docs: migrate HowToGalaxy URLs/prose to PyAutoLabs/HowToGalaxy repo (#363)

Sub-task 3 of 3 of the HowToGalaxy extraction. The HowToGalaxy tutorial
series now lives in its own repository at
https://github.com/PyAutoLabs/HowToGalaxy; this commit points every
HowToGalaxy reference in PyAutoGalaxy at the new location (tag
2026.4.13.6, matching autogalaxy_workspace at extraction).

- Rewrite Colab URLs across docs/howtogalaxy/chapter_{1..4,optional}.rst
  from `autogalaxy_workspace/blob/.../notebooks/howtogalaxy/chapter_X/`
  to `HowToGalaxy/blob/.../notebooks/chapter_X/` (drops redundant
  howtogalaxy/ segment since the new repo root is the tutorial series).
- Reword the README, docs/index.rst and docs/overview/overview_2
  new-starter/"HowToGalaxy" sections to frame HowToGalaxy as a standalone
  repo alongside autogalaxy_workspace.
- Rewrite docs/howtogalaxy/howtogalaxy.rst, docs/general/workspace.rst
  and paper/paper.md with the same framing; fix the broken local
  `file:///Users/...` readthedocs path in workspace.rst.
- Remove the `scripts/howtogalaxy/` bullet from CLAUDE.md's workspace
  overview and add a short note that HowToGalaxy now lives externally.
- Restore missing `|` separator between "Introduction on Colab" and
  "HowToGalaxy" links at the top of README.rst (pre-existing drift).

Closes #362

Co-authored-by: Jammy2211 <JNightingale2211@gmail.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

Author: 3 people

CLAUDE.md

@@ -199,11 +199,12 @@ The `autogalaxy_workspace` at `/mnt/c/Users/Jammy/Code/PyAutoLabs/autogalaxy_wor
 - `start_here.ipynb` / `start_here.py` – entry point overview of the API
 - `scripts/imaging/` – end-to-end scripts: `simulator.py`, `fit.py`, `modeling.py`, `likelihood_function.py`, `features/`
 - `scripts/interferometer/`, `scripts/ellipse/`, `scripts/multi/` – same structure for other dataset types
-- `scripts/howtogalaxy/` – chapter-by-chapter tutorial scripts (chapters 1–4 + optional)
 - `notebooks/` – Jupyter notebook equivalents of all `scripts/`
 - `scripts/guides/` – topic guides (e.g. linear profiles, pixelizations, chaining)
 - `config/` – workspace-level config that overrides package defaults when running workspace scripts
 
+The **HowToGalaxy** tutorial series lives in its own repository at `PyAutoLabs/HowToGalaxy` (no longer under the workspace).
+
 ### Namespace Conventions
 
 When importing `autogalaxy as ag`:

README.rst

@@ -34,7 +34,7 @@ PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
 
 `Installation Guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_ |
 `readthedocs <https://pyautogalaxy.readthedocs.io/en/latest/index.html>`_ |
-`Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/start_here.ipynb>`_
+`Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/start_here.ipynb>`_ |
 `HowToGalaxy <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_
 
 **PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:
@@ -56,7 +56,9 @@ The following links are useful for new starters:
 
 - `The introduction Jupyter Notebook on Google Colab <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/start_here.ipynb>`_, where you can try **PyAutoGalaxy** in a web browser (without installation).
 
-- `The autogalaxy_workspace GitHub repository <https://github.com/Jammy2211/autogalaxy_workspace>`_, which includes example scripts and the `HowToGalaxy Jupyter notebook lectures <https://github.com/Jammy2211/autogalaxy_workspace/tree/main/notebooks/howtogalaxy>`_ which give new users a step-by-step introduction to **PyAutoGalaxy**.
+- `The autogalaxy_workspace GitHub repository <https://github.com/PyAutoLabs/autogalaxy_workspace>`_: example scripts covering every **PyAutoGalaxy** use case.
+
+- `The HowToGalaxy GitHub repository <https://github.com/PyAutoLabs/HowToGalaxy>`_: a Jupyter notebook lecture series teaching galaxy modeling from the ground up.
 
 Core Aims
 ---------
@@ -81,16 +83,14 @@ Slack is invitation-only. If you’d like to join, please send an email requesti
 
 For installation issues, bug reports, or feature requests, please raise an issue on the `GitHub issues page <https://github.com/Jammy2211/PyAutoGalaxy/issues>`_.
 
-Here’s a clean, **AutoGalaxy**-appropriate rewrite, keeping the same structure and tone but removing lensing-specific language:
-
 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 <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_
+A complete overview of the lectures `is provided on the HowToGalaxy readthedocs page <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_, and the notebooks themselves live in the `PyAutoLabs/HowToGalaxy <https://github.com/PyAutoLabs/HowToGalaxy>`_ repository.
 
