# Copyright (c) 2012-2020 Jicamarca Radio Observatory
# All rights reserved.
#
# Distributed under the terms of the BSD 3-clause license.
"""Spectra processing Unit and operations

Here you will find the processing unit `SpectraProc` and several operations
to work with Spectra data type
"""

import time
import itertools

import numpy
import math

from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation
from schainpy.model.data.jrodata import Spectra
from schainpy.model.data.jrodata import hildebrand_sekhon
from schainpy.utils import log

from scipy.optimize import curve_fit


class SpectraProc(ProcessingUnit):

    def __init__(self):

        ProcessingUnit.__init__(self)

        self.buffer = None
        self.firstdatatime = None
        self.profIndex = 0
        self.dataOut = Spectra()
        self.id_min = None
        self.id_max = None
        self.setupReq = False #Agregar a todas las unidades de proc

    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.nProfiles = self.dataOut.nFFTPoints
        self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
        self.dataOut.utctime = self.firstdatatime
        self.dataOut.flagDecodeData = self.dataIn.flagDecodeData
        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.codeList = self.dataIn.codeList
        self.dataOut.azimuthList = self.dataIn.azimuthList
        self.dataOut.elevationList = self.dataIn.elevationList

    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 = False

    def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False):

        if self.dataIn.type == "Spectra":
            self.dataOut.copy(self.dataIn)
            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.data_cspc, shift, axis=1)
            if pairsList:
                self.__selectPairs(pairsList)

        elif self.dataIn.type == "Voltage":

            self.dataOut.flagNoData = True

            if nFFTPoints == None:
                raise ValueError("This SpectraProc.run() need nFFTPoints input variable")

            if nProfiles == None:
                nProfiles = nFFTPoints

            if ippFactor == None:
                self.dataOut.ippFactor = 1

            self.dataOut.nFFTPoints = nFFTPoints

            if self.buffer is None:
                self.buffer = numpy.zeros((self.dataIn.nChannels,
                                           nProfiles,
                                           self.dataIn.nHeights),
                                          dtype='complex')

            if self.dataIn.flagDataAsBlock:
                nVoltProfiles = self.dataIn.data.shape[1]

                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
            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()
                if pairsList == None:
                    self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)]
                else:
                    self.dataOut.pairsList = pairsList
                self.__getFft()
                self.dataOut.flagNoData = False
                self.firstdatatime = None
                self.profIndex = 0
        else:
            raise ValueError("The type of input object '%s' is not valid".format(
                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 selectFFTs(self, minFFT, maxFFT ):
        """
        Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango
        minFFT<= FFT <= maxFFT
        """

        if (minFFT > maxFFT):
            raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT))

        if (minFFT < self.dataOut.getFreqRange()[0]):
            minFFT = self.dataOut.getFreqRange()[0]

        if (maxFFT > self.dataOut.getFreqRange()[-1]):
            maxFFT = self.dataOut.getFreqRange()[-1]

        minIndex = 0
        maxIndex = 0
        FFTs = self.dataOut.getFreqRange()

        inda = numpy.where(FFTs >= minFFT)
        indb = numpy.where(FFTs <= maxFFT)

        try:
            minIndex = inda[0][0]
        except:
            minIndex = 0

        try:
            maxIndex = indb[0][-1]
        except:
            maxIndex = len(FFTs)

        self.selectFFTsByIndex(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 selectFFTsByIndex(self, minIndex, maxIndex):
        """

        """

        if (minIndex < 0) or (minIndex > maxIndex):
            raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex))

        if (maxIndex >= self.dataOut.nProfiles):
            maxIndex = self.dataOut.nProfiles-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.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1])
        self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1]
        self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1]

        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, :, :]
            sortdata = numpy.sort(daux, axis=None)
            noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt)

        self.dataOut.noise_estimation = noise.copy()

        return 1

class removeDC(Operation):

    def run(self, dataOut, mode=2):
        self.dataOut = dataOut
        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 = int(jspectra.shape[1] / 2)
        ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
        ind_vel = ind_vel.astype(int)

        if ind_vel[0] < 0:
            ind_vel[list(range(0, 1))] = ind_vel[list(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(list(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 self.dataOut

