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<h1>Source code for networkx.classes.multigraph</h1><div class="highlight"><pre>
<span></span><span class="sd">"""Base class for MultiGraph."""</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">cached_property</span>
<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="kn">import</span> <span class="nn">networkx.convert</span> <span class="k">as</span> <span class="nn">convert</span>
<span class="kn">from</span> <span class="nn">networkx</span> <span class="kn">import</span> <span class="n">NetworkXError</span>
<span class="kn">from</span> <span class="nn">networkx.classes.coreviews</span> <span class="kn">import</span> <span class="n">MultiAdjacencyView</span>
<span class="kn">from</span> <span class="nn">networkx.classes.graph</span> <span class="kn">import</span> <span class="n">Graph</span>
<span class="kn">from</span> <span class="nn">networkx.classes.reportviews</span> <span class="kn">import</span> <span class="n">MultiDegreeView</span><span class="p">,</span> <span class="n">MultiEdgeView</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"MultiGraph"</span><span class="p">]</span>
<div class="viewcode-block" id="MultiGraph"><a class="viewcode-back" href="../../../reference/classes/multigraph.html#networkx.MultiGraph">[docs]</a><span class="k">class</span> <span class="nc">MultiGraph</span><span class="p">(</span><span class="n">Graph</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> An undirected graph class that can store multiedges.</span>
<span class="sd"> Multiedges are multiple edges between two nodes. Each edge</span>
<span class="sd"> can hold optional data or attributes.</span>
<span class="sd"> A MultiGraph holds undirected edges. Self loops are allowed.</span>
<span class="sd"> Nodes can be arbitrary (hashable) Python objects with optional</span>
<span class="sd"> key/value attributes. By convention `None` is not used as a node.</span>
<span class="sd"> Edges are represented as links between nodes with optional</span>
<span class="sd"> key/value attributes, in a MultiGraph each edge has a key to</span>
<span class="sd"> distinguish between multiple edges that have the same source and</span>
<span class="sd"> destination nodes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> incoming_graph_data : input graph (optional, default: None)</span>
<span class="sd"> Data to initialize graph. If None (default) an empty</span>
<span class="sd"> graph is created. The data can be any format that is supported</span>
<span class="sd"> by the to_networkx_graph() function, currently including edge list,</span>
<span class="sd"> dict of dicts, dict of lists, NetworkX graph, 2D NumPy array,</span>
<span class="sd"> SciPy sparse array, or PyGraphviz graph.</span>
<span class="sd"> multigraph_input : bool or None (default None)</span>
<span class="sd"> Note: Only used when `incoming_graph_data` is a dict.</span>
<span class="sd"> If True, `incoming_graph_data` is assumed to be a</span>
<span class="sd"> dict-of-dict-of-dict-of-dict structure keyed by</span>
<span class="sd"> node to neighbor to edge keys to edge data for multi-edges.</span>
<span class="sd"> A NetworkXError is raised if this is not the case.</span>
<span class="sd"> If False, :func:`to_networkx_graph` is used to try to determine</span>
<span class="sd"> the dict's graph data structure as either a dict-of-dict-of-dict</span>
<span class="sd"> keyed by node to neighbor to edge data, or a dict-of-iterable</span>
<span class="sd"> keyed by node to neighbors.</span>
<span class="sd"> If None, the treatment for True is tried, but if it fails,</span>
<span class="sd"> the treatment for False is tried.</span>
<span class="sd"> attr : keyword arguments, optional (default= no attributes)</span>
<span class="sd"> Attributes to add to graph as key=value pairs.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> Graph</span>
<span class="sd"> DiGraph</span>
<span class="sd"> MultiDiGraph</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> Create an empty graph structure (a "null graph") with no nodes and</span>
<span class="sd"> no edges.</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> G can be grown in several ways.</span>
<span class="sd"> **Nodes:**</span>
<span class="sd"> Add one node at a time:</span>
<span class="sd"> >>> G.add_node(1)</span>
<span class="sd"> Add the nodes from any container (a list, dict, set or</span>
<span class="sd"> even the lines from a file or the nodes from another graph).</span>
<span class="sd"> >>> G.add_nodes_from([2, 3])</span>
<span class="sd"> >>> G.add_nodes_from(range(100, 110))</span>
<span class="sd"> >>> H = nx.path_graph(10)</span>
<span class="sd"> >>> G.add_nodes_from(H)</span>
<span class="sd"> In addition to strings and integers any hashable Python object</span>
<span class="sd"> (except None) can represent a node, e.g. a customized node object,</span>
<span class="sd"> or even another Graph.</span>
<span class="sd"> >>> G.add_node(H)</span>
<span class="sd"> **Edges:**</span>
<span class="sd"> G can also be grown by adding edges.</span>
<span class="sd"> Add one edge,</span>
<span class="sd"> >>> key = G.add_edge(1, 2)</span>
<span class="sd"> a list of edges,</span>
<span class="sd"> >>> keys = G.add_edges_from([(1, 2), (1, 3)])</span>
<span class="sd"> or a collection of edges,</span>
<span class="sd"> >>> keys = G.add_edges_from(H.edges)</span>
<span class="sd"> If some edges connect nodes not yet in the graph, the nodes</span>
<span class="sd"> are added automatically. If an edge already exists, an additional</span>
<span class="sd"> edge is created and stored using a key to identify the edge.</span>
<span class="sd"> By default the key is the lowest unused integer.</span>
<span class="sd"> >>> keys = G.add_edges_from([(4, 5, {"route": 28}), (4, 5, {"route": 37})])</span>
<span class="sd"> >>> G[4]</span>
<span class="sd"> AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 2: {'route': 37}}})</span>
<span class="sd"> **Attributes:**</span>
<span class="sd"> Each graph, node, and edge can hold key/value attribute pairs</span>
<span class="sd"> in an associated attribute dictionary (the keys must be hashable).</span>
<span class="sd"> By default these are empty, but can be added or changed using</span>
<span class="sd"> add_edge, add_node or direct manipulation of the attribute</span>
<span class="sd"> dictionaries named graph, node and edge respectively.</span>
<span class="sd"> >>> G = nx.MultiGraph(day="Friday")</span>
<span class="sd"> >>> G.graph</span>
<span class="sd"> {'day': 'Friday'}</span>
<span class="sd"> Add node attributes using add_node(), add_nodes_from() or G.nodes</span>
<span class="sd"> >>> G.add_node(1, time="5pm")</span>
<span class="sd"> >>> G.add_nodes_from([3], time="2pm")</span>
<span class="sd"> >>> G.nodes[1]</span>
<span class="sd"> {'time': '5pm'}</span>
<span class="sd"> >>> G.nodes[1]["room"] = 714</span>
<span class="sd"> >>> del G.nodes[1]["room"] # remove attribute</span>
<span class="sd"> >>> list(G.nodes(data=True))</span>
<span class="sd"> [(1, {'time': '5pm'}), (3, {'time': '2pm'})]</span>
<span class="sd"> Add edge attributes using add_edge(), add_edges_from(), subscript</span>
<span class="sd"> notation, or G.edges.</span>
<span class="sd"> >>> key = G.add_edge(1, 2, weight=4.7)</span>
<span class="sd"> >>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")</span>
<span class="sd"> >>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])</span>
<span class="sd"> >>> G[1][2][0]["weight"] = 4.7</span>
<span class="sd"> >>> G.edges[1, 2, 0]["weight"] = 4</span>
<span class="sd"> Warning: we protect the graph data structure by making `G.edges[1,</span>
<span class="sd"> 2, 0]` a read-only dict-like structure. However, you can assign to</span>
<span class="sd"> attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets</span>
<span class="sd"> to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`.</span>
<span class="sd"> **Shortcuts:**</span>
<span class="sd"> Many common graph features allow python syntax to speed reporting.</span>
<span class="sd"> >>> 1 in G # check if node in graph</span>
<span class="sd"> True</span>
<span class="sd"> >>> [n for n in G if n < 3] # iterate through nodes</span>
<span class="sd"> [1, 2]</span>
<span class="sd"> >>> len(G) # number of nodes in graph</span>
<span class="sd"> 5</span>
<span class="sd"> >>> G[1] # adjacency dict-like view mapping neighbor -> edge key -> edge attributes</span>
<span class="sd"> AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})</span>
