##// END OF EJS Templates
schain-jc
RSS
Adicion del factor de normalizacion en la clase Spectra....
Daniel Valdez -
r245:0a5b40329524
parent child
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@@ -315,6 +315,11 class Axes:
315 ylabel=ylabel,
315 ylabel=ylabel,
316 title=title,
316 title=title,
317 **kwargs)
317 **kwargs)
318 if self.xmin == None: self.xmin = xmin
319 if self.xmax == None: self.xmax = xmax
320 if self.ymin == None: self.ymin = ymin
321 if self.ymax == None: self.ymax = ymax
322
318 self.__firsttime = False
323 self.__firsttime = False
319 return
324 return
320
325
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@@ -1,7 +1,11
1 import numpy
1 import numpy
2 import datetime
2 import datetime
3 import sys
3 import matplotlib
4 import matplotlib
4 matplotlib.use("GTKAgg")
5 if sys.platform == 'linux':
6 matplotlib.use("GTKAgg")
7 if sys.platform == 'darwin':
8 matplotlib.use("TKAgg")
5 import matplotlib.pyplot
9 import matplotlib.pyplot
6 import matplotlib.dates
10 import matplotlib.dates
7 #import scitools.numpyutils
11 #import scitools.numpyutils
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@@ -161,6 +161,8 class JROData:
161
161
162 noise = None
162 noise = None
163
163
164 windowOfFilter = 1
165
164 #Speed of ligth
166 #Speed of ligth
165 C = 3e8
167 C = 3e8
166
168
@@ -448,7 +450,7 class Spectra(JROData):
448 if type == 3:
450 if type == 3:
449 noise = self.getNoisebyWindow()
451 noise = self.getNoisebyWindow()
450
452
451 return 10*numpy.log10(noise)
453 return noise
452
454
453
455
454 def getFreqRange(self, extrapoints=0):
456 def getFreqRange(self, extrapoints=0):
@@ -473,8 +475,17 class Spectra(JROData):
473
475
474 return range(self.nPairs)
476 return range(self.nPairs)
475
477
478 def getNormFactor(self):
479 pwcode = 1
480 if self.flagDecodeData:
481 pwcode = numpy.sum(self.code[0]**2)
482 normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*self.windowOfFilter*pwcode
483
484 return normFactor
485
476 nPairs = property(getNPairs, "I'm the 'nPairs' property.")
486 nPairs = property(getNPairs, "I'm the 'nPairs' property.")
477 pairsIndexList = property(getPairsIndexList, "I'm the 'pairsIndexList' property.")
487 pairsIndexList = property(getPairsIndexList, "I'm the 'pairsIndexList' property.")
488 normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.")
478
489
479 class SpectraHeis(JROData):
490 class SpectraHeis(JROData):
480
491
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@@ -86,14 +86,18 class CrossSpectraPlot(Figure):
86
86
87 if len(pairsIndexList) > 4:
87 if len(pairsIndexList) > 4:
88 pairsIndexList = pairsIndexList[0:4]
88 pairsIndexList = pairsIndexList[0:4]
89
89 factor = dataOut.normFactor
90 x = dataOut.getVelRange(1)
90 x = dataOut.getVelRange(1)
91 y = dataOut.getHeiRange()
91 y = dataOut.getHeiRange()
92 z = 10.*numpy.log10(dataOut.data_spc[:,:,:])
92 z = dataOut.data_spc[:,:,:]/factor
93 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
93 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
94 avg = numpy.average(numpy.abs(z), axis=1)
94 avg = numpy.average(numpy.abs(z), axis=1)
95 noise = dataOut.getNoise()/factor
96
97 zdB = 10*numpy.log10(z)
98 avgdB = 10*numpy.log10(avg)
99 noisedB = 10*numpy.log10(noise)
95
100
