def test_ordinary(self):
for Model in self.cov_models:
model = Model(dim=1,
var=0.5,
len_scale=2,
anis=[0.1, 1],
angles=[0.5, 0, 0])
srf = SRF(model, seed=19970221)
srf.set_condition(self.cond_pos[0], self.cond_val, "ordinary")
field_1 = srf.unstructured(self.pos[0])
field_2 = srf.structured(self.pos[0])
for i, val in enumerate(self.cond_val):
self.assertAlmostEqual(val, field_1[i], places=2)
self.assertAlmostEqual(val, field_2[(i, )], places=2)
for dim in self.dims[1:]:
model = Model(
dim=dim,
var=0.5,
len_scale=2,
anis=[0.1, 1],
angles=[0.5, 0, 0],
)
srf = SRF(model, seed=19970221)
srf.set_condition(self.cond_pos[:dim], self.cond_val,
"ordinary")
field_1 = srf.unstructured(self.pos[:dim])
field_2 = srf.structured(self.pos[:dim])
for i, val in enumerate(self.cond_val):
self.assertAlmostEqual(val, field_1[i], places=2)
self.assertAlmostEqual(val, field_2[dim * (i, )], places=2)
import numpy as np
from gstools import Gaussian, SRF
import matplotlib.pyplot as plt
# condtions
cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
gridx = np.linspace(0.0, 15.0, 151)
# spatial random field class
model = Gaussian(dim=1, var=0.5, len_scale=2)
srf = SRF(model)
srf.set_condition(cond_pos, cond_val, "ordinary")
fields = []
for i in range(100):
if i % 10 == 0:
print(i)
fields.append(srf(gridx, seed=i))
label = "Conditioned ensemble" if i == 0 else None
plt.plot(gridx, fields[i], color="k", alpha=0.1, label=label)
plt.plot(gridx, np.full_like(gridx, srf.mean), label="estimated mean")
plt.plot(gridx, np.mean(fields, axis=0), linestyle=":", label="Ensemble mean")
plt.plot(gridx, srf.krige_field, linestyle="dashed", label="kriged field")
plt.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions")
plt.legend()
plt.show()
