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import numpy
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import math
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from jroproc_base import ProcessingUnit, Operation
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from schainpy.model.data.jrodata import Spectra
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from schainpy.model.data.jrodata import hildebrand_sekhon
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class SpectraProc(ProcessingUnit):
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def __init__(self):
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ProcessingUnit.__init__(self)
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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 = Spectra()
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self.id_min = None
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self.id_max = None
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def __updateObjFromInput(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.channelIndexList = self.dataIn.channelIndexList
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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.flagShiftFFT = self.dataIn.flagShiftFFT
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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*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
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self.dataOut.frequency = self.dataIn.frequency
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self.dataOut.realtime = self.dataIn.realtime
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self.dataOut.azimuth = self.dataIn.azimuth
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self.dataOut.zenith = self.dataIn.zenith
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self.dataOut.beam.codeList = self.dataIn.beam.codeList
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self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
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self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
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def __getFft(self):
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"""
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Convierte valores de Voltaje a Spectra
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Affected:
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self.dataOut.data_spc
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self.dataOut.data_cspc
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self.dataOut.data_dc
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self.dataOut.heightList
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self.profIndex
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self.buffer
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self.dataOut.flagNoData
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"""
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fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1)
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fft_volt = fft_volt.astype(numpy.dtype('complex'))
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dc = fft_volt[:,0,:]
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#calculo de self-spectra
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fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
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spc = fft_volt * numpy.conjugate(fft_volt)
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spc = spc.real
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blocksize = 0
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blocksize += dc.size
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blocksize += spc.size
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cspc = None
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pairIndex = 0
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if self.dataOut.pairsList != None:
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#calculo de cross-spectra
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cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
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for pair in self.dataOut.pairsList:
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if pair[0] not in self.dataOut.channelList:
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raise ValueError, "Error getting CrossSpectra: pair 0 of (%s) is not in channelList=%s" %(str(pair), str(self.dataOut.channelList))
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if pair[1] not in self.dataOut.channelList:
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raise ValueError, "Error getting CrossSpectra: pair 1 of (%s) is not in channelList=%s" %(str(pair), str(self.dataOut.channelList))
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cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
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pairIndex += 1
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blocksize += cspc.size
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self.dataOut.data_spc = spc
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self.dataOut.data_cspc = cspc
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self.dataOut.data_dc = dc
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self.dataOut.blockSize = blocksize
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self.dataOut.flagShiftFFT = False
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def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None):
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self.dataOut.flagNoData = True
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if self.dataIn.type == "Spectra":