96 noise = dataOut.getNoise()
97
101
98 thisDatetime = dataOut.datatime
102 thisDatetime = dataOut.datatime
99 title = "Cross-Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
103 title = "Cross-Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
@@ -113,8 +117,8 class CrossSpectraPlot(Figure):
113 if xmax == None: xmax = numpy.nanmax(x)
117 if xmax == None: xmax = numpy.nanmax(x)
114 if ymin == None: ymin = numpy.nanmin(y)
118 if ymin == None: ymin = numpy.nanmin(y)
115 if ymax == None: ymax = numpy.nanmax(y)
119 if ymax == None: ymax = numpy.nanmax(y)
116 if zmin == None: zmin = numpy.nanmin(avg)*0.9
120 if zmin == None: zmin = numpy.nanmin(avgdB)*0.9
117 if zmax == None: zmax = numpy.nanmax(avg)*0.9
121 if zmax == None: zmax = numpy.nanmax(avgdB)*0.9
118
122
119 self.__isConfig = True
123 self.__isConfig = True
120
124
@@ -123,18 +127,18 class CrossSpectraPlot(Figure):
123 for i in range(self.nplots):
127 for i in range(self.nplots):
124 pair = dataOut.pairsList[pairsIndexList[i]]
128 pair = dataOut.pairsList[pairsIndexList[i]]
125
129
126 title = "Channel %d: %4.2fdB" %(pair[0], noise[pair[0]])
130 title = "Channel %d: %4.2fdB" %(pair[0], noisedB[pair[0]])
127 z = 10.*numpy.log10(dataOut.data_spc[pair[0],:,:])
131 zdB = 10.*numpy.log10(dataOut.data_spc[pair[0],:,:]/factor)
128 axes0 = self.axesList[i*self.__nsubplots]
132 axes0 = self.axesList[i*self.__nsubplots]
129 axes0.pcolor(x, y, z,
133 axes0.pcolor(x, y, zdB,
130 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
134 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
131 xlabel=xlabel, ylabel=ylabel, title=title,
135 xlabel=xlabel, ylabel=ylabel, title=title,
132 ticksize=9, cblabel='')
136 ticksize=9, cblabel='')
133
137
134 title = "Channel %d: %4.2fdB" %(pair[1], noise[pair[1]])
138 title = "Channel %d: %4.2fdB" %(pair[1], noisedB[pair[1]])
135 z = 10.*numpy.log10(dataOut.data_spc[pair[1],:,:])
139 zdB = 10.*numpy.log10(dataOut.data_spc[pair[1],:,:]/factor)
136 axes0 = self.axesList[i*self.__nsubplots+1]
140 axes0 = self.axesList[i*self.__nsubplots+1]
137 axes0.pcolor(x, y, z,
141 axes0.pcolor(x, y, zdB,
138 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
142 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
139 xlabel=xlabel, ylabel=ylabel, title=title,
143 xlabel=xlabel, ylabel=ylabel, title=title,
140 ticksize=9, cblabel='')
144 ticksize=9, cblabel='')
@@ -265,13 +269,18 class RTIPlot(Figure):
265
269
266 tmin = None
270 tmin = None
267 tmax = None
271 tmax = None
272 factor = dataOut.normFactor
268 x = dataOut.getTimeRange()
273 x = dataOut.getTimeRange()
269 y = dataOut.getHeiRange()
274 y = dataOut.getHeiRange()
270 z = 10.*numpy.log10(dataOut.data_spc[channelIndexList,:,:])
275
271 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
276 z = dataOut.data_spc[channelIndexList,:,:]/factor
277 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
272 avg = numpy.average(z, axis=1)
278 avg = numpy.average(z, axis=1)
279 noise = dataOut.getNoise()/factor
273
280
274 noise = dataOut.getNoise()
281 # zdB = 10.*numpy.log10(z)
282 avgdB = 10.*numpy.log10(avg)
283 noisedB = 10.*numpy.log10(noise)
275
284
276 thisDatetime = dataOut.datatime
285 thisDatetime = dataOut.datatime
277 title = "RTI: %s" %(thisDatetime.strftime("%d-%b-%Y"))