<span class="sd"> Often the best way to traverse all edges of a graph is via the neighbors.</span>
<span class="sd"> The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`.</span>
<span class="sd"> >>> for n, nbrsdict in G.adjacency():</span>
<span class="sd"> ... for nbr, keydict in nbrsdict.items():</span>
<span class="sd"> ... for key, eattr in keydict.items():</span>
<span class="sd"> ... if "weight" in eattr:</span>
<span class="sd"> ... # Do something useful with the edges</span>
<span class="sd"> ... pass</span>
<span class="sd"> But the edges() method is often more convenient:</span>
<span class="sd"> >>> for u, v, keys, weight in G.edges(data="weight", keys=True):</span>
<span class="sd"> ... if weight is not None:</span>
<span class="sd"> ... # Do something useful with the edges</span>
<span class="sd"> ... pass</span>
<span class="sd"> **Reporting:**</span>
<span class="sd"> Simple graph information is obtained using methods and object-attributes.</span>
<span class="sd"> Reporting usually provides views instead of containers to reduce memory</span>
<span class="sd"> usage. The views update as the graph is updated similarly to dict-views.</span>
<span class="sd"> The objects `nodes`, `edges` and `adj` provide access to data attributes</span>
<span class="sd"> via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration</span>
<span class="sd"> (e.g. `nodes.items()`, `nodes.data('color')`,</span>
<span class="sd"> `nodes.data('color', default='blue')` and similarly for `edges`)</span>
<span class="sd"> Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.</span>
<span class="sd"> For details on these and other miscellaneous methods, see below.</span>
<span class="sd"> **Subclasses (Advanced):**</span>
<span class="sd"> The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.</span>
<span class="sd"> The outer dict (node_dict) holds adjacency information keyed by node.</span>
<span class="sd"> The next dict (adjlist_dict) represents the adjacency information</span>
<span class="sd"> and holds edge_key dicts keyed by neighbor. The edge_key dict holds</span>
<span class="sd"> each edge_attr dict keyed by edge key. The inner dict</span>
<span class="sd"> (edge_attr_dict) represents the edge data and holds edge attribute</span>
<span class="sd"> values keyed by attribute names.</span>
<span class="sd"> Each of these four dicts in the dict-of-dict-of-dict-of-dict</span>
<span class="sd"> structure can be replaced by a user defined dict-like object.</span>
<span class="sd"> In general, the dict-like features should be maintained but</span>
<span class="sd"> extra features can be added. To replace one of the dicts create</span>
<span class="sd"> a new graph class by changing the class(!) variable holding the</span>
<span class="sd"> factory for that dict-like structure. The variable names are</span>
<span class="sd"> node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,</span>
<span class="sd"> adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory</span>
<span class="sd"> and graph_attr_dict_factory.</span>
<span class="sd"> node_dict_factory : function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the dict containing node</span>
<span class="sd"> attributes, keyed by node id.</span>
<span class="sd"> It should require no arguments and return a dict-like object</span>
<span class="sd"> node_attr_dict_factory: function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the node attribute</span>
<span class="sd"> dict which holds attribute values keyed by attribute name.</span>
<span class="sd"> It should require no arguments and return a dict-like object</span>
<span class="sd"> adjlist_outer_dict_factory : function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the outer-most dict</span>
<span class="sd"> in the data structure that holds adjacency info keyed by node.</span>
<span class="sd"> It should require no arguments and return a dict-like object.</span>
<span class="sd"> adjlist_inner_dict_factory : function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the adjacency list</span>
<span class="sd"> dict which holds multiedge key dicts keyed by neighbor.</span>
<span class="sd"> It should require no arguments and return a dict-like object.</span>
<span class="sd"> edge_key_dict_factory : function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the edge key dict</span>
<span class="sd"> which holds edge data keyed by edge key.</span>
<span class="sd"> It should require no arguments and return a dict-like object.</span>
<span class="sd"> edge_attr_dict_factory : function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the edge attribute</span>
<span class="sd"> dict which holds attribute values keyed by attribute name.</span>
<span class="sd"> It should require no arguments and return a dict-like object.</span>
<span class="sd"> graph_attr_dict_factory : function, (default: dict)</span>
<span class="sd"> Factory function to be used to create the graph attribute</span>
<span class="sd"> dict which holds attribute values keyed by attribute name.</span>
<span class="sd"> It should require no arguments and return a dict-like object.</span>
<span class="sd"> Typically, if your extension doesn't impact the data structure all</span>
<span class="sd"> methods will inherited without issue except: `to_directed/to_undirected`.</span>
<span class="sd"> By default these methods create a DiGraph/Graph class and you probably</span>
<span class="sd"> want them to create your extension of a DiGraph/Graph. To facilitate</span>
<span class="sd"> this we define two class variables that you can set in your subclass.</span>
<span class="sd"> to_directed_class : callable, (default: DiGraph or MultiDiGraph)</span>
<span class="sd"> Class to create a new graph structure in the `to_directed` method.</span>
<span class="sd"> If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.</span>
<span class="sd"> to_undirected_class : callable, (default: Graph or MultiGraph)</span>
<span class="sd"> Class to create a new graph structure in the `to_undirected` method.</span>
<span class="sd"> If `None`, a NetworkX class (Graph or MultiGraph) is used.</span>
<span class="sd"> **Subclassing Example**</span>
<span class="sd"> Create a low memory graph class that effectively disallows edge</span>
<span class="sd"> attributes by using a single attribute dict for all edges.</span>
<span class="sd"> This reduces the memory used, but you lose edge attributes.</span>
<span class="sd"> >>> class ThinGraph(nx.Graph):</span>
<span class="sd"> ... all_edge_dict = {"weight": 1}</span>
<span class="sd"> ...</span>
<span class="sd"> ... def single_edge_dict(self):</span>
<span class="sd"> ... return self.all_edge_dict</span>
<span class="sd"> ...</span>
<span class="sd"> ... edge_attr_dict_factory = single_edge_dict</span>
<span class="sd"> >>> G = ThinGraph()</span>
<span class="sd"> >>> G.add_edge(2, 1)</span>
<span class="sd"> >>> G[2][1]</span>
<span class="sd"> {'weight': 1}</span>
<span class="sd"> >>> G.add_edge(2, 2)</span>
<span class="sd"> >>> G[2][1] is G[2][2]</span>
<span class="sd"> True</span>
<span class="sd"> """</span>
<span class="c1"># node_dict_factory = dict # already assigned in Graph</span>
<span class="c1"># adjlist_outer_dict_factory = dict</span>
<span class="c1"># adjlist_inner_dict_factory = dict</span>
<span class="n">edge_key_dict_factory</span> <span class="o">=</span> <span class="nb">dict</span>
<span class="c1"># edge_attr_dict_factory = dict</span>
<span class="k">def</span> <span class="nf">to_directed_class</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns the class to use for empty directed copies.</span>
<span class="sd"> If you subclass the base classes, use this to designate</span>
<span class="sd"> what directed class to use for `to_directed()` copies.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">MultiDiGraph</span>
<span class="k">def</span> <span class="nf">to_undirected_class</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns the class to use for empty undirected copies.</span>
<span class="sd"> If you subclass the base classes, use this to designate</span>
<span class="sd"> what directed class to use for `to_directed()` copies.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">MultiGraph</span>
<div class="viewcode-block" id="MultiGraph.__init__"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.__init__.html#networkx.MultiGraph.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">multigraph_input</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Initialize a graph with edges, name, or graph attributes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> incoming_graph_data : input graph</span>
<span class="sd"> Data to initialize graph. If incoming_graph_data=None (default)</span>
<span class="sd"> an empty graph is created. The data can be an edge list, or any</span>
<span class="sd"> NetworkX graph object. If the corresponding optional Python</span>
<span class="sd"> packages are installed the data can also be a 2D NumPy array, a</span>
<span class="sd"> SciPy sparse array, or a PyGraphviz graph.</span>
<span class="sd"> multigraph_input : bool or None (default None)</span>
<span class="sd"> Note: Only used when `incoming_graph_data` is a dict.</span>
<span class="sd"> If True, `incoming_graph_data` is assumed to be a</span>
<span class="sd"> dict-of-dict-of-dict-of-dict structure keyed by</span>
<span class="sd"> node to neighbor to edge keys to edge data for multi-edges.</span>
<span class="sd"> A NetworkXError is raised if this is not the case.</span>
<span class="sd"> If False, :func:`to_networkx_graph` is used to try to determine</span>
<span class="sd"> the dict's graph data structure as either a dict-of-dict-of-dict</span>
<span class="sd"> keyed by node to neighbor to edge data, or a dict-of-iterable</span>
<span class="sd"> keyed by node to neighbors.</span>
<span class="sd"> If None, the treatment for True is tried, but if it fails,</span>
<span class="sd"> the treatment for False is tried.</span>
<span class="sd"> attr : keyword arguments, optional (default= no attributes)</span>
<span class="sd"> Attributes to add to graph as key=value pairs.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> convert</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> G = nx.MultiGraph(name="my graph")</span>
<span class="sd"> >>> e = [(1, 2), (1, 2), (2, 3), (3, 4)] # list of edges</span>
<span class="sd"> >>> G = nx.MultiGraph(e)</span>
<span class="sd"> Arbitrary graph attribute pairs (key=value) may be assigned</span>
<span class="sd"> >>> G = nx.MultiGraph(e, day="Friday")</span>
<span class="sd"> >>> G.graph</span>
<span class="sd"> {'day': 'Friday'}</span>
<span class="sd"> """</span>
<span class="c1"># multigraph_input can be None/True/False. So check "is not False"</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">incoming_graph_data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">and</span> <span class="n">multigraph_input</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
<span class="n">Graph</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">convert</span><span class="o">.</span><span class="n">from_dict_of_dicts</span><span class="p">(</span>
<span class="n">incoming_graph_data</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="n">multigraph_input</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">attr</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
<span class="k">if</span> <span class="n">multigraph_input</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"converting multigraph_input raised:</span><span class="se">\n</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">err</span><span class="p">)</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">err</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="n">Graph</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">Graph</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming_graph_data</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">)</span></div>
<span class="nd">@cached_property</span>
<span class="k">def</span> <span class="nf">adj</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Graph adjacency object holding the neighbors of each node.</span>
<span class="sd"> This object is a read-only dict-like structure with node keys</span>
<span class="sd"> and neighbor-dict values. The neighbor-dict is keyed by neighbor</span>
<span class="sd"> to the edgekey-data-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets</span>
<span class="sd"> the color of the edge `(3, 2, 0)` to `"blue"`.</span>
<span class="sd"> Iterating over G.adj behaves like a dict. Useful idioms include</span>
<span class="sd"> `for nbr, edgesdict in G.adj[n].items():`.</span>
<span class="sd"> The neighbor information is also provided by subscripting the graph.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> e = [(1, 2), (1, 2), (1, 3), (3, 4)] # list of edges</span>
<span class="sd"> >>> G = nx.MultiGraph(e)</span>
<span class="sd"> >>> G.edges[1, 2, 0]["weight"] = 3</span>
<span class="sd"> >>> result = set()</span>
<span class="sd"> >>> for edgekey, data in G[1][2].items():</span>
<span class="sd"> ... result.add(data.get('weight', 1))</span>
<span class="sd"> >>> result</span>
<span class="sd"> {1, 3}</span>
<span class="sd"> For directed graphs, `G.adj` holds outgoing (successor) info.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">MultiAdjacencyView</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">)</span>
<div class="viewcode-block" id="MultiGraph.new_edge_key"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.new_edge_key.html#networkx.MultiGraph.new_edge_key">[docs]</a> <span class="k">def</span> <span class="nf">new_edge_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns an unused key for edges between nodes `u` and `v`.</span>
<span class="sd"> The nodes `u` and `v` do not need to be already in the graph.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> In the standard MultiGraph class the new key is the number of existing</span>
<span class="sd"> edges between `u` and `v` (increased if necessary to ensure unused).</span>
<span class="sd"> The first edge will have key 0, then 1, etc. If an edge is removed</span>
<span class="sd"> further new_edge_keys may not be in this order.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> u, v : nodes</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> key : int</span>
<span class="sd"> """</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">keydict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="n">key</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">keydict</span><span class="p">)</span>
<span class="k">while</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keydict</span><span class="p">:</span>
<span class="n">key</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">key</span></div>
<div class="viewcode-block" id="MultiGraph.add_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.add_edge.html#networkx.MultiGraph.add_edge">[docs]</a> <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u_for_edge</span><span class="p">,</span> <span class="n">v_for_edge</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Add an edge between u and v.</span>
<span class="sd"> The nodes u and v will be automatically added if they are</span>
<span class="sd"> not already in the graph.</span>
<span class="sd"> Edge attributes can be specified with keywords or by directly</span>
<span class="sd"> accessing the edge's attribute dictionary. See examples below.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> u_for_edge, v_for_edge : nodes</span>
<span class="sd"> Nodes can be, for example, strings or numbers.</span>
<span class="sd"> Nodes must be hashable (and not None) Python objects.</span>
<span class="sd"> key : hashable identifier, optional (default=lowest unused integer)</span>
<span class="sd"> Used to distinguish multiedges between a pair of nodes.</span>
<span class="sd"> attr : keyword arguments, optional</span>
<span class="sd"> Edge data (or labels or objects) can be assigned using</span>
<span class="sd"> keyword arguments.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> The edge key assigned to the edge.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> add_edges_from : add a collection of edges</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> To replace/update edge data, use the optional key argument</span>
<span class="sd"> to identify a unique edge. Otherwise a new edge will be created.</span>
<span class="sd"> NetworkX algorithms designed for weighted graphs cannot use</span>
<span class="sd"> multigraphs directly because it is not clear how to handle</span>
<span class="sd"> multiedge weights. Convert to Graph using edge attribute</span>
<span class="sd"> 'weight' to enable weighted graph algorithms.</span>
<span class="sd"> Default keys are generated using the method `new_edge_key()`.</span>
<span class="sd"> This method can be overridden by subclassing the base class and</span>
