# Copyright (C) 2004-2017 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
#
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
"""Base class for directed graphs."""
from copy import deepcopy
import networkx as nx
from networkx.classes.graph import Graph
from networkx.classes.coreviews import AdjacencyView
from networkx.classes.reportviews import OutEdgeView, InEdgeView, \
DiDegreeView, InDegreeView, OutDegreeView
from networkx.exception import NetworkXError
import networkx.convert as convert
[docs]class DiGraph(Graph):
"""
Base class for directed graphs.
A DiGraph stores nodes and edges with optional data, or attributes.
DiGraphs hold directed edges. Self loops are allowed but multiple
(parallel) edges are not.
Nodes can be arbitrary (hashable) Python objects with optional
key/value attributes. By convention `None` is not used as a node.
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 any format that is supported
by the to_networkx_graph() function, currently including edge list,
dict of dicts, dict of lists, NetworkX graph, NumPy matrix
or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
MultiGraph
MultiDiGraph
OrderedDiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.DiGraph()
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.path_graph(10)
>>> 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. There are no errors when adding
nodes or edges that already exist.
**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.DiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time='5pm')
>>> G.add_nodes_from([3], time='2pm')
>>> G.nodes[1]
{'time': '5pm'}
>>> G.nodes[1]['room'] = 714
>>> del G.nodes[1]['room'] # remove attribute
>>> list(G.nodes(data=True))
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edges.
>>> 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]['weight'] = 4.7
>>> G.edges[1, 2]['weight'] = 4
Warning: we protect the graph data structure by making `G.edges[1, 2]` a
read-only dict-like structure. Use 2 sets of brackets to add/change
data attributes. (For multigraphs: `MG.edges[u, v, key][name] = value`).
**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
Often the best way to traverse all edges of a graph is via the neighbors.
The neighbors are reported as an adjacency-dict `G.adj` or as `G.adjacency()`
>>> for n, nbrsdict in G.adjacency():
... for nbr, eattr in nbrsdict.items():
... if 'weight' in eattr:
... # Do something useful with the edges
... pass
But the edges reporting object is often more convenient:
>>> for u, v, weight in G.edges(data='weight'):
... if weight is not None:
... # Do something useful with the edges
... pass
**Reporting:**
Simple graph information is obtained using object-attributes and methods.
Reporting usually provides views instead of containers to reduce memory
usage. The views update as the graph is updated similarly to dict-views.
The objects `nodes, `edges` and `adj` provide access to data attributes
via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration
(e.g. `nodes.items()`, `nodes.data('color')`,
`nodes.data('color', default='blue')` and similarly for `edges`)
Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
For details on these and other miscellaneous methods, see below.
**Subclasses (Advanced):**
The Graph class uses a dict-of-dict-of-dict data structure.
The outer dict (node_dict) holds adjacency information keyed by node.
The next dict (adjlist_dict) represents the adjacency information and holds
edge data keyed by neighbor. The inner dict (edge_attr_dict) represents
the edge data and holds edge attribute values keyed by attribute names.
Each of these three dicts can be replaced in a subclass 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_inner_dict_factory, adjlist_outer_dict_factory,
and edge_attr_dict_factory.
node_dict_factory : function, (default: dict)
Factory function to be used to create the dict containing node
attributes, keyed by node id.
It should require no arguments and return a dict-like object
adjlist_outer_dict_factory : function, (default: dict)
Factory function to be used to create the outer-most dict
in the data structure that holds adjacency info keyed by node.
It should require no arguments and return a dict-like object.
adjlist_inner_dict_factory : function, optional (default: dict)
Factory function to be used to create the adjacency list
dict which holds edge data keyed by neighbor.
It should require no arguments and return a dict-like object
edge_attr_dict_factory : function, optional (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 low memory graph class that effectively disallows edge
attributes by using a single attribute dict for all edges.
This reduces the memory used, but you lose edge attributes.
>>> class ThinGraph(nx.Graph):
... all_edge_dict = {'weight': 1}
... def single_edge_dict(self):
... return self.all_edge_dict
... edge_attr_dict_factory = single_edge_dict
>>> G = ThinGraph()
>>> G.add_edge(2, 1)
>>> G[2][1]
{'weight': 1}
>>> G.add_edge(2, 2)
>>> G[2][1] is G[2][2]
True
Please see :mod:`~networkx.classes.ordered` for more examples of
creating graph subclasses by overwriting the base class `dict` with
a dictionary-like object.
"""
def __getstate__(self):
attr = self.__dict__.copy()
# remove lazy property attributes
if 'nodes' in attr:
del attr['nodes']
if 'edges' in attr:
del attr['edges']
if 'out_edges' in attr:
del attr['out_edges']
if 'in_edges' in attr:
del attr['in_edges']
if 'degree' in attr:
del attr['degree']
if 'in_degree' in attr:
del attr['in_degree']
if 'out_degree' in attr:
del attr['out_degree']
return attr
[docs] def __init__(self, data=None, **attr):
"""Initialize a graph with edges, name, or graph 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
--------
convert
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G = nx.Graph(name='my graph')
>>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
>>> G = nx.Graph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.Graph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}
"""
self.node_dict_factory = ndf = self.node_dict_factory
self.adjlist_outer_dict_factory = self.adjlist_outer_dict_factory
self.adjlist_inner_dict_factory = self.adjlist_inner_dict_factory
self.edge_attr_dict_factory = self.edge_attr_dict_factory
self.root_graph = self
self.graph = {} # dictionary for graph attributes
self._node = ndf() # dictionary for node attributes
# We store two adjacency lists:
# the predecessors of node n are stored in the dict self._pred
# the successors of node n are stored in the dict self._succ=self._adj
self._adj = ndf() # empty adjacency dictionary
self._pred = ndf() # predecessor
self._succ = self._adj # successor
# attempt to load graph with data
if data is not None:
convert.to_networkx_graph(data, create_using=self)
# load graph attributes (must be after convert)
self.graph.update(attr)
@property
def adj(self):
"""Graph adjacency object holding the neighbors of each node.
This object is a read-only dict-like structure with node keys
and neighbor-dict values. The neighbor-dict is keyed by neighbor
to the edge-data-dict. So `G.adj[3][2]['color'] = 'blue'` sets
the color of the edge `(3, 2)` to `"blue"`.
Iterating over G.adj behaves like a dict. Useful idioms include
`for nbr, datadict in G.adj[n].items():`.
The neighbor information is also provided by subscripting the graph.
So `for nbr, foovalue in G[node].data('foo', default=1):` works.
For directed graphs, `G.adj` holds outgoing (successor) info.
"""
return AdjacencyView(self._succ)
@property
def succ(self):
"""Graph adjacency object holding the successors of each node.
This object is a read-only dict-like structure with node keys
and neighbor-dict values. The neighbor-dict is keyed by neighbor
to the edge-data-dict. So `G.succ[3][2]['color'] = 'blue'` sets
the color of the edge `(3, 2)` to `"blue"`.
Iterating over G.succ behaves like a dict. Useful idioms include
`for nbr, datadict in G.succ[n].items():`. A data-view not provided
by dicts also exists: `for nbr, foovalue in G.succ[node].data('foo'):`
and a default can be set via a `default` argument to the `data` method.
The neighbor information is also provided by subscripting the graph.
So `for nbr, foovalue in G[node].data('foo', default=1):` works.
For directed graphs, `G.adj` is identical to `G.succ`.
"""
return AdjacencyView(self._succ)
@property
def pred(self):
"""Graph adjacency object holding the predecessors of each node.
This object is a read-only dict-like structure with node keys
and neighbor-dict values. The neighbor-dict is keyed by neighbor
to the edge-data-dict. So `G.pred[2][3]['color'] = 'blue'` sets
the color of the edge `(3, 2)` to `"blue"`.
Iterating over G.pred behaves like a dict. Useful idioms include
`for nbr, datadict in G.pred[n].items():`. A data-view not provided
by dicts also exists: `for nbr, foovalue in G.pred[node].data('foo'):`
A default can be set via a `default` argument to the `data` method.
