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])

(Inverse) Box-Cox transformation to denormalize data.

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)