PyAutoLens

@Jammy2211

Submitting Author: (@Jammy2211)
All current maintainers: (@Jammy2211)
Package Name: PyAutoLens
One-Line Description of Package: Astronomy software for strong gravitational lensing analysis and Bayesian inference
Repository Link: https://github.com/Jammy2211/PyAutoLens
Version submitted: 2026.5.11.1
EiC: @kysolvik
Editor: @dhomeier
Reviewer 1: @crhea93
Archive: TBD
JOSS DOI: https://joss.theoj.org/papers/10.21105/joss.02825
Version accepted: TBD
Date accepted (month/day/year): TBD

Code of Conduct & Commitment to Maintain Package

I agree to abide by [pyOpenSci's Code of Conduct][PyOpenSciCodeOfConduct] during the review process and in future interactions in spaces supported by pyOpenSci should it be accepted.
I have read and will commit to package maintenance after the review as per the [pyOpenSci Policies Guidelines][Commitment].

Description

PyAutoLens is an open-source Python package for analysing strong gravitational lenses, where the gravity of a foreground galaxy distorts and magnifies the light of a background source. The software supports automated Bayesian model-fitting of imaging and interferometer datasets, pixelized and parametric source reconstruction, hierarchical Bayesian inference, and large-scale analysis of strong lens samples. PyAutoLens places a focus on reproducible and scalable scientific workflows, including support for massively parallel model-fitting, graphical models and SQLite3 databases for managing and analysing large suites of lens models. The software is used for research on dark matter, galaxy evolution, cosmology and supermassive black holes using datasets from facilities including Hubble, ALMA, Euclid and JWST.

Scope

Please indicate which category or categories.
- Data retrieval
- Data extraction
- Data processing/munging
- Data deposition
- Data validation and testing
- Data visualization¹
- Workflow automation
- Citation management and bibliometrics
- Scientific software wrappers
- Database interoperability

Domain Specific

Geospatial
Education

Community Partnerships

Astropy:My package adheres to Astropy community standards
Pangeo: My package adheres to the [Pangeo standards listed in the pyOpenSci peer review guidebook][PangeoCollaboration]

For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
- Who is the target audience and what are scientific applications of this package?

Astronomers studying strong gravitational lensing, dark matter, galaxy evolution, supermassive black holes and cosmology, as well as undergraduates and researchers interested in learning computational Bayesian inference techniques for astronomy. The package analyses strong lens imaging data and extracts information on galaxies, dark matter and the distant Universe.

Are there other Python packages that accomplish the same thing? If so, how does yours differ?

Examples include lenstronomy, GLEE and gravlens.

PyAutoLens places a strong emphasis on automated Bayesian inference, scalable analysis of large strong lens samples, pixelized source reconstruction, hierarchical modeling and reproducible workflows. The software also includes comprehensive simulation tools, database functionality and support for both imaging and interferometer datasets.

If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

This package was first submitted to Astropy years ago, before the review process transitioned to PyOpenSci:

astropy/astropy.github.com#491

Technical checks

For details about the pyOpenSci packaging requirements, see our [packaging guide][PackagingGuide]. Confirm each of the following by checking the box. This package:

does not violate the Terms of Service of any service it interacts with.
uses an [OSI approved license][OsiApprovedLicense].
contains a README with instructions for installing the development version.
includes documentation with examples for all functions.
contains a tutorial with examples of its essential functions and uses.
has a test suite.
has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

Do you wish to automatically submit to the [Journal of Open Source Software][JournalOfOpenSourceSoftware]?

Already published in JOSS: https://joss.theoj.org/papers/10.21105/joss.02825

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

I have read the author guide.
I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

Last but not least please fill out our pre-review survey. This helps us track submission and improve our peer review process. We will also ask our reviewers and editors to fill this out.

Please fill out a pre-submission inquiry before submitting a data visualization package. ↩

@Jammy2211

Hi @Jammy2211,

My name is Kylen, I'm a member of the EiC team here at pyOpenSci. Thanks for submitting PyAutoLens! It looks like a great package and a good fit for pyOpenSci. Next step is pre-review checks, which I'll be tackling in the coming days. Thanks for your patience, I'll be back in touch asap!

One other thing: we've recently updated our guidelines around generative AI. We're still in the process of updating all our templates, but when you have the chance could respond in this issue with our updated gen-AI checks template (at the bottom of the page)?

@Jammy2211

Editor in Chief checks

Hi there! Thank you for submitting your package for pyOpenSci
review. Below are the basic checks that your package needs to pass
to begin our review. If some of these are missing, we will ask you
to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements
below.

