From 6a8beed4e1c63432c7ceaef6115913b5e001631d Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Fri, 15 May 2026 08:27:53 +0100 Subject: [PATCH] =?UTF-8?q?docs:=20audit-driven=20URL=20fixes=20(Jammy2211?= =?UTF-8?q?=20=E2=86=92=20PyAutoLabs,=20/release/=20=E2=86=92=20/main/,=20?= =?UTF-8?q?etc.)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Apply scripted URL rewrites surfaced by the new admin_jammy/software/url_check/ audit tool. All changes are doc-only (README, *.md, *.rst, plus docstring URLs in source files). No behaviour changes. Line endings preserved. Patterns applied: - hhttps:// → https:// (user-reported typo in overview_2_new_user_guide.md) - 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 moved to /docs/CODE_OF_CONDUCT.md - numfocus CoC moved to 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.) - autofit_workspace overview/{simple,complex}/{fit,result}.ipynb → new flat structure - workspaces modeling/imaging/features/.ipynb → imaging/features//modeling.ipynb - workspaces multi/modeling/features/.ipynb → multi/features//modeling.ipynb - workspaces multi/modeling/start_here.ipynb → multi/start_here.ipynb - workspaces tree/main/notebooks/plot → notebooks/guides/plot - Colab badge URL: workspace-root start_here.ipynb → notebooks//start_here.ipynb Tool + report: PyAutoLabs/admin_jammy#21 Issue: PyAutoLabs/PyAutoLens#508 Co-Authored-By: Claude Opus 4.7 (1M context) --- CITATIONS.md | 6 +++--- CODE_OF_CONDUCT.md | 4 ++-- CONTRIBUTING.md | 4 ++-- README.md | 12 ++++++------ autofit/mapper/prior_model/collection.py | 2 +- autofit/mapper/prior_model/prior_model.py | 2 +- docs/api/analysis.rst | 6 +++--- docs/api/database.rst | 2 +- docs/api/model.rst | 12 ++++++------ docs/api/plot.rst | 4 ++-- docs/api/priors.rst | 10 +++++----- docs/api/samples.rst | 6 +++--- docs/api/searches.rst | 8 ++++---- docs/conf.py | 4 ++-- docs/cookbooks/multiple_datasets.md | 18 +++++++++--------- docs/cookbooks/samples.md | 4 ++-- docs/features/graphical.md | 6 +++--- docs/features/interpolate.md | 2 +- docs/features/search_chaining.md | 10 +++++----- docs/features/search_grid_search.md | 6 +++--- docs/features/sensitivity_mapping.md | 10 +++++----- docs/general/citations.md | 6 +++--- docs/general/configs.md | 2 +- docs/general/roadmap.md | 2 +- docs/general/software.md | 4 ++-- docs/general/workspace.md | 4 ++-- docs/index.md | 6 +++--- docs/installation/conda.md | 2 +- docs/installation/overview.md | 2 +- docs/installation/pip.md | 2 +- docs/installation/source.md | 2 +- docs/installation/troubleshooting.md | 2 +- docs/overview/backup.md | 4 ++-- docs/overview/scientific_workflow.md | 2 +- docs/overview/statistical_methods.md | 12 ++++++------ docs/overview/the_basics.md | 10 +++++----- docs/science_examples/astronomy.md | 14 +++++++------- files/citations.md | 4 ++-- paper/README.md | 2 +- paper/paper.md | 12 ++++++------ 40 files changed, 116 insertions(+), 116 deletions(-) diff --git a/CITATIONS.md b/CITATIONS.md index 1000bcca5..fb057a03e 100644 --- a/CITATIONS.md +++ b/CITATIONS.md @@ -1,9 +1,9 @@ # Citations & References The bibtex entries for **PyAutoFit** and its affiliated software packages can be found -[here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citations.bib), with example text for citing **PyAutoFit** -in [.tex format here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citation.tex) format here and -[.md format here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citations.md). As shown in the examples, we +[here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citations.bib), with example text for citing **PyAutoFit** +in [.tex format here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citation.tex) format here and +[.md format here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citations.md). As shown in the examples, we would greatly appreciate it if you mention **PyAutoFit** by name and include a link to our GitHub page! **PyAutoFit** is published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.02550#) and its diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index d8befc974..8f60bb6d3 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -302,7 +302,7 @@ the situation is not yet resolved. ## License -This code of conduct has been adapted from [*NUMFOCUS code of conduct*](https://github.com/numfocus/numfocus/blob/main/manual/numfocus-coc.md#the-short-version), -which is adapted from numerous sources, including the [*Geek Feminism wiki, created by the Ada Initiative and other volunteers, which is under a Creative Commons Zero license*](http://geekfeminism.wikia.com/wiki/Fiterence_anti-harassment/Policy), the [*Contributor Covenant version 1.2.0*](http://contributor-covenant.org/version/1/2/0/), the [*Bokeh Code of Conduct*](https://github.com/bokeh/bokeh/blob/main/CODE_OF_CONDUCT.md), the [*SciPy Code of Conduct*](https://github.com/jupyter/governance/blob/main/conduct/enforcement.md), the [*Carpentries Code of Conduct*](https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html#enforcement-manual), and the [*NeurIPS Code of Conduct*](https://neurips.cc/public/CodeOfConduct). +This code of conduct has been adapted from [*NUMFOCUS code of conduct*](https://numfocus.org/code-of-conduct), +which is adapted from numerous sources, including the [*Geek Feminism wiki, created by the Ada Initiative and other volunteers, which is under a Creative Commons Zero license*](http://geekfeminism.wikia.com/wiki/Conference_anti-harassment/Policy), the [*Contributor Covenant version 1.2.0*](http://contributor-covenant.org/version/1/2/0/), the [*Bokeh Code of Conduct*](https://github.com/bokeh/bokeh/blob/main/docs/CODE_OF_CONDUCT.md), the [*SciPy Code of Conduct*](https://github.com/jupyter/governance/blob/main/conduct/enforcement.md), the [*Carpentries Code of Conduct*](https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html#enforcement-manual), and the [*NeurIPS Code of Conduct*](https://neurips.cc/public/CodeOfConduct). **PyAutoFit Code of Conduct is licensed under the [Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/).