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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.
Source code for networkx.classes.multigraph
"""Base class for MultiGraph."""
# Copyright (C) 2004-2015 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
from copy import deepcopy
import networkx as nx
from networkx.classes.graph import Graph
from networkx import NetworkXError
__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
'Pieter Swart (swart@lanl.gov)',
'Dan Schult(dschult@colgate.edu)'])
[docs]class MultiGraph(Graph):
"""
An undirected graph class that can store multiedges.
Multiedges are multiple edges between two nodes. Each edge
can hold optional data or attributes.
A MultiGraph holds undirected edges. Self loops are allowed.
Nodes can be arbitrary (hashable) Python objects with optional
key/value attributes.
Edges are represented as links between nodes with optional
key/value attributes.
Parameters
----------
data : input graph
Data to initialize graph. If data=None (default) an empty
graph is created. The data can be an edge list, or any
NetworkX graph object. If the corresponding optional Python
packages are installed the data can also be a NumPy matrix
or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
DiGraph
MultiDiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.MultiGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2,3])
>>> G.add_nodes_from(range(100,110))
>>> H=nx.Graph()
>>> H.add_path([0,1,2,3,4,5,6,7,8,9])
>>> G.add_nodes_from(H)
In addition to strings and integers any hashable Python object
(except None) can represent a node, e.g. a customized node object,
or even another Graph.
>>> G.add_node(H)
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> G.add_edge(1, 2)
a list of edges,
>>> G.add_edges_from([(1,2),(1,3)])
or a collection of edges,
>>> G.add_edges_from(H.edges())
If some edges connect nodes not yet in the graph, the nodes
are added automatically. If an edge already exists, an additional
edge is created and stored using a key to identify the edge.
By default the key is the lowest unused integer.
>>> G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))])
>>> G[4]
{3: {0: {}}, 5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}}
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node or direct manipulation of the attribute
dictionaries named graph, node and edge respectively.
>>> G = nx.MultiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.node
>>> G.add_node(1, time='5pm')
>>> G.add_nodes_from([3], time='2pm')
>>> G.node[1]
{'time': '5pm'}
>>> G.node[1]['room'] = 714
>>> del G.node[1]['room'] # remove attribute
>>> G.nodes(data=True)
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Warning: adding a node to G.node does not add it to the graph.
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edge.
>>> G.add_edge(1, 2, weight=4.7 )
>>> G.add_edges_from([(3,4),(4,5)], color='red')
>>> G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
>>> G[1][2][0]['weight'] = 4.7
>>> G.edge[1][2][0]['weight'] = 4
**Shortcuts:**
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph
True
>>> [n for n in G if n<3] # iterate through nodes
[1, 2]
>>> len(G) # number of nodes in graph
5
>>> G[1] # adjacency dict keyed by neighbor to edge attributes
... # Note: you should not change this dict manually!
{2: {0: {'weight': 4}, 1: {'color': 'blue'}}}
The fastest way to traverse all edges of a graph is via
adjacency_iter(), but the edges() method is often more convenient.
>>> for n,nbrsdict in G.adjacency_iter():
... for nbr,keydict in nbrsdict.items():
... for key,eattr in keydict.items():
... if 'weight' in eattr:
... (n,nbr,key,eattr['weight'])
(1, 2, 0, 4)
(2, 1, 0, 4)
(2, 3, 0, 8)
(3, 2, 0, 8)
>>> G.edges(data='weight', keys=True)
[(1, 2, 0, 4), (1, 2, 1, None), (2, 3, 0, 8), (3, 4, 0, None), (4, 5, 0, None)]
**Reporting:**
Simple graph information is obtained using methods.
Iterator versions of many reporting methods exist for efficiency.
Methods exist for reporting nodes(), edges(), neighbors() and degree()
as well as the number of nodes and edges.
For details on these and other miscellaneous methods, see below.
**Subclasses (Advanced):**
The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
The outer dict (node_dict) holds adjacency lists keyed by node.
The next dict (adjlist) represents the adjacency list and holds
edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr
dict keyed by edge key. The inner dict (edge_attr) represents
the edge data and holds edge attribute values keyed by attribute names.
Each of these four dicts in the dict-of-dict-of-dict-of-dict
structure can be replaced by a user defined dict-like object.
