#!/usr/bin/env python # # Copyright 2009,2012 Free Software Foundation, Inc. # # This file is part of GNU Radio # # GNU Radio is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3, or (at your option) # any later version. # # GNU Radio is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GNU Radio; see the file COPYING. If not, write to # the Free Software Foundation, Inc., 51 Franklin Street, # Boston, MA 02110-1301, USA. # from gnuradio import gr from gnuradio import filter import sys, time try: import scipy from scipy import fftpack except ImportError: print "Error: Program requires scipy (see: www.scipy.org)." sys.exit(1) try: import pylab from pylab import mlab except ImportError: print "Error: Program requires matplotlib (see: matplotlib.sourceforge.net)." sys.exit(1) class pfb_top_block(gr.top_block): def __init__(self): gr.top_block.__init__(self) self._N = 100000 # number of samples to use self._fs = 2000 # initial sampling rate self._interp = 5 # Interpolation rate for PFB interpolator self._ainterp = 5.5 # Resampling rate for the PFB arbitrary resampler # Frequencies of the signals we construct freq1 = 100 freq2 = 200 # Create a set of taps for the PFB interpolator # This is based on the post-interpolation sample rate self._taps = filter.firdes.low_pass_2(self._interp, self._interp*self._fs, freq2+50, 50, attenuation_dB=120, window=filter.firdes.WIN_BLACKMAN_hARRIS) # Create a set of taps for the PFB arbitrary resampler # The filter size is the number of filters in the filterbank; 32 will give very low side-lobes, # and larger numbers will reduce these even farther # The taps in this filter are based on a sampling rate of the filter size since it acts # internally as an interpolator. flt_size = 32 self._taps2 = filter.firdes.low_pass_2(flt_size, flt_size*self._fs, freq2+50, 150, attenuation_dB=120, window=filter.firdes.WIN_BLACKMAN_hARRIS) # Calculate the number of taps per channel for our own information tpc = scipy.ceil(float(len(self._taps)) / float(self._interp)) print "Number of taps: ", len(self._taps) print "Number of filters: ", self._interp print "Taps per channel: ", tpc # Create a couple of signals at different frequencies self.signal1 = gr.sig_source_c(self._fs, gr.GR_SIN_WAVE, freq1, 0.5) self.signal2 = gr.sig_source_c(self._fs, gr.GR_SIN_WAVE, freq2, 0.5) self.signal = gr.add_cc() self.head = gr.head(gr.sizeof_gr_complex, self._N) # Construct the PFB interpolator filter self.pfb = filter.pfb.interpolator_ccf(self._interp, self._taps) # Construct the PFB arbitrary resampler filter self.pfb_ar = filter.pfb.arb_resampler_ccf(self._ainterp, self._taps2, flt_size) self.snk_i = gr.vector_sink_c() #self.pfb_ar.pfb.print_taps() #self.pfb.pfb.print_taps() # Connect the blocks self.connect(self.signal1, self.head, (self.signal,0)) self.connect(self.signal2, (self.signal,1)) self.connect(self.signal, self.pfb) self.connect(self.signal, self.pfb_ar) self.connect(self.signal, self.snk_i) # Create the sink for the interpolated signals self.snk1 = gr.vector_sink_c() self.snk2 = gr.vector_sink_c() self.connect(self.pfb, self.snk1) self.connect(self.pfb_ar, self.snk2) def main(): tb = pfb_top_block() tstart = time.time() tb.run() tend = time.time() print "Run time: %f" % (tend - tstart) if 