jroproc_parameters.py
3108 lines
| 121.0 KiB
| text/x-python
|
PythonLexer
ebocanegra
|
r965 | import numpy | |
| import math | |||
| from scipy import optimize, interpolate, signal, stats, ndimage | |||
| import re | |||
| import datetime | |||
| import copy | |||
| import sys | |||
| import importlib | |||
| import itertools | |||
| from jroproc_base import ProcessingUnit, Operation | |||
| from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon | |||
| from scipy import asarray as ar,exp | |||
| from scipy.optimize import curve_fit | |||
| SPEED_OF_LIGHT = 299792458 | |||
| 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','<f4'),('imag','<f4')]) | |||
| # self.dataOut.nHeights = self.dataIn.nHeights | |||
| # self.dataOut.nChannels = self.dataIn.nChannels | |||
| self.dataOut.nBaud = self.dataIn.nBaud | |||
| self.dataOut.nCode = self.dataIn.nCode | |||
| self.dataOut.code = self.dataIn.code | |||
| # self.dataOut.nProfiles = self.dataOut.nFFTPoints | |||
| self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |||
| # self.dataOut.utctime = self.firstdatatime | |||
| self.dataOut.utctime = self.dataIn.utctime | |||
| self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada | |||
| self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip | |||
| self.dataOut.nCohInt = self.dataIn.nCohInt | |||
| # self.dataOut.nIncohInt = 1 | |||
| self.dataOut.ippSeconds = self.dataIn.ippSeconds | |||
| # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |||
| self.dataOut.timeInterval = self.dataIn.timeInterval | |||
| self.dataOut.heightList = self.dataIn.getHeiRange() | |||
| self.dataOut.frequency = self.dataIn.frequency | |||
| self.dataOut.noise = self.dataIn.noise | |||
| def run(self): | |||
| #---------------------- Voltage Data --------------------------- | |||
| if self.dataIn.type == "Voltage": | |||
| self.__updateObjFromInput() | |||
| self.dataOut.data_pre = self.dataIn.data.copy() | |||
| self.dataOut.flagNoData = False | |||
| self.dataOut.utctimeInit = self.dataIn.utctime | |||
| self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds | |||
| return | |||
| #---------------------- Spectra Data --------------------------- | |||
| if self.dataIn.type == "Spectra": | |||
| self.dataOut.data_pre = (self.dataIn.data_spc,self.dataIn.data_cspc) | |||
| self.dataOut.abscissaList = self.dataIn.getVelRange(1) | |||
| self.dataOut.noise = self.dataIn.getNoise() | |||
| self.dataOut.normFactor = self.dataIn.normFactor | |||
| self.dataOut.outputInterval = self.dataIn.outputInterval | |||
| self.dataOut.groupList = self.dataIn.pairsList | |||
| self.dataOut.flagNoData = False | |||
| if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels | |||
| self.dataOut.ChanDist = self.dataIn.ChanDist | |||
| if hasattr(self.dataIn, 'VelRange'): #Velocities range | |||
| self.dataOut.VelRange = self.dataIn.VelRange | |||
| if hasattr(self.dataIn, 'RadarConst'): #Radar Constant | |||
| self.dataOut.RadarConst = self.dataIn.RadarConst | |||
| if hasattr(self.dataIn, 'NPW'): #NPW | |||
| self.dataOut.NPW = self.dataIn.NPW | |||
| if hasattr(self.dataIn, 'COFA'): #COFA | |||
| self.dataOut.COFA = self.dataIn.COFA | |||
| #---------------------- Correlation Data --------------------------- | |||
| if self.dataIn.type == "Correlation": | |||
| acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() | |||
| self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) | |||
| self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) | |||
| self.dataOut.groupList = (acf_pairs, ccf_pairs) | |||
| self.dataOut.abscissaList = self.dataIn.lagRange | |||
| self.dataOut.noise = self.dataIn.noise | |||
| self.dataOut.data_SNR = self.dataIn.SNR | |||
| self.dataOut.flagNoData = False | |||
| self.dataOut.nAvg = self.dataIn.nAvg | |||
| #---------------------- Parameters Data --------------------------- | |||
| if self.dataIn.type == "Parameters": | |||
| self.dataOut.copy(self.dataIn) | |||
| self.dataOut.flagNoData = False | |||
| return True | |||
| self.__updateObjFromInput() | |||
| self.dataOut.utctimeInit = self.dataIn.utctime | |||
| self.dataOut.paramInterval = self.dataIn.timeInterval | |||
| return | |||
| class PrecipitationProc(Operation): | |||
| ''' | |||
| Funtion that uses Reflectivity factor (Z), and estimates rainfall Rate | |||
| Input: | |||
| self.dataOut.data_pre : SelfSpectra | |||
| Output: | |||
| self.dataOut.data_output : Reflectivity factor, rainfall Rate | |||
| Parameters affected: Winds, height range, SNR | |||
| ''' | |||
| def run(self, dataOut): | |||
| #numpy.set_printoptions(threshold=numpy.NaN) | |||
| spc = dataOut.data_pre[0].copy() | |||
| NPW = dataOut.NPW | |||
| COFA = dataOut.COFA | |||
| #print 'COFA',COFA.shape | |||
| #spc = numpy.where(spc<0,spc, numpy.NaN ) | |||
| SNR = numpy.array([spc[0,:,:] / NPW[0] , spc[1,:,:] / NPW[1]]) | |||
| #print 'SNR',SNR.shape | |||
| #print ' ' | |||
| RadarConst = dataOut.RadarConst | |||
| #frequency = 34.85*10**9 | |||
| #Lambda = SPEED_OF_LIGHT/frequency | |||
| Num_Hei = spc.shape[2] | |||
| Num_Bin = spc.shape[1] | |||
| Num_Chn = spc.shape[0] | |||
| ETA = numpy.zeros(([Num_Chn ,Num_Hei])) | |||
| data_output = numpy.ones([Num_Chn ,Num_Hei])*numpy.NaN | |||
| Km = 0.93 | |||
| ETA = numpy.sum(SNR,1) | |||
| ETA = numpy.where(ETA > 0. , ETA, numpy.NaN) | |||
| Ze = numpy.ones([Num_Chn,Num_Hei] ) | |||
| for r in range(Num_Hei): | |||
| Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) | |||
| Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) | |||
| dBZe = 10*numpy.log10(Ze) | |||
| dataOut.data_output = Ze | |||
| dataOut.data_param = dBZe | |||
| print 'dBZe',dBZe[0,:] | |||
| class FullSpectralAnalysis(Operation): | |||
| """ | |||
| Function that implements Full Spectral Analisys technique. | |||
| Input: | |||
| self.dataOut.data_pre : SelfSpectra and CrossSPectra data | |||
| self.dataOut.groupList : Pairlist of channels | |||
| self.dataOut.ChanDist : Physical distance between receivers | |||
| Output: | |||
| self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind | |||
| Parameters affected: Winds, height range, SNR | |||
| """ | |||
| def run(self, dataOut): | |||
| spc = dataOut.data_pre[0].copy() | |||
| cspc = dataOut.data_pre[1].copy() | |||
| nChannel = spc.shape[0] | |||
| nProfiles = spc.shape[1] | |||
| nHeights = spc.shape[2] | |||
| pairsList = dataOut.groupList | |||
| ChanDist = dataOut.ChanDist | |||
| VelRange= dataOut.VelRange | |||
| ySamples=numpy.ones([nChannel,nProfiles]) | |||
| phase=numpy.ones([nChannel,nProfiles]) | |||
| CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_) | |||
| coherence=numpy.ones([nChannel,nProfiles]) | |||
| PhaseSlope=numpy.ones(nChannel) | |||
| PhaseInter=numpy.ones(nChannel) | |||
| data = dataOut.data_pre | |||
| noise = dataOut.noise | |||
| SNRdB = 10*numpy.log10(dataOut.data_SNR) | |||
| FirstMoment = [] | |||
| SNRdBMean = [] | |||
| for j in range(nHeights): | |||
| FirstMoment = numpy.append(FirstMoment,numpy.mean([dataOut.data_param[0,1,j],dataOut.data_param[1,1,j],dataOut.data_param[2,1,j]])) | |||
| SNRdBMean = numpy.append(SNRdBMean,numpy.mean([SNRdB[0,j],SNRdB[1,j],SNRdB[2,j]])) | |||
| data_output=numpy.ones([3,spc.shape[2]])*numpy.NaN | |||
| velocityX=[] | |||
| velocityY=[] | |||
| velocityV=[] | |||
| for Height in range(nHeights): | |||
| [Vzon,Vmer,Vver, GaussCenter]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, VelRange) | |||
| if abs(Vzon)<100 and abs(Vzon)> 0.: | |||
| velocityX=numpy.append(velocityX, Vzon)#Vmag | |||
| else: | |||
| velocityX=numpy.append(velocityX, numpy.NaN) | |||
| if abs(Vmer)<100 and abs(Vmer) > 0.: | |||
| velocityY=numpy.append(velocityY, Vmer)#Vang | |||
