NNPDF/nnpdf
Oct 2024 – Oct 2025
13 Months active
Languages Used
PythonYAMLBashHCLpythonyamlRSTShell
Technical Skills
Backend DevelopmentConfiguration ManagementData ManagementData ProcessingPython DevelopmentCI/CD
juacrumar
PROFILE
Juacrumar
Juan Cruz-Martinez contributed to the NNPDF/nnpdf repository by engineering robust data analysis and modeling workflows for high-energy physics. He developed and maintained backend systems for PDF evolution, dataset configuration, and reproducible fitting pipelines, leveraging Python and YAML for configuration management and scientific computing. His work included integrating JAX and TensorFlow backends, optimizing training and prediction routines, and enhancing CI/CD pipelines for cross-platform reliability. By modularizing data loading, standardizing metadata, and improving uncertainty handling, Juan ensured maintainable, scalable code that supports both legacy and modern analyses. His technical depth is reflected in thorough documentation, rigorous testing, and reproducibility-focused design.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
172Total
Bugs
20
Commits
172
Features
79
Lines of code
180,001
Activity Months13
Work History
October 2025
2 Commits • 2 Features
Oct 1, 2025Monthly summary for 2025-10 (NNPDF/nnpdf). Delivered two high-impact updates that improve documentation accuracy and Monte Carlo (MC) uncertainty handling, with positive implications for data provenance, model evaluation, and reproducibility across analyses. No major bugs fixed this month. Key work focused on aligning collaborator affiliations and refining ATLAS Z0J 8 TeV MC uncertainty handling.
2 Commits • 2 Features
Oct 1, 2025Monthly summary for 2025-10 (NNPDF/nnpdf). Delivered two high-impact updates that improve documentation accuracy and Monte Carlo (MC) uncertainty handling, with positive implications for data provenance, model evaluation, and reproducibility across analyses. No major bugs fixed this month. Key work focused on aligning collaborator affiliations and refining ATLAS Z0J 8 TeV MC uncertainty handling.
October 2025
September 2025
8 Commits • 4 Features
Sep 1, 2025In September 2025, the NNPDF/nnpdf effort delivered a cohesive set of dataset configuration enhancements, a baseline runcard for the NNPDF4.1 series, and CI/CD improvements that collectively improve data analysis reliability, reproducibility, and maintainability. The work emphasized compatibility with existing workflows while enabling new physics analyses, and it was complemented by clear documentation improvements to boost discoverability and collaboration.
September 2025
8 Commits • 4 Features
Sep 1, 2025In September 2025, the NNPDF/nnpdf effort delivered a cohesive set of dataset configuration enhancements, a baseline runcard for the NNPDF4.1 series, and CI/CD improvements that collectively improve data analysis reliability, reproducibility, and maintainability. The work emphasized compatibility with existing workflows while enabling new physics analyses, and it was complemented by clear documentation improvements to boost discoverability and collaboration.
