import numpy from jroproc_base import ProcessingUnit, Operation from model.data.jrodata import Voltage class VoltageProc(ProcessingUnit): def __init__(self): ProcessingUnit.__init__(self) # self.objectDict = {} self.dataOut = Voltage() self.flip = 1 def run(self): if self.dataIn.type == 'AMISR': self.__updateObjFromAmisrInput() if self.dataIn.type == 'Voltage': self.dataOut.copy(self.dataIn) # self.dataOut.copy(self.dataIn) def __updateObjFromAmisrInput(self): self.dataOut.timeZone = self.dataIn.timeZone self.dataOut.dstFlag = self.dataIn.dstFlag self.dataOut.errorCount = self.dataIn.errorCount self.dataOut.useLocalTime = self.dataIn.useLocalTime self.dataOut.flagNoData = self.dataIn.flagNoData self.dataOut.data = self.dataIn.data self.dataOut.utctime = self.dataIn.utctime self.dataOut.channelList = self.dataIn.channelList self.dataOut.timeInterval = self.dataIn.timeInterval self.dataOut.heightList = self.dataIn.heightList self.dataOut.nProfiles = self.dataIn.nProfiles self.dataOut.nCohInt = self.dataIn.nCohInt self.dataOut.ippSeconds = self.dataIn.ippSeconds self.dataOut.frequency = self.dataIn.frequency self.dataOut.azimuth = self.dataIn.azimuth self.dataOut.zenith = self.dataIn.zenith self.dataOut.beam.codeList = self.dataIn.beam.codeList self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList self.dataOut.beam.zenithList = self.dataIn.beam.zenithList # # pass# # # def init(self): # # # if self.dataIn.type == 'AMISR': # self.__updateObjFromAmisrInput() # # if self.dataIn.type == 'Voltage': # self.dataOut.copy(self.dataIn) # # No necesita copiar en cada init() los atributos de dataIn # # la copia deberia hacerse por cada nuevo bloque de datos def selectChannels(self, channelList): channelIndexList = [] for channel in channelList: 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=None, maxHei=None): """ Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango minHei <= height <= maxHei Input: minHei : valor minimo de altura a considerar maxHei : valor maximo de altura a considerar Affected: Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex Return: 1 si el metodo se ejecuto con exito caso contrario devuelve 0 """ if minHei == None: minHei = self.dataOut.heightList[0] if maxHei == None: maxHei = self.dataOut.heightList[-1] if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei) if (maxHei > self.dataOut.heightList[-1]): maxHei = self.dataOut.heightList[-1] # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei) minIndex = 0 maxIndex = 0 heights = self.dataOut.heightList inda = numpy.where(heights >= minHei) indb = numpy.where(heights <= maxHei) try: minIndex = inda[0][0] except: minIndex = 0 try: maxIndex = indb[0][-1] except: maxIndex = len(heights) self.selectHeightsByIndex(minIndex, maxIndex) return 1 def selectHeightsByIndex(self, minIndex, maxIndex): """ Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango minIndex <= index <= maxIndex Input: minIndex : valor de indice minimo de altura a considerar maxIndex : valor de indice maximo de altura a considerar Affected: self.dataOut.data self.dataOut.heightList Return: 1 si el metodo se ejecuto con exito caso contrario devuelve 0 """ if (minIndex < 0) or (minIndex > maxIndex): raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) if (maxIndex >= self.dataOut.nHeights): maxIndex = self.dataOut.nHeights-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, axis=1): 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.data.shape[axis] % window if axis == 1: buffer = self.dataOut.data[:,0:self.dataOut.data.shape[axis]-r] buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[axis]/window,window) buffer = numpy.sum(buffer,axis+1) elif axis == 2: buffer = self.dataOut.data[:, :, 0:self.dataOut.data.shape[axis]-r] buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1],self.dataOut.data.shape[axis]/window,window) buffer = numpy.sum(buffer,axis+1) else: raise ValueError, "axis value should be 1 or 2, the input value %d is not valid" % (axis) self.dataOut.data = buffer.copy() self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*(self.dataOut.nHeights-r)/window,newdelta) self.dataOut.windowOfFilter = window return 1 def deFlip(self): self.dataOut.data *= self.flip self.flip *= -1. def setRadarFrequency(self, frequency=None): if frequency != None: self.dataOut.frequency = frequency return 1 class CohInt(Operation): isConfig = False __profIndex = 0 __withOverapping = False __byTime = False __initime = None __lastdatatime = None __integrationtime = None __buffer = None __dataReady = False n = None def __init__(self): Operation.