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_intersection(populated_digraph):
another_digraph = sn.DiGraph()
a = another_digraph.add_node({"type": "A"}, '3caaa8c09148493dbdf02c574b95526c')
b = another_digraph.add_node({"type": "B"}, '2cdfebf3bf9547f19f0412ccdfbe03b7')
d = another_digraph.add_node({"type": "D"}, 'da30015efe3c44dbb0b3b3862cef704a')
another_digraph.add_edge(a, b, {"type": "normal"}, '5f5f44ec7c0144e29c5b7d513f92d9ab')
another_digraph.add_edge(b, a, {"type": "normal"}, 'f3674fcc691848ebbd478b1bfb3e84c3')
another_digraph.add_edge(a, d, {"type": "normal"}, 'f3674fcc691848ebbd478b1bfb3e84c3')
another_digraph.add_edge(d, b, {"type": "irregular"}, 'f3674fcc691848ebbd478b1bfb3e84c3')
I = sn.intersection(populated_digraph, another_digraph)
correct_nodes = {
uuid.UUID('3caaa8c09148493dbdf02c574b95526c'): {
"type": "A",
"id": uuid.UUID('3caaa8c09148493dbdf02c574b95526c')
},
uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'): {
"type": "B",
"id": uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7')
}
}
assert I.get_nodes() == correct_nodes
correct_edges = {
# a,b
uuid.UUID('5f5f44ec7c0144e29c5b7d513f92d9ab'): {
"type": "normal",
"src": uuid.UUID('3caaa8c09148493dbdf02c574b95526c'),
"dst": uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
"id": uuid.UUID('5f5f44ec7c0144e29c5b7d513f92d9ab')
},
# b,a
uuid.UUID('f3674fcc691848ebbd478b1bfb3e84c3'): {
"type": "normal",
"src": uuid.UUID('2cdfebf3bf9547f19f0412ccdfbe03b7'),
"dst": uuid.UUID('3caaa8c09148493dbdf02c574b95526c'),
"id": uuid.UUID('f3674fcc691848ebbd478b1bfb3e84c3')
}
}
assert I.get_edges() == correct_edges