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multinomialrvsrwreshaper6insertrrrIrK) r"NrrrrMs2rs3r s4res4r'r'r(*test_contingency_table_with_zero_rows_colsXs(        z6TestSomersD.test_contingency_table_with_zero_rows_colsc Csd}d}t|}tjdtjj|t||d|}|d}d}t t |d t |Wdn1s;wY|d}d }t t |d t |Wdn1s\wYd }t t |dt ggWdn1szwYt t |dt d ggWdn1swYt d }t t |d t |Wdn1swYd |d <t t |dt |WddS1swYdS)NrorSrrTr*z:All elements of the contingency table must be non-negativerEr]z5All elements of the contingency table must be integerz?At least two elements of the contingency table must be nonzero.r)rrr) rrUr8r9r rVrWrwrXrPrQr6r) r"rZrrrs5messages6s7r'r'r(test_invalid_contingency_tablesws<        "z+TestSomersD.test_invalid_contingency_tablescCsbgd}ddtjg}gd}ddtj g}t||}t||}t|j|jt|j|jdS)Nrrg@)rr*rrrF)rrOr r6rrIrK)r"r#rHr$y2rMrr'r'r(test_only_ranks_matters   z"TestSomersD.test_only_ranks_mattercCs6td}td}t||}t|jtddS)Nr.)rr:r r6rreyerLr'r'r(test_contingency_table_returns   z)TestSomersD.test_contingency_table_returncCs`gd}gd}tj||dd}|jdksJtj||dd}t|j|jt|jd|jdtj||d d}t|j|jt|j|jd|tj||dd}|jdks\Jtj||d d}t|j|jt|jd|jdtj||dd}t|j|jt|j|jdtjt d d tj||d dWddS1swYdS) Nr)r,r-r/r0r/rrrrrr*rz`alternative` must be...rE ekki-ekki) r r6rIrrrKreverserGrrQ)r"rGrHrrMr'r'r(test_somersd_alternatives,"z$TestSomersD.test_somersd_alternativepositive_correlation)FTcCstd}|r |nt|}|rdnd}tj||dd}|j|ks#J|jdks*Jtj||dd}|j|ks9J|j|r?dndksDJtj||dd}|j|ksSJ|j|rYdndks^JdS) Nr.rrrrrrr)rr:flipr r6rIrK)r"rlrGrHexpected_statisticrMr'r'r( test_somersd_perfect_correlations  z,TestSomersD.test_somersd_perfect_correlationcCsVddg}d}tdtj||d}tj||d}d}t||j}t||dddS) Nrr*@Bi_rgHz Yr%r2)r8r9choicesr r6rIr)r"classes n_samplesr#r$ val_sklearn val_scipyr'r'r(!test_somersd_large_inputs_gh18132s z-TestSomersD.test_somersd_large_inputs_gh18132N)rWrXrYr9r;rBrIrMrRr_rdrfrhrkrGrrrorvr'r'r'r(r.s o#  )  r.c@seZdZdZejdddgddggdfdd gd d ggd fd d gdd ggdfd dgddggdfd dgddggdfd dgddggdfdd gddggdfd dgddggdfddgdd ggdfdd gddggdfd d gdd ggdfg d d!Zejdddgddggd"fdd gd d ggd#fd d gdd ggd$fd dgddggd%fd dgddggd&fd dgddggd'fdd gddggd(fd dgddggd)fddgdd ggd*fdd gddggd+fd d gdd ggd$fg d,d-Zd.d/Z ejdddgddggd0fgd1d2Z ejddd gddggd3e j ffd dgddggd3e j ffgd4d5Z ejdd d gdd ggd6fd d7gd8dggd9fd:d;gdd?d@gdAdBZdCS)DTestBarnardExactz8Some tests to show that barnard_exact() works correctly.input_sample,expected+rr.')gXyq @g{2s&Q7?ror*rfr,)gllgEA]0K?r/r0)*)1%g_?r)g_c1?g=?rT)g5PyQgQ@2?rr<)ggJ"?)g_c1gwݝل?rr+)g7@g?r)g~t,?3O?r-)gr?~CY7?cCs(t|}|j|j}}t||g|dS)zThe expected values have been generated by R, using a resolution for the nuisance parameter of 1e-6 : ```R library(Barnard) options(digits=10) barnard.test(43, 40, 10, 39, dp=1e-6, pooled=TRUE) ``` NrrIrKrr" input_samplerrMrIrKr'r'r( test_precise szTestBarnardExact.test_precise)g7\@gA2?)gXS;gh?)g>!