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self.dataOut.copy(self.dataIn)
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return True
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if self.dataIn.type == "Voltage":
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if nFFTPoints == None:
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raise ValueError, "This SpectraProc.run() need nFFTPoints input variable"
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if nProfiles == None:
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nProfiles = nFFTPoints
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# raise ValueError, "This SpectraProc.run() need nProfiles input variable"
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if ippFactor == None:
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ippFactor = 1
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self.dataOut.ippFactor = ippFactor
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self.dataOut.nFFTPoints = nFFTPoints
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self.dataOut.pairsList = pairsList
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if self.buffer == None:
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self.buffer = numpy.zeros((self.dataIn.nChannels,
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nProfiles,
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self.dataIn.nHeights),
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dtype='complex')
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self.id_min = 0
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self.id_max = self.dataIn.data.shape[1]
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if len(self.dataIn.data.shape) == 2:
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self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
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self.profIndex += 1
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else:
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if self.dataIn.data.shape[1] == nProfiles:
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self.buffer = self.dataIn.data.copy()
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self.profIndex = nProfiles
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elif self.dataIn.data.shape[1] < nProfiles:
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self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data
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self.profIndex += self.dataIn.data.shape[1]
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self.id_min += self.dataIn.data.shape[1]
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self.id_max += self.dataIn.data.shape[1]
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else:
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raise ValueError, "The type object %s has %d profiles, it should be equal to %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles)
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self.dataOut.flagNoData = True
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return 0
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if self.firstdatatime == None:
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self.firstdatatime = self.dataIn.utctime
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if self.profIndex == nProfiles:
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self.__updateObjFromInput()
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self.__getFft()
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self.dataOut.flagNoData = False
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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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return True
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raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type)
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def __selectPairs(self, channelList=None):
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if channelList == None:
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return
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pairsIndexListSelected = []
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for pairIndex in self.dataOut.pairsIndexList:
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#First pair
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if self.dataOut.pairsList[pairIndex][0] not in channelList:
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continue
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#Second pair
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if self.dataOut.pairsList[pairIndex][1] not in channelList:
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continue
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pairsIndexListSelected.append(pairIndex)
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if not pairsIndexListSelected:
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self.dataOut.data_cspc = None
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self.dataOut.pairsList = []
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return
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self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
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self.dataOut.pairsList = self.dataOut.pairsList[pairsIndexListSelected]
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return
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def selectChannels(self, channelList):
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channelIndexList = []
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for channel in channelList:
