def _contextual_partition(expected, observed, start=None, end=None):
edges = set()
if start is not None:
edges.add(start)
if end is not None:
edges.add(end)
for edge in expected + observed:
edges.update(edge)
partitions = list()
edges = sorted(edges)
last = edges[0]
for edge in edges[1:]:
partitions.append((last, edge))
last = edge
expected_parts = list()
observed_parts = list()
weights = list()
for part in partitions:
weights.append(part[1] - part[0])
expected_parts.append(_any_overlap(part, expected))
observed_parts.append(_any_overlap(part, observed))
return expected_parts, observed_parts, weights
def test__any_overlap_false(expected, observed):
part = expected["false"][0]
interval = observed["false"]
expected_return = 0
returned = _any_overlap(part, interval)
assert returned == expected_return