jroproc_spectra.py
1059 lines
| 35.3 KiB
| text/x-python
|
PythonLexer
|
r1062 | import itertools | |
|
r487 | import numpy | |
|
r1171 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation | |
|
r568 | from schainpy.model.data.jrodata import Spectra | |
from schainpy.model.data.jrodata import hildebrand_sekhon | |||
|
r1171 | from schainpy.utils import log | |
|
r1120 | ||
|
r1171 | @MPDecorator | |
|
r487 | class SpectraProc(ProcessingUnit): | |
|
r897 | ||
|
r1179 | def __init__(self): | |
|
r1171 | ||
|
r1179 | ProcessingUnit.__init__(self) | |
|
r897 | ||
|
r487 | self.buffer = None | |
self.firstdatatime = None | |||
self.profIndex = 0 | |||
self.dataOut = Spectra() | |||
|
r495 | self.id_min = None | |
self.id_max = None | |||
|
r1171 | self.setupReq = False #Agregar a todas las unidades de proc | |
|
r487 | ||
|
r623 | def __updateSpecFromVoltage(self): | |
|
r897 | ||
|
r487 | self.dataOut.timeZone = self.dataIn.timeZone | |
self.dataOut.dstFlag = self.dataIn.dstFlag | |||
self.dataOut.errorCount = self.dataIn.errorCount | |||
self.dataOut.useLocalTime = self.dataIn.useLocalTime | |||
|
r1120 | try: | |
self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() | |||
except: | |||
pass | |||
|
r487 | 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 | |||
|
r1120 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) | |
|
r897 | ||
|
r487 | self.dataOut.nBaud = self.dataIn.nBaud | |
self.dataOut.nCode = self.dataIn.nCode | |||
self.dataOut.code = self.dataIn.code | |||
self.dataOut.nProfiles = self.dataOut.nFFTPoints | |||
|
r897 | ||
|
r568 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
|
r487 | self.dataOut.utctime = self.firstdatatime | |
|
r1120 | # asumo q la data esta decodificada | |
self.dataOut.flagDecodeData = self.dataIn.flagDecodeData | |||
# asumo q la data esta sin flip | |||
self.dataOut.flagDeflipData = self.dataIn.flagDeflipData | |||
|
r623 | self.dataOut.flagShiftFFT = False | |
|
r897 | ||
|
r487 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
self.dataOut.nIncohInt = 1 | |||
|
r897 | ||
|
r487 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |
|
r897 | ||
|
r487 | self.dataOut.frequency = self.dataIn.frequency | |
self.dataOut.realtime = self.dataIn.realtime | |||
|
r897 | ||
|
r499 | self.dataOut.azimuth = self.dataIn.azimuth | |
self.dataOut.zenith = self.dataIn.zenith | |||
|
r897 | ||
|
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 | |||
|
r897 | ||
|
r487 | def __getFft(self): | |
""" | |||
Convierte valores de Voltaje a Spectra | |||
|
r897 | ||
|
r487 | Affected: | |
self.dataOut.data_spc | |||
self.dataOut.data_cspc | |||
self.dataOut.data_dc | |||
self.dataOut.heightList | |||
|
r897 | self.profIndex | |
|
r487 | self.buffer | |
self.dataOut.flagNoData | |||
""" | |||
|
r1120 | fft_volt = numpy.fft.fft( | |
self.buffer, n=self.dataOut.nFFTPoints, axis=1) | |||
|
r487 | fft_volt = fft_volt.astype(numpy.dtype('complex')) | |
|
r1120 | dc = fft_volt[:, 0, :] | |
|
r897 | ||
|
r1120 | # calculo de self-spectra | |
fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) | |||
|
r487 | spc = fft_volt * numpy.conjugate(fft_volt) | |
spc = spc.real | |||
|
r897 | ||
|
r487 | blocksize = 0 | |
blocksize += dc.size | |||
blocksize += spc.size | |||
|
r897 | ||
|
r487 | cspc = None | |
pairIndex = 0 | |||
if self.dataOut.pairsList != None: | |||
|
r1120 | # calculo de cross-spectra | |
cspc = numpy.zeros( | |||
(self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') | |||
|
r487 | for pair in self.dataOut.pairsList: | |
|
r587 | if pair[0] not in self.dataOut.channelList: | |
|
r1167 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( | |
str(pair), str(self.dataOut.channelList))) | |||
|
r587 | if pair[1] not in self.dataOut.channelList: | |
|
r1167 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( | |
str(pair), str(self.dataOut.channelList))) | |||
|
r897 | ||
|
r1120 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ | |
numpy.conjugate(fft_volt[pair[1], :, :]) | |||
|
r487 | pairIndex += 1 | |
blocksize += cspc.size | |||
|
r897 | ||
|
r487 | self.dataOut.data_spc = spc | |
self.dataOut.data_cspc = cspc | |||
self.dataOut.data_dc = dc | |||
self.dataOut.blockSize = blocksize | |||
|
r623 | self.dataOut.flagShiftFFT = True | |
|
r897 | ||
r1132 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None, shift_fft=False): | ||
|
r897 | ||
|
r487 | if self.dataIn.type == "Spectra": | |
self.dataOut.copy(self.dataIn) | |||
r1132 | if shift_fft: | ||
#desplaza a la derecha en el eje 2 determinadas posiciones | |||
shift = int(self.dataOut.nFFTPoints/2) | |||
self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) | |||
if self.dataOut.data_cspc is not None: | |||
#desplaza a la derecha en el eje 2 determinadas posiciones | |||
r1151 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) | ||
|
r1171 | ||
|
r487 | return True | |
|
r897 | ||
|
r487 | if self.dataIn.type == "Voltage": | |
|
r897 | ||
|
r1183 | self.dataOut.flagNoData = True | |
|
r487 | if nFFTPoints == None: | |
|
r1167 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") | |
|
r897 | ||
|
r495 | if nProfiles == None: | |
|
r568 | nProfiles = nFFTPoints | |
|
r897 | ||
|
r487 | if ippFactor == None: | |
ippFactor = 1 | |||
|
r897 | ||
|
r487 | self.dataOut.ippFactor = ippFactor | |
|
r897 | ||
|
r487 | self.dataOut.nFFTPoints = nFFTPoints | |
self.dataOut.pairsList = pairsList | |||
|
r495 | ||
|
r611 | if self.buffer is None: | |
|
r1120 | self.buffer = numpy.zeros((self.dataIn.nChannels, | |
nProfiles, | |||
self.dataIn.nHeights), | |||
|
r623 | dtype='complex') | |
|
r487 | ||
|
r623 | if self.dataIn.flagDataAsBlock: | |
|
r720 | nVoltProfiles = self.dataIn.data.shape[1] | |
|
r897 | ||
|
r720 | if nVoltProfiles == nProfiles: | |
|
r495 | self.buffer = self.dataIn.data.copy() | |
|
r720 | self.profIndex = nVoltProfiles | |
|
r897 | ||
|
r720 | elif nVoltProfiles < nProfiles: | |
|
r897 | ||
|
r623 | if self.profIndex == 0: | |
self.id_min = 0 | |||
|
r720 | self.id_max = nVoltProfiles | |
|
r897 | ||
|
r1120 | self.buffer[:, self.id_min:self.id_max, | |
:] = self.dataIn.data | |||
|
r720 | self.profIndex += nVoltProfiles | |
self.id_min += nVoltProfiles | |||
self.id_max += nVoltProfiles | |||
|
r495 | else: | |
|
r1167 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( | |
self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) | |||
|
r495 | self.dataOut.flagNoData = True | |
return 0 | |||
|
r897 | else: | |
|
r1120 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() | |
|
r623 | self.profIndex += 1 | |
|
r897 | ||
|
r487 | if self.firstdatatime == None: | |
self.firstdatatime = self.dataIn.utctime | |||
|
r897 | ||
|
r487 | if self.profIndex == nProfiles: | |
|
r623 | self.__updateSpecFromVoltage() | |
|
r487 | self.__getFft() | |
|
r897 | ||
|
r487 | self.dataOut.flagNoData = False | |
self.firstdatatime = None | |||
self.profIndex = 0 | |||
|
r897 | ||
|
r487 | return True | |
|
r897 | ||
|
r1167 | raise ValueError("The type of input object '%s' is not valid" % ( | |
self.dataIn.type)) | |||
|
r897 | ||
|
r730 | def __selectPairs(self, pairsList): | |
|
r897 | ||
|
r1120 | if not pairsList: | |
|
r730 | return | |
|
r897 | ||
|
r1062 | pairs = [] | |
pairsIndex = [] | |||
|
r897 | ||
|
r1062 | for pair in pairsList: | |
if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: | |||
|
r730 | continue | |
|
r1062 | pairs.append(pair) | |
pairsIndex.append(pairs.index(pair)) | |||
|
r1120 | ||
|
r1062 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] | |
self.dataOut.pairsList = pairs | |||
|
r897 | ||
|
r730 | return | |
|
r897 | ||
|
r730 | def __selectPairsByChannel(self, channelList=None): | |
|
r897 | ||
|
r587 | if channelList == None: | |
return | |||
|
r897 | ||
|
r587 | pairsIndexListSelected = [] | |
for pairIndex in self.dataOut.pairsIndexList: | |||
|
r1120 | # First pair | |
|
r587 | if self.dataOut.pairsList[pairIndex][0] not in channelList: | |
continue | |||
|
r1120 | # Second pair | |
|
r587 | if self.dataOut.pairsList[pairIndex][1] not in channelList: | |
continue | |||
|
r897 | ||
|
r587 | pairsIndexListSelected.append(pairIndex) | |
|
r897 | ||
|
r587 | if not pairsIndexListSelected: | |
self.dataOut.data_cspc = None | |||
