NNPDF/nnpdf
Nov 2024 – Mar 2026
6 Months active
Languages Used
CSVPythonYAMLpythonyamlFortrandata
Technical Skills
CSV ParsingCode RefactoringConfiguration ManagementData AnalysisData ConfigurationData Formatting
ecole41
PROFILE
Ecole41
Worked on the NNPDF/nnpdf repository to deliver robust data engineering and backend features supporting high energy physics analysis. Built and refined data ingestion pipelines, metadata management workflows, and uncertainty modeling, leveraging Python, YAML, and CSV parsing. Enhanced reproducibility and downstream analytics by standardizing dataset metadata, automating data exports, and improving kinematic and uncertainty data integration. Refactored Keras backend training APIs for maintainability and streamlined replica generation with improved seed handling and positivity controls. Collaborated on Level1 data generation enhancements, focusing on data fidelity and maintainability. The work emphasized configuration management, scientific computing, and reliable data processing for physics workflows.
Overall Statistics
Feature vs Bugs
86%Features
Repository Contributions
73Total
Bugs
3
Commits
73
Features
19
Lines of code
22,829
Activity Months6
Your Network
25 peopleShared Repositories
25
Andrew PietraszkiewiczMember
achiefaMember
Giovanni De CrescenzoMember
Eva GroenendijkMember
Ella ColeMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
enoceraMember
Work History
March 2026
3 Commits • 2 Features
Mar 1, 2026March 2026 (NNPDF/nnpdf) focused on delivering feature work that improves data fidelity and API usability for Level1 outputs. Key work included Level1 Data Generation Enhancements and a Positivity Mask Creation Refactor, with changes implemented in validphys2/pseudodata.py. The work lays a stronger foundation for uncertainty modeling and downstream analyses while improving maintainability and collaborator onboarding.
3 Commits • 2 Features
Mar 1, 2026March 2026 (NNPDF/nnpdf) focused on delivering feature work that improves data fidelity and API usability for Level1 outputs. Key work included Level1 Data Generation Enhancements and a Positivity Mask Creation Refactor, with changes implemented in validphys2/pseudodata.py. The work lays a stronger foundation for uncertainty modeling and downstream analyses while improving maintainability and collaborator onboarding.
March 2026
February 2026
9 Commits • 2 Features
Feb 1, 2026February 2026 (NNPDF/nnpdf) delivered targeted enhancements to replica generation and positivity/pseudodata handling, reinforcing reproducibility, API clarity, and data integrity. The work strengthens our ability to propagate uncertainties reliably and reduces ambiguity in downstream tooling, setting the stage for scalable analyses and Colibri-compatible workflows.
February 2026
9 Commits • 2 Features
Feb 1, 2026February 2026 (NNPDF/nnpdf) delivered targeted enhancements to replica generation and positivity/pseudodata handling, reinforcing reproducibility, API clarity, and data integrity. The work strengthens our ability to propagate uncertainties reliably and reduces ambiguity in downstream tooling, setting the stage for scalable analyses and Colibri-compatible workflows.
June 2025
1 Commits • 1 Features
Jun 1, 20252025-06 monthly summary for NNPDF/nnpdf: Delivered a focused feature to refactor the Keras backend training API, removing stopping functionality, consolidating fit parameter passing into a dictionary, and disabling monitoring and chi-squared logging. This work simplifies the training workflow, reduces overhead, and improves reproducibility and integration with external orchestration. Impact: Streamlined training runs with fewer configuration edge cases, improved reliability for automated pipelines, and a clear API path for future monitoring enhancements. Note: No major bugs logged this month for this module; effort centered on API cleanup, maintainability, and aligning with broader performance review goals.
1 Commits • 1 Features
Jun 1, 20252025-06 monthly summary for NNPDF/nnpdf: Delivered a focused feature to refactor the Keras backend training API, removing stopping functionality, consolidating fit parameter passing into a dictionary, and disabling monitoring and chi-squared logging. This work simplifies the training workflow, reduces overhead, and improves reproducibility and integration with external orchestration. Impact: Streamlined training runs with fewer configuration edge cases, improved reliability for automated pipelines, and a clear API path for future monitoring enhancements. Note: No major bugs logged this month for this module; effort centered on API cleanup, maintainability, and aligning with broader performance review goals.