| else: | |||
| velocityY=numpy.append(velocityY, numpy.NaN) | |||
| if abs(GaussCenter)<10: | |||
| velocityV=numpy.append(velocityV, Vver) | |||
| else: | |||
| velocityV=numpy.append(velocityV, numpy.NaN) | |||
| #FirstMoment[Height]= numpy.NaN | |||
| if SNRdBMean[Height] <12: | |||
| FirstMoment[Height] = numpy.NaN | |||
| velocityX[Height] = numpy.NaN | |||
| velocityY[Height] = numpy.NaN | |||
| data_output[0]=numpy.array(velocityX) | |||
| data_output[1]=numpy.array(velocityY) | |||
| data_output[2]=-FirstMoment | |||
| print ' ' | |||
| #print 'FirstMoment' | |||
| #print FirstMoment | |||
| print ' ' | |||
| #print 'velocityY' | |||
| #print numpy.array(velocityY) | |||
| print ' ' | |||
| #print 'SNR' | |||
| #print 10*numpy.log10(dataOut.data_SNR) | |||
| #print numpy.shape(10*numpy.log10(dataOut.data_SNR)) | |||
| print ' ' | |||
| dataOut.data_output=data_output | |||
| return | |||
| def moving_average(self,x, N=2): | |||
| return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] | |||
| def gaus(self,xSamples,a,x0,sigma): | |||
| return a*exp(-(xSamples-x0)**2/(2*sigma**2)) | |||
| def Find(self,x,value): | |||
| for index in range(len(x)): | |||
| if x[index]==value: | |||
| return index | |||
| def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, VelRange): | |||
| ySamples=numpy.ones([spc.shape[0],spc.shape[1]]) | |||
| phase=numpy.ones([spc.shape[0],spc.shape[1]]) | |||
| CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) | |||
| coherence=numpy.ones([spc.shape[0],spc.shape[1]]) | |||
| PhaseSlope=numpy.ones(spc.shape[0]) | |||
| PhaseInter=numpy.ones(spc.shape[0]) | |||
| xFrec=VelRange | |||
| '''Getting Eij and Nij''' | |||
| E01=ChanDist[0][0] | |||
| N01=ChanDist[0][1] | |||
| E02=ChanDist[1][0] | |||
| N02=ChanDist[1][1] | |||
| E12=ChanDist[2][0] | |||
| N12=ChanDist[2][1] | |||
| z = spc.copy() | |||
| z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |||
| for i in range(spc.shape[0]): | |||
| '''****** Line of Data SPC ******''' | |||
| zline=z[i,:,Height] | |||
| '''****** SPC is normalized ******''' | |||
| FactNorm= zline.copy() / numpy.sum(zline.copy()) | |||
| FactNorm= FactNorm/numpy.sum(FactNorm) | |||
| SmoothSPC=self.moving_average(FactNorm,N=3) | |||
| xSamples = ar(range(len(SmoothSPC))) | |||
| ySamples[i] = SmoothSPC-noise[i] | |||
| for i in range(spc.shape[0]): | |||
| '''****** Line of Data CSPC ******''' | |||
| cspcLine=cspc[i,:,Height].copy() | |||
| '''****** CSPC is normalized ******''' | |||
| chan_index0 = pairsList[i][0] | |||
| chan_index1 = pairsList[i][1] | |||
| CSPCFactor= numpy.sum(ySamples[chan_index0]) * numpy.sum(ySamples[chan_index1]) | |||
| CSPCNorm= cspcLine.copy() / numpy.sqrt(CSPCFactor) | |||
| CSPCSamples[i] = CSPCNorm-noise[i] | |||
| coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor) | |||
| coherence[i]= self.moving_average(coherence[i],N=2) | |||
| phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi | |||
| '''****** Getting fij width ******''' | |||
| yMean=[] | |||
| yMean2=[] | |||
| for j in range(len(ySamples[1])): | |||
| yMean=numpy.append(yMean,numpy.mean([ySamples[0,j],ySamples[1,j],ySamples[2,j]])) | |||
| '''******* Getting fitting Gaussian ******''' | |||
| meanGauss=sum(xSamples*yMean) / len(xSamples) | |||
| sigma=sum(yMean*(xSamples-meanGauss)**2) / len(xSamples) | |||
| if (abs(meanGauss/sigma**2) > 0.00001) : | |||
| try: | |||
| popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=[1,meanGauss,sigma]) | |||
| if numpy.amax(popt)>numpy.amax(yMean)*0.3: | |||
| FitGauss=self.gaus(xSamples,*popt) | |||
| else: | |||
| FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) | |||
| print 'Verificador: Dentro', Height | |||
| except RuntimeError: | |||
| FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) | |||
| # | |||
| else: | |||
| FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) | |||
| Maximun=numpy.amax(yMean) | |||
| eMinus1=Maximun*numpy.exp(-1)*0.8 | |||
| HWpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-eMinus1))) | |||
| HalfWidth= xFrec[HWpos] | |||
| GCpos=self.Find(FitGauss, numpy.amax(FitGauss)) | |||
| Vpos=self.Find(FactNorm, numpy.amax(FactNorm)) | |||
| #Vpos=FirstMoment[] | |||
| '''****** Getting Fij ******''' | |||
| GaussCenter=xFrec[GCpos] | |||
| if (GaussCenter<0 and HalfWidth>0) or (GaussCenter>0 and HalfWidth<0): | |||
| Fij=abs(GaussCenter)+abs(HalfWidth)+0.0000001 | |||
| else: | |||
| Fij=abs(GaussCenter-HalfWidth)+0.0000001 | |||
| '''****** Getting Frecuency range of significant data ******''' | |||
| Rangpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-Maximun*0.10))) | |||
| if Rangpos<GCpos: | |||
| Range=numpy.array([Rangpos,2*GCpos-Rangpos]) | |||
| else: | |||
| Range=numpy.array([2*GCpos-Rangpos,Rangpos]) | |||
| FrecRange=xFrec[Range[0]:Range[1]] | |||
| '''****** Getting SCPC Slope ******''' | |||
| for i in range(spc.shape[0]): | |||
| if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.5: | |||
| PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3) | |||
| slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange,PhaseRange) | |||
| PhaseSlope[i]=slope | |||
| PhaseInter[i]=intercept | |||
| else: | |||
| PhaseSlope[i]=0 | |||
| PhaseInter[i]=0 | |||
| '''Getting constant C''' | |||
| cC=(Fij*numpy.pi)**2 | |||
| '''****** Getting constants F and G ******''' | |||
| MijEijNij=numpy.array([[E02,N02], [E12,N12]]) | |||
| MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi) | |||
| MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi) | |||
| MijResults=numpy.array([MijResult0,MijResult1]) | |||
| (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |||
| '''****** Getting constants A, B and H ******''' | |||
| W01=numpy.amax(coherence[0]) | |||
| W02=numpy.amax(coherence[1]) | |||
| W12=numpy.amax(coherence[2]) | |||
| WijResult0=((cF*E01+cG*N01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC)) | |||
| WijResult1=((cF*E02+cG*N02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC)) | |||
| WijResult2=((cF*E12+cG*N12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC)) | |||
| WijResults=numpy.array([WijResult0, WijResult1, WijResult2]) | |||
| WijEijNij=numpy.array([ [E01**2, N01**2, 2*E01*N01] , [E02**2, N02**2, 2*E02*N02] , [E12**2, N12**2, 2*E12*N12] ]) | |||
| (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) | |||
| VxVy=numpy.array([[cA,cH],[cH,cB]]) | |||
| VxVyResults=numpy.array([-cF,-cG]) | |||
| (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) | |||
| Vzon = Vy | |||
| Vmer = Vx | |||
| Vmag=numpy.sqrt(Vzon**2+Vmer**2) | |||
| Vang=numpy.arctan2(Vmer,Vzon) | |||
| Vver=xFrec[Vpos] | |||
| return Vzon, Vmer, Vver, GaussCenter | |||
| class SpectralMoments(Operation): | |||
| ''' | |||
| Function SpectralMoments() | |||
| Calculates moments (power, mean, standard deviation) and SNR of the signal | |||
| Type of dataIn: Spectra | |||
| Configuration Parameters: | |||
| dirCosx : Cosine director in X axis | |||
| dirCosy : Cosine director in Y axis | |||
| elevation : | |||
| azimuth : | |||
| Input: | |||
| channelList : simple channel list to select e.g. [2,3,7] | |||
| self.dataOut.data_pre : Spectral data | |||
| self.dataOut.abscissaList : List of frequencies | |||
| self.dataOut.noise : Noise level per channel | |||
| Affected: | |||
| self.dataOut.data_param : Parameters per channel | |||
| self.dataOut.data_SNR : SNR per channel | |||
| ''' | |||
| def run(self, dataOut): | |||
| #dataOut.data_pre = dataOut.data_pre[0] | |||
| data = dataOut.data_pre[0] | |||
| absc = dataOut.abscissaList[:-1] | |||
| noise = dataOut.noise | |||
| nChannel = data.shape[0] | |||
