<h3 dir="auto">Describe your issue.</h3>
<p dir="auto">In the <code class="notranslate">cp310-macosx_arm64</code> test on cirrus-ci there are 14 failing tests in scipy.stats.<br>
<a href="https://cirrus-ci.com/task/6051468460425216" rel="nofollow">https://cirrus-ci.com/task/6051468460425216</a></p>
<h3 dir="auto">Reproducing Code Example</h3>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="Running the test suite"><pre class="notranslate"><span class="pl-v">Running</span> <span class="pl-s1">the</span> <span class="pl-s1">test</span> <span class="pl-s1">suite</span></pre></div>
<h3 dir="auto">Error message</h3>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="=================================== FAILURES ===================================
_________________ test_stats_boost_ufunc[_beta_ppf-args1-0.5] __________________
scipy/stats/tests/test_boost_ufuncs.py:41: in test_stats_boost_ufunc
    value = func(*args)
E   RuntimeWarning: overflow encountered in _beta_ppf
        args       = [0.6875, 2.0, 3.0]
        expected   = 0.5
        func       = &lt;ufunc '_beta_ppf'&gt;
        rtol       = 3.814697265625e-06
        typ        = &lt;class 'numpy.float32'&gt;
        type_char  = 'f'
        type_chars = ['f', 'd', 'g']
        type_sigs  = ['fff-&gt;f', 'ddd-&gt;d', 'ggg-&gt;g']
______________________ test_cont_basic[500-200-ncf-arg74] ______________________
scipy/stats/tests/test_continuous_basic.py:162: in test_cont_basic
    check_cdf_ppf(distfn, arg, distname)
        arg        = (27, 27, 0.41578441799226107)
        distfn     = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        distname   = 'ncf'
        m          = 1.0966313767196902
        n_fit_samples = 200
        rng        = RandomState(MT19937) at 0x14A668E40
        rvs        = array([0.90888841, 0.87523216, 0.73228753, 1.33584017, 0.93548576,
       1.09504212, 1.15670442, 0.36786914, 0.791225...03, 1.73540023, 0.87035866, 0.73702581, 0.81477748,
       0.87362182, 1.58673099, 1.88455316, 0.61564337, 1.09912842])
        sn         = 500
        v          = 0.2013794025952613
scipy/stats/tests/test_continuous_basic.py:516: in check_cdf_ppf
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
        arg        = (27, 27, 0.41578441799226107)
        distfn     = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        msg        = 'ncf'
        values     = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2333: in ppf
    place(output, cond, self._ppf(*goodargs) * scale + loc)
        _a         = 0.0
        _b         = inf
        args       = (array(27), array(27), array(0.41578442))
        cond       = array([ True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True])
        cond2      = array([False, False, False])
        cond3      = array([False, False, False])
        goodargs   = [array([0.001, 0.5  , 0.999]), array([27, 27, 27]), array([27, 27, 27]), array([0.41578442, 0.41578442, 0.41578442])]
        kwds       = {}
        loc        = array([0, 0, 0])
        lower_bound = 0.0
        output     = array([nan, nan, nan])
        q          = array([0.001, 0.5  , 0.999])
        scale      = array([1, 1, 1])
        self       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        upper_bound = inf
scipy/stats/_continuous_distns.py:6604: in _ppf
    return _boost._ncf_ppf(q, dfn, dfd, nc)
E   RuntimeWarning: overflow encountered in _ncf_ppf
        dfd        = array([27, 27, 27])
        dfn        = array([27, 27, 27])
        nc         = array([0.41578442, 0.41578442, 0.41578442])
        q          = array([0.001, 0.5  , 0.999])
        self       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
______________________ test_cont_basic[500-200-nct-arg75] ______________________
scipy/stats/tests/test_continuous_basic.py:162: in test_cont_basic
    check_cdf_ppf(distfn, arg, distname)
        arg        = (14, 0.24045031331198066)
        distfn     = &lt;scipy.stats._continuous_distns.nct_gen object at 0x1231046d0&gt;
        distname   = 'nct'
        m          = 0.2543673273832776
        n_fit_samples = 200
        rng        = RandomState(MT19937) at 0x17F91B140
        rvs        = array([ 8.73534081e-01, -1.04204572e+00,  8.32185889e-01,  1.83689614e+00,
       -1.81244028e-01,  7.69549733e-01,  8...9293e+00,  8.20972491e-01, -7.63240067e-01,
        1.27150283e+00,  1.30239072e-01,  1.64179170e+00,  1.18497579e+00])
        sn         = 500
        v          = 1.1694163414603564
scipy/stats/tests/test_continuous_basic.py:516: in check_cdf_ppf
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
        arg        = (14, 0.24045031331198066)
        distfn     = &lt;scipy.stats._continuous_distns.nct_gen object at 0x1231046d0&gt;
        msg        = 'nct'
        values     = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2333: in ppf
    place(output, cond, self._ppf(*goodargs) * scale + loc)
        _a         = -inf
        _b         = inf
        args       = (array(14), array(0.24045031))
        cond       = array([ True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True])
        cond2      = array([False, False, False])
        cond3      = array([False, False, False])
        goodargs   = [array([0.001, 0.5  , 0.999]), array([14, 14, 14]), array([0.24045031, 0.24045031, 0.24045031])]
        kwds       = {}
        loc        = array([0, 0, 0])
        lower_bound = -inf
        output     = array([nan, nan, nan])
        q          = array([0.001, 0.5  , 0.999])
        scale      = array([1, 1, 1])
        self       = &lt;scipy.stats._continuous_distns.nct_gen object at 0x1231046d0&gt;
        upper_bound = inf
scipy/stats/_continuous_distns.py:6800: in _ppf
    return _boost._nct_ppf(q, df, nc)
E   RuntimeWarning: overflow encountered in _nct_ppf
        df         = array([14, 14, 14])
        nc         = array([0.24045031, 0.24045031, 0.24045031])
        q          = array([0.001, 0.5  , 0.999])
        self       = &lt;scipy.stats._continuous_distns.nct_gen object at 0x1231046d0&gt;
_____________________ test_cont_basic[500-200-ncx2-arg76] ______________________
scipy/stats/tests/test_continuous_basic.py:162: in test_cont_basic
    check_cdf_ppf(distfn, arg, distname)
        arg        = (21, 1.0560465975116415)
        distfn     = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        distname   = 'ncx2'
        m          = 22.056046597511642
        n_fit_samples = 200
        rng        = RandomState(MT19937) at 0x17F91B240
        rvs        = array([24.18228087, 28.6893038 , 24.17456852, 14.84747842, 18.70623905,
       16.16763421,  9.29241683, 14.62038405, ...716689, 25.11697161, 27.96343751, 35.36238425,
       17.36403196, 25.81839268, 23.01798747, 22.53534591, 20.49078605])
        sn         = 500
        v          = 46.22418639004657
scipy/stats/tests/test_continuous_basic.py:516: in check_cdf_ppf
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
        arg        = (21, 1.0560465975116415)
        distfn     = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        msg        = 'ncx2'
        values     = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2160: in cdf
    place(output, cond, self._cdf(*goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(21), array(1.0560466))
        cond       = array([ True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True])
        cond2      = array([False, False, False])
        dtyp       = dtype('float64')
        goodargs   = [array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466])]
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0.])
