# Source code for networkx.classes.function

```
"""Functional interface to graph methods and assorted utilities.
"""
from collections import Counter
from itertools import chain
import networkx as nx
from networkx.utils import pairwise, not_implemented_for
from networkx.classes.graphviews import subgraph_view, reverse_view
__all__ = ['nodes', 'edges', 'degree', 'degree_histogram', 'neighbors',
'number_of_nodes', 'number_of_edges', 'density',
'is_directed', 'info', 'freeze', 'is_frozen',
'subgraph', 'subgraph_view', 'induced_subgraph', 'reverse_view',
'edge_subgraph', 'restricted_view',
'to_directed', 'to_undirected',
'add_star', 'add_path', 'add_cycle',
'create_empty_copy', 'set_node_attributes',
'get_node_attributes', 'set_edge_attributes',
'get_edge_attributes', 'all_neighbors', 'non_neighbors',
'non_edges', 'common_neighbors', 'is_weighted',
'is_negatively_weighted', 'is_empty',
'selfloop_edges', 'nodes_with_selfloops', 'number_of_selfloops',
]
[docs]def edges(G, nbunch=None):
"""Returns an edge view of edges incident to nodes in nbunch.
Return all edges if nbunch is unspecified or nbunch=None.
For digraphs, edges=out_edges
"""
return G.edges(nbunch)
[docs]def degree(G, nbunch=None, weight=None):
"""Returns a degree view of single node or of nbunch of nodes.
If nbunch is omitted, then return degrees of *all* nodes.
"""
return G.degree(nbunch, weight)
[docs]def neighbors(G, n):
"""Returns a list of nodes connected to node n. """
return G.neighbors(n)
[docs]def number_of_nodes(G):
"""Returns the number of nodes in the graph."""
return G.number_of_nodes()
[docs]def number_of_edges(G):
"""Returns the number of edges in the graph. """
return G.number_of_edges()
[docs]def density(G):
r"""Returns the density of a graph.
The density for undirected graphs is
.. math::
d = \frac{2m}{n(n-1)},
and for directed graphs is
.. math::
d = \frac{m}{n(n-1)},
where `n` is the number of nodes and `m` is the number of edges in `G`.
Notes
-----
The density is 0 for a graph without edges and 1 for a complete graph.
The density of multigraphs can be higher than 1.
Self loops are counted in the total number of edges so graphs with self
loops can have density higher than 1.
"""
n = number_of_nodes(G)
m = number_of_edges(G)
if m == 0 or n <= 1:
return 0
d = m / (n * (n - 1))
if not G.is_directed():
d *= 2
return d
[docs]def degree_histogram(G):
"""Returns a list of the frequency of each degree value.
Parameters
----------
G : Networkx graph
A graph
Returns
-------
hist : list
A list of frequencies of degrees.
The degree values are the index in the list.
Notes
-----
Note: the bins are width one, hence len(list) can be large
(Order(number_of_edges))
"""
counts = Counter(d for n, d in G.degree())
return [counts.get(i, 0) for i in range(max(counts) + 1)]
def frozen(*args, **kwargs):
"""Dummy method for raising errors when trying to modify frozen graphs"""
raise nx.NetworkXError("Frozen graph can't be modified")
[docs]def freeze(G):
"""Modify graph to prevent further change by adding or removing
nodes or edges.
Node and edge data can still be modified.
Parameters
----------
G : graph
A NetworkX graph
Examples
--------
>>> G = nx.path_graph(4)
>>> G = nx.freeze(G)
>>> try:
... G.add_edge(4, 5)
... except nx.NetworkXError as e:
... print(str(e))
Frozen graph can't be modified
Notes
-----
To "unfreeze" a graph you must make a copy by creating a new graph object:
>>> graph = nx.path_graph(4)
>>> frozen_graph = nx.freeze(graph)
>>> unfrozen_graph = nx.Graph(frozen_graph)
>>> nx.is_frozen(unfrozen_graph)
False
See Also
--------
is_frozen
"""
G.add_node = frozen
G.add_nodes_from = frozen
G.remove_node = frozen
G.remove_nodes_from = frozen
G.add_edge = frozen
G.add_edges_from = frozen
G.add_weighted_edges_from = frozen
G.remove_edge = frozen
G.remove_edges_from = frozen
G.clear = frozen
G.frozen = True
return G
[docs]def is_frozen(G):
"""Returns True if graph is frozen.
Parameters
----------
G : graph
A NetworkX graph
See Also
--------
freeze
"""
try:
return G.frozen
except AttributeError:
return False
[docs]def add_star(G_to_add_to, nodes_for_star, **attr):
"""Add a star to Graph G_to_add_to.
The first node in `nodes_for_star` is the middle of the star.
It is connected to all other nodes.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_star : iterable container
A container of nodes.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in star.
See Also
--------
add_path, add_cycle
Examples
--------
>>> G = nx.Graph()
>>> nx.add_star(G, [0, 1, 2, 3])
>>> nx.add_star(G, [10, 11, 12], weight=2)
"""
nlist = iter(nodes_for_star)
try:
v = next(nlist)
except StopIteration:
return
G_to_add_to.add_node(v)
edges = ((v, n) for n in nlist)
G_to_add_to.add_edges_from(edges, **attr)
[docs]def add_path(G_to_add_to, nodes_for_path, **attr):
"""Add a path to the Graph G_to_add_to.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_path : iterable container
A container of nodes. A path will be constructed from
the nodes (in order) and added to the graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in path.
See Also
--------
add_star, add_cycle
Examples
--------
>>> G = nx.Graph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> nx.add_path(G, [10, 11, 12], weight=7)
"""
nlist = iter(nodes_for_path)
try:
first_node = next(nlist)
except StopIteration:
return
G_to_add_to.add_node(first_node)
G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist)), **attr)
[docs]def add_cycle(G_to_add_to, nodes_for_cycle, **attr):
"""Add a cycle to the Graph G_to_add_to.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_cycle: iterable container
A container of nodes. A cycle will be constructed from
the nodes (in order) and added to the graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in cycle.
See Also
--------
add_path, add_star
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> nx.add_cycle(G, [0, 1, 2, 3])
>>> nx.add_cycle(G, [10, 11, 12], weight=7)
"""
nlist = iter(nodes_for_cycle)
try:
first_node = next(nlist)
except StopIteration:
return
G_to_add_to.add_node(first_node)
G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist), cyclic=True), **attr)
[docs]def subgraph(G, nbunch):
"""Returns the subgraph induced on nodes in nbunch.
Parameters
----------
G : graph
A NetworkX graph
nbunch : list, iterable
A container of nodes that will be iterated through once (thus
it should be an iterator or be iterable). Each element of the
container should be a valid node type: any hashable type except
None. If nbunch is None, return all edges data in the graph.
Nodes in nbunch that are not in the graph will be (quietly)
ignored.
Notes
-----
subgraph(G) calls G.subgraph()
"""
return G.subgraph(nbunch)
[docs]def induced_subgraph(G, nbunch):
"""Returns a SubGraph view of `G` showing only nodes in nbunch.
The induced subgraph of a graph on a set of nodes N is the
graph with nodes N and edges from G which have both ends in N.
Parameters
----------
G : NetworkX Graph
nbunch : node, container of nodes or None (for all nodes)
Returns
-------
subgraph : SubGraph View
A read-only view of the subgraph in `G` induced by the nodes.
Changes to the graph `G` will be reflected in the view.
Notes
-----
To create a mutable subgraph with its own copies of nodes
edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
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))`
If you are going to compute subgraphs of your subgraphs you could
end up with a chain of views that can be very slow once the chain
has about 15 views in it. If they are all induced subgraphs, you
can short-cut the chain by making them all subgraphs of the original
graph. The graph class method `G.subgraph` does this when `G` is
a subgraph. In contrast, this function allows you to choose to build
chains or not, as you wish. The returned subgraph is a view on `G`.
Examples
--------
>>> import networkx as nx
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> H = G.subgraph([0, 1, 2])
>>> list(H.edges)
[(0, 1), (1, 2)]
"""
induced_nodes = nx.filters.show_nodes(G.nbunch_iter(nbunch))
return nx.graphviews.subgraph_view(G, induced_nodes)
[docs]def edge_subgraph(G, edges):
"""Returns a view of the subgraph induced by the specified edges.
