# pymovements: A Python Package for Eye Movement Data Processing

arXiv: https://arxiv.org/abs/2304.09859 · HTML: https://mlsanity.com/paper/2304.09859/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2023.

## Built on by (most cited first, 5 of 5)

- [PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus](https://mlsanity.com/paper/2403.00506/) (2024)
- [Reporting Eye-Tracking Data Quality: Towards a New Standard](https://mlsanity.com/paper/2404.00620/) (2024)
- [Controlling Reading Ease with Gaze-Guided Text Generation](https://mlsanity.com/paper/2601.17781/) (2026)
- [Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models](https://mlsanity.com/paper/2304.13536/) (2023)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)

## Abstract (arXiv)

We introduce pymovements: a Python package for analyzing eye-tracking data that follows best practices in software development, including rigorous testing and adherence to coding standards. The package provides functionality for key processes along the entire preprocessing pipeline. This includes parsing of eye tracker data files, transforming positional data into velocity data, detecting gaze events like saccades and fixations, computing event properties like saccade amplitude and fixational dispersion and visualizing data and results with several types of plotting methods. Moreover, pymovements also provides an easily accessible interface for downloading and processing publicly available datasets. Additionally, we emphasize how rigorous testing in scientific software packages is critical to the reproducibility and transparency of research, enabling other researchers to verify and build upon previous findings.