def __init__(self, numchans, taps=None, oversample_rate=1, atten=100):
gr.hier_block2.__init__(
self, "pfb_channelizer_ccf",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(numchans, numchans, gr.sizeof_gr_complex))
self._nchans = numchans
self._oversample_rate = oversample_rate
if (taps is not None) and (len(taps) > 0):
self._taps = taps
else:
# Create a filter that covers the full bandwidth of the input signal
bw = 0.4
tb = 0.2
ripple = 0.1
made = False
while not made:
try:
self._taps = optfir.low_pass(1, self._nchans, bw, bw + tb,
ripple, atten)
made = True
except RuntimeError:
ripple += 0.01
made = False
print(
"Warning: set ripple to %.4f dB. If this is a problem, adjust the attenuation or create your own filter taps."
% (ripple))
# Build in an exit strategy; if we've come this far, it ain't working.
if (ripple >= 1.0):
raise RuntimeError(
"optfir could not generate an appropriate filter.")
self.s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex,
self._nchans)
self.pfb = filter.pfb_channelizer_ccf(self._nchans, self._taps,
self._oversample_rate)
self.connect(self, self.s2ss)
for i in xrange(self._nchans):
self.connect((self.s2ss, i), (self.pfb, i))
self.connect((self.pfb, i), (self, i))
def __init__(self, numchans, taps=None, oversample_rate=1, atten=100):
gr.hier_block2.__init__(self, "pfb_channelizer_ccf",
gr.io_signature(1, 1, gr.sizeof_gr_complex),
gr.io_signature(numchans, numchans, gr.sizeof_gr_complex))
self._nchans = numchans
self._oversample_rate = oversample_rate
if (taps is not None) and (len(taps) > 0):
self._taps = taps
else:
# Create a filter that covers the full bandwidth of the input signal
bw = 0.4
tb = 0.2
ripple = 0.1
made = False
while not made:
try:
self._taps = optfir.low_pass(1, self._nchans, bw, bw+tb, ripple, atten)
made = True
except RuntimeError:
ripple += 0.01
made = False
print("Warning: set ripple to %.4f dB. If this is a problem, adjust the attenuation or create your own filter taps." % (ripple))
# Build in an exit strategy; if we've come this far, it ain't working.
if(ripple >= 1.0):
raise RuntimeError("optfir could not generate an appropriate filter.")
self.s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, self._nchans)
self.pfb = filter.pfb_channelizer_ccf(self._nchans, self._taps,
self._oversample_rate)
self.connect(self, self.s2ss)
for i in xrange(self._nchans):
self.connect((self.s2ss,i), (self.pfb,i))
self.connect((self.pfb,i), (self,i))
def test_000(self):
N = 1000 # number of samples to use
M = 5 # Number of channels to channelize
fs = 1000 # baseband sampling rate
ifs = M * fs # input samp rate to channelizer
taps = filter.firdes.low_pass_2(
1,
ifs,
500,
50,
attenuation_dB=80,
window=filter.firdes.WIN_BLACKMAN_hARRIS)
signals = list()
add = gr.add_cc()
freqs = [-200, -100, 0, 100, 200]
for i in xrange(len(freqs)):
f = freqs[i] + (M / 2 - M + i + 1) * fs
signals.append(gr.sig_source_c(ifs, gr.GR_SIN_WAVE, f, 1))
self.tb.connect(signals[i], (add, i))
head = gr.head(gr.sizeof_gr_complex, N)
s2ss = gr.stream_to_streams(gr.sizeof_gr_complex, M)
pfb = filter.pfb_channelizer_ccf(M, taps, 1)
self.tb.connect(add, head, s2ss)
snks = list()
for i in xrange(M):
snks.append(gr.vector_sink_c())
self.tb.connect((s2ss, i), (pfb, i))
self.tb.connect((pfb, i), snks[i])
self.tb.run()
Ntest = 50
L = len(snks[0].data())
t = map(lambda x: float(x) / fs, xrange(L))
