diff options
Diffstat (limited to 'gr-digital/python/qa_mpsk_snr_est.py')
| -rwxr-xr-x | gr-digital/python/qa_mpsk_snr_est.py | 63 |
1 files changed, 31 insertions, 32 deletions
diff --git a/gr-digital/python/qa_mpsk_snr_est.py b/gr-digital/python/qa_mpsk_snr_est.py index d392567bfd..c976bf21a8 100755 --- a/gr-digital/python/qa_mpsk_snr_est.py +++ b/gr-digital/python/qa_mpsk_snr_est.py @@ -1,6 +1,6 @@ #!/usr/bin/env python # -# Copyright 2011 Free Software Foundation, Inc. +# Copyright 2011,2012 Free Software Foundation, Inc. # # This file is part of GNU Radio # @@ -29,94 +29,93 @@ def get_cplx(): 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 () +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): + def tearDown(self): self.tb = None - def mpsk_snr_est_setup (self, op): + 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) + 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 + 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): + 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) + 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) + self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2) - def test_mpsk_snr_est_skew (self): + 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) + 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) + self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2) - def test_mpsk_snr_est_m2m4 (self): + 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) + 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) + self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2) - def test_mpsk_snr_est_svn (self): + 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) + 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) + self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2) - def test_probe_mpsk_snr_est_m2m4 (self): + 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) + 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) + 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 + 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) - + self.assertFloatTuplesAlmostEqual(expected_result, actual_result, 2) if __name__ == '__main__': # Test various SNR estimators; we're not using a Gaussian |