IMSA-CMS/CMSAnalysis
Oct 2024 – May 2025
7 Months active
Languages Used
C++JSONC
Technical Skills
Data OrganizationFile ManagementASCII ArtC++C++ DevelopmentCode Organization
Cameron Eddington
PROFILE
Cameron Eddington
Over seven months, Charles Eddington engineered robust data analysis and visualization features for the IMSA-CMS/CMSAnalysis repository, focusing on scale factor workflows, systematic uncertainty handling, and histogram management. He centralized and standardized data and histogram naming, refactored code for maintainability, and implemented modules for residual visualization and systematic scale factor application. Using C++ and the ROOT analysis framework, Charles improved data integrity, reproducibility, and analysis reliability by enhancing error handling, modularizing configuration, and cleaning legacy code. His work enabled accurate multi-year physics analyses, streamlined data-driven tuning, and reduced technical debt, demonstrating depth in scientific computing, data organization, and object-oriented programming.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
26Total
Bugs
2
Commits
26
Features
10
Lines of code
6,238
Activity Months7
Your Network
34 people
Work History
May 2025
4 Commits • 2 Features
May 1, 2025Month: 2025-05 — Summary of work in IMSA-CMS/CMSAnalysis. Delivered two major features around histogram naming and systematics, refactored the naming framework to centralize HistNameFinder usage, and removed legacy path management via FilePathMapper. These changes improve reliability, consistency, and support for systematic variations, enabling safer downstream analytics and easier maintenance. Key value: fewer naming errors, streamlined SuperPlot usage, and better reuse of components across analyses.
4 Commits • 2 Features
May 1, 2025Month: 2025-05 — Summary of work in IMSA-CMS/CMSAnalysis. Delivered two major features around histogram naming and systematics, refactored the naming framework to centralize HistNameFinder usage, and removed legacy path management via FilePathMapper. These changes improve reliability, consistency, and support for systematic variations, enabling safer downstream analytics and easier maintenance. Key value: fewer naming errors, streamlined SuperPlot usage, and better reuse of components across analyses.
May 2025
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 IMSA-CMS/CMSAnalysis: Delivered focused improvements to histogram generation under Up/Down systematics and completed targeted codebase cleanup. The changes enhance the accuracy of systematic weight application, reduce technical debt, and lay groundwork for more robust future physics analyses.
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 IMSA-CMS/CMSAnalysis: Delivered focused improvements to histogram generation under Up/Down systematics and completed targeted codebase cleanup. The changes enhance the accuracy of systematic weight application, reduce technical debt, and lay groundwork for more robust future physics analyses.
March 2025
2 Commits • 1 Features
Mar 1, 2025In March 2025, IMSA-CMS/CMSAnalysis delivered a robust Systematic Scale Factor Framework with plotting enhancements that enable more accurate data analysis by incorporating systematic uncertainties. The work included refactoring of scale-factor loading and plotting to support muon and electron scale factors, enhanced error handling, and improved debugging for file/histogram loading. This work lays the groundwork for more reliable physics analyses and scalable workflows across datasets.
2 Commits • 1 Features
Mar 1, 2025In March 2025, IMSA-CMS/CMSAnalysis delivered a robust Systematic Scale Factor Framework with plotting enhancements that enable more accurate data analysis by incorporating systematic uncertainties. The work included refactoring of scale-factor loading and plotting to support muon and electron scale factors, enhanced error handling, and improved debugging for file/histogram loading. This work lays the groundwork for more reliable physics analyses and scalable workflows across datasets.
March 2025
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for IMSA-CMS/CMSAnalysis. Focused on delivering robust histogram tooling and stabilizing scale-factor workflows for Higgs analysis. Implemented a histogram scaling feature and fixed configuration-related data issues to improve accuracy, reproducibility, and reliability of data visualization and analysis.
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for IMSA-CMS/CMSAnalysis. Focused on delivering robust histogram tooling and stabilizing scale-factor workflows for Higgs analysis. Implemented a histogram scaling feature and fixed configuration-related data issues to improve accuracy, reproducibility, and reliability of data visualization and analysis.
December 2024
4 Commits • 1 Features
Dec 1, 2024December 2024 highlights for IMSA-CMS/CMSAnalysis: delivered Scale Factor Analysis Module with Residual Visualization and completed targeted refactoring of the SuperimposeRatio function to compute and visualize residuals, enabling clearer assessment of scale-factor impact on physics measurements with and without their application. Fixed Scale Factor Retrieval by Period in MultiYearScaleFactor and cleaned up debug prints and obsolete scale-factor additions, reducing noise and maintenance burden. These changes improve measurement fidelity, reproducibility, and support for data-driven tuning across analysis periods.
