sans" svg:font-family="&apos;DejaVu Sans&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Lohit Devanagari" svg:font-family="&apos;Lohit Devanagari&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="Tahoma2" svg:font-family="Tahoma" style:font-family-generic="system" style:font-pitch="variable"/>
-  <style:font-face style:name="WenQuanYi Zen Hei Sharp" svg:font-family="&apos;WenQuanYi Zen Hei Sharp&apos;" style:font-family-generic="system" style:font-pitch="variable"/>
- </office:font-face-decls>
- <office:styles>
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-  <draw:gradient draw:name="redpurple" draw:style="linear" draw:start-color="#ff3366" draw:end-color="#b80047" draw:start-intensity="100%" draw:end-intensity="100%" draw:angle="900" draw:border="0%"/>
-  <draw:marker draw:name="Arrow" svg:viewBox="0 0 20 30" svg:d="M10 0l-10 30h20z"/>
-  <draw:marker draw:name="Dimension_20_Lines" draw:display-name="Dimension Lines" svg:viewBox="0 0 836 110" svg:d="M0 0h278 278 280v36 36 38h-278-278-280v-36-36z"/>
-  <draw:marker draw:name="Short_20_line_20_Arrow" draw:display-name="Short line Arrow" svg:viewBox="0 0 3000 3000" svg:d="M1500 0l1500 2789v211h-114l-1286-2392v2392h-200v-2392l-1286 2392h-114v-211z"/>
-  <style:default-style style:family="graphic">
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-   <style:paragraph-properties style:text-autospace="ideograph-alpha" style:punctuation-wrap="simple" style:line-break="strict" style:writing-mode="lr-tb" style:font-independent-line-spacing="false">
-    <style:tab-stops/>
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-  </style:default-style>
-  <style:style style:name="standard" style:family="graphic">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0cm" svg:stroke-color="#808080" draw:marker-start-width="0.2cm" draw:marker-start-center="false" draw:marker-end-width="0.2cm" draw:marker-end-center="false" draw:fill="solid" draw:fill-color="#cfe7f5" draw:textarea-horizontal-align="justify" fo:padding-top="0.125cm" fo:padding-bottom="0.125cm" fo:padding-left="0.25cm" fo:padding-right="0.25cm" draw:shadow="hidden" draw:shadow-offset-x="0.2cm" draw:shadow-offset-y="0.2cm" draw:shadow-color="#808080">
-    <text:list-style style:name="standard">
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-   <style:page-layout-properties fo:margin-top="0cm" fo:margin-bottom="0cm" fo:margin-left="0cm" fo:margin-right="0cm" fo:page-width="21.59cm" fo:page-height="27.94cm" style:print-orientation="portrait"/>
-  </style:page-layout>
-  <style:page-layout style:name="PM1">
-   <style:page-layout-properties fo:margin-top="0cm" fo:margin-bottom="0cm" fo:margin-left="0cm" fo:margin-right="0cm" fo:page-width="28cm" fo:page-height="21cm" style:print-orientation="landscape"/>
-  </style:page-layout>
-  <style:style style:name="dp1" style:family="drawing-page">
-   <style:drawing-page-properties draw:background-size="border" draw:fill="none"/>
-  </style:style>
-  <style:style style:name="dp2" style:family="drawing-page">
-   <style:drawing-page-properties draw:background-size="border" draw:fill="gradient" draw:fill-gradient-name="new" draw:fill-image-width="0cm" draw:fill-image-height="0cm"/>
-  </style:style>
-  <style:style style:name="dp3" style:family="drawing-page">
-   <style:drawing-page-properties draw:background-size="border" draw:fill="solid" draw:fill-color="#c0c0c0" draw:fill-image-width="0cm" draw:fill-image-height="0cm"/>
-  </style:style>
-  <style:style style:name="dp4" style:family="drawing-page">
-   <style:drawing-page-properties presentation:display-header="true" presentation:display-footer="true" presentation:display-page-number="false" presentation:display-date-time="true"/>
-  </style:style>
-  <style:style style:name="dp5" style:family="drawing-page">
-   <style:drawing-page-properties presentation:background-visible="true" presentation:background-objects-visible="true" presentation:display-footer="true" presentation:display-page-number="false" presentation:display-date-time="true"/>
-  </style:style>
-  <style:style style:name="dp6" style:family="drawing-page">
-   <style:drawing-page-properties presentation:visibility="hidden" presentation:background-visible="true" presentation:background-objects-visible="true" presentation:display-footer="true" presentation:display-page-number="false" presentation:display-date-time="true"/>
-  </style:style>
-  <style:style style:name="gr1" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="gr2" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:textarea-vertical-align="bottom" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="gr3" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr4" style:family="graphic">
-   <style:graphic-properties style:protect="size"/>
-  </style:style>
-  <style:style style:name="gr5" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="0.713cm"/>
-  </style:style>
-  <style:style style:name="gr6" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="15.539cm"/>
-  </style:style>
-  <style:style style:name="gr7" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.949cm"/>
-  </style:style>
-  <style:style style:name="gr8" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="8.534cm"/>
-  </style:style>
-  <style:style style:name="gr9" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="7.254cm"/>
-  </style:style>
-  <style:style style:name="gr10" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.869cm"/>
-  </style:style>
-  <style:style style:name="gr11" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="6.706cm"/>
-  </style:style>
-  <style:style style:name="gr12" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="6.503cm"/>
-  </style:style>
-  <style:style style:name="gr13" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="1.423cm"/>
-  </style:style>
-  <style:style style:name="gr14" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" fo:min-height="5.961cm"/>
-  </style:style>
-  <style:style style:name="gr15" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties OOoLatexArgs="28§display§\left (&#x0a;\begin{array}{ccc}&#x0a; 1 &amp; 2 &amp; 3 \\&#x0a;  4 &amp; 5 &amp; 6&#x0a;\end{array}&#x0a;\right )" draw:stroke="none" draw:fill="none" draw:textarea-horizontal-align="center" draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr16" style:family="graphic" style:parent-style-name="objectwithoutfill">
-   <style:graphic-properties svg:stroke-width="0.051cm" svg:stroke-color="#ff0000" draw:marker-start-width="0.279cm" draw:marker-end="Short_20_line_20_Arrow" draw:marker-end-width="0.381cm" draw:fill="none" draw:textarea-vertical-align="middle" fo:padding-top="0.15cm" fo:padding-bottom="0.15cm" fo:padding-left="0.275cm" fo:padding-right="0.275cm"/>
-  </style:style>
-  <style:style style:name="gr17" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="0.712cm"/>
-  </style:style>
-  <style:style style:name="gr18" style:family="graphic" style:parent-style-name="Object_20_with_20_no_20_fill_20_and_20_no_20_line">
-   <style:graphic-properties draw:textarea-vertical-align="middle" draw:color-mode="standard" draw:luminance="0%" draw:contrast="0%" draw:gamma="100%" draw:red="0%" draw:green="0%" draw:blue="0%" fo:clip="rect(0cm, 0cm, 0cm, 0cm)" draw:image-opacity="100%" style:mirror="none"/>
-  </style:style>
-  <style:style style:name="gr19" style:family="graphic" style:parent-style-name="standard">
-   <style:graphic-properties draw:stroke="none" svg:stroke-color="#000000" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="true" draw:auto-grow-width="false" fo:max-height="0cm" fo:min-height="9.686cm"/>
-  </style:style>
-  <style:style style:name="pr1" style:family="presentation" style:parent-style-name="Default-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill-color="#ff3366" draw:textarea-vertical-align="middle" draw:auto-grow-height="false"/>
-  </style:style>
-  <style:style style:name="pr2" style:family="presentation" style:parent-style-name="Default-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.449cm"/>
-  </style:style>
-  <style:style style:name="pr3" style:family="presentation" style:parent-style-name="Default-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="0.768cm"/>
-  </style:style>
-  <style:style style:name="pr4" style:family="presentation" style:parent-style-name="Default-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr5" style:family="presentation" style:parent-style-name="Default-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:textarea-vertical-align="bottom" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr6" style:family="presentation" style:parent-style-name="standard_25_20talk-backgroundobjects">
-   <style:graphic-properties draw:stroke="solid" svg:stroke-width="0.051cm" draw:marker-start-width="0.279cm" draw:marker-end-width="0.279cm" draw:fill="gradient" draw:fill-color="#ff3366" draw:fill-gradient-name="redpurple" draw:textarea-vertical-align="middle" draw:auto-grow-height="false" fo:padding-top="0.025cm" fo:padding-bottom="0.025cm" fo:padding-left="0.025cm" fo:padding-right="0.025cm"/>
-  </style:style>
-  <style:style style:name="pr7" style:family="presentation" style:parent-style-name="standard_25_20talk-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.449cm"/>
-  </style:style>
-  <style:style style:name="pr8" style:family="presentation" style:parent-style-name="standard_25_20talk-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="0.768cm"/>
-  </style:style>
-  <style:style style:name="pr9" style:family="presentation" style:parent-style-name="standard_25_20talk-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr10" style:family="presentation" style:parent-style-name="standard_25_20talk-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:textarea-vertical-align="bottom" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr11" style:family="presentation" style:parent-style-name="standard_25_20talk1-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.449cm"/>
-  </style:style>
-  <style:style style:name="pr12" style:family="presentation" style:parent-style-name="standard_25_20talk1-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr13" style:family="presentation" style:parent-style-name="standard_25_20talk1-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:textarea-vertical-align="bottom" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr14" style:family="presentation" style:parent-style-name="_31_1.02x8.27_25_20title_25_20standard-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.449cm"/>
-  </style:style>
-  <style:style style:name="pr15" style:family="presentation" style:parent-style-name="_31_1.02x8.27_25_20title_25_20standard-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr16" style:family="presentation" style:parent-style-name="_31_1.02x8.27_25_20title_25_20standard-backgroundobjects">
-   <style:graphic-properties draw:stroke="none" draw:fill="none" draw:fill-color="#ffffff" draw:textarea-vertical-align="bottom" draw:auto-grow-height="false" fo:min-height="1.397cm"/>
-  </style:style>
-  <style:style style:name="pr17" style:family="presentation" style:parent-style-name="Default-title">
-   <style:graphic-properties draw:auto-grow-height="true" fo:min-height="1.957cm"/>
-  </style:style>
-  <style:style style:name="pr18" style:family="presentation" style:parent-style-name="Default-outline1">
-   <style:graphic-properties fo:min-height="11.93cm"/>
-  </style:style>
-  <style:style style:name="pr19" style:family="presentation" style:parent-style-name="Default-notes">
-   <style:graphic-properties draw:fill-color="#ffffff" draw:auto-grow-height="true" fo:min-height="12.572cm"/>
-  </style:style>
-  <style:style style:name="pr20" style:family="presentation" style:parent-style-name="_31_1.02x8.27_25_20title_25_20standard-title">
-   <style:graphic-properties draw:auto-grow-height="true" fo:min-height="3.505cm"/>
-  </style:style>
-  <style:style style:name="pr21" style:family="presentation" style:parent-style-name="_31_1.02x8.27_25_20title_25_20standard-notes">
-   <style:graphic-properties draw:fill-color="#ffffff" draw:auto-grow-height="true" fo:min-height="12.572cm"/>
-  </style:style>
-  <style:style style:name="pr22" style:family="presentation" style:parent-style-name="Default-title">
-   <style:graphic-properties fo:min-height="1.957cm"/>
-  </style:style>
-  <style:style style:name="pr23" style:family="presentation" style:parent-style-name="Default-outline1">
-   <style:graphic-properties fo:min-height="16.006cm"/>
-  </style:style>
-  <style:style style:name="pr24" style:family="presentation" style:parent-style-name="Default-notes">
-   <style:graphic-properties draw:fill-color="#ffffff" fo:min-height="12.572cm"/>
-  </style:style>
-  <style:style style:name="pr25" style:family="presentation" style:parent-style-name="Default-outline1">
-   <style:graphic-properties fo:min-height="11.929cm"/>
-  </style:style>
-  <style:style style:name="P1" style:family="paragraph">
-   <style:text-properties fo:font-size="14pt" style:font-size-asian="14pt" style:font-size-complex="14pt"/>
-  </style:style>
-  <style:style style:name="P2" style:family="paragraph">
-   <style:paragraph-properties fo:text-align="end"/>
-   <style:text-properties fo:font-size="14pt" style:font-size-asian="14pt" style:font-size-complex="14pt"/>
-  </style:style>
-  <style:style style:name="P3" style:family="paragraph">
-   <style:paragraph-properties fo:text-align="center"/>
-  </style:style>
-  <style:style style:name="P4" style:family="paragraph">
-   <style:paragraph-properties fo:text-align="center"/>
-   <style:text-properties fo:font-size="14pt" style:font-size-asian="14pt" style:font-size-complex="14pt"/>
-  </style:style>
-  <style:style style:name="P5" style:family="paragraph">
-   <style:paragraph-properties fo:text-align="start"/>
-  </style:style>
-  <style:style style:name="P6" style:family="paragraph">
-   <style:paragraph-properties fo:text-align="start"/>
-   <style:text-properties style:font-name="Inconsolata1" fo:font-size="11pt" style:font-name-asian="Liberation Mono1" style:font-size-asian="11pt" style:font-name-complex="Liberation Mono1" style:font-size-complex="11pt"/>
-  </style:style>
-  <style:style style:name="P7" style:family="paragraph">
-   <style:text-properties fo:font-size="12pt" style:font-size-asian="12pt" style:font-size-complex="12pt"/>
-  </style:style>
-  <style:style style:name="P8" style:family="paragraph">
-   <style:text-properties fo:font-size="20pt"/>
-  </style:style>
-  <style:style style:name="P9" style:family="paragraph">
-   <style:text-properties style:font-name="Inconsolata1" fo:font-size="12pt" style:font-size-asian="12pt" style:font-size-complex="12pt"/>
-  </style:style>
-  <style:style style:name="P10" style:family="paragraph">
-   <style:text-properties fo:color="#0000ff" fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
-  </style:style>
-  <style:style style:name="P11" style:family="paragraph">
-   <style:text-properties style:font-name="Inconsolata1"/>
-  </style:style>
-  <style:style style:name="P12" style:family="paragraph">
-   <style:text-properties style:font-name="Carlito1" fo:font-style="italic" style:font-style-asian="italic" style:font-style-complex="italic"/>
-  </style:style>
-  <style:style style:name="P13" style:family="paragraph">
-   <style:text-properties style:font-name="Carlito1"/>
-  </style:style>
-  <style:style style:name="P14" style:family="paragraph">
-   <style:text-properties style:font-name="Carlito1" fo:font-size="40pt" style:font-size-asian="40pt" style:font-size-complex="40pt"/>
-  </style:style>
-  <style:style style:name="P15" style:family="paragraph">
-   <style:text-properties fo:font-size="26.3999996185303pt"/>
-  </style:style>
-  <style:style style:name="P16" style:family="paragraph">
-   <style:text-properties fo:font-size="18pt"/>
-  </style:style>
-  <style:style style:name="P17" style:family="paragraph">
-   <style:text-properties fo:font-size="40pt" style:font-size-asian="40pt" style:font-size-complex="40pt"/>
-  </style:style>
-  <style:style style:name="P18" style:family="paragraph">
-   <style:text-properties fo:font-size="44pt" style:font-size-asian="44pt" style:font-size-complex="44pt"/>
-  </style:style>
-  <style:style style:name="T1" style:family="text">
-   <style:text-properties fo:font-size="14pt" style:font-size-asian="14pt" style:font-size-complex="14pt"/>
-  </style:style>
-  <style:style style:name="T2" style:family="text">
-   <style:text-properties fo:color="#0000ff"/>
-  </style:style>
-  <style:style style:name="T3" style:family="text">
-   <style:text-properties fo:color="#0000ff" style:font-name="Inconsolata1"/>
-  </style:style>
-  <style:style style:name="T4" style:family="text">
-   <style:text-properties style:font-name="Inconsolata1" fo:font-size="11pt" style:font-name-asian="Liberation Mono1" style:font-size-asian="11pt" style:font-name-complex="Liberation Mono1" style:font-size-complex="11pt"/>
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-  <style:style style:name="T5" style:family="text">
-   <style:text-properties fo:font-size="12pt" style:font-size-asian="12pt" style:font-size-complex="12pt"/>
-  </style:style>
-  <style:style style:name="T6" style:family="text">
-   <style:text-properties style:font-name="Inconsolata1"/>
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-  <style:style style:name="T7" style:family="text">
-   <style:text-properties style:font-name="Inconsolata1" fo:font-size="12pt" style:font-size-asian="12pt" style:font-size-complex="12pt"/>
-  </style:style>
-  <style:style style:name="T8" style:family="text">
-   <style:text-properties fo:color="#0000ff" fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
-  </style:style>
-  <style:style style:name="T9" style:family="text">
-   <style:text-properties style:font-name="Carlito1" fo:font-style="italic" style:font-style-asian="italic" style:font-style-complex="italic"/>
-  </style:style>
-  <style:style style:name="T10" style:family="text">
-   <style:text-properties style:font-name="Carlito1"/>
-  </style:style>
-  <style:style style:name="T11" style:family="text">
-   <style:text-properties style:font-name="Carlito1" fo:font-size="40pt" style:font-size-asian="40pt" style:font-size-complex="40pt"/>
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-  <style:style style:name="T12" style:family="text">
-   <style:text-properties fo:font-size="18pt"/>
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-  <style:style style:name="T13" style:family="text">
-   <style:text-properties fo:font-size="22pt" style:font-size-asian="22pt" style:font-size-complex="22pt"/>
-  </style:style>
-  <style:style style:name="T14" style:family="text">
-   <style:text-properties fo:font-size="20pt" style:font-size-asian="20pt" style:font-size-complex="20pt"/>
-  </style:style>
-  <style:style style:name="T15" style:family="text">
-   <style:text-properties fo:font-size="18pt" style:font-size-asian="18pt" style:font-size-complex="18pt"/>
-  </style:style>
-  <style:style style:name="T16" style:family="text">
-   <style:text-properties style:font-name="Inconsolata1" fo:font-size="20pt" style:font-size-asian="20pt" style:font-size-complex="20pt"/>
-  </style:style>
-  <style:style style:name="T17" style:family="text">
-   <style:text-properties fo:font-size="40pt" style:font-size-asian="40pt" style:font-size-complex="40pt"/>
-  </style:style>
-  <style:style style:name="T18" style:family="text">
-   <style:text-properties style:font-name="Inconsolata1" fo:font-size="40pt" style:font-size-asian="40pt" style:font-size-complex="40pt"/>
-  </style:style>
-  <style:style style:name="T19" style:family="text">
-   <style:text-properties fo:color="#ff0000"/>
-  </style:style>
-  <style:style style:name="T20" style:family="text">
-   <style:text-properties fo:font-size="44pt" style:font-size-asian="44pt" style:font-size-complex="44pt"/>
-  </style:style>
-  <style:style style:name="T21" style:family="text">
-   <style:text-properties fo:color="#000000"/>
-  </style:style>
-  <text:list-style style:name="L1">
-   <text:list-level-style-bullet text:level="1" text:bullet-char="●">
-    <style:list-level-properties text:min-label-width="0.6cm"/>
-    <style:text-properties fo:font-family="StarSymbol" style:use-window-font-color="true" fo:font-size="45%"/>
-   </text:list-level-style-bullet>
-   <text:list-level-style-bullet text:level="2" text:bullet-char="●">
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-      <text:p>Why?</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Generally, there are two reasons for wanting to use a compiled language from python</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Speed </text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Reusing existing code</text:p>
-          <text:p/>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
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-      <text:p>Extension Methods</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>C</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T2">C-API</text:span>: Brute force use the python/NumPy headers and C-API to access your python data structures</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">ctypes</text:span>: a module that allows you to easily call functions in shared libraries </text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">Cython</text:span>: superset of python that can convert python into compiled C code (a fork of the Pyrex language)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Fortran</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">f2py</text:span>: part of NumPy. <text:s/>Allows for easy calling of Fortran from python</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>No discussed</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">weave</text:span>: part of SciPy. <text:s/>Allows for inlining of C code in python. <text:s/>Seems to be deprecated</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">Forthon</text:span>: a separate effort from f2py—not clear what the differences are)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-      <text:p>C-API</text:p>
-     </draw:text-box>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>The standard python interpreter (Cpython) is written in C. <text:s/></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You can access the API directly to include your own C functions.</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Pros</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Very powerful</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Underlies most (if not all) of the other techniques we&apos;ll see</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Cons</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Harder to write, many need to change to keep up with API changes</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Use requires compilation</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Limited to specific python implementations (CPython, not PyPy)</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T2">These days, there are better methods for most applications</text:span></text:p>
-       </text:list-item>
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-      <text:p>See http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html#id5</text:p>
-     </draw:text-box>
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-      <text:p>C-API Example</text:p>
-     </draw:text-box>
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-      <text:p text:style-name="P5"><text:span text:style-name="T4">/* <text:s/>Example of wrapping cos function from math.h with the Python-C-API. */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">#include &lt;Python.h&gt;</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">#include &lt;math.h&gt;</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"/></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">/* <text:s/>wrapped cosine function */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">static PyObject* cos_func(PyObject* self, PyObject* args)</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">{</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">double value;</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">double answer;</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">/* <text:s/>parse the input, from python float to c double */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">if (!PyArg_ParseTuple(args, &quot;d&quot;, &amp;value))</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="8"/></text:span><text:span text:style-name="T4">return NULL;</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">/* if the above function returns -1, an appropriate Python exception will</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="5"/></text:span><text:span text:style-name="T4">* have been set, and the function simply returns NULL</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="5"/></text:span><text:span text:style-name="T4">*/</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">/* call cos from libm */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">answer = cos(value);</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">/* <text:s/>construct the output from cos, from c double to python float */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="4"/></text:span><text:span text:style-name="T4">return Py_BuildValue(&quot;f&quot;, answer);</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">}</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"/></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">/* <text:s/>define functions in module */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">static PyMethodDef CosMethods[] =</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">{</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="5"/></text:span><text:span text:style-name="T4">{&quot;cos_func&quot;, cos_func, METH_VARARGS, &quot;evaluate the cosine&quot;},</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="5"/></text:span><text:span text:style-name="T4">{NULL, NULL, 0, NULL}</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">};</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"/></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">/* module initialization */</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">PyMODINIT_FUNC</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">initcos_module(void)</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">{</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4"><text:s text:c="5"/></text:span><text:span text:style-name="T4">(void) Py_InitModule(&quot;cos_module&quot;, CosMethods);</text:span></text:p>
-      <text:p text:style-name="P5"><text:span text:style-name="T4">}</text:span></text:p>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T5">Example from:</text:span></text:p>
-      <text:p><text:span text:style-name="T5">http://scipy-lectures.github.io/advanced/interfacing_with_c/interfacing_with_c.html</text:span></text:p>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p>C-API Example</text:p>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Things to be careful about:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Multi-dimensional arrays in C require care</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Standard approach for 2-d is to define a <text:span text:style-name="T6">double **</text:span> type and allocate a vector of pointers that point to the proper location in a 1-d contiguous memory block.</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Building:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Python has a <text:span text:style-name="T6">distutils</text:span> package that makes it easy to build the shared-object library</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>ctypes</text:p>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T6">ctypes</text:span> allows you to interface with an existing library. <text:s/></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You likely don&apos;t need to modify your C code</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Simply need to define the interface to the C function in python</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Support for NumPy through <text:span text:style-name="T6">numpy.ctypeslib</text:span></text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Pros:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>You don&apos;t need to explicitly write the interfaces to your libraries</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Part of standard python</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Cons:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Still need to compile a shared-object library</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p>ctypes Example</text:p>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T7">import numpy as np</text:span></text:p>
-      <text:p><text:span text:style-name="T7">import ctypes as C</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7">_cfunc = np.ctypeslib.load_library(&apos;cfunc&apos;, &apos;.&apos;)</text:span></text:p>
-      <text:p><text:span text:style-name="T7">_cfunc.my_subroutine.restype = C.c_int</text:span></text:p>
-      <text:p><text:span text:style-name="T7">_cfunc.my_subroutine.argtypes = [C.POINTER(C.c_double), C.c_int]</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7">def my_subroutine(A):</text:span></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="4"/></text:span><text:span text:style-name="T7">return _cfunc.my_subroutine(A.ctypes.data_as(C.POINTER(C.c_double)), len(A))</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7">A = np.arange(10, dtype=np.float64)</text:span></text:p>
-      <text:p><text:span text:style-name="T7">n = my_subroutine(A)</text:span></text:p>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T7">#include &lt;stdio.h&gt;</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7">int my_subroutine(double *array, int len) {</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="2"/></text:span><text:span text:style-name="T7">printf(&quot;in my_subroutine\n&quot;);</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="2"/></text:span><text:span text:style-name="T7">int i;</text:span></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="2"/></text:span><text:span text:style-name="T7">for (i = 0; i &lt; len; i++) {</text:span></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="4"/></text:span><text:span text:style-name="T7">printf(&quot;%3d: %f\n&quot;, i, array[i]);</text:span></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="2"/></text:span><text:span text:style-name="T7">}</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7"><text:s text:c="2"/></text:span><text:span text:style-name="T7">return 0;</text:span></text:p>
-      <text:p><text:span text:style-name="T7"/></text:p>
-      <text:p><text:span text:style-name="T7">}</text:span></text:p>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T8">Python:</text:span></text:p>
-     </draw:text-box>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T8">C:</text:span></text:p>
-     </draw:text-box>
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-    <presentation:notes draw:style-name="dp4">
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-     <draw:text-box>
-      <text:p>f2py</text:p>
-     </draw:text-box>
-    </draw:frame>
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-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Very easy way to interface with Fortran</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Fortran and C use different orderings of array data</text:p>
-        <text:list>
-         <text:list-header>
-          <text:p/>
-         </text:list-header>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T6">&gt;&gt;&gt; a = numpy.zeros((6,6), dtype=numpy.float64)</text:span></text:p>
-      <text:p><text:span text:style-name="T6">&gt;&gt;&gt; a.flags</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">C_CONTIGUOUS : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">F_CONTIGUOUS : False</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">OWNDATA : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">WRITEABLE : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">ALIGNED : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">UPDATEIFCOPY : False</text:span></text:p>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T9">“</text:span><text:span text:style-name="T9">Usually there is no need to worry about how the arrays are stored in memory and whether the wrapped functions, being either Fortran or C functions, assume one or another storage order. F2PY automatically ensures that wrapped functions get arguments with proper storage order; the corresponding algorithm is designed to make copies of arrays only when absolutely necessary. However, when dealing with very large multidimensional input arrays with sizes close to the size of the physical memory in your computer, then a care must be taken to use always proper-contiguous and proper type arguments.”</text:span></text:p>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T10">http://docs.scipy.org/doc/numpy-dev/f2py/python-usage.html</text:span></text:p>
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-     <draw:text-box>
-      <text:p>f2py</text:p>
-     </draw:text-box>
-    </draw:frame>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>You can initialize NumPy arrays in Fortran order from the start</text:p>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-        <text:p/>
-       </text:list-item>
-       <text:list-item>
-        <text:p>f2py takes directives (as comments) to help decide what to do</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>For more advanced usage, you can manually create a signature file</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>To see the signature file from your Fortran routine, do:</text:p>
-            <text:p><text:span text:style-name="T6">f2py -h numpy_in_f.pyf numpy_in_f.f90</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
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-     <draw:text-box>
-      <text:p><text:span text:style-name="T10">http://docs.scipy.org/doc/numpy-dev/f2py/python-usage.html</text:span></text:p>
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-      <text:p><text:span text:style-name="T6">&gt;&gt;&gt; a = np.zeros( (4,6), dtype=np.float64, order=&quot;F&quot;)</text:span></text:p>
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-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">OWNDATA : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">WRITEABLE : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">ALIGNED : True</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s text:c="2"/></text:span><text:span text:style-name="T6">UPDATEIFCOPY : False</text:span></text:p>
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-      <text:p><text:span text:style-name="T11">Extending Python through</text:span><text:span text:style-name="T11"><text:line-break/></text:span><text:span text:style-name="T11">C and Fortran</text:span></text:p>
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-      <text:p>Arrays</text:p>
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-        <text:p>Row vs. Column major: A(m,n)</text:p>
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-         <text:list-item>
-          <text:p>First index is called the row</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Second index is called the column</text:p>
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-         <text:list-item>
-          <text:p><text:span text:style-name="T2">Multi-dimensional arrays are flattened into a one-dimensional sequence for storage</text:span></text:p>
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-          <text:p>Row-major (C, python): rows are stored one after the other</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Column-major (Fortran, matlab): columns are stored one after the other</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Ordering matters for:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Passing arrays between languages</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Deciding which index to loop over first</text:p>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T6">f2py</text:span> handles the translation automatically, if needed, but that may involve an implicit copy</text:p>
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-     <svg:title>TexMaths</svg:title>
-     <svg:desc>24§display§u_{i,j} \leftarrow \frac{ \left [ u_{i+1,j} + u_{i-1,j} \right ] \Delta y^2 +
-                                                      \left [ u_{i,j+1} + u_{i,j-1} \right ] \Delta x^2}
-                                       {2 \left [ \Delta x^2 + \Delta y^2] }</svg:desc>
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-      <text:p>Performance</text:p>
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-       <text:list-item>
-        <text:p>Aside: </text:p>
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-         <text:list-item>
-          <text:p>Gauss-Seidel makes use of the new data as soon as it is available.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>There is another type of iteration called Jacobi iteration. <text:s/>The difference is that each zone is updated using only the old data:</text:p>
-          <text:p/>
-          <text:p/>
-          <text:p/>
-          <text:p/>
-         </text:list-item>
-         <text:list-item>
-          <text:p>These methods have different convergence properties</text:p>
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-      <text:p><text:span text:style-name="T6">Cython: 0.0472810268402 s</text:span></text:p>
-      <text:p><text:span text:style-name="T6">f2py: 0.0393579006195 s</text:span></text:p>
-      <text:p><text:span text:style-name="T6">ctypes: 0.149159908295 s</text:span></text:p>
-      <text:p><text:span text:style-name="T6">C-API: 0.0446910858154 s</text:span></text:p>
-      <text:p><text:span text:style-name="T6"><text:s/></text:span></text:p>
-      <text:p><text:span text:style-name="T6">max diff NumPy: 0.0877975718815</text:span></text:p>
-      <text:p><text:span text:style-name="T6">max diff Cython: 0.0</text:span></text:p>
-      <text:p><text:span text:style-name="T6">max diff F90: 0.0</text:span></text:p>
-      <text:p><text:span text:style-name="T6">max diff ctypes: 0.0</text:span></text:p>
-      <text:p><text:span text:style-name="T6">max diff C-API: 0.0</text:span></text:p>
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-          <text:p>Older way: <text:span text:style-name="T6">os.system()</text:span></text:p>
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-          <text:p><text:span text:style-name="T6">subprocess</text:span> is the modern replacement (see: <text:span text:style-name="T15">https://docs.python.org/2/library/subprocess.html#subprocess-replacements</text:span>)</text:p>
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-        <text:p>Example: issue a <text:span text:style-name="T6">git</text:span> command and capture <text:span text:style-name="T6">stdout</text:span> and <text:span text:style-name="T6">stderr</text:span></text:p>
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-         <text:list-item>
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-      <text:p>What Next?</text:p>
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-        <text:p>What other tasks do we want to discuss for our last class? (the make-up)</text:p>
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-        <text:p>Do we want to setup a way to keep the discussion going after the semester?</text:p>
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-          <text:p>There is a campus-wide google group as part of our Google Apps called SBPyG (Python interest group) that was started through GeoSci.</text:p>
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diff --git a/lectures/08-packages/packaging.fodp b/lectures/08-packages/packaging.fodp
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-      <text:p><text:span text:style-name="T2">Packaging and Applications</text:span></text:p>
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-      <text:p>Modules</text:p>
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-       <text:list-item>
-        <text:p>See <text:a xlink:href="https://docs.python.org/2/tutorial/modules.html" xlink:type="simple">https://docs.python.org/2/tutorial/modules.html</text:a> </text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>We follow this explicitly here</text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p>Simplest form: single file</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">import </text:span><text:span text:style-name="T4">file_base_name</text:span> to access the module&apos;s contents</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Module&apos;s name is available as <text:span text:style-name="T5">__name__</text:span> in the module</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Separate namespace by default</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Can have a block that is executed if run from the commandline</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>If run as “<text:span text:style-name="T5">python modulename.py args</text:span>” then <text:span text:style-name="T5">__name__</text:span> is set to “<text:span text:style-name="T5">__main__</text:span>”</text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>“private variables”</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>If you do: “<text:span text:style-name="T3">from modulename import *</text:span>”, then everything is imported into the current namespace <text:span text:style-name="T6">except</text:span><text:span text:style-name="T7"> for any names that start with “</text:span><text:span text:style-name="T4">_</text:span><text:span text:style-name="T7">”</text:span></text:p>
-         </text:list-item>
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-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T7">If you change a file, you can re-import it as: </text:span><text:span text:style-name="T8">reload(modulename)</text:span></text:p>
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-      <text:p>Search Path</text:p>
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-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p><text:span text:style-name="T7">Just like UNIX has a path that it searches, in order, for an executable, python uses this to find modules</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">Search order:</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T7">Current directory</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T9">PYTHONPATH</text:span><text:span text:style-name="T7"> environment variable</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T7">System-wide python installation default path</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T4">sys.path</text:span><text:span text:style-name="T7"> will show the path</text:span></text:p>
-         </text:list-item>
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-      <text:p>Compilation</text:p>
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-        <text:p><text:span text:style-name="T7">Python is interpreted, but it creates byte-code files (</text:span><text:span text:style-name="T8">.pyc</text:span><text:span text:style-name="T7"> instead of </text:span><text:span text:style-name="T8">.py</text:span><text:span text:style-name="T7">) when a module is first imported</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">This speeds up the loading of the module—it does not change the speed of execution</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T4">.pyc</text:span><text:span text:style-name="T7"> is automatically recreated based on the file modification times</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">Note that </text:span><text:span text:style-name="T4">.pyc</text:span><text:span text:style-name="T7"> files are not, in general, portable</text:span></text:p>
-         </text:list-item>
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-      <text:p>Packaging</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T10">Often you separate a project into multiple files / directories</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T11">Example from python docs:</text:span></text:p>
-         </text:list-item>
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-       </text:list-item>
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-      <text:p><text:span text:style-name="T12">sound/ <text:s text:c="25"/>Top-level package</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="6"/></text:span><text:span text:style-name="T12">__init__.py <text:s text:c="14"/>Initialize the sound package</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="6"/></text:span><text:span text:style-name="T12">formats/ <text:s text:c="17"/>Subpackage for file format conversions</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">__init__.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">wavread.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">wavwrite.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">aiffread.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">aiffwrite.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">auread.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">auwrite.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">...</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="6"/></text:span><text:span text:style-name="T12">effects/ <text:s text:c="17"/>Subpackage for sound effects</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">__init__.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">echo.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">surround.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">reverse.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">...</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="6"/></text:span><text:span text:style-name="T12">filters/ <text:s text:c="17"/>Subpackage for filters</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">__init__.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">equalizer.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">vocoder.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">karaoke.py</text:span></text:p>
-      <text:p><text:span text:style-name="T12"><text:s text:c="14"/></text:span><text:span text:style-name="T12">...</text:span></text:p>
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-      <text:p>Packaging</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T10">You can do:</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T13">import sound.effects.echo</text:span><text:span text:style-name="T11"> (just get that single module)</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T11">Access as </text:span><text:span text:style-name="T13">sound.effects.echo.echofilter(...)</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T13">from sound.effects import echo</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T11">Access as </text:span><text:span text:style-name="T13">echo.echofilter(...)</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T13">from sound.effects.echo import echofilter</text:span><text:span text:style-name="T11"> (make that specific function available)</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T11">Access as </text:span><text:span text:style-name="T13">echofilter(...)</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p><text:span text:style-name="T11">In order to do </text:span><text:span text:style-name="T13">from sound.effects import *</text:span><text:span text:style-name="T11">, we need to define what we mean by all</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T11">The package should have an __init__.py file—this tells python that a directory contains packages</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p><text:span text:style-name="T11">Can be empty, but needs to be present (</text:span><text:span text:style-name="T14">example with our previous module</text:span><text:span text:style-name="T11">)</text:span></text:p>
-           </text:list-item>
-           <text:list-item>
-            <text:p><text:span text:style-name="T11">Set </text:span><text:span text:style-name="T13">__all__</text:span><text:span text:style-name="T11"> to the list of modules that should be imported by default</text:span></text:p>
-           </text:list-item>
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-     <draw:text-box>
-      <text:p>Packaging</text:p>
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-       <text:list-item>
-        <text:p>There are several different options for packaging your python code for other users</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>Unfortunately, these appear to be in a state of flux at the moment</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>We already saw how <text:span text:style-name="T3">distutils</text:span> and <text:span text:style-name="T3">setup.py</text:span> makes writing extensions easy</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Main methods (at the moment):</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">distutils</text:span>: this is part of python (2 and 3)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">setuptools</text:span>: newer than <text:span text:style-name="T3">distutils</text:span>, offers more functionality. <text:s/>Introduced easy_install, and <text:span text:style-name="T3">setuptools</text:span> module to import into <text:span text:style-name="T3">setup.py</text:span>. </text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>See: <text:span text:style-name="T15"><text:a xlink:href="http://stackoverflow.com/questions/6344076/differences-between-distribute-distutils-setuptools-and-distutils2" xlink:type="simple">http://stackoverflow.com/questions/6344076/differences-between-distribute-distutils-setuptools-and-distutils2</text:a></text:span><text:span text:style-name="T15"> </text:span><text:span text:style-name="T10"><text:s/></text:span>and <text:span text:style-name="T15"><text:a xlink:href="https://python-packaging-user-guide.readthedocs.org/en/latest/tutorial.html" xlink:type="simple">https://python-packaging-user-guide.readthedocs.org/en/latest/tutorial.html</text:a></text:span><text:span text:style-name="T15"> </text:span></text:p>
-         </text:list-item>
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-     <draw:text-box>
-      <text:p>Commandline Arguments</text:p>
-     </draw:text-box>
-    </draw:frame>
-    <draw:frame presentation:style-name="pr20" draw:layer="layout" svg:width="26.924cm" svg:height="13.005cm" svg:x="0.508cm" svg:y="3.556cm" presentation:class="outline" presentation:user-transformed="true">
-     <draw:text-box>
-      <text:list text:style-name="L4">
-       <text:list-item>
-        <text:p>Python provides the usual argc/v variables through the sys module</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Several modules exist that help you parse these variables</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">getopt:</text:span> the original module—need to do a lot manually</text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Based on the C <text:span text:style-name="T5">getopt()</text:span></text:p>
-           </text:list-item>
-          </text:list>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">optparse</text:span>: an alternative, but this is supposed to be deprecated</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T3">argparse</text:span> automates a lot of things for you (including help and usage)</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>See: <text:span text:style-name="T16"><text:a xlink:href="http://legacy.python.org/dev/peps/pep-0389/#why-aren-t-getopt-and-optparse-enough" xlink:type="simple">http://legacy.python.org/dev/peps/pep-0389/#why-aren-t-getopt-and-optparse-enough</text:a></text:span><text:span text:style-name="T16"> </text:span></text:p>
-         </text:list-item>
-        </text:list>
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diff --git a/lectures/09-mayavi/mayavi.fodp b/lectures/09-mayavi/mayavi.fodp
deleted file mode 100644
index 14e8e2c6..00000000
--- a/lectures/09-mayavi/mayavi.fodp
+++ /dev/null
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- <office:meta><meta:initial-creator>Michael Zingale</meta:initial-creator><meta:creation-date>2013-01-02T12:36:14</meta:creation-date><dc:date>2014-01-27T12:02:42.334206274</dc:date><dc:creator>Michael Zingale</dc:creator><meta:editing-duration>PT15H1M48S</meta:editing-duration><meta:editing-cycles>51</meta:editing-cycles><meta:generator>LibreOffice/4.2.3.3$Linux_X86_64 LibreOffice_project/420$Build-3</meta:generator><meta:document-statistic meta:object-count="66"/></office:meta>
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-      <text:p>Mayavi</text:p>
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-      <text:p>Intro to Mayavi</text:p>
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-        <text:p>Mayavi allows for plotting of three-dimensional data (i.e. x, y, z + value)</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Provides (<text:span text:style-name="T3">http://docs.enthought.com/mayavi/mayavi/overview.html#introduction</text:span>):</text:p>
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-         <text:list-item>
-          <text:p>Visualization of scalar, vector and tensor data in 2 and 3 dimensions.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Easy scriptability using Python.</text:p>
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-         <text:list-item>
-          <text:p>Easy extendibility via custom sources, modules, and data filters.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Reading several file formats: VTK (legacy and XML), PLOT3D, etc.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Saving of visualizations.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Saving rendered visualization in a variety of image formats.</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Convenient functionality for rapid scientific plotting via mlab (see mlab: Python scripting for 3D plotting).</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-       <text:list-item>
-        <text:p>Installing:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>If you are not using one of the python distributions, you may need to install it manually</text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p>Fedora: <text:span text:style-name="T4">yum install Mayavi</text:span></text:p>
-          <text:list>
-           <text:list-item>
-            <text:p>Note: you may need to enable the <text:span text:style-name="T5">updates-testing</text:span> repo to get the latest version because of a change to VTK</text:p>
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-      <text:p>Starting Python for Mayavi</text:p>
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-        <text:p>Mayavi is interactive</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>See <text:span text:style-name="T3"><text:a xlink:href="http://docs.enthought.com/mayavi/mayavi/mlab_running_scripts.html#running-mlab-scripts" xlink:type="simple">http://docs.enthought.com/mayavi/mayavi/mlab_running_scripts.html#running-mlab-scripts</text:a></text:span></text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T6">Interactive use:</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T4">ipython --gui=wx –pylab=wx</text:span></text:p>
-         </text:list-item>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">This will use a different output window to render the results</text:span></text:p>
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-        <text:p><text:span text:style-name="T6">In scripts:</text:span></text:p>
-        <text:list>
-         <text:list-item>
-          <text:p><text:span text:style-name="T7">Use </text:span><text:span text:style-name="T4">mlab.show()</text:span><text:span text:style-name="T7"> to show the output window and allow for interaction</text:span></text:p>
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-      <text:p>Interactivity</text:p>
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-       <text:list-item>
-        <text:p><text:span text:style-name="T8">From http://docs.enthought.com/mayavi/mayavi/mlab_case_studies.html:</text:span></text:p>
-       </text:list-item>
-      </text:list>
-      <text:p><text:span text:style-name="T9">...it is important to remember that Mayavi is an interactive program, and that the properties of these objects can be modified interactively, as described in Changing object properties interactively. It is often impossible to choose the best parameters for a visualization before hand. Colors, contour values, colormap, view angle, etc... should be chosen interactively. If reproducibiles are required, the chosen values can be added in the original script.</text:span></text:p>
-      <text:p><text:span text:style-name="T9">Moreover, the mlab functions expose only a small fraction of the possibilities of the visualization objects. The dialogs expose more of these functionalities, that are entirely controlled by the attributes of the objects returned by the mlab functions. These objects are very rich, as they are built from VTK objects. It can be hard to find the right attribute to modify when exploring them, or in the VTK documentation, thus the easiest way is to modify them interactively using the pipeline view dialog and use the record feature to find out the corresponding lines of code. See Organisation of Mayavi visualizations: the pipeline to understand better the link between the lines of code generated by the record feature and mlab. .</text:span></text:p>
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-      <text:p>Some Basics</text:p>
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-        <text:p>Mayavi is a big library. <text:s/>We&apos;ll look at some simple examples that show some basics.</text:p>
-       </text:list-item>
-       <text:list-item>
-        <text:p>See the docs for more examples/detail:</text:p>
-        <text:list>
-         <text:list-item>
-          <text:p>http://docs.enthought.com/mayavi/mayavi/mlab.html</text:p>
-         </text:list-item>
-        </text:list>
-       </text:list-item>
-      </text:list>
-     </draw:text-box>
-    </draw:frame>
-    <presentation:notes draw:style-name="dp3">
-     <office:forms form:automatic-focus="false" form:apply-design-mode="false"/>
-     <draw:page-thumbnail draw:style-name="gr3" draw:layer="layout" svg:width="13.968cm" svg:height="10.476cm" svg:x="3.81cm" svg:y="2.123cm" draw:page-number="5" presentation:class="page"/>
-     <draw:frame presentation:style-name="pr12" draw:layer="layout" svg:width="17.271cm" svg:height="12.572cm" svg:x="2.159cm" svg:y="13.271cm" presentation:class="notes" presentation:placeholder="true">
-      <draw:text-box/>
-     </draw:frame>
-    </presentation:notes>
-   </draw:page>
-   <presentation:settings presentation:mouse-visible="false"/>
-  </office:presentation>
- </office:body>
-</office:document>
\ No newline at end of file
diff --git a/lectures/CHANGES b/lectures/CHANGES
deleted file mode 100644
index a5e2b130..00000000
--- a/lectures/CHANGES
+++ /dev/null
@@ -1,3 +0,0 @@
--- make this S/U grading
-
--- perhaps have BB "grade" participation on the forum?
\ No newline at end of file
diff --git a/lectures/09-mayavi/NOTES b/other/50-mayavi/NOTES
similarity index 100%
rename from lectures/09-mayavi/NOTES
rename to other/50-mayavi/NOTES
diff --git a/other/50-mayavi/mayavi.pdf b/other/50-mayavi/mayavi.pdf
new file mode 100644
index 00000000..e0c76650
Binary files /dev/null and b/other/50-mayavi/mayavi.pdf differ
diff --git a/other/60-GUI/gui-notebook.ipynb b/other/60-GUI/gui-notebook.ipynb
new file mode 100644
index 00000000..352e5e08
--- /dev/null
+++ b/other/60-GUI/gui-notebook.ipynb
@@ -0,0 +1,665 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:281d1c65366416dcc4292e69c11c442c945332985f5acfcdcd0b150daccabd0e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "Depending of the version of python, the tkinter module has a different name"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "if sys.version_info[0] < 3:\n",
+      "    import Tkinter as Tk\n",
+      "else:\n",
+      "    import tkinter as Tk\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Geometry Managers"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Grid"
+     ]
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "see http://userpages.umbc.edu/~dhood2/courses/cmsc433/spring2010/?section=Notes&topic=Python&notes=94\n",
+      "http://effbot.org/tkinterbook/grid.htm"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "help(Tk.Label.grid)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Help on method grid_configure in module Tkinter:\n",
+        "\n",
+        "grid_configure(self, cnf={}, **kw) unbound Tkinter.Label method\n",
+        "    Position a widget in the parent widget in a grid. Use as options:\n",
+        "    column=number - use cell identified with given column (starting with 0)\n",
+        "    columnspan=number - this widget will span several columns\n",
+        "    in=master - use master to contain this widget\n",
+        "    in_=master - see 'in' option description\n",
+        "    ipadx=amount - add internal padding in x direction\n",
+        "    ipady=amount - add internal padding in y direction\n",
+        "    padx=amount - add padding in x direction\n",
+        "    pady=amount - add padding in y direction\n",
+        "    row=number - use cell identified with given row (starting with 0)\n",
+        "    rowspan=number - this widget will span several rows\n",
+        "    sticky=NSEW - if cell is larger on which sides will this\n",
+        "                  widget stick to the cell boundary\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class GridExample:\n",
+      "    def __init__(self, parent):\n",
+      "        \n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        # first row\n",
+      "        self.l1 = Tk.Label(self.parent, text=\"Entry 1: \")\n",
+      "        self.e1 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l1.grid(row=0, column=0)\n",
+      "        self.e1.grid(row=0, column=1)\n",
+      "        \n",
+      "        # second row\n",
+      "        self.l2 = Tk.Label(self.parent, text=\"Long Entry 2: \")\n",
+      "        self.e2 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l2.grid(row=2, column=0)\n",
+      "        self.e2.grid(row=2, column=1)\n",
+      "\n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"my simple GUI\")\n",
+      "\n",
+      "gui_test = GridExample(root)\n",
+      "\n",
+      "root.mainloop()   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "Widgets are centered in their cells, but you can use sticky and N, S, E, W directions to say where to anchor it"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class GridExample:\n",
+      "    def __init__(self, parent):\n",
+      "        \n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        # first row\n",
+      "        self.l1 = Tk.Label(self.parent, text=\"Entry 1: \")\n",
+      "        self.e1 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l1.grid(row=0, column=0, sticky=Tk.E)\n",
+      "        self.e1.grid(row=0, column=1)\n",
+      "        \n",
+      "        # second row\n",
+      "        self.l2 = Tk.Label(self.parent, text=\"Long Entry 2: \")\n",
+      "        self.e2 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l2.grid(row=2, column=0, sticky=Tk.E)\n",
+      "        self.e2.grid(row=2, column=1)\n",
+      "\n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"my simple GUI\")\n",
+      "\n",
+      "gui_test = GridExample(root)\n",
+      "\n",
+      "root.mainloop()   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Pack"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "help(Tk.Button.pack)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Help on method pack_configure in module Tkinter:\n",
+        "\n",
+        "pack_configure(self, cnf={}, **kw) unbound Tkinter.Button method\n",
+        "    Pack a widget in the parent widget. Use as options:\n",
+        "    after=widget - pack it after you have packed widget\n",
+        "    anchor=NSEW (or subset) - position widget according to\n",
+        "                              given direction\n",
+        "    before=widget - pack it before you will pack widget\n",
+        "    expand=bool - expand widget if parent size grows\n",
+        "    fill=NONE or X or Y or BOTH - fill widget if widget grows\n",
+        "    in=master - use master to contain this widget\n",
+        "    in_=master - see 'in' option description\n",
+        "    ipadx=amount - add internal padding in x direction\n",
+        "    ipady=amount - add internal padding in y direction\n",
+        "    padx=amount - add padding in x direction\n",
+        "    pady=amount - add padding in y direction\n",
+        "    side=TOP or BOTTOM or LEFT or RIGHT -  where to add this widget.\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class PackExample:\n",
+      "    def __init__(self, parent):\n",
+      "        \n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        # first row\n",
+      "        self.l1 = Tk.Label(self.parent, text=\"Entry 1: \")\n",
+      "        self.e1 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l2 = Tk.Label(self.parent, text=\"Long Entry 2: \")\n",
+      "        self.e2 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l1.pack()\n",
+      "        self.e1.pack()\n",
+      "        self.l2.pack()\n",
+      "        self.e2.pack()\n",
+      "\n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"my simple GUI\")\n",
+      "\n",
+      "gui_test = PackExample(root)\n",
+      "\n",
+      "root.mainloop()   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "We can use side to specify where the widget lives.  Notice how things behave when you expand the widget"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class PackExample:\n",
+      "    def __init__(self, parent):\n",
+      "        \n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        # first row\n",
+      "        self.l1 = Tk.Label(self.parent, text=\"Entry 1: \")\n",
+      "        self.e1 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l2 = Tk.Label(self.parent, text=\"Long Entry 2: \")\n",
+      "        self.e2 = Tk.Entry(self.parent)\n",
+      "        \n",
+      "        self.l1.pack(side=Tk.LEFT)\n",
+      "        self.e1.pack(side=Tk.LEFT)\n",
+      "        self.l2.pack(side=Tk.RIGHT)\n",
+      "        self.e2.pack(side=Tk.RIGHT)\n",
+      "\n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"my simple GUI\")\n",
+      "\n",
+      "gui_test = PackExample(root)\n",
+      "\n",
+      "root.mainloop()   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "A simple GUI"
+     ]
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "Here we create a very simple GUI that allows you to enter a string, and outputs the value when you click \"show\".\n",
+      "\n",
+      "Notice that we create the GUI as a class -- this makes it easy to deal with all the interactions, since the different\n",
+      "widget objects and methods they expose (e.g. to get data from the field) are all together in the object's namespace.\n",
+      "\n",
+      "Some people have the class itself inherit from Tk.Frame, see, e.g., http://stackoverflow.com/questions/17466561/best-way-to-structure-a-tkinter-application"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class MyGUI:\n",
+      "    def __init__(self, parent):\n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        self.frame_a = Tk.Frame(self.parent)\n",
+      "        self.frame_a.pack(fill=Tk.BOTH, expand=1)                                                                        \n",
+      "        \n",
+      "        self.label_a = Tk.Label(self.frame_a, text=\"enter some text here: \")\n",
+      "        self.label_a.grid(row=0, column=0, sticky=Tk.W)\n",
+      "        \n",
+      "        self.label_str = Tk.StringVar()\n",
+      "        \n",
+      "        self.field_a = Tk.Entry(self.frame_a, textvariable=self.label_str)\n",
+      "        self.field_a.grid(row=0, column=1, sticky=Tk.W)\n",
+      "        \n",
+      "        self.frame_buttons = Tk.Frame(self.parent)\n",
+      "        self.frame_buttons.pack(fill=Tk.BOTH, expand=1)\n",
+      "\n",
+      "        self.ok_button = Tk.Button(self.frame_buttons, text=\"show\", command=self._show)\n",
+      "        self.ok_button.pack(side=Tk.RIGHT, padx=5, pady=5)\n",
+      "        \n",
+      "    def _show(self):\n",
+      "        str = self.field_a.get()\n",
+      "        print \"you entered {}\".format(str)      "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"my simple GUI\")\n",
+      "\n",
+      "gui_test = MyGUI(root)\n",
+      "\n",
+      "root.mainloop()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "you entered this is my test\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "you entered this is my test"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Windows Popping Out Windows"
+     ]
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "Now we can try to make it more complex -- let's have the show button pop up a new window.  We'll define a new class to manage that.  It will take the parent widget and also the MyGUI object itself, so we can access that data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class Response:\n",
+      "    def __init__(self, parent, main_obj):\n",
+      "        self.parent = parent\n",
+      "        self.main_obj = main_obj\n",
+      "        \n",
+      "        self.frame = Tk.Frame(self.parent)\n",
+      "        self.frame.pack(fill=Tk.BOTH, expand=1)\n",
+      "        \n",
+      "        str = self.main_obj.field_a.get()\n",
+      "        self.label = Tk.Label(self.frame, text=\"You entered {}\".format(str))\n",
+      "        self.label.grid(row=0, column=0, sticky=Tk.W)\n",
+      "\n",
+      "        self.frame_buttons = Tk.Frame(self.parent)\n",
+      "        self.frame_buttons.pack(fill=Tk.BOTH, expand=1)\n",
+      "        \n",
+      "        self.ok_button = Tk.Button(self.frame_buttons, text=\"Ok\", command=self._ok)\n",
+      "        self.ok_button.pack(side=Tk.RIGHT, padx=5, pady=5)\n",
+      "        \n",
+      "    def _ok(self):\n",
+      "        self.parent.destroy()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "we'll need to modify the show() function in our previous class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class MyGUI2:\n",
+      "    def __init__(self, parent):\n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        self.frame_a = Tk.Frame(self.parent)\n",
+      "        self.frame_a.pack(fill=Tk.BOTH, expand=1)                                                                        \n",
+      "        \n",
+      "        self.label_a = Tk.Label(self.frame_a, text=\"enter some text here: \")\n",
+      "        self.label_a.grid(row=0, column=0, sticky=Tk.W)\n",
+      "        \n",
+      "        self.label_str = Tk.StringVar()\n",
+      "        \n",
+      "        self.field_a = Tk.Entry(self.frame_a, textvariable=self.label_str)\n",
+      "        self.field_a.grid(row=0, column=1, sticky=Tk.W)\n",
+      "        \n",
+      "        self.frame_buttons = Tk.Frame(self.parent)\n",
+      "        self.frame_buttons.pack(fill=Tk.BOTH, expand=1)\n",
+      "\n",
+      "        self.show_button = Tk.Button(self.frame_buttons, text=\"show\", command=self._show)\n",
+      "        self.show_button.pack(side=Tk.RIGHT, padx=5, pady=5)\n",
+      "        \n",
+      "    def _show(self):\n",
+      "        self.new_window = Tk.Toplevel(self.parent)\n",
+      "        self.show = Response(self.new_window, self)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"my simple GUI\")\n",
+      "\n",
+      "gui_test = MyGUI2(root)\n",
+      "\n",
+      "root.mainloop()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Message Boxes"
+     ]
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "a messagebox is a useful way to give feedback"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import tkMessageBox\n",
+      "\n",
+      "class MyApp:\n",
+      "    def __init__(self, parent):\n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        self.button = Tk.Button(self.parent, text=\"press me\", command=self.doit)\n",
+      "        self.button.pack()\n",
+      "        \n",
+      "    def doit(self):\n",
+      "        tkMessageBox.showwarning(\"Warning\", \"you did something bad!\")    \n",
+      "                \n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"main application\")\n",
+      "\n",
+      "my_app = MyApp(root)\n",
+      "\n",
+      "root.mainloop()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Styling"
+     ]
+    },
+    {
+     "cell_type": "raw",
+     "metadata": {},
+     "source": [
+      "sometimes we want to make our widgets look more native.  Tkinter supports this via ttk"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import ttk\n",
+      "import tkFileDialog\n",
+      "\n",
+      "class MyApp:\n",
+      "    def __init__(self, parent):\n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        self.style = ttk.Style()\n",
+      "        self.style.theme_use(\"default\")\n",
+      "        \n",
+      "        self.button1 = ttk.Button(self.parent, text=\"button 1\", command=self.file)\n",
+      "        self.button1.pack()\n",
+      "        self.button2 = ttk.Button(self.parent, text=\"button 2\")\n",
+      "        self.button2.pack()\n",
+      "        self.button3 = ttk.Button(self.parent, text=\"button 3\")\n",
+      "        self.button3.pack()        \n",
+      "        \n",
+      "    def file(self):\n",
+      "        self.file_opts = {}\n",
+      "        return tkFileDialog.askopenfile(mode='r', **self.file_opts)\n",
+      "    \n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"main application\")\n",
+      "\n",
+      "my_app = MyApp(root)\n",
+      "\n",
+      "root.mainloop()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class MyApp:\n",
+      "    def __init__(self, parent):\n",
+      "        self.parent = parent\n",
+      "        \n",
+      "        self.button1 = Tk.Button(self.parent, text=\"button 1\")\n",
+      "        self.button1.pack()\n",
+      "        self.button2 = Tk.Button(self.parent, text=\"button 2\")\n",
+      "        self.button2.pack()\n",
+      "        self.button3 = Tk.Button(self.parent, text=\"button 3\")\n",
+      "        self.button3.pack()        \n",
+      "                \n",
+      "root = Tk.Tk()\n",
+      "root.wm_title(\"main application\")\n",
+      "\n",
+      "my_app = MyApp(root)\n",
+      "\n",
+      "root.mainloop()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/lectures/notebooks/LectureNotebooks b/other/LectureNotebooks
similarity index 100%
rename from lectures/notebooks/LectureNotebooks
rename to other/LectureNotebooks
diff --git a/other/projectile-motion.ipynb b/other/projectile-motion.ipynb
new file mode 100644
index 00000000..7345931f
--- /dev/null
+++ b/other/projectile-motion.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Projectile Motion"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "This notebook shows off some basic IPython notebook features.  We create a simple class to describe a projectile, given an angle and initial velocity and then instantiate some objects and make plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class Projectile(object):\n",
+    "\n",
+    "    # this is called every time a new object is created\n",
+    "    def __init__(self, v=1.0, theta=45, grav=9.81):\n",
+    "\n",
+    "        self.v = v           # velocity m/s\n",
+    "        self.theta = theta   # angle (degrees)\n",
+    "        \n",
+    "        self.theta_rad = math.radians(theta)\n",
+    "        self.vx = v*math.cos(self.theta_rad)\n",
+    "        self.vy = v*math.sin(self.theta_rad)\n",
+    "\n",
+    "        self.g = grav\n",
+    "\n",
+    "        self.npts = 1000\n",
+    "\n",
+    "    def height(self):\n",
+    "\n",
+    "        # how high does this projectile go?\n",
+    "        # vf_y^2 = 0 = vi_y^2 - 2 g h\n",
+    "        h = self.vy**2/(2.0*self.g)\n",
+    "\n",
+    "        return h\n",
+    "\n",
+    "    def time(self):\n",
+    "        \n",
+    "        # time of flight up\n",
+    "        # vf_y = 0 = vi_y - g t\n",
+    "        t = self.vy/self.g\n",
+    "\n",
+    "        # total time = up + down\n",
+    "        t = 2.0*t\n",
+    "\n",
+    "        return t\n",
+    "        \n",
+    "    def distance(self):\n",
+    "        \n",
+    "        d = self.vx*self.time()\n",
+    "\n",
+    "        return d\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        # a string representation for this class -- so we can print it\n",
+    "        str = \" v: {} m/s\\n theta: {} (degrees)\\n height: {} m\\n distance: {} m\\n\".format(\n",
+    "            self.v, self.theta, self.height(), self.distance())\n",
+    "            \n",
+    "        return str\n",
+    "    \n",
+    "    def t(self):\n",
+    "        return numpy.linspace(0.0, self.time(), num=self.npts)\n",
+    "    \n",
+    "    def x(self):\n",
+    "        return self.vx*self.t()\n",
+    "    \n",
+    "    def y(self):\n",
+    "        return self.vy*self.t() - 0.5*self.g*self.t()**2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "p1 = Projectile(theta=80, v=10)\n",
+    "p2 = Projectile(theta=45, v=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " v: 10 m/s\n",
+      " theta: 80 (degrees)\n",
+      " height: 4.943151429118012 m\n",
+      " distance: 3.4864438667244526 m\n",
+      "\n",
+      " v: 10 m/s\n",
+      " theta: 45 (degrees)\n",
+      " height: 2.54841997961264 m\n",
+      " distance: 10.19367991845056 m\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(p1)\n",
+    "print(p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fea4666ed30>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vpJvgiofNuZYPLICWvVZXZHuOCe5DM0ps2Cuu22R+4bpazEXI4jOsrmhYitPj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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fea4666ed68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pylab.plot(p1.x(), p1.y())\n",
+    "pylab.plot(p2.x(), p2.y())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lectures/notebooks/scientific-python-lectures-master/.gitignore b/other/scientific-python-lectures-master/.gitignore
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/.gitignore
rename to other/scientific-python-lectures-master/.gitignore
