CLIMADA-project/climada_python
Sep 2025 – Sep 2025
1 Month active
Languages Used
Python
Technical Skills
Data AlignmentGeospatial AnalysisGrid DefinitionPythonRaster Data Processing
Zélie Stalhandske
PROFILE
Zélie Stalhandske
Zelie Stalhandske developed a custom grid feature for LitPop exposure calculations in the CLIMADA-project/climada_python repository, focusing on enhancing geospatial exposure modeling. She implemented functionality that allows users to define target grids or align with existing population and nightlight datasets, improving the flexibility and accuracy of spatial resampling. Using Python and leveraging skills in raster data processing and grid definition, Zelie’s work addressed the challenge of integrating diverse gridded data sources for regional analyses. The feature improved interoperability and modeling precision, laying a foundation for more adaptable exposure assessments and supporting robust, user-driven workflows in geospatial data alignment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
812
Activity Months1
Your Network
18 peopleSame Organization
@usys.ethz.ch5
Work History
September 2025
1 Commits • 1 Features
Sep 1, 20252025-09 monthly summary: Delivered a key feature for CLIMADA Python that enhances geospatial exposure modeling flexibility and accuracy. Implemented Custom grids for LitPop exposure calculations, enabling users to specify target grids or align with existing population or nightlight datasets, improving spatial resampling and integration of diverse gridded data sources. This work lays groundwork for more precise regional analyses and better interoperability across datasets. No major bugs fixed this month. Overall impact includes improved modeling accuracy, expanded workflow interoperability, and measurable business value for users conducting exposure assessments.
1 Commits • 1 Features
Sep 1, 20252025-09 monthly summary: Delivered a key feature for CLIMADA Python that enhances geospatial exposure modeling flexibility and accuracy. Implemented Custom grids for LitPop exposure calculations, enabling users to specify target grids or align with existing population or nightlight datasets, improving spatial resampling and integration of diverse gridded data sources. This work lays groundwork for more precise regional analyses and better interoperability across datasets. No major bugs fixed this month. Overall impact includes improved modeling accuracy, expanded workflow interoperability, and measurable business value for users conducting exposure assessments.
September 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AlignmentGeospatial AnalysisGrid DefinitionPythonRaster Data Processing
Repositories Contributed To
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