Skip to main content

Geostatistical expansion in the scipy style

Project description

Info: scikit-gstat needs Python >= 3.5! Codacy Badge Codecov

How to cite

In case you use SciKit-GStat in other software or scientific publications, please reference this module. It is published and has a DOI. It can be cited as:

Mirko Mälicke, & Helge David Schneider. (2019, November 25). Scikit-GStat 0.2.7: A scipy flavored geostatistical analysis toolbox written in Python. (Version v.0.2.7). Zenodo.

Full Documentation

The full documentation can be found at:


SciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes two base classes Variogram and OrdinaryKriging. Additionally, various variogram classes inheriting from Variogram are available for solving directional or space-time related tasks. The module makes use of a rich selection of semi-variance estimators and variogram model functions, while being extensible at the same time. The estimators include:

  • matheron
  • cressie
  • dowd
  • genton
  • entropy
  • two experimental ones: quantiles, minmax

The models include:

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

with all of them in a nugget and no-nugget variation. All the estimator are implemented using numba’s jit decorator. The usage of numba might be subject to change in future versions.



pip install scikit-gstat


git clone
cd scikit-gstat
pip install -r requirements.txt
pip install -e .

Note: It can happen that the installation of shapely, numba or numpy is failing using pip. Especially on Windows systems. Usually, a missing Dll (see eg. #31) or visual c++ redistributable is the reason. These errors are not caused by pip, scikit-gstat or the respective packages and there are a lot of issues in the shapely and numpy repo concerning these problems. Usually, the best workaround is to install especially shapely independent from scikit-gstat. As far as I know, these problems do not apply if anaconda is used like:

conda install shapely numpy


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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for scikit-gstat, version 0.3.7
Filename, size File type Python version Upload date Hashes
Filename, size scikit-gstat-0.3.7.tar.gz (71.3 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page