jroproc_spectra_acf.py
735 lines
| 25.5 KiB
| text/x-python
|
PythonLexer
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r794 | import numpy | |
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r1167 | from .jroproc_base import ProcessingUnit, Operation | |
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r794 | from schainpy.model.data.jrodata import Spectra | |
from schainpy.model.data.jrodata import hildebrand_sekhon | |||
class SpectraAFCProc(ProcessingUnit): | |||
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r897 | ||
def __init__(self, **kwargs): | |||
ProcessingUnit.__init__(self, **kwargs) | |||
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r794 | self.buffer = None | |
self.firstdatatime = None | |||
self.profIndex = 0 | |||
self.dataOut = Spectra() | |||
self.id_min = None | |||
self.id_max = None | |||
def __updateSpecFromVoltage(self): | |||
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r897 | ||
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r794 | self.dataOut.plotting = "spectra_acf" | |
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r897 | ||
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r794 | 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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r794 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |
self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | |||
self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15 | |||
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r897 | ||
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r794 | 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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r794 | 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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r794 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
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 | |||
self.dataOut.flagShiftFFT = False | |||
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r897 | ||
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r794 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
self.dataOut.nIncohInt = 1 | |||
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r897 | ||
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r794 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |
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r897 | ||
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r794 | self.dataOut.frequency = self.dataIn.frequency | |
self.dataOut.realtime = self.dataIn.realtime | |||
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r897 | ||
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r794 | self.dataOut.azimuth = self.dataIn.azimuth | |
self.dataOut.zenith = self.dataIn.zenith | |||
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r897 | ||
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r794 | 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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r794 | def __decodeData(self, nProfiles, code): | |
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r897 | ||
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r794 | if code is None: | |
return | |||
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r897 | ||
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r794 | for i in range(nProfiles): | |
self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i] | |||
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r897 | ||
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r794 | def __getFft(self): | |
""" | |||
Convierte valores de Voltaje a Spectra | |||
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r897 | ||
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r794 | 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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r794 | self.buffer | |
self.dataOut.flagNoData | |||
""" | |||
nsegments = self.dataOut.nHeights | |||
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r897 | ||
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r794 | _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex') | |
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r897 | ||
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r794 | for i in range(nsegments): | |
try: | |||
_fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles] | |||
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r897 | ||
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r794 | if self.code is not None: | |
_fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0] | |||
except: | |||
pass | |||
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r897 | ||
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r794 | fft_volt = numpy.fft.fft(_fft_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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r794 | #calculo de self-spectra | |
# fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) | |||
spc = fft_volt * numpy.conjugate(fft_volt) | |||
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r897 | ||
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r794 | data = numpy.fft.ifft(spc, axis=1) | |
data = numpy.fft.fftshift(data, axes=(1,)) | |||
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r897 | ||
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r794 | spc = data | |
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r897 | ||
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r794 | blocksize = 0 | |
blocksize += dc.size | |||
blocksize += spc.size | |||
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r897 | ||
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r794 | cspc = None | |
pairIndex = 0 | |||
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r897 | ||
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r794 | 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: | |||
if pair[0] not in self.dataOut.channelList: | |||
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r1167 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList))) | |
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r794 | if pair[1] not in self.dataOut.channelList: | |
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r1167 | 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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r794 | chan_index0 = self.dataOut.channelList.index(pair[0]) | |
chan_index1 = self.dataOut.channelList.index(pair[1]) | |||
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r897 | ||
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r794 | cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:]) | |
pairIndex += 1 | |||
blocksize += cspc.size | |||
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r897 | ||
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r794 | self.dataOut.data_spc = spc | |
