Python networkx.shortest_simple_paths() Examples

def k_shortest_paths(self, source, target):
        """
        如果源宿点是None，则返回len为1的None数组
        :param source:
        :param target:
        :return:
        """
        if source is None:
            return [None]
        generator = shortest_simple_paths(self, source, target, weight=self.weight)
        rtn = []
        index = 0
        for i in generator:
            index += 1
            if index > self.k:
                break
            rtn.append(i)
        return rtn 

def k_shortest_paths(self, source, target):
        """
        如果源宿点是None，则返回len为1的None数组
        :param source:
        :param target:
        :return:
        """
        if source is None:
            return [None]
        generator = shortest_simple_paths(self.net, source, target, weight=self.weight)
        rtn = []
        index = 0
        for i in generator:
            index += 1
            if index > self.k:
                break
            rtn.append(i)
        return rtn 

def visualize_frags(outdir, graphs, options):
    from rpy2.robjects import r

    utilities.ensure_dir(outdir)

    for i, graph in enumerate(graphs):
        r.pdf(os.path.join(outdir, "fragments.cluster_{}.pdf".format(i)))

        for component in networkx.connected_components(graph):
            subgraph = graph.subgraph(component)
            
            ends = [node for node,degree in subgraph.degree_iter() if degree==1]
            breakends = [node for node in list(networkx.shortest_simple_paths(subgraph, ends[0], ends[1]))[0]]
            # breakends = [breakend_from_label(node) for node in breakends]
            breakends = breakends[:-1:2] + breakends[-1:]
            # print ")"*100, breakends

            for sample, dataset in sorted(options.iter_10xdatasets()):
                plot_frags(breakends, options, sample, dataset)
        # plot_frags(breakpoints, options, sample, dataset)
        r["dev.off"]() 

def visualize_frag_cluster(breakpoints, options):
    """
    breakpoints are (chromx, x, chromy, y, orientation)
    """

    graph = networkx.Graph()
    for breakpoint in breakpoints:
        chromx, x, chromy, y, orientation = breakpoint
        graph.add_edge((chromx, x, orientation[0]), (chromy, y, orientation[1]))

    ends = [node for node,degree in graph.degree_iter() if degree==1]
    breakends = [node for node in list(networkx.shortest_simple_paths(graph, ends[0], ends[1]))[0]]
    # breakends = [breakend_from_label(node) for node in breakends]
    breakends = breakends[:-1:2] + breakends[-1:]
    # print ")"*100, breakends

    for sample, dataset in sorted(options.iter_10xdatasets()):
        plot_frags(breakends, options, sample, dataset) 

def test_weighted_shortest_simple_path():
    def cost_func(path):
        return sum(G.adj[u][v]['weight'] for (u, v) in zip(path, path[1:]))

    G = nx.complete_graph(5)
    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
    nx.set_edge_attributes(G, weight, 'weight')
    cost = 0
    for path in nx.shortest_simple_paths(G, 0, 3, weight='weight'):
        this_cost = cost_func(path)
        assert_true(cost <= this_cost)
        cost = this_cost 

def test_ssp_source_missing():
    G = nx.Graph()
    nx.add_path(G, [0, 1, 2])
    nx.add_path(G, [3, 4, 5])
    paths = list(nx.shortest_simple_paths(G, 0, 3)) 

def test_directed_weighted_shortest_simple_path():
    def cost_func(path):
        return sum(G.adj[u][v]['weight'] for (u, v) in zip(path, path[1:]))

    G = nx.complete_graph(5)
    G = G.to_directed()
    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
    nx.set_edge_attributes(G, weight, 'weight')
    cost = 0
    for path in nx.shortest_simple_paths(G, 0, 3, weight='weight'):
        this_cost = cost_func(path)
        assert_true(cost <= this_cost)
        cost = this_cost 

