schain Commit - r1240:ce4b76019d00 · Jicamarca Repository

Update ACF first step

avaldez -

r1240:ce4b76019d00

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r1240:ce4b76019d00

schainpy/model/data/jrodata.py +3 0

                     noise = numpy.zeros(self.nChannels)
                     for channel in range(self.nChannels):
+                        #print "confuse",self.data_spc.dtype
                         daux = self.data_spc[channel,
                                              xmin_index:xmax_index, ymin_index:ymax_index]
+                        #print "HI3.0",(daux.dtype),daux.shape
                         noise[channel] = hildebrand_sekhon(daux, self.nIncohInt)
                     return noise

schainpy/model/graphics/jroplot_spectra.py +4 -3

                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     zdB = 10*numpy.log10(z)
+                    #print "a000",dataOut.data_spc.dtype
                     avg = numpy.average(z, axis=1)
                     avgdB = 10*numpy.log10(avg)
+                    #print "before plot"
                     noise = dataOut.getNoise()/factor
                     noisedB = 10*numpy.log10(noise)
                     if len(pairsIndexList) > 4:
                         pairsIndexList = pairsIndexList[0:4]
                     if normFactor is None:
                         factor = dataOut.normFactor
                     else:
                     #colormap = kwargs.get('colormap', 'jet')
                     if HEIGHT is not None:
                         self.HEIGHT  = HEIGHT
                     if not isTimeInHourRange(dataOut.datatime, xmin, xmax):
                         return

schainpy/model/proc/jroproc_spectra.py +2 0

                     # calculo de self-spectra
                     fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
+                    #print "spec dtype 0",fft_volt.dtype
                     spc = fft_volt * numpy.conjugate(fft_volt)
                     spc = spc.real
+                    #print "spec dtype 1",spc.dtype
                     blocksize = 0
                     blocksize += dc.size

schainpy/model/proc/jroproc_spectra_acf.py +22 -1

@@ -102,10 +102,13 class SpectraAFCProc(ProcessingUnit):
102	# fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))	102	# fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
103	spc = fft_volt * numpy.conjugate(fft_volt)	103	spc = fft_volt * numpy.conjugate(fft_volt)
104		104
		105
105	data = numpy.fft.ifft(spc, axis=1)	106	data = numpy.fft.ifft(spc, axis=1)
106	data = numpy.fft.fftshift(data, axes=(1,))	107	data = numpy.fft.fftshift(data, axes=(1,))
107		108
108	spc = data	109	spc = data.real
		110
		111
109		112
110	blocksize = 0	113	blocksize = 0
111	blocksize += dc.size	114	blocksize += dc.size
@@ -140,6 +143,24 class SpectraAFCProc(ProcessingUnit):
140		143
141	self.dataOut.flagNoData = True	144	self.dataOut.flagNoData = True
142		145
		146	if self.dataIn.type == "Spectra":
		147	self.dataOut.copy(self.dataIn)
		148	spc= self.dataOut.data_spc
		149	data = numpy.fft.ifft(spc, axis=1)
		150	data = numpy.fft.fftshift(data, axes=(1,))
		151	spc = data.real
		152	spc = spc[0,:,0] / numpy.max(numpy.abs(spc[0,:,0]))
		153	print spc
		154	import matplotlib.pyplot as plt
		155	#plt.plot(spc[10:])
		156	plt.show()
		157
		158
		159	self.dataOut.data_spc = spc
		160
		161	return True
		162
		163
143	if code is not None:	164	if code is not None:
144	self.code = numpy.array(code).reshape(nCode,nBaud)	165	self.code = numpy.array(code).reshape(nCode,nBaud)
145	else:	166	else:

schainpy/model/proc/jroproc_voltage.py +2 -2

                     self.__nProfiles = dataOut.nProfiles
                     self.__nHeis     = dataOut.nHeights
                     shape            = dataOut.data.shape #nchannels, nprofiles, nsamples
+                    print  "shape",shape
                     #last test
                     residue =  (shape[1] - self.nsamples) % self.step
                     if residue != 0:
                     numberProfile   = self.nsamples
                     numberSamples   = (shape[1] - self.nsamples)/self.step
-                    print "New number of profile: %d, number of height: %d, Resolution %d"%(numberProfile,numberSamples,deltaHeight*self.step)
+                    print "New number of profile: %d, number of height: %d, Resolution %d Km"%(numberProfile,numberSamples,deltaHeight*self.step)
                     self.bufferShape  = shape[0], numberSamples, numberProfile  # nchannels, nsamples , nprofiles
                     self.profileShape = shape[0], numberProfile, numberSamples  # nchannels, nprofiles, nsamples

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