schain Commit - r836:0804a6804e7a · Jicamarca Repository

-Function to interpolate corrupted profiles

Julio Valdez -

r836:0804a6804e7a

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r836:0804a6804e7a

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schainpy/model/io/jroIO_voltage.py +1 -1

             '''
             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 Voltage
             # from _sha import blocksize
             class VoltageReader(JRODataReader, ProcessingUnit):
                 """
                 Esta clase permite leer datos de voltage desde archivos en formato rawdata (.r). La lectura
                 de los datos siempre se realiza por bloques. Los datos leidos (array de 3 dimensiones:
                 perfiles*alturas*canales) son almacenados en la variable "buffer".
                                          perfiles * alturas * canales
                 Esta clase contiene instancias (objetos) de las clases BasicHeader, SystemHeader,
                 RadarControllerHeader y Voltage. Los tres primeros se usan para almacenar informacion de la
                 cabecera de datos (metadata), y el cuarto (Voltage) para obtener y almacenar un perfil 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 = VoltageReader()
                     readerObj.setup(dpath, startTime, endTime)
                     while(True):
                         #to get one profile
                         profile =  readerObj.getData()
                         #print the profile
                         print profile
                         #If you want to see all datablock
                         print readerObj.datablock
                         if readerObj.flagNoMoreFiles:
                             break
                 """
                 ext = ".r"
                 optchar = "D"
                 dataOut = None
                 def __init__(self):
                     """
                     Inicializador de la clase VoltageReader para la lectura de datos de voltage.
                     Input:
                         dataOut    :    Objeto de la clase Voltage. 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.
                     Variables afectadas:
                         self.dataOut
                     Return:
                         None
                     """
                     ProcessingUnit.__init__(self)
                     self.isConfig = False
                     self.datablock = None
                     self.utc = 0
                     self.ext = ".r"
                     self.optchar = "D"
                     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.filenameList = []
                     self.lastUTTime = 0
                     self.maxTimeStep = 30
                     self.flagNoMoreFiles = 0
                     self.set = 0
                     self.path = None
                     self.profileIndex = 2**32-1
                     self.delay  = 3   #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.nTxs = 1
                     self.txIndex = 0
                 def createObjByDefault(self):
                     dataObj = Voltage()
                     return dataObj
                 def __hasNotDataInBuffer(self):
                     if self.profileIndex >= self.processingHeaderObj.profilesPerBlock*self.nTxs:
                         return 1
                     return 0
                 def getBlockDimension(self):
                     """
                     Obtiene la cantidad de puntos a leer por cada bloque de datos
                     Affected:
                         self.blocksize
                     Return:
                         None
                     """
                     pts2read = self.processingHeaderObj.profilesPerBlock * self.processingHeaderObj.nHeights * self.systemHeaderObj.nChannels
                     self.blocksize = pts2read
                 def readBlock(self):
                     """
                     readBlock 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
                     Inputs:
                         None
                     Return:
                         None
                     Affected:
                         self.profileIndex
                         self.datablock
                         self.flagIsNewFile
                         self.flagIsNewBlock
                         self.nTotalBlocks
                     Exceptions:
                         Si un bloque leido no es un bloque valido
                     """
                     current_pointer_location = self.fp.tell()
                     junk = numpy.fromfile( self.fp, self.dtype, self.blocksize )
                     try:
                         junk = junk.reshape( (self.processingHeaderObj.profilesPerBlock, self.processingHeaderObj.nHeights, self.systemHeaderObj.nChannels) )
                     except:
                         #print "The read block (%3d) has not enough data" %self.nReadBlocks
                         if self.waitDataBlock(pointer_location=current_pointer_location):
                             junk = numpy.fromfile( self.fp, self.dtype, self.blocksize )
                             junk = junk.reshape( (self.processingHeaderObj.profilesPerBlock, self.processingHeaderObj.nHeights, self.systemHeaderObj.nChannels) )
             #             return 0
                     #Dimensions : nChannels, nProfiles, nSamples
                     junk = numpy.transpose(junk, (2,0,1))
                     self.datablock = junk['real'] + junk['imag']*1j
                     self.profileIndex = 0
                     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()
                     if self.nTxs > 1:
                         self.dataOut.radarControllerHeaderObj.ippSeconds = self.radarControllerHeaderObj.ippSeconds/self.nTxs
                     #Time interval and code are propierties of dataOut. Its value depends of radarControllerHeaderObj.
