def test_interleaving(self):
# Takes 3 streams (a, b and c)
# Outputs 2 streams.
# First (d) is interleaving of a and b.
# Second (e) is interleaving of a and b and c. c is taken in
# chunks of 2 which are reversed.
A = (1, 2, 3, 4, 5)
B = (11, 12, 13, 14, 15)
C = (99, 98, 97, 96, 95, 94, 93, 92, 91, 90)
expected_D = (1, 11, 2, 12, 3, 13, 4, 14, 5, 15)
expected_E = (1, 11, 98, 99, 2, 12, 96, 97, 3, 13, 94, 95, 4, 14, 92,
93, 5, 15, 90, 91)
mapping = [
[(0, 0), (1, 0)], # mapping to produce D
[(0, 0), (1, 0), (2, 1), (2, 0)], # mapping to produce E
]
srcA = gr.vector_source_f(A, False, 1)
srcB = gr.vector_source_f(B, False, 1)
srcC = gr.vector_source_f(C, False, 2)
vmap = gr.vector_map(gr.sizeof_int, (1, 1, 2), mapping)
dstD = gr.vector_sink_f(2)
dstE = gr.vector_sink_f(4)
self.tb.connect(srcA, (vmap, 0))
self.tb.connect(srcB, (vmap, 1))
self.tb.connect(srcC, (vmap, 2))
self.tb.connect((vmap, 0), dstD)
self.tb.connect((vmap, 1), dstE)
self.tb.run()
self.assertEqual(expected_D, dstD.data())
self.assertEqual(expected_E, dstE.data())
def test_stretch_01(self):
tb = self.tb
data = 10*[1,]
data0 = map(lambda x: x/20.0, data)
data1 = map(lambda x: x/10.0, data)
expected_result0 = 10*[0.05,]
expected_result1 = 10*[0.1,]
src0 = gr.vector_source_f(data0, False)
src1 = gr.vector_source_f(data1, False)
inter = gr.streams_to_vector(gr.sizeof_float, 2)
op = blocks.stretch_ff(0.1, 2)
deinter = gr.vector_to_streams(gr.sizeof_float, 2)
dst0 = gr.vector_sink_f()
dst1 = gr.vector_sink_f()
tb.connect(src0, (inter,0))
tb.connect(src1, (inter,1))
tb.connect(inter, op)
tb.connect(op, deinter)
tb.connect((deinter,0), dst0)
tb.connect((deinter,1), dst1)
tb.run()
dst0_data = dst0.data()
dst1_data = dst1.data()
self.assertFloatTuplesAlmostEqual(expected_result0, dst0_data, 4)
self.assertFloatTuplesAlmostEqual(expected_result1, dst1_data, 4)
def __init__(self,
N,
sps,
rolloff,
ntaps,
bw,
noise,
foffset,
toffset,
poffset,
mode=0):
gr.top_block.__init__(self)
rrc_taps = gr.firdes.root_raised_cosine(sps, sps, 1.0, rolloff, ntaps)
gain = 2 * scipy.pi / 100.0
nfilts = 32
rrc_taps_rx = gr.firdes.root_raised_cosine(nfilts, sps * nfilts, 1.0,
rolloff, ntaps * nfilts)
data = 2.0 * scipy.random.randint(0, 2, N) - 1.0
data = scipy.exp(1j * poffset) * data
self.src = gr.vector_source_c(data.tolist(), False)
self.rrc = gr.interp_fir_filter_ccf(sps, rrc_taps)
self.chn = gr.channel_model(noise, foffset, toffset)
self.off = gr.fractional_interpolator_cc(0.20, 1.0)
if mode == 0:
self.clk = gr.pfb_clock_sync_ccf(sps, gain, rrc_taps_rx, nfilts,
nfilts // 2, 3.5)
self.taps = self.clk.get_taps()
self.dtaps = self.clk.get_diff_taps()
self.vsnk_err = gr.vector_sink_f()
self.vsnk_rat = gr.vector_sink_f()
self.vsnk_phs = gr.vector_sink_f()
self.connect((self.clk, 1), self.vsnk_err)
self.connect((self.clk, 2), self.vsnk_rat)
self.connect((self.clk, 3), self.vsnk_phs)
else: # mode == 1
mu = 0.5
gain_mu = 0.1
gain_omega = 0.25 * gain_mu * gain_mu
omega_rel_lim = 0.02
self.clk = digital.clock_recovery_mm_cc(sps, gain_omega, mu,
gain_mu, omega_rel_lim)
self.vsnk_err = gr.vector_sink_f()
self.connect((self.clk, 1), self.vsnk_err)
self.vsnk_src = gr.vector_sink_c()
self.vsnk_clk = gr.vector_sink_c()
self.connect(self.src, self.rrc, self.chn, self.off, self.clk,
self.vsnk_clk)
self.connect(self.off, self.vsnk_src)
def __init__(self, N, sps, rolloff, ntaps, bw, noise, foffset, toffset, poffset):
gr.top_block.__init__(self)
rrc_taps = gr.firdes.root_raised_cosine(
sps, sps, 1.0, rolloff, ntaps)
data = 2.0*scipy.random.randint(0, 2, N) - 1.0
data = scipy.exp(1j*poffset) * data
self.src = gr.vector_source_c(data.tolist(), False)
self.rrc = filter.interp_fir_filter_ccf(sps, rrc_taps)
self.chn = filter.channel_model(noise, foffset, toffset)
self.fll = digital.fll_band_edge_cc(sps, rolloff, ntaps, bw)
self.vsnk_src = gr.vector_sink_c()
self.vsnk_fll = gr.vector_sink_c()
self.vsnk_frq = gr.vector_sink_f()
self.vsnk_phs = gr.vector_sink_f()
self.vsnk_err = gr.vector_sink_f()
self.connect(self.src, self.rrc, self.chn, self.fll, self.vsnk_fll)
self.connect(self.rrc, self.vsnk_src)
self.connect((self.fll,1), self.vsnk_frq)
self.connect((self.fll,2), self.vsnk_phs)
self.connect((self.fll,3), self.vsnk_err)
def test_interleaving(self):
