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 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. http://doi.org/10.5281/zenodo.3552235
The full documentation can be found at: https://mmaelicke.github.io/scikit-gstat
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.
pip install scikit-gstat
git clone https://github.com/mmaelicke/scikit-gstat.git 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) print(V)
spherical Variogram ------------------- Estimator: matheron Range: 1.64 Sill: 5.35 Nugget: 0.00
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