def test_causal_source(self):
g = BELGraph()
g.add_edge(1, 2, relation=INCREASES)
g.add_edge(2, 3, relation=INCREASES)
self.assertTrue(is_causal_source(g, 1))
self.assertFalse(is_causal_central(g, 1))
self.assertFalse(is_causal_sink(g, 1))
self.assertFalse(is_causal_source(g, 2))
self.assertTrue(is_causal_central(g, 2))
self.assertFalse(is_causal_sink(g, 2))
self.assertFalse(is_causal_source(g, 3))
self.assertFalse(is_causal_central(g, 3))
self.assertTrue(is_causal_sink(g, 3))
def test_causal_source(self):
g = BELGraph()
a, b, c = (protein(n(), n()) for _ in range(3))
g.add_increases(a, b, n(), n())
g.add_increases(b, c, n(), n())
self.assertTrue(is_causal_source(g, a))
self.assertFalse(is_causal_central(g, a))
self.assertFalse(is_causal_sink(g, a))
self.assertFalse(is_causal_source(g, b))
self.assertTrue(is_causal_central(g, b))
self.assertFalse(is_causal_sink(g, b))
self.assertFalse(is_causal_source(g, c))
self.assertFalse(is_causal_central(g, c))
self.assertTrue(is_causal_sink(g, c))
def get_causal_sink_nodes(graph, function):
"""Returns a set of all ABUNDANCE nodes that have an causal out-degree of 0, which likely means that the knowledge
assembly is incomplete, or there is a curation error.
:param pybel.BELGraph graph: A BEL graph
:param str function: The BEL function to filter by
:return: A set of sink ABUNDANCE nodes
:rtype: set[tuple]
"""
return {
node
for node, data in graph.iter_node_data_pairs()
if data[FUNCTION] == function and is_causal_sink(graph, node)
}