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#!/usr/bin/env python
#
# Copyright 2011 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
from gnuradio import gr, gr_unittest
import digital_swig as digital
import math, random
def get_cplx():
return complex(2*random.randint(0,1) - 1, 0)
def get_n_cplx():
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 i in xrange(N)]
self._bits = [get_cplx() for i in xrange(N)]
def tearDown (self):
self.tb = None
def mpsk_snr_est_setup (self, op):
result = []
for i in xrange(1,6):
src_data = [b+(i*n) for b,n in zip(self._bits, self._noise)]
src = gr.vector_source_c (src_data)
dst = gr.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 = [11.48, 5.91, 3.30, 2.08, 1.46]
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 = [11.48, 5.91, 3.30, 2.08, 1.46]
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 = [11.02, 6.20, 4.98, 5.16, 5.66]
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 = [10.90, 6.00, 4.76, 4.97, 5.49]
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 = [11.02, 6.20, 4.98, 5.16, 5.66]
actual_result = []
for i in xrange(1,6):
src_data = [b+(i*n) for b,n in zip(self._bits, self._noise)]
src = gr.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, "test_mpsk_snr_est.xml")
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