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if channel not in self.dataOut.channelList:
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raise ValueError, "Error selecting channels: The value %d in channelList is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList))
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index = self.dataOut.channelList.index(channel)
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channelIndexList.append(index)
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self.selectChannelsByIndex(channelIndexList)
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def selectChannelsByIndex(self, channelIndexList):
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"""
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Selecciona un bloque de datos en base a canales segun el channelIndexList
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Input:
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channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
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Affected:
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self.dataOut.data_spc
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self.dataOut.channelIndexList
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self.dataOut.nChannels
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Return:
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None
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"""
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for channelIndex in channelIndexList:
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if channelIndex not in self.dataOut.channelIndexList:
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raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList)
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# nChannels = len(channelIndexList)
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data_spc = self.dataOut.data_spc[channelIndexList,:]
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data_dc = self.dataOut.data_dc[channelIndexList,:]
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self.dataOut.data_spc = data_spc
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self.dataOut.data_dc = data_dc
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self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
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# self.dataOut.nChannels = nChannels
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self.__selectPairs(self.dataOut.channelList)
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return 1
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def selectHeights(self, minHei, maxHei):
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"""
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Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
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minHei <= height <= maxHei
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Input:
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minHei : valor minimo de altura a considerar
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maxHei : valor maximo de altura a considerar
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Affected:
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Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
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Return:
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1 si el metodo se ejecuto con exito caso contrario devuelve 0
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"""
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if (minHei > maxHei):
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raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei)
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if (minHei < self.dataOut.heightList[0]):
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minHei = self.dataOut.heightList[0]
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if (maxHei > self.dataOut.heightList[-1]):
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maxHei = self.dataOut.heightList[-1]
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# raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
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minIndex = 0
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maxIndex = 0
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heights = self.dataOut.heightList
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inda = numpy.where(heights >= minHei)
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indb = numpy.where(heights <= maxHei)
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try:
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minIndex = inda[0][0]
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except:
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minIndex = 0
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try:
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maxIndex = indb[0][-1]
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except:
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maxIndex = len(heights)
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self.selectHeightsByIndex(minIndex, maxIndex)
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return 1
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def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None):
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newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
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if hei_ref != None:
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newheis = numpy.where(self.dataOut.heightList>hei_ref)
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minIndex = min(newheis[0])
