igmhub/picca
Nov 2024 – Sep 2025
3 Months active
Languages Used
PythonTOMLText
Technical Skills
Astronomy Data ProcessingData EngineeringFile I/OAstrophysics SoftwareCommand-line Interface DevelopmentData Analysis
HiramHerrera
PROFILE
Hiramherrera
Over a three-month period, contributed to the igmhub/picca repository by developing features that enhanced data ingestion, analysis, and reproducibility in astrophysics workflows. Built a flexible data ingestion system using Python that accepts multiple column naming conventions, improving compatibility and data integrity. Implemented redshift range filtering through new command-line flags, enabling precise selection of forest pixels for correlation functions and distortion matrices. Added CLI tools for covariance data generation and stabilized build configuration by refining pyproject.toml and managing dependencies with TOML and SciPy. Demonstrated strengths in data engineering, scripting, and build system configuration, resulting in more robust and maintainable software.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
141
Activity Months3
Your Network
10 peopleShared Repositories
10
Work History
September 2025
3 Commits • 1 Features
Sep 1, 20252025-09 monthly summary for igmhub/picca: Delivered covariance tooling CLI capabilities and stabilised build/config to enable reliable CLI-based covariance data generation, improving reproducibility and reducing manual steps in analytics workflows. Demonstrated strong packaging, Python scripting, and linting discipline.
3 Commits • 1 Features
Sep 1, 20252025-09 monthly summary for igmhub/picca: Delivered covariance tooling CLI capabilities and stabilised build/config to enable reliable CLI-based covariance data generation, improving reproducibility and reducing manual steps in analytics workflows. Demonstrated strong packaging, Python scripting, and linting discipline.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 — igmhub/picca: Implemented redshift range filtering with new CLI flags and integrated calculation paths, enabling precise, reproducible analysis across correlation functions and distortion matrices. This feature enables users to specify min/max redshift for forest pixels via --z-min-pixels and --z-max-pixels across picca_cf, picca_dmat, picca_xcf, and picca_xdmat. Commit 316c7d99a892b1a539447e7f5edf23e941ec8fdb.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 — igmhub/picca: Implemented redshift range filtering with new CLI flags and integrated calculation paths, enabling precise, reproducible analysis across correlation functions and distortion matrices. This feature enables users to specify min/max redshift for forest pixels via --z-min-pixels and --z-max-pixels across picca_cf, picca_dmat, picca_xcf, and picca_xdmat. Commit 316c7d99a892b1a539447e7f5edf23e941ec8fdb.
November 2024
1 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11: igmhub/picca delivered a Flexible Data Ingestion feature enabling catalog and transmission data to accept either RA/DEC or TARGET_RA/TARGET_DEC column name pairs. The implementation includes conditional logic to support both naming conventions and raises a ValueError when neither pair is found, improving data ingestion flexibility and data integrity. This work reduces onboarding friction for new data sources and lowers downstream data quality issues.
1 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11: igmhub/picca delivered a Flexible Data Ingestion feature enabling catalog and transmission data to accept either RA/DEC or TARGET_RA/TARGET_DEC column name pairs. The implementation includes conditional logic to support both naming conventions and raises a ValueError when neither pair is found, improving data ingestion flexibility and data integrity. This work reduces onboarding friction for new data sources and lowers downstream data quality issues.
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture88.0%
Performance88.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonTOMLText
Technical Skills
Astronomy Data ProcessingAstrophysics SoftwareBuild ConfigurationBuild System ConfigurationCommand-line Interface DevelopmentData AnalysisData EngineeringDependency ManagementFile I/OScripting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline