Agent-based modeling (ABM) in Python 3+
Project description
Mesa: Agent-based modeling in Python 3+
=========================================
.. image:: https://github.com/projectmesa/mesa/workflows/build/badge.svg
:target: https://github.com/projectmesa/mesa/actions
.. image:: https://codecov.io/gh/projectmesa/mesa/branch/main/graph/badge.svg
:target: https://codecov.io/gh/projectmesa/mesa
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
.. image:: https://img.shields.io/matrix/project-mesa:matrix.org?label=chat&logo=Matrix
:target: https://matrix.to/#/#project-mesa:matrix.org
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.
.. image:: https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/Mesa_Screenshot.png
:width: 100%
:scale: 100%
:alt: 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.*
.. _`Mesa` : https://github.com/projectmesa/mesa/
Features
------------
* Modular components
* Browser-based visualization
* Built-in tools for analysis
* Example model library
Using Mesa
------------
Getting started quickly:
.. code-block:: bash
$ pip install mesa
You can also use `pip` to install the github version:
.. code-block:: bash
$ pip install -U -e git+https://github.com/projectmesa/mesa@main#egg=mesa
Or any other (development) branch on this repo or your own fork:
.. code-block:: bash
$ pip install -U -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa
Take a look at the `examples <https://github.com/projectmesa/mesa/tree/main/examples>`_ folder for sample models demonstrating Mesa features.
For more help on using Mesa, check out the following resources:
* `Intro to Mesa Tutorial`_
* `Docs`_
* `Email list for users`_
* `PyPI`_
.. _`Intro to Mesa Tutorial` : http://mesa.readthedocs.org/en/main/tutorials/intro_tutorial.html
.. _`Docs` : http://mesa.readthedocs.org/en/main/
.. _`Email list for users` : https://groups.google.com/d/forum/projectmesa
.. _`PyPI` : https://pypi.python.org/pypi/Mesa/
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 <https://docs.docker.com/compose/install/>`_ and then, in the folder containing the Mesa Git repository, you run:
.. code-block:: bash
$ docker compose up
# If you want to make it run in the background, you instead run
$ docker compose up -d
This runs the wolf-sheep predation model, as an example.
With the docker-compose.yml file in this Git repository, the `docker compose up` command does two important things:
* 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.
* 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
If you are a model developer that wants to run Mesa on a model, you need to:
* make sure that your model folder is inside the folder containing the docker-compose.yml file
* change the ``MODEL_DIR`` variable in docker-compose.yml to point to the path of your model
* make sure that the model folder contains a run.py file
Then, you just need to run `docker compose up -d` to make it accessible from ``localhost:8521``.
Contributing to Mesa
----------------------------
Want to join the Mesa team or just curious about what is happening with Mesa? You can...
* Join our `Matrix chat room`_ in which questions, issues, and ideas can be (informally) discussed.
* Come to a monthly dev session (you can find dev session times, agendas and notes on `Mesa discussions`_).
* Just check out the code on `GitHub`_.
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 join a dev session (see `Mesa discussions`_).
A feature is most likely to be added if you build it!
Don't forget to checkout the `Contributors guide`_.
.. _`Matrix chat room` : https://matrix.to/#/#project-mesa:matrix.org
.. _`Mesa discussions` : https://github.com/projectmesa/mesa/discussions
.. _`GitHub` : https://github.com/projectmesa/mesa/
.. _`ticket` : https://github.com/projectmesa/mesa/issues
.. _`Contributors guide` : https://github.com/projectmesa/mesa/blob/main/CONTRIBUTING.rst
Citing Mesa
----------------------------
To cite Mesa in your publication, you can use the `CITATION.bib`_.
.. _`CITATION.bib` : https://github.com/projectmesa/mesa/blob/main/CITATION.bib
=========================================
.. image:: https://github.com/projectmesa/mesa/workflows/build/badge.svg
:target: https://github.com/projectmesa/mesa/actions
.. image:: https://codecov.io/gh/projectmesa/mesa/branch/main/graph/badge.svg
:target: https://codecov.io/gh/projectmesa/mesa
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
.. image:: https://img.shields.io/matrix/project-mesa:matrix.org?label=chat&logo=Matrix
:target: https://matrix.to/#/#project-mesa:matrix.org
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.
.. image:: https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/Mesa_Screenshot.png
:width: 100%
:scale: 100%
:alt: 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.*
.. _`Mesa` : https://github.com/projectmesa/mesa/
Features
------------
* Modular components
* Browser-based visualization
* Built-in tools for analysis
* Example model library
Using Mesa
------------
Getting started quickly:
.. code-block:: bash
$ pip install mesa
You can also use `pip` to install the github version:
.. code-block:: bash
$ pip install -U -e git+https://github.com/projectmesa/mesa@main#egg=mesa
Or any other (development) branch on this repo or your own fork:
.. code-block:: bash
$ pip install -U -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa
Take a look at the `examples <https://github.com/projectmesa/mesa/tree/main/examples>`_ folder for sample models demonstrating Mesa features.
For more help on using Mesa, check out the following resources:
* `Intro to Mesa Tutorial`_
* `Docs`_
* `Email list for users`_
* `PyPI`_
.. _`Intro to Mesa Tutorial` : http://mesa.readthedocs.org/en/main/tutorials/intro_tutorial.html
.. _`Docs` : http://mesa.readthedocs.org/en/main/
.. _`Email list for users` : https://groups.google.com/d/forum/projectmesa
.. _`PyPI` : https://pypi.python.org/pypi/Mesa/
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 <https://docs.docker.com/compose/install/>`_ and then, in the folder containing the Mesa Git repository, you run:
.. code-block:: bash
$ docker compose up
# If you want to make it run in the background, you instead run
$ docker compose up -d
This runs the wolf-sheep predation model, as an example.
With the docker-compose.yml file in this Git repository, the `docker compose up` command does two important things:
* 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.
* 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
If you are a model developer that wants to run Mesa on a model, you need to:
* make sure that your model folder is inside the folder containing the docker-compose.yml file
* change the ``MODEL_DIR`` variable in docker-compose.yml to point to the path of your model
* make sure that the model folder contains a run.py file
Then, you just need to run `docker compose up -d` to make it accessible from ``localhost:8521``.
Contributing to Mesa
----------------------------
Want to join the Mesa team or just curious about what is happening with Mesa? You can...
* Join our `Matrix chat room`_ in which questions, issues, and ideas can be (informally) discussed.
* Come to a monthly dev session (you can find dev session times, agendas and notes on `Mesa discussions`_).
* Just check out the code on `GitHub`_.
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 join a dev session (see `Mesa discussions`_).
A feature is most likely to be added if you build it!
Don't forget to checkout the `Contributors guide`_.
.. _`Matrix chat room` : https://matrix.to/#/#project-mesa:matrix.org
.. _`Mesa discussions` : https://github.com/projectmesa/mesa/discussions
.. _`GitHub` : https://github.com/projectmesa/mesa/
.. _`ticket` : https://github.com/projectmesa/mesa/issues
.. _`Contributors guide` : https://github.com/projectmesa/mesa/blob/main/CONTRIBUTING.rst
Citing Mesa
----------------------------
To cite Mesa in your publication, you can use the `CITATION.bib`_.
.. _`CITATION.bib` : https://github.com/projectmesa/mesa/blob/main/CITATION.bib
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