             #         self.dataOut.timeInterval = self.radarControllerHeaderObj.ippSeconds * self.processingHeaderObj.nCohInt
             #
             #         if self.radarControllerHeaderObj.code is not None:
             #
             #             self.dataOut.nCode = self.radarControllerHeaderObj.nCode
             #
             #             self.dataOut.nBaud = self.radarControllerHeaderObj.nBaud
             #
             #             self.dataOut.code = self.radarControllerHeaderObj.code
                     self.dataOut.dtype = self.dtype
                     self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock
                     self.dataOut.heightList = numpy.arange(self.processingHeaderObj.nHeights) *self.processingHeaderObj.deltaHeight +  self.processingHeaderObj.firstHeight
                     self.dataOut.channelList = range(self.systemHeaderObj.nChannels)
                     self.dataOut.nCohInt = self.processingHeaderObj.nCohInt
                     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 no esta sin flip
                     self.dataOut.flagShiftFFT = self.processingHeaderObj.shif_fft
                 def reshapeData(self):
                     if self.nTxs < 0:
                         return
                     if self.nTxs == 1:
                         return
                     if self.nTxs < 1 and self.processingHeaderObj.profilesPerBlock % (1./self.nTxs) != 0:
                         raise ValueError, "1./nTxs (=%f), should be a multiple of nProfiles (=%d)" %(1./self.nTxs, self.processingHeaderObj.profilesPerBlock)
                     if self.nTxs > 1 and self.processingHeaderObj.nHeights % self.nTxs != 0:
                         raise ValueError, "nTxs (=%d), should be a multiple of nHeights (=%d)" %(self.nTxs, self.processingHeaderObj.nHeights)
                     self.datablock = self.datablock.reshape((self.systemHeaderObj.nChannels, self.processingHeaderObj.profilesPerBlock*self.nTxs, self.processingHeaderObj.nHeights/self.nTxs))
                     self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock*self.nTxs
                     self.dataOut.heightList = numpy.arange(self.processingHeaderObj.nHeights/self.nTxs) *self.processingHeaderObj.deltaHeight +  self.processingHeaderObj.firstHeight
                     self.dataOut.radarControllerHeaderObj.ippSeconds = self.radarControllerHeaderObj.ippSeconds/self.nTxs
                     return
                 def getData(self):
                     """
                     getData obtiene una unidad de datos del buffer de lectura, un perfil,  y la copia al objeto self.dataOut
                     del tipo "Voltage" 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"
                     Ademas incrementa el contador del buffer "self.profileIndex" en 1.
                     Return:
                         Si el flag self.getByBlock ha sido seteado el bloque completo es copiado a self.dataOut y el self.profileIndex
                         es igual al total de perfiles leidos desde el archivo.
                         Si self.getByBlock == False:
                             self.dataOut.data = buffer[:, thisProfile, :]
                             shape = [nChannels, nHeis]
                         Si self.getByBlock == True:
                             self.dataOut.data = buffer[:, :, :]
                             shape = [nChannels, nProfiles, nHeis]
                     Variables afectadas:
                         self.dataOut
                         self.profileIndex
                     Affected:
                         self.dataOut
                         self.profileIndex
                         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() ):
                             return 0
                         self.getFirstHeader()
                         self.reshapeData()
                     if self.datablock is None:
                         self.dataOut.flagNoData = True
                         return 0
                     if not self.getByBlock:
                         """
                         Return profile by profile
                         If nTxs > 1 then one profile is divided by nTxs and number of total
                         blocks is increased by nTxs (nProfiles *= nTxs)
                         """
                         self.dataOut.flagDataAsBlock = False
                         self.dataOut.data = self.datablock[:,self.profileIndex,:]
                         self.dataOut.profileIndex = self.profileIndex
                         self.profileIndex += 1
             #         elif self.selBlocksize==None or self.selBlocksize==self.dataOut.nProfiles:
             #             """
             #             Return all block
             #             """
             #             self.dataOut.flagDataAsBlock = True
             #             self.dataOut.data = self.datablock
             #             self.dataOut.profileIndex = self.dataOut.nProfiles - 1
             #
             #             self.profileIndex = self.dataOut.nProfiles
                     else:
                         """
                         Return a block
                         """
                         if self.selBlocksize == None:   self.selBlocksize = self.dataOut.nProfiles
                         if self.selBlocktime != None:   self.selBlocksize = int(self.dataOut.nProfiles*round(self.selBlocktime/(self.dataOut.ippSeconds*self.dataOut.nProfiles)))
                         self.dataOut.data = self.datablock[:,self.profileIndex:self.profileIndex+self.selBlocksize,:]
                         self.profileIndex += self.selBlocksize
                         datasize = self.dataOut.data.shape[1]
                         if datasize < self.selBlocksize:
                             buffer = numpy.zeros((self.dataOut.data.shape[0],self.selBlocksize,self.dataOut.data.shape[2]), dtype = 'complex')
                             buffer[:,:datasize,:] = self.dataOut.data
                             while datasize < self.selBlocksize: #Not enough profiles to fill the block
                                 if not( self.readNextBlock() ):
                                     return 0
                                 self.getFirstHeader()
                                 self.reshapeData()
                                 if self.datablock is None:
                                     self.dataOut.flagNoData = True
                                     return 0
                                 #stack data
                                 blockIndex = self.selBlocksize - datasize
                                 datablock1 = self.datablock[:,:blockIndex,:]
                                 buffer[:,datasize:datasize+datablock1.shape[1],:] = datablock1
                                 datasize += datablock1.shape[1]
                             self.dataOut.data = buffer
                             self.profileIndex = blockIndex
                         self.dataOut.flagDataAsBlock = True
-                        self.dataOut.nProfiles = self.selBlocksize
+                        self.dataOut.nProfiles = self.dataOut.data.shape[1]
                     self.dataOut.flagNoData = False
                     self.getBasicHeader()
                     self.dataOut.realtime = self.online
                     return self.dataOut.data
             class VoltageWriter(JRODataWriter, Operation):
                 """
                 Esta clase permite escribir datos de voltajes a archivos procesados (.r). La escritura
                 de los datos siempre se realiza por bloques.
                 """
                 ext = ".r"
                 optchar = "D"
                 shapeBuffer = None
                 def __init__(self):
                     """
                     Inicializador de la clase VoltageWriter para la escritura de datos de espectros.
                     Affected:
                         self.dataOut
                     Return: None
                     """
                     Operation.__init__(self)
                     self.nTotalBlocks = 0
                     self.profileIndex = 0
                     self.isConfig = False
                     self.fp = None
                     self.flagIsNewFile = 1
                     self.blockIndex = 0
                     self.flagIsNewBlock = 0
                     self.setFile = None
                     self.dtype = None
                     self.path = None
                     self.filename = None
                     self.basicHeaderObj = BasicHeader(LOCALTIME)
                     self.systemHeaderObj = SystemHeader()
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.processingHeaderObj = ProcessingHeader()
                 def hasAllDataInBuffer(self):
                     if self.profileIndex >= self.processingHeaderObj.profilesPerBlock:
                         return 1
                     return 0
                 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.shapeBuffer = (self.processingHeaderObj.profilesPerBlock,
                                         self.processingHeaderObj.nHeights,
                                         self.systemHeaderObj.nChannels)
                     self.datablock = numpy.zeros((self.systemHeaderObj.nChannels,
                                                  self.processingHeaderObj.profilesPerBlock,
                                                  self.processingHeaderObj.nHeights),
                                                  dtype=numpy.dtype('complex64'))
                 def writeBlock(self):
                     """
                     Escribe el buffer en el file designado
                     Affected:
                         self.profileIndex
                         self.flagIsNewFile
                         self.flagIsNewBlock
                         self.nTotalBlocks
                         self.blockIndex
                     Return: None
                     """
                     data = numpy.zeros( self.shapeBuffer, self.dtype )
                     junk = numpy.transpose(self.datablock, (1,2,0))
                     data['real'] = junk.real
                     data['imag'] = junk.imag
                     data = data.reshape( (-1) )
                     data.tofile( self.fp )
                     self.datablock.fill(0)
                     self.profileIndex = 0
                     self.flagIsNewFile = 0
                     self.flagIsNewBlock = 1
                     self.blockIndex += 1
                     self.nTotalBlocks += 1
             #         print "[Writing] Block = %04d" %self.blockIndex
                 def putData(self):
                     """
                     Setea un bloque de datos y luego los escribe en un file
                     Affected:
                         self.flagIsNewBlock
                         self.profileIndex
                     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.datablock.fill(0)
                         self.profileIndex = 0
                         self.setNextFile()
                     if self.profileIndex == 0:
                         self.setBasicHeader()
                     self.datablock[:,self.profileIndex,:] = self.dataOut.data
                     self.profileIndex += 1
                     if self.hasAllDataInBuffer():
                         #if self.flagIsNewFile:
                         self.writeNextBlock()
             #            self.setFirstHeader()
                     return 1
                 def __getBlockSize(self):
                     '''
                     Este metodos determina el cantidad de bytes para un bloque de datos de tipo Voltage
                     '''
                     dtype_width = self.getDtypeWidth()
                     blocksize = int(self.dataOut.nHeights * self.dataOut.nChannels * self.profilesPerBlock * dtype_width * 2)
                     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 = 0 # Voltage
                     self.processingHeaderObj.blockSize = self.__getBlockSize()
                     self.processingHeaderObj.profilesPerBlock = self.profilesPerBlock
                     self.processingHeaderObj.dataBlocksPerFile = self.blocksPerFile
                     self.processingHeaderObj.nWindows = 1 #podria ser 1 o self.dataOut.processingHeaderObj.nWindows
                     self.processingHeaderObj.nCohInt = self.dataOut.nCohInt
                     self.processingHeaderObj.nIncohInt = 1 # Cuando la data de origen es de tipo Voltage
                     self.processingHeaderObj.totalSpectra = 0 # Cuando la data de origen es de tipo Voltage
                     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_voltage.py +16 -2

             import sys
             import numpy
             from jroproc_base import ProcessingUnit, Operation
             from schainpy.model.data.jrodata import Voltage
             class VoltageProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
             #         self.objectDict = {}
                     self.dataOut = Voltage()
                     self.flip = 1
                 def run(self):
                     if self.dataIn.type == 'AMISR':
                         self.__updateObjFromAmisrInput()
                     if self.dataIn.type == 'Voltage':
                         self.dataOut.copy(self.dataIn)
             #         self.dataOut.copy(self.dataIn)
                 def __updateObjFromAmisrInput(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
                     self.dataOut.flagNoData = self.dataIn.flagNoData
                     self.dataOut.data = self.dataIn.data
                     self.dataOut.utctime = self.dataIn.utctime
                     self.dataOut.channelList = self.dataIn.channelList
             #         self.dataOut.timeInterval = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.nProfiles = self.dataIn.nProfiles
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.ippSeconds = self.dataIn.ippSeconds
                     self.dataOut.frequency = self.dataIn.frequency
                     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
             #
             #        pass#
             #
             #    def init(self):
             #
             #
             #        if self.dataIn.type == 'AMISR':
             #            self.__updateObjFromAmisrInput()
             #
             #        if self.dataIn.type == 'Voltage':
             #            self.dataOut.copy(self.dataIn)
             #        # No necesita copiar en cada init() los atributos de dataIn
             #        # la copia deberia hacerse por cada nuevo bloque de datos
                 def selectChannels(self, channelList):
                     channelIndexList = []
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError, "Channel %d is not in %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
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                         self.dataOut.m_ProcessingHeader.totalSpectra
                         self.dataOut.systemHeaderObj.numChannels
                         self.dataOut.m_ProcessingHeader.blockSize
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             print channelIndexList
                             raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         data = self.dataOut.data[channelIndexList,:,:]
                     else:
                         data = self.dataOut.data[channelIndexList,:]
                     self.dataOut.data = data
                     self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
             #        self.dataOut.nChannels = nChannels
                     return 1
                 def selectHeights(self, minHei=None, maxHei=None):
                     """
                     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 == None:
                         minHei = self.dataOut.heightList[0]
                     if maxHei == None:
                         maxHei = self.dataOut.heightList[-1]
                     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 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
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights
                     #voltage
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         data = self.dataOut.data[:,:, minIndex:maxIndex]
                     else:
                         data = self.dataOut.data[:, minIndex:maxIndex]
             #         firstHeight = self.dataOut.heightList[minIndex]
                     self.dataOut.data = data
                     self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
                     if self.dataOut.nHeights <= 1:
                         raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
                     return 1
                 def filterByHeights(self, window):
                     deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                     if window == None:
                         window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
                     newdelta = deltaHeight * window
                     r = self.dataOut.nHeights % window
                     newheights = (self.dataOut.nHeights-r)/window
                     if newheights <= 1:
                         raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
                         buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
                         buffer = numpy.sum(buffer,3)
                     else:
                         buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
                         buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
                         buffer = numpy.sum(buffer,2)
                     self.dataOut.data = buffer
                     self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
                     self.dataOut.windowOfFilter = window
                 def setH0(self, h0, deltaHeight = None):
                     if not deltaHeight:
                         deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                     nHeights = self.dataOut.nHeights
                     newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
                     self.dataOut.heightList = newHeiRange
                 def deFlip(self, channelList = []):
                     data = self.dataOut.data.copy()
                     if self.dataOut.flagDataAsBlock:
                         flip = self.flip
                         profileList = range(self.dataOut.nProfiles)
                         if not channelList:
                             for thisProfile in profileList:
                                 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
                                 flip *= -1.0
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in self.dataOut.channelList:
                                     continue
                                 for thisProfile in profileList:
                                     data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
                                     flip *= -1.0
                         self.flip = flip
                     else:
                         if not channelList:
                             data[:,:] = data[:,:]*self.flip
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in self.dataOut.channelList:
                                     continue
                                 data[thisChannel,:] = data[thisChannel,:]*self.flip
                         self.flip *= -1.
                     self.dataOut.data = data
                 def setRadarFrequency(self, frequency=None):
                     if frequency != None:
                         self.dataOut.frequency = frequency
                     return 1
+                def interpolateHeights(self, topLim, botLim):
+                    #69 al 72 para julia
+                    #82-84 para meteoros
+                    if len(numpy.shape(self.dataOut.data))==2:
+                        sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
+                        sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
+                        self.dataOut.data[:,botLim:limSup+1] = sampInterp
+                    else:
+                        sampInterp = (self.dataOut.data[:,:,botLim-1] + self.dataOut.data[:,:,topLim+1])/2
+                        nInt = topLim - botLim + 1
+                        for i in range(nInt):
+                            self.dataOut.data[:,:,botLim+i] = sampInterp
+            # import collections
             class CohInt(Operation):
                 isConfig = False
                 __profIndex = 0
                 __withOverapping  = False
                 __byTime = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __dataReady = False
                 n = None
                 def __init__(self):
                     Operation.__init__(self)
             #         self.isConfig = False
                 def setup(self, n=None, timeInterval=None, overlapping=False, byblock=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 = None
                     self.__dataReady = False
                     self.byblock = byblock
                     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 #* 60. #if (type(timeInterval)!=integer) -> change this line
                         self.n = 9999
                         self.__byTime = True
                     if overlapping:
                         self.__withOverapping = True
                         self.__buffer = None
                     else:
                         self.__withOverapping = False
                         self.__buffer = 0
                     self.__profIndex = 0
                 def putData(self, data):
                     """
                     Add a profile to the __buffer and increase in one the __profileIndex
                     """
                     if not self.__withOverapping:
                         self.__buffer += data.copy()
                         self.__profIndex += 1
                         return
                     #Overlapping data
                     nChannels, nHeis = data.shape
                     data = numpy.reshape(data, (1, nChannels, nHeis))
                     #If the buffer is empty then it takes the data value
                     if self.__buffer is None:
                         self.__buffer = data
                         self.__profIndex += 1
                         return
                     #If the buffer length is lower than n then stakcing the data value
                     if self.__profIndex < self.n:
                         self.__buffer = numpy.vstack((self.__buffer, data))
                         self.__profIndex += 1
                         return
                     #If the buffer length is equal to n then replacing the last buffer value with the data value
                     self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
                     self.__buffer[self.n-1] = data
                     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
                     """
                     if not self.__withOverapping:
                         data = self.__buffer
                         n = self.__profIndex
                         self.__buffer = 0
                         self.__profIndex = 0
                         return data, n
                     #Integration with Overlapping
                     data = numpy.sum(self.__buffer, axis=0)
                     n = self.__profIndex
                     return data, n
                 def byProfiles(self, data):
                     self.__dataReady = False
                     avgdata = None
             #         n = None
                     self.putData(data)
                     if self.__profIndex == self.n:
                         avgdata, n = self.pushData()
                         self.__dataReady = True
                     return avgdata
                 def byTime(self, data, datatime):
                     self.__dataReady = False
                     avgdata = None
                     n = None
                     self.putData(data)
                     if (datatime - self.__initime) >= self.__integrationtime:
                         avgdata, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata
                 def integrate(self, data, datatime=None):
                     if self.__initime == None:
                         self.__initime = datatime
                     if self.__byTime:
                         avgdata = self.byTime(data, datatime)
                     else:
                         avgdata = self.byProfiles(data)
                     self.__lastdatatime = datatime
                     if avgdata is None:
                         return None, None
                     avgdatatime = self.__initime
                     deltatime = datatime -self.__lastdatatime
                     if not self.__withOverapping:
                         self.__initime = datatime
                     else:
                         self.__initime += deltatime
                     return avgdata, avgdatatime
                 def integrateByBlock(self, dataOut):
                     times = int(dataOut.data.shape[1]/self.n)
                     avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
                     id_min = 0
                     id_max = self.n
                     for i in range(times):
                         junk = dataOut.data[:,id_min:id_max,:]
                         avgdata[:,i,:] = junk.sum(axis=1)
                         id_min += self.n
                         id_max += self.n
                     timeInterval = dataOut.ippSeconds*self.n
                     avgdatatime = (times - 1) * timeInterval + dataOut.utctime
                     self.__dataReady = True
                     return avgdata, avgdatatime
                 def run(self, dataOut, **kwargs):
                     if not self.isConfig:
                         self.setup(**kwargs)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         avgdata, avgdatatime = self.integrateByBlock(dataOut)
                         dataOut.nProfiles /= self.n
                     else:
                         avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
             #        dataOut.timeInterval *= n
                     dataOut.flagNoData = True
                     if self.__dataReady:
                         dataOut.data = avgdata
                         dataOut.nCohInt *= self.n
                         dataOut.utctime = avgdatatime
             #             dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
                         dataOut.flagNoData = False
             class Decoder(Operation):
                 isConfig = False
                 __profIndex = 0
                 code = None
                 nCode = None
                 nBaud = None
                 def __init__(self):
                     Operation.__init__(self)
                     self.times = None
                     self.osamp = None
             #         self.__setValues = False
                     self.isConfig = False
                 def setup(self, code, osamp, dataOut):
                     self.__profIndex = 0
                     self.code = code
                     self.nCode = len(code)
                     self.nBaud = len(code[0])
                     if (osamp != None) and (osamp >1):
                         self.osamp = osamp
                         self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
                         self.nBaud = self.nBaud*self.osamp
                     self.__nChannels = dataOut.nChannels
                     self.__nProfiles = dataOut.nProfiles
                     self.__nHeis = dataOut.nHeights
                     if self.__nHeis < self.nBaud:
                         raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
                     #Frequency
                     __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
                     __codeBuffer[:,0:self.nBaud] = self.code
                     self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
                     if dataOut.flagDataAsBlock:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
                     else:
                         #Time
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
                 def __convolutionInFreq(self, data):
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=1)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=1)
                     return data
                 def __convolutionInFreqOpt(self, data):
                     raise NotImplementedError
                 def __convolutionInTime(self, data):
                     code = self.code[self.__profIndex]
                     for i in range(self.__nChannels):
                         self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInTime(self, data):
                     repetitions = self.__nProfiles / self.nCode
                     junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
                     junk = junk.flatten()
                     code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
                     for i in range(self.__nChannels):
                         for j in range(self.__nProfiles):
                             self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInFreq(self, data):
                     raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=2)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=2)
                     return data
                 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
                     if dataOut.flagDecodeData:
                         print "This data is already decoded, recoding again ..."
                     if not self.isConfig:
                         if code is None:
                             if dataOut.code is None:
                                 raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
                             code = dataOut.code
                         else:
                             code = numpy.array(code).reshape(nCode,nBaud)
                         self.setup(code, osamp, dataOut)
                         self.isConfig = True
                         if mode == 3:
                             sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
                         if times != None:
                             sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
                     if self.code is None:
                         print "Fail decoding: Code is not defined."
                         return
                     datadec = None
                     if mode == 3:
                         mode = 0
                     if dataOut.flagDataAsBlock:
                         """
                         Decoding when data have been read as block,
                         """
                         if mode == 0:
                             datadec = self.__convolutionByBlockInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionByBlockInFreq(dataOut.data)
                     else:
                         """
                         Decoding when data have been read profile by profile
                         """
                         if mode == 0:
                             datadec = self.__convolutionInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionInFreq(dataOut.data)
                         if mode == 2:
                             datadec = self.__convolutionInFreqOpt(dataOut.data)
                     if datadec is None:
                         raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
                     dataOut.code = self.code
                     dataOut.nCode = self.nCode
                     dataOut.nBaud = self.nBaud
                     dataOut.data = datadec
                     dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
                     dataOut.flagDecodeData = True #asumo q la data esta decodificada
                     if self.__profIndex == self.nCode-1:
                         self.__profIndex = 0
                         return 1
                     self.__profIndex += 1
                     return 1
             #        dataOut.flagDeflipData = True #asumo q la data no esta sin flip
             class ProfileConcat(Operation):
                 isConfig = False
                 buffer = None
                 def __init__(self):
                     Operation.__init__(self)
                     self.profileIndex = 0
                 def reset(self):
                     self.buffer = numpy.zeros_like(self.buffer)
                     self.start_index = 0
                     self.times = 1
                 def setup(self, data, m, n=1):
                     self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
                     self.nHeights = data.shape[1]#.nHeights
                     self.start_index = 0
                     self.times = 1
                 def concat(self, data):
                     self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
                     self.start_index = self.start_index + self.nHeights
                 def run(self, dataOut, m):
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         self.setup(dataOut.data, m, 1)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
                     else:
                         self.concat(dataOut.data)
                         self.times += 1
                         if self.times > m:
                             dataOut.data = self.buffer
                             self.reset()
                             dataOut.flagNoData = False
                             # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
                             deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                             xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
                             dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
                             dataOut.ippSeconds *= m
             class ProfileSelector(Operation):
                 profileIndex = None
                 # Tamanho total de los perfiles
                 nProfiles = None
                 def __init__(self):
                     Operation.__init__(self)
                     self.profileIndex = 0
                 def incProfileIndex(self):
                     self.profileIndex += 1
                     if self.profileIndex >= self.nProfiles:
                         self.profileIndex = 0
                 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
                     if profileIndex < minIndex:
                         return False
                     if profileIndex > maxIndex:
                         return False
                     return True
                 def isThisProfileInList(self, profileIndex, profileList):
                     if profileIndex not in profileList:
                         return False
                     return True
                 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
                     """
                     ProfileSelector:
                     Inputs:
                         profileList        :    Index of profiles selected. Example: profileList = (0,1,2,7,8)
                         profileRangeList    :    Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
                         rangeList            :    List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
                     """
                     if rangeList is not None:
                         if type(rangeList[0]) not in (tuple, list):
                             rangeList = [rangeList]
                     dataOut.flagNoData = True
                     if dataOut.flagDataAsBlock:
                         """
                         data dimension  = [nChannels, nProfiles, nHeis]
                         """
                         if profileList != None:
                             dataOut.data = dataOut.data[:,profileList,:]
                         if profileRangeList != None:
                             minIndex = profileRangeList[0]
                             maxIndex = profileRangeList[1]
                             profileList = range(minIndex, maxIndex+1)
                             dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
                         if rangeList != None:
                             profileList = []
                             for thisRange in rangeList:
                                 minIndex = thisRange[0]
                                 maxIndex = thisRange[1]
                                 profileList.extend(range(minIndex, maxIndex+1))
                             dataOut.data = dataOut.data[:,profileList,:]
                         dataOut.nProfiles = len(profileList)
                         dataOut.profileIndex = dataOut.nProfiles - 1
                         dataOut.flagNoData = False
                         return True
                     """
                     data dimension  = [nChannels, nHeis]
                     """
                     if profileList != None:
                         if self.isThisProfileInList(dataOut.profileIndex, profileList):
                             self.nProfiles = len(profileList)
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return True
                     if profileRangeList != None:
                         minIndex = profileRangeList[0]
                         maxIndex = profileRangeList[1]
                         if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                             self.nProfiles = maxIndex - minIndex + 1
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return True
                     if rangeList != None:
                         nProfiles = 0
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             nProfiles += maxIndex - minIndex + 1
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                                 self.nProfiles = nProfiles
                                 dataOut.nProfiles = self.nProfiles
                                 dataOut.profileIndex = self.profileIndex
                                 dataOut.flagNoData = False
                                 self.incProfileIndex()
                                 break
                         return True
                     if beam != None: #beam is only for AMISR data
                         if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
                             dataOut.flagNoData = False
                             dataOut.profileIndex = self.profileIndex
                             self.incProfileIndex()
                         return True
                     raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
                     return False
             class Reshaper(Operation):
                 def __init__(self):
                     Operation.__init__(self)
                     self.__buffer = None
                     self.__nitems = 0
                 def __appendProfile(self, dataOut, nTxs):
                     if self.__buffer is None:
                         shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
                         self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
                     ini = dataOut.nHeights * self.__nitems
                     end = ini + dataOut.nHeights
                     self.__buffer[:, ini:end] = dataOut.data
                     self.__nitems += 1
                     return int(self.__nitems*nTxs)
                 def __getBuffer(self):
                     if self.__nitems == int(1./self.__nTxs):
                         self.__nitems = 0
                         return self.__buffer.copy()
                     return None
                 def __checkInputs(self, dataOut, shape, nTxs):
                     if shape is None and nTxs is None:
                         raise ValueError, "Reshaper: shape of factor should be defined"
                     if nTxs:
                         if nTxs < 0:
                             raise ValueError, "nTxs should be greater than 0"
                         if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
                             raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
                         shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
                         return shape, nTxs
                     if len(shape) != 2 and len(shape) !=  3:
                         raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
                     if len(shape) == 2:
                         shape_tuple = [dataOut.nChannels]
                         shape_tuple.extend(shape)
                     else:
                         shape_tuple = list(shape)
                     nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
                     return shape_tuple, nTxs
                 def run(self, dataOut, shape=None, nTxs=None):
                     shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
                     else:
                         if self.__nTxs < 1:
                             self.__appendProfile(dataOut, self.__nTxs)
                             new_data = self.__getBuffer()
                             if new_data is not None:
                                 dataOut.data = new_data
                                 dataOut.flagNoData = False
                                 profileIndex = dataOut.profileIndex*nTxs
                         else:
                             raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= self.__nTxs
             class SplitProfiles(Operation):
                 def __init__(self):
                     Operation.__init__(self)
