peyes 0.0.8

A package for eye tracking data analysis and visualization

pEYES

A Python Package for Eye-Tracking Researchers

This package is intended for scientific use when processing and analyzing eye-tracking data.
It provides easy-to-use functions for:

• Downloading & parsing publicly available annotated eye-tracking datasets.
• Configuring eye-tracking detection algorithms, and running them on the datasets or your own data.
• Analyzing the results of the detection algorithms, and comparing them to ground truth data or to a different detection algorithm.
• Visualizing the results of the detection algorithms.

Installation Instructions

This package has been created and tested with python 3.12.

To install this package as a user, use

pip install peyes

To install this package as a developer, clone the package from GitHub and run the following commands:

python -m venv env
git pull
pip install -e .
git checkout -b dev

Usage

This package is intended for scientific use, and is designed to be easy to use for anyone with basic python knowledge.
Most of the functions in this package are documented, and can be accessed by running:

import peyes
help(peyes)

For more detailed information, please refer to the user tutorials provided in the docs directory of this repository.

Citation & License

This package is distributed under the MIT License, but some of the datasets & detection algorithms that are implemented in this package are distributed under different licenses. Please refer to the documentation of the specific dataset or detection algorithm for more information.

If you use this package in your research, please cite it as follows:

# TBD

If you use a specific dataset or detection algorithm that is implemented in this package, please also cite the original authors of that dataset or detection algorithm. This information, like the license, can be found in the documentation of the specific dataset or detection algorithm.

Acknowledgements

We are grateful for the support of the Center for Interdisciplinary Data Science Research (CIDR) at the Hebrew University of Jerusalem. In particular, we would like to thank Haimasree Bhattacharya from CIDR for her contributions.