# import matplotlib.pyplot as plt

def fit_func( x, a0, a1, a2): #, a3, a4, a5):
    z = (x - a1) / a2
    y = a0 * numpy.exp(-z**2 / a2)  #+ a3 + a4 * x + a5 * x**2
    return y


class CleanRayleigh(Operation):

    def __init__(self):

        Operation.__init__(self)
        self.i=0
        self.isConfig = False
        self.__dataReady = False
        self.__profIndex = 0
        self.byTime = False
        self.byProfiles = False

        self.bloques =  None
        self.bloque0 = None

        self.index = 0

        self.buffer = 0
        self.buffer2 = 0
        self.buffer3 = 0


    def setup(self,dataOut,min_hei,max_hei,n, timeInterval,factor_stdv):

        self.nChannels = dataOut.nChannels
        self.nProf = dataOut.nProfiles
        self.nPairs = dataOut.data_cspc.shape[0]
        self.pairsArray = numpy.array(dataOut.pairsList)
        self.spectra = dataOut.data_spc
        self.cspectra = dataOut.data_cspc
        self.heights = dataOut.heightList #alturas totales
        self.nHeights =  len(self.heights)
        self.min_hei = min_hei
        self.max_hei = max_hei
        if (self.min_hei == None):
            self.min_hei = 0
        if (self.max_hei == None):
            self.max_hei = dataOut.heightList[-1]
        self.hval = ((self.max_hei>=self.heights) & (self.heights >= self.min_hei)).nonzero()
        self.heightsClean = self.heights[self.hval] #alturas filtradas
        self.hval = self.hval[0] # forma (N,), an solo N elementos -> Indices de alturas
        self.nHeightsClean = len(self.heightsClean)
        self.channels = dataOut.channelList
        self.nChan = len(self.channels)
        self.nIncohInt = dataOut.nIncohInt
        self.__initime = dataOut.utctime
        self.maxAltInd = self.hval[-1]+1
        self.minAltInd = self.hval[0]

        self.crosspairs = dataOut.pairsList
        self.nPairs = len(self.crosspairs)
        self.normFactor = dataOut.normFactor
        self.nFFTPoints = dataOut.nFFTPoints
        self.ippSeconds = dataOut.ippSeconds
        self.currentTime = self.__initime
        self.pairsArray = numpy.array(dataOut.pairsList)
        self.factor_stdv = factor_stdv
        #print("CHANNELS: ",[x for x in self.channels])

        if n != None :
            self.byProfiles = True
            self.nIntProfiles = n
        else:
            self.__integrationtime = timeInterval

        self.__dataReady = False
        self.isConfig = True



    def run(self, dataOut,min_hei=None,max_hei=None, n=None, timeInterval=10,factor_stdv=2.5):
        #print (dataOut.utctime)
        if not self.isConfig :
            #print("Setting config")
            self.setup(dataOut, min_hei,max_hei,n,timeInterval,factor_stdv)
            #print("Config Done")
        tini=dataOut.utctime

        if self.byProfiles:
            if self.__profIndex ==  self.nIntProfiles:
                self.__dataReady = True
        else:
            if (tini - self.__initime) >= self.__integrationtime:
                #print(tini - self.__initime,self.__profIndex)
                self.__dataReady = True
                self.__initime = tini

        #if (tini.tm_min % 2) == 0 and (tini.tm_sec < 5 and self.fint==0):

        if self.__dataReady:
            #print("Data ready",self.__profIndex)
            self.__profIndex = 0
            jspc = self.buffer
            jcspc = self.buffer2
            #jnoise = self.buffer3
            self.buffer = dataOut.data_spc
            self.buffer2 = dataOut.data_cspc
            #self.buffer3 = dataOut.noise
            self.currentTime = dataOut.utctime
            if numpy.any(jspc) :
                #print( jspc.shape, jcspc.shape)
                jspc = numpy.reshape(jspc,(int(len(jspc)/self.nChannels),self.nChannels,self.nFFTPoints,self.nHeights))
                jcspc= numpy.reshape(jcspc,(int(len(jcspc)/self.nPairs),self.nPairs,self.nFFTPoints,self.nHeights))
                self.__dataReady = False
                #print( jspc.shape, jcspc.shape)
                dataOut.flagNoData = False
            else:
                dataOut.flagNoData = True
                self.__dataReady = False
                return dataOut
        else:
            #print( len(self.buffer))
            if numpy.any(self.buffer):
                self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0)
                self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0)
                self.buffer3 += dataOut.data_dc
            else:
                self.buffer = dataOut.data_spc
                self.buffer2 = dataOut.data_cspc
                self.buffer3 = dataOut.data_dc
            #print self.index, self.fint
            #print self.buffer2.shape
            dataOut.flagNoData = True                   ## NOTE: ?? revisar LUEGO
            self.__profIndex += 1
            return dataOut                             ## NOTE: REV


        #index = tini.tm_hour*12+tini.tm_min/5
        '''REVISAR'''
        # jspc = jspc/self.nFFTPoints/self.normFactor
        # jcspc = jcspc/self.nFFTPoints/self.normFactor



        tmp_spectra,tmp_cspectra = self.cleanRayleigh(dataOut,jspc,jcspc,self.factor_stdv)
        dataOut.data_spc = tmp_spectra
        dataOut.data_cspc = tmp_cspectra

        #dataOut.data_spc,dataOut.data_cspc = self.cleanRayleigh(dataOut,jspc,jcspc,self.factor_stdv)

        dataOut.data_dc = self.buffer3
        dataOut.nIncohInt *= self.nIntProfiles
        dataOut.utctime = self.currentTime             #tiempo promediado
        #print("Time: ",time.localtime(dataOut.utctime))
        # dataOut.data_spc = sat_spectra
        # dataOut.data_cspc = sat_cspectra
        self.buffer = 0
        self.buffer2 = 0
        self.buffer3 = 0

        return dataOut

    def cleanRayleigh(self,dataOut,spectra,cspectra,factor_stdv):
        #print("OP cleanRayleigh")
        #import matplotlib.pyplot as plt
        #for k in range(149):
        #channelsProcssd = []
        #channelA_ok = False
        #rfunc = cspectra.copy() #self.bloques
        rfunc = spectra.copy()
        #rfunc = cspectra
        #val_spc = spectra*0.0 #self.bloque0*0.0
        #val_cspc = cspectra*0.0 #self.bloques*0.0
        #in_sat_spectra = spectra.copy()  #self.bloque0
        #in_sat_cspectra = cspectra.copy()  #self.bloques


        ###ONLY FOR TEST:
        raxs = math.ceil(math.sqrt(self.nPairs))
        caxs = math.ceil(self.nPairs/raxs)
        if self.nPairs <4:
            raxs = 2
            caxs = 2
        #print(raxs, caxs)
        fft_rev = 14 #nFFT to plot
        hei_rev = ((self.heights >= 550) & (self.heights <= 551)).nonzero() #hei to plot
        hei_rev = hei_rev[0]
        #print(hei_rev)

        #print numpy.absolute(rfunc[:,0,0,14])

        gauss_fit, covariance = None, None
        for ih in range(self.minAltInd,self.maxAltInd):
            for ifreq in range(self.nFFTPoints):
                '''
                ###ONLY FOR TEST:
                if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY
                    fig, axs = plt.subplots(raxs, caxs)
                    fig2, axs2 = plt.subplots(raxs, caxs)
                col_ax = 0
                row_ax = 0
                '''
                #print(self.nPairs)
                for ii in range(self.nChan):  #PARES DE CANALES SELF y CROSS
                    # if self.crosspairs[ii][1]-self.crosspairs[ii][0] > 1: # APLICAR SOLO EN PARES CONTIGUOS
                    #     continue
                    # if not self.crosspairs[ii][0] in channelsProcssd:
                    #     channelA_ok = True
                    #print("pair: ",self.crosspairs[ii])
                    '''
                    ###ONLY FOR TEST:
                    if (col_ax%caxs==0 and col_ax!=0 and self.nPairs !=1):
                        col_ax = 0
                        row_ax += 1
                    '''
                    func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih]))           #Potencia?
                    #print(func2clean.shape)
                    val = (numpy.isfinite(func2clean)==True).nonzero()