__init__(self) # self.isConfig = False def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False): """ Set the parameters of the integration class. Inputs: n : Number of coherent integrations timeInterval : Time of integration. If the parameter "n" is selected this one does not work overlapping : """ self.__initime = None self.__lastdatatime = 0 self.__buffer = None self.__dataReady = False self.byblock = byblock if n == None and timeInterval == None: raise ValueError, "n or timeInterval should be specified ..." if n != None: self.n = n self.__byTime = False else: self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line self.n = 9999 self.__byTime = True if overlapping: self.__withOverapping = True self.__buffer = None else: self.__withOverapping = False self.__buffer = 0 self.__profIndex = 0 def putData(self, data): """ Add a profile to the __buffer and increase in one the __profileIndex """ if not self.__withOverapping: self.__buffer += data.copy() self.__profIndex += 1 return #Overlapping data nChannels, nHeis = data.shape data = numpy.reshape(data, (1, nChannels, nHeis)) #If the buffer is empty then it takes the data value if self.__buffer == None: self.__buffer = data self.__profIndex += 1 return #If the buffer length is lower than n then stakcing the data value if self.__profIndex < self.n: self.__buffer = numpy.vstack((self.__buffer, data)) self.__profIndex += 1 return #If the buffer length is equal to n then replacing the last buffer value with the data value self.__buffer = numpy.roll(self.__buffer, -1, axis=0) self.__buffer[self.n-1] = data self.__profIndex = self.n return def pushData(self): """ Return the sum of the last profiles and the profiles used in the sum. Affected: self.__profileIndex """ if not self.__withOverapping: data = self.__buffer n = self.__profIndex self.__buffer = 0 self.__profIndex = 0 return data, n #Integration with Overlapping data = numpy.sum(self.__buffer, axis=0) n = self.__profIndex return data, n def byProfiles(self, data): self.__dataReady = False avgdata = None # n = None self.putData(data) if self.__profIndex == self.n: avgdata, n = self.pushData() self.__dataReady = True return avgdata def byTime(self, data, datatime): self.__dataReady = False avgdata = None n = None self.putData(data) if (datatime - self.__initime) >= self.__integrationtime: avgdata, n = self.pushData() self.n = n self.__dataReady = True return avgdata def integrate(self, data, datatime=None): if self.__initime == None: self.__initime = datatime if self.__byTime: avgdata = self.byTime(data, datatime) else: avgdata = self.byProfiles(data) self.__lastdatatime = datatime if avgdata == None: return None, None avgdatatime = self.__initime deltatime = datatime -self.__lastdatatime if not self.__withOverapping: self.__initime = datatime else: self.__initime += deltatime return avgdata, avgdatatime def integrateByBlock(self, dataOut): times = int(dataOut.data.shape[1]/self.n) avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) id_min = 0 id_max = self.n for i in range(times): junk = dataOut.data[:,id_min:id_max,:] avgdata[:,i,:] = junk.sum(axis=1) id_min += self.n id_max += self.n timeInterval = dataOut.ippSeconds*self.n avgdatatime = (times - 1) * timeInterval + dataOut.utctime self.__dataReady = True return avgdata, avgdatatime def run(self, dataOut, **kwargs): if not self.isConfig: self.setup(**kwargs) self.isConfig = True if self.byblock: avgdata, avgdatatime = self.integrateByBlock(dataOut) else: avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) # dataOut.timeInterval *= n dataOut.flagNoData = True if self.__dataReady: dataOut.data = avgdata dataOut.nCohInt *= self.n dataOut.utctime = avgdatatime dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt dataOut.flagNoData = False class Decoder(Operation): isConfig = False __profIndex = 0 code = None nCode = None nBaud = None def __init__(self): Operation.__init__(self) self.times = None self.osamp = None self.__setValues = False # self.isConfig = False def setup(self, code, shape, times, osamp): self.__profIndex = 0 self.code = code self.nCode = len(code) self.nBaud = len(code[0]) if times != None: self.times = times 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 if len(shape) == 2: self.__nChannels, self.__nHeis = shape __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) __codeBuffer[:,0:self.nBaud] = self.code self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) self.ndatadec = self.__nHeis - self.nBaud + 1 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) else: self.__nChannels, self.__nProfiles, self.__nHeis = shape self.ndatadec = self.__nHeis - self.nBaud + 1 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) def convolutionInFreq(self, data): fft_code = self.fft_code[self.__profIndex].reshape(1,-1) fft_data = numpy.fft.fft(data, axis=1) conv = fft_data*fft_code data = numpy.fft.ifft(conv,axis=1) datadec = data[:,:-self.nBaud+1] return datadec def convolutionInFreqOpt(self, data): fft_code = self.fft_code[self.__profIndex].reshape(1,-1) data = cfunctions.decoder(fft_code, data) datadec = data[:,:-self.nBaud+1] return datadec def convolutionInTime(self, data): code = self.code[self.__profIndex] for i in range(self.__nChannels): self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='valid') return self.datadecTime def convolutionByBlockInTime(self, data): junk = numpy.lib.stride_tricks.as_strided(self.code, (self.times, self.code.size), (0, self.code.itemsize)) junk = junk.flatten() code_block = numpy.reshape(junk, (self.nCode*self.times,self.nBaud)) for i in range(self.__nChannels): for j in range(self.__nProfiles): self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='valid') return self.datadecTime def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, times=None, osamp=None): if code == None: code = dataOut.code else: code = numpy.array(code).reshape(nCode,nBaud) if not self.isConfig: self.setup(code, dataOut.data.shape, times, osamp) dataOut.code = code dataOut.nCode = nCode dataOut.nBaud = nBaud dataOut.radarControllerHeaderObj.code = code dataOut.radarControllerHeaderObj.nCode = nCode dataOut.radarControllerHeaderObj.nBaud = nBaud self.isConfig = True 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 mode == 3: datadec = self.convolutionByBlockInTime(dataOut.data) if not(self.__setValues): dataOut.code = self.code dataOut.nCode = self.nCode dataOut.nBaud = self.nBaud dataOut.radarControllerHeaderObj.code = self.code dataOut.radarControllerHeaderObj.nCode = self.nCode dataOut.radarControllerHeaderObj.nBaud = self.nBaud self.__setValues = True dataOut.data = datadec dataOut.heightList = dataOut.heightList[0:self.ndatadec] dataOut.flagDecodeData = True #asumo q la data no esta decodificada if self.__profIndex == self.nCode-1: self.__profIndex = 0 return 1 self.__profIndex += 1 return 1 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip class ProfileConcat(Operation): isConfig = False buffer = None def __init__(self): Operation.__init__(self) self.profileIndex = 0 def reset(self): self.buffer = numpy.zeros_like(self.buffer) self.start_index = 0 self.times = 1 def setup(self, data, m, n=1): self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) self.profiles = data.shape[1] self.start_index = 0 self.times = 1 def concat(self, data): self.buffer[:,self.start_index:self.profiles*self.times] = data.copy() self.start_index = self.start_index + self.profiles def run(self, dataOut, m): dataOut.flagNoData = True if not self.isConfig: self.setup(dataOut.data, m, 1) self.isConfig = True 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 * 5 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) class ProfileSelector(Operation): profileIndex = None # Tamanho total de los perfiles nProfiles = None def __init__(self): Operation.__init__(self) self.profileIndex = 0 def incIndex(self): self.profileIndex += 1 if self.profileIndex >= self.nProfiles: self.profileIndex = 0 def 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, beam=None, byblock=False): dataOut.flagNoData = True self.nProfiles = dataOut.nProfiles if byblock: if profileList != None: dataOut.data = dataOut.data[:,profileList,:] pass else: pmin = profileRangeList[0] pmax = profileRangeList[1] dataOut.data = dataOut.data[:,pmin:pmax+1,:] dataOut.flagNoData = False self.profileIndex = 0 return 1 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 elif beam != None: #beam is only for AMISR data if self.isProfileInList(dataOut.beamRangeDict[beam]): dataOut.flagNoData = False self.incIndex() return 1 else: raise ValueError, "ProfileSelector needs profileList or profileRangeList" return 0 class Reshaper(Operation): def __init__(self): Operation.__init__(self) self.updateNewHeights = False def run(self, dataOut, shape): shape_tuple = tuple(shape) dataOut.data = numpy.reshape(dataOut.data, shape_tuple) dataOut.flagNoData = False if not(self.updateNewHeights): old_nheights = dataOut.nHeights new_nheights = dataOut.data.shape[2] factor = new_nheights / old_nheights deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * factor dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)