diff --git a/CITATIONS.md b/CITATIONS.md index 5544893d..c0cbb44f 100644 --- a/CITATIONS.md +++ b/CITATIONS.md @@ -1,9 +1,9 @@ # Citations & References The bibtex entries for **PyAutoGalaxy** and its affiliated software packages can be found -[here](https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.bib), with example text for citing **PyAutoGalaxy** -in [.tex format here](https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.tex) format here and -[.md format here](https://github.com/Jammy2211/PyAutoGalaxy/blob/main/files/citations.md). +[here](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/files/citations.bib), with example text for citing **PyAutoGalaxy** +in [.tex format here](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/files/citations.tex) format here and +[.md format here](https://github.com/PyAutoLabs/PyAutoGalaxy/blob/main/files/citations.md). As shown in the examples, we would greatly appreciate it if you mention **PyAutoGalaxy** by name and include a link to our GitHub page! diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 250f744d..e0fccad3 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). **PyAutoGalaxy 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 2f35dd7c..1612fdae 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [Installation Guide](https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html) | [readthedocs](https://pyautogalaxy.readthedocs.io/en/latest/index.html) | -[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/start_here.ipynb) | +[Introduction on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb) | [HowToGalaxy](https://github.com/PyAutoLabs/HowToGalaxy) Welcome to the **PyAutoGalaxy** Workspace! @@ -15,7 +15,7 @@ You can get set up on your personal computer by following the installation guide our [readthedocs](https://pyautogalaxy.readthedocs.io/). Alternatively, you can try **PyAutoGalaxy** out in a web browser by going to -the [autogalaxy workspace on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/start_here.ipynb). +the [autogalaxy workspace on Colab](https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb). ## New Users @@ -24,7 +24,7 @@ overview of **PyAutoGalaxy**'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/autogalaxy_workspace/blob/2026.5.14.2/start_here.ipynb +https://colab.research.google.com/github/PyAutoLabs/autogalaxy_workspace/blob/2026.5.14.2/notebooks/imaging/start_here.ipynb Then checkout the [new user starting guide](https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html) to navigate the workspace for your science case. @@ -77,7 +77,7 @@ galaxy modeling and 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/PyAutoGalaxy/issues). +For installation issues, bug reports, or feature requests, please raise an issue on the [GitHub issues page](https://github.com/PyAutoLabs/PyAutoGalaxy/issues). ## Contribution diff --git a/notebooks/guides/hpc/README_Repos.md b/notebooks/guides/hpc/README_Repos.md index 1f792239..afa54fa3 100644 --- a/notebooks/guides/hpc/README_Repos.md +++ b/notebooks/guides/hpc/README_Repos.md @@ -118,10 +118,10 @@ 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/PyAutoLabs/PyAutoConf +git clone https://github.com/PyAutoLabs/PyAutoFit +git clone https://github.com/PyAutoLabs/PyAutoArray +git clone https://github.com/PyAutoLabs/PyAutoGalaxy ``` ## 9. Install Python Dependencies diff --git a/notebooks/guides/modeling/cookbook.ipynb b/notebooks/guides/modeling/cookbook.ipynb index 84fc4b89..6d6ff73e 100644 --- a/notebooks/guides/modeling/cookbook.ipynb +++ b/notebooks/guides/modeling/cookbook.ipynb @@ -363,8 +363,8 @@ "\n", "The following example notebooks show how to compose and fit these models:\n", "\n", - "https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/multi_gaussian_expansion.ipynb\n", - "https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/shapelets.ipynb\n", + "https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/multi_gaussian_expansion/modeling.ipynb\n", + "https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/shapelets/modeling.ipynb\n", "\n", "__Model Linking (Advanced)__\n", "\n", @@ -372,7 +372,7 @@ "\n", "The following example notebooks show how to compose and fit these models:\n", "\n", - "https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/imaging/advanced/guides/modeling/chaining.ipynb\n", + "https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/advanced/guides/modeling/chaining.ipynb\n", "\n", "__Across Datasets (Advanced)__\n", "\n", @@ -381,7 +381,7 @@ "\n", "The following example notebooks show how to compose and fit these models:\n", "\n", - "https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/start_here.ipynb\n", + "https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/start_here.ipynb\n", "\n", "__Relations (Advanced)__\n", "\n", @@ -390,7 +390,7 @@ "\n", "The following example notebooks show how to compose and fit these models:\n", "\n", - "https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/features/wavelength_dependence.ipynb\n", + "https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/features/wavelength_dependence/modeling.ipynb\n", "\n", "__PyAutoFit API__\n", "\n", diff --git a/notebooks/imaging/data_preparation/examples/data.ipynb b/notebooks/imaging/data_preparation/examples/data.ipynb index 825b3fef..e7777445 100644 --- a/notebooks/imaging/data_preparation/examples/data.ipynb +++ b/notebooks/imaging/data_preparation/examples/data.ipynb @@ -307,7 +307,7 @@ "\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 f4afaf94..13fe0ab3 100644 --- a/notebooks/imaging/data_preparation/examples/noise_map.ipynb +++ b/notebooks/imaging/data_preparation/examples/noise_map.ipynb @@ -155,7 +155,7 @@ "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 1044246e..f4398376 100644 --- a/notebooks/imaging/data_preparation/examples/psf.ipynb +++ b/notebooks/imaging/data_preparation/examples/psf.ipynb @@ -183,7 +183,7 @@ "\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 8df1cb4c..d2f272a2 100644 --- a/notebooks/imaging/features/linear_light_profiles/likelihood_function.ipynb +++ b/notebooks/imaging/features/linear_light_profiles/likelihood_function.ipynb @@ -925,7 +925,7 @@ "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 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 5dd2db1a..4a86a766 100644 --- a/notebooks/imaging/features/multi_gaussian_expansion/likelihood_function.ipynb +++ b/notebooks/imaging/features/multi_gaussian_expansion/likelihood_function.ipynb @@ -1116,7 +1116,7 @@ "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", "__Wrap Up__\n", diff --git a/notebooks/imaging/features/pixelization/likelihood_function.ipynb b/notebooks/imaging/features/pixelization/likelihood_function.ipynb index ca2f185e..b6bd00fd 100644 --- a/notebooks/imaging/features/pixelization/likelihood_function.ipynb +++ b/notebooks/imaging/features/pixelization/likelihood_function.ipynb @@ -1333,7 +1333,7 @@ "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", "__Log Likelihood Function: Pixelization With Light Profile__\n", diff --git a/notebooks/imaging/likelihood_function.ipynb b/notebooks/imaging/likelihood_function.ipynb index baa1217a..e58023f0 100644 --- a/notebooks/imaging/likelihood_function.ipynb +++ b/notebooks/imaging/likelihood_function.ipynb @@ -606,7 +606,7 @@ "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", "__Wrap Up__\n", diff --git a/notebooks/interferometer/features/pixelization/likelihood_function.ipynb b/notebooks/interferometer/features/pixelization/likelihood_function.ipynb index c089f536..329da6a1 100644 --- a/notebooks/interferometer/features/pixelization/likelihood_function.ipynb +++ b/notebooks/interferometer/features/pixelization/likelihood_function.ipynb @@ -1355,7 +1355,7 @@ "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", "__Log Likelihood Function: Pixelization With Light Profile__\n", diff --git a/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb b/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb index f989b313..5027600d 100644 --- a/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb +++ b/notebooks/interferometer/features/pixelization/many_visibilities_preparation.ipynb @@ -49,7 +49,7 @@ "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 `autogalaxy_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 3ba2a25f..66e27daa 100644 --- a/notebooks/interferometer/features/pixelization/modeling.ipynb +++ b/notebooks/interferometer/features/pixelization/modeling.ipynb @@ -126,7 +126,7 @@ "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 `autogalaxy_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 878e21c4..beca23e9 100644 --- a/notebooks/interferometer/likelihood_function.ipynb +++ b/notebooks/interferometer/likelihood_function.ipynb @@ -589,7 +589,7 @@ "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", "__Wrap Up__\n", diff --git a/scripts/guides/hpc/README_Repos.md b/scripts/guides/hpc/README_Repos.md index 1f792239..afa54fa3 100644 --- a/scripts/guides/hpc/README_Repos.md +++ b/scripts/guides/hpc/README_Repos.md @@ -118,10 +118,10 @@ 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/PyAutoLabs/PyAutoConf +git clone https://github.com/PyAutoLabs/PyAutoFit +git clone https://github.com/PyAutoLabs/PyAutoArray +git clone https://github.com/PyAutoLabs/PyAutoGalaxy ``` ## 9. Install Python Dependencies diff --git a/scripts/guides/modeling/cookbook.py b/scripts/guides/modeling/cookbook.py index 4744cb4b..a960bc34 