** \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 8e0e352c9..159ef8308 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -56,7 +56,7 @@ Contributions are welcome and greatly appreciated! ### Report Bugs -Report bugs at https://github.com/rhayes777/PyAutoFit/issues +Report bugs at https://github.com/PyAutoLabs/PyAutoFit/issues If you are playing with the PyAutoFit library and find a bug, please reporting it including: @@ -68,7 +68,7 @@ reporting it including: ### Propose New `NonLinearSearch` or Features The best way to send feedback is to open an issue at -https://github.com/rhayes777/PyAutoFit/issues +https://github.com/PyAutoLabs/PyAutoFit/issues with tag *enhancement*. If you are proposing a new `NonLinearSearch` or a new feature: diff --git a/README.md b/README.md index 27ddb1a54..9675efddd 100644 --- a/README.md +++ b/README.md @@ -3,9 +3,9 @@ [![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Python Versions](https://img.shields.io/pypi/pyversions/autofit)](https://pypi.org/project/autofit/) [![PyPI Version](https://img.shields.io/pypi/v/autofit.svg)](https://pypi.org/project/autofit/) -[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/start_here.ipynb) -[![Tests](https://github.com/rhayes777/PyAutoFit/actions/workflows/main.yml/badge.svg)](https://github.com/rhayes777/PyAutoFit/actions) -[![Build](https://github.com/rhayes777/PyAutoBuild/actions/workflows/release.yml/badge.svg)](https://github.com/rhayes777/PyAutoBuild/actions) +[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb) +[![Tests](https://github.com/PyAutoLabs/PyAutoFit/actions/workflows/main.yml/badge.svg)](https://github.com/PyAutoLabs/PyAutoFit/actions) +[![Build](https://github.com/PyAutoLabs/PyAutoBuild/actions/workflows/release.yml/badge.svg)](https://github.com/PyAutoLabs/PyAutoBuild/actions) [![Documentation Status](https://readthedocs.org/projects/pyautofit/badge/?version=latest)](https://pyautofit.readthedocs.io/en/latest/?badge=latest) [![JOSS](https://joss.theoj.org/papers/10.21105/joss.02550/status.svg)](https://doi.org/10.21105/joss.02550) @@ -32,13 +32,13 @@ The following links are useful for new starters: - [The PyAutoFit readthedocs](https://pyautofit.readthedocs.io/en/latest), which includes an [installation guide](https://pyautofit.readthedocs.io/en/latest/installation/overview.html) and an overview of **PyAutoFit**'s core features. - [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb), where you can try **PyAutoFit** in a web browser (without installation). -- [The autofit_workspace GitHub repository](https://github.com/Jammy2211/autofit_workspace), which includes example scripts demonstrating **PyAutoFit**'s features. +- [The autofit_workspace GitHub repository](https://github.com/PyAutoLabs/autofit_workspace), which includes example scripts demonstrating **PyAutoFit**'s features. - [The standalone HowToFit repository](https://github.com/PyAutoLabs/HowToFit), a series of Jupyter notebook lectures which give new users a step-by-step introduction to **PyAutoFit**. ## Support Support for installation issues, help with Fit modeling and using **PyAutoFit** is available by -[raising an issue on the GitHub issues page](https://github.com/rhayes777/PyAutoFit/issues). +[raising an issue on the GitHub issues page](https://github.com/PyAutoLabs/PyAutoFit/issues). We also offer support on the **PyAutoFit** [Slack channel](https://pyautoFit.slack.com/), where we also provide the latest updates on **PyAutoFit**. Slack is invitation-only, so if you'd like to join send @@ -57,7 +57,7 @@ The lectures are available in the [standalone HowToFit repository](https://githu To illustrate the **PyAutoFit** API, we use an illustrative toy model of fitting a one-dimensional Gaussian to noisy 1D data. Here's the `data` (black) and the model (red) we'll fit: - + We define our model, a 1D Gaussian by writing a Python class using the format below: diff --git a/autofit/mapper/prior_model/collection.py b/autofit/mapper/prior_model/collection.py index ed6f19beb..b06d8878b 100644 --- a/autofit/mapper/prior_model/collection.py +++ b/autofit/mapper/prior_model/collection.py @@ -137,7 +137,7 @@ def __init__( For a complete description of the model composition API, see the **PyAutoFit** model API cookbooks: - https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html + https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html The Python class input into a ``Model`` to create a model component is written using the following format: diff --git a/autofit/mapper/prior_model/prior_model.py b/autofit/mapper/prior_model/prior_model.py index 756899185..c0153e8ae 100644 --- a/autofit/mapper/prior_model/prior_model.py +++ b/autofit/mapper/prior_model/prior_model.py @@ -66,7 +66,7 @@ def __init__( For a complete description of the model composition API, see the **PyAutoFit** model API cookbooks: - https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html + https://pyautofit.readthedocs.io/en/latest/cookbooks/model.html The Python class input into a ``Model`` to create a model component is written using the following format: diff --git a/docs/api/analysis.rst b/docs/api/analysis.rst index cc035b214..24425acfc 100644 --- a/docs/api/analysis.rst +++ b/docs/api/analysis.rst @@ -8,9 +8,9 @@ It acts as an interface between the data, model and the non-linear search. **Examples / Tutorials:** -- `readthedocs: example using Analysis object `_. -- `autofit_workspace: simple tutorial `_ -- `autofit_workspace: complex tutorial `_ +- `readthedocs: example using Analysis object `_. +- `autofit_workspace: simple tutorial `_ +- `autofit_workspace: complex tutorial `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ -------- diff --git a/docs/api/database.rst b/docs/api/database.rst index 613b3f27f..1f656d835 100644 --- a/docs/api/database.rst +++ b/docs/api/database.rst @@ -9,7 +9,7 @@ inspection, analysis and interpretation. **Examples / Tutorials:** - `readthedocs: example using database functionality `_ -- `autofit_workspace: tutorial using database `_ +- `autofit_workspace: tutorial using database `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ ---------- diff --git a/docs/api/model.rst b/docs/api/model.rst index 3db8331b6..d69f7068b 100644 --- a/docs/api/model.rst +++ b/docs/api/model.rst @@ -4,16 +4,16 @@ Models Model objects are used for composing models that are fitted to data. -It is recommended the `model API cookbooks `_ are used for guidance on building complex model. +It is recommended the `model API cookbooks `_ are used for guidance on building complex model. **Examples / Tutorials:** -- `Model API Cookbooks (recommended) `_. +- `Model API Cookbooks (recommended) `_. -- `readthedocs: example using Model object `_. -- `readthedocs: example using Collection object `_. -- `autofit_workspace: simple tutorial `_ -- `autofit_workspace: complex tutorial `_ +- `readthedocs: example using Model object `_. +- `readthedocs: example using Collection object `_. +- `autofit_workspace: simple tutorial `_ +- `autofit_workspace: complex tutorial `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ ------ diff --git a/docs/api/plot.rst b/docs/api/plot.rst index b33d9b94d..da930f2a6 100644 --- a/docs/api/plot.rst +++ b/docs/api/plot.rst @@ -7,8 +7,8 @@ by **PyAutoFit**. **Examples / Tutorials:** -- `readthedocs: non-linear search example `_ -- `autofit_workspace: plot tutorials `_ +- `readthedocs: non-linear search example `_ +- `autofit_workspace: plot tutorials `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ -------- diff --git a/docs/api/priors.rst b/docs/api/priors.rst index 89d0980ec..7b6bacc84 100644 --- a/docs/api/priors.rst +++ b/docs/api/priors.rst @@ -6,12 +6,12 @@ The priors of parameters of every component of a mdoel, which is fitted to data, **Examples / Tutorials:** -- `Model API Cookbooks (recommended) `_. +- `Model API Cookbooks (recommended) `_. -- `readthedocs: example using Model object `_. -- `readthedocs: example using Collection object `_. -- `autofit_workspace: simple tutorial `_ -- `autofit_workspace: complex tutorial `_ +- `readthedocs: example using Model object `_. +- `readthedocs: example using Collection object `_. +- `autofit_workspace: simple tutorial `_ +- `autofit_workspace: complex tutorial `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ Priors diff --git a/docs/api/samples.rst b/docs/api/samples.rst index 01ce64087..cdbd545e7 100644 --- a/docs/api/samples.rst +++ b/docs/api/samples.rst @@ -9,9 +9,9 @@ For example, for an MCMC model-fit, the ``Samples`` objects contains every sampl **Examples / Tutorials:** -- `readthedocs: example on using results `_. -- `autofit_workspace: simple results tutorial `_ -- `autofit_workspace: complex result tutorial `_ +- `readthedocs: example on using results `_. +- `autofit_workspace: simple results tutorial `_ +- `autofit_workspace: complex result tutorial `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ Samples diff --git a/docs/api/searches.rst b/docs/api/searches.rst index 64ded83a6..77debb1b4 100644 --- a/docs/api/searches.rst +++ b/docs/api/searches.rst @@ -9,9 +9,9 @@ Markov Chain Monte Carlo (MCMC) and Maximum Likelihood Estimators (MLE). **Examples / Tutorials:** -- `readthedocs: example using non-linear searches `_. -- `autofit_workspace: simple tutorial `_ -- `autofit_workspace: complex tutorial `_ +- `readthedocs: example using non-linear searches `_. +- `autofit_workspace: simple tutorial `_ +- `autofit_workspace: complex tutorial `_ - `HowToFit: tutorial lectures (detailed step-by-step examples) `_ Nested Samplers @@ -79,7 +79,7 @@ model are fitted in over a discrete grid. **Examples / Tutorials:** - `readthedocs: example using a non-linear search grid search `_. -- `autofit_workspace: example using a non-linear search grid search `_ +- `autofit_workspace: example using a non-linear search grid search `_ GridSearch ---------- diff --git a/docs/conf.py b/docs/conf.py index c26a291bf..8321da9a7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -3,7 +3,7 @@ # # This file only contains a selection of the most common options. For a full # list see the documentation: -# https://www.sphinx-doc.org/en/main/usage/configuration.html +# https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- @@ -61,7 +61,7 @@ intersphinx_mapping = { "python": ("https://docs.python.org/3", None), - "sphinx": ("https://www.sphinx-doc.org/en/main", None), + "sphinx": ("https://www.sphinx-doc.org/en/master", None), } # -- Options for TODOs ------------------------------------------------------- diff --git a/docs/cookbooks/multiple_datasets.md b/docs/cookbooks/multiple_datasets.md index 3cb4abde9..4ea94cfa2 100644 --- a/docs/cookbooks/multiple_datasets.md +++ b/docs/cookbooks/multiple_datasets.md @@ -89,17 +89,17 @@ for data, noise_map in zip(data_list, noise_map_list): Here is what the plots look like: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_data_0.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_data_0.png :alt: Alternative text :width: 300 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_data_1.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_data_1.png :alt: Alternative text :width: 300 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_data_2.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_data_2.png :alt: Alternative text :width: 300 ``` @@ -246,17 +246,17 @@ for data, result in zip(data_list, result_list): The image appears as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_0.