Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pypi.python.org/pypi/Mesa/ below:

Mesa · PyPI

Mesa 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-based alternative to NetLogo, Repast, or MASON.

Above: A Mesa implementation of the WolfSheep model, this can be displayed in browser windows or Jupyter.

• Modular components
• Browser-based visualization
• Built-in tools for analysis
• Example model library

To install our latest stable release, run:

pip install -U mesa

Starting with Mesa 3.0, we don't install all our dependencies anymore by default.

# You can customize the additional dependencies you need, if you want. Available are:
pip install -U mesa[network,viz]

# This is equivalent to our recommended dependencies:
pip install -U mesa[rec]

# To install all, including developer, dependencies:
pip install -U mesa[all]

You can also use pip to install the latest GitHub version:

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:

pip install -U -e git+https://github.com/YOUR_FORK/mesa@YOUR_BRANCH#egg=mesa

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

• Intro to Mesa Tutorial (An introductory model, the Boltzmann Wealth Model, for beginners or those new to Mesa.)
• Visualization Tutorial (An introduction into our Solara visualization)
• Complexity Explorer Tutorial (An advanced-beginner model, SugarScape with Traders, with instructional videos)
• Mesa Examples (A repository of seminal ABMs using Mesa and examples of employing specific Mesa Features)
• Docs (Mesa's documentation, API and useful snippets)
  • Development version docs (the latest version docs if you're using a pre-release Mesa version)
• Discussions (GitHub threaded discussions about Mesa)
• Matrix Chat (Chat Forum via Matrix to talk about Mesa)

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

If you are a Mesa developer, first install Docker Compose and then, in the folder containing the Mesa Git repository, you run:

$ docker compose up
# If you want to make it run in the background, you instead run
$ docker compose up -d

This runs the Schelling 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 8765 to your host system's port 8765 so you can interact with the running model as usual by visiting localhost:8765 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 an app.py file

Then, you just need to run docker compose up -d to have it accessible from localhost:8765.

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.

To cite Mesa in your publication, you can refer to our peer-reviewed article in the Journal of Open Source Software (JOSS):

• ter Hoeven, E., Kwakkel, J., Hess, V., Pike, T., Wang, B., rht, & Kazil, J. (2025). Mesa 3: Agent-based modeling with Python in 2025. Journal of Open Source Software, 10(107), 7668. https://doi.org/10.21105/joss.07668

Our CITATION.cff can be used to generate APA, BibTeX and other citation formats.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4