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-0-Scientific-Computing-with-Python.ipynb b/other/scientific-python-lectures-master/Lecture-0-Scientific-Computing-with-Python.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-0-Scientific-Computing-with-Python.ipynb
rename to other/scientific-python-lectures-master/Lecture-0-Scientific-Computing-with-Python.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-1-Introduction-to-Python-Programming.ipynb b/other/scientific-python-lectures-master/Lecture-1-Introduction-to-Python-Programming.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-1-Introduction-to-Python-Programming.ipynb
rename to other/scientific-python-lectures-master/Lecture-1-Introduction-to-Python-Programming.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-2-Numpy.ipynb b/other/scientific-python-lectures-master/Lecture-2-Numpy.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-2-Numpy.ipynb
rename to other/scientific-python-lectures-master/Lecture-2-Numpy.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-3-Scipy.ipynb b/other/scientific-python-lectures-master/Lecture-3-Scipy.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-3-Scipy.ipynb
rename to other/scientific-python-lectures-master/Lecture-3-Scipy.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-4-Matplotlib.ipynb b/other/scientific-python-lectures-master/Lecture-4-Matplotlib.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-4-Matplotlib.ipynb
rename to other/scientific-python-lectures-master/Lecture-4-Matplotlib.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-5-Sympy.ipynb b/other/scientific-python-lectures-master/Lecture-5-Sympy.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-5-Sympy.ipynb
rename to other/scientific-python-lectures-master/Lecture-5-Sympy.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-6A-Fortran-and-C.ipynb b/other/scientific-python-lectures-master/Lecture-6A-Fortran-and-C.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-6A-Fortran-and-C.ipynb
rename to other/scientific-python-lectures-master/Lecture-6A-Fortran-and-C.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-6B-HPC.ipynb b/other/scientific-python-lectures-master/Lecture-6B-HPC.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-6B-HPC.ipynb
rename to other/scientific-python-lectures-master/Lecture-6B-HPC.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/Lecture-7-Revision-Control-Software.ipynb b/other/scientific-python-lectures-master/Lecture-7-Revision-Control-Software.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/Lecture-7-Revision-Control-Software.ipynb
rename to other/scientific-python-lectures-master/Lecture-7-Revision-Control-Software.ipynb
diff --git a/lectures/notebooks/scientific-python-lectures-master/README.md b/other/scientific-python-lectures-master/README.md
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/README.md
rename to other/scientific-python-lectures-master/README.md
diff --git a/other/scientific-python-lectures-master/cy_dcumsum.c b/other/scientific-python-lectures-master/cy_dcumsum.c
new file mode 100644
index 00000000..a0c4b1f9
--- /dev/null
+++ b/other/scientific-python-lectures-master/cy_dcumsum.c
@@ -0,0 +1,5941 @@
+/* Generated by Cython 0.29.21 */
+
+#define PY_SSIZE_T_CLEAN
+#include "Python.h"
+#ifndef Py_PYTHON_H
+    #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
+    #error Cython requires Python 2.6+ or Python 3.3+.
+#else
+#define CYTHON_ABI "0_29_21"
+#define CYTHON_HEX_VERSION 0x001D15F0
+#define CYTHON_FUTURE_DIVISION 0
+#include <stddef.h>
+#ifndef offsetof
+  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+  #ifndef __stdcall
+    #define __stdcall
+  #endif
+  #ifndef __cdecl
+    #define __cdecl
+  #endif
+  #ifndef __fastcall
+    #define __fastcall
+  #endif
+#endif
+#ifndef DL_IMPORT
+  #define DL_IMPORT(t) t
+#endif
+#ifndef DL_EXPORT
+  #define DL_EXPORT(t) t
+#endif
+#define __PYX_COMMA ,
+#ifndef HAVE_LONG_LONG
+  #if PY_VERSION_HEX >= 0x02070000
+    #define HAVE_LONG_LONG
+  #endif
+#endif
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#ifndef Py_HUGE_VAL
+  #define Py_HUGE_VAL HUGE_VAL
+#endif
+#ifdef PYPY_VERSION
+  #define CYTHON_COMPILING_IN_PYPY 1
+  #define CYTHON_COMPILING_IN_PYSTON 0
+  #define CYTHON_COMPILING_IN_CPYTHON 0
+  #undef CYTHON_USE_TYPE_SLOTS
+  #define CYTHON_USE_TYPE_SLOTS 0
+  #undef CYTHON_USE_PYTYPE_LOOKUP
+  #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #if PY_VERSION_HEX < 0x03050000
+    #undef CYTHON_USE_ASYNC_SLOTS
+    #define CYTHON_USE_ASYNC_SLOTS 0
+  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+    #define CYTHON_USE_ASYNC_SLOTS 1
+  #endif
+  #undef CYTHON_USE_PYLIST_INTERNALS
+  #define CYTHON_USE_PYLIST_INTERNALS 0
+  #undef CYTHON_USE_UNICODE_INTERNALS
+  #define CYTHON_USE_UNICODE_INTERNALS 0
+  #undef CYTHON_USE_UNICODE_WRITER
+  #define CYTHON_USE_UNICODE_WRITER 0
+  #undef CYTHON_USE_PYLONG_INTERNALS
+  #define CYTHON_USE_PYLONG_INTERNALS 0
+  #undef CYTHON_AVOID_BORROWED_REFS
+  #define CYTHON_AVOID_BORROWED_REFS 1
+  #undef CYTHON_ASSUME_SAFE_MACROS
+  #define CYTHON_ASSUME_SAFE_MACROS 0
+  #undef CYTHON_UNPACK_METHODS
+  #define CYTHON_UNPACK_METHODS 0
+  #undef CYTHON_FAST_THREAD_STATE
+  #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_PYCALL
+  #define CYTHON_FAST_PYCALL 0
+  #undef CYTHON_PEP489_MULTI_PHASE_INIT
+  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+  #undef CYTHON_USE_TP_FINALIZE
+  #define CYTHON_USE_TP_FINALIZE 0
+  #undef CYTHON_USE_DICT_VERSIONS
+  #define CYTHON_USE_DICT_VERSIONS 0
+  #undef CYTHON_USE_EXC_INFO_STACK
+  #define CYTHON_USE_EXC_INFO_STACK 0
+#elif defined(PYSTON_VERSION)
+  #define CYTHON_COMPILING_IN_PYPY 0
+  #define CYTHON_COMPILING_IN_PYSTON 1
+  #define CYTHON_COMPILING_IN_CPYTHON 0
+  #ifndef CYTHON_USE_TYPE_SLOTS
+    #define CYTHON_USE_TYPE_SLOTS 1
+  #endif
+  #undef CYTHON_USE_PYTYPE_LOOKUP
+  #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #undef CYTHON_USE_ASYNC_SLOTS
+  #define CYTHON_USE_ASYNC_SLOTS 0
+  #undef CYTHON_USE_PYLIST_INTERNALS
+  #define CYTHON_USE_PYLIST_INTERNALS 0
+  #ifndef CYTHON_USE_UNICODE_INTERNALS
+    #define CYTHON_USE_UNICODE_INTERNALS 1
+  #endif
+  #undef CYTHON_USE_UNICODE_WRITER
+  #define CYTHON_USE_UNICODE_WRITER 0
+  #undef CYTHON_USE_PYLONG_INTERNALS
+  #define CYTHON_USE_PYLONG_INTERNALS 0
+  #ifndef CYTHON_AVOID_BORROWED_REFS
+    #define CYTHON_AVOID_BORROWED_REFS 0
+  #endif
+  #ifndef CYTHON_ASSUME_SAFE_MACROS
+    #define CYTHON_ASSUME_SAFE_MACROS 1
+  #endif
+  #ifndef CYTHON_UNPACK_METHODS
+    #define CYTHON_UNPACK_METHODS 1
+  #endif
+  #undef CYTHON_FAST_THREAD_STATE
+  #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_PYCALL
+  #define CYTHON_FAST_PYCALL 0
+  #undef CYTHON_PEP489_MULTI_PHASE_INIT
+  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+  #undef CYTHON_USE_TP_FINALIZE
+  #define CYTHON_USE_TP_FINALIZE 0
+  #undef CYTHON_USE_DICT_VERSIONS
+  #define CYTHON_USE_DICT_VERSIONS 0
+  #undef CYTHON_USE_EXC_INFO_STACK
+  #define CYTHON_USE_EXC_INFO_STACK 0
+#else
+  #define CYTHON_COMPILING_IN_PYPY 0
+  #define CYTHON_COMPILING_IN_PYSTON 0
+  #define CYTHON_COMPILING_IN_CPYTHON 1
+  #ifndef CYTHON_USE_TYPE_SLOTS
+    #define CYTHON_USE_TYPE_SLOTS 1
+  #endif
+  #if PY_VERSION_HEX < 0x02070000
+    #undef CYTHON_USE_PYTYPE_LOOKUP
+    #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
+    #define CYTHON_USE_PYTYPE_LOOKUP 1
+  #endif
+  #if PY_MAJOR_VERSION < 3
+    #undef CYTHON_USE_ASYNC_SLOTS
+    #define CYTHON_USE_ASYNC_SLOTS 0
+  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+    #define CYTHON_USE_ASYNC_SLOTS 1
+  #endif
+  #if PY_VERSION_HEX < 0x02070000
+    #undef CYTHON_USE_PYLONG_INTERNALS
+    #define CYTHON_USE_PYLONG_INTERNALS 0
+  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
+    #define CYTHON_USE_PYLONG_INTERNALS 1
+  #endif
+  #ifndef CYTHON_USE_PYLIST_INTERNALS
+    #define CYTHON_USE_PYLIST_INTERNALS 1
+  #endif
+  #ifndef CYTHON_USE_UNICODE_INTERNALS
+    #define CYTHON_USE_UNICODE_INTERNALS 1
+  #endif
+  #if PY_VERSION_HEX < 0x030300F0
+    #undef CYTHON_USE_UNICODE_WRITER
+    #define CYTHON_USE_UNICODE_WRITER 0
+  #elif !defined(CYTHON_USE_UNICODE_WRITER)
+    #define CYTHON_USE_UNICODE_WRITER 1
+  #endif
+  #ifndef CYTHON_AVOID_BORROWED_REFS
+    #define CYTHON_AVOID_BORROWED_REFS 0
+  #endif
+  #ifndef CYTHON_ASSUME_SAFE_MACROS
+    #define CYTHON_ASSUME_SAFE_MACROS 1
+  #endif
+  #ifndef CYTHON_UNPACK_METHODS
+    #define CYTHON_UNPACK_METHODS 1
+  #endif
+  #ifndef CYTHON_FAST_THREAD_STATE
+    #define CYTHON_FAST_THREAD_STATE 1
+  #endif
+  #ifndef CYTHON_FAST_PYCALL
+    #define CYTHON_FAST_PYCALL 1
+  #endif
+  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
+    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)
+  #endif
+  #ifndef CYTHON_USE_TP_FINALIZE
+    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
+  #endif
+  #ifndef CYTHON_USE_DICT_VERSIONS
+    #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1)
+  #endif
+  #ifndef CYTHON_USE_EXC_INFO_STACK
+    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)
+  #endif
+#endif
+#if !defined(CYTHON_FAST_PYCCALL)
+#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
+#endif
+#if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+  #undef SHIFT
+  #undef BASE
+  #undef MASK
+  #ifdef SIZEOF_VOID_P
+    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
+  #endif
+#endif
+#ifndef __has_attribute
+  #define __has_attribute(x) 0
+#endif
+#ifndef __has_cpp_attribute
+  #define __has_cpp_attribute(x) 0
+#endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+#     define CYTHON_UNUSED __attribute__ ((__unused__))
+#   else
+#     define CYTHON_UNUSED
+#   endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+#ifndef CYTHON_MAYBE_UNUSED_VAR
+#  if defined(__cplusplus)
+     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
+#  else
+#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
+#  endif
+#endif
+#ifndef CYTHON_NCP_UNUSED
+# if CYTHON_COMPILING_IN_CPYTHON
+#  define CYTHON_NCP_UNUSED
+# else
+#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
+# endif
+#endif
+#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
+#ifdef _MSC_VER
+    #ifndef _MSC_STDINT_H_
+        #if _MSC_VER < 1300
+           typedef unsigned char     uint8_t;
+           typedef unsigned int      uint32_t;
+        #else
+           typedef unsigned __int8   uint8_t;
+           typedef unsigned __int32  uint32_t;
+        #endif
+    #endif
+#else
+   #include <stdint.h>
+#endif
+#ifndef CYTHON_FALLTHROUGH
+  #if defined(__cplusplus) && __cplusplus >= 201103L
+    #if __has_cpp_attribute(fallthrough)
+      #define CYTHON_FALLTHROUGH [[fallthrough]]
+    #elif __has_cpp_attribute(clang::fallthrough)
+      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
+    #elif __has_cpp_attribute(gnu::fallthrough)
+      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
+    #endif
+  #endif
+  #ifndef CYTHON_FALLTHROUGH
+    #if __has_attribute(fallthrough)
+      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
+    #else
+      #define CYTHON_FALLTHROUGH
+    #endif
+  #endif
+  #if defined(__clang__ ) && defined(__apple_build_version__)
+    #if __apple_build_version__ < 7000000
+      #undef  CYTHON_FALLTHROUGH
+      #define CYTHON_FALLTHROUGH
+    #endif
+  #endif
+#endif
+
+#ifndef CYTHON_INLINE
+  #if defined(__clang__)
+    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
+  #elif defined(__GNUC__)
+    #define CYTHON_INLINE __inline__
+  #elif defined(_MSC_VER)
+    #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
+  #else
+    #define CYTHON_INLINE
+  #endif
+#endif
+
+#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
+  #define Py_OptimizeFlag 0
+#endif
+#define __PYX_BUILD_PY_SSIZE_T "n"
+#define CYTHON_FORMAT_SSIZE_T "z"
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyClass_Type
+#else
+  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+#else
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+#endif
+  #define __Pyx_DefaultClassType PyType_Type
+#endif
+#ifndef Py_TPFLAGS_CHECKTYPES
+  #define Py_TPFLAGS_CHECKTYPES 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_INDEX
+  #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
+  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_FINALIZE
+  #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
+#ifndef METH_STACKLESS
+  #define METH_STACKLESS 0
+#endif
+#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
+  #ifndef METH_FASTCALL
+     #define METH_FASTCALL 0x80
+  #endif
+  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
+  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
+                                                          Py_ssize_t nargs, PyObject *kwnames);
+#else
+  #define __Pyx_PyCFunctionFast _PyCFunctionFast
+  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
+#endif
+#if CYTHON_FAST_PYCCALL
+#define __Pyx_PyFastCFunction_Check(func)\
+    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
+#else
+#define __Pyx_PyFastCFunction_Check(func) 0
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
+  #define PyObject_Malloc(s)   PyMem_Malloc(s)
+  #define PyObject_Free(p)     PyMem_Free(p)
+  #define PyObject_Realloc(p)  PyMem_Realloc(p)
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
+  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)
+  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)
+  #define PyMem_RawFree(p)             PyMem_Free(p)
+#endif
+#if CYTHON_COMPILING_IN_PYSTON
+  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
+#else
+  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
+#endif
+#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
+  #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#elif PY_VERSION_HEX >= 0x03060000
+  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
+#elif PY_VERSION_HEX >= 0x03000000
+  #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#else
+  #define __Pyx_PyThreadState_Current _PyThreadState_Current
+#endif
+#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
+#include "pythread.h"
+#define Py_tss_NEEDS_INIT 0
+typedef int Py_tss_t;
+static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
+  *key = PyThread_create_key();
+  return 0;
+}
+static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
+  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
+  *key = Py_tss_NEEDS_INIT;
+  return key;
+}
+static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
+  PyObject_Free(key);
+}
+static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
+  return *key != Py_tss_NEEDS_INIT;
+}
+static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
+  PyThread_delete_key(*key);
+  *key = Py_tss_NEEDS_INIT;
+}
+static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
+  return PyThread_set_key_value(*key, value);
+}
+static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
+  return PyThread_get_key_value(*key);
+}
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
+#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
+#else
+#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
+#endif
+#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
+#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
+#else
+#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
+#endif
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+  #define CYTHON_PEP393_ENABLED 1
+  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
+                                              0 : _PyUnicode_Ready((PyObject *)(op)))
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
+  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
+  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
+  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
+  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
+  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
+  #else
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
+  #endif
+#else
+  #define CYTHON_PEP393_ENABLED 0
+  #define PyUnicode_1BYTE_KIND  1
+  #define PyUnicode_2BYTE_KIND  2
+  #define PyUnicode_4BYTE_KIND  4
+  #define __Pyx_PyUnicode_READY(op)       (0)
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
+  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
+  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
+  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
+#else
+  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
+      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
+  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
+  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
+  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
+#endif
+#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
+#else
+  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
+#endif
+#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
+  #define PyObject_ASCII(o)            PyObject_Repr(o)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBaseString_Type            PyUnicode_Type
+  #define PyStringObject               PyUnicodeObject
+  #define PyString_Type                PyUnicode_Type
+  #define PyString_Check               PyUnicode_Check
+  #define PyString_CheckExact          PyUnicode_CheckExact
+#ifndef PyObject_Unicode
+  #define PyObject_Unicode             PyObject_Str
+#endif
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+#else
+  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
+  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+#endif
+#ifndef PySet_CheckExact
+  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
+#endif
+#if PY_VERSION_HEX >= 0x030900A4
+  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
+  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
+#else
+  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
+  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
+#endif
+#if CYTHON_ASSUME_SAFE_MACROS
+  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
+#else
+  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyIntObject                  PyLongObject
+  #define PyInt_Type                   PyLong_Type
+  #define PyInt_Check(op)              PyLong_Check(op)
+  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
+  #define PyInt_FromString             PyLong_FromString
+  #define PyInt_FromUnicode            PyLong_FromUnicode
+  #define PyInt_FromLong               PyLong_FromLong
+  #define PyInt_FromSize_t             PyLong_FromSize_t
+  #define PyInt_FromSsize_t            PyLong_FromSsize_t
+  #define PyInt_AsLong                 PyLong_AsLong
+  #define PyInt_AS_LONG                PyLong_AS_LONG
+  #define PyInt_AsSsize_t              PyLong_AsSsize_t
+  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
+  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define PyNumber_Int                 PyNumber_Long
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBoolObject                 PyLongObject
+#endif
+#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
+  #ifndef PyUnicode_InternFromString
+    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
+  #endif
+#endif
+#if PY_VERSION_HEX < 0x030200A4
+  typedef long Py_hash_t;
+  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
+#else
+  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
+#else
+  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
+#endif
+#if CYTHON_USE_ASYNC_SLOTS
+  #if PY_VERSION_HEX >= 0x030500B1
+    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
+    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
+  #else
+    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
+  #endif
+#else
+  #define __Pyx_PyType_AsAsync(obj) NULL
+#endif
+#ifndef __Pyx_PyAsyncMethodsStruct
+    typedef struct {
+        unaryfunc am_await;
+        unaryfunc am_aiter;
+        unaryfunc am_anext;
+    } __Pyx_PyAsyncMethodsStruct;
+#endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+  #define _USE_MATH_DEFINES
+#endif
+#include <math.h>
+#ifdef NAN
+#define __PYX_NAN() ((float) NAN)
+#else
+static CYTHON_INLINE float __PYX_NAN() {
+  float value;
+  memset(&value, 0xFF, sizeof(value));
+  return value;
+}
+#endif
+#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
+#define __Pyx_truncl trunc
+#else
+#define __Pyx_truncl truncl
+#endif
+
+#define __PYX_MARK_ERR_POS(f_index, lineno) \
+    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
+#define __PYX_ERR(f_index, lineno, Ln_error) \
+    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
+
+#ifndef __PYX_EXTERN_C
+  #ifdef __cplusplus
+    #define __PYX_EXTERN_C extern "C"
+  #else
+    #define __PYX_EXTERN_C extern
+  #endif
+#endif
+
+#define __PYX_HAVE__cy_dcumsum
+#define __PYX_HAVE_API__cy_dcumsum
+/* Early includes */
+#include <string.h>
+#include <stdio.h>
+#include "numpy/arrayobject.h"
+#include "numpy/ndarrayobject.h"
+#include "numpy/ndarraytypes.h"
+#include "numpy/arrayscalars.h"
+#include "numpy/ufuncobject.h"
+
+    /* NumPy API declarations from "numpy/__init__.pxd" */
+    
+#ifdef _OPENMP
+#include <omp.h>
+#endif /* _OPENMP */
+
+#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
+                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
+
+#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
+#define __PYX_DEFAULT_STRING_ENCODING ""
+#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_uchar_cast(c) ((unsigned char)c)
+#define __Pyx_long_cast(x) ((long)x)
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
+    (sizeof(type) < sizeof(Py_ssize_t))  ||\
+    (sizeof(type) > sizeof(Py_ssize_t) &&\
+          likely(v < (type)PY_SSIZE_T_MAX ||\
+                 v == (type)PY_SSIZE_T_MAX)  &&\
+          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
+                                v == (type)PY_SSIZE_T_MIN)))  ||\
+    (sizeof(type) == sizeof(Py_ssize_t) &&\
+          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
+                               v == (type)PY_SSIZE_T_MAX)))  )
+static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
+    return (size_t) i < (size_t) limit;
+}
+#if defined (__cplusplus) && __cplusplus >= 201103L
+    #include <cstdlib>
+    #define __Pyx_sst_abs(value) std::abs(value)
+#elif SIZEOF_INT >= SIZEOF_SIZE_T
+    #define __Pyx_sst_abs(value) abs(value)
+#elif SIZEOF_LONG >= SIZEOF_SIZE_T
+    #define __Pyx_sst_abs(value) labs(value)
+#elif defined (_MSC_VER)
+    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
+#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define __Pyx_sst_abs(value) llabs(value)
+#elif defined (__GNUC__)
+    #define __Pyx_sst_abs(value) __builtin_llabs(value)
+#else
+    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
+#endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+#define __Pyx_PyBytes_FromString        PyBytes_FromString
+#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
+#if PY_MAJOR_VERSION < 3
+    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#else
+    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+#endif
+#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
+#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
+#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
+#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
+#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
+    const Py_UNICODE *u_end = u;
+    while (*u_end++) ;
+    return (size_t)(u_end - u - 1);
+}
+#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
+#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
+#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
+static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
+#define __Pyx_PySequence_Tuple(obj)\
+    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+#if CYTHON_ASSUME_SAFE_MACROS
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+#else
+#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+#endif
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
+#else
+#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
+#endif
+#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+static int __Pyx_sys_getdefaultencoding_not_ascii;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    PyObject* ascii_chars_u = NULL;
+    PyObject* ascii_chars_b = NULL;
+    const char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    if (strcmp(default_encoding_c, "ascii") == 0) {
+        __Pyx_sys_getdefaultencoding_not_ascii = 0;
+    } else {
+        char ascii_chars[128];
+        int c;
+        for (c = 0; c < 128; c++) {
+            ascii_chars[c] = c;
+        }
+        __Pyx_sys_getdefaultencoding_not_ascii = 1;
+        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+        if (!ascii_chars_u) goto bad;
+        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+            PyErr_Format(
+                PyExc_ValueError,
+                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+                default_encoding_c);
+            goto bad;
+        }
+        Py_DECREF(ascii_chars_u);
+        Py_DECREF(ascii_chars_b);
+    }
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    Py_XDECREF(ascii_chars_u);
+    Py_XDECREF(ascii_chars_b);
+    return -1;
+}
+#endif
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
+#else
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+static char* __PYX_DEFAULT_STRING_ENCODING;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
+    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
+    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    return -1;
+}
+#endif
+#endif
+
+
+/* Test for GCC > 2.95 */
+#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
+  #define likely(x)   __builtin_expect(!!(x), 1)
+  #define unlikely(x) __builtin_expect(!!(x), 0)
+#else /* !__GNUC__ or GCC < 2.95 */
+  #define likely(x)   (x)
+  #define unlikely(x) (x)
+#endif /* __GNUC__ */
+static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
+
+static PyObject *__pyx_m = NULL;
+static PyObject *__pyx_d;
+static PyObject *__pyx_b;
+static PyObject *__pyx_cython_runtime = NULL;
+static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
+static PyObject *__pyx_empty_unicode;
+static int __pyx_lineno;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+
+/* Header.proto */
+#if !defined(CYTHON_CCOMPLEX)
+  #if defined(__cplusplus)
+    #define CYTHON_CCOMPLEX 1
+  #elif defined(_Complex_I)
+    #define CYTHON_CCOMPLEX 1
+  #else
+    #define CYTHON_CCOMPLEX 0
+  #endif
+#endif
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    #include <complex>
+  #else
+    #include <complex.h>
+  #endif
+#endif
+#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
+  #undef _Complex_I
+  #define _Complex_I 1.0fj
+#endif
+
+
+static const char *__pyx_f[] = {
+  "cy_dcumsum.pyx",
+  "__init__.pxd",
+  "type.pxd",
+};
+/* BufferFormatStructs.proto */
+#define IS_UNSIGNED(type) (((type) -1) > 0)
+struct __Pyx_StructField_;
+#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
+typedef struct {
+  const char* name;
+  struct __Pyx_StructField_* fields;
+  size_t size;
+  size_t arraysize[8];
+  int ndim;
+  char typegroup;
+  char is_unsigned;
+  int flags;
+} __Pyx_TypeInfo;
+typedef struct __Pyx_StructField_ {
+  __Pyx_TypeInfo* type;
+  const char* name;
+  size_t offset;
+} __Pyx_StructField;
+typedef struct {
+  __Pyx_StructField* field;
+  size_t parent_offset;
+} __Pyx_BufFmt_StackElem;
+typedef struct {
+  __Pyx_StructField root;
+  __Pyx_BufFmt_StackElem* head;
+  size_t fmt_offset;
+  size_t new_count, enc_count;
+  size_t struct_alignment;
+  int is_complex;
+  char enc_type;
+  char new_packmode;
+  char enc_packmode;
+  char is_valid_array;
+} __Pyx_BufFmt_Context;
+
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":690
+ * # in Cython to enable them only on the right systems.
+ * 
+ * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ */
+typedef npy_int8 __pyx_t_5numpy_int8_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":691
+ * 
+ * ctypedef npy_int8       int8_t
+ * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int32      int32_t
+ * ctypedef npy_int64      int64_t
+ */
+typedef npy_int16 __pyx_t_5numpy_int16_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":692
+ * ctypedef npy_int8       int8_t
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int64      int64_t
+ * #ctypedef npy_int96      int96_t
+ */
+typedef npy_int32 __pyx_t_5numpy_int32_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":693
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_int96      int96_t
+ * #ctypedef npy_int128     int128_t
+ */
+typedef npy_int64 __pyx_t_5numpy_int64_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":697
+ * #ctypedef npy_int128     int128_t
+ * 
+ * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t
+ */
+typedef npy_uint8 __pyx_t_5numpy_uint8_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":698
+ * 
+ * ctypedef npy_uint8      uint8_t
+ * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint32     uint32_t
+ * ctypedef npy_uint64     uint64_t
+ */
+typedef npy_uint16 __pyx_t_5numpy_uint16_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":699
+ * ctypedef npy_uint8      uint8_t
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint64     uint64_t
+ * #ctypedef npy_uint96     uint96_t
+ */
+typedef npy_uint32 __pyx_t_5numpy_uint32_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":700
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t
+ * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_uint96     uint96_t
+ * #ctypedef npy_uint128    uint128_t
+ */
+typedef npy_uint64 __pyx_t_5numpy_uint64_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":704
+ * #ctypedef npy_uint128    uint128_t
+ * 
+ * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_float64    float64_t
+ * #ctypedef npy_float80    float80_t
+ */
+typedef npy_float32 __pyx_t_5numpy_float32_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":705
+ * 
+ * ctypedef npy_float32    float32_t
+ * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_float80    float80_t
+ * #ctypedef npy_float128   float128_t
+ */
+typedef npy_float64 __pyx_t_5numpy_float64_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":714
+ * # The int types are mapped a bit surprising --
+ * # numpy.int corresponds to 'l' and numpy.long to 'q'
+ * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longlong   long_t
+ * ctypedef npy_longlong   longlong_t
+ */
+typedef npy_long __pyx_t_5numpy_int_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":715
+ * # numpy.int corresponds to 'l' and numpy.long to 'q'
+ * ctypedef npy_long       int_t
+ * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longlong   longlong_t
+ * 
+ */
+typedef npy_longlong __pyx_t_5numpy_long_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":716
+ * ctypedef npy_long       int_t
+ * ctypedef npy_longlong   long_t
+ * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_ulong      uint_t
+ */
+typedef npy_longlong __pyx_t_5numpy_longlong_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":718
+ * ctypedef npy_longlong   longlong_t
+ * 
+ * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_ulonglong  ulong_t
+ * ctypedef npy_ulonglong  ulonglong_t
+ */
+typedef npy_ulong __pyx_t_5numpy_uint_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":719
+ * 
+ * ctypedef npy_ulong      uint_t
+ * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_ulonglong  ulonglong_t
+ * 
+ */
+typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":720
+ * ctypedef npy_ulong      uint_t
+ * ctypedef npy_ulonglong  ulong_t
+ * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_intp       intp_t
+ */
+typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":722
+ * ctypedef npy_ulonglong  ulonglong_t
+ * 
+ * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uintp      uintp_t
+ * 
+ */
+typedef npy_intp __pyx_t_5numpy_intp_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":723
+ * 
+ * ctypedef npy_intp       intp_t
+ * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_double     float_t
+ */
+typedef npy_uintp __pyx_t_5numpy_uintp_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":725
+ * ctypedef npy_uintp      uintp_t
+ * 
+ * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_double     double_t
+ * ctypedef npy_longdouble longdouble_t
+ */
+typedef npy_double __pyx_t_5numpy_float_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":726
+ * 
+ * ctypedef npy_double     float_t
+ * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longdouble longdouble_t
+ * 
+ */
+typedef npy_double __pyx_t_5numpy_double_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":727
+ * ctypedef npy_double     float_t
+ * ctypedef npy_double     double_t
+ * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_cfloat      cfloat_t
+ */
+typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
+/* Declarations.proto */
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    typedef ::std::complex< float > __pyx_t_float_complex;
+  #else
+    typedef float _Complex __pyx_t_float_complex;
+  #endif
+#else
+    typedef struct { float real, imag; } __pyx_t_float_complex;
+#endif
+static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+
+/* Declarations.proto */
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+  #ifdef __cplusplus
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+  #else
+    typedef double _Complex __pyx_t_double_complex;
+  #endif
+#else
+    typedef struct { double real, imag; } __pyx_t_double_complex;
+#endif
+static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
+
+
+/*--- Type declarations ---*/
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":729
+ * ctypedef npy_longdouble longdouble_t
+ * 
+ * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_cdouble     cdouble_t
+ * ctypedef npy_clongdouble clongdouble_t
+ */
+typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":730
+ * 
+ * ctypedef npy_cfloat      cfloat_t
+ * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_clongdouble clongdouble_t
+ * 
+ */
+typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":731
+ * ctypedef npy_cfloat      cfloat_t
+ * ctypedef npy_cdouble     cdouble_t
+ * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_cdouble     complex_t
+ */
+typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
+
+/* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":733
+ * ctypedef npy_clongdouble clongdouble_t
+ * 
+ * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew1(a):
+ */
+typedef npy_cdouble __pyx_t_5numpy_complex_t;
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+  #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
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+  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
+  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
+  #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
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+  #define __Pyx_RefNannyDeclarations
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)
+  #define __Pyx_RefNannyFinishContext()
+  #define __Pyx_INCREF(r) Py_INCREF(r)
+  #define __Pyx_DECREF(r) Py_DECREF(r)
+  #define __Pyx_GOTREF(r)
+  #define __Pyx_GIVEREF(r)
+  #define __Pyx_XINCREF(r) Py_XINCREF(r)
+  #define __Pyx_XDECREF(r) Py_XDECREF(r)
+  #define __Pyx_XGOTREF(r)
+  #define __Pyx_XGIVEREF(r)
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+#define __Pyx_XDECREF_SET(r, v) do {\
+        PyObject *tmp = (PyObject *) r;\
+        r = v; __Pyx_XDECREF(tmp);\
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+#define __Pyx_DECREF_SET(r, v) do {\
+        PyObject *tmp = (PyObject *) r;\
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+#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
+
+/* RaiseArgTupleInvalid.proto */
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
+
+/* RaiseDoubleKeywords.proto */
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
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+/* ParseKeywords.proto */
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\
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+    const char* function_name);
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+/* ArgTypeTest.proto */
+#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
+    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
+        __Pyx__ArgTypeTest(obj, type, name, exact))
+static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
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+static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
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+/* BufferFormatCheck.proto */
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type);
+
+/* BufferGetAndValidate.proto */
+#define __Pyx_GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack)\
+    ((obj == Py_None || obj == NULL) ?\
+    (__Pyx_ZeroBuffer(buf), 0) :\
+    __Pyx__GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack))
+static int  __Pyx__GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
+    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
+static void __Pyx_ZeroBuffer(Py_buffer* buf);
+static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
+static Py_ssize_t __Pyx_minusones[] = { -1, -1, -1, -1, -1, -1, -1, -1 };
+static Py_ssize_t __Pyx_zeros[] = { 0, 0, 0, 0, 0, 0, 0, 0 };
+
+/* BufferIndexError.proto */
+static void __Pyx_RaiseBufferIndexError(int axis);
+
+#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
+/* PyThreadStateGet.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyThreadState_declare  PyThreadState *__pyx_tstate;
+#define __Pyx_PyThreadState_assign  __pyx_tstate = __Pyx_PyThreadState_Current;
+#define __Pyx_PyErr_Occurred()  __pyx_tstate->curexc_type
+#else
+#define __Pyx_PyThreadState_declare
+#define __Pyx_PyThreadState_assign
+#define __Pyx_PyErr_Occurred()  PyErr_Occurred()
+#endif
+
+/* PyErrFetchRestore.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
+#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
+#else
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#endif
+#else
+#define __Pyx_PyErr_Clear() PyErr_Clear()
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
+#endif
+
+/* PyObjectGetAttrStr.proto */
+#if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name);
+#else
+#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
+#endif
+
+/* GetBuiltinName.proto */
+static PyObject *__Pyx_GetBuiltinName(PyObject *name);
+
+/* GetTopmostException.proto */
+#if CYTHON_USE_EXC_INFO_STACK
+static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
+#endif
+
+/* SaveResetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
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+#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
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+#else
+#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
+#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
+#endif
+
+/* PyErrExceptionMatches.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
+#else
+#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
+#endif
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+/* GetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
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+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
+#endif
+
+/* PyObjectCall.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
+#else
+#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
+#endif
+
+/* RaiseException.proto */
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+
+/* TypeImport.proto */
+#ifndef __PYX_HAVE_RT_ImportType_proto
+#define __PYX_HAVE_RT_ImportType_proto
