Release 2026.5.1.4: bump Colab URL tag refs

Author: rhayes777

README.rst

@@ -5,7 +5,7 @@ PyAutoLens-JAX: Open-Source Strong Lensing
     :trim:
 
 .. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
-   :target: https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.1/start_here.ipynb
+   :target: https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb
 
 .. |RTD| image:: https://readthedocs.org/projects/pyautolens/badge/?version=latest
     :target: https://pyautolens.readthedocs.io/en/latest/?badge=latest
@@ -46,7 +46,7 @@ PyAutoLens-JAX: Open-Source Strong Lensing
 
 `Installation Guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>`_ |
 `readthedocs <https://pyautolens.readthedocs.io/en/latest/index.html>`_ |
-`Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.1/start_here.ipynb>`_ |
+`Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb>`_ |
 `HowToLens <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>`_
 
 .. image:: https://github.com/Jammy2211/PyAutoLogo/blob/main/gifs/pyautolens.gif?raw=true
@@ -63,7 +63,7 @@ The following links are useful for new starters:
 
 - `The PyAutoLens readthedocs <https://pyautolens.readthedocs.io/en/latest>`_: which includes `an overview of PyAutoLens's core features <https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html>`_, `a new user starting guide <https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html>`_ and `an installation guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>`_.
 
-- `The introduction Jupyter Notebook on Google Colab <https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.1/start_here.ipynb>`_: try **PyAutoLens** in a web browser (without installation).
+- `The introduction Jupyter Notebook on Google Colab <https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb>`_: try **PyAutoLens** in a web browser (without installation).
 
 - `The autolens_workspace GitHub repository <https://github.com/PyAutoLabs/autolens_workspace>`_: example scripts covering every **PyAutoLens** use case.
 

docs/howtolens/chapter_1_introduction.rst

@@ -7,29 +7,29 @@ In chapter 1, we introduce you to strong gravitational lensing and the core **Py
 
 The chapter contains the following tutorials:
 
-`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb>`_
+`Tutorial 0: Visualization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb>`_
 - Setting up **PyAutoLens**'s visualization library.
 
-`Tutorial 1: Grids And Galaxies <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb>`_
+`Tutorial 1: Grids And Galaxies <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/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: Ray Tracing <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_2_ray_tracing.ipynb>`_
+`Tutorial 2: Ray Tracing <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_2_ray_tracing.ipynb>`_
 - Using grids, galaxies and mass profiles to perform strong lens ray-tracing.
 
-`Tutorial 3: More Ray Tracing <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_3_more_ray_tracing.ipynb>`_
+`Tutorial 3: More Ray Tracing <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_3_more_ray_tracing.ipynb>`_
 - Advanced strong lens ray-tracing.
 
-`Tutorial 4: Point Sources <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb>`_
+`Tutorial 4: Point Sources <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_4_point_sources.ipynb>`_
 - How lensing calculations when the source galaxy is a point-source (e.g. a quasar).
 
-`Tutorial 5: Lensing Formalism <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb>`_
+`Tutorial 5: Lensing Formalism <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb>`_
 - The algebraic lensing formalism used to describe strong lensing.
 
-`Tutorial 6: Data <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_6_data.ipynb>`_
+`Tutorial 6: Data <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_6_data.ipynb>`_
 - Loading and inspecting telescope imaging data of a strong lens.
 
-`Tutorial 7: Fitting <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_7_fitting.ipynb>`_
+`Tutorial 7: Fitting <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_7_fitting.ipynb>`_
 - Fitting data with a strong lens model.
 
-`Tutorial 8: Summary <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb>`_
+`Tutorial 8: Summary <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb>`_
 - A summary of the chapter.

docs/howtolens/chapter_2_lens_modeling.rst

@@ -5,26 +5,26 @@ In chapter 2, we'll take you through how to model strong lenses using a non-line
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_1_non_linear_search.ipynb>`_
+`Tutorial 1: Non-linear Search <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_1_non_linear_search.ipynb>`_
 - How a non-linear search is used to fit a lens model and the concepts of a parameter space and priors.
 
-`Tutorial 2: Practicalities <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_2_practicalities.ipynb>`_
+`Tutorial 2: Practicalities <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_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/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.ipynb>`_
+`Tutorial 3: Realism and Complexity <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_3_realism_and_complexity.ipynb>`_
 - Finding a balance between realism and complexity when composing and fitting a lens model.
 
-`Tutorial 4: Dealing with Failure <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.ipynb>`_
+`Tutorial 4: Dealing with Failure <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.ipynb>`_
 - What to do when PyAutoLens finds an inaccurate lens model.
 
-`Tutorial 5: Linear Profiles <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb>`_
+`Tutorial 5: Linear Profiles <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_5_linear_profiles.ipynb>`_
 - Light profiles which capture complex morphologies in a reduced number of non-linear parameters.
 
