roman-corgi/corgidrp
Oct 2025 – Feb 2026
3 Months active
Languages Used
PythonreStructuredText
Technical Skills
Data ProcessingImage CorrectionImage RotationPytestScientific ComputingTesting
clarissardoo
PROFILE
Clarissardoo
Clarissa Rizzo contributed to the roman-corgi/corgidrp repository by developing and enhancing polarization data support in image processing workflows, focusing on robust handling of 3D data shapes and WCS updates. She implemented dual-pass distortion correction for polarization frames and expanded end-to-end test coverage using Pytest and Python, ensuring reliable validation and maintainability. Clarissa improved bad pixel correction testing by introducing mock polarization data and cleaning test outputs, which increased test reliability and CI stability. She also addressed image header error handling for L4/L3 datasets and updated documentation in reStructuredText, strengthening error reporting, processing history, and overall code hygiene.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
4
Lines of code
994
Activity Months3
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36 peopleShared Repositories
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Ezar ShinbaroMember
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Work History
February 2026
3 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for roman-corgi/corgidrp: Delivered targeted bug fixes for L4/L3 image header handling, updated documentation for error reporting and processing history, and strengthened test coverage and code hygiene. These changes enhance data processing reliability for non-coron and polar L4 datasets and improve maintainability and troubleshooting.
3 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for roman-corgi/corgidrp: Delivered targeted bug fixes for L4/L3 image header handling, updated documentation for error reporting and processing history, and strengthened test coverage and code hygiene. These changes enhance data processing reliability for non-coron and polar L4 datasets and improve maintainability and troubleshooting.
February 2026
November 2025
3 Commits • 1 Features
Nov 1, 2025November 2025 performance summary for roman-corgi/corgidrp: focused on strengthening test coverage for bad pixel correction and improving test stability in CI. Implemented Bad Pixel Correction Testing Improvements by using mock polarization data across multiple frames to reflect production scenarios, increasing validation reliability and reducing regression risk. Cleaned test output by removing noise (no print statements) and added comments for maintainability. These changes reduce cycle time for bug fixes and PR reviews while aligning validation with real-world data.
November 2025
3 Commits • 1 Features
Nov 1, 2025November 2025 performance summary for roman-corgi/corgidrp: focused on strengthening test coverage for bad pixel correction and improving test stability in CI. Implemented Bad Pixel Correction Testing Improvements by using mock polarization data across multiple frames to reflect production scenarios, increasing validation reliability and reducing regression risk. Cleaned test output by removing noise (no print statements) and added comments for maintainability. These changes reduce cycle time for bug fixes and PR reviews while aligning validation with real-world data.
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 performance summary for roman-corgi/corgidrp: Delivered polarization data support across Northup processing and distortion correction with end-to-end test coverage and robust data shape handling. Implemented pol_data flag validation, ensured correct rotation and WCS updates across all polarization planes, and extended distortion correction to process polarization data by iterating twice for both frames. These changes enable researchers to analyze polarized data more efficiently and with higher confidence, while preserving compatibility with existing workflows.
3 Commits • 2 Features
Oct 1, 2025October 2025 performance summary for roman-corgi/corgidrp: Delivered polarization data support across Northup processing and distortion correction with end-to-end test coverage and robust data shape handling. Implemented pol_data flag validation, ensured correct rotation and WCS updates across all polarization planes, and extended distortion correction to process polarization data by iterating twice for both frames. These changes enable researchers to analyze polarized data more efficiently and with higher confidence, while preserving compatibility with existing workflows.
October 2025
Activity
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Quality Metrics
Correctness92.2%
Maintainability88.8%
Architecture88.8%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonreStructuredText
Technical Skills
Data ProcessingImage CorrectionImage RotationPytestPythonScientific ComputingTestingUnit TestingWCS Handlingdata processingdata validationdebuggingdocumentationerror handlingimage processing
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