import numpy import math from scipy import optimize from scipy import interpolate from scipy import signal from scipy import stats import re import datetime import copy from jroproc_base import ProcessingUnit, Operation from model.data.jrodata import Parameters class ParametersProc(ProcessingUnit): nSeconds = None def __init__(self): ProcessingUnit.__init__(self) self.objectDict = {} self.buffer = None self.firstdatatime = None self.profIndex = 0 self.dataOut = Parameters() def __updateObjFromInput(self): self.dataOut.inputUnit = self.dataIn.type self.dataOut.timeZone = self.dataIn.timeZone self.dataOut.dstFlag = self.dataIn.dstFlag self.dataOut.errorCount = self.dataIn.errorCount self.dataOut.useLocalTime = self.dataIn.useLocalTime self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() self.dataOut.channelList = self.dataIn.channelList self.dataOut.heightList = self.dataIn.heightList self.dataOut.dtype = numpy.dtype([('real',' m): ss1 = m valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 power = ((spec2[valid] - n0)*fwindow[valid]).sum() fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) snr = (spec2.mean()-n0)/n0 if (snr < 1.e-20) : snr = 1.e-20 vec_power[ind] = power vec_fd[ind] = fd vec_w[ind] = w vec_snr[ind] = snr moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) return moments #------------------- Get Lags ---------------------------------- def GetLags(self): ''' Function GetMoments() Input: self.dataOut.data_pre self.dataOut.abscissaRange self.dataOut.noise self.dataOut.normFactor self.dataOut.SNR self.dataOut.pairsList self.dataOut.nChannels Affected: self.dataOut.data_param ''' data = self.dataOut.data_pre normFactor = self.dataOut.normFactor nHeights = self.dataOut.nHeights absc = self.dataOut.abscissaRange[:-1] noise = self.dataOut.noise SNR = self.dataOut.SNR pairsList = self.dataOut.pairsList nChannels = self.dataOut.nChannels pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) self.dataOut.data_param = numpy.zeros((len(pairsCrossCorr)*2 + 1, nHeights)) dataNorm = numpy.abs(data) for l in range(len(pairsList)): dataNorm[l,:,:] = dataNorm[l,:,:]/normFactor[l,:] self.dataOut.data_param[:-1,:] = self.__calculateTaus(dataNorm, pairsCrossCorr, pairsAutoCorr, absc) self.dataOut.data_param[-1,:] = self.__calculateLag1Phase(data, pairsAutoCorr, absc) return def __getPairsAutoCorr(self, pairsList, nChannels): pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan for l in range(len(pairsList)): firstChannel = pairsList[l][0] secondChannel = pairsList[l][1] #Obteniendo pares de Autocorrelacion if firstChannel == secondChannel: pairsAutoCorr[firstChannel] = int(l) pairsAutoCorr = pairsAutoCorr.astype(int) pairsCrossCorr = range(len(pairsList)) pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) return pairsAutoCorr, pairsCrossCorr def __calculateTaus(self, data, pairsCrossCorr, pairsAutoCorr, lagTRange): Pt0 = data.shape[1]/2 #Funcion de Autocorrelacion dataAutoCorr = stats.nanmean(data[pairsAutoCorr,:,:], axis = 0) #Obtencion Indice de TauCross indCross = data[pairsCrossCorr,:,:].argmax(axis = 1) #Obtencion Indice de TauAuto indAuto = numpy.zeros(indCross.shape,dtype = 'int') CCValue = data[pairsCrossCorr,Pt0,:] for i in range(pairsCrossCorr.size): indAuto[i,:] = numpy.abs(dataAutoCorr - CCValue[i,:]).argmin(axis = 0) #Obtencion de TauCross y TauAuto tauCross = lagTRange[indCross] tauAuto = lagTRange[indAuto] Nan1, Nan2 = numpy.where(tauCross == lagTRange[0]) tauCross[Nan1,Nan2] = numpy.nan tauAuto[Nan1,Nan2] = numpy.nan tau = numpy.vstack((tauCross,tauAuto)) return tau def __calculateLag1Phase(self, data, pairs, lagTRange): data1 = stats.nanmean(data[pairs,:,:], axis = 0) lag1 = numpy.where(lagTRange == 0)[0][0] + 1 phase = numpy.angle(data1[lag1,:]) return phase #------------------- Detect Meteors ------------------------------ def DetectMeteors(self, hei_ref = None, tauindex = 0, predefinedPhaseShifts = None, centerReceiverIndex = 2, cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, noise_timeStep = 4, noise_multiple = 4, multDet_timeLimit = 1, multDet_rangeLimit = 3, phaseThresh = 20, SNRThresh = 8, hmin = 70, hmax=110, azimuth = 0) : ''' Function DetectMeteors() Project developed with paper: HOLDSWORTH ET AL. 2004 Input: self.dataOut.data_pre centerReceiverIndex: From the channels, which is the center receiver hei_ref: Height reference for the Beacon signal extraction tauindex: predefinedPhaseShifts: Predefined phase offset for the voltge signals cohDetection: Whether to user Coherent detection or not cohDet_timeStep: Coherent