NNPDF/nnpdf
Nov 2024 – May 2025
7 Months active
Languages Used
PythonYAMLyamlTextpythonMatplotlibdataTOML
Technical Skills
Configuration ManagementData AnalysisData ConfigurationData ManagementData ProcessingExperimental Data Analysis
Mark Nestor Costantini
PROFILE
Mark Nestor Costantini
Over seven months, Michael Clark contributed to the NNPDF/nnpdf repository by engineering robust data ingestion, configuration, and testing frameworks for high-energy physics analyses. He developed end-to-end pipelines for ATLAS and EICC datasets, integrating raw data, metadata, and systematic uncertainty handling using Python, YAML, and scientific libraries like NumPy and Matplotlib. Michael refactored code for maintainability, standardized metadata, and improved reproducibility through strict validation and CI/CD enhancements. He implemented multiclosure testing modules and advanced data filtering, supporting reliable statistical modeling. His work emphasized modular design, documentation, and regression safety, resulting in scalable, uncertainty-aware workflows and improved data quality across the codebase.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
98Total
Bugs
7
Commits
98
Features
25
Lines of code
141,397
Activity Months7
Your Network
88 people
Work History
May 2025
3 Commits • 1 Features
May 1, 2025May 2025: Focused on strengthening test coverage and CI reliability for NNPDF/nnpdf. Delivered concrete features and fixes with clear business value: improved confidence in t0_sampling behavior through dedicated tests, and reduced CI noise by updating the fitbot reference dataset in GitHub Actions. These changes improve regression safety, speed up PR validation, and reinforce measurement reliability across the t0_sampling workflow.
3 Commits • 1 Features
May 1, 2025May 2025: Focused on strengthening test coverage and CI reliability for NNPDF/nnpdf. Delivered concrete features and fixes with clear business value: improved confidence in t0_sampling behavior through dedicated tests, and reduced CI noise by updating the fitbot reference dataset in GitHub Actions. These changes improve regression safety, speed up PR validation, and reinforce measurement reliability across the t0_sampling workflow.
May 2025
April 2025
8 Commits • 2 Features
Apr 1, 2025April 2025 (2025-04) monthly summary for NNPDF/nnpdf: Delivered key feature work on T0 Sampling Configuration Lifecycle and Defaults and implemented targeted code quality improvements in the closure testing framework. The changes stabilize configuration management across the repository, clarify defaults for new fits, and remove ambiguity in the overfit metric by eliminating an unused parameter. Completed several important test stabilizations and framework enhancements that improve reliability, maintainability, and CI readiness.
April 2025
8 Commits • 2 Features
Apr 1, 2025April 2025 (2025-04) monthly summary for NNPDF/nnpdf: Delivered key feature work on T0 Sampling Configuration Lifecycle and Defaults and implemented targeted code quality improvements in the closure testing framework. The changes stabilize configuration management across the repository, clarify defaults for new fits, and remove ambiguity in the overfit metric by eliminating an unused parameter. Completed several important test stabilizations and framework enhancements that improve reliability, maintainability, and CI readiness.
March 2025
22 Commits • 9 Features
Mar 1, 2025March 2025 (2025-03) monthly summary for NNPDF/nnpdf focused on delivering production-ready sampling, cleanup, and documentation to enable reliable experimentation and onboarding. Key changes center on T0 sampling integration, codebase cleanup to reduce clutter, and improved data import paths and documentation to support reproducibility and alignment with published conventions.
22 Commits • 9 Features
Mar 1, 2025March 2025 (2025-03) monthly summary for NNPDF/nnpdf focused on delivering production-ready sampling, cleanup, and documentation to enable reliable experimentation and onboarding. Key changes center on T0 sampling integration, codebase cleanup to reduce clutter, and improved data import paths and documentation to support reproducibility and alignment with published conventions.
March 2025
February 2025
12 Commits • 4 Features
Feb 1, 2025February 2025 highlights for NNPDF/nnpdf: delivered major data quality and packaging improvements, aligning outputs with the latest paper draft and enabling broader data coverage. Key features delivered include robust inconsistent closure data handling with unified filtering, new NNPDF POS/NF data (F2U, F2D) plus MSbar POS datasets, and enhanced plot labeling for closure plots. Major bugs fixed include strict validation to raise errors on unknown dataset keys, reducing misconfigurations. The work improved data integrity, reproducibility, and packaging reliability, supporting more accurate phenomenology and easier maintenance. Technologies demonstrated include Python-based data filtering, YAML/config validation, plotting aesthetics, dataset metadata management, and modern packaging workflows.
February 2025
12 Commits • 4 Features
Feb 1, 2025February 2025 highlights for NNPDF/nnpdf: delivered major data quality and packaging improvements, aligning outputs with the latest paper draft and enabling broader data coverage. Key features delivered include robust inconsistent closure data handling with unified filtering, new NNPDF POS/NF data (F2U, F2D) plus MSbar POS datasets, and enhanced plot labeling for closure plots. Major bugs fixed include strict validation to raise errors on unknown dataset keys, reducing misconfigurations. The work improved data integrity, reproducibility, and packaging reliability, supporting more accurate phenomenology and easier maintenance. Technologies demonstrated include Python-based data filtering, YAML/config validation, plotting aesthetics, dataset metadata management, and modern packaging workflows.
