schain-jc Commit - r1132:b323193ace39

Fix saving shifted pdata, add shift_fft parameter in SpectraProc to correct shifted pdata

jespinoza -

r1132:b323193ace39

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r1132:b323193ace39

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schainpy/model/io/jroIO_spectra.py +3 -3

             '''
             Created on Jul 2, 2014
             @author: roj-idl71
             '''
             import numpy
             from jroIO_base import LOCALTIME, JRODataReader, JRODataWriter
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
             from schainpy.model.data.jroheaderIO import PROCFLAG, BasicHeader, SystemHeader, RadarControllerHeader, ProcessingHeader
             from schainpy.model.data.jrodata import Spectra
             class SpectraReader(JRODataReader, ProcessingUnit):
                 """
                 Esta clase permite leer datos de espectros desde archivos procesados (.pdata). La lectura
                 de los datos siempre se realiza por bloques. Los datos leidos (array de 3 dimensiones)
                 son almacenados en tres buffer's para el Self Spectra, el Cross Spectra y el DC Channel.
                                     paresCanalesIguales    * alturas * perfiles  (Self Spectra)
                                     paresCanalesDiferentes * alturas * perfiles  (Cross Spectra)
                                     canales * alturas                            (DC Channels)
                 Esta clase contiene instancias (objetos) de las clases BasicHeader, SystemHeader,
                 RadarControllerHeader y Spectra. Los tres primeros se usan para almacenar informacion de la
                 cabecera de datos (metadata), y el cuarto (Spectra) para obtener y almacenar un bloque de
                 datos desde el "buffer" cada vez que se ejecute el metodo "getData".
                 Example:
                     dpath = "/home/myuser/data"
                     startTime = datetime.datetime(2010,1,20,0,0,0,0,0,0)
                     endTime = datetime.datetime(2010,1,21,23,59,59,0,0,0)
                     readerObj = SpectraReader()
                     readerObj.setup(dpath, startTime, endTime)
                     while(True):
                         readerObj.getData()
                         print readerObj.data_spc
                         print readerObj.data_cspc
                         print readerObj.data_dc
                         if readerObj.flagNoMoreFiles:
                             break
                 """
                 pts2read_SelfSpectra = 0
                 pts2read_CrossSpectra = 0
                 pts2read_DCchannels = 0
                 ext = ".pdata"
                 optchar = "P"
                 dataOut = None
                 nRdChannels = None
                 nRdPairs = None
                 rdPairList = []
                 def __init__(self, **kwargs):
                     """
                     Inicializador de la clase SpectraReader para la lectura de datos de espectros.
                     Inputs:
                         dataOut    :    Objeto de la clase Spectra. Este objeto sera utilizado para
                                           almacenar un perfil de datos cada vez que se haga un requerimiento
                                           (getData). El perfil sera obtenido a partir del buffer de datos,
                                           si el buffer esta vacio se hara un nuevo proceso de lectura de un
                                           bloque de datos.
                                           Si este parametro no es pasado se creara uno internamente.
                     Affected:
                         self.dataOut
                     Return      : None
                     """
                     #Eliminar de la base la herencia
                     ProcessingUnit.__init__(self, **kwargs)
             #         self.isConfig = False
                     self.pts2read_SelfSpectra = 0
                     self.pts2read_CrossSpectra = 0
                     self.pts2read_DCchannels = 0
                     self.datablock = None
                     self.utc = None
                     self.ext = ".pdata"
                     self.optchar = "P"
                     self.basicHeaderObj = BasicHeader(LOCALTIME)
                     self.systemHeaderObj = SystemHeader()
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.processingHeaderObj = ProcessingHeader()
                     self.online = 0
                     self.fp = None
                     self.idFile = None
                     self.dtype = None
                     self.fileSizeByHeader = None
                     self.filenameList = []
                     self.filename = None
                     self.fileSize = None
                     self.firstHeaderSize = 0
                     self.basicHeaderSize = 24
                     self.pathList = []
                     self.lastUTTime = 0
                     self.maxTimeStep = 30
                     self.flagNoMoreFiles = 0
                     self.set = 0
                     self.path = None
                     self.delay  = 60   #seconds
                     self.nTries = 3  #quantity tries
