gstools.transform¶
GStools subpackage providing transformations to post-process normal fields.
Field-Transformations¶
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Binary transformation. |
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Discrete transformation. |
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(Inverse) Box-Cox transformation to denormalize data. |
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Zinn and Harvey transformation to connect low or high values. |
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Force moments of a normal distributed field. |
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Transform normal distribution to log-normal distribution. |
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Transform normal distribution to uniform distribution on [0, 1]. |
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Transform normal distribution to the bimodal arcsin distribution. |
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Transform normal distribution to U-quadratic distribution. |
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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)
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gstools.transform.
boxcox
(fld, lmbda=1, shift=0)[source]¶ (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. Forlmbda=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
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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 takethresholds (
str
ornumpy.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”
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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.
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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 varianceb (
float
, optional) – Parameter b of the arcsin distribution (upper bound). Default: keep mean and variance
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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.
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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.
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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 varianceb (
float
, optional) – Parameter b of the U-quadratic distribution (upper bound). Default: keep mean and variance