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_0.png :alt: Alternative text :width: 300 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_1.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_1.png :alt: Alternative text :width: 300 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_2.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_2.png :alt: Alternative text :width: 300 ``` @@ -305,17 +305,17 @@ for data, noise_map in zip(data_list, noise_map_list): The images appear as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_0.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_0.png :alt: Alternative text :width: 300 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_1.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_1.png :alt: Alternative text :width: 300 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/feature/docs_update/docs/images/multi_model_data_2.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/multi_model_data_2.png :alt: Alternative text :width: 300 ``` diff --git a/docs/cookbooks/samples.md b/docs/cookbooks/samples.md index 4bb1593b0..dc505e5c7 100644 --- a/docs/cookbooks/samples.md +++ b/docs/cookbooks/samples.md @@ -154,7 +154,7 @@ plt.close() This plot appears as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/toy_model_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/toy_model_fit.png :alt: Alternative text :width: 600 ``` @@ -299,7 +299,7 @@ plotter.corner() This plot appears as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/corner.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/corner.png :alt: Alternative text :width: 600 ``` diff --git a/docs/features/graphical.md b/docs/features/graphical.md index 23f3169e7..7f83dcb62 100644 --- a/docs/features/graphical.md +++ b/docs/features/graphical.md @@ -52,17 +52,17 @@ for dataset_index in range(total_gaussians): This is what our three Gaussians look like: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_1__low_snr.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_1__low_snr.png :alt: Alternative text :width: 600 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_2__low_snr.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_2__low_snr.png :alt: Alternative text :width: 600 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_3__low_snr.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_3__low_snr.png :alt: Alternative text :width: 600 ``` diff --git a/docs/features/interpolate.md b/docs/features/interpolate.md index d84526d13..51164784e 100644 --- a/docs/features/interpolate.md +++ b/docs/features/interpolate.md @@ -48,7 +48,7 @@ for time in range(3): Visual comparison of the datasets shows that the `centre` of each `Gaussian` varies smoothly over time, with it moving from pixel 40 at t=0 to pixel 60 at t=2. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/hi.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/hi.png :alt: Alternative text :width: 600 ``` diff --git a/docs/features/search_chaining.md b/docs/features/search_chaining.md index 96bfd26d6..09497a674 100644 --- a/docs/features/search_chaining.md +++ b/docs/features/search_chaining.md @@ -27,7 +27,7 @@ precise, most accurate model that best fits the dataset available. In this example we demonstrate search chaining using the example data where there are two `Gaussians` that are visibly split: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x2_split.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x2_split.png :alt: Alternative text :width: 600 ``` @@ -49,7 +49,7 @@ procedure. To fit the left `Gaussian`, our first `analysis` receive only half data removing the right `Gaussian`. Note that this give a speed-up in log likelihood evaluation. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x2_left.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x2_left.png :alt: Alternative text :width: 600 ``` @@ -92,7 +92,7 @@ result_1 = search_1.fit(model=model_1, analysis=analysis_1) By plotting the result we can see we have fitted the left `Gaussian` reasonably well. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x2_left_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x2_left_fit.png :alt: Alternative text :width: 600 ``` @@ -149,7 +149,7 @@ result_2 = search_2.fit(model=model_2, analysis=analysis_2) We can now see our model has successfully fitted both Gaussian's: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x2_right_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x2_right_fit.png :alt: Alternative text :width: 600 ``` @@ -206,7 +206,7 @@ result_3 = search_3.fit(model=model_3, analysis=analysis_3) We can now see our model has successfully fitted both Gaussians simultaneously: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x2_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x2_fit.png :alt: Alternative text :width: 600 ``` diff --git a/docs/features/search_grid_search.md b/docs/features/search_grid_search.md index 2373150d3..0f79d473c 100644 --- a/docs/features/search_grid_search.md +++ b/docs/features/search_grid_search.md @@ -24,7 +24,7 @@ In this example we will demonstrate the search grid search feature, again using in noisy data. This 1D data includes a small feature to the right of the central `Gaussian`, a second `Gaussian` centred on pixel 70. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature.png :alt: Alternative text :width: 600 ``` @@ -53,7 +53,7 @@ which the non linear search may miss. The image below shows a fit where we failed to detect the feature: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature_fit_no_feature.