```
%matplotlib widget
import matplotlib.pyplot as plt
plt.ioff()
# turn of warnings
import warnings
warnings.filterwarnings('ignore')
```

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)
print(model)
print(model.anis)
print(model.len_scale_vec)
```

```
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])
model.plot("cov_spatial")
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 $x$, $y$ and $z$. Therefore you can provide rotation angles for the model:

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