Release 2026.5.1.4: bump Colab URL tag refs

Author: rhayes777

notebooks/chapter_1_introduction/README.rst

@@ -5,20 +5,20 @@ In chapter 1, we introduce you to strong gravitational lensing and the core **Py
 Files
 -----
 
-`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb>`_
+`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
+`Tutorial 1: Grids And Galaxies <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
 - How grids of (y,x) coordinates are used to create images of galaxies that ultimately quantify their morphology.
 
-`Tutorial 2: Data <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_2_data.ipynb>`_
+`Tutorial 2: Data <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_2_data.ipynb>`_
 - Simulating and inspecting telescope imaging data of a galaxy, for example from the Hubble Space Telescope.
 
-`Tutorial 3: Fitting <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb>`_
+`Tutorial 3: Fitting <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb>`_
 - How to fit imaging data of a galaxy and quantify whether a fit is good or bad.
 
-`Tutorial 4: Methods <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb>`_
+`Tutorial 4: Methods <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_1_introduction/tutorial_5_summary.ipynb>`_
+`Tutorial 5: Summary <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb>`_
 - A summary of the chapter.

notebooks/chapter_2_modeling/README.rst

@@ -5,26 +5,26 @@ In chapter 2, we'll take you through how to model galaxies using a non-linear se
 Files
 -----
 
-`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb>`_
+`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_2_practicalities.ipynb>`_
+`Tutorial 2: Practicalities <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb>`_
+`Tutorial 3: Realism and Complexity <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb>`_
+`Tutorial 4: Dealing with Failure <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_5_linear_profiles.ipynb>`_
+`Tutorial 5: Linear Profiles <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_6_masking.ipynb>`_
+`Tutorial 6: Masking <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_7_results.ipynb>`_
+`Tutorial 7: Results <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_8_need_for_speed.ipynb>`_
+`Tutorial 8: Need for Speed <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb>`_
 - How to fit complex models whilst balancing efficiency and run-time.

notebooks/chapter_3_search_chaining/README.rst

@@ -6,20 +6,20 @@ different model.
 Files
 -----
 
-`Tutorial 1: Search Chaining <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb>`_
+`Tutorial 1: Search Chaining <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb>`_
+`Tutorial 2: Prior Passing <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb>`_
 - How the results of earlier searches are passed to later searches.
 
-`Tutorial 3: Lens  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb>`_
+`Tutorial 3: Lens  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb>`_
 - Fitting the galaxy's light followed by its mass using chained searches.
 
-`Tutorial 4: Two Lens galaxies <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb>`_
+`Tutorial 4: Two Lens galaxies <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb>`_
 - Modeling a galaxy with two lens galaxies using chained searches.
 
-`Tutorial 5: Complex Source <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_4_complex_source.ipynb>`_
+`Tutorial 5: Complex Source <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_4_complex_source.ipynb>`_
 - Using multiple light profiles to fit a complex and irregular source using chained searches.
 
-`Tutorial 6: SLaM <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb>`_
+`Tutorial 6: SLaM <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb>`_
 - Template pipelines for fitting model is standardized ways.

notebooks/chapter_4_pixelizations/README.rst

@@ -5,26 +5,26 @@ In chapter 4, we use **Pixelizations** to reconstruct complex source galaxies on
 Files
 -----
 
-`Tutorial 1: Pixelizations <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb>`_
+`Tutorial 1: Pixelizations <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb>`_
 - Creating a pixel-grid in the source-plane.
 
-`Tutorial 2: Mappers <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb>`_
+`Tutorial 2: Mappers <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb>`_
 - How a pixelization maps source-pixels to image-pixels.
 
-`Tutorial 3: Inversions <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb>`_
+`Tutorial 3: Inversions <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb>`_
 - Inverting the mappings to reconstruct the source's light.
 
-`Tutorial 4: Bayesian Regularization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb>`_
+`Tutorial 4: Bayesian Regularization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb>`_
 - Smoothing the source within a Bayesian framework.
 
-`Tutorial 5: Borders <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_5_borders.ipynb>`_
+`Tutorial 5: Borders <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_5_borders.ipynb>`_
 - Preventing highly demagnified image-pixels ruining the inversion.
 
-`Tutorial 6: Lens Modeling  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_6_modeling.ipynb>`_
+`Tutorial 6: Lens Modeling  <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_6_modeling.ipynb>`_
 - How to use inversions to fit a model.
 
-`Tutorial 7: Adaptive Pixelization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.ipynb>`_
+`Tutorial 7: Adaptive Pixelization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.ipynb>`_
 - A Voronoi mesh which adapts to the mass model's magnification.
 
-`Tutorial 8: Model Fit <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb>`_
+`Tutorial 8: Model Fit <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb>`_
 - An example modeling pipeline which uses an inversion.

scripts/chapter_1_introduction/README.rst

@@ -5,20 +5,20 @@ In chapter 1, we introduce you to strong gravitational lensing and the core **Py
 Files
 -----
 
-`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb>`_
+`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
+`Tutorial 1: Grids And Galaxies <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
 - How grids of (y,x) coordinates are used to create images of galaxies that ultimately quantify their morphology.
 
-`Tutorial 2: Data <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_2_data.ipynb>`_
+`Tutorial 2: Data <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_2_data.ipynb>`_
 - Simulating and inspecting telescope imaging data of a galaxy, for example from the Hubble Space Telescope.
 
-`Tutorial 3: Fitting <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb>`_
+`Tutorial 3: Fitting <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_3_fitting.ipynb>`_
 - How to fit imaging data of a galaxy and quantify whether a fit is good or bad.
 
-`Tutorial 4: Methods <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_1_introduction/tutorial_4_methods.ipynb>`_
+`Tutorial 4: Methods <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_1_introduction/tutorial_5_summary.ipynb>`_
+`Tutorial 5: Summary <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_5_summary.ipynb>`_
 - A summary of the chapter.

scripts/chapter_2_modeling/README.rst

@@ -5,26 +5,26 @@ In chapter 2, we'll take you through how to model galaxies using a non-linear se
 Files
 -----
 
-`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.4.13.6/notebooks/chapter_2_modeling/tutorial_1_non_linear_search.ipynb>`_
+`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_2_practicalities.ipynb>`_
+`Tutorial 2: Practicalities <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_3_realism_and_complexity.ipynb>`_
+`Tutorial 3: Realism and Complexity <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_4_dealing_with_failure.ipynb>`_
+`Tutorial 4: Dealing with Failure <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_5_linear_profiles.ipynb>`_
+`Tutorial 5: Linear Profiles <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_6_masking.ipynb>`_
+`Tutorial 6: Masking <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_7_results.ipynb>`_
+`Tutorial 7: Results <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/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/chapter_2_modeling/tutorial_8_need_for_speed.ipynb>`_
+`Tutorial 8: Need for Speed <https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.1.4/notebooks/chapter_2_modeling/tutorial_8_need_for_speed.ipynb>`_
 - How to fit complex models whilst balancing efficiency and run-time.