import numpy

from jroproc_base import ProcessingUnit, Operation
from schainpy.model.data.jrodata import Spectra
from schainpy.model.data.jrodata import hildebrand_sekhon

import matplotlib.pyplot as plt

class SpectraProc(ProcessingUnit):

    def __init__(self, **kwargs):

        ProcessingUnit.__init__(self, **kwargs)

        self.buffer = None
        self.firstdatatime = None
        self.profIndex = 0
        self.dataOut = Spectra()
        self.id_min = None
        self.id_max = None

    def __updateSpecFromVoltage(self):

        self.dataOut.timeZone = self.dataIn.timeZone
        self.dataOut.dstFlag = self.dataIn.dstFlag
        self.dataOut.errorCount = self.dataIn.errorCount
        self.dataOut.useLocalTime = self.dataIn.useLocalTime

        self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
        self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
        self.dataOut.channelList = self.dataIn.channelList
        self.dataOut.heightList = self.dataIn.heightList
        self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])

        self.dataOut.nBaud = self.dataIn.nBaud
        self.dataOut.nCode = self.dataIn.nCode
        self.dataOut.code = self.dataIn.code
        self.dataOut.nProfiles = self.dataOut.nFFTPoints

        self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
        self.dataOut.utctime = self.firstdatatime
        self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
        self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
        self.dataOut.flagShiftFFT = False

        self.dataOut.nCohInt = self.dataIn.nCohInt
        self.dataOut.nIncohInt = 1

        self.dataOut.windowOfFilter = self.dataIn.windowOfFilter

        self.dataOut.frequency = self.dataIn.frequency
        self.dataOut.realtime = self.dataIn.realtime

        self.dataOut.azimuth = self.dataIn.azimuth
        self.dataOut.zenith = self.dataIn.zenith

        self.dataOut.beam.codeList = self.dataIn.beam.codeList
        self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
        self.dataOut.beam.zenithList = self.dataIn.beam.zenithList

    def __getFft(self):
        """
        Convierte valores de Voltaje a Spectra

        Affected:
            self.dataOut.data_spc
            self.dataOut.data_cspc
            self.dataOut.data_dc
            self.dataOut.heightList
            self.profIndex
            self.buffer
            self.dataOut.flagNoData
        """
        fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1)
        
        fft_volt = fft_volt.astype(numpy.dtype('complex'))
        dc = fft_volt[:,0,:]

        #calculo de self-spectra
        fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
        spc = fft_volt * numpy.conjugate(fft_volt)
        spc = spc.real

        blocksize = 0
        blocksize += dc.size
        blocksize += spc.size

        cspc = None
        pairIndex = 0
        if self.dataOut.pairsList != None:
            #calculo de cross-spectra
            cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
            for pair in self.dataOut.pairsList:
                if pair[0] not in self.dataOut.channelList:
                    raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList))
                if pair[1] not in self.dataOut.channelList:
                    raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList))

                cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
                pairIndex += 1
            blocksize += cspc.size

        self.dataOut.data_spc = spc
        self.dataOut.data_cspc = cspc
        self.dataOut.data_dc = dc
        self.dataOut.blockSize = blocksize
        self.dataOut.flagShiftFFT = True

    def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None):

        self.dataOut.flagNoData = True

        if self.dataIn.type == "Spectra":
            self.dataOut.copy(self.dataIn)
#             self.__selectPairs(pairsList)
            return True

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

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

            if nProfiles == None:
                nProfiles = nFFTPoints

            if ippFactor == None:
                ippFactor = 1

            self.dataOut.ippFactor = ippFactor

            self.dataOut.nFFTPoints = nFFTPoints
            self.dataOut.pairsList = pairsList

