roman-corgi/corgidrp
Aug 2025 – Jun 2026
4 Months active
Languages Used
FITSJavaScriptNumPyPytestPythonHTMLShellfits
Technical Skills
Aperture PhotometryAstronomyAstronomy SoftwareAstrophysicsBug FixingCalibration
Eric Shen
PROFILE
Eric Shen
Developed and enhanced the roman-corgi/corgidrp data processing pipeline, focusing on scientific reliability and maintainability for astronomical calibration and polarization workflows. Over four months, delivered 41 features and resolved 22 bugs, implementing robust error propagation, warning suppression, and end-to-end test suites to ensure data integrity and reproducibility. Leveraged Python, NumPy, and Astropy to build modular calibration routines, automate image processing, and streamline FITS file handling. Expanded test coverage with Pytest and mock data, improved logging for diagnostics, and refactored code for clarity. These efforts accelerated production readiness, reduced debugging time, and strengthened the accuracy of scientific data products.
Overall Statistics
Feature vs Bugs
65%Features
Repository Contributions
115Total
Bugs
22
Commits
115
Features
41
Lines of code
587,214
Activity Months4
Your Network
36 peopleShared Repositories
36
maillardMember
Alexis LauMember
Aneesh BaburajMember
bensutlieffMember
clarissardooMember
Ell BogatMember
Ezar ShinbaroMember
Jason WangMember
juliamiltonMember
Work History
June 2026
3 Commits • 1 Features
Jun 1, 2026June 2026: Delivered critical test coverage and tooling improvements for the calibration workflow in roman-corgi/corgidrp. Implemented an End-to-End Throughput Calibration Test Suite for the L1 to CoreThroughput pipeline with mock-data handling, calibration product generation, and header validation. Fixed a typo in the End-to-End Test Argument Parser Description from 'l21' to 'l1' to reflect the test purpose. These changes increase reliability, reduce debugging time, and improve confidence in calibration results.
3 Commits • 1 Features
Jun 1, 2026June 2026: Delivered critical test coverage and tooling improvements for the calibration workflow in roman-corgi/corgidrp. Implemented an End-to-End Throughput Calibration Test Suite for the L1 to CoreThroughput pipeline with mock-data handling, calibration product generation, and header validation. Fixed a typo in the End-to-End Test Argument Parser Description from 'l21' to 'l1' to reflect the test purpose. These changes increase reliability, reduce debugging time, and improve confidence in calibration results.
June 2026
October 2025
12 Commits • 5 Features
Oct 1, 2025Monthly performance summary for 2025-10 focused on delivering robust polarization data processing in roman-corgi/corgidrp. Key features delivered improved reliability and scientific value by strengthening error handling, data integrity, and the accuracy of Stokes datacube reconstruction. The work also included targeted testing enhancements and lint-cleanup to improve maintainability and CI robustness.
October 2025
12 Commits • 5 Features
Oct 1, 2025Monthly performance summary for 2025-10 focused on delivering robust polarization data processing in roman-corgi/corgidrp. Key features delivered improved reliability and scientific value by strengthening error handling, data integrity, and the accuracy of Stokes datacube reconstruction. The work also included targeted testing enhancements and lint-cleanup to improve maintainability and CI robustness.
September 2025
53 Commits • 17 Features
Sep 1, 2025September 2025 (roman-corgi/corgidrp) delivered foundational feature enhancements, reliability improvements, and expanded testing to strengthen product quality and maintainability. Core feature work includes an Auto size selector, updated cropping scenarios with new image save behavior, and added separation/angle kwargs to enable flexible processing; these changes enable faster preprocessing, more accurate outputs, and easier customization for customers. The data processing pipeline gained DPAM flux calibration support with a usage guard and optional header matching to improve accuracy and safety. Testing and observability were significantly enhanced through mock-data-driven E2E testing, expanded unit-test infrastructure, Mueller matrix compatibility alignment, and a new runtime logger. These efforts collectively reduce release risk, improve test stability, and accelerate delivery of high-value features to customers.
53 Commits • 17 Features
Sep 1, 2025September 2025 (roman-corgi/corgidrp) delivered foundational feature enhancements, reliability improvements, and expanded testing to strengthen product quality and maintainability. Core feature work includes an Auto size selector, updated cropping scenarios with new image save behavior, and added separation/angle kwargs to enable flexible processing; these changes enable faster preprocessing, more accurate outputs, and easier customization for customers. The data processing pipeline gained DPAM flux calibration support with a usage guard and optional header matching to improve accuracy and safety. Testing and observability were significantly enhanced through mock-data-driven E2E testing, expanded unit-test infrastructure, Mueller matrix compatibility alignment, and a new runtime logger. These efforts collectively reduce release risk, improve test stability, and accelerate delivery of high-value features to customers.
September 2025
August 2025
47 Commits • 18 Features
Aug 1, 2025In August 2025, roman-corgi/corgidrp delivered a set of stability, reliability, and test-coverage improvements across the data-processing and calibration pipelines. The month focused on suppressing noisy warnings, repairing data integrity, and accelerating pipeline readiness for production use, with a strong emphasis on test-driven validation and code quality.
August 2025
47 Commits • 18 Features
Aug 1, 2025In August 2025, roman-corgi/corgidrp delivered a set of stability, reliability, and test-coverage improvements across the data-processing and calibration pipelines. The month focused on suppressing noisy warnings, repairing data integrity, and accelerating pipeline readiness for production use, with a strong emphasis on test-driven validation and code quality.
Activity
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Quality Metrics
Correctness86.6%
Maintainability84.4%
Architecture78.6%
Performance75.4%
AI Usage21.8%
Skills & Technologies
Programming Languages
FITSHTMLJavaScriptNumPyNumpyPytestPythonShellfits
Technical Skills
Aperture PhotometryAstronomyAstronomy Data ReductionAstronomy SoftwareAstronomy Software DevelopmentAstrophysicsAstropyBackend DevelopmentBug FixingCalibrationCode CleanupCode DocumentationCode RefactoringConfigurationConfiguration Management
Repositories Contributed To
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