+enum __Pyx_ImportType_CheckSize {
+   __Pyx_ImportType_CheckSize_Error = 0,
+   __Pyx_ImportType_CheckSize_Warn = 1,
+   __Pyx_ImportType_CheckSize_Ignore = 2
+};
+static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size);
+#endif
+
+/* PyDictVersioning.proto */
+#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
+#define __PYX_DICT_VERSION_INIT  ((PY_UINT64_T) -1)
+#define __PYX_GET_DICT_VERSION(dict)  (((PyDictObject*)(dict))->ma_version_tag)
+#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\
+    (version_var) = __PYX_GET_DICT_VERSION(dict);\
+    (cache_var) = (value);
+#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\
+    static PY_UINT64_T __pyx_dict_version = 0;\
+    static PyObject *__pyx_dict_cached_value = NULL;\
+    if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
+        (VAR) = __pyx_dict_cached_value;\
+    } else {\
+        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
+        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
+    }\
+}
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
+static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
+#else
+#define __PYX_GET_DICT_VERSION(dict)  (0)
+#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
+#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
+#endif
+
+/* CLineInTraceback.proto */
+#ifdef CYTHON_CLINE_IN_TRACEBACK
+#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
+#else
+static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
+#endif
+
+/* CodeObjectCache.proto */
+typedef struct {
+    PyCodeObject* code_object;
+    int code_line;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+    int count;
+    int max_count;
+    __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+/* AddTraceback.proto */
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename);
+
+/* BufferStructDeclare.proto */
+typedef struct {
+  Py_ssize_t shape, strides, suboffsets;
+} __Pyx_Buf_DimInfo;
+typedef struct {
+  size_t refcount;
+  Py_buffer pybuffer;
+} __Pyx_Buffer;
+typedef struct {
+  __Pyx_Buffer *rcbuffer;
+  char *data;
+  __Pyx_Buf_DimInfo diminfo[8];
+} __Pyx_LocalBuf_ND;
+
+#if PY_MAJOR_VERSION < 3
+    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
+    static void __Pyx_ReleaseBuffer(Py_buffer *view);
+#else
+    #define __Pyx_GetBuffer PyObject_GetBuffer
+    #define __Pyx_ReleaseBuffer PyBuffer_Release
+#endif
+
+
+/* RealImag.proto */
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    #define __Pyx_CREAL(z) ((z).real())
+    #define __Pyx_CIMAG(z) ((z).imag())
+  #else
+    #define __Pyx_CREAL(z) (__real__(z))
+    #define __Pyx_CIMAG(z) (__imag__(z))
+  #endif
+#else
+    #define __Pyx_CREAL(z) ((z).real)
+    #define __Pyx_CIMAG(z) ((z).imag)
+#endif
+#if defined(__cplusplus) && CYTHON_CCOMPLEX\
+        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
+    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
+    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
+#else
+    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
+    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
+#endif
+
+/* Arithmetic.proto */
+#if CYTHON_CCOMPLEX
+    #define __Pyx_c_eq_float(a, b)   ((a)==(b))
+    #define __Pyx_c_sum_float(a, b)  ((a)+(b))
+    #define __Pyx_c_diff_float(a, b) ((a)-(b))
+    #define __Pyx_c_prod_float(a, b) ((a)*(b))
+    #define __Pyx_c_quot_float(a, b) ((a)/(b))
+    #define __Pyx_c_neg_float(a)     (-(a))
+  #ifdef __cplusplus
+    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)
+    #define __Pyx_c_conj_float(z)    (::std::conj(z))
+    #if 1
+        #define __Pyx_c_abs_float(z)     (::std::abs(z))
+        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))
+    #endif
+  #else
+    #define __Pyx_c_is_zero_float(z) ((z)==0)
+    #define __Pyx_c_conj_float(z)    (conjf(z))
+    #if 1
+        #define __Pyx_c_abs_float(z)     (cabsf(z))
+        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))
+    #endif
+ #endif
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
+    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
+    #if 1
+        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
+        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
+    #endif
+#endif
+
+/* Arithmetic.proto */
+#if CYTHON_CCOMPLEX
+    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
+    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
+    #define __Pyx_c_diff_double(a, b) ((a)-(b))
+    #define __Pyx_c_prod_double(a, b) ((a)*(b))
+    #define __Pyx_c_quot_double(a, b) ((a)/(b))
+    #define __Pyx_c_neg_double(a)     (-(a))
+  #ifdef __cplusplus
+    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
+    #define __Pyx_c_conj_double(z)    (::std::conj(z))
+    #if 1
+        #define __Pyx_c_abs_double(z)     (::std::abs(z))
+        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
+    #endif
+  #else
+    #define __Pyx_c_is_zero_double(z) ((z)==0)
+    #define __Pyx_c_conj_double(z)    (conj(z))
+    #if 1
+        #define __Pyx_c_abs_double(z)     (cabs(z))
+        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
+    #endif
+ #endif
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
+    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
+    #if 1
+        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
+        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
+    #endif
+#endif
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+/* FastTypeChecks.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
+#else
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
+#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
+#endif
+#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+
+/* CheckBinaryVersion.proto */
+static int __Pyx_check_binary_version(void);
+
+/* InitStrings.proto */
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
+
+
+/* Module declarations from 'cpython.buffer' */
+
+/* Module declarations from 'libc.string' */
+
+/* Module declarations from 'libc.stdio' */
+
+/* Module declarations from '__builtin__' */
+
+/* Module declarations from 'cpython.type' */
+static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
+
+/* Module declarations from 'cpython' */
+
+/* Module declarations from 'cpython.object' */
+
+/* Module declarations from 'cpython.ref' */
+
+/* Module declarations from 'cpython.mem' */
+
+/* Module declarations from 'numpy' */
+
+/* Module declarations from 'numpy' */
+static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
+static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
+static PyTypeObject *__pyx_ptype_5numpy_generic = 0;
+static PyTypeObject *__pyx_ptype_5numpy_number = 0;
+static PyTypeObject *__pyx_ptype_5numpy_integer = 0;
+static PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0;
+static PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0;
+static PyTypeObject *__pyx_ptype_5numpy_inexact = 0;
+static PyTypeObject *__pyx_ptype_5numpy_floating = 0;
+static PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0;
+static PyTypeObject *__pyx_ptype_5numpy_flexible = 0;
+static PyTypeObject *__pyx_ptype_5numpy_character = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
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+/* Module declarations from 'cy_dcumsum' */
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+#define __Pyx_MODULE_NAME "cy_dcumsum"
+extern int __pyx_module_is_main_cy_dcumsum;
+int __pyx_module_is_main_cy_dcumsum = 0;
+
+/* Implementation of 'cy_dcumsum' */
+static PyObject *__pyx_builtin_ImportError;
+static const char __pyx_k_a[] = "a";
+static const char __pyx_k_b[] = "b";
+static const char __pyx_k_i[] = "i";
+static const char __pyx_k_n[] = "n";
+static const char __pyx_k_main[] = "__main__";
+static const char __pyx_k_name[] = "__name__";
+static const char __pyx_k_test[] = "__test__";
+static const char __pyx_k_dcumsum[] = "dcumsum";
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+static const char __pyx_k_ImportError[] = "ImportError";
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+static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
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+static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
+static PyObject *__pyx_n_s_ImportError;
+static PyObject *__pyx_n_s_a;
+static PyObject *__pyx_n_s_b;
+static PyObject *__pyx_n_s_cline_in_traceback;
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+static PyObject *__pyx_kp_s_cy_dcumsum_pyx;
+static PyObject *__pyx_n_s_dcumsum;
+static PyObject *__pyx_n_s_i;
+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_n;
+static PyObject *__pyx_n_s_name;
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+static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;
+static PyObject *__pyx_n_s_test;
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+static PyObject *__pyx_tuple__2;
+static PyObject *__pyx_tuple__3;
+static PyObject *__pyx_codeobj__4;
+/* Late includes */
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+/* "cy_dcumsum.pyx":4
+ * cimport numpy
+ * 
+ * def dcumsum(numpy.ndarray[numpy.float64_t, ndim=1] a, numpy.ndarray[numpy.float64_t, ndim=1] b):             # <<<<<<<<<<<<<<
+ *     cdef int i, n = len(a)
+ *     b[0] = a[0]
+ */
+
+/* Python wrapper */
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+  __pyx_pybuffer_b.pybuffer.buf = NULL;
+  __pyx_pybuffer_b.refcount = 0;
+  __pyx_pybuffernd_b.data = NULL;
+  __pyx_pybuffernd_b.rcbuffer = &__pyx_pybuffer_b;
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+}
+
+static int __Pyx_modinit_variable_import_code(void) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__Pyx_modinit_variable_import_code", 0);
+  /*--- Variable import code ---*/
+  __Pyx_RefNannyFinishContext();
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+}
+
+static int __Pyx_modinit_function_import_code(void) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__Pyx_modinit_function_import_code", 0);
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+}
+
+
+#ifndef CYTHON_NO_PYINIT_EXPORT
+#define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC
+#elif PY_MAJOR_VERSION < 3
+#ifdef __cplusplus
+#define __Pyx_PyMODINIT_FUNC extern "C" void
+#else
+#define __Pyx_PyMODINIT_FUNC void
+#endif
+#else
+#ifdef __cplusplus
+#define __Pyx_PyMODINIT_FUNC extern "C" PyObject *
+#else
+#define __Pyx_PyMODINIT_FUNC PyObject *
+#endif
+#endif
+
+
+#if PY_MAJOR_VERSION < 3
+__Pyx_PyMODINIT_FUNC initcy_dcumsum(void) CYTHON_SMALL_CODE; /*proto*/
+__Pyx_PyMODINIT_FUNC initcy_dcumsum(void)
+#else
+__Pyx_PyMODINIT_FUNC PyInit_cy_dcumsum(void) CYTHON_SMALL_CODE; /*proto*/
+__Pyx_PyMODINIT_FUNC PyInit_cy_dcumsum(void)
+#if CYTHON_PEP489_MULTI_PHASE_INIT
+{
+  return PyModuleDef_Init(&__pyx_moduledef);
+}
+static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) {
+    #if PY_VERSION_HEX >= 0x030700A1
+    static PY_INT64_T main_interpreter_id = -1;
+    PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp);
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+        main_interpreter_id = current_id;
+        return (unlikely(current_id == -1)) ? -1 : 0;
+    } else if (unlikely(main_interpreter_id != current_id))
+    #else
+    static PyInterpreterState *main_interpreter = NULL;
+    PyInterpreterState *current_interpreter = PyThreadState_Get()->interp;
+    if (!main_interpreter) {
+        main_interpreter = current_interpreter;
+    } else if (unlikely(main_interpreter != current_interpreter))
+    #endif
+    {
+        PyErr_SetString(
+            PyExc_ImportError,
+            "Interpreter change detected - this module can only be loaded into one interpreter per process.");
+        return -1;
+    }
+    return 0;
+}
+static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) {
+    PyObject *value = PyObject_GetAttrString(spec, from_name);
+    int result = 0;
+    if (likely(value)) {
+        if (allow_none || value != Py_None) {
+            result = PyDict_SetItemString(moddict, to_name, value);
+        }
+        Py_DECREF(value);
+    } else if (PyErr_ExceptionMatches(PyExc_AttributeError)) {
+        PyErr_Clear();
+    } else {
+        result = -1;
+    }
+    return result;
+}
+static CYTHON_SMALL_CODE PyObject* __pyx_pymod_create(PyObject *spec, CYTHON_UNUSED PyModuleDef *def) {
+    PyObject *module = NULL, *moddict, *modname;
+    if (__Pyx_check_single_interpreter())
+        return NULL;
+    if (__pyx_m)
+        return __Pyx_NewRef(__pyx_m);
+    modname = PyObject_GetAttrString(spec, "name");
+    if (unlikely(!modname)) goto bad;
+    module = PyModule_NewObject(modname);
+    Py_DECREF(modname);
+    if (unlikely(!module)) goto bad;
+    moddict = PyModule_GetDict(module);
+    if (unlikely(!moddict)) goto bad;
+    if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "loader", "__loader__", 1) < 0)) goto bad;
+    if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "origin", "__file__", 1) < 0)) goto bad;
+    if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "parent", "__package__", 1) < 0)) goto bad;
+    if (unlikely(__Pyx_copy_spec_to_module(spec, moddict, "submodule_search_locations", "__path__", 0) < 0)) goto bad;
+    return module;
+bad:
+    Py_XDECREF(module);
+    return NULL;
+}
+
+
+static CYTHON_SMALL_CODE int __pyx_pymod_exec_cy_dcumsum(PyObject *__pyx_pyinit_module)
+#endif
+#endif
+{
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannyDeclarations
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+  if (__pyx_m) {
+    if (__pyx_m == __pyx_pyinit_module) return 0;
+    PyErr_SetString(PyExc_RuntimeError, "Module 'cy_dcumsum' has already been imported. Re-initialisation is not supported.");
+    return -1;
+  }
+  #elif PY_MAJOR_VERSION >= 3
+  if (__pyx_m) return __Pyx_NewRef(__pyx_m);
+  #endif
+  #if CYTHON_REFNANNY
+__Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny");
+if (!__Pyx_RefNanny) {
+  PyErr_Clear();
+  __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
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+  __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) __PYX_ERR(0, 2, __pyx_L1_error)
+  __pyx_empty_unicode = PyUnicode_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_unicode)) __PYX_ERR(0, 2, __pyx_L1_error)
+  #ifdef __Pyx_CyFunction_USED
+  if (__pyx_CyFunction_init() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
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+  if (__pyx_FusedFunction_init() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
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+  #ifdef __Pyx_Coroutine_USED
+  if (__pyx_Coroutine_init() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+  #ifdef __Pyx_Generator_USED
+  if (__pyx_Generator_init() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+  #ifdef __Pyx_AsyncGen_USED
+  if (__pyx_AsyncGen_init() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+  #ifdef __Pyx_StopAsyncIteration_USED
+  if (__pyx_StopAsyncIteration_init() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+  /*--- Library function declarations ---*/
+  /*--- Threads initialization code ---*/
+  #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
+  #ifdef WITH_THREAD /* Python build with threading support? */
+  PyEval_InitThreads();
+  #endif
+  #endif
+  /*--- Module creation code ---*/
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+  __pyx_m = __pyx_pyinit_module;
+  Py_INCREF(__pyx_m);
+  #else
+  #if PY_MAJOR_VERSION < 3
+  __pyx_m = Py_InitModule4("cy_dcumsum", __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
+  #else
+  __pyx_m = PyModule_Create(&__pyx_moduledef);
+  #endif
+  if (unlikely(!__pyx_m)) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+  __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 2, __pyx_L1_error)
+  Py_INCREF(__pyx_d);
+  __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 2, __pyx_L1_error)
+  Py_INCREF(__pyx_b);
+  __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 2, __pyx_L1_error)
+  Py_INCREF(__pyx_cython_runtime);
+  if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 2, __pyx_L1_error);
+  /*--- Initialize various global constants etc. ---*/
+  if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
+  if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+  if (__pyx_module_is_main_cy_dcumsum) {
+    if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name, __pyx_n_s_main) < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  }
+  #if PY_MAJOR_VERSION >= 3
+  {
+    PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 2, __pyx_L1_error)
+    if (!PyDict_GetItemString(modules, "cy_dcumsum")) {
+      if (unlikely(PyDict_SetItemString(modules, "cy_dcumsum", __pyx_m) < 0)) __PYX_ERR(0, 2, __pyx_L1_error)
+    }
+  }
+  #endif
+  /*--- Builtin init code ---*/
+  if (__Pyx_InitCachedBuiltins() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  /*--- Constants init code ---*/
+  if (__Pyx_InitCachedConstants() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  /*--- Global type/function init code ---*/
+  (void)__Pyx_modinit_global_init_code();
+  (void)__Pyx_modinit_variable_export_code();
+  (void)__Pyx_modinit_function_export_code();
+  (void)__Pyx_modinit_type_init_code();
+  if (unlikely(__Pyx_modinit_type_import_code() < 0)) __PYX_ERR(0, 2, __pyx_L1_error)
+  (void)__Pyx_modinit_variable_import_code();
+  (void)__Pyx_modinit_function_import_code();
+  /*--- Execution code ---*/
+  #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
+  if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  #endif
+
+  /* "cy_dcumsum.pyx":4
+ * cimport numpy
+ * 
+ * def dcumsum(numpy.ndarray[numpy.float64_t, ndim=1] a, numpy.ndarray[numpy.float64_t, ndim=1] b):             # <<<<<<<<<<<<<<
+ *     cdef int i, n = len(a)
+ *     b[0] = a[0]
+ */
+  __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_10cy_dcumsum_1dcumsum, NULL, __pyx_n_s_cy_dcumsum); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 4, __pyx_L1_error)
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_dcumsum, __pyx_t_1) < 0) __PYX_ERR(0, 4, __pyx_L1_error)
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+
+  /* "cy_dcumsum.pyx":2
+ * 
+ * cimport numpy             # <<<<<<<<<<<<<<
+ * 
+ * def dcumsum(numpy.ndarray[numpy.float64_t, ndim=1] a, numpy.ndarray[numpy.float64_t, ndim=1] b):
+ */
+  __pyx_t_1 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 2, __pyx_L1_error)
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+
+  /* "../../../../.local/lib/python3.9/site-packages/numpy/__init__.pxd":1014
+ * 
+ * 
+ * cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil:             # <<<<<<<<<<<<<<
+ *     """
+ *     returns the unit part of the dtype for a numpy datetime64 object.
+ */
+
+  /*--- Wrapped vars code ---*/
+
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  if (__pyx_m) {
+    if (__pyx_d) {
+      __Pyx_AddTraceback("init cy_dcumsum", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    }
+    Py_CLEAR(__pyx_m);
+  } else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_ImportError, "init cy_dcumsum");
+  }
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+  return (__pyx_m != NULL) ? 0 : -1;
+  #elif PY_MAJOR_VERSION >= 3
+  return __pyx_m;
+  #else
+  return;
+  #endif
+}
+
+/* --- Runtime support code --- */
+/* Refnanny */
+#if CYTHON_REFNANNY
+static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
+    PyObject *m = NULL, *p = NULL;
+    void *r = NULL;
+    m = PyImport_ImportModule(modname);
+    if (!m) goto end;
+    p = PyObject_GetAttrString(m, "RefNannyAPI");
+    if (!p) goto end;
+    r = PyLong_AsVoidPtr(p);
+end:
+    Py_XDECREF(p);
+    Py_XDECREF(m);
+    return (__Pyx_RefNannyAPIStruct *)r;
+}
+#endif
+
+/* RaiseArgTupleInvalid */
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *more_or_less;
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    PyErr_Format(PyExc_TypeError,
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 func_name, more_or_less, num_expected,
+                 (num_expected == 1) ? "" : "s", num_found);
+}
+
+/* RaiseDoubleKeywords */
+static void __Pyx_RaiseDoubleKeywordsError(
+    const char* func_name,
+    PyObject* kw_name)
+{
+    PyErr_Format(PyExc_TypeError,
+        #if PY_MAJOR_VERSION >= 3
+        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
+        #else
+        "%s() got multiple values for keyword argument '%s'", func_name,
+        PyString_AsString(kw_name));
+        #endif
+}
+
+/* ParseKeywords */
+static int __Pyx_ParseOptionalKeywords(
+    PyObject *kwds,
+    PyObject **argnames[],
+    PyObject *kwds2,
+    PyObject *values[],
+    Py_ssize_t num_pos_args,
+    const char* function_name)
+{
+    PyObject *key = 0, *value = 0;
+    Py_ssize_t pos = 0;
+    PyObject*** name;
+    PyObject*** first_kw_arg = argnames + num_pos_args;
+    while (PyDict_Next(kwds, &pos, &key, &value)) {
+        name = first_kw_arg;
+        while (*name && (**name != key)) name++;
+        if (*name) {
+            values[name-argnames] = value;
+            continue;
+        }
+        name = first_kw_arg;
+        #if PY_MAJOR_VERSION < 3
+        if (likely(PyString_Check(key))) {
+            while (*name) {
+                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
+                        && _PyString_Eq(**name, key)) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    if ((**argname == key) || (
+                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
+                             && _PyString_Eq(**argname, key))) {
+                        goto arg_passed_twice;
+                    }
+                    argname++;
+                }
+            }
+        } else
+        #endif
+        if (likely(PyUnicode_Check(key))) {
+            while (*name) {
+                int cmp = (**name == key) ? 0 :
+                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
+                #endif
+                    PyUnicode_Compare(**name, key);
+                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                if (cmp == 0) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    int cmp = (**argname == key) ? 0 :
+                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
+                    #endif
+                        PyUnicode_Compare(**argname, key);
+                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                    if (cmp == 0) goto arg_passed_twice;
+                    argname++;
+                }
+            }
+        } else
+            goto invalid_keyword_type;
+        if (kwds2) {
+            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
+        } else {
+            goto invalid_keyword;
+        }
+    }
+    return 0;
+arg_passed_twice:
+    __Pyx_RaiseDoubleKeywordsError(function_name, key);
+    goto bad;
+invalid_keyword_type:
+    PyErr_Format(PyExc_TypeError,
+        "%.200s() keywords must be strings", function_name);
+    goto bad;
+invalid_keyword:
+    PyErr_Format(PyExc_TypeError,
+    #if PY_MAJOR_VERSION < 3
+        "%.200s() got an unexpected keyword argument '%.200s'",
+        function_name, PyString_AsString(key));
+    #else
+        "%s() got an unexpected keyword argument '%U'",
+        function_name, key);
+    #endif
+bad:
+    return -1;
+}
+
+/* ArgTypeTest */
+static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
+{
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    else if (exact) {
+        #if PY_MAJOR_VERSION == 2
+        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
+        #endif
+    }
+    else {
+        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
+    }
+    PyErr_Format(PyExc_TypeError,
+        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
+        name, type->tp_name, Py_TYPE(obj)->tp_name);
+    return 0;
+}
+
+/* IsLittleEndian */
+static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
+{
+  union {
+    uint32_t u32;
+    uint8_t u8[4];
+  } S;
+  S.u32 = 0x01020304;
+  return S.u8[0] == 4;
+}
+
+/* BufferFormatCheck */
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type) {
+  stack[0].field = &ctx->root;
+  stack[0].parent_offset = 0;
+  ctx->root.type = type;
+  ctx->root.name = "buffer dtype";
+  ctx->root.offset = 0;
+  ctx->head = stack;
+  ctx->head->field = &ctx->root;
+  ctx->fmt_offset = 0;
+  ctx->head->parent_offset = 0;
+  ctx->new_packmode = '@';
+  ctx->enc_packmode = '@';
+  ctx->new_count = 1;
+  ctx->enc_count = 0;
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  ctx->is_valid_array = 0;
+  ctx->struct_alignment = 0;
+  while (type->typegroup == 'S') {
+    ++ctx->head;
+    ctx->head->field = type->fields;
+    ctx->head->parent_offset = 0;
+    type = type->fields->type;
+  }
+}
+static int __Pyx_BufFmt_ParseNumber(const char** ts) {
+    int count;
+    const char* t = *ts;
+    if (*t < '0' || *t > '9') {
+      return -1;
+    } else {
+        count = *t++ - '0';
+        while (*t >= '0' && *t <= '9') {
+            count *= 10;
+            count += *t++ - '0';
+        }
+    }
+    *ts = t;
+    return count;
+}
+static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
+    int number = __Pyx_BufFmt_ParseNumber(ts);
+    if (number == -1)
+        PyErr_Format(PyExc_ValueError,\
+                     "Does not understand character buffer dtype format string ('%c')", **ts);
+    return number;
+}
+static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
+  PyErr_Format(PyExc_ValueError,
+               "Unexpected format string character: '%c'", ch);
+}
+static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
+  switch (ch) {
+    case '?': return "'bool'";
+    case 'c': return "'char'";
+    case 'b': return "'signed char'";
+    case 'B': return "'unsigned char'";
+    case 'h': return "'short'";
+    case 'H': return "'unsigned short'";
+    case 'i': return "'int'";
+    case 'I': return "'unsigned int'";
+    case 'l': return "'long'";
+    case 'L': return "'unsigned long'";
+    case 'q': return "'long long'";
+    case 'Q': return "'unsigned long long'";
+    case 'f': return (is_complex ? "'complex float'" : "'float'");
+    case 'd': return (is_complex ? "'complex double'" : "'double'");
+    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
+    case 'T': return "a struct";
+    case 'O': return "Python object";
+    case 'P': return "a pointer";
+    case 's': case 'p': return "a string";
+    case 0: return "end";
+    default: return "unparseable format string";
+  }
+}
+static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return 2;
+    case 'i': case 'I': case 'l': case 'L': return 4;
+    case 'q': case 'Q': return 8;
+    case 'f': return (is_complex ? 8 : 4);
+    case 'd': return (is_complex ? 16 : 8);
+    case 'g': {
+      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
+      return 0;
+    }
+    case 'O': case 'P': return sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(short);
+    case 'i': case 'I': return sizeof(int);
+    case 'l': case 'L': return sizeof(long);
+    #ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
+    #endif
+    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
+    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
+    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
+    case 'O': case 'P': return sizeof(void*);
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+typedef struct { char c; short x; } __Pyx_st_short;
+typedef struct { char c; int x; } __Pyx_st_int;
+typedef struct { char c; long x; } __Pyx_st_long;
+typedef struct { char c; float x; } __Pyx_st_float;
+typedef struct { char c; double x; } __Pyx_st_double;
+typedef struct { char c; long double x; } __Pyx_st_longdouble;
+typedef struct { char c; void *x; } __Pyx_st_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+/* These are for computing the padding at the end of the struct to align
+   on the first member of the struct. This will probably the same as above,
+   but we don't have any guarantees.
+ */
+typedef struct { short x; char c; } __Pyx_pad_short;
+typedef struct { int x; char c; } __Pyx_pad_int;
+typedef struct { long x; char c; } __Pyx_pad_long;
+typedef struct { float x; char c; } __Pyx_pad_float;
+typedef struct { double x; char c; } __Pyx_pad_double;
+typedef struct { long double x; char c; } __Pyx_pad_longdouble;
+typedef struct { void *x; char c; } __Pyx_pad_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
+  switch (ch) {
+    case 'c':
+        return 'H';
+    case 'b': case 'h': case 'i':
+    case 'l': case 'q': case 's': case 'p':
+        return 'I';
+    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
+        return 'U';
+    case 'f': case 'd': case 'g':
+        return (is_complex ? 'C' : 'R');
+    case 'O':
+        return 'O';
+    case 'P':
+        return 'P';
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
+  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
+    const char* expected;
+    const char* quote;
+    if (ctx->head == NULL) {
+      expected = "end";
+      quote = "";
+    } else {
+      expected = ctx->head->field->type->name;
+      quote = "'";
+    }
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected %s%s%s but got %s",
+                 quote, expected, quote,
+                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
+  } else {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_StructField* parent = (ctx->head - 1)->field;
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
+                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
+                 parent->type->name, field->name);
+  }
+}
+static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
+  char group;
+  size_t size, offset, arraysize = 1;
+  if (ctx->enc_type == 0) return 0;
+  if (ctx->head->field->type->arraysize[0]) {
+    int i, ndim = 0;
+    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
+        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
+        ndim = 1;
+        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
+            PyErr_Format(PyExc_ValueError,
+                         "Expected a dimension of size %zu, got %zu",
+                         ctx->head->field->type->arraysize[0], ctx->enc_count);
+            return -1;
+        }
+    }
+    if (!ctx->is_valid_array) {
+      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
+                   ctx->head->field->type->ndim, ndim);
+      return -1;
+    }
+    for (i = 0; i < ctx->head->field->type->ndim; i++) {
+      arraysize *= ctx->head->field->type->arraysize[i];
+    }
+    ctx->is_valid_array = 0;
+    ctx->enc_count = 1;
+  }
+  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
+  do {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_TypeInfo* type = field->type;
+    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
+      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
+    } else {
+      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
+    }
+    if (ctx->enc_packmode == '@') {
+      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
+      size_t align_mod_offset;
+      if (align_at == 0) return -1;
+      align_mod_offset = ctx->fmt_offset % align_at;
+      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
+      if (ctx->struct_alignment == 0)
+          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
+                                                                 ctx->is_complex);
+    }
+    if (type->size != size || type->typegroup != group) {
+      if (type->typegroup == 'C' && type->fields != NULL) {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        ++ctx->head;
+        ctx->head->field = type->fields;
+        ctx->head->parent_offset = parent_offset;
+        continue;
+      }
+      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
+      } else {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+      }
+    }
+    offset = ctx->head->parent_offset + field->offset;
+    if (ctx->fmt_offset != offset) {
+      PyErr_Format(PyExc_ValueError,
+                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
+                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
+      return -1;
+    }
+    ctx->fmt_offset += size;
+    if (arraysize)
+      ctx->fmt_offset += (arraysize - 1) * size;
+    --ctx->enc_count;
+    while (1) {
+      if (field == &ctx->root) {
+        ctx->head = NULL;
+        if (ctx->enc_count != 0) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+        }
+        break;
+      }
+      ctx->head->field = ++field;
+      if (field->type == NULL) {
+        --ctx->head;
+        field = ctx->head->field;
+        continue;
+      } else if (field->type->typegroup == 'S') {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        if (field->type->fields->type == NULL) continue;
+        field = field->type->fields;
+        ++ctx->head;
+        ctx->head->field = field;
+        ctx->head->parent_offset = parent_offset;
+        break;
+      } else {
+        break;
+      }
+    }
+  } while (ctx->enc_count);
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  return 0;
+}
+static PyObject *
+__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
+{
+    const char *ts = *tsp;
+    int i = 0, number, ndim;
+    ++ts;
+    if (ctx->new_count != 1) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Cannot handle repeated arrays in format string");
+        return NULL;
+    }
+    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+    ndim = ctx->head->field->type->ndim;
+    while (*ts && *ts != ')') {
+        switch (*ts) {
+            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
+            default:  break;
+        }
+        number = __Pyx_BufFmt_ExpectNumber(&ts);
+        if (number == -1) return NULL;
+        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
+            return PyErr_Format(PyExc_ValueError,
+                        "Expected a dimension of size %zu, got %d",
+                        ctx->head->field->type->arraysize[i], number);
+        if (*ts != ',' && *ts != ')')
+            return PyErr_Format(PyExc_ValueError,
+                                "Expected a comma in format string, got '%c'", *ts);
+        if (*ts == ',') ts++;
+        i++;
+    }
+    if (i != ndim)
+        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
+                            ctx->head->field->type->ndim, i);
+    if (!*ts) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Unexpected end of format string, expected ')'");
+        return NULL;
+    }
+    ctx->is_valid_array = 1;
+    ctx->new_count = 1;
+    *tsp = ++ts;
+    return Py_None;
+}
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
+  int got_Z = 0;
+  while (1) {
+    switch(*ts) {
+      case 0:
+        if (ctx->enc_type != 0 && ctx->head == NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        if (ctx->head != NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        return ts;
+      case ' ':
+      case '\r':
+      case '\n':
+        ++ts;
+        break;
+      case '<':
+        if (!__Pyx_Is_Little_Endian()) {
+          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '>':
+      case '!':
+        if (__Pyx_Is_Little_Endian()) {
+          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '=':
+      case '@':
+      case '^':
+        ctx->new_packmode = *ts++;
+        break;
+      case 'T':
+        {
+          const char* ts_after_sub;
+          size_t i, struct_count = ctx->new_count;
+          size_t struct_alignment = ctx->struct_alignment;
+          ctx->new_count = 1;
+          ++ts;
+          if (*ts != '{') {
+            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
+            return NULL;
+          }
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          ctx->enc_count = 0;
+          ctx->struct_alignment = 0;
+          ++ts;
+          ts_after_sub = ts;
+          for (i = 0; i != struct_count; ++i) {
+            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
+            if (!ts_after_sub) return NULL;
+          }
+          ts = ts_after_sub;
+          if (struct_alignment) ctx->struct_alignment = struct_alignment;
+        }
+        break;
+      case '}':
+        {
+          size_t alignment = ctx->struct_alignment;
+          ++ts;
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          if (alignment && ctx->fmt_offset % alignment) {
+            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
+          }
+        }
+        return ts;
+      case 'x':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->fmt_offset += ctx->new_count;
+        ctx->new_count = 1;
+        ctx->enc_count = 0;
+        ctx->enc_type = 0;
+        ctx->enc_packmode = ctx->new_packmode;
+        ++ts;
+        break;
+      case 'Z':
+        got_Z = 1;
+        ++ts;
+        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
+          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
+          return NULL;
+        }
+        CYTHON_FALLTHROUGH;
+      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
+      case 'l': case 'L': case 'q': case 'Q':
+      case 'f': case 'd': case 'g':
+      case 'O': case 'p':
+        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
+            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
+          ctx->enc_count += ctx->new_count;
+          ctx->new_count = 1;
+          got_Z = 0;
+          ++ts;
+          break;
+        }
+        CYTHON_FALLTHROUGH;
+      case 's':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->enc_count = ctx->new_count;
+        ctx->enc_packmode = ctx->new_packmode;
+        ctx->enc_type = *ts;
+        ctx->is_complex = got_Z;
+        ++ts;
+        ctx->new_count = 1;
+        got_Z = 0;
+        break;
+      case ':':
+        ++ts;
+        while(*ts != ':') ++ts;
+        ++ts;
+        break;
+      case '(':
+        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
+        break;
+      default:
+        {
+          int number = __Pyx_BufFmt_ExpectNumber(&ts);
+          if (number == -1) return NULL;
+          ctx->new_count = (size_t)number;
+        }
+    }
+  }
+}
+
+/* BufferGetAndValidate */
+  static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
+  if (unlikely(info->buf == NULL)) return;
+  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
+  __Pyx_ReleaseBuffer(info);
+}
+static void __Pyx_ZeroBuffer(Py_buffer* buf) {
+  buf->buf = NULL;
+  buf->obj = NULL;
+  buf->strides = __Pyx_zeros;
+  buf->shape = __Pyx_zeros;
+  buf->suboffsets = __Pyx_minusones;
+}
+static int __Pyx__GetBufferAndValidate(
+        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
+        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
+{
+  buf->buf = NULL;
+  if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {
+    __Pyx_ZeroBuffer(buf);
+    return -1;
+  }
+  if (unlikely(buf->ndim != nd)) {
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer has wrong number of dimensions (expected %d, got %d)",
+                 nd, buf->ndim);
+    goto fail;
+  }
+  if (!cast) {
+    __Pyx_BufFmt_Context ctx;
+    __Pyx_BufFmt_Init(&ctx, stack, dtype);
+    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
+  }
+  if (unlikely((size_t)buf->itemsize != dtype->size)) {
+    PyErr_Format(PyExc_ValueError,
+      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
+      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
+      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
+    goto fail;
+  }
+  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
+  return 0;
+fail:;
+  __Pyx_SafeReleaseBuffer(buf);
