jroproc_spectra.py
1064 lines
| 36.2 KiB
| text/x-python
|
PythonLexer
|
r487 | import numpy | ||
from jroproc_base import ProcessingUnit, Operation | ||||
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r568 | from schainpy.model.data.jrodata import Spectra | ||
from schainpy.model.data.jrodata import hildebrand_sekhon | ||||
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r487 | |||
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r1123 | import matplotlib.pyplot as plt | ||
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r487 | class SpectraProc(ProcessingUnit): | ||
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r897 | |||
def __init__(self, **kwargs): | ||||
ProcessingUnit.__init__(self, **kwargs) | ||||
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r487 | self.buffer = None | ||
self.firstdatatime = None | ||||
self.profIndex = 0 | ||||
self.dataOut = Spectra() | ||||
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r495 | self.id_min = None | ||
self.id_max = None | ||||
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r487 | |||
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r623 | def __updateSpecFromVoltage(self): | ||
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r897 | |||
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r487 | self.dataOut.timeZone = self.dataIn.timeZone | ||
self.dataOut.dstFlag = self.dataIn.dstFlag | ||||
self.dataOut.errorCount = self.dataIn.errorCount | ||||
self.dataOut.useLocalTime = self.dataIn.useLocalTime | ||||
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r897 | |||
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r487 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | ||
self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | ||||
self.dataOut.channelList = self.dataIn.channelList | ||||
self.dataOut.heightList = self.dataIn.heightList | ||||
self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) | ||||
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r897 | |||
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r487 | self.dataOut.nBaud = self.dataIn.nBaud | ||
self.dataOut.nCode = self.dataIn.nCode | ||||
self.dataOut.code = self.dataIn.code | ||||
self.dataOut.nProfiles = self.dataOut.nFFTPoints | ||||
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r897 | |||
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r568 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | ||
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r487 | self.dataOut.utctime = self.firstdatatime | ||
self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada | ||||
self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip | ||||
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r623 | self.dataOut.flagShiftFFT = False | ||
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r897 | |||
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r487 | self.dataOut.nCohInt = self.dataIn.nCohInt | ||
self.dataOut.nIncohInt = 1 | ||||
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r897 | |||
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r487 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | ||
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r897 | |||
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r487 | self.dataOut.frequency = self.dataIn.frequency | ||
self.dataOut.realtime = self.dataIn.realtime | ||||
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r897 | |||
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r499 | self.dataOut.azimuth = self.dataIn.azimuth | ||
self.dataOut.zenith = self.dataIn.zenith | ||||
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r897 | |||
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r501 | self.dataOut.beam.codeList = self.dataIn.beam.codeList | ||
self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | ||||
self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | ||||
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r897 | |||
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r487 | def __getFft(self): | ||
""" | ||||
Convierte valores de Voltaje a Spectra | ||||
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r897 | |||
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r487 | Affected: | ||
self.dataOut.data_spc | ||||
self.dataOut.data_cspc | ||||
self.dataOut.data_dc | ||||
self.dataOut.heightList | ||||
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r897 | self.profIndex | ||
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r487 | self.buffer | ||
self.dataOut.flagNoData | ||||
""" | ||||
fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1) | ||||
fft_volt = fft_volt.astype(numpy.dtype('complex')) | ||||
dc = fft_volt[:,0,:] | ||||
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r897 | |||
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r487 | #calculo de self-spectra | ||
fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,)) | ||||
spc = fft_volt * numpy.conjugate(fft_volt) | ||||
spc = spc.real | ||||
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r897 | |||
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r487 | blocksize = 0 | ||
blocksize += dc.size | ||||
blocksize += spc.size | ||||
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r897 | |||
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r487 | cspc = None | ||
pairIndex = 0 | ||||
if self.dataOut.pairsList != None: | ||||
#calculo de cross-spectra | ||||
cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') | ||||
for pair in self.dataOut.pairsList: | ||||
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r587 | if pair[0] not in self.dataOut.channelList: | ||
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r600 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) | ||
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r587 | if pair[1] not in self.dataOut.channelList: | ||