The induced subgraph contains each edge in `edges` and each
node incident to any of those edges.
Parameters
----------
G : NetworkX Graph
edges : iterable
An iterable of edges. Edges not present in `G` are ignored.
Returns
-------
subgraph : SubGraph View
A read-only edge-induced subgraph of `G`.
Changes to `G` are reflected in the view.
Notes
-----
To create a mutable subgraph with its own copies of nodes
edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
If you create a subgraph of a subgraph recursively you can end up
with a chain of subgraphs that becomes very slow with about 15
nested subgraph views. Luckily the edge_subgraph filter nests
nicely so you can use the original graph as G in this function
to avoid chains. We do not rule out chains programmatically so
that odd cases like an `edge_subgraph` of a `restricted_view`
can be created.
Examples
--------
>>> import networkx as nx
>>> G = nx.path_graph(5)
>>> H = G.edge_subgraph([(0, 1), (3, 4)])
>>> list(H.nodes)
[0, 1, 3, 4]
>>> list(H.edges)
[(0, 1), (3, 4)]
"""
nxf = nx.filters
edges = set(edges)
nodes = set()
for e in edges:
nodes.update(e[:2])
induced_nodes = nxf.show_nodes(nodes)
if G.is_multigraph():
if G.is_directed():
induced_edges = nxf.show_multidiedges(edges)
else:
induced_edges = nxf.show_multiedges(edges)
else:
if G.is_directed():
induced_edges = nxf.show_diedges(edges)
else:
induced_edges = nxf.show_edges(edges)
return nx.graphviews.subgraph_view(G, induced_nodes, induced_edges)
[docs]def restricted_view(G, nodes, edges):
"""Returns a view of `G` with hidden nodes and edges.
The resulting subgraph filters out node `nodes` and edges `edges`.
Filtered out nodes also filter out any of their edges.
Parameters
----------
G : NetworkX Graph
nodes : iterable
An iterable of nodes. Nodes not present in `G` are ignored.
edges : iterable
An iterable of edges. Edges not present in `G` are ignored.
Returns
-------
subgraph : SubGraph View
A read-only restricted view of `G` filtering out nodes and edges.
Changes to `G` are reflected in the view.
Notes
-----
To create a mutable subgraph with its own copies of nodes
edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
If you create a subgraph of a subgraph recursively you may end up
with a chain of subgraph views. Such chains can get quite slow
for lengths near 15. To avoid long chains, try to make your subgraph
based on the original graph. We do not rule out chains programmatically
so that odd cases like an `edge_subgraph` of a `restricted_view`
can be created.
Examples
--------
>>> import networkx as nx
>>> G = nx.path_graph(5)
>>> H = nx.restricted_view(G, [0], [(1, 2), (3, 4)])
>>> list(H.nodes)
[1, 2, 3, 4]
>>> list(H.edges)
[(2, 3)]
"""
nxf = nx.filters
hide_nodes = nxf.hide_nodes(nodes)
if G.is_multigraph():
if G.is_directed():
hide_edges = nxf.hide_multidiedges(edges)
else:
hide_edges = nxf.hide_multiedges(edges)
else:
if G.is_directed():
hide_edges = nxf.hide_diedges(edges)
else:
hide_edges = nxf.hide_edges(edges)
return nx.graphviews.subgraph_view(G, hide_nodes, hide_edges)
[docs]def to_directed(graph):
"""Returns a directed view of the graph `graph`.
Identical to graph.to_directed(as_view=True)
Note that graph.to_directed defaults to `as_view=False`
while this function always provides a view.
"""
return graph.to_directed(as_view=True)
[docs]def to_undirected(graph):
"""Returns an undirected view of the graph `graph`.
Identical to graph.to_undirected(as_view=True)
Note that graph.to_undirected defaults to `as_view=False`
while this function always provides a view.
"""
return graph.to_undirected(as_view=True)
[docs]def create_empty_copy(G, with_data=True):
"""Returns a copy of the graph G with all of the edges removed.
Parameters
----------
G : graph
A NetworkX graph
with_data : bool (default=True)
Propagate Graph and Nodes data to the new graph.