In general, the dict-like features should be maintained but
extra features can be added. To replace one of the dicts create
a new graph class by changing the class(!) variable holding the
factory for that dict-like structure. The variable names
are node_dict_factory, adjlist_dict_factory, edge_key_dict_factory
and edge_attr_dict_factory.
node_dict_factory : function, (default: dict)
Factory function to be used to create the outer-most dict
in the data structure that holds adjacency lists keyed by node.
It should require no arguments and return a dict-like object.
adjlist_dict_factory : function, (default: dict)
Factory function to be used to create the adjacency list
dict which holds multiedge key dicts keyed by neighbor.
It should require no arguments and return a dict-like object.
edge_key_dict_factory : function, (default: dict)
Factory function to be used to create the edge key dict
which holds edge data keyed by edge key.
It should require no arguments and return a dict-like object.
edge_attr_dict_factory : function, (default: dict)
Factory function to be used to create the edge attribute
dict which holds attrbute values keyed by attribute name.
It should require no arguments and return a dict-like object.
Examples
--------
Create a multigraph object that tracks the order nodes are added.
>>> from collections import OrderedDict
>>> class OrderedGraph(nx.MultiGraph):
... node_dict_factory = OrderedDict
>>> G = OrderedGraph()
>>> G.add_nodes_from( (2,1) )
>>> G.nodes()
[2, 1]
>>> G.add_edges_from( ((2,2), (2,1), (2,1), (1,1)) )
>>> G.edges()
[(2, 1), (2, 1), (2, 2), (1, 1)]
Create a multgraph object that tracks the order nodes are added
and for each node track the order that neighbors are added and for
each neighbor tracks the order that multiedges are added.
>>> class OrderedGraph(nx.MultiGraph):
... node_dict_factory = OrderedDict
... adjlist_dict_factory = OrderedDict
... edge_key_dict_factory = OrderedDict
>>> G = OrderedGraph()
>>> G.add_nodes_from( (2,1) )
>>> G.nodes()
[2, 1]
>>> G.add_edges_from( ((2,2), (2,1,2,{'weight':0.1}), (2,1,1,{'weight':0.2}), (1,1)) )
>>> G.edges(keys=True)
[(2, 2, 0), (2, 1, 2), (2, 1, 1), (1, 1, 0)]
"""
# node_dict_factory=dict # already assigned in Graph
# adjlist_dict_factory=dict
edge_key_dict_factory = dict
# edge_attr_dict_factory=dict
[docs] def __init__(self, data=None, **attr):
self.edge_key_dict_factory = self.edge_key_dict_factory
Graph.__init__(self, data, **attr)
[docs] def add_edge(self, u, v, key=None, attr_dict=None, **attr):
"""Add an edge between u and v.
The nodes u and v will be automatically added if they are
not already in the graph.
Edge attributes can be specified with keywords or by providing
a dictionary with key/value pairs. See examples below.
Parameters
----------
u,v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
key : hashable identifier, optional (default=lowest unused integer)
Used to distinguish multiedges between a pair of nodes.
attr_dict : dictionary, optional (default= no attributes)
Dictionary of edge attributes. Key/value pairs will
update existing data associated with the edge.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
To replace/update edge data, use the optional key argument
to identify a unique edge. Otherwise a new edge will be created.
NetworkX algorithms designed for weighted graphs cannot use
multigraphs directly because it is not clear how to handle
multiedge weights. Convert to Graph using edge attribute
'weight' to enable weighted graph algorithms.
Examples
--------
The following all add the edge e=(1,2) to graph G:
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> e = (1,2)
>>> G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes
>>> G.add_edges_from( [(1,2)] ) # add edges from iterable container
Associate data to edges using keywords:
>>> G.add_edge(1, 2, weight=3)
>>> G.add_edge(1, 2, key=0, weight=4) # update data for key=0
>>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
"""
# set up attribute dict
if attr_dict is None:
attr_dict = attr
else:
try:
attr_dict.update(attr)
except AttributeError:
raise NetworkXError(
"The attr_dict argument must be a dictionary.")
# add nodes
if u not in self.adj:
self.adj[u] = self.adjlist_dict_factory()
self.node[u] = {}
if v not in self.adj:
self.adj[v] = self.adjlist_dict_factory()
self.node[v] = {}
if v in self.adj[u]:
keydict = self.adj[u][v]
if key is None:
# find a unique integer key
# other methods might be better here?
key = len(keydict)
while key in keydict:
key += 1
datadict = keydict.get(key, self.edge_attr_dict_factory())
datadict.update(attr_dict)
keydict[key] = datadict
else:
# selfloops work this way without special treatment
if key is None:
key = 0
datadict = self.edge_attr_dict_factory()
datadict.update(attr_dict)
keydict = self.edge_key_dict_factory()
keydict[key] = datadict
self.adj[u][v] = keydict
self.adj[v][u] = keydict
[docs] def add_edges_from(self, ebunch, attr_dict=None, **attr):
"""Add all the edges in ebunch.
