U Kv f@@sLdZddlZddlZddlmZddlmZmZm Z d dd Z d d d Z dS)zb Created on Sun Sep 25 21:23:38 2011 Author: Josef Perktold and Scipy developers License : BSD-3 N)stats) array_like bool_likeint_likenormTc Cst|ddd}t|d}t|d}tj||d}|j|}|r|dkrttj||d|}ttj|d|d |}|||} t j | } qt |r| |}|j |f|} qtd n"t |r|j |f|} ntd td|d} dg|j} td| |<t| } tdg|j} tddd | |<t| } tPtjd ddd| | d|t| t| |  }tj||d}W5QRX| |}|S)a Calculate the Anderson-Darling a2 statistic. Parameters ---------- x : array_like The data to test. dist : {'norm', callable} The assumed distribution under the null of test statistic. fit : bool If True, then the distribution parameters are estimated. Currently only for 1d data x, except in case dist='norm'. params : tuple The optional distribution parameters if fit is False. axis : int If dist is 'norm' or fit is False, then data can be an n-dimensional and axis specifies the axis of a variable. Returns ------- {float, ndarray} The Anderson-Darling statistic. xN)ndimfitaxis)r r)Zddofr z!dist must be 'norm' or a Callablez+if fit is false, then dist must be callableignorez#divide by zero encountered in log1p)messageg?)rrrnpsortshapeZ expand_dimsZmeanZstdrrZcdfcallabler ValueErrorZaranger slicetuplewarningscatch_warningsfilterwarningsloglog1psum)rdistr paramsr yZnobsZxbarswziZsl1Zsl2Z ad_valuesZa2rr=/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/stats/_adnorm.pyanderson_statisticsD         . r&cCst|dd|d}|j|}|dd|d|d}t|dkr|dkrx|d krxdtd d |d |d}n|d krdtdd|d|d}nT|dkrtdd|d|d}n,|dkrtdd|d|d}nd}ntdd d dg}dd}dd}dd} dd} dd} ||| | | g} tj||dd } tjt|}t d!D]"}| |k}| |||||<qj||fS)"a Anderson-Darling test for normal distribution unknown mean and variance. Parameters ---------- x : array_like The data array. axis : int The axis to perform the test along. Returns ------- ad2 : float Anderson Darling test statistic. pval : float The pvalue for hypothesis that the data comes from a normal distribution with unknown mean and variance. See Also -------- statsmodels.stats.diagnostic.anderson_statistic The Anderson-Darling a2 statistic. statsmodels.stats.diagnostic.kstest_fit Kolmogorov-Smirnov test with estimated parameters for Normal or Exponential distributions. rT)rr r r g?g@rgg?Zd;*)\HY@(\k@g(\?A`Т SeE@MbM@g333333? Ac]?V-@Gz? \m?t@Έ ?cSstjt|S)N)rnan ones_likead2arrr%~znormal_ad..cSs"dtdd|d|dS)Nr r'r(r)rrexpr6rrr%r8scSs"dtdd|d|dS)Nr r*r+r,rr:r6rrr%r8scSstdd|d|dS)Nr-r.r/rr:r6rrr%r8r9cSstdd|d|dS)Nr1r2r3rr:r6rrr%r8r9right)Zside) r&rrsizer;arrayZ searchsortedr4r5range)rr Zad2nr7ZpvalZboundsZpval0Zpval1Zpval2Zpval3Zpval4Zpvalliidxr$maskrrr% normal_adOs4 $$   rD)rTrr)r) __doc__rZnumpyrZscipyrZstatsmodels.tools.validationrrrr&rDrrrr%s   ?