_Mh,|RddlZddlZddlmZddlmZmZddlm Z m Z m Z ddl m Z mZddlZdZdZdZd ZGd d eZd(d Zd)d Zd)dZd)dZd*dZdZdZdZdZdZdZ d!dZ"de e"fdZ#dZ dZ$d!dZ"ee e$e"Z%eje ejj&dZ'd Z d!Z$d"!dZ"ee e$e"Z(eje ejj&dZ)d#!dZ* d+d'Z+dS),N)wraps) FunctionDoc Parameter) _contains_nan AxisError_get_nan)array_namespaceis_numpyz{One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.zAfter omitting NaNs, one or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.zAll axis-slices of one or more sample arguments are too small; all elements of returned arrays will be NaN. See documentation for sample size requirements.zAfter omitting NaNs, one or more axis-slices of one or more sample arguments is too small; corresponding elements of returned arrays will be NaN. See documentation for sample size requirements.ceZdZdS)SmallSampleWarningN)__name__ __module__ __qualname__\/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/scipy/stats/_axis_nan_policy.pyr r %sDrr c|s|S t|nfd|D}d|D}t||}||gt|z}fdt||DS)zP Broadcast shapes of arrays, ignoring incompatibility of specified axes Nc:g|]}|Sr)asarray).0arrxps r z%_broadcast_arrays..0s# 0 0 0#bjjoo 0 0 0rcg|] }|j Srshaperrs rrz%_broadcast_arrays..1 * * *Cci * * *rcBg|]\}}||Sr) broadcast_to)rarray new_shapers rrz%_broadcast_arrays..5s; = = = y OOE9 - - = = =r)r _broadcast_shapeslenzip)arraysaxisrshapes new_shapess ` r_broadcast_arraysr*)s  %'Z& ! !RB 0 0 0 0 0 0 0F * *6 * * *F"6400J | \#f++-  = = = =$' $;$; = = ==rc |s|S|tj|}d} tjd5|t}dddn #1swxYwYn"#t $r}t ||d}~wwxYwtj||st ||}td|D}tj t||ft}t||D])\}}||t|t|z d<*||||dkz||dk<tj |}|d|ks |ddkrd |}t |ttj |t|krt d |dd|f} tj||d }tj|d  |d z tj|d k| kzrt d |5|tjt|z fd| D}|St% S)zF Broadcast shapes, ignoring incompatibility of specified axes Nz:`axis` must be an integer, a tuple of integers, or `None`.ignore)invalidc,g|]}t|Sr)r$)rrs rrz%_broadcast_shapes..Ns111#e**111r)dtyperz/`axis` is out of bounds for array of dimension z*`axis` must contain only distinct elementsr'z/Array shapes are incompatible for broadcasting.c Vg|]%}ttj|&Sr)tuplenpinsert)r removed_shapenew_axisr"s rrz%_broadcast_shapes..rs?;;;'BIi=IIJJ;;;r)r5 atleast_1derrstateastypeint ValueErrorr array_equalmaxonesr$r%sortuniquedeleteallanyaranger4) r(r'messageaxis_inten_dimsr)rowrremoved_shapesr8r"s @@rr#r#9s    }T""N ,X... , ,;;s++ , , , , , , , , , , , , , , , , , ,G$$! + ,~h-- %G$$ $11&111 2 2F#f++v.c:::J*f--** U$)CHHSZZ  !! $tax.0TAXwt}} 8v  a1:17::GG$$ $ ry  3t99 , ,HII I#AAAtG,Yz4a888 z***I Q'''I  vqZ9%<=>??LJKKK ")CII...;;;;;+9;;; Ys:A(A A(A  A(#A $A(( B2BBc:d|D}t||S)aJ 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 -------- >>> import numpy as np >>> from scipy.stats._axis_nan_policy import _broadcast_array_shapes_remove_axis >>> a = np.zeros((5, 2, 1)) >>> b = np.zeros((9, 3)) >>> _broadcast_array_shapes_remove_axis((a, b), 1) (5, 3) cg|] }|j Srrrs rrz7_broadcast_array_shapes_remove_axis..rr)_broadcast_shapes_remove_axis)r&r'r(s r#_broadcast_array_shapes_remove_axisrPys'(+ *6 * * *F ( 6 66rc~t||}|d}|tj||}t|S)z Broadcast shapes, dropping specified axes Same as _broadcast_array_shapes_remove_axis, but given a sequence of array shapes `shapes` instead of the arrays themselves. r)r#r5rCr4)r(r'rs rrOrOs?vt , ,F 1IE  %&& <<rFcZt||s|nd}tj||}|S)z3Concatenate arrays along an axis with broadcasting.Nr2)r*r5 concatenate)r&r'pairedress r_broadcast_concatenaterVs3 v6'Cttt D DF .d + + +C Jrc|s d|DStj|d}|ddD]}|tj|z}|fd|DS)z.Remove nans from paired or unpaired 1D samplescDg|]}|tj|Srr5isnanrsamples rrz _remove_nans..s*@@@f((()@@@rrr1Nc g|] }| Srr)rr\not_nanss rrz _remove_nans..s 3 3 3F8  3 3 3rrY)samplesrTnansr\r^s @r _remove_nansras A@@@@@@ 8GAJ  D!""