| data_param = numpy.zeros((nChannel, 4, data.shape[2])) | |||
| for ind in range(nChannel): | |||
| data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) | |||
| dataOut.data_param = data_param[:,1:,:] | |||
| dataOut.data_SNR = data_param[:,0] | |||
| return | |||
| def __calculateMoments(self, oldspec, oldfreq, n0, | |||
| nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): | |||
| if (nicoh == None): nicoh = 1 | |||
| if (graph == None): graph = 0 | |||
| if (smooth == None): smooth = 0 | |||
| elif (self.smooth < 3): smooth = 0 | |||
| if (type1 == None): type1 = 0 | |||
| if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 | |||
| if (snrth == None): snrth = -3 | |||
| if (dc == None): dc = 0 | |||
| if (aliasing == None): aliasing = 0 | |||
| if (oldfd == None): oldfd = 0 | |||
| if (wwauto == None): wwauto = 0 | |||
| if (n0 < 1.e-20): n0 = 1.e-20 | |||
| freq = oldfreq | |||
| vec_power = numpy.zeros(oldspec.shape[1]) | |||
| vec_fd = numpy.zeros(oldspec.shape[1]) | |||
| vec_w = numpy.zeros(oldspec.shape[1]) | |||
| vec_snr = numpy.zeros(oldspec.shape[1]) | |||
| for ind in range(oldspec.shape[1]): | |||
| spec = oldspec[:,ind] | |||
| aux = spec*fwindow | |||
| max_spec = aux.max() | |||
| m = list(aux).index(max_spec) | |||
| #Smooth | |||
| if (smooth == 0): spec2 = spec | |||
| else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) | |||
| # Calculo de Momentos | |||
| bb = spec2[range(m,spec2.size)] | |||
| bb = (bb<n0).nonzero() | |||
| bb = bb[0] | |||
| ss = spec2[range(0,m + 1)] | |||
| ss = (ss<n0).nonzero() | |||
| ss = ss[0] | |||
| if (bb.size == 0): | |||
| bb0 = spec.size - 1 - m | |||
| else: | |||
| bb0 = bb[0] - 1 | |||
| if (bb0 < 0): | |||
| bb0 = 0 | |||
| if (ss.size == 0): ss1 = 1 | |||
| else: ss1 = max(ss) + 1 | |||
| if (ss1 > 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 SA Parameters -------------------------- | |||
| def GetSAParameters(self): | |||
| #SA en frecuencia | |||
| pairslist = self.dataOut.groupList | |||
| num_pairs = len(pairslist) | |||
| vel = self.dataOut.abscissaList | |||
| spectra = self.dataOut.data_pre | |||
| cspectra = self.dataIn.data_cspc | |||
| delta_v = vel[1] - vel[0] | |||
| #Calculating the power spectrum | |||
| spc_pow = numpy.sum(spectra, 3)*delta_v | |||
| #Normalizing Spectra | |||
| norm_spectra = spectra/spc_pow | |||
| #Calculating the norm_spectra at peak | |||
| max_spectra = numpy.max(norm_spectra, 3) | |||
| #Normalizing Cross Spectra | |||
| norm_cspectra = numpy.zeros(cspectra.shape) | |||
| for i in range(num_chan): | |||
| norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) | |||
| max_cspectra = numpy.max(norm_cspectra,2) | |||
| max_cspectra_index = numpy.argmax(norm_cspectra, 2) | |||
| for i in range(num_pairs): | |||
| cspc_par[i,:,:] = __calculateMoments(norm_cspectra) | |||
| #------------------- Get Lags ---------------------------------- | |||
| class SALags(Operation): | |||
| ''' | |||
| Function GetMoments() | |||
| Input: | |||
| self.dataOut.data_pre | |||
| self.dataOut.abscissaList | |||
| self.dataOut.noise | |||
| self.dataOut.normFactor | |||
| self.dataOut.data_SNR | |||
| self.dataOut.groupList | |||
| self.dataOut.nChannels | |||
| Affected: | |||
| self.dataOut.data_param | |||
| ''' | |||
| def run(self, dataOut): | |||
| data_acf = dataOut.data_pre[0] | |||
| data_ccf = dataOut.data_pre[1] | |||
| normFactor_acf = dataOut.normFactor[0] | |||
| normFactor_ccf = dataOut.normFactor[1] | |||
| pairs_acf = dataOut.groupList[0] | |||
| pairs_ccf = dataOut.groupList[1] | |||
| nHeights = dataOut.nHeights | |||
| absc = dataOut.abscissaList | |||
| noise = dataOut.noise | |||
| SNR = dataOut.data_SNR | |||
| nChannels = dataOut.nChannels | |||
| # pairsList = dataOut.groupList | |||
| # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) | |||
| for l in range(len(pairs_acf)): | |||
| data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] | |||
| for l in range(len(pairs_ccf)): | |||
| data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] | |||
| dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) | |||
| dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) | |||
| dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, 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_acf, data_ccf, lagRange): | |||
| lag0 = data_acf.shape[1]/2 | |||
| #Funcion de Autocorrelacion | |||
| mean_acf = stats.nanmean(data_acf, axis = 0) | |||
| #Obtencion Indice de TauCross | |||
| ind_ccf = data_ccf.argmax(axis = 1) | |||
| #Obtencion Indice de TauAuto | |||
| ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') | |||
| ccf_lag0 = data_ccf[:,lag0,:] | |||
| for i in range(ccf_lag0.shape[0]): | |||
| ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) | |||
| #Obtencion de TauCross y TauAuto | |||
| tau_ccf = lagRange[ind_ccf] | |||
| tau_acf = lagRange[ind_acf] | |||
| Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) | |||
| tau_ccf[Nan1,Nan2] = numpy.nan | |||
| tau_acf[Nan1,Nan2] = numpy.nan | |||
| tau = numpy.vstack((tau_ccf,tau_acf)) | |||
| return tau | |||
| def __calculateLag1Phase(self, data, lagTRange): | |||
| data1 = stats.nanmean(data, axis = 0) | |||
| lag1 = numpy.where(lagTRange == 0)[0][0] + 1 | |||
| phase = numpy.angle(data1[lag1,:]) | |||
| return phase | |||
| class SpectralFitting(Operation): | |||
| ''' | |||
| Function GetMoments() | |||
| Input: | |||
| Output: | |||
| Variables modified: | |||
| ''' | |||
| def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): | |||
| if path != None: | |||
| sys.path.append(path) | |||
| self.dataOut.library = importlib.import_module(file) | |||
| #To be inserted as a parameter | |||
| groupArray = numpy.array(groupList) | |||
| # groupArray = numpy.array([[0,1],[2,3]]) | |||
| self.dataOut.groupList = groupArray | |||
| nGroups = groupArray.shape[0] | |||
| nChannels = self.dataIn.nChannels | |||
| nHeights=self.dataIn.heightList.size | |||
| #Parameters Array | |||
| self.dataOut.data_param = None | |||
| #Set constants | |||
| constants = self.dataOut.library.setConstants(self.dataIn) | |||
| self.dataOut.constants = constants | |||
| M = self.dataIn.normFactor | |||
| N = self.dataIn.nFFTPoints | |||
| ippSeconds = self.dataIn.ippSeconds | |||
| K = self.dataIn.nIncohInt | |||
| pairsArray = numpy.array(self.dataIn.pairsList) | |||
| #List of possible combinations | |||
| listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) | |||
| indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |||
| if getSNR: | |||
| listChannels = groupArray.reshape((groupArray.size)) | |||
| listChannels.sort() | |||
| noise = self.dataIn.getNoise() | |||
| self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) | |||
| for i in range(nGroups): | |||
| coord = groupArray[i,:] | |||
| #Input data array | |||
| data = self.dataIn.data_spc[coord,:,:]/(M*N) | |||
| data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) | |||
| #Cross Spectra data array for Covariance Matrixes | |||
| ind = 0 | |||
| for pairs in listComb: | |||
| pairsSel = numpy.array([coord[x],coord[y]]) | |||
| indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) | |||
| ind += 1 | |||
| dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) | |||
| dataCross = dataCross**2/K | |||
| for h in range(nHeights): | |||
| # print self.dataOut.heightList[h] | |||
| #Input | |||
| d = data[:,h] | |||
| #Covariance Matrix | |||
| D = numpy.diag(d**2/K) | |||
| ind = 0 | |||
| for pairs in listComb: | |||
| #Coordinates in Covariance Matrix | |||
| x = pairs[0] | |||
| y = pairs[1] | |||
| #Channel Index | |||
| S12 = dataCross[ind,:,h] | |||
| D12 = numpy.diag(S12) | |||