# Adjusted phase rotations for data
p0 = 0
p1 = math.pi * 0.51998885
p2 = -math.pi * 0.96002233
p3 = math.pi * 0.96002233
p4 = -math.pi * 0.51998885
# Create known data as complex sinusoids at the different baseband freqs
# the different channel numbering is due to channelizer output order.
expected0_data = map(lambda x: math.cos(2.*math.pi*freqs[2]*x+p0) + \
1j*math.sin(2.*math.pi*freqs[2]*x+p0), t)
expected1_data = map(lambda x: math.cos(2.*math.pi*freqs[3]*x+p1) + \
1j*math.sin(2.*math.pi*freqs[3]*x+p1), t)
expected2_data = map(lambda x: math.cos(2.*math.pi*freqs[4]*x+p2) + \
1j*math.sin(2.*math.pi*freqs[4]*x+p2), t)
expected3_data = map(lambda x: math.cos(2.*math.pi*freqs[0]*x+p3) + \
1j*math.sin(2.*math.pi*freqs[0]*x+p3), t)
expected4_data = map(lambda x: math.cos(2.*math.pi*freqs[1]*x+p4) + \
1j*math.sin(2.*math.pi*freqs[1]*x+p4), t)
dst0_data = snks[0].data()
dst1_data = snks[1].data()
dst2_data = snks[2].data()
dst3_data = snks[3].data()
dst4_data = snks[4].data()
self.assertComplexTuplesAlmostEqual(expected0_data[-Ntest:],
dst0_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected1_data[-Ntest:],
dst1_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected2_data[-Ntest:],
dst2_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected3_data[-Ntest:],
dst3_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected4_data[-Ntest:],
dst4_data[-Ntest:], 3)
def test_000(self):
N = 1000 # number of samples to use
M = 5 # Number of channels to channelize
fs = 1000 # baseband sampling rate
ifs = M*fs # input samp rate to channelizer
taps = filter.firdes.low_pass_2(1, ifs, 500, 50,
attenuation_dB=80,
window=filter.firdes.WIN_BLACKMAN_hARRIS)
signals = list()
add = blocks.add_cc()
freqs = [-200, -100, 0, 100, 200]
for i in xrange(len(freqs)):
f = freqs[i] + (M/2-M+i+1)*fs
data = sig_source_c(ifs, f, 1, N)
signals.append(blocks.vector_source_c(data))
self.tb.connect(signals[i], (add,i))
s2ss = blocks.stream_to_streams(gr.sizeof_gr_complex, M)
pfb = filter.pfb_channelizer_ccf(M, taps, 1)
self.tb.connect(add, s2ss)
snks = list()
for i in xrange(M):
snks.append(blocks.vector_sink_c())
self.tb.connect((s2ss,i), (pfb,i))
self.tb.connect((pfb, i), snks[i])
self.tb.run()
Ntest = 50
L = len(snks[0].data())
t = map(lambda x: float(x)/fs, xrange(L))
# Adjusted phase rotations for data
p0 = 0
p1 = math.pi*0.51998885
p2 = -math.pi*0.96002233
p3 = math.pi*0.96002233
p4 = -math.pi*0.51998885
# Create known data as complex sinusoids at the different baseband freqs
# the different channel numbering is due to channelizer output order.
expected0_data = map(lambda x: math.cos(2.*math.pi*freqs[2]*x+p0) + \
1j*math.sin(2.*math.pi*freqs[2]*x+p0), t)
expected1_data = map(lambda x: math.cos(2.*math.pi*freqs[3]*x+p1) + \
1j*math.sin(2.*math.pi*freqs[3]*x+p1), t)
expected2_data = map(lambda x: math.cos(2.*math.pi*freqs[4]*x+p2) + \
1j*math.sin(2.*math.pi*freqs[4]*x+p2), t)
expected3_data = map(lambda x: math.cos(2.*math.pi*freqs[0]*x+p3) + \
1j*math.sin(2.*math.pi*freqs[0]*x+p3), t)
expected4_data = map(lambda x: math.cos(2.*math.pi*freqs[1]*x+p4) + \
1j*math.sin(2.*math.pi*freqs[1]*x+p4), t)
dst0_data = snks[0].data()
dst1_data = snks[1].data()
dst2_data = snks[2].data()
dst3_data = snks[3].data()
dst4_data = snks[4].data()
self.assertComplexTuplesAlmostEqual(expected0_data[-Ntest:], dst0_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected1_data[-Ntest:], dst1_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected2_data[-Ntest:], dst2_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected3_data[-Ntest:], dst3_data[-Ntest:], 3)
self.assertComplexTuplesAlmostEqual(expected4_data[-Ntest:], dst4_data[-Ntest:], 3)