August 2025
4 Commits • 3 Features
Aug 1, 2025Concise monthly summary for 2025-08 focusing on key business value and technical achievements for NNPDF/nnpdf. 1) Key features delivered - JAX backend integration and training optimization: Added CI job to run tests with the JAX backend, configured environment for JAX, installed dependencies, and adapted MetaModel to correctly handle JAX backend when determining training replicas. Commit: 072673d0dd7a71c0b178a0663293201bd78a9ab7 ("add jax backend to the CI tests"). - JAX backend training step optimization: Introduced STEPS_PER_EPOCH = 100 with a JAX-specific override to use 1 step per epoch; simplified training step logic to leverage the constant or 1 when epochs are fewer than the constant to reduce overhead. Commit: d2aa5569b72f7b8bfbef634753f215c83c2aead9 ("fix STEPS_PER_EPOCH"). - NNPDF configuration: ekos_path option: Updated nnprofile_example.yaml to add ekos_path configuration and clarified that downloaded theories, ekos, and related data are stored under subdirectories in the NNPDF share path. Commit: a768963799014ee9fef1f911ab716070de513914 ("Update nnprofile_example.yaml"). 2) Major bugs fixed - N3LHAPDFSet t0 central value handling bug fix: Ensure N3LHAPDFSet correctly returns only the central PDF value when is_t0 is true, avoiding processing all replicas. Commit: 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 ("fix t0"). 3) Overall impact and accomplishments - Strengthened reliability and reproducibility by expanding automated testing to include JAX backend and by clarifying data storage paths for NNPDF components. - Reduced training overhead for JAX runs by introducing a fixed-step training regime per epoch, enabling faster iteration cycles and lower CI runtime cost. - Fixed correctness issue in central value selection for t0 scenarios, preventing incorrect replica processing and ensuring accurate downstream results. 4) Technologies, skills demonstrated - CI integration and environment management for ML backends (JAX) - ML training optimization and pipeline simplification - YAML configuration management for data and theory storage paths - Bug diagnosis and targeted fixes in PDF set handling Commit references: - 072673d0dd7a71c0b178a0663293201bd78a9ab7 - d2aa5569b72f7b8bfbef634753f215c83c2aead9 - 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 - a768963799014ee9fef1f911ab716070de513914
4 Commits • 3 Features
Aug 1, 2025Concise monthly summary for 2025-08 focusing on key business value and technical achievements for NNPDF/nnpdf. 1) Key features delivered - JAX backend integration and training optimization: Added CI job to run tests with the JAX backend, configured environment for JAX, installed dependencies, and adapted MetaModel to correctly handle JAX backend when determining training replicas. Commit: 072673d0dd7a71c0b178a0663293201bd78a9ab7 ("add jax backend to the CI tests"). - JAX backend training step optimization: Introduced STEPS_PER_EPOCH = 100 with a JAX-specific override to use 1 step per epoch; simplified training step logic to leverage the constant or 1 when epochs are fewer than the constant to reduce overhead. Commit: d2aa5569b72f7b8bfbef634753f215c83c2aead9 ("fix STEPS_PER_EPOCH"). - NNPDF configuration: ekos_path option: Updated nnprofile_example.yaml to add ekos_path configuration and clarified that downloaded theories, ekos, and related data are stored under subdirectories in the NNPDF share path. Commit: a768963799014ee9fef1f911ab716070de513914 ("Update nnprofile_example.yaml"). 2) Major bugs fixed - N3LHAPDFSet t0 central value handling bug fix: Ensure N3LHAPDFSet correctly returns only the central PDF value when is_t0 is true, avoiding processing all replicas. Commit: 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 ("fix t0"). 3) Overall impact and accomplishments - Strengthened reliability and reproducibility by expanding automated testing to include JAX backend and by clarifying data storage paths for NNPDF components. - Reduced training overhead for JAX runs by introducing a fixed-step training regime per epoch, enabling faster iteration cycles and lower CI runtime cost. - Fixed correctness issue in central value selection for t0 scenarios, preventing incorrect replica processing and ensuring accurate downstream results. 4) Technologies, skills demonstrated - CI integration and environment management for ML backends (JAX) - ML training optimization and pipeline simplification - YAML configuration management for data and theory storage paths - Bug diagnosis and targeted fixes in PDF set handling Commit references: - 072673d0dd7a71c0b178a0663293201bd78a9ab7 - d2aa5569b72f7b8bfbef634753f215c83c2aead9 - 6b6ddf441838ef1f57b99c4db7a410b88ad199b2 - a768963799014ee9fef1f911ab716070de513914
August 2025
July 2025
8 Commits • 6 Features
Jul 1, 2025Concise monthly summary for 2025-07 focusing on stability, reproducibility, and maintainability for NNPDF/nnpdf. Key features delivered include: (1) JAX compatibility enhancements and compute_loss stability achieved by making input a class attribute to avoid tracking and saving weights as NumPy objects, and investigating spurious compute_loss calls each epoch; (2) JSON logging of chi2 values and dependency pinning to exact version to ensure stable builds; (3) LHAPDF compatibility layer moved to wrappers for maintainability; (4) Testing workflow enhancement with postfit integration to Nolha tests and lhapdf-like functions; (5) CI workflow alignment to use the latest stable reference set (fitbot 4.1.0 tag). Additional improvements include refactoring for rule uniqueness checks in FilterRule/CoreConfig. Major bugs fixed include test suite compatibility with dependencies (eko 0.15.2, numpy >=2.0) and corrections in-data metric calculation and data filtering in ModelTrainer. Overall impact: increased reliability and reproducibility of experiments, tighter validation, and streamlined dependency management, enabling faster iteration and onboarding. Technologies demonstrated: Python, JAX, LHAPDF integration, dependency pinning, CI/CD (fitbot), and test modernization.
