""" Tests for the stats.mstats module (support for maskd arrays) """ from __future__ import division, print_function, absolute_import import warnings import numpy as np from numpy import nan import numpy.ma as ma from numpy.ma import masked, nomask import scipy.stats.mstats as mstats from scipy import stats from numpy.testing import TestCase, run_module_suite from numpy.testing.decorators import skipif from numpy.ma.testutils import (assert_equal, assert_almost_equal, assert_array_almost_equal, assert_array_almost_equal_nulp, assert_, assert_allclose, assert_raises) class TestMquantiles(TestCase): def test_mquantiles_limit_keyword(self): # Regression test for Trac ticket #867 data = np.array([[6., 7., 1.], [47., 15., 2.], [49., 36., 3.], [15., 39., 4.], [42., 40., -999.], [41., 41., -999.], [7., -999., -999.], [39., -999., -999.], [43., -999., -999.], [40., -999., -999.], [36., -999., -999.]]) desired = [[19.2, 14.6, 1.45], [40.0, 37.5, 2.5], [42.8, 40.05, 3.55]] quants = mstats.mquantiles(data, axis=0, limit=(0, 50)) assert_almost_equal(quants, desired) class TestGMean(TestCase): def test_1D(self): a = (1,2,3,4) actual = mstats.gmean(a) desired = np.power(1*2*3*4,1./4.) assert_almost_equal(actual, desired,decimal=14) desired1 = mstats.gmean(a,axis=-1) assert_almost_equal(actual, desired1, decimal=14) assert_(not isinstance(desired1, ma.MaskedArray)) a = ma.array((1,2,3,4),mask=(0,0,0,1)) actual = mstats.gmean(a) desired = np.power(1*2*3,1./3.) assert_almost_equal(actual, desired,decimal=14) desired1 = mstats.gmean(a,axis=-1) assert_almost_equal(actual, desired1, decimal=14) @skipif(not hasattr(np, 'float96'), 'cannot find float96 so skipping') def test_1D_float96(self): a = ma.array((1,2,3,4), mask=(0,0,0,1)) actual_dt = mstats.gmean(a, dtype=np.float96) desired_dt = np.power(1 * 2 * 3, 1. / 3.).astype(np.float96) assert_almost_equal(actual_dt, desired_dt, decimal=14) assert_(actual_dt.dtype == desired_dt.dtype) def test_2D(self): a = ma.array(((1, 2, 3, 4), (1, 2, 3, 4), (1, 2, 3, 4)), mask=((0, 0, 0, 0), (1, 0, 0, 1), (0, 1, 1, 0))) actual = mstats.gmean(a) desired = np.array((1,2,3,4)) assert_array_almost_equal(actual, desired, decimal=14) desired1 = mstats.gmean(a,axis=0) assert_array_almost_equal(actual, desired1, decimal=14) actual = mstats.gmean(a, -1) desired = ma.array((np.power(1*2*3*4,1./4.), np.power(2*3,1./2.), np.power(1*4,1./2.))) assert_array_almost_equal(actual, desired, decimal=14) class TestHMean(TestCase): def test_1D(self): a = (1,2,3,4) actual = mstats.hmean(a) desired = 4. / (1./1 + 1./2 + 1./3 + 1./4) assert_almost_equal(actual, desired, decimal=14) desired1 = mstats.hmean(ma.array(a),axis=-1) assert_almost_equal(actual, desired1, decimal=14) a = ma.array((1,2,3,4),mask=(0,0,0,1)) actual = mstats.hmean(a) desired = 3. / (1./1 + 1./2 + 1./3) assert_almost_equal(actual, desired,decimal=14) desired1 = mstats.hmean(a,axis=-1) assert_almost_equal(actual, desired1, decimal=14) @skipif(not hasattr(np, 'float96'), 'cannot find float96 so skipping') def test_1D_float96(self): a = ma.array((1,2,3,4), mask=(0,0,0,1)) actual_dt = mstats.hmean(a, dtype=np.float96) desired_dt = np.asarray(3. / (1./1 + 1./2 + 1./3), dtype=np.float96) assert_almost_equal(actual_dt, desired_dt, decimal=14) assert_(actual_dt.dtype == desired_dt.dtype) def test_2D(self): a = ma.array(((1,2,3,4),(1,2,3,4),(1,2,3,4)), mask=((0,0,0,0),(1,0,0,1),(0,1,1,0))) actual = mstats.hmean(a) desired = ma.array((1,2,3,4)) assert_array_almost_equal(actual, desired, decimal=14) actual1 = mstats.hmean(a,axis=-1) desired = (4./(1/1.