Geostatistical expansion in the scipy style
Info: scikit-gstat needs Python >= 3.6!
How to cite
In case you use SciKit-GStat in other software or scientific publications, please reference this module. There is a GMD publication in discussion. Please cite it like:
MÃ¤licke, M.: SciKit-GStat 1.0: A SciPy flavoured geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-174, in review, 2021.
The code itself is published and has a DOI. It can be cited as:
Mirko MÃ¤licke, Romain Hugonnet, Helge David Schneider, Sebastian MÃ¼ller, Egil MÃ¶ller, & Johan Van de Wauw. (2022). mmaelicke/scikit-gstat: Version 1.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5970098
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
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 clone https://github.com/mmaelicke/scikit-gstat.git cd scikit-gstat pip install -r requirements.txt pip install -e .
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
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()
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