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version 0.1.1

Geostatistical expansion in the scipy style

People: Mirko Maelicke



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
- mat\xe9rn

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.


.. code-block:: bash

pip install scikit-gstat


.. code-block:: bash

git clone
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:

.. code-block:: python

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)

.. code-block:: bash

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



You can download the latest distribution from PyPI here:

Using pip

You can install scikit-gstat for yourself from the terminal by running:

pip install --user scikit-gstat

If you want to install it for all users on your machine, do:

pip install scikit-gstat
On Linux, do sudo pip install scikit-gstat.

If you don't yet have the pip tool, you can get it following these instructions.

This package was discovered in PyPI.