%matplotlib widget
import matplotlib.pyplot as plt
plt.ioff()
# turn of warnings
import warnings
warnings.filterwarnings('ignore')Let's create an ensemble of conditioned random fields in 2D.
import numpy as np
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(20220425)
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=(7, 7))
# 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()Loading...
To check if the generated fields are correct, we can have a look at their names:
print(cond_srf.field_names)['fld0', 'fld1', 'fld2', 'fld3']