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 tm_reflectivity(size, wl, n, ar=1.0): # Takes size and wl in meters
scatt = tmatrix.Scatterer(
radius=0.5e3 * size, # conversion to millimeter radius
radius_type=tmatrix.Scatterer.RADIUS_MAXIMUM,
wavelength=wl * 1.0e3, # conversion to millimeters
m=n,
axis_ratio=1.0 / ar)
scatt.set_geometry(tmatrix_aux.geom_vert_back)
return radar.radar_xsect(
scatt
) # mm**2 ... need just to integrate and multiply by Rayleigh factor
def _compute_single_size(self, d):
ar = self.aspect_ratio_func(d)
rain = Scatterer(radius=0.5 * d,
wavelength=self.wl,
m=self.n,
axis_ratio=1.0 / ar)
rain.Kw_sqr = self.K2
if self.canting is not None:
rain.or_pdf = orientation.gaussian_pdf(std=self.canting)
rain.orient = orientation.orient_averaged_fixed
# Set backward scattering for reflectivity calculations
rain.set_geometry((self._theta0, self._theta0, 0., 180., 0., 0.))
rxsh = radar.radar_xsect(rain, h_pol=True)
rxsv = radar.radar_xsect(rain, h_pol=False)
# Set forward scattering for attenuation and phase computing
rain.set_geometry((self._theta0, self._theta0, 0., 0., 0., 0.))
ext = scatter.ext_xsect(rain)
skdp = radar.Kdp(rain)
# Calculate Rayleigh approximation for reference
ray = self.prefactor * d**6
#print(d, rxsh, rxsv, ext, ray, skdp, ar)
return rxsh, rxsv, ext, ray, skdp, ar
def test_radar(self):
"""Test that the radar properties are computed correctly
"""
tm = TMatrixPSD(lam=tmatrix_aux.wl_C,
m=refractive.m_w_10C[tmatrix_aux.wl_C], suppress_warning=True)
tm.psd = psd.GammaPSD(D0=2.0, Nw=1e3, mu=4)
tm.psd_eps_func = lambda D: 1.0/drop_ar(D)
tm.D_max = 10.0
tm.or_pdf = orientation.gaussian_pdf(20.0)
tm.orient = orientation.orient_averaged_fixed
tm.geometries = (tmatrix_aux.geom_horiz_back,
tmatrix_aux.geom_horiz_forw)
tm.init_scatter_table()
radar_xsect_h = radar.radar_xsect(tm)
Z_h = radar.refl(tm)
Z_v = radar.refl(tm, False)
ldr = radar.ldr(tm)
Zdr = radar.Zdr(tm)
delta_hv = radar.delta_hv(tm)
rho_hv = radar.rho_hv(tm)
tm.set_geometry(tmatrix_aux.geom_horiz_forw)
Kdp = radar.Kdp(tm)
A_h = radar.Ai(tm)
A_v = radar.Ai(tm, False)
radar_xsect_h_ref = 0.22176446239750278
Z_h_ref = 6383.7337897299258
Z_v_ref = 5066.721040036321
ldr_ref = 0.0021960626647629547
Zdr_ref = 1.2599339374097778
delta_hv_ref = -0.00021227778705544846
rho_hv_ref = 0.99603080460983828
Kdp_ref = 0.19334678024367824
A_h_ref = 0.018923976733777458
A_v_ref = 0.016366340549483317
for (val, ref) in zip(
(radar_xsect_h, Z_h, Z_v, ldr, Zdr, delta_hv, rho_hv, Kdp, A_h,
A_v),
(radar_xsect_h_ref, Z_h_ref, Z_v_ref, ldr_ref, Zdr_ref,
delta_hv_ref, rho_hv_ref, Kdp_ref, A_h_ref, A_v_ref)):
test_relative(self, val, ref)
def test_radar(self):
"""Test that the radar properties are computed correctly
"""
tm = TMatrixPSD(lam=tmatrix_aux.wl_C,
m=refractive.m_w_10C[tmatrix_aux.wl_C],
suppress_warning=True)
tm.psd = psd.GammaPSD(D0=2.0, Nw=1e3, mu=4)
tm.psd_eps_func = lambda D: 1.0 / drop_ar(D)
tm.D_max = 10.0
tm.or_pdf = orientation.gaussian_pdf(20.0)
tm.orient = orientation.orient_averaged_fixed
tm.geometries = (tmatrix_aux.geom_horiz_back,
