cositools/cosipy
Jan 2025 – Mar 2025
3 Months active
Languages Used
JSONPythonOri
Technical Skills
Bug FixingData HandlingDocumentationTechnical WritingClass ModificationData Engineering
Christopher M. Karwin
PROFILE
Christopher M. Karwin
Over three months, Chris Karwin enhanced the cositools/cosipy repository by building and refining data processing and visualization features for scientific workflows. He implemented energy-based filtering for unbinned data and introduced South Atlantic Anomaly event filtering, enabling more accurate and targeted analyses. His work included integrating livetime data into processing pipelines, improving documentation and tutorials, and expanding unit test coverage to ensure reliability. Using Python and leveraging data engineering and scientific computing skills, Chris also refactored plotting logic for PsiChi maps to support robust coordinate handling and external data inputs, resulting in more maintainable, reproducible, and user-friendly analytics pipelines.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
3
Lines of code
290
Activity Months3
Your Network
25 peopleSame Organization
@discover11.prv.cube1
Work History
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for cosipy (cositools/cosipy). Key outcomes focused on delivering business-value features and robustness for data processing and visualization. Key features delivered: - Energy-based data filtering for UnBinnedData: added select_data_energy to dataIO to filter unbinned data by energy range (emin, emax); supports reading input data from a file and saving filtered results to a new file, enabling targeted energy-domain analyses in downstream pipelines. - PsiChi map plotting robustness and external data support: improved plotting for PsiChi maps with local coordinates; refactored plotting logic to correctly handle coordinate systems and ensure Galactic coordinates ('G') are used when appropriate; added capability for plot_psichi_map to ingest external binned data for visualization and comparison. Major bugs fixed: - Fixed plotting for PsiChi map with local coordinates; improved coordinate-system handling and ensured correct use of Galactic coordinates where applicable; enhanced compatibility with external binned data inputs. Overall impact and accomplishments: - Enabled precise, repeatable energy-filtered data workflows and more reliable, coordinate-system-robust visualizations. These changes reduce manual data wrangling, improve the accuracy of analytics, and enhance interoperability with external datasets, supporting faster, more confident decision-making and pipeline automation. Technologies/skills demonstrated: - Python data I/O and data processing (dataIO energy selection), plotting and coordinate system handling, plotting robustness, support for external data inputs, and refactoring for maintainability.
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for cosipy (cositools/cosipy). Key outcomes focused on delivering business-value features and robustness for data processing and visualization. Key features delivered: - Energy-based data filtering for UnBinnedData: added select_data_energy to dataIO to filter unbinned data by energy range (emin, emax); supports reading input data from a file and saving filtered results to a new file, enabling targeted energy-domain analyses in downstream pipelines. - PsiChi map plotting robustness and external data support: improved plotting for PsiChi maps with local coordinates; refactored plotting logic to correctly handle coordinate systems and ensure Galactic coordinates ('G') are used when appropriate; added capability for plot_psichi_map to ingest external binned data for visualization and comparison. Major bugs fixed: - Fixed plotting for PsiChi map with local coordinates; improved coordinate-system handling and ensured correct use of Galactic coordinates where applicable; enhanced compatibility with external binned data inputs. Overall impact and accomplishments: - Enabled precise, repeatable energy-filtered data workflows and more reliable, coordinate-system-robust visualizations. These changes reduce manual data wrangling, improve the accuracy of analytics, and enhance interoperability with external datasets, supporting faster, more confident decision-making and pipeline automation. Technologies/skills demonstrated: - Python data I/O and data processing (dataIO energy selection), plotting and coordinate system handling, plotting robustness, support for external data inputs, and refactoring for maintainability.
March 2025
February 2025
6 Commits • 1 Features
Feb 1, 2025February 2025: Delivered SAA Event Filtering and Livetime Integration for cosipy. Implemented filtering of zero livetime intervals from ori files, propagated livetime data into SpacecraftFile, and updated weighting to enable accurate time-based data analysis. This work, together with expanded test coverage and tutorials, improves data reliability, reproducibility, and scientific value for mission analyses.
February 2025
6 Commits • 1 Features
Feb 1, 2025February 2025: Delivered SAA Event Filtering and Livetime Integration for cosipy. Implemented filtering of zero livetime intervals from ori files, propagated livetime data into SpacecraftFile, and updated weighting to enable accurate time-based data analysis. This work, together with expanded test coverage and tutorials, improves data reliability, reproducibility, and scientific value for mission analyses.
January 2025
2 Commits • 1 Features
Jan 1, 2025Monthly summary for 2025-01 (cositools/cosipy): Key features delivered: - Improved background estimation notebook documentation: clarified the on-off analysis method and updated kernel and language details in the example notebook to improve clarity and reproducibility. Major bugs fixed: - Fixed FullDetectorResponse label handling: removed an unnecessary label from the labels tuple, correcting the output representation in response handling. Overall impact and accomplishments: - Enhances data integrity and user onboarding; reduces downstream errors and support time, strengthening cosipy’s reliability for analytics pipelines. Technologies/skills demonstrated: - Python data handling, Jupyter notebook documentation, clear commit-driven development, and attention to data structure correctness.
2 Commits • 1 Features
Jan 1, 2025Monthly summary for 2025-01 (cositools/cosipy): Key features delivered: - Improved background estimation notebook documentation: clarified the on-off analysis method and updated kernel and language details in the example notebook to improve clarity and reproducibility. Major bugs fixed: - Fixed FullDetectorResponse label handling: removed an unnecessary label from the labels tuple, correcting the output representation in response handling. Overall impact and accomplishments: - Enhances data integrity and user onboarding; reduces downstream errors and support time, strengthening cosipy’s reliability for analytics pipelines. Technologies/skills demonstrated: - Python data handling, Jupyter notebook documentation, clear commit-driven development, and attention to data structure correctness.
January 2025
Activity
Loading activity data...
Quality Metrics
Correctness88.2%
Maintainability89.0%
Architecture81.8%
Performance81.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONOriPython
Technical Skills
Backend DevelopmentBug FixingClass ModificationData EngineeringData FilteringData HandlingData IOData ProcessingData ValidationData VisualizationDebuggingDocumentationFile ParsingPythonScientific Computing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline