# gstools.transform

GStools subpackage providing transformations to post-process normal fields.

## Wrapper

 `apply`(fld, method[, field, store, process]) Apply field transformation.

## Field 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[, low, high, field, ...]) 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.

## Array Transformations

 `array_discrete`(field, values[, thresholds, ...]) Discrete transformation. `array_boxcox`(field[, lmbda, shift]) (Inverse) Box-Cox transformation to denormalize data. `array_zinnharvey`(field[, conn, mean, var]) Zinn and Harvey transformation to connect low or high values. `array_force_moments`(field[, mean, var]) Force moments of a normal distributed field. `array_to_lognormal`(field) Transform normal distribution to log-normal distribution. `array_to_uniform`(field[, mean, var, low, high]) Transform normal distribution to uniform distribution on [low, high]. `array_to_arcsin`(field[, mean, var, a, b]) Transform normal distribution to arcsin distribution. `array_to_uquad`(field[, mean, var, a, b]) Transform normal distribution to U-quadratic distribution.