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
During two months on the LDMX-Software/pflib repository, this developer delivered five core features focused on data acquisition, analysis, and calibration for embedded detector systems. They implemented configurable pre-charge injection for flexible test cycles, developed a parameter-driven timescan analysis framework with enhanced Python-based visualization, and introduced multi-file comparison for robust calibration. Their work included C++ task adjustments for parameter sweeps, refactored plotting logic for code reuse, and a new TOA calibration workflow using object-oriented buffering. By integrating C++, Python, and CMake, they enabled faster, more reliable analytics and calibration, supporting improved experimental readiness and more efficient detector tuning without reported defects.
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
16 peopleSame Organization
@student.lu.se7
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
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