M/Ph}&PdZddlZddlZd dZd dZGddZ d d ZdS) zqAnova k-sample comparison without and with trimming Created on Sun Jun 09 23:51:34 2013 Author: Josef Perktold NcLtj|}||}d}|j|}t ||z}||z }||krt dt dg|jz}t ||||<|t|S)a Slices off a proportion of items from both ends of an array. Slices off the passed proportion of items from both ends of the passed array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and** rightmost 10% of scores). You must pre-sort the array if you want 'proper' trimming. Slices off less if proportion results in a non-integer slice index (i.e., conservatively slices off `proportiontocut`). Parameters ---------- a : array_like Data to trim. proportiontocut : float or int Proportion of data to trim at each end. axis : int or None Axis along which the observations are trimmed. The default is to trim along axis=0. If axis is None then the array will be flattened before trimming. Returns ------- out : array-like Trimmed version of array `a`. Examples -------- >>> from scipy import stats >>> a = np.arange(20) >>> b = stats.trimboth(a, 0.1) >>> b.shape (16,) NrProportion too big.) npasarrayravelshapeint ValueErrorslicendimtuple)aproportiontocutaxisnobslowercutuppercutsls `/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/stats/robust_compare.pytrimbothrsH 1 A | GGII 74=D?T)**HhHH./// ++ BXx((BtH U2YY<cxttj||||}tj||S)a Return mean of array after trimming observations from both tails. If `proportiontocut` = 0.1, slices off 'leftmost' and 'rightmost' 10% of scores. Slices off LESS if proportion results in a non-integer slice index (i.e., conservatively slices off `proportiontocut` ). Parameters ---------- a : array_like Input array proportiontocut : float Fraction to cut off at each tail of the sorted observations. axis : int or None Axis along which the trimmed means are computed. The default is axis=0. If axis is None then the trimmed mean will be computed for the flattened array. Returns ------- trim_mean : ndarray Mean of trimmed array. r)rrsortmean)rrrnewas r trim_meanrBs92 BGAt$$oD A A AD 74d # # ##rceZdZdZddZedZedZedZedZ ed Z ed Z ed Z ddZ dZdS) TrimmedMeana class for trimmed and winsorized one sample statistics axis is None, i.e. ravelling, is not supported Parameters ---------- data : array-like The data, observations to analyze. fraction : float in (0, 0.5) The fraction of observations to trim at each tail. The number of observations trimmed at each tail is ``int(fraction * nobs)`` is_sorted : boolean Indicator if data is already sorted. By default the data is sorted along ``axis``. axis : int The axis of reduce operations. By default axis=0, that is observations are along the zero dimension, i.e. rows if 2-dim. Frctj||_||_||_|jj|x|_}t||zx|_}||z x|_ }||krtd|d|zz |_ tdg|jj z|_t|j|j |j|<t|j|_|s!tj|j||_n |j|_tj|j|||_tj|j|dz ||_dS)Nrr)rrdatarfractionrrr rrr nobs_reducedr r rr r data_sortedtake lowerbound upperbound)selfr#r$ is_sortedrrrrs r__init__zTrimmedMean.__init__us9Jt$$    9?400 D#&x$#7#77 #'(?2   233 3 1x</;;-$).0dmT];; .. )!wtyt<<z!scale_transform..s !a%ridentityc|Srfr_rgs rriz!scale_transform..s!rz&transform should be abs, square or expr`rrrEz(center should be median, mean or trimmed) rrracallabler r1r`rr isinstancenumbersNumber)r#centerrP trim_fracrrhtfuncrTs rscale_transformrssm2 4AE h   j   )  CABBB eAry'>'>'>EEEFF 6  eArwqt'<'<'rus 0000f$$$$:\\\\\\\\~GJ222222r