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maxIndex = max(newheis[0])
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data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
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heightList = self.dataOut.heightList[minIndex:maxIndex+1]
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# determina indices
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nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0]))
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avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0))
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beacon_dB = numpy.sort(avg_dB)[-nheis:]
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beacon_heiIndexList = []
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for val in avg_dB.tolist():
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if val >= beacon_dB[0]:
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beacon_heiIndexList.append(avg_dB.tolist().index(val))
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#data_spc = data_spc[:,:,beacon_heiIndexList]
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data_cspc = None
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if self.dataOut.data_cspc != None:
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data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
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#data_cspc = data_cspc[:,:,beacon_heiIndexList]
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data_dc = None
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if self.dataOut.data_dc != None:
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data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
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#data_dc = data_dc[:,beacon_heiIndexList]
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self.dataOut.data_spc = data_spc
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self.dataOut.data_cspc = data_cspc
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self.dataOut.data_dc = data_dc
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self.dataOut.heightList = heightList
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self.dataOut.beacon_heiIndexList = beacon_heiIndexList
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return 1
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def selectHeightsByIndex(self, minIndex, maxIndex):
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"""
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Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
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minIndex <= index <= maxIndex
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Input:
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minIndex : valor de indice minimo de altura a considerar
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maxIndex : valor de indice maximo de altura a considerar
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Affected:
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self.dataOut.data_spc
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self.dataOut.data_cspc
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self.dataOut.data_dc
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self.dataOut.heightList
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Return:
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1 si el metodo se ejecuto con exito caso contrario devuelve 0
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"""
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if (minIndex < 0) or (minIndex > maxIndex):
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raise ValueError, "Error selecting heights by index: Index range in (%d,%d) is not valid" % (minIndex, maxIndex)
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if (maxIndex >= self.dataOut.nHeights):
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maxIndex = self.dataOut.nHeights-1
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# raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
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# nHeights = maxIndex - minIndex + 1
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#Spectra
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data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
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data_cspc = None
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if self.dataOut.data_cspc != None:
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data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
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data_dc = None
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if self.dataOut.data_dc != None:
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data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
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self.dataOut.data_spc = data_spc
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self.dataOut.data_cspc = data_cspc
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self.dataOut.data_dc = data_dc
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self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
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return 1
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def removeDC(self, mode = 2):
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jspectra = self.dataOut.data_spc
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jcspectra = self.dataOut.data_cspc
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num_chan = jspectra.shape[0]
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num_hei = jspectra.shape[2]