| #Completing Covariance Matrix with Cross Spectras | |||
| D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 | |||
| D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 | |||
| ind += 1 | |||
| Dinv=numpy.linalg.inv(D) | |||
| L=numpy.linalg.cholesky(Dinv) | |||
| LT=L.T | |||
| dp = numpy.dot(LT,d) | |||
| #Initial values | |||
| data_spc = self.dataIn.data_spc[coord,:,h] | |||
| if (h>0)and(error1[3]<5): | |||
| p0 = self.dataOut.data_param[i,:,h-1] | |||
| else: | |||
| p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) | |||
| try: | |||
| #Least Squares | |||
| minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) | |||
| # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) | |||
| #Chi square error | |||
| error0 = numpy.sum(infodict['fvec']**2)/(2*N) | |||
| #Error with Jacobian | |||
| error1 = self.dataOut.library.errorFunction(minp,constants,LT) | |||
| except: | |||
| minp = p0*numpy.nan | |||
| error0 = numpy.nan | |||
| error1 = p0*numpy.nan | |||
| #Save | |||
| if self.dataOut.data_param == None: | |||
| self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan | |||
| self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan | |||
| self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) | |||
| self.dataOut.data_param[i,:,h] = minp | |||
| return | |||
| def __residFunction(self, p, dp, LT, constants): | |||
| fm = self.dataOut.library.modelFunction(p, constants) | |||
| fmp=numpy.dot(LT,fm) | |||
| return dp-fmp | |||
| def __getSNR(self, z, noise): | |||
| avg = numpy.average(z, axis=1) | |||
| SNR = (avg.T-noise)/noise | |||
| SNR = SNR.T | |||
| return SNR | |||
| def __chisq(p,chindex,hindex): | |||
| #similar to Resid but calculates CHI**2 | |||
| [LT,d,fm]=setupLTdfm(p,chindex,hindex) | |||
| dp=numpy.dot(LT,d) | |||
| fmp=numpy.dot(LT,fm) | |||
| chisq=numpy.dot((dp-fmp).T,(dp-fmp)) | |||
| return chisq | |||
| 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): | |||
| def techniqueDBS(self, kwargs): | |||
| """ | |||
| 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 | |||
| """ | |||
| velRadial0 = kwargs['velRadial'] | |||
| heiRang = kwargs['heightList'] | |||
| SNR0 = kwargs['SNR'] | |||
| 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] | |||
| azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |||
| heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*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, pairs_ccf, azimuth = None): | |||
| nPairs = len(pairs_ccf) | |||
| 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(nPairs) | |||
| disty = numpy.zeros(nPairs) | |||
| dist = numpy.zeros(nPairs) | |||
| ang = numpy.zeros(nPairs) | |||
| for i in range(nPairs): | |||
| distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] | |||
| disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] | |||
| dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) | |||
| ang[i] = numpy.arctan2(disty[i],distx[i]) | |||
| return distx, disty, dist, ang | |||
| #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] | |||
| nHeights = tau1.shape[1] | |||
| vel = numpy.zeros((nPairs,3,nHeights)) | |||
| dist1 = numpy.reshape(dist, (dist.size,1)) | |||
| angCos = numpy.cos(ang) | |||
| angSin = numpy.sin(ang) | |||
| vel0 = dist1*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): | |||
| def techniqueSA(self, kwargs): | |||
| """ | |||
| 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 | |||
| """ | |||
| position_x = kwargs['positionX'] | |||
| position_y = kwargs['positionY'] | |||
| azimuth = kwargs['azimuth'] | |||
| if kwargs.has_key('correctFactor'): | |||
| correctFactor = kwargs['correctFactor'] | |||
| else: | |||
| correctFactor = 1 | |||
| groupList = kwargs['groupList'] | |||
| pairs_ccf = groupList[1] | |||
| tau = kwargs['tau'] | |||
| _lambda = kwargs['_lambda'] | |||
| #Cross Correlation pairs obtained | |||
| # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, 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, pairs_ccf,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, outputInterval): | |||
| dataTime = currentTime + paramInterval | |||
| deltaTime = dataTime - self.__initime | |||
| if deltaTime >= outputInterval 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 | |||
| ''' | |||
| # print arrayMeteor.shape | |||
| #Settings | |||
| nInt = (heightMax - heightMin)/2 | |||
| # print nInt | |||
| nInt = int(nInt) | |||
| # print nInt | |||
| winds = numpy.zeros((2,nInt))*numpy.nan | |||
| #Filter errors | |||
| error = numpy.where(arrayMeteor[:,-1] == 0)[0] | |||
| finalMeteor = arrayMeteor[error,:] | |||
| #Meteor Histogram | |||
| finalHeights = finalMeteor[:,2] | |||
| 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[:, 6] | |||
| zen = meteorAux[:, 4]*numpy.pi/180 | |||
| azim = meteorAux[:, 3]*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 techniqueNSM_SA(self, **kwargs): | |||
| metArray = kwargs['metArray'] | |||
| heightList = kwargs['heightList'] | |||
| timeList = kwargs['timeList'] | |||
| rx_location = kwargs['rx_location'] | |||
| groupList = kwargs['groupList'] | |||
| azimuth = kwargs['azimuth'] | |||
| dfactor = kwargs['dfactor'] | |||
| k = kwargs['k'] | |||
| azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) | |||
| d = dist*dfactor | |||
| #Phase calculation | |||
| metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) | |||
| metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities | |||
| velEst = numpy.zeros((heightList.size,2))*numpy.nan | |||
| azimuth1 = azimuth1*numpy.pi/180 | |||
| for i in range(heightList.size): | |||
| h = heightList[i] | |||
| indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] | |||
| metHeight = metArray1[indH,:] | |||
| if metHeight.shape[0] >= 2: | |||
| velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities | |||
| iazim = metHeight[:,1].astype(int) | |||
| azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths | |||
| A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) | |||
| A = numpy.asmatrix(A) | |||
| A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() | |||
| velHor = numpy.dot(A1,velAux) | |||
| velEst[i,:] = numpy.squeeze(velHor) | |||
| return velEst | |||
| def __getPhaseSlope(self, metArray, heightList, timeList): | |||
| meteorList = [] | |||
| #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 | |||
| #Putting back together the meteor matrix | |||
| utctime = metArray[:,0] | |||
| uniqueTime = numpy.unique(utctime) | |||
| phaseDerThresh = 0.5 | |||
| ippSeconds = timeList[1] - timeList[0] | |||
| sec = numpy.where(timeList>1)[0][0] | |||
| nPairs = metArray.shape[1] - 6 | |||
| nHeights = len(heightList) | |||
| for t in uniqueTime: | |||
| metArray1 = metArray[utctime==t,:] | |||
| # phaseDerThresh = numpy.pi/4 #reducir Phase thresh | |||
| tmet = metArray1[:,1].astype(int) | |||
| hmet = metArray1[:,2].astype(int) | |||
| metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) | |||
| metPhase[:,:] = numpy.nan | |||
| metPhase[:,hmet,tmet] = metArray1[:,6:].T | |||
| #Delete short trails | |||
| metBool = ~numpy.isnan(metPhase[0,:,:]) | |||
| heightVect = numpy.sum(metBool, axis = 1) | |||
| metBool[heightVect<sec,:] = False | |||
| metPhase[:,heightVect<sec,:] = numpy.nan | |||
| #Derivative | |||
| metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) | |||
| phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) | |||
| metPhase[phDerAux] = numpy.nan | |||
| #--------------------------METEOR DETECTION ----------------------------------------- | |||
| indMet = numpy.where(numpy.any(metBool,axis=1))[0] | |||
| for p in numpy.arange(nPairs): | |||
| phase = metPhase[p,:,:] | |||
| phDer = metDer[p,:,:] | |||
