Source code for networkx.classes.multidigraph

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

import networkx as nx
from networkx.classes.digraph import DiGraph
from networkx.classes.multigraph import MultiGraph
from networkx.classes.coreviews import MultiAdjacencyView
from networkx.classes.reportviews import OutMultiEdgeView, InMultiEdgeView, \
    DiMultiDegreeView, OutMultiDegreeView, InMultiDegreeView
from networkx.exception import NetworkXError


[docs]class MultiDiGraph(MultiGraph, DiGraph): """A directed graph class that can store multiedges. Multiedges are multiple edges between two nodes. Each edge can hold optional data or attributes. A MultiDiGraph holds directed 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. See Also -------- Graph DiGraph MultiGraph OrderedMultiDiGraph Examples -------- Create an empty graph structure (a "null graph") with no nodes and no edges. >>> G = nx.MultiDiGraph() 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, >>> key = G.add_edge(1, 2) a list of edges, >>> keys = G.add_edges_from([(1, 2), (1, 3)]) or a collection of edges, >>> keys = G.add_edges_from(H.edges) If some edges connect nodes not yet in the graph, the nodes are added automatically. If an edge already exists, an additional edge is created and stored using a key to identify the edge. By default the key is the lowest unused integer. >>> keys = G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))]) >>> G[4] AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}}) **Attributes:** Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but can be added or changed using add_edge, add_node or direct manipulation of the attribute dictionaries named graph, node and edge respectively. >>> G = nx.MultiDiGraph(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. >>> key = G.add_edge(1, 2, weight=4.7 ) >>> keys = G.add_edges_from([(3, 4), (4, 5)], color='red') >>> keys = G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})]) >>> 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 available as an adjacency-view `G.adj` object or via the method `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. **Subclasses (Advanced):** The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure. The outer dict (node_dict) holds adjacency information keyed by node. The next dict (adjlist_dict) represents the adjacency information and holds edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr dict keyed by edge key. The inner dict (edge_attr_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 node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory, adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory 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 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, (default: dict) Factory function to be used to create the adjacency list dict which holds multiedge key dicts keyed by neighbor. It should require no arguments and return a dict-like object. edge_key_dict_factory : function, (default: dict) Factory function to be used to create the edge key dict which holds edge data keyed by edge key. It should require no arguments and return a dict-like object. edge_attr_dict_factory : function, (default: dict) Factory function to be used to create the edge attribute dict which holds 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 # adjlist_outer_dict_factory = dict # adjlist_inner_dict_factory = dict edge_key_dict_factory = dict # edge_attr_dict_factory = dict
[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. 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.edge_key_dict_factory = self.edge_key_dict_factory DiGraph.__init__(self, incoming_graph_data, **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 edgekey-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, 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 MultiAdjacencyView(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 edgekey-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, 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.succ` is identical to `G.adj`. """ return MultiAdjacencyView(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 edgekey-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, datadict in G.adj[n].items():`. """ return MultiAdjacencyView(self._pred)
[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 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_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_dict : dictionary, optional (default= no attributes) Dictionary of edge attributes. Key/value pairs will update existing data associated with the edge. attr : keyword arguments, optional Edge data (or labels or objects) can be assigned using keyword arguments. Returns ------- The edge key assigned to the edge. See Also -------- add_edges_from : add a collection of edges Notes ----- To replace/update edge data, use the optional key argument to identify a unique edge. Otherwise a new edge will be created. NetworkX algorithms designed for weighted graphs cannot use multigraphs directly because it is not clear how to handle multiedge weights. Convert to Graph using edge attribute 'weight' to enable weighted graph algorithms. 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.MultiDiGraph() >>> e = (1, 2) >>> key = G.add_edge(1, 2) # explicit two-node form >>> G.add_edge(*e) # single edge as tuple of two nodes 1 >>> G.add_edges_from( [(1, 2)] ) # add edges from iterable container [2] Associate data to edges using keywords: >>> key = G.add_edge(1, 2, weight=3) >>> key = G.add_edge(1, 2, key=0, weight=4) # update data for key=0 >>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7) For non-string attribute keys, use subscript notation. >>> ekey = G.add_edge(1, 2) >>> G[1][2][0].update({0: 5}) >>> G.edges[1, 2, 0].update({0: 5}) """ u, v = u_for_edge, v_for_edge # 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] = self.node_attr_dict_factory() 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] = self.node_attr_dict_factory() if key is None: key = self.new_edge_key(u, v) if v in self._succ[u]: keydict = self._adj[u][v] datadict = keydict.get(key, self.edge_key_dict_factory()) datadict.update(attr) keydict[key] = datadict else: # selfloops work this way without special treatment datadict = self.edge_attr_dict_factory() datadict.update(attr) keydict = self.edge_key_dict_factory() keydict[key] = datadict self._succ[u][v] = keydict self._pred[v][u] = keydict return key
