PyAutoLens-JAX: Open-Source Strong Lensing
Installation Guide | readthedocs | Introduction on Colab | HowToLens
When two or more galaxies are aligned perfectly down our line-of-sight, the background galaxy appears multiple times.
This is called strong gravitational lensing and PyAutoLens makes it simple to model strong gravitational lenses, using JAX to accelerate lens modeling on GPUs.
Getting Started
Human-Readable Documentation and Examples
The following human-readable documentation and examples are useful for new starters:
- The PyAutoLens readthedocs: which includes an overview of PyAutoLens's core features, a new user starting guide and an installation guide.
- The introduction Jupyter Notebook on Google Colab: try PyAutoLens in a web browser (without installation).
- The autolens_workspace GitHub repository: example scripts covering every PyAutoLens use case.
- The HowToLens GitHub repository: a Jupyter notebook lecture series teaching strong lensing and lens modeling from the ground up.
PyAutoLens AI Assistant
The PyAutoLens AI Assistant lets you do gravitational lensing science in natural language from inside an AI coding agent. You can get started simply by asking it a question about gravitational lensing or describing the task you would like to perform with PyAutoLens. See the autolens_assistant GitHub page for its full scope and instructions.
The assistant runs inside an AI coding agent: Claude Code or Codex are recommended. Sustained scientific use normally needs paid access to one of them (a personal subscription, institutional access or API billing). OpenCode is an experimental alternative whose client is free but whose model access, cost and capability depend on the provider. Browser chat routes (ChatGPT or Claude with a GitHub connector) are no longer supported.
Community & Contributing
PyAutoLens is built in the open by its users: everyone is welcome to ask questions, share what they have made with it, and contribute.
Questions, ideas and bug reports: the PyAutoLabs Discussions. Chat with us on Slack.
Community-built tools and tutorials, and how to contribute: the PyAutoLens community page.
HowToLens
For users less familiar with gravitational lensing, Bayesian inference and scientific analysis you may wish to read through the HowToLens lectures. These teach you the basic principles of gravitational lensing and Bayesian inference, with the content pitched at undergraduate level and above.
A complete overview of the lectures is provided on the HowToLens readthedocs page, and the notebooks themselves live in the PyAutoLabs/HowToLens repository.
Citations
Information on how to cite PyAutoLens in publications can be found on the citations page.
i found you, i found you, i found you beautiful, exploding, i found you
Release files for autolens 2026.10.12.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autolens-2026.10.12.1.tar.gz | 7.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autolens-2026.10.12.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.2 MB
Release files / autolens-2026.10.12.1.tar.gz
| Download URL | autolens-2026.10.12.1.tar.gz |
|---|---|
| Size | 7.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c3e055c24b0fd6a15634516407234068a64ed850429ce033e4a80b77ecf1da3b
|
|
BLAKE2b-256 checksum How to use checksums |
d28373ba26dcc34fc8472f662560975e47d778e068e602e91680549abef949ed
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.15
|
Release files / autolens-2026.10.12.1-py3-none-any.whl
| Download URL | autolens-2026.10.12.1-py3-none-any.whl |
|---|---|
| Size | 330.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
84a78779f94875066a9b852a5bec6eae4327c43240d30e78cf36932754f8a95e
|
|
BLAKE2b-256 checksum How to use checksums |
1859c65658e6e4cc77ce4a687c6e70d8b8ce94c8963bd01b256f4d58322c39d7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.12.15
|
