Python networkx.set_edge_attributes() Examples

def test_get_edge_attributes():
    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
    for G in graphs:
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 100
        nx.set_edge_attributes(G, vals, attr)
        attrs = nx.get_edge_attributes(G, attr)

        assert_equal(len(attrs), 2)
        if G.is_multigraph():
            keys = [(0, 1, 0), (1, 2, 0)]
            for u, v, k in keys:
                try:
                    assert_equal(attrs[(u, v, k)], 100)
                except KeyError:
                    assert_equal(attrs[(v, u, k)], 100)
        else:
            keys = [(0, 1), (1, 2)]
            for u, v in keys:
                try:
                    assert_equal(attrs[(u, v)], 100)
                except KeyError:
                    assert_equal(attrs[(v, u)], 100) 

def test_get_edge_attributes():
    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
    for G in graphs:
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 100
        nx.set_edge_attributes(G, vals, attr)
        attrs = nx.get_edge_attributes(G, attr)

        assert_equal(len(attrs), 2)
        if G.is_multigraph():
            keys = [(0, 1, 0), (1, 2, 0)]
            for u, v, k in keys:
                try:
                    assert_equal(attrs[(u, v, k)], 100)
                except KeyError:
                    assert_equal(attrs[(v, u, k)], 100)
        else:
            keys = [(0, 1), (1, 2)]
            for u, v in keys:
                try:
                    assert_equal(attrs[(u, v)], 100)
                except KeyError:
                    assert_equal(attrs[(v, u)], 100) 

def test_networkx2igraph(self):
        import networkx as nx
        ng = nx.complete_graph(3)
        [x, y] = [int(x) for x in nx.__version__.split('.')]
        if x == 1:
            nx.set_node_attributes(ng, 'vattrib', 0)
            nx.set_edge_attributes(ng, 'eattrib', 1)
        else:
            nx.set_node_attributes(ng, 0, 'vattrib')
            nx.set_edge_attributes(ng, 1, 'eattrib')
        (e, n) = graphistry.bind(source='src', destination='dst').networkx2pandas(ng)

        edges = pd.DataFrame({
            'dst': {0: 1, 1: 2, 2: 2},
            'src': {0: 0, 1: 0, 2: 1},
            'eattrib': {0: 1, 1: 1, 2: 1}
        })
        nodes = pd.DataFrame({
            '__nodeid__': {0: 0, 1: 1, 2: 2},
            'vattrib': {0: 0, 1: 0, 2: 0}
        })

        assertFrameEqual(e, edges)
        assertFrameEqual(n, nodes) 

def create_star_graph(n_nodes=10, ring=True):
    """
    Create a star graph with the points connected by
    Args:
        n_nodes (int): number of nodes in graph (max 26)
        ring (Boolean): add ring around the border with low (distance=2) weights

    Returns:
        networkx MultiGraoh in the shape of a star

    """
    graph = nx.MultiGraph()
    node_names = list(string.ascii_lowercase)[:n_nodes]
    graph.add_star(node_names)
    nx.set_edge_attributes(graph, 10, 'distance')
    nx.set_edge_attributes(graph, 1, 'required')
    nx.set_edge_attributes(graph, 'solid', 'style')
    if ring:
        for e in list(zip(node_names[1:-1] + [node_names[1]], node_names[2:] + [node_names[-1]])):
            graph.add_edge(e[0], e[1], distance=2, required=0, style='dashed')
    return graph 

def test_edge_attribute(self):

        g = nx.karate_club_graph()
        attr = {(u, v): {"even": int((u+v) % 2)} for (u, v) in g.edges()}
        nx.set_edge_attributes(g, attr)

        model = gc.CompositeModel(g)
        model.add_status("Susceptible")
        model.add_status("Infected")

        c = cpm.EdgeCategoricalAttribute("even", "0", probability=0.6)
        model.add_rule("Susceptible", "Infected", c)

        config = mc.Configuration()
        config.add_model_parameter('fraction_infected', 0.1)

        model.set_initial_status(config)
        iterations = model.iteration_bunch(10)
        self.assertEqual(len(iterations), 10) 

def calculate_edge_lengths(G, verbose=True):

    # Calculate the lengths of the edges

    if verbose:
        print('Calculating edge lengths...')

