docs: audit-driven URL fixes across docs, READMEs, and docstrings

CITATIONS.md

@@ -1,9 +1,9 @@
 # Citations & References
 
 The bibtex entries for **PyAutoFit** and its affiliated software packages can be found
-[here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citations.bib), with example text for citing **PyAutoFit**
-in [.tex format here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citation.tex) format here and
-[.md format here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citations.md). As shown in the examples, we
+[here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citations.bib), with example text for citing **PyAutoFit**
+in [.tex format here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citation.tex) format here and
+[.md format here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citations.md). As shown in the examples, we
 would greatly appreciate it if you mention **PyAutoFit** by name and include a link to our GitHub page!
 
 **PyAutoFit** is published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.02550#) and its

CODE_OF_CONDUCT.md

@@ -302,7 +302,7 @@ the situation is not yet resolved.
 
 ## License
 
-This code of conduct has been adapted from [*NUMFOCUS code of conduct*](https://github.com/numfocus/numfocus/blob/main/manual/numfocus-coc.md#the-short-version),
-which is adapted from numerous sources, including the [*Geek Feminism wiki, created by the Ada Initiative and other volunteers, which is under a Creative Commons Zero license*](http://geekfeminism.wikia.com/wiki/Fiterence_anti-harassment/Policy), the [*Contributor Covenant version 1.2.0*](http://contributor-covenant.org/version/1/2/0/), the [*Bokeh Code of Conduct*](https://github.com/bokeh/bokeh/blob/main/CODE_OF_CONDUCT.md), the [*SciPy Code of Conduct*](https://github.com/jupyter/governance/blob/main/conduct/enforcement.md), the [*Carpentries Code of Conduct*](https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html#enforcement-manual), and the [*NeurIPS Code of Conduct*](https://neurips.cc/public/CodeOfConduct).
+This code of conduct has been adapted from [*NUMFOCUS code of conduct*](https://numfocus.org/code-of-conduct),
+which is adapted from numerous sources, including the [*Geek Feminism wiki, created by the Ada Initiative and other volunteers, which is under a Creative Commons Zero license*](http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy), the [*Contributor Covenant version 1.2.0*](http://contributor-covenant.org/version/1/2/0/), the [*Bokeh Code of Conduct*](https://github.com/bokeh/bokeh/blob/main/docs/CODE_OF_CONDUCT.md), the [*SciPy Code of Conduct*](https://github.com/jupyter/governance/blob/main/conduct/enforcement.md), the [*Carpentries Code of Conduct*](https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html#enforcement-manual), and the [*NeurIPS Code of Conduct*](https://neurips.cc/public/CodeOfConduct).
 
 **PyAutoFit Code of Conduct is licensed under the [Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/).**

CONTRIBUTING.md

@@ -56,7 +56,7 @@ Contributions are welcome and greatly appreciated!
 
 ### Report Bugs
 
-Report bugs at https://github.com/rhayes777/PyAutoFit/issues
+Report bugs at https://github.com/PyAutoLabs/PyAutoFit/issues
 
 If you are playing with the PyAutoFit library and find a bug, please
 reporting it including:
@@ -68,7 +68,7 @@ reporting it including:
 ### Propose New `NonLinearSearch` or Features
 
 The best way to send feedback is to open an issue at
-https://github.com/rhayes777/PyAutoFit/issues
+https://github.com/PyAutoLabs/PyAutoFit/issues
 with tag *enhancement*.
 
 If you are proposing a new `NonLinearSearch` or a new feature:

README.md

@@ -3,9 +3,9 @@
 [![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 [![Python Versions](https://img.shields.io/pypi/pyversions/autofit)](https://pypi.org/project/autofit/)
 [![PyPI Version](https://img.shields.io/pypi/v/autofit.svg)](https://pypi.org/project/autofit/)
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/start_here.ipynb)
-[![Tests](https://github.com/rhayes777/PyAutoFit/actions/workflows/main.yml/badge.svg)](https://github.com/rhayes777/PyAutoFit/actions)
-[![Build](https://github.com/rhayes777/PyAutoBuild/actions/workflows/release.yml/badge.svg)](https://github.com/rhayes777/PyAutoBuild/actions)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb)
+[![Tests](https://github.com/PyAutoLabs/PyAutoFit/actions/workflows/main.yml/badge.svg)](https://github.com/PyAutoLabs/PyAutoFit/actions)
+[![Build](https://github.com/PyAutoLabs/PyAutoBuild/actions/workflows/release.yml/badge.svg)](https://github.com/PyAutoLabs/PyAutoBuild/actions)
 [![Documentation Status](https://readthedocs.org/projects/pyautofit/badge/?version=latest)](https://pyautofit.readthedocs.io/en/latest/?badge=latest)
 [![JOSS](https://joss.theoj.org/papers/10.21105/joss.02550/status.svg)](https://doi.org/10.21105/joss.02550)
 
@@ -32,13 +32,13 @@ The following links are useful for new starters:
 
