nu-radio/NuRadioMC
Jan 2025 – Feb 2025
2 Months active
Languages Used
C++JSONPythonJupyter Notebook
Technical Skills
Code RefactoringConfiguration ManagementDocumentationGeospatial AnalysisPhysics SimulationSimulation
karenterveer
PROFILE
Karenterveer
Contributed to the nu-radio/NuRadioMC repository by delivering four features over two months, focusing on maintainability, configuration efficiency, and streamlined data workflows. Enhanced simulation precision and code clarity through Python-based refactoring, including improved docstrings, JSON configuration cleanup, and millimeter-scale geospatial accuracy. Developed an interactive Jupyter Notebook for LOFAR data processing, integrating NuRadioReco to enable end-to-end workflows from raw TBB files to .nur format with filtering, calibration, and visualization of traces and polarization. The work emphasized reproducibility and onboarding, leveraging Python, JSON, and scientific computing skills to simplify configuration, improve memory usage, and support advanced geospatial analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
4
Lines of code
65,403
Activity Months2
Your Network
57 peopleShared Repositories
39
Arthur CorstanjeMember
alisanozdrinaMember
Alisa NozdrinaMember
BryanMember
Christian GlaserMember
Christian GlaserMember
Cosmin DeaconuMember
boboeyenMember
Felix SchlüterMember
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered an interactive LOFAR data processing notebook integrated with NuRadioReco, enabling end-to-end LOFAR workflows from LOFAR TBB files to NuRadioReco .nur format with filtering, calibration, and in-notebook data analysis. The notebook exposes traces, frequency spectra, and polarization footprint visualizations. All work is backed by a dedicated notebook file added to nu-radio/NuRadioMC (commit 990b01539f1e5b27639ffdc00e1f4898b6a22f0d). Major bugs fixed: none reported this month. Overall impact: improves onboarding, reproducibility, and efficiency for LOFAR data analysis; sets foundation for broader LOFAR data processing pipelines. Technologies/skills demonstrated: Python, Jupyter notebooks, NuRadioReco integration, LOFAR data handling, file format conversions (.tbb to .nur), data filtering and calibration, visualization of traces and polarization.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered an interactive LOFAR data processing notebook integrated with NuRadioReco, enabling end-to-end LOFAR workflows from LOFAR TBB files to NuRadioReco .nur format with filtering, calibration, and in-notebook data analysis. The notebook exposes traces, frequency spectra, and polarization footprint visualizations. All work is backed by a dedicated notebook file added to nu-radio/NuRadioMC (commit 990b01539f1e5b27639ffdc00e1f4898b6a22f0d). Major bugs fixed: none reported this month. Overall impact: improves onboarding, reproducibility, and efficiency for LOFAR data analysis; sets foundation for broader LOFAR data processing pipelines. Technologies/skills demonstrated: Python, Jupyter notebooks, NuRadioReco integration, LOFAR data handling, file format conversions (.tbb to .nur), data filtering and calibration, visualization of traces and polarization.
February 2025
January 2025
3 Commits • 3 Features
Jan 1, 2025Month: 2025-01 — NuRadioMC focused on maintainability, configuration efficiency, and simulation precision. Delivered three targeted enhancements with measurable impact on clarity, parsing efficiency, memory usage, and fidelity. Overall, improved maintainability and efficiency, with clearer code, streamlined configuration loading, and a more accurate, simpler simulation model. Technologies demonstrated include Python docstrings, JSON config hygiene, and high-precision data handling.
January 2025
3 Commits • 3 Features
Jan 1, 2025Month: 2025-01 — NuRadioMC focused on maintainability, configuration efficiency, and simulation precision. Delivered three targeted enhancements with measurable impact on clarity, parsing efficiency, memory usage, and fidelity. Overall, improved maintainability and efficiency, with clearer code, streamlined configuration loading, and a more accurate, simpler simulation model. Technologies demonstrated include Python docstrings, JSON config hygiene, and high-precision data handling.
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Quality Metrics
Correctness85.0%
Maintainability90.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++JSONJupyter NotebookPython
Technical Skills
Code RefactoringConfiguration ManagementData ProcessingDocumentationGeospatial AnalysisJupyter NotebookLOFAR DataNuRadioRecoPhysics SimulationPythonScientific ComputingSimulation
Repositories Contributed To
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