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: 19 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: 2026-05-08T19:07:53.000Z (25 days ago)
- Last Synced: 2026-05-08T20:34:57.688Z (25 days ago)
- Topics: astronomy, astrophysics, galaxies, image-processing, morphology, python
- Language: Python
- Homepage: https://pyautogalaxy.readthedocs.io/
- Size: 28.3 MB
- Stars: 32
- Watchers: 3
- Forks: 14
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATIONS.rst
- Agents: AGENTS.md
Awesome Lists containing this project
README
PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
========================================================================
.. image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/start_here.ipynb
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:target: https://pyautogalaxy.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
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:target: https://doi.org/10.21105/joss.04475
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:alt: Python Versions
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:alt: PyPI Version
`Installation Guide `_ |
`readthedocs `_ |
`Introduction on Colab `_ |
`HowToGalaxy `_
**PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:
.. image:: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/hstcombined.png?raw=true
:target: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/hstcombined.png
**PyAutoGalaxy** also fits interferometer data from observatories such as ALMA:
.. image:: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/almacombined.png?raw=true
:target: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/almacombined.png
Getting Started
---------------
The following links 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.
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 `_.
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 `_.
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 `_, and the notebooks themselves live in the `PyAutoLabs/HowToGalaxy `_ repository.
Citations
---------
Information on how to cite **PyAutoGalaxy** in publications can be found `on the citations page `_.
Contributing
------------
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.