dataforgoodfr/13_potentiel_solaire
Feb 2025 – Feb 2025
1 Month active
Languages Used
JSONJupyter NotebookPython
Technical Skills
Data AnalysisData EngineeringData VisualizationFile ConversionFoliumGIS
Neghad
PROFILE
Neghad
During February 2025, Neghadly contributed to the dataforgoodfr/13_potentiel_solaire repository by developing a GeoJSON to GeoPackage (GPKG) data conversion feature within the extract_sample_data.py pipeline. This work introduced idempotent conversion logic, selective processing, and data preservation to streamline geospatial workflows and ensure reliable, revision-safe data packaging. Neghadly also implemented shadow analysis notebooks for assessing solar potential in Saint-Denis, using Python, Geopandas, and Jupyter Notebooks to calculate and visualize seasonal shadow projections for school buildings. Maintenance included resolving merge conflicts and removing obsolete notebooks, resulting in a more robust, reproducible data pipeline for solar energy analysis and GIS applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
2
Lines of code
6,848
Activity Months1
Your Network
18 peopleWork History
February 2025
10 Commits • 2 Features
Feb 1, 2025February 2025 – Monthly summary for dataforgoodfr/13_potentiel_solaire Key deliverables: - GeoJSON to GPKG data conversion for solar data: Extended extract_sample_data.py to support GeoJSON→GPKG conversion with a dedicated convert_geojson_to_gpkg function, skip-if-exists to avoid redundant downloads, and options for selective conversion and data preservation. This streamlines GIS workflows and ensures consistent, revision-safe data packaging. - Shadow analysis notebooks and tooling for solar potential in Saint-Denis: Implemented shadow projection calculations for school buildings and created notebooks to analyze and visualize shadows across seasons, enabling more accurate solar potential assessments. Included cleanup of obsolete notebooks and merge-conflict resolutions to maintain notebook integrity. Major bugs fixed / maintenance: - Resolved notebook conflicts and recovered the latest versions; removed obsolete notebooks to reduce confusion. - Introduced idempotent data conversion behavior (skip-if-exists) to enhance robustness and prevent unnecessary recomputation. Overall impact and accomplishments: - Strengthened the data processing pipeline from raw GeoJSON sources to GIS-ready GPKG packages, improving reliability, reproducibility, and data usability for solar potential analyses. - Accelerated decision-making support by delivering ready-to-use data formats and insightful seasonal shadow analyses for Saint-Denis. Technologies / skills demonstrated: - Python scripting and code refactoring; GIS data formats (GeoJSON, GeoPackage GPKG); data conversion tooling with selective processing and data preservation. - Git-based collaboration, conflict resolution, and maintenance of notebooks; Jupyter notebooks for data analysis and visualization; shadow projection algorithms. Business value: - Reduced data processing time and redundancy, enabling faster GIS workflows and more accurate solar potential assessments for targeted locations.
10 Commits • 2 Features
Feb 1, 2025February 2025 – Monthly summary for dataforgoodfr/13_potentiel_solaire Key deliverables: - GeoJSON to GPKG data conversion for solar data: Extended extract_sample_data.py to support GeoJSON→GPKG conversion with a dedicated convert_geojson_to_gpkg function, skip-if-exists to avoid redundant downloads, and options for selective conversion and data preservation. This streamlines GIS workflows and ensures consistent, revision-safe data packaging. - Shadow analysis notebooks and tooling for solar potential in Saint-Denis: Implemented shadow projection calculations for school buildings and created notebooks to analyze and visualize shadows across seasons, enabling more accurate solar potential assessments. Included cleanup of obsolete notebooks and merge-conflict resolutions to maintain notebook integrity. Major bugs fixed / maintenance: - Resolved notebook conflicts and recovered the latest versions; removed obsolete notebooks to reduce confusion. - Introduced idempotent data conversion behavior (skip-if-exists) to enhance robustness and prevent unnecessary recomputation. Overall impact and accomplishments: - Strengthened the data processing pipeline from raw GeoJSON sources to GIS-ready GPKG packages, improving reliability, reproducibility, and data usability for solar potential analyses. - Accelerated decision-making support by delivering ready-to-use data formats and insightful seasonal shadow analyses for Saint-Denis. Technologies / skills demonstrated: - Python scripting and code refactoring; GIS data formats (GeoJSON, GeoPackage GPKG); data conversion tooling with selective processing and data preservation. - Git-based collaboration, conflict resolution, and maintenance of notebooks; Jupyter notebooks for data analysis and visualization; shadow projection algorithms. Business value: - Reduced data processing time and redundancy, enabling faster GIS workflows and more accurate solar potential assessments for targeted locations.
February 2025
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance76.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
Data AnalysisData EngineeringData VisualizationFile ConversionFoliumGISGeopandasGeospatial AnalysisGeospatial Data ProcessingGitJupyter NotebooksMatplotlibMerge Conflict ResolutionPandasPvlib
Repositories Contributed To
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