See Also
-----
empty_graph
"""
H = G.__class__()
H.add_nodes_from(G.nodes(data=with_data))
if with_data:
H.graph.update(G.graph)
return H
[docs]def info(G, n=None):
"""Return a summary of information for the graph G or a single node n.
The summary includes the number of nodes and edges (or neighbours for a single
node), and their average degree.
Parameters
----------
G : Networkx graph
A graph
n : node (any hashable)
A node in the graph G
Returns
-------
info : str
A string containing the short summary
Raises
------
NetworkXError
If n is not in the graph G
"""
info = '' # append this all to a string
if n is None:
info += f"Name: {G.name}\n"
type_name = [type(G).__name__]
info += f"Type: {','.join(type_name)}\n"
info += f"Number of nodes: {G.number_of_nodes()}\n"
info += f"Number of edges: {G.number_of_edges()}\n"
nnodes = G.number_of_nodes()
if len(G) > 0:
if G.is_directed():
deg = sum(d for n, d in G.in_degree()) / float(nnodes)
info += f"Average in degree: {deg:8.4f}\n"
deg = sum(d for n, d in G.out_degree()) / float(nnodes)
info += f"Average out degree: {deg:8.4f}"
else:
s = sum(dict(G.degree()).values())
info += f"Average degree: {(float(s) / float(nnodes)):8.4f}"
else:
if n not in G:
raise nx.NetworkXError(f"node {n} not in graph")
info += f"Node {n} has the following properties:\n"
info += f"Degree: {G.degree(n)}\n"
info += "Neighbors: "
info += ' '.join(str(nbr) for nbr in G.neighbors(n))
return info
[docs]def set_node_attributes(G, values, name=None):
"""Sets node attributes from a given value or dictionary of values.
.. Warning:: The call order of arguments `values` and `name`
switched between v1.x & v2.x.
Parameters
----------
G : NetworkX Graph
values : scalar value, dict-like
What the node attribute should be set to. If `values` is
not a dictionary, then it is treated as a single attribute value
that is then applied to every node in `G`. This means that if
you provide a mutable object, like a list, updates to that object
will be reflected in the node attribute for every node.
The attribute name will be `name`.
If `values` is a dict or a dict of dict, it should be keyed
by node to either an attribute value or a dict of attribute key/value
pairs used to update the node's attributes.
name : string (optional, default=None)
Name of the node attribute to set if values is a scalar.
Examples
--------
After computing some property of the nodes of a graph, you may want
to assign a node attribute to store the value of that property for
each node::
>>> G = nx.path_graph(3)
>>> bb = nx.betweenness_centrality(G)
>>> isinstance(bb, dict)
True
>>> nx.set_node_attributes(G, bb, 'betweenness')
>>> G.nodes[1]['betweenness']
1.0
If you provide a list as the second argument, updates to the list
will be reflected in the node attribute for each node::
>>> G = nx.path_graph(3)
>>> labels = []
>>> nx.set_node_attributes(G, labels, 'labels')
>>> labels.append('foo')
>>> G.nodes[0]['labels']
['foo']
>>> G.nodes[1]['labels']
['foo']
>>> G.nodes[2]['labels']
['foo']
If you provide a dictionary of dictionaries as the second argument,
the outer dictionary is assumed to be keyed by node to an inner
dictionary of node attributes for that node::
>>> G = nx.path_graph(3)
>>> attrs = {0: {'attr1': 20, 'attr2': 'nothing'}, 1: {'attr2': 3}}
>>> nx.set_node_attributes(G, attrs)
>>> G.nodes[0]['attr1']
20
>>> G.nodes[0]['attr2']
'nothing'
>>> G.nodes[1]['attr2']
3
>>> G.nodes[2]
{}
"""
# Set node attributes based on type of `values`
if name is not None: # `values` must not be a dict of dict
try: # `values` is a dict
for n, v in values.items():
try:
G.nodes[n][name] = values[n]
except KeyError:
pass
except AttributeError: # `values` is a constant
for n in G:
G.nodes[n][name] = values
else: # `values` must be dict of dict
for n, d in values.items():
try:
G.nodes[n].update(d)
except KeyError:
pass
[docs]def get_node_attributes(G, name):
"""Get node attributes from graph
Parameters
----------
G : NetworkX Graph
name : string
Attribute name
Returns
-------
Dictionary of attributes keyed by node.
