""" Test functions for stats module

"""
import warnings
import re
import sys
import pickle
import os

from numpy.testing import (assert_equal, assert_array_equal,
                           assert_almost_equal, assert_array_almost_equal,
                           assert_allclose, assert_, assert_warns,
                           assert_array_less, suppress_warnings)
import pytest
from pytest import raises as assert_raises

import numpy
import numpy as np
from numpy import typecodes, array
from numpy.lib.recfunctions import rec_append_fields
from scipy import special
from scipy._lib._util import check_random_state
from scipy.integrate import IntegrationWarning
import scipy.stats as stats
from scipy.stats._distn_infrastructure import argsreduce
import scipy.stats.distributions

from scipy.special import xlogy
from .test_continuous_basic import distcont

# python -OO strips docstrings
DOCSTRINGS_STRIPPED = sys.flags.optimize > 1


def _assert_hasattr(a, b, msg=None):
    if msg is None:
        msg = '%s does not have attribute %s' % (a, b)
    assert_(hasattr(a, b), msg=msg)


def test_api_regression():
    # https://github.com/scipy/scipy/issues/3802
    _assert_hasattr(scipy.stats.distributions, 'f_gen')


def check_vonmises_pdf_periodic(k, l, s, x):
    vm = stats.vonmises(k, loc=l, scale=s)
    assert_almost_equal(vm.pdf(x), vm.pdf(x % (2*numpy.pi*s)))


def check_vonmises_cdf_periodic(k, l, s, x):
    vm = stats.vonmises(k, loc=l, scale=s)
    assert_almost_equal(vm.cdf(x) % 1, vm.cdf(x % (2*numpy.pi*s)) % 1)


def test_vonmises_pdf_periodic():
    for k in [0.1, 1, 101]:
        for x in [0, 1, numpy.pi, 10, 100]:
            check_vonmises_pdf_periodic(k, 0, 1, x)
            check_vonmises_pdf_periodic(k, 1, 1, x)
            check_vonmises_pdf_periodic(k, 0, 10, x)

            check_vonmises_cdf_periodic(k, 0, 1, x)
            check_vonmises_cdf_periodic(k, 1, 1, x)
            check_vonmises_cdf_periodic(k, 0, 10, x)


def test_vonmises_line_support():
    assert_equal(stats.vonmises_line.a, -np.pi)
    assert_equal(stats.vonmises_line.b, np.pi)


def test_vonmises_numerical():
    vm = stats.vonmises(800)
    assert_almost_equal(vm.cdf(0), 0.5)


@pytest.mark.parametrize('dist',
                         ['alpha', 'betaprime',
                          'fatiguelife', 'invgamma', 'invgauss', 'invweibull',
                          'johnsonsb', 'levy', 'levy_l', 'lognorm', 'gilbrat',
                          'powerlognorm', 'rayleigh', 'wald'])
def test_support(dist):
    """gh-6235"""
    dct = dict(distcont)
    args = dct[dist]

    dist = getattr(stats, dist)

    assert_almost_equal(dist.pdf(dist.a, *args), 0)
    assert_equal(dist.logpdf(dist.a, *args), -np.inf)
    assert_almost_equal(dist.pdf(dist.b, *args), 0)
    assert_equal(dist.logpdf(dist.b, *args), -np.inf)


class TestRandInt(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.randint.rvs(5, 30, size=100)
        assert_(numpy.all(vals < 30) & numpy.all(vals >= 5))
        assert_(len(vals) == 100)
        vals = stats.randint.rvs(5, 30, size=(2, 50))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.randint.rvs(15, 46)
        assert_((val >= 15) & (val < 46))
        assert_(isinstance(val, numpy.ScalarType), msg=repr(type(val)))
        val = stats.randint(15, 46).rvs(3)
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_pdf(self):
        k = numpy.r_[0:36]
        out = numpy.where((k >= 5) & (k < 30), 1.0/(30-5), 0)
        vals = stats.randint.pmf(k, 5, 30)
        assert_array_almost_equal(vals, out)

    def test_cdf(self):
        x = np.linspace(0, 36, 100)
        k = numpy.floor(x)
        out = numpy.select([k >= 30, k >= 5], [1.0, (k-5.0+1)/(30-5.0)], 0)
        vals = stats.randint.cdf(x, 5, 30)
        assert_array_almost_equal(vals, out, decimal=12)


class TestBinom(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.binom.rvs(10, 0.75, size=(2, 50))
        assert_(numpy.all(vals >= 0) & numpy.all(vals <= 10))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.binom.rvs(10, 0.75)
        assert_(isinstance(val, int))
        val = stats.binom(10, 0.75).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_pmf(self):
        # regression test for Ticket #1842
        vals1 = stats.binom.pmf(100, 100, 1)
        vals2 = stats.binom.pmf(0, 100, 0)
        assert_allclose(vals1, 1.0, rtol=1e-15, atol=0)
        assert_allclose(vals2, 1.0, rtol=1e-15, atol=0)

    def test_entropy(self):
        # Basic entropy tests.
        b = stats.binom(2, 0.5)
        expected_p = np.array([0.25, 0.5, 0.25])
        expected_h = -sum(xlogy(expected_p, expected_p))
        h = b.entropy()
        assert_allclose(h, expected_h)

        b = stats.binom(2, 0.0)
        h = b.entropy()
        assert_equal(h, 0.0)

        b = stats.binom(2, 1.0)
        h = b.entropy()
        assert_equal(h, 0.0)

    def test_warns_p0(self):
        # no spurious warnigns are generated for p=0; gh-3817
        with warnings.catch_warnings():
            warnings.simplefilter("error", RuntimeWarning)
            assert_equal(stats.binom(n=2, p=0).mean(), 0)
            assert_equal(stats.binom(n=2, p=0).std(), 0)


class TestBernoulli(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.bernoulli.rvs(0.75, size=(2, 50))
        assert_(numpy.all(vals >= 0) & numpy.all(vals <= 1))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.bernoulli.rvs(0.75)
        assert_(isinstance(val, int))
        val = stats.bernoulli(0.75).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_entropy(self):
        # Simple tests of entropy.
        b = stats.bernoulli(0.25)
        expected_h = -0.25*np.log(0.25) - 0.75*np.log(0.75)
        h = b.entropy()
        assert_allclose(h, expected_h)

        b = stats.bernoulli(0.0)
        h = b.entropy()
        assert_equal(h, 0.0)

        b = stats.bernoulli(1.0)
        h = b.entropy()
        assert_equal(h, 0.0)


class TestBradford(object):
    # gh-6216
    def test_cdf_ppf(self):
        c = 0.1
        x = np.logspace(-20, -4)
        q = stats.bradford.cdf(x, c)
        xx = stats.bradford.ppf(q, c)
        assert_allclose(x, xx)


class TestNBinom(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.nbinom.rvs(10, 0.75, size=(2, 50))
        assert_(numpy.all(vals >= 0))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.nbinom.rvs(10, 0.75)
        assert_(isinstance(val, int))
        val = stats.nbinom(10, 0.75).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_pmf(self):
        # regression test for ticket 1779
        assert_allclose(np.exp(stats.nbinom.logpmf(700, 721, 0.52)),
                        stats.nbinom.pmf(700, 721, 0.52))
        # logpmf(0,1,1) shouldn't return nan (regression test for gh-4029)
        val = scipy.stats.nbinom.logpmf(0, 1, 1)
        assert_equal(val, 0)


class TestGenInvGauss(object):
    def setup_method(self):
        np.random.seed(1234)

    @pytest.mark.slow
    def test_rvs_with_mode_shift(self):
        # ratio_unif w/ mode shift
        gig = stats.geninvgauss(2.3, 1.5)
        _, p = stats.kstest(gig.rvs(size=1500, random_state=1234), gig.cdf)
        assert_equal(p > 0.05, True)

    @pytest.mark.slow
    def test_rvs_without_mode_shift(self):
        # ratio_unif w/o mode shift
        gig = stats.geninvgauss(0.9, 0.75)
        _, p = stats.kstest(gig.rvs(size=1500, random_state=1234), gig.cdf)
        assert_equal(p > 0.05, True)

    @pytest.mark.slow
    def test_rvs_new_method(self):
        # new algorithm of Hoermann / Leydold
        gig = stats.geninvgauss(0.1, 0.2)
        _, p = stats.kstest(gig.rvs(size=1500, random_state=1234), gig.cdf)
        assert_equal(p > 0.05, True)

    @pytest.mark.slow
    def test_rvs_p_zero(self):
        def my_ks_check(p, b):
            gig = stats.geninvgauss(p, b)
            rvs = gig.rvs(size=1500, random_state=1234)
            return stats.kstest(rvs, gig.cdf)[1] > 0.05
        # boundary cases when p = 0
        assert_equal(my_ks_check(0, 0.2), True)  # new algo
        assert_equal(my_ks_check(0, 0.9), True)  # ratio_unif w/o shift
        assert_equal(my_ks_check(0, 1.5), True)  # ratio_unif with shift

    def test_rvs_negative_p(self):
        # if p negative, return inverse
        assert_equal(
                stats.geninvgauss(-1.5, 2).rvs(size=10, random_state=1234),
                1 / stats.geninvgauss(1.5, 2).rvs(size=10, random_state=1234))

    def test_invgauss(self):
        # test that invgauss is special case
        ig = stats.geninvgauss.rvs(size=1500, p=-0.5, b=1, random_state=1234)
        assert_equal(stats.kstest(ig, 'invgauss', args=[1])[1] > 0.15, True)
        # test pdf and cdf
        mu, x = 100, np.linspace(0.01, 1, 10)
        pdf_ig = stats.geninvgauss.pdf(x, p=-0.5, b=1 / mu, scale=mu)
        assert_allclose(pdf_ig, stats.invgauss(mu).pdf(x))
        cdf_ig = stats.geninvgauss.cdf(x, p=-0.5, b=1 / mu, scale=mu)
        assert_allclose(cdf_ig, stats.invgauss(mu).cdf(x))

    def test_pdf_R(self):
        # test against R package GIGrvg
        # x <- seq(0.01, 5, length.out = 10)
        # GIGrvg::dgig(x, 0.5, 1, 1)
        vals_R = np.array([2.081176820e-21, 4.488660034e-01, 3.747774338e-01,
                           2.693297528e-01, 1.905637275e-01, 1.351476913e-01,
                           9.636538981e-02, 6.909040154e-02, 4.978006801e-02,
                           3.602084467e-02])
        x = np.linspace(0.01, 5, 10)
        assert_allclose(vals_R, stats.geninvgauss.pdf(x, 0.5, 1))

    def test_pdf_zero(self):
        # pdf at 0 is 0, needs special treatment to avoid 1/x in pdf
        assert_equal(stats.geninvgauss.pdf(0, 0.5, 0.5), 0)
        # if x is large and p is moderate, make sure that pdf does not
        # overflow because of x**(p-1); exp(-b*x) forces pdf to zero
        assert_equal(stats.geninvgauss.pdf(2e6, 50, 2), 0)


class TestNormInvGauss(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_cdf_R(self):
        # test pdf and cdf vals against R
        # require("GeneralizedHyperbolic")
        # x_test <- c(-7, -5, 0, 8, 15)
        # r_cdf <- GeneralizedHyperbolic::pnig(x_test, mu = 0, a = 1, b = 0.5)
        # r_pdf <- GeneralizedHyperbolic::dnig(x_test, mu = 0, a = 1, b = 0.5)
        r_cdf = np.array([8.034920282e-07, 2.512671945e-05, 3.186661051e-01,
                          9.988650664e-01, 9.999848769e-01])
        x_test = np.array([-7, -5, 0, 8, 15])
        vals_cdf = stats.norminvgauss.cdf(x_test, a=1, b=0.5)
        assert_allclose(vals_cdf, r_cdf, atol=1e-9)

    def test_pdf_R(self):
        # values from R as defined in test_cdf_R
        r_pdf = np.array([1.359600783e-06, 4.413878805e-05, 4.555014266e-01,
                          7.450485342e-04, 8.917889931e-06])
        x_test = np.array([-7, -5, 0, 8, 15])
        vals_pdf = stats.norminvgauss.pdf(x_test, a=1, b=0.5)
        assert_allclose(vals_pdf, r_pdf, atol=1e-9)

    def test_stats(self):
        a, b = 1, 0.5
        gamma = np.sqrt(a**2 - b**2)
        v_stats = (b / gamma, a**2 / gamma**3, 3.0 * b / (a * np.sqrt(gamma)),
                   3.0 * (1 + 4 * b**2 / a**2) / gamma)
        assert_equal(v_stats, stats.norminvgauss.stats(a, b, moments='mvsk'))

    def test_ppf(self):
        a, b = 1, 0.5
        x_test = np.array([0.001, 0.5, 0.999])
        vals = stats.norminvgauss.ppf(x_test, a, b)
        assert_allclose(x_test, stats.norminvgauss.cdf(vals, a, b))


class TestGeom(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.geom.rvs(0.75, size=(2, 50))
        assert_(numpy.all(vals >= 0))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.geom.rvs(0.75)
        assert_(isinstance(val, int))
        val = stats.geom(0.75).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_pmf(self):
        vals = stats.geom.pmf([1, 2, 3], 0.5)
        assert_array_almost_equal(vals, [0.5, 0.25, 0.125])

    def test_logpmf(self):
        # regression test for ticket 1793
        vals1 = np.log(stats.geom.pmf([1, 2, 3], 0.5))
        vals2 = stats.geom.logpmf([1, 2, 3], 0.5)
        assert_allclose(vals1, vals2, rtol=1e-15, atol=0)

        # regression test for gh-4028
        val = stats.geom.logpmf(1, 1)
        assert_equal(val, 0.0)

    def test_cdf_sf(self):
        vals = stats.geom.cdf([1, 2, 3], 0.5)
        vals_sf = stats.geom.sf([1, 2, 3], 0.5)
        expected = array([0.5, 0.75, 0.875])
        assert_array_almost_equal(vals, expected)
        assert_array_almost_equal(vals_sf, 1-expected)

    def test_logcdf_logsf(self):
        vals = stats.geom.logcdf([1, 2, 3], 0.5)
        vals_sf = stats.geom.logsf([1, 2, 3], 0.5)
        expected = array([0.5, 0.75, 0.875])
        assert_array_almost_equal(vals, np.log(expected))
        assert_array_almost_equal(vals_sf, np.log1p(-expected))

    def test_ppf(self):
        vals = stats.geom.ppf([0.5, 0.75, 0.875], 0.5)
        expected = array([1.0, 2.0, 3.0])
        assert_array_almost_equal(vals, expected)

    def test_ppf_underflow(self):
        # this should not underflow
        assert_allclose(stats.geom.ppf(1e-20, 1e-20), 1.0, atol=1e-14)


class TestPlanck(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_sf(self):
        vals = stats.planck.sf([1, 2, 3], 5.)
        expected = array([4.5399929762484854e-05,
                          3.0590232050182579e-07,
                          2.0611536224385579e-09])
        assert_array_almost_equal(vals, expected)

    def test_logsf(self):
        vals = stats.planck.logsf([1000., 2000., 3000.], 1000.)
        expected = array([-1001000., -2001000., -3001000.])
        assert_array_almost_equal(vals, expected)


class TestGennorm(object):
    def test_laplace(self):
        # test against Laplace (special case for beta=1)
        points = [1, 2, 3]
        pdf1 = stats.gennorm.pdf(points, 1)
        pdf2 = stats.laplace.pdf(points)
        assert_almost_equal(pdf1, pdf2)

    def test_norm(self):
        # test against normal (special case for beta=2)
        points = [1, 2, 3]
        pdf1 = stats.gennorm.pdf(points, 2)
        pdf2 = stats.norm.pdf(points, scale=2**-.5)
        assert_almost_equal(pdf1, pdf2)


class TestHalfgennorm(object):
    def test_expon(self):
        # test against exponential (special case for beta=1)
        points = [1, 2, 3]
        pdf1 = stats.halfgennorm.pdf(points, 1)
        pdf2 = stats.expon.pdf(points)
        assert_almost_equal(pdf1, pdf2)

    def test_halfnorm(self):
        # test against half normal (special case for beta=2)
        points = [1, 2, 3]
        pdf1 = stats.halfgennorm.pdf(points, 2)
        pdf2 = stats.halfnorm.pdf(points, scale=2**-.5)
        assert_almost_equal(pdf1, pdf2)

    def test_gennorm(self):
        # test against generalized normal
        points = [1, 2, 3]
        pdf1 = stats.halfgennorm.pdf(points, .497324)
        pdf2 = stats.gennorm.pdf(points, .497324)
        assert_almost_equal(pdf1, 2*pdf2)


class TestTruncnorm(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_ppf_ticket1131(self):
        vals = stats.truncnorm.ppf([-0.5, 0, 1e-4, 0.5, 1-1e-4, 1, 2], -1., 1.,
                                   loc=[3]*7, scale=2)
        expected = np.array([np.nan, 1, 1.00056419, 3, 4.99943581, 5, np.nan])
        assert_array_almost_equal(vals, expected)

    def test_isf_ticket1131(self):
        vals = stats.truncnorm.isf([-0.5, 0, 1e-4, 0.5, 1-1e-4, 1, 2], -1., 1.,
                                   loc=[3]*7, scale=2)
        expected = np.array([np.nan, 5, 4.99943581, 3, 1.00056419, 1, np.nan])
        assert_array_almost_equal(vals, expected)

    def test_gh_2477_small_values(self):
        # Check a case that worked in the original issue.
        low, high = -11, -10
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low < x.min() < x.max() < high)
        # Check a case that failed in the original issue.
        low, high = 10, 11
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low < x.min() < x.max() < high)

    # @pytest.mark.xfail(reason="truncnorm rvs is know to fail at extreme tails")
    def test_gh_2477_large_values(self):
        # Check a case that used to fail because of extreme tailness.
        low, high = 100, 101
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low <= x.min() <= x.max() <= high), str([low, high, x])

        # Check some additional extreme tails
        low, high = 1000, 1001
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low < x.min() < x.max() < high)

        low, high = 10000, 10001
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low < x.min() < x.max() < high)

        low, high = -10001, -10000
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low < x.min() < x.max() < high)

    def test_gh_9403_nontail_values(self):
        for low, high in [[3, 4], [-4, -3]]:
            xvals = np.array([-np.inf, low, high, np.inf])
            xmid = (high+low)/2.0
            cdfs = stats.truncnorm.cdf(xvals, low, high)
            sfs = stats.truncnorm.sf(xvals, low, high)
            pdfs = stats.truncnorm.pdf(xvals, low, high)
            expected_cdfs = np.array([0, 0, 1, 1])
            expected_sfs = np.array([1.0, 1.0, 0.0, 0.0])
            expected_pdfs = np.array([0, 3.3619772, 0.1015229, 0])
            if low < 0:
                expected_pdfs = np.array([0, 0.1015229, 3.3619772, 0])
            assert_almost_equal(cdfs, expected_cdfs)
            assert_almost_equal(sfs, expected_sfs)
            assert_almost_equal(pdfs, expected_pdfs)
            assert_almost_equal(np.log(expected_pdfs[1]/expected_pdfs[2]), low+0.5)
            pvals = np.array([0, 0.5, 1.0])
            ppfs = stats.truncnorm.ppf(pvals, low, high)
            expected_ppfs = np.array([low, np.sign(low)*3.1984741, high])
            assert_almost_equal(ppfs, expected_ppfs)

            if low < 0:
                assert_almost_equal(stats.truncnorm.sf(xmid, low, high), 0.8475544278436675)
                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high), 0.1524455721563326)
            else:
                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high), 0.8475544278436675)
                assert_almost_equal(stats.truncnorm.sf(xmid, low, high), 0.1524455721563326)
            pdf = stats.truncnorm.pdf(xmid, low, high)
            assert_almost_equal(np.log(pdf/expected_pdfs[2]), (xmid+0.25)/2)

    def test_gh_9403_medium_tail_values(self):
        for low, high in [[39, 40], [-40, -39]]:
            xvals = np.array([-np.inf, low, high, np.inf])
            xmid = (high+low)/2.0
            cdfs = stats.truncnorm.cdf(xvals, low, high)
            sfs = stats.truncnorm.sf(xvals, low, high)
            pdfs = stats.truncnorm.pdf(xvals, low, high)
            expected_cdfs = np.array([0, 0, 1, 1])
            expected_sfs = np.array([1.0, 1.0, 0.0, 0.0])
            expected_pdfs = np.array([0, 3.90256074e+01, 2.73349092e-16, 0])
            if low < 0:
                expected_pdfs = np.array([0, 2.73349092e-16, 3.90256074e+01, 0])
            assert_almost_equal(cdfs, expected_cdfs)
            assert_almost_equal(sfs, expected_sfs)
            assert_almost_equal(pdfs, expected_pdfs)
            assert_almost_equal(np.log(expected_pdfs[1]/expected_pdfs[2]), low+0.5)
            pvals = np.array([0, 0.5, 1.0])
            ppfs = stats.truncnorm.ppf(pvals, low, high)
            expected_ppfs = np.array([low, np.sign(low)*39.01775731, high])
            assert_almost_equal(ppfs, expected_ppfs)
            cdfs = stats.truncnorm.cdf(ppfs, low, high)
            assert_almost_equal(cdfs, pvals)

            if low < 0:
                assert_almost_equal(stats.truncnorm.sf(xmid, low, high), 0.9999999970389126)
                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high), 2.961048103554866e-09)
            else:
                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high), 0.9999999970389126)
                assert_almost_equal(stats.truncnorm.sf(xmid, low, high), 2.961048103554866e-09)
            pdf = stats.truncnorm.pdf(xmid, low, high)
            assert_almost_equal(np.log(pdf/expected_pdfs[2]), (xmid+0.25)/2)

            xvals = np.linspace(low, high, 11)
            xvals2 = -xvals[::-1]
            assert_almost_equal(stats.truncnorm.cdf(xvals, low, high), stats.truncnorm.sf(xvals2, -high, -low)[::-1])
            assert_almost_equal(stats.truncnorm.sf(xvals, low, high), stats.truncnorm.cdf(xvals2, -high, -low)[::-1])
            assert_almost_equal(stats.truncnorm.pdf(xvals, low, high), stats.truncnorm.pdf(xvals2, -high, -low)[::-1])

    def _test_moments_one_range(self, a, b, expected, decimal_s=7):
        m0, v0, s0, k0 = expected[:4]
        m, v, s, k = stats.truncnorm.stats(a, b, moments='mvsk')
        assert_almost_equal(m, m0)
        assert_almost_equal(v, v0)
        assert_almost_equal(s, s0, decimal=decimal_s)
        assert_almost_equal(k, k0)

