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