neuroinformatics-unit/movement
Feb 2026 – Feb 2026
1 Month active
Languages Used
MarkdownPython
Technical Skills
PythonPython programmingdata analysisdata verificationdocumentationunit testing
Eduardo Augusto Almeida lira
PROFILE
Eduardo Augusto Almeida Lira
Eduardo developed two core features for the neuroinformatics-unit/movement repository, focusing on data verification and pose-based analytics. He implemented a Python function to convert pose keypoints into 2D bounding boxes, handling centroid and shape calculations, padding, and NaN values, and backed this with comprehensive unit tests. Eduardo also enhanced contributor onboarding by updating documentation, relocating dataset verification examples, and automating quality checks with pre-commit CI. His work emphasized robust code organization and data integrity, leveraging Python, xarray, and Markdown. These contributions improved the reliability of downstream analysis and streamlined the contributor workflow, reflecting thoughtful engineering and attention to maintainability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
405
Activity Months1
Your Network
19 peopleWork History
February 2026
2 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary: Delivered two high-impact features in neuroinformatics-unit/movement that enhance data verification and pose-based analytics, improved contributor docs and onboarding, and strengthened test coverage. These changes improve data integrity, reduce contributor friction, and provide robust primitives for downstream analysis such as 2D bounding boxes from pose keypoints. Tech stack and practices demonstrated include Python, testing, code organization, and documentation updates, with strong emphasis on automated quality checks via pre-commit CI.
2 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary: Delivered two high-impact features in neuroinformatics-unit/movement that enhance data verification and pose-based analytics, improved contributor docs and onboarding, and strengthened test coverage. These changes improve data integrity, reduce contributor friction, and provide robust primitives for downstream analysis such as 2D bounding boxes from pose keypoints. Tech stack and practices demonstrated include Python, testing, code organization, and documentation updates, with strong emphasis on automated quality checks via pre-commit CI.
February 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
PythonPython programmingdata analysisdata verificationdocumentationunit testingxarray
Repositories Contributed To
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