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import numpy
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import math
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from scipy import optimize, interpolate, signal, stats, ndimage
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import re
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import datetime
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import copy
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import sys
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import importlib
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import itertools
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from jroproc_base import ProcessingUnit, Operation
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from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
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from scipy import asarray as ar,exp
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from scipy.optimize import curve_fit
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SPEED_OF_LIGHT = 299792458
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class ParametersProc(ProcessingUnit):
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nSeconds = None
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def __init__(self):
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ProcessingUnit.__init__(self)
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# self.objectDict = {}
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self.buffer = None
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self.firstdatatime = None
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self.profIndex = 0
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self.dataOut = Parameters()
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def __updateObjFromInput(self):
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self.dataOut.inputUnit = self.dataIn.type
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self.dataOut.timeZone = self.dataIn.timeZone
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self.dataOut.dstFlag = self.dataIn.dstFlag
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self.dataOut.errorCount = self.dataIn.errorCount
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self.dataOut.useLocalTime = self.dataIn.useLocalTime
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self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
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self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
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self.dataOut.channelList = self.dataIn.channelList
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self.dataOut.heightList = self.dataIn.heightList
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self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
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# self.dataOut.nHeights = self.dataIn.nHeights
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# self.dataOut.nChannels = self.dataIn.nChannels
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self.dataOut.nBaud = self.dataIn.nBaud
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self.dataOut.nCode = self.dataIn.nCode
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self.dataOut.code = self.dataIn.code
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# self.dataOut.nProfiles = self.dataOut.nFFTPoints
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self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
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# self.dataOut.utctime = self.firstdatatime
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self.dataOut.utctime = self.dataIn.utctime
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self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
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self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
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self.dataOut.nCohInt = self.dataIn.nCohInt
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# self.dataOut.nIncohInt = 1
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self.dataOut.ippSeconds = self.dataIn.ippSeconds
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# self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
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self.dataOut.timeInterval = self.dataIn.timeInterval
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self.dataOut.heightList = self.dataIn.getHeiRange()
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self.dataOut.frequency = self.dataIn.frequency
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self.dataOut.noise = self.dataIn.noise
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def run(self):
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#---------------------- Voltage Data ---------------------------
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if self.dataIn.type == "Voltage":
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self.__updateObjFromInput()
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self.dataOut.data_pre = self.dataIn.data.copy()
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self.dataOut.flagNoData = False
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self.dataOut.utctimeInit = self.dataIn.utctime
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self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds
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return
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#---------------------- Spectra Data ---------------------------
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if self.dataIn.type == "Spectra":
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self.dataOut.data_pre = (self.dataIn.data_spc,self.dataIn.data_cspc)
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self.dataOut.abscissaList = self.dataIn.getVelRange(1)
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self.dataOut.noise = self.dataIn.getNoise()
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self.dataOut.normFactor = self.dataIn.normFactor
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self.dataOut.outputInterval = self.dataIn.outputInterval
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self.dataOut.groupList = self.dataIn.pairsList
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self.dataOut.flagNoData = False
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if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
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self.dataOut.ChanDist = self.dataIn.ChanDist
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if hasattr(self.dataIn, 'VelRange'): #Velocities range
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self.dataOut.VelRange = self.dataIn.VelRange
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if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
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self.dataOut.RadarConst = self.dataIn.RadarConst
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if hasattr(self.dataIn, 'NPW'): #NPW
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self.dataOut.NPW = self.dataIn.NPW
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if hasattr(self.dataIn, 'COFA'): #COFA
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self.dataOut.COFA = self.dataIn.COFA
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#---------------------- Correlation Data ---------------------------
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if self.dataIn.type == "Correlation":
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acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
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self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
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self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
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self.dataOut.groupList = (acf_pairs, ccf_pairs)
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self.dataOut.abscissaList = self.dataIn.lagRange
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self.dataOut.noise = self.dataIn.noise
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self.dataOut.data_SNR = self.dataIn.SNR
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self.dataOut.flagNoData = False
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self.dataOut.nAvg = self.dataIn.nAvg