1: fig1 = pylab.figure(1, figsize=(12,10), facecolor="w") fig2 = pylab.figure(2, figsize=(12,10), facecolor="w") fig3 = pylab.figure(3, figsize=(12,10), facecolor="w") Ns = 10000 Ne = 10000 fftlen = 8192 winfunc = scipy.blackman # Plot input signal fs = tb._fs d = tb.snk_i.data()[Ns:Ns+Ne] sp1_f = fig1.add_subplot(2, 1, 1) X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen/4, Fs=fs, window = lambda d: d*winfunc(fftlen), scale_by_freq=True) X_in = 10.0*scipy.log10(abs(fftpack.fftshift(X))) f_in = scipy.arange(-fs/2.0, fs/2.0, fs/float(X_in.size)) p1_f = sp1_f.plot(f_in, X_in, "b") sp1_f.set_xlim([min(f_in), max(f_in)+1]) sp1_f.set_ylim([-200.0, 50.0]) sp1_f.set_title("Input Signal", weight="bold") sp1_f.set_xlabel("Frequency (Hz)") sp1_f.set_ylabel("Power (dBW)") Ts = 1.0/fs Tmax = len(d)*Ts t_in = scipy.arange(0, Tmax, Ts) x_in = scipy.array(d) sp1_t = fig1.add_subplot(2, 1, 2) p1_t = sp1_t.plot(t_in, x_in.real, "b-o") #p1_t = sp1_t.plot(t_in, x_in.imag, "r-o") sp1_t.set_ylim([-2.5, 2.5]) sp1_t.set_title("Input Signal", weight="bold") sp1_t.set_xlabel("Time (s)") sp1_t.set_ylabel("Amplitude") # Plot output of PFB interpolator fs_int = tb._fs*tb._interp sp2_f = fig2.add_subplot(2, 1, 1) d = tb.snk1.data()[Ns:Ns+(tb._interp*Ne)] X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen/4, Fs=fs, window = lambda d: d*winfunc(fftlen), scale_by_freq=True) X_o = 10.0*scipy.log10(abs(fftpack.fftshift(X))) f_o = scipy.arange(-fs_int/2.0, fs_int/2.0, fs_int/float(X_o.size)) p2_f = sp2_f.plot(f_o, X_o, "b") sp2_f.set_xlim([min(f_o), max(f_o)+1]) sp2_f.set_ylim([-200.0, 50.0]) sp2_f.set_title("Output Signal from PFB Interpolator", weight="bold") sp2_f.set_xlabel("Frequency (Hz)") sp2_f.set_ylabel("Power (dBW)") Ts_int = 1.0/fs_int Tmax = len(d)*Ts_int t_o = scipy.arange(0, Tmax, Ts_int) x_o1 = scipy.array(d) sp2_t = fig2.add_subplot(2, 1, 2) p2_t = sp2_t.plot(t_o, x_o1.real, "b-o") #p2_t = sp2_t.plot(t_o, x_o.imag, "r-o") sp2_t.set_ylim([-2.5, 2.5]) sp2_t.set_title("Output Signal from PFB Interpolator", weight="bold") sp2_t.set_xlabel("Time (s)") sp2_t.set_ylabel("Amplitude") # Plot output of PFB arbitrary resampler fs_aint = tb._fs * tb._ainterp sp3_f = fig3.add_subplot(2, 1, 1) d = tb.snk2.data()[Ns:Ns+(tb._interp*Ne)] X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen/4, Fs=fs, window = lambda d: d*winfunc(fftlen), scale_by_freq=True) X_o = 10.0*scipy.log10(abs(fftpack.fftshift(X))) f_o = scipy.arange(-fs_aint/2.0, fs_aint/2.0, fs_aint/float(X_o.size)) p3_f = sp3_f.plot(f_o, X_o, "b") sp3_f.set_xlim([min(f_o), max(f_o)+1]) sp3_f.set_ylim([-200.0, 50.0]) sp3_f.set_title("Output Signal from PFB Arbitrary Resampler", weight="bold") sp3_f.set_xlabel("Frequency (Hz)") sp3_f.set_ylabel("Power (dBW)") Ts_aint = 1.0/fs_aint Tmax = len(d)*Ts_aint t_o = scipy.arange(0, Tmax, Ts_aint) x_o2 = scipy.array(d) sp3_f = fig3.add_subplot(2, 1, 2) p3_f = sp3_f.plot(t_o, x_o2.real, "b-o") p3_f = sp3_f.plot(t_o, x_o1.real, "m-o") #p3_f = sp3_f.plot(t_o, x_o2.imag, "r-o") sp3_f.set_ylim([-2.5, 2.5]) sp3_f.set_title("Output Signal from PFB Arbitrary Resampler", weight="bold") sp3_f.set_xlabel("Time (s)") sp3_f.set_ylabel("Amplitude") pylab.show() if __name__ == "__main__": try: main() except KeyboardInterrupt: pass