| for h in indMet: | |||
| height = heightList[h] | |||
| phase1 = phase[h,:] #82 | |||
| phDer1 = phDer[h,:] | |||
| phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap | |||
| indValid = numpy.where(~numpy.isnan(phase1))[0] | |||
| initMet = indValid[0] | |||
| endMet = 0 | |||
| for i in range(len(indValid)-1): | |||
| #Time difference | |||
| inow = indValid[i] | |||
| inext = indValid[i+1] | |||
| idiff = inext - inow | |||
| #Phase difference | |||
| phDiff = numpy.abs(phase1[inext] - phase1[inow]) | |||
| if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor | |||
| sizeTrail = inow - initMet + 1 | |||
| if sizeTrail>3*sec: #Too short meteors | |||
| x = numpy.arange(initMet,inow+1)*ippSeconds | |||
| y = phase1[initMet:inow+1] | |||
| ynnan = ~numpy.isnan(y) | |||
| x = x[ynnan] | |||
| y = y[ynnan] | |||
| slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) | |||
| ylin = x*slope + intercept | |||
| rsq = r_value**2 | |||
| if rsq > 0.5: | |||
| vel = slope#*height*1000/(k*d) | |||
| estAux = numpy.array([utctime,p,height, vel, rsq]) | |||
| meteorList.append(estAux) | |||
| initMet = inext | |||
| metArray2 = numpy.array(meteorList) | |||
| return metArray2 | |||
| def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): | |||
| azimuth1 = numpy.zeros(len(pairslist)) | |||
| dist = numpy.zeros(len(pairslist)) | |||
| for i in range(len(rx_location)): | |||
| ch0 = pairslist[i][0] | |||
| ch1 = pairslist[i][1] | |||
| diffX = rx_location[ch0][0] - rx_location[ch1][0] | |||
| diffY = rx_location[ch0][1] - rx_location[ch1][1] | |||
| azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi | |||
| dist[i] = numpy.sqrt(diffX**2 + diffY**2) | |||
| azimuth1 -= azimuth0 | |||
| return azimuth1, dist | |||
| def techniqueNSM_DBS(self, **kwargs): | |||
| metArray = kwargs['metArray'] | |||
| heightList = kwargs['heightList'] | |||
| timeList = kwargs['timeList'] | |||
| zenithList = kwargs['zenithList'] | |||
| nChan = numpy.max(cmet) + 1 | |||
| nHeights = len(heightList) | |||
| utctime = metArray[:,0] | |||
| cmet = metArray[:,1] | |||
| hmet = metArray1[:,3].astype(int) | |||
| h1met = heightList[hmet]*zenithList[cmet] | |||
| vmet = metArray1[:,5] | |||
| for i in range(nHeights - 1): | |||
| hmin = heightList[i] | |||
| hmax = heightList[i + 1] | |||
| vthisH = vmet[(h1met>=hmin) & (h1met<hmax)] | |||
| return data_output | |||
| def run(self, dataOut, technique, **kwargs): | |||
| param = dataOut.data_param | |||
| if dataOut.abscissaList != None: | |||
| absc = dataOut.abscissaList[:-1] | |||
| noise = dataOut.noise | |||
| heightList = dataOut.heightList | |||
| SNR = dataOut.data_SNR | |||
| if technique == 'DBS': | |||
| kwargs['velRadial'] = param[:,1,:] #Radial velocity | |||
| kwargs['heightList'] = heightList | |||
| kwargs['SNR'] = SNR | |||
| dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function | |||
| dataOut.utctimeInit = dataOut.utctime | |||
| dataOut.outputInterval = dataOut.paramInterval | |||
| 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.groupList | |||
| # nChannels = dataOut.nChannels | |||
| kwargs['groupList'] = dataOut.groupList | |||
| kwargs['tau'] = dataOut.data_param | |||
| kwargs['_lambda'] = dataOut.C/dataOut.frequency | |||
| # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) | |||
| dataOut.data_output = self.techniqueSA(kwargs) | |||
| dataOut.utctimeInit = dataOut.utctime | |||
| dataOut.outputInterval = 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.outputInterval = nHours*3600 | |||
| if self.__isConfig == False: | |||
| # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |||
| #Get Initial LTC time | |||
| self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |||
| self.__initime = (self.__initime.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.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |||
| if self.__dataReady: | |||
| dataOut.utctimeInit = self.__initime | |||
| self.__initime += dataOut.outputInterval #to erase time offset | |||
| dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) | |||
| dataOut.flagNoData = False | |||
| self.__buffer = None | |||
| elif technique == 'Meteors1': | |||
| dataOut.flagNoData = True | |||
| self.__dataReady = False | |||
| if kwargs.has_key('nMins'): | |||
| nMins = kwargs['nMins'] | |||
| else: nMins = 20 | |||
| if kwargs.has_key('rx_location'): | |||
| rx_location = kwargs['rx_location'] | |||
| else: rx_location = [(0,1),(1,1),(1,0)] | |||
| if kwargs.has_key('azimuth'): | |||
| azimuth = kwargs['azimuth'] | |||
| else: azimuth = 51 | |||
| if kwargs.has_key('dfactor'): | |||
| dfactor = kwargs['dfactor'] | |||
| if kwargs.has_key('mode'): | |||
| mode = kwargs['mode'] | |||
| else: mode = 'SA' | |||
| #Borrar luego esto | |||
| if dataOut.groupList == None: | |||
| dataOut.groupList = [(0,1),(0,2),(1,2)] | |||
| groupList = dataOut.groupList | |||
| C = 3e8 | |||
| freq = 50e6 | |||
| lamb = C/freq | |||
| k = 2*numpy.pi/lamb | |||
| timeList = dataOut.abscissaList | |||
| heightList = dataOut.heightList | |||
| if self.__isConfig == False: | |||
| dataOut.outputInterval = nMins*60 | |||
| # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |||
| #Get Initial LTC time | |||
| initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |||
| minuteAux = initime.minute | |||
| minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) | |||
| self.__initime = (initime.replace(minute = minuteNew, 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.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |||
| if self.__dataReady: | |||
| dataOut.utctimeInit = self.__initime | |||
| self.__initime += dataOut.outputInterval #to erase time offset | |||
| metArray = self.__buffer | |||
| if mode == 'SA': | |||
| dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) | |||
| elif mode == 'DBS': | |||
| dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList) | |||
| dataOut.data_output = dataOut.data_output.T | |||
| dataOut.flagNoData = False | |||
| self.__buffer = None | |||
| return | |||
| class EWDriftsEstimation(Operation): | |||
| def __init__(self): | |||
| Operation.__init__(self) | |||
| 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 run(self, dataOut, zenith, zenithCorrection): | |||
| heiRang = dataOut.heightList | |||
| velRadial = dataOut.data_param[:,3,:] | |||
| SNR = dataOut.data_SNR | |||
| zenith = numpy.array(zenith) | |||
| zenith -= zenithCorrection | |||
| zenith *= numpy.pi/180 | |||
| heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) | |||
| alp = zenith[0] | |||
| bet = zenith[1] | |||
| w_w = velRadial1[0,:] | |||
| w_e = velRadial1[1,:] | |||
| w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) | |||
| u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) | |||
| winds = numpy.vstack((u,w)) | |||
| dataOut.heightList = heiRang1 | |||
| dataOut.data_output = winds | |||
| dataOut.data_SNR = SNR1 | |||
| dataOut.utctimeInit = dataOut.utctime | |||
| dataOut.outputInterval = dataOut.timeInterval | |||
| return | |||
| #--------------- Non Specular Meteor ---------------- | |||
| class NonSpecularMeteorDetection(Operation): | |||
| def run(self, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): | |||
| data_acf = self.dataOut.data_pre[0] | |||
| data_ccf = self.dataOut.data_pre[1] | |||
| lamb = self.dataOut.C/self.dataOut.frequency | |||
| tSamp = self.dataOut.ippSeconds*self.dataOut.nCohInt | |||
| paramInterval = self.dataOut.paramInterval | |||
| nChannels = data_acf.shape[0] | |||
| nLags = data_acf.shape[1] | |||
| nProfiles = data_acf.shape[2] | |||
| nHeights = self.dataOut.nHeights | |||
| nCohInt = self.dataOut.nCohInt | |||