# Takes 3 streams (a, b and c)
# Outputs 2 streams.
# First (d) is interleaving of a and b.
# Second (e) is interleaving of a and b and c. c is taken in
# chunks of 2 which are reversed.
A = (1, 2, 3, 4, 5)
B = (11, 12, 13, 14, 15)
C = (99, 98, 97, 96, 95, 94, 93, 92, 91, 90)
expected_D = (1, 11, 2, 12, 3, 13, 4, 14, 5, 15)
expected_E = (1, 11, 98, 99, 2, 12, 96, 97, 3, 13, 94, 95,
4, 14, 92, 93, 5, 15, 90, 91)
mapping = [[(0, 0), (1, 0)], # mapping to produce D
[(0, 0), (1, 0), (2, 1), (2, 0)], # mapping to produce E
]
srcA = gr.vector_source_f(A, False, 1)
srcB = gr.vector_source_f(B, False, 1)
srcC = gr.vector_source_f(C, False, 2)
vmap = gr.vector_map(gr.sizeof_int, (1, 1, 2), mapping)
dstD = gr.vector_sink_f(2)
dstE = gr.vector_sink_f(4)
self.tb.connect(srcA, (vmap, 0))
self.tb.connect(srcB, (vmap, 1))
self.tb.connect(srcC, (vmap, 2))
self.tb.connect((vmap, 0), dstD)
self.tb.connect((vmap, 1), dstE)
self.tb.run()
self.assertEqual(expected_D, dstD.data())
self.assertEqual(expected_E, dstE.data())
def test_deint_001(self):
lenx = 64
src = gr.vector_source_f(range(lenx))
op = gr.deinterleave(gr.sizeof_float)
dst0 = gr.vector_sink_f()
dst1 = gr.vector_sink_f()
dst2 = gr.vector_sink_f()
dst3 = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect((op, 0), dst0)
self.tb.connect((op, 1), dst1)
self.tb.connect((op, 2), dst2)
self.tb.connect((op, 3), dst3)
self.tb.run()
expected_result0 = tuple(range(0, lenx, 4))
expected_result1 = tuple(range(1, lenx, 4))
expected_result2 = tuple(range(2, lenx, 4))
expected_result3 = tuple(range(3, lenx, 4))
self.assertFloatTuplesAlmostEqual(expected_result0, dst0.data())
self.assertFloatTuplesAlmostEqual(expected_result1, dst1.data())
self.assertFloatTuplesAlmostEqual(expected_result2, dst2.data())
self.assertFloatTuplesAlmostEqual(expected_result3, dst3.data())
def test_deint_001 (self):
lenx = 64
src = gr.vector_source_f (range (lenx))
op = gr.deinterleave (gr.sizeof_float)
dst0 = gr.vector_sink_f ()
dst1 = gr.vector_sink_f ()
dst2 = gr.vector_sink_f ()
dst3 = gr.vector_sink_f ()
self.tb.connect (src, op)
self.tb.connect ((op, 0), dst0)
self.tb.connect ((op, 1), dst1)
self.tb.connect ((op, 2), dst2)
self.tb.connect ((op, 3), dst3)
self.tb.run ()
expected_result0 = tuple (range (0, lenx, 4))
expected_result1 = tuple (range (1, lenx, 4))
expected_result2 = tuple (range (2, lenx, 4))
expected_result3 = tuple (range (3, lenx, 4))
self.assertFloatTuplesAlmostEqual (expected_result0, dst0.data ())
self.assertFloatTuplesAlmostEqual (expected_result1, dst1.data ())
self.assertFloatTuplesAlmostEqual (expected_result2, dst2.data ())
self.assertFloatTuplesAlmostEqual (expected_result3, dst3.data ())
def test_deint_001 (self):
lenx = 64
src0 = gr.vector_source_f (range (lenx))
op = gr.deinterleave (gr.sizeof_float,4)
dst0 = gr.vector_sink_f ()
dst1 = gr.vector_sink_f ()
dst2 = gr.vector_sink_f ()
dst3 = gr.vector_sink_f ()
self.tb.connect (src0, op)
op.connect(dst0,usesPortName="float_out_1")
op.connect(dst1,usesPortName="float_out_2")
op.connect(dst2,usesPortName="float_out_3")
op.connect(dst3,usesPortName="float_out_4")
self.tb.run ()
expected_result0 = tuple (range (0, lenx, 4))
expected_result1 = tuple (range (1, lenx, 4))
expected_result2 = tuple (range (2, lenx, 4))
expected_result3 = tuple (range (3, lenx, 4))
self.assertFloatTuplesAlmostEqual (expected_result0, dst0.data())
self.assertFloatTuplesAlmostEqual (expected_result1, dst1.data())
self.assertFloatTuplesAlmostEqual (expected_result2, dst2.data ())
self.assertFloatTuplesAlmostEqual (expected_result3, dst3.data ())
def test_deint_001(self):
lenx = 64
src0 = gr.vector_source_f(range(lenx))
op = gr.deinterleave(gr.sizeof_float, 4)
dst0 = gr.vector_sink_f()
dst1 = gr.vector_sink_f()
dst2 = gr.vector_sink_f()
dst3 = gr.vector_sink_f()
self.tb.connect(src0, op)
op.connect(dst0, usesPortName="float_out_1")
op.connect(dst1, usesPortName="float_out_2")
op.connect(dst2, usesPortName="float_out_3")
op.connect(dst3, usesPortName="float_out_4")
self.tb.run()
expected_result0 = tuple(range(0, lenx, 4))
expected_result1 = tuple(range(1, lenx, 4))
expected_result2 = tuple(range(2, lenx, 4))
expected_result3 = tuple(range(3, lenx, 4))
self.assertFloatTuplesAlmostEqual(expected_result0, dst0.data())
self.assertFloatTuplesAlmostEqual(expected_result1, dst1.data())
self.assertFloatTuplesAlmostEqual(expected_result2, dst2.data())
self.assertFloatTuplesAlmostEqual(expected_result3, dst3.data())
def __init__(self, N, sps, rolloff, ntaps, bw, noise, foffset, toffset, poffset):
gr.top_block.__init__(self)
rrc_taps = gr.firdes.root_raised_cosine(
sps, sps, 1.0, rolloff, ntaps)
