Python networkx.OrderedGraph() Examples

def test_differential_operator(self, n_vertices=98):
        r"""The Laplacian must always be the divergence of the gradient,
        whether the Laplacian is combinatorial or normalized, and whether the
        graph is directed or weighted."""
        def test_incidence_nx(graph):
            r"""Test that the incidence matrix corresponds to NetworkX."""
            incidence_pg = np.sign(graph.D.toarray())
            G = nx.OrderedDiGraph if graph.is_directed() else nx.OrderedGraph
            graph_nx = nx.from_scipy_sparse_matrix(graph.W, create_using=G)
            incidence_nx = nx.incidence_matrix(graph_nx, oriented=True)
            np.testing.assert_equal(incidence_pg, incidence_nx.toarray())
        for graph in [graphs.Graph(np.zeros((n_vertices, n_vertices))),
                      graphs.Graph(np.identity(n_vertices)),
                      graphs.Graph([[0, 0.8], [0.8, 0]]),
                      graphs.Graph([[1.3, 0], [0.4, 0.5]]),
                      graphs.ErdosRenyi(n_vertices, directed=False, seed=42),
                      graphs.ErdosRenyi(n_vertices, directed=True, seed=42)]:
            for lap_type in ['combinatorial', 'normalized']:
                graph.compute_laplacian(lap_type)
                graph.compute_differential_operator()
                L = graph.D.dot(graph.D.T)
                np.testing.assert_allclose(L.toarray(), graph.L.toarray())
                test_incidence_nx(graph) 

def test_tuplelabels(self):
        # https://github.com/networkx/networkx/pull/1048
        # Writing tuple labels to GML failed.
        G = nx.OrderedGraph()
        G.add_edge((0, 1), (1, 0))
        data = '\n'.join(nx.generate_gml(G, stringizer=literal_stringizer))
        answer = """graph [
  node [
    id 0
    label "(0,1)"
  ]
  node [
    id 1
    label "(1,0)"
  ]
  edge [
    source 0
    target 1
  ]
]"""
        assert_equal(data, answer) 

def test_tuplelabels(self):
        # https://github.com/networkx/networkx/pull/1048
        # Writing tuple labels to GML failed.
        G = nx.OrderedGraph()
        G.add_edge((0, 1), (1, 0))
        data = '\n'.join(nx.generate_gml(G, stringizer=literal_stringizer))
        answer = """graph [
  node [
    id 0
    label "(0,1)"
  ]
  node [
    id 1
    label "(1,0)"
  ]
  edge [
    source 0
    target 1
  ]
]"""
        assert_equal(data, answer) 

def test_graph(self):
        G = nx.OrderedGraph() 

def test_graph():
        G = nx.OrderedGraph() 

def test_attribute_dict_integrity(self):
        # we must not replace dict-like graph data structures with dicts
        G = nx.OrderedGraph()
        G.add_nodes_from("abc")
        H = to_networkx_graph(G, create_using=nx.OrderedGraph())
        assert_equal(list(H.nodes), list(G.nodes))
        H = nx.OrderedDiGraph(G)
        assert_equal(list(H.nodes), list(G.nodes)) 

def test_write_edgelist_4(self):
        fh=io.BytesIO()
        G=nx.OrderedGraph()
        G.add_edge(1,2,weight=2.0)
        G.add_edge(2,3,weight=3.0)
        nx.write_edgelist(G,fh,data=[('weight')])
        fh.seek(0)
        assert_equal(fh.read(),b"1 2 2.0\n2 3 3.0\n") 

def test_write_edgelist_3(self):
        fh=io.BytesIO()
        G=nx.OrderedGraph()
        G.add_edge(1,2,weight=2.0)
        G.add_edge(2,3,weight=3.0)
        nx.write_edgelist(G,fh,data=True)
        fh.seek(0)
        assert_equal(fh.read(),b"1 2 {'weight': 2.0}\n2 3 {'weight': 3.0}\n") 

def test_write_edgelist_2(self):
        fh=io.BytesIO()
        G=nx.OrderedGraph()
        G.add_edges_from([(1,2),(2,3)])
        nx.write_edgelist(G,fh,data=True)
        fh.seek(0)
        assert_equal(fh.read(),b"1 2 {}\n2 3 {}\n") 

def test_write_edgelist_1(self):
        fh=io.BytesIO()
        G=nx.OrderedGraph()
        G.add_edges_from([(1,2),(2,3)])
        nx.write_edgelist(G,fh,data=False)
        fh.seek(0)
        assert_equal(fh.read(),b"1 2\n2 3\n") 

def test_graph2(self):
        G = nx.OrderedGraph([
            (1, 2), (4, 13), (0, 13), (4, 5), (7, 10), (1, 7), (0, 3), (2, 6),
            (5, 6), (7, 13), (4, 8), (0, 8), (0, 9), (2, 13), (6, 7), (3, 6),
            (2, 8)
        ])
        self.check_graph(G, is_planar=False) 

def test_graph1(self):
        G = nx.OrderedGraph([
            (3, 10), (2, 13), (1, 13), (7, 11), (0, 8), (8, 13), (0, 2),
            (0, 7), (0, 10), (1, 7)
        ])
        self.check_graph(G, is_planar=True) 

def test_reversed(self):
        C33 = nx.OrderedGraph()
        C33.add_nodes_from(reversed(range(3*3*4)))
        C33.add_edges_from(dnx.chimera_graph(3, 3, 4).edges)
        coord = chimera_coordinates(3, 3, 4)

        labels = canonical_chimera_labeling(C33)
        labels = {v: coord.chimera_to_linear(labels[v]) for v in labels}

