o tBh:@sddlZddlZddlZddlmZddlmZm Z ddl Z dddZ dddZ dd Z d d Zd d ZdZdZddZe eeeZe j ee j jddZdZdZddZe eeeZe j ee j jddZ  dddZdS)N)wraps) FunctionDoc ParametercCsdd|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`. In other words, return output shape of a typical hypothesis test on `arrays` vectorized along `axis`. Examples -------- >>> a = np.zeros((5, 2, 1)) >>> b = np.zeros((9, 3)) >>> _broadcast_array_shapes((a, b), 1) (5, 3) cSg|]}|jqSshape).0arrrrs/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/scipy/stats/_axis_nan_policy.py !z7_broadcast_array_shapes_remove_axis..)_broadcast_shapes_remove_axis)arraysaxisshapesrrr #_broadcast_array_shapes_remove_axiss rcCstdd|D}tjt||ftd}t||D]\}}||t|t|d<q|dur6tj||dd}tj|dd}||jdd9}t|dk||kBrUt dt |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. cSsg|]}t|qSrlen)r rrrr r ,z1_broadcast_shapes_remove_axis..)dtypeNrrz/Array shapes are incompatible for broadcasting.) maxnponesrintzipdeleteallany ValueErrortuple)rrn_dims new_shapesrowr new_shaperrr r%srcsttdd|Dfdd|D}fdd|D}t|ddfdd|D}tj|dd}t|d}|S)z3Concatenate arrays along an axis with broadcasting.cSrrndimr xrrr r <r z*_broadcast_concatenate..cs,g|]}|dg|jt|jqS)r)reshaper(listrr)r'rr r =s,csg|] }t|dqS)rswapaxesr)rrr r ?sr.rcs$g|]}t||jdfqSr-)r broadcast_torr)rrr r Cs$)rrr concatenater/)xsrresr)rr(rr _broadcast_concatenate9s r4csX|s dd|DSt|d}|ddD] }|t|B}q|fdd|DS)z+Remove nans from paired or unpaired samplescSsg|] }|t|qSrrisnanr samplerrr r Psz _remove_nans..rrNcsg|]}|qSrrr7not_nansrr r Xrr5)samplespairednansr8rr9r _remove_nansLsr>cCs4tdd|Ds dSt||}t|tj}|S)zU Check for empty sample; return appropriate output for a vectorized hypotest css|]}|jdkVqdS)rN)sizer7rrr `sz&_check_empty_inputs..N)r rrrnan)r;r output_shapeoutputrrr _check_empty_inputs[s  rDrzint or None, default: 0zIf an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If ``None``, the input will be raveled before computing the statistic. )default 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. propagaterFcs6durddfddfdd}|S)aBFactory for a wrapper that adds axis/nan_policy params to a function. Parameters ---------- result_object : callable Callable that returns an object of the type returned by the function being wrapped (e.g. the namedtuple or dataclass returned by a statistical test) provided the separate components (e.g. statistic, pvalue). default_axis : int, default: 0 The default value of the axis argument. 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_unpacker : callable, optional Function that unpacks the results of the function being wrapped into a tuple. This is essentially the inverse of `result_object`. 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. 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