4 Commits • 1 Features
Dec 1, 2024December 2024 highlights for IMSA-CMS/CMSAnalysis: delivered Scale Factor Analysis Module with Residual Visualization and completed targeted refactoring of the SuperimposeRatio function to compute and visualize residuals, enabling clearer assessment of scale-factor impact on physics measurements with and without their application. Fixed Scale Factor Retrieval by Period in MultiYearScaleFactor and cleaned up debug prints and obsolete scale-factor additions, reducing noise and maintenance burden. These changes improve measurement fidelity, reproducibility, and support for data-driven tuning across analysis periods.
December 2024
November 2024
6 Commits • 2 Features
Nov 1, 2024Monthly summary for IMSA-CMS/CMSAnalysis - 2024-11 Key features delivered: - Scale Factor Data Reorganization and Cross-Year Loading: Centralized scale factor data in a dedicated folder and updated loading to support multi-year analyses across particle types. This change enables year-over-year comparisons and more scalable analyses. (Commits: 33f63d69bc9cf2bbdd3cc2e6d895092a865700e8; 78767acaafcadd0fd87c6bd60091ad411ee1dd0b) - Vincent ASCII Art Visual Enhancement in Scale Factor Printing: Enhanced scale factor printing with Vincent-themed ASCII art and improved output readability. (Commits: 12a2cdc090ae6092559c1e6a6671d2fba8eaa266; dadefd51a2c3d495e97a564d4a719cd73ab0a22d; 55477a25e34b37e4842dcaef46f13297726a204c; 3cc9d39de094f588eb6e0b6a3ba3248bbbe11308) Major bugs fixed: - Scale factor printing stability: fixed print formatting issues to ensure consistent output across runs and environments, improving reliability of analysis reporting. (Commits: 55477a25e34b37e4842dcaef46f13297726a204c; 3cc9d39de094f588eb6e0b6a3ba3248bbbe11308) Overall impact and accomplishments: - Business value: Enabled reliable multi-year scale factor analysis and enhanced output presentation, reducing analysis time and improving decision making with clearer visuals. - Technical impact: Improved code organization, data loading paths, and output rendering; strengthened maintainability via clearer commit history and centralized data assets. Technologies/skills demonstrated: - Data organization and modularization, multi-year data loading, output formatting, ASCII art integration, version control hygiene, and cross-functional collaboration.
November 2024
6 Commits • 2 Features
Nov 1, 2024Monthly summary for IMSA-CMS/CMSAnalysis - 2024-11 Key features delivered: - Scale Factor Data Reorganization and Cross-Year Loading: Centralized scale factor data in a dedicated folder and updated loading to support multi-year analyses across particle types. This change enables year-over-year comparisons and more scalable analyses. (Commits: 33f63d69bc9cf2bbdd3cc2e6d895092a865700e8; 78767acaafcadd0fd87c6bd60091ad411ee1dd0b) - Vincent ASCII Art Visual Enhancement in Scale Factor Printing: Enhanced scale factor printing with Vincent-themed ASCII art and improved output readability. (Commits: 12a2cdc090ae6092559c1e6a6671d2fba8eaa266; dadefd51a2c3d495e97a564d4a719cd73ab0a22d; 55477a25e34b37e4842dcaef46f13297726a204c; 3cc9d39de094f588eb6e0b6a3ba3248bbbe11308) Major bugs fixed: - Scale factor printing stability: fixed print formatting issues to ensure consistent output across runs and environments, improving reliability of analysis reporting. (Commits: 55477a25e34b37e4842dcaef46f13297726a204c; 3cc9d39de094f588eb6e0b6a3ba3248bbbe11308) Overall impact and accomplishments: - Business value: Enabled reliable multi-year scale factor analysis and enhanced output presentation, reducing analysis time and improving decision making with clearer visuals. - Technical impact: Improved code organization, data loading paths, and output rendering; strengthened maintainability via clearer commit history and centralized data assets. Technologies/skills demonstrated: - Data organization and modularization, multi-year data loading, output formatting, ASCII art integration, version control hygiene, and cross-functional collaboration.
October 2024
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10 – IMSA-CMS/CMSAnalysis focused on data hygiene and governance. Implemented a standardized file naming convention for Data Collection assets (H++toLL path) to Run_2_Year_XXXX.txt, consolidating Year_XXXX_Run_2 references across years and runs to improve data reliability, discoverability, and downstream automation.
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10 – IMSA-CMS/CMSAnalysis focused on data hygiene and governance. Implemented a standardized file naming convention for Data Collection assets (H++toLL path) to Run_2_Year_XXXX.txt, consolidating Year_XXXX_Run_2 references across years and runs to improve data reliability, discoverability, and downstream automation.
October 2024
Activity
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Quality Metrics
Correctness85.4%
Maintainability85.4%
Architecture78.4%
Performance72.4%
AI Usage21.6%
Skills & Technologies
Programming Languages
CC++JSON
Technical Skills
ASCII ArtC++C++ DevelopmentCode CleanupCode OrganizationConfiguration ManagementData AnalysisData ManagementData OrganizationData VisualizationDebuggingFile ManagementHistogrammingJSON ParsingObject-Oriented Programming
Repositories Contributed To
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