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r600 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) | ||
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r897 | |||
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r487 | cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:]) | ||
pairIndex += 1 | ||||
blocksize += cspc.size | ||||
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r897 | |||
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r487 | self.dataOut.data_spc = spc | ||
self.dataOut.data_cspc = cspc | ||||
self.dataOut.data_dc = dc | ||||
self.dataOut.blockSize = blocksize | ||||
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r623 | self.dataOut.flagShiftFFT = True | ||
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r897 | |||
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r487 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None): | ||
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r897 | |||
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r487 | self.dataOut.flagNoData = True | ||
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r897 | |||
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r487 | if self.dataIn.type == "Spectra": | ||
self.dataOut.copy(self.dataIn) | ||||
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r730 | # self.__selectPairs(pairsList) | ||
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r487 | return True | ||
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r897 | |||
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r487 | if self.dataIn.type == "Voltage": | ||
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r897 | |||
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r487 | if nFFTPoints == None: | ||
raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" | ||||
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r897 | |||
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r495 | if nProfiles == None: | ||
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r568 | nProfiles = nFFTPoints | ||
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r897 | |||
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r487 | if ippFactor == None: | ||
ippFactor = 1 | ||||
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r897 | |||
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r487 | self.dataOut.ippFactor = ippFactor | ||
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r897 | |||
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r487 | self.dataOut.nFFTPoints = nFFTPoints | ||
self.dataOut.pairsList = pairsList | ||||
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r495 | |||
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r611 | if self.buffer is None: | ||
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r623 | self.buffer = numpy.zeros( (self.dataIn.nChannels, | ||
nProfiles, | ||||
self.dataIn.nHeights), | ||||
dtype='complex') | ||||
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r487 | |||
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r623 | if self.dataIn.flagDataAsBlock: | ||
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r720 | #data dimension: [nChannels, nProfiles, nSamples] | ||
nVoltProfiles = self.dataIn.data.shape[1] | ||||
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r730 | # nVoltProfiles = self.dataIn.nProfiles | ||
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r897 | |||
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r720 | if nVoltProfiles == nProfiles: | ||
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r495 | self.buffer = self.dataIn.data.copy() | ||
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r720 | self.profIndex = nVoltProfiles | ||
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r897 | |||
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r720 | elif nVoltProfiles < nProfiles: | ||
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r897 | |||
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r623 | if self.profIndex == 0: | ||
self.id_min = 0 | ||||
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r720 | self.id_max = nVoltProfiles | ||
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r897 | |||
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r495 | self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data | ||
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r720 | self.profIndex += nVoltProfiles | ||
self.id_min += nVoltProfiles | ||||
self.id_max += nVoltProfiles | ||||
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r495 | else: | ||
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r750 | raise ValueError, "The type object %s has %d profiles, it should just has %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles) | ||
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r495 | self.dataOut.flagNoData = True | ||
return 0 | ||||
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r897 | else: | ||
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r623 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() | ||
self.profIndex += 1 | ||||
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r897 | |||
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r487 | if self.firstdatatime == None: | ||
self.firstdatatime = self.dataIn.utctime | ||||
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r897 | |||
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r487 | if self.profIndex == nProfiles: | ||
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r623 | self.__updateSpecFromVoltage() | ||
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r487 | self.__getFft() | ||
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r897 | |||
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r487 | self.dataOut.flagNoData = False | ||
self.firstdatatime = None | ||||
self.profIndex = 0 | ||||
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r897 | |||
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r487 | return True | ||
