blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Code RefactoringData AnalysisData VisualizationMachine LearningMatplotlibPandas
Quenard Stanislas
PROFILE
Quenard Stanislas
Developed foundational data visualization and uncertainty analysis features for the blackSwanCS/Higgs_collaboration_B repository, focusing on the Higgs dataset. Built an integrated pipeline that ingests data, generates pair plots for key features, and configures systematic uncertainty tests using Python, Pandas, and Matplotlib. Introduced utilities for TES and JES uncertainty analysis, enabling per-bin variation studies, histogram generation, and flexible plotting with optional background overlays. Refactored legacy analysis code to improve maintainability and reproducibility across experiments. The work established robust groundwork for future scientific computing tasks, emphasizing clean code practices and reproducible workflows without addressing bug fixes during the development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
6,282
Activity Months1
Your Network
41 peopleShared Repositories
21
Work History
June 2025
7 Commits • 2 Features
Jun 1, 2025June 2025: Delivered foundational visualization and uncertainty-analysis capabilities for the Higgs dataset project. Implemented an integrated visualization and testing pipeline and launched TES/JES uncertainty analysis utilities, with initial data ingestion, robust test configurations, and groundwork for per-bin uncertainty quantification. Cleaned up legacy analysis code and assets to improve maintainability and reproducibility across experiments.
7 Commits • 2 Features
Jun 1, 2025June 2025: Delivered foundational visualization and uncertainty-analysis capabilities for the Higgs dataset project. Implemented an integrated visualization and testing pipeline and launched TES/JES uncertainty analysis utilities, with initial data ingestion, robust test configurations, and groundwork for per-bin uncertainty quantification. Cleaned up legacy analysis code and assets to improve maintainability and reproducibility across experiments.
June 2025
Activity
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Quality Metrics
Correctness85.8%
Maintainability85.8%
Architecture82.8%
Performance80.0%
AI Usage25.8%
Skills & Technologies
Programming Languages
Python
Technical Skills
Code RefactoringData AnalysisData VisualizationMachine LearningMatplotlibPandasPlottingPythonScientific Computing
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