+1/2.+1/3.+1/4.), 2./(1/2.+1/3.), 2./(1/1.+1/4.) ) assert_array_almost_equal(actual1, desired, decimal=14) class TestRanking(TestCase): def __init__(self, *args, **kwargs): TestCase.__init__(self, *args, **kwargs) def test_ranking(self): x = ma.array([0,1,1,1,2,3,4,5,5,6,]) assert_almost_equal(mstats.rankdata(x),[1,3,3,3,5,6,7,8.5,8.5,10]) x[[3,4]] = masked assert_almost_equal(mstats.rankdata(x),[1,2.5,2.5,0,0,4,5,6.5,6.5,8]) assert_almost_equal(mstats.rankdata(x,use_missing=True), [1,2.5,2.5,4.5,4.5,4,5,6.5,6.5,8]) x = ma.array([0,1,5,1,2,4,3,5,1,6,]) assert_almost_equal(mstats.rankdata(x),[1,3,8.5,3,5,7,6,8.5,3,10]) x = ma.array([[0,1,1,1,2], [3,4,5,5,6,]]) assert_almost_equal(mstats.rankdata(x),[[1,3,3,3,5],[6,7,8.5,8.5,10]]) assert_almost_equal(mstats.rankdata(x,axis=1),[[1,3,3,3,5],[1,2,3.5,3.5,5]]) assert_almost_equal(mstats.rankdata(x,axis=0),[[1,1,1,1,1],[2,2,2,2,2,]]) class TestCorr(TestCase): def test_pearsonr(self): # Tests some computations of Pearson's r x = ma.arange(10) with warnings.catch_warnings(): # The tests in this context are edge cases, with perfect # correlation or anticorrelation, or totally masked data. # None of these should trigger a RuntimeWarning. warnings.simplefilter("error", RuntimeWarning) assert_almost_equal(mstats.pearsonr(x, x)[0], 1.0) assert_almost_equal(mstats.pearsonr(x, x[::-1])[0], -1.0) x = ma.array(x, mask=True) pr = mstats.pearsonr(x, x) assert_(pr[0] is masked) assert_(pr[1] is masked) x1 = ma.array([-1.0, 0.0, 1.0]) y1 = ma.array([0, 0, 3]) r, p = mstats.pearsonr(x1, y1) assert_almost_equal(r, np.sqrt(3)/2) assert_almost_equal(p, 1.0/3) # (x2, y2) have the same unmasked data as (x1, y1). mask = [False, False, False, True] x2 = ma.array([-1.0, 0.0, 1.0, 99.0], mask=mask) y2 = ma.array([0, 0, 3, -1], mask=mask) r, p = mstats.pearsonr(x2, y2) assert_almost_equal(r, np.sqrt(3)/2) assert_almost_equal(p, 1.0/3) def test_spearmanr(self): # Tests some computations of Spearman's rho (x, y) = ([5.05,6.75,3.21,2.66],[1.65,2.64,2.64,6.95]) assert_almost_equal(mstats.spearmanr(x,y)[0], -0.6324555) (x, y) = ([5.05,6.75,3.21,2.66,np.nan],[1.65,2.64,2.64,6.95,np.nan]) (x, y) = (ma.fix_invalid(x), ma.fix_invalid(y)) assert_almost_equal(mstats.spearmanr(x,y)[0], -0.6324555) x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1, 1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7] y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6, 0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4] assert_almost_equal(mstats.spearmanr(x,y)[0], 0.6887299) x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1, 1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7, np.nan] y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6, 0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4, np.nan] (x, y) = (ma.fix_invalid(x), ma.fix_invalid(y)) assert_almost_equal(mstats.spearmanr(x,y)[0], 0.6887299) def test_kendalltau(self): # Tests some computations of Kendall's tau x = ma.fix_invalid([5.05, 6.75, 3.21, 2.66,np.nan]) y = ma.fix_invalid([1.65, 26.5, -5.93, 7.96, np.nan]) z = ma.fix_invalid([1.65, 2.64, 2.64, 6.95, np.nan]) assert_almost_equal(np.asarray(mstats.kendalltau(x,y)), [+0.3333333,0.4969059]) assert_almost_equal(np.asarray(mstats.kendalltau(x,z)), [-0.5477226,0.2785987]) # x = ma.fix_invalid([0, 0, 0, 0,20,20, 0,60, 0,20, 10,10, 0,40, 0,20, 0, 0, 0, 0, 0, np.nan]) y = ma.fix_invalid([0,80,80,80,10,33,60, 0,67,27, 25,80,80,80,80,80,80, 0,10,45, np.nan, 0]) result = mstats.kendalltau(x,y) assert_almost_equal(np.asarray(result), [-0.1585188, 0.4128009]) def test_kendalltau_seasonal(self): # Tests the seasonal Kendall tau. x = [[nan,nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1], [4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3], [3, 2, 5, 6, 18, 4, 9, 1, 1,nan, 1, 1,nan], [nan, 6, 11, 4, 17,nan, 6, 1, 1, 2, 5, 1, 1]] x = ma.fix_invalid(x).T output = mstats.kendalltau_seasonal(x) assert_almost_equal(output['global p-value (indep)'], 0.008, 3) assert_almost_equal(output['seasonal p-value'].round(2), [0.18,0.53,0.20,0.04]) def test_pointbiserial(self): x = [1,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0, 0,0,0,0,1,-1] y = [14.8,13.8,12.4,10.1,7.1,6.1,5.8,4.6,4.3,3.5,3.3,3.2,3.0, 2.8,2.8,2.5,2.4,2.3,2.1,1.7,1.7,1.5,1.3,1.3,1.2,1.2,1.1, 0.8,0.7,0.6,0.5,0.2,0.2,0.1,np.nan] assert_almost_equal(mstats.pointbiserialr(x, y)[0], 0.36149, 5) class TestTrimming(TestCase): def test_trim(self): a = ma.arange(10) assert_equal(mstats.trim(a), [0,1,2,3,4,5,6,7,8,9]) a = ma.arange(10) assert_equal(mstats.trim(a,(2,8)), [None,None,2,3,4,5,6,7,8,None]) a = ma.arange(10) assert_equal(mstats.trim(a,limits=(2,8),inclusive=(False,False)), [None,None,None,3,4,5,6,7,None,None]) a = ma.arange(10) assert_equal(mstats.trim(a,limits=(0.1,0.2),relative=True), [None,1,2,3,4,5,6,7,None,None]) a = ma.arange(12) a[[0,-1]] = a[5] = masked assert_equal(mstats.trim(a,(2,8)), [None,None,2,3,4,None,6,7,8,None,None,None]) x = ma.arange(100).reshape(10,10) trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=None) assert_equal(trimx._mask.ravel(),[1]*10+[0]*70+[1]*20) trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=0) assert_equal(trimx._mask.ravel(),[1]*10+[0]*70+[1]*20) trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=-1) assert_equal(trimx._mask.T.ravel(),[1]*10+[0]*70+[1]*20) x = ma.arange(110).reshape(11,10) x[1] = masked trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=None) assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20) trimx = mstats.trim(x,(0.1,0.2),relative=True,axis=0) assert_equal(trimx._mask.ravel(),[1]*20+[0]*70+[1]*20) trimx = mstats.trim(x.T,(0.1,0.2),relative=True,axis=-1) assert_equal(trimx.T._mask.ravel(),[1]*20+[0]*70+[1]*20) def test_trim_old(self): x = ma.arange(100) assert_equal(mstats.trimboth(x).count(), 60) assert_equal(mstats.trimtail(x,tail='r').count(), 80) x[50:70] = masked trimx = mstats.trimboth(x) assert_equal(trimx.count(), 48) assert_equal(trimx._mask, [1]*16 + [0]*34 + [1]*20 + [0]*14 + [1]*16) x._mask = nomask x.shape = (10,10) assert_equal(mstats.trimboth(x).count(), 60) assert_equal(mstats.trimtail(x).count(), 80) def test_trimmedmean(self): 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(mstats.trimmed_mean(data,0.1), 343, 0) assert_almost_equal(mstats.trimmed_mean(data,(0.1,0.1)), 343, 