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

            if self.dataIn.flagDataAsBlock:
                #data dimension: [nChannels, nProfiles, nSamples]
                
                nVoltProfiles = self.dataIn.data.shape[1]
#                 nVoltProfiles = self.dataIn.nProfiles

                if nVoltProfiles == nProfiles:
                    self.buffer = self.dataIn.data.copy()
                    self.profIndex = nVoltProfiles

                elif nVoltProfiles < nProfiles:

                    if self.profIndex == 0:
                        self.id_min = 0
                        self.id_max = nVoltProfiles

                    self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data
                    self.profIndex += nVoltProfiles
                    self.id_min += nVoltProfiles
                    self.id_max += nVoltProfiles
                else:
                    raise ValueError, "The type object %s has %d profiles, it should just has %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles)
                    self.dataOut.flagNoData = True
                    return 0
            else:
               
                self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
                self.profIndex += 1

            if self.firstdatatime == None:
                self.firstdatatime = self.dataIn.utctime

            if self.profIndex == nProfiles:
                self.__updateSpecFromVoltage()
                self.__getFft()

                self.dataOut.flagNoData = False
                self.firstdatatime = None
                self.profIndex = 0

            return True

        raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type)

    def __selectPairs(self, pairsList):

        if channelList == None:
            return

        pairsIndexListSelected = []

        for thisPair in pairsList:

            if thisPair not in self.dataOut.pairsList:
                continue

            pairIndex = self.dataOut.pairsList.index(thisPair)

            pairsIndexListSelected.append(pairIndex)

        if not pairsIndexListSelected:
            self.dataOut.data_cspc = None
            self.dataOut.pairsList = []
            return

        self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
        self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]

        return

    def __selectPairsByChannel(self, channelList=None):

        if channelList == None:
            return

        pairsIndexListSelected = []
        for pairIndex in self.dataOut.pairsIndexList:
            #First pair
            if self.dataOut.pairsList[pairIndex][0] not in channelList:
                continue
            #Second pair
            if self.dataOut.pairsList[pairIndex][1] not in channelList:
                continue

            pairsIndexListSelected.append(pairIndex)

        if not pairsIndexListSelected:
            self.dataOut.data_cspc = None
            self.dataOut.pairsList = []
            return

        self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
        self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]

        return

    def selectChannels(self, channelList):

        channelIndexList = []

        for channel in channelList:
            if channel not in self.dataOut.channelList:
                raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList))

            index = self.dataOut.channelList.index(channel)
            channelIndexList.append(index)

        self.selectChannelsByIndex(channelIndexList)

    def selectChannelsByIndex(self, channelIndexList):
        """
        Selecciona un bloque de datos en base a canales segun el channelIndexList

        Input:
            channelIndexList    :    lista sencilla de canales a seleccionar por ej. [2,3,7]

        Affected:
            self.dataOut.data_spc
            self.dataOut.channelIndexList
            self.dataOut.nChannels

        Return:
            None
        """

        for channelIndex in channelIndexList:
            if channelIndex not in self.dataOut.channelIndexList:
                raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList)

#         nChannels = len(channelIndexList)

        data_spc = self.dataOut.data_spc[channelIndexList,:]
        data_dc = self.dataOut.data_dc[channelIndexList,:]

        self.dataOut.data_spc = data_spc
        self.dataOut.data_dc = data_dc

        self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
#        self.dataOut.nChannels = nChannels

        self.__selectPairsByChannel(self.dataOut.channelList)

        return 1
    
    
    def 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 selectHeights(self, minHei, maxHei):
        """
        Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
        minHei <= height <= maxHei

        Input:
            minHei    :    valor minimo de altura a considerar
            maxHei    :    valor maximo de altura a considerar

        Affected:
            Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex

        Return:
            1 si el metodo se ejecuto con exito caso contrario devuelve 0
        """

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

        if (minHei < self.dataOut.heightList[0]):
            minHei = self.dataOut.heightList[0]

        if (maxHei > self.dataOut.heightList[-1]):
            maxHei = self.dataOut.heightList[-1]

        minIndex = 0
        maxIndex = 0
        heights = self.dataOut.heightList

        inda = numpy.where(heights >= minHei)
        indb = numpy.where(heights <= maxHei)

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

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

        self.selectHeightsByIndex(minIndex, maxIndex)
        

        return 1

    def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None):
        newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])

        if hei_ref != None:
            newheis = numpy.where(self.dataOut.heightList>hei_ref)

        minIndex = min(newheis[0])
        maxIndex = max(newheis[0])
        data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
        heightList = self.dataOut.heightList[minIndex:maxIndex+1]

        # determina indices
        nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0]))
        avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0))
        beacon_dB = numpy.sort(avg_dB)[-nheis:]
        beacon_heiIndexList = []
        for val in avg_dB.tolist():
            if val >= beacon_dB[0]:
                beacon_heiIndexList.append(avg_dB.tolist().index(val))

        #data_spc = data_spc[:,:,beacon_heiIndexList]
        data_cspc = None
        if self.dataOut.data_cspc is not None:
            data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
            #data_cspc = data_cspc[:,:,beacon_heiIndexList]

        data_dc = None
        if self.dataOut.data_dc is not None:
            data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
            #data_dc = data_dc[:,beacon_heiIndexList]

        self.dataOut.data_spc = data_spc
        self.dataOut.data_cspc = data_cspc
        self.dataOut.data_dc = data_dc
        self.dataOut.heightList = heightList
        self.dataOut.beacon_heiIndexList = beacon_heiIndexList

        return 1

    def 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]

        #self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]

        return 1



    def selectHeightsByIndex(self, minIndex, maxIndex):
        """
        Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
        minIndex <= index <= maxIndex

        Input:
            minIndex    :    valor de indice minimo de altura a considerar
            maxIndex    :    valor de indice maximo de altura a considerar

        Affected:
            self.dataOut.data_spc
            self.dataOut.data_cspc
            self.dataOut.data_dc
            self.dataOut.heightList

        Return:
            1 si el metodo se ejecuto con exito caso contrario devuelve 0
        """

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

        if (maxIndex >= self.dataOut.nHeights):
            maxIndex = self.dataOut.nHeights-1

        #Spectra
        data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]

        data_cspc = None
        if self.dataOut.data_cspc is not None:
            data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]

        data_dc = None
        if self.dataOut.data_dc is not None:
            data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]

        self.dataOut.data_spc = data_spc
        self.dataOut.data_cspc = data_cspc
        self.dataOut.data_dc = data_dc

        self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]

        return 1
    
    
    def removeDC(self, mode = 2):
        jspectra = self.dataOut.data_spc
        jcspectra = self.dataOut.data_cspc


        num_chan = jspectra.shape[0]
        num_hei = jspectra.shape[2]

        if jcspectra is not None:
            jcspectraExist = True
            num_pairs = jcspectra.shape[0]
        else:   jcspectraExist = False

        freq_dc = jspectra.shape[1]/2
        ind_vel = numpy.array([-2,-1,1,2]) + freq_dc

        if ind_vel[0]<0:
            ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof

        if mode == 1:
            jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION

            if jcspectraExist:
                jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2

        if mode == 2:

            vel = numpy.array([-2,-1,1,2])
            xx = numpy.zeros([4,4])

            for fil in range(4):
                xx[fil,:] = vel[fil]**numpy.asarray(range(4))

            xx_inv = numpy.linalg.inv(xx)
            xx_aux = xx_inv[0,:]

            for ich in range(num_chan):
                yy = jspectra[ich,ind_vel,:]
                jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy)

                junkid = jspectra[ich,freq_dc,:]<=0
                cjunkid = sum(junkid)

                if cjunkid.any():
                    jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2

            if jcspectraExist:
                for ip in range(num_pairs):
                    yy = jcspectra[ip,ind_vel,:]
                    jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy)


        self.dataOut.data_spc = jspectra
        self.dataOut.data_cspc = jcspectra

        return 1

    def removeInterference2(self):
        
        cspc = self.dataOut.data_cspc
        spc = self.dataOut.data_spc
        print numpy.shape(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 ) ]
            #print numpy.shape(realCspc)
            #print '',SelectedHeights, '', numpy.shape(realCspc[i,:,SelectedHeights])
            InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 )
            print SelectedHeights
            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
            
            print '########################################################################################'
            print 'Len interference sum',len(InterferenceSum)
            print 'InterferenceThresholdMin', InterferenceThresholdMin, 'InterferenceThresholdMax', InterferenceThresholdMax
            print 'InterferenceRange',InterferenceRange
            print '########################################################################################'
            
            ''' Ploteo '''
            
            #for i in range(3):
            #print 'FASE', numpy.shape(phase), y[25]
            #print numpy.shape(coherence)
            #fig = plt.figure(10+ int(numpy.random.rand()*100))
            #plt.plot( x[0:256],coherence[:,25] )
            #cohAv = numpy.average(coherence[i],1)
            #Pendiente = FrecRange * PhaseSlope[i]         
            #plt.plot( InterferenceSum)
            #plt.plot( numpy.sort(InterferenceSum))
            #plt.plot( LinePower ) 
            #plt.plot( xFrec,phase[i])
            
            #CSPCmean = numpy.mean(numpy.abs(CSPCSamples),0)
            #plt.plot(xFrec, FitGauss01)
            #plt.plot(xFrec, CSPCmean)
            #plt.plot(xFrec, numpy.abs(CSPCSamples[0]))
            #plt.plot(xFrec, FitGauss)
            #plt.plot(xFrec, yMean)
            #plt.plot(xFrec, numpy.abs(coherence[0]))
            
            #plt.axis([-12, 12, 15, 50])
            #plt.title("%s" %(  '%s %s, Channel %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S") , i)))
 
                  
            #fig.savefig('/home/erick/Documents/Pics/nom{}.png'.format(int(numpy.random.rand()*100)))
                  
            #plt.show()
                #self.indice=self.indice+1 
        #raise
        
            
        self.dataOut.data_cspc = cspc
        
#         for i in range(spc.shape[0]):
#             LinePower= numpy.sum(spc[i], axis=0)
#             Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)]
#             SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ]
#             #print numpy.shape(realCspc)
#             #print '',SelectedHeights, '', numpy.shape(realCspc[i,:,SelectedHeights])
#             InterferenceSum = numpy.sum( spc[i,:,SelectedHeights], axis=0 )
#             InterferenceThreshold = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)]
#             InterferenceRange = numpy.where( InterferenceSum > InterferenceThreshold )
#             if len(InterferenceRange)<int(spc.shape[1]*0.03):
#                 spc[i,InterferenceRange,:] = numpy.NaN
            
        #self.dataOut.data_spc = spc

    def removeInterference(self,  interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):

        jspectra = self.dataOut.data_spc
        jcspectra = self.dataOut.data_cspc
        jnoise = self.dataOut.getNoise()
        num_incoh = self.dataOut.nIncohInt

        num_channel  = jspectra.shape[0]
        num_prof  = jspectra.shape[1]
        num_hei   = jspectra.shape[2]

        #hei_interf
        if hei_interf is None:
            count_hei = num_hei/2   #Como es entero no importa
            hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei
            hei_interf = numpy.asarray(hei_interf)[0]
        #nhei_interf
        if (nhei_interf == None):
            nhei_interf = 5
        if (nhei_interf < 1):
            nhei_interf = 1
        if (nhei_interf > count_hei):
            nhei_interf = count_hei
        if (offhei_interf == None):
            offhei_interf = 0

        ind_hei = range(num_hei)
#         mask_prof = numpy.asarray(range(num_prof - 2)) + 1
#         mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
        mask_prof = numpy.asarray(range(num_prof))
        num_mask_prof = mask_prof.size
        comp_mask_prof = [0, num_prof/2]


        #noise_exist:    Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
        if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
            jnoise = numpy.nan
        noise_exist = jnoise[0] < numpy.Inf

        #Subrutina de Remocion de la Interferencia
        for ich in range(num_channel):
            #Se ordena los espectros segun su potencia (menor a mayor)
            power = jspectra[ich,mask_prof,:]
            power = power[:,hei_interf]
            power = power.sum(axis = 0)
            psort = power.ravel().argsort()

            #Se estima la interferencia promedio en los Espectros de Potencia empleando
            junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]]

            if noise_exist:
            #    tmp_noise = jnoise[ich] / num_prof
                tmp_noise = jnoise[ich]
            junkspc_interf = junkspc_interf - tmp_noise
            #junkspc_interf[:,comp_mask_prof] = 0

            jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf
            jspc_interf = jspc_interf.transpose()
            #Calculando el espectro de interferencia promedio
            noiseid =  numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh))
            noiseid = noiseid[0]
            cnoiseid = noiseid.size
            interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh))
            interfid = interfid[0]
            cinterfid = interfid.size

            if (cnoiseid > 0):   jspc_interf[noiseid] = 0

            #Expandiendo los perfiles a limpiar
            if (cinterfid > 0):
                new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof
                new_interfid = numpy.asarray(new_interfid)
                new_interfid = {x for x in new_interfid}
                new_interfid = numpy.array(list(new_interfid))
                new_cinterfid = new_interfid.size
            else: new_cinterfid = 0

            for ip in range(new_cinterfid):
                ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort()
                jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]]


            jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices

            #Removiendo la interferencia del punto de mayor interferencia
            ListAux = jspc_interf[mask_prof].tolist()
            maxid = ListAux.index(max(ListAux))


            if cinterfid > 0:
                for ip in range(cinterfid*(interf == 2) - 1):
                    ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero()
                    cind = len(ind)

                    if (cind > 0):
                        jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh))

                ind = numpy.array([-2,-1,1,2])
                xx = numpy.zeros([4,4])

                for id1 in range(4):
                    xx[:,id1] = ind[id1]**numpy.asarray(range(4))

                xx_inv = numpy.linalg.inv(xx)
                xx = xx_inv[:,0]
                ind = (ind + maxid + num_mask_prof)%num_mask_prof
                yy = jspectra[ich,mask_prof[ind],:]
                jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)


            indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero()
            jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh))

        #Remocion de Interferencia en el Cross Spectra
        if jcspectra is None: return jspectra, jcspectra
        num_pairs = jcspectra.size/(num_prof*num_hei)
        jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)

        for ip in range(num_pairs):

            #-------------------------------------------

            cspower = numpy.abs(jcspectra[ip,mask_prof,:])
            cspower = cspower[:,hei_interf]
            cspower = cspower.sum(axis = 0)

            cspsort = cspower.ravel().argsort()
            junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]]
            junkcspc_interf = junkcspc_interf.transpose()
            jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf

            ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()

            median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
            median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
            junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag)

            for iprof in range(num_prof):
                ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort()
                jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]]

            #Removiendo la Interferencia
            jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf

            ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
            maxid = ListAux.index(max(ListAux))

            ind = numpy.array([-2,-1,1,2])
            xx = numpy.zeros([4,4])

            for id1 in range(4):
                xx[:,id1] = ind[id1]**numpy.asarray(range(4))

            xx_inv = numpy.linalg.inv(xx)
            xx = xx_inv[:,0]

            ind = (ind + maxid + num_mask_prof)%num_mask_prof
            yy = jcspectra[ip,mask_prof[ind],:]
            jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)

        #Guardar Resultados
        self.dataOut.data_spc = jspectra
        self.dataOut.data_cspc = jcspectra

        return 1

    def setRadarFrequency(self, frequency=None):

        if frequency != None:
            self.dataOut.frequency = frequency

        return 1

    def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
        #validacion de rango
        if minHei == None:
            minHei = self.dataOut.heightList[0]

        if maxHei == None:
            maxHei = self.dataOut.heightList[-1]

        if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
            print 'minHei: %.2f is out of the heights range'%(minHei)
            print 'minHei is setting to %.2f'%(self.dataOut.heightList[0])
            minHei = self.dataOut.heightList[0]

        if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
            print 'maxHei: %.2f is out of the heights range'%(maxHei)
            print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1])
            maxHei = self.dataOut.heightList[-1]

        # validacion de velocidades
        velrange = self.dataOut.getVelRange(1)

        if minVel == None:
            minVel = velrange[0]

        if maxVel == None:
            maxVel = velrange[-1]

        if (minVel < velrange[0]) or (minVel > maxVel):
            print 'minVel: %.2f is out of the velocity range'%(minVel)
            print 'minVel is setting to %.2f'%(velrange[0])
            minVel = velrange[0]

        if (maxVel > velrange[-1]) or (maxVel < minVel):
            print 'maxVel: %.2f is out of the velocity range'%(maxVel)
            print 'maxVel is setting to %.2f'%(velrange[-1])
            maxVel = velrange[-1]

        # seleccion de indices para rango
        minIndex = 0
        maxIndex = 0
        heights = self.dataOut.heightList

        inda = numpy.where(heights >= minHei)
        indb = numpy.where(heights <= maxHei)

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

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

        if (minIndex < 0) or (minIndex > maxIndex):
            raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)

        if (maxIndex >= self.dataOut.nHeights):
            maxIndex = self.dataOut.nHeights-1

