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import numpy
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from .jroproc_base import ProcessingUnit, Operation
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from schainpy.model.data.jrodata import Correlation, hildebrand_sekhon
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class CorrelationProc(ProcessingUnit):
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pairsList = None
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data_cf = None
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def __init__(self, **kwargs):
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ProcessingUnit.__init__(self, **kwargs)
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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 = Correlation()
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def __updateObjFromVoltage(self):
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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.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.nProfiles = self.dataIn.nProfiles
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self.dataOut.utctime = self.dataIn.utctime
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# self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
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# self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nPoints
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def removeDC(self, jspectra):
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nChannel = jspectra.shape[0]
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for i in range(nChannel):
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jspectra_tmp = jspectra[i,:,:]
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jspectra_DC = numpy.mean(jspectra_tmp,axis = 0)
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jspectra_tmp = jspectra_tmp - jspectra_DC
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jspectra[i,:,:] = jspectra_tmp
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return jspectra
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def removeNoise(self, mode = 2):
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indR = numpy.where(self.dataOut.lagR == 0)[0][0]
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indT = numpy.where(self.dataOut.lagT == 0)[0][0]
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jspectra = self.dataOut.data_corr[:,:,indR,:]
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num_chan = jspectra.shape[0]
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num_hei = jspectra.shape[2]
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freq_dc = indT
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ind_vel = numpy.array([-2,-1,1,2]) + freq_dc
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NPot = self.dataOut.getNoise(mode)
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jspectra[:,freq_dc,:] = jspectra[:,freq_dc,:] - NPot
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SPot = jspectra[:,freq_dc,:]
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pairsAutoCorr = self.dataOut.getPairsAutoCorr()
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# self.dataOut.signalPotency = SPot
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self.dataOut.noise = NPot
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self.dataOut.SNR = (SPot/NPot)[pairsAutoCorr]
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self.dataOut.data_corr[:,:,indR,:] = jspectra
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return 1
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def run(self, lags=None, mode = 'time', pairsList=None, fullBuffer=False, nAvg = 1, removeDC = False, splitCF=False):
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self.dataOut.flagNoData = True
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if self.dataIn.type == "Correlation":
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self.dataOut.copy(self.dataIn)
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return
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if self.dataIn.type == "Voltage":
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nChannels = self.dataIn.nChannels
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nProfiles = self.dataIn.nProfiles
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nHeights = self.dataIn.nHeights
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data_pre = self.dataIn.data
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#--------------- Remover DC ------------
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if removeDC:
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data_pre = self.removeDC(data_pre)
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#---------------------------------------------
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# pairsList = list(ccfList)
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# for i in acfList:
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# pairsList.append((i,i))
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#
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# ccf_pairs = numpy.arange(len(ccfList))
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# acf_pairs = numpy.arange(len(ccfList),len(pairsList))
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self.__updateObjFromVoltage()
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#----------------------------------------------------------------------
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#Creating temporal buffers
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if fullBuffer:
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tmp = numpy.zeros((len(pairsList), len(lags), nProfiles, nHeights), dtype = 'complex')*numpy.nan
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elif mode == 'time':
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if lags == None:
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lags = numpy.arange(-nProfiles+1, nProfiles)
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tmp = numpy.zeros((len(pairsList), len(lags), nHeights),dtype='complex')
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elif mode == 'height':
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if lags == None:
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lags = numpy.arange(-nHeights+1, nHeights)
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tmp = numpy.zeros(len(pairsList), (len(lags), nProfiles),dtype='complex')
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#For loop
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for l in range(len(pairsList)):
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ch0 = pairsList[l][0]
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ch1 = pairsList[l][1]
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for i in range(len(lags)):
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idx = lags[i]
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if idx >= 0:
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if mode == 'time':
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ccf0 = data_pre[ch0,:nProfiles-idx,:]*numpy.conj(data_pre[ch1,idx:,:]) #time
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else:
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ccf0 = data_pre[ch0,:,nHeights-idx]*numpy.conj(data_pre[ch1,:,idx:]) #heights
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else:
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if mode == 'time':
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ccf0 = data_pre[ch0,-idx:,:]*numpy.conj(data_pre[ch1,:nProfiles+idx,:]) #time
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else:
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ccf0 = data_pre[ch0,:,-idx:]*numpy.conj(data_pre[ch1,:,:nHeights+idx]) #heights
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if fullBuffer:
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tmp[l,i,:ccf0.shape[0],:] = ccf0
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else:
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tmp[l,i,:] = numpy.sum(ccf0, axis=0)
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#-----------------------------------------------------------------
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if fullBuffer:
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tmp = numpy.sum(numpy.reshape(tmp,(tmp.shape[0],tmp.shape[1],tmp.shape[2]/nAvg,nAvg,tmp.shape[3])),axis=3)
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self.dataOut.nAvg = nAvg
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self.dataOut.data_cf = tmp
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self.dataOut.mode = mode
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self.dataOut.nLags = len(lags)
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self.dataOut.pairsList = pairsList
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self.dataOut.nPairs = len(pairsList)
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#Se Calcula los factores de Normalizacion
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if mode == 'time':
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delta = self.dataIn.ippSeconds*self.dataIn.nCohInt
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else:
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delta = self.dataIn.heightList[1] - self.dataIn.heightList[0]
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self.dataOut.lagRange = numpy.array(lags)*delta
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# self.dataOut.nCohInt = self.dataIn.nCohInt*nAvg
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self.dataOut.flagNoData = False
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# a = self.dataOut.normFactor
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return
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