Creating an Ensemble of conditioned 2D Fields

Let’s create an ensemble of conditioned random fields in 2D.

import numpy as np
import matplotlib.pyplot as plt
import gstools as gs


# conditioning data (x, y, value)
cond_pos = [[0.3, 1.9, 1.1, 3.3, 4.7], [1.2, 0.6, 3.2, 4.4, 3.8]]
cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]

# grid definition for output field
x = np.arange(0, 5, 0.1)
y = np.arange(0, 5, 0.1)

model = gs.Gaussian(dim=2, var=0.5, len_scale=5, anis=0.5, angles=-0.5)
krige = gs.Krige(model, cond_pos=cond_pos, cond_val=cond_val)
cond_srf = gs.CondSRF(krige)
cond_srf.set_pos([x, y], "structured")

To generate the ensemble we will use a seed-generator. By specifying store=[f"fld{i}", False, False], only the conditioned field is stored with the specified name. The raw random field and the raw kriging field is not stored. This way, we can access each conditioned field by index cond_srf[i]:

seed = gs.random.MasterRNG(20170519)
ens_no = 4
for i in range(ens_no):
    cond_srf(seed=seed(), store=[f"fld{i}", False, False])

Now let’s have a look at the pairwise differences between the generated fields. We will see, that they coincide at the given conditions.

fig, ax = plt.subplots(ens_no + 1, ens_no + 1, figsize=(8, 8))
# plotting kwargs for scatter and image
vmax = np.max(cond_srf.all_fields)
sc_kw = dict(c=cond_val, edgecolors="k", vmin=0, vmax=vmax)
im_kw = dict(extent=2 * [0, 5], origin="lower", vmin=0, vmax=vmax)
for i in range(ens_no):
    # conditioned fields and conditions
    ax[i + 1, 0].imshow(cond_srf[i].T, **im_kw)
    ax[i + 1, 0].scatter(*cond_pos, **sc_kw)
    ax[i + 1, 0].set_ylabel(f"Field {i}", fontsize=10)
    ax[0, i + 1].imshow(cond_srf[i].T, **im_kw)
    ax[0, i + 1].scatter(*cond_pos, **sc_kw)
    ax[0, i + 1].set_title(f"Field {i}", fontsize=10)
    # absolute differences
    for j in range(ens_no):
        ax[i + 1, j + 1].imshow(np.abs(cond_srf[i] - cond_srf[j]).T, **im_kw)

# beautify plots
ax[0, 0].axis("off")
for a in ax.flatten():
    a.set_xticklabels([]), a.set_yticklabels([])
    a.set_xticks([]), a.set_yticks([])
fig.subplots_adjust(wspace=0, hspace=0)
fig.show()
Field 0, Field 1, Field 2, Field 3

To check if the generated fields are correct, we can have a look at their names:

print(cond_srf.field_names)

Out:

['fld0', 'fld1', 'fld2', 'fld3']

Total running time of the script: ( 0 minutes 1.159 seconds)

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