gstools.transform

GStools subpackage providing transformations.

Field-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.

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

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.discrete(fld, values, thresholds='arithmetic')[source]

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.boxcox(fld, lmbda=1, shift=0)[source]

Box-Cox transformation.

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.zinnharvey(fld, conn='high')[source]

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”
gstools.transform.normal_force_moments(fld)[source]

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_lognormal(fld)[source]

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)[source]

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_arcsin(fld, a=None, b=None)[source]

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_uquad(fld, a=None, b=None)[source]

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