Optional but recommended:

Linting The package uses automated linting (e.g., ruff) to enforce code style and quality standards, ideally integrated with CI.
Type checking The package includes type hints and uses a type checker (e.g., mypy) to catch type-related issues, ideally integrated with CI. (EiC Note: Type hints mostly present, but not universal. No type checking that I can see).
Modern tooling The package uses modern Python development tools (e.g., uv for dependency management) and Hatch for building publishing and running development tasks to streamline development workflows. See the pyOpenSci packaging guide for more on modern packaging tools.

Initial onboarding survey was filled out
We appreciate each maintainer of the package filling out this survey individually. 🙌
A big thank you in advance to authors for setting aside five to ten minutes to do this. It truly helps our organization! 🙌

Editor comments

Hi again @Jammy2211! PyAutoLens looks great. Since it's already been published in JOSS, it will undergo an accelerated review with just one editor and one reviewer.

Just a few things:

README; Could be helpful to include the Install guide link under "Getting Started" in the readme alongside the other links.
DOI badge: The DOI badge is rendering inconsistently, seems to be a Zenodo issue. Can be resolved by using shields.io badge. Note this isn't a requirement, so I'll leave it up to you.
API Docs: Noticed the occasional weird formatting in the API docs, could be to do inconsistencies in type hints? E.g. Mask2D.elliptical_annular params.
Gen AI: We've recently updated our guidelines around generative AI. We're still in the process of updating all our templates, but when you have the chance could respond in this issue with our updated gen-AI checks template (at the bottom of the page)? I know that PyAutoLens is using an AI-first development workflow, and so some of the questions in the checklist may be hard to answer. Just do your best to give an overall picture, and you could link to your Contributing guide for a description of the process. This is all to help reviewers with their process.

1 & 2 are optional, so once 3 & 4 are addressed we'll start looking for an editor!

I will fix 1, 2 and 3 later today or next week :).

This package has a public development history spanning 3-6 months, with commits distributed over time that reflect iterative, thoughtful development.
All code in this package has been carefully reviewed by a human. Its implementation is also understood by the authors submitting the package.
All communication on this issue will be written by a human (someone on your maintainer team). We embrace the use of LLMs for translation and grammar correction. We prefer honest interactions over ones that prioritize perfect language and grammar. Use as little aid from a LLM as possible.
Generative AI tools were used to develop and maintain this package.

Please list the tools and frameworks that you used below.

If you checked the first box above, please describe the tools and frameworks that you used below including:

Which parts of the submission were generated (e.g., documentation, tests, code). In addition to a general description, please specifically indicate any substantial portions of code (classes, modules, subpackages) that were wholly or primarily generated by AI.

PyCharm
pytest
sphinx
GitHub w/ PRs: https://github.com/PyAutoLabs/PyAutoLens/pulls
PyPI: https://pypi.org/project/autolens/
test PyPI

PyAutoLens was developed by human between 2018-2023, between 2023-2025 many functions were converted using ChatGPT to support JAX, albeit with most implementation still completed by a human (e.g. copy and paste). PyAutoLens (and its parent packages) have always used test driven development and thus have an extensive unit test library.

In March 2026 PyAutoLens switched to a full agentic AI development model, which is still being developed and expanded on as the lead maintainer learns agentic AI.

Currently, this workflow involves writing natural language development prompts on a parent project (https://github.com/PyAutoLabs/PyAutoPrompt), which has an AI development workflow which implements the feature in the source code.

PyAutoLens has a workspace with end-to-end science pipelines all of which are used as integration tests and context for the agentic AI workflow (https://github.com/PyAutoLabs/autolens_workspace), with over 80% of examples human written typically before 2022 when AI took over the world. There is also a dediciated testing workspace for tests not reflective of science analysis (https://github.com/PyAutoLabs/autolens_workspace_test) which are a mix of AI generated and human cheked, and fully human written Jupyter Notebook lectures (https://github.com/PyAutoLabs/HowToLens).

Every agentic AI feature is fully tracked via GitHub issues and PR's, so you can go back to our PR's from March 6th and see a full open history of when development began to become fully agentic: https://github.com/PyAutoLabs/PyAutoLens/pulls?page=6&q=is%3Apr+is%3Aclosed

For the sake of full disclosure, I have also developed an autolens_assistant which is a natural language assistant that allows a human to use PyAutoLens via a conversation analogous to a ChatGPT conversation of Claude / codex: https://github.com/PyAutoLabs/PyAutoLens/blob/main/CONTRIBUTING.md

The approximate scale of the generated portions (e.g., "all of the tests were generated and then checked by a human," "small routines were generated and copied into the code").

The code design, abstractions and architecture still fully reflect PyAutoLens as of ~2022 (e.g. when published in JOSS). The majority of source code is still unchanged after switching to agentic AI. Docstrings were extensive before siwtching to AI agentic, but had significant additional text added , partly to provide AI with a lot more context. Looking at the PR history, the majority of agentic-AI only work so far has been massively expanding the testing infrastructure, major refactors removing huge portions of code (removing the whole visualization / plot packages and modules for a much more light weight matplotlib-only API) and ensuring JAX support is integrated throughout all of the source code.

How the generative AI was used (e.g., line completion, help with translation, queried separately and integrated, agentic workflow).

This is described above. In short, generative AI was used first for assistance with JAX-related conversions, and later through an agentic development workflow based on natural-language prompts, GitHub issues, pull requests, and human review.

If you have a policy around generative AI use in your project, please provide a link to it below:

I think the closest thing I have currently is just the standard contributing guidelines, the agentic AI workflow is still being developed so I havent formalised AI guidelines yet: https://github.com/PyAutoLabs/PyAutoLens/blob/main/CONTRIBUTING.md

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

As the reviewer, I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

A statement of need clearly stating problems the software is designed to solve and its target audience in the README file.
Installation instructions: for the development version of the package and any non-standard dependencies in README.
Short quickstart tutorials demonstrating significant functionality that successfully runs locally.
Function Documentation: for all user-facing functions.
Examples for all user-facing functions.
Community guidelines including contribution guidelines in the README or CONTRIBUTING.
Metadata including author(s), author e-mail(s), a URL, and any other relevant metadata, for example, in a pyproject.toml file or elsewhere.

Readme file requirements
The package meets the readme requirements below:

Package has a README.md file in the root directory.

The README should include, from top to bottom:

The package name
Badges for:
- Continuous integration and test coverage,
- Docs building (if you have a documentation website),
- Python versions supported,
- Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be wider than high. A badge for pyOpenSci peer review will be provided when the package is accepted.

Short description of package goals.
Package installation instructions
Any additional setup required to use the package (authentication tokens, etc.)
Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
- Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
Link to your documentation website.
If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole.
The package structure should follow the general community best practices. In general, please consider whether:

Package documentation is clear and easy to find and use.
The need for the package is clear
All functions have documentation and associated examples for use
The package is easy to install

Functionality

Final approval (post-review)

The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 3

Review Comments

Thank you for your submission of PyAutoLens! The code is already in an extremely mature state. Since this is an expedited review, I will be focusing on the tutorials and readability. I will not be reviewing the natural language extension.

Minor comments:

The JOSS DOI in the header of this issue brings me to a different JOSS paper called 'bacting'
There is an error rendering the first image in https://pyautolens.readthedocs.io/en/latest/general/demagnified_solutions.html

@Jammy2211

Hi @Jammy2211, apologies for the delay in proceeding with your submission; after we had a hold on assigning new reviewers in the summer to prioritise completing the open reviews, I did not find the time move on with this one!
I am happy to let you know we do have a reviewer now, and hopefully will proceed swiftly from here.

If you have a policy around generative AI use in your project, please provide a link to it below:

I think the closest thing I have currently is just the standard contributing guidelines, the agentic AI workflow is still being developed so I havent formalised AI guidelines yet: https://github.com/PyAutoLabs/PyAutoLens/blob/main/CONTRIBUTING.md

Editor's note: it seems you have now adopted a common policy in https://github.com/PyAutoLabs/PyAutoScientist/blob/main/AI_POLICY.md .
Is it correct we can take this for the project policy?

@Jammy2211

The JOSS DOI in the header of this issue brings me to a different JOSS paper called 'bacting'

There is an error rendering the first image in https://pyautolens.readthedocs.io/en/latest/general/demagnified_solutions.html

@Jammy2211 I have fixed the JOSS link in this issue, as the repository page has the correct link.
That just leaves the doc page above where I now get broken links for the first two images.

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PyAutoLens #305

Code of Conduct & Commitment to Maintain Package

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PyAutoLens #305

Description

Code of Conduct & Commitment to Maintain Package

Description

Scope

Domain Specific

Community Partnerships

Technical checks

Publication Options

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

Please fill out our survey

Footnotes

Activity

kysolvik commented on May 28, 2026

kysolvik commented on Jun 9, 2026

Editor in Chief checks

Editor comments

Jammy2211 commented on Jun 19, 2026

1 remaining item

crhea93 commented on Oct 7, 2026

Package Review

Documentation

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Final approval (post-review)

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Minor comments:

dhomeier commented on Oct 7, 2026

dhomeier commented on Oct 7, 2026

dhomeier commented on Oct 30, 2026

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