jroproc_spectra.py
935 lines
| 33.5 KiB
| text/x-python
|
PythonLexer
Daniel Valdez
|
r487 | import numpy | ||
Julio Valdez
|
r502 | import math | ||
|
Daniel Valdez
|
r487 | |||
| from jroproc_base import ProcessingUnit, Operation | ||||
| from model.data.jrodata import Spectra | ||||
|
Daniel Valdez
|
r491 | from model.data.jrodata import hildebrand_sekhon | ||
|
Daniel Valdez
|
r487 | |||
| class SpectraProc(ProcessingUnit): | ||||
| def __init__(self): | ||||
| ProcessingUnit.__init__(self) | ||||
| self.buffer = None | ||||
| self.firstdatatime = None | ||||
| self.profIndex = 0 | ||||
| self.dataOut = Spectra() | ||||
|
Daniel Valdez
|
r495 | self.id_min = None | ||
| self.id_max = None | ||||
|
Daniel Valdez
|
r487 | |||
| def __updateObjFromInput(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.nHeights = self.dataIn.nHeights | ||||
| # self.dataOut.nChannels = self.dataIn.nChannels | ||||
| 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.channelIndexList = self.dataIn.channelIndexList | ||||
| self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock | ||||
| 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 = self.dataIn.flagShiftFFT | ||||
| self.dataOut.nCohInt = self.dataIn.nCohInt | ||||
| self.dataOut.nIncohInt = 1 | ||||
| # self.dataOut.ippSeconds = self.dataIn.ippSeconds | ||||
| self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | ||||
Miguel Valdez
|
r528 | # self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt | ||
|
Daniel Valdez
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r487 | self.dataOut.frequency = self.dataIn.frequency | ||
| self.dataOut.realtime = self.dataIn.realtime | ||||
|
Daniel Valdez
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r499 | self.dataOut.azimuth = self.dataIn.azimuth | ||
| self.dataOut.zenith = self.dataIn.zenith | ||||
|
Daniel Valdez
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r501 | self.dataOut.beam.codeList = self.dataIn.beam.codeList | ||
| self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | ||||
| self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | ||||
|
Daniel Valdez
|
r487 | 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: | ||||
| cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:]) | ||||
| pairIndex += 1 | ||||
| blocksize += cspc.size | ||||
| self.dataOut.data_spc = spc | ||||
| self.dataOut.data_cspc = cspc | ||||
| self.dataOut.data_dc = dc | ||||
| self.dataOut.blockSize = blocksize | ||||
| self.dataOut.flagShiftFFT = False | ||||
| def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None): | ||||
| self.dataOut.flagNoData = True | ||||
| if self.dataIn.type == "Spectra": | ||||
| self.dataOut.copy(self.dataIn) | ||||
| return True | ||||
| if self.dataIn.type == "Voltage": | ||||
| if nFFTPoints == None: | ||||
| raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" | ||||
|
Daniel Valdez
|
r495 | if nProfiles == None: | ||
| raise ValueError, "This SpectraProc.run() need nProfiles input variable" | ||||
|
Daniel Valdez
|
r487 | |||
| if ippFactor == None: | ||||
| ippFactor = 1 | ||||
| self.dataOut.ippFactor = ippFactor | ||||
| self.dataOut.nFFTPoints = nFFTPoints | ||||
| self.dataOut.pairsList = pairsList | ||||
|
Daniel Valdez
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r495 | |||
|
Daniel Valdez
|
r487 | if self.buffer == None: | ||
| self.buffer = numpy.zeros((self.dataIn.nChannels, | ||||
| nProfiles, | ||||
| self.dataIn.nHeights), | ||||
|
Daniel Valdez
|
r495 | dtype='complex') | ||
| self.id_min = 0 | ||||
| self.id_max = self.dataIn.data.shape[1] | ||||
|
Daniel Valdez
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r487 | |||
|
Daniel Valdez
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r495 | if len(self.dataIn.data.shape) == 2: | ||
| self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() | ||||
| self.profIndex += 1 | ||||
| else: | ||||
| if self.dataIn.data.shape[1] == nProfiles: | ||||
| self.buffer = self.dataIn.data.copy() | ||||
| self.profIndex = nProfiles | ||||
| elif self.dataIn.data.shape[1] < nProfiles: | ||||
| self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data | ||||
| self.profIndex += self.dataIn.data.shape[1] | ||||
| self.id_min += self.dataIn.data.shape[1] | ||||
| self.id_max += self.dataIn.data.shape[1] | ||||
| else: | ||||
| raise ValueError, "The type object %s has %d profiles, it should be equal to %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles) | ||||
| self.dataOut.flagNoData = True | ||||
| return 0 | ||||
|
Daniel Valdez
|
r487 | |||
| if self.firstdatatime == None: | ||||
| self.firstdatatime = self.dataIn.utctime | ||||
| if self.profIndex == nProfiles: | ||||
| self.__updateObjFromInput() | ||||
| self.__getFft() | ||||
| self.dataOut.flagNoData = False | ||||
| self.buffer = None | ||||
| self.firstdatatime = None | ||||
| self.profIndex = 0 | ||||
| return True | ||||
| raise ValueError, "The type object %s is not valid"%(self.dataIn.type) | ||||
| def selectChannels(self, channelList): | ||||
| channelIndexList = [] | ||||
| for channel in 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: | ||||
| print channelIndexList | ||||
| raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex | ||||
| # nChannels = len(channelIndexList) | ||||
| data_spc = self.dataOut.data_spc[channelIndexList,:] | ||||
| self.dataOut.data_spc = data_spc | ||||
| self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | ||||
| # self.dataOut.nChannels = nChannels | ||||
| 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 < self.dataOut.heightList[0]) or (minHei > maxHei): | ||||
| raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei) | ||||
| if (maxHei > self.dataOut.heightList[-1]): | ||||
| maxHei = self.dataOut.heightList[-1] | ||||
| # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei) | ||||
| 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 != None: | ||||
| data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | ||||
| #data_cspc = data_cspc[:,:,beacon_heiIndexList] | ||||
| data_dc = None | ||||
| if self.dataOut.data_dc != 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, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) | ||||
| if (maxIndex >= self.dataOut.nHeights): | ||||
| maxIndex = self.dataOut.nHeights-1 | ||||
| # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) | ||||
| # nHeights = maxIndex - minIndex + 1 | ||||
| #Spectra | ||||
| data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] | ||||
| data_cspc = None | ||||
| if self.dataOut.data_cspc != None: | ||||
| data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | ||||
| data_dc = None | ||||
| if self.dataOut.data_dc != 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 != 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 == 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/ math.sqrt(num_incoh)) | ||||
| noiseid = noiseid[0] | ||||
| cnoiseid = noiseid.size | ||||
| interfid = numpy.where(jspc_interf > tmp_noise/ math.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/math.sqrt(num_incoh))).nonzero() | ||||
| cind = len(ind) | ||||
| if (cind > 0): | ||||
| jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/math.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/math.sqrt(num_incoh))).nonzero() | ||||
| jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/math.sqrt(num_incoh)) | ||||
| #Remocion de Interferencia en el Cross Spectra | ||||
| if jcspectra == 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): | ||||
| Operation.__init__(self) | ||||
| # 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 = None | ||||
| self.__buffer_cspc = None | ||||
| self.__buffer_dc = None | ||||
| self.__dataReady = False | ||||
| if n == None and timeInterval == None: | ||||
| raise ValueError, "n or timeInterval should be specified ..." | ||||
| if n != None: | ||||
| self.n = n | ||||
| self.__byTime = False | ||||
| else: | ||||
| self.__integrationtime = timeInterval #if (type(timeInterval)!=integer) -> change this line | ||||
| self.n = 9999 | ||||
| self.__byTime = True | ||||
| if overlapping: | ||||
| self.__withOverapping = True | ||||
| else: | ||||
| self.__withOverapping = False | ||||
| self.__buffer_spc = 0 | ||||
| self.__buffer_cspc = 0 | ||||
| self.__buffer_dc = 0 | ||||
| self.__profIndex = 0 | ||||
| def putData(self, data_spc, data_cspc, data_dc): | ||||
| """ | ||||
| Add a profile to the __buffer_spc and increase in one the __profileIndex | ||||
| """ | ||||
| if not self.__withOverapping: | ||||
| self.__buffer_spc += data_spc | ||||
| if data_cspc == None: | ||||
| self.__buffer_cspc = None | ||||