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature_fit_no_feature.png :alt: Alternative text :width: 600 ``` @@ -110,7 +110,7 @@ This shows a peak evidence value on the 4th cell of grid-search, where the `Unif 60 -> 80 and therefore included the Gaussian feature. By plotting this model-fit we can see it has successfully detected the feature. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature_fit_feature.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature_fit_feature.png :alt: Alternative text :width: 600 ``` diff --git a/docs/features/sensitivity_mapping.md b/docs/features/sensitivity_mapping.md index 04a3884d1..404a5ac6a 100644 --- a/docs/features/sensitivity_mapping.md +++ b/docs/features/sensitivity_mapping.md @@ -19,7 +19,7 @@ evidence increase we should expect for datasets of varying quality and / or mode To illustrate sensitivity mapping we will again use the example of fitting 1D Gaussian's in noisy data. This 1D data includes a small feature to the right of the central `Gaussian`, a second `Gaussian` centred on pixel 70. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/gaussian_x1_with_feature.png :alt: Alternative text :width: 600 ``` @@ -175,12 +175,12 @@ def __call__(instance, simulate_path): Here are what the two most extreme simulated datasets look like, corresponding to the highest and lowest normalization values -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/sensitivity_data_low.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/sensitivity_data_low.png :alt: Alternative text :width: 600 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/sensitivity_data_high.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/sensitivity_data_high.png :alt: Alternative text :width: 600 ``` @@ -218,12 +218,12 @@ sensitivity_result = sensitivity.run() Here are what the fits to the two most extreme simulated datasets look like, for the models including the Gaussian feature. -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/sensitivity_data_low_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/sensitivity_data_low_fit.png :alt: Alternative text :width: 600 ``` -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/features/images/sensitivity_data_high_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/features/images/sensitivity_data_high_fit.png :alt: Alternative text :width: 600 ``` diff --git a/docs/general/citations.md b/docs/general/citations.md index 962e2d0e0..29342bf6c 100644 --- a/docs/general/citations.md +++ b/docs/general/citations.md @@ -3,9 +3,9 @@ # Citations & References The bibtex entries for **PyAutoFit** and its affiliated software packages can be found -[here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citations.bib), with example text for citing **PyAutoFit** -in [.tex format here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citation.tex) format here and -[.md format here](https://github.com/rhayes777/PyAutoFit/blob/main/files/citations.md). As shown in the examples, we +[here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citations.bib), with example text for citing **PyAutoFit** +in [.tex format here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citation.tex) format here and +[.md format here](https://github.com/PyAutoLabs/PyAutoFit/blob/main/files/citations.md). As shown in the examples, we would greatly appreciate it if you mention **PyAutoFit** by name and include a link to our GitHub page! **PyAutoFit** is published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.02550#) and its diff --git a/docs/general/configs.md b/docs/general/configs.md index a1c90be7e..2095367c3 100644 --- a/docs/general/configs.md +++ b/docs/general/configs.md @@ -4,7 +4,7 @@ visualization and other aspects of **PyAutoFit**. Descriptions of every configuration file and their input parameters are provided in the `README.md` in -the [config directory of the workspace](https://github.com/Jammy2211/autofit_workspace/tree/release/config) +the [config directory of the workspace](https://github.com/PyAutoLabs/autofit_workspace/tree/main/config) ## Setup diff --git a/docs/general/roadmap.md b/docs/general/roadmap.md index 3d7f6b63f..2fdfeb013 100644 --- a/docs/general/roadmap.md +++ b/docs/general/roadmap.md @@ -13,7 +13,7 @@ Supported for autodiff is nearly implemented, including JAX fits. We are always striving to add new non-linear searches to PyAutoFit\*. In the short term, we aim to provide a wrapper to the many method available in the `scipy.optimize` library with support for outputting results to hard-disk. -If you would like to see a non-linear search implemented in **PyAutoFit** please [raise an issue on GitHub](https://github.com/rhayes777/PyAutoFit/issues)! +If you would like to see a non-linear search implemented in **PyAutoFit** please [raise an issue on GitHub](https://github.com/PyAutoLabs/PyAutoFit/issues)! **Approximate Bayesian Computation** diff --git a/docs/general/software.md b/docs/general/software.md index e17b0d6d9..60c9077dc 100644 --- a/docs/general/software.md +++ b/docs/general/software.md @@ -4,10 +4,10 @@ The following software projects use **PyAutoFit**: -[PyAutoLens](https://github.com/Jammy2211/PyAutoLens) - +[PyAutoLens](https://github.com/PyAutoLabs/PyAutoLens) - Astronomy software for modeling Strong Gravitational Lenses. -[PyAutoGalaxy](https://github.com/Jammy2211/PyAutoGalaxy) - +[PyAutoGalaxy](https://github.com/PyAutoLabs/PyAutoGalaxy) - Astronomy software for modeling galaxy light profiles and dynamics. [PyAutoCTI](https://github.com/Jammy2211/PyAutoCTI) - diff --git a/docs/general/workspace.md b/docs/general/workspace.md index aa2367ab6..83fb1a35d 100644 --- a/docs/general/workspace.md +++ b/docs/general/workspace.md @@ -2,7 +2,7 @@ # Workspace Tour -You should have downloaded and configured the [autofit_workspace](https://github.com/Jammy2211/autofit_workspace) +You should have downloaded and configured the [autofit_workspace](https://github.com/PyAutoLabs/autofit_workspace) when