Ɏg6?)gSy@?g^F?)g-gXI#?)gaЍgo?)gb]?gFugH ?)g 6ҭ@g?)gi( r})gNXzr~cCs,t|dd}|j|j}}t||g|dS)zThe expected values have been generated by R, using a resolution for the nuisance parameter of 1e-6 : ```R library(Barnard) options(digits=10) barnard.test(43, 40, 10, 39, dp=1e-6, pooled=FALSE) ``` F)pooledNrrr'r'r(test_pooled_param's z"TestBarnardExact.test_pooled_paramcCd}tt|dtddgddggddWdn1swYd }tt|dttd ddWdn1sBwYd }tt|dtd dgddggWdn1sdwYd }tt|dtddgddggdWddS1swYdS)N7Number of points `n` must be strictly positive, found 0rErr*rr+rrx,The input `table` must be of shape \(2, 2\).r-*All values in `table` must be nonnegative.rzI`alternative` should be one of {'two-sided', 'less', 'greater'}, found .* not-correct)rPrQrrr:rXr" error_msgr'r'r( test_raisesD$"zTestBarnardExact.test_raisesrEcC6t|}|j|j}}t||dt||ddSNrrrrIrKrrr'r'r(test_edge_cases^sz TestBarnardExact.test_edge_casesrrcCrrrrr'r'r(test_row_or_col_zerojz%TestBarnardExact.test_row_or_col_zero)r{gE\/??,)ggQ5rDr7i)g&X}>rDrrrc Csf|\}}|dkrt|dddddf}| }t||d}|j|j}}t||g||gdddS)a "The expected values have been generated by R, using a resolution for the nuisance parameter of 1e-6 : ```R library(Barnard) options(digits=10) a = barnard.test(2, 7, 8, 2, dp=1e-6, pooled=TRUE) a$p.value[1] ``` In this test, we are using the "one-sided" return value `a$p.value[1]` to test our pvalue. rNrrHz>r2)rr rrIrKr) r"rrr expected_statless_pvalue_expectrMrIrKr'r'r(test_less_greaterws  z"TestBarnardExact.test_less_greaterN)rWrXrY__doc__rGrrrrrrrrJrrr'r'r'r(rwsr      rwc@seZdZdZdZejdddgddggdfdd gd d ggd fdd gd dggdfd dgd d ggdfddgd dggdfdd gddggdfddgddggdfddgddggdfd dgddggdfg ddZejdddgd dggd fddgddggd!fdd gd d ggd"fdd#gd d ggd$fdd gd dggd%fddgd dggd&fdd gddggdfddgd'dggdfddgddggd!fddgddggd(fd dgddggd)fg d*d+Z ejdddgd dggd,fddgddggd-fdd gd d ggd.fdd gd dggd/fddgd dggd0fdd gddggd1fddgddggd-fddgddggd2fgd3d4Z d5d6Z ejdddgdd gge j e j ffddgd dgge j e j ffgd7d8Zd9d:Zejd;d<d=d>Zd?S)@TestBoschlooExactz9Some tests to show that boschloo_exact() works correctly.rrxr*r/r0)<vB\?g/??r,rr.)gM?gA>?rrTr<)_VѶ?g֭?)u%?gc'?rr+rrr)r_g?rt)+f?gXc}v?%)gZыD?ggi]?cC2t|dd}|j|j}}t||g||jddS)aThe expected values have been generated by R, using a resolution for the nuisance parameter of 1e-8 : ```R library(Exact) options(digits=10) data <- matrix(c(43, 10, 40, 39), 2, 2, byrow=TRUE) a = exact.test(data, method="Boschloo", alternative="less", tsmethod="central", np.interval=TRUE, beta=1e-8) ``` rrr2NrrIrKrATOLrr'r'r( test_lesss zTestBoschlooExact.test_lessryrrz) k\2?g0,%?)gKv?gN3?)rg'&5?r|)gw@_?g7?)gi{?gɑ)z?)