        # seleccion de indices para velocidades
        indminvel = numpy.where(velrange >= minVel)
        indmaxvel = numpy.where(velrange <= maxVel)
        try:
            minIndexVel = indminvel[0][0]
        except:
            minIndexVel = 0

        try:
            maxIndexVel = indmaxvel[0][-1]
        except:
            maxIndexVel = len(velrange)

        #seleccion del espectro
        data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1]
        #estimacion de ruido
        noise = numpy.zeros(self.dataOut.nChannels)

        for channel in range(self.dataOut.nChannels):
            daux = data_spc[channel,:,:]
            noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)

        self.dataOut.noise_estimation = noise.copy()

        return 1

class IncohInt(Operation):


    __profIndex = 0
    __withOverapping  = False

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

    __buffer_spc = None
    __buffer_cspc = None
    __buffer_dc = None

    __dataReady = False

    __timeInterval = None

    n = None



    def __init__(self, **kwargs):

        Operation.__init__(self, **kwargs)
#         self.isConfig = False

    def setup(self, n=None, timeInterval=None, overlapping=False):
        """
        Set the parameters of the integration class.

        Inputs:

            n        :    Number of coherent integrations
            timeInterval   :    Time of integration. If the parameter "n" is selected this one does not work
            overlapping    :

        """

        self.__initime = None
        self.__lastdatatime = 0

        self.__buffer_spc = 0
        self.__buffer_cspc = 0
        self.__buffer_dc = 0

        self.__profIndex = 0
        self.__dataReady = False
        self.__byTime = False

        if n is None and timeInterval is None:
            raise ValueError, "n or timeInterval should be specified ..."

        if n is not None:
            self.n = int(n)
        else:
            self.__integrationtime = int(timeInterval) #if (type(timeInterval)!=integer) -> change this line
            self.n = None
            self.__byTime = True

    def putData(self, data_spc, data_cspc, data_dc):

        """
        Add a profile to the __buffer_spc and increase in one the __profileIndex

        """

        self.__buffer_spc += data_spc

        if data_cspc is None:
            self.__buffer_cspc = None
        else:
            self.__buffer_cspc += data_cspc

        if data_dc is None:
            self.__buffer_dc = None
        else:
            self.__buffer_dc += data_dc

        self.__profIndex += 1

        return

    def pushData(self):
        """
        Return the sum of the last profiles and the profiles used in the sum.

        Affected:

        self.__profileIndex

        """

        data_spc = self.__buffer_spc
        data_cspc = self.__buffer_cspc
        data_dc = self.__buffer_dc
        n = self.__profIndex

        self.__buffer_spc = 0
        self.__buffer_cspc = 0
        self.__buffer_dc = 0
        self.__profIndex = 0

        return data_spc, data_cspc, data_dc, n

    def byProfiles(self, *args):

        self.__dataReady = False
        avgdata_spc = None
        avgdata_cspc = None
        avgdata_dc = None

        self.putData(*args)

        if self.__profIndex == self.n:

            avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
            self.n = n
            self.__dataReady = True

        return avgdata_spc, avgdata_cspc, avgdata_dc

    def byTime(self, datatime, *args):

        self.__dataReady = False
        avgdata_spc = None
        avgdata_cspc = None
        avgdata_dc = None

        self.putData(*args)

        if (datatime - self.__initime) >= self.__integrationtime:
            avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
            self.n = n
            self.__dataReady = True

        return avgdata_spc, avgdata_cspc, avgdata_dc

    def integrate(self, datatime, *args):

        if self.__profIndex == 0:
            self.__initime = datatime

        if self.__byTime:
            avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
        else:
            avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)

        if not self.__dataReady:
            return None, None, None, None

        return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc

    def run(self, dataOut, n=None, timeInterval=None, overlapping=False):

        if n==1:
            return

        dataOut.flagNoData = True

        if not self.isConfig:
            self.setup(n, timeInterval, overlapping)
            self.isConfig = True

        avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
                                                                            dataOut.data_spc,
                                                                            dataOut.data_cspc,
                                                                            dataOut.data_dc)

        if self.__dataReady:

            dataOut.data_spc = avgdata_spc
            dataOut.data_cspc = avgdata_cspc
            dataOut.data_dc = avgdata_dc

            dataOut.nIncohInt *= self.n
            dataOut.utctime = avgdatatime
            dataOut.flagNoData = False