LDMX-Software/pflib
Jun 2025 – Jul 2025
2 Months active
Languages Used
C++PythonCMake
Technical Skills
C++Command Line InterfaceData AnalysisData VisualizationEmbedded SystemsFirmware Development
andpet1324
PROFILE
Andpet1324
Over a two-month period, contributed to the LDMX-Software/pflib repository by developing five core features focused on data acquisition, analysis, and calibration for embedded detector systems. Leveraging C++, Python, and CMake, introduced configurable pre-charge injection for flexible test scenarios and built a parameter timescan analysis framework with enhanced plotting and multi-file comparison. Delivered tools for Time-over-Threshold analytics, including threshold scanning and TOA calibration workflows, using both Python scripting and C++ implementations. Refactored plotting logic for greater code reuse and maintainability, enabling faster, more reliable analytics and calibration cycles without reported defects, and supporting robust experimental validation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
5
Lines of code
1,274
Activity Months2
Your Network
18 peopleSame Organization
@student.lu.se7
Shared Repositories
11
Andreas PetterssonMember
tomeichlersmithMember
Will HaseckeMember
Jeremiah MansMember
Jeremiah MansMember
Erik WallinMember
Tamas VamiMember
Tamas VamiMember
verenamhMember
Work History
July 2025
4 Commits • 3 Features
Jul 1, 2025In 2025-07, delivered three core features in the LDMX-Software/pflib project, focused on TOT analytics, threshold scanning, and TOA calibration. These efforts deliver measurable business value by enhancing data-driven TOT characterization, reducing calibration time, and improving code reuse. No major bugs were reported/fixed this month; maintenance focused on stabilizing new functionality and ensuring reliable operation across plotting, scanning, and calibration workflows. Overall impact: faster, more reliable TOT analytics and calibration, enabling tighter detector tuning and better experimental results. Technologies demonstrated: Python plotting, C++ implementations for scanners, binary/bisection search methods, and an object-oriented DecodeAndBuffer design for buffering-before-write workflows.
4 Commits • 3 Features
Jul 1, 2025In 2025-07, delivered three core features in the LDMX-Software/pflib project, focused on TOT analytics, threshold scanning, and TOA calibration. These efforts deliver measurable business value by enhancing data-driven TOT characterization, reducing calibration time, and improving code reuse. No major bugs were reported/fixed this month; maintenance focused on stabilizing new functionality and ensuring reliable operation across plotting, scanning, and calibration workflows. Overall impact: faster, more reliable TOT analytics and calibration, enabling tighter detector tuning and better experimental results. Technologies demonstrated: Python plotting, C++ implementations for scanners, binary/bisection search methods, and an object-oriented DecodeAndBuffer design for buffering-before-write workflows.
July 2025
June 2025
4 Commits • 2 Features
Jun 1, 2025Month: 2025-06 Overview: This period focused on delivering core enhancements in the pflib component of the LDMX-Software repository, driving business value through flexible test configurations and scalable parameter-driven analysis. The work enhances experiment readiness, data quality, and insight generation with traceable changes and clear delivery milestones. Key features delivered: - Pre-CC Charge Injection option for charge_timescan: Adds a configurable pre-charge injection option and updates prompts/config to support flexible pre-CC testing scenarios, enabling more versatile validation and faster test cycles. - Commit: eee353d371d880afa1e5197f78b1d7299a03311f - Parameter Timescan Analysis Framework and Enhanced Visualization: Introduces parameter_timescan to analyze pulse shapes under varying parameters, adds a plotting script, and adjusts C++ tasks for parameter sweeps. Visualization is enhanced with a new SCATTER plot option and multi-file comparison support. - Commits: 205fd6d884251b647d172a9c39dbfc1e7ba255c5; 433ac29e20d6be4dccb33e20c94a426218f5c50a; a6b0707f6cb424c3dddba712c3e4ade054970467 Major bugs fixed: - No critical defects reported for this period. Overall impact and accomplishments: - Reduced testing friction and enabled more data-driven decision-making through configurable pre-charge testing and robust parametric analysis workflows. The enhancements empower rapid iteration, better parameter coverage, and improved cross-file comparisons for more reliable calibration and validation. Technologies/skills demonstrated: - Python-based visualization and plotting (SCATTER option), - C++ task adjustments for parameter sweeps, - Scripted analysis workflows and enhanced analyzer logic for parametric timescans, - Documentation and configuration management to support flexible test scenarios.
June 2025
4 Commits • 2 Features
Jun 1, 2025Month: 2025-06 Overview: This period focused on delivering core enhancements in the pflib component of the LDMX-Software repository, driving business value through flexible test configurations and scalable parameter-driven analysis. The work enhances experiment readiness, data quality, and insight generation with traceable changes and clear delivery milestones. Key features delivered: - Pre-CC Charge Injection option for charge_timescan: Adds a configurable pre-charge injection option and updates prompts/config to support flexible pre-CC testing scenarios, enabling more versatile validation and faster test cycles. - Commit: eee353d371d880afa1e5197f78b1d7299a03311f - Parameter Timescan Analysis Framework and Enhanced Visualization: Introduces parameter_timescan to analyze pulse shapes under varying parameters, adds a plotting script, and adjusts C++ tasks for parameter sweeps. Visualization is enhanced with a new SCATTER plot option and multi-file comparison support. - Commits: 205fd6d884251b647d172a9c39dbfc1e7ba255c5; 433ac29e20d6be4dccb33e20c94a426218f5c50a; a6b0707f6cb424c3dddba712c3e4ade054970467 Major bugs fixed: - No critical defects reported for this period. Overall impact and accomplishments: - Reduced testing friction and enabled more data-driven decision-making through configurable pre-charge testing and robust parametric analysis workflows. The enhancements empower rapid iteration, better parameter coverage, and improved cross-file comparisons for more reliable calibration and validation. Technologies/skills demonstrated: - Python-based visualization and plotting (SCATTER option), - C++ task adjustments for parameter sweeps, - Scripted analysis workflows and enhanced analyzer logic for parametric timescans, - Documentation and configuration management to support flexible test scenarios.
Activity
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Quality Metrics
Correctness82.6%
Maintainability82.6%
Architecture82.6%
Performance65.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakePython
Technical Skills
C++C++ DevelopmentCMakeCommand Line InterfaceData AcquisitionData AnalysisData VisualizationEmbedded SystemsFirmware DevelopmentHardware TestingPlottingPythonRefactoringScientific ComputingScripting
Repositories Contributed To
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