| @@ -297,7 +297,6 class JROData(GenericData): | |||
|
|
297 | 297 | |
|
|
298 | 298 | def getFmax(self): |
|
|
299 | 299 | PRF = 1. / (self.ippSeconds * self.nCohInt) |
|
|
300 | ||
|
|
301 | 300 | fmax = PRF |
|
|
302 | 301 | return fmax |
|
|
303 | 302 | |
| @@ -605,11 +604,9 class Spectra(JROData): | |||
|
|
605 | 604 | noise = numpy.zeros(self.nChannels) |
|
|
606 | 605 | |
|
|
607 | 606 | for channel in range(self.nChannels): |
|
|
608 | #print "confuse",self.data_spc.dtype | |
|
|
609 | 607 | daux = self.data_spc[channel, |
|
|
610 | 608 | xmin_index:xmax_index, ymin_index:ymax_index] |
|
|
611 | 609 | |
|
|
612 | #print "HI3.0",(daux.dtype),daux.shape | |
|
|
613 | 610 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
|
|
614 | 611 | |
|
|
615 | 612 | return noise |
| @@ -634,9 +631,7 class Spectra(JROData): | |||
|
|
634 | 631 | return freqrange |
|
|
635 | 632 | |
|
|
636 | 633 | def getAcfRange(self, extrapoints=0): |
|
|
637 | #print "miay",self.ippFactor | |
|
|
638 | 634 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
|
639 | #print deltafreq | |
|
|
640 | 635 | freqrange = deltafreq * \ |
|
|
641 | 636 | (numpy.arange(self.nFFTPoints + extrapoints) - |
|
|
642 | 637 | self.nFFTPoints / 2.) - deltafreq / 2 |
| @@ -646,10 +641,11 class Spectra(JROData): | |||
|
|
646 | 641 | def getFreqRange(self, extrapoints=0): |
|
|
647 | 642 | |
|
|
648 | 643 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
|
644 | #print "deltafreq", deltafreq | |
|
|
649 | 645 | freqrange = deltafreq * \ |
|
|
650 | 646 | (numpy.arange(self.nFFTPoints + extrapoints) - |
|
|
651 | 647 | self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
652 | ||
|
|
648 | #print "freqrange",freqrange | |
|
|
653 | 649 | return freqrange |
|
|
654 | 650 | |
|
|
655 | 651 | def getVelRange(self, extrapoints=0): |
| @@ -274,7 +274,6 class ACFPlot(Figure): | |||
|
|
274 | 274 | folder=None, username=None, password=None, |
|
|
275 | 275 | xaxis="frequency"): |
|
|
276 | 276 | |
|
|
277 | ||
|
|
278 | 277 | channel0 = channel |
|
|
279 | 278 | nSamples = nSamples |
|
|
280 | 279 | resFactor = resolutionFactor |
| @@ -313,12 +312,6 class ACFPlot(Figure): | |||
|
|
313 | 312 | shape = dataOut.data_acf.shape |
|
|
314 | 313 | hei_index = numpy.arange(shape[2]) |
|
|
315 | 314 | hei_plot = numpy.arange(nSamples)*resFactor |
|
|
316 | #print hei_plot | |
|
|
317 | #import matplotlib.pyplot as plt | |
|
|
318 | #c=z[0,:,0]*15+15 | |
|
|
319 | #plt.plot(c) | |
|
|
320 | #plt.show() | |
|
|
321 | #print "HOLA# | |
|
|
322 | 315 | |
|
|
323 | 316 | if nSampleList is not None: |
|
|
324 | 317 | for nsample in nSampleList: |
| @@ -335,18 +328,9 class ACFPlot(Figure): | |||
|
|
335 | 328 | #escalamiento -1 a 1 a resolucion (factor de resolucion en altura)* deltaHeight |
|
|
336 | 329 | min = numpy.min(z[0,:,0]) |
|
|
337 | 330 | max =numpy.max(z[0,:,0]) |
|
|
338 | ||
|
|
339 | 331 | for i in range(shape[0]): |
|
|
340 | 332 | for j in range(shape[2]): |
|
|
341 | 333 | z[i,:,j]= (((z[i,:,j]-min)/(max-min))*deltaHeight*resFactor + j*deltaHeight) |
|
|
342 | #z[i,:,j]= (z[i,:,j]+1.0)*deltaHeight*dataOut.step/2.0 + j*deltaHeight*dataOut.step | |
|
|
343 | ||
|
|
344 | #print deltaHeight | |
|
|
345 | #print resFactor | |
|
|
346 | #print numpy.max(z[0,:,0]) | |
|
|
347 | #import matplotlib.pyplot as plt | |
|
|
348 | #plt.plot((z[0,:,0])*deltaHeight) | |
|
|
349 | #plt.show() | |
|
|
350 | 334 | |
|
|
351 | 335 | if xaxis == "frequency": |
|
|
352 | 336 | x = dataOut.getFreqRange()/1000. |
| @@ -355,7 +339,8 class ACFPlot(Figure): | |||
|
|
355 | 339 | ylabel = "Power (dB)" |
|
|
356 | 340 | |
|
|
357 | 341 | elif xaxis == "time": |
|
|
358 |
|
|
|
|
342 | delta= dataOut.getAcfRange()[1]-dataOut.getAcfRange()[0] | |
|
|
343 | x = dataOut.getAcfRange()+delta/2.0 | |
|
|
359 | 344 | zdB = z[channel0,:,hei_plot] |
|
|
360 | 345 | xlabel = "Time (ms)" |
|
|
361 | 346 | ylabel = "ACF" |
| @@ -34,7 +34,6 class SpectraProc(ProcessingUnit): | |||
|
|
34 | 34 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
35 | 35 | self.dataOut.channelList = self.dataIn.channelList |
|
|
36 | 36 | self.dataOut.heightList = self.dataIn.heightList |
|
|
37 | #print self.dataOut.heightList.shape,"spec4" | |
|
|
38 | 37 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
