From d753a86ed33ab629233dbcf9130666e395c76b95 Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Thu, 9 Jul 2026 16:29:06 +0100 Subject: [PATCH] Fix dead chapter README Colab links Chapter 1's entry for the removed tutorial_4_complex_models now points at its replacement tutorial_4_why_modeling_is_hard (retitled to match); chapter 3's hierarchical-models link now matches the notebook's actual (typo'd) filename tutorial_4_hierachical_models.ipynb. Both grandfathered Colab entries leave .url_check_allowlist.txt. Part of PyAutoLabs/HowToLens#21 (tracker PyAutoLabs/PyAutoBuild#124) Co-Authored-By: Claude Fable 5 --- .url_check_allowlist.txt | 4 ---- notebooks/chapter_1_introduction/README.md | 2 +- notebooks/chapter_3_graphical_models/README.md | 2 +- scripts/chapter_1_introduction/README.md | 2 +- scripts/chapter_3_graphical_models/README.md | 2 +- 5 files changed, 4 insertions(+), 8 deletions(-) diff --git a/.url_check_allowlist.txt b/.url_check_allowlist.txt index 7601887..488d82b 100644 --- a/.url_check_allowlist.txt +++ b/.url_check_allowlist.txt @@ -9,10 +9,6 @@ # 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/HowToFit/blob/2026.5.14.2/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb # scripts/chapter_1_introduction/README.md:16 -https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.5.14.2/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb # scripts/chapter_3_graphical_models/README.md:17 - # GitHub refs (workspaces / removed tutorials) https://github.com/Jammy2211/PyAutoLogo/blob/main/gifs/pyautofit.gif?raw=true # README.md:7 https://github.com/PyAutoLabs/HowToFit/blob/main/scripts/chapter_1_introduction/images/bad_fit.png?raw=true # scripts/chapter_1_introduction/tutorial_4_why_modeling_is_hard.py:452 diff --git a/notebooks/chapter_1_introduction/README.md b/notebooks/chapter_1_introduction/README.md index badc17a..354cbd4 100644 --- a/notebooks/chapter_1_introduction/README.md +++ b/notebooks/chapter_1_introduction/README.md @@ -13,6 +13,6 @@ The chapter contains the following tutorials: - [Tutorial 3: Non Linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb) — Searching non-linear parameter spaces to find the best-fit model. -- [Tutorial 4: Complex Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb) — Composing and fitting more complex models in a scalable and extensible way. +- [Tutorial 4: Why Modeling Is Hard](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_why_modeling_is_hard.ipynb) — Why fitting complex models is challenging and how a good scientific approach overcomes common problems. - [Tutorial 5: Results and Samples](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb) — Interpreting model-fit results and using the samples for scientific analysis. diff --git a/notebooks/chapter_3_graphical_models/README.md b/notebooks/chapter_3_graphical_models/README.md index 1acd444..f81a63e 100644 --- a/notebooks/chapter_3_graphical_models/README.md +++ b/notebooks/chapter_3_graphical_models/README.md @@ -14,6 +14,6 @@ The chapter contains the following tutorials: - [Tutorial 3: Graphical Benefits](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb) — Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one. -- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework. +- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierachical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework. - [Tutorial 5: Expectation Propagation](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP). diff --git a/scripts/chapter_1_introduction/README.md b/scripts/chapter_1_introduction/README.md index badc17a..354cbd4 100644 --- a/scripts/chapter_1_introduction/README.md +++ b/scripts/chapter_1_introduction/README.md @@ -13,6 +13,6 @@ The chapter contains the following tutorials: - [Tutorial 3: Non Linear Search](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_3_non_linear_search.ipynb) — Searching non-linear parameter spaces to find the best-fit model. -- [Tutorial 4: Complex Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_complex_models.ipynb) — Composing and fitting more complex models in a scalable and extensible way. +- [Tutorial 4: Why Modeling Is Hard](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_4_why_modeling_is_hard.ipynb) — Why fitting complex models is challenging and how a good scientific approach overcomes common problems. - [Tutorial 5: Results and Samples](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_1_introduction/tutorial_5_results_and_samples.ipynb) — Interpreting model-fit results and using the samples for scientific analysis. diff --git a/scripts/chapter_3_graphical_models/README.md b/scripts/chapter_3_graphical_models/README.md index 1acd444..f81a63e 100644 --- a/scripts/chapter_3_graphical_models/README.md +++ b/scripts/chapter_3_graphical_models/README.md @@ -14,6 +14,6 @@ The chapter contains the following tutorials: - [Tutorial 3: Graphical Benefits](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_3_graphical_benefits.ipynb) — Illustrating the benefits of graphical modeling over fitting individual datasets one-by-one. -- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierarchical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework. +- [Tutorial 4: Hierarchical Models](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_4_hierachical_models.ipynb) — Fitting hierarchical models using the graphical modeling framework. - [Tutorial 5: Expectation Propagation](https://colab.research.google.com/github/PyAutoLabs/HowToFit/blob/2026.7.6.649/notebooks/chapter_3_graphical_models/tutorial_5_expectation_propagation.ipynb) — Scaling graphical models up to fit extremely large datasets using Expectation Propagation (EP).