data = 2.0*scipy.random.randint(0, 2, N) - 1.0
data = scipy.exp(1j*poffset) * data
self.src = gr.vector_source_c(data.tolist(), False)
self.rrc = gr.interp_fir_filter_ccf(sps, rrc_taps)
self.chn = gr.channel_model(noise, foffset, toffset)
self.fll = digital.fll_band_edge_cc(sps, rolloff, ntaps, bw)
self.vsnk_src = gr.vector_sink_c()
self.vsnk_fll = gr.vector_sink_c()
self.vsnk_frq = gr.vector_sink_f()
self.vsnk_phs = gr.vector_sink_f()
self.vsnk_err = gr.vector_sink_f()
self.connect(self.src, self.rrc, self.chn, self.fll, self.vsnk_fll)
self.connect(self.rrc, self.vsnk_src)
self.connect((self.fll,1), self.vsnk_frq)
self.connect((self.fll,2), self.vsnk_phs)
self.connect((self.fll,3), self.vsnk_err)
def __init__(self, N, sps, rolloff, ntaps, bw, noise,
foffset, toffset, poffset, mode=0):
gr.top_block.__init__(self)
rrc_taps = gr.firdes.root_raised_cosine(
sps, sps, 1.0, rolloff, ntaps)
gain = 2*scipy.pi/100.0
nfilts = 32
rrc_taps_rx = gr.firdes.root_raised_cosine(
nfilts, sps*nfilts, 1.0, rolloff, ntaps*nfilts)
data = 2.0*scipy.random.randint(0, 2, N) - 1.0
data = scipy.exp(1j*poffset) * data
self.src = gr.vector_source_c(data.tolist(), False)
self.rrc = gr.interp_fir_filter_ccf(sps, rrc_taps)
self.chn = gr.channel_model(noise, foffset, toffset)
self.off = gr.fractional_interpolator_cc(0.20, 1.0)
if mode == 0:
self.clk = gr.pfb_clock_sync_ccf(sps, gain, rrc_taps_rx,
nfilts, nfilts//2, 3.5)
self.taps = self.clk.get_taps()
self.dtaps = self.clk.get_diff_taps()
self.vsnk_err = gr.vector_sink_f()
self.vsnk_rat = gr.vector_sink_f()
self.vsnk_phs = gr.vector_sink_f()
self.connect((self.clk,1), self.vsnk_err)
self.connect((self.clk,2), self.vsnk_rat)
self.connect((self.clk,3), self.vsnk_phs)
else: # mode == 1
mu = 0.5
gain_mu = 0.1
gain_omega = 0.25*gain_mu*gain_mu
omega_rel_lim = 0.02
self.clk = digital.clock_recovery_mm_cc(sps, gain_omega,
mu, gain_mu,
omega_rel_lim)
self.vsnk_err = gr.vector_sink_f()
self.connect((self.clk,1), self.vsnk_err)
self.vsnk_src = gr.vector_sink_c()
self.vsnk_clk = gr.vector_sink_c()
self.connect(self.src, self.rrc, self.chn, self.off, self.clk, self.vsnk_clk)
self.connect(self.off, self.vsnk_src)
def test_010_run(self):
expected = (1.0, 2.0, 3.0, 4.0)
hblock = gr.top_block("test_block")
src = gr.vector_source_f(expected, False)
sink1 = gr.vector_sink_f()
sink2 = gr.vector_sink_f()
hblock.connect(src, sink1)
hblock.connect(src, sink2)
hblock.run()
actual1 = sink1.data()
actual2 = sink2.data()
self.assertEquals(expected, actual1)
self.assertEquals(expected, actual2)
def test_010_run(self):
expected = (1.0, 2.0, 3.0, 4.0)
hblock = gr.top_block("test_block")
src = gr.vector_source_f(expected, False)
sink1 = gr.vector_sink_f()
sink2 = gr.vector_sink_f()
hblock.connect(src, sink1)
hblock.connect(src, sink2)
hblock.run()
actual1 = sink1.data()
actual2 = sink2.data()
self.assertEquals(expected, actual1)
self.assertEquals(expected, actual2)
def test_complex_to_float_2(self):
src_data = (1 + 2j, 3 + 4j, 5 + 6j, 7 + 8j, 9 + 10j)
expected_data1 = (1.0, 3.0, 5.0, 7.0, 9.0)
expected_data2 = (2.0, 4.0, 6.0, 8.0, 10.0)
src = gr.vector_source_c(src_data)
op = blocks_swig.complex_to_float()
dst1 = gr.vector_sink_f()
dst2 = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect((op, 0), dst1)
self.tb.connect((op, 1), dst2)
self.tb.run()
self.assertFloatTuplesAlmostEqual(expected_data1, dst1.data())
self.assertFloatTuplesAlmostEqual(expected_data2, dst2.data())
def test_complex_to_float_2(self):
src_data = (1+2j, 3+4j, 5+6j, 7+8j, 9+10j)
expected_data1 = (1.0, 3.0, 5.0, 7.0, 9.0)
expected_data2 = (2.0, 4.0, 6.0, 8.0, 10.0)
src = gr.vector_source_c(src_data)
op = blocks_swig.complex_to_float()
dst1 = gr.vector_sink_f()
dst2 = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect((op, 0), dst1)
self.tb.connect((op, 1), dst2)
self.tb.run()
self.assertFloatTuplesAlmostEqual(expected_data1, dst1.data())
self.assertFloatTuplesAlmostEqual(expected_data2, dst2.data())
def test__002_t(self):
"""
Generate a signal with SNR as given below.
Calculate the RMSE.
"""
nsamples = 1024
n_trials = 100
samp_rate = 32000
SNR = 20 # in dB
self.siggen = siggen.signal_generator(n_sinusoids=1,
SNR=SNR,
samp_rate=samp_rate,
nsamples=nsamples * n_trials)
self.stream = gr.stream_to_vector(gr.sizeof_gr_complex, nsamples)
self.esprit = specest.esprit_vcf(n=1, m=64, nsamples=nsamples)
self.sink = gr.vector_sink_f(vlen=1)
# wire it up ...
self.tb.connect(self.siggen, self.stream, self.esprit, self.sink)
self.tb.run()
MSE = 0.0
omega = self.siggen.omegas()[0]
for i in range(n_trials):
MSE += (omega - self.sink.data()[i])**2.0
print '\n' + 70 * '-'
print 'Testing specest_esprit_vcf ...'
