Skip to main content

Agent-based modeling (ABM) in Python 3+

Project description

https://api.travis-ci.org/projectmesa/mesa.svg?branch=master https://codecov.io/gh/projectmesa/mesa/branch/master/graph/badge.svg https://img.shields.io/badge/code%20style-black-000000.svg

Mesa is an Apache2 licensed agent-based modeling (or ABM) framework in Python.

It allows users to quickly create agent-based models using built-in core components (such as spatial grids and agent schedulers) or customized implementations; visualize them using a browser-based interface; and analyze their results using Python’s data analysis tools. Its goal is to be the Python 3-based alternative to NetLogo, Repast, or MASON.

A screenshot of the Schelling Model in Mesa

Above: A Mesa implementation of the Schelling segregation model, being visualized in a browser window and analyzed in a Jupyter notebook.

Features

  • Modular components

  • Browser-based visualization

  • Built-in tools for analysis

  • Example model library

Using Mesa

Getting started quickly:

$ pip install mesa

You can also use pip to install the github version:

$ pip install -e git+https://github.com/projectmesa/mesa#egg=mesa

Take a look at the examples folder for sample models demonstrating Mesa features.

For more help on using Mesa, check out the following resources:

Running Mesa in Docker

You can run Mesa in a Docker container in a few ways.

If you are a Mesa developer, first install docker-compose and then run:

$ docker-compose build --pull
...
$ docker-compose up -d dev # start the docker container
$ docker-compose exec dev bash # enter the docker container that has your current version of Mesa installed at /opt/mesa
$ mesa runserver examples/Schelling # or any other example model in examples

The docker-compose file does two important things:

  • It binds the docker container’s port 8521 to your host system’s port 8521 so you can interact with the running model as usual by visiting localhost:8521 on your browser

  • It mounts the mesa root directory (relative to the docker-compose.yml file) into /opt/mesa and runs pip install -e on that directory so your changes to mesa should be reflected in the running container.

If you are a model developer that wants to run Mesa on a model (assuming you are currently in your top-level model directory with the run.py file):

$ docker run --rm -it -p127.0.0.1:8521:8521 -v${PWD}:/code comses/mesa:dev mesa runserver /code

Contributing back to Mesa

If you run into an issue, please file a ticket for us to discuss. If possible, follow up with a pull request.

If you would like to add a feature, please reach out via ticket or the dev email list for discussion. A feature is most likely to be added if you build it!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Mesa-0.8.8.1.tar.gz (646.2 kB view hashes)

Uploaded Source

Built Distribution

Mesa-0.8.8.1-py3-none-any.whl (667.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page