Detection calculation time step cohDet_thresh: Coherent Detection phase threshold to correct phases noise_timeStep: Noise calculation time step noise_multiple: Noise multiple to define signal threshold multDet_timeLimit: Multiple Detection Removal time limit in seconds multDet_rangeLimit: Multiple Detection Removal range limit in km phaseThresh: Maximum phase difference between receiver to be consider a meteor SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor hmin: Minimum Height of the meteor to use it in the further wind estimations hmax: Maximum Height of the meteor to use it in the further wind estimations azimuth: Azimuth angle correction Affected: self.dataOut.data_param Rejection Criteria (Errors): 0: No error; analysis OK 1: SNR < SNR threshold 2: angle of arrival (AOA) ambiguously determined 3: AOA estimate not feasible 4: Large difference in AOAs obtained from different antenna baselines 5: echo at start or end of time series 6: echo less than 5 examples long; too short for analysis 7: echo rise exceeds 0.3s 8: echo decay time less than twice rise time 9: large power level before echo 10: large power level after echo 11: poor fit to amplitude for estimation of decay time 12: poor fit to CCF phase variation for estimation of radial drift velocity 13: height unresolvable echo: not valid height within 70 to 110 km 14: height ambiguous echo: more then one possible height within 70 to 110 km 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s 16: oscilatory echo, indicating event most likely not an underdense echo 17: phase difference in meteor Reestimation Data Storage: Meteors for Wind Estimation (8): Day Hour | Range Height Azimuth Zenith errorCosDir VelRad errorVelRad TypeError ''' #Get Beacon signal newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) if hei_ref != None: newheis = numpy.where(self.dataOut.heightList>hei_ref) heiRang = self.dataOut.getHeiRange() #Pairs List pairslist = [] nChannel = self.dataOut.nChannels for i in range(nChannel): if i != centerReceiverIndex: pairslist.append((centerReceiverIndex,i)) #****************REMOVING HARDWARE PHASE DIFFERENCES*************** # see if the user put in pre defined phase shifts voltsPShift = self.dataOut.data_pre.copy() if predefinedPhaseShifts != None: hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 else: #get hardware phase shifts using beacon signal hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') for i in range(self.dataOut.data_pre.shape[0]): voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* #Remove DC voltsDC = numpy.mean(voltsPShift,1) voltsDC = numpy.mean(voltsDC,1) for i in range(voltsDC.shape[0]): voltsPShift[i] = voltsPShift[i] - voltsDC[i] #Don't considerate last heights, theyre used to calculate Hardware Phase Shift voltsPShift = voltsPShift[:,:,:newheis[0][0]] #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** #Coherent Detection if cohDetection: #use coherent detection to get the net power cohDet_thresh = cohDet_thresh*numpy.pi/180 voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, self.dataOut.timeInterval, pairslist, cohDet_thresh) #Non-coherent detection! powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) #********** END OF COH/NON-COH POWER CALCULATION********************** #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** #Get noise noise, noise1 = self.__getNoise(powerNet, noise_timeStep, self.dataOut.timeInterval) # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) #Get signal threshold signalThresh = noise_multiple*noise #Meteor echoes detection listMeteors = self.__findMeteors(powerNet, signalThresh) #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** #Parameters heiRange = self.dataOut.getHeiRange() rangeInterval = heiRange[1] - heiRange[0] rangeLimit = multDet_rangeLimit/rangeInterval timeLimit = multDet_timeLimit/self.dataOut.timeInterval #Multiple detection removals listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) #************ END OF REMOVE MULTIPLE DETECTIONS ********************** #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** #Parameters phaseThresh = phaseThresh*numpy.pi/180 thresh = [phaseThresh, noise_multiple, SNRThresh] #Meteor reestimation (Errors N 1, 6, 12, 17) listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist, thresh, noise, self.dataOut.timeInterval, self.dataOut.frequency) # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) #Estimation of decay times (Errors N 7, 8, 11) listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, self.dataOut.timeInterval, self.dataOut.frequency) #******************* END OF METEOR REESTIMATION ******************* #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** #Calculating Radial Velocity (Error N 15) radialStdThresh = 10 listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist, self.dataOut.timeInterval) if len(listMeteors4) > 0: #Setting New Array date = repr(self.dataOut.datatime) arrayMeteors4, arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) #Calculate AOA (Error N 3, 4) #JONES ET AL. 1998 AOAthresh = numpy.pi/8 error = arrayParameters[:,-1] phases = -arrayMeteors4[:,9:13] pairsList = [] pairsList.append((0,3)) pairsList.append((1,2)) arrayParameters[:,4:7], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, error, AOAthresh, azimuth) #Calculate Heights (Error N 13 and 14) error = arrayParameters[:,-1] Ranges = arrayParameters[:,2] zenith = arrayParameters[:,5] arrayParameters[:,3], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) #********************* END OF PARAMETERS CALCULATION ************************** #***************************+ SAVE DATA IN HDF5 FORMAT ********************** self.dataOut.data_param = arrayParameters return def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): minIndex = min(newheis[0]) maxIndex = max(newheis[0]) voltage = voltage0[:,:,minIndex:maxIndex+1] nLength = voltage.shape[1]/n nMin = 0 nMax = 0 phaseOffset = numpy.zeros((len(pairslist),n)) for i in range(n): nMax += nLength phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) phaseCCF = numpy.mean(phaseCCF, axis = 2) phaseOffset[:,i] = phaseCCF.transpose() nMin = nMax # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) #Remove Outliers factor = 2 wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) dw = numpy.std(wt,axis = 1) dw = dw.reshape((dw.size,1)) ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) phaseOffset[ind] = numpy.nan phaseOffset = stats.nanmean(phaseOffset, axis=1) return phaseOffset def __shiftPhase(self, data, phaseShift): #this will shift the phase of a complex number dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) return dataShifted def __estimatePhaseDifference(self, array, pairslist): nChannel = array.shape[0] nHeights = array.shape[2] numPairs = len(pairslist) # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) #Correct phases derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) if indDer[0].shape[0] > 0: for i in range(indDer[0].shape[0]): signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi # for j in range(numSides): # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) # #Linear phaseInt = numpy.zeros((numPairs,1)) angAllCCF = phaseCCF[:,[0,1,3,4],0] for j in range(numPairs): fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) phaseInt[j] = fit[1] #Phase Differences phaseDiff = phaseInt - phaseCCF[:,2,:] phaseArrival = phaseInt.reshape(phaseInt.size) #Dealias indAlias = numpy.where(phaseArrival > numpy.pi) phaseArrival[indAlias] -= 2*numpy.pi indAlias = numpy.where(phaseArrival < -numpy.pi) phaseArrival[indAlias] += 2*numpy.pi return phaseDiff, phaseArrival def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power #find the phase shifts of each channel over 1 second intervals #only look at ranges below the beacon signal numProfPerBlock = numpy.ceil(timeSegment/timeInterval) numBlocks = int(volts.shape[1]/numProfPerBlock) numHeights = volts.shape[2] nChannel = volts.shape[0] voltsCohDet = volts.copy() pairsarray = numpy.array(pairslist) indSides = pairsarray[:,1] # indSides = numpy.array(range(nChannel)) # indSides = numpy.delete(indSides, indCenter) # # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) listBlocks = numpy.array_split(volts, numBlocks, 1) startInd = 0 endInd = 0 for i in range(numBlocks): startInd = endInd endInd = endInd + listBlocks[i].shape[1] arrayBlock = listBlocks[i] # arrayBlockCenter = listCenter[i] #Estimate the Phase Difference phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) #Phase Difference RMS arrayPhaseRMS = numpy.abs(phaseDiff) phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) indPhase = numpy.where(phaseRMSaux==4) #Shifting if indPhase[0].shape[0] > 0: for j in range(indSides.size): arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) voltsCohDet[:,startInd:endInd,:] = arrayBlock return voltsCohDet def __calculateCCF(self, volts, pairslist ,laglist): nHeights = volts.shape[2] nPoints = volts.shape[1] voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') for i in range(len(pairslist)): volts1 = volts[pairslist[i][0]] volts2 = volts[pairslist[i][1]] for