self.dataOut.data_cspc = cspc | |||
self.dataOut.data_dc = dc | |||
self.dataOut.blockSize = blocksize | |||
self.dataOut.flagShiftFFT = True | |||
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r897 | ||
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r794 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1): | |
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r897 | ||
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r794 | self.dataOut.flagNoData = True | |
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r897 | ||
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r794 | if code is not None: | |
self.code = numpy.array(code).reshape(nCode,nBaud) | |||
else: | |||
self.code = None | |||
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r897 | ||
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r794 | if self.dataIn.type == "Voltage": | |
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r897 | ||
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r794 | if nFFTPoints == None: | |
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r1167 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") | |
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r897 | ||
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r794 | if nProfiles == None: | |
nProfiles = nFFTPoints | |||
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r897 | ||
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r794 | self.dataOut.ippFactor = 1 | |
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r897 | ||
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r794 | self.dataOut.nFFTPoints = nFFTPoints | |
self.dataOut.nProfiles = nProfiles | |||
self.dataOut.pairsList = pairsList | |||
# if self.buffer is None: | |||
# self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights), | |||
# dtype='complex') | |||
if not self.dataIn.flagDataAsBlock: | |||
self.buffer = self.dataIn.data.copy() | |||
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r897 | ||
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r794 | # for i in range(self.dataIn.nHeights): | |
# self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex] | |||
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r897 | # | |
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r794 | # self.profIndex += 1 | |
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r897 | ||
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r794 | else: | |
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r1167 | raise ValueError("") | |
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r897 | ||
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r794 | self.firstdatatime = self.dataIn.utctime | |
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r897 | ||
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r794 | self.profIndex == nProfiles | |
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r897 | ||
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r794 | self.__updateSpecFromVoltage() | |
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r897 | ||
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r794 | self.__getFft() | |
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r897 | ||
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r794 | self.dataOut.flagNoData = False | |
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r897 | ||
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r794 | return True | |
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r897 | ||
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r1167 | raise ValueError("The type of input object '%s' is not valid"%(self.dataIn.type)) | |
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r897 | ||
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r794 | def __selectPairs(self, pairsList): | |
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r897 | ||
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r794 | if channelList == None: | |
return | |||
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r897 | ||
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r794 | pairsIndexListSelected = [] | |
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r897 | ||
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r794 | for thisPair in pairsList: | |
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r897 | ||
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r794 | if thisPair not in self.dataOut.pairsList: | |
continue | |||
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r897 | ||
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r794 | pairIndex = self.dataOut.pairsList.index(thisPair) | |
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r897 | ||
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r794 | pairsIndexListSelected.append(pairIndex) | |
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r897 | ||
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r794 | if not pairsIndexListSelected: | |
self.dataOut.data_cspc = None | |||
self.dataOut.pairsList = [] | |||
return | |||
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r897 | ||
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r794 | 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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r794 | return | |
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r897 | ||
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r794 | def __selectPairsByChannel(self, channelList=None): | |
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r897 | ||
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r794 | if channelList == None: | |
return | |||
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r897 | ||
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r794 | 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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r794 | pairsIndexListSelected.append(pairIndex) | |
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r897 | ||
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r794 | if not pairsIndexListSelected: | |
self.dataOut.data_cspc = None | |||
self.dataOut.pairsList = [] | |||
return | |||
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r897 | ||
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r794 | 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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r794 | return | |
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r897 | ||
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r794 | def selectChannels(self, channelList): | |
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r897 | ||
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r794 | channelIndexList = [] | |
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r897 | ||
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r794 | for channel in channelList: | |
