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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 SpectraHeis
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class SpectraHeisProc(ProcessingUnit):
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def __init__(self, **kwargs):
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ProcessingUnit.__init__(self, **kwargs)
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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 = SpectraHeis()
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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 = self.dataIn.dtype
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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 = 1
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self.dataOut.ippFactor = 1
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self.dataOut.noise_estimation = None
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# self.dataOut.nProfiles = self.dataOut.nFFTPoints
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self.dataOut.nFFTPoints = self.dataIn.nHeights
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# self.dataOut.channelIndexList = self.dataIn.channelIndexList
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# self.dataOut.flagNoData = self.dataIn.flagNoData
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self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
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self.dataOut.utctime = self.dataIn.utctime
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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.nIncohInt
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# self.dataOut.set=self.dataIn.set
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# self.dataOut.deltaHeight=self.dataIn.deltaHeight
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def __updateObjFromFits(self):
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self.dataOut.utctime = self.dataIn.utctime
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# self.dataOut.channelIndexList = self.dataIn.channelIndexList
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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.data_spc = self.dataIn.data
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self.dataOut.ippSeconds = self.dataIn.ippSeconds
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self.dataOut.nCohInt = self.dataIn.nCohInt
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self.dataOut.nIncohInt = self.dataIn.nIncohInt
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# self.dataOut.timeInterval = self.dataIn.timeInterval
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self.dataOut.timeZone = self.dataIn.timeZone
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self.dataOut.useLocalTime = True
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# self.dataOut.
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# self.dataOut.
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def __getFft(self):
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fft_volt = numpy.fft.fft(self.dataIn.data, axis=1)
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fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
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spc = numpy.abs(fft_volt * numpy.conjugate(fft_volt))/(self.dataOut.nFFTPoints)
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self.dataOut.data_spc = spc
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def run(self):
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self.dataOut.flagNoData = True
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if self.dataIn.type == "Fits":
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self.__updateObjFromFits()
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self.dataOut.flagNoData = False
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return
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if self.dataIn.type == "SpectraHeis":
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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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self.__updateObjFromVoltage()
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self.__getFft()
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self.dataOut.flagNoData = False
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return
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raise ValueError, "The type object %s is not valid"%(self.dataIn.type)
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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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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
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self.dataOut.channelIndexList
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self.dataOut.nChannels
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self.dataOut.m_ProcessingHeader.totalSpectra
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self.dataOut.systemHeaderObj.numChannels
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self.dataOut.m_ProcessingHeader.blockSize
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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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print channelIndexList
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raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
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# nChannels = len(channelIndexList)
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data_spc = self.dataOut.data_spc[channelIndexList,:]
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self.dataOut.data_spc = data_spc
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self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
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return 1
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class IncohInt4SpectraHeis(Operation):
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isConfig = False
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__profIndex = 0
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__withOverapping = False
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__byTime = False
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__initime = None
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__lastdatatime = None
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__integrationtime = None
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__buffer = None
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__dataReady = False
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n = None
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def __init__(self, **kwargs):
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Operation.__init__(self, **kwargs)
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# self.isConfig = False
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def setup(self, n=None, timeInterval=None, overlapping=False):
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"""
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Set the parameters of the integration class.
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Inputs:
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n : Number of coherent integrations
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timeInterval : Time of integration. If the parameter "n" is selected this one does not work
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overlapping :
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"""
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self.__initime = None
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self.__lastdatatime = 0
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self.__buffer = None
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self.__dataReady = False
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if n == None and timeInterval == None:
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raise ValueError, "n or timeInterval should be specified ..."
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if n != None:
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self.n = n
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self.__byTime = False
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else:
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self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
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self.n = 9999
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self.__byTime = True
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if overlapping:
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self.__withOverapping = True
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self.__buffer = None
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else:
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self.__withOverapping = False
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self.__buffer = 0
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self.__profIndex = 0
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def putData(self, data):
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"""
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Add a profile to the __buffer and increase in one the __profileIndex
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"""
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if not self.__withOverapping:
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self.__buffer += data.copy()
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self.__profIndex += 1
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return
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#Overlapping data
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nChannels, nHeis = data.shape
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data = numpy.reshape(data, (1, nChannels, nHeis))
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#If the buffer is empty then it takes the data value
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if self.__buffer is None:
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self.__buffer = data
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self.__profIndex += 1
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return
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#If the buffer length is lower than n then stakcing the data value
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if self.__profIndex < self.n:
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self.__buffer = numpy.vstack((self.__buffer, data))
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self.__profIndex += 1
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return
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#If the buffer length is equal to n then replacing the last buffer value with the data value
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self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
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self.__buffer[self.n-1] = data
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self.__profIndex = self.n
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return
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def pushData(self):
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"""
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Return the sum of the last profiles and the profiles used in the sum.
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Affected:
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self.__profileIndex
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"""
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if not self.__withOverapping:
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data = self.__buffer
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n = self.__profIndex
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self.__buffer = 0
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self.__profIndex = 0
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return data, n
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#Integration with Overlapping
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data = numpy.sum(self.__buffer, axis=0)
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n = self.__profIndex
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return data, n
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def byProfiles(self, data):
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self.__dataReady = False
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avgdata = None
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# n = None
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self.putData(data)
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if self.__profIndex == self.n:
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avgdata, n = self.pushData()
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self.__dataReady = True
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return avgdata
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def byTime(self, data, datatime):
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self.__dataReady = False
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avgdata = None
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n = None
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self.putData(data)
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if (datatime - self.__initime) >= self.__integrationtime:
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avgdata, n = self.pushData()
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self.n = n
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self.__dataReady = True
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return avgdata
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def integrate(self, data, datatime=None):
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if self.__initime == None:
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self.__initime = datatime
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if self.__byTime:
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avgdata = self.byTime(data, datatime)
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else:
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avgdata = self.byProfiles(data)
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self.__lastdatatime = datatime
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if avgdata is None:
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return None, None
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avgdatatime = self.__initime
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deltatime = datatime -self.__lastdatatime
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if not self.__withOverapping:
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self.__initime = datatime
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else:
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self.__initime += deltatime
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return avgdata, avgdatatime
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def run(self, dataOut, n=None, timeInterval=None, overlapping=False, **kwargs):
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if not self.isConfig:
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self.setup(n=n, timeInterval=timeInterval, overlapping=overlapping)
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self.isConfig = True
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avgdata, avgdatatime = self.integrate(dataOut.data_spc, dataOut.utctime)
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# dataOut.timeInterval *= n
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dataOut.flagNoData = True
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if self.__dataReady:
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dataOut.data_spc = avgdata
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dataOut.nIncohInt *= self.n
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# dataOut.nCohInt *= self.n
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dataOut.utctime = avgdatatime
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# dataOut.timeInterval = dataOut.ippSeconds * dataOut.nIncohInt
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# dataOut.timeInterval = self.__timeInterval*self.n
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dataOut.flagNoData = False
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