gstools.field.generator.Generator

class gstools.field.generator.Generator(model, **kwargs)

Bases: ABC

Abstract generator class.

Parameters:

• model (CovModel) – Covariance model

• **kwargs – Placeholder for keyword-args

Attributes:

model

CovModel: Covariance model of the spatial random field.

name

str: Name of the generator.

value_type

str: Type of the field values (scalar, vector).

zero_var

bool: Whether Covariance model has zero variance.

Methods

__call__(pos[, add_nugget])	Generate the field.
get_nugget(shape)	Generate normal distributed values for the nugget simulation.
update([model, seed])	Update the model and the seed.

abstractmethod __call__(pos, add_nugget=True)

Generate the field.

Parameters:

• pos ((d, n), numpy.ndarray) – the position tuple with d dimensions and n points.

• add_nugget (bool) – Whether to add nugget noise to the field.

Returns:

the random modes

Return type:

numpy.ndarray

abstractmethod get_nugget(shape)

Generate normal distributed values for the nugget simulation.

Parameters:

shape (tuple) – the shape of the summed modes

Returns:

nugget – the nugget in the same shape as the summed modes

Return type:

numpy.ndarray

abstractmethod update(model=None, seed=nan)

Update the model and the seed.

If model and seed are not different, nothing will be done.

Parameters:

• model (CovModel or None, optional) – covariance model. Default: None

• seed (int or None or numpy.nan, optional) – the seed of the random number generator. If None, a random seed is used. If numpy.nan, the actual seed will be kept. Default: numpy.nan

abstract property model

Covariance model of the spatial random field.

Type:

CovModel

property name

Name of the generator.

Type:

str

abstract property value_type

Type of the field values (scalar, vector).

Type:

str

property zero_var

Whether Covariance model has zero variance.

Type:

bool