    @pytest.mark.xfail_on_32bit("reduced accuracy with 32bit platforms.")
    def test_moments(self):
        # Values validated by changing TRUNCNORM_TAIL_X so as to evaluate
        # using both the _norm_XXX() and _norm_logXXX() functions, and by
        # removing the _stats and _munp methods in truncnorm tp force
        # numerical quadrature.
        # For m,v,s,k expect k to have the largest error as it is
        # constructed from powers of lower moments

        self._test_moments_one_range(-30, 30, [0, 1, 0.0, 0.0])
        self._test_moments_one_range(-10, 10, [0, 1, 0.0, 0.0])
        self._test_moments_one_range(-3, 3, [0.0000000000000000, 0.9733369246625415, 0.0000000000000000, -0.1711144363977444])
        self._test_moments_one_range(-2, 2, [0.0000000000000000, 0.7737413035499232, 0.0000000000000000, -0.6344632828703505])

        self._test_moments_one_range(0, np.inf, [0.7978845608028654, 0.3633802276324186, 0.9952717464311565, 0.8691773036059725])
        self._test_moments_one_range(-np.inf, 0, [-0.7978845608028654, 0.3633802276324186, -0.9952717464311565, 0.8691773036059725])

        self._test_moments_one_range(-1, 3, [0.2827861107271540, 0.6161417353578292, 0.5393018494027878, -0.2058206513527461])
        self._test_moments_one_range(-3, 1, [-0.2827861107271540, 0.6161417353578292, -0.5393018494027878, -0.2058206513527461])

        self._test_moments_one_range(-10, -9, [-9.1084562880124764, 0.0114488058210104, -1.8985607337519652, 5.0733457094223553])
        self._test_moments_one_range(-20, -19, [-19.0523439459766628, 0.0027250730180314, -1.9838694022629291, 5.8717850028287586])
        self._test_moments_one_range(-30, -29, [-29.0344012377394698, 0.0011806603928891, -1.9930304534611458, 5.8854062968996566], decimal_s=6)
        self._test_moments_one_range(-40, -39, [-39.0256074199326264, 0.0006548826719649, -1.9963146354109957, 5.6167758371700494])
        self._test_moments_one_range(39, 40, [39.0256074199326264, 0.0006548826719649, 1.9963146354109957, 5.6167758371700494])

    def test_9902_moments(self):
        m, v = stats.truncnorm.stats(0, np.inf, moments='mv')
        assert_almost_equal(m, 0.79788456)
        assert_almost_equal(v, 0.36338023)

    def test_gh_1489_trac_962_rvs(self):
        # Check the original example.
        low, high = 10, 15
        x = stats.truncnorm.rvs(low, high, 0, 1, size=10)
        assert_(low < x.min() < x.max() < high)

    def test_gh_11299_rvs(self):
        # Arose from investigating gh-11299
        # Test multiple shape parameters simultaneously.
        low = [-10, 10, -np.inf, -5, -np.inf, -np.inf, -45, -45, 40, -10, 40]
        high = [-5, 11, 5, np.inf, 40, -40, 40, -40, 45, np.inf, np.inf]
        x = stats.truncnorm.rvs(low, high, size=(5, len(low)))
        assert np.shape(x) == (5, len(low))
        assert_(np.all(low <= x.min(axis=0)))
        assert_(np.all(x.max(axis=0) <= high))

class TestHypergeom(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.hypergeom.rvs(20, 10, 3, size=(2, 50))
        assert_(numpy.all(vals >= 0) &
                numpy.all(vals <= 3))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.hypergeom.rvs(20, 3, 10)
        assert_(isinstance(val, int))
        val = stats.hypergeom(20, 3, 10).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_precision(self):
        # comparison number from mpmath
        M = 2500
        n = 50
        N = 500
        tot = M
        good = n
        hgpmf = stats.hypergeom.pmf(2, tot, good, N)
        assert_almost_equal(hgpmf, 0.0010114963068932233, 11)

    def test_args(self):
        # test correct output for corner cases of arguments
        # see gh-2325
        assert_almost_equal(stats.hypergeom.pmf(0, 2, 1, 0), 1.0, 11)
        assert_almost_equal(stats.hypergeom.pmf(1, 2, 1, 0), 0.0, 11)

        assert_almost_equal(stats.hypergeom.pmf(0, 2, 0, 2), 1.0, 11)
        assert_almost_equal(stats.hypergeom.pmf(1, 2, 1, 0), 0.0, 11)

    def test_cdf_above_one(self):
        # for some values of parameters, hypergeom cdf was >1, see gh-2238
        assert_(0 <= stats.hypergeom.cdf(30, 13397950, 4363, 12390) <= 1.0)

    def test_precision2(self):
        # Test hypergeom precision for large numbers.  See #1218.
        # Results compared with those from R.
        oranges = 9.9e4
        pears = 1.1e5
        fruits_eaten = np.array([3, 3.8, 3.9, 4, 4.1, 4.2, 5]) * 1e4
        quantile = 2e4
        res = [stats.hypergeom.sf(quantile, oranges + pears, oranges, eaten)
               for eaten in fruits_eaten]
        expected = np.array([0, 1.904153e-114, 2.752693e-66, 4.931217e-32,
                             8.265601e-11, 0.1237904, 1])
        assert_allclose(res, expected, atol=0, rtol=5e-7)

        # Test with array_like first argument
        quantiles = [1.9e4, 2e4, 2.1e4, 2.15e4]
        res2 = stats.hypergeom.sf(quantiles, oranges + pears, oranges, 4.2e4)
        expected2 = [1, 0.1237904, 6.511452e-34, 3.277667e-69]
        assert_allclose(res2, expected2, atol=0, rtol=5e-7)

    def test_entropy(self):
        # Simple tests of entropy.
        hg = stats.hypergeom(4, 1, 1)
        h = hg.entropy()
        expected_p = np.array([0.75, 0.25])
        expected_h = -np.sum(xlogy(expected_p, expected_p))
        assert_allclose(h, expected_h)

        hg = stats.hypergeom(1, 1, 1)
        h = hg.entropy()
        assert_equal(h, 0.0)

    def test_logsf(self):
        # Test logsf for very large numbers. See issue #4982
        # Results compare with those from R (v3.2.0):
        # phyper(k, n, M-n, N, lower.tail=FALSE, log.p=TRUE)
        # -2239.771

        k = 1e4
        M = 1e7
        n = 1e6
        N = 5e4

        result = stats.hypergeom.logsf(k, M, n, N)
        expected = -2239.771   # From R
        assert_almost_equal(result, expected, decimal=3)

        k = 1
        M = 1600
        n = 600
        N = 300

        result = stats.hypergeom.logsf(k, M, n, N)
        expected = -2.566567e-68   # From R
        assert_almost_equal(result, expected, decimal=15)

    def test_logcdf(self):
        # Test logcdf for very large numbers. See issue #8692
        # Results compare with those from R (v3.3.2):
        # phyper(k, n, M-n, N, lower.tail=TRUE, log.p=TRUE)
        # -5273.335

        k = 1
        M = 1e7
        n = 1e6
        N = 5e4

        result = stats.hypergeom.logcdf(k, M, n, N)
        expected = -5273.335   # From R
        assert_almost_equal(result, expected, decimal=3)

        # Same example as in issue #8692
        k = 40
        M = 1600
        n = 50
        N = 300

        result = stats.hypergeom.logcdf(k, M, n, N)
        expected = -7.565148879229e-23    # From R
        assert_almost_equal(result, expected, decimal=15)

        k = 125
        M = 1600
        n = 250
        N = 500

        result = stats.hypergeom.logcdf(k, M, n, N)
        expected = -4.242688e-12    # From R
        assert_almost_equal(result, expected, decimal=15)

        # test broadcasting robustness based on reviewer
        # concerns in PR 9603; using an array version of
        # the example from issue #8692
        k = np.array([40, 40, 40])
        M = 1600
        n = 50
        N = 300

        result = stats.hypergeom.logcdf(k, M, n, N)
        expected = np.full(3, -7.565148879229e-23)  # filled from R result
        assert_almost_equal(result, expected, decimal=15)


class TestLoggamma(object):

    def test_stats(self):
        # The following precomputed values are from the table in section 2.2
        # of "A Statistical Study of Log-Gamma Distribution", by Ping Shing
        # Chan (thesis, McMaster University, 1993).
        table = np.array([
                # c,    mean,   var,    skew,    exc. kurt.
                0.5, -1.9635, 4.9348, -1.5351, 4.0000,
                1.0, -0.5772, 1.6449, -1.1395, 2.4000,
                12.0, 2.4427, 0.0869, -0.2946, 0.1735,
            ]).reshape(-1, 5)
        for c, mean, var, skew, kurt in table:
            computed = stats.loggamma.stats(c, moments='msvk')
            assert_array_almost_equal(computed, [mean, var, skew, kurt],
                                      decimal=4)


class TestLogistic(object):
    # gh-6226
    def test_cdf_ppf(self):
        x = np.linspace(-20, 20)
        y = stats.logistic.cdf(x)
        xx = stats.logistic.ppf(y)
        assert_allclose(x, xx)

    def test_sf_isf(self):
        x = np.linspace(-20, 20)
        y = stats.logistic.sf(x)
        xx = stats.logistic.isf(y)
        assert_allclose(x, xx)

    def test_extreme_values(self):
        # p is chosen so that 1 - (1 - p) == p in double precision
        p = 9.992007221626409e-16
        desired = 34.53957599234088
        assert_allclose(stats.logistic.ppf(1 - p), desired)
        assert_allclose(stats.logistic.isf(p), desired)


class TestLogser(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.logser.rvs(0.75, size=(2, 50))
        assert_(numpy.all(vals >= 1))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.logser.rvs(0.75)
        assert_(isinstance(val, int))
        val = stats.logser(0.75).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_pmf_small_p(self):
        m = stats.logser.pmf(4, 1e-20)
        # The expected value was computed using mpmath:
        #   >>> import mpmath
        #   >>> mpmath.mp.dps = 64
        #   >>> k = 4
        #   >>> p = mpmath.mpf('1e-20')
        #   >>> float(-(p**k)/k/mpmath.log(1-p))
        #   2.5e-61
        # It is also clear from noticing that for very small p,
        # log(1-p) is approximately -p, and the formula becomes
        #    p**(k-1) / k
        assert_allclose(m, 2.5e-61)

    def test_mean_small_p(self):
        m = stats.logser.mean(1e-8)
        # The expected mean was computed using mpmath:
        #   >>> import mpmath
        #   >>> mpmath.dps = 60
        #   >>> p = mpmath.mpf('1e-8')
        #   >>> float(-p / ((1 - p)*mpmath.log(1 - p)))
        #   1.000000005
        assert_allclose(m, 1.000000005)


class TestPareto(object):
    def test_stats(self):
        # Check the stats() method with some simple values. Also check
        # that the calculations do not trigger RuntimeWarnings.
        with warnings.catch_warnings():
            warnings.simplefilter("error", RuntimeWarning)

            m, v, s, k = stats.pareto.stats(0.5, moments='mvsk')
            assert_equal(m, np.inf)
            assert_equal(v, np.inf)
            assert_equal(s, np.nan)
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(1.0, moments='mvsk')
            assert_equal(m, np.inf)
            assert_equal(v, np.inf)
            assert_equal(s, np.nan)
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(1.5, moments='mvsk')
            assert_equal(m, 3.0)
            assert_equal(v, np.inf)
            assert_equal(s, np.nan)
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(2.0, moments='mvsk')
            assert_equal(m, 2.0)
            assert_equal(v, np.inf)
            assert_equal(s, np.nan)
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(2.5, moments='mvsk')
            assert_allclose(m, 2.5 / 1.5)
            assert_allclose(v, 2.5 / (1.5*1.5*0.5))
            assert_equal(s, np.nan)
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(3.0, moments='mvsk')
            assert_allclose(m, 1.5)
            assert_allclose(v, 0.75)
            assert_equal(s, np.nan)
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(3.5, moments='mvsk')
            assert_allclose(m, 3.5 / 2.5)
            assert_allclose(v, 3.5 / (2.5*2.5*1.5))
            assert_allclose(s, (2*4.5/0.5)*np.sqrt(1.5/3.5))
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(4.0, moments='mvsk')
            assert_allclose(m, 4.0 / 3.0)
            assert_allclose(v, 4.0 / 18.0)
            assert_allclose(s, 2*(1+4.0)/(4.0-3) * np.sqrt((4.0-2)/4.0))
            assert_equal(k, np.nan)

            m, v, s, k = stats.pareto.stats(4.5, moments='mvsk')
            assert_allclose(m, 4.5 / 3.5)
            assert_allclose(v, 4.5 / (3.5*3.5*2.5))
            assert_allclose(s, (2*5.5/1.5) * np.sqrt(2.5/4.5))
            assert_allclose(k, 6*(4.5**3 + 4.5**2 - 6*4.5 - 2)/(4.5*1.5*0.5))

    def test_sf(self):
        x = 1e9
        b = 2
        scale = 1.5
        p = stats.pareto.sf(x, b, loc=0, scale=scale)
        expected = (scale/x)**b   # 2.25e-18
        assert_allclose(p, expected)


class TestGenpareto(object):
    def test_ab(self):
        # c >= 0: a, b = [0, inf]
        for c in [1., 0.]:
            c = np.asarray(c)
            a, b = stats.genpareto._get_support(c)
            assert_equal(a, 0.)
            assert_(np.isposinf(b))

        # c < 0: a=0, b=1/|c|
        c = np.asarray(-2.)
        a, b = stats.genpareto._get_support(c)
        assert_allclose([a, b], [0., 0.5])

    def test_c0(self):
        # with c=0, genpareto reduces to the exponential distribution
        # rv = stats.genpareto(c=0.)
        rv = stats.genpareto(c=0.)
        x = np.linspace(0, 10., 30)
        assert_allclose(rv.pdf(x), stats.expon.pdf(x))
        assert_allclose(rv.cdf(x), stats.expon.cdf(x))
        assert_allclose(rv.sf(x), stats.expon.sf(x))

        q = np.linspace(0., 1., 10)
        assert_allclose(rv.ppf(q), stats.expon.ppf(q))

    def test_cm1(self):
        # with c=-1, genpareto reduces to the uniform distr on [0, 1]
        rv = stats.genpareto(c=-1.)
        x = np.linspace(0, 10., 30)
        assert_allclose(rv.pdf(x), stats.uniform.pdf(x))
        assert_allclose(rv.cdf(x), stats.uniform.cdf(x))
        assert_allclose(rv.sf(x), stats.uniform.sf(x))

        q = np.linspace(0., 1., 10)
        assert_allclose(rv.ppf(q), stats.uniform.ppf(q))

        # logpdf(1., c=-1) should be zero
        assert_allclose(rv.logpdf(1), 0)

    def test_x_inf(self):
        # make sure x=inf is handled gracefully
        rv = stats.genpareto(c=0.1)
        assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
        assert_(np.isneginf(rv.logpdf(np.inf)))

        rv = stats.genpareto(c=0.)
        assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
        assert_(np.isneginf(rv.logpdf(np.inf)))

        rv = stats.genpareto(c=-1.)
        assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
        assert_(np.isneginf(rv.logpdf(np.inf)))

    def test_c_continuity(self):
        # pdf is continuous at c=0, -1
        x = np.linspace(0, 10, 30)
        for c in [0, -1]:
            pdf0 = stats.genpareto.pdf(x, c)
            for dc in [1e-14, -1e-14]:
                pdfc = stats.genpareto.pdf(x, c + dc)
                assert_allclose(pdf0, pdfc, atol=1e-12)

            cdf0 = stats.genpareto.cdf(x, c)
            for dc in [1e-14, 1e-14]:
                cdfc = stats.genpareto.cdf(x, c + dc)
                assert_allclose(cdf0, cdfc, atol=1e-12)

    def test_c_continuity_ppf(self):
        q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
                  np.linspace(0.01, 1, 30, endpoint=False),
                  1. - np.logspace(1e-12, 0.01, base=0.1)]
        for c in [0., -1.]:
            ppf0 = stats.genpareto.ppf(q, c)
            for dc in [1e-14, -1e-14]:
                ppfc = stats.genpareto.ppf(q, c + dc)
                assert_allclose(ppf0, ppfc, atol=1e-12)

    def test_c_continuity_isf(self):
        q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
                  np.linspace(0.01, 1, 30, endpoint=False),
                  1. - np.logspace(1e-12, 0.01, base=0.1)]
        for c in [0., -1.]:
            isf0 = stats.genpareto.isf(q, c)
            for dc in [1e-14, -1e-14]:
                isfc = stats.genpareto.isf(q, c + dc)
                assert_allclose(isf0, isfc, atol=1e-12)

    def test_cdf_ppf_roundtrip(self):
        # this should pass with machine precision. hat tip @pbrod
        q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
                  np.linspace(0.01, 1, 30, endpoint=False),
                  1. - np.logspace(1e-12, 0.01, base=0.1)]
        for c in [1e-8, -1e-18, 1e-15, -1e-15]:
            assert_allclose(stats.genpareto.cdf(stats.genpareto.ppf(q, c), c),
                            q, atol=1e-15)

    def test_logsf(self):
        logp = stats.genpareto.logsf(1e10, .01, 0, 1)
        assert_allclose(logp, -1842.0680753952365)

    # Values in 'expected_stats' are
    # [mean, variance, skewness, excess kurtosis].
    @pytest.mark.parametrize(
        'c, expected_stats',
        [(0, [1, 1, 2, 6]),
         (1/4, [4/3, 32/9, 10/np.sqrt(2), np.nan]),
         (1/9, [9/8, (81/64)*(9/7), (10/9)*np.sqrt(7), 754/45]),
         (-1, [1/2, 1/12, 0, -6/5])])
    def test_stats(self, c, expected_stats):
        result = stats.genpareto.stats(c, moments='mvsk')
        assert_allclose(result, expected_stats, rtol=1e-13, atol=1e-15)

    def test_var(self):
        # Regression test for gh-11168.
        v = stats.genpareto.var(1e-8)
        assert_allclose(v, 1.000000040000001, rtol=1e-13)


class TestPearson3(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.pearson3.rvs(0.1, size=(2, 50))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllFloat'])
        val = stats.pearson3.rvs(0.5)
        assert_(isinstance(val, float))
        val = stats.pearson3(0.5).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllFloat'])
        assert_(len(val) == 3)

    def test_pdf(self):
        vals = stats.pearson3.pdf(2, [0.0, 0.1, 0.2])
        assert_allclose(vals, np.array([0.05399097, 0.05555481, 0.05670246]),
                        atol=1e-6)
        vals = stats.pearson3.pdf(-3, 0.1)
        assert_allclose(vals, np.array([0.00313791]), atol=1e-6)
        vals = stats.pearson3.pdf([-3, -2, -1, 0, 1], 0.1)
        assert_allclose(vals, np.array([0.00313791, 0.05192304, 0.25028092,
                                        0.39885918, 0.23413173]), atol=1e-6)

    def test_cdf(self):
        vals = stats.pearson3.cdf(2, [0.0, 0.1, 0.2])
        assert_allclose(vals, np.array([0.97724987, 0.97462004, 0.97213626]),
                        atol=1e-6)
        vals = stats.pearson3.cdf(-3, 0.1)
        assert_allclose(vals, [0.00082256], atol=1e-6)
        vals = stats.pearson3.cdf([-3, -2, -1, 0, 1], 0.1)
        assert_allclose(vals, [8.22563821e-04, 1.99860448e-02, 1.58550710e-01,
                               5.06649130e-01, 8.41442111e-01], atol=1e-6)


class TestKappa4(object):
    def test_cdf_genpareto(self):
        # h = 1 and k != 0 is generalized Pareto
        x = [0.0, 0.1, 0.2, 0.5]
        h = 1.0
        for k in [-1.9, -1.0, -0.5, -0.2, -0.1, 0.1, 0.2, 0.5, 1.0,
                  1.9]:
            vals = stats.kappa4.cdf(x, h, k)
            # shape parameter is opposite what is expected
            vals_comp = stats.genpareto.cdf(x, -k)
            assert_allclose(vals, vals_comp)

    def test_cdf_genextreme(self):
        # h = 0 and k != 0 is generalized extreme value
        x = np.linspace(-5, 5, 10)
        h = 0.0
        k = np.linspace(-3, 3, 10)
        vals = stats.kappa4.cdf(x, h, k)
        vals_comp = stats.genextreme.cdf(x, k)
        assert_allclose(vals, vals_comp)

    def test_cdf_expon(self):
        # h = 1 and k = 0 is exponential
        x = np.linspace(0, 10, 10)
        h = 1.0
        k = 0.0
        vals = stats.kappa4.cdf(x, h, k)
        vals_comp = stats.expon.cdf(x)
        assert_allclose(vals, vals_comp)

    def test_cdf_gumbel_r(self):
        # h = 0 and k = 0 is gumbel_r
        x = np.linspace(-5, 5, 10)
        h = 0.0
        k = 0.0
        vals = stats.kappa4.cdf(x, h, k)
        vals_comp = stats.gumbel_r.cdf(x)
        assert_allclose(vals, vals_comp)

    def test_cdf_logistic(self):
        # h = -1 and k = 0 is logistic
        x = np.linspace(-5, 5, 10)
        h = -1.0
        k = 0.0
        vals = stats.kappa4.cdf(x, h, k)
        vals_comp = stats.logistic.cdf(x)
        assert_allclose(vals, vals_comp)

    def test_cdf_uniform(self):
        # h = 1 and k = 1 is uniform
        x = np.linspace(-5, 5, 10)
        h = 1.0
        k = 1.0
        vals = stats.kappa4.cdf(x, h, k)
        vals_comp = stats.uniform.cdf(x)
        assert_allclose(vals, vals_comp)

    def test_integers_ctor(self):
        # regression test for gh-7416: _argcheck fails for integer h and k
        # in numpy 1.12
        stats.kappa4(1, 2)


class TestPoisson(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_pmf_basic(self):
        # Basic case
        ln2 = np.log(2)
        vals = stats.poisson.pmf([0, 1, 2], ln2)
        expected = [0.5, ln2/2, ln2**2/4]
        assert_allclose(vals, expected)

    def test_mu0(self):
        # Edge case: mu=0
        vals = stats.poisson.pmf([0, 1, 2], 0)
        expected = [1, 0, 0]
        assert_array_equal(vals, expected)

        interval = stats.poisson.interval(0.95, 0)
        assert_equal(interval, (0, 0))

