python类has_path()的实例源码

def add_cycle_edges_by_path(g,number_of_edges,path_length = 5):
    number = 0
    num_nodes = g.number_of_nodes()
    nodes = g.nodes()
    extra_edges = []
    while number < number_of_edges:
        u,v = np.random.randint(0,num_nodes,2)
        u = nodes[u]
        v = nodes[v]
        if nx.has_path(g,u,v):
            length = nx.shortest_path_length(g,source = u,target = v)
            if length <= path_length:
                extra_edges.append((v,u))
                number += 1
        if nx.has_path(g,v,u):
            length = nx.shortest_path_length(g,source = v,target = u)
            if length <= path_length:
                extra_edges.append((u,v))
                number += 1
    print("# extra edges added with path length <= %d: %d" % (path_length,len(extra_edges)))
    return extra_edges

def add_extra_edges(g,number_of_edges):
    number = 0
    num_nodes = g.number_of_nodes()
    nodes = g.nodes()
    extra_edges = set()
    while len(extra_edges) < number_of_edges:
        u,v = np.random.randint(0,num_nodes,2)
        u = nodes[u]
        v = nodes[v]
        if nx.has_path(g,u,v):
            if (v,u) not in extra_edges:
                extra_edges.add((v,u))  
        if nx.has_path(g,v,u):
            if (u,v) not in extra_edges:
                extra_edges.add((u,v))
    extra_edges = list(extra_edges)
    print("# extra edges added (path lenght unconstrainted): %d" % (len(extra_edges)))
    return extra_edges

def _all_simple_paths_multigraph(G, source, target, cutoff=None):
    if cutoff < 1:
        return
    visited = [source]
    stack = [(v for u,v,k in list(G.edges(source, keys=True)))]
    while stack:
        children = stack[-1]
        child = next(children, None)
        if child is None:
            stack.pop()
            visited.pop()
        elif nx.has_path(G, child, target) == False: # added kaklise
            pass
        elif len(visited) < cutoff:
            if child == target:
                yield visited + [target]
            elif child not in visited:
                visited.append(child)
                stack.append((v for u,v in list(G.edges(child))))
        else: #len(visited) == cutoff:
            count = ([child]+list(children)).count(target)
            for i in range(count):
                yield visited + [target]
            stack.pop()
            visited.pop()

def hasLogicalError(self):
        for type in self.types:
            for charge_type in ['X','Z']:
                LogicalLattice = self.Primal.copy()
                for node in self.Primal.nodes():
                    if self.Primal.node[node]['charge'][charge_type] == 0:
                        LogicalLattice.remove_node(node)
                for node1 in LogicalLattice.nodes():
                    for node2 in LogicalLattice.nodes():
                        if node1 in self.Boundary[type] and node2 in self.Boundary[type]:
                            if self.Boundary[type][node1] != self.Boundary[type][node2]:
                                start, end = node1, node2
                                if start in LogicalLattice.nodes() and end in LogicalLattice.nodes():
                                    if nx.has_path(LogicalLattice, start, end):
                                        return True

        return False

def has_path(self, source_address, target_address):
        """ True if there is a connecting path regardless of the number of hops. """
        try:
            return networkx.has_path(self.graph, source_address, target_address)
        except (networkx.NodeNotFound, networkx.NetworkXNoPath):
            return False

def has_sense_path(G, source, target):
    if source not in G.senses or target not in G.senses:
        return False

    for source_sense, target_sense in itertools.product(G.senses[source], G.senses[target]):
        if nx.has_path(G, source_sense, target_sense):
            return True

    return False

def get_shortest_path(self, nx_graph, src_dpid, dst_dpid):

        if nx.has_path(nx_graph, src_dpid, dst_dpid):
            return nx.shortest_path(nx_graph, src_dpid, dst_dpid)

        return None

def get_path(self,q0,qf):
        """
        Searches for a path in the PRM between configurations q0 and qf.
        Returns a list of configuration tuples describing the path or []
        if no path is found.
        """
        q0 = np.reshape(np.array(q0),3)
        qf = np.reshape(np.array(qf),3)
        if all(q0 == qf):
            return [qf]

        n0 = len(self.G.node)
        nf = n0 + 1

        # Add the start and end configs to G, so we can just search it
        self.G.add_node(n0,cfg=q0)
        self.G.add_node(nf,cfg=qf)
        for k in [n0,nf]:
            self._connect(k,DobotModel.forward_kinematics(self.G.node[k]['cfg']))

        if not nx.has_path(self.G,n0,nf):
            path = [] # could not find a path
        else:
            nodes = nx.dijkstra_path(self.G,n0,nf,'weight')
            path = [self.G.node[k]['cfg'] for k in nodes]

