NNPDF/nnpdf
Jul 2024 – Jan 2026
12 Months active
Languages Used
PythonYAMLBinarypythonyamlJupyter NotebookCSVreStructuredText
Technical Skills
Python scriptingconfiguration managementdata processingdata analysismachine learningsoftware development
achiefa
PROFILE
Achiefa
Amedeo Chiefa contributed to the NNPDF/nnpdf repository by engineering robust data processing pipelines and enhancing scientific analysis workflows for high-energy physics. He refactored metadata and uncertainty management, modernized dataset configurations using YAML and Python, and improved the reliability of replica-based fitting and photon computation. His work included implementing version-aware file handling, optimizing data extraction with caching, and strengthening error handling for reproducibility. Amedeo also centralized documentation for QED fits and photon PDFs, streamlining onboarding and reducing misconfiguration. Through systematic code cleanup, testing, and configuration management, he delivered maintainable solutions that improved data integrity and accelerated research iteration cycles.
Overall Statistics
Feature vs Bugs
65%Features
Repository Contributions
117Total
Bugs
23
Commits
117
Features
43
Lines of code
142,412
Activity Months12
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 (2026-01) – NNPDF/nnpdf: Delivered QED fit configuration documentation improvements, including new parameters and fiatlux usage guidance. No major bugs fixed this month. Impact: improved onboarding, reproducibility, and readiness for future experimentation. Technologies demonstrated: documentation best practices, version control, and domain knowledge in QED fits. Business value includes faster onboarding and fewer misconfigurations, enabling quicker experimentation cycles.
1 Commits • 1 Features
Jan 1, 2026January 2026 (2026-01) – NNPDF/nnpdf: Delivered QED fit configuration documentation improvements, including new parameters and fiatlux usage guidance. No major bugs fixed this month. Impact: improved onboarding, reproducibility, and readiness for future experimentation. Technologies demonstrated: documentation best practices, version control, and domain knowledge in QED fits. Business value includes faster onboarding and fewer misconfigurations, enabling quicker experimentation cycles.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12. Focused on enhancing end-user documentation for LuxQED QED fits and photon PDF usage in NNPDF/nnpdf. Delivered a dedicated LuxQED QED Fits Documentation and Photon PDF Usage Guide detailing setup, usage, and generation/download of photon PDFs. No major bugs fixed this month. Overall impact: improved onboarding, reproducibility, and adoption of LuxQED workflows, reducing setup time and support overhead. Technologies/skills demonstrated: technical writing, documentation tooling, QED fits knowledge, LuxQED, photon PDFs, and Git-based collaboration.
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12. Focused on enhancing end-user documentation for LuxQED QED fits and photon PDF usage in NNPDF/nnpdf. Delivered a dedicated LuxQED QED Fits Documentation and Photon PDF Usage Guide detailing setup, usage, and generation/download of photon PDFs. No major bugs fixed this month. Overall impact: improved onboarding, reproducibility, and adoption of LuxQED workflows, reducing setup time and support overhead. Technologies/skills demonstrated: technical writing, documentation tooling, QED fits knowledge, LuxQED, photon PDFs, and Git-based collaboration.
November 2025
28 Commits • 7 Features
Nov 1, 2025Month 2025-11 — NNPDF/nnpdf: Delivered a comprehensive set of photon workflow improvements, focused on reliability, performance, and maintainability. Centralized photon computation and path handling to streamline processing, strengthened guards and checks, and improved testing and logging to ensure robust operation in production. Key accomplishments and business value: - Stabilized and accelerated photon-related workflows, enabling safer replicas and consistent results across runs, which reduces debugging time in production and accelerates feature delivery for downstream analytics. - Enhanced system observability and test fidelity, improving confidence in results and lowering the risk of regression across major photon workflows. - Reduced maintenance burden through code quality cleanups, serialization stability improvements, and clearer data structures for photon replicas. Top features delivered (business value in parentheses): - Photon download and path infrastructure (ensures correct photon provisioning and controlled source of photon data). - Photon computation relocation to vp-setupfit (centralizes compute workflow for consistency and easier maintenance). - Photon compute checks and enforcement (ensures correctness and configurable compute behavior). - Photon computation correctness and API changes (improves shape handling, naming, performance, and support for replicas). - Loader/resource handling improvements and non-photon guards (reliable initialization and resource filtering). - Logging improvements and data structure enhancements (better runtime insight and faster access to photon replicas). Major bugs fixed (quality and stability): - Photon resource naming fixes and adjusted naming behavior for computed photons. - Compute/index photon fixes and enforcement logic (including force_compute when specified). - Testing assets and plotting alignment (shapes, logs, and test visuals). - Fiatlux execution error handling enhancements. - Joblib serialization stability and class isolation to avoid pickling issues. - Code quality and configuration cleanup (unused imports removed, pyproject cleanup, pre-commit setup). Overall impact and accomplishments: - Reduced end-to-end photon workflow friction, enabling more predictable results and faster iteration cycles. - Improved reliability and maintainability of the NNPDF photon pipeline, including better support for replicas and distributed execution. - Clearer instrumentation and test alignment, reducing post-release hotfixes and improving confidence in production deployments. Technologies/skills demonstrated: - Python refactoring, API design, and architectural consolidation (vp-setupfit integration, replica support). - Data structure optimization (photon replicas stored in a dictionary) for faster access. - Serialization and debugging improvements (joblib handling, pickling stability). - Observability, logging strategy, and test asset management. - Build quality enhancements (pre-commit, configuration cleanup) and loader/resource handling improvements.