+''bhv&&&uH 3 3 3 37 3 3 33rc|sfd|DS|dk}|ddD] }||kz} |fd|DS)z9Remove sentinel values from paired or unpaired 1D samplesc(g|]}||kSrr)rr\sentinels rrz$_remove_sentinel..s#AAAvv)*AAArrr1Nc g|] }| Srr)rr\ not_sentinelss rrz$_remove_sentinel..s 8 8 8fF= ! 8 8 8rr)r_rTrd sentinelsr\rfs ` @r_remove_sentinelrhs BAAAAAAAAx'I!""+558!34 JM 8 8 8 8 8 8 88rcd}|D])}t|dd}|ptj|}*|s|dfStj|}tj|tjr|n tj}tt|D]"}|| |d||<#tj|tj }|r tj n tj }||j ||j} }|r tjnd} |} | | kr=|D]3}tj|| kr| | tj } n4n| | k=d} t#| g} |D]}t|dd}|gtj||j}tj|r|jn|j}tj|}| ||<| || | fS)NFmaskcopyc |dz S)Nr1r)x_s rz2_masked_arrays_2_sentinel_arrays..s 1q5rzThis function replaces masked elements with sentinel values, but the data contains all distinct values of this data type. Consider promoting the dtype to `np.float64`.)getattrr5rE result_type issubdtypenumberfloat64ranger$r;inexactfinfoiinfor?min nextafterinfr=r rdatarlrappend)r_has_maskr\rjr/irwinfo max_possible min_possibler{rdrG out_sampless r _masked_arrays_2_sentinel_arraysrs2 H,,vvu--+rvd|| } NG $E]5")44 DEE"*E 3w<< ::QZ&&u5&99 mE2:..G ,288BHD!%e$$u++/,L 'A .@.@IH \ ! !  Fvf()) $9Xw77   \ ! !N!!!K##vvt,,  ?466D+-6$<<HV[%%'''V[FZ''F#F4L6""""   rctd|DsdSt||}tj|t |z}|S)zU Check for empty sample; return appropriate output for a vectorized hypotest c3,K|]}|jdkVdS)rN)sizer[s r z&_check_empty_inputs..s)66Fv{a666666rN)rErPr5r@r)r_r' output_shapeoutputs r_check_empty_inputsrsW 66g666 6 6t7wEEL W\ " "Xw%7 7F Mrc(|rfd|Dn|S)z^ Add reduced axes back to all the arrays in the result object if keepdims = True. chg|].}t|tstj|n|/Sr) isinstancer<r5 expand_dims)rr reduced_axess rrz%_add_reduced_axes..sO K K K>>O  //r 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. propagaterrzbool, default: FalsezIf 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.a Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new code) are converted to ``np.ndarray`` before the calculation is performed. In this case, the output will be a scalar or ``np.ndarray`` of appropriate shape rather than a 2D ``np.matrix``. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a masked array with ``mask=False``.r1rc   pi} ddd| dttjjdkrfdt s dd fd n  f d } | S) a' Factory for a wrapper that adds axis/nan_policy params to a function. Parameters ---------- tuple_to_result : 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_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 or callable, 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. If callable, it must accept a list of samples, axis, and a dictionary of keyword arguments passed to the wrapper function as arguments and return a bool indicating weather the samples passed are too small. 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``. Alternatively, may be a callable that accepts a dictionary of arguments passed into the wrapped function and returns the number of outputs corresponding with those arguments. kwd_samples : sequence, default: () The names of keyword parameters that should be treated as samples. For example, `gmean` accepts as its first argument a sample `a` but also `weights` as a fourth, optional keyword argument. In this case, we use `n_samples=1` and kwd_samples=['weights']. override : dict, default: {'vectorization': False, 'nan_propagation': True} Pass a dictionary with ``'vectorization': True`` to ensure that the decorator overrides the function's behavior for multimensional input. 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