Installation Guide | readthedocs | Introduction on Colab | HowToGalaxy
Welcome to the PyAutoGalaxy Workspace!
You can get set up on your personal computer by following the installation guide on our readthedocs.
Alternatively, you can try PyAutoGalaxy out in a web browser by going to the autogalaxy workspace on Colab.
New users should read the autogalaxy_workspace/start_here.ipynb notebook, which will give you a concise
overview of PyAutoGalaxy’s core features and API.
This can be done via a web browser by going to the following Google Colab link:
Then checkout the new user starting guide to navigate the workspace for your science case.
If you are new to galaxy modeling or the statistical techniques it relies on, the HowToGalaxy lecture series takes you from first principles through to modeling real galaxy imaging data. It now lives in its own repository:
https://github.com/PyAutoLabs/HowToGalaxy
The workspace includes the following main directories:
notebooks: PyAutoGalaxy examples written as Jupyter notebooks.scripts: PyAutoGalaxy examples written as Python scripts.config: Configuration files which customize PyAutoGalaxy’s behaviour.dataset: Where data is stored, including example datasets.output: Where PyAutoGalaxy analysis and visualization outputs are written.
The examples in the notebooks and scripts folders are structured as follows:
guides: Guides which introduce the core features of PyAutoGalaxy, including the core galaxy modeling API.imaging: Examples for galaxy modeling using CCD imaging (e.g. Hubble, James Webb, Euclid).interferometer: Examples for galaxies observed with an interferometer (e.g. ALMA, JVLA).multi: Examples for modeling galaxies observed in multiple wavebands.
The dataset packages (e.g. imaging, interferometer and multi) include the following types of examples:
modeling: Performing galaxy modeling using that type of data.simulators: Simulating galaxy images.fit: How to compute residuals, chi-squared maps, and likelihoods.data_preparation: Preparing real datasets for PyAutoGalaxy analysis.features: Advanced modeling features (e.g. Multi Gaussian Expansion, priors, constraints).likelihood_function: Step-by-step guides to the likelihood function.
The guides package contains important subpackages, including:
results: How to load, inspect, and analyze results from many galaxy fits efficiently.modeling: Customizing galaxy models and building automated modeling pipelines.plot: How to visualize galaxy images, profiles, and residuals.
The files README.rst distributed throughout the workspace describe what is in each folder.
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/Jammy2211/PyAutoGalaxy/issues).
To make changes in the tutorial notebooks, please make changes in the corresponding Python files (.py)
present in the scripts folder of each chapter. The marker # %% alternates between code cells and
markdown cells.
The config/ directory contains two files used by the automated build and test system
(CI, smoke tests, and pre-release checks). These are not relevant to normal workspace usage.
config/build/no_run.yaml— scripts to skip during automated runs. Each entry is a filename stem or path pattern with an inline comment explaining why it is skipped.config/build/env_vars.yaml— environment variables applied to each script during automated runs. Defines default values (e.g. test mode, small datasets) and per-script overrides for scripts that need different settings.