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Welcome to SciKit GStat

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SciKit-Gstat is a scipy-styled analysis module for variogram analysis. The base class is called Variogram, which is probably the only import needed. However, several other classes exist:

• DirectionalVariogram for directional variograms

• SpaceTimeVariogram for spatio-temporal variograms

• OrdinaryKriging for interpolation

• MetricSpace for pre-computed spatial samples

The variogram classes have a similar interface and can compute experimental variograms and fit theoretical variogram model functions. The module makes use of a rich selection of semi-variance estimators, variogram model functions and sptial binning functions, while being extensible at the same time.

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, 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

Contents:

• Installation
  • PyPI
  • GitHub
  • Conda-Forge
  • Note
• Getting Started
  • Load the class and data
  • Plot
  • Mutating
• User Guide
  • Introduction
    • General
    • What is geostatistics?
    • Geostatistical Tools
    • How to use this guide
  • Variography
    • The variogram
    • When direction matters
  • Interpolation
    • Spatial interpolation
    • Using a spatial model
    • Kriging equation system
    • Kriging error
    • Example
• Tutorials
• Technical Notes
  • Fitting a theoretical model
    • General
    • Default fit
    • Constrained fit
    • Distance weighted fit
  • Directional Variograms
    • General
    • Understanding Search Area
    • Directional variograms
  • Kriging estimate mode
    • General
    • Why not calculate?
    • Example
• Code Reference
  • Variogram Class
  • DirectionalVariogram Class
  • SpaceTimeVariogram class
  • Binning functions
  • Estimator Functions
    • Matheron
    • Cressie
    • Dowd
    • Genton
    • Shannon Entropy
    • MinMax
    • Percentile
  • Variogram models
    • Spherical model
    • Exponential model
    • Gaussian model
    • Cubic model
    • Stable model
    • Matérn model
  • Kriging Class
  • Example data sets
    • Datasets
    • Utility Functions
  • MetricSpace - Coordinate representation
    • MetricSpace
    • MetricSpacePair
  • Utility Functions
    • Shannon Entropy
    • Cross Validation
    • Uncertainty Propagation
    • Maximum Likelihood Estimation
• Changelog
  • Version 1.0.0
  • Version 0.6.14
  • Version 0.6.13
  • Version 0.6.12
  • Version 0.6.11
  • Version 0.6.10
  • Version 0.6.9
  • Version 0.6.8
  • Version 0.6.7
  • Version 0.6.6
  • Version 0.6.5
  • Version 0.6.4
  • Version 0.6.3
  • Version 0.6.2
  • Version 0.6.1
  • Version 0.6.0
  • Version 0.5.6
  • Version 0.5.5
  • Version 0.5.4
  • Version 0.5.3
  • Version 0.5.2
  • Version 0.5.1
  • Version 0.5.0
  • Version 0.4.4
  • Version 0.4.3
  • Version 0.4.2
  • Version 0.4.1
  • Version 0.4.0
  • Version 0.3.11
  • Version 0.3.10
  • Version 0.3.9
  • Version 0.3.8
  • Version 0.3.7
  • Version 0.3.6
  • Version 0.3.5
  • Version 0.3.4
  • Version 0.3.3
  • Version 0.3.2
  • Version 0.3.1
  • Version 0.3.0
  • Version 0.2.8
  • Version 0.2.7
  • Version 0.2.6
  • Version 0.2.5
  • Version 0.2.4
  • Version 0.2.3
  • Version 0.2.2
  • Version 0.2.1
  • Version 0.2.0
• PDF

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Installation