t in range(len(laglist)): idxT = laglist[t] if idxT >= 0: vStacked = numpy.vstack((volts2[idxT:,:], numpy.zeros((idxT, nHeights),dtype='complex'))) else: vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), volts2[:(nPoints + idxT),:])) voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) vStacked = None return voltsCCF def __getNoise(self, power, timeSegment, timeInterval): numProfPerBlock = numpy.ceil(timeSegment/timeInterval) numBlocks = int(power.shape[0]/numProfPerBlock) numHeights = power.shape[1] listPower = numpy.array_split(power, numBlocks, 0) noise = numpy.zeros((power.shape[0], power.shape[1])) noise1 = numpy.zeros((power.shape[0], power.shape[1])) startInd = 0 endInd = 0 for i in range(numBlocks): #split por canal startInd = endInd endInd = endInd + listPower[i].shape[0] arrayBlock = listPower[i] noiseAux = numpy.mean(arrayBlock, 0) # noiseAux = numpy.median(noiseAux) # noiseAux = numpy.mean(arrayBlock) noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux noiseAux1 = numpy.mean(arrayBlock) noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 return noise, noise1 def __findMeteors(self, power, thresh): nProf = power.shape[0] nHeights = power.shape[1] listMeteors = [] for i in range(nHeights): powerAux = power[:,i] threshAux = thresh[:,i] indUPthresh = numpy.where(powerAux > threshAux)[0] indDNthresh = numpy.where(powerAux <= threshAux)[0] j = 0 while (j < indUPthresh.size - 2): if (indUPthresh[j + 2] == indUPthresh[j] + 2): indDNAux = numpy.where(indDNthresh > indUPthresh[j]) indDNthresh = indDNthresh[indDNAux] if (indDNthresh.size > 0): indEnd = indDNthresh[0] - 1 indInit = indUPthresh[j] meteor = powerAux[indInit:indEnd + 1] indPeak = meteor.argmax() + indInit FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! j = numpy.where(indUPthresh == indEnd)[0] + 1 else: j+=1 else: j+=1 return listMeteors def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): arrayMeteors = numpy.asarray(listMeteors) listMeteors1 = [] while arrayMeteors.shape[0] > 0: FLAs = arrayMeteors[:,4] maxFLA = FLAs.argmax() listMeteors1.append(arrayMeteors[maxFLA,:]) MeteorInitTime = arrayMeteors[maxFLA,1] MeteorEndTime = arrayMeteors[maxFLA,3] MeteorHeight = arrayMeteors[maxFLA,0] #Check neighborhood maxHeightIndex = MeteorHeight + rangeLimit minHeightIndex = MeteorHeight - rangeLimit minTimeIndex = MeteorInitTime - timeLimit maxTimeIndex = MeteorEndTime + timeLimit #Check Heights indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) return listMeteors1 def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): numHeights = volts.shape[2] nChannel = volts.shape[0] thresholdPhase = thresh[0] thresholdNoise = thresh[1] thresholdDB = float(thresh[2]) thresholdDB1 = 10**(thresholdDB/10) pairsarray = numpy.array(pairslist) indSides = pairsarray[:,1] pairslist1 = list(pairslist) pairslist1.append((0,1)) pairslist1.append((3,4)) listMeteors1 = [] listPowerSeries = [] listVoltageSeries = [] #volts has the war data if frequency == 30e6: timeLag = 45*10**-3 else: timeLag = 15*10**-3 lag = numpy.ceil(timeLag/timeInterval) for i in range(len(listMeteors)): ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### meteorAux = numpy.zeros(16) #Loading meteor Data (mHeight, mStart, mPeak, mEnd) mHeight = listMeteors[i][0] mStart = listMeteors[i][1] mPeak = listMeteors[i][2] mEnd = listMeteors[i][3] #get the volt data between the start and end times of the meteor meteorVolts = volts[:,mStart:mEnd+1,mHeight] meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) #3.6. Phase Difference estimation phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) #3.7. Phase difference removal & meteor start, peak and end times reestimated #meteorVolts0.- all Channels, all Profiles meteorVolts0 = volts[:,:,mHeight] meteorThresh = noise[:,mHeight]*thresholdNoise meteorNoise = noise[:,mHeight] meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power #Times reestimation mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] if mStart1.size > 0: mStart1 = mStart1[-1] + 1 else: mStart1 = mPeak mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] if mEndDecayTime1.size == 0: mEndDecayTime1 = powerNet0.size else: mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() #meteorVolts1.