 - [The PyAutoFit readthedocs](https://pyautofit.readthedocs.io/en/latest), which includes an [installation guide](https://pyautofit.readthedocs.io/en/latest/installation/overview.html) and an overview of **PyAutoFit**'s core features.
 - [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb), where you can try **PyAutoFit** in a web browser (without installation).
-- [The autofit_workspace GitHub repository](https://github.com/Jammy2211/autofit_workspace), which includes example scripts demonstrating **PyAutoFit**'s features.
+- [The autofit_workspace GitHub repository](https://github.com/PyAutoLabs/autofit_workspace), which includes example scripts demonstrating **PyAutoFit**'s features.
 - [The standalone HowToFit repository](https://github.com/PyAutoLabs/HowToFit), a series of Jupyter notebook lectures which give new users a step-by-step introduction to **PyAutoFit**.
 
 ## Support
 
 Support for installation issues, help with Fit modeling and using **PyAutoFit** is available by
-[raising an issue on the GitHub issues page](https://github.com/rhayes777/PyAutoFit/issues).
+[raising an issue on the GitHub issues page](https://github.com/PyAutoLabs/PyAutoFit/issues).
 
 We also offer support on the **PyAutoFit** [Slack channel](https://pyautoFit.slack.com/), where we also provide the
 latest updates on **PyAutoFit**. Slack is invitation-only, so if you'd like to join send
@@ -57,7 +57,7 @@ The lectures are available in the [standalone HowToFit repository](https://githu
 To illustrate the **PyAutoFit** API, we use an illustrative toy model of fitting a one-dimensional Gaussian to
 noisy 1D data. Here's the `data` (black) and the model (red) we'll fit:
 
-<img src="https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/files/toy_model_fit.png" width="400" />
+<img src="https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/files/toy_model_fit.png" width="400" />
 
 We define our model, a 1D Gaussian by writing a Python class using the format below:
 

autofit/mapper/prior_model/collection.py

@@ -137,7 +137,7 @@ def __init__(
 
         For a complete description of the model composition API, see the **PyAutoFit** model API cookbooks:
 
-        https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html
+        https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html
 
         The Python class input into a ``Model`` to create a model component is written using the following format:
 

autofit/mapper/prior_model/prior_model.py

@@ -66,7 +66,7 @@ def __init__(
 
         For a complete description of the model composition API, see the **PyAutoFit** model API cookbooks:
 
-        https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html
+        https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html
 
         The Python class input into a ``Model`` to create a model component is written using the following format:
 

docs/api/analysis.rst

@@ -8,9 +8,9 @@ It acts as an interface between the data, model and the non-linear search.
 
 **Examples / Tutorials:**
 
-- `readthedocs: example using Analysis object <https://pyautofit.readthedocs.io/en/latest/overview/model_fit.html>`_.
-- `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
-- `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
+- `readthedocs: example using Analysis object <https://pyautofit.readthedocs.io/en/latest/overview/the_basics.html>`_.
+- `autofit_workspace: simple tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_1_the_basics.ipynb>`_
+- `autofit_workspace: complex tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_2_scientific_workflow.ipynb>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 --------

docs/api/database.rst

@@ -9,7 +9,7 @@ inspection, analysis and interpretation.
 **Examples / Tutorials:**
 
 - `readthedocs: example using database functionality <https://pyautofit.readthedocs.io/en/latest/features/database.html>`_
-- `autofit_workspace: tutorial using database <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/features/database.ipynb>`_
+- `autofit_workspace: tutorial using database <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/features/database.ipynb>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 ----------

docs/api/model.rst

@@ -4,16 +4,16 @@ Models
 
 Model objects are used for composing models that are fitted to data.
 
-It is recommended the `model API cookbooks <https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html>`_ are used for guidance on building complex model.
+It is recommended the `model API cookbooks <https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html>`_ are used for guidance on building complex model.
 
 **Examples / Tutorials:**
 
-- `Model API Cookbooks (recommended) <https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html>`_.
+- `Model API Cookbooks (recommended) <https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html>`_.
 
-- `readthedocs: example using Model object <https://pyautofit.readthedocs.io/en/latest/overview/model_fit.html>`_.
-- `readthedocs: example using Collection object <https://pyautofit.readthedocs.io/en/latest/overview/model_complex.html>`_.
-- `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
-- `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
+- `readthedocs: example using Model object <https://pyautofit.readthedocs.io/en/latest/overview/the_basics.html>`_.
+- `readthedocs: example using Collection object <https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html>`_.
+- `autofit_workspace: simple tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_1_the_basics.ipynb>`_
+- `autofit_workspace: complex tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_2_scientific_workflow.ipynb>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 ------

docs/api/plot.rst

@@ -7,8 +7,8 @@ by **PyAutoFit**.
 