tmatrix_aux.geom_horiz_forw)
tm.init_scatter_table()
radar_xsect_h = radar.radar_xsect(tm)
Z_h = radar.refl(tm)
Z_v = radar.refl(tm, False)
ldr = radar.ldr(tm)
Zdr = radar.Zdr(tm)
delta_hv = radar.delta_hv(tm)
rho_hv = radar.rho_hv(tm)
tm.set_geometry(tmatrix_aux.geom_horiz_forw)
Kdp = radar.Kdp(tm)
A_h = radar.Ai(tm)
A_v = radar.Ai(tm, False)
radar_xsect_h_ref = 0.22176446239750278
Z_h_ref = 6383.7337897299258
Z_v_ref = 5066.721040036321
ldr_ref = 0.0021960626647629547
Zdr_ref = 1.2599339374097778
delta_hv_ref = -0.00021227778705544846
rho_hv_ref = 0.99603080460983828
Kdp_ref = 0.19334678024367824
A_h_ref = 0.018923976733777458
A_v_ref = 0.016366340549483317
for (val, ref) in zip(
(radar_xsect_h, Z_h, Z_v, ldr, Zdr, delta_hv, rho_hv, Kdp, A_h,
A_v), (radar_xsect_h_ref, Z_h_ref, Z_v_ref, ldr_ref, Zdr_ref,
delta_hv_ref, rho_hv_ref, Kdp_ref, A_h_ref, A_v_ref)):
test_relative(self, val, ref)
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)
]
def calcScatPropOneFreq(wl,
radii,
as_ratio,
rho,
elv,
ndgs=30,
canting=False,
cantingStd=1,
meanAngle=0,
safeTmatrix=False):
"""
Calculates the Ze at H and V polarization, Kdp for one wavelength
TODO: LDR???
Parameters
----------
wl: wavelength [mm] (single value)
radii: radius [mm] of the particle (array[n])
as_ratio: aspect ratio of the super particle (array[n])
rho: density [g/mmˆ3] of the super particle (array[n])
elv: elevation angle [°]
ndgs: division points used to integrate over the particle surface
canting: boolean (default = False)
cantingStd: standard deviation of the canting angle [°] (default = 1)
meanAngle: mean value of the canting angle [°] (default = 0)
Returns
-------
reflect_h: super particle horizontal reflectivity[mm^6/m^3] (array[n])
reflect_v: super particle vertical reflectivity[mm^6/m^3] (array[n])
refIndex: refractive index from each super particle (array[n])
kdp: calculated kdp from each particle (array[n])
"""
#---pyTmatrix setup
# initialize a scatterer object
scatterer = Scatterer(wavelength=wl)
scatterer.radius_type = Scatterer.RADIUS_MAXIMUM
scatterer.ndgs = ndgs
scatterer.ddelta = 1e-6
if canting == True:
scatterer.or_pdf = orientation.gaussian_pdf(std=cantingStd,
mean=meanAngle)
# scatterer.orient = orientation.orient_averaged_adaptive
scatterer.orient = orientation.orient_averaged_fixed
# geometric parameters - incident direction
scatterer.thet0 = 90. - elv
scatterer.phi0 = 0.
# parameters for backscattering
refIndex = np.ones_like(radii, np.complex128) * np.nan
reflect_h = np.ones_like(radii) * np.nan
reflect_v = np.ones_like(radii) * np.nan
# S matrix for Kdp
sMat = np.ones_like(radii) * np.nan
for i, radius in enumerate(radii[::5]): #TODO remove [::5]
# A quick function to save the distribution of values used in the test
#with open('/home/dori/table_McRadar.txt', 'a') as f:
# f.write('{0:f} {1:f} {2:f} {3:f} {4:f} {5:f} {6:f}\n'.format(wl, elv,
# meanAngle,
# cantingStd,
# radius,
# rho[i],
# as_ratio[i]))
# scattering geometry backward
# radius = 100.0 # just a test to force nans
scatterer.thet = 180. - scatterer.thet0
scatterer.phi = (180. + scatterer.phi0) % 360.