<span class="sd"> providing a custom `new_edge_key()` method.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> The following each add an additional edge e=(1, 2) to graph G:</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> e = (1, 2)</span>
<span class="sd"> >>> ekey = G.add_edge(1, 2) # explicit two-node form</span>
<span class="sd"> >>> G.add_edge(*e) # single edge as tuple of two nodes</span>
<span class="sd"> 1</span>
<span class="sd"> >>> G.add_edges_from([(1, 2)]) # add edges from iterable container</span>
<span class="sd"> [2]</span>
<span class="sd"> Associate data to edges using keywords:</span>
<span class="sd"> >>> ekey = G.add_edge(1, 2, weight=3)</span>
<span class="sd"> >>> ekey = G.add_edge(1, 2, key=0, weight=4) # update data for key=0</span>
<span class="sd"> >>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)</span>
<span class="sd"> For non-string attribute keys, use subscript notation.</span>
<span class="sd"> >>> ekey = G.add_edge(1, 2)</span>
<span class="sd"> >>> G[1][2][0].update({0: 5})</span>
<span class="sd"> >>> G.edges[1, 2, 0].update({0: 5})</span>
<span class="sd"> """</span>
<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">u_for_edge</span><span class="p">,</span> <span class="n">v_for_edge</span>
<span class="c1"># add nodes</span>
<span class="k">if</span> <span class="n">u</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">:</span>
<span class="k">if</span> <span class="n">u</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"None cannot be a node"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">adjlist_inner_dict_factory</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">[</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_attr_dict_factory</span><span class="p">()</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">:</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"None cannot be a node"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">adjlist_inner_dict_factory</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_attr_dict_factory</span><span class="p">()</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_edge_key</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">]:</span>
<span class="n">keydict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="n">datadict</span> <span class="o">=</span> <span class="n">keydict</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">edge_attr_dict_factory</span><span class="p">())</span>
<span class="n">datadict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">attr</span><span class="p">)</span>
<span class="n">keydict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">datadict</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># selfloops work this way without special treatment</span>
<span class="n">datadict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">edge_attr_dict_factory</span><span class="p">()</span>
<span class="n">datadict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">attr</span><span class="p">)</span>
<span class="n">keydict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">edge_key_dict_factory</span><span class="p">()</span>
<span class="n">keydict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">datadict</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="n">keydict</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span> <span class="o">=</span> <span class="n">keydict</span>
<span class="k">return</span> <span class="n">key</span></div>
<div class="viewcode-block" id="MultiGraph.add_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.add_edges_from.html#networkx.MultiGraph.add_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">add_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch_to_add</span><span class="p">,</span> <span class="o">**</span><span class="n">attr</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Add all the edges in ebunch_to_add.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ebunch_to_add : container of edges</span>
<span class="sd"> Each edge given in the container will be added to the</span>
<span class="sd"> graph. The edges can be:</span>
<span class="sd"> - 2-tuples (u, v) or</span>
<span class="sd"> - 3-tuples (u, v, d) for an edge data dict d, or</span>
<span class="sd"> - 3-tuples (u, v, k) for not iterable key k, or</span>
<span class="sd"> - 4-tuples (u, v, k, d) for an edge with data and key k</span>
<span class="sd"> attr : keyword arguments, optional</span>
<span class="sd"> Edge data (or labels or objects) can be assigned using</span>
<span class="sd"> keyword arguments.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> A list of edge keys assigned to the edges in `ebunch`.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> add_edge : add a single edge</span>
<span class="sd"> add_weighted_edges_from : convenient way to add weighted edges</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Adding the same edge twice has no effect but any edge data</span>
<span class="sd"> will be updated when each duplicate edge is added.</span>
<span class="sd"> Edge attributes specified in an ebunch take precedence over</span>
<span class="sd"> attributes specified via keyword arguments.</span>
<span class="sd"> Default keys are generated using the method ``new_edge_key()``.</span>
<span class="sd"> This method can be overridden by subclassing the base class and</span>
<span class="sd"> providing a custom ``new_edge_key()`` method.</span>
<span class="sd"> When adding edges from an iterator over the graph you are changing,</span>
<span class="sd"> a `RuntimeError` can be raised with message:</span>
<span class="sd"> `RuntimeError: dictionary changed size during iteration`. This</span>
<span class="sd"> happens when the graph's underlying dictionary is modified during</span>
<span class="sd"> iteration. To avoid this error, evaluate the iterator into a separate</span>
<span class="sd"> object, e.g. by using `list(iterator_of_edges)`, and pass this</span>
<span class="sd"> object to `G.add_edges_from`.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc</span>
<span class="sd"> >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples</span>
<span class="sd"> >>> e = zip(range(0, 3), range(1, 4))</span>
<span class="sd"> >>> G.add_edges_from(e) # Add the path graph 0-1-2-3</span>
<span class="sd"> Associate data to edges</span>
<span class="sd"> >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)</span>
<span class="sd"> >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")</span>
<span class="sd"> Evaluate an iterator over a graph if using it to modify the same graph</span>
<span class="sd"> >>> G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])</span>
<span class="sd"> >>> # Grow graph by one new node, adding edges to all existing nodes.</span>
<span class="sd"> >>> # wrong way - will raise RuntimeError</span>
<span class="sd"> >>> # G.add_edges_from(((5, n) for n in G.nodes))</span>
<span class="sd"> >>> # right way - note that there will be no self-edge for node 5</span>
<span class="sd"> >>> assigned_keys = G.add_edges_from(list((5, n) for n in G.nodes))</span>
<span class="sd"> """</span>
<span class="n">keylist</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">ebunch_to_add</span><span class="p">:</span>
<span class="n">ne</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ne</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">dd</span> <span class="o">=</span> <span class="n">e</span>
<span class="k">elif</span> <span class="n">ne</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">dd</span> <span class="o">=</span> <span class="n">e</span>
<span class="n">key</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">elif</span> <span class="n">ne</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">e</span>
<span class="n">dd</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">key</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Edge tuple </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2"> must be a 2-tuple, 3-tuple or 4-tuple."</span>
<span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
<span class="n">ddd</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">ddd</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">attr</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">ddd</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">dd</span><span class="p">)</span>
<span class="k">except</span> <span class="p">(</span><span class="ne">TypeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span>
