def disambiguate(self):
gd = GraphDisambiguator()
gd.find_instances(self)
self.nodes = TopographicalSorter(
self.nodes,
dependencies=lambda n: tuple(
e.related for e in n.related(backward=False))).sorted()
def process(self, normalize=True, prune=True, disambiguate=True):
# TODO move models' normalize methods to regulator steps (SHARE-1023)
if normalize:
self.normalize()
gd = GraphDisambiguator()
# TODO move pruning to regulator step (SHARE-1024)
if prune:
gd.prune(self)
if disambiguate:
gd.find_instances(self)
self.nodes = TopographicalSorter(self.nodes, dependencies=lambda n: tuple(e.related for e in n.related(backward=False))).sorted()
def process(self, normalize=True, prune=True, disambiguate=True):
if normalize:
self.normalize()
gd = GraphDisambiguator()
if prune:
gd.prune(self)
if disambiguate:
gd.find_instances(self)
self.nodes = TopographicalSorter(
self.nodes,
dependencies=lambda n: tuple(
e.related for e in n.related(backward=False))).sorted()
def __init__(self, data, namespace=None):
self.nodes = []
self.relations = {}
self._lookup = {}
self.namespace = namespace
hints, relations = {}, set()
for blob in copy.deepcopy(data):
id, type = blob.pop('@id'), blob.pop('@type').lower()
self._lookup[id, type] = ChangeNode(self,
id,
type,
blob,
namespace=namespace)
self.relations[self._lookup[id, type]] = set()
self.nodes.append(self._lookup[id, type])
for k, v in tuple(blob.items()):
if isinstance(v,
dict) and k != 'extra' and not k.startswith('@'):
related = (v.pop('@id'), v.pop('@type').lower())
hints[(id, type), related] = k
relations.add(((id, type), related))
blob.pop(k)
if isinstance(v, list):
for rel in v:
subject = (rel.pop('@id'), rel.pop('@type').lower())
relations.add((subject, (id, type)))
blob.pop(k)
for subject, related in relations:
try:
edge = GraphEdge(self._lookup[subject],
self._lookup[related],
hint=hints.get((subject, related)))
except KeyError as e:
raise UnresolvableReference(*e.args)
self.relations[self._lookup[subject]].add(edge)
self.relations[self._lookup[related]].add(edge)
self.nodes = TopographicalSorter(
self.nodes,
dependencies=lambda n: tuple(
e.related for e in n.related(backward=False))).sorted()