nu-radio/NuRadioMC
Jan 2025 – Feb 2025
2 Months active
Languages Used
PythonC++
Technical Skills
Data AnalysisModule RefactoringNumerical AnalysisScientific ComputingSignal ProcessingPython
Alisa Nozdrina
PROFILE
Alisa Nozdrina
Anna Nozdrina enhanced the nu-radio/NuRadioMC repository by developing advanced features for spectral and continuous wave noise subtraction in radio data analysis. She refactored the Channel Sinewave Subtraction module in Python, introducing dynamic runtime configurability, standalone execution, and improved amplitude and phase fitting using numerical analysis techniques. In subsequent work, Anna implemented robust convergence checks, baseline RMS recalculation, and an IIR-filter-based Goertzel amplitude estimator to increase the accuracy and reliability of continuous wave noise removal. Her contributions focused on scientific computing and signal processing, resulting in more accurate spectral analysis and streamlined integration for downstream data processing pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
315
Activity Months2
Work History
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 — nu-radio/NuRadioMC: Delivered robust continuous wave (CW) noise removal enhancements, significantly improving accuracy and robustness of CW subtraction in radio data. Key changes include convergence checks for the subtraction loop, revised baseline RMS handling, an IIR-filter-based Goertzel amplitude estimator, and phase estimation to improve sine fitting. Added frequency-band RMS calculation for more reliable baseline tracking and stability. These changes reduce noise leakage, increase trust in automated cleaning, and enable more reliable downstream analyses.
3 Commits • 1 Features
Feb 1, 2025February 2025 — nu-radio/NuRadioMC: Delivered robust continuous wave (CW) noise removal enhancements, significantly improving accuracy and robustness of CW subtraction in radio data. Key changes include convergence checks for the subtraction loop, revised baseline RMS handling, an IIR-filter-based Goertzel amplitude estimator, and phase estimation to improve sine fitting. Added frequency-band RMS calculation for more reliable baseline tracking and stability. These changes reduce noise leakage, increase trust in automated cleaning, and enable more reliable downstream analyses.
February 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) monthly performance snapshot for nu-radio/NuRadioMC: Delivered enhancements to Channel Sinewave Subtraction with usability improvements, standalone execution, and improved amplitude/fitting accuracy. The work enabled dynamic adjustment of peak prominence at runtime and added a standalone executable mode, simplifying testing and deployment. Implemented spectral amplitude normalization fixes and advanced sine fitting, including phase-shifted sine fitting and a switch from cosine to sine in the fit definition, aligning with standard representations and increasing fitting reliability. Consolidated work across two commits to improve usability and deployment readiness. Business value includes more accurate spectral analysis, easier integration into pipelines, and reduced time-to-value for users deploying NuRadioMC in diverse environments.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) monthly performance snapshot for nu-radio/NuRadioMC: Delivered enhancements to Channel Sinewave Subtraction with usability improvements, standalone execution, and improved amplitude/fitting accuracy. The work enabled dynamic adjustment of peak prominence at runtime and added a standalone executable mode, simplifying testing and deployment. Implemented spectral amplitude normalization fixes and advanced sine fitting, including phase-shifted sine fitting and a switch from cosine to sine in the fit definition, aligning with standard representations and increasing fitting reliability. Consolidated work across two commits to improve usability and deployment readiness. Business value includes more accurate spectral analysis, easier integration into pipelines, and reduced time-to-value for users deploying NuRadioMC in diverse environments.
Activity
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Quality Metrics
Correctness84.0%
Maintainability80.0%
Architecture80.0%
Performance68.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Data AnalysisModule RefactoringNumerical AnalysisPythonPython DevelopmentScientific ComputingSignal Processing
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