286 title = "RTI: %s" %(thisDatetime.strftime("%d-%b-%Y"))
@@ -290,8 +299,8 class RTIPlot(Figure):
290 tmin, tmax = self.getTimeLim(x, xmin, xmax)
299 tmin, tmax = self.getTimeLim(x, xmin, xmax)
291 if ymin == None: ymin = numpy.nanmin(y)
300 if ymin == None: ymin = numpy.nanmin(y)
292 if ymax == None: ymax = numpy.nanmax(y)
301 if ymax == None: ymax = numpy.nanmax(y)
293 if zmin == None: zmin = numpy.nanmin(avg)*0.9
302 if zmin == None: zmin = numpy.nanmin(avgdB)*0.9
294 if zmax == None: zmax = numpy.nanmax(avg)*0.9
303 if zmax == None: zmax = numpy.nanmax(avgdB)*0.9
295
304
296 self.name = thisDatetime.strftime("%Y%m%d_%H%M%S")
305 self.name = thisDatetime.strftime("%Y%m%d_%H%M%S")
297 self.__isConfig = True
306 self.__isConfig = True
@@ -302,15 +311,15 class RTIPlot(Figure):
302 for i in range(self.nplots):
311 for i in range(self.nplots):
303 title = "Channel %d: %s" %(dataOut.channelList[i], thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
312 title = "Channel %d: %s" %(dataOut.channelList[i], thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
304 axes = self.axesList[i*self.__nsubplots]
313 axes = self.axesList[i*self.__nsubplots]
305 z = avg[i].reshape((1,-1))
314 zdB = avgdB[i].reshape((1,-1))
306 axes.pcolor(x, y, z,
315 axes.pcolor(x, y, zdB,
307 xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
316 xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
308 xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,
317 xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,
309 ticksize=9, cblabel='', cbsize="1%")
318 ticksize=9, cblabel='', cbsize="1%")
310
319
311 if self.__showprofile:
320 if self.__showprofile:
312 axes = self.axesList[i*self.__nsubplots +1]
321 axes = self.axesList[i*self.__nsubplots +1]
313 axes.pline(avg[i], y,
322 axes.pline(avgdB[i], y,
314 xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax,
323 xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax,
315 xlabel='dB', ylabel='', title='',
324 xlabel='dB', ylabel='', title='',
316 ytick_visible=False,
325 ytick_visible=False,
@@ -415,15 +424,18 class SpectraPlot(Figure):
415 if channel not in dataOut.channelList:
424 if channel not in dataOut.channelList:
416 raise ValueError, "Channel %d is not in dataOut.channelList"
425 raise ValueError, "Channel %d is not in dataOut.channelList"
417 channelIndexList.append(dataOut.channelList.index(channel))
426 channelIndexList.append(dataOut.channelList.index(channel))
418
427 factor = dataOut.normFactor
419 x = dataOut.getVelRange(1)
428 x = dataOut.getVelRange(1)
420 y = dataOut.getHeiRange()
429 y = dataOut.getHeiRange()
421
430
422 z = 10.*numpy.log10(dataOut.data_spc[channelIndexList,:,:])
431 z = dataOut.data_spc[channelIndexList,:,:]/factor
423 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
432 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
424 avg = numpy.average(z, axis=1)
433 avg = numpy.average(z, axis=1)
434 noise = dataOut.getNoise()/factor
425
435
426 noise = dataOut.getNoise()
436 zdB = 10*numpy.log10(z)
437 avgdB = 10*numpy.log10(avg)
438 noisedB = 10*numpy.log10(noise)
427
439
428 thisDatetime = dataOut.datatime
440 thisDatetime = dataOut.datatime
429 title = "Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