<span class="k">if</span> <span class="n">ne</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">raise</span>
<span class="n">key</span> <span class="o">=</span> <span class="n">dd</span> <span class="c1"># ne == 3 with 3rd value not dict, must be a key</span>
<span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
<span class="bp">self</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">][</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">ddd</span><span class="p">)</span>
<span class="n">keylist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="k">return</span> <span class="n">keylist</span></div>
<div class="viewcode-block" id="MultiGraph.remove_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.remove_edge.html#networkx.MultiGraph.remove_edge">[docs]</a> <span class="k">def</span> <span class="nf">remove_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Remove an edge between u and v.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> u, v : nodes</span>
<span class="sd"> Remove an edge between nodes u and v.</span>
<span class="sd"> key : hashable identifier, optional (default=None)</span>
<span class="sd"> Used to distinguish multiple edges between a pair of nodes.</span>
<span class="sd"> If None, remove a single edge between u and v. If there are</span>
<span class="sd"> multiple edges, removes the last edge added in terms of</span>
<span class="sd"> insertion order.</span>
<span class="sd"> Raises</span>
<span class="sd"> ------</span>
<span class="sd"> NetworkXError</span>
<span class="sd"> If there is not an edge between u and v, or</span>
<span class="sd"> if there is no edge with the specified key.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> remove_edges_from : remove a collection of edges</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> nx.add_path(G, [0, 1, 2, 3])</span>
<span class="sd"> >>> G.remove_edge(0, 1)</span>
<span class="sd"> >>> e = (1, 2)</span>
<span class="sd"> >>> G.remove_edge(*e) # unpacks e from an edge tuple</span>
<span class="sd"> For multiple edges</span>
<span class="sd"> >>> G = nx.MultiGraph() # or MultiDiGraph, etc</span>
<span class="sd"> >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned</span>
<span class="sd"> [0, 1, 2]</span>
<span class="sd"> When ``key=None`` (the default), edges are removed in the opposite</span>
<span class="sd"> order that they were added:</span>
<span class="sd"> >>> G.remove_edge(1, 2)</span>
<span class="sd"> >>> G.edges(keys=True)</span>
<span class="sd"> MultiEdgeView([(1, 2, 0), (1, 2, 1)])</span>
<span class="sd"> >>> G.remove_edge(2, 1) # edges are not directed</span>
<span class="sd"> >>> G.edges(keys=True)</span>
<span class="sd"> MultiEdgeView([(1, 2, 0)])</span>
<span class="sd"> For edges with keys</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> G.add_edge(1, 2, key="first")</span>
<span class="sd"> 'first'</span>
<span class="sd"> >>> G.add_edge(1, 2, key="second")</span>
<span class="sd"> 'second'</span>
<span class="sd"> >>> G.remove_edge(1, 2, key="first")</span>
<span class="sd"> >>> G.edges(keys=True)</span>
<span class="sd"> MultiEdgeView([(1, 2, 'second')])</span>
<span class="sd"> """</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The edge </span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2"> is not in the graph."</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
<span class="c1"># remove the edge with specified data</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">d</span><span class="o">.</span><span class="n">popitem</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">del</span> <span class="n">d</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
<span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"The edge </span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2"> with key </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2"> is not in the graph."</span>
<span class="k">raise</span> <span class="n">NetworkXError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># remove the key entries if last edge</span>
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="k">if</span> <span class="n">u</span> <span class="o">!=</span> <span class="n">v</span><span class="p">:</span> <span class="c1"># check for selfloop</span>
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="n">u</span><span class="p">]</span></div>
<div class="viewcode-block" id="MultiGraph.remove_edges_from"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.remove_edges_from.html#networkx.MultiGraph.remove_edges_from">[docs]</a> <span class="k">def</span> <span class="nf">remove_edges_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ebunch</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Remove all edges specified in ebunch.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> ebunch: list or container of edge tuples</span>
<span class="sd"> Each edge given in the list or container will be removed</span>
<span class="sd"> from the graph. The edges can be:</span>
<span class="sd"> - 2-tuples (u, v) A single edge between u and v is removed.</span>
<span class="sd"> - 3-tuples (u, v, key) The edge identified by key is removed.</span>
<span class="sd"> - 4-tuples (u, v, key, data) where data is ignored.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> remove_edge : remove a single edge</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Will fail silently if an edge in ebunch is not in the graph.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc</span>
<span class="sd"> >>> ebunch = [(1, 2), (2, 3)]</span>
<span class="sd"> >>> G.remove_edges_from(ebunch)</span>
<span class="sd"> Removing multiple copies of edges</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)])</span>
<span class="sd"> >>> G.remove_edges_from([(1, 2), (2, 1)]) # edges aren't directed</span>
<span class="sd"> >>> list(G.edges())</span>
<span class="sd"> [(1, 2)]</span>
<span class="sd"> >>> G.remove_edges_from([(1, 2), (1, 2)]) # silently ignore extra copy</span>
<span class="sd"> >>> list(G.edges) # now empty graph</span>
<span class="sd"> []</span>
<span class="sd"> When the edge is a 2-tuple ``(u, v)`` but there are multiple edges between</span>
<span class="sd"> u and v in the graph, the most recent edge (in terms of insertion</span>
<span class="sd"> order) is removed.</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> for key in ("x", "y", "a"):</span>
<span class="sd"> ... k = G.add_edge(0, 1, key=key)</span>
<span class="sd"> >>> G.edges(keys=True)</span>
<span class="sd"> MultiEdgeView([(0, 1, 'x'), (0, 1, 'y'), (0, 1, 'a')])</span>
<span class="sd"> >>> G.remove_edges_from([(0, 1)])</span>
<span class="sd"> >>> G.edges(keys=True)</span>
<span class="sd"> MultiEdgeView([(0, 1, 'x'), (0, 1, 'y')])</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">ebunch</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">remove_edge</span><span class="p">(</span><span class="o">*</span><span class="n">e</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
<span class="k">except</span> <span class="n">NetworkXError</span><span class="p">:</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="MultiGraph.has_edge"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.has_edge.html#networkx.MultiGraph.has_edge">[docs]</a> <span class="k">def</span> <span class="nf">has_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns True if the graph has an edge between nodes u and v.</span>
<span class="sd"> This is the same as `v in G[u] or key in G[u][v]`</span>
<span class="sd"> without KeyError exceptions.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> u, v : nodes</span>
<span class="sd"> Nodes can be, for example, strings or numbers.</span>
<span class="sd"> key : hashable identifier, optional (default=None)</span>
<span class="sd"> If specified return True only if the edge with</span>
<span class="sd"> key is found.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> edge_ind : bool</span>
<span class="sd"> True if edge is in the graph, False otherwise.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> Can be called either using two nodes u, v, an edge tuple (u, v),</span>
<span class="sd"> or an edge tuple (u, v, key).</span>
<span class="sd"> >>> G = nx.MultiGraph() # or MultiDiGraph</span>
<span class="sd"> >>> nx.add_path(G, [0, 1, 2, 3])</span>
<span class="sd"> >>> G.has_edge(0, 1) # using two nodes</span>
<span class="sd"> True</span>
<span class="sd"> >>> e = (0, 1)</span>
<span class="sd"> >>> G.has_edge(*e) # e is a 2-tuple (u, v)</span>
