Python networkx.weakly_connected_component_subgraphs方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def plan(config, state_info):
    """Defines a startup plan for the MineMeld graph.

    Args:
        config (MineMeldConfig): config
        state_info (dict): state_info for each node

    Returns a dictionary where keys are node names and
    values the satrtup command for the node.
    """
    plan = {}

    graph = _build_graph(config)

    for subgraph in nx.weakly_connected_component_subgraphs(graph, copy=True):
        plan.update(_plan_subgraph(subgraph, config, state_info))

    return plan 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def setUp(self):
        self.undirected = [
            nx.connected_component_subgraphs,
            nx.biconnected_component_subgraphs,
        ]
        self.directed = [
            nx.weakly_connected_component_subgraphs,
            nx.strongly_connected_component_subgraphs,
            nx.attracting_component_subgraphs,
        ]
        self.subgraph_funcs = self.undirected + self.directed
        
        self.D = nx.DiGraph()
        self.D.add_edge(1, 2, eattr='red')
        self.D.add_edge(2, 1, eattr='red')
        self.D.node[1]['nattr'] = 'blue'
        self.D.graph['gattr'] = 'green'

        self.G = nx.Graph()
        self.G.add_edge(1, 2, eattr='red')
        self.G.node[1]['nattr'] = 'blue'
        self.G.graph['gattr'] = 'green' 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def setUp(self):
        self.undirected = [
            nx.connected_component_subgraphs,
            nx.biconnected_component_subgraphs,
        ]
        self.directed = [
            nx.weakly_connected_component_subgraphs,
            nx.strongly_connected_component_subgraphs,
            nx.attracting_component_subgraphs,
        ]
        self.subgraph_funcs = self.undirected + self.directed

        self.D = nx.DiGraph()
        self.D.add_edge(1, 2, eattr='red')
        self.D.add_edge(2, 1, eattr='red')
        self.D.nodes[1]['nattr'] = 'blue'
        self.D.graph['gattr'] = 'green'

        self.G = nx.Graph()
        self.G.add_edge(1, 2, eattr='red')
        self.G.nodes[1]['nattr'] = 'blue'
        self.G.graph['gattr'] = 'green' 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def setUp(self):
        self.undirected = [
            nx.connected_component_subgraphs,
            nx.biconnected_component_subgraphs,
        ]
        self.directed = [
            nx.weakly_connected_component_subgraphs,
            nx.strongly_connected_component_subgraphs,
            nx.attracting_component_subgraphs,
        ]
        self.subgraph_funcs = self.undirected + self.directed
        
        self.D = nx.DiGraph()
        self.D.add_edge(1, 2, eattr='red')
        self.D.add_edge(2, 1, eattr='red')
        self.D.nodes[1]['nattr'] = 'blue'
        self.D.graph['gattr'] = 'green'

        self.G = nx.Graph()
        self.G.add_edge(1, 2, eattr='red')
        self.G.nodes[1]['nattr'] = 'blue'
        self.G.graph['gattr'] = 'green' 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def get_lcc(di_graph):
    di_graph = di_graph.to_undirected().to_directed()
    di_graph = max(nx.weakly_connected_component_subgraphs(di_graph), key=len)
    tdl_nodes = di_graph.nodes()
    nodeListMap = dict(zip(tdl_nodes, range(len(tdl_nodes))))
    di_graph = nx.relabel_nodes(di_graph, nodeListMap, copy=True)
    return di_graph, nodeListMap 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def ok_to_remove_node(g,n):
    g0 = copy.deepcopy(g)
    n0 = get_node_by_addr(g0,n.addr)
    g0.remove_node(n0)
    return len([x for x in networkx.weakly_connected_component_subgraphs(g0)]) <= 1 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def cluster_padding_nodes(cfg,addrs):
    nodes = [get_node_by_addr(cfg,x) for x in addrs]
    sg = cfg.subgraph(nodes)
    gs = [g for g in networkx.weakly_connected_component_subgraphs(sg)]
    return sorted(gs,key=lambda x:len(x)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def _handle_contained_in(ctx):
    # for each 'contained' tree, recursively build new trees based on
    # scaling groups with generated ids
    for contained_tree in nx.weakly_connected_component_subgraphs(
            ctx.plan_contained_graph.reverse(copy=True)):
        # extract tree root node id
        node_id = nx.topological_sort(contained_tree)[0]
        _build_multi_instance_node_tree_rec(
            node_id=node_id,
            contained_tree=contained_tree,
            ctx=ctx)
    ctx.deployment_contained_graph = ctx.deployment_node_graph.copy() 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def components(self) -> List['DAGCircuit']:
        """Split DAGCircuit into independent components"""
        comps = nx.weakly_connected_component_subgraphs(self.graph)
        return [DAGCircuit(comp) for comp in comps] 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def get_lcc(di_graph):
    di_graph = max(nx.weakly_connected_component_subgraphs(di_graph), key=len)
    tdl_nodes = list(di_graph.nodes())
    nodeListMap = dict(zip(tdl_nodes, range(len(tdl_nodes))))
    nx.relabel_nodes(di_graph, nodeListMap, copy=False)
    return di_graph, nodeListMap 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def test_connected_component_subgraphs(self):
        wcc = nx.weakly_connected_component_subgraphs
        cc = nx.connected_component_subgraphs
        for G, C in self.gc:
            U = G.to_undirected()
            w = {frozenset(g) for g in wcc(G)}
            c = {frozenset(g) for g in cc(U)}
            assert_equal(w, c) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def test_weakly_connected_component_subgraphs(self):
        wcc = nx.weakly_connected_component_subgraphs
        cc = nx.connected_component_subgraphs
        for G, C in self.gc:
            U = G.to_undirected()
            w = {frozenset(g) for g in wcc(G)}
            c = {frozenset(g) for g in cc(U)}
            assert_equal(w, c) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def is_forest(G):
    """
    Returns True if `G` is a forest.