+  return -1;
+}
+
+/* BufferIndexError */
+  static void __Pyx_RaiseBufferIndexError(int axis) {
+  PyErr_Format(PyExc_IndexError,
+     "Out of bounds on buffer access (axis %d)", axis);
+}
+
+/* PyErrFetchRestore */
+  #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    tmp_type = tstate->curexc_type;
+    tmp_value = tstate->curexc_value;
+    tmp_tb = tstate->curexc_traceback;
+    tstate->curexc_type = type;
+    tstate->curexc_value = value;
+    tstate->curexc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    *type = tstate->curexc_type;
+    *value = tstate->curexc_value;
+    *tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+}
+#endif
+
+/* PyObjectGetAttrStr */
+  #if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_getattro))
+        return tp->tp_getattro(obj, attr_name);
+#if PY_MAJOR_VERSION < 3
+    if (likely(tp->tp_getattr))
+        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
+#endif
+    return PyObject_GetAttr(obj, attr_name);
+}
+#endif
+
+/* GetBuiltinName */
+  static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
+    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
+    if (unlikely(!result)) {
+        PyErr_Format(PyExc_NameError,
+#if PY_MAJOR_VERSION >= 3
+            "name '%U' is not defined", name);
+#else
+            "name '%.200s' is not defined", PyString_AS_STRING(name));
+#endif
+    }
+    return result;
+}
+
+/* GetTopmostException */
+  #if CYTHON_USE_EXC_INFO_STACK
+static _PyErr_StackItem *
+__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
+{
+    _PyErr_StackItem *exc_info = tstate->exc_info;
+    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
+           exc_info->previous_item != NULL)
+    {
+        exc_info = exc_info->previous_item;
+    }
+    return exc_info;
+}
+#endif
+
+/* SaveResetException */
+  #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    #if CYTHON_USE_EXC_INFO_STACK
+    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
+    *type = exc_info->exc_type;
+    *value = exc_info->exc_value;
+    *tb = exc_info->exc_traceback;
+    #else
+    *type = tstate->exc_type;
+    *value = tstate->exc_value;
+    *tb = tstate->exc_traceback;
+    #endif
+    Py_XINCREF(*type);
+    Py_XINCREF(*value);
+    Py_XINCREF(*tb);
+}
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    #if CYTHON_USE_EXC_INFO_STACK
+    _PyErr_StackItem *exc_info = tstate->exc_info;
+    tmp_type = exc_info->exc_type;
+    tmp_value = exc_info->exc_value;
+    tmp_tb = exc_info->exc_traceback;
+    exc_info->exc_type = type;
+    exc_info->exc_value = value;
+    exc_info->exc_traceback = tb;
+    #else
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = type;
+    tstate->exc_value = value;
+    tstate->exc_traceback = tb;
+    #endif
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+#endif
+
+/* PyErrExceptionMatches */
+  #if CYTHON_FAST_THREAD_STATE
+static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+    Py_ssize_t i, n;
+    n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+    for (i=0; i<n; i++) {
+        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
+    }
+#endif
+    for (i=0; i<n; i++) {
+        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
+    }
+    return 0;
+}
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
+    PyObject *exc_type = tstate->curexc_type;
+    if (exc_type == err) return 1;
+    if (unlikely(!exc_type)) return 0;
+    if (unlikely(PyTuple_Check(err)))
+        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
+    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
+}
+#endif
+
+/* GetException */
+  #if CYTHON_FAST_THREAD_STATE
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
+#endif
+{
+    PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_FAST_THREAD_STATE
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    local_type = tstate->curexc_type;
+    local_value = tstate->curexc_value;
+    local_tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_FAST_THREAD_STATE
+    if (unlikely(tstate->curexc_type))
+#else
+    if (unlikely(PyErr_Occurred()))
+#endif
+        goto bad;
+    #if PY_MAJOR_VERSION >= 3
+    if (local_tb) {
+        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+            goto bad;
+    }
+    #endif
+    Py_XINCREF(local_tb);
+    Py_XINCREF(local_type);
+    Py_XINCREF(local_value);
+    *type = local_type;
+    *value = local_value;
+    *tb = local_tb;
+#if CYTHON_FAST_THREAD_STATE
+    #if CYTHON_USE_EXC_INFO_STACK
+    {
+        _PyErr_StackItem *exc_info = tstate->exc_info;
+        tmp_type = exc_info->exc_type;
+        tmp_value = exc_info->exc_value;
+        tmp_tb = exc_info->exc_traceback;
+        exc_info->exc_type = local_type;
+        exc_info->exc_value = local_value;
+        exc_info->exc_traceback = local_tb;
+    }
+    #else
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = local_type;
+    tstate->exc_value = local_value;
+    tstate->exc_traceback = local_tb;
+    #endif
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+    return 0;
+bad:
+    *type = 0;
+    *value = 0;
+    *tb = 0;
+    Py_XDECREF(local_type);
+    Py_XDECREF(local_value);
+    Py_XDECREF(local_tb);
+    return -1;
+}
+
+/* PyObjectCall */
+  #if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+    PyObject *result;
+    ternaryfunc call = func->ob_type->tp_call;
+    if (unlikely(!call))
+        return PyObject_Call(func, arg, kw);
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+    result = (*call)(func, arg, kw);
+    Py_LeaveRecursiveCall();
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+/* RaiseException */
+  #if PY_MAJOR_VERSION < 3
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
+                        CYTHON_UNUSED PyObject *cause) {
+    __Pyx_PyThreadState_declare
+    Py_XINCREF(type);
+    if (!value || value == Py_None)
+        value = NULL;
+    else
+        Py_INCREF(value);
+    if (!tb || tb == Py_None)
+        tb = NULL;
+    else {
+        Py_INCREF(tb);
+        if (!PyTraceBack_Check(tb)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: arg 3 must be a traceback or None");
+            goto raise_error;
+        }
+    }
+    if (PyType_Check(type)) {
+#if CYTHON_COMPILING_IN_PYPY
+        if (!value) {
+            Py_INCREF(Py_None);
+            value = Py_None;
+        }
+#endif
+        PyErr_NormalizeException(&type, &value, &tb);
+    } else {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto raise_error;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(type);
+        Py_INCREF(type);
+        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: exception class must be a subclass of BaseException");
+            goto raise_error;
+        }
+    }
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrRestore(type, value, tb);
+    return;
+raise_error:
+    Py_XDECREF(value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
+    return;
+}
+#else
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
+    PyObject* owned_instance = NULL;
+    if (tb == Py_None) {
+        tb = 0;
+    } else if (tb && !PyTraceBack_Check(tb)) {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: arg 3 must be a traceback or None");
+        goto bad;
+    }
+    if (value == Py_None)
+        value = 0;
+    if (PyExceptionInstance_Check(type)) {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto bad;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(value);
+    } else if (PyExceptionClass_Check(type)) {
+        PyObject *instance_class = NULL;
+        if (value && PyExceptionInstance_Check(value)) {
+            instance_class = (PyObject*) Py_TYPE(value);
+            if (instance_class != type) {
+                int is_subclass = PyObject_IsSubclass(instance_class, type);
+                if (!is_subclass) {
+                    instance_class = NULL;
+                } else if (unlikely(is_subclass == -1)) {
+                    goto bad;
+                } else {
+                    type = instance_class;
+                }
+            }
+        }
+        if (!instance_class) {
+            PyObject *args;
+            if (!value)
+                args = PyTuple_New(0);
+            else if (PyTuple_Check(value)) {
+                Py_INCREF(value);
+                args = value;
+            } else
+                args = PyTuple_Pack(1, value);
+            if (!args)
+                goto bad;
+            owned_instance = PyObject_Call(type, args, NULL);
+            Py_DECREF(args);
+            if (!owned_instance)
+                goto bad;
+            value = owned_instance;
+            if (!PyExceptionInstance_Check(value)) {
+                PyErr_Format(PyExc_TypeError,
+                             "calling %R should have returned an instance of "
+                             "BaseException, not %R",
+                             type, Py_TYPE(value));
+                goto bad;
+            }
+        }
+    } else {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: exception class must be a subclass of BaseException");
+        goto bad;
+    }
+    if (cause) {
+        PyObject *fixed_cause;
+        if (cause == Py_None) {
+            fixed_cause = NULL;
+        } else if (PyExceptionClass_Check(cause)) {
+            fixed_cause = PyObject_CallObject(cause, NULL);
+            if (fixed_cause == NULL)
+                goto bad;
+        } else if (PyExceptionInstance_Check(cause)) {
+            fixed_cause = cause;
+            Py_INCREF(fixed_cause);
+        } else {
+            PyErr_SetString(PyExc_TypeError,
+                            "exception causes must derive from "
+                            "BaseException");
+            goto bad;
+        }
+        PyException_SetCause(value, fixed_cause);
+    }
+    PyErr_SetObject(type, value);
+    if (tb) {
+#if CYTHON_COMPILING_IN_PYPY
+        PyObject *tmp_type, *tmp_value, *tmp_tb;
+        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
+        Py_INCREF(tb);
+        PyErr_Restore(tmp_type, tmp_value, tb);
+        Py_XDECREF(tmp_tb);
+#else
+        PyThreadState *tstate = __Pyx_PyThreadState_Current;
+        PyObject* tmp_tb = tstate->curexc_traceback;
+        if (tb != tmp_tb) {
+            Py_INCREF(tb);
+            tstate->curexc_traceback = tb;
+            Py_XDECREF(tmp_tb);
+        }
+#endif
+    }
+bad:
+    Py_XDECREF(owned_instance);
+    return;
+}
+#endif
+
+/* TypeImport */
+  #ifndef __PYX_HAVE_RT_ImportType
+#define __PYX_HAVE_RT_ImportType
+static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,
+    size_t size, enum __Pyx_ImportType_CheckSize check_size)
+{
+    PyObject *result = 0;
+    char warning[200];
+    Py_ssize_t basicsize;
+#ifdef Py_LIMITED_API
+    PyObject *py_basicsize;
+#endif
+    result = PyObject_GetAttrString(module, class_name);
+    if (!result)
+        goto bad;
+    if (!PyType_Check(result)) {
+        PyErr_Format(PyExc_TypeError,
+            "%.200s.%.200s is not a type object",
+            module_name, class_name);
+        goto bad;
+    }
+#ifndef Py_LIMITED_API
+    basicsize = ((PyTypeObject *)result)->tp_basicsize;
+#else
+    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
+    if (!py_basicsize)
+        goto bad;
+    basicsize = PyLong_AsSsize_t(py_basicsize);
+    Py_DECREF(py_basicsize);
+    py_basicsize = 0;
+    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
+        goto bad;
+#endif
+    if ((size_t)basicsize < size) {
+        PyErr_Format(PyExc_ValueError,
+            "%.200s.%.200s size changed, may indicate binary incompatibility. "
+            "Expected %zd from C header, got %zd from PyObject",
+            module_name, class_name, size, basicsize);
+        goto bad;
+    }
+    if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {
+        PyErr_Format(PyExc_ValueError,
+            "%.200s.%.200s size changed, may indicate binary incompatibility. "
+            "Expected %zd from C header, got %zd from PyObject",
+            module_name, class_name, size, basicsize);
+        goto bad;
+    }
+    else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {
+        PyOS_snprintf(warning, sizeof(warning),
+            "%s.%s size changed, may indicate binary incompatibility. "
+            "Expected %zd from C header, got %zd from PyObject",
+            module_name, class_name, size, basicsize);
+        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
+    }
+    return (PyTypeObject *)result;
+bad:
+    Py_XDECREF(result);
+    return NULL;
+}
+#endif
+
+/* PyDictVersioning */
+  #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
+    PyObject *dict = Py_TYPE(obj)->tp_dict;
+    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
+}
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
+    PyObject **dictptr = NULL;
+    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
+    if (offset) {
+#if CYTHON_COMPILING_IN_CPYTHON
+        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
+#else
+        dictptr = _PyObject_GetDictPtr(obj);
+#endif
+    }
+    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
+}
+static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
+    PyObject *dict = Py_TYPE(obj)->tp_dict;
+    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
+        return 0;
+    return obj_dict_version == __Pyx_get_object_dict_version(obj);
+}
+#endif
+
+/* CLineInTraceback */
+  #ifndef CYTHON_CLINE_IN_TRACEBACK
+static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
+    PyObject *use_cline;
+    PyObject *ptype, *pvalue, *ptraceback;
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject **cython_runtime_dict;
+#endif
+    if (unlikely(!__pyx_cython_runtime)) {
+        return c_line;
+    }
+    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
+#if CYTHON_COMPILING_IN_CPYTHON
+    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
+    if (likely(cython_runtime_dict)) {
+        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
+            use_cline, *cython_runtime_dict,
+            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
+    } else
+#endif
+    {
+      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
+      if (use_cline_obj) {
+        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
+        Py_DECREF(use_cline_obj);
+      } else {
+        PyErr_Clear();
+        use_cline = NULL;
+      }
+    }
+    if (!use_cline) {
+        c_line = 0;
+        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
+    }
+    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
+        c_line = 0;
+    }
+    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
+    return c_line;
+}
+#endif
+
+/* CodeObjectCache */
+  static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+    int start = 0, mid = 0, end = count - 1;
+    if (end >= 0 && code_line > entries[end].code_line) {
+        return count;
+    }
+    while (start < end) {
+        mid = start + (end - start) / 2;
+        if (code_line < entries[mid].code_line) {
+            end = mid;
+        } else if (code_line > entries[mid].code_line) {
+             start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    if (code_line <= entries[mid].code_line) {
+        return mid;
+    } else {
+        return mid + 1;
+    }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+    PyCodeObject* code_object;
+    int pos;
+    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+        return NULL;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+        return NULL;
+    }
+    code_object = __pyx_code_cache.entries[pos].code_object;
+    Py_INCREF(code_object);
+    return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+    int pos, i;
+    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+    if (unlikely(!code_line)) {
+        return;
+    }
+    if (unlikely(!entries)) {
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (likely(entries)) {
+            __pyx_code_cache.entries = entries;
+            __pyx_code_cache.max_count = 64;
+            __pyx_code_cache.count = 1;
+            entries[0].code_line = code_line;
+            entries[0].code_object = code_object;
+            Py_INCREF(code_object);
+        }
+        return;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+        PyCodeObject* tmp = entries[pos].code_object;
+        entries[pos].code_object = code_object;
+        Py_DECREF(tmp);
+        return;
+    }
+    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+        int new_max = __pyx_code_cache.max_count + 64;
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
+        if (unlikely(!entries)) {
+            return;
+        }
+        __pyx_code_cache.entries = entries;
+        __pyx_code_cache.max_count = new_max;
+    }
+    for (i=__pyx_code_cache.count; i>pos; i--) {
+        entries[i] = entries[i-1];
+    }
+    entries[pos].code_line = code_line;
+    entries[pos].code_object = code_object;
+    __pyx_code_cache.count++;
+    Py_INCREF(code_object);
+}
+
+/* AddTraceback */
+  #include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+            const char *funcname, int c_line,
+            int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    #if PY_MAJOR_VERSION < 3
+    py_srcfile = PyString_FromString(filename);
+    #else
+    py_srcfile = PyUnicode_FromString(filename);
+    #endif
+    if (!py_srcfile) goto bad;
+    if (c_line) {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #else
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #endif
+    }
+    else {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromString(funcname);
+        #else
+        py_funcname = PyUnicode_FromString(funcname);
+        #endif
+    }
+    if (!py_funcname) goto bad;
+    py_code = __Pyx_PyCode_New(
+        0,
+        0,
+        0,
+        0,
+        0,
+        __pyx_empty_bytes, /*PyObject *code,*/
+        __pyx_empty_tuple, /*PyObject *consts,*/
+        __pyx_empty_tuple, /*PyObject *names,*/
+        __pyx_empty_tuple, /*PyObject *varnames,*/
+        __pyx_empty_tuple, /*PyObject *freevars,*/
+        __pyx_empty_tuple, /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        py_line,
+        __pyx_empty_bytes  /*PyObject *lnotab*/
+    );
+    Py_DECREF(py_srcfile);
+    Py_DECREF(py_funcname);
+    return py_code;
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyFrameObject *py_frame = 0;
+    PyThreadState *tstate = __Pyx_PyThreadState_Current;
+    if (c_line) {
+        c_line = __Pyx_CLineForTraceback(tstate, c_line);
+    }
+    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
+    if (!py_code) {
+        py_code = __Pyx_CreateCodeObjectForTraceback(
+            funcname, c_line, py_line, filename);
+        if (!py_code) goto bad;
+        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
+    }
+    py_frame = PyFrame_New(
+        tstate,            /*PyThreadState *tstate,*/
+        py_code,           /*PyCodeObject *code,*/
+        __pyx_d,    /*PyObject *globals,*/
+        0                  /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
+
+#if PY_MAJOR_VERSION < 3
+static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
+    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
+    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
+    return -1;
+}
+static void __Pyx_ReleaseBuffer(Py_buffer *view) {
+    PyObject *obj = view->obj;
+    if (!obj) return;
+    if (PyObject_CheckBuffer(obj)) {
+        PyBuffer_Release(view);
+        return;
+    }
+    if ((0)) {}
+    view->obj = NULL;
+    Py_DECREF(obj);
+}
+#endif
+
+
+  /* Declarations */
+  #if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      return ::std::complex< float >(x, y);
+    }
+  #else
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      return x + y*(__pyx_t_float_complex)_Complex_I;
+    }
+  #endif
+#else
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      __pyx_t_float_complex z;
+      z.real = x;
+      z.imag = y;
+      return z;
+    }
+#endif
+
+/* Arithmetic */
+  #if CYTHON_CCOMPLEX
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+       return (a.real == b.real) && (a.imag == b.imag);
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real + b.real;
+        z.imag = a.imag + b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real - b.real;
+        z.imag = a.imag - b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real * b.real - a.imag * b.imag;
+        z.imag = a.real * b.imag + a.imag * b.real;
+        return z;
+    }
+    #if 1
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else if (fabsf(b.real) >= fabsf(b.imag)) {
+            if (b.real == 0 && b.imag == 0) {
+                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
+            } else {
+                float r = b.imag / b.real;
+                float s = (float)(1.0) / (b.real + b.imag * r);
+                return __pyx_t_float_complex_from_parts(
+                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
+            }
+        } else {
+            float r = b.real / b.imag;
+            float s = (float)(1.0) / (b.imag + b.real * r);
+            return __pyx_t_float_complex_from_parts(
+                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
+        }
+    }
+    #else
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else {
+            float denom = b.real * b.real + b.imag * b.imag;
+            return __pyx_t_float_complex_from_parts(
+                (a.real * b.real + a.imag * b.imag) / denom,
+                (a.imag * b.real - a.real * b.imag) / denom);
+        }
+    }
+    #endif
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
+        __pyx_t_float_complex z;
+        z.real = -a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
+       return (a.real == 0) && (a.imag == 0);
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
+        __pyx_t_float_complex z;
+        z.real =  a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    #if 1
+        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
+          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
+            return sqrtf(z.real*z.real + z.imag*z.imag);
+          #else
+            return hypotf(z.real, z.imag);
+          #endif
+        }
+        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+            __pyx_t_float_complex z;
+            float r, lnr, theta, z_r, z_theta;
+            if (b.imag == 0 && b.real == (int)b.real) {
+                if (b.real < 0) {
+                    float denom = a.real * a.real + a.imag * a.imag;
+                    a.real = a.real / denom;
+                    a.imag = -a.imag / denom;
+                    b.real = -b.real;
+                }
+                switch ((int)b.real) {
+                    case 0:
+                        z.real = 1;
+                        z.imag = 0;
+                        return z;
+                    case 1:
+                        return a;
+                    case 2:
+                        return __Pyx_c_prod_float(a, a);
+                    case 3:
+                        z = __Pyx_c_prod_float(a, a);
+                        return __Pyx_c_prod_float(z, a);
+                    case 4:
+                        z = __Pyx_c_prod_float(a, a);
+                        return __Pyx_c_prod_float(z, z);
+                }
+            }
+            if (a.imag == 0) {
+                if (a.real == 0) {
+                    return a;
+                } else if (b.imag == 0) {
+                    z.real = powf(a.real, b.real);
+                    z.imag = 0;
+                    return z;
+                } else if (a.real > 0) {
+                    r = a.real;
+                    theta = 0;
+                } else {
+                    r = -a.real;
+                    theta = atan2f(0.0, -1.0);
+                }
+            } else {
+                r = __Pyx_c_abs_float(a);
+                theta = atan2f(a.imag, a.real);
+            }
+            lnr = logf(r);
+            z_r = expf(lnr * b.real - theta * b.imag);
+            z_theta = theta * b.real + lnr * b.imag;
+            z.real = z_r * cosf(z_theta);
+            z.imag = z_r * sinf(z_theta);
+            return z;
+        }
+    #endif
+#endif
+
+/* Declarations */
+  #if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      return ::std::complex< double >(x, y);
+    }
+  #else
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      return x + y*(__pyx_t_double_complex)_Complex_I;
+    }
+  #endif
+#else
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      __pyx_t_double_complex z;
+      z.real = x;
+      z.imag = y;
+      return z;
+    }
+#endif
+
+/* Arithmetic */
+  #if CYTHON_CCOMPLEX
+#else
+    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+       return (a.real == b.real) && (a.imag == b.imag);
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real + b.real;
+        z.imag = a.imag + b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real - b.real;
+        z.imag = a.imag - b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real * b.real - a.imag * b.imag;
+        z.imag = a.real * b.imag + a.imag * b.real;
+        return z;
+    }
+    #if 1
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else if (fabs(b.real) >= fabs(b.imag)) {
+            if (b.real == 0 && b.imag == 0) {
+                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
+            } else {
+                double r = b.imag / b.real;
+                double s = (double)(1.0) / (b.real + b.imag * r);
+                return __pyx_t_double_complex_from_parts(
+                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
+            }
+        } else {
+            double r = b.real / b.imag;
+            double s = (double)(1.0) / (b.imag + b.real * r);
+            return __pyx_t_double_complex_from_parts(
+                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
+        }
+    }
+    #else
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        if (b.imag == 0) {
+            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
+        } else {
+            double denom = b.real * b.real + b.imag * b.imag;
+            return __pyx_t_double_complex_from_parts(
+                (a.real * b.real + a.imag * b.imag) / denom,
+                (a.imag * b.real - a.real * b.imag) / denom);
+        }
+    }
+    #endif
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
+        __pyx_t_double_complex z;
+        z.real = -a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
+       return (a.real == 0) && (a.imag == 0);
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
+        __pyx_t_double_complex z;
+        z.real =  a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    #if 1
+        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
+          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
+            return sqrt(z.real*z.real + z.imag*z.imag);
+          #else
+            return hypot(z.real, z.imag);
+          #endif
+        }
+        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+            __pyx_t_double_complex z;
+            double r, lnr, theta, z_r, z_theta;
+            if (b.imag == 0 && b.real == (int)b.real) {
+                if (b.real < 0) {
+                    double denom = a.real * a.real + a.imag * a.imag;
+                    a.real = a.real / denom;
+                    a.imag = -a.imag / denom;
+                    b.real = -b.real;
+                }
+                switch ((int)b.real) {
+                    case 0:
+                        z.real = 1;
+                        z.imag = 0;
+                        return z;
+                    case 1:
+                        return a;
+                    case 2:
+                        return __Pyx_c_prod_double(a, a);
+                    case 3:
+                        z = __Pyx_c_prod_double(a, a);
+                        return __Pyx_c_prod_double(z, a);
+                    case 4:
+                        z = __Pyx_c_prod_double(a, a);
+                        return __Pyx_c_prod_double(z, z);
+                }
+            }
+            if (a.imag == 0) {
+                if (a.real == 0) {
+                    return a;
+                } else if (b.imag == 0) {
+                    z.real = pow(a.real, b.real);
+                    z.imag = 0;
+                    return z;
+                } else if (a.real > 0) {
+                    r = a.real;
+                    theta = 0;
+                } else {
+                    r = -a.real;
+                    theta = atan2(0.0, -1.0);
+                }
+            } else {
+                r = __Pyx_c_abs_double(a);
+                theta = atan2(a.imag, a.real);
+            }
+            lnr = log(r);
+            z_r = exp(lnr * b.real - theta * b.imag);
+            z_theta = theta * b.real + lnr * b.imag;
+            z.real = z_r * cos(z_theta);
+            z.imag = z_r * sin(z_theta);
+            return z;
+        }
+    #endif
+#endif
+
+/* CIntToPy */
+  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(long),
+                                     little, !is_unsigned);
+    }
+}
+
+/* CIntFromPyVerify */
+  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
+    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
+#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
+    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
+#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
+    {\
+        func_type value = func_value;\
+        if (sizeof(target_type) < sizeof(func_type)) {\
+            if (unlikely(value != (func_type) (target_type) value)) {\
+                func_type zero = 0;\
+                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
+                    return (target_type) -1;\
+                if (is_unsigned && unlikely(value < zero))\
+                    goto raise_neg_overflow;\
+                else\
+                    goto raise_overflow;\
+            }\
+        }\
+        return (target_type) value;\
+    }
+
+/* CIntFromPy */
+  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (long) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (long) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
+                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
+                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
+                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (long) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (long) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            long val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (long) -1;
+        }
+    } else {
+        long val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (long) -1;
+        val = __Pyx_PyInt_As_long(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to long");
+    return (long) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to long");
+    return (long) -1;
+}
+
+/* CIntFromPy */
+  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(int) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (int) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (int) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
+                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
+                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
+                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (int) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(int) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (int) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            int val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (int) -1;
+        }
+    } else {
+        int val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (int) -1;
+        val = __Pyx_PyInt_As_int(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to int");
+    return (int) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to int");
+    return (int) -1;
+}
+
+/* FastTypeChecks */
+  #if CYTHON_COMPILING_IN_CPYTHON
+static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
+    while (a) {
+        a = a->tp_base;
+        if (a == b)
+            return 1;
+    }
+    return b == &PyBaseObject_Type;
+}
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
+    PyObject *mro;
+    if (a == b) return 1;
+    mro = a->tp_mro;
+    if (likely(mro)) {
+        Py_ssize_t i, n;
+        n = PyTuple_GET_SIZE(mro);
+        for (i = 0; i < n; i++) {
+            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
+                return 1;
+        }
+        return 0;
+    }
+    return __Pyx_InBases(a, b);
+}
+#if PY_MAJOR_VERSION == 2
+static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
+    PyObject *exception, *value, *tb;
+    int res;
+    __Pyx_PyThreadState_declare
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrFetch(&exception, &value, &tb);
+    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
+    if (unlikely(res == -1)) {
+        PyErr_WriteUnraisable(err);
+        res = 0;
+    }
+    if (!res) {
+        res = PyObject_IsSubclass(err, exc_type2);
+        if (unlikely(res == -1)) {
+            PyErr_WriteUnraisable(err);
+            res = 0;
+        }
+    }
+    __Pyx_ErrRestore(exception, value, tb);
+    return res;
+}
+#else
+static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
+    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
+    if (!res) {
+        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
+    }
+    return res;
+}
+#endif
+static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+    Py_ssize_t i, n;
+    assert(PyExceptionClass_Check(exc_type));
+    n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+    for (i=0; i<n; i++) {
+        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
+    }
+#endif
+    for (i=0; i<n; i++) {
+        PyObject *t = PyTuple_GET_ITEM(tuple, i);
+        #if PY_MAJOR_VERSION < 3
+        if (likely(exc_type == t)) return 1;
+        #endif
+        if (likely(PyExceptionClass_Check(t))) {
+            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
+        } else {
+        }
+    }
+    return 0;
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
+    if (likely(err == exc_type)) return 1;
+    if (likely(PyExceptionClass_Check(err))) {
+        if (likely(PyExceptionClass_Check(exc_type))) {
+            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
+        } else if (likely(PyTuple_Check(exc_type))) {
+            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
+        } else {
+        }
+    }
+    return PyErr_GivenExceptionMatches(err, exc_type);
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
+    assert(PyExceptionClass_Check(exc_type1));
+    assert(PyExceptionClass_Check(exc_type2));
+    if (likely(err == exc_type1 || err == exc_type2)) return 1;
+    if (likely(PyExceptionClass_Check(err))) {
+        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
+    }
+    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
+}
+#endif
+
+/* CheckBinaryVersion */
+  static int __Pyx_check_binary_version(void) {
+    char ctversion[4], rtversion[4];
+    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
+    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
+    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
+        char message[200];
+        PyOS_snprintf(message, sizeof(message),
+                      "compiletime version %s of module '%.100s' "
+                      "does not match runtime version %s",
+                      ctversion, __Pyx_MODULE_NAME, rtversion);
+        return PyErr_WarnEx(NULL, message, 1);
+    }
+    return 0;
+}
+
+/* InitStrings */
+  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        #if PY_MAJOR_VERSION < 3
+        if (t->is_unicode) {
+            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+        } else if (t->intern) {
+            *t->p = PyString_InternFromString(t->s);
+        } else {
+            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        }
+        #else
+        if (t->is_unicode | t->is_str) {
+            if (t->intern) {
+                *t->p = PyUnicode_InternFromString(t->s);
+            } else if (t->encoding) {
+                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+            } else {
+                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+            }
+        } else {
+            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+        }
+        #endif
+        if (!*t->p)
+            return -1;
+        if (PyObject_Hash(*t->p) == -1)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
+    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
+}
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
+    Py_ssize_t ignore;
+    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
+}
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+#if !CYTHON_PEP393_ENABLED
+static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+    char* defenc_c;
+    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+    if (!defenc) return NULL;
+    defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+    {
+        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+        char* c;
+        for (c = defenc_c; c < end; c++) {
+            if ((unsigned char) (*c) >= 128) {
+                PyUnicode_AsASCIIString(o);
+                return NULL;
+            }
+        }
+    }
+#endif
+    *length = PyBytes_GET_SIZE(defenc);
+    return defenc_c;
+}
+#else
+static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+    if (likely(PyUnicode_IS_ASCII(o))) {
+        *length = PyUnicode_GET_LENGTH(o);
+        return PyUnicode_AsUTF8(o);
+    } else {
+        PyUnicode_AsASCIIString(o);
+        return NULL;
+    }
+#else
+    return PyUnicode_AsUTF8AndSize(o, length);
+#endif
+}
+#endif
+#endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+    if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+            __Pyx_sys_getdefaultencoding_not_ascii &&
+#endif
+            PyUnicode_Check(o)) {
+        return __Pyx_PyUnicode_AsStringAndSize(o, length);
+    } else
+#endif
+#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
+    if (PyByteArray_Check(o)) {
+        *length = PyByteArray_GET_SIZE(o);
+        return PyByteArray_AS_STRING(o);
+    } else
+#endif
+    {
+        char* result;
+        int r = PyBytes_AsStringAndSize(o, &result, length);
+        if (unlikely(r < 0)) {
+            return NULL;
+        } else {
+            return result;
+        }
+    }
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+   int is_true = x == Py_True;
+   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
+   else return PyObject_IsTrue(x);
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
+    int retval;
+    if (unlikely(!x)) return -1;
+    retval = __Pyx_PyObject_IsTrue(x);
+    Py_DECREF(x);
+    return retval;
+}
+static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
+#if PY_MAJOR_VERSION >= 3
+    if (PyLong_Check(result)) {
+        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
+                "__int__ returned non-int (type %.200s).  "
+                "The ability to return an instance of a strict subclass of int "
+                "is deprecated, and may be removed in a future version of Python.",
+                Py_TYPE(result)->tp_name)) {
+            Py_DECREF(result);
+            return NULL;
+        }
+        return result;
+    }
+#endif
+    PyErr_Format(PyExc_TypeError,
