gstools.field.generator.RandMeth
- class gstools.field.generator.RandMeth(model, *, mode_no=1000, seed=None, sampling='auto', **kwargs)[source]
Bases:
Generator
Randomization method for calculating isotropic random fields.
- Parameters
model (
CovModel
) – Covariance modelmode_no (
int
, optional) – Number of Fourier modes. Default:1000
seed (
int
orNone
, optional) – The seed of the random number generator. If “None”, a random seed is used. Default:None
sampling (
str
, optional) –Sampling strategy. Either
“auto”: select best strategy depending on given model
“inversion”: use inversion method
“mcmc”: use mcmc sampling
**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 [Hesse2014]:
\[u\left(x\right)= \sqrt{\frac{\sigma^{2}}{N}}\cdot \sum_{i=1}^{N}\left( Z_{1,i}\cdot\cos\left(\left\langle k_{i},x\right\rangle \right)+ Z_{2,i}\cdot\sin\left(\left\langle k_{i},x\right\rangle \right) \right)\]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
References
- Hesse2014
Heße, F., Prykhodko, V., Schlüter, S., and Attinger, S., “Generating random fields with a truncated power-law variogram: A comparison of several numerical methods”, Environmental Modelling & Software, 55, 32-48., (2014)
- Attributes
Methods
__call__
(pos[, add_nugget])Calculate the random modes for the randomization method.
get_nugget
(shape)Generate normal distributed values for the nugget simulation.
reset_seed
([seed])Recalculate the random amplitudes and wave numbers with the given seed.
update
([model, seed])Update the model and the seed.
- __call__(pos, add_nugget=True)[source]
Calculate the random modes for the randomization method.
This method calls the summate_* Cython methods, which are the heart of the randomization method.
- 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
- get_nugget(shape)[source]
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
- reset_seed(seed=nan)[source]
Recalculate the random amplitudes and wave numbers with the given seed.
- Parameters
seed (
int
orNone
ornumpy.nan
, optional) – the seed of the random number generator. IfNone
, a random seed is used. Ifnumpy.nan
, the actual seed will be kept. Default:numpy.nan
Notes
Even if the given seed is the present one, modes will be recalculated.
- update(model=None, seed=nan)[source]
Update the model and the seed.
If model and seed are not different, nothing will be done.
- property seed
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