GSTools API

Purpose

GeoStatTools is a library providing geostatistical tools for random field generation, conditioned field generation, kriging and variogram estimation based on a list of provided or even user-defined covariance models.

The following functionalities are directly provided on module-level.

Subpackages

`covmodel`	GStools subpackage providing a set of handy covariance models.
`field`	GStools subpackage providing tools for spatial random fields.
`variogram`	GStools subpackage providing tools for estimating and fitting variograms.
`krige`	GStools subpackage providing kriging.
`random`	GStools subpackage for random number generation.
`tools`	GStools subpackage providing miscellaneous tools.
`transform`	GStools subpackage providing transformations to post-process normal fields.
`normalizer`	GStools subpackage providing normalization routines.

Classes

Kriging

Swiss-Army-Knife for Kriging. For short cut classes see: gstools.krige

Krige(model, cond_pos, cond_val[, ...])

A Swiss Army knife for kriging.

Spatial Random Field

Classes for (conditioned) random field generation

`SRF`(model[, mean, normalizer, trend, ...])	A class to generate spatial random fields (SRF).
`CondSRF`(krige[, generator])	A class to generate conditioned spatial random fields (SRF).

Covariance Base-Class

Class to construct user defined covariance models

CovModel([dim, var, len_scale, nugget, ...])

Base class for the GSTools covariance models.

Covariance Models

Standard Covariance Models

`Gaussian`([dim, var, len_scale, nugget, ...])	The Gaussian covariance model.
`Exponential`([dim, var, len_scale, nugget, ...])	The Exponential covariance model.
`Matern`([dim, var, len_scale, nugget, anis, ...])	The Matérn covariance model.
`Integral`([dim, var, len_scale, nugget, ...])	The Exponential Integral covariance model.
`Stable`([dim, var, len_scale, nugget, anis, ...])	The stable covariance model.
`Rational`([dim, var, len_scale, nugget, ...])	The rational quadratic covariance model.
`Cubic`([dim, var, len_scale, nugget, anis, ...])	The Cubic covariance model.
`Linear`([dim, var, len_scale, nugget, anis, ...])	The bounded linear covariance model.
`Circular`([dim, var, len_scale, nugget, ...])	The circular covariance model.
`Spherical`([dim, var, len_scale, nugget, ...])	The Spherical covariance model.
`HyperSpherical`([dim, var, len_scale, ...])	The Hyper-Spherical covariance model.
`SuperSpherical`([dim, var, len_scale, ...])	The Super-Spherical covariance model.
`JBessel`([dim, var, len_scale, nugget, anis, ...])	The J-Bessel hole model.

Truncated Power Law Covariance Models

`TPLGaussian`([dim, var, len_scale, nugget, ...])	Truncated-Power-Law with Gaussian modes.
`TPLExponential`([dim, var, len_scale, ...])	Truncated-Power-Law with Exponential modes.
`TPLStable`([dim, var, len_scale, nugget, ...])	Truncated-Power-Law with Stable modes.
`TPLSimple`([dim, var, len_scale, nugget, ...])	The simply truncated power law model.

Functions

VTK-Export

Routines to export fields to the vtk format

`vtk_export`(filename, pos, fields[, mesh_type])	Export a field to vtk.
`to_vtk`(pos, fields[, mesh_type])	Create a VTK/PyVista grid.

Geometric

Some convenient functions for geometric operations

`rotated_main_axes`(dim, angles)	Create list of the main axis defined by the given system rotations.
`generate_grid`(pos)	Generate grid from a structured position tuple.
`generate_st_grid`(pos, time[, mesh_type])	Generate spatio-temporal grid from a position tuple and time array.

Variogram Estimation

Estimate the variogram of a given field with these routines

`vario_estimate`(pos, field[, bin_edges, ...])	Estimates the empirical variogram.
`vario_estimate_axis`(field[, direction, ...])	Estimates the variogram along array axis.
`standard_bins`([pos, dim, latlon, mesh_type, ...])	Get standard binning.

Misc

`EARTH_RADIUS`	earth radius for WGS84 ellipsoid in km
`KM_SCALE`	earth radius for WGS84 ellipsoid in km
`DEGREE_SCALE`	radius for unit sphere in degree
`RADIAN_SCALE`	radius for unit sphere