July 2025
8 Commits • 6 Features
Jul 1, 2025Concise monthly summary for 2025-07 focusing on stability, reproducibility, and maintainability for NNPDF/nnpdf. Key features delivered include: (1) JAX compatibility enhancements and compute_loss stability achieved by making input a class attribute to avoid tracking and saving weights as NumPy objects, and investigating spurious compute_loss calls each epoch; (2) JSON logging of chi2 values and dependency pinning to exact version to ensure stable builds; (3) LHAPDF compatibility layer moved to wrappers for maintainability; (4) Testing workflow enhancement with postfit integration to Nolha tests and lhapdf-like functions; (5) CI workflow alignment to use the latest stable reference set (fitbot 4.1.0 tag). Additional improvements include refactoring for rule uniqueness checks in FilterRule/CoreConfig. Major bugs fixed include test suite compatibility with dependencies (eko 0.15.2, numpy >=2.0) and corrections in-data metric calculation and data filtering in ModelTrainer. Overall impact: increased reliability and reproducibility of experiments, tighter validation, and streamlined dependency management, enabling faster iteration and onboarding. Technologies demonstrated: Python, JAX, LHAPDF integration, dependency pinning, CI/CD (fitbot), and test modernization.
June 2025
13 Commits • 5 Features
Jun 1, 2025June 2025 (2025-06) monthly summary for NNPDF/nnpdf focusing on delivering business value through a modernized, robust PDF evolution workflow, expanded documentation, and stronger test coverage. The work emphasizes maintainability, reproducibility, and build stability, with clear traceability to commits.
13 Commits • 5 Features
Jun 1, 2025June 2025 (2025-06) monthly summary for NNPDF/nnpdf focusing on delivering business value through a modernized, robust PDF evolution workflow, expanded documentation, and stronger test coverage. The work emphasizes maintainability, reproducibility, and build stability, with clear traceability to commits.
June 2025
May 2025
11 Commits • 5 Features
May 1, 2025May 2025 delivered targeted feature enhancements and performance optimizations for NNPDF/nnpdf, with a focus on reproducibility, efficiency, and maintainability. Key deliverables include: 1) Extended Legacy Data/Theory support for ATLAS_Z0J_8TEV_PT-M, including a legacy_data_10 variant to ensure reproducible analyses across legacy and updated grids; 2) Covariance Matrix Generation API improvements enabling construction from a list of DataSetSpecs with optional data_input and added safety checks; 3) Memoization of predictions/central_predictions to reduce recomputation in plotting and analysis workflows; 4) Speedups in Pineappl theories convolutions via pre-ordered fktables and einsum; 5) Documentation and code comments improvements for EKO/Evolven3fit. Business value realized includes more reliable cross-grid reproducibility, faster analysis pipelines, and clearer developer/docs. No major bugs fixed this month; primary focus on feature delivery and performance gains.
May 2025
11 Commits • 5 Features
May 1, 2025May 2025 delivered targeted feature enhancements and performance optimizations for NNPDF/nnpdf, with a focus on reproducibility, efficiency, and maintainability. Key deliverables include: 1) Extended Legacy Data/Theory support for ATLAS_Z0J_8TEV_PT-M, including a legacy_data_10 variant to ensure reproducible analyses across legacy and updated grids; 2) Covariance Matrix Generation API improvements enabling construction from a list of DataSetSpecs with optional data_input and added safety checks; 3) Memoization of predictions/central_predictions to reduce recomputation in plotting and analysis workflows; 4) Speedups in Pineappl theories convolutions via pre-ordered fktables and einsum; 5) Documentation and code comments improvements for EKO/Evolven3fit. Business value realized includes more reliable cross-grid reproducibility, faster analysis pipelines, and clearer developer/docs. No major bugs fixed this month; primary focus on feature delivery and performance gains.