 Citations
 ---------

docs/general/workspace.rst

@@ -50,9 +50,10 @@ The folder where  modeling results are stored.
 HowToGalaxy
 -----------
 
-The **HowToGalaxy** lecture series are a collection of Jupyter notebooks describing how to build a **PyAutoGalaxy** model
-fitting project and giving illustrations of different statistical methods and techniques.
+The **HowToGalaxy** lecture series is a collection of Jupyter notebooks describing how to build a **PyAutoGalaxy** model
+fitting project and giving illustrations of different statistical methods and techniques. It ships as a standalone
+repository at `PyAutoLabs/HowToGalaxy <https://github.com/PyAutoLabs/HowToGalaxy>`_ (separate from the workspace).
 
 Checkout the
-`tutorials section <file:///Users/Jammy/Code/PyAutoLabs/PyAutoGalaxy/docs/_build/howtogalaxy/howtogalaxy.html>`_ for a
+`tutorials section <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_ for a
 full description of the lectures and online examples of every notebook.

docs/howtogalaxy/chapter_1_introduction.rst

@@ -7,20 +7,20 @@ In chapter 1, we introduce you to galaxy morphology, structure and the core **Py
 
 The chapter contains the following tutorials:
 
-`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_1_introduction/tutorial_0_visualization.ipynb>`_
+`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb>`_
 - Setting up **PyAutoGalaxy**'s visualization library.
 
-`Tutorial 1: Grids And Galaxies <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
+`Tutorial 1: Grids And Galaxies <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
 - Using grids of (y,x) coordinates with galaxies made up of light profiles.
 
-`Tutorial 2: Data <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_1_introduction/tutorial_2_data.ipynb>`_
+`Tutorial 2: Data <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_2_data.ipynb>`_
 - Simulating and inspecting telescope imaging data of a galaxy.
 
-`Tutorial 3: Fitting <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_1_introduction/tutorial_3_fitting.ipynb>`_
+`Tutorial 3: Fitting <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb>`_
 - Fitting data with a galaxy model.
 
-`Tutorial 4: Methods <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_1_introduction/tutorial_4_methods.ipynb>`_
+`Tutorial 4: Methods <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb>`_
 - An overview of the different methods used to fit galaxies with.
 
-`Tutorial 5: Summary <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_1_introduction/tutorial_5_summary.ipynb>`_
+`Tutorial 5: Summary <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb>`_
 - A summary of the chapter.

docs/howtogalaxy/chapter_2_modeling.rst

@@ -5,27 +5,27 @@ In chapter 2, we'll take you through how to model galaxies using a non-linear se
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_1_non_linear_search.ipynb>`_
+`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb>`_
 - How a non-linear search is used to fit a model and the concepts of a parameter space and priors.
 
-`Tutorial 2: Practicalities <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_2_practicalities.ipynb>`_
+`Tutorial 2: Practicalities <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_2_practicalities.ipynb>`_
 - Practicalities of performing model-fitting, like how to inspect the results on your hard-disk.
 
-`Tutorial 3: Realism and Complexity <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb>`_
+`Tutorial 3: Realism and Complexity <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb>`_
 - Finding a balance between realism and complexity when composing and fitting a model.
 
-`Tutorial 4: Dealing with Failure <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb>`_
+`Tutorial 4: Dealing with Failure <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb>`_
 - What to do when PyAutoGalaxy finds an inaccurate model.
 
-`Tutorial 5: Linear Profiles <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_5_linear_profiles.ipynb>`_
+`Tutorial 5: Linear Profiles <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_5_linear_profiles.ipynb>`_
 - Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
 
-`Tutorial 6: Masking <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_6_masking.ipynb>`_
+`Tutorial 6: Masking <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_6_masking.ipynb>`_
 - How to mask your data to improve the model.
 
-`Tutorial 7: Results <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_7_results.ipynb>`_
+`Tutorial 7: Results <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_7_results.ipynb>`_
 - Overview of the results available after successfully fitting a model.
 
-`Tutorial 8: Need for Speed <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_2_modeling/tutorial_8_need_for_speed.ipynb>`_
+`Tutorial 8: Need for Speed <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb>`_
 - How to fit complex models whilst balancing efficiency and run-time.
 

docs/howtogalaxy/chapter_3_search_chaining.rst

@@ -6,11 +6,11 @@ robust modeling of large galaxy samples.
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Search Chaining <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb>`_
+`Tutorial 1: Search Chaining <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb>`_
 - Breaking the modeling procedure into a chained sequence of model-fits.
 
-`Tutorial 2: Prior Passing <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb>`_
+`Tutorial 2: Prior Passing <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb>`_
 - How the results of earlier searches are passed to later searches.
 
