schain Commit - r1050:d6a89c93744f · Jicamarca Repository

remove profilehooks imports

Juan C. Espinoza -

r1050:d6a89c93744f

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r1050:d6a89c93744f

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schainpy/model/proc/jroproc_voltage.py 0 -23

             import sys
             import numpy
-            from profilehooks import profile
             from scipy import interpolate
             from schainpy import cSchain
             from jroproc_base import ProcessingUnit, Operation
             from schainpy.model.data.jrodata import Voltage
             from time import time
             class VoltageProc(ProcessingUnit):
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
             #         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
                     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:
 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]):
                         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 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:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights
                     #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
                 def interpolateHeights(self, topLim, botLim):
                     #69 al 72 para julia
                     #82-84 para meteoros
                     if len(numpy.shape(self.dataOut.data))==2:
                         sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
                         sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
                         #self.dataOut.data[:,botLim:limSup+1] = sampInterp
                         self.dataOut.data[:,botLim:topLim+1] = sampInterp
                     else:
                         nHeights = self.dataOut.data.shape[2]
                         x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
                         y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)]
                         f = interpolate.interp1d(x, y, axis = 2)
                         xnew = numpy.arange(botLim,topLim+1)
                         ynew = f(xnew)
                         self.dataOut.data[:,:,botLim:topLim+1]  = ynew
             # import collections
             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, **kwargs):
                     Operation.__init__(self, **kwargs)
             #         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 is 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 is 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, n=None, timeInterval=None, overlapping=False, byblock=False, **kwargs):
                     if not self.isConfig:
                         self.setup(n=n, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         avgdata, avgdatatime = self.integrateByBlock(dataOut)
                         dataOut.nProfiles /= self.n
                     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, **kwargs):
                     Operation.__init__(self, **kwargs)
                     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 self.__nHeis < self.nBaud:
                         raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
                     #Frequency
                     __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))
                     if dataOut.flagDataAsBlock:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
                     else:
                         #Time
                         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)
                     return data
                 def __convolutionInFreqOpt(self, data):
                     raise NotImplementedError
                 def __convolutionInTime(self, data):
                     code = self.code[self.__profIndex]
                     for i in range(self.__nChannels):
                         self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
                     return self.datadecTime
-                #@profile
-                def oldCorrelate(self, i, data, code_block):
-                    profilesList = xrange(self.__nProfiles)
-                    for j in profilesList:
-                            self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
-                #@profile
                 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))
                     profilesList = xrange(self.__nProfiles)
-                    # def toVectorize(a,b):
-                    #     return numpy.correlate(a,b, mode='full')
-                    # vectorized = numpy.vectorize(toVectorize, signature='(n),(m)->(k)')
                     for i in range(self.__nChannels):
-                    #     self.datadecTime[i,:,:] = numpy.array([numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] for j in profilesList ])
-                        # def func(i, j):
-                        #     self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
-                        # map(lambda j: func(i, j), range(self.__nProfiles))
-                        #print data[i,:,:].shape
-                        # self.datadecTime[i,:,:] = vectorized(data[i,:,:], code_block[:,:])[:,self.nBaud-1:]
                         for j in profilesList:
                             self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
-                        # print data[i,:,:]
-                        # print cSchain.correlateByBlock(data[i,:,:], code_block, 2)
-                        # self.datadecTime[i,:,:] = cSchain.correlateByBlock(data[i,:,:], code_block, 2)
-                        # print self.datadecTime[i,:,:]
-                        #print self.datadecTime[i,:,:].shape
                     return self.datadecTime
                 def __convolutionByBlockInFreq(self, data):
                     raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=2)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=2)
                     return data
                 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
                     if dataOut.flagDecodeData:
                         print "This data is already decoded, recoding again ..."
                     if not self.isConfig:
                         if code is None:
                             if dataOut.code is None:
                                 raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
                             code = dataOut.code
                         else:
                             code = numpy.array(code).reshape(nCode,nBaud)
                         self.setup(code, osamp, dataOut)
                         self.isConfig = True
                         if mode == 3:
                             sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
                         if times != None:
                             sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
                     if self.code is None:
                         print "Fail decoding: Code is not defined."