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#---------------------- Parameters Data ---------------------------
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if self.dataIn.type == "Parameters":
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self.dataOut.copy(self.dataIn)
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self.dataOut.flagNoData = False
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return True
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self.__updateObjFromInput()
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self.dataOut.utctimeInit = self.dataIn.utctime
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self.dataOut.paramInterval = self.dataIn.timeInterval
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return
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class PrecipitationProc(Operation):
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'''
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Funtion that uses Reflectivity factor (Z), and estimates rainfall Rate
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Input:
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self.dataOut.data_pre : SelfSpectra
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Output:
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self.dataOut.data_output : Reflectivity factor, rainfall Rate
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Parameters affected: Winds, height range, SNR
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'''
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def run(self, dataOut):
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#numpy.set_printoptions(threshold=numpy.NaN)
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spc = dataOut.data_pre[0].copy()
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NPW = dataOut.NPW
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COFA = dataOut.COFA
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#print 'COFA',COFA.shape
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#spc = numpy.where(spc<0,spc, numpy.NaN )
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SNR = numpy.array([spc[0,:,:] / NPW[0] , spc[1,:,:] / NPW[1]])
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#print 'SNR',SNR.shape
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#print ' '
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RadarConst = dataOut.RadarConst
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#frequency = 34.85*10**9
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#Lambda = SPEED_OF_LIGHT/frequency
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Num_Hei = spc.shape[2]
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Num_Bin = spc.shape[1]
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Num_Chn = spc.shape[0]
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ETA = numpy.zeros(([Num_Chn ,Num_Hei]))
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data_output = numpy.ones([Num_Chn ,Num_Hei])*numpy.NaN
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Km = 0.93
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ETA = numpy.sum(SNR,1)
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ETA = numpy.where(ETA > 0. , ETA, numpy.NaN)
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Ze = numpy.ones([Num_Chn,Num_Hei] )
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for r in range(Num_Hei):
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Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
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Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
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dBZe = 10*numpy.log10(Ze)
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dataOut.data_output = Ze
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dataOut.data_param = dBZe
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print 'dBZe',dBZe[0,:]
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class FullSpectralAnalysis(Operation):
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"""
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Function that implements Full Spectral Analisys technique.
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Input:
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self.dataOut.data_pre : SelfSpectra and CrossSPectra data
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self.dataOut.groupList : Pairlist of channels
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self.dataOut.ChanDist : Physical distance between receivers
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Output:
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self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind
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Parameters affected: Winds, height range, SNR
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"""
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def run(self, dataOut):
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spc = dataOut.data_pre[0].copy()
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cspc = dataOut.data_pre[1].copy()
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nChannel = spc.shape[0]
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nProfiles = spc.shape[1]
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nHeights = spc.shape[2]
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pairsList = dataOut.groupList
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ChanDist = dataOut.ChanDist
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VelRange= dataOut.VelRange
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ySamples=numpy.ones([nChannel,nProfiles])
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phase=numpy.ones([nChannel,nProfiles])
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CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_)
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coherence=numpy.ones([nChannel,nProfiles])
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PhaseSlope=numpy.ones(nChannel)
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PhaseInter=numpy.ones(nChannel)
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data = dataOut.data_pre
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noise = dataOut.noise
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SNRdB = 10*numpy.log10(dataOut.data_SNR)
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FirstMoment = []
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SNRdBMean = []
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for j in range(nHeights):
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FirstMoment = numpy.append(FirstMoment,numpy.mean([dataOut.data_param[0,1,j],dataOut.data_param[1,1,j],dataOut.data_param[2,1,j]]))
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SNRdBMean = numpy.append(SNRdBMean,numpy.mean([SNRdB[0,j],SNRdB[1,j],SNRdB[2,j]]))
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data_output=numpy.ones([3,spc.shape[2]])*numpy.NaN
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velocityX=[]
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velocityY=[]
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velocityV=[]
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for Height in range(nHeights):
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[Vzon,Vmer,Vver, GaussCenter]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, VelRange)
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if abs(Vzon)<100 and abs(Vzon)> 0.:
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velocityX=numpy.append(velocityX, Vzon)#Vmag
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else:
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velocityX=numpy.append(velocityX, numpy.NaN)
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if abs(Vmer)<100 and abs(Vmer) > 0.:
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velocityY=numpy.append(velocityY, Vmer)#Vang
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else:
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velocityY=numpy.append(velocityY, numpy.NaN)
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if abs(GaussCenter)<10:
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velocityV=numpy.append(velocityV, Vver)
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else:
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velocityV=numpy.append(velocityV, numpy.NaN)
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#FirstMoment[Height]= numpy.NaN
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if SNRdBMean[Height] <12:
