def compute(i):
s = res.loc[i, 'Dmax']
start = time.time()
print(res.loc[i])
if res.loc[i].hasnans():
print('computing')
density = rho(s)
m = refractive.mi(wl, density)
scatterer = Scatterer(radius=0.5 * s,
wavelength=wl,
m=m,
axis_ratio=1.0 / ar,
radius_type=Scatterer.RADIUS_MAXIMUM)
scatterer.set_geometry(tmatrix_aux.geom_vert_back)
scatterer.orient = orientation.orient_averaged_fixed
scatterer.or_pdf = orientation.uniform_pdf()
res.loc[i, 'f'] = f
res.loc[i, 'wl'] = wl
res.loc[i, 'rho'] = density
res.loc[i, 'mr'] = m.real
res.loc[i, 'mi'] = m.imag
res.loc[i, 'Dmax'] = s
res.loc[i, 'Csca'] = scatter.sca_xsect(scatterer)
res.loc[i, 'Cbk'] = scatter.sca_intensity(scatterer)
res.loc[i, 'sigmabk'] = radar.radar_xsect(scatterer)
res.loc[i, 'Cext'] = scatter.ext_xsect(scatterer)
res.loc[i, 'g'] = scatter.asym(scatterer)
res.to_csv(savename)
end = time.time()
print(s, end - start)
def test_against_mie(self):
"""Test scattering parameters against Mie results
"""
# Reference values computed with the Mie code of Maetzler
sca_xsect_ref = 4.4471684294079958
ext_xsect_ref = 7.8419745883848435
asym_ref = 0.76146646088675629
sca = Scatterer(wavelength=1, radius=1, m=complex(3.0,0.5))
sca_xsect = scatter.sca_xsect(sca)
ext_xsect = scatter.ext_xsect(sca)
asym = scatter.asym(sca)
test_less(self, abs(1-sca_xsect/sca_xsect_ref), 1e-6)
test_less(self, abs(1-ext_xsect/ext_xsect_ref), 1e-6)
test_less(self, abs(1-asym/asym_ref), 1e-6)
def test_against_mie(self):
"""Test scattering parameters against Mie results
"""
# Reference values computed with the Mie code of Maetzler
sca_xsect_ref = 4.4471684294079958
ext_xsect_ref = 7.8419745883848435
asym_ref = 0.76146646088675629
sca = Scatterer(wavelength=1, radius=1, m=complex(3.0, 0.5))
sca_xsect = scatter.sca_xsect(sca)
ext_xsect = scatter.ext_xsect(sca)
asym = scatter.asym(sca)
test_less(self, abs(1 - sca_xsect / sca_xsect_ref), 1e-6)
test_less(self, abs(1 - ext_xsect / ext_xsect_ref), 1e-6)
test_less(self, abs(1 - asym / asym_ref), 1e-6)
def test_integrated_x_sca(self):
"""Test Rayleigh scattering cross section integrated over sizes.
"""
m = complex(3.0,0.5)
K = (m**2-1)/(m**2+2)
N0 = 10
Lambda = 1e4
sca = Scatterer(wavelength=1, m=m)
sca.psd_integrator = psd.PSDIntegrator()
sca.psd = psd.ExponentialPSD(N0=N0, Lambda=Lambda)
sca.psd.D_max = 0.002
sca.psd_integrator.D_max = sca.psd.D_max
# 256 is quite low, but we want to run the test reasonably fast
sca.psd_integrator.num_points = 256
sca.psd_integrator.init_scatter_table(sca, angular_integration=True)
# This size-integrated scattering cross section has an analytical value.
# Check that we can reproduce it.
sca_xsect_ref = 480*N0*np.pi**5*abs(K)**2/Lambda**7
sca_xsect = scatter.sca_xsect(sca)
test_less(self, abs(1-sca_xsect/sca_xsect_ref), 1e-3)
def test_integrated_x_sca(self):
"""Test Rayleigh scattering cross section integrated over sizes.
"""
m = complex(3.0, 0.5)
K = (m**2 - 1) / (m**2 + 2)
N0 = 10
Lambda = 1e4
sca = Scatterer(wavelength=1, m=m)
sca.psd_integrator = psd.PSDIntegrator()
sca.psd = psd.ExponentialPSD(N0=N0, Lambda=Lambda)
sca.psd.D_max = 0.002
sca.psd_integrator.D_max = sca.psd.D_max
# 256 is quite low, but we want to run the test reasonably fast
sca.psd_integrator.num_points = 256
sca.psd_integrator.init_scatter_table(sca, angular_integration=True)
# This size-integrated scattering cross section has an analytical value.
# Check that we can reproduce it.
sca_xsect_ref = 480 * N0 * np.pi**5 * abs(K)**2 / Lambda**7
sca_xsect = scatter.sca_xsect(sca)
test_less(self, abs(1 - sca_xsect / sca_xsect_ref), 1e-3)
def init_scatter_table(self, tm, angular_integration=False, verbose=False):
"""Initialize the scattering lookup tables.
