Python networkx.union方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def __call__(self, code, charge_type):
        l,d = code.depth, code.dimension
        s = {}
        for type in code.types:
            s[type] = code.Syndrome(type, charge_type)

        uc = nx.union(s['green'], nx.union(s['red'], s['blue']))
        for edge in code.Dual['red'].edges():
            break
        scale = common.euclidean_dist(edge[0], edge[1])+.1

        i = 1

        while uc.nodes() != []:
        	clusters = GCC_Partition(uc, i*scale)
        	for cluster in clusters:
        		code, uc = GCC_Annihilate(cluster, code, uc, charge_type, i*scale)
        	i += 1
        return code 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def test_union_attributes():
    g = nx.Graph()
    g.add_node(0, x=4)
    g.add_node(1, x=5)
    g.add_edge(0, 1, size=5)
    g.graph['name'] = 'g'

    h = g.copy()
    h.graph['name'] = 'h'
    h.graph['attr'] = 'attr'
    h.node[0]['x'] = 7

    gh = nx.union(g, h, rename=('g', 'h'))
    assert_equal( set(gh.nodes()) , set(['h0', 'h1', 'g0', 'g1']) )
    for n in gh:
        graph, node = n
        assert_equal( gh.node[n], eval(graph).node[int(node)] )

    assert_equal(gh.graph['attr'],'attr')
    assert_equal(gh.graph['name'],'h') # h graph attributes take precendent 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def setUp(self):
        # G is the example graph in Figure 1 from Batagelj and
        # Zaversnik's paper titled An O(m) Algorithm for Cores
        # Decomposition of Networks, 2003,
        # http://arXiv.org/abs/cs/0310049.  With nodes labeled as
        # shown, the 3-core is given by nodes 1-8, the 2-core by nodes
        # 9-16, the 1-core by nodes 17-20 and node 21 is in the
        # 0-core.
        t1=nx.convert_node_labels_to_integers(nx.tetrahedral_graph(),1)
        t2=nx.convert_node_labels_to_integers(t1,5)
        G=nx.union(t1,t2)
        G.add_edges_from( [(3,7), (2,11), (11,5), (11,12), (5,12), (12,19),
                           (12,18), (3,9), (7,9), (7,10), (9,10), (9,20),
                           (17,13), (13,14), (14,15), (15,16), (16,13)])
        G.add_node(21)
        self.G=G

        # Create the graph H resulting from the degree sequence
        # [0,1,2,2,2,2,3] when using the Havel-Hakimi algorithm. 

        degseq=[0,1,2,2,2,2,3]
        H = nx.havel_hakimi_graph(degseq)
        mapping = {6:0, 0:1, 4:3, 5:6, 3:4, 1:2, 2:5 }
        self.H = nx.relabel_nodes(H, mapping) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def test_union_attributes():
    g = nx.Graph()
    g.add_node(0, x=4)
    g.add_node(1, x=5)
    g.add_edge(0, 1, size=5)
    g.graph['name'] = 'g'

    h = g.copy()
    h.graph['name'] = 'h'
    h.graph['attr'] = 'attr'
    h.nodes[0]['x'] = 7

    gh = nx.union(g, h, rename=('g', 'h'))
    assert_equal(set(gh.nodes()), set(['h0', 'h1', 'g0', 'g1']))
    for n in gh:
        graph, node = n
        assert_equal(gh.nodes[n], eval(graph).nodes[int(node)])

    assert_equal(gh.graph['attr'], 'attr')
    assert_equal(gh.graph['name'], 'h')  # h graph attributes take precendent 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def setUp(self):
        # G is the example graph in Figure 1 from Batagelj and
        # Zaversnik's paper titled An O(m) Algorithm for Cores
        # Decomposition of Networks, 2003,
        # http://arXiv.org/abs/cs/0310049.  With nodes labeled as
        # shown, the 3-core is given by nodes 1-8, the 2-core by nodes
        # 9-16, the 1-core by nodes 17-20 and node 21 is in the
        # 0-core.
        t1 = nx.convert_node_labels_to_integers(nx.tetrahedral_graph(), 1)
        t2 = nx.convert_node_labels_to_integers(t1, 5)
        G = nx.union(t1, t2)
        G.add_edges_from([(3, 7), (2, 11), (11, 5), (11, 12), (5, 12),
                          (12, 19), (12, 18), (3, 9), (7, 9), (7, 10),
                          (9, 10), (9, 20), (17, 13), (13, 14), (14, 15),
                          (15, 16), (16, 13)])
        G.add_node(21)
        self.G = G

        # Create the graph H resulting from the degree sequence
        # [0, 1, 2, 2, 2, 2, 3] when using the Havel-Hakimi algorithm.

        degseq = [0, 1, 2, 2, 2, 2, 3]
        H = nx.havel_hakimi_graph(degseq)
        mapping = {6: 0, 0: 1, 4: 3, 5: 6, 3: 4, 1: 2, 2: 5}
        self.H = nx.relabel_nodes(H, mapping) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def test_disconnected_graph(self):
        """Test for a graph with multiple connected components."""
        G = nx.barbell_graph(3, 0)
        H = nx.barbell_graph(3, 0)
        mapping = dict(zip(range(6), 'abcdef'))
        nx.relabel_nodes(H, mapping, copy=False)
        G = nx.union(G, H)
        chains = list(nx.chain_decomposition(G))
        expected = [
            [(0, 1), (1, 2), (2, 0)],
            [(3, 4), (4, 5), (5, 3)],
            [('a', 'b'), ('b', 'c'), ('c', 'a')],
            [('d', 'e'), ('e', 'f'), ('f', 'd')],
        ]
        self.assertEqual(len(chains), len(expected))
        for chain in chains:
            self.assertContainsChain(chain, expected) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def test_union_attributes():
    g = nx.Graph()
    g.add_node(0, x=4)
    g.add_node(1, x=5)
    g.add_edge(0, 1, size=5)
    g.graph['name'] = 'g'

    h = g.copy()
    h.graph['name'] = 'h'
    h.graph['attr'] = 'attr'
    h.nodes[0]['x'] = 7

    gh = nx.union(g, h, rename=('g', 'h'))
    assert_equal( set(gh.nodes()) , set(['h0', 'h1', 'g0', 'g1']) )
    for n in gh:
        graph, node = n
        assert_equal( gh.nodes[n], eval(graph).nodes[int(node)] )

    assert_equal(gh.graph['attr'],'attr')
    assert_equal(gh.graph['name'],'h') # h graph attributes take precendent 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def __call__(self, code, charge_type):
        errors = {}
        for type in code.types:
            errors[type] = code.Syndrome(type, charge_type)

        shrunk_errs, shrunk_exts, matches = {}, {}, {}
        loops_graph = nx.Graph()
        for t1 in code.types:
            [t2, t3] = code.complementaryTypes(t1)
            shrunk_errs[t1] = nx.union(errors[t2], errors[t3])
            shrunk_exts[t1] = code.External[t2] + code.External[t3]
            alt_ext = code.External[t1][0]
            matches[t1] = DSP_Matching(shrunk_errs[t1], shrunk_exts[t1], 2, alt_ext)

            for start in matches[t1]:
                end = matches[t1][start]
                chain = DSP_Path(code.Dual[t1], start, end)
                links = len(chain) -1

                for i in range(links):
                    node1, node2 = chain[i], chain[i+1]
                    edge = (node1, node2)
                    if edge in loops_graph.edges():
                        loops_graph.remove_edge(*edge)
                    else:
                        loops_graph.add_edge(*edge)
        Exts = code.External['red']+code.External['blue']+code.External['green']

        code, loops_graph = correctLoops(code, loops_graph, charge_type)
        while hasConnectedBoundaries(code, loops_graph, Exts):
            ext1, ext2 = connectedBoundaries(loops_graph, Exts)
            code, loops_graph = makeBoundLoop(code, loops_graph, ext1, ext2)
            code, loops_graph = correctLoops(code, loops_graph, charge_type)
        return code 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def merge_graphs_to(graph, graphs):
    head_node = graph.graph['head_node']
    for i, g in enumerate(graphs):
        graph = nx.union(graph, g)
        graph.add_edge(head_node, g.graph['head_node'], arg=i)
    graph.graph['head_node'] = head_node
    return graph 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def disjoint_union_all(graphs):
    """Return the disjoint union of all graphs.

    This operation forces distinct integer node labels starting with 0
    for the first graph in the list and numbering consecutively.

    Parameters
    ----------
    graphs : list
       List of NetworkX graphs

    Returns
    -------
    U : A graph with the same type as the first graph in list

    Notes
    -----
    It is recommended that the graphs be either all directed or all undirected.

    Graph, edge, and node attributes are propagated to the union graph.
    If a graph attribute is present in multiple graphs, then the value
    from the last graph in the list with that attribute is used.
    """
    graphs = iter(graphs)
    U = next(graphs)
    for H in graphs:
        U = nx.disjoint_union(U, H)
    return U 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def compose_all(graphs, name=None):
    """Return the composition of all graphs.

