Creating Fancier Fields

Only using Gaussian covariance fields gets boring. Now we are going to create much rougher random fields by using an exponential covariance model and we are going to make them anisotropic.

The code is very similar to the previous examples, but with a different covariance model class Exponential. As model parameters we a using following

  • variance \sigma^2=1

  • correlation length \lambda=(12, 3)^T

  • rotation angle \theta=\pi/8

import numpy as np

import gstools as gs

x = y = np.arange(100)
model = gs.Exponential(dim=2, var=1, len_scale=[12.0, 3.0], angles=np.pi / 8)
srf = gs.SRF(model, seed=20170519)
srf.structured([x, y])
srf.plot()
Field 2D structured: (100, 100)

The anisotropy ratio could also have been set with

model = gs.Exponential(dim=2, var=1, len_scale=12, anis=0.25, angles=np.pi / 8)

Total running time of the script: ( 0 minutes 1.058 seconds)

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