Initialize the scattering lookup tables for the different geometries.
Before calling this, the following attributes must be set:
num_points, m_func, axis_ratio_func, D_max, geometries
and additionally, all the desired attributes of the Scatterer class
(e.g. wavelength, aspect ratio).
Args:
tm: a Scatterer instance.
angular_integration: If True, also calculate the
angle-integrated quantities (scattering cross section,
extinction cross section, asymmetry parameter). These are
needed to call the corresponding functions in the scatter
module when PSD integration is active. The default is False.
verbose: if True, print information about the progress of the
calculation (which may take a while). If False (default),
run silently.
"""
self._psd_D = np.linspace(self.D_max / self.num_points, self.D_max,
self.num_points)
self._S_table = {}
self._Z_table = {}
self._previous_psd = None
self._m_table = np.empty(self.num_points, dtype=complex)
if angular_integration:
self._angular_table = {
"sca_xsect": {},
"ext_xsect": {},
"asym": {}
}
else:
self._angular_table = None
(old_m, old_axis_ratio, old_radius, old_geom, old_psd_integrator) = \
(tm.m, tm.axis_ratio, tm.radius, tm.get_geometry(),
tm.psd_integrator)
try:
# temporarily disable PSD integration to avoid recursion
tm.psd_integrator = None
for geom in self.geometries:
self._S_table[geom] = \
np.empty((2,2,self.num_points), dtype=complex)
self._Z_table[geom] = np.empty((4, 4, self.num_points))
if angular_integration:
for int_var in ["sca_xsect", "ext_xsect", "asym"]:
self._angular_table[int_var][geom] = \
np.empty(self.num_points)
for (i, D) in enumerate(self._psd_D):
if verbose:
print("Computing point {i} at D={D}...".format(i=i, D=D))
if self.m_func != None:
tm.m = self.m_func(D)
if self.axis_ratio_func != None:
tm.axis_ratio = self.axis_ratio_func(D)
self._m_table[i] = tm.m
tm.radius = D / 2.0
for geom in self.geometries:
tm.set_geometry(geom)
(S, Z) = tm.get_SZ_orient()
self._S_table[geom][:, :, i] = S
self._Z_table[geom][:, :, i] = Z
if angular_integration:
self._angular_table["sca_xsect"][geom][i] = \
scatter.sca_xsect(tm)
self._angular_table["ext_xsect"][geom][i] = \
scatter.ext_xsect(tm)
self._angular_table["asym"][geom][i] = \
scatter.asym(tm)
finally:
#restore old values
(tm.m, tm.axis_ratio, tm.radius, tm.psd_integrator) = \
(old_m, old_axis_ratio, old_radius, old_psd_integrator)
tm.set_geometry(old_geom)
) # ar
ones = np.ones_like(volume_fractions)
ref_soft = snowScatt.refractiveIndex.mixing.eps(
[ref_index**2 * ones, complex(1.0, 0.0) * ones],
[volume_fractions, 1.0 - volume_fractions])
ref_soft = np.sqrt(ref_soft)
for iD, D in enumerate(Dmax):
spheroid = tmatrix.Scatterer(radius=0.5 * D,
radius_type=tmatrix.Scatterer.RADIUS_MAXIMUM,
wavelength=wavelength,
m=ref_soft[iD],
axis_ratio=1.0 / aspect_ratio)
spheroid.set_geometry(tmatrix_aux.geom_vert_back)
spheroidCs[iD] = scatter.sca_xsect(spheroid)
spheroidCb[iD] = radar.radar_xsect(spheroid)
spheroidQb = spheroidCb / (np.pi * reff_sph**2)
spheroidQs = spheroidCs / (np.pi * reff_sph**2)
spheroid_x = 2.0 * np.pi * reff_sph / wavelength
axs[0].plot(spheroid_x, spheroidQb, c='m', label='soft-spheroid')
axs[1].plot(spheroid_x, spheroidQs, c='m', label='soft-spheroid')
###############################################################################
for ipt, (particle_type, ssrga_particle) in enumerate(
zip(particle_types, ssrga_particle_names)):
part_sel = [
str(v.values) for v in Cext.particle_name
if str(v.values).startswith(particle_type)
Nc = 1
Nl = 1
xr = np.ndarray((Nc, Nl), dtype=np.float64)
mr = np.ndarray((Nc, Nl), dtype=np.complex128)
xr[:, 0] = x
mr[:, 0] = m
thetas = np.linspace(0.0, np.pi, 180)
terms, Qe, Qs, Qa, Qb, Qp, g, ssa, S1, S2 = scattnlay(xr, mr, theta=thetas)
print(S1[0, -1] / k, S2[0, -1] / k)
s1 = S1[0, 0] / k
s2 = S2[0, 0] / k
SM = 0.0 * S
SM[0, 0] = -1.0j * s2
SM[1, 1] = -1.0j * s1
print("Cext = ", scatter.ext_xsect(scatterer), Qe[0] * r * r * np.pi)
print("Csca = ", scatter.sca_xsect(scatterer), Qs[0] * r * r * np.pi)
print("Cbck = ", radar.radar_xsect(scatterer), Qb[0] * r * r * np.pi)
print(S[0, 0].real / S1[0, 0].real)
print(S[0, 0].imag / S1[0, 0].imag)
print(S[1, 1].real / S2[0, 0].real)
print(S[1, 1].imag / S2[0, 0].imag)
Z11 = 0.5 * (abs(s1)**2 + abs(s2)**2)
Z33 = 0.5 * (s1 * s2.conjugate() + s1.conjugate() * s2)
print(Z[0, 0] / Z11)
print(Z[2, 2] / Z33)
def init_scatter_table(self, tm, angular_integration=False, verbose=False):
"""Initialize the scattering lookup tables.
