ó áp7]c@svdZddlZddlmZdd„Zdefd„ƒYZdefd „ƒYZe d „Z e d krrdd l m Z ddljZe d ƒZejeƒZedZeeƒZejƒeeƒZejeeddƒeeƒ\ZZejeedddƒejeedddƒejddƒejddƒejeddƒejƒndS(s Empirical CDF Functions iÿÿÿÿN(tinterp1dgš™™™™™©?cCslt|ƒ}tjtjd|ƒd|ƒ}tj||ddƒ}tj||ddƒ}||fS(sõ Constructs a Dvoretzky-Kiefer-Wolfowitz confidence band for the eCDF. Parameters ---------- F : array-like The empirical distributions alpha : float Set alpha for a (1 - alpha) % confidence band. Notes ----- Based on the DKW inequality. .. math:: P \left( \sup_x \left| F(x) - \hat(F)_n(X) \right| > \epsilon \right) \leq 2e^{-2n\epsilon^2} References ---------- Wasserman, L. 2006. `All of Nonparametric Statistics`. Springer. g@iii(tlentnptsqrttlogtclip(tFtalphatnobstepsilontlowertupper((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyt _conf_sets  $t StepFunctioncBs)eZdZdedd„Zd„ZRS(s/ A basic step function. Values at the ends are handled in the simplest way possible: everything to the left of x[0] is set to ival; everything to the right of x[-1] is set to y[-1]. Parameters ---------- x : array-like y : array-like ival : float ival is the value given to the values to the left of x[0]. Default is 0. sorted : bool Default is False. side : {'left', 'right'}, optional Default is 'left'. Defines the shape of the intervals constituting the steps. 'right' correspond to [a, b) intervals and 'left' to (a, b]. Examples -------- >>> import numpy as np >>> from statsmodels.distributions.empirical_distribution import StepFunction >>> >>> x = np.arange(20) >>> y = np.arange(20) >>> f = StepFunction(x, y) >>> >>> print(f(3.2)) 3.0 >>> print(f([[3.2,4.5],[24,-3.1]])) [[ 3. 4.] [ 19. 0.]] >>> f2 = StepFunction(x, y, side='right') >>> >>> print(f(3.0)) 2.0 >>> print(f2(3.0)) 3.0 gtleftc Cs7|jƒdkr'd}t|ƒ‚n||_tj|ƒ}tj|ƒ}|j|jkrud}t|ƒ‚nt|jƒdkrŸd}t|ƒ‚ntjtj |f|_ tj||f|_ |s tj |j ƒ} tj |j | dƒ|_ tj |j | dƒ|_ n|j jd|_ dS( NtrightRs*side can take the values 'right' or 'left's"x and y do not have the same shapeisx and y must be 1-dimensionali(RR(R t ValueErrortsideRtasarraytshapeRtr_tinftxtytargsortttaketn( tselfRRtivaltsortedRtmsgt_xt_ytasort((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyt__init__Ms& cCs*tj|j||jƒd}|j|S(Ni(Rt searchsortedRRR(Rttimettind((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyt__call__gs(t__name__t __module__t__doc__tFalseR"R&(((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyR "s)tECDFcBseZdZdd„ZRS(s… Return the Empirical CDF of an array as a step function. Parameters ---------- x : array-like Observations side : {'left', 'right'}, optional Default is 'right'. Defines the shape of the intervals constituting the steps. 'right' correspond to [a, b) intervals and 'left' to (a, b]. Returns ------- Empirical CDF as a step function. Examples -------- >>> import numpy as np >>> from statsmodels.distributions.empirical_distribution import ECDF >>> >>> ecdf = ECDF([3, 3, 1, 4]) >>> >>> ecdf([3, 55, 0.5, 1.5]) array([ 0.75, 1. , 0. , 0.25]) RcCsmtj|dtƒ}|jƒt|ƒ}tjd|d|ƒ}tt|ƒj||d|dtƒdS(Ntcopygð?iRR( RtarraytTruetsortRtlinspacetsuperR+R"(RRRRR((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyR"†s   (R'R(R)R"(((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyR+lscKs‡tj|ƒ}|r'|||}n<g}x$|D]}|j|||ƒq4Wtj|ƒ}tj|ƒ}t||||ƒS(sÅ Given a monotone function fn (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x. (RRtappendR-RR(tfnRt vectorizedtkeywordsRRta((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pytmonotone_fn_inverter”s t__main__(turlopens-http://www.statsci.org/data/general/nerve.txtgI@twheretposttrigø?gÍÌÌÌÌÌð?(R)tnumpyRtscipy.interpolateRR tobjectR R+R.R7R'tstatsmodels.compat.pythonR9tmatplotlib.pyplottpyplottpltt nerve_datatloadtxtRtcdfR/RtstepR R txlimtylimtvlinestshow(((sOlib/python2.7/site-packages/statsmodels/distributions/empirical_distribution.pyts.  J(