Welcome to the PyAutoLens Workspace. You can get started right away by going to the autolens workspace Binder. Alternatively, you can get set up by following the installation guide on our readthedocs.
If you haven't already, install PyAutoLens via pip or conda.
Next, clone the
autolens workspace (the line
--depth 1 clones only the most recent branch on
autolens_workspace, reducing the download size):
cd /path/on/your/computer/you/want/to/put/the/autolens_workspace git clone https://github.com/Jammy2211/autolens_workspace --depth 1 cd autolens_workspace
welcome.py script to get started!
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 with PyAutoLens.
output: Where the PyAutoLens analysis and visualization are output.
slam: The Source, Light and Mass (SLaM) lens modeling pipelines, which are scripts for experienced users.
The examples in the
scripts folders are structured as follows:
overview: Examples giving an overview of PyAutoLens's core features.
howtolens: Detailed step-by-step Jupyter notebook tutorials on how to use PyAutoLens.
imaging: Examples for analysing and simulating CCD imaging data.
interferometer: Examples for analysing and simulating interferometer.
database: Examples for using database tools which load libraries of model-fits to large datasets.
plot: An API reference guide for PyAutoLens's plotting tools.
misc: Miscelaneous scripts for specific lens analysis.
interferometer folders you'll find the following packages:
modeling: Examples of how to fit a lens model to data via a non-linear search.
chaining: Advanced modeling scripts which chain together multiple non-linear searches.
simulators: Scripts for simulating realistic imaging and interferometer data of strong lenses.
preprocess: Tools to preprocess
databefore an analysis (e.g. convert units, create masks).
chaining sections are for users familiar with PyAutoLens and contain:
pipelines: Example pipelines for modeling strong lenses using non-linear search chaining.
hyper_mode: Examples using hyper-mode, which adapts the lens model to the data being fitted.
slam: Example scripts that fit lens datasets using the SLaM pipelines.
We recommend new users begin with the example notebooks / scripts in the overview folder and the HowToLens tutorials.
This version of the workspace are built and tested for using PyAutoLens v1.12.2.
Included with PyAutoLens is the
HowToLens lecture series, which provides an introduction to strong gravitational
lens modeling with PyAutoLens. It can be found in the workspace & consists of 5 chapters:
Introduction: An introduction to strong gravitational lensing & PyAutoLens.
Lens Modeling: How to model strong lenses, including a primer on Bayesian non-linear analysis.
Pipelines: How to build model-fitting pipelines & tailor them to your own science case.
Inversions: How to perform pixelized reconstructions of the source-galaxy.
Hyper-Mode: How to use PyAutoLens advanced modeling features that adapt the model to the strong lens being analysed.
To make changes in the tutorial notebooks, please make changes in the 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 for installation issues, help with lens modeling and using PyAutoLens is available by raising an issue on the autolens_workspace GitHub page. or joining the PyAutoLens Slack channel, where we also provide the latest updates on PyAutoLens.
Slack is invitation-only, so if you'd like to join send an email requesting an invite.