- all Channels, from start to end meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] if meteorVolts2.shape[1] == 0: meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) ##################### END PARAMETERS REESTIMATION ######################### ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis if meteorVolts2.shape[1] > 0: #Phase Difference re-estimation phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting #Phase Difference RMS phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) #Data from Meteor mPeak1 = powerNet1.argmax() + mStart1 mPeakPower1 = powerNet1.max() noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] #Vectorize meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] meteorAux[7:11] = phaseDiffint[0:4] #Rejection Criterions if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation meteorAux[-1] = 17 elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB meteorAux[-1] = 1 else: meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis PowerSeries = 0 listMeteors1.append(meteorAux) listPowerSeries.append(PowerSeries) listVoltageSeries.append(meteorVolts1) return listMeteors1, listPowerSeries, listVoltageSeries def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): threshError = 10 #Depending if it is 30 or 50 MHz if frequency == 30e6: timeLag = 45*10**-3 else: timeLag = 15*10**-3 lag = numpy.ceil(timeLag/timeInterval) listMeteors1 = [] for i in range(len(listMeteors)): meteorPower = listPower[i] meteorAux = listMeteors[i] if meteorAux[-1] == 0: try: indmax = meteorPower.argmax() indlag = indmax + lag y = meteorPower[indlag:] x = numpy.arange(0, y.size)*timeLag #first guess a = y[0] tau = timeLag #exponential fit popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) y1 = self.__exponential_function(x, *popt) #error estimation error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) decayTime = popt[1] riseTime = indmax*timeInterval meteorAux[11:13] = [decayTime, error] #Table items 7, 8 and 11 if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s meteorAux[-1] = 7 elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time meteorAux[-1] = 8 if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time meteorAux[-1] = 11 except: meteorAux[-1] = 11 listMeteors1.append(meteorAux) return listMeteors1 #Exponential Function def __exponential_function(self, x, a, tau): y = a*numpy.exp(-x/tau) return y def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): pairslist1 = list(pairslist) pairslist1.append((0,1)) pairslist1.append((3,4)) numPairs = len(pairslist1) #Time Lag timeLag = 45*10**-3 c = 3e8 lag = numpy.ceil(timeLag/timeInterval) freq = 30e6 listMeteors1 = [] for i in range(len(listMeteors)): meteor = listMeteors[i] meteorAux = numpy.hstack((meteor[:-1], 0, 0, meteor[-1])) if meteor[-1] == 0: mStart = listMeteors[i][1] mPeak = listMeteors[i][2] mLag = mPeak - mStart + lag #get the volt data between the start and end times of the meteor meteorVolts = listVolts[i] meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) #Get CCF allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) #Method 2 slopes = numpy.zeros(numPairs) time = numpy.array([-2,-1,1,2])*timeInterval angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) #Correct phases derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) if indDer[0].shape[0] > 0: for i in range(indDer[0].shape[0]): signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) for j in range(numPairs): fit = stats.linregress(time, angAllCCF[j,:]) slopes[j] = fit[0] #Remove Outlier # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) # slopes = numpy.delete(slopes,indOut) # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) # slopes = numpy.delete(slopes,indOut) radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) meteorAux[-2] = radialError meteorAux[-3] = radialVelocity #Setting Error #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s if numpy.abs(radialVelocity) > 200: meteorAux[-1] = 15 #Number 12: Poor fit to CCF variation for estimation of radial drift velocity elif radialError > radialStdThresh: meteorAux[-1] = 12 listMeteors1.append(meteorAux) return listMeteors1 def __setNewArrays(self, listMeteors, date, heiRang): #New arrays arrayMeteors = numpy.array(listMeteors) arrayParameters = numpy.zeros((len(listMeteors),10)) #Date inclusion date = re.findall(r'\((.*?)