Python networkx.DiGraph方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def prep_ws(inpath):
    """
    Preprocess web spam graph.
    """
    # Create an empty digraph
    G = nx.DiGraph()

    # Read the file and create the graph
    src = 0
    f = open(inpath, 'r')
    for line in f:
        if src != 0:
            arr = line.split()
            for dst in arr:
                dst_id = int(dst.split(':')[0])
                # We consider the graph unweighted
                G.add_edge(src, dst_id)
        src += 1
    # G.add_node(src-2)

    # Preprocess the graph
    G, ids = pp.prep_graph(G, relabel=True, del_self_loops=False)

    # Return the preprocessed graph
    return G 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def read_graph(edgeList,weighted=False, directed=False):
    '''
    Reads the input network in networkx.
    '''
    if weighted:
        G = nx.read_edgelist(edgeList, nodetype=str, data=(('type',int),('weight',float),('id',int)), create_using=nx.DiGraph())
    else:
        G = nx.read_edgelist(edgeList, nodetype=str,data=(('type',int),('id',int)), create_using=nx.DiGraph())
        for edge in G.edges():
            G[edge[0]][edge[1]]['weight'] = 1.0

    if not directed:
        G = G.to_undirected()

    # print (G.edges(data = True))
    return G 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def __init__(self, cardinalities=[2], edges=[]):
        self.K = cardinalities  # Cardinalities for each task
        self.t = len(cardinalities)  # Total number of tasks
        self.edges = edges

        # Create the graph of tasks
        self.G = nx.DiGraph()
        self.G.add_nodes_from(range(self.t))
        self.G.add_edges_from(edges)

        # Pre-compute parents, children, and leaf nodes
        self.leaf_nodes = [i for i in self.G.nodes() if self.G.out_degree(i) == 0]
        self.parents = {t: self.get_parent(t) for t in range(self.t)}
        self.children = {t: self.get_children(t) for t in range(self.t)}

        # Save the cardinality of the feasible set
        self.k = len(list(self.feasible_set())) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def _build_tree(self, root):
        g = nx.DiGraph()
        def _rec_build(nid, node):
            for child in node:
                cid = g.number_of_nodes()
                if isinstance(child[0], str) or isinstance(child[0], bytes):
                    # leaf node
                    word = self.vocab.get(child[0].lower(), self.UNK_WORD)
                    g.add_node(cid, x=word, y=int(child.label()), mask=1)
                else:
                    g.add_node(cid, x=SST.PAD_WORD, y=int(child.label()), mask=0)
                    _rec_build(cid, child)
                g.add_edge(cid, nid)
        # add root
        g.add_node(0, x=SST.PAD_WORD, y=int(root.label()), mask=0)
        _rec_build(0, root)
        ret = DGLGraph()
        ret.from_networkx(g, node_attrs=['x', 'y', 'mask'])
        return ret 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def to_networkx(self):
        """Convert to networkx graph.

        The edge id will be saved as the 'id' edge attribute.

        Returns
        -------
        networkx.DiGraph
            The nx graph
        """
        src, dst, eid = self.edges()
        # xiangsx: Always treat graph as multigraph
        ret = nx.MultiDiGraph()
        ret.add_nodes_from(range(self.number_of_nodes()))
        for u, v, e in zip(src, dst, eid):
            ret.add_edge(u, v, id=e)
        return ret 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def create_test_heterograph2(index_dtype):
    plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
    wishes_nx = nx.DiGraph()
    wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
    wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
    wishes_nx.add_edge('u0', 'g1', id=0)
    wishes_nx.add_edge('u2', 'g0', id=1)
    develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')

    g = dgl.heterograph({
        ('user', 'follows', 'user'): [(0, 1), (1, 2)],
        ('user', 'plays', 'game'): plays_spmat,
        ('user', 'wishes', 'game'): wishes_nx,
        ('developer', 'develops', 'game'): develops_g,
        })
    return g 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def create_test_heterograph3(index_dtype):
    plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
    wishes_nx = nx.DiGraph()
    wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
    wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
    wishes_nx.add_edge('u0', 'g1', id=0)
    wishes_nx.add_edge('u2', 'g0', id=1)

    follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows', _restrict_format='coo')
    plays_g = dgl.bipartite(
        [(0, 0), (1, 0), (2, 1), (1, 1)], 'user', 'plays', 'game', _restrict_format='coo')
    wishes_g = dgl.bipartite([(0, 1), (2, 0)], 'user', 'wishes', 'game', _restrict_format='coo')
    develops_g = dgl.bipartite(
        [(0, 0), (1, 1)], 'developer', 'develops', 'game', _restrict_format='coo')
    g = dgl.hetero_from_relations([follows_g, plays_g, wishes_g, develops_g])
    return g 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def test_pickling_heterograph():
    # copied from test_heterograph.create_test_heterograph()
    plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
    wishes_nx = nx.DiGraph()
    wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
    wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
    wishes_nx.add_edge('u0', 'g1', id=0)
    wishes_nx.add_edge('u2', 'g0', id=1)

    follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows')
    plays_g = dgl.bipartite(plays_spmat, 'user', 'plays', 'game')
    wishes_g = dgl.bipartite(wishes_nx, 'user', 'wishes', 'game')
    develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
    g = dgl.hetero_from_relations([follows_g, plays_g, wishes_g, develops_g])

    g.nodes['user'].data['u_h'] = F.randn((3, 4))
    g.nodes['game'].data['g_h'] = F.randn((2, 5))
    g.edges['plays'].data['p_h'] = F.randn((4, 6))

    new_g = _reconstruct_pickle(g)
    _assert_is_identical_hetero(g, new_g) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def test_pickling_heterograph_index_compatibility():
    plays_spmat = ssp.coo_matrix(([1, 1, 1, 1], ([0, 1, 2, 1], [0, 0, 1, 1])))
    wishes_nx = nx.DiGraph()
    wishes_nx.add_nodes_from(['u0', 'u1', 'u2'], bipartite=0)
    wishes_nx.add_nodes_from(['g0', 'g1'], bipartite=1)
    wishes_nx.add_edge('u0', 'g1', id=0)
    wishes_nx.add_edge('u2', 'g0', id=1)

    follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows')
    plays_g = dgl.bipartite(plays_spmat, 'user', 'plays', 'game')
    wishes_g = dgl.bipartite(wishes_nx, 'user', 'wishes', 'game')
    develops_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
    g = dgl.hetero_from_relations([follows_g, plays_g, wishes_g, develops_g])

    with open("tests/compute/hetero_pickle_old.pkl", "rb") as f:
        gi = pickle.load(f)
        f.close()
    new_g = dgl.DGLHeteroGraph(gi, g.ntypes, g.etypes)
    _assert_is_identical_hetero(g, new_g) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def topological_sort_of_nucleotides(graph: nx.DiGraph) -> List[Nucleotide]:
    """
    Perform topological order of the graph

    Parameters
    ----------
    graph

    Returns
    -------
    sorted_nucleotides
        list of nucleotides sorted in topological order
    """
    nucleotides_without_inputs = [
        each_nucleotide for each_nucleotide in graph
        if not list(graph.predecessors(each_nucleotide))]
    nucleotides_without_inputs_sorted = sorted(
        nucleotides_without_inputs, key=lambda x: x.name)
    topological_order = list(nx.topological_sort(graph))
    topological_order_only_with_inputs = [
        each_nucleotide for each_nucleotide in topological_order
        if each_nucleotide not in nucleotides_without_inputs]
    topological_order_sorted = (nucleotides_without_inputs_sorted
                                + topological_order_only_with_inputs)
    return topological_order_sorted 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def _add_update_events(subplot: plt_axes.Subplot, dna_helix_graph: nx.DiGraph,
                       nucleotide_plots: Dict[Nucleotide, _NUCLEOTIDE_PLOT]):
    subplot.figure.canvas.mpl_connect(
        'draw_event', lambda x: subplot.pchanged())
    subplot.figure.canvas.mpl_connect(
        'resize_event', lambda x: subplot.pchanged())

    text_initial_position = list(nucleotide_plots.values())[0].body.center
    text_object = subplot.text(
        text_initial_position[0], text_initial_position[1], "",
        ha="right", va="top", ma="left",
        bbox=dict(facecolor='white', edgecolor='blue', pad=5.0))
    text_object.set_visible(False)

    subplot.figure.canvas.mpl_connect(
        'button_press_event',
        partial(_remove_nucleotide_info_text, text_object=text_object))
    subplot.figure.canvas.mpl_connect(
        'pick_event',
        partial(_draw_nucleotide_info, dna_helix_graph=dna_helix_graph,
                text_object=text_object, subplot=subplot)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def _create_subgraph_plot(event, dna_helix_graph: nx.DiGraph):
    mouseevent = event.mouseevent
    if not mouseevent.dblclick or mouseevent.button != 1:
        return

    logger = logging.getLogger(__name__)
    nucleotide_name = event.artist.get_label().split(":")[-1]
    nucleotide = _get_nucleotide_by_name(nucleotide_name, dna_helix_graph)
    logger.info("Create subgraph plot for %s", nucleotide_name)
    figure, subplot = _create_figure_with_subplot()
    figure.suptitle("Subgraph of nucleotide {}".format(nucleotide_name))

    nucleotide_with_neighbors_subgraph = _get_nucleotide_subgraph(
        dna_helix_graph, nucleotide)
    draw_dna_helix_on_subplot(
        nucleotide_with_neighbors_subgraph, subplot, verbosity=1)
    _draw_click_instructions(subplot, doubleclick=False)
    plt.draw()
    logger.info("Done!") 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def analyze_calls(module: str, all_calls=False) -> nx.DiGraph:
    """
    Analyze and build call graph of simple module.

    Parameters
    ----------
    module : str
        Module path
    all_calls : bool
        Return graph of all calls, default is False

    Returns
    -------
    nx.DiGraph
        Directed graph of functions and call

    """
    file_analysis = analyze(module)

    # create a set of all imports
    return _create_call_graph(file_analysis["calls"], file_analysis["funcs"], all_calls) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def _add_call_edge(
    call_graph: nx.DiGraph,
    call_name: str,
    func_span: Dict[str, Any],
    call_lines,
    call_type="local",
):
    call_graph.add_node(call_name, call_type=call_type)
    for line in call_lines:
        calling_func = [
            func for func, span in func_span.items() if span[0] <= line <= span[1]
        ]
        if calling_func:
            call_graph.add_edge(calling_func[0], call_name, line=line)
        else:
            call_graph.add_edge("ext", call_name, line=line) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import DiGraph [as 别名]
def build_mol_graph(self):
        mol = self.mol
        graph = nx.DiGraph(Chem.rdmolops.GetAdjacencyMatrix(mol))
        for atom in mol.GetAtoms():
            graph.nodes[atom.GetIdx()]['label'] = (atom.GetSymbol(), atom.GetFormalCharge())

        for bond in mol.GetBonds():
            a1 = bond.GetBeginAtom().GetIdx()
            a2 = bond.GetEndAtom().GetIdx()
            btype = MolGraph.BOND_LIST.index( bond.GetBondType() )
            graph[a1][a2]['label'] = btype
            graph[a2][a1]['label'] = btype

        return graph