28 Commits • 7 Features
Nov 1, 2025Month 2025-11 — NNPDF/nnpdf: Delivered a comprehensive set of photon workflow improvements, focused on reliability, performance, and maintainability. Centralized photon computation and path handling to streamline processing, strengthened guards and checks, and improved testing and logging to ensure robust operation in production. Key accomplishments and business value: - Stabilized and accelerated photon-related workflows, enabling safer replicas and consistent results across runs, which reduces debugging time in production and accelerates feature delivery for downstream analytics. - Enhanced system observability and test fidelity, improving confidence in results and lowering the risk of regression across major photon workflows. - Reduced maintenance burden through code quality cleanups, serialization stability improvements, and clearer data structures for photon replicas. Top features delivered (business value in parentheses): - Photon download and path infrastructure (ensures correct photon provisioning and controlled source of photon data). - Photon computation relocation to vp-setupfit (centralizes compute workflow for consistency and easier maintenance). - Photon compute checks and enforcement (ensures correctness and configurable compute behavior). - Photon computation correctness and API changes (improves shape handling, naming, performance, and support for replicas). - Loader/resource handling improvements and non-photon guards (reliable initialization and resource filtering). - Logging improvements and data structure enhancements (better runtime insight and faster access to photon replicas). Major bugs fixed (quality and stability): - Photon resource naming fixes and adjusted naming behavior for computed photons. - Compute/index photon fixes and enforcement logic (including force_compute when specified). - Testing assets and plotting alignment (shapes, logs, and test visuals). - Fiatlux execution error handling enhancements. - Joblib serialization stability and class isolation to avoid pickling issues. - Code quality and configuration cleanup (unused imports removed, pyproject cleanup, pre-commit setup). Overall impact and accomplishments: - Reduced end-to-end photon workflow friction, enabling more predictable results and faster iteration cycles. - Improved reliability and maintainability of the NNPDF photon pipeline, including better support for replicas and distributed execution. - Clearer instrumentation and test alignment, reducing post-release hotfixes and improving confidence in production deployments. Technologies/skills demonstrated: - Python refactoring, API design, and architectural consolidation (vp-setupfit integration, replica support). - Data structure optimization (photon replicas stored in a dictionary) for faster access. - Serialization and debugging improvements (joblib handling, pickling stability). - Observability, logging strategy, and test asset management. - Build quality enhancements (pre-commit, configuration cleanup) and loader/resource handling improvements.
November 2025
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10 (NNPDF/nnpdf): Key features delivered: - Reimplemented the ATLAS Z0J 8 TeV dataset, updating data structures and uncertainty definitions; removed legacy data. Introduced YAML configurations for central data and uncertainties to ensure compatibility with latest analysis methods. Result: improved accuracy, usability, and future-proofing of the dataset. Major bugs fixed: - No major bugs reported this month. Overall impact and accomplishments: - Strengthened data reliability and reproducibility for the ATLAS Z0J analyses, enabling more robust physics results and smoother onboarding for new contributors. The work aligns NNPDF/nnpdf with current analysis workflows, reducing legacy data debt and enabling faster iteration in upcoming cycles. Technologies/skills demonstrated: - Data engineering and schema modernization, YAML-driven configuration, uncertainty framework design, dataset reimplementation, Git-based collaboration, and cross-team integration with analysis pipelines.
