# Source code for networkx.classes.multigraph

"""Base class for MultiGraph."""
from copy import deepcopy

import networkx as nx
from networkx.classes.graph import Graph
from networkx.classes.reportviews import MultiEdgeView, MultiDegreeView
from networkx import NetworkXError

[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. By convention None is not used as a node.

Edges are represented as links between nodes with optional
key/value attributes.

Parameters
----------
incoming_graph_data : input graph (optional, default: None)
Data to initialize graph. If 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.

--------
Graph
DiGraph
MultiDiGraph
OrderedMultiGraph

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:

Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).

>>> H = nx.path_graph(10)

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.

**Edges:**

G can also be grown by adding edges.

a list of edges,

>>> keys = G.add_edges_from([(1, 2), (1, 3)])

or a collection of edges,

If some edges connect nodes not yet in the graph, the nodes
edge is created and stored using a key to identify the edge.
By default the key is the lowest unused integer.

>>> G[4]
AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 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
dictionaries named graph, node and edge respectively.

>>> G = nx.MultiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}

>>> 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'})]

notation, or G.edges.

>>> key = G.add_edge(1, 2, weight=4.7 )
>>> keys = G.add_edges_from([(3, 4), (4, 5)], color='red')
>>> G[1][2][0]['weight'] = 4.7
>>> G.edges[1, 2, 0]['weight'] = 4

Warning: we protect the graph data structure by making G.edges[1, 2] a
read-only dict-like structure. However, you can assign to attributes
in e.g. G.edges[1, 2]. Thus, use 2 sets of brackets to add/change
data attributes: G.edges[1, 2]['weight'] = 4
(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
>>> G[1] # adjacency dict-like view keyed by neighbor to edge attributes
AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})

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 G.adjacency().

>>> for n, nbrsdict in G.adjacency():
...     for nbr, keydict in nbrsdict.items():
...        for key, eattr in keydict.items():
...            if 'weight' in eattr:
...                # Do something useful with the edges
...                pass

But the edges() method is often more convenient:

>>> for u, v, keys, weight in G.edges(data='weight', keys=True):
...     if weight is not None:
...         # Do something useful with the edges
...         pass

**Reporting:**

Simple graph information is obtained using methods and object-attributes.
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.

The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
The outer dict (node_dict) holds adjacency information keyed by node.
edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr
dict keyed by edge key. The inner dict (edge_attr_dict) 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
and graph_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

node_attr_dict_factory: function, (default: dict)
Factory function to be used to create the node attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object

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.

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 attribute values keyed by attribute name.
It should require no arguments and return a dict-like object.

graph_attr_dict_factory : function, (default: dict)
Factory function to be used to create the graph attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object.

Typically, if your extension doesn't impact the data structure all
methods will inherited without issue except: to_directed/to_undirected.
By default these methods create a DiGraph/Graph class and you probably
want them to create your extension of a DiGraph/Graph. To facilitate
this we define two class variables that you can set in your subclass.

to_directed_class : callable, (default: DiGraph or MultiDiGraph)
Class to create a new graph structure in the to_directed method.
If None, a NetworkX class (DiGraph or MultiDiGraph) is used.

to_undirected_class : callable, (default: Graph or MultiGraph)
Class to create a new graph structure in the to_undirected method.
If None, a NetworkX class (Graph or MultiGraph) is used.

Examples
--------

Please see :mod:~networkx.classes.ordered for examples of
creating graph subclasses by overwriting the base class dict with
a dictionary-like object.
"""
# node_dict_factory = dict    # already assigned in Graph
edge_key_dict_factory = dict
# edge_attr_dict_factory = dict

def to_directed_class(self):
"""Returns the class to use for empty directed copies.

If you subclass the base classes, use this to designate
what directed class to use for to_directed() copies.
"""
return nx.MultiDiGraph

def to_undirected_class(self):
"""Returns the class to use for empty undirected copies.

If you subclass the base classes, use this to designate
what directed class to use for to_directed() copies.
"""
return MultiGraph

[docs]    def __init__(self, incoming_graph_data=None, **attr):
"""Initialize a graph with edges, name, or graph attributes.

Parameters
----------
incoming_graph_data : input graph
Data to initialize graph.  If incoming_graph_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.

--------
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.edge_key_dict_factory = self.edge_key_dict_factory
Graph.__init__(self, incoming_graph_data, **attr)

@property
"""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 edgekey-data-dict.  So G.adj[3][2][0]['color'] = 'blue' sets
the color of the edge (3, 2, 0) to "blue".

Iterating over G.adj behaves like a dict. Useful idioms include
for nbr, nbrdict 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.
"""

[docs]    def new_edge_key(self, u, v):
"""Returns an unused key for edges between nodes u and v.

The nodes u and v do not need to be already in the graph.

Notes
-----
In the standard MultiGraph class the new key is the number of existing
edges between u and v (increased if necessary to ensure unused).
The first edge will have key 0, then 1, etc. If an edge is removed
further new_edge_keys may not be in this order.

Parameters
----------
u, v : nodes

Returns
-------
key : int
"""
try:
except KeyError:
return 0
key = len(keydict)
while key in keydict:
key += 1
return key

[docs]    def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
"""Add an edge between u and v.

The nodes u and v will be automatically added if they are

Edge attributes can be specified with keywords or by directly
accessing the edge's attribute dictionary. See examples below.

Parameters
----------
u_for_edge, v_for_edge : 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 : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.

Returns
-------
The edge key assigned to the edge.

--------

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.

Default keys are generated using the method new_edge_key().
This method can be overridden by subclassing the base class and
providing a custom new_edge_key() method.

Examples
--------
The following all add the edge e=(1, 2) to graph G:

>>> G = nx.MultiGraph()
>>> e = (1, 2)
>>> ekey = G.add_edge(1, 2)           # explicit two-node form
>>> G.add_edge(*e)             # single edge as tuple of two nodes
1
[2]

Associate data to edges using keywords:

>>> ekey = G.add_edge(1, 2, weight=3)
>>> ekey = G.add_edge(1, 2, key=0, weight=4)   # update data for key=0
>>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)

For non-string attribute keys, use subscript notation.

>>> G[1][2][0].update({0: 5})
>>> G.edges[1, 2, 0].update({0: 5})
"""
u, v = u_for_edge, v_for_edge
self._node[u] = self.node_attr_dict_factory()
self._node[v] = self.node_attr_dict_factory()
if key is None:
key = self.new_edge_key(u, v)
else:
# selfloops work this way without special treatment
keydict = self.edge_key_dict_factory()
return key

Parameters
----------
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 data dict d, or
- 3-tuples (u, v, k) for not iterable key k, or
- 4-tuples (u, v, k, d) for an edge with data and key k

attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.

Returns
-------
A list of edge keys assigned to the edges in ebunch.

--------

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.

Default keys are generated using the method new_edge_key().
This method can be overridden by subclassing the base class and
providing a custom new_edge_key() method.

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))

Associate data to edges

>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
>>> G.add_edges_from([(3, 4), (1, 4)], label='WN2898')
"""
keylist = []
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:
msg = f"Edge tuple {e} must be a 2-tuple, 3-tuple or 4-tuple."
raise NetworkXError(msg)
ddd = {}
ddd.update(attr)
try:
ddd.update(dd)
except:
if ne != 3:
raise
key = dd
self[u][v][key].update(ddd)
keylist.append(key)
return keylist

[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 (arbitrary) 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.

--------
remove_edges_from : remove a collection of edges

Examples
--------
>>> G = nx.MultiGraph()
>>> 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

For multiple edges

>>> G = nx.MultiGraph()   # or MultiDiGraph, etc
>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)])  # key_list returned
[0, 1, 2]
>>> G.remove_edge(1, 2) # remove a single (arbitrary) edge

For edges with keys

>>> G = nx.MultiGraph()   # or MultiDiGraph, etc
'first'
'second'
>>> G.remove_edge(1, 2, key='second')

"""
try:
except KeyError:
raise NetworkXError(f"The edge {u}-{v} is not in the graph.")
# remove the edge with specified data
if key is None:
d.popitem()
else:
try:
del d[key]
except KeyError:
msg = f"The edge {u}-{v} with key {key} is not in the graph."
raise NetworkXError(msg)
if len(d) == 0:
# remove the key entries if last edge
if u != v:  # check for selfloop

[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.

--------
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)

Removing multiple copies of edges

>>> G = nx.MultiGraph()
>>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)])
>>> G.remove_edges_from([(1, 2), (1, 2)])
>>> list(G.edges())
[(1, 2)]
>>> G.remove_edges_from([(1, 2), (1, 2)]) # silently ignore extra copy
>>> list(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):
"""Returns True if the graph has an edge between nodes u and v.

This is the same as v in G[u] or key in G[u][v]
without KeyError exceptions.

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
>>> nx.add_path(G, [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
'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 :exc:KeyError if 0 not in G
True

"""
try:
if key is None:
else:
except KeyError:
return False

@property
def edges(self):
"""Returns an iterator over the edges.

edges(self, nbunch=None, data=False, keys=False, default=None)

The EdgeView provides set-like operations on the edge-tuples
as well as edge attribute lookup. When called, it also provides
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.

Edges are returned as tuples with optional data and keys
in the order (node, neighbor, key, data).

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).
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
-------
edges : MultiEdgeView
A view of edge attributes, usually it iterates over (u, v)
(u, v, k) or (u, v, k, d) tuples of edges, but can also be
used for attribute lookup as edges[u, v, k]['foo'].

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
>>> key = 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)
MultiEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
>>> G.edges.data('weight', default=1)
MultiEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
>>> G.edges(keys=True) # default keys are integers
MultiEdgeView([(0, 1, 0), (1, 2, 0), (2, 3, 0)])
>>> G.edges.data(keys=True)
MultiEdgeDataView([(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})])
>>> G.edges.data('weight', default=1, keys=True)
MultiEdgeDataView([(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)])
>>> G.edges([0, 3])
MultiEdgeDataView([(0, 1), (3, 2)])
>>> G.edges(0)
MultiEdgeDataView([(0, 1)])
"""
return MultiEdgeView(self)

[docs]    def get_edge_data(self, u, v, key=None, default=None):
"""Returns the attribute dictionary associated with edge (u, v).

This is identical to G[u][v][key] except the default is returned
instead of an exception is the edge doesn't exist.

Parameters
----------
u, v : nodes

default :  any Python object (default=None)

key : hashable identifier, optional (default=None)
Return data only for the edge with specified key.

Returns
-------
edge_dict : dictionary
The edge attribute dictionary.

Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> key = G.add_edge(0, 1, key='a', weight=7)
>>> G[0][1]['a']  # key='a'
{'weight': 7}
>>> G.edges[0, 1, 'a']  # key='a'
{'weight': 7}

Warning: we protect the graph data structure by making
G.edges and G[1][2] read-only dict-like structures.
However, you can assign values to attributes in e.g.
G.edges[1, 2, 'a'] or G[1][2]['a'] using an additional
bracket as shown next. You need to specify all edge info
to assign to the edge data associated with an edge.

>>> G[0][1]['a']['weight'] = 10
>>> G.edges[0, 1, 'a']['weight'] = 10
>>> G[0][1]['a']['weight']
10
>>> G.edges[1, 0, 'a']['weight']
10

>>> G = nx.MultiGraph() # or MultiDiGraph
>>> nx.add_path(G, [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:
else:
except KeyError:
return default

@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, if a single node is passed as argument.

OR if multiple nodes are requested
nd_iter : iterator
The iterator returns two-tuples of (node, degree).

Examples
--------
>>> G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.degree(0) # node 0 with degree 1
1
>>> list(G.degree([0, 1]))
[(0, 1), (1, 2)]

"""
return MultiDegreeView(self)

def is_multigraph(self):
"""Returns True if graph is a multigraph, False otherwise."""
return True

def is_directed(self):
"""Returns True if graph is directed, False otherwise."""
return False

[docs]    def copy(self, as_view=False):
"""Returns a copy of the graph.

The copy method by default returns an independent 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 -- A "deepcopy" copies the graph structure as well as
all data attributes and any objects they might contain.
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.__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.__class__()

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.

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.

--------
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.generic_graph_view(self)
G = self.__class__()
G.graph.update(self.graph)
G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
for v, keydict in nbrs.items()
return G

[docs]    def to_directed(self, as_view=False):
"""Returns 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.

and deep copies, https://docs.python.org/2/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
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]

If already directed, return a (deep) copy

>>> G = nx.DiGraph()   # or MultiDiGraph, etc
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1)]
"""
graph_class = self.to_directed_class()
if as_view is True:
return nx.graphviews.generic_graph_view(self, graph_class)
# deepcopy when not a view
G = graph_class()
G.graph.update(deepcopy(self.graph))
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
for v, keydict in nbrs.items()
return G

[docs]    def to_undirected(self, as_view=False):
"""Returns an undirected copy of the graph.

Returns
-------
G : Graph/MultiGraph
A deepcopy of the graph.

--------

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 G = nx.MultiGraph(D)
which returns a shallow copy of the data.

and deep copies, https://docs.python.org/2/library/copy.html.

Warning: If you have subclassed MultiiGraph to use dict-like
objects in the data structure, those changes do not transfer
to the MultiGraph 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)]
"""
graph_class = self.to_undirected_class()
if as_view is True:
return nx.graphviews.generic_graph_view(self, graph_class)
# deepcopy when not a view
G = graph_class()
G.graph.update(deepcopy(self.graph))
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
for v, keydict in nbrs.items()
return G

[docs]    def number_of_edges(self, u=None, v=None):
"""Returns the number of edges between two nodes.

Parameters
----------
u, v : nodes, optional (Gefault=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. If
the graph is directed, this only returns the number of edges
from u to v.

--------
size

Examples
--------
For undirected multigraphs, this method counts the total number
of edges in the graph::

>>> G = nx.MultiGraph()
>>> G.add_edges_from([(0, 1), (0, 1), (1, 2)])
[0, 1, 0]
>>> G.number_of_edges()
3

If you specify two nodes, this counts the total number of edges
joining the two nodes::

>>> G.number_of_edges(0, 1)
2

For directed multigraphs, this method can count the total number
of directed edges from u to v::

>>> G = nx.MultiDiGraph()
>>> G.add_edges_from([(0, 1), (0, 1), (1, 0)])
[0, 1, 0]
>>> G.number_of_edges(0, 1)
2
>>> G.number_of_edges(1, 0)
1

"""
if u is None:
return self.size()
try: