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Claire, Erick Bocanegra 21-02-18
Claire, Erick Bocanegra 21-02-18
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jroproc_spectra.py
1068 lines | 36.3 KiB | text/x-python | PythonLexer
/ schainpy / model / proc / jroproc_spectra.py
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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:
print 'DATA shape', self.dataIn.data.shape
sadsdf
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