def test_universal(self):
# "quad" -> to few conditional points
for drift in ["linear", 0, 1, trend]:
for Model in self.cov_models:
for dim in self.dims:
model = Model(
dim=dim,
var=2,
len_scale=10,
anis=[0.9, 0.8],
angles=[2, 1, 0.5],
)
universal = krige.Universal(
model, self.cond_pos[:dim], self.cond_val, drift
)
field_1, __ = universal.unstructured(self.grids[dim - 1])
field_1 = field_1.reshape(self.grid_shape[:dim])
field_2, __ = universal.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_2[self.data_idx[:dim]][i], val, places=2
)
was added. The resulting samples are then used as input for Universal kriging. The "linear" drift is then estimated during the interpolation. To access only the estimated mean/drift, we provide a switch `only_mean` in the call routine. """ import numpy as np from gstools import SRF, Gaussian, krige # synthetic condtions with a drift drift_model = Gaussian(dim=1, var=0.1, len_scale=2) drift = SRF(drift_model, seed=101) cond_pos = np.linspace(0.1, 8, 10) cond_val = drift(cond_pos) + cond_pos * 0.1 + 1 # resulting grid gridx = np.linspace(0.0, 15.0, 151) drift_field = drift(gridx) + gridx * 0.1 + 1 # kriging model = Gaussian(dim=1, var=0.1, len_scale=2) krig = krige.Universal(model, cond_pos, cond_val, "linear") krig(gridx) ax = krig.plot() ax.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions") ax.plot(gridx, gridx * 0.1 + 1, ":", label="linear drift") ax.plot(gridx, drift_field, "--", label="original field") mean = krig(gridx, only_mean=True) ax.plot(gridx, mean, label="estimated drift") ax.legend()