当前位置: 首页>>代码示例>>Python>>正文


Python networkx.to_directed方法代码示例

本文整理汇总了Python中networkx.to_directed方法的典型用法代码示例。如果您正苦于以下问题:Python networkx.to_directed方法的具体用法?Python networkx.to_directed怎么用?Python networkx.to_directed使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在networkx的用法示例。


在下文中一共展示了networkx.to_directed方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: setUp

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def setUp(self):
        self.G = nx.path_graph(9)
        self.DG = nx.path_graph(9, create_using=nx.DiGraph())
        self.MG = nx.path_graph(9, create_using=nx.MultiGraph())
        self.MDG = nx.path_graph(9, create_using=nx.MultiDiGraph())
        self.Gv = nx.to_undirected(self.DG)
        self.DGv = nx.to_directed(self.G)
        self.MGv = nx.to_undirected(self.MDG)
        self.MDGv = nx.to_directed(self.MG)
        self.Rv = self.DG.reverse()
        self.MRv = self.MDG.reverse()
        self.graphs = [self.G, self.DG, self.MG, self.MDG,
                       self.Gv, self.DGv, self.MGv, self.MDGv,
                       self.Rv, self.MRv]
        for G in self.graphs:
            G.edges, G.nodes, G.degree 
开发者ID:holzschu,项目名称:Carnets,代码行数:18,代码来源:test_graphviews.py

示例2: setup

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def setup(self):
        self.G = nx.path_graph(9)
        self.dv = nx.to_directed(self.G)
        self.MG = nx.path_graph(9, create_using=nx.MultiGraph())
        self.Mdv = nx.to_directed(self.MG) 
开发者ID:holzschu,项目名称:Carnets,代码行数:7,代码来源:test_graphviews.py

示例3: test_already_directed

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def test_already_directed(self):
        dd = nx.to_directed(self.dv)
        Mdd = nx.to_directed(self.Mdv)
        assert_edges_equal(dd.edges, self.dv.edges)
        assert_edges_equal(Mdd.edges, self.Mdv.edges) 
开发者ID:holzschu,项目名称:Carnets,代码行数:7,代码来源:test_graphviews.py

示例4: test_subgraph_todirected

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def test_subgraph_todirected(self):
        SG = nx.induced_subgraph(self.G, [4, 5, 6])
        SSG = SG.to_directed()
        assert_equal(sorted(SSG), [4, 5, 6])
        assert_equal(sorted(SSG.edges), [(4, 5), (5, 4), (5, 6), (6, 5)]) 
开发者ID:holzschu,项目名称:Carnets,代码行数:7,代码来源:test_graphviews.py

示例5: save_graph

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def save_graph(G, output_path, delimiter=',', write_stats=True, write_weights=False, write_dir=True):
    r"""
    Saves a graph to a file as an edgelist of weighted edgelist. If the stats parameter is set to True the file
    will include several lines containing the same basic graph statistics as provided by the get_stats function.
    For undirected graphs, the method stores both directions of every edge.

    Parameters
    ----------
    G : graph
       A NetworkX graph
    output_path : file or string
       File or filename to write. If a file is provided, it must be
       opened in 'wb' mode.
    delimiter : string, optional
       The string used to separate values. Default is ','.
    write_stats : bool, optional
        Sets if graph statistics should be added to the edgelist or not. Default is True.
    write_weights : bool, optional
        If True data will be stored as weighted edgelist (e.g. triplets src, dst, weight) otherwise as normal edgelist.
        If the graph edges have no weight attribute and this parameter is set to True,
        a weight of 1 will be assigned to each edge. Default is False.
    write_dir : bool, optional
        This option is only relevant for undirected graphs. If False, the graph will be stored with a single
        direction of the edges. If True, both directions of edges will be stored. Default is True.
    """
    # Write the graph stats in the file if required
    if write_stats:
        get_stats(G, output_path)