you installed **PyAutoFit**. If you didn't, checkout the [installation instructions](https://pyautofit.readthedocs.io/en/latest/general/installation.html#installation-with-pip) for how to downloaded and configure the workspace. @@ -26,7 +26,7 @@ There are numerous example describing how perform model-fitting with **PyAutoFit advanced model-fitting features. All examples are provided as Python scripts and Jupyter notebooks. Descriptions of every configuration file and their input parameters are provided in the `README.md` in -the [config directory of the workspace](https://github.com/Jammy2211/autofit_workspace/tree/release/config) +the [config directory of the workspace](https://github.com/PyAutoLabs/autofit_workspace/tree/main/config) ## Config diff --git a/docs/index.md b/docs/index.md index 8ca9fd8bf..8cd62bf8e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -18,13 +18,13 @@ The following links are useful for new starters: - [The PyAutoFit readthedocs](https://pyautofit.readthedocs.io/en/latest), which includes an [installation guide](https://pyautofit.readthedocs.io/en/latest/installation/overview.html) and an overview of **PyAutoFit**'s core features. - [The introduction Jupyter Notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb), where you can try **PyAutoFit** in a web browser (without installation). -- [The autofit_workspace GitHub repository](https://github.com/Jammy2211/autofit_workspace), which includes example scripts demonstrating **PyAutoFit**'s features. +- [The autofit_workspace GitHub repository](https://github.com/PyAutoLabs/autofit_workspace), which includes example scripts demonstrating **PyAutoFit**'s features. - [The standalone HowToFit repository](https://github.com/PyAutoLabs/HowToFit), a series of Jupyter notebook lectures which give new users a step-by-step introduction to **PyAutoFit**. ## Support Support for installation issues, help with Fit modeling and using **PyAutoFit** is available by -[raising an issue on the GitHub issues page](https://github.com/rhayes777/PyAutoFit/issues). +[raising an issue on the GitHub issues page](https://github.com/PyAutoLabs/PyAutoFit/issues). We also offer support on the **PyAutoFit** [Slack channel](https://pyautoFit.slack.com/), where we also provide the latest updates on **PyAutoFit**. Slack is invitation-only, so if you'd like to join send @@ -43,7 +43,7 @@ The lectures are available in the [standalone HowToFit repository](https://githu To illustrate the **PyAutoFit** API, we use an illustrative toy model of fitting a one-dimensional Gaussian to noisy 1D data. Here's the `data` (black) and the model (red) we'll fit: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/files/toy_model_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/files/toy_model_fit.png :width: 400 ``` diff --git a/docs/installation/conda.md b/docs/installation/conda.md index 3d8d940e7..fd93a378e 100644 --- a/docs/installation/conda.md +++ b/docs/installation/conda.md @@ -32,7 +32,7 @@ the `autofit_workspace`, reducing the download size): ```bash cd /path/on/your/computer/you/want/to/put/the/autofit_workspace -git clone https://github.com/Jammy2211/autofit_workspace --depth 1 +git clone https://github.com/PyAutoLabs/autofit_workspace --depth 1 cd autofit_workspace ``` diff --git a/docs/installation/overview.md b/docs/installation/overview.md index e89a4f3b1..5b3194f50 100644 --- a/docs/installation/overview.md +++ b/docs/installation/overview.md @@ -24,7 +24,7 @@ There are currently no known issues with installing **PyAutoFit**. ## Dependencies -**PyAutoConf** +**PyAutoConf** **dynesty** diff --git a/docs/installation/pip.md b/docs/installation/pip.md index cd5e056c3..3610c68bc 100644 --- a/docs/installation/pip.md +++ b/docs/installation/pip.md @@ -27,7 +27,7 @@ the `autofit_workspace`, reducing the download size): ```bash cd /path/on/your/computer/you/want/to/put/the/autofit_workspace -git clone https://github.com/Jammy2211/autofit_workspace --depth 1 +git clone https://github.com/PyAutoLabs/autofit_workspace --depth 1 cd autofit_workspace ``` diff --git a/docs/installation/source.md b/docs/installation/source.md index 7e4c5ca3b..359998a8b 100644 --- a/docs/installation/source.md +++ b/docs/installation/source.md @@ -9,7 +9,7 @@ contribute the development **PyAutoFit** or experiment with yourself! First, clone (or fork) the **PyAutoFit** GitHub repository: ```bash -git clone https://github.com/Jammy2211/PyAutoFit +git clone https://github.com/PyAutoLabs/PyAutoFit ``` Next, install the **PyAutoFit** dependencies via pip: diff --git a/docs/installation/troubleshooting.md b/docs/installation/troubleshooting.md index 232c9cef2..ecba4eb35 100644 --- a/docs/installation/troubleshooting.md +++ b/docs/installation/troubleshooting.md @@ -25,5 +25,5 @@ are not running the script with the `autofit_workspace` as the working directory ## Support If you are still having issues with installation or using **PyAutoFit** in general, please raise an issue on the -[autofit_workspace issues page](https://github.com/Jammy2211/autofit_workspace/issues) with a description of the +[autofit_workspace issues page](https://github.com/PyAutoLabs/autofit_workspace/issues) with a description of the problem and your system setup (operating system, Python version, etc.). diff --git a/docs/overview/backup.md b/docs/overview/backup.md index fb179fa37..548c63fff 100644 --- a/docs/overview/backup.md +++ b/docs/overview/backup.md @@ -24,7 +24,7 @@ plt.close() The data appear as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/data_2.