NNPDF/nnpdf
Oct 2024 – Aug 2025
7 Months active
Languages Used
PythonTOMLYAMLBashShellpythonyamlJupyter Notebook
Technical Skills
Bug FixingData VisualizationDependency ManagementPackage ManagementPlotting LibrariesScientific Computing
Radonirinaunimi
PROFILE
Radonirinaunimi
Rado Rabemananjara contributed to the NNPDF/nnpdf repository by developing and refining data processing pipelines, hyperparameter optimization modules, and plotting utilities for physics data analysis. He implemented robust uncertainty data handling and standardized metadata across datasets, improving data fidelity and reducing misconfigurations. Using Python, YAML, and Pandas, Rado enhanced compatibility with evolving dependencies like Matplotlib and PineAPPL, ensuring reliable visualization and analysis workflows. His work on the HyperLoss module introduced new loss functions and improved reward calculations, supporting more precise model tuning. Throughout, he emphasized maintainable code, commit-level traceability, and workflow automation, demonstrating depth in scientific computing and DevOps.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
51Total
Bugs
7
Commits
51
Features
12
Lines of code
217,276
Activity Months7
Your Network
30 peopleSame Organization
@nikhef.nl5
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered the Chi2p Loss Function for Hyper-Optimization in NNPDF/nnpdf, expanding the loss options and integrating its calculation into the HyperLoss class to enable finer model tuning and metric-based optimization. There were no major bugs fixed this month; the focus was on feature delivery, integration, and code quality. Overall, this work strengthens the model selection pipeline by providing a richer optimization signal, enabling more reliable hyperparameter searches and potential gains in generalization. Technologies demonstrated include Python-based metric integration, modular hyper-optimization workflows, and version-control discipline (e.g., clear commit history).
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered the Chi2p Loss Function for Hyper-Optimization in NNPDF/nnpdf, expanding the loss options and integrating its calculation into the HyperLoss class to enable finer model tuning and metric-based optimization. There were no major bugs fixed this month; the focus was on feature delivery, integration, and code quality. Overall, this work strengthens the model selection pipeline by providing a richer optimization signal, enabling more reliable hyperparameter searches and potential gains in generalization. Technologies demonstrated include Python-based metric integration, modular hyper-optimization workflows, and version-control discipline (e.g., clear commit history).
August 2025
June 2025
10 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary highlighting key business value and technical accomplishments across the NNPDF/nnpdf repository. Focused on enabling reliable experiments, faster iteration, and robust data handling in the PineAPPL-enabled workflow while strengthening core utilities and correctness in hyperparameter optimization.
June 2025
10 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary highlighting key business value and technical accomplishments across the NNPDF/nnpdf repository. Focused on enabling reliable experiments, faster iteration, and robust data handling in the PineAPPL-enabled workflow while strengthening core utilities and correctness in hyperparameter optimization.
April 2025
3 Commits
Apr 1, 2025April 2025 monthly summary for NNPDF/nnpdf: Implemented critical bug fixes to improve reliability and data handling across rapidity configurations. Delivered corrections to hyperparameter optimization phi metric in HyperLoss (phi2) and aligned rapidity data processing by swapping configuration files and standardizing eta bin definitions. These changes reduce erroneous rewards, improve data consistency, and strengthen the foundation for robust experiments and model tuning.
3 Commits
Apr 1, 2025April 2025 monthly summary for NNPDF/nnpdf: Implemented critical bug fixes to improve reliability and data handling across rapidity configurations. Delivered corrections to hyperparameter optimization phi metric in HyperLoss (phi2) and aligned rapidity data processing by swapping configuration files and standardizing eta bin definitions. These changes reduce erroneous rewards, improve data consistency, and strengthen the foundation for robust experiments and model tuning.
April 2025
February 2025
4 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for NNPDF/nnpdf: Delivered targeted fixes to PineAPPL data parsing and structure function extraction to ensure correct Q2 and x binning, proper polarized/unpolarized PDF identification, and adaptation to API changes. Upgraded Matplotlib compatibility to support newer versions and preserve downstream library compatibility. These efforts improve data accuracy, reduce downstream errors, and enable more reliable analyses and visualization workflows.
February 2025
4 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for NNPDF/nnpdf: Delivered targeted fixes to PineAPPL data parsing and structure function extraction to ensure correct Q2 and x binning, proper polarized/unpolarized PDF identification, and adaptation to API changes. Upgraded Matplotlib compatibility to support newer versions and preserve downstream library compatibility. These efforts improve data accuracy, reduce downstream errors, and enable more reliable analyses and visualization workflows.
