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jrodata.py: el atributo ippFactor se inicia en 1...
Daniel Valdez -
r448:318977a743a9
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@@ -420,6 +420,8 class Spectra(JROData):
420 self.flagDeflipData = False #asumo q la data no esta sin flip
420 self.flagDeflipData = False #asumo q la data no esta sin flip
421
421
422 self.flagShiftFFT = False
422 self.flagShiftFFT = False
423
424 self.ippFactor = 1
423
425
424 def getNoisebyHildebrand(self):
426 def getNoisebyHildebrand(self):
425 """
427 """
@@ -464,19 +466,19 class Spectra(JROData):
464 return self.noise
466 return self.noise
465
467
466 def getNoise(self, type = 1):
468 def getNoise(self, type = 1):
469 if self.noise == None:
470 self.noise = numpy.zeros(self.nChannels)
471
472 if type == 1:
473 self.noise = self.getNoisebyHildebrand()
474
475 if type == 2:
476 self.noise = self.getNoisebySort()
477
478 if type == 3:
479 self.noise = self.getNoisebyWindow()
467
480
468 self.noise = numpy.zeros(self.nChannels)
481 return self.noise
469
470 if type == 1:
471 noise = self.getNoisebyHildebrand()
472
473 if type == 2:
474 noise = self.getNoisebySort()
475
476 if type == 3:
477 noise = self.getNoisebyWindow()
478
479 return noise
480
482
481
483
482 def getFreqRange(self, extrapoints=0):
484 def getFreqRange(self, extrapoints=0):
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@@ -102,13 +102,13 class CrossSpectraPlot(Figure):
102
102
103 if len(pairsIndexList) > 4:
103 if len(pairsIndexList) > 4:
104 pairsIndexList = pairsIndexList[0:4]
104 pairsIndexList = pairsIndexList[0:4]
105 factor = dataOut.normFactor
105 #factor = dataOut.normFactor
106 x = dataOut.getVelRange(1)
106 x = dataOut.getVelRange(1)
107 y = dataOut.getHeiRange()
107 y = dataOut.getHeiRange()
108 z = dataOut.data_spc[:,:,:]/factor
108 z = dataOut.data_spc[:,:,:]#/factor
109 # z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
109 # z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
110 avg = numpy.abs(numpy.average(z, axis=1))
110 avg = numpy.abs(numpy.average(z, axis=1))
111 noise = dataOut.getNoise()/factor
111 noise = dataOut.getNoise()#/factor
112
112
113 zdB = 10*numpy.log10(z)
113 zdB = 10*numpy.log10(z)
114 avgdB = 10*numpy.log10(avg)
114 avgdB = 10*numpy.log10(avg)
@@ -151,7 +151,7 class CrossSpectraPlot(Figure):
151 pair = dataOut.pairsList[pairsIndexList[i]]
151 pair = dataOut.pairsList[pairsIndexList[i]]
152 str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S"))
152 str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S"))
153 title = "Ch%d: %4.2fdB: %s" %(pair[0], noisedB[pair[0]], str_datetime)
153 title = "Ch%d: %4.2fdB: %s" %(pair[0], noisedB[pair[0]], str_datetime)
154 zdB = 10.*numpy.log10(dataOut.data_spc[pair[0],:,:]/factor)
154 zdB = 10.*numpy.log10(dataOut.data_spc[pair[0],:,:])
155 axes0 = self.axesList[i*self.__nsubplots]
155 axes0 = self.axesList[i*self.__nsubplots]
156 axes0.pcolor(x, y, zdB,
156 axes0.pcolor(x, y, zdB,
157 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
157 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
@@ -159,7 +159,7 class CrossSpectraPlot(Figure):
159 ticksize=9, colormap=power_cmap, cblabel='')
159 ticksize=9, colormap=power_cmap, cblabel='')
160
160
161 title = "Ch%d: %4.2fdB: %s" %(pair[1], noisedB[pair[1]], str_datetime)
161 title = "Ch%d: %4.2fdB: %s" %(pair[1], noisedB[pair[1]], str_datetime)
162 zdB = 10.*numpy.log10(dataOut.data_spc[pair[1],:,:]/factor)
162 zdB = 10.*numpy.log10(dataOut.data_spc[pair[1],:,:])
163 axes0 = self.axesList[i*self.__nsubplots+1]
163 axes0 = self.axesList[i*self.__nsubplots+1]
164 axes0.pcolor(x, y, zdB,
164 axes0.pcolor(x, y, zdB,
165 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
165 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
@@ -318,11 +318,11 class RTIPlot(Figure):
318
318
319 tmin = None
319 tmin = None
320 tmax = None
320 tmax = None
321 factor = dataOut.normFactor
321 #factor = dataOut.normFactor
322 x = dataOut.getTimeRange()
322 x = dataOut.getTimeRange()
323 y = dataOut.getHeiRange()
323 y = dataOut.getHeiRange()
324
324
325 z = dataOut.data_spc[channelIndexList,:,:]/factor
325 z = dataOut.data_spc[channelIndexList,:,:]#/factor
326 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
326 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