                    if len(val)>0:                                                              #limitador
                        min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40)
                        if min_val <= -40 :
                            min_val = -40
                        max_val = numpy.around(numpy.amax(func2clean)+2) #< 200
                        if max_val >= 200 :
                            max_val = 200
                        #print min_val, max_val
                        step = 1
                        #print("Getting bins and the histogram")
                        x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step
                        y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step))
                        #print(len(y_dist),len(binstep[:-1]))
                        #print(row_ax,col_ax, " ..")
                        #print(self.pairsArray[ii][0],self.pairsArray[ii][1])
                        mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist)
                        sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist))
                        parg = [numpy.amax(y_dist),mean,sigma]

                        newY = None

                        try :
                            gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg)
                            mode = gauss_fit[1]
                            stdv = gauss_fit[2]
                            #print(" FIT OK",gauss_fit)
                            '''
                            ###ONLY FOR TEST:
                            if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY
                                newY = fit_func(x_dist,gauss_fit[0],gauss_fit[1],gauss_fit[2])
                                axs[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green')
                                axs[row_ax,col_ax].plot(binstep[:-1],newY,color='red')
                                axs[row_ax,col_ax].set_title("CH "+str(self.channels[ii]))
                            '''
                        except:
                            mode = mean
                            stdv = sigma
                            #print("FIT FAIL")
                            #continue


                        #print(mode,stdv)
                        #Removing echoes greater than mode + std_factor*stdv
                        noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero()
                        #noval tiene los indices que se van a remover
                        #print("Chan ",ii," novals: ",len(noval[0]))
                        if len(noval[0]) > 0: #forma de array (N,) es igual a longitud (N)
                            novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero()
                            #print(novall)
                            #print("   ",self.pairsArray[ii])
                            #cross_pairs = self.pairsArray[ii]
                                #Getting coherent echoes which are removed.
                            # if len(novall[0]) > 0:
                            #
                            #     val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1
                            #     val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1
                            #     val_cspc[novall[0],ii,ifreq,ih] = 1
                                #print("OUT NOVALL 1")
                            try:
                                pair = (self.channels[ii],self.channels[ii + 1])
                            except:
                                pair = (99,99)
                            #print("par ", pair)
                            if ( pair in self.crosspairs):
                                q = self.crosspairs.index(pair)
                                #print("está aqui: ", q, (ii,ii + 1))
                                new_a = numpy.delete(cspectra[:,q,ifreq,ih], noval[0])
                                cspectra[noval,q,ifreq,ih] = numpy.mean(new_a) #mean CrossSpectra

                            #if channelA_ok:
                            #chA = self.channels.index(cross_pairs[0])
                            new_b = numpy.delete(spectra[:,ii,ifreq,ih], noval[0])
                            spectra[noval,ii,ifreq,ih] = numpy.mean(new_b) #mean Spectra Pair A
                            #channelA_ok = False

                            # chB = self.channels.index(cross_pairs[1])
                            # new_c = numpy.delete(spectra[:,chB,ifreq,ih], noval[0])
                            # spectra[noval,chB,ifreq,ih] = numpy.mean(new_c) #mean Spectra Pair B
                            #
                            # channelsProcssd.append(self.crosspairs[ii][0]) # save channel A
                            # channelsProcssd.append(self.crosspairs[ii][1]) # save channel B
                        '''
                        ###ONLY FOR TEST:
                        if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY
                            func2clean = 10*numpy.log10(numpy.absolute(spectra[:,ii,ifreq,ih]))
                            y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step))
                            axs2[row_ax,col_ax].plot(binstep[:-1],newY,color='red')
                            axs2[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green')
                            axs2[row_ax,col_ax].set_title("CH "+str(self.channels[ii]))
                        '''
            '''
                ###ONLY FOR TEST:
                    col_ax += 1    #contador de ploteo columnas
                    ##print(col_ax)
                ###ONLY FOR TEST:
                if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY
                    title = str(dataOut.datatime)+" nFFT: "+str(ifreq)+"   Alt: "+str(self.heights[ih])+ " km"
                    title2 = str(dataOut.datatime)+" nFFT: "+str(ifreq)+"   Alt: "+str(self.heights[ih])+ " km  CLEANED"
                    fig.suptitle(title)
                    fig2.suptitle(title2)
                    plt.show()
            '''
            ##################################################################################################