print 'Ran %u trials to estimate the frequency' % n_trials
print 'Used %u samples to estimate the frequency' % nsamples
print 'Sampling rate %s' % eng_notation.num_to_str(samp_rate)
print 'SNR of %u dB' % SNR
print 'Root mean square error %g' % numpy.sqrt(MSE / n_trials)
print 'Cramer-Rao Bound %g' % numpy.sqrt(
6 / 10**(SNR / 10.0) / nsamples**3)
print 70 * '-'
def test_fff_000(self):
N = 1000 # number of samples to use
fs = 1000 # baseband sampling rate
rrate = 1.123 # resampling rate
nfilts = 32
freq = 100
signal = gr.sig_source_f(fs, gr.GR_SIN_WAVE, freq, 1)
head = gr.head(gr.sizeof_float, N)
pfb = filter.pfb_arb_resampler_fff(rrate, taps)
snk = gr.vector_sink_f()
self.tb.connect(signal, head, pfb, snk)
self.tb.run()
Ntest = 50
L = len(snk.data())
t = map(lambda x: float(x)/(fs*rrate), xrange(L))
phase = 0.53013
expected_data = map(lambda x: math.sin(2.*math.pi*freq*x+phase), t)
dst_data = snk.data()
self.assertFloatTuplesAlmostEqual(expected_data[-Ntest:], dst_data[-Ntest:], 3)
def test_003(self):
stream = [[float(0)] * 10, [float(1)] * 10, [float(2)] * 10]
mux = [
0, 2, 2, 1, 1, 2, 0, 2, 0, 0, 1, 0, 1, 0, 1, 2, 1, 2, 1, 1, 2, 0,
0, 2, 1, 2, 0, 1, 2, 0
]
imux = []
for x in mux:
imux.append(int(x))
data = [
gr.vector_source_f(stream[0]),
gr.vector_source_f(stream[1]),
gr.vector_source_f(stream[2])
]
dst = gr.vector_sink_f()
uut = ofdm.static_mux_v(gr.sizeof_float, imux)
self.fg.connect(data[0], (uut, 0))
self.fg.connect(data[1], (uut, 1))
self.fg.connect(data[2], (uut, 2))
self.fg.connect(uut, dst)
self.fg.run()
self.assertEqual(map(float, mux), list(dst.data()))
def test_001_detect(self):
""" Send two bursts, with zeros in between, and check
they are both detected at the correct position and no
false alarms occur """
n_zeros = 15
fft_len = 32
cp_len = 4
sig_len = (fft_len + cp_len) * 10
sync_symbol = [(random.randint(0, 1) * 2) - 1
for x in range(fft_len / 2)] * 2
tx_signal = [0,] * n_zeros + \
sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(sig_len)]
tx_signal = tx_signal * 2
add = gr.add_cc()
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
sink_freq = gr.vector_sink_f()
sink_detect = gr.vector_sink_b()
self.tb.connect(gr.vector_source_c(tx_signal), (add, 0))
self.tb.connect(gr.noise_source_c(gr.GR_GAUSSIAN, .005), (add, 1))
self.tb.connect(add, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
sig1_detect = sink_detect.data()[0:len(tx_signal) / 2]
sig2_detect = sink_detect.data()[len(tx_signal) / 2:]
self.assertTrue(
abs(sig1_detect.index(1) - (n_zeros + fft_len + cp_len)) < cp_len)
self.assertTrue(
abs(sig2_detect.index(1) - (n_zeros + fft_len + cp_len)) < cp_len)
self.assertEqual(numpy.sum(sig1_detect), 1)
self.assertEqual(numpy.sum(sig2_detect), 1)
def test_002(self):
udp_rcv = gr.udp_source( gr.sizeof_float, '0.0.0.0', 0, eof=False )
rcv_port = udp_rcv.get_port()
udp_snd = gr.udp_sink( gr.sizeof_float, '127.0.0.1', 65500 )
udp_snd.connect( 'localhost', rcv_port )
n_data = 16
src_data = [float(x) for x in range(n_data)]
expected_result = tuple(src_data)
src = gr.vector_source_f(src_data)
dst = gr.vector_sink_f()
self.tb_snd.connect( src, udp_snd )
self.tb_rcv.connect( udp_rcv, dst )
self.tb_rcv.start()
self.tb_snd.run()
udp_snd.disconnect()
self.timeout = False
q = Timer(3.0,self.stop_rcv)
q.start()
self.tb_rcv.wait()
q.cancel()
result_data = dst.data()
self.assertEqual(expected_result, result_data)
self.assert_(self.timeout) # source ignores EOF?
def test_quad_demod_001(self):
f = 1000.0
fs = 8000.0
src_data = []
for i in xrange(200):
ti = i / fs
src_data.append(cmath.exp(2j * cmath.pi * f * ti))
# f/fs is a quarter turn per sample.
# Set the gain based on this to get 1 out.
gain = 1.0 / (cmath.pi / 4)
expected_result = [
0,
] + 199 * [1.0]
src = gr.vector_source_c(src_data)
op = analog.quadrature_demod_cf(gain)
dst = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
result_data = dst.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_001_ff(self):
N = 10000 # number of samples to use
fs = 1000 # baseband sampling rate
rrate = 1.123 # resampling rate
freq = 10
signal = gr.sig_source_f(fs, gr.GR_SIN_WAVE, freq, 1)
head = gr.head(gr.sizeof_float, N)
op = filter.fractional_interpolator_ff(0, rrate)
snk = gr.vector_sink_f()
self.tb.connect(signal, head, op, snk)
self.tb.run()
import time
time.sleep(2)
Ntest = 5000
L = len(snk.data())
t = map(lambda x: float(x)/(fs/rrate), xrange(L))
phase = 0.1884
expected_data = map(lambda x: math.sin(2.*math.pi*freq*x+phase), t)
dst_data = snk.data()
self.assertFloatTuplesAlmostEqual(expected_data[-Ntest:], dst_data[-Ntest:], 3)
def test_complex_to_float_2 (self):
src_data = (0, 1, -1, 3+4j, -3-4j, -3+4j)
expected_result0 = (0, 1, -1, 3, -3, -3)
expected_result1 = (0, 0, 0, 4, -4, 4)
src = gr.vector_source_c (src_data)
op = gr.complex_to_float ()
dst0 = gr.vector_sink_f ()
dst1 = gr.vector_sink_f ()
self.tb.connect (src, op)
self.tb.connect ((op, 0), dst0)
self.tb.connect ((op, 1), dst1)
self.tb.run ()
actual_result = dst0.data ()
self.assertFloatTuplesAlmostEqual (expected_result0, actual_result)
actual_result = dst1.data ()
self.assertFloatTuplesAlmostEqual (expected_result1, actual_result)
def __init__(self):