+                 "__%.4s__ returned non-%.4s (type %.200s)",
+                 type_name, type_name, Py_TYPE(result)->tp_name);
+    Py_DECREF(result);
+    return NULL;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
+#if CYTHON_USE_TYPE_SLOTS
+  PyNumberMethods *m;
+#endif
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diff --git a/other/scientific-python-lectures-master/cy_dcumsum.pyx b/other/scientific-python-lectures-master/cy_dcumsum.pyx
new file mode 100644
index 00000000..c63ccafe
--- /dev/null
+++ b/other/scientific-python-lectures-master/cy_dcumsum.pyx
@@ -0,0 +1,9 @@
+
+cimport numpy
+
+def dcumsum(numpy.ndarray[numpy.float64_t, ndim=1] a, numpy.ndarray[numpy.float64_t, ndim=1] b):
+    cdef int i, n = len(a)
+    b[0] = a[0]
+    for i from 1 <= i < n:
+        b[i] = b[i-1] + a[i]
+    return b
diff --git a/other/scientific-python-lectures-master/dcumsum.f b/other/scientific-python-lectures-master/dcumsum.f
new file mode 100644
index 00000000..ebab9719
--- /dev/null
+++ b/other/scientific-python-lectures-master/dcumsum.f
@@ -0,0 +1,14 @@
+c File dcumsum.f
+       subroutine dcumsum(a, b, n)
+       double precision a(n)
+       double precision b(n)
+       integer n
+cf2py  intent(in) :: a
+cf2py  intent(out) :: b
+cf2py  intent(hide) :: n
+
+       b(1) = a(1)
+       do 100 i=2, n
+           b(i) = b(i-1) + a(i)
+100    continue
+       end
diff --git a/other/scientific-python-lectures-master/dprod.f b/other/scientific-python-lectures-master/dprod.f
new file mode 100644
index 00000000..9e18f71e
--- /dev/null
+++ b/other/scientific-python-lectures-master/dprod.f
@@ -0,0 +1,10 @@
+
+       subroutine dprod(x, y, n)
+    
+       double precision x(n), y
+       y = 1.0
+    
+       do 100 i=1, n
+           y = y * x(i)
+100    continue
+       end
diff --git a/other/scientific-python-lectures-master/dprod.pyf b/other/scientific-python-lectures-master/dprod.pyf
new file mode 100644
index 00000000..9659de3c
--- /dev/null
+++ b/other/scientific-python-lectures-master/dprod.pyf
@@ -0,0 +1,9 @@
+python module dprod ! in 
+    interface  ! in :dprod
+        subroutine dprod(x,y,n) ! in :dprod:dprod.f
+            double precision dimension(n), intent(in) :: x
+            double precision, intent(out) :: y
+            integer, optional,check(len(x)>=n),depend(x),intent(in) :: n=len(x)
+        end subroutine dprod
+    end interface 
+end python module dprod
diff --git a/other/scientific-python-lectures-master/filename.png b/other/scientific-python-lectures-master/filename.png
new file mode 100644
index 00000000..0e2295a8
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diff --git a/other/scientific-python-lectures-master/filename.svg b/other/scientific-python-lectures-master/filename.svg
new file mode 100644
index 00000000..a859d319
--- /dev/null
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+    <!-- title -->
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+     <use x="133.984375" xlink:href="#DejaVuSans-65"/>
+    </g>
+   </g>
+  </g>
+ </g>
+ <defs>
+  <clipPath id="p46e5680ee2">
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+</svg>
diff --git a/other/scientific-python-lectures-master/functions.c b/other/scientific-python-lectures-master/functions.c
new file mode 100644
index 00000000..3203d14f
--- /dev/null
+++ b/other/scientific-python-lectures-master/functions.c
@@ -0,0 +1,46 @@
+
+#include <stdio.h>
+
+void hello(int n);
+
+double dprod(double *x, int n);
+
+void dcumsum(double *a, double *b, int n);
+
+void
+hello(int n)
+{
+    int i;
+    
+    for (i = 0; i < n; i++)
+    {
+        printf("C says hello\n");
+    }
+}
+
+
+double 
+dprod(double *x, int n)
+{
+    int i;
+    double y = 1.0;
+    
+    for (i = 0; i < n; i++)
+    {
+        y *= x[i];
+    }
+
+    return y;
+}
+
+void
+dcumsum(double *a, double *b, int n)
+{
+    int i;
+    
+    b[0] = a[0];
+    for (i = 1; i < n; i++)
+    {
+        b[i] = a[i] + b[i-1];
+    }
+}
diff --git a/other/scientific-python-lectures-master/functions.o b/other/scientific-python-lectures-master/functions.o
new file mode 100644
index 00000000..968fc6fd
Binary files /dev/null and b/other/scientific-python-lectures-master/functions.o differ
diff --git a/other/scientific-python-lectures-master/functions.py b/other/scientific-python-lectures-master/functions.py
new file mode 100644
index 00000000..f204b893
--- /dev/null
+++ b/other/scientific-python-lectures-master/functions.py
@@ -0,0 +1,29 @@
+
+import numpy
+import ctypes
+
+_libfunctions = numpy.ctypeslib.load_library('libfunctions', '.')
+
+_libfunctions.hello.argtypes = [ctypes.c_int]
+_libfunctions.hello.restype  =  ctypes.c_void_p
+
+_libfunctions.dprod.argtypes = [numpy.ctypeslib.ndpointer(dtype=numpy.float), ctypes.c_int]
+_libfunctions.dprod.restype  = ctypes.c_double
+
+_libfunctions.dcumsum.argtypes = [numpy.ctypeslib.ndpointer(dtype=numpy.float), numpy.ctypeslib.ndpointer(dtype=numpy.float), ctypes.c_int]
+_libfunctions.dcumsum.restype  = ctypes.c_void_p
+
+def hello(n):
+    return _libfunctions.hello(int(n))
+
+def dprod(x, n=None):
+    if n is None:
+        n = len(x)
+    x = numpy.asarray(x, dtype=numpy.float)
+    return _libfunctions.dprod(x, int(n))
+
+def dcumsum(a, n):
+    a = numpy.asarray(a, dtype=numpy.float)
+    b = numpy.empty(len(a), dtype=numpy.float)
+    _libfunctions.dcumsum(a, b, int(n))
+    return b
diff --git a/other/scientific-python-lectures-master/hello.py b/other/scientific-python-lectures-master/hello.py
new file mode 100644
index 00000000..c5e2790a
--- /dev/null
+++ b/other/scientific-python-lectures-master/hello.py
@@ -0,0 +1,3 @@
+import hellofortran
+
+hellofortran.hellofortran(5)
diff --git a/other/scientific-python-lectures-master/hellofortran.f b/other/scientific-python-lectures-master/hellofortran.f
new file mode 100644
index 00000000..b5e154e2
--- /dev/null
+++ b/other/scientific-python-lectures-master/hellofortran.f
@@ -0,0 +1,8 @@
+C File  hellofortran.f
+        subroutine hellofortran (n)
+        integer n
+       
+        do 100 i=0, n
+            print *, "Fortran says hello"
+100     continue
+        end
diff --git a/lectures/notebooks/scientific-python-lectures-master/images/github-diff.png b/other/scientific-python-lectures-master/images/github-diff.png
similarity index 100%
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diff --git a/lectures/notebooks/scientific-python-lectures-master/images/github-project-page.png b/other/scientific-python-lectures-master/images/github-project-page.png
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rename from lectures/notebooks/scientific-python-lectures-master/images/github-project-page.png
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diff --git a/lectures/notebooks/scientific-python-lectures-master/images/scientific-python-stack.svg b/other/scientific-python-lectures-master/images/scientific-python-stack.svg
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diff --git a/lectures/notebooks/scientific-python-lectures-master/images/spyder-screenshot.jpg b/other/scientific-python-lectures-master/images/spyder-screenshot.jpg
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diff --git a/lectures/notebooks/scientific-python-lectures-master/images/theory-experiment-computation.png b/other/scientific-python-lectures-master/images/theory-experiment-computation.png
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rename from lectures/notebooks/scientific-python-lectures-master/images/theory-experiment-computation.png
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diff --git a/lectures/notebooks/scientific-python-lectures-master/images/theory-experiment-computation.svg b/other/scientific-python-lectures-master/images/theory-experiment-computation.svg
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/images/theory-experiment-computation.svg
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diff --git a/lectures/notebooks/scientific-python-lectures-master/ipython-example.ipynb b/other/scientific-python-lectures-master/ipython-example.ipynb
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/ipython-example.ipynb
rename to other/scientific-python-lectures-master/ipython-example.ipynb
diff --git a/other/scientific-python-lectures-master/run_hello_c.py b/other/scientific-python-lectures-master/run_hello_c.py
new file mode 100644
index 00000000..176bc707
--- /dev/null
+++ b/other/scientific-python-lectures-master/run_hello_c.py
@@ -0,0 +1,4 @@
+
+import functions
+
+functions.hello(3)
diff --git a/lectures/notebooks/scientific-python-lectures-master/scripts/hello-world-in-swedish.py b/other/scientific-python-lectures-master/scripts/hello-world-in-swedish.py
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/scripts/hello-world-in-swedish.py
rename to other/scientific-python-lectures-master/scripts/hello-world-in-swedish.py
diff --git a/lectures/notebooks/scientific-python-lectures-master/scripts/hello-world.py b/other/scientific-python-lectures-master/scripts/hello-world.py
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/scripts/hello-world.py
rename to other/scientific-python-lectures-master/scripts/hello-world.py
diff --git a/other/scientific-python-lectures-master/setup.py b/other/scientific-python-lectures-master/setup.py
new file mode 100644
index 00000000..3afee6d2
--- /dev/null
+++ b/other/scientific-python-lectures-master/setup.py
@@ -0,0 +1,9 @@
+
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+setup(
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("cy_dcumsum", ["cy_dcumsum.pyx"])]
+)
diff --git a/lectures/notebooks/scientific-python-lectures-master/stockholm_td_adj.dat b/other/scientific-python-lectures-master/stockholm_td_adj.dat
similarity index 100%
rename from lectures/notebooks/scientific-python-lectures-master/stockholm_td_adj.dat
rename to other/scientific-python-lectures-master/stockholm_td_adj.dat
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..bb321245
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,4 @@
+[tool.codespell]
+skip = "*.fodp,*.pdf,*.sty,.git,*/_build,*.c,*.f90,other"
+#ignore-words-list = "pres"
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..5ca519bb
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+jupyter-book<2
+matplotlib
+basemap
+numpy
+sympy
+scipy
+keras
+tensorflow
+pydot
+graphviz
+scikit-learn
+sphinx-copybutton
+sphinx-prompt
+jupyterlab_myst
+
diff --git a/syllabus/GNUmakefile b/syllabus/GNUmakefile
new file mode 100644
index 00000000..a2caff50
--- /dev/null
+++ b/syllabus/GNUmakefile
@@ -0,0 +1,13 @@
+EPStoPDF = epstopdf
+
+ALL: syllabus.pdf
+
+%.pdf: %.tex
+	pdflatex $<
+	pdflatex $<
+
+clean:
+	$(RM) *.aux *.log
+	$(RM) *~
+
+.PHONY: clean
diff --git a/syllabus/accessibility.sty b/syllabus/accessibility.sty
new file mode 100644
index 00000000..afefecfa
--- /dev/null
+++ b/syllabus/accessibility.sty
@@ -0,0 +1,2103 @@
+%%
+%% This is file `accessibility.sty',
+%% generated with the docstrip utility.
+%%
+%% The original source files were:
+%%
+%% accessibility.dtx  (with options: `package')
+%% 
+%% This is a generated file.
+%% 
+%% Copyright 2007 Babett Schalitz
+%% Copyright 2019 Andrew Clifton
+%% 
+%% This material is subject to the LaTeX Project Public License. See
+%% http://www.ctan.org/tex-archive/help/Catalogue/licenses.lppl.html
+%% for the details of that license.
+%% 
+%% This package allows to produce tagged PDF output following Adobe's
+%% PDF-1.5 and 1.6 specifications.
+%% 
+%% For more details see the package description on CTAN.
+%% 
+\iffalse
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% accessibility.sty
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% Copyright 2007 Babett Schalitz
+%% Copyright 2019 Andrew Clifton
+%%
+%% This material is subject to the LaTeX Project Public License. See
+%% http://www.ctan.org/tex-archive/help/Catalogue/licenses.lppl.html
+%% for the details of that license.
+%%
+%% This package allows to produce tagged PDF output following Adobe's
+%% PDF-1.5 and 1.6 specifications.
+%%
+%% %^^ VERSION INFO 1 OF 3
+%% This is accessibility version 2.0.3 Backward compatibility with prior
+%% versions is not assured.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% \CheckSum{2882}
+%% \CharacterTable
+%%  {Upper-case    \A\B\C\D\E\F\G\H\I\J\K\L\M\N\O\P\Q\R\S\T\U\V\W\X\Y\Z
+%%   Lower-case    \a\b\c\d\e\f\g\h\i\j\k\l\m\n\o\p\q\r\s\t\u\v\w\x\y\z
+%%   Digits        \0\1\2\3\4\5\6\7\8\9
+%%   Exclamation   \!     Double quote  \"     Hash (number) \#
+%%   Dollar        \$     Percent       \%     Ampersand     \&
+%%   Acute accent  \'     Left paren    \(     Right paren   \)
+%%   Asterisk      \*     Plus          \+     Comma         \,
+%%   Minus         \-     Point         \.     Solidus       \/
+%%   Colon         \:     Semicolon     \;     Less than     \<
+%%   Equals        \=     Greater than  \>     Question mark \?
+%%   Commercial at \@     Left bracket  \[     Backslash     \\
+%%   Right bracket \]     Circumflex    \^     Underscore    \_
+%%   Grave accent  \`     Left brace    \{     Vertical bar  \|
+%%   Right brace   \}     Tilde         \~}
+%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\fi
+
+\ProvidesPackage{accessibility}[2019/11/02 v. 2.0.3]
+\NeedsTeXFormat{LaTeX2e}
+
+\RequirePackage{xkeyval}
+\RequirePackage{ifthen}
+
+\newcounter{StructTree}%
+\pdfobj reserveobjnum%
+\setcounter{StructTree}{\pdflastobj}%
+\xdef\Karray{}%
+
+\newcounter{ObjHelp}%
+
+\newcounter{TaggedObj}%[page]
+
+\newboolean{ACCESSProblems} \setboolean{ACCESSProblems}{false}%
+\gdef\DocumentLanguage{}%
+\gdef\ActualLanguage{}%
+\newif\ifLanguageDiff \global\LanguageDifffalse%
+\gdef\LanguageCode{}%
+\newboolean{@tagged@pdf} \setboolean{@tagged@pdf}{false}%
+\newboolean{@right@pdfversion} \setboolean{@tagged@pdf}{false}%
+\newboolean{@Access@pdf} \setboolean{@Access@pdf}{false}%
+\newif\ifPDFDetailedStructure \global\PDFDetailedStructuretrue%
+\DeclareOption{flatstructure}{\global\PDFDetailedStructurefalse}%
+\DeclareOption{highstructure}{\global\PDFDetailedStructuretrue}%
+\DeclareOption{tagged}{\setboolean{@tagged@pdf}{true}}%
+\DeclareOption{untagged}{\setboolean{@tagged@pdf}{false}}%
+\DeclareOption*{%
+   \PackageWarning{accessibility}{Unknown Option \CurrentOption}}%
+\ProcessOptions\relax%
+
+
+\ifthenelse{\isundefined{\pdfoutput}}{%
+  %latex with dvips
+  \setboolean{@right@pdfversion}{false}%
+  }{\ifthenelse{\number\pdfoutput<1}{%
+      %pdflatex in DVI mode
+      \setboolean{@right@pdfversion}{false}%
+      }{%pdflatex in PDF mode
+      \ifthenelse{\pdftexversion<120}{%
+          \PackageError{accessibility}%
+          {pdfTeX/pdfLaTeX version >= 1.20 required for direct PDF outut}%
+          {Try to install a more recent version!}%
+      }{%
+      %It is the right version
+      \setboolean{@right@pdfversion}{true}%
+    }%
+  }%
+}
+
+\ifthenelse{\boolean{@right@pdfversion} \and \boolean{@tagged@pdf}}{%
+      \setboolean{@Access@pdf}{true}%
+}{%
+      \setboolean{@Access@pdf}{false}%
+}
+
+\newboolean{@KOMAScriptClass} \setboolean{@KOMAScriptClass}{false}%
+
+\@ifclassloaded{scrreprt} {\setboolean{@KOMAScriptClass}{true}}{}%
+\@ifclassloaded{scrbook}  {\setboolean{@KOMAScriptClass}{true}}{}%
+\@ifclassloaded{scrartcl} {\setboolean{@KOMAScriptClass}{true}}{}%
+\ifthenelse{\boolean{@KOMAScriptClass}}{%
+                \PackageInfo{accessibility}{KOMAscript Klasse}}{}%
+
+\newboolean{@StandardClass} \setboolean{@StandardClass}{false}%
+
+\@ifclassloaded{report} {\setboolean{@StandardClass}{true}}{}%
+\@ifclassloaded{book}   {\setboolean{@StandardClass}{true}}{}%
+\@ifclassloaded{article}{\setboolean{@StandardClass}{true}}{}%
+
+\ifthenelse{\boolean{@StandardClass}}{%
+               \PackageInfo{accessibility}{Standardklasse}}{}%
+
+\pdfcompresslevel=0% Damit wird die PDF-Quelldatei lesbar
+\pdfminorversion=6% Bestimmt die PDF - Version der Ausgabe
+
+\newcommand{\alt}[1]{\xdef\altAttr{#1}}%
+\newcommand{\newhref}[3]{\xdef\altAttr{#2}\href{#1}{#3}}%
+  %
+\@ifundefined{thead}{%
+  \newcommand{\thead}[1]{%
+    \global\TableHeadCelltrue%
+    \textbf{#1}}%
+}{%
+  \let\originalthead\thead
+  \renewcommand{\thead}{%
+    \global\TableHeadCelltrue%
+    \originalthead}%
+}
+
+\newcount\@stackdepth \@stackdepth=0%
+\def\accessPushStack#1#2#3#4{%
+   \ifnum \@stackdepth >15\relax%
+        \PackageWarning{accessibility}{too deep}%
+   \else%
+        \global\advance\@stackdepth\@ne%
+   \fi%
+    {\expandafter\xdef\csname StackA\romannumeral\the\@stackdepth\endcsname{#1}}%
+   {\expandafter\xdef\csname StackB\romannumeral\the\@stackdepth\endcsname{#2}}%
+   {\expandafter\xdef\csname StackC\romannumeral\the\@stackdepth\endcsname{#3}}%
+   {\expandafter\xdef\csname StackD\romannumeral\the\@stackdepth\endcsname{#4}}%
+}%
+
+\def\accessPopStack#1#2#3#4{%
+  \ifnum \the\@stackdepth <1\relax%
+     \global\let#1\empty%
+     \global\let#2\empty%
+     \global\let#3\empty%
+     \global\let#4\empty%
+  \else%
+     \xdef#1{\csname StackA\romannumeral\the\@stackdepth\endcsname}%
+     \xdef#2{\csname StackB\romannumeral\the\@stackdepth\endcsname}%
+     \xdef#3{\csname StackC\romannumeral\the\@stackdepth\endcsname}%
+     \xdef#4{\csname StackD\romannumeral\the\@stackdepth\endcsname}%
+   %Variablen wieder leeren
+   {\expandafter\xdef\csname StackA\romannumeral\the\@stackdepth\endcsname{}}%
+   {\expandafter\xdef\csname StackB\romannumeral\the\@stackdepth\endcsname{}}%
+   {\expandafter\xdef\csname StackC\romannumeral\the\@stackdepth\endcsname{}}%
+   {\expandafter\xdef\csname StackD\romannumeral\the\@stackdepth\endcsname{}}%
+     \global\advance\@stackdepth\m@ne%
+  \fi%
+}%
+
+\def\accessReadTopStack#1#2#3{%
+  \ifnum \the\@stackdepth <1\relax%
+     \global\let#1\empty%
+     \global\let#2\empty%
+     \global\let#3\empty%
+  \else%
+     \xdef#1{\csname StackA\romannumeral\the\@stackdepth\endcsname}%
+     \xdef#2{\csname StackB\romannumeral\the\@stackdepth\endcsname}%
+     \xdef#3{\csname StackC\romannumeral\the\@stackdepth\endcsname}%
+  \fi%
+}%
+
+
+\newenvironment{PDFStructObj}[2]{% #1 StructType #2 Title
+  \ifTextActive{\endPDFMarkContent\endPDFTextObj}\fi%
+  \pdfobj reserveobjnum% Objektnummer reservieren
+  \setcounter{ObjHelp}{\pdflastobj}%
+  \expandafter\xdef\csname PDF@#1@Array\endcsname{}%
+  \accessPushStack{#1}{\theObjHelp}{PDF@#1@Array}{#2}%drauftun
+}%
+{%andere Ebenen Schließen
+  \accessPopStack\StructElem\Objnum\KidsArray\Title %runterholen
+  \ifx \StructElem\empty%
+    \PackageWarning{accessibility}{empty \string\PopStack ?}%
+  \else%
+    %\convertLanguageInCode{\languagename}%
+    \gdef\TitleHelp{}%
+    \if \Title\empty \else%
+        \gdef\TitleHelp{/T (\Title)}%
+    \fi%
+     \ifnum \@stackdepth <1\relax%
+          \xdef\ParentElem{\theStructTree}%
+          \xdef\ParentArray{Karray}%
+     \else%
+          \accessReadTopStack\ParentStructElem\ParentElem\ParentArray%
+     \fi%
+    \immediate \pdfobj useobjnum\number\Objnum{<</Type /StructElem %
+        /P \ParentElem\space 0 R %
+        \TitleHelp %
+        /C /Normal %
+        \space\LanguageCode %
+        /K [\csname \KidsArray\endcsname] %
+        /S /\StructElem>>}%
+    \pdfrefobj\Objnum%
+    \expandafter\xdef\csname \ParentArray\endcsname{%
+         \csname \ParentArray\endcsname \space \Objnum\space 0 R}%
+  \fi%
+}
+
+\newcommand{\sectionInDepth}[2]{%
+   \csname #2\endcsname=100%
+   \ifthenelse{\equal{#1}{Document}}{\csname #2\endcsname=\m@ne}{}%
+   \ifthenelse{\equal{#1}{Part}}{\csname #2\endcsname=0}{}%
+   \ifthenelse{\equal{#1}{Chapter}}{\csname #2\endcsname=1}{}%
+   \ifthenelse{\equal{#1}{Section}}{\csname #2\endcsname=2}{}%
+   \ifthenelse{\equal{#1}{Subsection}}{\csname #2\endcsname=3}{}%
+   \ifthenelse{\equal{#1}{Subsubsection}}{\csname #2\endcsname=4}{}%
+   \ifthenelse{\equal{#1}{Paragraph}}{\csname #2\endcsname=5}{}%
+   \ifthenelse{\equal{#1}{Subparagraph}}{\csname #2\endcsname=6}{}%
+   \ifthenelse{\equal{#1}{Div}}{\csname #2\endcsname=7}{}%
+}
+
+
+\newcount\@bool%
+\newcount\@elem%
+\newcount\@elemi%
+\newcommand{\closeUntilPDFStruct}[1]{%
+      \ifTextActive%
+          \endPDFMarkContent%
+          \endPDFTextObj%
+      \fi%
+     %Schleife
+     \@bool=0%
+        \sectionInDepth{#1}{@elemi}%
+        \ifnum \@elemi=100 \@bool=1 \fi%
+        \global\advance\@elemi\m@ne%
+     \@whilenum \@bool =0\do{%
+        \accessReadTopStack\StructElem\Objnum\KidsArray%
+        \sectionInDepth{\StructElem}{@elem}%
+        \ifthenelse{\@elem >\@elemi}{%
+             \endPDFStructObj%
+        }{%
+             \@bool=1%
+        }%
+        \if #1\empty \@bool=1 \fi%
+        \ifthenelse{\equal{#1}{\StructElem}}{\@bool=1}{}%
+     }%
+}
+
+\xdef\TextType{}%
+\newcounter{TextObjNum}%
+\xdef\TextArray{}%
+\newif\ifTextActive \TextActivefalse%
+\newif\ifSpezialTextActive \SpezialTextActivefalse%
+
+\newenvironment*{PDFTextObj}{%
+  %altes Textobj beenden, immer max. ein Textobj aktiv
+  \ifTextActive \endPDFMarkContent\endPDFTextObj\fi%
+  %neues anfangen
+  \global\TextActivetrue%
+  \pdfobj reserveobjnum% Objektnummer reservieren
+  \setcounter{TextObjNum}{\pdflastobj}%
+  \xdef\TextArray{}%
+  \xdef\TextType{P}% kein TextTxp --> P
+}%
+{%
+ \ifTextActive%
+  \ifnum \@stackdepth <1\relax%
+       \xdef\ParentElem{\theStructTree}%
+       \xdef\ParentArray{Karray}%
+       %\PackageWarning{accessibility}{stackdepth<1}%
+  \else%
+       \accessReadTopStack\ParentStructElem\ParentElem\ParentArray%
+  \fi%
+  %\convertLanguageInCode{\languagename}%
+  \immediate \pdfobj useobjnum\theTextObjNum{<</Type /StructElem %
+     /P \ParentElem \space 0 R %
+     /C /Normal %
+     /K [\TextArray] %
+     /S /\TextType %
+    \space\LanguageCode>>}%
+  \pdfrefobj \theTextObjNum%
+  \expandafter\xdef\csname \ParentArray\endcsname{%
+       \csname \ParentArray\endcsname \space \theTextObjNum\space 0 R}%
+  \global\TextActivefalse%
+ \fi%
+}
+
+
+\xdef\altAttr{}%
+\xdef\titleAttr{}%
+\newenvironment*{PDFSpezialTextObj}[1]{%
+  \ifTextActive \endPDFMarkContent\endPDFTextObj\fi%
+  %neues anfangen
+  \global\SpezialTextActivetrue%
+  \pdfobj reserveobjnum% Objektnummer reservieren
+  \setcounter{TextObjNum}{\pdflastobj}%
+  \xdef\TextArray{}%
+  \xdef\TextType{#1}%
+}%
+{%
+  %\gdef\LanguageHelp{}%
+  %\ifLanguageDiff%
+  %    \gdef\LanguageHelp{\LanguageCode}%
+  %\fi%
+  %  \convertLanguageInCode{\languagename}%
+    \gdef\AltHelp{}%
+  \ifthenelse{\equal{\altAttr}{}}{}{%
+     \gdef\AltHelp{/Alt(\altAttr)}%
+  }%
+  \gdef\TitleHelp{}%
+  \ifthenelse{\equal{\titleAttr}{}}{}{%
+     \gdef\TitleHelp{/T(\titleAttr)}%
+  }%
+  \ifnum \@stackdepth <1\relax%
+       \xdef\ParentElem{\theStructTree}%
+       \xdef\ParentArray{Karray}%
+  \else%
+       \accessReadTopStack\ParentStructElem\ParentElem\ParentArray%
+  \fi%
+  \immediate \pdfobj useobjnum\theTextObjNum{<</Type /StructElem %
+     /P \ParentElem \space 0 R %
+     /C /Normal %
+     /K [\TextArray] %
+     /S /\TextType %
+    \space\LanguageCode %
+    \space\TitleHelp %
+    \space\AltHelp>>}%
+  \pdfrefobj \theTextObjNum%
+  \expandafter\xdef\csname \ParentArray\endcsname{%
+      \csname \ParentArray\endcsname \space \theTextObjNum\space 0 R}%
+  \global\SpezialTextActivefalse%
+  \xdef\TextType{}%
+  \EveryparReset%
+  \xdef\altAttr{}% wieder leeren
+  \xdef\titleAttr{}% wieder leeren
+}
+
+\xdef\lastEveryparType{}%
+\xdef\HelpBool{}%
+\xdef\Type{}%
+\newcounter{PDFReferenceObjNum}%
+\xdef\ReferenceArray{}%
+\newenvironment*{PDFInlineObjInText}[1]{%
+  %alte einstellung merken
+  \xdef\lastEveryparType{\everyparStructElem}%
+  \xdef\HelpBool{\InlineObj}%
+  \xdef\Type{#1}%
+  \ifthenelse{\equal{\Type}{Reference} \or \equal{\Type}{Link}}{%
+    \pdfobj reserveobjnum%
+    \setcounter{PDFReferenceObjNum}{\pdflastobj}%
+     \setcounter{ObjNum}{\theTaggedObj}%
+     \EveryparConfig{#1}{obj}%
+  }{%
+     \EveryparConfig{#1}{true}%
+  }%
+  \PDFMarkContent% kein everypar
+}%
+{%
+  \endPDFMarkContent%
+  \ifthenelse{\equal{\Type}{Reference} \or \equal{\Type}{Link}}{%
+    \xdef\ReferenceArray{<</Type /MCR /Pg \pdfpageref\thepage %
+         \space \space 0 R /MCID \theObjNum>>}%
+    \ifthenelse{\pdftexversion>139}{%
+      \setcounter{ObjHelp}{\pdflastlink}%%<</Type /OBJR /Obj 600 0 R>>
+      %\PackageWarning{accessibility}%
+      %{Objektnummer vom letzten Link: \theObjHelp}%
+      \ifthenelse{\theObjHelp>0}{%
+        \xdef\ReferenceArray{\ReferenceArray\space %
+              <</Type /OBJR /Obj \theObjHelp\space 0 R>>}%
+      }{}%
+   }{}%
+   \writeComplexTextObj{\thePDFReferenceObjNum}%
+         {\ReferenceArray}%
+         {/Reference}{\theTextObjNum}{NoPage}%
+   \xdef\TextArray{\TextArray \theObjHelp\space 0 R \space}%
+  }{}%
+  % alte einstellung wiederherstellen
+  \EveryparConfig{\lastEveryparType}{\HelpBool}%
+  \PDFMarkContent%
+}
+
+
+\newcommand{\EveryparConfig}[2]{%
+   \xdef\everyparStructElem{#1}%
+   \xdef\InlineObj{#2}%
+}
+
+%%Setzt die Variablen auf die Standardwerte für eine normale Texterkennung.
+\newcommand{\EveryparReset}{%
+   \xdef\everyparStructElem{P}%
+   \xdef\InlineObj{false}%
+}
+
+\newif\ifMarkContentOpen \MarkContentOpenfalse%
+\xdef\everyparStructElem{P}%
+\xdef\InlineObj{false}%
+\newenvironment{PDFMarkContent}{%
+  \ifMarkContentOpen \endPDFMarkContent\fi%
+  \global\MarkContentOpentrue%
+  \ifthenelse{\equal{\everyparStructElem}{}}{\EveryparReset}{}%
+  \pdfliteral{/\everyparStructElem\space <</MCID \theTaggedObj>> BDC}%
+  \ifthenelse{\equal{\InlineObj}{false}}{% Text
+    %\ifthenelse{\equal{\themypage}{\thepage}}{}{%
+       %\PackageWarning{accessibility}{unterschiedlich %
+       %\thepage<>\themypage; MCID \theTaggedObj}
+    %}%
+    \xdef\TextArray{\TextArray \space <</Type /MCR %
+        %/Pg \pdfpageref\thepage \space \space 0 R %
+        /Pg \pdfpageref\themypage \space \space 0 R %
+        /MCID \theTaggedObj>>}%
+  }{}%
+  \ifthenelse{\equal{\InlineObj}{true}}{%  inline-Objekt
+    \writePDFLeafObj{\theTaggedObj}{\everyparStructElem}%
+  }{}%
+  \stepcounter{TaggedObj}%
+}%
+{%
+ \ifMarkContentOpen%
+    \pdfliteral{EMC}%
+    \global\MarkContentOpenfalse%
+ \fi%
+}
+
+\newcommand*{\writePDFLeafObj}[2]{%
+  %\gdef\LanguageHelp{}%
+  %\ifLanguageDiff%
+  %    \gdef\LanguageHelp{\LanguageCode}%
+  %\fi%
+  %  \convertLanguageInCode{\languagename}%
+  \gdef\AltHelp{}%
+  \ifthenelse{\equal{\altAttr}{}}{}{%
+     \gdef\AltHelp{/Alt(\altAttr)}%
+  }%
+  \gdef\TitleHelp{}%
+  \ifthenelse{\equal{\titleAttr}{}}{}{%
+     \gdef\TitleHelp{/T(\titleAttr)}%
+  }%
+  \immediate \pdfobj {<</Type /StructElem %
+    /P \theTextObjNum \space 0 R %
+    /C /Normal %
+    /K <</Type /MCR %
+         /Pg \pdfpageref\thepage \space \space 0 R %
+         /MCID #1>> %
+    /S /#2 %
+    \space\LanguageCode %
+    \space\TitleHelp %
+    \space\AltHelp>>}%
+  \setcounter{ObjHelp}{\pdflastobj}%
+  \pdfrefobj \pdflastobj%
+  \xdef\TextArray{\TextArray \space \theObjHelp\space 0 R}%
+  \xdef\altAttr{}%
+  \xdef\titleAttr{}%
+}
+
+
+\newcommand*{\writeComplexTextObj}[5]{%
+  \ifthenelse{\equal{#5}{Page}}{% Seitenreferenz angeben
+    \immediate \pdfobj useobjnum#1{<</Type /StructElem %
+      /P #4\space 0 R %
+      /Pg \pdfpageref\thepage \space \space 0 R %
+      /C /Normal %
+      /K [#2] %
+      /S #3 \space\LanguageCode>>}%
+    }{%keine Seitenreferenz angeben
+    \immediate \pdfobj useobjnum#1{<</Type /StructElem %
+      /P #4\space 0 R %
+      /C /Normal %
+      /K [#2] %
+      /S #3>>}%
+  }%
+  \setcounter{ObjHelp}{\pdflastobj}%
+  \pdfrefobj \pdflastobj%
+}
+
+  %\newcounter{ParCounter}[page]%
+\newcounter{ParCounter}%
+\newif\ifnumberingpars \numberingparstrue%
+\newif\ifnpar \npartrue%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originaleverypar\everypar%
+  \newtoks\npeverypar%
+  \npeverypar{}%
+  % Call everypar with the argument extended with the toks
+  \def\everypar#1{%
+    \originaleverypar{#1\ifnumberingpars\the\npeverypar\fi}}%
+  \npeverypar{%
+    \ifnpar{%
+      \stepcounter{ParCounter}%
+     %\pdfliteral{/Artifact BMC}%
+     % \llap{\small\arabic{ParCounter}\qquad}%
+     %\pdfliteral{EMC}%
+      \ifSpezialTextActive\else \PDFTextObj \fi%
+      \PDFMarkContent%
+    }\fi%
+  }%
+}{}
+\AtBeginDocument{%
+\PDFStructObj{Document}{\empty}%
+\everypar{}%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \AtEndDocument{%
+  \closeUntilPDFStruct{Document}%
+  \newcounter{ClassMap}%
+  \pdfobj reserveobjnum% neues Objekt Reservieren
+  \setcounter{ClassMap}{\pdflastobj}%
+  \immediate \pdfobj useobjnum \theClassMap{<<%
+      /Normal <</O /Layout /EndIndent 0.0 %
+                /SpaceAfter 0.0 /SpaceBefore 0.0 %
+                /StartIndent 0.0 /WritingMode %
+                /LrTb /TextAlign /Start>> %
+      /CM1 <</O /Layout /TextAlign /Justify>> %
+      /CM2 <</O /Layout /TextAlign /Center>> %
+      /CM3 <</O /Layout /TextAlign /Start>> %
+      /CM4 <</O /Layout /InlineAlign /Center %
+                /Placement /Block /SpaceAfter 12.125 %
+                /BBox [266 314 329 336]>> %
+       >>} \pdfrefobj \pdflastobj%
+   %\global\setcounter{ClassMap}{\pdflastobj}%
+  \pdfobj {<<%
+            /IndexItem /Span % Indexeinträge
+            /TOF /TOC % Table of Figures
+            /TOFI /TOCI % Table of Figures Eintrag
+            /TOT /TOC % Table of Tables
+            /TOTI /TOCI % Table of Tables Eintrag++
+            /Titlepage /Sect % Titlepage
+            /Bibliography /L % Bibligraphy
+            /BibItem /LI % BibligraphyItem
+            /ParagraphSpan /Span % geteilte Paragraphen am Seitenumbruch
+            /Footnote /Note %  Fußnotentext
+            /Chapter /Sect%
+            /Section /Sect%
+            /Subsection /Sect%
+            /Subsubsection /Sect%
+            /Float /Div%
+            /L1 /L%
+            /L2 /L%
+            /L3 /L%
+            /L4 /L%
+            /L5 /L%
+          >>} \pdfrefobj \pdflastobj%
+  \setcounter{ObjHelp}{\pdflastobj}%
+  \immediate \pdfobj useobjnum \theStructTree{%
+     <</Type /StructTreeRoot %
+       /RoleMap \theObjHelp \space 0 R %
+       /ClassMap \theClassMap \space 0 R %
+       /ParentTree <</Nums [0 [\Karray]]>> % TODO Viel komplizierter
+       /ParentTreeNextKey 1 % berechnen
+       /K [\Karray] %
+     >>}\pdfrefobj\pdflastobj%
+  \pdfcatalog{%
+      /StructTreeRoot \theStructTree\space 0 R%
+      /MarkInfo <</Marked true /LetterspaceFlags 0>>%
+      %/PieceInfo<</MarkedPDF>>%
+      %/MarkInfo <</Marked true>>%
+      %/Metadata \theStructTree\space 0 R%
+      }%
+  \ifthenelse{\boolean{ACCESSProblems}}{%
+     \PackageWarningNoLine{accessibility}{%
+        @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\MessageBreak
+        @@ There were non-defined Figure Alt-Tags! @@\MessageBreak
+        @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@}%
+  }{}%
+  }%
+}{}
+
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\newcount\linenopenalty\linenopenalty=-100000%
+\mathchardef\linenopenaltypar=32000%
+  %
+  %%%%%%%%%%%%%Verändern der Outputroutine%%%%%%%%%%%%%%%%%%%%%%%%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\@tempa\output%
+  \newtoks\output%
+  \let\@AC@output\output%
+  \output=\expandafter{\the\@tempa}%
+  %
+  \@tempa={%
+    % LineNoTest
+    \let\@@par\@@@par%
+    \ifnum\interlinepenalty<-\linenopenaltypar%
+       \advance\interlinepenalty-\linenopenalty%
+       \@AC@nobreaktrue%
+    \fi%
+    \@tempswatrue%
+    \ifnum\outputpenalty>-\linenopenaltypar\else%
+       \ifnum\outputpenalty>-188000\relax%
+         \@tempswafalse%
+         \fi%
+       \fi%
+    \if@tempswa%
+         % LineNoLaTeXOutput
+         \ifnum \holdinginserts=\thr@@ %
+           \global\holdinginserts-\thr@@ %
+           \unvbox\@cclv %
+           \ifnum \outputpenalty=\@M \else \penalty\outputpenalty \fi %
+         \else%
+           \if@twocolumn \let\@makecol\@AC@makecol \fi%
+           \the\@AC@output %
+           \ifnum \holdinginserts=-\thr@@ %
+             \global\holdinginserts\thr@@ \fi %
+         \fi%
+    \else  %
+       %MakeLineNo
+       \boxmaxdepth\maxdimen\setbox\z@\vbox{\unvbox\@cclv}%
+       \@tempdima\dp\z@ \unvbox\z@%
+       \sbox\@tempboxa{\hb@xt@\z@{\makeLineNumber}}%
+       \stepcounter{linenumber}%
+       \stepcounter{abslinenumber}%
+       \ht\@tempboxa\z@ \@AC@depthbox %
+       \count@\lastpenalty %
+       \ifnum\outputpenalty=-\linenopenaltypar %
+         \ifnum\count@=\z@ \else %
+           \xdef\@AC@parpgbrk{%
+             \penalty\the\count@%
+             \global\let\noexpand\@AC@parpgbrk%
+                        \noexpand\@AC@screenoff@pen}%
+         \fi%
+       \else%
+         \@tempcnta\outputpenalty%
+         \advance\@tempcnta -\linenopenalty%
+         \penalty \ifnum\count@<\@tempcnta \@tempcnta \else \count@ \fi %
+       \fi%
+    \fi%
+  }%
+  \def\@AC@nobreaktrue{\let\if@nobreak\iftrue} %
+  %
+  \def\@AC@depthbox{%
+    \dp\@tempboxa=\@tempdima%
+    \nointerlineskip \kern-\@tempdima \box\@tempboxa} %
+  %
+  \def\@AC@screenoff@pen{%
+    \ifdim\lastskip=\z@ %
+      \@tempdima\prevdepth \setbox\@tempboxa\null %
+      \@AC@depthbox                           \fi}%
+  %
+  \global\let\@AC@parpgbrk\@AC@screenoff@pen %
+}{}%
+  %
+  %%%%%%%%%%%%%Initializieren der Variablen%%%%%%%%%%%%%%%%%%%%%%%
+  %
+  %Zeilennummer
+  \newcounter{linenumber}%
+  \newcounter{abslinenumber}%
+  %Seitennummer
+  \newcount\c@AC@truepage %
+  \global\advance\c@AC@truepage\@ne %mit eins beginnen
+  %\g@addto@macro\cl@page{\global\c@AC@truepage\c@page}%
+  \g@addto@macro\cl@page{\global\advance\c@AC@truepage\@ne}%
+  \@addtoreset{AC@truepage}{@ckpt}%
+  %
+  \newcounter{mypage}%
+  \setcounter{mypage}{\@ne}%
+  \g@addto@macro\cl@page{\global\c@mypage\c@page}%
+  \@addtoreset{mypage}{@ckpt}%
+  %
+  %%%%%%%%%%%%%Schreiben der Zeilennummmern%%%%%%%%%%%%%%%%%%%%%%%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \def\makeLineNumber{%
+    \protected@write\@auxout{}{\string\@AC{\the\c@linenumber}%
+                              {\noexpand\the\c@AC@truepage}}%
+    \testNumberedPage%
+    %Schreibt die Zeilennummern
+    %\hss{\normalfont\tiny\sffamily\thelinenumber\quad}%
+  }%
+}{}%
+  %
+  %%%%%%%%%%%%%%Absatz
+\newif\ifLastLineStop \LastLineStopfalse%
+\newcounter{LastPar}%
+
+\newcommand{\EndPage}[1]{%
+    \ifMarkContentOpen%
+       \endPDFMarkContent%
+       \global\LastLineStoptrue%
+       \setcounter{LastPar}{\noexpand\theParCounter}%
+    \else%
+       \global\LastLineStopfalse%
+    \fi%
+    \if@twocolumn \else%
+       \stepcounter{mypage}%
+    \fi%
+}%
+
+\newcommand{\BeginPage}{%
+   \ifLastLineStop%
+     \ifnum \theParCounter=\theLastPar%
+        \pdfliteral{/P <</MCID \theTaggedObj>> BDC}%
+        \global\MarkContentOpentrue%
+         \xdef\TextArray{\TextArray \space <</Type /MCR %
+                /Pg \pdfpageref\the\c@AC@truepage \space \space 0 R %
+                /MCID \theTaggedObj>>}%
+        \stepcounter{TaggedObj}%
+     \fi%
+   \fi%
+}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\original@startcolumn\@startcolumn%
+  \renewcommand{\@startcolumn}{%
+    \BeginPage%
+    \original@startcolumn%
+  }%
+}{}%
+
+  %%%%%%%%%%%%%%Berichtigung der Seitennummer%%%%%%%%%%%%%%%%%%%%%%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \def\LastNumberedPage{first}%
+  \def\AC@Pfirst{\nextAC\relax}%
+  %
+  \let\lastAC\relax  % compare to last line on this page
+  \let\firstAC\relax % compare to first line on this page
+  \let\pageAC\relax  % get the page number, compute the linenumber
+  \let\nextAC\relax  % move to the next page
+  %
+  \AtEndDocument{\let\@AC\@gobbletwo} %
+  %
+  \def\@AC#1#2{{\expandafter\@@AC%
+                   \csname AC@P#2C\@AC@column\expandafter\endcsname%
+                   \csname AC@PO#2\endcsname%
+                   {#1}{#2}}}%
+  %
+  \def\@@AC#1#2#3#4{\ifx#1\relax%
+    \ifx#2\relax\gdef#2{#3}\fi%
+      \expandafter\@@@AC\csname AC@P\LastNumberedPage\endcsname#1%
+      \xdef#1{\lastAC{#3}\firstAC{#3}%
+              \pageAC{#4}{\@AC@column}{#2}\nextAC\relax}%
+    \else%
+      \def\lastAC##1{\noexpand\lastAC{#3}}%
+      \xdef#1{#1}%
+    \fi%
+    \xdef\LastNumberedPage{#4C\@AC@column}%
+  }%
+  %
+  \def\@@@AC#1#2{{\def\nextAC##1{\noexpand\nextAC\noexpand#2}%
+                  \xdef#1{#1}}}%
+  %
+  \def\NumberedPageCache{\AC@Pfirst}%
+  %
+  \def\testLastNumberedPage#1{%
+    \ifnum#1<\c@linenumber%
+      \let\firstAC\@gobble%
+    \fi%
+    \ifnum#1=\c@linenumber%
+      \EndPage{#1}%
+    \fi%
+  }%
+  %
+  \def\testFirstNumberedPage#1{%
+    \ifnum#1>\c@linenumber%
+       \def\nextAC##1{\testNextNumberedPage\AC@Pfirst}%
+    \else%
+      \let\nextAC\@gobble%
+      \def\pageAC{\gotNumberedPage{#1}}%
+    \fi%
+  }%
+  %
+  \long\def \@gobblethree #1#2#3{}%
+  %
+  \def\testNumberedPage{%
+    \let\lastAC\testLastNumberedPage%
+    \let\firstAC\testFirstNumberedPage%
+    \let\pageAC\@gobblethree%
+    \let\nextAC\testNextNumberedPage%
+    \NumberedPageCache%
+  }%
+  %
+  \def\testNextNumberedPage#1{%
+   \ifx#1\relax%
+     \global\def\NumberedPageCache{\gotNumberedPage0000}%
+    \PackageWarning{accessibility}{Changed paragraphs, rerun to get it right}%
+   \else%
+     \global\let\NumberedPageCache#1%
+   \fi%
+   \testNumberedPage%
+  }%
+  %
+  \def\gotNumberedPage#1#2#3#4{%
+    \ifodd \if@twocolumn #3\else #2\fi\relax\fi%
+    \advance\c@linenumber\@ne % Nummerierung ab 1 sonst ab 0
+    \advance\c@linenumber-#4\relax%
+  }%
+}{}%
+  %
+  %%%%%%%%%%%%%%Spaltenerkennung%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \def\@AC@col{\def\@AC@column} %
+  \@AC@col{1}%
+  %
+  \AtBeginDocument{\let\@AC@orig@makecol\@makecol} %
+  %
+  \def\@AC@makecol{%
+     \@AC@orig@makecol
+     \setbox\@outputbox \vbox{%
+        \boxmaxdepth \@maxdepth%
+        \protected@write\@auxout{}{%
+            \string\@AC@col{\if@firstcolumn1\else2\fi}%
+        }%
+        \box\@outputbox %
+  }}%
+}{}%
+  %
+  %%%%%%%%%%%%%%Absatzerkennung%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\@@@par\@@par%
+  \newcount\linenoprevgraf%
+  %
+  \def\linenumberpar{%
+    \ifvmode \@@@par \else %
+      \ifinner \@@@par \else%
+        \xdef\@AC@outer@holdins{\the\holdinginserts}%
+        \advance \interlinepenalty \linenopenalty%
+        \linenoprevgraf \prevgraf%
+        \global \holdinginserts \thr@@ %
+        \@@@par%
+        \ifnum\prevgraf>\linenoprevgraf%
+          \penalty-\linenopenaltypar%
+        \fi%
+        \@AC@parpgbrk %
+        \global\holdinginserts\@AC@outer@holdins%
+        \advance\interlinepenalty -\linenopenalty%
+      \fi%
+    \fi}%
+  %
+  \AtEndOfPackage{%
+    \xdef\@AC@outer@holdins{\the\holdinginserts}%
+    \let\@@par\linenumberpar%
+    \ifx\@par\@@@par\let\@par\linenumberpar\fi%
+    \ifx\par\@@@par\let\par\linenumberpar\fi%
+  }%
+}{}%
+  %
+  %%%%%%%%%%%%%%Formelbehandlung%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\def\linenomath{%
+  \ifnum\interlinepenalty>-\linenopenaltypar
+    \global\holdinginserts\thr@@
+    \advance\interlinepenalty \linenopenalty
+   \ifhmode                                   %
+    \advance\predisplaypenalty \linenopenalty
+   \fi
+    \advance\postdisplaypenalty \linenopenalty
+    \advance\interdisplaylinepenalty \linenopenalty
+  \fi
+  \ignorespaces
+}%
+
+\def\endlinenomath{%
+    \global\holdinginserts\@AC@outer@holdins %
+    \global\@ignoretrue
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifundefined{mathindent}{%
+    \let\AC@displaymath\[%
+    \let\AC@enddisplaymath\]%
+    \renewcommand\[{\begin{linenomath}\AC@displaymath}%
+    \renewcommand\]{\AC@enddisplaymath\end{linenomath}}%
+    %
+    \let\AC@equation\equation%
+    \let\AC@endequation\endequation%
+    \renewenvironment{equation}%
+       {\linenomath\AC@equation}%
+       {\AC@endequation\endlinenomath}%
+  }{}%
+  %
+  \let\AC@eqnarray\eqnarray%
+  \let\AC@endeqnarray\endeqnarray%
+  \renewenvironment{eqnarray}%
+     {\linenomath\AC@eqnarray}%
+     {\AC@endeqnarray\endlinenomath}%
+  %
+  \advance\maxdeadcycles 100%
+}{}%
+  %
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalnewpage\newpage%
+  \renewcommand{\newpage}{%
+    \endPDFMarkContent%
+    \originalnewpage%
+  }%
+}{}%
+
+\newenvironment{PDFSect}[2]{%
+  \ifPDFDetailedStructure%
+      \closeUntilPDFStruct{#1}%
+      \PDFStructObj{#1}{#2}%
+  \fi%
+  \PDFSpezialTextObj{H}%
+  \EveryparConfig{H}{false}%
+}%
+{%
+  \endPDFMarkContent%
+  \endPDFSpezialTextObj%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifundefined{chapter}{% es gibt keine Chapter z.B. in Article-Klassen
+    }{%
+    \let\originalchapter\chapter%
+    \renewcommand{\chapter}{%Sortiert die verschiedenen Aufrufe
+      \@ifstar{\originalchapterWithStar}%\chapter*{Beispielkapitel}
+      {\@ifnextchar[%]
+          {\originalchapterWithTwoOption}%\chapter[BspKap]{Beispielkapitel}
+          {\originalchapterWithOption}%\chapter{Beispielkapitel}
+      }%
+    }%
+  }%
+}{}
+
+\newcommand{\originalchapterWithStar}[1]{%
+  \PDFSect{Chapter}{#1}\originalchapter*{#1}\endPDFSect}%
+\newcommand{\originalchapterWithTwoOption}[2]{%
+  \PDFSect{Chapter}{#1}\originalchapter[#1]{#2}\endPDFSect}%
+\newcommand{\originalchapterWithOption}[1]{%
+  \PDFSect{Chapter}{#1}\originalchapter{#1}\endPDFSect}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifundefined{addchap}{% es gibt keine Chapter z.B. in Article-Klassen
+  }{%
+    \let\originaladdchap\addchap%
+    \renewcommand{\addchap}{%
+      \@ifstar{\originaladdchapWithStar}%
+      {\@ifnextchar[%]
+          {\originaladdchapWithTwoOption}%
+          {\originaladdchapWithOption}%
+      }%
+    }%
+  }%
+}{}
+
+\newcommand{\originaladdchapWithStar}[1]{%
+  \PDFSect{Chapter}{#1} \originaladdchap*{#1} \endPDFSect}%
+\newcommand{\originaladdchapWithTwoOption}[2]{%
+  \PDFSect{Chapter}{#1} \originaladdchap[#1]{#2} \endPDFSect}%
+\newcommand{\originaladdchapWithOption}[1]{%
+  \PDFSect{Chapter}{#1} \originaladdchap{#1} \endPDFSect}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifundefined{set@preamble}{% es gibt kein set@preamble%
+    }{%  %außerhalb des KOMA-Scripts
+    \let\originaluse@preamble\use@preamble%
+    \renewcommand{\use@preamble}[1]{%
+        \EveryparConfig{P}{true}%
+        \originaluse@preamble{#1}%
+        \EveryparConfig{H}{false}%
+    }%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalsection\section%
+  \renewcommand{\section}{%
+    \@ifstar{\originalsectionWithStar}%
+    {\@ifnextchar[%]
+        {\originalsectionWithTwoOption}%
+        {\originalsectionWithOption}%
+    }%
+  }%
+}{}
+
+\newcommand{\originalsectionWithStar}[1]%
+  {\PDFSect{Section}{#1} \originalsection*{#1} \endPDFSect}%
+\newcommand{\originalsectionWithTwoOption}[2]%
+  {\PDFSect{Section}{#1} \originalsection[#1]{#2} \endPDFSect}%
+\newcommand{\originalsectionWithOption}[1]%
+  {\PDFSect{Section}{#1} \originalsection{#1} \endPDFSect}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalsubsection\subsection%
+  \renewcommand{\subsection}{%
+    \@ifstar{\originalsubsectionWithStar}%
+    {\@ifnextchar[%]
+        {\originalsubsectionWithTwoOption}%
+        {\originalsubsectionWithOption}%
+    }%
+  }%
+}{}
+
+\newcommand{\originalsubsectionWithStar}[1]%
+  {\PDFSect{Subsection}{#1} \originalsubsection*{#1} \endPDFSect}%
+\newcommand{\originalsubsectionWithTwoOption}[2]%
+  {\PDFSect{Subsection}{#1} \originalsubsection[#1]{#2} \endPDFSect}%
+\newcommand{\originalsubsectionWithOption}[1]%
+  {\PDFSect{Subsection}{#1} \originalsubsection{#1} \endPDFSect}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalsubsubsection\subsubsection%
+  \renewcommand{\subsubsection}{%
+    \@ifstar{\originalsubsubsectionWithStar}%
+      {\@ifnextchar[%]
+        {\originalsubsubsectionWithTwoOption}%
+        {\originalsubsubsectionWithOption}%
+    }%
+  }%
+}{}
+
+\newcommand{\originalsubsubsectionWithStar}[1]%
+  {\PDFSect{Subsubsection}{#1} \originalsubsubsection*{#1} \endPDFSect}%
+\newcommand{\originalsubsubsectionWithTwoOption}[2]%
+  {\PDFSect{Subsubsection}{#1} \originalsubsubsection[#1]{#2} \endPDFSect}%
+\newcommand{\originalsubsubsectionWithOption}[1]%
+  {\PDFSect{Subsubsection}{#1} \originalsubsubsection{#1} \endPDFSect}%
+
+\newenvironment{PDFParSect}[2]{%
+  %\ifPDFDetailedStructure%
+  %    \closeUntilPDFStruct{#1}%
+  %    \PDFStructObj{#1}{#2}%
+  %\fi%
+  \PDFTextObj%
+  \EveryparConfig{P}{false}%
+  \PDFMarkContent%
+}%
+{%
+  %\endPDFMarkContent% erst durch everypar
+  %\endPDFTextObj%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalparagraph\paragraph%
+  \renewcommand{\paragraph}{%
+    \@ifstar{\originalparagraphWithStar}%
+    {\@ifnextchar[%]
+        {\originalparagraphWithTwoOption}%
+        {\originalparagraphWithOption}%
+    }%
+  }%
+}{}
+
+\newcommand{\originalparagraphWithStar}[1]%
+  {\PDFParSect{Paragraph}{#1} \originalparagraph*{#1} \endPDFParSect}%
+\newcommand{\originalparagraphWithTwoOption}[2]%
+  {\PDFParSect{Paragraph}{#1} \originalparagraph[#1]{#2} \endPDFParSect}%
+\newcommand{\originalparagraphWithOption}[1]%
+  {\PDFParSect{Paragraph}{#1} \originalparagraph{#1} \endPDFParSect}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalsubparagraph\subparagraph%
+  \renewcommand{\subparagraph}{%
+    \@ifstar{\originalsubparagraphWithStar}%
+    {\@ifnextchar[%]
+        {\originalsubparagraphWithTwoOption}%
+        {\originalsubparagraphWithOption}%
+    }%
+  }%
+}{}
+
+\newcommand{\originalsubparagraphWithStar}[1]%
+  {\PDFParSect{Subparagraph}{#1} \originalsubparagraph*{#1} \endPDFParSect}%
+\newcommand{\originalsubparagraphWithTwoOption}[2]%
+  {\PDFParSect{Subparagraph}{#1} \originalsubparagraph[#1]{#2} \endPDFParSect}%
+\newcommand{\originalsubparagraphWithOption}[1]%
+  {\PDFParSect{Subparagraph}{#1} \originalsubparagraph{#1} \endPDFParSect}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifundefined{minisec}{}{%
+   \let\originalminisec\minisec%
+   \renewcommand{\minisec}{%
+     \@ifstar{\originalminisecWithStar}%
+     {\@ifnextchar[%]
+         {\originalminisecWithTwoOption}%
+         {\originalminisecWithOption}%
+     }%
+   }%
+  }%
+}{}
+
+\newcommand{\originalminisecWithStar}[1]%
+  {\PDFSpezialTextObj{H}\EveryparConfig{H}{false}%
+        \originalminisec*{#1} \endPDFSpezialTextObj}%
+\newcommand{\originalminisecWithTwoOption}[2]%
+  {\PDFSpezialTextObj{H}\EveryparConfig{H}{false}%
+        \originalminisec[#1]{#2} \endPDFSpezialTextObj}%
+\newcommand{\originalminisecWithOption}[1]%
+  {\PDFSpezialTextObj{H}\EveryparConfig{H}{false}%
+        \originalminisec{#1} \endPDFSpezialTextObj}%
+
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalquote\quote%
+  \let\originalendquote\endquote%
+  \renewenvironment*{quote}%
+    {\PDFSpezialTextObj{Quote}\EveryparConfig{Quote}{false}\originalquote}%
+    {\endPDFMarkContent\originalendquote\endPDFSpezialTextObj}%
+  %
+  \let\originalquotation\quotation%
+  \let\originalendquotation\endquotation%
+  \renewenvironment*{quotation}%
+    {\PDFSpezialTextObj{Quote}\EveryparConfig{Quote}{false}\originalquotation}%
+    {\endPDFMarkContent\originalendquotation\endPDFSpezialTextObj}%
+  %
+  \let\originalverse\verse%
+  \let\originalendverse\endverse%
+  \renewenvironment*{verse}%
+    {\PDFSpezialTextObj{Quote}\EveryparConfig{Quote}{false}\originalverse}%
+    {\endPDFMarkContent\originalendverse\endPDFSpezialTextObj}%
+}{}
+
+%%begin{verbatim}
+%%end{verbatim}
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalverbatim\@verbatim%
+  \renewcommand{\@verbatim}{%
+  %\PDFStructObj{Div}{\empty}%
+  \PDFSpezialTextObj{Code}
+    \originalverbatim%
+  }%
+  \let\originalendverbatim\endverbatim%
+  \renewcommand{\endverbatim}{%
+    \endPDFMarkContent%
+    \originalendverbatim%
+    \endPDFSpezialTextObj%
+    %\endPDFStructObj%
+  }%
+  \expandafter\let\csname endverbatim*\endcsname =\endverbatim%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifpackageloaded{thmbox}{%
+    \PackageWarning{accessibility}%
+        {The thmbox-package isn't yet supported.}%
+  }{}%
+  \@ifpackageloaded{theorem}{%
+    \newcommand{\@myendtheorem}{%
+      \@endtheorem%
+      \endPDFSpezialTextObj%
+    }%TODO ungetestet
+    \let\original@thm\@thm%
+    \gdef\@thm#1#2{%
+      \PDFSpezialTextObj{Div}%
+      \EveryparConfig{H}{true}%
+      \PDFMarkContent%
+      \global \expandafter \let \csname end#1\endcsname \@myendtheorem%
+      \original@thm{#1}{#2}%
+    }%
+  }{%without theorem-package
+    \let\original@begintheorem\@begintheorem%
+    \renewcommand{\@begintheorem}{%
+      \PDFSpezialTextObj{Div}%
+      \EveryparConfig{H}{true}%
+      \PDFMarkContent%
+      \EveryparConfig{P}{true}%
+      \original@begintheorem%
+    }%
+    \let\original@opargbegintheorem\@opargbegintheorem%
+    \renewcommand{\@opargbegintheorem}{%
+      \PDFSpezialTextObj{Div}%
+      \EveryparConfig{H}{true}%
+      \PDFMarkContent%
+      \EveryparConfig{P}{true}%
+      \original@opargbegintheorem%
+    }%
+    \let\original@endtheorem\@endtheorem%
+    \renewcommand{\@endtheorem}{%
+      \original@endtheorem%
+      \endPDFSpezialTextObj%
+    }%
+  }%
+}{}
+
+\newif\ifItemActive \ItemActivefalse%
+\newcounter{ListDepth}%
+
+\newenvironment{PDFList}{%
+  \ifItemActive \closeItem\fi%
+  %Liste beginnen
+  \addtocounter{ListDepth}{1}%
+  %\PDFStructObj{L}{\empty}% Sonst Fehler bei Zugriffsprüfung AA
+  \PDFStructObj{L\arabic{ListDepth}}{\empty}%
+  %\PDFStructObj{L\romannumeral\theListDepth}{\empty}%
+}{%
+  \ifItemActive \closeItem\fi%
+  %Liste beenden
+  \endPDFStructObj%
+  \addtocounter{ListDepth}{-1}%
+}
+
+\newenvironment{PDFListLabel}{%
+  \ifItemActive \closeItem\fi%
+  \PDFStructObj{LI}{\empty}%
+  \global\ItemActivetrue%
+  \PDFSpezialTextObj{Lbl}%
+  \EveryparConfig{Lbl}{false}%
+  \PDFMarkContent%
+}{%
+  \endPDFMarkContent%
+  \endPDFSpezialTextObj%
+  \PDFSpezialTextObj{LBody}%
+  \EveryparConfig{LBody}{false}%
+  %\PDFMarkContent{LBody}% wird über everypar erledigt
+}%
+
+\newcommand{\closeItem}{% Altes Item abschließen
+  \endPDFMarkContent%
+  \endPDFSpezialTextObj%{LBody}
+  \endPDFStructObj%
+  \global\ItemActivefalse%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalitemize\itemize%
+  \let\originalenditemize\enditemize%
+  \renewenvironment{itemize}%
+    {\begin{PDFList}\originalitemize}%
+    {%\ifItemActive \closeItem\fi%
+     \originalenditemize\end{PDFList}}%
+  %
+  \let\originallabelitemi\labelitemi%
+  \renewcommand{\labelitemi}{%
+     \begin{PDFListLabel} \originallabelitemi \end{PDFListLabel}}%
+  \let\originallabelitemii\labelitemii%
+  \renewcommand{\labelitemii}{%
+     \begin{PDFListLabel} \originallabelitemii \end{PDFListLabel}}%
+  \let\originallabelitemiii\labelitemiii%
+  \renewcommand{\labelitemiii}{%
+     \begin{PDFListLabel} \originallabelitemiii \end{PDFListLabel}}%
+  \let\originallabelitemiv\labelitemiv%
+  \renewcommand{\labelitemiv}{%
+     \begin{PDFListLabel} \originallabelitemiv \end{PDFListLabel}}%
+  %
+  \let\originalenumerate\enumerate%
+  \let\originalendenumerate\endenumerate%
+  \renewenvironment{enumerate}%
+    {\begin{PDFList}\originalenumerate}%
+    {%\ifItemActive \closeItem\fi%
+     \originalendenumerate\end{PDFList}}%
+  %
+  \let\originallabelenumi\labelenumi%
+  \renewcommand{\labelenumi}{%
+    \begin{PDFListLabel} \originallabelenumi \end{PDFListLabel}}%
+  \let\originallabelenumii\labelenumii%
+  \renewcommand{\labelenumii}{%
+    \begin{PDFListLabel} \originallabelenumii \end{PDFListLabel}}%
+  \let\originallabelenumiii\labelenumiii%
+  \renewcommand{\labelenumiii}{%
+    \begin{PDFListLabel} \originallabelenumiii \end{PDFListLabel}}%
+  \let\originallabelenumiv\labelenumiv%
+  \renewcommand{\labelenumiv}{%
+    \begin{PDFListLabel} \originallabelenumiv \end{PDFListLabel}}%
+  %
+  \let\originaldescription\description%
+  \let\originalenddescription\enddescription%
+  \renewenvironment{description}%
+    {\begin{PDFList}\originaldescription}%
+    {%\ifItemActive \closeItem\fi%
+     \originalenddescription\end{PDFList}}%
+  %
+  \let\originaldescriptionlabel\descriptionlabel% aus scrrept
+  \renewcommand{\descriptionlabel}[1]{%
+     \begin{PDFListLabel} \originaldescriptionlabel{#1} \end{PDFListLabel}}%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalFormulaBegin\[%
+  \renewcommand*{\[}{%
+      \PDFSpezialTextObj{Formula}
+      \EveryparConfig{Formula}{false}%%
+      \originalFormulaBegin%
+  }%
+  \let\originalFormulaEnd\]%
+  \renewcommand*{\]}{%
+      \endPDFMarkContent
+      \originalFormulaEnd%
+      \endPDFSpezialTextObj%
+  }%
+  \let\originalequation\equation%
+  \let\originalendequation\endequation%
+  \renewenvironment{equation}%
+    {\PDFSpezialTextObj{Formula}\EveryparConfig{Formula}{false}\originalequation}%
+    {\endPDFMarkContent\originalendequation\endPDFSpezialTextObj}%
+    %
+  \let\originaleqnarray\eqnarray%
+  \let\originalendeqnarray\endeqnarray%
+  \renewenvironment{eqnarray}%
+   {%\def&{\originalamp}%  --> das bringt den Fehler inaccessibile
+    \PackageWarning{accessibilty}{The `eqnarray` environment should not be used anymore. It is deprecated.}%
+     \PDFSpezialTextObj{Formula}%
+     \EveryparConfig{Formula}{false}\originaleqnarray}%
+   {\endPDFMarkContent\originalendeqnarray\endPDFSpezialTextObj}%
+}{}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\original@float\@float%
+  \let\originalend@float\end@float%
+  \renewenvironment*{@float}[1]{%
+    \PDFStructObj{Float}{\csname #1name\endcsname}%
+    %\global\numberingparsfalse%
+    \original@float{#1}%
+  }{%
+    \originalend@float%
+    \endPDFMarkContent%
+    %\global\numberingparstrue%
+    \endPDFStructObj%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\original@@makecaption\@makecaption%
+ %  \renewcommand{\@makecaption}[3]{%
+  \renewcommand{\@makecaption}[2]{%
+    \global\numberingparsfalse%
+    \PDFSpezialTextObj{Caption}%
+    \EveryparConfig{Caption}{false}%
+    \PDFMarkContent%
+      \PackageWarning{accessibility}{begin makecaption}%
+      \original@@makecaption{#1}{#2}%{#3}%
+      \PackageWarning{accessibility}{end makecaption}%
+    \endPDFMarkContent%
+    \endPDFSpezialTextObj%{Caption}%
+    \global\numberingparstrue%
+  }%
+}{}
+
+\newif\ifTableHeadCell \global\TableHeadCellfalse%
+\newif\ifTableLineActive \global\TableLineActivefalse%
+\newif\ifTableCellActive \global\TableCellActivefalse%
+\newif\ifAfterKill \global\AfterKillfalse%
+
+\newenvironment{PDFTable}{%
+  \global\numberingparsfalse%
+  \PDFStructObj{Table}{\empty}%
+  \PDFStructObj{TBody}{\empty}%
+  \global\TableLineActivefalse%
+  \global\TableCellActivefalse%
+}{%
+  \ifTableLineActive\endPDFTableLine\fi%
+  \endPDFStructObj%{TBody}{\empty}%
+  \endPDFStructObj%{Table}{\empty}%
+  \global\numberingparstrue%
+}%
+
+\newenvironment{PDFTableLine}{%
+  \ifTableCellActive\endPDFTableCell\fi%
+  \ifTableLineActive\endPDFTableLine\fi%
+  \global\TableLineActivetrue%
+  \PDFStructObj{TR}{\empty}%
+}{%
+  \ifTableLineActive%
+    \endPDFStructObj%
+    \global\TableLineActivefalse%
+  \fi%
+}%
+
+\newenvironment{PDFTableCell}{%
+  \ifTableCellActive\endPDFTableCell\fi%
+  \global\TableCellActivetrue%
+  \PDFSpezialTextObj{TD}%
+  \EveryparConfig{TD}{false}%
+  \PDFMarkContent%
+}{%
+  \ifTableCellActive%
+    \endPDFMarkContent%
+    \ifTableHeadCell%
+       \xdef\TextType{TH}%
+       \global\TableHeadCellfalse%
+    \fi%
+    \endPDFSpezialTextObj%{TD}%
+    \global\TableCellActivefalse%
+  \fi%
+}%
+
+\def\originalamp{&}%
+\catcode`\&=\active%
+\def&{\originalamp}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originaltabular\tabular%
+  \let\originalendtabular\endtabular%
+  \renewenvironment*{tabular}{%
+    \def&{\endPDFTableCell\originalamp\PDFTableCell}%
+    \PDFTable%
+    \PDFTableLine%
+    \PDFTableCell%
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    \originaltabular%
+  }{%
+    %\pdfliteral{EMC}%
+    \def&{\originalamp}%
+    \originalendtabular%
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    \ifTableCellActive\endPDFTableCell\fi%
+    \ifTableLineActive\endPDFTableLine\fi%
+    \endPDFTable%
+  }%
+  \@ifpackageloaded{array}{%
+    \let\originalaryend\@arraycr%
+    \renewcommand*{\@arraycr}{\endPDFTableCell%
+       \endPDFTableLine\PDFTableLine\PDFTableCell\originalaryend}%
+  }{% wenn kein anderes Tabellen-Package
+    \let\originaltabend\@tabularcr%
+    \renewcommand*{\@tabularcr}{\endPDFTableCell%
+       \endPDFTableLine\PDFTableLine\PDFTableCell\originaltabend}%
+  }%
+ % \@ifpackageloaded{tabularx}{%
+ %   \PackageWarning{accessibity}%
+ %       {The tabularx-package isn't yet fully supported.%
+ %        You can use the tabular-environemt but not the tabularx.}
+ % }{}%
+ % \@ifpackageloaded{longtable}{%
+ %   \PackageWarning{accessibity}%
+ %        {The longtable-package isn't yet supported.}
+ %   %\tabularnewline  \endhead\endfirsthead\endfoot\endlastfoor
+ % }{}%
+}{}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originaltabbing\tabbing%
+  \let\originalendtabbing\endtabbing%
+  \renewenvironment*{tabbing}{%
+    \PDFTable%
+    \let\originalkill\kill%
+    \renewcommand{\kill}{\global\AfterKilltrue%
+      \originalkill%%
+    }%
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    \originaltabbing%
+  }{%
+    \originalendtabbing%
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    \endPDFTable%
+  }%
+  \let\original@startfield\@startfield%
+  \renewcommand{\@startfield}{%
+    \original@startfield \ifAfterKill\PDFTableCell\fi%
+  }%
+  \let\original@stopfield\@stopfield%
+  \renewcommand{\@stopfield}{%
+    \ifAfterKill\endPDFTableCell\fi \original@stopfield%
+  }%
+  \let\original@startline\@startline%
+  \renewcommand{\@startline}{%
+    \ifAfterKill\PDFTableLine\fi \original@startline%
+  }%
+  \let\original@stopline\@stopline%
+  \renewcommand{\@stopline}{%
+    \original@stopline \ifAfterKill\endPDFTableLine\fi%
+  }%
+}{}
+
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalemph\emph%
+  \renewcommand{\emph}[1]{%
+    \begin{PDFInlineObjInText}{Span}%
+    \originalemph{#1}%
+    \end{PDFInlineObjInText}%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifpackageloaded{hyperref}{%
+    \let\original@setref\@setref%
+    \renewcommand{\@setref}[3]{%
+        \begin{PDFInlineObjInText}{Reference}%
+        \original@setref{#1}{#2}{#3}%
+        \end{PDFInlineObjInText}}%
+    %Linkziele%
+    %\let\originalhyper@anchorstart\hyper@anchorstart%
+    %\renewcommand{\hyper@anchorstart}%
+    %{\pdfliteral{/Span <</E (anchorstart)>> BDC EMC}%
+    %\originalhyper@anchorstart}%
+    %\let\originalhyper@anchorend\hyper@anchorend%
+    %\renewcommand{\hyper@anchorend}{\originalhyper@anchorend
+    %\pdfliteral{/Span <</E (anchorend)>> BDC EMC}}%
+    % Einträge im TOC, LOF, LOT
+    %\let\originalhyper@linkstart\hyper@linkstart%
+    %\renewcommand{\hyper@linkstart}{%
+    %    \begin{PDFInlineObjInText}{Reference}%
+    %     \originalhyper@linkstart}%
+    %\let\originalhyper@linkend\hyper@linkend%
+    %\renewcommand{\hyper@linkend}{%
+    %     \originalhyper@linkend%
+    %    \end{PDFInlineObjInText}}%
+    %\useacronym --> Kurzform, Glossarseitezahlen,
+    %Indexseitenzahlen, Glossareinträge, Hyperlink
+    \let\originalhyperlink\hyperlink%
+    \renewcommand*{\hyperlink}[2]{%
+        \ifIndexItemActive\else\begin{PDFInlineObjInText}{Reference}\fi%
+        %Wenn Index -- folgender Aufruf
+        % hyperlink{page.\the\toks@}{\the\toks@}%
+        %Bringt Fehler
+        \originalhyperlink{#1}{#2}%\relax%
+        \ifIndexItemActive\else\end{PDFInlineObjInText}\fi%
+    }%
+    %href  pdfobleme mit pdf 1.3 \@urlbordercolor nicht definiert
+    \let\originalhyper@linkurl\hyper@linkurl%
+    \renewcommand{\hyper@linkurl}[2]{%
+        \begin{PDFInlineObjInText}{Link}%
+        \originalhyper@linkurl{#1}{#2}%
+        \end{PDFInlineObjInText}}%
+    %
+    \let\originalhyper@linkfile\hyper@linkfile%
+    \renewcommand{\hyper@linkfile}[3]{%
+        \begin{PDFInlineObjInText}{Link}%
+        \originalhyper@linkfile{#1}{#2}{#3}%
+        \end{PDFInlineObjInText}}%
+    %Seitenzahlen in Index, anders da anschließend
+    %keine Texterkennung nötig.
+    %eigentlich über hyperlink möglich
+    \let\originalhyperpage\hyperpage%
+    \renewcommand{\hyperpage}[1]{%
+        \EveryparConfig{Reference}{true}%
+        \PDFMarkContent% kein everypar
+        \originalhyperpage{#1}%
+        \endPDFMarkContent}%
+    % URL
+    \let\originalnolinkurl\nolinkurl%
+    \renewcommand{\nolinkurl}[1]{%
+        \begin{PDFInlineObjInText}{Link}%
+        \originalnolinkurl{#1}%
+        \end{PDFInlineObjInText}}%
+  }{% ohne hyperref
+      \let\originalref\ref%
+      \renewcommand{\ref}[1]{%
+        \begin{PDFInlineObjInText}{Reference}%
+        \originalref{#1}%
+        \end{PDFInlineObjInText}}%
+      %
+      \let\originalpageref\pageref%
+      \renewcommand{\pageref}[1]{%
+        \begin{PDFInlineObjInText}{Reference}%
+        \originalpageref{#1}%
+        \end{PDFInlineObjInText}}%
+  }%
+}{}
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalcite\cite%
+  \renewcommand{\cite}[2][__empty__]{% #1 Name des Eintages
+    \begin{PDFInlineObjInText}{Reference}%
+    \ifthenelse{\equal{#1}{__empty__}}%
+        {\originalcite{#2}}%
+        {\originalcite[#1]{#2}}%
+    \end{PDFInlineObjInText}%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalverb\verb%
+  \renewcommand{\verb}{%
+    \begin{PDFInlineObjInText}{Code}%
+    \originalverb%
+  }%
+  \let\originalverb@egroup\verb@egroup%
+  \renewcommand{\verb@egroup}{%
+   \originalverb@egroup%
+    \end{PDFInlineObjInText}%
+  }%
+}{}
+
+  \let\originalFormulaTextBegin\(%
+  \renewcommand*{\(}{%
+      \PDFInlineObjInText{Formula}%
+      \originalFormulaTextBegin%
+  }%
+  \let\originalFormulaTextEnd\)%
+  \renewcommand*{\)}{%
+      \originalFormulaTextEnd%
+      \endPDFInlineObjInText%
+  }%
+
+
+\newcounter{PDFFootnotemark}%
+\newcounter{PDFFootnotetext}%
+\newcounter{ObjNum}
+
+\newenvironment{PDFFootnote}{%
+  \global\numberingparsfalse%
+  \pdfobj reserveobjnum%
+  \setcounter{PDFFootnotemark}{\pdflastobj}%
+  \pdfobj reserveobjnum%
+  \setcounter{PDFFootnotetext}{\pdflastobj}%
+}{%
+  %\EveryparConfig{\lastEveryparType}{\HelpBool}%
+  \global\numberingparstrue%
+  \EveryparConfig{\lastEveryparType}{false}%
+  \PDFMarkContent%
+}
+
+\newenvironment{PDFFootnoteReference}{%
+    \xdef\HelpBool{\InlineObj}%
+    \EveryparConfig{Reference}{obj}%
+    \setcounter{ObjNum}{\theTaggedObj}%
+    \PDFMarkContent%
+}{%
+    \endPDFMarkContent%
+    \writeComplexTextObj{\thePDFFootnotemark}%
+        {\theObjNum \space \thePDFFootnotetext \space 0 R}%
+        {/Reference}{\theTextObjNum}{Page}%
+    \xdef\TextArray{\TextArray \theObjHelp\space 0 R \space}%
+}
+
+\newenvironment{PDFFootnoteText}{%
+   \EveryparConfig{Note}{obj}%
+   \setcounter{ObjNum}{\theTaggedObj}%
+   \PDFMarkContent%
+}{%
+   \endPDFMarkContent%
+   \writeComplexTextObj%
+          {\thePDFFootnotetext}{\theObjNum}%
+          {/Footnote}{\thePDFFootnotemark}{Page}%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\original@footnotemark\@footnotemark%
+  %Fußnotenreferenz im Text
+  \renewcommand{\@footnotemark}{%
+    \begin{PDFFootnoteReference}%
+    \original@footnotemark%
+    \end{PDFFootnoteReference}%
+  }%
+  \let\original@makefntext\@makefntext%
+  %Fußnotentext am Seitenende
+  \renewcommand{\@makefntext}[1]{%
+    \begin{PDFFootnoteText}%
+    \original@makefntext{#1}%
+    \end{PDFFootnoteText}%
+  }%
+  \let\originalfootnote\footnote%
+  \def\footnote{\@ifnextchar[{\@@xxfootnote}{\@@xfootnote}}%
+  \def\@@xxfootnote[#1]#2{%
+    \begin{PDFFootnote}%
+    \originalfootnote[#1]{#2}%
+    \end{PDFFootnote}%
+  }%
+  \def\@@xfootnote#1{%
+    \begin{PDFFootnote}%
+    \originalfootnote{#1}%
+    \end{PDFFootnote}%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\original@starttoc\@starttoc%
+  \renewcommand{\@starttoc}[1]{%
+    \ifthenelse{\equal{#1}{toc}}{% Table of content
+        \PDFSpezialTextObj{TOC}\EveryparConfig{TOCI}{true}%
+    }{}%
+    \ifthenelse{\equal{#1}{lot}}{% List of Tables
+        \PDFSpezialTextObj{TOT}\EveryparConfig{TOTI}{true}%
+    }{}%
+    \ifthenelse{\equal{#1}{lof}}{% List of figures
+        \PDFSpezialTextObj{TOF}\EveryparConfig{TOFI}{true}%
+    }{}%
+    %\ifthenelse{\equal{#1}{brf}}{}}{}% Bibliography
+    \original@starttoc{#1}%
+    \ifthenelse{\equal{#1}{toc} \or \equal{#1}{lot} \or \equal{#1}{lof}}{%
+      \endPDFMarkContent%
+      \endPDFSpezialTextObj%
+    }{}%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalcontentsline\contentsline
+  \@ifpackageloaded{hyperref}{%then: Mit hyperref
+    \renewcommand{\contentsline}[4]{%
+        \originalcontentsline{#1}{#2}{#3\protect\endPDFMarkContent}{#4}%
+    }%
+  }{%else: ohne Hyperref
+    \renewcommand{\contentsline}[3]{%
+        \originalcontentsline{#1}{#2}{#3\protect\endPDFMarkContent}%
+   }%
+  }%
+}{}
+
+\newif\ifBibItemActive \BibItemActivefalse%
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalthebibliography\thebibliography%
+  \let\originalendthebibliography\endthebibliography%
+  \renewenvironment{thebibliography}{%
+      \originalthebibliography%
+      %\PDFStructObj{Bibliography}% geht hier nicht in bibitem realisiert
+  }{%
+      \originalendthebibliography%
+      \endPDFMarkContent%
+      \endPDFSpezialTextObj%{\LBody}
+      \endPDFStructObj%{\BibItem}
+      \global\BibItemActivefalse%
+      \endPDFStructObj%{Bibliography}
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalbibitem\bibitem%
+  \renewcommand{\bibitem}[2][__empty__]{% #1 [Label] #2 Eintrag
+    \ifBibItemActive% schon welche
+      \endPDFMarkContent%
+      \endPDFSpezialTextObj%{\LBody}
+      \endPDFStructObj%{\BibItem}
+      \global\BibItemActivefalse%
+    \else% erstes Item
+      \PDFStructObj{Bibliography}{\empty}%
+    \fi%
+    \global\BibItemActivetrue%
+    \PDFStructObj{BibItem}{\empty}%
+    \PDFSpezialTextObj{Lbl}%
+    \EveryparConfig{Lbl}{false}%
+    \PDFMarkContent%
+    \ifthenelse{\equal{#1}{__empty__}}%
+        {\originalbibitem{#2}}%
+        {\originalbibitem[#1]{#2}}%
+    %\endPDFMarkContent% Zu früh, Text wird erst mit everypar gestetzt
+    \endPDFSpezialTextObj%
+    \PDFSpezialTextObj{LBody}%
+    \EveryparConfig{LBody}{false}%
+   %\PDFMarkContent{LBody}% wird über everypar erledigt
+  }%
+}{}
+\newif\ifIndexItemActive \IndexItemActivefalse%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originaltheindex\theindex%
+  \let\originalendtheindex\endtheindex%
+  \renewenvironment{theindex}{%
+    \expandafter\originaltheindex\relax%
+  }{%
+      \endPDFMarkContent%
+    \originalendtheindex%
+    \ifIndexItemActive%
+      \endPDFSpezialTextObj%
+      \global\IndexItemActivefalse%
+    \fi
+    \endPDFStructObj%{Index}%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\original@idxitem\@idxitem%
+  \renewcommand*\@idxitem{%
+    \ifIndexItemActive% schon welche
+      \endPDFMarkContent%
+      \endPDFSpezialTextObj%
+      \global\IndexItemActivefalse%
+    \else% erstes Item
+      \PDFStructObj{Index}%
+    \fi%
+    \global\IndexItemActivetrue%
+    \PDFSpezialTextObj{IndexEntry}%
+    \EveryparConfig{IndexEntry}{false}%
+    \original@idxitem%
+  }%
+}{}
+
+\newenvironment*{PDFPageArtefakt}{%
+  \pdfliteral{/Artifact <</Type /Pagination>> BDC}%
+}{%
+  \pdfliteral{EMC}%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+   \let\original@thehead\@thehead%
+   \renewcommand*{\@thehead}{%
+      \ifthenelse{\equal{\original@thehead}{\empty}}{}{%
+          \begin{PDFPageArtefakt}%
+          \original@thehead%
+          \end{PDFPageArtefakt}%
+      }%
+   }%
+   \let\original@thefoot\@thefoot%
+   \renewcommand*{\@thefoot}{%
+      \ifthenelse{\equal{\original@thefoot}{\empty}}{}{%
+          \begin{PDFPageArtefakt}%
+          \original@thefoot%
+          \end{PDFPageArtefakt}%
+       }%
+   }%
+}{}
+\newenvironment*{PDFLayoutArtefakt}{%
+  \numberingparsfalse%
+  \pdfliteral{/Artifact <</Type /Layout>> BDC}%
+}{%
+  \pdfliteral{EMC}%
+  \numberingparstrue%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originaldotfill\dotfill%
+  \renewcommand*{\dotfill}{%
+    \begin{PDFLayoutArtefakt}%
+    \originaldotfill%
+    \end{PDFLayoutArtefakt}%
+  }%
+  \let\originalfootnoterule\footnoterule%
+  \renewcommand*\footnoterule{%
+    \let\hrule\originalhrule%
+    \begin{PDFLayoutArtefakt}%
+    \originalfootnoterule%
+    \end{PDFLayoutArtefakt}%
+    \let\originalhrule\hrule%
+  }%
+   %\vrule height1ex depth0pt width1ex
+   %\hrule height1ex depth0pt width1ex
+   %
+   %hrulefill, hline cline, toprule, midrule, bottomrule, cmidrule? greifen auf hrule zu
+  %Klappt nicht immer mit Argumentübergabe
+  \let\originalhrule\hrule%
+  \def\hrule#1#2{%
+    \ifthenelse{\equal{#2}{\z@}}{}{\begin{PDFLayoutArtefakt}}%
+    \originalhrule#1#2%
+    \ifthenelse{\equal{#2}{\z@}}{}{\end{PDFLayoutArtefakt}}%
+  }%
+
+
+  %vline (2), @arrayrule(2?) greift auf vrule zu
+  %Klappt nicht mit Argumentübergabe
+  %\let\originalvrule\vrule%
+  %\def\vrule#1#2{%
+  %  \begin{PDFLayoutArtefakt}%
+  %  \originalvrule#1#2%
+  %  \end{PDFLayoutArtefakt}%
+}{}
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \def\@dottedtocline#1#2#3#4#5{%
+    \ifnum #1>\c@tocdepth \else%
+      \vskip \z@ \@plus.2\p@%
+      {\leftskip #2\relax \rightskip \@tocrmarg %
+      \parfillskip -\rightskip%
+      \parindent #2\relax\@afterindenttrue%
+      \interlinepenalty\@M%
+      \leavevmode%
+      \@tempdima #3\relax%
+      \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip%
+      {#4}\nobreak%
+      \begin{PDFLayoutArtefakt}%
+      \leaders\hbox{$\m@th \mkern %
+        \@dotsep mu\hbox{.}\mkern \@dotsep mu$}\hfill%
+      \end{PDFLayoutArtefakt}%
+      \nobreak%
+      \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}%
+      \par}%
+    \fi%
+  }%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \let\originalmaketitle\maketitle%
+  \renewcommand{\maketitle}{%
+    \PDFStructObj{Div}{Titlepage}%
+    \EveryparConfig{P}{false}%
+    %
+    \originalmaketitle%
+    \endPDFMarkContent%
+    \endPDFStructObj%
+  }%
+}{}%
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifpackageloaded{graphicx}{%
+    \let\originalincludegraphics\includegraphics%
+    \renewcommand{\includegraphics}[2][__empty__]{%
+    \global\numberingparsfalse%
+     % \PDFInlineObjInText{Figure}%
+    \PDFSpezialTextObj{Figure}%
+    \EveryparConfig{Figure}{false}%
+    \PDFMarkContent%
+      \ifthenelse{\equal{#1}{__empty__}}%
+          {\originalincludegraphics{#2}}%
+          {\originalincludegraphics[#1]{#2}}%
+    %  \endPDFInlineObjInText%
+    \endPDFMarkContent%
+    \endPDFSpezialTextObj%{Figure}%
+    \global\numberingparstrue%
+    }%
+  }{}%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+    \let\originalpicture\picture%
+    \let\originalendpicture\endpicture%
+    \renewenvironment{picture}{%
+    \global\numberingparsfalse%
+    \PDFSpezialTextObj{Figure}%
+    \EveryparConfig{Figure}{false}%
+    \PDFMarkContent%
+    \originalpicture%
+ }{%
+    \originalendpicture%
+    \endPDFMarkContent%
+    \endPDFSpezialTextObj%{Figure}%
+    \global\numberingparstrue%
+    }%
+}{}
+
+
+\newcommand{\convertLanguageInCode}[1]{%
+  \gdef\LanguageCode{}%
+  \ifthenelse{\equal{#1}{\string danish}}{\gdef\LanguageCode{/Lang(DA)}}{}%
+  \ifthenelse{\equal{#1}{\string german}}{\gdef\LanguageCode{/Lang(DE)}}{}%
+  \ifthenelse{\equal{#1}{\string ngerman}}{\gdef\LanguageCode{/Lang(DE)}}{}%
+  \ifthenelse{\equal{#1}{\string germanb}}{\gdef\LanguageCode{/Lang(DE)}}{}%
+  \ifthenelse{\equal{#1}{\string austrian}}{\gdef\LanguageCode{/Lang(DE)}}{}%
+  \ifthenelse{\equal{#1}{\string naustrian}}{\gdef\LanguageCode{/Lang(DE)}}{}%
+  \ifthenelse{\equal{#1}{\string english}}{\gdef\LanguageCode{/Lang(EN)}}{}%
+  \ifthenelse{\equal{#1}{\string USenglish}}{\gdef\LanguageCode{/Lang(EN-US)}}{}%
+  \ifthenelse{\equal{#1}{\string american}}{\gdef\LanguageCode{/Lang(EN-US)}}{}%
+  \ifthenelse{\equal{#1}{\string UKenglish}}{\gdef\LanguageCode{/Lang(EN-GB)}}{}%
+  \ifthenelse{\equal{#1}{\string british}}{\gdef\LanguageCode{/Lang(EN-GB)}}{}%
+  \ifthenelse{\equal{#1}{\string canadian}}{\gdef\LanguageCode{/Lang(EN)}}{}%
+  \ifthenelse{\equal{#1}{\string australian}}{\gdef\LanguageCode{/Lang(EN)}}{}%
+  \ifthenelse{\equal{#1}{\string newzealand}}{\gdef\LanguageCode{/Lang(EN)}}{}%
+  \ifthenelse{\equal{#1}{\string finnish}}{\gdef\LanguageCode{/Lang(FI)}}{}%
+  \ifthenelse{\equal{#1}{\string french}}{\gdef\LanguageCode{/Lang(FR)}}{}%
+  \ifthenelse{\equal{#1}{\string francais}}{\gdef\LanguageCode{/Lang(FR)}}{}%
+  \ifthenelse{\equal{#1}{\string canadien}}{\gdef\LanguageCode{/Lang(FR)}}{}%
+  \ifthenelse{\equal{#1}{\string acadian}}{\gdef\LanguageCode{/Lang(FR)}}{}%
+  \ifthenelse{\equal{#1}{\string italian}}{\gdef\LanguageCode{/Lang(IT)}}{}%
+  \ifthenelse{\equal{#1}{\string norsk}}{\gdef\LanguageCode{/Lang(NO)}}{}%
+  \ifthenelse{\equal{#1}{\string nynorsk}}{\gdef\LanguageCode{/Lang(NO)}}{}%
+  \ifthenelse{\equal{#1}{\string portuges}}{\gdef\LanguageCode{/Lang(PT)}}{}%
+  \ifthenelse{\equal{#1}{\string portuguese}}{\gdef\LanguageCode{/Lang(PT)}}{}%
+  \ifthenelse{\equal{#1}{\string brazilian}}{\gdef\LanguageCode{/Lang(PT-BR)}}{}%
+  \ifthenelse{\equal{#1}{\string brazil}}{\gdef\LanguageCode{/Lang(PT-BR)}}{}%
+  \ifthenelse{\equal{#1}{\string swedish}}{\gdef\LanguageCode{/Lang(SV)}}{}%
+  \ifthenelse{\equal{#1}{\string spanish}}{\gdef\LanguageCode{/Lang(ES)}}{}%
+   % not surreported in babel:
+  \ifthenelse{\equal{\LanguageCode}{}}{%
+\PackageWarning{accessibility}{The chosen language (#1) is not supported %
+by Adobe Reader 6.0.}%
+}{}%
+}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \AtBeginDocument{%
+    \gdef\DocumentLanguage{\languagename}%
+    \gdef\ActualLanguage{\languagename}%
+    \convertLanguageInCode{\languagename}%
+    \pdfcatalog{% Catalog dictionary of PDF output.
+      \LanguageCode% Setzt die Sprache
+    }%
+  }%
+}{}
+
+\newcommand{\recognizeLanguageChange}[1]{%
+  \ifthenelse{\equal{#1}{\ActualLanguage}}{%
+     %keine änderung zu vorher
+  }{%
+     \gdef\ActualLanguage{#1}%
+     \convertLanguageInCode{\languagename}}%
+  \ifthenelse{\equal{#1}{\DocumentLanguage}}{%
+     \global\LanguageDifffalse%
+  }{%
+     \global\LanguageDifftrue%
+  }%
+}
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifpackageloaded{babel}{%
+    \let\originalselectlanguage\selectlanguage%
+    \renewcommand{\selectlanguage}[1]{%
+      \originalselectlanguage{#1}%
+      \recognizeLanguageChange{#1}%
+    }%
+    \let\originalotherlanguage\otherlanguage%
+    \let\originalendotherlanguage\otherlanguage%
+    \long\def\otherlanguage#1{%
+      \csname selectlanguage \endcsname{#1}%
+      \ignorespaces%
+      \recognizeLanguageChange{#1}%
+    }%
+      \long\def\endotherlanguage{%
+      \originalTeX%
+      \global\@ignoretrue\ignorespaces%
+      \recognizeLanguageChange{\languagename}%
+    }%
+    \let\originalforeignlanguage\foreignlanguage%
+    \renewcommand{\foreignlanguage}[2]{%
+      \convertLanguageInCode{\string #1}%
+      \pdfliteral{/Span <<\LanguageCode>> BDC}%
+      \originalforeignlanguage{#1}{#2}%
+      \pdfliteral{EMC}%
+      \convertLanguageInCode{\languagename}%
+    }%
+  }{}%
+}{}
+
+\ifthenelse{\boolean{@Access@pdf}}{%
+  \@ifpackageloaded{glossary}{%
+    \let\originalnewacronym\newacronym%
+    \renewcommand{\newacronym}[4][]{%
+  %%%%% Originaldefinition
+\ifthenelse{\equal{#1}{}}{\renewcommand\@acrnmcmd{#2}}{%
+\renewcommand\@acrnmcmd{#1}}%
+    \xdef\expansion{#3}%
+\@ifundefined{\@acrnmcmd}{%
+\expandafter\newcommand\csname\@acrnmcmd short\endcsname{%
+\protect\pdfliteral{/Span <</E (\expansion)>> BDC}%
+#2%
+\protect\pdfliteral{EMC}%
+\protect\glsxspace}%
+\expandafter\newcommand\csname\@acrnmcmd @nx@short\endcsname{%
+\protect\pdfliteral{/Span <</E (\expansion)>> BDC}%
+#2%
+\protect\pdfliteral{EMC}}%
+\expandafter\newcommand\csname\@acrnmcmd long\endcsname{%
+#3\protect\glsxspace}
+\expandafter\newcommand\csname\@acrnmcmd @nx@long\endcsname{#3}
+\def\@acrn@entry{#4}%
+{%
+\expandafter\@gls@getdescr\expandafter{\@acrn@entry}%
+\let\glodesc\@glo@desc%
+\def\glolong{#3}%
+\@onelevel@sanitize\glolong
+\def\gloshort{\noexpand\acronymfont{#2}}%
+\@onelevel@sanitize\gloshort
+\expandafter\protected@xdef\expandafter\@acrnamefmt{\@acronymnamefmt}
+\expandafter\protected@xdef\expandafter\@acrdesc{\@acronymdescfmt}
+}%
+\@acr@addtolist{\@acrnmcmd}
+\@glo@tb=\expandafter{\@acrn@entry}%
+\protected@edef\@acr@glsentry{name={\@acrnamefmt},%
+format=glsnumformat,sort={\@acrnmcmd},\the\@glo@tb,%
+description={\@acrdesc}}%
+\@glo@tb=\expandafter{\@acr@glsentry}%
+\newboolean{\@acrnmcmd first}\setboolean{\@acrnmcmd first}{true}
+\expandafter\protected@edef\csname \@acrnmcmd\endcsname{%
+\noexpand\@ifstar{\csname @s@\@acrnmcmd\endcsname}{%
+\csname @\@acrnmcmd\endcsname}}
+\ifglshyperacronym % hyperlinks
+\expandafter\protected@edef\csname @\@acrnmcmd\endcsname{%
+\noexpand\ifthenelse{\noexpand\boolean{\@acrnmcmd first}}{%
+\csname\@acrnmcmd @nx@long\endcsname\noexpand\@acrnmins\
+(\noexpand\xacronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\csname\@acrnmcmd @nx@short\endcsname}%
+})\noexpand\unsetacronym{\@acrnmcmd}%
+}{\noexpand\xacronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\csname\@acrnmcmd @nx@short\endcsname}%
+\noexpand\@acrnmins}}\noexpand\glsxspace}
+\expandafter\protected@edef\csname @s@\@acrnmcmd\endcsname{%
+\noexpand\ifthenelse{\noexpand\boolean{\@acrnmcmd first}}{%
+\noexpand\expandafter\noexpand\MakeUppercase
+\csname\@acrnmcmd @nx@long\endcsname\noexpand\@acrnmins\
+(\noexpand\xacronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\csname\@acrnmcmd @nx@short\endcsname}%
+})%
+\noexpand\unsetacronym{\@acrnmcmd}}{%
+\noexpand\xacronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\noexpand\expandafter\noexpand\MakeUppercase
+\csname\@acrnmcmd @nx@short\endcsname}%
+\noexpand\@acrnmins}}\noexpand\glsxspace}
+\else % no hyperlinks
+\expandafter\protected@edef\csname @\@acrnmcmd\endcsname{%
+\noexpand\ifthenelse{\noexpand\boolean{\@acrnmcmd first}}{%
+\csname\@acrnmcmd @nx@long\endcsname\noexpand\@acrnmins\
+(\noexpand\acronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\csname\@acrnmcmd @nx@short\endcsname}%
+})\noexpand\unsetacronym{\@acrnmcmd}%
+}{\noexpand\acronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\csname\@acrnmcmd @nx@short\endcsname}%
+\noexpand\@acrnmins}}%
+\noexpand\glsxspace}
+\expandafter\protected@edef\csname @s@\@acrnmcmd\endcsname{%
+\noexpand\ifthenelse{\noexpand\boolean{\@acrnmcmd first}}{%
+\noexpand\expandafter
+\noexpand\MakeUppercase
+\csname\@acrnmcmd @nx@long\endcsname\noexpand\@acrnmins\
+(\noexpand\acronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\csname\@acrnmcmd @nx@short\endcsname}%
+})%
+\noexpand\unsetacronym{\@acrnmcmd}}{%
+\noexpand\acronym{\the\@glo@tb}{%
+\noexpand\acronymfont{\noexpand\expandafter\noexpand\MakeUppercase
+\csname\@acrnmcmd @nx@short\endcsname}%
+\noexpand\@acrnmins}}\noexpand\glsxspace}
+\fi
+}{%
+\PackageError{glossary}{Command '\expandafter\string
+\csname\@acrnmcmd\endcsname' already defined}{%
+The command name specified by \string\newacronym already exists.}}}
+  %%%%% Originaldefinition
+  }{}%
+}{}
+
+\endinput
+%%
+%% End of file `accessibility.sty'.
diff --git a/syllabus/syllabus.tex b/syllabus/syllabus.tex
index 864853c8..ee345a8e 100644
--- a/syllabus/syllabus.tex
+++ b/syllabus/syllabus.tex
@@ -1,248 +1,252 @@
-\documentclass[11pt]{article}
+\documentclass[10pt]{article}
 
