import math

import pytest

np = pytest.importorskip("numpy")
pytest.importorskip("scipy")


import networkx as nx


class TestEigenvectorCentrality:
    def test_K5(self):
        """Eigenvector centrality: K5"""
        G = nx.complete_graph(5)
        b = nx.eigenvector_centrality(G)
        v = math.sqrt(1 / 5.0)
        b_answer = dict.fromkeys(G, v)
        for n in sorted(G):
            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)
        nstart = {n: 1 for n in G}
        b = nx.eigenvector_centrality(G, nstart=nstart)
        for n in sorted(G):
            assert b[n] == pytest.approx(b_answer[n], abs=1e-7)

        b = nx.eigenvector_centrality_numpy(G)
        for n in sorted(G):
            assert b[n] == pytest.approx(b_answer[n], abs=1e-3)

    def test_P3(self):
        """Eigenvector centrality: P3"""
        G = nx.path_graph(3)
        b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
        b = nx.eigenvector_centrality_numpy(G)
        for n in sorted(G):
            assert b[n] == pytest.approx(b_answer[n], abs=1e-4)
        b = nx.eigenvector_centrality(G)
        for n in sorted(G):
            assert b[n] == pytest.approx(b_answer[n], abs=1e-4)

    def test_P3_unweighted(self):
        """Eigenvector centrality: P3"""
        G = nx.path_graph(3)
        b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
        b = nx.eigenvector_centrality_numpy(G, weight=None)
        for n in sorted(G):
            assert b[n] == pytest.approx(b_answer[n], abs=1e-4)

    def test_maxiter(self):
        with pytest.raises(nx.PowerIterationFailedConvergence):
            G = nx.path_graph(3)
            nx.eigenvector_centrality(G, max_iter=0)


class TestEigenvectorCentralityDirected:
    @classmethod
    def setup_class(cls):
        G = nx.DiGraph()

        edges = [
            (1, 2),
            (1, 3),
            (2, 4),
            (3, 2),
            (3, 5),
            (4, 2),
            (4, 5),
            (4, 6),
            (5, 6),
            (5, 7),
            (5, 8),
            (6, 8),
            (7, 1),
            (7, 5),
            (7, 8),
            (8, 6),
            (8, 7),
        ]

        G.add_edges_from(edges, weight=2.0)
        cls.G = G.reverse()
        cls.G.evc = [
            0.25368793,
            0.19576478,
            0.32817092,
            0.40430835,
            0.48199885,
            0.15724483,
            0.51346196,
            0.32475403,
        ]

        H = nx.DiGraph()

        edges = [
            (1, 2),
            (1, 3),
            (2, 4),
            (3, 2),
            (3, 5),
            (4, 2),
            (4, 5),
            (4, 6),
            (5, 6),
            (5, 7),
            (5, 8),
            (6, 8),
            (7, 1),
            (7, 5),
            (7, 8),
            (8, 6),
            (8, 7),
        ]

        G.add_edges_from(edges)
        cls.H = G.reverse()
        cls.H.evc = [
            0.25368793,
            0.19576478,
            0.32817092,
            0.40430835,
            0.48199885,
            0.15724483,
            0.51346196,
            0.32475403,
        ]

    def test_eigenvector_centrality_weighted(self):
        G = self.G
        p = nx.eigenvector_centrality(G)
        for a, b in zip(list(p.values()), self.G.evc):
            assert a == pytest.approx(b, abs=1e-4)

    def test_eigenvector_centrality_weighted_numpy(self):
        G = self.G
        p = nx.eigenvector_centrality_numpy(G)
        for a, b in zip(list(p.values()), self.G.evc):
            assert a == pytest.approx(b, abs=1e-7)

    def test_eigenvector_centrality_unweighted(self):
        G = self.H
        p = nx.eigenvector_centrality(G)
        for a, b in zip(list(p.values()), self.G.evc):
            assert a == pytest.approx(b, abs=1e-4)

    def test_eigenvector_centrality_unweighted_numpy(self):
        G = self.H
        p = nx.eigenvector_centrality_numpy(G)
        for a, b in zip(list(p.values()), self.G.evc):
            assert a == pytest.approx(b, abs=1e-7)


class TestEigenvectorCentralityExceptions:
    def test_multigraph(self):
        with pytest.raises(nx.NetworkXException):
            nx.eigenvector_centrality(nx.MultiGraph())

    def test_multigraph_numpy(self):
        with pytest.raises(nx.NetworkXException):
            nx.eigenvector_centrality_numpy(nx.MultiGraph())

    def test_null(self):
        with pytest.raises(nx.NetworkXException):
            nx.eigenvector_centrality(nx.Graph())

    def test_null_numpy(self):
        with pytest.raises(nx.NetworkXException):
            nx.eigenvector_centrality_numpy(nx.Graph())

    @pytest.mark.parametrize(
        "G",
        [
            nx.empty_graph(3),
            nx.DiGraph([(0, 1), (1, 2)]),
        ],
    )
    def test_disconnected_numpy(self, G):
        msg = "does not give consistent results for disconnected"
        with pytest.raises(nx.AmbiguousSolution, match=msg):
            nx.eigenvector_centrality_numpy(G)

    def test_zero_nstart(self):
        G = nx.Graph([(1, 2), (1, 3), (2, 3)])
        with pytest.raises(
            nx.NetworkXException, match="initial vector cannot have all zero values"
        ):
            nx.eigenvector_centrality(G, nstart={v: 0 for v in G})