    def test_rvs(self):
        vals = stats.poisson.rvs(0.5, size=(2, 50))
        assert_(numpy.all(vals >= 0))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.poisson.rvs(0.5)
        assert_(isinstance(val, int))
        val = stats.poisson(0.5).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_stats(self):
        mu = 16.0
        result = stats.poisson.stats(mu, moments='mvsk')
        assert_allclose(result, [mu, mu, np.sqrt(1.0/mu), 1.0/mu])

        mu = np.array([0.0, 1.0, 2.0])
        result = stats.poisson.stats(mu, moments='mvsk')
        expected = (mu, mu, [np.inf, 1, 1/np.sqrt(2)], [np.inf, 1, 0.5])
        assert_allclose(result, expected)


class TestKSTwo(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_cdf(self):
        for n in [1, 2, 3, 10, 100, 1000]:
            # Test x-values:
            #  0, 1/2n, where the cdf should be 0
            #  1/n, where the cdf should be n!/n^n
            #  0.5, where the cdf should match ksone.cdf
            # 1-1/n, where cdf = 1-2/n^n
            # 1, where cdf == 1
            # (E.g. Exact values given by Eqn 1 in Simard / L'Ecuyer)
            x = np.array([0, 0.5/n, 1/n, 0.5, 1-1.0/n, 1])
            v1 = (1.0/n)**n
            lg = scipy.special.gammaln(n+1)
            elg = (np.exp(lg) if v1 != 0 else 0)
            expected = np.array([0, 0, v1 * elg,
                                 1 - 2*stats.ksone.sf(0.5, n),
                                 max(1 - 2*v1, 0.0),
                                 1.0])
            vals_cdf = stats.kstwo.cdf(x, n)
            assert_allclose(vals_cdf, expected)

    def test_sf(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            # Same x values as in test_cdf, and use sf = 1 - cdf
            x = np.array([0, 0.5/n, 1/n, 0.5, 1-1.0/n, 1])
            v1 = (1.0/n)**n
            lg = scipy.special.gammaln(n+1)
            elg = (np.exp(lg) if v1 != 0 else 0)
            expected = np.array([1.0, 1.0,
                                 1 - v1 * elg,
                                 2*stats.ksone.sf(0.5, n),
                                 min(2*v1, 1.0), 0])
            vals_sf = stats.kstwo.sf(x, n)
            assert_allclose(vals_sf, expected)

    def test_cdf_sqrtn(self):
        # For fixed a, cdf(a/sqrt(n), n) -> kstwobign(a) as n->infinity
        # cdf(a/sqrt(n), n) is an increasing function of n (and a)
        # Check that the function is indeed increasing (allowing for some
        # small floating point and algorithm differences.)
        x = np.linspace(0, 2, 11)[1:]
        ns = [50, 100, 200, 400, 1000, 2000]
        for _x in x:
            xn = _x / np.sqrt(ns)
            probs = stats.kstwo.cdf(xn, ns)
            diffs = np.diff(probs)
            assert_array_less(diffs, 1e-8)

    def test_cdf_sf(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            vals_cdf = stats.kstwo.cdf(x, n)
            vals_sf = stats.kstwo.sf(x, n)
            assert_array_almost_equal(vals_cdf, 1 - vals_sf)

    def test_cdf_sf_sqrtn(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            xn = x / np.sqrt(n)
            vals_cdf = stats.kstwo.cdf(xn, n)
            vals_sf = stats.kstwo.sf(xn, n)
            assert_array_almost_equal(vals_cdf, 1 - vals_sf)

    def test_ppf_of_cdf(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            xn = x[x > 0.5/n]
            vals_cdf = stats.kstwo.cdf(xn, n)
            # CDFs close to 1 are better dealt with using the SF
            cond = (0 < vals_cdf) & (vals_cdf < 0.99)
            vals = stats.kstwo.ppf(vals_cdf, n)
            assert_allclose(vals[cond], xn[cond], rtol=1e-4)

    def test_isf_of_sf(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            xn = x[x > 0.5/n]
            vals_isf = stats.kstwo.isf(xn, n)
            cond = (0 < vals_isf) & (vals_isf < 1.0)
            vals = stats.kstwo.sf(vals_isf, n)
            assert_allclose(vals[cond], xn[cond], rtol=1e-4)

    def test_ppf_of_cdf_sqrtn(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            xn = (x / np.sqrt(n))[x > 0.5/n]
            vals_cdf = stats.kstwo.cdf(xn, n)
            cond = (0 < vals_cdf) & (vals_cdf < 1.0)
            vals = stats.kstwo.ppf(vals_cdf, n)
            assert_allclose(vals[cond], xn[cond])

    def test_isf_of_sf_sqrtn(self):
        x = np.linspace(0, 1, 11)
        for n in [1, 2, 3, 10, 100, 1000]:
            xn = (x / np.sqrt(n))[x > 0.5/n]
            vals_sf = stats.kstwo.sf(xn, n)
            # SFs close to 1 are better dealt with using the CDF
            cond = (0 < vals_sf) & (vals_sf < 0.95)
            vals = stats.kstwo.isf(vals_sf, n)
            assert_allclose(vals[cond], xn[cond])

    def test_ppf(self):
        probs = np.linspace(0, 1, 11)[1:]
        for n in [1, 2, 3, 10, 100, 1000]:
            xn = stats.kstwo.ppf(probs, n)
            vals_cdf = stats.kstwo.cdf(xn, n)
            assert_allclose(vals_cdf, probs)

    def test_simard_lecuyer_table1(self):
        # Compute the cdf for values near the mean of the distribution.
        # The mean u ~ log(2)*sqrt(pi/(2n))
        # Compute for x in [u/4, u/3, u/2, u, 2u, 3u]
        # This is the computation of Table 1 of Simard, R., L'Ecuyer, P. (2011)
        #  "Computing the Two-Sided Kolmogorov-Smirnov Distribution".
        # Except that the values below are not from the published table, but
        # were generated using an independent SageMath implementation of
        # Durbin's algorithm (with the exponentiation and scaling of
        # Marsaglia/Tsang/Wang's version) using 500 bit arithmetic.
        # Some of the values in the published table have relative
        # errors greater than 1e-4.
        ns = [10, 50, 100, 200, 500, 1000]
        ratios = np.array([1.0/4, 1.0/3, 1.0/2, 1, 2, 3])
        expected = np.array([
            [1.92155292e-08, 5.72933228e-05, 2.15233226e-02, 6.31566589e-01, 9.97685592e-01, 9.99999942e-01],
            [2.28096224e-09, 1.99142563e-05, 1.42617934e-02, 5.95345542e-01, 9.96177701e-01, 9.99998662e-01],
            [1.00201886e-09, 1.32673079e-05, 1.24608594e-02, 5.86163220e-01, 9.95866877e-01, 9.99998240e-01],
            [4.93313022e-10, 9.52658029e-06, 1.12123138e-02, 5.79486872e-01, 9.95661824e-01, 9.99997964e-01],
            [2.37049293e-10, 6.85002458e-06, 1.01309221e-02, 5.73427224e-01, 9.95491207e-01, 9.99997750e-01],
            [1.56990874e-10, 5.71738276e-06, 9.59725430e-03, 5.70322692e-01, 9.95409545e-01, 9.99997657e-01]
        ])
        for idx, n in enumerate(ns):
            x = ratios * np.log(2) * np.sqrt(np.pi/2/n)
            vals_cdf = stats.kstwo.cdf(x, n)
            assert_allclose(vals_cdf, expected[idx], rtol=1e-5)


class TestZipf(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.zipf.rvs(1.5, size=(2, 50))
        assert_(numpy.all(vals >= 1))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.zipf.rvs(1.5)
        assert_(isinstance(val, int))
        val = stats.zipf(1.5).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])

    def test_moments(self):
        # n-th moment is finite iff a > n + 1
        m, v = stats.zipf.stats(a=2.8)
        assert_(np.isfinite(m))
        assert_equal(v, np.inf)

        s, k = stats.zipf.stats(a=4.8, moments='sk')
        assert_(not np.isfinite([s, k]).all())


class TestDLaplace(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        vals = stats.dlaplace.rvs(1.5, size=(2, 50))
        assert_(numpy.shape(vals) == (2, 50))
        assert_(vals.dtype.char in typecodes['AllInteger'])
        val = stats.dlaplace.rvs(1.5)
        assert_(isinstance(val, int))
        val = stats.dlaplace(1.5).rvs(3)
        assert_(isinstance(val, numpy.ndarray))
        assert_(val.dtype.char in typecodes['AllInteger'])
        assert_(stats.dlaplace.rvs(0.8) is not None)

    def test_stats(self):
        # compare the explicit formulas w/ direct summation using pmf
        a = 1.
        dl = stats.dlaplace(a)
        m, v, s, k = dl.stats('mvsk')

        N = 37
        xx = np.arange(-N, N+1)
        pp = dl.pmf(xx)
        m2, m4 = np.sum(pp*xx**2), np.sum(pp*xx**4)
        assert_equal((m, s), (0, 0))
        assert_allclose((v, k), (m2, m4/m2**2 - 3.), atol=1e-14, rtol=1e-8)

    def test_stats2(self):
        a = np.log(2.)
        dl = stats.dlaplace(a)
        m, v, s, k = dl.stats('mvsk')
        assert_equal((m, s), (0., 0.))
        assert_allclose((v, k), (4., 3.25))


class TestLaplace(object):
    @pytest.mark.parametrize("rvs_loc", [-5, 0, 1, 2])
    @pytest.mark.parametrize("rvs_scale", [1, 2, 3, 10])
    def test_fit(self, rvs_loc, rvs_scale):
        # tests that various inputs follow expected behavior
        # for a variety of `loc` and `scale`.
        data = stats.laplace.rvs(size=100, loc=rvs_loc, scale=rvs_scale)

        # MLE estimates are given by
        loc_mle = np.median(data)
        scale_mle = np.sum(np.abs(data - loc_mle)) / len(data)

        # standard outputs should match MLE
        loc, scale = stats.laplace.fit(data)
        assert_allclose(loc, loc_mle, atol=1e-15, rtol=1e-15)
        assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)

        # fixed parameter should use MLE for other
        loc, scale = stats.laplace.fit(data, floc=loc_mle)
        assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)
        loc, scale = stats.laplace.fit(data, fscale=scale_mle)
        assert_allclose(loc, loc_mle)

        # test with non-mle fixed parameter
        # create scale with non-median loc
        loc = rvs_loc * 2
        scale_mle = np.sum(np.abs(data - loc)) / len(data)

        # fixed loc to non median, scale should match
        # scale calculation with modified loc
        loc, scale = stats.laplace.fit(data, floc=loc)
        assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)

        # fixed scale created with non median loc,
        # loc output should still be the data median.
        loc, scale = stats.laplace.fit(data, fscale=scale_mle)
        assert_allclose(loc_mle, loc, atol=1e-15, rtol=1e-15)

        # error raised when both `floc` and `fscale` are fixed
        assert_raises(RuntimeError, stats.laplace.fit, data, floc=loc_mle,
                      fscale=scale_mle)

        # error is raised with non-finite values
        assert_raises(RuntimeError, stats.laplace.fit, [np.nan])
        assert_raises(RuntimeError, stats.laplace.fit, [np.inf])

    @pytest.mark.parametrize("rvs_scale,rvs_loc", [(10, -5),
                                                   (5, 10),
                                                   (.2, .5)])
    def test_fit_MLE_comp_optimzer(self, rvs_loc, rvs_scale):
        data = stats.laplace.rvs(size=1000, loc=rvs_loc, scale=rvs_scale)

        # the log-likelihood function for laplace is given by
        def ll(loc, scale, data):
            return -1 * (- (len(data)) * np.log(2*scale) -
                         (1/scale)*np.sum(np.abs(data - loc)))

        # test that the objective function result of the analytical MLEs is
        # less than or equal to that of the numerically optimized estimate
        loc, scale = stats.laplace.fit(data)
        loc_opt, scale_opt = super(type(stats.laplace),
                                   stats.laplace).fit(data)
        ll_mle = ll(loc, scale, data)
        ll_opt = ll(loc_opt, scale_opt, data)
        assert ll_mle < ll_opt or np.allclose(ll_mle, ll_opt,
                                              atol=1e-15, rtol=1e-15)

    def test_fit_simple_non_random_data(self):
        data = np.array([1.0, 1.0, 3.0, 5.0, 8.0, 14.0])
        # with `floc` fixed to 6, scale should be 4.
        loc, scale = stats.laplace.fit(data, floc=6)
        assert_allclose(scale, 4, atol=1e-15, rtol=1e-15)
        # with `fscale` fixed to 6, loc should be 4.
        loc, scale = stats.laplace.fit(data, fscale=6)
        assert_allclose(loc, 4, atol=1e-15, rtol=1e-15)


class TestInvGamma(object):
    def test_invgamma_inf_gh_1866(self):
        # invgamma's moments are only finite for a>n
        # specific numbers checked w/ boost 1.54
        with warnings.catch_warnings():
            warnings.simplefilter('error', RuntimeWarning)
            mvsk = stats.invgamma.stats(a=19.31, moments='mvsk')
            expected = [0.05461496450, 0.0001723162534, 1.020362676,
                        2.055616582]
            assert_allclose(mvsk, expected)

            a = [1.1, 3.1, 5.6]
            mvsk = stats.invgamma.stats(a=a, moments='mvsk')
            expected = ([10., 0.476190476, 0.2173913043],       # mmm
                        [np.inf, 0.2061430632, 0.01312749422],  # vvv
                        [np.nan, 41.95235392, 2.919025532],     # sss
                        [np.nan, np.nan, 24.51923076])          # kkk
            for x, y in zip(mvsk, expected):
                assert_almost_equal(x, y)

    def test_cdf_ppf(self):
        # gh-6245
        x = np.logspace(-2.6, 0)
        y = stats.invgamma.cdf(x, 1)
        xx = stats.invgamma.ppf(y, 1)
        assert_allclose(x, xx)

    def test_sf_isf(self):
        # gh-6245
        if sys.maxsize > 2**32:
            x = np.logspace(2, 100)
        else:
            # Invgamme roundtrip on 32-bit systems has relative accuracy
            # ~1e-15 until x=1e+15, and becomes inf above x=1e+18
            x = np.logspace(2, 18)

        y = stats.invgamma.sf(x, 1)
        xx = stats.invgamma.isf(y, 1)
        assert_allclose(x, xx, rtol=1.0)


class TestF(object):
    def test_endpoints(self):
        # Compute the pdf at the left endpoint dst.a.
        data = [[stats.f, (2, 1), 1.0]]
        for _f, _args, _correct in data:
            ans = _f.pdf(_f.a, *_args)
            print(_f, (_args), ans, _correct, ans == _correct)

        ans = [_f.pdf(_f.a, *_args) for _f, _args, _ in data]
        correct = [_correct_ for _f, _args, _correct_ in data]
        assert_array_almost_equal(ans, correct)

    def test_f_moments(self):
        # n-th moment of F distributions is only finite for n < dfd / 2
        m, v, s, k = stats.f.stats(11, 6.5, moments='mvsk')
        assert_(np.isfinite(m))
        assert_(np.isfinite(v))
        assert_(np.isfinite(s))
        assert_(not np.isfinite(k))

    def test_moments_warnings(self):
        # no warnings should be generated for dfd = 2, 4, 6, 8 (div by zero)
        with warnings.catch_warnings():
            warnings.simplefilter('error', RuntimeWarning)
            stats.f.stats(dfn=[11]*4, dfd=[2, 4, 6, 8], moments='mvsk')

    def test_stats_broadcast(self):
        dfn = np.array([[3], [11]])
        dfd = np.array([11, 12])
        m, v, s, k = stats.f.stats(dfn=dfn, dfd=dfd, moments='mvsk')
        m2 = [dfd / (dfd - 2)]*2
        assert_allclose(m, m2)
        v2 = 2 * dfd**2 * (dfn + dfd - 2) / dfn / (dfd - 2)**2 / (dfd - 4)
        assert_allclose(v, v2)
        s2 = ((2*dfn + dfd - 2) * np.sqrt(8*(dfd - 4)) /
              ((dfd - 6) * np.sqrt(dfn*(dfn + dfd - 2))))
        assert_allclose(s, s2)
        k2num = 12 * (dfn * (5*dfd - 22) * (dfn + dfd - 2) +
                      (dfd - 4) * (dfd - 2)**2)
        k2den = dfn * (dfd - 6) * (dfd - 8) * (dfn + dfd - 2)
        k2 = k2num / k2den
        assert_allclose(k, k2)


def test_rvgeneric_std():
    # Regression test for #1191
    assert_array_almost_equal(stats.t.std([5, 6]), [1.29099445, 1.22474487])


def test_moments_t():
    # regression test for #8786
    assert_equal(stats.t.stats(df=1, moments='mvsk'),
                 (np.inf, np.nan, np.nan, np.nan))
    assert_equal(stats.t.stats(df=1.01, moments='mvsk'),
                 (0.0, np.inf, np.nan, np.nan))
    assert_equal(stats.t.stats(df=2, moments='mvsk'),
                 (0.0, np.inf, np.nan, np.nan))
    assert_equal(stats.t.stats(df=2.01, moments='mvsk'),
                 (0.0, 2.01/(2.01-2.0), np.nan, np.inf))
    assert_equal(stats.t.stats(df=3, moments='sk'), (np.nan, np.inf))
    assert_equal(stats.t.stats(df=3.01, moments='sk'), (0.0, np.inf))
    assert_equal(stats.t.stats(df=4, moments='sk'), (0.0, np.inf))
    assert_equal(stats.t.stats(df=4.01, moments='sk'), (0.0, 6.0/(4.01 - 4.0)))


class TestRvDiscrete(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_rvs(self):
        states = [-1, 0, 1, 2, 3, 4]
        probability = [0.0, 0.3, 0.4, 0.0, 0.3, 0.0]
        samples = 1000
        r = stats.rv_discrete(name='sample', values=(states, probability))
        x = r.rvs(size=samples)
        assert_(isinstance(x, numpy.ndarray))

        for s, p in zip(states, probability):
            assert_(abs(sum(x == s)/float(samples) - p) < 0.05)

        x = r.rvs()
        assert_(isinstance(x, int))

    def test_entropy(self):
        # Basic tests of entropy.
        pvals = np.array([0.25, 0.45, 0.3])
        p = stats.rv_discrete(values=([0, 1, 2], pvals))
        expected_h = -sum(xlogy(pvals, pvals))
        h = p.entropy()
        assert_allclose(h, expected_h)

        p = stats.rv_discrete(values=([0, 1, 2], [1.0, 0, 0]))
        h = p.entropy()
        assert_equal(h, 0.0)

    def test_pmf(self):
        xk = [1, 2, 4]
        pk = [0.5, 0.3, 0.2]
        rv = stats.rv_discrete(values=(xk, pk))

        x = [[1., 4.],
             [3., 2]]
        assert_allclose(rv.pmf(x),
                        [[0.5, 0.2],
                         [0., 0.3]], atol=1e-14)

    def test_cdf(self):
        xk = [1, 2, 4]
        pk = [0.5, 0.3, 0.2]
        rv = stats.rv_discrete(values=(xk, pk))

        x_values = [-2, 1., 1.1, 1.5, 2.0, 3.0, 4, 5]
        expected = [0, 0.5, 0.5, 0.5, 0.8, 0.8, 1, 1]
        assert_allclose(rv.cdf(x_values), expected, atol=1e-14)

        # also check scalar arguments
        assert_allclose([rv.cdf(xx) for xx in x_values],
                        expected, atol=1e-14)

    def test_ppf(self):
        xk = [1, 2, 4]
        pk = [0.5, 0.3, 0.2]
        rv = stats.rv_discrete(values=(xk, pk))

        q_values = [0.1, 0.5, 0.6, 0.8, 0.9, 1.]
        expected = [1, 1, 2, 2, 4, 4]
        assert_allclose(rv.ppf(q_values), expected, atol=1e-14)

        # also check scalar arguments
        assert_allclose([rv.ppf(q) for q in q_values],
                        expected, atol=1e-14)

    def test_cdf_ppf_next(self):
        # copied and special cased from test_discrete_basic
        vals = ([1, 2, 4, 7, 8], [0.1, 0.2, 0.3, 0.3, 0.1])
        rv = stats.rv_discrete(values=vals)

        assert_array_equal(rv.ppf(rv.cdf(rv.xk[:-1]) + 1e-8),
                           rv.xk[1:])

    def test_expect(self):
        xk = [1, 2, 4, 6, 7, 11]
        pk = [0.1, 0.2, 0.2, 0.2, 0.2, 0.1]
        rv = stats.rv_discrete(values=(xk, pk))

        assert_allclose(rv.expect(), np.sum(rv.xk * rv.pk), atol=1e-14)

    def test_multidimension(self):
        xk = np.arange(12).reshape((3, 4))
        pk = np.array([[0.1, 0.1, 0.15, 0.05],
                       [0.1, 0.1, 0.05, 0.05],
                       [0.1, 0.1, 0.05, 0.05]])
        rv = stats.rv_discrete(values=(xk, pk))

        assert_allclose(rv.expect(), np.sum(rv.xk * rv.pk), atol=1e-14)

    def test_bad_input(self):
        xk = [1, 2, 3]
        pk = [0.5, 0.5]
        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))

        pk = [1, 2, 3]
        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))

        xk = [1, 2, 3]
        pk = [0.5, 1.2, -0.7]
        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))

        xk = [1, 2, 3, 4, 5]
        pk = [0.3, 0.3, 0.3, 0.3, -0.2]
        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))

    def test_shape_rv_sample(self):
        # tests added for gh-9565

        # mismatch of 2d inputs
        xk, pk = np.arange(4).reshape((2, 2)), np.full((2, 3), 1/6)
        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))

        # same number of elements, but shapes not compatible
        xk, pk = np.arange(6).reshape((3, 2)), np.full((2, 3), 1/6)
        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))

        # same shapes => no error
        xk, pk = np.arange(6).reshape((3, 2)), np.full((3, 2), 1/6)
        assert_equal(stats.rv_discrete(values=(xk, pk)).pmf(0), 1/6)


class TestSkewNorm(object):
    def setup_method(self):
        self.rng = check_random_state(1234)

    def test_normal(self):
        # When the skewness is 0 the distribution is normal
        x = np.linspace(-5, 5, 100)
        assert_array_almost_equal(stats.skewnorm.pdf(x, a=0),
                                  stats.norm.pdf(x))

    def test_rvs(self):
        shape = (3, 4, 5)
        x = stats.skewnorm.rvs(a=0.75, size=shape, random_state=self.rng)
        assert_equal(shape, x.shape)

        x = stats.skewnorm.rvs(a=-3, size=shape, random_state=self.rng)
        assert_equal(shape, x.shape)

    def test_moments(self):
        X = stats.skewnorm.rvs(a=4, size=int(1e6), loc=5, scale=2,
                               random_state=self.rng)
        expected = [np.mean(X), np.var(X), stats.skew(X), stats.kurtosis(X)]
        computed = stats.skewnorm.stats(a=4, loc=5, scale=2, moments='mvsk')
        assert_array_almost_equal(computed, expected, decimal=2)