April 2025
15 Commits • 9 Features
Apr 1, 2025April 2025 performance summary for NNPDF/nnpdf: Delivered a cohesive set of features, reliability fixes, and performance improvements that streamline onboarding, standardize configuration, and enhance the accuracy and scalability of modeling workflows. Focused on improving installation ease, dataset configurability, hyperparameter optimization relevance, and CI/CD robustness. Demonstrated strong collaboration between configuration-driven design, reproducible experiments, and robust deployment practices to accelerate deliverables and maintainability.
15 Commits • 9 Features
Apr 1, 2025April 2025 performance summary for NNPDF/nnpdf: Delivered a cohesive set of features, reliability fixes, and performance improvements that streamline onboarding, standardize configuration, and enhance the accuracy and scalability of modeling workflows. Focused on improving installation ease, dataset configurability, hyperparameter optimization relevance, and CI/CD robustness. Demonstrated strong collaboration between configuration-driven design, reproducible experiments, and robust deployment practices to accelerate deliverables and maintainability.
April 2025
March 2025
41 Commits • 19 Features
Mar 1, 2025Monthly summary for 2025-03 (NNPDF/nnpdf): Delivered user-facing onboarding improvements, developer-oriented quality upgrades, and stability fixes that collectively reduce setup friction, accelerate releases, and improve maintainability. Highlights include onboarding-focused doc updates, API-aligned tutorials, and a new theory card enriching the content library. Implemented robust CI/CD and packaging enhancements to shorten release cycles and ensure cross-platform reliability, while updating dependencies to maintain security and compatibility.
March 2025
41 Commits • 19 Features
Mar 1, 2025Monthly summary for 2025-03 (NNPDF/nnpdf): Delivered user-facing onboarding improvements, developer-oriented quality upgrades, and stability fixes that collectively reduce setup friction, accelerate releases, and improve maintainability. Highlights include onboarding-focused doc updates, API-aligned tutorials, and a new theory card enriching the content library. Implemented robust CI/CD and packaging enhancements to shorten release cycles and ensure cross-platform reliability, while updating dependencies to maintain security and compatibility.
February 2025
14 Commits • 4 Features
Feb 1, 2025February 2025 (NNPDF/nnpdf): Delivered core API enhancements, data processing robustness, and CI/config improvements, resulting in more reliable fits, consistent data naming, and easier maintenance. Key features include: 1) ValidPhys: Flexible parse_pdf API now accepts PDF objects directly, returning the PDF instance when applicable; type hints added and unnecessary annotation removed to streamline usage. 2) Dataset naming convention modernization in n3fit: default enforcement of new data names, deprecation of legacy names, and removal of the old fallback/config option to improve data consistency and user guidance. 3) Data handling and fitting robustness across single-point and replicated data: unified treatment of 1-point datasets for sequential and parallel fits; per-replica pseudodata storage; improved masking, covariance handling, chi2 calculations, and reinforced reliability through testing. 4) CI/Dependency/Config hygiene and testing infrastructure: optional pymongo dependency, aligned fitbot environment, ignoring untracked files, and enforced import sorting for maintainability. These changes collectively improve data integrity, reproduceability, and operational reliability, enabling faster, more reliable model validation and easier future maintenance.
14 Commits • 4 Features
Feb 1, 2025February 2025 (NNPDF/nnpdf): Delivered core API enhancements, data processing robustness, and CI/config improvements, resulting in more reliable fits, consistent data naming, and easier maintenance. Key features include: 1) ValidPhys: Flexible parse_pdf API now accepts PDF objects directly, returning the PDF instance when applicable; type hints added and unnecessary annotation removed to streamline usage. 2) Dataset naming convention modernization in n3fit: default enforcement of new data names, deprecation of legacy names, and removal of the old fallback/config option to improve data consistency and user guidance. 3) Data handling and fitting robustness across single-point and replicated data: unified treatment of 1-point datasets for sequential and parallel fits; per-replica pseudodata storage; improved masking, covariance handling, chi2 calculations, and reinforced reliability through testing. 4) CI/Dependency/Config hygiene and testing infrastructure: optional pymongo dependency, aligned fitbot environment, ignoring untracked files, and enforced import sorting for maintainability. These changes collectively improve data integrity, reproduceability, and operational reliability, enabling faster, more reliable model validation and easier future maintenance.
