def main():
N = 1000000
fs = 8000
freqs = [100, 200, 300, 400, 500]
nchans = 7
sigs = list()
for fi in freqs:
s = gr.sig_source_c(fs, gr.GR_SIN_WAVE, fi, 1)
sigs.append(s)
taps = gr.firdes.low_pass_2(len(freqs), fs, fs / float(nchans) / 2, 100,
100)
print "Num. Taps = %d (taps per filter = %d)" % (len(taps),
len(taps) / nchans)
filtbank = gr.pfb_synthesizer_ccf(nchans, taps)
head = gr.head(gr.sizeof_gr_complex, N)
snk = gr.vector_sink_c()
tb = gr.top_block()
tb.connect(filtbank, head, snk)
for i, si in enumerate(sigs):
tb.connect(si, (filtbank, i))
tb.run()
if 1:
f1 = pylab.figure(1)
s1 = f1.add_subplot(1, 1, 1)
s1.plot(snk.data()[1000:])
fftlen = 2048
f2 = pylab.figure(2)
s2 = f2.add_subplot(1, 1, 1)
winfunc = scipy.blackman
s2.psd(snk.data()[10000:],
NFFT=fftlen,
Fs=nchans * fs,
noverlap=fftlen / 4,
window=lambda d: d * winfunc(fftlen))
pylab.show()
def main():
N = 1000000
fs = 8000
freqs = [100, 200, 300, 400, 500]
nchans = 7
sigs = list()
for fi in freqs:
s = gr.sig_source_c(fs, gr.GR_SIN_WAVE, fi, 1)
sigs.append(s)
taps = gr.firdes.low_pass_2(len(freqs), fs, fs/float(nchans)/2, 100, 100)
print "Num. Taps = %d (taps per filter = %d)" % (len(taps),
len(taps)/nchans)
filtbank = gr.pfb_synthesizer_ccf(nchans, taps)
head = gr.head(gr.sizeof_gr_complex, N)
snk = gr.vector_sink_c()
tb = gr.top_block()
tb.connect(filtbank, head, snk)
for i,si in enumerate(sigs):
tb.connect(si, (filtbank, i))
tb.run()
if 1:
f1 = pylab.figure(1)
s1 = f1.add_subplot(1,1,1)
s1.plot(snk.data()[1000:])
fftlen = 2048
f2 = pylab.figure(2)
s2 = f2.add_subplot(1,1,1)
winfunc = scipy.blackman
s2.psd(snk.data()[10000:], NFFT=fftlen,
Fs = nchans*fs,
noverlap=fftlen/4,
window = lambda d: d*winfunc(fftlen))
pylab.show()
def main():
N = 1000000
fs = 8000
freqs = [100, 200, 300, 400, 500]
nchans = 7
sigs = list()
fmtx = list()
for fi in freqs:
s = gr.sig_source_f(fs, gr.GR_SIN_WAVE, fi, 1)
fm = blks2.nbfm_tx (fs, 4*fs, max_dev=10000, tau=75e-6)
sigs.append(s)
fmtx.append(fm)
syntaps = gr.firdes.low_pass_2(len(freqs), fs, fs/float(nchans)/2, 100, 100)
print "Synthesis Num. Taps = %d (taps per filter = %d)" % (len(syntaps),
len(syntaps)/nchans)
chtaps = gr.firdes.low_pass_2(len(freqs), fs, fs/float(nchans)/2, 100, 100)
print "Channelizer Num. Taps = %d (taps per filter = %d)" % (len(chtaps),
len(chtaps)/nchans)
filtbank = gr.pfb_synthesizer_ccf(nchans, syntaps)
channelizer = blks2.pfb_channelizer_ccf(nchans, chtaps)
noise_level = 0.01
head = gr.head(gr.sizeof_gr_complex, N)
noise = gr.noise_source_c(gr.GR_GAUSSIAN, noise_level)
addnoise = gr.add_cc()
snk_synth = gr.vector_sink_c()
tb = gr.top_block()
tb.connect(noise, (addnoise,0))
tb.connect(filtbank, head, (addnoise, 1))
tb.connect(addnoise, channelizer)
tb.connect(addnoise, snk_synth)
snk = list()
for i,si in enumerate(sigs):
tb.connect(si, fmtx[i], (filtbank, i))
for i in xrange(nchans):
snk.append(gr.vector_sink_c())
tb.connect((channelizer, i), snk[i])
tb.run()
if 1:
channel = 1
data = snk[channel].data()[1000:]
f1 = pylab.figure(1)
s1 = f1.add_subplot(1,1,1)
s1.plot(data[10000:10200] )
s1.set_title(("Output Signal from Channel %d" % channel))
fftlen = 2048
winfunc = scipy.blackman
#winfunc = scipy.hamming
f2 = pylab.figure(2)
s2 = f2.add_subplot(1,1,1)
s2.psd(data, NFFT=fftlen,
Fs = nchans*fs,
noverlap=fftlen/4,
window = lambda d: d*winfunc(fftlen))
s2.set_title(("Output PSD from Channel %d" % channel))
f3 = pylab.figure(3)
s3 = f3.add_subplot(1,1,1)
s3.psd(snk_synth.data()[1000:], NFFT=fftlen,
Fs = nchans*fs,
noverlap=fftlen/4,
window = lambda d: d*winfunc(fftlen))
s3.set_title("Output of Synthesis Filter")
pylab.show()
def main():
N = 10000
fs = 2000.0
Ts = 1.0/fs
t = scipy.arange(0, N*Ts, Ts)
# When playing with the number of channels, be careful about the filter
# specs and the channel map of the synthesizer set below.
nchans = 10
# Build the filter(s)
bw = 1000
tb = 400
proto_taps = gr.firdes.low_pass_2(1, nchans*fs, bw, tb, 80,
gr.firdes.WIN_BLACKMAN_hARRIS)
print "Filter length: ", len(proto_taps)
# Create a modulated signal
npwr = 0.01
data = scipy.random.randint(0, 256, N)
rrc_taps = gr.firdes.root_raised_cosine(1, 2, 1, 0.35, 41)
src = gr.vector_source_b(data.astype(scipy.uint8).tolist(), False)
mod = digital.bpsk_mod(samples_per_symbol=2)
chan = gr.channel_model(npwr)
rrc = gr.fft_filter_ccc(1, rrc_taps)
# Split it up into pieces
channelizer = blks2.pfb_channelizer_ccf(nchans, proto_taps, 2)
# Put the pieces back together again
syn_taps = [nchans*t for t in proto_taps]
synthesizer = gr.pfb_synthesizer_ccf(nchans, syn_taps, True)
src_snk = gr.vector_sink_c()
snk = gr.vector_sink_c()
# Remap the location of the channels
# Can be done in synth or channelizer (watch out for rotattions in
# the channelizer)
synthesizer.set_channel_map([ 0, 1, 2, 3, 4,
15, 16, 17, 18, 19])
tb = gr.top_block()
tb.connect(src, mod, chan, rrc, channelizer)
tb.connect(rrc, src_snk)
vsnk = []
for i in xrange(nchans):
tb.connect((channelizer,i), (synthesizer, i))
vsnk.append(gr.vector_sink_c())
tb.connect((channelizer,i), vsnk[i])
tb.connect(synthesizer, snk)
tb.run()
sin = scipy.array(src_snk.data()[1000:])
sout = scipy.array(snk.data()[1000:])
# Plot original signal
fs_in = nchans*fs
f1 = pylab.figure(1, figsize=(16,12), facecolor='w')
s11 = f1.add_subplot(2,2,1)
s11.psd(sin, NFFT=fftlen, Fs=fs_in)
s11.set_title("PSD of Original Signal")
s11.set_ylim([-200, -20])
s12 = f1.add_subplot(2,2,2)
s12.plot(sin.real[1000:1500], "o-b")
s12.plot(sin.imag[1000:1500], "o-r")
s12.set_title("Original Signal in Time")
start = 1
skip = 4
s13 = f1.add_subplot(2,2,3)
s13.plot(sin.real[start::skip], sin.imag[start::skip], "o")
s13.set_title("Constellation")
s13.set_xlim([-2, 2])
s13.set_ylim([-2, 2])
# Plot channels
nrows = int(scipy.sqrt(nchans))
ncols = int(scipy.ceil(float(nchans)/float(nrows)))
f2 = pylab.figure(2, figsize=(16,12), facecolor='w')
for n in xrange(nchans):
s = f2.add_subplot(nrows, ncols, n+1)
s.psd(vsnk[n].data(), NFFT=fftlen, Fs=fs_in)
s.set_title("Channel {0}".format(n))
s.set_ylim([-200, -20])
# Plot reconstructed signal
fs_out = 2*nchans*fs
f3 = pylab.figure(3, figsize=(16,12), facecolor='w')
s31 = f3.add_subplot(2,2,1)
s31.psd(sout, NFFT=fftlen, Fs=fs_out)
s31.set_title("PSD of Reconstructed Signal")
s31.set_ylim([-200, -20])
s32 = f3.add_subplot(2,2,2)
s32.plot(sout.real[1000:1500], "o-b")
s32.plot(sout.imag[1000:1500], "o-r")
s32.set_title("Reconstructed Signal in Time")
start = 2
skip = 4
s33 = f3.add_subplot(2,2,3)
s33.plot(sout.real[start::skip], sout.imag[start::skip], "o")
s33.set_title("Constellation")
s33.set_xlim([-2, 2])
s33.set_ylim([-2, 2])
pylab.show()
def main():
N = 10000
fs = 2000.0
Ts = 1.0 / fs
t = scipy.arange(0, N * Ts, Ts)
# When playing with the number of channels, be careful about the filter
# specs and the channel map of the synthesizer set below.
nchans = 10
# Build the filter(s)
bw = 1000
tb = 400
proto_taps = gr.firdes.low_pass_2(1, nchans * fs, bw, tb, 80,
gr.firdes.WIN_BLACKMAN_hARRIS)
print "Filter length: ", len(proto_taps)
# Create a modulated signal
npwr = 0.01
data = scipy.random.randint(0, 256, N)
rrc_taps = gr.firdes.root_raised_cosine(1, 2, 1, 0.35, 41)
src = gr.vector_source_b(data.astype(scipy.uint8).tolist(), False)
mod = digital.bpsk_mod(samples_per_symbol=2)
chan = gr.channel_model(npwr)
rrc = gr.fft_filter_ccc(1, rrc_taps)
# Split it up into pieces
channelizer = blks2.pfb_channelizer_ccf(nchans, proto_taps, 2)
# Put the pieces back together again
syn_taps = [nchans * t for t in proto_taps]
synthesizer = gr.pfb_synthesizer_ccf(nchans, syn_taps, True)
src_snk = gr.vector_sink_c()
snk = gr.vector_sink_c()
# Remap the location of the channels
# Can be done in synth or channelizer (watch out for rotattions in
# the channelizer)
synthesizer.set_channel_map([0, 1, 2, 3, 4, 15, 16, 17, 18, 19])
tb = gr.top_block()
tb.connect(src, mod, chan, rrc, channelizer)
tb.connect(rrc, src_snk)
vsnk = []
for i in xrange(nchans):
tb.connect((channelizer, i), (synthesizer, i))
vsnk.append(gr.vector_sink_c())
tb.connect((channelizer, i), vsnk[i])
tb.connect(synthesizer, snk)
tb.run()
sin = scipy.array(src_snk.data()[1000:])
sout = scipy.array(snk.data()[1000:])
# Plot original signal
fs_in = nchans * fs
f1 = pylab.figure(1, figsize=(16, 12), facecolor='w')
s11 = f1.add_subplot(2, 2, 1)
s11.psd(sin, NFFT=fftlen, Fs=fs_in)
s11.set_title("PSD of Original Signal")
s11.set_ylim([-200, -20])
s12 = f1.add_subplot(2, 2, 2)
s12.plot(sin.real[1000:1500], "o-b")
s12.plot(sin.imag[1000:1500], "o-r")
s12.set_title("Original Signal in Time")
start = 1
skip = 4
s13 = f1.add_subplot(2, 2, 3)
s13.plot(sin.real[start::skip], sin.imag[start::skip], "o")
s13.set_title("Constellation")
s13.set_xlim([-2, 2])
s13.set_ylim([-2, 2])
# Plot channels
nrows = int(scipy.sqrt(nchans))
ncols = int(scipy.ceil(float(nchans) / float(nrows)))
f2 = pylab.figure(2, figsize=(16, 12), facecolor='w')
for n in xrange(nchans):
s = f2.add_subplot(nrows, ncols, n + 1)
s.psd(vsnk[n].data(), NFFT=fftlen, Fs=fs_in)
s.set_title("Channel {0}".format(n))
s.set_ylim([-200, -20])
# Plot reconstructed signal
fs_out = 2 * nchans * fs
f3 = pylab.figure(3, figsize=(16, 12), facecolor='w')
s31 = f3.add_subplot(2, 2, 1)
s31.psd(sout, NFFT=fftlen, Fs=fs_out)
s31.set_title("PSD of Reconstructed Signal")
s31.set_ylim([-200, -20])
s32 = f3.add_subplot(2, 2, 2)
s32.plot(sout.real[1000:1500], "o-b")
s32.plot(sout.imag[1000:1500], "o-r")
s32.set_title("Reconstructed Signal in Time")
start = 2
skip = 4
s33 = f3.add_subplot(2, 2, 3)
s33.plot(sout.real[start::skip], sout.imag[start::skip], "o")
s33.set_title("Constellation")
s33.set_xlim([-2, 2])
s33.set_ylim([-2, 2])
pylab.show()
