def _compute_gautschi_canting(list_of_std):
"""
Computes the quadrature points and weights for a list of standard
deviations for the Gaussian distribution of canting angles
Args:
list_of_std: list of standard deviations for which a
quadrature rule must be computed (one set of points and weights
per standard deviation)
Returns:
A tuple, containing two lists, one with the quadrature points
for every stdev, one with the quadrature weights for every
stdev
"""
scatt = Scatterer(radius=5.0)
gautschi_pts = []
gautschi_w = []
for l in list_of_std:
scatt.or_pdf = orientation.gaussian_pdf(std=l)
scatt.orient = orientation.orient_averaged_fixed
scatt._init_orient()
gautschi_pts.append(scatt.beta_p)
# Check if sum of weights if 1, if not set it to 1
scatt.beta_w /= np.sum(scatt.beta_w)
gautschi_w.append(scatt.beta_w)
return (gautschi_pts, gautschi_w)
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_backend():
"""Replicate the test run of the backend T-Matrix code.
Replicates the results of the default test run of the backend code
by Mishchenko. The user can use the function to manually check that
the results match. Small errors may be present due to different compiler
optimizations.
"""
scatterer = Scatterer(radius=10.0,
rat=0.1,
wavelength=2 * np.pi,
m=complex(1.5, 0.02),
axis_ratio=0.5,
ddelt=1e-3,
ndgs=2,
np=-1)
scatterer.thet0 = 56.0
scatterer.thet = 65.0
scatterer.phi0 = 114.0
scatterer.phi = 128.0
scatterer.alpha = 145.0
scatterer.beta = 52.0
print("Amplitude matrix S:")
print(scatterer.get_S())
print("Phase matrix Z:")
print(scatterer.get_Z())
def scatter_off_2dvd_packed(dicc):
def drop_ar(D_eq):
if D_eq < 0.7:
return 1.0
elif D_eq < 1.5:
return 1.173 - 0.5165*D_eq + 0.4698*D_eq**2 - 0.1317*D_eq**3 - \
8.5e-3*D_eq**4
else:
return 1.065 - 6.25e-2*D_eq - 3.99e-3*D_eq**2 + 7.66e-4*D_eq**3 - \
4.095e-5*D_eq**4
d_diameters = dicc['1']
d_densities = dicc['2']
mypds = interpolate.interp1d(d_diameters,
d_densities,
bounds_error=False,
fill_value=0.0)
scatterer = Scatterer(wavelength=tmatrix_aux.wl_C,
m=refractive.m_w_10C[tmatrix_aux.wl_C])
scatterer.psd_integrator = PSDIntegrator()
scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0 / drop_ar(D)
scatterer.psd_integrator.D_max = 10.0
scatterer.psd_integrator.geometries = (tmatrix_aux.geom_horiz_back,
tmatrix_aux.geom_horiz_forw)
scatterer.or_pdf = orientation.gaussian_pdf(20.0)
scatterer.orient = orientation.orient_averaged_fixed
scatterer.psd_integrator.init_scatter_table(scatterer)
scatterer.psd = mypds # GammaPSD(D0=2.0, Nw=1e3, mu=4)
radar.refl(scatterer)
zdr = radar.Zdr(scatterer)
z = radar.refl(scatterer)
scatterer.set_geometry(tmatrix_aux.geom_horiz_forw)
kdp = radar.Kdp(scatterer)
A = radar.Ai(scatterer)
return z, zdr, kdp, A
def _setup_scattering(self, wavelength, dsr_func):
""" Internal Function to create scattering tables.
This internal function sets up the scattering table. It takes a
wavelength as an argument where wavelength is one of the pytmatrix
accepted wavelengths.
Parameters:
-----------
wavelength : tmatrix wavelength
PyTmatrix wavelength.
dsr_func : function
Drop Shape Relationship function. Several built-in are available in the `DSR` module.