-`Tutorial 3: x2 Galaxies <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb>`_
+`Tutorial 3: x2 Galaxies <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_3_x2_galaxies.ipynb>`_
 - Modeling a dataset with two galaxies using chained searches.

docs/howtogalaxy/chapter_4_pixelizations.rst

@@ -5,17 +5,17 @@ In chapter 4, we use **Pixelizations** to reconstruct complex galaxies on pixeli
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Pixelizations <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb>`_
+`Tutorial 1: Pixelizations <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb>`_
 - Creating a pixel-grid which will reconstruct a galaxy's light.
 
-`Tutorial 2: Mappers <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_4_pixelizations/tutorial_2_mappers.ipynb>`_
+`Tutorial 2: Mappers <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb>`_
 - How a pixelization maps between the data and pixelization.
 
-`Tutorial 3: Inversions <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_4_pixelizations/tutorial_3_inversions.ipynb>`_
+`Tutorial 3: Inversions <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb>`_
 - Inverting the mappings to reconstruct the galaxy's light.
 
-`Tutorial 4: Bayesian Regularization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb>`_
+`Tutorial 4: Bayesian Regularization <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb>`_
 - Smoothing the source within a Bayesian framework.
 
-`Tutorial 6: Model Fit <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_4_pixelizations/tutorial_6_model_fit.ipynb>`_
+`Tutorial 6: Model Fit <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_6_model_fit.ipynb>`_
 - An example modeling pipeline which uses an inversion.

docs/howtogalaxy/chapter_optional.rst

@@ -5,12 +5,12 @@ This chapter contains optional tutorials expanding on different aspects of how *
 
 The chapter contains the following tutorials:
 
-`Tutorial: Mass Profiles  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_optional/tutorial_mass_profiles.ipynb>`_
+`Tutorial: Mass Profiles  <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_optional/tutorial_mass_profiles.ipynb>`_
 - A description of mass profiles implemented in PyAutoGalaxy, which are currently only used by its child project PyAutoLens.
 
-`Tutorial: Sub-grids  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_optional/tutorial_sub_grids.ipynb>`_
+`Tutorial: Sub-grids  <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_optional/tutorial_sub_grids.ipynb>`_
 - Use sub-grids to perform more accurate and precise calculations.
 
-`Tutorial: Searches  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/howtogalaxy/chapter_optional/tutorial_searches.ipynb>`_
+`Tutorial: Searches  <https://colab.research.google.com/github/PyAutoLabs/HowToGalaxy/blob/2026.4.13.6/notebooks/chapter_optional/tutorial_searches.ipynb>`_
 - Alternative non-linear searches to sample parameter space.
 

docs/howtogalaxy/howtogalaxy.rst

@@ -3,8 +3,8 @@
 HowToGalaxy Lectures
 ====================
 
-The best way to learn **PyAutoGalaxy** is by going through the **HowToGalaxy** lecture series on the
-`autogalaxy workspace <https://github.com/Jammy2211/autogalaxy_workspace>`_.
+The best way to learn **PyAutoGalaxy** is by going through the **HowToGalaxy** lecture series, which lives in its own
+repository at `PyAutoLabs/HowToGalaxy <https://github.com/PyAutoLabs/HowToGalaxy>`_.
 
 The lectures are provided as Jupyter notebooks (and Python scripts), and they are linked to via this readthedocs. The
 lectures are composed of five chapters
@@ -23,7 +23,8 @@ in the later chapters are pretty challenging, and familiarity and modeling is de
 you tackle them.
 
 Therefore, we recommend that you complete chapters 1 & 2 and then apply what you've learnt to the modeling of simulated
-and real galaxy data, using the scripts found in the 'autogalaxy_workspace'. Once you're happy
+and real galaxy data, using the scripts found in the
+`autogalaxy_workspace <https://github.com/PyAutoLabs/autogalaxy_workspace>`_. Once you're happy
 with the results and confident with your use of **PyAutoGalaxy**, you can then begin to cover the advanced functionality
 covered in chapters 3 & 4.
 
@@ -40,8 +41,8 @@ Notebooks are a different way to write, view and use Python code. Compared to th
 This makes them an ideal way for us to present the HowToFit lecture series, therefore I recommend you get yourself
 a Jupyter notebook viewer (https://jupyter.org/) if you havent done so already.
 
-If you *really* want to use Python scripts, all tutorials are supplied a .py python files in the 'scripts' folder of
-the workspace.
+If you *really* want to use Python scripts, all tutorials are also supplied as ``.py`` files in the ``scripts`` folder
+of the `HowToGalaxy repository <https://github.com/PyAutoLabs/HowToGalaxy>`_.
 
 Visualization
 -------------