def test_union_idempotent(populated_digraph):
g1 = populated_digraph
g2 = g1.copy()
gu = sn.union(g1, g2)
assert gu.get_nodes() == g1.get_nodes()
assert gu.get_nodes() == g2.get_nodes()
assert gu.get_edges() == g1.get_edges()
assert gu.get_edges() == g2.get_edges()
def diff(A, B, context=False, mods=False):
'''Given two graphs A and B, where it is generally assumed that B is a "newer" version of A,
returns a new graph which captures information about which nodes and edges of A were
removed, added, and remain the same in B.
Specifically, it returns A ∪ B, such that:
1. Nodes in A - B are given the "diffstatus" attribute "removed"
2. Nodes in B - A are given the "diffstatus" attribute "added"
3. Nodes in A ∩ B are given the "diffstatus" attribute "same"
Notice that the union of 1 - 3 equals A ∪ B.
The optional parameter context, when true, will prune the graph so that nodes/edges which are
the same are only present in the diff graph if:
1. An edge incident on/to/from it has been changed, or
2. it is connected to a changed node.
The optional parameter mods, when true, will check for attribute modifications on nodes and
edges, in addition to new/removed nodes. Any nodes/edges that have had their attributes
changed between A and B are marked with the "diffstatus" attribute as "modified."
WARNING: Currently, this method only works if both A and B were generated with unique IDs in a
deterministic fashion; i.e., two identical nodes are given the same ID at both points in time.
This means that diff() will not work on graphs which were generated with automatic random UUIDs.
'''
# must take their union first, then mark appropriate nodes/edges
AB = sn.union(A, B)
# any edges incident on, to, or from the removed nodes will not be in the removed graph,
# since we cannot have edges incident on, to, or from non-existent nodes
removed = sn.difference(A, B)
_mark_nodes_edges_as(AB, removed, 'removed')
_mark_incident_edges_as(AB, removed, 'removed')
added = sn.difference(B, A)
_mark_nodes_edges_as(AB, added, 'added')
_mark_incident_edges_as(AB, added, 'added')
same = sn.intersection(B, A)
_mark_nodes_edges_as(AB, same, 'same')
_check_changed_edges(A, B, AB, same)
if mods:
_check_mods(A, B, AB, same)
if context:
_clear_clutter(AB)
return AB
def test_union_partial(populated_digraph):
g1 = populated_digraph
g2 = sn.DiGraph()
a = g2.add_node({"type": "A"}, '3caaa8c09148493dbdf02c574b95526c')
b = g2.add_node({"type": "B"}, '2cdfebf3bf9547f19f0412ccdfbe03b7')
d = g2.add_node({"type": "D"})
ab = g2.add_edge(a, b, {"type": "normal"}, '5f5f44ec7c0144e29c5b7d513f92d9ab')
bd = g2.add_edge(b, d, {"type": "irregular"})
gu = sn.union(g1, g2)
correct_nodes = {
a: {
'id': a,
'type': 'A'
},
b: {
'id': b,
'type': 'B'
},
uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'): {
'id': uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'),
'type': 'C'
},
d: {
'id': d,
'type': 'D'
}
}
assert gu.get_nodes() == correct_nodes
correct_edges = {
ab: {
'id': ab,
'src': a,
'dst': b,
'type': 'normal'
},
uuid.UUID('f3674fcc691848ebbd478b1bfb3e84c3'): {
'id': uuid.UUID('f3674fcc691848ebbd478b1bfb3e84c3'),
'src': b,
'dst': a,
'type': 'normal'
},
uuid.UUID('7eb91be54d3746b89a61a282bcc207bb'): {
'id': uuid.UUID('7eb91be54d3746b89a61a282bcc207bb'),
'src': a,
'dst': uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'),
'type': 'normal'
},
uuid.UUID('c172a3599b7d4ef3bbb688277276b763'): {
'id': uuid.UUID('c172a3599b7d4ef3bbb688277276b763'),
'src': uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
'dst': uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'),
'type': 'irregular'
},
bd: {
'id': bd,
'src': b,
'dst': d,
'type': 'irregular'
}
}
gu.get_nodes() == correct_nodes
def test_union_disjoint(populated_digraph):
g1 = populated_digraph
g2 = sn.DiGraph()
a = g2.add_node({"label" : "A"}, 'a')
b = g2.add_node({"label" : "B"}, 'b')
c = g2.add_node({"label" : "C"}, 'c')
ab = g2.add_edge(a, b, {"type" : "belongs"}, 'belongs')
bc = g2.add_edge(b, c, {"type" : "owns"}, 'owns')
ca = g2.add_edge(c, a, {"type" : "has"}, 'has')
gu = sn.union(g1, g2)
correct_nodes = {
uuid.UUID('3caaa8c09148493dbdf02c574b95526c'): {
'id': uuid.UUID('3caaa8c09148493dbdf02c574b95526c'),
'type': 'A'
},
uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'): {
'id': uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
'type': 'B'
},
uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'): {
'id': uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'),
'type': 'C'
},
'a': {
'id': 'a',
'label': 'A'
},
'b': {
'id': 'b',
'label': 'B'
},
'c': {
'id': 'c',
'label': 'C'
}
}
assert gu.get_nodes() == correct_nodes
correct_edges = {
uuid.UUID('5f5f44ec7c0144e29c5b7d513f92d9ab'): {
'id': uuid.UUID('5f5f44ec7c0144e29c5b7d513f92d9ab'),
'src': uuid.UUID('3caaa8c09148493dbdf02c574b95526c'),
'dst': uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
'type': 'normal'
},
uuid.UUID('f3674fcc691848ebbd478b1bfb3e84c3'): {
'id': uuid.UUID('f3674fcc691848ebbd478b1bfb3e84c3'),
'src': uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
'dst': uuid.UUID('3caaa8c09148493dbdf02c574b95526c'),
'type': 'normal'
},
uuid.UUID('7eb91be54d3746b89a61a282bcc207bb'): {
'id': uuid.UUID('7eb91be54d3746b89a61a282bcc207bb'),
'src': uuid.UUID('3caaa8c09148493dbdf02c574b95526c'),
'dst': uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'),
'type': 'normal'
},
uuid.UUID('c172a3599b7d4ef3bbb688277276b763'): {
'id': uuid.UUID('c172a3599b7d4ef3bbb688277276b763'),
'src': uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
'dst': uuid.UUID('3cd197c2cf5e42dc9ccd0c2adcaf4bc2'),
'type': 'irregular'
},
ab: {
'id': ab,
'src': a,
'dst': b,
'type': 'belongs'
},
bc: {
'id': bc,
'src': b,
'dst': c,
'type': 'owns'
},
ca: {
'id': ca,
'src': c,
'dst': a,
'type': 'has'
}
}
assert gu.get_edges() == correct_edges