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if jcspectra != None:
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jcspectraExist = True
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num_pairs = jcspectra.shape[0]
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else: jcspectraExist = False
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freq_dc = jspectra.shape[1]/2
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ind_vel = numpy.array([-2,-1,1,2]) + freq_dc
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if ind_vel[0]<0:
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ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof
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if mode == 1:
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jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION
|
|
|
|
|
|
if jcspectraExist:
|
|
|
jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2
|
|
|
|
|
|
if mode == 2:
|
|
|
|
|
|
vel = numpy.array([-2,-1,1,2])
|
|
|
xx = numpy.zeros([4,4])
|
|
|
|
|
|
for fil in range(4):
|
|
|
xx[fil,:] = vel[fil]**numpy.asarray(range(4))
|
|
|
|
|
|
xx_inv = numpy.linalg.inv(xx)
|
|
|
xx_aux = xx_inv[0,:]
|
|
|
|
|
|
for ich in range(num_chan):
|
|
|
yy = jspectra[ich,ind_vel,:]
|
|
|
jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy)
|
|
|
|
|
|
junkid = jspectra[ich,freq_dc,:]<=0
|
|
|
cjunkid = sum(junkid)
|
|
|
|
|
|
if cjunkid.any():
|
|
|
jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2
|
|
|
|
|
|
if jcspectraExist:
|
|
|
for ip in range(num_pairs):
|
|
|
yy = jcspectra[ip,ind_vel,:]
|
|
|
jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy)
|
|
|
|
|
|
|
|
|
self.dataOut.data_spc = jspectra
|
|
|
self.dataOut.data_cspc = jcspectra
|
|
|
|
|
|
return 1
|
|
|
|
|
|
def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):
|
|
|
|
|
|
jspectra = self.dataOut.data_spc
|
|
|
jcspectra = self.dataOut.data_cspc
|
|
|
jnoise = self.dataOut.getNoise()
|
|
|
num_incoh = self.dataOut.nIncohInt
|
|
|
|
|
|
num_channel = jspectra.shape[0]
|
|
|
num_prof = jspectra.shape[1]
|
|
|
num_hei = jspectra.shape[2]
|
|
|
|
|
|
#hei_interf
|
|
|
if hei_interf == None:
|
|
|
count_hei = num_hei/2 #Como es entero no importa
|
|
|
hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei
|
|
|
hei_interf = numpy.asarray(hei_interf)[0]
|
|
|
#nhei_interf
|
|
|
if (nhei_interf == None):
|
|
|
nhei_interf = 5
|
|
|
if (nhei_interf < 1):
|
|
|
nhei_interf = 1
|
|
|
if (nhei_interf > count_hei):
|
|
|
nhei_interf = count_hei
|
|
|
if (offhei_interf == None):
|
|
|
offhei_interf = 0
|
|
|
|
|
|
ind_hei = range(num_hei)
|
|
|
# mask_prof = numpy.asarray(range(num_prof - 2)) + 1
|
|
|
# mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
|
|
|
mask_prof = numpy.asarray(range(num_prof))
|
|
|
num_mask_prof = mask_prof.size
|
|
|
comp_mask_prof = [0, num_prof/2]
|
|
|
|
|
|
|
|
|
#noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
|
|
|
if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
|
|
|
jnoise = numpy.nan
|
|
|
noise_exist = jnoise[0] < numpy.Inf
|
|
|
|
|
|
#Subrutina de Remocion de la Interferencia
|
|
|
for ich in range(num_channel):
|
|
|
#Se ordena los espectros segun su potencia (menor a mayor)
|
|
|
power = jspectra[ich,mask_prof,:]
|
|
|
power = power[:,hei_interf]
|
|
|
power = power.sum(axis = 0)
|
|
|
psort = power.ravel().argsort()
|
|
|
|
|
|
#Se estima la interferencia promedio en los Espectros de Potencia empleando
|
|
|
junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]]
|
|
|
|
|
|
if noise_exist:
|
|
|
# tmp_noise = jnoise[ich] / num_prof
|
|
|
tmp_noise = jnoise[ich]
|
|
|
junkspc_interf = junkspc_interf - tmp_noise
|
|
|
#junkspc_interf[:,comp_mask_prof] = 0
|
|
|
|
|
|
jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf
|
|
|
jspc_interf = jspc_interf.transpose()
|
|
|
#Calculando el espectro de interferencia promedio
|
|
|
noiseid = numpy.where(jspc_interf <= tmp_noise/ math.sqrt(num_incoh))
|
|
|
noiseid = noiseid[0]
|
|
|
cnoiseid = noiseid.size
|
|
|
interfid = numpy.where(jspc_interf > tmp_noise/ math.sqrt(num_incoh))
|
|
|
interfid = interfid[0]
|
|
|
cinterfid = interfid.size
|
|
|
|
|
|
if (cnoiseid > 0): jspc_interf[noiseid] = 0
|
|
|
|
|
|
#Expandiendo los perfiles a limpiar
|
|
|
if (cinterfid > 0):
|
|
|
new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof
|
|
|
new_interfid = numpy.asarray(new_interfid)
|
|
|
new_interfid = {x for x in new_interfid}
|
|
|
new_interfid = numpy.array(list(new_interfid))
|
|
|
new_cinterfid = new_interfid.size
|
|
|
else: new_cinterfid = 0
|
|
|
|
|
|
for ip in range(new_cinterfid):
|
|
|
ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort()
|
|
|
jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]]
|
|
|
|
|
|
|
|
|
jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices
|
|
|
|
|
|
#Removiendo la interferencia del punto de mayor interferencia
|
|
|
ListAux = jspc_interf[mask_prof].tolist()
|
|
|
maxid = ListAux.index(max(ListAux))
|
|
|
|
|
|
|
|
|
if cinterfid > 0:
|
|
|
for ip in range(cinterfid*(interf == 2) - 1):
|
|
|
ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/math.sqrt(num_incoh))).nonzero()
|
|
|
cind = len(ind)
|
|
|
|
|
|
if (cind > 0):
|
|
|
jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/math.sqrt(num_incoh))
|
|
|
|
|
|
ind = numpy.array([-2,-1,1,2])
|
|
|
xx = numpy.zeros([4,4])
|
|
|
|
|
|
for id1 in range(4):
|
|
|
xx[:,id1] = ind[id1]**numpy.asarray(range(4))
|
|
|
|
|
|
xx_inv = numpy.linalg.inv(xx)
|
|
|
xx = xx_inv[:,0]
|
|
|
ind = (ind + maxid + num_mask_prof)%num_mask_prof
|
|
|
yy = jspectra[ich,mask_prof[ind],:]
|
|
|
jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
|
|
|
|
|
|
|
|
|
indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/math.sqrt(num_incoh))).nonzero()
|
|
|
jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/math.sqrt(num_incoh))