<span class="sd"> True</span>
<span class="sd"> >>> G.add_edge(0, 1, key="a")</span>
<span class="sd"> 'a'</span>
<span class="sd"> >>> G.has_edge(0, 1, key="a") # specify key</span>
<span class="sd"> True</span>
<span class="sd"> >>> G.has_edge(1, 0, key="a") # edges aren't directed</span>
<span class="sd"> True</span>
<span class="sd"> >>> e = (0, 1, "a")</span>
<span class="sd"> >>> G.has_edge(*e) # e is a 3-tuple (u, v, 'a')</span>
<span class="sd"> True</span>
<span class="sd"> The following syntax are equivalent:</span>
<span class="sd"> >>> G.has_edge(0, 1)</span>
<span class="sd"> True</span>
<span class="sd"> >>> 1 in G[0] # though this gives :exc:`KeyError` if 0 not in G</span>
<span class="sd"> True</span>
<span class="sd"> >>> 0 in G[1] # other order; also gives :exc:`KeyError` if 0 not in G</span>
<span class="sd"> True</span>
<span class="sd"> """</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">False</span></div>
<span class="nd">@cached_property</span>
<span class="k">def</span> <span class="nf">edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns an iterator over the edges.</span>
<span class="sd"> edges(self, nbunch=None, data=False, keys=False, default=None)</span>
<span class="sd"> The MultiEdgeView provides set-like operations on the edge-tuples</span>
<span class="sd"> as well as edge attribute lookup. When called, it also provides</span>
<span class="sd"> an EdgeDataView object which allows control of access to edge</span>
<span class="sd"> attributes (but does not provide set-like operations).</span>
<span class="sd"> Hence, ``G.edges[u, v, k]['color']`` provides the value of the color</span>
<span class="sd"> attribute for the edge from ``u`` to ``v`` with key ``k`` while</span>
<span class="sd"> ``for (u, v, k, c) in G.edges(data='color', keys=True, default="red"):``</span>
<span class="sd"> iterates through all the edges yielding the color attribute with</span>
<span class="sd"> default `'red'` if no color attribute exists.</span>
<span class="sd"> Edges are returned as tuples with optional data and keys</span>
<span class="sd"> in the order (node, neighbor, key, data). If ``keys=True`` is not</span>
<span class="sd"> provided, the tuples will just be (node, neighbor, data), but</span>
<span class="sd"> multiple tuples with the same node and neighbor will be generated</span>
<span class="sd"> when multiple edges exist between two nodes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> nbunch : single node, container, or all nodes (default= all nodes)</span>
<span class="sd"> The view will only report edges from these nodes.</span>
<span class="sd"> data : string or bool, optional (default=False)</span>
<span class="sd"> The edge attribute returned in 3-tuple (u, v, ddict[data]).</span>
<span class="sd"> If True, return edge attribute dict in 3-tuple (u, v, ddict).</span>
<span class="sd"> If False, return 2-tuple (u, v).</span>
<span class="sd"> keys : bool, optional (default=False)</span>
<span class="sd"> If True, return edge keys with each edge, creating (u, v, k)</span>
<span class="sd"> tuples or (u, v, k, d) tuples if data is also requested.</span>
<span class="sd"> default : value, optional (default=None)</span>
<span class="sd"> Value used for edges that don't have the requested attribute.</span>
<span class="sd"> Only relevant if data is not True or False.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> edges : MultiEdgeView</span>
<span class="sd"> A view of edge attributes, usually it iterates over (u, v)</span>
<span class="sd"> (u, v, k) or (u, v, k, d) tuples of edges, but can also be</span>
<span class="sd"> used for attribute lookup as ``edges[u, v, k]['foo']``.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Nodes in nbunch that are not in the graph will be (quietly) ignored.</span>
<span class="sd"> For directed graphs this returns the out-edges.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> nx.add_path(G, [0, 1, 2])</span>
<span class="sd"> >>> key = G.add_edge(2, 3, weight=5)</span>
<span class="sd"> >>> key2 = G.add_edge(2, 1, weight=2) # multi-edge</span>
<span class="sd"> >>> [e for e in G.edges()]</span>
<span class="sd"> [(0, 1), (1, 2), (1, 2), (2, 3)]</span>
<span class="sd"> >>> G.edges.data() # default data is {} (empty dict)</span>
<span class="sd"> MultiEdgeDataView([(0, 1, {}), (1, 2, {}), (1, 2, {'weight': 2}), (2, 3, {'weight': 5})])</span>
<span class="sd"> >>> G.edges.data("weight", default=1)</span>
<span class="sd"> MultiEdgeDataView([(0, 1, 1), (1, 2, 1), (1, 2, 2), (2, 3, 5)])</span>
<span class="sd"> >>> G.edges(keys=True) # default keys are integers</span>
<span class="sd"> MultiEdgeView([(0, 1, 0), (1, 2, 0), (1, 2, 1), (2, 3, 0)])</span>
<span class="sd"> >>> G.edges.data(keys=True)</span>
<span class="sd"> MultiEdgeDataView([(0, 1, 0, {}), (1, 2, 0, {}), (1, 2, 1, {'weight': 2}), (2, 3, 0, {'weight': 5})])</span>
<span class="sd"> >>> G.edges.data("weight", default=1, keys=True)</span>
<span class="sd"> MultiEdgeDataView([(0, 1, 0, 1), (1, 2, 0, 1), (1, 2, 1, 2), (2, 3, 0, 5)])</span>
<span class="sd"> >>> G.edges([0, 3]) # Note ordering of tuples from listed sources</span>
<span class="sd"> MultiEdgeDataView([(0, 1), (3, 2)])</span>
<span class="sd"> >>> G.edges([0, 3, 2, 1]) # Note ordering of tuples</span>
<span class="sd"> MultiEdgeDataView([(0, 1), (3, 2), (2, 1), (2, 1)])</span>
<span class="sd"> >>> G.edges(0)</span>
<span class="sd"> MultiEdgeDataView([(0, 1)])</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">MultiEdgeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<div class="viewcode-block" id="MultiGraph.get_edge_data"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.get_edge_data.html#networkx.MultiGraph.get_edge_data">[docs]</a> <span class="k">def</span> <span class="nf">get_edge_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns the attribute dictionary associated with edge (u, v,</span>
<span class="sd"> key).</span>
<span class="sd"> If a key is not provided, returns a dictionary mapping edge keys</span>
<span class="sd"> to attribute dictionaries for each edge between u and v.</span>
<span class="sd"> This is identical to `G[u][v][key]` except the default is returned</span>
<span class="sd"> instead of an exception is the edge doesn't exist.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> u, v : nodes</span>
<span class="sd"> default : any Python object (default=None)</span>
<span class="sd"> Value to return if the specific edge (u, v, key) is not</span>
<span class="sd"> found, OR if there are no edges between u and v and no key</span>
<span class="sd"> is specified.</span>
<span class="sd"> key : hashable identifier, optional (default=None)</span>
<span class="sd"> Return data only for the edge with specified key, as an</span>
<span class="sd"> attribute dictionary (rather than a dictionary mapping keys</span>
<span class="sd"> to attribute dictionaries).</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> edge_dict : dictionary</span>
<span class="sd"> The edge attribute dictionary, OR a dictionary mapping edge</span>
<span class="sd"> keys to attribute dictionaries for each of those edges if no</span>
<span class="sd"> specific key is provided (even if there's only one edge</span>
<span class="sd"> between u and v).</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.MultiGraph() # or MultiDiGraph</span>
<span class="sd"> >>> key = G.add_edge(0, 1, key="a", weight=7)</span>
<span class="sd"> >>> G[0][1]["a"] # key='a'</span>
<span class="sd"> {'weight': 7}</span>
<span class="sd"> >>> G.edges[0, 1, "a"] # key='a'</span>
<span class="sd"> {'weight': 7}</span>
<span class="sd"> Warning: we protect the graph data structure by making</span>
<span class="sd"> `G.edges` and `G[1][2]` read-only dict-like structures.</span>
<span class="sd"> However, you can assign values to attributes in e.g.</span>
<span class="sd"> `G.edges[1, 2, 'a']` or `G[1][2]['a']` using an additional</span>
<span class="sd"> bracket as shown next. You need to specify all edge info</span>
<span class="sd"> to assign to the edge data associated with an edge.</span>
<span class="sd"> >>> G[0][1]["a"]["weight"] = 10</span>
<span class="sd"> >>> G.edges[0, 1, "a"]["weight"] = 10</span>
<span class="sd"> >>> G[0][1]["a"]["weight"]</span>
<span class="sd"> 10</span>
<span class="sd"> >>> G.edges[1, 0, "a"]["weight"]</span>
<span class="sd"> 10</span>
<span class="sd"> >>> G = nx.MultiGraph() # or MultiDiGraph</span>
<span class="sd"> >>> nx.add_path(G, [0, 1, 2, 3])</span>
<span class="sd"> >>> G.edges[0, 1, 0]["weight"] = 5</span>