        X = stats.skewnorm.rvs(a=-4, size=int(1e6), loc=5, scale=2,
                               random_state=self.rng)
        expected = [np.mean(X), np.var(X), stats.skew(X), stats.kurtosis(X)]
        computed = stats.skewnorm.stats(a=-4, loc=5, scale=2, moments='mvsk')
        assert_array_almost_equal(computed, expected, decimal=2)

    def test_cdf_large_x(self):
        # Regression test for gh-7746.
        # The x values are large enough that the closest 64 bit floating
        # point representation of the exact CDF is 1.0.
        p = stats.skewnorm.cdf([10, 20, 30], -1)
        assert_allclose(p, np.ones(3), rtol=1e-14)
        p = stats.skewnorm.cdf(25, 2.5)
        assert_allclose(p, 1.0, rtol=1e-14)

    def test_cdf_sf_small_values(self):
        # Triples are [x, a, cdf(x, a)].  These values were computed
        # using CDF[SkewNormDistribution[0, 1, a], x] in Wolfram Alpha.
        cdfvals = [
            [-8, 1, 3.870035046664392611e-31],
            [-4, 2, 8.1298399188811398e-21],
            [-2, 5, 1.55326826787106273e-26],
            [-9, -1, 2.257176811907681295e-19],
            [-10, -4, 1.523970604832105213e-23],
        ]
        for x, a, cdfval in cdfvals:
            p = stats.skewnorm.cdf(x, a)
            assert_allclose(p, cdfval, rtol=1e-8)
            # For the skew normal distribution, sf(-x, -a) = cdf(x, a).
            p = stats.skewnorm.sf(-x, -a)
            assert_allclose(p, cdfval, rtol=1e-8)


class TestExpon(object):
    def test_zero(self):
        assert_equal(stats.expon.pdf(0), 1)

    def test_tail(self):  # Regression test for ticket 807
        assert_equal(stats.expon.cdf(1e-18), 1e-18)
        assert_equal(stats.expon.isf(stats.expon.sf(40)), 40)

    def test_nan_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
        assert_raises(RuntimeError, stats.expon.fit, x)

    def test_inf_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
        assert_raises(RuntimeError, stats.expon.fit, x)


class TestNorm(object):
    def test_nan_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
        assert_raises(RuntimeError, stats.norm.fit, x)

    def test_inf_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
        assert_raises(RuntimeError, stats.norm.fit, x)

    def test_bad_keyword_arg(self):
        x = [1, 2, 3]
        assert_raises(TypeError, stats.norm.fit, x, plate="shrimp")


class TestUniform(object):
    """gh-10300"""
    def test_nan_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
        assert_raises(RuntimeError, stats.uniform.fit, x)

    def test_inf_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
        assert_raises(RuntimeError, stats.uniform.fit, x)


class TestExponNorm(object):
    def test_moments(self):
        # Some moment test cases based on non-loc/scaled formula
        def get_moms(lam, sig, mu):
            # See wikipedia for these formulae
            #  where it is listed as an exponentially modified gaussian
            opK2 = 1.0 + 1 / (lam*sig)**2
            exp_skew = 2 / (lam * sig)**3 * opK2**(-1.5)
            exp_kurt = 6.0 * (1 + (lam * sig)**2)**(-2)
            return [mu + 1/lam, sig*sig + 1.0/(lam*lam), exp_skew, exp_kurt]

        mu, sig, lam = 0, 1, 1
        K = 1.0 / (lam * sig)
        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
        assert_almost_equal(sts, get_moms(lam, sig, mu))
        mu, sig, lam = -3, 2, 0.1
        K = 1.0 / (lam * sig)
        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
        assert_almost_equal(sts, get_moms(lam, sig, mu))
        mu, sig, lam = 0, 3, 1
        K = 1.0 / (lam * sig)
        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
        assert_almost_equal(sts, get_moms(lam, sig, mu))
        mu, sig, lam = -5, 11, 3.5
        K = 1.0 / (lam * sig)
        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
        assert_almost_equal(sts, get_moms(lam, sig, mu))

    def test_nan_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
        assert_raises(RuntimeError, stats.exponnorm.fit, x, floc=0, fscale=1)

    def test_inf_raises_error(self):
        # see gh-issue 10300
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
        assert_raises(RuntimeError, stats.exponnorm.fit, x, floc=0, fscale=1)

    def test_extremes_x(self):
        # Test for extreme values against overflows
        assert_almost_equal(stats.exponnorm.pdf(-900, 1), 0.0)
        assert_almost_equal(stats.exponnorm.pdf(+900, 1), 0.0)
        assert_almost_equal(stats.exponnorm.pdf(1, 0.01), 0.0)
        assert_almost_equal(stats.exponnorm.pdf(-900, 0.01), 0.0)
        assert_almost_equal(stats.exponnorm.pdf(+900, 0.01), 0.0)


class TestGenExpon(object):
    def test_pdf_unity_area(self):
        from scipy.integrate import simps
        # PDF should integrate to one
        p = stats.genexpon.pdf(numpy.arange(0, 10, 0.01), 0.5, 0.5, 2.0)
        assert_almost_equal(simps(p, dx=0.01), 1, 1)

    def test_cdf_bounds(self):
        # CDF should always be positive
        cdf = stats.genexpon.cdf(numpy.arange(0, 10, 0.01), 0.5, 0.5, 2.0)
        assert_(numpy.all((0 <= cdf) & (cdf <= 1)))


class TestExponpow(object):
    def test_tail(self):
        assert_almost_equal(stats.exponpow.cdf(1e-10, 2.), 1e-20)
        assert_almost_equal(stats.exponpow.isf(stats.exponpow.sf(5, .8), .8),
                            5)


class TestSkellam(object):
    def test_pmf(self):
        # comparison to R
        k = numpy.arange(-10, 15)
        mu1, mu2 = 10, 5
        skpmfR = numpy.array(
                   [4.2254582961926893e-005, 1.1404838449648488e-004,
                    2.8979625801752660e-004, 6.9177078182101231e-004,
                    1.5480716105844708e-003, 3.2412274963433889e-003,
                    6.3373707175123292e-003, 1.1552351566696643e-002,
                    1.9606152375042644e-002, 3.0947164083410337e-002,
                    4.5401737566767360e-002, 6.1894328166820688e-002,
                    7.8424609500170578e-002, 9.2418812533573133e-002,
                    1.0139793148019728e-001, 1.0371927988298846e-001,
                    9.9076583077406091e-002, 8.8546660073089561e-002,
                    7.4187842052486810e-002, 5.8392772862200251e-002,
                    4.3268692953013159e-002, 3.0248159818374226e-002,
                    1.9991434305603021e-002, 1.2516877303301180e-002,
                    7.4389876226229707e-003])

        assert_almost_equal(stats.skellam.pmf(k, mu1, mu2), skpmfR, decimal=15)

    def test_cdf(self):
        # comparison to R, only 5 decimals
        k = numpy.arange(-10, 15)
        mu1, mu2 = 10, 5
        skcdfR = numpy.array(
                   [6.4061475386192104e-005, 1.7810985988267694e-004,
                    4.6790611790020336e-004, 1.1596768997212152e-003,
                    2.7077485103056847e-003, 5.9489760066490718e-003,
                    1.2286346724161398e-002, 2.3838698290858034e-002,
                    4.3444850665900668e-002, 7.4392014749310995e-002,
                    1.1979375231607835e-001, 1.8168808048289900e-001,
                    2.6011268998306952e-001, 3.5253150251664261e-001,
                    4.5392943399683988e-001, 5.5764871387982828e-001,
                    6.5672529695723436e-001, 7.4527195703032389e-001,
                    8.1945979908281064e-001, 8.7785257194501087e-001,
                    9.2112126489802404e-001, 9.5136942471639818e-001,
                    9.7136085902200120e-001, 9.8387773632530240e-001,
                    9.9131672394792536e-001])

        assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)


class TestLognorm(object):
    def test_pdf(self):
        # Regression test for Ticket #1471: avoid nan with 0/0 situation
        # Also make sure there are no warnings at x=0, cf gh-5202
        with warnings.catch_warnings():
            warnings.simplefilter('error', RuntimeWarning)
            pdf = stats.lognorm.pdf([0, 0.5, 1], 1)
            assert_array_almost_equal(pdf, [0.0, 0.62749608, 0.39894228])

    def test_logcdf(self):
        # Regression test for gh-5940: sf et al would underflow too early
        x2, mu, sigma = 201.68, 195, 0.149
        assert_allclose(stats.lognorm.sf(x2-mu, s=sigma),
                        stats.norm.sf(np.log(x2-mu)/sigma))
        assert_allclose(stats.lognorm.logsf(x2-mu, s=sigma),
                        stats.norm.logsf(np.log(x2-mu)/sigma))


class TestBeta(object):
    def test_logpdf(self):
        # Regression test for Ticket #1326: avoid nan with 0*log(0) situation
        logpdf = stats.beta.logpdf(0, 1, 0.5)
        assert_almost_equal(logpdf, -0.69314718056)
        logpdf = stats.beta.logpdf(0, 0.5, 1)
        assert_almost_equal(logpdf, np.inf)

    def test_logpdf_ticket_1866(self):
        alpha, beta = 267, 1472
        x = np.array([0.2, 0.5, 0.6])
        b = stats.beta(alpha, beta)
        assert_allclose(b.logpdf(x).sum(), -1201.699061824062)
        assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))

    def test_fit_bad_keyword_args(self):
        x = [0.1, 0.5, 0.6]
        assert_raises(TypeError, stats.beta.fit, x, floc=0, fscale=1,
                      plate="shrimp")

    def test_fit_duplicated_fixed_parameter(self):
        # At most one of 'f0', 'fa' or 'fix_a' can be given to the fit method.
        # More than one raises a ValueError.
        x = [0.1, 0.5, 0.6]
        assert_raises(ValueError, stats.beta.fit, x, fa=0.5, fix_a=0.5)


class TestBetaPrime(object):
    def test_logpdf(self):
        alpha, beta = 267, 1472
        x = np.array([0.2, 0.5, 0.6])
        b = stats.betaprime(alpha, beta)
        assert_(np.isfinite(b.logpdf(x)).all())
        assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))

    def test_cdf(self):
        # regression test for gh-4030: Implementation of
        # scipy.stats.betaprime.cdf()
        x = stats.betaprime.cdf(0, 0.2, 0.3)
        assert_equal(x, 0.0)

        alpha, beta = 267, 1472
        x = np.array([0.2, 0.5, 0.6])
        cdfs = stats.betaprime.cdf(x, alpha, beta)
        assert_(np.isfinite(cdfs).all())

        # check the new cdf implementation vs generic one:
        gen_cdf = stats.rv_continuous._cdf_single
        cdfs_g = [gen_cdf(stats.betaprime, val, alpha, beta) for val in x]
        assert_allclose(cdfs, cdfs_g, atol=0, rtol=2e-12)


class TestGamma(object):
    def test_pdf(self):
        # a few test cases to compare with R
        pdf = stats.gamma.pdf(90, 394, scale=1./5)
        assert_almost_equal(pdf, 0.002312341)

        pdf = stats.gamma.pdf(3, 10, scale=1./5)
        assert_almost_equal(pdf, 0.1620358)

    def test_logpdf(self):
        # Regression test for Ticket #1326: cornercase avoid nan with 0*log(0)
        # situation
        logpdf = stats.gamma.logpdf(0, 1)
        assert_almost_equal(logpdf, 0)

    def test_fit_bad_keyword_args(self):
        x = [0.1, 0.5, 0.6]
        assert_raises(TypeError, stats.gamma.fit, x, floc=0, plate="shrimp")


class TestChi2(object):
    # regression tests after precision improvements, ticket:1041, not verified
    def test_precision(self):
        assert_almost_equal(stats.chi2.pdf(1000, 1000), 8.919133934753128e-003,
                            decimal=14)
        assert_almost_equal(stats.chi2.pdf(100, 100), 0.028162503162596778,
                            decimal=14)

    def test_ppf(self):
        # Expected values computed with mpmath.
        df = 4.8
        x = stats.chi2.ppf(2e-47, df)
        assert_allclose(x, 1.098472479575179840604902808e-19, rtol=1e-10)
        x = stats.chi2.ppf(0.5, df)
        assert_allclose(x, 4.15231407598589358660093156, rtol=1e-10)

        df = 13
        x = stats.chi2.ppf(2e-77, df)
        assert_allclose(x, 1.0106330688195199050507943e-11, rtol=1e-10)
        x = stats.chi2.ppf(0.1, df)
        assert_allclose(x, 7.041504580095461859307179763, rtol=1e-10)


class TestGumbelL(object):
    # gh-6228
    def test_cdf_ppf(self):
        x = np.linspace(-100, -4)
        y = stats.gumbel_l.cdf(x)
        xx = stats.gumbel_l.ppf(y)
        assert_allclose(x, xx)

    def test_logcdf_logsf(self):
        x = np.linspace(-100, -4)
        y = stats.gumbel_l.logcdf(x)
        z = stats.gumbel_l.logsf(x)
        u = np.exp(y)
        v = -special.expm1(z)
        assert_allclose(u, v)

    def test_sf_isf(self):
        x = np.linspace(-20, 5)
        y = stats.gumbel_l.sf(x)
        xx = stats.gumbel_l.isf(y)
        assert_allclose(x, xx)

class TestLevyStable(object):

    def test_fit(self):
        # construct data to have percentiles that match
        # example in McCulloch 1986.
        x = [-.05413,-.05413,
               0.,0.,0.,0.,
               .00533,.00533,.00533,.00533,.00533,
               .03354,.03354,.03354,.03354,.03354,
               .05309,.05309,.05309,.05309,.05309]
        alpha1, beta1, loc1, scale1 = stats.levy_stable._fitstart(x)
        assert_allclose(alpha1, 1.48, rtol=0, atol=0.01)
        assert_almost_equal(beta1, -.22, 2)
        assert_almost_equal(scale1, 0.01717, 4)
        assert_almost_equal(loc1, 0.00233, 2)  # to 2 dps due to rounding error in McCulloch86

        # cover alpha=2 scenario
        x2 = x + [.05309,.05309,.05309,.05309,.05309]
        alpha2, beta2, loc2, scale2 = stats.levy_stable._fitstart(x2)
        assert_equal(alpha2, 2)
        assert_equal(beta2, -1)
        assert_almost_equal(scale2, .02503, 4)
        assert_almost_equal(loc2, .03354, 4)

    @pytest.mark.slow
    def test_pdf_nolan_samples(self):
        """ Test pdf values against Nolan's stablec.exe output
            see - http://fs2.american.edu/jpnolan/www/stable/stable.html

            There's a known limitation of Nolan's executable for alpha < 0.2.

            Repeat following with beta = -1, -.5, 0, .5 and 1
                stablec.exe <<
                1 # pdf
                1 # Nolan S equivalent to S0 in scipy
                .25,2,.25 # alpha
                -1,-1,0 # beta
                -10,10,1 # x
                1,0 # gamma, delta
                2 # output file
        """
        data = np.load(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                                 'data/stable-pdf-sample-data.npy')))

        data = np.core.records.fromarrays(data.T, names='x,p,alpha,beta')

        # support numpy 1.8.2 for travis
        npisin = np.isin if hasattr(np, "isin") else np.in1d

        tests = [
            # best selects
            ['best', None, 8, None],

            # quadrature is accurate for most alpha except 0.25; perhaps limitation of Nolan stablec?
            # we reduce size of x to speed up computation as numerical integration slow.
            ['quadrature', None, 8, lambda r: (r['alpha'] > 0.25) & (npisin(r['x'], [-10,-5,0,5,10]))],

            # zolatarev is accurate except at alpha==1, beta != 0
            ['zolotarev', None, 8, lambda r: r['alpha'] != 1],
            ['zolotarev', None, 8, lambda r: (r['alpha'] == 1) & (r['beta'] == 0)],
            ['zolotarev', None, 1, lambda r: (r['alpha'] == 1) & (r['beta'] != 0)],

            # fft accuracy reduces as alpha decreases, fails at low values of alpha and x=0
            ['fft', 0, 4, lambda r: r['alpha'] > 1],
            ['fft', 0, 3, lambda r: (r['alpha'] < 1) & (r['alpha'] > 0.25)],
            ['fft', 0, 1, lambda r: (r['alpha'] == 0.25) & (r['x'] != 0)],  # not useful here
        ]
        for ix, (default_method, fft_min_points, decimal_places, filter_func) in enumerate(tests):
            stats.levy_stable.pdf_default_method = default_method
            stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
            subdata = data[filter_func(data)] if filter_func is not None else data
            with suppress_warnings() as sup:
                sup.record(RuntimeWarning, "Density calculation unstable for alpha=1 and beta!=0.*")
                sup.record(RuntimeWarning, "Density calculations experimental for FFT method.*")
                p = stats.levy_stable.pdf(subdata['x'], subdata['alpha'], subdata['beta'], scale=1, loc=0)
                subdata2 = rec_append_fields(subdata, 'calc', p)
                failures = subdata2[(np.abs(p-subdata['p']) >= 1.5*10.**(-decimal_places)) | np.isnan(p)]
                assert_almost_equal(p, subdata['p'], decimal_places, "pdf test %s failed with method '%s'\n%s" % (ix, default_method, failures), verbose=False)

    @pytest.mark.slow
    def test_cdf_nolan_samples(self):
        """ Test cdf values against Nolan's stablec.exe output
            see - http://fs2.american.edu/jpnolan/www/stable/stable.html

            There's a known limitation of Nolan's executable for alpha < 0.2.

            Repeat following with beta = -1, -.5, 0, .5 and 1
                stablec.exe <<
                2 # cdf
                1 # Nolan S equivalent to S0 in scipy
                .25,2,.25 # alpha
                -1,-1,0 # beta
                -10,10,1 # x
                1,0 # gamma, delta
                2 # output file
        """
        data = np.load(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                                 'data/stable-cdf-sample-data.npy')))

        data = np.core.records.fromarrays(data.T, names='x,p,alpha,beta')

        tests = [
            # zolatarev is accurate for all values
            ['zolotarev', None, 8, None],

            # fft accuracy poor, very poor alpha < 1
            ['fft', 0, 2, lambda r: r['alpha'] > 1],
        ]
        for ix, (default_method, fft_min_points, decimal_places, filter_func) in enumerate(tests):
            stats.levy_stable.pdf_default_method = default_method
            stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
            subdata = data[filter_func(data)] if filter_func is not None else data
            with suppress_warnings() as sup:
                sup.record(RuntimeWarning, 'FFT method is considered ' +
                           'experimental for cumulative distribution ' +
                           'function evaluations.*')
                p = stats.levy_stable.cdf(subdata['x'], subdata['alpha'], subdata['beta'], scale=1, loc=0)
                subdata2 = rec_append_fields(subdata, 'calc', p)
                failures = subdata2[(np.abs(p-subdata['p']) >= 1.5*10.**(-decimal_places)) | np.isnan(p)]
                assert_almost_equal(p, subdata['p'], decimal_places, "cdf test %s failed with method '%s'\n%s" % (ix, default_method, failures), verbose=False)

    def test_pdf_alpha_equals_one_beta_non_zero(self):
        """ sample points extracted from Tables and Graphs of Stable Probability
            Density Functions - Donald R Holt - 1973 - p 187.
        """
        xs = np.array([0, 0, 0, 0,
                       1, 1, 1, 1,
                       2, 2, 2, 2,
                       3, 3, 3, 3,
                       4, 4, 4, 4])
        density = np.array([.3183, .3096, .2925, .2622,
                            .1591, .1587, .1599, .1635,
                            .0637, .0729, .0812, .0955,
                            .0318, .0390, .0458, .0586,
                            .0187, .0236, .0285, .0384])
        betas = np.array([0, .25, .5, 1,
                          0, .25, .5, 1,
                          0, .25, .5, 1,
                          0, .25, .5, 1,
                          0, .25, .5, 1])

        tests = [
            ['quadrature', None, 4],
            #['fft', 0, 4],
            ['zolotarev', None, 1],
        ]

        with np.errstate(all='ignore'), suppress_warnings() as sup:
            sup.filter(category=RuntimeWarning, message="Density calculation unstable.*")
            for default_method, fft_min_points, decimal_places in tests:
                stats.levy_stable.pdf_default_method = default_method
                stats.levy_stable.pdf_fft_min_points_threshold = fft_min_points
                #stats.levy_stable.fft_grid_spacing = 0.0001
                pdf = stats.levy_stable.pdf(xs, 1, betas, scale=1, loc=0)
                assert_almost_equal(pdf, density, decimal_places, default_method)

    def test_stats(self):
        param_sets = [
            [(1.48,-.22, 0, 1), (0,np.inf,np.NaN,np.NaN)],
            [(2,.9, 10, 1.5), (10,4.5,0,0)]
        ]
        for args, exp_stats in param_sets:
            calc_stats = stats.levy_stable.stats(args[0], args[1], loc=args[2], scale=args[3], moments='mvsk')
            assert_almost_equal(calc_stats, exp_stats)

class TestArrayArgument(object):  # test for ticket:992
    def setup_method(self):
        np.random.seed(1234)

    def test_noexception(self):
        rvs = stats.norm.rvs(loc=(np.arange(5)), scale=np.ones(5),
                             size=(10, 5))
        assert_equal(rvs.shape, (10, 5))


class TestDocstring(object):
    def test_docstrings(self):
        # See ticket #761
        if stats.rayleigh.__doc__ is not None:
            assert_("rayleigh" in stats.rayleigh.__doc__.lower())
        if stats.bernoulli.__doc__ is not None:
            assert_("bernoulli" in stats.bernoulli.__doc__.lower())

    def test_no_name_arg(self):
        # If name is not given, construction shouldn't fail.  See #1508.
        stats.rv_continuous()
        stats.rv_discrete()


class TestEntropy(object):
    def test_entropy_positive(self):
        # See ticket #497
        pk = [0.5, 0.2, 0.3]
        qk = [0.1, 0.25, 0.65]
        eself = stats.entropy(pk, pk)
        edouble = stats.entropy(pk, qk)
        assert_(0.0 == eself)
        assert_(edouble >= 0.0)

    def test_entropy_base(self):
        pk = np.ones(16, float)
        S = stats.entropy(pk, base=2.)
        assert_(abs(S - 4.) < 1.e-5)

        qk = np.ones(16, float)
        qk[:8] = 2.
        S = stats.entropy(pk, qk)
        S2 = stats.entropy(pk, qk, base=2.)
        assert_(abs(S/S2 - np.log(2.)) < 1.e-5)

    def test_entropy_zero(self):
        # Test for PR-479
        assert_almost_equal(stats.entropy([0, 1, 2]), 0.63651416829481278,
                            decimal=12)

    def test_entropy_2d(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        qk = [[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]]
        assert_array_almost_equal(stats.entropy(pk, qk),
                                  [0.1933259, 0.18609809])

    def test_entropy_2d_zero(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        qk = [[0.0, 0.1], [0.3, 0.6], [0.5, 0.3]]
        assert_array_almost_equal(stats.entropy(pk, qk),
                                  [np.inf, 0.18609809])

        pk[0][0] = 0.0
        assert_array_almost_equal(stats.entropy(pk, qk),
                                  [0.17403988, 0.18609809])

    def test_entropy_base_2d_nondefault_axis(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        assert_array_almost_equal(stats.entropy(pk, axis=1),
                                  [0.63651417, 0.63651417, 0.66156324])

    def test_entropy_2d_nondefault_axis(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        qk = [[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]]
        assert_array_almost_equal(stats.entropy(pk, qk, axis=1),
                                  [0.231049, 0.231049, 0.127706])

    def test_entropy_raises_value_error(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        qk = [[0.1, 0.2], [0.6, 0.3]]
        assert_raises(ValueError, stats.entropy, pk, qk)

    def test_base_entropy_with_axis_0_is_equal_to_default(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        assert_array_almost_equal(stats.entropy(pk, axis=0),
                                  stats.entropy(pk))

    def test_entropy_with_axis_0_is_equal_to_default(self):
        pk = [[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]]
        qk = [[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]]
        assert_array_almost_equal(stats.entropy(pk, qk, axis=0),
                                  stats.entropy(pk, qk))

    def test_base_entropy_transposed(self):
        pk = np.array([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
        assert_array_almost_equal(stats.entropy(pk.T).T,
                                  stats.entropy(pk, axis=1))

    def test_entropy_transposed(self):
        pk = np.array([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
        qk = np.array([[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]])
        assert_array_almost_equal(stats.entropy(pk.T, qk.T).T,
                                  stats.entropy(pk, qk, axis=1))


def TestArgsreduce():
    a = array([1, 3, 2, 1, 2, 3, 3])
    b, c = argsreduce(a > 1, a, 2)

    assert_array_equal(b, [3, 2, 2, 3, 3])
    assert_array_equal(c, [2, 2, 2, 2, 2])

    b, c = argsreduce(2 > 1, a, 2)
    assert_array_equal(b, a[0])
    assert_array_equal(c, [2])

    b, c = argsreduce(a > 0, a, 2)
    assert_array_equal(b, a)
    assert_array_equal(c, [2] * numpy.size(a))


class TestFitMethod(object):
    skip = ['ncf', 'ksone', 'kstwo']

    def setup_method(self):
        np.random.seed(1234)

