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Python networkx.to_directed方法代碼示例

本文整理匯總了Python中networkx.to_directed方法的典型用法代碼示例。如果您正苦於以下問題:Python networkx.to_directed方法的具體用法?Python networkx.to_directed怎麽用?Python networkx.to_directed使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在networkx的用法示例。


在下文中一共展示了networkx.to_directed方法的8個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: setUp

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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 
開發者ID:holzschu,項目名稱:Carnets,代碼行數:18,代碼來源:test_graphviews.py

示例2: setup

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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) 
開發者ID:holzschu,項目名稱:Carnets,代碼行數:7,代碼來源:test_graphviews.py

示例3: test_already_directed

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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) 
開發者ID:holzschu,項目名稱:Carnets,代碼行數:7,代碼來源:test_graphviews.py

示例4: test_subgraph_todirected

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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)]) 
開發者ID:holzschu,項目名稱:Carnets,代碼行數:7,代碼來源:test_graphviews.py

示例5: save_graph

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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) 
開發者ID:Dru-Mara,項目名稱:EvalNE,代碼行數:56,代碼來源:preprocess.py

示例6: caveman_graph

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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 
開發者ID:SpaceGroupUCL,項目名稱:qgisSpaceSyntaxToolkit,代碼行數:47,代碼來源:community.py

示例7: connected_caveman_graph

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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 
開發者ID:SpaceGroupUCL,項目名稱:qgisSpaceSyntaxToolkit,代碼行數:44,代碼來源:community.py

示例8: caveman_graph

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import to_directed [as 別名]
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 
開發者ID:holzschu,項目名稱:Carnets,代碼行數:46,代碼來源:community.py


注:本文中的networkx.to_directed方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。