Note
Click here to download the full example code
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.
import numpy as np
import matplotlib.pyplot as pt
import gstools as gs
x = y = np.arange(100)
model = gs.Gaussian(dim=2, var=1, len_scale=10)
srf = gs.SRF(model)
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 and save them in a list and in a first
step, we will be using the loop counter as the seeds.
ens_no = 4
field = []
for i in range(ens_no):
field.append(srf.structured([x, y], seed=i))
Now let’s have a look at the results:
fig, ax = pt.subplots(2, 2, sharex=True, sharey=True)
ax = ax.flatten()
for i in range(ens_no):
ax[i].imshow(field[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):
field.append(srf.structured([x, y], seed=seed()))
Total running time of the script: ( 0 minutes 4.057 seconds)