Пример #1
0
    def edges(self):
        """Return an iterator over the edges.

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

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

        Parameters
        ----------
        nbunch : iterable container, optional (default= all nodes)
            A container of nodes.  The container will be iterated
            through once.
        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 dont have the requested attribute.
            Only relevant if data is not True or False.

        Returns
        -------
        edge : iterator
            An iterator over (u, v), (u, v, d) or (u, v, key, d) edge tuples.

        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)) # default keys are integers
        [(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
        """
        self.__dict__['edges'] = edges = OutMultiEdgeView(self)
        self.__dict__['out_edges'] = edges
        return edges
Пример #2
0
    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)
Пример #3
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 def edge(self):
     return OutMultiEdgeView(self)