|
|
|
|
|
|
#Remocion de Interferencia en el Cross Spectra
|
|
|
if jcspectra == None: return jspectra, jcspectra
|
|
|
num_pairs = jcspectra.size/(num_prof*num_hei)
|
|
|
jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
|
|
|
|
|
|
for ip in range(num_pairs):
|
|
|
|
|
|
#-------------------------------------------
|
|
|
|
|
|
cspower = numpy.abs(jcspectra[ip,mask_prof,:])
|
|
|
cspower = cspower[:,hei_interf]
|
|
|
cspower = cspower.sum(axis = 0)
|
|
|
|
|
|
cspsort = cspower.ravel().argsort()
|
|
|
junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]]
|
|
|
junkcspc_interf = junkcspc_interf.transpose()
|
|
|
jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf
|
|
|
|
|
|
ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
|
|
|
|
|
|
median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
|
|
|
median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
|
|
|
junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag)
|
|
|
|
|
|
for iprof in range(num_prof):
|
|
|
ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort()
|
|
|
jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]]
|
|
|
|
|
|
#Removiendo la Interferencia
|
|
|
jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf
|
|
|
|
|
|
ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
|
|
|
maxid = ListAux.index(max(ListAux))
|
|
|
|
|
|
ind = numpy.array([-2,-1,1,2])
|
|
|
xx = numpy.zeros([4,4])
|
|
|
|
|
|
for id1 in range(4):
|
|
|
xx[:,id1] = ind[id1]**numpy.asarray(range(4))
|
|
|
|
|
|
xx_inv = numpy.linalg.inv(xx)
|
|
|
xx = xx_inv[:,0]
|
|
|
|
|
|
ind = (ind + maxid + num_mask_prof)%num_mask_prof
|
|
|
yy = jcspectra[ip,mask_prof[ind],:]
|
|
|
jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
|
|
|
|
|
|
#Guardar Resultados
|
|
|
self.dataOut.data_spc = jspectra
|
|
|
self.dataOut.data_cspc = jcspectra
|
|
|
|
|
|
return 1
|
|
|
|
|
|
def setRadarFrequency(self, frequency=None):
|
|
|
if frequency != None:
|
|
|
self.dataOut.frequency = frequency
|
|
|
|
|
|
return 1
|
|
|
|
|
|
def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
|
|
|
#validacion de rango
|
|
|
if minHei == None:
|
|
|
minHei = self.dataOut.heightList[0]
|
|
|
|
|
|
if maxHei == None:
|
|
|
maxHei = self.dataOut.heightList[-1]
|
|
|
|
|
|
if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
|
|
|
print 'minHei: %.2f is out of the heights range'%(minHei)
|
|
|
print 'minHei is setting to %.2f'%(self.dataOut.heightList[0])
|
|
|
minHei = self.dataOut.heightList[0]
|
|
|
|
|
|
if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
|
|
|
print 'maxHei: %.2f is out of the heights range'%(maxHei)
|
|
|
print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1])
|
|
|
maxHei = self.dataOut.heightList[-1]
|
|
|
|
|
|
# validacion de velocidades
|
|
|
velrange = self.dataOut.getVelRange(1)
|
|
|
|
|
|
if minVel == None:
|
|
|
minVel = velrange[0]
|
|
|
|
|
|
if maxVel == None:
|
|
|
maxVel = velrange[-1]
|
|
|
|
|
|
if (minVel < velrange[0]) or (minVel > maxVel):
|
|
|
print 'minVel: %.2f is out of the velocity range'%(minVel)
|
|
|
print 'minVel is setting to %.2f'%(velrange[0])
|
|
|
minVel = velrange[0]
|
|
|
|
|
|
if (maxVel > velrange[-1]) or (maxVel < minVel):
|
|
|
print 'maxVel: %.2f is out of the velocity range'%(maxVel)
|
|
|
print 'maxVel is setting to %.2f'%(velrange[-1])
|
|
|
maxVel = velrange[-1]
|
|
|
|
|
|
# seleccion de indices para rango
|
|
|
minIndex = 0
|
|
|
maxIndex = 0
|
|
|
heights = self.dataOut.heightList
|
|
|
|
|
|
inda = numpy.where(heights >= minHei)
|
|
|
indb = numpy.where(heights <= maxHei)
|
|
|
|
|
|
try:
|
|
|
minIndex = inda[0][0]
|
|
|
except:
|
|
|
minIndex = 0
|
|
|
|
|
|
try:
|
|
|
maxIndex = indb[0][-1]
|
|
|
except:
|
|
|
maxIndex = len(heights)
|
|
|
|
|
|
if (minIndex < 0) or (minIndex > maxIndex):
|
|
|
raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
|
|
|
|
|
|
if (maxIndex >= self.dataOut.nHeights):
|
|
|
maxIndex = self.dataOut.nHeights-1
|
|
|
|
|
|
# seleccion de indices para velocidades
|
|
|
indminvel = numpy.where(velrange >= minVel)
|
|
|
indmaxvel = numpy.where(velrange <= maxVel)
|
|
|
try:
|
|
|
minIndexVel = indminvel[0][0]
|
|
|
except:
|
|
|
minIndexVel = 0
|
|
|
|
|
|
try:
|
|
|
maxIndexVel = indmaxvel[0][-1]
|
|
|
except:
|
|
|
maxIndexVel = len(velrange)
|
|
|
|
|
|
#seleccion del espectro
|
|
|
data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1]
|
|
|
#estimacion de ruido
|
|
|
noise = numpy.zeros(self.dataOut.nChannels)
|
|
|
|
|
|
for channel in range(self.dataOut.nChannels):
|
|
|
daux = data_spc[channel,:,:]
|
|
|
noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
|
|
|
|
|
|
self.dataOut.noise_estimation = noise.copy()
|
|
|
|
|
|
return 1
|
|
|
|
|
|
class IncohInt(Operation):
|
|
|
|
|
|
|
|
|
__profIndex = 0
|
|
|
__withOverapping = False
|
|
|
|
|
|
__byTime = False
|
|
|
__initime = None
|
|
|
__lastdatatime = None
|
|
|
__integrationtime = None
|
|
|
|
|
|
__buffer_spc = None
|
|
|
__buffer_cspc = None
|
|
|
__buffer_dc = None
|
|
|
|
|
|
__dataReady = False
|
|
|
|
|
|
__timeInterval = None
|
|
|
|
|
|
n = None
|
|
|
|
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
|
Operation.__init__(self)
|
|
|
# self.isConfig = False
|
|
|
|
|
|
def setup(self, n=None, timeInterval=None, overlapping=False):
|
|
|
"""
|
|
|
Set the parameters of the integration class.
|
|
|
|
|
|
Inputs:
|
|
|
|
|
|
n : Number of coherent integrations
|
|
|
timeInterval : Time of integration. If the parameter "n" is selected this one does not work
|
|
|
overlapping :
|
|
|
|
|
|
"""
|
|
|
|
|
|
self.__initime = None
|
|
|
self.__lastdatatime = 0
|
|
|
self.__buffer_spc = None
|
|
|
self.__buffer_cspc = None
|
|
|
self.__buffer_dc = None
|
|
|
self.__dataReady = False
|
|
|
|
|
|
|
|
|
if n == None and timeInterval == None:
|
|
|
raise ValueError, "n or timeInterval should be specified ..."
|
|
|
|
|
|
if n != None:
|
|
|
self.n = n
|
|
|
self.__byTime = False
|
|
|
else:
|
|
|
self.__integrationtime = timeInterval #if (type(timeInterval)!=integer) -> change this line
|
|
|
self.n = 9999
|
|
|
self.__byTime = True
|
|
|
|
|
|
if overlapping:
|
|
|
self.__withOverapping = True
|
|
|
else:
|
|
|
self.__withOverapping = False
|
|
|
self.__buffer_spc = 0
|
|
|
self.__buffer_cspc = 0
|
|
|
self.__buffer_dc = 0
|
|
|
|
|
|
self.__profIndex = 0
|
|
|
|
|
|
def putData(self, data_spc, data_cspc, data_dc):
|
|
|
|
|
|
"""
|
|
|
Add a profile to the __buffer_spc and increase in one the __profileIndex
|
|
|
|
|
|
"""
|
|
|
|
|
|
if not self.__withOverapping:
|
|
|
self.__buffer_spc += data_spc
|
|
|
|
|
|
if data_cspc == None:
|
|
|
self.__buffer_cspc = None
|
|
|
else:
|
|
|
self.__buffer_cspc += data_cspc
|
|
|
|
|
|
if data_dc == None:
|
|
|
self.__buffer_dc = None
|
|
|
else:
|
|
|
self.__buffer_dc += data_dc
|
|
|
|
|
|
self.__profIndex += 1
|
|
|
return
|
|
|
|
|
|
#Overlapping data
|
|
|
nChannels, nFFTPoints, nHeis = data_spc.shape
|
|
|
data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
|
|
|
if data_cspc != None:
|
|
|
data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
|
|
|
if data_dc != None:
|
|
|
data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
|
|
|
|
|
|
#If the buffer is empty then it takes the data value
|
|
|
if self.__buffer_spc == None:
|
|
|
self.__buffer_spc = data_spc
|
|
|
|
|
|
if data_cspc == None:
|
|
|
self.__buffer_cspc = None
|
|
|
else:
|
|
|
self.__buffer_cspc += data_cspc
|
|
|
|
|
|
if data_dc == None:
|
|
|
self.__buffer_dc = None
|
|
|
else:
|
|
|
self.__buffer_dc += data_dc
|
|
|
|
|
|
self.__profIndex += 1
|
|
|
return
|
|
|
|
|
|
#If the buffer length is lower than n then stakcing the data value
|
|
|
if self.__profIndex < self.n:
|
|
|
self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
|
|
|
|
|
|
if data_cspc != None:
|
|
|
self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
|
|
|
|
|
|
if data_dc != None:
|
|
|
self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
|
|
|
|
|
|
self.__profIndex += 1
|
|
|
return
|
|
|
|
|
|
#If the buffer length is equal to n then replacing the last buffer value with the data value
|
|
|
self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
|
|
|
self.__buffer_spc[self.n-1] = data_spc
|
|
|
|
|
|
if data_cspc != None:
|
|
|
self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
|
|
|
self.__buffer_cspc[self.n-1] = data_cspc
|
|
|
|
|
|
if data_dc != None:
|
|
|
self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
|
|
|
self.__buffer_dc[self.n-1] = data_dc
|
|
|
|
|
|
self.__profIndex = self.n
|
|
|
return
|
|
|
|
|
|
|
|
|
def pushData(self):
|
|
|
"""
|
|
|
Return the sum of the last profiles and the profiles used in the sum.
