Python networkx.shortest_path方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def verify(prog, src_name, dst_name):
    src = prog.subs.find(src_name)
    dst = prog.subs.find(dst_name)
    if src is None or dst is None:
        return None

    graphs = GraphsBuilder()
    graphs.run(prog)
    cg = graphs.callgraph

    if nx.has_path(cg, src.id.number, dst.id.number):
        return ('calls', nx.shortest_path(cg, src.id.number, dst.id.number))

    calls = CallsitesCollector(graphs.callgraph, src.id.number, dst.id.number)

    for sub in prog.subs:
        calls.run(sub)
        cfg = graphs.callgraph.nodes[sub.id.number]['cfg']
        for src in calls.srcs:
            for dst in calls.dsts:
                if src != dst and nx.has_path(cfg, src, dst):
                    return ('sites', nx.shortest_path(cfg, src, dst))
        calls.clear()

    return None 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_path_node_ids(self, leaf_id: NodeId, root_id: NodeId = None) -> List[NodeId]:
        """
        Get the data of the path between ``leaf_id`` and ``root_id``.

        Args:
            leaf_id: Id that identifies the leaf of the tree. \
                     If ``leaf_id`` is the hash of a walker state ``from_hash`` \
                     needs to be ``True``. Otherwise it refers to a node id of \
                     the leaf node.
            root_id: Node id of the root node of the tree.

        Returns:
            List of the node ids between ``root`` and ``leaf_id``.

        """
        root = root_id if root_id is not None else self.root_id
        nodes = nx.shortest_path(self.data, root, leaf_id)
        return nodes 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_seed_scaffold():

    g_idx = 1
    seed_scaffolds = {} #this stores initial long scaffolds
    to_merge = set()

    for subg in nx.connected_component_subgraphs(G):
        p = []
        for node in subg.nodes():
            if subg.degree(node) == 1:
                p.append(node)

        #If this is 2 then we have found the path!
        if len(p) == 2:
            path = nx.shortest_path(subg,p[0],p[1])
            seed_scaffolds[g_idx] = path
            g_idx += 1


        #else try to insert these contigs in the long scaffolds generated previously
        else:
            for node in subg.nodes():
                to_merge.add(node.split(':')[0])

    return seed_scaffolds, to_merge 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_typing(self, source, target):
        """Get a typing dict associated to the edge 'source->target'."""
        if (source, target) in self.edges():
            if self.is_graph(source):
                return self.get_edge(source, target)["mapping"]
            else:
                edge = self.get_edge(source, target)
                return (edge["lhs_mapping"], edge["rhs_mapping"])
        else:
            try:
                path = nx.shortest_path(self._graph, source, target)
            except:
                raise HierarchyError(
                    "No path from '{}' to '{}' in the hierarchy".format(
                        source, target))
            return self.compose_path_typing(path) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_event_path(self, current_state, target_state):
        path_events = []
        try:
            states = nx.shortest_path(G=self.G, source=current_state.state_str, target=target_state.state_str)
            if not isinstance(states, list) or len(states) < 2:
                self.logger.warning("Error getting path from %s to %s" %
                                    (current_state.state_str, target_state.state_str))
            start_state = states[0]
            for state in states[1:]:
                edge = self.G[start_state][state]
                edge_event_strs = list(edge["events"].keys())
                if self.random_input:
                    random.shuffle(edge_event_strs)
                path_events.append(edge["events"][edge_event_strs[0]]["event"])
                start_state = state
        except Exception as e:
            print(e)
            self.logger.warning("Cannot find a path from %s to %s" % (current_state.state_str, target_state.state_str))
        return path_events 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def shortest_undirected_path(self, physical_qubit1, physical_qubit2):
        """Returns the shortest undirected path between physical_qubit1 and physical_qubit2.

        Args:
            physical_qubit1 (int): A physical qubit
            physical_qubit2 (int): Another physical qubit
        Returns:
            List: The shortest undirected path
        Raises:
            CouplingError: When there is no path between physical_qubit1, physical_qubit2.
        """
        try:
            return nx.shortest_path(self.graph.to_undirected(as_view=True), source=physical_qubit1,
                                    target=physical_qubit2)
        except nx.exception.NetworkXNoPath:
            raise CouplingError(
                "Nodes %s and %s are not connected" % (str(physical_qubit1), str(physical_qubit2))) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_path(self, sim, app_name, message, topology_src, alloc_DES, alloc_module, traffic, from_des):
        """
        Get the path between a node of the topology and a module deployed in a node. Furthermore it chooses the process deployed in that node.
        """
        node_src = topology_src  # TOPOLOGY SOURCE where the message is generated

        # print "Node (Topo id): %s" %node_src
        # print "Service DST: %s "%message.dst
        DES_dst = alloc_module[app_name][message.dst]
        # print "DES DST: %s" % DES_dst
        minLenPath = float('inf')
        minPath = []
        bestDES = 0
        for des in DES_dst:
            node_dst = sim.alloc_DES[des]
            path = list(nx.shortest_path(sim.topology.G, source=node_src, target=node_dst))
            if len(path) < minLenPath:
                minLenPath = len(path)
                minPath = [path]
                bestDES = [des]

        return minPath, bestDES 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def compute_BEST_DES(self, node_src, alloc_DES, sim, DES_dst,message):
        try:

            bestLong = float('inf')
            minPath = []
            bestDES = []
            #print len(DES_dst)
            for dev in DES_dst:
                #print "DES :",dev
                node_dst = alloc_DES[dev]
                path = list(nx.shortest_path(sim.topology.G, source=node_src, target=node_dst))
                long = len(path)

                if  long < bestLong:
                    bestLong = long
                    minPath = path
                    bestDES = dev

