(ph,|PSSKrSSKrSSKJr SSKJrJr SSKJ r J r J r SSK J r Jr SSKrSrSrSrS r"S S \5rS%S jrS&S jrS&SjrS&SjrS'SjrSrSrSrSrSrSr SRCS5r"S\ \"4Sjr#Sr Sr$SRCS5r"\"\ \$\"5r%\R"\ \RRLSS9r'S r S!r$S"RCS5r"\"\ \$\"5r(\R"\ \RRLSS9r)S#RCS5r*S(S$jr+g))N)wraps) FunctionDoc Parameter) _contains_nan AxisError_get_nan)array_namespaceis_numpyz{One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.zAfter omitting NaNs, one or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.zAll axis-slices of one or more sample arguments are too small; all elements of returned arrays will be NaN. See documentation for sample size requirements.zAfter omitting NaNs, one or more axis-slices of one or more sample arguments is too small; corresponding elements of returned arrays will be NaN. See documentation for sample size requirements.c\rSrSrSrg)SmallSampleWarning%N)__name__ __module__ __qualname____firstlineno____static_attributes__rO/var/www/html/venv/lib/python3.13/site-packages/scipy/stats/_axis_nan_policy.pyr r %srr c\U(dU$Uc[U6OUnUVs/sHo2RU5PM nnUVs/sHo3RPM nn[XA5nUcU/[ U5-n[ X5VVs/sHupgUR Xg5PM snn$s snfs snfs snnf)zH Broadcast shapes of arrays, ignoring incompatibility of specified axes )r asarrayshape_broadcast_shapeslenzip broadcast_to)arraysaxisxparrshapes new_shapesarray new_shapes r_broadcast_arraysr%)s  %'Z& !RB)/ 0#jjoF 0#) *6Cii6F *"60J | \#f+- $'$; =$;  OOE -$; == 1 * =sBB#=B(c U(dU$Ubt[R"U5nSn[R"SS9 UR[5nSSS5 [R"WU5(d [ U5eUn[UVs/sHn[U5PM sn5n[R"[U5U4[S9n[Xp5HupXX[U5[U5- S&M UbXaUS:-XS:'[R"U5nUSU:d USS:aSU3n[ U5e[[R"U55[U5:wa [ S 5eUSS2U4n [R"XqS S 9n[R"USS 9n XRSS 9-n [R "US :HXz:H-)5(a [ S 5eUbTU[R""[U55- n W V s/sH#n [%[R&"XU 55PM% nn U$[%U 5$!,(df  GN=f![ an[ U5UeSnAff=fs snfs sn f) z> Broadcast shapes, ignoring incompatibility of specified axes Nz:`axis` must be an integer, a tuple of integers, or `None`.ignore)invalid)dtyperz/`axis` is out of bounds for array of dimension z*`axis` must contain only distinct elementsrz/Array shapes are incompatible for broadcasting.)np atleast_1derrstateastypeint ValueErrorr array_equalmaxronesrsortuniquedeleteallanyarangetupleinsert) r!rmessageaxis_intern_dimsr"rowremoved_shapesr$new_axis removed_shapes rrr9sN   }}T"N ,X.;;s+/~~h--G$ $&1&#e*&1 2F#f+v.c:J*- $)CHSZ  !. tax.0AXwwt} 8v a1117:GG$ $ ryy 3t9 ,HI I#AtG,YYza8 z*I Q''I  vvqZ%<=>??JKK "))CI..+9;+9-BIIi=IJ+9 ;Yc/. ,G$! + ,2H;s@II I I=*J III I:) I55I:cXUVs/sHo"RPM nn[X15$s snf)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 -------- >>> 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) )r_broadcast_shapes_remove_axis)rrr r!s r#_broadcast_array_shapes_remove_axisrHys*($* *6Cii6F * ( 66+s'cj[X5nUSnUb[R"X!5n[U5$)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)rr-r8r<)r!rrs rrGrGs5v ,F 1IE  %& <rFcV[X(dUOS5n[R"XS9nU$)z3Concatenate arrays along an axis with broadcasting.Nr,)r%r- concatenate)rrpairedress r_broadcast_concatenaterNs% v6tt DF .. +C Jrc$U(d*UVs/sHo"[R"U5)PM sn$[R"US5nUSSHnU[R"U5-nM U)nUVs/sHo"UPM sn$s snfs snf)z.Remove nans from paired or unpaired 1D samplesrr+N)r-isnan)samplesrLsamplenansnot_nanss r _remove_nansrUs 8?@f(()@@ 88GAJ D!"+bhhv&&uH+2 378 7 33A 4s #B7B cU(dUVs/sH o3X2:gPM sn$USU:HnUSSH nXCU:H-nM U)nUVs/sHo3UPM sn$s snfs snf)z9Remove sentinel values from paired or unpaired 1D samplesrr+Nr)rQrLsentinelrR sentinels not_sentinelss r_remove_sentinelrZsy 9@Avv)*AAx'I!"+8!34 JM07 8f= ! 88B 9s AAcSnUH/n[USS5nU=(d [R"U5nM1 U(dUS4$[R"U6n[R"U[R 5(aUO[R n[[U55HnXRUSS9X'M [R"U[R5nU(a[RO[RnU"U5RU"U5RpU(a[ROSn Un X:aHUH:n[R"X+:H5(dM"U "U [R *5n O OX:aMHSn [#U 5e/n UHn[USS5nUb{[R$"X2R&5n[R"U5(aUR(R+5O UR(n[R,"U5nXU'U R/U5 M X4$)NFmask)copyc US- $)Nr+r)x_s r2_masked_arrays_2_sentinel_arrays..s1q5rzThis 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`.)getattrr-r: result_type issubdtypenumberfloat64rangerr0inexactfinfoiinfor4min nextafterinfr2rrdatar]rappend)rQhas_maskrRr\r)iriinfo max_possible min_possiblermrWr> out_sampless r _masked_arrays_2_sentinel_arraysrws Hvvu-+rvvd| } NNG $E]]5"))44E"**E 3w< Z&&u5&9 ! mmE2::.G288BHHD!%e$u+//, ' .@IH  !Fvvf())$Xw7   !N!!Kvvt,  ??46D+-66$<# UHoRS:Hv M g7f)rN)size).0rRs r &_check_empty_inputs..s6gF{{agsN)r:rHr-r5r)rQr output_shapeoutputs r_check_empty_inputsrsA 6g6 6 67wEL WW\ "Xw%7 7F MrcU(a>UVs/sH0n[U[5(d[R"X15OUPM2 sn$U$s snf)zR Add reduced axes back to all the arrays in the result object if keepdims = True. ) isinstancer1r- expand_dims)rM reduced_axeskeepdimsrs r_add_reduced_axesrsXGJ KFIFJvs++^^F 1178FI K" " Ks7ArzIf 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. cSU3n[XU5n[R"U[RRUS9nXE4$)Nzint or None, default: default)rinspect KEYWORD_ONLY) default_axis_name_desc_type_axis_parameter_doc_axis_parameters r_get_axis_paramsr"sJ$\N 3E#E%8''(/(9(9(F(F0<>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``.c $^^^^^^^^^^ T=(d 0n SSS.mTRU 5 TcSm[[R"T5R5S:XaT4Sjm[ T5(d SS.U4S jjm OTm UU UUUUUUU4 S jn U $) aS 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. Use ``'nan_propagation': False`` to ensure that the decorator does not override the function's behavior for ``nan_policy='propagate'``. 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