Parameters
----------
ebunch : container of edges
Each edge given in the container will be added to the
graph. The edges can be:
- 2-tuples (u,v) or
- 3-tuples (u,v,d) for an edge attribute dict d, or
- 4-tuples (u,v,k,d) for an edge identified by key k
attr_dict : dictionary, optional (default= no attributes)
Dictionary of edge attributes. Key/value pairs will
update existing data associated with each edge.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
See Also
--------
add_edge : add a single edge
add_weighted_edges_from : convenient way to add weighted edges
Notes
-----
Adding the same edge twice has no effect but any edge data
will be updated when each duplicate edge is added.
Edge attributes specified in edges take precedence
over attributes specified generally.
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edges_from([(0,1),(1,2)]) # using a list of edge tuples
>>> e = zip(range(0,3),range(1,4))
>>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1,2),(2,3)], weight=3)
>>> G.add_edges_from([(3,4),(1,4)], label='WN2898')
"""
# set up attribute dict
if attr_dict is None:
attr_dict = attr
else:
try:
attr_dict.update(attr)
except AttributeError:
raise NetworkXError(
"The attr_dict argument must be a dictionary.")
# process ebunch
for e in ebunch:
ne = len(e)
if ne == 4:
u, v, key, dd = e
elif ne == 3:
u, v, dd = e
key = None
elif ne == 2:
u, v = e
dd = {}
key = None
else:
raise NetworkXError(
"Edge tuple %s must be a 2-tuple, 3-tuple or 4-tuple." % (e,))
ddd = {}
ddd.update(attr_dict)
ddd.update(dd)
self.add_edge(u, v, key, ddd)
[docs] def remove_edge(self, u, v, key=None):
"""Remove an edge between u and v.
Parameters
----------
u,v: nodes
Remove an edge between nodes u and v.
key : hashable identifier, optional (default=None)
Used to distinguish multiple edges between a pair of nodes.
If None remove a single (abritrary) edge between u and v.
Raises
------
NetworkXError
If there is not an edge between u and v, or
if there is no edge with the specified key.
See Also
--------
remove_edges_from : remove a collection of edges
Examples
--------
>>> G = nx.MultiGraph()
>>> G.add_path([0,1,2,3])
>>> G.remove_edge(0,1)
>>> e = (1,2)
>>> G.remove_edge(*e) # unpacks e from an edge tuple
For multiple edges
>>> G = nx.MultiGraph() # or MultiDiGraph, etc
>>> G.add_edges_from([(1,2),(1,2),(1,2)])
>>> G.remove_edge(1,2) # remove a single (arbitrary) edge
For edges with keys
>>> G = nx.MultiGraph() # or MultiDiGraph, etc
>>> G.add_edge(1,2,key='first')
>>> G.add_edge(1,2,key='second')
>>> G.remove_edge(1,2,key='second')
"""
try:
d = self.adj[u][v]
except (KeyError):
raise NetworkXError(
"The edge %s-%s is not in the graph." % (u, v))
# remove the edge with specified data
if key is None:
d.popitem()
else:
try:
del d[key]
except (KeyError):
raise NetworkXError(
"The edge %s-%s with key %s is not in the graph." % (
u, v, key))
if len(d) == 0:
# remove the key entries if last edge
del self.adj[u][v]
if u!=v: # check for selfloop
del self.adj[v][u]
[docs] def remove_edges_from(self, ebunch):
"""Remove all edges specified in ebunch.
Parameters
----------
ebunch: list or container of edge tuples
Each edge given in the list or container will be removed
from the graph. The edges can be:
- 2-tuples (u,v) All edges between u and v are removed.
- 3-tuples (u,v,key) The edge identified by key is removed.
- 4-tuples (u,v,key,data) where data is ignored.
See Also
--------
remove_edge : remove a single edge
Notes
-----
Will fail silently if an edge in ebunch is not in the graph.
