def test_003(self): offsets = (6, 3, 8) key = pmt.string_to_symbol('key') srcid = pmt.string_to_symbol('qa_tag_utils') tags = [] for k in offsets: t = gr.tag_t() t.offset = k t.key = key t.value = pmt.from_long(k) t.srcid = srcid tags.append(t) for k, t in zip(sorted(offsets), sorted(tags, key=gr.tag_t_offset_compare_key())): self.assertEqual(t.offset, k) self.assertTrue(pmt.equal(t.key, key)) self.assertTrue(pmt.equal(t.value, pmt.from_long(k))) self.assertTrue(pmt.equal(t.srcid, srcid)) tmin = min(tags, key=gr.tag_t_offset_compare_key()) self.assertEqual(tmin.offset, min(offsets)) self.assertTrue(pmt.equal(tmin.key, key)) self.assertTrue(pmt.equal(tmin.value, pmt.from_long(min(offsets)))) self.assertTrue(pmt.equal(tmin.srcid, srcid)) tmax = max(tags, key=gr.tag_t_offset_compare_key()) self.assertEqual(tmax.offset, max(offsets)) self.assertTrue(pmt.equal(tmax.key, key)) self.assertTrue(pmt.equal(tmax.value, pmt.from_long(max(offsets)))) self.assertTrue(pmt.equal(tmax.srcid, srcid))
def test_tag_propagation(self): ''' test_tag_propagation: test that non length tags are handled correctly ''' prepad = 10 postpad = 10 length1 = 15 length2 = 25 gap_len = 5 lentag1_offset = 0 lentag2_offset = length1 + gap_len tag1_offset = 0 # accompanies first length tag tag2_offset = length1 + gap_len # accompanies second length tag tag3_offset = 2 # in ramp-up state tag4_offset = length1 + 2 # in gap; tag will be dropped tag5_offset = length1 + gap_len + 7 # in copy state data = np.concatenate( (np.ones(length1), np.zeros(gap_len), -1.0 * np.ones(length2))) window = np.concatenate((-2.0 * np.ones(5), -4.0 * np.ones(5))) tags = (make_length_tag(lentag1_offset, length1), make_length_tag(lentag2_offset, length2), make_tag(tag1_offset, 'head', pmt.intern('tag1')), make_tag(tag2_offset, 'head', pmt.intern('tag2')), make_tag(tag3_offset, 'body', pmt.intern('tag3')), make_tag(tag4_offset, 'body', pmt.intern('tag4')), make_tag(tag5_offset, 'body', pmt.intern('tag5'))) expected = np.concatenate( (np.zeros(prepad), window[0:5], np.ones(length1 - len(window)), window[5:10], np.zeros(postpad + prepad), -1.0 * window[0:5], -1.0 * np.ones(length2 - len(window)), -1.0 * window[5:10], np.zeros(postpad))) elentag1_offset = 0 elentag2_offset = length1 + prepad + postpad etag1_offset = 0 etag2_offset = elentag2_offset etag3_offset = prepad + tag3_offset etag5_offset = 2 * prepad + postpad + tag5_offset - gap_len etags = (make_length_tag(elentag1_offset, length1 + prepad + postpad), make_length_tag(elentag2_offset, length2 + prepad + postpad), make_tag(etag1_offset, 'head', pmt.intern('tag1')), make_tag(etag2_offset, 'head', pmt.intern('tag2')), make_tag(etag3_offset, 'body', pmt.intern('tag3')), make_tag(etag5_offset, 'body', pmt.intern('tag5'))) # flowgraph source = blocks.vector_source_f(data, tags=tags) shaper = digital.burst_shaper_ff(window, pre_padding=prepad, post_padding=postpad) sink = blocks.vector_sink_f() self.tb.connect(source, shaper, sink) self.tb.run() # checks self.assertFloatTuplesAlmostEqual(sink.data(), expected, 6) for x, y in zip(sorted(sink.tags(), key=gr.tag_t_offset_compare_key()), sorted(etags, key=gr.tag_t_offset_compare_key())): self.assertTrue(compare_tags(x, y))
def test_tag_propagation (self): ''' test_tag_propagation: test that non length tags are handled correctly ''' prepad = 10 postpad = 10 length1 = 15 length2 = 25 gap_len = 5 lentag1_offset = 0 lentag2_offset = length1 + gap_len tag1_offset = 0 # accompanies first length tag tag2_offset = length1 + gap_len # accompanies second length tag tag3_offset = 2 # in ramp-up state tag4_offset = length1 + 2 # in gap; tag will be dropped tag5_offset = length1 + gap_len + 7 # in copy state data = np.concatenate((np.ones(length1), np.zeros(gap_len), -1.0*np.ones(length2))) window = np.concatenate((-2.0*np.ones(5), -4.0*np.ones(5))) tags = (make_length_tag(lentag1_offset, length1), make_length_tag(lentag2_offset, length2), make_tag(tag1_offset, 'head', pmt.intern('tag1')), make_tag(tag2_offset, 'head', pmt.intern('tag2')), make_tag(tag3_offset, 'body', pmt.intern('tag3')), make_tag(tag4_offset, 'body', pmt.intern('tag4')), make_tag(tag5_offset, 'body', pmt.intern('tag5'))) expected = np.concatenate((np.zeros(prepad), window[0:5], np.ones(length1 - len(window)), window[5:10], np.zeros(postpad + prepad), -1.0*window[0:5], -1.0*np.ones(length2 - len(window)), -1.0*window[5:10], np.zeros(postpad))) elentag1_offset = 0 elentag2_offset = length1 + prepad + postpad etag1_offset = 0 etag2_offset = elentag2_offset etag3_offset = prepad + tag3_offset etag5_offset = 2*prepad + postpad + tag5_offset - gap_len etags = (make_length_tag(elentag1_offset, length1 + prepad + postpad), make_length_tag(elentag2_offset, length2 + prepad + postpad), make_tag(etag1_offset, 'head', pmt.intern('tag1')), make_tag(etag2_offset, 'head', pmt.intern('tag2')), make_tag(etag3_offset, 'body', pmt.intern('tag3')), make_tag(etag5_offset, 'body', pmt.intern('tag5'))) # flowgraph source = blocks.vector_source_f(data, tags=tags) shaper = digital.burst_shaper_ff(window, pre_padding=prepad, post_padding=postpad) sink = blocks.vector_sink_f() self.tb.connect(source, shaper, sink) self.tb.run () # checks self.assertFloatTuplesAlmostEqual(sink.data(), expected, 6) for x, y in zip(sorted(sink.tags(), key=gr.tag_t_offset_compare_key()), sorted(etags, key=gr.tag_t_offset_compare_key())): self.assertTrue(compare_tags(x, y))