def test_iem_fung92_brogioni10():
eps_r = 3 + 0.1j
iem_fung = IEM_Fung92_Briogoni10(roughness_rms=0.429e-2, corr_length=30e-2)
frequency = 2.2e9
mu = np.cos(np.deg2rad([30, 50, 60]))
R = iem_fung.diffuse_reflection_matrix(frequency,
1,
eps_r,
mu,
mu,
np.pi,
2,
debug=True)
sigma_vv = dB(4 * np.pi * mu * R[0])
sigma_hh = dB(4 * np.pi * mu * R[1])
print(sigma_vv)
print(sigma_hh)
assert np.all(
np.abs(sigma_vv - [-25.8475821, -28.09794986, -27.1320767]) < 1e-2)
assert np.all(
np.abs(sigma_hh - [-31.30415086, -40.67474292, -29.06341978]) < 1e-2)
def test_iem_fung92():
eps_r = 3 + 0.1j
iem_fung = IEM_Fung92(roughness_rms=0.429e-2, corr_length=3e-2)
frequency = 2.2e9
mu = np.cos(np.deg2rad([30, 50, 60]))
R = iem_fung.diffuse_reflection_matrix(frequency, 1, eps_r, mu, mu, np.pi, 2, debug=True)
sigma_vv = dB(4*np.pi * mu * R[0])
sigma_hh = dB(4*np.pi * mu * R[1])
print(sigma_vv)
print(sigma_hh)
assert np.all(np.abs(sigma_vv - [-20.25297061, -24.35232625, -26.74346526]) < 1e-2)
assert np.all(np.abs(sigma_hh - [-22.10327899, -28.69367149, -32.53013663]) < 1e-2)
def sel_data(self, channel=None, return_backscatter=False, **kwargs):
# this function allows selection as xarray.DataArray.sel and in addition by channel if a channel_map is defined.
# ffilter the variables of channel_map[channel] that are effectively in self.data.dims
# and apply them to the selector sel in addition to kwargs
if channel is not None:
kwargs.update({
k: v
for k, v in self.channel_map[channel].items()
if k in self.data.dims
})
if return_backscatter:
# get theta
theta = kwargs.pop('theta', None)
theta_inc = kwargs.pop('theta_inc', None)
if theta is not None and theta_inc is not None:
if not np.all(theta_inc == theta):
raise SMRTError(
'theta and theta_inc must be the same when returning backscatter'
)
if theta is None:
theta = theta_inc
if theta is None:
theta = self.data.theta_inc
def select_theta(x, theta, **kwargs):
# select by theta and deal with cases where theta is in the coords or not
if 'theta' in x.coords:
return x.sel(theta=theta, theta_inc=theta, **kwargs)
else:
return x.sel(theta_inc=theta, **kwargs)
if lib.is_sequence(theta):
# now select all the theta if it is a sequence
x = xr.concat([
select_theta(self.data, t, drop=True, **kwargs)
for t in theta
], pd.Index(theta, 'theta_inc'))
else:
x = select_theta(self.data, theta, drop=True, **kwargs)
else:
x = self.data.sel(drop=True, **kwargs)
if return_backscatter:
x = (4 * np.pi * np.cos(np.deg2rad(theta))) * x
return dB(x) if return_backscatter == "dB" else x
else:
return x
def test_iem_fung92_biogoni10_continuty():
eps_r = 3 + 0.1j
iem_fung = IEM_Fung92(roughness_rms=0.429e-2, corr_length=3e-2)
iem_fung_brogioni = IEM_Fung92_Briogoni10(roughness_rms=0.429e-2,
corr_length=3e-2)
frequency = 2.2e9
mu = np.cos(np.deg2rad([30, 50, 60]))
R = iem_fung.diffuse_reflection_matrix(frequency,
1,
eps_r,
mu,
mu,
np.pi,
2,
debug=True)
R2 = iem_fung_brogioni.diffuse_reflection_matrix(frequency,
1,
eps_r,
mu,
mu,
np.pi,
2,
debug=True)
sigma_vv = dB(4 * np.pi * mu * R[0])
sigma_hh = dB(4 * np.pi * mu * R[1])
sigma_vv2 = dB(4 * np.pi * mu * R2[0])
sigma_hh2 = dB(4 * np.pi * mu * R2[1])
assert np.allclose(sigma_vv, sigma_vv2)
assert np.allclose(sigma_hh, sigma_hh2)
def sigmaVH_dB(self, **kwargs):
"""Return VH backscattering coefficient in dB. Any parameter can be added to slice the results (e.g. frequency=37e9).
See xarray slicing with sel method (to document)"""
return dB(self.sigmaVH(**kwargs))
def sigma_dB(self, **kwargs):
"""Return backscattering coefficient. Any parameter can be added to slice the results (e.g. frequency=37e9, polarization_inc='V', polarization='V').
See xarray slicing with sel method (to document)"""
return dB(self.sigma(**kwargs))
