verde

name	verde
description	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.
version	1.0.0
author	Geoscience Skills
license	MIT
tags	["Gridding","Interpolation","Spatial Analysis","Fatiando","Cross-Validation","Verde","Spline","Block Reduction"]
dependencies	["verde>=1.8.0","numpy","scipy"]
complements	["harmonica","geostatspy","scikit-gstat","pyvista"]
workflow_role	analysis

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  np

reducer = vd.BlockReduce(reduction=np.median, spacing=)
coords_reduced, values_reduced = reducer.(coordinates, values)