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verde

Data & analytics

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Updated 10-16 01:50

About this skill

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

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

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

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

When to Use vs Alternatives

Use Case Tool Why
General spatial gridding Verde ML-style API, pipelines, cross-validation
Basic 1D/2D interpolation scipy.interpolate Simpler API, no spatial focus
Potential field gridding Harmonica Equivalent sources designed for gravity/magnetics
Command-line batch gridding GMT Powerful CLI, good for automation scripts
Geostatistical interpolation scikit-gstat / pykrige Variogram-based with uncertainty
Very large datasets (10M+ pts) GMT / GDAL Better memory handling at scale
Vector data (GPS velocities) Verde (Vector) Built-in 2-component vector gridding
Trend removal + gridding Verde (Chain) Pipeline combines steps cleanly

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 cross_val_score()
  • 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

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

Scripts