    # skip these b/c deprecated, or only loc and scale arguments
    fitSkipNonFinite = ['frechet_l', 'frechet_r', 'expon', 'norm', 'uniform', ]

    @pytest.mark.parametrize('dist,args', distcont)
    def test_fit_w_non_finite_data_values(self, dist, args):
        """gh-10300"""
        if dist in self.fitSkipNonFinite:
            pytest.skip("%s fit known to fail or deprecated" % dist)
        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
        y = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
        distfunc = getattr(stats, dist)
        assert_raises(RuntimeError, distfunc.fit, x, floc=0, fscale=1)
        assert_raises(RuntimeError, distfunc.fit, y, floc=0, fscale=1)

    def test_fix_fit_2args_lognorm(self):
        # Regression test for #1551.
        np.random.seed(12345)
        with np.errstate(all='ignore'):
            x = stats.lognorm.rvs(0.25, 0., 20.0, size=20)
            expected_shape = np.sqrt(((np.log(x) - np.log(20))**2).mean())
            assert_allclose(np.array(stats.lognorm.fit(x, floc=0, fscale=20)),
                            [expected_shape, 0, 20], atol=1e-8)

    def test_fix_fit_norm(self):
        x = np.arange(1, 6)

        loc, scale = stats.norm.fit(x)
        assert_almost_equal(loc, 3)
        assert_almost_equal(scale, np.sqrt(2))

        loc, scale = stats.norm.fit(x, floc=2)
        assert_equal(loc, 2)
        assert_equal(scale, np.sqrt(3))

        loc, scale = stats.norm.fit(x, fscale=2)
        assert_almost_equal(loc, 3)
        assert_equal(scale, 2)

    def test_fix_fit_gamma(self):
        x = np.arange(1, 6)
        meanlog = np.log(x).mean()

        # A basic test of gamma.fit with floc=0.
        floc = 0
        a, loc, scale = stats.gamma.fit(x, floc=floc)
        s = np.log(x.mean()) - meanlog
        assert_almost_equal(np.log(a) - special.digamma(a), s, decimal=5)
        assert_equal(loc, floc)
        assert_almost_equal(scale, x.mean()/a, decimal=8)

        # Regression tests for gh-2514.
        # The problem was that if `floc=0` was given, any other fixed
        # parameters were ignored.
        f0 = 1
        floc = 0
        a, loc, scale = stats.gamma.fit(x, f0=f0, floc=floc)
        assert_equal(a, f0)
        assert_equal(loc, floc)
        assert_almost_equal(scale, x.mean()/a, decimal=8)

        f0 = 2
        floc = 0
        a, loc, scale = stats.gamma.fit(x, f0=f0, floc=floc)
        assert_equal(a, f0)
        assert_equal(loc, floc)
        assert_almost_equal(scale, x.mean()/a, decimal=8)

        # loc and scale fixed.
        floc = 0
        fscale = 2
        a, loc, scale = stats.gamma.fit(x, floc=floc, fscale=fscale)
        assert_equal(loc, floc)
        assert_equal(scale, fscale)
        c = meanlog - np.log(fscale)
        assert_almost_equal(special.digamma(a), c)

    def test_fix_fit_beta(self):
        # Test beta.fit when both floc and fscale are given.

        def mlefunc(a, b, x):
            # Zeros of this function are critical points of
            # the maximum likelihood function.
            n = len(x)
            s1 = np.log(x).sum()
            s2 = np.log(1-x).sum()
            psiab = special.psi(a + b)
            func = [s1 - n * (-psiab + special.psi(a)),
                    s2 - n * (-psiab + special.psi(b))]
            return func

        # Basic test with floc and fscale given.
        x = np.array([0.125, 0.25, 0.5])
        a, b, loc, scale = stats.beta.fit(x, floc=0, fscale=1)
        assert_equal(loc, 0)
        assert_equal(scale, 1)
        assert_allclose(mlefunc(a, b, x), [0, 0], atol=1e-6)

        # Basic test with f0, floc and fscale given.
        # This is also a regression test for gh-2514.
        x = np.array([0.125, 0.25, 0.5])
        a, b, loc, scale = stats.beta.fit(x, f0=2, floc=0, fscale=1)
        assert_equal(a, 2)
        assert_equal(loc, 0)
        assert_equal(scale, 1)
        da, db = mlefunc(a, b, x)
        assert_allclose(db, 0, atol=1e-5)

        # Same floc and fscale values as above, but reverse the data
        # and fix b (f1).
        x2 = 1 - x
        a2, b2, loc2, scale2 = stats.beta.fit(x2, f1=2, floc=0, fscale=1)
        assert_equal(b2, 2)
        assert_equal(loc2, 0)
        assert_equal(scale2, 1)
        da, db = mlefunc(a2, b2, x2)
        assert_allclose(da, 0, atol=1e-5)
        # a2 of this test should equal b from above.
        assert_almost_equal(a2, b)

        # Check for detection of data out of bounds when floc and fscale
        # are given.
        assert_raises(ValueError, stats.beta.fit, x, floc=0.5, fscale=1)
        y = np.array([0, .5, 1])
        assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1)
        assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1, f0=2)
        assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1, f1=2)

        # Check that attempting to fix all the parameters raises a ValueError.
        assert_raises(ValueError, stats.beta.fit, y, f0=0, f1=1,
                      floc=2, fscale=3)

    def test_expon_fit(self):
        x = np.array([2, 2, 4, 4, 4, 4, 4, 8])

        loc, scale = stats.expon.fit(x)
        assert_equal(loc, 2)    # x.min()
        assert_equal(scale, 2)  # x.mean() - x.min()

        loc, scale = stats.expon.fit(x, fscale=3)
        assert_equal(loc, 2)    # x.min()
        assert_equal(scale, 3)  # fscale

        loc, scale = stats.expon.fit(x, floc=0)
        assert_equal(loc, 0)    # floc
        assert_equal(scale, 4)  # x.mean() - loc

    def test_lognorm_fit(self):
        x = np.array([1.5, 3, 10, 15, 23, 59])
        lnxm1 = np.log(x - 1)

        shape, loc, scale = stats.lognorm.fit(x, floc=1)
        assert_allclose(shape, lnxm1.std(), rtol=1e-12)
        assert_equal(loc, 1)
        assert_allclose(scale, np.exp(lnxm1.mean()), rtol=1e-12)

        shape, loc, scale = stats.lognorm.fit(x, floc=1, fscale=6)
        assert_allclose(shape, np.sqrt(((lnxm1 - np.log(6))**2).mean()),
                        rtol=1e-12)
        assert_equal(loc, 1)
        assert_equal(scale, 6)

        shape, loc, scale = stats.lognorm.fit(x, floc=1, fix_s=0.75)
        assert_equal(shape, 0.75)
        assert_equal(loc, 1)
        assert_allclose(scale, np.exp(lnxm1.mean()), rtol=1e-12)

    def test_uniform_fit(self):
        x = np.array([1.0, 1.1, 1.2, 9.0])

        loc, scale = stats.uniform.fit(x)
        assert_equal(loc, x.min())
        assert_equal(scale, x.ptp())

        loc, scale = stats.uniform.fit(x, floc=0)
        assert_equal(loc, 0)
        assert_equal(scale, x.max())

        loc, scale = stats.uniform.fit(x, fscale=10)
        assert_equal(loc, 0)
        assert_equal(scale, 10)

        assert_raises(ValueError, stats.uniform.fit, x, floc=2.0)
        assert_raises(ValueError, stats.uniform.fit, x, fscale=5.0)

    def test_fshapes(self):
        # take a beta distribution, with shapes='a, b', and make sure that
        # fa is equivalent to f0, and fb is equivalent to f1
        a, b = 3., 4.
        x = stats.beta.rvs(a, b, size=100, random_state=1234)
        res_1 = stats.beta.fit(x, f0=3.)
        res_2 = stats.beta.fit(x, fa=3.)
        assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)

        res_2 = stats.beta.fit(x, fix_a=3.)
        assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)

        res_3 = stats.beta.fit(x, f1=4.)
        res_4 = stats.beta.fit(x, fb=4.)
        assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)

        res_4 = stats.beta.fit(x, fix_b=4.)
        assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)

        # cannot specify both positional and named args at the same time
        assert_raises(ValueError, stats.beta.fit, x, fa=1, f0=2)

        # check that attempting to fix all parameters raises a ValueError
        assert_raises(ValueError, stats.beta.fit, x, fa=0, f1=1,
                      floc=2, fscale=3)

        # check that specifying floc, fscale and fshapes works for
        # beta and gamma which override the generic fit method
        res_5 = stats.beta.fit(x, fa=3., floc=0, fscale=1)
        aa, bb, ll, ss = res_5
        assert_equal([aa, ll, ss], [3., 0, 1])

        # gamma distribution
        a = 3.
        data = stats.gamma.rvs(a, size=100)
        aa, ll, ss = stats.gamma.fit(data, fa=a)
        assert_equal(aa, a)

    def test_extra_params(self):
        # unknown parameters should raise rather than be silently ignored
        dist = stats.exponnorm
        data = dist.rvs(K=2, size=100)
        dct = dict(enikibeniki=-101)
        assert_raises(TypeError, dist.fit, data, **dct)


class TestFrozen(object):
    def setup_method(self):
        np.random.seed(1234)

    # Test that a frozen distribution gives the same results as the original
    # object.
    #
    # Only tested for the normal distribution (with loc and scale specified)
    # and for the gamma distribution (with a shape parameter specified).
    def test_norm(self):
        dist = stats.norm
        frozen = stats.norm(loc=10.0, scale=3.0)

        result_f = frozen.pdf(20.0)
        result = dist.pdf(20.0, loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.cdf(20.0)
        result = dist.cdf(20.0, loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.ppf(0.25)
        result = dist.ppf(0.25, loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.isf(0.25)
        result = dist.isf(0.25, loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.sf(10.0)
        result = dist.sf(10.0, loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.median()
        result = dist.median(loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.mean()
        result = dist.mean(loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.var()
        result = dist.var(loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.std()
        result = dist.std(loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.entropy()
        result = dist.entropy(loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        result_f = frozen.moment(2)
        result = dist.moment(2, loc=10.0, scale=3.0)
        assert_equal(result_f, result)

        assert_equal(frozen.a, dist.a)
        assert_equal(frozen.b, dist.b)

    def test_gamma(self):
        a = 2.0
        dist = stats.gamma
        frozen = stats.gamma(a)

        result_f = frozen.pdf(20.0)
        result = dist.pdf(20.0, a)
        assert_equal(result_f, result)

        result_f = frozen.cdf(20.0)
        result = dist.cdf(20.0, a)
        assert_equal(result_f, result)

        result_f = frozen.ppf(0.25)
        result = dist.ppf(0.25, a)
        assert_equal(result_f, result)

        result_f = frozen.isf(0.25)
        result = dist.isf(0.25, a)
        assert_equal(result_f, result)

        result_f = frozen.sf(10.0)
        result = dist.sf(10.0, a)
        assert_equal(result_f, result)

        result_f = frozen.median()
        result = dist.median(a)
        assert_equal(result_f, result)

        result_f = frozen.mean()
        result = dist.mean(a)
        assert_equal(result_f, result)

        result_f = frozen.var()
        result = dist.var(a)
        assert_equal(result_f, result)

        result_f = frozen.std()
        result = dist.std(a)
        assert_equal(result_f, result)

        result_f = frozen.entropy()
        result = dist.entropy(a)
        assert_equal(result_f, result)

        result_f = frozen.moment(2)
        result = dist.moment(2, a)
        assert_equal(result_f, result)

        assert_equal(frozen.a, frozen.dist.a)
        assert_equal(frozen.b, frozen.dist.b)

    def test_regression_ticket_1293(self):
        # Create a frozen distribution.
        frozen = stats.lognorm(1)
        # Call one of its methods that does not take any keyword arguments.
        m1 = frozen.moment(2)
        # Now call a method that takes a keyword argument.
        frozen.stats(moments='mvsk')
        # Call moment(2) again.
        # After calling stats(), the following was raising an exception.
        # So this test passes if the following does not raise an exception.
        m2 = frozen.moment(2)
        # The following should also be true, of course.  But it is not
        # the focus of this test.
        assert_equal(m1, m2)

    def test_ab(self):
        # test that the support of a frozen distribution
        # (i) remains frozen even if it changes for the original one
        # (ii) is actually correct if the shape parameters are such that
        #      the values of [a, b] are not the default [0, inf]
        # take a genpareto as an example where the support
        # depends on the value of the shape parameter:
        # for c > 0: a, b = 0, inf
        # for c < 0: a, b = 0, -1/c

        c = -0.1
        rv = stats.genpareto(c=c)
        a, b = rv.dist._get_support(c)
        assert_equal([a, b], [0., 10.])

        c = 0.1
        stats.genpareto.pdf(0, c=c)
        assert_equal(rv.dist._get_support(c), [0, np.inf])

        c = -0.1
        rv = stats.genpareto(c=c)
        a, b = rv.dist._get_support(c)
        assert_equal([a, b], [0., 10.])

        c = 0.1
        stats.genpareto.pdf(0, c)  # this should NOT change genpareto.b
        assert_equal((rv.dist.a, rv.dist.b), stats.genpareto._get_support(c))

        rv1 = stats.genpareto(c=0.1)
        assert_(rv1.dist is not rv.dist)

        # c >= 0: a, b = [0, inf]
        for c in [1., 0.]:
            c = np.asarray(c)
            rv = stats.genpareto(c=c)
            a, b = rv.a, rv.b
            assert_equal(a, 0.)
            assert_(np.isposinf(b))

            # c < 0: a=0, b=1/|c|
            c = np.asarray(-2.)
            a, b = stats.genpareto._get_support(c)
            assert_allclose([a, b], [0., 0.5])

    def test_rv_frozen_in_namespace(self):
        # Regression test for gh-3522
        assert_(hasattr(stats.distributions, 'rv_frozen'))

    def test_random_state(self):
        # only check that the random_state attribute exists,
        frozen = stats.norm()
        assert_(hasattr(frozen, 'random_state'))

        # ... that it can be set,
        frozen.random_state = 42
        assert_equal(frozen.random_state.get_state(),
                     np.random.RandomState(42).get_state())

        # ... and that .rvs method accepts it as an argument
        rndm = np.random.RandomState(1234)
        frozen.rvs(size=8, random_state=rndm)

    def test_pickling(self):
        # test that a frozen instance pickles and unpickles
        # (this method is a clone of common_tests.check_pickling)
        beta = stats.beta(2.3098496451481823, 0.62687954300963677)
        poiss = stats.poisson(3.)
        sample = stats.rv_discrete(values=([0, 1, 2, 3],
                                           [0.1, 0.2, 0.3, 0.4]))

        for distfn in [beta, poiss, sample]:
            distfn.random_state = 1234
            distfn.rvs(size=8)
            s = pickle.dumps(distfn)
            r0 = distfn.rvs(size=8)

            unpickled = pickle.loads(s)
            r1 = unpickled.rvs(size=8)
            assert_equal(r0, r1)

            # also smoke test some methods
            medians = [distfn.ppf(0.5), unpickled.ppf(0.5)]
            assert_equal(medians[0], medians[1])
            assert_equal(distfn.cdf(medians[0]),
                         unpickled.cdf(medians[1]))

    def test_expect(self):
        # smoke test the expect method of the frozen distribution
        # only take a gamma w/loc and scale and poisson with loc specified
        def func(x):
            return x

        gm = stats.gamma(a=2, loc=3, scale=4)
        gm_val = gm.expect(func, lb=1, ub=2, conditional=True)
        gamma_val = stats.gamma.expect(func, args=(2,), loc=3, scale=4,
                                       lb=1, ub=2, conditional=True)
        assert_allclose(gm_val, gamma_val)

        p = stats.poisson(3, loc=4)
        p_val = p.expect(func)
        poisson_val = stats.poisson.expect(func, args=(3,), loc=4)
        assert_allclose(p_val, poisson_val)


class TestExpect(object):
    # Test for expect method.
    #
    # Uses normal distribution and beta distribution for finite bounds, and
    # hypergeom for discrete distribution with finite support
    def test_norm(self):
        v = stats.norm.expect(lambda x: (x-5)*(x-5), loc=5, scale=2)
        assert_almost_equal(v, 4, decimal=14)

        m = stats.norm.expect(lambda x: (x), loc=5, scale=2)
        assert_almost_equal(m, 5, decimal=14)

        lb = stats.norm.ppf(0.05, loc=5, scale=2)
        ub = stats.norm.ppf(0.95, loc=5, scale=2)
        prob90 = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub)
        assert_almost_equal(prob90, 0.9, decimal=14)

        prob90c = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub,
                                    conditional=True)
        assert_almost_equal(prob90c, 1., decimal=14)

    def test_beta(self):
        # case with finite support interval
        v = stats.beta.expect(lambda x: (x-19/3.)*(x-19/3.), args=(10, 5),
                              loc=5, scale=2)
        assert_almost_equal(v, 1./18., decimal=13)

        m = stats.beta.expect(lambda x: x, args=(10, 5), loc=5., scale=2.)
        assert_almost_equal(m, 19/3., decimal=13)

        ub = stats.beta.ppf(0.95, 10, 10, loc=5, scale=2)
        lb = stats.beta.ppf(0.05, 10, 10, loc=5, scale=2)
        prob90 = stats.beta.expect(lambda x: 1., args=(10, 10), loc=5.,
                                   scale=2., lb=lb, ub=ub, conditional=False)
        assert_almost_equal(prob90, 0.9, decimal=13)

        prob90c = stats.beta.expect(lambda x: 1, args=(10, 10), loc=5,
                                    scale=2, lb=lb, ub=ub, conditional=True)
        assert_almost_equal(prob90c, 1., decimal=13)

    def test_hypergeom(self):
        # test case with finite bounds

        # without specifying bounds
        m_true, v_true = stats.hypergeom.stats(20, 10, 8, loc=5.)
        m = stats.hypergeom.expect(lambda x: x, args=(20, 10, 8), loc=5.)
        assert_almost_equal(m, m_true, decimal=13)

        v = stats.hypergeom.expect(lambda x: (x-9.)**2, args=(20, 10, 8),
                                   loc=5.)
        assert_almost_equal(v, v_true, decimal=14)

        # with bounds, bounds equal to shifted support
        v_bounds = stats.hypergeom.expect(lambda x: (x-9.)**2,
                                          args=(20, 10, 8),
                                          loc=5., lb=5, ub=13)
        assert_almost_equal(v_bounds, v_true, decimal=14)

        # drop boundary points
        prob_true = 1-stats.hypergeom.pmf([5, 13], 20, 10, 8, loc=5).sum()
        prob_bounds = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
                                             loc=5., lb=6, ub=12)
        assert_almost_equal(prob_bounds, prob_true, decimal=13)

        # conditional
        prob_bc = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8), loc=5.,
                                         lb=6, ub=12, conditional=True)
        assert_almost_equal(prob_bc, 1, decimal=14)

