tKg,bddlZddlmZddlmZmZddlmZedddd d d d Z y) N)_get_nan)array_namespace xp_copysign)_axis_nan_policy_factoryc|SNxs Z/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/scipy/stats/_variation.pyr sac|fSr r r s r rr sr) n_outputsresult_to_tupleF)keepdimsc t|}|j|}||j|d}d}|j|}t |}|j dk(s||kDrOt |j}|j||j||} | jdk(r| dS| S|j||} ||k(rc|j||d} |j| dkDt|j|j| |} | jdk(r| dS| Stj dd 5|j|||} | | z } ddd jdk(r| dS| S#1swYxYw) a 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. 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 ]) N)r) fill_valuer )axis)r correctionignore)divideinvalid)rasarrayreshapeshapersizelistpopfullndimmeanstdwhererinfnperrstate) ar nan_policyddofrxpnNaNshpresultmean_astd_as r variationr4 skp  B 1 A | JJq%   A 1+Cvv{dQh177m  -#[[A-vbz969 WWQTW "F qyqt2%!)[BFF1CV%LcR#[[A-vbz969 Hh 7qt5 8 )6":5v5 8 7s E::F)r propagater) numpyr(scipy._lib._utilrscipy._lib._array_apirr_axis_nan_policyrr4r rr r:s;%>61nt6Ut6t6r