scikit-gstat 1.0.19

Geostatistical expansion in the scipy style

How to cite

In case you use SciKit-GStat in other software or scientific publications, please reference this module. There is a GMD publication. Please cite it like:

Mälicke, M.: SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, 2022.

The code itself is published and has a DOI. It can be cited as:

Mirko Mälicke, Romain Hugonnet, Helge David Schneider, Sebastian Müller, Egil Möller, & Johan Van de Wauw. (2022). mmaelicke/scikit-gstat: Version 1.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5970098

Full Documentation

The full documentation can be found at: https://mmaelicke.github.io/scikit-gstat

Description

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.

Installation

PyPI

pip install scikit-gstat

Note: It can happen that the installation of 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.

GIT:

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

Conda-Forge:

From Version 0.5.5 on scikit-gstat is also available on conda-forge. Note that for versions < 1.0 conda-forge will not always be up to date, but from 1.0 on, each minor release will be available.

conda install -c conda-forge scikit-gstat

Quickstart

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 using the skgstat.data sub-module, that includes a growing list of sample data.

import skgstat as skg

# the data functions return a dict of 'sample' and 'description'
coordinates, values = skg.data.pancake(N=300).get('sample')

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

spherical Variogram
-------------------
Estimator:         matheron
Effective Range:   353.64
Sill:              1512.24
Nugget:            0.00

All variogram parameters can be changed in place and the class will automatically invalidate and update dependent results and parameters.

V.model = 'exponential'
V.n_lags = 15
V.maxlag = 500

# plot - matplotlib and plotly are available backends
fig = V.plot()