def test_simple(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])
simple = krige.Simple(model, self.mean, self.cond_pos[0],
self.cond_val)
field_1, __ = simple.unstructured(self.pos[0])
field_2, __ = simple.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)
self.assertAlmostEqual(self.mean, field_1[-1], places=2)
self.assertAlmostEqual(self.mean, field_2[(-1, )], 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],
)
simple = krige.Simple(model, self.mean, self.cond_pos[:dim],
self.cond_val)
field_1, __ = simple.unstructured(self.pos[:dim])
field_2, __ = simple.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)
self.assertAlmostEqual(self.mean, field_1[-1], places=2)
self.assertAlmostEqual(self.mean,
field_2[dim * (-1, )],
places=2)
def test_simple(self):
for Model in self.cov_models:
for dim in self.dims:
model = Model(
dim=dim,
var=2,
len_scale=2,
anis=[0.9, 0.8],
angles=[2, 1, 0.5],
)
simple = krige.Simple(model, self.cond_pos[:dim],
self.cond_val, self.mean)
field_1, __ = simple.unstructured(self.grids[dim - 1])
field_1 = field_1.reshape(self.grid_shape[:dim])
field_2, __ = simple.structured(self.pos[:dim])
self.assertAlmostEqual(np.max(np.abs(field_1 - field_2)),
0.0,
places=2)
for i, val in enumerate(self.cond_val):
self.assertAlmostEqual(field_1[self.data_idx[:dim]][i],
val,
places=2)
Thereby :math:`c(x_i,x_j)` is the covariance of the given observations. Example ^^^^^^^ Here we use simple kriging in 1D (for plotting reasons) with 5 given observations/conditions. The mean of the field has to be given beforehand. """ import numpy as np from gstools import Gaussian, krige # 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] # resulting grid gridx = np.linspace(0.0, 15.0, 151) # spatial random field class model = Gaussian(dim=1, var=0.5, len_scale=2) ############################################################################### krig = krige.Simple(model, mean=1, cond_pos=cond_pos, cond_val=cond_val) krig(gridx) ############################################################################### ax = krig.plot() ax.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions") ax.legend()