| sec = numpy.round(nProfiles/self.dataOut.paramInterval) | |||
| heightList = self.dataOut.heightList | |||
| ippSeconds = self.dataOut.ippSeconds*self.dataOut.nCohInt*self.dataOut.nAvg | |||
| utctime = self.dataOut.utctime | |||
| self.dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) | |||
| #------------------------ SNR -------------------------------------- | |||
| power = data_acf[:,0,:,:].real | |||
| noise = numpy.zeros(nChannels) | |||
| SNR = numpy.zeros(power.shape) | |||
| for i in range(nChannels): | |||
| noise[i] = hildebrand_sekhon(power[i,:], nCohInt) | |||
| SNR[i] = (power[i]-noise[i])/noise[i] | |||
| SNRm = numpy.nanmean(SNR, axis = 0) | |||
| SNRdB = 10*numpy.log10(SNR) | |||
| if mode == 'SA': | |||
| nPairs = data_ccf.shape[0] | |||
| #---------------------- Coherence and Phase -------------------------- | |||
| phase = numpy.zeros(data_ccf[:,0,:,:].shape) | |||
| # phase1 = numpy.copy(phase) | |||
| coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) | |||
| for p in range(nPairs): | |||
| ch0 = self.dataOut.groupList[p][0] | |||
| ch1 = self.dataOut.groupList[p][1] | |||
| ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) | |||
| phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter | |||
| # phase1[p,:,:] = numpy.angle(ccf) #median filter | |||
| coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter | |||
| # coh1[p,:,:] = numpy.abs(ccf) #median filter | |||
| coh = numpy.nanmax(coh1, axis = 0) | |||
| # struc = numpy.ones((5,1)) | |||
| # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) | |||
| #---------------------- Radial Velocity ---------------------------- | |||
| phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) | |||
| velRad = phaseAux*lamb/(4*numpy.pi*tSamp) | |||
| if allData: | |||
| boolMetFin = ~numpy.isnan(SNRm) | |||
| # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) | |||
| else: | |||
| #------------------------ Meteor mask --------------------------------- | |||
| # #SNR mask | |||
| # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) | |||
| # | |||
| # #Erase small objects | |||
| # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) | |||
| # | |||
| # auxEEJ = numpy.sum(boolMet1,axis=0) | |||
| # indOver = auxEEJ>nProfiles*0.8 #Use this later | |||
| # indEEJ = numpy.where(indOver)[0] | |||
| # indNEEJ = numpy.where(~indOver)[0] | |||
| # | |||
| # boolMetFin = boolMet1 | |||
| # | |||
| # if indEEJ.size > 0: | |||
| # boolMet1[:,indEEJ] = False #Erase heights with EEJ | |||
| # | |||
| # boolMet2 = coh > cohThresh | |||
| # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) | |||
| # | |||
| # #Final Meteor mask | |||
| # boolMetFin = boolMet1|boolMet2 | |||
| #Coherence mask | |||
| boolMet1 = coh > 0.75 | |||
| struc = numpy.ones((30,1)) | |||
| boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) | |||
| #Derivative mask | |||
| derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) | |||
| boolMet2 = derPhase < 0.2 | |||
| # boolMet2 = ndimage.morphology.binary_opening(boolMet2) | |||
| # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) | |||
| boolMet2 = ndimage.median_filter(boolMet2,size=5) | |||
| boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) | |||
| # #Final mask | |||
| # boolMetFin = boolMet2 | |||
| boolMetFin = boolMet1&boolMet2 | |||
| # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) | |||
| #Creating data_param | |||
| coordMet = numpy.where(boolMetFin) | |||
| tmet = coordMet[0] | |||
| hmet = coordMet[1] | |||
| data_param = numpy.zeros((tmet.size, 6 + nPairs)) | |||
| data_param[:,0] = utctime | |||
| data_param[:,1] = tmet | |||
| data_param[:,2] = hmet | |||
| data_param[:,3] = SNRm[tmet,hmet] | |||
| data_param[:,4] = velRad[tmet,hmet] | |||
| data_param[:,5] = coh[tmet,hmet] | |||
| data_param[:,6:] = phase[:,tmet,hmet].T | |||
| elif mode == 'DBS': | |||
| self.dataOut.groupList = numpy.arange(nChannels) | |||
| #Radial Velocities | |||
| # phase = numpy.angle(data_acf[:,1,:,:]) | |||
| phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) | |||
| velRad = phase*lamb/(4*numpy.pi*tSamp) | |||
| #Spectral width | |||
| acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) | |||
| acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) | |||
| spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) | |||
| # velRad = ndimage.median_filter(velRad, size = (1,5,1)) | |||
| if allData: | |||
| boolMetFin = ~numpy.isnan(SNRdB) | |||
| else: | |||
| #SNR | |||
| boolMet1 = (SNRdB>SNRthresh) #SNR mask | |||
| boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) | |||
| #Radial velocity | |||
| boolMet2 = numpy.abs(velRad) < 30 | |||
| boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) | |||
| #Spectral Width | |||
| boolMet3 = spcWidth < 30 | |||
| boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) | |||
| # boolMetFin = self.__erase_small(boolMet1, 10,5) | |||
| boolMetFin = boolMet1&boolMet2&boolMet3 | |||
| #Creating data_param | |||
| coordMet = numpy.where(boolMetFin) | |||
| cmet = coordMet[0] | |||
| tmet = coordMet[1] | |||
| hmet = coordMet[2] | |||
| data_param = numpy.zeros((tmet.size, 7)) | |||
| data_param[:,0] = utctime | |||
| data_param[:,1] = cmet | |||
| data_param[:,2] = tmet | |||
| data_param[:,3] = hmet | |||
| data_param[:,4] = SNR[cmet,tmet,hmet].T | |||
| data_param[:,5] = velRad[cmet,tmet,hmet].T | |||
| data_param[:,6] = spcWidth[cmet,tmet,hmet].T | |||
| # self.dataOut.data_param = data_int | |||
| if len(data_param) == 0: | |||
| self.dataOut.flagNoData = True | |||
| else: | |||
| self.dataOut.data_param = data_param | |||
| def __erase_small(self, binArray, threshX, threshY): | |||
| labarray, numfeat = ndimage.measurements.label(binArray) | |||
| binArray1 = numpy.copy(binArray) | |||
| for i in range(1,numfeat + 1): | |||
| auxBin = (labarray==i) | |||
| auxSize = auxBin.sum() | |||
| x,y = numpy.where(auxBin) | |||
| widthX = x.max() - x.min() | |||
| widthY = y.max() - y.min() | |||
| #width X: 3 seg -> 12.5*3 | |||
| #width Y: | |||
| if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): | |||
| binArray1[auxBin] = False | |||
| return binArray1 | |||
| #--------------- Specular Meteor ---------------- | |||
| class SMDetection(Operation): | |||
| ''' | |||
| 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): | |||
| Utc Time | Range Height | |||
| Azimuth Zenith errorCosDir | |||
| VelRad errorVelRad | |||
| Phase0 Phase1 Phase2 Phase3 | |||
| TypeError | |||
| ''' | |||
| def run(self, dataOut, hei_ref = None, tauindex = 0, | |||
| phaseOffsets = None, | |||
| cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, | |||
| noise_timeStep = 4, noise_multiple = 4, | |||
| multDet_timeLimit = 1, multDet_rangeLimit = 3, | |||
| phaseThresh = 20, SNRThresh = 5, | |||
| hmin = 50, hmax=150, azimuth = 0, | |||
| channelPositions = None) : | |||
| #Getting Pairslist | |||
| if channelPositions == None: | |||
| # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |||
| channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |||
| meteorOps = SMOperations() | |||
| pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |||
| heiRang = dataOut.getHeiRange() | |||
| #Get Beacon signal - No Beacon signal anymore | |||
| # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) | |||
| # | |||
| # if hei_ref != None: | |||
| # newheis = numpy.where(self.dataOut.heightList>hei_ref) | |||
| # | |||
| #****************REMOVING HARDWARE PHASE DIFFERENCES*************** | |||
| # see if the user put in pre defined phase shifts | |||
| voltsPShift = dataOut.data_pre.copy() | |||
| # if predefinedPhaseShifts != None: | |||
| # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 | |||
| # | |||
| # # elif beaconPhaseShifts: | |||
| # # #get hardware phase shifts using beacon signal | |||
| # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) | |||
| # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) | |||
| # | |||
| # else: | |||
| # hardwarePhaseShifts = numpy.zeros(5) | |||
| # | |||
| # 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, dataOut.timeInterval, pairslist0, 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, 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 = dataOut.getHeiRange() | |||
| rangeInterval = heiRange[1] - heiRange[0] | |||
| rangeLimit = multDet_rangeLimit/rangeInterval | |||
| timeLimit = multDet_timeLimit/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, pairslist0, thresh, noise, dataOut.timeInterval, 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, dataOut.timeInterval, 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, pairslist0, dataOut.timeInterval) | |||
| if len(listMeteors4) > 0: | |||
| #Setting New Array | |||
| date = dataOut.utctime | |||
| arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) | |||
| #Correcting phase offset | |||
| if phaseOffsets != None: | |||
| phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |||
| arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |||
| #Second Pairslist | |||
| pairsList = [] | |||
| pairx = (0,1) | |||
| pairy = (2,3) | |||
| pairsList.append(pairx) | |||
| pairsList.append(pairy) | |||
| jph = numpy.array([0,0,0,0]) | |||
| h = (hmin,hmax) | |||
| arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |||
| # #Calculate AOA (Error N 3, 4) | |||
| # #JONES ET AL. 1998 | |||
| # error = arrayParameters[:,-1] | |||
| # AOAthresh = numpy.pi/8 | |||
| # phases = -arrayParameters[:,9:13] | |||
| # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.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] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) | |||
| # error = arrayParameters[:,-1] | |||
| #********************* END OF PARAMETERS CALCULATION ************************** | |||
| #***************************+ PASS DATA TO NEXT STEP ********************** | |||
| # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) | |||
| dataOut.data_param = arrayParameters | |||
| if arrayParameters == None: | |||
| dataOut.flagNoData = True | |||
| else: | |||
| dataOut.flagNoData = True | |||
| 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 | |||
| phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) | |||
| # 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)): | |||
| meteorAux = listMeteors[i] | |||
| if meteorAux[-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), 13)) | |||
| #Date inclusion | |||
| # date = re.findall(r'\((.*?)\)', date) | |||
| # date = date[0].split(',') | |||
| # date = map(int, date) | |||
| # | |||
| # if len(date)<6: | |||
| # date.append(0) | |||
| # | |||
| # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] | |||
| # arrayDate = numpy.tile(date, (len(listMeteors), 1)) | |||
| arrayDate = numpy.tile(date, (len(listMeteors))) | |||
| #Meteor array | |||
| # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] | |||
| # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) | |||
| #Parameters Array | |||
| arrayParameters[:,0] = arrayDate #Date | |||
| arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range | |||
| arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error | |||
| arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases | |||
| arrayParameters[:,-1] = arrayMeteors[:,-1] #Error | |||
| return arrayParameters | |||
| class CorrectSMPhases(Operation): | |||
| def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): | |||
| arrayParameters = dataOut.data_param | |||
| pairsList = [] | |||
| pairx = (0,1) | |||
| pairy = (2,3) | |||
| pairsList.append(pairx) | |||
| pairsList.append(pairy) | |||
| jph = numpy.zeros(4) | |||
| phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |||
| # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |||
| arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) | |||
| meteorOps = SMOperations() | |||
| if channelPositions == None: | |||
| # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |||
| channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |||
| pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |||
| h = (hmin,hmax) | |||
| arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |||
| dataOut.data_param = arrayParameters | |||
| return | |||
| class SMPhaseCalibration(Operation): | |||
| __buffer = None | |||
| __initime = None | |||
| __dataReady = False | |||
| __isConfig = False | |||
| def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): | |||
| dataTime = currentTime + paramInterval | |||
| deltaTime = dataTime - initTime | |||
| if deltaTime >= outputInterval or deltaTime < 0: | |||
| return True | |||
| return False | |||
| def __getGammas(self, pairs, d, phases): | |||
| gammas = numpy.zeros(2) | |||
| for i in range(len(pairs)): | |||
| pairi = pairs[i] | |||
| phip3 = phases[:,pairi[1]] | |||
| d3 = d[pairi[1]] | |||
| phip2 = phases[:,pairi[0]] | |||
| d2 = d[pairi[0]] | |||
| #Calculating gamma | |||
| # jdcos = alp1/(k*d1) | |||
| # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) | |||
| jgamma = -phip2*d3/d2 - phip3 | |||
| jgamma = numpy.angle(numpy.exp(1j*jgamma)) | |||
| # jgamma[jgamma>numpy.pi] -= 2*numpy.pi | |||
| # jgamma[jgamma<-numpy.pi] += 2*numpy.pi | |||
| #Revised distribution | |||
| jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) | |||
| #Histogram | |||
| nBins = 64.0 | |||
| rmin = -0.5*numpy.pi | |||
| rmax = 0.5*numpy.pi | |||
| phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) | |||
| meteorsY = phaseHisto[0] | |||
| phasesX = phaseHisto[1][:-1] | |||
| width = phasesX[1] - phasesX[0] | |||
| phasesX += width/2 | |||
| #Gaussian aproximation | |||
| bpeak = meteorsY.argmax() | |||
| peak = meteorsY.max() | |||
| jmin = bpeak - 5 | |||
| jmax = bpeak + 5 + 1 | |||
| if jmin<0: | |||
| jmin = 0 | |||
| jmax = 6 | |||
| elif jmax > meteorsY.size: | |||
| jmin = meteorsY.size - 6 | |||
| jmax = meteorsY.size | |||
| x0 = numpy.array([peak,bpeak,50]) | |||
| coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) | |||
| #Gammas | |||
| gammas[i] = coeff[0][1] | |||
| return gammas | |||
| def __residualFunction(self, coeffs, y, t): | |||
| return y - self.__gauss_function(t, coeffs) | |||
| def __gauss_function(self, t, coeffs): | |||
| return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) | |||
| def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): | |||
| meteorOps = SMOperations() | |||
| nchan = 4 | |||
| pairx = pairsList[0] | |||
| pairy = pairsList[1] | |||
| center_xangle = 0 | |||
| center_yangle = 0 | |||
| range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) | |||
| ntimes = len(range_angle) | |||
| nstepsx = 20.0 | |||
| nstepsy = 20.0 | |||
| for iz in range(ntimes): | |||
| min_xangle = -range_angle[iz]/2 + center_xangle | |||
| max_xangle = range_angle[iz]/2 + center_xangle | |||
| min_yangle = -range_angle[iz]/2 + center_yangle | |||
| max_yangle = range_angle[iz]/2 + center_yangle | |||
| inc_x = (max_xangle-min_xangle)/nstepsx | |||
| inc_y = (max_yangle-min_yangle)/nstepsy | |||
| alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle | |||
| alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle | |||
| penalty = numpy.zeros((nstepsx,nstepsy)) | |||
| jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) | |||
| jph = numpy.zeros(nchan) | |||
| # Iterations looking for the offset | |||
| for iy in range(int(nstepsy)): | |||
| for ix in range(int(nstepsx)): | |||
