Spacial data interpolation

This package provides single interface for different interpolation methods.

Example

Regular usage of ordinary kriging method

import numpy as np
from sdinterp.variogram import calc_empirical, get_cov_model, fit_cov_model
from sdinterp.interpolation.kriging import Ordinary

# prepare data
n = 100
points = np.random.random((n, 2))
target = np.random.random((n,))

# get empirical variogram and fit model to it
bin_center, gamma = calc_empirical(points, target)
cov_model = get_cov_model("exponential")
fit_cov_model(cov_model, bin_center, gamma)

# init interpolation model
model = Ordinary(cov_model, points, target)

# evaluate model
prediction = model(np.random.random((1000, 2)))

Supported methods

• Simple Kriging
• Ordinary Kriging
• Universal Kriging
• Inverse Distance Weighting
• Nearest Neighbours
• Linear
• Radial basis function

Supported covariance models

• Gaussian
• Exponential
• Matern
• Integral
• Stable
• Rational
• Cubic
• Linear
• Circular
• Spherical

Requirements

• gstools >= 1.4.1
• numpy >= 1.23.5
• scipy >= 1.10.0

Develop and test

• Download project
• Create new virtualenv and install requirements.txt
• For testing run pytest from root

Build and release

• Build with python -m build
• Deploy to test.pypi.org with twine upload --repository testpypi dist/*
• Deploy to pypi.org with twine upload dist/*

Contact

You can contact me via popov.aleksandr.v01@gmail.com