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r897 | |||
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r587 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) | ||
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r897 | |||
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r730 | def __selectPairs(self, pairsList): | ||
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r897 | |||
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r730 | if channelList == None: | ||
return | ||||
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r897 | |||
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r730 | pairsIndexListSelected = [] | ||
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r897 | |||
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r730 | for thisPair in pairsList: | ||
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r897 | |||
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r730 | if thisPair not in self.dataOut.pairsList: | ||
continue | ||||
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r897 | |||
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r730 | pairIndex = self.dataOut.pairsList.index(thisPair) | ||
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r897 | |||
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r730 | pairsIndexListSelected.append(pairIndex) | ||
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r897 | |||
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r730 | if not pairsIndexListSelected: | ||
self.dataOut.data_cspc = None | ||||
self.dataOut.pairsList = [] | ||||
return | ||||
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r897 | |||
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r730 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | ||
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r897 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] | ||
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r730 | return | ||
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r897 | |||
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r730 | def __selectPairsByChannel(self, channelList=None): | ||
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r897 | |||
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r587 | if channelList == None: | ||
return | ||||
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r897 | |||
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r587 | pairsIndexListSelected = [] | ||
for pairIndex in self.dataOut.pairsIndexList: | ||||
#First pair | ||||
if self.dataOut.pairsList[pairIndex][0] not in channelList: | ||||
continue | ||||
#Second pair | ||||
if self.dataOut.pairsList[pairIndex][1] not in channelList: | ||||
continue | ||||
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r897 | |||
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r587 | pairsIndexListSelected.append(pairIndex) | ||
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r897 | |||
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r587 | if not pairsIndexListSelected: | ||
self.dataOut.data_cspc = None | ||||
self.dataOut.pairsList = [] | ||||
return | ||||
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r897 | |||
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r587 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | ||
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r897 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] | ||
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r587 | return | ||
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r897 | |||
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r487 | def selectChannels(self, channelList): | ||
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r897 | |||
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r487 | channelIndexList = [] | ||
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r897 | |||
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r487 | for channel in channelList: | ||
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r586 | if channel not in self.dataOut.channelList: | ||
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r730 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) | ||
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r897 | |||
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r487 | index = self.dataOut.channelList.index(channel) | ||
channelIndexList.append(index) | ||||
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r897 | |||
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r487 | self.selectChannelsByIndex(channelIndexList) | ||
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r897 | |||
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r487 | def selectChannelsByIndex(self, channelIndexList): | ||
""" | ||||
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r897 | Selecciona un bloque de datos en base a canales segun el channelIndexList | ||
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r487 | Input: | ||
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r897 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | ||
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r487 | Affected: | ||
self.dataOut.data_spc | ||||
self.dataOut.channelIndexList | ||||
self.dataOut.nChannels | ||||
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r897 | |||
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r487 | Return: | ||
None | ||||
""" | ||||
for channelIndex in channelIndexList: | ||||
if channelIndex not in self.dataOut.channelIndexList: | ||||
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r587 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) | ||
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r897 | |||
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r487 | # nChannels = len(channelIndexList) | ||
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r897 | |||