    Composition is the simple union of the node sets and edge sets.
    The node sets of the supplied graphs need not be disjoint.

    Parameters
    ----------
    graphs : list
       List of NetworkX graphs

    name : string
       Specify name for new graph

    Returns
    -------
    C : A graph with the same type as the first graph in list

    Notes
    -----
    It is recommended that the supplied graphs be either all directed or all
    undirected.

    Graph, edge, and node attributes are propagated to the union graph.
    If a graph attribute is present in multiple graphs, then the value
    from the last graph in the list with that attribute is used.
    """
    graphs = iter(graphs)
    C = next(graphs)
    for H in graphs:
        C = nx.compose(C, H, name=name)
    return C 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def test_mixed_type_union():
    G = nx.Graph()
    H = nx.MultiGraph()
    U = nx.union(G,H) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def setUp(self):
        G1 = cnlti(nx.grid_2d_graph(2, 2), first_label=0, ordering="sorted")
        G2 = cnlti(nx.lollipop_graph(3, 3), first_label=4, ordering="sorted")
        G3 = cnlti(nx.house_graph(), first_label=10, ordering="sorted")
        self.G = nx.union(G1, G2)
        self.G = nx.union(self.G, G3)
        self.DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])
        self.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1)

        self.gc = []
        G = nx.DiGraph()
        G.add_edges_from([(1, 2), (2, 3), (2, 8), (3, 4), (3, 7), (4, 5),
                          (5, 3), (5, 6), (7, 4), (7, 6), (8, 1), (8, 7)])
        C = [[3, 4, 5, 7], [1, 2, 8], [6]]
        self.gc.append((G, C))

        G = nx.DiGraph()
        G.add_edges_from([(1, 2), (1, 3), (1, 4), (4, 2), (3, 4), (2, 3)])
        C = [[2, 3, 4],[1]]
        self.gc.append((G, C))

        G = nx.DiGraph()
        G.add_edges_from([(1, 2), (2, 3), (3, 2), (2, 1)])
        C = [[1, 2, 3]]
        self.gc.append((G,C))

        # Eppstein's tests
        G = nx.DiGraph({0:[1], 1:[2, 3], 2:[4, 5], 3:[4, 5], 4:[6], 5:[], 6:[]})
        C = [[0], [1], [2],[ 3], [4], [5], [6]]
        self.gc.append((G,C))

        G = nx.DiGraph({0:[1], 1:[2, 3, 4], 2:[0, 3], 3:[4], 4:[3]})
        C = [[0, 1, 2], [3, 4]]
        self.gc.append((G, C)) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def disjoint_union_all(graphs):
    """Returns the disjoint union of all graphs.

    This operation forces distinct integer node labels starting with 0
    for the first graph in the list and numbering consecutively.

    Parameters
    ----------
    graphs : list
       List of NetworkX graphs

    Returns
    -------
    U : A graph with the same type as the first graph in list

    Raises
    ------
    ValueError
       If `graphs` is an empty list.

    Notes
    -----
    It is recommended that the graphs be either all directed or all undirected.

    Graph, edge, and node attributes are propagated to the union graph.
    If a graph attribute is present in multiple graphs, then the value
    from the last graph in the list with that attribute is used.
    """
    if not graphs:
        raise ValueError('cannot apply disjoint_union_all to an empty list')
    graphs = iter(graphs)
    U = next(graphs)
    for H in graphs:
        U = nx.disjoint_union(U, H)
    return U 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import union [as 別名]
def compose_all(graphs):
    """Returns the composition of all graphs.

    Composition is the simple union of the node sets and edge sets.
    The node sets of the supplied graphs need not be disjoint.

    Parameters
    ----------
    graphs : list
       List of NetworkX graphs

    Returns
    -------
    C : A graph with the same type as the first graph in list

    Raises
    ------
    ValueError
       If `graphs` is an empty list.

    Notes
    -----
    It is recommended that the supplied graphs be either all directed or all
    undirected.

    Graph, edge, and node attributes are propagated to the union graph.
    If a graph attribute is present in multiple graphs, then the value
    from the last graph in the list with that attribute is used.
    """
    if not graphs:
        raise ValueError('cannot apply compose_all to an empty list')
    graphs = iter(graphs)
    C = next(graphs)
    for H in graphs:
        C = nx.compose(C, H)
    return C