grc_wxgui.top_block_gui.__init__(self, title="Top Block")
##################################################
# Variables
##################################################
self.samp_rate = samp_rate = 32000
##################################################
# Blocks
##################################################
self.plot_sink_0 = plot_sink.plot_sink_f(
self.GetWin(),
title="Scope Plot",
vlen=1,
decim=1,
)
self.Add(self.plot_sink_0.win)
self.gr_vector_sink_x_0 = gr.vector_sink_f(1)
self.gr_serial_0 = gr_ser.ser()
self.gr_serial_0.set_parameters("/dev/ttyACM1", 9600, 8, "N", 1)
self.const_source_x_0 = gr.sig_source_f(0, gr.GR_CONST_WAVE, 0, 0, 1)
##################################################
# Connections
##################################################
self.connect((self.gr_serial_0, 0), (self.gr_vector_sink_x_0, 0))
self.connect((self.gr_serial_0, 0), (self.plot_sink_0, 0))
self.connect((self.const_source_x_0, 0), (self.gr_serial_0, 0))
def test_001 (self):
src_data = (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
expected_data = ( 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 1.384, 0.794, 1.384, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.794, 1.384, 2.411, 1.384, 0.794, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.794, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000 )
Np = 4
P = 4
L = 2
src = gr.vector_source_c(src_data, False)
cyclo_fam = specest.cyclo_fam(Np, P, L)
sink = gr.vector_sink_f(2*Np)
self.tb.connect(src, cyclo_fam, sink)
self.tb.run()
estimated_data = sink.data()[-2*P*L*(2*Np):]
self.assertFloatTuplesAlmostEqual(expected_data,estimated_data,3)
def __init__(self, dBm, pfa, pfd, useless_bw, plot_histogram):
gr.top_block.__init__(self)
# Constants
samp_rate = 2.4e6
samples_per_band = 16
tcme = 1.9528
output_pfa = True
debug_stats = False
self.histogram = plot_histogram
primary_user_location = 5
mu = 0
fft_size = 4096
nframes_to_check = 1
nframes_to_average = 1
downconverter = 1
src_data = self.generateRandomSignalSource(
dBm, fft_size, mu, nframes_to_check * nframes_to_average)
# Blocks
src = gr.vector_source_c(src_data)
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, fft_size)
fftb = fft.fft_vcc(fft_size, True, (window.blackmanharris(1024)),
False, 1)
self.ss = howto.spectrum_sensing_cf(samp_rate, fft_size,
samples_per_band, pfd, pfa, tcme,
output_pfa, debug_stats,
primary_user_location, useless_bw,
self.histogram, nframes_to_check,
nframes_to_average, downconverter)
self.sink = gr.vector_sink_f()
# Connections
self.connect(src, s2v, fftb, self.ss, self.sink)
def xtest_fff_004(self):
random.seed(0)
for i in xrange(25):
sys.stderr.write("\n>>> Loop = %d\n" % (i,))
src_len = 4096
src_data = make_random_float_tuple(src_len)
ntaps = int(random.uniform(2, 1000))
taps = make_random_float_tuple(ntaps)
expected_result = reference_filter_fff(1, taps, src_data)
src = gr.vector_source_f(src_data)
op = gr.fft_filter_fff(1, taps)
dst = gr.vector_sink_f()
tb = gr.top_block()
tb.connect(src, op, dst)
tb.run()
result_data = dst.data()
#print "src_len =", src_len, " ntaps =", ntaps
try:
self.assert_fft_float_ok2(expected_result, result_data, abs_eps=1.0)
except:
expected = open('expected', 'w')
for x in expected_result:
expected.write(`x` + '\n')
actual = open('actual', 'w')
for x in result_data:
actual.write(`x` + '\n')
raise
def test_complex_to_float_2(self):
src_data = (0, 1, -1, 3 + 4j, -3 - 4j, -3 + 4j)
expected_result0 = (0, 1, -1, 3, -3, -3)
expected_result1 = (0, 0, 0, 4, -4, 4)
src = gr.vector_source_c(src_data)
op = gr.complex_to_float()
dst0 = gr.vector_sink_f()
dst1 = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect((op, 0), dst0)
self.tb.connect((op, 1), dst1)
self.tb.run()
actual_result = dst0.data()
self.assertFloatTuplesAlmostEqual(expected_result0, actual_result)
actual_result = dst1.data()
self.assertFloatTuplesAlmostEqual(expected_result1, actual_result)
def test_complex_to_arg (self):
pi = math.pi
input_data = (0, pi/6, pi/4, pi/2, 3*pi/4, 7*pi/8,
-pi/6, -pi/4, -pi/2, -3*pi/4, -7*pi/8)
expected_result = (0.0, # 0
0.52382522821426392, # pi/6
0.78539806604385376, # pi/4
1.5707963705062866, # pi/2
2.3561947345733643, # 3pi/4
2.7491819858551025, # 7pi/8
-0.52382522821426392, # -pi/6
-0.78539806604385376, # -pi/4
-1.5707963705062866, # -pi/2
-2.3561947345733643, # -3pi/4
-2.7491819858551025) # -7pi/8
src_data = tuple ([math.cos (x) + math.sin (x) * 1j for x in input_data])
src = gr.vector_source_c (src_data)
op = gr.complex_to_arg ()
dst = gr.vector_sink_f ()
self.tb.connect (src, op)
self.tb.connect (op, dst)
self.tb.run ()
actual_result = dst.data ()
self.assertFloatTuplesAlmostEqual (expected_result, actual_result, 5)
def xtest_004_decim_random_vals(self):
MAX_TAPS = 9
MAX_DECIM = 7
OUTPUT_LEN = 9
random.seed(0) # we want reproducibility
for ntaps in xrange(1, MAX_TAPS + 1):
for decim in xrange(1, MAX_DECIM+1):
for ilen in xrange(ntaps + decim, ntaps + OUTPUT_LEN*decim):
src_data = random_floats(ilen)
taps = random_floats(ntaps)
expected_result = reference_dec_filter(src_data, decim, taps)
tb = gr.top_block()
src = gr.vector_source_f(src_data)
op = gr.rational_resampler_base_fff(1, decim, taps)
dst = gr.vector_sink_f()
tb.connect(src, op, dst)
