def tmatrix(self, wl, pluvio_filter=True, pip_filter=False, density=None):
"""Calculate radar reflectivity at requested wavelength wl [mm] using
T-matrix"""
name = switch_wl(wl) + "reflTM"
if density is None:
density = self.density(pluvio_filter=pluvio_filter,
pip_filter=pip_filter)
z_serie = pd.Series(density)
dBin = self.instr['dsd'].bin_width()
edges = self.instr['dsd'].bin_cen() + 0.5 * dBin
grp = self.instr['dsd'].grouped(rule=self.rule,
varinterval=self.varinterval,
col=self.dsd.bin_cen())
psd_values = grp.mean()
for item in density.iteritems():
if item[1] > 0.0:
ref = refractive.mi(wl, 0.001 * item[1])
print(ref)
flake = tmatrix.Scatterer(wavelength=wl,
m=ref,
axis_ratio=1.0 / 1.0)
flake.psd_integrator = psd.PSDIntegrator()
flake.psd_integrator.D_max = 35.0
flake.psd = psd.BinnedPSD(
bin_edges=edges, bin_psd=psd_values.loc[item[0]].values)
flake.psd_integrator.init_scatter_table(flake)
Z = 10.0 * np.log10(radar.refl(flake))
else:
Z = np.nan
z_serie.loc[item[0]] = Z
z_serie.name = name
return z_serie
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 tmatrix_stuffses(dsd):
drops = tmatrix.Scatterer(wavelength=aux.wl_C, m=ref.m_w_10C[aux.wl_C])
drops.Kw_sqr = aux.K_w_sqr[aux.wl_C]
drops.or_pdf = ori.gaussian_pdf(std=7.0)
drops.orient = ori.orient_averaged_fixed
drops.psd_integrator = psd.PSDIntegrator()
drops.psd_integrator.D_max = 10.0
drops.psd_integrator.axis_ratio_func = read.ar
back = aux.geom_horiz_back
forw = aux.geom_horiz_forw
drops.psd_integrator.geometries = (back, forw)
drops.psd_integrator.init_scatter_table(drops)
psds = dsd.to_tm_series(resample=None)
drops.set_geometry(back)
zh = []
zv = []
zdr = []
rho_hv = []
ldr = []
for tm_psd in psds:
drops.psd = tm_psd
zh.append(db(radar.refl(drops)))
zv.append(db(radar.refl(drops, False)))
zdr.append(db(radar.Zdr(drops)))
rho_hv.append(radar.rho_hv(drops))
ldr.append(db(radar.ldr(drops)))
d = {
'R': dsd.intensity(),
'Zh': zh,
'Zv': zv,
'Zdr': zdr,
'rho_hv': rho_hv,
'LDR': ldr
}
return pd.DataFrame(data=d, index=psds.index)
Dmass, _, _ = snowScatt.snowMassVelocityArea(Dmax, 'vonTerzi_needle')
reff_sph = mass2reff(Dmass)
volume_fractions = 6.0 * Dmass / (ice_density * np.pi * aspect_ratio * Dmax**3
) # 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(
'wl', # [31.557101, 8.565499, 3.155710]
'elv', # always 90
'meanAngle', # it is used only for prolate
'cantingStd', # always 1.0
'radius', # 8660
'rho', # 8706
'as_ratio' # 8769
]
data = pd.read_csv('/home/dori/table_McRadar.txt',
delim_whitespace=True,
header=None,
names=column_names) # 26463 lines
data = data[np.abs((data.wl - wls[fi]) / wls[fi]) < 0.05]
data = data[data.elv == e]
scatterer = tmatrix.Scatterer(wavelength=1.0)
scatterer.radius_type = tmatrix.Scatterer.RADIUS_MAXIMUM
scatterer.ndgs = 30
scatterer.ddelta = 1e-6
i = 0
if len(data):
for index, row in data.iterrows():
coords = Z11.sel(wavelength=row.wl,
size=row.radius,
aspect=row.as_ratio,
density=row.rho,
elevation=row.elv,
canting=row.cantingStd,
method='nearest').coords
if np.isnan(Z11.loc[coords]):
print(index, i)
den = rhoD(D)
return refractive.mi(w,den)
return m_func
lambdas = np.linspace(1,10,20)
D0s = 3.67/lambdas
fig, (ax1,ax2,ax3) = plt.subplots(3,1)#,sharex=True)
Zdr_array = []
Z_array = []
for wl in wavelengths:
ref_func = m_rho_func(wl)
for lamb in lambdas:
scatterer = tmatrix.Scatterer(wavelength=wl,
axis_ratio=1.0/ar,
