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jroprocessing.py
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'''
$Author: dsuarez $
$Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
'''
import os
import numpy
import datetime
import time
from jrodata import *
from jrodataIO import *
from jroplot import *
class ProcessingUnit:
"""
Esta es la clase base para el procesamiento de datos.
Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
- Metodos internos (callMethod)
- Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
tienen que ser agreagados con el metodo "add".
"""
# objeto de datos de entrada (Voltage, Spectra o Correlation)
dataIn = None
# objeto de datos de entrada (Voltage, Spectra o Correlation)
dataOut = None
objectDict = None
def __init__(self):
self.objectDict = {}
def init(self):
raise ValueError, "Not implemented"
def addOperation(self, object, objId):
"""
Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
identificador asociado a este objeto.
Input:
object : objeto de la clase "Operation"
Return:
objId : identificador del objeto, necesario para ejecutar la operacion
"""
self.objectDict[objId] = object
return objId
def operation(self, **kwargs):
"""
Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
atributos del objeto dataOut
Input:
**kwargs : Diccionario de argumentos de la funcion a ejecutar
"""
raise ValueError, "ImplementedError"
def callMethod(self, name, **kwargs):
"""
Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
Input:
name : nombre del metodo a ejecutar
**kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
"""
if name != 'run':
if name == 'init' and self.dataIn.isEmpty():
self.dataOut.flagNoData = True
return False
if name != 'init' and self.dataOut.isEmpty():
return False
methodToCall = getattr(self, name)
methodToCall(**kwargs)
if name != 'run':
return True
if self.dataOut.isEmpty():
return False
return True
def callObject(self, objId, **kwargs):
"""
Ejecuta la operacion asociada al identificador del objeto "objId"
Input:
objId : identificador del objeto a ejecutar
**kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
Return:
None
"""
if self.dataOut.isEmpty():
return False
object = self.objectDict[objId]
object.run(self.dataOut, **kwargs)
return True
def call(self, operationConf, **kwargs):
"""
Return True si ejecuta la operacion "operationConf.name" con los
argumentos "**kwargs". False si la operacion no se ha ejecutado.
La operacion puede ser de dos tipos:
1. Un metodo propio de esta clase:
operation.type = "self"
2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
operation.type = "other".
Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
"addOperation" e identificado con el operation.id
con el id de la operacion.
Input:
Operation : Objeto del tipo operacion con los atributos: name, type y id.
"""
if operationConf.type == 'self':
sts = self.callMethod(operationConf.name, **kwargs)
if operationConf.type == 'other':
sts = self.callObject(operationConf.id, **kwargs)
return sts
def setInput(self, dataIn):
self.dataIn = dataIn
def getOutput(self):
return self.dataOut
class Operation():
"""
Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
acumulacion dentro de esta clase
Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
"""
__buffer = None
__isConfig = False
def __init__(self):
pass
def run(self, dataIn, **kwargs):
"""
Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
Input:
dataIn : objeto del tipo JROData
Return:
None
Affected:
__buffer : buffer de recepcion de datos.
"""
raise ValueError, "ImplementedError"
class VoltageProc(ProcessingUnit):
def __init__(self):
self.objectDict = {}
self.dataOut = Voltage()
self.flip = 1
def init(self):
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:
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)
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, maxHei):
"""
Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
minHei <= height <= maxHei
Input:
minHei : valor minimo de altura a considerar
maxHei : valor maximo de altura a considerar
Affected:
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
Return:
1 si el metodo se ejecuto con exito caso contrario devuelve 0
"""
if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
if (maxHei > self.dataOut.heightList[-1]):
maxHei = self.dataOut.heightList[-1]
# raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
minIndex = 0
maxIndex = 0
heights = self.dataOut.heightList
inda = numpy.where(heights >= minHei)
indb = numpy.where(heights <= maxHei)
try:
minIndex = inda[0][0]
except:
minIndex = 0
try:
maxIndex = indb[0][-1]
except:
maxIndex = len(heights)
self.selectHeightsByIndex(minIndex, maxIndex)
return 1
def 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-1
# raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
nHeights = maxIndex - minIndex + 1
#voltage
data = self.dataOut.data[:,minIndex:maxIndex+1]
firstHeight = self.dataOut.heightList[minIndex]
self.dataOut.data = data
self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
return 1
def filterByHeights(self, window):
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
if window == None:
window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
newdelta = deltaHeight * window
r = self.dataOut.data.shape[1] % window
buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
buffer = numpy.sum(buffer,2)
self.dataOut.data = buffer
self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
self.dataOut.windowOfFilter = window
def deFlip(self):
self.dataOut.data *= self.flip
self.flip *= -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):
self.__isConfig = False
def setup(self, n=None, timeInterval=None, overlapping=False):
"""
Set the parameters of the integration class.
Inputs:
n : Number of coherent integrations
timeInterval : Time of integration. If the parameter "n" is selected this one does not work
overlapping :
"""
self.__initime = None
self.__lastdatatime = 0
self.__buffer = None
self.__dataReady = False
if n == None and timeInterval == None:
raise ValueError, "n or timeInterval should be specified ..."
if n != None:
self.n = n
self.__byTime = False
else:
self.__integrationtime = timeInterval * 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 run(self, dataOut, **kwargs):
if not self.__isConfig:
self.setup(**kwargs)
self.__isConfig = True
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):
self.__isConfig = False
def setup(self, code):
self.__profIndex = 0
self.code = code
self.nCode = len(code)
self.nBaud = len(code[0])
def convolutionInFreq(self, data):
nchannel, ndata = data.shape
newcode = numpy.zeros(ndata)
newcode[0:self.nBaud] = self.code[self.__profIndex]
fft_data = numpy.fft.fft(data, axis=1)
fft_code = numpy.conj(numpy.fft.fft(newcode))
fft_code = fft_code.reshape(1,len(fft_code))
# conv = fft_data.copy()
# conv.fill(0)
conv = fft_data*fft_code
data = numpy.fft.ifft(conv,axis=1)
datadec = data[:,:-self.nBaud+1]
ndatadec = ndata - self.nBaud + 1
if self.__profIndex == self.nCode-1:
self.__profIndex = 0
return ndatadec, datadec
self.__profIndex += 1
return ndatadec, datadec
def convolutionInTime(self, data):
nchannel, ndata = data.shape
newcode = self.code[self.__profIndex]
ndatadec = ndata - self.nBaud + 1
datadec = numpy.zeros((nchannel, ndatadec))
for i in range(nchannel):
datadec[i,:] = numpy.correlate(data[i,:], newcode)
if self.__profIndex == self.nCode-1:
self.__profIndex = 0
return ndatadec, datadec
self.__profIndex += 1
return ndatadec, datadec
def run(self, dataOut, code=None, mode = 0):
if not self.__isConfig:
if code == None:
code = dataOut.code
self.setup(code)
self.__isConfig = True
if mode == 0:
ndatadec, datadec = self.convolutionInFreq(dataOut.data)
if mode == 1:
print "This function is not implemented"
# ndatadec, datadec = self.convolutionInTime(dataOut.data)
dataOut.data = datadec
dataOut.heightList = dataOut.heightList[0:ndatadec]
dataOut.flagDecodeData = True #asumo q la data no esta decodificada
# dataOut.flagDeflipData = True #asumo q la data no esta sin flip
class SpectraProc(ProcessingUnit):
def __init__(self):
self.objectDict = {}
self.buffer = None
self.firstdatatime = None
self.profIndex = 0
self.dataOut = Spectra()
def __updateObjFromInput(self):
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 = self.dataIn.dtype
# self.dataOut.nHeights = self.dataIn.nHeights
# self.dataOut.nChannels = self.dataIn.nChannels
self.dataOut.nBaud = self.dataIn.nBaud
self.dataOut.nCode = self.dataIn.nCode
self.dataOut.code = self.dataIn.code
self.dataOut.nProfiles = self.dataOut.nFFTPoints
# self.dataOut.channelIndexList = self.dataIn.channelIndexList
self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
self.dataOut.utctime = self.firstdatatime
self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
self.dataOut.nCohInt = self.dataIn.nCohInt
self.dataOut.nIncohInt = 1
self.dataOut.ippSeconds = self.dataIn.ippSeconds
self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
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,axis=1)
dc = fft_volt[:,0,:]
#calculo de self-spectra
fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
spc = fft_volt * numpy.conjugate(fft_volt)
spc = spc.real
blocksize = 0
blocksize += dc.size
blocksize += spc.size
cspc = None
pairIndex = 0
if self.dataOut.pairsList != None:
#calculo de cross-spectra
cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
for pair in self.dataOut.pairsList:
cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
pairIndex += 1
blocksize += cspc.size
self.dataOut.data_spc = spc
self.dataOut.data_cspc = cspc
self.dataOut.data_dc = dc
self.dataOut.blockSize = blocksize
def init(self, nFFTPoints=None, pairsList=None):
self.dataOut.flagNoData = True
if self.dataIn.type == "Spectra":
self.dataOut.copy(self.dataIn)
return
if self.dataIn.type == "Voltage":
if nFFTPoints == None:
raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
if pairsList == None:
nPairs = 0
else:
nPairs = len(pairsList)
self.dataOut.nFFTPoints = nFFTPoints
self.dataOut.pairsList = pairsList
self.dataOut.nPairs = nPairs
if self.buffer == None:
self.buffer = numpy.zeros((self.dataIn.nChannels,
self.dataOut.nFFTPoints,
self.dataIn.nHeights),
dtype='complex')
self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
self.profIndex += 1
if self.firstdatatime == None:
self.firstdatatime = self.dataIn.utctime
if self.profIndex == self.dataOut.nFFTPoints:
self.__updateObjFromInput()
self.__getFft()
self.dataOut.flagNoData = False
self.buffer = None
self.firstdatatime = None
self.profIndex = 0
return
raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
def selectChannels(self, channelList):
channelIndexList = []
for channel in channelList:
index = self.dataOut.channelList.index(channel)
channelIndexList.append(index)
self.selectChannelsByIndex(channelIndexList)
def selectChannelsByIndex(self, channelIndexList):
"""
Selecciona un bloque de datos en base a canales segun el channelIndexList
Input:
channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
Affected:
self.dataOut.data_spc
self.dataOut.channelIndexList
self.dataOut.nChannels
Return:
None
"""
for channelIndex in channelIndexList:
if channelIndex not in self.dataOut.channelIndexList:
print channelIndexList
raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
nChannels = len(channelIndexList)
data_spc = self.dataOut.data_spc[channelIndexList,:]
self.dataOut.data_spc = data_spc
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
# self.dataOut.nChannels = nChannels
return 1
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
n = None
def __init__(self):
self.__isConfig = False
def setup(self, n=None, timeInterval=None, overlapping=False):
"""
Set the parameters of the integration class.
Inputs:
n : Number of coherent integrations
timeInterval : Time of integration. If the parameter "n" is selected this one does not work
overlapping :
"""
self.__initime = None
self.__lastdatatime = 0
self.__buffer_spc = None
self.__buffer_cspc = None
self.__buffer_dc = None
self.__dataReady = False
if n == None and timeInterval == None:
raise ValueError, "n or timeInterval should be specified ..."
if n != None:
self.n = n
self.__byTime = False
else:
self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
self.n = 9999
self.__byTime = True
if overlapping:
self.__withOverapping = True
else:
self.__withOverapping = False
self.__buffer_spc = 0
self.__buffer_cspc = 0
self.__buffer_dc = 0
self.__profIndex = 0
def putData(self, data_spc, data_cspc, data_dc):
"""
Add a profile to the __buffer_spc and increase in one the __profileIndex
"""
if not self.__withOverapping:
self.__buffer_spc += data_spc
if data_cspc == None:
self.__buffer_cspc = None
else:
self.__buffer_cspc += data_cspc
if data_dc == None:
self.__buffer_dc = None
else:
self.__buffer_dc += data_dc
self.__profIndex += 1
return
#Overlapping data
nChannels, nFFTPoints, nHeis = data_spc.shape
data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
if data_cspc != None:
data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
if data_dc != None:
data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
#If the buffer is empty then it takes the data value
if self.__buffer_spc == None:
self.__buffer_spc = data_spc
if data_cspc == None:
self.__buffer_cspc = None
else:
self.__buffer_cspc += data_cspc
if data_dc == None:
self.__buffer_dc = None
else:
self.__buffer_dc += data_dc
self.__profIndex += 1
return
#If the buffer length is lower than n then stakcing the data value
if self.__profIndex < self.n:
self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
if data_cspc != None:
self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
if data_dc != None:
self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
self.__profIndex += 1
return
#If the buffer length is equal to n then replacing the last buffer value with the data value
self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
self.__buffer_spc[self.n-1] = data_spc
if data_cspc != None:
self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
self.__buffer_cspc[self.n-1] = data_cspc
if data_dc != None:
self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
self.__buffer_dc[self.n-1] = data_dc
self.__profIndex = self.n
return
def pushData(self):
"""
Return the sum of the last profiles and the profiles used in the sum.
