def test_pushout_symmetry_directed(self):
A = nx.DiGraph()
A.add_nodes_from(["a", "b"])
A.add_edges_from([("a", "b")])
B = nx.DiGraph()
B.add_nodes_from([1, 2, 3])
B.add_edges_from([(2, 3), (3, 2), (1, 3)])
C = nx.DiGraph()
C.add_nodes_from(["x", "y"])
C.add_edges_from([("x", "x"), ("x", "y")])
homAB = {"a": 2, "b": 3}
homAC = {"a": "x", "b": "x"}
D, homBD, homCD = pushout(
A, B, C, homAB, homAC
)
D_inv, homCD_inv, homBD_inv = pushout(
A, C, B, homAC, homAB
)
assert_equals(len(D.nodes()), len(D_inv.nodes()))
assert_equals(len(D.edges()), len(D_inv.edges()))
def test_pushout(self):
D, homBD, homCD = pushout(self.A, self.B, self.C, self.homAB,
self.homAC)
assert_equals(type(D), nx.DiGraph)
assert_equals(len(D.nodes()), len(self.D.nodes()))
assert_equals(len(D.edges()), len(self.D.edges()))
assert (id(self.B) != id(D))
def test_pushout(self):
D, homBD, homCD = pushout(
self.A, self.B, self.C, self.homAB, self.homAC
)
assert_equals(type(D), nx.DiGraph)
assert_equals(len(D.nodes()),
len(self.D.nodes()))
assert_equals(len(D.edges()),
len(self.D.edges()))
assert(id(self.B) != id(D))
def test_pushout_symmetry_directed(self):
A = nx.DiGraph()
A.add_nodes_from(["a", "b"])
A.add_edges_from([("a", "b")])
B = nx.DiGraph()
B.add_nodes_from([1, 2, 3])
B.add_edges_from([(2, 3), (3, 2), (1, 3)])
C = nx.DiGraph()
C.add_nodes_from(["x", "y"])
C.add_edges_from([("x", "x"), ("x", "y")])
homAB = {"a": 2, "b": 3}
homAC = {"a": "x", "b": "x"}
D, homBD, homCD = pushout(A, B, C, homAB, homAC)
D_inv, homCD_inv, homBD_inv = pushout(A, C, B, homAC, homAB)
assert_equals(len(D.nodes()), len(D_inv.nodes()))
assert_equals(len(D.edges()), len(D_inv.edges()))
def test_pushout_inplace(self):
B_copy = copy.deepcopy(self.B)
D, homBD, homCD = pushout(
self.A, B_copy, self.C, self.homAB, self.homAC, inplace=True
)
assert_equals(type(D), nx.DiGraph)
assert_equals(len(D.nodes()),
len(self.D.nodes()))
assert_equals(len(D.edges()),
len(self.D.edges()))
assert(id(B_copy) == id(D))
def test_pushout_inplace(self):
B_copy = copy.deepcopy(self.B)
D, homBD, homCD = pushout(self.A,
B_copy,
self.C,
self.homAB,
self.homAC,
inplace=True)
assert_equals(type(D), nx.DiGraph)
assert_equals(len(D.nodes()), len(self.D.nodes()))
assert_equals(len(D.edges()), len(self.D.edges()))
assert (id(B_copy) == id(D))
def _get_rule_projections(hierarchy,
origin_id,
rule,
instance,
rhs_typing=None,
ignore=None):
if ignore is None:
ignore = []
if rhs_typing is None:
rhs_typing = {}
projections = {}
if rule.is_relaxing():
for graph, origin_typing in hierarchy.get_descendants(
origin_id).items():
if graph not in ignore:
if hierarchy.is_graph(graph):
# Compute canonical P_T
l_t, l_l_t, l_t_t = image_factorization(
rule.lhs, hierarchy.get_graph(graph),
compose(instance, origin_typing))
# Compute canonical R_T
r_t, l_t_r_t, r_r_t = pushout(rule.p, l_t, rule.rhs, l_l_t,
rule.p_rhs)
# Modify P_T and R_T according to the controlling
# relation rhs_typing
if graph in rhs_typing.keys():
r_t_factorization = {
r_r_t[k]: v
for k, v in rhs_typing[graph].items()
}
added_t_nodes = set()
for n in r_t.nodes():
if n in r_t_factorization.keys():
# If corresponding R_T node is specified in
# the controlling relation add nodes of T
# that type it to P
t_nodes = r_t_factorization[n]
for t_node in t_nodes:
if t_node not in l_t_t.values() and\
t_node not in added_t_nodes:
new_p_node = primitives.generate_new_node_id(
l_t, t_node)
primitives.add_node(l_t, new_p_node)
added_t_nodes.add(t_node)
l_t_r_t[new_p_node] = n
l_t_t[new_p_node] = t_node
else:
l_t_r_t[keys_by_value(l_t_t,
t_node)[0]] = n
projections[graph] = {
"rule": Rule(lhs=l_t, p=l_t, rhs=r_t, p_rhs=l_t_r_t),
"instance": l_t_t,
"l_l_t": l_l_t,
"p_p_t": {k: l_l_t[v]
for k, v in rule.p_lhs.items()},
"r_r_t": r_r_t
}
return projections
def _rewrite_base(hierarchy,
graph_id,
rule,
instance,
rhs_typing,
inplace=False):
g_m, p_g_m, g_m_g =\
pullback_complement(rule.p, rule.lhs, hierarchy.get_graph(graph_id),
rule.p_lhs, instance, inplace)
g_prime, g_m_g_prime, r_g_prime = pushout(rule.p, g_m, rule.rhs, p_g_m,
rule.p_rhs, inplace)
relation_updates = []
for related_g in hierarchy.adjacent_relations(graph_id):
relation_updates.append((graph_id, related_g))
updated_homomorphisms = dict()
for typing_graph in hierarchy.successors(graph_id):
new_hom = copy.deepcopy(
hierarchy.adj[graph_id][typing_graph]["mapping"])
removed_nodes = set()
new_nodes = dict()
for node in rule.lhs.nodes():
p_keys = keys_by_value(rule.p_lhs, node)
# nodes that were removed
if len(p_keys) == 0:
removed_nodes.add(instance[node])
elif len(p_keys) == 1:
if typing_graph not in rhs_typing.keys() or\
rule.p_rhs[p_keys[0]] not in rhs_typing[typing_graph].keys():
if r_g_prime[rule.p_rhs[p_keys[0]]] in new_hom.keys():
removed_nodes.add(r_g_prime[rule.p_rhs[p_keys[0]]])
# nodes were clonned
elif len(p_keys) > 1:
for k in p_keys:
if typing_graph in rhs_typing.keys() and\
rule.p_rhs[k] in rhs_typing[typing_graph].keys():
new_nodes[r_g_prime[rule.p_rhs[k]]] =\
list(rhs_typing[typing_graph][rule.p_rhs[k]])[0]
else:
removed_nodes.add(r_g_prime[rule.p_rhs[k]])
for node in rule.rhs.nodes():
p_keys = keys_by_value(rule.p_rhs, node)
# nodes that were added
if len(p_keys) == 0:
if typing_graph in rhs_typing.keys():
if node in rhs_typing[typing_graph].keys():
new_nodes[node] = list(
rhs_typing[typing_graph][node])[0]
# nodes that were merged
elif len(p_keys) > 1:
for k in p_keys:
removed_nodes.add(p_g_m[k])
# assign new type of node
if typing_graph in rhs_typing.keys():
if node in rhs_typing[typing_graph].keys():
new_type = list(rhs_typing[typing_graph][node])
new_nodes[r_g_prime[node]] = new_type
# update homomorphisms
for n in removed_nodes:
if n in new_hom.keys():
del new_hom[n]
new_hom.update(new_nodes)
updated_homomorphisms.update({(graph_id, typing_graph): new_hom})
return {
"graph": (g_m, p_g_m, g_m_g, g_prime, g_m_g_prime, r_g_prime),
"homomorphisms": updated_homomorphisms,
"relations": relation_updates
}
def get_rule_projections(tx, hierarchy, graph_id, rule, instance, rhs_typing=None):
"""Execute the query finding rule liftings."""
if rhs_typing is None:
rhs_typing = {}
projections = {}
if rule.is_relaxing():
if len(rule.lhs.nodes()) > 0:
lhs_instance = {
n: instance[n] for n in rule.lhs.nodes()
}
lhs_vars = {
n: n for n in rule.lhs.nodes()}
match_instance_vars = {
v: lhs_instance[k] for k, v in lhs_vars.items()
}
# Match nodes
query = "// Match nodes the instance of the rewritten graph \n"
query += "MATCH {}".format(
", ".join([
"({}:{} {{id: '{}'}})".format(k, graph_id, v)
for k, v in match_instance_vars.items()
])
)
query += "\n\n"
carry_vars = list(lhs_vars.values())
for k, v in lhs_vars.items():
query += (
"OPTIONAL MATCH (n)<-[:typing*1..]-({})\n".format(v) +
"WITH {} \n".format(
", ".join(
carry_vars +
["collect(DISTINCT {{type:'node', origin: {}.id, id: n.id, graph:labels(n)[0], attrs: properties(n)}}) as {}_dict\n".format(
v, v)])
)
)
carry_vars.append("{}_dict".format(v))
# Match edges
for (u, v) in rule.p.edges():
edge_var = "{}_{}".format(lhs_vars[u], lhs_vars[v])
query += "OPTIONAL MATCH ({}_instance)-[{}:edge]->({}_instance)\n".format(
lhs_vars[u],
edge_var,
lhs_vars[v])
query += "WHERE ({})<-[:typing*1..]-({}) AND ({})<-[:typing*1..]-({})\n".format(
"{}_instance".format(lhs_vars[u]), lhs_vars[u],
"{}_instance".format(lhs_vars[v]), lhs_vars[v])
query += (
"WITH {} \n".format(
", ".join(carry_vars + [
"collect({{type: 'edge', source: {}.id, target: {}.id, graph:labels({})[0], attrs: properties({})}}) as {}\n".format(
"{}_instance".format(lhs_vars[u]),
"{}_instance".format(lhs_vars[v]),
"{}_instance".format(lhs_vars[u]),
edge_var,
edge_var)
])
)
)
carry_vars.append(edge_var)
query += "RETURN {}".format(
", ".join(
["{}_dict as {}".format(v, v) for v in lhs_vars.values()] +
["{}_{}".format(lhs_vars[u], lhs_vars[v]) for u, v in rule.p.edges()]))
result = tx.run(query)
record = result.single()
l_l_ts = {}
l_nodes = {}
l_edges = {}
for k, v in record.items():
if len(v) > 0:
if v[0]["type"] == "node":
for el in v:
l_node = keys_by_value(instance, el["origin"])[0]
if el["graph"] not in l_nodes:
l_nodes[el["graph"]] = {}
l_l_ts[el["graph"]] = {}
if el["id"] not in l_nodes[el["graph"]]:
l_nodes[el["graph"]][el["id"]] = {}
l_nodes[el["graph"]][el["id"]] = attrs_union(
l_nodes[el["graph"]][el["id"]],
attrs_intersection(
generic.convert_props_to_attrs(el["attrs"]),
get_node(rule.lhs, l_node)))
l_l_ts[el["graph"]][l_node] = el["id"]
else:
for el in v:
l_sources = keys_by_value(l_l_ts[el["graph"]], el["source"])
l_targets = keys_by_value(l_l_ts[el["graph"]], el["target"])
for l_source in l_sources:
for l_target in l_targets:
if exists_edge(rule.l, l_source, l_target):
if el["graph"] not in l_edges:
l_edges[el["graph"]] = {}
if (el["source"], el["target"]) not in l_edges[el["graph"]]:
l_edges[el["graph"]][(el["source"], el["target"])] = {}
l_edges[el["graph"]][(el["source"], el["target"])] =\
attrs_union(
l_edges[el["graph"]][(el["source"], el["target"])],
attrs_intersection(
generic.convert_props_to_attrs(el["attrs"]),
get_edge(rule.lhs, l_source, l_target)))
for graph, typing in hierarchy.get_descendants(graph_id).items():
if graph in l_nodes:
nodes = l_nodes[graph]
else:
nodes = {}
if graph in l_edges:
edges = l_edges[graph]
else:
edges = {}
l = nx.DiGraph()
add_nodes_from(l, [(k, v) for k, v in nodes.items()])
if graph in l_edges:
add_edges_from(
l,
[(s, t, v) for (s, t), v in edges.items()])
rhs, p_rhs, r_r_t = pushout(
rule.p, l, rule.rhs, compose(rule.p_lhs, l_l_ts[graph]), rule.p_rhs)
l_t_t = {n: n for n in nodes}
# Modify P_T and R_T according to the controlling
# relation rhs_typing
if graph in rhs_typing.keys():
r_t_factorization = {
r_r_t[k]: v
for k, v in rhs_typing[graph].items()
}
added_t_nodes = set()
for n in rhs.nodes():
if n in r_t_factorization.keys():
# If corresponding R_T node is specified in
# the controlling relation add nodes of T
# that type it to P
t_nodes = r_t_factorization[n]
for t_node in t_nodes:
if t_node not in l_t_t.values() and\
t_node not in added_t_nodes:
new_p_node = generate_new_id(
l.nodes(), t_node)
l.add_node(new_p_node)
added_t_nodes.add(t_node)
p_rhs[new_p_node] = n
l_t_t[new_p_node] = t_node
else:
p_rhs[keys_by_value(l_t_t, t_node)[0]] = n
projections[graph] = {
"rule": Rule(p=l, rhs=rhs, p_rhs=p_rhs),
"instance": l_t_t,
"l_l_t": l_l_ts[graph],
"p_p_t": {k: l_l_ts[graph][v] for k, v in rule.p_lhs.items()},
"r_r_t": r_r_t
}
return projections