"""
self.scatterer = Scatterer(wavelength=wavelength, m=self.m_w)
self.scatterer.psd_integrator = PSDIntegrator()
self.scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0 / dsr_func(
D)
self.dsr_func = dsr_func
self.scatterer.psd_integrator.D_max = 10.0
self.scatterer.psd_integrator.geometries = (
tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw)
self.scatterer.or_pdf = orientation.gaussian_pdf(20.0)
self.scatterer.orient = orientation.orient_averaged_fixed
self.scatterer.psd_integrator.init_scatter_table(self.scatterer)
def test_psd(self):
"""Test a case that integrates over a particle size distribution
"""
tm = Scatterer(wavelength=6.5,
m=complex(1.5, 0.5),
axis_ratio=1.0 / 0.6)
tm.psd_integrator = psd.PSDIntegrator()
tm.psd_integrator.num_points = 500
tm.psd = psd.GammaPSD(D0=1.0, Nw=1e3, mu=4)
tm.psd_integrator.D_max = 10.0
tm.psd_integrator.init_scatter_table(tm)
(S, Z) = tm.get_SZ()
S_ref = np.array([[
complex(1.02521928e+00, 6.76066598e-01),
complex(6.71933838e-24, 6.83819665e-24)
],
[
complex(-6.71933678e-24, -6.83813546e-24),
complex(-1.10464413e+00, -1.05571494e+00)
]])
Z_ref = np.array([
[7.20540295e-02, -1.54020475e-02, -9.96222107e-25, 8.34246458e-26],
[-1.54020475e-02, 7.20540295e-02, 1.23279391e-25, 1.40049088e-25],
[9.96224596e-25, -1.23291269e-25, -6.89739108e-02, 1.38873290e-02],
[8.34137617e-26, 1.40048866e-25, -1.38873290e-02, -6.89739108e-02]
])
test_relative(self, S, S_ref)
test_relative(self, Z, Z_ref)
def _setup_scattering(self, wavelength, dsr_func, max_diameter):
""" Internal Function to create scattering tables.
This internal function sets up the scattering table. It takes a
wavelength as an argument where wavelength is one of the pytmatrix
accepted wavelengths.
Parameters:
wavelength : tmatrix wavelength
PyTmatrix wavelength.
dsr_func : function
Drop Shape Relationship function. Several built-in are available in the `DSR` module.
max_diameter: float
Maximum drop diameter to generate scattering table for.
"""
self.scatterer = Scatterer(wavelength=wavelength,
m=self.scattering_params["m_w"])
self.scatterer.psd_integrator = PSDIntegrator()
self.scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0 / dsr_func(
D)
self.dsr_func = dsr_func
self.scatterer.psd_integrator.D_max = max_diameter
self.scatterer.psd_integrator.geometries = (
tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw)
self.scatterer.or_pdf = orientation.gaussian_pdf(
self.scattering_params["canting_angle"])
self.scatterer.orient = orientation.orient_averaged_fixed
self.scatterer.psd_integrator.init_scatter_table(self.scatterer)
self.scattering_table_consistent = True
def test_optical_theorem(self):
"""Optical theorem: test that for a lossless particle, Csca=Cext
"""
tm = Scatterer(radius=4.0,
wavelength=6.5,
m=complex(1.5, 0.0),
axis_ratio=1.0 / 0.6)
tm.set_geometry(tmatrix_aux.geom_horiz_forw)
ssa_h = scatter.ssa(tm, True)
ssa_v = scatter.ssa(tm, False)
test_less(self, abs(1.0 - ssa_h), 1e-6)
test_less(self, abs(1.0 - ssa_v), 1e-6)
def __init__(self, wl=tmatrix_aux.wl_X, dr =1, shape='bc'):
DSR_list = {'tb':DSR.tb, 'bc': DSR.bc, 'pb': DSR.pb}
self.scatterer = Scatterer(wavelength=wl, m=refractive.m_w_10C[wl])
self.scatterer.psd_integrator = PSDIntegrator()
self.scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0/DSR_list[shape](D)
self.scatterer.psd_integrator.D_max = 10.0
self.scatterer.psd_integrator.geometries = (tmatrix_aux.geom_horiz_back,
tmatrix_aux.geom_horiz_forw)
self.scatterer.or_pdf = orientation.gaussian_pdf(20.0)
self.scatterer.orient = orientation.orient_averaged_fixed
self.scatterer.psd_integrator.init_scatter_table(self.scatterer)
self.dr=dr
def compute_gautschi_canting(list_of_std):
scatt = Scatterer(radius = 5.0)
gautschi_pts = []
gautschi_w = []
for l in list_of_std:
scatt.or_pdf = orientation.gaussian_pdf(std=l)
scatt.orient = orientation.orient_averaged_fixed
scatt._init_orient()
gautschi_pts.append(scatt.beta_p)
gautschi_w.append(scatt.beta_w)
return(gautschi_pts,gautschi_w)
def _setup_scattering(self, wavelength):
self.scatterer = Scatterer(wavelength=wavelength,
m=refractive.m_w_10C[wavelength])
self.scatterer.psd_integrator = PSDIntegrator()
self.scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0 / \
axis_ratio.axis_ratio_THBRS07(D)
self.scatterer.psd_integrator.D_max = 8.0
self.scatterer.psd_integrator.geometries = (
tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw)
self.scatterer.or_pdf = orientation.gaussian_pdf(20.0)
self.scatterer.orient = orientation.orient_averaged_fixed
print "MADE IT CC!!!!!!!!"