    x = np.matrix(G.nodes.data('x'))[:, 1]
    y = np.matrix(G.nodes.data('y'))[:, 1]

    node_coordinates = np.concatenate([x, y], axis=1)
    node_distances = squareform(pdist(node_coordinates, 'euclidean'))

    adjacency_matrix = np.array(nx.adjacency_matrix(G).todense())
    adjacency_matrix = adjacency_matrix.astype('float')
    adjacency_matrix[adjacency_matrix == 0] = np.nan

    edge_lengths = np.multiply(node_distances, adjacency_matrix)

    edge_attr_dict = {index: v for index, v in np.ndenumerate(edge_lengths) if ~np.isnan(v)}
    nx.set_edge_attributes(G, edge_attr_dict, 'length')

    return G 

def load_graphml(self,filename):
        warnings.warn("The load_graphml function is deprecated and "
                      "will be removed in version 2.0.0. "
                      "Use NX.READ_GRAPHML function instead.",
                      FutureWarning,
                      stacklevel=8
                      )

        self.G = nx.read_graphml(filename)
        attEdges = {}
        for k in self.G.edges():
            attEdges[k] = {"BW": 1, "PR": 1}
        nx.set_edge_attributes(self.G, values=attEdges)
        attNodes = {}
        for k in self.G.nodes():
            attNodes[k] = {"IPT": 1}
        nx.set_node_attributes(self.G, values=attNodes)
        for k in self.G.nodes():
            self.nodeAttributes[k] = self.G.node[k] #it has "id" att. TODO IMPROVE 

def test_typical_cases(self):

        G = nx.complete_graph(10)

        S = dnx.maximum_cut(G, ExactSolver())
        self.assertTrue(len(S) == 5)  # half of the nodes

        with self.assertRaises(dnx.DWaveNetworkXException):
            S = dnx.weighted_maximum_cut(G, ExactSolver())

        nx.set_edge_attributes(G, 1, 'weight')
        S = dnx.weighted_maximum_cut(G, ExactSolver())
        self.assertTrue(len(S) == 5)  # half of the nodes

        G = nx.Graph()
        G.add_edges_from([(0, 1), (0, 2), (1, 2), (1, 3), (3, 4), (2, 4)])
        S = dnx.maximum_cut(G, ExactSolver())
        self.assertTrue(len(S) in (2, 3))

        # this needs another one for weight 

def test_weighted_input():
    G1 = nx.karate_club_graph()
    G2 = nx.karate_club_graph()
    rand = np.random.RandomState(seed=42)
    edge_weights = {e: rand.randint(0, 1000) for e in G2.edges}
    nx.set_edge_attributes(G2, edge_weights, "weight")
    assert nx.is_isomorphic(G1, G2)

    for label, obj in distance.__dict__.items():
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter("always")
            if isinstance(obj, type) and BaseDistance in obj.__bases__:
                dist = obj().dist(G1, G2)
                warning_triggered = False
                for warning in w:
                    if "weighted" in str(warning.message):
                        warning_triggered = True
                if not warning_triggered:
                    assert not np.isclose(dist, 0.0)
                else:
                    assert np.isclose(dist, 0.0) 

def test_set_edge_attributes():
    graphs = [nx.Graph(), nx.DiGraph()]
    for G in graphs:
        G = nx.path_graph(3, create_using=G)

        # Test single value
        attr = 'hello'
        vals = 3
        nx.set_edge_attributes(G, attr, vals)
        assert_equal(G[0][1][attr], vals)
        assert_equal(G[1][2][attr], vals)

        # Test multiple values
        attr = 'hi'
        edges = [(0,1), (1,2)]
        vals = dict(zip(edges, range(len(edges))))
        nx.set_edge_attributes(G, attr, vals)
        assert_equal(G[0][1][attr], 0)
        assert_equal(G[1][2][attr], 1) 

def test_set_edge_attributes_multi():
    graphs = [nx.MultiGraph(), nx.MultiDiGraph()]
    for G in graphs:
        G = nx.path_graph(3, create_using=G)

        # Test single value
        attr = 'hello'
        vals = 3
        nx.set_edge_attributes(G, attr, vals)
        assert_equal(G[0][1][0][attr], vals)
        assert_equal(G[1][2][0][attr], vals)

        # Test multiple values
        attr = 'hi'
        edges = [(0,1,0), (1,2,0)]
        vals = dict(zip(edges, range(len(edges))))
        nx.set_edge_attributes(G, attr, vals)
        assert_equal(G[0][1][0][attr], 0)
        assert_equal(G[1][2][0][attr], 1) 

def test_get_edge_attributes():
    graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
    for G in graphs:
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 100
        nx.set_edge_attributes(G, attr, vals)
        attrs = nx.get_edge_attributes(G, attr)

        assert_equal(len(attrs), 2)
        if G.is_multigraph():
            keys = [(0,1,0), (1,2,0)]
        else:
            keys = [(0,1), (1,2)]
        for key in keys:
            assert_equal(attrs[key], 100) 

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_set_edge_attributes_multi():
    graphs = [nx.MultiGraph(), nx.MultiDiGraph()]
    for G in graphs:
        # Test single value
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 3
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][0][attr], vals)
        assert_equal(G[1][2][0][attr], vals)