327 avg = numpy.average(z, axis=1)
327 avg = numpy.average(z, axis=1)
328
328
@@ -506,14 +506,14 class SpectraPlot(Figure):
506 if channel not in dataOut.channelList:
506 if channel not in dataOut.channelList:
507 raise ValueError, "Channel %d is not in dataOut.channelList"
507 raise ValueError, "Channel %d is not in dataOut.channelList"
508 channelIndexList.append(dataOut.channelList.index(channel))
508 channelIndexList.append(dataOut.channelList.index(channel))
509 factor = dataOut.normFactor
509 #factor = dataOut.normFactor
510 x = dataOut.getVelRange(1)
510 x = dataOut.getVelRange(1)
511 y = dataOut.getHeiRange()
511 y = dataOut.getHeiRange()
512
512
513 z = dataOut.data_spc[channelIndexList,:,:]/factor
513 z = dataOut.data_spc[channelIndexList,:,:]#/factor
514 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
514 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
515 avg = numpy.average(z, axis=1)
515 avg = numpy.average(z, axis=1)
516 noise = dataOut.getNoise()/factor
516 noise = dataOut.getNoise()#/factor
517
517
518 zdB = 10*numpy.log10(z)
518 zdB = 10*numpy.log10(z)
519 avgdB = 10*numpy.log10(avg)
519 avgdB = 10*numpy.log10(avg)
@@ -765,9 +765,9 class PowerProfilePlot(Figure):
765 raise ValueError, "Channel %d is not in dataOut.channelList"
765 raise ValueError, "Channel %d is not in dataOut.channelList"
766 channelIndexList.append(dataOut.channelList.index(channel))
766 channelIndexList.append(dataOut.channelList.index(channel))
767
767
768 factor = dataOut.normFactor
768 #factor = dataOut.normFactor
769 y = dataOut.getHeiRange()
769 y = dataOut.getHeiRange()
770 x = dataOut.data_spc[channelIndexList,:,:]/factor
770 x = dataOut.data_spc[channelIndexList,:,:]#/factor
771 x = numpy.where(numpy.isfinite(x), x, numpy.NAN)
771 x = numpy.where(numpy.isfinite(x), x, numpy.NAN)
772 avg = numpy.average(x, axis=1)
772 avg = numpy.average(x, axis=1)
773
773
@@ -1107,8 +1107,8 class Noise(Figure):
1107 tmax = None
1107 tmax = None
1108 x = dataOut.getTimeRange()
1108 x = dataOut.getTimeRange()
1109 y = dataOut.getHeiRange()
1109 y = dataOut.getHeiRange()
1110 factor = dataOut.normFactor
1110 #factor = dataOut.normFactor
1111 noise = dataOut.getNoise()/factor
1111 noise = dataOut.getNoise()#/factor
1112 noisedB = 10*numpy.log10(noise)
1112 noisedB = 10*numpy.log10(noise)
1113
1113
1114 #thisDatetime = dataOut.datatime
1114 #thisDatetime = dataOut.datatime
@@ -1433,8 +1433,8 class RTIfromSpectraHeis(Figure):
1433 x = dataOut.getTimeRange()
1433 x = dataOut.getTimeRange()
1434 y = dataOut.getHeiRange()
1434 y = dataOut.getHeiRange()
1435
1435
1436 factor = 1
1436 #factor = 1
1437 data = dataOut.data_spc/factor
1437 data = dataOut.data_spc#/factor
1438 data = numpy.average(data,axis=1)
1438 data = numpy.average(data,axis=1)
1439 datadB = 10*numpy.log10(data)
1439 datadB = 10*numpy.log10(data)
1440
1440
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@@ -1194,6 +1194,49 class SpectraProc(ProcessingUnit):
1194 self.dataOut.frequency = frequency
1194 self.dataOut.frequency = frequency
1195
1195
1196 return 1
1196 return 1
1197
1198 def getNoise(self, minHei, maxHei):
1199
1200 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
1201 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
1202
1203 if (maxHei > self.dataOut.heightList[-1]):
1204 maxHei = self.dataOut.heightList[-1]
1205
1206 minIndex = 0
1207 maxIndex = 0
1208 heights = self.dataOut.heightList
1209
1210 inda = numpy.where(heights >= minHei)
1211 indb = numpy.where(heights <= maxHei)
1212
1213 try:
1214 minIndex = inda[0][0]
1215 except:
1216 minIndex = 0
1217
1218 try:
1219 maxIndex = indb[0][-1]
1220 except:
1221 maxIndex = len(heights)
1222
1223 if (minIndex < 0) or (minIndex > maxIndex):
1224 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
1225
1226 if (maxIndex >= self.dataOut.nHeights):
1227 maxIndex = self.dataOut.nHeights-1
1228
1229 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
1230
1231 noise = numpy.zeros(self.dataOut.nChannels)
1232
1233 for channel in range(self.dataOut.nChannels):
1234 daux = data_spc[channel,:,:]
1235 noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
1236
1237 self.dataOut.noise = noise.copy()
1238
1239 return 1
1197
1240
1198
1241
1199 class IncohInt(Operation):
1242 class IncohInt(Operation):
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