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r487 | data_spc = self.dataOut.data_spc[channelIndexList,:] | ||
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r587 | data_dc = self.dataOut.data_dc[channelIndexList,:] | ||
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r897 | |||
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r487 | self.dataOut.data_spc = data_spc | ||
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r587 | self.dataOut.data_dc = data_dc | ||
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r897 | |||
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r487 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | ||
# self.dataOut.nChannels = nChannels | ||||
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r897 | |||
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r730 | self.__selectPairsByChannel(self.dataOut.channelList) | ||
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r897 | |||
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r487 | return 1 | ||
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r1123 | |||
def selectFFTs(self, minFFT, maxFFT ): | ||||
""" | ||||
Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango | ||||
minFFT<= FFT <= maxFFT | ||||
""" | ||||
if (minFFT > maxFFT): | ||||
raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT) | ||||
if (minFFT < self.dataOut.getFreqRange()[0]): | ||||
minFFT = self.dataOut.getFreqRange()[0] | ||||
if (maxFFT > self.dataOut.getFreqRange()[-1]): | ||||
maxFFT = self.dataOut.getFreqRange()[-1] | ||||
minIndex = 0 | ||||
maxIndex = 0 | ||||
FFTs = self.dataOut.getFreqRange() | ||||
inda = numpy.where(FFTs >= minFFT) | ||||
indb = numpy.where(FFTs <= maxFFT) | ||||
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r487 | |||
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r1123 | try: | ||
minIndex = inda[0][0] | ||||
except: | ||||
minIndex = 0 | ||||
try: | ||||
maxIndex = indb[0][-1] | ||||
except: | ||||
maxIndex = len(FFTs) | ||||
self.selectFFTsByIndex(minIndex, maxIndex) | ||||
return 1 | ||||
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r487 | def selectHeights(self, minHei, maxHei): | ||
""" | ||||
Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango | ||||
minHei <= height <= maxHei | ||||
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r897 | |||
|
r487 | Input: | ||
|
r897 | minHei : valor minimo de altura a considerar | ||
|
r487 | maxHei : valor maximo de altura a considerar | ||
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r897 | |||
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r487 | Affected: | ||
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | ||||
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r897 | |||
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r487 | Return: | ||
1 si el metodo se ejecuto con exito caso contrario devuelve 0 | ||||
""" | ||||
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r897 | |||
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r1123 | |||
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r587 | if (minHei > maxHei): | ||
raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) | ||||
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r897 | |||
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r587 | if (minHei < self.dataOut.heightList[0]): | ||
minHei = self.dataOut.heightList[0] | ||||
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r897 | |||
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r487 | if (maxHei > self.dataOut.heightList[-1]): | ||
maxHei = self.dataOut.heightList[-1] | ||||
minIndex = 0 | ||||
maxIndex = 0 | ||||
heights = self.dataOut.heightList | ||||
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r897 | |||
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r487 | inda = numpy.where(heights >= minHei) | ||
indb = numpy.where(heights <= maxHei) | ||||
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r897 | |||
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r487 | try: | ||
minIndex = inda[0][0] | ||||
except: | ||||
minIndex = 0 | ||||
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r897 | |||
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r487 | try: | ||
maxIndex = indb[0][-1] | ||||
except: | ||||
maxIndex = len(heights) | ||||
self.selectHeightsByIndex(minIndex, maxIndex) | ||||
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r1123 | |||
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r897 | |||
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r487 | return 1 | ||
def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): | ||||
newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) | ||||
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r897 | |||
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r487 | if hei_ref != None: | ||
newheis = numpy.where(self.dataOut.heightList>hei_ref) | ||||
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r897 | |||
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r487 | minIndex = min(newheis[0]) | ||
maxIndex = max(newheis[0]) | ||||
data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] | ||||
heightList = self.dataOut.heightList[minIndex:maxIndex+1] | ||||
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r897 | |||
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r487 | # determina indices | ||
nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) | ||||
avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) | ||||
beacon_dB = numpy.sort(avg_dB)[-nheis:] | ||||
beacon_heiIndexList = [] | ||||
for val in avg_dB.tolist(): | ||||
if val >= beacon_dB[0]: | ||||
beacon_heiIndexList.append(avg_dB.tolist().index(val)) | ||||
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r897 | |||
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r487 | #data_spc = data_spc[:,:,beacon_heiIndexList] | ||
data_cspc = None | ||||
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r612 | if self.dataOut.data_cspc is not None: | ||
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r487 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | ||
#data_cspc = data_cspc[:,:,beacon_heiIndexList] | ||||
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r897 | |||
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r487 | data_dc = None | ||
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r612 | if self.dataOut.data_dc is not None: | ||
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r487 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] | ||
#data_dc = data_dc[:,beacon_heiIndexList] | ||||
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r897 | |||
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r487 | self.dataOut.data_spc = data_spc | ||
self.dataOut.data_cspc = data_cspc | ||||
self.dataOut.data_dc = data_dc | ||||
self.dataOut.heightList = heightList | ||||
self.dataOut.beacon_heiIndexList = beacon_heiIndexList | ||||
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r897 | |||
|
r487 | return 1 | ||
|
r897 | |||
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r1123 | def selectFFTsByIndex(self, minIndex, maxIndex): | ||
""" | ||||
""" | ||||
if (minIndex < 0) or (minIndex > maxIndex): | ||||
raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) | ||||
if (maxIndex >= self.dataOut.nProfiles): | ||||
maxIndex = self.dataOut.nProfiles-1 | ||||
#Spectra | ||||
data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] | ||||
data_cspc = None | ||||
if self.dataOut.data_cspc is not None: | ||||
data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] | ||||
data_dc = None | ||||
if self.dataOut.data_dc is not None: | ||||
data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] | ||||
self.dataOut.data_spc = data_spc | ||||
self.dataOut.data_cspc = data_cspc | ||||
self.dataOut.data_dc = data_dc | ||||
self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) | ||||
self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] | ||||
self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] | ||||
#self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] | ||||
return 1 | ||||
|
r897 | |||
|
r487 | def selectHeightsByIndex(self, minIndex, maxIndex): | ||
""" | ||||
Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango | ||||
minIndex <= index <= maxIndex | ||||
|
r897 | |||
|
r487 | Input: | ||
|
r897 | minIndex : valor de indice minimo de altura a considerar | ||
|
r487 | maxIndex : valor de indice maximo de altura a considerar | ||
|
r897 | |||
|
r487 | Affected: | ||
self.dataOut.data_spc | ||||
self.dataOut.data_cspc | ||||
self.dataOut.data_dc | ||||
self.dataOut.heightList | ||||
|
r897 | |||
|
r487 | Return: | ||
1 si el metodo se ejecuto con exito caso contrario devuelve 0 | ||||
""" | ||||
|
r897 | |||
|
r487 | if (minIndex < 0) or (minIndex > maxIndex): | ||
|
r730 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) | ||
|
r897 | |||
|
r487 | if (maxIndex >= self.dataOut.nHeights): | ||
maxIndex = self.dataOut.nHeights-1 | ||||
#Spectra | ||||
data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] | ||||
|
r897 | |||
|
r487 | data_cspc = None | ||
|
r612 | if self.dataOut.data_cspc is not None: | ||
|
r487 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | ||
|
r897 | |||
|
r487 | data_dc = None | ||
|
r612 | if self.dataOut.data_dc is not None: | ||
|
r487 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] | ||
|
r897 | |||
|
r487 | self.dataOut.data_spc = data_spc | ||
self.dataOut.data_cspc = data_cspc | ||||
self.dataOut.data_dc = data_dc | ||||
|
r897 | |||
|
r487 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] | ||
|
r897 | |||
|
r487 | return 1 | ||
|
r1123 | |||
|
r487 | def removeDC(self, mode = 2): | ||
jspectra = self.dataOut.data_spc | ||||
jcspectra = self.dataOut.data_cspc | ||||
|
r897 | |||
|
r487 | num_chan = jspectra.shape[0] | ||
num_hei = jspectra.shape[2] | ||||
|
r897 | |||
|
r612 | if jcspectra is not None: | ||
|
r487 | jcspectraExist = True | ||
num_pairs = jcspectra.shape[0] | ||||
else: jcspectraExist = False | ||||
|
r897 | |||
|
r487 | freq_dc = jspectra.shape[1]/2 | ||
|
r897 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc | ||
|
r487 | if ind_vel[0]<0: | ||
ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof | ||||
|
r897 | |||
if mode == 1: | ||||
|
r487 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION | ||
|
r897 | |||
|
r487 | if jcspectraExist: | ||
jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 | ||||
|
r897 | |||
|
r487 | if mode == 2: | ||
|
r897 | |||
|
r487 | vel = numpy.array([-2,-1,1,2]) | ||
xx = numpy.zeros([4,4]) | ||||
|
r897 | |||
|
r487 | for fil in range(4): | ||
xx[fil,:] = vel[fil]**numpy.asarray(range(4)) | ||||
|
r897 | |||
|
r487 | xx_inv = numpy.linalg.inv(xx) | ||
xx_aux = xx_inv[0,:] | ||||
|
r897 | |||
|
r487 | for ich in range(num_chan): | ||
yy = jspectra[ich,ind_vel,:] | ||||
jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) | ||||
junkid = jspectra[ich,freq_dc,:]<=0 | ||||
cjunkid = sum(junkid) | ||||
|
r897 | |||
|
r487 | if cjunkid.any(): | ||
jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 | ||||
|
r897 | |||
|
r487 | if jcspectraExist: | ||
for ip in range(num_pairs): | ||||
yy = jcspectra[ip,ind_vel,:] | ||||
jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) | ||||
|
r897 | |||
|
r487 | self.dataOut.data_spc = jspectra | ||
self.dataOut.data_cspc = jcspectra | ||||
|
r897 | |||
|
r487 | return 1 | ||
|
r897 | |||
|
r1123 | def removeInterference2(self): | ||
cspc = self.dataOut.data_cspc | ||||
spc = self.dataOut.data_spc | ||||
print numpy.shape(spc) | ||||
Heights = numpy.arange(cspc.shape[2]) | ||||
realCspc = numpy.abs(cspc) | ||||
for i in range(cspc.shape[0]): | ||||
LinePower= numpy.sum(realCspc[i], axis=0) | ||||
Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] | ||||
SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] | ||||
#print numpy.shape(realCspc) | ||||
#print '',SelectedHeights, '', numpy.shape(realCspc[i,:,SelectedHeights]) | ||||
InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 ) | ||||
print SelectedHeights | ||||
InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] | ||||
InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] | ||||
InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) | ||||
#InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) | ||||
if len(InterferenceRange)<int(cspc.shape[1]*0.3): | ||||
cspc[i,InterferenceRange,:] = numpy.NaN | ||||
print '########################################################################################' | ||||
print 'Len interference sum',len(InterferenceSum) | ||||
print 'InterferenceThresholdMin', InterferenceThresholdMin, 'InterferenceThresholdMax', InterferenceThresholdMax | ||||
print 'InterferenceRange',InterferenceRange | ||||
print '########################################################################################' | ||||
''' Ploteo ''' | ||||
#for i in range(3): | ||||
#print 'FASE', numpy.shape(phase), y[25] | ||||
#print numpy.shape(coherence) | ||||
#fig = plt.figure(10+ int(numpy.random.rand()*100)) | ||||
#plt.plot( x[0:256],coherence[:,25] ) | ||||
#cohAv = numpy.average(coherence[i],1) | ||||
#Pendiente = FrecRange * PhaseSlope[i] | ||||
#plt.plot( InterferenceSum) | ||||
#plt.plot( numpy.sort(InterferenceSum)) | ||||
#plt.plot( LinePower ) | ||||
#plt.plot( xFrec,phase[i]) | ||||
#CSPCmean = numpy.mean(numpy.abs(CSPCSamples),0) | ||||
#plt.plot(xFrec, FitGauss01) | ||||
#plt.plot(xFrec, CSPCmean) | ||||
#plt.plot(xFrec, numpy.abs(CSPCSamples[0])) | ||||
#plt.plot(xFrec, FitGauss) | ||||
#plt.plot(xFrec, yMean) | ||||
#plt.plot(xFrec, numpy.abs(coherence[0])) | ||||
#plt.axis([-12, 12, 15, 50]) | ||||
#plt.title("%s" %( '%s %s, Channel %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S") , i))) | ||||
#fig.savefig('/home/erick/Documents/Pics/nom{}.png'.format(int(numpy.random.rand()*100))) | ||||
#plt.show() | ||||
#self.indice=self.indice+1 | ||||
#raise | ||||
self.dataOut.data_cspc = cspc | ||||
# for i in range(spc.shape[0]): | ||||
# LinePower= numpy.sum(spc[i], axis=0) | ||||
# Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] | ||||
# SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] | ||||
# #print numpy.shape(realCspc) | ||||
# #print '',SelectedHeights, '', numpy.shape(realCspc[i,:,SelectedHeights]) | ||||
# InterferenceSum = numpy.sum( spc[i,:,SelectedHeights], axis=0 ) | ||||
# InterferenceThreshold = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] | ||||
# InterferenceRange = numpy.where( InterferenceSum > InterferenceThreshold ) | ||||
# if len(InterferenceRange)<int(spc.shape[1]*0.03): | ||||
# spc[i,InterferenceRange,:] = numpy.NaN | ||||
#self.dataOut.data_spc = spc | ||||
|
r487 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): | ||
|
r897 | |||
|
r487 | jspectra = self.dataOut.data_spc | ||
jcspectra = self.dataOut.data_cspc | ||||
jnoise = self.dataOut.getNoise() | ||||
num_incoh = self.dataOut.nIncohInt | ||||
|
r897 | |||
|
r487 | num_channel = jspectra.shape[0] | ||
num_prof = jspectra.shape[1] | ||||
num_hei = jspectra.shape[2] | ||||
|
r897 | |||
|
r487 | #hei_interf | ||
|
r612 | if hei_interf is None: | ||
|
r487 | count_hei = num_hei/2 #Como es entero no importa | ||
hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei | ||||
hei_interf = numpy.asarray(hei_interf)[0] | ||||
|
r897 | #nhei_interf | ||
|
r487 | if (nhei_interf == None): | ||
nhei_interf = 5 | ||||
if (nhei_interf < 1): | ||||
|
r897 | nhei_interf = 1 | ||
|
r487 | if (nhei_interf > count_hei): | ||
nhei_interf = count_hei | ||||
|
r897 | if (offhei_interf == None): | ||
|
r487 | offhei_interf = 0 | ||
|
r897 | |||
|
r487 | ind_hei = range(num_hei) | ||
|
r897 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 | ||
|
r487 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 | ||
|
r897 | mask_prof = numpy.asarray(range(num_prof)) | ||
|
r487 | num_mask_prof = mask_prof.size | ||
comp_mask_prof = [0, num_prof/2] | ||||
|
r897 | |||
|
r487 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal | ||
if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): | ||||
jnoise = numpy.nan | ||||
noise_exist = jnoise[0] < numpy.Inf | ||||
|
r897 | |||
|
r487 | #Subrutina de Remocion de la Interferencia | ||
for ich in range(num_channel): | ||||
#Se ordena los espectros segun su potencia (menor a mayor) | ||||
power = jspectra[ich,mask_prof,:] | ||||
power = power[:,hei_interf] | ||||
power = power.sum(axis = 0) | ||||
psort = power.ravel().argsort() | ||||
|
r897 | |||
|
r487 | #Se estima la interferencia promedio en los Espectros de Potencia empleando | ||
junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] | ||||
|
r897 | |||
|
r487 | if noise_exist: | ||
# tmp_noise = jnoise[ich] / num_prof | ||||
tmp_noise = jnoise[ich] | ||||
junkspc_interf = junkspc_interf - tmp_noise | ||||
#junkspc_interf[:,comp_mask_prof] = 0 | ||||
|
r897 | |||
|
r487 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf | ||
jspc_interf = jspc_interf.transpose() | ||||
#Calculando el espectro de interferencia promedio | ||||
|
r722 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) | ||
|
r487 | noiseid = noiseid[0] | ||
cnoiseid = noiseid.size | ||||
|
r722 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) | ||
|
r487 | interfid = interfid[0] | ||
cinterfid = interfid.size | ||||
|
r897 | |||
|
r487 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 | ||
|
r897 | |||
|
r487 | #Expandiendo los perfiles a limpiar | ||
if (cinterfid > 0): | ||||
new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof | ||||
|
r897 | new_interfid = numpy.asarray(new_interfid) | ||
|
r487 | new_interfid = {x for x in new_interfid} | ||
new_interfid = numpy.array(list(new_interfid)) | ||||
new_cinterfid = new_interfid.size | ||||
else: new_cinterfid = 0 | ||||
|
r897 | |||
|
r487 | for ip in range(new_cinterfid): | ||
|
r897 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() | ||
|
r487 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] | ||
|
r897 | |||
|
r487 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices | ||
|
r897 | |||
|
r487 | #Removiendo la interferencia del punto de mayor interferencia | ||
ListAux = jspc_interf[mask_prof].tolist() | ||||
maxid = ListAux.index(max(ListAux)) | ||||
|
r897 | |||
|
r487 | if cinterfid > 0: | ||
for ip in range(cinterfid*(interf == 2) - 1): | ||||
|
r722 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() | ||
|
r487 | cind = len(ind) | ||
|
r897 | |||
|
r487 | if (cind > 0): | ||
|
r722 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) | ||
|
r897 | |||
|
r487 | ind = numpy.array([-2,-1,1,2]) | ||
xx = numpy.zeros([4,4]) | ||||
|
r897 | |||
|
r487 | for id1 in range(4): | ||
xx[:,id1] = ind[id1]**numpy.asarray(range(4)) | ||||
|
r897 | |||
|
r487 | xx_inv = numpy.linalg.inv(xx) | ||
xx = xx_inv[:,0] | ||||
ind = (ind + maxid + num_mask_prof)%num_mask_prof | ||||
yy = jspectra[ich,mask_prof[ind],:] | ||||
jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) | ||||
|
r897 | |||
indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() | ||||
|
r722 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) | ||
|
r897 | |||
|
r487 | #Remocion de Interferencia en el Cross Spectra | ||
|
r612 | if jcspectra is None: return jspectra, jcspectra | ||
|
r487 | num_pairs = jcspectra.size/(num_prof*num_hei) | ||
jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) | ||||
|
r897 | |||
|
r487 | for ip in range(num_pairs): | ||
|
r897 | |||
|
r487 | #------------------------------------------- | ||
|
r897 | |||
|
r487 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) | ||
cspower = cspower[:,hei_interf] | ||||
cspower = cspower.sum(axis = 0) | ||||
|
r897 | |||
|
r487 | cspsort = cspower.ravel().argsort() | ||
junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] | ||||
junkcspc_interf = junkcspc_interf.transpose() | ||||
jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf | ||||
|
r897 | |||
|
r487 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() | ||
|
r897 | |||
|
r487 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) | ||
median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) | ||||
junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) | ||||
|
r897 | |||
|
r487 | for iprof in range(num_prof): | ||
ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() | ||||
jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] | ||||
|
r897 | |||
|
r487 | #Removiendo la Interferencia | ||
jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf | ||||
|
r897 | |||
|
r487 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() | ||
maxid = ListAux.index(max(ListAux)) | ||||
|
r897 | |||
|
r487 | ind = numpy.array([-2,-1,1,2]) | ||
xx = numpy.zeros([4,4]) | ||||
|
r897 | |||
|
r487 | for id1 in range(4): | ||
xx[:,id1] = ind[id1]**numpy.asarray(range(4)) | ||||
|
r897 | |||
|
r487 | xx_inv = numpy.linalg.inv(xx) | ||
xx = xx_inv[:,0] | ||||
|
r897 | |||
|
r487 | ind = (ind + maxid + num_mask_prof)%num_mask_prof | ||
yy = jcspectra[ip,mask_prof[ind],:] | ||||
jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) | ||||
|
r897 | |||
|
r487 | #Guardar Resultados | ||
self.dataOut.data_spc = jspectra | ||||
self.dataOut.data_cspc = jcspectra | ||||
|
r897 | |||
|
r487 | return 1 | ||
|
r897 | |||
|
r487 | def setRadarFrequency(self, frequency=None): | ||
|
r897 | |||
|
r487 | if frequency != None: | ||
self.dataOut.frequency = frequency | ||||
|
r897 | |||
|
r487 | return 1 | ||
|
r897 | |||
def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): | ||||
|
r487 | #validacion de rango | ||
if minHei == None: | ||||
minHei = self.dataOut.heightList[0] | ||||
|
r897 | |||
|
r487 | if maxHei == None: | ||
maxHei = self.dataOut.heightList[-1] | ||||
|
r897 | |||
|
r487 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): | ||
print 'minHei: %.2f is out of the heights range'%(minHei) | ||||
print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) | ||||
minHei = self.dataOut.heightList[0] | ||||
|
r897 | |||
|
r487 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): | ||
print 'maxHei: %.2f is out of the heights range'%(maxHei) | ||||
print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) | ||||
maxHei = self.dataOut.heightList[-1] | ||||
|
r897 | |||
|
r487 | # validacion de velocidades | ||
velrange = self.dataOut.getVelRange(1) | ||||
|
r897 | |||
|
r487 | if minVel == None: | ||
minVel = velrange[0] | ||||
|
r897 | |||
|
r487 | if maxVel == None: | ||
maxVel = velrange[-1] | ||||
|
r897 | |||
|
r487 | if (minVel < velrange[0]) or (minVel > maxVel): | ||
print 'minVel: %.2f is out of the velocity range'%(minVel) | ||||
print 'minVel is setting to %.2f'%(velrange[0]) | ||||
minVel = velrange[0] | ||||
|
r897 | |||
|
r487 | if (maxVel > velrange[-1]) or (maxVel < minVel): | ||
print 'maxVel: %.2f is out of the velocity range'%(maxVel) | ||||
print 'maxVel is setting to %.2f'%(velrange[-1]) | ||||
maxVel = velrange[-1] | ||||
|
r897 | |||
# seleccion de indices para rango | ||||
|
r487 | minIndex = 0 | ||
maxIndex = 0 | ||||
heights = self.dataOut.heightList | ||||
|
r897 | |||
|
r487 | inda = numpy.where(heights >= minHei) | ||
indb = numpy.where(heights <= maxHei) | ||||
|
r897 | |||
|
r487 | try: | ||
minIndex = inda[0][0] | ||||
except: | ||||
minIndex = 0 | ||||
|
r897 | |||
|
r487 | try: | ||
maxIndex = indb[0][-1] | ||||
except: | ||||
maxIndex = len(heights) | ||||
if (minIndex < 0) or (minIndex > maxIndex): | ||||
raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) | ||||
|
r897 | |||
|
r487 | if (maxIndex >= self.dataOut.nHeights): | ||
maxIndex = self.dataOut.nHeights-1 | ||||
# seleccion de indices para velocidades | ||||
indminvel = numpy.where(velrange >= minVel) | ||||
indmaxvel = numpy.where(velrange <= maxVel) | ||||
try: | ||||
minIndexVel = indminvel[0][0] | ||||
except: | ||||
minIndexVel = 0 | ||||
|
r897 | |||
|
r487 | try: | ||
maxIndexVel = indmaxvel[0][-1] | ||||
|
r897 | except: | ||
|
r487 | maxIndexVel = len(velrange) | ||
|
r897 | |||
|
r487 | #seleccion del espectro | ||
data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] | ||||
#estimacion de ruido | ||||
noise = numpy.zeros(self.dataOut.nChannels) | ||||
|
r897 | |||
|
r487 | for channel in range(self.dataOut.nChannels): | ||
daux = data_spc[channel,:,:] | ||||
noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) | ||||
|
r897 | |||
|
r487 | self.dataOut.noise_estimation = noise.copy() | ||
|
r897 | |||
|
r487 | return 1 | ||
|
r897 | |||
|
r487 | class IncohInt(Operation): | ||
|
r897 | |||
|
r487 | __profIndex = 0 | ||
__withOverapping = False | ||||
|
r897 | |||
|
r487 | __byTime = False | ||
__initime = None | ||||
__lastdatatime = None | ||||
__integrationtime = None | ||||
|
r897 | |||
|
r487 | __buffer_spc = None | ||
__buffer_cspc = None | ||||
__buffer_dc = None | ||||
|
r897 | |||
|
r487 | __dataReady = False | ||
|
r897 | |||
|
r487 | __timeInterval = None | ||
|
r897 | |||
|
r487 | n = None | ||
|
r897 | |||
def __init__(self, **kwargs): | ||||
Operation.__init__(self, **kwargs) | ||||
|
r487 | # self.isConfig = False | ||
|
r897 | |||
|
r487 | def setup(self, n=None, timeInterval=None, overlapping=False): | ||
""" | ||||
Set the parameters of the integration class. | ||||
|
r897 | |||
|
r487 | Inputs: | ||
|
r897 | |||
|
r487 | n : Number of coherent integrations | ||
timeInterval : Time of integration. If the parameter "n" is selected this one does not work | ||||
|
r897 | overlapping : | ||
|
r487 | """ | ||
|
r897 | |||
|
r487 | self.__initime = None | ||
self.__lastdatatime = 0 | ||||
|
r897 | |||
|
r623 | self.__buffer_spc = 0 | ||
self.__buffer_cspc = 0 | ||||
self.__buffer_dc = 0 | ||||
|
r897 | |||
|
r623 | self.__profIndex = 0 | ||
self.__dataReady = False | ||||
self.__byTime = False | ||||
|
r897 | |||
|
r623 | if n is None and timeInterval is None: | ||
|
r897 | raise ValueError, "n or timeInterval should be specified ..." | ||
|
r623 | if n is not None: | ||
self.n = int(n) | ||||
|
r487 | else: | ||
|
r623 | self.__integrationtime = int(timeInterval) #if (type(timeInterval)!=integer) -> change this line | ||
self.n = None | ||||
|
r487 | self.__byTime = True | ||
|
r897 | |||
|
r487 | def putData(self, data_spc, data_cspc, data_dc): | ||
|
r897 | |||
|
r487 | """ | ||
Add a profile to the __buffer_spc and increase in one the __profileIndex | ||||
|
r897 | |||
|
r487 | """ | ||
|
r897 | |||
|
r623 | self.__buffer_spc += data_spc | ||
|
r897 | |||
|
r623 | if data_cspc is None: | ||
self.__buffer_cspc = None | ||||
else: | ||||
self.__buffer_cspc += data_cspc | ||||
|
r897 | |||
|
r623 | if data_dc is None: | ||
self.__buffer_dc = None | ||||
else: | ||||
self.__buffer_dc += data_dc | ||||
|
r897 | |||
|
r623 | self.__profIndex += 1 | ||
|
r897 | |||
|
r487 | return | ||
|
r897 | |||
|
r487 | def pushData(self): | ||
""" | ||||
Return the sum of the last profiles and the profiles used in the sum. | ||||
|
r897 | |||
|
r487 | Affected: | ||
|
r897 | |||
|
r487 | self.__profileIndex | ||
|
r897 | |||
|
r487 | """ | ||
|
r897 | |||
|
r623 | data_spc = self.__buffer_spc | ||
data_cspc = self.__buffer_cspc | ||||
data_dc = self.__buffer_dc | ||||
|
r487 | n = self.__profIndex | ||
|
r897 | |||
|
r623 | self.__buffer_spc = 0 | ||
self.__buffer_cspc = 0 | ||||
self.__buffer_dc = 0 | ||||
self.__profIndex = 0 | ||||
|
r897 | |||
|
r487 | return data_spc, data_cspc, data_dc, n | ||
|
r897 | |||
|
r487 | def byProfiles(self, *args): | ||
|
r897 | |||
|
r487 | self.__dataReady = False | ||
|
r624 | avgdata_spc = None | ||
avgdata_cspc = None | ||||
avgdata_dc = None | ||||
|
r897 | |||
|
r487 | self.putData(*args) | ||
|
r897 | |||
|
r487 | if self.__profIndex == self.n: | ||
|
r897 | |||
|
r487 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | ||
|
r623 | self.n = n | ||
|
r487 | self.__dataReady = True | ||
|
r897 | |||
|
r487 | return avgdata_spc, avgdata_cspc, avgdata_dc | ||
|
r897 | |||
|
r487 | def byTime(self, datatime, *args): | ||
|
r897 | |||
|
r487 | self.__dataReady = False | ||
|
r624 | avgdata_spc = None | ||
avgdata_cspc = None | ||||
avgdata_dc = None | ||||
|
r897 | |||
|
r487 | self.putData(*args) | ||
|
r897 | |||
|
r487 | if (datatime - self.__initime) >= self.__integrationtime: | ||
avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | ||||
self.n = n | ||||
self.__dataReady = True | ||||
|
r897 | |||
|
r487 | return avgdata_spc, avgdata_cspc, avgdata_dc | ||
|
r897 | |||
|
r487 | def integrate(self, datatime, *args): | ||
|
r897 | |||
|
r623 | if self.__profIndex == 0: | ||
|
r487 | self.__initime = datatime | ||
|
r897 | |||
|
r487 | if self.__byTime: | ||
avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args) | ||||
else: | ||||
avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) | ||||
|
r897 | |||
|
r623 | if not self.__dataReady: | ||
|
r487 | return None, None, None, None | ||
|
r897 | |||
|
r623 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc | ||
|
r897 | |||
|
r487 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): | ||
|
r897 | |||
|
r487 | if n==1: | ||
return | ||||
|
r897 | |||
|
r623 | dataOut.flagNoData = True | ||
|
r897 | |||
|
r487 | if not self.isConfig: | ||
self.setup(n, timeInterval, overlapping) | ||||
self.isConfig = True | ||||
|
r897 | |||
|
r487 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, | ||
dataOut.data_spc, | ||||
dataOut.data_cspc, | ||||
dataOut.data_dc) | ||||
|
r897 | |||
|
r487 | if self.__dataReady: | ||
|
r897 | |||
|
r487 | dataOut.data_spc = avgdata_spc | ||
dataOut.data_cspc = avgdata_cspc | ||||
dataOut.data_dc = avgdata_dc | ||||
|
r897 | |||
|
r487 | dataOut.nIncohInt *= self.n | ||
dataOut.utctime = avgdatatime | ||||
dataOut.flagNoData = False | ||||
|
r1001 | |||