Dj'HdZddlZddlZddZddZGddZ d dZy) zqAnova k-sample comparison without and with trimming Created on Sun Jun 09 23:51:34 2013 Author: Josef Perktold Nc$tj|}||j}d}|j|}t ||z}||z }||k\r t dt dg|jz}t ||||<|t|S)a Slices off a proportion of items from both ends of an array. Slices off the passed proportion of items from both ends of the passed array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and** rightmost 10% of scores). You must pre-sort the array if you want 'proper' trimming. Slices off less if proportion results in a non-integer slice index (i.e., conservatively slices off `proportiontocut`). Parameters ---------- a : array_like Data to trim. proportiontocut : float or int Proportion of data to trim at each end. axis : int or None Axis along which the observations are trimmed. The default is to trim along axis=0. If axis is None then the array will be flattened before trimming. Returns ------- out : array-like Trimmed version of array `a`. Examples -------- >>> from scipy import stats >>> a = np.arange(20) >>> b = stats.trimboth(a, 0.1) >>> b.shape (16,) NrProportion too big.) npasarrayravelshapeint ValueErrorslicendimtuple)aproportiontocutaxisnobslowercutuppercutsls dC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels\stats\robust_compare.pytrimbothrsH 1 A | GGI 774=D?T)*HhHH.// + BXx(BtH U2Y<ctttj||||}tj||S)a Return mean of array after trimming observations from both tails. If `proportiontocut` = 0.1, slices off 'leftmost' and 'rightmost' 10% of scores. Slices off LESS if proportion results in a non-integer slice index (i.e., conservatively slices off `proportiontocut` ). Parameters ---------- a : array_like Input array proportiontocut : float Fraction to cut off at each tail of the sorted observations. axis : int or None Axis along which the trimmed means are computed. The default is axis=0. If axis is None then the trimmed mean will be computed for the flattened array. Returns ------- trim_mean : ndarray Mean of trimmed array. r)rrsortmean)rrrnewas r trim_meanrBs.2 BGGAt$oD AD 774d ##rceZdZdZd dZedZedZedZedZ edZ edZ ed Z dd Z d Zy ) 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. ctj||_||_||_|jj |x|_}t||zx|_}||z x|_ }||k\r td|d|zz |_ tdg|jjz|_t|j|j|j|<t|j|_|s'tj |j||_n|j|_tj$|j"|||_tj$|j"|dz ||_y)Nrr)rrdatarfractionrrr rrr nobs_reducedr r rr r data_sortedtake lowerbound upperbound)selfr#r$ is_sortedrrrrs r__init__zTrimmedMean.__init__us*JJt$    99??400 D#&x$#77 #'(?2   23 3 1x</;-$))..0dmmT]]; .!wwtyytz!scale_transform..s !a%ridentityc|Srcr^rds rrfz!scale_transform..s!rz&transform should be abs, square or expmedianrrrFz(center should be median, mean or trimmed) rrr`callabler r1rirr isinstancenumbersNumber)r#centerrO trim_fracrretfuncrSs rscale_transformrqs,2 4AE h  j  ) ABB Aryy'>EEF J 6 Arwwqt'rss9 0f$:\\~GJ2r