                     self.nFiles = 3   #number of files for searching
                     self.nReadBlocks = 0
                     self.flagIsNewFile = 1
                     self.__isFirstTimeOnline = 1
             #         self.ippSeconds = 0
                     self.flagDiscontinuousBlock = 0
                     self.flagIsNewBlock = 0
                     self.nTotalBlocks = 0
                     self.blocksize = 0
                     self.dataOut = self.createObjByDefault()
                     self.profileIndex = 1 #Always
                 def createObjByDefault(self):
                     dataObj = Spectra()
                     return dataObj
                 def __hasNotDataInBuffer(self):
                     return 1
                 def getBlockDimension(self):
                     """
                     Obtiene la cantidad de puntos a leer por cada bloque de datos
                     Affected:
                         self.nRdChannels
                         self.nRdPairs
                         self.pts2read_SelfSpectra
                         self.pts2read_CrossSpectra
                         self.pts2read_DCchannels
                         self.blocksize
                         self.dataOut.nChannels
                         self.dataOut.nPairs
                     Return:
                         None
                     """
                     self.nRdChannels = 0
                     self.nRdPairs = 0
                     self.rdPairList = []
                     for i in range(0, self.processingHeaderObj.totalSpectra*2, 2):
                         if self.processingHeaderObj.spectraComb[i] == self.processingHeaderObj.spectraComb[i+1]:
                             self.nRdChannels = self.nRdChannels + 1 #par de canales iguales
                         else:
                             self.nRdPairs = self.nRdPairs + 1 #par de canales diferentes
                             self.rdPairList.append((self.processingHeaderObj.spectraComb[i], self.processingHeaderObj.spectraComb[i+1]))
                     pts2read = self.processingHeaderObj.nHeights * self.processingHeaderObj.profilesPerBlock
                     self.pts2read_SelfSpectra = int(self.nRdChannels * pts2read)
                     self.blocksize = self.pts2read_SelfSpectra
                     if self.processingHeaderObj.flag_cspc:
                         self.pts2read_CrossSpectra = int(self.nRdPairs * pts2read)
                         self.blocksize += self.pts2read_CrossSpectra
                     if self.processingHeaderObj.flag_dc:
                         self.pts2read_DCchannels = int(self.systemHeaderObj.nChannels * self.processingHeaderObj.nHeights)
                         self.blocksize += self.pts2read_DCchannels
             #        self.blocksize = self.pts2read_SelfSpectra + self.pts2read_CrossSpectra + self.pts2read_DCchannels
                 def readBlock(self):
                     """
                     Lee el bloque de datos desde la posicion actual del puntero del archivo
                     (self.fp) y actualiza todos los parametros relacionados al bloque de datos
                     (metadata + data). La data leida es almacenada en el buffer y el contador del buffer
                     es seteado a 0
                     Return: None
                     Variables afectadas:
                         self.flagIsNewFile
                         self.flagIsNewBlock
                         self.nTotalBlocks
                         self.data_spc
                         self.data_cspc
                         self.data_dc
                     Exceptions:
                         Si un bloque leido no es un bloque valido
                     """
                     blockOk_flag = False
                     fpointer = self.fp.tell()
                     spc = numpy.fromfile( self.fp, self.dtype[0], self.pts2read_SelfSpectra )
                     spc = spc.reshape( (self.nRdChannels, self.processingHeaderObj.nHeights, self.processingHeaderObj.profilesPerBlock) ) #transforma a un arreglo 3D
                     if self.processingHeaderObj.flag_cspc:
                         cspc = numpy.fromfile( self.fp, self.dtype, self.pts2read_CrossSpectra )
                         cspc = cspc.reshape( (self.nRdPairs, self.processingHeaderObj.nHeights, self.processingHeaderObj.profilesPerBlock) ) #transforma a un arreglo 3D
                     if self.processingHeaderObj.flag_dc:
                         dc = numpy.fromfile( self.fp, self.dtype, self.pts2read_DCchannels ) #int(self.processingHeaderObj.nHeights*self.systemHeaderObj.nChannels) )
                         dc = dc.reshape( (self.systemHeaderObj.nChannels, self.processingHeaderObj.nHeights) ) #transforma a un arreglo 2D
-                    if not(self.processingHeaderObj.shif_fft):
+                    if self.processingHeaderObj.shif_fft:
                         #desplaza a la derecha en el eje 2 determinadas posiciones
                         shift = int(self.processingHeaderObj.profilesPerBlock/2)
                         spc = numpy.roll( spc, shift , axis=2 )
                         if self.processingHeaderObj.flag_cspc:
                             #desplaza a la derecha en el eje 2 determinadas posiciones
                             cspc = numpy.roll( cspc, shift, axis=2 )
                     #Dimensions : nChannels, nProfiles, nSamples
                     spc = numpy.transpose( spc, (0,2,1) )
                     self.data_spc = spc
                     if self.processingHeaderObj.flag_cspc:
                         cspc = numpy.transpose( cspc, (0,2,1) )
                         self.data_cspc = cspc['real'] + cspc['imag']*1j
                     else:
                         self.data_cspc = None
                     if self.processingHeaderObj.flag_dc:
                         self.data_dc = dc['real'] + dc['imag']*1j
                     else:
                         self.data_dc = None
                     self.flagIsNewFile = 0
                     self.flagIsNewBlock = 1
                     self.nTotalBlocks += 1
                     self.nReadBlocks += 1
                     return 1
                 def getFirstHeader(self):
                     self.getBasicHeader()
                     self.dataOut.systemHeaderObj = self.systemHeaderObj.copy()
                     self.dataOut.radarControllerHeaderObj = self.radarControllerHeaderObj.copy()
             #         self.dataOut.ippSeconds = self.ippSeconds
             #         self.dataOut.timeInterval = self.radarControllerHeaderObj.ippSeconds * self.processingHeaderObj.nCohInt * self.processingHeaderObj.nIncohInt * self.processingHeaderObj.profilesPerBlock
                     self.dataOut.dtype = self.dtype
             #         self.dataOut.nPairs = self.nPairs
                     self.dataOut.pairsList = self.rdPairList
                     self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock
                     self.dataOut.nFFTPoints = self.processingHeaderObj.profilesPerBlock
                     self.dataOut.nCohInt = self.processingHeaderObj.nCohInt
                     self.dataOut.nIncohInt = self.processingHeaderObj.nIncohInt
                     xf = self.processingHeaderObj.firstHeight + self.processingHeaderObj.nHeights*self.processingHeaderObj.deltaHeight
                     self.dataOut.heightList = numpy.arange(self.processingHeaderObj.firstHeight, xf, self.processingHeaderObj.deltaHeight)
                     self.dataOut.channelList = range(self.systemHeaderObj.nChannels)
                     self.dataOut.flagShiftFFT = True    #Data is always shifted
                     self.dataOut.flagDecodeData = self.processingHeaderObj.flag_decode #asumo q la data no esta decodificada
                     self.dataOut.flagDeflipData = self.processingHeaderObj.flag_deflip #asumo q la data esta sin flip
                 def getData(self):
                     """
                     First method to execute before "RUN" is called.
                     Copia el buffer de lectura a la clase "Spectra",
                     con todos los parametros asociados a este (metadata). cuando no hay datos en el buffer de
                     lectura es necesario hacer una nueva lectura de los bloques de datos usando "readNextBlock"
                     Return:
 :    Si no hay mas archivos disponibles
 :    Si hizo una buena copia del buffer
                     Affected:
                         self.dataOut
                         self.flagDiscontinuousBlock
                         self.flagIsNewBlock
                     """
                     if self.flagNoMoreFiles:
                         self.dataOut.flagNoData = True
                         print 'Process finished'
                         return 0
                     self.flagDiscontinuousBlock = 0
                     self.flagIsNewBlock = 0
                     if self.__hasNotDataInBuffer():
                         if not( self.readNextBlock() ):
                             self.dataOut.flagNoData = True
                             return 0
                     #data es un numpy array de 3 dmensiones (perfiles, alturas y canales)
                     if self.data_spc is None:
                         self.dataOut.flagNoData = True
                         return 0
                     self.getBasicHeader()
                     self.getFirstHeader()
                     self.dataOut.data_spc = self.data_spc
                     self.dataOut.data_cspc = self.data_cspc
                     self.dataOut.data_dc = self.data_dc
                     self.dataOut.flagNoData = False
                     self.dataOut.realtime = self.online
                     return self.dataOut.data_spc
             class SpectraWriter(JRODataWriter, Operation):
                 """
                 Esta clase permite escribir datos de espectros a archivos procesados (.pdata). La escritura
                 de los datos siempre se realiza por bloques.
                 """
                 ext = ".pdata"
                 optchar = "P"
                 shape_spc_Buffer = None
                 shape_cspc_Buffer = None
                 shape_dc_Buffer = None
                 data_spc = None
                 data_cspc = None
                 data_dc = None
             #    dataOut = None
                 def __init__(self, **kwargs):
                     """
                     Inicializador de la clase SpectraWriter para la escritura de datos de espectros.
                     Affected:
                         self.dataOut
                         self.basicHeaderObj
                         self.systemHeaderObj
                         self.radarControllerHeaderObj
                         self.processingHeaderObj
                     Return: None
                     """
                     Operation.__init__(self, **kwargs)
                     self.isConfig = False
                     self.nTotalBlocks = 0
                     self.data_spc = None
                     self.data_cspc = None
                     self.data_dc = None
                     self.fp = None
                     self.flagIsNewFile = 1
                     self.nTotalBlocks = 0
                     self.flagIsNewBlock = 0
                     self.setFile = None
                     self.dtype = None
                     self.path = None
                     self.noMoreFiles = 0
                     self.filename = None
                     self.basicHeaderObj = BasicHeader(LOCALTIME)
                     self.systemHeaderObj = SystemHeader()
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.processingHeaderObj = ProcessingHeader()
                 def hasAllDataInBuffer(self):
                     return 1
                 def setBlockDimension(self):
                     """
                     Obtiene las formas dimensionales del los subbloques de datos que componen un bloque
                     Affected:
                         self.shape_spc_Buffer
                         self.shape_cspc_Buffer
                         self.shape_dc_Buffer
                     Return: None
                     """
                     self.shape_spc_Buffer = (self.dataOut.nChannels,
                                              self.processingHeaderObj.nHeights,
                                              self.processingHeaderObj.profilesPerBlock)
                     self.shape_cspc_Buffer = (self.dataOut.nPairs,
                                               self.processingHeaderObj.nHeights,
                                               self.processingHeaderObj.profilesPerBlock)
                     self.shape_dc_Buffer = (self.dataOut.nChannels,
                                             self.processingHeaderObj.nHeights)
                 def writeBlock(self):
                     """
                     Escribe el buffer en el file designado
                     Affected:
                         self.data_spc
                         self.data_cspc
                         self.data_dc
                         self.flagIsNewFile
                         self.flagIsNewBlock
                         self.nTotalBlocks
                         self.nWriteBlocks
                     Return: None
                     """
                     spc = numpy.transpose( self.data_spc, (0,2,1) )
-                    if not( self.processingHeaderObj.shif_fft ):
+                    if self.processingHeaderObj.shif_fft:
                         spc = numpy.roll( spc, self.processingHeaderObj.profilesPerBlock/2, axis=2 ) #desplaza a la derecha en el eje 2 determinadas posiciones
                     data = spc.reshape((-1))
                     data = data.astype(self.dtype[0])
                     data.tofile(self.fp)
                     if self.data_cspc is not None:
                         data = numpy.zeros( self.shape_cspc_Buffer, self.dtype )
                         cspc = numpy.transpose( self.data_cspc, (0,2,1) )
-                        if not( self.processingHeaderObj.shif_fft ):
+                        if self.processingHeaderObj.shif_fft:
                             cspc = numpy.roll( cspc, self.processingHeaderObj.profilesPerBlock/2, axis=2 ) #desplaza a la derecha en el eje 2 determinadas posiciones
                         data['real'] = cspc.real
                         data['imag'] = cspc.imag
                         data = data.reshape((-1))
                         data.tofile(self.fp)
                     if self.data_dc is not None:
                         data = numpy.zeros( self.shape_dc_Buffer, self.dtype )
                         dc = self.data_dc
                         data['real'] = dc.real
                         data['imag'] = dc.imag
                         data = data.reshape((-1))
                         data.tofile(self.fp)
             #         self.data_spc.fill(0)
             #
             #         if self.data_dc is not None:
             #             self.data_dc.fill(0)
             #
             #         if self.data_cspc is not None:
             #             self.data_cspc.fill(0)
                     self.flagIsNewFile = 0
                     self.flagIsNewBlock = 1
                     self.nTotalBlocks += 1
                     self.nWriteBlocks += 1
                     self.blockIndex += 1
             #         print "[Writing] Block = %d04" %self.blockIndex
                 def putData(self):
                     """
                     Setea un bloque de datos y luego los escribe en un file
                     Affected:
                         self.data_spc
                         self.data_cspc
                         self.data_dc
                     Return:
 :    Si no hay data o no hay mas files que puedan escribirse
 :    Si se escribio la data de un bloque en un file
                     """
                     if self.dataOut.flagNoData:
                         return 0
                     self.flagIsNewBlock = 0
                     if self.dataOut.flagDiscontinuousBlock:
                         self.data_spc.fill(0)
                         if self.dataOut.data_cspc is not None:
                             self.data_cspc.fill(0)
                         if self.dataOut.data_dc is not None:
                             self.data_dc.fill(0)
                         self.setNextFile()
                     if self.flagIsNewFile == 0:
                         self.setBasicHeader()
                     self.data_spc = self.dataOut.data_spc.copy()
                     if self.dataOut.data_cspc is not None:
                         self.data_cspc = self.dataOut.data_cspc.copy()
                     if self.dataOut.data_dc is not None:
                         self.data_dc = self.dataOut.data_dc.copy()
                     # #self.processingHeaderObj.dataBlocksPerFile)
                     if self.hasAllDataInBuffer():
             #            self.setFirstHeader()
                         self.writeNextBlock()
                     return 1
                 def __getBlockSize(self):
                     '''
                     Este metodos determina el cantidad de bytes para un bloque de datos de tipo Spectra
                     '''
                     dtype_width = self.getDtypeWidth()
                     pts2write = self.dataOut.nHeights * self.dataOut.nFFTPoints
                     pts2write_SelfSpectra = int(self.dataOut.nChannels * pts2write)
                     blocksize = (pts2write_SelfSpectra*dtype_width)
                     if self.dataOut.data_cspc is not None:
                         pts2write_CrossSpectra = int(self.dataOut.nPairs * pts2write)
                         blocksize += (pts2write_CrossSpectra*dtype_width*2)
                     if self.dataOut.data_dc is not None:
                         pts2write_DCchannels = int(self.dataOut.nChannels * self.dataOut.nHeights)
                         blocksize += (pts2write_DCchannels*dtype_width*2)
             #         blocksize = blocksize #* datatypeValue * 2 #CORREGIR ESTO
                     return blocksize
                 def setFirstHeader(self):
                     """
                     Obtiene una copia del First Header
                     Affected:
                         self.systemHeaderObj
                         self.radarControllerHeaderObj
                         self.dtype
                     Return:
                         None
                     """
                     self.systemHeaderObj = self.dataOut.systemHeaderObj.copy()
                     self.systemHeaderObj.nChannels = self.dataOut.nChannels
                     self.radarControllerHeaderObj = self.dataOut.radarControllerHeaderObj.copy()
                     self.processingHeaderObj.dtype = 1 # Spectra
                     self.processingHeaderObj.blockSize = self.__getBlockSize()
                     self.processingHeaderObj.profilesPerBlock = self.dataOut.nFFTPoints
                     self.processingHeaderObj.dataBlocksPerFile = self.blocksPerFile
                     self.processingHeaderObj.nWindows = 1 #podria ser 1 o self.dataOut.processingHeaderObj.nWindows
                     self.processingHeaderObj.nCohInt = self.dataOut.nCohInt# Se requiere para determinar el valor de timeInterval
                     self.processingHeaderObj.nIncohInt = self.dataOut.nIncohInt
                     self.processingHeaderObj.totalSpectra = self.dataOut.nPairs + self.dataOut.nChannels
                     self.processingHeaderObj.shif_fft = self.dataOut.flagShiftFFT
                     if self.processingHeaderObj.totalSpectra > 0:
                         channelList = []
                         for channel in range(self.dataOut.nChannels):
                             channelList.append(channel)
                             channelList.append(channel)
                         pairsList = []
                         if self.dataOut.nPairs > 0:
                             for pair in self.dataOut.pairsList:
                                 pairsList.append(pair[0])
                                 pairsList.append(pair[1])
                         spectraComb = channelList + pairsList
                         spectraComb = numpy.array(spectraComb, dtype="u1")
                         self.processingHeaderObj.spectraComb = spectraComb
                     if self.dataOut.code is not None:
                         self.processingHeaderObj.code = self.dataOut.code
                         self.processingHeaderObj.nCode = self.dataOut.nCode
                         self.processingHeaderObj.nBaud = self.dataOut.nBaud
                     if self.processingHeaderObj.nWindows != 0:
                         self.processingHeaderObj.firstHeight = self.dataOut.heightList[0]
                         self.processingHeaderObj.deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                         self.processingHeaderObj.nHeights = self.dataOut.nHeights
                         self.processingHeaderObj.samplesWin = self.dataOut.nHeights
                     self.processingHeaderObj.processFlags = self.getProcessFlags()
                     self.setBasicHeader()

schainpy/model/proc/jroproc_spectra.py +10 -1

             import itertools
             import numpy
             from jroproc_base import ProcessingUnit, Operation
             from schainpy.model.data.jrodata import Spectra
             from schainpy.model.data.jrodata import hildebrand_sekhon
             class SpectraProc(ProcessingUnit):
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
                     self.buffer = None
                     self.firstdatatime = None
                     self.profIndex = 0
                     self.dataOut = Spectra()
                     self.id_min = None
                     self.id_max = None
                 def __updateSpecFromVoltage(self):
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     try:
                         self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
                     except:
                         pass
                     self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
                     self.dataOut.nBaud = self.dataIn.nBaud
                     self.dataOut.nCode = self.dataIn.nCode
                     self.dataOut.code = self.dataIn.code
                     self.dataOut.nProfiles = self.dataOut.nFFTPoints
                     self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
                     self.dataOut.utctime = self.firstdatatime
                     # asumo q la data esta decodificada
                     self.dataOut.flagDecodeData = self.dataIn.flagDecodeData
                     # asumo q la data esta sin flip
                     self.dataOut.flagDeflipData = self.dataIn.flagDeflipData
                     self.dataOut.flagShiftFFT = False
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.nIncohInt = 1
                     self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.realtime = self.dataIn.realtime
                     self.dataOut.azimuth = self.dataIn.azimuth
                     self.dataOut.zenith = self.dataIn.zenith
                     self.dataOut.beam.codeList = self.dataIn.beam.codeList
                     self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
                     self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
                 def __getFft(self):
                     """
                     Convierte valores de Voltaje a Spectra
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                         self.profIndex
                         self.buffer
                         self.dataOut.flagNoData
                     """
                     fft_volt = numpy.fft.fft(
                         self.buffer, n=self.dataOut.nFFTPoints, axis=1)
                     fft_volt = fft_volt.astype(numpy.dtype('complex'))
                     dc = fft_volt[:, 0, :]
                     # calculo de self-spectra
                     fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
                     spc = fft_volt * numpy.conjugate(fft_volt)
                     spc = spc.real
                     blocksize = 0
                     blocksize += dc.size
                     blocksize += spc.size
                     cspc = None
                     pairIndex = 0
                     if self.dataOut.pairsList != None:
                         # calculo de cross-spectra
                         cspc = numpy.zeros(
                             (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
                         for pair in self.dataOut.pairsList:
                             if pair[0] not in self.dataOut.channelList:
                                 raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % (
                                     str(pair), str(self.dataOut.channelList))
                             if pair[1] not in self.dataOut.channelList:
                                 raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % (
                                     str(pair), str(self.dataOut.channelList))
                             cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \
                                 numpy.conjugate(fft_volt[pair[1], :, :])
                             pairIndex += 1
                         blocksize += cspc.size
                     self.dataOut.data_spc = spc
                     self.dataOut.data_cspc = cspc
                     self.dataOut.data_dc = dc
                     self.dataOut.blockSize = blocksize
                     self.dataOut.flagShiftFFT = True
-                def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None):
+                def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None, shift_fft=False):
                     self.dataOut.flagNoData = True
                     if self.dataIn.type == "Spectra":
                         self.dataOut.copy(self.dataIn)
                         if not pairsList:
                             pairsList = itertools.combinations(self.dataOut.channelList, 2)
                         if self.dataOut.data_cspc is not None:
                             self.__selectPairs(pairsList)
+                        if shift_fft:
+                            #desplaza a la derecha en el eje 2 determinadas posiciones
+                            shift = int(self.dataOut.nFFTPoints/2)
+                            self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1)
+                            if self.dataOut.data_cspc is not None:
+                                #desplaza a la derecha en el eje 2 determinadas posiciones
+                                self.dataOut.data_cspc = numpy.roll(self.dataOut.cspc, shift, axis=1)
                         return True
                     if self.dataIn.type == "Voltage":
                         if nFFTPoints == None:
                             raise ValueError, "This SpectraProc.run() need nFFTPoints input variable"
                         if nProfiles == None:
                             nProfiles = nFFTPoints
                         if ippFactor == None:
                             ippFactor = 1
                         self.dataOut.ippFactor = ippFactor
                         self.dataOut.nFFTPoints = nFFTPoints
                         self.dataOut.pairsList = pairsList
                         if self.buffer is None:
                             self.buffer = numpy.zeros((self.dataIn.nChannels,
                                                        nProfiles,
                                                        self.dataIn.nHeights),
                                                       dtype='complex')
                         if self.dataIn.flagDataAsBlock:
                             # data dimension: [nChannels, nProfiles, nSamples]
                             nVoltProfiles = self.dataIn.data.shape[1]
                         #                 nVoltProfiles = self.dataIn.nProfiles
                             if nVoltProfiles == nProfiles:
                                 self.buffer = self.dataIn.data.copy()
                                 self.profIndex = nVoltProfiles
                             elif nVoltProfiles < nProfiles:
                                 if self.profIndex == 0:
                                     self.id_min = 0
                                     self.id_max = nVoltProfiles
                                 self.buffer[:, self.id_min:self.id_max,
                                             :] = self.dataIn.data
                                 self.profIndex += nVoltProfiles
                                 self.id_min += nVoltProfiles
                                 self.id_max += nVoltProfiles
                             else:
                                 raise ValueError, "The type object %s has %d profiles, it should just has %d profiles" % (
