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| author | Johnathan Corgan <johnathan@corganlabs.com> | 2017-10-05 14:44:45 -0700 |
|---|---|---|
| committer | Johnathan Corgan <johnathan@corganlabs.com> | 2017-10-05 14:44:45 -0700 |
| commit | eda2ad20ab579057e9acc1aa76f1178a6071e1d4 (patch) | |
| tree | 7526e9d16871ea2cf1478229eba5d187e9b30e2c /gr-blocks/python | |
| parent | be8f0c0cadca9ffa92a982e27d7e9a88753b6fac (diff) | |
| parent | 6d2221196082a4954c249dc6955e33d5832a56f2 (diff) | |
Merge branch 'maint'
Diffstat (limited to 'gr-blocks/python')
| -rw-r--r-- | gr-blocks/python/blocks/qa_moving_average.py | 65 |
1 files changed, 65 insertions, 0 deletions
diff --git a/gr-blocks/python/blocks/qa_moving_average.py b/gr-blocks/python/blocks/qa_moving_average.py index 2c58805925..53b240fe7f 100644 --- a/gr-blocks/python/blocks/qa_moving_average.py +++ b/gr-blocks/python/blocks/qa_moving_average.py @@ -45,6 +45,10 @@ class test_moving_average(gr_unittest.TestCase): def tearDown(self): self.tb = None + # These tests will always pass and are therefore useless. 100 random numbers [-1,1) are + # getting summed up and scaled with 0.001. Then, an assertion verifies a result near 0, + # which is the case even if the block is malfunctioning. + def test_01(self): tb = self.tb @@ -87,5 +91,66 @@ class test_moving_average(gr_unittest.TestCase): # make sure result is close to zero self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 1) + # This tests implement own moving average to verify correct behaviour of the block + + def test_03(self): + tb = self.tb + + N = 10000 # number of samples + history = 100 # num of samples to average + data = make_random_float_tuple(N, 1) # generate random data + + # pythonic MA filter + data_padded = (history-1)*[0.0]+list(data) # history + expected_result = [] + moving_sum = sum(data_padded[:history-1]) + for i in range(N): + moving_sum += data_padded[i+history-1] + expected_result.append(moving_sum) + moving_sum -= data_padded[i] + + src = blocks.vector_source_f(data, False) + op = blocks.moving_average_ff(history, 1) + dst = blocks.vector_sink_f() + + tb.connect(src, op) + tb.connect(op, dst) + tb.run() + + dst_data = dst.data() + + # make sure result is close to zero + self.assertFloatTuplesAlmostEqual(expected_result, dst_data, 4) + + def test_04(self): + tb = self.tb + + N = 10000 # number of samples + history = 100 # num of samples to average + data = make_random_complex_tuple(N, 1) # generate random data + + # pythonic MA filter + data_padded = (history-1)*[0.0+1j*0.0]+list(data) # history + expected_result = [] + moving_sum = sum(data_padded[:history-1]) + for i in range(N): + moving_sum += data_padded[i+history-1] + expected_result.append(moving_sum) + moving_sum -= data_padded[i] + + src = blocks.vector_source_c(data, False) + op = blocks.moving_average_cc(history, 1) + dst = blocks.vector_sink_c() + + tb.connect(src, op) + tb.connect(op, dst) + tb.run() + + dst_data = dst.data() + + # make sure result is close to zero + self.assertComplexTuplesAlmostEqual(expected_result, dst_data, 4) + + if __name__ == '__main__': gr_unittest.run(test_moving_average, "test_moving_average.xml") |