aeye-lab/pymovements
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data GenerationData VisualizationDocumentationJupyter NotebookNumPyPolars
Sasho
PROFILE
Sasho
Saso Grm developed a comprehensive tutorial feature for the aeye-lab/pymovements repository, focusing on onboarding and validation for synthetic gaze data workflows. Using Python and Jupyter Notebook, Saso guided users through importing libraries, defining experiment metadata, generating synthetic gaze positions with NumPy, and organizing results in a Polars DataFrame. The tutorial culminated in initializing a Gaze object and visualizing gaze traces, providing an end-to-end example for reproducibility and testing. This work enhanced the documentation by offering a practical, hands-on guide, addressing common user challenges and improving the accessibility of synthetic data generation within the pymovements ecosystem.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
185
Activity Months1
Work History
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for aeye-lab/pymovements: Delivered a new tutorials feature to improve onboarding and validation by demonstrating how to generate synthetic gaze data with pymovements. The docs tutorial walks users through end-to-end workflow: importing libraries, defining experiment metadata, generating synthetic gaze positions with a step function, creating a Polars DataFrame, initializing a Gaze object, and visualizing the gaze trace. This enhances reproducibility, testing, and user adoption.
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for aeye-lab/pymovements: Delivered a new tutorials feature to improve onboarding and validation by demonstrating how to generate synthetic gaze data with pymovements. The docs tutorial walks users through end-to-end workflow: importing libraries, defining experiment metadata, generating synthetic gaze positions with a step function, creating a Polars DataFrame, initializing a Gaze object, and visualizing the gaze trace. This enhances reproducibility, testing, and user adoption.
August 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data GenerationData VisualizationDocumentationJupyter NotebookNumPyPolarsPython
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