        scale      = array(1)
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([ 6.77671634, 21.36270226, 49.09451392])
scipy/stats/_continuous_distns.py:6502: in _cdf
    return _lazywhere(cond, (x, df, nc), f=_boost._ncx2_cdf,
        cond       = array([ True,  True,  True])
        df         = array([21, 21, 21])
        nc         = array([1.0560466, 1.0560466, 1.0560466])
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([ 6.77671634, 21.36270226, 49.09451392])
scipy/_lib/_util.py:69: in _lazywhere
    np.place(out, cond, f(*temp))
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        args       = [array([ True,  True,  True]), array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466])]
        arrays     = [array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466])]
        cond       = array([ True,  True,  True])
        f          = &lt;ufunc '_ncx2_cdf'&gt;
        f2         = &lt;function ncx2_gen._cdf.&lt;locals&gt;.&lt;lambda&gt; at 0x17f9825f0&gt;
        fillvalue  = nan
        out        = array([nan, nan, nan])
        tcode      = 'd'
        temp       = (array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466]))
___________________ test_methods_with_lists[ncf-args75-ppf] ____________________
scipy/stats/tests/test_continuous_basic.py:714: in test_methods_with_lists
    result = f(x, *shape2, loc=loc, scale=scale)
        args       = (27, 27, 0.41578441799226107)
        dist       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        distname   = 'ncf'
        f          = &lt;bound method rv_continuous.ppf of &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;&gt;
        loc        = [0, 0.1]
        method     = 'ppf'
        scale      = [1, 1.01]
        shape2     = [[27, 27], [27, 27], [0.41578441799226107, 0.41578441799226107]]
        x          = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2333: in ppf
    place(output, cond, self._ppf(*goodargs) * scale + loc)
        _a         = 0.0
        _b         = inf
        args       = (array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442]))
        cond       = array([ True,  True])
        cond0      = array([ True,  True])
        cond1      = array([ True,  True])
        cond2      = array([False, False])
        cond3      = array([False, False])
        goodargs   = [array([0.1, 0.2]), array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442])]
        kwds       = {'loc': [0, 0.1], 'scale': [1, 1.01]}
        loc        = array([0. , 0.1])
        lower_bound = array([0. , 0.1])
        output     = array([nan, nan])
        q          = array([0.1, 0.2])
        scale      = array([1.  , 1.01])
        self       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        upper_bound = array([inf, inf])
scipy/stats/_continuous_distns.py:6604: in _ppf
    return _boost._ncf_ppf(q, dfn, dfd, nc)
E   RuntimeWarning: overflow encountered in _ncf_ppf
        dfd        = array([27, 27])
        dfn        = array([27, 27])
        nc         = array([0.41578442, 0.41578442])
        q          = array([0.1, 0.2])
        self       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
___________________ test_methods_with_lists[ncf-args75-isf] ____________________
scipy/stats/tests/test_continuous_basic.py:714: in test_methods_with_lists
    result = f(x, *shape2, loc=loc, scale=scale)
        args       = (27, 27, 0.41578441799226107)
        dist       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        distname   = 'ncf'
        f          = &lt;bound method rv_continuous.isf of &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;&gt;
        loc        = [0, 0.1]
        method     = 'isf'
        scale      = [1, 1.01]
        shape2     = [[27, 27], [27, 27], [0.41578441799226107, 0.41578441799226107]]
        x          = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2378: in isf
    place(output, cond, self._isf(*goodargs) * scale + loc)
        _a         = 0.0
        _b         = inf
        args       = (array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442]))
        cond       = array([ True,  True])
        cond0      = array([ True,  True])
        cond1      = array([ True,  True])
        cond2      = array([False, False])
        cond3      = array([False, False])
        goodargs   = [array([0.1, 0.2]), array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442])]
        kwds       = {'loc': [0, 0.1], 'scale': [1, 1.01]}
        loc        = array([0. , 0.1])
        lower_bound = array([0. , 0.1])
        output     = array([nan, nan])
        q          = array([0.1, 0.2])
        scale      = array([1.  , 1.01])
        self       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        upper_bound = array([inf, inf])
scipy/stats/_continuous_distns.py:6610: in _isf
    return _boost._ncf_isf(x, dfn, dfd, nc)
E   RuntimeWarning: overflow encountered in _ncf_isf
        dfd        = array([27, 27])
        dfn        = array([27, 27])
        nc         = array([0.41578442, 0.41578442])
        self       = &lt;scipy.stats._continuous_distns.ncf_gen object at 0x123106c20&gt;
        x          = array([0.1, 0.2])
___________________ test_discrete_basic[skellam-arg17-True] ____________________
scipy/stats/tests/test_discrete_basic.py:47: in test_discrete_basic
    check_cdf_ppf(distfn, arg, supp, distname + ' cdf_ppf')
        arg        = (15, 8)
        distfn     = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        distname   = 'skellam'
        first_case = True
        m          = 7.0
        rvs        = array([ 4,  6, 10, ...,  5, 14, 15])
        supp       = array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,
        18,  19,  20,  21,  22,  25])
        v          = 23.0
scipy/stats/tests/test_discrete_basic.py:185: in check_cdf_ppf
    npt.assert_array_equal(distfn.ppf(distfn.cdf(supp, *arg), *arg),
        arg        = (15, 8)
        distfn     = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        msg        = 'skellam cdf_ppf'
        supp       = array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,
        18,  19,  20,  21,  22,  25])
scipy/stats/_distn_infrastructure.py:3433: in cdf
    place(output, cond, np.clip(self._cdf(*goodargs), 0, 1))
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(15), array(8))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,... True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,... True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False,...False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False])
        cond3      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False,...False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False])
        goodargs   = [array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12..., 15]), array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 8, 8, 8])]
        k          = array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,
        18,  19,  20,  21,  22,  25])
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_discrete_distns.py:1437: in _cdf
    _boost._ncx2_cdf(2*mu2, -2*x, 2*mu1),
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        mu1        = array([15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
       15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15])
        mu2        = array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 8, 8, 8])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        x          = array([-10.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,   2.,
         3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,  13.,
        14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  25.])
_________________________ test_moments[skellam-arg17] __________________________
scipy/stats/tests/test_discrete_basic.py:89: in test_moments
    check_normalization(distfn, arg, distname)
        arg        = (15, 8)
        distfn     = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        distname   = 'skellam'
        k          = 0.043478260869565216
        m          = 7.0
        s          = 0.06346090862606624
        v          = 23.0
scipy/stats/tests/common_tests.py:32: in check_normalization
    normalization_expect = distfn.expect(lambda x: 1, args=args)
        args       = (15, 8)
        atol       = 1e-07
        distfn     = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        distname   = 'skellam'
        norm_moment = 1.0
        rtol       = 1e-07
scipy/stats/_distn_infrastructure.py:3739: in expect
    x0 = self.ppf(0.5, *args)
        _a         = -inf
        _b         = inf
        args       = (15, 8)
        chunksize  = 32
        conditional = False
        fun        = &lt;function rv_discrete.expect.&lt;locals&gt;.fun at 0x2840aaa70&gt;
        func       = &lt;function check_normalization.&lt;locals&gt;.&lt;lambda&gt; at 0x2840aa8c0&gt;
        invfac     = 1.0
        lb         = -inf
        loc        = 0
        maxcount   = 1000
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        tolerance  = 1e-10
        ub         = inf
scipy/stats/_distn_infrastructure.py:3592: in ppf
    place(output, cond, self._ppf(*goodargs) + loc)
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(15), array(8))
        cond       = True
        cond0      = True
        cond1      = True
        cond2      = False
        goodargs   = [array([0.5]), array([15]), array([8])]
        kwds       = {}
        loc        = array([0])
        output     = array(nan)
        q          = array(0.5)
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_distn_infrastructure.py:989: in _ppf
    return self._ppfvec(q, *args)
        args       = (array([15]), array([8]))
        q          = array([0.5])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2328: in __call__
    return self._vectorize_call(func=func, args=vargs)
        args       = (&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;, array([0.5]), array([15]), array([8]))
        excluded   = set()
        func       = &lt;function _drv2_ppfsingle at 0x126867c70&gt;
        kwargs     = {}
        self       = &lt;numpy.vectorize object at 0x1269cd960&gt;
        vargs      = (&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;, array([0.5]), array([15]), array([8]))
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2411: in _vectorize_call
    outputs = ufunc(*inputs)
        args       = (&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;, array([0.5]), array([15]), array([8]))
        func       = &lt;function _drv2_ppfsingle at 0x126867c70&gt;
        inputs     = [array(&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;,
      dtype=object), array([0.5], dtype=object), array([15], dtype=object), array([8], dtype=object)]
        otypes     = 'd'
        self       = &lt;numpy.vectorize object at 0x1269cd960&gt;
        ufunc      = &lt;ufunc '_drv2_ppfsingle (vectorized)'&gt;
scipy/stats/_distn_infrastructure.py:2943: in _drv2_ppfsingle
    qb = self._cdf(b, *args)
        _a         = -inf
        _b         = inf
        a          = -inf
        args       = (15, 8)
        b          = 50
        q          = 0.5
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_discrete_distns.py:1438: in _cdf
    1 - _boost._ncx2_cdf(2*mu1, 2*(x+1), 2*mu2))
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        mu1        = 15
        mu2        = 8
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        x          = 50.0
_____________________________ test_skellam_gh11474 _____________________________
scipy/stats/tests/test_discrete_distns.py:238: in test_skellam_gh11474
    cdf = skellam.cdf(0, mu, mu)
        mu         = [1, 10, 100, 1000, 5000, 5050, ...]
scipy/stats/_distn_infrastructure.py:3433: in cdf
    place(output, cond, np.clip(self._cdf(*goodargs), 0, 1))
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000]), array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000]))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True])
        cond0      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True])
        cond1      = True
        cond2      = False
        cond3      = False
        goodargs   = [array([0, 0, 0, 0, 0, 0, 0, 0, 0]), array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000]), array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000])]
        k          = array(0)
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0.])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_discrete_distns.py:1438: in _cdf
    1 - _boost._ncx2_cdf(2*mu1, 2*(x+1), 2*mu2))
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        mu1        = array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000])
        mu2        = array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        x          = array([0., 0., 0., 0., 0., 0., 0., 0., 0.])
_____________________________ TestSkellam.test_cdf _____________________________
scipy/stats/tests/test_distributions.py:3249: in test_cdf
    assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)
        k          = array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14])
        mu1        = 10
        mu2        = 5
        self       = &lt;scipy.stats.tests.test_distributions.TestSkellam object at 0x14aba4520&gt;
        skcdfR     = array([6.40614754e-05, 1.78109860e-04, 4.67906118e-04, 1.15967690e-03,
       2.70774851e-03, 5.94897601e-03, 1.228634...799e-01, 8.77852572e-01,
       9.21121265e-01, 9.51369425e-01, 9.71360859e-01, 9.83877736e-01,
       9.91316724e-01])
scipy/stats/_distn_infrastructure.py:3433: in cdf
    place(output, cond, np.clip(self._cdf(*goodargs), 0, 1))
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(10), array(5))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False])
        cond3      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False])
        goodargs   = [array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9,  10...0, 10, 10, 10, 10, 10, 10]), array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
       5, 5, 5])]
        k          = array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14])
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0.])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_discrete_distns.py:1437: in _cdf
    _boost._ncx2_cdf(2*mu2, -2*x, 2*mu1),
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        mu1        = array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
       10, 10, 10, 10, 10, 10, 10, 10])
        mu2        = array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
       5, 5, 5])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        x          = array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,
         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
        12.,  13.,  14.])
___________________________ TestExpect.test_skellam ____________________________
scipy/stats/tests/test_distributions.py:4833: in test_skellam
    m1 = stats.skellam.expect(lambda x: x, args=(p1, p2))
        p1         = 18
        p2         = 22
        self       = &lt;scipy.stats.tests.test_distributions.TestExpect object at 0x149f402b0&gt;
scipy/stats/_distn_infrastructure.py:3739: in expect
    x0 = self.ppf(0.5, *args)
        _a         = -inf
        _b         = inf
        args       = (18, 22)
        chunksize  = 32
        conditional = False
        fun        = &lt;function rv_discrete.expect.&lt;locals&gt;.fun at 0x2852a5120&gt;
        func       = &lt;function TestExpect.test_skellam.&lt;locals&gt;.&lt;lambda&gt; at 0x2852a4ca0&gt;
        invfac     = 1.0
        lb         = -inf
        loc        = 0
        maxcount   = 1000
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        tolerance  = 1e-10
        ub         = inf
scipy/stats/_distn_infrastructure.py:3592: in ppf
    place(output, cond, self._ppf(*goodargs) + loc)
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(18), array(22))
        cond       = True
        cond0      = True
        cond1      = True
        cond2      = False
        goodargs   = [array([0.5]), array([18]), array([22])]
        kwds       = {}
        loc        = array([0])
        output     = array(nan)
        q          = array(0.5)
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_distn_infrastructure.py:989: in _ppf
    return self._ppfvec(q, *args)
        args       = (array([18]), array([22]))
        q          = array([0.5])
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2328: in __call__
    return self._vectorize_call(func=func, args=vargs)
        args       = (&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;, array([0.5]), array([18]), array([22]))
        excluded   = set()
        func       = &lt;function _drv2_ppfsingle at 0x126867c70&gt;
        kwargs     = {}
        self       = &lt;numpy.vectorize object at 0x1269cd960&gt;
        vargs      = (&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;, array([0.5]), array([18]), array([22]))
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2411: in _vectorize_call
    outputs = ufunc(*inputs)
        args       = (&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;, array([0.5]), array([18]), array([22]))
        func       = &lt;function _drv2_ppfsingle at 0x126867c70&gt;
        inputs     = [array(&lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;,
      dtype=object), array([0.5], dtype=object), array([18], dtype=object), array([22], dtype=object)]
        otypes     = 'd'
        self       = &lt;numpy.vectorize object at 0x1269cd960&gt;
        ufunc      = &lt;ufunc '_drv2_ppfsingle (vectorized)'&gt;
scipy/stats/_distn_infrastructure.py:2943: in _drv2_ppfsingle
    qb = self._cdf(b, *args)
        _a         = -inf
        _b         = inf
        a          = -inf
        args       = (18, 22)
        b          = 50
        q          = 0.5
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
scipy/stats/_discrete_distns.py:1438: in _cdf
    1 - _boost._ncx2_cdf(2*mu1, 2*(x+1), 2*mu2))
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        mu1        = 18
        mu2        = 22
        self       = &lt;scipy.stats._discrete_distns.skellam_gen object at 0x1269cde70&gt;
        x          = 50.0
__________________________ test_ncx2_tails_ticket_955 __________________________
scipy/stats/tests/test_distributions.py:6292: in test_ncx2_tails_ticket_955
    a = stats.ncx2.cdf(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
scipy/stats/_distn_infrastructure.py:2160: in cdf
    place(output, cond, self._cdf(*goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(2), array(107.458615))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False])
        dtyp       = dtype('float64')
        goodargs   = [array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 2...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])]
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0.])
        scale      = array(1)
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2,
       24.4, 24.6, 24.8])
scipy/stats/_continuous_distns.py:6502: in _cdf
    return _lazywhere(cond, (x, df, nc), f=_boost._ncx2_cdf,
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        df         = array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2])
        nc         = array([107.458615, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.4586...15, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2,
       24.4, 24.6, 24.8])
scipy/_lib/_util.py:69: in _lazywhere
    np.place(out, cond, f(*temp))
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        args       = [array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])]
        arrays     = [array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 2...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])]
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        f          = &lt;ufunc '_ncx2_cdf'&gt;
        f2         = &lt;function ncx2_gen._cdf.&lt;locals&gt;.&lt;lambda&gt; at 0x2852a6f80&gt;
        fillvalue  = nan
        out        = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
        tcode      = 'd'
        temp       = (array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 2...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615]))
______________________________ test_ncx2_gh12731 _______________________________
scipy/stats/tests/test_distributions.py:6345: in test_ncx2_gh12731
    assert_equal(stats.ncx2.cdf(1e4, df=1, nc=nc), 0)
        nc         = array([    100000,    1000000,   10000000,  100000000, 1000000000])
scipy/stats/_distn_infrastructure.py:2160: in cdf
    place(output, cond, self._cdf(*goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(1), array([    100000,    1000000,   10000000,  100000000, 1000000000]))
        cond       = array([ True,  True,  True,  True,  True])
        cond0      = array([ True,  True,  True,  True,  True])
        cond1      = True
        cond2      = array([False, False, False, False, False])
        dtyp       = dtype('float64')
        goodargs   = [array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000])]
        kwds       = {'df': 1, 'nc': array([    100000,    1000000,   10000000,  100000000, 1000000000])}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0.])
        scale      = array(1)
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array(10000.)
scipy/stats/_continuous_distns.py:6502: in _cdf
    return _lazywhere(cond, (x, df, nc), f=_boost._ncx2_cdf,
        cond       = array([ True,  True,  True,  True,  True])
        df         = array([1, 1, 1, 1, 1])
        nc         = array([    100000,    1000000,   10000000,  100000000, 1000000000])
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([10000., 10000., 10000., 10000., 10000.])
scipy/_lib/_util.py:69: in _lazywhere
    np.place(out, cond, f(*temp))
E   RuntimeWarning: overflow encountered in _ncx2_cdf
        args       = [array([ True,  True,  True,  True,  True]), array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000])]
        arrays     = [array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000])]
        cond       = array([ True,  True,  True,  True,  True])
        f          = &lt;ufunc '_ncx2_cdf'&gt;
        f2         = &lt;function ncx2_gen._cdf.&lt;locals&gt;.&lt;lambda&gt; at 0x2852a6830&gt;
        fillvalue  = nan
        out        = array([nan, nan, nan, nan, nan])
        tcode      = 'd'
        temp       = (array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000]))
_______________________________ test_ncx2_gh8665 _______________________________
scipy/stats/tests/test_distributions.py:6357: in test_ncx2_gh8665
    sf = stats.ncx2.sf(x, df=nu, nc=lam)
        lam        = 499.51538166556196
        nu         = 20
        x          = array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
       2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
       4.99515382e+04])
scipy/stats/_distn_infrastructure.py:2243: in sf
    place(output, cond, self._sf(*goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(20), array(499.51538167))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False])
        dtyp       = dtype('float64')
        goodargs   = [array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])]
        kwds       = {'df': 20, 'nc': 499.51538166556196}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
        scale      = array(1)
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
       2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
       4.99515382e+04])
scipy/stats/_continuous_distns.py:6515: in _sf
    return _lazywhere(cond, (x, df, nc), f=_boost._ncx2_sf,
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        df         = array([20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20])
        nc         = array([499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])
        self       = &lt;scipy.stats._continuous_distns.ncx2_gen object at 0x123168490&gt;
        x          = array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
       2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
       4.99515382e+04])
scipy/_lib/_util.py:69: in _lazywhere
    np.place(out, cond, f(*temp))
E   RuntimeWarning: overflow encountered in _ncx2_sf
        args       = [array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True]), array([4.9...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])]
        arrays     = [array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])]
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        f          = &lt;ufunc '_ncx2_sf'&gt;
        f2         = &lt;function ncx2_gen._sf.&lt;locals&gt;.&lt;lambda&gt; at 0x2852a7be0&gt;
        fillvalue  = nan
        out        = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
        tcode      = 'd'
        temp       = (array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167]))
=========================== short test summary info ============================
FAILED scipy/stats/tests/test_boost_ufuncs.py::test_stats_boost_ufunc[_beta_ppf-args1-0.5]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-ncf-arg74]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-nct-arg75]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-ncx2-arg76]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[ncf-args75-ppf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[ncf-args75-isf]
FAILED scipy/stats/tests/test_discrete_basic.py::test_discrete_basic[skellam-arg17-True]
FAILED scipy/stats/tests/test_discrete_basic.py::test_moments[skellam-arg17]
FAILED scipy/stats/tests/test_discrete_distns.py::test_skellam_gh11474 - Runt...
FAILED scipy/stats/tests/test_distributions.py::TestSkellam::test_cdf - Runti...
FAILED scipy/stats/tests/test_distributions.py::TestExpect::test_skellam - Ru...
FAILED scipy/stats/tests/test_distributions.py::test_ncx2_tails_ticket_955 - ...
FAILED scipy/stats/tests/test_distributions.py::test_ncx2_gh12731 - RuntimeWa...
FAILED scipy/stats/tests/test_distributions.py::test_ncx2_gh8665 - RuntimeWar..."><pre class="notranslate">=================================== FAILURES ===================================
_________________ test_stats_boost_ufunc[_beta_ppf-args1-0.5] __________________
scipy/stats/tests/test_boost_ufuncs.py:41: <span class="pl-k">in</span> test_stats_boost_ufunc
    value = func(<span class="pl-k">*</span>args)
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _beta_ppf
        args       = [0.6875, 2.0, 3.0]
        expected   = 0.5
        func       = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_beta_ppf<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
        rtol       = 3.814697265625e-06
        typ        = <span class="pl-k">&lt;</span>class <span class="pl-s"><span class="pl-pds">'</span>numpy.float32<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
        type_char  = <span class="pl-s"><span class="pl-pds">'</span>f<span class="pl-pds">'</span></span>
        type_chars = [<span class="pl-s"><span class="pl-pds">'</span>f<span class="pl-pds">'</span></span>, <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>, <span class="pl-s"><span class="pl-pds">'</span>g<span class="pl-pds">'</span></span>]
        type_sigs  = [<span class="pl-s"><span class="pl-pds">'</span>fff-&gt;f<span class="pl-pds">'</span></span>, <span class="pl-s"><span class="pl-pds">'</span>ddd-&gt;d<span class="pl-pds">'</span></span>, <span class="pl-s"><span class="pl-pds">'</span>ggg-&gt;g<span class="pl-pds">'</span></span>]
______________________ test_cont_basic[500-200-ncf-arg74] ______________________
scipy/stats/tests/test_continuous_basic.py:162: <span class="pl-k">in</span> test_cont_basic
    check_cdf_ppf(distfn, arg, distname)
        arg        = (27, 27, 0.41578441799226107)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>ncf<span class="pl-pds">'</span></span>
        m          = 1.0966313767196902
        n_fit_samples = 200
        rng        = RandomState(MT19937) at 0x14A668E40
        rvs        = array([0.90888841, 0.87523216, 0.73228753, 1.33584017, 0.93548576,
       1.09504212, 1.15670442, 0.36786914, 0.791225...03, 1.73540023, 0.87035866, 0.73702581, 0.81477748,
       0.87362182, 1.58673099, 1.88455316, 0.61564337, 1.09912842])
        sn         = 500
        v          = 0.2013794025952613
scipy/stats/tests/test_continuous_basic.py:516: <span class="pl-k">in</span> check_cdf_ppf
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, <span class="pl-k">*</span>arg), <span class="pl-k">*</span>arg),
        arg        = (27, 27, 0.41578441799226107)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        msg        = <span class="pl-s"><span class="pl-pds">'</span>ncf<span class="pl-pds">'</span></span>
        values     = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2333: <span class="pl-k">in</span> ppf
    place(output, cond, self._ppf(<span class="pl-k">*</span>goodargs) <span class="pl-k">*</span> scale + loc)
        _a         = 0.0
        _b         = inf
        args       = (array(27), array(27), array(0.41578442))
        cond       = array([ True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True])
        cond2      = array([False, False, False])
        cond3      = array([False, False, False])
        goodargs   = [array([0.001, 0.5  , 0.999]), array([27, 27, 27]), array([27, 27, 27]), array([0.41578442, 0.41578442, 0.41578442])]
        kwds       = {}
        loc        = array([0, 0, 0])
        lower_bound = 0.0
        output     = array([nan, nan, nan])
        q          = array([0.001, 0.5  , 0.999])
        scale      = array([1, 1, 1])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        upper_bound = inf
scipy/stats/_continuous_distns.py:6604: <span class="pl-k">in</span> _ppf
    <span class="pl-k">return</span> _boost._ncf_ppf(q, dfn, dfd, nc)
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncf_ppf
        dfd        = array([27, 27, 27])
        dfn        = array([27, 27, 27])
        nc         = array([0.41578442, 0.41578442, 0.41578442])
        q          = array([0.001, 0.5  , 0.999])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
______________________ test_cont_basic[500-200-nct-arg75] ______________________
scipy/stats/tests/test_continuous_basic.py:162: <span class="pl-k">in</span> test_cont_basic
    check_cdf_ppf(distfn, arg, distname)
        arg        = (14, 0.24045031331198066)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.nct_gen object at 0x1231046d<span class="pl-k">0&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>nct<span class="pl-pds">'</span></span>
        m          = 0.2543673273832776
        n_fit_samples = 200
        rng        = RandomState(MT19937) at 0x17F91B140
        rvs        = array([ 8.73534081e-01, -1.04204572e+00,  8.32185889e-01,  1.83689614e+00,
       -1.81244028e-01,  7.69549733e-01,  8...9293e+00,  8.20972491e-01, -7.63240067e-01,
        1.27150283e+00,  1.30239072e-01,  1.64179170e+00,  1.18497579e+00])
        sn         = 500
        v          = 1.1694163414603564
scipy/stats/tests/test_continuous_basic.py:516: <span class="pl-k">in</span> check_cdf_ppf
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, <span class="pl-k">*</span>arg), <span class="pl-k">*</span>arg),
        arg        = (14, 0.24045031331198066)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.nct_gen object at 0x1231046d<span class="pl-k">0&gt;</span>
        msg        = <span class="pl-s"><span class="pl-pds">'</span>nct<span class="pl-pds">'</span></span>
        values     = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2333: <span class="pl-k">in</span> ppf
    place(output, cond, self._ppf(<span class="pl-k">*</span>goodargs) <span class="pl-k">*</span> scale + loc)
        _a         = -inf
        _b         = inf
        args       = (array(14), array(0.24045031))
        cond       = array([ True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True])
        cond2      = array([False, False, False])
        cond3      = array([False, False, False])
        goodargs   = [array([0.001, 0.5  , 0.999]), array([14, 14, 14]), array([0.24045031, 0.24045031, 0.24045031])]
        kwds       = {}
        loc        = array([0, 0, 0])
        lower_bound = -inf
        output     = array([nan, nan, nan])
        q          = array([0.001, 0.5  , 0.999])
        scale      = array([1, 1, 1])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.nct_gen object at 0x1231046d<span class="pl-k">0&gt;</span>
        upper_bound = inf
scipy/stats/_continuous_distns.py:6800: <span class="pl-k">in</span> _ppf
    <span class="pl-k">return</span> _boost._nct_ppf(q, df, nc)
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _nct_ppf
        df         = array([14, 14, 14])
        nc         = array([0.24045031, 0.24045031, 0.24045031])
        q          = array([0.001, 0.5  , 0.999])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.nct_gen object at 0x1231046d<span class="pl-k">0&gt;</span>
_____________________ test_cont_basic[500-200-ncx2-arg76] ______________________
scipy/stats/tests/test_continuous_basic.py:162: <span class="pl-k">in</span> test_cont_basic
    check_cdf_ppf(distfn, arg, distname)
        arg        = (21, 1.0560465975116415)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>ncx2<span class="pl-pds">'</span></span>
        m          = 22.056046597511642
        n_fit_samples = 200
        rng        = RandomState(MT19937) at 0x17F91B240
        rvs        = array([24.18228087, 28.6893038 , 24.17456852, 14.84747842, 18.70623905,
       16.16763421,  9.29241683, 14.62038405, ...716689, 25.11697161, 27.96343751, 35.36238425,
       17.36403196, 25.81839268, 23.01798747, 22.53534591, 20.49078605])
        sn         = 500
        v          = 46.22418639004657
scipy/stats/tests/test_continuous_basic.py:516: <span class="pl-k">in</span> check_cdf_ppf
    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, <span class="pl-k">*</span>arg), <span class="pl-k">*</span>arg),
        arg        = (21, 1.0560465975116415)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        msg        = <span class="pl-s"><span class="pl-pds">'</span>ncx2<span class="pl-pds">'</span></span>
        values     = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2160: <span class="pl-k">in</span> cdf
    place(output, cond, self._cdf(<span class="pl-k">*</span>goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(21), array(1.0560466))
        cond       = array([ True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True])
        cond2      = array([False, False, False])
        dtyp       = dtype(<span class="pl-s"><span class="pl-pds">'</span>float64<span class="pl-pds">'</span></span>)
        goodargs   = [array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466])]
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0.])
        scale      = array(1)
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([ 6.77671634, 21.36270226, 49.09451392])
scipy/stats/_continuous_distns.py:6502: <span class="pl-k">in</span> _cdf
    <span class="pl-k">return</span> _lazywhere(cond, (x, df, nc), f=_boost._ncx2_cdf,
        cond       = array([ True,  True,  True])
        df         = array([21, 21, 21])
        nc         = array([1.0560466, 1.0560466, 1.0560466])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([ 6.77671634, 21.36270226, 49.09451392])
scipy/_lib/_util.py:69: <span class="pl-k">in</span> _lazywhere
    np.place(out, cond, f(<span class="pl-k">*</span>temp))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        args       = [array([ True,  True,  True]), array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466])]
        arrays     = [array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466])]
        cond       = array([ True,  True,  True])
        f          = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_ncx2_cdf<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
        f2         = <span class="pl-k">&lt;</span>function ncx2_gen._cdf.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.<span class="pl-k">&lt;</span>lambda<span class="pl-k">&gt;</span> at 0x17f9825f<span class="pl-k">0&gt;</span>
        fillvalue  = nan
        out        = array([nan, nan, nan])
        tcode      = <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>
        temp       = (array([ 6.77671634, 21.36270226, 49.09451392]), array([21, 21, 21]), array([1.0560466, 1.0560466, 1.0560466]))
___________________ test_methods_with_lists[ncf-args75-ppf] ____________________
scipy/stats/tests/test_continuous_basic.py:714: <span class="pl-k">in</span> test_methods_with_lists
    result = f(x, <span class="pl-k">*</span>shape2, loc=loc, scale=scale)
        args       = (27, 27, 0.41578441799226107)
        dist       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>ncf<span class="pl-pds">'</span></span>
        f          = <span class="pl-k">&lt;</span>bound method rv_continuous.ppf of <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;&gt;</span>
        loc        = [0, 0.1]
        method     = <span class="pl-s"><span class="pl-pds">'</span>ppf<span class="pl-pds">'</span></span>
        scale      = [1, 1.01]
        shape2     = [[27, 27], [27, 27], [0.41578441799226107, 0.41578441799226107]]
        x          = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2333: <span class="pl-k">in</span> ppf
    place(output, cond, self._ppf(<span class="pl-k">*</span>goodargs) <span class="pl-k">*</span> scale + loc)
        _a         = 0.0
        _b         = inf
        args       = (array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442]))
        cond       = array([ True,  True])
        cond0      = array([ True,  True])
        cond1      = array([ True,  True])
        cond2      = array([False, False])
        cond3      = array([False, False])
        goodargs   = [array([0.1, 0.2]), array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442])]
        kwds       = {<span class="pl-s"><span class="pl-pds">'</span>loc<span class="pl-pds">'</span></span>: [0, 0.1], <span class="pl-s"><span class="pl-pds">'</span>scale<span class="pl-pds">'</span></span>: [1, 1.01]}
        loc        = array([0. , 0.1])
        lower_bound = array([0. , 0.1])
        output     = array([nan, nan])
        q          = array([0.1, 0.2])
        scale      = array([1.  , 1.01])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        upper_bound = array([inf, inf])
scipy/stats/_continuous_distns.py:6604: <span class="pl-k">in</span> _ppf
    <span class="pl-k">return</span> _boost._ncf_ppf(q, dfn, dfd, nc)
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncf_ppf
        dfd        = array([27, 27])
        dfn        = array([27, 27])
        nc         = array([0.41578442, 0.41578442])
        q          = array([0.1, 0.2])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
___________________ test_methods_with_lists[ncf-args75-isf] ____________________
scipy/stats/tests/test_continuous_basic.py:714: <span class="pl-k">in</span> test_methods_with_lists
    result = f(x, <span class="pl-k">*</span>shape2, loc=loc, scale=scale)
        args       = (27, 27, 0.41578441799226107)
        dist       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>ncf<span class="pl-pds">'</span></span>
        f          = <span class="pl-k">&lt;</span>bound method rv_continuous.isf of <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;&gt;</span>
        loc        = [0, 0.1]
        method     = <span class="pl-s"><span class="pl-pds">'</span>isf<span class="pl-pds">'</span></span>
        scale      = [1, 1.01]
        shape2     = [[27, 27], [27, 27], [0.41578441799226107, 0.41578441799226107]]
        x          = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2378: <span class="pl-k">in</span> isf
    place(output, cond, self._isf(<span class="pl-k">*</span>goodargs) <span class="pl-k">*</span> scale + loc)
        _a         = 0.0
        _b         = inf
        args       = (array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442]))
        cond       = array([ True,  True])
        cond0      = array([ True,  True])
        cond1      = array([ True,  True])
        cond2      = array([False, False])
        cond3      = array([False, False])
        goodargs   = [array([0.1, 0.2]), array([27, 27]), array([27, 27]), array([0.41578442, 0.41578442])]
        kwds       = {<span class="pl-s"><span class="pl-pds">'</span>loc<span class="pl-pds">'</span></span>: [0, 0.1], <span class="pl-s"><span class="pl-pds">'</span>scale<span class="pl-pds">'</span></span>: [1, 1.01]}
        loc        = array([0. , 0.1])
        lower_bound = array([0. , 0.1])
        output     = array([nan, nan])
        q          = array([0.1, 0.2])
        scale      = array([1.  , 1.01])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        upper_bound = array([inf, inf])
scipy/stats/_continuous_distns.py:6610: <span class="pl-k">in</span> _isf
    <span class="pl-k">return</span> _boost._ncf_isf(x, dfn, dfd, nc)
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncf_isf
        dfd        = array([27, 27])
        dfn        = array([27, 27])
        nc         = array([0.41578442, 0.41578442])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncf_gen object at 0x123106c<span class="pl-k">20&gt;</span>
        x          = array([0.1, 0.2])
___________________ test_discrete_basic[skellam-arg17-True] ____________________
scipy/stats/tests/test_discrete_basic.py:47: <span class="pl-k">in</span> test_discrete_basic
    check_cdf_ppf(distfn, arg, supp, distname + <span class="pl-s"><span class="pl-pds">'</span> cdf_ppf<span class="pl-pds">'</span></span>)
        arg        = (15, 8)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>skellam<span class="pl-pds">'</span></span>
        first_case = True
        m          = 7.0
        rvs        = array([ 4,  6, 10, ...,  5, 14, 15])
        supp       = array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,
        18,  19,  20,  21,  22,  25])
        v          = 23.0
scipy/stats/tests/test_discrete_basic.py:185: <span class="pl-k">in</span> check_cdf_ppf
    npt.assert_array_equal(distfn.ppf(distfn.cdf(supp, <span class="pl-k">*</span>arg), <span class="pl-k">*</span>arg),
        arg        = (15, 8)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        msg        = <span class="pl-s"><span class="pl-pds">'</span>skellam cdf_ppf<span class="pl-pds">'</span></span>
        supp       = array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,
        18,  19,  20,  21,  22,  25])
scipy/stats/_distn_infrastructure.py:3433: <span class="pl-k">in</span> cdf
    place(output, cond, np.clip(self._cdf(<span class="pl-k">*</span>goodargs), 0, 1))
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(15), array(8))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,... True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,... True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False,...False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False])
        cond3      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False,...False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False])
        goodargs   = [array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12..., 15]), array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 8, 8, 8])]
        k          = array([-10,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,
         5,   6,   7,   8,   9,  10,  11,  12,  13,  14,  15,  16,  17,
        18,  19,  20,  21,  22,  25])
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_discrete_distns.py:1437: <span class="pl-k">in</span> _cdf
    _boost._ncx2_cdf(2<span class="pl-k">*</span>mu2, -2<span class="pl-k">*</span>x, 2<span class="pl-k">*</span>mu1),
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        mu1        = array([15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
       15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15])
        mu2        = array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 8, 8, 8])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        x          = array([-10.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,   2.,
         3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,  13.,
        14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  25.])
