|
|
import itertools
|
|
|
|
|
|
import numpy
|
|
|
|
|
|
from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation
|
|
|
from schainpy.model.data.jrodata import Spectra
|
|
|
from schainpy.model.data.jrodata import hildebrand_sekhon
|
|
|
from schainpy.utils import log
|
|
|
|
|
|
@MPDecorator
|
|
|
class SpectraProc(ProcessingUnit):
|
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
|
ProcessingUnit.__init__(self)
|
|
|
|
|
|
self.buffer = None
|
|
|
self.firstdatatime = None
|
|
|
self.profIndex = 0
|
|
|
self.dataOut = Spectra()
|
|
|
self.id_min = None
|
|
|
self.id_max = None
|
|
|
self.setupReq = False #Agregar a todas las unidades de proc
|
|
|
|
|
|
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
|
|
|
try:
|
|
|
self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
|
|
|
except:
|
|
|
pass
|
|
|
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
|
|
|
# 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
|
|
|
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, shift_fft=False):
|
|
|
|
|
|
if self.dataIn.type == "Spectra":
|
|
|
self.dataOut.copy(self.dataIn)
|
|
|
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
|
|
|
self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1)
|
|
|
|
|
|
return True
|
|
|
|
|
|
if self.dataIn.type == "Voltage":
|
|
|
|
|
|
self.dataOut.flagNoData = True
|
|
|
|
|
|
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:
|
|
|
nVoltProfiles = self.dataIn.data.shape[1]
|
|
|
|
|
|
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:
|
|
|
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 not pairsList:
|
|
|
return
|
|
|
|
|
|
pairs = []
|
|
|
pairsIndex = []
|
|
|
|
|
|
for pair in pairsList:
|
|
|
if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList:
|
|
|
continue
|
|
|
pairs.append(pair)
|
|
|
pairsIndex.append(pairs.index(pair))
|
|
|
|
|
|
self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex]
|
|
|
self.dataOut.pairsList = pairs
|
|
|
|
|
|
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))
|
|
|
|
|
|
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.channelList = range(len(channelIndexList))
|
|
|
self.__selectPairsByChannel(channelIndexList)
|
|
|
|
|
|
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 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
|
|
|
|
|
|
|
|
|
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]
|
|
|
|
|
|
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 = int(jspectra.shape[1] / 2)
|
|
|
ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
|
|
|
ind_vel = ind_vel.astype(int)
|
|
|
|
|
|
if ind_vel[0] < 0:
|
|
|
ind_vel[list(range(0, 1))] = ind_vel[list(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(list(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
|
|
|
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
|
|
|
|
|
|
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 = int(num_hei / 2)
|
|
|
hei_interf = numpy.asmatrix(list(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 = list(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(list(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[list(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(list(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 = int(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[list(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 = int(numpy.median(numpy.real(
|
|
|
junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
|
|
|
median_imag = int(numpy.median(numpy.imag(
|
|
|
junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
|
|
|
comp_mask_prof = [int(e) for e in comp_mask_prof]
|
|
|
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(list(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)
|
|
|
|
|
|
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)
|
|
|
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
|
|
|
|
|
|
return dataOut
|
|
|
|