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/data_2.png :alt: Alternative text :width: 600 ``` @@ -371,7 +371,7 @@ plt.close() The plot appears as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/toy_model_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/toy_model_fit.png :alt: Alternative text :width: 600 ``` diff --git a/docs/overview/scientific_workflow.md b/docs/overview/scientific_workflow.md index 06f18aec4..7bc451898 100644 --- a/docs/overview/scientific_workflow.md +++ b/docs/overview/scientific_workflow.md @@ -141,7 +141,7 @@ search = af.Emcee( The screenshot below shows the output folder where all output is enabled: -```{image} https://raw.githubusercontent.com/Jammy2211/PyAutoFit/main/docs/overview/image/output_example.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/overview/image/output_example.png :alt: Alternative text :width: 400 ``` diff --git a/docs/overview/statistical_methods.md b/docs/overview/statistical_methods.md index 285279b92..a40d4dc20 100644 --- a/docs/overview/statistical_methods.md +++ b/docs/overview/statistical_methods.md @@ -14,7 +14,7 @@ This is achieved through a concise API and scientific workflow that ensures scal A full description of using graphical models is given below: - + ## Hierarchical Models @@ -36,7 +36,7 @@ and comparing how well they fit the data. A full description of using model comparison is given below: - + ## Interpolation @@ -51,7 +51,7 @@ to determine the most likely model parameters at any point in time. **PyAutoFit**'s interpolation feature allows for this, and a full description of its use is given below: - + ## Search Grid Search @@ -70,7 +70,7 @@ space to further aid the fitting process. A full description of using search grid searches is given below: - + ## Search Chaining @@ -85,7 +85,7 @@ of **bite-sized** searches which are faster and more reliable than fitting the m A full description of using search chaining is given below: - + ## Sensitivity Mapping @@ -102,4 +102,4 @@ how much the change in the model led to a measurable change in the data. This is A full description of using sensitivity mapping is given below: - + diff --git a/docs/overview/the_basics.md b/docs/overview/the_basics.md index c57d1f322..97005e549 100644 --- a/docs/overview/the_basics.md +++ b/docs/overview/the_basics.md @@ -36,7 +36,7 @@ import numpy as np The example `data` with errors (black) is shown below: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/data.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/data.png :alt: Alternative text :width: 600 ``` @@ -277,7 +277,7 @@ plt.clf() Here is what the plot looks like: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/model_gaussian.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/model_gaussian.png :alt: Alternative text :width: 600 ``` @@ -518,7 +518,7 @@ plt.close() The plot appears as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/toy_model_fit.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/toy_model_fit.png :alt: Alternative text :width: 600 ``` @@ -545,7 +545,7 @@ plotter.corner_anesthetic() The plot appears as follows: -```{image} https://raw.githubusercontent.com/rhayes777/PyAutoFit/main/docs/images/corner.png +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoFit/main/docs/images/corner.png :alt: Alternative text :width: 600 ``` @@ -640,7 +640,7 @@ interpretation of the results remains feasible and insightful. ## Resources -The [autofit_workspace:](https://github.com/Jammy2211/autofit_workspace/) repository on GitHub provides numerous +The [autofit_workspace:](https://github.com/PyAutoLabs/autofit_workspace/) repository on GitHub provides numerous examples demonstrating more complex model-fitting tasks. This includes cookbooks, which provide a concise reference guide to the **PyAutoFit** API for advanced model-fitting: diff --git a/docs/science_examples/astronomy.md b/docs/science_examples/astronomy.md index ecde1848b..7641d7b3b 100644 --- a/docs/science_examples/astronomy.md +++ b/docs/science_examples/astronomy.md @@ -4,11 +4,11 @@ This example illustrates model-component and fitting for an Astronomy science case, based are the phenomena of strong gravitational lensing. This is the science case that sparked the development of **PyAutoFit** as a spin -off of our astronomy software [PyAutoLens](https://github.com/Jammy2211/PyAutoLens). +off of our astronomy software [PyAutoLens](https://github.com/PyAutoLabs/PyAutoLens). The schematic below depicts a strong gravitational lens: -```{image} https://raw.githubusercontent.com/Jammy2211/PyAutoLens/main/docs/overview/images/overview_1_lensing/schematic.jpg +```{image} https://raw.githubusercontent.com/PyAutoLabs/PyAutoLens/main/docs/overview/images/overview_1_lensing/schematic.jpg :alt: Alternative text :width: 600 ``` @@ -25,7 +25,7 @@ lens. The amount light is deflected by is defined by the distances between each We therefore need a model which contains separate model-components for every galaxy, and where each galaxy contains separate model-components describing its light and mass. A multi-level representation of this model is as follows: -```{image} https://github.com/rhayes777/PyAutoFit/blob/main/docs/overview/image/lens_model.png?raw=true +```{image} https://github.com/PyAutoLabs/PyAutoFit/blob/main/docs/overview/image/lens_model.png?raw=true :alt: Alternative text :width: 600 ``` @@ -259,7 +259,7 @@ modeling features to compose and fits models of arbitrary complexity and dimensi To illustrate this further, consider the following dataset which is called a **strong lens galaxy cluster**: -```{image} https://github.com/rhayes777/PyAutoFit/blob/main/docs/overview/image/cluster_example.jpg?raw=true +```{image} https://github.com/PyAutoLabs/PyAutoFit/blob/main/docs/overview/image/cluster_example.jpg?raw=true :alt: Alternative text :width: 600 ``` @@ -304,7 +304,7 @@ model = af.Collection( Here is an illustration of this model's graph: -```{image} https://github.com/rhayes777/PyAutoFit/blob/main/docs/overview/image/lens_model_cluster.png?raw=true +```{image} https://github.com/PyAutoLabs/PyAutoFit/blob/main/docs/overview/image/lens_model_cluster.png?raw=true :alt: Alternative text :width: 600 ``` @@ -318,8 +318,8 @@ An example project on the **autofit_workspace** shows how to use **PyAutoFit** t lensing data, using **multi-level model composition**. If you'd like to perform the fit shown in this script, checkout the -[simple examples](https://github.com/Jammy2211/autofit_workspace/tree/main/notebooks/overview/simplee) on the +[simple examples](https://github.com/PyAutoLabs/autofit_workspace/tree/main/notebooks/overview) on the `autofit_workspace`. We detail how **PyAutoFit** works in the first 3 tutorials of the [HowToFit lecture series](https://github.com/PyAutoLabs/HowToFit). - + diff --git a/files/citations.md b/files/citations.md index 6bb86280c..2e554bb7f 100644 --- a/files/citations.md +++ b/files/citations.md @@ -1,6 +1,6 @@ I**nesrt in the main body of the paper:** -We use the probabilistic programming language `PyAutoFit` https://github.com/rhayes777/PyAutoFit) [@pyautofit] to... +We use the probabilistic programming language `PyAutoFit` https://github.com/PyAutoLabs/PyAutoFit) [@pyautofit] to... **At the end of the paper (delete as appropriate, see https://pyautofit.readthedocs.io/en/latest/general/citations.html):** @@ -13,7 +13,7 @@ This work uses the following software packages: - `emcee` https://github.com/dfm/emcee [@emcee] - `matplotlib` https://github.com/matplotlib/matplotlib [@matplotlib] - `NumPy` https://github.com/numpy/numpy [@numpy] -- `PyAutoFit` https://github.com/rhayes777/PyAutoFit [@pyautofit] +- `PyAutoFit` https://github.com/PyAutoLabs/PyAutoFit [@pyautofit] - `PyMultiNest` https://github.com/JohannesBuchner/PyMultiNest [@multinest] [@pymultinest] - `Python` https://www.python.org/ [@python] diff --git a/paper/README.md b/paper/README.md index 2e22d9dc6..b39481882 100644 --- a/paper/README.md +++ b/paper/README.md @@ -1,5 +1,5 @@ PyAutoFit JOSS Paper ==================== -Paper accompanying [PyAutoFit](https://github.com/rhayes777/PyAutoFit) for submission to the Journal of Open Source +Paper accompanying [PyAutoFit](https://github.com/PyAutoLabs/PyAutoFit) for submission to the Journal of Open Source Software (JOSS). \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index efd01fada..8d3c10933 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,7 +21,7 @@ affiliations: - name: ConcR Ltd, London, UK index: 2 date: 17 July 2020 -codeRepository: https://github.com/rhayes777/PyAutoFit +codeRepository: https://github.com/PyAutoLabs/PyAutoFit license: MIT bibliography: paper.bib --- @@ -35,11 +35,11 @@ interfaces with all aspects of the modeling (e.g., the model, data, fitting proc therefore provides complete management of every aspect of modeling. This includes composing high-dimensionality models from individual model components, customizing the fitting procedure and performing data augmentation before a model-fit. Advanced features include database tools for analysing large suites of modeling results and exploiting domain-specific -knowledge of a problem via non-linear search chaining. Accompanying `PyAutoFit` is the [autofit workspace](https://github.com/Jammy2211/autofit_workspace), +knowledge of a problem via non-linear search chaining. Accompanying `PyAutoFit` is the [autofit workspace](https://github.com/PyAutoLabs/autofit_workspace), which includes example scripts, together with the standalone [HowToFit](https://github.com/PyAutoLabs/HowToFit) lecture series which introduces non-experts to model-fitting and provides a guide on how to begin a project using `PyAutoFit`. Readers can try `PyAutoFit` right now by -going to [the introduction Jupyter notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/start_here.ipynb) +going to [the introduction Jupyter notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb) or checkout our [readthedocs](https://pyautofit.readthedocs.io/en/latest/) for a complete overview of **PyAutoFit**'s features. @@ -118,7 +118,7 @@ and thus enable accurate fitting of models of arbitrary complexity. # History -`PyAutoFit` is a generalization of [PyAutoLens](https://github.com/Jammy2211/PyAutoLens), an Astronomy package +`PyAutoFit` is a generalization of [PyAutoLens](https://github.com/PyAutoLabs/PyAutoLens), an Astronomy package developed to analyse images of gravitationally lensed galaxies. Modeling gravitational lenses historically requires large amounts of human time and supervision, an approach which does not scale to the incoming samples of 100000 objects. Domain exploitation enabled full automation of the lens modeling procedure [@Nightingale2015; @Nightingale2018], with @@ -128,13 +128,13 @@ of cancer tumour growth. # Workspace and HowToFit Tutorials -`PyAutoFit` is distributed with the [autofit workspace](https://github.com/Jammy2211/autofit_workspace), which +`PyAutoFit` is distributed with the [autofit workspace](https://github.com/PyAutoLabs/autofit_workspace), which contains example scripts for composing a model, performing a fit, using the `Aggregator` and `PyAutoFit`'s advanced statistical inference methods. Complementing the workspace is the standalone [HowToFit](https://github.com/PyAutoLabs/HowToFit) repository, a series of Jupyter notebook tutorials aimed at non-experts, introducing them to model-fitting and Bayesian inference. They teach users how to write model-components and `Analysis` classes in `PyAutoFit`, use these to fit a dataset and interpret the model-fitting results. The lectures -are available on our [Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/start_here.ipynb) and may therefore be +are available on our [Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.14.2/notebooks/overview/overview_1_the_basics.ipynb) and may therefore be taken without a local `PyAutoFit` installation. # Software Citations