tb.run()
tb = None
result_data = dst.data()
L1 = len(result_data)
L2 = len(expected_result)
L = min(L1, L2)
if False:
sys.stderr.write('delta = %2d: ntaps = %d decim = %d ilen = %d\n' % (L2 - L1, ntaps, decim, ilen))
sys.stderr.write(' len(result_data) = %d len(expected_result) = %d\n' %
(len(result_data), len(expected_result)))
self.assertEqual(expected_result[0:L], result_data[0:L])
def xtest_fff_003(self):
random.seed(0)
for i in xrange(25):
sys.stderr.write("\n>>> Loop = %d\n" % (i,))
src_len = 4096
src_data = make_random_float_tuple(src_len)
ntaps = int(random.uniform(2, 1000))
taps = make_random_float_tuple(ntaps)
expected_result = reference_filter_fff(1, taps, src_data)
src = gr.vector_source_f(src_data)
op = gr.fft_filter_fff(1, taps)
dst = gr.vector_sink_f()
tb = gr.top_block()
tb.connect(src, op, dst)
tb.run()
result_data = dst.data()
#print "src_len =", src_len, " ntaps =", ntaps
try:
self.assert_fft_float_ok2(expected_result, result_data, abs_eps=1.0)
except:
expected = open('expected', 'w')
for x in expected_result:
expected.write(`x` + '\n')
actual = open('actual', 'w')
for x in result_data:
actual.write(`x` + '\n')
raise
def test_001_t(self):
# set up fg
src_data1 = [0] * 100
src_data2 = [1] * 1000
src_data = tuple(src_data1 + src_data2)
src = gr.vector_source_f(src_data)
sqr = multiorder_tf()
sqr.set_parameters(1, 0, 0, 0.5, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1,
1100)
#Preload
sqr.input_config(1).preload_items = 1
dst = gr.vector_sink_f()
self.tb.connect(src, sqr)
self.tb.connect(sqr, dst)
self.tb.run()
# check data
result_data = dst.data()
import matplotlib.pyplot as plt
plt.plot(result_data)
plt.show()
def test_006_interp_decim(self):
taps = (0,1,0,0)
src_data = range(10000)
interp = 3
decimation = 2
expected_result = reference_interp_dec_filter(src_data, interp, decimation, taps)
tb = gr.top_block()
src = gr.vector_source_f(src_data)
op = gr.rational_resampler_base_fff(interp, decimation, taps)
dst = gr.vector_sink_f()
tb.connect(src, op)
tb.connect(op, dst)
tb.run()
result_data = dst.data()
L1 = len(result_data)
L2 = len(expected_result)
L = min(L1, L2)
if False:
sys.stderr.write('delta = %2d: ntaps = %d decim = %d ilen = %d\n' %
(L2 - L1, len(taps), decimation, len(src_data)))
sys.stderr.write(' len(result_data) = %d len(expected_result) = %d\n' %
(len(result_data), len(expected_result)))
self.assertEqual(expected_result[1:L], result_data[1:L])
def test_001(self):
port = 65500
n_data = 16
src_data = [float(x) for x in range(n_data)]
expected_result = tuple(src_data)
src = gr.vector_source_f(src_data)
udp_snd = gr.udp_sink( gr.sizeof_float, 'localhost', port )
self.tb_snd.connect( src, udp_snd )
udp_rcv = gr.udp_source( gr.sizeof_float, 'localhost', port )
dst = gr.vector_sink_f()
self.tb_rcv.connect( udp_rcv, dst )
self.tb_rcv.start()
self.tb_snd.run()
udp_snd.disconnect()
self.timeout = False
q = Timer(3.0,self.stop_rcv)
q.start()
self.tb_rcv.wait()
q.cancel()
result_data = dst.data()
self.assertEqual(expected_result, result_data)
self.assert_(not self.timeout)
def test__002_t (self):
"""
Generate a signal with SNR as given below.
Calculate the RMSE.
"""
nsamples = 1024
n_trials = 100
samp_rate = 32000
SNR = 20 # in dB
self.siggen = siggen.signal_generator(n_sinusoids = 1,
SNR = SNR, samp_rate = samp_rate,
nsamples = nsamples * n_trials)
self.stream = gr.stream_to_vector(gr.sizeof_gr_complex, nsamples)
self.esprit = specest.esprit_vcf(n=1, m=64, nsamples = nsamples)
self.sink = gr.vector_sink_f(vlen=1)
# wire it up ...
self.tb.connect(self.siggen, self.stream, self.esprit, self.sink)
self.tb.run()
MSE = 0.0
omega = self.siggen.omegas()[0]
for i in range(n_trials):
MSE += (omega - self.sink.data()[i])**2.0
print '\n' + 70*'-'
print 'Testing specest_esprit_vcf ...'
print 'Ran %u trials to estimate the frequency' % n_trials
print 'Used %u samples to estimate the frequency' % nsamples
print 'Sampling rate %s' % eng_notation.num_to_str(samp_rate)
print 'SNR of %u dB' % SNR
print 'Root mean square error %g' % numpy.sqrt(MSE/n_trials)
print 'Cramer-Rao Bound %g' % numpy.sqrt(6/10**(SNR/10.0)/nsamples**3)
print 70*'-'
def test_003_multiburst(self):
""" Send several bursts, see if the number of detects is correct.
Burst lengths and content are random.
"""
n_bursts = 42
fft_len = 32
cp_len = 4
tx_signal = []
for i in xrange(n_bursts):
sync_symbol = [(random.randint(0, 1) * 2) - 1
for x in range(fft_len / 2)] * 2
tx_signal += [0,] * random.randint(0, 2*fft_len) + \
sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(fft_len * random.randint(5,23))]
add = gr.add_cc()
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len)
sink_freq = gr.vector_sink_f()
sink_detect = gr.vector_sink_b()
channel = gr.channel_model(0.005)
self.tb.connect(gr.vector_source_c(tx_signal), channel, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
n_bursts_detected = numpy.sum(sink_detect.data())
# We allow for one false alarm or missed burst
self.assertTrue(
abs(n_bursts_detected - n_bursts) <= 1,
msg="""Because of statistics, it is possible (though unlikely)
that the number of detected bursts differs slightly. If the number of detects is
off by one or two, run the test again and see what happen.
