Installation Guide | PyAutoGalaxy readthedocs | autogalaxy_workspace
Welcome to HowToGalaxy — the tutorial lecture series for PyAutoGalaxy, an open-source library for modeling the light of galaxies.
HowToGalaxy teaches new users how to model galaxy morphologies from scratch. It assumes minimal prior knowledge of astronomy or statistics and takes you from first principles all the way to using PyAutoGalaxy for professional scientific research.
For experienced scientists who already know the fundamentals of galaxy light profile fitting and Bayesian modeling, the autogalaxy_workspace examples will be more appropriate — they are concise and assume the concepts taught in HowToGalaxy as background.
chapter_1_introduction— An introduction to galaxy morphology and PyAutoGalaxy: grids, light profiles, galaxies, simulated imaging data, and fitting.chapter_2_modeling— Bayesian inference, non-linear searches, and how to fit a galaxy model to CCD imaging data with PyAutoGalaxy.chapter_3_search_chaining— Chaining multiple non-linear searches together to build automated galaxy modeling pipelines for complex systems.chapter_4_pixelizations— Pixelized source reconstructions (inversions) for galaxies with irregular morphologies.chapter_optional— Optional tutorials on alternative non-linear searches and other advanced topics.
HowToGalaxy currently sits at four chapters. Each chapter will take around a day to work through.
We recommend completing chapters 1 and 2, then applying what you've learned to real galaxy modeling in the
autogalaxy_workspace before returning for the more advanced material in chapters 3 and 4.
You can run the tutorials on your own machine by following the PyAutoGalaxy installation guide, then cloning this repository:
git clone https://github.com/PyAutoLabs/HowToGalaxy.git
cd HowToGalaxyAlternatively, every tutorial can be opened directly in Google Colab via the links in each chapter's
README.md.
The tutorials are distributed as both Jupyter notebooks (notebooks/) and Python scripts (scripts/).
We recommend the notebooks for reading — images and plots render inline, and you can step through small
code blocks interactively. Use the Python scripts for actual PyAutoGalaxy use, which is the workflow
chapter 3 onwards transitions you to.
Before starting chapter 1, complete scripts/chapter_1_introduction/tutorial_0_visualization.py
(or the equivalent notebook). This confirms your PyAutoGalaxy installation, walks you through how
images and figures display in Jupyter, and configures matplotlib for the rest of the tutorial series.
scripts/— Runnable Python tutorial scripts, one subfolder per chapter.notebooks/— Jupyter notebook versions of the scripts (auto-generated; see below).config/— PyAutoGalaxy configuration YAML files used by the tutorials.dataset/— Tutorial datasets are generated at runtime by scripts inscripts/simulators/— no.fitsfiles are committed.output/— Model-fit results (generated at runtime, not committed).
Notebooks in notebooks/ are generated from the Python files in scripts/. Always edit the ``.py``
scripts, never the notebooks directly. The # %% markers in each script alternate between code and
markdown cells, which PyAutoBuild uses to produce the
.ipynb files.
autogalaxy_workspace is the main user-facing
workspace for PyAutoGalaxy — concise examples, guides, and science templates aimed at users who have
a working understanding of galaxy morphology and light profile fitting. HowToGalaxy is the teaching
companion. Many tutorials in chapters 2–4 reference autogalaxy_workspace scripts as the next place to
go after the relevant concept has been introduced.
If you use HowToGalaxy or PyAutoGalaxy in your research, please cite the references listed in
CITATIONS.rst.
Support for PyAutoGalaxy is available via our Slack workspace. Slack is invitation-only; send an email if you'd like an invite.
For installation issues, bug reports, or feature requests, raise an issue on the PyAutoGalaxy GitHub issues page (for library issues) or the HowToGalaxy GitHub issues page (for tutorial content issues).