DUNE/waffles
Aug 2025 – Jan 2026
4 Months active
Languages Used
PythonYAML
Technical Skills
Configuration ManagementData AnalysisData ConfigurationData ProcessingDetector MappingScientific Computing
Gloria Piemonti
PROFILE
Gloria Piemonti
Worked on the DUNE/waffles repository to deliver data processing and analysis enhancements over four months, focusing on scientific computing and detector analytics. Developed features such as per-channel waveform analysis, mean peak computation, and enhanced SNR configuration, leveraging Python, YAML, and Jupyter Notebooks. Improved configuration management and data organization by introducing optimized YAML mappings and a new CSV schema for detector responses, enabling faster retrieval and reproducibility. Enhanced debugging and observability through structured logging and verbose output, while targeted bug fixes stabilized peak fitting and plotting workflows. The work emphasized maintainable code, robust data pipelines, and clear, actionable analytics for end users.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
4
Lines of code
6,463
Activity Months4
Your Network
21 peopleSame Organization
@campus.unimib.it1
Shared Repositories
20
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Francesca AlemannoMember
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Work History
January 2026
3 Commits • 1 Features
Jan 1, 2026January 2026: Delivered key data-organization and analysis enhancements for DUNE/waffles, along with a targeted bug fix to stabilize processing visuals. The work enables faster data retrieval, more reproducible detector analyses, and a cleaner end-user plotting experience during long-running workflows. Technologies demonstrated include Python-based CSV schema design, Jupyter notebook development for interpolation workflows, and robust plotting control.
3 Commits • 1 Features
Jan 1, 2026January 2026: Delivered key data-organization and analysis enhancements for DUNE/waffles, along with a targeted bug fix to stabilize processing visuals. The work enables faster data retrieval, more reproducible detector analyses, and a cleaner end-user plotting experience during long-running workflows. Technologies demonstrated include Python-based CSV schema design, Jupyter notebook development for interpolation workflows, and robust plotting control.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly performance summary for DUNE/waffles focusing on feature delivery and technical impact. Delivered a per-channel analysis capability to improve waveform analytics, with enhanced data processing and visualization. Overall, this work shifts measurement toward more accurate channel-level insights and faster interpretation of complex data.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly performance summary for DUNE/waffles focusing on feature delivery and technical impact. Delivered a per-channel analysis capability to improve waveform analytics, with enhanced data processing and visualization. Overall, this work shifts measurement toward more accurate channel-level insights and faster interpretation of complex data.
September 2025
1 Commits • 1 Features
Sep 1, 2025Summary for 2025-09: Key feature delivered in DUNE/waffles was Fithist Logging Verbose Output Enhancement, which augments fithist output with endpoint and channel context to improve debugging and data analysis. This was implemented via commit 36724330adc476d3a8566c9ccf6fef6bb24da2ed ('improving output print'). No major bugs were reported in the provided data. Overall, the work enhances observability, accelerates triage, and supports richer analytics, contributing to stability and faster data-driven decisions. Technologies demonstrated include Python logging augmentation, structured log messages, and end-to-end traceability of changes in the repository.
1 Commits • 1 Features
Sep 1, 2025Summary for 2025-09: Key feature delivered in DUNE/waffles was Fithist Logging Verbose Output Enhancement, which augments fithist output with endpoint and channel context to improve debugging and data analysis. This was implemented via commit 36724330adc476d3a8566c9ccf6fef6bb24da2ed ('improving output print'). No major bugs were reported in the provided data. Overall, the work enhances observability, accelerates triage, and supports richer analytics, contributing to stability and faster data-driven decisions. Technologies demonstrated include Python logging augmentation, structured log messages, and end-to-end traceability of changes in the repository.
September 2025
August 2025
6 Commits • 1 Features
Aug 1, 2025Summary for 2025-08: Delivered significant improvements to data processing in DUNE/waffles. Key features were enhanced SNR configuration and waveform analysis with updated channel mappings and parameter tuning; major bug fix improved robustness of peak fitting and baseline calibration by constraining positive parameter values. Additional config and map updates (YAML optimization and NP02 map) improved reproducibility and maintainability. Overall impact: higher data quality, more reliable signal extraction, and a more maintainable configuration framework. Technologies demonstrated include YAML configuration optimization, advanced parameterization of SNR and waveform analysis, constraint-based fitting, and detector mapping updates.
August 2025
6 Commits • 1 Features
Aug 1, 2025Summary for 2025-08: Delivered significant improvements to data processing in DUNE/waffles. Key features were enhanced SNR configuration and waveform analysis with updated channel mappings and parameter tuning; major bug fix improved robustness of peak fitting and baseline calibration by constraining positive parameter values. Additional config and map updates (YAML optimization and NP02 map) improved reproducibility and maintainability. Overall impact: higher data quality, more reliable signal extraction, and a more maintainable configuration framework. Technologies demonstrated include YAML configuration optimization, advanced parameterization of SNR and waveform analysis, constraint-based fitting, and detector mapping updates.
Activity
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Quality Metrics
Correctness83.6%
Maintainability83.6%
Architecture80.0%
Performance72.8%
AI Usage21.8%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
CSV handlingConfiguration ManagementData AnalysisData ConfigurationData ProcessingDebuggingDetector MappingJupyterJupyter NotebookLoggingPythonPython scriptingScientific ComputingSignal Processingdata analysis
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