import numpy

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

class SpectraLagsProc(ProcessingUnit):
    
    def __init__(self):
        
        ProcessingUnit.__init__(self)
        
        self.__input_buffer = None
        self.firstdatatime = None
        self.profIndex = 0
        self.dataOut = Spectra()
        self.id_min = None
        self.id_max = None
        self.__codeIndex = 0
        
        self.__lags_buffer = None

    def __updateSpecFromVoltage(self):
        
        self.dataOut.plotting = "spectra_lags"
        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 __createLagsBlock(self, voltages):
        
        if self.__lags_buffer is None:
            self.__lags_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, self.dataOut.nHeights), dtype='complex')
        
        nsegments = self.dataOut.nHeights - self.dataOut.nProfiles
        
#         codes = numpy.conjugate(self.__input_buffer[:,9:169])/10000
        
        for i in range(nsegments):
            self.__lags_buffer[:,:,i] = voltages[:,i:i+self.dataOut.nProfiles]#*codes
        
        return self.__lags_buffer
        
    def __decodeData(self, volt_buffer, pulseIndex=None):
        
        if pulseIndex is None:
            return volt_buffer
        
        codes = numpy.conjugate(self.__input_buffer[:,pulseIndex[0]:pulseIndex[1]])/10000
        
        nsegments = self.dataOut.nHeights - self.dataOut.nProfiles
            
        for i in range(nsegments):
            volt_buffer[:,:,i] = volt_buffer[:,:,i]*codes
        
        return volt_buffer
    
    def __getFft(self, datablock):
        """
        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.__input_buffer
            self.dataOut.flagNoData
        """
        
        fft_volt = numpy.fft.fft(datablock, n=self.dataOut.nFFTPoints, axis=1)
        
        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 != []:
            #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=None, nBaud=None, codeFromHeader=False, pulseIndex=None):
        
        self.dataOut.flagNoData = True
        
        self.code = None
            
        if codeFromHeader:
            if self.dataIn.code is not None:
                self.code = self.dataIn.code
        
        if code is not None:
            self.code = numpy.array(code).reshape(nCode,nBaud)
            
        if self.dataIn.type == "Voltage":
            
            if nFFTPoints == None:
                raise ValueError, "This SpectraProc.run() need nFFTPoints input variable"
            
            if nProfiles == None:
                nProfiles = nFFTPoints
            
            self.profIndex == nProfiles
            self.firstdatatime = self.dataIn.utctime
            
            self.dataOut.ippFactor = 1
            self.dataOut.nFFTPoints = nFFTPoints
            self.dataOut.nProfiles = nProfiles
            self.dataOut.pairsList = pairsList
            
            self.__updateSpecFromVoltage()
            
            if not self.dataIn.flagDataAsBlock:
                self.__input_buffer = self.dataIn.data.copy()
                
                lags_block = self.__createLagsBlock(self.__input_buffer)
            
                lags_block = self.__decodeData(lags_block, pulseIndex)
            
            else:
                self.__input_buffer = self.dataIn.data.copy()
            
            self.__getFft(lags_block)
            
            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