441 title = "Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
@@ -443,30 +455,30 class SpectraPlot(Figure):
443 if xmax == None: xmax = numpy.nanmax(x)
455 if xmax == None: xmax = numpy.nanmax(x)
444 if ymin == None: ymin = numpy.nanmin(y)
456 if ymin == None: ymin = numpy.nanmin(y)
445 if ymax == None: ymax = numpy.nanmax(y)
457 if ymax == None: ymax = numpy.nanmax(y)
446 if zmin == None: zmin = numpy.nanmin(avg)*0.9
458 if zmin == None: zmin = numpy.nanmin(avgdB)*0.9
447 if zmax == None: zmax = numpy.nanmax(avg)*0.9
459 if zmax == None: zmax = numpy.nanmax(avgdB)*0.9
448
460
449 self.__isConfig = True
461 self.__isConfig = True
450
462
451 self.setWinTitle(title)
463 self.setWinTitle(title)
452
464
453 for i in range(self.nplots):
465 for i in range(self.nplots):
454 title = "Channel %d: %4.2fdB" %(dataOut.channelList[i], noise[i])
466 title = "Channel %d: %4.2fdB" %(dataOut.channelList[i], noisedB[i])
455 axes = self.axesList[i*self.__nsubplots]
467 axes = self.axesList[i*self.__nsubplots]
456 axes.pcolor(x, y, z[i,:,:],
468 axes.pcolor(x, y, zdB[i,:,:],
457 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
469 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
458 xlabel=xlabel, ylabel=ylabel, title=title,
470 xlabel=xlabel, ylabel=ylabel, title=title,
459 ticksize=9, cblabel='')
471 ticksize=9, cblabel='')
460
472
461 if self.__showprofile:
473 if self.__showprofile:
462 axes = self.axesList[i*self.__nsubplots +1]
474 axes = self.axesList[i*self.__nsubplots +1]
463 axes.pline(avg[i], y,
475 axes.pline(avgdB[i], y,
464 xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax,
476 xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax,
465 xlabel='dB', ylabel='', title='',
477 xlabel='dB', ylabel='', title='',
466 ytick_visible=False,
478 ytick_visible=False,
467 grid='x')
479 grid='x')
468
480
469 noiseline = numpy.repeat(noise[i], len(y))
481 noiseline = numpy.repeat(noisedB[i], len(y))
470 axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2)
482 axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2)
471
483
472 self.draw()
484 self.draw()
@@ -631,11 +643,14 class ProfilePlot(Figure):
631 raise ValueError, "Channel %d is not in dataOut.channelList"
643 raise ValueError, "Channel %d is not in dataOut.channelList"
632 channelIndexList.append(dataOut.channelList.index(channel))
644 channelIndexList.append(dataOut.channelList.index(channel))
633
645
634
646 factor = dataOut.normFactor
635 y = dataOut.getHeiRange()
647 y = dataOut.getHeiRange()
636 x = 10.*numpy.log10(dataOut.data_spc[channelIndexList,:,:])
648 x = dataOut.data_spc[channelIndexList,:,:]/factor
649 x = numpy.where(numpy.isfinite(x), x, numpy.NAN)
637 avg = numpy.average(x, axis=1)
650 avg = numpy.average(x, axis=1)
638
651
652 avgdB = 10*numpy.log10(avg)
653
639 thisDatetime = dataOut.datatime
654 thisDatetime = dataOut.datatime
640 title = "Power Profile"
655 title = "Power Profile"
641 xlabel = "dB"
656 xlabel = "dB"
@@ -651,8 +666,8 class ProfilePlot(Figure):
651
666
652 if ymin == None: ymin = numpy.nanmin(y)
667 if ymin == None: ymin = numpy.nanmin(y)
653 if ymax == None: ymax = numpy.nanmax(y)
668 if ymax == None: ymax = numpy.nanmax(y)
654 if xmin == None: xmin = numpy.nanmin(avg)*0.9
669 if xmin == None: xmin = numpy.nanmin(avgdB)*0.9
655 if xmax == None: xmax = numpy.nanmax(avg)*0.9
670 if xmax == None: xmax = numpy.nanmax(avgdB)*0.9
656
671
657 self.__isConfig = True
672 self.__isConfig = True
658
673
@@ -663,7 +678,7 class ProfilePlot(Figure):
663 axes = self.axesList[0]
678 axes = self.axesList[0]
664
679
665 legendlabels = ["channel %d"%x for x in channelList]
680 legendlabels = ["channel %d"%x for x in channelList]
666 axes.pmultiline(avg, y,
681 axes.pmultiline(avgdB, y,
667 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
682 xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax,
668 xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels,
683 xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels,
669 ytick_visible=True, nxticks=5,
684 ytick_visible=True, nxticks=5,
@@ -852,6 +867,10 class RTIfromNoise(Figure):
852
867
853 self.WIDTH = 820
868 self.WIDTH = 820
854 self.HEIGHT = 200
869 self.HEIGHT = 200
870 self.WIDTHPROF = 120
871 self.HEIGHTPROF = 0
872 self.xdata = None
873 self.ydata = None
855
874
856 def getSubplots(self):
875 def getSubplots(self):
857
876
@@ -865,17 +884,19 class RTIfromNoise(Figure):
865 self.__showprofile = showprofile
884 self.__showprofile = showprofile
866 self.nplots = nplots
885 self.nplots = nplots
867
886
868 ncolspan = 1
887 ncolspan = 7
869 colspan = 1
888 colspan = 6
870
889 self.__nsubplots = 2
890
871 self.createFigure(idfigure = idfigure,
891 self.createFigure(idfigure = idfigure,
872 wintitle = wintitle,
892 wintitle = wintitle,
873 widthplot = self.WIDTH,
893 widthplot = self.WIDTH+self.WIDTHPROF,
874 heightplot = self.HEIGHT)
894 heightplot = self.HEIGHT+self.HEIGHTPROF)
875
895
876 nrow, ncol = self.getSubplots()
896 nrow, ncol = self.getSubplots()
877
897
878 self.addAxes(nrow, ncol, 0, 0, 1, 1)
898 self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1)
899
879
900
880 def run(self, dataOut, idfigure, wintitle="", channelList=None, showprofile='True',
901 def run(self, dataOut, idfigure, wintitle="", channelList=None, showprofile='True',
881 xmin=None, xmax=None, ymin=None, ymax=None,
902 xmin=None, xmax=None, ymin=None, ymax=None,
@@ -899,12 +920,13 class RTIfromNoise(Figure):
899 tmax = None
920 tmax = None
900 x = dataOut.getTimeRange()
921 x = dataOut.getTimeRange()
901 y = dataOut.getHeiRange()
922 y = dataOut.getHeiRange()
902
923 factor = dataOut.normFactor
903 noise = dataOut.getNoise()
924 noise = dataOut.getNoise()/factor
925 noisedB = 10*numpy.log10(noise)
904
926
905 thisDatetime = dataOut.datatime
927 thisDatetime = dataOut.datatime
906 title = "RTI: %s" %(thisDatetime.strftime("%d-%b-%Y"))
928 title = "RTI: %s" %(thisDatetime.strftime("%d-%b-%Y"))
907 xlabel = "Velocity (m/s)"
929 xlabel = ""
908 ylabel = "Range (Km)"
930 ylabel = "Range (Km)"
909
931
910 if not self.__isConfig:
932 if not self.__isConfig:
@@ -917,12 +939,14 class RTIfromNoise(Figure):
917 showprofile=showprofile)
939 showprofile=showprofile)
918
940
919 tmin, tmax = self.getTimeLim(x, xmin, xmax)
941 tmin, tmax = self.getTimeLim(x, xmin, xmax)
920 if ymin == None: ymin = numpy.nanmin(noise)
942 if ymin == None: ymin = numpy.nanmin(noisedB)
921 if ymax == None: ymax = numpy.nanmax(noise)
943 if ymax == None: ymax = numpy.nanmax(noisedB)
922
944
923 self.name = thisDatetime.strftime("%Y%m%d_%H%M%S")