|
39 | 38 | |
|
|
40 | 39 | self.dataOut.nBaud = self.dataIn.nBaud |
| @@ -65,8 +64,6 class SpectraProc(ProcessingUnit): | |||
|
|
65 | 64 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
66 | 65 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
67 | 66 | |
|
|
68 | self.dataOut.step = self.dataIn.step # | |
|
|
69 | ||
|
|
70 | 67 | def __getFft(self): |
|
|
71 | 68 | """ |
|
|
72 | 69 | Convierte valores de Voltaje a Spectra |
| @@ -125,7 +122,7 class SpectraProc(ProcessingUnit): | |||
|
|
125 | 122 | |
|
|
126 | 123 | if self.dataIn.type == "Spectra": |
|
|
127 | 124 | self.dataOut.copy(self.dataIn) |
|
|
128 | print "hi",self.dataOut.ippSeconds | |
|
|
125 | ||
|
|
129 | 126 | if shift_fft: |
|
|
130 | 127 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
131 | 128 | shift = int(self.dataOut.nFFTPoints/2) |
| @@ -156,7 +153,7 class SpectraProc(ProcessingUnit): | |||
|
|
156 | 153 | if self.buffer is None: |
|
|
157 | 154 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
158 | 155 | nProfiles, |
|
|
159 | self.dataIn.heightList.shape[0]), | |
|
|
156 | self.dataIn.heightList.shape[0]),#update heightlist | |
|
|
160 | 157 | dtype='complex') |
|
|
161 | 158 | |
|
|
162 | 159 | |
| @@ -134,34 +134,27 class SpectraAFCProc(ProcessingUnit): | |||
|
|
134 | 134 | self.dataOut.blockSize = blocksize |
|
|
135 | 135 | self.dataOut.flagShiftFFT = True |
|
|
136 | 136 | |
|
|
137 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1): | |
|
|
137 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1,real= None, imag=None): | |
|
|
138 | 138 | |
|
|
139 | 139 | self.dataOut.flagNoData = True |
|
|
140 | 140 | |
|
|
141 | 141 | if self.dataIn.type == "Spectra": |
|
|
142 | 142 | self.dataOut.copy(self.dataIn) |
|
|
143 | #print "hi",self.dataOut.ippSeconds | |
|
|
144 | ||
|
|
145 | 143 | spc = self.dataOut.data_spc |
|
|
146 | 144 | data = numpy.fft.ifft(spc, axis=1) |
|
|
147 | 145 | data = numpy.fft.fftshift(data, axes=(1,)) |
|
|
148 | 146 | acf = numpy.abs(data) # Autocorrelacion LLAMAR A ESTE VALOR ACF |
|
|
149 | #acf = data.imag | |
|
|
147 | if real: | |
|
|
148 | acf = data.real | |
|
|
149 | if imag: | |
|
|
150 | acf = data.imag | |
|
|
150 | 151 | shape = acf.shape #nchannels, nprofiles, nsamples |
|
|
151 | 152 | |
|
|
152 | #import matplotlib.pyplot as plt | |
|
|
153 | #plt.plot(acf[0,:,0] / numpy.max(numpy.abs(acf[0,:,0]))) | |
|
|
154 | #plt.show() | |
|
|
155 | ||
|
|
156 | 153 | # Normalizando |
|
|
157 | 154 | for i in range(shape[0]): |
|
|
158 | 155 | for j in range(shape[2]): |
|
|
159 | 156 | acf[i,:,j]= acf[i,:,j] / numpy.max(numpy.abs(acf[i,:,j])) |
|
|
160 | 157 | |
|
|
161 | #import matplotlib.pyplot as plt | |
|
|
162 | #plt.plot(acf[0,:,0]) | |
|
|
163 | #plt.show() | |
|
|
164 | ||
|
|
165 | 158 | self.dataOut.data_acf = acf |
|
|
166 | 159 | return True |
|
|
167 | 160 | |
| @@ -1209,8 +1209,8 class SSheightProfiles(Operation): | |||
|
|
1209 | 1209 | self.__nProfiles = dataOut.nProfiles |
|
|
1210 | 1210 | self.__nHeis = dataOut.nHeights |
|
|
1211 | 1211 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples |
|
|
1212 | #print "shape",shape | |
|
|
1213 | #last test | |
|
|
1212 | ||
|
|
1213 | ||
|
|
1214 | 1214 | residue = (shape[1] - self.nsamples) % self.step |
|
|
1215 | 1215 | if residue != 0: |
|
|
1216 | 1216 | print "The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue) |
| @@ -1248,8 +1248,6 class SSheightProfiles(Operation): | |||
|
|
1248 | 1248 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1249 | 1249 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
|
|
1250 | 1250 | |
|
|
1251 | #print "hi",dataOut.ippSeconds | |
|
|
1252 | #print ippSeconds | |
|
|
1253 | 1251 | dataOut.data = self.sshProfiles |
|
|
1254 | 1252 | dataOut.flagNoData = False |
|
|
1255 | 1253 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] |
| @@ -1258,7 +1256,6 class SSheightProfiles(Operation): | |||
|
|
1258 | 1256 | dataOut.flagDataAsBlock = True |
|
|
1259 | 1257 | dataOut.ippSeconds = ippSeconds |
|
|
1260 | 1258 | dataOut.step = self.step |
|
|
1261 | #print dataOut.ippSeconds | |
|
|
1262 | 1259 | |
|
|
1263 | 1260 | |
|
|
1264 | 1261 | # import collections |
General Comments 0
You need to be logged in to leave comments.
Login now