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
Worked on the dataforgoodfr/13_potentiel_solaire repository to enhance solar potential analysis by refining measurement accuracy, optimizing performance, and improving data reliability. Focused on backend development and data engineering, the work included refactoring height analysis functions, implementing caching for WNS data to reduce per-building processing time, and strengthening data validation for geospatial datasets. Addressed bugs related to WMS URL changes and cache handling, ensuring robust data retrieval and repeatable notebook execution. Leveraged Python, Pandas, and GeoPandas to process large geospatial datasets efficiently, resulting in a faster, more scalable analysis pipeline with improved data integrity and reproducibility across workflows.
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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