| @@ -3392,6 +3392,7 class SpectralFitting(Operation): | |||
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3392 | 3392 | incoh_aver[pair[1],incoh_echoes]=1 |
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3393 | 3393 | return my_incoh_spectra ,my_incoh_cspectra,my_incoh_aver,my_coh_aver, incoh_spectra, coh_spectra, incoh_cspectra, coh_cspectra, incoh_aver, coh_aver |
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3394 | 3394 | |
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3395 | ||
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3395 | 3396 | def __CleanCoherent(self,snrth, spectra, cspectra, coh_aver,dataOut, noise,clean_coh_echoes,index): |
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3396 | 3397 | |
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3397 | 3398 | nProf = dataOut.nProfiles |
| @@ -3718,14 +3719,6 class SpectralFitting(Operation): | |||
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3718 | 3719 | dataOut.clean_num_aver = clean_num_aver |
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3719 | 3720 | dataOut.coh_num_aver = coh_num_aver |
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3720 | 3721 | |
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3721 | #List of possible combinations | |
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3722 | #listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) | |
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3723 | #indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |
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3724 | #if getSNR: | |
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3725 | # listChannels = groupArray.reshape((groupArray.size)) | |
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3726 | # listChannels.sort() | |
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3727 | # print("AQUI ESTOY") | |
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3728 | # dataOut.data_SNR = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise[listChannels]) | |
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3729 | 3722 | else: |
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3730 | 3723 | clean_num_aver = dataOut.clean_num_aver |
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3731 | 3724 | coh_num_aver = dataOut.coh_num_aver |
| @@ -3940,13 +3933,12 class SpectralFitting(Operation): | |||
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3940 | 3933 | dataOut.spc_noise = my_noises*self.nProf*self.M |
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3941 | 3934 | if numpy.any(proc): dataOut.spc_noise = my_noises*self.nProf*self.M |
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3942 | 3935 | if getSNR: |
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3943 | print("self.groupArray",self.groupArray.size,self.groupArray) | |
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3944 | 3936 | listChannels = self.groupArray.reshape((self.groupArray.size)) |
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3945 | 3937 | listChannels.sort() |
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3946 | 3938 | # TEST |
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3947 | 3939 | noise_C = numpy.zeros(self.nChannels) |
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3948 | 3940 | noise_C = dataOut.getNoise() |
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3949 | print("noise_C",noise_C) | |
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3941 | #print("noise_C",noise_C) | |
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3950 | 3942 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:],noise_C/(600.0*1.15))# PRUEBA *nProf*M |
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3951 | 3943 | #dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise_C[listChannels])# PRUEBA *nProf*M |
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3952 | 3944 | dataOut.flagNoData = False |
| @@ -3962,11 +3954,6 class SpectralFitting(Operation): | |||
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3962 | 3954 | |
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3963 | 3955 | avg = numpy.average(z, axis=1) |
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3964 | 3956 | SNR = (avg.T-noise)/noise |
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3965 | print("------------------------------------------------------") | |
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3966 | print("T - Noise", noise.shape) | |
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3967 | print("T - Noise", 10*numpy.log10(noise[0])) | |
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3968 | print("T - avg.T" , avg.T.shape) | |
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3969 | print("T - avg.T" , 10*numpy.log10(avg.T[:,0])) | |
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3970 | 3957 | SNR = SNR.T |
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3971 | 3958 | return SNR |
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3972 | 3959 | |
| @@ -4736,7 +4723,7 class EWDriftsEstimation(Operation): | |||
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4736 | 4723 | dataOut.data_output = winds |
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4737 | 4724 | #snr1 = 10*numpy.log10(SNR1[0])# estaba comentado |
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4738 | 4725 | dataOut.data_snr1 = numpy.reshape(snr1,(1,snr1.shape[0])) |
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4739 | print("data_snr1",dataOut.data_snr1) | |
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4726 | #print("data_snr1",dataOut.data_snr1) | |
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4740 | 4727 | dataOut.utctimeInit = dataOut.utctime |
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4741 | 4728 | dataOut.outputInterval = dataOut.timeInterval |
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4742 | 4729 | |
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