def test_directed_weighted_shortest_simple_path_issue2427():
    G = nx.DiGraph()
    G.add_edge('IN', 'OUT', weight=2)
    G.add_edge('IN', 'A', weight=1)
    G.add_edge('IN', 'B', weight=2)
    G.add_edge('B', 'OUT', weight=2)
    assert_equal(list(nx.shortest_simple_paths(G, 'IN', 'OUT', weight="weight")),
                 [['IN', 'OUT'], ['IN', 'B', 'OUT']])
    G = nx.DiGraph()
    G.add_edge('IN', 'OUT', weight=10)
    G.add_edge('IN', 'A', weight=1)
    G.add_edge('IN', 'B', weight=1)
    G.add_edge('B', 'OUT', weight=1)
    assert_equal(list(nx.shortest_simple_paths(G, 'IN', 'OUT', weight="weight")),
                 [['IN', 'B', 'OUT'], ['IN', 'OUT']]) 

def test_weight_name():
    G = nx.cycle_graph(7)
    nx.set_edge_attributes(G, 1, 'weight')
    nx.set_edge_attributes(G, 1, 'foo')
    G.adj[1][2]['foo'] = 7
    paths = list(nx.shortest_simple_paths(G, 0, 3, weight='foo'))
    solution = [[0, 6, 5, 4, 3], [0, 1, 2, 3]]
    assert_equal(paths, solution) 

def test_ssp_source_missing():
    G = nx.Graph()
    nx.add_path(G, [1, 2, 3])
    paths = list(nx.shortest_simple_paths(G, 0, 3)) 

def test_ssp_target_missing():
    G = nx.Graph()
    nx.add_path(G, [1, 2, 3])
    paths = list(nx.shortest_simple_paths(G, 1, 4)) 

def test_ssp_multigraph():
    G = nx.MultiGraph()
    nx.add_path(G, [1, 2, 3])
    paths = list(nx.shortest_simple_paths(G, 1, 4)) 

def test_shortest_simple_paths():
    G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
    paths = nx.shortest_simple_paths(G, 1, 12)
    assert_equal(next(paths), [1, 2, 3, 4, 8, 12])
    assert_equal(next(paths), [1, 5, 6, 7, 8, 12])
    assert_equal([len(path) for path in nx.shortest_simple_paths(G, 1, 12)],
                 sorted([len(path) for path in nx.all_simple_paths(G, 1, 12)])) 

def test_shortest_simple_paths_directed():
    G = nx.cycle_graph(7, create_using=nx.DiGraph())
    paths = nx.shortest_simple_paths(G, 0, 3)
    assert_equal([path for path in paths], [[0, 1, 2, 3]]) 

def test_Greg_Bernstein():
    g1 = nx.Graph()
    g1.add_nodes_from(["N0", "N1", "N2", "N3", "N4"])
    g1.add_edge("N4", "N1", weight=10.0, capacity=50, name="L5")
    g1.add_edge("N4", "N0", weight=7.0, capacity=40, name="L4")
    g1.add_edge("N0", "N1", weight=10.0, capacity=45, name="L1")
    g1.add_edge("N3", "N0", weight=10.0, capacity=50, name="L0")
    g1.add_edge("N2", "N3", weight=12.0, capacity=30, name="L2")
    g1.add_edge("N1", "N2", weight=15.0, capacity=42, name="L3")
    solution = [['N1', 'N0', 'N3'], ['N1', 'N2', 'N3'], ['N1', 'N4', 'N0', 'N3']]
    result = list(nx.shortest_simple_paths(g1, 'N1', 'N3', weight='weight'))
    assert_equal(result, solution) 

def test_weighted_shortest_simple_path():
    def cost_func(path):
        return sum(G.adj[u][v]['weight'] for (u, v) in zip(path, path[1:]))
    G = nx.complete_graph(5)
    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
    nx.set_edge_attributes(G, weight, 'weight')
    cost = 0
    for path in nx.shortest_simple_paths(G, 0, 3, weight='weight'):
        this_cost = cost_func(path)
        assert_true(cost <= this_cost)
        cost = this_cost 

def test_directed_weighted_shortest_simple_path():
    def cost_func(path):
        return sum(G.adj[u][v]['weight'] for (u, v) in zip(path, path[1:]))
    G = nx.complete_graph(5)
    G = G.to_directed()
    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
    nx.set_edge_attributes(G, weight, 'weight')
    cost = 0
    for path in nx.shortest_simple_paths(G, 0, 3, weight='weight'):
        this_cost = cost_func(path)
        assert_true(cost <= this_cost)
        cost = this_cost 