Examples
--------
>>> G = nx.Graph()
>>> G.add_nodes_from([1, 2, 3], color='red')
>>> color = nx.get_node_attributes(G, 'color')
>>> color[1]
'red'
"""
return {n: d[name] for n, d in G.nodes.items() if name in d}
[docs]def set_edge_attributes(G, values, name=None):
"""Sets edge attributes from a given value or dictionary of values.
.. Warning:: The call order of arguments `values` and `name`
switched between v1.x & v2.x.
Parameters
----------
G : NetworkX Graph
values : scalar value, dict-like
What the edge attribute should be set to. If `values` is
not a dictionary, then it is treated as a single attribute value
that is then applied to every edge in `G`. This means that if
you provide a mutable object, like a list, updates to that object
will be reflected in the edge attribute for each edge. The attribute
name will be `name`.
If `values` is a dict or a dict of dict, it should be keyed
by edge tuple to either an attribute value or a dict of attribute
key/value pairs used to update the edge's attributes.
For multigraphs, the edge tuples must be of the form ``(u, v, key)``,
where `u` and `v` are nodes and `key` is the edge key.
For non-multigraphs, the keys must be tuples of the form ``(u, v)``.
name : string (optional, default=None)
Name of the edge attribute to set if values is a scalar.
Examples
--------
After computing some property of the edges of a graph, you may want
to assign a edge attribute to store the value of that property for
each edge::
>>> G = nx.path_graph(3)
>>> bb = nx.edge_betweenness_centrality(G, normalized=False)
>>> nx.set_edge_attributes(G, bb, 'betweenness')
>>> G.edges[1, 2]['betweenness']
2.0
If you provide a list as the second argument, updates to the list
will be reflected in the edge attribute for each edge::
>>> labels = []
>>> nx.set_edge_attributes(G, labels, 'labels')
>>> labels.append('foo')
>>> G.edges[0, 1]['labels']
['foo']
>>> G.edges[1, 2]['labels']
['foo']
If you provide a dictionary of dictionaries as the second argument,
the entire dictionary will be used to update edge attributes::
>>> G = nx.path_graph(3)
>>> attrs = {(0, 1): {'attr1': 20, 'attr2': 'nothing'},
... (1, 2): {'attr2': 3}}
>>> nx.set_edge_attributes(G, attrs)
>>> G[0][1]['attr1']
20
>>> G[0][1]['attr2']
'nothing'
>>> G[1][2]['attr2']
3
"""
if name is not None:
# `values` does not contain attribute names
try:
# if `values` is a dict using `.items()` => {edge: value}
if G.is_multigraph():
for (u, v, key), value in values.items():
try:
G[u][v][key][name] = value
except KeyError:
pass
else:
for (u, v), value in values.items():
try:
G[u][v][name] = value
except KeyError:
pass
except AttributeError:
# treat `values` as a constant
for u, v, data in G.edges(data=True):
data[name] = values
else:
# `values` consists of doct-of-dict {edge: {attr: value}} shape
if G.is_multigraph():
for (u, v, key), d in values.items():
try:
G[u][v][key].update(d)
except KeyError:
pass
else:
for (u, v), d in values.items():
try:
G[u][v].update(d)
except KeyError:
pass
[docs]def get_edge_attributes(G, name):
"""Get edge attributes from graph
Parameters
----------
G : NetworkX Graph
name : string
Attribute name
Returns
-------
Dictionary of attributes keyed by edge. For (di)graphs, the keys are
2-tuples of the form: (u, v). For multi(di)graphs, the keys are 3-tuples of
the form: (u, v, key).
Examples
--------
>>> G = nx.Graph()
>>> nx.add_path(G, [1, 2, 3], color='red')
>>> color = nx.get_edge_attributes(G, 'color')
>>> color[(1, 2)]
'red'
"""
if G.is_multigraph():
edges = G.edges(keys=True, data=True)
else:
edges = G.edges(data=True)
return {x[:-1]: x[-1][name] for x in edges if name in x[-1]}
[docs]def all_neighbors(graph, node):
"""Returns all of the neighbors of a node in the graph.
If the graph is directed returns predecessors as well as successors.
Parameters
----------
graph : NetworkX graph
Graph to find neighbors.
node : node
The node whose neighbors will be returned.
Returns
-------
neighbors : iterator
Iterator of neighbors
"""
if graph.is_directed():
values = chain(graph.predecessors(node), graph.successors(node))
else:
values = graph.neighbors(node)
return values
[docs]def non_neighbors(graph, node):
"""Returns the non-neighbors of the node in the graph.
Parameters
----------
graph : NetworkX graph
Graph to find neighbors.
node : node
The node whose neighbors will be returned.
Returns
-------
non_neighbors : iterator
Iterator of nodes in the graph that are not neighbors of the node.
"""
nbors = set(neighbors(graph, node)) | {node}
return (nnode for nnode in graph if nnode not in nbors)
[docs]def non_edges(graph):
"""Returns the non-existent edges in the graph.
Parameters
----------
graph : NetworkX graph.
Graph to find non-existent edges.
Returns
-------
non_edges : iterator
Iterator of edges that are not in the graph.
"""
if graph.is_directed():
for u in graph:
for v in non_neighbors(graph, u):
yield (u, v)
else:
nodes = set(graph)
while nodes:
u = nodes.pop()
for v in nodes - set(graph[u]):
yield (u, v)
[docs]@not_implemented_for('directed')
def common_neighbors(G, u, v):
"""Returns the common neighbors of two nodes in a graph.
Parameters
----------
G : graph
A NetworkX undirected graph.
u, v : nodes
Nodes in the graph.
Returns
-------
cnbors : iterator
Iterator of common neighbors of u and v in the graph.
Raises
------
NetworkXError
If u or v is not a node in the graph.
Examples
--------
>>> G = nx.complete_graph(5)
>>> sorted(nx.common_neighbors(G, 0, 1))
[2, 3, 4]
"""
if u not in G:
raise nx.NetworkXError('u is not in the graph.')
if v not in G:
raise nx.NetworkXError('v is not in the graph.')
# Return a generator explicitly instead of yielding so that the above
# checks are executed eagerly.
return (w for w in G[u] if w in G[v] and w not in (u, v))
[docs]def is_weighted(G, edge=None, weight='weight'):
"""Returns True if `G` has weighted edges.
Parameters
----------
G : graph
A NetworkX graph.
edge : tuple, optional
A 2-tuple specifying the only edge in `G` that will be tested. If
None, then every edge in `G` is tested.
weight: string, optional
The attribute name used to query for edge weights.
Returns
-------
bool
A boolean signifying if `G`, or the specified edge, is weighted.
Raises
------
NetworkXError
If the specified edge does not exist.
Examples
--------
>>> G = nx.path_graph(4)
>>> nx.is_weighted(G)
False
>>> nx.is_weighted(G, (2, 3))
False
>>> G = nx.DiGraph()
>>> G.add_edge(1, 2, weight=1)
>>> nx.is_weighted(G)
True
"""
if edge is not None:
data = G.get_edge_data(*edge)
if data is None:
msg = f'Edge {edge!r} does not exist.'
raise nx.NetworkXError(msg)
return weight in data
if is_empty(G):
# Special handling required since: all([]) == True
return False
return all(weight in data for u, v, data in G.edges(data=True))
[docs]def is_negatively_weighted(G, edge=None, weight='weight'):
"""Returns True if `G` has negatively weighted edges.
Parameters
----------
G : graph
A NetworkX graph.
edge : tuple, optional
A 2-tuple specifying the only edge in `G` that will be tested. If
None, then every edge in `G` is tested.
weight: string, optional
The attribute name used to query for edge weights.
Returns
-------
bool
A boolean signifying if `G`, or the specified edge, is negatively
weighted.
Raises
------
NetworkXError
If the specified edge does not exist.
