GerryChain

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GerryChain is a library for using Markov Chain Monte Carlo methods to study the problem of political redistricting. Development of the library began during the 2018 Voting Rights Data Institute (VRDI).

The project is in active development in the mggg/GerryChain GitHub repository, where bug reports and feature requests, as well as contributions, are welcome.

Installation

Supported Python Versions

The most recent version of GerryChain (as of April 2024) supports

  • Python 3.9

  • Python 3.10

  • Python 3.11

  • Python 3.12

If you do not have one of these versions installed on you machine, we recommend that you go to the Python website and download the installer for one of these versions. 1

A Note For Windows Users

If you are using Windows and are new to Python, we recommend that you still install Python using the installation package available on the Python website. There are several versions of Python available on the Windows Store, but they can be… finicky, and experience seems to suggest that downloadable available on the Python website produce better results.

In addition, we recommend that you install the Windows Terminal from the Microsoft Store. It is still possible to use PowerShell or the Command Prompt, but Windows Terminal tends to be more beginner friendly and allows for a greater range of utility than the natively installed terminal options (for example, it allows for you to install the more recent version of PowerShell, PowerShell 7, and for the use of the Linux Subsystem for Windows).

Setting Up a Virtual Environment

Once Python is installed on your system, you will want to open the terminal and navigate to the working directory of your project. Here are some brief instructions for doing so on different systems:

  • MacOS: To open the terminal, you will likely want to use the Spotlight Search (the magnifying glass in the top right corner of your screen) to find the “Terminal” application (you can also access Spotlight Search by pressing “Command (⌘) + Space”). Once you have the terminal open, type cd followed by the path to your working directory. For example, if you are working on a project called my_project in your Documents folder, you may access by typing the command

    cd ~/Documents/my_project

    into the terminal (here the ~ is a shortcut for your home directory). If you do not know what your working directory is, you can find it by navigating to the desired folder in your file explorer, and clicking on “Get Info”. The path will be labeled “Where” and from there you can copy the path to your clipboard and paste it in the terminal.

  • Linux: Most Linux distributions have the keyboard shortcut Ctrl + Alt + T set to open the terminal. From there you may navigate to your working directory by typing cd followed by the path to your working directory. For example, if you are working on a project called my_project in your Documents folder, you may access this via the command

    cd ~/Documents/my_project

    (here the ~ is a shortcut for your home directory). If you do not know what your working directory is, you can find it by navigating to the desired folder in your file explorer, and clicking on “Properties”. The path will be labeled “Location” and from there you can copy the path to your clipboard and paste it in the terminal (to paste in the terminal in Linux, you will need to use the keyboard shortcut Ctrl + Shift + V instead of Ctrl + V).

  • Windows: Open the Windows Terminal and type cd followed by the path to your working directory. For example, if you are working on a project called my_project in your Documents folder, you may access this by typing the command

    cd ~\Documents\my_project

    into the terminal (here the ~ is a shortcut for your home directory). If you do not know what your working directory is, you can find it by navigating to the desired folder in your file explorer, and clicking on “Properties”. The path will be labeled “Location” and from there you can copy the path to your clipboard and paste it in the terminal.

Once you have navigated to your working directory, you will want to set up a virtual environment. This is a way of isolating the Python packages you install for this project from the packages you have installed globally on your system. This is useful because it allows you to install different versions of packages for different projects without worrying about compatibility issues. To set up a virtual environment, type the following command into the terminal:

python -m venv .venv

This will create a virtual environment in your working directory which you can see if you list all the files in your working directory via the command ls -a (dir on Windows). Now we need to activate the virtual environment. To do this, type the following command into the terminal:

  • Windows: .venv\Scripts\activate

  • MacOS/Linux: source .venv/bin/activate

You should now see (.venv) at the beginning of your terminal prompt. This indicates that you are in the virtual environment, and are now ready to install GerryChain.

To install GerryChain from PyPI, run pip install gerrychain from the command line.

If you plan on using GerryChain’s GIS functions, such as computing adjacencies or reading in shapefiles, then run pip install gerrychain[geo] from the command line.

This approach sometimes fails due to compatibility issues between our different Python GIS dependencies, like geopandas, pyproj, fiona, and shapely. If you run into this issue, try installing the dependencies using the geo_settings.txt file. To do this, run pip install -r geo_settings.txt from the command line.

Note

If you plan on following through the tutorials present within the remainder of this documentation, you will also need to install matplotlib from PyPI. This can also be accomplished with a simple invocation of pip install matplotlib from the command line.

1

Of course, if you are using a Linux system, you will either need to use your system’s package manager or install from source. You may also find luck installing Python directly from the package manager if you find installing from source to be troublesome.

User Guide

We also highly recommend the resources prepared by Daryl R. DeFord of MGGG for the 2019 MIT IAP course Computational Approaches for Political Redistricting.

API Reference

Topics

Index