|
|
import numpy
|
|
|
|
|
|
from jroproc_base import ProcessingUnit, Operation
|
|
|
from model.data.jrodata import SpectraHeis
|
|
|
|
|
|
class SpectraHeisProc(ProcessingUnit):
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
|
ProcessingUnit.__init__(self)
|
|
|
|
|
|
# self.buffer = None
|
|
|
# self.firstdatatime = None
|
|
|
# self.profIndex = 0
|
|
|
self.dataOut = SpectraHeis()
|
|
|
|
|
|
def __updateObjFromInput(self):
|
|
|
|
|
|
self.dataOut.timeZone = self.dataIn.timeZone
|
|
|
self.dataOut.dstFlag = self.dataIn.dstFlag
|
|
|
self.dataOut.errorCount = self.dataIn.errorCount
|
|
|
self.dataOut.useLocalTime = self.dataIn.useLocalTime
|
|
|
|
|
|
self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()#
|
|
|
self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()#
|
|
|
self.dataOut.channelList = self.dataIn.channelList
|
|
|
self.dataOut.heightList = self.dataIn.heightList
|
|
|
# self.dataOut.dtype = self.dataIn.dtype
|
|
|
self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
|
|
|
# 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 = 1
|
|
|
# self.dataOut.nProfiles = self.dataOut.nFFTPoints
|
|
|
self.dataOut.nFFTPoints = self.dataIn.nHeights
|
|
|
# self.dataOut.channelIndexList = self.dataIn.channelIndexList
|
|
|
# self.dataOut.flagNoData = self.dataIn.flagNoData
|
|
|
self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
|
|
|
self.dataOut.utctime = self.dataIn.utctime
|
|
|
# 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.nIncohInt
|
|
|
# self.dataOut.set=self.dataIn.set
|
|
|
# self.dataOut.deltaHeight=self.dataIn.deltaHeight
|
|
|
|
|
|
|
|
|
def __updateObjFromFits(self):
|
|
|
self.dataOut.utctime = self.dataIn.utctime
|
|
|
self.dataOut.channelIndexList = self.dataIn.channelIndexList
|
|
|
|
|
|
self.dataOut.channelList = self.dataIn.channelList
|
|
|
self.dataOut.heightList = self.dataIn.heightList
|
|
|
self.dataOut.data_spc = self.dataIn.data
|
|
|
self.dataOut.timeInterval = self.dataIn.timeInterval
|
|
|
self.dataOut.timeZone = self.dataIn.timeZone
|
|
|
self.dataOut.useLocalTime = True
|
|
|
# self.dataOut.
|
|
|
# self.dataOut.
|
|
|
|
|
|
def __getFft(self):
|
|
|
|
|
|
fft_volt = numpy.fft.fft(self.dataIn.data, axis=1)
|
|
|
fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
|
|
|
spc = numpy.abs(fft_volt * numpy.conjugate(fft_volt))/(self.dataOut.nFFTPoints)
|
|
|
self.dataOut.data_spc = spc
|
|
|
|
|
|
def run(self):
|
|
|
|
|
|
self.dataOut.flagNoData = True
|
|
|
|
|
|
if self.dataIn.type == "Fits":
|
|
|
self.__updateObjFromFits()
|
|
|
self.dataOut.flagNoData = False
|
|
|
return
|
|
|
|
|
|
if self.dataIn.type == "SpectraHeis":
|
|
|
self.dataOut.copy(self.dataIn)
|
|
|
return
|
|
|
|
|
|
if self.dataIn.type == "Voltage":
|
|
|
self.__updateObjFromInput()
|
|
|
self.__getFft()
|
|
|
self.dataOut.flagNoData = False
|
|
|
|
|
|
return
|
|
|
|
|
|
raise ValueError, "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
|
|
|
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_spc = self.dataOut.data_spc[channelIndexList,:]
|
|
|
|
|
|
self.dataOut.data_spc = data_spc
|
|
|
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
|
|
|
|
|
|
return 1
|
|
|
|
|
|
class IncohInt4SpectraHeis(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):
|
|
|
"""
|
|
|
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_spc, dataOut.utctime)
|
|
|
|
|
|
# dataOut.timeInterval *= n
|
|
|
dataOut.flagNoData = True
|
|
|
|
|
|
if self.__dataReady:
|
|
|
dataOut.data_spc = avgdata
|
|
|
dataOut.nIncohInt *= self.n
|
|
|
# dataOut.nCohInt *= self.n
|
|
|
dataOut.utctime = avgdatatime
|
|
|
dataOut.timeInterval = dataOut.ippSeconds * dataOut.nIncohInt
|
|
|
# dataOut.timeInterval = self.__timeInterval*self.n
|
|
|
dataOut.flagNoData = False
|
