diff --git a/schainpy/model/proc/__init__.py b/schainpy/model/proc/__init__.py
index fd9d020..a17c2ba 100644
--- a/schainpy/model/proc/__init__.py
+++ b/schainpy/model/proc/__init__.py
@@ -10,4 +10,5 @@ from jroproc_heispectra import *
 from jroproc_amisr import *
 from jroproc_correlation import *
 from jroproc_parameters import *
-from jroproc_spectra_lags import *
\ No newline at end of file
+from jroproc_spectra_lags import *
+from jroproc_spectra_acf import *
\ No newline at end of file
diff --git a/schainpy/model/proc/jroproc_spectra_acf.py b/schainpy/model/proc/jroproc_spectra_acf.py
new file mode 100644
index 0000000..d257d7e
--- /dev/null
+++ b/schainpy/model/proc/jroproc_spectra_acf.py
@@ -0,0 +1,736 @@
+import numpy
+
+from jroproc_base import ProcessingUnit, Operation
+from schainpy.model.data.jrodata import Spectra
+from schainpy.model.data.jrodata import hildebrand_sekhon
+
+class SpectraAFCProc(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
+
+    def __updateSpecFromVoltage(self):
+        
+        self.dataOut.plotting = "spectra_acf"
+        
+        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.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15
+        
+        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 __decodeData(self, nProfiles, code):
+        
+        if code is None:
+            return
+        
+        for i in range(nProfiles):
+            self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i]
+        
+    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
+        """
+        nsegments = self.dataOut.nHeights
+        
+        _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex')
+        
+        for i in range(nsegments):
+            try:
+                _fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles]
+                
+                if self.code is not None:
+                    _fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0]
+            except:
+                pass
+        
+        fft_volt = numpy.fft.fft(_fft_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)
+        
+        data = numpy.fft.ifft(spc, axis=1)
+        data = numpy.fft.fftshift(data, axes=(1,))
+        
+        spc = data
+        
+        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))
+                
+                chan_index0 = self.dataOut.channelList.index(pair[0])
+                chan_index1 = self.dataOut.channelList.index(pair[1])
+                
+                cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:])
+                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=[], code=None, nCode=1, nBaud=1):
+        
+        self.dataOut.flagNoData = True
+        
+        if code is not None:
+            self.code = numpy.array(code).reshape(nCode,nBaud)
+        else:
+            self.code = None
+        
+        if self.dataIn.type == "Voltage":
+            
+            if nFFTPoints == None:
+                raise ValueError, "This SpectraProc.run() need nFFTPoints input variable"
+            
+            if nProfiles == None:
+                nProfiles = nFFTPoints
+                
+            self.dataOut.ippFactor = 1
+            
+            self.dataOut.nFFTPoints = nFFTPoints
+            self.dataOut.nProfiles = nProfiles
+            self.dataOut.pairsList = pairsList
+
+#             if self.buffer is None:
+#                 self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights),
+#                                           dtype='complex')
+
+            if not self.dataIn.flagDataAsBlock:
+                self.buffer = self.dataIn.data.copy()
+                
+#                 for i in range(self.dataIn.nHeights):
+#                     self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex]
+#                 
+#                 self.profIndex += 1
+            
+            else:
+                raise ValueError, ""
+            
+            self.firstdatatime = self.dataIn.utctime
+            
+            self.profIndex == nProfiles
+            
+            self.__updateSpecFromVoltage()
+            
+            self.__getFft()
+            
+            self.dataOut.flagNoData = False
+            
+            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 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 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 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
\ No newline at end of file
diff --git a/schainpy/model/proc/jroproc_spectra_lags.py b/schainpy/model/proc/jroproc_spectra_lags.py
index ff764e8..19ed0c5 100644
--- a/schainpy/model/proc/jroproc_spectra_lags.py
+++ b/schainpy/model/proc/jroproc_spectra_lags.py
@@ -106,7 +106,7 @@ class SpectraLagsProc(ProcessingUnit):
         
         fft_volt = numpy.fft.fft(datablock, n=self.dataOut.nFFTPoints, axis=1)
         
-#         dc = fft_volt[:,0,:]
+        dc = fft_volt[:,0,:]
         
         #calculo de self-spectra
         fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
@@ -114,13 +114,13 @@ class SpectraLagsProc(ProcessingUnit):
         spc = spc.real
         
         blocksize = 0
-#         blocksize += dc.size
+        blocksize += dc.size
         blocksize += spc.size
         
         cspc = None
         pairIndex = 0
         
-        if self.dataOut.pairsList != None:
+        if self.dataOut.pairsList != []:
             #calculo de cross-spectra
             cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
             for pair in self.dataOut.pairsList:
@@ -138,7 +138,7 @@ class SpectraLagsProc(ProcessingUnit):
         
         self.dataOut.data_spc = spc
         self.dataOut.data_cspc = cspc
-#         self.dataOut.data_dc = dc
+        self.dataOut.data_dc = dc
         self.dataOut.blockSize = blocksize
         self.dataOut.flagShiftFFT = True