0) assert_almost_equal(mstats.trimmed_mean(data,(0.2,0.2)), 283, 0) def test_trimmed_stde(self): 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(mstats.trimmed_stde(data,(0.2,0.2)), 56.13193, 5) assert_almost_equal(mstats.trimmed_stde(data,0.2), 56.13193, 5) def test_winsorization(self): 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(mstats.winsorize(data,(0.2,0.2)).var(ddof=1), 21551.4, 1) data[5] = masked winsorized = mstats.winsorize(data) assert_equal(winsorized.mask, data.mask) class TestMoments(TestCase): # Comparison numbers are found using R v.1.5.1 # note that length(testcase) = 4 # testmathworks comes from documentation for the # Statistics Toolbox for Matlab and can be found at both # http://www.mathworks.com/access/helpdesk/help/toolbox/stats/kurtosis.shtml # http://www.mathworks.com/access/helpdesk/help/toolbox/stats/skewness.shtml # Note that both test cases came from here. testcase = [1,2,3,4] testmathworks = ma.fix_invalid([1.165, 0.6268, 0.0751, 0.3516, -0.6965, np.nan]) testcase_2d = ma.array( np.array([[0.05245846, 0.50344235, 0.86589117, 0.36936353, 0.46961149], [0.11574073, 0.31299969, 0.45925772, 0.72618805, 0.75194407], [0.67696689, 0.91878127, 0.09769044, 0.04645137, 0.37615733], [0.05903624, 0.29908861, 0.34088298, 0.66216337, 0.83160998], [0.64619526, 0.94894632, 0.27855892, 0.0706151, 0.39962917]]), mask=np.array([[True, False, False, True, False], [True, True, True, False, True], [False, False, False, False, False], [True, True, True, True, True], [False, False, True, False, False]], dtype=np.bool)) def test_moment(self): y = mstats.moment(self.testcase,1) assert_almost_equal(y,0.0,10) y = mstats.moment(self.testcase,2) assert_almost_equal(y,1.25) y = mstats.moment(self.testcase,3) assert_almost_equal(y,0.0) y = mstats.moment(self.testcase,4) assert_almost_equal(y,2.5625) def test_variation(self): y = mstats.variation(self.testcase) assert_almost_equal(y,0.44721359549996, 10) def test_skewness(self): y = mstats.skew(self.testmathworks) assert_almost_equal(y,-0.29322304336607,10) y = mstats.skew(self.testmathworks,bias=0) assert_almost_equal(y,-0.437111105023940,10) y = mstats.skew(self.testcase) assert_almost_equal(y,0.0,10) def test_kurtosis(self): # Set flags for axis = 0 and fisher=0 (Pearson's definition of kurtosis # for compatibility with Matlab) y = mstats.kurtosis(self.testmathworks,0,fisher=0,bias=1) assert_almost_equal(y, 2.1658856802973,10) # Note that MATLAB has confusing docs for the following case # kurtosis(x,0) gives an unbiased estimate of Pearson's skewness # kurtosis(x) gives a biased estimate of Fisher's skewness (Pearson-3) # The MATLAB docs imply that both should give Fisher's y = mstats.kurtosis(self.testmathworks,fisher=0, bias=0) assert_almost_equal(y, 3.663542721189047,10) y = mstats.kurtosis(self.testcase,0,0) assert_almost_equal(y,1.64) # test that kurtosis works on multidimensional masked arrays correct_2d = ma.array(np.array([-1.5, -3., -1.47247052385, 0., -1.26979517952]), mask=np.array([False, False, False, True, False], dtype=np.bool)) assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1), correct_2d) for i, row in enumerate(self.testcase_2d): assert_almost_equal(mstats.kurtosis(row), correct_2d[i]) correct_2d_bias_corrected = ma.array( np.array([-1.5, -3., -1.88988209538, 