Transform 22

Anisotropy and Rotation

%matplotlib widget
import matplotlib.pyplot as plt
# turn of warnings
import warnings

The internally used (semi-) variogram represents the isotropic case for the model. Nevertheless, you can provide anisotropy ratios by:

import gstools as gs

model = gs.Gaussian(dim=3, var=2.0, len_scale=10, anis=0.5)
Gaussian(dim=3, var=2.0, len_scale=10.0, nugget=0.0, anis=[1.0, 0.5])
[1.  0.5]
[10. 10.  5.]

As you can see, we defined just one anisotropy-ratio and the second transversal direction was filled up with 1.. You can get the length-scales in each direction by the attribute :any:CovModel.len_scale_vec. For full control you can set a list of anistropy ratios: anis=[0.5, 0.4].

Alternatively you can provide a list of length-scales:

model = gs.Gaussian(dim=3, var=2.0, len_scale=[10, 5, 4])
print("Anisotropy representations:")
print("Anis. ratios:", model.anis)
print("Main length scale", model.len_scale)
print("All length scales", model.len_scale_vec)

Rotation Angles

The main directions of the field don't have to coincide with the spatial directions xx, yy and zz. Therefore you can provide rotation angles for the model:

model = gs.Gaussian(dim=3, var=2.0, len_scale=[10, 2], angles=2.5)
print("Rotation angles", model.angles)

Again, the angles were filled up with 0. to match the dimension and you could also provide a list of angles. The number of angles depends on the given dimension:

  • in 1D: no rotation performable
  • in 2D: given as rotation around z-axis
  • in 3D: given by yaw, pitch, and roll (known as Tait–Bryan angles)
  • in nD: See the random field example about higher dimensions