_________________________ test_moments[skellam-arg17] __________________________
scipy/stats/tests/test_discrete_basic.py:89: <span class="pl-k">in</span> test_moments
    check_normalization(distfn, arg, distname)
        arg        = (15, 8)
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>skellam<span class="pl-pds">'</span></span>
        k          = 0.043478260869565216
        m          = 7.0
        s          = 0.06346090862606624
        v          = 23.0
scipy/stats/tests/common_tests.py:32: <span class="pl-k">in</span> check_normalization
    normalization_expect = distfn.expect(lambda x: 1, args=args)
        args       = (15, 8)
        atol       = 1e-07
        distfn     = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        distname   = <span class="pl-s"><span class="pl-pds">'</span>skellam<span class="pl-pds">'</span></span>
        norm_moment = 1.0
        rtol       = 1e-07
scipy/stats/_distn_infrastructure.py:3739: <span class="pl-k">in</span> expect
    x0 = self.ppf(0.5, <span class="pl-k">*</span>args)
        _a         = -inf
        _b         = inf
        args       = (15, 8)
        chunksize  = 32
        conditional = False
        fun        = <span class="pl-k">&lt;</span>function rv_discrete.expect.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.fun at 0x2840aaa<span class="pl-k">70&gt;</span>
        func       = <span class="pl-k">&lt;</span>function check_normalization.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.<span class="pl-k">&lt;</span>lambda<span class="pl-k">&gt;</span> at 0x2840aa8c<span class="pl-k">0&gt;</span>
        invfac     = 1.0
        lb         = -inf
        loc        = 0
        maxcount   = 1000
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        tolerance  = 1e-10
        ub         = inf
scipy/stats/_distn_infrastructure.py:3592: <span class="pl-k">in</span> ppf
    place(output, cond, self._ppf(<span class="pl-k">*</span>goodargs) + loc)
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(15), array(8))
        cond       = True
        cond0      = True
        cond1      = True
        cond2      = False
        goodargs   = [array([0.5]), array([15]), array([8])]
        kwds       = {}
        loc        = array([0])
        output     = array(nan)
        q          = array(0.5)
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_distn_infrastructure.py:989: <span class="pl-k">in</span> _ppf
    <span class="pl-k">return</span> self._ppfvec(q, <span class="pl-k">*</span>args)
        args       = (array([15]), array([8]))
        q          = array([0.5])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2328: <span class="pl-k">in</span> __call__
    <span class="pl-k">return</span> self._vectorize_call(func=func, args=vargs)
        args       = (<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>, array([0.5]), array([15]), array([8]))
        excluded   = <span class="pl-en">set</span>()
        func       = <span class="pl-k">&lt;</span>function _drv2_ppfsingle at 0x126867c<span class="pl-k">70&gt;</span>
        kwargs     = {}
        self       = <span class="pl-k">&lt;</span>numpy.vectorize object at 0x1269cd<span class="pl-k">960&gt;</span>
        vargs      = (<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>, array([0.5]), array([15]), array([8]))
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2411: <span class="pl-k">in</span> _vectorize_call
    outputs = ufunc(<span class="pl-k">*</span>inputs)
        args       = (<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>, array([0.5]), array([15]), array([8]))
        func       = <span class="pl-k">&lt;</span>function _drv2_ppfsingle at 0x126867c<span class="pl-k">70&gt;</span>
        inputs     = [array(<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>,
      dtype=object), array([0.5], dtype=object), array([15], dtype=object), array([8], dtype=object)]
        otypes     = <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>
        self       = <span class="pl-k">&lt;</span>numpy.vectorize object at 0x1269cd<span class="pl-k">960&gt;</span>
        ufunc      = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_drv2_ppfsingle (vectorized)<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
scipy/stats/_distn_infrastructure.py:2943: <span class="pl-k">in</span> _drv2_ppfsingle
    qb = self._cdf(b, <span class="pl-k">*</span>args)
        _a         = -inf
        _b         = inf
        a          = -inf
        args       = (15, 8)
        b          = 50
        q          = 0.5
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_discrete_distns.py:1438: <span class="pl-k">in</span> _cdf
    1 - _boost._ncx2_cdf(2<span class="pl-k">*</span>mu1, 2<span class="pl-k">*</span>(x+1), 2<span class="pl-k">*</span>mu2))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        mu1        = 15
        mu2        = 8
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        x          = 50.0
_____________________________ test_skellam_gh11474 _____________________________
scipy/stats/tests/test_discrete_distns.py:238: <span class="pl-k">in</span> test_skellam_gh11474
    cdf = skellam.cdf(0, mu, mu)
        mu         = [1, 10, 100, 1000, 5000, 5050, ...]
scipy/stats/_distn_infrastructure.py:3433: <span class="pl-k">in</span> cdf
    place(output, cond, np.clip(self._cdf(<span class="pl-k">*</span>goodargs), 0, 1))
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000]), array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000]))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True])
        cond0      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True])
        cond1      = True
        cond2      = False
        cond3      = False
        goodargs   = [array([0, 0, 0, 0, 0, 0, 0, 0, 0]), array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000]), array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000])]
        k          = array(0)
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0.])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_discrete_distns.py:1438: <span class="pl-k">in</span> _cdf
    1 - _boost._ncx2_cdf(2<span class="pl-k">*</span>mu1, 2<span class="pl-k">*</span>(x+1), 2<span class="pl-k">*</span>mu2))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        mu1        = array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000])
        mu2        = array([   1,   10,  100, 1000, 5000, 5050, 5100, 5250, 6000])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        x          = array([0., 0., 0., 0., 0., 0., 0., 0., 0.])
_____________________________ TestSkellam.test_cdf _____________________________
scipy/stats/tests/test_distributions.py:3249: <span class="pl-k">in</span> test_cdf
    assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)
        k          = array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14])
        mu1        = 10
        mu2        = 5
        self       = <span class="pl-k">&lt;</span>scipy.stats.tests.test_distributions.TestSkellam object at 0x14aba<span class="pl-k">4520&gt;</span>
        skcdfR     = array([6.40614754e-05, 1.78109860e-04, 4.67906118e-04, 1.15967690e-03,
       2.70774851e-03, 5.94897601e-03, 1.228634...799e-01, 8.77852572e-01,
       9.21121265e-01, 9.51369425e-01, 9.71360859e-01, 9.83877736e-01,
       9.91316724e-01])
scipy/stats/_distn_infrastructure.py:3433: <span class="pl-k">in</span> cdf
    place(output, cond, np.clip(self._cdf(<span class="pl-k">*</span>goodargs), 0, 1))
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(10), array(5))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False])
        cond3      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False])
        goodargs   = [array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9,  10...0, 10, 10, 10, 10, 10, 10]), array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
       5, 5, 5])]
        k          = array([-10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,
         3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14])
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0.])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_discrete_distns.py:1437: <span class="pl-k">in</span> _cdf
    _boost._ncx2_cdf(2<span class="pl-k">*</span>mu2, -2<span class="pl-k">*</span>x, 2<span class="pl-k">*</span>mu1),
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        mu1        = array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
       10, 10, 10, 10, 10, 10, 10, 10])
        mu2        = array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
       5, 5, 5])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        x          = array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,
         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
        12.,  13.,  14.])
