def _fill_holes(of_instance, threshold=0):
# calculate velocity scalar
vlcty = np.sqrt(of_instance[::, ::, 0]**2 + of_instance[::, ::, 1]**2)
# zero mask
zero_holes = vlcty <= threshold
# targets
coord_target_i, coord_target_j = np.meshgrid(range(of_instance.shape[1]),
range(of_instance.shape[0]))
# source
coord_source_i, coord_source_j = coord_target_i[~zero_holes], coord_target_j[~zero_holes]
delta_x_source = of_instance[::, ::, 0][~zero_holes]
delta_y_source = of_instance[::, ::, 1][~zero_holes]
# reshape
src = np.vstack((coord_source_i.ravel(), coord_source_j.ravel())).T
trg = np.vstack((coord_target_i.ravel(), coord_target_j.ravel())).T
# create an object
interpolator = ipol.Idw(src, trg)
#
delta_x_target = interpolator(delta_x_source.ravel())
delta_y_target = interpolator(delta_y_source.ravel())
# reshape output
delta_x_target = delta_x_target.reshape(of_instance.shape[0],
of_instance.shape[1])
delta_y_target = delta_y_target.reshape(of_instance.shape[0],
of_instance.shape[1])
return np.stack([delta_x_target, delta_y_target], axis=-1)
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_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([]))
def test_Idw_1(self):
"""testing the basic behaviour of the Idw class"""
ip = ipol.Idw(self.src, self.trg)
# input more than one dataset
res = ip(self.vals)
self.assertTrue(
np.allclose(
res,
np.array([[1., 2., 3.], [2., 2., 2.], [1.2, 2., 2.8],
[3., 2., 1.]])))
# input only one flat array
res = ip(self.vals[:, 2])
self.assertTrue(np.allclose(res, np.array([3., 2., 2.8, 1.])))
def _interpolator(points, coord_source, coord_target, method="idw"):
coord_source_i, coord_source_j = coord_source
coord_target_i, coord_target_j = coord_target
# reshape
trg = np.vstack((coord_source_i.ravel(), coord_source_j.ravel())).T
src = np.vstack((coord_target_i.ravel(), coord_target_j.ravel())).T
if method == "nearest":
interpolator = NearestNDInterpolator(
src, points.ravel(), tree_options={"balanced_tree": False})
points_interpolated = interpolator(trg)
elif method == "linear":
interpolator = LinearNDInterpolator(src, points.ravel(), fill_value=0)
points_interpolated = interpolator(trg)
elif method == "idw":
interpolator = ipol.Idw(src, trg)
points_interpolated = interpolator(points.ravel())
# reshape output
points_interpolated = points_interpolated.reshape(points.shape)
return points_interpolated.astype(points.dtype)
trg = np.vstack((trg[0].ravel(), trg[1].ravel())).T
# Plot Definition
def gridplot(interpolated, title=""):
plt.pcolormesh(xtrg, ytrg, interpolated.reshape((len(xtrg), len(ytrg))))
plt.axis("tight")
plt.scatter(src[:, 0], src[:, 1], facecolor="None", s=50, marker='s')
plt.title(title)
plt.xlabel("X")
plt.ylabel("Y")
# 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")