        #print("Getting average of the spectra and cross-spectra from incoherent echoes.")
        out_spectra = numpy.zeros([self.nChan,self.nFFTPoints,self.nHeights], dtype=float) #+numpy.nan
        out_cspectra = numpy.zeros([self.nPairs,self.nFFTPoints,self.nHeights], dtype=complex) #+numpy.nan
        for ih in range(self.nHeights):
            for ifreq in range(self.nFFTPoints):
                for ich in range(self.nChan):
                    tmp = spectra[:,ich,ifreq,ih]
                    valid = (numpy.isfinite(tmp[:])==True).nonzero()

                    if len(valid[0]) >0 :
                        out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0])

                for icr in range(self.nPairs):
                    tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih])
                    valid = (numpy.isfinite(tmp)==True).nonzero()
                    if len(valid[0]) > 0:
                        out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0])

        return out_spectra, out_cspectra

    def REM_ISOLATED_POINTS(self,array,rth):
#         import matplotlib.pyplot as plt
        if rth == None :
            rth = 4
        #print("REM ISO")
        num_prof = len(array[0,:,0])
        num_hei = len(array[0,0,:])
        n2d = len(array[:,0,0])

        for ii in range(n2d) :
            #print ii,n2d
            tmp = array[ii,:,:]
            #print tmp.shape, array[ii,101,:],array[ii,102,:]

          # fig = plt.figure(figsize=(6,5))
          # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
          # ax = fig.add_axes([left, bottom, width, height])
          # x = range(num_prof)
          # y = range(num_hei)
          # cp = ax.contour(y,x,tmp)
          # ax.clabel(cp, inline=True,fontsize=10)
          # plt.show()

            #indxs = WHERE(FINITE(tmp) AND tmp GT 0,cindxs)
            tmp = numpy.reshape(tmp,num_prof*num_hei)
            indxs1 = (numpy.isfinite(tmp)==True).nonzero()
            indxs2 = (tmp > 0).nonzero()

            indxs1 = (indxs1[0])
            indxs2 = indxs2[0]
            #indxs1 = numpy.array(indxs1[0])
            #indxs2 = numpy.array(indxs2[0])
            indxs = None
            #print indxs1 , indxs2
            for iv in range(len(indxs2)):
                indv = numpy.array((indxs1 == indxs2[iv]).nonzero())
                #print len(indxs2), indv
                if len(indv[0]) > 0  :
                    indxs =  numpy.concatenate((indxs,indxs2[iv]), axis=None)
  #           print indxs
            indxs = indxs[1:]
            #print(indxs, len(indxs))
            if len(indxs) < 4 :
                array[ii,:,:] = 0.
                return

            xpos = numpy.mod(indxs ,num_hei)
            ypos = (indxs / num_hei)
            sx = numpy.argsort(xpos) # Ordering respect to "x" (time)
            #print sx
            xpos = xpos[sx]
            ypos = ypos[sx]