    A forest is a graph with no undirected cycles.

    For directed graphs, `G` is a forest if the underlying graph is a forest.
    The underlying graph is obtained by treating each directed edge as a single
    undirected edge in a multigraph.

    Parameters
    ----------
    G : graph
        The graph to test.

    Returns
    -------
    b : bool
        A boolean that is True if `G` is a forest.

    Notes
    -----
    In another convention, a directed forest is known as a *polyforest* and
    then *forest* corresponds to a *branching*.

    See Also
    --------
    is_branching

    """
    if len(G) == 0:
        raise nx.exception.NetworkXPointlessConcept('G has no nodes.')

    if G.is_directed():
        components = nx.weakly_connected_component_subgraphs
    else:
        components = nx.connected_component_subgraphs

    return all(len(c) - 1 == c.number_of_edges() for c in components(G)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def test_connected_raise(self):
        G = nx.Graph()
        assert_raises(NetworkXNotImplemented, nx.weakly_connected_components, G)
        assert_raises(NetworkXNotImplemented, nx.number_weakly_connected_components, G)
        assert_raises(NetworkXNotImplemented, nx.is_weakly_connected, G)
        # deprecated
        assert_raises(NetworkXNotImplemented, nx.weakly_connected_component_subgraphs, G) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import weakly_connected_component_subgraphs [as 别名]
def prep_graph(G, relabel=True, del_self_loops=True, maincc=True):
    r"""
    Preprocess a graphs according to the parameters provided.
    By default the (digraphs) graphs are restricted to their main (weakly) connected component.
    Trying to embed graphs with several CCs may cause some algorithms to put them infinitely far away.

    Parameters
    ----------
    G : graph
        A NetworkX graph
    relabel : bool, optional
        Determines if the nodes are relabeled with consecutive integers 0..N
    del_self_loops : bool, optional
        Determines if self loops should be deleted from the graph. Default is True.
    maincc : bool, optional
        Determines if the graphs should be restricted to the main connected component or not. Default is True.

    Returns
    -------
    G : graph
       A preprocessed NetworkX graph
    Ids : list of tuples
       A list of (OldNodeID, NewNodeID). Returns None if relabel=False.
    """
    # Remove self loops
    if del_self_loops:
        G.remove_edges_from(G.selfloop_edges())

    # Restrict graph to its main connected component
    if maincc:
        if G.is_directed():
            Gcc = max(nx.weakly_connected_component_subgraphs(G), key=len)
        else:
            Gcc = max(nx.connected_component_subgraphs(G), key=len)
    else:
        Gcc = G

    # Relabel graph nodes in 0...N
    if relabel:
        Grl = nx.convert_node_labels_to_integers(Gcc, first_label=0, ordering='sorted')
        # A list of (oldNodeID, newNodeID)
        ids = list(zip(sorted(Gcc.nodes), sorted(Grl.nodes)))
        return Grl, ids
    else:
        return Gcc, None