PyAutoFit Workspace

Installation Guide | readthedocs | Introduction on Binder | HowToFit

Welcome to the PyAutoFit Workspace!

Getting Started

You can get set up on your personal computer by following the installation guide on our readthedocs.

Alternatively, you can try PyAutoFit out in a web browser by going to the autofit workspace Binder.

Where To Go?

We recommend that you start with the autofit_workspace/notebooks/overview/overview_1_the_basics.ipynb notebook, which will give you a concise overview of PyAutoFit's core features and API.

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

Then, you may wish to implement your own model in PyAutoFit, using the cookbooks for help with the API. Alternative, you may want to checkout the features package for a list of advanced statistical modeling features.

HowToFit

For users less familiar with Bayesian inference and scientific analysis you may wish to read through the HowToFits lectures. These teach you the basic principles of Bayesian inference, with the content pitched at undergraduate level and above.

A complete overview of the lectures is provided on the HowToFit readthedocs page

Workspace Structure

The workspace includes the following main directories:

notebooks: PyAutoFit examples written as Jupyter notebooks.
scipts: PyAutoFit examples written as Python scripts.
projects: Example projects which use PyAutoFit, which serve as a illustration of model-fitting problems and the PyAutoFit API.
config: Configuration files which customize PyAutoFit's behaviour.
dataset: Where data is stored, including example datasets distributed with PyAutoFit.
output: Where the PyAutoFit analysis and visualization are output.

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

overview: Examples using PyAutoFit to compose and fit a model to data via a non-linear search.
cookbooks: Concise API reference guides for PyAutoFit's core features.
features: Examples of PyAutoFit's advanced modeling features.
howtofit: Detailed step-by-step tutorials.
searches: Example scripts of every non-linear search supported by PyAutoFit.
plot: An API reference guide for PyAutoFits's plotting tools.

The following projects are available in the project folder:

astro: An Astronomy project which fits images of gravitationally lensed galaxies.

Workspace Version

This version of the workspace are built and tested for using PyAutoFit v2023.7.5.2.

Support

Support for installation issues and integrating your modeling software with PyAutoFit is available by raising an issue on the autofit_workspace GitHub page. or joining the PyAutoFit Slack channel, where we also provide the latest updates on PyAutoFit.

Slack is invitation-only, so if you'd like to join send an email requesting an invite.

File	Mode	Size
config
dataset
notebooks
projects
scripts
.gitignore	-rw-r--r--	102 bytes
CITATIONS.rst	-rw-r--r--	811 bytes
CODE_OF_CONDUCT.md	-rw-r--r--	18.4 KB
README.rst	-rw-r--r--	4.1 KB
requirements.txt	-rw-r--r--	7 bytes
runtime.txt	-rw-r--r--	10 bytes
simulate_datasets_include.sh	-rw-r--r--	0 bytes
simulate_datasets_missing.sh	-rw-r--r--	0 bytes
welcome.py	-rw-r--r--	3.2 KB

README.rst