self.dataOut.pairsList = [] | |||
return | |||
|
r897 | ||
|
r587 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | |
|
r1120 | self.dataOut.pairsList = [self.dataOut.pairsList[i] | |
for i in pairsIndexListSelected] | |||
|
r897 | ||
|
r587 | return | |
|
r897 | ||
|
r487 | def selectChannels(self, channelList): | |
|
r897 | ||
|
r487 | channelIndexList = [] | |
|
r897 | ||
|
r487 | for channel in channelList: | |
|
r586 | if channel not in self.dataOut.channelList: | |
|
r1167 | raise ValueError("Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" % ( | |
channel, str(self.dataOut.channelList))) | |||
|
r897 | ||
|
r487 | index = self.dataOut.channelList.index(channel) | |
channelIndexList.append(index) | |||
|
r897 | ||
|
r487 | self.selectChannelsByIndex(channelIndexList) | |
|
r897 | ||
|
r487 | def selectChannelsByIndex(self, channelIndexList): | |
""" | |||
|
r897 | Selecciona un bloque de datos en base a canales segun el channelIndexList | |
|
r487 | Input: | |
|
r897 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | |
|
r487 | Affected: | |
self.dataOut.data_spc | |||
self.dataOut.channelIndexList | |||
self.dataOut.nChannels | |||
|
r897 | ||
|
r487 | Return: | |
None | |||
""" | |||
for channelIndex in channelIndexList: | |||
if channelIndex not in self.dataOut.channelIndexList: | |||
|
r1167 | raise ValueError("Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " % ( | |
channelIndex, self.dataOut.channelIndexList)) | |||
|
r897 | ||
|
r487 | # nChannels = len(channelIndexList) | |
|
r897 | ||
|
r1120 | data_spc = self.dataOut.data_spc[channelIndexList, :] | |
data_dc = self.dataOut.data_dc[channelIndexList, :] | |||
|
r897 | ||
|
r487 | self.dataOut.data_spc = data_spc | |
|
r587 | self.dataOut.data_dc = data_dc | |
|
r897 | ||
|
r1120 | self.dataOut.channelList = [ | |
self.dataOut.channelList[i] for i in channelIndexList] | |||
|
r487 | # self.dataOut.nChannels = nChannels | |
|
r897 | ||
|
r730 | self.__selectPairsByChannel(self.dataOut.channelList) | |
|
r897 | ||
|
r487 | return 1 | |
|
r1123 | ||
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): | |||
|
r1188 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) | |
|
r1123 | ||
if (minFFT < self.dataOut.getFreqRange()[0]): | |||
minFFT = self.dataOut.getFreqRange()[0] | |||
|
r487 | ||
|
r1123 | 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) | |||
|
r487 | ||
|
r1123 | try: | |
minIndex = inda[0][0] | |||
except: | |||
minIndex = 0 | |||
try: | |||
maxIndex = indb[0][-1] | |||
except: | |||
maxIndex = len(FFTs) | |||
self.selectFFTsByIndex(minIndex, maxIndex) | |||
return 1 | |||
|
r1157 | def setH0(self, h0, deltaHeight = None): | |
if not deltaHeight: | |||
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] | |||
nHeights = self.dataOut.nHeights | |||
newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight | |||
self.dataOut.heightList = newHeiRange | |||
|
r1123 | ||
|
r487 | 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 | |||
|
r897 | ||
|
r487 | Input: | |
|
r897 | minHei : valor minimo de altura a considerar | |
|
r487 | maxHei : valor maximo de altura a considerar | |
|
r897 | ||
|
r487 | Affected: | |
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | |||
|
r897 | ||
|
r487 | Return: | |
1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |||
""" | |||
|
r897 | ||
|
r1123 | ||
|
r587 | if (minHei > maxHei): | |
|
r1188 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei)) | |
|
r897 | ||
|
r587 | if (minHei < self.dataOut.heightList[0]): | |
minHei = self.dataOut.heightList[0] | |||
|
r897 | ||
|
r487 | if (maxHei > self.dataOut.heightList[-1]): | |
maxHei = self.dataOut.heightList[-1] | |||
minIndex = 0 | |||
maxIndex = 0 | |||
heights = self.dataOut.heightList | |||
|
r897 | ||
|
r487 | inda = numpy.where(heights >= minHei) | |
indb = numpy.where(heights <= maxHei) | |||
|
r897 | ||
|
r487 | try: | |
minIndex = inda[0][0] | |||
except: | |||
minIndex = 0 | |||
|
r897 | ||
|
r487 | try: | |
maxIndex = indb[0][-1] | |||
except: | |||
maxIndex = len(heights) | |||
self.selectHeightsByIndex(minIndex, maxIndex) | |||
|
r1123 | ||
|
r897 | ||
|
r487 | return 1 | |
|
r1120 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): | |
newheis = numpy.where( | |||
self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) | |||
|
r897 | ||
|
r487 | if hei_ref != None: | |
|
r1120 | newheis = numpy.where(self.dataOut.heightList > hei_ref) | |
|
r897 | ||
|
r487 | minIndex = min(newheis[0]) | |
maxIndex = max(newheis[0]) | |||
|
r1120 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] | |
heightList = self.dataOut.heightList[minIndex:maxIndex + 1] | |||
|
r897 | ||
|
r487 | # determina indices | |
|
r1120 | 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)) | |||
|
r487 | 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)) | |||
|
r897 | ||
|
r487 | #data_spc = data_spc[:,:,beacon_heiIndexList] | |
data_cspc = None | |||
|
r612 | if self.dataOut.data_cspc is not None: | |
|
r1120 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] | |
|
r487 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] | |
|
r897 | ||
|
r487 | data_dc = None | |
|
r612 | if self.dataOut.data_dc is not None: | |
|
r1120 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] | |
|
r487 | #data_dc = data_dc[:,beacon_heiIndexList] | |
|
r897 | ||
|
r487 | 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 | |||
|
r897 | ||
|
r487 | return 1 | |
|
r897 | ||
|
r1123 | def selectFFTsByIndex(self, minIndex, maxIndex): | |
""" | |||
""" | |||
if (minIndex < 0) or (minIndex > maxIndex): | |||
|
r1188 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) | |
|
r1123 | ||
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] | |||
return 1 | |||
|
r897 | ||
|
r487 | def selectHeightsByIndex(self, minIndex, maxIndex): | |
""" | |||
Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango | |||
minIndex <= index <= maxIndex | |||
|
r897 | ||
|
r487 | Input: | |
|
r897 | minIndex : valor de indice minimo de altura a considerar | |
|
r487 | maxIndex : valor de indice maximo de altura a considerar | |
|
r897 | ||
|
r487 | Affected: | |
self.dataOut.data_spc | |||
self.dataOut.data_cspc | |||
self.dataOut.data_dc | |||
self.dataOut.heightList | |||
|
r897 | ||
|
r487 | Return: | |
1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |||
""" | |||
|
r897 | ||
|
r487 | if (minIndex < 0) or (minIndex > maxIndex): | |
|
r1167 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( | |
minIndex, maxIndex)) | |||
|
r897 | ||
|
r487 | if (maxIndex >= self.dataOut.nHeights): | |
|
r1120 | maxIndex = self.dataOut.nHeights - 1 | |
|
r487 | ||
|
r1120 | # Spectra | |
data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] | |||
|
r897 | ||
|
r487 | data_cspc = None | |
|
r612 | if self.dataOut.data_cspc is not None: | |
|
r1120 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] | |
|
r897 | ||
|
r487 | data_dc = None | |
|
r612 | if self.dataOut.data_dc is not None: | |
|
r1120 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] | |
|
r897 | ||
|
r487 | self.dataOut.data_spc = data_spc | |
self.dataOut.data_cspc = data_cspc | |||
self.dataOut.data_dc = data_dc | |||
|
r897 | ||
|
r1120 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] | |
|
r897 | ||
|
r487 | return 1 | |
|
r897 | ||
|
r1120 | def removeDC(self, mode=2): | |
|
r487 | jspectra = self.dataOut.data_spc | |
jcspectra = self.dataOut.data_cspc | |||
|
r897 | ||
|
r487 | num_chan = jspectra.shape[0] | |
num_hei = jspectra.shape[2] | |||
|
r897 | ||
|
r612 | if jcspectra is not None: | |
|
r487 | jcspectraExist = True | |
num_pairs = jcspectra.shape[0] | |||
|
r1120 | else: | |
jcspectraExist = False | |||
|
r897 | ||
|
r1167 | freq_dc = int(jspectra.shape[1] / 2) | |
|
r1120 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc | |
|
r1167 | ind_vel = ind_vel.astype(int) | |
|
r897 | ||
|
r1120 | if ind_vel[0] < 0: | |
|
r1167 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof | |
|
r897 | ||
if mode == 1: | |||
|
r1120 | jspectra[:, freq_dc, :] = ( | |
jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION | |||
|
r897 | ||
|
r487 | if jcspectraExist: | |
|
r1120 | jcspectra[:, freq_dc, :] = ( | |
jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 | |||
