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import numpy
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import scipy.signal
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import matplotlib
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matplotlib.use("TKAgg")
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import pylab as pl
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import time
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def getInverseFilter(code, lenfilter=8*28):
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nBauds = len(code)
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if lenfilter == None:
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lenfilter = 10*nBauds
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codeBuffer = numpy.zeros((lenfilter), dtype=numpy.float32)
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codeBuffer[0:nBauds] = code
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inverse_filter = numpy.real(numpy.fft.ifft(1.0/numpy.fft.fft(codeBuffer)))
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inverse_filter = numpy.roll(inverse_filter, shift=120)
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# pl.plot(codeBuffer)
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# pl.plot(inverse_filter)
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# pl.show()
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return inverse_filter
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def getSignal(nChannels, nHeis):
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u = numpy.complex(1,2)
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u /= numpy.abs(u)
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signal = numpy.random.rand(nChannels, nHeis)
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signal = signal.astype(numpy.complex)
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signal *= u
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return signal
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def time_decoding(signal, code):
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ini = time.time()
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nBauds = len(code)
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nChannels, nHeis = signal.shape
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datadec = numpy.zeros((nChannels, nHeis - nBauds + 1), dtype=numpy.complex)
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tmpcode = code.astype(numpy.complex)
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#######################################
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ini = time.time()
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for i in range(nChannels):
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datadec[i,:] = numpy.correlate(signal[i,:], code, mode='valid')/nBauds
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print time.time() - ini
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return datadec
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def freq_decoding(signal, code):
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ini = time.time()
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nBauds = len(code)
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nChannels, nHeis = signal.shape
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codeBuffer = numpy.zeros((nHeis), dtype=numpy.float32)
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codeBuffer[0:nBauds] = code
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fft_code = numpy.conj(numpy.fft.fft(codeBuffer)).reshape(1, -1)
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######################################
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ini = time.time()
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fft_data = numpy.fft.fft(signal, axis=1)
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conv = fft_data*fft_code
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data = numpy.fft.ifft(conv, axis=1)/nBauds
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datadec = data[:,:-nBauds+1]
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print time.time() - ini
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return datadec
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def fftconvol_decoding(signal, code):
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ini = time.time()
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nBauds = len(code)
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nChannels, nHeis = signal.shape
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datadec = numpy.zeros((nChannels, nHeis - nBauds + 1), dtype=numpy.complex)
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tmpcode = code.astype(numpy.complex)
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#######################################
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ini = time.time()
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for i in range(nChannels):
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datadec[i,:] = scipy.signal.fftconvolve(signal[i,:], code[-1::-1], mode='valid')/nBauds
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print time.time() - ini
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return datadec
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def filter_decoding(signal, code):
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ini = time.time()
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nBauds = len(code)
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nChannels, nHeis = signal.shape
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inverse_filter = getInverseFilter(code)
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datadec = numpy.zeros((nChannels, nHeis + len(inverse_filter) - 1), dtype=numpy.complex)
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#######################################
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ini = time.time()
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for i in range(nChannels):
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datadec[i,:] = numpy.convolve(signal[i,:], inverse_filter, mode='full')
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datadec = datadec[:,120:120+nHeis]
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print time.time() - ini
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return datadec
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nChannels, nHeis = 8, 3900
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index = 300
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code = numpy.array([1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1])
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signal = getSignal(nChannels, nHeis)
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signal[0,index:index+len(code)] = code*10
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signalout = time_decoding(signal, code)
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signalout1 = freq_decoding(signal, code)
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signalout2 = fftconvol_decoding(signal, code)
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signalout3 = filter_decoding(signal, code)
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#pl.plot(numpy.abs(signal[0]))
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pl.plot(numpy.abs(signalout[0]))
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#pl.plot(numpy.abs(signalout1[0]))
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#pl.plot(numpy.abs(signalout2[0]))
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pl.plot(numpy.abs(signalout3[0])+0.5)
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pl.show()
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