        # check simple integral
        prob_b = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
                                        lb=0, ub=8)
        assert_almost_equal(prob_b, 1, decimal=13)

    def test_poisson(self):
        # poisson, use lower bound only
        prob_bounds = stats.poisson.expect(lambda x: 1, args=(2,), lb=3,
                                           conditional=False)
        prob_b_true = 1-stats.poisson.cdf(2, 2)
        assert_almost_equal(prob_bounds, prob_b_true, decimal=14)

        prob_lb = stats.poisson.expect(lambda x: 1, args=(2,), lb=2,
                                       conditional=True)
        assert_almost_equal(prob_lb, 1, decimal=14)

    def test_genhalflogistic(self):
        # genhalflogistic, changes upper bound of support in _argcheck
        # regression test for gh-2622
        halflog = stats.genhalflogistic
        # check consistency when calling expect twice with the same input
        res1 = halflog.expect(args=(1.5,))
        halflog.expect(args=(0.5,))
        res2 = halflog.expect(args=(1.5,))
        assert_almost_equal(res1, res2, decimal=14)

    def test_rice_overflow(self):
        # rice.pdf(999, 0.74) was inf since special.i0 silentyly overflows
        # check that using i0e fixes it
        assert_(np.isfinite(stats.rice.pdf(999, 0.74)))

        assert_(np.isfinite(stats.rice.expect(lambda x: 1, args=(0.74,))))
        assert_(np.isfinite(stats.rice.expect(lambda x: 2, args=(0.74,))))
        assert_(np.isfinite(stats.rice.expect(lambda x: 3, args=(0.74,))))

    def test_logser(self):
        # test a discrete distribution with infinite support and loc
        p, loc = 0.3, 3
        res_0 = stats.logser.expect(lambda k: k, args=(p,))
        # check against the correct answer (sum of a geom series)
        assert_allclose(res_0,
                        p / (p - 1.) / np.log(1. - p), atol=1e-15)

        # now check it with `loc`
        res_l = stats.logser.expect(lambda k: k, args=(p,), loc=loc)
        assert_allclose(res_l, res_0 + loc, atol=1e-15)

    def test_skellam(self):
        # Use a discrete distribution w/ bi-infinite support. Compute two first
        # moments and compare to known values (cf skellam.stats)
        p1, p2 = 18, 22
        m1 = stats.skellam.expect(lambda x: x, args=(p1, p2))
        m2 = stats.skellam.expect(lambda x: x**2, args=(p1, p2))
        assert_allclose(m1, p1 - p2, atol=1e-12)
        assert_allclose(m2 - m1**2, p1 + p2, atol=1e-12)

    def test_randint(self):
        # Use a discrete distribution w/ parameter-dependent support, which
        # is larger than the default chunksize
        lo, hi = 0, 113
        res = stats.randint.expect(lambda x: x, (lo, hi))
        assert_allclose(res,
                        sum(_ for _ in range(lo, hi)) / (hi - lo), atol=1e-15)

    def test_zipf(self):
        # Test that there is no infinite loop even if the sum diverges
        assert_warns(RuntimeWarning, stats.zipf.expect,
                     lambda x: x**2, (2,))

    def test_discrete_kwds(self):
        # check that discrete expect accepts keywords to control the summation
        n0 = stats.poisson.expect(lambda x: 1, args=(2,))
        n1 = stats.poisson.expect(lambda x: 1, args=(2,),
                                  maxcount=1001, chunksize=32, tolerance=1e-8)
        assert_almost_equal(n0, n1, decimal=14)

    def test_moment(self):
        # test the .moment() method: compute a higher moment and compare to
        # a known value
        def poiss_moment5(mu):
            return mu**5 + 10*mu**4 + 25*mu**3 + 15*mu**2 + mu

        for mu in [5, 7]:
            m5 = stats.poisson.moment(5, mu)
            assert_allclose(m5, poiss_moment5(mu), rtol=1e-10)


class TestNct(object):
    def test_nc_parameter(self):
        # Parameter values c<=0 were not enabled (gh-2402).
        # For negative values c and for c=0 results of rv.cdf(0) below were nan
        rv = stats.nct(5, 0)
        assert_equal(rv.cdf(0), 0.5)
        rv = stats.nct(5, -1)
        assert_almost_equal(rv.cdf(0), 0.841344746069, decimal=10)

    def test_broadcasting(self):
        res = stats.nct.pdf(5, np.arange(4, 7)[:, None],
                            np.linspace(0.1, 1, 4))
        expected = array([[0.00321886, 0.00557466, 0.00918418, 0.01442997],
                          [0.00217142, 0.00395366, 0.00683888, 0.01126276],
                          [0.00153078, 0.00291093, 0.00525206, 0.00900815]])
        assert_allclose(res, expected, rtol=1e-5)

    def test_variance_gh_issue_2401(self):
        # Computation of the variance of a non-central t-distribution resulted
        # in a TypeError: ufunc 'isinf' not supported for the input types,
        # and the inputs could not be safely coerced to any supported types
        # according to the casting rule 'safe'
        rv = stats.nct(4, 0)
        assert_equal(rv.var(), 2.0)

    def test_nct_inf_moments(self):
        # n-th moment of nct only exists for df > n
        m, v, s, k = stats.nct.stats(df=1.9, nc=0.3, moments='mvsk')
        assert_(np.isfinite(m))
        assert_equal([v, s, k], [np.inf, np.nan, np.nan])

        m, v, s, k = stats.nct.stats(df=3.1, nc=0.3, moments='mvsk')
        assert_(np.isfinite([m, v, s]).all())
        assert_equal(k, np.nan)


class TestRice(object):
    def test_rice_zero_b(self):
        # rice distribution should work with b=0, cf gh-2164
        x = [0.2, 1., 5.]
        assert_(np.isfinite(stats.rice.pdf(x, b=0.)).all())
        assert_(np.isfinite(stats.rice.logpdf(x, b=0.)).all())
        assert_(np.isfinite(stats.rice.cdf(x, b=0.)).all())
        assert_(np.isfinite(stats.rice.logcdf(x, b=0.)).all())

        q = [0.1, 0.1, 0.5, 0.9]
        assert_(np.isfinite(stats.rice.ppf(q, b=0.)).all())

        mvsk = stats.rice.stats(0, moments='mvsk')
        assert_(np.isfinite(mvsk).all())

        # furthermore, pdf is continuous as b\to 0
        # rice.pdf(x, b\to 0) = x exp(-x^2/2) + O(b^2)
        # see e.g. Abramovich & Stegun 9.6.7 & 9.6.10
        b = 1e-8
        assert_allclose(stats.rice.pdf(x, 0), stats.rice.pdf(x, b),
                        atol=b, rtol=0)

    def test_rice_rvs(self):
        rvs = stats.rice.rvs
        assert_equal(rvs(b=3.).size, 1)
        assert_equal(rvs(b=3., size=(3, 5)).shape, (3, 5))


class TestErlang(object):
    def setup_method(self):
        np.random.seed(1234)

    def test_erlang_runtimewarning(self):
        # erlang should generate a RuntimeWarning if a non-integer
        # shape parameter is used.
        with warnings.catch_warnings():
            warnings.simplefilter("error", RuntimeWarning)

            # The non-integer shape parameter 1.3 should trigger a
            # RuntimeWarning
            assert_raises(RuntimeWarning,
                          stats.erlang.rvs, 1.3, loc=0, scale=1, size=4)

            # Calling the fit method with `f0` set to an integer should
            # *not* trigger a RuntimeWarning.  It should return the same
            # values as gamma.fit(...).
            data = [0.5, 1.0, 2.0, 4.0]
            result_erlang = stats.erlang.fit(data, f0=1)
            result_gamma = stats.gamma.fit(data, f0=1)
            assert_allclose(result_erlang, result_gamma, rtol=1e-3)

    def test_gh_pr_10949_argcheck(self):
        assert_equal(stats.erlang.pdf(0.5, a=[1, -1]), stats.gamma.pdf(0.5, a=[1, -1]))


class TestRayleigh(object):
    # gh-6227
    def test_logpdf(self):
        y = stats.rayleigh.logpdf(50)
        assert_allclose(y, -1246.0879769945718)

    def test_logsf(self):
        y = stats.rayleigh.logsf(50)
        assert_allclose(y, -1250)


class TestExponWeib(object):

    def test_pdf_logpdf(self):
        # Regression test for gh-3508.
        x = 0.1
        a = 1.0
        c = 100.0
        p = stats.exponweib.pdf(x, a, c)
        logp = stats.exponweib.logpdf(x, a, c)
        # Expected values were computed with mpmath.
        assert_allclose([p, logp],
                        [1.0000000000000054e-97, -223.35075402042244])

    def test_a_is_1(self):
        # For issue gh-3508.
        # Check that when a=1, the pdf and logpdf methods of exponweib are the
        # same as those of weibull_min.
        x = np.logspace(-4, -1, 4)
        a = 1
        c = 100

        p = stats.exponweib.pdf(x, a, c)
        expected = stats.weibull_min.pdf(x, c)
        assert_allclose(p, expected)

        logp = stats.exponweib.logpdf(x, a, c)
        expected = stats.weibull_min.logpdf(x, c)
        assert_allclose(logp, expected)

    def test_a_is_1_c_is_1(self):
        # When a = 1 and c = 1, the distribution is exponential.
        x = np.logspace(-8, 1, 10)
        a = 1
        c = 1

        p = stats.exponweib.pdf(x, a, c)
        expected = stats.expon.pdf(x)
        assert_allclose(p, expected)

        logp = stats.exponweib.logpdf(x, a, c)
        expected = stats.expon.logpdf(x)
        assert_allclose(logp, expected)


class TestWeibull(object):

    def test_logpdf(self):
        # gh-6217
        y = stats.weibull_min.logpdf(0, 1)
        assert_equal(y, 0)

    def test_with_maxima_distrib(self):
        # Tests for weibull_min and weibull_max.
        # The expected values were computed using the symbolic algebra
        # program 'maxima' with the package 'distrib', which has
        # 'pdf_weibull' and 'cdf_weibull'.  The mapping between the
        # scipy and maxima functions is as follows:
        # -----------------------------------------------------------------
        # scipy                              maxima
        # ---------------------------------  ------------------------------
        # weibull_min.pdf(x, a, scale=b)     pdf_weibull(x, a, b)
        # weibull_min.logpdf(x, a, scale=b)  log(pdf_weibull(x, a, b))
        # weibull_min.cdf(x, a, scale=b)     cdf_weibull(x, a, b)
        # weibull_min.logcdf(x, a, scale=b)  log(cdf_weibull(x, a, b))
        # weibull_min.sf(x, a, scale=b)      1 - cdf_weibull(x, a, b)
        # weibull_min.logsf(x, a, scale=b)   log(1 - cdf_weibull(x, a, b))
        #
        # weibull_max.pdf(x, a, scale=b)     pdf_weibull(-x, a, b)
        # weibull_max.logpdf(x, a, scale=b)  log(pdf_weibull(-x, a, b))
        # weibull_max.cdf(x, a, scale=b)     1 - cdf_weibull(-x, a, b)
        # weibull_max.logcdf(x, a, scale=b)  log(1 - cdf_weibull(-x, a, b))
        # weibull_max.sf(x, a, scale=b)      cdf_weibull(-x, a, b)
        # weibull_max.logsf(x, a, scale=b)   log(cdf_weibull(-x, a, b))
        # -----------------------------------------------------------------
        x = 1.5
        a = 2.0
        b = 3.0

        # weibull_min

        p = stats.weibull_min.pdf(x, a, scale=b)
        assert_allclose(p, np.exp(-0.25)/3)

        lp = stats.weibull_min.logpdf(x, a, scale=b)
        assert_allclose(lp, -0.25 - np.log(3))

        c = stats.weibull_min.cdf(x, a, scale=b)
        assert_allclose(c, -special.expm1(-0.25))

        lc = stats.weibull_min.logcdf(x, a, scale=b)
        assert_allclose(lc, np.log(-special.expm1(-0.25)))

        s = stats.weibull_min.sf(x, a, scale=b)
        assert_allclose(s, np.exp(-0.25))

        ls = stats.weibull_min.logsf(x, a, scale=b)
        assert_allclose(ls, -0.25)

        # Also test using a large value x, for which computing the survival
        # function using the CDF would result in 0.
        s = stats.weibull_min.sf(30, 2, scale=3)
        assert_allclose(s, np.exp(-100))

        ls = stats.weibull_min.logsf(30, 2, scale=3)
        assert_allclose(ls, -100)

        # weibull_max
        x = -1.5

        p = stats.weibull_max.pdf(x, a, scale=b)
        assert_allclose(p, np.exp(-0.25)/3)

        lp = stats.weibull_max.logpdf(x, a, scale=b)
        assert_allclose(lp, -0.25 - np.log(3))

        c = stats.weibull_max.cdf(x, a, scale=b)
        assert_allclose(c, np.exp(-0.25))

        lc = stats.weibull_max.logcdf(x, a, scale=b)
        assert_allclose(lc, -0.25)

        s = stats.weibull_max.sf(x, a, scale=b)
        assert_allclose(s, -special.expm1(-0.25))

        ls = stats.weibull_max.logsf(x, a, scale=b)
        assert_allclose(ls, np.log(-special.expm1(-0.25)))

        # Also test using a value of x close to 0, for which computing the
        # survival function using the CDF would result in 0.
        s = stats.weibull_max.sf(-1e-9, 2, scale=3)
        assert_allclose(s, -special.expm1(-1/9000000000000000000))

        ls = stats.weibull_max.logsf(-1e-9, 2, scale=3)
        assert_allclose(ls, np.log(-special.expm1(-1/9000000000000000000)))


class TestRdist(object):
    def test_rdist_cdf_gh1285(self):
        # check workaround in rdist._cdf for issue gh-1285.
        distfn = stats.rdist
        values = [0.001, 0.5, 0.999]
        assert_almost_equal(distfn.cdf(distfn.ppf(values, 541.0), 541.0),
                            values, decimal=5)

    def test_rdist_beta(self):
        # rdist is a special case of stats.beta
        x = np.linspace(-0.99, 0.99, 10)
        c = 2.7
        assert_almost_equal(0.5*stats.beta(c/2, c/2).pdf((x + 1)/2),
                            stats.rdist(c).pdf(x))


class TestTrapz(object):
    def test_reduces_to_triang(self):
        modes = [0, 0.3, 0.5, 1]
        for mode in modes:
            x = [0, mode, 1]
            assert_almost_equal(stats.trapz.pdf(x, mode, mode),
                                stats.triang.pdf(x, mode))
            assert_almost_equal(stats.trapz.cdf(x, mode, mode),
                                stats.triang.cdf(x, mode))

    def test_reduces_to_uniform(self):
        x = np.linspace(0, 1, 10)
        assert_almost_equal(stats.trapz.pdf(x, 0, 1), stats.uniform.pdf(x))
        assert_almost_equal(stats.trapz.cdf(x, 0, 1), stats.uniform.cdf(x))

    def test_cases(self):
        # edge cases
        assert_almost_equal(stats.trapz.pdf(0, 0, 0), 2)
        assert_almost_equal(stats.trapz.pdf(1, 1, 1), 2)
        assert_almost_equal(stats.trapz.pdf(0.5, 0, 0.8),
                            1.11111111111111111)
        assert_almost_equal(stats.trapz.pdf(0.5, 0.2, 1.0),
                            1.11111111111111111)

        # straightforward case
        assert_almost_equal(stats.trapz.pdf(0.1, 0.2, 0.8), 0.625)
        assert_almost_equal(stats.trapz.pdf(0.5, 0.2, 0.8), 1.25)
        assert_almost_equal(stats.trapz.pdf(0.9, 0.2, 0.8), 0.625)

        assert_almost_equal(stats.trapz.cdf(0.1, 0.2, 0.8), 0.03125)
        assert_almost_equal(stats.trapz.cdf(0.2, 0.2, 0.8), 0.125)
        assert_almost_equal(stats.trapz.cdf(0.5, 0.2, 0.8), 0.5)
        assert_almost_equal(stats.trapz.cdf(0.9, 0.2, 0.8), 0.96875)
        assert_almost_equal(stats.trapz.cdf(1.0, 0.2, 0.8), 1.0)

    def test_moments_and_entropy(self):
        # issue #11795: improve precision of trapz stats
        # Apply formulas from Wikipedia for the following parameters:
        a, b, c, d = -3, -1, 2, 3  # => 1/3, 5/6, -3, 6
        p1, p2, loc, scale = (b-a) / (d-a), (c-a) / (d-a), a, d-a
        h = 2 / (d+c-b-a)
        moment = lambda n: h * ((d**(n+2) - c**(n+2)) / (d-c)
                                - (b**(n+2) - a**(n+2)) / (b-a)) / (n+1) / (n+2)
        mean = moment(1)
        var = moment(2) - mean**2
        entropy = 0.5 * (d-c+b-a) / (d+c-b-a) + np.log(0.5 * (d+c-b-a))
        assert_almost_equal(stats.trapz.mean(p1, p2, loc, scale),
                            mean, decimal=13)
        assert_almost_equal(stats.trapz.var(p1, p2, loc, scale),
                            var, decimal=13)
        assert_almost_equal(stats.trapz.entropy(p1, p2, loc, scale),
                            entropy, decimal=13)

        # Check boundary cases where scipy d=0 or d=1.
        assert_almost_equal(stats.trapz.mean(0, 0, -3, 6), -1, decimal=13)
        assert_almost_equal(stats.trapz.mean(0, 1, -3, 6), 0, decimal=13)
        assert_almost_equal(stats.trapz.var(0, 1, -3, 6), 3, decimal=13)

    def test_trapz_vect(self):
        # test that array-valued shapes and arguments are handled
        c = np.array([0.1, 0.2, 0.3])
        d = np.array([0.5, 0.6])[:, None]
        x = np.array([0.15, 0.25, 0.9])
        v = stats.trapz.pdf(x, c, d)

        cc, dd, xx = np.broadcast_arrays(c, d, x)

        res = np.empty(xx.size, dtype=xx.dtype)
        ind = np.arange(xx.size)
        for i, x1, c1, d1 in zip(ind, xx.ravel(), cc.ravel(), dd.ravel()):
            res[i] = stats.trapz.pdf(x1, c1, d1)

        assert_allclose(v, res.reshape(v.shape), atol=1e-15)

        # Check that the stats() method supports vector arguments.
        v = np.asarray(stats.trapz.stats(c, d, moments="mvsk"))
        cc, dd = np.broadcast_arrays(c, d)
        res = np.empty((cc.size, 4))  # 4 stats returned per value
        ind = np.arange(cc.size)
        for i, c1, d1 in zip(ind, cc.ravel(), dd.ravel()):
            res[i] = stats.trapz.stats(c1, d1, moments="mvsk")

        assert_allclose(v, res.T.reshape(v.shape), atol=1e-15)


class TestTriang(object):
    def test_edge_cases(self):
        with np.errstate(all='raise'):
            assert_equal(stats.triang.pdf(0, 0), 2.)
            assert_equal(stats.triang.pdf(0.5, 0), 1.)
            assert_equal(stats.triang.pdf(1, 0), 0.)

            assert_equal(stats.triang.pdf(0, 1), 0)
            assert_equal(stats.triang.pdf(0.5, 1), 1.)
            assert_equal(stats.triang.pdf(1, 1), 2)

            assert_equal(stats.triang.cdf(0., 0.), 0.)
            assert_equal(stats.triang.cdf(0.5, 0.), 0.75)
            assert_equal(stats.triang.cdf(1.0, 0.), 1.0)

            assert_equal(stats.triang.cdf(0., 1.), 0.)
            assert_equal(stats.triang.cdf(0.5, 1.), 0.25)
            assert_equal(stats.triang.cdf(1., 1.), 1)


class TestMielke(object):
    def test_moments(self):
        k, s = 4.642, 0.597
        # n-th moment exists only if n < s
        assert_equal(stats.mielke(k, s).moment(1), np.inf)
        assert_equal(stats.mielke(k, 1.0).moment(1), np.inf)
        assert_(np.isfinite(stats.mielke(k, 1.01).moment(1)))

    def test_burr_equivalence(self):
        x = np.linspace(0.01, 100, 50)
        k, s = 2.45, 5.32
        assert_allclose(stats.burr.pdf(x, s, k/s), stats.mielke.pdf(x, k, s))


class TestBurr(object):
    def test_endpoints_7491(self):
        # gh-7491
        # Compute the pdf at the left endpoint dst.a.
        data = [
            [stats.fisk, (1,), 1],
            [stats.burr, (0.5, 2), 1],
            [stats.burr, (1, 1), 1],
            [stats.burr, (2, 0.5), 1],
            [stats.burr12, (1, 0.5), 0.5],
            [stats.burr12, (1, 1), 1.0],
            [stats.burr12, (1, 2), 2.0]]

        ans = [_f.pdf(_f.a, *_args) for _f, _args, _ in data]
        correct = [_correct_ for _f, _args, _correct_ in data]
        assert_array_almost_equal(ans, correct)

        ans = [_f.logpdf(_f.a, *_args) for _f, _args, _ in data]
        correct = [np.log(_correct_) for _f, _args, _correct_ in data]
        assert_array_almost_equal(ans, correct)

    def test_burr_stats_9544(self):
        # gh-9544.  Test from gh-9978
        c, d = 5.0, 3
        mean, variance = stats.burr(c, d).stats()
        # mean = sc.beta(3 + 1/5, 1. - 1/5) * 3  = 1.4110263...
        # var =  sc.beta(3 + 2 / 5, 1. - 2 / 5) * 3 - (sc.beta(3 + 1 / 5, 1. - 1 / 5) * 3) ** 2
        mean_hc, variance_hc = 1.4110263183925857, 0.22879948026191643
        assert_allclose(mean, mean_hc)
        assert_allclose(variance, variance_hc)

    def test_burr_nan_mean_var_9544(self):
        # gh-9544.  Test from gh-9978
        c, d = 0.5, 3
        mean, variance = stats.burr(c, d).stats()
        assert_(np.isnan(mean))
        assert_(np.isnan(variance))
        c, d = 1.5, 3
        mean, variance = stats.burr(c, d).stats()
        assert_(np.isfinite(mean))
        assert_(np.isnan(variance))

        c, d = 0.5, 3
        e1, e2, e3, e4 = stats.burr._munp(np.array([1, 2, 3, 4]), c, d)
        assert_(np.isnan(e1))
        assert_(np.isnan(e2))
        assert_(np.isnan(e3))
        assert_(np.isnan(e4))
        c, d = 1.5, 3
        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
        assert_(np.isfinite(e1))
        assert_(np.isnan(e2))
        assert_(np.isnan(e3))
        assert_(np.isnan(e4))
        c, d = 2.5, 3
        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
        assert_(np.isfinite(e1))
        assert_(np.isfinite(e2))
        assert_(np.isnan(e3))
        assert_(np.isnan(e4))
        c, d = 3.5, 3
        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
        assert_(np.isfinite(e1))
        assert_(np.isfinite(e2))
        assert_(np.isfinite(e3))
        assert_(np.isnan(e4))
        c, d = 4.5, 3
        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
        assert_(np.isfinite(e1))
        assert_(np.isfinite(e2))
        assert_(np.isfinite(e3))
        assert_(np.isfinite(e4))


def test_540_567():
    # test for nan returned in tickets 540, 567
    assert_almost_equal(stats.norm.cdf(-1.7624320982), 0.03899815971089126,
                        decimal=10, err_msg='test_540_567')
    assert_almost_equal(stats.norm.cdf(-1.7624320983), 0.038998159702449846,
                        decimal=10, err_msg='test_540_567')
    assert_almost_equal(stats.norm.cdf(1.38629436112, loc=0.950273420309,
                                       scale=0.204423758009),
                        0.98353464004309321,
                        decimal=10, err_msg='test_540_567')


def test_regression_ticket_1316():
    # The following was raising an exception, because _construct_default_doc()
    # did not handle the default keyword extradoc=None.  See ticket #1316.
    stats._continuous_distns.gamma_gen(name='gamma')


def test_regression_ticket_1326():
    # adjust to avoid nan with 0*log(0)
    assert_almost_equal(stats.chi2.pdf(0.0, 2), 0.5, 14)


def test_regression_tukey_lambda():
    # Make sure that Tukey-Lambda distribution correctly handles
    # non-positive lambdas.
    x = np.linspace(-5.0, 5.0, 101)

    with np.errstate(divide='ignore'):
        for lam in [0.0, -1.0, -2.0, np.array([[-1.0], [0.0], [-2.0]])]:
            p = stats.tukeylambda.pdf(x, lam)
            assert_((p != 0.0).all())
            assert_(~np.isnan(p).all())

        lam = np.array([[-1.0], [0.0], [2.0]])
        p = stats.tukeylambda.pdf(x, lam)

    assert_(~np.isnan(p).all())
    assert_((p[0] != 0.0).all())
    assert_((p[1] != 0.0).all())
    assert_((p[2] != 0.0).any())
    assert_((p[2] == 0.0).any())


