DUNE/ndlar_flow
Mar 2025 – Jul 2025
4 Months active
Languages Used
PythonShellYAML
Technical Skills
CalibrationConfiguration ManagementData ConfigurationData ProcessingData ReconstructionEvent Filtering
Kevin Wood
PROFILE
Kevin Wood
K. Wood contributed to the DUNE/ndlar_flow repository by developing and refining calibration and configuration management systems to enhance data integrity and reproducibility. Over four months, Wood implemented features such as timestamp synchronization, YAML-based data configuration, and parameterized ADC calibration, using Python and Shell scripting to streamline workflows and improve energy reconstruction accuracy. By updating event filtering logic and stabilizing FSD calibration through careful version control and configuration alignment, Wood addressed both hardware and software challenges. The work demonstrated depth in data processing, detector calibration, and time series analysis, resulting in more reliable data pipelines and reduced systematic uncertainties for downstream analyses.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
12Total
Bugs
3
Commits
12
Features
5
Lines of code
74
Activity Months4
Your Network
78 people
Work History
July 2025
3 Commits • 1 Features
Jul 1, 2025July 2025 (DUNE/ndlar_flow) monthly summary focusing on stabilizing FSD calibration and improving pedestal data accuracy to strengthen data quality and pipeline reliability. Key changes delivered: - FSD calibration stability: reverted to a known-good state by restoring vcm_mv and vcm_dac values and pedestal file paths (CalibHitBuilderData.yaml and GeneratePedestals.yaml), including reverting pedestal_file to the correct configuration. - Pedestal data update for accuracy: pointed the FSD hit builder to the most recent pedestal file in CalibHitBuilderData.yaml and CalibHitBuilderMC.yaml to improve calibration accuracy. Impact and outcomes: - Restored calibration stability and reproducibility, reducing risk of drift in FSD workflows. - Improved calibration precision through updated pedestal data, benefiting downstream analyses. - Achieved consistent configuration across related YAML files (CalibHitBuilderData.yaml, CalibHitBuilderMC.yaml, GeneratePedestals.yaml). Technologies/skills demonstrated: - Version control and commit traceability (git commits referenced). - YAML-based configuration management for calibration pipelines. - Debugging and revert strategy to restore validated working state. - Data calibration workflow discipline enabling repeatable, reliable deployments.
3 Commits • 1 Features
Jul 1, 2025July 2025 (DUNE/ndlar_flow) monthly summary focusing on stabilizing FSD calibration and improving pedestal data accuracy to strengthen data quality and pipeline reliability. Key changes delivered: - FSD calibration stability: reverted to a known-good state by restoring vcm_mv and vcm_dac values and pedestal file paths (CalibHitBuilderData.yaml and GeneratePedestals.yaml), including reverting pedestal_file to the correct configuration. - Pedestal data update for accuracy: pointed the FSD hit builder to the most recent pedestal file in CalibHitBuilderData.yaml and CalibHitBuilderMC.yaml to improve calibration accuracy. Impact and outcomes: - Restored calibration stability and reproducibility, reducing risk of drift in FSD workflows. - Improved calibration precision through updated pedestal data, benefiting downstream analyses. - Achieved consistent configuration across related YAML files (CalibHitBuilderData.yaml, CalibHitBuilderMC.yaml, GeneratePedestals.yaml). Technologies/skills demonstrated: - Version control and commit traceability (git commits referenced). - YAML-based configuration management for calibration pipelines. - Debugging and revert strategy to restore validated working state. - Data calibration workflow discipline enabling repeatable, reliable deployments.
July 2025
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05 Key features delivered: - Charge Readout System ADC Calibration Parameter Update in DUNE/ndlar_flow, introducing configuration parameters for reference voltage, common-mode voltage, ADC counts, and gain to enable precise calibration. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Enables more accurate charge measurements and energy reconstruction through configuration-driven ADC calibration, improving data quality and reducing systematic uncertainties. Calibration workflow is streamlined via parameterization, accelerating deployment to production environments. Technologies/skills demonstrated: - Calibration parameterization, hardware/software co-design, configuration management, traceability (linked commit 829ef190f550991b0630ccda4b384db09969d737), cross-repo coordination, and diligent change management.
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05 Key features delivered: - Charge Readout System ADC Calibration Parameter Update in DUNE/ndlar_flow, introducing configuration parameters for reference voltage, common-mode voltage, ADC counts, and gain to enable precise calibration. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Enables more accurate charge measurements and energy reconstruction through configuration-driven ADC calibration, improving data quality and reducing systematic uncertainties. Calibration workflow is streamlined via parameterization, accelerating deployment to production environments. Technologies/skills demonstrated: - Calibration parameterization, hardware/software co-design, configuration management, traceability (linked commit 829ef190f550991b0630ccda4b384db09969d737), cross-repo coordination, and diligent change management.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for DUNE/ndlar_flow focusing on feature delivery and reliability improvements in event reconstruction timing and windowing. Implemented a critical enhancement to RawEvent reconstruction timing and windowing: fixed a default shifted event delta to zero to prevent unintended time shifts; expanded the event window from 2000 to 2400 to align with the updated pixel response and ensure proper data capture. Changes were implemented through two commits, with clear, targeted messages. Overall impact includes more accurate timing, improved data capture fidelity, and stronger alignment between acquisition and downstream analysis pipelines.
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for DUNE/ndlar_flow focusing on feature delivery and reliability improvements in event reconstruction timing and windowing. Implemented a critical enhancement to RawEvent reconstruction timing and windowing: fixed a default shifted event delta to zero to prevent unintended time shifts; expanded the event window from 2000 to 2400 to align with the updated pixel response and ensure proper data capture. Changes were implemented through two commits, with clear, targeted messages. Overall impact includes more accurate timing, improved data capture fidelity, and stronger alignment between acquisition and downstream analysis pipelines.
April 2025
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly wrap-up for DUNE/ndlar_flow: delivered key features to improve data integrity and reproducibility, fixed critical energy and filtering bugs, and advanced configuration management. Notable outcomes include timestamp synchronization improvements, default truth-reference generation, and data YAML-based configurations enabling more reliable analyses and smoother workflows across datasets. Impact: higher data quality, more accurate energy reporting, and streamlined development and deployment.
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly wrap-up for DUNE/ndlar_flow: delivered key features to improve data integrity and reproducibility, fixed critical energy and filtering bugs, and advanced configuration management. Notable outcomes include timestamp synchronization improvements, default truth-reference generation, and data YAML-based configurations enabling more reliable analyses and smoother workflows across datasets. Impact: higher data quality, more accurate energy reporting, and streamlined development and deployment.
Activity
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Quality Metrics
Correctness90.0%
Maintainability91.6%
Architecture85.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonShellYAML
Technical Skills
CalibrationConfiguration ManagementData ConfigurationData ProcessingData ReconstructionDetector CalibrationEvent FilteringMonte Carlo SimulationScriptingSoftware DevelopmentTime Series Analysis
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