                                     self.dataIn.type, self.dataIn.data.shape[1], nProfiles)
                                 self.dataOut.flagNoData = True
                                 return 0
                         else:
                             self.buffer[:, self.profIndex, :] = self.dataIn.data.copy()
                             self.profIndex += 1
                         if self.firstdatatime == None:
                             self.firstdatatime = self.dataIn.utctime
                         if self.profIndex == nProfiles:
                             self.__updateSpecFromVoltage()
                             self.__getFft()
                             self.dataOut.flagNoData = False
                             self.firstdatatime = None
                             self.profIndex = 0
                         return True
                     raise ValueError, "The type of input object '%s' is not valid" % (
                         self.dataIn.type)
                 def __selectPairs(self, pairsList):
                     if not pairsList:
                         return
                     pairs = []
                     pairsIndex = []
                     for pair in pairsList:
                         if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList:
                             continue
                         pairs.append(pair)
                         pairsIndex.append(pairs.index(pair))
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex]
                     self.dataOut.pairsList = pairs
                     return
                 def __selectPairsByChannel(self, channelList=None):
                     if channelList == None:
                         return
                     pairsIndexListSelected = []
                     for pairIndex in self.dataOut.pairsIndexList:
                         # First pair
                         if self.dataOut.pairsList[pairIndex][0] not in channelList:
                             continue
                         # Second pair
                         if self.dataOut.pairsList[pairIndex][1] not in channelList:
                             continue
                         pairsIndexListSelected.append(pairIndex)
                     if not pairsIndexListSelected:
                         self.dataOut.data_cspc = None
                         self.dataOut.pairsList = []
                         return
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
                     self.dataOut.pairsList = [self.dataOut.pairsList[i]
                                               for i in pairsIndexListSelected]
                     return
                 def selectChannels(self, channelList):
                     channelIndexList = []
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" % (
                                 channel, str(self.dataOut.channelList))
                         index = self.dataOut.channelList.index(channel)
                         channelIndexList.append(index)
                     self.selectChannelsByIndex(channelIndexList)
                 def selectChannelsByIndex(self, channelIndexList):
                     """
                     Selecciona un bloque de datos en base a canales segun el channelIndexList
                     Input:
                         channelIndexList    :    lista sencilla de canales a seleccionar por ej. [2,3,7]
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " % (
                                 channelIndex, self.dataOut.channelIndexList)
             #         nChannels = len(channelIndexList)
                     data_spc = self.dataOut.data_spc[channelIndexList, :]
                     data_dc = self.dataOut.data_dc[channelIndexList, :]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.channelList = [
                         self.dataOut.channelList[i] for i in channelIndexList]
             #        self.dataOut.nChannels = nChannels
                     self.__selectPairsByChannel(self.dataOut.channelList)
                     return 1
                 def selectHeights(self, minHei, maxHei):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
                     minHei <= height <= maxHei
                     Input:
                         minHei    :    valor minimo de altura a considerar
                         maxHei    :    valor maximo de altura a considerar
                     Affected:
                         Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minHei > maxHei):
                         raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (
                             minHei, maxHei)
                     if (minHei < self.dataOut.heightList[0]):
                         minHei = self.dataOut.heightList[0]
                     if (maxHei > self.dataOut.heightList[-1]):
                         maxHei = self.dataOut.heightList[-1]
                     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)
                     self.selectHeightsByIndex(minIndex, maxIndex)
                     return 1
                 def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None):
                     newheis = numpy.where(
                         self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex])
                     if hei_ref != None:
                         newheis = numpy.where(self.dataOut.heightList > hei_ref)
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                     heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     # determina indices
                     nheis = int(self.dataOut.radarControllerHeaderObj.txB /
                                 (self.dataOut.heightList[1] - self.dataOut.heightList[0]))
                     avg_dB = 10 * \
                         numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0))
                     beacon_dB = numpy.sort(avg_dB)[-nheis:]
                     beacon_heiIndexList = []
                     for val in avg_dB.tolist():
                         if val >= beacon_dB[0]:
                             beacon_heiIndexList.append(avg_dB.tolist().index(val))