<span class="sd"> >>> G.get_edge_data(0, 1)</span>
<span class="sd"> {0: {'weight': 5}}</span>
<span class="sd"> >>> e = (0, 1)</span>
<span class="sd"> >>> G.get_edge_data(*e) # tuple form</span>
<span class="sd"> {0: {'weight': 5}}</span>
<span class="sd"> >>> G.get_edge_data(3, 0) # edge not in graph, returns None</span>
<span class="sd"> >>> G.get_edge_data(3, 0, default=0) # edge not in graph, return default</span>
<span class="sd"> 0</span>
<span class="sd"> >>> G.get_edge_data(1, 0, 0) # specific key gives back</span>
<span class="sd"> {'weight': 5}</span>
<span class="sd"> """</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">][</span><span class="n">key</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
<span class="k">return</span> <span class="n">default</span></div>
<span class="nd">@cached_property</span>
<span class="k">def</span> <span class="nf">degree</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""A DegreeView for the Graph as G.degree or G.degree().</span>
<span class="sd"> The node degree is the number of edges adjacent to the node.</span>
<span class="sd"> The weighted node degree is the sum of the edge weights for</span>
<span class="sd"> edges incident to that node.</span>
<span class="sd"> This object provides an iterator for (node, degree) as well as</span>
<span class="sd"> lookup for the degree for a single node.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> nbunch : single node, container, or all nodes (default= all nodes)</span>
<span class="sd"> The view will only report edges incident to these nodes.</span>
<span class="sd"> weight : string or None, optional (default=None)</span>
<span class="sd"> The name of an edge attribute that holds the numerical value used</span>
<span class="sd"> as a weight. If None, then each edge has weight 1.</span>
<span class="sd"> The degree is the sum of the edge weights adjacent to the node.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> MultiDegreeView or int</span>
<span class="sd"> If multiple nodes are requested (the default), returns a `MultiDegreeView`</span>
<span class="sd"> mapping nodes to their degree.</span>
<span class="sd"> If a single node is requested, returns the degree of the node as an integer.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc</span>
<span class="sd"> >>> nx.add_path(G, [0, 1, 2, 3])</span>
<span class="sd"> >>> G.degree(0) # node 0 with degree 1</span>
<span class="sd"> 1</span>
<span class="sd"> >>> list(G.degree([0, 1]))</span>
<span class="sd"> [(0, 1), (1, 2)]</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">MultiDegreeView</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">is_multigraph</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns True if graph is a multigraph, False otherwise."""</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="k">def</span> <span class="nf">is_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns True if graph is directed, False otherwise."""</span>
<span class="k">return</span> <span class="kc">False</span>
<div class="viewcode-block" id="MultiGraph.copy"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.copy.html#networkx.MultiGraph.copy">[docs]</a> <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns a copy of the graph.</span>
<span class="sd"> The copy method by default returns an independent shallow copy</span>
<span class="sd"> of the graph and attributes. That is, if an attribute is a</span>
<span class="sd"> container, that container is shared by the original an the copy.</span>
<span class="sd"> Use Python's `copy.deepcopy` for new containers.</span>
<span class="sd"> If `as_view` is True then a view is returned instead of a copy.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> All copies reproduce the graph structure, but data attributes</span>
<span class="sd"> may be handled in different ways. There are four types of copies</span>
<span class="sd"> of a graph that people might want.</span>
<span class="sd"> Deepcopy -- A "deepcopy" copies the graph structure as well as</span>
<span class="sd"> all data attributes and any objects they might contain.</span>
<span class="sd"> The entire graph object is new so that changes in the copy</span>
<span class="sd"> do not affect the original object. (see Python's copy.deepcopy)</span>
<span class="sd"> Data Reference (Shallow) -- For a shallow copy the graph structure</span>
<span class="sd"> is copied but the edge, node and graph attribute dicts are</span>
<span class="sd"> references to those in the original graph. This saves</span>
<span class="sd"> time and memory but could cause confusion if you change an attribute</span>
<span class="sd"> in one graph and it changes the attribute in the other.</span>
<span class="sd"> NetworkX does not provide this level of shallow copy.</span>
<span class="sd"> Independent Shallow -- This copy creates new independent attribute</span>
<span class="sd"> dicts and then does a shallow copy of the attributes. That is, any</span>
<span class="sd"> attributes that are containers are shared between the new graph</span>
<span class="sd"> and the original. This is exactly what `dict.copy()` provides.</span>
<span class="sd"> You can obtain this style copy using:</span>
<span class="sd"> >>> G = nx.path_graph(5)</span>
<span class="sd"> >>> H = G.copy()</span>
<span class="sd"> >>> H = G.copy(as_view=False)</span>
<span class="sd"> >>> H = nx.Graph(G)</span>
<span class="sd"> >>> H = G.__class__(G)</span>
<span class="sd"> Fresh Data -- For fresh data, the graph structure is copied while</span>
<span class="sd"> new empty data attribute dicts are created. The resulting graph</span>
<span class="sd"> is independent of the original and it has no edge, node or graph</span>
<span class="sd"> attributes. Fresh copies are not enabled. Instead use:</span>
<span class="sd"> >>> H = G.__class__()</span>
<span class="sd"> >>> H.add_nodes_from(G)</span>
<span class="sd"> >>> H.add_edges_from(G.edges)</span>
<span class="sd"> View -- Inspired by dict-views, graph-views act like read-only</span>
<span class="sd"> versions of the original graph, providing a copy of the original</span>
<span class="sd"> structure without requiring any memory for copying the information.</span>
<span class="sd"> See the Python copy module for more information on shallow</span>
<span class="sd"> and deep copies, https://docs.python.org/3/library/copy.html.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> as_view : bool, optional (default=False)</span>
<span class="sd"> If True, the returned graph-view provides a read-only view</span>
<span class="sd"> of the original graph without actually copying any data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> G : Graph</span>
<span class="sd"> A copy of the graph.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> to_directed: return a directed copy of the graph.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc</span>
<span class="sd"> >>> H = G.copy()</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">as_view</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">graphviews</span><span class="o">.</span><span class="n">generic_graph_view</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="n">G</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">()</span>
<span class="n">G</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
<span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">datadict</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
<span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">nbrs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">keydict</span> <span class="ow">in</span> <span class="n">nbrs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">datadict</span> <span class="ow">in</span> <span class="n">keydict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">G</span></div>
<div class="viewcode-block" id="MultiGraph.to_directed"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.to_directed.html#networkx.MultiGraph.to_directed">[docs]</a> <span class="k">def</span> <span class="nf">to_directed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns a directed representation of the graph.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> G : MultiDiGraph</span>
<span class="sd"> A directed graph with the same name, same nodes, and with</span>
<span class="sd"> each edge (u, v, k, data) replaced by two directed edges</span>
<span class="sd"> (u, v, k, data) and (v, u, k, data).</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> This returns a "deepcopy" of the edge, node, and</span>
