def edge(self, edge_to_find, key=0):
"""returns edge in this graph with same src and dst"""
# TODO: check if this even needed - will be if searching nidb specifically
# but that's so rare (that's a design stage if anywhere)
src_id = edge_to_find.src
dst_id = edge_to_find.dst
if self.is_multigraph():
for (src, dst, rkey) in self._graph.edges(src_id, keys=True):
if dst == dst_id and rkey == search_key:
return DmEdge(self._anm, src, dst, search_key)
for (src, dst) in self._graph.edges(src_id):
if dst == dst_id:
return DmEdge(self._anm, src, dst)
def edges(self, nbunch=None, *args, **kwargs):
# nbunch may be single node
# TODO: Apply edge filters
if nbunch:
try:
nbunch = nbunch.node_id
except AttributeError:
# only store the id in overlay
nbunch = (n.node_id for n in nbunch)
def filter_func(edge):
return (
all(getattr(edge, key) for key in args) and
all(getattr(edge, key) == val for key, val in kwargs.items())
)
# TODO: See if more efficient way to access underlying data structure
# rather than create overlay to throw away
if self.is_multigraph():
valid_edges = list((src, dst, key) for (src, dst, key) in
self._graph.edges(nbunch, keys=True))
else:
default_key = 0
valid_edges = list((src, dst, default_key) for (src, dst) in
self._graph.edges(nbunch))
all_edges = [DmEdge(self, src, dst, key)
for src, dst, key in valid_edges]
return (edge for edge in all_edges if filter_func(edge))
def boundary_edges(self, nbunch, nbunch2=None):
nbunch = (n.node_id for n in nbunch) # only store the id in overlay
return iter(
DmEdge(self, src, dst)
for (src, dst) in nx.edge_boundary(self._graph, nbunch, nbunch2))