օa?g1|?r-)gY<;?gND?)ge?gG`?cCr)aThe expected values have been generated by R, using a resolution for the nuisance parameter of 1e-8 : ```R library(Exact) options(digits=10) data <- matrix(c(43, 10, 40, 39), 2, 2, byrow=TRUE) a = exact.test(data, method="Boschloo", alternative="greater", tsmethod="central", np.interval=TRUE, beta=1e-8) ``` rrr2Nrrr'r'r( test_greaters zTestBoschlooExact.test_greater)rgqQS,5?)rgG?/??)rgKE`?)rghr1ֽ?)rgrfb?)r_g?)rgP:pRv?cCs4t|ddd}|j|j}}t||g||jddS)aThe expected values have been generated by R, using a resolution for the nuisance parameter of 1e-8 : ```R library(Exact) options(digits=10) data <- matrix(c(43, 10, 40, 39), 2, 2, byrow=TRUE) a = exact.test(data, method="Boschloo", alternative="two.sided", tsmethod="central", np.interval=TRUE, beta=1e-8) ``` r@)rrxr2Nrrr'r'r(test_two_sidedsz TestBoschlooExact.test_two_sidedcCr)NrrErr*rr+rrrr-rrzK`alternative` should be one of \('two-sided', 'less', 'greater'\), found .*r)rPrQrrr:rXrr'r'r(rrzTestBoschlooExact.test_raisescCrr)rrIrKrrr'r'r(rrz&TestBoschlooExact.test_row_or_col_zerocCs`ddgddgg}t|ddj}t|ddj}dt||dks!Jt|ddj}|d ks.JdS) Nrrrtrrrr*rrr)rrKr")r"tblplpgptr'r'r(test_two_sided_gt_1s z%TestBoschlooExact.test_two_sided_gt_1r)rrcCs>ddgddgg}t||dj}tj||dd}t||dS)Nr*r/r0rr)rrIr  fisher_exactr)r"rr boschloo_statfisher_pr'r'r(test_against_fisher_exact)sz+TestBoschlooExact.test_against_fisher_exactN)rWrXrYrrrGrrrrrrrrJrrrr'r'r'r(rsr     rc@seZdZejdgedfeddgfgddZddZ dd Z d d Z ejd gd ddZ ejj ddZddZddZddZdS) TestCvm_2samprr,rcCsXtjttdt|}t|jtjt|j tjWddS1s%wYdS)NrE) rGrHrrr rrIrrJrKr:r'r'r(test_too_small_input4s "z"TestCvm_2samp.test_too_small_inputcCsNtd}d}tjt|dt||dWddS1s wYdS)Nr,z/method must be either auto, exact or asymptoticrExyz)rr:rGrrQr )r"r$msgr'r'r(r|<s "z TestCvm_2samp.test_invalid_inputcCsNgd}gd}t||}tt|t|}t|j|jf|j|jfdS)N)r*rr+r/r-)rrrtr>)r rr rrIrKr"r#r$rrr'r'r(test_list_inputBs  zTestCvm_2samp.test_list_inputcCs>gd}gd}t||}t|jdddt|jddddS)N) gffffff@g @rgffffff!@皙"@g#@g333333$@g333333%@gffffff&@)g@g@g@@333333@gffffff @g333333"@g#@g%@g&@g'@g(@g)@g*@g333333-@gS㥛?r1r2g ףp= ?r])r rrIrKr"r#r$rr'r'r(test_example_conoverIs  z"TestCvm_2samp.test_example_conoverzstatistic, m, n, pval))ir,r-gcj`?)iir/r/gtE]t?)i@r+r-g88?)ir-r/gXwS?cCstt||||dSr)rr )r"rIrrxpvalr'r'r(test_exact_pvalueSs zTestCvm_2samp.test_exact_pvaluecCstjdtjjdd}tjjdd}t||}td|jko$dknt||d}td|jko=dkdSdS)Nirpri rrr}) rr8r9rrvrWr rrKrr'r'r(test_large_sample^s  (zTestCvm_2samp.test_large_samplecCsdtjdtjd}tjd}t||dd}t||dd}t|j|jt|j|jdddS) Nrr/r0rrrr]r2) rr8r9rr rrIrrKrr'r'r(test_exact_vs_asymptoticjs   z&TestCvm_2samp.test_exact_vs_asymptoticcCsttd}gd}t||dd}t||dd}t|j|jtd}t||dd}t||dd}t|j|jdS)NrT)r_g@g333333*@rrrrr)rr:r rrKrr'r'r(test_method_autoss  zTestCvm_2samp.test_method_autocCsVtd}t||}t|j|jfdt|dd|dd}t|j|jfddS)Nr|rCr+)rr:r