# Create a gaussian process from a cov. model using HMatrixΒΆ

In this basic example we are going to build a gaussian process from its covariance model and using the `HMatrix`

as sampling method.

```
[1]:
```

```
from __future__ import print_function
import openturns as ot
```

Define the covariance model :

```
[2]:
```

```
dimension = 1
amplitude = [1.0] * dimension
scale = [10] * dimension
covarianceModel = ot.AbsoluteExponential(scale, amplitude)
```

Define the time grid on which we want to sample the gaussian process :

```
[3]:
```

```
# define a mesh
tmin = 0.0
step = 0.01
n = 10001
timeGrid = ot.RegularGrid(tmin, step, n)
```

Finally define the gaussian process :

```
[4]:
```

```
# create the process
process = ot.GaussianProcess(covarianceModel, timeGrid)
print(process)
```

```
GaussianProcess(trend=[x0]->[0.0], covariance=AbsoluteExponential(scale=[10], amplitude=[1]))
```

We set the sampling method to `HMAT`

```
[5]:
```

```
process.setSamplingMethod(1)
```

We sample the process :

```
[6]:
```

```
# draw a sample
sample = process.getSample(6)
sample.drawMarginal(0)
```

```
[6]:
```

We notice here that we are able to sample the covariance model over a mesh of size `10000`

, which is usually tricky on laptop. This is mainly due to the compression.