"""
return AdjacencyView(self._pred)
[docs] def add_node(self, n, **attr):
"""Add a single node n and update node attributes.
Parameters
----------
n : node
A node can be any hashable Python object except None.
attr : keyword arguments, optional
Set or change node attributes using key=value.
See Also
--------
add_nodes_from
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_node(1)
>>> G.add_node('Hello')
>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
>>> G.add_node(K3)
>>> G.number_of_nodes()
3
Use keywords set/change node attributes:
>>> G.add_node(1, size=10)
>>> G.add_node(3, weight=0.4, UTM=('13S', 382871, 3972649))
Notes
-----
A hashable object is one that can be used as a key in a Python
dictionary. This includes strings, numbers, tuples of strings
and numbers, etc.
On many platforms hashable items also include mutables such as
NetworkX Graphs, though one should be careful that the hash
doesn't change on mutables.
"""
if n not in self._succ:
self._succ[n] = self.adjlist_inner_dict_factory()
self._pred[n] = self.adjlist_inner_dict_factory()
self._node[n] = attr
else: # update attr even if node already exists
self._node[n].update(attr)
[docs] def add_nodes_from(self, nodes, **attr):
"""Add multiple nodes.
Parameters
----------
nodes : iterable container
A container of nodes (list, dict, set, etc.).
OR
A container of (node, attribute dict) tuples.
Node attributes are updated using the attribute dict.
attr : keyword arguments, optional (default= no attributes)
Update attributes for all nodes in nodes.
Node attributes specified in nodes as a tuple take
precedence over attributes specified via keyword arguments.
See Also
--------
add_node
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_nodes_from('Hello')
>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
>>> G.add_nodes_from(K3)
>>> sorted(G.nodes(), key=str)
[0, 1, 2, 'H', 'e', 'l', 'o']
Use keywords to update specific node attributes for every node.
>>> G.add_nodes_from([1, 2], size=10)
>>> G.add_nodes_from([3, 4], weight=0.4)
Use (node, attrdict) tuples to update attributes for specific nodes.
>>> G.add_nodes_from([(1, dict(size=11)), (2, {'color':'blue'})])
>>> G.nodes[1]['size']
11
>>> H = nx.Graph()
>>> H.add_nodes_from(G.nodes(data=True))
>>> H.nodes[1]['size']
11
"""
for n in nodes:
# keep all this inside try/except because
# CPython throws TypeError on n not in self._succ,
# while pre-2.7.5 ironpython throws on self._succ[n]
try:
if n not in self._succ:
self._succ[n] = self.adjlist_inner_dict_factory()
self._pred[n] = self.adjlist_inner_dict_factory()
self._node[n] = attr.copy()
else:
self._node[n].update(attr)
except TypeError:
nn, ndict = n
if nn not in self._succ:
self._succ[nn] = self.adjlist_inner_dict_factory()
self._pred[nn] = self.adjlist_inner_dict_factory()
newdict = attr.copy()
newdict.update(ndict)
self._node[nn] = newdict
else:
olddict = self._node[nn]
olddict.update(attr)
olddict.update(ndict)
[docs] def remove_node(self, n):
"""Remove node n.
Removes the node n and all adjacent edges.
Attempting to remove a non-existent node will raise an exception.
Parameters
----------
n : node
A node in the graph
Raises
-------
NetworkXError
If n is not in the graph.
See Also
--------
remove_nodes_from
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> list(G.edges)
[(0, 1), (1, 2)]
>>> G.remove_node(1)
>>> list(G.edges)
[]
"""
try:
nbrs = self._succ[n]
del self._node[n]
except KeyError: # NetworkXError if n not in self
raise NetworkXError("The node %s is not in the digraph." % (n,))
for u in nbrs:
del self._pred[u][n] # remove all edges n-u in digraph
del self._succ[n] # remove node from succ
for u in self._pred[n]:
del self._succ[u][n] # remove all edges n-u in digraph
del self._pred[n] # remove node from pred
[docs] def remove_nodes_from(self, nodes):
"""Remove multiple nodes.