rrIrKr{r'r'r(test_same_inputs  zTestCvm_2samp.test_same_inputN)rWrXrYrGrrrr:rr|rrrxslowrrrrr'r'r'r(r3s"      rc@s.eZdZgdgdgdfZgdgdgdfZgdgdgdfZdZdZd Ze j j d eed feed feed ffgd dddZ dZ dZe j j d ee dfeedffddgdddZddZddZddZdd Zd!d"Zd#d$Ze j d%d&d'd(Ze j d)gd*d+d,Zd-d.Zd/S)0 TestTukeyHSD)r 7@ffffff:@皙;@fffff=@)ffffff<@皙A@=@皙@@皙>@)g:@gL<@gL8@g333333:@g;@)r rgHzG:@rrrrr)r rr) rrrrrrrrrraK Comparison LowerCL Difference UpperCL Significance 2 - 3 0.6908830568 4.34 7.989116943 1 2 - 1 0.9508830568 4.6 8.249116943 1 3 - 2 -7.989116943 -4.34 -0.6908830568 1 3 - 1 -3.389116943 0.26 3.909116943 0 1 - 2 -8.249116943 -4.6 -0.9508830568 1 1 - 3 -3.909116943 -0.26 3.389116943 0 aS Comparison LowerCL Difference UpperCL Significance 2 - 1 0.2679292645 3.645 7.022070736 1 2 - 3 0.5934764007 4.34 8.086523599 1 1 - 2 -7.022070736 -3.645 -0.2679292645 1 1 - 3 -2.682070736 0.695 4.072070736 0 3 - 2 -8.086523599 -4.34 -0.5934764007 1 3 - 1 -4.072070736 -0.695 2.682070736 0 aS Comparison LowerCL Difference UpperCL Significance 2 - 3 1.561605075 4.34 7.118394925 1 2 - 1 2.740784879 6.08 9.419215121 1 3 - 2 -7.118394925 -4.34 -1.561605075 1 3 - 1 -1.964526566 1.74 5.444526566 0 1 - 2 -9.419215121 -6.08 -2.740784879 1 1 - 3 -5.444526566 -1.74 1.964526566 0 zdata,res_expect_str,atolr`g|=)equal size samplezunequal sample sizezextreme sample size differences)idsc Cstj|ddddtdd}tj|}|}|D]G\}}} } } } t |dt |d}}t |j ||f| |dt |j ||f| |dt |j ||f| |dt |j||fd k| dkqdS) a SAS code used to generate results for each sample: DATA ACHE; INPUT BRAND RELIEF; CARDS; 1 24.5 ... 3 27.8 ; ods graphics on; ODS RTF;ODS LISTING CLOSE; PROC ANOVA DATA=ACHE; CLASS BRAND; MODEL RELIEF=BRAND; MEANS BRAND/TUKEY CLDIFF; TITLE 'COMPARE RELIEF ACROSS MEDICINES - ANOVA EXAMPLE'; ods output CLDiffs =tc; proc print data=tc; format LowerCL 17.16 UpperCL 17.16 Difference 17.16; title "Output with many digits"; RUN; QUIT; ODS RTF close; ODS LISTING;  -  r,Ndtype)r-r-rr2r^rasarrayreplacesplitfloatrXr  tukey_hsdconfidence_intervalintrlowrIhighrK) r"datares_expect_strr3 res_expect res_tukeyconfrjlrhsigr'r'r(test_compare_sass! zTestTukeyHSD.test_compare_sasz 1 2 -8.2491590248597 -4.6 -0.9508409751403 0.0144483269098 1 3 -3.9091590248597 -0.26 3.3891590248597 0.9803107240900 2 3 0.6908409751403 4.34 7.9891590248597 0.0203311368795 z 1 2 -7.02207069748501 -3.645 -0.26792930251500 0.03371498443080 1 3 -2.68207069748500 0.695 4.07207069748500 0.85572267328807 2 3 0.59347644287720 4.34 8.08652355712281 0.02259047020620 rrrzunequal size samplec Cstj|tdd}tj|}|}|D]E\}}} } } } t|dt|d}}t |j ||f| |dt |j ||f| |dt |j ||f| |dt |j ||f| |dqdS)an vals = [24.5, 23.5, 26.4, 27.1, 29.9, 28.4, 34.2, 29.5, 32.2, 30.1, 