[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. See Also -------- remove_edges_from : remove a collection of edges Examples -------- >>> G = nx.MultiDiGraph() >>> 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.MultiDiGraph() >>> 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.MultiDiGraph() >>> G.add_edge(1, 2, key='first') 'first' >>> G.add_edge(1, 2, key='second') 'second' >>> G.remove_edge(1, 2, key='second') """ try: d = self._adj[u][v] 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 del self._succ[u][v] del self._pred[v][u]
@property def edges(self): """An OutMultiEdgeView of the Graph as G.edges or G.edges(). edges(self, nbunch=None, data=False, keys=False, default=None) The OutMultiEdgeView 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. 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 : EdgeView 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.MultiDiGraph() >>> nx.add_path(G, [0, 1, 2]) >>> key = G.add_edge(2, 3, weight=5) >>> [e for e in G.edges()] [(0, 1), (1, 2), (2, 3)] >>> list(G.edges(data=True)) # default data is {} (empty dict) [(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})] >>> list(G.edges(data='weight', default=1)) [(0, 1, 1), (1, 2, 1), (2, 3, 5)] >>> list(G.edges(keys=True)) # default keys are integers [(0, 1, 0), (1, 2, 0), (2, 3, 0)] >>> list(G.edges(data=True, keys=True)) [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})] >>> list(G.edges(data='weight', default=1, keys=True)) [(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)] >>> list(G.edges([0, 2])) [(0, 1), (2, 3)] >>> list(G.edges(0)) [(0, 1)] See Also -------- in_edges, out_edges """ return OutMultiEdgeView(self) # alias out_edges to edges out_edges = edges @property def in_edges(self): """An InMultiEdgeView of the Graph as G.in_edges or G.in_edges(). in_edges(self, nbunch=None, data=False, keys=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). 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 ------- in_edges : InMultiEdgeView A view of edge attributes, usually it iterates over (u, v) or (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']`. See Also -------- edges """ return InMultiEdgeView(self) @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 nodes 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 -------- out_degree, in_degree Examples -------- >>> G = nx.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)] """ return DiMultiDegreeView(self) @property def in_degree(self): """A DegreeView for (node, in_degree) or in_degree for single node. The node in-degree is the number of edges pointing in 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 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, in-degree). See Also -------- degree, out_degree Examples -------- >>> G = nx.MultiDiGraph() >>> 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)] """ return InMultiDegreeView(self) @property def out_degree(self): """Returns an iterator for (node, out-degree) or out-degree for single node. out_degree(self, nbunch=None, weight=None) The node out-degree is the number of edges pointing out of the node. This function returns the out-degree for a single node or an iterator for a bunch of nodes or if nothing is passed as argument. 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 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. 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, out-degree). See Also -------- degree, in_degree Examples -------- >>> G = nx.MultiDiGraph() >>> 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)] """ return OutMultiDegreeView(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 True
[docs] def to_undirected(self, reciprocal=False, as_view=False): """Returns 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 : MultiGraph 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 -------- MultiGraph, copy, add_edge, add_edges_from 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=MultiiGraph(G) 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 MultiDiGraph 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()) if reciprocal is True: G.add_edges_from((u, v, key, deepcopy(data)) for u, nbrs in self._adj.items() for v, keydict in nbrs.items() for key, data in keydict.items() if v in self._pred[u] and key in self._pred[u][v]) else: G.add_edges_from((u, v, key, deepcopy(data)) for u, nbrs in self._adj.items() for v, keydict in nbrs.items() for key, data in keydict.items()) return G
[docs] def reverse(self, copy=True): """Returns 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.__class__() H.graph.update(deepcopy(self.graph)) H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items()) H.add_edges_from((v, u, k, deepcopy(d)) for u, v, k, d in self.edges(keys=True, data=True)) return H return nx.graphviews.reverse_view(self)