0., -0.5234638463918877]), mask=np.array([False, False, False, True, False], dtype=np.bool)) assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1, bias=False), correct_2d_bias_corrected) for i, row in enumerate(self.testcase_2d): assert_almost_equal(mstats.kurtosis(row, bias=False), correct_2d_bias_corrected[i]) # Check consistency between stats and mstats implementations assert_array_almost_equal_nulp(mstats.kurtosis(self.testcase_2d[2, :]), stats.kurtosis(self.testcase_2d[2, :])) def test_mode(self): a1 = [0,0,0,1,1,1,2,3,3,3,3,4,5,6,7] a2 = np.reshape(a1, (3,5)) a3 = np.array([1,2,3,4,5,6]) a4 = np.reshape(a3, (3,2)) ma1 = ma.masked_where(ma.array(a1) > 2, a1) ma2 = ma.masked_where(a2 > 2, a2) ma3 = ma.masked_where(a3 < 2, a3) ma4 = ma.masked_where(ma.array(a4) < 2, a4) assert_equal(mstats.mode(a1, axis=None), (3,4)) assert_equal(mstats.mode(a1, axis=0), (3,4)) assert_equal(mstats.mode(ma1, axis=None), (0,3)) assert_equal(mstats.mode(a2, axis=None), (3,4)) assert_equal(mstats.mode(ma2, axis=None), (0,3)) assert_equal(mstats.mode(a3, axis=None), (1,1)) assert_equal(mstats.mode(ma3, axis=None), (2,1)) assert_equal(mstats.mode(a2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]])) assert_equal(mstats.mode(ma2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]])) assert_equal(mstats.mode(a2, axis=-1), ([[0],[3],[3]], [[3],[3],[1]])) assert_equal(mstats.mode(ma2, axis=-1), ([[0],[1],[0]], [[3],[1],[0]])) assert_equal(mstats.mode(ma4, axis=0), ([[3,2]], [[1,1]])) assert_equal(mstats.mode(ma4, axis=-1), ([[2],[3],[5]], [[1],[1],[1]])) class TestPercentile(TestCase): def setUp(self): self.a1 = [3,4,5,10,-3,-5,6] self.a2 = [3,-6,-2,8,7,4,2,1] self.a3 = [3.,4,5,10,-3,-5,-6,7.0] def test_percentile(self): x = np.arange(8) * 0.5 assert_equal(mstats.scoreatpercentile(x, 0), 0.) assert_equal(mstats.scoreatpercentile(x, 100), 3.5) assert_equal(mstats.scoreatpercentile(x, 50), 1.75) def test_2D(self): x = ma.array([[1, 1, 1], [1, 1, 1], [4, 4, 3], [1, 1, 1], [1, 1, 1]]) assert_equal(mstats.scoreatpercentile(x,50), [1,1,1]) class TestVariability(TestCase): """ Comparison numbers are found using R v.1.5.1 note that length(testcase) = 4 """ testcase = ma.fix_invalid([1,2,3,4,np.nan]) def test_signaltonoise(self): # This is not in R, so used: # mean(testcase, axis=0) / (sqrt(var(testcase)*3/4)) y = mstats.signaltonoise(self.testcase) assert_almost_equal(y,2.236067977) def test_sem(self): # This is not in R, so used: sqrt(var(testcase)*3/4) / sqrt(3) y = mstats.sem(self.testcase) assert_almost_equal(y, 0.6454972244) n = self.testcase.count() assert_allclose(mstats.sem(self.testcase, ddof=0) * np.sqrt(n/(n-2)), mstats.sem(self.testcase, ddof=2)) def test_zmap(self): # This is not in R, so tested by using: # (testcase[i]-mean(testcase,axis=0)) / sqrt(var(testcase)*3/4) y = mstats.zmap(self.testcase, self.testcase) desired_unmaskedvals = ([-1.3416407864999, -0.44721359549996, 0.44721359549996, 1.3416407864999]) assert_array_almost_equal(desired_unmaskedvals, y.data[y.mask == False], decimal=12) def test_zscore(self): # This is not in R, so tested by using: # (testcase[i]-mean(testcase,axis=0)) / sqrt(var(testcase)*3/4) y = mstats.zscore(self.testcase) desired = ma.fix_invalid([-1.3416407864999, -0.44721359549996, 0.44721359549996, 