# Creating an Ensemble of Fields

Creating an ensemble of random fields would also be a great idea. Let’s reuse most of the previous code.

We will set the position tuple pos before generation to reuse it afterwards.

```import matplotlib.pyplot as pt
import numpy as np

import gstools as gs

x = y = np.arange(100)

model = gs.Gaussian(dim=2, var=1, len_scale=10)
srf = gs.SRF(model)
srf.set_pos([x, y], "structured")
```

This time, we did not provide a seed to `SRF`, as the seeds will used during the actual computation of the fields. We will create four ensemble members, for better visualisation, save them in to srf class and in a first step, we will be using the loop counter as the seeds.

```ens_no = 4
for i in range(ens_no):
srf(seed=i, store=f"field{i}")
```

Now let’s have a look at the results. We can access the fields by name or index:

```fig, ax = pt.subplots(2, 2, sharex=True, sharey=True)
ax = ax.flatten()
for i in range(ens_no):
ax[i].imshow(srf[i].T, origin="lower")
pt.show()
```

## Using better Seeds

It is not always a good idea to use incrementing seeds. Therefore GSTools provides a seed generator `MasterRNG`. The loop, in which the fields are generated would then look like

```from gstools.random import MasterRNG

seed = MasterRNG(20170519)
for i in range(ens_no):
srf(seed=seed(), store=f"better_field{i}")
```

Total running time of the script: ( 0 minutes 4.162 seconds)

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