PyAutoLens Workspace

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Installation Guide <https://pyautolens.readthedocs.io/en/latest/installation/overview.html>_ | readthedocs <https://pyautolens.readthedocs.io/en/latest/index.html>_ | Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/release?filepath=introduction.ipynb>_ | HowToLens <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>_

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Welcome to the PyAutoLens Workspace!

Getting Started

You can get set up on your personal computer by following the installation guide on our readthedocs <https://pyautolens.readthedocs.io/>_.

Alternatively, you can try PyAutoLens out in a web browser by going to the autolens workspace Binder <https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/release?filepath=introduction.ipynb>_.

Where To Go?

We recommend that you start with the autolens_workspace/introduction.ipynb notebook, which will give you a concise overview of PyAutoLens's core features and API.

Next, read through the overview example notebooks of features you are interested in, in the folder: autolens_workspace/notebooks/overview.

Then, find the start_here.ipynb notebook for the feature you are interested in (for example, if you are looking to model CCD imaging data, go to autolens_workspace/notebooks/imaging/modeling/start_here.ipynb).

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 <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>_

Workspace Structure

The workspace includes the following main directories:

• notebooks: PyAutoLens examples written as Jupyter notebooks.
• scripts: PyAutoLens examples written as Python scripts.
• config: Configuration files which customize PyAutoLens's behaviour.
• dataset: Where data is stored, including example datasets distributed.
• output: Where the PyAutoLens analysis and visualization are output.
• slam: The Source, Light and Mass (SLaM) pipelines to model strong lens imaging (Advanced).

The examples in the notebooks and scripts folders are structured as follows:

• overview: Examples giving an overview of PyAutoLens's core features.

• howtolens: Detailed step-by-step Jupyter notebook lectures on how to use PyAutoLens.

• imaging: Examples for analysing and simulating CCD imaging data (e.g. Hubble, Euclid).

• interferometer: Examples for analysing and simulating interferometer datasets (e.g. ALMA, JVLA).

• multi: Modeling multiple datasets simultaneously (E.g. multi-wavelength imaging, imaging and interferometry).

• point_source: Modeling strong lens point source datasets.

• group: Group-scale lens modeling and simulations examples.

• plot: An API reference guide for PyAutoLens's plotting tools.

• misc: Miscellaneous scripts for specific lens analysis.

Inside these packages are packages titled advanced which only users familiar PyAutoLens should check out.

In the imaging, interferometer, point_source, multi and group folders you'll find the following packages:

• modeling: Examples of how to fit a lens model to data via a non-linear search.
• simulators: Scripts for simulating realistic imaging and interferometer data of strong lenses.
• data_preparation: Tools to preprocess data before an analysis (e.g. convert units, create masks).
• results: Examples using the results of a model-fit.
• advanced: Advanced modeling scripts which use PyAutoLens's advanced features.

The files README.rst distributed throughout the workspace describe what is in each folder.

Workspace Version

This version of the workspace is built and tested for using PyAutoLens v2024.5.16.0.

Contribution

To make changes in the tutorial notebooks, please make changes in the corresponding python files(.py) present in the scripts folder of each chapter. Please note that marker # %% alternates between code cells and markdown cells.

Support

Support for installation issues, help with lens modeling and using PyAutoLens is available by raising an issue on the autolens_workspace GitHub page <https://github.com/Jammy2211/autolens_workspace/issues>. or joining the PyAutoLens Slack channel <https://pyautolens.slack.com/>, where we also provide the latest updates on PyAutoLens.

Slack is invitation-only, so if you'd like to join send an email <https://github.com/Jammy2211>_ requesting an invite.

Galaxy-Scale vs Group Scale Lenses

The imaging, interferometer and point_source packages provides scripts for modeling galaxy-scale lenses, whereas the group package provides scripts for modeling group-scale lenses.

But what are the defitions of a galaxy scale and group scale lens? The line between the two is blurry, but is defined roughly as follows:

• A galaxy-scale lens is a system which can be modeled to a high level of accuracy using a single mass distribution for the main lens galaxy. There are examples which include additional galaxies in the model to make small improvements on the overall lens model, but for many science cases this is not stricly necessary.

• A group scale lens is a system which cannot be modeled to a high level of accuracy using a single mass distribution. The notion of a 'main' lens galaxy is illposed and two or more main lens galaxies are required to fit an accurate model.

If you have data which requires the lens model to include additional galaxies, whether it be a galaxy or group scale system, keep an eye out for PyAutoLens's 'clump API' which is designed to facilitate this.