Basic MethodsΒΆ

The covariance model class CovModel of GSTools provides a set of handy methods.

One of the following functions defines the main characterization of the variogram:

  • CovModel.variogram : The variogram of the model given by

    \gamma\left(r\right)=
\sigma^2\cdot\left(1-\rho\left(r\right)\right)+n

  • CovModel.covariance : The (auto-)covariance of the model given by

    C\left(r\right)= \sigma^2\cdot\rho\left(r\right)

  • CovModel.correlation : The (auto-)correlation (or normalized covariance) of the model given by

    \rho\left(r\right)

  • CovModel.cor : The normalized correlation taking a normalized range given by:

    \mathrm{cor}\left(\frac{r}{\ell}\right) = \rho\left(r\right)

As you can see, it is the easiest way to define a covariance model by giving a correlation function as demonstrated in the introductory example. If one of the above functions is given, the others will be determined:

../../_images/sphx_glr_01_basic_methods_001.png
import gstools as gs

model = gs.Exponential(dim=3, var=2.0, len_scale=10, nugget=0.5)
ax = model.plot("variogram")
model.plot("covariance", ax=ax)
model.plot("correlation", ax=ax)

Total running time of the script: ( 0 minutes 0.118 seconds)

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