Detection error was: %d """ % (numpy.sum(sink_detect.data()) - n_bursts))
def __init__(self, dBm, pfa, pfd, useless_bw):
gr.top_block.__init__(self)
# Constants
samp_rate = 2.4e6
samples_per_band = 16
tcme = 1.9528
output_pfa = True
debug_stats = False
histogram = True
primary_user_location = 0
mu = 0
fft_size = 16
history = 3
src_data = [1+1j]*fft_size*history
# Blocks
src = gr.vector_source_c(src_data)
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, fft_size)
self.ss = howto.spectrum_sensing_cf(samp_rate,fft_size,samples_per_band,pfd,pfa,tcme,output_pfa,debug_stats,primary_user_location,useless_bw,histogram,history)
self.sink = gr.vector_sink_f()
# Connections
self.connect(src, s2v, self.ss, self.sink)
def test_002_freq(self):
""" Add a fine frequency offset and see if that get's detected properly """
fft_len = 32
cp_len = 4
# This frequency offset is normalized to rads, i.e. \pi == f_s/2
max_freq_offset = 2 * numpy.pi / fft_len # Otherwise, it's coarse
freq_offset = ((2 * random.random()) - 1) * max_freq_offset
sig_len = (fft_len + cp_len) * 10
sync_symbol = [(random.randint(0, 1) * 2) - 1
for x in range(fft_len / 2)] * 2
tx_signal = sync_symbol[-cp_len:] + \
sync_symbol + \
[(random.randint(0, 1)*2)-1 for x in range(sig_len)]
mult = gr.multiply_cc()
add = gr.add_cc()
sync = digital.ofdm_sync_sc_cfb(fft_len, cp_len, True)
channel = gr.channel_model(0.005, freq_offset / 2.0 / numpy.pi)
sink_freq = gr.vector_sink_f()
sink_detect = gr.vector_sink_b()
self.tb.connect(gr.vector_source_c(tx_signal), channel, sync)
self.tb.connect((sync, 0), sink_freq)
self.tb.connect((sync, 1), sink_detect)
self.tb.run()
phi_hat = sink_freq.data()[sink_detect.data().index(1)]
est_freq_offset = 2 * phi_hat / fft_len
self.assertAlmostEqual(est_freq_offset, freq_offset, places=2)
def __init__(self, dBm, pfa, pfd, useless_bw, plot_histogram):
gr.top_block.__init__(self)
# Constants
samp_rate = 2.4e6
samples_per_band = 16
tcme = 1.9528
output_pfa = True
debug_stats = False
self.histogram = plot_histogram
primary_user_location = 5
mu = 0
fft_size = 4096
nframes_to_check = 1
nframes_to_average = 1
downconverter = 1
src_data = self.generateRandomSignalSource(dBm, fft_size, mu, nframes_to_check*nframes_to_average)
# Blocks
src = gr.vector_source_c(src_data)
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, fft_size)
fftb = fft.fft_vcc(fft_size, True, (window.blackmanharris(1024)), False, 1)
self.ss = howto.spectrum_sensing_cf(samp_rate,fft_size,samples_per_band,pfd,pfa,tcme,output_pfa,debug_stats,primary_user_location,useless_bw,self.histogram,nframes_to_check,nframes_to_average,downconverter)
self.sink = gr.vector_sink_f()
# Connections
self.connect(src, s2v, fftb, self.ss, self.sink)
def test_001(self):
""" Run test:
- No overlap
- Hamming window from Python
- Constant input signal of amplitude 1
The given input signal has a known power of 1. Therefore, the integral of the
PSD estimation result should be pretty close to 1."""
fft_len = 256
overlap = 0
ma_len = 1
window = hamming(fft_len)
src_data = (1,) * ((ma_len + 1) * fft_len)
src = gr.vector_source_c(src_data, False)
welch = specest.welch(fft_len, overlap, ma_len, False, window)
sink = gr.vector_sink_f(fft_len)
self.tb.connect(src, welch, sink)
self.tb.run()
dst_data = sink.data()
dst_data = array(dst_data[-fft_len:])
power_est = sum(dst_data) * 2 * pi / fft_len
self.assertAlmostEqual(power_est, 1, 5)
def test_005_(self):
src_data = (1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0)
dwav = numpy.array(src_data)
wvps = numpy.zeros(3)
# wavelet power spectrum
scl = 1.0/sqr(dwav[0])
k = 1
for e in range(len(wvps)):
wvps[e] = scl*sqr(dwav[k:k+(01<<e)]).sum()
k += 01<<e
src = gr.vector_source_f(src_data, False, len(src_data))
kon = wavelet_swig.wvps_ff(len(src_data))
dst = gr.vector_sink_f(int(math.ceil(math.log(len(src_data), 2))))
self.tb.connect(src, kon)
self.tb.connect(kon, dst)
self.tb.run()
snk_data = dst.data()
sum = 0
for (u,v) in zip(snk_data, wvps):
w = u - v
sum += w * w
sum /= float(len(snk_data))
assert sum < 1e-6
def test__001_t(self):
"""
Generate a signal with n_sinusoids sinusoids
and a SNR of SNR.
Another Check to see if it's working at all.
"""
n_sinusoids = 2
nsamples = 2048
samp_rate = 32000
SNR = 10 # in dB
decimals = 3
self.siggen = siggen.signal_generator(n_sinusoids=n_sinusoids,
SNR=SNR,
samp_rate=samp_rate,
nsamples=nsamples)
self.stream = gr.stream_to_vector(gr.sizeof_gr_complex, nsamples)
self.esprit = specest.esprit_vcf(n=n_sinusoids,
m=100,
nsamples=nsamples)
self.sink = gr.vector_sink_f(vlen=n_sinusoids)
# wire it up ...
self.tb.connect(self.siggen, self.stream, self.esprit, self.sink)
for i in range(100):
self.tb.run()
for (g, e) in zip(sorted(list(self.sink.data())),
self.siggen.omegas()):
self.assertAlmostEqual(g, e, decimals)
def test02(self):
# Test float/float version
omega = 2
gain_omega = 0.01
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital_swig.clock_recovery_mm_ff(omega, gain_omega, mu, gain_mu, omega_rel_lim)
data = 100 * [1]
self.src = gr.vector_source_f(data, False)
self.snk = gr.vector_sink_f()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 100 * [0.99972] # doesn't quite get to 1.0
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 30
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp :]
dst_data = dst_data[len_d - Ncmp :]
# print expected_result
# print dst_data
self.assertFloatTuplesAlmostEqual(expected_result, dst_data, 5)
def test_complex_to_arg(self):
pi = math.pi
input_data = (0, pi / 6, pi / 4, pi / 2, 3 * pi / 4, 7 * pi / 8,
-pi / 6, -pi / 4, -pi / 2, -3 * pi / 4, -7 * pi / 8)
expected_result = (
0.0, # 0
0.52382522821426392, # pi/6
0.78539806604385376, # pi/4
1.5707963705062866, # pi/2
2.3561947345733643, # 3pi/4
2.7491819858551025, # 7pi/8
-0.52382522821426392, # -pi/6
-0.78539806604385376, # -pi/4
-1.5707963705062866, # -pi/2
-2.3561947345733643, # -3pi/4
-2.7491819858551025) # -7pi/8
src_data = tuple([math.cos(x) + math.sin(x) * 1j for x in input_data])
src = gr.vector_source_c(src_data)
op = gr.complex_to_arg()
dst = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
actual_result = dst.data()
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 3)
def __init__(self, n_sinusoids = 1, SNR = 10, samp_rate = 32e3, nsamples = 2048):
gr.hier_block2.__init__(self, "ESPRIT/MUSIC signal generator",
gr.io_signature(0, 0, gr.sizeof_float),
gr.io_signature(1, 1, gr.sizeof_gr_complex))
sigampl = 10.0**(SNR/10.0) # noise power is 1
self.srcs = list()
self.n_sinusoids = n_sinusoids
self.samp_rate = samp_rate
# create our signals ...
for s in range(n_sinusoids):
self.srcs.append(gr.sig_source_c(samp_rate,
gr.GR_SIN_WAVE,1000 * s + 2000,
numpy.sqrt(sigampl/n_sinusoids)))
seed = ord(os.urandom(1))
self.noise = gr.noise_source_c(gr.GR_GAUSSIAN, 1, seed)
self.add = gr.add_cc()
self.head = gr.head(gr.sizeof_gr_complex, nsamples)
self.sink = gr.vector_sink_f(vlen=n_sinusoids)
# wire it up ...
for s in range(n_sinusoids):
self.connect(self.srcs[s], (self.add, s))
# Additive noise
self.connect(self.noise, (self.add, n_sinusoids))
self.connect(self.add, self.head, self)
def test_002_interp(self):
taps = random_floats(31)
#src_data = random_floats(10000) # FIXME the 10k case fails!
