9=e TddlZddlmZddlmZmZddlmZdddddZ d dd d Z dS) N)normalize_axis_index) _nan_allsame _contains_nan) _chk_asarrayFaxisddofkeepdimsc tj|}||d}tj||j}||dkz|d|z}|j|tjtj|||z }||k} ||k} t||d} |} d| |<tj | |d} d| tj| | j| z<tj d5tj | ||d}d d d n #1swxYwY~ tj ||ddk}d| |<|| z }tj || |z<| | z}tj| |tj z||<||z| z| | zz}tj||<|s)tj||}|jd kr|d }|S) z Private version of `variation` that ignores nan. `a` must be a numpy array. `axis` is assumed to be normalized, i.e. 0 <= axis < a.ndim. T)r r rr g?ignore)invalidrN)npisnanall broadcast_toshape expand_dims count_nonzerorcopynanmeanerrstatenanstdnansuminfsignnansqueeze)ar r r a_isnanall_nan all_nan_fullall_zerongood ddof_too_big ddof_equal_nis_consta2mean_astd_asum_zeroresultsigned_inf_masknan_masks 6lib/python3.11/site-packages/scipy/stats/_variation.py _nanvariationr2sp:hqkkGkktdk33G?7AG44L16"''TD'AAWHLHWT] ^B,W4@@@$ G GHE% X & & &CC "4dTBBBCCCCCCCCCCCCCCC y555:HF8V^F$&6FH9x  i,.O gf_&=>>GF?'!L0L84KLHvF8  F... <2  BZF Ms"EE E  propagater ct||\}}t||j}|j|}t ||\}}|r|dkrt ||||S|jdks||krat|j}|rd||<n||=t|dkr tj }n tj |tj }|S| |d} ||kr| |dd} tj| tj }tj| tjzj|j| jdk<|jd kr|d }|Stjd d 5| ||d } | | z }d d d n #1swxYwY|s)tj||}|jd kr|d }|S)am Compute the coefficient of variation. The coefficient of variation is the standard deviation divided by the mean. This function is equivalent to:: np.std(x, axis=axis, ddof=ddof) / np.mean(x) The default for ``ddof`` is 0, but many definitions of the coefficient of variation use the square root of the unbiased sample variance for the sample standard deviation, which corresponds to ``ddof=1``. The function does not take the absolute value of the mean of the data, so the return value is negative if the mean is negative. Parameters ---------- a : array_like Input array. axis : int or None, optional Axis along which to calculate the coefficient of variation. Default is 0. If None, compute over the whole array `a`. nan_policy : {'propagate', 'raise', 'omit'}, optional Defines how to handle when input contains ``nan``. The following options are available: * 'propagate': return ``nan`` * 'raise': raise an exception * 'omit': perform the calculation with ``nan`` values omitted The default is 'propagate'. ddof : int, optional Gives the "Delta Degrees Of Freedom" used when computing the standard deviation. The divisor used in the calculation of the standard deviation is ``N - ddof``, where ``N`` is the number of elements. `ddof` must be less than ``N``; if it isn't, the result will be ``nan`` or ``inf``, depending on ``N`` and the values in the array. By default `ddof` is zero for backwards compatibility, but it is recommended to use ``ddof=1`` to ensure that the sample standard deviation is computed as the square root of the unbiased sample variance. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. Returns ------- variation : ndarray The calculated variation along the requested axis. Notes ----- There are several edge cases that are handled without generating a warning: * If both the mean and the standard deviation are zero, ``nan`` is returned. * If the mean is zero and the standard deviation is nonzero, ``inf`` is returned. * If the input has length zero (either because the array has zero length, or all the input values are ``nan`` and ``nan_policy`` is ``'omit'``), ``nan`` is returned. * If the input contains ``inf``, ``nan`` is returned. References ---------- .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall: New York. 2000. Examples -------- >>> import numpy as np >>> from scipy.stats import variation >>> variation([1, 2, 3, 4, 5], ddof=1) 0.5270462766947299 Compute the variation along a given dimension of an array that contains a few ``nan`` values: >>> x = np.array([[ 10.0, np.nan, 11.0, 19.0, 23.0, 29.0, 98.0], ... [ 29.0, 30.0, 32.0, 33.0, 35.0, 56.0, 57.0], ... [np.nan, np.nan, 12.0, 13.0, 16.0, 16.0, 17.0]]) >>> variation(x, axis=1, ddof=1, nan_policy='omit') array([1.05109361, 0.31428986, 0.146483 ]) )ndimomitrrr) fill_valueTr4rr)divider)r r Nr )rrr6rrr2sizelistlenrrfullmeanstd full_likerrflatrr ) r!r nan_policyr r n contains_nanshpr.r+r,s r1 variationrF[s1r1d##GAt 16 2 2 2D  A,Q ;;L*I f,,QTxHHHHv{{dQhh17mm  CIID s88q==VFFWSRV444F VVD4V ( (F qyy4a$77e777')wv'?&E EJN# <2  BZF Hh 7 7 7  d55                 F... <2  BZF MsF88F<?F<)rr3r) numpyrnumpy.core.multiarrayrscipy._lib._utilrr _stats_pyrr2rFrr1rLs66666688888888######QQQQQQhDUDDDDDDDrK