26.1, 28.3, 24.3, 26.2, 27.8] names = {'zero', 'zero', 'zero', 'zero', 'zero', 'one', 'one', 'one', 'one', 'one', 'two', 'two', 'two', 'two', 'two'} [p,t,stats] = anova1(vals,names,"off"); [c,m,h,nms] = multcompare(stats, "CType","hsd"); rrr-rr2N)rrrrrXr rrrrrrIrrK) r"rrr3rrrrrrrrr&r'r'r(test_compare_matlabs  z TestTukeyHSD.test_compare_matlabc Csd}tj|ddddtdd}gdgd gd f}tj|}|}|D]E\}}}} } } t |d t |d }}t |j ||f| d d t |j ||f|dd t |j ||f| dd t |j||f| d d q,dS)a+ Testing against results and p-values from R: from: https://www.rdocumentation.org/packages/stats/versions/3.6.2/ topics/TukeyHSD > require(graphics) > summary(fm1 <- aov(breaks ~ tension, data = warpbreaks)) > TukeyHSD(fm1, "tension", ordered = TRUE) > plot(TukeyHSD(fm1, "tension")) Tukey multiple comparisons of means 95% family-wise confidence level factor levels have been ordered Fit: aov(formula = breaks ~ tension, data = warpbreaks) $tension z diff lwr upr p adj 2 - 3 4.722222 -4.8376022 14.28205 0.4630831 1 - 3 14.722222 5.1623978 24.28205 0.0014315 1 - 2 10.000000 0.4401756 19.55982 0.0384598 rrr,Nrr)r66r<F43rCrmr=r)rT,)r>rrr?rtr>rr6$*rrrrzr7rrzr)rrrr>r.ryr7r|rrTrrr?rr|r|rr7rrr2rgh㈵>r) r"str_resrrrrrrrrrr&r'r'r(test_compare_r s& zTestTukeyHSD.test_compare_rc Csgd}gd}gd}gd}t||||}|}tgdgdgdgdg}tgd gd gd gdg}d D]$\} } t|j| | f|| | fd dt|j| | f|| | fd dq@dS)zp Example sourced from: https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm )皙@g@333333@gffffff@g@)g @rg333333@gffffff"@r)g @g%@g333333 @rr)rgffffff@gffffff@gffffff@g@)rrrg)g(\?rgq= ףpgp= ף?)gGz?rrg ףp= ?)rrrr)rrrgzG?)gzG@rg?g= ףp=@)g= ףp=@rrg@)rr)r*r)rrrrJr]r2N)r rrrrrrr) r"group1group2group3group4rMrlowerupperrrr'r'r(test_engineering_stat_handbook2s,  "z+TestTukeyHSD.test_engineering_stat_handbookcCstjdtjdd}tj|}|}t|j|j j tt |j |j dtt |j|jdt|j |j j tt |j dt|j |j j tt |j ddS)Nr5rrorrrr)rr8r9rr rrrrrrOdiagonalrIrK)r"rrMrr'r'r(test_rand_symmLs  zTestTukeyHSD.test_rand_symmcCsNttddtgddtjggdWddS1s wYdS)Nz...must be finite.rErr*)r-r/r)rPrQr rrrOrbr'r'r( test_no_inf_s"zTestTukeyHSD.test_no_infcCsTttddtddgddggddggdWddS1s#wYdS)Nz...must be one-dimensionalrErr*rr,)r,rr-rPrQr rrbr'r'r( test_is_1dcs$"zTestTukeyHSD.test_is_1dcCsHttddtgddggdWddS1swYdS)Nz...must be greater than onerEr*r,)r+r,r-r rbr'r'r( test_no_emptygs"zTestTukeyHSD.test_no_emptynargsrcCsFttddtjgdg|WddS1swYdS)Nz...more than 1 treatment.rErr/rr )r"rr'r'r(test_not_enough_treatmentsks"z'TestTukeyHSD.test_not_enough_treatmentscl)rFrrr*cCsVttddtgdddgddg}||WddS1s$wYdS)Nzmust be between 0 and 1rErrr+r)rPrQr rr)r"rrr'r'r(test_conf_level_invalidps "z$TestTukeyHSD.test_conf_level_invalidcCsPtj|jdd}tj|jdd}t|j|jdt|j|jddS)Nr*rr)r rdata_diff_size ttest_indrrK)r"r res_ttestr'r'r(test_2_args_ttestvszTestTukeyHSD.test_2_args_ttestN)rWrXrYdata_same_sizer extreme_size sas_same_size sas_diff_size sas_extremerGrrr matlab_sm_sizmatlab_diff_szrrrr r rrrrrr'r'r'r(rs\    % )    rc @seZdZejdgdgdfddZejdgdgdgd gd gd gd gd gdfddZddZddZ ddZ dS)TestPoissonMeansTestzc1, n1, c2, n2, p_expect)rrorrogea?)r*ror-rog c?cCs$t||||}t|j|dddS)Nr`r2r poisson_means_testrrK)r"c1n1c2n2p_expectrMr'r'r(test_paper_examplessz(TestPoissonMeansTest.test_paper_examplesz c1, n1, c2, n2, p_expect, alt, d)rTr.rTr.g{}?rr)r.r.r.r.goPF?rr)2r|rrgae?rr^)rrorTrg/V-=?rr)rrtr+rTg")?rr)r+rTrrog_'Qm~?rr)r+rTrr.g|?rr)rrr)r|g 0ݷ?rrc Cs,tj||||||d}t|j|ddddS)N)rdiffg>gؗҜ<r3rr!) r"r#r$r%r&r'altdrMr'r'r(test_fortran_authorssz)TestPoissonMeansTest.test_fortran_authorscCs0d\}}d\}}t||||}t|jddS)N)rprprr!r"count1count2nobs1nobs2rMr'r'r(test_different_resultssz+TestPoissonMeansTest.test_different_resultscCs0d\}}d\}}t||||}t|jddS)Nr rrr!r/r'r'r(test_less_than_zero_lambda_hat2sz4TestPoissonMeansTest.test_less_than_zero_lambda_hat2cCsd\}}d\}}d}tt|dtd|||Wdn1s#wYtt|dt||d|Wdn1sAwYd}tt|dtd|||Wdn1sawYtt|dt||d|Wdn1swYd}tt|dt|d||Wdn1swYtt|dt|||dWdn1swYd }tt|dtj||||dd Wdn1swYd }tt|dtjd d d d ddWddS1swYdS)Nr rz`k1` and `k2` must be integers.rErz1`k1` and `k2` must be greater than or equal to 0.rz%`n1` and `n2` must be greater than 0.z(diff must be greater than or equal to 0.)r*zAlternative must be one of ...rr*errorr)rP TypeErrorr r"rQ)r"r0r1r2r3rar'r'r(rs>$z*TestPoissonMeansTest.test_input_validationN) rWrXrYrGrrr(r.r4r5rr'r'r'r(r ~s(   r c@s`eZdZddZddZejdgdddZejdgd d d Z d d Z ddZ dS) TestBWSTestcCsltjd}|jdd\}}d}tjt|dt||g||gWdn1s,wYd}tjt|dttjg|Wdn1sMwYd}tjt|dt|gWdn1slwYd}tjt|dtj||d d Wdn1swYd }tjt|dtj||d d WddS1swYdS)Ng(?cCsRtjd}|jdd\}}tj|||d}t|j|ddt|j|ddd dS) Nr9r:rrvIh%<=rr]r}r+rr8rr r;rrIrKr"rrIrKr#r#r$rMr'r'r(test_against_Rs zTestBWSTest.test_against_R))rgD5H?gdԕ?)r`ч?gȲךX?)rrCg4)?cCsZtjd}|jdd}|jdd}tj|||d}t|j|ddt|j|dd d dS) Nl.sZrrr0rr?rr]r}r+r@rAr'r'r(test_against_R_imbalanced s  z%TestBWSTest.test_against_R_imbalancedcCstjd}|jdd\}}tjd}tjd|d}tj|||d}t|jdks,Jtjd}tjd|d}tj|||d}t|j|jtjd}tjd|d}tj|||d}t |j|jrgJdS)N/HN()r*r.rr.) n_resamplesr#rlVC A) rr8rr r&r;rnull_distributionrallclose)r"r#r#r$rrrr r'r'r( test_methods    zTestBWSTest.test_methodcCstjd}|jdd}|d}tj||dd}|jdksJt|jdt|j tj||dd}|jdks9Jt|jdtj||dd}|jdksNJt|jdt|j tj||dd}|jdkshJt|jddS) NrEr,rrrrrr) rr8rr r;rIrrKrrG)r"r#r#r$rMr'r'r(test_directions2s   zTestBWSTest.test_directionsN) rWrXrYr<r=rGrrrBrDrIrJr'r'r'r(r8s     r8). itertoolsrnumpyrr8r4rG numpy.testingrrrrrrP scipy.statsr rscipy.stats._hypotestsr r r r r rrscipy.stats._mannwhitneyurrr common_testsrscipy._lib._testutilsrscipy.stats._axis_nan_policyrrrrZrr.rwrrrr r8r'r'r'r(s@    $  7]roXtT