DUNE/waffles
Apr 2025 – Feb 2026
7 Months active
Languages Used
MarkdownRSTPythonJupyter NotebookreStructuredTextrstYAML
Technical Skills
DocumentationTechnical WritingData ProcessingData VisualizationFile ManagementScripting
Francesca Alemanno
PROFILE
Francesca Alemanno
Francesca Alemanno developed and enhanced data analysis and visualization workflows for the DUNE/waffles repository, focusing on scientific computing challenges in waveform and calibration data. She designed Jupyter Notebook-based pipelines for multi-channel waveform analysis, implemented YAML-driven configuration for PMT calibration, and improved onboarding through comprehensive documentation. Using Python, Pandas, and Matplotlib, Francesca introduced features such as templated normalization, rise/fall time analysis, and scalable plotting for large datasets. Her work emphasized reproducibility, maintainability, and usability, streamlining data extraction, processing, and visualization. The depth of her contributions enabled faster, more reliable analysis and improved the overall developer and researcher experience.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
29Total
Bugs
0
Commits
29
Features
14
Lines of code
78,659
Activity Months7
Work History
February 2026
3 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) monthly summary for DUNE/waffles focused on delivering scalable PMT calibration configuration and improved waveform analysis workflow, with emphasis on reproducibility, maintainability, and data quality.
3 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) monthly summary for DUNE/waffles focused on delivering scalable PMT calibration configuration and improved waveform analysis workflow, with emphasis on reproducibility, maintainability, and data quality.
February 2026
January 2026
8 Commits • 2 Features
Jan 1, 2026January 2026 was driven by delivering a comprehensive enhancement to the DUNE/waffles waveform analysis workflow, paired with onboarding and setup improvements. The work increased analysis speed, reproducibility, and usability for multi-channel data through templated normalization, template management, and improved notebook readability. No major bug fixes were logged; ongoing maintenance included code and notebook cleanup.
January 2026
8 Commits • 2 Features
Jan 1, 2026January 2026 was driven by delivering a comprehensive enhancement to the DUNE/waffles waveform analysis workflow, paired with onboarding and setup improvements. The work increased analysis speed, reproducibility, and usability for multi-channel data through templated normalization, template management, and improved notebook readability. No major bug fixes were logged; ongoing maintenance included code and notebook cleanup.
December 2025
4 Commits • 3 Features
Dec 1, 2025December 2025 (DUNE/waffles): Delivered key plotting and waveform-analysis enhancements that enable faster, more reliable cross-channel exploration and reproducible workflows. Implemented a return_figs option in plot_grid and plot_detectors to support figure overlays and retrieval, along with a refactor of PlotUtils to improve performance and stability. Added YAML-driven cuts and a waveform selection class to compute and visualize average waveforms across multiple channels, plus a dedicated folder for signal-shape studies. Launched a Jupyter notebook workflow to clean, average, and visualize waveforms in a persistence plot for streamlined analysis. These changes enhance business value by accelerating data exploration, improving visualization reliability, and enabling configurable analysis pipelines.
4 Commits • 3 Features
Dec 1, 2025December 2025 (DUNE/waffles): Delivered key plotting and waveform-analysis enhancements that enable faster, more reliable cross-channel exploration and reproducible workflows. Implemented a return_figs option in plot_grid and plot_detectors to support figure overlays and retrieval, along with a refactor of PlotUtils to improve performance and stability. Added YAML-driven cuts and a waveform selection class to compute and visualize average waveforms across multiple channels, plus a dedicated folder for signal-shape studies. Launched a Jupyter notebook workflow to clean, average, and visualize waveforms in a persistence plot for streamlined analysis. These changes enhance business value by accelerating data exploration, improving visualization reliability, and enabling configurable analysis pipelines.
