DUNE/ndlar_flow
Feb 2025 – Apr 2025
3 Months active
Languages Used
PythonYAMLyaml
Technical Skills
Code MaintenanceCode RefactoringConfiguration ManagementData AnalysisData FilteringData Processing
Noe Roy
PROFILE
Noe Roy
Worked on the DUNE/ndlar_flow repository to enhance signal processing and event reconstruction pipelines, focusing on reliability, scalability, and maintainability. Delivered eight new features and fixed two bugs over three months, including dynamic thresholding for hit detection, alternate Monte Carlo light event workflows, and streamlined noise filtering. Applied Python and YAML to refactor code, modernize configuration management, and optimize workflow performance. Addressed critical issues such as division-by-zero errors and peak timing offsets, improving data quality and reducing maintenance overhead. The work resulted in more accurate event reconstruction, higher processing capacity, and a leaner, easier-to-maintain codebase for scientific computing.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
22Total
Bugs
2
Commits
22
Features
8
Lines of code
1,243
Activity Months3
Your Network
25 peopleSame Organization
@yorku.ca1
Shared Repositories
24
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Work History
April 2025
1 Commits
Apr 1, 2025April 2025 — DUNE/ndlar_flow focused on improving waveform peak timing accuracy by removing an off-by-one offset in peak-based calculations, aligning calculations with the actual peak position for sample_idx and busy_ns. This change enhances data quality for downstream analytics with minimal surface area impact.
1 Commits
Apr 1, 2025April 2025 — DUNE/ndlar_flow focused on improving waveform peak timing accuracy by removing an off-by-one offset in peak-based calculations, aligning calculations with the actual peak position for sample_idx and busy_ns. This change enhances data quality for downstream analytics with minimal surface area impact.
April 2025
March 2025
9 Commits • 4 Features
Mar 1, 2025Summary for 2025-03: Delivered substantial modernization and tuning of the DUNE/ndlar_flow signal processing and event reconstruction pipeline, with a focus on performance, robustness, and maintainability. Implemented high-impact feature upgrades (FlashFinder tuning, FSD pipeline upgrade, deconv stage removal, baselining overhaul) and fixed critical reliability bugs (hit finding division-by-zero, dynamic sizing checks). Result: higher processing capacity and sensitivity, streamlined data paths, more consistent baselining, and reduced risk of runtime errors. Key outcomes align with business goals of faster, more accurate event reconstruction and easier future maintenance.
March 2025
9 Commits • 4 Features
Mar 1, 2025Summary for 2025-03: Delivered substantial modernization and tuning of the DUNE/ndlar_flow signal processing and event reconstruction pipeline, with a focus on performance, robustness, and maintainability. Implemented high-impact feature upgrades (FlashFinder tuning, FSD pipeline upgrade, deconv stage removal, baselining overhaul) and fixed critical reliability bugs (hit finding division-by-zero, dynamic sizing checks). Result: higher processing capacity and sensitivity, streamlined data paths, more consistent baselining, and reduced risk of runtime errors. Key outcomes align with business goals of faster, more accurate event reconstruction and easier future maintenance.
February 2025
12 Commits • 4 Features
Feb 1, 2025February 2025: Focused delivery and cleanup for DUNE/ndlar_flow to improve reliability and scalability of the hit-detection and MC processing pipelines. Key features delivered include Hit Finder Enhancements with dynamic thresholding and code cleanup, a new Alternate Monte Carlo Light Event Reconstruction Workflow, and targeted noise-filtering adjustments, along with comprehensive codebase cleanup to remove legacy scripts and configurations. These changes reduce maintenance overhead, improve processing accuracy, and simplify future feature work. Business impact includes fewer missed events due to more robust hit detection, clearer MC workflow for simulations, and a leaner repository that lowers deployment risk.
12 Commits • 4 Features
Feb 1, 2025February 2025: Focused delivery and cleanup for DUNE/ndlar_flow to improve reliability and scalability of the hit-detection and MC processing pipelines. Key features delivered include Hit Finder Enhancements with dynamic thresholding and code cleanup, a new Alternate Monte Carlo Light Event Reconstruction Workflow, and targeted noise-filtering adjustments, along with comprehensive codebase cleanup to remove legacy scripts and configurations. These changes reduce maintenance overhead, improve processing accuracy, and simplify future feature work. Business impact includes fewer missed events due to more robust hit detection, clearer MC workflow for simulations, and a leaner repository that lowers deployment risk.
February 2025
Activity
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Quality Metrics
Correctness85.4%
Maintainability90.0%
Architecture81.8%
Performance77.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAMLyaml
Technical Skills
Code CleanupCode MaintenanceCode RefactoringConfiguration ManagementData AnalysisData FilteringData ProcessingData Processing ConfigurationData Processing PipelineNumerical AnalysisNumerical ComputingPythonRefactoringScientific ComputingSignal Processing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline