def test_007_constellation_type(self):
dm = symbolmapping.SymbolMapping(4, "BORonka")
self.assertEqual(dm.constellationType(), "BORONKA")
dm = symbolmapping.SymbolMapping(4, "carson")
self.assertEqual(dm.constellationType(), "CARSON")
dm = symbolmapping.SymbolMapping(2, "carson")
self.assertEqual(dm.constellationType(), "GRAY")
def test_003_constellation_set(self):
dm = symbolmapping.SymbolMapping(1)
orders = np.array([1, 2, 4, 6])
for co in orders:
dm.setConstellationOrder(co)
c, b = generate_constellation(co)
self.assertVectorAlmostEqual(c, dm.constellation())
def test_001_constellation_ref(self):
orders = np.array([1, 2, 4, 6])
for co in self._orders:
dm = symbolmapping.SymbolMapping(co)
c, b = generate_constellation(co)
self.assertVectorAlmostEqual(c, dm.constellation())
self.assertEqual(dm.constellationOrder(), co)
def test_006_llr_calculation(self):
for co in self._orders:
dm = symbolmapping.SymbolMapping(co)
bits = np.random.randint(0, 2, co * 5).astype(np.uint8)
symbols = dm.map_to_constellation(bits)
llrs_d = dm.demap_llrs(symbols, float(3.0))
if co in (1, 3) or co > 6:
rb = np.sign(llrs_d) - 1.
rb /= -2.
rb = rb.astype(np.uint8)
np.testing.assert_array_equal(bits, rb)
continue
ref_ln_probs = calculate_symbol_log_probabilities(
symbols, dm.constellation(), float(3.0))
ref_llrs = calculate_llrs(ref_ln_probs) * .5
if co > 3:
rb = np.sign(llrs_d) - 1.
rb /= -2.
rb = rb.astype(np.uint8)
self.assertTrue(np.all(np.sign(ref_llrs) == np.sign(llrs_d)))
else:
self.assertTrue(
np.all(np.abs(ref_llrs - llrs_d) < self._precision))
def test_004_mapping(self):
for co in self._orders:
dm = symbolmapping.SymbolMapping(co)
bits = np.random.randint(0, 2, co * 500).astype(np.uint8)
ref_syms = map_to_constellation(bits, dm.constellation())
uut_syms = dm.map_to_constellation(bits)
self.assertVectorAlmostEqual(uut_syms, ref_syms)
def test_005_ln_prob_calculation(self):
snr_db = float(3.0)
for co in self._orders:
dm = symbolmapping.SymbolMapping(co)
bits = np.random.randint(0, 2, co * 500).astype(np.uint8)
symbols = dm.map_to_constellation(bits)
ln_probs = dm.calculate_ln_probabilities(symbols, snr_db)
ref_ln_probs = calculate_symbol_log_probabilities(
symbols, dm.constellation(), snr_db).flatten()
self.assertFloatTuplesAlmostEqual(ref_ln_probs * .5, ln_probs, 4)
def test_006_llr_calculation(self):
for co in (2, 4, 6):
dm = symbolmapping.SymbolMapping(co)
bits = np.random.randint(0, 2, co * 5).astype(np.uint8)
symbols = dm.map_to_constellation(bits)
ref_ln_probs = calculate_symbol_log_probabilities(
symbols, dm.constellation(), float(3.0))
ref_llrs = calculate_llrs(ref_ln_probs) * .5
llrs_d = dm.demap_llrs(symbols, float(3.0))
if co > 3:
self.assertTrue(np.all(np.sign(ref_llrs) == np.sign(llrs_d)))
else:
self.assertTrue(
np.all(np.abs(ref_llrs - llrs_d) < self._precision))
def test_009_constellation_carson(self):
dm = symbolmapping.SymbolMapping(4, "carSon")
self.assertEqual(dm.constellationType(), "CARSON")
res = dm.constellation()
ref, bins = generate_constellation(4, 'cArSoN')
self.assertTrue(np.all(np.abs(res - ref) < 1e-7))
def test_008_constellation_boronka(self):
dm = symbolmapping.SymbolMapping(4, "BORonka")
self.assertEqual(dm.constellationType(), "BORONKA")
res = dm.constellation()
ref, bins = generate_constellation(4, 'BoRoNkA')
self.assertTrue(np.all(np.abs(res - ref) < 1e-7))