___________________________ TestExpect.test_skellam ____________________________
scipy/stats/tests/test_distributions.py:4833: <span class="pl-k">in</span> test_skellam
    m1 = stats.skellam.expect(lambda x: x, args=(p1, p2))
        p1         = 18
        p2         = 22
        self       = <span class="pl-k">&lt;</span>scipy.stats.tests.test_distributions.TestExpect object at 0x149f402b<span class="pl-k">0&gt;</span>
scipy/stats/_distn_infrastructure.py:3739: <span class="pl-k">in</span> expect
    x0 = self.ppf(0.5, <span class="pl-k">*</span>args)
        _a         = -inf
        _b         = inf
        args       = (18, 22)
        chunksize  = 32
        conditional = False
        fun        = <span class="pl-k">&lt;</span>function rv_discrete.expect.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.fun at 0x2852a<span class="pl-k">5120&gt;</span>
        func       = <span class="pl-k">&lt;</span>function TestExpect.test_skellam.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.<span class="pl-k">&lt;</span>lambda<span class="pl-k">&gt;</span> at 0x2852a4ca<span class="pl-k">0&gt;</span>
        invfac     = 1.0
        lb         = -inf
        loc        = 0
        maxcount   = 1000
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        tolerance  = 1e-10
        ub         = inf
scipy/stats/_distn_infrastructure.py:3592: <span class="pl-k">in</span> ppf
    place(output, cond, self._ppf(<span class="pl-k">*</span>goodargs) + loc)
        _          = 1
        _a         = -inf
        _b         = inf
        args       = (array(18), array(22))
        cond       = True
        cond0      = True
        cond1      = True
        cond2      = False
        goodargs   = [array([0.5]), array([18]), array([22])]
        kwds       = {}
        loc        = array([0])
        output     = array(nan)
        q          = array(0.5)
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_distn_infrastructure.py:989: <span class="pl-k">in</span> _ppf
    <span class="pl-k">return</span> self._ppfvec(q, <span class="pl-k">*</span>args)
        args       = (array([18]), array([22]))
        q          = array([0.5])
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2328: <span class="pl-k">in</span> __call__
    <span class="pl-k">return</span> self._vectorize_call(func=func, args=vargs)
        args       = (<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>, array([0.5]), array([18]), array([22]))
        excluded   = <span class="pl-en">set</span>()
        func       = <span class="pl-k">&lt;</span>function _drv2_ppfsingle at 0x126867c<span class="pl-k">70&gt;</span>
        kwargs     = {}
        self       = <span class="pl-k">&lt;</span>numpy.vectorize object at 0x1269cd<span class="pl-k">960&gt;</span>
        vargs      = (<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>, array([0.5]), array([18]), array([22]))
/Users/admin/scipy-dev/lib/python3.10/site-packages/numpy/lib/function_base.py:2411: <span class="pl-k">in</span> _vectorize_call
    outputs = ufunc(<span class="pl-k">*</span>inputs)
        args       = (<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>, array([0.5]), array([18]), array([22]))
        func       = <span class="pl-k">&lt;</span>function _drv2_ppfsingle at 0x126867c<span class="pl-k">70&gt;</span>
        inputs     = [array(<span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>,
      dtype=object), array([0.5], dtype=object), array([18], dtype=object), array([22], dtype=object)]
        otypes     = <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>
        self       = <span class="pl-k">&lt;</span>numpy.vectorize object at 0x1269cd<span class="pl-k">960&gt;</span>
        ufunc      = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_drv2_ppfsingle (vectorized)<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
scipy/stats/_distn_infrastructure.py:2943: <span class="pl-k">in</span> _drv2_ppfsingle
    qb = self._cdf(b, <span class="pl-k">*</span>args)
        _a         = -inf
        _b         = inf
        a          = -inf
        args       = (18, 22)
        b          = 50
        q          = 0.5
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
scipy/stats/_discrete_distns.py:1438: <span class="pl-k">in</span> _cdf
    1 - _boost._ncx2_cdf(2<span class="pl-k">*</span>mu1, 2<span class="pl-k">*</span>(x+1), 2<span class="pl-k">*</span>mu2))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        mu1        = 18
        mu2        = 22
        self       = <span class="pl-k">&lt;</span>scipy.stats._discrete_distns.skellam_gen object at 0x1269cde<span class="pl-k">70&gt;</span>
        x          = 50.0
__________________________ test_ncx2_tails_ticket_955 __________________________
scipy/stats/tests/test_distributions.py:6292: <span class="pl-k">in</span> test_ncx2_tails_ticket_955
    a = stats.ncx2.cdf(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
scipy/stats/_distn_infrastructure.py:2160: <span class="pl-k">in</span> cdf
    place(output, cond, self._cdf(<span class="pl-k">*</span>goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(2), array(107.458615))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False])
        dtyp       = dtype(<span class="pl-s"><span class="pl-pds">'</span>float64<span class="pl-pds">'</span></span>)
        goodargs   = [array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 2...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])]
        kwds       = {}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0.])
        scale      = array(1)
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2,
       24.4, 24.6, 24.8])
scipy/stats/_continuous_distns.py:6502: <span class="pl-k">in</span> _cdf
    <span class="pl-k">return</span> _lazywhere(cond, (x, df, nc), f=_boost._ncx2_cdf,
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        df         = array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2])
        nc         = array([107.458615, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.4586...15, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2,
       24.4, 24.6, 24.8])
scipy/_lib/_util.py:69: <span class="pl-k">in</span> _lazywhere
    np.place(out, cond, f(<span class="pl-k">*</span>temp))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        args       = [array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])]
        arrays     = [array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 2...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615])]
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True])
        f          = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_ncx2_cdf<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
        f2         = <span class="pl-k">&lt;</span>function ncx2_gen._cdf.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.<span class="pl-k">&lt;</span>lambda<span class="pl-k">&gt;</span> at 0x2852a6f<span class="pl-k">80&gt;</span>
        fillvalue  = nan
        out        = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
        tcode      = <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>
        temp       = (array([20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. ,
       22.2, 22.4, 22.6, 22.8, 23. , 23.2, 2...5, 107.458615, 107.458615, 107.458615, 107.458615,
       107.458615, 107.458615, 107.458615, 107.458615, 107.458615]))
______________________________ test_ncx2_gh12731 _______________________________
scipy/stats/tests/test_distributions.py:6345: <span class="pl-k">in</span> test_ncx2_gh12731
    assert_equal(stats.ncx2.cdf(1e4, df=1, nc=nc), 0)
        nc         = array([    100000,    1000000,   10000000,  100000000, 1000000000])
scipy/stats/_distn_infrastructure.py:2160: <span class="pl-k">in</span> cdf
    place(output, cond, self._cdf(<span class="pl-k">*</span>goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(1), array([    100000,    1000000,   10000000,  100000000, 1000000000]))
        cond       = array([ True,  True,  True,  True,  True])
        cond0      = array([ True,  True,  True,  True,  True])
        cond1      = True
        cond2      = array([False, False, False, False, False])
        dtyp       = dtype(<span class="pl-s"><span class="pl-pds">'</span>float64<span class="pl-pds">'</span></span>)
        goodargs   = [array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000])]
        kwds       = {<span class="pl-s"><span class="pl-pds">'</span>df<span class="pl-pds">'</span></span>: 1, <span class="pl-s"><span class="pl-pds">'</span>nc<span class="pl-pds">'</span></span>: array([    100000,    1000000,   10000000,  100000000, 1000000000])}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0.])
        scale      = array(1)
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array(10000.)
scipy/stats/_continuous_distns.py:6502: <span class="pl-k">in</span> _cdf
    <span class="pl-k">return</span> _lazywhere(cond, (x, df, nc), f=_boost._ncx2_cdf,
        cond       = array([ True,  True,  True,  True,  True])
        df         = array([1, 1, 1, 1, 1])
        nc         = array([    100000,    1000000,   10000000,  100000000, 1000000000])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([10000., 10000., 10000., 10000., 10000.])
scipy/_lib/_util.py:69: <span class="pl-k">in</span> _lazywhere
    np.place(out, cond, f(<span class="pl-k">*</span>temp))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_cdf
        args       = [array([ True,  True,  True,  True,  True]), array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000])]
        arrays     = [array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000])]
        cond       = array([ True,  True,  True,  True,  True])
        f          = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_ncx2_cdf<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
        f2         = <span class="pl-k">&lt;</span>function ncx2_gen._cdf.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.<span class="pl-k">&lt;</span>lambda<span class="pl-k">&gt;</span> at 0x2852a<span class="pl-k">6830&gt;</span>
        fillvalue  = nan
        out        = array([nan, nan, nan, nan, nan])
        tcode      = <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>
        temp       = (array([10000., 10000., 10000., 10000., 10000.]), array([1, 1, 1, 1, 1]), array([    100000,    1000000,   10000000,  100000000, 1000000000]))
_______________________________ test_ncx2_gh8665 _______________________________
scipy/stats/tests/test_distributions.py:6357: <span class="pl-k">in</span> test_ncx2_gh8665
    sf = stats.ncx2.sf(x, df=nu, nc=lam)
        lam        = 499.51538166556196
        nu         = 20
        x          = array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
       2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
       4.99515382e+04])
scipy/stats/_distn_infrastructure.py:2243: <span class="pl-k">in</span> sf
    place(output, cond, self._sf(<span class="pl-k">*</span>goodargs))
        _a         = 0.0
        _b         = inf
        args       = (array(20), array(499.51538167))
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        cond0      = True
        cond1      = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        cond2      = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False])
        dtyp       = dtype(<span class="pl-s"><span class="pl-pds">'</span>float64<span class="pl-pds">'</span></span>)
        goodargs   = [array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])]
        kwds       = {<span class="pl-s"><span class="pl-pds">'</span>df<span class="pl-pds">'</span></span>: 20, <span class="pl-s"><span class="pl-pds">'</span>nc<span class="pl-pds">'</span></span>: 499.51538166556196}
        loc        = array(0)
        output     = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
        scale      = array(1)
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
       2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
       4.99515382e+04])
scipy/stats/_continuous_distns.py:6515: <span class="pl-k">in</span> _sf
    <span class="pl-k">return</span> _lazywhere(cond, (x, df, nc), f=_boost._ncx2_sf,
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        df         = array([20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20])
        nc         = array([499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])
        self       = <span class="pl-k">&lt;</span>scipy.stats._continuous_distns.ncx2_gen object at 0x<span class="pl-k">123168490&gt;</span>
        x          = array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
       2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
       4.99515382e+04])
scipy/_lib/_util.py:69: <span class="pl-k">in</span> _lazywhere
    np.place(out, cond, f(<span class="pl-k">*</span>temp))
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _ncx2_sf
        args       = [array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True]), array([4.9...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])]
        arrays     = [array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167])]
        cond       = array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True])
        f          = <span class="pl-k">&lt;</span>ufunc <span class="pl-s"><span class="pl-pds">'</span>_ncx2_sf<span class="pl-pds">'</span></span><span class="pl-k">&gt;</span>
        f2         = <span class="pl-k">&lt;</span>function ncx2_gen._sf.<span class="pl-k">&lt;</span>locals<span class="pl-k">&gt;</span>.<span class="pl-k">&lt;</span>lambda<span class="pl-k">&gt;</span> at 0x2852a7be<span class="pl-k">0&gt;</span>
        fillvalue  = nan
        out        = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
        tcode      = <span class="pl-s"><span class="pl-pds">'</span>d<span class="pl-pds">'</span></span>
        temp       = (array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
       1.07617327e+02, 2.31854502e+02, 4.99515...8167, 499.51538167, 499.51538167,
       499.51538167, 499.51538167, 499.51538167, 499.51538167,
       499.51538167]))
=========================== short <span class="pl-c1">test</span> summary info ============================
FAILED scipy/stats/tests/test_boost_ufuncs.py::test_stats_boost_ufunc[_beta_ppf-args1-0.5]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-ncf-arg74]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-nct-arg75]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-ncx2-arg76]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[ncf-args75-ppf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[ncf-args75-isf]
FAILED scipy/stats/tests/test_discrete_basic.py::test_discrete_basic[skellam-arg17-True]
FAILED scipy/stats/tests/test_discrete_basic.py::test_moments[skellam-arg17]
FAILED scipy/stats/tests/test_discrete_distns.py::test_skellam_gh11474 - Runt...
