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.
For experienced scientists, the run through above will have been a breeze. Concepts surrounding galaxy structure and morphology were already familiar and the statistical techniques used for fitting and modeling already understood.
For those less familiar with these concepts (e.g. undergraduate students, new PhD students or interested members of the public), things may have been less clear and a slower more detailed explanation of each concept would be beneficial.
The HowToGalaxy Jupyter Notebook lectures are provide exactly this They are a 3+ chapter guide which thoroughly take you through the core concepts of galaxy light profiles, teach you the principles ofthe statistical techniques used in modeling and ultimately will allow you to undertake scientific research like a professional astronomer.
If this sounds like it suits you, checkout the autogalaxy_workspace/notebooks/howtogalaxy package now, its it recommended you go here before anywhere else!
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.howtogalaxy: Conceptual tutorials introducing galaxy modeling and inference with PyAutoGalaxy.
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.