From 3fb4fd953d617ca8e856840cbe248b3cb9cea72c Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Fri, 15 May 2026 08:56:19 +0100 Subject: [PATCH] docs: audit-driven URL fixes (workspace phase) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Apply scripted URL rewrites surfaced by admin_jammy/software/url_check. This is the workspace counterpart to the library-side cleanup that shipped under the same task. All changes are doc-only: URL strings in .md / .rst / .ipynb / .py docstrings and comments. No code behaviour changes. Line endings preserved. Patterns applied: - Jammy2211/ → PyAutoLabs/ (workspaces migrated orgs) - Jammy2211|rhayes777/ → PyAutoLabs/ - /blob/release/ and /tree/release/ → /main/ (release branch removed) - joshspeagle/nautilus → johannesulf/nautilus (sampler moved orgs) - rhayes777/PyAutoBuild → PyAutoLabs/PyAutoBuild - bokeh CoC → /docs/CODE_OF_CONDUCT.md - numfocus CoC → numfocus.org/code-of-conduct - www.sphinx-doc.org /en/main → /en/master - pyautofit.readthedocs.io renames (cookbook_1_basics → cookbooks/model, overview/model_fit → overview/the_basics, etc.) - Workspace notebook reorganisation (overview/{simple,complex}/{fit,result} → new flat structure; modeling/imaging/features/.ipynb → imaging/features//modeling.ipynb; tree/main/notebooks/plot → notebooks/guides/plot) Tool + report: PyAutoLabs/admin_jammy#21 (merged) Library wave: PyAutoLabs/PyAutoLens#509 (and paired PRs, all merged) Issue: PyAutoLabs/PyAutoLens#508 Co-Authored-By: Claude Opus 4.7 (1M context) --- CITATIONS.md | 6 ++--- CODE_OF_CONDUCT.md | 4 +-- README.md | 8 +++--- notebooks/group/likelihood_function.ipynb | 2 +- notebooks/guides/advanced/add_a_profile.ipynb | 16 ++++++------ .../guides/advanced/custom_analysis.ipynb | 12 ++++----- notebooks/guides/advanced/multi_plane.ipynb | 2 +- notebooks/guides/hpc/README_Repos.md | 10 +++---- notebooks/guides/modeling/cookbook.ipynb | 10 +++---- notebooks/guides/tracer.ipynb | 2 +- .../data_preparation/examples/data.ipynb | 2 +- .../data_preparation/examples/noise_map.ipynb | 2 +- .../data_preparation/examples/psf.ipynb | 2 +- .../likelihood_function.ipynb | 2 +- .../likelihood_function.ipynb | 2 +- .../features/pixelization/delaunay.ipynb | 8 +++--- .../pixelization/likelihood_function.ipynb | 2 +- notebooks/imaging/likelihood_function.ipynb | 2 +- .../features/pixelization/delaunay.ipynb | 2 +- .../pixelization/likelihood_function.ipynb | 2 +- .../many_visibilities_preparation.ipynb | 2 +- .../features/pixelization/modeling.ipynb | 2 +- .../features/pixelization/slam.ipynb | 2 +- .../interferometer/likelihood_function.ipynb | 2 +- notebooks/interferometer/simulator.ipynb | 2 +- scripts/group/likelihood_function.py | 2 +- scripts/guides/advanced/add_a_profile.py | 16 ++++++------ scripts/guides/advanced/custom_analysis.py | 12 ++++----- scripts/guides/advanced/multi_plane.py | 2 +- scripts/guides/hpc/README_Repos.md | 10 +++---- scripts/guides/modeling/cookbook.py | 10 +++---- scripts/guides/tracer.py | 2 +- .../imaging/data_preparation/examples/data.py | 2 +- .../data_preparation/examples/noise_map.py | 2 +- .../imaging/data_preparation/examples/psf.py | 2 +- .../likelihood_function.py | 2 +- .../likelihood_function.py | 2 +- .../imaging/features/pixelization/delaunay.py | 8 +++--- .../pixelization/likelihood_function.py | 2 +- scripts/imaging/likelihood_function.py | 2 +- .../features/pixelization/delaunay.py | 2 +- .../pixelization/likelihood_function.py | 2 +- .../many_visibilities_preparation.py | 2 +- .../features/pixelization/modeling.py | 2 +- .../features/pixelization/slam.py | 2 +- scripts/interferometer/likelihood_function.py | 2 +- scripts/interferometer/simulator.py | 2 +- start_here.ipynb | 26 +++++++++---------- start_here.py | 26 +++++++++---------- 49 files changed, 125 insertions(+), 125 deletions(-) diff --git a/CITATIONS.md b/CITATIONS.md index c2eba2c4e..512e66eb6 100644 --- a/CITATIONS.md +++ b/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 diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 1548dfed0..b7898bb53 100644 --- a/CODE_OF_CONDUCT.md +++ b/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/).** \ No newline at end of file diff --git a/README.md b/README.md index 1607ac6a8..2e65fcbd8 100644 --- a/README.md +++ b/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) @@ -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 diff --git a/notebooks/group/likelihood_function.ipynb b/notebooks/group/likelihood_function.ipynb index a0d88aacc..e4ac4a1d4 100644 --- a/notebooks/group/likelihood_function.ipynb +++ b/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", diff --git a/notebooks/guides/advanced/add_a_profile.ipynb b/notebooks/guides/advanced/add_a_profile.ipynb index f74120937..af1eb0ecf 100644 --- a/notebooks/guides/advanced/add_a_profile.ipynb +++ b/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", diff --git a/notebooks/guides/advanced/custom_analysis.ipynb b/notebooks/guides/advanced/custom_analysis.ipynb index f4d06e993..d087f63ce 100644 --- a/notebooks/guides/advanced/custom_analysis.ipynb +++ b/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", diff --git a/notebooks/guides/advanced/multi_plane.ipynb b/notebooks/guides/advanced/multi_plane.ipynb index bae866939..878659970 100644 --- a/notebooks/guides/advanced/multi_plane.ipynb +++ b/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", diff --git a/notebooks/guides/hpc/README_Repos.md b/notebooks/guides/hpc/README_Repos.md index c13d140a2..c748fda53 100644 --- a/notebooks/guides/hpc/README_Repos.md +++ b/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 diff --git a/notebooks/guides/modeling/cookbook.ipynb b/notebooks/guides/modeling/cookbook.ipynb index c8725e171..b00a672b3 100644 --- a/notebooks/guides/modeling/cookbook.ipynb +++ b/notebooks/guides/modeling/cookbook.ipynb @@ -544,8 +544,8 @@ "\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", @@ -553,7 +553,7 @@ "\n", "The following example notebooks show how to compose and fit these models:\n", "\n", - "https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/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", diff --git a/notebooks/guides/tracer.ipynb b/notebooks/guides/tracer.ipynb index 40b6e0d3a..07faa80dd 100644 --- a/notebooks/guides/tracer.ipynb +++ b/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" ] }, { diff --git a/notebooks/imaging/data_preparation/examples/data.ipynb b/notebooks/imaging/data_preparation/examples/data.ipynb index efbd2f4b6..03a158d2c 100644 --- a/notebooks/imaging/data_preparation/examples/data.ipynb +++ b/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", diff --git a/notebooks/imaging/data_preparation/examples/noise_map.ipynb b/notebooks/imaging/data_preparation/examples/noise_map.ipynb index 46f436230..f41c67165 100644 --- a/notebooks/imaging/data_preparation/examples/noise_map.ipynb +++ b/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", diff --git a/notebooks/imaging/data_preparation/examples/psf.ipynb b/notebooks/imaging/data_preparation/examples/psf.ipynb index 95cc9fee3..997f2a87d 100644 --- a/notebooks/imaging/data_preparation/examples/psf.ipynb +++ b/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", diff --git a/notebooks/imaging/features/linear_light_profiles/likelihood_function.ipynb b/notebooks/imaging/features/linear_light_profiles/likelihood_function.ipynb index 3980d8024..399339fe2 100644 --- a/notebooks/imaging/features/linear_light_profiles/likelihood_function.ipynb +++ b/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", diff --git a/notebooks/imaging/features/multi_gaussian_expansion/likelihood_function.ipynb b/notebooks/imaging/features/multi_gaussian_expansion/likelihood_function.ipynb index 5cf124a61..42ee1ac88 100644 --- a/notebooks/imaging/features/multi_gaussian_expansion/likelihood_function.ipynb +++ b/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", diff --git a/notebooks/imaging/features/pixelization/delaunay.ipynb b/notebooks/imaging/features/pixelization/delaunay.ipynb index 5e4827a71..d707e5b53 100644 --- a/notebooks/imaging/features/pixelization/delaunay.ipynb +++ b/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", diff --git a/notebooks/imaging/features/pixelization/likelihood_function.ipynb b/notebooks/imaging/features/pixelization/likelihood_function.ipynb index a73367fd0..195073c28 100644 --- a/notebooks/imaging/features/pixelization/likelihood_function.ipynb +++ b/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", diff --git a/notebooks/imaging/likelihood_function.ipynb b/notebooks/imaging/likelihood_function.ipynb index a93b8aebb..03bd3a232 100644 --- a/notebooks/imaging/likelihood_function.ipynb +++ b/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", diff --git a/notebooks/interferometer/features/pixelization/delaunay.ipynb b/notebooks/interferometer/features/pixelization/delaunay.ipynb index 34123059c..d6d24aad7 100644 --- a/notebooks/interferometer/features/pixelization/delaunay.ipynb +++ b/notebooks/interferometer/features/pixelization/delaunay.ipynb @@ -1673,7 +1673,7 @@ "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", "__Log Likelihood Function: Source Code Speed Up__\n", diff --git a/notebooks/interferometer/features/pixelization/likelihood_function.ipynb b/notebooks/interferometer/features/pixelization/likelihood_function.ipynb index 6ebfb6d95..22593026d 100644 --- a/notebooks/interferometer/features/pixelization/likelihood_function.ipynb +++ b/notebooks/interferometer/features/pixelization/likelihood_function.ipynb @@ -1506,7 +1506,7 @@ "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", "__Log Likelihood Function: Source Code Speed Up__\n", diff --git a/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb b/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb index e8a40f0a0..6f9a6ad00 100644 --- a/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb +++ b/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb @@ -48,7 +48,7 @@ "A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which\n", "are too big to include in the main `autolens_workspace` repository:\n", "\n", - "https://github.com/Jammy2211/autolens_workspace_large_files\n", + "https://github.com/PyAutoLabs/autolens_workspace_large_files\n", "\n", "After downloading the file, place it in the directory:\n", "\n", diff --git a/notebooks/interferometer/features/pixelization/modeling.ipynb b/notebooks/interferometer/features/pixelization/modeling.ipynb index 4fe7c3b6a..5f9d00757 100644 --- a/notebooks/interferometer/features/pixelization/modeling.ipynb +++ b/notebooks/interferometer/features/pixelization/modeling.ipynb @@ -125,7 +125,7 @@ "A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which\n", "are too big to include in the main `autolens_workspace` repository:\n", "\n", - "https://github.com/Jammy2211/autolens_workspace_large_files\n", + "https://github.com/PyAutoLabs/autolens_workspace_large_files\n", "\n", "After downloading the file, place it in the directory:\n", "\n", diff --git a/notebooks/interferometer/features/pixelization/slam.ipynb b/notebooks/interferometer/features/pixelization/slam.ipynb index b25eba825..5106e23e5 100644 --- a/notebooks/interferometer/features/pixelization/slam.ipynb +++ b/notebooks/interferometer/features/pixelization/slam.ipynb @@ -75,7 +75,7 @@ "A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which\n", "are too big to include in the main `autolens_workspace` repository:\n", "\n", - "https://github.com/Jammy2211/autolens_workspace_large_files\n", + "https://github.com/PyAutoLabs/autolens_workspace_large_files\n", "\n", "After downloading the file, place it in the directory:\n", "\n", diff --git a/notebooks/interferometer/likelihood_function.ipynb b/notebooks/interferometer/likelihood_function.ipynb index a10c95c9e..d09649381 100644 --- a/notebooks/interferometer/likelihood_function.ipynb +++ b/notebooks/interferometer/likelihood_function.ipynb @@ -772,7 +772,7 @@ "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", diff --git a/notebooks/interferometer/simulator.ipynb b/notebooks/interferometer/simulator.ipynb index 88b7f9fe6..45af55974 100644 --- a/notebooks/interferometer/simulator.ipynb +++ b/notebooks/interferometer/simulator.ipynb @@ -419,7 +419,7 @@ "A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which\n", "are too big to include in the main `autolens_workspace` repository:\n", "\n", - "https://github.com/Jammy2211/autolens_workspace_large_files\n", + "https://github.com/PyAutoLabs/autolens_workspace_large_files\n", "\n", "After downloading the file, place it in the directory:\n", "\n", diff --git a/scripts/group/likelihood_function.py b/scripts/group/likelihood_function.py index 0ccf34239..b435195ea 100644 --- a/scripts/group/likelihood_function.py +++ b/scripts/group/likelihood_function.py @@ -458,7 +458,7 @@ To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus) +The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Wrap Up__ diff --git a/scripts/guides/advanced/add_a_profile.py b/scripts/guides/advanced/add_a_profile.py index b0dc65a98..bd490dd9c 100644 --- a/scripts/guides/advanced/add_a_profile.py +++ b/scripts/guides/advanced/add_a_profile.py @@ -51,22 +51,22 @@ The mass profiles available in **PyAutoLens** are located in its parent package, **PyAutoGalaxy**: - https://github.com/Jammy2211/PyAutoGalaxy + https://github.com/PyAutoLabs/PyAutoGalaxy All light and mass profiles are found in the following python package: - https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/profiles + https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/profiles Mass profiles are in the following package: - https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass + https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass Lets look at an example mass profile. We'll use the `Isothermal` profile, which is located in the `total` package because it represents a total (stars + dark matter) mass distribution: - https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass/total + https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/profiles/mass/total - https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/total/isothermal.py + https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/total/isothermal.py For simplicity, a shortened version of the `Isothermal` profile is shown below. @@ -223,7 +223,7 @@ class Isothermal(MassProfile): All mass profiles in **PyAutoLens** inherit from the `MassProfile` abstract base class, which is located in the following package: - https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/abstract/abstract.py + https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/profiles/mass/abstract/abstract.py This contains functions which are useful for any mass profile, which your custom mass profile will inherit. @@ -234,7 +234,7 @@ class Isothermal(MassProfile): The `MassProfile` class inherits from the `GeometryProfile` abstract base class, which is located here: - https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/profiles/geometry_profiles.py + https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/profiles/geometry_profiles.py This contains functions which are useful for any elliptical (and spherical) profile, which your custom mass profile will again inherit (e.g. `radial_grid_from`). @@ -254,7 +254,7 @@ class Isothermal(MassProfile): Mass profiles therefore also inherit from the `OperateDeflections` abstract base class, which contains numerous functions for computing these lensing quantities from the deflection angles. This is located here: -https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py +https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py This means that once you've implemented a deflections angles calculation for your mass profile, you can compute all lensing quantities from it without having to write any additional code! diff --git a/scripts/guides/advanced/custom_analysis.py b/scripts/guides/advanced/custom_analysis.py index b14b24a85..1bf5c5523 100644 --- a/scripts/guides/advanced/custom_analysis.py +++ b/scripts/guides/advanced/custom_analysis.py @@ -78,18 +78,18 @@ The `Analysis` classes available in **PyAutoLens** are actually located in both **PyAutoLens** and its parent package, **PyAutoGalaxy**: - https://github.com/Jammy2211/PyAutoGalaxy - https://github.com/Jammy2211/PyAutoLens + https://github.com/PyAutoLabs/PyAutoGalaxy + https://github.com/PyAutoLabs/PyAutoLens All classes used for lens modeling are found in the following packages: - https://github.com/Jammy2211/PyAutoGalaxy/tree/main/autogalaxy/imaging/model - https://github.com/Jammy2211/PyAutoLens/tree/main/autolens/imaging/model + https://github.com/PyAutoLabs/PyAutoGalaxy/tree/main/autogalaxy/imaging/model + https://github.com/PyAutoLabs/PyAutoLens/tree/main/autolens/imaging/model The `AnalysisImaging` classes are found in the following modules: - https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/imaging/model/analysis.py - https://github.com/Jammy2211/PyAutoLens/blob/main/autolens/imaging/model/analysis.py + https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/imaging/model/analysis.py + https://github.com/PyAutoLabs/PyAutoLens/blob/main/autolens/imaging/model/analysis.py __Lens Model__ diff --git a/scripts/guides/advanced/multi_plane.py b/scripts/guides/advanced/multi_plane.py index ecd004c1e..80500d421 100644 --- a/scripts/guides/advanced/multi_plane.py +++ b/scripts/guides/advanced/multi_plane.py @@ -64,7 +64,7 @@ Multi-plane ray tracing is implemented in the `tracer_util.py` module of the following package: -https://github.com/Jammy2211/PyAutoLens/blob/main/autolens/lens/tracer_util.py +https://github.com/PyAutoLabs/PyAutoLens/blob/main/autolens/lens/tracer_util.py It uses the function `traced_grid_2d_list_from`. diff --git a/scripts/guides/hpc/README_Repos.md b/scripts/guides/hpc/README_Repos.md index c13d140a2..c748fda53 100644 --- a/scripts/guides/hpc/README_Repos.md +++ b/scripts/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 diff --git a/scripts/guides/modeling/cookbook.py b/scripts/guides/modeling/cookbook.py index f1e800399..2e1e4e399 100644 --- a/scripts/guides/modeling/cookbook.py +++ b/scripts/guides/modeling/cookbook.py @@ -389,8 +389,8 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/modeling/features/multi_gaussian_expansion.ipynb -https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/modeling/features/shapelets.ipynb +https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/modeling/features/multi_gaussian_expansion.ipynb +https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/modeling/features/shapelets.ipynb __Model Linking (Advanced)__ @@ -398,7 +398,7 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/imaging/advanced/guides/modeling/chaining.ipynb +https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/imaging/advanced/guides/modeling/chaining.ipynb __Across Datasets (Advanced)__ @@ -407,7 +407,7 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/multi/modeling/start_here.ipynb +https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/multi/start_here.ipynb __Relations (Advanced)__ @@ -416,7 +416,7 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/multi/modeling/features/wavelength_dependence.ipynb +https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/multi/features/wavelength_dependence/modeling.ipynb __PyAutoFit API__ diff --git a/scripts/guides/tracer.py b/scripts/guides/tracer.py index 7d2d490c8..ead909736 100644 --- a/scripts/guides/tracer.py +++ b/scripts/guides/tracer.py @@ -493,7 +493,7 @@ A full description of each can be found in the docstring of the source code of each function: - https://github.com/Jammy2211/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py + https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/autogalaxy/operate/deflections.py """ tangential_critical_curve = lens_calc.tangential_critical_curve_list_from(grid=grid) diff --git a/scripts/imaging/data_preparation/examples/data.py b/scripts/imaging/data_preparation/examples/data.py index cf8928a93..176bf9aec 100644 --- a/scripts/imaging/data_preparation/examples/data.py +++ b/scripts/imaging/data_preparation/examples/data.py @@ -205,7 +205,7 @@ The preprocess module is found here: -https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py +https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py Functions related to background subtraction are: diff --git a/scripts/imaging/data_preparation/examples/noise_map.py b/scripts/imaging/data_preparation/examples/noise_map.py index 88f9bc73c..e3a154b60 100644 --- a/scripts/imaging/data_preparation/examples/noise_map.py +++ b/scripts/imaging/data_preparation/examples/noise_map.py @@ -118,7 +118,7 @@ from the data you currently have (if it is not already RMS values including the Poisson noise contribution and background sky contribution). -https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py +https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py Functions related to the noise map are: diff --git a/scripts/imaging/data_preparation/examples/psf.py b/scripts/imaging/data_preparation/examples/psf.py index fb0820ab3..5960a9836 100644 --- a/scripts/imaging/data_preparation/examples/psf.py +++ b/scripts/imaging/data_preparation/examples/psf.py @@ -126,7 +126,7 @@ The preprocess module contains functions for converting an even-sized PSF to an odd-sized PSF. -https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/dataset/preprocess.py +https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/dataset/preprocess.py - `psf_with_odd_dimensions_from` diff --git a/scripts/imaging/features/linear_light_profiles/likelihood_function.py b/scripts/imaging/features/linear_light_profiles/likelihood_function.py index 0375a8730..b64fb9209 100644 --- a/scripts/imaging/features/linear_light_profiles/likelihood_function.py +++ b/scripts/imaging/features/linear_light_profiles/likelihood_function.py @@ -665,7 +665,7 @@ To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus) +The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. For linear light profiles, the reduced number of free parameters (e.g. the `intensity` values are solved for diff --git a/scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py b/scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py index e716f3ca6..009e2445a 100644 --- a/scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py +++ b/scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py @@ -805,7 +805,7 @@ To fit a galaxy model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus) +The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. For an MGE, the reduced number of free parameters (e.g. the `intensity` values are solved for diff --git a/scripts/imaging/features/pixelization/delaunay.py b/scripts/imaging/features/pixelization/delaunay.py index a9947242f..17f808d33 100644 --- a/scripts/imaging/features/pixelization/delaunay.py +++ b/scripts/imaging/features/pixelization/delaunay.py @@ -1447,7 +1447,7 @@ def mass_total( To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus) +The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Sub Gridding__ @@ -1458,7 +1458,7 @@ def mass_total( **PyAutoLens** has alternative methods of computing the lens galaxy images above, which uses a grid whose sub-size adaptively increases depending on a required fractional accuracy of the light profile. - https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/structures/grids/two_d/grid_iterate.py + https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/structures/grids/two_d/grid_iterate.py __Sourrce Plane Interpolation__ @@ -1468,10 +1468,10 @@ def mass_total( which uses natural neighbor interpolation). `MapperVoronoiNoInterp.pix_index_for_sub_slim_index`: -https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/inversion/mappers/voronoi.py +https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/inversion/mappers/voronoi.py `pixelization_index_for_voronoi_sub_slim_index_from`: - https://github.com/Jammy2211/PyAutoArray/blob/main/autoarray/util/mapper_util.py + https://github.com/PyAutoLabs/PyAutoArray/blob/main/autoarray/util/mapper_util.py The number of pixels that each sub-pixel maps too is also stored and extracted. This is used for speeding up the calculation of the `mapping_matrix` described next. diff --git a/scripts/imaging/features/pixelization/likelihood_function.py b/scripts/imaging/features/pixelization/likelihood_function.py index 9e5155a6c..36f1ad1d9 100644 --- a/scripts/imaging/features/pixelization/likelihood_function.py +++ b/scripts/imaging/features/pixelization/likelihood_function.py @@ -960,7 +960,7 @@ To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus) +The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Wrap Up__ diff --git a/scripts/imaging/likelihood_function.py b/scripts/imaging/likelihood_function.py index e0d4f9975..412dce7ea 100644 --- a/scripts/imaging/likelihood_function.py +++ b/scripts/imaging/likelihood_function.py @@ -492,7 +492,7 @@ To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/joshspeagle/Nautilus) +The default sampler is the nested sampling algorithm `Nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Wrap Up__ diff --git a/scripts/interferometer/features/pixelization/delaunay.py b/scripts/interferometer/features/pixelization/delaunay.py index 6ad2a335b..9f4d382b1 100644 --- a/scripts/interferometer/features/pixelization/delaunay.py +++ b/scripts/interferometer/features/pixelization/delaunay.py @@ -1293,7 +1293,7 @@ def mass_total( To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus) +The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Log Likelihood Function: Source Code Speed Up__ diff --git a/scripts/interferometer/features/pixelization/likelihood_function.py b/scripts/interferometer/features/pixelization/likelihood_function.py index 78850e3f3..e4dea96c8 100644 --- a/scripts/interferometer/features/pixelization/likelihood_function.py +++ b/scripts/interferometer/features/pixelization/likelihood_function.py @@ -974,7 +974,7 @@ To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus) +The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Log Likelihood Function: Source Code Speed Up__ diff --git a/scripts/interferometer/features/pixelization/many_visibilities_preparation.py b/scripts/interferometer/features/pixelization/many_visibilities_preparation.py index bdc022d3e..d633683c0 100644 --- a/scripts/interferometer/features/pixelization/many_visibilities_preparation.py +++ b/scripts/interferometer/features/pixelization/many_visibilities_preparation.py @@ -43,7 +43,7 @@ A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which are too big to include in the main `autolens_workspace` repository: -https://github.com/Jammy2211/autolens_workspace_large_files +https://github.com/PyAutoLabs/autolens_workspace_large_files After downloading the file, place it in the directory: diff --git a/scripts/interferometer/features/pixelization/modeling.py b/scripts/interferometer/features/pixelization/modeling.py index b7378591b..aa8396a53 100644 --- a/scripts/interferometer/features/pixelization/modeling.py +++ b/scripts/interferometer/features/pixelization/modeling.py @@ -120,7 +120,7 @@ A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which are too big to include in the main `autolens_workspace` repository: -https://github.com/Jammy2211/autolens_workspace_large_files +https://github.com/PyAutoLabs/autolens_workspace_large_files After downloading the file, place it in the directory: diff --git a/scripts/interferometer/features/pixelization/slam.py b/scripts/interferometer/features/pixelization/slam.py index b8039af99..4ced545c3 100644 --- a/scripts/interferometer/features/pixelization/slam.py +++ b/scripts/interferometer/features/pixelization/slam.py @@ -70,7 +70,7 @@ A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which are too big to include in the main `autolens_workspace` repository: -https://github.com/Jammy2211/autolens_workspace_large_files +https://github.com/PyAutoLabs/autolens_workspace_large_files After downloading the file, place it in the directory: diff --git a/scripts/interferometer/likelihood_function.py b/scripts/interferometer/likelihood_function.py index e09525aef..c6f7ab24d 100644 --- a/scripts/interferometer/likelihood_function.py +++ b/scripts/interferometer/likelihood_function.py @@ -486,7 +486,7 @@ To fit a lens model to data, the likelihood function illustrated in this tutorial is sampled using a non-linear search algorithm. -The default sampler is the nested sampling algorithm `nautilus` (https://github.com/joshspeagle/nautilus) +The default sampler is the nested sampling algorithm `nautilus` (https://github.com/johannesulf/nautilus) multiple