scikit-gstat 0.2.0

Geostatistical expansion in the scipy style

Info: scikit-gstat needs Python >= 3.4!

New Version 0.2

Scikit-gstat was rewritten in major parts. Most of the changes are internal, but the attributes and behaviour of the Variogram has also changed substantially. A detailed description of of the new versions usage will follow. The last version of the old Variogram class, 0.1.8, is kept in the version-0.1.8 branch on GitHub, but not developed any further. Those two versions are not compatible.

Description

At current state, this module offers a scipy-styled Variogram class for performing geostatistical analysis. This class 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

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

Installation

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 .

Usage

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