Python networkx.MultiDiGraph方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def nx_graph(self):
        """Convert the data in a ``nodelist`` into a networkx labeled directed graph."""
        import networkx

        nx_nodelist = list(range(1, len(self.nodes)))
        nx_edgelist = [
            (n, self._hd(n), self._rel(n))
            for n in nx_nodelist if self._hd(n)
        ]
        self.nx_labels = {}
        for n in nx_nodelist:
            self.nx_labels[n] = self.nodes[n]['word']

        g = networkx.MultiDiGraph()
        g.add_nodes_from(nx_nodelist)
        g.add_edges_from(nx_edgelist)

        return g 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
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 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def _to_graphml_simple(graph: BELGraph) -> nx.MultiDiGraph:
    """Convert a BEL graph to a simple graph.

    :param graph: A BEL graph
    """
    rv = nx.MultiDiGraph()

    for node in graph:
        rv.add_node(node.as_bel(), function=node.function)

    for u, v, key, edge_data in graph.edges(data=True, keys=True):
        u_key, v_key = u.as_bel(), v.as_bel()
        rv.add_edge(
            u_key,
            v_key,
            key=key,
            interaction=edge_data[RELATION],
            bel=graph.edge_to_bel(u, v, edge_data),
        )

    return rv 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def _to_graphml_umbrella(graph: BELGraph) -> nx.MultiDiGraph:
    """Convert a BEL graph to a new graph the nodes as original BEL terms strings.

    :param graph: A BEL graph
    """
    rv = nx.MultiDiGraph()

    for u, v, key, edge_data in graph.edges(data=True, keys=True):
        u_key, _, v_key = edge_to_tuple(u, v, edge_data)

        rv.add_edge(
            u_key,
            v_key,
            key=key,
            relation=edge_data[RELATION],
            bel=graph.edge_to_bel(u, v, edge_data),
        )

    return rv 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def test_random_nodes(self):
        """Test getting random nodes."""
        graph = nx.MultiDiGraph()

        graph.add_edge(1, 2)
        graph.add_edge(1, 3)
        graph.add_edge(1, 4)
        graph.add_edge(1, 5)

        n = 30000
        r = Counter(
            get_random_node(graph, set(), invert_degrees=False)
            for _ in range(n)
        )

        degree_sum = 4 + 1 + 1 + 1 + 1

        self.assertAlmostEqual(4 / degree_sum, r[1] / n, places=2)
        self.assertAlmostEqual(1 / degree_sum, r[2] / n, places=2)
        self.assertAlmostEqual(1 / degree_sum, r[3] / n, places=2)
        self.assertAlmostEqual(1 / degree_sum, r[4] / n, places=2)
        self.assertAlmostEqual(1 / degree_sum, r[5] / n, places=2) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def test_random_nodes_inverted(self):
        """Test getting random nodes."""
        graph = nx.MultiDiGraph()

        graph.add_edge(1, 2)
        graph.add_edge(1, 3)
        graph.add_edge(1, 4)
        graph.add_edge(1, 5)

        n = 30000
        r = Counter(
            get_random_node(graph, set(), invert_degrees=True)
            for _ in range(n)
        )

        degree_sum = (1 / 4) + (1 / 1) + (1 / 1) + (1 / 1) + (1 / 1)

        self.assertAlmostEqual((1 / 4) / degree_sum, r[1] / n, places=2)
        self.assertAlmostEqual((1 / 1) / degree_sum, r[2] / n, places=2)
        self.assertAlmostEqual((1 / 1) / degree_sum, r[3] / n, places=2)
        self.assertAlmostEqual((1 / 1) / degree_sum, r[4] / n, places=2)
        self.assertAlmostEqual((1 / 1) / degree_sum, r[5] / n, places=2) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def read_graph(self, subgraph_file=None):
        if subgraph_file is None:
            subraph_file = self.context_graph_file
        logging.info("Writing graph.")
        # write the graph out
        file_format = subgraph_file.split(".")[-1]
        if file_format == "graphml":
            return nx.read_graphml(subgraph_file)
        elif file_format == "gml":
            return nx.read_gml(subgraph_file)
        elif file_format == "gexf":
            return nx.read_gexf(subgraph_file)
        elif file_format == "net":
            return nx.read_pajek(subgraph_file)
        elif file_format == "yaml":
            return nx.read_yaml(subgraph_file)
        elif file_format == "gpickle":
            return nx.read_gpickle(subgraph_file)
        else:
            logging.warning("File format not found, returning empty graph.")
        return nx.MultiDiGraph() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def anonymize_graph(self) -> "nx.MultiDiGraph":
        """Anonymizes the underlying graph before sending to Graphistry.

        Returns
        -------
        nx.MultiDiGraph
            The same graph structure, but without attributes.
        """

        json_graph = self.to_json()

        # Remove all properties from nodes, leave only IDs

        json_graph["nodes"] = [{"id": node["id"]} for node in json_graph["nodes"]]
        json_graph["links"] = [
            {"source": edge["source"], "target": edge["target"]} for edge in json_graph["links"]
        ]

        return nx.readwrite.json_graph.node_link_graph(json_graph) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def graph(self) -> nx.MultiDiGraph:
        """Generates the MultiDiGraph.