                         return
                     self.__nProfiles = dataOut.nProfiles
                     datadec = None
                     if mode == 3:
                         mode = 0
                     if dataOut.flagDataAsBlock:
                         """
                         Decoding when data have been read as block,
                         """
                         if mode == 0:
                             datadec = self.__convolutionByBlockInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionByBlockInFreq(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)
                     if datadec is None:
                         raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
                     dataOut.code = self.code
                     dataOut.nCode = self.nCode
                     dataOut.nBaud = self.nBaud
                     dataOut.data = datadec
                     dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
                     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, **kwargs):
                     Operation.__init__(self, **kwargs)
                     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.shape[1]#.nHeights
                     self.start_index = 0
                     self.times = 1
                 def concat(self, data):
                     self.buffer[:,self.start_index:self.nHeights*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, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def incProfileIndex(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, nProfiles=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))
                     """
                     if rangeList is not None:
                         if type(rangeList[0]) not in (tuple, list):
                             rangeList = [rangeList]
                     dataOut.flagNoData = True
                     if dataOut.flagDataAsBlock:
                         """
                         data dimension  = [nChannels, nProfiles, nHeis]
                         """
                         if profileList != None:
                             dataOut.data = dataOut.data[:,profileList,:]
                         if profileRangeList != None:
                             minIndex = profileRangeList[0]
                             maxIndex = profileRangeList[1]
                             profileList = range(minIndex, maxIndex+1)
                             dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
                         if rangeList != None:
                             profileList = []
                             for thisRange in rangeList:
                                 minIndex = thisRange[0]
                                 maxIndex = thisRange[1]
                                 profileList.extend(range(minIndex, maxIndex+1))
                             dataOut.data = dataOut.data[:,profileList,:]
                         dataOut.nProfiles = len(profileList)
                         dataOut.profileIndex = dataOut.nProfiles - 1
                         dataOut.flagNoData = False
                         return True
                     """
                     data dimension  = [nChannels, nHeis]
                     """
                     if profileList != None:
                         if self.isThisProfileInList(dataOut.profileIndex, profileList):
                             self.nProfiles = len(profileList)
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return True
                     if profileRangeList != None:
                         minIndex = profileRangeList[0]
                         maxIndex = profileRangeList[1]
                         if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                             self.nProfiles = maxIndex - minIndex + 1
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return True
                     if rangeList != None:
                         nProfiles = 0
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             nProfiles += maxIndex - minIndex + 1
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                                 self.nProfiles = nProfiles
                                 dataOut.nProfiles = self.nProfiles
                                 dataOut.profileIndex = self.profileIndex
                                 dataOut.flagNoData = False
                                 self.incProfileIndex()
                                 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.incProfileIndex()
                         return True
                     raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
                     return False
             class Reshaper(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__buffer = None
                     self.__nitems = 0
                 def __appendProfile(self, dataOut, nTxs):
                     if self.__buffer is None:
                         shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
                         self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
                     ini = dataOut.nHeights * self.__nitems
                     end = ini + dataOut.nHeights
                     self.__buffer[:, ini:end] = dataOut.data
                     self.__nitems += 1
                     return int(self.__nitems*nTxs)
                 def __getBuffer(self):
                     if self.__nitems == int(1./self.__nTxs):
                         self.__nitems = 0
                         return self.__buffer.copy()
                     return None
                 def __checkInputs(self, dataOut, shape, nTxs):
                     if shape is None and nTxs is None:
                         raise ValueError, "Reshaper: shape of factor should be defined"
                     if nTxs:
                         if nTxs < 0:
                             raise ValueError, "nTxs should be greater than 0"
                         if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
                             raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
                         shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
                         return shape, nTxs
                     if len(shape) != 2 and len(shape) !=  3:
                         raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
                     if len(shape) == 2:
                         shape_tuple = [dataOut.nChannels]
                         shape_tuple.extend(shape)
                     else:
                         shape_tuple = list(shape)
                     nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
                     return shape_tuple, nTxs
                 def run(self, dataOut, shape=None, nTxs=None):