scatterer.radius = radius
scatterer.axis_ratio = 1. / as_ratio[i]
scatterer.m = refractive.mi(wl, rho[i])
refIndex[i] = refractive.mi(wl, rho[i])
if safeTmatrix:
inputs = [
str(scatterer.radius),
str(scatterer.wavelength),
str(scatterer.m),
str(scatterer.axis_ratio),
str(int(canting)),
str(cantingStd),
str(meanAngle),
str(ndgs),
str(scatterer.thet0),
str(scatterer.phi0)
]
arguments = ' '.join(inputs)
a = subprocess.run(
['spheroidMcRadar'] +
inputs, # this script should be installed by McRadar
capture_output=True)
# print(str(a))
try:
back_hh, back_vv, sMatrix, _ = str(
a.stdout).split('Results ')[-1].split()
back_hh = float(back_hh)
back_vv = float(back_vv)
sMatrix = float(sMatrix)
except:
back_hh = np.nan
back_vv = np.nan
sMatrix = np.nan
# print(back_hh, radar.radar_xsect(scatterer, True))
# print(back_vv, radar.radar_xsect(scatterer, False))
reflect_h[i] = scatterer.wavelength**4 / (
np.pi**5 * scatterer.Kw_sqr
) * back_hh # radar.radar_xsect(scatterer, True) # Kwsqrt is not correct by default at every frequency
reflect_v[i] = scatterer.wavelength**4 / (
np.pi**5 * scatterer.Kw_sqr
) * back_vv # radar.radar_xsect(scatterer, False)
# scattering geometry forward
# scatterer.thet = scatterer.thet0
# scatterer.phi = (scatterer.phi0) % 360. #KDP geometry
# S = scatterer.get_S()
sMat[i] = sMatrix # (S[1,1]-S[0,0]).real
# print(sMatrix, sMat[i])
# print(sMatrix)
else:
reflect_h[i] = scatterer.wavelength**4 / (
np.pi**5 * scatterer.Kw_sqr) * radar.radar_xsect(
scatterer, True
) # Kwsqrt is not correct by default at every frequency
reflect_v[i] = scatterer.wavelength**4 / (
np.pi**5 * scatterer.Kw_sqr) * radar.radar_xsect(
scatterer, False)
# scattering geometry forward
scatterer.thet = scatterer.thet0
scatterer.phi = (scatterer.phi0) % 360. #KDP geometry
S = scatterer.get_S()
sMat[i] = (S[1, 1] - S[0, 0]).real
kdp = 1e-3 * (180.0 / np.pi) * scatterer.wavelength * sMat
del scatterer # TODO: Evaluate the chance to have one Scatterer object already initiated instead of having it locally
return reflect_h, reflect_v, refIndex, kdp
def calcScatPropOneFreq(wl, radii, as_ratio,
rho, elv, ndgs=30,
canting=False, cantingStd=1,
meanAngle=0):
"""
Calculates the Ze of one particle
Parameter
---------
wl: wavelenght [mm] (single value)
radii: radius [mm] of the particle (array[n])
as_ratio: aspect ratio of the super particle (array[n])
rho: density [g/mmˆ3] of the super particle (array[n])
elv: elevation angle [°]
ndgs: number of division points used to integrate over
the particle surface (default= 30 it is already high)
canting: boolean (default = False)
cantingStd: standard deviation of the canting angle [°] (default = 1)
meanAngle: mean value of the canting angle [°] (default = 0)
Returns
-------
reflect: horizontal reflectivity[mm^6/m^3] from each super particle (array[n])
reflect_v: vertical reflectivity[mm^6/m^3] from each super particle (array[n])
refIndex: refractive index from each super particle (array[n])
"""
#---pyTmatrix setup
# initialize a scatterer object
scatterer = Scatterer(wavelength=wl)
scatterer.radius_type = Scatterer.RADIUS_MAXIMUM
scatterer.ndgs = ndgs
scatterer.ddelta = 1e-6
if canting==True:
scatterer.or_pdf = orientation.gaussian_pdf(std=cantingStd, mean=meanAngle)
# scatterer.orient = orientation.orient_averaged_adaptive
scatterer.orient = orientation.orient_averaged_fixed
# geometric parameters
scatterer.thet0 = 90. - elv
scatterer.phi0 = 0.