self.scatterer.psd_integrator.init_scatter_table(self.scatterer)
print "MADE IT HERE!!!!!!!!"
def _create_scatterer(wavelength, orientation_std):
"""
Create a scatterer instance that will be used later
Args:
wavelength: wavelength in mm
orientation_std: standard deviation of the Gaussian distribution
of orientations (canting angles)
Returns:
scatt: a pytmatrix Scatterer class instance
"""
scatt = Scatterer(radius=1.0, wavelength=wavelength, ndgs=10)
scatt.or_pdf = orientation.gaussian_pdf(std=orientation_std)
scatt.orient = orientation.orient_averaged_fixed
return scatt
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_rayleigh(self):
"""Test match with Rayleigh scattering for small spheres
"""
wl = 100.0
r = 1.0
eps = 1.0
m = complex(1.5, 0.5)
tm = Scatterer(wavelength=wl, radius=r, axis_ratio=eps, m=m)
S = tm.get_S()
k = 2 * np.pi / wl
S_ray = k**2 * (m**2 - 1) / (m**2 + 2) * r
test_relative(self, S[0, 0], S_ray, limit=1e-3)
test_relative(self, S[1, 1], -S_ray, limit=1e-3)
test_less(self, abs(S[0, 1]), 1e-25)
test_less(self, abs(S[1, 0]), 1e-25)
def setup_scatterer_rain(elev_radar):
scatterer = Scatterer()
scatterer.psd_integrator = psd.PSDIntegrator()
scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0/drop_ar(D)
scatterer.alpha = 0.0
scatterer.beta = 0.0
scatterer.phi0 = 0.0
scatterer.thet = 90.0 - elev_radar[0]
scatterer.thet0 = 90.0 - elev_radar[0]
scatterer.phi = 0.0
geom_forw = scatterer.get_geometry()
scatterer.phi = 180.0
geom_back = scatterer.get_geometry()
scatterer.or_pdf = orientation.gaussian_pdf(7.0) # orientation PDF according to Bringi and Chandrasekar (2001)
scatterer.orient = orientation.orient_averaged_fixed # averaging method
return [scatterer, geom_forw, geom_back]
def setup_scatterer_grau(elev_radar):
scatterer = Scatterer()
scatterer.psd_integrator = psd.PSDIntegrator()
scatterer.axis_ratio = 1. # 1./0.8 (original);
scatterer.alpha = 0.0
scatterer.beta = 0.0
scatterer.phi0 = 0.0
scatterer.thet = 90.0 - elev_radar[0]
scatterer.thet0 = 90.0 - elev_radar[0]
scatterer.phi = 0.0
geom_forw = scatterer.get_geometry()
scatterer.phi = 180.0
geom_back = scatterer.get_geometry()
# set up orientation averaging, Gaussian PDF with mean=0 and std=7 deg
scatterer.or_pdf = orientation.gaussian_pdf(1) # orientation PDF according to Bringi and Chandrasekar (2001)
scatterer.orient = orientation.orient_averaged_fixed # averaging method
return [scatterer, geom_forw, geom_back]
def test_asymmetry(self):
"""Test calculation of the asymmetry parameter
"""
tm = Scatterer(radius=4.0,
wavelength=6.5,
m=complex(1.5, 0.5),
axis_ratio=1.0)
tm.set_geometry(tmatrix_aux.geom_horiz_forw)
asym_horiz = scatter.asym(tm)
tm.set_geometry(tmatrix_aux.geom_vert_forw)
asym_vert = scatter.asym(tm)