945 self.name = thisDatetime.strftime("%Y%m%d_%H%M%S")
924 self.__isConfig = True
946 self.__isConfig = True
925
947
948 self.xdata = numpy.array([])
949 self.ydata = numpy.array([])
926
950
927 self.setWinTitle(title)
951 self.setWinTitle(title)
928
952
@@ -931,15 +955,21 class RTIfromNoise(Figure):
931
955
932 legendlabels = ["channel %d"%idchannel for idchannel in channelList]
956 legendlabels = ["channel %d"%idchannel for idchannel in channelList]
933 axes = self.axesList[0]
957 axes = self.axesList[0]
934 xdata = x[0:1]
958
935 ydata = noise[channelIndexList].reshape(-1,1)
959 self.xdata = numpy.hstack((self.xdata, x[0:1]))
936 axes.pmultilineyaxis(x=xdata, y=ydata,
960
961 if len(self.ydata)==0:
962 self.ydata = noisedB[channelIndexList].reshape(-1,1)
963 else:
964 self.ydata = numpy.hstack((self.ydata, noisedB[channelIndexList].reshape(-1,1)))
965
966
967 axes.pmultilineyaxis(x=self.xdata, y=self.ydata,
937 xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax,
968 xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax,
938 xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid",
969 xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid",
939 XAxisAsTime=True
970 XAxisAsTime=True
940 )
971 )
941
972
942
943 self.draw()
973 self.draw()
944
974
945 if save:
975 if save:
@@ -951,4 +981,6 class RTIfromNoise(Figure):
951
981
952 if x[1] + (x[1]-x[0]) >= self.axesList[0].xmax:
982 if x[1] + (x[1]-x[0]) >= self.axesList[0].xmax:
953 self.__isConfig = False
983 self.__isConfig = False
984 del self.xdata
985 del self.ydata
954 No newline at end of file
986
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@@ -359,9 +359,10 class VoltageProc(ProcessingUnit):
359 r = self.dataOut.data.shape[1] % window
359 r = self.dataOut.data.shape[1] % window
360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
362 buffer = numpy.average(buffer,2)
362 buffer = numpy.sum(buffer,2)
363 self.dataOut.data = buffer
363 self.dataOut.data = buffer
364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
365 self.dataOut.windowOfFilter = window
365
366
366 def deFlip(self):
367 def deFlip(self):
367 self.dataOut.data *= self.flip
368 self.dataOut.data *= self.flip
@@ -694,6 +695,7 class SpectraProc(ProcessingUnit):
694 self.dataOut.nCohInt = self.dataIn.nCohInt
695 self.dataOut.nCohInt = self.dataIn.nCohInt
695 self.dataOut.nIncohInt = 1
696 self.dataOut.nIncohInt = 1
696 self.dataOut.ippSeconds = self.dataIn.ippSeconds
697 self.dataOut.ippSeconds = self.dataIn.ippSeconds
698 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
697
699
698 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
700 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
699
701
@@ -1086,9 +1088,9 class IncohInt(Operation):
1086
1088
1087 if self.__dataReady:
1089 if self.__dataReady:
1088
1090
1089 dataOut.data_spc = avgdata_spc / self.n
1091 dataOut.data_spc = avgdata_spc
1090 dataOut.data_cspc = avgdata_cspc / self.n
1092 dataOut.data_cspc = avgdata_cspc
1091 dataOut.data_dc = avgdata_dc / self.n
1093 dataOut.data_dc = avgdata_dc
1092
1094
1093 dataOut.nIncohInt *= self.n
1095 dataOut.nIncohInt *= self.n
1094 dataOut.utctime = avgdatatime
1096 dataOut.utctime = avgdatatime
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