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FirstMoment[Height] = numpy.NaN
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velocityX[Height] = numpy.NaN
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velocityY[Height] = numpy.NaN
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data_output[0]=numpy.array(velocityX)
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data_output[1]=numpy.array(velocityY)
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data_output[2]=-FirstMoment
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print ' '
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#print 'FirstMoment'
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#print FirstMoment
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print ' '
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#print 'velocityY'
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#print numpy.array(velocityY)
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print ' '
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#print 'SNR'
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#print 10*numpy.log10(dataOut.data_SNR)
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#print numpy.shape(10*numpy.log10(dataOut.data_SNR))
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print ' '
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dataOut.data_output=data_output
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return
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def moving_average(self,x, N=2):
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return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
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def gaus(self,xSamples,a,x0,sigma):
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return a*exp(-(xSamples-x0)**2/(2*sigma**2))
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def Find(self,x,value):
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for index in range(len(x)):
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if x[index]==value:
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return index
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def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, VelRange):
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ySamples=numpy.ones([spc.shape[0],spc.shape[1]])
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phase=numpy.ones([spc.shape[0],spc.shape[1]])
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CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_)
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coherence=numpy.ones([spc.shape[0],spc.shape[1]])
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PhaseSlope=numpy.ones(spc.shape[0])
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PhaseInter=numpy.ones(spc.shape[0])
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xFrec=VelRange
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'''Getting Eij and Nij'''
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E01=ChanDist[0][0]
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N01=ChanDist[0][1]
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E02=ChanDist[1][0]
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N02=ChanDist[1][1]
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E12=ChanDist[2][0]
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N12=ChanDist[2][1]
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z = spc.copy()
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z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
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for i in range(spc.shape[0]):
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'''****** Line of Data SPC ******'''
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zline=z[i,:,Height]
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'''****** SPC is normalized ******'''
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FactNorm= zline.copy() / numpy.sum(zline.copy())
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FactNorm= FactNorm/numpy.sum(FactNorm)
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SmoothSPC=self.moving_average(FactNorm,N=3)
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xSamples = ar(range(len(SmoothSPC)))
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ySamples[i] = SmoothSPC-noise[i]
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for i in range(spc.shape[0]):
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'''****** Line of Data CSPC ******'''
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cspcLine=cspc[i,:,Height].copy()
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'''****** CSPC is normalized ******'''
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chan_index0 = pairsList[i][0]
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chan_index1 = pairsList[i][1]
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CSPCFactor= numpy.sum(ySamples[chan_index0]) * numpy.sum(ySamples[chan_index1])
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CSPCNorm= cspcLine.copy() / numpy.sqrt(CSPCFactor)
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CSPCSamples[i] = CSPCNorm-noise[i]
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coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor)
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coherence[i]= self.moving_average(coherence[i],N=2)
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phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi
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'''****** Getting fij width ******'''
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yMean=[]
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yMean2=[]
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for j in range(len(ySamples[1])):
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yMean=numpy.append(yMean,numpy.mean([ySamples[0,j],ySamples[1,j],ySamples[2,j]]))
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'''******* Getting fitting Gaussian ******'''
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meanGauss=sum(xSamples*yMean) / len(xSamples)
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sigma=sum(yMean*(xSamples-meanGauss)**2) / len(xSamples)
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if (abs(meanGauss/sigma**2) > 0.00001) :
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try:
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popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=[1,meanGauss,sigma])
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if numpy.amax(popt)>numpy.amax(yMean)*0.3:
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FitGauss=self.gaus(xSamples,*popt)
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else:
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FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
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print 'Verificador: Dentro', Height
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except RuntimeError:
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FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
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#
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else:
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FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
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Maximun=numpy.amax(yMean)
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eMinus1=Maximun*numpy.exp(-1)*0.8
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HWpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-eMinus1)))
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HalfWidth= xFrec[HWpos]
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GCpos=self.Find(FitGauss, numpy.amax(FitGauss))
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Vpos=self.Find(FactNorm, numpy.amax(FactNorm))
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#Vpos=FirstMoment[]
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'''****** 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]
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# error[indInvalid2] = 14
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# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
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# error[indInvalid1] = 13
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#
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# return heights, error
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