        # Remove the start and end configs so G remains consistent with tree
        self.G.remove_node(n0)
        self.G.remove_node(nf)

        return path

def get_shortest_path(self, nx_graph, src_dpid, dst_dpid):

        if nx.has_path(nx_graph, src_dpid, dst_dpid):
            return nx.shortest_path(nx_graph, src_dpid, dst_dpid)

        return None

def get_shortest_path(self, nx_graph, src_dpid, dst_dpid):

        if nx.has_path(nx_graph, src_dpid, dst_dpid):
            return nx.shortest_path(nx_graph, src_dpid, dst_dpid)

        return None

def _remove_unreachable_nodes(graph):
    """Remove all data structures that are not reachable
    from modules or from stacks.
    """
    removed = 0
    for node in graph.nodes()[:]:
        # check if reachable from globals
        for root in graph.root_nodes:
            if nx.has_path(graph, root, node):
                break
        else:
            graph.remove_node(node)
            removed += 1
    return removed

def find_affected_but_not_down(netbox_going_down, netboxes):
    """Mark affected but not down because of redundancy boxes"""
    graph = build_layer2_graph()
    if not graph.has_node(netbox_going_down):
        return [netbox_going_down]
    graph.remove_node(netbox_going_down)
    masters = find_uplink_nodes(netbox_going_down)
    redundant = []
    for netbox in netboxes:
        if netbox_going_down == netbox:
            continue
        if any(nx.has_path(graph, master, netbox) for master in masters):
            redundant.append(netbox)

    return redundant

def guess(graph, question, choices):
    MAX = 9999
    SUBMAX = 999
    ques_node = find_node(graph, question)
    dists = []
    for choice in choices:
        choice_node = find_node(graph, choice)
        if ques_node is None and choice_node is None:
            dist = MAX
        elif ques_node is None and choice_node is not None:
            dist = SUBMAX
        elif ques_node is not None and choice_node is None:
            dist = MAX
        else:
            if nx.has_path(graph, ques_node, choice_node):
                pl = len(nx.shortest_path(graph, ques_node, choice_node))
                dist = pl
            else:
                dist = MAX
        dists.append(dist)
    answer, dist = min(enumerate(dists), key=lambda x: x[1])
    max_dist = max(dists)
    if dist == MAX:
        return None
    if dist == max_dist:
        return None
    return answer

def links_in_simple_paths(self, sources, sinks):
        """
        Count all links in a simple path between sources and sinks

        Parameters
        -----------
        sources : list
            List of source nodes
        sinks : list
            List of sink nodes
        sinks : list
            List of sink nodes

        Returns
        -------
        link_count : dict
            A dictonary with the number of times each link is involved in a path
        """
        link_names = [name for (node1, node2, name) in list(self.edges(keys=True))]
        link_count = pd.Series(data = 0, index=link_names)

        for sink in sinks:
            for source in sources:
                if nx.has_path(self, source, sink):
                    paths = _all_simple_paths(self,source,target=sink)
                    for path in paths:
                        for i in range(len(path)-1):
                            links = list(self[path[i]][path[i+1]].keys())
                            for link in links:
                                link_count[link] = link_count[link]+1

        return link_count

def hasConnectedBoundaries(code, loops_graph, Exts):
    if len(loops_graph.edges()) <= 1:
        return False
    for ext1 in loops_graph.nodes():
        for ext2 in loops_graph.nodes():
            if ext1 in Exts and ext2 in Exts and ext1 != ext2:
                if nx.has_path(loops_graph,ext1,ext2):
                    path = nx.shortest_path(loops_graph, ext1, ext2)
                    for node in path:
                        if node not in Exts:
                            return True
    return False

def connectedBoundaries(loops_graph, Exts):
    for ext1 in loops_graph.nodes():
        for ext2 in loops_graph.nodes():
            if ext1 in Exts and ext2 in Exts and ext1 != ext2:
                if nx.has_path(loops_graph,ext1,ext2):
                    path = nx.shortest_path(loops_graph, ext1, ext2)
                    for node in path:
                        if node not in Exts:
                            return ext1, ext2

def extract_diff_graph(graph, diff):
    diff_graph = graph.copy()
    diff_graph.root_nodes = list()