January 2025
9 Commits • 2 Features
Jan 1, 2025Monthly summary for 2025-01 for repository NNPDF/nnpdf. Focused on stabilizing uncertainty data handling, standardizing metadata, and enabling DYP_FT integration. Deliveries reduce misconfigurations, improve data fidelity across datasets, and lay groundwork for scalable uncertainty analyses and downstream workflows.
9 Commits • 2 Features
Jan 1, 2025Monthly summary for 2025-01 for repository NNPDF/nnpdf. Focused on stabilizing uncertainty data handling, standardizing metadata, and enabling DYP_FT integration. Deliveries reduce misconfigurations, improve data fidelity across datasets, and lay groundwork for scalable uncertainty analyses and downstream workflows.
January 2025
November 2024
20 Commits • 3 Features
Nov 1, 2024November 2024 performance summary for NNPDF/nnpdf focused on data integrity, reliability, and CI/CD efficiency. Delivered cross-dataset data integrity and metadata standardization for CMS and LHC datasets, refined uncertainties and YAML precision, and aligned kinematic variables in LHCb/CMS configurations to improve data consistency and downstream analysis reliability. Strengthened plotting robustness with NaN-safe handling, data-vs-theory visualization testing, and standardized dataset configurations, increasing confidence in figures used for reports and publications. Improved FKTableData convolution typing, parsing, and safety with object-based convolutions, stricter error handling, and immutability guarantees to reduce runtime errors and simplify maintenance. Advanced CI/CD automation and workflow enhancements, including regenerating common data, CI git config adjustments, PR checkout fixes, and dependency updates, resulting in reduced CI flakiness and faster feedback cycles.
November 2024
20 Commits • 3 Features
Nov 1, 2024November 2024 performance summary for NNPDF/nnpdf focused on data integrity, reliability, and CI/CD efficiency. Delivered cross-dataset data integrity and metadata standardization for CMS and LHC datasets, refined uncertainties and YAML precision, and aligned kinematic variables in LHCb/CMS configurations to improve data consistency and downstream analysis reliability. Strengthened plotting robustness with NaN-safe handling, data-vs-theory visualization testing, and standardized dataset configurations, increasing confidence in figures used for reports and publications. Improved FKTableData convolution typing, parsing, and safety with object-based convolutions, stricter error handling, and immutability guarantees to reduce runtime errors and simplify maintenance. Advanced CI/CD automation and workflow enhancements, including regenerating common data, CI git config adjustments, PR checkout fixes, and dependency updates, resulting in reduced CI flakiness and faster feedback cycles.
October 2024
4 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for NNPDF/nnpdf focusing on plotting enhancements and reliability. Key accomplishments include upgrading Matplotlib to 3.9 across project artifacts, fixing a Matplotlib 3.8+ offset calculation bug that affected the first data point and axis relimitation, and introducing a manual y-range computation utility (extract_ylims) to ensure correct y-axis scaling for ratio plots using ScaledTranslation. These changes improve plotting accuracy, stability, and interoperability with latest dependencies, reducing manual maintenance and enabling more reliable data visualizations in downstream analyses. Commit-level traceability is provided for changes: Matplotlib upgrade (3fa95bc... and 721932b...), offset bug fix (2fb62b5...), and y-range utility (3625d3c...goa85).
4 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for NNPDF/nnpdf focusing on plotting enhancements and reliability. Key accomplishments include upgrading Matplotlib to 3.9 across project artifacts, fixing a Matplotlib 3.8+ offset calculation bug that affected the first data point and axis relimitation, and introducing a manual y-range computation utility (extract_ylims) to ensure correct y-axis scaling for ratio plots using ScaledTranslation. These changes improve plotting accuracy, stability, and interoperability with latest dependencies, reducing manual maintenance and enabling more reliable data visualizations in downstream analyses. Commit-level traceability is provided for changes: Matplotlib upgrade (3fa95bc... and 721932b...), offset bug fix (2fb62b5...), and y-range utility (3625d3c...goa85).
October 2024
Activity
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Quality Metrics
Correctness86.2%
Maintainability86.2%
Architecture83.2%
Performance75.0%
AI Usage21.2%
Skills & Technologies
Programming Languages
BashJupyter NotebookNumPyPandasPythonShellTOMLYAMLpythonyaml
Technical Skills
API IntegrationBackend DevelopmentBash ScriptingBug FixingBuild ConfigurationCI/CDCode RefactoringConfigurationConfiguration ManagementData AnalysisData ConfigurationData FormattingData ManagementData ProcessingData Validation
Repositories Contributed To
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