i4Ds/Karabo-Pipeline
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Astronomy Data ProcessingData ConfigurationData SimulationMetadata ManagementPython ScriptingScientific Computing
Michel Pluess
PROFILE
Michel Pluess
Michel Pluess developed advanced simulation and data management features for the i4Ds/Karabo-Pipeline repository, focusing on radio astronomy workflows. He delivered new SKA-MID-AAstar telescope layouts for OSKAR simulations and built a Python-based converter to map array configurations to OSKAR model directories, updating Jupyter notebooks and scripts to support these enhancements. Additionally, Michel refactored the PI24 run script to automate metadata generation for simulated data, integrating ObsCore metadata and preparing outputs for Rucio-based ingestion. His work demonstrated depth in astronomy data processing, metadata management, and scientific computing, resulting in higher-fidelity simulations and improved data provenance within the pipeline.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
3,191
Activity Months1
Your Network
9 peopleSame Organization
@fhnw.ch4
Work History
November 2024
2 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for i4Ds/Karabo-Pipeline. Focus areas included feature delivery for simulation fidelity and data management readiness. Key outcomes: SKA-MID-AAstar layouts for OSKAR with a Python converter to map arrays to OSKAR models, updated notebooks and scripts; PI24 metadata generation and Rucio ingestion prep through ObsCore metadata integration. Overall impact: higher-fidelity simulations, improved data provenance, and streamlined data ingestion pipelines. Technologies demonstrated: Python, OSKAR, ObsCore metadata, and Rucio-based data management.
2 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for i4Ds/Karabo-Pipeline. Focus areas included feature delivery for simulation fidelity and data management readiness. Key outcomes: SKA-MID-AAstar layouts for OSKAR with a Python converter to map arrays to OSKAR models, updated notebooks and scripts; PI24 metadata generation and Rucio ingestion prep through ObsCore metadata integration. Overall impact: higher-fidelity simulations, improved data provenance, and streamlined data ingestion pipelines. Technologies demonstrated: Python, OSKAR, ObsCore metadata, and Rucio-based data management.
November 2024
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Astronomy Data ProcessingData ConfigurationData SimulationMetadata ManagementPython ScriptingScientific ComputingSimulationTelescope Modeling
Repositories Contributed To
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