October 2024
1 Commits • 1 Features
Oct 1, 2024Monthly summary for 2024-10 (NNPDF/nnpdf): Key features delivered: - Reimplemented the ATLAS Z0J 8 TeV dataset, updating data structures and uncertainty definitions; removed legacy data. Introduced YAML configurations for central data and uncertainties to ensure compatibility with latest analysis methods. Result: improved accuracy, usability, and future-proofing of the dataset. Major bugs fixed: - No major bugs reported this month. Overall impact and accomplishments: - Strengthened data reliability and reproducibility for the ATLAS Z0J analyses, enabling more robust physics results and smoother onboarding for new contributors. The work aligns NNPDF/nnpdf with current analysis workflows, reducing legacy data debt and enabling faster iteration in upcoming cycles. Technologies/skills demonstrated: - Data engineering and schema modernization, YAML-driven configuration, uncertainty framework design, dataset reimplementation, Git-based collaboration, and cross-team integration with analysis pipelines.
August 2024
2 Commits • 1 Features
Aug 1, 2024August 2024 monthly summary for NNPDF/nnpdf: Delivered Replica Training Workflow Enhancements, refactoring the handling of training and validation pseudodata collection for replicas to simplify logic and ensure consistent namespace usage. Also corrected naming conventions for training and validation actions related to replicas in the N3Fit configuration to improve clarity and reduce misconfiguration risk. Implemented via two commits that moved collect over replicas in n3fit_data and fixed related bug, enhancing reliability of replica experiments.
2 Commits • 1 Features
Aug 1, 2024August 2024 monthly summary for NNPDF/nnpdf: Delivered Replica Training Workflow Enhancements, refactoring the handling of training and validation pseudodata collection for replicas to simplify logic and ensure consistent namespace usage. Also corrected naming conventions for training and validation actions related to replicas in the N3Fit configuration to improve clarity and reduce misconfiguration risk. Implemented via two commits that moved collect over replicas in n3fit_data and fixed related bug, enhancing reliability of replica experiments.
August 2024
July 2024
1 Commits • 1 Features
Jul 1, 2024Month: 2024-07 Key feature delivered: - Pseudodata Saving for Multi-Replica Fitting in NNPDF/nnpdf: Added capability to save pseudodata during the fitting of multiple replicas to enhance flexibility and analysis. This enables better cross-replica comparison and reproducibility of fitting results. Commit reference captured for traceability: 5911f561a21f35768cc95240d1f1fcead728d6ae (First attempt for saving pseudodata with multiple replicas). Major bugs fixed: - No major bugs fixed this month (feature-focused delivery). Overall impact and accomplishments: - Delivered a targeted feature that extends the fitting workflow by persisting pseudodata across multiple replicas, enabling deeper analysis, validation, and reproducibility of results. - The work reduces manual work and potential inconsistencies when comparing replica fits, accelerating research cycles and improving data integrity. Technologies/skills demonstrated: - Feature design and implementation in a collaborative, multi-replica fitting context. - Version control discipline with traceable commits and change history. - Emphasis on data management and experimentation workflows relevant to uncertainty quantification. - Ability to translate research requirements into an actionable software capability that adds value to the analysis pipeline. Business value: - Improves robustness of replica-based analyses, enhances decision-making with more reliable uncertainty estimates, and supports faster iteration in model development and validation.
July 2024
1 Commits • 1 Features
Jul 1, 2024Month: 2024-07 Key feature delivered: - Pseudodata Saving for Multi-Replica Fitting in NNPDF/nnpdf: Added capability to save pseudodata during the fitting of multiple replicas to enhance flexibility and analysis. This enables better cross-replica comparison and reproducibility of fitting results. Commit reference captured for traceability: 5911f561a21f35768cc95240d1f1fcead728d6ae (First attempt for saving pseudodata with multiple replicas). Major bugs fixed: - No major bugs fixed this month (feature-focused delivery). Overall impact and accomplishments: - Delivered a targeted feature that extends the fitting workflow by persisting pseudodata across multiple replicas, enabling deeper analysis, validation, and reproducibility of results. - The work reduces manual work and potential inconsistencies when comparing replica fits, accelerating research cycles and improving data integrity. Technologies/skills demonstrated: - Feature design and implementation in a collaborative, multi-replica fitting context. - Version control discipline with traceable commits and change history. - Emphasis on data management and experimentation workflows relevant to uncertainty quantification. - Ability to translate research requirements into an actionable software capability that adds value to the analysis pipeline. Business value: - Improves robustness of replica-based analyses, enhances decision-making with more reliable uncertainty estimates, and supports faster iteration in model development and validation.
Activity
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Quality Metrics
Correctness88.4%
Maintainability87.4%
Architecture86.2%
Performance81.2%
AI Usage21.0%
Skills & Technologies
Programming Languages
BinaryCSVJupyter NotebookPythonYAMLpythonreStructuredTextyaml
Technical Skills
API DevelopmentAPI integrationBug FixingCI/CDCode CleanupCode RefactoringConfiguration ManagementConfiguration managementData AnalysisData CleanupData ConfigurationData HandlingData ManagementData ProcessingData Validation
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline