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Wind and rainfall processing of CLAIRE radar with V3.0
George Yong -
r1205:45d75be01895
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@@ -526,36 +526,29 class Spectra(JROData):
526 526 def getFreqRangeTimeResponse(self, extrapoints=0):
527 527
528 528 deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor)
529 freqrange = deltafreq * \
530 (numpy.arange(self.nFFTPoints + extrapoints) -
531 self.nFFTPoints / 2.) - deltafreq / 2
529 freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
532 530
533 531 return freqrange
534 532
535 533 def getAcfRange(self, extrapoints=0):
536 534
537 535 deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor))
538 freqrange = deltafreq * \
539 (numpy.arange(self.nFFTPoints + extrapoints) -
540 self.nFFTPoints / 2.) - deltafreq / 2
536 freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
541 537
542 538 return freqrange
543 539
544 540 def getFreqRange(self, extrapoints=0):
545 541
546 542 deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor)
547 freqrange = deltafreq * \
548 (numpy.arange(self.nFFTPoints + extrapoints) -
549 self.nFFTPoints / 2.) - deltafreq / 2
543 freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
550 544
551 545 return freqrange
552 546
553 547 def getVelRange(self, extrapoints=0):
554 548
555 549 deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor)
556 velrange = deltav * (numpy.arange(self.nFFTPoints +
557 extrapoints) - self.nFFTPoints / 2.)
558
550 velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.)
551
559 552 if self.nmodes:
560 553 return velrange/self.nmodes
561 554 else:
@@ -576,8 +569,7 class Spectra(JROData):
576 569 if self.flagDecodeData:
577 570 pwcode = numpy.sum(self.code[0]**2)
578 571 #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter
579 normFactor = self.nProfiles * self.nIncohInt * \
580 self.nCohInt * pwcode * self.windowOfFilter
572 normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter
581 573
582 574 return normFactor
583 575
@@ -597,8 +589,7 class Spectra(JROData):
597 589
598 590 def getTimeInterval(self):
599 591
600 timeInterval = self.ippSeconds * self.nCohInt * \
601 self.nIncohInt * self.nProfiles * self.ippFactor
592 timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor
602 593
603 594 return timeInterval
604 595
@@ -625,8 +616,7 class Spectra(JROData):
625 616 pairsIndexList.append(self.pairsList.index(pair))
626 617 for i in range(len(pairsIndexList)):
627 618 pair = self.pairsList[pairsIndexList[i]]
628 ccf = numpy.average(
629 self.data_cspc[pairsIndexList[i], :, :], axis=0)
619 ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0)
630 620 powa = numpy.average(self.data_spc[pair[0], :, :], axis=0)
631 621 powb = numpy.average(self.data_spc[pair[1], :, :], axis=0)
632 622 avgcoherenceComplex = ccf / numpy.sqrt(powa * powb)
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@@ -735,7 +735,7 class SkyMapPlot_(Figure):
735 735
736 736
737 737
738
738 @MPDecorator
739 739 class WindProfilerPlot_(Figure):
740 740
741 741 __isConfig = None
@@ -745,8 +745,8 class WindProfilerPlot_(Figure):
745 745 HEIGHTPROF = None
746 746 PREFIX = 'wind'
747 747
748 def __init__(self, **kwargs):
749 Figure.__init__(self, **kwargs)
748 def __init__(self):
749 Figure.__init__(self)
750 750 self.timerange = None
751 751 self.isConfig = False
752 752 self.__nsubplots = 1
@@ -825,6 +825,9 class WindProfilerPlot_(Figure):
825 825 zmax : None
826 826 """
827 827
828 if dataOut.flagNoData:
829 return dataOut
830
828 831 # if timerange is not None:
829 832 # self.timerange = timerange
830 833 #
@@ -836,8 +839,8 class WindProfilerPlot_(Figure):
836 839 z = dataOut.data_output.copy()
837 840 nplots = z.shape[0] #Number of wind dimensions estimated
838 841 nplotsw = nplots
839
840
842
843
841 844 #If there is a SNR function defined
842 845 if dataOut.data_SNR is not None:
843 846 nplots += 1
@@ -948,6 +951,8 class WindProfilerPlot_(Figure):
948 951 self.isConfig = False
949 952 update_figfile = True
950 953
954 return dataOut
955
951 956 @MPDecorator
952 957 class ParametersPlot_(Figure):
953 958
@@ -1051,7 +1056,7 class ParametersPlot_(Figure):
1051 1056
1052 1057 if not isTimeInHourRange(dataOut.datatime, xmin, xmax):
1053 1058 return
1054
1059
1055 1060 if channelList == None:
1056 1061 channelIndexList = list(range(dataOut.data_param.shape[0]))
1057 1062 else:
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@@ -226,7 +226,7 def createPcolor(ax, x, y, z, xmin, xmax, ymin, ymax, zmin, zmax,
226 226
227 227 z = numpy.ma.masked_invalid(z)
228 228 cmap = matplotlib.pyplot.get_cmap(colormap)
229 cmap.set_bad('black', 1.)
229 cmap.set_bad('white', 1.)
230 230 imesh = ax.pcolormesh(x, y, z.T, vmin=zmin, vmax=zmax, cmap=cmap)
231 231 cb = matplotlib.pyplot.colorbar(imesh, cax=ax_cb)
232 232 cb.set_label(cblabel)
@@ -291,7 +291,7 def addpcolorbuffer(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', col
291 291 z = numpy.ma.masked_invalid(z)
292 292
293 293 cmap = matplotlib.pyplot.get_cmap(colormap)
294 cmap.set_bad('black', 1.)
