image-match/python/py/Lib/site-packages/scipy/stats/tests/test_mstats_extras.py

import numpy as np
import numpy.ma as ma
import scipy.stats.mstats as ms

from numpy.testing import (assert_equal, assert_almost_equal, assert_,
                           assert_allclose)


def test_compare_medians_ms():
    x = np.arange(7)
    y = x + 10
    assert_almost_equal(ms.compare_medians_ms(x, y), 0)

    y2 = np.linspace(0, 1, num=10)
    assert_almost_equal(ms.compare_medians_ms(x, y2), 0.017116406778)


def test_hdmedian():
    # 1-D array
    x = ma.arange(11)
    assert_allclose(ms.hdmedian(x), 5, rtol=1e-14)
    x.mask = ma.make_mask(x)
    x.mask[:7] = False
    assert_allclose(ms.hdmedian(x), 3, rtol=1e-14)

    # Check that `var` keyword returns a value.  TODO: check whether returned
    # value is actually correct.
    assert_(ms.hdmedian(x, var=True).size == 2)

    # 2-D array
    x2 = ma.arange(22).reshape((11, 2))
    assert_allclose(ms.hdmedian(x2, axis=0), [10, 11])
    x2.mask = ma.make_mask(x2)
    x2.mask[:7, :] = False
    assert_allclose(ms.hdmedian(x2, axis=0), [6, 7])


def test_rsh():
    rng = np.random.default_rng(806795795)
    x = rng.standard_normal(100)
    res = ms.rsh(x)
    # Just a sanity check that the code runs and output shape is correct.
    # TODO: check that implementation is correct.
    assert_(res.shape == x.shape)

    # Check points keyword
    res = ms.rsh(x, points=[0, 1.])
    assert_(res.size == 2)


def test_mjci():
    # Tests the Marits-Jarrett estimator
    data = ma.array([77, 87, 88,114,151,210,219,246,253,262,
                     296,299,306,376,428,515,666,1310,2611])
    assert_almost_equal(ms.mjci(data),[55.76819,45.84028,198.87875],5)


def test_trimmed_mean_ci():
    # Tests the confidence intervals of the trimmed mean.
    data = ma.array([545,555,558,572,575,576,578,580,
                     594,605,635,651,653,661,666])
    assert_almost_equal(ms.trimmed_mean(data,0.2), 596.2, 1)
    assert_equal(np.round(ms.trimmed_mean_ci(data,(0.2,0.2)),1),
                 [561.8, 630.6])


def test_idealfourths():
    # Tests ideal-fourths
    test = np.arange(100)
    assert_almost_equal(np.asarray(ms.idealfourths(test)),
                        [24.416667,74.583333],6)
    test_2D = test.repeat(3).reshape(-1,3)
    assert_almost_equal(ms.idealfourths(test_2D, axis=0),
                        [[24.416667,24.416667,24.416667],
                         [74.583333,74.583333,74.583333]],6)
    assert_almost_equal(ms.idealfourths(test_2D, axis=1),
                        test.repeat(2).reshape(-1,2))
    test = [0, 0]
    _result = ms.idealfourths(test)
    assert_(np.isnan(_result).all())


class TestQuantiles:
    data = [0.706560797,0.727229578,0.990399276,0.927065621,0.158953014,
            0.887764025,0.239407086,0.349638551,0.972791145,0.149789972,
            0.936947700,0.132359948,0.046041972,0.641675031,0.945530547,
            0.224218684,0.771450991,0.820257774,0.336458052,0.589113496,
            0.509736129,0.696838829,0.491323573,0.622767425,0.775189248,
            0.641461450,0.118455200,0.773029450,0.319280007,0.752229111,
            0.047841438,0.466295911,0.583850781,0.840581845,0.550086491,
            0.466470062,0.504765074,0.226855960,0.362641207,0.891620942,
            0.127898691,0.490094097,0.044882048,0.041441695,0.317976349,
            0.504135618,0.567353033,0.434617473,0.636243375,0.231803616,
            0.230154113,0.160011327,0.819464108,0.854706985,0.438809221,
            0.487427267,0.786907310,0.408367937,0.405534192,0.250444460,
            0.995309248,0.144389588,0.739947527,0.953543606,0.680051621,
            0.388382017,0.863530727,0.006514031,0.118007779,0.924024803,
            0.384236354,0.893687694,0.626534881,0.473051932,0.750134705,
            0.241843555,0.432947602,0.689538104,0.136934797,0.150206859,
            0.474335206,0.907775349,0.525869295,0.189184225,0.854284286,
            0.831089744,0.251637345,0.587038213,0.254475554,0.237781276,
            0.827928620,0.480283781,0.594514455,0.213641488,0.024194386,
            0.536668589,0.699497811,0.892804071,0.093835427,0.731107772]

    def test_hdquantiles(self):
        data = self.data
        assert_almost_equal(ms.hdquantiles(data,[0., 1.]),
                            [0.006514031, 0.995309248])
        hdq = ms.hdquantiles(data,[0.25, 0.5, 0.75])
        assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,])

        data = np.array(data).reshape(10,10)
        hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0)
        assert_almost_equal(hdq[:,0], ms.hdquantiles(data[:,0],[0.25,0.5,0.75]))
        assert_almost_equal(hdq[:,-1], ms.hdquantiles(data[:,-1],[0.25,0.5,0.75]))
        hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0,var=True)
        assert_almost_equal(hdq[...,0],
                            ms.hdquantiles(data[:,0],[0.25,0.5,0.75],var=True))
        assert_almost_equal(hdq[...,-1],
                            ms.hdquantiles(data[:,-1],[0.25,0.5,0.75], var=True))

    def test_hdquantiles_sd(self):
        # Standard deviation is a jackknife estimator, so we can check if
        # the efficient version (hdquantiles_sd) matches a rudimentary,
        # but clear version here.

        hd_std_errs = ms.hdquantiles_sd(self.data)

        # jacknnife standard error, Introduction to the Bootstrap Eq. 11.5
        n = len(self.data)
        jdata = np.broadcast_to(self.data, (n, n))
        jselector = np.logical_not(np.eye(n))  # leave out one sample each row
        jdata = jdata[jselector].reshape(n, n-1)
        jdist = ms.hdquantiles(jdata, axis=1)
        jdist_mean = np.mean(jdist, axis=0)
        jstd = ((n-1)/n * np.sum((jdist - jdist_mean)**2, axis=0))**.5

        assert_almost_equal(hd_std_errs, jstd)
        # Test actual values for good measure
        assert_almost_equal(hd_std_errs, [0.0379258, 0.0380656, 0.0380013])

        two_data_points = ms.hdquantiles_sd([1, 2])
        assert_almost_equal(two_data_points, [0.5, 0.5, 0.5])

    def test_mquantiles_cimj(self):
        # Only test that code runs, implementation not checked for correctness
        ci_lower, ci_upper = ms.mquantiles_cimj(self.data)
        assert_(ci_lower.size == ci_upper.size == 3)


def test_median_cihs():
    # Basic test against R library EnvStats function `eqnpar`, e.g.
    # library(EnvStats)
    # options(digits=8)
    # x = c(0.88612955, 0.35242375, 0.66240904, 0.94617974, 0.10929913,
    #       0.76699506, 0.88550655, 0.62763754, 0.76818588, 0.68506508,
    #       0.88043148, 0.03911248, 0.93805564, 0.95326961, 0.25291112,
    #       0.16128487, 0.49784577, 0.24588924, 0.6597, 0.92239679)
    # eqnpar(x, p=0.5,
    #        ci.method = "interpolate", approx.conf.level = 0.95, ci = TRUE)
    rng = np.random.default_rng(8824288259505800535)
    x = rng.random(size=20)
    assert_allclose(ms.median_cihs(x), (0.38663198, 0.88431272))

    # SciPy's 90% CI upper limit doesn't match that of EnvStats eqnpar. SciPy
    # doesn't look wrong, and it agrees with a different reference,
    # `median_confint_hs` from `hoehleatsu/quantileCI`.
    # In (e.g.) Colab with R runtime:
    # devtools::install_github("hoehleatsu/quantileCI")
    # library(quantileCI)
    # median_confint_hs(x=x, conf.level=0.90, interpolate=TRUE)
    assert_allclose(ms.median_cihs(x, 0.1), (0.48319773366, 0.88094268050))