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

We are going to start with a very simple example of a spatial random field with an isotropic Gaussian covariance model and following parameters:

- variance $\sigma^2=1$
- correlation length $\lambda=10$

First, we set things up and create the axes for the field. We are going to need the `SRF`

class for the actual generation of the spatial random field.
But `SRF`

also needs a covariance model and we will simply take the `Gaussian`

model.

```
import gstools as gs
x = y = range(101)
```

Now we create the covariance model with the parameters $\sigma^2$ and
$\lambda$ and hand it over to :any:`SRF`

. By specifying a seed,
we make sure to create reproducible results:

```
model = gs.Gaussian(dim=2, var=1, len_scale=10, nugget=0)
srf = gs.SRF(model, seed=20220425)
```

With these simple steps, everything is ready to create our first random field.
We will create the field on a structured grid (as you might have guessed from
the `x`

and `y`

), which makes it easier to plot.

```
field = srf.structured([x, y])
srf.plot()
```

Wow, that was pretty easy!

`model.plot()`