Examples
--------
>>> G = nx.Graph()
>>> G.add_edges_from([(1, 3), (2, 4), (2, 6)])
>>> G.add_edge(1, 2, weight=4)
>>> nx.is_negatively_weighted(G, (1, 2))
False
>>> G[2][4]['weight'] = -2
>>> nx.is_negatively_weighted(G)
True
>>> G = nx.DiGraph()
>>> edges = [('0', '3', 3), ('0', '1', -5), ('1', '0', -2)]
>>> G.add_weighted_edges_from(edges)
>>> nx.is_negatively_weighted(G)
True
"""
if edge is not None:
data = G.get_edge_data(*edge)
if data is None:
msg = f'Edge {edge!r} does not exist.'
raise nx.NetworkXError(msg)
return weight in data and data[weight] < 0
return any(weight in data and data[weight] < 0
for u, v, data in G.edges(data=True))
[docs]def is_empty(G):
"""Returns True if `G` has no edges.
Parameters
----------
G : graph
A NetworkX graph.
Returns
-------
bool
True if `G` has no edges, and False otherwise.
Notes
-----
An empty graph can have nodes but not edges. The empty graph with zero
nodes is known as the null graph. This is an $O(n)$ operation where n
is the number of nodes in the graph.
"""
return not any(G.adj.values())
[docs]def nodes_with_selfloops(G):
"""Returns an iterator over nodes with self loops.
A node with a self loop has an edge with both ends adjacent
to that node.
Returns
-------
nodelist : iterator
A iterator over nodes with self loops.
See Also
--------
selfloop_edges, number_of_selfloops
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edge(1, 1)
>>> G.add_edge(1, 2)
>>> list(nx.nodes_with_selfloops(G))
[1]
"""
return (n for n, nbrs in G.adj.items() if n in nbrs)
[docs]def selfloop_edges(G, data=False, keys=False, default=None):
"""Returns an iterator over selfloop edges.
A selfloop edge has the same node at both ends.
Parameters
----------
data : string or 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')
keys : bool, optional (default=False)
If True, return edge keys with each edge.
default : value, optional (default=None)
Value used for edges that don't have the requested attribute.
Only relevant if data is not True or False.
Returns
-------
edgeiter : iterator over edge tuples
An iterator over all selfloop edges.
See Also
--------
nodes_with_selfloops, number_of_selfloops
Examples
--------
>>> G = nx.MultiGraph() # or Graph, DiGraph, MultiDiGraph, etc
>>> ekey = G.add_edge(1, 1)
>>> ekey = G.add_edge(1, 2)
>>> list(nx.selfloop_edges(G))
[(1, 1)]
>>> list(nx.selfloop_edges(G, data=True))
[(1, 1, {})]
>>> list(nx.selfloop_edges(G, keys=True))
[(1, 1, 0)]
>>> list(nx.selfloop_edges(G, keys=True, data=True))
[(1, 1, 0, {})]
"""
if data is True:
if G.is_multigraph():
if keys is True:
return ((n, n, k, d)
for n, nbrs in G.adj.items()
if n in nbrs for k, d in nbrs[n].items())
else:
return ((n, n, d)
for n, nbrs in G.adj.items()
if n in nbrs for d in nbrs[n].values())
else:
return ((n, n, nbrs[n]) for n, nbrs in G.adj.items() if n in nbrs)
elif data is not False:
if G.is_multigraph():
if keys is True:
return ((n, n, k, d.get(data, default))
for n, nbrs in G.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 G.adj.items()
if n in nbrs for d in nbrs[n].values())
else:
return ((n, n, nbrs[n].get(data, default))
for n, nbrs in G.adj.items() if n in nbrs)
else:
if G.is_multigraph():
if keys is True:
return ((n, n, k)
for n, nbrs in G.adj.items()
if n in nbrs for k in nbrs[n])
else:
return ((n, n)
for n, nbrs in G.adj.items()
if n in nbrs for d in nbrs[n].values())
else:
return ((n, n) for n, nbrs in G.adj.items() if n in nbrs)
[docs]def number_of_selfloops(G):
"""Returns the number of selfloop edges.
A selfloop edge has the same node at both ends.
Returns
-------
nloops : int
The number of selfloops.
See Also
--------
nodes_with_selfloops, selfloop_edges
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edge(1, 1)
>>> G.add_edge(1, 2)
>>> nx.number_of_selfloops(G)
1
"""
return sum(1 for _ in nx.selfloop_edges(G))
```