def to_directed(self):
"""Return a directed representation of the graph.
Returns
-------
G : MultiDiGraph
A directed graph with the same name, same nodes, and with
each edge (u,v,data) replaced by two directed edges
(u,v,data) and (v,u,data).
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar D=DiGraph(G) which returns a
shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, http://docs.python.org/library/copy.html.
Warning
-------
If you have subclassed MultiGraph to use dict-like objects in the
data structure, those changes do not transfer to the MultiDiGraph
created by this method.
Examples
--------
>>> G = nx.Graph() # or MultiGraph, etc
>>> G.add_path([0,1])
>>> H = G.to_directed()
>>> H.edges()
[(0, 1), (1, 0)]
If already directed, return a (deep) copy
>>> G = nx.DiGraph() # or MultiDiGraph, etc
>>> G.add_path([0,1])
>>> H = G.to_directed()
>>> H.edges()
[(0, 1)]
"""
from networkx.classes.multidigraph import MultiDiGraph
G=MultiDiGraph()
G.add_nodes_from(self)
G.add_edges_from( (u,v,key,deepcopy(datadict))
for u,nbrs in self.adjacency_iter()
for v,keydict in nbrs.items()
for key,datadict in keydict.items() )
G.graph=deepcopy(self.graph)
G.node=deepcopy(self.node)
return G
def add_snapshot(self,
ebunch=None,
graph=None,
start=None,
end=None,
time=None,
multi=False):
"""Add a snapshot with a bunch of edge values.
Parameters
----------
ebunch : container of edges, optional (default= None)
Each edge in the ebunch list will be included to all added graphs.
graph : networkx graph object, optional (default= None)
networkx graph to be inserted into snapshot graph.
start: start timestamp, inclusive
end: end timestamp, exclusive
time: timestamp for impulses, cannot be used together with (start, end)
multi : boolean, optional (default= False)
Determines if type of graphs in snapshot are DiGraphs for MultiDiGraphs
Returns
-------
None
Examples
--------
>>> G = dnx.SnapshotGraph()
>>> G.add_snapshot([(1, 4), (1, 3)])
"""
if not graph:
if multi is True:
g = MultiDiGraph()
else:
g = DiGraph()
g.add_edges_from(ebunch)
else:
g = graph
if time is not None and (start or end):
raise ValueError(
'Time and (start or end) cannot both be specified.')
elif time is not None:
self.insert(g, time=time)
elif (start is None and end is not None) or (start is not None
and end is None):
raise ValueError(
'Start and end must both be specified for intervals.')
else:
self.insert(g, start=start, end=end)
def to_subgraph(self,
begin,
end,
multigraph=False,
edge_data=False,
edge_interval_data=False,
node_data=False):
"""Return a networkx DiGraph or MultiDiGraph which includes all the nodes and
edges which have overlapping intervals with the given interval.
Parameters
----------
begin: int or float
Inclusive beginning time of the edge appearing in the interval graph.
end: int or float
Non-inclusive ending time of the edge appearing in the interval graph.
Must be bigger than or equal to begin.
multigraph: bool, optional (default= False)
If True, a networkx MultiGraph will be returned. If False, networkx Graph.
edge_data: bool, optional (default= False)
If True, edges will keep their attributes.
edge_interval_data: bool, optional (default= False)
If True, each edge's attribute will also include its begin and end interval data.
If `edge_data= True` and there already exist edge attributes with names begin and end,
they will be overwritten.
node_data : bool, optional (default= False)
If True, each node's attributes will be included.
See Also
--------
to_snapshots : divide the interval graph to snapshots
Notes
-----
If multigraph= False, and edge_data=True or edge_interval_data=True,
in case there are multiple edges, only one will show with one of the edge's attributes.
Note: nodes with no edges will not appear in any subgraph.