    #     pnodes = collections.defaultdict(set)
    #     for m in diff[0] | diff[2]:
    #         for n in diff_graph.nodes():
    #             if nx.has_path(diff_graph, n, m):
    #                 pnodes[m].add(n)
    #
    #     nodes = set.union(*pnodes.values())
    nodes = set()
    logging.debug('Searching on-paths nodes...')
    for m in graph.root_nodes:
        for i in diff[0] | diff[2]:
            if m == i:
                nodes.add(m)
            try:
                nodes.update(*list(search_paths(diff_graph, m, i)))
            except:
                pass
        if m in nodes:
            diff_graph.root_nodes.append(m)

    logging.debug('Removing nodes off-path...')
    for n in diff_graph.nodes()[:]:
        if n not in nodes:
            diff_graph.remove_node(n)
    logging.debug('Setting node colors...')
    for n in diff_graph.nodes():
        diff_graph.node[n]['color'] = 'turquoise'
    for n in diff[0]:
        diff_graph.node[n]['color'] = 'red'
        if isinstance(n, Stack):
            diff_graph.node[n]['color'] = 'deeppink'
    for n in diff[2]:
        diff_graph.node[n]['color'] = 'green'
    logging.debug('Exporting diff_graph.dot...')
    nx.write_dot(diff_graph, 'diff_graph.dot')
    return diff_graph

# internal classes


# internal functions

def __validate_coloring(self):
        log.debug("validating red-blue coloring; this could take a while (forever) if your graph is too big")
        # All red paths to a vertex should be disjoint from all blue paths
        # to the same vertex, except for red-blue links and their incident nodes
        red_dag = self.get_red_graph()
        blue_dag = self.get_blue_graph()
        source = self.root

        for dst in self.graph.nodes():
            if dst == source:
                continue

            red_paths = list(nx.all_simple_paths(red_dag, source, dst))
            red_nodes = set(n for p in red_paths for n in p)
            red_edges = set(e for p in red_paths for e in zip(p, p[1:]))
            blue_paths = list(nx.all_simple_paths(blue_dag, source, dst))
            blue_nodes = set(n for p in blue_paths for n in p)
            blue_edges = set(e for p in blue_paths for e in zip(p, p[1:]))

            redblue_edges = red_edges.intersection(blue_edges)
            redblue_nodes = red_nodes.intersection(blue_nodes)
            redblue_nodes.remove(source)
            redblue_nodes.remove(dst)

            assert all(self._get_edge_color(e) == 'red-blue' for e in redblue_edges),\
                "invalid coloring: non cut link shared by red and blue paths!"
            # every shared node has at least one incident cut link
            # TODO: finish this?  unclear it's necessary as it just validates consistency of coloring not actual correctness of properties
            # assert all(any(self._get_edge_color(e) == 'red-blue' for e in
            #                list(self.graph.successors(n)) + list(self.graph.predecessors(n)))
            #            for n in redblue_nodes), "invalid coloring of nodes: shares a non-cut node!"

            # verify each red-blue edge or node is a cut edge/node
            for cut_node in redblue_nodes:
                g = self.graph.subgraph(n for n in self.graph.nodes() if n != cut_node)
                # could induce an empty (or near-empty) graph
                if source not in g or dst not in g:
                    continue
                assert not nx.has_path(g, source, dst), "invalid coloring: non cut node shared by red and blue paths!"
            for cut_link in redblue_edges:
                g = self.graph.edge_subgraph(e for e in self.graph.edges() if e != cut_link)
                # could induce an empty (or near-empty) graph
                if source not in g or dst not in g:
                    continue
                assert not nx.has_path(g, source, dst), "invalid coloring: non cut link shared by red and blue paths!"
        # draw_overlaid_graphs(self.graph, [red_dag, blue_dag])

        return True