Parameters
----------
nodes : iterable container
A container of nodes (list, dict, set, etc.). If a node
in the container is not in the graph it is silently ignored.
See Also
--------
remove_node
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> e = list(G.nodes)
>>> e
[0, 1, 2]
>>> G.remove_nodes_from(e)
>>> list(G.nodes)
[]
"""
for n in nodes:
try:
succs = self._succ[n]
del self._node[n]
for u in succs:
del self._pred[u][n] # remove all edges n-u in digraph
del self._succ[n] # now remove node
for u in self._pred[n]:
del self._succ[u][n] # remove all edges n-u in digraph
del self._pred[n] # now remove node
except KeyError:
pass # silent failure on remove
[docs] def add_edge(self, u, v, **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 directly
accessing the edge's attribute dictionary. See examples below.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
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
-----
Adding an edge that already exists updates the edge data.
Many NetworkX algorithms designed for weighted graphs use
an edge attribute (by default `weight`) to hold a numerical value.
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, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> G.add_edge(1, 2)
>>> G[1][2].update({0: 5})
>>> G.edges[1, 2].update({0: 5})
"""
# add nodes
if u not in self._succ:
self._succ[u] = self.adjlist_inner_dict_factory()
self._pred[u] = self.adjlist_inner_dict_factory()
self._node[u] = {}
if v not in self._succ:
self._succ[v] = self.adjlist_inner_dict_factory()
self._pred[v] = self.adjlist_inner_dict_factory()
self._node[v] = {}
# add the edge
datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
datadict.update(attr)
self._succ[u][v] = datadict
self._pred[v][u] = datadict
[docs] def add_edges_from(self, ebunch, **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 must be given as 2-tuples (u, v) or
3-tuples (u, v, d) where d is a dictionary containing edge data.
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 an ebunch take precedence over
attributes specified via keyword arguments.
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')
"""
# process ebunch
for e in ebunch:
ne = len(e)
if ne == 3:
u, v, dd = e
elif ne == 2:
u, v = e
dd = {}
else:
raise NetworkXError(
"Edge tuple %s must be a 2-tuple or 3-tuple." % (e,))
if u not in self._succ:
self._succ[u] = self.adjlist_inner_dict_factory()
self._pred[u] = self.adjlist_inner_dict_factory()
self._node[u] = {}
if v not in self._succ:
self._succ[v] = self.adjlist_inner_dict_factory()
self._pred[v] = self.adjlist_inner_dict_factory()
self._node[v] = {}
datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
datadict.update(attr)
datadict.update(dd)
self._succ[u][v] = datadict
self._pred[v][u] = datadict
[docs] def remove_edge(self, u, v):
"""Remove the edge between u and v.
Parameters
----------
u, v : nodes
Remove the edge between nodes u and v.
Raises
------
NetworkXError
If there is not an edge between u and v.
See Also
--------
remove_edges_from : remove a collection of edges
Examples
--------
>>> G = nx.Graph() # or DiGraph, etc
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.remove_edge(0, 1)
>>> e = (1, 2)
>>> G.remove_edge(*e) # unpacks e from an edge tuple
>>> e = (2, 3, {'weight':7}) # an edge with attribute data
>>> G.remove_edge(*e[:2]) # select first part of edge tuple
"""
try:
del self._succ[u][v]
del self._pred[v][u]
except KeyError:
raise NetworkXError("The edge %s-%s not in graph." % (u, v))
[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) edge between u and v.
- 3-tuples (u, v, k) where k 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.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)
"""
for e in ebunch:
u, v = e[:2] # ignore edge data
if u in self._succ and v in self._succ[u]:
del self._succ[u][v]
del self._pred[v][u]
def has_successor(self, u, v):
"""Return True if node u has successor v.
This is true if graph has the edge u->v.
"""
return (u in self._succ and v in self._succ[u])
def has_predecessor(self, u, v):
"""Return True if node u has predecessor v.
This is true if graph has the edge u<-v.