if channel not in self.dataOut.channelList: | |||
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r1167 | 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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r794 | index = self.dataOut.channelList.index(channel) | |
channelIndexList.append(index) | |||
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r897 | ||
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r794 | self.selectChannelsByIndex(channelIndexList) | |
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r897 | ||
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r794 | 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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r794 | Input: | |
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r897 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | |
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r794 | Affected: | |
self.dataOut.data_spc | |||
self.dataOut.channelIndexList | |||
self.dataOut.nChannels | |||
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r897 | ||
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r794 | Return: | |
None | |||
""" | |||
for channelIndex in channelIndexList: | |||
if channelIndex not in self.dataOut.channelIndexList: | |||
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r1167 | 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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r794 | # nChannels = len(channelIndexList) | |
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r897 | ||
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r794 | data_spc = self.dataOut.data_spc[channelIndexList,:] | |
data_dc = self.dataOut.data_dc[channelIndexList,:] | |||
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r897 | ||
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r794 | self.dataOut.data_spc = data_spc | |
self.dataOut.data_dc = data_dc | |||
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r897 | ||
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r794 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |
# self.dataOut.nChannels = nChannels | |||
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r897 | ||
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r794 | self.__selectPairsByChannel(self.dataOut.channelList) | |
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r897 | ||
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r794 | return 1 | |
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 | ||
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r794 | Input: | |
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r897 | minHei : valor minimo de altura a considerar | |
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r794 | maxHei : valor maximo de altura a considerar | |
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r897 | ||
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r794 | Affected: | |
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | |||
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r897 | ||
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r794 | Return: | |
1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |||
""" | |||
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r897 | ||
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r794 | if (minHei > maxHei): | |
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r1167 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei)) | |
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r897 | ||
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r794 | if (minHei < self.dataOut.heightList[0]): | |
minHei = self.dataOut.heightList[0] | |||
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r897 | ||
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r794 | 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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r794 | inda = numpy.where(heights >= minHei) | |
indb = numpy.where(heights <= maxHei) | |||
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r897 | ||
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r794 | try: | |
minIndex = inda[0][0] | |||
except: | |||
minIndex = 0 | |||
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r897 | ||
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r794 | try: | |
maxIndex = indb[0][-1] | |||
except: | |||
maxIndex = len(heights) | |||
self.selectHeightsByIndex(minIndex, maxIndex) | |||
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r897 | ||
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r794 | 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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r794 | if hei_ref != None: | |
newheis = numpy.where(self.dataOut.heightList>hei_ref) | |||
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r897 | ||
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r794 | 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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r794 | # 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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r794 | #data_spc = data_spc[:,:,beacon_heiIndexList] | |
data_cspc = None | |||
if self.dataOut.data_cspc is not None: | |||
data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | |||
#data_cspc = data_cspc[:,:,beacon_heiIndexList] | |||
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r897 | ||
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r794 | data_dc = None | |
if self.dataOut.data_dc is not None: | |||
data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] | |||
#data_dc = data_dc[:,beacon_heiIndexList] | |||
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r897 | ||
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r794 | 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 | ||
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r794 | return 1 | |
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r897 | ||
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r794 | def selectHeightsByIndex(self, minIndex, maxIndex): | |
""" | |||
Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango | |||
minIndex <= index <= maxIndex | |||
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r897 | ||
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r794 | Input: | |
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r897 | minIndex : valor de indice minimo de altura a considerar | |
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r794 | maxIndex : valor de indice maximo de altura a considerar | |
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r897 | ||
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r794 | Affected: | |
self.dataOut.data_spc | |||
self.dataOut.data_cspc | |||
self.dataOut.data_dc | |||
self.dataOut.heightList | |||
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r897 | ||
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r794 | Return: | |
1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |||
""" | |||
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r897 | ||
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r794 | if (minIndex < 0) or (minIndex > maxIndex): | |
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r1167 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) | |
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r897 | ||
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r794 | if (maxIndex >= self.dataOut.nHeights): | |
maxIndex = self.dataOut.nHeights-1 | |||
#Spectra | |||
data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] | |||
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r897 | ||
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r794 | data_cspc = None | |
if self.dataOut.data_cspc is not None: | |||
data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | |||
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r897 | ||
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r794 | data_dc = None | |
if self.dataOut.data_dc is not None: | |||
data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] | |||
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r897 | ||
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r794 | self.dataOut.data_spc = data_spc | |
self.dataOut.data_cspc = data_cspc | |||
self.dataOut.data_dc = data_dc | |||
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r897 | ||
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r794 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] | |
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r897 | ||
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r794 | return 1 | |
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r897 | ||
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r794 | def removeDC(self, mode = 2): | |
jspectra = self.dataOut.data_spc | |||
jcspectra = self.dataOut.data_cspc | |||
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r897 | ||
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r794 | num_chan = jspectra.shape[0] | |
num_hei = jspectra.shape[2] | |||
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r897 | ||
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r794 | if jcspectra is not None: | |
jcspectraExist = True | |||
num_pairs = jcspectra.shape[0] | |||
else: jcspectraExist = False | |||
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r897 | ||
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r794 | freq_dc = jspectra.shape[1]/2 | |
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r897 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc | |
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r794 | if ind_vel[0]<0: | |
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r1167 | ind_vel[list(range(0,1))] = ind_vel[list(range(0,1))] + self.num_prof | |
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r897 | ||
if mode == 1: | |||
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r794 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION | |
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r897 | ||
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r794 | if jcspectraExist: | |
jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 | |||
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r897 | ||
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r794 | if mode == 2: | |
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r897 | ||
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r794 | vel = numpy.array([-2,-1,1,2]) | |
xx = numpy.zeros([4,4]) | |||
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r897 | ||
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r794 | for fil in range(4): | |
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r1167 | xx[fil,:] = vel[fil]**numpy.asarray(list(range(4))) | |
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r897 | ||
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r794 | xx_inv = numpy.linalg.inv(xx) | |
xx_aux = xx_inv[0,:] | |||
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r897 | ||
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r794 | 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) | |||
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r897 | ||
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r794 | if cjunkid.any(): | |
jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 | |||
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r897 | ||
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r794 | if jcspectraExist: | |
for ip in range(num_pairs): | |||
yy = jcspectra[ip,ind_vel,:] | |||
jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) | |||
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r897 | ||
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r794 | self.dataOut.data_spc = jspectra | |
self.dataOut.data_cspc = jcspectra | |||
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r897 | ||
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r794 | return 1 | |
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r897 | ||
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r794 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): | |
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r897 | ||
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r794 | jspectra = self.dataOut.data_spc | |
jcspectra = self.dataOut.data_cspc | |||
jnoise = self.dataOut.getNoise() | |||
num_incoh = self.dataOut.nIncohInt | |||
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r897 | ||
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r794 | num_channel = jspectra.shape[0] | |
num_prof = jspectra.shape[1] | |||
num_hei = jspectra.shape[2] | |||
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r897 | ||
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r794 | #hei_interf | |
if hei_interf is None: | |||
count_hei = num_hei/2 #Como es entero no importa | |||
|
r1167 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei | |
|
r794 | hei_interf = numpy.asarray(hei_interf)[0] | |
|
r897 | #nhei_interf | |
|
r794 | if (nhei_interf == None): | |
nhei_interf = 5 | |||
if (nhei_interf < 1): | |||
|
r897 | nhei_interf = 1 | |
|
r794 | if (nhei_interf > count_hei): | |
nhei_interf = count_hei | |||
|
r897 | if (offhei_interf == None): | |
|
r794 | offhei_interf = 0 | |
|
r897 | ||
|
r1167 | ind_hei = list(range(num_hei)) | |
|
r897 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 | |
|
r794 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 | |
|
r1167 | mask_prof = numpy.asarray(list(range(num_prof))) | |
|
r794 | num_mask_prof = mask_prof.size | |
comp_mask_prof = [0, num_prof/2] | |||
|
r897 | ||
|
r794 | #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 | ||
