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#!/usr/bin/env python
#
# Copyright 2011-2013,2018 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
""" Test digital.mpsk_snr_est_cc """
import random
from gnuradio import gr, gr_unittest, digital, blocks
def random_bit():
"""Create random bits using random() rather than randint(). The latter
changed for Python 3.2."""
return random.random() > .5
def get_cplx():
"Return a BPSK symbol (complex)"
return complex(2 * random_bit() - 1, 0)
def get_n_cplx():
"Return random, normal-distributed complex number"
return complex(random.random() - 0.5, random.random() - 0.5)
class test_mpsk_snr_est(gr_unittest.TestCase):
def setUp(self):
self.tb = gr.top_block()
random.seed(0) # make repeatable
N = 10000
self._noise = [get_n_cplx() for _ in range(N)]
self._bits = [get_cplx() for _ in range(N)]
def tearDown(self):
self.tb = None
def mpsk_snr_est_setup(self, op):
result = []
for i in range(1, 6):
src_data = [b + (i * n) for b, n in zip(self._bits, self._noise)]
src = blocks.vector_source_c(src_data)
dst = blocks.null_sink(gr.sizeof_gr_complex)
tb = gr.top_block()
tb.connect(src, op)
tb.connect(op, dst)
tb.run() # run the graph and wait for it to finish
result.append(op.snr())
return result
def test_mpsk_snr_est_simple(self):
expected_result = [8.20, 4.99, 3.23, 2.01, 1.03]
N = 10000
alpha = 0.001
op = digital.mpsk_snr_est_cc(digital.SNR_EST_SIMPLE, N, alpha)
actual_result = self.mpsk_snr_est_setup(op)
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2)
def test_mpsk_snr_est_skew(self):
expected_result = [8.31, 1.83, -1.68, -3.56, -4.68]
N = 10000
alpha = 0.001
op = digital.mpsk_snr_est_cc(digital.SNR_EST_SKEW, N, alpha)
actual_result = self.mpsk_snr_est_setup(op)
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2)
def test_mpsk_snr_est_m2m4(self):
expected_result = [8.01, 3.19, 1.97, 2.15, 2.65]
N = 10000
alpha = 0.001
op = digital.mpsk_snr_est_cc(digital.SNR_EST_M2M4, N, alpha)
actual_result = self.mpsk_snr_est_setup(op)
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2)
def test_mpsk_snr_est_svn(self):
expected_result = [7.91, 3.01, 1.77, 1.97, 2.49]
N = 10000
alpha = 0.001
op = digital.mpsk_snr_est_cc(digital.SNR_EST_SVR, N, alpha)
actual_result = self.mpsk_snr_est_setup(op)
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2)
def test_probe_mpsk_snr_est_m2m4(self):
expected_result = [8.01, 3.19, 1.97, 2.15, 2.65]
actual_result = []
for i in range(1, 6):
src_data = [b + (i * n) for b, n in zip(self._bits, self._noise)]
src = blocks.vector_source_c(src_data)
N = 10000
alpha = 0.001
op = digital.probe_mpsk_snr_est_c(digital.SNR_EST_M2M4, N, alpha)
tb = gr.top_block()
tb.connect(src, op)
tb.run() # run the graph and wait for it to finish
actual_result.append(op.snr())
self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2)
if __name__ == '__main__':
# Test various SNR estimators; we're not using a Gaussian
# noise source, so these estimates have no real meaning;
# just a sanity check.
gr_unittest.run(test_mpsk_snr_est)
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