def test03(self):
# Test complex/complex version with varying input
omega = 2
gain_omega = 0.01
mu = 0.25
gain_mu = 0.1
omega_rel_lim = 0.0001
self.test = digital_swig.clock_recovery_mm_cc(omega, gain_omega, mu, gain_mu, omega_rel_lim)
data = 1000 * [complex(1, 1), complex(1, 1), complex(-1, -1), complex(-1, -1)]
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(-1.2, -1.2), complex(1.2, 1.2)]
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 :]
# print expected_result
# print dst_data
self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1)
def test01(self):
# Test complex/complex version
omega = 2
gain_omega = 0.001
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital.clock_recovery_mm_cc(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 100*[complex(1, 1),]
self.src = blocks.vector_source_c(data, False)
self.snk = blocks.vector_sink_c()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 100*[complex(0.99972, 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.assertComplexTuplesAlmostEqual(expected_result, dst_data, 5)
def test01(self):
# Test complex/complex version
omega = 2
gain_omega = 0.001
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital_swig.clock_recovery_mm_cc(omega, gain_omega, mu, gain_mu, omega_rel_lim)
data = 100 * [complex(1, 1)]
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 = 100 * [complex(0.99972, 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.assertComplexTuplesAlmostEqual(expected_result, dst_data, 5)
def test03(self):
# Test complex/complex version with varying input
omega = 2
gain_omega = 0.01
mu = 0.25
gain_mu = 0.1
omega_rel_lim = 0.0001
self.test = digital.clock_recovery_mm_cc(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 1000*[complex(1, 1), complex(1, 1), complex(-1, -1), complex(-1, -1)]
self.src = blocks.vector_source_c(data, False)
self.snk = blocks.vector_sink_c()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 1000*[complex(-1.2, -1.2), complex(1.2, 1.2)]
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:]
#print expected_result
#print dst_data
self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1)
