dataforgoodfr/13_potentiel_solaire
Mar 2025 – Mar 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
API IntegrationBackend DevelopmentCachingData AnalysisData EngineeringData Wrangling
Luc J
PROFILE
Luc J
During March 2025, Kelu worked on the dataforgoodfr/13_potentiel_solaire repository, delivering enhancements to solar potential analysis workflows. He refactored height analysis logic in Python and Jupyter Notebook, improving measurement accuracy and validation for building roofs over four meters. By introducing caching for WNS data and flux readings, he reduced per-building processing time and improved pipeline scalability. Kelu also addressed data retrieval issues by updating WMS URLs and fixing import errors, ensuring reliable notebook execution. Additionally, he implemented ID-based reconciliation for associating buildings with schools, strengthening data integrity. The work demonstrated depth in geospatial data processing and performance optimization.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
8Total
Bugs
2
Commits
8
Features
3
Lines of code
1,616
Activity Months1
Your Network
18 peopleWork History
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 (dataforgoodfr/13_potentiel_solaire) delivered notable improvements in measurement accuracy, performance, and data reliability for solar potential analysis. Major work spanned feature refinements, caching optimizations, and data integrity enhancements, with targeted fixes to WMS-based retrieval and notebook execution reliability. The overall impact is a faster, more scalable analysis pipeline with stronger data provenance and repeatable results across large datasets.
8 Commits • 3 Features
Mar 1, 2025March 2025 (dataforgoodfr/13_potentiel_solaire) delivered notable improvements in measurement accuracy, performance, and data reliability for solar potential analysis. Major work spanned feature refinements, caching optimizations, and data integrity enhancements, with targeted fixes to WMS-based retrieval and notebook execution reliability. The overall impact is a faster, more scalable analysis pipeline with stronger data provenance and repeatable results across large datasets.
March 2025
Activity
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Quality Metrics
Correctness77.4%
Maintainability80.0%
Architecture77.6%
Performance77.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
API IntegrationBackend DevelopmentCachingData AnalysisData EngineeringData WranglingFile I/OGeoPandasGeopandasGeospatial AnalysisGeospatial DataGeospatial Data ProcessingJupyter NotebookMatplotlibPandas
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