\)', date) date = date[0].split(',') date = map(int, date) date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] arrayDate = numpy.tile(date, (len(listMeteors), 1)) #Meteor array arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) #Parameters Array arrayParameters[:,0:3] = arrayMeteors[:,0:3] arrayParameters[:,-3:] = arrayMeteors[:,-3:] return arrayMeteors, arrayParameters def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): arrayAOA = numpy.zeros((phases.shape[0],3)) cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) arrayAOA[:,2] = cosDirError azimuthAngle = arrayAOA[:,0] zenithAngle = arrayAOA[:,1] #Setting Error #Number 3: AOA not fesible indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] error[indInvalid] = 3 #Number 4: Large difference in AOAs obtained from different antenna baselines indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] error[indInvalid] = 4 return arrayAOA, error def __getDirectionCosines(self, arrayPhase, pairsList): #Initializing some variables ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi ang_aux = ang_aux.reshape(1,ang_aux.size) cosdir = numpy.zeros((arrayPhase.shape[0],2)) cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) for i in range(2): #First Estimation phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] #Dealias indcsi = numpy.where(phi0_aux > numpy.pi) phi0_aux[indcsi] -= 2*numpy.pi indcsi = numpy.where(phi0_aux < -numpy.pi) phi0_aux[indcsi] += 2*numpy.pi #Direction Cosine 0 cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) #Most-Accurate Second Estimation phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] phi1_aux = phi1_aux.reshape(phi1_aux.size,1) #Direction Cosine 1 cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) #Searching the correct Direction Cosine cosdir0_aux = cosdir0[:,i] cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) #Minimum Distance cosDiff = (cosdir1 - cosdir0_aux)**2 indcos = cosDiff.argmin(axis = 1) #Saving Value obtained cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] return cosdir0, cosdir def __calculateAOA(self, cosdir, azimuth): cosdirX = cosdir[:,0] cosdirY = cosdir[:,1] zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() return angles def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): Ramb = 375 #Ramb = c/(2*PRF) Re = 6371 #Earth Radius heights = numpy.zeros(Ranges.shape) R_aux = numpy.array([0,1,2])*Ramb R_aux = R_aux.reshape(1,R_aux.size) Ranges = Ranges.reshape(Ranges.size,1) Ri = Ranges + R_aux hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re #Check if there is a height between 70 and 110 km h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) ind_h = numpy.where(h_bool == 1)[0] hCorr = hi[ind_h, :] ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) hCorr = hi[ind_hCorr] heights[ind_h] = hCorr #Setting Error #Number 13: Height unresolvable echo: not valid height within 70 to 110 km #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] error[indInvalid2] = 14 indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] error[indInvalid1] = 13 return heights, error class WindProfiler(Operation): __isConfig = False __initime = None __lastdatatime = None __integrationtime = None __buffer = None __dataReady = False __firstdata = None n = None def __init__(self): Operation.__init__(self) def __calculateCosDir(self, elev, azim): zen = (90 - elev)*numpy.pi/180 azim = azim*numpy.pi/180 cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) signX = numpy.sign(numpy.cos(azim)) signY = numpy.sign(numpy.sin(azim)) cosDirX = numpy.copysign(cosDirX, signX) cosDirY = numpy.copysign(cosDirY, signY) return cosDirX, cosDirY def __calculateAngles(self, theta_x, theta_y, azimuth): dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) zenith_arr = numpy.arccos(dir_cosw) azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): # if horOnly: A = numpy.c_[dir_cosu,dir_cosv] else: A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] A = numpy.asmatrix(A) A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() return A1 def __correctValues(self, heiRang, phi, velRadial, SNR): listPhi = phi.tolist() maxid = listPhi.index(max(listPhi)) minid = listPhi.index(min(listPhi)) rango = range(len(phi)) # rango = numpy.delete(rango,maxid) heiRang1 = heiRang*math.cos(phi[maxid]) heiRangAux = heiRang*math.cos(phi[minid]) indOut = (heiRang1 < heiRangAux[0]).nonzero() heiRang1 = numpy.delete(heiRang1,indOut) velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) SNR1 = numpy.zeros([len(phi),len(heiRang1)]) for i in rango: x = heiRang*math.cos(phi[i]) y1 = velRadial[i,:] f1 = interpolate.interp1d(x,y1,kind = 'cubic') x1 = heiRang1 y11 = f1(x1) y2 = SNR[i,:] f2 = interpolate.interp1d(x,y2,kind = 'cubic') y21 = f2(x1) velRadial1[i,:] = y11 SNR1[i,:] = y21 return heiRang1, velRadial1, SNR1 def __calculateVelUVW(self, A, velRadial): #Operacion Matricial # velUVW = numpy.zeros((velRadial.shape[1],3)) # for ind in range(velRadial.shape[1]): # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) # velUVW = velUVW.transpose() velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) velUVW[:,:] = numpy.dot(A,velRadial) return velUVW def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): """ Function that implements Doppler Beam Swinging (DBS) technique. Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, Direction correction (if necessary), Ranges and SNR Output: Winds estimation (Zonal, Meridional and Vertical) Parameters affected: Winds, height range, SNR """ azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(dirCosx, disrCosy, azimuth) heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correct*velRadial0, SNR0) A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) #Calculo de Componentes de la velocidad con DBS winds = self.__calculateVelUVW(A,velRadial1) return winds, heiRang1, SNR1 def __calculateDistance(self, posx, posy, pairsCrossCorr, pairsList, pairs, azimuth = None): posx = numpy.asarray(posx) posy = numpy.asarray(posy) #Rotacion Inversa para alinear con el azimuth if azimuth!= None: azimuth = azimuth*math.pi/180 posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) else: posx1 = posx posy1 = posy #Calculo de Distancias distx = numpy.zeros(pairsCrossCorr.size) disty = numpy.zeros(pairsCrossCorr.size) dist = numpy.zeros(pairsCrossCorr.size) ang = numpy.zeros(pairsCrossCorr.size) for i in range(pairsCrossCorr.size): distx[i] = posx1[pairsList[pairsCrossCorr[i]][1]] - posx1[pairsList[pairsCrossCorr[i]][0]] disty[i] = posy1[pairsList[pairsCrossCorr[i]][1]] - posy1[pairsList[pairsCrossCorr[i]][0]] dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) ang[i] = numpy.arctan2(disty[i],distx[i]) #Calculo de Matrices nPairs = len(pairs) ang1 = numpy.zeros((nPairs, 2, 1)) dist1 = numpy.zeros((nPairs, 2, 1)) for j in range(nPairs): dist1[j,0,0] = dist[pairs[j][0]] dist1[j,1,0] = dist[pairs[j][1]] ang1[j,0,0] = ang[pairs[j][0]] ang1[j,1,0] = ang[pairs[j][1]] return distx,disty, dist1,ang1 def __calculateVelVer(self, phase, lagTRange, _lambda): Ts = lagTRange[1] - lagTRange[0] velW = -_lambda*phase/(4*math.pi*Ts) return velW def __calculateVelHorDir(self, dist, tau1, tau2, ang): nPairs = tau1.shape[0] vel = numpy.zeros((nPairs,3,tau1.shape[2])) angCos = numpy.cos(ang) angSin = numpy.sin(ang) vel0 = dist*tau1/(2*tau2**2) vel[:,0,:] = (vel0*angCos).sum(axis = 1) vel[:,1,:] = (vel0*angSin).sum(axis = 1) ind = numpy.where(numpy.isinf(vel)) vel[ind] = numpy.nan return vel def __getPairsAutoCorr(self, pairsList, nChannels): pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan for l in range(len(pairsList)): firstChannel = pairsList[l][0] secondChannel = pairsList[l][1] #Obteniendo pares de Autocorrelacion if firstChannel == secondChannel: pairsAutoCorr[firstChannel] = int(l) pairsAutoCorr = pairsAutoCorr.astype(int) pairsCrossCorr = range(len(pairsList)) pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) return pairsAutoCorr, pairsCrossCorr def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): """ Function that implements Spaced Antenna (SA) technique. Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, Direction correction (if necessary), Ranges and SNR Output: Winds estimation (Zonal, Meridional and Vertical) Parameters affected: Winds """ #Cross Correlation pairs obtained pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) pairsArray = numpy.array(pairsList)[pairsCrossCorr] pairsSelArray = numpy.array(pairsSelected) pairs = [] #Wind estimation pairs obtained for i in range(pairsSelArray.shape[0]/2): ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] pairs.append((ind1,ind2)) indtau = tau.shape[0]/2 tau1 = tau[:indtau,:] tau2 = tau[indtau:-1,:] tau1 = tau1[pairs,:] tau2 = tau2[pairs,:] phase1 = tau[-1,:] #--------------------------------------------------------------------- #Metodo Directo distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairsCrossCorr, pairsList, pairs,azimuth) winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) winds = stats.nanmean(winds, axis=0) #--------------------------------------------------------------------- #Metodo General # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) # #Calculo Coeficientes de Funcion de Correlacion # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) # #Calculo de Velocidades # winds = self.calculateVelUV(F,G,A,B,H) #--------------------------------------------------------------------- winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) winds = correctFactor*winds return winds def __checkTime(self, currentTime, paramInterval, windsInterval): dataTime = currentTime + paramInterval deltaTime = dataTime - self.