Installation Guide | readthedocs | Introduction on Colab | HowToGalaxy
PyAutoGalaxy is software for analysing the morphologies and structures of galaxies:
PyAutoGalaxy also fits interferometer data from observatories such as ALMA:
The following human-readable documentation and examples are useful for new starters:
- The PyAutoGalaxy readthedocs, which includes an overview of PyAutoGalaxy's core features, a new user starting guide and an installation guide.
- The introduction Jupyter Notebook on Google Colab, where you can try PyAutoGalaxy in a web browser (without installation).
- The autogalaxy_workspace GitHub repository: example scripts covering every PyAutoGalaxy use case.
- The HowToGalaxy GitHub repository: a Jupyter notebook lecture series teaching galaxy modeling from the ground up.
The PyAutoGalaxy AI Assistant lets you do galaxy structure and morphology science in natural language from inside an AI coding agent. You can get started simply by asking it a question about galaxy structure or describing the task you would like to perform with PyAutoGalaxy. See the assistant for its full scope and instructions.
The assistant runs inside an AI coding agent: Claude Code or Codex are recommended. Sustained scientific use normally needs paid access to one of them (a personal subscription, institutional access or API billing). OpenCode is an experimental alternative whose client is free but whose model access, cost and capability depend on the provider. Browser chat routes (ChatGPT or Claude with a GitHub connector) are no longer supported.
PyAutoGalaxy has three core aims:
- Big Data: Scaling automated Sérsic fitting to extremely large datasets, accelerated with JAX on GPUs and using tools like an SQL database to build a scalable scientific workflow.
- Model Complexity: Fitting complex galaxy morphology models (e.g. Multi Gaussian Expansion, Shapelets, Ellipse Fitting, Irregular Meshes) that go beyond just simple Sérsic fitting.
- Data Variety: Support for many data types (e.g. CCD imaging, interferometry, multi-band imaging) which can be fitted independently or simultaneously.
A complete overview of the software's aims is provided in our Journal of Open Source Software paper.
Questions, help with your code or your analysis, and ideas: the PyAutoLabs Discussions. Bug reports with a reproducer (a snippet, the traceback, your versions): an issue on the library's tracker. The Slack is for collaborators, by invitation.
The collaborator Slack workspace shares project updates and discussions about galaxy modeling and analysis.
For users less familiar with galaxy analysis, Bayesian inference, and scientific analysis, you may wish to read through the HowToGalaxy lectures. These introduce the basic principles of galaxy modeling and Bayesian inference, with the material pitched at undergraduate level and above.
A complete overview of the lectures is provided on the HowToGalaxy readthedocs page, and the notebooks themselves live in the PyAutoLabs/HowToGalaxy repository.
Information on how to cite PyAutoGalaxy in publications can be found on the citations page.
Information on how to contribute to PyAutoGalaxy can be found on the contributing page.
Hands on support for contributions is available via our Slack workspace, again please email to request an invite.