MCMC and optimization algorithms are supported. __Wrap Up__ diff --git a/scripts/interferometer/simulator.py b/scripts/interferometer/simulator.py index 68bfcfd99..fcd80dad1 100644 --- a/scripts/interferometer/simulator.py +++ b/scripts/interferometer/simulator.py @@ -250,7 +250,7 @@ A high-resolution `uv_wavelengths` file for ALMA is available in a separate repository that hosts large files which are too big to include in the main `autolens_workspace` repository: -https://github.com/Jammy2211/autolens_workspace_large_files +https://github.com/PyAutoLabs/autolens_workspace_large_files After downloading the file, place it in the directory: diff --git a/start_here.ipynb b/start_here.ipynb index 452cf75c1..ea20116bf 100644 --- a/start_here.ipynb +++ b/start_here.ipynb @@ -12,7 +12,7 @@ "\n", "Here is a schematic of a strong gravitational lens:\n", "\n", - "![Schematic of Gravitational Lensing](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_1_lensing/schematic.jpg)\n", + "![Schematic of Gravitational Lensing](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_1_lensing/schematic.jpg)\n", "**Credit: F. Courbin, S. G. Djorgovski, G. Meylan, et al., Caltech / EPFL / WMKO**\n", "https://www.astro.caltech.edu/~george/qsolens/\n", "\n", @@ -555,7 +555,7 @@ "reconstructed on a Voronoi mesh adapted to the source morphology, revealing it to be a grand-design face on spiral\n", "galaxy:\n", "\n", - "![Pixelized Source](https://github.com/Jammy2211/PyAutoLens/blob/main/files/imageaxis.png?raw=true)\n", + "![Pixelized Source](https://github.com/PyAutoLabs/PyAutoLens/blob/main/files/imageaxis.png?raw=true)\n", "\n", "A complete overview of pixelized source reconstructions can be found\n", "at `notebooks/overview/overview_5_pixelizations.ipynb`.\n", @@ -574,11 +574,11 @@ "Instead, we assume that our source is a point source with a centre (y,x), and ray-trace triangles at iteratively\n", "higher resolutions to determine the source's exact locations in the image-plane:\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "Note that the image positions above include the fifth central image of the strong lens, which is often not seen in \n", "strong lens imaging data. It is easy to disable this image in the point source modeling.\n", @@ -589,7 +589,7 @@ "\n", "Modeling of interferometer data from submillimeter (e.g. ALMA) and radio (e.g. LOFAR) observatories:\n", "\n", - "![ALMA Image](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/paper/almacombined.png)\n", + "![ALMA Image](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/paper/almacombined.png)\n", "\n", "Visibilities data is fitted directly in the uv-plane, circumventing issues that arise when fitting a dirty image\n", "such as correlated noise. This uses the non-uniform fast fourier transform algorithm\n", @@ -602,7 +602,7 @@ "\n", "An MGE decomposes the light of a galaxy into tens or hundreds of two dimensional Gaussians:\n", "\n", - "![MGE](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/mge.png)\n", + "![MGE](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/mge.png)\n", "\n", "In the image above, 30 Gaussians are shown, where their sizes go from below the pixel scale (in order to resolve\n", "point emission) to beyond the size of the galaxy (to capture its extended emission).\n", @@ -624,7 +624,7 @@ "The strong lenses we've discussed so far have just a single lens galaxy responsible for the lensing. Group-scale\n", "strong lenses are systems where there two or more lens galaxies deflecting one or more background sources:\n", "\n", - "![Group](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/group.png)\n", + "![Group](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/group.png)\n", "\n", "**PyAutoLens** has built in tools for modeling group-scale lenses, with no limit on the number of\n", "lens and source galaxies!\n", @@ -638,9 +638,9 @@ "Modeling imaging datasets observed at different wavelengths (e.g. HST F814W and F150W) simultaneously or simultaneously\n", "analysing imaging and interferometer data:\n", "\n", - "![g-band](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/g_image.png)\n", + "![g-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/g_image.png)\n", "\n", - "![r-band](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/r_image.png)\n", + "![r-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/r_image.png)\n", "\n", "The appearance of the strong changes as a function of wavelength, therefore multi-wavelength analysis means we can learn\n", "more about the different components in a galaxy (e.g a redder bulge and bluer disk) or when imaging and interferometer\n", @@ -655,7 +655,7 @@ "Ellipse fitting is a technique which fits many ellipses to a galaxy's emission to determine its ellipticity, position\n", "angle