cta-observatory/cta-lstchain
Dec 2025 – Feb 2026
3 Months active
Languages Used
Python
Technical Skills
Jupyter NotebookPythonPython scriptingdata analysisdata processingdata quality analysis
Arnau Aguasca-Cabot
PROFILE
Arnau Aguasca-cabot
Worked on stabilizing and enhancing data processing pipelines for the cta-lstchain repository, focusing on data quality, ingestion, and simulation workflows. Addressed issues in Cherenkov transparency analysis by correcting parameter handling, improving the accuracy of cosmic ray event processing. Improved file handling by implementing directory pattern matching and deduplication logic, ensuring only unique datacheck files were processed per run and restoring stable data access in Jupyter Notebooks. Leveraged Python, HDF5, and scientific computing techniques to skip unnecessary columns in simulation data, maintaining data integrity and performance. Prioritized maintainability, reproducibility, and scalable data handling throughout the three-month contribution period.
Overall Statistics
Feature vs Bugs
20%Features
Repository Contributions
9Total
Bugs
4
Commits
9
Features
1
Lines of code
272
Activity Months3
Your Network
12 peopleShared Repositories
12
Maria LainezMember
Mathieu de BonyMember
morcuendedMember
Daniel MorcuendeMember
Guillaume GrolleronMember
Maximilian LinhoffMember
moralejoMember
Paula KorneckiMember
rcervinoucmMember
Work History
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for cta-lstchain: Delivered a targeted data integrity improvement by skipping run_number in the HDF5 simulation data processing. The change ensures run_number is not read into downstream logic, preventing data misalignment and improving processing performance for large-scale simulations. Implemented as a bug fix with commit ebcee0247f43297119ae0b8c9cf13aa77e4e3969, following standard contribution practices (Signed-off-by). This update enhances reliability and scalability of the simulation data pipeline and reduces downstream risk.
1 Commits
Feb 1, 2026February 2026 monthly summary for cta-lstchain: Delivered a targeted data integrity improvement by skipping run_number in the HDF5 simulation data processing. The change ensures run_number is not read into downstream logic, preventing data misalignment and improving processing performance for large-scale simulations. Implemented as a bug fix with commit ebcee0247f43297119ae0b8c9cf13aa77e4e3969, following standard contribution practices (Signed-off-by). This update enhances reliability and scalability of the simulation data pipeline and reduces downstream risk.
February 2026
January 2026
2 Commits
Jan 1, 2026January 2026 focused on stabilizing data quality file handling in the cta-lstchain repository to improve data integrity and reproducibility. Delivered a bug fix that ensures only unique datacheck files are processed per run ID and reverted changes in the data quality notebook to restore stable file paths and data access behavior. The changes reduce data duplication risk, improve reliability of run-based analytics, and provide a clean baseline for future improvements.
January 2026
2 Commits
Jan 1, 2026January 2026 focused on stabilizing data quality file handling in the cta-lstchain repository to improve data integrity and reproducibility. Delivered a bug fix that ensures only unique datacheck files are processed per run ID and reverted changes in the data quality notebook to restore stable file paths and data access behavior. The changes reduce data duplication risk, improve reliability of run-based analytics, and provide a clean baseline for future improvements.
December 2025
6 Commits • 1 Features
Dec 1, 2025December 2025 focused on stabilizing data processing pipelines and improving data quality for the CTA-LSTChain workflow. Delivered targeted fixes and robust ingestion capabilities to support reliable cosmic-ray data analysis, with a clear emphasis on maintainability and scalable data handling.
6 Commits • 1 Features
Dec 1, 2025December 2025 focused on stabilizing data processing pipelines and improving data quality for the CTA-LSTChain workflow. Delivered targeted fixes and robust ingestion capabilities to support reliable cosmic-ray data analysis, with a clear emphasis on maintainability and scalable data handling.
December 2025
Activity
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Quality Metrics
Correctness97.8%
Maintainability91.2%
Architecture91.2%
Performance91.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
HDF5Jupyter NotebookPythonPython scriptingdata analysisdata processingdata quality analysiserror handlingfile handlingloggingscientific computing
Repositories Contributed To
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