verde

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Spatial data gridding and interpolation with a machine-learning style API. Process geographic and Cartesian point data onto regular grids. Use when Claude needs to: (1) Grid scattered spatial data onto regular grids, (2) Interpolate point data using splines, linear, or cubic methods, (3) Process geographic coordinates with projections, (4) Reduce large datasets using block averaging, (5) Remove polynomial trends from spatial data, (6) Cross-validate gridding parameters, (7) Create processing pipelines with Chain, (8) Grid vector data like GPS velocities.

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Sourcesteadfastasart/geoscience-skills

Added on2026-05-16

NPX Install

npx skill4agent add steadfastasart/geoscience-skills verde

SKILL.md Content

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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

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

Gridders - Available gridders and parameters
Cross-Validation - Parameter tuning methods

Scripts

scripts/grid_data.py - Grid scattered data to NetCDF

verde

NPX Install

Tags

SKILL.md Content

Verde - Spatial Data Gridding

Quick Reference

Key Classes

Essential Operations

Grid Scattered Data

Project to Cartesian

Block Reduce Large Datasets

Remove Trend Before Gridding

Processing Pipeline

Cross-Validation

Mask Far from Data

Grid Parameters

Gridder Comparison

When to Use vs Alternatives

Common Workflows

Grid Scattered Spatial Data with Validation

Common Issues

References

Scripts