            #print bestDES,minPath
            return minPath, bestDES

        except (nx.NetworkXNoPath, nx.NodeNotFound) as e:
            self.logger.warning("There is no path between two nodes: %s - %s " % (node_src, node_dst))
            # print "Simulation ends?"
            return [], None 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_path(self, sim, app_name, message, topology_src, alloc_DES, alloc_module, traffic,from_des):

        node_src = topology_src
        DES_dst = alloc_module[app_name][message.dst]  # returns an array with all DES process serving

        if message.dst not in self.rr.keys():
            self.rr[message.dst] = 0

        # print "GET PATH"
        # print "\tNode _ src (id_topology): %i" % node_src
        # print "\tRequest service: %s " % (message.dst)
        # print "\tProcess serving that service: %s (pos ID: %i)" % (DES_dst, self.rr[message.dst])

        next_DES_dst =DES_dst[self.rr[message.dst]]

        dst_node = alloc_DES[next_DES_dst]
        path = list(nx.shortest_path(sim.topology.G, source=node_src, target=dst_node))
        bestPath = [path]
        bestDES = [next_DES_dst]
        self.rr[message.dst] = (self.rr[message.dst] + 1) % len(DES_dst)

        return bestPath, bestDES 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def compute_most_near(self,node_src,alloc_DES,sim,DES_dst):
        """
        This functions caches the minimun path among client-devices and fog-devices-Module Calculator and it chooses the best calculator process deployed in that node
        """
        #By Placement policy we know that:
        try:
            minLenPath = float('inf')
            minPath = []
            bestDES = []
            for dev in DES_dst:
                node_dst = alloc_DES[dev]
                path = list(nx.shortest_path(sim.topology.G, source=node_src, target=node_dst))
                if len(path)<minLenPath:
                    minLenPath = len(path)
                    minPath = path
                    bestDES = dev

            return minPath,bestDES
        except nx.NetworkXNoPath:
            self.logger.warning("There is no path between two nodes: %s - %s "%(node_src,node_dst))
            print "Simulation ends?"
            return [],None 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_path(self, sim, app_name, message, topology_src, alloc_DES, alloc_module, traffic, from_des):

        node_src = topology_src

        DES_dst = alloc_module[app_name][message.dst]

        bestPath = []
        bestDES = []
        for des in DES_dst:
            dst_node = alloc_DES[des]

       #     print type(node_src)
        #    print type(dst_node)
         #   print "NODE SRC: %s" %node_src
          #  print "NODE DST: %s" %dst_node

            path = list(nx.shortest_path(sim.topology.G, source=node_src, target=str(dst_node)))
           # print path

            bestPath = [path]
            bestDES  = [des]

        return bestPath,bestDES 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_path(self, sim, app_name, message, topology_src, alloc_DES, alloc_module, traffic, from_des):

        node_src = topology_src
        DES_dst = alloc_module[app_name][message.dst]  # returns an array with all DES process serving

        if message.dst not in self.rr.keys():
            self.rr[message.dst] = 0

        # print "GET PATH"
        # print "\tNode _ src (id_topology): %i" % node_src
        # print "\tRequest service: %s " % (message.dst)
        # print "\tProcess serving that service: %s (pos ID: %i)" % (DES_dst, self.rr[message.dst])

        next_DES_dst =DES_dst[self.rr[message.dst]]

        dst_node = alloc_DES[next_DES_dst]
        path = list(nx.shortest_path(sim.topology.G, source=node_src, target=dst_node))
        bestPath = [path]
        bestDES = [next_DES_dst]
        self.rr[message.dst] = (self.rr[message.dst] + 1) % len(DES_dst)

        return bestPath, bestDES 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def compute_most_near(self,node_src,alloc_DES,sim,DES_dst):
        """
        This functions caches the minimun path among client-devices and fog-devices-Module Calculator and it chooses the best calculator process deployed in that node
        """
        #By Placement policy we know that:

        minLenPath = float('inf')
        minPath = []
        bestDES = []
        for dev in DES_dst:
            node_dst = alloc_DES[dev]
            path = list(nx.shortest_path(sim.topology.G, source=node_src, target=node_dst))
            if len(path)<minLenPath:
                minLenPath = len(path)
                minPath = path
                bestDES = dev

        return minPath,bestDES 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def get_path(self, sim, app_name,message, topology_src, alloc_DES, alloc_module, traffic,from_des):
        paths = []
        dst_idDES = []

        node_src = topology_src #TOPOLOGY SOURCE where the message is generated
        DES_dst = alloc_module[app_name][message.dst]

        #Among all possible path we choose the smallest
        bestPath = []
        bestDES = []
        print (DES_dst)
        for des in DES_dst:
            dst_node = alloc_DES[des]
            # print "DES Node %i " %dst_node

            path = list(nx.shortest_path(sim.topology.G, source=node_src, target=dst_node))
            bestPath = [path]
            bestDES  = [des]
            print (path)


        return bestPath,bestDES 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import shortest_path [as 別名]
def bring_farthest_pair_together(self, pairs: Sequence[QidPair]):
        """Adds SWAPs to bring the farthest-apart pair of logical qubits
        together."""
        distances = [self.distance(pair) for pair in pairs]
        assert distances
        max_distance = min(distances)
        farthest_pairs = [
            pair for pair, d in zip(pairs, distances) if d == max_distance
        ]
        choice = self.prng.choice(len(farthest_pairs))
        farthest_pair = farthest_pairs[choice]
        edge = self.log_to_phys(*farthest_pair)
        shortest_path = nx.shortest_path(self.device_graph, *edge)
        assert len(shortest_path) - 1 == max_distance
        midpoint = max_distance // 2
        self.swap_along_path(shortest_path[:midpoint])
        self.swap_along_path(shortest_path[midpoint:])