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)