SBNSoftware/sbndcode
Sep 2025 – Sep 2025
1 Month active
Languages Used
Python
Technical Skills
Command-line ScriptingData VisualizationROOT AnalysisScientific Computing
Francisco Javier Nicolás-Arnaldos
PROFILE
Francisco Javier Nicolás-arnaldos
Developed a PTB Trigger Event Visualization Tool for the SBNSoftware/sbndcode repository, focusing on enabling analysts to efficiently assess trigger behavior in PTB data. The tool was implemented in Python and leverages command-line scripting and ROOT analysis to generate stacked histograms of Low Level Trigger and High Level Trigger events relative to a reference time. Customization options were provided through a command-line interface, and the tool outputs visualizations as PDF files for streamlined analyst review. The work emphasized feature delivery and code quality, supporting faster data quality checks and contributing to safer, more efficient scientific data processing pipelines. No bugs were addressed.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
233
Activity Months1
Your Network
61 peopleSame Organization
@ugr.es3
Shared Repositories
58
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Alicia Vázquez-RamosMember
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AndyChappellMember
Alejandro Sánchez CastilloMember
Bear CarlsonMember
Brinden CarlsonMember
Brinden CarlsonMember
Carlos Martin MoralesMember
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for SBNSoftware/sbndcode focused on feature delivery and enabling analysts to quickly assess trigger behavior in PTB data. The main deliverable was a visualization tool that enhances data interpretation and supports faster data quality checks, contributing to safer and more efficient data processing pipelines.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for SBNSoftware/sbndcode focused on feature delivery and enabling analysts to quickly assess trigger behavior in PTB data. The main deliverable was a visualization tool that enhances data interpretation and supports faster data quality checks, contributing to safer and more efficient data processing pipelines.
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Command-line ScriptingData VisualizationROOT AnalysisScientific Computing
Repositories Contributed To
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