|
r897 | ||
|
r487 | if mode == 2: | |
|
r897 | ||
|
r1120 | vel = numpy.array([-2, -1, 1, 2]) | |
xx = numpy.zeros([4, 4]) | |||
|
r897 | ||
|
r487 | for fil in range(4): | |
|
r1167 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) | |
|
r897 | ||
|
r487 | xx_inv = numpy.linalg.inv(xx) | |
|
r1120 | xx_aux = xx_inv[0, :] | |
|
r897 | ||
|
r1167 | for ich in range(num_chan): | |
|
r1120 | yy = jspectra[ich, ind_vel, :] | |
jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) | |||
|
r487 | ||
|
r1120 | junkid = jspectra[ich, freq_dc, :] <= 0 | |
|
r487 | cjunkid = sum(junkid) | |
|
r897 | ||
|
r487 | if cjunkid.any(): | |
|
r1120 | jspectra[ich, freq_dc, junkid.nonzero()] = ( | |
jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 | |||
|
r897 | ||
|
r487 | if jcspectraExist: | |
for ip in range(num_pairs): | |||
|
r1120 | yy = jcspectra[ip, ind_vel, :] | |
jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) | |||
|
r897 | ||
|
r487 | self.dataOut.data_spc = jspectra | |
self.dataOut.data_cspc = jcspectra | |||
|
r897 | ||
|
r487 | return 1 | |
|
r897 | ||
|
r1123 | def removeInterference2(self): | |
cspc = self.dataOut.data_cspc | |||
spc = self.dataOut.data_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 ) ] | |||
InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 ) | |||
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 | |||
self.dataOut.data_cspc = cspc | |||
|
r487 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): | |
|
r897 | ||
|
r487 | jspectra = self.dataOut.data_spc | |
jcspectra = self.dataOut.data_cspc | |||
jnoise = self.dataOut.getNoise() | |||
num_incoh = self.dataOut.nIncohInt | |||
|
r897 | ||
|
r1120 | num_channel = jspectra.shape[0] | |
num_prof = jspectra.shape[1] | |||
num_hei = jspectra.shape[2] | |||
|
r897 | ||
|
r1120 | # hei_interf | |
|
r612 | if hei_interf is None: | |
|
r1120 | count_hei = num_hei / 2 # Como es entero no importa | |
|
r1167 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei | |
|
r487 | hei_interf = numpy.asarray(hei_interf)[0] | |
|
r1120 | # nhei_interf | |
|
r487 | if (nhei_interf == None): | |
nhei_interf = 5 | |||
if (nhei_interf < 1): | |||
|
r897 | nhei_interf = 1 | |
|
r487 | if (nhei_interf > count_hei): | |
nhei_interf = count_hei | |||
|
r897 | if (offhei_interf == None): | |
|
r487 | offhei_interf = 0 | |
|
r897 | ||
|
r1167 | ind_hei = list(range(num_hei)) | |
|
r897 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 | |
|
r487 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 | |
|
r1167 | mask_prof = numpy.asarray(list(range(num_prof))) | |
|
r487 | num_mask_prof = mask_prof.size | |
|
r1120 | comp_mask_prof = [0, num_prof / 2] | |
|
r897 | ||
|
r1120 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal | |
|
r487 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): | |
jnoise = numpy.nan | |||
noise_exist = jnoise[0] < numpy.Inf | |||
|
r897 | ||
|
r1120 | # Subrutina de Remocion de la Interferencia | |
|
r487 | for ich in range(num_channel): | |
|
r1120 | # Se ordena los espectros segun su potencia (menor a mayor) | |
power = jspectra[ich, mask_prof, :] | |||
power = power[:, hei_interf] | |||
power = power.sum(axis=0) | |||
|
r487 | psort = power.ravel().argsort() | |
|
r897 | ||
|
r1120 | # Se estima la interferencia promedio en los Espectros de Potencia empleando | |
|
r1167 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( | |
offhei_interf, nhei_interf + offhei_interf))]]] | |||
|
r897 | ||
|
r487 | if noise_exist: | |
|
r1120 | # tmp_noise = jnoise[ich] / num_prof | |
|
r487 | tmp_noise = jnoise[ich] | |
junkspc_interf = junkspc_interf - tmp_noise | |||
#junkspc_interf[:,comp_mask_prof] = 0 | |||
|
r897 | ||
|
r1120 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf | |
|
r487 | jspc_interf = jspc_interf.transpose() | |
|
r1120 | # Calculando el espectro de interferencia promedio | |
noiseid = numpy.where( | |||
jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) | |||
|
r487 | noiseid = noiseid[0] | |
cnoiseid = noiseid.size | |||
|
r1120 | interfid = numpy.where( | |
jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) | |||
|
r487 | interfid = interfid[0] | |
cinterfid = interfid.size | |||
|
r897 | ||
|
r1120 | if (cnoiseid > 0): | |
jspc_interf[noiseid] = 0 | |||
|
r897 | ||
|
r1120 | # Expandiendo los perfiles a limpiar | |
|
r487 | if (cinterfid > 0): | |
|
r1120 | new_interfid = ( | |
numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof | |||
|
r897 | new_interfid = numpy.asarray(new_interfid) | |
|
r487 | new_interfid = {x for x in new_interfid} | |
new_interfid = numpy.array(list(new_interfid)) | |||
new_cinterfid = new_interfid.size | |||
|
r1120 | else: | |
new_cinterfid = 0 | |||
|
r897 | ||
|
r487 | for ip in range(new_cinterfid): | |
|
r1120 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() | |
jspc_interf[new_interfid[ip] | |||
] = junkspc_interf[ind[nhei_interf / 2], new_interfid[ip]] | |||
|
r897 | ||
|
r1120 | jspectra[ich, :, ind_hei] = jspectra[ich, :, | |
ind_hei] - jspc_interf # Corregir indices | |||
|
r897 | ||
|
r1120 | # Removiendo la interferencia del punto de mayor interferencia | |
|
r487 | ListAux = jspc_interf[mask_prof].tolist() | |
maxid = ListAux.index(max(ListAux)) | |||
|
r897 | ||
|
r487 | if cinterfid > 0: | |
|
r1120 | for ip in range(cinterfid * (interf == 2) - 1): | |
ind = (jspectra[ich, interfid[ip], :] < tmp_noise * | |||
(1 + 1 / numpy.sqrt(num_incoh))).nonzero() | |||
|
r487 | cind = len(ind) | |
|
r897 | ||
|
r487 | if (cind > 0): | |
|
r1120 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ | |
(1 + (numpy.random.uniform(cind) - 0.5) / | |||
numpy.sqrt(num_incoh)) | |||
|
r897 | ||
|
r1120 | ind = numpy.array([-2, -1, 1, 2]) | |
xx = numpy.zeros([4, 4]) | |||
|
r897 | ||
|
r487 | for id1 in range(4): | |
|
r1167 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) | |
|
r897 | ||
|
r487 | xx_inv = numpy.linalg.inv(xx) | |
|
r1120 | 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) | |||
|
r487 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) | |
|
r897 | ||
|
r487 | for ip in range(num_pairs): | |
|
r897 | ||
|
r487 | #------------------------------------------- | |
|
r897 | ||
|
r1120 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) | |
cspower = cspower[:, hei_interf] | |||
cspower = cspower.sum(axis=0) | |||
|
r897 | ||
|
r487 | cspsort = cspower.ravel().argsort() | |
|
r1167 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( | |
offhei_interf, nhei_interf + offhei_interf))]]] | |||
|
r487 | junkcspc_interf = junkcspc_interf.transpose() | |
|
r1120 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf | |
|
r897 | ||
|
r487 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() | |
|
r897 | ||
|
r1120 | median_real = numpy.median(numpy.real( | |
|
r1167 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :])) | |
|
r1120 | median_imag = numpy.median(numpy.imag( | |
|
r1167 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :])) | |
|
r1120 | junkcspc_interf[comp_mask_prof, :] = numpy.complex( | |
median_real, median_imag) | |||
|
r897 | ||
|
r487 | for iprof in range(num_prof): | |
|
r1120 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() | |
jcspc_interf[iprof] = junkcspc_interf[iprof, | |||
ind[nhei_interf / 2]] | |||
|
r897 | ||
|
r1120 | # Removiendo la Interferencia | |
jcspectra[ip, :, ind_hei] = jcspectra[ip, | |||
:, ind_hei] - jcspc_interf | |||
|
r897 | ||
|
r487 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() | |
maxid = ListAux.index(max(ListAux)) | |||
|
r897 | ||
|
r1120 | ind = numpy.array([-2, -1, 1, 2]) | |
xx = numpy.zeros([4, 4]) | |||
|
r897 | ||
|
r487 | for id1 in range(4): | |
|
r1167 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) | |
|
r897 | ||
|
r487 | xx_inv = numpy.linalg.inv(xx) | |
|
r1120 | xx = xx_inv[:, 0] | |
|
r897 | ||
|
r1120 | 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) | |||
|
r897 | ||
|
r1120 | # Guardar Resultados | |
|
r487 | self.dataOut.data_spc = jspectra | |
self.dataOut.data_cspc = jcspectra | |||
|
r897 | ||
|
r487 | return 1 | |
|
r897 | ||
|
r487 | def setRadarFrequency(self, frequency=None): | |
|
r897 | ||
|
r487 | if frequency != None: | |
self.dataOut.frequency = frequency | |||
|
r897 | ||
|
r487 | return 1 | |
|
r897 | ||
def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): | |||
|
r1120 | # validacion de rango | |
|
r487 | if minHei == None: | |
minHei = self.dataOut.heightList[0] | |||
|
r897 | ||
|
r487 | if maxHei == None: | |
maxHei = self.dataOut.heightList[-1] | |||
|
r897 | ||
|
r487 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): | |
|
r1167 | print('minHei: %.2f is out of the heights range' % (minHei)) | |
print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) | |||
|
r487 | minHei = self.dataOut.heightList[0] | |
|
r897 | ||
|
r487 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): | |
|
r1167 | print('maxHei: %.2f is out of the heights range' % (maxHei)) | |
print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) | |||
|
r487 | maxHei = self.dataOut.heightList[-1] | |
|
r897 | ||
|
r487 | # validacion de velocidades | |
velrange = self.dataOut.getVelRange(1) | |||
|
r897 | ||
|
r487 | if minVel == None: | |
minVel = velrange[0] | |||
|
r897 | ||
|
r487 | if maxVel == None: | |
maxVel = velrange[-1] | |||
|
r897 | ||
|
r487 | if (minVel < velrange[0]) or (minVel > maxVel): | |
|
r1167 | print('minVel: %.2f is out of the velocity range' % (minVel)) | |
print('minVel is setting to %.2f' % (velrange[0])) | |||
|
r487 | minVel = velrange[0] | |
|
r897 | ||
|
r487 | if (maxVel > velrange[-1]) or (maxVel < minVel): | |
|
r1167 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) | |
print('maxVel is setting to %.2f' % (velrange[-1])) | |||
|
r487 | maxVel = velrange[-1] | |
|
r897 | ||
# seleccion de indices para rango | |||
|
r487 | minIndex = 0 | |
maxIndex = 0 | |||
heights = self.dataOut.heightList | |||
|
r897 | ||
|
r487 | inda = numpy.where(heights >= minHei) | |
indb = numpy.where(heights <= maxHei) | |||
|
r897 | ||
|
r487 | try: | |
minIndex = inda[0][0] | |||
except: | |||
minIndex = 0 | |||
|
r897 | ||
|
r487 | try: | |
maxIndex = indb[0][-1] | |||
except: | |||
maxIndex = len(heights) | |||
if (minIndex < 0) or (minIndex > maxIndex): | |||
|
r1167 | raise ValueError("some value in (%d,%d) is not valid" % ( | |
minIndex, maxIndex)) | |||
|
r897 | ||
|
r487 | if (maxIndex >= self.dataOut.nHeights): | |
|
r1120 | maxIndex = self.dataOut.nHeights - 1 | |
|
r487 | ||
# seleccion de indices para velocidades | |||
indminvel = numpy.where(velrange >= minVel) | |||
indmaxvel = numpy.where(velrange <= maxVel) | |||
try: | |||
minIndexVel = indminvel[0][0] | |||
except: | |||
minIndexVel = 0 | |||
|
r897 | ||
|
r487 | try: | |
maxIndexVel = indmaxvel[0][-1] | |||
|
r897 | except: | |
|
r487 | maxIndexVel = len(velrange) | |
|
r897 | ||
|
r1120 | # seleccion del espectro | |
data_spc = self.dataOut.data_spc[:, | |||
minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] | |||
# estimacion de ruido | |||
|
r487 | noise = numpy.zeros(self.dataOut.nChannels) | |
|
r897 | ||
|
r487 | for channel in range(self.dataOut.nChannels): | |
|
r1120 | daux = data_spc[channel, :, :] | |
|
r487 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) | |
|
r897 | ||
|
r487 | self.dataOut.noise_estimation = noise.copy() | |
|
r897 | ||
|
r487 | return 1 | |
|
r897 | ||
|
r1177 | ||
|
r1120 | class IncohInt(Operation): | |
|
r897 | ||
|
r487 | __profIndex = 0 | |
|
r1120 | __withOverapping = False | |
|
r897 | ||
|
r487 | __byTime = False | |
__initime = None | |||
__lastdatatime = None | |||
__integrationtime = None | |||
|
r897 | ||
|
r487 | __buffer_spc = None | |
__buffer_cspc = None | |||
__buffer_dc = None | |||
|
r897 | ||
|
r487 | __dataReady = False | |
|
r897 | ||
|
r487 | __timeInterval = None | |
|
r897 | ||
|
r487 | n = None | |
|
r897 | ||
|
r1179 | def __init__(self): | |
|
r1171 | ||
|
r1179 | Operation.__init__(self) | |
|
r897 | ||
|
r487 | def setup(self, n=None, timeInterval=None, overlapping=False): | |
""" | |||
Set the parameters of the integration class. | |||
|
r897 | ||
|
r487 | Inputs: | |
|
r897 | ||
|
r487 | n : Number of coherent integrations | |
timeInterval : Time of integration. If the parameter "n" is selected this one does not work | |||
|
r897 | overlapping : | |
|
r487 | """ | |
|
r897 | ||
|
r487 | self.__initime = None | |
self.__lastdatatime = 0 | |||
|
r897 | ||
|
r623 | self.__buffer_spc = 0 | |
self.__buffer_cspc = 0 | |||
self.__buffer_dc = 0 | |||
|
r897 | ||
|
r623 | self.__profIndex = 0 | |
self.__dataReady = False | |||
self.__byTime = False | |||
|
r897 | ||
|
r623 | if n is None and timeInterval is None: | |
|
r1167 | raise ValueError("n or timeInterval should be specified ...") | |
|
r897 | ||
|
r623 | if n is not None: | |
self.n = int(n) | |||
|
r487 | else: | |
|
r1179 | ||
|
r1120 | self.__integrationtime = int(timeInterval) | |
|
r623 | self.n = None | |
|
r487 | self.__byTime = True | |
|
r897 | ||
|
r487 | def putData(self, data_spc, data_cspc, data_dc): | |
""" | |||
Add a profile to the __buffer_spc and increase in one the __profileIndex | |||
|
r897 | ||
|
r487 | """ | |
|
r897 | ||
|
r623 | self.__buffer_spc += data_spc | |
|
r897 | ||
|
r623 | if data_cspc is None: | |
self.__buffer_cspc = None | |||
else: | |||
self.__buffer_cspc += data_cspc | |||
|
r897 | ||
|
r623 | if data_dc is None: | |
self.__buffer_dc = None | |||
else: | |||
self.__buffer_dc += data_dc | |||
|
r897 | ||
|
r623 | self.__profIndex += 1 | |
|
r897 | ||
|
r487 | return | |
|
r897 | ||
|
r487 | def pushData(self): | |
""" | |||
Return the sum of the last profiles and the profiles used in the sum. | |||
|
r897 | ||
|
r487 | Affected: | |
|
r897 | ||
|
r487 | self.__profileIndex | |
|
r897 | ||
|
r487 | """ | |
|
r897 | ||
|
r623 | data_spc = self.__buffer_spc | |
data_cspc = self.__buffer_cspc | |||
data_dc = self.__buffer_dc | |||
|
r487 | n = self.__profIndex | |
|
r897 | ||
|
r623 | self.__buffer_spc = 0 | |
self.__buffer_cspc = 0 | |||
self.__buffer_dc = 0 | |||
self.__profIndex = 0 | |||
|
r897 | ||
|
r487 | return data_spc, data_cspc, data_dc, n | |
|
r897 | ||
|
r487 | def byProfiles(self, *args): | |
|
r897 | ||
|
r487 | self.__dataReady = False | |
|
r624 | avgdata_spc = None | |
avgdata_cspc = None | |||
avgdata_dc = None | |||
|
r897 | ||
|
r487 | self.putData(*args) | |
|
r897 | ||
|
r487 | if self.__profIndex == self.n: | |
|
r897 | ||
|
r487 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | |
|
r623 | self.n = n | |
|
r487 | self.__dataReady = True | |
|
r897 | ||
|
r487 | return avgdata_spc, avgdata_cspc, avgdata_dc | |
|
r897 | ||
|
r487 | def byTime(self, datatime, *args): | |
|
r897 | ||
|
r487 | self.__dataReady = False | |
|
r624 | avgdata_spc = None | |
avgdata_cspc = None | |||
avgdata_dc = None | |||
|
r897 | ||
|
r487 | self.putData(*args) | |
|
r897 | ||
|
r487 | if (datatime - self.__initime) >= self.__integrationtime: | |
avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | |||
self.n = n | |||
self.__dataReady = True | |||
|
r897 | ||
|
r487 | return avgdata_spc, avgdata_cspc, avgdata_dc | |
|
r897 | ||
|
r487 | def integrate(self, datatime, *args): | |
|
r897 | ||
|
r623 | if self.__profIndex == 0: | |
|
r487 | self.__initime = datatime | |
|
r897 | ||
|
r487 | if self.__byTime: | |
|
r1120 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( | |
datatime, *args) | |||
|
r487 | else: | |
avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) | |||
|
r897 | ||
|
r623 | if not self.__dataReady: | |
|
r487 | return None, None, None, None | |
|
r897 | ||
|
r623 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc | |
|
r897 | ||
|
r487 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): | |
|
r1120 | if n == 1: | |
|
r487 | return | |
|
r1171 | ||
|
r623 | dataOut.flagNoData = True | |
|
r897 | ||
|
r487 | if not self.isConfig: | |
self.setup(n, timeInterval, overlapping) | |||
self.isConfig = True | |||
|
r897 | ||
|
r487 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, | |
dataOut.data_spc, | |||
dataOut.data_cspc, | |||
dataOut.data_dc) | |||
|
r897 | ||
|
r487 | if self.__dataReady: | |
|
r897 | ||
|
r487 | dataOut.data_spc = avgdata_spc | |
dataOut.data_cspc = avgdata_cspc | |||
|
r1183 | dataOut.data_dc = avgdata_dc | |
|
r487 | dataOut.nIncohInt *= self.n | |
dataOut.utctime = avgdatatime | |||
|
r1171 | dataOut.flagNoData = False | |
|
r1177 | ||
|
r1171 | return dataOut |