annotate env/lib/python3.9/site-packages/networkx/algorithms/approximation/tests/test_connectivity.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

import pytest

import networkx as nx
from networkx.algorithms import approximation as approx


def test_global_node_connectivity():
    # Figure 1 chapter on Connectivity
    G = nx.Graph()
    G.add_edges_from(
        [
            (1, 2),
            (1, 3),
            (1, 4),
            (1, 5),
            (2, 3),
            (2, 6),
            (3, 4),
            (3, 6),
            (4, 6),
            (4, 7),
            (5, 7),
            (6, 8),
            (6, 9),
            (7, 8),
            (7, 10),
            (8, 11),
            (9, 10),
            (9, 11),
            (10, 11),
        ]
    )
    assert 2 == approx.local_node_connectivity(G, 1, 11)
    assert 2 == approx.node_connectivity(G)
    assert 2 == approx.node_connectivity(G, 1, 11)


def test_white_harary1():
    # Figure 1b white and harary (2001)
    # A graph with high adhesion (edge connectivity) and low cohesion
    # (node connectivity)
    G = nx.disjoint_union(nx.complete_graph(4), nx.complete_graph(4))
    G.remove_node(7)
    for i in range(4, 7):
        G.add_edge(0, i)
    G = nx.disjoint_union(G, nx.complete_graph(4))
    G.remove_node(G.order() - 1)
    for i in range(7, 10):
        G.add_edge(0, i)
    assert 1 == approx.node_connectivity(G)


def test_complete_graphs():
    for n in range(5, 25, 5):
        G = nx.complete_graph(n)
        assert n - 1 == approx.node_connectivity(G)
        assert n - 1 == approx.node_connectivity(G, 0, 3)


def test_empty_graphs():
    for k in range(5, 25, 5):
        G = nx.empty_graph(k)
        assert 0 == approx.node_connectivity(G)
        assert 0 == approx.node_connectivity(G, 0, 3)


def test_petersen():
    G = nx.petersen_graph()
    assert 3 == approx.node_connectivity(G)
    assert 3 == approx.node_connectivity(G, 0, 5)


# Approximation fails with tutte graph
# def test_tutte():
#    G = nx.tutte_graph()
#    assert_equal(3, approx.node_connectivity(G))


def test_dodecahedral():
    G = nx.dodecahedral_graph()
    assert 3 == approx.node_connectivity(G)
    assert 3 == approx.node_connectivity(G, 0, 5)


def test_octahedral():
    G = nx.octahedral_graph()
    assert 4 == approx.node_connectivity(G)
    assert 4 == approx.node_connectivity(G, 0, 5)


# Approximation can fail with icosahedral graph depending
# on iteration order.
# def test_icosahedral():
#    G=nx.icosahedral_graph()
#    assert_equal(5, approx.node_connectivity(G))
#    assert_equal(5, approx.node_connectivity(G, 0, 5))


def test_only_source():
    G = nx.complete_graph(5)
    pytest.raises(nx.NetworkXError, approx.node_connectivity, G, s=0)


def test_only_target():
    G = nx.complete_graph(5)
    pytest.raises(nx.NetworkXError, approx.node_connectivity, G, t=0)


def test_missing_source():
    G = nx.path_graph(4)
    pytest.raises(nx.NetworkXError, approx.node_connectivity, G, 10, 1)


def test_missing_target():
    G = nx.path_graph(4)
    pytest.raises(nx.NetworkXError, approx.node_connectivity, G, 1, 10)


def test_source_equals_target():
    G = nx.complete_graph(5)
    pytest.raises(nx.NetworkXError, approx.local_node_connectivity, G, 0, 0)


def test_directed_node_connectivity():
    G = nx.cycle_graph(10, create_using=nx.DiGraph())  # only one direction
    D = nx.cycle_graph(10).to_directed()  # 2 reciprocal edges
    assert 1 == approx.node_connectivity(G)
    assert 1 == approx.node_connectivity(G, 1, 4)
    assert 2 == approx.node_connectivity(D)
    assert 2 == approx.node_connectivity(D, 1, 4)


class TestAllPairsNodeConnectivityApprox:
    @classmethod
    def setup_class(cls):
        cls.path = nx.path_graph(7)
        cls.directed_path = nx.path_graph(7, create_using=nx.DiGraph())
        cls.cycle = nx.cycle_graph(7)
        cls.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
        cls.gnp = nx.gnp_random_graph(30, 0.1)
        cls.directed_gnp = nx.gnp_random_graph(30, 0.1, directed=True)
        cls.K20 = nx.complete_graph(20)
        cls.K10 = nx.complete_graph(10)
        cls.K5 = nx.complete_graph(5)
        cls.G_list = [
            cls.path,
            cls.directed_path,
            cls.cycle,
            cls.directed_cycle,
            cls.gnp,
            cls.directed_gnp,
            cls.K10,
            cls.K5,
            cls.K20,
        ]

    def test_cycles(self):
        K_undir = approx.all_pairs_node_connectivity(self.cycle)
        for source in K_undir:
            for target, k in K_undir[source].items():
                assert k == 2
        K_dir = approx.all_pairs_node_connectivity(self.directed_cycle)
        for source in K_dir:
            for target, k in K_dir[source].items():
                assert k == 1

    def test_complete(self):
        for G in [self.K10, self.K5, self.K20]:
            K = approx.all_pairs_node_connectivity(G)
            for source in K:
                for target, k in K[source].items():
                    assert k == len(G) - 1

    def test_paths(self):
        K_undir = approx.all_pairs_node_connectivity(self.path)
        for source in K_undir:
            for target, k in K_undir[source].items():
                assert k == 1
        K_dir = approx.all_pairs_node_connectivity(self.directed_path)
        for source in K_dir:
            for target, k in K_dir[source].items():
                if source < target:
                    assert k == 1
                else:
                    assert k == 0

    def test_cutoff(self):
        for G in [self.K10, self.K5, self.K20]:
            for mp in [2, 3, 4]:
                paths = approx.all_pairs_node_connectivity(G, cutoff=mp)
                for source in paths:
                    for target, K in paths[source].items():
                        assert K == mp

    def test_all_pairs_connectivity_nbunch(self):
        G = nx.complete_graph(5)
        nbunch = [0, 2, 3]
        C = approx.all_pairs_node_connectivity(G, nbunch=nbunch)
        assert len(C) == len(nbunch)