January 2025
20 Commits • 2 Features
Jan 1, 2025January 2025: Delivered key features and data-quality improvements for the NNPDF/nnpdf codebase. Implemented a robust Multiclosure nsigma closure testing framework with new modules for multiclosure calculations, enhanced plotting, API label usage, and TPR/TPR metrics, accompanied by comprehensive documentation. Completed dataset metadata and uncertainty naming cleanup to standardize terminology (stat -> stat_mult), corrected the ATLAS luminosity uncertainty type, updated metadata URL/version, and refreshed dataset metadata for ATLAS Z0 analyses. Reduced maintenance overhead by removing an unused rawdata file and refactoring helpers and plots for consistency and reuse.
20 Commits • 2 Features
Jan 1, 2025January 2025: Delivered key features and data-quality improvements for the NNPDF/nnpdf codebase. Implemented a robust Multiclosure nsigma closure testing framework with new modules for multiclosure calculations, enhanced plotting, API label usage, and TPR/TPR metrics, accompanied by comprehensive documentation. Completed dataset metadata and uncertainty naming cleanup to standardize terminology (stat -> stat_mult), corrected the ATLAS luminosity uncertainty type, updated metadata URL/version, and refreshed dataset metadata for ATLAS Z0 analyses. Reduced maintenance overhead by removing an unused rawdata file and refactoring helpers and plots for consistency and reuse.
January 2025
December 2024
16 Commits • 4 Features
Dec 1, 2024December 2024 highlights for NNPDF/nnpdf: Delivered end-to-end data configuration and metadata improvements enabling robust Z/W analyses across multiple energies. Restored EICC raw data for 15/22 GeV configurations and removed outdated data configurations. Harmonized Z boson dataset metadata (naming, processing types) and cleaned legacy configurations. Implemented 13 TeV Z boson data processing scripts and configurations, including central values, uncertainties, kinematics, and new data/files. Enhanced uncertainty handling for Z/W datasets with refined correlation treatment. Fixed documentation and units for W/Z metadata by correcting LaTeX labels and GeV^2 units.
December 2024
16 Commits • 4 Features
Dec 1, 2024December 2024 highlights for NNPDF/nnpdf: Delivered end-to-end data configuration and metadata improvements enabling robust Z/W analyses across multiple energies. Restored EICC raw data for 15/22 GeV configurations and removed outdated data configurations. Harmonized Z boson dataset metadata (naming, processing types) and cleaned legacy configurations. Implemented 13 TeV Z boson data processing scripts and configurations, including central values, uncertainties, kinematics, and new data/files. Enhanced uncertainty handling for Z/W datasets with refined correlation treatment. Fixed documentation and units for W/Z metadata by correcting LaTeX labels and GeV^2 units.
November 2024
17 Commits • 3 Features
Nov 1, 2024November 2024 — Delivered end-to-end data ingestion, metadata linkage, and uncertainty configuration for three major datasets in NNPDF/nnpdf (ATLAS Z0 8 TeV LowMass, ATLAS Z0 8 TeV HiMass, and WPWM 13 TeV). Key outcomes include ingestion of raw data and kinematics, robust metadata references, and initial systematics integration to enable analysis and simulation workflows; covariance-based uncertainty processing introduced for WPWM 13 TeV; metadata and luminosity configurations updated for HiMass analyses; and targeted bug fixes to improve reproducibility (e.g., Stat to stat rename, lumi uncertainty type corrections) along with versioned checks and filter-file dependencies. These efforts collectively enhance data quality, reproducibility, and speed to analysis, delivering uncertainty-aware results across datasets and enabling scalable, business-value-driven research.
17 Commits • 3 Features
Nov 1, 2024November 2024 — Delivered end-to-end data ingestion, metadata linkage, and uncertainty configuration for three major datasets in NNPDF/nnpdf (ATLAS Z0 8 TeV LowMass, ATLAS Z0 8 TeV HiMass, and WPWM 13 TeV). Key outcomes include ingestion of raw data and kinematics, robust metadata references, and initial systematics integration to enable analysis and simulation workflows; covariance-based uncertainty processing introduced for WPWM 13 TeV; metadata and luminosity configurations updated for HiMass analyses; and targeted bug fixes to improve reproducibility (e.g., Stat to stat rename, lumi uncertainty type corrections) along with versioned checks and filter-file dependencies. These efforts collectively enhance data quality, reproducibility, and speed to analysis, delivering uncertainty-aware results across datasets and enabling scalable, business-value-driven research.
November 2024
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Quality Metrics
Correctness91.6%
Maintainability92.2%
Architecture90.0%
Performance83.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVMatplotlibPythonRSTTOMLTextYAMLdatapythonreStructuredText
Technical Skills
API IntegrationCI/CDClosure TestingCode CleanupCode ConsolidationCode NavigationCode OrganizationCode ReadabilityCode RefactoringCode ReviewConfigurationConfiguration ManagementData AnalysisData ConfigurationData Filtering
Repositories Contributed To
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