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Jammy2211 merged 1 commit into from
Jul 9, 2026
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fix: pin chapter README Colab URLs to the release tag + dead filenames #22

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5 changes: 0 additions & 5 deletions .url_check_allowlist.txt
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Expand Up @@ -14,11 +14,6 @@ https://academic.oup.com/mnras/article/488/1/1387/5526256 # CITATIONS.md:22
# Code of Conduct boilerplate
http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy # CODE_OF_CONDUCT.md:305

# Colab refs to notebooks no longer in HowTo/workspace
https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/main/notebooks/chapter_3_search_chaining/tutorial_4_complex_source.ipynb # scripts/chapter_3_search_chaining/README.md:16
https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/main/notebooks/chapter_4_pixelizations/tutorial_11_adapt_regularization.py.ipynb # scripts/chapter_4_pixelizations/README.md:27
https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/main/notebooks/chapter_4_pixelizations/tutorial_3_pixelizations.ipynb # scripts/chapter_4_pixelizations/README.md:11

# External dead / paywalled / departmental pages
http://www.ita.uni-heidelberg.de/~massimo/sub/Lectures/gl_all.pdf # README.md:66

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18 changes: 9 additions & 9 deletions notebooks/chapter_1_introduction/README.md
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Expand Up @@ -4,20 +4,20 @@ 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/HowToLens/blob/main/notebooks/chapter_1_introduction/tutorial_0_visualization.ipynb) — Setting up **PyAutoLens**'s visualization library.
- [Tutorial 0: Visualization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_1_grids_and_galaxies.ipynb) — Using grids of (y,x) coordinates with galaxies made up of light profiles.
- [Tutorial 1: Grids And Galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_2_ray_tracing.ipynb) — Using grids, galaxies and mass profiles to perform strong lens ray-tracing.
- [Tutorial 2: Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_3_more_ray_tracing.ipynb) — Advanced strong lens ray-tracing.
- [Tutorial 3: More Ray Tracing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/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 4: Point Sources](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_5_lensing_formalism.ipynb) — The algebraic lensing formalism used to describe strong lensing.
- [Tutorial 5: Lensing Formalism](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_6_data.ipynb) — Loading and inspecting telescope imaging data of a strong lens.
- [Tutorial 6: Data](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_7_fitting.ipynb) — Fitting data with a strong lens model.
- [Tutorial 7: Fitting](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb) — A summary of the chapter.
- [Tutorial 8: Summary](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_8_summary.ipynb) — A summary of the chapter.
16 changes: 8 additions & 8 deletions notebooks/chapter_2_lens_modeling/README.md
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Expand Up @@ -4,18 +4,18 @@ In chapter 2, we'll take you through how to model strong lenses using a non-line

# Files

- [Tutorial 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/main/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 1: Non-linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/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 2: Practicalities](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/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 3: Realism and Complexity](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.ipynb) — What to do when PyAutoLens finds an inaccurate lens model.
- [Tutorial 4: Dealing with Failure](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/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 5: Linear Profiles](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/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 6: Masking and Positions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_2_lens_modeling/tutorial_7_results.ipynb) — Overview of the results available after successfully fitting a lens model.
- [Tutorial 7: Results](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb) — How to fit complex models whilst balancing efficiency and run-time.
- [Tutorial 8: Need for Speed](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/notebooks/chapter_2_lens_modeling/tutorial_8_need_for_speed.ipynb) — How to fit complex models whilst balancing efficiency and run-time.
12 changes: 6 additions & 6 deletions notebooks/chapter_3_search_chaining/README.md
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Expand Up @@ -5,14 +5,14 @@ different lens model.

# Files

- [Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/main/notebooks/chapter_3_search_chaining/tutorial_1_search_chaining.ipynb) — Breaking the lens modeling procedure into a chained sequence of model-fits.
- [Tutorial 1: Search Chaining](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_3_search_chaining/tutorial_2_prior_passing.ipynb) — How the results of earlier searches are passed to later searches.
- [Tutorial 2: Prior Passing](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_3_search_chaining/tutorial_3_lens_and_source.ipynb) — Fitting the lens's light followed by its mass using chained searches.
- [Tutorial 3: Lens and Source](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_3_search_chaining/tutorial_4_x2_lens_galaxies.ipynb) — Modeling a strong lens with two lens galaxies using chained searches.
- [Tutorial 4: Two Lens galaxies](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/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 5: Complex Source](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/notebooks/chapter_3_search_chaining/tutorial_5_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/main/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb) — Template pipelines for fitting lens model is standardized ways.
- [Tutorial 6: SLaM](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/notebooks/chapter_3_search_chaining/tutorial_6_slam.ipynb) — Template pipelines for fitting lens model is standardized ways.
22 changes: 11 additions & 11 deletions notebooks/chapter_4_pixelizations/README.md
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Expand Up @@ -4,24 +4,24 @@ In chapter 4, we use **Pixelizations** to reconstruct complex source galaxies on

# Files

- [Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/main/notebooks/chapter_4_pixelizations/tutorial_1_pixelizations.ipynb) — Creating a pixel-grid in the source-plane.
- [Tutorial 1: Pixelizations](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_2_mappers.ipynb) — How a pixelization maps source-pixels to image-pixels.
- [Tutorial 2: Mappers](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_3_pixelizations.ipynb) — Inverting the mappings to reconstruct the source's light.
- [Tutorial 3: Inversions](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_4_bayesian_regularization.ipynb) — Smoothing the source within a Bayesian framework.
- [Tutorial 4: Bayesian Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_5_borders.ipynb) — Preventing highly demagnified image-pixels ruining the inversion.
- [Tutorial 5: Borders](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_6_lens_modeling.ipynb) — How to use inversions to fit a lens model.
- [Tutorial 6: Lens Modeling](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.ipynb) — A Voronoi mesh which adapts to the mass model's magnification.
- [Tutorial 7: Adaptive Pixelization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_8_model_fit.ipynb) — An example lens modeling pipeline which uses an inversion.
- [Tutorial 8: Model Fit](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_9_fit_problems.ipynb) — The shortcomings of our lens models and inversions.
- [Tutorial 9: Fit Problems](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_10_brightness_adaption.ipynb) — Adapting the pixelization to the source's morphology.
- [Tutorial 10: Brightness Adaption](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/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/main/notebooks/chapter_4_pixelizations/tutorial_11_adapt_regularization.py.ipynb) — Adapting the regularization to the source's morphology.
- [Tutorial 11: Adaptive Regularization](https://colab.research.google.com/github/PyAutoLabs/HowToLens/blob/2026.7.6.649/notebooks/chapter_4_pixelizations/tutorial_11_adaptive_regularization.ipynb) — Adapting the regularization to the source's morphology.
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