        # Test multiple values
        G = nx.path_graph(3, create_using=G)
        attr = 'hi'
        edges = [(0, 1, 0), (1, 2, 0)]
        vals = dict(zip(edges, range(len(edges))))
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][0][attr], 0)
        assert_equal(G[1][2][0][attr], 1)

        # Test dictionary of dictionaries
        G = nx.path_graph(3, create_using=G)
        d = {'hi': 0, 'hello': 200}
        edges = [(0, 1, 0)]
        vals = dict.fromkeys(edges, d)
        nx.set_edge_attributes(G, vals)
        assert_equal(G[0][1][0]['hi'], 0)
        assert_equal(G[0][1][0]['hello'], 200)
        assert_equal(G[1][2][0], {}) 

def test_effective_size_weighted_undirected(self):
        G = self.G.copy()
        nx.set_edge_attributes(G, self.G_weights, 'weight')
        effective_size = nx.effective_size(G, weight='weight')
        assert_almost_equal(round(effective_size['G'], 2), 5.47)
        assert_almost_equal(round(effective_size['A'], 2), 2.47)
        assert_almost_equal(round(effective_size['C'], 2), 1) 

def test_set_edge_attributes():
    graphs = [nx.Graph(), nx.DiGraph()]
    for G in graphs:
        # Test single value
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 3
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][attr], vals)
        assert_equal(G[1][2][attr], vals)

        # Test multiple values
        G = nx.path_graph(3, create_using=G)
        attr = 'hi'
        edges = [(0, 1), (1, 2)]
        vals = dict(zip(edges, range(len(edges))))
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][attr], 0)
        assert_equal(G[1][2][attr], 1)

        # Test dictionary of dictionaries
        G = nx.path_graph(3, create_using=G)
        d = {'hi': 0, 'hello': 200}
        edges = [(0, 1)]
        vals = dict.fromkeys(edges, d)
        nx.set_edge_attributes(G, vals)
        assert_equal(G[0][1]['hi'], 0)
        assert_equal(G[0][1]['hello'], 200)
        assert_equal(G[1][2], {}) 

def test_florentine_families_graph(self):
        G = nx.florentine_families_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        for flow_func in flow_funcs:
            T = nx.gomory_hu_tree(G, flow_func=flow_func)
            assert_true(nx.is_tree(T))
            for u, v in combinations(G, 2):
                cut_value, edge = self.minimum_edge_weight(T, u, v)
                assert_equal(nx.minimum_cut_value(G, u, v),
                             cut_value) 

def test_complete_graph_cutoff(self):
        G = nx.complete_graph(5)
        nx.set_edge_attributes(G, dict(((u, v), 1) for u, v in G.edges()),
                               'capacity')
        for flow_func in [shortest_augmenting_path, edmonds_karp]:
            for cutoff in [3, 2, 1]:
                result = nx.maximum_flow_value(G, 0, 4, flow_func=flow_func,
                                               cutoff=cutoff)
                assert_equal(cutoff, result,
                            msg="cutoff error in {0}".format(flow_func.__name__)) 

def test_davis_southern_women_graph(self):
        G = nx.davis_southern_women_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        for flow_func in flow_funcs:
            T = nx.gomory_hu_tree(G, flow_func=flow_func)
            assert_true(nx.is_tree(T))
            for u, v in combinations(G, 2):
                cut_value, edge = self.minimum_edge_weight(T, u, v)
                assert_equal(nx.minimum_cut_value(G, u, v),
                             cut_value) 

def test_karate_club_graph(self):
        G = nx.karate_club_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        for flow_func in flow_funcs:
            T = nx.gomory_hu_tree(G, flow_func=flow_func)
            assert_true(nx.is_tree(T))
            for u, v in combinations(G, 2):
                cut_value, edge = self.minimum_edge_weight(T, u, v)
                assert_equal(nx.minimum_cut_value(G, u, v),
                             cut_value) 

def test_default_flow_function_karate_club_graph(self):
        G = nx.karate_club_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        T = nx.gomory_hu_tree(G)
        assert_true(nx.is_tree(T))
        for u, v in combinations(G, 2):
            cut_value, edge = self.minimum_edge_weight(T, u, v)
            assert_equal(nx.minimum_cut_value(G, u, v),
                         cut_value) 

def test_karate_club_graph(self):
        G = nx.karate_club_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        for flow_func in flow_funcs:
            T = nx.gomory_hu_tree(G, flow_func=flow_func)
            assert_true(nx.is_tree(T))
            for u, v in combinations(G, 2):
                cut_value, edge = self.minimum_edge_weight(T, u, v)
                assert_equal(nx.minimum_cut_value(G, u, v),
                             cut_value) 

def test_davis_southern_women_graph(self):
        G = nx.davis_southern_women_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        for flow_func in flow_funcs:
            T = nx.gomory_hu_tree(G, flow_func=flow_func)
            assert_true(nx.is_tree(T))
            for u, v in combinations(G, 2):
                cut_value, edge = self.minimum_edge_weight(T, u, v)
                assert_equal(nx.minimum_cut_value(G, u, v),
                             cut_value) 

def test_florentine_families_graph(self):
        G = nx.florentine_families_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        for flow_func in flow_funcs:
            T = nx.gomory_hu_tree(G, flow_func=flow_func)
            assert_true(nx.is_tree(T))
            for u, v in combinations(G, 2):
                cut_value, edge = self.minimum_edge_weight(T, u, v)
                assert_equal(nx.minimum_cut_value(G, u, v),
                             cut_value) 

def test_complete_graph_cutoff(self):
        G = nx.complete_graph(5)
        nx.set_edge_attributes(G, {(u, v): 1 for u, v in G.edges()},
                               'capacity')
        for flow_func in [shortest_augmenting_path, edmonds_karp]:
            for cutoff in [3, 2, 1]:
                result = nx.maximum_flow_value(G, 0, 4, flow_func=flow_func,
                                               cutoff=cutoff)
                assert_equal(cutoff, result,
                             msg="cutoff error in {0}".format(flow_func.__name__)) 

def test_karate_club_graph_cutset(self):
        G = nx.karate_club_graph()
        nx.set_edge_attributes(G, 1, 'capacity')
        T = nx.gomory_hu_tree(G)
        assert_true(nx.is_tree(T))
        u, v = 0, 33
        cut_value, edge = self.minimum_edge_weight(T, u, v)
        cutset = self.compute_cutset(G, T, edge)
        assert_equal(cut_value, len(cutset)) 

def test_set_edge_attributes_multi():
    graphs = [nx.MultiGraph(), nx.MultiDiGraph()]
    for G in graphs:
        # Test single value
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 3
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][0][attr], vals)
        assert_equal(G[1][2][0][attr], vals)

        # Test multiple values
        G = nx.path_graph(3, create_using=G)
        attr = 'hi'
        edges = [(0, 1, 0), (1, 2, 0)]
        vals = dict(zip(edges, range(len(edges))))
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][0][attr], 0)
        assert_equal(G[1][2][0][attr], 1)

        # Test dictionary of dictionaries
        G = nx.path_graph(3, create_using=G)
        d = {'hi': 0, 'hello': 200}
        edges = [(0, 1, 0)]
        vals = dict.fromkeys(edges, d)
        nx.set_edge_attributes(G, vals)
        assert_equal(G[0][1][0]['hi'], 0)
        assert_equal(G[0][1][0]['hello'], 200)
        assert_equal(G[1][2][0], {}) 

def test_set_edge_attributes():
    graphs = [nx.Graph(), nx.DiGraph()]
    for G in graphs:
        # Test single value
        G = nx.path_graph(3, create_using=G)
        attr = 'hello'
        vals = 3
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][attr], vals)
        assert_equal(G[1][2][attr], vals)

        # Test multiple values
        G = nx.path_graph(3, create_using=G)
        attr = 'hi'
        edges = [(0, 1), (1, 2)]
        vals = dict(zip(edges, range(len(edges))))
        nx.set_edge_attributes(G, vals, attr)
        assert_equal(G[0][1][attr], 0)
        assert_equal(G[1][2][attr], 1)

        # Test dictionary of dictionaries
        G = nx.path_graph(3, create_using=G)
        d = {'hi': 0, 'hello': 200}
        edges = [(0, 1)]
        vals = dict.fromkeys(edges, d)
        nx.set_edge_attributes(G, vals)
        assert_equal(G[0][1]['hi'], 0)
        assert_equal(G[0][1]['hello'], 200)
        assert_equal(G[1][2], {}) 

def test_effective_size_weighted_directed(self):
        D = self.D.copy()
        nx.set_edge_attributes(D, self.D_weights, 'weight')
        effective_size = nx.effective_size(D, weight='weight')
        assert_almost_equal(round(effective_size[0], 3), 1.567)
        assert_almost_equal(round(effective_size[1], 3), 1.083)
        assert_almost_equal(round(effective_size[2], 3), 1) 

def test_constraint_weighted_directed(self):
        D = self.D.copy()
        nx.set_edge_attributes(D, self.D_weights, 'weight')
        constraint = nx.constraint(D, weight='weight')
        assert_almost_equal(round(constraint[0], 3), 0.840)
        assert_almost_equal(round(constraint[1], 3), 1.143)
        assert_almost_equal(round(constraint[2], 3), 1.378)