You should have downloaded and configured the autolens workspace when you installed PyAutoLens. If you didn’t, checkout the installation instructions for how to downloaded and configure the workspace.
README.rst files distributed throughout the workspace describe every folder and file, and specify if
examples are for beginner or advanced users.
New users should begin by checking out the following parts of the workspace.
The HowToLens lecture series are a collection of Jupyter notebooks describing how to build a PyAutoLens model fitting project and giving illustrations of different statistical methods and techniques.
Checkout the tutorials section for a full description of the lectures and online examples of every notebook.
Scripts / Notebooks#
There are numerous example describing how to perform lensing calculations, lens modeling, and many other PyAutoLens features. All examples are provided as Python scripts and Jupyter notebooks.
Descriptions of every configuration file and their input parameters are provided in the
the config directory of the workspace
Here, you’ll find the configuration files used by PyAutoLens which customize:
The default settings used by every non-linear search.
Visualization, including the backend used by matplotlib.
The priors and notation configs associated with the light and mass profiles used for lens modeling.
The behaviour of different (y,x) Cartesian grids used to perform lens calculations.
The general.yaml config which customizes other aspects of PyAutoLens.
Checkout the configuration section of the readthedocs for a complete description of every configuration file.
Contains the dataset’s used to perform lens modeling. Example datasets using simulators included with the workspace are included here by default.
The folder where lens modeling results are stored.
Advanced lens modeling pipelines that use the Source, Light and Mass (SLaM) approach to lens modeling.
See here for an overview.