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# Mesa: Agent-based modeling in Python
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# Synthetic movement data generation using Mesa
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## About Mesa
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Mesa allows users to quickly create agent-based models using built-in
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[Mesa](https://github.com/projectmesa/mesa) allows users to quickly create agent-based models using built-in
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core components (such as spatial grids and agent schedulers) or
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customized implementations; visualize them using a browser-based
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interface; and analyze their results using Python's data analysis
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tools. Its goal is to be the Python-based alternative to NetLogo,
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Repast, or MASON.
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![A screenshot of the WolfSheep Model in Mesa](https://raw.githubusercontent.com/projectmesa/mesa/main/docs/images/wolf_sheep.png)
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*Above: A Mesa implementation of the WolfSheep model, this
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can be displayed in browser windows or Jupyter.*
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## Features
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- Modular components
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- Browser-based visualization
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- Built-in tools for analysis
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- Example model library
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## Using Mesa
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To install our latest stable release (3.0.x), run:
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## Installation
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``` bash
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pip install -U mesa
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pip install mesa==2.4.0 shapely movingpandas
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```
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To install our latest pre-release, run:
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``` bash
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pip install -U --pre mesa
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```
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Starting with Mesa 3.0, we don't install all our dependencies anymore by default.
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```bash
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# You can customize the additional dependencies you need, if you want. Available are:
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pip install -U --pre mesa[network,viz]
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# This is equivalent to our recommended dependencies:
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pip install -U --pre mesa[rec]
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# To install all, including developer, dependencies:
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pip install -U --pre mesa[all]
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```
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You can also use `pip` to install the latest GitHub version:
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``` bash
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pip install -U -e git+https://github.com/projectmesa/mesa@main#egg=mesa
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```
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Or any other (development) branch on this repo or your own fork:
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``` bash
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pip install -U -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa
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```
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## Resources
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For resources or help on using Mesa, check out the following:
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- [Intro to Mesa Tutorial](http://mesa.readthedocs.org/en/stable/tutorials/intro_tutorial.html) (An introductory model, the Boltzmann
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Wealth Model, for beginners or those new to Mesa.)
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- [Visualization Tutorial](https://mesa.readthedocs.io/stable/tutorials/visualization_tutorial.html) (An introduction into our Solara visualization)
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- [Complexity Explorer Tutorial](https://www.complexityexplorer.org/courses/172-agent-based-models-with-python-an-introduction-to-mesa) (An advanced-beginner model,
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SugarScape with Traders, with instructional videos)
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- [Mesa Examples](https://github.com/projectmesa/mesa-examples) (A repository of seminal ABMs using Mesa and
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examples of employing specific Mesa Features)
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- [Docs](http://mesa.readthedocs.org/) (Mesa's documentation, API and useful snippets)
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- [Development version docs](https://mesa.readthedocs.io/latest/) (the latest version docs if you're using a pre-release Mesa version)
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- [Discussions](https://github.com/projectmesa/mesa/discussions) (GitHub threaded discussions about Mesa)
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- [Matrix Chat](https://matrix.to/#/#project-mesa:matrix.org) (Chat Forum via Matrix to talk about Mesa)
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## Running Mesa in Docker
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You can run Mesa in a Docker container in a few ways.
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If you are a Mesa developer, first [install Docker
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Compose](https://docs.docker.com/compose/install/) and then, in the
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folder containing the Mesa Git repository, you run:
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``` bash
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$ docker compose up
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# If you want to make it run in the background, you instead run
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$ docker compose up -d
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```
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This runs the Schelling model, as an example.
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With the docker-compose.yml file in this Git repository, the `docker compose up` command does two important things:
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- It mounts the mesa root directory (relative to the
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docker-compose.yml file) into /opt/mesa and runs pip install -e on
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that directory so your changes to mesa should be reflected in the
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running container.
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- It binds the docker container's port 8765 to your host system's
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port 8765 so you can interact with the running model as usual by
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visiting localhost:8765 on your browser
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If you are a model developer that wants to run Mesa on a model, you need
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to:
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- make sure that your model folder is inside the folder containing the
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docker-compose.yml file
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- change the `MODEL_DIR` variable in docker-compose.yml to point to
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the path of your model
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- make sure that the model folder contains an app.py file
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Then, you just need to run `docker compose up -d` to have it
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accessible from `localhost:8765`.
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## Contributing to Mesa
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Want to join the Mesa team or just curious about what is happening with
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Mesa? You can\...
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> - Join our [Matrix chat room](https://matrix.to/#/#project-mesa:matrix.org) in which questions, issues, and
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> ideas can be (informally) discussed.
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> - Come to a monthly dev session (you can find dev session times,
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> agendas and notes on [Mesa discussions](https://github.com/projectmesa/mesa/discussions)).
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> - Just check out the code on [GitHub](https://github.com/projectmesa/mesa/).
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If you run into an issue, please file a [ticket](https://github.com/projectmesa/mesa/issues) for us to discuss. If
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possible, follow up with a pull request.
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## User guide
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If you would like to add a feature, please reach out via [ticket](https://github.com/projectmesa/mesa/issues) or
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join a dev session (see [Mesa discussions](https://github.com/projectmesa/mesa/discussions)). A feature is most likely
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to be added if you build it!
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To use the generators, follow the instructions in corresponding directory:
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Don't forget to checkout the [Contributors guide](https://github.com/projectmesa/mesa/blob/main/CONTRIBUTING.md).
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- [Ship movement simulator](https://github.com/movingpandas/synth-data-generator/tree/main/mesa/examples/basic/ships_hybrid_algorithm)
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## Citing Mesa
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## Acknowledgements
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To cite Mesa in your publication, you can use the [CITATION.bib](https://github.com/projectmesa/mesa/blob/main/CITATION.bib).
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This work was supported in part by the Horizon Framework Programme of the European Union under grant agreement No. 101070279 ([MobiSpaces](https://mobispaces.eu)).

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