        G = nx.relabel_nodes(C33, labels, copy=True)

        self.assertTrue(nx.is_isomorphic(G, C33)) 

def test_subgraph_copy(self):
        for origG in self.graphs:
            G = nx.OrderedGraph(origG)
            SG = G.subgraph([4, 5, 6])
            H = SG.copy()
            assert_equal(type(G), type(H)) 

def test_attribute_dict_integrity(self):
        # we must not replace dict-like graph data structures with dicts
        G = nx.OrderedGraph()
        G.add_nodes_from("abc")
        H = to_networkx_graph(G, create_using=nx.OrderedGraph)
        assert_equal(list(H.nodes), list(G.nodes))
        H = nx.OrderedDiGraph(G)
        assert_equal(list(H.nodes), list(G.nodes)) 

def test_write_edgelist_4(self):
        fh = io.BytesIO()
        G = nx.OrderedGraph()
        G.add_edge(1, 2, weight=2.0)
        G.add_edge(2, 3, weight=3.0)
        nx.write_edgelist(G, fh, data=[('weight')])
        fh.seek(0)
        assert_equal(fh.read(), b"1 2 2.0\n2 3 3.0\n") 

def test_write_edgelist_3(self):
        fh = io.BytesIO()
        G = nx.OrderedGraph()
        G.add_edge(1, 2, weight=2.0)
        G.add_edge(2, 3, weight=3.0)
        nx.write_edgelist(G, fh, data=True)
        fh.seek(0)
        assert_equal(fh.read(), b"1 2 {'weight': 2.0}\n2 3 {'weight': 3.0}\n") 

def test_write_edgelist_2(self):
        fh = io.BytesIO()
        G = nx.OrderedGraph()
        G.add_edges_from([(1, 2), (2, 3)])
        nx.write_edgelist(G, fh, data=True)
        fh.seek(0)
        assert_equal(fh.read(), b"1 2 {}\n2 3 {}\n") 

def test_write_edgelist_1(self):
        fh = io.BytesIO()
        G = nx.OrderedGraph()
        G.add_edges_from([(1, 2), (2, 3)])
        nx.write_edgelist(G, fh, data=False)
        fh.seek(0)
        assert_equal(fh.read(), b"1 2\n2 3\n") 

def test_graph():
        G = nx.OrderedGraph() 

def busmap_by_linemask(network, mask):
    mask = network.lines.loc[:,['bus0', 'bus1']].assign(mask=mask).set_index(['bus0','bus1'])['mask']
    G = nx.OrderedGraph()
    G.add_nodes_from(network.buses.index)
    G.add_edges_from(mask.index[mask])
    return pd.Series(OrderedDict((n, str(i))
                                 for i, g in enumerate(nx.connected_components(G))
                                 for n in g),
                     name='name') 

def find_cycles(sub_network, weight='x_pu'):
    """
    Find all cycles in the sub_network and record them in sub_network.C.

    networkx collects the cycles with more than 2 edges; then the 2-edge cycles
    from the MultiGraph must be collected separately (for cases where there
    are multiple lines between the same pairs of buses).

    Cycles with infinite impedance are skipped.
    """
    branches_bus0 = sub_network.branches()["bus0"]
    branches_i = branches_bus0.index

    #reduce to a non-multi-graph for cycles with > 2 edges
    mgraph = sub_network.graph(weight=weight, inf_weight=False)
    graph = nx.OrderedGraph(mgraph)

    cycles = nx.cycle_basis(graph)

    #number of 2-edge cycles
    num_multi = len(mgraph.edges()) - len(graph.edges())

    sub_network.C = dok_matrix((len(branches_bus0),len(cycles)+num_multi))

    for j,cycle in enumerate(cycles):

        for i in range(len(cycle)):
            branch = next(iterkeys(mgraph[cycle[i]][cycle[(i+1)%len(cycle)]]))
            branch_i = branches_i.get_loc(branch)
            sign = +1 if branches_bus0.iat[branch_i] == cycle[i] else -1
            sub_network.C[branch_i,j] += sign

    #counter for multis
    c = len(cycles)

    #add multi-graph 2-edge cycles for multiple branches between same pairs of buses
    for u,v in graph.edges():
        bs = list(mgraph[u][v].keys())
        if len(bs) > 1:
            first = bs[0]
            first_i = branches_i.get_loc(first)
            for b in bs[1:]:
                b_i = branches_i.get_loc(b)
                sign = -1 if branches_bus0.iat[b_i] == branches_bus0.iat[first_i] else +1
                sub_network.C[first_i,c] = 1
                sub_network.C[b_i,c] = sign
                c+=1