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authorMarcus Müller <marcus@hostalia.de>2020-06-19 11:07:54 +0200
committerMarcus Müller <marcus@hostalia.de>2020-06-19 11:07:54 +0200
commite76d04ca2f4f15e3b1a1ab2a81dd52c4e6d2472c (patch)
tree1d86f68fceed9cd7204d9a79e816dc06c15feaf4 /gr-digital/python/digital/qa_linear_equalizer.py
parent98348e37209aa7daeb96fe5ead815e5b083dc6da (diff)
parent39311758cb1e6a7424d3213b3eb2c65c8c4dcfe1 (diff)
Move from SWIG to Pybind11
Goodbye, and thanks for all the fish, SWIG. Please refer to docs/PYBIND11.md for details on how to deal with Pybind.
Diffstat (limited to 'gr-digital/python/digital/qa_linear_equalizer.py')
-rwxr-xr-xgr-digital/python/digital/qa_linear_equalizer.py6
1 files changed, 3 insertions, 3 deletions
diff --git a/gr-digital/python/digital/qa_linear_equalizer.py b/gr-digital/python/digital/qa_linear_equalizer.py
index d7a4a597f3..522575db54 100755
--- a/gr-digital/python/digital/qa_linear_equalizer.py
+++ b/gr-digital/python/digital/qa_linear_equalizer.py
@@ -104,7 +104,7 @@ class qa_linear_equalizer(gr_unittest.TestCase):
preamble_symbols = self.map_symbols_to_constellation(self.unpack_values(self.preamble, 8, 2), cons)
alg = digital.adaptive_algorithm_lms(cons, gain).base()
- evm = digital.meas_evm_cc(cons, digital.evm_measurement_t_EVM_PERCENT)
+ evm = digital.meas_evm_cc(cons, digital.evm_measurement_t.EVM_PERCENT)
leq = digital.linear_equalizer(num_taps, self.sps, alg, False, preamble_symbols, 'corr_est')
correst = digital.corr_est_cc(modulated_sync_word, self.sps, 12, corr_calc, digital.THRESHOLD_ABSOLUTE)
constmod = digital.generic_mod(
@@ -130,8 +130,8 @@ class qa_linear_equalizer(gr_unittest.TestCase):
self.tb.run()
# look at the last 1000 samples, should converge quickly, below 5% EVM
- upper_bound = tuple(20.0*numpy.ones((num_test,)))
- lower_bound = tuple(0.0*numpy.zeros((num_test,)))
+ upper_bound = list(20.0*numpy.ones((num_test,)))
+ lower_bound = list(0.0*numpy.zeros((num_test,)))
output_data = vsi.data()
output_data = output_data[-num_test:]
self.assertLess(output_data, upper_bound)