NNPDF/nnpdf
Nov 2024 – Oct 2025
6 Months active
Languages Used
PythonYAMLBinarypythonyamlJupyter NotebookCSV
Technical Skills
Configuration ManagementData AnalysisData ManagementData ProcessingMetadata ConfigurationPhysics Analysis
achiefa
PROFILE
Achiefa
Amedeo Chiefa contributed to the NNPDF/nnpdf repository by engineering robust data processing and analysis workflows for high energy physics datasets. Over six months, he modernized metadata management, refactored data extraction pipelines, and enhanced uncertainty quantification, using Python and YAML to streamline configuration and improve reproducibility. His work included implementing version-aware covariance matrix loading, optimizing file path handling, and overhauling statistical reporting for sum rules. By integrating caching, error handling, and legacy support, Amedeo improved both performance and reliability. These efforts resulted in more maintainable code, faster data ingestion, and greater accuracy in physics analysis and downstream scientific workflows.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
83Total
Bugs
18
Commits
83
Features
31
Lines of code
87,480
Activity Months6
Work History
October 2025
4 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — NNPDF/nnpdf. Key accomplishment: Sum Rules Statistical Reporting Overhaul; refactor to MCStats and SymmHessianStats; ensure scalar stats; remove unused imports; update regression tests for Hessian calculations. This month focused on improving accuracy, reliability, and maintainability of statistics reporting and laying groundwork for expanded Hessian analytics.
4 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — NNPDF/nnpdf. Key accomplishment: Sum Rules Statistical Reporting Overhaul; refactor to MCStats and SymmHessianStats; ensure scalar stats; remove unused imports; update regression tests for Hessian calculations. This month focused on improving accuracy, reliability, and maintainability of statistics reporting and laying groundwork for expanded Hessian analytics.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for NNPDF/nnpdf: Delivered a key feature to improve theoretical covariance handling for fits. Implemented theory covariance matrix file loading and path resolution that identifies and loads the correct covariance file by version, using presence in the tables directory, with dynamic selection based on user-provided covariance data and point prescriptions. This enhances accuracy of theoretical covariance calculations and adds flexibility to fit configuration. Included focused bug fixes and workflow improvements to improve reliability and reproducibility. Business impact: reduces manual intervention, shortens iteration cycles, and supports more robust covariance-based fits.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for NNPDF/nnpdf: Delivered a key feature to improve theoretical covariance handling for fits. Implemented theory covariance matrix file loading and path resolution that identifies and loads the correct covariance file by version, using presence in the tables directory, with dynamic selection based on user-provided covariance data and point prescriptions. This enhances accuracy of theoretical covariance calculations and adds flexibility to fit configuration. Included focused bug fixes and workflow improvements to improve reliability and reproducibility. Business impact: reduces manual intervention, shortens iteration cycles, and supports more robust covariance-based fits.
April 2025
4 Commits • 3 Features
Apr 1, 2025April 2025 | NNPDF/nnpdf developed features to improve API usability and analysis reproducibility, fixed portability issues, and clarified the QCD-only convolution workflow. Key features delivered include: (1) Pineappl C-factor support added to the API extension with a single-factor enforcement warning and adjusted data access/concatenation to align with the single-factor assumption; (2) Convolution workflow updated to isolate NNLO QCD contributions by excluding Electroweak corrections in the example notebook; (3) Notebook portability improvements by removing hardcoded local paths, introducing placeholders, and standardizing execution counts and numerical outputs. Overall, these changes enhance reliability, reproducibility, and collaboration readiness, reducing misconfiguration and environment-specific failures.
4 Commits • 3 Features
Apr 1, 2025April 2025 | NNPDF/nnpdf developed features to improve API usability and analysis reproducibility, fixed portability issues, and clarified the QCD-only convolution workflow. Key features delivered include: (1) Pineappl C-factor support added to the API extension with a single-factor enforcement warning and adjusted data access/concatenation to align with the single-factor assumption; (2) Convolution workflow updated to isolate NNLO QCD contributions by excluding Electroweak corrections in the example notebook; (3) Notebook portability improvements by removing hardcoded local paths, introducing placeholders, and standardizing execution counts and numerical outputs. Overall, these changes enhance reliability, reproducibility, and collaboration readiness, reducing misconfiguration and environment-specific failures.