"""
return (u in self._pred and v in self._pred[u])
[docs] def successors(self, n):
"""Return an iterator over successor nodes of n.
neighbors() and successors() are the same.
"""
try:
return iter(self._succ[n])
except KeyError:
raise NetworkXError("The node %s is not in the digraph." % (n,))
# digraph definitions
neighbors = successors
[docs] def predecessors(self, n):
"""Return an iterator over predecessor nodes of n."""
try:
return iter(self._pred[n])
except KeyError:
raise NetworkXError("The node %s is not in the digraph." % (n,))
@property
def edges(self):
"""An OutEdgeView of the DiGraph as G.edges or G.edges().
edges(self, nbunch=None, data=False, default=None)
The OutEdgeView provides set-like operations on the edge-tuples
as well as edge attribute lookup. When called, it also provides
an EdgeDataView object which allows control of access to edge
attributes (but does not provide set-like operations).
Hence, `G.edges[u, v]['color']` provides the value of the color
attribute for edge `(u, v)` while
`for (u, v, c) in G.edges.data('color', default='red'):`
iterates through all the edges yielding the color attribute
with default `'red'` if no color attribute exists.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
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.
Returns
-------
edges : OutEdgeView
A view of edge attributes, usually it iterates over (u, v)
or (u, v, d) tuples of edges, but can also be used for
attribute lookup as `edges[u, v]['foo']`.
See Also
--------
in_edges, out_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.DiGraph() # or MultiDiGraph, etc
>>> nx.add_path(G, [0, 1, 2])
>>> G.add_edge(2, 3, weight=5)
>>> [e for e in G.edges]
[(0, 1), (1, 2), (2, 3)]
>>> G.edges.data() # default data is {} (empty dict)
OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
>>> G.edges.data('weight', default=1)
OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
>>> G.edges([0, 2]) # only edges incident to these nodes
OutEdgeDataView([(0, 1), (2, 3)])
>>> G.edges(0) # only edges incident to a single node (use G.adj[0]?)
OutEdgeDataView([(0, 1)])
"""
self.__dict__['edges'] = edges = OutEdgeView(self)
self.__dict__['out_edges'] = edges
return edges
# alias out_edges to edges
out_edges = edges
@property
def in_edges(self):
"""An InEdgeView of the Graph as G.in_edges or G.in_edges().
in_edges(self, nbunch=None, data=False, default=None):
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
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.
Returns
-------
in_edges : InEdgeView
A view of edge attributes, usually it iterates over (u, v)
or (u, v, d) tuples of edges, but can also be used for
attribute lookup as `edges[u, v]['foo']`.
See Also
--------
edges
"""
self.__dict__['in_edges'] = in_edges = InEdgeView(self)
return in_edges
@property
def degree(self):
"""A DegreeView for the Graph as G.degree or G.degree().
The node degree is the number of edges adjacent to the node.
The weighted node degree is the sum of the edge weights for
edges incident to that node.
This object provides an iterator for (node, degree) as well as
lookup for the degree for a single node.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
weight : string or None, optional (default=None)
The name of an 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
-------
If a single node is requested
deg : int
Degree of the node
OR if multiple nodes are requested
nd_iter : iterator
The iterator returns two-tuples of (node, degree).
See Also
--------
in_degree, out_degree
Examples
--------
>>> G = nx.DiGraph() # or MultiDiGraph
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.degree(0) # node 0 with degree 1
1
>>> list(G.degree([0, 1, 2]))
[(0, 1), (1, 2), (2, 2)]
"""
self.__dict__['degree'] = degree = DiDegreeView(self)
return degree
@property
def in_degree(self):
"""An InDegreeView for (node, in_degree) or in_degree for single node.
The node in_degree is the number of edges pointing to the node.
The weighted node degree is the sum of the edge weights for
edges incident to that node.
This object provides an iteration over (node, in_degree) as well as
lookup for the degree for a single node.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
weight : string or None, optional (default=None)
The name of an 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
-------
If a single node is requested
deg : int
In-degree of the node
OR if multiple nodes are requested
nd_iter : iterator
The iterator returns two-tuples of (node, in-degree).
