jroproc_voltage.py
753 lines
| 22.8 KiB
| text/x-python
|
PythonLexer
Daniel Valdez
|
r487 | 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): | |||
|
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r491 | if self.dataIn.type == 'AMISR': | |
| self.__updateObjFromAmisrInput() | |||
| if self.dataIn.type == 'Voltage': | |||
| self.dataOut.copy(self.dataIn) | |||
| # self.dataOut.copy(self.dataIn) | |||
|
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|
r487 | ||
|
Daniel Valdez
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r491 | 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 | |||
|
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r499 | ||
| self.dataOut.azimuth = self.dataIn.azimuth | |||
| self.dataOut.zenith = self.dataIn.zenith | |||
|
Daniel Valdez
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r487 | # | |
| # 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 | |||
|
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r495 | def filterByHeights(self, window, axis=1): | |
|
Daniel Valdez
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r487 | 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 | |||
|
Daniel Valdez
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r495 | 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() | |||
|
Daniel Valdez
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r487 | self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*(self.dataOut.nHeights-r)/window,newdelta) | |
| self.dataOut.windowOfFilter = window | |||
|
Daniel Valdez
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r495 | ||
| return 1 | |||
|
Daniel Valdez
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r487 | 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 | |||
|
Daniel Valdez
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r495 | def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False): | |
|
Daniel Valdez
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r487 | """ | |
| 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 | |||
|
Daniel Valdez
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r495 | self.byblock = byblock | |
|
Daniel Valdez
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r487 | ||
| 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 | |||
|
Daniel Valdez
|
r495 | ||
| 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 | |||
|
Daniel Valdez
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r487 | def run(self, dataOut, **kwargs): | |
| if not self.isConfig: | |||
| self.setup(**kwargs) | |||
| self.isConfig = True | |||
|
Daniel Valdez
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r495 | ||
| if self.byblock: | |||
| avgdata, avgdatatime = self.integrateByBlock(dataOut) | |||
| else: | |||
| avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) | |||
|
Daniel Valdez
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r487 | ||
| # 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 | |||
|
Daniel Valdez
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r495 | ||
|
Daniel Valdez
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r487 | def __init__(self): | |
| Operation.__init__(self) | |||
|
Daniel Valdez
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r495 | ||
| self.times = None | |||
| self.osamp = None | |||
| self.__setValues = False | |||
|
Daniel Valdez
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r487 | # self.isConfig = False | |
|
Daniel Valdez
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r495 | def setup(self, code, shape, times, osamp): | |
|
Daniel Valdez
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r487 | ||
| self.__profIndex = 0 | |||
| self.code = code | |||
| self.nCode = len(code) | |||
| self.nBaud = len(code[0]) | |||
|
Daniel Valdez
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r495 | if times != None: | |
| self.times = times | |||
|
Daniel Valdez
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r487 | ||
|
Daniel Valdez
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r495 | 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 | |||
|
Daniel Valdez
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r487 | ||
|
Daniel Valdez
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r495 | 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) | |||
|
Daniel Valdez
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r487 | ||
|
Daniel Valdez
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r495 | ||
|
Daniel Valdez
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r487 | ||
| 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 | |||
|
Daniel Valdez
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r495 | 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): | |||
|
Daniel Valdez
|
r487 | ||
| if code == None: | |||
| code = dataOut.code | |||
| else: | |||
| code = numpy.array(code).reshape(nCode,nBaud) | |||
|
Daniel Valdez
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r495 | ||
| if not self.isConfig: | |||
| self.setup(code, dataOut.data.shape, times, osamp) | |||
|
Daniel Valdez
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r487 | 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) | |||
|
Daniel Valdez
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r495 | ||
| 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 | |||
|
Daniel Valdez
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r487 | ||
| 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 | |||
|
Daniel Valdez
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r495 | ||
| 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) |