src_data = random_floats(1000)
interpolation = 3
expected_result = reference_interp_filter(src_data, interpolation, taps)
tb = gr.top_block()
src = gr.vector_source_f(src_data)
op = gr.rational_resampler_base_fff(interpolation, 1, taps)
dst = gr.vector_sink_f()
tb.connect(src, op)
tb.connect(op, dst)
tb.run()
result_data = dst.data()
L1 = len(result_data)
L2 = len(expected_result)
L = min(L1, L2)
if False:
sys.stderr.write('delta = %2d: ntaps = %d interp = %d ilen = %d\n' %
(L2 - L1, len(taps), interpolation, len(src_data)))
sys.stderr.write(' len(result_data) = %d len(expected_result) = %d\n' %
(len(result_data), len(expected_result)))
#self.assertEqual(expected_result[0:L], result_data[0:L])
# FIXME check first 3 answers
self.assertEqual(expected_result[3:L], result_data[3:L])
def __init__(self, dBm, pfa, pfd, nTrials):
gr.top_block.__init__(self)
# Constants
samp_rate = 2.4e6
fft_size = 4096
samples_per_band = 16
tcme = 1.9528
output_pfa = True
debug_stats = False
histogram = False
primary_user_location = 0
useless_bw = 200000.0
src_data = [0+0j]*fft_size*nTrials
voltage = self.powerToAmplitude(dBm);
# Blocks
src = gr.vector_source_c(src_data)
noise = gr.noise_source_c(gr.GR_GAUSSIAN, voltage, 42)
add = gr.add_vcc()
s2v = gr.stream_to_vector(gr.sizeof_gr_complex, fft_size)
fftb = fft.fft_vcc(fft_size, True, (window.blackmanharris(1024)), False, 1)
ss = howto.spectrum_sensing_cf(samp_rate,fft_size,samples_per_band,pfd,pfa,tcme,output_pfa,debug_stats,primary_user_location,useless_bw,histogram)
self.sink = gr.vector_sink_f()
# Connections
self.connect(src, add, s2v, fftb, ss, self.sink)
self.connect(noise, (add, 1))
def xtest_005_interp_random_vals(self):
MAX_TAPS = 9
MAX_INTERP = 7
INPUT_LEN = 9
random.seed(0) # we want reproducibility
for ntaps in xrange(1, MAX_TAPS + 1):
for interp in xrange(1, MAX_INTERP+1):
for ilen in xrange(ntaps, ntaps + INPUT_LEN):
src_data = random_floats(ilen)
taps = random_floats(ntaps)
expected_result = reference_interp_filter(src_data, interp, taps)
tb = gr.top_block()
src = gr.vector_source_f(src_data)
op = gr.rational_resampler_base_fff(interp, 1, taps)
dst = gr.vector_sink_f()
tb.connect(src, op, dst)
tb.run()
tb = None
result_data = dst.data()
L1 = len(result_data)
L2 = len(expected_result)
L = min(L1, L2)
#if True or abs(L1-L2) > 1:
if False:
sys.stderr.write('delta = %2d: ntaps = %d interp = %d ilen = %d\n' % (L2 - L1, ntaps, interp, ilen))
#sys.stderr.write(' len(result_data) = %d len(expected_result) = %d\n' %
# (len(result_data), len(expected_result)))
#self.assertEqual(expected_result[0:L], result_data[0:L])
# FIXME check first ntaps+1 answers
self.assertEqual(expected_result[ntaps+1:L], result_data[ntaps+1:L])
def test_001_t (self):
src_data = [0]*10
src_data1 = [1]*20
src_data = src_data+src_data1
#expected_result = (-2.0, 0.0, 5.0, 8.0, 9.0, 11.0, 14.0, 18.0)
src0 = gr.vector_source_f(src_data)
sqr = dsim()
sqr.set_parameters(2,0.5,1,.1,10,1)
#Preload
sqr.input_config(1).preload_items = 1
dst = gr.vector_sink_f()
self.tb.connect(src0, (sqr,0)) # src0(vector_source) -> sqr_input_0
self.tb.connect((sqr,0), (sqr,1)) # sqr_output_0 -> sqr_input_1
self.tb.connect(sqr,dst) # sqr_output_0 -> dst (vector_source)
self.tb.run()
result_data = dst.data()
import matplotlib.pyplot as plt
plt.plot(result_data)
print "result", result_data
plt.show()
def test_quad_demod_001(self):
f = 1000.0
fs = 8000.0
src_data = []
for i in xrange(200):
ti = i/fs
src_data.append(cmath.exp(2j*cmath.pi*f*ti))
# f/fs is a quarter turn per sample.
# Set the gain based on this to get 1 out.
gain = 1.0/(cmath.pi/4)
expected_result = [0,] + 199*[1.0]
src = gr.vector_source_c(src_data)
op = analog.quadrature_demod_cf(gain)
dst = gr.vector_sink_f()
self.tb.connect(src, op)
self.tb.connect(op, dst)
self.tb.run()
result_data = dst.data()
self.assertComplexTuplesAlmostEqual(expected_result, result_data, 5)
def test_fir_filter_fff_001(self):
src_data = 40*[1, 2, 3, 4]
expected_data = (0.5, 1.5, 3.0, 5.0, 5.5, 6.5, 8.0, 10.0,
10.5, 11.5, 13.0, 15.0, 15.5, 16.5, 18.0,
20.0, 20.5, 21.5, 23.0, 25.0, 25.5, 26.5,
28.0, 30.0, 30.5, 31.5, 33.0, 35.0, 35.5,
36.5, 38.0, 40.0, 40.5, 41.5, 43.0, 45.0,
45.5, 46.5, 48.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0,
50.0, 50.0, 50.0, 50.0, 50.0)
src = gr.vector_source_f(src_data)
op = filter.fir_filter_fff(1, 20*[0.5, 0.5])
dst = gr.vector_sink_f()
self.tb.connect(src, op, dst)
self.tb.run()
result_data = dst.data()
self.assertFloatTuplesAlmostEqual(expected_data, result_data, 5)
