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'])