class TestdDEM:
dem_2009 = xdem.DEM(xdem.examples.get_path("longyearbyen_ref_dem"))
dem_1990 = xdem.DEM(xdem.examples.get_path("longyearbyen_tba_dem"))
outlines_1990 = gu.Vector(xdem.examples.get_path("longyearbyen_glacier_outlines"))
ddem = xdem.dDEM(
dem_2009 - dem_1990,
start_time=np.datetime64("1990-08-01"),
end_time=np.datetime64("2009-08-01")
)
def test_init(self):
"""Test that the dDEM object was instantiated correctly."""
assert isinstance(self.ddem, xdem.dDEM)
assert isinstance(self.ddem.data, np.ma.masked_array)
def test_copy(self):
"""Test that copying works as it should."""
ddem2 = self.ddem.copy()
assert isinstance(ddem2, xdem.dDEM)
ddem2.data += 1
assert self.ddem != ddem2
def test_filled_data(self):
"""Test that the filled_data property points to the right data."""
ddem2 = self.ddem.copy()
assert not np.any(np.isnan(ddem2.data)) or np.all(~ddem2.data.mask)
assert ddem2.filled_data is not None
assert np.count_nonzero(np.isnan(ddem2.data)) == 0
ddem2.data.ravel()[0] = np.nan
assert np.count_nonzero(np.isnan(ddem2.data)) == 1
assert ddem2.filled_data is None
ddem2.interpolate(method="linear")
assert ddem2.fill_method is not None
def test_regional_hypso(self):
"""Test the regional hypsometric approach."""
ddem = self.ddem.copy()
ddem.data.mask = np.zeros_like(ddem.data, dtype=bool)
ddem.data.mask.ravel()[np.random.choice(ddem.data.size, 50000, replace=False)] = True
assert np.count_nonzero(ddem.data.mask) > 0
assert ddem.filled_data is None
ddem.interpolate(
method="regional_hypsometric",
reference_elevation=self.dem_2009,
mask=self.outlines_1990
)
assert ddem._filled_data is not None
assert type(ddem.filled_data) == np.ndarray
assert ddem.filled_data.shape == ddem.data.shape
assert np.abs(np.nanmean(self.ddem.data - ddem.filled_data)) < 1
def test_local_hypso(self):
"""Test the local hypsometric approach."""
ddem = self.ddem.copy()
scott_1990 = self.outlines_1990.query("NAME == 'Scott Turnerbreen'")
ddem.data.mask = np.zeros_like(ddem.data, dtype=bool)
ddem.data.mask.ravel()[np.random.choice(ddem.data.size, 50000, replace=False)] = True
assert np.count_nonzero(ddem.data.mask) > 0
assert ddem.filled_data is None
ddem.interpolate(
method="local_hypsometric",
reference_elevation=self.dem_2009.data,
mask=self.outlines_1990
)
assert np.abs(np.mean(self.ddem.data - ddem.filled_data)) < 1
"""Plot an example of spatial interpolation of randomly generated errors."""
import geoutils as gu
import matplotlib.pyplot as plt
import numpy as np
import xdem
dem_2009 = xdem.DEM(xdem.examples.get_path("longyearbyen_ref_dem"))
dem_1990 = xdem.DEM(xdem.examples.get_path("longyearbyen_tba_dem"))
outlines_1990 = gu.Vector(
xdem.examples.get_path("longyearbyen_glacier_outlines"))
ddem = xdem.dDEM(dem_2009 - dem_1990,
start_time=np.datetime64("1990-08-01"),
end_time=np.datetime64("2009-08-01"))
# The example DEMs are void-free, so let's make some random voids.
ddem.data.mask = np.zeros_like(ddem.data, dtype=bool) # Reset the mask
# Introduce 50000 nans randomly throughout the dDEM.
ddem.data.mask.ravel()[np.random.choice(ddem.data.size, 50000,
replace=False)] = True
ddem.interpolate(method="linear")
ylim = (300, 100)
xlim = (800, 1050)
plt.figure(figsize=(8, 5))
plt.subplot(121)
plt.imshow(ddem.data.squeeze(), cmap="coolwarm_r", vmin=-50, vmax=50)
plt.ylim(ylim)
plt.xlim(xlim)
dem1, dem2 = dem_2009, dem_1990
ddem_data = dem1.data - dem2.data
# If we want to inherit the georeferencing information:
ddem_raster = xdem.DEM.from_array(ddem_data, dem1.transform, dem2.crs)
# TEXT HERE
ddem_raster = dem1 - dem2
#############################
# SECTION: dDEM interpolation
#############################
ddem = xdem.dDEM(raster=dem_2009 - dem_1990,
start_time=dem_1990.datetime,
end_time=dem_2009.datetime)
# The example DEMs are void-free, so let's make some random voids.
ddem.data.mask = np.zeros_like(ddem.data, dtype=bool) # Reset the mask
# Introduce 50000 nans randomly throughout the dDEM.
ddem.data.mask.ravel()[np.random.choice(ddem.data.size, 50000,
replace=False)] = True
# SUBSECTION: Linear spatial interpolation
ddem.interpolate(method="linear")
# SUBSECTION: Local hypsometric interpolation
ddem.interpolate(method="local_hypsometric",