1.3416407864999, np.nan]) assert_almost_equal(desired, y, decimal=12) class TestMisc(TestCase): def test_obrientransform(self): args = [[5]*5+[6]*11+[7]*9+[8]*3+[9]*2+[10]*2, [6]+[7]*2+[8]*4+[9]*9+[10]*16] result = [5*[3.1828]+11*[0.5591]+9*[0.0344]+3*[1.6086]+2*[5.2817]+2*[11.0538], [10.4352]+2*[4.8599]+4*[1.3836]+9*[0.0061]+16*[0.7277]] assert_almost_equal(np.round(mstats.obrientransform(*args).T,4), result,4) def test_kstwosamp(self): x = [[nan,nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1], [4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3], [3, 2, 5, 6, 18, 4, 9, 1, 1,nan, 1, 1,nan], [nan, 6, 11, 4, 17,nan, 6, 1, 1, 2, 5, 1, 1]] x = ma.fix_invalid(x).T (winter,spring,summer,fall) = x.T assert_almost_equal(np.round(mstats.ks_twosamp(winter,spring),4), (0.1818,0.9892)) assert_almost_equal(np.round(mstats.ks_twosamp(winter,spring,'g'),4), (0.1469,0.7734)) assert_almost_equal(np.round(mstats.ks_twosamp(winter,spring,'l'),4), (0.1818,0.6744)) def test_friedmanchisq(self): # No missing values args = ([9.0,9.5,5.0,7.5,9.5,7.5,8.0,7.0,8.5,6.0], [7.0,6.5,7.0,7.5,5.0,8.0,6.0,6.5,7.0,7.0], [6.0,8.0,4.0,6.0,7.0,6.5,6.0,4.0,6.5,3.0]) result = mstats.friedmanchisquare(*args) assert_almost_equal(result[0], 10.4737, 4) assert_almost_equal(result[1], 0.005317, 6) # Missing values x = [[nan,nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1], [4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3], [3, 2, 5, 6, 18, 4, 9, 1, 1,nan, 1, 1,nan], [nan, 6, 11, 4, 17,nan, 6, 1, 1, 2, 5, 1, 1]] x = ma.fix_invalid(x) result = mstats.friedmanchisquare(*x) assert_almost_equal(result[0], 2.0156, 4) assert_almost_equal(result[1], 0.5692, 4) def test_regress_simple(): # Regress a line with sinusoidal noise. Test for #1273. x = np.linspace(0, 100, 100) y = 0.2 * np.linspace(0, 100, 100) + 10 y += np.sin(np.linspace(0, 20, 100)) slope, intercept, r_value, p_value, sterr = mstats.linregress(x, y) assert_almost_equal(slope, 0.19644990055858422) assert_almost_equal(intercept, 10.211269918932341) def test_plotting_positions(): # Regression test for #1256 pos = mstats.plotting_positions(np.arange(3), 0, 0) assert_array_almost_equal(pos.data, np.array([0.25, 0.5, 0.75])) class TestNormalitytests(): def test_vs_nonmasked(self): x = np.array((-2,-1,0,1,2,3)*4)**2 assert_array_almost_equal(mstats.normaltest(x), stats.normaltest(x)) assert_array_almost_equal(mstats.skewtest(x), stats.skewtest(x)) assert_array_almost_equal(mstats.kurtosistest(x), stats.kurtosistest(x)) funcs = [stats.normaltest, stats.skewtest, stats.kurtosistest] mfuncs = [mstats.normaltest, mstats.skewtest, mstats.kurtosistest] x = [1, 2, 3, 4] for func, mfunc in zip(funcs, mfuncs): assert_raises(ValueError, func, x) assert_raises(ValueError, mfunc, x) def test_axis_None(self): # Test axis=None (equal to axis=0 for 1-D input) x = np.array((-2,-1,0,1,2,3)*4)**2 assert_allclose(mstats.normaltest(x, axis=None), mstats.normaltest(x)) assert_allclose(mstats.skewtest(x, axis=None), mstats.skewtest(x)) assert_allclose(mstats.kurtosistest(x, axis=None), mstats.kurtosistest(x)) def test_maskedarray_input(self): # Add some masked values, test result doesn't change x = np.array((-2,-1,0,1,2,3)*4)**2 xm = np.ma.array(np.r_[np.inf, x, 