@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstrings stripped")
def test_regression_ticket_1421():
    assert_('pdf(x, mu, loc=0, scale=1)' not in stats.poisson.__doc__)
    assert_('pmf(x,' in stats.poisson.__doc__)


def test_nan_arguments_gh_issue_1362():
    with np.errstate(invalid='ignore'):
        assert_(np.isnan(stats.t.logcdf(1, np.nan)))
        assert_(np.isnan(stats.t.cdf(1, np.nan)))
        assert_(np.isnan(stats.t.logsf(1, np.nan)))
        assert_(np.isnan(stats.t.sf(1, np.nan)))
        assert_(np.isnan(stats.t.pdf(1, np.nan)))
        assert_(np.isnan(stats.t.logpdf(1, np.nan)))
        assert_(np.isnan(stats.t.ppf(1, np.nan)))
        assert_(np.isnan(stats.t.isf(1, np.nan)))

        assert_(np.isnan(stats.bernoulli.logcdf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.cdf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.logsf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.sf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.pmf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.logpmf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.ppf(np.nan, 0.5)))
        assert_(np.isnan(stats.bernoulli.isf(np.nan, 0.5)))


def test_frozen_fit_ticket_1536():
    np.random.seed(5678)
    true = np.array([0.25, 0., 0.5])
    x = stats.lognorm.rvs(true[0], true[1], true[2], size=100)

    with np.errstate(divide='ignore'):
        params = np.array(stats.lognorm.fit(x, floc=0.))

    assert_almost_equal(params, true, decimal=2)

    params = np.array(stats.lognorm.fit(x, fscale=0.5, loc=0))
    assert_almost_equal(params, true, decimal=2)

    params = np.array(stats.lognorm.fit(x, f0=0.25, loc=0))
    assert_almost_equal(params, true, decimal=2)

    params = np.array(stats.lognorm.fit(x, f0=0.25, floc=0))
    assert_almost_equal(params, true, decimal=2)

    np.random.seed(5678)
    loc = 1
    floc = 0.9
    x = stats.norm.rvs(loc, 2., size=100)
    params = np.array(stats.norm.fit(x, floc=floc))
    expected = np.array([floc, np.sqrt(((x-floc)**2).mean())])
    assert_almost_equal(params, expected, decimal=4)


def test_regression_ticket_1530():
    # Check the starting value works for Cauchy distribution fit.
    np.random.seed(654321)
    rvs = stats.cauchy.rvs(size=100)
    params = stats.cauchy.fit(rvs)
    expected = (0.045, 1.142)
    assert_almost_equal(params, expected, decimal=1)


def test_gh_pr_4806():
    # Check starting values for Cauchy distribution fit.
    np.random.seed(1234)
    x = np.random.randn(42)
    for offset in 10000.0, 1222333444.0:
        loc, scale = stats.cauchy.fit(x + offset)
        assert_allclose(loc, offset, atol=1.0)
        assert_allclose(scale, 0.6, atol=1.0)


def test_tukeylambda_stats_ticket_1545():
    # Some test for the variance and kurtosis of the Tukey Lambda distr.
    # See test_tukeylamdba_stats.py for more tests.

    mv = stats.tukeylambda.stats(0, moments='mvsk')
    # Known exact values:
    expected = [0, np.pi**2/3, 0, 1.2]
    assert_almost_equal(mv, expected, decimal=10)

    mv = stats.tukeylambda.stats(3.13, moments='mvsk')
    # 'expected' computed with mpmath.
    expected = [0, 0.0269220858861465102, 0, -0.898062386219224104]
    assert_almost_equal(mv, expected, decimal=10)

    mv = stats.tukeylambda.stats(0.14, moments='mvsk')
    # 'expected' computed with mpmath.
    expected = [0, 2.11029702221450250, 0, -0.02708377353223019456]
    assert_almost_equal(mv, expected, decimal=10)


def test_poisson_logpmf_ticket_1436():
    assert_(np.isfinite(stats.poisson.logpmf(1500, 200)))


def test_powerlaw_stats():
    """Test the powerlaw stats function.

    This unit test is also a regression test for ticket 1548.

    The exact values are:
    mean:
        mu = a / (a + 1)
    variance:
        sigma**2 = a / ((a + 2) * (a + 1) ** 2)
    skewness:
        One formula (see https://en.wikipedia.org/wiki/Skewness) is
            gamma_1 = (E[X**3] - 3*mu*E[X**2] + 2*mu**3) / sigma**3
        A short calculation shows that E[X**k] is a / (a + k), so gamma_1
        can be implemented as
            n = a/(a+3) - 3*(a/(a+1))*a/(a+2) + 2*(a/(a+1))**3
            d = sqrt(a/((a+2)*(a+1)**2)) ** 3
            gamma_1 = n/d
        Either by simplifying, or by a direct calculation of mu_3 / sigma**3,
        one gets the more concise formula:
            gamma_1 = -2.0 * ((a - 1) / (a + 3)) * sqrt((a + 2) / a)
    kurtosis: (See https://en.wikipedia.org/wiki/Kurtosis)
        The excess kurtosis is
            gamma_2 = mu_4 / sigma**4 - 3
        A bit of calculus and algebra (sympy helps) shows that
            mu_4 = 3*a*(3*a**2 - a + 2) / ((a+1)**4 * (a+2) * (a+3) * (a+4))
        so
            gamma_2 = 3*(3*a**2 - a + 2) * (a+2) / (a*(a+3)*(a+4)) - 3
        which can be rearranged to
            gamma_2 = 6 * (a**3 - a**2 - 6*a + 2) / (a*(a+3)*(a+4))
    """
    cases = [(1.0, (0.5, 1./12, 0.0, -1.2)),
             (2.0, (2./3, 2./36, -0.56568542494924734, -0.6))]
    for a, exact_mvsk in cases:
        mvsk = stats.powerlaw.stats(a, moments="mvsk")
        assert_array_almost_equal(mvsk, exact_mvsk)


def test_powerlaw_edge():
    # Regression test for gh-3986.
    p = stats.powerlaw.logpdf(0, 1)
    assert_equal(p, 0.0)


def test_exponpow_edge():
    # Regression test for gh-3982.
    p = stats.exponpow.logpdf(0, 1)
    assert_equal(p, 0.0)

    # Check pdf and logpdf at x = 0 for other values of b.
    p = stats.exponpow.pdf(0, [0.25, 1.0, 1.5])
    assert_equal(p, [np.inf, 1.0, 0.0])
    p = stats.exponpow.logpdf(0, [0.25, 1.0, 1.5])
    assert_equal(p, [np.inf, 0.0, -np.inf])


def test_gengamma_edge():
    # Regression test for gh-3985.
    p = stats.gengamma.pdf(0, 1, 1)
    assert_equal(p, 1.0)

    # Regression tests for gh-4724.
    p = stats.gengamma._munp(-2, 200, 1.)
    assert_almost_equal(p, 1./199/198)

    p = stats.gengamma._munp(-2, 10, 1.)
    assert_almost_equal(p, 1./9/8)


def test_ksone_fit_freeze():
    # Regression test for ticket #1638.
    d = np.array(
        [-0.18879233, 0.15734249, 0.18695107, 0.27908787, -0.248649,
         -0.2171497, 0.12233512, 0.15126419, 0.03119282, 0.4365294,
          0.08930393, -0.23509903, 0.28231224, -0.09974875, -0.25196048,
          0.11102028, 0.1427649, 0.10176452, 0.18754054, 0.25826724,
          0.05988819, 0.0531668, 0.21906056, 0.32106729, 0.2117662,
          0.10886442, 0.09375789, 0.24583286, -0.22968366, -0.07842391,
         -0.31195432, -0.21271196, 0.1114243, -0.13293002, 0.01331725,
         -0.04330977, -0.09485776, -0.28434547, 0.22245721, -0.18518199,
         -0.10943985, -0.35243174, 0.06897665, -0.03553363, -0.0701746,
         -0.06037974, 0.37670779, -0.21684405])

    with np.errstate(invalid='ignore'):
        with suppress_warnings() as sup:
            sup.filter(IntegrationWarning,
                       "The maximum number of subdivisions .50. has been "
                       "achieved.")
            sup.filter(RuntimeWarning,
                       "floating point number truncated to an integer")
            stats.ksone.fit(d)


def test_norm_logcdf():
    # Test precision of the logcdf of the normal distribution.
    # This precision was enhanced in ticket 1614.
    x = -np.asarray(list(range(0, 120, 4)))
    # Values from R
    expected = [-0.69314718, -10.36010149, -35.01343716, -75.41067300,
                -131.69539607, -203.91715537, -292.09872100, -396.25241451,
                -516.38564863, -652.50322759, -804.60844201, -972.70364403,
                -1156.79057310, -1356.87055173, -1572.94460885, -1805.01356068,
                -2053.07806561, -2317.13866238, -2597.19579746, -2893.24984493,
                -3205.30112136, -3533.34989701, -3877.39640444, -4237.44084522,
                -4613.48339520, -5005.52420869, -5413.56342187, -5837.60115548,
                -6277.63751711, -6733.67260303]

    assert_allclose(stats.norm().logcdf(x), expected, atol=1e-8)

    # also test the complex-valued code path
    assert_allclose(stats.norm().logcdf(x + 1e-14j).real, expected, atol=1e-8)

    # test the accuracy: d(logcdf)/dx = pdf / cdf \equiv exp(logpdf - logcdf)
    deriv = (stats.norm.logcdf(x + 1e-10j)/1e-10).imag
    deriv_expected = np.exp(stats.norm.logpdf(x) - stats.norm.logcdf(x))
    assert_allclose(deriv, deriv_expected, atol=1e-10)


def test_levy_cdf_ppf():
    # Test levy.cdf, including small arguments.
    x = np.array([1000, 1.0, 0.5, 0.1, 0.01, 0.001])

    # Expected values were calculated separately with mpmath.
    # E.g.
    # >>> mpmath.mp.dps = 100
    # >>> x = mpmath.mp.mpf('0.01')
    # >>> cdf = mpmath.erfc(mpmath.sqrt(1/(2*x)))
    expected = np.array([0.9747728793699604,
                         0.3173105078629141,
                         0.1572992070502851,
                         0.0015654022580025495,
                         1.523970604832105e-23,
                         1.795832784800726e-219])

    y = stats.levy.cdf(x)
    assert_allclose(y, expected, rtol=1e-10)

    # ppf(expected) should get us back to x.
    xx = stats.levy.ppf(expected)
    assert_allclose(xx, x, rtol=1e-13)


def test_hypergeom_interval_1802():
    # these two had endless loops
    assert_equal(stats.hypergeom.interval(.95, 187601, 43192, 757),
                 (152.0, 197.0))
    assert_equal(stats.hypergeom.interval(.945, 187601, 43192, 757),
                 (152.0, 197.0))
    # this was working also before
    assert_equal(stats.hypergeom.interval(.94, 187601, 43192, 757),
                 (153.0, 196.0))

    # degenerate case .a == .b
    assert_equal(stats.hypergeom.ppf(0.02, 100, 100, 8), 8)
    assert_equal(stats.hypergeom.ppf(1, 100, 100, 8), 8)


def test_distribution_too_many_args():
    np.random.seed(1234)

    # Check that a TypeError is raised when too many args are given to a method
    # Regression test for ticket 1815.
    x = np.linspace(0.1, 0.7, num=5)
    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, loc=1.0)
    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, 4, loc=1.0)
    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, 4, 5)
    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, loc=1.0, scale=0.5)
    assert_raises(TypeError, stats.gamma.rvs, 2., 3, loc=1.0, scale=0.5)
    assert_raises(TypeError, stats.gamma.cdf, x, 2., 3, loc=1.0, scale=0.5)
    assert_raises(TypeError, stats.gamma.ppf, x, 2., 3, loc=1.0, scale=0.5)
    assert_raises(TypeError, stats.gamma.stats, 2., 3, loc=1.0, scale=0.5)
    assert_raises(TypeError, stats.gamma.entropy, 2., 3, loc=1.0, scale=0.5)
    assert_raises(TypeError, stats.gamma.fit, x, 2., 3, loc=1.0, scale=0.5)

    # These should not give errors
    stats.gamma.pdf(x, 2, 3)  # loc=3
    stats.gamma.pdf(x, 2, 3, 4)  # loc=3, scale=4
    stats.gamma.stats(2., 3)
    stats.gamma.stats(2., 3, 4)
    stats.gamma.stats(2., 3, 4, 'mv')
    stats.gamma.rvs(2., 3, 4, 5)
    stats.gamma.fit(stats.gamma.rvs(2., size=7), 2.)

    # Also for a discrete distribution
    stats.geom.pmf(x, 2, loc=3)  # no error, loc=3
    assert_raises(TypeError, stats.geom.pmf, x, 2, 3, 4)
    assert_raises(TypeError, stats.geom.pmf, x, 2, 3, loc=4)

    # And for distributions with 0, 2 and 3 args respectively
    assert_raises(TypeError, stats.expon.pdf, x, 3, loc=1.0)
    assert_raises(TypeError, stats.exponweib.pdf, x, 3, 4, 5, loc=1.0)
    assert_raises(TypeError, stats.exponweib.pdf, x, 3, 4, 5, 0.1, 0.1)
    assert_raises(TypeError, stats.ncf.pdf, x, 3, 4, 5, 6, loc=1.0)
    assert_raises(TypeError, stats.ncf.pdf, x, 3, 4, 5, 6, 1.0, scale=0.5)
    stats.ncf.pdf(x, 3, 4, 5, 6, 1.0)  # 3 args, plus loc/scale


def test_ncx2_tails_ticket_955():
    # Trac #955 -- check that the cdf computed by special functions
    # matches the integrated pdf
    a = stats.ncx2.cdf(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
    b = stats.ncx2._cdfvec(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
    assert_allclose(a, b, rtol=1e-3, atol=0)


def test_ncx2_tails_pdf():
    # ncx2.pdf does not return nans in extreme tails(example from gh-1577)
    # NB: this is to check that nan_to_num is not needed in ncx2.pdf
    with suppress_warnings() as sup:
        sup.filter(RuntimeWarning, "divide by zero encountered in log")
        assert_equal(stats.ncx2.pdf(1, np.arange(340, 350), 2), 0)
        logval = stats.ncx2.logpdf(1, np.arange(340, 350), 2)

    assert_(np.isneginf(logval).all())


@pytest.mark.parametrize('method, expected', [
    ('cdf', np.array([2.497951336e-09, 3.437288941e-10])),
    ('pdf', np.array([1.238579980e-07, 1.710041145e-08])),
    ('logpdf', np.array([-15.90413011, -17.88416331])),
    ('ppf', np.array([4.865182052, 7.017182271]))
])
def test_ncx2_zero_nc(method, expected):
    # gh-5441
    # ncx2 with nc=0 is identical to chi2
    # Comparison to R (v3.5.1)
    # > options(digits=10)
    # > pchisq(0.1, df=10, ncp=c(0,4))
    # > dchisq(0.1, df=10, ncp=c(0,4))
    # > dchisq(0.1, df=10, ncp=c(0,4), log=TRUE)
    # > qchisq(0.1, df=10, ncp=c(0,4))

    result = getattr(stats.ncx2, method)(0.1, nc=[0, 4], df=10)
    assert_allclose(result, expected, atol=1e-15)


def test_ncx2_zero_nc_rvs():
    # gh-5441
    # ncx2 with nc=0 is identical to chi2
    result = stats.ncx2.rvs(df=10, nc=0, random_state=1)
    expected = stats.chi2.rvs(df=10, random_state=1)
    assert_allclose(result, expected, atol=1e-15)


def test_foldnorm_zero():
    # Parameter value c=0 was not enabled, see gh-2399.
    rv = stats.foldnorm(0, scale=1)
    assert_equal(rv.cdf(0), 0)  # rv.cdf(0) previously resulted in: nan


def test_stats_shapes_argcheck():
    # stats method was failing for vector shapes if some of the values
    # were outside of the allowed range, see gh-2678
    mv3 = stats.invgamma.stats([0.0, 0.5, 1.0], 1, 0.5)  # 0 is not a legal `a`
    mv2 = stats.invgamma.stats([0.5, 1.0], 1, 0.5)
    mv2_augmented = tuple(np.r_[np.nan, _] for _ in mv2)
    assert_equal(mv2_augmented, mv3)

    # -1 is not a legal shape parameter
    mv3 = stats.lognorm.stats([2, 2.4, -1])
    mv2 = stats.lognorm.stats([2, 2.4])
    mv2_augmented = tuple(np.r_[_, np.nan] for _ in mv2)
    assert_equal(mv2_augmented, mv3)

    # FIXME: this is only a quick-and-dirty test of a quick-and-dirty bugfix.
    # stats method with multiple shape parameters is not properly vectorized
    # anyway, so some distributions may or may not fail.


# Test subclassing distributions w/ explicit shapes

class _distr_gen(stats.rv_continuous):
    def _pdf(self, x, a):
        return 42


class _distr2_gen(stats.rv_continuous):
    def _cdf(self, x, a):
        return 42 * a + x


class _distr3_gen(stats.rv_continuous):
    def _pdf(self, x, a, b):
        return a + b

    def _cdf(self, x, a):
        # Different # of shape params from _pdf, to be able to check that
        # inspection catches the inconsistency."""
        return 42 * a + x


class _distr6_gen(stats.rv_continuous):
    # Two shape parameters (both _pdf and _cdf defined, consistent shapes.)
    def _pdf(self, x, a, b):
        return a*x + b

    def _cdf(self, x, a, b):
        return 42 * a + x


class TestSubclassingExplicitShapes(object):
    # Construct a distribution w/ explicit shapes parameter and test it.

    def test_correct_shapes(self):
        dummy_distr = _distr_gen(name='dummy', shapes='a')
        assert_equal(dummy_distr.pdf(1, a=1), 42)

    def test_wrong_shapes_1(self):
        dummy_distr = _distr_gen(name='dummy', shapes='A')
        assert_raises(TypeError, dummy_distr.pdf, 1, **dict(a=1))

    def test_wrong_shapes_2(self):
        dummy_distr = _distr_gen(name='dummy', shapes='a, b, c')
        dct = dict(a=1, b=2, c=3)
        assert_raises(TypeError, dummy_distr.pdf, 1, **dct)

    def test_shapes_string(self):
        # shapes must be a string
        dct = dict(name='dummy', shapes=42)
        assert_raises(TypeError, _distr_gen, **dct)

    def test_shapes_identifiers_1(self):
        # shapes must be a comma-separated list of valid python identifiers
        dct = dict(name='dummy', shapes='(!)')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_identifiers_2(self):
        dct = dict(name='dummy', shapes='4chan')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_identifiers_3(self):
        dct = dict(name='dummy', shapes='m(fti)')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_identifiers_nodefaults(self):
        dct = dict(name='dummy', shapes='a=2')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_args(self):
        dct = dict(name='dummy', shapes='*args')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_kwargs(self):
        dct = dict(name='dummy', shapes='**kwargs')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_keywords(self):
        # python keywords cannot be used for shape parameters
        dct = dict(name='dummy', shapes='a, b, c, lambda')
        assert_raises(SyntaxError, _distr_gen, **dct)

    def test_shapes_signature(self):
        # test explicit shapes which agree w/ the signature of _pdf
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, a):
                return stats.norm._pdf(x) * a

        dist = _dist_gen(shapes='a')
        assert_equal(dist.pdf(0.5, a=2), stats.norm.pdf(0.5)*2)

    def test_shapes_signature_inconsistent(self):
        # test explicit shapes which do not agree w/ the signature of _pdf
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, a):
                return stats.norm._pdf(x) * a

        dist = _dist_gen(shapes='a, b')
        assert_raises(TypeError, dist.pdf, 0.5, **dict(a=1, b=2))

    def test_star_args(self):
        # test _pdf with only starargs
        # NB: **kwargs of pdf will never reach _pdf
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, *args):
                extra_kwarg = args[0]
                return stats.norm._pdf(x) * extra_kwarg

        dist = _dist_gen(shapes='extra_kwarg')
        assert_equal(dist.pdf(0.5, extra_kwarg=33), stats.norm.pdf(0.5)*33)
        assert_equal(dist.pdf(0.5, 33), stats.norm.pdf(0.5)*33)
        assert_raises(TypeError, dist.pdf, 0.5, **dict(xxx=33))

    def test_star_args_2(self):
        # test _pdf with named & starargs
        # NB: **kwargs of pdf will never reach _pdf
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, offset, *args):
                extra_kwarg = args[0]
                return stats.norm._pdf(x) * extra_kwarg + offset

        dist = _dist_gen(shapes='offset, extra_kwarg')
        assert_equal(dist.pdf(0.5, offset=111, extra_kwarg=33),
                     stats.norm.pdf(0.5)*33 + 111)
        assert_equal(dist.pdf(0.5, 111, 33),
                     stats.norm.pdf(0.5)*33 + 111)

    def test_extra_kwarg(self):
        # **kwargs to _pdf are ignored.
        # this is a limitation of the framework (_pdf(x, *goodargs))
        class _distr_gen(stats.rv_continuous):
            def _pdf(self, x, *args, **kwargs):
                # _pdf should handle *args, **kwargs itself.  Here "handling"
                # is ignoring *args and looking for ``extra_kwarg`` and using
                # that.
                extra_kwarg = kwargs.pop('extra_kwarg', 1)
                return stats.norm._pdf(x) * extra_kwarg

        dist = _distr_gen(shapes='extra_kwarg')
        assert_equal(dist.pdf(1, extra_kwarg=3), stats.norm.pdf(1))

    def shapes_empty_string(self):
        # shapes='' is equivalent to shapes=None
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x):
                return stats.norm.pdf(x)

        dist = _dist_gen(shapes='')
        assert_equal(dist.pdf(0.5), stats.norm.pdf(0.5))


class TestSubclassingNoShapes(object):
    # Construct a distribution w/o explicit shapes parameter and test it.

