Tutorial 7: Field transformations

The generated fields of gstools are ordinary Gaussian random fields. In application there are several transformations to describe real world problems in an appropriate manner.

GStools provides a submodule gstools.transform with a range of common transformations:

binary(fld[, divide, upper, lower]) Binary transformation.
discrete(fld, values[, thresholds]) Discrete transformation.
boxcox(fld[, lmbda, shift]) Box-Cox transformation.
zinnharvey(fld[, conn]) Zinn and Harvey transformation to connect low or high values.
normal_force_moments(fld) Force moments of a normal distributed field.
normal_to_lognormal(fld) Transform normal distribution to log-normal distribution.
normal_to_uniform(fld) Transform normal distribution to uniform distribution on [0, 1].
normal_to_arcsin(fld[, a, b]) Transform normal distribution to the bimodal arcsin distribution.
normal_to_uquad(fld[, a, b]) Transform normal distribution to U-quadratic distribution.

All the transformations take a field class, that holds a generated field, as input and will manipulate this field inplace.

Simply import the transform submodule and apply a transformation to the srf class:

from gstools import transform as tf
...
tf.normal_to_lognormal(srf)