autogalaxy_workspace

The PyAutoGalaxy Workspace: contains example scripts, datasets and more

https://github.com/jammy2211/autogalaxy_workspace

Repository

The PyAutoGalaxy Workspace: contains example scripts, datasets and more

README.rst

PyAutoGalaxy Workspace
======================

.. |binder| image:: https://mybinder.org/badge_logo.svg
   :target: https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/HEAD

.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.02825/status.svg
   :target: https://doi.org/10.21105/joss.02825

|binder| |JOSS|

`Installation Guide `_ |
`readthedocs `_ |
`Introduction on Binder `_ |
`HowToGalaxy `_

Welcome to the **PyAutoGalaxy** Workspace. You can get started right away by going to the `autogalaxy workspace
Binder `_.
Alternatively, you can get set up by following the installation guide on our `readthedocs `_.

Getting Started
---------------

We recommend new users begin by looking at the following notebooks: 

- ``notebooks/overview``: Examples giving an overview of **PyAutoGalaxy**'s core features.
- ``notebooks/howtogalaxy``: Detailed step-by-step Jupyter notebook lectures on how to use **PyAutoGalaxy**.

Installation
------------

If you haven't already, install `PyAutoGalaxy via pip or conda `_.

Next, clone the ``autogalaxy workspace`` (the line ``--depth 1`` clones only the most recent branch on
the ``autogalaxy_workspace``, reducing the download size):

.. code-block:: bash

   cd /path/on/your/computer/you/want/to/put/the/autogalaxy_workspace
   git clone https://github.com/Jammy2211/autogalaxy_workspace --depth 1
   cd autogalaxy_workspace

Run the ``welcome.py`` script to get started!

.. code-block:: bash

   python3 welcome.py

Workspace Structure
-------------------

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 distributed.
- ``output``: Where the **PyAutoGalaxy** analysis and visualization are output.

The examples in the ``notebooks`` and ``scripts`` folders are structured as follows:

- ``overview``: Examples giving an overview of **PyAutoGalaxy**'s core features.
- ``howtogalaxy``: Detailed step-by-step Jupyter notebook lectures on how to use **PyAutoGalaxy**.

- ``imaging``: Examples for analysing and simulating CCD imaging data (e.g. Hubble, Euclid).
- ``interferometer``: Examples for analysing and simulating interferometer datasets (e.g. ALMA, JVLA).
- ``multi``: Modeling multiple datasets simultaneously (E.g. multi-wavelength imaging, imaging and interferometry).

- ``plot``: An API reference guide for **PyAutoGalaxy**'s plotting tools.
- ``misc``: Miscellaneous scripts for specific galaxy analysis.

Inside these packages are packages titled ``advanced`` which only users familiar **PyAutoGalaxy** should check out.

In the ``imaging``, ``interferometer``, and ``multi`` folders you'll find the following packages:

- ``modeling``: Examples of how to fit a galaxy model to data via a non-linear search.
- ``simulators``: Scripts for simulating realistic imaging and interferometer data of strong galaxies.
- ``data_preparation``: Tools to preprocess ``data`` before an analysis (e.g. convert units, create masks).
- ``results``: Examples using the results of a model-fit.
- ``advanced``: Advanced modeling scripts which use **PyAutoGalaxy**'s advanced features.


The files ``README.rst`` distributed throughout the workspace describe what is in each folder.

Getting Started
---------------

We recommend new users begin with the example notebooks / scripts in the *overview* folder and the **HowToGalaxy**
tutorials.

Workspace Version
-----------------

This version of the workspace is built and tested for using **PyAutoGalaxy v2025.5.7.16**.

HowToGalaxy
-----------

Included is the ``HowToGalaxy`` lecture series, which provides an introduction to strong gravitational
galaxy modeling. It can be found in the workspace & consists of 5 chapters:

- ``Introduction``: An introduction to galaxy morphology & **PyAutoGalaxy**.
- ``Galaxy Modeling``: How to model strong galaxies, including a primer on Bayesian analysis and model-fitting via a non-linear search .
- ``Search Chaining``: Chaining non-linear searches together to build model-fitting pipelines & tailor them to your own science case.
- ``Pixelizations``: How to perform pixelized reconstructions of a galaxy.

Contribution
------------
To make changes in the tutorial notebooks, please make changes in the corresponding python files(.py) present in the
``scripts`` folder of each chapter. Please note that  marker ``# %%`` alternates between code cells and markdown cells.

Support
-------

Support for installation issues, help with galaxy modeling and using **PyAutoGalaxy** is available by
`raising an issue on the autogalaxy_workspace GitHub page `_. or
joining the **PyAutoGalaxy** `Slack channel `_, where we also provide the latest updates on
**PyAutoGalaxy**.

Slack is invitation-only, so if you'd like to join send an `email `_ requesting an
invite.

Owner

Citation (CITATIONS.rst)

.. _references:

Citations & References
======================

The bibtex entries for **PyAutoGalaxy** and its affiliated software packages can be found
`here <https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.bib>`_, with example text for citing **PyAutoGalaxy**
in `.tex format here <https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.tex>`_ format here and
`.md format here <https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.md>`_.

As shown in the examples, we would greatly appreciate it if you mention **PyAutoGalaxy** by name and include a link to
our GitHub page!

**PyAutoGalaxy** is published in the `Journal of Open Source Software <https://joss.theoj.org/papers/10.21105/joss.02825#>`_ and its
entry in the above .bib file is under the citation key ``pyautogalaxy``.

You should also specify the non-linear search(es) you use in your analysis (e.g. Dynesty, Emcee, PySwarms, etc) in
the main body of text, and delete as appropriate any packages your analysis did not use. The citations.bib file includes
the citation key for all of these projects.

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