import numpy as np
import pytest
from numpy.testing import assert_almost_equal
from scipy import stats

from statsmodels.stats._lilliefors import lilliefors, get_lilliefors_table, \
    kstest_fit, ksstat


class TestLilliefors(object):

    def test_normal(self):
        np.random.seed(3975)
        x_n = stats.norm.rvs(size=500)

        # R function call:
        # require(nortest)
        # lillie.test(x_n)

        d_ks_norm, p_norm = lilliefors(x_n, dist='norm', pvalmethod='approx')
        # shift normal distribution > 0 to exactly mirror R `KScorrect` test
        # R `KScorrect` requires all values tested for exponential
        # distribution to be > 0
        # R function call:
        # require(KScorrect)
        # LcKS(x_n+abs(min(x_n))+0.001, 'pexp')

        d_ks_exp, p_exp = lilliefors(x_n + np.abs(x_n.min()) + 0.001,
                                     dist='exp', pvalmethod='approx')
        # assert normal
        assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
        assert_almost_equal(p_norm, 0.64175, decimal=3)
        # assert exp
        assert_almost_equal(d_ks_exp, 0.3436007, decimal=3)
        assert_almost_equal(p_exp, 0.001, decimal=3)

    def test_normal_table(self):
        np.random.seed(3975)
        x_n = stats.norm.rvs(size=500)

        d_ks_norm, p_norm = lilliefors(x_n, dist='norm', pvalmethod='table')
        # assert normal
        assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
        assert_almost_equal(p_norm, 0.64175, decimal=3)

    def test_expon(self):
        np.random.seed(3975)
        x_e = stats.expon.rvs(size=500)
        # R function call:
        # require(nortest)
        # lillie.test(x_n)
        d_ks_norm, p_norm = lilliefors(x_e, dist='norm', pvalmethod='approx')
        # R function call:
        # require(KScorrect)
        # LcKS(x_e, 'pexp')
        d_ks_exp, p_exp = lilliefors(x_e, dist='exp', pvalmethod='approx')
        # assert normal
        assert_almost_equal(d_ks_norm, 0.15581, decimal=3)
        assert_almost_equal(p_norm, 2.2e-16, decimal=3)
        # assert exp
        assert_almost_equal(d_ks_exp, 0.02763748, decimal=3)
        assert_almost_equal(p_exp, 0.72540, decimal=3)

    def test_pval_bounds(self):
        x = stats.norm.ppf((np.arange(10.) + 0.5) / 10)
        d_ks_n, p_n = lilliefors(x, dist='norm', pvalmethod='approx')
        x = stats.expon.ppf((np.arange(10.) + 0.5) / 10)
        d_ks_e, p_e = lilliefors(x, dist='exp', pvalmethod='approx')

        assert_almost_equal(p_n, 0.99, decimal=7)
        assert_almost_equal(p_e, 0.99, decimal=7)

    def test_min_nobs(self):
        x = np.arange(3.)
        with pytest.raises(ValueError):
            lilliefors(x, dist='norm', pvalmethod='approx')
        x = np.arange(2.)
        with pytest.raises(ValueError):
            lilliefors(x, dist='exp', pvalmethod='approx')

    @pytest.mark.smoke
    def test_large_sample(self, reset_randomstate):
        x = np.random.randn(10000)
        lilliefors(x, pvalmethod='approx')


def test_get_lilliefors_errors(reset_randomstate):
    with pytest.raises(ValueError):
        get_lilliefors_table(dist='unknown')
    with pytest.raises(ValueError):
        kstest_fit(np.random.standard_normal(100), dist='unknown',
                   pvalmethod='table')


def test_ksstat(reset_randomstate):
    x = np.random.uniform(0, 1, 100)
    two_sided = ksstat(x, 'uniform', alternative='two_sided')
    greater = ksstat(x, 'uniform', alternative='greater')
    lower = ksstat(x, stats.uniform, alternative='lower')
    print(two_sided, greater, lower)
    assert lower <= two_sided
    assert greater <= two_sided
