def calcParameters(self,D0, Nw, mu):
self.moments = {}
self.scatterer.psd = GammaPSD(D0=D0, Nw=10**(Nw), mu=mu)
self.scatterer.set_geometry(tmatrix_aux.geom_horiz_back)
self.moments['Zh'] = 10*np.log10(radar.refl(self.scatterer))
self.moments['Zdr'] = 10*np.log10(radar.Zdr(self.scatterer))
self.moments['delta_hv'] = radar.delta_hv(self.scatterer)
self.moments['ldr_h'] = radar.ldr(self.scatterer)
self.moments['ldr_v'] = radar.ldr(self.scatterer, h_pol=False)
self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw)
self.moments['Kdp'] = radar.Kdp(self.scatterer)
self.moments['Ah'] = radar.Ai(self.scatterer)
self.moments['Adr'] = self.moments['Ah']-radar.Ai(self.scatterer, h_pol=False)
return self.moments
def cal_tm4(n0,lamda,u,scatterer):
rain_zdr=[]
rain_zv=[]
rain_ldr=[]
rain_rhv=[]
rain_zh=[]
length=len(n0)
for i in range(length):
scatterer.psd=UnnormalizedGammaPSD(N0=n0[i],Lambda=lamda[i],mu=u)
rain_zh.append(radar.refl(scatterer))
rain_zdr.append(radar.Zdr(scatterer))
rain_zv.append(radar.refl(scatterer,h_pol=False))
rain_ldr.append(radar.ldr(scatterer))
rain_rhv.append(radar.rho_hv(scatterer))
if i%1000==0:
print '\r|'+'='*(50*i/length)+' '*(50-50*i/length)+'|'+'%1.2f%%' %(100.0*i/length),
print ' '
rain_zh = np.array(rain_zh)
rain_zdr = np.array(rain_zdr)
rain_zv = np.array(rain_zv)
rain_ldr = np.array(rain_ldr)
rain_rhv = np.array(rain_rhv)
return rain_zh,rain_zdr,rain_zv,rain_ldr,rain_rhv
def get_radar_variables_unnormalizedGamma(N0=None,
Lambda=None,
mu=None,
D_max=None):
scatterer.psd = psd.UnnormalizedGammaPSD(N0=N0,
Lambda=Lambda,
mu=mu,
D_max=D_max)
return [radar.refl(scatterer), radar.Zdr(scatterer), radar.ldr(scatterer)]
def get_radar_variables_ThomPSD(N1=None,
N2=None,
Lambda1=None,
Lambda2=None,
mu=None,
D_max=None):
scatterer.psd = ThomPSD(N1=N1,
N2=N2,
Lambda1=Lambda1,
Lambda2=Lambda2,
mu=mu,
D_max=D_max)
return [radar.refl(scatterer), radar.Zdr(scatterer), radar.ldr(scatterer)]
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)
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)
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)
def get_radar_variables_Exponential(N0=None,Lambda=None,D_max=None):
scatterer.psd = psd.ExponentialPSD(N0=N0, Lambda=Lambda, D_max=D_max)
return [radar.refl(scatterer), radar.Zdr(scatterer), radar.ldr(scatterer)]
def calculate_radar_parameters(self,
dsr_func=DSR.bc,
scatter_time_range=None):
''' Calculates radar parameters for the Drop Size Distribution.
Calculates the radar parameters and stores them in the object.
Defaults to X-Band,Beard and Chuang 10C setup.
Sets the dictionary parameters in fields dictionary:
Zh, Zv, Zdr, Kdp, Ai, Av(hor. and vert. Attenuation),
Adr (diff. attenuation), cross_correlation_ratio_hv (rhohv),
LDR, Kdp
Parameters:
----------
wavelength: optional, pytmatrix wavelength
Wavelength to calculate scattering coefficients at.
dsr_func: optional, function
Drop Shape Relationship function. Several are available
in the `DSR` module.
Defaults to Beard and Chuang
scatter_time_range: optional, tuple
Parameter to restrict the scattering to a time interval.
The first element is the start time,
while the second is the end time.
'''
self._setup_scattering(SPEED_OF_LIGHT / self.scattering_freq * 1000.0,
dsr_func)
self._setup_empty_fields()
if scatter_time_range is None:
self.scatter_start_time = 0
self.scatter_end_time = self.numt
else:
if scatter_time_range[0] < 0:
print("Invalid Start time specified, aborting")
return
self.scatter_start_time = scatter_time_range[0]
self.scatter_end_time = scatter_time_range[1]
if scatter_time_range[1] > self.numt:
print("End of Scatter time is greater than end of file. " +
"Scattering to end of included time.")
self.scatter_end_time = self.numt
# We break up scattering to avoid regenerating table.
self.scatterer.set_geometry(tmatrix_aux.geom_horiz_back)
print('Calculating backward scattering parameters ...')
for t in range(self.scatter_start_time, self.scatter_end_time):
if np.sum(self.Nd['data'][t]) is 0:
continue
BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges['data'],
self.Nd['data'][t])
self.scatterer.psd = BinnedDSD
self.fields['Zh']['data'][t] = 10 * \
np.log10(radar.refl(self.scatterer))
self.fields['Zv']['data'][t] = 10 * \
np.log10(radar.refl(self.scatterer, h_pol=False))
self.fields['Zdr']['data'][t] = 10 * \
np.log10(radar.Zdr(self.scatterer))
self.fields['cross_correlation_ratio_hv']['data'][t] = \
radar.rho_hv(self.scatterer)
self.fields['specific_differential_phase_hv']['data'][t] = \
radar.delta_hv(self.scatterer)
self.fields['LDR']['data'][t] = 10 * \
np.log10(radar.ldr(self.scatterer))
self.scatterer.set_geometry(tmatrix_aux.geom_horiz_forw)
print('Calculating forward scattering parameters ...')
for t in range(self.scatter_start_time, self.scatter_end_time):
BinnedDSD = pytmatrix.psd.BinnedPSD(self.bin_edges['data'],
self.Nd['data'][t])
self.scatterer.psd = BinnedDSD
self.fields['Kdp']['data'][t] = radar.Kdp(self.scatterer)
self.fields['Ai']['data'][t] = radar.Ai(self.scatterer)
self.fields['Av']['data'][t] = radar.Ai(self.scatterer,
h_pol=False)
self.fields['Adr']['data'][t] = radar.Ai(self.scatterer) - \
radar.Ai(self.scatterer, h_pol=False)
# Mask all values where no precipitation present or when ice present
params_list = [
'Zh', 'Zv', 'Zdr', 'Kdp', 'Ai', 'Av', 'Adr',
'cross_correlation_ratio_hv', 'LDR',
'specific_differential_phase_hv'
]
l = np.empty(len(self.fields['Precip_Code']['data']), dtype=bool)
j = 0
for i in self.fields['Precip_Code']['data']:
l[j] = 'N' in i or 'G' in i
j += 1
for param in params_list:
self.fields[param]['data'] = \
np.ma.masked_where(l, self.fields[param]['data'])