-`Tutorial 6: Masking and Positions <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_6_masking_and_positions.ipynb>`_
+`Tutorial 6: Masking and Positions <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_6_masking_and_positions.ipynb>`_
 - How to mask and mark positions on your data to improve the lens model.
 
-`Tutorial 7: Results <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_7_results.ipynb>`_
+`Tutorial 7: Results <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_7_results.ipynb>`_
 - Overview of the results available after successfully fitting a lens model.
 
-`Tutorial 8: Need for Speed <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb>`_
+`Tutorial 8: Need for Speed <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb>`_
 - How to fit complex models whilst balancing efficiency and run-time.

docs/howtolens/chapter_3_search_chaining.rst

@@ -6,20 +6,20 @@ robust modeling of large strong lens samples.
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Search Chaining <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb>`_
+`Tutorial 1: Search Chaining <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb>`_
 - Breaking the lens modeling procedure into a chained sequence of model-fits.
 
-`Tutorial 2: Prior Passing <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb>`_
+`Tutorial 2: Prior Passing <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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 and Source <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb>`_
+`Tutorial 3: Lens and Source <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb>`_
 - Fitting the lens's light followed by its mass using chained searches.
 
-`Tutorial 4: Two Lens galaxies <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb>`_
+`Tutorial 4: Two Lens galaxies <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb>`_
 - Modeling a strong lens with two lens galaxies using chained searches.
 
-`Tutorial 5: Complex Source <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_3_search_chaining/tutorial_4_complex_source.ipynb>`_
+`Tutorial 5: Complex Source <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb>`_
+`Tutorial 6: SLaM <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb>`_
 - Template pipelines for fitting lens model is standardized ways.

docs/howtolens/chapter_4_pixelizations.rst

@@ -5,35 +5,35 @@ In chapter 4, we use **Pixelizations** to reconstruct complex source galaxies on
 
 The chapter contains the following tutorials:
 
-`Tutorial 1: Pixelizations <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb>`_
+`Tutorial 1: Pixelizations <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb>`_
+`Tutorial 2: Mappers <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_3_inversions.ipynb>`_
+`Tutorial 3: Inversions <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb>`_
+`Tutorial 4: Bayesian Regularization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_5_borders.ipynb>`_
+`Tutorial 5: Borders <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_6_lens_modeling.ipynb>`_
+`Tutorial 6: Lens Modeling  <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_6_lens_modeling.ipynb>`_
 - How to use inversions to fit a lens model.
 
-`Tutorial 7: Adaptive Pixelization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.ipynb>`_
+`Tutorial 7: Adaptive Pixelization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/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/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb>`_
+`Tutorial 8: Model Fit <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb>`_
 - An example lens modeling pipeline which uses an inversion.
 
-`Tutorial 9: Fit Problems <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_9_fit_problems.ipynb>`_
+`Tutorial 9: Fit Problems <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_9_fit_problems.ipynb>`_
 - The shortcomings of our lens models and inversions.
 
-`Tutorial 10: Brightness Adaption <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_10_brightness_adaption.ipynb>`_
+`Tutorial 10: Brightness Adaption <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_10_brightness_adaption.ipynb>`_
 - Adapting the pixelization to the source's morphology.
 
-`Tutorial 11: Adaptive Regularization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_4_pixelizations/tutorial_11_adapt_regularization.py.ipynb>`_
+`Tutorial 11: Adaptive Regularization <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_4_pixelizations/tutorial_11_adapt_regularization.py.ipynb>`_
 - Adapting the regularization to the source's morphology.

docs/howtolens/chapter_optional.rst

@@ -5,9 +5,9 @@ This chapter contains optional tutorials expanding on different aspects of how *
 
 The chapter contains the following tutorials:
 
-`Tutorial: Sub-grids  <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_optional/tutorial_sub_grids.ipynb>`_
+`Tutorial: Sub-grids  <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_optional/tutorial_sub_grids.ipynb>`_
 - Use sub-grids to perform more accuratee and precise lensing calculations.
 
-`Tutorial: Searches  <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.1/notebooks/chapter_optional/tutorial_searches.ipynb>`_
+`Tutorial: Searches  <https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.5.1.4/notebooks/chapter_optional/tutorial_searches.ipynb>`_
 - Alternative non-linear searches to sample parameter space.
 

docs/index.rst

@@ -16,7 +16,7 @@ The following links are useful for new starters:
 
 - `The PyAutoLens readthedocs <https://pyautolens.readthedocs.io/en/latest>`_: which includes `an overview of PyAutoLens's core features <https://pyautolens.readthedocs.io/en/latest/overview/overview_1_start_here.html>`_, `a new user starting guide <https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html>`_ and `an installation guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>`_.
 
-- `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.1/start_here.ipynb>`_, where you can try **PyAutoLens** in a web browser (without installation).
+- `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.1.4/start_here.ipynb>`_, where you can try **PyAutoLens** in a web browser (without installation).
 
 - `The autolens_workspace GitHub repository <https://github.com/PyAutoLabs/autolens_workspace>`_, which includes example scripts covering every **PyAutoLens** use case.