10], mask=np.r_[True, [False] * x.size, True]) assert_allclose(mstats.normaltest(xm), stats.normaltest(x)) assert_allclose(mstats.skewtest(xm), stats.skewtest(x)) assert_allclose(mstats.kurtosistest(xm), stats.kurtosistest(x)) def test_nd_input(self): x = np.array((-2,-1,0,1,2,3)*4)**2 x_2d = np.vstack([x] * 2).T for func in [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]: res_1d = func(x) res_2d = func(x_2d) assert_allclose(res_2d[0], [res_1d[0]] * 2) assert_allclose(res_2d[1], [res_1d[1]] * 2) #TODO: for all ttest functions, add tests with masked array inputs class TestTtest_rel(): def test_vs_nonmasked(self): np.random.seed(1234567) outcome = np.random.randn(20, 4) + [0, 0, 1, 2] # 1-D inputs res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1]) res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1]) assert_allclose(res1, res2) # 2-D inputs res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None) res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None) assert_allclose(res1, res2) res1 = stats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0) res2 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0) assert_allclose(res1, res2) # Check default is axis=0 res3 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:]) assert_allclose(res2, res3) def test_invalid_input_size(self): assert_raises(ValueError, mstats.ttest_rel, np.arange(10), np.arange(11)) x = np.arange(24) assert_raises(ValueError, mstats.ttest_rel, x.reshape(2, 3, 4), x.reshape(2, 4, 3), axis=1) assert_raises(ValueError, mstats.ttest_rel, x.reshape(2, 3, 4), x.reshape(2, 4, 3), axis=2) def test_empty(self): res1 = mstats.ttest_rel([], []) assert_(np.all(np.isnan(res1))) class TestTtest_ind(): def test_vs_nonmasked(self): np.random.seed(1234567) outcome = np.random.randn(20, 4) + [0, 0, 1, 2] # 1-D inputs res1 = stats.ttest_ind(outcome[:, 0], outcome[:, 1]) res2 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1]) assert_allclose(res1, res2) # 2-D inputs res1 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], axis=None) res2 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], axis=None) assert_allclose(res1, res2) res1 = stats.ttest_ind(outcome[:, :2], outcome[:, 2:], axis=0) res2 = mstats.ttest_ind(outcome[:, :2], outcome[:, 2:], axis=0) assert_allclose(res1, res2) # Check default is axis=0 res3 = mstats.ttest_ind(outcome[:, :2], outcome[:, 2:]) assert_allclose(res2, res3) def test_empty(self): res1 = mstats.ttest_ind([], []) assert_(np.all(np.isnan(res1))) class TestTtest_1samp(): def test_vs_nonmasked(self): np.random.seed(1234567) outcome = np.random.randn(20, 4) + [0, 0, 1, 2] # 1-D inputs res1 = stats.ttest_1samp(outcome[:, 0], 1) res2 = mstats.ttest_1samp(outcome[:, 0], 1) assert_allclose(res1, res2) # 2-D inputs res1 = stats.ttest_1samp(outcome[:, 0], outcome[:, 1], axis=None) res2 = mstats.ttest_1samp(outcome[:, 0], outcome[:, 1], axis=None) assert_allclose(res1, res2) res1 = stats.ttest_1samp(outcome[:, :2], outcome[:, 2:], axis=0) res2 = mstats.ttest_1samp(outcome[:, :2], outcome[:, 2:], axis=0) assert_allclose(res1, res2) # Check default is axis=0 res3 = mstats.ttest_1samp(outcome[:, :2], outcome[:, 2:]) assert_allclose(res2, res3) def test_empty(self): res1 = mstats.ttest_1samp([], 1) assert_(np.all(np.isnan(res1))) if __name__ == "__main__": run_module_suite()