        Places the nodes in the Graph.

        Returns
        -------
        nx.MultiDiGraph
            The generated NetworkX object.
        """

        logger.info("Beginning graph generation.")

        # De-duplicate nodes.
        self.nodes = dedup_nodes(self.nodes)

        for node in self.nodes:
            # Insert the node into the graph.
            # This also takes care of edges.
            self.insert_node(node, hash(node))

        logger.info("Completed graph generation.")
        logger.info(f"Graph contains {len(self.G.nodes())} nodes and {len(self.G.edges())} edges.")

        return self.G 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def multi_graph_to_log_graph(digraph: nx.MultiDiGraph):
    """
    This does not work with the default version of Networkx, but with the fork available at wardbradt/Networkx

    Given weighted MultiDigraph m1, returns a MultiDigraph m2 where for each edge e1 in each edge bunch eb1 of m1, the
    weight w1 of e1 is replaced with log(w1) and the weight w2 of each edge e2 in the opposite edge bunch of eb is
    log(1/w2)

    This function is not optimized.

    todo: allow this function to be used with Networkx DiGraph objects. Should not be that hard, simply return seen
    from self._report in the iterator for digraph's edges() in reportviews.py as it is done for multidigraph's
    edge_bunches()
    """
    result_graph = nx.MultiDiGraph()
    for bunch in digraph.edge_bunches(data=True, seen=True):
        for data_dict in bunch[2]:
            weight = data_dict.pop('weight')
            # if not seen
            if not bunch[3]:
                result_graph.add_edge(bunch[0], bunch[1], -math.log(weight), **data_dict)
            else:
                result_graph.add_edge(bunch[0], bunch[1], -math.log(1/weight), **data_dict) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def format_graph_for_json(graph, raise_errors=True):
    """
    Currently, only supported types for graph are Graph, DiGraph, MultiGraph, and MultiDiGraph. graph must
    be an instance of one of these types, not a class that inherits from one.
    """
    graph_dict = nx.to_dict_of_dicts(graph)
    graph_type = ''

    for key, value in accepted_types.items():
        if type(graph) == key:
            graph_type = value
            break

    if graph_type == '':
        if raise_errors:
            raise TypeError('parameter graph is not of the accepted types.graph is of'
                            'type {}'.format(str(type(graph))))
        else:
            graph_type = 'other'

    return {'graph_type': graph_type, 'graph_dict': graph_dict} 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def from_networkx(cls, graph):
        """Take a networkx MultiDigraph and create a new DAGCircuit.

        Args:
            graph (networkx.MultiDiGraph): The graph to create a DAGCircuit
                object from. The format of this MultiDiGraph format must be
                in the same format as returned by to_networkx.

        Returns:
            DAGCircuit: The dagcircuit object created from the networkx
                MultiDiGraph.
        """

        dag = DAGCircuit()
        for node in nx.topological_sort(graph):
            if node.type == 'out':
                continue
            if node.type == 'in':
                dag._add_wire(node.wire)
            elif node.type == 'op':
                dag.apply_operation_back(node.op.copy(), node.qargs,
                                         node.cargs, node.condition)
        return dag 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def _gather_succ(self, node_id, direct_succ):
        """
        Function set an attribute successors and gather multiple lists
        of direct successors into a single one.

        Args:
            node_id (int): label of the considered node in the DAG
            direct_succ (list): list of direct successors for the given node

        Returns:
            MultiDiGraph: with update of the attribute ['predecessors']
            the lists of direct successors are put into a single one
        """
        gather = self._multi_graph
        for d_succ in direct_succ:
            gather.get_node_data(node_id).successors.append([d_succ])
            succ = gather.get_node_data(d_succ).successors
            gather.get_node_data(node_id).successors.append(succ)
        return gather 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def _add_default_nodes(graph: 'networkx.MultiDiGraph',
                       fontsize: int = 12) -> None:
    """Add the standard nodes we need."""

    graph.add_node(START_NODE_ID,
                   label="START",
                   fillcolor="green", style="filled", fontsize=fontsize,
                   **{"class": "start active"})
    graph.add_node(END_NODE_ID,
                   label="END",
                   fillcolor="red", style="filled", fontsize=fontsize,
                   **{"class": "end"})
    graph.add_node(TMP_NODE_ID,
                   label="TMP",
                   style="invis",
                   **{"class": "invisible"}) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import MultiDiGraph [as 別名]
def _add_message_edge(graph: 'networkx.MultiDiGraph',
                      message: Dict[Text, Any],
                      current_node: int,
                      next_node_idx: int,
                      is_current: bool
                      ):
    """Create an edge based on the user message."""

    if message:
        message_key = sanitize(message.get("intent", {}).get("name", None))
        message_label = sanitize(message.get("text", None))
    else:
        message_key = None
        message_label = None

    _add_edge(graph, current_node, next_node_idx, message_key,
              message_label,
              **{"class": "active" if is_current else ""})