gstools.field.generator¶

GStools subpackage providing generators for spatial random fields.

The following classes are provided

RandMeth(model[, mode_no, seed, …]) Randomization method for calculating isotropic spatial random fields.

class gstools.field.generator.RandMeth(model, mode_no=1000, seed=None, chunk_tmp_size=10000000.0, verbose=False, **kwargs)[source]¶

Randomization method for calculating isotropic spatial random fields.

Parameters:

model (CovModel) – covariance model
mode_no (int, optional) – number of Fourier modes. Default: 1000
seed (int or None, optional) – the seed of the random number generator. If “None”, a random seed is used. Default: None
chunk_tmp_size (int, optional) – Number of points (number of coordinates * mode_no) to be handled by one chunk while creating the fild. This is used to prevent memory overflows while generating the field. Default: 1e7
verbose (bool, optional) – State if there should be output during the generation. Default: False
**kwargs – Placeholder for keyword-args

Notes

The Randomization method is used to generate isotropic spatial random fields characterized by a given covariance model. The calculation looks like:

$u\left(x\right)= \sqrt{\frac{\sigma^{2}}{N}}\cdot \sum_{i=1}^{N}\left( Z_{1,i}\cdot\cos\left(\left\langle k_{i},x\right\rangle \right)+ Z_{2,i}\cdot\sin\left(\left\langle k_{i},x\right\rangle \right) \right)$

where:

$N$ : fourier mode number

$Z_{j,i}$ : random samples from a normal distribution

$k_i$ : samples from the spectral density distribution of the covariance model

Attributes:	`chunk_tmp_size` `int`: temporary chunk size `mode_no` `int`: The number of modes in the randomization method. `model` `CovModel`: The covariance model of the spatial random field. `name` `str`: The name of the generator `seed` `int`: the seed of the master RNG `verbose` `bool`: verbosity of the generator

Methods

`__call__`(x[, y, z])	Calculates the random modes for the randomization method.
`reset_seed`([seed])	Recalculate the random amplitudes and wave numbers with the given seed.
`update`([model, seed])	Update the model and the seed.

__call__(x, y=None, z=None)[source]¶

Calculates the random modes for the randomization method.

Parameters:	x (`float`, `numpy.ndarray`) – the x components of the position tuple, the shape has to be (len(x), 1, 1) for 3d and accordingly shorter for lower dimensions y (`float`, `numpy.ndarray`, optional) – the y components of the pos. tupls z (`float`, `numpy.ndarray`, optional) – the z components of the pos. tuple
Returns:	the random modes
Return type:	`numpy.ndarray`

reset_seed(seed=nan)[source]¶

Recalculate the random amplitudes and wave numbers with the given seed.

Parameters:	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`

Notes

Even if the given seed is the present one, modes will be racalculated.

update(model=None, seed=nan)[source]¶

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`

chunk_tmp_size¶

temporary chunk size

Type:	`int`

mode_no¶

The number of modes in the randomization method.

Type:	`int`

model¶

The covariance model of the spatial random field.

Type:	`CovModel`

name¶

The name of the generator

Type:	`str`

seed¶

the seed of the master RNG

Notes

If a new seed is given, the setter property not only saves the new seed, but also creates new random modes with the new seed.

Type:	`int`

verbose¶

verbosity of the generator

Type:	`bool`