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| author | Sebastian Müller <senpo@posteo.de> | 2017-09-30 23:50:21 +0200 |
|---|---|---|
| committer | Sebastian Müller <senpo@posteo.de> | 2017-09-30 23:50:21 +0200 |
| commit | e30ce800d374db2e0b0e7c0ee9f3d6acf0a12161 (patch) | |
| tree | 50ffdd22ea40461f5e70f7f0c4154467dba8f13f /gr-blocks/python | |
| parent | bbed667ecc7ff1ced67fbc87ac66f2b5299a4dc0 (diff) | |
[gr-blocks] improve moving_average unit test
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") |