    def test_only__pdf(self):
        dummy_distr = _distr_gen(name='dummy')
        assert_equal(dummy_distr.pdf(1, a=1), 42)

    def test_only__cdf(self):
        # _pdf is determined from _cdf by taking numerical derivative
        dummy_distr = _distr2_gen(name='dummy')
        assert_almost_equal(dummy_distr.pdf(1, a=1), 1)

    @pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
    def test_signature_inspection(self):
        # check that _pdf signature inspection works correctly, and is used in
        # the class docstring
        dummy_distr = _distr_gen(name='dummy')
        assert_equal(dummy_distr.numargs, 1)
        assert_equal(dummy_distr.shapes, 'a')
        res = re.findall(r'logpdf\(x, a, loc=0, scale=1\)',
                         dummy_distr.__doc__)
        assert_(len(res) == 1)

    @pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
    def test_signature_inspection_2args(self):
        # same for 2 shape params and both _pdf and _cdf defined
        dummy_distr = _distr6_gen(name='dummy')
        assert_equal(dummy_distr.numargs, 2)
        assert_equal(dummy_distr.shapes, 'a, b')
        res = re.findall(r'logpdf\(x, a, b, loc=0, scale=1\)',
                         dummy_distr.__doc__)
        assert_(len(res) == 1)

    def test_signature_inspection_2args_incorrect_shapes(self):
        # both _pdf and _cdf defined, but shapes are inconsistent: raises
        assert_raises(TypeError, _distr3_gen, name='dummy')

    def test_defaults_raise(self):
        # default arguments should raise
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, a=42):
                return 42
        assert_raises(TypeError, _dist_gen, **dict(name='dummy'))

    def test_starargs_raise(self):
        # without explicit shapes, *args are not allowed
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, a, *args):
                return 42
        assert_raises(TypeError, _dist_gen, **dict(name='dummy'))

    def test_kwargs_raise(self):
        # without explicit shapes, **kwargs are not allowed
        class _dist_gen(stats.rv_continuous):
            def _pdf(self, x, a, **kwargs):
                return 42
        assert_raises(TypeError, _dist_gen, **dict(name='dummy'))


@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
def test_docstrings():
    badones = [r',\s*,', r'\(\s*,', r'^\s*:']
    for distname in stats.__all__:
        dist = getattr(stats, distname)
        if isinstance(dist, (stats.rv_discrete, stats.rv_continuous)):
            for regex in badones:
                assert_(re.search(regex, dist.__doc__) is None)


def test_infinite_input():
    assert_almost_equal(stats.skellam.sf(np.inf, 10, 11), 0)
    assert_almost_equal(stats.ncx2._cdf(np.inf, 8, 0.1), 1)


def test_lomax_accuracy():
    # regression test for gh-4033
    p = stats.lomax.ppf(stats.lomax.cdf(1e-100, 1), 1)
    assert_allclose(p, 1e-100)


def test_gompertz_accuracy():
    # Regression test for gh-4031
    p = stats.gompertz.ppf(stats.gompertz.cdf(1e-100, 1), 1)
    assert_allclose(p, 1e-100)


def test_truncexpon_accuracy():
    # regression test for gh-4035
    p = stats.truncexpon.ppf(stats.truncexpon.cdf(1e-100, 1), 1)
    assert_allclose(p, 1e-100)


def test_rayleigh_accuracy():
    # regression test for gh-4034
    p = stats.rayleigh.isf(stats.rayleigh.sf(9, 1), 1)
    assert_almost_equal(p, 9.0, decimal=15)


def test_genextreme_give_no_warnings():
    """regression test for gh-6219"""

    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")

        stats.genextreme.cdf(.5, 0)
        stats.genextreme.pdf(.5, 0)
        stats.genextreme.ppf(.5, 0)
        stats.genextreme.logpdf(-np.inf, 0.0)
        number_of_warnings_thrown = len(w)
        assert_equal(number_of_warnings_thrown, 0)


def test_genextreme_entropy():
    # regression test for gh-5181
    euler_gamma = 0.5772156649015329

    h = stats.genextreme.entropy(-1.0)
    assert_allclose(h, 2*euler_gamma + 1, rtol=1e-14)

    h = stats.genextreme.entropy(0)
    assert_allclose(h, euler_gamma + 1, rtol=1e-14)

    h = stats.genextreme.entropy(1.0)
    assert_equal(h, 1)

    h = stats.genextreme.entropy(-2.0, scale=10)
    assert_allclose(h, euler_gamma*3 + np.log(10) + 1, rtol=1e-14)

    h = stats.genextreme.entropy(10)
    assert_allclose(h, -9*euler_gamma + 1, rtol=1e-14)

    h = stats.genextreme.entropy(-10)
    assert_allclose(h, 11*euler_gamma + 1, rtol=1e-14)


def test_genextreme_sf_isf():
    # Expected values were computed using mpmath:
    #
    #    import mpmath
    #
    #    def mp_genextreme_sf(x, xi, mu=0, sigma=1):
    #        # Formula from wikipedia, which has a sign convention for xi that
    #        # is the opposite of scipy's shape parameter.
    #        if xi != 0:
    #            t = mpmath.power(1 + ((x - mu)/sigma)*xi, -1/xi)
    #        else:
    #            t = mpmath.exp(-(x - mu)/sigma)
    #        return 1 - mpmath.exp(-t)
    #
    # >>> mpmath.mp.dps = 1000
    # >>> s = mp_genextreme_sf(mpmath.mp.mpf("1e8"), mpmath.mp.mpf("0.125"))
    # >>> float(s)
    # 1.6777205262585625e-57
    # >>> s = mp_genextreme_sf(mpmath.mp.mpf("7.98"), mpmath.mp.mpf("-0.125"))
    # >>> float(s)
    # 1.52587890625e-21
    # >>> s = mp_genextreme_sf(mpmath.mp.mpf("7.98"), mpmath.mp.mpf("0"))
    # >>> float(s)
    # 0.00034218086528426593

    x = 1e8
    s = stats.genextreme.sf(x, -0.125)
    assert_allclose(s, 1.6777205262585625e-57)
    x2 = stats.genextreme.isf(s, -0.125)
    assert_allclose(x2, x)

    x = 7.98
    s = stats.genextreme.sf(x, 0.125)
    assert_allclose(s, 1.52587890625e-21)
    x2 = stats.genextreme.isf(s, 0.125)
    assert_allclose(x2, x)

    x = 7.98
    s = stats.genextreme.sf(x, 0)
    assert_allclose(s, 0.00034218086528426593)
    x2 = stats.genextreme.isf(s, 0)
    assert_allclose(x2, x)


def test_burr12_ppf_small_arg():
    prob = 1e-16
    quantile = stats.burr12.ppf(prob, 2, 3)
    # The expected quantile was computed using mpmath:
    #   >>> import mpmath
    #   >>> mpmath.mp.dps = 100
    #   >>> prob = mpmath.mpf('1e-16')
    #   >>> c = mpmath.mpf(2)
    #   >>> d = mpmath.mpf(3)
    #   >>> float(((1-prob)**(-1/d) - 1)**(1/c))
    #   5.7735026918962575e-09
    assert_allclose(quantile, 5.7735026918962575e-09)


def test_crystalball_function():
    """
    All values are calculated using the independent implementation of the
    ROOT framework (see https://root.cern.ch/).
    Corresponding ROOT code is given in the comments.
    """
    X = np.linspace(-5.0, 5.0, 21)[:-1]

    # for(float x = -5.0; x < 5.0; x+=0.5)
    #   std::cout << ROOT::Math::crystalball_pdf(x, 1.0, 2.0, 1.0) << ", ";
    calculated = stats.crystalball.pdf(X, beta=1.0, m=2.0)
    expected = np.array([0.0202867, 0.0241428, 0.0292128, 0.0360652, 0.045645,
                         0.059618, 0.0811467, 0.116851, 0.18258, 0.265652,
                         0.301023, 0.265652, 0.18258, 0.097728, 0.0407391,
                         0.013226, 0.00334407, 0.000658486, 0.000100982,
                         1.20606e-05])
    assert_allclose(expected, calculated, rtol=0.001)

    # for(float x = -5.0; x < 5.0; x+=0.5)
    #   std::cout << ROOT::Math::crystalball_pdf(x, 2.0, 3.0, 1.0) << ", ";
    calculated = stats.crystalball.pdf(X, beta=2.0, m=3.0)
    expected = np.array([0.0019648, 0.00279754, 0.00417592, 0.00663121,
                         0.0114587, 0.0223803, 0.0530497, 0.12726, 0.237752,
                         0.345928, 0.391987, 0.345928, 0.237752, 0.12726,
                         0.0530497, 0.0172227, 0.00435458, 0.000857469,
                         0.000131497, 1.57051e-05])
    assert_allclose(expected, calculated, rtol=0.001)

    # for(float x = -5.0; x < 5.0; x+=0.5) {
    #   std::cout << ROOT::Math::crystalball_pdf(x, 2.0, 3.0, 2.0, 0.5);
    #   std::cout << ", ";
    # }
    calculated = stats.crystalball.pdf(X, beta=2.0, m=3.0, loc=0.5, scale=2.0)
    expected = np.array([0.00785921, 0.0111902, 0.0167037, 0.0265249,
                         0.0423866, 0.0636298, 0.0897324, 0.118876, 0.147944,
                         0.172964, 0.189964, 0.195994, 0.189964, 0.172964,
                         0.147944, 0.118876, 0.0897324, 0.0636298, 0.0423866,
                         0.0265249])
    assert_allclose(expected, calculated, rtol=0.001)

    # for(float x = -5.0; x < 5.0; x+=0.5)
    #   std::cout << ROOT::Math::crystalball_cdf(x, 1.0, 2.0, 1.0) << ", ";
    calculated = stats.crystalball.cdf(X, beta=1.0, m=2.0)
    expected = np.array([0.12172, 0.132785, 0.146064, 0.162293, 0.18258,
                         0.208663, 0.24344, 0.292128, 0.36516, 0.478254,
                         0.622723, 0.767192, 0.880286, 0.94959, 0.982834,
                         0.995314, 0.998981, 0.999824, 0.999976, 0.999997])
    assert_allclose(expected, calculated, rtol=0.001)

    # for(float x = -5.0; x < 5.0; x+=0.5)
    #   std::cout << ROOT::Math::crystalball_cdf(x, 2.0, 3.0, 1.0) << ", ";
    calculated = stats.crystalball.cdf(X, beta=2.0, m=3.0)
    expected = np.array([0.00442081, 0.00559509, 0.00730787, 0.00994682,
                         0.0143234, 0.0223803, 0.0397873, 0.0830763, 0.173323,
                         0.320592, 0.508717, 0.696841, 0.844111, 0.934357,
                         0.977646, 0.993899, 0.998674, 0.999771, 0.999969,
                         0.999997])
    assert_allclose(expected, calculated, rtol=0.001)

    # for(float x = -5.0; x < 5.0; x+=0.5) {
    #   std::cout << ROOT::Math::crystalball_cdf(x, 2.0, 3.0, 2.0, 0.5);
    #   std::cout << ", ";
    # }
    calculated = stats.crystalball.cdf(X, beta=2.0, m=3.0, loc=0.5, scale=2.0)
    expected = np.array([0.0176832, 0.0223803, 0.0292315, 0.0397873, 0.0567945,
                         0.0830763, 0.121242, 0.173323, 0.24011, 0.320592,
                         0.411731, 0.508717, 0.605702, 0.696841, 0.777324,
                         0.844111, 0.896192, 0.934357, 0.960639, 0.977646])
    assert_allclose(expected, calculated, rtol=0.001)


def test_crystalball_function_moments():
    """
    All values are calculated using the pdf formula and the integrate function
    of Mathematica
    """
    # The Last two (alpha, n) pairs test the special case n == alpha**2
    beta = np.array([2.0, 1.0, 3.0, 2.0, 3.0])
    m = np.array([3.0, 3.0, 2.0, 4.0, 9.0])

    # The distribution should be correctly normalised
    expected_0th_moment = np.array([1.0, 1.0, 1.0, 1.0, 1.0])
    calculated_0th_moment = stats.crystalball._munp(0, beta, m)
    assert_allclose(expected_0th_moment, calculated_0th_moment, rtol=0.001)

    # calculated using wolframalpha.com
    # e.g. for beta = 2 and m = 3 we calculate the norm like this:
    #   integrate exp(-x^2/2) from -2 to infinity +
    #   integrate (3/2)^3*exp(-2^2/2)*(3/2-2-x)^(-3) from -infinity to -2
    norm = np.array([2.5511, 3.01873, 2.51065, 2.53983, 2.507410455])

    a = np.array([-0.21992, -3.03265, np.inf, -0.135335, -0.003174])
    expected_1th_moment = a / norm
    calculated_1th_moment = stats.crystalball._munp(1, beta, m)
    assert_allclose(expected_1th_moment, calculated_1th_moment, rtol=0.001)

    a = np.array([np.inf, np.inf, np.inf, 3.2616, 2.519908])
    expected_2th_moment = a / norm
    calculated_2th_moment = stats.crystalball._munp(2, beta, m)
    assert_allclose(expected_2th_moment, calculated_2th_moment, rtol=0.001)

    a = np.array([np.inf, np.inf, np.inf, np.inf, -0.0577668])
    expected_3th_moment = a / norm
    calculated_3th_moment = stats.crystalball._munp(3, beta, m)
    assert_allclose(expected_3th_moment, calculated_3th_moment, rtol=0.001)

    a = np.array([np.inf, np.inf, np.inf, np.inf, 7.78468])
    expected_4th_moment = a / norm
    calculated_4th_moment = stats.crystalball._munp(4, beta, m)
    assert_allclose(expected_4th_moment, calculated_4th_moment, rtol=0.001)

    a = np.array([np.inf, np.inf, np.inf, np.inf, -1.31086])
    expected_5th_moment = a / norm
    calculated_5th_moment = stats.crystalball._munp(5, beta, m)
    assert_allclose(expected_5th_moment, calculated_5th_moment, rtol=0.001)


@pytest.mark.parametrize(
    'df1,df2,x',
    [(2, 2, [-0.5, 0.2, 1.0, 2.3]),
     (4, 11, [-0.5, 0.2, 1.0, 2.3]),
     (7, 17, [1, 2, 3, 4, 5])]
)
def test_ncf_edge_case(df1, df2, x):
    # Test for edge case described in gh-11660.
    # Non-central Fisher distribution when nc = 0
    # should be the same as Fisher distribution.
    nc = 0
    expected_cdf = stats.f.cdf(x, df1, df2)
    calculated_cdf = stats.ncf.cdf(x, df1, df2, nc)
    assert_allclose(expected_cdf, calculated_cdf, rtol=1e-14)

    # when ncf_gen._skip_pdf will be used instead of generic pdf,
    # this additional test will be useful.
    expected_pdf = stats.f.pdf(x, df1, df2)
    calculated_pdf = stats.ncf.pdf(x, df1, df2, nc)
    assert_allclose(expected_pdf, calculated_pdf, rtol=1e-6)


def test_ncf_variance():
    # Regression test for gh-10658 (incorrect variance formula for ncf).
    # The correct value of ncf.var(2, 6, 4), 42.75, can be verified with, for
    # example, Wolfram Alpha with the expression
    #     Variance[NoncentralFRatioDistribution[2, 6, 4]]
    # or with the implementation of the noncentral F distribution in the C++
    # library Boost.
    v = stats.ncf.var(2, 6, 4)
    assert_allclose(v, 42.75, rtol=1e-14)


class TestHistogram(object):
    def setup_method(self):
        np.random.seed(1234)

        # We have 8 bins
        # [1,2), [2,3), [3,4), [4,5), [5,6), [6,7), [7,8), [8,9)
        # But actually np.histogram will put the last 9 also in the [8,9) bin!
        # Therefore there is a slight difference below for the last bin, from
        # what you might have expected.
        histogram = np.histogram([1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5,
                                  6, 6, 6, 6, 7, 7, 7, 8, 8, 9], bins=8)
        self.template = stats.rv_histogram(histogram)

        data = stats.norm.rvs(loc=1.0, scale=2.5, size=10000, random_state=123)
        norm_histogram = np.histogram(data, bins=50)
        self.norm_template = stats.rv_histogram(norm_histogram)

    def test_pdf(self):
        values = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
                           5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5])
        pdf_values = np.asarray([0.0/25.0, 0.0/25.0, 1.0/25.0, 1.0/25.0,
                                 2.0/25.0, 2.0/25.0, 3.0/25.0, 3.0/25.0,
                                 4.0/25.0, 4.0/25.0, 5.0/25.0, 5.0/25.0,
                                 4.0/25.0, 4.0/25.0, 3.0/25.0, 3.0/25.0,
                                 3.0/25.0, 3.0/25.0, 0.0/25.0, 0.0/25.0])
        assert_allclose(self.template.pdf(values), pdf_values)

        # Test explicitly the corner cases:
        # As stated above the pdf in the bin [8,9) is greater than
        # one would naively expect because np.histogram putted the 9
        # into the [8,9) bin.
        assert_almost_equal(self.template.pdf(8.0), 3.0/25.0)
        assert_almost_equal(self.template.pdf(8.5), 3.0/25.0)
        # 9 is outside our defined bins [8,9) hence the pdf is already 0
        # for a continuous distribution this is fine, because a single value
        # does not have a finite probability!
        assert_almost_equal(self.template.pdf(9.0), 0.0/25.0)
        assert_almost_equal(self.template.pdf(10.0), 0.0/25.0)

        x = np.linspace(-2, 2, 10)
        assert_allclose(self.norm_template.pdf(x),
                        stats.norm.pdf(x, loc=1.0, scale=2.5), rtol=0.1)

    def test_cdf_ppf(self):
        values = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
                           5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5])
        cdf_values = np.asarray([0.0/25.0, 0.0/25.0, 0.0/25.0, 0.5/25.0,
                                 1.0/25.0, 2.0/25.0, 3.0/25.0, 4.5/25.0,
                                 6.0/25.0, 8.0/25.0, 10.0/25.0, 12.5/25.0,
                                 15.0/25.0, 17.0/25.0, 19.0/25.0, 20.5/25.0,
                                 22.0/25.0, 23.5/25.0, 25.0/25.0, 25.0/25.0])
        assert_allclose(self.template.cdf(values), cdf_values)
        # First three and last two values in cdf_value are not unique
        assert_allclose(self.template.ppf(cdf_values[2:-1]), values[2:-1])

        # Test of cdf and ppf are inverse functions
        x = np.linspace(1.0, 9.0, 100)
        assert_allclose(self.template.ppf(self.template.cdf(x)), x)
        x = np.linspace(0.0, 1.0, 100)
        assert_allclose(self.template.cdf(self.template.ppf(x)), x)

        x = np.linspace(-2, 2, 10)
        assert_allclose(self.norm_template.cdf(x),
                        stats.norm.cdf(x, loc=1.0, scale=2.5), rtol=0.1)

    def test_rvs(self):
        N = 10000
        sample = self.template.rvs(size=N, random_state=123)
        assert_equal(np.sum(sample < 1.0), 0.0)
        assert_allclose(np.sum(sample <= 2.0), 1.0/25.0 * N, rtol=0.2)
        assert_allclose(np.sum(sample <= 2.5), 2.0/25.0 * N, rtol=0.2)
        assert_allclose(np.sum(sample <= 3.0), 3.0/25.0 * N, rtol=0.1)
        assert_allclose(np.sum(sample <= 3.5), 4.5/25.0 * N, rtol=0.1)
        assert_allclose(np.sum(sample <= 4.0), 6.0/25.0 * N, rtol=0.1)
        assert_allclose(np.sum(sample <= 4.5), 8.0/25.0 * N, rtol=0.1)
        assert_allclose(np.sum(sample <= 5.0), 10.0/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 5.5), 12.5/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 6.0), 15.0/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 6.5), 17.0/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 7.0), 19.0/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 7.5), 20.5/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 8.0), 22.0/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 8.5), 23.5/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 9.0), 25.0/25.0 * N, rtol=0.05)
        assert_allclose(np.sum(sample <= 9.0), 25.0/25.0 * N, rtol=0.05)
        assert_equal(np.sum(sample > 9.0), 0.0)

    def test_munp(self):
        for n in range(4):
            assert_allclose(self.norm_template._munp(n),
                            stats.norm(1.0, 2.5).moment(n), rtol=0.05)

    def test_entropy(self):
        assert_allclose(self.norm_template.entropy(),
                        stats.norm.entropy(loc=1.0, scale=2.5), rtol=0.05)


def test_loguniform():
    # This test makes sure the alias of "loguniform" is log-uniform
    rv = stats.loguniform(10 ** -3, 10 ** 0)
    rvs = rv.rvs(size=10000, random_state=42)
    vals, _ = np.histogram(np.log10(rvs), bins=10)
    assert 900 <= vals.min() <= vals.max() <= 1100
    assert np.abs(np.median(vals) - 1000) <= 10


class TestArgus(object):
    def test_argus_rvs_large_chi(self):
        # test that the algorithm can handle large values of chi
        x = stats.argus.rvs(50, size=500, random_state=325)
        assert_almost_equal(stats.argus(50).mean(), x.mean(), decimal=4)

    def test_argus_rvs_ratio_uniforms(self):
        # test that the ratio of uniforms algorithms works for chi > 2.611
        x = stats.argus.rvs(3.5, size=1500, random_state=1535)
        assert_almost_equal(stats.argus(3.5).mean(), x.mean(), decimal=3)
        assert_almost_equal(stats.argus(3.5).std(), x.std(), decimal=3)


def test_rvs_no_size_warning():
    class rvs_no_size_gen(stats.rv_continuous):
        def _rvs(self):
            return 1

    rvs_no_size = rvs_no_size_gen(name='rvs_no_size')

    with assert_warns(np.VisibleDeprecationWarning):
        rvs_no_size.rvs()