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         if shape[2] % n != 0:
                             raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])
                         new_shape = shape[0], shape[1]*n, shape[2]/n
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles/n) - 1
                     else:
                         raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)"
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= n
             class CombineProfiles(Operation):
                 def __init__(self):
                     Operation.__init__(self)
                     self.__remData = None
                     self.__profileIndex = 0
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         new_shape = shape[0], shape[1]/n, shape[2]*n
                         if shape[1] % n != 0:
                             raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*n) - 1
                     else:
                         #nchannels, nsamples
                         if self.__remData is None:
                             newData = dataOut.data
                         else:
                             newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
                         self.__profileIndex += 1
                         if self.__profileIndex < n:
                             self.__remData = newData
                             #continue
                             return
                         self.__profileIndex = 0
                         self.__remData = None
                         dataOut.data = newData
                         dataOut.flagNoData = False
                         profileIndex = dataOut.profileIndex/n
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles/n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds *= n
             # import collections
             # from scipy.stats import mode
             #
             # class Synchronize(Operation):
             #
             #     isConfig = False
             #     __profIndex = 0
             #
             #     def __init__(self):
             #
             #         Operation.__init__(self)
             # #         self.isConfig = False
             #         self.__powBuffer = None
             #         self.__startIndex = 0
             #         self.__pulseFound = False
             #
             #     def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
             #
             #         #Read data
             #
             #         powerdB = dataOut.getPower(channel = channel)
             #         noisedB = dataOut.getNoise(channel = channel)[0]
             #
             #         self.__powBuffer.extend(powerdB.flatten())
             #
             #         dataArray = numpy.array(self.__powBuffer)
             #
             #         filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
             #
             #         maxValue = numpy.nanmax(filteredPower)
             #
             #         if maxValue < noisedB + 10:
             #             #No se encuentra ningun pulso de transmision
             #             return None
             #
             #         maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
             #
             #         if len(maxValuesIndex) < 2:
             #             #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
             #             return None
             #
             #         phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
             #
             #         #Seleccionar solo valores con un espaciamiento de nSamples
             #         pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
             #
             #         if len(pulseIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         spacing = pulseIndex[1:] - pulseIndex[:-1]
             #
             #         #remover senales que se distancien menos de 10 unidades o muestras
             #         #(No deberian existir IPP menor a 10 unidades)
             #
             #         realIndex = numpy.where(spacing > 10 )[0]
             #
             #         if len(realIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
             #         realPulseIndex = pulseIndex[realIndex]
             #
             #         period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
             #
             #         print "IPP = %d samples" %period
             #
             #         self.__newNSamples = dataOut.nHeights #int(period)
             #         self.__startIndex = int(realPulseIndex[0])
             #
             #         return 1
             #
             #
             #     def setup(self, nSamples, nChannels, buffer_size = 4):
             #
             #         self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
             #                                           maxlen = buffer_size*nSamples)
             #
             #         bufferList = []
             #
             #         for i in range(nChannels):
             #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) +  numpy.NAN,
             #                                           maxlen = buffer_size*nSamples)
             #
             #             bufferList.append(bufferByChannel)
             #
             #         self.__nSamples = nSamples
             #         self.__nChannels = nChannels
             #         self.__bufferList = bufferList
             #
             #     def run(self, dataOut, channel = 0):
             #
             #         if not self.isConfig:
             #             nSamples = dataOut.nHeights
             #             nChannels = dataOut.nChannels
             #             self.setup(nSamples, nChannels)
             #             self.isConfig = True
             #
             #         #Append new data to internal buffer
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             bufferByChannel.extend(dataOut.data[thisChannel])
             #
             #         if self.__pulseFound:
             #             self.__startIndex -= self.__nSamples
             #
             #         #Finding Tx Pulse
             #         if not self.__pulseFound:
             #             indexFound = self.__findTxPulse(dataOut, channel)
             #
             #             if indexFound == None:
             #                 dataOut.flagNoData = True
             #                 return
             #
             #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
             #             self.__pulseFound = True
             #             self.__startIndex = indexFound
             #
             #         #If pulse was found ...
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             #print self.__startIndex
             #             x = numpy.array(bufferByChannel)
             #             self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
             #
             #         deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
             #         dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
             # #             dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
             #
             #         dataOut.data = self.__arrayBuffer
             #
             #         self.__startIndex += self.__newNSamples
             #
             #         return

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