                     #data_spc = data_spc[:,:,beacon_heiIndexList]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                         #data_cspc = data_cspc[:,:,beacon_heiIndexList]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                         #data_dc = data_dc[:,beacon_heiIndexList]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = heightList
                     self.dataOut.beacon_heiIndexList = beacon_heiIndexList
                     return 1
                 def selectHeightsByIndex(self, minIndex, maxIndex):
                     """
                     Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
                     minIndex <= index <= maxIndex
                     Input:
                         minIndex    :    valor de indice minimo de altura a considerar
                         maxIndex    :    valor de indice maximo de altura a considerar
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (
                             minIndex, maxIndex)
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights - 1
                     # Spectra
                     data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     return 1
                 def removeDC(self, mode=2):
                     jspectra = self.dataOut.data_spc
                     jcspectra = self.dataOut.data_cspc
                     num_chan = jspectra.shape[0]
                     num_hei = jspectra.shape[2]
                     if jcspectra is not None:
                         jcspectraExist = True
                         num_pairs = jcspectra.shape[0]
                     else:
                         jcspectraExist = False
                     freq_dc = jspectra.shape[1] / 2
                     ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
                     if ind_vel[0] < 0:
                         ind_vel[range(0, 1)] = ind_vel[range(0, 1)] + self.num_prof
                     if mode == 1:
                         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 is 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 / numpy.sqrt(num_incoh))
                         noiseid = noiseid[0]
                         cnoiseid = noiseid.size
                         interfid = numpy.where(
                             jspc_interf > tmp_noise / numpy.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 / numpy.sqrt(num_incoh))).nonzero()
                                 cind = len(ind)
                                 if (cind > 0):
                                     jspectra[ich, interfid[ip], ind] = tmp_noise * \
                                         (1 + (numpy.random.uniform(cind) - 0.5) /
                                          numpy.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 / numpy.sqrt(num_incoh))).nonzero()
                         jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \
                             (1 - 1 / numpy.sqrt(num_incoh))
                     # Remocion de Interferencia en el Cross Spectra
                     if jcspectra is 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, **kwargs):
                     Operation.__init__(self, **kwargs)
             #         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 = 0
                     self.__buffer_cspc = 0
                     self.__buffer_dc = 0
                     self.__profIndex = 0
                     self.__dataReady = False
                     self.__byTime = False
                     if n is None and timeInterval is None:
                         raise ValueError, "n or timeInterval should be specified ..."
                     if n is not None:
                         self.n = int(n)
                     else:
                         # if (type(timeInterval)!=integer) -> change this line
                         self.__integrationtime = int(timeInterval)
                         self.n = None
                         self.__byTime = True
                 def putData(self, data_spc, data_cspc, data_dc):
                     """
                     Add a profile to the __buffer_spc and increase in one the __profileIndex
                     """
                     self.__buffer_spc += data_spc
                     if data_cspc is None:
                         self.__buffer_cspc = None
                     else:
                         self.__buffer_cspc += data_cspc
                     if data_dc is None:
                         self.__buffer_dc = None
                     else:
                         self.__buffer_dc += data_dc
                     self.__profIndex += 1
                     return
                 def pushData(self):
                     """
                     Return the sum of the last profiles and the profiles used in the sum.
                     Affected:
                     self.__profileIndex
                     """
                     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
                 def byProfiles(self, *args):
                     self.__dataReady = False
                     avgdata_spc = None
                     avgdata_cspc = None
                     avgdata_dc = None
                     self.putData(*args)
                     if self.__profIndex == self.n:
                         avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
                         self.n = n
                         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
                     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.__profIndex == 0:
                         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)
                     if not self.__dataReady:
                         return None, None, None, None
                     return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc
                 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
                     if n == 1:
                         return
                     dataOut.flagNoData = True
                     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)
                     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.flagNoData = False

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