294 cmap.set_bad('white', 1.)
295 295
296 296 ax.pcolormesh(x, y, z.T, vmin=zmin, vmax=zmax, cmap=cmap)
297 297
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@@ -192,10 +192,10 class MADReader(JRODataReader, ProcessingUnit):
192 192 self.parameters = one + two
193 193 self.parameters_d = one_d + two_d
194 194
195 log.success('Parameters found: {}'.format(','.join(self.parameters)),
195 log.success('Parameters found: {}'.format(','.join(str(self.parameters))),
196 196 'MADReader')
197 197 if s_parameters:
198 log.success('Spatial parameters: {}'.format(','.join(s_parameters)),
198 log.success('Spatial parameters: {}'.format(','.join(str(s_parameters))),
199 199 'MADReader')
200 200
201 201 for param in list(self.oneDDict.keys()):
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@@ -123,7 +123,7 class ParametersProc(ProcessingUnit):
123 123 self.dataOut.ippFactor = self.dataIn.ippFactor
124 124 self.dataOut.abscissaList = self.dataIn.getVelRange(1)
125 125 self.dataOut.spc_noise = self.dataIn.getNoise()
126 self.dataOut.spc_range = (self.dataIn.getFreqRange(1)/1000. , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
126 self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
127 127 # self.dataOut.normFactor = self.dataIn.normFactor
128 128 self.dataOut.pairsList = self.dataIn.pairsList
129 129 self.dataOut.groupList = self.dataIn.pairsList
@@ -239,18 +239,18 class SpectralFilters(Operation):
239 239
240 240
241 241 '''Reacomodando SPCrange'''
242
243 VelRange=numpy.roll(VelRange,-(int(self.Num_Bin/2)) ,axis=0)
242 244
243 VelRange=numpy.roll(VelRange,-(self.Num_Bin/2) ,axis=0)
244
245 VelRange[-(self.Num_Bin/2):]+= Vmax
245 VelRange[-(int(self.Num_Bin/2)):]+= Vmax
246 246
247 FrecRange=numpy.roll(FrecRange,-(self.Num_Bin/2),axis=0)
247 FrecRange=numpy.roll(FrecRange,-(int(self.Num_Bin/2)),axis=0)
248 248
249 FrecRange[-(self.Num_Bin/2):]+= Fmax
249 FrecRange[-(int(self.Num_Bin/2)):]+= Fmax
250 250
251 TimeRange=numpy.roll(TimeRange,-(self.Num_Bin/2),axis=0)
251 TimeRange=numpy.roll(TimeRange,-(int(self.Num_Bin/2)),axis=0)
252 252
253 TimeRange[-(self.Num_Bin/2):]+= Tmax
253 TimeRange[-(int(self.Num_Bin/2)):]+= Tmax
254 254
255 255 ''' ------------------ '''
256 256
@@ -258,7 +258,7 class SpectralFilters(Operation):
258 258 Breaker2R=numpy.where(VelRange == Breaker2R)
259 259
260 260
261 SPCroll = numpy.roll(self.spc,-(self.Num_Bin/2) ,axis=1)
261 SPCroll = numpy.roll(self.spc,-(int(self.Num_Bin/2)) ,axis=1)
262 262
263 263 SPCcut = SPCroll.copy()
264 264 for i in range(self.Num_Chn):
@@ -285,7 +285,7 class SpectralFilters(Operation):
285 285 dataOut.spcparam_range[2]=VelRange
286 286 dataOut.spcparam_range[1]=TimeRange
287 287 dataOut.spcparam_range[0]=FrecRange
288
288 return dataOut
289 289
290 290 class GaussianFit(Operation):
291 291
@@ -660,7 +660,7 class PrecipitationProc(Operation):
660 660
661 661 Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
662 662 Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR)
663 RadarConstant = 5e-26 * Numerator / Denominator #
663 RadarConstant = 10e-26 * Numerator / Denominator #
664 664
665 665 ''' ============================= '''
666 666
@@ -753,6 +753,7 class PrecipitationProc(Operation):
753 753 dataOut.data_param[1]=V_mean
754 754 dataOut.data_param[2]=RR
755 755
756 return dataOut
756 757
757 758 def dBZeMODE2(self, dataOut): # Processing for MIRA35C
758 759
@@ -833,7 +834,7 class FullSpectralAnalysis(Operation):
833 834
834 835 SNRspc = spc.copy()
835 836 SNRspc[:,:,0:7]= numpy.NaN
836
837
837 838 """##########################################"""
838 839
839 840
@@ -876,8 +877,8 class FullSpectralAnalysis(Operation):
876 877 dbSNR = numpy.average(dbSNR,0)
877 878
878 879 for Height in range(nHeights):
879
880 [Vzon,Vmer,Vver, GaussCenter, PhaseSlope, FitGaussCSPC]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, dataOut.spc_range.copy(), dbSNR[Height], SNRlimit)
880
881 [Vzon,Vmer,Vver, GaussCenter, PhaseSlope, FitGaussCSPC]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit)
881 882 PhaseLine = numpy.append(PhaseLine, PhaseSlope)
882 883
883 884 if abs(Vzon)<100. and abs(Vzon)> 0.:
@@ -908,7 +909,7 class FullSpectralAnalysis(Operation):
908 909
909 910 dataOut.data_output=data_output
910 911
911 return
912 return dataOut
912 913
913 914
914 915 def moving_average(self,x, N=2):
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