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

All the transformations take a field class, that holds a generated field, as input and will manipulate this field inplace or store it with a given name.

Simply apply a transformation to a field class:

```import gstools as gs
...
srf = gs.SRF(model)
srf(...)
gs.transform.normal_to_lognormal(srf)
```

Or use the provided wrapper:

```import gstools as gs
...
srf = gs.SRF(model)
srf(...)
srf.transform("lognormal")
```

Examples

log-normal fields

log-normal fields

binary fields

binary fields

Discrete fields

Discrete fields

Zinn & Harvey transformation

Zinn & Harvey transformation

Bimodal fields

Bimodal fields

Combinations

Combinations

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