U Gv fh@sBddlZddlmZddlmZmZddlmZddl Z d.ddZ d/ddZ d0d d Z d1d d Z d2d dZddZddZddZddZddZddZdZddZdeefddZd Zd!Zd"dZeeeeZe jee jjd#d$Zd%Zd&Zd'dZeeeeZe jee jjd(d$Zd)dZdd*d(ddd+gfd,d-Z dS)3N)wraps) FunctionDoc Parameter) _contains_nancCs6t||d}|dkr"|gt|}ddt||DS)P Broadcast shapes of arrays, ignoring incompatibility of specified axes axisNcSsg|]\}}t||qS)np broadcast_to).0array new_shaper r @/tmp/pip-unpacked-wheel-96ln3f52/scipy/stats/_axis_nan_policy.py sz%_broadcast_arrays..)_broadcast_array_shapeslenzip)arraysr new_shapesr r r_broadcast_arrayss  rcCsdd|D}t||S)rcSsg|]}t|jqSr )r asarrayshaper arrr r rrsz+_broadcast_array_shapes..)_broadcast_shapesrrshapesr r rrsrc s|s|S|dk r>t|}|t}t||s:td|}tdd|D}tjt||ftd}t ||D] \}}||t|t|d<qp|dk r,|||dk||dk<t |}|d|ks|ddkrd|}t|tt |t|kr td |dd|f}tj ||d d }tj|dd |j dd 9t|d k|kBrltd |dk r|tt|fd d|D}|StSdS)zF Broadcast shapes, ignoring incompatibility of specified axes Nz:`axis` must be an integer, a tuple of integers, or `None`.cSsg|] }t|qSr rr rr r rr2sz%_broadcast_shapes..)dtyperz/`axis` is out of bounds for array of dimension z*`axis` must contain only distinct elementsrz/Array shapes are incompatible for broadcasting.csg|]}tt|qSr )tupler insert)r Z removed_shapeZnew_axisrr rrVs)r atleast_1dastypeintZ array_equalZ AxisErrormaxonesrrsortuniquedeleteallany ValueErrorZaranger#) rrZaxis_intn_dimsrrowrmessageZremoved_shapesr r%rr!sB           rcCsdd|D}t||S)a Broadcast shapes of arrays, dropping specified axes Given a sequence of arrays `arrays` and an integer or tuple `axis`, find the shape of the broadcast result after consuming/dropping `axis`. 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Examples -------- >>> import numpy as np >>> a = np.zeros((5, 2, 1)) >>> b = np.zeros((9, 3)) >>> _broadcast_array_shapes((a, b), 1) (5, 3) cSsg|] }|jqSr rrr r rrpsz7_broadcast_array_shapes_remove_axis..)_broadcast_shapes_remove_axisrr r r#_broadcast_array_shapes_remove_axis]sr6cCs.t||}|d}|dk r&t||}t|S)z Broadcast shapes, dropping specified axes Same as _broadcast_array_shapes, but given a sequence of array shapes `shapes` instead of the arrays themselves. rN)rr r-r#)rrrr r rr5ts   r5cCst||}tj||d}|S)z3Concatenate arrays along an axis with broadcasting.r)rr Z concatenate)rrresr r r_broadcast_concatenates r8csX|sdd|DSt|d}|ddD]}|t|B}q,|fdd|DS)z.Remove nans from paired or unpaired 1D samplescSsg|]}|t|qSr r isnanr sampler r rrsz _remove_nans..rr"Ncsg|] }|qSr r r;Znot_nansr rrsr9)samplespairedZnansr<r r=r _remove_nanssr@csX|sfdd|DS|dk}|ddD]}||kB}q.|fdd|DS)z9Remove sentinel values from paired or unpaired 1D samplescsg|]}||kqSr r r;)sentinelr rrsz$_remove_sentinel..rr"Ncsg|] }|qSr r r;) not_sentinelsr rrsr )r>r?rAZ sentinelsr<r )rBrAr_remove_sentinels rCcCsd}|D]}t|dd}|p$t|}q|s4|dfStj|}t|tjrP|ntj}tt|D]}||j |dd||<qbt|tj }|rtj ntj }||j ||j}} |rtjndd} |} | | kr|D]$}t|| kr| | tj } qq֐qqd} t| g} |D]b}t|dd}|dk rlt||j}t|rT|jn|j}t|}| ||<| |q| | fS)NFmaskcopycSs|dS)Nr"r )x_r r rz2_masked_arrays_2_sentinel_arrays..zThis function replaces masked elements with sentinel values, but the data contains all distinct values of this data type. 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If ``None``, the input will be raveled before computing the statistic. cCs2d|}t|||}tj|tjj|d}||fS)Nzint or None, default: default)rinspect KEYWORD_ONLY) default_axis_name_desc_type_axis_parameter_doc_axis_parameterr r r_get_axis_paramss  rk nan_policyz{'propagate', 'omit', 'raise'}aDefines how to handle input NaNs. - ``propagate``: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN. - ``omit``: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN. - ``raise``: if a NaN is present, a ``ValueError`` will be raised. propagaterar^zbool, default: FalsezIf this is set to True, the axes which are reduced are left in the result as dimensions with size one. 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Standard is 0 except when backwards compatibility demands otherwise (e.g. `None`). n_samples : int or callable, default: 1 The number of data samples accepted by the function (e.g. `mannwhitneyu`), a callable that accepts a dictionary of parameters passed into the function and returns the number of data samples (e.g. `wilcoxon`), or `None` to indicate an arbitrary number of samples (e.g. `kruskal`). paired : {False, True} Whether the function being wrapped treats the samples as paired (i.e. corresponding elements of each sample should be considered as different components of the same sample.) result_to_tuple : callable, optional Function that unpacks the results of the function being wrapped into a tuple. This is essentially the inverse of `tuple_to_result`. Default is `None`, which is appropriate for statistical tests that return a statistic, pvalue tuple (rather than, e.g., a non-iterable datalass). too_small : int, default: 0 The largest unnacceptably small sample for the function being wrapped. For example, some functions require samples of size two or more or they raise an error. This argument prevents the error from being raised when input is not 1D and instead places a NaN in the corresponding element of the result. n_outputs : int or callable, default: 2 The number of outputs produced by the function given 1d sample(s). For example, hypothesis tests that return a namedtuple or result object with attributes ``statistic`` and ``pvalue`` use the default ``n_outputs=2``; summary statistics with scalar output use ``n_outputs=1``. 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