def test_simple_peak():
pileup = SparseValues([0, 5, 35], [False, True, False])
score_pileup = SparseValues([0, 5, 10, 20, 30, 35], [0, 1, 2, 3, 4, 0])
score_pileup.track_size = 100
pileup.track_size = 100
max_paths = SparseMaxPaths(pileup, graph, score_pileup).run()
assert max_paths == [Peak(5, 5, [1, 3, 4], graph=graph)]
def test_offset_end_peak():
graph = offsetgraph()
pileup = SparseValues([0, 85], [False, True])
score_pileup = SparseValues([0, 85], [0, 10])
score_pileup.track_size = 100
pileup.track_size = 100
max_paths = SparseMaxPaths(pileup, graph, score_pileup).run()
print(max_paths)
assert max_paths == [Peak(5, 10, [109, 110], graph=graph)]
def test_trailing_zeros():
graph = offsetgraph()
pileup = SparseValues([0, 5, 35], [False, True, False])
score_pileup = SparseValues([0, 15, 30], [0, 10, 0])
score_pileup.track_size = 100
pileup.track_size = 100
max_paths = SparseMaxPaths(pileup, graph, score_pileup).run()
print(max_paths)
assert max_paths == [Peak(5, 5, [101, 103, 104], graph=graph)]
def test_multiple_peak():
graph = complicated_offset()
pileup = SparseValues([0, 1, 7, 8, 10, 12, 14], [0, 1, 0, 1, 0, 1, 0])
score_pileup = SparseValues([0, 4], [5, 6])
score_pileup.track_size = 20
pileup.track_size = 20
max_paths = SparseMaxPaths(pileup, graph, score_pileup).run()
max_paths.sort(key=lambda x: x.region_paths[0])
print(max_paths)
assert max_paths == [
Peak(1, 1, [101, 103, 104], graph=graph),
Peak(0, 2, [105, 107], graph=graph)
]
def test_to_from_file(self):
indexes = np.array([1, 2, 3, 4, 5, 6, 7, 8])
values = np.array([1, 2, 3, 4, 5, 6, 7, 8])
sv = SparseValues(indexes, values)
sv.track_size = 10
sv.to_sparse_files("test_sparsevalues.tmp")
new = sv.from_sparse_files("test_sparsevalues.tmp")
self.assertEqual(sv, new)
def test_single_peak():
indices = np.array([0, 3, 32], dtype="int")
values = np.array([False, True, False], dtype="bool")
pileup = SparseValues(indices, values)
indices = np.arange(0, 40, 2)
values = (np.arange(0, 40, 2) + 1) % 5
score_pileup = SparseValues(indices, values)
score_pileup.track_size = 100
graph.node_indexes = np.arange(0, 110, 10)
max_paths = SparseMaxPaths(pileup, graph, score_pileup)
print(max_paths.run())
def convert_old_sparse(old_sparse):
graph = old_sparse.graph
indices = [0]
values = [0]
for key in sorted(old_sparse.data.keys()):
vi = old_sparse.data[key]
node_idx = graph.node_indexes[key - graph.min_node]
indices.append(node_idx)
values.append(vi.start_value)
indices.extend(vi.indexes + node_idx)
values.extend(vi.values)
sv = SparseValues(indices, values, sanitize=True)
sv.track_size = graph.node_indexes[-1]
return sv
def test_get_summits(self):
qvalues = SparseValues(np.array([0]), np.array([3]))
qvalues.track_size = 22
qvalues.to_sparse_files("tests/test_qvalues")
run_argument_parser([
"create_ob_graph", "-o", "tests/testgraph.obg",
"tests/vg_test_graph.json"
])
max_paths = PeakCollection([Peak(0, 2, [1, 2], score=3)])
PeakFasta(self.correct_sequence_graph).write_max_path_sequences(
"tests/test_max_paths.fasta", max_paths)
run_argument_parser([
"get_summits", "-g", "tests/testgraph.obg",
"tests/test_max_paths.fasta", "tests/test_qvalues", "2"
])
result = PeakCollection.from_fasta_file(
"tests/test_max_paths_summits.fasta")
self.assertEqual(result.intervals[0], Peak(2, 6, [1]))
self.assertEqual(result.intervals[0].sequence.lower(), "tccc")
def offset_areas():
sv = SparseValues([0, 10, 11, 20, 29, 31], [1, 0, 1, 0, 1, 0])
sv.track_size = 80
return sv
def areas():
sv = SparseValues([0, 15, 42, 48, 72], [0, 1, 0, 1, 0])
sv.track_size = 80
return sv
def nonvar_pileup():
sv = SparseValues([0, 15, 45, 50, 60, 70], [0, 1, 4, 1, 3, 5])
sv.track_size = 80
return sv
def pileup():
sv = SparseValues([0, 15, 30, 45, 50, 60, 70], [0, 1, 2, 6, 2, 3, 6])
sv.track_size = 80
return sv