Info: scikit-gstat needs Python >= 3.6!
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, Egil Möller, Helge David Schneider, & Sebastian Müller. (2021, May 28).
mmaelicke/scikit-gstat: A scipy flavoured geostatistical variogram analysis toolbox (Version v0.6.0). Zenodo. http://doi.org/10.5281/zenodo.4835779
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:
- 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.
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.
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.
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.
All variogram parameters can be changed in place and the class will automatically invalidate and update dependent results and parameters.