| jph[pairy[1]] = alpha_y[iy] | |||
| jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] | |||
| jph[pairx[1]] = alpha_x[ix] | |||
| jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] | |||
| jph_array[:,ix,iy] = jph | |||
| meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) | |||
| error = meteorsArray1[:,-1] | |||
| ind1 = numpy.where(error==0)[0] | |||
| penalty[ix,iy] = ind1.size | |||
| i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) | |||
| phOffset = jph_array[:,i,j] | |||
| center_xangle = phOffset[pairx[1]] | |||
| center_yangle = phOffset[pairy[1]] | |||
| phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) | |||
| phOffset = phOffset*180/numpy.pi | |||
| return phOffset | |||
| def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): | |||
| dataOut.flagNoData = True | |||
| self.__dataReady = False | |||
| dataOut.outputInterval = nHours*3600 | |||
| if self.__isConfig == False: | |||
| # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |||
| #Get Initial LTC time | |||
| self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |||
| self.__initime = (self.__initime.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.copy() | |||
| else: | |||
| self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |||
| self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |||
| if self.__dataReady: | |||
| dataOut.utctimeInit = self.__initime | |||
| self.__initime += dataOut.outputInterval #to erase time offset | |||
| freq = dataOut.frequency | |||
| c = dataOut.C #m/s | |||
| lamb = c/freq | |||
| k = 2*numpy.pi/lamb | |||
| azimuth = 0 | |||
| h = (hmin, hmax) | |||
| pairs = ((0,1),(2,3)) | |||
| if channelPositions == None: | |||
| # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |||
| channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |||
| meteorOps = SMOperations() | |||
| pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |||
| # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] | |||
| meteorsArray = self.__buffer | |||
| error = meteorsArray[:,-1] | |||
| boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) | |||
| ind1 = numpy.where(boolError)[0] | |||
| meteorsArray = meteorsArray[ind1,:] | |||
| meteorsArray[:,-1] = 0 | |||
| phases = meteorsArray[:,8:12] | |||
| #Calculate Gammas | |||
| gammas = self.__getGammas(pairs, distances, phases) | |||
| # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 | |||
| #Calculate Phases | |||
| phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) | |||
| phasesOff = phasesOff.reshape((1,phasesOff.size)) | |||
| dataOut.data_output = -phasesOff | |||
| dataOut.flagNoData = False | |||
| self.__buffer = None | |||
| return | |||
| class SMOperations(): | |||
| def __init__(self): | |||
| return | |||
| def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): | |||
| arrayParameters = arrayParameters0.copy() | |||
| hmin = h[0] | |||
| hmax = h[1] | |||
| #Calculate AOA (Error N 3, 4) | |||
| #JONES ET AL. 1998 | |||
| AOAthresh = numpy.pi/8 | |||
| error = arrayParameters[:,-1] | |||
| phases = -arrayParameters[:,8:12] + jph | |||
| # phases = numpy.unwrap(phases) | |||
| arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) | |||
| #Calculate Heights (Error N 13 and 14) | |||
| error = arrayParameters[:,-1] | |||
| Ranges = arrayParameters[:,1] | |||
| zenith = arrayParameters[:,4] | |||
| arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) | |||
| #----------------------- Get Final data ------------------------------------ | |||
| # error = arrayParameters[:,-1] | |||
| # ind1 = numpy.where(error==0)[0] | |||
| # arrayParameters = arrayParameters[ind1,:] | |||
| return arrayParameters | |||
| def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): | |||
| arrayAOA = numpy.zeros((phases.shape[0],3)) | |||
| cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) | |||
| 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 | |||
| indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] | |||
| error[indError] = 0 | |||
| #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, distances): | |||
| #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): | |||
| ph0 = arrayPhase[:,pairsList[i][0]] | |||
| ph1 = arrayPhase[:,pairsList[i][1]] | |||
| d0 = distances[pairsList[i][0]] | |||
| d1 = distances[pairsList[i][1]] | |||
| ph0_aux = ph0 + ph1 | |||
| ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) | |||
| # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi | |||
| # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi | |||
| #First Estimation | |||
| cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) | |||
| #Most-Accurate Second Estimation | |||
| phi1_aux = ph0 - ph1 | |||
| phi1_aux = phi1_aux.reshape(phi1_aux.size,1) | |||
| #Direction Cosine 1 | |||
| cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) | |||
| #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 | |||
| indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] | |||
| error[indError] = 0 | |||
| 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 | |||
| def getPhasePairs(self, channelPositions): | |||
| chanPos = numpy.array(channelPositions) | |||
| listOper = list(itertools.combinations(range(5),2)) | |||
| distances = numpy.zeros(4) | |||
| axisX = [] | |||
| axisY = [] | |||
| distX = numpy.zeros(3) | |||
| distY = numpy.zeros(3) | |||
| ix = 0 | |||
| iy = 0 | |||
| pairX = numpy.zeros((2,2)) | |||
| pairY = numpy.zeros((2,2)) | |||
| for i in range(len(listOper)): | |||
| pairi = listOper[i] | |||
| posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) | |||
| if posDif[0] == 0: | |||
| axisY.append(pairi) | |||
| distY[iy] = posDif[1] | |||
| iy += 1 | |||
| elif posDif[1] == 0: | |||
| axisX.append(pairi) | |||
| distX[ix] = posDif[0] | |||
| ix += 1 | |||
| for i in range(2): | |||
| if i==0: | |||
| dist0 = distX | |||
| axis0 = axisX | |||
| else: | |||
| dist0 = distY | |||
| axis0 = axisY | |||
| side = numpy.argsort(dist0)[:-1] | |||
| axis0 = numpy.array(axis0)[side,:] | |||
| chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) | |||
| axis1 = numpy.unique(numpy.reshape(axis0,4)) | |||
| side = axis1[axis1 != chanC] | |||
| diff1 = chanPos[chanC,i] - chanPos[side[0],i] | |||
| diff2 = chanPos[chanC,i] - chanPos[side[1],i] | |||
| if diff1<0: | |||
| chan2 = side[0] | |||
| d2 = numpy.abs(diff1) | |||
| chan1 = side[1] | |||
| d1 = numpy.abs(diff2) | |||
| else: | |||
| chan2 = side[1] | |||
| d2 = numpy.abs(diff2) | |||
| chan1 = side[0] | |||
| d1 = numpy.abs(diff1) | |||
| if i==0: | |||
| chanCX = chanC | |||
| chan1X = chan1 | |||
| chan2X = chan2 | |||
| distances[0:2] = numpy.array([d1,d2]) | |||
| else: | |||
| chanCY = chanC | |||
| chan1Y = chan1 | |||
| chan2Y = chan2 | |||
| distances[2:4] = numpy.array([d1,d2]) | |||
| # axisXsides = numpy.reshape(axisX[ix,:],4) | |||
| # | |||
| # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) | |||
| # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) | |||
| # | |||
| # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] | |||
| # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] | |||
| # channel25X = int(pairX[0,ind25X]) | |||
| # channel20X = int(pairX[1,ind20X]) | |||
| # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] | |||
| # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] | |||
| # channel25Y = int(pairY[0,ind25Y]) | |||
| # channel20Y = int(pairY[1,ind20Y]) | |||
| # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] | |||
| pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] | |||
| return pairslist, distances | |||
| # 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 | |||