scikit-gstat

name	scikit-gstat
description	Geostatistical analysis with scikit-learn style API. Compute variograms, kriging interpolation, and spatial correlation analysis. Use when Claude needs to: (1) Compute experimental variograms from spatial data, (2) Fit variogram models (spherical, exponential, gaussian, matern), (3) Perform Ordinary or Universal Kriging interpolation, (4) Assess spatial anisotropy with directional variograms, (5) Cross-validate spatial models, (6) Analyze spatio-temporal data, (7) Export variogram parameters for other geostatistical software.
version	1.0.0
author	Geoscience Skills
license	MIT
tags	["Geostatistics","Variogram","Kriging","Scikit-Learn","Spatial Statistics","Scikit-Gstat","Interpolation","Directional Analysis"]
dependencies	["scikit-gstat>=1.0.0","numpy","scipy","scikit-learn"]
complements	["verde","geostatspy"]
workflow_role	analysis

SciKit-GStat - Geostatistics

Quick Reference

import skgstat as skg
import numpy as np

# Create variogram
V = skg.Variogram(coordinates=coords, values=values, n_lags=15)

# Fit model
V.model = 'spherical'
print(f"Range: {V.parameters[0]:.2f}, Sill: {V.parameters[1]:.2f}")

# Kriging interpolation
ok = skg.OrdinaryKriging(V)
predictions = ok.transform(grid_coords)

Key Classes

Class	Purpose
Variogram	Empirical and theoretical variograms
OrdinaryKriging	Interpolation with spatial correlation
DirectionalVariogram	Anisotropic variograms
SpaceTimeVariogram	Spatio-temporal analysis

Essential Operations

Create and Fit Variogram

import skgstat as skg

V = skg.Variogram(
    coordinates=coords,      # (n, 2) array of x, y
    values=values,           # (n,) array of measurements
    n_lags=15,
    maxlag='median'          # or specific distance
)

# Fit model: 'spherical', 'exponential', 'gaussian', 'matern', 'stable'
V.model = 'spherical'

# Get parameters
print(f"Range: {V.parameters[0]:.2f}")
()
()
()