PyAutoCTI Workspace

Installation Guide | readthedocs |

Welcome to the PyAutoCTI Workspace. You can get started right away by going to the autocti workspace Binder. Alternatively, you can get set up by following the installation guide on our readthedocs.

Getting Started

We recommend new users begin by looking at the following notebooks:

• notebooks/overview: Examples giving an overview of PyAutoCTI's core features.

Installation

If you haven't already, install PyAutoCTI via pip or conda.

Next, clone the autocti workspace (the line --depth 1 clones only the most recent branch on the autocti_workspace, reducing the download size):

cd /path/on/your/computer/you/want/to/put/the/autocti_workspace
git clone https://github.com/Jammy2211/autocti_workspace --depth 1
cd autocti_workspace

Run the welcome.py script to get started!

python3 welcome.py

Workspace Structure

The workspace includes the following main directories:

• notebooks: PyAutoCTI examples written as Jupyter notebooks.
• scripts: PyAutoCTI examples written as Python scripts.
• config: Configuration files which customize PyAutoCTI's behaviour.
• dataset: Where data is stored, including example datasets distributed.
• output: Where the PyAutoCTI analysis and visualization are output.

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

• overview: Examples giving an overview of PyAutoCTI's core features.
• dataset_1d: Examples for analysing and simulating 1D CTI datasets (e.g. warm pixels).
• imaging_ci: Examples for analysing and simulating CCD charge injection imaging data (e.g. Euclid).
• results: Examples using the results of a model-fit, including database tools which loads results from hard-disk.
• plot: An API reference guide for PyAutoCTI's plotting tools.
• misc: Miscellaneous scripts for specific cti analysis.

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

In the dataset_1d and imaging_ci folders you'll find the following packages:

• correction: Examples of how to correct data with a CTI model.
• modeling: Examples of how to fit a CTI model to data via a non-linear search.
• simulators: Scripts for simulating realistic CTI calibration datasets.
• data_preparation: Tools to preprocess CTI calibration data before an analysis (e.g. cosmic ray flagging).
• advanced/chaining: Advanced modeling scripts which chain together multiple non-linear searches.

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

Getting Started

We recommend new users begin with the example notebooks / scripts in the overview folder and the HowToCTI tutorials.

Workspace Version

This version of the workspace is built and tested for using PyAutoCTI 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 cti modeling and using PyAutoCTI is available by raising an issue on the autocti_workspace GitHub page. or joining the PyAutoCTI Slack channel, where we also provide the latest updates on PyAutoCTI.

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