December 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly performance summary for 2025-10 focusing on feature delivery and impact. In DUNE/waffles, introduced the Periodic Calibration Results Visualization Notebook to enable visual analysis of calibration data, leveraging pandas for data handling and matplotlib/seaborn for plotting. This delivers improved visibility into calibration behavior and supports faster debugging and decision-making for calibration pipelines. Commit reference for the deliverable: 54ea669d4914b5bc33ee009f1a9100e112df9c1c.
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly performance summary for 2025-10 focusing on feature delivery and impact. In DUNE/waffles, introduced the Periodic Calibration Results Visualization Notebook to enable visual analysis of calibration data, leveraging pandas for data handling and matplotlib/seaborn for plotting. This delivers improved visibility into calibration behavior and supports faster debugging and decision-making for calibration pipelines. Commit reference for the deliverable: 54ea669d4914b5bc33ee009f1a9100e112df9c1c.
September 2025
8 Commits • 3 Features
Sep 1, 2025In Sep 2025, the DUNE/waffles work focused on improving developer onboarding, expanding data analysis capabilities, and enhancing reproducibility. Key activities included extensive documentation consolidation for environment setup, library usage, data access, and storage/server access; introduction of an offset scan feature with a Jupyter Notebook to analyze ADC offsets across channels/modules from waveform data; and significant enhancements to beam histograms notebooks for photo-electron studies, including a new notebook and visuals. These efforts reduce onboarding time, enable more robust data analysis, and provide reusable analysis notebooks for researchers.
8 Commits • 3 Features
Sep 1, 2025In Sep 2025, the DUNE/waffles work focused on improving developer onboarding, expanding data analysis capabilities, and enhancing reproducibility. Key activities included extensive documentation consolidation for environment setup, library usage, data access, and storage/server access; introduction of an offset scan feature with a Jupyter Notebook to analyze ADC offsets across channels/modules from waveform data; and significant enhancements to beam histograms notebooks for photo-electron studies, including a new notebook and visuals. These efforts reduce onboarding time, enable more robust data analysis, and provide reusable analysis notebooks for researchers.
September 2025
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for DUNE/waffles. Delivered two major enhancements focused on visualization capabilities and data organization, driving faster analysis and improving data governance for large datasets. No major bugs fixed this month; stability of the data processing and plotting pipelines was maintained. Outcomes strengthen data accessibility, reproducibility, and analyst efficiency, with a focus on scalable visualization and organized data workflows.
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for DUNE/waffles. Delivered two major enhancements focused on visualization capabilities and data organization, driving faster analysis and improving data governance for large datasets. No major bugs fixed this month; stability of the data processing and plotting pipelines was maintained. Outcomes strengthen data accessibility, reproducibility, and analyst efficiency, with a focus on scalable visualization and organized data workflows.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 focused on documentation improvements for the DUNE/waffles Python library to support data extraction workflows, NP02 scope, and streamlined developer setup. No major bugs fixed this month; emphasis on polishing docs, improving onboarding, and stabilizing guidance for data access and development work. Overall, these efforts enhance reproducibility, reduce setup time for new contributors, and clarify data extraction procedures across HDF5, RUCIO paths, and NP02 scope.
3 Commits • 1 Features
Apr 1, 2025April 2025 focused on documentation improvements for the DUNE/waffles Python library to support data extraction workflows, NP02 scope, and streamlined developer setup. No major bugs fixed this month; emphasis on polishing docs, improving onboarding, and stabilizing guidance for data access and development work. Overall, these efforts enhance reproducibility, reduce setup time for new contributors, and clarify data extraction procedures across HDF5, RUCIO paths, and NP02 scope.
April 2025
Activity
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Quality Metrics
Correctness87.0%
Maintainability84.8%
Architecture80.0%
Performance78.0%
AI Usage24.2%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonRSTYAMLreStructuredTextrst
Technical Skills
Data AnalysisData ProcessingData VisualizationDocumentationFile ManagementHDF5Jupyter NotebookJupyter NotebooksJupyter notebooksMatplotlibNumPyPandasPythonPython programmingPython scripting
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