# Is the asymmetry parameter the same for a sphere in two directions?
test_less(self, abs(1 - asym_horiz / asym_vert), 1e-6)
# Is the asymmetry parameter zero for small particles?
tm.radius = 0.0004
tm.set_geometry(tmatrix_aux.geom_horiz_forw)
asym_horiz = scatter.asym(tm)
test_less(self, abs(asym_horiz), 1e-8)
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_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 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
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
# and/or phase matrices. So, initialize a scatterer object for RAIN
# INFO ON GEOMETRY: geom_tuple = (thet0, thet, phi0, phi, alpha, beta) where,
# The Euler angle alpha of the scatterer orientation (alpha)
# The Euler angle beta of the scatterer orientation (beta)
# The zenith angle of the incident beam (thet0)
# The zenith angle of the scattered beam (thet)
# The azimuth angle of the incident beam (phi0)
# The azimuth angle of the scattered beam (phi)
# e.g. geom_horiz_back is (90.0, 90.0, 0.0, 180.0, 0.0, 0.0)
ref_indices_rain = [
complex(8.983, 0.989),
complex(8.590, 1.670),
complex(7.718, 2.473)
]
scatterer = Scatterer()
theta_radar = (0.5, 0.9, 1.3, 1.9, 2.3, 3, 3.5, 5, 6.9, 9.1, 11.8, 15.1
) # The angles for the radar are:
scatterer.alpha = 0.0
scatterer.beta = 0.0
scatterer.phi0 = 0.0
scatterer.thet = 90.0 - theta_radar[0]
scatterer.thet0 = 90.0 - theta_radar[0]
scatterer.phi = 180.0
geom_back = scatterer.get_geometry()
scatterer.phi = 0.0
geom_forw = scatterer.get_geometry()
# so assuming perfect backscattering (no gaussian function)
# geom_tuple = (theta_radar, theta_radar, 0.0, 180.0, 0.0, 0.0)
# Set geometry to backscattering!
def calcKdpPropOneFreq(wl,
radii,
as_ratio,
rho,
elv,
ndgs=2,
canting=False,
cantingStd=1,
meanAngle=0):
"""
Calculation of the KDP of one particle
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: 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
-------
kdp: calculated kdp from each particle (array[n])
"""
scatterer = Scatterer(wavelength=wl) #, axis_ratio=1./as_ratio)
scatterer.radius_type = Scatterer.RADIUS_MAXIMUM
scatterer.set_geometry(tmatrix_aux.geom_horiz_forw)
scatterer.ndgs = ndgs
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 = scatterer.thet0
scatterer.phi = (scatterer.phi0) % 360. #KDP geometry
sMat = np.ones_like(radii) * np.nan
for i, radius in enumerate(radii):
scatterer.axis_ratio = 1. / as_ratio[i]
scatterer.radius = radius
scatterer.m = refractive.mi(wl, rho[i])
S = scatterer.get_S()
sMat[i] = (S[1, 1] - S[0, 0]).real
kdp = 1e-3 * (180.0 / np.pi) * scatterer.wavelength * (sMat)
return kdp
def create_scatterer(wavelength,orientation_std):
scatt = Scatterer(radius = 1.0, wavelength = wavelength)
scatt.or_pdf = orientation.gaussian_pdf(std=orientation_std)
scatt.orient = orientation.orient_averaged_fixed
return scatt
if ptype == 'rain':
rads = np.linspace(0.1, 4.0, nr)
else:
rads = np.linspace(1, nr, nr)
sv = np.zeros((nr, niZe, nsZe, nsAz))
sh = np.zeros((nr, niZe, nsZe, nsAz))
# zdr = np.zeros((nr,niZe,nsZe,nsAz))
for n, r in enumerate(rads):