__initime if deltaTime >= windsInterval or deltaTime < 0: self.__dataReady = True return def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): ''' Function that implements winds estimation technique with detected meteors. Input: Detected meteors, Minimum meteor quantity to wind estimation Output: Winds estimation (Zonal and Meridional) Parameters affected: Winds ''' #Settings nInt = (heightMax - heightMin)/2 winds = numpy.zeros((2,nInt))*numpy.nan #Filter errors error = numpy.where(arrayMeteor[:,-1] == 0)[0] finalMeteor = arrayMeteor[error,:] #Meteor Histogram finalHeights = finalMeteor[:,3] hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) nMeteorsPerI = hist[0] heightPerI = hist[1] #Sort of meteors indSort = finalHeights.argsort() finalMeteor2 = finalMeteor[indSort,:] # Calculating winds ind1 = 0 ind2 = 0 for i in range(nInt): nMet = nMeteorsPerI[i] ind1 = ind2 ind2 = ind1 + nMet meteorAux = finalMeteor2[ind1:ind2,:] if meteorAux.shape[0] >= meteorThresh: vel = meteorAux[:, 7] zen = meteorAux[:, 5]*numpy.pi/180 azim = meteorAux[:, 4]*numpy.pi/180 n = numpy.cos(zen) # m = (1 - n**2)/(1 - numpy.tan(azim)**2) # l = m*numpy.tan(azim) l = numpy.sin(zen)*numpy.sin(azim) m = numpy.sin(zen)*numpy.cos(azim) A = numpy.vstack((l, m)).transpose() A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) windsAux = numpy.dot(A1, vel) winds[0,i] = windsAux[0] winds[1,i] = windsAux[1] return winds, heightPerI[:-1] def run(self, dataOut, technique, **kwargs): param = dataOut.data_param if dataOut.abscissaRange != None: absc = dataOut.abscissaRange[:-1] noise = dataOut.noise heightRange = dataOut.getHeiRange() SNR = dataOut.SNR if technique == 'DBS': if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): theta_x = numpy.array(kwargs['dirCosx']) theta_y = numpy.array(kwargs['dirCosy']) else: elev = numpy.array(kwargs['elevation']) azim = numpy.array(kwargs['azimuth']) theta_x, theta_y = self.__calculateCosDir(elev, azim) azimuth = kwargs['correctAzimuth'] if kwargs.has_key('horizontalOnly'): horizontalOnly = kwargs['horizontalOnly'] else: horizontalOnly = False if kwargs.has_key('correctFactor'): correctFactor = kwargs['correctFactor'] else: correctFactor = 1 if kwargs.has_key('channelList'): channelList = kwargs['channelList'] if len(channelList) == 2: horizontalOnly = True arrayChannel = numpy.array(channelList) param = param[arrayChannel,:,:] theta_x = theta_x[arrayChannel] theta_y = theta_y[arrayChannel] velRadial0 = param[:,1,:] #Radial velocity dataOut.winds, dataOut.heightRange, dataOut.SNR = self.techniqueDBS(velRadial0, theta_x, theta_y, azimuth, correctFactor, horizontalOnly, heightRange, SNR) #DBS Function dataOut.initUtcTime = dataOut.ltctime dataOut.windsInterval = dataOut.timeInterval elif technique == 'SA': #Parameters position_x = kwargs['positionX'] position_y = kwargs['positionY'] azimuth = kwargs['azimuth'] if kwargs.has_key('crosspairsList'): pairs = kwargs['crosspairsList'] else: pairs = None if kwargs.has_key('correctFactor'): correctFactor = kwargs['correctFactor'] else: correctFactor = 1 tau = dataOut.data_param _lambda = dataOut.C/dataOut.frequency pairsList = dataOut.pairsList nChannels = dataOut.nChannels dataOut.winds = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) dataOut.initUtcTime = dataOut.ltctime dataOut.windsInterval = dataOut.timeInterval elif technique == 'Meteors': dataOut.flagNoData = True self.__dataReady = False if kwargs.has_key('nHours'): nHours = kwargs['nHours'] else: nHours = 1 if kwargs.has_key('meteorsPerBin'): meteorThresh = kwargs['meteorsPerBin'] else: meteorThresh = 6 if kwargs.has_key('hmin'): hmin = kwargs['hmin'] else: hmin = 70 if kwargs.has_key('hmax'): hmax = kwargs['hmax'] else: hmax = 110 dataOut.windsInterval = nHours*3600 if self.__isConfig == False: # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) #Get Initial LTC time self.__initime = (dataOut.datatime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() self.__isConfig = True if self.__buffer == None: self.__buffer = dataOut.data_param self.__firstdata = copy.copy(dataOut) else: self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) self.__checkTime(dataOut.ltctime, dataOut.paramInterval, dataOut.windsInterval) #Check if the buffer is ready if self.__dataReady: dataOut.initUtcTime = self.__initime self.__initime = self.__initime + dataOut.windsInterval #to erase time offset dataOut.winds, dataOut.heightRange = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) dataOut.flagNoData = False self.__buffer = None return