     # *********************************** Cleaning isolated points **********************************
            ic = 0
            while True :
                r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2)))
                #no_coh = WHERE(FINITE(r) AND (r LE rth),cno_coh)
                #plt.plot(r)
                #plt.show()
                no_coh1 = (numpy.isfinite(r)==True).nonzero()
                no_coh2 = (r <= rth).nonzero()
                #print r, no_coh1, no_coh2
                no_coh1 = numpy.array(no_coh1[0])
                no_coh2 = numpy.array(no_coh2[0])
                no_coh = None
                #print valid1 , valid2
                for iv in range(len(no_coh2)):
                    indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero())
                    if len(indv[0]) > 0  :
                        no_coh =  numpy.concatenate((no_coh,no_coh2[iv]), axis=None)
                no_coh = no_coh[1:]
                #print len(no_coh), no_coh
                if len(no_coh) < 4 :
                    #print xpos[ic], ypos[ic], ic
    #                plt.plot(r)
    #                plt.show()
                    xpos[ic] = numpy.nan
                    ypos[ic] = numpy.nan

                ic = ic + 1
                if  (ic == len(indxs)) :
                    break
                #print( xpos, ypos)

            indxs = (numpy.isfinite(list(xpos))==True).nonzero()
            #print indxs[0]
            if len(indxs[0]) < 4 :
                array[ii,:,:] = 0.
                return

            xpos = xpos[indxs[0]]
            ypos = ypos[indxs[0]]
            for i in range(0,len(ypos)):
                ypos[i]=int(ypos[i])
            junk = tmp
            tmp = junk*0.0

            tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))]
            array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei))

            #print array.shape
            #tmp = numpy.reshape(tmp,(num_prof,num_hei))
            #print tmp.shape

            # fig = plt.figure(figsize=(6,5))
            # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
            # ax = fig.add_axes([left, bottom, width, height])
            # x = range(num_prof)
            # y = range(num_hei)
            # cp = ax.contour(y,x,array[ii,:,:])
            # ax.clabel(cp, inline=True,fontsize=10)
            # plt.show()
        return array

class removeInterference(Operation):

    def removeInterference2(self):

        cspc = self.dataOut.data_cspc
        spc = self.dataOut.data_spc
        Heights = numpy.arange(cspc.shape[2])
        realCspc = numpy.abs(cspc)

        for i in range(cspc.shape[0]):
            LinePower= numpy.sum(realCspc[i], axis=0)
            Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)]
            SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ]
            InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 )
            InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)]
            InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)]


            InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) )
            #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax]))
            if len(InterferenceRange)<int(cspc.shape[1]*0.3):
                cspc[i,InterferenceRange,:] = numpy.NaN

        self.dataOut.data_cspc = cspc

    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 = int(num_hei / 2)
            hei_interf = numpy.asmatrix(list(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 = list(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(list(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[list(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(list(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 = int(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[list(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 = int(numpy.median(numpy.real(
                junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
            median_imag = int(numpy.median(numpy.imag(
                junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
            comp_mask_prof = [int(e) for e in comp_mask_prof]
            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(list(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 run(self, dataOut, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None, mode=1):

        self.dataOut = dataOut

        if mode == 1:
            self.removeInterference(interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None)
        elif mode == 2:
            self.removeInterference2()

        return self.dataOut


class IncohInt(Operation):

    __profIndex = 0
    __withOverapping = False

    __byTime = False
    __initime = None
    __lastdatatime = None
    __integrationtime = None

    __buffer_spc = None
    __buffer_cspc = None
    __buffer_dc = None

    __dataReady = False

    __timeInterval = None

    n = None

    def __init__(self):

        Operation.__init__(self)

    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:

            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

        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

        return dataOut

class dopplerFlip(Operation):

    def run(self, dataOut):
        # arreglo 1: (num_chan, num_profiles, num_heights)
        self.dataOut = dataOut
        # JULIA-oblicua, indice 2
        # arreglo 2: (num_profiles, num_heights)
        jspectra = self.dataOut.data_spc[2]
        jspectra_tmp = numpy.zeros(jspectra.shape)
        num_profiles = jspectra.shape[0]
        freq_dc = int(num_profiles / 2)
        # Flip con for
        for j in range(num_profiles):
            jspectra_tmp[num_profiles-j-1]= jspectra[j]
        # Intercambio perfil de DC con perfil inmediato anterior
        jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1]
        jspectra_tmp[freq_dc]= jspectra[freq_dc]
        # canal modificado es re-escrito en el arreglo de canales
        self.dataOut.data_spc[2] = jspectra_tmp

        return self.dataOut