    # Open the file where data should be stored
    f = open(output_path, 'a+b')

    # Write the graph to a file and use both edge directions if graph is undirected
    if G.is_directed():
        # Store edgelist
        if write_weights:
            J = nx.DiGraph()
            J.add_weighted_edges_from(G.edges.data('weight', 1))
            nx.write_weighted_edgelist(J, f, delimiter=delimiter)
        else:
            nx.write_edgelist(G, f, delimiter=delimiter, data=False)
    else:
        if write_dir:
            H = nx.to_directed(G)
            J = nx.DiGraph()
        else:
            H = G
            J = nx.DiGraph()
        # Store edgelist
        if write_weights:
            J.add_weighted_edges_from(H.edges.data('weight', 1))
            nx.write_weighted_edgelist(J, f, delimiter=delimiter)
        else:
            nx.write_edgelist(H, f, delimiter=delimiter, data=False) 
开发者ID:Dru-Mara,项目名称:EvalNE,代码行数:56,代码来源:preprocess.py

示例6: caveman_graph

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def caveman_graph(l, k):
    """Returns a caveman graph of ``l`` cliques of size ``k``.

    Parameters
    ----------
    l : int
      Number of cliques
    k : int
      Size of cliques

    Returns
    -------
    G : NetworkX Graph
      caveman graph

    Notes
    -----
    This returns an undirected graph, it can be converted to a directed
    graph using :func:`nx.to_directed`, or a multigraph using
    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
    described in [1]_ and it is unclear which of the directed
    generalizations is most useful.

    Examples
    --------
    >>> G = nx.caveman_graph(3, 3)

    See also
    --------

    connected_caveman_graph

    References
    ----------
    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
       Amer. J. Soc. 105, 493-527, 1999.
    """
    # l disjoint cliques of size k
    G = nx.empty_graph(l*k)
    G.name = "caveman_graph(%s,%s)" % (l*k, k)
    if k > 1:
        for start in range(0, l*k, k):
            edges = itertools.combinations(range(start, start+k), 2)
            G.add_edges_from(edges)
    return G 
开发者ID:SpaceGroupUCL,项目名称:qgisSpaceSyntaxToolkit,代码行数:47,代码来源:community.py

示例7: connected_caveman_graph

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def connected_caveman_graph(l, k):
    """Returns a connected caveman graph of ``l`` cliques of size ``k``.

    The connected caveman graph is formed by creating ``n`` cliques of size
    ``k``, then a single edge in each clique is rewired to a node in an
    adjacent clique.

    Parameters
    ----------
    l : int
      number of cliques
    k : int
      size of cliques

    Returns
    -------
    G : NetworkX Graph
      connected caveman graph

    Notes
    -----
    This returns an undirected graph, it can be converted to a directed
    graph using :func:`nx.to_directed`, or a multigraph using
    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
    described in [1]_ and it is unclear which of the directed
    generalizations is most useful.

    Examples
    --------
    >>> G = nx.connected_caveman_graph(3, 3)

    References
    ----------
    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
       Amer. J. Soc. 105, 493-527, 1999.
    """
    G = nx.caveman_graph(l, k)
    G.name = "connected_caveman_graph(%s,%s)" % (l, k)
    for start in range(0, l*k, k):
        G.remove_edge(start, start+1)
        G.add_edge(start, (start-1) % (l*k))
    return G 
开发者ID:SpaceGroupUCL,项目名称:qgisSpaceSyntaxToolkit,代码行数:44,代码来源:community.py

示例8: caveman_graph

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import to_directed [as 别名]
def caveman_graph(l, k):
    """Returns a caveman graph of `l` cliques of size `k`.

    Parameters
    ----------
    l : int
      Number of cliques
    k : int
      Size of cliques

    Returns
    -------
    G : NetworkX Graph
      caveman graph

    Notes
    -----
    This returns an undirected graph, it can be converted to a directed
    graph using :func:`nx.to_directed`, or a multigraph using
    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
    described in [1]_ and it is unclear which of the directed
    generalizations is most useful.

    Examples
    --------
    >>> G = nx.caveman_graph(3, 3)

    See also
    --------

    connected_caveman_graph

    References
    ----------
    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
       Amer. J. Soc. 105, 493-527, 1999.
    """
    # l disjoint cliques of size k
    G = nx.empty_graph(l * k)
    if k > 1:
        for start in range(0, l * k, k):
            edges = itertools.combinations(range(start, start + k), 2)
            G.add_edges_from(edges)
    return G 
开发者ID:holzschu,项目名称:Carnets,代码行数:46,代码来源:community.py


注:本文中的networkx.to_directed方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。