|
r794 | #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 | ||
|
r794 | #Se estima la interferencia promedio en los Espectros de Potencia empleando | |
|
r1167 | junkspc_interf = jspectra[ich,:,hei_interf[psort[list(range(offhei_interf, nhei_interf + offhei_interf))]]] | |
|
r897 | ||
|
r794 | 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 | ||
|
r794 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf | |
jspc_interf = jspc_interf.transpose() | |||
#Calculando el espectro de interferencia promedio | |||
noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) | |||
noiseid = noiseid[0] | |||
cnoiseid = noiseid.size | |||
interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) | |||
interfid = interfid[0] | |||
cinterfid = interfid.size | |||
|
r897 | ||
|
r794 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 | |
|
r897 | ||
|
r794 | #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) | |
|
r794 | 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 | ||
|
r794 | for ip in range(new_cinterfid): | |
|
r897 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() | |
|
r794 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] | |
|
r897 | ||
|
r794 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices | |
|
r897 | ||
|
r794 | #Removiendo la interferencia del punto de mayor interferencia | |
ListAux = jspc_interf[mask_prof].tolist() | |||
maxid = ListAux.index(max(ListAux)) | |||
|
r897 | ||
|
r794 | if cinterfid > 0: | |
for ip in range(cinterfid*(interf == 2) - 1): | |||
ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() | |||
cind = len(ind) | |||
|
r897 | ||
|
r794 | if (cind > 0): | |
jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) | |||
|
r897 | ||
|
r794 | ind = numpy.array([-2,-1,1,2]) | |
xx = numpy.zeros([4,4]) | |||
|
r897 | ||
|
r794 | for id1 in range(4): | |
|
r1167 | xx[:,id1] = ind[id1]**numpy.asarray(list(range(4))) | |
|
r897 | ||
|
r794 | 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() | |||
|
r794 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) | |
|
r897 | ||
|
r794 | #Remocion de Interferencia en el Cross Spectra | |
if jcspectra is None: return jspectra, jcspectra | |||
num_pairs = jcspectra.size/(num_prof*num_hei) | |||
jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) | |||
|
r897 | ||
|
r794 | for ip in range(num_pairs): | |
|
r897 | ||
|
r794 | #------------------------------------------- | |
|
r897 | ||
|
r794 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) | |
cspower = cspower[:,hei_interf] | |||
cspower = cspower.sum(axis = 0) | |||
|
r897 | ||
|
r794 | cspsort = cspower.ravel().argsort() | |
|
r1167 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[list(range(offhei_interf, nhei_interf + offhei_interf))]]] | |
|
r794 | junkcspc_interf = junkcspc_interf.transpose() | |
jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf | |||
|
r897 | ||
|
r794 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() | |
|
r897 | ||
|
r1167 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[list(range(3*num_prof/4))]],:])) | |
median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[list(range(3*num_prof/4))]],:])) | |||
|
r794 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) | |
|
r897 | ||
|
r794 | 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 | ||
|
r794 | #Removiendo la Interferencia | |
jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf | |||
|
r897 | ||
|
r794 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() | |
maxid = ListAux.index(max(ListAux)) | |||
|
r897 | ||
|
r794 | ind = numpy.array([-2,-1,1,2]) | |
xx = numpy.zeros([4,4]) | |||
|
r897 | ||
|
r794 | for id1 in range(4): | |
|
r1167 | xx[:,id1] = ind[id1]**numpy.asarray(list(range(4))) | |
|
r897 | ||
|
r794 | xx_inv = numpy.linalg.inv(xx) | |
xx = xx_inv[:,0] | |||
|
r897 | ||
|
r794 | 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 | ||
|
r794 | #Guardar Resultados | |
self.dataOut.data_spc = jspectra | |||
self.dataOut.data_cspc = jcspectra | |||
|
r897 | ||
|
r794 | return 1 | |
|
r897 | ||
|
r794 | def setRadarFrequency(self, frequency=None): | |
|
r897 | ||
|
r794 | if frequency != None: | |
self.dataOut.frequency = frequency | |||
|
r897 | ||
|
r794 | return 1 | |
|
r897 | ||
def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): | |||
|
r794 | #validacion de rango | |
if minHei == None: | |||
minHei = self.dataOut.heightList[0] | |||
|
r897 | ||
|
r794 | if maxHei == None: | |
maxHei = self.dataOut.heightList[-1] | |||
|
r897 | ||
|
r794 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): | |
|
r1167 | print('minHei: %.2f is out of the heights range'%(minHei)) | |
print('minHei is setting to %.2f'%(self.dataOut.heightList[0])) | |||
|
r794 | minHei = self.dataOut.heightList[0] | |
|
r897 | ||
|
r794 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): | |
|
r1167 | print('maxHei: %.2f is out of the heights range'%(maxHei)) | |
print('maxHei is setting to %.2f'%(self.dataOut.heightList[-1])) | |||
|
r794 | maxHei = self.dataOut.heightList[-1] | |
|
r897 | ||
|
r794 | # validacion de velocidades | |
velrange = self.dataOut.getVelRange(1) | |||
|
r897 | ||
|
r794 | if minVel == None: | |
minVel = velrange[0] | |||
|
r897 | ||
|
r794 | if maxVel == None: | |
maxVel = velrange[-1] | |||
|
r897 | ||
|
r794 | if (minVel < velrange[0]) or (minVel > maxVel): | |
|
r1167 | print('minVel: %.2f is out of the velocity range'%(minVel)) | |
print('minVel is setting to %.2f'%(velrange[0])) | |||
|
r794 | minVel = velrange[0] | |
|
r897 | ||
|
r794 | if (maxVel > velrange[-1]) or (maxVel < minVel): | |
|
r1167 | print('maxVel: %.2f is out of the velocity range'%(maxVel)) | |
print('maxVel is setting to %.2f'%(velrange[-1])) | |||
|
r794 | maxVel = velrange[-1] | |
|
r897 | ||
# seleccion de indices para rango | |||
|
r794 | minIndex = 0 | |
maxIndex = 0 | |||
heights = self.dataOut.heightList | |||
|
r897 | ||
|
r794 | inda = numpy.where(heights >= minHei) | |
indb = numpy.where(heights <= maxHei) | |||
|
r897 | ||
|
r794 | try: | |
minIndex = inda[0][0] | |||
except: | |||
minIndex = 0 | |||
|
r897 | ||
|
r794 | try: | |
maxIndex = indb[0][-1] | |||
except: | |||
maxIndex = len(heights) | |||
if (minIndex < 0) or (minIndex > maxIndex): | |||
|
r1167 | raise ValueError("some value in (%d,%d) is not valid" % (minIndex, maxIndex)) | |
|
r897 | ||
|
r794 | 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 | ||
|
r794 | try: | |
maxIndexVel = indmaxvel[0][-1] | |||
|
r897 | except: | |
|
r794 | maxIndexVel = len(velrange) | |
|
r897 | ||
|
r794 | #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 | ||
|
r794 | for channel in range(self.dataOut.nChannels): | |
daux = data_spc[channel,:,:] | |||
noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) | |||
|
r897 | ||
|
r794 | self.dataOut.noise_estimation = noise.copy() | |
|
r897 | ||
|
r1167 | return 1 |