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_path([0,1,2,3])
>>> ebunch=[(1,2),(2,3)]
>>> G.remove_edges_from(ebunch)
Removing multiple copies of edges
>>> G = nx.MultiGraph()
>>> G.add_edges_from([(1,2),(1,2),(1,2)])
>>> G.remove_edges_from([(1,2),(1,2)])
>>> G.edges()
[(1, 2)]
>>> G.remove_edges_from([(1,2),(1,2)]) # silently ignore extra copy
>>> G.edges() # now empty graph
[]
"""
for e in ebunch:
try:
self.remove_edge(*e[:3])
except NetworkXError:
pass
[docs] def has_edge(self, u, v, key=None):
"""Return True if the graph has an edge between nodes u and v.
Parameters
----------
u,v : nodes
Nodes can be, for example, strings or numbers.
key : hashable identifier, optional (default=None)
If specified return True only if the edge with
key is found.
Returns
-------
edge_ind : bool
True if edge is in the graph, False otherwise.
Examples
--------
Can be called either using two nodes u,v, an edge tuple (u,v),
or an edge tuple (u,v,key).
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_path([0,1,2,3])
>>> G.has_edge(0,1) # using two nodes
True
>>> e = (0,1)
>>> G.has_edge(*e) # e is a 2-tuple (u,v)
True
>>> G.add_edge(0,1,key='a')
>>> G.has_edge(0,1,key='a') # specify key
True
>>> e=(0,1,'a')
>>> G.has_edge(*e) # e is a 3-tuple (u,v,'a')
True
The following syntax are equivalent:
>>> G.has_edge(0,1)
True
>>> 1 in G[0] # though this gives KeyError if 0 not in G
True
"""
try:
if key is None:
return v in self.adj[u]
else:
return key in self.adj[u][v]
except KeyError:
return False
[docs] def edges(self, nbunch=None, data=False, keys=False, default=None):
"""Return a list of edges.
Edges are returned as tuples with optional data and keys
in the order (node, neighbor, key, data).
Parameters
----------
nbunch : iterable container, optional (default= all nodes)
A container of nodes. The container will be iterated
through once.
data : bool, optional (default=False)
Return two tuples (u,v) (False) or three-tuples (u,v,data) (True).
keys : bool, optional (default=False)
Return two tuples (u,v) (False) or three-tuples (u,v,key) (True).
Returns
--------
edge_list: list of edge tuples
Edges that are adjacent to any node in nbunch, or a list
of all edges if nbunch is not specified.
See Also
--------
edges_iter : return an iterator over the edges
Notes
-----
Nodes in nbunch that are not in the graph will be (quietly) ignored.
For directed graphs this returns the out-edges.
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_path([0,1,2])
>>> G.add_edge(2,3,weight=5)
>>> G.edges()
[(0, 1), (1, 2), (2, 3)]
>>> G.edges(data=True) # default edge data is {} (empty dictionary)
[(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
>>> list(G.edges_iter(data='weight', default=1))
[(0, 1, 1), (1, 2, 1), (2, 3, 5)]
>>> G.edges(keys=True) # default keys are integers
[(0, 1, 0), (1, 2, 0), (2, 3, 0)]
>>> G.edges(data=True,keys=True) # default keys are integers
[(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})]
>>> list(G.edges(data='weight',default=1,keys=True))
[(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)]
>>> G.edges([0,3])
[(0, 1), (3, 2)]
>>> G.edges(0)
[(0, 1)]
"""
return list(self.edges_iter(nbunch, data, keys, default))
[docs] def edges_iter(self, nbunch=None, data=False, keys=False, default=None):
"""Return an iterator over the edges.
Edges are returned as tuples with optional data and keys
in the order (node, neighbor, key, data).
Parameters
----------
nbunch : iterable container, optional (default= all nodes)
A container of nodes. The container will be iterated
through once.
data : string or bool, optional (default=False)
The edge attribute returned in 3-tuple (u,v,ddict[data]).
If True, return edge attribute dict in 3-tuple (u,v,ddict).
If False, return 2-tuple (u,v).
default : value, optional (default=None)
Value used for edges that dont have the requested attribute.
Only relevant if data is not True or False.
keys : bool, optional (default=False)
If True, return edge keys with each edge.
Returns
-------
edge_iter : iterator
An iterator of (u,v), (u,v,d) or (u,v,key,d) tuples of edges.
See Also
--------
edges : return a list of edges
Notes
-----
Nodes in nbunch that are not in the graph will be (quietly) ignored.
For directed graphs this returns the out-edges.
