Python networkx.to_directed() Examples

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 

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 

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) 

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) 

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)]) 

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) 

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) 

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)]) 

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) 

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 

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 

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 

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