pymovements

[dkrako]

Submitting Author: (@dkrako)
All current maintainers: (@dkrako, @cbueth, @SiQube)
Package Name: pymovements
One-Line Description of Package: A Python package for processing eye tracking data
Repository Link: https://github.com/pymovements/pymovements
Version submitted: v0.28.0
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

pymovements is an open-source Python package for downloading, preprocessing, and analyzing eye movement data. It covers the whole processing pipeline: downloading publicly available eye tracking datasets from a built-in dataset catalog, parsing raw eye tracker output files, transforming gaze coordinates (for example pixel coordinates to degrees of visual angle, positions to velocities), detecting oculomotor events such as fixations, saccades, and blinks with established algorithms, computing event properties like amplitude and dispersion, and visualizing gaze data as heatmaps, scanpaths, and time series plots. All data is held in polars DataFrames for fast, memory-efficient processing of large recordings.

Associated Publication

Publication Title: pymovements: A Python Package for Processing Eye Movement Data
Publication DOI: 10.1145/3588015.3590134
Journal/Venue: ETRA '23: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications

Publication Title: The More the Merrier: Boost Your Dataset Visibility and Discover Eye-Tracking Datasets with pymovements
Publication DOI: 10.1145/3715669.3726810
Journal/Venue: ETRA '25: Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

Scope

• Data retrieval
• Data extraction
• Data processing/munging
• Data deposition
• Data validation and testing
• Data visualization¹
• Workflow automation
• Citation management and bibliometrics
• Scientific software wrappers
• Database interoperability

This package falls under data retrieval, extraction, processing, and visualization because it covers the full eye tracking analysis pipeline: retrieving public datasets from a curated dataset library, extracting gaze samples and metadata from raw eye tracker output files, transforming and processing gaze signals into oculomotor events and their properties, and visualizing the results. The package additionally supports data validation and testing through built-in gaze data quality measures and data quality reporting.

• Who is the target audience and what are scientific applications of this package?

The target audience is researchers working with eye tracking data in psychology, cognitive science, psycholinguistics, neuroscience, and machine learning. Scientific applications include reading research, scanpath and fixation analysis, data quality assessment, and preparing gaze datasets for machine learning.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

Existing packages cover parts of the pipeline: eyekit specializes in reading experiments, and MNE-Python's eye tracking support is only rudimentary as it focuses on electrophysiology. pymovements differs by providing the complete pipeline from dataset download to event-level analysis in a single consistent API, together with a curated catalog of public datasets.

Domain Specific

• Geospatial
• Education

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Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩

[cbueth]

I confirm to be co-maintainer of pymovements and co-author of the submitted manuscript.

[SiQube]

I confirm to be co-maintainer of pymovements and co-author of the submitted manuscript.