DUNE/waffles
Sep 2025 – Dec 2025
4 Months active
Languages Used
CSVNonePythonUnknownYAML
Technical Skills
CSV handlingPythonPython programmingYAML configurationbackend developmentbatch processing
Julio
PROFILE
Julio
Over four months, contributed to the DUNE/waffles repository by developing and refining calibration and data analysis workflows for scientific signal processing. Built robust Python-based utilities for LED calibration, waveform alignment, and batch data handling, leveraging Pandas and YAML for configuration and data management. Enhanced calibration accuracy through per-channel configurability, Poisson-weighted fitting, and vendor-aware data curation, while implementing safeguards for edge-case failures and improving visualization with Plotly. Focused on clean code practices, function encapsulation, and comprehensive documentation, the work improved reliability, maintainability, and throughput of calibration pipelines, enabling clearer diagnostics and faster iteration for large-scale experimental data analysis.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
110Total
Bugs
10
Commits
110
Features
45
Lines of code
17,994
Activity Months4
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Work History
December 2025
5 Commits • 2 Features
Dec 1, 2025This monthly summary highlights key feature deliveries, major bug fixes, and overall impact for DUNE/waffles in 2025-12, with an emphasis on business value, reliability, and signal processing accuracy. The work focused on robust data analysis safeguards, calibration improvements, and improved data curation to enable clearer, faster insights.
5 Commits • 2 Features
Dec 1, 2025This monthly summary highlights key feature deliveries, major bug fixes, and overall impact for DUNE/waffles in 2025-12, with an emphasis on business value, reliability, and signal processing accuracy. The work focused on robust data analysis safeguards, calibration improvements, and improved data curation to enable clearer, faster insights.
December 2025
November 2025
41 Commits • 17 Features
Nov 1, 2025Month: 2025-11 Key features delivered - Weigh fitting by Poisson sigmas: added weigh_fit_by_poisson_sigmas parameter to the fitting functions and propagated to related areas including led_calibration. - Alignment seeds utilities: defined get_alignment_seeds_dataframe() and get_alignment_seeds(), with seed helpers and improvements; tolerant to NaNs. - LED calibration waveform alignment: implemented waveform alignment in led_calibration analysis and related utilities (align_waveforms_by_correlation) with updated default parameters. - Vendor-aware data handling: introduced vendor dataframe utilities (get_sipm_vendor_dataframe, get_vendor) and enforced vendor metadata in alignment seeds. - Alignment logic improvements and plotting: derived integration limits from data, computed average pulse position from data, and added warnings for cross-channel seeds; disabled legends in plots; overlayed average waveform on persistence heatmaps. - Entire-pulse-integration and seed enforcement: added entire-pulse-integration feature in alignment workflow and vendor-based seed enforcement when vendor is available. - Fine-selection enhancements and robustness: added get_fine_selection_regions() and regions_limits_are_consistent(); switched alignment and fine-selection order; saved/ plotted fine-selection limits. - Documentation and maintenance: updated led_calibration/params.yml with documentation; performed code cleanup (remove unused imports); improved exception handling in get_pulse_window_limits; addressed edge case of sliced waveforms shorter than SPE template. Major bugs fixed - Fixed missing import in led_calibration/utils.py. - Deleted unused imports and performed general code cleanup. - Improved exception handling in get_pulse_window_limits and handled edge cases where waveforms are shorter than the SPE template. Overall impact and accomplishments - Significantly increased calibration accuracy and alignment robustness through data-driven limits, Poisson-weighted fitting, and waveform alignment. - Strengthened data integrity with vendor-aware utilities and seed enforcement, reducing downstream errors in analysis pipelines. - Improved maintainability, documentation, and visualization quality, enabling faster iteration and clearer communication of results. Technologies/skills demonstrated - Python, pandas/dataframes, waveform analysis, Poisson-based weighting, alignment algorithms, data validation, and software maintenance. - End-to-end feature delivery from data handling to visualization and documentation, reflecting strong cross-cutting engineering impact.
