roman-corgi/corgidrp
Sep 2025 – Sep 2025
1 Month active
Languages Used
FITSPython
Technical Skills
Astronomy SoftwareCalibrationCoordinate SystemsData AnalysisData CalibrationData Processing
Neil Zimmerman
PROFILE
Neil Zimmerman
Worked on the roman-corgi/corgidrp repository to enhance zeropoint and wavelength calibration for astronomical data processing. Developed features that corrected zeropoint coordinates by accounting for filter offsets and introduced cross-filter offset compensation, improving the accuracy of wavelength location. Added user override options for zeropoint offsets, allowing researchers to manually refine wavelength zero-point determination. Maintained robust documentation and managed test data to ensure reliability and clarity in calibration workflows. Utilized Python and FITS formats, applying skills in scientific computing, data calibration, and testing. The work resulted in more precise spectral analyses and a cleaner, more maintainable codebase for ongoing research.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
400
Activity Months1
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Work History
September 2025
5 Commits • 2 Features
Sep 1, 2025In September 2025, the team concentrated on enhancing zeropoint calibration for the roman-corgi/corgidrp repository, introducing user overrides for more precise wavelength zero-point determination, and maintaining robust docs/test data. The changes improved calibration accuracy, added flexible controls for researchers, and strengthened test reliability and documentation. This work translates to higher confidence in spectral analyses, more efficient troubleshooting, and a cleaner, more maintainable codebase.
5 Commits • 2 Features
Sep 1, 2025In September 2025, the team concentrated on enhancing zeropoint calibration for the roman-corgi/corgidrp repository, introducing user overrides for more precise wavelength zero-point determination, and maintaining robust docs/test data. The changes improved calibration accuracy, added flexible controls for researchers, and strengthened test reliability and documentation. This work translates to higher confidence in spectral analyses, more efficient troubleshooting, and a cleaner, more maintainable codebase.
September 2025
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture76.0%
Performance68.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
FITSPython
Technical Skills
Astronomy SoftwareCalibrationCoordinate SystemsData AnalysisData CalibrationData ProcessingDocumentationScientific ComputingTesting
Repositories Contributed To
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