Text and supporting code for Modeling and Simulation in Python
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Modeling and Simulation in Python is an introduction to physical modeling using a computational approach. It is organized in three parts:
The first part presents discrete models, including a bikeshare system and world population growth.
The second part introduces first-order systems, including models of infectious disease, thermal systems, and chemical kinetics.
The third part is about second-order systems, including mechanical systems like projectiles, celestial mechanics, and rotating rigid bodies.
Taking a computational approach makes it possible to work with more realistic models than what you typically see in a first-year physics class, with the option to include features like friction and drag.
Python is an ideal programming language for this material. It is a good first language for people who have not programmed before, and it provides high-level data structures that are well-suited to express solutions to the problems we are interested in.
Modeling and Simulation in Python is a Free Book. It is available under the Creative Commons Attribution-NonCommercial 4.0 Unported License, which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don’t use it for commercial purposes.
To run the examples and work on the exercises in this book, you have to:
Install Python on your computer, along with the libraries we will use.
Copy my files onto your computer.
Run Jupyter, which is a tool for running and writing programs, and load a notebook, which is a file that contains code and text.
The next three sections provide details for these steps. I wish there were an easier way to get started; it’s regrettable that you have to do so much work before you write your first program. Be persistent!
You might already have Python installed on your computer, but you might not have the latest version. To use the code in this book, you need Python 3.6 or later. Even if you have the latest version, you probably don’t have all of the libraries we need.
You could update Python and install these libraries, but I strongly recommend that you don’t go down that road. I think you will find it easier to use Anaconda, which is a free Python distribution that includes all the libraries you need for this book (and more).
Anaconda is available for Linux, macOS, and Windows. By default, it puts all files in your home directory, so you don’t need administrator (root) permission to install it, and if you have a version of Python already, Anaconda will not remove or modify it.
Start at https://www.anaconda.com/download. Download the installer for your system and run it. You don’t need administrative privileges to install Anaconda, so I recommend you run the installer as a normal user, not as administrator or root.
I suggest you accept the recommended options. On Windows you have the option to install Visual Studio Code, which is an interactive environment for writing programs. You won’t need it for this book, but you might want it for other projects.
By default, Anaconda installs most of the packages you need, but there are a few more you might have to add. Once the installation is complete, open a command window. On macOS or Linux, you can use Terminal. On Windows, open Git Bash.
Run the following command (copy and paste it if you can, to avoid typos):
conda install jupyterlab pandas seaborn sympy beautifulsoup4 lxml
Some of these packages might already be installed. Then run this command:
conda install -c unidata pint
That should be everything you need.
Copying my files
The code for this book is available from https://github.com/AllenDowney/ModSimPy, which is a Git repository. Git is a software tool that helps you keep track of the programs and other files that make up a project. A collection of files under Git’s control is called a repository (the cool kids call it a “repo"). GitHub is a hosting service that provides storage for Git repositories and a convenient web interface.
Before you download these files, I suggest you copy my repository on GitHub, which is called forking. If you don’t already have a GitHub account, you’ll need to create one.
Use a browser to view the homepage of my repository at https://github.com/AllenDowney/ModSimPy. You should see a gray button in the upper right that says Fork. If you press it, GitHub will create a copy of my repository that belongs to you.
Now, the best way to download the files is to use a Git client, which is a program that manages git repositories. You can get installation instructions for Windows, macOS, and Linux at http://modsimpy.com/getgit.
In Windows, I suggest you accept the options recommended by the installer, with two exceptions:
As the default editor, choose instead of .
For “Configuring line ending conversions", select “Check out as is, commit as is".
For macOS and Linux, I suggest you accept the recommended options.
Once the installation is complete, open a command window. On Windows, open Git Bash, which should be in your Start menu. On macOS or Linux, you can use Terminal.
To find out what directory you are in, type , which stands for “print working directory". On Windows, most likely you are in . On MacOS or Linux, you are probably in your home directory, .
The next step is to copy files from your repository on GitHub to your computer; in Git vocabulary, this process is called cloning. Run this command:
git clone https://github.com/YourGitHubUserName/ModSimPy
Of course, you should replace with your GitHub user name. After cloning, you should have a new directory called .
If you don’t want to use Git, you can download my files in a Zip archive from http://modsimpy.com/zip. You will need a program like WinZip or gzip to unpack the Zip file. Make a note of the location of the files you download.
The code for each chapter, and starter code for the exercises, is in Jupyter notebooks. If you have not used Jupyter before, you can read about it at https://jupyter.org.
To start Jupyter on macOS or Linux, open a Terminal; on Windows, open Git Bash. Use to “change directory" into the code directory in the repository:
Then launch the Jupyter notebook server:
Jupyter should open a window in a browser, and you should see the list of notebooks in my repository. Click on the first notebook, and follow the instructions to run the first few “cells". The first time you run a notebook, it might take several seconds to start, while some Python files get initialized. After that, it should run faster.
Feel free to read through the notebook, but it might not make sense until you read Chapter 1.
You can also launch Jupyter from the Start menu on Windows, the Dock on macOS, or the Anaconda Navigator on any system. If you do that, Jupyter might start in your home directory or somewhere else in your file system, so you might have to navigate to find the directory.