FAILED scipy/stats/tests/test_distributions.py::TestSkellam::test_cdf - Runti...
FAILED scipy/stats/tests/test_distributions.py::TestExpect::test_skellam - Ru...
FAILED scipy/stats/tests/test_distributions.py::test_ncx2_tails_ticket_955 - ...
FAILED scipy/stats/tests/test_distributions.py::test_ncx2_gh12731 - RuntimeWa...
FAILED scipy/stats/tests/test_distributions.py::test_ncx2_gh8665 - RuntimeWar...</pre></div>
<h3 dir="auto">SciPy/NumPy/Python version information</h3>
<p dir="auto">cp310-macosx_arm64</p>
<h3 dir="auto">Describe your issue</h3>
<p dir="auto">Several statistical distribution methods emit warnings stemming from Boost. This issue tracks the status of all of them.</p>
<ul class="contains-task-list">
<li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> <code class="notranslate">beta.ppf</code> as reported here; should be fixed by <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1393910253" data-permission-text="Title is private" data-url="https://github.com/boostorg/math/issues/827" data-hovercard-type="pull_request" data-hovercard-url="/boostorg/math/pull/827/hovercard" href="https://github.com/boostorg/math/pull/827">boostorg/math#827</a></li>
<li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> <code class="notranslate">ncf</code> as reported in <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1388256302" data-permission-text="Title is private" data-url="https://github.com/scipy/scipy/issues/17101" data-hovercard-type="issue" data-hovercard-url="/scipy/scipy/issues/17101/hovercard" href="https://github.com/scipy/scipy/issues/17101">gh-17101</a>, should be fixed by <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1410540033" data-permission-text="Title is private" data-url="https://github.com/boostorg/math/issues/846" data-hovercard-type="pull_request" data-hovercard-url="/boostorg/math/pull/846/hovercard" href="https://github.com/boostorg/math/pull/846">boostorg/math#846</a></li>
<li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> <code class="notranslate">nct</code> - I'm not sure that this has been reported separately yet. Is it caused by the same sort of thing?</li>
<li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox"> <code class="notranslate">ncx2</code> - Ditto.</li>
</ul>
<p dir="auto"><a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1418900239" data-permission-text="Title is private" data-url="https://github.com/scipy/scipy/issues/17272" data-hovercard-type="pull_request" data-hovercard-url="/scipy/scipy/pull/17272/hovercard" href="https://github.com/scipy/scipy/pull/17272">gh-17272</a> will silence <a href="https://github.com/scipy/scipy/issues/14901#issuecomment-1272504168" data-hovercard-type="issue" data-hovercard-url="/scipy/scipy/issues/14901/hovercard">the failures in SciPy's tests</a> temporarily, but as many of these marks as possible should be removed when <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1405303062" data-permission-text="Title is private" data-url="https://github.com/scipy/scipy/issues/17207" data-hovercard-type="pull_request" data-hovercard-url="/scipy/scipy/pull/17207/hovercard" href="https://github.com/scipy/scipy/pull/17207">gh-17207</a> merges.</p>
<hr>
<p dir="auto"><em>Original Post</em></p>
<h3 dir="auto">Describe your issue.</h3>
<p dir="auto">macOS CI is failing due to an overflow. I am still trying to reproduce locally.</p>
<p dir="auto">NumPy was updated to 1.21.3, could be this or another dependency.</p>
<p dir="auto"><a href="https://github.com/scipy/scipy/runs/3960228039">https://github.com/scipy/scipy/runs/3960228039</a></p>
<h3 dir="auto">Reproducing Code Example</h3>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# from scipy/stats/tests/test_distributions.py:2878: in test_issue_12796
import numpy as np
from scipy import stats
q = 0.999995
a = np.array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
b = np.array([99999, 99998, 99997, 99996, 99995, 99994, 99993, 99992, 99991, 99990, 99989, 99988, 99987, 99986, 99985, 99984, 99983, 99982, 99981])
stats.beta.ppf(q, a, b)"><pre class="notranslate"><span class="pl-c"># from scipy/stats/tests/test_distributions.py:2878: in test_issue_12796</span>
<span class="pl-k">import</span> <span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">np</span>
<span class="pl-k">from</span> <span class="pl-s1">scipy</span> <span class="pl-k">import</span> <span class="pl-s1">stats</span>
<span class="pl-s1">q</span> <span class="pl-c1">=</span> <span class="pl-c1">0.999995</span>
<span class="pl-s1">a</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-en">array</span>([ <span class="pl-c1">2</span>,  <span class="pl-c1">3</span>,  <span class="pl-c1">4</span>,  <span class="pl-c1">5</span>,  <span class="pl-c1">6</span>,  <span class="pl-c1">7</span>,  <span class="pl-c1">8</span>,  <span class="pl-c1">9</span>, <span class="pl-c1">10</span>, <span class="pl-c1">11</span>, <span class="pl-c1">12</span>, <span class="pl-c1">13</span>, <span class="pl-c1">14</span>, <span class="pl-c1">15</span>, <span class="pl-c1">16</span>, <span class="pl-c1">17</span>, <span class="pl-c1">18</span>, <span class="pl-c1">19</span>, <span class="pl-c1">20</span>])
<span class="pl-s1">b</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-en">array</span>([<span class="pl-c1">99999</span>, <span class="pl-c1">99998</span>, <span class="pl-c1">99997</span>, <span class="pl-c1">99996</span>, <span class="pl-c1">99995</span>, <span class="pl-c1">99994</span>, <span class="pl-c1">99993</span>, <span class="pl-c1">99992</span>, <span class="pl-c1">99991</span>, <span class="pl-c1">99990</span>, <span class="pl-c1">99989</span>, <span class="pl-c1">99988</span>, <span class="pl-c1">99987</span>, <span class="pl-c1">99986</span>, <span class="pl-c1">99985</span>, <span class="pl-c1">99984</span>, <span class="pl-c1">99983</span>, <span class="pl-c1">99982</span>, <span class="pl-c1">99981</span>])
<span class="pl-s1">stats</span>.<span class="pl-s1">beta</span>.<span class="pl-en">ppf</span>(<span class="pl-s1">q</span>, <span class="pl-s1">a</span>, <span class="pl-s1">b</span>)</pre></div>
<h3 dir="auto">Error message</h3>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="FAILED scipy/stats/tests/test_distributions.py::TestBeta::test_issue_12635 - ...
927
FAILED scipy/stats/tests/test_distributions.py::TestBeta::test_issue_12794 - ...
928
FAILED scipy/stats/tests/test_distributions.py::TestBeta::test_issue_12796 - ...
...
scipy/stats/_continuous_distns.py:626: in _ppf
916
    return _boost._beta_ppf(q, a, b)
917
E   RuntimeWarning: overflow encountered in _beta_ppf"><pre class="notranslate">FAILED scipy/stats/tests/test_distributions.py::TestBeta::test_issue_12635 - ...
927
FAILED scipy/stats/tests/test_distributions.py::TestBeta::test_issue_12794 - ...
928
FAILED scipy/stats/tests/test_distributions.py::TestBeta::test_issue_12796 - ...
...
scipy/stats/_continuous_distns.py:626: <span class="pl-k">in</span> _ppf
916
    <span class="pl-k">return</span> _boost._beta_ppf(q, a, b)
917
E   RuntimeWarning: overflow encountered <span class="pl-k">in</span> _beta_ppf</pre></div>
<h3 dir="auto">SciPy/NumPy/Python version information</h3>
<p dir="auto">master on macOS/CI</p>