aeye-lab/pymovements
Mar 2025 – Feb 2026
4 Months active
Languages Used
BibTeXCSVPythonYAML
Technical Skills
Configuration ManagementData EngineeringDataset ManagementPython DevelopmentJupyter NotebooksPython programming
Paul Prasse
PROFILE
Paul Prasse
Paul Prasse contributed to the aeye-lab/pymovements repository by developing and refining features for eye-tracking data workflows. He expanded dataset coverage with new definitions for Potsdam Binge PVT, enhanced scanpath visualizations by adding directional arrows, and improved data consistency through updates to dataset schemas. Paul also authored a Jupyter-based tutorial for parsing SR Research EyeLink data, streamlining onboarding and reproducibility. His work emphasized maintainable Python code, robust configuration management, and clear API design, including a targeted refactor of plotting arguments. Across these efforts, Paul demonstrated depth in data engineering, visualization with matplotlib, and collaborative documentation-driven development practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
5
Lines of code
1,391
Activity Months4
Your Network
11 peopleShared Repositories
11
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Focused API refinement in pymovements with a Scanpathplot argument refactor to improve clarity, consistency, and maintainability. No major bug fixes were merged this month. This work reduces onboarding time for users and lowers future maintenance costs.
1 Commits • 1 Features
Feb 1, 2026February 2026: Focused API refinement in pymovements with a Scanpathplot argument refactor to improve clarity, consistency, and maintainability. No major bug fixes were merged this month. This work reduces onboarding time for users and lowers future maintenance costs.
February 2026
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01): Delivered two user-facing features in pymovements, improving data quality and visualization fidelity, with enhancements to dataset definitions and scanpath visualization. These efforts strengthen data integrity for Potsdam binge datasets and provide clearer gaze-sequence representations in plots, enabling more accurate behavioral analysis and repeatable research workflows. Team collaboration and quality practices were reinforced through documentation updates and pre-commit hygiene.
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01): Delivered two user-facing features in pymovements, improving data quality and visualization fidelity, with enhancements to dataset definitions and scanpath visualization. These efforts strengthen data integrity for Potsdam binge datasets and provide clearer gaze-sequence representations in plots, enabling more accurate behavioral analysis and repeatable research workflows. Team collaboration and quality practices were reinforced through documentation updates and pre-commit hygiene.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary: Delivered the EyeLink Data Parsing Tutorial for pymovements, introducing a step-by-step workflow for parsing SR Research EyeLink data. The tutorial covers handling raw eye-tracking files, extracting experiment metadata, and creating custom dataset definitions. This documentation-driven release improves onboarding, reproducibility, and accelerates the setup of end-to-end EyeLink data pipelines. No major bugs fixed this month.
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary: Delivered the EyeLink Data Parsing Tutorial for pymovements, introducing a step-by-step workflow for parsing SR Research EyeLink data. The tutorial covers handling raw eye-tracking files, extracting experiment metadata, and creating custom dataset definitions. This documentation-driven release improves onboarding, reproducibility, and accelerates the setup of end-to-end EyeLink data pipelines. No major bugs fixed this month.
November 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03 | Repo: aeye-lab/pymovements. Focused on expanding dataset coverage and strengthening test infrastructure. Delivered Potsdam Binge PVT dataset definitions for remote eye-tracking and wearable eye-tracking, including configuration files, Python definitions, and an example CSV for testing. Updated the dataset library and functional tests to recognize the new additions. No major bugs fixed this month; primary value came from feature delivery and validation tooling. Impact: Enables broader research and product use cases requiring Potsdam Binge PVT data, improves testing coverage, and accelerates onboarding for new datasets. Technologies/skills demonstrated: Python, configuration management, dataset library integration, test-driven development, and repository collaboration.
March 2025
1 Commits • 1 Features
Mar 1, 2025Month: 2025-03 | Repo: aeye-lab/pymovements. Focused on expanding dataset coverage and strengthening test infrastructure. Delivered Potsdam Binge PVT dataset definitions for remote eye-tracking and wearable eye-tracking, including configuration files, Python definitions, and an example CSV for testing. Updated the dataset library and functional tests to recognize the new additions. No major bugs fixed this month; primary value came from feature delivery and validation tooling. Impact: Enables broader research and product use cases requiring Potsdam Binge PVT data, improves testing coverage, and accelerates onboarding for new datasets. Technologies/skills demonstrated: Python, configuration management, dataset library integration, test-driven development, and repository collaboration.
Activity
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Quality Metrics
Correctness100.0%
Maintainability96.0%
Architecture96.0%
Performance96.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
BibTeXCSVPythonYAML
Technical Skills
Configuration ManagementData EngineeringDataset ManagementJupyter NotebooksPythonPython DevelopmentPython programmingdata analysisdata managementdata parsingdata visualizationdataset definitionmatplotlibunit testing
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