def test02(self):
# Test QPSK sync
M = 4
theta = 0
loop_bw = cmath.pi/100.0
fmin = -0.5
fmax = 0.5
mu = 0.5
gain_mu = 0.01
omega = 2
gain_omega = 0.001
omega_rel = 0.001
self.test = digital.mpsk_receiver_cc(M, theta, loop_bw,
fmin, fmax, mu, gain_mu,
omega, gain_omega,
omega_rel)
data = 10000*[complex( 0.707, 0.707),
complex(-0.707, 0.707),
complex(-0.707, -0.707),
complex( 0.707, -0.707)]
data = [0.5*d for d in data]
self.src = gr.vector_source_c(data, False)
self.snk = gr.vector_sink_c()
# pulse shaping interpolation filter
nfilts = 32
excess_bw = 0.35
ntaps = 11 * int(omega*nfilts)
rrc_taps0 = filter.firdes.root_raised_cosine(
nfilts, nfilts, 1.0, excess_bw, ntaps)
rrc_taps1 = filter.firdes.root_raised_cosine(
1, omega, 1.0, excess_bw, 11*omega)
self.rrc0 = filter.pfb_arb_resampler_ccf(omega, rrc_taps0)
self.rrc1 = filter.fir_filter_ccf(1, rrc_taps1)
self.tb.connect(self.src, self.rrc0, self.rrc1, self.test, self.snk)
self.tb.run()
expected_result = 10000*[complex(-0.5, +0.0), complex(+0.0, -0.5),
complex(+0.5, +0.0), complex(+0.0, +0.5)]
# get data after a settling period
dst_data = self.snk.data()[200:]
# Only compare last Ncmp samples
Ncmp = 1000
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp - 1:-1]
dst_data = dst_data[len_d - Ncmp:]
#for e,d in zip(expected_result, dst_data):
# print "{0:+.02f} {1:+.02f}".format(e, d)
self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1)
def test02(self):
# Test QPSK sync
M = 4
theta = 0
loop_bw = 2 * cmath.pi / 100.0
fmin = -0.5
fmax = 0.5
mu = 0.25
gain_mu = 0.01
omega = 2
gain_omega = 0.001
omega_rel = 0.001
self.test = digital_swig.mpsk_receiver_cc(M, theta, loop_bw, fmin,
fmax, mu, gain_mu, omega,
gain_omega, omega_rel)
data = 1000 * [
complex(0.707, 0.707),
complex(0.707, 0.707),
complex(-0.707, 0.707),
complex(-0.707, 0.707),
complex(-0.707, -0.707),
complex(-0.707, -0.707),
complex(0.707, -0.707),
complex(0.707, -0.707)
]
self.src = gr.vector_source_c(data, False)
self.snk = gr.vector_sink_c()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 1000 * [
complex(0, -1.0),
complex(1.0, 0),
complex(0, 1.0),
complex(-1.0, 0)
]
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 100
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#for e,d in zip(expected_result, dst_data):
# print e, d
self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1)
def test01(self):
# Test BPSK sync
M = 2
theta = 0
loop_bw = cmath.pi / 100.0
fmin = -0.5
fmax = 0.5
mu = 0.5
gain_mu = 0.01
omega = 2
gain_omega = 0.001
omega_rel = 0.001
self.test = digital.mpsk_receiver_cc(M, theta, loop_bw, fmin, fmax, mu,
gain_mu, omega, gain_omega,
omega_rel)
data = 10000 * [complex(1, 0), complex(-1, 0)]
#data = [2*random.randint(0,1)-1 for x in xrange(10000)]
self.src = blocks.vector_source_c(data, False)
self.snk = blocks.vector_sink_c()
# pulse shaping interpolation filter
nfilts = 32
excess_bw = 0.35
ntaps = 11 * int(omega * nfilts)
rrc_taps0 = filter.firdes.root_raised_cosine(nfilts, nfilts, 1.0,
excess_bw, ntaps)
rrc_taps1 = filter.firdes.root_raised_cosine(1, omega, 1.0, excess_bw,
11 * omega)
self.rrc0 = filter.pfb_arb_resampler_ccf(omega, rrc_taps0)
self.rrc1 = filter.fir_filter_ccf(1, rrc_taps1)
self.tb.connect(self.src, self.rrc0, self.rrc1, self.test, self.snk)
self.tb.run()
expected_result = [0.5 * d for d in data]
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 1000
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp - 1:-1]
dst_data = dst_data[len_d - Ncmp:]
#for e,d in zip(expected_result, dst_data):
# print "{0:+.02f} {1:+.02f}".format(e, d)
self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1)
def test02 (self):
# Test QPSK sync
M = 4
theta = 0
loop_bw = 2*cmath.pi/100.0
fmin = -0.5
fmax = 0.5
mu = 0.25
gain_mu = 0.01
omega = 2
gain_omega = 0.001
omega_rel = 0.001
self.test = digital_swig.mpsk_receiver_cc(M, theta, loop_bw,
fmin, fmax, mu, gain_mu,
omega, gain_omega,
omega_rel)
data = 1000*[complex( 0.707, 0.707), complex( 0.707, 0.707),
complex(-0.707, 0.707), complex(-0.707, 0.707),
complex(-0.707, -0.707), complex(-0.707, -0.707),
complex( 0.707, -0.707), complex( 0.707, -0.707)]
self.src = gr.vector_source_c(data, False)
self.snk = gr.vector_sink_c()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 1000*[complex(0, -1.0), complex(1.0, 0),
complex(0, 1.0), complex(-1.0, 0)]
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 100
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#for e,d in zip(expected_result, dst_data):
# print e, d
self.assertComplexTuplesAlmostEqual (expected_result, dst_data, 1)
