Geostatistical expansion in the scipy style
Info: scikit-gstat needs Python >= 3.5!
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 MÃ¤licke, HelgeDavidSchneider, & Codacy Badger. (2019, November 7). mmaelicke/scikit-gstat: Version 0.2.6 (Version v0.2.6). Zenodo. http://doi.org/10.5281/zenodo.3531816
The full documentation can be found at: https://mmaelicke.github.io/scikit-gstat
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.
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:
- two experimental ones: quantiles, minmax
The models include:
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.
pip install scikit-gstat
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
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