                     shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
                     else:
                         if self.__nTxs < 1:
                             self.__appendProfile(dataOut, self.__nTxs)
                             new_data = self.__getBuffer()
                             if new_data is not None:
                                 dataOut.data = new_data
                                 dataOut.flagNoData = False
                                 profileIndex = dataOut.profileIndex*nTxs
                         else:
                             raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= self.__nTxs
             class SplitProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         if shape[2] % n != 0:
                             raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])
                         new_shape = shape[0], shape[1]*n, shape[2]/n
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles/n) - 1
                     else:
                         raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)"
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= n
             class CombineProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__remData = None
                     self.__profileIndex = 0
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         new_shape = shape[0], shape[1]/n, shape[2]*n
                         if shape[1] % n != 0:
                             raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*n) - 1
                     else:
                         #nchannels, nsamples
                         if self.__remData is None:
                             newData = dataOut.data
                         else:
                             newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
                         self.__profileIndex += 1
                         if self.__profileIndex < n:
                             self.__remData = newData
                             #continue
                             return
                         self.__profileIndex = 0
                         self.__remData = None
                         dataOut.data = newData
                         dataOut.flagNoData = False
                         profileIndex = dataOut.profileIndex/n
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles/n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds *= n
             # import collections
             # from scipy.stats import mode
             #
             # class Synchronize(Operation):
             #
             #     isConfig = False
             #     __profIndex = 0
             #
             #     def __init__(self, **kwargs):
             #
             #         Operation.__init__(self, **kwargs)
             # #         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

schainpy/model/utils/jroutils_publish.py 0 -1

             '''
             @author: Juan C. Espinoza
             '''
             import time
             import json
             import numpy
             import paho.mqtt.client as mqtt
             import zmq
-            from profilehooks import profile
             import datetime
             from zmq.utils.monitor import recv_monitor_message
             from functools import wraps
             from threading import Thread
             from multiprocessing import Process
             from schainpy.model.proc.jroproc_base import Operation, ProcessingUnit
             from schainpy.model.data.jrodata import JROData
             MAXNUMX = 100
             MAXNUMY = 100
             class PrettyFloat(float):
                 def __repr__(self):
                     return '%.2f' % self
             def roundFloats(obj):
                 if isinstance(obj, list):
                     return map(roundFloats, obj)
                 elif isinstance(obj, float):
                     return round(obj, 2)
             def decimate(z, MAXNUMY):
                 # dx = int(len(self.x)/self.__MAXNUMX) + 1
                 dy = int(len(z[0])/MAXNUMY) + 1
                 return z[::, ::dy]
             class throttle(object):
                 """Decorator that prevents a function from being called more than once every
                 time period.
                 To create a function that cannot be called more than once a minute, but
                 will sleep until it can be called:
                 @throttle(minutes=1)
                 def foo():
                   pass
                 for i in range(10):
                   foo()
                   print "This function has run %s times." % i
                 """
                 def __init__(self, seconds=0, minutes=0, hours=0):
                     self.throttle_period = datetime.timedelta(
                         seconds=seconds, minutes=minutes, hours=hours
                     )
                     self.time_of_last_call = datetime.datetime.min
                 def __call__(self, fn):
                     @wraps(fn)
                     def wrapper(*args, **kwargs):
                         now = datetime.datetime.now()
                         time_since_last_call = now - self.time_of_last_call
                         time_left = self.throttle_period - time_since_last_call
                         if time_left > datetime.timedelta(seconds=0):
                             return
                         self.time_of_last_call = datetime.datetime.now()
                         return fn(*args, **kwargs)
                     return wrapper
             class PublishData(Operation):
                 """Clase publish."""
                 def __init__(self, **kwargs):
                     """Inicio."""
                     Operation.__init__(self, **kwargs)
                     self.isConfig = False
                     self.client = None
                     self.zeromq = None
                     self.mqtt = None
                 def on_disconnect(self, client, userdata, rc):
                     if rc != 0:
                         print("Unexpected disconnection.")
                     self.connect()
                 def connect(self):
                     print 'trying to connect'
                     try:
                         self.client.connect(
                             host=self.host,
                             port=self.port,
                             keepalive=60*10,
                             bind_address='')
                         self.client.loop_start()
                         # self.client.publish(
                         #     self.topic + 'SETUP',
                         #     json.dumps(setup),
                         #     retain=True
                         #     )
                     except:
                         print "MQTT Conection error."
                         self.client = False
                 def setup(self, port=1883, username=None, password=None, clientId="user", zeromq=1, verbose=True, **kwargs):
                     self.counter = 0
                     self.topic = kwargs.get('topic', 'schain')
                     self.delay = kwargs.get('delay', 0)
                     self.plottype = kwargs.get('plottype', 'spectra')
                     self.host = kwargs.get('host', "10.10.10.82")
                     self.port = kwargs.get('port', 3000)
                     self.clientId = clientId
                     self.cnt = 0
                     self.zeromq = zeromq
                     self.mqtt = kwargs.get('plottype', 0)
                     self.client = None
                     self.verbose = verbose
                     self.dataOut.firstdata = True
                     setup = []
                     if mqtt is 1:
                         self.client = mqtt.Client(
                             client_id=self.clientId + self.topic + 'SCHAIN',
                             clean_session=True)
                         self.client.on_disconnect = self.on_disconnect
                         self.connect()
                         for plot in self.plottype:
                             setup.append({
                                 'plot': plot,
                                 'topic': self.topic + plot,
                                 'title': getattr(self, plot + '_' + 'title', False),
                                 'xlabel': getattr(self, plot + '_' + 'xlabel', False),
                                 'ylabel': getattr(self, plot + '_' + 'ylabel', False),
                                 'xrange': getattr(self, plot + '_' + 'xrange', False),
                                 'yrange': getattr(self, plot + '_' + 'yrange', False),
                                 'zrange': getattr(self, plot + '_' + 'zrange', False),
                             })
                     if zeromq is 1:
                         context = zmq.Context()
                         self.zmq_socket = context.socket(zmq.PUSH)
                         server = kwargs.get('server', 'zmq.pipe')
                         if 'tcp://' in server:
                             address = server
                         else:
                             address = 'ipc:///tmp/%s' % server
                         self.zmq_socket.connect(address)
                         time.sleep(1)
                 def publish_data(self):
                     self.dataOut.finished = False
                     if self.mqtt is 1:
                         yData = self.dataOut.heightList[:2].tolist()
                         if self.plottype == 'spectra':
                             data = getattr(self.dataOut, 'data_spc')
                             z = data/self.dataOut.normFactor
                             zdB = 10*numpy.log10(z)
                             xlen, ylen = zdB[0].shape
                             dx = int(xlen/MAXNUMX) + 1
                             dy = int(ylen/MAXNUMY) + 1
                             Z = [0 for i in self.dataOut.channelList]
                             for i in self.dataOut.channelList:
                                 Z[i] = zdB[i][::dx, ::dy].tolist()
                             payload = {
                                 'timestamp': self.dataOut.utctime,
                                 'data': roundFloats(Z),
                                 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList],
                                 'interval': self.dataOut.getTimeInterval(),
                                 'type': self.plottype,
                                 'yData': yData
                             }
                             # print payload
                         elif self.plottype in ('rti', 'power'):
                             data = getattr(self.dataOut, 'data_spc')
                             z = data/self.dataOut.normFactor
                             avg = numpy.average(z, axis=1)
                             avgdB = 10*numpy.log10(avg)
                             xlen, ylen = z[0].shape
                             dy = numpy.floor(ylen/self.__MAXNUMY) + 1
                             AVG = [0 for i in self.dataOut.channelList]
                             for i in self.dataOut.channelList:
                                 AVG[i] = avgdB[i][::dy].tolist()
                             payload = {
                                 'timestamp': self.dataOut.utctime,
                                 'data': roundFloats(AVG),
                                 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList],
                                 'interval': self.dataOut.getTimeInterval(),
                                 'type': self.plottype,
                                 'yData': yData
                             }
                         elif self.plottype == 'noise':
                             noise = self.dataOut.getNoise()/self.dataOut.normFactor
                             noisedB = 10*numpy.log10(noise)
                             payload = {
                                 'timestamp': self.dataOut.utctime,
                                 'data': roundFloats(noisedB.reshape(-1, 1).tolist()),
                                 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList],
                                 'interval': self.dataOut.getTimeInterval(),
                                 'type': self.plottype,
                                 'yData': yData
                             }
                         elif self.plottype == 'snr':
                             data = getattr(self.dataOut, 'data_SNR')
                             avgdB = 10*numpy.log10(data)
                             ylen = data[0].size
                             dy = numpy.floor(ylen/self.__MAXNUMY) + 1
                             AVG = [0 for i in self.dataOut.channelList]
                             for i in self.dataOut.channelList:
                                 AVG[i] = avgdB[i][::dy].tolist()
                             payload = {
                                 'timestamp': self.dataOut.utctime,
                                 'data': roundFloats(AVG),
                                 'channels': ['Ch %s' % ch for ch in self.dataOut.channelList],
                                 'type': self.plottype,
                                 'yData': yData
                             }
                         else:
                             print "Tipo de grafico invalido"
                             payload = {
                                 'data': 'None',
                                 'timestamp': 'None',
                                 'type': None
                             }
                             # print 'Publishing data to {}'.format(self.host)
                         self.client.publish(self.topic + self.plottype, json.dumps(payload), qos=0)
                     if self.zeromq is 1:
                         if self.verbose:
                             print '[Sending] {} - {}'.format(self.dataOut.type, self.dataOut.datatime)
                         self.zmq_socket.send_pyobj(self.dataOut)
                         self.dataOut.firstdata = False
                 def run(self, dataOut, **kwargs):
                     self.dataOut = dataOut
                     if not self.isConfig:
                         self.setup(**kwargs)
                         self.isConfig = True
                     self.publish_data()
                     time.sleep(self.delay)
                 def close(self):
                     if self.zeromq is 1:
                         self.dataOut.finished = True
                         self.zmq_socket.send_pyobj(self.dataOut)
                         self.zmq_socket.close()
                     if self.client:
                         self.client.loop_stop()
                         self.client.disconnect()
             class ReceiverData(ProcessingUnit):
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
                     self.isConfig = False
                     server = kwargs.get('server', 'zmq.pipe')
                     if 'tcp://' in server:
                         address = server
                     else:
                         address = 'ipc:///tmp/%s' % server
                     self.address = address
                     self.dataOut = JROData()
                 def setup(self):
                     self.context = zmq.Context()
                     self.receiver = self.context.socket(zmq.PULL)
                     self.receiver.bind(self.address)
                     time.sleep(0.5)
                     print '[Starting] ReceiverData from {}'.format(self.address)
                 def run(self):
                     if not self.isConfig:
                         self.setup()
                         self.isConfig = True
                     self.dataOut = self.receiver.recv_pyobj()
                     print '[Receiving] {} - {}'.format(self.dataOut.type,
                                                            self.dataOut.datatime.ctime())
             class PlotterReceiver(ProcessingUnit, Process):
                 throttle_value = 5
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
                     Process.__init__(self)
                     self.mp = False
                     self.isConfig = False
                     self.isWebConfig = False
                     self.plottypes = []
                     self.connections = 0
                     server = kwargs.get('server', 'zmq.pipe')
                     plot_server = kwargs.get('plot_server', 'zmq.web')
                     if 'tcp://' in server:
                         address = server
                     else:
                         address = 'ipc:///tmp/%s' % server
                     if 'tcp://' in plot_server:
                         plot_address = plot_server
                     else:
                         plot_address = 'ipc:///tmp/%s' % plot_server
                     self.address = address
                     self.plot_address = plot_address
                     self.plottypes = [s.strip() for s in kwargs.get('plottypes', 'rti').split(',')]
                     self.realtime = kwargs.get('realtime', False)
                     self.throttle_value = kwargs.get('throttle', 5)
                     self.sendData = self.initThrottle(self.throttle_value)
                     self.setup()
                 def setup(self):
                     self.data = {}
                     self.data['times'] = []
                     for plottype in self.plottypes:
                         self.data[plottype] = {}
                     self.data['noise'] = {}
                     self.data['throttle'] = self.throttle_value
                     self.data['ENDED'] = False
                     self.isConfig = True
                     self.data_web = {}
                 def event_monitor(self, monitor):
                     events = {}
                     for name in dir(zmq):
                         if name.startswith('EVENT_'):
                             value = getattr(zmq, name)
                             events[value] = name
                     while monitor.poll():
                         evt = recv_monitor_message(monitor)
                         if evt['event'] == 32:
                             self.connections += 1
                         if evt['event'] == 512:
                             pass
                         if self.connections == 0 and self.started is True:
                             self.ended = True
                         evt.update({'description': events[evt['event']]})
                         if evt['event'] == zmq.EVENT_MONITOR_STOPPED:
                             break
                     monitor.close()
                     print("event monitor thread done!")
