This module offers at the current state a scipy-styled Variogram class for performing geostatistical analysis. This class is can be used to derive variograms. Key benefits are a number of semivariance estimators and theoretical variogram functions. The module is planned to be hold in the manner of scikit modules and be based upon numpy and scipy whenever possible. There is also a distance matrix extension available, with a function for calculating n.dimensional distance matrices for the variogram. The estimators include:

• matheron
• cressie
• dowd
• genton (still buggy)
• entropy (not tested)

The models include:

• sperical
• exponential
• gaussian
• cubic
• stable
• matérn

with all of them in a nugget and no-nugget variation. All the estimator functions are written numba compatible, therefore you can just download it and include the @jit decorator. This can speed up the calculation for bigger data sets up to 100x. Nevertheless, this is not included in this sckit-gstat version as these functions might be re-implemented using Cython. This is still under evaluation.

At the current stage, the package does not inlcude any kriging. This is planned for a future release.

You can either install scikit-gstat using pip or you download the latest version from github.

PyPI:

pip install scikit-gstat

GIT:

git clone https://github.com/mmaelicke/scikit-gstat.git
cd scikit-gstat
pip install -r requirements.txt
pip install -e .

The Variogram class needs at least a list of coordiantes and values. All other attributes are set by default. You can easily set up an example by generating some random data:

import numpy as np
import skgstat as skg

coordinates = np.random.gamma(0.7, 2, (30,2))
values = np.random.gamma(2, 2, 30)

V = skg.Variogram(coordinates=coordinates, values=values)
print(V)

spherical Variogram
-------------------
Estimator:    matheron
Range:        1.64
Sill:         5.35
Nugget:       0.00