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_source_nodes(graph, function):
"""Returns a set of all nodes that have an in-degree of 0, which likely means that it is an external
perturbagen and is not known to have any causal origin from within the biological system.
These nodes are useful to identify because they generally don't provide any mechanistic insight.
:param pybel.BELGraph graph: A BEL graph
:param str function: The BEL function to filter by
:return: A set of source nodes
:rtype: set[tuple]
"""
return {
node
for node, data in graph.iter_node_data_pairs()
if data[FUNCTION] == function and is_causal_source(graph, node)
}
