gstools.transform

GStools subpackage providing transformations to post-process normal fields.

Field-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.

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gstools.transform.binary(fld, divide=None, upper=None, lower=None)

Binary transformation.

After this transformation, the field only has two values.

Parameters

• fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

• divide (float, optional) – The dividing value. Default: fld.mean

• upper (float, optional) – The resulting upper value of the field. Default: mean + sqrt(fld.model.sill)

• lower (float, optional) – The resulting lower value of the field. Default: mean - sqrt(fld.model.sill)

gstools.transform.boxcox(fld, lmbda=1, shift=0)

(Inverse) Box-Cox transformation to denormalize data.

After this transformation, the again Box-Cox transformed field is normal distributed.

See: https://en.wikipedia.org/wiki/Power_transform#Box%E2%80%93Cox_transformation

Parameters

• fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

• lmbda (float, optional) – The lambda parameter of the Box-Cox transformation. For lmbda=0 one obtains the log-normal transformation. Default: 1

• shift (float, optional) – The shift parameter from the two-parametric Box-Cox transformation. The field will be shifted by that value before transformation. Default: 0

gstools.transform.discrete(fld, values, thresholds='arithmetic')

Discrete transformation.

After this transformation, the field has only len(values) discrete values.

Parameters

• fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

• values (numpy.ndarray) – The discrete values the field will take

• thresholds (str or numpy.ndarray, optional) – the thresholds, where the value classes are separated possible values are: * “arithmetic”: the mean of the 2 neighbouring values * “equal”: devide the field into equal parts * an array of explicitly given thresholds Default: “arithmetic”

gstools.transform.normal_force_moments(fld)

Force moments of a normal distributed field.

After this transformation, the field is still normal distributed.

Parameters

fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

gstools.transform.normal_to_arcsin(fld, a=None, b=None)

Transform normal distribution to the bimodal arcsin distribution.

See: https://en.wikipedia.org/wiki/Arcsine_distribution

After this transformation, the field is arcsin-distributed on [a, b].

Parameters

• fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

• a (float, optional) – Parameter a of the arcsin distribution (lower bound). Default: keep mean and variance

• b (float, optional) – Parameter b of the arcsin distribution (upper bound). Default: keep mean and variance

gstools.transform.normal_to_lognormal(fld)

Transform normal distribution to log-normal distribution.

After this transformation, the field is log-normal distributed.

Parameters

fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

gstools.transform.normal_to_uniform(fld)

Transform normal distribution to uniform distribution on [0, 1].

After this transformation, the field is uniformly distributed on [0, 1].

Parameters

fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

gstools.transform.normal_to_uquad(fld, a=None, b=None)

Transform normal distribution to U-quadratic distribution.

See: https://en.wikipedia.org/wiki/U-quadratic_distribution

After this transformation, the field is U-quadratic-distributed on [a, b].

Parameters

• fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

• a (float, optional) – Parameter a of the U-quadratic distribution (lower bound). Default: keep mean and variance

• b (float, optional) – Parameter b of the U-quadratic distribution (upper bound). Default: keep mean and variance

gstools.transform.zinnharvey(fld, conn='high')

Zinn and Harvey transformation to connect low or high values.

After this transformation, the field is still normal distributed.

Parameters

• fld (Field) – Spatial Random Field class containing a generated field. Field will be transformed inplace.

• conn (str, optional) – Desired connectivity. Either “low” or “high”. Default: “high”