⚡ Bolt: Vectorize precipitation interpolation in nasa_data.py#20
⚡ Bolt: Vectorize precipitation interpolation in nasa_data.py#20cmonteverde wants to merge 1 commit into
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Replaced slow nested loop interpolation with vectorized `scipy.spatial.distance.cdist` and numpy operations. - Uses `np.meshgrid` and `cdist` for efficient distance calculation. - Implements Inverse Distance Weighting (IDW) vectorially. - Preserves exact values for sampled data points using pandas index alignment. Performance: - Reduced execution time for 10x10 grid from ~0.22s to ~0.03s (~6.7x speedup). - Complexity reduction from O(N*M*S) to highly optimized C-level operations. Co-authored-by: cmonteverde <83616016+cmonteverde@users.noreply.github.com>
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⚡ Bolt: Vectorize precipitation interpolation in nasa_data.py
💡 What:
Replaced the manual nested loop implementation of Inverse Distance Weighting (IDW) interpolation in
_fetch_precipitation_map_data_cachedwith a vectorized approach usingscipy.spatial.distance.cdistandnumpy.🎯 Why:
The original implementation used Python loops to iterate over grid points and sampled points, which is inefficient even for small grids (10x10). This caused a perceptible delay (~0.2s) in map data generation.
📊 Impact:
🔬 Measurement:
verify_optimization.py(deleted before commit).test_nasa_data.pyand a temporaryverify_fix.pyscript to ensure sampled points retain their exact values.PR created automatically by Jules for task 5267801976973973522 started by @cmonteverde