FRBs/FRB
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPythonShell
Technical Skills
API IntegrationAstronomyAstrophysicsData AnalysisData VisualizationLikelihood Maximization
Vic
PROFILE
Vic
Vicco contributed to the FRBs/FRB repository by developing analytics capabilities for fast radio burst data analysis. He implemented new Python scripts in Jupyter Notebook and Shell to support likelihood evaluation, scanning, parameter optimization, and redshift probability computation from dispersion measure, addressing key scientific computing needs. To improve maintainability, he reorganized the repository structure, introducing a crossmatching folder and relocating files to better reflect updated workflows. Additionally, he removed unnecessary artifacts to streamline continuous integration. Vicco’s work enabled faster, more reproducible data analysis and reduced repository clutter, demonstrating a solid grasp of data analysis, astrophysics, and scientific software engineering.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
2,592
Activity Months1
Your Network
10 people
Work History
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for FRBs/FRB repository. Focused on delivering analytics capabilities and improving maintainability. Implemented new Python scripts for FRB data analysis (likelihood evaluation, scanning, and parameter optimization) and a script to compute redshift probability from DM; reorganized repository with a crossmatching folder and file moves; cleaned up artifacts by removing a dummy.txt file. These efforts accelerate data analysis, improve reproducibility, and reduce CI noise.
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for FRBs/FRB repository. Focused on delivering analytics capabilities and improving maintainability. Implemented new Python scripts for FRB data analysis (likelihood evaluation, scanning, and parameter optimization) and a script to compute redshift probability from DM; reorganized repository with a crossmatching folder and file moves; cleaned up artifacts by removing a dummy.txt file. These efforts accelerate data analysis, improve reproducibility, and reduce CI noise.
May 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability86.6%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonShell
Technical Skills
API IntegrationAstronomyAstrophysicsData AnalysisData VisualizationLikelihood MaximizationPython ScriptingScientific Computing
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