February 2025
January 2025
22 Commits • 8 Features
Jan 1, 2025January 2025 performance summary for NNPDF/nnpdf focused on delivering a stable, scalable data foundation for downstream analyses and improved packaging for broader distribution. The month emphasized data organization, metadata consistency, dataset freshness, and targeted bug fixes to enhance reliability and scientific rigor, while improving developer efficiency through tooling and labeling improvements.
January 2025
22 Commits • 8 Features
Jan 1, 2025January 2025 performance summary for NNPDF/nnpdf focused on delivering a stable, scalable data foundation for downstream analyses and improved packaging for broader distribution. The month emphasized data organization, metadata consistency, dataset freshness, and targeted bug fixes to enhance reliability and scientific rigor, while improving developer efficiency through tooling and labeling improvements.
December 2024
4 Commits • 4 Features
Dec 1, 2024December 2024 Monthly Summary for repository NNPDF/nnpdf. This period focused on delivering robust backend improvements, standardizing plotting behavior, and improving documentation and version reporting to enhance reproducibility and developer experience.
4 Commits • 4 Features
Dec 1, 2024December 2024 Monthly Summary for repository NNPDF/nnpdf. This period focused on delivering robust backend improvements, standardizing plotting behavior, and improving documentation and version reporting to enhance reproducibility and developer experience.
December 2024
November 2024
28 Commits • 9 Features
Nov 1, 2024November 2024 (2024-11) monthly summary for NNPDF/nnpdf focused on delivering business value through robustness, reproducibility, and expanded experimentation. Key efforts spanned CI/CD reliability, code quality, testing, and data packaging, with targeted fixes to ensure correctness and clearer diagnostics. The team stabilized the core workflow, broadened testing and experimental scope, and introduced new capabilities for performance and variant analysis, while addressing foundational bugs that affected input handling, kinematics, error messages, and tolerances.
November 2024
28 Commits • 9 Features
Nov 1, 2024November 2024 (2024-11) monthly summary for NNPDF/nnpdf focused on delivering business value through robustness, reproducibility, and expanded experimentation. Key efforts spanned CI/CD reliability, code quality, testing, and data packaging, with targeted fixes to ensure correctness and clearer diagnostics. The team stabilized the core workflow, broadened testing and experimental scope, and introduced new capabilities for performance and variant analysis, while addressing foundational bugs that affected input handling, kinematics, error messages, and tolerances.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for NNPDF/nnpdf: Delivered two high-impact items: backward compatibility for legacy theory cards in EKO and FIATLUX_NOTFIXED dataset support. Implemented through deprecation-aware key mapping and new meta package, enhancing data ingestion reliability, maintaining compatibility with older EKO configurations, and enabling handling of FIATLUX_NOTFIXED datasets.
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for NNPDF/nnpdf: Delivered two high-impact items: backward compatibility for legacy theory cards in EKO and FIATLUX_NOTFIXED dataset support. Implemented through deprecation-aware key mapping and new meta package, enhancing data ingestion reliability, maintaining compatibility with older EKO configurations, and enabling handling of FIATLUX_NOTFIXED datasets.
October 2024
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Quality Metrics
Correctness86.2%
Maintainability87.0%
Architecture82.2%
Performance76.8%
AI Usage20.2%
Skills & Technologies
Programming Languages
BashBibTeXC++HCLJupyter NotebookMakefileMarkdownPythonRSTShell
Technical Skills
API DevelopmentAPI IntegrationBackend DevelopmentBug FixingBuild ConfigurationBuild System ConfigurationBuild SystemsCI/CDCI/CD ConfigurationCLI DevelopmentCachingCode CleanupCode CoverageCode DocumentationCode Formatting
Repositories Contributed To
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