1#!/usr/bin/env python
2#
3# Copyright 2009,2012,2013 Free Software Foundation, Inc.
4#
5# This file is part of GNU Radio
6#
7# GNU Radio is free software; you can redistribute it and/or modify
8# it under the terms of the GNU General Public License as published by
9# the Free Software Foundation; either version 3, or (at your option)
10# any later version.
11#
12# GNU Radio is distributed in the hope that it will be useful,
13# but WITHOUT ANY WARRANTY; without even the implied warranty of
14# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15# GNU General Public License for more details.
16#
17# You should have received a copy of the GNU General Public License
18# along with GNU Radio; see the file COPYING.  If not, write to
19# the Free Software Foundation, Inc., 51 Franklin Street,
20# Boston, MA 02110-1301, USA.
21#
22
23from __future__ import print_function
24from __future__ import division
25from __future__ import unicode_literals
26from gnuradio import gr
27from gnuradio import blocks
28from gnuradio import filter
29import sys, time
30import numpy
31
32try:
33    from gnuradio import analog
34except ImportError:
35    sys.stderr.write("Error: Program requires gr-analog.\n")
36    sys.exit(1)
37
38try:
39    import pylab
40    from pylab import mlab
41except ImportError:
42    sys.stderr.write("Error: Program requires matplotlib (see: matplotlib.sourceforge.net).\n")
43    sys.exit(1)
44
45class pfb_top_block(gr.top_block):
46    def __init__(self):
47        gr.top_block.__init__(self)
48
49        self._N = 100000        # number of samples to use
50        self._fs = 2000         # initial sampling rate
51        self._interp = 5        # Interpolation rate for PFB interpolator
52        self._ainterp = 5.5       # Resampling rate for the PFB arbitrary resampler
53
54        # Frequencies of the signals we construct
55        freq1 = 100
56        freq2 = 200
57
58        # Create a set of taps for the PFB interpolator
59        # This is based on the post-interpolation sample rate
60        self._taps = filter.firdes.low_pass_2(self._interp,
61                                              self._interp*self._fs,
62                                              freq2+50, 50,
63                                              attenuation_dB=120,
64                                              window=filter.firdes.WIN_BLACKMAN_hARRIS)
65
66        # Create a set of taps for the PFB arbitrary resampler
67        # The filter size is the number of filters in the filterbank; 32 will give very low side-lobes,
68        # and larger numbers will reduce these even farther
69        # The taps in this filter are based on a sampling rate of the filter size since it acts
70        # internally as an interpolator.
71        flt_size = 32
72        self._taps2 = filter.firdes.low_pass_2(flt_size,
73                                               flt_size*self._fs,
74                                               freq2+50, 150,
75                                               attenuation_dB=120,
76                                               window=filter.firdes.WIN_BLACKMAN_hARRIS)
77
78        # Calculate the number of taps per channel for our own information
79        tpc = numpy.ceil(float(len(self._taps)) / float(self._interp))
80        print("Number of taps:     ", len(self._taps))
81        print("Number of filters:  ", self._interp)
82        print("Taps per channel:   ", tpc)
83
84        # Create a couple of signals at different frequencies
85        self.signal1 = analog.sig_source_c(self._fs, analog.GR_SIN_WAVE, freq1, 0.5)
86        self.signal2 = analog.sig_source_c(self._fs, analog.GR_SIN_WAVE, freq2, 0.5)
87        self.signal = blocks.add_cc()
88
89        self.head = blocks.head(gr.sizeof_gr_complex, self._N)
90
91        # Construct the PFB interpolator filter
92        self.pfb = filter.pfb.interpolator_ccf(self._interp, self._taps)
93
94        # Construct the PFB arbitrary resampler filter
95        self.pfb_ar = filter.pfb.arb_resampler_ccf(self._ainterp, self._taps2, flt_size)
96        self.snk_i = blocks.vector_sink_c()
97
98        #self.pfb_ar.pfb.print_taps()
99        #self.pfb.pfb.print_taps()
100
101        # Connect the blocks
102        self.connect(self.signal1, self.head, (self.signal,0))
103        self.connect(self.signal2, (self.signal,1))
104        self.connect(self.signal, self.pfb)
105        self.connect(self.signal, self.pfb_ar)
106        self.connect(self.signal, self.snk_i)
107
108        # Create the sink for the interpolated signals
109        self.snk1 = blocks.vector_sink_c()
110        self.snk2 = blocks.vector_sink_c()
111        self.connect(self.pfb, self.snk1)
112        self.connect(self.pfb_ar, self.snk2)
113
114
115def main():
116    tb = pfb_top_block()
117
118    tstart = time.time()
119    tb.run()
120    tend = time.time()
121    print("Run time: %f" % (tend - tstart))
122
123
124    if 1:
125        fig1 = pylab.figure(1, figsize=(12,10), facecolor="w")
126        fig2 = pylab.figure(2, figsize=(12,10), facecolor="w")
127        fig3 = pylab.figure(3, figsize=(12,10), facecolor="w")
128
129        Ns = 10000
130        Ne = 10000
131
132        fftlen = 8192
133        winfunc = numpy.blackman
134
135        # Plot input signal
136        fs = tb._fs
137
138        d = tb.snk_i.data()[Ns:Ns+Ne]
139        sp1_f = fig1.add_subplot(2, 1, 1)
140
141        X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen / 4, Fs=fs,
142                          window = lambda d: d*winfunc(fftlen),
143                          scale_by_freq=True)
144        X_in = 10.0*numpy.log10(abs(numpy.fft.fftshift(X)))
145        f_in = numpy.arange(-fs / 2.0, fs / 2.0, fs / float(X_in.size))
146        p1_f = sp1_f.plot(f_in, X_in, "b")
147        sp1_f.set_xlim([min(f_in), max(f_in)+1])
148        sp1_f.set_ylim([-200.0, 50.0])
149
150
151        sp1_f.set_title("Input Signal", weight="bold")
152        sp1_f.set_xlabel("Frequency (Hz)")
153        sp1_f.set_ylabel("Power (dBW)")
154
155        Ts = 1.0 / fs
156        Tmax = len(d)*Ts
157
158        t_in = numpy.arange(0, Tmax, Ts)
159        x_in = numpy.array(d)
160        sp1_t = fig1.add_subplot(2, 1, 2)
161        p1_t = sp1_t.plot(t_in, x_in.real, "b-o")
162        #p1_t = sp1_t.plot(t_in, x_in.imag, "r-o")
163        sp1_t.set_ylim([-2.5, 2.5])
164
165        sp1_t.set_title("Input Signal", weight="bold")
166        sp1_t.set_xlabel("Time (s)")
167        sp1_t.set_ylabel("Amplitude")
168
169
170        # Plot output of PFB interpolator
171        fs_int = tb._fs*tb._interp
172
173        sp2_f = fig2.add_subplot(2, 1, 1)
174        d = tb.snk1.data()[Ns:Ns+(tb._interp*Ne)]
175        X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen / 4, Fs=fs,
176                          window = lambda d: d*winfunc(fftlen),
177                          scale_by_freq=True)
178        X_o = 10.0*numpy.log10(abs(numpy.fft.fftshift(X)))
179        f_o = numpy.arange(-fs_int / 2.0, fs_int / 2.0, fs_int / float(X_o.size))
180        p2_f = sp2_f.plot(f_o, X_o, "b")
181        sp2_f.set_xlim([min(f_o), max(f_o)+1])
182        sp2_f.set_ylim([-200.0, 50.0])
183
184        sp2_f.set_title("Output Signal from PFB Interpolator", weight="bold")
185        sp2_f.set_xlabel("Frequency (Hz)")
186        sp2_f.set_ylabel("Power (dBW)")
187
188        Ts_int = 1.0 / fs_int
189        Tmax = len(d)*Ts_int
190
191        t_o = numpy.arange(0, Tmax, Ts_int)
192        x_o1 = numpy.array(d)
193        sp2_t = fig2.add_subplot(2, 1, 2)
194        p2_t = sp2_t.plot(t_o, x_o1.real, "b-o")
195        #p2_t = sp2_t.plot(t_o, x_o.imag, "r-o")
196        sp2_t.set_ylim([-2.5, 2.5])
197
198        sp2_t.set_title("Output Signal from PFB Interpolator", weight="bold")
199        sp2_t.set_xlabel("Time (s)")
200        sp2_t.set_ylabel("Amplitude")
201
202
203        # Plot output of PFB arbitrary resampler
204        fs_aint = tb._fs * tb._ainterp
205
206        sp3_f = fig3.add_subplot(2, 1, 1)
207        d = tb.snk2.data()[Ns:Ns+(tb._interp*Ne)]
208        X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen / 4, Fs=fs,
209                          window = lambda d: d*winfunc(fftlen),
210                          scale_by_freq=True)
211        X_o = 10.0*numpy.log10(abs(numpy.fft.fftshift(X)))
212        f_o = numpy.arange(-fs_aint / 2.0, fs_aint / 2.0, fs_aint / float(X_o.size))
213        p3_f = sp3_f.plot(f_o, X_o, "b")
214        sp3_f.set_xlim([min(f_o), max(f_o)+1])
215        sp3_f.set_ylim([-200.0, 50.0])
216
217        sp3_f.set_title("Output Signal from PFB Arbitrary Resampler", weight="bold")
218        sp3_f.set_xlabel("Frequency (Hz)")
219        sp3_f.set_ylabel("Power (dBW)")
220
221        Ts_aint = 1.0 / fs_aint
222        Tmax = len(d)*Ts_aint
223
224        t_o = numpy.arange(0, Tmax, Ts_aint)
225        x_o2 = numpy.array(d)
226        sp3_f = fig3.add_subplot(2, 1, 2)
227        p3_f = sp3_f.plot(t_o, x_o2.real, "b-o")
228        p3_f = sp3_f.plot(t_o, x_o1.real, "m-o")
229        #p3_f = sp3_f.plot(t_o, x_o2.imag, "r-o")
230        sp3_f.set_ylim([-2.5, 2.5])
231
232        sp3_f.set_title("Output Signal from PFB Arbitrary Resampler", weight="bold")
233        sp3_f.set_xlabel("Time (s)")
234        sp3_f.set_ylabel("Amplitude")
235
236        pylab.show()
237
238
239if __name__ == "__main__":
240    try:
241        main()
242    except KeyboardInterrupt:
243        pass
244
245