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#!/usr/bin/env python
#
# Copyright 2009 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, blks2
import os
import scipy, pylab
from scipy import fftpack
from pylab import mlab
import time
#print os.getpid()
#raw_input()
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 = gr.firdes.low_pass_2(self._interp, self._interp*self._fs, freq2+50, 50,
attenuation_dB=120, window=gr.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 = gr.firdes.low_pass_2(flt_size, flt_size*self._fs, freq2+50, 150,
attenuation_dB=120, window=gr.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 = blks2.pfb_interpolator_ccf(self._interp, self._taps)
# Construct the PFB arbitrary resampler filter
self.pfb_ar = blks2.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
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