testcode.py
145 lines
| 3.5 KiB
| text/x-python
|
PythonLexer
Miguel Valdez
|
r320 | import numpy | |
| import scipy.signal | |||
| import matplotlib | |||
| matplotlib.use("TKAgg") | |||
| import pylab as pl | |||
| import time | |||
|
Miguel Valdez
|
r328 | def getInverseFilter(code, lenfilter=8*28): | |
|
Miguel Valdez
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r320 | ||
| nBauds = len(code) | |||
| if lenfilter == None: | |||
| lenfilter = 10*nBauds | |||
| codeBuffer = numpy.zeros((lenfilter), dtype=numpy.float32) | |||
| codeBuffer[0:nBauds] = code | |||
| inverse_filter = numpy.real(numpy.fft.ifft(1.0/numpy.fft.fft(codeBuffer))) | |||
| inverse_filter = numpy.roll(inverse_filter, shift=120) | |||
| # pl.plot(codeBuffer) | |||
| # pl.plot(inverse_filter) | |||
| # pl.show() | |||
| return inverse_filter | |||
| def getSignal(nChannels, nHeis): | |||
| u = numpy.complex(1,2) | |||
| u /= numpy.abs(u) | |||
| signal = numpy.random.rand(nChannels, nHeis) | |||
| signal = signal.astype(numpy.complex) | |||
| signal *= u | |||
| return signal | |||
| def time_decoding(signal, code): | |||
| ini = time.time() | |||
| nBauds = len(code) | |||
| nChannels, nHeis = signal.shape | |||
| datadec = numpy.zeros((nChannels, nHeis - nBauds + 1), dtype=numpy.complex) | |||
| tmpcode = code.astype(numpy.complex) | |||
| ####################################### | |||
| ini = time.time() | |||
| for i in range(nChannels): | |||
| datadec[i,:] = numpy.correlate(signal[i,:], code, mode='valid')/nBauds | |||
| print time.time() - ini | |||
| return datadec | |||
| def freq_decoding(signal, code): | |||
| ini = time.time() | |||
| nBauds = len(code) | |||
| nChannels, nHeis = signal.shape | |||
| codeBuffer = numpy.zeros((nHeis), dtype=numpy.float32) | |||
| codeBuffer[0:nBauds] = code | |||
| fft_code = numpy.conj(numpy.fft.fft(codeBuffer)).reshape(1, -1) | |||
| ###################################### | |||
| ini = time.time() | |||
| fft_data = numpy.fft.fft(signal, axis=1) | |||
| conv = fft_data*fft_code | |||
| data = numpy.fft.ifft(conv, axis=1)/nBauds | |||
| datadec = data[:,:-nBauds+1] | |||
| print time.time() - ini | |||
| return datadec | |||
| def fftconvol_decoding(signal, code): | |||
| ini = time.time() | |||
| nBauds = len(code) | |||
| nChannels, nHeis = signal.shape | |||
| datadec = numpy.zeros((nChannels, nHeis - nBauds + 1), dtype=numpy.complex) | |||
| tmpcode = code.astype(numpy.complex) | |||
| ####################################### | |||
| ini = time.time() | |||
| for i in range(nChannels): | |||
| datadec[i,:] = scipy.signal.fftconvolve(signal[i,:], code[-1::-1], mode='valid')/nBauds | |||
| print time.time() - ini | |||
| return datadec | |||
| def filter_decoding(signal, code): | |||
| ini = time.time() | |||
| nBauds = len(code) | |||
| nChannels, nHeis = signal.shape | |||
| inverse_filter = getInverseFilter(code) | |||
| datadec = numpy.zeros((nChannels, nHeis + len(inverse_filter) - 1), dtype=numpy.complex) | |||
| ####################################### | |||
| ini = time.time() | |||
| for i in range(nChannels): | |||
| datadec[i,:] = numpy.convolve(signal[i,:], inverse_filter, mode='full') | |||
| datadec = datadec[:,120:120+nHeis] | |||
| print time.time() - ini | |||
| return datadec | |||
| nChannels, nHeis = 8, 3900 | |||
| index = 300 | |||
| 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]) | |||
| signal = getSignal(nChannels, nHeis) | |||
| signal[0,index:index+len(code)] = code*10 | |||
| signalout = time_decoding(signal, code) | |||
| signalout1 = freq_decoding(signal, code) | |||
| signalout2 = fftconvol_decoding(signal, code) | |||
| signalout3 = filter_decoding(signal, code) | |||
| #pl.plot(numpy.abs(signal[0])) | |||
| pl.plot(numpy.abs(signalout[0])) | |||
| #pl.plot(numpy.abs(signalout1[0])) | |||
| #pl.plot(numpy.abs(signalout2[0])) | |||
|
Miguel Valdez
|
r328 | pl.plot(numpy.abs(signalout3[0])+0.5) | |
|
Miguel Valdez
|
r320 | pl.show() | |