schain Commit - r1558:0436325fab68 · Jicamarca Repository

minor changes spectra plot

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r1558:0436325fab68

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r1558:0436325fab68

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schainpy/model/data/jrodata.py +1 -1

                     for channel in range(self.nChannels):
                         daux = self.data_spc[channel,xmin_index:xmax_index, ymin_index:ymax_index]
-                        noise[channel] = hildebrand_sekhon(daux, self.max_nIncohInt)
+                        noise[channel] = hildebrand_sekhon(daux, self.max_nIncohInt[channel])
                     return noise

schainpy/model/graphics/jroplot_spectra.py +10 -5

                     noise = 10*numpy.log10(dataOut.getNoise()/norm)
-                    z = []
+                    z = numpy.zeros((dataOut.nChannels, dataOut.nFFTPoints, dataOut.nHeights))
                     for ch in range(dataOut.nChannels):
-                        if hasattr(dataOut.normFactor,'shape'):
+                        if hasattr(dataOut.normFactor,'ndim'):
-                            z.append(numpy.divide(dataOut.data_spc[ch],dataOut.normFactor[ch]))
+                            if dataOut.normFactor.ndim > 1:
+                                z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor[ch]))
+                            else:
+                                z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor))
                         else:
-                            z.append(numpy.divide(dataOut.data_spc[ch],dataOut.normFactor))
+                            z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor))
-                    z = numpy.asarray(z)
                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     spc = 10*numpy.log10(z)
                     data['spc'] = spc
+                    print(spc[0].shape)
                     data['rti'] = spc.mean(axis=1)
                     data['noise'] = noise
                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))

schainpy/model/io/jroIO_kamisr.py +11 -4

                     #Dimensions : nChannels, nProfiles, nSamples
                     new_block = numpy.empty((nblocks, nchan, numpy.int_(self.newProfiles), nsamples), dtype="complex64")
                     ############################################
-                    profPerCH = int(self.profPerBlockRAW / (self.nFFT * self.nChannels))
+                    profPerCH = int(self.profPerBlockRAW / (self.nFFT* self.nChannels))
                     for thisChannel in range(nchan):
-                        idx_ch = [thisChannel+k*nchan for k in range(profPerCH)]
+                        idx_ch = [thisChannel+(k*nchan*self.nFFT) for k in range(profPerCH)]
-                        idx_ch = numpy.array(idx_ch, dtype=int)
+                        if self.nFFT > 1:
-                        #print(thisChannel,idx_ch)
+                            aux = [numpy.arange(i, i+self.nFFT) for i in idx_ch]
+                            idx_ch  = None
+                            idx_ch =aux
+                            idx_ch = numpy.array(idx_ch, dtype=int).flatten()
+                        else:
+                            idx_ch = numpy.array(idx_ch, dtype=int)
+                        #print(thisChannel,profPerCH,idx_ch)
                         #print(numpy.where(channels==self.beamCode[thisChannel])[0])
                         #new_block[:,thisChannel,:,:] = self.dataset[:,numpy.where(channels==self.beamCode[thisChannel])[0],:]
                         new_block[:,thisChannel,:,:] = self.dataset[:,idx_ch,:]

schainpy/model/io/jroIO_param.py +4 -3

                         grp = fp.create_group('Metadata')
                     for i in range(len(self.metadataList)):
-                        if not hasattr(self.dataOut, self.metadataList[i]):
+                        attribute = self.metadataList[i]
-                            log.warning('Metadata: `{}` not found'.format(self.metadataList[i]), self.name)
+                        if not hasattr(self.dataOut,attribute ):
+                            log.warning('Metadata: `{}` not found'.format(attribute), self.name)
                             continue
-                        value = getattr(self.dataOut, self.metadataList[i])
+                        value = getattr(self.dataOut, attribute)
                         if isinstance(value, bool):
                             if value is True:
                                 value = 1

schainpy/model/proc/jroproc_parameters.py +6 -8

                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     self.dataOut.radar_ipp = self.dataIn.radar_ipp
                     self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
-                    self.dataOut.radarControllerHeaderTxt = self.dataOut.radarControllerHeaderObj.toString()
+                    #self.dataOut.radarControllerHeaderTxt = self.dataOut.radarControllerHeaderObj.toString()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.heightList = self.dataIn.heightList
                         self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
                         self.dataOut.ippFactor = self.dataIn.ippFactor
                         self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
-                        self.dataOut.radarControllerHeaderTxt = self.dataOut.radarControllerHeaderObj.toString()
+                        #self.dataOut.radarControllerHeaderTxt = self.dataOut.radarControllerHeaderObj.toString()
                         self.dataOut.ipp = self.dataIn.ipp
                         self.dataOut.radar_ipp = self.dataIn.radar_ipp
                         self.dataOut.sampled_heightsFFT = self.dataIn.sampled_heightsFFT
                         max_spec = aux.max()
                         m = aux.tolist().index(max_spec)
-                        if hasattr(normFactor, "shape"):
+                        if hasattr(normFactor, "ndim"):
-                            norm = normFactor[ind]
+                            if normFactor.ndim > 1:
+                                norm = normFactor[ind]
                         # Smooth
                         if (smooth == 0):
                             spec2 = spec
                         spec2 *= (norm*norm)   #compensation for sats remove
                         #snr = (spec2.mean()-n0)/n0
                         snr = (spec2.mean())/n0
-                        #print(snr)
-                        # if  snr < 1.0:  # By definition SNR must be higher than 1
-                        #     snr = 0
                         # vec_power[ind] = power    #D. Scipión replaced with the line below
                         vec_power[ind] = total_power

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