June 2025
January 2025
13 Commits • 2 Features
Jan 1, 2025Month: 2025-01 focused on stabilizing data interpretation, standardizing dataset metadata, and aligning luminosity uncertainties across ATLAS datasets for NNPDF/nnpdf. Key bug fixed, metadata quality improved, and uncertainties reconciled to support reliable downstream analytics and reporting. Demonstrated strong data engineering, version-controlled metadata changes, and cross-team collaboration across dataset labeling and luminosity calibration efforts. These changes improve data quality, reproducibility, and visualization readiness with clear business value for analysis pipelines.
January 2025
13 Commits • 2 Features
Jan 1, 2025Month: 2025-01 focused on stabilizing data interpretation, standardizing dataset metadata, and aligning luminosity uncertainties across ATLAS datasets for NNPDF/nnpdf. Key bug fixed, metadata quality improved, and uncertainties reconciled to support reliable downstream analytics and reporting. Demonstrated strong data engineering, version-controlled metadata changes, and cross-team collaboration across dataset labeling and luminosity calibration efforts. These changes improve data quality, reproducibility, and visualization readiness with clear business value for analysis pipelines.
December 2024
30 Commits • 8 Features
Dec 1, 2024December 2024: NNPDF/nnpdf monthly highlights focused on strengthening data handling, metadata quality, and automation, with measurable business value in reproducibility and downstream analysis. Delivered a robust data ingestion and export path, expanded uncertainty and kinematics support, and comprehensive metadata alignment across data references. Implemented automated data.yaml generation, raw data support, and updated validphys processing options, while modernizing parameter naming (m_ll, m_ll2, eta) and metadata workflows. Enhanced data/metadata integration (central data function, inspire/hep links) and produced YAML/CSV artifacts for uncertainties and kinematics. Fixed critical plotting/metadata bugs and LaTeX-related issues to ensure stable reports and figures.
30 Commits • 8 Features
Dec 1, 2024December 2024: NNPDF/nnpdf monthly highlights focused on strengthening data handling, metadata quality, and automation, with measurable business value in reproducibility and downstream analysis. Delivered a robust data ingestion and export path, expanded uncertainty and kinematics support, and comprehensive metadata alignment across data references. Implemented automated data.yaml generation, raw data support, and updated validphys processing options, while modernizing parameter naming (m_ll, m_ll2, eta) and metadata workflows. Enhanced data/metadata integration (central data function, inspire/hep links) and produced YAML/CSV artifacts for uncertainties and kinematics. Fixed critical plotting/metadata bugs and LaTeX-related issues to ensure stable reports and figures.
December 2024
November 2024
17 Commits • 4 Features
Nov 1, 2024November 2024 — NNPDF/nnpdf monthly performance summary highlighting delivery of ATLAS data integration capabilities, data quality improvements, and metadata governance. Focused on enabling downstream analysis, reproducibility, and faster data-driven decisions.
November 2024
17 Commits • 4 Features
Nov 1, 2024November 2024 — NNPDF/nnpdf monthly performance summary highlighting delivery of ATLAS data integration capabilities, data quality improvements, and metadata governance. Focused on enabling downstream analysis, reproducibility, and faster data-driven decisions.
Activity
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Quality Metrics
Correctness88.0%
Maintainability89.4%
Architecture86.6%
Performance80.6%
AI Usage20.6%
Skills & Technologies
Programming Languages
CSVFortranPythonYAMLdatapythonyaml
Technical Skills
Backend DevelopmentCSV ParsingCode RefactoringConfigurationConfiguration ManagementData AnalysisData ConfigurationData CurationData EngineeringData FormattingData IntegrationData ManagementData ProcessingData VisualizationExperimental Physics Data
Repositories Contributed To
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