-\usepackage[margin=0.75in,tmargin=0.5in]{geometry}
-\usepackage{mathpazo}
+\usepackage[tagged]{accessibility}
 
-\usepackage{inconsolata}
-\usepackage[T1]{fontenc}
+%\usepackage[cm]{fullpage}
+\usepackage[lmargin=0.65in,rmargin=0.65in,
+            tmargin=0.5in,bmargin=0.75in]{geometry}
 
-\usepackage{hyperref}
+\usepackage[parfill]{parskip}
 
-\usepackage{fancyhdr}
+\usepackage{hyperref}
 
-\pagestyle{fancy}
+\usepackage{palatino}
 
-\fancyhead{}
-\fancyfoot[LO,LE]{Python for Scientific Computing/Spring 2015}
-\fancyfoot[RO,RE]{revision 1.03 (2015-01-23)}
-\fancyfoot[CO,CE]{\thepage}
-\renewcommand{\headrulewidth}{0.0pt}
-\renewcommand{\footrulewidth}{0.0pt}
+% URLs (special font for monospace)
+\usepackage[defaultsans]{cantarell}
+\usepackage[T1]{fontenc}
 
 
-\newenvironment{squishlist}
-  {\begin{itemize}
+\newenvironment{itemsquish}
+  { \begin{itemize}
     % set spacing between items
-    \addtolength{\itemsep}{-0.33\baselineskip}
+    \addtolength{\itemsep}{-0.25\baselineskip}
     % set spacing between lines
-    %\addtolength{\baselineskip}{-0.25\baselineskip}
-   }
+    \addtolength{\baselineskip}{-0.25\baselineskip} }
   { \end{itemize} }
 
+\hypersetup{
+  pdftitle={PHY 546: Python for Scientific Computing (Spring 2025)},
+  pdflang={en-US}
+}
 
 \begin{document}
 
 \begin{center}
-{\Large \bf PHY 683: Python for Scientific Computing} \\[0.25em]
-{\em instructor}\/: Michael Zingale, ESS 452, michael.zingale@stonybrook.edu \\
-{\em date/location}\/: Mondays, 3:00--3:53~pm in Physics P112
+{\LARGE \sffamily \bfseries PHY 546: Python for Scientific Computing} \\[1mm]
+{\sffamily Spring 2025} \\[3mm]
+{\em Instructor}\/: Michael Zingale, ESS 452, michael.zingale@stonybrook.edu \\
+{\em Date/Location}\/: Mondays, 2:00--2:55~pm in Humanities 3018 \\
+{\em Web:}\/ {\small \url{https://sbu-python-class.github.io/python-science}}
 \end{center}
 
-\noindent {\bf Learning Goals:} \\
+\subsection*{Learning Goals:}
+
 The learning goal of this course is to enhance scholarship in an area
 of active research, by learning how to apply python to problems
 in your field.
 
-\ \\[-2mm]
-\noindent {\bf Credit:} \\
+
+\subsection*{Format:}
+
+The class will be interactive throughout, with students working
+through Jupyter notebooks together with the instructor during the
+class.  Students will be expected to read and work through some basic
+notebooks on their own before class, and then, in class, we will work
+on projects together, sharing what we learn.
+
+This class is {\em heavily} dependent on out-of-class discussion,
+managed via slack.  All students are expected to participate in this
+online discussion.
+
+\subsection*{Credit:}
+
 This is a 1-credit course.
 
 
-\ \\[-2mm]
-\noindent {\bf Contacting the Instructor:}  \\
-{\em e-mail:} michael.zingale@stonybrook.edu ({add ``PHY 683'' to the
+\subsection*{Contacting the Instructor:}
+
+{\em e-mail:} michael.zingale@stonybrook.edu ({add ``PHY 546'' to the
  start of the subject line of any e-mail}).
 %
-\begin{tabbing}
-\noindent {\em office hours:} \= Tues.~~  \=1:00~pm \=to \=2:30~pm \\
-                              \> Thurs.\ \>1:00~pm \>to \>2:30~pm
-\end{tabbing}
-Generally, I am in my office every day this semester, except for Wednesdays.
-
-\ \\[-2mm]
-\noindent {\bf Webpage:} \\
-The webpage for this class is \url{http://bender.astro.sunysb.edu/classes/python-science} .
-
-\ \\[-2mm]
-\noindent {\bf Texts:} \\
-There are no textbooks for this class.  Online information
+%% \begin{tabbing}
+%% \noindent {\em office hours:} \= Tues.~~  \=1:00~pm \=to \=2:30~pm \\
+%%                               \> Thurs.\ \>1:00~pm \>to \>2:30~pm
+%% \end{tabbing}
+%% Generally, I am in my office every day this semester, except for Wednesdays.
+
+
+
+\subsection*{Texts:}
+
+There are no required textbooks for this class.  Online information
 will be linked to from the course webpage.
 
-\ \\[-2mm]
-\noindent {\bf Lecture Topics:} \\
+A nice text for you to read along with the class, if desired, is
+{\em Effective Computation in Physics}\/ by Scopatz \& Huff.
+
+
+
+\subsection*{Lecture Topics:}
+
 We will (try to) discuss the following topics in the course:
 %
-\begin{squishlist}
-\item {\em Basics of computing} (0.5 lectures)
-
-\item {\em Introduction to python} (3.5 lectures) \\
-  data structures and control statements, functions, classes, popular modules \\
-  IPython and notebooks
+\begin{itemsquish}
+\item {\em Introduction to python} (4 lectures) \\ data structures and
+  control statements, functions, classes, popular modules, Jupyter
+  notebooks
 
 \item {\em Software engineering practices} (1 lecture) \\
-  including {\tt git} and github
+  including {\tt git} and github, and unit testing (with {\tt pytest})
 
-\item {\em Introduction to the NumPy array library} (1 lecture)
+\item {\em Introduction to the NumPy array library} (2 lecture)
 
-\item {\em matplotlib for visualization} (1 lecture)
+\item {\em matplotlib / bokeh / plot.ly for visualization} (1 lecture)
 
-\item {\em SciPy and numerical methods} (3 lectures)
+\item {\em SciPy and numerical methods} (2 lectures)
 
 \item {\em Introduction to SymPy} (1 lecture)
 
-\item {\em MayaVi for 3-d visualization} (0.5 lectures)
-
-\item {\em f2py and C extensions} (1.5 lectures)
+\item {\em Machine learning} (1 lecture)
 
 \item {\em Building applications / packaging} (1 lecture)
-\end{squishlist}
+\end{itemsquish}
 
 \noindent Time-permitting, we will also discuss:
-\begin{squishlist}
-\item {\em GUIs} (1 lecture)
+\begin{itemsquish}
+\item {\em NetworkX}
 
-\item {\em Unit testing}
+\item {\em Pandas and the data frame} (1 lecture)
 
-\item {\em Interacting with forms on a webpage}
 
-\item {\em Julia}
-\end{squishlist}
-%
+\item {\em Cython / Numba / extensions}
+
+\end{itemsquish}
+
 \noindent The actual course topics and time spent on each topic will depend on the
 interest and the participation level of the class.
 
-\ \\[-2mm]
-\noindent {\bf Computers:} \\
+
+\subsection*{Computers:}
 %
 As we all use different systems for our research, we are not meeting
 in a computer lab.  Instead, you should bring your own laptop to
 class.  Information on how to install python on Windows, Mac OSX, and
-Linux will be posted on the class webpage. \\
+Linux will be posted on the class webpage.
 
 \noindent
-If you don't have access to a computer that can run python, see me,
-and I can try to arrange something for the semester.
+If you don't have access to a computer that can run python, then you will be
+able to follow along for most of the course content using a cloud platform.
 
-\ \\[-2mm]
-\noindent {\bf Evaluation:} \\
+
+\subsection*{Slack:}
 %
+We will use the slack team communication tool for all discussion.  You
+will be added to the slack team at the start of the semester and then
+can join in on the conversation at: {\sf phy546-S25}
+
+
+\subsection*{Evaluation:}
+
 Students are expected to attend the class and to contribute
-to the discussions (by asking questions, proposing examples, or
+to the slack discussions (by asking questions, proposing examples, or
 providing demonstrations of their own).  As we meet only one hour per
 week, students show plan on spending time outside of class reviewing
-and practicing the material we discussed. \\
+and practicing the material we discussed.
 
-\noindent {\bf The primary place for participation is the online forum
-  on the class blackboard page}.  This is the place to interact with
-me and your classmates---ask anything, share examples, etc. \\
+\noindent {\em \bfseries The primary place for participation is the slack team chat}.  This is the place to interact with
+me and your classmates---ask anything, share examples, etc.
 
 \noindent Letter grades will be based on the online participation.  A
 rough guide is presented below:
 \begin{itemize}
-\item {\sf A\phantom{+}}: 10 postings to the discussion board
-  {\bf plus} a small example showing how you can apply what
-  we've discussed in class to your field.
+\item {\sf A\phantom{+}}: 10 (meaningful) postings to slack
 
-\item {\sf A$-$}: 8 postings to the discussion board
+\item {\sf A$-$}: 8 (meaningful) postings to slack
 
-\item {\sf B$+$}: 5 postings to the discussion board
+\item {\sf B$+$}: 5 postings to the slack
 
-\item {\sf B\phantom{+}}:  2 postings to the discussion board
+\item {\sf B\phantom{+}}:  3 postings to the slack
 \end{itemize}
 A post does not mean a ``me to''-type post, but something either
 demonstrating a problem you don't understand (giving code), asking for
 some detail from the lecture to be explained, sharing a neat trick you
-found, answering a classmate's question, etc.  Note that asking
+found, answering a classmate's question, etc.  {\em \bfseries Note that asking
 questions about the content counts just as much as providing
-answers---the idea is to have a discussion outside of class on the
+answers}---the idea is to have a discussion outside of class on the
 material.
 
-\ \\[2mm]
-\noindent {\bf Required Syllabus Statements:} \\
 
-\noindent
-{\em Americans with Disabilities Act: }
-%
-If you have a physical, psychological, medical or learning disability
-that may impact your course work, please contact Disability Support
-Services, ECC (Educational Communications Center) Building, Room 128,
-(631) 632-6748. They will determine with you what accommodations, if
-any, are necessary and appropriate. All information and documentation
-is confidential.
 
+\subsection*{Student Accessibility Support Center Statement}
+
+If you have a physical, psychological, medical, or learning disability
+that may impact your course work, please contact the Student
+Accessibility Support Center, Stony Brook Union Suite 107, (631)
+632-6748, or at sasc@stonybrook.edu. They will determine with you what
+accommodations are necessary and appropriate. All information and
+documentation is confidential.
+
+Students who require assistance during emergency evacuation are
+encouraged to discuss their needs with their professors and the
+Student Accessibility Support Center. For procedures and information
+go to the following website:\\
+{\small \url{https://ehs.stonybrook.edu//programs/fire-safety/emergency-evacuation/evacuation-guide-disabilities}}\linebreak
+and search Fire Safety and Evacuation and Disabilities.
+
+
+\subsection*{Academic Integrity}
 
-\ \\[-2mm]
-\noindent
-{\em Academic Integrity: }
-%
 Each student must pursue his or her academic goals honestly and be
-personally accountable for all submitted work.  Representing another
-person's work as your own is always wrong.  Faculty are required to
+personally accountable for all submitted work. Representing another
+person's work as your own is always wrong. Faculty is required to
 report any suspected instances of academic dishonesty to the Academic
 Judiciary. Faculty in the Health Sciences Center (School of Health
-Technology \& Management, Nursing, Social Welfare, Dental Medicine) and
-School of Medicine are required to follow their school-specific
-procedures. For more comprehensive information on academic integrity,
-including categories of academic dishonesty, please refer to the
-academic judiciary website at
-\url{http://www.stonybrook.edu/uaa/academicjudiciary/}
-
-\ \\[-2mm]
-\noindent
-{\em Critical Incident Management: }
-%
+Professions, Nursing, Social Welfare, Dental Medicine) and School of
+Medicine are required to follow their school-specific procedures. For
+more comprehensive information on academic integrity, including
+categories of academic dishonesty please refer to the academic
+judiciary website at\\
+{\small \url{http://www.stonybrook.edu/commcms/academic\_integrity/}}
+
+
+\subsection*{Critical Incident Management}
+
 Stony Brook University expects students to respect the rights,
 privileges, and property of other people. Faculty are required to
-report to the Office of Judicial Affairs any disruptive behavior that
-interrupts their ability to teach, compromises the safety of the
-learning environment, or inhibits students' ability to learn.  Faculty
-in the HSC Schools and the School of Medicine are required to follow
-their school-specific procedures.
-
-\ \\[-2mm]
-{\em Electronic Communication: }
-%
-Email to your University email account is an important way of
-communicating with you for this course.  For most students the email
-address is:\\[0.25em] `firstname.lastname@stonybrook.edu'\\[0.25em]
-and the account can be
-accessed here.
+report to the Office of Student Conduct and Community Standards any
+disruptive behavior that interrupts their ability to teach,
+compromises the safety of the learning environment, or inhibits
+students' ability to learn. Faculty in the HSC Schools and the School
+of Medicine are required to follow their school-specific
+procedures. Further information about most academic matters can be
+found in the Undergraduate Bulletin, the Undergraduate Class Schedule,
+and the Faculty-Employee Handbook.
+
+
+\subsection*{Electronic Communication}
+
+Email to your University email account is an important way
+of communicating with you for this course.  For most students the
+email address is `{\tt firstname.lastname@stonybrook.edu}'.
+%, and the account can be accessed here.
 {\em It is your responsibility to read your email received at this
-  account.}
-For instructions about how to verify your University email address see
-this: \\[0.25em]
-\url{http://it.stonybrook.edu/help/kb/checking-or-changing-your-mail-forwarding-address-in-the-epo} \\[0.25em]
-%
-You can set up email forwarding using instructions here: \\[0.25em]
-\url{http://it.stonybrook.edu/help/kb/setting-up-mail-forwarding-in-google-mail}
-\\[0.25em]
-%
+  account.}  For instructions about how to verify your University
+email address see this: \\
+{\small \url{http://it.stonybrook.edu/help/kb/checking-or-changing-your-mail-forwarding-address-in-the-epo}}\\
 If you choose to forward your University email to another account, we
-are not responsible for any undeliverable messages.
+are not responsible for undeliverable messages.
 
-\ \\[-2mm]
-\noindent
-{\em Religious Observances: }
-%
-See the policy statement regarding religious holidays at \\[0.25em]
-{\footnotesize \url{http://www.stonybrook.edu/commcms/provost/pdfs/Policy\%20Statement\%20Regarding\%20Religious\%20Holidays\%20revised.docx}}
-\\[0.25em]
+
+\subsection*{Religious Observances}
+
+See the policy statement regarding religious holidays at\hfill\\
+{\small \url{https://www.stonybrook.edu/commcms/registrar/calendars/religious_holidays.php#2022}} \linebreak
 %
+
 Students are expected to notify the course professors by email of
 their intention to take time out for religious observance.  This
 should be done as soon as possible but definitely before the end of
 the `add/drop' period.  At that time they can discuss with the
 instructor(s) how they will be able to make up the work covered.
-
-
-
-
-
-
-\ \\[3 mm]
-\noindent
-(information on the above required syllabus statements can be found at \\
-\url{http://www.stonybrook.edu/commcms/provost/category/faculty/policies.html}\,)
-
-
 \end{document}
diff --git a/topics.txt b/topics.txt
index d4c7ef26..6da20798 100644
--- a/topics.txt
+++ b/topics.txt
@@ -1,3 +1,5 @@
+pycharm edu? https://www.jetbrains.com/pycharm-edu/
+
 general:
 
    python: all a scientist needs