100644 --- a/scripts/guides/modeling/cookbook.py +++ b/scripts/guides/modeling/cookbook.py @@ -237,8 +237,8 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/multi_gaussian_expansion.ipynb -https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/modeling/imaging/features/shapelets.ipynb +https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/multi_gaussian_expansion/modeling.ipynb +https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/features/shapelets/modeling.ipynb __Model Linking (Advanced)__ @@ -246,7 +246,7 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/imaging/advanced/guides/modeling/chaining.ipynb +https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/imaging/advanced/guides/modeling/chaining.ipynb __Across Datasets (Advanced)__ @@ -255,7 +255,7 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/start_here.ipynb +https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/start_here.ipynb __Relations (Advanced)__ @@ -264,7 +264,7 @@ The following example notebooks show how to compose and fit these models: -https://github.com/Jammy2211/autogalaxy_workspace/blob/release/notebooks/multi/modeling/features/wavelength_dependence.ipynb +https://github.com/PyAutoLabs/autogalaxy_workspace/blob/main/notebooks/multi/features/wavelength_dependence/modeling.ipynb __PyAutoFit API__ diff --git a/scripts/imaging/data_preparation/examples/data.py b/scripts/imaging/data_preparation/examples/data.py index 5122fa87..6db27ea1 100644 --- a/scripts/imaging/data_preparation/examples/data.py +++ b/scripts/imaging/data_preparation/examples/data.py @@ -203,7 +203,7 @@ 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 043ebe27..d198db86 100644 --- a/scripts/imaging/data_preparation/examples/noise_map.py +++ b/scripts/imaging/data_preparation/examples/noise_map.py @@ -117,7 +117,7 @@ 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 c2d574d3..741548bb 100644 --- a/scripts/imaging/data_preparation/examples/psf.py +++ b/scripts/imaging/data_preparation/examples/psf.py @@ -123,7 +123,7 @@ 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 caea900e..498011d8 100644 --- a/scripts/imaging/features/linear_light_profiles/likelihood_function.py +++ b/scripts/imaging/features/linear_light_profiles/likelihood_function.py @@ -583,7 +583,7 @@ 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 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 0e43dbab..2cb4cdda 100644 --- a/scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py +++ b/scripts/imaging/features/multi_gaussian_expansion/likelihood_function.py @@ -715,7 +715,7 @@ 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. __Wrap Up__ diff --git a/scripts/imaging/features/pixelization/likelihood_function.py b/scripts/imaging/features/pixelization/likelihood_function.py index cc065298..b85fdd29 100644 --- a/scripts/imaging/features/pixelization/likelihood_function.py +++ b/scripts/imaging/features/pixelization/likelihood_function.py @@ -822,7 +822,7 @@ 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. __Log Likelihood Function: Pixelization With Light Profile__ diff --git a/scripts/imaging/likelihood_function.py b/scripts/imaging/likelihood_function.py index 11b5a864..effbc84b 100644 --- a/scripts/imaging/likelihood_function.py +++ b/scripts/imaging/likelihood_function.py @@ -360,7 +360,7 @@ 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. __Wrap Up__ diff --git a/scripts/interferometer/features/pixelization/likelihood_function.py b/scripts/interferometer/features/pixelization/likelihood_function.py index a43d0d9c..5bdb085b 100644 --- a/scripts/interferometer/features/pixelization/likelihood_function.py +++ b/scripts/interferometer/features/pixelization/likelihood_function.py @@ -867,7 +867,7 @@ 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. __Log Likelihood Function: Pixelization With Light Profile__ diff --git a/scripts/interferometer/features/pixelization/many_visibilities_preparation.py b/scripts/interferometer/features/pixelization/many_visibilities_preparation.py index 98c38ecf..de84d655 100644 --- a/scripts/interferometer/features/pixelization/many_visibilities_preparation.py +++ b/scripts/interferometer/features/pixelization/many_visibilities_preparation.py @@ -44,7 +44,7 @@ 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 `autogalaxy_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 0bf6a01f..04bf898b 100644 --- a/scripts/interferometer/features/pixelization/modeling.py +++ b/scripts/interferometer/features/pixelization/modeling.py @@ -121,7 +121,7 @@ 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 `autogalaxy_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 c984834f..d2a2e43d 100644 --- a/scripts/interferometer/likelihood_function.py +++ b/scripts/interferometer/likelihood_function.py @@ -367,7 +367,7 @@ 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. __Wrap Up__ diff --git a/start_here.ipynb b/start_here.ipynb index 4a0e8ef6..f2a6ffa0 100644 --- a/start_here.ipynb +++ b/start_here.ipynb @@ -9,7 +9,7 @@ "\n", "**PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:\n", "\n", - "![HST Image](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/paper/hstcombined.png)\n", + "![HST Image](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/paper/hstcombined.png)\n", "\n", "**PyAutoGalaxy** has three core aims:\n", "\n", @@ -455,7 +455,7 @@ "\n", "Modeling 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", @@ -469,9 +469,9 @@ "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/PyAutoGalaxy/main/docs/overview/images/overview_3/g_image.png)\n", + "![g-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/docs/overview/images/overview_3/g_image.png)\n", "\n", - "![r-band](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/docs/overview/images/overview_3/r_image.png)\n", + "![r-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/docs/overview/images/overview_3/r_image.png)\n", "\n", "The appearance of the galaxy 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", @@ -486,7 +486,7 @@ "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. a Sersic profile):\n", "\n", - "![ellipse](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/docs/overview/images/overview_3/ellipse.png)\n", + "![ellipse](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/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", @@ -503,7 +503,7 @@ "\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/PyAutoGalaxy/main/docs/overview/images/overview_3/mge.png)\n", + "![MGE](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/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", @@ -560,7 +560,7 @@ "fitted accurately using light profiles, whereas its asymmetric and irregular spiral arm features are accurately\n", "captured using a rectangular mesh:\n", "\n", - "![HST Image](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/paper/hstcombined.png)\n", + "![HST Image](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/paper/hstcombined.png)\n", "\n", "Checkout `autogalaxy_workspace/notebooks/features/pixelizations.ipynb` to learn how to use a pixelization, however\n", "this is a more advanced feature and it is recommended you first get to grips with the core API.\n", diff --git a/start_here.py b/start_here.py index 4f48886d..036f9e02 100644 --- a/start_here.py +++ b/start_here.py @@ -4,7 +4,7 @@ **PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies: -![HST Image](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/paper/hstcombined.png) +![HST Image](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/paper/hstcombined.png) **PyAutoGalaxy** has three core aims: @@ -340,7 +340,7 @@ Modeling 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 @@ -354,9 +354,9 @@ 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/PyAutoGalaxy/main/docs/overview/images/overview_3/g_image.png) +![g-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/docs/overview/images/overview_3/g_image.png) -![r-band](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/docs/overview/images/overview_3/r_image.png) +![r-band](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/docs/overview/images/overview_3/r_image.png) The appearance of the galaxy 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 @@ -371,7 +371,7 @@ 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. a Sersic profile): -![ellipse](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/docs/overview/images/overview_3/ellipse.png) +![ellipse](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/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. @@ -388,7 +388,7 @@ An MGE decomposes the light of a galaxy into tens or hundreds of two dimensional Gaussians: -![MGE](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/docs/overview/images/overview_3/mge.png) +![MGE](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/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). @@ -445,7 +445,7 @@ fitted accurately using light profiles, whereas its asymmetric and irregular spiral arm features are accurately captured using a rectangular mesh: -![HST Image](https://raw.githubusercontent.com/Jammy2211/PyAutoGalaxy/main/paper/hstcombined.png) +![HST Image](https://raw.githubusercontent.com/PyAutoLabs/PyAutoGalaxy/main/paper/hstcombined.png) Checkout `autogalaxy_workspace/notebooks/features/pixelizations.ipynb` to learn how to use a pixelization, however this is a more advanced feature and it is recommended you first get to grips with the core API.