Verde - Spatial Data Gridding

Quick Reference

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

Essential Operations

Grid Scattered Data

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

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

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

trend = vd.Trend(degree=2)  # Quadratic
trend.fit(coordinates, values)
residuals = values - trend.predict(coordinates)

# Grid residuals, then add trend back

Processing Pipeline

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

spline = vd.Spline()
scores = vd.cross_val_score(spline, coordinates, values, cv=5)
print(f"Mean R2: {scores.mean():.3f}")

Mask Far from Data

grid = spline.grid(spacing=0.1)
mask = vd.distance_mask(coordinates, maxdist=0.2, grid=grid)
grid_masked = grid.where(mask)

Grid Parameters

Gridder Comparison

When to Use vs Alternatives

Choose Verde when: You need a Pythonic, scikit-learn-style API for gridding scattered spatial data with built-in cross-validation, trend removal, and pipelines. Ideal for exploratory analysis and reproducible workflows.

Choose scipy.interpolate when: You have a simple interpolation task without spatial coordinates, projections, or need for validation.

Choose GMT when: You need command-line batch processing of large datasets or are integrating with shell-based workflows and need surface or nearneighbor.

Common Workflows

Grid Scattered Spatial Data with Validation

• Load scattered point data (coordinates + values)
• Project geographic coordinates to Cartesian if needed
• Inspect data distribution and identify clusters or gaps
• Apply BlockReduce to decimate dense clusters
• Remove regional trend with Trend(degree=1) or Trend(degree=2)
• Cross-validate gridder parameters with crossvalscore()
• Tune Spline(damping=...) or Spline(mindist=...) based on CV scores
• Fit chosen gridder (Spline, Linear, or Cubic) on residuals
• Grid onto regular spacing with .grid()
• Add trend back to gridded residuals
• Apply distance_mask() to clip extrapolation artifacts
• Visualize grid with xarray plotting: grid.elevation.plot()
• Save result to NetCDF with grid.to_netcdf()

Common Issues

References

• [Gridders](references/gridders.md) - Available gridders and parameters
• [Cross-Validation](references/cross_validation.md) - Parameter tuning methods

Scripts

• [scripts/griddata.py](scripts/griddata.py) - Grid scattered data to NetCDF