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Updating chapters
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‎chapters/chap01.ipynb‎

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" local, _ = urlretrieve(url, filename)\n",
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" print('Downloaded ' + local)\n",
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" \n",
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"download('https://raw.githubusercontent.com/AllenDowney/' +\n",
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" 'ModSimPy/master/modsim.py')"
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"download('https://github.com/AllenDowney/ModSimPy/raw/master/' +\n",
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" 'modsim.py')"
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]
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},
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{
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"This book is about modeling and simulation of physical systems. The\n",
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"following diagram shows what I mean by \"modeling\":\n",
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"\n",
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"<img src =\"https://github.com/AllenDowney/ModSimPy/raw/master/book/figs/modeling_framework.png\" width=\"400\">"
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"![Diagram of the modeling framework](https://github.com/AllenDowney/ModSim/raw/main/figs/modeling_framework.png)"
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]
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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},
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"nbformat": 4,

‎chapters/chap05.ipynb‎

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{
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"cell_type": "markdown",
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"id": "continuing-cancellation",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"source": [
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"The following cell downloads a copy of https://en.wikipedia.org/wiki/World_population_estimates"
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]
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"cell_type": "code",
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"execution_count": 4,
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"id": "convenient-stroke",
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"download('https://raw.githubusercontent.com/AllenDowney/' +\n",
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"metadata": {},
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"source": [
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"To read this data, we will use Pandas, which provides functions for\n",
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"working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it. You have already seen this import\n",
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"statement:"
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"working with data. The function we'll use is `read_html`, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it."
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]
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},
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{
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}
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],
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"metadata": {
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"celltoolbar": "Tags",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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},
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"nbformat": 4,

‎chapters/chap09.ipynb‎

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"source": [
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"**Exercise:** Use SymPy to solve the quadratic growth equation using the alternative parameterization\n",
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"\n",
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"$\\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $"
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"$$ \\frac{df(t)}{dt} = \\alpha f(t) + \\beta f^2(t) $$\n"
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]
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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},
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"nbformat": 4,

‎chapters/chap10.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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"nbformat": 4,

‎chapters/chap11.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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"nbformat": 4,

‎chapters/chap17.ipynb‎

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"License: [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
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]
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "electoral-turkey",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# install Pint if necessary\n",
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"\n",
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"try:\n",
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" import pint\n",
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"except ImportError:\n",
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" !pip install pint"
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]
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},
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{
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"cell_type": "code",
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"id": "close-payday",
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"metadata": {},
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"source": [
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"`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is the concentration of insulin in the blood in μU/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
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"`data` has two columns: `glucose` is the concentration of blood glucose in mg/dL; `insulin` is the concentration of insulin in the blood in $\\mu$U/mL (a medical \"unit\", denoted U, is an amount defined by convention in context). The index is time in minutes.\n",
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"\n",
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"This dataset represents glucose and insulin concentrations over\n",
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"182 min for a subject with normal insulin production and sensitivity."
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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"nbformat": 4,

‎chapters/chap19.ipynb‎

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"## Low-Pass Filter\n",
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"\n",
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"The following circuit diagram (from <http://modsimpy.com/divider>) shows a low-pass filter built with one resistor and one capacitor.\n",
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"The following circuit diagram (from [Wikipedia](https://commons.wikimedia.org/wiki/File:1st_Order_Lowpass_Filter_RC.svg)) shows a low-pass filter built with one resistor and one capacitor.\n",
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"\n",
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"![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/RC_Divider.svg)\n",
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"![Circuit diagram of a low-pass filter](https://github.com/AllenDowney/ModSim/raw/main/figs/Lowpass_Filter_RC.png)\n",
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"\n",
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"A \"filter\" is a circuit takes a signal, $V_{in}$, as input and produces a signal, $V_{out}$, as output. In this context, a \"signal\" is a voltage that changes over time.\n",
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"\n",
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"\n",
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"$$I_R = (V_{in} - V_{out}) / R$$ \n",
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"\n",
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"where $R$ is resistance in ohms (Ω).\n",
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"where $R$ is resistance in ohms ($\\Omega$).\n",
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"\n",
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"Assuming that no current flows through the output of the circuit,\n",
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"Kirchhoff's current law implies that the current through the capacitor\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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"nbformat": 4,

‎chapters/chap22.ipynb‎

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"source": [
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"**Exercise:** The model so far is based on the assumption that coefficient of drag does not depend on velocity, but in reality it does. The following figure, from Adair, [*The Physics of Baseball*](https://books.google.com/books/about/The_Physics_of_Baseball.html?id=4xE4Ngpk_2EC), shows coefficient of drag as a function of velocity.\n",
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"\n",
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"<img src=\"https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png\" width=\"400\">\n",
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"![Graph of drag coefficient versus velocity](https://github.com/AllenDowney/ModSimPy/raw/master/figs/baseball_drag.png)\n",
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"\n",
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"I used [an online graph digitizer](https://automeris.io/WebPlotDigitizer/) to extract the data and save it in a CSV file. "
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.7.9"
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}
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"nbformat": 4,

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