Kw_sqr=tmatrix_aux.K_w_sqr[wl])
scatterer.psd_integrator = psd.PSDIntegrator()
scatterer.psd_integrator.m_func = ref_func
scatterer.psd_integrator.geometries=[tmatrix_aux.geom_horiz_back,tmatrix_aux.geom_horiz_forw,]
scatterer.set_geometry(tmatrix_aux.geom_horiz_back)
scatterer.psd = psd.ExponentialPSD(N0=1.0,Lambda=lamb,D_max=20.0)
scatterer.psd_integrator.D_max = 20.0
scatterer.psd_integrator.init_scatter_table(scatterer)
Zdr = 10.0*np.log10(radar.Zdr(scatterer))
scatterer.set_geometry(tmatrix_aux.geom_horiz_forw)
W = np.pi*1e3*(1.0/lamb)**4
Kdp = 1.0e6*radar.Kdp(scatterer)/W
Zdr_array.append(Zdr)
Z_array.append(Kdp)
ax1.plot(D0s,Zdr_array,label=wl)
@author: dori
"""
import sys
sys.path.append('/work/DBs/scattnlay')
from scattnlay import scattnlay
from pytmatrix import tmatrix, scatter, radar
import numpy as np
r = 2.0
wl = 100.0
m = complex(1.5, 0.5)
x = 2. * np.pi * r / wl
k = 2. * np.pi / wl
scatterer = tmatrix.Scatterer(radius=r, wavelength=wl, m=m, axis_ratio=1.0)
S, Z = scatterer.get_SZ()
print(S[0, 0], S[1, 1])
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
import numpy as np
from pytmatrix import refractive, scatter, tmatrix, tmatrix_aux
sys.path.append('../')
wl = tmatrix_aux.wl_S
m = refractive.mi(tmatrix_aux.wl_S,refractive.ice_density)
pi5 = np.pi**5.0
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])
-Lambda * D) / gamma(mu + 4.0)
#Lambda = (3.67+mu)/D0
D0s = np.linspace(0.75, 2.2, 10)
Zhh = 0.0 * D0s
Ai = 0.0 * D0s
wl = tmatrix_aux.wl_W
el = 0.
scatterer = tmatrix.Scatterer(
wavelength=wl,
m=refractive.m_w_10C[wl],
#kw_sqr=tmatrix_aux.K_w_sqr[wl],
radius_type=tmatrix.Scatterer.RADIUS_MAXIMUM,
#or_pdf=orientation.gaussian_pdf(std=1.0),
#orient=orientation.orient_averaged_fixed,
thet0=90.0 - el,
thet=90.0 + el)
Nw = 8000.0 # mm-2 m-3
mu = 5
scatterer.psd_integrator = psd.PSDIntegrator(
D_max=8.0,
geometries=(tmatrix_aux.geom_horiz_back, tmatrix_aux.geom_horiz_forw))
scatterer.psd_integrator.init_scatter_table(scatterer)
for i, D0 in enumerate(D0s):
scatterer.psd = genGamma(Nw, D0, mu) #psd.GammaPSD(D0=D0, mu=mu, Nw=Nw)
scatterer.set_geometry(tmatrix_aux.geom_horiz_back)
Zhh[i] = 10.0 * np.log10(radar.refl(scatterer))
scatterer.set_geometry(tmatrix_aux.geom_horiz_forw)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 7 16:30:05 2018
@author: dori
"""
from pytmatrix import tmatrix
scatt = tmatrix.Scatterer(radius=0.01)
def Sorinet(scatt, t0=None, t=None, p0=None, p=None):
if t0 is not None:
scatt.thet0 = t0
if p0 is not None:
scatt.p0 = p0
if t is not None:
scatt.thet = t
if p is not None:
scatt.phi = p
return scatt.get_S()[0,0]
import matplotlib.pyplot as plt
from pytmatrix import tmatrix
from pytmatrix import tmatrix_aux
from pytmatrix import radar
from pytmatrix import refractive
from pytmatrix import orientation
# Reference Mie calculation
wl = tmatrix_aux.wl_W
el = 0.
m = refractive.m_w_10C[wl]
scatterer = tmatrix.Scatterer(wavelength=wl,
m=m,
axis_ratio=1.0,
radius_type=tmatrix.Scatterer.RADIUS_MAXIMUM,
thet0=90.0 - el,
thet=90.0 + el)
Dmax = np.linspace(0.1, 2.7, 100)
Zhh = 0.0 * Dmax
Zvv = 0.0 * Dmax
Zdr = 0.0 * Dmax
for i, D in enumerate(Dmax):
scatterer.radius = 0.5 * D
Zdr[i] = 10.0 * np.log10(radar.Zdr(scatterer))
Zhh[i] = 10.0 * np.log10(radar.refl(scatterer, h_pol=True))
Zvv[i] = 10.0 * np.log10(radar.refl(scatterer, h_pol=False))
plt.figure()
ax0 = plt.gca()
