pyneon 0.0.1

A Python library for reading, preprocessing and exporting Pupil Labs Neon data

PyNeon

PyNeon is a lightweight Python package designed to streamline the processing and analysis of multimodal data from the Neon eye-tracking system (Pupil Labs GmbH). This community-driven effort provides a versatile set of tools to work with Neon's rich data, including gaze, eye states, IMU, video, events, and more.

PyNeon supports both native (data stored in the companion device) and Pupil Cloud data formats. We want to acknowledge the pupil-labs/pl-neon-recording project, which inspired the design of PyNeon.

Documentation for PyNeon is available at https://ncc-brain.github.io/PyNeon/ which includes detailed references for classes and functions, as well as step-by-step tutorials presented as Jupyter notebooks.

We also created a few sample datasets containing short Neon recordings for testing and tutorial purposes. These datasets can be found on OSF. We also provide a utility function get_sample_data() to download these sample datasets directly from PyNeon.

Key Features

• Easy API for reading in datasets, recordings, or individual modalities of data.
  • Tutorial for reading data in Pupil Cloud format
  • Tutorial for reading data in native format
• (Tutorial) Various preprocessing functions, including data cropping, interpolation, concatenation, etc.
• (Tutorial) Flexible epoching of data for trial-based analysis.
• (Tutorial) Methods for working with scene video, including scanpath estimation and AprilTags-based mapping.
• (Tutorial) Exportation to Motion-BIDS and Eye-Tracking-BIDS formats for interoperability across the cognitive neuroscience community.

Installation

To install the development version of PyNeon:

pip install git+https://github.com/ncc-brain/PyNeon.git

A PyPI release is planned for the future.

Citing PyNeon

If you use PyNeon in your research, please cite the accompanying paper as follows:

@misc{pyneon,
    title={PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data},
    url={osf.io/preprints/psyarxiv/y5jmg_v2},
    DOI={10.31234/osf.io/y5jmg_v2},
    publisher={PsyArXiv},
    author={Chu, Qian and Hartel, Jan-Gabriel and Lepauvre, Alex and Melloni, Lucia},
    year={2025},
    month={August}
}