November 2025
41 Commits • 17 Features
Nov 1, 2025Month: 2025-11 Key features delivered - Weigh fitting by Poisson sigmas: added weigh_fit_by_poisson_sigmas parameter to the fitting functions and propagated to related areas including led_calibration. - Alignment seeds utilities: defined get_alignment_seeds_dataframe() and get_alignment_seeds(), with seed helpers and improvements; tolerant to NaNs. - LED calibration waveform alignment: implemented waveform alignment in led_calibration analysis and related utilities (align_waveforms_by_correlation) with updated default parameters. - Vendor-aware data handling: introduced vendor dataframe utilities (get_sipm_vendor_dataframe, get_vendor) and enforced vendor metadata in alignment seeds. - Alignment logic improvements and plotting: derived integration limits from data, computed average pulse position from data, and added warnings for cross-channel seeds; disabled legends in plots; overlayed average waveform on persistence heatmaps. - Entire-pulse-integration and seed enforcement: added entire-pulse-integration feature in alignment workflow and vendor-based seed enforcement when vendor is available. - Fine-selection enhancements and robustness: added get_fine_selection_regions() and regions_limits_are_consistent(); switched alignment and fine-selection order; saved/ plotted fine-selection limits. - Documentation and maintenance: updated led_calibration/params.yml with documentation; performed code cleanup (remove unused imports); improved exception handling in get_pulse_window_limits; addressed edge case of sliced waveforms shorter than SPE template. Major bugs fixed - Fixed missing import in led_calibration/utils.py. - Deleted unused imports and performed general code cleanup. - Improved exception handling in get_pulse_window_limits and handled edge cases where waveforms are shorter than the SPE template. Overall impact and accomplishments - Significantly increased calibration accuracy and alignment robustness through data-driven limits, Poisson-weighted fitting, and waveform alignment. - Strengthened data integrity with vendor-aware utilities and seed enforcement, reducing downstream errors in analysis pipelines. - Improved maintainability, documentation, and visualization quality, enabling faster iteration and clearer communication of results. Technologies/skills demonstrated - Python, pandas/dataframes, waveform analysis, Poisson-based weighting, alignment algorithms, data validation, and software maintenance. - End-to-end feature delivery from data handling to visualization and documentation, reflecting strong cross-cutting engineering impact.
October 2025
54 Commits • 25 Features
Oct 1, 2025October 2025 monthly summary for DUNE/waffles: Delivered key features across the led_calibration core, data handling, and visualization, while strengthening data quality controls and configuration management. The work enabled more accurate calibration results, robust data exports, and richer diagnostics, driving faster iteration and clearer stakeholder insights.
54 Commits • 25 Features
Oct 1, 2025October 2025 monthly summary for DUNE/waffles: Delivered key features across the led_calibration core, data handling, and visualization, while strengthening data quality controls and configuration management. The work enabled more accurate calibration results, robust data exports, and richer diagnostics, driving faster iteration and clearer stakeholder insights.
October 2025
September 2025
10 Commits • 1 Features
Sep 1, 20252025-09 monthly summary for DUNE/waffles focusing on calibration histogram enhancements, per-channel configurability, and batch-processing reliability. Delivered per-channel histogram configuration with gain-seed integration, added channel-wise nbins and domain computation utilities, and improved robustness in gain/SNR calibration processing. Fixed batch processing channel handling and recovered missing channels to ensure reliable batch runs across APA modules. Global impact includes improved calibration accuracy, robustness, and data processing throughput for batch workflows.
September 2025
10 Commits • 1 Features
Sep 1, 20252025-09 monthly summary for DUNE/waffles focusing on calibration histogram enhancements, per-channel configurability, and batch-processing reliability. Delivered per-channel histogram configuration with gain-seed integration, added channel-wise nbins and domain computation utilities, and improved robustness in gain/SNR calibration processing. Fixed batch processing channel handling and recovered missing channels to ensure reliable batch runs across APA modules. Global impact includes improved calibration accuracy, robustness, and data processing throughput for batch workflows.
Activity
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Quality Metrics
Correctness93.8%
Maintainability87.0%
Architecture87.4%
Performance86.2%
AI Usage22.0%
Skills & Technologies
Programming Languages
CSVNonePythonUnknownYAML
Technical Skills
CSV handlingCode RefactoringData AnalysisNumPyPandasPlotlyPythonPython programmingPython scriptingSoftware DevelopmentYAMLYAML configurationbackend developmentbatch processingcalibration techniques
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