jroproc_voltage.py
1282 lines
| 41.2 KiB
| text/x-python
|
PythonLexer
ebocanegra
|
r965 | import sys | |
| import numpy | |||
| from scipy import interpolate | |||
| from jroproc_base import ProcessingUnit, Operation | |||
| from schainpy.model.data.jrodata import Voltage | |||
| class VoltageProc(ProcessingUnit): | |||
| def __init__(self): | |||
| ProcessingUnit.__init__(self) | |||
| # self.objectDict = {} | |||
| self.dataOut = Voltage() | |||
| self.flip = 1 | |||
| def run(self): | |||
| if self.dataIn.type == 'AMISR': | |||
| self.__updateObjFromAmisrInput() | |||
| if self.dataIn.type == 'Voltage': | |||
| self.dataOut.copy(self.dataIn) | |||
| # self.dataOut.copy(self.dataIn) | |||
| def __updateObjFromAmisrInput(self): | |||
| self.dataOut.timeZone = self.dataIn.timeZone | |||
| self.dataOut.dstFlag = self.dataIn.dstFlag | |||
| self.dataOut.errorCount = self.dataIn.errorCount | |||
| self.dataOut.useLocalTime = self.dataIn.useLocalTime | |||
| self.dataOut.flagNoData = self.dataIn.flagNoData | |||
| self.dataOut.data = self.dataIn.data | |||
| self.dataOut.utctime = self.dataIn.utctime | |||
| self.dataOut.channelList = self.dataIn.channelList | |||
| # self.dataOut.timeInterval = self.dataIn.timeInterval | |||
| self.dataOut.heightList = self.dataIn.heightList | |||
| self.dataOut.nProfiles = self.dataIn.nProfiles | |||
| self.dataOut.nCohInt = self.dataIn.nCohInt | |||
| self.dataOut.ippSeconds = self.dataIn.ippSeconds | |||
| self.dataOut.frequency = self.dataIn.frequency | |||
| self.dataOut.azimuth = self.dataIn.azimuth | |||
| self.dataOut.zenith = self.dataIn.zenith | |||
| self.dataOut.beam.codeList = self.dataIn.beam.codeList | |||
| self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | |||
| self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | |||
| # | |||
| # pass# | |||
| # | |||
| # def init(self): | |||
| # | |||
| # | |||
| # if self.dataIn.type == 'AMISR': | |||
| # self.__updateObjFromAmisrInput() | |||
| # | |||
| # if self.dataIn.type == 'Voltage': | |||
| # self.dataOut.copy(self.dataIn) | |||
| # # No necesita copiar en cada init() los atributos de dataIn | |||
| # # la copia deberia hacerse por cada nuevo bloque de datos | |||
| def selectChannels(self, channelList): | |||
| channelIndexList = [] | |||
| for channel in channelList: | |||
| if channel not in self.dataOut.channelList: | |||
| raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList)) | |||
| index = self.dataOut.channelList.index(channel) | |||
| channelIndexList.append(index) | |||
| self.selectChannelsByIndex(channelIndexList) | |||
| def selectChannelsByIndex(self, channelIndexList): | |||
| """ | |||
| Selecciona un bloque de datos en base a canales segun el channelIndexList | |||
| Input: | |||
| channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | |||
| Affected: | |||
| self.dataOut.data | |||
| self.dataOut.channelIndexList | |||
| self.dataOut.nChannels | |||
| self.dataOut.m_ProcessingHeader.totalSpectra | |||
| self.dataOut.systemHeaderObj.numChannels | |||
| self.dataOut.m_ProcessingHeader.blockSize | |||
| Return: | |||
| None | |||
| """ | |||
| for channelIndex in channelIndexList: | |||
| if channelIndex not in self.dataOut.channelIndexList: | |||
| print channelIndexList | |||
| raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex | |||
| if self.dataOut.flagDataAsBlock: | |||
| """ | |||
| Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |||
| """ | |||
| data = self.dataOut.data[channelIndexList,:,:] | |||
| else: | |||
| data = self.dataOut.data[channelIndexList,:] | |||
| self.dataOut.data = data | |||
| self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |||
| # self.dataOut.nChannels = nChannels | |||
| return 1 | |||
| def selectHeights(self, minHei=None, maxHei=None): | |||
| """ | |||
| Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango | |||
| minHei <= height <= maxHei | |||
| Input: | |||
| minHei : valor minimo de altura a considerar | |||
| maxHei : valor maximo de altura a considerar | |||
| Affected: | |||
| Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | |||
| Return: | |||
| 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |||
| """ | |||
| if minHei == None: | |||
| minHei = self.dataOut.heightList[0] | |||
| if maxHei == None: | |||
| maxHei = self.dataOut.heightList[-1] | |||
| if (minHei < self.dataOut.heightList[0]): | |||
| 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: | |||
| 1 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): | |||
| 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 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, **kwargs): | |||
| if not self.isConfig: | |||
| self.setup(**kwargs) | |||
| self.isConfig = True | |||
| if dataOut.flagDataAsBlock: | |||
| """ | |||
| Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] | |||
| """ | |||
| avgdata, avgdatatime = self.integrateByBlock(dataOut) | |||
| 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): | |||
| Operation.__init__(self) | |||
| self.times = None | |||
| self.osamp = None | |||
| # self.__setValues = False | |||
| self.isConfig = False | |||
| def setup(self, code, osamp, dataOut): | |||
| self.__profIndex = 0 | |||
| self.code = code | |||
| self.nCode = len(code) | |||
| self.nBaud = len(code[0]) | |||
| if (osamp != None) and (osamp >1): | |||
| self.osamp = osamp | |||
| self.code = numpy.repeat(code, repeats=self.osamp, axis=1) | |||
| self.nBaud = self.nBaud*self.osamp | |||
| self.__nChannels = dataOut.nChannels | |||
| self.__nProfiles = dataOut.nProfiles | |||
| self.__nHeis = dataOut.nHeights | |||
| if 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 | |||
| def __convolutionByBlockInTime(self, data): | |||
| repetitions = self.__nProfiles / self.nCode | |||
| junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) | |||
| junk = junk.flatten() | |||
| code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) | |||
| for i in range(self.__nChannels): | |||
| for j in range(self.__nProfiles): | |||
| self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |||
| 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 | |||
| 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): | |||
| Operation.__init__(self) | |||
| self.profileIndex = 0 | |||
| def reset(self): | |||
| self.buffer = numpy.zeros_like(self.buffer) | |||
| self.start_index = 0 | |||
| self.times = 1 | |||
| def setup(self, data, m, n=1): | |||
| self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) | |||
| self.nHeights = data.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): | |||
| Operation.__init__(self) | |||
| 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): | |||
| Operation.__init__(self) | |||
| 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): | |||
| Operation.__init__(self) | |||
| 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): | |||
| Operation.__init__(self) | |||
| 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): | |||
| # | |||
| # Operation.__init__(self) | |||
| # # self.isConfig = False | |||
| # self.__powBuffer = None | |||
| # self.__startIndex = 0 | |||
| # self.__pulseFound = False | |||
| # | |||
| # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): | |||
| # | |||
| # #Read data | |||
| # | |||
| # powerdB = dataOut.getPower(channel = channel) | |||
| # noisedB = dataOut.getNoise(channel = channel)[0] | |||
| # | |||
| # self.__powBuffer.extend(powerdB.flatten()) | |||
| # | |||
| # dataArray = numpy.array(self.__powBuffer) | |||
| # | |||
| # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") | |||
| # | |||
| # maxValue = numpy.nanmax(filteredPower) | |||
| # | |||
| # if maxValue < noisedB + 10: | |||
| # #No se encuentra ningun pulso de transmision | |||
| # return None | |||
| # | |||
| # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] | |||
| # | |||
| # if len(maxValuesIndex) < 2: | |||
| # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX | |||
| # return None | |||
| # | |||
| # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples | |||
| # | |||
| # #Seleccionar solo valores con un espaciamiento de nSamples | |||
| # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) | |||
| # | |||
| # if len(pulseIndex) < 2: | |||
| # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |||
| # return None | |||
| # | |||
| # spacing = pulseIndex[1:] - pulseIndex[:-1] | |||
| # | |||
| # #remover senales que se distancien menos de 10 unidades o muestras | |||
| # #(No deberian existir IPP menor a 10 unidades) | |||
| # | |||
| # realIndex = numpy.where(spacing > 10 )[0] | |||
| # | |||
| # if len(realIndex) < 2: | |||
| # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |||
| # return None | |||
| # | |||
| # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) | |||
| # realPulseIndex = pulseIndex[realIndex] | |||
| # | |||
| # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] | |||
| # | |||
| # print "IPP = %d samples" %period | |||
| # | |||
| # self.__newNSamples = dataOut.nHeights #int(period) | |||
| # self.__startIndex = int(realPulseIndex[0]) | |||
| # | |||
| # return 1 | |||
| # | |||
| # | |||
| # def setup(self, nSamples, nChannels, buffer_size = 4): | |||
| # | |||
| # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), | |||
| # maxlen = buffer_size*nSamples) | |||
| # | |||
| # bufferList = [] | |||
| # | |||
| # for i in range(nChannels): | |||
| # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, | |||
| # maxlen = buffer_size*nSamples) | |||
| # | |||
| # bufferList.append(bufferByChannel) | |||
| # | |||
| # self.__nSamples = nSamples | |||
| # self.__nChannels = nChannels | |||
| # self.__bufferList = bufferList | |||
| # | |||
| # def run(self, dataOut, channel = 0): | |||
| # | |||
| # if not self.isConfig: | |||
| # nSamples = dataOut.nHeights | |||
| # nChannels = dataOut.nChannels | |||
| # self.setup(nSamples, nChannels) | |||
| # self.isConfig = True | |||
| # | |||
| # #Append new data to internal buffer | |||
| # for thisChannel in range(self.__nChannels): | |||
| # bufferByChannel = self.__bufferList[thisChannel] | |||
| # bufferByChannel.extend(dataOut.data[thisChannel]) | |||
| # | |||
| # if self.__pulseFound: | |||
| # self.__startIndex -= self.__nSamples | |||
| # | |||
| # #Finding Tx Pulse | |||
| # if not self.__pulseFound: | |||
| # indexFound = self.__findTxPulse(dataOut, channel) | |||
| # | |||
| # if indexFound == None: | |||
| # dataOut.flagNoData = True | |||
| # return | |||
| # | |||
| # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) | |||
| # self.__pulseFound = True | |||
| # self.__startIndex = indexFound | |||
| # | |||
| # #If pulse was found ... | |||
| # for thisChannel in range(self.__nChannels): | |||
| # bufferByChannel = self.__bufferList[thisChannel] | |||
| # #print self.__startIndex | |||
| # x = numpy.array(bufferByChannel) | |||
| # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] | |||
| # | |||
| # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |||
| # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight | |||
| # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 | |||
| # | |||
| # dataOut.data = self.__arrayBuffer | |||
| # | |||
| # self.__startIndex += self.__newNSamples | |||
| # | |||
| # return |