diff options
Diffstat (limited to 'gr-blocks/python')
| -rw-r--r-- | gr-blocks/python/blocks/qa_moving_average.py | 35 |
1 files changed, 32 insertions, 3 deletions
diff --git a/gr-blocks/python/blocks/qa_moving_average.py b/gr-blocks/python/blocks/qa_moving_average.py index 7f9aa5777..4319b40b0 100644 --- a/gr-blocks/python/blocks/qa_moving_average.py +++ b/gr-blocks/python/blocks/qa_moving_average.py @@ -81,7 +81,7 @@ class test_moving_average(gr_unittest.TestCase): # This tests implement own moving average to verify correct behaviour of the block - def test_03(self): + def test_vector_int(self): tb = self.tb vlen = 5 @@ -111,7 +111,36 @@ class test_moving_average(gr_unittest.TestCase): # make sure result is close to zero self.assertTupleEqual(dut_data, ref_data) - def test_04(self): + def test_vector_complex(self): + tb = self.tb + + vlen = 5 + N = 10*vlen + data = make_random_complex_tuple(N, 2**10) + src = blocks.vector_source_c(data, False) + one_to_many = blocks.stream_to_streams(gr.sizeof_gr_complex, vlen) + one_to_vector = blocks.stream_to_vector(gr.sizeof_gr_complex, vlen) + many_to_vector = blocks.streams_to_vector(gr.sizeof_gr_complex, vlen) + isolated = [ blocks.moving_average_cc(100, 1) for i in range(vlen)] + dut = blocks.moving_average_cc(100, 1, vlen=vlen) + dut_dst = blocks.vector_sink_c(vlen=vlen) + ref_dst = blocks.vector_sink_c(vlen=vlen) + + tb.connect(src, one_to_many) + tb.connect(src, one_to_vector, dut, dut_dst) + tb.connect(many_to_vector, ref_dst) + for idx, single in enumerate(isolated): + tb.connect((one_to_many,idx), single, (many_to_vector,idx)) + + tb.run() + + dut_data = dut_dst.data() + ref_data = ref_dst.data() + + # make sure result is close to zero + self.assertTupleEqual(dut_data, ref_data) + + def test_complex_scalar(self): tb = self.tb N = 10000 # number of samples @@ -119,7 +148,7 @@ class test_moving_average(gr_unittest.TestCase): 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 + data_padded = (history-1)*[complex(0.0, 0.0)]+list(data) # history expected_result = [] moving_sum = sum(data_padded[:history-1]) for i in range(N): |