| else: | ||||
| self.__buffer_cspc += data_cspc | ||||
| if data_dc == None: | ||||
| self.__buffer_dc = None | ||||
| else: | ||||
| self.__buffer_dc += data_dc | ||||
| self.__profIndex += 1 | ||||
| return | ||||
| #Overlapping data | ||||
| nChannels, nFFTPoints, nHeis = data_spc.shape | ||||
| data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis)) | ||||
| if data_cspc != None: | ||||
| data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis)) | ||||
| if data_dc != None: | ||||
| data_dc = numpy.reshape(data_dc, (1, -1, nHeis)) | ||||
| #If the buffer is empty then it takes the data value | ||||
| if self.__buffer_spc == None: | ||||
| self.__buffer_spc = data_spc | ||||
| if data_cspc == None: | ||||
| self.__buffer_cspc = None | ||||
| else: | ||||
| self.__buffer_cspc += data_cspc | ||||
| if data_dc == None: | ||||
| self.__buffer_dc = None | ||||
| else: | ||||
| self.__buffer_dc += data_dc | ||||
| self.__profIndex += 1 | ||||
| return | ||||
| #If the buffer length is lower than n then stakcing the data value | ||||
| if self.__profIndex < self.n: | ||||
| self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc)) | ||||
| if data_cspc != None: | ||||
| self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc)) | ||||
| if data_dc != None: | ||||
| self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc)) | ||||
| self.__profIndex += 1 | ||||
| return | ||||
| #If the buffer length is equal to n then replacing the last buffer value with the data value | ||||
| self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0) | ||||
| self.__buffer_spc[self.n-1] = data_spc | ||||
| if data_cspc != None: | ||||
| self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0) | ||||
| self.__buffer_cspc[self.n-1] = data_cspc | ||||
| if data_dc != None: | ||||
| self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0) | ||||
| self.__buffer_dc[self.n-1] = data_dc | ||||
| self.__profIndex = self.n | ||||
| return | ||||
| def pushData(self): | ||||
| """ | ||||
| Return the sum of the last profiles and the profiles used in the sum. | ||||
| Affected: | ||||
| self.__profileIndex | ||||
| """ | ||||
| data_spc = None | ||||
| data_cspc = None | ||||
| data_dc = None | ||||
| if not self.__withOverapping: | ||||
| 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 | ||||
| #Integration with Overlapping | ||||
| data_spc = numpy.sum(self.__buffer_spc, axis=0) | ||||
| if self.__buffer_cspc != None: | ||||
| data_cspc = numpy.sum(self.__buffer_cspc, axis=0) | ||||
| if self.__buffer_dc != None: | ||||
| data_dc = numpy.sum(self.__buffer_dc, axis=0) | ||||
| n = self.__profIndex | ||||
| return data_spc, data_cspc, data_dc, n | ||||
| def byProfiles(self, *args): | ||||
| self.__dataReady = False | ||||
| avgdata_spc = None | ||||
| avgdata_cspc = None | ||||
| avgdata_dc = None | ||||
| # n = None | ||||
| self.putData(*args) | ||||
| if self.__profIndex == self.n: | ||||
| avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | ||||
| 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 | ||||
| n = 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.__initime == None: | ||||
| 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) | ||||
| self.__lastdatatime = datatime | ||||
| if avgdata_spc == None: | ||||
| return None, None, None, None | ||||
| avgdatatime = self.__initime | ||||
| try: | ||||
| self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1) | ||||
| except: | ||||
| self.__timeInterval = self.__lastdatatime - self.__initime | ||||
| deltatime = datatime -self.__lastdatatime | ||||
| if not self.__withOverapping: | ||||
| self.__initime = datatime | ||||
| else: | ||||
| self.__initime += deltatime | ||||
| return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc | ||||
| def run(self, dataOut, n=None, timeInterval=None, overlapping=False): | ||||
| if n==1: | ||||
| dataOut.flagNoData = False | ||||
| return | ||||
| 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) | ||||
| # dataOut.timeInterval *= n | ||||
| dataOut.flagNoData = True | ||||
| 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.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints | ||||
|
Miguel Valdez
|
r528 | # dataOut.timeInterval = self.__timeInterval*self.n | ||
|
Daniel Valdez
|
r487 | dataOut.flagNoData = False | ||