See Also
--------
degree, out_degree
Examples
--------
>>> G = nx.DiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.in_degree(0) # node 0 with degree 0
0
>>> list(G.in_degree([0, 1, 2]))
[(0, 0), (1, 1), (2, 1)]
"""
self.__dict__['in_degree'] = in_degree = InDegreeView(self)
return in_degree
@property
def out_degree(self):
"""An OutDegreeView for (node, out_degree)
The node out_degree is the number of edges pointing out of the node.
The weighted node degree is the sum of the edge weights for
edges incident to that node.
This object provides an iterator over (node, out_degree) as well as
lookup for the degree for a single node.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
weight : string or None, optional (default=None)
The name of an 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
-------
If a single node is requested
deg : int
Out-degree of the node
OR if multiple nodes are requested
nd_iter : iterator
The iterator returns two-tuples of (node, out-degree).
See Also
--------
degree, in_degree
Examples
--------
>>> G = nx.DiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.out_degree(0) # node 0 with degree 1
1
>>> list(G.out_degree([0, 1, 2]))
[(0, 1), (1, 1), (2, 1)]
"""
self.__dict__['out_degree'] = out_degree = OutDegreeView(self)
return out_degree
[docs] def clear(self):
"""Remove all nodes and edges from the graph.
This also removes the name, and all graph, node, and edge attributes.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.clear()
>>> list(G.nodes)
[]
>>> list(G.edges)
[]
"""
self._succ.clear()
self._pred.clear()
self._node.clear()
self.graph.clear()
def is_multigraph(self):
"""Return True if graph is a multigraph, False otherwise."""
return False
def is_directed(self):
"""Return True if graph is directed, False otherwise."""
return True
[docs] def fresh_copy(self):
"""Return a fresh copy graph with the same data structure.
A fresh copy has no nodes, edges or graph attributes. It is
the same data structure as the current graph. This method is
typically used to create an empty version of the graph.
Notes
=====
If you subclass the base class you should overwrite this method
to return your class of graph.
"""
return DiGraph()
[docs] def copy(self, as_view=False):
"""Return a copy of the graph.
The copy method by default returns a shallow copy of the graph
and attributes. That is, if an attribute is a container, that
container is shared by the original an the copy.
Use Python's `copy.deepcopy` for new containers.
If `as_view` is True then a view is returned instead of a copy.
Notes
=====
All copies reproduce the graph structure, but data attributes
may be handled in different ways. There are four types of copies
of a graph that people might want.
Deepcopy -- The default behavior is a "deepcopy" where the graph
structure as well as all data attributes and any objects they might
contain are copied. The entire graph object is new so that changes
in the copy do not affect the original object. (see Python's
copy.deepcopy)
Data Reference (Shallow) -- For a shallow copy the graph structure
is copied but the edge, node and graph attribute dicts are
references to those in the original graph. This saves
time and memory but could cause confusion if you change an attribute
in one graph and it changes the attribute in the other.
NetworkX does not provide this level of shallow copy.
Independent Shallow -- This copy creates new independent attribute
dicts and then does a shallow copy of the attributes. That is, any
attributes that are containers are shared between the new graph
and the original. This is exactly what `dict.copy()` provides.
You can obtain this style copy using:
>>> G = nx.path_graph(5)
>>> H = G.copy()
>>> H = G.copy(as_view=False)
>>> H = nx.Graph(G)
>>> H = G.fresh_copy().__class__(G)
Fresh Data -- For fresh data, the graph structure is copied while
new empty data attribute dicts are created. The resulting graph
is independent of the original and it has no edge, node or graph
attributes. Fresh copies are not enabled. Instead use:
>>> H = G.fresh_copy()
>>> H.add_nodes_from(G)
>>> H.add_edges_from(G.edges)
View -- Inspired by dict-views, graph-views act like read-only
versions of the original graph, providing a copy of the original
structure without requiring any memory for copying the information.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/2/library/copy.html.
Parameters
----------
as_view : bool, optional (default=False)
If True, the returned graph-view provides a read-only view
of the original graph without actually copying any data.