                 def initThrottle(self, throttle_value):
                     @throttle(seconds=throttle_value)
                     def sendDataThrottled(fn_sender, data):
                         fn_sender(data)
                     return sendDataThrottled
                 def send(self, data):
                     # print '[sending] data=%s size=%s' % (data.keys(), len(data['times']))
                     self.sender.send_pyobj(data)
                 def update(self):
                     t = self.dataOut.utctime
                     if t in self.data['times']:
                         return
                     self.data['times'].append(t)
                     self.data['dataOut'] = self.dataOut
                     for plottype in self.plottypes:
                         if plottype == 'spc':
                             z = self.dataOut.data_spc/self.dataOut.normFactor
                             self.data[plottype] = 10*numpy.log10(z)
                             self.data['noise'][t] = 10*numpy.log10(self.dataOut.getNoise()/self.dataOut.normFactor)
                         if plottype == 'cspc':
                             jcoherence = self.dataOut.data_cspc/numpy.sqrt(self.dataOut.data_spc*self.dataOut.data_spc)
                             self.data['cspc_coh'] = numpy.abs(jcoherence)
                             self.data['cspc_phase'] = numpy.arctan2(jcoherence.imag, jcoherence.real)*180/numpy.pi
                         if plottype == 'rti':
                             self.data[plottype][t] = self.dataOut.getPower()
                         if plottype == 'snr':
                             self.data[plottype][t] = 10*numpy.log10(self.dataOut.data_SNR)
                         if plottype == 'dop':
                             self.data[plottype][t] = 10*numpy.log10(self.dataOut.data_DOP)
                         if plottype == 'mean':
                             self.data[plottype][t] = self.dataOut.data_MEAN
                         if plottype == 'std':
                             self.data[plottype][t] = self.dataOut.data_STD
                         if plottype == 'coh':
                             self.data[plottype][t] = self.dataOut.getCoherence()
                         if plottype == 'phase':
                             self.data[plottype][t] = self.dataOut.getCoherence(phase=True)
                         if plottype == 'output':
                             self.data[plottype][t] = self.dataOut.data_output
                         if plottype == 'param':
                             self.data[plottype][t] = self.dataOut.data_param
                         if self.realtime:
                             self.data_web['timestamp'] = t
                             if plottype == 'spc':
                                 self.data_web[plottype] = roundFloats(decimate(self.data[plottype]).tolist())
                             elif plottype == 'cspc':
                                 self.data_web['cspc_coh'] = roundFloats(decimate(self.data['cspc_coh']).tolist())
                                 self.data_web['cspc_phase'] = roundFloats(decimate(self.data['cspc_phase']).tolist())
                             elif plottype == 'noise':
                                 self.data_web['noise'] = roundFloats(self.data['noise'][t].tolist())
                             else:
                                 self.data_web[plottype] = roundFloats(decimate(self.data[plottype][t]).tolist())
                             self.data_web['interval'] = self.dataOut.getTimeInterval()
                             self.data_web['type'] = plottype
                 def run(self):
                     print '[Starting] {} from {}'.format(self.name, self.address)
                     self.context = zmq.Context()
                     self.receiver = self.context.socket(zmq.PULL)
                     self.receiver.bind(self.address)
                     monitor = self.receiver.get_monitor_socket()
                     self.sender = self.context.socket(zmq.PUB)
                     if self.realtime:
                         self.sender_web = self.context.socket(zmq.PUB)
                         self.sender_web.connect(self.plot_address)
                         time.sleep(1)
                     if 'server' in self.kwargs:
                         self.sender.bind("ipc:///tmp/{}.plots".format(self.kwargs['server']))
                     else:
                         self.sender.bind("ipc:///tmp/zmq.plots")
                     time.sleep(3)
                     t = Thread(target=self.event_monitor, args=(monitor,))
                     t.start()
                     while True:
                         self.dataOut = self.receiver.recv_pyobj()
                         # print '[Receiving] {} - {}'.format(self.dataOut.type,
                         #                                    self.dataOut.datatime.ctime())
                         self.update()
                         if self.dataOut.firstdata is True:
                             self.data['STARTED'] = True
                         if self.dataOut.finished is True:
                             self.send(self.data)
                             self.connections -= 1
                             if self.connections == 0 and self.started:
                                 self.ended = True
                                 self.data['ENDED'] = True
                                 self.send(self.data)
                                 self.setup()
                                 self.started = False
                         else:
                             if self.realtime:
                                 self.send(self.data)
                                 self.sender_web.send_string(json.dumps(self.data_web))
                             else:
                                 self.sendData(self.send, self.data)
                             self.started = True
                         self.data['STARTED'] = False
                     return
                 def sendToWeb(self):
                     if not self.isWebConfig:
                         context = zmq.Context()
                         sender_web_config = context.socket(zmq.PUB)
                         if 'tcp://' in self.plot_address:
                             dum, address, port = self.plot_address.split(':')
                             conf_address = '{}:{}:{}'.format(dum, address, int(port)+1)
                         else:
                             conf_address = self.plot_address + '.config'
                         sender_web_config.bind(conf_address)
                         time.sleep(1)
                         for kwargs in self.operationKwargs.values():
                             if 'plot' in kwargs:
                                 print '[Sending] Config data to web for {}'.format(kwargs['code'].upper())
                                 sender_web_config.send_string(json.dumps(kwargs))
                         self.isWebConfig = True

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