def test_ssp_source_missing():
    G = nx.Graph()
    nx.add_path(G, [1, 2, 3])
    paths = list(nx.shortest_simple_paths(G, 0, 3)) 

def test_ssp_target_missing():
    G = nx.Graph()
    nx.add_path(G, [1, 2, 3])
    paths = list(nx.shortest_simple_paths(G, 1, 4)) 

def test_ssp_multigraph():
    G = nx.MultiGraph()
    nx.add_path(G, [1, 2, 3])
    paths = list(nx.shortest_simple_paths(G, 1, 4)) 

def k_shortest_paths(G, source, target, k, weight=None):
    return list(islice(nx.shortest_simple_paths(G, source, target, weight=weight), k))

# DE PM! LO Ordena de menor a menos segun el weight 

def test_Greg_Bernstein():
    g1 = nx.Graph()
    g1.add_nodes_from(["N0", "N1", "N2", "N3", "N4"])
    g1.add_edge("N4", "N1", weight=10.0, capacity=50, name="L5")
    g1.add_edge("N4", "N0", weight=7.0, capacity=40, name="L4")
    g1.add_edge("N0", "N1", weight=10.0, capacity=45, name="L1")
    g1.add_edge("N3", "N0", weight=10.0, capacity=50, name="L0")
    g1.add_edge("N2", "N3", weight=12.0, capacity=30, name="L2")
    g1.add_edge("N1", "N2", weight=15.0, capacity=42, name="L3")
    solution = [['N1', 'N0', 'N3'], ['N1', 'N2', 'N3'], ['N1', 'N4', 'N0', 'N3']]
    result = list(nx.shortest_simple_paths(g1, 'N1', 'N3', weight='weight'))
    assert_equal(result, solution) 

def test_shortest_simple_paths_directed():
    G = nx.cycle_graph(7, create_using=nx.DiGraph())
    paths = nx.shortest_simple_paths(G, 0, 3)
    assert_equal([path for path in paths], [[0, 1, 2, 3]]) 

def test_shortest_simple_paths():
    G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
    paths = nx.shortest_simple_paths(G, 1, 12)
    assert_equal(next(paths), [1, 2, 3, 4, 8, 12])
    assert_equal(next(paths), [1, 5, 6, 7, 8, 12])
    assert_equal([len(path) for path in nx.shortest_simple_paths(G, 1, 12)],
                 sorted([len(path) for path in nx.all_simple_paths(G, 1, 12)])) 

def test_ssp_source_missing():
    G = nx.Graph()
    G.add_path([0, 1, 2])
    G.add_path([3, 4, 5])
    paths = list(nx.shortest_simple_paths(G, 0, 3)) 

def test_ssp_multigraph():
    G = nx.MultiGraph()
    G.add_path([1,2,3])
    paths = list(nx.shortest_simple_paths(G, 1, 4)) 

def test_ssp_target_missing():
    G = nx.Graph()
    G.add_path([1,2,3])
    paths = list(nx.shortest_simple_paths(G, 1, 4)) 

def test_ssp_source_missing():
    G = nx.Graph()
    G.add_path([1,2,3])
    paths = list(nx.shortest_simple_paths(G, 0, 3)) 

def test_directed_weighted_shortest_simple_path():
    def cost_func(path):
        return sum(G.edge[u][v]['weight'] for (u, v) in zip(path, path[1:]))
    G = nx.complete_graph(5)
    G = G.to_directed()
    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
    nx.set_edge_attributes(G, 'weight', weight)
    cost = 0
    for path in nx.shortest_simple_paths(G, 0, 3, weight='weight'):
        this_cost = cost_func(path)
        assert_true(cost <= this_cost)
        cost = this_cost 

def test_weighted_shortest_simple_path():
    def cost_func(path):
        return sum(G.edge[u][v]['weight'] for (u, v) in zip(path, path[1:]))
    G = nx.complete_graph(5)
    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
    nx.set_edge_attributes(G, 'weight', weight)
    cost = 0
    for path in nx.shortest_simple_paths(G, 0, 3, weight='weight'):
        this_cost = cost_func(path)
        assert_true(cost <= this_cost)
        cost = this_cost