diff --git a/schainpy/model/graphics/jroplot_spectra.py b/schainpy/model/graphics/jroplot_spectra.py index da589d6..56dd0fa 100644 --- a/schainpy/model/graphics/jroplot_spectra.py +++ b/schainpy/model/graphics/jroplot_spectra.py @@ -324,8 +324,8 @@ class WpowerPlot_(Figure): zmin : None, zmax : None """ - print("***************PLOTEO******************") - print("DATAOUT SHAPE : ",dataOut.data.shape) + #print("***************PLOTEO******************") + #print("DATAOUT SHAPE : ",dataOut.data.shape) if dataOut.flagNoData: return dataOut @@ -344,7 +344,7 @@ class WpowerPlot_(Figure): channelIndexList.append(dataOut.channelList.index(channel)) - print("channelIndexList",channelIndexList) + #print("channelIndexList",channelIndexList) if normFactor is None: factor = dataOut.normFactor else: @@ -364,7 +364,7 @@ class WpowerPlot_(Figure): ylabel = "Range (km)" y = dataOut.getHeiRange() - print("factor",factor) + #print("factor",factor) z = dataOut.data/factor # dividido /factor z = numpy.where(numpy.isfinite(z), z, numpy.NAN) diff --git a/schainpy/model/proc/jroproc_spectra.py b/schainpy/model/proc/jroproc_spectra.py index 5bc04b3..60b8215 100644 --- a/schainpy/model/proc/jroproc_spectra.py +++ b/schainpy/model/proc/jroproc_spectra.py @@ -88,6 +88,7 @@ class SpectraProc(ProcessingUnit): # calculo de self-spectra fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) spc = fft_volt * numpy.conjugate(fft_volt) + #print("spcch0",spc[0]) spc = spc.real blocksize = 0 @@ -1154,6 +1155,7 @@ class PulsePair(Operation): self.__buffer = data*numpy.conjugate(data) self.__bufferV = data[:,(self.__nProf-1):,:]*numpy.conjugate(data[:,1:,:]) self.__profIndex = self.n + #print("spcch0",self.__buffer) return def pushData(self): @@ -1162,9 +1164,13 @@ class PulsePair(Operation): data_IV = numpy.zeros((self.__nch,self.__nHeis)) for i in range(self.__nch): - data_I[i,:] = numpy.sum(numpy.sum(self.__buffer[i],axis=0),axis=0)/self.n - data_IV[i,:] = numpy.sum(numpy.sum(self.__bufferV[i],axis=0),axis=0)/(self.n-1) - + data_I[i,:] = numpy.sum(self.__buffer[i],axis=0)/self.n + data_IV[i,:] = numpy.sum(self.__bufferV[i],axis=0)/(self.n-1) + ##print("******") + #print("data_I",data_I[0]) + #print(self.__buffer.shape) + #a=numpy.average(self.__buffer,axis=1) + #print("average", a) n = self.__profIndex ####data_intensity = numpy.sum(numpy.sum(self.__buffer,axis=0),axis=0)/self.n #print("data_intensity push data",data_intensity.shape)