Initialize the scattering lookup tables for the different geometries.
Before calling this, the following attributes must be set:
num_points, m_func, axis_ratio_func, D_max, geometries
and additionally, all the desired attributes of the Scatterer class
(e.g. wavelength, aspect ratio).
Args:
tm: a Scatterer instance.
angular_integration: If True, also calculate the
angle-integrated quantities (scattering cross section,
extinction cross section, asymmetry parameter). These are
needed to call the corresponding functions in the scatter
module when PSD integration is active. The default is False.
verbose: if True, print information about the progress of the
calculation (which may take a while). If False (default),
run silently.
"""
self._psd_D = np.linspace(self.D_max/self.num_points, self.D_max,
self.num_points)
self._S_table = {}
self._Z_table = {}
self._previous_psd = None
self._m_table = np.empty(self.num_points, dtype=complex)
if angular_integration:
self._angular_table = {"sca_xsect": {}, "ext_xsect": {},
"asym": {}}
else:
self._angular_table = None
(old_m, old_axis_ratio, old_radius, old_geom, old_psd_integrator) = \
(tm.m, tm.axis_ratio, tm.radius, tm.get_geometry(),
tm.psd_integrator)
try:
# temporarily disable PSD integration to avoid recursion
tm.psd_integrator = None
for geom in self.geometries:
self._S_table[geom] = \
np.empty((2,2,self.num_points), dtype=complex)
self._Z_table[geom] = np.empty((4,4,self.num_points))
if angular_integration:
for int_var in ["sca_xsect", "ext_xsect", "asym"]:
self._angular_table[int_var][geom] = \
np.empty(self.num_points)
for (i,D) in enumerate(self._psd_D):
if verbose:
print("Computing point {i} at D={D}...".format(i=i, D=D))
if self.m_func != None:
tm.m = self.m_func(D)
if self.axis_ratio_func != None:
tm.axis_ratio = self.axis_ratio_func(D)
self._m_table[i] = tm.m
tm.radius = D/2.0
for geom in self.geometries:
tm.set_geometry(geom)
(S, Z) = tm.get_SZ_orient()
self._S_table[geom][:,:,i] = S
self._Z_table[geom][:,:,i] = Z
if angular_integration:
self._angular_table["sca_xsect"][geom][i] = \
scatter.sca_xsect(tm)
self._angular_table["ext_xsect"][geom][i] = \
scatter.ext_xsect(tm)
self._angular_table["asym"][geom][i] = \
scatter.asym(tm)
finally:
#restore old values
(tm.m, tm.axis_ratio, tm.radius, tm.psd_integrator) = \
(old_m, old_axis_ratio, old_radius, old_psd_integrator)
tm.set_geometry(old_geom)
wl4 = wl**4.0
K = (m**2+1.0)/(m**2-2)
K2 = (K*K.conj()).real
pref = pi5*K2/wl4
scatt = tmatrix.Scatterer(radius=1.0,
wavelength=wl,
m=m,
axis_ratio=1.0)
plt.figure()
ax = plt.gca()
for th in np.linspace(0.0,180):
scatt.thet = th
[[S11,S12],[S21,S22]] = scatt.get_S()
sig = scatter.sca_xsect(scatt)
# ax.scatter(th,1e6*sig,c='m')
ax.scatter(th,S11,c='b')
ax.scatter(th,S12,c='r')
ax.scatter(th,S21,c='g',marker='x')
ax.scatter(th,S22,c='k')
ax.set_ylim([-0.0015,0.0015])
ax.set_ylim([-0.001355,-0.00135])
plt.figure()
ax = plt.gca()
for s in np.linspace(1,10,50):
scatt = tmatrix.Scatterer(radius=s,
wavelength=wl,
m=m,
axis_ratio=1.0)