April 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for NNPDF/nnpdf focusing on data extraction and loading improvements. Delivered a set of enhancements to the data ingestion pipeline that improve portability, performance, reliability, and legacy variant support. Key outcomes include refactoring data extraction to use relative paths for metadata and raw data, implementing caching for table retrieval to boost load times, strengthening error handling for data consistency checks, and introducing a data_central configuration to ensure proper loading for legacy variants. These changes reduce pipeline fragility, speed up data processing, and improve compatibility with legacy datasets, accompanied by code quality improvements through clean-up and pre-commit updates.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for NNPDF/nnpdf focusing on data extraction and loading improvements. Delivered a set of enhancements to the data ingestion pipeline that improve portability, performance, reliability, and legacy variant support. Key outcomes include refactoring data extraction to use relative paths for metadata and raw data, implementing caching for table retrieval to boost load times, strengthening error handling for data consistency checks, and introducing a data_central configuration to ensure proper loading for legacy variants. These changes reduce pipeline fragility, speed up data processing, and improve compatibility with legacy datasets, accompanied by code quality improvements through clean-up and pre-commit updates.
December 2024
67 Commits • 22 Features
Dec 1, 2024December 2024 monthly work summary for NNPDF/nnpdf: Delivered a metadata modernization and filtering pipeline overhaul with centralized metadata, naming cleanup, and legacy data removal, plus targeted metadata updates for CMS_WPWM_7TEV/8TEV configurations. Completed substantial refactoring of kinematics and kin-related naming, integrated CMS prescription into the filtering workflow, added generated data for new scenarios, and refreshed HepData integration. Executed comprehensive bug fixes across metadata references, read_csv keyword handling, statistics uncertainties, and filter logic; regenerated tests and plots to ensure correctness. Result: higher data quality, reproducibility, and faster, more reliable data processing for downstream analyses and business decisions.
67 Commits • 22 Features
Dec 1, 2024December 2024 monthly work summary for NNPDF/nnpdf: Delivered a metadata modernization and filtering pipeline overhaul with centralized metadata, naming cleanup, and legacy data removal, plus targeted metadata updates for CMS_WPWM_7TEV/8TEV configurations. Completed substantial refactoring of kinematics and kin-related naming, integrated CMS prescription into the filtering workflow, added generated data for new scenarios, and refreshed HepData integration. Executed comprehensive bug fixes across metadata references, read_csv keyword handling, statistics uncertainties, and filter logic; regenerated tests and plots to ensure correctness. Result: higher data quality, reproducibility, and faster, more reliable data processing for downstream analyses and business decisions.
December 2024
November 2024
4 Commits • 3 Features
Nov 1, 2024In November 2024, NNPDF/nnpdf delivered significant data curation and uncertainty-management enhancements. Key features include ATLAS WJ 8 TeV dataset metadata modernization with versioning, external links, and HEPData integration; addition of WM-PT units and table references; updates to YAML metadata. Central data and kinematics for ATLAS W+/W- (8 TeV) were generated with new YAML files and Python scripts for processing and cross-validation against legacy data, centralizing these assets within nnpdf. Uncertainty handling for ATLAS W+jet 8 TeV analysis was strengthened through definitions and variants, a refactor of filter_utils.py to manage systematic and statistical uncertainties, and the addition of new data files for W+ and W-; this improves the robustness of systematic treatment. These efforts collectively improve data provenance, reproducibility, and analysis reliability, enabling more accurate physics results and streamlined downstream usage.
November 2024
4 Commits • 3 Features
Nov 1, 2024In November 2024, NNPDF/nnpdf delivered significant data curation and uncertainty-management enhancements. Key features include ATLAS WJ 8 TeV dataset metadata modernization with versioning, external links, and HEPData integration; addition of WM-PT units and table references; updates to YAML metadata. Central data and kinematics for ATLAS W+/W- (8 TeV) were generated with new YAML files and Python scripts for processing and cross-validation against legacy data, centralizing these assets within nnpdf. Uncertainty handling for ATLAS W+jet 8 TeV analysis was strengthened through definitions and variants, a refactor of filter_utils.py to manage systematic and statistical uncertainties, and the addition of new data files for W+ and W-; this improves the robustness of systematic treatment. These efforts collectively improve data provenance, reproducibility, and analysis reliability, enabling more accurate physics results and streamlined downstream usage.
Activity
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Quality Metrics
Correctness87.6%
Maintainability88.0%
Architecture85.8%
Performance79.4%
AI Usage20.2%
Skills & Technologies
Programming Languages
BinaryCSVJupyter NotebookPythonYAMLpythonyaml
Technical Skills
API DevelopmentBug FixingCI/CDCode CleanupCode RefactoringConfiguration ManagementData AnalysisData CleanupData ConfigurationData HandlingData ManagementData ProcessingData ValidationError HandlingExperimental Physics Data
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