https://github.com/pyautolabs/pyautogalaxy
PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology
https://github.com/pyautolabs/pyautogalaxy
astronomy astrophysics galaxies image-processing morphology python
Last synced: 22 days ago
JSON representation
PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology
- Host: GitHub
- URL: https://github.com/pyautolabs/pyautogalaxy
- Owner: PyAutoLabs
- License: mit
- Created: 2019-10-19T10:45:44.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2026-06-09T09:20:29.000Z (about 1 month ago)
- Last Synced: 2026-06-18T22:09:33.019Z (30 days ago)
- Topics: astronomy, astrophysics, galaxies, image-processing, morphology, python
- Language: Python
- Homepage: https://pyautogalaxy.readthedocs.io/
- Size: 28.6 MB
- Stars: 33
- Watchers: 3
- Forks: 14
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATIONS.md
- Agents: AGENTS.md
Awesome Lists containing this project
README
# PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
[](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.6.26.642/notebooks/imaging/start_here.ipynb)
[](https://pyautogalaxy.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/PyAutoLabs/PyAutoGalaxy/actions)
[](https://github.com/Jammy2211/PyAutoBuild/actions)
[](https://github.com/psf/black)
[](https://doi.org/10.21105/joss.04475)
[](https://github.com/pyOpenSci/software-submission/issues/235)
[](https://doi.org/10.5281/zenodo.7546914)
[](https://www.repostatus.org/#active)
[](https://pypi.org/project/autogalaxy/)
[](https://pypi.org/project/autogalaxy/)
[Installation Guide](https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html) |
[readthedocs](https://pyautogalaxy.readthedocs.io/en/latest/index.html) |
[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.6.26.642/notebooks/imaging/start_here.ipynb) |
[HowToGalaxy](https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html)
**PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:
[](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/paper/hstcombined.png)
**PyAutoGalaxy** also fits interferometer data from observatories such as ALMA:
[](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/paper/almacombined.png)
## Getting Started
The following links are useful for new starters:
- [The PyAutoGalaxy readthedocs](https://pyautogalaxy.readthedocs.io/en/latest), which includes [an overview of PyAutoGalaxy's core features](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_start_here.html), [a new user starting guide](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) and [an installation guide](https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html).
- [The introduction Jupyter Notebook on Google Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.6.26.642/notebooks/imaging/start_here.ipynb), where you can try **PyAutoGalaxy** in a web browser (without installation).
- [The autogalaxy_workspace GitHub repository](https://github.com/PyAutoLabs/autogalaxy_workspace): example scripts covering every **PyAutoGalaxy** use case.
- [The HowToGalaxy GitHub repository](https://github.com/PyAutoLabs/HowToGalaxy): a Jupyter notebook lecture series teaching galaxy modeling from the ground up.
## Core Aims
**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](https://joss.theoj.org/papers/10.21105/joss.04475).
## Community & Support
Support for **PyAutoGalaxy** is available via our Slack workspace, where the community shares updates, discusses
galaxy modeling and analysis, and helps troubleshoot problems.
Slack is invitation-only. If you'd like to join, please send an email requesting an invite.
For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/PyAutoLabs/PyAutoGalaxy/issues).
## HowToGalaxy
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](https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html), and the notebooks themselves live in the [PyAutoLabs/HowToGalaxy](https://github.com/PyAutoLabs/HowToGalaxy) repository.
## Citations
Information on how to cite **PyAutoGalaxy** in publications can be found [on the citations page](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/CITATIONS.md).
## Contributing
Information on how to contribute to **PyAutoGalaxy** can be found [on the contributing page](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/CONTRIBUTING.md).
Hands on support for contributions is available via our Slack workspace, again please email to request an invite.