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Modificaciones en el numero de intergraciones, en la escala de db Range
Modificaciones en el numero de intergraciones, en la escala de db Range
Miguel Valdez - - Load All Authors

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jroproc_voltage.py
1050 lines | 33.4 KiB | text/x-python | PythonLexer
/ schainpy / model / proc / jroproc_voltage.py
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import numpy
from jroproc_base import ProcessingUnit, Operation
from schainpy.model.data.jrodata import Voltage
class VoltageProc(ProcessingUnit):
def __init__(self):
ProcessingUnit.__init__(self)
# self.objectDict = {}
self.dataOut = Voltage()
self.flip = 1
def run(self):
if self.dataIn.type == 'AMISR':
self.__updateObjFromAmisrInput()
if self.dataIn.type == 'Voltage':
self.dataOut.copy(self.dataIn)
# self.dataOut.copy(self.dataIn)
def __updateObjFromAmisrInput(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.flagNoData = self.dataIn.flagNoData
self.dataOut.data = self.dataIn.data
self.dataOut.utctime = self.dataIn.utctime
self.dataOut.channelList = self.dataIn.channelList
# self.dataOut.timeInterval = self.dataIn.timeInterval
self.dataOut.heightList = self.dataIn.heightList
self.dataOut.nProfiles = self.dataIn.nProfiles
self.dataOut.nCohInt = self.dataIn.nCohInt
self.dataOut.ippSeconds = self.dataIn.ippSeconds
self.dataOut.frequency = self.dataIn.frequency
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
#
# pass#
#
# def init(self):
#
#
# if self.dataIn.type == 'AMISR':
# self.__updateObjFromAmisrInput()
#
# if self.dataIn.type == 'Voltage':
# self.dataOut.copy(self.dataIn)
# # No necesita copiar en cada init() los atributos de dataIn
# # la copia deberia hacerse por cada nuevo bloque de datos
def selectChannels(self, channelList):
channelIndexList = []
for channel in channelList:
if channel not in self.dataOut.channelList:
raise ValueError, "Channel %d is not in %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
self.dataOut.channelIndexList
self.dataOut.nChannels
self.dataOut.m_ProcessingHeader.totalSpectra
self.dataOut.systemHeaderObj.numChannels
self.dataOut.m_ProcessingHeader.blockSize
Return:
None
"""
for channelIndex in channelIndexList:
if channelIndex not in self.dataOut.channelIndexList:
print channelIndexList
raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
# nChannels = len(channelIndexList)
if self.dataOut.flagDataAsBlock:
"""
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
data = self.dataOut.data[channelIndexList,:,:]
else:
data = self.dataOut.data[channelIndexList,:]
self.dataOut.data = data
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
# self.dataOut.nChannels = nChannels
return 1
def selectHeights(self, minHei=None, maxHei=None):
"""
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 == None:
minHei = self.dataOut.heightList[0]
if maxHei == None:
maxHei = self.dataOut.heightList[-1]
if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
if (maxHei > self.dataOut.heightList[-1]):
maxHei = self.dataOut.heightList[-1]
# raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
minIndex = 0
maxIndex = 0
heights = self.dataOut.heightList
inda = numpy.where(heights >= minHei)
indb = numpy.where(heights <= maxHei)
try:
minIndex = inda[0][0]
except:
minIndex = 0
try:
maxIndex = indb[0][-1]
except:
maxIndex = len(heights)
self.selectHeightsByIndex(minIndex, maxIndex)
return 1
def 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
self.dataOut.heightList
Return:
1 si el metodo se ejecuto con exito caso contrario devuelve 0
"""
if (minIndex < 0) or (minIndex > maxIndex):
raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
if (maxIndex >= self.dataOut.nHeights):
maxIndex = self.dataOut.nHeights
# raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
# nHeights = maxIndex - minIndex + 1
#voltage
if self.dataOut.flagDataAsBlock:
"""
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
data = self.dataOut.data[:,minIndex:maxIndex,:]
else:
data = self.dataOut.data[:,minIndex:maxIndex]
# firstHeight = self.dataOut.heightList[minIndex]
self.dataOut.data = data
self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
if self.dataOut.nHeights <= 1:
raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
return 1
def filterByHeights(self, window):
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
if window == None:
window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
newdelta = deltaHeight * window
r = self.dataOut.nHeights % window
newheights = (self.dataOut.nHeights-r)/window
if newheights <= 1:
raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
if self.dataOut.flagDataAsBlock:
"""
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
buffer = numpy.sum(buffer,3)
else:
buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
buffer = numpy.sum(buffer,2)
self.dataOut.data = buffer
self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
self.dataOut.windowOfFilter = window
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 deFlip(self, channelList = []):
data = self.dataOut.data.copy()
if self.dataOut.flagDataAsBlock:
flip = self.flip
profileList = range(self.dataOut.nProfiles)
if not channelList:
for thisProfile in profileList:
data[:,thisProfile,:] = data[:,thisProfile,:]*flip
flip *= -1.0
else:
for thisChannel in channelList:
if thisChannel not in self.dataOut.channelList:
continue
for thisProfile in profileList:
data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
flip *= -1.0
self.flip = flip
else:
if not channelList:
data[:,:] = data[:,:]*self.flip
else:
for thisChannel in channelList:
if thisChannel not in self.dataOut.channelList:
continue
data[thisChannel,:] = data[thisChannel,:]*self.flip
self.flip *= -1.
self.dataOut.data = data
def setRadarFrequency(self, frequency=None):
if frequency != None:
self.dataOut.frequency = frequency
return 1
class CohInt(Operation):
isConfig = False
__profIndex = 0
__withOverapping = False
__byTime = False
__initime = None
__lastdatatime = None
__integrationtime = None
__buffer = None
__dataReady = False
n = None
def __init__(self):
Operation.__init__(self)
# self.isConfig = False
def setup(self, n=None, timeInterval=None, overlapping=False, byblock=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 = None
self.__dataReady = False
self.byblock = byblock
if n == None and timeInterval == None:
raise ValueError, "n or timeInterval should be specified ..."
if n != None:
self.n = n
self.__byTime = False
else:
self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
self.n = 9999
self.__byTime = True
if overlapping:
self.__withOverapping = True
self.__buffer = None
else:
self.__withOverapping = False
self.__buffer = 0
self.__profIndex = 0
def putData(self, data):
"""
Add a profile to the __buffer and increase in one the __profileIndex
"""
if not self.__withOverapping:
self.__buffer += data.copy()
self.__profIndex += 1
return
#Overlapping data
nChannels, nHeis = data.shape
data = numpy.reshape(data, (1, nChannels, nHeis))
#If the buffer is empty then it takes the data value
if self.__buffer == None:
self.__buffer = data
self.__profIndex += 1
return
#If the buffer length is lower than n then stakcing the data value
if self.__profIndex < self.n:
self.__buffer = numpy.vstack((self.__buffer, data))
self.__profIndex += 1
return
#If the buffer length is equal to n then replacing the last buffer value with the data value
self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
self.__buffer[self.n-1] = data
self.__profIndex = self.n
return
def pushData(self):
"""
Return the sum of the last profiles and the profiles used in the sum.
Affected:
self.__profileIndex
"""
if not self.__withOverapping:
data = self.__buffer
n = self.__profIndex
self.__buffer = 0
self.__profIndex = 0
return data, n
#Integration with Overlapping
data = numpy.sum(self.__buffer, axis=0)
n = self.__profIndex
return data, n
def byProfiles(self, data):
self.__dataReady = False
avgdata = None
# n = None
self.putData(data)
if self.__profIndex == self.n:
avgdata, n = self.pushData()
self.__dataReady = True
return avgdata
def byTime(self, data, datatime):
self.__dataReady = False
avgdata = None
n = None
self.putData(data)
if (datatime - self.__initime) >= self.__integrationtime:
avgdata, n = self.pushData()
self.n = n
self.__dataReady = True
return avgdata
def integrate(self, data, datatime=None):
if self.__initime == None:
self.__initime = datatime
if self.__byTime:
avgdata = self.byTime(data, datatime)
else:
avgdata = self.byProfiles(data)
self.__lastdatatime = datatime
if avgdata == None:
return None, None
avgdatatime = self.__initime
deltatime = datatime -self.__lastdatatime
if not self.__withOverapping:
self.__initime = datatime
else:
self.__initime += deltatime
return avgdata, avgdatatime
def integrateByBlock(self, dataOut):
times = int(dataOut.data.shape[1]/self.n)
avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
id_min = 0
id_max = self.n
for i in range(times):
junk = dataOut.data[:,id_min:id_max,:]
avgdata[:,i,:] = junk.sum(axis=1)
id_min += self.n
id_max += self.n
timeInterval = dataOut.ippSeconds*self.n
avgdatatime = (times - 1) * timeInterval + dataOut.utctime
self.__dataReady = True
return avgdata, avgdatatime
def run(self, dataOut, **kwargs):
if not self.isConfig:
self.setup(**kwargs)
self.isConfig = True
if dataOut.flagDataAsBlock:
"""
Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
avgdata, avgdatatime = self.integrateByBlock(dataOut)
else:
avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
# dataOut.timeInterval *= n
dataOut.flagNoData = True
if self.__dataReady:
dataOut.data = avgdata
dataOut.nCohInt *= self.n
dataOut.utctime = avgdatatime
# dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
dataOut.flagNoData = False
class Decoder(Operation):
isConfig = False
__profIndex = 0
code = None
nCode = None
nBaud = None
def __init__(self):
Operation.__init__(self)
self.times = None
self.osamp = None
# self.__setValues = False
self.isConfig = False
def setup(self, code, osamp, dataOut):
self.__profIndex = 0
self.code = code
self.nCode = len(code)
self.nBaud = len(code[0])
if (osamp != None) and (osamp >1):
self.osamp = osamp
self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
self.nBaud = self.nBaud*self.osamp
self.__nChannels = dataOut.nChannels
self.__nProfiles = dataOut.nProfiles
self.__nHeis = dataOut.nHeights
if dataOut.flagDataAsBlock:
self.ndatadec = self.__nHeis #- self.nBaud + 1
self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
else:
__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
__codeBuffer[:,0:self.nBaud] = self.code
self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
self.ndatadec = self.__nHeis #- self.nBaud + 1
self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
def convolutionInFreq(self, data):
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
fft_data = numpy.fft.fft(data, axis=1)
conv = fft_data*fft_code
data = numpy.fft.ifft(conv,axis=1)
datadec = data#[:,:]
return datadec
def convolutionInFreqOpt(self, data):
raise NotImplementedError
# fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
#
# data = cfunctions.decoder(fft_code, data)
#
# datadec = data#[:,:]
#
# return datadec
def convolutionInTime(self, data):
code = self.code[self.__profIndex]
for i in range(self.__nChannels):
self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='same')
return self.datadecTime
def convolutionByBlockInTime(self, data):
repetitions = self.__nProfiles / self.nCode
junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
junk = junk.flatten()
code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
for i in range(self.__nChannels):
for j in range(self.__nProfiles):
self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='same')
return self.datadecTime
def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None):
if not self.isConfig:
if code == None:
code = dataOut.code
else:
code = numpy.array(code).reshape(nCode,nBaud)
self.setup(code, osamp, dataOut)
self.isConfig = True
if dataOut.flagDataAsBlock:
"""
Decoding when data have been read as block,
"""
datadec = self.convolutionByBlockInTime(dataOut.data)
else:
"""
Decoding when data have been read profile by profile
"""
if mode == 0:
datadec = self.convolutionInTime(dataOut.data)
if mode == 1:
datadec = self.convolutionInFreq(dataOut.data)
if mode == 2:
datadec = self.convolutionInFreqOpt(dataOut.data)
dataOut.code = self.code
dataOut.nCode = self.nCode
dataOut.nBaud = self.nBaud
dataOut.data = datadec
dataOut.heightList = dataOut.heightList[0:self.ndatadec]
dataOut.flagDecodeData = True #asumo q la data esta decodificada
if self.__profIndex == self.nCode-1:
self.__profIndex = 0
return 1
self.__profIndex += 1
return 1
# dataOut.flagDeflipData = True #asumo q la data no esta sin flip
class ProfileConcat(Operation):
isConfig = False
buffer = None
def __init__(self):
Operation.__init__(self)
self.profileIndex = 0
def reset(self):
self.buffer = numpy.zeros_like(self.buffer)
self.start_index = 0
self.times = 1
def setup(self, data, m, n=1):
self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
self.nHeights = data.nHeights
self.start_index = 0
self.times = 1
def concat(self, data):
self.buffer[:,self.start_index:self.profiles*self.times] = data.copy()
self.start_index = self.start_index + self.nHeights
def run(self, dataOut, m):
dataOut.flagNoData = True
if not self.isConfig:
self.setup(dataOut.data, m, 1)
self.isConfig = True
if dataOut.flagDataAsBlock:
raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
else:
self.concat(dataOut.data)
self.times += 1
if self.times > m:
dataOut.data = self.buffer
self.reset()
dataOut.flagNoData = False
# se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
dataOut.ippSeconds *= m
class ProfileSelector(Operation):
profileIndex = None
# Tamanho total de los perfiles
nProfiles = None
def __init__(self):
Operation.__init__(self)
self.profileIndex = 0
def incIndex(self):
self.profileIndex += 1
if self.profileIndex >= self.nProfiles:
self.profileIndex = 0
def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
if profileIndex < minIndex:
return False
if profileIndex > maxIndex:
return False
return True
def isThisProfileInList(self, profileIndex, profileList):
if profileIndex not in profileList:
return False
return True
def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None):
"""
ProfileSelector:
Inputs:
profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
"""
dataOut.flagNoData = True
self.nProfiles = dataOut.nProfiles
if dataOut.flagDataAsBlock:
"""
data dimension = [nChannels, nProfiles, nHeis]
"""
if profileList != None:
dataOut.data = dataOut.data[:,profileList,:]
dataOut.nProfiles = len(profileList)
dataOut.profileIndex = dataOut.nProfiles - 1
if profileRangeList != None:
minIndex = profileRangeList[0]
maxIndex = profileRangeList[1]
dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
dataOut.nProfiles = maxIndex - minIndex + 1
dataOut.profileIndex = dataOut.nProfiles - 1
if rangeList != None:
raise ValueError, "Profile Selector: Not implemented for rangeList yet"
dataOut.flagNoData = False
return True
else:
"""
data dimension = [nChannels, nHeis]
"""
if profileList != None:
dataOut.nProfiles = len(profileList)
if self.isThisProfileInList(dataOut.profileIndex, profileList):
dataOut.flagNoData = False
dataOut.profileIndex = self.profileIndex
self.incIndex()
return True
if profileRangeList != None:
minIndex = profileRangeList[0]
maxIndex = profileRangeList[1]
dataOut.nProfiles = maxIndex - minIndex + 1
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
dataOut.flagNoData = False
dataOut.profileIndex = self.profileIndex
self.incIndex()
return True
if rangeList != None:
nProfiles = 0
for thisRange in rangeList:
minIndex = thisRange[0]
maxIndex = thisRange[1]
nProfiles += maxIndex - minIndex + 1
dataOut.nProfiles = nProfiles
for thisRange in rangeList:
minIndex = thisRange[0]
maxIndex = thisRange[1]
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
# print "profileIndex = ", dataOut.profileIndex
dataOut.flagNoData = False
dataOut.profileIndex = self.profileIndex
self.incIndex()
break
return True
if beam != None: #beam is only for AMISR data
if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
dataOut.flagNoData = False
dataOut.profileIndex = self.profileIndex
self.incIndex()
return 1
raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
return 0
class Reshaper(Operation):
def __init__(self):
Operation.__init__(self)
self.updateNewHeights = True
def run(self, dataOut, shape):
if not dataOut.flagDataAsBlock:
raise ValueError, "Reshaper can only be used when voltage have been read as Block, getBlock = True"
if len(shape) != 3:
raise ValueError, "shape len should be equal to 3, (nChannels, nProfiles, nHeis)"
shape_tuple = tuple(shape)
dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
dataOut.flagNoData = False
if self.updateNewHeights:
old_nheights = dataOut.nHeights
new_nheights = dataOut.data.shape[2]
factor = 1.0*new_nheights / old_nheights
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * factor
dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
dataOut.nProfiles = dataOut.data.shape[1]
dataOut.ippSeconds *= factor
import collections
from scipy.stats import mode
class Synchronize(Operation):
isConfig = False
__profIndex = 0
def __init__(self):
Operation.__init__(self)
# self.isConfig = False
self.__powBuffer = None
self.__startIndex = 0
self.__pulseFound = False
def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
#Read data
powerdB = dataOut.getPower(channel = channel)
noisedB = dataOut.getNoise(channel = channel)[0]
self.__powBuffer.extend(powerdB.flatten())
dataArray = numpy.array(self.__powBuffer)
filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
maxValue = numpy.nanmax(filteredPower)
if maxValue < noisedB + 10:
#No se encuentra ningun pulso de transmision
return None
maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
if len(maxValuesIndex) < 2:
#Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
return None
phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
#Seleccionar solo valores con un espaciamiento de nSamples
pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
if len(pulseIndex) < 2:
#Solo se encontro un pulso de transmision con ancho mayor a 1
return None
spacing = pulseIndex[1:] - pulseIndex[:-1]
#remover senales que se distancien menos de 10 unidades o muestras
#(No deberian existir IPP menor a 10 unidades)
realIndex = numpy.where(spacing > 10 )[0]
if len(realIndex) < 2:
#Solo se encontro un pulso de transmision con ancho mayor a 1
return None
#Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
realPulseIndex = pulseIndex[realIndex]
period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
print "IPP = %d samples" %period
self.__newNSamples = dataOut.nHeights #int(period)
self.__startIndex = int(realPulseIndex[0])
return 1
def setup(self, nSamples, nChannels, buffer_size = 4):
self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
maxlen = buffer_size*nSamples)
bufferList = []
for i in range(nChannels):
bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
maxlen = buffer_size*nSamples)
bufferList.append(bufferByChannel)
self.__nSamples = nSamples
self.__nChannels = nChannels
self.__bufferList = bufferList
def run(self, dataOut, channel = 0):
if not self.isConfig:
nSamples = dataOut.nHeights
nChannels = dataOut.nChannels
self.setup(nSamples, nChannels)
self.isConfig = True
#Append new data to internal buffer
for thisChannel in range(self.__nChannels):
bufferByChannel = self.__bufferList[thisChannel]
bufferByChannel.extend(dataOut.data[thisChannel])
if self.__pulseFound:
self.__startIndex -= self.__nSamples
#Finding Tx Pulse
if not self.__pulseFound:
indexFound = self.__findTxPulse(dataOut, channel)
if indexFound == None:
dataOut.flagNoData = True
return
self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
self.__pulseFound = True
self.__startIndex = indexFound
#If pulse was found ...
for thisChannel in range(self.__nChannels):
bufferByChannel = self.__bufferList[thisChannel]
#print self.__startIndex
x = numpy.array(bufferByChannel)
self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
# dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
dataOut.data = self.__arrayBuffer
self.__startIndex += self.__newNSamples
return