+
    Û,½i×  ã                   óâ   € ^ RI t^ RI Ht ^ RIHt . ROt. ROt]! ]RRR1,          4      t]! ]RRR1,          4      tR t	. R	Ot
. R
Ot]! ]
RRR1,          4      t]! ]RRR1,          4      tR tR# )é    N)Úpoly1d)Úbetac                óÒ  € \         P                  ! V 4      p V P                  p\         P                  ! V 4      P	                  \         P
                  4      p RpV R8  pV R8H  p\         P                  ! V 4      V8  pW4,          V,          ( pW,          pW,          p\         P                  ! V 4      p	\         P                  W“&   \         P                  W”&   VP                  ^ 8”  d   \        V4      \        V4      ,          W•&   VP                  ^ 8”  dN   RV^,          ,          RR^V,          ,           ,          \        V^,           V^,           4      ,
          ,          W–&   Wn        V	# )a«  Variance of the Tukey Lambda distribution.

Parameters
----------
lam : array_like
    The lambda values at which to compute the variance.

Returns
-------
v : ndarray
    The variance.  For lam < -0.5, the variance is not defined, so
    np.nan is returned.  For lam = 0.5, np.inf is returned.

Notes
-----
In an interval around lambda=0, this function uses the [4,4] Pade
approximation to compute the variance.  Otherwise it uses the standard
formula (https://en.wikipedia.org/wiki/Tukey_lambda_distribution).  The
Pade approximation is used because the standard formula has a removable
discontinuity at lambda = 0, and does not produce accurate numerical
results near lambda = 0.
g333333³?g       @ç      ð?g      à¿)ÚnpÚasarrayÚshapeÚ
atleast_1dÚastypeÚfloat64ÚabsÚ
empty_likeÚnanÚinfÚsizeÚ_tukeylambda_var_pÚ_tukeylambda_var_qr   )
ÚlamÚshpÚ	thresholdÚlow_maskÚneghalf_maskÚ
small_maskÚreg_maskÚsmallÚregÚvs
   &         Ú@/usr/lib/python3/dist-packages/scipy/stats/_tukeylambda_stats.pyÚtukeylambda_variancer   +   s  € ô. *Š*S‹/€CØ
)‰)€CÜ
-Š-˜Ó
×
#Ñ
#¤B§J¡JÓ
/€Cð €Ið
 T‰z€Hà˜$‘;€Lä—’˜“˜yÑ(€JàÕ(¨:Õ5Ð6€Hð O€EØ
-€Cô 	ŠcÓ€AÜ—&‘&€AKÜ—f‘f€AOØ‡zzA„~ä*¨5Ó1Ô4FÀuÓ4MÕMˆ‰Ø
‡xx!„|Ø˜S !V•|¨¨s°Q¸µW­}Õ(=Ü(,¨S°1­W°c¸AµgÓ(>õ)?õ @ˆ‰à„GØ€Hó    c                óÒ  € \         P                  ! V 4      p V P                  p\         P                  ! V 4      P	                  \         P
                  4      p RpV R8  pV R8H  p\         P                  ! V 4      V8  pW4,          V,          ( pW,          pW,          p\         P                  ! V 4      p	\         P                  W“&   \         P                  W”&   VP                  ^ 8”  d   \        V4      \        V4      ,          W•&   VP                  ^ 8”  dÎ   R^V,          ^,           ,          ^\        ^V,          ^,           V^,           4      ,          ,
          ^\        ^V,          ^,           ^V,          ^,           4      ,          ,           p
^R^V,          ^,           ,          \        V^,           V^,           4      ,
          ^,          ,          pW«,          ^,
          W–&   Wn        V	# )a*  Kurtosis of the Tukey Lambda distribution.

Parameters
----------
lam : array_like
    The lambda values at which to compute the variance.

Returns
-------
v : ndarray
    The variance.  For lam < -0.25, the variance is not defined, so
    np.nan is returned.  For lam = 0.25, np.inf is returned.

g)\Âõ(¬?r   g      Ð¿)r   r   r	   r
   r   r   r   r   r   r   r   Ú_tukeylambda_kurt_pÚ_tukeylambda_kurt_qr   )r   r   r   r   Únegqrtr_maskr   r   r   r   ÚkÚnumerÚdenoms   &           r   Útukeylambda_kurtosisr(   “   st  € ô *Š*S‹/€CØ
)‰)€CÜ
-Š-˜Ó
×
#Ñ
#¤B§J¡JÓ
/€Cð €Ið U‰{€Hà˜%‘<€Lä—’˜“˜yÑ(€JàÕ(¨:Õ5Ð6€Hð O€EØ
-€Cô 	ŠcÓ€AÜ—&‘&€AKÜ—f‘f€AOØ‡zzA„~Ü+¨EÓ2Ô5HÈÓ5OÕOˆ‰Ø
‡xx!„|Ø˜˜C !Õ$ q¬4°°Cµ¸!µ¸SÀ1½WÓ+EÕ'EÕEØ”T˜!˜c' A+ q¨3¥w°¥{Ó3Õ3õ4ˆàS˜!˜c' A+Õ&¬¨c°A­g°s¸QµwÓ)?Õ?À!ÕCÕCˆØ•m aÕ'ˆ‰ð „GØ€Hr    )gÓSb¦Q
@gŸo€|-aç?g_3
L¶/á¿g†|úA"Æ?gCÜUÑG˜¿)r   g<ŽŸ*x@gÿÍ y’¼@g`B{dêAü?g-¡~éëÐ?éÿÿÿÿ)g333333ó?g6|ýÃòiÀgeÛSH§6ÀgÑí™˜^Ê?gË)kPd@)r   g?…Ý»A¯@gID@êî)@g¢€¤Pr‘Û?g`2Ž¿fQÀ)Únumpyr   r   Úscipy.specialr   Ú_tukeylambda_var_pcÚ_tukeylambda_var_qcr   r   r   Ú_tukeylambda_kurt_pcÚ_tukeylambda_kurt_qcr"   r#   r(   © r    r   Ú<module>r1      s’   ðÛ Ý Ý ò8-Ð ò?Ð ñ
 Ð/±°"°Õ5Ó6Ð ÙÐ/±°"°Õ5Ó6Ð ò9òz?Ð òAÐ ñ
 Ð1±$°B°$Õ7Ó8Ð ÙÐ1±$°B°$Õ7Ó8Ð ô4r    