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 new tutorials feature for the aeye-lab/pymovements repository, focusing on onboarding and validation for synthetic gaze data workflows. The work involved creating a comprehensive Jupyter Notebook tutorial that guides users through importing Python libraries, defining experiment metadata, generating synthetic gaze positions using NumPy and a step function, and organizing the data with Polars. The tutorial then demonstrates initializing a Gaze object and visualizing the resulting gaze trace. This addition improved documentation quality and reproducibility for synthetic data scenarios, providing users with a clear, end-to-end example that supports testing and encourages broader adoption of the pymovements library.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
185
Activity Months1
Your Network
11 peopleShared Repositories
11
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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