def test_nnearest_warning(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
ipol.Idw(self.src, self.trg, nnearest=len(self.src) + 1)
ipol.OrdinaryKriging(self.src, self.trg, nnearest=len(self.src) + 1)
ipol.ExternalDriftKriging(self.src, self.trg, nnearest=len(self.src) + 1)
for item in w:
assert issubclass(item.category, UserWarning)
assert "nnearest" in str(item.message)
def test_OrdinaryKriging_1(self):
"""testing the basic behaviour of the OrdinaryKriging class"""
ip = ipol.OrdinaryKriging(self.src, self.trg, '1.0 Lin(2.0)')
res = ip(self.vals)
self.assertTrue(np.all(res == np.array([[1., 2., 3.],
[2., 2., 2.],
[1.5, 2., 2.5],
[3., 2., 1.]])))
def test_OrdinaryKriging_1(self):
"""testing the basic behaviour of the OrdinaryKriging class"""
ip = ipol.OrdinaryKriging(self.src, self.trg, "1.0 Lin(2.0)")
# input more than one dataset
res = ip(self.vals)
assert np.all(res == np.array([[1.0, 2.0, 3.0], [2.0, 2.0, 2.0],
[1.5, 2.0, 2.5], [3.0, 2.0, 1.0]]))
# input only one flat array
res = ip(self.vals[:, 2])
assert np.allclose(res, np.array([3.0, 2.0, 2.5, 1.0]))
def test_MissingErrors(self):
with pytest.raises(ipol.MissingSourcesError):
ipol.Nearest(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Nearest(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.Idw(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Idw(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.Linear(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Linear(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.OrdinaryKriging(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.OrdinaryKriging(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.ExternalDriftKriging(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.ExternalDriftKriging(self.src, np.array([]))
# Interpolation IDW
idw = ipol.Idw(src, trg)
gridplot(idw(vals.ravel()), "IDW")
# Other approach for IDW
ip_near = ipol.Nearest(src, trg)
maxdist = trg[1, 0] - trg[0, 0]
result_near = ip_near(vals.ravel(), maxdist=maxdist)
# 5.2.3 Kriging ===============================
df_meg_nodes_np = np.array(df_meg_nodes)
gridx = np.arange(0.0, 5.5, 0.5)
gridy = np.arange(0.0, 5.5, 0.5)
# Ordninary Kriging
ok = ipol.OrdinaryKriging(src, trg)
gridplot(ok(vals.ravel()), "Ordinary Kriging")
# Universal Kriging
UK = UniversalKriging(df_meg_nodes_np[:, 0],
df_meg_nodes_np[:, 4],
df_meg_nodes_np[:, 1],
variogram_model='linear',
drift_terms=['regional_linear'])
z, ss = UK.execute('grid', xtrg, ytrg)
# Variogram KDE Sample Points
from skgstat import Variogram
data = np.array(df_meg_nodes_np[:, [5, 6, 7]])
coordinates = np.array(data[:, [0, 1]])
