========== GerryChain

.. image:: https://circleci.com/gh/mggg/GerryChain.svg?style=svg :target: https://circleci.com/gh/mggg/GerryChain :alt: Build Status .. image:: https://codecov.io/gh/mggg/GerryChain/branch/master/graph/badge.svg :target: https://codecov.io/gh/mggg/GerryChain :alt: Code Coverage .. image:: https://readthedocs.org/projects/gerrychain/badge/?version=latest :target: https://gerrychain.readthedocs.io/en/latest :alt: Documentation Status .. image:: https://badge.fury.io/py/gerrychain.svg :target: https://pypi.org/project/gerrychain/ :alt: PyPI Package .. image:: https://img.shields.io/conda/vn/conda-forge/gerrychain.svg?color=%230099cd :target: https://anaconda.org/conda-forge/gerrychain :alt: conda-forge Package

GerryChain is a Python library for building ensembles of districting plans using Markov chain Monte Carlo. It is developed and maintained by the Metric Geometry and Gerrymandering Group and our network of volunteers. It is distributed under the 3-Clause BSD License_.

The basic workflow is to start with the geometry of an initial plan and generate a large collection of sample plans for comparison. Usually, we will constrain these sampled plans in such a way that they perform at least as well as the initial plan according to traditional districting principles, such as population balance or compactness. Comparing the initial plan to the ensemble provides quantitative tools for measuring whether or not it is an outlier among the sampled plans.

.. _Voting Rights Data Institute: http://gerrydata.org/ .. _chain: https://github.com/gerrymandr/cfp_mcmc .. _Markov chain Monte Carlo: https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo .. _Metric Geometry and Gerrymandering Group: https://www.mggg.org/ .. _3-Clause BSD License: https://opensource.org/licenses/BSD-3-Clause

Getting started

See our Getting started guide_ for the basics of using GerryChain.

.. _Getting started guide: https://gerrychain.readthedocs.io/en/latest/user/quickstart.html

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

.. _Computational Approaches for Political Redistricting: https://people.csail.mit.edu/ddeford//CAPR.php

Useful links

• Documentation_
• Bug reports and feature requests_
• Contributions welcome!_

.. _Documentation: https://gerrychain.readthedocs.io/en/latest/ .. _Bug reports and feature requests: https://github.com/mggg/gerrychain/issues .. _Contributions welcome!: https://github.com/mggg/gerrychain/pulls

Installation

Using conda (recommended)

To install GerryChain from conda-forge_ using conda_, run

.. code-block:: console

conda install -c conda-forge gerrychain

For this command to work as intended, you will first need to activate the conda environment that you want to install GerryChain in. If the environment you want to activate is called vrdi (for example), then you can do this by running

.. code-block:: console

conda activate vrdi

If this command causes problems, make sure conda is up-to-date by running

.. code-block:: console

conda update conda
conda init

For more information on using conda to install packages and manage dependencies, see Getting started with conda_.

.. _Getting started with conda: https://conda.io/projects/conda/en/latest/user-guide/getting-started.html .. _conda: https://conda.io/projects/conda/en/latest/ .. _conda-forge: https://conda-forge.org/

Using pip

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

This approach often fails due to compatibility issues between our different Python GIS dependencies, like geopandas, pyproj, fiona, and shapely. For this reason, we recommend installing from conda-forge for most users.

.. _PyPI: https://pypi.org/