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_path([0,1,2])
>>> G.add_edge(2,3,weight=5)
>>> [e for e in G.edges_iter()]
[(0, 1), (1, 2), (2, 3)]
>>> list(G.edges_iter(data=True)) # default data is {} (empty dict)
[(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
>>> list(G.edges_iter(data='weight', default=1))
[(0, 1, 1), (1, 2, 1), (2, 3, 5)]
>>> list(G.edges(keys=True)) # default keys are integers
[(0, 1, 0), (1, 2, 0), (2, 3, 0)]
>>> list(G.edges(data=True,keys=True)) # default keys are integers
[(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})]
>>> list(G.edges(data='weight',default=1,keys=True))
[(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)]
>>> list(G.edges_iter([0,3]))
[(0, 1), (3, 2)]
>>> list(G.edges_iter(0))
[(0, 1)]
"""
seen = {} # helper dict to keep track of multiply stored edges
if nbunch is None:
nodes_nbrs = self.adj.items()
else:
nodes_nbrs = ((n, self.adj[n]) for n in self.nbunch_iter(nbunch))
if data is True:
for n, nbrs in nodes_nbrs:
for nbr, keydict in nbrs.items():
if nbr not in seen:
for key, ddict in keydict.items():
yield (n, nbr, key, ddict) if keys else (n, nbr, ddict)
seen[n] = 1
elif data is not False:
for n, nbrs in nodes_nbrs:
for nbr, keydict in nbrs.items():
if nbr not in seen:
for key, ddict in keydict.items():
d = ddict[data] if data in ddict else default
yield (n, nbr, key, d) if keys else (n, nbr, d)
seen[n] = 1
else:
for n, nbrs in nodes_nbrs:
for nbr, keydict in nbrs.items():
if nbr not in seen:
for key in keydict:
yield (n, nbr, key) if keys else (n, nbr)
seen[n] = 1
del seen
[docs] def get_edge_data(self, u, v, key=None, default=None):
"""Return the attribute dictionary associated with edge (u,v).
Parameters
----------
u,v : nodes
default: any Python object (default=None)
Value to return if the edge (u,v) is not found.
key : hashable identifier, optional (default=None)
Return data only for the edge with specified key.
Returns
-------
edge_dict : dictionary
The edge attribute dictionary.
Notes
-----
It is faster to use G[u][v][key].
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_edge(0,1,key='a',weight=7)
>>> G[0][1]['a'] # key='a'
{'weight': 7}
Warning: Assigning G[u][v][key] corrupts the graph data structure.
But it is safe to assign attributes to that dictionary,
>>> G[0][1]['a']['weight'] = 10
>>> G[0][1]['a']['weight']
10
>>> G[1][0]['a']['weight']
10
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_path([0,1,2,3])
>>> G.get_edge_data(0,1)
{0: {}}
>>> e = (0,1)
>>> G.get_edge_data(*e) # tuple form
{0: {}}
>>> G.get_edge_data('a','b',default=0) # edge not in graph, return 0
0
"""
try:
if key is None:
return self.adj[u][v]
else:
return self.adj[u][v][key]
except KeyError:
return default
[docs] def degree_iter(self, nbunch=None, weight=None):
"""Return an iterator for (node, degree).
The node degree is the number of edges adjacent to the node.
Parameters
----------
nbunch : iterable container, optional (default=all nodes)
A container of nodes. The container will be iterated
through once.
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
Returns
-------
nd_iter : an iterator
The iterator returns two-tuples of (node, degree).
See Also
--------
degree
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_path([0,1,2,3])
>>> list(G.degree_iter(0)) # node 0 with degree 1
[(0, 1)]
>>> list(G.degree_iter([0,1]))
[(0, 1), (1, 2)]
"""
if nbunch is None:
nodes_nbrs = self.adj.items()
else:
nodes_nbrs = ((n, self.adj[n]) for n in self.nbunch_iter(nbunch))
if weight is None:
for n, nbrs in nodes_nbrs:
deg = sum([len(data) for data in nbrs.values()])
yield (n, deg + (n in nbrs and len(nbrs[n])))
else:
# edge weighted graph - degree is sum of nbr edge weights
for n, nbrs in nodes_nbrs:
deg = sum([d.get(weight, 1)
for data in nbrs.values()
for d in data.values()])
if n in nbrs:
deg += sum([d.get(weight, 1)
for key, d in nbrs[n].items()])
yield (n, deg)
def is_multigraph(self):
"""Return True if graph is a multigraph, False otherwise."""
return True
def is_directed(self):
"""Return True if graph is directed, False otherwise."""
return False
[docs] 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
[docs] def selfloop_edges(self, data=False, keys=False, default=None):
"""Return a list of selfloop edges.
A selfloop edge has the same node at both ends.
Parameters
----------
data : bool, optional (default=False)
Return selfloop edges as two tuples (u,v) (data=False)
or three-tuples (u,v,datadict) (data=True)
or three-tuples (u,v,datavalue) (data='attrname')
default : value, optional (default=None)
Value used for edges that dont have the requested attribute.
Only relevant if data is not True or False.
keys : bool, optional (default=False)
If True, return edge keys with each edge.
Returns
-------
edgelist : list of edge tuples
A list of all selfloop edges.
See Also
--------
nodes_with_selfloops, number_of_selfloops
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> G.add_edge(1,1)
>>> G.add_edge(1,2)
>>> G.selfloop_edges()
[(1, 1)]
>>> G.selfloop_edges(data=True)
[(1, 1, {})]
>>> G.selfloop_edges(keys=True)
[(1, 1, 0)]
>>> G.selfloop_edges(keys=True, data=True)
[(1, 1, 0, {})]
"""
if data is True:
if keys:
return [(n, n, k, d)
for n, nbrs in self.adj.items()
if n in nbrs for k, d in nbrs[n].items()]
else:
return [(n, n, d)
for n, nbrs in self.adj.items()
if n in nbrs for d in nbrs[n].values()]
elif data is not False:
if keys:
return [(n, n, k, d.get(data, default))
for n, nbrs in self.adj.items()
if n in nbrs for k, d in nbrs[n].items()]
else:
return [(n, n, d.get(data, default))
for n, nbrs in self.adj.items()
if n in nbrs for d in nbrs[n].values()]
else:
if keys:
return [(n, n, k)
for n, nbrs in self.adj.items()
if n in nbrs for k in nbrs[n].keys()]
else:
return [(n, n)
for n, nbrs in self.adj.items()
if n in nbrs for d in nbrs[n].values()]
[docs] def number_of_edges(self, u=None, v=None):
"""Return the number of edges between two nodes.
Parameters
----------
u,v : nodes, optional (default=all edges)
If u and v are specified, return the number of edges between
u and v. Otherwise return the total number of all edges.
Returns
-------
nedges : int
The number of edges in the graph. If nodes u and v are specified
return the number of edges between those nodes.
See Also
--------
size
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_path([0,1,2,3])
>>> G.number_of_edges()
3
>>> G.number_of_edges(0,1)
1
>>> e = (0,1)
>>> G.number_of_edges(*e)
1
"""
if u is None: return self.size()
try:
edgedata = self.adj[u][v]
except KeyError:
return 0 # no such edge
return len(edgedata)
[docs] def subgraph(self, nbunch):
"""Return the subgraph induced on nodes in nbunch.
The induced subgraph of the graph contains the nodes in nbunch
and the edges between those nodes.
Parameters
----------
nbunch : list, iterable
A container of nodes which will be iterated through once.
Returns
-------
G : Graph
A subgraph of the graph with the same edge attributes.
Notes
-----
The graph, edge or node attributes just point to the original graph.
So changes to the node or edge structure will not be reflected in
the original graph while changes to the attributes will.
To create a subgraph with its own copy of the edge/node attributes use:
nx.Graph(G.subgraph(nbunch))
If edge attributes are containers, a deep copy can be obtained using:
G.subgraph(nbunch).copy()
For an inplace reduction of a graph to a subgraph you can remove nodes:
G.remove_nodes_from([ n in G if n not in set(nbunch)])
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_path([0,1,2,3])
>>> H = G.subgraph([0,1,2])
>>> H.edges()
[(0, 1), (1, 2)]
"""
bunch = self.nbunch_iter(nbunch)
# create new graph and copy subgraph into it
H = self.__class__()
# copy node and attribute dictionaries
for n in bunch:
H.node[n] = self.node[n]
# namespace shortcuts for speed
H_adj = H.adj
self_adj = self.adj
# add nodes and edges (undirected method)
for n in H:
Hnbrs = H.adjlist_dict_factory()
H_adj[n] = Hnbrs
for nbr, edgedict in self_adj[n].items():
if nbr in H_adj:
# add both representations of edge: n-nbr and nbr-n
# they share the same edgedict
ed = edgedict.copy()
Hnbrs[nbr] = ed
H_adj[nbr][n] = ed
H.graph = self.graph
return H