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])
krige = gs.krige.Ordinary(model, self.cond_pos[0], self.cond_val)
crf = gs.CondSRF(krige, seed=19970221)
field_1 = crf.unstructured(self.pos[0])
field_2 = crf.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],
)
krige = gs.krige.Ordinary(model, self.cond_pos[:dim],
self.cond_val)
crf = gs.CondSRF(krige, seed=19970221)
field_1 = crf.unstructured(self.pos[:dim])
field_2 = crf.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)
def test_setter(self):
krige1 = gs.krige.Krige(gs.Exponential(), self.cond_pos, self.cond_val)
krige2 = gs.krige.Krige(
gs.Gaussian(var=2),
self.cond_pos,
self.cond_val,
mean=-1,
trend=-2,
normalizer=gs.normalizer.YeoJohnson(),
)
crf1 = gs.CondSRF(krige1)
crf2 = gs.CondSRF(krige2, seed=19970221)
# update settings
crf1.model = gs.Gaussian(var=2)
crf1.mean = -1
crf1.trend = -2
# also checking correctly setting uninitialized normalizer
crf1.normalizer = gs.normalizer.YeoJohnson
# check if setting went right
self.assertTrue(crf1.model == crf2.model)
self.assertTrue(crf1.normalizer == crf2.normalizer)
self.assertAlmostEqual(crf1.mean, crf2.mean)
self.assertAlmostEqual(crf1.trend, crf2.trend)
# reset kriging
crf1.krige.set_condition()
# compare fields
field1 = crf1(self.pos, seed=19970221)
field2 = crf2(self.pos)
self.assertTrue(np.all(np.isclose(field1, field2)))
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])
krige = gs.krige.Simple(model, self.cond_pos[0], self.cond_val,
self.mean)
crf = gs.CondSRF(krige, seed=19970221)
field_1 = crf.unstructured(self.pos[0])
field_2 = crf.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],
)
krige = gs.krige.Simple(model, self.cond_pos[:dim],
self.cond_val, self.mean)
crf = gs.CondSRF(krige, seed=19970221)
field_1 = crf.unstructured(self.pos[:dim])
field_2 = crf.structured(self.pos[:dim])
# check reuse
raw_kr2 = copy(crf["raw_krige"])
crf(seed=19970222)
self.assertTrue(np.allclose(raw_kr2, crf["raw_krige"]))
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)
def test_cond_srf(self):
bin_max = np.deg2rad(8)
bin_edges = np.linspace(0, bin_max, 5)
emp_vario = gs.vario_estimate(
(self.data[:, 0], self.data[:, 1]),
self.data[:, 2],
bin_edges,
latlon=True,
)
mod = gs.Spherical(latlon=True, rescale=gs.EARTH_RADIUS)
mod.fit_variogram(*emp_vario, nugget=False)
krige = gs.krige.Ordinary(mod, (self.data[:, 0], self.data[:, 1]),
self.data[:, 2])
crf = gs.CondSRF(krige)
field = crf((self.data[:, 0], self.data[:, 1]))
for i, dat in enumerate(self.data[:, 2]):
self.assertAlmostEqual(field[i], dat, 3)
def test_nugget(self):
model = gs.Gaussian(
nugget=0.01,
var=0.5,
len_scale=2,
anis=[0.1, 1],
angles=[0.5, 0, 0],
)
krige = gs.krige.Ordinary(model,
self.cond_pos,
self.cond_val,
exact=True)
crf = gs.CondSRF(krige, seed=19970221)
field_1 = crf.unstructured(self.pos)
field_2 = crf.structured(self.pos)
for i, val in enumerate(self.cond_val):
self.assertAlmostEqual(val, field_1[i], places=2)
self.assertAlmostEqual(val, field_2[3 * (i, )], places=2)
"""
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)
###############################################################################
# We create a list containing the generated conditioned fields.
ens_no = 4
field = []
for i in range(ens_no):
field.append(cond_srf.structured([x, y], seed=i))
###############################################################################
# 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