 **Examples / Tutorials:**
 
-- `readthedocs: non-linear search example <https://pyautofit.readthedocs.io/en/latest/overview/non_linear_search.html>`_
-- `autofit_workspace: plot tutorials <https://github.com/Jammy2211/autofit_workspace/tree/release/notebooks/plot>`_
+- `readthedocs: non-linear search example <https://pyautofit.readthedocs.io/en/latest/cookbooks/search.html>`_
+- `autofit_workspace: plot tutorials <https://github.com/PyAutoLabs/autofit_workspace/tree/main/notebooks/guides/plot>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 --------

docs/api/priors.rst

@@ -6,12 +6,12 @@ The priors of parameters of every component of a mdoel, which is fitted to data,
 
 **Examples / Tutorials:**
 
-- `Model API Cookbooks (recommended) <https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html>`_.
+- `Model API Cookbooks (recommended) <https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html>`_.
 
-- `readthedocs: example using Model object <https://pyautofit.readthedocs.io/en/latest/overview/model_fit.html>`_.
-- `readthedocs: example using Collection object <https://pyautofit.readthedocs.io/en/latest/overview/model_complex.html>`_.
-- `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
-- `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
+- `readthedocs: example using Model object <https://pyautofit.readthedocs.io/en/latest/overview/the_basics.html>`_.
+- `readthedocs: example using Collection object <https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html>`_.
+- `autofit_workspace: simple tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_1_the_basics.ipynb>`_
+- `autofit_workspace: complex tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_2_scientific_workflow.ipynb>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 Priors

docs/api/samples.rst

@@ -9,9 +9,9 @@ For example, for an MCMC model-fit, the ``Samples`` objects contains every sampl
 
 **Examples / Tutorials:**
 
-- `readthedocs: example on using results <https://pyautofit.readthedocs.io/en/latest/overview/result.html>`_.
-- `autofit_workspace: simple results tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/result.ipynb>`_
-- `autofit_workspace: complex result tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/result.ipynb>`_
+- `readthedocs: example on using results <https://pyautofit.readthedocs.io/en/latest/cookbooks/result.html>`_.
+- `autofit_workspace: simple results tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/cookbooks/result.ipynb>`_
+- `autofit_workspace: complex result tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/cookbooks/result.ipynb>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 Samples

docs/api/searches.rst

@@ -9,9 +9,9 @@ Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimators (MLE).
 
 **Examples / Tutorials:**
 
-- `readthedocs: example using non-linear searches <https://pyautofit.readthedocs.io/en/latest/overview/non_linear_search.html>`_.
-- `autofit_workspace: simple tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/simple/fit.ipynb>`_
-- `autofit_workspace: complex tutorial <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/overview/complex/fit.ipynb>`_
+- `readthedocs: example using non-linear searches <https://pyautofit.readthedocs.io/en/latest/cookbooks/search.html>`_.
+- `autofit_workspace: simple tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_1_the_basics.ipynb>`_
+- `autofit_workspace: complex tutorial <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/overview/overview_2_scientific_workflow.ipynb>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
 Nested Samplers
@@ -79,7 +79,7 @@ model are fitted in over a discrete grid.
 **Examples / Tutorials:**
 
 - `readthedocs: example using a non-linear search grid search <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_.
-- `autofit_workspace: example using a non-linear search grid search <https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/features/search_grid_search.ipynb>`_
+- `autofit_workspace: example using a non-linear search grid search <https://github.com/PyAutoLabs/autofit_workspace/blob/main/notebooks/features/search_grid_search.ipynb>`_
 
 GridSearch
 ----------

docs/conf.py

@@ -3,7 +3,7 @@
 #
 # This file only contains a selection of the most common options. For a full
 # list see the documentation:
-# https://www.sphinx-doc.org/en/main/usage/configuration.html
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 # -- Path setup --------------------------------------------------------------
 
@@ -61,7 +61,7 @@
 
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3", None),
-    "sphinx": ("https://www.sphinx-doc.org/en/main", None),
+    "sphinx": ("https://www.sphinx-doc.org/en/master", None),
 }
 
 # -- Options for TODOs -------------------------------------------------------

docs/cookbooks/multiple_datasets.md

@@ -89,17 +89,17 @@ for data, noise_map in zip(data_list, noise_map_list):
 
 Here is what the plots look like:
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_data_0.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_data_0.png
 :alt: Alternative text
 :width: 300
 ```
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_data_1.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_data_1.png
 :alt: Alternative text
 :width: 300
 ```
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_data_2.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_data_2.png
 :alt: Alternative text
 :width: 300
 ```
@@ -246,17 +246,17 @@ for data, result in zip(data_list, result_list):
 
 The image appears as follows:
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_0.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_0.png
 :alt: Alternative text
 :width: 300
 ```
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_1.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_1.png
 :alt: Alternative text
 :width: 300
 ```
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_2.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_2.png
 :alt: Alternative text
 :width: 300
 ```
@@ -305,17 +305,17 @@ for data, noise_map in zip(data_list, noise_map_list):
 
 The images appear as follows:
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_0.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_0.png
 :alt: Alternative text
 :width: 300
 ```
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_1.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_1.png
 :alt: Alternative text
 :width: 300
 ```
 
-```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_2.png
+```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_2.png
 :alt: Alternative text
 :width: 300
 ```