Returns
-------
G : Graph
A copy of the graph.
See Also
--------
to_directed: return a directed copy of the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> H = G.copy()
"""
if as_view is True:
return nx.graphviews.DiGraphView(self)
G = self.fresh_copy()
G.graph.update(self.graph)
G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
G.add_edges_from((u, v, datadict.copy())
for u, nbrs in self.adj.items()
for v, datadict in nbrs.items())
return G
[docs] def to_undirected(self, reciprocal=False, as_view=False):
"""Return an undirected representation of the digraph.
Parameters
----------
reciprocal : bool (optional)
If True only keep edges that appear in both directions
in the original digraph.
as_view : bool (optional, default=False)
If True return an undirected view of the original directed graph.
Returns
-------
G : Graph
An undirected graph with the same name and nodes and
with edge (u, v, data) if either (u, v, data) or (v, u, data)
is in the digraph. If both edges exist in digraph and
their edge data is different, only one edge is created
with an arbitrary choice of which edge data to use.
You must check and correct for this manually if desired.
See Also
--------
Graph, copy, add_edge, add_edges_from
Notes
-----
If edges in both directions (u, v) and (v, u) exist in the
graph, attributes for the new undirected edge will be a combination of
the attributes of the directed edges. The edge data is updated
in the (arbitrary) order that the edges are encountered. For
more customized control of the edge attributes use add_edge().
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 G=DiGraph(D) which returns a
shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/2/library/copy.html.
Warning: If you have subclassed DiGraph to use dict-like objects
in the data structure, those changes do not transfer to the
Graph created by this method.
Examples
--------
>>> G = nx.path_graph(2) # or MultiGraph, etc
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1)]
"""
if as_view is True:
return nx.graphviews.GraphView(self)
# deepcopy when not a view
G = Graph()
G.graph.update(deepcopy(self.graph))
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
if reciprocal is True:
G.add_edges_from((u, v, deepcopy(d))
for u, nbrs in self.adj.items()
for v, d in nbrs.items()
if v in self._pred[u])
else:
G.add_edges_from((u, v, deepcopy(d))
for u, nbrs in self.adj.items()
for v, d in nbrs.items())
return G
[docs] def subgraph(self, nodes):
"""Return a SubGraph view of the subgraph induced on `nodes`.
The induced subgraph of the graph contains the nodes in `nodes`
and the edges between those nodes.
Parameters
----------
nodes : list, iterable
A container of nodes which will be iterated through once.
Returns
-------
G : SubGraph View
A subgraph view of the graph. The graph structure cannot be
changed but node/edge attributes can and are shared with the
original graph.
Notes
-----
The graph, edge and node attributes are shared with the original graph.
Changes to the graph structure is ruled out by the view, but changes
to attributes are reflected in the original graph.
To create a subgraph with its own copy of the edge/node attributes use:
G.subgraph(nodes).copy()
For an inplace reduction of a graph to a subgraph you can remove nodes:
G.remove_nodes_from([n for n in G if n not in set(nodes)])
Examples
--------
>>> 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(self.nbunch_iter(nodes))
SubGraph = nx.graphviews.SubDiGraph
# if already a subgraph, don't make a chain
if hasattr(self, '_NODE_OK'):
return SubGraph(self._graph, induced_nodes, self._EDGE_OK)
return SubGraph(self, induced_nodes)
[docs] def reverse(self, copy=True):
"""Return the reverse of the graph.
The reverse is a graph with the same nodes and edges
but with the directions of the edges reversed.
Parameters
----------
copy : bool optional (default=True)
If True, return a new DiGraph holding the reversed edges.
If False, the reverse graph is created using a view of
the original graph.
"""
if copy:
H = self.fresh_copy()
H.graph.update(deepcopy(self.graph))
if 'name' in H.graph:
H.name = "Reverse of (%s)" % H.name
H.add_nodes_from((n, deepcopy(d)) for n, d in self.node.items())
H.add_edges_from((v, u, deepcopy(d)) for u, v, d
in self.edges(data=True))
return H
return nx.graphviews.ReverseView(self)