|
|
|
|
|
|
Affected:
|
|
|
|
|
|
self.__profileIndex
|
|
|
|
|
|
"""
|
|
|
data_spc = None
|
|
|
data_cspc = None
|
|
|
data_dc = None
|
|
|
|
|
|
if not self.__withOverapping:
|
|
|
data_spc = self.__buffer_spc
|
|
|
data_cspc = self.__buffer_cspc
|
|
|
data_dc = self.__buffer_dc
|
|
|
|
|
|
n = self.__profIndex
|
|
|
|
|
|
self.__buffer_spc = 0
|
|
|
self.__buffer_cspc = 0
|
|
|
self.__buffer_dc = 0
|
|
|
self.__profIndex = 0
|
|
|
|
|
|
return data_spc, data_cspc, data_dc, n
|
|
|
|
|
|
#Integration with Overlapping
|
|
|
data_spc = numpy.sum(self.__buffer_spc, axis=0)
|
|
|
|
|
|
if self.__buffer_cspc != None:
|
|
|
data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
|
|
|
|
|
|
if self.__buffer_dc != None:
|
|
|
data_dc = numpy.sum(self.__buffer_dc, axis=0)
|
|
|
|
|
|
n = self.__profIndex
|
|
|
|
|
|
return data_spc, data_cspc, data_dc, n
|
|
|
|
|
|
def byProfiles(self, *args):
|
|
|
|
|
|
self.__dataReady = False
|
|
|
avgdata_spc = None
|
|
|
avgdata_cspc = None
|
|
|
avgdata_dc = None
|
|
|
# n = None
|
|
|
|
|
|
self.putData(*args)
|
|
|
|
|
|
if self.__profIndex == self.n:
|
|
|
|
|
|
avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
|
|
|
self.__dataReady = True
|
|
|
|
|
|
return avgdata_spc, avgdata_cspc, avgdata_dc
|
|
|
|
|
|
def byTime(self, datatime, *args):
|
|
|
|
|
|
self.__dataReady = False
|
|
|
avgdata_spc = None
|
|
|
avgdata_cspc = None
|
|
|
avgdata_dc = None
|
|
|
n = None
|
|
|
|
|
|
self.putData(*args)
|
|
|
|
|
|
if (datatime - self.__initime) >= self.__integrationtime:
|
|
|
avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
|
|
|
self.n = n
|
|
|
self.__dataReady = True
|
|
|
|
|
|
return avgdata_spc, avgdata_cspc, avgdata_dc
|
|
|
|
|
|
def integrate(self, datatime, *args):
|
|
|
|
|
|
if self.__initime == None:
|
|
|
self.__initime = datatime
|
|
|
|
|
|
if self.__byTime:
|
|
|
avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
|
|
|
else:
|
|
|
avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
|
|
|
|
|
|
self.__lastdatatime = datatime
|
|
|
|
|
|
if avgdata_spc == None:
|
|
|
return None, None, None, None
|
|
|
|
|
|
avgdatatime = self.__initime
|
|
|
try:
|
|
|
self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
|
|
|
except:
|
|
|
self.__timeInterval = self.__lastdatatime - self.__initime
|
|
|
|
|
|
deltatime = datatime -self.__lastdatatime
|
|
|
|
|
|
if not self.__withOverapping:
|
|
|
self.__initime = datatime
|
|
|
else:
|
|
|
self.__initime += deltatime
|
|
|
|
|
|
return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
|
|
|
|
|
|
def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
|
|
|
|
|
|
if n==1:
|
|
|
dataOut.flagNoData = False
|
|
|
return
|
|
|
|
|
|
if not self.isConfig:
|
|
|
self.setup(n, timeInterval, overlapping)
|
|
|
self.isConfig = True
|
|
|
|
|
|
avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
|
|
|
dataOut.data_spc,
|
|
|
dataOut.data_cspc,
|
|
|
dataOut.data_dc)
|
|
|
|
|
|
# dataOut.timeInterval *= n
|
|
|
dataOut.flagNoData = True
|
|
|
|
|
|
if self.__dataReady:
|
|
|
|
|
|
dataOut.data_spc = avgdata_spc
|
|
|
dataOut.data_cspc = avgdata_cspc
|
|
|
dataOut.data_dc = avgdata_dc
|
|
|
|
|
|
dataOut.nIncohInt *= self.n
|
|
|
dataOut.utctime = avgdatatime
|
|
|
#dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
|
|
|
# dataOut.timeInterval = self.__timeInterval*self.n
|
|
|
dataOut.flagNoData = False
|
|
|
|
