
    =hu                     (   S SK JrJr  S SKrS SKr\" \R                  5      r\\" S5      :  r\(       + r	\\" S5      :  r
\\" S5      :  r\\" S5      :  r\\" S5      :  rS r\R                  S4S	 jr\
(       a  S
SKJr  OS SKJr   SSS.S jjrg)    )VersionparseNz1.4.99z1.5.99z1.6.99z1.8.99z1.15.99c                 *   U S::  a  U $ X S-
  -  (       d  U $ [        S5      nSnX :  ac  UnX0:  aB  U * U-  * nSUS-
  R                  5       -  nXS-  nX`:X  a  U$ Xa:  a  UnUS-  nX0:X  a  U$ X0:  a  MB  X1:  a  UnUS-  nX :X  a  U$ X :  a  Mc  X!:  a  UnU$ )a	  
Find the next regular number greater than or equal to target.
Regular numbers are composites of the prime factors 2, 3, and 5.
Also known as 5-smooth numbers or Hamming numbers, these are the optimal
size for inputs to FFTPACK.

Target must be a positive integer.
      inf         )float
bit_length)targetmatchp5p35quotientp2Ns          kC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\statsmodels/compat/scipy.py_next_regularr      s     { qj!%LE	
B
+l !C(H10023BA{1HC}
 l ;E
a<I+ +, 
zL    c                     [         R                  " U [        S9U-  nUb  UR                  U5      n[	        U[         R
                  5      (       d  [         R                  " U5      nU$ )zReturn an array of all value.)dtype)nponesboolastype
isinstancendarrayasarray)shapevaluetypecodeouts       r   	_valarrayr%   <   sP     ''%t
$u
,Cjj"c2::&&jjoJr   r   )multivariate_t
fill_valuec                    SSK Js  Jn  UR                  XX#US9$ ! [        [
        4 a    SSKJn  U" XX$U5      s $ f = f)ay  
Run one of two elementwise functions depending on a condition.

Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise
when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.

Parameters
----------
cond : array
    The condition, expressed as a boolean array.
args : Array or tuple of Arrays
    Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.
f1 : callable
    Elementwise function of `args`, returning a single array.
    Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.
f2 : callable, optional
    Elementwise function of `args`, returning a single array.
    Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.
    Mutually exclusive with `fill_value`.
fill_value : Array or scalar, optional
    If provided, value with which to fill output array where `cond` is False.
    It does not need to be scalar; it needs however to be broadcastable with
    `cond` and `args`.
    Mutually exclusive with `f2`. You must provide one or the other.
xp : array_namespace, optional
    The standard-compatible namespace for `cond` and `args`. Default: infer.

Returns
-------
Array
    An array with elements from the output of `f1` where `cond` is True and either
    the output of `f2` or `fill_value` where `cond` is False. The returned array has
    data type determined by type promotion rules between the output of `f1` and
    either `fill_value` or the output of `f2`.

Notes
-----
Falls back to _lazywhere if xpx.apply_where is not available.

``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even
when `cond` is False, and `f2` when cond is True. This function evaluates each
function only for their matching condition, if the backend allows for it.

On Dask, `f1` and `f2` are applied to the individual chunks and should use functions
from the namespace of the chunks.

r   Nr'   )
_lazywhere)scipy._lib.array_api_extra_libarray_api_extraapply_whereImportErrorAttributeErrorscipy._lib._utilr*   )condargsf1f2r(   xpxr*   s          r   r.   r.   N   sI    d:00t2jII( :/$bb99:s     ??)N)packaging.versionr   r   numpyr   scipy__version__
SP_VERSIONSP_LT_15SCIPY_GT_14SP_LT_16SP_LT_17SP_LT_19	SP_LT_116r   nanr%   _scipy_multivariate_tr&   scipy.statsr.    r   r   <module>rF      s    ,  5$$%
))l))))))++	*Z 66D  5* 9:.29:r   