and centre, without assuming a parametric form for its light (e.g. like a Seisc profile):\n", "\n", - "![ellipse](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/ellipse.png)\n", + "![ellipse](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/ellipse.png)\n", "\n", "This provides complementary information to parametric light profile fitting, for example giving insights on whether\n", "the ellipticity and position angle are constant with radius or if the galaxy's emission is lopsided. \n", diff --git a/start_here.py b/start_here.py index aea6a6a0d..fe883873b 100644 --- a/start_here.py +++ b/start_here.py @@ -7,7 +7,7 @@ Here is a schematic of a strong gravitational lens: -![Schematic of Gravitational Lensing](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_1_lensing/schematic.jpg) +![Schematic of Gravitational Lensing](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_1_lensing/schematic.jpg) **Credit: F. Courbin, S. G. Djorgovski, G. Meylan, et al., Caltech / EPFL / WMKO** https://www.astro.caltech.edu/~george/qsolens/ @@ -418,7 +418,7 @@ reconstructed on a Voronoi mesh adapted to the source morphology, revealing it to be a grand-design face on spiral galaxy: -![Pixelized Source](https://github.com/Jammy2211/PyAutoLens/blob/main/files/imageaxis.png?raw=true) +![Pixelized Source](https://github.com/PyAutoLabs/PyAutoLens/blob/main/files/imageaxis.png?raw=true) A complete overview of pixelized source reconstructions can be found at `notebooks/overview/overview_5_pixelizations.ipynb`. @@ -437,11 +437,11 @@ Instead, we assume that our source is a point source with a centre (y,x), and ray-trace triangles at iteratively higher resolutions to determine the source's exact locations in the image-plane: - - - - - + + + + + Note that the image positions above include the fifth central image of the strong lens, which is often not seen in strong lens imaging data. It is easy to disable this image in the point source modeling. @@ -452,7 +452,7 @@ Modeling of interferometer data from submillimeter (e.g. ALMA) and radio (e.g. LOFAR) observatories: -![ALMA Image](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/paper/almacombined.png) +![ALMA Image](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/paper/almacombined.png) Visibilities data is fitted directly in the uv-plane, circumventing issues that arise when fitting a dirty image such as correlated noise. This uses the non-uniform fast fourier transform algorithm @@ -465,7 +465,7 @@ An MGE decomposes the light of a galaxy into tens or hundreds of two dimensional Gaussians: -![MGE](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/mge.png) +![MGE](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/mge.png) In the image above, 30 Gaussians are shown, where their sizes go from below the pixel scale (in order to resolve point emission) to beyond the size of the galaxy (to capture its extended emission). @@ -487,7 +487,7 @@ The strong lenses we've discussed so far have just a single lens galaxy responsible for the lensing. Group-scale strong lenses are systems where there two or more lens galaxies deflecting one or more background sources: -![Group](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/group.png) +![Group](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/group.png) **PyAutoLens** has built in tools for modeling group-scale lenses, with no limit on the number of lens and source galaxies! @@ -501,9 +501,9 @@ Modeling imaging datasets observed at different wavelengths (e.g. HST F814W and F150W) simultaneously or simultaneously analysing imaging and interferometer data: -![g-band](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/g_image.png) +![g-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/g_image.png) -![r-band](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/r_image.png) +![r-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/r_image.png) The appearance of the strong changes as a function of wavelength, therefore multi-wavelength analysis means we can learn more about the different components in a galaxy (e.g a redder bulge and bluer disk) or when imaging and interferometer @@ -518,7 +518,7 @@ Ellipse fitting is a technique which fits many ellipses to a galaxy's emission to determine its ellipticity, position angle and centre, without assuming a parametric form for its light (e.g. like a Seisc profile): -![ellipse](https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_3/ellipse.png) +![ellipse](https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_3/ellipse.png) This provides complementary information to parametric light profile fitting, for example giving insights on whether the ellipticity and position angle are constant with radius or if the galaxy's emission is lopsided.