<span class="sd"> graph attributes which attempts to completely copy</span>
<span class="sd"> all of the data and references.</span>
<span class="sd"> This is in contrast to the similar D=MultiDiGraph(G) which</span>
<span class="sd"> returns a shallow copy of the data.</span>
<span class="sd"> See the Python copy module for more information on shallow</span>
<span class="sd"> and deep copies, https://docs.python.org/3/library/copy.html.</span>
<span class="sd"> Warning: If you have subclassed MultiGraph to use dict-like objects</span>
<span class="sd"> in the data structure, those changes do not transfer to the</span>
<span class="sd"> MultiDiGraph created by this method.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> G.add_edge(0, 1)</span>
<span class="sd"> 0</span>
<span class="sd"> >>> G.add_edge(0, 1)</span>
<span class="sd"> 1</span>
<span class="sd"> >>> H = G.to_directed()</span>
<span class="sd"> >>> list(H.edges)</span>
<span class="sd"> [(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1)]</span>
<span class="sd"> If already directed, return a (deep) copy</span>
<span class="sd"> >>> G = nx.MultiDiGraph()</span>
<span class="sd"> >>> G.add_edge(0, 1)</span>
<span class="sd"> 0</span>
<span class="sd"> >>> H = G.to_directed()</span>
<span class="sd"> >>> list(H.edges)</span>
<span class="sd"> [(0, 1, 0)]</span>
<span class="sd"> """</span>
<span class="n">graph_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_directed_class</span><span class="p">()</span>
<span class="k">if</span> <span class="n">as_view</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">graphviews</span><span class="o">.</span><span class="n">generic_graph_view</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph_class</span><span class="p">)</span>
<span class="c1"># deepcopy when not a view</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">graph_class</span><span class="p">()</span>
<span class="n">G</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">d</span><span class="p">))</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
<span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">datadict</span><span class="p">))</span>
<span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">nbrs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">adj</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">keydict</span> <span class="ow">in</span> <span class="n">nbrs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">datadict</span> <span class="ow">in</span> <span class="n">keydict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">G</span></div>
<div class="viewcode-block" id="MultiGraph.to_undirected"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.to_undirected.html#networkx.MultiGraph.to_undirected">[docs]</a> <span class="k">def</span> <span class="nf">to_undirected</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">as_view</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns an undirected copy of the graph.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> G : Graph/MultiGraph</span>
<span class="sd"> A deepcopy of the graph.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> copy, add_edge, add_edges_from</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> This returns a "deepcopy" of the edge, node, and</span>
<span class="sd"> graph attributes which attempts to completely copy</span>
<span class="sd"> all of the data and references.</span>
<span class="sd"> This is in contrast to the similar `G = nx.MultiGraph(D)`</span>
<span class="sd"> which returns a shallow copy of the data.</span>
<span class="sd"> See the Python copy module for more information on shallow</span>
<span class="sd"> and deep copies, https://docs.python.org/3/library/copy.html.</span>
<span class="sd"> Warning: If you have subclassed MultiGraph to use dict-like</span>
<span class="sd"> objects in the data structure, those changes do not transfer</span>
<span class="sd"> to the MultiGraph created by this method.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> G = nx.MultiGraph([(0, 1), (0, 1), (1, 2)])</span>
<span class="sd"> >>> H = G.to_directed()</span>
<span class="sd"> >>> list(H.edges)</span>
<span class="sd"> [(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 2, 0), (2, 1, 0)]</span>
<span class="sd"> >>> G2 = H.to_undirected()</span>
<span class="sd"> >>> list(G2.edges)</span>
<span class="sd"> [(0, 1, 0), (0, 1, 1), (1, 2, 0)]</span>
<span class="sd"> """</span>
<span class="n">graph_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_undirected_class</span><span class="p">()</span>
<span class="k">if</span> <span class="n">as_view</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
<span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">graphviews</span><span class="o">.</span><span class="n">generic_graph_view</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph_class</span><span class="p">)</span>
<span class="c1"># deepcopy when not a view</span>
<span class="n">G</span> <span class="o">=</span> <span class="n">graph_class</span><span class="p">()</span>
<span class="n">G</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">d</span><span class="p">))</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
<span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">datadict</span><span class="p">))</span>
<span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">nbrs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">keydict</span> <span class="ow">in</span> <span class="n">nbrs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">datadict</span> <span class="ow">in</span> <span class="n">keydict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">G</span></div>
<div class="viewcode-block" id="MultiGraph.number_of_edges"><a class="viewcode-back" href="../../../reference/classes/generated/networkx.MultiGraph.number_of_edges.html#networkx.MultiGraph.number_of_edges">[docs]</a> <span class="k">def</span> <span class="nf">number_of_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">v</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Returns the number of edges between two nodes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> u, v : nodes, optional (Gefault=all edges)</span>
<span class="sd"> If u and v are specified, return the number of edges between</span>
<span class="sd"> u and v. Otherwise return the total number of all edges.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> nedges : int</span>
<span class="sd"> The number of edges in the graph. If nodes `u` and `v` are</span>
<span class="sd"> specified return the number of edges between those nodes. If</span>
<span class="sd"> the graph is directed, this only returns the number of edges</span>
<span class="sd"> from `u` to `v`.</span>
<span class="sd"> See Also</span>
<span class="sd"> --------</span>
<span class="sd"> size</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> For undirected multigraphs, this method counts the total number</span>
<span class="sd"> of edges in the graph::</span>
<span class="sd"> >>> G = nx.MultiGraph()</span>
<span class="sd"> >>> G.add_edges_from([(0, 1), (0, 1), (1, 2)])</span>
<span class="sd"> [0, 1, 0]</span>
<span class="sd"> >>> G.number_of_edges()</span>
<span class="sd"> 3</span>
<span class="sd"> If you specify two nodes, this counts the total number of edges</span>
<span class="sd"> joining the two nodes::</span>
<span class="sd"> >>> G.number_of_edges(0, 1)</span>
<span class="sd"> 2</span>
<span class="sd"> For directed multigraphs, this method can count the total number</span>
<span class="sd"> of directed edges from `u` to `v`::</span>
<span class="sd"> >>> G = nx.MultiDiGraph()</span>
<span class="sd"> >>> G.add_edges_from([(0, 1), (0, 1), (1, 0)])</span>
<span class="sd"> [0, 1, 0]</span>
<span class="sd"> >>> G.number_of_edges(0, 1)</span>
<span class="sd"> 2</span>
<span class="sd"> >>> G.number_of_edges(1, 0)</span>
<span class="sd"> 1</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">u</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">edgedata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adj</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="n">v</span><span class="p">]</span>
<span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span> <span class="c1"># no such edge</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">edgedata</span><span class="p">)</span></div></div>
</pre></div>
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