docs: audit-driven URL fixes (workspace phase)

CITATIONS.md

@@ -1,9 +1,9 @@
 # Citations & References
 
 The bibtex entries for **PyAutoLens** and its affiliated software packages can be found
-[here](https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.bib), with example text for citing **PyAutoLens**
-in [.tex format here](https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.tex) format here and
-[.md format here](https://github.com/Jammy2211/PyAutoLens/blob/main/files/citations.md). As shown in the examples, we
+[here](https://github.com/PyAutoLabs/PyAutoLens/blob/main/files/citations.bib), with example text for citing **PyAutoLens**
+in [.tex format here](https://github.com/PyAutoLabs/PyAutoLens/blob/main/files/citations.tex) format here and
+[.md format here](https://github.com/PyAutoLabs/PyAutoLens/blob/main/files/citations.md). As shown in the examples, we
 would greatly appreciate it if you mention **PyAutoLens** by name and include a link to our GitHub page!
 
 **PyAutoLens** is published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.02825#) and its

CODE_OF_CONDUCT.md

@@ -301,7 +301,7 @@ 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/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/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).
 
 **PyAutoLens Code of Conduct is licensed under the [Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/).**

README.md

@@ -4,7 +4,7 @@
 
 [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/2026.5.14.2/start_here.ipynb) |
+[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb) |
 [HowToLens](https://github.com/PyAutoLabs/HowToLens)
 
 <img src="https://github.com/Jammy2211/PyAutoLogo/blob/main/gifs/pyautolens.gif?raw=true" width="900" />
@@ -17,7 +17,7 @@ You can get set up on your personal computer by following the installation guide
 our [readthedocs](https://pyautolens.readthedocs.io/).
 
 Alternatively, you can try **PyAutoLens** out in a web browser by going to
-the [autolens workspace on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.14.2/start_here.ipynb).
+the [autolens workspace on Colab](https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb).
 
 ## New Users
 
@@ -26,7 +26,7 @@ overview of **PyAutoLens**'s core features and API.
 
 This can be done via a web browser by going to the following Google Colab link:
 
-https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.14.2/start_here.ipynb
+https://colab.research.google.com/github/PyAutoLabs/autolens_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb
 
 Then checkout the [new user starting guide](https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) to navigate the
 workspace for your science case.
@@ -86,7 +86,7 @@ gravitational lensing analysis, and helps troubleshoot problems.
 
 Slack is invitation-only. If you'd like to join, please send an email requesting an invite.
 
-For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/Jammy2211/PyAutoLens/issues).
+For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/PyAutoLabs/PyAutoLens/issues).
 
 ## Contribution
 

notebooks/group/likelihood_function.ipynb

@@ -726,7 +726,7 @@
         "To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
         "non-linear search algorithm.\n",
         "\n",
-        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus)\n",
+        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus)\n",
         "multiple MCMC and optimization algorithms are supported.\n",
         "\n",
         "__Wrap Up__\n",

notebooks/guides/advanced/add_a_profile.ipynb

@@ -67,22 +67,22 @@
         "\n",
         "The mass profiles available in **PyAutoLens** are located in its parent package, **PyAutoGalaxy**: \n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy\n",
         "\n",
         "All light and mass profiles are found in the following python package:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/profiles\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/profiles\n",
         "\n",
         "Mass profiles are in the following package:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass\n",
         "\n",
         "Lets look at an example mass profile. We'll use the `Isothermal` profile, which is located in the `total` package\n",
         "because it represents a total (stars + dark matter) mass distribution:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass/total\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass/total\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/total/isothermal.py\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/total/isothermal.py\n",
         "\n",
         "For simplicity, a shortened version of the `Isothermal` profile is shown below. \n",
         "\n",
@@ -249,7 +249,7 @@
         "All mass profiles in **PyAutoLens** inherit from the `MassProfile` abstract base class, which is located in the\n",
         "following package:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/abstract/abstract.py\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/abstract/abstract.py\n",
         "\n",
         "This contains functions which are useful for any mass profile, which your custom mass profile will inherit.\n",
         "\n",
@@ -260,7 +260,7 @@
         "\n",
         "The `MassProfile` class inherits from the `GeometryProfile` abstract base class, which is located here:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/profiles/geometry_profiles.py\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/profiles/geometry_profiles.py\n",
         "\n",
         "This contains functions which are useful for any elliptical (and spherical) profile, which your custom \n",
         "mass profile will again inherit (e.g. `radial_grid_from`).\n",
@@ -280,7 +280,7 @@
         "Mass profiles therefore also inherit from the `OperateDeflections` abstract base class, which contains numerous \n",
         "functions for computing these lensing quantities from the deflection angles. This is located here:\n",
         "\n",
-        "https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py\n",
+        "https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py\n",
         "\n",
         "This means that once you've implemented a deflections angles calculation for your mass profile, you can compute all\n",
         "lensing quantities from it without having to write any additional code!\n",

notebooks/guides/advanced/custom_analysis.ipynb

@@ -94,18 +94,18 @@
         "The `Analysis` classes available in **PyAutoLens** are actually located in both **PyAutoLens** and its parent \n",
         "package, **PyAutoGalaxy**: \n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy\n",
-        " https://github.com/Jammy2211/PyAutoLens\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy\n",
+        " https://github.com/PyAutoLabs/PyAutoLens\n",
         "\n",
         "All classes used for lens modeling are found in the following packages:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/imaging/model\n",
-        " https://github.com/Jammy2211/PyAutoLens/tree/main/autolens/imaging/model\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/imaging/model\n",
+        " https://github.com/PyAutoLabs/PyAutoLens/tree/main/autolens/imaging/model\n",
         "\n",
         "The `AnalysisImaging` classes are found in the following modules:\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/imaging/model/analysis.py\n",
-        " https://github.com/Jammy2211/PyAutoLens/blob/main/autolens/imaging/model/analysis.py\n",
+        " https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/imaging/model/analysis.py\n",
+        " https://github.com/PyAutoLabs/PyAutoLens/blob/main/autolens/imaging/model/analysis.py\n",
         "\n",
         "__Lens Model__\n",
         "\n",

notebooks/guides/advanced/multi_plane.ipynb

@@ -91,7 +91,7 @@
         "\n",
         "Multi-plane ray tracing is implemented in the `tracer_util.py` module of the following package:\n",
         "\n",
-        "https://github.com/Jammy2211/PyAutoLens/blob/main/autolens/lens/tracer_util.py\n",
+        "https://github.com/PyAutoLabs/PyAutoLens/blob/main/autolens/lens/tracer_util.py\n",
         "\n",
         "It uses the function `traced_grid_2d_list_from`.\n",
         "\n",

notebooks/guides/hpc/README_Repos.md

@@ -119,11 +119,11 @@ cd $HOME/PyAuto
 Clone all required repositories:
 
 ```
-git clone https://github.com/rhayes777/PyAutoConf
-git clone https://github.com/rhayes777/PyAutoFit
-git clone https://github.com/Jammy2211/PyAutoArray
-git clone https://github.com/Jammy2211/PyAutoGalaxy
-git clone https://github.com/Jammy2211/PyAutoLens
+git clone https://github.com/PyAutoLabs/PyAutoConf
+git clone https://github.com/PyAutoLabs/PyAutoFit
+git clone https://github.com/PyAutoLabs/PyAutoArray
+git clone https://github.com/PyAutoLabs/PyAutoGalaxy
+git clone https://github.com/PyAutoLabs/PyAutoLens
 ```
 
 ## 9. Install Python Dependencies

notebooks/guides/modeling/cookbook.ipynb

@@ -544,16 +544,16 @@
         "\n",
         "The following example notebooks show how to compose and fit these models:\n",
         "\n",
-        "https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/modeling/features/multi_gaussian_expansion.ipynb\n",
-        "https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/modeling/features/shapelets.ipynb\n",
+        "https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/modeling/features/multi_gaussian_expansion.ipynb\n",
+        "https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/modeling/features/shapelets.ipynb\n",
         "\n",
         "__Model Linking (Advanced)__\n",
         "\n",
         "When performing non-linear search chaining, the inferred model of one phase can be linked to the model.\n",
         "\n",
         "The following example notebooks show how to compose and fit these models:\n",
         "\n",
-        "https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/imaging/advanced/guides/modeling/chaining.ipynb\n",
+        "https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/imaging/advanced/guides/modeling/chaining.ipynb\n",
         "\n",
         "__Across Datasets (Advanced)__\n",
         "\n",
@@ -562,7 +562,7 @@
         "\n",
         "The following example notebooks show how to compose and fit these models:\n",
         "\n",
-        "https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/multi/modeling/start_here.ipynb\n",
+        "https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/multi/start_here.ipynb\n",
         "\n",
         "__Relations (Advanced)__\n",
         "\n",
@@ -571,7 +571,7 @@
         "\n",
         "The following example notebooks show how to compose and fit these models:\n",
         "\n",
-        "https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/multi/modeling/features/wavelength_dependence.ipynb\n",
+        "https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/multi/features/wavelength_dependence/modeling.ipynb\n",
         "\n",
         "__PyAutoFit API__\n",
         "\n",

notebooks/guides/tracer.ipynb

@@ -815,7 +815,7 @@
         "\n",
         "A full description of each can be found in the docstring of the source code of each function:\n",
         "\n",
-        "   https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py"
+        "   https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py"
       ]
     },
     {

notebooks/imaging/data_preparation/examples/data.ipynb

@@ -309,7 +309,7 @@
         "\n",
         "The preprocess module is found here:\n",
         "\n",
-        "https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
+        "https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
         "\n",
         "Functions related to background subtraction are:\n",
         "\n",

notebooks/imaging/data_preparation/examples/noise_map.ipynb

@@ -156,7 +156,7 @@
         "from the data you currently have (if it is not already RMS values including the Poisson noise contribution and\n",
         "background sky contribution).\n",
         "\n",
-        "https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
+        "https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
         "\n",
         "Functions related to the noise map are:\n",
         "\n",

notebooks/imaging/data_preparation/examples/psf.ipynb

@@ -186,7 +186,7 @@
         "\n",
         "The preprocess module contains functions for converting an even-sized PSF to an odd-sized PSF.\n",
         "\n",
-        "https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
+        "https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py\n",
         "\n",
         "- `psf_with_odd_dimensions_from`\n",
         "\n",

notebooks/imaging/features/linear_light_profiles/likelihood_function.ipynb

@@ -1050,7 +1050,7 @@
         "To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
         "non-linear search algorithm.\n",
         "\n",
-        "The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
+        "The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
         "multiple MCMC and optimization algorithms are supported.\n",
         "\n",
         "For linear light profiles, the reduced number of free parameters (e.g. the `intensity` values are solved for\n",

notebooks/imaging/features/multi_gaussian_expansion/likelihood_function.ipynb

@@ -1221,7 +1221,7 @@
         "To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
         "non-linear search algorithm.\n",
         "\n",
-        "The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus)\n",
+        "The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus)\n",
         "multiple MCMC and optimization algorithms are supported.\n",
         "\n",
         "For an MGE, the reduced number of free parameters (e.g. the `intensity` values are solved for\n",

notebooks/imaging/features/pixelization/delaunay.ipynb

@@ -1873,7 +1873,7 @@
         "To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
         "non-linear search algorithm.\n",
         "\n",
-        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus)\n",
+        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus)\n",
         "multiple MCMC and optimization algorithms are supported.\n",
         "\n",
         "__Sub Gridding__\n",
@@ -1884,7 +1884,7 @@
         "**PyAutoLens** has alternative methods of computing the lens galaxy images above, which uses a grid whose sub-size\n",
         "adaptively increases depending on a required fractional accuracy of the light profile.\n",
         "\n",
-        " https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/structures/grids/two_d/grid_iterate.py\n",
+        " https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/structures/grids/two_d/grid_iterate.py\n",
         "\n",
         "__Sourrce Plane Interpolation__\n",
         "\n",
@@ -1894,10 +1894,10 @@
         "which uses natural neighbor interpolation).\n",
         "\n",
         "`MapperVoronoiNoInterp.pix_index_for_sub_slim_index`:\n",
-        "https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/inversion/mappers/voronoi.py\n",
+        "https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/inversion/mappers/voronoi.py\n",
         "\n",
         "`pixelization_index_for_voronoi_sub_slim_index_from`:\n",
-        " https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/util/mapper_util.py\n",
+        " https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/util/mapper_util.py\n",
         "\n",
         "The number of pixels that each sub-pixel maps too is also stored and extracted. This is used for speeding up\n",
         "the calculation of the `mapping_matrix` described next.\n",

notebooks/imaging/features/pixelization/likelihood_function.ipynb

@@ -1543,7 +1543,7 @@
         "To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
         "non-linear search algorithm.\n",
         "\n",
-        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus)\n",
+        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus)\n",
         "multiple MCMC and optimization algorithms are supported.\n",
         "\n",
         "__Wrap Up__\n",

notebooks/imaging/likelihood_function.ipynb

@@ -800,7 +800,7 @@
         "To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a\n",
         "non-linear search algorithm.\n",
         "\n",
-        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus)\n",
+        "The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus)\n",
         "multiple MCMC and optimization algorithms are supported.\n",
         "\n",
         "__Wrap Up__\n",