Verde - Spatial Data Gridding
Quick Reference
python
import verde as vd
# Basic gridding
spline = vd.Spline()
spline.fit(coordinates, values) # coordinates = (lon, lat) tuple
grid = spline.grid(spacing=0.1) # Returns xarray Dataset
# Access result
elevation = grid.elevation.values
# Save output
grid.to_netcdf('output.nc')
Key Classes
| Class | Purpose |
|---|---|
Spline | Bi-harmonic spline interpolation (smooth, good extrapolation) |
Linear | Delaunay triangulation (fast, no extrapolation) |
Cubic | Cubic interpolation (medium smoothness) |
Chain | Pipeline of processing steps |
BlockReduce | Decimate data to block means/medians |
Trend | Polynomial trend fitting and removal |
Vector | Grid 2-component vector data |
Essential Operations
Grid Scattered Data
python
coordinates = (longitude, latitude) # Tuple of 1D arrays values = elevation # 1D array spline = vd.Spline() spline.fit(coordinates, values) grid = spline.grid(spacing=0.1, data_names=['elevation'])
Project to Cartesian
python
import pyproj projection = pyproj.Proj(proj='merc', lat_ts=data_lat.mean()) proj_coords = projection(longitude, latitude) spline = vd.Spline() spline.fit(proj_coords, values) grid = spline.grid(spacing=1000) # 1000m spacing
Block Reduce Large Datasets
python
import numpy as np reducer = vd.BlockReduce(reduction=np.median, spacing=0.1) coords_reduced, values_reduced = reducer.filter(coordinates, values)
Remove Trend Before Gridding
python
trend = vd.Trend(degree=2) # Quadratic trend.fit(coordinates, values) residuals = values - trend.predict(coordinates) # Grid residuals, then add trend back
Processing Pipeline
python
chain = vd.Chain([
('trend', vd.Trend(degree=1)),
('reduce', vd.BlockReduce(np.median, spacing=0.05)),
('spline', vd.Spline())
])
chain.fit(coordinates, values)
grid = chain.grid(spacing=0.01)
Cross-Validation
python
spline = vd.Spline()
scores = vd.cross_val_score(spline, coordinates, values, cv=5)
print(f"Mean R2: {scores.mean():.3f}")
Mask Far from Data
python
grid = spline.grid(spacing=0.1) mask = vd.distance_mask(coordinates, maxdist=0.2, grid=grid) grid_masked = grid.where(mask)
Grid Parameters
| Parameter | Description |
|---|---|
spacing | Grid cell size (same units as coordinates) |
region | (west, east, south, north) bounds |
shape | (n_north, n_east) grid dimensions |
adjust | 'spacing' or 'region' - which to adjust for exact fit |
Gridder Comparison
| Gridder | Speed | Smoothness | Extrapolation |
|---|---|---|---|
Spline | Medium | High | Good |
Linear | Fast | Low | None |
Cubic | Fast | Medium | None |
Common Issues
| Issue | Solution |
|---|---|
| Poor extrapolation | Use distance_mask() to mask far from data |
| Slow with large data | Use BlockReduce first |
| Regional trends | Remove with Trend before gridding |
| Wrong spacing | Check coordinate units (degrees vs meters) |
References
- •Gridders - Available gridders and parameters
- •Cross-Validation - Parameter tuning methods
Scripts
- •scripts/grid_data.py - Grid scattered data to NetCDF