# geometric parameters
scatterer.thet = 180. - scatterer.thet0
scatterer.phi = (180. + scatterer.phi0) % 360.
refIndex = np.ones_like(radii, np.complex128)*np.nan
reflect = np.ones_like(radii)*np.nan
reflect_v = np.ones_like(radii)*np.nan
for i, radius in enumerate(radii):
scatterer.radius = radius
scatterer.axis_ratio = 1./as_ratio[i]
scatterer.m = refractive.mi(wl, rho[i])
refIndex[i] = refractive.mi(wl, rho[i])
reflect[i] = scatterer.wavelength**4/(np.pi**5*scatterer.Kw_sqr) * radar.radar_xsect(scatterer, True)
reflect_v[i] = scatterer.wavelength**4/(np.pi**5*scatterer.Kw_sqr) * radar.radar_xsect(scatterer, False)
del scatterer
return reflect, reflect_v, refIndex
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 calcScatPropOneFreq(wl,
radii,
as_ratio,
rho,
elv,
ndgs=30,
canting=False,
cantingStd=1,
meanAngle=0):
"""
Calculates the Ze at H and V polarization, Kdp for one wavelength
TODO: LDR???
Parameters
----------
wl: wavelenght [mm] (single value)
radii: radius [mm] of the particle (array[n])
as_ratio: aspect ratio of the super particle (array[n])
rho: density [g/mmˆ3] of the super particle (array[n])
elv: elevation angle [°]
ndgs: division points used to integrate over the particle surface
canting: boolean (default = False)
cantingStd: standard deviation of the canting angle [°] (default = 1)
meanAngle: mean value of the canting angle [°] (default = 0)
Returns
-------
reflect_h: super particle horizontal reflectivity[mm^6/m^3] (array[n])
reflect_v: super particle vertical reflectivity[mm^6/m^3] (array[n])
refIndex: refractive index from each super particle (array[n])
kdp: calculated kdp from each particle (array[n])
"""
#---pyTmatrix setup
# initialize a scatterer object
scatterer = Scatterer(wavelength=wl)
scatterer.radius_type = Scatterer.RADIUS_MAXIMUM
scatterer.ndgs = ndgs
scatterer.ddelta = 1e-6
if canting == True:
scatterer.or_pdf = orientation.gaussian_pdf(std=cantingStd,
mean=meanAngle)
# scatterer.orient = orientation.orient_averaged_adaptive
scatterer.orient = orientation.orient_averaged_fixed
# geometric parameters - incident direction
scatterer.thet0 = 90. - elv
scatterer.phi0 = 0.
# parameters for backscattering
refIndex = np.ones_like(radii, np.complex128) * np.nan
reflect_h = np.ones_like(radii) * np.nan
reflect_v = np.ones_like(radii) * np.nan
# S matrix for Kdp
sMat = np.ones_like(radii) * np.nan
for i, radius in enumerate(radii):
# A quick function to save the distribution of values used in the test
#with open('/home/dori/table_McRadar.txt', 'a') as f:
# f.write('{0:f} {1:f} {2:f} {3:f} {4:f} {5:f} {6:f}\n'.format(wl, elv,
# meanAngle,
# cantingStd,
# radius,
# rho[i],
# as_ratio[i]))
# scattering geometry backward
scatterer.thet = 180. - scatterer.thet0 # Is it????
scatterer.phi = (180. + scatterer.phi0) % 360.
scatterer.radius = radius
scatterer.axis_ratio = 1. / as_ratio[i]
scatterer.m = refractive.mi(wl, rho[i])
refIndex[i] = refractive.mi(wl, rho[i])
reflect_h[i] = scatterer.wavelength**4 / (
np.pi**5 * scatterer.Kw_sqr) * radar.radar_xsect(
scatterer,
True) # Kwsqrt is not correct by default at every frequency
reflect_v[i] = scatterer.wavelength**4 / (
np.pi**5 * scatterer.Kw_sqr) * radar.radar_xsect(scatterer, False)
# scattering geometry forward
scatterer.thet = scatterer.thet0
scatterer.phi = (scatterer.phi0) % 360. #KDP geometry
S = scatterer.get_S()
sMat[i] = (S[1, 1] - S[0, 0]).real
kdp = 1e-3 * (180.0 / np.pi) * scatterer.wavelength * sMat
del scatterer # TODO: Evaluate the chance to have one Scatterer object already initiated instead of having it locally
return reflect_h, reflect_v, refIndex, kdp