print(r)
if ptype == 'rain':
ar = 1.0 / drop_ar(2 * r)
else:
ar = 1.0 / hail_ar(2 * r)
scatterer = Scatterer(radius=r,
axis_ratio=0.6,
wavelength=Lambda,
m=m,
ndgs=3)
#scatterer.psd_integrator = PSDIntegrator()
#scatterer.psd_integrator.axis_ratio_func = lambda D: 1.0/drop_ar(D)
#scatterer.psd_integrator.axis_ratio_func = lambda D: 0.8
#scatterer.psd_integrator.D_max = 100.0
#scatterer.psd_integrator.num_points = 16
#scatterer.psd_integrator.geometries = ()
geometries = ()
ize = np.linspace(0, 90, niZe) + 90
sze = np.linspace(0, 180, nsZe)
saz = np.linspace(0, 180, nsAz)
sze, ize, saz = np.meshgrid(sze, ize, saz)
A = backscatter.createDimension('Mass Parameter a', len(a.tolist()))
D = backscatter.createDimension('Maximum Diameter', len(d.tolist()))
WAVE = backscatter.createDimension('Wavelength', len(wave))
xsections = backscatter.createVariable(
'backscat', 'f4', ('Area Ratio', 'Mass Parameter b', 'Mass Parameter a',
'Maximum Diameter', 'Wavelength'))
for i in range(len(a)):
print(a[i])
for j in range(len(b)):
print(b[j])
for k in range(len(ar)):
print(ar[k])
for l in range(len(d)):
for n in range(len(wave)):
if wave[n] / d[l] > 0.15:
dmod = d[l] * (ar[k]**(1. / 3.))
newm = mix(a[i], b[j], d[l] * 0.1, ar[k])
oblate = Scatterer(radius=dmod / 2.,
wavelength=wave[n],
m=newm,
axis_ratio=1 / ar[k])
xsection = 10 * np.log10(radar.refl(oblate))
xsections[i, j, k, l,
n] = 10 * np.log10(radar.refl(oblate))
else:
xsections[i, j, k, l, n] = np.nan
backscatter.close()
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
plt.figure()
plt.plot(D,a1.a*D**a1.b)
plt.plot(D,a2.a*D**a2.b)
#
plt.figure()
plt.plot(D,diel1(D),D,diel2(D))
from pytmatrix import orientation
from pytmatrix.tmatrix import Scatterer
f=9.41
wavelength=constants.C/(f*1E09)*1000 # in mm
scatt = Scatterer(radius = 1.0, wavelength = wavelength)
scatt.or_pdf = orientation.gaussian_pdf(std=20)
scatt.orient = orientation.orient_averaged_fixed
list_SZ_1=[]
list_SZ_2=[]
elevation = 5
m_func1= a1.get_m_func(270,f)
m_func2= a2.get_m_func(270,f)
geom_back=(90-elevation, 180-(90-elevation), 0., 180, 0.0,0.0)
geom_forw=(90-elevation, 90-elevation, 0., 0.0, 0.0,0.0)
for i,d in enumerate(D):
if __name__ == "__main__":
TIME_UNIT = "seconds since 1970-01-01 00:00"
OUTDIR = "."
# Radar band in mm.
flist = glob.glob(
"/g/data/kl02/vhl548/data_for_others/disdro2/*psd_na.txt")
for infile in flist:
for RADAR_BAND in [
tmatrix_aux.wl_S, tmatrix_aux.wl_C, tmatrix_aux.wl_X,
tmatrix_aux.wl_Ku, tmatrix_aux.wl_Ka, tmatrix_aux.wl_W
]:
print("Looking at wavelength {} mm.".format(RADAR_BAND))
SCATTERER = Scatterer(wavelength=RADAR_BAND,
m=refractive.m_w_10C[RADAR_BAND])
SCATTERER.psd_integrator = PSDIntegrator()
SCATTERER.psd_integrator.axis_ratio_func = lambda D: drop_axis_ratio(
D)
SCATTERER.psd_integrator.D_max = 8
SCATTERER.psd_integrator.geometries = (
tmatrix_aux.geom_horiz_back,
tmatrix_aux.geom_horiz_forw,
)
SCATTERER.or_pdf = orientation.gaussian_pdf(10.0)
SCATTERER.orient = orientation.orient_averaged_fixed
SCATTERER.psd_integrator.init_scatter_table(SCATTERER)
main(infile, RADAR_BAND)