Examples
--------
>>> G = dnx.IntervalGraph()
>>> G.add_edges_from([(1, 2, 3, 10), (2, 4, 1, 11), (6, 4, 12, 19), (2, 4, 8, 15)])
>>> H = G.to_subgraph(4, 12)
>>> type(H)
<class 'networkx.classes.graph.DiGraph'>
>>> list(H.edges(data=True))
[(1, 2, {}), (2, 4, {})]
>>> H = G.to_subgraph(4, 12, edge_interval_data=True)
>>> type(H)
<class 'networkx.classes.graph.DiGraph'>
>>> list(H.edges(data=True))
[(1, 2, {'end': 10, 'begin': 3}), (2, 4, {'end': 15, 'begin': 8})]
>>> M = G.to_subgraph(4, 12, multigraph=True, edge_interval_data=True)
>>> type(M)
<class 'networkx.classes.multigraph.MultiDiGraph'>
>>> list(M.edges(data=True))
[(1, 2, {'end': 10, 'begin': 3}), (2, 4, {'end': 11, 'begin': 1}), (2, 4, {'end': 15, 'begin': 8})]
"""
if begin and end and begin > end:
raise NetworkXError(
"IntervalGraph: interval end must be bigger than or equal to begin: "
"begin: {}, end: {}.".format(begin, end))
iedges = self.tree[begin:end]
if multigraph:
G = MultiDiGraph()
else:
G = DiGraph()
if edge_data and edge_interval_data:
G.add_edges_from((iedge.data[0], iedge.data[1],
dict(self._adj[iedge.data[0]][iedge],
begin=iedge.begin,
end=iedge.end)) for iedge in iedges)
elif edge_data:
G.add_edges_from((iedge.data[0], iedge.data[1],
self._adj[iedge.data[0]][iedge].copy())
for iedge in iedges)
elif edge_interval_data:
G.add_edges_from((iedge.data[0], iedge.data[1], {
'begin': iedge.begin,
'end': iedge.end
}) for iedge in iedges)
else:
G.add_edges_from(
(iedge.data[0], iedge.data[1]) for iedge in iedges)
# include node attributes
if node_data:
G.add_nodes_from((n, self._node[n].copy()) for n in G.nodes)
return G
def to_subgraph(self,
begin,
end,
inclusive=(True, False),
multigraph=False,
edge_data=False,
edge_timestamp_data=False,
node_data=False):
"""Return a networkx Graph or MultiGraph which includes all the nodes and
edges which have timestamps within the given interval.
Parameters
----------
begin: int or float
end: int or float
Must be bigger than or equal to begin.
inclusive: 2-tuple boolean that determines inclusivity of begin and end
multigraph: bool, optional (default= False)
If True, a networkx MultiGraph will be returned. If False, networkx Graph.
edge_data: bool, optional (default= False)
If True, edges will keep their attributes.
edge_timestamp_data: bool, optional (default= False)
If True, each edge's attribute will also include its timestamp data.
If `edge_data= True` and there already exist edge attributes named timestamp
it will be overwritten.
node_data : bool, optional (default= False)
if True, each node's attributes will be included.
See Also
--------
to_snapshots : divide the impulse graph to snapshots
Notes
-----
If multigraph= False, and edge_data=True or edge_interval_data=True,
in case there are multiple edges, only one will show with one of the edge's attributes.
Note: nodes with no edges will not appear in any subgraph.
Examples
--------
>>> G = dnx.ImpulseGraph()
>>> G.add_edges_from([(1, 2, 10), (2, 4, 11), (6, 4, 19), (2, 4, 15)])
>>> H = G.to_subgraph(4, 12)
>>> type(H)
<class 'networkx.classes.graph.DiGraph'>
>>> list(H.edges(data=True))
[(1, 2, {}), (2, 4, {})]
>>> H = G.to_subgraph(10, 12, edge_timestamp_data=True)
>>> type(H)
<class 'networkx.classes.graph.DiGraph'>
>>> list(H.edges(data=True))
[(1, 2, {'timestamp': 10}), (2, 4, {'timestamp': 11})]
>>> M = G.to_subgraph(4, 12, multigraph=True, edge_timestamp_data=True)
>>> type(M)
<class 'networkx.classes.multigraph.MultiDiGraph'>
>>> list(M.edges(data=True))
[(1, 2, {'timestamp': 10}), (2, 4, {'timestamp': 11})]
"""
iedges = self.__search_tree(begin, end, inclusive=inclusive)
if multigraph:
G = MultiDiGraph()
else:
G = DiGraph()
if edge_data and edge_timestamp_data:
G.add_edges_from((iedge[0], iedge[1],
dict(self._pred[iedge[0]][iedge[1]][iedge],
timestamp=iedge[3])) for iedge in iedges)
elif edge_data:
G.add_edges_from(
(iedge[0], iedge[1], self._pred[iedge[0]][iedge[1]][iedge])
for iedge in iedges)
elif edge_timestamp_data:
G.add_edges_from((iedge[0], iedge[1], {
'timestamp': iedge[3]
}) for iedge in iedges)
else:
G.add_edges_from((iedge[0], iedge[1]) for iedge in iedges)
if node_data:
G.add_nodes_from((n, self._node[n].copy()) for n in G.nodes)
return G
