Let us start with a short example of a self defined model (Of course, we
provide a lot of predefined models [See:
but they all work the same way).
Therefore we reimplement the Gaussian covariance model
by defining just the “normalized”
import numpy as np import gstools as gs # use CovModel as the base-class class Gau(gs.CovModel): def cor(self, h): return np.exp(-h ** 2)
Here the parameter
h stands for the normalized range
r / len_scale.
Now we can instantiate this model:
model = Gau(dim=2, var=2.0, len_scale=10)
To have a look at the variogram, let’s plot it:
This is almost identical to the already provided
There, a scaling factor is implemented so the len_scale coincides with the
gau_model = gs.Gaussian(dim=2, var=2.0, len_scale=10) gau_model.plot()
We already used some parameters, which every covariance models has. The basic ones are:
- dim : dimension of the model
- var : variance of the model (on top of the subscale variance)
- len_scale : length scale of the model
- nugget : nugget (subscale variance) of the model
These are the common parameters used to characterize a covariance model and are therefore used by every model in GSTools. You can also access and reset them:
print("old model:", model) model.dim = 3 model.var = 1 model.len_scale = 15 model.nugget = 0.1 print("new model:", model)
old model: Gau(dim=2, var=2.0, len_scale=10, nugget=0.0, anis=[1.], angles=[0.]) new model: Gau(dim=3, var=1.0, len_scale=15, nugget=0.1, anis=[1. 1.], angles=[0. 0. 0.])
- The sill of the variogram is calculated by
sill = variance + nuggetSo we treat the variance as everything above the nugget, which is sometimes called partial sill.
- A covariance model can also have additional parameters.
Total running time of the script: ( 0 minutes 0.244 seconds)