Affected:
self.__profileIndex
"""
data_spc = None
data_cspc = None
data_dc = None
if not self.__withOverapping:
data_spc = self.__buffer_spc
data_cspc = self.__buffer_cspc
data_dc = self.__buffer_dc
n = self.__profIndex
self.__buffer_spc = 0
self.__buffer_cspc = 0
self.__buffer_dc = 0
self.__profIndex = 0
return data_spc, data_cspc, data_dc, n
#Integration with Overlapping
data_spc = numpy.sum(self.__buffer_spc, axis=0)
if self.__buffer_cspc != None:
data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
if self.__buffer_dc != None:
data_dc = numpy.sum(self.__buffer_dc, axis=0)
n = self.__profIndex
return data_spc, data_cspc, data_dc, n
def byProfiles(self, *args):
self.__dataReady = False
avgdata_spc = None
avgdata_cspc = None
avgdata_dc = None
n = None
self.putData(*args)
if self.__profIndex == self.n:
avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
self.__dataReady = True
return avgdata_spc, avgdata_cspc, avgdata_dc
def byTime(self, datatime, *args):
self.__dataReady = False
avgdata_spc = None
avgdata_cspc = None
avgdata_dc = None
n = None
self.putData(*args)
if (datatime - self.__initime) >= self.__integrationtime:
avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
self.n = n
self.__dataReady = True
return avgdata_spc, avgdata_cspc, avgdata_dc
def integrate(self, datatime, *args):
if self.__initime == None:
self.__initime = datatime
if self.__byTime:
avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
else:
avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
self.__lastdatatime = datatime
if avgdata_spc == None:
return None, None, None, None
avgdatatime = self.__initime
deltatime = datatime -self.__lastdatatime
if not self.__withOverapping:
self.__initime = datatime
else:
self.__initime += deltatime
return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
if not self.__isConfig:
self.setup(n, timeInterval, overlapping)
self.__isConfig = True
avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
dataOut.data_spc,
dataOut.data_cspc,
dataOut.data_dc)
# dataOut.timeInterval *= n
dataOut.flagNoData = True
if self.__dataReady:
dataOut.data_spc = avgdata_spc
dataOut.data_cspc = avgdata_cspc
dataOut.data_dc = avgdata_dc
dataOut.nIncohInt *= self.n
dataOut.utctime = avgdatatime
dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
dataOut.flagNoData = False
class ProfileSelector(Operation):
profileIndex = None
# Tamanho total de los perfiles
nProfiles = None
def __init__(self):
self.profileIndex = 0
def incIndex(self):
self.profileIndex += 1
if self.profileIndex >= self.nProfiles:
self.profileIndex = 0
def isProfileInRange(self, minIndex, maxIndex):
if self.profileIndex < minIndex:
return False
if self.profileIndex > maxIndex:
return False
return True
def isProfileInList(self, profileList):
if self.profileIndex not in profileList:
return False
return True
def run(self, dataOut, profileList=None, profileRangeList=None):
dataOut.flagNoData = True
self.nProfiles = dataOut.nProfiles
if profileList != None:
if self.isProfileInList(profileList):
dataOut.flagNoData = False
self.incIndex()
return 1
elif profileRangeList != None:
minIndex = profileRangeList[0]
maxIndex = profileRangeList[1]
if self.isProfileInRange(minIndex, maxIndex):
dataOut.flagNoData = False
self.incIndex()
return 1
else:
raise ValueError, "ProfileSelector needs profileList or profileRangeList"
return 0