cositools/cosipy
Mar 2026 – Apr 2026
2 Months active
Languages Used
Python
Technical Skills
Pythondata analysisstatistical modelingCI/CDDocumentation GenerationMachine Learning
Pascal Janowski
PROFILE
Pascal Janowski
Worked on the cositools/cosipy repository over a two-month period, delivering targeted enhancements to both performance and development workflows. Improved the UnbinnedLikelihood component by introducing cached density value usage and optional batch sizing, reducing redundant computations and increasing flexibility for large-scale data analysis in Python and numpy. Subsequently, established automated CI/CD pipelines using GitHub Actions, integrating documentation generation and unit testing to ensure cross-environment reliability, particularly for macOS and machine learning dependencies. These efforts streamlined release processes, improved maintainability, and enhanced quality control, demonstrating a focus on robust statistical modeling, automation, and sustainable open-source development practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
142,030
Activity Months2
Your Network
39 peopleSame Organization
@etik.com4
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35
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Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 — Monthly summary for cositools/cosipy highlighting CI/CD automation, documentation/testing workflows, and cross-environment reliability; established automated pipelines to improve quality, release velocity, and maintainability across macOS and ML dependencies.
1 Commits • 1 Features
Apr 1, 2026April 2026 — Monthly summary for cositools/cosipy highlighting CI/CD automation, documentation/testing workflows, and cross-environment reliability; established automated pipelines to improve quality, release velocity, and maintainability across macOS and ML dependencies.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — cositools/cosipy (UnbinnedLikelihood) delivered targeted performance improvements and enhanced API flexibility. Key features delivered: UnbinnedLikelihood now uses cached density values, reducing recomputation when density data is cached. The batch_size parameter is now optional and density handling uses asarray when cached, increasing robustness and compatibility with varied data pipelines. Major bugs fixed: None reported; no regressions observed during caching path integration. Overall impact and accomplishments: Improved runtime efficiency for large datasets, lower compute usage, and smoother integration into caching-enabled workflows. Technologies/skills demonstrated: Python, numpy (asarray usage), caching strategies, performance optimization, clean API design, and maintainability.
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — cositools/cosipy (UnbinnedLikelihood) delivered targeted performance improvements and enhanced API flexibility. Key features delivered: UnbinnedLikelihood now uses cached density values, reducing recomputation when density data is cached. The batch_size parameter is now optional and density handling uses asarray when cached, increasing robustness and compatibility with varied data pipelines. Major bugs fixed: None reported; no regressions observed during caching path integration. Overall impact and accomplishments: Improved runtime efficiency for large datasets, lower compute usage, and smoother integration into caching-enabled workflows. Technologies/skills demonstrated: Python, numpy (asarray usage), caching strategies, performance optimization, clean API design, and maintainability.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
CI/CDDocumentation GenerationMachine LearningPythonUnit Testingdata analysisstatistical modeling
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