
    Ug;
                    ~   d dl Z d dlmZ d dlmZmZ d dlZd dlZd dl	m
Z
 d dlmZ d dlmZmZmZ d dlmZ d dlmZ d d	lmZ d dlmZ d dlmc mZ d d
lmZmZ ddlm Z  ddl!m"Z#m$Z% ddl&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/ ddl0m1Z1m2Z2m3Z3 ddl4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z; ddl<m=Z= d dl>m?Z? d dl@mAZA d dlBmCZC d ZDd ZEddZF G d de+          ZG eGddd          ZH G d de+          ZI eId ddd           ZJ G d! d"e+          ZK eKdd#$          ZL ejM        d%ejN        z            ZO ejP        eO          ZQd& ZRd' ZSd( ZTd) ZUd* ZVd+ ZWd, ZXd- ZY G d. d/e+          ZZ eZd01          Z[ G d2 d3e+          Z\ e\dd4$          Z] G d5 d6e+          Z^ e^ejN         d7z  ejN        d7z  d8          Z_ G d9 d:e+          Z` e`ddd;          Za G d< d=eb          Zc G d> d?eA          Zdd@ ZedA Zf G dB dCe+          Zg egdddD          Zh G dE dFe+          Zi eiddG$          Zj G dH dIe+          Zk ekdddJ          Zl G dK dLe+          Zm emddM$          Zn G dN dOe+          Zo eoddP$          Zp G dQ dRem          Zq eqddS$          Zr G dT dUe+          Zs esdV1          Zt G dW dXe+          Zu euddY$          Zv G dZ d[e+          Zw ewdd\$          Zx G d] d^e+          Zy eyejN         ejN        d_          Zz G d` dae+          Z{ e{db1          Z| G dc dde+          Z} e}de1          Z~ G df dge+          Z eddh$          Z G di dje+          Z edk1          Zdl Z G dm dne+          Z eddo$          Z G dp dqe+          Z eddr$          Z G ds dte+          Z eddu$          Z G dv dwe+          Z eddx$          Z G dy dze+          Z edd{$          Z G d| d}e+          Z edd~$          Z G d de+          Z edd$          Z G d de+          Z ed1          Z G d de+          Z edd          Z G d de+          Z ed1          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z ed1          Zd Z G d de+          Z edd$          Z G d de          Z edd$          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z ed1          Z G d de+          Z edd$          Zd Z G d de+          Z ed1          Z G d de+          Z ed1          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z ed1          Z G d de+          Z eddd          Z G d de+          Z edd$          Z G d de+          Z eddì$          Z G dĄ de+          Z eddƬ$          Z G dǄ de+          Z edɬ1          Z G dʄ de+          Z ed d̬$          Z G d̈́ de+          Z edϬ1          Z G dЄ de+          Z edddҬ          Z G dӄ de+          Z edլ1          Z G dք de+          Z edج1          Z G dل de+          Z ed۬1          Zd܄ Z G d݄ de+          Z edd߬$          Z G d de+          Z edd⬈          Z G d de+          Z ed1          Z G d de+          Z ed1          Z G d de+          Z edd$          Zd Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z ed1          Z G d de+          Z edd$          Z G d d e+          Z ed1          Z G d de+          Z edd$          Zd Z G d de+          Z edd$          Z G d	 d
e+          Z edd$          Z G d de+          Z ed1          Z G d de+          Z ed1          Z G d de+          Z edd$          Z G d de+          Z edd$          Z G d de+          Z ed1          Z G d de+          Z eddd          Z G d de+          Z edd $          Z G d! d"e+          Z ed#1          Z G d$ d%e+          Z ed&dd'          Z  G d( d)e+          Z edd*$          Z G d+ d,e+          Z ed-1          Z ed.1          Z G d/ d0e+          Z edd1$          Z G d2 d3e+          Z ed41          Z	 G d5 d6e+          Z
 e
dd7$          Z G d8 d9e+          Z ed&dd:          Z G d; d<e+          Z ed=1          Z G d> d?e+          Z ed@1          Z G dA dBe+          ZdCZ G dD dEe          Z edddF          Z edddG          Zg dHZ G dI dJ          ZeD ]Z eee ee                      G dK dLe+          Z edddM          Z G dN dOe+          Z eddP$          ZdQ ZdR Z dS Z! G dT dUe+          Z" e"dVdW          Z# G dX dYe+          Z$ e$ddZ$          Z% G d[ d\e+          Z& e&d]1          Z' G d^ d_ec          Z( G d` dae+          Z) e)dddb          Z* G dc dde+          Z+ e+de1          Z, e+ejN         ejN        df          Z- G dg dhe          Z. e.ddi$          Z/ G dj dke+          Z0 e0dd%ejN        z  dl          Z1 G dm dne+          Z2 e2do1          Z3 G dp dqe+          Z4 e4d dr$          Z5 G ds dte+          Z6 e6dudvw          Z7dx Z8 G dy dze+          Z9 e9d{d|dd}          Z: G d~ de+          Z; G d de+          Z< e<dd ej=                  Z> G d de+          Z? e?dd$          Z@ eA eB            C                                D                                          ZE e(eEe+          \  ZFZGeFeGz   dgz   ZHdS (      N)Iterable)wrapscached_property
Polynomial)BSpline)extend_notes_in_docstringreplace_notes_in_docstringinherit_docstring_from)LowLevelCallable)optimize)	integrate)_lazyselect
_lazywhere   )_stats)tukeylambda_variancetukeylambda_kurtosis)	_vectorize_rvs_over_shapesget_distribution_names	_kurtosis_isintegralrv_continuous_skew_get_fixed_fit_value_check_shape
_ShapeInfo)kolmognkolmognpkolmogni)_XMIN_LOGXMIN_EULER_ZETA3_SQRT_PI_SQRT_2_OVER_PI_LOG_SQRT_2_OVER_PI)CensoredData)root_scalar)FitErrorc                     |                      dd           |                      dd           |                      dd           |                      dd           | rt          d| z            dS )a  
    Remove the optimizer-related keyword arguments 'loc', 'scale' and
    'optimizer' from `kwds`.  Then check that `kwds` is empty, and
    raise `TypeError("Unknown arguments: %s." % kwds)` if it is not.

    This function is used in the fit method of distributions that override
    the default method and do not use the default optimization code.

    `kwds` is modified in-place.
    locNscale	optimizermethodzUnknown arguments: %s.)pop	TypeError)kwdss    ]/var/www/surfInsights/venv3-11/lib/python3.11/site-packages/scipy/stats/_continuous_distns.py_remove_optimizer_parametersr4   '   sz     	HHUDHHWdHH[$HHXt 90478889 9    c                 <     t                      fd            }|S )Nc                 B   |                     dd                                          }t          |t                    }|dk    s|rD|                                dk    r, t          t          |           |           j        |g|R i |S |r|j        } | |g|R i |S )Nr/   mlemmr   )	getlower
isinstancer(   num_censoredsupertypefit_uncensored)selfdataargsr2   r/   censoredfuns         r3   wrapperz _call_super_mom.<locals>.wrapper>   s    (E**0022dL11T>>h>4+<+<+>+>+B+B.5dT**.tCdCCCdCCC ( '3tT1D111D111r5   )r   )rF   rG   s   ` r3   _call_super_momrH   :   s5     3ZZ2 2 2 2 Z2 Nr5   c                      |p|dz
  }||z
  } fd} |||          s;|dz  }||z
  }d}t          j        |          rt          |           |||          ;|S )Nr   c                 |    t          j         |                     t          j         |                    k    S Nnpsign)lbrackrbrackrF   s     r3   interval_contains_rootz1_get_left_bracket.<locals>.interval_contains_rootV   s2    wss6{{##rwss6{{';';;;r5      zVThe solver could not find a bracket containing a root to an MLE first order condition.)rM   isinfFitSolverError)rF   rP   rO   diffrQ   msgs   `     r3   _get_left_bracketrW   O   s    !vzFF?D< < < < < %$VV44 &	$78F 	& %%% %$VV44 & Mr5   c                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
	ksone_gena  Kolmogorov-Smirnov one-sided test statistic distribution.

    This is the distribution of the one-sided Kolmogorov-Smirnov (KS)
    statistics :math:`D_n^+` and :math:`D_n^-`
    for a finite sample size ``n >= 1`` (the shape parameter).

    %(before_notes)s

    See Also
    --------
    kstwobign, kstwo, kstest

    Notes
    -----
    :math:`D_n^+` and :math:`D_n^-` are given by

    .. math::

        D_n^+ &= \text{sup}_x (F_n(x) - F(x)),\\
        D_n^- &= \text{sup}_x (F(x) - F_n(x)),\\

    where :math:`F` is a continuous CDF and :math:`F_n` is an empirical CDF.
    `ksone` describes the distribution under the null hypothesis of the KS test
    that the empirical CDF corresponds to :math:`n` i.i.d. random variates
    with CDF :math:`F`.

    %(after_notes)s

    References
    ----------
    .. [1] Birnbaum, Z. W. and Tingey, F.H. "One-sided confidence contours
       for probability distribution functions", The Annals of Mathematical
       Statistics, 22(4), pp 592-596 (1951).

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.stats import ksone
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)

    Display the probability density function (``pdf``):

    >>> n = 1e+03
    >>> x = np.linspace(ksone.ppf(0.01, n),
    ...                 ksone.ppf(0.99, n), 100)
    >>> ax.plot(x, ksone.pdf(x, n),
    ...         'r-', lw=5, alpha=0.6, label='ksone pdf')

    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.

    Freeze the distribution and display the frozen ``pdf``:

    >>> rv = ksone(n)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()

    Check accuracy of ``cdf`` and ``ppf``:

    >>> vals = ksone.ppf([0.001, 0.5, 0.999], n)
    >>> np.allclose([0.001, 0.5, 0.999], ksone.cdf(vals, n))
    True

    c                 @    |dk    |t          j        |          k    z  S Nr   rM   roundrB   ns     r3   	_argcheckzksone_gen._argcheck       Q1+,,r5   c                 @    t          dddt          j        fd          gS Nr_   Tr   TFr   rM   infrB   s    r3   _shape_infozksone_gen._shape_info       3q"&k=AABBr5   c                 .    t          j        ||           S rK   )scu	_smirnovprB   xr_   s      r3   _pdfzksone_gen._pdf   s    a####r5   c                 ,    t          j        ||          S rK   )rk   	_smirnovcrm   s      r3   _cdfzksone_gen._cdf   s    }Q"""r5   c                 ,    t          j        ||          S rK   )scsmirnovrm   s      r3   _sfzksone_gen._sf   s    z!Qr5   c                 ,    t          j        ||          S rK   )rk   
_smirnovcirB   qr_   s      r3   _ppfzksone_gen._ppf   s    ~a###r5   c                 ,    t          j        ||          S rK   )rt   smirnoviry   s      r3   _isfzksone_gen._isf       {1a   r5   N)__name__
__module____qualname____doc__r`   rh   ro   rr   rv   r{   r~    r5   r3   rY   rY   f   s        B BF- - -C C C$ $ $# # #     $ $ $! ! ! ! !r5   rY                 ?ksone)abnamec                   B    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
S )	kstwo_genad  Kolmogorov-Smirnov two-sided test statistic distribution.

    This is the distribution of the two-sided Kolmogorov-Smirnov (KS)
    statistic :math:`D_n` for a finite sample size ``n >= 1``
    (the shape parameter).

    %(before_notes)s

    See Also
    --------
    kstwobign, ksone, kstest

    Notes
    -----
    :math:`D_n` is given by

    .. math::

        D_n = \text{sup}_x |F_n(x) - F(x)|

    where :math:`F` is a (continuous) CDF and :math:`F_n` is an empirical CDF.
    `kstwo` describes the distribution under the null hypothesis of the KS test
    that the empirical CDF corresponds to :math:`n` i.i.d. random variates
    with CDF :math:`F`.

    %(after_notes)s

    References
    ----------
    .. [1] Simard, R., L'Ecuyer, P. "Computing the Two-Sided
       Kolmogorov-Smirnov Distribution",  Journal of Statistical Software,
       Vol 39, 11, 1-18 (2011).

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.stats import kstwo
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)

    Display the probability density function (``pdf``):

    >>> n = 10
    >>> x = np.linspace(kstwo.ppf(0.01, n),
    ...                 kstwo.ppf(0.99, n), 100)
    >>> ax.plot(x, kstwo.pdf(x, n),
    ...         'r-', lw=5, alpha=0.6, label='kstwo pdf')

    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.

    Freeze the distribution and display the frozen ``pdf``:

    >>> rv = kstwo(n)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()

    Check accuracy of ``cdf`` and ``ppf``:

    >>> vals = kstwo.ppf([0.001, 0.5, 0.999], n)
    >>> np.allclose([0.001, 0.5, 0.999], kstwo.cdf(vals, n))
    True

    c                 @    |dk    |t          j        |          k    z  S r[   r\   r^   s     r3   r`   zkstwo_gen._argcheck  ra   r5   c                 @    t          dddt          j        fd          gS rc   re   rg   s    r3   rh   zkstwo_gen._shape_info	  ri   r5   c                 b    dt          |t                    s|nt          j        |          z  dfS N      ?r   )r<   r   rM   
asanyarrayr^   s     r3   _get_supportzkstwo_gen._get_support  s4    jH55KQQ2=;K;KL 	r5   c                 "    t          ||          S rK   )r   rm   s      r3   ro   zkstwo_gen._pdf  s    1~~r5   c                 "    t          ||          S rK   r   rm   s      r3   rr   zkstwo_gen._cdf  s    q!}}r5   c                 &    t          ||d          S NFcdfr   rm   s      r3   rv   zkstwo_gen._sf  s    q!''''r5   c                 &    t          ||d          S )NTr   r    ry   s      r3   r{   zkstwo_gen._ppf  s    1$''''r5   c                 &    t          ||d          S r   r   ry   s      r3   r~   zkstwo_gen._isf  s    1%((((r5   N)r   r   r   r   r`   rh   r   ro   rr   rv   r{   r~   r   r5   r3   r   r      s        A AD- - -C C C      ( ( (( ( () ) ) ) )r5   r   kstwo)momtyper   r   r   c                   6    e Zd ZdZd Zd Zd Zd Zd Zd Z	dS )	kstwobign_gena  Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic.

    This is the asymptotic distribution of the two-sided Kolmogorov-Smirnov
    statistic :math:`\sqrt{n} D_n` that measures the maximum absolute
    distance of the theoretical (continuous) CDF from the empirical CDF.
    (see `kstest`).

    %(before_notes)s

    See Also
    --------
    ksone, kstwo, kstest

    Notes
    -----
    :math:`\sqrt{n} D_n` is given by

    .. math::

        D_n = \text{sup}_x |F_n(x) - F(x)|

    where :math:`F` is a continuous CDF and :math:`F_n` is an empirical CDF.
    `kstwobign`  describes the asymptotic distribution (i.e. the limit of
    :math:`\sqrt{n} D_n`) under the null hypothesis of the KS test that the
    empirical CDF corresponds to i.i.d. random variates with CDF :math:`F`.

    %(after_notes)s

    References
    ----------
    .. [1] Feller, W. "On the Kolmogorov-Smirnov Limit Theorems for Empirical
       Distributions",  Ann. Math. Statist. Vol 19, 177-189 (1948).

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zkstwobign_gen._shape_infoI      	r5   c                 ,    t          j        |           S rK   )rk   _kolmogprB   rn   s     r3   ro   zkstwobign_gen._pdfL  s    Qr5   c                 *    t          j        |          S rK   )rk   _kolmogcr   s     r3   rr   zkstwobign_gen._cdfO  s    |Ar5   c                 *    t          j        |          S rK   )rt   
kolmogorovr   s     r3   rv   zkstwobign_gen._sfR  s    }Qr5   c                 *    t          j        |          S rK   )rk   	_kolmogcirB   rz   s     r3   r{   zkstwobign_gen._ppfU  s    }Qr5   c                 *    t          j        |          S rK   )rt   kolmogir   s     r3   r~   zkstwobign_gen._isfX  s    z!}}r5   N)
r   r   r   r   rh   ro   rr   rv   r{   r~   r   r5   r3   r   r   $  sy        # #H                       r5   r   	kstwobign)r   r   rR   c                 H    t          j        | dz   dz            t          z  S NrR          @)rM   exp_norm_pdf_Crn   s    r3   	_norm_pdfr   h  s!    61a4%){**r5   c                 $    | dz   dz  t           z
  S r   )_norm_pdf_logCr   s    r3   _norm_logpdfr   l  s    qD53;''r5   c                 *    t          j        |           S rK   )rt   ndtrr   s    r3   	_norm_cdfr   p  s    71::r5   c                 *    t          j        |           S rK   )rt   log_ndtrr   s    r3   _norm_logcdfr   t  s    ;q>>r5   c                 *    t          j        |           S rK   )rt   ndtrirz   s    r3   	_norm_ppfr   x  s    8A;;r5   c                 "    t          |            S rK   r   r   s    r3   _norm_sfr   |  s    aR==r5   c                 "    t          |            S rK   r   r   s    r3   _norm_logsfr     s    r5   c                 "    t          |            S rK   r   r   s    r3   	_norm_isfr     s    aLL=r5   c                       e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd Zd Zd Ze eed          d                         Zd ZdS )norm_gena  A normal continuous random variable.

    The location (``loc``) keyword specifies the mean.
    The scale (``scale``) keyword specifies the standard deviation.

    %(before_notes)s

    Notes
    -----
    The probability density function for `norm` is:

    .. math::

        f(x) = \frac{\exp(-x^2/2)}{\sqrt{2\pi}}

    for a real number :math:`x`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   znorm_gen._shape_info  r   r5   Nc                 ,    |                     |          S rK   )standard_normalrB   sizerandom_states      r3   _rvsznorm_gen._rvs  s    ++D111r5   c                      t          |          S rK   r   r   s     r3   ro   znorm_gen._pdf  s    ||r5   c                      t          |          S rK   r   r   s     r3   _logpdfznorm_gen._logpdf      Ar5   c                      t          |          S rK   r   r   s     r3   rr   znorm_gen._cdf      ||r5   c                      t          |          S rK   r   r   s     r3   _logcdfznorm_gen._logcdf  r   r5   c                      t          |          S rK   r   r   s     r3   rv   znorm_gen._sf  s    {{r5   c                      t          |          S rK   )r   r   s     r3   _logsfznorm_gen._logsf  s    1~~r5   c                      t          |          S rK   r   r   s     r3   r{   znorm_gen._ppf  r   r5   c                      t          |          S rK   r   r   s     r3   r~   znorm_gen._isf  r   r5   c                     dS )N)r   r   r   r   r   rg   s    r3   r   znorm_gen._stats      !!r5   c                 P    dt          j        dt           j        z            dz   z  S Nr   rR   r   rM   logpirg   s    r3   _entropyznorm_gen._entropy  s     BF1RU7OOA%&&r5   a}          For the normal distribution, method of moments and maximum likelihood
        estimation give identical fits, and explicit formulas for the estimates
        are available.
        This function uses these explicit formulas for the maximum likelihood
        estimation of the normal distribution parameters, so the
        `optimizer` and `method` arguments are ignored.

notesc                    |                     dd           }|                     dd           }t          |           ||t          d          t          j        |          }t          j        |                                          st          d          ||                                }n|}|-t          j        ||z
  dz                                            }n|}||fS )Nflocfscale3All parameters fixed. There is nothing to optimize.$The data contains non-finite values.rR   )	r0   r4   
ValueErrorrM   asarrayisfiniteallmeansqrt)rB   rC   r2   r   r   r,   r-   s          r3   r@   znorm_gen.fit  s     xx%%(D))$T*** 2  ) * * * z${4  $$&& 	ECDDD<))++CCC>GdSj1_224455EEEEzr5   c                 F    |dz  dk    rt          j        |dz
            S dS )z
        @returns Moments of standard normal distribution for integer n >= 0

        See eq. 16 of https://arxiv.org/abs/1209.4340v2
        rR   r   r   r   )rt   
factorial2r^   s     r3   _munpznorm_gen._munp  s*     q5A::=Q'''2r5   NN)r   r   r   r   rh   r   ro   r   rr   r   rv   r   r{   r~   r   r   rH   r
   r   r@   r   r   r5   r3   r   r     s,        ,  2 2 2 2                " " "' ' '  6? @ @ @ @ @ _<	 	 	 	 	r5   r   norm)r   c                   D    e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd ZdS )		alpha_gena&  An alpha continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `alpha` ([1]_, [2]_) is:

    .. math::

        f(x, a) = \frac{1}{x^2 \Phi(a) \sqrt{2\pi}} *
                  \exp(-\frac{1}{2} (a-1/x)^2)

    where :math:`\Phi` is the normal CDF, :math:`x > 0`, and :math:`a > 0`.

    `alpha` takes ``a`` as a shape parameter.

    %(after_notes)s

    References
    ----------
    .. [1] Johnson, Kotz, and Balakrishnan, "Continuous Univariate
           Distributions, Volume 1", Second Edition, John Wiley and Sons,
           p. 173 (1994).
    .. [2] Anthony A. Salvia, "Reliability applications of the Alpha
           Distribution", IEEE Transactions on Reliability, Vol. R-34,
           No. 3, pp. 251-252 (1985).

    %(example)s

    c                 @    t          dddt          j        fd          gS Nr   Fr   FFre   rg   s    r3   rh   zalpha_gen._shape_info      326{NCCDDr5   c                 ^    d|dz  z  t          |          z  t          |d|z  z
            z  S Nr   rR   )r   r   rB   rn   r   s      r3   ro   zalpha_gen._pdf  s0    AqDz)A,,&y3q5'9'999r5   c                     dt          j        |          z  t          |d|z  z
            z   t          j        t          |                    z
  S )Nr   )rM   r   r   r   r
  s      r3   r   zalpha_gen._logpdf"  s>    "&))|l1SU7333bfYq\\6J6JJJr5   c                 L    t          |d|z  z
            t          |          z  S Nr   r   r
  s      r3   rr   zalpha_gen._cdf%  s#    3q5!!IaLL00r5   c           
      p    dt          j        |t          |t          |          z            z
            z  S r  )rM   r   r   r   rB   rz   r   s      r3   r{   zalpha_gen._ppf(  s.    2:a)AillN";";;<<<<r5   c                 D    t           j        gdz  t           j        gdz  z   S NrR   rM   rf   nanrB   r   s     r3   r   zalpha_gen._stats+  s    xzRVHQJ&&r5   N)r   r   r   r   r   _open_support_mask_support_maskrh   ro   r   rr   r{   r   r   r5   r3   r  r    s         > "4ME E E: : :K K K1 1 1= = =' ' ' ' 'r5   r  alphac                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )

anglit_gena  An anglit continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `anglit` is:

    .. math::

        f(x) = \sin(2x + \pi/2) = \cos(2x)

    for :math:`-\pi/4 \le x \le \pi/4`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zanglit_gen._shape_infoF  r   r5   c                 0    t          j        d|z            S r  )rM   cosr   s     r3   ro   zanglit_gen._pdfI  s    vac{{r5   c                 P    t          j        |t           j        dz  z             dz  S N   r   rM   sinr   r   s     r3   rr   zanglit_gen._cdfM  s!    vaai  #%%r5   c                 P    t          j        |t           j        dz  z             dz  S r  )rM   r  r   r   s     r3   rv   zanglit_gen._sfP  s!    va"%!)m$$++r5   c                 n    t          j        t          j        |                    t           j        dz  z
  S Nr   )rM   arcsinr   r   r   s     r3   r{   zanglit_gen._ppfS  s%    y$$RU1W,,r5   c                     dt           j        t           j        z  dz  dz
  ddt           j        dz  dz
  z  t           j        t           j        z  dz
  dz  z  fS )	Nr      r   r  r   `      rR   rM   r   rg   s    r3   r   zanglit_gen._statsV  sH    BE"%KN3&RB-?ruQQR@R-RRRr5   c                 0    dt          j        d          z
  S Nr   rR   rM   r   rg   s    r3   r   zanglit_gen._entropyY      {r5   N)r   r   r   r   rh   ro   rr   rv   r{   r   r   r   r5   r3   r  r  2  s         &    & & &, , ,- - -S S S    r5   r  r   anglitc                   6    e Zd ZdZd Zd Zd Zd Zd Zd Z	dS )	arcsine_gena  An arcsine continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `arcsine` is:

    .. math::

        f(x) = \frac{1}{\pi \sqrt{x (1-x)}}

    for :math:`0 < x < 1`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zarcsine_gen._shape_infot  r   r5   c                     t          j        d          5  dt           j        z  t          j        |d|z
  z            z  cd d d            S # 1 swxY w Y   d S )Nignoredivider   r   )rM   errstater   r   r   s     r3   ro   zarcsine_gen._pdfw  s    [))) 	. 	.ru9RWQ!W---	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	. 	.   *AAAc                 n    dt           j        z  t          j        t          j        |                    z  S Nr   )rM   r   r&  r   r   s     r3   rr   zarcsine_gen._cdf|  s%    25y271::....r5   c                 P    t          j        t           j        dz  |z            dz  S r;  r!  r   s     r3   r{   zarcsine_gen._ppf  s!    vbeCik""C''r5   c                     d}d}d}d}||||fS )Nr   g      ?r         r   rB   mumu2g1g2s        r3   r   zarcsine_gen._stats  s$    3Br5   c                     dS )Ngοr   rg   s    r3   r   zarcsine_gen._entropy  s    &&r5   N
r   r   r   r   rh   ro   rr   r{   r   r   r   r5   r3   r2  r2  `  sx         &  . . .
/ / /( ( (  ' ' ' ' 'r5   r2  arcsinec                       e Zd ZdZd ZdS )FitDataErrorz=Raised when input data is inconsistent with fixed parameters.c                 *    d|d|d|df| _         d S )Nz>Invalid values in `data`.  Maximum likelihood estimation with z requires that z < (x - loc)/scale  < z for each x in `data`.rD   )rB   distrr;   uppers       r3   __init__zFitDataError.__init__  sH    B$B B7<B B"'B B B
			r5   Nr   r   r   r   rM  r   r5   r3   rH  rH    s)        GG
 
 
 
 
r5   rH  c                       e Zd ZdZd ZdS )rT   zN
    Raised when a solver fails to converge while fitting a distribution.
    c                 L    d}||                     dd          z  }|f| _        d S )Nz1Solver for the MLE equations failed to converge: 
 )replacerD   )rB   mesgemsgs      r3   rM  zFitSolverError.__init__  s,    BT2&&&G			r5   NrN  r   r5   r3   rT   rT     s-         
    r5   rT   c                 p    t          j        | |z             }||| t          j        |           z   z  z
  }|S rK   rt   psi)r   r   r_   s1psiabfuncs         r3   _beta_mle_ar\    s8     F1q5MMEeVbfQii'((DKr5   c                     | \  }}t          j        ||z             }||| t          j        |          z   z  z
  ||| t          j        |          z   z  z
  g}|S rK   rW  )thetar_   rY  s2r   r   rZ  r[  s           r3   _beta_mle_abr`    sb     DAqF1q5MMEufrvayy())ufrvayy())+DKr5   c                        e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Z fdZe eed           fd                        Zd Z xZS )beta_gena  A beta continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `beta` is:

    .. math::

        f(x, a, b) = \frac{\Gamma(a+b) x^{a-1} (1-x)^{b-1}}
                          {\Gamma(a) \Gamma(b)}

    for :math:`0 <= x <= 1`, :math:`a > 0`, :math:`b > 0`, where
    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).

    `beta` takes :math:`a` and :math:`b` as shape parameters.

    %(after_notes)s

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS Nr   Fr   r  r   re   rB   iaibs      r3   rh   zbeta_gen._shape_info  =    UQK@@UQK@@Bxr5   Nc                 0    |                     |||          S rK   beta)rB   r   r   r   r   s        r3   r   zbeta_gen._rvs  s      At,,,r5   c                     t          j        d          5  t          j        |||          cd d d            S # 1 swxY w Y   d S Nr5  over)rM   r8  rk   	_beta_pdfrB   rn   r   r   s       r3   ro   zbeta_gen._pdf  s     [h''' 	* 	*=Aq))	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	* 	*   9= =c                     t          j        |dz
  |           t          j        |dz
  |          z   }|t          j        ||          z  }|S r  )rt   xlog1pyxlogybetaln)rB   rn   r   r   lPxs        r3   r   zbeta_gen._logpdf  sG    jS1"%%S!(<(<<ryA
r5   c                 .    t          j        |||          S rK   )rt   betaincrq  s       r3   rr   zbeta_gen._cdf  s    z!Q"""r5   c                 .    t          j        |||          S rK   )rt   betainccrq  s       r3   rv   zbeta_gen._sf  s    {1a###r5   c                 .    t          j        |||          S rK   )rt   betainccinvrq  s       r3   r~   zbeta_gen._isf  s    ~aA&&&r5   c                 .    t          j        |||          S rK   )rk   	_beta_ppfrB   rz   r   r   s       r3   r{   zbeta_gen._ppf  s    }Q1%%%r5   c                 &   ||z   }||z  }||z  |dz  |dz   z  z  }d||z
  z  t          j        |dz             z  |dz   t          j        ||z            z  z  }d||z
  dz  |dz   z  ||z  |dz   z  z
  z  }||z  |dz   z  |dz   z  }||z  }	||||	fS )NrR   r         rM   r   )
rB   r   r   a_plus_b
_beta_mean_beta_variance_beta_skewness_beta_kurtosis_excess_n_beta_kurtosis_excess_d_beta_kurtosis_excesss
             r3   r   zbeta_gen._stats  s    q5xZ
1!x!| <=A;A)>)>>$qLBGAENN:<"#AzX\'B'(1u1'=(> #?"#a%8a<"8HqL"I 7:Q Q!	# 	#r5   c                    t          |t                    r|                                }t          |          t	          |          fd}t          j        |d          \  }}t                                          |||f          S )Nc                 F   | \  }}d||z
  z  t          j        ||z   dz             z  ||z   dz   z  t          j        ||z            z  }|dz  |dz  d|z  dz
  z  z
  |dz  |dz   z  z   d|z  |z  |dz   z  z
  }|||z  ||z   dz   z  ||z   dz   z  z  }|dz  }|z
  |z
  gS )NrR   r   r  r  r  )rn   r   r   skkurB  rC  s        r3   r[  z beta_gen._fitstart.<locals>.func  s    DAqAaCQ+++q1uqy9BGAaCLLHBA1ac!e$q!tQqSz1AaCE1Q3K?B!A#qs1u+qs1u%%B!GBrE2b5>!r5   )r   r   rJ  )	r<   r(   	_uncensorr   r   r   fsolver>   	_fitstart)rB   rC   r[  r   r   rB  rC  	__class__s        @@r3   r  zbeta_gen._fitstart  s    dL)) 	$>>##D4[[t__	" 	" 	" 	" 	" 	" tZ001ww  QF 333r5   z        In the special case where `method="MLE"` and
        both `floc` and `fscale` are given, a
        `ValueError` is raised if any value `x` in `data` does not satisfy
        `floc < x < floc + fscale`.

r   c           	         |                     dd           }|                     dd           }|| t                      j        |g|R i |S |                    dd            |                    dd            t	          |g d          }t	          |g d          }t          |           ||t          d          t          j        |          	                                st          d          t          j
        |          |z
  |z  }t          j        |dk              st          j        |dk              rt          d	|||z   
          |                                }||||}	d|z
  }d|z
  }n|}	|	|z  d|z
  z  }
t          j        t           |
|	t#          |          t          j        |                                          fd          \  }}}}|dk    rt)          |          |d         }
||	|
}	}
nt          j        |                                          }t+          j        |                                           }|d|z
  z  |                    d          z  dz
  }||z  }
d|z
  |z  }	t          j        t0          |
|	gt#          |          ||fd          \  }}}}|dk    rt)          |          |\  }
}	|
|	||fS )Nr   r   f0fafix_a)f1fbfix_br   r   r   r   rk  r;   rL  T)rD   full_output)rT  )ddof)r:   r>   r@   r0   r   r4   r   rM   r   r   ravelanyrH  r   r   r  r\  lenr   sumrT   rt   log1pvarr`  )rB   rC   rD   r2   r   r   r  r  xbarr   r   r^  infoierrT  rY  r_  facr  s                     r3   r@   zbeta_gen.fit  s'    xx%%(D))<6>577;t3d333d333 	4   !$(=(=(=>>!$(=(=(=>>$T***>bn ) * * * {4  $$&& 	ECDDD %/6$!) 	Htqy 1 1 	HvTGGGGyy{{>R^ ~ 4x4x DAH%A &._QTBF4LL$4$4$6$67 & & &"E4d
 axx$$////aA~ !1 !!##B4%$$&&B !d(#dhhAh&6&66:Cs
ATS A &._q!f$iiR( & & &"E4d
 axx$$////DAq!T6!!r5   c                 
   d }d }d }d }|dk    r|dk    r |||          S |dk    r$||z
  dk    r| ||          k    r |||          S |dk    r$||z
  dk    r| ||          k    r |||          S  |||          S )Nc                     t          j        | |          | dz
  t          j        |           z  z
  |dz
  t          j        |          z  z
  | |z   dz
  t          j        | |z             z  z   S r-  )rt   rv  rX  r   r   s     r3   regularz"beta_gen._entropy.<locals>.regular  sf    IaOOq1uq		&99UbfQii'(+,q519q1u*EF Gr5   c                    | |z   }dt          j        dt           j        z            t          j        |           z   t          j        |          z   dt          j        |          z  z
  dz   z  }d|z  d|dz  z  z   |dz  z   d|d	z  z  z
  }d
| z  d| dz  z  z
  | dz  z
  | d	z  z   }d
|z  d|dz  z  z
  |dz  z
  |d	z  z   }|||z   |z   dz  z   S )Nr   rR   r  r   n                         i
   x   r   )r   r   sum_ablog_termt1t2t3s          r3   asymptotic_ab_largez.beta_gen._entropy.<locals>.asymptotic_ab_large  s    UFqw"&))+bfQii7!BF6NN:JJQNH Vbo-<q~MBQAtG#ag-47BQAtG#ag-47BrBw|s222r5   c                    | |z   }t          j        |           | dz
  t          j        |           z  z
  }dd|z  z  dd|z  z  z   |dz  dz  z
  |dz  dz  z
  |dz  dz  z   |d	z  d
z  z   |dz  d
z  z
  d|z  z   dd|z  z  z
  |dz  dz  z   |dz  dz  z   |dz  dz  z
  |d	z  d
z  z
  |dz  dz  z   }|t          j        | |z            z  t          j        |          z   dt          j        |          z  z
  }||z   |z   S )Nr   rR      r  r  r  r                 r  <   ~   )rt   gammalnrX  rM   r  r   )r   r   r  r  r  r  s         r3   asymptotic_b_largez-beta_gen._entropy.<locals>.asymptotic_b_large  sM   UFA!a%26!99!44BQqS	Ar!tH$q$wrz1AtGCK?!T'#+MT'#+ !4,./h79:BvIG$,q.!#)4<#346<dl2oF $,s"# &,T\#%56  bhqsmm+bfQii7!BF6NN:JJH7X%%r5   c                     | dk    rdS t          j        |           }t          |          }t          | d|z  z            dz   }|dd|z   z  z  S )Nr   i  r  rR      )rM   log10int)vjdigitsds       r3   threshold_largez*beta_gen._entropy.<locals>.threshold_large  sU    CxxtAVVFAf$%%)AR!a%[= r5   g    RAg    (RAg    .Ar   )rB   r   r   r  r  r  r  s          r3   r   zbeta_gen._entropy  s    	G 	G 	G	3 	3 	3
	& 
	& 
	&	! 	! 	! ;;1;;&&q!,,,%ZZAESLLQ//!2D2D-D-D%%a+++%ZZAESLLQ//!2D2D-D-D%%a+++71a== r5   r   )r   r   r   r   rh   r   ro   r   rr   rv   r~   r{   r   r  rH   r	   r   r@   r   __classcell__r  s   @r3   rb  rb    s.        .  
- - - -* * *  
# # #$ $ $' ' '& & &# # # 4 4 4 4 4" } 5+ , , ,
c" c" c" c", , _c"J+! +! +! +! +! +! +!r5   rb  rk  c                   R    e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 ZdS )betaprime_gena  A beta prime continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `betaprime` is:

    .. math::

        f(x, a, b) = \frac{x^{a-1} (1+x)^{-a-b}}{\beta(a, b)}

    for :math:`x >= 0`, :math:`a > 0`, :math:`b > 0`, where
    :math:`\beta(a, b)` is the beta function (see `scipy.special.beta`).

    `betaprime` takes ``a`` and ``b`` as shape parameters.

    The distribution is related to the `beta` distribution as follows:
    If :math:`X` follows a beta distribution with parameters :math:`a, b`,
    then :math:`Y = X/(1-X)` has a beta prime distribution with
    parameters :math:`a, b` ([1]_).

    The beta prime distribution is a reparametrized version of the
    F distribution.  The beta prime distribution with shape parameters
    ``a`` and ``b`` and ``scale = s`` is equivalent to the F distribution
    with parameters ``d1 = 2*a``, ``d2 = 2*b`` and ``scale = (a/b)*s``.
    For example,

    >>> from scipy.stats import betaprime, f
    >>> x = [1, 2, 5, 10]
    >>> a = 12
    >>> b = 5
    >>> betaprime.pdf(x, a, b, scale=2)
    array([0.00541179, 0.08331299, 0.14669185, 0.03150079])
    >>> f.pdf(x, 2*a, 2*b, scale=(a/b)*2)
    array([0.00541179, 0.08331299, 0.14669185, 0.03150079])

    %(after_notes)s

    References
    ----------
    .. [1] Beta prime distribution, Wikipedia,
           https://en.wikipedia.org/wiki/Beta_prime_distribution

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS rd  re   re  s      r3   rh   zbetaprime_gen._shape_info  rh  r5   Nc                     t                               |||          }t                               |||          }||z  S Nr   r   )gammarvs)rB   r   r   r   r   u1u2s          r3   r   zbetaprime_gen._rvs  s9    YYqt,Y??YYqt,Y??Bwr5   c                 T    t          j        |                     |||                    S rK   rM   r   r   rq  s       r3   ro   zbetaprime_gen._pdf  "    vdll1a++,,,r5   c                     t          j        |dz
  |          t          j        ||z   |          z
  t          j        ||          z
  S r  )rt   ru  rt  rv  rq  s       r3   r   zbetaprime_gen._logpdf  s<    xC##bjQ&:&::RYq!__LLr5   c                 :    t          |dk    |||gd d           S )Nr   c                 F    t                               dd| z   z  ||          S r[   rk  rv   x_a_b_s      r3   <lambda>z$betaprime_gen._cdf.<locals>.<lambda>  s    txx1R4"b99 r5   c                 F    t                               | d| z   z  ||          S r[   rk  rr   r  s      r3   r  z$betaprime_gen._cdf.<locals>.<lambda>  s    $))B"Ir2">"> r5   f2r   rq  s       r3   rr   zbetaprime_gen._cdf  s:     EAq!999>>@ @ @ 	@r5   c                 :    t          |dk    |||gd d           S )Nr   c                 F    t                               dd| z   z  ||          S r[   r  r  s      r3   r  z#betaprime_gen._sf.<locals>.<lambda>  s    tyyAbD2r:: r5   c                 F    t                               | d| z   z  ||          S r[   r  r  s      r3   r  z#betaprime_gen._sf.<locals>.<lambda>	  s    $((2qt9b""="= r5   r  r  rq  s       r3   rv   zbetaprime_gen._sf  s4    EAq!9::==
 
 
 	
r5   c                 j   t          j        |||          \  }}}t          j                            |||          }t          j        d          5  |d|z
  z  }d d d            n# 1 swxY w Y   |dk    }dt          j                            ||         ||         ||                   z  dz
  ||<   |S )Nr5  r6  r   gH.?)rM   broadcast_arraysstatsrk  r{   r8  r~   )rB   pr   r   routis          r3   r{   zbetaprime_gen._ppf  s    %aA..1a JOOAq!$$[))) 	 	q1u+C	 	 	 	 	 	 	 	 	 	 	 	 	 	 	Z5:??1Q41qt444q8A
s   	A&&A*-A*c                 P    t          |k    ||ffdt          j                  S )Nc                 p     t          j         fdt          ddz             D             d          S )Nc                 ,    g | ]}|z   d z
  |z
  z  S r   r   ).0r  r   r   s     r3   
<listcomp>z9betaprime_gen._munp.<locals>.<lambda>.<locals>.<listcomp>  s)    !G!G!GA1Q3q51Q3-!G!G!Gr5   r   r   axis)rM   prodrange)r   r   r_   s   ``r3   r  z%betaprime_gen._munp.<locals>.<lambda>  s<    !G!G!G!G!Gq!A#!G!G!GaPPP r5   	fillvaluer   rM   rf   )rB   r_   r   r   s    `  r3   r   zbetaprime_gen._munp  s8    EAq6PPPPf   	r5   r   )r   r   r   r   r   r  r  rh   r   ro   r   rr   rv   r{   r   r   r5   r3   r  r    s        . .^ "4M  
   
- - -M M M@ @ @
 
 
      r5   r  	betaprimec                   8    e Zd ZdZd Zd Zd Zd Zd
dZd Z	d	S )bradford_genab  A Bradford continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `bradford` is:

    .. math::

        f(x, c) = \frac{c}{\log(1+c) (1+cx)}

    for :math:`0 <= x <= 1` and :math:`c > 0`.

    `bradford` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS NcFr   r  re   rg   s    r3   rh   zbradford_gen._shape_info:  r  r5   c                 B    |||z  dz   z  t          j        |          z  S r  rt   r  rB   rn   r  s      r3   ro   zbradford_gen._pdf=  s!    AaC#I!,,r5   c                 Z    t          j        ||z            t          j        |          z  S rK   r	  r
  s      r3   rr   zbradford_gen._cdfA  s!    x!}}rx{{**r5   c                 Z    t          j        |t          j        |          z            |z  S rK   rt   expm1r  rB   rz   r  s      r3   r{   zbradford_gen._ppfD  s#    xBHQKK((1,,r5   mvc                 d   t          j        d|z             }||z
  ||z  z  }|dz   |z  d|z  z
  d|z  |z  |z  z  }d }d }d|v ryt          j        d          d|z  |z  d|z  |z  |dz   z  z
  d|z  |z  ||dz   z  dz   z  z   z  }|t          j        |||dz
  z  d|z  z   z            d|z  |dz
  z  d|z  z   z  z  }d	|v rj|dz  |dz
  z  |d|z  d
z
  z  dz   z  d|z  |z  |z  |dz
  z  |dz
  z  z   d|z  |z  |z  d|z  dz
  z  z   d|dz  z  z   }|d|z  ||dz
  z  d|z  z   dz  z  z  }||||fS )Nr   r   rR   sr  	   r  r  kr(     r      )rM   r   r   )rB   r  momentsr  r@  rA  rB  rC  s           r3   r   zbradford_gen._statsG  s   F3q5MMcAaC[#qyQ1Qq)'>>RT!VAaCE1Q3K/!Aq!A#wqy0AABB"'!Q!WQqS[/**AaC1IacM::B'>>Q$!*a1Rjm,RT!VAXqs^QqS-AAA#a%'1Q3r6"#%'1W-B!A#q!A#wqs{Q&&&B3Br5   c                 j    t          j        d|z             }|dz  t          j        ||z            z
  S Nr   r   r.  )rB   r  r  s      r3   r   zbradford_gen._entropyV  s.    F1Q3KKurvac{{""r5   Nr  rE  r   r5   r3   r  r  $  s         *E E E- - -+ + +- - -   # # # # #r5   r  bradfordc                   T    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Zd ZdS )burr_gena  A Burr (Type III) continuous random variable.

    %(before_notes)s

    See Also
    --------
    fisk : a special case of either `burr` or `burr12` with ``d=1``
    burr12 : Burr Type XII distribution
    mielke : Mielke Beta-Kappa / Dagum distribution

    Notes
    -----
    The probability density function for `burr` is:

    .. math::

        f(x; c, d) = c d \frac{x^{-c - 1}}
                              {{(1 + x^{-c})}^{d + 1}}

    for :math:`x >= 0` and :math:`c, d > 0`.

    `burr` takes ``c`` and ``d`` as shape parameters for :math:`c` and
    :math:`d`.

    This is the PDF corresponding to the third CDF given in Burr's list;
    specifically, it is equation (11) in Burr's paper [1]_. The distribution
    is also commonly referred to as the Dagum distribution [2]_. If the
    parameter :math:`c < 1` then the mean of the distribution does not
    exist and if :math:`c < 2` the variance does not exist [2]_.
    The PDF is finite at the left endpoint :math:`x = 0` if :math:`c * d >= 1`.

    %(after_notes)s

    References
    ----------
    .. [1] Burr, I. W. "Cumulative frequency functions", Annals of
       Mathematical Statistics, 13(2), pp 215-232 (1942).
    .. [2] https://en.wikipedia.org/wiki/Dagum_distribution
    .. [3] Kleiber, Christian. "A guide to the Dagum distributions."
       Modeling Income Distributions and Lorenz Curves  pp 97-117 (2008).

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS Nr  Fr   r  r  re   rB   icids      r3   rh   zburr_gen._shape_info  rh  r5   c                 d    t          |dk    |||gd d           }|j        dk    r|d         S |S )Nr   c                 6    ||z  | ||z  dz
  z  z  d| |z  z   z  S r[   r   r  c_d_s      r3   r  zburr_gen._pdf.<locals>.<lambda>  s(    rBw"r"uQw-8ABJG r5   c                 @    ||z  | | dz
  z  z  d| | z  z   |dz   z  z  S Nr   r   r   r%  s      r3   r  zburr_gen._pdf.<locals>.<lambda>  s6    27bbS3Y.?#@%&_"s($C$E r5   r  r   r   ndimrB   rn   r  r  outputs        r3   ro   zburr_gen._pdf  sX    FQ1IGGF FG G G
 ;!":r5   c                 d    t          |dk    |||gd d           }|j        dk    r|d         S |S )Nr   c                     t          j        |          t          j        |          z   t          j        ||z  dz
  |           z   |dz   t          j        | |z            z  z
  S r[   )rM   r   rt   ru  r  r%  s      r3   r  z"burr_gen._logpdf.<locals>.<lambda>  sS    r

RVBZZ 7"(2b519b:Q:Q Q#%a428BH+=+="=!> r5   c                     t          j        |          t          j        |          z   t          j        | dz
  |           z   t          j        |dz   | | z            z
  S r[   rM   r   rt   ru  rt  r%  s      r3   r  z"burr_gen._logpdf.<locals>.<lambda>  sT    26"::r

#:%'XrcAgr%:%:$;%'Z1bB3i%@%@$A r5   r  r   r*  r,  s        r3   r   zburr_gen._logpdf  s\    FQ1I? ?B B	C C C ;!":r5   c                     d|| z  z   | z  S r[   r   rB   rn   r  r  s       r3   rr   zburr_gen._cdf  s    AGr""r5   c                 :    t          j        || z            | z  S rK   r	  r3  s       r3   r   zburr_gen._logcdf  s    xQB  QB''r5   c                 T    t          j        |                     |||                    S rK   rM   r   r   r3  s       r3   rv   zburr_gen._sf  "    vdkk!Q**+++r5   c                 B    t          j        d|| z  z   | z             S r[   rM   r  r3  s       r3   r   zburr_gen._logsf  s&    x1qA2w;1"--...r5   c                 $    |d|z  z  dz
  d|z  z  S N      r   r   rB   rz   r  r  s       r3   r{   zburr_gen._ppf  s    DFa46**r5   c                 h    t          j        d|z  |           }t          j        |          d|z  z  S Nr<  rt   rt  r  )rB   rz   r  r  _qs        r3   r~   zburr_gen._isf  s0    Zq1"%%x||q))r5   c           	         t          j        dd                              dd          |z  }t          j        ||z   d|z
            |z  \  }}}}t          j        |dk    |t           j                  }||dz  z
  }	t          j        |dk    |	t           j                  }
t          |dk    |||||	fd t           j        	          }t          |d
k    ||||||	fd t           j        	          }t          j        |          dk    rN|	                                |
	                                |	                                |	                                fS ||
||fS )Nr      r   r   rR   r         @c                 Z    |d|z  |z  z
  d|dz  z  z   t          j        |dz            z  S )Nr  rR   r  )r  e1e2e3mu2_if_cs        r3   r  z!burr_gen._stats.<locals>.<lambda>  s6    b1R47lQr1uW.D/1w1}/E/E.F r5   r        @c                 T    |d|z  |z  z
  d|z  |dz  z  z   d|dz  z  z
  |dz  z  dz
  S )Nr   r  rR   r  r   )r  rF  rG  rH  e4rI  s         r3   r  z!burr_gen._stats.<locals>.<lambda>  sB    qtBw,2b!e+aAg51DI r5   r   )
rM   arangereshapert   rk  wherer  r   r+  item)rB   r  r  ncrF  rG  rH  rL  r@  rI  rA  rB  rC  s                r3   r   zburr_gen._stats  sS   Yq!__$$Qq))A-Rb11A5BBXa#gr26**A:hq3w"&11GBH%G Gf   GBB)K Kf   71::??7799chhjj"''))RWWYY>>3Br5   c                     d t          j        |          t          j        |          t          j        |          }}}t          ||k    ||k    z  ||k    z  |||ffdt           j                  S )Nc                 N    d| z  |z  }|t          j        d|z
  ||z             z  S r  rt   rk  r_   r  r  rQ  s       r3   __munpzburr_gen._munp.<locals>.__munp  .    a!BrwsRxR0000r5   c                      || |          S rK   r   )r  r  r_   _burr_gen__munps      r3   r  z burr_gen._munp.<locals>.<lambda>  s    &&Aq// r5   )rM   r   r   r  )rB   r_   r  r  rY  s       @r3   r   zburr_gen._munp  s|    	1 	1 	1 *Q--A
1a11q5Q!V,Q7!Q9999&" " 	"r5   N)r   r   r   r   rh   ro   r   rr   r   rv   r   r{   r~   r   r   r   r5   r3   r  r  ^  s        + +`  
	 	 	
 
 
# # #( ( (, , ,/ / /+ + +* * *  ." " " " "r5   r  burrc                   N    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd ZdS )
burr12_gena}  A Burr (Type XII) continuous random variable.

    %(before_notes)s

    See Also
    --------
    fisk : a special case of either `burr` or `burr12` with ``d=1``
    burr : Burr Type III distribution

    Notes
    -----
    The probability density function for `burr12` is:

    .. math::

        f(x; c, d) = c d \frac{x^{c-1}}
                              {(1 + x^c)^{d + 1}}

    for :math:`x >= 0` and :math:`c, d > 0`.

    `burr12` takes ``c`` and ``d`` as shape parameters for :math:`c`
    and :math:`d`.

    This is the PDF corresponding to the twelfth CDF given in Burr's list;
    specifically, it is equation (20) in Burr's paper [1]_.

    %(after_notes)s

    The Burr type 12 distribution is also sometimes referred to as
    the Singh-Maddala distribution from NIST [2]_.

    References
    ----------
    .. [1] Burr, I. W. "Cumulative frequency functions", Annals of
       Mathematical Statistics, 13(2), pp 215-232 (1942).

    .. [2] https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/b12pdf.htm

    .. [3] "Burr distribution",
       https://en.wikipedia.org/wiki/Burr_distribution

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS r  re   r   s      r3   rh   zburr12_gen._shape_info  rh  r5   c                 T    t          j        |                     |||                    S rK   r  r3  s       r3   ro   zburr12_gen._pdf  r  r5   c                     t          j        |          t          j        |          z   t          j        |dz
  |          z   t          j        | dz
  ||z            z   S r[   r1  r3  s       r3   r   zburr12_gen._logpdf  sN    vayy26!99$rxAq'9'99BJr!tQPQT<R<RRRr5   c                 V    t          j        |                     |||                     S rK   rt   r  r   r3  s       r3   rr   zburr12_gen._cdf  %    Q1--....r5   c                 @    t          j        d||z  z   | z             S r[   r	  r3  s       r3   r   zburr12_gen._logcdf  s$    x!ad(qb))***r5   c                 T    t          j        |                     |||                    S rK   r6  r3  s       r3   rv   zburr12_gen._sf!  r7  r5   c                 4    t          j        | ||z            S rK   rt   rt  r3  s       r3   r   zburr12_gen._logsf$  s    z1"ad###r5   c                 h    t          j        d|z  t          j        |           z            d|z  z  S Nr  r   r  r=  s       r3   r{   zburr12_gen._ppf'  s0     x1rx||+,,qs33r5   c                 f    t          j        d|z  t          j        |          z            d|z  z  S rh  )rt   r  rM   r   )rB   r  r  r  s       r3   r~   zburr12_gen._isf-  s,    x1rvayy())AaC00r5   c                 V    d }t          ||z  |k    |||f|t          j                  S )Nc                 N    d| z  |z  }|t          j        d|z   ||z
            z  S r  rT  rU  s       r3   moment_if_existsz*burr12_gen._munp.<locals>.moment_if_exists1  rW  r5   r  )r   rM   r  )rB   r_   r  r  rl  s        r3   r   zburr12_gen._munp0  sD    	1 	1 	1 !a%!)aAY0@$&F, , , 	,r5   N)r   r   r   r   rh   ro   r   rr   r   rv   r   r{   r~   r   r   r5   r3   r\  r\    s        + +X  
- - -S S S/ / /+ + +, , ,$ $ $4 4 41 1 1, , , , ,r5   r\  burr12c                   Z    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Zd Zd ZdS )fisk_gena  A Fisk continuous random variable.

    The Fisk distribution is also known as the log-logistic distribution.

    %(before_notes)s

    See Also
    --------
    burr

    Notes
    -----
    The probability density function for `fisk` is:

    .. math::

        f(x, c) = \frac{c x^{c-1}}
                       {(1 + x^c)^2}

    for :math:`x >= 0` and :math:`c > 0`.

    Please note that the above expression can be transformed into the following
    one, which is also commonly used:

    .. math::

        f(x, c) = \frac{c x^{-c-1}}
                       {(1 + x^{-c})^2}

    `fisk` takes ``c`` as a shape parameter for :math:`c`.

    `fisk` is a special case of `burr` or `burr12` with ``d=1``.

    Suppose ``X`` is a logistic random variable with location ``l``
    and scale ``s``. Then ``Y = exp(X)`` is a Fisk (log-logistic)
    random variable with ``scale = exp(l)`` and shape ``c = 1/s``.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zfisk_gen._shape_infog  r  r5   c                 :    t                               ||d          S r  )rZ  ro   r
  s      r3   ro   zfisk_gen._pdfj  s    yyAs###r5   c                 :    t                               ||d          S r  )rZ  rr   r
  s      r3   rr   zfisk_gen._cdfn      yyAs###r5   c                 :    t                               ||d          S r  )rZ  rv   r
  s      r3   rv   zfisk_gen._sfq  s    xx1c"""r5   c                 :    t                               ||d          S r  )rZ  r   r
  s      r3   r   zfisk_gen._logpdft  s    ||Aq#&&&r5   c                 :    t                               ||d          S r  )rZ  r   r
  s      r3   r   zfisk_gen._logcdfx  s    ||Aq#&&&r5   c                 :    t                               ||d          S r  )rZ  r   r
  s      r3   r   zfisk_gen._logsf{  s    {{1a%%%r5   c                 :    t                               ||d          S r  )rZ  r{   r
  s      r3   r{   zfisk_gen._ppf~  rs  r5   c                 :    t                               ||d          S r  )rZ  r~   r  s      r3   r~   zfisk_gen._isf  rs  r5   c                 :    t                               ||d          S r  )rZ  r   rB   r_   r  s      r3   r   zfisk_gen._munp  s    zz!Q$$$r5   c                 8    t                               |d          S r  )rZ  r   rB   r  s     r3   r   zfisk_gen._stats  s    {{1c"""r5   c                 0    dt          j        |          z
  S r  r.  r}  s     r3   r   zfisk_gen._entropy      26!99}r5   N)r   r   r   r   rh   ro   rr   rv   r   r   r   r{   r~   r   r   r   r   r5   r3   ro  ro  <  s        ) )TE E E$ $ $$ $ $# # #' ' '' ' '& & &$ $ $$ $ $% % %# # #    r5   ro  fiskc                   J    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 ZddZd
S )
cauchy_gena	  A Cauchy continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `cauchy` is

    .. math::

        f(x) = \frac{1}{\pi (1 + x^2)}

    for a real number :math:`x`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zcauchy_gen._shape_info  r   r5   c                 2    dt           j        z  d||z  z   z  S r  r+  r   s     r3   ro   zcauchy_gen._pdf      25y#ac'""r5   c                 P    ddt           j        z  t          j        |          z  z   S r   rM   r   arctanr   s     r3   rr   zcauchy_gen._cdf       SYry||+++r5   c                 d    t          j        t           j        |z  t           j        dz  z
            S r;  rM   tanr   r   s     r3   r{   zcauchy_gen._ppf  s#    vbeAgbeCi'(((r5   c                 P    ddt           j        z  t          j        |          z  z
  S r   r  r   s     r3   rv   zcauchy_gen._sf  r  r5   c                 d    t          j        t           j        dz  t           j        |z  z
            S r;  r  r   s     r3   r~   zcauchy_gen._isf  s#    vbeCia'(((r5   c                 ^    t           j        t           j        t           j        t           j        fS rK   rM   r  rg   s    r3   r   zcauchy_gen._stats      vrvrvrv--r5   c                 D    t          j        dt           j        z            S r%  r   rg   s    r3   r   zcauchy_gen._entropy      vagr5   Nc                     t          |t                    r|                                }t          j        |g d          \  }}}|||z
  dz  fS )N   2   K   rR   r<   r(   r  rM   
percentile)rB   rC   rD   p25p50p75s         r3   r  zcauchy_gen._fitstart  sQ    dL)) 	$>>##DdLLL99S#S3YM!!r5   rK   )r   r   r   r   rh   ro   rr   r{   rv   r~   r   r   r  r   r5   r3   r  r    s         &  # # #, , ,) ) ), , ,) ) ). . .  " " " " " "r5   r  cauchyc                   P    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )chi_gena  A chi continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `chi` is:

    .. math::

        f(x, k) = \frac{1}{2^{k/2-1} \Gamma \left( k/2 \right)}
                   x^{k-1} \exp \left( -x^2/2 \right)

    for :math:`x >= 0` and :math:`k > 0` (degrees of freedom, denoted ``df``
    in the implementation). :math:`\Gamma` is the gamma function
    (`scipy.special.gamma`).

    Special cases of `chi` are:

        - ``chi(1, loc, scale)`` is equivalent to `halfnorm`
        - ``chi(2, 0, scale)`` is equivalent to `rayleigh`
        - ``chi(3, 0, scale)`` is equivalent to `maxwell`

    `chi` takes ``df`` as a shape parameter.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS NdfFr   r  re   rg   s    r3   rh   zchi_gen._shape_info      4BF^DDEEr5   Nc                 `    t          j        t                              |||                    S r  )rM   r   chi2r  rB   r  r   r   s       r3   r   zchi_gen._rvs  s$    wtxxLxIIJJJr5   c                 R    t          j        |                     ||                    S rK   r  rB   rn   r  s      r3   ro   zchi_gen._pdf  s"     vdll1b))***r5   c                     t          j        d          dt          j        d          z  |z  z
  t          j        d|z            z
  }|t          j        |dz
  |          z   d|dz  z  z
  S )NrR   r   r   )rM   r   rt   r  ru  )rB   rn   r  ls       r3   r   zchi_gen._logpdf  s_    F1II26!99R'"*RU*;*;;28BGQ'''"QT'11r5   c                 >    t          j        d|z  d|dz  z            S Nr   rR   rt   gammaincr  s      r3   rr   zchi_gen._cdf  s     {2b5"QT'***r5   c                 >    t          j        d|z  d|dz  z            S r  rt   	gammainccr  s      r3   rv   zchi_gen._sf  s     |BrE2ad7+++r5   c                 \    t          j        dt          j        d|z  |          z            S NrR   r   rM   r   rt   gammaincinvrB   rz   r  s      r3   r{   zchi_gen._ppf  s'    wq2q111222r5   c                 \    t          j        dt          j        d|z  |          z            S r  rM   r   rt   gammainccinvr  s      r3   r~   zchi_gen._isf  s'    wqB222333r5   c                 p   t          j        d          t          j        d|z  d          z  }|||z  z
  }d|dz  z  |dd|z  z
  z  z   t          j        t          j        |d                    z  }d|z  d|z
  z  d|dz  z  z
  d|dz  z  d|z  dz
  z  z   }|t          j        |d	z            z  }||||fS )
NrR   r   rD  r         ?r   r  r   r   )rM   r   rt   pochr   powerrB   r  r@  rA  rB  rC  s         r3   r   zchi_gen._stats  s    WQZZ"'#(C0002b5jCi"a"f+%rz"(32D2D'E'EErT3r6]1RU7"Qr1uW"Q%77
bjc"""3Br5   c                 >    d }d }t          |dk     |f||          S )Nc                     t          j        d| z            d| t          j        d          z
  | dz
  t          j        d| z            z  z
  z  z   S r   )rt   r  rM   r   digammar  s    r3   regular_formulaz)chi_gen._entropy.<locals>.regular_formula  sO    JrBw''R"&))^rAvC"H9M9M.MMNO Pr5   c                     dt          j        t           j                  dz  z   | dz  dz  z
  | dz  dz  z
  d| dz  z  z
  | dz  d	z  z   S )
Nr   rR   r  r  r  gll?   r   r  s    r3   asymptotic_formulaz,chi_gen._entropy.<locals>.asymptotic_formula  sW    "&--/)RVQJ6"b&!CBFm$')2vrk2 3r5   g     r@r  r  )rB   r  r  r  s       r3   r   zchi_gen._entropy  sK    	P 	P 	P	3 	3 	3 "s(RFO/1 1 1 	1r5   r   r   r   r   r   rh   r   ro   r   rr   rv   r{   r~   r   r   r   r5   r3   r  r    s         <F F FK K K K+ + +2 2 2+ + +, , ,3 3 34 4 4  1 1 1 1 1r5   r  chic                   P    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )chi2_gena  A chi-squared continuous random variable.

    For the noncentral chi-square distribution, see `ncx2`.

    %(before_notes)s

    See Also
    --------
    ncx2

    Notes
    -----
    The probability density function for `chi2` is:

    .. math::

        f(x, k) = \frac{1}{2^{k/2} \Gamma \left( k/2 \right)}
                   x^{k/2-1} \exp \left( -x/2 \right)

    for :math:`x > 0`  and :math:`k > 0` (degrees of freedom, denoted ``df``
    in the implementation).

    `chi2` takes ``df`` as a shape parameter.

    The chi-squared distribution is a special case of the gamma
    distribution, with gamma parameters ``a = df/2``, ``loc = 0`` and
    ``scale = 2``.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zchi2_gen._shape_info@  r  r5   Nc                 .    |                     ||          S rK   )	chisquarer  s       r3   r   zchi2_gen._rvsC  s    %%b$///r5   c                 R    t          j        |                     ||                    S rK   r  r  s      r3   ro   zchi2_gen._pdfF  s     vdll1b))***r5   c                     t          j        |dz  dz
  |          |dz  z
  t          j        |dz            z
  t          j        d          |z  dz  z
  S )Nr   r   rR   )rt   ru  r  rM   r   r  s      r3   r   zchi2_gen._logpdfJ  sO    x2a##ad*RZ2->->>"&))B,PRARRRr5   c                 ,    t          j        ||          S rK   )rt   chdtrr  s      r3   rr   zchi2_gen._cdfM      xAr5   c                 ,    t          j        ||          S rK   )rt   chdtrcr  s      r3   rv   zchi2_gen._sfP      yQr5   c                 ,    t          j        ||          S rK   )rt   chdtrirB   r  r  s      r3   r~   zchi2_gen._isfS  r  r5   c                 8    dt          j        |dz  |          z  S r  rt   r  r  s      r3   r{   zchi2_gen._ppfV  s    1a((((r5   c                 Z    |}d|z  }dt          j        d|z            z  }d|z  }||||fS )NrR   r         (@r  r  s         r3   r   zchi2_gen._statsY  s=    drws2v"W3Br5   c                 H    d|z  }d }d }t          |dk     |f||          S )Nr   c                     | t          j        d          z   t          j        |           z   d| z
  t          j        |           z  z   S NrR   r   )rM   r   rt   r  rX  )half_dfs    r3   r  z*chi2_gen._entropy.<locals>.regular_formulac  s?    bfQii'"*W*=*==[BF7OO34 5r5   c                     t          j        d          ddt          j        dt           j        z            z   z  z   }d| z  }|d|d|d|dz  z   z  z   z  z   z  dt          j        |           z  z   |z   S )NrR   r   r   gUUUUUUUUUUUUտgllg      @r   )r  r  hs      r3   r  z-chi2_gen._entropy.<locals>.asymptotic_formulag  s}     q		CRVAbeG__!455AGAta51S5=(9!9::;w'(*+, -r5   }   r  r  )rB   r  r  r  r  s        r3   r   zchi2_gen._entropy`  sR    (	5 	5 	5		- 		- 		- 'C-')/1 1 1 	1r5   r   )r   r   r   r   rh   r   ro   r   rr   rv   r~   r{   r   r   r   r5   r3   r  r    s           BF F F0 0 0 0+ + +S S S            ) ) )  1 1 1 1 1r5   r  r  c                   H    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 ZdS )
cosine_gena\  A cosine continuous random variable.

    %(before_notes)s

    Notes
    -----
    The cosine distribution is an approximation to the normal distribution.
    The probability density function for `cosine` is:

    .. math::

        f(x) = \frac{1}{2\pi} (1+\cos(x))

    for :math:`-\pi \le x \le \pi`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zcosine_gen._shape_info  r   r5   c                 P    dt           j        z  dt          j        |          z   z  S Nr   r   rM   r   r  r   s     r3   ro   zcosine_gen._pdf  s    RU{AbfQiiK((r5   c                 r    t          j        |          }t          |dk    |fd t           j                   S )Nr  c                 n    t          j        |           t          j        dt           j        z            z
  S r  )rM   r  r   r   r  s    r3   r  z$cosine_gen._logpdf.<locals>.<lambda>  s!    BHQKK"&25//$A r5   r  )rM   r  r   rf   r
  s      r3   r   zcosine_gen._logpdf  s=    F1II!r'A4AA%'VG- - - 	-r5   c                 *    t          j        |          S rK   rk   _cosine_cdfr   s     r3   rr   zcosine_gen._cdf  s    q!!!r5   c                 ,    t          j        |           S rK   r  r   s     r3   rv   zcosine_gen._sf  s    r"""r5   c                 *    t          j        |          S rK   rk   _cosine_invcdfrB   r  s     r3   r{   zcosine_gen._ppf  s    !!$$$r5   c                 ,    t          j        |           S rK   r  r  s     r3   r~   zcosine_gen._isf  s    "1%%%%r5   c                     t           j        t           j        z  dz  dz
  }dt           j        dz  dz
  z  dt           j        t           j        z  dz
  dz  z  z  }d	|d	|fS )
NrD  r   r  r   Z         @r  rR   r   r+  )rB   r  r  s      r3   r   zcosine_gen._stats  sW    URU]S C'BE1HrM"cRURU]Q->,B&BCAsA~r5   c                 J    t          j        dt           j        z            dz
  S )Nr   r   r   rg   s    r3   r   zcosine_gen._entropy  s    vags""r5   Nr   r   r   r   rh   ro   r   rr   rv   r{   r~   r   r   r   r5   r3   r  r  z  s         (  ) ) )- - -" " "# # #% % %& & &  
# # # # #r5   r  cosinec                   P    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )
dgamma_gena  A double gamma continuous random variable.

    The double gamma distribution is also known as the reflected gamma
    distribution [1]_.

    %(before_notes)s

    Notes
    -----
    The probability density function for `dgamma` is:

    .. math::

        f(x, a) = \frac{1}{2\Gamma(a)} |x|^{a-1} \exp(-|x|)

    for a real number :math:`x` and :math:`a > 0`. :math:`\Gamma` is the
    gamma function (`scipy.special.gamma`).

    `dgamma` takes ``a`` as a shape parameter for :math:`a`.

    %(after_notes)s

    References
    ----------
    .. [1] Johnson, Kotz, and Balakrishnan, "Continuous Univariate
           Distributions, Volume 1", Second Edition, John Wiley and Sons
           (1994).

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zdgamma_gen._shape_info  r  r5   Nc                     |                     |          }t                              |||          }|t          j        |dk    dd          z  S Nr   r  r   r   r  )uniformr  r  rM   rO  )rB   r   r   r   ugms         r3   r   zdgamma_gen._rvs  sL      d ++YYqt,Y??BHQ#Xq"----r5   c                     t          |          }ddt          j        |          z  z  ||dz
  z  z  t          j        |           z  S r	  )absrt   r  rM   r   rB   rn   r   axs       r3   ro   zdgamma_gen._pdf  s@    VVAbhqkkM"2#;.<<r5   c                     t          |          }t          j        |dz
  |          |z
  t          j        d          z
  t          j        |          z
  S r	  )r
  rt   ru  rM   r   r  r  s       r3   r   zdgamma_gen._logpdf  sB    VVxC$$r)BF1II5
1EEr5   c           	          t          j        |dk    ddt          j        ||          z  z   dt          j        ||           z            S Nr   r   )rM   rO  rt   r  r  r
  s      r3   rr   zdgamma_gen._cdf  sK    xAc"+a"3"333BLQB///1 1 	1r5   c           
          t          j        |dk    dt          j        ||          z  ddt          j        ||           z  z             S r  )rM   rO  rt   r  r  r
  s      r3   rv   zdgamma_gen._sf  sK    xABLA...c"+a!"4"4446 6 	6r5   c                 j    t           j                            |          t          j        d          z
  S Nr   )r  r  r   rM   r   r  s     r3   r   zdgamma_gen._entropy  s%    {##A&&44r5   c           	          t          j        |dk    t          j        |d|z  dz
            t          j        |d|z                       S r   rM   rO  rt   r  r  r  s      r3   r{   zdgamma_gen._ppf  sI    xCq!A#'22AaC0002 2 	2r5   c           	          t          j        |dk    t          j        |d|z  dz
             t          j        |d|z                      S r   r  r  s      r3   r~   zdgamma_gen._isf  sI    xC1Q373331Q3//1 1 	1r5   c                 <    ||dz   z  }d|d|dz   |dz   z  |z  dz
  fS )Nr   r   r   rD  r   )rB   r   rA  s      r3   r   zdgamma_gen._stats  s4    3iCququoc1#555r5   r   )r   r   r   r   rh   r   ro   r   rr   rv   r   r{   r~   r   r   r5   r3   r  r    s         >E E E. . . .
= = =
F F F1 1 1
6 6 6
5 5 52 2 2
1 1 1
6 6 6 6 6r5   r  dgammac                   V    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd Zd ZdS )dweibull_genav  A double Weibull continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `dweibull` is given by

    .. math::

        f(x, c) = c / 2 |x|^{c-1} \exp(-|x|^c)

    for a real number :math:`x` and :math:`c > 0`.

    `dweibull` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zdweibull_gen._shape_info  r  r5   Nc                     |                     |          }t                              |||          }|t          j        |dk    dd          z  S r  )r  weibull_minr  rM   rO  )rB   r  r   r   r  ws         r3   r   zdweibull_gen._rvs  sL      d ++OOAD|ODDBHQ#Xq"--..r5   c                 r    t          |          }|dz  ||dz
  z  z  t          j        ||z             z  }|S Nr   r   )r
  rM   r   )rB   rn   r  r  Pxs        r3   ro   zdweibull_gen._pdf"  s;    VVWrAcE{"RVRUF^^3	r5   c                     t          |          }t          j        |          t          j        d          z
  t          j        |dz
  |          z   ||z  z
  S r  )r
  rM   r   rt   ru  )rB   rn   r  r  s       r3   r   zdweibull_gen._logpdf(  sF    VVvayy26#;;&!c'2)>)>>QFFr5   c                     dt          j        t          |          |z             z  }t          j        |dk    d|z
  |          S Nr   r   r   )rM   r   r
  rO  )rB   rn   r  Cx1s       r3   rr   zdweibull_gen._cdf,  s>    BFCFFAI:&&&xAq3w,,,r5   c                     dt          j        |dk    |d|z
            z  }t          j        t          j        |           d|z            }t          j        |dk    ||           S Nr   r   r   )rM   rO  r  r   )rB   rz   r  r  s       r3   r{   zdweibull_gen._ppf0  s[    28AHaa000hs|S1W--xCsd+++r5   c                     dt           j                            t          j        |          |          z  }t          j        |dk    |d|z
            S r#  )r  r  rv   rM   r
  rO  )rB   rn   r  half_weibull_min_sfs       r3   rv   zdweibull_gen._sf5  sG    !E$5$9$9"&))Q$G$GGxA2A8K4KLLLr5   c                     dt          j        |dk    |d|z
            z  }t          j                            ||          }t          j        |dk    | |          S r&  )rM   rO  r  r  r~   )rB   rz   r  double_qweibull_min_isfs        r3   r~   zdweibull_gen._isf9  sU    c1b1f555+001==xC/!1?CCCr5   c                 N    d|dz  z
  t          j        dd|z  |z  z             z  S )Nr   rR   r   rt   r  r{  s      r3   r   zdweibull_gen._munp>  s,    QUrxcAgk(9::::r5   c                     dS N)r   Nr   Nr   r}  s     r3   r   zdweibull_gen._statsD      r5   c                 n    t           j                            |          t          j        d          z
  }|S r  )r  r  r   rM   r   )rB   r  r  s      r3   r   zdweibull_gen._entropyG  s*    &&q))BF3KK7r5   r   )r   r   r   r   rh   r   ro   r   rr   r{   rv   r~   r   r   r   r   r5   r3   r  r    s         *E E E/ / / /
  G G G- - -, , ,
M M MD D D
; ; ;         r5   r  dweibullc                       e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd Zd Ze eed          d                         ZdS )	expon_genaE  An exponential continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `expon` is:

    .. math::

        f(x) = \exp(-x)

    for :math:`x \ge 0`.

    %(after_notes)s

    A common parameterization for `expon` is in terms of the rate parameter
    ``lambda``, such that ``pdf = lambda * exp(-lambda * x)``. This
    parameterization corresponds to using ``scale = 1 / lambda``.

    The exponential distribution is a special case of the gamma
    distributions, with gamma shape parameter ``a = 1``.

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zexpon_gen._shape_infoj  r   r5   Nc                 ,    |                     |          S rK   )standard_exponentialr   s      r3   r   zexpon_gen._rvsm  s    00666r5   c                 ,    t          j        |           S rK   rM   r   r   s     r3   ro   zexpon_gen._pdfp  s    vqbzzr5   c                     | S rK   r   r   s     r3   r   zexpon_gen._logpdft  	    r	r5   c                 .    t          j        |            S rK   rt   r  r   s     r3   rr   zexpon_gen._cdfw      !}r5   c                 .    t          j        |            S rK   r	  r   s     r3   r{   zexpon_gen._ppfz  r>  r5   c                 ,    t          j        |           S rK   r9  r   s     r3   rv   zexpon_gen._sf}  s    vqbzzr5   c                     | S rK   r   r   s     r3   r   zexpon_gen._logsf  r;  r5   c                 ,    t          j        |           S rK   r.  r   s     r3   r~   zexpon_gen._isf      q		zr5   c                     dS )N)r   r   r         @r   rg   s    r3   r   zexpon_gen._stats  r   r5   c                     dS r  r   rg   s    r3   r   zexpon_gen._entropy      sr5   z        When `method='MLE'`,
        this function uses explicit formulas for the maximum likelihood
        estimation of the exponential distribution parameters, so the
        `optimizer`, `loc` and `scale` keyword arguments are
        ignored.

r   c                 b   t          |          dk    rt          d          |                    dd           }|                    dd           }t          |           ||t	          d          t          j        |          }t          j        |                                          st	          d          |	                                }||}n$|}||k     rt          d|t
          j                  ||                                |z
  }n|}t          |          t          |          fS )	Nr   Too many arguments.r   r   r   r   exponr  )r  r1   r0   r4   r   rM   r   r   r   minrH  rf   r   float)	rB   rC   rD   r2   r   r   data_minr,   r-   s	            r3   r@   zexpon_gen.fit  s,    t99q==1222xx%%(D))$T*** 2 ) * * * z${4  $$&& 	ECDDD88::<CCC#~~"7$bfEEEE>IIKK#%EEE Szz5<<''r5   r   )r   r   r   r   rh   r   ro   r   rr   r{   rv   r   r~   r   r   rH   r
   r   r@   r   r5   r3   r4  r4  O  s	        4  7 7 7 7              " " "    6   &( &(  _&( &( &(r5   r4  rJ  c                   >    e Zd ZdZd Zd
dZd Zd Zd Zd Z	d	 Z
dS )exponnorm_gena  An exponentially modified Normal continuous random variable.

    Also known as the exponentially modified Gaussian distribution [1]_.

    %(before_notes)s

    Notes
    -----
    The probability density function for `exponnorm` is:

    .. math::

        f(x, K) = \frac{1}{2K} \exp\left(\frac{1}{2 K^2} - x / K \right)
                  \text{erfc}\left(-\frac{x - 1/K}{\sqrt{2}}\right)

    where :math:`x` is a real number and :math:`K > 0`.

    It can be thought of as the sum of a standard normal random variable
    and an independent exponentially distributed random variable with rate
    ``1/K``.

    %(after_notes)s

    An alternative parameterization of this distribution (for example, in
    the Wikipedia article [1]_) involves three parameters, :math:`\mu`,
    :math:`\lambda` and :math:`\sigma`.

    In the present parameterization this corresponds to having ``loc`` and
    ``scale`` equal to :math:`\mu` and :math:`\sigma`, respectively, and
    shape parameter :math:`K = 1/(\sigma\lambda)`.

    .. versionadded:: 0.16.0

    References
    ----------
    .. [1] Exponentially modified Gaussian distribution, Wikipedia,
           https://en.wikipedia.org/wiki/Exponentially_modified_Gaussian_distribution

    %(example)s

    c                 @    t          dddt          j        fd          gS )NKFr   r  re   rg   s    r3   rh   zexponnorm_gen._shape_info  r  r5   Nc                 f    |                     |          |z  }|                    |          }||z   S rK   )r7  r   )rB   rQ  r   r   expvalgvals         r3   r   zexponnorm_gen._rvs  s7    224881<++D11}r5   c                 R    t          j        |                     ||                    S rK   r  )rB   rn   rQ  s      r3   ro   zexponnorm_gen._pdf       vdll1a(()))r5   c                 v    d|z  }|d|z  |z
  z  }|t          ||z
            z   t          j        |          z
  S Nr   r   r   rM   r   )rB   rn   rQ  invKexpargs        r3   r   zexponnorm_gen._logpdf  sA    Qwta(QX...::r5   c                     d|z  }|d|z  |z
  z  }|t          ||z
            z   }t          |          t          j        |          z
  S rX  r   r   rM   r   rB   rn   rQ  rZ  rS  logprods         r3   rr   zexponnorm_gen._cdf  sL    Qwta(<D111||bfWoo--r5   c                     d|z  }|d|z  |z
  z  }|t          ||z
            z   }t          |           t          j        |          z   S rX  r]  r^  s         r3   rv   zexponnorm_gen._sf  sN    Qwta(<D111!}}rvg..r5   c                 Z    ||z  }d|z   }d|dz  z  |dz  z  }d|z  |z  |dz  z  }||||fS )Nr   rR   r  r>  rE  r  r   )rB   rQ  K2opK2skwkrts         r3   r   zexponnorm_gen._stats  sO    URx!Q$h%BhmdRj($S  r5   r   )r   r   r   r   rh   r   ro   r   rr   rv   r   r   r5   r3   rO  rO    s        ( (RE E E   
* * *; ; ;
. . ./ / /! ! ! ! !r5   rO  	exponnormc                 P    t          j        t          j        ||                     S )a'  
    Compute (1 + x)**y - 1.

    Uses expm1 and xlog1py to avoid loss of precision when
    (1 + x)**y is close to 1.

    Note that the inverse of this function with respect to x is
    ``_pow1pm1(x, 1/y)``.  That is, if

        t = _pow1pm1(x, y)

    then

        x = _pow1pm1(t, 1/y)
    )rM   r  rt   rt  rn   ys     r3   _pow1pm1rj    s       8BJq!$$%%%r5   c                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
exponweib_gena  An exponentiated Weibull continuous random variable.

    %(before_notes)s

    See Also
    --------
    weibull_min, numpy.random.Generator.weibull

    Notes
    -----
    The probability density function for `exponweib` is:

    .. math::

        f(x, a, c) = a c [1-\exp(-x^c)]^{a-1} \exp(-x^c) x^{c-1}

    and its cumulative distribution function is:

    .. math::

        F(x, a, c) = [1-\exp(-x^c)]^a

    for :math:`x > 0`, :math:`a > 0`, :math:`c > 0`.

    `exponweib` takes :math:`a` and :math:`c` as shape parameters:

    * :math:`a` is the exponentiation parameter,
      with the special case :math:`a=1` corresponding to the
      (non-exponentiated) Weibull distribution `weibull_min`.
    * :math:`c` is the shape parameter of the non-exponentiated Weibull law.

    %(after_notes)s

    References
    ----------
    https://en.wikipedia.org/wiki/Exponentiated_Weibull_distribution

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS Nr   Fr   r  r  re   rB   rf  r!  s      r3   rh   zexponweib_gen._shape_infoL  rh  r5   c                 T    t          j        |                     |||                    S rK   r  rB   rn   r   r  s       r3   ro   zexponweib_gen._pdfQ  $     vdll1a++,,,r5   c                     ||z   }t          j        |           }t          j        |          t          j        |          z   t          j        |dz
  |          z   |z   t          j        |dz
  |          z   }|S r  )rt   r  rM   r   ru  )rB   rn   r   r  negxcexm1clogps          r3   r   zexponweib_gen._logpdfV  sq    A% q		BF1II%S%(@(@@S!,,-r5   c                 >    t          j        ||z              }||z  S rK   r=  )rB   rn   r   r  ru  s        r3   rr   zexponweib_gen._cdf]  s!    1a4% axr5   c                 j    t          j        |d|z  z              t          j        d|z            z  S r  )rt   r  rM   r   )rB   rz   r   r  s       r3   r{   zexponweib_gen._ppfa  s2    1s1u:+&&&CE):):::r5   c                 R    t          t          j        ||z              |           S rK   )rj  rM   r   rq  s       r3   rv   zexponweib_gen._sfd  s%    "&!Q$--++++r5   c                 ^    t          j        t          | d|z                        d|z  z  S r[   )rM   r   rj  )rB   r  r   r  s       r3   r~   zexponweib_gen._isfg  s1    1"ac***+++qs33r5   Nr   r   r   r   rh   ro   r   rr   r{   rv   r~   r   r5   r3   rl  rl  #  s        ' 'P  
- - -
    ; ; ;, , ,4 4 4 4 4r5   rl  	exponweibc                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
exponpow_gena  An exponential power continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `exponpow` is:

    .. math::

        f(x, b) = b x^{b-1} \exp(1 + x^b - \exp(x^b))

    for :math:`x \ge 0`, :math:`b > 0`.  Note that this is a different
    distribution from the exponential power distribution that is also known
    under the names "generalized normal" or "generalized Gaussian".

    `exponpow` takes ``b`` as a shape parameter for :math:`b`.

    %(after_notes)s

    References
    ----------
    http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Exponentialpower.pdf

    %(example)s

    c                 @    t          dddt          j        fd          gS Nr   Fr   r  re   rg   s    r3   rh   zexponpow_gen._shape_info  r  r5   c                 R    t          j        |                     ||                    S rK   r  rB   rn   r   s      r3   ro   zexponpow_gen._pdf       vdll1a(()))r5   c                     ||z  }dt          j        |          z   t          j        |dz
  |          z   |z   t          j        |          z
  }|S Nr   r   )rM   r   rt   ru  r   )rB   rn   r   xbfs        r3   r   zexponpow_gen._logpdf  sH    Tq		MBHQWa00025r

Br5   c                 X    t          j        t          j        ||z                        S rK   r=  r  s      r3   rr   zexponpow_gen._cdf  s#    "(1a4..))))r5   c                 V    t          j        t          j        ||z                       S rK   rM   r   rt   r  r  s      r3   rv   zexponpow_gen._sf  s     vrx1~~o&&&r5   c                 \    t          j        t          j        |                     d|z  z  S r  rt   r  rM   r   r  s      r3   r~   zexponpow_gen._isf  s%    "&))$$1--r5   c                 t    t          t          j        t          j        |                      d|z            S r  powrt   r  rB   rz   r   s      r3   r{   zexponpow_gen._ppf  s,    28RXqb\\M**CE222r5   N)r   r   r   r   rh   ro   r   rr   rv   r~   r{   r   r5   r3   r~  r~  n  s         6E E E* * *  
* * *' ' '. . .3 3 3 3 3r5   r~  exponpowc                   X    e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 Zd ZdS )fatiguelife_gena0  A fatigue-life (Birnbaum-Saunders) continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `fatiguelife` is:

    .. math::

        f(x, c) = \frac{x+1}{2c\sqrt{2\pi x^3}} \exp(-\frac{(x-1)^2}{2x c^2})

    for :math:`x >= 0` and :math:`c > 0`.

    `fatiguelife` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    References
    ----------
    .. [1] "Birnbaum-Saunders distribution",
           https://en.wikipedia.org/wiki/Birnbaum-Saunders_distribution

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zfatiguelife_gen._shape_info  r  r5   Nc                     |                     |          }d|z  |z  }||z  }dd|z  z   d|z  t          j        d|z             z  z   }|S )Nr   r   rR   r   )r   rM   r   )rB   r  r   r   zrn   x2ts           r3   r   zfatiguelife_gen._rvs  sW    ((..E!GqS!B$J1RWQV__,,r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zfatiguelife_gen._pdf  s"     vdll1a(()))r5   c                    t          j        |dz             |dz
  dz  d|z  |dz  z  z  z
  t          j        d|z            z
  dt          j        dt           j        z            dt          j        |          z  z   z  z
  S )Nr   rR   r   r   r  r   r
  s      r3   r   zfatiguelife_gen._logpdf  sq    qsqsQh#a%1*55qsCRVAbeG__q{234 	5r5   c                     t          d|z  t          j        |          dt          j        |          z  z
  z            S r  )r   rM   r   r
  s      r3   rr   zfatiguelife_gen._cdf  s2    qBGAJJRWQZZ$?@AAAr5   c                 l    |t          |          z  }d|t          j        |dz  dz             z   dz  z  S N      ?rR   r   r   rM   r   rB   rz   r  tmps       r3   r{   zfatiguelife_gen._ppf  s9    )A,,sRWS!VaZ0001444r5   c                     t          d|z  t          j        |          dt          j        |          z  z
  z            S r  )r   rM   r   r
  s      r3   rv   zfatiguelife_gen._sf  s2    a271::BGAJJ#>?@@@r5   c                 n    | t          |          z  }d|t          j        |dz  dz             z   dz  z  S r  r  r  s       r3   r~   zfatiguelife_gen._isf  s;    b9Q<<sRWS!VaZ0001444r5   c                     ||z  }|dz  dz   }d|z  dz   }||z  dz  }d|z  d|z  dz   z  t          j        |d          z  }d	|z  d
|z  dz   z  |dz  z  }||||fS )Nr   r   r  rJ  r      rE  r  r  ]   g      D@rM   r  )rB   r  c2r@  denrA  rB  rC  s           r3   r   zfatiguelife_gen._stats  s     qS#X^BhnfslUbeck"RXc3%7%77Vr"ut|$sCx/3Br5   r   )r   r   r   r   r   r  r  rh   r   ro   r   rr   r{   rv   r~   r   r   r5   r3   r  r    s         4 "4ME E E   * * *
5 5 5B B B5 5 5A A A5 5 5    r5   r  fatiguelifec                   >    e Zd ZdZd Zd Zd
dZd Zd Zd Z	d	 Z
dS )foldcauchy_genao  A folded Cauchy continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `foldcauchy` is:

    .. math::

        f(x, c) = \frac{1}{\pi (1+(x-c)^2)} + \frac{1}{\pi (1+(x+c)^2)}

    for :math:`x \ge 0` and :math:`c \ge 0`.

    `foldcauchy` takes ``c`` as a shape parameter for :math:`c`.

    %(example)s

    c                     |dk    S Nr   r   r}  s     r3   r`   zfoldcauchy_gen._argcheck
	      Avr5   c                 @    t          dddt          j        fd          gS Nr  Fr   rd   re   rg   s    r3   rh   zfoldcauchy_gen._shape_info	      326{MBBCCr5   Nc                 V    t          t                              |||                    S )Nr,   r   r   )r
  r  r  rB   r  r   r   s       r3   r   zfoldcauchy_gen._rvs	  s0    6::!$+7  9 9 : : 	:r5   c                 \    dt           j        z  dd||z
  dz  z   z  dd||z   dz  z   z  z   z  S Nr   r   rR   r+  r
  s      r3   ro   zfoldcauchy_gen._pdf	  s:    25y#q!A#z*S!QqS1H*-==>>r5   c                     dt           j        z  t          j        ||z
            t          j        ||z             z   z  S r  r  r
  s      r3   rr   zfoldcauchy_gen._cdf	  s0    25y")AaC..29QqS>>9::r5   c                 ~    t          j        d||z
            t          j        d||z             z   t           j        z  S r[   )rM   arctan2r   r
  s      r3   rv   zfoldcauchy_gen._sf	  s6    
 
1a!e$$rz!QU';';;RUBBr5   c                 ^    t           j        t           j        t           j        t           j        fS rK   r  r}  s     r3   r   zfoldcauchy_gen._stats"	  r  r5   r   r   r   r   r   r`   rh   r   ro   rr   rv   r   r   r5   r3   r  r    s         &  D D D: : : :? ? ?; ; ;C C C. . . . .r5   r  
foldcauchyc                   J    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd ZdS )f_gena  An F continuous random variable.

    For the noncentral F distribution, see `ncf`.

    %(before_notes)s

    See Also
    --------
    ncf

    Notes
    -----
    The F distribution with :math:`df_1 > 0` and :math:`df_2 > 0` degrees of freedom is
    the distribution of the ratio of two independent chi-squared distributions with
    :math:`df_1` and :math:`df_2` degrees of freedom, after rescaling by
    :math:`df_2 / df_1`.

    The probability density function for `f` is:

    .. math::

        f(x, df_1, df_2) = \frac{df_2^{df_2/2} df_1^{df_1/2} x^{df_1 / 2-1}}
                                {(df_2+df_1 x)^{(df_1+df_2)/2}
                                 B(df_1/2, df_2/2)}

    for :math:`x > 0`.

    `f` accepts shape parameters ``dfn`` and ``dfd`` for :math:`df_1`, the degrees of
    freedom of the chi-squared distribution in the numerator, and :math:`df_2`, the
    degrees of freedom of the chi-squared distribution in the denominator, respectively.

    %(after_notes)s

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )NdfnFr   r  dfdre   )rB   idfnidfds      r3   rh   zf_gen._shape_infoN	  s>    %BF^DD%BF^DDd|r5   Nc                 0    |                     |||          S rK   )r  )rB   r  r  r   r   s        r3   r   z
f_gen._rvsS	  s    ~~c3---r5   c                 T    t          j        |                     |||                    S rK   r  rB   rn   r  r  s       r3   ro   z
f_gen._pdfV	  s$     vdll1c3//000r5   c                 <   d|z  }d|z  }|dz  t          j        |          z  |dz  t          j        |          z  z   t          j        |dz  dz
  |          z   ||z   dz  t          j        |||z  z             z  t          j        |dz  |dz            z   z
  }|S Nr   rR   r   )rM   r   rt   ru  rv  )rB   rn   r  r  r_   mrw  s          r3   r   zf_gen._logpdf\	  s    #I#IsRVAYY1rvayy028AaC!GQ3G3GGaC7bfQ1Woo-	!A#qs0C0CCE
r5   c                 .    t          j        |||          S rK   )rt   fdtrr  s       r3   rr   z
f_gen._cdfc	  s    wsC###r5   c                 .    t          j        |||          S rK   )rt   fdtrcr  s       r3   rv   z	f_gen._sff	      xS!$$$r5   c                 .    t          j        |||          S rK   )rt   fdtri)rB   rz   r  r  s       r3   r{   z
f_gen._ppfi	  r  r5   c                    d|z  d|z  }}|dz
  |dz
  |dz
  |dz
  f\  }}}}t          |dk    ||fd t          j                  }	t          |dk    ||||fd	 t          j                  }
t          |d
k    ||||fd t          j                  }|t          j        d          z  }t          |dk    |||fd t          j                  }|dz  }|	|
||fS )Nr   r   rJ  rE         @rR   c                     | |z  S rK   r   )v2v2_2s     r3   r  zf_gen._stats.<locals>.<lambda>r	  s
    R$Y r5   r   c                 6    d|z  |z  | |z   z  | |dz  z  |z  z  S r  r   )v1r  r  v2_4s       r3   r  zf_gen._stats.<locals>.<lambda>w	  s-    FRK29%dAg)<= r5   r  c                 T    d| z  |z   |z  t          j        || | |z   z  z            z  S r  r  )r  r  r  v2_6s       r3   r  zf_gen._stats.<locals>.<lambda>}	  s4    Vd]d"RWTR295E-F%G%GG r5   r*  c                     d| | z  |z  z   |z  S )Nr*  r   )rB  r  v2_8s      r3   r  zf_gen._stats.<locals>.<lambda>	  s    AR$$6$#> r5   r  )r   rM   rf   r  r   )rB   r  r  r  r  r  r  r  r  r@  rA  rB  rC  s                r3   r   zf_gen._statsl	  s   c28B!#b"r'27BG!CdD$FRJ&&F 
 FRT4(> >F	  FRtT*H HF	 
 	bgbkkFRt$>>F  	g3Br5   c                 @   d|z  }d|z  }d||z   z  }t          j        |          t          j        |          z
  t          j        ||          z   d|z
  t          j        |          z  z   d|z   t          j        |          z  z
  |t          j        |          z  z   S r  )rM   r   rt   rv  rX  )rB   r  r  half_dfnhalf_dfdhalf_sums         r3   r   zf_gen._entropy	  s     99#)$sbfSkk)BIh,I,IIX!1!11256\x  5!!#+bfX.>.>#>? 	@r5   r   )r   r   r   r   rh   r   ro   r   rr   rv   r{   r   r   r   r5   r3   r  r  )	  s        # #H  
. . . .1 1 1  $ $ $% % %% % %  <
@ 
@ 
@ 
@ 
@r5   r  r  c                   >    e Zd ZdZd Zd Zd
dZd Zd Zd Z	d	 Z
dS )foldnorm_genaz  A folded normal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `foldnorm` is:

    .. math::

        f(x, c) = \sqrt{2/\pi} cosh(c x) \exp(-\frac{x^2+c^2}{2})

    for :math:`x \ge 0` and :math:`c \ge 0`.

    `foldnorm` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                     |dk    S r  r   r}  s     r3   r`   zfoldnorm_gen._argcheck	  r  r5   c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zfoldnorm_gen._shape_info	  r  r5   Nc                 L    t          |                    |          |z             S rK   r
  r   r  s       r3   r   zfoldnorm_gen._rvs	  s#    <//559:::r5   c                 L    t          ||z             t          ||z
            z   S rK   r   r
  s      r3   ro   zfoldnorm_gen._pdf	  s#    Q)AaC..00r5   c                     t          j        d          }dt          j        ||z
  |z            t          j        ||z   |z            z   z  S r  )rM   r   rt   erf)rB   rn   r  sqrt_twos       r3   rr   zfoldnorm_gen._cdf	  sE    71::bfa!eX-..Q8H1I1IIJJr5   c                 L    t          ||z
            t          ||z             z   S rK   r   r
  s      r3   rv   zfoldnorm_gen._sf	  s!    A!a%00r5   c                    ||z  }t          j        d|z            t          j        dt           j        z            z  }d|z  |t	          j        |t          j        d          z            z  z   }|dz   ||z  z
  }d||z  |z  ||z  z
  |z
  z  }|t          j        |d          z  }||dz   z  dz   d|z  |z  z   }|d|d	z
  z  d	|dz  z  z
  |dz  z  z  }||dz  z  d	z
  }||||fS )
N      r   rR   r   r  rE  r  r  rD  )rM   r   r   r   rt   r  r  )rB   r  r  expfacr@  rA  rB  rC  s           r3   r   zfoldnorm_gen._stats	  s
    qSR272be8#4#44YRVAbgajjL11111fr"un2b58be#f,-
bhsC   27^a"V)B,.
rR"W~RU
*b!e33#s(]R3Br5   r   r  r   r5   r3   r  r  	  s         *  D D D; ; ; ;1 1 1K K K1 1 1    r5   r  foldnormc                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Z eed           fd            Z xZS )weibull_min_gena  Weibull minimum continuous random variable.

    The Weibull Minimum Extreme Value distribution, from extreme value theory
    (Fisher-Gnedenko theorem), is also often simply called the Weibull
    distribution. It arises as the limiting distribution of the rescaled
    minimum of iid random variables.

    %(before_notes)s

    See Also
    --------
    weibull_max, numpy.random.Generator.weibull, exponweib

    Notes
    -----
    The probability density function for `weibull_min` is:

    .. math::

        f(x, c) = c x^{c-1} \exp(-x^c)

    for :math:`x > 0`, :math:`c > 0`.

    `weibull_min` takes ``c`` as a shape parameter for :math:`c`.
    (named :math:`k` in Wikipedia article and :math:`a` in
    ``numpy.random.weibull``).  Special shape values are :math:`c=1` and
    :math:`c=2` where Weibull distribution reduces to the `expon` and
    `rayleigh` distributions respectively.

    Suppose ``X`` is an exponentially distributed random variable with
    scale ``s``. Then ``Y = X**k`` is `weibull_min` distributed with shape
    ``c = 1/k`` and scale ``s**k``.

    %(after_notes)s

    References
    ----------
    https://en.wikipedia.org/wiki/Weibull_distribution

    https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zweibull_min_gen._shape_info
  r  r5   c                 v    |t          ||dz
            z  t          j        t          ||                     z  S r[   r  rM   r   r
  s      r3   ro   zweibull_min_gen._pdf
  s1    Q!}RVSAYYJ////r5   c                 ~    t          j        |          t          j        |dz
  |          z   t	          ||          z
  S r[   rM   r   rt   ru  r  r
  s      r3   r   zweibull_min_gen._logpdf
  s2    vayy28AE1---Aq		99r5   c                 J    t          j        t          ||                      S rK   rt   r  r  r
  s      r3   rr   zweibull_min_gen._cdf
  s    #a))$$$$r5   c                 P    t          t          j        |            d|z            S r  r  r  s      r3   r{   zweibull_min_gen._ppf
  s"    BHaRLL=#a%(((r5   c                 R    t          j        |                     ||                    S rK   r6  r
  s      r3   rv   zweibull_min_gen._sf 
       vdkk!Q''(((r5   c                 $    t          ||           S rK   r  r
  s      r3   r   zweibull_min_gen._logsf#
  s    Aq		zr5   c                 8    t          j        |           d|z  z  S r[   r.  r  s      r3   r~   zweibull_min_gen._isf&
  s    
ac""r5   c                 <    t          j        d|dz  |z  z             S r  r-  r{  s      r3   r   zweibull_min_gen._munp)
  s    xAcE!G$$$r5   c                 X    t            |z  t          j        |          z
  t           z   dz   S r[   r#   rM   r   r}  s     r3   r   zweibull_min_gen._entropy,
  %    w{RVAYY&/!33r5   a          If ``method='mm'``, parameters fixed by the user are respected, and the
        remaining parameters are used to match distribution and sample moments
        where possible. For example, if the user fixes the location with
        ``floc``, the parameters will only match the distribution skewness and
        variance to the sample skewness and variance; no attempt will be made
        to match the means or minimize a norm of the errors.
        

r   c           	         t          |t                    rJ|                                dk    r|                                }n t	                      j        |g|R i |S |                    dd          r t	                      j        |g|R i |S t          | |||          \  }}}}|                    dd          	                                }d t          j        |          d} |          }	|	k     r'|dk    r!||s t	                      j        |g|R i |S |dk    rd	\  }
}}nEt          |          r|d         nd }
|                    d
d           }|                    dd           }| |
t          fdd|gd          j        }
n||}
|d|bt          j        |          }t          j        |t%          j        dd|
z  z             t%          j        dd|
z  z             dz  z
  z            }n||}|7|5t          j        |          }||t%          j        dd|
z  z             z  z
  }n||}|dk    r|
||fS  t	                      j        ||
f||d|S )Nr   superfitFr/   r8   c                     t          j        dd| z  z             }t          j        dd| z  z             }t          j        dd| z  z             }d|dz  z  d|z  |z  z
  |z   }||dz  z
  dz  }||z  S )Nr   rR   r  r  r-  )r  gamma1gamma2gamma3numr  s         r3   skewz!weibull_min_gen.fit.<locals>.skewJ
  s|    Xa!e__FXa!e__FXa!e__Ffai-!F(6/1F:CFAI%-Cs7Nr5   g     @r9   NNNr,   r-   c                       |           z
  S rK   r   )r  r  r  s    r3   r  z%weibull_min_gen.fit.<locals>.<lambda>k
  s    dd1ggk r5   g{Gz?bisect)bracketr/   r   rR   r,   r-   )r<   r(   r=   r  r>   r@   r0   _check_fit_input_parametersr:   r;   r  r  r  r)   rootrM   r  r   rt   r  r   )rB   rC   rD   r2   fcr   r   r/   max_cs_minr  r,   r-   r  r  r  r  r  s                  @@r3   r@   zweibull_min_gen.fit/
  s    dL)) 	8  ""a''~~''"uww{47$777$77788J&& 	4577;t3d333d333 "=T4=A4"I "Ib$(E**0022	 	 	 JtUu994BJtJ577;t3d333d333 T>>,MAsEEt99.Q$A((5$''CHHWd++E:!) 11111D%=#+- - --1 A^A>emtAGA!AaC%28AacE??A3E!EFGGEEE<CKAeBHQ1W----CCCT>>c5=  577;tQECuEEEEEr5   )r   r   r   r   rh   ro   r   rr   r{   rv   r   r~   r   r   r	   r   r@   r  r  s   @r3   r  r  	  s       + +XE E E0 0 0: : :% % %) ) )) ) )  # # #% % %4 4 4 } 5   JF JF JF JF JF JF JF JF JFr5   r  r  c                   j     e Zd ZdZd Zd Z fdZd Zd Zd Z	d Z
d	 Zd
 Zd Zd Zd Zd Z xZS )truncweibull_min_gena9  A doubly truncated Weibull minimum continuous random variable.

    %(before_notes)s

    See Also
    --------
    weibull_min, truncexpon

    Notes
    -----
    The probability density function for `truncweibull_min` is:

    .. math::

        f(x, a, b, c) = \frac{c x^{c-1} \exp(-x^c)}{\exp(-a^c) - \exp(-b^c)}

    for :math:`a < x <= b`, :math:`0 \le a < b` and :math:`c > 0`.

    `truncweibull_min` takes :math:`a`, :math:`b`, and :math:`c` as shape
    parameters.

    Notice that the truncation values, :math:`a` and :math:`b`, are defined in
    standardized form:

    .. math::

        a = (u_l - loc)/scale
        b = (u_r - loc)/scale

    where :math:`u_l` and :math:`u_r` are the specific left and right
    truncation values, respectively. In other words, the support of the
    distribution becomes :math:`(a*scale + loc) < x <= (b*scale + loc)` when
    :math:`loc` and/or :math:`scale` are provided.

    %(after_notes)s

    References
    ----------

    .. [1] Rinne, H. "The Weibull Distribution: A Handbook". CRC Press (2009).

    %(example)s

    c                 *    |dk    ||k    z  |dk    z  S Nr   r   rB   r  r   r   s       r3   r`   ztruncweibull_min_gen._argcheck
  s    RAE"a"f--r5   c                     t          dddt          j        fd          }t          dddt          j        fd          }t          dddt          j        fd          }|||gS )Nr  Fr   r  r   rd   r   re   )rB   r!  rf  rg  s       r3   rh   z truncweibull_min_gen._shape_info
  sY    UQK@@UQK??UQK@@B|r5   c                 J    t                                          |d          S )N)r   r   r   rJ  r>   r  rB   rC   r  s     r3   r  ztruncweibull_min_gen._fitstart
  s     ww  I 666r5   c                 
    ||fS rK   r   r  s       r3   r   z!truncweibull_min_gen._get_support
      !tr5   c                 
   t          j        t          ||                     t          j        t          ||                     z
  }|t          ||dz
            z  t          j        t          ||                     z  |z  S r[   rM   r   r  )rB   rn   r  r   r   denums         r3   ro   ztruncweibull_min_gen._pdf
  sh    Q
##bfc!QiiZ&8&88C1Q3KK"&#a))"4"44==r5   c           	      6   t          j        t          j        t          ||                     t          j        t          ||                     z
            }t          j        |          t	          j        |dz
  |          z   t          ||          z
  |z
  S r[   )rM   r   r   r  rt   ru  )rB   rn   r  r   r   logdenums         r3   r   ztruncweibull_min_gen._logpdf
  ss    6"&#a)),,rvs1ayyj/A/AABBvayy28AE1---Aq		9HDDr5   c                 (   t          j        t          ||                     t          j        t          ||                     z
  }t          j        t          ||                     t          j        t          ||                     z
  }||z  S rK   r"  rB   rn   r  r   r   r
  r#  s          r3   rr   ztruncweibull_min_gen._cdf
  p    vs1ayyj!!BFC1II:$6$66Q
##bfc!QiiZ&8&88U{r5   c           	      p   t          j        t          j        t          ||                     t          j        t          ||                     z
            }t          j        t          j        t          ||                     t          j        t          ||                     z
            }||z
  S rK   rM   r   r   r  rB   rn   r  r   r   lognumr%  s          r3   r   ztruncweibull_min_gen._logcdf
      Aq		z**RVSAYYJ-?-??@@6"&#a)),,rvs1ayyj/A/AABB  r5   c                 (   t          j        t          ||                     t          j        t          ||                     z
  }t          j        t          ||                     t          j        t          ||                     z
  }||z  S rK   r"  r'  s          r3   rv   ztruncweibull_min_gen._sf
  r(  r5   c           	      p   t          j        t          j        t          ||                     t          j        t          ||                     z
            }t          j        t          j        t          ||                     t          j        t          ||                     z
            }||z
  S rK   r*  r+  s          r3   r   ztruncweibull_min_gen._logsf
  r-  r5   c                     t          t          j        d|z
  t          j        t          ||                     z  |t          j        t          ||                     z  z              d|z            S r[   r  rM   r   r   rB   rz   r  r   r   s        r3   r~   ztruncweibull_min_gen._isf
  e    VQUbfc!QiiZ0001rvs1ayyj7I7I3IIJJJAaC  	r5   c                     t          t          j        d|z
  t          j        t          ||                     z  |t          j        t          ||                     z  z              d|z            S r[   r1  r2  s        r3   r{   ztruncweibull_min_gen._ppf
  r3  r5   c           	      v   t          j        ||z  dz             t          j        ||z  dz   t          ||                    t          j        ||z  dz   t          ||                    z
  z  }t	          j        t          ||                     t	          j        t          ||                     z
  }||z  S r  )rt   r  r  r  rM   r   )rB   r_   r  r   r   	gamma_funr#  s          r3   r   ztruncweibull_min_gen._munp
  s    HQqS2X&&K!b#a)),,r{1Q38SAYY/O/OO	 Q
##bfc!QiiZ&8&885  r5   )r   r   r   r   r`   rh   r  r   ro   r   rr   r   rv   r   r~   r{   r   r  r  s   @r3   r  r  
  s        + +X. . .  7 7 7 7 7  > > >E E E  
! ! !
  
! ! !
  
  
! ! ! ! ! ! !r5   r  truncweibull_minc                   H    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 ZdS )weibull_max_gena0  Weibull maximum continuous random variable.

    The Weibull Maximum Extreme Value distribution, from extreme value theory
    (Fisher-Gnedenko theorem), is the limiting distribution of rescaled
    maximum of iid random variables. This is the distribution of -X
    if X is from the `weibull_min` function.

    %(before_notes)s

    See Also
    --------
    weibull_min

    Notes
    -----
    The probability density function for `weibull_max` is:

    .. math::

        f(x, c) = c (-x)^{c-1} \exp(-(-x)^c)

    for :math:`x < 0`, :math:`c > 0`.

    `weibull_max` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    References
    ----------
    https://en.wikipedia.org/wiki/Weibull_distribution

    https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zweibull_max_gen._shape_info  r  r5   c                 z    |t          | |dz
            z  t          j        t          | |                     z  S r[   r  r
  s      r3   ro   zweibull_max_gen._pdf  s5    aR1~bfc1"ajj[1111r5   c                     t          j        |          t          j        |dz
  |           z   t	          | |          z
  S r[   r  r
  s      r3   r   zweibull_max_gen._logpdf!  s6    vayy28AaC!,,,sA2qzz99r5   c                 J    t          j        t          | |                     S rK   r"  r
  s      r3   rr   zweibull_max_gen._cdf$  s    vsA2qzzk"""r5   c                 &    t          | |           S rK   r  r
  s      r3   r   zweibull_max_gen._logcdf'  s    QB

{r5   c                 L    t          j        t          | |                      S rK   r  r
  s      r3   rv   zweibull_max_gen._sf*  s!    #qb!**%%%%r5   c                 P    t          t          j        |           d|z             S r  )r  rM   r   r  s      r3   r{   zweibull_max_gen._ppf-  s#    RVAYYJA&&&&r5   c                 t    t          j        d|dz  |z  z             }t          |          dz  rd}nd}||z  S )Nr   rR   r  r   )rt   r  r  )rB   r_   r  valsgns        r3   r   zweibull_max_gen._munp0  sE    hs1S57{##q66A: 	CCCSyr5   c                 X    t            |z  t          j        |          z
  t           z   dz   S r[   r  r}  s     r3   r   zweibull_max_gen._entropy8  r  r5   N)r   r   r   r   rh   ro   r   rr   r   rv   r{   r   r   r   r5   r3   r9  r9  
  s        # #HE E E2 2 2: : :# # #  & & &' ' '  4 4 4 4 4r5   r9  weibull_max)r   r   c                   N    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd ZdS )genlogistic_gena  A generalized logistic continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `genlogistic` is:

    .. math::

        f(x, c) = c \frac{\exp(-x)}
                         {(1 + \exp(-x))^{c+1}}

    for real :math:`x` and :math:`c > 0`. In literature, different
    generalizations of the logistic distribution can be found. This is the type 1
    generalized logistic distribution according to [1]_. It is also referred to
    as the skew-logistic distribution [2]_.

    `genlogistic` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    References
    ----------
    .. [1] Johnson et al. "Continuous Univariate Distributions", Volume 2,
           Wiley. 1995.
    .. [2] "Generalized Logistic Distribution", Wikipedia,
           https://en.wikipedia.org/wiki/Generalized_logistic_distribution

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zgenlogistic_gen._shape_info`  r  r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zgenlogistic_gen._pdfc  r  r5   c                     |dz
   |dk     z  dz
  }t          j        |          }t          j        |          ||z  z   |dz   t          j        t          j        |                     z  z
  S Nr   r   )rM   r
  r   rt   r  r   )rB   rn   r  multabsxs        r3   r   zgenlogistic_gen._logpdfg  sc     Qx1q5!A%vayyvayy49$!rxu/F/F'FFFr5   c                 >    dt          j        |           z   | z  }|S r[   r9  )rB   rn   r  Cxs       r3   rr   zgenlogistic_gen._cdfo  s!    r

lqb!	r5   c                 X    | t          j        t          j        |                     z  S rK   )rM   r  r   r
  s      r3   r   zgenlogistic_gen._logcdfs  s#    rBHRVQBZZ((((r5   c                 X    t          j        t          j        |d|z                       S r?  )rM   r   rt   powm1r  s      r3   r{   zgenlogistic_gen._ppfv  s%    rx46**++++r5   c                 T    t          j        |                     ||                     S rK   rt   r  r   r
  s      r3   rv   zgenlogistic_gen._sfy  #    a++,,,,r5   c                 4    |                      d|z
  |          S r[   r{   r  s      r3   r~   zgenlogistic_gen._isf|  s    yyQ"""r5   c                    t           t          j        |          z   }t          j        t          j        z  dz  t          j        d|          z   }dt          j        d|          z  dt          z  z   }|t          j        |d          z  }t          j        dz  dz  dt          j        d|          z  z   }||d	z  z  }||||fS )
NrE  rR   r  r  r  r         .@r  r   )r#   rt   rX  rM   r   zetar$   r  rB   r  r@  rA  rB  rC  s         r3   r   zgenlogistic_gen._stats  s    bfQiieBEk#o1-1&(
bhsC   UAXd]Qrwq!}}_,
c3h3Br5   c                 6    t          |dk     |fd d           S )Ng    ^Ac                 r    t          j        |            t          j        | dz             z   t          z   dz   S r[   )rM   r   rt   rX  r#   r  s    r3   r  z*genlogistic_gen._entropy.<locals>.<lambda>  s+    RVAYYJA$>$G!$K r5   c                 (    dd| z  z  t           z   dz   S r-  r#   r  s    r3   r  z*genlogistic_gen._entropy.<locals>.<lambda>  s    q!a%y6'9A'= r5   r  r  r}  s     r3   r   zgenlogistic_gen._entropy  s1    !c'A5KK >=? ? ? 	?r5   N)r   r   r   r   rh   ro   r   rr   r   r{   rv   r~   r   r   r   r5   r3   rG  rG  ?  s         @E E E* * *G G G  ) ) ), , ,- - -# # #  ? ? ? ? ?r5   rG  genlogisticc                   b    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd ZddZd Zd ZdS )genpareto_gena  A generalized Pareto continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `genpareto` is:

    .. math::

        f(x, c) = (1 + c x)^{-1 - 1/c}

    defined for :math:`x \ge 0` if :math:`c \ge 0`, and for
    :math:`0 \le x \le -1/c` if :math:`c < 0`.

    `genpareto` takes ``c`` as a shape parameter for :math:`c`.

    For :math:`c=0`, `genpareto` reduces to the exponential
    distribution, `expon`:

    .. math::

        f(x, 0) = \exp(-x)

    For :math:`c=-1`, `genpareto` is uniform on ``[0, 1]``:

    .. math::

        f(x, -1) = 1

    %(after_notes)s

    %(example)s

    c                 *    t          j        |          S rK   rM   r   r}  s     r3   r`   zgenpareto_gen._argcheck      {1~~r5   c                 V    t          ddt          j         t          j        fd          gS Nr  Fr  re   rg   s    r3   rh   zgenpareto_gen._shape_info  $    3'8.IIJJr5   c                     t          j        |          }t          |dk     |fd t           j                  }t          j        |dk    | j        | j                  }||fS )Nr   c                     d| z  S r?  r   r  s    r3   r  z,genpareto_gen._get_support.<locals>.<lambda>  s
    q r5   )rM   r   r   rf   rO  r   )rB   r  r   r   s       r3   r   zgenpareto_gen._get_support  sY    JqMMq1uqd((v  HQ!VTVTV,,!tr5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zgenpareto_gen._pdf  r  r5   c                 D    t          ||k    |dk    z  ||fd |           S )Nr   c                 @    t          j        |dz   || z             |z  S r  rf  rn   r  s     r3   r  z'genpareto_gen._logpdf.<locals>.<lambda>  s"    
1r61Q3(?(?'?!'C r5   r  r
  s      r3   r   zgenpareto_gen._logpdf  s4    16a1f-1vCC"  	r5   c                 2    t          j        | |            S rK   )rt   inv_boxcox1pr
  s      r3   rr   zgenpareto_gen._cdf  s    QB''''r5   c                 0    t          j        | |           S rK   )rt   
inv_boxcoxr
  s      r3   rv   zgenpareto_gen._sf  s    }aR!$$$r5   c                 D    t          ||k    |dk    z  ||fd |           S )Nr   c                 8    t          j        || z             |z  S rK   r	  rn  s     r3   r  z&genpareto_gen._logsf.<locals>.<lambda>  s    1~'9 r5   r  r
  s      r3   r   zgenpareto_gen._logsf  s4    16a1f-1v99"  	r5   c                 2    t          j        | |            S rK   )rt   boxcox1pr  s      r3   r{   zgenpareto_gen._ppf  s    QB####r5   c                 0    t          j        ||            S rK   )rt   boxcoxr  s      r3   r~   zgenpareto_gen._isf  s    	!aR    r5   r  c                 V   d|vrd }n"t          |dk     |fd t          j                  }d|vrd }n"t          |dk     |fd t          j                  }d|vrd }n"t          |dk     |fd	 t          j                  }d
|vrd }n"t          |dk     |fd t          j                  }||||fS )Nr  r   c                     dd| z
  z  S r[   r   xis    r3   r  z&genpareto_gen._stats.<locals>.<lambda>  s    aRj r5   r  r   c                 *    dd| z
  dz  z  dd| z  z
  z  S r-  r   r{  s    r3   r  z&genpareto_gen._stats.<locals>.<lambda>  s    a1r6A+oQrT&B r5   r  gUUUUUU?c                 Z    dd| z   z  t          j        dd| z  z
            z  dd| z  z
  z  S )NrR   r   r  r  r{  s    r3   r  z&genpareto_gen._stats.<locals>.<lambda>  s5    qAF|bga!B$h6G6G'G()AbD(2 r5   r  r  c                 `    ddd| z  z
  z  d| dz  z  | z   dz   z  dd| z  z
  z  dd| z  z
  z  dz
  S )Nr  r   rR   r   r   r{  s    r3   r  z&genpareto_gen._stats.<locals>.<lambda>  sQ    qA"H~2q529I'J()AbD(2562X(?AB(C r5   r   rM   rf   r  )rB   r  r  r  r  r  r  s          r3   r   zgenpareto_gen._stats  s    gAA1q51$006# #A gAA1s7QDBB6# #A gAA1s7QD3 36# #A gAA1s7QDD D6# #A !Qzr5   c           	      l    d t          |dk    |ffdt          j        dz                       S )Nc                    d}t          j        d| dz             }t          |t          j        | |                    D ]\  }}||d|z  z  d||z  z
  z  z   }t          j        || z  dk     |d|z  | z  z  t           j                  S )Nr   r   r   r  r   r<  )rM   rM  ziprt   combrO  rf   )r_   r  rB  r  kicnks         r3   rV  z#genpareto_gen._munp.<locals>.__munp   s    C	!QU##Aq"'!Q--00 > >CC2"*,a"f==8AEAIsdQh1_'<bfEEEr5   r   c                      |           S rK   r   )r  _genpareto_gen__munpr_   s    r3   r  z%genpareto_gen._munp.<locals>.<lambda>  s    FF1aLL r5   r   )r   rt   r  )rB   r_   r  r  s    ` @r3   r   zgenpareto_gen._munp  sR    	F 	F 	F !q&1$00000(1q5//+ + 	+r5   c                     d|z   S r  r   r}  s     r3   r   zgenpareto_gen._entropy
  s    Avr5   Nr  )r   r   r   r   r`   rh   r   ro   r   rr   rv   r   r{   r~   r   r   r   r   r5   r3   rb  rb    s        " "F  K K K  * * *  
( ( (% % %  
$ $ $! ! !   :	+ 	+ 	+    r5   rb  	genparetoc                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
genexpon_gena!  A generalized exponential continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `genexpon` is:

    .. math::

        f(x, a, b, c) = (a + b (1 - \exp(-c x)))
                        \exp(-a x - b x + \frac{b}{c}  (1-\exp(-c x)))

    for :math:`x \ge 0`, :math:`a, b, c > 0`.

    `genexpon` takes :math:`a`, :math:`b` and :math:`c` as shape parameters.

    %(after_notes)s

    References
    ----------
    H.K. Ryu, "An Extension of Marshall and Olkin's Bivariate Exponential
    Distribution", Journal of the American Statistical Association, 1993.

    N. Balakrishnan, Asit P. Basu (editors), *The Exponential Distribution:
    Theory, Methods and Applications*, Gordon and Breach, 1995.
    ISBN 10: 2884491929

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }t          dddt          j        fd          }|||gS )Nr   Fr   r  r   r  re   )rB   rf  rg  r!  s       r3   rh   zgenexpon_gen._shape_info1  sY    UQK@@UQK@@UQK@@B|r5   c           	          ||t          j        | |z             z  z   t          j        | |z
  |z  |t          j        | |z             z  |z  z             z  S rK   rt   r  rM   r   rB   rn   r   r   r  s        r3   ro   zgenexpon_gen._pdf7  sl     A!A''!Aq01BHaRTNN?0CA0E1F *G *G G 	Gr5   c                     t          j        ||t          j        | |z             z  z             | |z
  |z  z   |t          j        | |z             z  |z  z   S rK   rM   r   rt   r  r  s        r3   r   zgenexpon_gen._logpdf=  sZ    vaBHaRTNN?++,,1ax7BHaRTNN?8KA8MMMr5   c                 z    t          j        | |z
  |z  |t          j        | |z             z  |z  z              S rK   r=  r  s        r3   rr   zgenexpon_gen._cdf@  s>    1"Q$A!A$7$99::::r5   c                     ||z   }||t          j        |           z  z
  |z  }|t          j        | |z  t          j        |           z            j        z   |z  S rK   )rM   r  rt   lambertwr   realrB   r  r   r   r  r  r  s          r3   r{   zgenexpon_gen._ppfC  sZ    E28QB<<"BK1rvqbzz 12277::r5   c                 x    t          j        | |z
  |z  |t          j        | |z             z  |z  z             S rK   r  r  s        r3   rv   zgenexpon_gen._sfH  s;    vr!tQhRXqbd^^O!4Q!66777r5   c                     ||z   }||t          j        |          z  z
  |z  }|t          j        | |z  t          j        |           z            j        z   |z  S rK   )rM   r   rt   r  r   r  r  s          r3   r~   zgenexpon_gen._isfK  sW    E26!99_aBK1rvqbzz 12277::r5   Nr{  r   r5   r3   r  r    s         >  G G GN N N; ; ;; ; ;
8 8 8; ; ; ; ;r5   r  genexponc                   v     e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Zd Zd Z fdZd Zd Z xZS )genextreme_genaB  A generalized extreme value continuous random variable.

    %(before_notes)s

    See Also
    --------
    gumbel_r

    Notes
    -----
    For :math:`c=0`, `genextreme` is equal to `gumbel_r` with
    probability density function

    .. math::

        f(x) = \exp(-\exp(-x)) \exp(-x),

    where :math:`-\infty < x < \infty`.

    For :math:`c \ne 0`, the probability density function for `genextreme` is:

    .. math::

        f(x, c) = \exp(-(1-c x)^{1/c}) (1-c x)^{1/c-1},

    where :math:`-\infty < x \le 1/c` if :math:`c > 0` and
    :math:`1/c \le x < \infty` if :math:`c < 0`.

    Note that several sources and software packages use the opposite
    convention for the sign of the shape parameter :math:`c`.

    `genextreme` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                 *    t          j        |          S rK   rd  r}  s     r3   r`   zgenextreme_gen._argcheck{  re  r5   c                 V    t          ddt          j         t          j        fd          gS rg  re   rg   s    r3   rh   zgenextreme_gen._shape_info~  rh  r5   c                 
   t          j        |dk    dt          j        |t                    z  t           j                  }t          j        |dk     dt          j        |t                     z  t           j                   }||fS Nr   r   )rM   rO  maximumr!   rf   minimum)rB   r  _b_as       r3   r   zgenextreme_gen._get_support  sc    Xa!eS2:a#7#77@@Xa!eS2:a%#8#8826'BB2vr5   c                 D    t          ||k    |dk    z  ||fd |           S )Nr   c                 8    t          j        | | z            |z  S rK   r	  rn  s     r3   r  z+genextreme_gen._loglogcdf.<locals>.<lambda>  s    rx1~~a'7 r5   r  r
  s      r3   
_loglogcdfzgenextreme_gen._loglogcdf  s3    16a1f-1v77!= = 	=r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zgenextreme_gen._pdf  s"     vdll1a(()))r5   c                    t          ||k    |dk    z  ||fd d          }t          j        |           }|                     ||          }t	          j        |          }t	          j        ||dk    |t          j         k    z  d           t          |dk    |t          j         k    z   |||fd t          j                   }t	          j        ||dk    |dk    z  d           |S )Nr   c                     || z  S rK   r   rn  s     r3   r  z(genextreme_gen._logpdf.<locals>.<lambda>  s
    !A# r5   r   r   c                     |  |z   |z
  S rK   r   )pex2lpex2lex2s      r3   r  z(genextreme_gen._logpdf.<locals>.<lambda>  s    teemd6J r5   r  )r   rt   r  r  rM   r   putmaskrf   )rB   rn   r  cxlogex2logpex2r  logpdfs           r3   r   zgenextreme_gen._logpdf  s    aAF+aV5E5EsKK2#//!Q''vg

7Q!VbfW5s;;;rQw2"&=9:!7F3JJ')vg/ / / 	
6AFqAv.444r5   c                 T    t          j        |                     ||                     S rK   )rM   r   r  r
  s      r3   r   zgenextreme_gen._logcdf  s#    tq!,,----r5   c                 R    t          j        |                     ||                    S rK   rM   r   r   r
  s      r3   rr   zgenextreme_gen._cdf  rV  r5   c                 T    t          j        |                     ||                     S rK   rT  r
  s      r3   rv   zgenextreme_gen._sf  rU  r5   c                     t          j        t          j        |                      }t          ||k    |dk    z  ||fd |          S )Nr   c                 :    t          j        | | z             |z  S rK   r=  rn  s     r3   r  z%genextreme_gen._ppf.<locals>.<lambda>      !a(8(8'81'< r5   )rM   r   r   rB   rz   r  rn   s       r3   r{   zgenextreme_gen._ppf  sP    VRVAYYJ16a1f-1v<<aA A 	Ar5   c                     t          j        t          j        |                       }t	          ||k    |dk    z  ||fd |          S )Nr   c                 :    t          j        | | z             |z  S rK   r=  rn  s     r3   r  z%genextreme_gen._isf.<locals>.<lambda>  r  r5   )rM   r   rt   r  r   r  s       r3   r~   zgenextreme_gen._isf  sR    VRXqb\\M"""16a1f-1v<<aA A 	Ar5   c                    fd} |d          } |d          } |d          } |d          }t          j        t                    dk     t           j        z  dz  dz  ||dz  z
            }t          j        t                    dk     t           j        dz  dz  t	          j        t	          j        dz  d	z             dt	          j        d	z             z  z
            dz  z            }d
}	t          j        t                    |	k     t           t	          j        t	          j        dz                       z            }
t          j        dk     t           j        |
           }t          j        dk     t           j        |dz  |z            }t          dk    ||||fd t           j                  }t          j        t                    |	dz  k    dt          j
        d          z  t          z  t           j        dz  z  |          }t          dk    |||||fd t           j                  }t          j        t                    |	dz  k    d|dz
            }||||fS )Nc                 8    t          j        | z  dz             S r[   r-  )r_   r  s    r3   gz genextreme_gen._stats.<locals>.g  s    8AEAI&&&r5   r   rR   r  r   gHz>r   rE  r   +=r<  r  r  c                 V    t          j        |           | |d|z  z   |z  z   z  |dz  z  S NrR   r  rL   )r  rB  rC  g3g2mg12s        r3   r  z'genextreme_gen._stats.<locals>.<lambda>  s4    WQZZ"QvXr/A)AB63;N r5   r  g(\?r  r  g      пc                 <    |d|z  d||z   z  | z  z   | z  z   |dz  z  S )Nr  r  rR   r   )rB  rC  r  g4r  s        r3   r  z'genextreme_gen._stats.<locals>.<lambda>  s3     BrEArF{OB,>$>#BBFAIM r5   gq=
ףp?333333@rD  )rM   rO  r
  r   rt   r  r  r#   r  r   r   r$   )rB   r  r  rB  rC  r  r  r  gam2kepsgamkr  r  sk1r  ku1r  s    `               r3   r   zgenextreme_gen._stats  sY   	' 	' 	' 	' 	'QqTTQqTTQqTTQqTT#a&&4-!BE'C);RCZHHQ$s
3"*SU3Y"7"7"*QW:M:M8M"MNNqRUvUW WxAvgrx
1q58I8I/J/J1/LMMHQXrvu--HQXrvr3wu}55 eRR0O O#%6	+ + +
 Xc!ffT	)2bgajj=+?q+H#NN eb"b&1N N#%6	+ + +
 Xc!fft+Xs3w??!R|r5   c                     t          |t                    r|                                }t          |          }|dk     rd}nd}t	                                          ||f          S )Nr   r   r  rJ  r<   r(   r  r   r>   r  )rB   rC   r  r   r  s       r3   r  zgenextreme_gen._fitstart  sc    dL)) 	$>>##D$KKq55AAAww  QD 111r5   c                 &   t          j        d|dz             }d||z  z  t          j        t          j        ||          d|z  z  t          j        ||z  dz             z  d          z  }t          j        ||z  dk    |t           j                  S )Nr   r   r   r  r  )rM   rM  r  rt   r  r  rO  rf   )rB   r_   r  r  valss        r3   r   zgenextreme_gen._munp  s    Ia11a4x"&GAqMMR!G#bhqsQw&7&77    x!b$///r5   c                 "    t           d|z
  z  dz   S r[   r_  r}  s     r3   r   zgenextreme_gen._entropy  s    q1u~!!r5   )r   r   r   r   r`   rh   r   r  ro   r   r   rr   rv   r{   r~   r   r  r   r   r  r  s   @r3   r  r  T  s       % %L  K K K  
= = =
* * *  . . .* * *- - -A A A
A A A
  B	2 	2 	2 	2 	20 0 0" " " " " " "r5   r  
genextremec                 Z    d} fd} dk    r7t          j                   dz   } dk     rt          j        ||d          }|S n* dk    rt          j         d	z            d
z   }n	d  |z
  z  }t          j        ||dd          \  }}}}|dk    rt          d z            |d         S )af  Inverse of the digamma function (real positive arguments only).

    This function is used in the `fit` method of `gamma_gen`.
    The function uses either optimize.fsolve or optimize.newton
    to solve `sc.digamma(x) - y = 0`.  There is probably room for
    improvement, but currently it works over a wide range of y:

    >>> import numpy as np
    >>> rng = np.random.default_rng()
    >>> y = 64*rng.standard_normal(1000000)
    >>> y.min(), y.max()
    (-311.43592651416662, 351.77388222276869)
    >>> x = [_digammainv(t) for t in y]
    >>> np.abs(sc.digamma(x) - y).max()
    1.1368683772161603e-13

    gox?c                 2    t          j        |           z
  S rK   )rt   r  rh  s    r3   r[  z_digammainv.<locals>.func   s    z!}}q  r5   g      r   r  绽|=)tolr  g-@g뭁,?r   dy=T)xtolr  r   z"_digammainv: fsolve failed, y = %rr   )rM   r   r   newtonr  RuntimeError)ri  _emr[  x0valuer  r  rT  s   `       r3   _digammainvr    s    $ &C! ! ! ! ! 	6zzVAYY_r66 OD"%888EL  
RVAeG__w&QBH%_T2E9=? ? ?E4d
axx?!CDDD8Or5   c                        e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd Z fdZ eed           fd            Z xZS )	gamma_gena  A gamma continuous random variable.

    %(before_notes)s

    See Also
    --------
    erlang, expon

    Notes
    -----
    The probability density function for `gamma` is:

    .. math::

        f(x, a) = \frac{x^{a-1} e^{-x}}{\Gamma(a)}

    for :math:`x \ge 0`, :math:`a > 0`. Here :math:`\Gamma(a)` refers to the
    gamma function.

    `gamma` takes ``a`` as a shape parameter for :math:`a`.

    When :math:`a` is an integer, `gamma` reduces to the Erlang
    distribution, and when :math:`a=1` to the exponential distribution.

    Gamma distributions are sometimes parameterized with two variables,
    with a probability density function of:

    .. math::

        f(x, \alpha, \beta) =
        \frac{\beta^\alpha x^{\alpha - 1} e^{-\beta x }}{\Gamma(\alpha)}

    Note that this parameterization is equivalent to the above, with
    ``scale = 1 / beta``.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zgamma_gen._shape_infoG  r  r5   Nc                 .    |                     ||          S rK   standard_gamma)rB   r   r   r   s       r3   r   zgamma_gen._rvsJ  s    **1d333r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zgamma_gen._pdfM  r  r5   c                 b    t          j        |dz
  |          |z
  t          j        |          z
  S r  )rt   ru  r  r
  s      r3   r   zgamma_gen._logpdfQ  s*    x#q!!A%
155r5   c                 ,    t          j        ||          S rK   r  r
  s      r3   rr   zgamma_gen._cdfT  r   r5   c                 ,    t          j        ||          S rK   r  r
  s      r3   rv   zgamma_gen._sfW  s    |Aq!!!r5   c                 ,    t          j        ||          S rK   r  r  s      r3   r{   zgamma_gen._ppfZ  s    ~a###r5   c                 ,    t          j        ||          S rK   rt   r  r  s      r3   r~   zgamma_gen._isf]  s    q!$$$r5   c                 >    ||dt          j        |          z  d|z  fS )Nr   rE  r  r  s     r3   r   zgamma_gen._stats`  s!    !S^SU**r5   c                 >    d }d }t          |dk     |f||          S )Nc                 f    t          j        |           d| z
  z  | z   t          j        |           z   S r[   rt   rX  r  r   s    r3   r  z+gamma_gen._entropy.<locals>.regular_formulae  s+    6!99!$q(2:a==88r5   c                     ddt          j        dt           j        z            z   t          j        |           z   z  dd| z  z  z
  | dz  dz  z
  | dz  d	z  z
  | d
z  dz  z   S )Nr   r   rR   r   r  r  r  r  r  r  r  r   r  s    r3   r  z.gamma_gen._entropy.<locals>.asymptotic_formulah  so    
 2qw/"&));<q!a%yH#vrk"%&VRK034c63,? @r5      r  r  )rB   r   r  r  s       r3   r   zgamma_gen._entropyc  sK    	9 	9 	9	@ 	@ 	@ !c'A5//1 1 1 	1r5   c                     t          |t                    r|                                }t          |          }dd|dz  z   z  }t	                                          ||f          S )Nr   :0yE>rR   rJ  r  )rB   rC   r  r   r  s       r3   r  zgamma_gen._fitstarts  sb     dL)) 	$>>##D4[[Aww  QD 111r5   a<          When the location is fixed by using the argument `floc`
        and `method='MLE'`, this
        function uses explicit formulas or solves a simpler numerical
        problem than the full ML optimization problem.  So in that case,
        the `optimizer`, `loc` and `scale` arguments are ignored.
        

r   c                 N   |                     dd           }|                     dd          }t          |t                    s|5|                                dk    r t	                      j        |g|R i |S |                    dd            t          |g d          }|                    dd           }t          |           |||t          d          t          j        |          }t          j        |                                          st          d          |                                dk    rt          j        |          }t          j        |          }	t          j        ||z
  d	z            }
|||}}}||
||
d
|	z  z  }||t          j        |	|z            }|
||	||z
  z  }|
||	|d
z  z  }|||z
  |z  }||||z  z
  }|||z
  |z  }|||fS t          j        ||k              rt%          d|t          j                  |dk    r||z
  }|                                }|||}nt          j        |          t          j        |                                          z
  d	z
  t          j        d	z
  d
z  dz  z             z   dz  z  }|dz  }|dz  }t+          j        fd||d          }||z  }nLt          j        |                                          t          j        |          z
  }t/          |          }|}|||fS )Nr   r/   r8   r9   r  r   r   r   r  rR   r  r  r   r  r  g333333?gffffff?c                 \    t          j        |           t          j        |           z
  z
  S rK   )rM   r   rt   r  )r   r  s    r3   r  zgamma_gen.fit.<locals>.<lambda>  s!    bfQii"*Q--.G!.K r5   )disp)r:   r<   r(   r;   r>   r@   r0   r   r4   r   rM   r   r   r   r   r  r   r  rH  rf   r   r   brentqr  )rB   rC   rD   r2   r   r/   r  r   m1m2m3r   r,   r-   r  aestxar  r  r  r  s                      @r3   r@   zgamma_gen.fit  sr    xx%%(E**t\** 	44!7!7 577;t3d333d333 	!$(=(=(=>>(D))$T***>d.63E  ) * * * z${4  $$&& 	ECDDD <<>>T!!BB$))**BfEsAyS[U]a"f{u}QyU]b3hyS[%1*%y#X&{1u9n}cQc5= 
 6$$, 	Bwd"&AAAA199 $;Dyy{{ >~ F4LL26$<<#4#4#6#66!bgqsQhAo6662a4@5\5\O$K$K$K$K$&4 4 4
 1HEE
 t!!##bfVnn4AAAE$~r5   r   )r   r   r   r   rh   r   ro   r   rr   rv   r{   r~   r   r   r  r	   r   r@   r  r  s   @r3   r  r    s+       ' 'PE E E4 4 4 4* * *6 6 6! ! !" " "$ $ $% % %+ + +1 1 1 
2 
2 
2 
2 
2 } 5   e e e e e e e e er5   r  r  c                   ^     e Zd ZdZd Zd Z fdZ eed           fd            Z	 xZ
S )
erlang_gena  An Erlang continuous random variable.

    %(before_notes)s

    See Also
    --------
    gamma

    Notes
    -----
    The Erlang distribution is a special case of the Gamma distribution, with
    the shape parameter `a` an integer.  Note that this restriction is not
    enforced by `erlang`. It will, however, generate a warning the first time
    a non-integer value is used for the shape parameter.

    Refer to `gamma` for examples.

    c                     t          j        t          j        |          |k              }|s"d|d}t          j        |t
          d           |dk    S )NzRThe shape parameter of the erlang distribution has been given a non-integer value .r  
stacklevelr   )rM   r   floorwarningswarnRuntimeWarning)rB   r   allintmessages       r3   r`   zerlang_gen._argcheck  sd    q()) 	AD=>D D DGM'>a@@@@1ur5   c                 @    t          dddt          j        fd          gS )Nr   Tr   rd   re   rg   s    r3   rh   zerlang_gen._shape_info  ri   r5   c                     t          |t                    r|                                }t          ddt	          |          dz  z   z            }t          t          |                               ||f          S )NrJ  r  rR   rJ  )r<   r(   r  r  r   r>   r  r  )rB   rC   r   r  s      r3   r  zerlang_gen._fitstart  sl     dL)) 	$>>##DteDkk1n,-..Y%%//A4/@@@r5   a          The Erlang distribution is generally defined to have integer values
        for the shape parameter.  This is not enforced by the `erlang` class.
        When fitting the distribution, it will generally return a non-integer
        value for the shape parameter.  By using the keyword argument
        `f0=<integer>`, the fit method can be constrained to fit the data to
        a specific integer shape parameter.r   c                 >     t                      j        |g|R i |S rK   )r>   r@   rB   rC   rD   r2   r  s       r3   r@   zerlang_gen.fit  s+     uww{4/$///$///r5   )r   r   r   r   r`   rh   r  r	   r   r@   r  r  s   @r3   r   r     s         &  C C CA A A A A } 5/ 0 0 00 0 0 00 00 0 0 0 0r5   r   erlangc                   V    e Zd ZdZd Zd Zd Zd Zd ZddZ	d	 Z
d
 Zd Zd Zd ZdS )gengamma_gena  A generalized gamma continuous random variable.

    %(before_notes)s

    See Also
    --------
    gamma, invgamma, weibull_min

    Notes
    -----
    The probability density function for `gengamma` is ([1]_):

    .. math::

        f(x, a, c) = \frac{|c| x^{c a-1} \exp(-x^c)}{\Gamma(a)}

    for :math:`x \ge 0`, :math:`a > 0`, and :math:`c \ne 0`.
    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).

    `gengamma` takes :math:`a` and :math:`c` as shape parameters.

    %(after_notes)s

    References
    ----------
    .. [1] E.W. Stacy, "A Generalization of the Gamma Distribution",
       Annals of Mathematical Statistics, Vol 33(3), pp. 1187--1192.

    %(example)s

    c                     |dk    |dk    z  S r  r   )rB   r   r  s      r3   r`   zgengamma_gen._argcheckK      A!q&!!r5   c                     t          dddt          j        fd          }t          ddt          j         t          j        fd          }||gS rn  re   ro  s      r3   rh   zgengamma_gen._shape_infoN  B    UQK@@UbfWbf$5~FFBxr5   c                 T    t          j        |                     |||                    S rK   r  rq  s       r3   ro   zgengamma_gen._pdfS  "    vdll1a++,,,r5   c                 `    t          |dk    |dk    z  ||ffdt          j                   S )Nr   c                     t          j        t          |                    t          j        |z  dz
  |           z   | |z  z
  t          j                  z
  S r[   )rM   r   r
  rt   ru  r  )rn   r  r   s     r3   r  z&gengamma_gen._logpdf.<locals>.<lambda>X  sJ    s1vv!A#'19M9M(M*+Q$)/13A)? r5   r  r  rq  s     ` r3   r   zgengamma_gen._logpdfV  sN    16a!e,q!f@ @ @ @%'VG- - - 	-r5   c                     ||z  }t          j        ||          }t          j        ||          }t          j        |dk    ||          S r  rt   r  r  rM   rO  rB   rn   r   r  xcval1val2s          r3   rr   zgengamma_gen._cdf\  E    T{1b!!|Ar""xAtT***r5   Nc                 @    |                     ||          }|d|z  z  S )Nr  r   r  )rB   r   r  r   r   r  s         r3   r   zgengamma_gen._rvsb  s(    '''552a4yr5   c                     ||z  }t          j        ||          }t          j        ||          }t          j        |dk    ||          S r  r  r  s          r3   rv   zgengamma_gen._sff  r   r5   c                     t          j        ||          }t          j        ||          }t          j        |dk    ||          d|z  z  S r  rt   r  r  rM   rO  rB   rz   r   r  r  r  s         r3   r{   zgengamma_gen._ppfl  E    ~a##q!$$xAtT**SU33r5   c                     t          j        ||          }t          j        ||          }t          j        |dk    ||          d|z  z  S r  r$  r%  s         r3   r~   zgengamma_gen._isfq  r&  r5   c                 8    t          j        ||dz  |z            S r  )rt   r  )rB   r_   r   r  s       r3   r   zgengamma_gen._munpv  s    wq!C%'"""r5   c                 D    d }d }t          |dk    ||f||          }|S )Nc                     t          j        |           }| d|z
  z  ||z  z   }t          j        |           t          j        t          |                    z
  }||z   }|S r[   )rt   rX  r  rM   r   r
  )r   r  rB  ABr  s         r3   r  z&gengamma_gen._entropy.<locals>.regular{  sS    &))CQWa'A
1s1vv.AAAHr5   c                 <   t                                           t          j        |           dz  z
  t          j        t          j        |                    z
  | dz  dz  z   | dz  dz  z
  t          j        |           | dz  dz  z
  | dz  dz  z
  | dz  d	z  z   |z  z   S )
NrR   r<  r  r  r  r  r  r  r  )r  r   rM   r   r
  )r   r  s     r3   
asymptoticz)gengamma_gen._entropy.<locals>.asymptotic  s    MMOObfQiik1fRVAYY''(+,c61*5893{CvayyAsFA:-C;q#vslJAMN Or5         i@r  r  r  )rB   r   r  r  r.  r  s         r3   r   zgengamma_gen._entropyz  sH    	 	 		O 	O 	O qCx!Q:'BBBr5   r   )r   r   r   r   r`   rh   ro   r   rr   r   rv   r{   r~   r   r   r   r5   r3   r  r  +  s         >" " "  
- - -- - -+ + +   + + +4 4 4
4 4 4
# # #    r5   r  gengammac                   6    e Zd ZdZd Zd Zd Zd Zd Zd Z	dS )	genhalflogistic_gena  A generalized half-logistic continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `genhalflogistic` is:

    .. math::

        f(x, c) = \frac{2 (1 - c x)^{1/(c-1)}}{[1 + (1 - c x)^{1/c}]^2}

    for :math:`0 \le x \le 1/c`, and :math:`c > 0`.

    `genhalflogistic` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zgenhalflogistic_gen._shape_info  r  r5   c                     | j         d|z  fS r  r  r}  s     r3   r   z genhalflogistic_gen._get_support  s    vs1u}r5   c                 v    d|z  }t          j        d||z  z
            }||dz
  z  }||z  }d|z  d|z   dz  z  S r  rM   r   )rB   rn   r  limitr  tmp0tmp2s          r3   ro   zgenhalflogistic_gen._pdf  sQ     Aj1Q3U1W~Cxv4!##r5   c                 `    d|z  }t          j        d||z  z
            }||z  }d|z
  d|z   z  S r)  r7  )rB   rn   r  r8  r  r:  s         r3   rr   zgenhalflogistic_gen._cdf  s>    Aj1Q3U|DQtV$$r5   c                 0    d|z  dd|z
  d|z   z  |z  z
  z  S r)  r   r  s      r3   r{   zgenhalflogistic_gen._ppf  s'    1ua#a%#a%1,,--r5   c                 B    dd|z  dz   t          j        d          z  z
  S r  r.  r}  s     r3   r   zgenhalflogistic_gen._entropy  s"    AaCE26!99$$$r5   N)
r   r   r   r   rh   r   ro   rr   r{   r   r   r5   r3   r3  r3    s{         *E E E  $ $ $% % %. . .% % % % %r5   r3  genhalflogisticc                   |     e Zd ZdZd Zd Z fdZd Zd Zd e	d                         Z
d	 Zd
 ZddZd Z xZS )genhyperbolic_genu  A generalized hyperbolic continuous random variable.

    %(before_notes)s

    See Also
    --------
    t, norminvgauss, geninvgauss, laplace, cauchy

    Notes
    -----
    The probability density function for `genhyperbolic` is:

    .. math::

        f(x, p, a, b) =
            \frac{(a^2 - b^2)^{p/2}}
            {\sqrt{2\pi}a^{p-1/2}
            K_p\Big(\sqrt{a^2 - b^2}\Big)}
            e^{bx} \times \frac{K_{p - 1/2}
            (a \sqrt{1 + x^2})}
            {(\sqrt{1 + x^2})^{1/2 - p}}

    for :math:`x, p \in ( - \infty; \infty)`,
    :math:`|b| < a` if :math:`p \ge 0`,
    :math:`|b| \le a` if :math:`p < 0`.
    :math:`K_{p}(.)` denotes the modified Bessel function of the second
    kind and order :math:`p` (`scipy.special.kv`)

    `genhyperbolic` takes ``p`` as a tail parameter,
    ``a`` as a shape parameter,
    ``b`` as a skewness parameter.

    %(after_notes)s

    The original parameterization of the Generalized Hyperbolic Distribution
    is found in [1]_ as follows

    .. math::

        f(x, \lambda, \alpha, \beta, \delta, \mu) =
           \frac{(\gamma/\delta)^\lambda}{\sqrt{2\pi}K_\lambda(\delta \gamma)}
           e^{\beta (x - \mu)} \times \frac{K_{\lambda - 1/2}
           (\alpha \sqrt{\delta^2 + (x - \mu)^2})}
           {(\sqrt{\delta^2 + (x - \mu)^2} / \alpha)^{1/2 - \lambda}}

    for :math:`x \in ( - \infty; \infty)`,
    :math:`\gamma := \sqrt{\alpha^2 - \beta^2}`,
    :math:`\lambda, \mu \in ( - \infty; \infty)`,
    :math:`\delta \ge 0, |\beta| < \alpha` if :math:`\lambda \ge 0`,
    :math:`\delta > 0, |\beta| \le \alpha` if :math:`\lambda < 0`.

    The location-scale-based parameterization implemented in
    SciPy is based on [2]_, where :math:`a = \alpha\delta`,
    :math:`b = \beta\delta`, :math:`p = \lambda`,
    :math:`scale=\delta` and :math:`loc=\mu`

    Moments are implemented based on [3]_ and [4]_.

    For the distributions that are a special case such as Student's t,
    it is not recommended to rely on the implementation of genhyperbolic.
    To avoid potential numerical problems and for performance reasons,
    the methods of the specific distributions should be used.

    References
    ----------
    .. [1] O. Barndorff-Nielsen, "Hyperbolic Distributions and Distributions
       on Hyperbolae", Scandinavian Journal of Statistics, Vol. 5(3),
       pp. 151-157, 1978. https://www.jstor.org/stable/4615705

    .. [2] Eberlein E., Prause K. (2002) The Generalized Hyperbolic Model:
        Financial Derivatives and Risk Measures. In: Geman H., Madan D.,
        Pliska S.R., Vorst T. (eds) Mathematical Finance - Bachelier
        Congress 2000. Springer Finance. Springer, Berlin, Heidelberg.
        :doi:`10.1007/978-3-662-12429-1_12`

    .. [3] Scott, David J, Würtz, Diethelm, Dong, Christine and Tran,
       Thanh Tam, (2009), Moments of the generalized hyperbolic
       distribution, MPRA Paper, University Library of Munich, Germany,
       https://EconPapers.repec.org/RePEc:pra:mprapa:19081.

    .. [4] E. Eberlein and E. A. von Hammerstein. Generalized hyperbolic
       and inverse Gaussian distributions: Limiting cases and approximation
       of processes. FDM Preprint 80, April 2003. University of Freiburg.
       https://freidok.uni-freiburg.de/fedora/objects/freidok:7974/datastreams/FILE1/content

    %(example)s

    c                     t          j        t          j        |          |k     |dk              t          j        t          j        |          |k    |dk               z  S r  )rM   logical_andr
  )rB   r  r   r   s       r3   r`   zgenhyperbolic_gen._argcheck  sJ    rvayy1}a1f55.aQ778 	9r5   c                     t          ddt          j         t          j        fd          }t          dddt          j        fd          }t          ddt          j         t          j        fd          }|||gS )Nr  Fr  r   r   rd   r   re   )rB   iprf  rg  s       r3   rh   zgenhyperbolic_gen._shape_info"  sc    UbfWbf$5~FFUQK??UbfWbf$5~FFB|r5   c                 J    t                                          |d          S )N)r   r   r   rJ  r  r  s     r3   r  zgenhyperbolic_gen._fitstart(  s"     ww  K 888r5   c                 H    t           j        d             } |||||          S )Nc                 0    t          j        | |||          S rK   )r   genhyperbolic_logpdfrn   r  r   r   s       r3   _logpdf_singlez1genhyperbolic_gen._logpdf.<locals>._logpdf_single0  s    .q!Q:::r5   rM   	vectorize)rB   rn   r  r   r   rJ  s         r3   r   zgenhyperbolic_gen._logpdf-  s7     
	; 	; 
	; ~aAq)))r5   c                 H    t           j        d             } |||||          S )Nc                 0    t          j        | |||          S rK   )r   genhyperbolic_pdfrI  s       r3   _pdf_singlez+genhyperbolic_gen._pdf.<locals>._pdf_single9  s    +Aq!Q777r5   rK  )rB   rn   r  r   r   rP  s         r3   ro   zgenhyperbolic_gen._pdf6  s7     
	8 	8 
	8 {1aA&&&r5   c                 t    t          j        |                     t                    t           j        g          S )Notypes)rM   rL  __get__objectfloat64)r[  s    r3   r  zgenhyperbolic_gen.<lambda>E  s%    ",t||F33RZLIII r5   c                    t          j        |||gt                    j                            t          j                  }t          j        t          d|          }t          j	        ||z   ||z
  z            }||z  t          j        |dz   |          z  t          j        ||          z  }d}	d}
| |cxk     r|k     rCn n@t          j        || ||	|
          d         t          j        ||||	|
          d         z   }nt          j        || ||	|
          d         }t          j        |          rd}t          j        |t"          d           t%          d	t'          d
|                    S )z
        Integrate the pdf of the genhyberbolic distribution from x0 to x1.
        This is a private function used by _cdf() and _sf() only; either x0
        will be -inf or x1 will be inf.
        _genhyperbolic_pdfr   r  r   )epsrelepsabszdInfinite values encountered in scipy.special.kve. Values replaced by NaN to avoid incorrect results.r  r  r   r   )rM   arrayrL  ctypesdata_asc_void_pr   from_cythonr   r   rt   kvr   quadisnanr  r  r  maxrK  )r  x1r  r   r   	user_datallcr  r   rY  rZ  intgrlrV   s                r3   _integrate_pdfz genhyperbolic_gen._integrate_pdfE  s    HaAY..5==foNN	*63G+46 6GQUQUO$$sRU1q5!__$ruQ{{2>>>>r>>>>>  nS"d,26C C CCDF!sD".4VE E EEFHHFF
 ^CR+1&B B BBCEF8F 	=HCM#~!<<<<3C(()))r5   c                 J    |                      t          j         ||||          S rK   rh  rM   rf   rB   rn   r  r   r   s        r3   rr   zgenhyperbolic_gen._cdfg  s"    ""BF7Aq!Q777r5   c                 H    |                      |t          j        |||          S rK   rj  rk  s        r3   rv   zgenhyperbolic_gen._sfj  s     ""1bfaA666r5   Nc                 \   t          j        |d          t          j        |d          z
  }t          j        |d          }t          j        |d          }t                              |||||          }	t                              ||          }
||	z  t          j        |	          |
z  z   S )NrR   r   r  )r  r   r-   r   r   r  )rM   float_powergeninvgaussr  r  r   )rB   r  r   r   r   r   r  r  r  gignormsts              r3   r   zgenhyperbolic_gen._rvsm  s    
 ^Aq!!BN1a$8$88^B$$^B&&oo%    t,??3w...r5   c                 v   t          j        |||          \  }}}t          j        |d          t          j        |d          z
  }t          j        |d          }t          j        dd          t          j        |d          z  }t          j        ddd          }|                    |j        d|j        z  z             }t          j        ||z   |          \  }}}	}
fd	|||	|
fD             \  }}}}||z  |z  }||z  t          j        |d          t          j        |d          z  |t          j        |d          z
  z  z   }t          j        |d
          t          j        |d
          z  |d
|z  |z  t          j        d          z  z
  dt          j        |d
          z  z   z  d
|z  t          j        |d          z  |t          j        |d          z
  z  z   }|t          j        |d          z  }t          j        |d          t          j        |d          z  |d|	z  |z  t          j        d          z  z
  d|z  t          j        |d          z  t          j        d          z  z   d
t          j        |d          z  z
  z  t          j        |d          t          j        |d
          z  d|z  d|z  |z  t          j        d          z  z
  dt          j        |d
          z  z   z  z   d
t          j        |d          z  |z  z   }|t          j        |d          z  d
z
  }||||fS )NrR   r   r   r  r   r   rC  r  c              3   "   K   | ]	}|z  V  
d S rK   r   )r  r   b0s     r3   	<genexpr>z+genhyperbolic_gen._stats.<locals>.<genexpr>  s'      ;;Q!b&;;;;;;r5   r  r  r>  r  r  r  )	rM   r  rn  linspacerN  shaper+  rt   r`  )rB   r  r   r   r  r  integersb1b2b3b4r1r2r3r4r  r  m3er  m4er  rt  s                        @r3   r   zgenhyperbolic_gen._stats  sE    %aA..1a^Aq!!BN1a$8$88^B$$^Aq!!BN2s$;$;;;q!Q''##HNTAF]$BCCU1x<44BB;;;;2r2r*:;;;BBFRKGbnQ**R^B-B-BB".Q''') ) 	

 N1a  2>"a#8#88!b&2+r2 6 666A&&&'( EBN2q)))".Q''')) 	 ".G,,,N1a  2>"a#8#88!b&2+r3 7 777VbnR+++bnR.E.EEFA&&&'( N1a  2>"a#8#88Vb2glR^B%<%<<<A&&&'(	( r1%%%*+ 	 ".B'''!+!Qzr5   r   )r   r   r   r   r`   rh   r  r   ro   staticmethodrh  rr   rv   r   r   r  r  s   @r3   r@  r@    s        W Wr9 9 9  9 9 9 9 9
* * *' ' ' JI* * \ JI*@8 8 87 7 7/ / / /*' ' ' ' ' ' 'r5   r@  genhyperbolicc                   B    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
S )gompertz_genaq  A Gompertz (or truncated Gumbel) continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `gompertz` is:

    .. math::

        f(x, c) = c \exp(x) \exp(-c (e^x-1))

    for :math:`x \ge 0`, :math:`c > 0`.

    `gompertz` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zgompertz_gen._shape_info  r  r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zgompertz_gen._pdf  r  r5   c                 `    t          j        |          |z   |t          j        |          z  z
  S rK   r  r
  s      r3   r   zgompertz_gen._logpdf  s%    vayy1}q28A;;..r5   c                 X    t          j        | t          j        |          z             S rK   r=  r
  s      r3   rr   zgompertz_gen._cdf  s$    !bhqkk)****r5   c                 \    t          j        d|z  t          j        |           z            S r?  r	  r  s      r3   r{   zgompertz_gen._ppf  s%    xq28QB<</000r5   c                 V    t          j        | t          j        |          z            S rK   r  r
  s      r3   rv   zgompertz_gen._sf  s!    vqb28A;;&'''r5   c                 V    t          j        t          j        |           |z            S rK   r  rB   r  r  s      r3   r~   zgompertz_gen._isf  s     x
1%%%r5   c                 v    dt          j        |          z
  t          j                            |          |z  z
  S r  )rM   r   rt   _ufuncs_scaled_exp1r}  s     r3   r   zgompertz_gen._entropy  s.    RVAYY!8!8!;!;A!===r5   Nr   r   r   r   rh   ro   r   rr   r{   rv   r~   r   r   r5   r3   r  r    s         *E E E* * */ / /+ + +1 1 1( ( (& & &> > > > >r5   r  gompertzc                     t          j        |           } t          j        |          }|                                }t          j        ||z
            }t          j        | |          S )N)weights)rM   r   rc  r   average)rn   
logweightsmaxlogwr  s       r3   _average_with_log_weightsr    sV    

1AJ''JnnGfZ')**G:a))))r5   c                       e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Ze ee          d                         ZdS )gumbel_r_gena  A right-skewed Gumbel continuous random variable.

    %(before_notes)s

    See Also
    --------
    gumbel_l, gompertz, genextreme

    Notes
    -----
    The probability density function for `gumbel_r` is:

    .. math::

        f(x) = \exp(-(x + e^{-x}))

    The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett
    distribution.  It is also related to the extreme value distribution,
    log-Weibull and Gompertz distributions.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zgumbel_r_gen._shape_info  r   r5   c                 P    t          j        |                     |                    S rK   r  r   s     r3   ro   zgumbel_r_gen._pdf      vdll1oo&&&r5   c                 4    | t          j        |           z
  S rK   r9  r   s     r3   r   zgumbel_r_gen._logpdf  s    rBFA2JJr5   c                 R    t          j        t          j        |                      S rK   r9  r   s     r3   rr   zgumbel_r_gen._cdf  s    vrvqbzzk"""r5   c                 .    t          j        |            S rK   r9  r   s     r3   r   zgumbel_r_gen._logcdf  s    r

{r5   c                 R    t          j        t          j        |                      S rK   r.  r   s     r3   r{   zgumbel_r_gen._ppf  s    q		z""""r5   c                 T    t          j        t          j        |                       S rK   r  r   s     r3   rv   zgumbel_r_gen._sf  s!    "&!**%%%%r5   c                 T    t          j        t          j        |                       S rK   rM   r   r  r  s     r3   r~   zgumbel_r_gen._isf  s!    !}%%%%r5   c                     t           t          j        t          j        z  dz  dt          j        d          z  t          j        dz  z  t          z  dfS )NrE  r  r  r  r  r#   rM   r   r   r$   rg   s    r3   r   zgumbel_r_gen._stats  s9    ruRU{3271::beQh(>(GOOr5   c                     t           dz   S r  r_  rg   s    r3   r   zgumbel_r_gen._entropy   s    {r5   c                    t          | ||          \  }}fd}||} ||          n|	|fdnfd|                    dd          }|dz  |dz  }
}	fd} ||	|
          sB|	dk    s|
t          j        k     r,|	dz  }	|
dz  }
 ||	|
          s|	dk    |
t          j        k     ,t	          j        |	|
fd	d	
          }|j        }||n
 ||          |fS )Nc                     |  t          j         | z            t          j        t	                              z
  z  S rK   )rt   	logsumexprM   r   r  )r-   rC   s    r3   get_loc_from_scalez,gumbel_r_gen.fit.<locals>.get_loc_from_scale1  s5    6R\4%%-8826#d));L;LLMMr5   c                     z
  t          j        z
  | z            z  z   }t                    | z   z  }|                                |z
  S rK   )rM   r   r  r  )r-   term1term2rC   r,   s      r3   r[  zgumbel_r_gen.fit.<locals>.funcD  sP     4Z263:2F+G+GG$NEIIu5E 99;;..r5   c                 f     | z  }t          |          }                                |z
  | z
  S )N)r  )r  r   )r-   sdatawavgrC   s      r3   r[  zgumbel_r_gen.fit.<locals>.funcK  s8    !EEME4TeLLLD99;;-55r5   r-   r   rR   c                 |    t          j         |                     t          j         |                    k    S rK   rL   )rO   rP   r[  s     r3   rQ   z0gumbel_r_gen.fit.<locals>.interval_contains_rootY  s7    V--V--. /r5   r   r  )r  rtolr  )r  r:   rM   rf   r   r)   r  )rB   rC   rD   r2   r   r   r  r-   brack_startrO   rP   rQ   resr[  r,   s    `           @@r3   r@   zgumbel_r_gen.fit$  s    9t9=tE EdF	N 	N 	N 	N 	N  E$$U++CC / / / / / / /6 6 6 6 6 ((7A..K(1_kAoFF
/ / / / / .-ff== 

frvoo!! .-ff== 

frvoo &tff5E,1? ? ?CHE*$$0B0B50I0ICEzr5   N)r   r   r   r   rh   ro   r   rr   r   r{   rv   r~   r   r   rH   r   r   r@   r   r5   r3   r  r    s         2  ' ' '  # # #  # # #& & && & &P P P   M**@ @ +* _@ @ @r5   r  gumbel_rc                       e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Ze ee          d                         ZdS )gumbel_l_gena  A left-skewed Gumbel continuous random variable.

    %(before_notes)s

    See Also
    --------
    gumbel_r, gompertz, genextreme

    Notes
    -----
    The probability density function for `gumbel_l` is:

    .. math::

        f(x) = \exp(x - e^x)

    The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett
    distribution.  It is also related to the extreme value distribution,
    log-Weibull and Gompertz distributions.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zgumbel_l_gen._shape_info  r   r5   c                 P    t          j        |                     |                    S rK   r  r   s     r3   ro   zgumbel_l_gen._pdf  r  r5   c                 0    |t          j        |          z
  S rK   r9  r   s     r3   r   zgumbel_l_gen._logpdf  r  r5   c                 R    t          j        t          j        |                      S rK   r  r   s     r3   rr   zgumbel_l_gen._cdf  s    "&))$$$$r5   c                 R    t          j        t          j        |                      S rK   rM   r   rt   r  r   s     r3   r{   zgumbel_l_gen._ppf  s    vrx||m$$$r5   c                 ,    t          j        |           S rK   r9  r   s     r3   r   zgumbel_l_gen._logsf  rC  r5   c                 P    t          j        t          j        |                     S rK   r9  r   s     r3   rv   zgumbel_l_gen._sf      vrvayyj!!!r5   c                 P    t          j        t          j        |                     S rK   r.  r   s     r3   r~   zgumbel_l_gen._isf  r  r5   c                     t            t          j        t          j        z  dz  dt          j        d          z  t          j        dz  z  t          z  dfS )NrE  r  r  r  r  rg   s    r3   r   zgumbel_l_gen._stats  s@    wbeC271::~beQh&/8 	8r5   c                     t           dz   S r  r_  rg   s    r3   r   zgumbel_l_gen._entropy  s    {r5   c                     |                     d          |d          |d<   t          j        t          j        |           g|R i |\  }}| |fS )Nr   )r:   r  r@   rM   r   )rB   rC   rD   r2   loc_rscale_rs         r3   r@   zgumbel_l_gen.fit  sa     88F' L=DL",
4(8(8'8H4HHH4HHwvwr5   N)r   r   r   r   rh   ro   r   rr   r{   r   rv   r~   r   r   rH   r   r   r@   r   r5   r3   r  r  l  s         4  ' ' '  % % %% % %  " " "" " "8 8 8   M**  +* _  r5   r  gumbel_lc                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Ze ee           fd                        Z xZS )halfcauchy_gena  A Half-Cauchy continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `halfcauchy` is:

    .. math::

        f(x) = \frac{2}{\pi (1 + x^2)}

    for :math:`x \ge 0`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zhalfcauchy_gen._shape_info  r   r5   c                 2    dt           j        z  d||z  z   z  S r  r+  r   s     r3   ro   zhalfcauchy_gen._pdf  r  r5   c                 t    t          j        dt           j        z            t          j        ||z            z
  S r;  rM   r   r   rt   r  r   s     r3   r   zhalfcauchy_gen._logpdf  s)    vc"%i  28AaC==00r5   c                 J    dt           j        z  t          j        |          z  S r;  r  r   s     r3   rr   zhalfcauchy_gen._cdf  s    25y1%%r5   c                 J    t          j        t           j        dz  |z            S r  r  r   s     r3   r{   zhalfcauchy_gen._ppf  s    vbeAgai   r5   c                 L    dt           j        z  t          j        d|          z  S Nr   r   )rM   r   r  r   s     r3   rv   zhalfcauchy_gen._sf  s    25y2:a++++r5   c                 P    dt          j        t           j        |z  dz            z  S r	  r  r  s     r3   r~   zhalfcauchy_gen._isf  s!    26"%'!)$$$$r5   c                 ^    t           j        t           j        t           j        t           j        fS rK   r  rg   s    r3   r   zhalfcauchy_gen._stats  r  r5   c                 D    t          j        dt           j        z            S r  r   rg   s    r3   r   zhalfcauchy_gen._entropy  r  r5   c                 @   |                     dd          r t                      j        |g|R i |S t          | |||          \  }}}t	          j        |          }|%||k     rt          d|t          j                  |}n|}d }||}	n |||          }	||	fS )Nr  F
halfcauchyr  c                     || z
  }|j         t          j        |          fd}t          j        d          j        dz  }t          ||t          j        |          f          }|j        S )Nc                 N    | dz  z   }dt          j        |z            z  z
  S r  rM   r  )r-   denominatorr_   shifted_data_squareds     r3   fun_to_solvez<halfcauchy_gen.fit.<locals>.find_scale.<locals>.fun_to_solve  s2    #Qh)==26"6{"BCCCaGGr5   r   r   r  )r   rM   squarefinfotinyr)   rc  r  )r,   rC   shifted_datar  smallr  r_   r  s         @@r3   
find_scalez&halfcauchy_gen.fit.<locals>.find_scale   s    #:L	A#%9\#:#: H H H H H H HSMM&+ElUBF<<P<P4QRRRC8Or5   r0   r>   r@   r  rM   rK  rH  rf   )rB   rC   rD   r2   r   r   rM  r,   r  r-   r  s             r3   r@   zhalfcauchy_gen.fit  s     88J&& 	4577;t3d333d3338t9=tE EdF 6$<<$"<t26JJJJCC C	 	 	 EEJsD))EEzr5   )r   r   r   r   rh   ro   r   rr   r{   rv   r~   r   r   rH   r   r   r@   r  r  s   @r3   r  r    s         &  # # #1 1 1& & &! ! !, , ,% % %. . .   M**% % % % +* _% % % % %r5   r  r  c                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Ze ee           fd                        Z xZS )halflogistic_gena  A half-logistic continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `halflogistic` is:

    .. math::

        f(x) = \frac{ 2 e^{-x} }{ (1+e^{-x})^2 }
             = \frac{1}{2} \text{sech}(x/2)^2

    for :math:`x \ge 0`.

    %(after_notes)s

    References
    ----------
    .. [1] Asgharzadeh et al (2011). "Comparisons of Methods of Estimation for the
           Half-Logistic Distribution". Selcuk J. Appl. Math. 93-108.

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zhalflogistic_gen._shape_info2  r   r5   c                 P    t          j        |                     |                    S rK   r  r   s     r3   ro   zhalflogistic_gen._pdf5  s     vdll1oo&&&r5   c                     t          j        d          |z
  dt          j        t          j        |                     z  z
  S r   )rM   r   rt   r  r   r   s     r3   r   zhalflogistic_gen._logpdf:  s2    vayy1}rBHRVQBZZ$8$8888r5   c                 0    t          j        |dz            S r;  )rM   tanhr   s     r3   rr   zhalflogistic_gen._cdf=  s    wqu~~r5   c                 0    dt          j        |          z  S r  rM   arctanhr   s     r3   r{   zhalflogistic_gen._ppf@  s    Ar5   c                 2    dt          j        |           z  S r  rt   expitr   s     r3   rv   zhalflogistic_gen._sfC  s    28QB<<r5   c                 6    t          |dk     |fd d           S )Nr   c                 2    t          j        d| z             S r  rt   logitr   s    r3   r  z'halflogistic_gen._isf.<locals>.<lambda>H  s    RXcAg%6%6$6 r5   c                 6    dt          j        d| z
            z  S r  r  r   s    r3   r  z'halflogistic_gen._isf.<locals>.<lambda>I  s    qAE):):': r5   r  r  r   s     r3   r~   zhalflogistic_gen._isfF  s/    !c'A566::< < < 	<r5   c                 d   |dk    rdt          j        d          z  S |dk    rt           j        t           j        z  dz  S |dk    r
dt          z  S |dk    rdt           j        dz  z  dz  S ddt	          d	d|z
            z
  z  t          j        |dz             z  t          j        |d          z  S )
Nr   rR   rD  r  r  r   r  rY  r   )rM   r   r   r$   r  rt   r  rZ  r^   s     r3   r   zhalflogistic_gen._munpK  s    66RVAYY;665;s?"66V8O66RUAX:$$!CQqSMM/"28AaC==0A>>r5   c                 0    dt          j        d          z
  S r  r.  rg   s    r3   r   zhalflogistic_gen._entropyV  r/  r5   c                 >   |                     dd          r t                      j        |g|R i |S t          | |||          \  }}}d }t	          j        |          }|%||k     rt          d|t          j                  |}n|}||n |||          }	||	fS )Nr  Fc                    | j         d         }t          j        | d          }t          j        d|dz             |dz   z  }d|z
  }d|z   }|d|z  |z  t          j        ||z            z  z
  }d|z  |z  }||z
  }dt          j        |dd          |dd          z            z  }	dt          j        |dd          |dd          dz  z            z  }
|	t          j        |	dz  d|z  |
z  z             z   d|z  z  }d}d}|                                }||k    rU|t          j	        | |z            z  }|d|z  |                                z  z
  }t          ||z
  |z            }|}||k    U|S )	Nr   r  r   r   rR   r*  r   r  )rw  rM   sortrM  r   r  r   r   rt   r  r
  )rC   r,   n_observationssorted_datar  rz   pp1r  rk  r,  Cr-   r  relative_residualshifted_meansum_term	scale_news                    r3   r  z(halflogistic_gen.fit.<locals>.find_scaleb  s    "Z]N'$Q///K	!^a/00.12DEAAAa%Ca#sQw77E7S=D%+KBF59{1226777ABF48k!""oq&88999A"'!Q$^);a)?"?@@@.(*E D !&++--L $d**&;,u2D)E)EE(1^+;hllnn+LL	$'):E(A$B$B!!	 $d**
 Lr5   halflogisticr  r  )rB   rC   rD   r2   r   r   r  rM  r,   r-   r  s             r3   r@   zhalflogistic_gen.fitY  s     88J&& 	4577;t3d333d3338t9=tE EdF	 	 	D 6$<<$">RVLLLLCC C !,**T32G2GEzr5   )r   r   r   r   rh   ro   r   rr   r{   rv   r~   r   r   rH   r   r   r@   r  r  s   @r3   r  r    s         2  ' ' '
9 9 9         < < <
	? 	? 	?   M**6 6 6 6 +* _6 6 6 6 6r5   r  r  c                        e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd Ze ee           fd                        Z xZS )halfnorm_genaF  A half-normal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `halfnorm` is:

    .. math::

        f(x) = \sqrt{2/\pi} \exp(-x^2 / 2)

    for :math:`x >= 0`.

    `halfnorm` is a special case of `chi` with ``df=1``.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zhalfnorm_gen._shape_info  r   r5   Nc                 H    t          |                    |                    S Nr  r  r   s      r3   r   zhalfnorm_gen._rvs  s!    <//T/::;;;r5   c                 |    t          j        dt           j        z            t          j        | |z  dz            z  S r;  rM   r   r   r   r   s     r3   ro   zhalfnorm_gen._pdf  s1    ws25y!!"&!Ac"2"222r5   c                 \    dt          j        dt           j        z            z  ||z  dz  z
  S Nr   r   r   r   s     r3   r   zhalfnorm_gen._logpdf  s*    RVCI&&&1S00r5   c                 T    t          j        |t          j        d          z            S r  rt   r  rM   r   r   s     r3   rr   zhalfnorm_gen._cdf  s    va"'!**n%%%r5   c                 ,    t          d|z   dz            S r  r   r   s     r3   r{   zhalfnorm_gen._ppf  s    !A#s###r5   c                 &    dt          |          z  S r  r   r   s     r3   rv   zhalfnorm_gen._sf  s    8A;;r5   c                 &    t          |dz            S r  r   r  s     r3   r~   zhalfnorm_gen._isf  s    1~~r5   c                    t          j        dt           j        z            ddt           j        z  z
  t          j        d          dt           j        z
  z  t           j        dz
  dz  z  dt           j        dz
  z  t           j        dz
  dz  z  fS )Nr   r   rR   r   r  r*  r  rM   r   r   rg   s    r3   r   zhalfnorm_gen._stats  sm    BE	""#be)

AbeG$beAg^3257RU1WqL(* 	*r5   c                 P    dt          j        t           j        dz            z  dz   S r  r   rg   s    r3   r   zhalfnorm_gen._entropy  s"    26"%)$$$S((r5   c                 V   |                     dd          r t                      j        |g|R i |S t          | |||          \  }}}t	          j        |          }|%||k     rt          d|t          j                  |}n|}||}nt          j	        |d|          dz  }||fS )Nr  Fhalfnormr  rR   )ordercenterr   )
r0   r>   r@   r  rM   rK  rH  rf   r  moment)
rB   rC   rD   r2   r   r   rM  r,   r-   r  s
            r3   r@   zhalfnorm_gen.fit  s     88J&& 	4577;t3d333d3338t9=tE EdF 6$<<$":THHHHCCCEELQs;;;S@EEzr5   r   )r   r   r   r   rh   r   ro   r   rr   r{   rv   r~   r   r   rH   r   r   r@   r  r  s   @r3   r	  r	    s         *  < < < <3 3 31 1 1& & &$ $ $    * * *) ) ) M**    +* _    r5   r	  r  c                   B    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
S )hypsecant_gena  A hyperbolic secant continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `hypsecant` is:

    .. math::

        f(x) = \frac{1}{\pi} \text{sech}(x)

    for a real number :math:`x`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zhypsecant_gen._shape_info  r   r5   c                 J    dt           j        t          j        |          z  z  S r  )rM   r   coshr   s     r3   ro   zhypsecant_gen._pdf  s    BE"'!**$%%r5   c                 n    dt           j        z  t          j        t          j        |                    z  S r;  rM   r   r  r   r   s     r3   rr   zhypsecant_gen._cdf  s%    25y26!99----r5   c                 n    t          j        t          j        t           j        |z  dz                      S r;  rM   r   r  r   r   s     r3   r{   zhypsecant_gen._ppf  s&    vbfRU1WS[))***r5   c                 p    dt           j        z  t          j        t          j        |                     z  S r;  r$  r   s     r3   rv   zhypsecant_gen._sf  s'    25y261"::....r5   c                 p    t          j        t          j        t           j        |z  dz                       S r;  r&  r   s     r3   r~   zhypsecant_gen._isf  s)    rvbeAgck**++++r5   c                 B    dt           j        t           j        z  dz  ddfS )Nr   r   rR   r+  rg   s    r3   r   zhypsecant_gen._stats  s    "%+a-A%%r5   c                 D    t          j        dt           j        z            S r  r   rg   s    r3   r   zhypsecant_gen._entropy  r  r5   N)r   r   r   r   rh   ro   rr   r{   rv   r~   r   r   r   r5   r3   r  r    s         &  & & &. . .+ + +/ / /, , ,& & &    r5   r  	hypsecantc                   *    e Zd ZdZd Zd Zd Zd ZdS )gausshyper_gena_  A Gauss hypergeometric continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `gausshyper` is:

    .. math::

        f(x, a, b, c, z) = C x^{a-1} (1-x)^{b-1} (1+zx)^{-c}

    for :math:`0 \le x \le 1`, :math:`a,b > 0`, :math:`c` a real number,
    :math:`z > -1`, and :math:`C = \frac{1}{B(a, b) F[2, 1](c, a; a+b; -z)}`.
    :math:`F[2, 1]` is the Gauss hypergeometric function
    `scipy.special.hyp2f1`.

    `gausshyper` takes :math:`a`, :math:`b`, :math:`c` and :math:`z` as shape
    parameters.

    %(after_notes)s

    References
    ----------
    .. [1] Armero, C., and M. J. Bayarri. "Prior Assessments for Prediction in
           Queues." *Journal of the Royal Statistical Society*. Series D (The
           Statistician) 43, no. 1 (1994): 139-53. doi:10.2307/2348939

    %(example)s

    c                 8    |dk    |dk    z  ||k    z  |dk    z  S )Nr   r  r   )rB   r   r   r  r  s        r3   r`   zgausshyper_gen._argcheck?  s'    A!a% AF+q2v66r5   c                    t          dddt          j        fd          }t          dddt          j        fd          }t          ddt          j         t          j        fd          }t          dddt          j        fd          }||||gS )	Nr   Fr   r  r   r  r  r  re   )rB   rf  rg  r!  izs        r3   rh   zgausshyper_gen._shape_infoC  sz    UQK@@UQK@@UbfWbf$5~FFURL.AABBr5   c                     t          j        ||          t          j        ||||z   |           z  }d|z  ||dz
  z  z  d|z
  |dz
  z  z  d||z  z   |z  z  S r  rt   rk  hyp2f1)rB   rn   r   r   r  r  normalization_constants          r3   ro   zgausshyper_gen._pdfJ  sn    !#A1aQ1K1K!K))ABK726QW:MM19q.! 	"r5   c                     t          j        ||z   |          t          j        ||          z  }t          j        |||z   ||z   |z   |           }t          j        ||||z   |           }||z  |z  S rK   r2  )	rB   r_   r   r   r  r  r  r
  r  s	            r3   r   zgausshyper_gen._munpO  sp    gac1oo1-i1Q3!Ar**i1acA2&&3w}r5   N)r   r   r   r   r`   rh   ro   r   r   r5   r3   r-  r-    s[         @7 7 7     " " "
    r5   r-  
gausshyperc                   X    e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd Zd Zdd
Zd ZdS )invgamma_gena_  An inverted gamma continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `invgamma` is:

    .. math::

        f(x, a) = \frac{x^{-a-1}}{\Gamma(a)} \exp(-\frac{1}{x})

    for :math:`x >= 0`, :math:`a > 0`. :math:`\Gamma` is the gamma function
    (`scipy.special.gamma`).

    `invgamma` takes ``a`` as a shape parameter for :math:`a`.

    `invgamma` is a special case of `gengamma` with ``c=-1``, and it is a
    different parameterization of the scaled inverse chi-squared distribution.
    Specifically, if the scaled inverse chi-squared distribution is
    parameterized with degrees of freedom :math:`\nu` and scaling parameter
    :math:`\tau^2`, then it can be modeled using `invgamma` with
    ``a=`` :math:`\nu/2` and ``scale=`` :math:`\nu \tau^2/2`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zinvgamma_gen._shape_infoy  r  r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zinvgamma_gen._pdf|  r  r5   c                 n    |dz    t          j        |          z  t          j        |          z
  d|z  z
  S r  rM   r   rt   r  r
  s      r3   r   zinvgamma_gen._logpdf  s1    1vq		!BJqMM1CE99r5   c                 2    t          j        |d|z            S r  r  r
  s      r3   rr   zinvgamma_gen._cdf  s    |AsQw'''r5   c                 2    dt          j        ||          z  S r  r  r  s      r3   r{   zinvgamma_gen._ppf  s    R_Q****r5   c                 2    t          j        |d|z            S r  r  r
  s      r3   rv   zinvgamma_gen._sf  s    {1cAg&&&r5   c                 2    dt          j        ||          z  S r  r  r  s      r3   r~   zinvgamma_gen._isf  s    R^Aq))))r5   mvskc                 8   t          |dk    |fd t          j                  }t          |dk    |fd t          j                  }d\  }}d|v r"t          |dk    |fd t          j                  }d	|v r"t          |d
k    |fd t          j                  }||||fS )Nr   c                     d| dz
  z  S r  r   r   s    r3   r  z%invgamma_gen._stats.<locals>.<lambda>  s    rQV} r5   rR   c                 $    d| dz
  dz  z  | dz
  z  S )Nr   rR   r   r   r   s    r3   r  z%invgamma_gen._stats.<locals>.<lambda>  s    rQVaK/?1r6/J r5   r   r  r  c                 B    dt          j        | dz
            z  | dz
  z  S )NrJ  r   rD  r  r   s    r3   r  z%invgamma_gen._stats.<locals>.<lambda>  s     "rwq2v.!b&9 r5   r  r   c                 0    dd| z  dz
  z  | dz
  z  | dz
  z  S )NrE  r  g      &@rD  rJ  r   r   s    r3   r  z%invgamma_gen._stats.<locals>.<lambda>  s%    "Q-R8AFC r5   r  )rB   r   r  r  r  rB  rC  s          r3   r   zinvgamma_gen._stats  s    At%<%<bfEEAt%J%J    B'>>At9926C CB '>>AtCCRVM MB 2r2~r5   c                 B    d }d }t          |dk    |f||          }|S )Nc                 j    | | dz   t          j        |           z  z
  t          j        |           z   }|S r  r  r   r  s     r3   r  z&invgamma_gen._entropy.<locals>.regular  s/    QWq		))BJqMM9AHr5   c                     ddt          j        |           z  z
  t          j        d          z   t          j        t           j                  z   dz  d| dz  z  z   | dz  dz  z   | dz  d	z  z
  | d
z  dz  z
  }|S )Nr   r  rR   UUUUUU?r<  r  r  r  r  r  r  r   rI  s     r3   r.  z)invgamma_gen._entropy.<locals>.asymptotic  s     aq		k/BF1II-ru=q@q#v: !3r	*,-sF2I6893s
CAHr5   r/  r0  r  )rB   r   r  r.  r  s        r3   r   zinvgamma_gen._entropy  sC    	 	 		 	 	 qCx!@@@r5   NrA  )r   r   r   r   r   r  r  rh   ro   r   rr   r{   rv   r~   r   r   r   r5   r3   r8  r8  Y  s         : "4ME E E* * *: : :( ( (+ + +' ' '* * *        r5   r8  invgammac                        e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 Z fdZ fdZd Z ee           fd            Zd Z xZS )invgauss_gena`  An inverse Gaussian continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `invgauss` is:

    .. math::

        f(x; \mu) = \frac{1}{\sqrt{2 \pi x^3}}
                    \exp\left(-\frac{(x-\mu)^2}{2 \mu^2 x}\right)

    for :math:`x \ge 0` and :math:`\mu > 0`.

    `invgauss` takes ``mu`` as a shape parameter for :math:`\mu`.

    %(after_notes)s

    A common shape-scale parameterization of the inverse Gaussian distribution
    has density

    .. math::

        f(x; \nu, \lambda) = \sqrt{\frac{\lambda}{2 \pi x^3}}
                    \exp\left( -\frac{\lambda(x-\nu)^2}{2 \nu^2 x}\right)

    Using ``nu`` for :math:`\nu` and ``lam`` for :math:`\lambda`, this
    parameterization is equivalent to the one above with ``mu = nu/lam``,
    ``loc = 0``, and ``scale = lam``.

    %(example)s

    c                 @    t          dddt          j        fd          gS Nr@  Fr   r  re   rg   s    r3   rh   zinvgauss_gen._shape_info  r  r5   Nc                 2    |                     |d|          S Nr   r  waldrB   r@  r   r   s       r3   r   zinvgauss_gen._rvs  s      St 444r5   c                     dt          j        dt           j        z  |dz  z            z  t          j        dd|z  z  ||z
  |z  dz  z            z  S )Nr   rR   rD  r<  r  rB   rn   r@  s      r3   ro   zinvgauss_gen._pdf  sO     271RU71c6>***26$!*qtRi!^2K+L+LLLr5   c                     dt          j        dt           j        z            z  dt          j        |          z  z
  ||z
  |z  dz  d|z  z  z
  S )Nr  rR   r  r   rX  s      r3   r   zinvgauss_gen._logpdf  sF    BF1RU7OO#c"&))m3"by1nac6JJJr5   c                     dt          j        |          z  }t          |||z  dz
  z            }d|z  t          | ||z  dz   z            z   }|t          j        t          j        ||z
                      z   S r-  )rM   r   r   r  r   rB   rn   r@  r  r   r   s         r3   r   zinvgauss_gen._logcdf  ss    "'!**nR1-..F\3$1r6Q,"788828BF1q5MM****r5   c                     dt          j        |          z  }t          |||z  dz
  z            }d|z  t          | ||z   z  |z            z   }|t          j        t          j        ||z
                       z   S r-  )rM   r   r   r   r  r   r[  s         r3   r   zinvgauss_gen._logsf  su    "'!**nB!|,--F\3$!b&/B"677728RVAE]]N++++r5   c                 R    t          j        |                     ||                    S rK   r6  rX  s      r3   rv   zinvgauss_gen._sf  s     vdkk!R(()))r5   c                 R    t          j        |                     ||                    S rK   r  rX  s      r3   rr   zinvgauss_gen._cdf       vdll1b))***r5   c                    t          j        ddd          5  t          j        ||          \  }}t          j        ||d          }|dk    }t          j        d||         z
  ||         d          ||<   t          j        |          }t                                          ||         ||                   ||<   d d d            n# 1 swxY w Y   |S Nr5  )r7  ro  invalidr   r   )	rM   r8  r  rk   _invgauss_ppf_invgauss_isfrb  r>   r{   )rB   rn   r@  ppfi_wti_nanr  s         r3   r{   zinvgauss_gen._ppf      [xJJJ 	; 	;'2..EAr#Ar1--Cs7D)!AdG)RXqAACIHSMMEah5	::CJ	; 	; 	; 	; 	; 	; 	; 	; 	; 	; 	; 	; 	; 	; 	; 
   B"CCCc                    t          j        ddd          5  t          j        ||          \  }}t          j        ||d          }|dk    }t          j        d||         z
  ||         d          ||<   t          j        |          }t                                          ||         ||                   ||<   d d d            n# 1 swxY w Y   |S ra  )	rM   r8  r  rk   rd  rc  rb  r>   r~   )rB   rn   r@  isfrf  rg  r  s         r3   r~   zinvgauss_gen._isf  rh  ri  c                 D    ||dz  dt          j        |          z  d|z  fS )NrD  r  r  r  )rB   r@  s     r3   r   zinvgauss_gen._stats  s%    2s7AbgbkkM2b500r5   c                 x   |                     dd          }t          |t                    s0t          |           t          k    s|                                dk    r t                      j        |g|R i |S t          | |||          \  }}}}	 || t                      j        |g|R i |S t          j
        ||z
  dk               rt          ddt          j                  ||z
  }t          j        |          }|-t          |          t          j        |dz  |dz  z
            z  }||z  }|||fS )Nr/   r8   r9   r   invgaussr  r  )r:   r<   r(   r?   wald_genr;   r>   r@   r  rM   r  rH  rf   r   r  r  )
rB   rC   rD   r2   r/   fshape_sr   r   fshape_nr  s
            r3   r@   zinvgauss_gen.fit  sU   (E**t\** 	4d4jjH.D.D<<>>T))577;t3d333d333'B4CG(O (O$hf	 <8/577;t3d333d333VD4K!O$$ 	)z"&AAAA$;Dwt}}H~TbfTRZ(b.-H&I&IJ&(Hv%%r5   c                     dt          j        dt           j        z            z   dt          j        |          z  z   }d|z  }t          j                            |          |z  }d|z  d|z  z
  S )zV
        Ref.: https://moser-isi.ethz.ch/docs/papers/smos-2012-10.pdf (eq. 9)
        r   rR   r  r   r  )rM   r   r   rt   r  r  )rB   r@  r   r  r   s        r3   r   zinvgauss_gen._entropy4  sg     BE	"""Q^3 bDJ##A&&q(Qwq  r5   r   )r   r   r   r   r   r  r  rh   r   ro   r   r   r   rv   rr   r{   r~   r   r   r@   r   r  r  s   @r3   rO  rO    s<       ! !D "4MF F F5 5 5 5M M M
K K K+ + +, , ,* * *+ + +        1 1 1 M**& & & & +*&B! ! ! ! ! ! !r5   rO  rn  c                   P    e Zd ZdZd Zd Zd Zd Zd Zd Z	dd	Z
d
 Zd Zd ZdS )geninvgauss_genaX  A Generalized Inverse Gaussian continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `geninvgauss` is:

    .. math::

        f(x, p, b) = x^{p-1} \exp(-b (x + 1/x) / 2) / (2 K_p(b))

    where `x > 0`, `p` is a real number and `b > 0`\([1]_).
    :math:`K_p` is the modified Bessel function of second kind of order `p`
    (`scipy.special.kv`).

    %(after_notes)s

    The inverse Gaussian distribution `stats.invgauss(mu)` is a special case of
    `geninvgauss` with `p = -1/2`, `b = 1 / mu` and `scale = mu`.

    Generating random variates is challenging for this distribution. The
    implementation is based on [2]_.

    References
    ----------
    .. [1] O. Barndorff-Nielsen, P. Blaesild, C. Halgreen, "First hitting time
       models for the generalized inverse gaussian distribution",
       Stochastic Processes and their Applications 7, pp. 49--54, 1978.

    .. [2] W. Hoermann and J. Leydold, "Generating generalized inverse Gaussian
       random variates", Statistics and Computing, 24(4), p. 547--557, 2014.

    %(example)s

    c                     ||k    |dk    z  S r  r   rB   r  r   s      r3   r`   zgeninvgauss_gen._argcheckj  s    Q1q5!!r5   c                     t          ddt          j         t          j        fd          }t          dddt          j        fd          }||gS )Nr  Fr  r   r   re   )rB   rD  rg  s      r3   rh   zgeninvgauss_gen._shape_infom  B    UbfWbf$5~FFUQK@@Bxr5   c                     d }t          j        |t           j        g          } ||||          }t          j        |                                          rd}t          j        |t          d           |S )Nc                 .    t          j        | ||          S rK   )r   geninvgauss_logpdfrn   r  r   s      r3   logpdf_singlez.geninvgauss_gen._logpdf.<locals>.logpdf_singlev  s    ,Q1555r5   rR  zjInfinite values encountered in scipy.special.kve(p, b). Values replaced by NaN to avoid incorrect results.r  r  )rM   rL  rV  rb  r  r  r  r  )rB   rn   r  r   r}  r  rV   s          r3   r   zgeninvgauss_gen._logpdfr  s|    	6 	6 	6 ]BJ<HHHM!Q""8A;;?? 	=HCM#~!<<<<r5   c                 T    t          j        |                     |||                    S rK   r  rB   rn   r  r   s       r3   ro   zgeninvgauss_gen._pdf  r  r5   c                 |     | j         | \  }fd}t          j        |t          j        g          } ||g|R  S )Nc                     |\  }}t          j        ||gt                    j                            t          j                  }t          j        t          d|          }t          j
        ||           d         S )N_geninvgauss_pdfr   )rM   r[  rL  r\  r]  r^  r   r_  r   r   ra  )rn   rD   r  r   re  rf  r  s         r3   _cdf_singlez)geninvgauss_gen._cdf.<locals>._cdf_single  si    DAq!Q//6>>vOOI".v7I/8: :C >#r1--a00r5   rR  )r   rM   rL  rV  )rB   rn   rD   r  r  r  s        @r3   rr   zgeninvgauss_gen._cdf  sa    ""D)B	1 	1 	1 	1 	1 l;
|DDD{1$t$$$$r5   c                 L    t          |dk    |||fd t          j                   S )Nr   c                 T    |dz
  t          j        |           z  || d| z  z   z  dz  z
  S r-  r.  r|  s      r3   r  z.geninvgauss_gen._logquasipdf.<locals>.<lambda>  s,    1q5"&))*;aQqSk!m*K r5   r  r  s       r3   _logquasipdfzgeninvgauss_gen._logquasipdf  s/    !a%!QKK6'# # 	#r5   Nc                   	
 t          j        |          r.t          j        |          r|                     ||||          }nk|j        dk    rI|j        dk    r>|                     |                                |                                ||          }nt          j        ||          \  }}t          |j        |          \  }	t          t          j	        |                    }t          j
        |          }t          j        ||gdgdgdgg          

j        st          	
fdt          t          |           d          D                       }|                     
d         
d         ||                              |          ||<   
                                 
j        |dk    r|                                }|S )Nr   multi_indexreadonlyflagsop_flagsc              3   `   K   | ](}|         sj         |         nt          d           V  )d S rK   r  slicer  r  bcits     r3   ru  z'geninvgauss_gen._rvs.<locals>.<genexpr>  R       ; ; ! 79eLR^A..t ; ; ; ; ; ;r5   r   r   )rM   isscalar_rvs_scalarr   rP  r  r   rw  r  r  emptynditerfinishedtupler  r  rN  iternext)rB   r  r   r   r   r  shp
numsamplesidxr  r  s            @@r3   r   zgeninvgauss_gen._rvs  s    ;q>> .	bk!nn .	""1a|<<CCVq[[QVq[[""16688QVVXXt\JJCC &q!,,DAq #17D11GC RWS\\**J (4..CAq6"/&0\J<$@B B BB k   ; ; ; ; ;%*CII:q%9%9; ; ; ; ;++BqE2a5*,8: ::A'#,, C k   2::((**C
r5   c           	      ~   3 d}|sd}dk     r d}                                }d}dk    sdk    rd}n4t          ddt          j        dz
            z  dz            k    rd}nd}t	          t          j        |                    }	t          j        |	          }
t          j        |
          }d}|r|rrddz   z  z  |z
  }d|z  dz
  z  z  dz
  }||dz  dz  z
  }d|dz  z  d	z  ||z  dz  z
  |z   }t          j        | t          j        d
|dz  z            z  dz            }t          j        d|z  dz             }|t          j	        |dz  t          j
        dz  z             z  |dz  z
  }| t          j	        |dz            z  |dz  z
  }                     |          3                     |          3z
  }                     |          3z
  }||z
  t          j        d|z            z  }||z
  t          j        d|z            z  }d}3 fd}|}nt          j        d                     |          z            }dz   t          j        dz   dz  dz  z             z   z  }d}|t          j        d                     |          z            z  }d} fd}||k    rt          d          |dk    rt          d          d}||
k     r|
|z
  }||                    |          z  }|                    |          } |||z
  | z  z   } | |z  |z   }!dt          j        |          z   ||!          k    }"t          j        |"          }#|#dk    r|!|"         ||||#z   <   ||#z  }|dk    r!||
z  dk    rd||
z   d}$t#          |$          |dz  }||
k     ̐nމdz
  z  }%t          j        |%dz  f          }&t          j                             |                    }'|'|%z  }(|%dz  k     rNt          j                   })dk    r|)dz  z  |%z  z
  z  z  }*n#|)t          j        ddz  z            z  }*nd\  })}*|&dz
  z  }+d|+z  t          j        |& z  dz            z  z  },|(|*z   |,z   }-||
k     r|
|z
  }t          j        |          t          j        |          }!}.|                    |          }|-|                    |          z  } | |(k    }/t          j        |/          | |(|*z   k    z  }0t          j        |/|0z            }1|%| |/         z  |(z  |!|/<   |'|.|/<   dk    r!|%z  | |0         |(z
  z  |)z  z   dz  z  |!|0<   n8t          j        | |0         |(z
  t          j                  z            z  |!|0<   |)|!|0         dz
  z  z  |.|0<   t          j        |& z  dz            | |1         |(z
  |*z
  z  d|+z  z  z
  }2dz  t          j        |2          z  |!|1<   |+t          j        |!|1          z  dz            z  |.|1<   t          j        ||.z                                 |!          k    }"t!          |"          }#|#dk    r|!|"         ||||#z   <   ||#z  }||
k     t          j        ||	          }!|rd|!z  }!|!S )NFr   r   Tr   rR   r  r     ir  c                 8                         |           z
  S rK   r  )rn   r   lmr  rB   s    r3   logqpdfz,geninvgauss_gen._rvs_scalar.<locals>.logqpdf  s     ,,Q155::r5   c                 2                         |           S rK   r  )rn   r   r  rB   s    r3   r  z,geninvgauss_gen._rvs_scalar.<locals>.logqpdf  s    ,,Q1555r5   zvmin must be smaller than vmax.zumax must be positive.r  iP  z2Not a single random variate could be generated in zH attempts. Sampling does not appear to work for the provided parameters.)r   r   )_moderK  rM   r   r  
atleast_1dr  zerosarccosr  r   r  r   r   r  r   r  r  rc  logical_notrN  )4rB   r  r   r  r   
invert_resr  
ratio_unif
mode_shiftsize1dNrn   	simulateda2a1p1q1phirY  root1root2d1d2vminvmaxumaxr  r  xplusr  r  r  r  r  accept
num_acceptrV   r  xsk1A1k2A2k3A3r+  r  cond1cond2cond3r  r  s4   ```                                                @r3   r  zgeninvgauss_gen._rvs_scalar  s    
 	Jq55AJJJq! 
66QUUJJ#c1rwq1u~~-12222JJ J r}Z0011GFOOHQKK	 t	, (61q5\A%)Ua!e_q(1,"a%!)^QY^b2gk1A5ibgcBEk&:&: :Q >??gb2gk***RVC!Gbeai$788826AbfS1Woo-Q6 &&q!Q//&&ua33b8&&ua33b8 	RVC"H%5%55	RVC"H%5%55; ; ; ; ; ; ; ;  vc$"3"3Aq!"<"<<==a%27AEA:1+<#=#==q@rvcD,=,=eQ,J,J&JKKK6 6 6 6 6 6 6 t|| !BCCCqyy !9:::Aa--	M<//Q/777 ((a(00D4K1,,!eaiBF1II+5VF^^
>><?KAiZ!789+INN1?!"1? ? ?C 's+++Q' a--, a!eBQU$$B))!Q2233BbBAEzzVQBZZq55AzBE12Q6BBbfQAX...BBBa!eBR"&"q1---1BR"A a--	M!bhqkk3 ((a(00,,!,444Ru--b2g>uu}55!E(]R/E
%q55"$a%1U8b=A*=*B"Ba!e!LCJJ!"RVQuX]bfQii,G%H%H!HCJE
QU 33%FB37Q;''!qx"}r/A*Ba"f*MM!VbfQii/E
E
{Q': ; ;;%&Q--4+<+<S!Q+G+GG [[
>><?KAiZ!789+I7 a--: jF## 	c'C
r5   c                     |dk     r)|t          j        |dz
  dz  |dz  z             dz   |z
  z  S t          j        d|z
  dz  |dz  z             d|z
  z
  |z  S r-  r  rv  s      r3   r  zgeninvgauss_gen._modek  sj    q55Q
QT 122Q6:;;GQUQJA-..!a%8A==r5   c                    t          j        ||z   |          }t          j        ||          }t          j        |          t          j        |          z  }|                                rad}t          j        |t          d           t          j        |t          j	        t          j
                  }||          ||          z  || <   n||z  }|S )NzInfinite values encountered in the moment calculation involving scipy.special.kve. Values replaced by NaN to avoid incorrect results.r  r  dtype)rt   kverM   rS   r  r  r  r  	full_liker  rV  )	rB   r_   r  r   r
  denominf_valsrV   r  s	            r3   r   zgeninvgauss_gen._munpr  s    fQUAq!8C==28E??2<<>> 	.C M#~!<<<<S"&
;;;Ay>E8),<<AxiLLeAr5   r   )r   r   r   r   r`   rh   r   ro   rr   r  r   r  r  r   r   r5   r3   rt  rt  E  s        # #H" " "  
   - - -% % %# # #5 5 5 5nW W Wr> > >    r5   rt  ro  c                   \     e Zd ZdZej        Zd Zd Z fdZ	d Z
d Zd Zdd	Zd
 Z xZS )norminvgauss_gena  A Normal Inverse Gaussian continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `norminvgauss` is:

    .. math::

        f(x, a, b) = \frac{a \, K_1(a \sqrt{1 + x^2})}{\pi \sqrt{1 + x^2}} \,
                     \exp(\sqrt{a^2 - b^2} + b x)

    where :math:`x` is a real number, the parameter :math:`a` is the tail
    heaviness and :math:`b` is the asymmetry parameter satisfying
    :math:`a > 0` and :math:`|b| <= a`.
    :math:`K_1` is the modified Bessel function of second kind
    (`scipy.special.k1`).

    %(after_notes)s

    A normal inverse Gaussian random variable `Y` with parameters `a` and `b`
    can be expressed as a normal mean-variance mixture:
    `Y = b * V + sqrt(V) * X` where `X` is `norm(0,1)` and `V` is
    `invgauss(mu=1/sqrt(a**2 - b**2))`. This representation is used
    to generate random variates.

    Another common parametrization of the distribution (see Equation 2.1 in
    [2]_) is given by the following expression of the pdf:

    .. math::

        g(x, \alpha, \beta, \delta, \mu) =
        \frac{\alpha\delta K_1\left(\alpha\sqrt{\delta^2 + (x - \mu)^2}\right)}
        {\pi \sqrt{\delta^2 + (x - \mu)^2}} \,
        e^{\delta \sqrt{\alpha^2 - \beta^2} + \beta (x - \mu)}

    In SciPy, this corresponds to
    `a = alpha * delta, b = beta * delta, loc = mu, scale=delta`.

    References
    ----------
    .. [1] O. Barndorff-Nielsen, "Hyperbolic Distributions and Distributions on
           Hyperbolae", Scandinavian Journal of Statistics, Vol. 5(3),
           pp. 151-157, 1978.

    .. [2] O. Barndorff-Nielsen, "Normal Inverse Gaussian Distributions and
           Stochastic Volatility Modelling", Scandinavian Journal of
           Statistics, Vol. 24, pp. 1-13, 1997.

    %(example)s

    c                 @    |dk    t          j        |          |k     z  S r  )rM   absoluterB   r   r   s      r3   r`   znorminvgauss_gen._argcheck  s    A"+a..1,--r5   c                     t          dddt          j        fd          }t          ddt          j         t          j        fd          }||gS rd  re   re  s      r3   rh   znorminvgauss_gen._shape_info  r  r5   c                 J    t                                          |d          S )N)r   r   rJ  r  r  s     r3   r  znorminvgauss_gen._fitstart  s"     ww  H 555r5   c                     t          j        |dz  |dz  z
            }|t           j        z  }t          j        d|          }|t	          j        ||z            z  t          j        ||z  ||z  z
  |z             z  |z  S r  )rM   r   r   hypotrt   k1er   )rB   rn   r   r   r  fac1sqs          r3   ro   znorminvgauss_gen._pdf  ss    1q!t$$25yXa^^bfQVnn$rvacAbDj5.@'A'AABFFr5   c           
         t          j        |          r/t          j        | j        |t           j        ||f          d         S t          j        |          }t          j        |          }g }t          |||          D ]H\  }}}|                    t          j        | j        |t           j        ||f          d                    It          j	        |          S )NrJ  r   )
rM   r  r   ra  ro   rf   r  r  appendr[  )rB   rn   r   r   resultr  a0rt  s           r3   rv   znorminvgauss_gen._sf  s    ;q>> 
	$>$)QaVDDDQGGa  Aa  AF #Aq! @ @RinTYBF35r(< < <<=? @ @ @ @8F###r5   c                       fd}t          j        |          r ||||          S g }t          |||          D ]&\  }}}|                     ||||                     't          j        |          S )Nc                    
fd}
                     ||          } |||||           }|dk    r|S |dk    r8d}|}||z   } |||||           dk    rd|z  }||z   } |||||           dk    n7d}|}||z
  } |||||           dk     rd|z  }||z
  } |||||           dk     t          j        |||||| f
j                  }	|	S )Nc                 8                         | ||          |z
  S rK   rv   )rn   r   r   rz   rB   s       r3   eqz6norminvgauss_gen._isf.<locals>._isf_scalar.<locals>.eq  s    xx1a((1,,r5   r   r   rR   )rD   r  )r   r   r  r  )rz   r   r   r  xmemdeltaleftrightr  rB   s             r3   _isf_scalarz*norminvgauss_gen._isf.<locals>._isf_scalar  sF   - - - - - 1aBB1aBQww	AvvU
b1a((1,,eGEJE b1a((1,,
 Ezbq!Q''!++eGE:D bq!Q''!++ _RuAq!9*.)5 5 5FMr5   )rM   r  r  r  r[  )	rB   rz   r   r   r  r  q0r  rt  s	   `        r3   r~   znorminvgauss_gen._isf  s    	 	 	 	 	B ;q>> 	$;q!Q'''F #Aq! 7 7Rkk"b"5566668F###r5   Nc                     t          j        |dz  |dz  z
            }t                              d|z  ||          }||z  t          j        |          t                              ||          z  z   S )NrR   r   )r@  r   r   r  )rM   r   rn  r  r  )rB   r   r   r   r   r  igs          r3   r   znorminvgauss_gen._rvs  sy     1q!t$$\\QuW4l\KK2vdhhD<H '/ 'J 'J J J 	Jr5   c                     t          j        |dz  |dz  z
            }||z  }|dz  |dz  z  }d|z  |t          j        |          z  z  }ddd|dz  z  |dz  z  z   z  |z  }||||fS )NrR   r  rD  r   r   r  )rB   r   r   r  r   varianceskewnesskurtosiss           r3   r   znorminvgauss_gen._stats  s    1q!t$$5ya4%(?7a"'%..01!a!Q$hAo-.6Xx11r5   r   )r   r   r   r   r   r  r  r`   rh   r  ro   rv   r~   r   r   r  r  s   @r3   r  r    s        4 4j "4M. . .  
6 6 6 6 6
G G G$ $ $($ ($ ($TJ J J J2 2 2 2 2 2 2r5   r  norminvgaussc                   b     e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd Zd Zd	 Zd fd	Z xZS )invweibull_genu  An inverted Weibull continuous random variable.

    This distribution is also known as the Fréchet distribution or the
    type II extreme value distribution.

    %(before_notes)s

    Notes
    -----
    The probability density function for `invweibull` is:

    .. math::

        f(x, c) = c x^{-c-1} \exp(-x^{-c})

    for :math:`x > 0`, :math:`c > 0`.

    `invweibull` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    References
    ----------
    F.R.S. de Gusmao, E.M.M Ortega and G.M. Cordeiro, "The generalized inverse
    Weibull distribution", Stat. Papers, vol. 52, pp. 591-619, 2011.

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zinvweibull_gen._shape_info;  r  r5   c                     t          j        || dz
            }t          j        ||           }t          j        |           }||z  |z  S r  rM   r  r   )rB   rn   r  xc1xc2s        r3   ro   zinvweibull_gen._pdf>  sG    hq1"s(##hq1"oofcTll3w}r5   c                 X    t          j        ||           }t          j        |           S rK   r  )rB   rn   r  r  s       r3   rr   zinvweibull_gen._cdfE  s#    hq1"oovsd||r5   c                 6    t          j        || z              S rK   )rM   r  r
  s      r3   rv   zinvweibull_gen._sfI  s    !aR%    r5   c                 X    t          j        t          j        |           d|z            S r?  )rM   r  r   r  s      r3   r{   zinvweibull_gen._ppfL  s"    x
DF+++r5   c                 :    t          j        |            d|z  z  S Nr  r9  r  s      r3   r~   zinvweibull_gen._isfO  s    1"A&&r5   c                 6    t          j        d||z  z
            S r[   r-  r{  s      r3   r   zinvweibull_gen._munpR  s    xAE	"""r5   c                 V    dt           z   t           |z  z   t          j        |          z
  S r[   r  r}  s     r3   r   zinvweibull_gen._entropyU  s"    x&1*$rvayy00r5   Nc                 V    |dn|}t                                          ||          S )N)r   rJ  r  )rB   rC   rD   r  s      r3   r  zinvweibull_gen._fitstartX  s-    vv4ww  D 111r5   rK   )r   r   r   r   r   r  r  rh   ro   rr   rv   r{   r~   r   r   r  r  r  s   @r3   r  r    s         : "4ME E E    ! ! !, , ,' ' '# # #1 1 12 2 2 2 2 2 2 2 2 2r5   r  
invweibullc                   8    e Zd ZdZd Zd Zd	dZd Zd Zd Z	dS )
jf_skew_t_gena  Jones and Faddy skew-t distribution.

    %(before_notes)s

    Notes
    -----
    The probability density function for `jf_skew_t` is:

    .. math::

        f(x; a, b) = C_{a,b}^{-1}
                    \left(1+\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{a+1/2}
                    \left(1-\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{b+1/2}

    for real numbers :math:`a>0` and :math:`b>0`, where
    :math:`C_{a,b} = 2^{a+b-1}B(a,b)(a+b)^{1/2}`, and :math:`B` denotes the
    beta function (`scipy.special.beta`).

    When :math:`a<b`, the distribution is negatively skewed, and when
    :math:`a>b`, the distribution is positively skewed. If :math:`a=b`, then
    we recover the `t` distribution with :math:`2a` degrees of freedom.

    `jf_skew_t` takes :math:`a` and :math:`b` as shape parameters.

    %(after_notes)s

    References
    ----------
    .. [1] M.C. Jones and M.J. Faddy. "A skew extension of the t distribution,
           with applications" *Journal of the Royal Statistical Society*.
           Series B (Statistical Methodology) 65, no. 1 (2003): 159-174.
           :doi:`10.1111/1467-9868.00378`

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS rd  re   re  s      r3   rh   zjf_skew_t_gen._shape_info  rh  r5   c                 (   d||z   dz
  z  t          j        ||          z  t          j        ||z             z  }d|t          j        ||z   |dz  z             z  z   |dz   z  }d|t          j        ||z   |dz  z             z  z
  |dz   z  }||z  |z  S NrR   r   r   )rt   rk  rM   r   )rB   rn   r   r   r  r  r  s          r3   ro   zjf_skew_t_gen._pdf  s    !a%!)rwq!}},rwq1u~~=!bga!ea1fn----1s7;!bga!ea1fn----1s7;Bw{r5   Nc                     |                     |||          }d|z  dz
  t          j        ||z             z  }dt          j        |d|z
  z            z  }||z  S r  )rk  rM   r   )rB   r   r   r   r   r  r  d3s           r3   r   zjf_skew_t_gen._rvs  s\    q!T**"fqjBGAENN*q2v'''Bwr5   c                 z    d|t          j        ||z   |dz  z             z  z   dz  }t          j        |||          S Nr   rR   r   )rM   r   rt   ry  )rB   rn   r   r   ri  s        r3   rr   zjf_skew_t_gen._cdf  s@    RWQUQ!V^,,,,3z!Q"""r5   c                     t                               |||          }d|z  dz
  t          j        ||z             z  }dt          j        |d|z
  z            z  }||z  S r  )rk  re  rM   r   )rB   rz   r   r   r  r  r
  s          r3   r{   zjf_skew_t_gen._ppf  sZ    XXaA"fqjBGAENN*q2v'''Bwr5   c                     d }|d|z  k    |d|z  k    z  |dk    z  }t          ||||ft          j        |t          j        g          t          j                  S )zReturns the n-th moment(s) where all the following hold:

        - n >= 0
        - a > n / 2
        - b > n / 2

        The result is np.nan in all other cases.
        c                 n   ||z   d| z  z  }d| z  t          j        ||          z  }t          j        | dz             }t          j        |dz  dk    dd          }t          j        |d| z  z   |z
  |d| z  z
  |z             }t          j        | |          |z  |z  }||z  |                                z  S )zgComputes E[T^(n_k)] where T is skew-t distributed with
            parameters a_k and b_k.
            r   rR   r   r   r  )rt   rk  rM   rM  rO  r  r  )	n_ka_kb_kr
  r  indicesrC  r  	sum_termss	            r3   
nth_momentz'jf_skew_t_gen._munp.<locals>.nth_moment  s     9#),CHrwsC000Eia((G(7Q;?B22CcCi'13s?W3LMMAW--3a7I;00r5   r   r   rR  r   rM   rL  rV  r  )rB   r_   r   r   r  nth_moment_valids         r3   r   zjf_skew_t_gen._munp  sp    	1 	1 	1 aKAaK8AFC1ILRZL999F	
 
 	
r5   r   )
r   r   r   r   rh   ro   r   rr   r{   r   r   r5   r3   r  r  a  s~        # #H  
     # # #  
 
 
 
 
r5   r  	jf_skew_tc                   J    e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd Zd Zd	S )
johnsonsb_gena!  A Johnson SB continuous random variable.

    %(before_notes)s

    See Also
    --------
    johnsonsu

    Notes
    -----
    The probability density function for `johnsonsb` is:

    .. math::

        f(x, a, b) = \frac{b}{x(1-x)}  \phi(a + b \log \frac{x}{1-x} )

    where :math:`x`, :math:`a`, and :math:`b` are real scalars; :math:`b > 0`
    and :math:`x \in [0,1]`.  :math:`\phi` is the pdf of the normal
    distribution.

    `johnsonsb` takes :math:`a` and :math:`b` as shape parameters.

    %(after_notes)s

    %(example)s

    c                     |dk    ||k    z  S r  r   r  s      r3   r`   zjohnsonsb_gen._argcheck  r  r5   c                     t          ddt          j         t          j        fd          }t          dddt          j        fd          }||gS Nr   Fr  r   r   re   re  s      r3   rh   zjohnsonsb_gen._shape_info  rx  r5   c                 r    t          ||t          j        |          z  z             }|dz  |d|z
  z  z  |z  S r)  )r   rt   r  )rB   rn   r   r   trms        r3   ro   zjohnsonsb_gen._pdf  s;    AbhqkkM)**ua1gs""r5   c                 P    t          ||t          j        |          z  z             S rK   )r   rt   r  rq  s       r3   rr   zjohnsonsb_gen._cdf  s!    Qrx{{]*+++r5   c                 V    t          j        d|z  t          |          |z
  z            S r  )rt   r  r   r  s       r3   r{   zjohnsonsb_gen._ppf  &    xa9Q<<!#34555r5   c                 P    t          ||t          j        |          z  z             S rK   )r   rt   r  rq  s       r3   rv   zjohnsonsb_gen._sf  s!    AbhqkkM)***r5   c                 V    t          j        d|z  t          |          |z
  z            S r  )rt   r  r   r  s       r3   r~   zjohnsonsb_gen._isf  r"  r5   N)r   r   r   r   r   r  r  r`   rh   ro   rr   r{   rv   r~   r   r5   r3   r  r    s         6 "4M" " "  
# # #
, , ,6 6 6+ + +6 6 6 6 6r5   r  	johnsonsbc                   D    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
dd
ZdS )johnsonsu_gena-  A Johnson SU continuous random variable.

    %(before_notes)s

    See Also
    --------
    johnsonsb

    Notes
    -----
    The probability density function for `johnsonsu` is:

    .. math::

        f(x, a, b) = \frac{b}{\sqrt{x^2 + 1}}
                     \phi(a + b \log(x + \sqrt{x^2 + 1}))

    where :math:`x`, :math:`a`, and :math:`b` are real scalars; :math:`b > 0`.
    :math:`\phi` is the pdf of the normal distribution.

    `johnsonsu` takes :math:`a` and :math:`b` as shape parameters.

    The first four central moments are calculated according to the formulas
    in [1]_.

    %(after_notes)s

    References
    ----------
    .. [1] Taylor Enterprises. "Johnson Family of Distributions".
       https://variation.com/wp-content/distribution_analyzer_help/hs126.htm

    %(example)s

    c                     |dk    ||k    z  S r  r   r  s      r3   r`   zjohnsonsu_gen._argcheck#  r  r5   c                     t          ddt          j         t          j        fd          }t          dddt          j        fd          }||gS r  re   re  s      r3   rh   zjohnsonsu_gen._shape_info&  rx  r5   c                     ||z  }t          ||t          j        |          z  z             }|dz  t          j        |dz             z  |z  S r  )r   rM   arcsinhr   )rB   rn   r   r   r  r  s         r3   ro   zjohnsonsu_gen._pdf+  sL     qSA
1--..uRWRV__$S((r5   c                 P    t          ||t          j        |          z  z             S rK   )r   rM   r+  rq  s       r3   rr   zjohnsonsu_gen._cdf2  s"    QA..///r5   c                 P    t          j        t          |          |z
  |z            S rK   )rM   sinhr   r  s       r3   r{   zjohnsonsu_gen._ppf5  "    w	!q(A-...r5   c                 P    t          ||t          j        |          z  z             S rK   )r   rM   r+  rq  s       r3   rv   zjohnsonsu_gen._sf8  s"    A
1--...r5   c                 P    t          j        t          |          |z
  |z            S rK   )rM   r.  r   rq  s       r3   r~   zjohnsonsu_gen._isf;  r/  r5   r  c                 t   d\  }}}}|dz  }t          j        |          }	||z  }
d|v r|	dz   t          j        |
          z  }d|v r5dt          j        |          z  |	t          j        d|
z            z  dz   z  }d|v r|	dz  t          j        |          dz  z  }d	t          j        |
          z  }|	|	dz   z  t          j        d	|
z            z  }t          j        d          d|	t          j        d|
z            z  z   d
z  z  }| ||z   z  |z  }d|v rd	d|	z  z   }d|	dz  z  |	dz   z  t          j        d|
z            z  }|	dz  t          j        d|
z            z  }dd	|	dz  z  z   d|	d	z  z  z   |	dz  z   }dd|	t          j        d|
z            z  z   dz  z  }||z   ||z  z   |z  d	z
  }||||fS )NNNNNr  r  r   r  rR   r   r  r  r  r  r  r   r  )rM   r   r.  rt   r  r"  r   )rB   r   r   r  r@  rA  rB  rC  bn2expbn2a_br  r  r  r  t4s                   r3   r   zjohnsonsu_gen._stats>  s    1CRf!e'>>#+,B'>>bhsmm#VBGAcENN%:Q%>?C'>>bhsmmS00B273<<B6A:&37BGAJJ!frwqu~~&="=!EEER5(B'>>QvXB619
+bgaenn<BRWQsU^^+Ba	k!AfaiK/&!);Bq6"'!C%..00144Er'BrE/U*Q.B3Br5   Nr  )r   r   r   r   r`   rh   ro   rr   r{   rv   r~   r   r   r5   r3   r'  r'    s        " "F" " "  
) ) )0 0 0/ / // / // / /     r5   r'  	johnsonsuc                       e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Ze eed          d                         ZdS )laplace_gena
  A Laplace continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `laplace` is

    .. math::

        f(x) = \frac{1}{2} \exp(-|x|)

    for a real number :math:`x`.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zlaplace_gen._shape_infos  r   r5   Nc                 2    |                     dd|          S )Nr   r   r  )laplacer   s      r3   r   zlaplace_gen._rvsv  s    ##Aqt#444r5   c                 L    dt          j        t          |                     z  S r  )rM   r   r
  r   s     r3   ro   zlaplace_gen._pdfy  s    263q66'??""r5   c           	          t          j        d          5  t          j        |dk    ddt          j        |           z  z
  dt          j        |          z            cd d d            S # 1 swxY w Y   d S )Nr5  rn  r   r   r   )rM   r8  rO  r   r   s     r3   rr   zlaplace_gen._cdf}  s    [h''' 	H 	H8AE3RVQBZZ#7RVAYYGG	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	H 	Hs   AA++A/2A/c                 .    |                      |           S rK   rr   r   s     r3   rv   zlaplace_gen._sf  s    yy!}}r5   c                     t          j        |dk    t          j        dd|z
  z             t          j        d|z                      S r   rM   rO  r   r   s     r3   r{   zlaplace_gen._ppf  s9    xC"&AaC//!126!A#;;???r5   c                 .    |                      |           S rK   rW  r   s     r3   r~   zlaplace_gen._isf  s    		!}r5   c                     dS )N)r   rR   r   r  r   rg   s    r3   r   zlaplace_gen._stats  s    zr5   c                 0    t          j        d          dz   S r  r.  rg   s    r3   r   zlaplace_gen._entropy  s    vayy{r5   z        This function uses explicit formulas for the maximum likelihood
        estimation of the Laplace distribution parameters, so the keyword
        arguments `loc`, `scale`, and `optimizer` are ignored.

r   c                     t          | |||          \  }}}|t          j        |          }|9t          j        t          j        ||z
                      t          |          z  }||fS rK   )r  rM   medianr  r
  r  )rB   rC   rD   r2   r   r   s         r3   r@   zlaplace_gen.fit  sp     9t9=tE EdF <9T??D>fRVD4K0011SYY>FV|r5   r   )r   r   r   r   rh   r   ro   rr   rv   r{   r~   r   r   rH   r
   r   r@   r   r5   r3   r:  r:  _  s         &  5 5 5 5# # #H H H  @ @ @        6F G G G 	G G _
  r5   r:  r=  c                   H    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 ZdS )laplace_asymmetric_genu  An asymmetric Laplace continuous random variable.

    %(before_notes)s

    See Also
    --------
    laplace : Laplace distribution

    Notes
    -----
    The probability density function for `laplace_asymmetric` is

    .. math::

       f(x, \kappa) &= \frac{1}{\kappa+\kappa^{-1}}\exp(-x\kappa),\quad x\ge0\\
                    &= \frac{1}{\kappa+\kappa^{-1}}\exp(x/\kappa),\quad x<0\\

    for :math:`-\infty < x < \infty`, :math:`\kappa > 0`.

    `laplace_asymmetric` takes ``kappa`` as a shape parameter for
    :math:`\kappa`. For :math:`\kappa = 1`, it is identical to a
    Laplace distribution.

    %(after_notes)s

    Note that the scale parameter of some references is the reciprocal of
    SciPy's ``scale``. For example, :math:`\lambda = 1/2` in the
    parameterization of [1]_ is equivalent to ``scale = 2`` with
    `laplace_asymmetric`.

    References
    ----------
    .. [1] "Asymmetric Laplace distribution", Wikipedia
            https://en.wikipedia.org/wiki/Asymmetric_Laplace_distribution

    .. [2] Kozubowski TJ and Podgórski K. A Multivariate and
           Asymmetric Generalization of Laplace Distribution,
           Computational Statistics 15, 531--540 (2000).
           :doi:`10.1007/PL00022717`

    %(example)s

    c                 @    t          dddt          j        fd          gS )NkappaFr   r  re   rg   s    r3   rh   z"laplace_asymmetric_gen._shape_info  s    7EArv;GGHHr5   c                 R    t          j        |                     ||                    S rK   r  rB   rn   rL  s      r3   ro   zlaplace_asymmetric_gen._pdf  s     vdll1e,,---r5   c                     d|z  }|t          j        |dk    | |          z  }|t          j        ||z             z  }|S rK  rC  )rB   rn   rL  kapinvrw  s        r3   r   zlaplace_asymmetric_gen._logpdf  sF    5"(16E66222rveFl###
r5   c                     d|z  }||z   }t          j        |dk    dt          j        | |z            ||z  z  z
  t          j        ||z            ||z  z            S rK  rM   rO  r   rB   rn   rL  rP  
kappkapinvs        r3   rr   zlaplace_asymmetric_gen._cdf  sk    56\
xQBFA2e8,,fZ.?@@qx((%
*:;= = 	=r5   c           	          d|z  }||z   }t          j        |dk    t          j        | |z            ||z  z  dt          j        ||z            ||z  z  z
            S rK  rR  rS  s        r3   rv   zlaplace_asymmetric_gen._sf  sn    56\
xQr%x((&*;<BF1V8,,eJ.>??A A 	Ar5   c                     d|z  }||z   }t          j        |||z  k    t          j        d|z
  |z  |z             |z  t          j        ||z  |z            |z            S r[   rC  rB   rz   rL  rP  rT  s        r3   r{   zlaplace_asymmetric_gen._ppf  sr    56\
xU:--Q
 25 8999&@q|E12258: : 	:r5   c                     d|z  }||z   }t          j        |||z  k    t          j        ||z  |z             |z  t          j        d|z
  |z  |z            |z            S r[   rC  rW  s        r3   r~   zlaplace_asymmetric_gen._isf  su    56\
xVJ..*U 2333F:Az1%788>@ @ 	@r5   c                 T   d|z  }||z
  }||z  ||z  z   }ddt          j        |d          z
  z  t          j        dt          j        |d          z   d          z  }ddt          j        |d          z   z  t          j        dt          j        |d          z   d          z  }||||fS )	Nr   r   r  r   r  rE  r*  rR   r  )rB   rL  rP  mnr  rB  rC  s          r3   r   zlaplace_asymmetric_gen._stats  s    5e^VmeEk)!BHUA&&&'28E13E3E1Es(K(KK!BHUA&&&'28E13E3E1Eq(I(II3Br5   c                 <    dt          j        |d|z  z             z   S r[   r.  rB   rL  s     r3   r   zlaplace_asymmetric_gen._entropy  s    26%%-((((r5   Nr  r   r5   r3   rJ  rJ    s        * *VI I I. . .  = = =A A A: : :@ @ @  ) ) ) ) )r5   rJ  laplace_asymmetricc                 *   t          |t                    st          j        |          }|                    dd           }|                    dd           }| j        r't          | j                            d                    nd}g }g }| j        r| j                            dd                                          }	t          |	          D ]c\  }
}dt          |
          z   }|d|z   d|z   g}t          ||          }|                    |           |                    |           ||||<   ddd	d
dddh|}t          |                              |          }|rt          d| d          t          |          |k    rt          d          d ||h|vrt!          d          t          |t                    r|                                n|}t          j        |                                          st)          d          |g|||R S )Nr   r   ,r    r  fix_r,   r-   r.   r/   zUnknown keyword arguments: r  zToo many positional arguments.r   r   )r<   r(   rM   r   r:   shapesr  splitrS  	enumeratestrr   r  set
differencer1   r  r  r   r   r   )distrC   rD   r2   r   r   
num_shapesfshape_keysfshapesrb  r  r  keynamesrB  
known_keysunknown_keys
uncensoreds                     r3   r  r    sD   dL))  z$88FD!!DXXh%%F04BT[&&s++,,,JKG
 { 	 $$S#..4466f%% 	  	 DAqA,C#'6A:.E&tU33Cs###NN3S	 +x(2%02Jt99''
33L GElEEEFFF
4yy:8999D&+7+++  ' ( ( 	( &0l%C%CM!!!J;z""&&(( A?@@@)7)D)&)))r5   c                   J    e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd Zd Zd	S )
levy_genag  A Levy continuous random variable.

    %(before_notes)s

    See Also
    --------
    levy_stable, levy_l

    Notes
    -----
    The probability density function for `levy` is:

    .. math::

        f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp\left(-\frac{1}{2x}\right)

    for :math:`x > 0`.

    This is the same as the Levy-stable distribution with :math:`a=1/2` and
    :math:`b=1`.

    %(after_notes)s

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.stats import levy
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)

    Calculate the first four moments:

    >>> mean, var, skew, kurt = levy.stats(moments='mvsk')

    Display the probability density function (``pdf``):

    >>> # `levy` is very heavy-tailed.
    >>> # To show a nice plot, let's cut off the upper 40 percent.
    >>> a, b = levy.ppf(0), levy.ppf(0.6)
    >>> x = np.linspace(a, b, 100)
    >>> ax.plot(x, levy.pdf(x),
    ...        'r-', lw=5, alpha=0.6, label='levy pdf')

    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.

    Freeze the distribution and display the frozen ``pdf``:

    >>> rv = levy()
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

    Check accuracy of ``cdf`` and ``ppf``:

    >>> vals = levy.ppf([0.001, 0.5, 0.999])
    >>> np.allclose([0.001, 0.5, 0.999], levy.cdf(vals))
    True

    Generate random numbers:

    >>> r = levy.rvs(size=1000)

    And compare the histogram:

    >>> # manual binning to ignore the tail
    >>> bins = np.concatenate((np.linspace(a, b, 20), [np.max(r)]))
    >>> ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
    >>> ax.set_xlim([x[0], x[-1]])
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()

    c                     g S rK   r   rg   s    r3   rh   zlevy_gen._shape_info  r   r5   c                     dt          j        dt           j        z  |z            z  |z  t          j        dd|z  z            z  S Nr   rR   r  r  r   s     r3   ro   zlevy_gen._pdf  s=    271RU719%%%)BF2qs8,<,<<<r5   c                 T    t          j        t          j        d|z                      S r  )rt   erfcrM   r   r   s     r3   rr   zlevy_gen._cdf  s     wrwsQw''(((r5   c                 T    t          j        t          j        d|z                      S r  r  r   s     r3   rv   zlevy_gen._sf  s     vbgcAg&&'''r5   c                 6    t          |dz            }d||z  z  S NrR   r   r   rB   rz   rB  s      r3   r{   zlevy_gen._ppf  s     !nncCi  r5   c                 <    ddt          j        |          dz  z  z  S r-  )rt   erfinvr  s     r3   r~   zlevy_gen._isf  s    !BIaLL!O#$$r5   c                 ^    t           j        t           j        t           j        t           j        fS rK   r  rg   s    r3   r   zlevy_gen._stats  r  r5   Nr   r   r   r   r   r  r  rh   ro   rr   rv   r{   r~   r   r   r5   r3   rr  rr  >  s        G GP "4M  = = =) ) )( ( (! ! !
% % %. . . . .r5   rr  levyc                   J    e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd Zd Zd	S )

levy_l_gena  A left-skewed Levy continuous random variable.

    %(before_notes)s

    See Also
    --------
    levy, levy_stable

    Notes
    -----
    The probability density function for `levy_l` is:

    .. math::
        f(x) = \frac{1}{|x| \sqrt{2\pi |x|}} \exp{ \left(-\frac{1}{2|x|} \right)}

    for :math:`x < 0`.

    This is the same as the Levy-stable distribution with :math:`a=1/2` and
    :math:`b=-1`.

    %(after_notes)s

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.stats import levy_l
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)

    Calculate the first four moments:

    >>> mean, var, skew, kurt = levy_l.stats(moments='mvsk')

    Display the probability density function (``pdf``):

    >>> # `levy_l` is very heavy-tailed.
    >>> # To show a nice plot, let's cut off the lower 40 percent.
    >>> a, b = levy_l.ppf(0.4), levy_l.ppf(1)
    >>> x = np.linspace(a, b, 100)
    >>> ax.plot(x, levy_l.pdf(x),
    ...        'r-', lw=5, alpha=0.6, label='levy_l pdf')

    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.

    Freeze the distribution and display the frozen ``pdf``:

    >>> rv = levy_l()
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

    Check accuracy of ``cdf`` and ``ppf``:

    >>> vals = levy_l.ppf([0.001, 0.5, 0.999])
    >>> np.allclose([0.001, 0.5, 0.999], levy_l.cdf(vals))
    True

    Generate random numbers:

    >>> r = levy_l.rvs(size=1000)

    And compare the histogram:

    >>> # manual binning to ignore the tail
    >>> bins = np.concatenate(([np.min(r)], np.linspace(a, b, 20)))
    >>> ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
    >>> ax.set_xlim([x[0], x[-1]])
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()

    c                     g S rK   r   rg   s    r3   rh   zlevy_l_gen._shape_info  r   r5   c                     t          |          }dt          j        dt          j        z  |z            z  |z  t          j        dd|z  z            z  S ru  )r
  rM   r   r   r   rB   rn   r  s      r3   ro   zlevy_l_gen._pdf  sH    VV25$$$R'r1R4y(9(999r5   c                 t    t          |          }dt          dt          j        |          z            z  dz
  S r  )r
  r   rM   r   r  s      r3   rr   zlevy_l_gen._cdf  s1    VV9Q_---11r5   c                 n    t          |          }dt          dt          j        |          z            z  S r  )r
  r   rM   r   r  s      r3   rv   zlevy_l_gen._sf  s,    VV8AO,,,,r5   c                 <    t          |dz   dz            }d||z  z  S )Nr   rR   r<  r   r{  s      r3   r{   zlevy_l_gen._ppf   s&    SA&&sSy!!r5   c                 2    dt          |dz            dz  z  S )Nr  rR   r   r  s     r3   r~   zlevy_l_gen._isf  s    )AaC..!###r5   c                 ^    t           j        t           j        t           j        t           j        fS rK   r  rg   s    r3   r   zlevy_l_gen._stats  r  r5   Nr  r   r5   r3   r  r    s        F FN "4M  : : :
2 2 2- - -" " "$ $ $. . . . .r5   r  levy_lc                        e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd Zd Zd Ze ee           fd                        Z xZS )logistic_gena  A logistic (or Sech-squared) continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `logistic` is:

    .. math::

        f(x) = \frac{\exp(-x)}
                    {(1+\exp(-x))^2}

    `logistic` is a special case of `genlogistic` with ``c=1``.

    Remark that the survival function (``logistic.sf``) is equal to the
    Fermi-Dirac distribution describing fermionic statistics.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zlogistic_gen._shape_info&  r   r5   Nc                 .    |                     |          S r  )logisticr   s      r3   r   zlogistic_gen._rvs)  s    $$$$///r5   c                 P    t          j        |                     |                    S rK   r  r   s     r3   ro   zlogistic_gen._pdf,  r  r5   c                     t          j        |           }|dt          j        t          j        |                    z  z
  S r;  )rM   r
  rt   r  r   )rB   rn   ri  s      r3   r   zlogistic_gen._logpdf0  s3    VAYYJ2+++++r5   c                 *    t          j        |          S rK   r  r   s     r3   rr   zlogistic_gen._cdf4      x{{r5   c                 *    t          j        |          S rK   rt   	log_expitr   s     r3   r   zlogistic_gen._logcdf7  s    |Ar5   c                 *    t          j        |          S rK   r  r   s     r3   r{   zlogistic_gen._ppf:  r  r5   c                 ,    t          j        |           S rK   r  r   s     r3   rv   zlogistic_gen._sf=  s    x||r5   c                 ,    t          j        |           S rK   r  r   s     r3   r   zlogistic_gen._logsf@  s    |QBr5   c                 ,    t          j        |           S rK   r  r   s     r3   r~   zlogistic_gen._isfC  s    |r5   c                 B    dt           j        t           j        z  dz  ddfS )Nr   rD  g333333?r+  rg   s    r3   r   zlogistic_gen._statsF  s    "%+c/1g--r5   c                     dS r;  r   rg   s    r3   r   zlogistic_gen._entropyI  s    sr5   c                   
 |                     dd          r t                      j        g|R i |S t          | ||          \  }}t	                    |                               \  }}|                    d|          |                    d|          }}|ffd	
|ffd	
fd}|(|&t          j        
|f          }	|	j	        d         }|}nK|(|&t          j        |f          }	|	j	        d         }|}n!t          j        |||f          }	|	j	        \  }}t          |          }|	j        r||fn t                      j        g|R i |S )	Nr  Fr,   r-   c                 l    | z
  |z  }t          j        t          j        |                    dz  z
  S r  )rM   r  rt   r  )r,   r-   r  rC   r_   s      r3   dl_dlocz!logistic_gen.fit.<locals>.dl_dloca  s2    u$A6"(1++&&1,,r5   c                 r    |z
  | z  }t          j        |t          j        |dz            z            z
  S r  )rM   r  r  )r-   r,   r  rC   r_   s      r3   	dl_dscalez#logistic_gen.fit.<locals>.dl_dscalee  s6    u$A6!BGAaCLL.))A--r5   c                 >    | \  }} ||           ||          fS rK   r   )paramsr,   r-   r  r  s      r3   r[  zlogistic_gen.fit.<locals>.funci  s/    JC73&&		%(=(===r5   r   )r0   r>   r@   r  r  r  r:   r   r  rn   r
  success)rB   rC   rD   r2   r   r   r,   r-   r[  r  r  r  r_   r  s    `        @@@r3   r@   zlogistic_gen.fitM  s    88J&& 	4577;t3d333d3338t9=tE EdFII ^^D))
UXXeS))488GU+C+CU  & 	- 	- 	- 	- 	- 	- 	- "& 	. 	. 	. 	. 	. 	. 	.	> 	> 	> 	> 	> 	> $,-#00C%(CEE&.-	E844CE!HECC-sEl33CJC E

 # 6e UWW[555555	7r5   r   )r   r   r   r   rh   r   ro   r   rr   r   r{   rv   r   r~   r   r   rH   r   r   r@   r  r  s   @r3   r  r    s        .  0 0 0 0' ' ', , ,               . . .   M**07 07 07 07 +* _07 07 07 07 07r5   r  r  c                   P    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )loggamma_gena  A log gamma continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `loggamma` is:

    .. math::

        f(x, c) = \frac{\exp(c x - \exp(x))}
                       {\Gamma(c)}

    for all :math:`x, c > 0`. Here, :math:`\Gamma` is the
    gamma function (`scipy.special.gamma`).

    `loggamma` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zloggamma_gen._shape_info  r  r5   Nc                     t          j        |                    |dz   |                    t          j        |                    |                    |z  z   S )Nr   r  )rM   r   r  r  r  s       r3   r   zloggamma_gen._rvs  sU     |))!a%d);;<<&--4-8899!;< 	=r5   c                     t          j        ||z  t          j        |          z
  t          j        |          z
            S rK   rM   r   rt   r  r
  s      r3   ro   zloggamma_gen._pdf  s/    vac"&))mBJqMM1222r5   c                 `    ||z  t          j        |          z
  t          j        |          z
  S rK   r  r
  s      r3   r   zloggamma_gen._logpdf  s%    sRVAYYA..r5   c                 B    t          |t          k     ||fd d           S )Nc                 `    t          j        || z  t          j        |dz             z
            S r[   r  rn  s     r3   r  z#loggamma_gen._cdf.<locals>.<lambda>  s%    rvacBJqsOO.C'D'D r5   c                 P    t          j        |t          j        |                     S rK   )rt   r  rM   r   rn  s     r3   r  z#loggamma_gen._cdf.<locals>.<lambda>  s    "+a*C*C r5   r  r   r"   r
  s      r3   rr   zloggamma_gen._cdf  s7     !h,ADDCCE E E 	Er5   c                 n    t          j        ||          }t          |t          k     |||fd d           S )Nc                 `    t          j        |          t          j        |dz             z   |z  S r[   r<  r  rz   r  s      r3   r  z#loggamma_gen._ppf.<locals>.<lambda>  s$    26!99rz!A#+F*I r5   c                 *    t          j        |           S rK   r.  r  s      r3   r  z#loggamma_gen._ppf.<locals>.<lambda>      RVAYY r5   r  )rt   r  r   r!   rB   rz   r  r  s       r3   r{   zloggamma_gen._ppf  sF     N1a  !e)aAYII668 8 8 	8r5   c                 B    t          |t          k     ||fd d           S )Nc                 b    t          j        || z  t          j        |dz             z
             S r[   )rM   r  rt   r  rn  s     r3   r  z"loggamma_gen._sf.<locals>.<lambda>  s(    1rz!A#1F(G(G'G r5   c                 P    t          j        |t          j        |                     S rK   )rt   r  rM   r   rn  s     r3   r  z"loggamma_gen._sf.<locals>.<lambda>  s    ",q"&))*D*D r5   r  r  r
  s      r3   rv   zloggamma_gen._sf  s5    !h,AGGDDF F F 	Fr5   c                 n    t          j        ||          }t          |t          k     |||fd d           S )Nc                 b    t          j        |           t          j        |dz             z   |z  S r[   )rM   r  rt   r  r  s      r3   r  z#loggamma_gen._isf.<locals>.<lambda>  s&    28QB<<"*QqS//+I1*L r5   c                 *    t          j        |           S rK   r.  r  s      r3   r  z#loggamma_gen._isf.<locals>.<lambda>  r  r5   r  )rt   r  r   r!   r  s       r3   r~   zloggamma_gen._isf  sF     OAq!!!e)aAYLL668 8 8 	8r5   c                     t          j        |          }t          j        d|          }t          j        d|          t          j        |d          z  }t          j        d|          ||z  z  }||||fS )Nr   rR   r  r  )rt   r  	polygammarM   r  )rB   r  r   r  r  excess_kurtosiss         r3   r   zloggamma_gen._stats  sm     z!}}l1a  <1%%c(:(::,q!,,C8S(O33r5   c                 B    d }d }t          |dk    |f||          }|S )Nc                 d    t          j        |           | t          j        |           z  z
  | z   }|S rK   )rt   r  r  )r  r  s     r3   r  z&loggamma_gen._entropy.<locals>.regular  s+    
1BJqMM 11A5AHr5   c                     dt          j        |           z  | dz  dz  z   | dz  dz  z
  | dz  dz  z   }t                                          |z   }|S )Nr  r<  r  r  r  r     )rM   r   r  r   )r  termr  s      r3   r.  z)loggamma_gen._entropy.<locals>.asymptotic  sQ    q		>AsF1H,q#vby81c6#:ED$&AHr5   -   r0  r  )rB   r  r  r.  r  s        r3   r   zloggamma_gen._entropy  sC    	 	 		 	 	 qBw@@@r5   r   r   r   r   r   rh   r   ro   r   rr   r{   rv   r~   r   r   r   r5   r3   r  r    s         0E E E= = = =3 3 3/ / /E E E&8 8 8F F F8 8 84 4 4    r5   r  loggammac                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Ze ee           fd
                        Z xZS )loglaplace_genaT  A log-Laplace continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `loglaplace` is:

    .. math::

        f(x, c) = \begin{cases}\frac{c}{2} x^{ c-1}  &\text{for } 0 < x < 1\\
                               \frac{c}{2} x^{-c-1}  &\text{for } x \ge 1
                  \end{cases}

    for :math:`c > 0`.

    `loglaplace` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    Suppose a random variable ``X`` follows the Laplace distribution with
    location ``a`` and scale ``b``.  Then ``Y = exp(X)`` follows the
    log-Laplace distribution with ``c = 1 / b`` and ``scale = exp(a)``.

    References
    ----------
    T.J. Kozubowski and K. Podgorski, "A log-Laplace growth rate model",
    The Mathematical Scientist, vol. 28, pp. 49-60, 2003.

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zloglaplace_gen._shape_info  r  r5   c                 X    |dz  }t          j        |dk     ||           }|||dz
  z  z  S r  rM   rO  )rB   rn   r  cd2s       r3   ro   zloglaplace_gen._pdf  s8     eHQUAr""1qs8|r5   c                 V    t          j        |dk     d||z  z  dd|| z  z  z
            S Nr   r   r  r
  s      r3   rr   zloglaplace_gen._cdf%  s0    xAs1a4x3qA2w;777r5   c                 V    t          j        |dk     dd||z  z  z
  d|| z  z            S r  r  r
  s      r3   rv   zloglaplace_gen._sf(  s0    xAq3q!t8|SaR[999r5   c                 `    t          j        |dk     d|z  d|z  z  dd|z
  z  d|z  z            S Nr   r   r   rR   r<  r  r  s      r3   r{   zloglaplace_gen._ppf+  s8    xC#a%3q5!1As1uIa3HIIIr5   c                 `    t          j        |dk    dd|z
  z  d|z  z  d|z  d|z  z            S r  r  r  s      r3   r~   zloglaplace_gen._isf.  s7    xC#sQw-3q5!9AaC46?KKKr5   c                     t          j        d          5  |dz  |dz  }}t          j        ||k     |||z
  z  t           j                  cd d d            S # 1 swxY w Y   d S )Nr5  r6  rR   )rM   r8  rO  rf   )rB   r_   r  r  n2s        r3   r   zloglaplace_gen._munp1  s    [))) 	= 	=T1a4B8BGR27^RV<<	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	= 	=s   4AAAc                 6    t          j        d|z            dz   S r  r.  r}  s     r3   r   zloglaplace_gen._entropy6  s    vc!e}}s""r5   c                    t          | |||          \  }}}}|, t          t          |           |           j        |g|R i |S t	          j        ||k              rt          d|t          j                  |dk    r||z
  }t                              t	          j	        |          |t	          j	        |          nd |d|z  nd d          \  }}|}	|t	          j
        |          n|}
|d|z  n|}||	|
fS )N
loglaplacer  r   r   r8   )r   r   r/   )r  r>   r?   r@   rM   r  rH  rf   r=  r   r   )rB   rC   rD   r2   r  r   r   r   r   r,   r-   r  r  s               r3   r@   zloglaplace_gen.fit9  s-    "=T4=A4"I "Ib$ <.5dT**.tCdCCCdCCC 6$$, 	G|4rvFFFF 199$;D {{26$<<282Dv$*,.!B$$d"'  ) )1 #^q			ZAEER#u}r5   )r   r   r   r   rh   ro   rr   rv   r{   r~   r   r   rH   r   r   r@   r  r  s   @r3   r  r    s         @E E E  8 8 8: : :J J JL L L= = =
# # # M**    +* _    r5   r  r  c                 J    t          | dk    | |fd t          j                   S )Nr   c                     t          j        |           dz   d|dz  z  z  t          j        || z  t          j        dt           j        z            z            z
  S r  )rM   r   r   r   rn   r  s     r3   r  z!_lognorm_logpdf.<locals>.<lambda>_  sM    RVAYY\MQAX$>&(fQURWQY5G5G-G&H&H%I r5   r  r  s     r3   _lognorm_logpdfr  ]  s3    a1fq!fJ Jvg  r5   c                        e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 Zd Zd Zd Zd Ze eed           fd                        Z xZS )lognorm_gena  A lognormal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `lognorm` is:

    .. math::

        f(x, s) = \frac{1}{s x \sqrt{2\pi}}
                  \exp\left(-\frac{\log^2(x)}{2s^2}\right)

    for :math:`x > 0`, :math:`s > 0`.

    `lognorm` takes ``s`` as a shape parameter for :math:`s`.

    %(after_notes)s

    Suppose a normally distributed random variable ``X`` has  mean ``mu`` and
    standard deviation ``sigma``. Then ``Y = exp(X)`` is lognormally
    distributed with ``s = sigma`` and ``scale = exp(mu)``.

    %(example)s

    The logarithm of a log-normally distributed random variable is
    normally distributed:

    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> from scipy import stats
    >>> fig, ax = plt.subplots(1, 1)
    >>> mu, sigma = 2, 0.5
    >>> X = stats.norm(loc=mu, scale=sigma)
    >>> Y = stats.lognorm(s=sigma, scale=np.exp(mu))
    >>> x = np.linspace(*X.interval(0.999))
    >>> y = Y.rvs(size=10000)
    >>> ax.plot(x, X.pdf(x), label='X (pdf)')
    >>> ax.hist(np.log(y), density=True, bins=x, label='log(Y) (histogram)')
    >>> ax.legend()
    >>> plt.show()

    c                 @    t          dddt          j        fd          gS )Nr  Fr   r  re   rg   s    r3   rh   zlognorm_gen._shape_info  r  r5   Nc                 V    t          j        ||                    |          z            S rK   rM   r   r   )rB   r  r   r   s       r3   r   zlognorm_gen._rvs  s%    va,66t<<<===r5   c                 R    t          j        |                     ||                    S rK   r  rB   rn   r  s      r3   ro   zlognorm_gen._pdf  r  r5   c                 "    t          ||          S rK   r  r  s      r3   r   zlognorm_gen._logpdf  s    q!$$$r5   c                 J    t          t          j        |          |z            S rK   r   rM   r   r  s      r3   rr   zlognorm_gen._cdf  s    Q'''r5   c                 J    t          t          j        |          |z            S rK   rY  r  s      r3   r   zlognorm_gen._logcdf  s    BF1IIM***r5   c                 J    t          j        |t          |          z            S rK   rM   r   r   rB   rz   r  s      r3   r{   zlognorm_gen._ppf      va)A,,&'''r5   c                 J    t          t          j        |          |z            S rK   r   rM   r   r  s      r3   rv   zlognorm_gen._sf  s    q		A&&&r5   c                 J    t          t          j        |          |z            S rK   )r   rM   r   r  s      r3   r   zlognorm_gen._logsf  s    26!99q=)))r5   c                 J    t          j        |t          |          z            S rK   rM   r   r   r  s      r3   r~   zlognorm_gen._isf  r  r5   c                     t          j        ||z            }t          j        |          }||dz
  z  }t          j        |dz
            d|z   z  }t          j        g d|          }||||fS Nr   rR   )r   rR   r  r   r  )rM   r   r   polyval)rB   r  r  r@  rA  rB  rC  s          r3   r   zlognorm_gen._stats  sm    F1Q3KKWQZZ1gWQqS\\1Q3Z***A..3Br5   c                     ddt          j        dt           j        z            z   dt          j        |          z  z   z  S Nr   r   rR   r   )rB   r  s     r3   r   zlognorm_gen._entropy  s1    a"&25//)Aq		M9::r5   aF          When `method='MLE'` and
        the location parameter is fixed by using the `floc` argument,
        this function uses explicit formulas for the maximum likelihood
        estimation of the log-normal shape and scale parameters, so the
        `optimizer`, `loc` and `scale` keyword arguments are ignored.
        If the location is free, a likelihood maximum is found by
        setting its partial derivative wrt to location to 0, and
        solving by substituting the analytical expressions of shape
        and scale (or provided parameters).
        See, e.g., equation 3.1 in
        A. Clifford Cohen & Betty Jones Whitten (1980)
        Estimation in the Three-Parameter Lognormal Distribution,
        Journal of the American Statistical Association, 75:370, 399-404
        https://doi.org/10.2307/2287466
        

r   c                 P    |                     dd          r t                      j        g|R i |S t           ||          }|\  }t	          j                  }fdfd} fd}|;t	          j        |          }	||	z
  }
 ||
          } ||
          }d|	z  }|dk    r||z
  }
 ||
          }|dz  }|dk    t	          j        |
          rt	          j        |          s t                      j        g|R i |S t	          j        t	          j	        |
t          j
                   |
dz
            } ||          }d|
|z
  z  }t	          j        |          rt	          j        |          rt	          j        |          t	          j        |          k    rg|
|z
  } ||          }|dz  }t	          j        |          r>t	          j        |          r*t	          j        |          t	          j        |          k    gt	          j        |          rt	          j        |          s t                      j        g|R i |S t          |||
f	          }|j        s t                      j        g|R i |S  ||j                  }||k    r|j        n||	z
  }n$||k    rt          d
dt          j
                  |} |          \  }}                     |          r|dk    s t                      j        g|R i |S |||fS )Nr  Fc                    t          j        | z
            }p%t          j        |                                          }p=t          j        t          j        |t          j        |          z
  dz                      }||fS r  )rM   r   r   r   r   )r,   lndatar-   rw  rC   r   fshapes       r3   get_shape_scalez(lognorm_gen.fit.<locals>.get_shape_scale  sw     ~s
++3bfV[[]]33EKbgbgvu/E.I&J&JKKE%<r5   c                      |           \  }}| z
  }t          j        dt          j        ||z            |dz  z  z   |z            S r-  rM   r  r   )r,   rw  r-   shiftedrC   r  s       r3   dL_dLocz lognorm_gen.fit.<locals>.dL_dLoc  sP    *?3//LE5SjG61rvgem44UAX==wFGGGr5   c                 T     |           \  }}                     || |f           S rK   )nnlf)r,   rw  r-   rC   r  rB   s      r3   llzlognorm_gen.fit.<locals>.ll  s4    *?3//LE5IIuc5148888r5   rR   gưr   r  lognormr   r  r   )r0   r>   r@   r  rM   rK  spacingr   r  	nextafterrf   rN   r)   	convergedr  rH  r`   )rB   rC   rD   r2   
parametersr   rM  r  r  r  rP   dL_dLoc_rbrack	ll_rbrackr  rO   dL_dLoc_lbrackr  ll_rootr,   rw  r-   r   r  r  r  s   ``                   @@@r3   r@   zlognorm_gen.fit  s   $ 88J&& 	4577;t3d333d3330tT4HH
%/"fdF6$<<	  	  	  	  	  	  	 	H 	H 	H 	H 	H 	H	9 	9 	9 	9 	9 	9 	9
 < j**G'F %WV__N6

IKE E))!E)!(
 !E))
 ;v&& 8bk..I.I 8 #uww{47$777$777
 ZVbfW = =vaxHHF$WV__N&)E;v&& 2;~+F+F w~.."'.2I2III%!(
	 ;v&& 2;~+F+F w~.."'.2I2III ;v&& 8bk..I.I 8"uww{47$777$777 g/?@@@C= 8"uww{47$777$777
 bllG%	11#((x7GCC x"9BbfEEEEC&s++uu%% 	4%!))577;t3d333d333c5  r5   r   )r   r   r   r   r   r  r  rh   r   ro   r   rr   r   r{   rv   r   r~   r   r   rH   r	   r@   r  r  s   @r3   r  r  d  sD       * *V "4ME E E> > > >* * *% % %( ( (+ + +( ( (' ' '* * *( ( (  ; ; ; } 5    Z! Z! Z! Z!!  _"Z! Z! Z! Z! Z!r5   r  r  c                   ^    e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 Zd Zd ZdS )
gibrat_genaB  A Gibrat continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `gibrat` is:

    .. math::

        f(x) = \frac{1}{x \sqrt{2\pi}} \exp(-\frac{1}{2} (\log(x))^2)

    `gibrat` is a special case of `lognorm` with ``s=1``.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zgibrat_gen._shape_infoC  r   r5   Nc                 P    t          j        |                    |                    S rK   r  r   s      r3   r   zgibrat_gen._rvsF  s     vl22488999r5   c                 P    t          j        |                     |                    S rK   r  r   s     r3   ro   zgibrat_gen._pdfI  r  r5   c                 "    t          |d          S r  r  r   s     r3   r   zgibrat_gen._logpdfM  s    q#&&&r5   c                 D    t          t          j        |                    S rK   r  r   s     r3   rr   zgibrat_gen._cdfP  s    ###r5   c                 D    t          j        t          |                    S rK   r  r   s     r3   r{   zgibrat_gen._ppfS      vill###r5   c                 D    t          t          j        |                    S rK   r  r   s     r3   rv   zgibrat_gen._sfV  s    q		"""r5   c                 D    t          j        t          |                    S rK   r  r  s     r3   r~   zgibrat_gen._isfY  r  r5   c                     t           j        }t          j        |          }||dz
  z  }t          j        |dz
            d|z   z  }t          j        g d|          }||||fS r  )rM   er   r  )rB   r  r@  rA  rB  rC  s         r3   r   zgibrat_gen._stats\  sc    DWQZZ1q5kWQU^^q1u%Z***A..3Br5   c                 P    dt          j        dt           j        z            z  dz   S r  r   rg   s    r3   r   zgibrat_gen._entropyd  s"    RVAI&&&,,r5   r   )r   r   r   r   r   r  r  rh   r   ro   r   rr   r{   rv   r~   r   r   r   r5   r3   r  r  -  s         & "4M  : : : :' ' '' ' '$ $ $$ $ $# # #$ $ $  - - - - -r5   r  gibratc                   P    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )maxwell_gena  A Maxwell continuous random variable.

    %(before_notes)s

    Notes
    -----
    A special case of a `chi` distribution,  with ``df=3``, ``loc=0.0``,
    and given ``scale = a``, where ``a`` is the parameter used in the
    Mathworld description [1]_.

    The probability density function for `maxwell` is:

    .. math::

        f(x) = \sqrt{2/\pi}x^2 \exp(-x^2/2)

    for :math:`x >= 0`.

    %(after_notes)s

    References
    ----------
    .. [1] http://mathworld.wolfram.com/MaxwellDistribution.html

    %(example)s
    c                     g S rK   r   rg   s    r3   rh   zmaxwell_gen._shape_info  r   r5   Nc                 <    t                               d||          S )NrD  r  r  r  r   s      r3   r   zmaxwell_gen._rvs  s    wwsLwAAAr5   c                 T    t           |z  |z  t          j        | |z  dz            z  S r;  )r&   rM   r   r   s     r3   ro   zmaxwell_gen._pdf  s+    q "261"Q$s(#3#333r5   c                     t          j        d          5  t          dt          j        |          z  z   d|z  |z  z
  cd d d            S # 1 swxY w Y   d S )Nr5  r6  rR   r   )rM   r8  r'   r   r   s     r3   r   zmaxwell_gen._logpdf  s    [))) 	? 	?&26!994s1uQw>	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	? 	?s   (AAAc                 8    t          j        d||z  dz            S Nr  r   r  r   s     r3   rr   zmaxwell_gen._cdf  s    {3!C(((r5   c                 V    t          j        dt          j        d|          z            S r  r  r   s     r3   r{   zmaxwell_gen._ppf  s#    wqQ///000r5   c                 8    t          j        d||z  dz            S r#  r  r   s     r3   rv   zmaxwell_gen._sf  s    |C1S)))r5   c                 V    t          j        dt          j        d|          z            S r  r  r   s     r3   r~   zmaxwell_gen._isf  s#    wqa000111r5   c                 R   dt           j        z  dz
  }dt          j        dt           j        z            z  ddt           j        z  z
  t          j        d          ddt           j        z  z
  z  |dz  z  dt           j        z  t           j        z  d	t           j        z  z   d
z
  |dz  z  fS )Nr  r*  rR   r       r  r  r     i  rM   r   r   rB   rB  s     r3   r   zmaxwell_gen._stats  s    gai"'#be)$$$!BE'	

Br"%xK(c1RU253ru9,s2c3h>@ 	@r5   c                 `    t           dt          j        dt          j        z            z  z   dz
  S r  )r#   rM   r   r   rg   s    r3   r   zmaxwell_gen._entropy  s%    BF1RU7OO++C//r5   r   r  r   r5   r3   r  r  k  s         4  B B B B4 4 4? ? ?
) ) )1 1 1* * *2 2 2@ @ @0 0 0 0 0r5   r  maxwellc                   6    e Zd ZdZd Zd Zd Zd Zd Zd Z	dS )	
mielke_gena  A Mielke Beta-Kappa / Dagum continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `mielke` is:

    .. math::

        f(x, k, s) = \frac{k x^{k-1}}{(1+x^s)^{1+k/s}}

    for :math:`x > 0` and :math:`k, s > 0`. The distribution is sometimes
    called Dagum distribution ([2]_). It was already defined in [3]_, called
    a Burr Type III distribution (`burr` with parameters ``c=s`` and
    ``d=k/s``).

    `mielke` takes ``k`` and ``s`` as shape parameters.

    %(after_notes)s

    References
    ----------
    .. [1] Mielke, P.W., 1973 "Another Family of Distributions for Describing
           and Analyzing Precipitation Data." J. Appl. Meteor., 12, 275-280
    .. [2] Dagum, C., 1977 "A new model for personal income distribution."
           Economie Appliquee, 33, 327-367.
    .. [3] Burr, I. W. "Cumulative frequency functions", Annals of
           Mathematical Statistics, 13(2), pp 215-232 (1942).

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )Nr  Fr   r  r  re   )rB   iki_ss      r3   rh   zmielke_gen._shape_info  >    UQK@@ea[.AACyr5   c                 B    |||dz
  z  z  d||z  z   d|dz  |z  z   z  z  S r  r   rB   rn   r  r  s       r3   ro   zmielke_gen._pdf  s2    QsU|s1a4x3quQw;777r5   c                     t          j        d          5  t          j        |          t          j        |          |dz
  z  z   t          j        ||z            d||z  z   z  z
  cd d d            S # 1 swxY w Y   d S )Nr5  r6  r   )rM   r8  r   r  r5  s       r3   r   zmielke_gen._logpdf  s    [))) 	L 	L6!99rvayy!a%0028AqD>>1qs73KK	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	L 	Ls   AA33A7:A7c                 0    ||z  d||z  z   |dz  |z  z  z  S r  r   r5  s       r3   rr   zmielke_gen._cdf  s&    !ts1a4x1S57+++r5   c                 `    t          ||dz  |z            }t          |d|z
  z  d|z            S r  r  )rB   rz   r  r  qsks        r3   r{   zmielke_gen._ppf  s3    !QsU1Woo3C=#a%(((r5   c                 N    d }t          ||k     |||f|t          j                  S )Nc                     t          j        || z   |z            t          j        d| |z  z
            z  t          j        ||z            z  S r[   r-  )r_   r  r  s      r3   r  z$mielke_gen._munp.<locals>.nth_moment  s@    8QqS!G$$RXa!e__4RXac]]BBr5   r  )rB   r_   r  r  r  s        r3   r   zmielke_gen._munp  s6    	C 	C 	C !a%!QJ???r5   N)
r   r   r   r   rh   ro   r   rr   r{   r   r   r5   r3   r/  r/    s           B  
8 8 8L L L
, , ,) ) )@ @ @ @ @r5   r/  mielkec                   T    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Zd ZdS )
kappa4_genap  Kappa 4 parameter distribution.

    %(before_notes)s

    Notes
    -----
    The probability density function for kappa4 is:

    .. math::

        f(x, h, k) = (1 - k x)^{1/k - 1} (1 - h (1 - k x)^{1/k})^{1/h-1}

    if :math:`h` and :math:`k` are not equal to 0.

    If :math:`h` or :math:`k` are zero then the pdf can be simplified:

    h = 0 and k != 0::

        kappa4.pdf(x, h, k) = (1.0 - k*x)**(1.0/k - 1.0)*
                              exp(-(1.0 - k*x)**(1.0/k))

    h != 0 and k = 0::

        kappa4.pdf(x, h, k) = exp(-x)*(1.0 - h*exp(-x))**(1.0/h - 1.0)

    h = 0 and k = 0::

        kappa4.pdf(x, h, k) = exp(-x)*exp(-exp(-x))

    kappa4 takes :math:`h` and :math:`k` as shape parameters.

    The kappa4 distribution returns other distributions when certain
    :math:`h` and :math:`k` values are used.

    +------+-------------+----------------+------------------+
    | h    | k=0.0       | k=1.0          | -inf<=k<=inf     |
    +======+=============+================+==================+
    | -1.0 | Logistic    |                | Generalized      |
    |      |             |                | Logistic(1)      |
    |      |             |                |                  |
    |      | logistic(x) |                |                  |
    +------+-------------+----------------+------------------+
    |  0.0 | Gumbel      | Reverse        | Generalized      |
    |      |             | Exponential(2) | Extreme Value    |
    |      |             |                |                  |
    |      | gumbel_r(x) |                | genextreme(x, k) |
    +------+-------------+----------------+------------------+
    |  1.0 | Exponential | Uniform        | Generalized      |
    |      |             |                | Pareto           |
    |      |             |                |                  |
    |      | expon(x)    | uniform(x)     | genpareto(x, -k) |
    +------+-------------+----------------+------------------+

    (1) There are at least five generalized logistic distributions.
        Four are described here:
        https://en.wikipedia.org/wiki/Generalized_logistic_distribution
        The "fifth" one is the one kappa4 should match which currently
        isn't implemented in scipy:
        https://en.wikipedia.org/wiki/Talk:Generalized_logistic_distribution
        https://www.mathwave.com/help/easyfit/html/analyses/distributions/gen_logistic.html
    (2) This distribution is currently not in scipy.

    References
    ----------
    J.C. Finney, "Optimization of a Skewed Logistic Distribution With Respect
    to the Kolmogorov-Smirnov Test", A Dissertation Submitted to the Graduate
    Faculty of the Louisiana State University and Agricultural and Mechanical
    College, (August, 2004),
    https://digitalcommons.lsu.edu/gradschool_dissertations/3672

    J.R.M. Hosking, "The four-parameter kappa distribution". IBM J. Res.
    Develop. 38 (3), 25 1-258 (1994).

    B. Kumphon, A. Kaew-Man, P. Seenoi, "A Rainfall Distribution for the Lampao
    Site in the Chi River Basin, Thailand", Journal of Water Resource and
    Protection, vol. 4, 866-869, (2012).
    :doi:`10.4236/jwarp.2012.410101`

    C. Winchester, "On Estimation of the Four-Parameter Kappa Distribution", A
    Thesis Submitted to Dalhousie University, Halifax, Nova Scotia, (March
    2000).
    http://www.nlc-bnc.ca/obj/s4/f2/dsk2/ftp01/MQ57336.pdf

    %(after_notes)s

    %(example)s

    c                 n    t          j        ||          d         j        }t          j        |d          S )Nr   T
fill_value)rM   r  rw  full)rB   r  r  rw  s       r3   r`   zkappa4_gen._argcheckI  s1    #Aq))!,2wu....r5   c                     t          ddt          j         t          j        fd          }t          ddt          j         t          j        fd          }||gS )Nr  Fr  r  re   )rB   ihr1  s      r3   rh   zkappa4_gen._shape_infoM  sG    UbfWbf$5~FFUbfWbf$5~FFBxr5   c           
         t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk               t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk               g}d }d }d }d }t          |||||||g||gt           j                  }d }d }t          |||||||g||gt           j                  }	||	fS )	Nr   c                 :    dt          j        | |           z
  |z  S r  )rM   rn  r  r  s     r3   r  z#kappa4_gen._get_support.<locals>.f0Z  s     ".QB///22r5   c                 *    t          j        |           S rK   r.  rG  s     r3   r  z#kappa4_gen._get_support.<locals>.f1]  s    6!99r5   c                 v    t          j        t          j        |                     }t           j         |d d <   |S rK   rM   r  rw  rf   r  r  r   s      r3   f3z#kappa4_gen._get_support.<locals>.f3`  s/    !%%AF7AaaaDHr5   c                     d|z  S r  r   rG  s     r3   f5z#kappa4_gen._get_support.<locals>.f5e      q5Lr5   defaultc                     d|z  S r  r   rG  s     r3   r  z#kappa4_gen._get_support.<locals>.f0m  rO  r5   c                 t    t          j        t          j        |                     }t           j        |d d <   |S rK   rJ  rK  s      r3   r  z#kappa4_gen._get_support.<locals>.f1p  s-    !%%A6AaaaDHr5   rM   rB  r   r  )
rB   r  r  condlistr  r  rL  rN  r  r  s
             r3   r   zkappa4_gen._get_supportR  s`   N1q5!a%00N1q5!q&11N1q5!a%00N161q511N161622N161q5113	3 	3 	3	 	 		 	 	
	 	 	 b"b"b1Q!#) ) )
	 	 		 	 	
 b"b"b1Q!#) ) ) 2vr5   c                 T    t          j        |                     |||                    S rK   r  rB   rn   r  r  s       r3   ro   zkappa4_gen._pdf{  rr  r5   c                 F   t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk              g}d }d }d }d }t          |||||g|||gt           j                  S )Nr   c                     t          j        d|z  dz
  | | z            t          j        d|z  dz
  | d|| z  z
  d|z  z  z            z   S )zpdf = (1.0 - k*x)**(1.0/k - 1.0)*(
                      1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h-1.0)
               logpdf = ...
            r   rf  rn   r  r  s      r3   r  zkappa4_gen._logpdf.<locals>.f0  s\    
 Js1us{QBqD11Js1us{QBac	SU/C,CDDE Fr5   c                 ^    t          j        d|z  dz
  | | z            d|| z  z
  d|z  z  z
  S )z~pdf = (1.0 - k*x)**(1.0/k - 1.0)*np.exp(-(
                      1.0 - k*x)**(1.0/k))
               logpdf = ...
            r   rf  rZ  s      r3   r  zkappa4_gen._logpdf.<locals>.f1  s:    
 :c!eckA2a400C!A#IQ3GGGr5   c                 n    |  t          j        d|z  dz
  | t          j        |            z            z   S )z]pdf = np.exp(-x)*(1.0 - h*np.exp(-x))**(1.0/h - 1.0)
               logpdf = ...
            r   )rt   rt  rM   r   rZ  s      r3   r  zkappa4_gen._logpdf.<locals>.f2  s5     2
3q53;261"::>>>>r5   c                 4    |  t          j        |            z
  S )zDpdf = np.exp(-x-np.exp(-x))
               logpdf = ...
            r9  rZ  s      r3   rL  zkappa4_gen._logpdf.<locals>.f3  s     2r

?"r5   rP  rT  	rB   rn   r  r  rU  r  r  r  rL  s	            r3   r   zkappa4_gen._logpdf  s    N161622N161622N161622N1616224
	F 	F 	F	H 	H 	H	? 	? 	?	# 	# 	# 8B+q!9#%6+ + + 	+r5   c                 T    t          j        |                     |||                    S rK   r  rW  s       r3   rr   zkappa4_gen._cdf  r  r5   c                 F   t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk              g}d }d }d }d }t          |||||g|||gt           j                  S )Nr   c                 V    d|z  t          j        | d|| z  z
  d|z  z  z            z  S )zVcdf = (1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h)
               logcdf = ...
            r   r	  rZ  s      r3   r  zkappa4_gen._logcdf.<locals>.f0  s5     E28QBac	SU';$;<<<<r5   c                      d|| z  z
  d|z  z   S )zLcdf = np.exp(-(1.0 - k*x)**(1.0/k))
               logcdf = ...
            r   r   rZ  s      r3   r  zkappa4_gen._logcdf.<locals>.f1  s     1Q3Y#a%(((r5   c                 d    d|z  t          j        | t          j        |            z            z  S )zLcdf = (1.0 - h*np.exp(-x))**(1.0/h)
               logcdf = ...
            r   )rt   r  rM   r   rZ  s      r3   r  zkappa4_gen._logcdf.<locals>.f2  s-     E28QBrvqbzzM2222r5   c                 .    t          j        |             S )zBcdf = np.exp(-np.exp(-x))
               logcdf = ...
            r9  rZ  s      r3   rL  zkappa4_gen._logcdf.<locals>.f3  s     FA2JJ;r5   rP  rT  r^  s	            r3   r   zkappa4_gen._logcdf  s    N161622N161622N161622N1616224
	= 	= 	=	) 	) 	)	3 	3 	3	 	 	 8B+q!9#%6+ + + 	+r5   c                 F   t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk              t          j        |dk    |dk              g}d }d }d }d }t          |||||g|||gt           j                  S )Nr   c                 0    d|z  dd| |z  z
  |z  |z  z
  z  S r  r   rz   r  r  s      r3   r  zkappa4_gen._ppf.<locals>.f0  s(    q5##A,!1A 5566r5   c                 D    d|z  dt          j        |            |z  z
  z  S r  r.  rg  s      r3   r  zkappa4_gen._ppf.<locals>.f1  s$    q5#"&))a/00r5   c                 ^    t          j        | |z              t          j        |          z   S )z,ppf = -np.log((1.0 - (q**h))/h)
            r  rg  s      r3   r  zkappa4_gen._ppf.<locals>.f2  s*     Hq!tW%%%q		11r5   c                 R    t          j        t          j        |                       S rK   r.  rg  s      r3   rL  zkappa4_gen._ppf.<locals>.f3  s    FBF1II:&&&&r5   rP  rT  )	rB   rz   r  r  rU  r  r  r  rL  s	            r3   r{   zkappa4_gen._ppf  s    N161622N161622N161622N1616224
	7 	7 	7	1 	1 	1	2 	2 	2
	' 	' 	' 8B+q!9#%6+ + + 	+r5   c                     t          j        |dk     |dk              |dk     g}d }d }t          |||g||gd          S )Nr   c                 B    d| z  |z                       t                    S r?  astyper  rG  s     r3   r  z&kappa4_gen._get_stats_info.<locals>.f0  s    F1H$$S)))r5   c                 <    d|z                       t                    S r?  rm  rG  s     r3   r  z&kappa4_gen._get_stats_info.<locals>.f1  s    F??3'''r5   rC  rP  )rM   rB  r   )rB   r  r  rU  r  r  s         r3   _get_stats_infozkappa4_gen._get_stats_info  sf    N1q5!q&))E

	* 	* 	*	( 	( 	( 8b"X1vqAAAAr5   c                 |    |                      ||          fdt          dd          D             }|d d          S )Nc                 \    g | ](}t          j        |k               rd nt           j        )S rK   rM   r  r  )r  r  maxrs     r3   r  z%kappa4_gen._stats.<locals>.<listcomp>  s2    MMMA26!d(++744MMMr5   r   rC  )rp  r  )rB   r  r  outputsrt  s       @r3   r   zkappa4_gen._stats  sG    ##Aq))MMMMq!MMMqqqzr5   c                     |                      |d         |d                   }||k    rt          j        S t          j        | j        dd|f|z             d         S Nr   r   rJ  )rp  rM   r  r   ra  _mom_integ1)rB   r  rD   rt  s       r3   _mom1_sczkappa4_gen._mom1_sc  sV    ##DGT!W55996M~d.1A49EEEaHHr5   N)r   r   r   r   r`   rh   r   ro   r   rr   r   r{   rp  r   ry  r   r5   r3   r>  r>    s        W Wp/ / /  
' ' 'R- - -
$+ $+ $+L- - -!+ !+ !+F+ + +2B B B  
I I I I Ir5   r>  kappa4c                   L     e Zd ZdZd Zd Zd Z fdZd Zd Z	d Z
d	 Z xZS )

kappa3_gena*  Kappa 3 parameter distribution.

    %(before_notes)s

    Notes
    -----
    The probability density function for `kappa3` is:

    .. math::

        f(x, a) = a (a + x^a)^{-(a + 1)/a}

    for :math:`x > 0` and :math:`a > 0`.

    `kappa3` takes ``a`` as a shape parameter for :math:`a`.

    References
    ----------
    P.W. Mielke and E.S. Johnson, "Three-Parameter Kappa Distribution Maximum
    Likelihood and Likelihood Ratio Tests", Methods in Weather Research,
    701-707, (September, 1973),
    :doi:`10.1175/1520-0493(1973)101<0701:TKDMLE>2.3.CO;2`

    B. Kumphon, "Maximum Entropy and Maximum Likelihood Estimation for the
    Three-Parameter Kappa Distribution", Open Journal of Statistics, vol 2,
    415-419 (2012), :doi:`10.4236/ojs.2012.24050`

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zkappa3_gen._shape_info#  r  r5   c                 *    ||||z  z   d|z  dz
  z  z  S r;  r   r
  s      r3   ro   zkappa3_gen._pdf&  s"    !ad(d1fQh'''r5   c                 $    ||||z  z   d|z  z  z  S r?  r   r
  s      r3   rr   zkappa3_gen._cdf*  s    !ad(d1f%%%r5   c           	      X   t          j        ||          \  }}t                                          ||          }d}||k     }t	          j        t	          j        d||         z  ||         ||         ||          z  z                       }||k    }||         |         ||<   |||<   |S )Ng{Gz?r<  )rM   r  r>   rv   rt   r  rt  )	rB   rn   r   sfcutoffr  sf2i2r  s	           r3   rv   zkappa3_gen._sf-  s    "1a((1WW[[A
 Kx
4!A$;!qtadU{0BCCDDD6\Q%)B1	r5   c                 &    ||| z  dz
  z  d|z  z  S r  r   r  s      r3   r{   zkappa3_gen._ppf=  s    1qb53;3q5))r5   c                 n    t          j        | |           }t          j        |          }||z  d|z  z  S r  r@  )rB   rz   r   lgr  s        r3   r~   zkappa3_gen._isf@  s7    ZQBE	S1W%%r5   c                 P    fdt          dd          D             }|d d          S )Nc                 \    g | ](}t          j        |k               rd nt           j        )S rK   rs  )r  r  r   s     r3   r  z%kappa3_gen._stats.<locals>.<listcomp>F  s0    JJJ26!a%==444bfJJJr5   r   rC  )r  )rB   r   ru  s    ` r3   r   zkappa3_gen._statsE  s2    JJJJeAqkkJJJqqqzr5   c                     t          j        ||d         k              rt           j        S t          j        | j        dd|f|z             d         S rw  )rM   r  r  r   ra  rx  )rB   r  rD   s      r3   ry  zkappa3_gen._mom1_scI  sJ    6!tAw, 	6M~d.1A49EEEaHHr5   )r   r   r   r   rh   ro   rr   rv   r{   r~   r   ry  r  r  s   @r3   r|  r|    s         @E E E( ( (& & &     * * *& & &
  I I I I I I Ir5   r|  kappa3c                   D    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 ZdS )	moyal_gena  A Moyal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `moyal` is:

    .. math::

        f(x) = \exp(-(x + \exp(-x))/2) / \sqrt{2\pi}

    for a real number :math:`x`.

    %(after_notes)s

    This distribution has utility in high-energy physics and radiation
    detection. It describes the energy loss of a charged relativistic
    particle due to ionization of the medium [1]_. It also provides an
    approximation for the Landau distribution. For an in depth description
    see [2]_. For additional description, see [3]_.

    References
    ----------
    .. [1] J.E. Moyal, "XXX. Theory of ionization fluctuations",
           The London, Edinburgh, and Dublin Philosophical Magazine
           and Journal of Science, vol 46, 263-280, (1955).
           :doi:`10.1080/14786440308521076` (gated)
    .. [2] G. Cordeiro et al., "The beta Moyal: a useful skew distribution",
           International Journal of Research and Reviews in Applied Sciences,
           vol 10, 171-192, (2012).
           http://www.arpapress.com/Volumes/Vol10Issue2/IJRRAS_10_2_02.pdf
    .. [3] C. Walck, "Handbook on Statistical Distributions for
           Experimentalists; International Report SUF-PFY/96-01", Chapter 26,
           University of Stockholm: Stockholm, Sweden, (2007).
           http://www.stat.rice.edu/~dobelman/textfiles/DistributionsHandbook.pdf

    .. versionadded:: 1.1.0

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zmoyal_gen._shape_info}  r   r5   Nc                 h    t                               dd||          }t          j        |           S )Nr   rR   )r   r-   r   r   )r  r  rM   r   )rB   r   r   r  s       r3   r   zmoyal_gen._rvs  s3    YYAD$0  2 2r

{r5   c                     t          j        d|t          j        |           z   z            t          j        dt           j        z            z  S Nr  rR   )rM   r   r   r   r   s     r3   ro   zmoyal_gen._pdf  s:    vda"&!**n-..251A1AAAr5   c                 ~    t          j        t          j        d|z            t          j        d          z            S r  )rt   rw  rM   r   r   r   s     r3   rr   zmoyal_gen._cdf  s-    wrvdQh''"'!**4555r5   c                 ~    t          j        t          j        d|z            t          j        d          z            S r  )rt   r  rM   r   r   r   s     r3   rv   zmoyal_gen._sf  s-    vbfTAX&&3444r5   c                 \    t          j        dt          j        |          dz  z             S r  )rM   r   rt   erfcinvr   s     r3   r{   zmoyal_gen._ppf  s'    q2:a==!++,,,,r5   c                     t          j        d          t           j        z   }t           j        dz  dz  }dt          j        d          z  t          j        d          z  t           j        dz  z  }d}||||fS )NrR      r  rJ  )rM   r   euler_gammar   r   rt   rZ  r?  s        r3   r   zmoyal_gen._stats  sa    VAYY'eQhl"'!**_rwqzz)BE1H43Br5   c                 b   |dk    r!t          j        d          t           j        z   S |dk    r7t           j        dz  dz  t          j        d          t           j        z   dz  z   S |dk    rwdt           j        dz  z  t          j        d          t           j        z   z  }t          j        d          t           j        z   dz  }dt	          j        d          z  }||z   |z   S |dk    rd	t	          j        d          z  t          j        d          t           j        z   z  }dt           j        dz  z  t          j        d          t           j        z   dz  z  }t          j        d          t           j        z   d
z  }dt           j        d
z  z  d
z  }||z   |z   |z   S |                     |          S )Nr   rR   r   rD  r  r  r  rJ  8   r   r  )rM   r   r  r   rt   rZ  ry  )rB   r_   tmp1r:  tmp3tmp4s         r3   r   zmoyal_gen._munp  sc   886!99r~--#XX5!8a<26!99r~#="AAA#XX>RVAYYr~%=>DF1IIbn,q0D

?D$;%%#XXBGAJJ&"&))bn*DEDruax<26!99r~#="AADF1II.2Druax<!#D$;%,, ==###r5   r   )r   r   r   r   rh   r   ro   rr   rv   r{   r   r   r   r5   r3   r  r  R  s        ) )T     
B B B6 6 65 5 5- - -  $ $ $ $ $r5   r  moyalc                   ^    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd ZddZddZdS )nakagami_gena`  A Nakagami continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `nakagami` is:

    .. math::

        f(x, \nu) = \frac{2 \nu^\nu}{\Gamma(\nu)} x^{2\nu-1} \exp(-\nu x^2)

    for :math:`x >= 0`, :math:`\nu > 0`. The distribution was introduced in
    [2]_, see also [1]_ for further information.

    `nakagami` takes ``nu`` as a shape parameter for :math:`\nu`.

    %(after_notes)s

    References
    ----------
    .. [1] "Nakagami distribution", Wikipedia
           https://en.wikipedia.org/wiki/Nakagami_distribution
    .. [2] M. Nakagami, "The m-distribution - A general formula of intensity
           distribution of rapid fading", Statistical methods in radio wave
           propagation, Pergamon Press, 1960, 3-36.
           :doi:`10.1016/B978-0-08-009306-2.50005-4`

    %(example)s

    c                     |dk    S r  r   )rB   nus     r3   r`   znakagami_gen._argcheck  s    Avr5   c                 @    t          dddt          j        fd          gS )Nr  Fr   r  re   rg   s    r3   rh   znakagami_gen._shape_info  r  r5   c                 R    t          j        |                     ||                    S rK   r  rB   rn   r  s      r3   ro   znakagami_gen._pdf  r_  r5   c                     t          j        d          t          j        ||          z   t          j        |          z
  t          j        d|z  dz
  |          z   ||dz  z  z
  S r  )rM   r   rt   ru  r  r  s      r3   r   znakagami_gen._logpdf  s^     q		BHR,,,rz"~~=21%%&(*1a40 	1r5   c                 8    t          j        |||z  |z            S rK   r  r  s      r3   rr   znakagami_gen._cdf  s    {2r!tAv&&&r5   c                 \    t          j        d|z  t          j        ||          z            S r  r  )rB   rz   r  s      r3   r{   znakagami_gen._ppf  s'    ws2vbnR333444r5   c                 8    t          j        |||z  |z            S rK   r  r  s      r3   rv   znakagami_gen._sf  s    |B1Q'''r5   c                 \    t          j        d|z  t          j        ||          z            S r[   r  )rB   r  r  s      r3   r~   znakagami_gen._isf  s'    wqtbob!444555r5   c                 "   t          j        |d          t          j        |          z  }d||z  z
  }|dd|z  |z  z
  z  dz  |z  t          j        |d          z  }d|dz  z  |z  d|z  d	z
  |d	z  z  z   d	|z  z
  dz   }|||dz  z  z  }||||fS )
Nr   r   r   r   r   r  r*  rR   )rt   r  rM   r   r  )rB   r  r@  rA  rB  rC  s         r3   r   znakagami_gen._stats  s    WRbgbkk)"R%i1qtCx< 3&+bhsC.@.@@AXb[AbDFBE>)!B$.2
bck3Br5   c                    t          j        |          }t          j        |          }t          j        |          }||dz
  t          j        |          z  z
  }dt          j        |          z  t          j        d          z
  }||z   |z   }t          j        	                                }|dk    }||         |z   dd||         z  z  z
  ||<   |
                    |          d         S )Nr   r  rR   g     j@r   r  r   )rM   rw  r  rt   r  r  r   r  r  r   rN  )	rB   r  rw  r+  r,  r  r  norm_entropyr  s	            r3   r   znakagami_gen._entropy  s    ]2JrNN"s(bjnn,,26"::q		)EAIz**,, Htl"Q2a5\1!yy##r5   Nc                 Z    t          j        |                    ||          |z            S r  )rM   r   r  )rB   r  r   r   s       r3   r   znakagami_gen._rvs  s*    w|222D2AABFGGGr5   c                    t          |t                    r|                                }|
d| j        z  }t	          j        |          }t	          j        t	          j        ||z
  dz            t          |          z            }|||fz   S )N)r   rR   )	r<   r(   r  numargsrM   rK  r   r  r  )rB   rC   rD   r,   r-   s        r3   r  znakagami_gen._fitstart	  s~    dL)) 	$>>##D<DL(D fTlls
Q//#d));<<sEl""r5   r   rK   )r   r   r   r   r`   rh   ro   r   rr   r{   rv   r~   r   r   r   r  r   r5   r3   r  r    s         >  F F F+ + +1 1 1' ' '5 5 5( ( (6 6 6  $ $ $"H H H H	# 	# 	# 	# 	# 	#r5   r  nakagamic                 0   |dz  dz
  }t          j        |           t          j        |          }}t          j        |dz  | |z            d||z
  dz  z  z
  }t          j        |||z            dz  }t          |dk    ||fd t           j                   S )Nr   r   r   rR   r   c                 0    | t          j        |          z   S rK   r.  )r  r  s     r3   r  z_ncx2_log_pdf.<locals>.<lambda>%  s    q26!99} r5   )r  r   )rM   r   rt   ru  iver   rf   )rn   r  rQ  df2r  nsr  corrs           r3   _ncx2_log_pdfr    s     S&3,CWQZZB
(3s7AbD
!
!Cb1$4
4C6#r"u#Dq	d
$
$6'	   r5   c                   P    e Zd ZdZd Zd ZddZd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )ncx2_gena  A non-central chi-squared continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `ncx2` is:

    .. math::

        f(x, k, \lambda) = \frac{1}{2} \exp(-(\lambda+x)/2)
            (x/\lambda)^{(k-2)/4}  I_{(k-2)/2}(\sqrt{\lambda x})

    for :math:`x >= 0`, :math:`k > 0` and :math:`\lambda \ge 0`.
    :math:`k` specifies the degrees of freedom (denoted ``df`` in the
    implementation) and :math:`\lambda` is the non-centrality parameter
    (denoted ``nc`` in the implementation). :math:`I_\nu` denotes the
    modified Bessel function of first order of degree :math:`\nu`
    (`scipy.special.iv`).

    `ncx2` takes ``df`` and ``nc`` as shape parameters.

    %(after_notes)s

    %(example)s

    c                 F    |dk    t          j        |          z  |dk    z  S r  rd  rB   r  rQ  s      r3   r`   zncx2_gen._argcheckE  s"    Q"+b//)R1W55r5   c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )Nr  Fr   r  rQ  rd   re   rB   idfincs      r3   rh   zncx2_gen._shape_infoH  s>    uq"&k>BBuq"&k=AASzr5   Nc                 0    |                     |||          S rK   )noncentral_chisquare)rB   r  rQ  r   r   s        r3   r   zncx2_gen._rvsM  s    00R>>>r5   c                 ~    t          j        |t                    |dk    z  }t          ||||ft          d           S )Nr  r   c                 8    t                               | |          S rK   )r  r   rn   r  _s      r3   r  z"ncx2_gen._logpdf.<locals>.<lambda>S  s    dll1b.A.A r5   r0  )rM   	ones_likeboolr   r  rB   rn   r  rQ  conds        r3   r   zncx2_gen._logpdfP  sL    |AT***bAg6$B}AAC C C 	Cr5   c                     t          j        |t                    |dk    z  }t          j        d          5  t	          ||||ft
          j        d           cd d d            S # 1 swxY w Y   d S )Nr  r   r5  rn  c                 8    t                               | |          S rK   )r  ro   r  s      r3   r  zncx2_gen._pdf.<locals>.<lambda>Y      $))Ar2B2B r5   r0  )rM   r  r  r8  r   rk   	_ncx2_pdfr  s        r3   ro   zncx2_gen._pdfU      |AT***bAg6[h''' 	D 	DdQBK3=!B!BD D D	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D 	D   !A&&A*-A*c                     t          j        |t                    |dk    z  }t          j        d          5  t	          ||||ft
          j        d           cd d d            S # 1 swxY w Y   d S )Nr  r   r5  rn  c                 8    t                               | |          S rK   )r  rr   r  s      r3   r  zncx2_gen._cdf.<locals>.<lambda>_  r  r5   r0  )rM   r  r  r8  r   rk   	_ncx2_cdfr  s        r3   rr   zncx2_gen._cdf[  r  r  c                     t          j        |t                    |dk    z  }t          j        d          5  t	          ||||ft
          j        d           cd d d            S # 1 swxY w Y   d S )Nr  r   r5  rn  c                 8    t                               | |          S rK   )r  r{   r  s      r3   r  zncx2_gen._ppf.<locals>.<lambda>e  r  r5   r0  )rM   r  r  r8  r   rk   	_ncx2_ppf)rB   rz   r  rQ  r  s        r3   r{   zncx2_gen._ppfa  r  r  c                     t          j        |t                    |dk    z  }t          j        d          5  t	          ||||ft
          j        d           cd d d            S # 1 swxY w Y   d S )Nr  r   r5  rn  c                 8    t                               | |          S rK   )r  rv   r  s      r3   r  zncx2_gen._sf.<locals>.<lambda>k  s    $((1b// r5   r0  )rM   r  r  r8  r   rk   _ncx2_sfr  s        r3   rv   zncx2_gen._sfg  s    |AT***bAg6[h''' 	C 	CdQBK3<!A!AC C C	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	C 	Cr  c                     t          j        |t                    |dk    z  }t          j        d          5  t	          ||||ft
          j        d           cd d d            S # 1 swxY w Y   d S )Nr  r   r5  rn  c                 8    t                               | |          S rK   )r  r~   r  s      r3   r  zncx2_gen._isf.<locals>.<lambda>q  r  r5   r0  )rM   r  r  r8  r   rk   	_ncx2_isfr  s        r3   r~   zncx2_gen._isfm  r  r  c                 
   ||z   }d }d |||d          z  }t          j        d           |||d          z  t          j         |||d          dz            z  }d |||d          z   |||d          dz  z  }||||fS )Nc                     | ||z  z   S rK   r   )r  r  r  s      r3   	k_plus_clz"ncx2_gen._stats.<locals>.k_plus_clu  s    qs7Nr5   r   r  r  r  rJ  rR   r  )rB   r  rQ  
_ncx2_meanr  _ncx2_variance_ncx2_skewness_ncx2_kurtosis_excesss           r3   r   zncx2_gen._statss  s    "W
	 	 			"b# 6 66'#,,2r1)=)=='))BC"8"8!";<<=!%		"b#(>(>!>!*2r3!7!7!:"; !	
 	
r5   r   )r   r   r   r   r`   rh   r   r   ro   rr   r{   rv   r~   r   r   r5   r3   r  r  )  s         66 6 6  
? ? ? ?C C C
D D DD D DD D DC C CD D D
 
 
 
 
r5   r  ncx2c                   R    e Zd ZdZd Zd ZddZd Zd Zd Z	d	 Z
d
 Zd ZddZdS )ncf_gena  A non-central F distribution continuous random variable.

    %(before_notes)s

    See Also
    --------
    scipy.stats.f : Fisher distribution

    Notes
    -----
    The probability density function for `ncf` is:

    .. math::

        f(x, n_1, n_2, \lambda) =
            \exp\left(\frac{\lambda}{2} +
                      \lambda n_1 \frac{x}{2(n_1 x + n_2)}
                \right)
            n_1^{n_1/2} n_2^{n_2/2} x^{n_1/2 - 1} \\
            (n_2 + n_1 x)^{-(n_1 + n_2)/2}
            \gamma(n_1/2) \gamma(1 + n_2/2) \\
            \frac{L^{\frac{n_1}{2}-1}_{n_2/2}
                \left(-\lambda n_1 \frac{x}{2(n_1 x + n_2)}\right)}
            {B(n_1/2, n_2/2)
                \gamma\left(\frac{n_1 + n_2}{2}\right)}

    for :math:`n_1, n_2 > 0`, :math:`\lambda \ge 0`.  Here :math:`n_1` is the
    degrees of freedom in the numerator, :math:`n_2` the degrees of freedom in
    the denominator, :math:`\lambda` the non-centrality parameter,
    :math:`\gamma` is the logarithm of the Gamma function, :math:`L_n^k` is a
    generalized Laguerre polynomial and :math:`B` is the beta function.

    `ncf` takes ``df1``, ``df2`` and ``nc`` as shape parameters. If ``nc=0``,
    the distribution becomes equivalent to the Fisher distribution.

    %(after_notes)s

    %(example)s

    c                 *    |dk    |dk    z  |dk    z  S r  r   )rB   df1r  rQ  s       r3   r`   zncf_gen._argcheck  s    aC!G$a00r5   c                     t          dddt          j        fd          }t          dddt          j        fd          }t          dddt          j        fd          }|||gS )Nr  Fr   r  r  rQ  rd   re   )rB   idf1idf2r  s       r3   rh   zncf_gen._shape_info  sZ    %BF^DD%BF^DDuq"&k=AAdC  r5   Nc                 2    |                     ||||          S rK   )noncentral_f)rB   r  r  rQ  r   r   s         r3   r   zncf_gen._rvs  s    ((c2t<<<r5   c                 0    t          j        ||||          S rK   )rk   _ncf_pdfrB   rn   r  r  rQ  s        r3   ro   zncf_gen._pdf  s     |AsC,,,r5   c                 0    t          j        ||||          S rK   )rk   _ncf_cdfr  s        r3   rr   zncf_gen._cdf  s    |AsC,,,r5   c                     t          j        d          5  t          j        ||||          cd d d            S # 1 swxY w Y   d S rm  )rM   r8  rk   _ncf_ppf)rB   rz   r  r  rQ  s        r3   r{   zncf_gen._ppf      [h''' 	1 	1<3R00	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1 	1   :>>c                 0    t          j        ||||          S rK   )rk   _ncf_sfr  s        r3   rv   zncf_gen._sf  s    {1c3+++r5   c                     t          j        d          5  t          j        ||||          cd d d            S # 1 swxY w Y   d S rm  )rM   r8  rk   _ncf_isfr  s        r3   r~   zncf_gen._isf  r  r  c                 <   |dz  |z  |z  }t          j        |d|z  z             t          j        d|z  |z
            z   t          j        |dz            z
  }|t          j        | dz  |z             z  }|t          j        |d|z  z   d|z  d|z            z  }|S )Nr   r   r   )rt   r  rM   r   hyp1f1)rB   r_   r  r  rQ  rB  r  s          r3   r   zncf_gen._munp  s    Sy}q z!CG)$$rz#c'!)'<'<<rz#c'?R?RRrvrcCin%%%ry3s7CGSV444
r5   r  c                     t          j        |||          }t          j        |||          }d|v rt          j        |||          nd }d|v rt          j        |||          nd }||||fS Nr  r  )rk   	_ncf_mean_ncf_variance_ncf_skewness_ncf_kurtosis_excess)	rB   r  r  rQ  r  r@  rA  rB  rC  s	            r3   r   zncf_gen._stats  s    ]3R((S"--03wSsC,,,D G^^ %b  15 	3Br5   r   r  )r   r   r   r   r`   rh   r   ro   rr   r{   rv   r~   r   r   r   r5   r3   r  r    s        ' 'P1 1 1! ! != = = =- - -- - -1 1 1, , ,1 1 1       r5   r  ncfc                   P    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
d
 Zd Zd ZdS )t_gena  A Student's t continuous random variable.

    For the noncentral t distribution, see `nct`.

    %(before_notes)s

    See Also
    --------
    nct

    Notes
    -----
    The probability density function for `t` is:

    .. math::

        f(x, \nu) = \frac{\Gamma((\nu+1)/2)}
                        {\sqrt{\pi \nu} \Gamma(\nu/2)}
                    (1+x^2/\nu)^{-(\nu+1)/2}

    where :math:`x` is a real number and the degrees of freedom parameter
    :math:`\nu` (denoted ``df`` in the implementation) satisfies
    :math:`\nu > 0`. :math:`\Gamma` is the gamma function
    (`scipy.special.gamma`).

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zt_gen._shape_info  r  r5   Nc                 0    |                     ||          S r  )
standard_tr  s       r3   r   z
t_gen._rvs  s    &&r&555r5   c                 R     t          |t          j        k    ||fd  fd          S )Nc                 6    t                               |           S rK   )r  ro   rn   r  s     r3   r  zt_gen._pdf.<locals>.<lambda>  s    DIIaLL r5   c                 T    t          j                            | |                    S rK   r  )rn   r  rB   s     r3   r  zt_gen._pdf.<locals>.<lambda>  s#    t||Ar**++ r5   r0  r  r  s   `  r3   ro   z
t_gen._pdf  sC    "&L1b'((   
 
 
 	
r5   c                 T    d }d }t          |t          j        k    ||f||          S )Nc                    t          j        t          j        d|z  d                    dt          j        |          t          j        t           j                  z   z  z
  |dz   dz  t          j        | | z  |z            z  z
  S r  )rM   r   rt   r  r   r  r	  s     r3   t_logpdfzt_gen._logpdf.<locals>.t_logpdf  so    F2738S1122RVBZZ"&--789Avqj!a%(!3!334 5r5   c                 6    t                               |           S rK   )r  r   r	  s     r3   norm_logpdfz"t_gen._logpdf.<locals>.norm_logpdf  s    <<??"r5   r0  r  )rB   rn   r  r	  r	  s        r3   r   zt_gen._logpdf  sC    	5 	5 	5
	# 	# 	# ",B	[XNNNNr5   c                 ,    t          j        ||          S rK   rt   stdtrr  s      r3   rr   z
t_gen._cdf   r  r5   c                 .    t          j        ||           S rK   r	  r  s      r3   rv   z	t_gen._sf#  s    xQBr5   c                 ,    t          j        ||          S rK   rt   stdtritr  s      r3   r{   z
t_gen._ppf&  s    z"a   r5   c                 .    t          j        ||           S rK   r	  r  s      r3   r~   z
t_gen._isf)  s    
2q!!!!r5   c                    t          j        |          }t          j        |dk    dt           j                  }|dk    |dk    z  |dk    t          j        |          z  |f}d d d f}t          |||ft           j                  }t          j        |dk    dt           j                  }|dk    |dk    z  |dk    t          j        |          z  |f}d	 d
 d f}t          |||ft           j                  }||||fS )Nr   r   rR   c                 J    t          j        t           j        | j                  S rK   rM   broadcast_torf   rw  r  s    r3   r  zt_gen._stats.<locals>.<lambda>5      !B!B r5   c                     | | dz
  z  S r;  r   r  s    r3   r  zt_gen._stats.<locals>.<lambda>6  s    r#v r5   c                 6    t          j        d| j                  S r[   rM   r	  rw  r  s    r3   r  zt_gen._stats.<locals>.<lambda>7      BH!=!= r5   r  r   c                 J    t          j        t           j        | j                  S rK   r	  r  s    r3   r  zt_gen._stats.<locals>.<lambda>?  r	  r5   c                     d| dz
  z  S )NrE  rJ  r   r  s    r3   r  zt_gen._stats.<locals>.<lambda>@  s    3 r5   c                 6    t          j        d| j                  S r  r"	  r  s    r3   r  zt_gen._stats.<locals>.<lambda>A  r#	  r5   )rM   isposinfrO  rf   r   r   r  )	rB   r  infinite_dfr@  rU  
choicelistrA  rB  rC  s	            r3   r   zt_gen._stats,  s   k"ooXb1fc26**!Va(!Vr{2.! CB..==?
 (Jrv>>Xb1fc26**!Va(!Vr{2.! CB//==?
 :ubf==3Br5   c                     |t           j        k    rt                                          S d }d }t	          |dk    |f||          }|S )Nc                     | dz  }| dz   dz  }|t          j        |          t          j        |          z
  z  t          j        t          j        |           t          j        |d          z            z   S r  )rt   r  rM   r   r   rk  )r  halfhalf1s      r3   r  zt_gen._entropy.<locals>.regularJ  si    a4D!VQJE2:e,,rz$/?/??@fRWR[[s););;<<= >r5   c                     t                                           d| z  z   | dz  dz  z   | dz  dz  z
  | dz  dz  z
  d| d	z  z  z   | d
z  dz  z   }|S )Nr   r  r   r  r  r  r*  g333333?r  r  )r  r   )r  r  s     r3   r.  z"t_gen._entropy.<locals>.asymptoticP  si     1R4'2s7A+5S!CGQ;!%r3w035s7A+>AHr5   d   r0  )rM   rf   r  r   r   )rB   r  r  r.  r  s        r3   r   zt_gen._entropyF  s\    <<==??"	> 	> 	>	 	 	 rSy2&J7CCCr5   r   r  r   r5   r3   r	  r	    s         <F F F6 6 6 6
 
 

O 
O 
O       ! ! !" " "  4    r5   r	  r  c                   L    e Zd ZdZd Zd ZddZd Zd Zd Z	d	 Z
d
 ZddZdS )nct_gena  A non-central Student's t continuous random variable.

    %(before_notes)s

    Notes
    -----
    If :math:`Y` is a standard normal random variable and :math:`V` is
    an independent chi-square random variable (`chi2`) with :math:`k` degrees
    of freedom, then

    .. math::

        X = \frac{Y + c}{\sqrt{V/k}}

    has a non-central Student's t distribution on the real line.
    The degrees of freedom parameter :math:`k` (denoted ``df`` in the
    implementation) satisfies :math:`k > 0` and the noncentrality parameter
    :math:`c` (denoted ``nc`` in the implementation) is a real number.

    %(after_notes)s

    %(example)s

    c                     |dk    ||k    z  S r  r   r  s      r3   r`   znct_gen._argcheckx  s    Q28$$r5   c                     t          dddt          j        fd          }t          ddt          j         t          j        fd          }||gS )Nr  Fr   r  rQ  re   r  s      r3   rh   znct_gen._shape_info{  sC    uq"&k>BBuw&7HHSzr5   Nc                     t                               |||          }t                              |||          }|t          j        |          z  t          j        |          z  S )Nr  r  )r  r  r  rM   r   )rB   r  rQ  r   r   r_   r  s          r3   r   znct_gen._rvs  sO    HH$\HBBXXbt,X??272;;,,r5   c                 D   |dz  }|dz  }||z  }||z  |z  }||z   }|dz  t          j        |          z  t          j        |dz             z   |t          j        d          z  ||z  dz  z   |dz  t          j        |          z  z   t          j        |dz            z   z
  }t          j        |          }	|d|z  z  }
t          j        d          |z  |z  t          j        |dz  dz   d|
          z  t          j        |t          j        |dz   dz            z            z  }t          j        |dz   dz  d|
          t          j        t          j        |          t          j        |dz  dz             z            z  }|	||z   z  }	t          j	        |	dd           S )Nr   r   r   rR   r  r   r   )
rM   r   rt   r  r   r   r  r   r  clip)rB   rn   r  rQ  r_   r  r  r  trm1r   valFtrm2s               r3   ro   znct_gen._pdf  s   sFVqS"uRx2v"RVAYYAaC0RVAYY;Bq(AaC+==Z!__%& VD\\qv

2a	!A#a%d ; ;;*T"(AaC7"3"33445	1Q3'3--*RWT]]28AaCE??:;;<
d4iwr1d###r5   c                     t          j        d          5  t          j        t          j        |||          dd          cd d d            S # 1 swxY w Y   d S Nr5  rn  r   r   )rM   r8  r6	  rk   _nct_cdfrB   rn   r  rQ  s       r3   rr   znct_gen._cdf  s    [h''' 	: 	:73<2r22Aq99	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	:r9  c                     t          j        d          5  t          j        |||          cd d d            S # 1 swxY w Y   d S rm  )rM   r8  rk   _nct_ppf)rB   rz   r  rQ  s       r3   r{   znct_gen._ppf      [h''' 	+ 	+<2r**	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+ 	+rr  c                     t          j        d          5  t          j        t          j        |||          dd          cd d d            S # 1 swxY w Y   d S r;	  )rM   r8  r6	  rk   _nct_sfr=	  s       r3   rv   znct_gen._sf  s    [h''' 	9 	973;q"b111a88	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9 	9r9  c                     t          j        d          5  t          j        |||          cd d d            S # 1 swxY w Y   d S rm  )rM   r8  rk   _nct_isfr=	  s       r3   r~   znct_gen._isf  r@	  rr  r  c                     t          j        ||          }t          j        ||          }d|v rt          j        ||          nd }d|v rt          j        ||          nd }||||fS r  )rk   	_nct_mean_nct_variance_nct_skewness_nct_kurtosis_excess)rB   r  rQ  r  r@  rA  rB  rC  s           r3   r   znct_gen._stats  sq    ]2r""B''*-..Sr2&&&d14S%b"---T3Br5   r   r  )r   r   r   r   r`   rh   r   ro   rr   r{   rv   r~   r   r   r5   r3   r1	  r1	  _  s         0% % %  
- - - -
$ $ $&: : :+ + +9 9 9+ + +     r5   r1	  nctc                        e Zd ZdZd Zd Zd Zd Zd Zd Z	dd	Z
d
 Ze ee           fd                        Z xZS )
pareto_genaL  A Pareto continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `pareto` is:

    .. math::

        f(x, b) = \frac{b}{x^{b+1}}

    for :math:`x \ge 1`, :math:`b > 0`.

    `pareto` takes ``b`` as a shape parameter for :math:`b`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zpareto_gen._shape_info  r  r5   c                     ||| dz
  z  z  S r[   r   r  s      r3   ro   zpareto_gen._pdf  s    1r!t9}r5   c                     d|| z  z
  S r[   r   r  s      r3   rr   zpareto_gen._cdf  s    1r7{r5   c                 .    t          d|z
  d|z            S )Nr   r<  r  r  s      r3   r{   zpareto_gen._ppf  s    1Q3Qr5   c                     || z  S rK   r   r  s      r3   rv   zpareto_gen._sf  s    A2wr5   c                 2    t          j        |d|z            S r?  r  r  s      r3   r~   zpareto_gen._isf  s    x4!8$$$r5   r  c                 X   d\  }}}}d|v ri|dk    }t          j        ||          }t          j        t          j        |          t           j                  }t          j        ||||dz
  z             d|v rr|dk    }t          j        ||          }t          j        t          j        |          t           j                  }t          j        ||||dz
  z  |dz
  dz  z             d	|v r|d
k    }t          j        ||          }t          j        t          j        |          t           j                  }d|dz   z  t          j        |dz
            z  |dz
  t          j        |          z  z  }	t          j        |||	           d|v r|dk    }t          j        ||          }t          j        t          j        |          t           j                  }dt          j        g d|          z  t          j        g d|          z  }	t          j        |||	           ||||fS )Nr3  r  r   r@  r   r  rR   r   r  r  rD  r  r   rE  )r   r   r  r  )r   g      r  r   )	rM   extractrB  rw  rf   placer  r   r  )
rB   r   r  r@  rA  rB  rC  maskbtr  s
             r3   r   zpareto_gen._stats  s   0CR'>>q5DD!$$B!888BHRrRV}---'>>q5DD!$$B'"(1++"&999CHS$bfC! ;<<<'>>q5DD!$$B!888BS>BGBH$5$55"s(bgbkk9QRDHRt$$$'>>q5DD!$$B!888B
#5#5#5r:::J555r::;DHRt$$$3Br5   c                 <    dd|z  z   t          j        |          z
  S r  r.  r}  s     r3   r   zpareto_gen._entropy      3q5y26!99$$r5   c                    t          | ||          }|\  }}|9t          j                  |z
  |pdk     rt          ddt          j                  j        d         fd||cxu rCn n?fdfdfdfd	}t          |                    d
d                    }|dz  |dz  }
}	 ||	|
          sB|	dk    s|
t          j        k     r,|	dz  }	|
dz  }
 ||	|
          s|	dk    |
t          j        k     ,t          |	|
g          }|j	        rx|j
        }t          j                  |z
  }p ||          }||z   t          j                  k     s,t          j                  |z
  }t          j        |d          }|||fS  t                      j        fi |S |t          j                  |z
  }n|}|pt          j                  |z
  }p ||          }|||fS )Nr   paretor   r  c                 b    t          j        t          j        |z
  | z                      z  S rK   r  )r-   locationrC   ndatas     r3   	get_shapez!pareto_gen.fit.<locals>.get_shape  s-     26"&$/U)B"C"CDDDDr5   c                     | z  |z  S rK   r   )rw  r-   r^	  s     r3   	dL_dScalez!pareto_gen.fit.<locals>.dL_dScale  s     u}u,,r5   c                 D    | dz   t          j        d|z
  z            z  S r[   r  )rw  r]	  rC   s     r3   dL_dLocationz$pareto_gen.fit.<locals>.dL_dLocation  s'     	RVA,A%B%BBBr5   c                     t          j                  | z
  }p | |          } ||           ||           z
  S rK   )rM   rK  )r-   r]	  rw  rc	  ra	  rC   r  r_	  s      r3   r  z$pareto_gen.fit.<locals>.fun_to_solve  sP     6$<<%/<))E8"<"<#|E844yy7N7NNNr5   c                 |    t          j         |                     t          j         |                    k    S rK   rL   rO   rP   r  s     r3   rQ   z.pareto_gen.fit.<locals>.interval_contains_root#  s;    V 4 455V 4 4556 7r5   r-   rR   r  )r  rM   rK  rH  rf   rw  rL  r:   r)   r  r  r  r>   r@   )rB   rC   rD   r2   r  r   r   rQ   r  rO   rP   r  r-   r,   rw  rc	  ra	  r  r  r_	  r^	  r  s    `             @@@@@@r3   r@   zpareto_gen.fit  s    1tT4HH
%/"fdF tt 3v{ C Cxq????
1	E 	E 	E 	E 	E 	E
 6!!!!!!!!- - - - -
C C C C C
O O O O O O O O O7 7 7 7 7  ! 4 455K(1_kAoFF .-ff== 

frvoo!! .-ff== 

frvoo lVV4DEEEC} 1fTllU*7))E3"7"7 rvd||33F4LL3.EL22Ec5(("uww{4004000\&,,'CCC ,"&,,,/))E3//c5  r5   r  )r   r   r   r   rh   ro   rr   r{   rv   r~   r   r   rH   r   r   r@   r  r  s   @r3   rL	  rL	    s         *E E E           % % %   6% % % M**R! R! R! R! +* _R! R! R! R! R!r5   rL	  r[	  c                   N    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd ZdS )	lomax_gena  A Lomax (Pareto of the second kind) continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `lomax` is:

    .. math::

        f(x, c) = \frac{c}{(1+x)^{c+1}}

    for :math:`x \ge 0`, :math:`c > 0`.

    `lomax` takes ``c`` as a shape parameter for :math:`c`.

    `lomax` is a special case of `pareto` with ``loc=-1.0``.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zlomax_gen._shape_infol  r  r5   c                 $    |dz  d|z   |dz   z  z  S r  r   r
  s      r3   ro   zlomax_gen._pdfo  s    uc!equ%%%r5   c                 `    t          j        |          |dz   t          j        |          z  z
  S r[   r  r
  s      r3   r   zlomax_gen._logpdfs  s&    vayyAaC!,,,r5   c                 X    t          j        | t          j        |          z             S rK   r  r
  s      r3   rr   zlomax_gen._cdfv  s#    !BHQKK((((r5   c                 V    t          j        | t          j        |          z            S rK   )rM   r   rt   r  r
  s      r3   rv   zlomax_gen._sfy  s     vqb!n%%%r5   c                 2    | t          j        |          z  S rK   r	  r
  s      r3   r   zlomax_gen._logsf|  s    r"(1++~r5   c                 X    t          j        t          j        |            |z            S rK   r  r  s      r3   r{   zlomax_gen._ppf  s"    x1"a(((r5   c                     |d|z  z  dz
  S r;  r   r  s      r3   r~   zlomax_gen._isf  s    4!8}q  r5   c                 R    t                               |dd          \  }}}}||||fS )Nr<  rA  )r,   r  )r[	  r  r[  s         r3   r   zlomax_gen._stats  s/     ,,qdF,CCCR3Br5   c                 <    dd|z  z   t          j        |          z
  S r  r.  r}  s     r3   r   zlomax_gen._entropy  s    Qwrvayy  r5   N)r   r   r   r   rh   ro   r   rr   rv   r   r{   r~   r   r   r   r5   r3   rh	  rh	  T  s         .E E E& & &- - -) ) )& & &  ) ) )! ! !  ! ! ! ! !r5   rh	  lomaxc                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 ZddZd Ze eed           fd                        Z xZS )pearson3_gena  A pearson type III continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `pearson3` is:

    .. math::

        f(x, \kappa) = \frac{|\beta|}{\Gamma(\alpha)}
                       (\beta (x - \zeta))^{\alpha - 1}
                       \exp(-\beta (x - \zeta))

    where:

    .. math::

            \beta = \frac{2}{\kappa}

            \alpha = \beta^2 = \frac{4}{\kappa^2}

            \zeta = -\frac{\alpha}{\beta} = -\beta

    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
    Pass the skew :math:`\kappa` into `pearson3` as the shape parameter
    ``skew``.

    %(after_notes)s

    %(example)s

    References
    ----------
    R.W. Vogel and D.E. McMartin, "Probability Plot Goodness-of-Fit and
    Skewness Estimation Procedures for the Pearson Type 3 Distribution", Water
    Resources Research, Vol.27, 3149-3158 (1991).

    L.R. Salvosa, "Tables of Pearson's Type III Function", Ann. Math. Statist.,
    Vol.1, 191-198 (1930).

    "Using Modern Computing Tools to Fit the Pearson Type III Distribution to
    Aviation Loads Data", Office of Aviation Research (2003).

    c                    d}d}d}t          j        d||          \  }}}|                                }t          j        |          |k     }| }d||         |z  z  }	||	z  dz  }
||
|	z  z
  }|	||         |z
  z  }||||||	|
|fS )Nr   r   g>r   rR   )rM   r  copyr  )rB   rn   r  r,   r-   norm2pearson_transitionansrV	  invmaskrk  r  rZ  transxs                r3   _preprocesszpearson3_gen._preprocess  s    
  #+*3488Qhhjj {4  #::%d7me+,!UT\!7d*+AvtWdE4??r5   c                 *    t          j        |          S rK   rd  )rB   r  s     r3   r`   zpearson3_gen._argcheck  s    
 {4   r5   c                 V    t          ddt          j         t          j        fd          gS )Nr  Fr  re   rg   s    r3   rh   zpearson3_gen._shape_info  s$    65BF7BF*;^LLMMr5   c                 *    d}d}|}d|dz  z  }||||fS )Nr   r   r  rR   r   )rB   r  r  r  r  r  s         r3   r   zpearson3_gen._stats  s,    aK!Qzr5   c                     t          j        |                     ||                    }|j        dk    rt          j        |          rdS |S d|t          j        |          <   |S )Nr   r   )rM   r   r   r+  rb  )rB   rn   r  ry	  s       r3   ro   zpearson3_gen._pdf  s]    
 fT\\!T**++8q==x}} sJ BHSMM
r5   c                    |                      ||          \  }}}}}}}}	t          j        t          ||                             ||<   t          j        t	          |                    t
                              ||          z   ||<   |S rK   )r|	  rM   r   r   r
  r  r  )
rB   rn   r  ry	  r{	  rV	  rz	  rk  r  r  s
             r3   r   zpearson3_gen._logpdf  s     Q%% 	6QgtUA F9QtW--..D	 vc$ii((5<<+F+FFG
r5   c                    |                      ||          \  }}}}}}}}t          ||                   ||<   t          j        ||j                  }t          j        ||dk              }	||         dk    }
t                              ||
         ||
                   ||	<   t          j        ||dk               }||         dk     }t                              ||         ||                   ||<   |S r  )	r|	  r   rM   r	  rw  rB  r  r   r  rB   rn   r  ry	  r{	  rV	  rz	  r  r  	invmask1a	invmask1b	invmask2a	invmask2bs                r3   rr   zpearson3_gen._cdf   s    Q%% 	3Qgq% ag&&D	tW]33N7D1H55	MA%	 6)#4eI6FGGI N7D1H55	MA%	&"3U95EFFI
r5   c                    |                      ||          \  }}}}}}}}t          ||                   ||<   t          j        ||j                  }t          j        ||dk              }	||         dk    }
t                              ||
         ||
                   ||	<   t          j        ||dk               }||         dk     }t                              ||         ||                   ||<   |S r  )	r|	  r   rM   r	  rw  rB  r  r  r   r	  s                r3   rv   zpearson3_gen._sf   s    Q%% 	3Qgq% QtW%%D	tW]33N7D1H55	MA%	&"3U95EFFIN7D1H55	MA%	6)#4eI6FGGI
r5   Nc                 6   t          j        ||          }|                     dg|          \  }}}}}}}	}
|                                }|j        |z
  }|                    |          ||<   |                    |	|          |z  |
z   ||<   |dk    r|d         }|S )Nr   r   )rM   r	  r|	  r  r   r   r  )rB   r  r   r   ry	  r  rV	  rz	  rk  r  rZ  nsmallnbigs                r3   r   zpearson3_gen._rvs0   s    tT**aS$'' 	4Q4$t y6! 0088D	#225$??DtKG2::a&C
r5   c                     |                      ||          \  }}}}}}}}	t          ||                   ||<   ||         }d||dk              z
  ||dk     <   t          j        ||          |z  |	z   ||<   |S rK  )r|	  r   rt   r  )
rB   rz   r  ry	  r  rV	  rz	  rk  r  rZ  s
             r3   r{   zpearson3_gen._ppf>   s    Q%% 	4Q4$tag&&D	gJ!D1H+o$(~eQ//4t;G
r5   ze        Note that method of moments (`method='MM'`) is not
        available for this distribution.

r   c                     |                     dd           dk    rt          d           t          t          |           |           j        |g|R i |S )Nr/   MMzhFit `method='MM'` is not available for the Pearson3 distribution. Please try the default `method='MLE'`.)r:   NotImplementedErrorr>   r?   r@   r  s       r3   r@   zpearson3_gen.fitG   sn    
 88Hd##t++% 'D E E E /5dT**.tCdCCCdCCCr5   r   )r   r   r   r   r|	  r`   rh   r   ro   r   rr   rv   r   r{   rH   r	   r   r@   r  r  s   @r3   ru	  ru	    s       , ,Z@ @ @8! ! !N N N        0  "      } 50 1 1 1D D D D1 1 _D D D D Dr5   ru	  pearson3c                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Z fdZe eed           fd                        Z xZS )powerlaw_gena  A power-function continuous random variable.

    %(before_notes)s

    See Also
    --------
    pareto

    Notes
    -----
    The probability density function for `powerlaw` is:

    .. math::

        f(x, a) = a x^{a-1}

    for :math:`0 \le x \le 1`, :math:`a > 0`.

    `powerlaw` takes ``a`` as a shape parameter for :math:`a`.

    %(after_notes)s

    For example, the support of `powerlaw` can be adjusted from the default
    interval ``[0, 1]`` to the interval ``[c, c+d]`` by setting ``loc=c`` and
    ``scale=d``. For a power-law distribution with infinite support, see
    `pareto`.

    `powerlaw` is a special case of `beta` with ``b=1``.

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zpowerlaw_gen._shape_infox   r  r5   c                     |||dz
  z  z  S r  r   r
  s      r3   ro   zpowerlaw_gen._pdf{   s    QsU|r5   c                 \    t          j        |          t          j        |dz
  |          z   S r[   )rM   r   rt   ru  r
  s      r3   r   zpowerlaw_gen._logpdf   s%    vayy28AE1----r5   c                     ||dz  z  S r  r   r
  s      r3   rr   zpowerlaw_gen._cdf   s    1S5zr5   c                 0    |t          j        |          z  S rK   r.  r
  s      r3   r   zpowerlaw_gen._logcdf   r/  r5   c                 (    t          |d|z            S r  r  r  s      r3   r{   zpowerlaw_gen._ppf   s    1c!e}}r5   c                 .    t          j        ||           S rK   )rt   rR  )rB   r  r   s      r3   rv   zpowerlaw_gen._sf   s    Ar5   c                     |||z   z  S rK   r   )rB   r_   r   s      r3   r   zpowerlaw_gen._munp   s    AE{r5   c                     ||dz   z  ||dz   z  |dz   dz  z  d|dz
  |dz   z  z  t          j        |dz   |z            z  dt          j        g d|          z  ||dz   z  |dz   z  z  fS )	Nr   r   rR   r  rD  r  )r   r  r  rR   r   )rM   r   r  r  s     r3   r   zpowerlaw_gen._stats   s    QWQWSQ.SQW-.!c'Q1G1GGBJ~~~q111Q!c']a!e5LMO 	Or5   c                 <    dd|z  z
  t          j        |          z
  S r  r.  r  s     r3   r   zpowerlaw_gen._entropy   rY	  r5   c                 d    t                                          ||          |dk    |dk    z  z  S Nr   r   )r>   r  )rB   rn   r   r  s      r3   r  zpowerlaw_gen._support_mask   s4    %%a++FqAv&( 	)r5   a:          Notes specifically for ``powerlaw.fit``: If the location is a free
        parameter and the value returned for the shape parameter is less than
        one, the true maximum likelihood approaches infinity. This causes
        numerical difficulties, and the resulting estimates are approximate.
        

r   c                 N   |                     dd          r t                      j        g|R i |S t          t	          j                            dk    r t                      j        g|R i |S t          | ||          \  }}|                               fg}|                     |i           d         }|W	                                |k    st          ddd          |,                                ||z   k    st          ddd          |>|dk    rt          d          |t	          j                  k    rd}t          |          d d	 || ||          ||fS |t	          j        	                                t          j                   }	p |	|          }
 ||
|	|f          }t	          j                                        |z
  t          j                  }p ||          } ||||f          }||k     r|
|	|fS |||fS |  |          }p ||          }|||fS fd
}d d fdfdfd}dk    r
 |            S dk    r
 |            S  |            }|                     |          } |            }|                     |          }||k    r|d         dk    r|S ||k    r|d         dk    r|S  t                      j        g|R i |S )Nr  Fr   powerlawr   zKNegative or zero `fscale` is outside the range allowed by the distribution.z0`fscale` must be greater than the range of data.c                     t          |           }| t          j        t          j        | |z
                      |t          j        |          z  z
  z  S rK   )r  rM   r  r   )rC   r,   r-   r  s       r3   r_	  z#powerlaw_gen.fit.<locals>.get_shape   sE     D		A3"&s
!3!344qFGGr5   c                 0    |                                  |z
  S rK   )rc  )rC   r,   s     r3   	get_scalez#powerlaw_gen.fit.<locals>.get_scale   s     88::##r5   c                     t          j                                        t           j                   } t          j        |           t          j        | j                  j        k     r3t          j        |           t          j        | j                  j        z  } t          j         |           t           j                  }p | |          }|| |fS rK   )	rM   r  rK  rf   r
  r  r  r  rN   )r,   r-   rw  rC   r  r	  r_	  s      r3   fit_loc_scale_w_shape_lt_1z4powerlaw_gen.fit.<locals>.fit_loc_scale_w_shape_lt_1!  s    ,txxzzBF733Cvc{{RXci00555gcllRXci%8%8%==L4!5!5rv>>E9iic599E#u$$r5   c                 *    | j         d          |z  |z  S r  )rw  )rC   rw  r-   s      r3   ra	  z#powerlaw_gen.fit.<locals>.dL_dScale !  s     JqM>E)E11r5   c                 B    |dz
  t          j        d|| z
  z            z  S r[   r  )rC   rw  r,   s      r3   rc	  z&powerlaw_gen.fit.<locals>.dL_dLocation%!  s&     AIS4Z(8!9!999r5   c                     t          j         |           t           j                   }p | |          } ||           S rK   rM   r  rf   )r,   r-   rw  rc	  rC   r  r	  r_	  s      r3   dL_dLocation_starz+powerlaw_gen.fit.<locals>.dL_dLocation_star*!  sR     L4!5!5w??E9iic599E<eS111r5   c                     t          j         |           t           j                   }p | |          } ||           ||           z
  S rK   r	  )	r,   r-   rw  rc	  ra	  rC   r  r	  r_	  s	      r3   r  z&powerlaw_gen.fit.<locals>.fun_to_solve1!  sj     L4!5!5w??E9iic599EIdE511"l4445 6r5   c                     t          j        
                                t           j                   } 
                                | z
  } 	|           dk    r+
                                |z
  } |dz  } 	|           dk    +fd}| dz
  }d} |||           sJ|t           j         k    r9
                                |z
  }|dz  } |||           s|t           j         k    9t	          j        || f          }t          j        |j        t           j                   }t          j         
|          t           j                  }p 
||          }|||fS )Nr   rR   c                 |    t          j         |                     t          j         |                    k    S rK   rL   rf	  s     r3   rQ   zTpowerlaw_gen.fit.<locals>.fit_loc_scale_w_shape_gt_1.<locals>.interval_contains_rootE!  s;    V 4 4557<<#7#7889 :r5   r   r   r  )rM   r  rK  rf   r   r)   r  )rP   r  rQ   rO   r  r  r,   r-   rw  r	  rC   r  r  r	  r_	  s            r3   fit_loc_scale_w_shape_gt_1z4powerlaw_gen.fit.<locals>.fit_loc_scale_w_shape_gt_19!  s    \$((**rvg66F XXZZ&(E##F++a//e+
 $#F++a//: : : : :
 aZF
 A--ff== "&((((**q.Q .-ff== "&(( 'vv>NOOOD,ty26'22CL4!5!5rv>>E9iic599E#u$$r5   )r0   r>   r@   r  rM   uniquer  r  _reduce_funcrK  rH  rc  r   ptpr  rf   r  )rB   rC   rD   r2   r   r   penalized_nllf_argspenalized_nllfrV   loc_lt1	shape_lt1ll_lt1loc_gt1	shape_gt1ll_gt1r-   rw  r	  r	  fit_shape_lt1fit_shape_gt1rc	  r	  ra	  r  r  r	  r_	  r  s    `                   @@@@@@@r3   r@   zpowerlaw_gen.fit   s   P 88J&& 	4577;t3d333d333ry1$$577;t3d333d333%@tAEt&M &M"fdF#dnnT&:&:%<=**+>CCAF
 88::$$":q!444!$((**v*E*E":q!444{{  "F G G G%%H oo%	H 	H 	H	$ 	$ 	$ $"29T400$>> l488::w77GB))D'6"B"BI#^Y$@$GGF l488::#6??GB))D'6"B"BI#^Y$@$GGF '611 '611 IdD))E:iidE::E$%%
	% 	% 	% 	% 	% 	% 	% 	%	2 	2 	2
	: 	: 	:
	2 	2 	2 	2 	2 	2 	2 	2 	2	6 	6 	6 	6 	6 	6 	6 	6 	6 	6!	% !	% !	% !	% !	% !	% !	% !	% !	% !	%H &A++--///FQJJ--/// 3244=$//2244=$//Va 0A 5 5  f__q!1A!5!5  577;t3d333d333r5   )r   r   r   r   rh   ro   r   rr   r   r{   rv   r   r   r   r  rH   r	   r   r@   r  r  s   @r3   r	  r	  W   s3        @E E E  . . .          O O O% % %) ) ) ) ) } 5   H4 H4 H4 H4  _H4 H4 H4 H4 H4r5   r	  r	  c                   P    e Zd ZdZej        Zd Zd Zd Z	d Z
d Zd Zd Zd	 Zd
S )powerlognorm_gena  A power log-normal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `powerlognorm` is:

    .. math::

        f(x, c, s) = \frac{c}{x s} \phi(\log(x)/s)
                     (\Phi(-\log(x)/s))^{c-1}

    where :math:`\phi` is the normal pdf, and :math:`\Phi` is the normal cdf,
    and :math:`x > 0`, :math:`s, c > 0`.

    `powerlognorm` takes :math:`c` and :math:`s` as shape parameters.

    %(after_notes)s

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )Nr  Fr   r  r  re   )rB   r!  r2  s      r3   rh   zpowerlognorm_gen._shape_info!  r3  r5   c                 T    t          j        |                     |||                    S rK   r  rB   rn   r  r  s       r3   ro   zpowerlognorm_gen._pdf!  r  r5   c                     t          j        |          t          j        |          z
  t          j        |          z
  t          t          j        |          |z            z   t          t          j        |           |z            |dz
  z  z   S r  rM   r   r   r   r	  s       r3   r   zpowerlognorm_gen._logpdf!  so    q		BF1II%q		1RVAYY]++,bfQiiZ!^,,B78 	9r5   c                 V    t          j        |                     |||                     S rK   ra  r	  s       r3   rr   zpowerlognorm_gen._cdf!  rb  r5   c                 6    |                      d|z
  ||          S r[   )r~   rB   rz   r  r  s       r3   r{   zpowerlognorm_gen._ppf!  s    yyQ1%%%r5   c                 T    t          j        |                     |||                    S rK   r6  r	  s       r3   rv   zpowerlognorm_gen._sf!  r7  r5   c                 R    t          t          j        |           |z            |z  S rK   rY  r	  s       r3   r   zpowerlognorm_gen._logsf!  s#    RVAYYJN++a//r5   c                 X    t          j        t          |d|z  z             |z            S r[   r  r	  s       r3   r~   zpowerlognorm_gen._isf!  s*    vyQqS***Q.///r5   N)r   r   r   r   r   r  r  rh   ro   r   rr   r{   rv   r   r~   r   r5   r3   r	  r	  t!  s         . "4M  
- - -9 9 9
/ / /& & &, , ,0 0 00 0 0 0 0r5   r	  powerlognormc                   B    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
S )powernorm_genah  A power normal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `powernorm` is:

    .. math::

        f(x, c) = c \phi(x) (\Phi(-x))^{c-1}

    where :math:`\phi` is the normal pdf, :math:`\Phi` is the normal cdf,
    :math:`x` is any real, and :math:`c > 0` [1]_.

    `powernorm` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    References
    ----------
    .. [1] NIST Engineering Statistics Handbook, Section 1.3.6.6.13,
           https://www.itl.nist.gov/div898/handbook//eda/section3/eda366d.htm

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zpowernorm_gen._shape_info!  r  r5   c                 T    |t          |          z  t          |           |dz
  z  z  S r  r   r   r
  s      r3   ro   zpowernorm_gen._pdf!  s(    1~A23!788r5   c                 x    t          j        |          t          |          z   |dz
  t          |           z  z   S r[   r	  r
  s      r3   r   zpowernorm_gen._logpdf!  s3    vayy<??*ac<3C3C-CCCr5   c                 T    t          j        |                     ||                     S rK   ra  r
  s      r3   rr   zpowernorm_gen._cdf!  s#    Q**++++r5   c                 J    t          t          d|z
  d|z                       S r  )r   r  r  s      r3   r{   zpowernorm_gen._ppf!  s%    #cAgsQw//0000r5   c                 R    t          j        |                     ||                    S rK   r6  r
  s      r3   rv   zpowernorm_gen._sf!  r  r5   c                 (    |t          |           z  S rK   r   r
  s      r3   r   zpowernorm_gen._logsf!  s    <####r5   c                 p    t          t          j        t          j        |          |z                       S rK   )r   rM   r   r   r  s      r3   r~   zpowernorm_gen._isf!  s)    "&Q//0000r5   N)r   r   r   r   rh   ro   r   rr   r{   rv   r   r~   r   r5   r3   r	  r	  !  s         6E E E9 9 9D D D, , ,1 1 1) ) )$ $ $1 1 1 1 1r5   r	  	powernormc                   D    e Zd ZdZd Zd Zd Zd Zd Zd Z	dd	Z
d
 ZdS )	rdist_gena/  An R-distributed (symmetric beta) continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `rdist` is:

    .. math::

        f(x, c) = \frac{(1-x^2)^{c/2-1}}{B(1/2, c/2)}

    for :math:`-1 \le x \le 1`, :math:`c > 0`. `rdist` is also called the
    symmetric beta distribution: if B has a `beta` distribution with
    parameters (c/2, c/2), then X = 2*B - 1 follows a R-distribution with
    parameter c.

    `rdist` takes ``c`` as a shape parameter for :math:`c`.

    This distribution includes the following distribution kernels as
    special cases::

        c = 2:  uniform
        c = 3:  `semicircular`
        c = 4:  Epanechnikov (parabolic)
        c = 6:  quartic (biweight)
        c = 8:  triweight

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zrdist_gen._shape_info	"  r  r5   c                 R    t          j        |                     ||                    S rK   r  r
  s      r3   ro   zrdist_gen._pdf"  rV  r5   c                 ~    t          j        d           t                              |dz   dz  |dz  |dz            z   S r  )rM   r   rk  r   r
  s      r3   r   zrdist_gen._logpdf"  s7    q		zDLL!a%AaC1====r5   c                 R    t                               |dz   dz  |dz  |dz            S r-  r  r
  s      r3   rr   zrdist_gen._cdf"  s(    yy!a%AaC1---r5   c                 R    t                               |dz   dz  |dz  |dz            S r-  r  r
  s      r3   rv   zrdist_gen._sf"  s(    xxQ	1Q3!,,,r5   c                 R    dt                               ||dz  |dz            z  dz
  S r  )rk  r{   r  s      r3   r{   zrdist_gen._ppf"  s*    1ac1Q3'''!++r5   Nc                 H    d|                     |dz  |dz  |          z  dz
  S r  rj  r  s       r3   r   zrdist_gen._rvs"  s,    <$$QqS!A#t444q88r5   c                     d|dz  z
  t          j        |dz   dz  |dz            z  }|t          j        d|dz            z  S )Nr   rR   r   r   r   rT  )rB   r_   r  	numerators       r3   r   zrdist_gen._munp"  sG    !a%[BGQWM1s7$C$CC	2761r62222r5   r   )r   r   r   r   rh   ro   r   rr   rv   r{   r   r   r   r5   r3   r	  r	  !  s           BE E E* * *> > >. . .- - -, , ,9 9 9 93 3 3 3 3r5   r	  r<  rdistc                        e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 Zd Zd Zd Ze eed           fd                        Z xZS )rayleigh_gena7  A Rayleigh continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `rayleigh` is:

    .. math::

        f(x) = x \exp(-x^2/2)

    for :math:`x \ge 0`.

    `rayleigh` is a special case of `chi` with ``df=2``.

    %(after_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zrayleigh_gen._shape_info?"  r   r5   Nc                 <    t                               d||          S )NrR   r  r  r   s      r3   r   zrayleigh_gen._rvsB"  s    wwqt,w???r5   c                 P    t          j        |                     |                    S rK   r  rB   r  s     r3   ro   zrayleigh_gen._pdfE"  r  r5   c                 <    t          j        |          d|z  |z  z
  S r  r.  r	  s     r3   r   zrayleigh_gen._logpdfI"  s    vayy37Q;&&r5   c                 8    t          j        d|dz  z             S r  r=  r	  s     r3   rr   zrayleigh_gen._cdfL"  s    1%%%%r5   c                 V    t          j        dt          j        |           z            S Nr  )rM   r   rt   r  r   s     r3   r{   zrayleigh_gen._ppfO"  s!    wrBHaRLL()))r5   c                 P    t          j        |                     |                    S rK   r6  r	  s     r3   rv   zrayleigh_gen._sfR"  s    vdkk!nn%%%r5   c                     d|z  |z  S )Nr  r   r	  s     r3   r   zrayleigh_gen._logsfU"  s    ax!|r5   c                 T    t          j        dt          j        |          z            S r	  )rM   r   r   r   s     r3   r~   zrayleigh_gen._isfX"  s    wrBF1II~&&&r5   c                    dt           j        z
  }t          j        t           j        dz            |dz  dt           j        dz
  z  t          j        t           j                  z  |dz  z  dt           j        z  |z  d|dz  z  z
  fS )Nr   rR   r  r  r  r(  r*  r+  s     r3   r   zrayleigh_gen._stats["  sp    "%ia  A257BGBENN*383"%BsAvI%' 	'r5   c                 L    t           dz  dz   dt          j        d          z  z
  S )Nr   r   r   rR   r  rg   s    r3   r   zrayleigh_gen._entropyb"  s!    czA~BF1II--r5   a          Notes specifically for ``rayleigh.fit``: If the location is fixed with
        the `floc` parameter, this method uses an analytical formula to find
        the scale.  Otherwise, this function uses a numerical root finder on
        the first order conditions of the log-likelihood function to find the
        MLE.  Only the (optional) `loc` parameter is used as the initial guess
        for the root finder; the `scale` parameter and any other parameters
        for the optimizer are ignored.

r   c                    |                     dd          r t                      j        g|R i |S t          | ||          \  }}fd}fd}|ffd	}|Dt	          j        |z
  dk              rt          ddt          j        	          | ||          fS |                    d
          }	|	| 	                              d         }	||n|}
t	          j
        t	          j                  t          j                   }t          |
|          }t          j        |
||f          }|j        st!          |j                  |j        }|p
 ||          }||fS )Nr  Fc                 d    t          j        | z
  dz            dt                    z  z  dz  S r  )rM   r  r  )r,   rC   s    r3   	scale_mlez#rayleigh_gen.fit.<locals>.scale_mlet"  s2     FD3J1,--SYY?BFFr5   c                     | z
  }|                                 }|dz                                   }d|z                                   }||dt                    z  z  |z  z
  S r  )r  r  )r,   r  rY  r_  s3rC   s        r3   loc_mlez!rayleigh_gen.fit.<locals>.loc_mley"  s\     BBa%BB$BAc$iiK(+++r5   c                 r    | z
  }|                                 |dz  d|z                                   z  z
  S r  )r  )r,   r-   r  rC   s      r3   loc_mle_scale_fixedz-rayleigh_gen.fit.<locals>.loc_mle_scale_fixed"  s6     B6688eQh!B$555r5   r   rayleighr   r  r,   r  )r0   r>   r@   r  rM   r  rH  rf   r:   r  r  rK  rW   r   r)   r  rT   flagr  )rB   rC   rD   r2   r   r   r	  r	  r	  loc0rF   rP   rO   r  r,   r-   r  s    `              r3   r@   zrayleigh_gen.fite"  s    88J&& 	4577;t3d333d3338t9=tE EdF	G 	G 	G 	G 	G
	, 	, 	, 	, 	, ,2 	6 	6 	6 	6 	6 	6 vdTkQ&'' -":QbfEEEEYYt__,, xx<>>$''*Dgg-@bfTllRVG44"3//"30@AAA} 	+ ***h())C..Ezr5   r   )r   r   r   r   r   r  r  rh   r   ro   r   rr   r{   rv   r   r~   r   r   rH   r	   r@   r  r  s   @r3   r	  r	  '"  s,        * "4M  @ @ @ @' ' '' ' '& & &* * *& & &  ' ' '' ' '. . . } 5. / / // / / // / _/ / / / /r5   r	  r	  c                        e Zd ZdZd Zd Z fdZd Zd Zd Z	d Z
d	 Zd
 Zd ZdZ eee           fd            Z xZS )reciprocal_gena,  A loguniform or reciprocal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for this class is:

    .. math::

        f(x, a, b) = \frac{1}{x \log(b/a)}

    for :math:`a \le x \le b`, :math:`b > a > 0`. This class takes
    :math:`a` and :math:`b` as shape parameters.

    %(after_notes)s

    %(example)s

    This doesn't show the equal probability of ``0.01``, ``0.1`` and
    ``1``. This is best when the x-axis is log-scaled:

    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.hist(np.log10(r))
    >>> ax.set_ylabel("Frequency")
    >>> ax.set_xlabel("Value of random variable")
    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
    >>> ticks = ["$10^{{ {} }}$".format(i) for i in [-2, -1, 0]]
    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
    >>> plt.show()

    This random variable will be log-uniform regardless of the base chosen for
    ``a`` and ``b``. Let's specify with base ``2`` instead:

    >>> rvs = %(name)s(2**-2, 2**0).rvs(size=1000)

    Values of ``1/4``, ``1/2`` and ``1`` are equally likely with this random
    variable.  Here's the histogram:

    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.hist(np.log2(rvs))
    >>> ax.set_ylabel("Frequency")
    >>> ax.set_xlabel("Value of random variable")
    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
    >>> ticks = ["$2^{{ {} }}$".format(i) for i in [-2, -1, 0]]
    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
    >>> plt.show()

    c                     |dk    ||k    z  S r  r   r  s      r3   r`   zreciprocal_gen._argcheck"      A!a%  r5   c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS rd  re   re  s      r3   rh   zreciprocal_gen._shape_info"  rh  r5   c                     t          |t                    r|                                }t                                          |t          j        |          t          j        |          f          S NrJ  r<   r(   r  r>   r  rM   rK  rc  r  s     r3   r  zreciprocal_gen._fitstart"  sV    dL)) 	$>>##Dww  RVD\\26$<<,H IIIr5   c                 
    ||fS rK   r   r  s      r3   r   zreciprocal_gen._get_support"  r   r5   c                 T    t          j        |                     |||                    S rK   r  rq  s       r3   ro   zreciprocal_gen._pdf"  r  r5   c                     t          j        |           t          j        t          j        |          t          j        |          z
            z
  S rK   r.  rq  s       r3   r   zreciprocal_gen._logpdf"  s6    q		zBF26!99rvayy#89999r5   c                     t          j        |          t          j        |          z
  t          j        |          t          j        |          z
  z  S rK   r.  rq  s       r3   rr   zreciprocal_gen._cdf"  s7    q		"&))#q		BF1II(=>>r5   c                     t          j        t          j        |          |t          j        |          t          j        |          z
  z  z             S rK   rM   r   r   r  s       r3   r{   zreciprocal_gen._ppf"  s9    vbfQii!RVAYY%:";;<<<r5   c                 &   dt          j        |          t          j        |          z
  z  |z  }t          j        t          j        t	          |t          j        |          z  |t          j        |          z                                }||z  S r[   )rM   r   r  r   	_log_diff)rB   r_   r   r   r  r  s         r3   r   zreciprocal_gen._munp"  sj    "&))bfQii'(1,WRVIa"&))mQrvayy[AABBCCBwr5   c                     dt          j        |          t          j        |          z   z  t          j        t          j        |          t          j        |          z
            z   S r  r.  r  s      r3   r   zreciprocal_gen._entropy"  sF    BF1IIq		)*RVBF1IIq		4I-J-JJJr5   z        `loguniform`/`reciprocal` is over-parameterized. `fit` automatically
         fixes `scale` to 1 unless `fscale` is provided by the user.

r   c                 n    |                     dd          } t                      j        |g|R d|i|S )Nr   r   )r0   r>   r@   )rB   rC   rD   r2   r   r  s        r3   r@   zreciprocal_gen.fit#  sA    (A&&uww{4>$>>>v>>>>r5   )r   r   r   r   r`   rh   r  r   ro   r   rr   r{   r   r   fit_noter	   r   r@   r  r  s   @r3   r	  r	  "  s       2 2f! ! !  
J J J J J  - - -: : :? ? ?= = =  
K K KLH }H===? ? ? ? >=? ? ? ? ?r5   r	  
loguniform
reciprocalc                   >    e Zd ZdZd Zd Zd
dZd Zd Zd Z	d	 Z
dS )rice_gena  A Rice continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `rice` is:

    .. math::

        f(x, b) = x \exp(- \frac{x^2 + b^2}{2}) I_0(x b)

    for :math:`x >= 0`, :math:`b > 0`. :math:`I_0` is the modified Bessel
    function of order zero (`scipy.special.i0`).

    `rice` takes ``b`` as a shape parameter for :math:`b`.

    %(after_notes)s

    The Rice distribution describes the length, :math:`r`, of a 2-D vector with
    components :math:`(U+u, V+v)`, where :math:`U, V` are constant, :math:`u,
    v` are independent Gaussian random variables with standard deviation
    :math:`s`.  Let :math:`R = \sqrt{U^2 + V^2}`. Then the pdf of :math:`r` is
    ``rice.pdf(x, R/s, scale=s)``.

    %(example)s

    c                     |dk    S r  r   rB   r   s     r3   r`   zrice_gen._argcheck,#  r  r5   c                 @    t          dddt          j        fd          gS )Nr   Fr   rd   re   rg   s    r3   rh   zrice_gen._shape_info/#  r  r5   Nc                     |t          j        d          z  |                    d|z             z   }t          j        ||z                      d                    S )NrR   )rR   r  r   r  )rM   r   r   r  )rB   r   r   r   r  s        r3   r   zrice_gen._rvs2#  sO    bgajjL<77TD[7IIIw!yyay(()))r5   c                 v    t          j        t          j        |          dt          j        |                    S r  )rt   chndtrrM   r  r  s      r3   rr   zrice_gen._cdf7#  s&    y1q")A,,777r5   c           	      v    t          j        t          j        |dt          j        |                              S r  )rM   r   rt   chndtrixr  r  s      r3   r{   zrice_gen._ppf:#  s(    wr{1a166777r5   c                 z    |t          j        ||z
   ||z
  z  dz            z  t          j        ||z            z  S r;  )rM   r   rt   i0er  s      r3   ro   zrice_gen._pdf=#  s=     26AaC&!A#,s*+++bfQqSkk99r5   c                     |dz  }d|z   }||z  dz  }d|z  t          j        |           z  t          j        |          z  t          j        |d|          z  S r  )rM   r   rt   r  r  )rB   r_   r   nd2n1rz  s         r3   r   zrice_gen._munpF#  s_    eWqSWc
RVRC[[(28B<<7	"a$$% 	&r5   r   )r   r   r   r   r`   rh   r   rr   r{   ro   r   r   r5   r3   r
  r
  #  s         8  D D D* * * *
8 8 88 8 8: : :& & & & &r5   r
  ricec                       e Zd ZdZ eed          d             Zd Zd Zd Z	d Z
ed	             Zd
 Zd Zd ZddZd ZdS )irwinhall_gena\
  An Irwin-Hall (Uniform Sum) continuous random variable.

    An `Irwin-Hall <https://en.wikipedia.org/wiki/Irwin-Hall_distribution/>`_
    continuous random variable is the sum of :math:`n` independent
    standard uniform random variables [1]_ [2]_.

    %(before_notes)s

    Notes
    -----
    Applications include `Rao's Spacing Test
    <https://jammalam.faculty.pstat.ucsb.edu/html/favorite/test.htm>`_,
    a more powerful alternative to the Rayleigh test
    when the data are not unimodal, and radar [3]_.

    Conveniently, the pdf and cdf are the :math:`n`-fold convolution of
    the ones for the standard uniform distribution, which is also the
    definition of the cardinal B-splines of degree :math:`n-1`
    having knots evenly spaced from :math:`1` to :math:`n` [4]_ [5]_.

    The Bates distribution, which represents the *mean* of statistically
    independent, uniformly distributed random variables, is simply the
    Irwin-Hall distribution scaled by :math:`1/n`. For example, the frozen
    distribution ``bates = irwinhall(10, scale=1/10)`` represents the
    distribution of the mean of 10 uniformly distributed random variables.
    
    %(after_notes)s

    References
    ----------
    .. [1] P. Hall, "The distribution of means for samples of size N drawn
            from a population in which the variate takes values between 0 and 1,
            all such values being equally probable",
            Biometrika, Volume 19, Issue 3-4, December 1927, Pages 240-244,
            :doi:`10.1093/biomet/19.3-4.240`.
    .. [2] J. O. Irwin, "On the frequency distribution of the means of samples
            from a population having any law of frequency with finite moments,
            with special reference to Pearson's Type II,
            Biometrika, Volume 19, Issue 3-4, December 1927, Pages 225-239,
            :doi:`0.1093/biomet/19.3-4.225`.
    .. [3] K. Buchanan, T. Adeyemi, C. Flores-Molina, S. Wheeland and D. Overturf, 
            "Sidelobe behavior and bandwidth characteristics
            of distributed antenna arrays,"
            2018 United States National Committee of
            URSI National Radio Science Meeting (USNC-URSI NRSM),
            Boulder, CO, USA, 2018, pp. 1-2.
            https://www.usnc-ursi-archive.org/nrsm/2018/papers/B15-9.pdf.
    .. [4] Amos Ron, "Lecture 1: Cardinal B-splines and convolution operators", p. 1
            https://pages.cs.wisc.edu/~deboor/887/lec1new.pdf.
    .. [5] Trefethen, N. (2012, July). B-splines and convolution. Chebfun. 
            Retrieved April 30, 2024, from http://www.chebfun.org/examples/approx/BSplineConv.html.

    %(example)s
    z        Raises a ``NotImplementedError`` for the Irwin-Hall distribution because
        the generic `fit` implementation is unreliable and no custom implementation
        is available. Consider using `scipy.stats.fit`.

r   c                 $    d}t          |          )NzThe generic `fit` implementation is unreliable for this distribution, and no custom implementation is available. Consider using `scipy.stats.fit`.)r	  )rB   rC   rD   r2   	fit_notess        r3   r@   zirwinhall_gen.fit#  s    
9	 "),,,r5   c                 X    |dk    t          |          z  t          j        |          z  S r  )r   rM   	isrealobjr^   s     r3   r`   zirwinhall_gen._argcheck#  s$    AQ'",q//99r5   c                 
    d|fS r  r   r^   s     r3   r   zirwinhall_gen._get_support#  r   r5   c                 @    t          dddt          j        fd          gS rc   re   rg   s    r3   rh   zirwinhall_gen._shape_info#  ri   r5   c                 ^    d } t          j        |t           j        g          ||          S )Nc                 l    t          j        || z   |d          t          j        || z   |d          z  S )NT)exact)rt   	stirling2r  )r  r_   s     r3   vmunpz"irwinhall_gen._munp.<locals>.vmunp#  s<    L5!4888gagq5556 7r5   rR  rM   rL  rV  )rB   r  r_   r-
  s       r3   r   zirwinhall_gen._munp#  s8    	7 	7 	7
 8r|E2:,777qAAAr5   c                 X    t          j        | dz             }t          j        |          S r[   )rM   rM  r   basis_element)r_   r  s     r3   	_cardbsplzirwinhall_gen._cardbspl#  s$    IacNN$Q'''r5   c                 d      fd} t          j        |t           j        g          ||          S )Nc                 @                          |          |           S rK   )r1
  rn   r_   rB   s     r3   vpdfz irwinhall_gen._pdf.<locals>.vpdf#  s    $4>>!$$Q'''r5   rR  r.
  )rB   rn   r_   r5
  s   `   r3   ro   zirwinhall_gen._pdf#  sA    	( 	( 	( 	( 	(6r|D"*666q!<<<r5   c                 d      fd} t          j        |t           j        g          ||          S )Nc                 d                          |                                          |           S rK   r1
  antiderivativer4
  s     r3   vcdfz irwinhall_gen._cdf.<locals>.vcdf#  s+    54>>!$$3355a888r5   rR  r.
  )rB   rn   r_   r:
  s   `   r3   rr   zirwinhall_gen._cdf#  sA    	9 	9 	9 	9 	96r|D"*666q!<<<r5   c                 d      fd} t          j        |t           j        g          ||          S )Nc                 j                          |                                          || z
            S rK   r8
  r4
  s     r3   vsfzirwinhall_gen._sf.<locals>.vsf#  s/    54>>!$$3355ac:::r5   rR  r.
  )rB   rn   r_   r=
  s   `   r3   rv   zirwinhall_gen._sf#  sA    	; 	; 	; 	; 	;5r|C555a;;;r5   Nc                 @    t           dd            } ||||          S )Nc                     t          j        |                               t                    } || fn| g|R }|                    |                              d          S )Nr  r   r  )rM   r  rn  r  r  r  )r_   r   r   usizes       r3   _rvs1z!irwinhall_gen._rvs.<locals>._rvs1#  s\    ""3''A LQDDqj4jjE''U'3377Q7???r5   r  r   )r   )rB   r_   r   r   rD   rA
  s         r3   r   zirwinhall_gen._rvs#  s>    	#	@ 	@ 	@ 
$	#	@ uQT====r5   c                 &    |dz  |dz  ddd|z  z  fS )NrR   r  r   r  rC  r   r^   s     r3   r   zirwinhall_gen._stats#  s#     sAbD!R1X%%r5   r   )r   r   r   r   r
   r   r@   r`   r   rh   r   r  r1
  ro   rr   rv   r   r   r   r5   r3   r"
  r"
  P#  s       5 5n   6? @ @ @- -	@ @-: : :  C C CB B B ( ( \(= = =
= = =
< < <
> > > >& & & & &r5   r"
  	irwinhallc                   8    e Zd ZdZd Zd Zd Zd Zd Zd	dZ	dS )
recipinvgauss_gena  A reciprocal inverse Gaussian continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `recipinvgauss` is:

    .. math::

        f(x, \mu) = \frac{1}{\sqrt{2\pi x}}
                    \exp\left(\frac{-(1-\mu x)^2}{2\mu^2x}\right)

    for :math:`x \ge 0`.

    `recipinvgauss` takes ``mu`` as a shape parameter for :math:`\mu`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS rQ  re   rg   s    r3   rh   zrecipinvgauss_gen._shape_info#  r  r5   c                 R    t          j        |                     ||                    S rK   r  rX  s      r3   ro   zrecipinvgauss_gen._pdf#  s"     vdll1b))***r5   c                 L    t          |dk    ||fd t          j                   S )Nr   c                     d|| z  z
  dz   d| z  |dz  z  z  dt          j        dt           j        z  | z            z  z
  S )Nr   r   rR   r   r   )rn   r@  s     r3   r  z+recipinvgauss_gen._logpdf.<locals>.<lambda>#  sH    1r!t8c/)9QqSS[)I+.rvagai/@/@+@*A r5   r  r  rX  s      r3   r   zrecipinvgauss_gen._logpdf#  s8    !a%!RB B%'VG- - - 	-r5   c                     d|z  |z
  }d|z  |z   }dt          j        |          z  }t          | |z            t          j        d|z            t          | |z            z  z
  S Nr   r   rM   r   r   r   rB   rn   r@  r7	  r9	  isqxs         r3   rr   zrecipinvgauss_gen._cdf#  se    2vz2vz271::~$t$$rvc"f~~id
6K6K'KKKr5   c                     d|z  |z
  }d|z  |z   }dt          j        |          z  }t          ||z            t          j        d|z            t          | |z            z  z   S rK
  rL
  rM
  s         r3   rv   zrecipinvgauss_gen._sf#  sc    2vz2vz271::~d##bfSVnnYuTz5J5J&JJJr5   Nc                 8    d|                     |d|          z  S rS  rT  rV  s       r3   r   zrecipinvgauss_gen._rvs$  s"    <$$R4$8888r5   r   )
r   r   r   r   rh   ro   r   rr   rv   r   r   r5   r3   rE
  rE
  #  s         ,F F F+ + +
- - -L L LK K K9 9 9 9 9 9r5   rE
  recipinvgaussc                   D    e Zd ZdZd Zd Zd Zd Zd ZddZ	d	 Z
d
 ZdS )semicircular_gena  A semicircular continuous random variable.

    %(before_notes)s

    See Also
    --------
    rdist

    Notes
    -----
    The probability density function for `semicircular` is:

    .. math::

        f(x) = \frac{2}{\pi} \sqrt{1-x^2}

    for :math:`-1 \le x \le 1`.

    The distribution is a special case of `rdist` with `c = 3`.

    %(after_notes)s

    References
    ----------
    .. [1] "Wigner semicircle distribution",
           https://en.wikipedia.org/wiki/Wigner_semicircle_distribution

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zsemicircular_gen._shape_info'$  r   r5   c                 V    dt           j        z  t          j        d||z  z
            z  S r  r*  r   s     r3   ro   zsemicircular_gen._pdf*$  s#    25y1Q3''r5   c                 |    t          j        dt           j        z            dt          j        | |z            z  z   S r  r  r   s     r3   r   zsemicircular_gen._logpdf-$  s.    vagRXqbd^^!333r5   c                     ddt           j        z  |t          j        d||z  z
            z  t          j        |          z   z  z   S )Nr   r   r   )rM   r   r   r&  r   s     r3   rr   zsemicircular_gen._cdf0$  s:    3ru9a!A#.1=>>>r5   c                 8    t                               |d          S Nr  )r	  r{   r   s     r3   r{   zsemicircular_gen._ppf3$  s    zz!Qr5   Nc                     t          j        |                    |                    }t          j        t           j        |                    |          z            }||z  S r  )rM   r   r  r  r   )rB   r   r   r  r   s        r3   r   zsemicircular_gen._rvs6$  sT     GL((d(3344F25<//T/:::;;1ur5   c                     dS )N)r   r  r   r<  r   rg   s    r3   r   zsemicircular_gen._stats=$  r0  r5   c                     dS )NgzCϑ?r   rg   s    r3   r   zsemicircular_gen._entropy@$  s    %%r5   r   )r   r   r   r   rh   ro   r   rr   r{   r   r   r   r   r5   r3   rS
  rS
  $  s         <  ( ( (4 4 4? ? ?             & & & & &r5   rS
  semicircularc                   >    e Zd ZdZd Zd Zd Zd Zd ZddZ	d	 Z
d
S )skewcauchy_gena  A skewed Cauchy random variable.

    %(before_notes)s

    See Also
    --------
    cauchy : Cauchy distribution

    Notes
    -----

    The probability density function for `skewcauchy` is:

    .. math::

        f(x) = \frac{1}{\pi \left(\frac{x^2}{\left(a\, \text{sign}(x) + 1
                                                   \right)^2} + 1 \right)}

    for a real number :math:`x` and skewness parameter :math:`-1 < a < 1`.

    When :math:`a=0`, the distribution reduces to the usual Cauchy
    distribution.

    %(after_notes)s

    References
    ----------
    .. [1] "Skewed generalized *t* distribution", Wikipedia
       https://en.wikipedia.org/wiki/Skewed_generalized_t_distribution#Skewed_Cauchy_distribution

    %(example)s

    c                 2    t          j        |          dk     S r[   )rM   r
  r  s     r3   r`   zskewcauchy_gen._argchecki$  s    vayy1}r5   c                 (    t          dddd          gS )Nr   F)r<  r   r  r   rg   s    r3   rh   zskewcauchy_gen._shape_infol$  s    3{NCCDDr5   c                 n    dt           j        |dz  |t          j        |          z  dz   dz  z  dz   z  z  S r-  )rM   r   rN   r
  s      r3   ro   zskewcauchy_gen._pdfo$  s8    BEQTQ^a%7!$;;a?@AAr5   c                    t          j        |dk    d|z
  dz  d|z
  t           j        z  t          j        |d|z
  z            z  z   d|z
  dz  d|z   t           j        z  t          j        |d|z   z            z  z             S Nr   r   rR   )rM   rO  r   r  r
  s      r3   rr   zskewcauchy_gen._cdfr$  s    xQQ!q1uo	!q1u+8N8N&NNQ!q1uo	!q1u+8N8N&NNP P 	Pr5   c           
      2   ||                      d|          k     }t          j        |t          j        t          j        d|z
  z  |d|z
  dz  z
  z            d|z
  z  t          j        t          j        d|z   z  |d|z
  dz  z
  z            d|z   z            S re
  )rr   rM   rO  r  r   )rB   rn   r   r  s       r3   r{   zskewcauchy_gen._ppfw$  s    		!QxruA!q1uk/BCCq1uMruA!q1uk/BCCq1uMO O 	Or5   rA  c                 ^    t           j        t           j        t           j        t           j        fS rK   r  )rB   r   r  s      r3   r   zskewcauchy_gen._stats}$  r  r5   c                     t          |t                    r|                                }t          j        |g d          \  }}}d|||z
  dz  fS )Nr  r   rR   r  )rB   rC   r  r  r  s        r3   r  zskewcauchy_gen._fitstart$  sU     dL)) 	$>>##DdLLL99S#C#)Q&&r5   NrL  )r   r   r   r   r`   rh   ro   rr   r{   r   r  r   r5   r3   r_
  r_
  G$  s           B  E E EB B BP P P
O O O. . . .' ' ' ' 'r5   r_
  
skewcauchyc                        e Zd ZdZd Zd Zd Zd Z fdZd Z	d Z
d	 ZddZddZed             Zd Z eed           fd            Z xZS )skewnorm_genaf  A skew-normal random variable.

    %(before_notes)s

    Notes
    -----
    The pdf is::

        skewnorm.pdf(x, a) = 2 * norm.pdf(x) * norm.cdf(a*x)

    `skewnorm` takes a real number :math:`a` as a skewness parameter
    When ``a = 0`` the distribution is identical to a normal distribution
    (`norm`). `rvs` implements the method of [1]_.

    %(after_notes)s

    %(example)s

    References
    ----------
    .. [1] A. Azzalini and A. Capitanio (1999). Statistical applications of
        the multivariate skew-normal distribution. J. Roy. Statist. Soc.,
        B 61, 579-602. :arxiv:`0911.2093`

    c                 *    t          j        |          S rK   rd  r  s     r3   r`   zskewnorm_gen._argcheck$  re  r5   c                 V    t          ddt          j         t          j        fd          gS )Nr   Fr  re   rg   s    r3   rh   zskewnorm_gen._shape_info$  rh  r5   c                 8    t          |dk    ||fd d           S )Nr   c                      t          |           S rK   r   rn   r   s     r3   r  z#skewnorm_gen._pdf.<locals>.<lambda>$  s    1 r5   c                 L    dt          |           z  t          || z            z  S r;  r	  rp
  s     r3   r  z#skewnorm_gen._pdf.<locals>.<lambda>$  s    By||OIacNN: r5   r  r  r
  s      r3   ro   zskewnorm_gen._pdf$  s2    FQF55::
 
 
 	
r5   c                 8    t          |dk    ||fd d           S )Nr   c                      t          |           S rK   r   rp
  s     r3   r  z&skewnorm_gen._logpdf.<locals>.<lambda>$  s    a r5   c                 p    t          j        d          t          |           z   t          || z            z   S r  r	  rp
  s     r3   r  z&skewnorm_gen._logpdf.<locals>.<lambda>$  s*    BF1IIl1oo5l1Q36G6GG r5   r  r  r
  s      r3   r   zskewnorm_gen._logpdf$  s2    FQF88GG
 
 
 	
r5   c                 6   t          j        |          }t          j        |dd|          }t          j        ||j                  }|dk     |dk    z  }t                                          ||         ||                   ||<   t          j        |dd          S )Nr   r   gư>r   r   )	rM   r  rk   _skewnorm_cdfr	  rw  r>   rr   r6	  )rB   rn   r   r   i_small_cdfr  s        r3   rr   zskewnorm_gen._cdf$  s    M!3Q//OAsy))Tza!e, 77<<++GGKwsAq!!!r5   c                 0    t          j        |dd|          S Nr   r   )rk   _skewnorm_ppfr
  s      r3   r{   zskewnorm_gen._ppf$       Ca000r5   c                 2    |                      | |           S rK   rA  r
  s      r3   rv   zskewnorm_gen._sf$  s     yy!aR   r5   c                 0    t          j        |dd|          S ry
  )rk   _skewnorm_isfr
  s      r3   r~   zskewnorm_gen._isf$  r{
  r5   Nc                    |                     |          }|                     |          }|t          j        d|dz  z             z  }||z  |t          j        d|dz  z
            z  z   }t          j        |dk    ||           S )Nr  r   rR   r   )normalrM   r   rO  )rB   r   r   r   u0r  r  r  s           r3   r   zskewnorm_gen._rvs$  s      d ++T**bga!Q$hrTAbga!Q$h''''xabS)))r5   rA  c                    g d}t          j        dt           j        z            |z  t          j        d|dz  z             z  }d|v r||d<   d|v rd|dz  z
  |d<   d|v r6dt           j        z
  dz  |t          j        d|dz  z
            z  d	z  z  |d<   d
|v r'dt           j        d	z
  z  |dz  d|dz  z
  dz  z  z  |d	<   |S )Nr3  rR   r   r  r   r  r  r   r  r  r  )rB   r   r  r-  consts        r3   r   zskewnorm_gen._stats$  s    )))"%  1$RWQAX%6%66'>>F1I'>>E1HF1I'>>be)Q5UAX1F1F+F*JJF1I'>>BEAI5!8Q\A4E+EFF1Ir5   c                 J   t          dg          t          ddg          t          g d          t          g d          t          g d          t          g d          t          g d          t          g d	          t          g d
          t          g d          d
}|S )Nr   r  r  )r  ir  )i   i?   i)i  iin  ir
  )(  iSi6Q  ii  iO)i iBi/ iio ir
  ) iԷi iYei{Hx ii i!)	i!iׅi쇀iiViX'ilir
  )
is_'il   </1 ldy( l   J8D l.~ l   -Rx iWi[i0)
r   r  rC  r  r  r     r        r   )rB   skewnorm_odd_momentss     r3   _skewnorm_odd_momentsz"skewnorm_gen._skewnorm_odd_moments$  s     1#1b'"",,,''...//77788EEEFF # # # $ $ 8 8 8 9 9 % % % & &  2 2 2 3 3 
  
$ $#r5   c                    |dz  rV|dk    rt          d          |t          j        d|dz  z             z  }| | j        |         |dz            z  t          z  S t          j        |dz   dz            d|dz  z  z  t          z  S )Nr   r
  zKskewnorm noncentral moments not implemented for odd orders greater than 19.rR   )r	  rM   r   r
  r&   rt   r  r%   )rB   r  r   r  s       r3   r   zskewnorm_gen._munp %  s    19 	Erzz) +5 6 6 6
 bga!Q$h'''E=D6u=eQhGGG%& ' 8UQYM**Qq\9HDDr5   a          If ``method='mm'``, parameters fixed by the user are respected, and the
        remaining parameters are used to match distribution and sample moments
        where possible. For example, if the user fixes the location with
        ``floc``, the parameters will only match the distribution skewness and
        variance to the sample skewness and variance; no attempt will be made
        to match the means or minimize a norm of the errors.
        Note that the maximum possible skewness magnitude of a
        `scipy.stats.skewnorm` distribution is approximately 0.9952717; if the
        magnitude of the data's sample skewness exceeds this, the returned
        shape parameter ``a`` will be infinite.
        

r   c           	         |                     dd          r t                      j        |g|R i |S t          |t                    rJ|                                dk    r|                                }n t                      j        |g|R i |S t          | |||          \  }}}}|                    dd          	                                }d }d }	|dk    rd	\  }
}}nEt          |          r|d         nd }
|                     d
d           }|                     dd           }||
t          j        |          }|dk    rt          j        |dd          }n" |d          }t          j        || |          } |	|          }t          j        d          5  t          j        t          j        |dz  d|dz  z
                      t          j        |          z  }
d d d            n# 1 swxY w Y   n#||n|
}
|
t          j        d|
dz  z             z  }|D|Bt          j        |          }t          j        |dd|dz  z  t          j        z  z
  z            }n||}|A|?t          j        |          }|||z  t          j        dt          j        z            z  z
  }n||}|dk    r|
||fS  t                      j        ||
f||d|S )Nr  Fr   r/   r8   c                     dt           j        z
  dz  | t          j        dt           j        z            z  dz  dd| dz  z  t           j        z  z
  dz  z  z  S )Nr   rR   r  r   r  r*  r  s    r3   skew_dz skewnorm_gen.fit.<locals>.skew_d1%  sX    beGQ;1rwq25y'9'9#9A"=%&1a4"%%73$?#@ A Ar5   c                     t          j        |           dz  }t          j        |           t          j        t           j        dz  |z  |dt           j        z
  dz  dz  z   z            z  S )NrK  rR   r   )rM   r
  rN   r   r   )r  s_23s     r3   d_skewz skewnorm_gen.fit.<locals>.d_skew4%  s]    6$<<#&D74==27a$$1ru9a-3)?"?@$ $  r5   r9   r  r,   r-   gGzgGz?r   r5  r6  rR   r  )r0   r>   r@   r<   r(   r=   r  r  r:   r;   r  r  r  rM   r6	  r8  r   r7  rN   r  r   r   )rB   rC   rD   r2   r  r   r   r/   r
  r
  r   r,   r-   r  s_maxr  r  r  r  s                     r3   r@   zskewnorm_gen.fit%  sl    88J&& 	4577;t3d333d333dL)) 	8  ""a''~~''"uww{47$777$777 "=T4=A4"I "Ib$(E**0022	A 	A 	A	 	 	 T>>,MAsEEt99.Q$A((5$''CHHWd++E:!) 
4  A GAud++q		GAvu--q		AH--- B BGBIadQq!tV5566rwqzzAB B B B B B B B B B B B B B B n!ABGA1H%%%A>emtAGAQq!tVBE\!1233EEE<CKAeAgbgag....CCCT>>c5=  577;tQECuEEEEEs   "AG44G8;G8r   rL  )r   r   r   r   r`   rh   ro   r   rr   r{   rv   r~   r   r   r   r
  r   r	   r   r@   r  r  s   @r3   rk
  rk
  $  sV        2  K K K
 
 

 
 
" " " " "1 1 1! ! !
1 1 1* * * *   * $ $ _$*E E E( } 5   FF FF FF FF FF FF FF FF FFr5   rk
  skewnormc                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
trapezoid_gena  A trapezoidal continuous random variable.

    %(before_notes)s

    Notes
    -----
    The trapezoidal distribution can be represented with an up-sloping line
    from ``loc`` to ``(loc + c*scale)``, then constant to ``(loc + d*scale)``
    and then downsloping from ``(loc + d*scale)`` to ``(loc+scale)``.  This
    defines the trapezoid base from ``loc`` to ``(loc+scale)`` and the flat
    top from ``c`` to ``d`` proportional to the position along the base
    with ``0 <= c <= d <= 1``.  When ``c=d``, this is equivalent to `triang`
    with the same values for `loc`, `scale` and `c`.
    The method of [1]_ is used for computing moments.

    `trapezoid` takes :math:`c` and :math:`d` as shape parameters.

    %(after_notes)s

    The standard form is in the range [0, 1] with c the mode.
    The location parameter shifts the start to `loc`.
    The scale parameter changes the width from 1 to `scale`.

    %(example)s

    References
    ----------
    .. [1] Kacker, R.N. and Lawrence, J.F. (2007). Trapezoidal and triangular
       distributions for Type B evaluation of standard uncertainty.
       Metrologia 44, 117-127. :doi:`10.1088/0026-1394/44/2/003`


    c                 F    |dk    |dk    z  |dk    z  |dk    z  ||k    z  S r	  r   rB   r  r  s      r3   r`   ztrapezoid_gen._argcheck%  s0    Q16"a1f-a8AFCCr5   c                 R    t          dddd          }t          dddd          }||gS )Nr  Fr   r   TTr  rb
  r   s      r3   rh   ztrapezoid_gen._shape_info%  s1    UHl;;UHl;;Bxr5   c                 z    d||z
  dz   z  }t          ||k     ||k    ||k    z  ||k    gd d d g||||f          S )NrR   r   c                     || z  |z  S rK   r   rn   r  r  r  s       r3   r  z$trapezoid_gen._pdf.<locals>.<lambda>%  s    q1uqy r5   c                     |S rK   r   r
  s       r3   r  z$trapezoid_gen._pdf.<locals>.<lambda>%  s    q r5   c                     |d| z
  z  d|z
  z  S r[   r   r
  s       r3   r  z$trapezoid_gen._pdf.<locals>.<lambda>%  s    qAaCyAaC/@ r5   r   )rB   rn   r  r  r  s        r3   ro   ztrapezoid_gen._pdf%  sn    1QKAE!VQ/E# 9800@@B q!Q<) ) 	)r5   c                 b    t          ||k     ||k    ||k    z  ||k    gd d d g|||f          S )Nc                 $    | dz  |z  ||z
  dz   z  S r  r   rn   r  r  s      r3   r  z$trapezoid_gen._cdf.<locals>.<lambda>%  s    AqD1H!A,> r5   c                 *    |d| |z
  z  z   ||z
  dz   z  S r  r   r
  s      r3   r  z$trapezoid_gen._cdf.<locals>.<lambda>%  s    Qac]qs1u,E r5   c                 6    dd| z
  dz  ||z
  dz   z  d|z
  z  z
  S r-  r   r
  s      r3   r  z$trapezoid_gen._cdf.<locals>.<lambda>%  s3    A!z23A#a%09<=aC0A -B r5   r
  r3  s       r3   rr   ztrapezoid_gen._cdf%  sa    AE!VQ/E# ?>EEB BC q!9& & 	&r5   c                 b   |                      |||          |                      |||          }}||k     ||k    ||k    g}t          j        ||z  d|z   |z
  z            d|z  d|z   |z
  z  d|z  z   dt          j        d|z
  ||z
  dz   z  d|z
  z            z
  g}t          j        ||          S r  )rr   rM   r   select)rB   rz   r  r  qcqdrU  r)	  s           r3   r{   ztrapezoid_gen._ppf%  s    1a##TYYq!Q%7%7BFAGQV,ga!eq1uqy122AgQ+cAg5"'1q5QUQY"71q5"ABBBD
 y:...r5   c                     |dz   z  }t          |dk    d|k     |dk     z  |dk    gd fdfdg|g          }dd|z   |z
  z  ||z
  z  dz   dz   z  z  }|S )	Nr   r   r   c                     dS r  r   r
  s    r3   r  z%trapezoid_gen._munp.<locals>.<lambda>%  s    s r5   c                 h    t          j        dz   t          j        |           z            | dz
  z  S rz  )rM   r  r   r  r_   s    r3   r  z%trapezoid_gen._munp.<locals>.<lambda>%  s+    rx1q		 122ae< r5   c                     dz   S r  r   r
  s    r3   r  z%trapezoid_gen._munp.<locals>.<lambda>%  s    qs r5   r   rR   r
  )rB   r_   r  r  ab_termdc_termrB  s    `     r3   r   ztrapezoid_gen._munp%  s     ac(#XaAG,a3h7]<<<<]]] C  SU1Wo7!23!!}E
r5   c                 f    dd|z
  |z   z  d|z   |z
  z  t          j        dd|z   |z
  z            z   S r   r.  r
  s      r3   r   ztrapezoid_gen._entropy%  s>     c!eAg#a%'*RVC3q57O-D-DDDr5   N)r   r   r   r   r`   rh   ro   rr   r{   r   r   r   r5   r3   r
  r
  l%  s           BD D D  
	) 	) 	)& & &/ / /  2E E E E Er5   r
  zS`trapz` is deprecated in favour of `trapezoid` and will be removed in SciPy 1.16.0.c                       e Zd ZdZd ZdS )	trapz_genz

    .. deprecated:: 1.14.0
        `trapz` is deprecated and will be removed in SciPy 1.16.
        Plese use `trapezoid` instead!
    c                 ^    t          j        t          t          d            | j        |i |S NrR   r  )r  r  deprmsgDeprecationWarningfreeze)rB   rD   r2   s      r3   __call__ztrapz_gen.__call__%  s1    g1a@@@@t{D)D)))r5   N)r   r   r   r   r
  r   r5   r3   r
  r
  %  s-         * * * * *r5   r
  	trapezoidtrapz)r   entropyexpectr@   intervalrk  logcdfr  logsfr   rH  r  pdfre  r  r  r  stdr  c                       e Zd Zd Zd ZdS )_DeprecationWrapperc                 V    d| d| d| _         t          t          |          | _        d S )Nz`trapz.z'` is deprecated in favour of trapezoid.zt. Please replace all uses of the distribution class `trapz` with `trapezoid`. `trapz` will be removed in SciPy 1.16.)rV   getattrr
  r/   )rB   r/   s     r3   rM  z_DeprecationWrapper.__init__%  sE    Xf X XV X X X i00r5   c                 ^    t          j        | j        t          d            | j        |i |S r
  )r  r  rV   r
  r/   )rB   rD   kwargss      r3   r
  z_DeprecationWrapper.__call__%  s3    dh 2qAAAAt{D+F+++r5   N)r   r   r   rM  r
  r   r5   r3   r
  r
  %  s2        1 1 1, , , , ,r5   r
  c                   D    e Zd ZdZddZd Zd Zd Zd Zd Z	d	 Z
d
 ZdS )
triang_gena5  A triangular continuous random variable.

    %(before_notes)s

    Notes
    -----
    The triangular distribution can be represented with an up-sloping line from
    ``loc`` to ``(loc + c*scale)`` and then downsloping for ``(loc + c*scale)``
    to ``(loc + scale)``.

    `triang` takes ``c`` as a shape parameter for :math:`0 \le c \le 1`.

    %(after_notes)s

    The standard form is in the range [0, 1] with c the mode.
    The location parameter shifts the start to `loc`.
    The scale parameter changes the width from 1 to `scale`.

    %(example)s

    Nc                 2    |                     d|d|          S r	  )
triangularr  s       r3   r   ztriang_gen._rvs&  s    &&q!Q555r5   c                     |dk    |dk    z  S r	  r   r}  s     r3   r`   ztriang_gen._argcheck&  s    Q16""r5   c                 (    t          dddd          gS )Nr  Fr
  r
  rb
  rg   s    r3   rh   ztriang_gen._shape_info!&  s    3x>>??r5   c                 r    t          |dk    ||k     ||k    |dk    z  |dk    gd d d d g||f          }|S )Nr   r   c                     dd| z  z
  S r  r   rn  s     r3   r  z!triang_gen._pdf.<locals>.<lambda>.&  s    a!a%i r5   c                     d| z  |z  S r  r   rn  s     r3   r  z!triang_gen._pdf.<locals>.<lambda>/&      a!eai r5   c                     dd| z
  z  d|z
  z  S r  r   rn  s     r3   r  z!triang_gen._pdf.<locals>.<lambda>0&  s    a1q5kQU&; r5   c                     d| z  S r  r   rn  s     r3   r  z!triang_gen._pdf.<locals>.<lambda>1&  
    a!e r5   r
  rB   rn   r  r  s       r3   ro   ztriang_gen._pdf$&  sk     aQq&Q!V,a! 0///;;++- A    r5   c                 r    t          |dk    ||k     ||k    |dk    z  |dk    gd d d d g||f          }|S )Nr   r   c                     d| z  | | z  z
  S r  r   rn  s     r3   r  z!triang_gen._cdf.<locals>.<lambda>:&  s    acAaCi r5   c                     | | z  |z  S rK   r   rn  s     r3   r  z!triang_gen._cdf.<locals>.<lambda>;&  r
  r5   c                 *    | | z  d| z  z
  |z   |dz
  z  S r  r   rn  s     r3   r  z!triang_gen._cdf.<locals>.<lambda><&  s    qsQqSy1}1&= r5   c                     | | z  S rK   r   rn  s     r3   r  z!triang_gen._cdf.<locals>.<lambda>=&  r
  r5   r
  r
  s       r3   rr   ztriang_gen._cdf5&  si    aQq&Q!V,a! 0///==++- A    r5   c           
          t          j        ||k     t          j        ||z            dt          j        d|z
  d|z
  z            z
            S r[   )rM   rO  r   r  s      r3   r{   ztriang_gen._ppfA&  sA    xArwq1u~~q!A#!A#1G1G/GHHHr5   c           	          |dz   dz  d|z
  ||z  z   dz  t          j        d          d|z  dz
  z  |dz   z  |dz
  z  dt          j        d|z
  ||z  z   d          z  z  dfS )	Nr   rD     rR   r   rC  r  g333333)rM   r   r  r}  s     r3   r   ztriang_gen._statsD&  sy    3QqsB

AaCE"AaC(!A#.!BHc!eAaCi#4N4N2NO 	r5   c                 0    dt          j        d          z
  S r  r.  r}  s     r3   r   ztriang_gen._entropyJ&  s    26!99}r5   r   )r   r   r   r   r   r`   rh   ro   rr   r{   r   r   r   r5   r3   r
  r
  &  s         *6 6 6 6# # #@ @ @  "
 
 
I I I      r5   r
  triangc                   X     e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Z fd
Zd Z xZS )truncexpon_genad  A truncated exponential continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `truncexpon` is:

    .. math::

        f(x, b) = \frac{\exp(-x)}{1 - \exp(-b)}

    for :math:`0 <= x <= b`.

    `truncexpon` takes ``b`` as a shape parameter for :math:`b`.

    %(after_notes)s

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   ztruncexpon_gen._shape_infog&  r  r5   c                     | j         |fS rK   r  r
  s     r3   r   ztruncexpon_gen._get_supportj&      vqyr5   c                 Z    t          j        |           t          j        |            z  S rK   r  r  s      r3   ro   ztruncexpon_gen._pdfm&  s#    vqbzzBHaRLL=))r5   c                 Z    | t          j        t          j        |                      z
  S rK   r  r  s      r3   r   ztruncexpon_gen._logpdfq&  s%    rBFBHaRLL=))))r5   c                 X    t          j        |           t          j        |           z  S rK   r=  r  s      r3   rr   ztruncexpon_gen._cdft&  s!    x||BHaRLL((r5   c                 X    t          j        |t          j        |           z             S rK   )rt   r  r  r  s      r3   r{   ztruncexpon_gen._ppfw&  s#    28QB<<((((r5   c                     t          j        |           t          j        |           z
  t          j        |           z  S rK   r  r  s      r3   rv   ztruncexpon_gen._sfz&  s0    r

RVQBZZ'1"55r5   c                     t          j        t          j        |           |t          j        |           z  z
             S rK   )rM   r   r   rt   r  r  s      r3   r~   ztruncexpon_gen._isf}&  s3    rvqbzzA!$445555r5   c                 R   |dk    r5d|dz   t          j        |           z  z
  t          j        |            z  S |dk    rDddd||z  d|z  z   dz   z  t          j        |           z  z
  z  t          j        |            z  S t	                                          ||          S r  )rM   r   rt   r  r>   r   )rB   r_   r   r  s      r3   r   ztruncexpon_gen._munp&  s     66qsBFA2JJ&&"(A2,,77!VVaQqS1WQYr

223bhrll]CC 77==A&&&r5   c                 |    t          j        |          }t          j        |dz
            d||dz
  z  z   d|z
  z  z   S r  r	
  )rB   r   eBs      r3   r   ztruncexpon_gen._entropy&  s;    VAYYvbd||Qr1S5z\CF333r5   )r   r   r   r   rh   r   ro   r   rr   r{   rv   r~   r   r   r  r  s   @r3   r
  r
  Q&  s         *E E E  * * ** * *) ) )) ) )6 6 66 6 6	' 	' 	' 	' 	'4 4 4 4 4 4 4r5   r
  
truncexponc                 2    t          j        | |gd          S )Nr   r  )rt   r  log_plog_qs     r3   _log_sumr
  &  s    <Q////r5   c                 R    t          j        | |t          j        dz  z   gd          S )N              ?r   r  )rt   r  rM   r   r
  s     r3   r
  r
  &  s&    <beBh/a8888r5   c                    t          j        | |          \  } }|dk    }| dk    }||z   }d fd}d }t          j        | t           j        t           j                  }| |         j        r | |         ||                   ||<   | |         j        r || |         ||                   ||<   | |         j        r || |         ||                   ||<   t          j        |          S )z3Log of Gaussian probability mass within an intervalr   c                 V    t          t          |          t          |                     S rK   )r
  r   r  s     r3   mass_case_leftz'_log_gauss_mass.<locals>.mass_case_left&  s    a,q//:::r5   c                       | |            S rK   r   )r   r   r
  s     r3   mass_case_rightz(_log_gauss_mass.<locals>.mass_case_right&  s    ~qb1"%%%r5   c                 h    t          j        t          |            t          |           z
            S rK   )rt   r  r   r  s     r3   mass_case_centralz*_log_gauss_mass.<locals>.mass_case_central&  s)     x1	1"5666r5   )rA  r  )rM   r  r  r  
complex128r   r  )	r   r   	case_left
case_rightcase_centralr
  r  r  r
  s	           @r3   _log_gauss_massr  &  s$   q!$$DAq QIQJ+,L; ; ;& & & & &7 7 7 ,qRV2=
A
A
AC| D')a	lCCI} H)/!J-:GGJ P--aoqOOL73<<r5   c                   x     e Zd ZdZd Zd Z fdZd Zd Zd Z	d Z
d	 Zd
 Zd Zd Zd Zd Zd ZddZ xZS )truncnorm_genaw
  A truncated normal continuous random variable.

    %(before_notes)s

    Notes
    -----
    This distribution is the normal distribution centered on ``loc`` (default
    0), with standard deviation ``scale`` (default 1), and truncated at ``a``
    and ``b`` *standard deviations* from ``loc``. For arbitrary ``loc`` and
    ``scale``, ``a`` and ``b`` are *not* the abscissae at which the shifted
    and scaled distribution is truncated.

    .. note::
        If ``a_trunc`` and ``b_trunc`` are the abscissae at which we wish
        to truncate the distribution (as opposed to the number of standard
        deviations from ``loc``), then we can calculate the distribution
        parameters ``a`` and ``b`` as follows::

            a, b = (a_trunc - loc) / scale, (b_trunc - loc) / scale

        This is a common point of confusion. For additional clarification,
        please see the example below.

    %(example)s

    In the examples above, ``loc=0`` and ``scale=1``, so the plot is truncated
    at ``a`` on the left and ``b`` on the right. However, suppose we were to
    produce the same histogram with ``loc = 1`` and ``scale=0.5``.

    >>> loc, scale = 1, 0.5
    >>> rv = truncnorm(a, b, loc=loc, scale=scale)
    >>> x = np.linspace(truncnorm.ppf(0.01, a, b),
    ...                 truncnorm.ppf(0.99, a, b), 100)
    >>> r = rv.rvs(size=1000)

    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
    >>> ax.set_xlim(a, b)
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()

    Note that the distribution is no longer appears to be truncated at
    abscissae ``a`` and ``b``. That is because the *standard* normal
    distribution is first truncated at ``a`` and ``b``, *then* the resulting
    distribution is scaled by ``scale`` and shifted by ``loc``. If we instead
    want the shifted and scaled distribution to be truncated at ``a`` and
    ``b``, we need to transform these values before passing them as the
    distribution parameters.

    >>> a_transformed, b_transformed = (a - loc) / scale, (b - loc) / scale
    >>> rv = truncnorm(a_transformed, b_transformed, loc=loc, scale=scale)
    >>> x = np.linspace(truncnorm.ppf(0.01, a, b),
    ...                 truncnorm.ppf(0.99, a, b), 100)
    >>> r = rv.rvs(size=10000)

    >>> fig, ax = plt.subplots(1, 1)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
    >>> ax.set_xlim(a-0.1, b+0.1)
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()
    c                     ||k     S rK   r   r  s      r3   r`   ztruncnorm_gen._argcheck'  s    1ur5   c                     t          ddt          j         t          j        fd          }t          ddt          j         t          j        fd          }||gS )Nr   Frd   r   )FTre   re  s      r3   rh   ztruncnorm_gen._shape_info	'  sG    UbfWbf$5}EEUbfWbf$5}EEBxr5   c                     t          |t                    r|                                }t                                          |t          j        |          t          j        |          f          S r
  r
  r  s     r3   r  ztruncnorm_gen._fitstart'  sV    dL)) 	$>>##Dww  RVD\\26$<<,H IIIr5   c                 
    ||fS rK   r   r  s      r3   r   ztruncnorm_gen._get_support'  r   r5   c                 T    t          j        |                     |||                    S rK   r  rq  s       r3   ro   ztruncnorm_gen._pdf'  r  r5   c                 B    t          |          t          ||          z
  S rK   )r   r  rq  s       r3   r   ztruncnorm_gen._logpdf'  s    AA!6!666r5   c                 T    t          j        |                     |||                    S rK   r  rq  s       r3   rr   ztruncnorm_gen._cdf'  r  r5   c           
      v   t          j        |||          \  }}}t          j        t          ||          t          ||          z
            }|dk    }t          j        |          rQt          j        t          j        |                     ||         ||         ||                                        ||<   |S Ng)rM   r  r   r  r  r  r   r   )rB   rn   r   r   r
  r  s         r3   r   ztruncnorm_gen._logcdf '  s    %aA..1aOAq11OAq4I4IIJJTM6!99 	I"&QqT1Q41)F)F"G"G!GHHF1Ir5   c                 T    t          j        |                     |||                    S rK   r6  rq  s       r3   rv   ztruncnorm_gen._sf('  r7  r5   c           
      v   t          j        |||          \  }}}t          j        t          ||          t          ||          z
            }|dk    }t          j        |          rQt          j        t          j        |                     ||         ||         ||                                        ||<   |S r  )rM   r  r   r  r  r  r   r   )rB   rn   r   r   r
  r  s         r3   r   ztruncnorm_gen._logsf+'  s    %aA..1a
?1a00?1a3H3HHIIDL6!99 	IxQqT1Q41(F(F!G!G GHHE!Hr5   c                 2   t          |          }t          |          }||z
  }t          j        t          j        dt          j        z  t          j        z            |z            }|t          |          z  |t          |          z  z
  d|z  z  }||z   }|S r  )r   rM   r   r   r   r  r   )	rB   r   r   r+  r,  Zr  Dr  s	            r3   r   ztruncnorm_gen._entropy3'  s    aLLaLLEF271ru9rt+,,q0111IaLL 00QU;Er5   c                 ,   t          j        |||          \  }}}|dk     }| }d }d }t          j        |          }||         }	||         }
|	j        r ||	||         ||                   ||<   |
j        r ||
||         ||                   ||<   |S )Nr   c                     t          t          |          t          j        |           t	          ||          z             }t          j        |          S rK   )r
  r   rM   r   r  rt   	ndtri_exprz   r   r   	log_Phi_xs       r3   ppf_leftz$truncnorm_gen._ppf.<locals>.ppf_leftB'  sE     a!#_Q-B-B!BD DI<	***r5   c                     t          t          |           t          j        |            t	          ||          z             }t          j        |           S rK   )r
  r   rM   r  r  rt   r  r  s       r3   	ppf_rightz%truncnorm_gen._ppf.<locals>.ppf_rightG'  sN     qb!1!1!#1"10E0E!EG GIL++++r5   rM   r  
empty_liker   )rB   rz   r   r   r  r  r  r  r  q_leftq_rights              r3   r{   ztruncnorm_gen._ppf<'  s    %aA..1aE	Z
	+ 	+ 	+
	, 	, 	,
 mA9J-; 	J%Xfa	lAiLIIC	N< 	O'i:*NNC
O
r5   c                 ,   t          j        |||          \  }}}|dk     }| }d }d }t          j        |          }||         }	||         }
|	j        r ||	||         ||                   ||<   |
j        r ||
||         ||                   ||<   |S )Nr   c                     t          t          |          t          j        |           t	          ||          z             }t          j        t          j        |                    S rK   )r
  r   rM   r   r  rt   r  r  r  s       r3   isf_leftz$truncnorm_gen._isf.<locals>.isf_left_'  sO    !,q//"$&))oa.C.C"CE EI<	 2 2333r5   c                     t          t          |           t          j        |            t	          ||          z             }t          j        t          j        |                     S rK   )r
  r   rM   r  r  rt   r  r  r  s       r3   	isf_rightz%truncnorm_gen._isf.<locals>.isf_rightd'  sX    !,r"2"2"$(A2,,A1F1F"FH HIL!3!34444r5   r  )rB   rz   r   r   r  r  r%  r'  r  r!  r"  s              r3   r~   ztruncnorm_gen._isfX'  s    %aA..1aE	Z
	4 	4 	4
	5 	5 	5
 mA9J-; 	J%Xfa	lAiLIIC	N< 	O'i:*NNC
O
r5   c                       fd}t          |dk    ||k    z  ||k    z  |||ft          j        |t          j        g          t          j                  S )Nc                 T  	 
                     t          j        ||g          ||          \  }}|| g}ddg}t          d| dz             D ]T	t	          ||||gg	fdd          }t          j        |          	dz
  |d         z  z   }|                    |           U|d         S )z
            Returns n-th moment. Defined only if n >= 0.
            Function cannot broadcast due to the loop over n
            r   r   c                     | |dz
  z  z  S r[   r   )rn   ri  r  s     r3   r  z:truncnorm_gen._munp.<locals>.n_th_moment.<locals>.<lambda>'  s    q1qs8| r5   r  r  r  )ro   rM   r   r  r   r  r  )r_   r   r   pApBprobsr  r  mkr  rB   s            @r3   n_th_momentz(truncnorm_gen._munp.<locals>.n_th_momentv'  s    
 YYrz1a&111a88FB"IE!fG1ac]] # #
 "%%!Q";";";";qJ J JVD\\QqSGBK$77r""""2;r5   r   rR  r  )rB   r_   r   r   r/  s   `    r3   r   ztruncnorm_gen._munpu'  sk    	 	 	 	 	& 16a1f-a81a),{BJ<HHH&" " 	"r5   r  c                     |                      t          j        ||g          ||          \  }}d }t          j        |d          } ||||||          S )Nc                 ^   ||z
  }|}|| g}t          ||| |ggd d          }dt          j        |          z   }	t          ||| |z
  ||z
  ggd d          }dt          j        |          z   }
t          ||| |ggd d          }d|z  t          j        |          z   }t          ||| |ggd d          }d	|	z  t          j        |          z   }||d
|	z  d|dz  z  z   z  z   }|t          j        |
d          z  }||d|z  d	|z  d|	z  |dz  z
  z  z   z  z   }||
dz  z  d	z
  }||
||fS )Nc                     | |z  S rK   r   rh  s     r3   r  zGtruncnorm_gen._stats.<locals>._truncnorm_stats_scalar.<locals>.<lambda>'  s
    1Q3 r5   r   r  r   c                     | |z  S rK   r   rh  s     r3   r  zGtruncnorm_gen._stats.<locals>._truncnorm_stats_scalar.<locals>.<lambda>'  s
    1 r5   c                     | |dz  z  S r  r   rh  s     r3   r  zGtruncnorm_gen._stats.<locals>._truncnorm_stats_scalar.<locals>.<lambda>'      1QT6 r5   rR   c                     | |dz  z  S rY
  r   rh  s     r3   r  zGtruncnorm_gen._stats.<locals>._truncnorm_stats_scalar.<locals>.<lambda>'  r5  r5   r  r  r  r  )r   rM   r  r  )r   r   r+  r,  r  r  r@  r-  r  r  rA  r  m4mu3rB  mu4rC  s                    r3   _truncnorm_stats_scalarz5truncnorm_gen._stats.<locals>._truncnorm_stats_scalar'  s   bBB"IEeeaV_6F6F()+ + +DRVD\\!BeeadAbD\%:<L<L()+ + +D bfTll"CeeaV_6I6I()+ + +D2t$BeeaV_6I6I()+ + +D2t$BrRUQr1uW_--CrxS)))Br2b51R42A#6677CsAv!BsB?"r5   )r  )excluded)r
  rM   r[  rL  )rB   r   r   r  r+  r,  r:  _truncnorm_statss           r3   r   ztruncnorm_gen._stats'  sp    "(Aq6**Aq11B	# 	# 	#4 <(?1=? ? ?1b"g666r5   r  )r   r   r   r   r`   rh   r  r   ro   r   rr   r   rv   r   r   r{   r~   r   r   r  r  s   @r3   r  r  &  s       > >@    
J J J J J  - - -7 7 7- - -  , , ,      8  :" " "07 7 7 7 7 7 7 7r5   r  	truncnorm)r   r   c                        e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 Zd Zd Zd Zd Zd Ze ee           fd                        Z xZS )truncpareto_genac  An upper truncated Pareto continuous random variable.

    %(before_notes)s

    See Also
    --------
    pareto : Pareto distribution

    Notes
    -----
    The probability density function for `truncpareto` is:

    .. math::

        f(x, b, c) = \frac{b}{1 - c^{-b}} \frac{1}{x^{b+1}}

    for :math:`b > 0`, :math:`c > 1` and :math:`1 \le x \le c`.

    `truncpareto` takes `b` and `c` as shape parameters for :math:`b` and
    :math:`c`.

    Notice that the upper truncation value :math:`c` is defined in
    standardized form so that random values of an unscaled, unshifted variable
    are within the range ``[1, c]``.
    If ``u_r`` is the upper bound to a scaled and/or shifted variable,
    then ``c = (u_r - loc) / scale``. In other words, the support of the
    distribution becomes ``(scale + loc) <= x <= (c*scale + loc)`` when
    `scale` and/or `loc` are provided.

    %(after_notes)s

    References
    ----------
    .. [1] Burroughs, S. M., and Tebbens S. F.
        "Upper-truncated power laws in natural systems."
        Pure and Applied Geophysics 158.4 (2001): 741-757.

    %(example)s

    c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )Nr   Fr   r  r  r   re   )rB   rg  r!  s      r3   rh   ztruncpareto_gen._shape_info'  s=    US"&M>BBUS"&M>BBBxr5   c                     |dk    |dk    z  S ry
  r   rB   r   r  s      r3   r`   ztruncpareto_gen._argcheck'  s    B1r6""r5   c                     | j         |fS rK   r  rB  s      r3   r   ztruncpareto_gen._get_support'  r
  r5   c                 2    |||dz    z  z  dd||z  z  z
  z  S r[   r   rB   rn   r   r  s       r3   ro   ztruncpareto_gen._pdf'  s'    1!f9}AadF
++r5   c           	          t          j        |          t          j        t          j        | t          j        |          z                       z
  |dz   t          j        |          z  z
  S r[   )rM   r   r  rE  s       r3   r   ztruncpareto_gen._logpdf'  sO    vayy2628QBrvayyL#9#9"9:::ac26!99_LLr5   c                 ,    d|| z  z
  dd||z  z  z
  z  S r[   r   rE  s       r3   rr   ztruncpareto_gen._cdf'  s#    ArE	a!AqD&j))r5   c                 j    t          j        || z             t          j        d||z  z            z
  S r  r9  rE  s       r3   r   ztruncpareto_gen._logcdf'  s1    xQB"(2ad7"3"333r5   c                 F    t          ddd||z  z  z
  |z  z
  d|z            S Nr   r  r  rB   rz   r   r  s       r3   r{   ztruncpareto_gen._ppf'  s+    1AadF
A~%r!t,,,r5   c                 8    || z  d||z  z  z
  dd||z  z  z
  z  S r[   r   rE  s       r3   rv   ztruncpareto_gen._sf'  s+    A2!Q$1qAv:..r5   c                 z    t          j        || z  d||z  z  z
            t          j        d||z  z            z
  S rJ  r  rE  s       r3   r   ztruncpareto_gen._logsf'  s;    va!ea1fn%%AqD(9(999r5   c                 R    t          d||z  z  dd||z  z  z
  |z  z   d|z            S rJ  r  rK  s       r3   r~   ztruncpareto_gen._isf'  s3    1QT6Q1a4ZN*BqD111r5   c                     t          j        |dd||z  z  z
  z            |dz   t          j        |          ||z  dz
  z  d|z  z
  z  z    S r[   r.  rB  s      r3   r   ztruncpareto_gen._entropy'  sX    1qAv:''aC"&))QTAX.1456 7 	7r5   c                     ||k                                     r#|t          j        |          z  dd||z  z  z
  z  S |||z
  z  ||z  ||z  z
  z  ||z  dz
  z  S r[   )r   rM   r   )rB   r_   r   r  s       r3   r   ztruncpareto_gen._munp(  se    F<<>> 	:RVAYY;!a1f*--!91q!t,1q99r5   c                     t          |t                    r|                                }t                              |          \  }}}t          |          |z
  |z  }||||fS rK   )r<   r(   r  r[	  r@   rc  )rB   rC   r   r,   r-   r  s         r3   r  ztruncpareto_gen._fitstart	(  s]    dL)) 	$>>##D

4((3YY_e#!S%r5   c                 |    !"#$% |                     dd          r t                      j        g|R i |S d #d ""#fd%fd $%fd}$fd!d !"fd		}d
 }& fd}t           ||          }|\  }	}
}}                                                                c$%t          j        $t          j                   }|	|
||t          d          |
|| |	$ !"fd}t          j        $t          j                   }|}d}|dz
  }|t          j         k    rd ||           ||          z  dk    rI|dz  }|t          j
        d|          z
  }|t          j         k    r ||           ||          z  dk    I|t          j         k    s |g|R i |S t          |||f          }|j        s |g|R i |S |j        dz
  }|dz
  }d}|t          j         k    rd ||           ||          z  dk    rI|dz  }|t          j
        d|          z
  }|t          j         k    r ||           ||          z  dk    I|t          j         k    s |g|R i |S t          |||f          }|j        s |g|R i |S |j        } !|          }  ||          } |||          }|z
  |z  }t	          d #|          z  d "|          dz
  z            }||k     s |g|R i |S n|}|dz
  }d}|t          j         k    rX |||	           |||	          z  dk    r;|dz  }|d|z  z
  }|t          j         k    r |||	           |||	          z  dk    ;|t          j         k    s |g|R i |S t          ||	f||f          }|j        s |g|R i |S |j        } !|          }  ||          }|	}nD||n ||
|          }|p
 !|          }|
p  ||          }|-                                |z
  dk     rt          dd|          |
r>|<|r:                                |
|z  |z   k    rt          dd  ||                    |	|z
  |z  } #|          }t          j        |          }d|z  |k     s |g|R i |S d|z  d||z
  z  z   }t          j        d|z  d          }	 t          |||f||f          }|j        s |g|R i |S |j        }n# t          $ r |}Y nw xY w|	}||z   $k     sC|r!t          j        |t          j                   }n  !|          }t          j        |d          }||z  |z   %k    s+  ||          }t          j        |t          j                  }t          j                             ||                    r|dk    s |g|R i |S ||||f}|B|@ |g|R i |}                     |          }                     |          }||k     r|S |S )Nr  Fc                 N    t          j        t          j        |                     S rK   )rM   r   r   r   s    r3   log_meanz%truncpareto_gen.fit.<locals>.log_mean(  s    726!99%%%r5   c                 6    dt          j        d| z            z  S r[   )rM   r   r   s    r3   	harm_meanz&truncpareto_gen.fit.<locals>.harm_mean(  s    RWQqS\\>!r5   c                     |z
  |z  } |          } 	|          }|dz
  |z  }d|dz
  |dd| z  z
  |z  t          j        |           z  z
  z  z
  |z  S r[   r.  )
r  r,   r-   r  harm_mlog_mquotrC   rV  rT  s
          r3   get_bz"truncpareto_gen.fit.<locals>.get_b(  sq    c5 AYq\\FHQKKE1He#DaDA!GV+;BF1II+E$EFFMMr5   c                     | z
  |z  S rK   r   )r,   r-   mxs     r3   get_cz"truncpareto_gen.fit.<locals>.get_c#(  s    He##r5   c                 >    |r|z
  }|S | r| z  z
  | dz
  z  }|S d S r[   r   )r  r   r,   rZ  r]  s      r3   get_locz$truncpareto_gen.fit.<locals>.get_loc&(  sF     6k
 "urzBF+
 r5   c                     | z
  S rK   r   )r,   rZ  s    r3   r	  z&truncpareto_gen.fit.<locals>.get_scale.(  s    8Or5   c                      	|           } | |          }| || |          n|} 
| z
  |z            }dd|dz
  ||dz   z  |z
  z  z   dd|dz   z  z
  z  |z  z
  S r[   r   )r,   r  r-   r  r   rX  rC   r[  r^  r	  rV  s         r3   r  z$truncpareto_gen.fit.<locals>.dL_dLoc4(  s     IcNNEc5!!A(*
ae$$$AYs
E122FQUQ1X\22q1ac7{CfLLLr5   c                 N    | t          j        | |z  d| |z  z
  z            |z  z
  S r[   r9  )r   logclogms      r3   dL_dBz"truncpareto_gen.fit.<locals>.dL_dB=(  s/     rx$!af* 566===r5   c                 L     t          t                    j        | g|R i |S rK   )r>   r?  r@   )rC   rD   r
  r  rB   s      r3   fallbackz%truncpareto_gen.fit.<locals>.fallbackC(  s0    35$//3DJ4JJJ6JJJr5   z2All parameters fixed.There is nothing to optimize.c                      |           } | |          } | z
  |z            }dd|dz
  z  z   t          j        |          z  |z  dz
  S r[   r.  )r,   r-   r  rX  rC   r^  r	  rV  s       r3   cond_bz#truncpareto_gen.fit.<locals>.cond_bT(  sb    %IcNNEc5))A&Ys
E'9::F1Q3K26!994v=AAr5   r   r   r   r  gMbP?rR   truncparetor  rK   )r0   r>   r@   r  rK  rc  rM   r  rf   r   r  r)   r  r  rH  r   r   r`   r  )'rB   rC   rD   r2   r`  r  rf  rh  r  r  r  r   r   mn_infrj  rP   r  rO   r  r,   r-   r  r   std_data
up_bound_bre  rd  params_overrideparams_supernllf_override
nllf_superr[  r^  r	  rV  rT  rZ  r]  r  s'   ``                             @@@@@@@r3   r@   ztruncpareto_gen.fit(  se	    88J&& 	4577;t3d333d333	& 	& 	&	" 	" 	"	N 	N 	N 	N 	N 	N 	N	$ 	$ 	$ 	$ 	$	 	 	 	 	 		 	 	 	 		M 	M 	M 	M 	M 	M 	M 	M 	M 	M	> 	> 	>	K 	K 	K 	K 	K 	K 1tT4HH
%/"b"dFTXXZZBb26'**NN$& = > > >ZDLV^zB B B B B B B B b26'22!"&(("F6NN66&>>9Q>>FA#bhr1oo5F "&(("F6NN66&>>9Q>> ''#8D848884888!&662BCCC} 9#8D848884888 D!"&((#GFOOGGFOO;q@@FA#bhr1oo5F "&((#GFOOGGFOO;q@@ ''#8D848884888!'FF3CDDD} 9#8D848884888h!	#E#u%%E!S%(( 3J- 88H#5#5!5!"IIh$7$7$9!:< <
J#8D848884888 '
  !''#GFB//%gfb112567 7FA#ad]F	 ''#GFB//%gfb112567 7 ''#8D848884888!'B5+16*:< < <} 9#8D848884888h!	#E#u%% *$$F0C0CC,iinnE'eeC''A DHHJJ$5$9$9"=CCCC  @t'V'88::6	D 000&}A-2U3->->@ @ @ @ z 3J-x))vayy$#8D8488848884!TD[/1afa00%edD\/5v.>@ @ @C = ='x<t<<<t<<<AA!   AAA  c	R /l300!	#UA..%r!!c5!!AQ''At~~a++,, 	1%!))8D040004000QU*<FN
 $8D84888488L IIot<<M<66JM))##s   0(T! T! !T0/T0)r   r   r   r   rh   r`   r   ro   r   rr   r   r{   rv   r   r~   r   r   r  rH   r   r   r@   r  r  s   @r3   r?  r?  '  sC       ' 'R  
# # #  , , ,M M M* * *4 4 4- - -/ / /: : :2 2 27 7 7: : :      M**Z Z Z Z +* _Z Z Z Z Zr5   r?  rk  c                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
tukeylambda_gena*  A Tukey-Lamdba continuous random variable.

    %(before_notes)s

    Notes
    -----
    A flexible distribution, able to represent and interpolate between the
    following distributions:

    - Cauchy                (:math:`lambda = -1`)
    - logistic              (:math:`lambda = 0`)
    - approx Normal         (:math:`lambda = 0.14`)
    - uniform from -1 to 1  (:math:`lambda = 1`)

    `tukeylambda` takes a real number :math:`lambda` (denoted ``lam``
    in the implementation) as a shape parameter.

    %(after_notes)s

    %(example)s

    c                 *    t          j        |          S rK   rd  rB   lams     r3   r`   ztukeylambda_gen._argcheck	)  s    {3r5   c                 V    t          ddt          j         t          j        fd          gS )Nrw  Fr  re   rg   s    r3   rh   ztukeylambda_gen._shape_info)  s$    5%26'26):NKKLLr5   c                 P   t          j        t          j        ||                    }||dz
  z  t          j        d|z
            |dz
  z  z   }dt          j        |          z  }t          j        |dk    t          |          dt          j        |          z  k     z  |d          S )Nr   r   r   r   )rM   r   rt   tklmbdarO  r
  )rB   rn   rw  Fxr   s        r3   ro   ztukeylambda_gen._pdf)  s    Z
1c**++#c']bj2..#c'::Bxc!ffs2:c??/B&BCRMMMr5   c                 ,    t          j        ||          S rK   )rt   rz  )rB   rn   rw  s      r3   rr   ztukeylambda_gen._cdf)  s    z!S!!!r5   c                 Z    t          j        ||          t          j        | |          z
  S rK   )rt   rx  rv  )rB   rz   rw  s      r3   r{   ztukeylambda_gen._ppf)  s'    yC  2;r3#7#777r5   c                 B    dt          |          dt          |          fS r  )_tlvar_tlkurtrv  s     r3   r   ztukeylambda_gen._stats)  s    &++q'#,,..r5   c                 F    fd}t          j        |dd          d         S )Nc                 |    t          j        t          | dz
            t          d| z
  dz
            z             S r[   )rM   r   r  )r  rw  s    r3   integz'tukeylambda_gen._entropy.<locals>.integ)  s4    6#aQ--AaCQ7888r5   r   r   )r   ra  )rB   rw  r  s    ` r3   r   ztukeylambda_gen._entropy)  s5    	9 	9 	9 	9 	9~eQ**1--r5   N)r   r   r   r   r`   rh   ro   rr   r{   r   r   r   r5   r3   rt  rt  (  s         ,     M M MN N N" " "8 8 8/ / /. . . . .r5   rt  tukeylambdac                       e Zd Zd ZdS )FitUniformFixedScaleDataErrorc                 "    d| d| d| _         d S )NzInvalid values in `data`.  Maximum likelihood estimation with the uniform distribution and fixed scale requires that np.ptp(data) <= fscale, but np.ptp(data) = z and fscale = r  rJ  )rB   r	  r   s      r3   rM  z&FitUniformFixedScaleDataError.__init__()  s3    ":=" " " " " 				r5   N)r   r   r   rM  r   r5   r3   r  r  ')  s#        
 
 
 
 
r5   r  c                   T    e Zd ZdZd ZddZd Zd Zd Zd Z	d	 Z
ed
             ZdS )uniform_gena  A uniform continuous random variable.

    In the standard form, the distribution is uniform on ``[0, 1]``. Using
    the parameters ``loc`` and ``scale``, one obtains the uniform distribution
    on ``[loc, loc + scale]``.

    %(before_notes)s

    %(example)s

    c                     g S rK   r   rg   s    r3   rh   zuniform_gen._shape_info=)  r   r5   Nc                 0    |                     dd|          S ry
  )r  r   s      r3   r   zuniform_gen._rvs@)  s    ##Cd333r5   c                     d||k    z  S r  r   r   s     r3   ro   zuniform_gen._pdfC)  s    AF|r5   c                     |S rK   r   r   s     r3   rr   zuniform_gen._cdfF)      r5   c                     |S rK   r   r   s     r3   r{   zuniform_gen._ppfI)  r  r5   c                     dS )N)r   gUUUUUU?r   g333333r   rg   s    r3   r   zuniform_gen._statsL)  s    ##r5   c                     dS r  r   rg   s    r3   r   zuniform_gen._entropyO)  rG  r5   c                 ,   t          |          dk    rt          d          |                    dd          }|                    dd          }t          |           ||t	          d          t          j        |          }t          j        |                                          st	          d          |r|)|	                                }t          j
        |          }n|}|                                |z
  }|	                                |k     rt          d|||z   	          nJt          j
        |          }||k    rt          ||
          |	                                d||z
  z  z
  }|}t          |          t          |          fS )a	  
        Maximum likelihood estimate for the location and scale parameters.

        `uniform.fit` uses only the following parameters.  Because exact
        formulas are used, the parameters related to optimization that are
        available in the `fit` method of other distributions are ignored
        here.  The only positional argument accepted is `data`.

        Parameters
        ----------
        data : array_like
            Data to use in calculating the maximum likelihood estimate.
        floc : float, optional
            Hold the location parameter fixed to the specified value.
        fscale : float, optional
            Hold the scale parameter fixed to the specified value.

        Returns
        -------
        loc, scale : float
            Maximum likelihood estimates for the location and scale.

        Notes
        -----
        An error is raised if `floc` is given and any values in `data` are
        less than `floc`, or if `fscale` is given and `fscale` is less
        than ``data.max() - data.min()``.  An error is also raised if both
        `floc` and `fscale` are given.

        Examples
        --------
        >>> import numpy as np
        >>> from scipy.stats import uniform

        We'll fit the uniform distribution to `x`:

        >>> x = np.array([2, 2.5, 3.1, 9.5, 13.0])

        For a uniform distribution MLE, the location is the minimum of the
        data, and the scale is the maximum minus the minimum.

        >>> loc, scale = uniform.fit(x)
        >>> loc
        2.0
        >>> scale
        11.0

        If we know the data comes from a uniform distribution where the support
        starts at 0, we can use `floc=0`:

        >>> loc, scale = uniform.fit(x, floc=0)
        >>> loc
        0.0
        >>> scale
        13.0

        Alternatively, if we know the length of the support is 12, we can use
        `fscale=12`:

        >>> loc, scale = uniform.fit(x, fscale=12)
        >>> loc
        1.5
        >>> scale
        12.0

        In that last example, the support interval is [1.5, 13.5].  This
        solution is not unique.  For example, the distribution with ``loc=2``
        and ``scale=12`` has the same likelihood as the one above.  When
        `fscale` is given and it is larger than ``data.max() - data.min()``,
        the parameters returned by the `fit` method center the support over
        the interval ``[data.min(), data.max()]``.

        r   rI  r   Nr   r   r   r  r  )r	  r   r   )r  r1   r0   r4   r   rM   r   r   r   rK  r	  rc  rH  r  rL  )	rB   rC   rD   r2   r   r   r,   r-   r	  s	            r3   r@   zuniform_gen.fitR)  s   V t99q==1222xx%%(D))$T*** 2 ) * * * z${4  $$&& 	ECDDD> >|hhjjt 

S(88::##&y3;OOOO $ &,,CV||3FKKKK ((**sFSL11CE Szz5<<''r5   r   )r   r   r   r   rh   r   ro   rr   r{   r   r   rH   r@   r   r5   r3   r  r  1)  s        
 
  4 4 4 4      $ $ $   R( R( _R( R( R(r5   r  r  c                        e Zd ZdZd Zd ZddZ ee           fd            Z	d Z
d Zd	 Zd
 Zd Z eed          	 	 d fd	            Ze eed           fd                        Z xZS )vonmises_genaU  A Von Mises continuous random variable.

    %(before_notes)s

    See Also
    --------
    scipy.stats.vonmises_fisher : Von-Mises Fisher distribution on a
                                  hypersphere

    Notes
    -----
    The probability density function for `vonmises` and `vonmises_line` is:

    .. math::

        f(x, \kappa) = \frac{ \exp(\kappa \cos(x)) }{ 2 \pi I_0(\kappa) }

    for :math:`-\pi \le x \le \pi`, :math:`\kappa \ge 0`. :math:`I_0` is the
    modified Bessel function of order zero (`scipy.special.i0`).

    `vonmises` is a circular distribution which does not restrict the
    distribution to a fixed interval. Currently, there is no circular
    distribution framework in SciPy. The ``cdf`` is implemented such that
    ``cdf(x + 2*np.pi) == cdf(x) + 1``.

    `vonmises_line` is the same distribution, defined on :math:`[-\pi, \pi]`
    on the real line. This is a regular (i.e. non-circular) distribution.

    Note about distribution parameters: `vonmises` and `vonmises_line` take
    ``kappa`` as a shape parameter (concentration) and ``loc`` as the location
    (circular mean). A ``scale`` parameter is accepted but does not have any
    effect.

    Examples
    --------
    Import the necessary modules.

    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> from scipy.stats import vonmises

    Define distribution parameters.

    >>> loc = 0.5 * np.pi  # circular mean
    >>> kappa = 1  # concentration

    Compute the probability density at ``x=0`` via the ``pdf`` method.

    >>> vonmises.pdf(0, loc=loc, kappa=kappa)
    0.12570826359722018

    Verify that the percentile function ``ppf`` inverts the cumulative
    distribution function ``cdf`` up to floating point accuracy.

    >>> x = 1
    >>> cdf_value = vonmises.cdf(x, loc=loc, kappa=kappa)
    >>> ppf_value = vonmises.ppf(cdf_value, loc=loc, kappa=kappa)
    >>> x, cdf_value, ppf_value
    (1, 0.31489339900904967, 1.0000000000000004)

    Draw 1000 random variates by calling the ``rvs`` method.

    >>> sample_size = 1000
    >>> sample = vonmises(loc=loc, kappa=kappa).rvs(sample_size)

    Plot the von Mises density on a Cartesian and polar grid to emphasize
    that it is a circular distribution.

    >>> fig = plt.figure(figsize=(12, 6))
    >>> left = plt.subplot(121)
    >>> right = plt.subplot(122, projection='polar')
    >>> x = np.linspace(-np.pi, np.pi, 500)
    >>> vonmises_pdf = vonmises.pdf(x, loc=loc, kappa=kappa)
    >>> ticks = [0, 0.15, 0.3]

    The left image contains the Cartesian plot.

    >>> left.plot(x, vonmises_pdf)
    >>> left.set_yticks(ticks)
    >>> number_of_bins = int(np.sqrt(sample_size))
    >>> left.hist(sample, density=True, bins=number_of_bins)
    >>> left.set_title("Cartesian plot")
    >>> left.set_xlim(-np.pi, np.pi)
    >>> left.grid(True)

    The right image contains the polar plot.

    >>> right.plot(x, vonmises_pdf, label="PDF")
    >>> right.set_yticks(ticks)
    >>> right.hist(sample, density=True, bins=number_of_bins,
    ...            label="Histogram")
    >>> right.set_title("Polar plot")
    >>> right.legend(bbox_to_anchor=(0.15, 1.06))

    c                 @    t          dddt          j        fd          gS )NrL  Fr   rd   re   rg   s    r3   rh   zvonmises_gen._shape_infoK*  s    7EArv;FFGGr5   c                     |dk    S r  r   r\  s     r3   r`   zvonmises_gen._argcheckN*  s    zr5   Nc                 2    |                     d||          S )Nr   r  )vonmises)rB   rL  r   r   s       r3   r   zvonmises_gen._rvsQ*  s    $$S%d$;;;r5   c                      t                      j        |i |}t          j        |t          j        z   dt          j        z            t          j        z
  S r  )r>   r  rM   modr   )rB   rD   r2   r  r  s       r3   r  zvonmises_gen.rvsT*  sB    eggk4(4((vcBEk1RU7++be33r5   c                     t          j        |t          j        |          z            dt           j        z  t          j        |          z  z  S r  )rM   r   rt   cosm1r   r
  rN  s      r3   ro   zvonmises_gen._pdfY*  s9    
 veBHQKK'((AbeGBF5MM,ABBr5   c                     |t          j        |          z  t          j        dt          j        z            z
  t          j        t          j        |                    z
  S r  )rt   r  rM   r   r   r
  rN  s      r3   r   zvonmises_gen._logpdf`*  s?    rx{{"RVAbeG__4rvbfUmm7L7LLLr5   c                 ,    t          j        ||          S rK   )r   von_mises_cdfrN  s      r3   rr   zvonmises_gen._cdfd*  s    #E1---r5   c                     dS r/  r   r\  s     r3   _stats_skipzvonmises_gen._stats_skipg*  r0  r5   c                     | t          j        |          z  t          j        |          z  t          j        dt          j        z  t          j        |          z            z   |z   S r  )rt   i1er
  rM   r   r   r\  s     r3   r   zvonmises_gen._entropyj*  sU     &6q25y26%==011249: 	;r5   z        The default limits of integration are endpoints of the interval
        of width ``2*pi`` centered at `loc` (e.g. ``[-pi, pi]`` when
        ``loc=0``).

r   r   r   r   Fc           	          t           j         t           j        }
}	|||	z   }|||
z   } t                      j        |||||||fi |S rK   )rM   r   r>   r
  )rB   r[  rD   r,   r-   lbubconditionalr2   r  r  r  s              r3   r
  zvonmises_gen.expectv*  sk     %B:rB:rBuww~dD##R[B B<@B B 	Br5   a          Fit data is assumed to represent angles and will be wrapped onto the
        unit circle. `f0` and `fscale` are ignored; the returned shape is
        always the maximum likelihood estimate and the scale is always
        1. Initial guesses are ignored.

c                    |                     dd          r t                      j        |g|R i |S t          | |||          \  }}}}| j        t
          j         k    r t                      j        |g|R i |S t          j        |dt
          j        z            }d }d }||n
 ||          }	||n |||	          }
t          j        |	t
          j        z   dt
          j        z            t
          j        z
  }	|
|	dfS )Nr  FrR   c                 *    t          j        |           S rK   )r  circmean)rC   s    r3   find_muz!vonmises_gen.fit.<locals>.find_mu*  s    >$'''r5   c                 x   t          j        t          j        || z
                      t          |           z  dk    rdS dk    rUfd}dz
  z  dz   z  }d|z  } ||          dk    r|S  ||          dk    r|S t	          |d||f          }|j        S t          j        t                    j        S )Nr   g 7yACr   c                 \    t          j        |           t          j        |           z  z
  S rK   )rt   r  r
  )rL  r  s    r3   solve_for_kappaz=vonmises_gen.fit.<locals>.find_kappa.<locals>.solve_for_kappa*  s#    6%==6::r5   rR   r  )r/   r  )	rM   r  r  r  r)   r  r  rL  r  )rC   r,   r  lower_boundupper_boundroot_resr  s         @r3   
find_kappaz$vonmises_gen.fit.<locals>.find_kappa*  s     rvcDj))**3t994A Avv tQ; ; ; ; ;  1gqsmm #?;//144&&$_[11Q66&&*?84?3M O  O  OH#=( x++r5   r   )r0   r>   r@   r  r   rM   r   r  )rB   rC   rD   r2   r  r   r   r  r  r,   rw  r  s              r3   r@   zvonmises_gen.fit*  s5    88J&& 	4577;t3d333d333%@tAEt&M &M"fdF6beV577;t3d333d333 vdAI&&	( 	( 	(6	, 6	, 6	,r &ddGGDMM ,**T32G2GfS25[!be),,ru4c1}r5   r   )Nr   r   r   NNF)r   r   r   r   rh   r`   r   r   r   r  ro   r   rr   r  r   r	   r
  rH   r@   r  r  s   @r3   r  r  )  s       ^ ^~H H H  < < < < M**4 4 4 4 +*4C C CM M M. . .     
; 
; 
; } 5    FJ 
B 
B 
B 
B 
B	 
B } 5/ 0 0 0
N N N N0 0 _N N N N Nr5   r  r  vonmises_linec                   j    e Zd ZdZej        Zd ZddZd Z	d Z
d Zd Zd	 Zd
 Zd Zd Zd Zd ZdS )ro  aX  A Wald continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `wald` is:

    .. math::

        f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp(- \frac{ (x-1)^2 }{ 2x })

    for :math:`x >= 0`.

    `wald` is a special case of `invgauss` with ``mu=1``.

    %(after_notes)s

    %(example)s
    c                     g S rK   r   rg   s    r3   rh   zwald_gen._shape_info*  r   r5   Nc                 2    |                     dd|          S rS  rT  r   s      r3   r   zwald_gen._rvs*  s      c 555r5   c                 8    t                               |d          S r  )rn  ro   r   s     r3   ro   zwald_gen._pdf*  s    }}Q$$$r5   c                 8    t                               |d          S r  )rn  rr   r   s     r3   rr   zwald_gen._cdf+      }}Q$$$r5   c                 8    t                               |d          S r  )rn  rv   r   s     r3   rv   zwald_gen._sf+  s    ||As###r5   c                 8    t                               |d          S r  )rn  r{   r   s     r3   r{   zwald_gen._ppf+  r  r5   c                 8    t                               |d          S r  )rn  r~   r   s     r3   r~   zwald_gen._isf+  r  r5   c                 8    t                               |d          S r  )rn  r   r   s     r3   r   zwald_gen._logpdf+      3'''r5   c                 8    t                               |d          S r  )rn  r   r   s     r3   r   zwald_gen._logcdf+  r  r5   c                 8    t                               |d          S r  )rn  r   r   s     r3   r   zwald_gen._logsf+  s    q#&&&r5   c                     dS )N)r   r   rD  rY  r   rg   s    r3   r   zwald_gen._stats+  s    ""r5   c                 6    t                               d          S r  )rn  r   rg   s    r3   r   zwald_gen._entropy+  s      %%%r5   r   )r   r   r   r   r   r  r  rh   r   ro   rr   rv   r{   r~   r   r   r   r   r   r   r5   r3   ro  ro  *  s         ( "4M  6 6 6 6% % %% % %$ $ $% % %% % %( ( (( ( (' ' '# # #& & & & &r5   ro  rU  c                   <    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	S )
wrapcauchy_gena  A wrapped Cauchy continuous random variable.

    %(before_notes)s

    Notes
    -----
    The probability density function for `wrapcauchy` is:

    .. math::

        f(x, c) = \frac{1-c^2}{2\pi (1+c^2 - 2c \cos(x))}

    for :math:`0 \le x \le 2\pi`, :math:`0 < c < 1`.

    `wrapcauchy` takes ``c`` as a shape parameter for :math:`c`.

    %(after_notes)s

    %(example)s

    c                     |dk    |dk     z  S r	  r   r}  s     r3   r`   zwrapcauchy_gen._argcheck7+  r	  r5   c                 (    t          dddd          gS )Nr  F)r   r   r  rb
  rg   s    r3   rh   zwrapcauchy_gen._shape_info:+  s    3v~>>??r5   c                 z    d||z  z
  dt           j        z  d||z  z   d|z  t          j        |          z  z
  z  z  S r  r  r
  s      r3   ro   zwrapcauchy_gen._pdf=+  s=    AaC!BE'1QqS51RVAYY#6788r5   c                 j    d }d }d|z   d|z
  z  }t          |t          j        k     ||f||          S )Nc                 z    dt           j        z  t          j        |t          j        | dz            z            z  S r-  rM   r   r  r  rn   crs     r3   r  zwrapcauchy_gen._cdf.<locals>.f1C+  s-    RU7RYr"&1++~6666r5   c           	          ddt           j        z  t          j        |t          j        dt           j        z  | z
  dz            z            z  z
  S r-  r  r  s     r3   r  zwrapcauchy_gen._cdf.<locals>.f2G+  s?    qw2bfagk1_.E.E+E!F!FFFFr5   r   r0  )r   rM   r   )rB   rn   r  r  r  r  s         r3   rr   zwrapcauchy_gen._cdfA+  sW    	7 	7 	7	G 	G 	G !ea!e_!be)aWr::::r5   c           
      V   d|z
  d|z   z  }dt          j        |t          j        t           j        |z            z            z  }dt           j        z  dt          j        |t          j        t           j        d|z
  z            z            z  z
  }t          j        |dk     ||          S )Nr   rR   r   r   )rM   r  r  r   rO  )rB   rz   r  rB  rcqrcmqs         r3   r{   zwrapcauchy_gen._ppfN+  s    1us1uo	#bfRU1Woo-...wq3rvbeQqSk':':#:;;;;xE	3---r5   c                 V    t          j        dt           j        z  d||z  z
  z            S r  r   r}  s     r3   r   zwrapcauchy_gen._entropyT+  s$    vagq1uo&&&r5   c                     t          |t                    r|                                }dt          j        |          t          j        |          dt          j        z  z  fS r  )r<   r(   r  rM   rK  r	  r   )rB   rC   s     r3   r  zwrapcauchy_gen._fitstartW+  sM     dL)) 	$>>##DBF4LL"&,,"%"888r5   N)r   r   r   r   r`   rh   ro   rr   r{   r   r  r   r5   r3   r  r  !+  s         *! ! !@ @ @9 9 9; ; ;. . .' ' '9 9 9 9 9r5   r  
wrapcauchyc                   P    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
 ZddZdS )gennorm_gena0  A generalized normal continuous random variable.

    %(before_notes)s

    See Also
    --------
    laplace : Laplace distribution
    norm : normal distribution

    Notes
    -----
    The probability density function for `gennorm` is [1]_:

    .. math::

        f(x, \beta) = \frac{\beta}{2 \Gamma(1/\beta)} \exp(-|x|^\beta),

    where :math:`x` is a real number, :math:`\beta > 0` and
    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).

    `gennorm` takes ``beta`` as a shape parameter for :math:`\beta`.
    For :math:`\beta = 1`, it is identical to a Laplace distribution.
    For :math:`\beta = 2`, it is identical to a normal distribution
    (with ``scale=1/sqrt(2)``).

    References
    ----------

    .. [1] "Generalized normal distribution, Version 1",
           https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1

    .. [2] Nardon, Martina, and Paolo Pianca. "Simulation techniques for
           generalized Gaussian densities." Journal of Statistical
           Computation and Simulation 79.11 (2009): 1317-1329

    .. [3] Wicklin, Rick. "Simulate data from a generalized Gaussian
           distribution" in The DO Loop blog, September 21, 2016,
           https://blogs.sas.com/content/iml/2016/09/21/simulate-generalized-gaussian-sas.html

    %(example)s

    c                 @    t          dddt          j        fd          gS Nrk  Fr   r  re   rg   s    r3   rh   zgennorm_gen._shape_info+      651bf+~FFGGr5   c                 R    t          j        |                     ||                    S rK   r  rB   rn   rk  s      r3   ro   zgennorm_gen._pdf+  s     vdll1d++,,,r5   c                     t          j        d|z            t          j        d|z            z
  t	          |          |z  z
  S r   )rM   r   rt   r  r
  r  s      r3   r   zgennorm_gen._logpdf+  s8    vc$h"*SX"6"66QEEr5   c                     dt          j        |          z  }d|z   |t          j        d|z  t	          |          |z            z  z
  S r   )rM   rN   rt   r  r
  rB   rn   rk  r  s       r3   rr   zgennorm_gen._cdf+  sB    "'!**a1r|CHc!ffdlCCCCCr5   c                     t          j        |dz
            }|t          j        d|z  d|z   d|z  |z  z
            d|z  z  z  S )Nr   r   r   )rM   rN   rt   r  r  s       r3   r{   zgennorm_gen._ppf+  sJ    GAG2?3t8cAgQq-@AACHMMMr5   c                 0    |                      | |          S rK   rA  r  s      r3   rv   zgennorm_gen._sf+  s    yy!T"""r5   c                 0    |                      ||           S rK   rW  r  s      r3   r~   zgennorm_gen._isf+  s    		!T""""r5   c                     t          j        d|z  d|z  d|z  g          \  }}}dt          j        ||z
            dt          j        ||z   d|z  z
            dz
  fS )Nr   rD  r  r   r   )rt   r  rM   r   )rB   rk  c1c3c5s        r3   r   zgennorm_gen._stats+  sa    ZT3t8SX >??
B26"r'??BrBwR/?(@(@2(EEEr5   c                 l    d|z  t          j        d|z            z
  t          j        d|z            z   S rX  r<  rB   rk  s     r3   r   zgennorm_gen._entropy+  s2    Dy26"t),,,rz"t)/D/DDDr5   Nc                     |                     d|z  |          }|d|z  z  }t          j        |          }|                    |j                  dk     }||          ||<   |S )Nr   r  r   )r  rM   r   randomrw  )rB   rk  r   r   r  ri  rV	  s          r3   r   zgennorm_gen._rvs+  sk     qvD11!D&MJqMM"""0036T7($r5   r   )r   r   r   r   rh   ro   r   rr   r{   rv   r~   r   r   r   r   r5   r3   r  r  c+  s        ) )TH H H- - -F F FD D D
N N N
# # ## # #F F FE E E	 	 	 	 	 	r5   r  gennormc                   B    e Zd ZdZd Zd Zd Zd Zd Zd Z	d Z
d	 Zd
S )halfgennorm_gena  The upper half of a generalized normal continuous random variable.

    %(before_notes)s

    See Also
    --------
    gennorm : generalized normal distribution
    expon : exponential distribution
    halfnorm : half normal distribution

    Notes
    -----
    The probability density function for `halfgennorm` is:

    .. math::

        f(x, \beta) = \frac{\beta}{\Gamma(1/\beta)} \exp(-|x|^\beta)

    for :math:`x, \beta > 0`. :math:`\Gamma` is the gamma function
    (`scipy.special.gamma`).

    `halfgennorm` takes ``beta`` as a shape parameter for :math:`\beta`.
    For :math:`\beta = 1`, it is identical to an exponential distribution.
    For :math:`\beta = 2`, it is identical to a half normal distribution
    (with ``scale=1/sqrt(2)``).

    References
    ----------

    .. [1] "Generalized normal distribution, Version 1",
           https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1

    %(example)s

    c                 @    t          dddt          j        fd          gS r  re   rg   s    r3   rh   zhalfgennorm_gen._shape_info+  r  r5   c                 R    t          j        |                     ||                    S rK   r  r  s      r3   ro   zhalfgennorm_gen._pdf+  s"     vdll1d++,,,r5   c                 f    t          j        |          t          j        d|z            z
  ||z  z
  S r  r<  r  s      r3   r   zhalfgennorm_gen._logpdf+  s,    vd||bjT222QW<<r5   c                 8    t          j        d|z  ||z            S r  r  r  s      r3   rr   zhalfgennorm_gen._cdf+  s    {3t8QW---r5   c                 >    t          j        d|z  |          d|z  z  S r  r  r  s      r3   r{   zhalfgennorm_gen._ppf+  s!    ~c$h**SX66r5   c                 8    t          j        d|z  ||z            S r  r  r  s      r3   rv   zhalfgennorm_gen._sf+  s    |CHag...r5   c                 >    t          j        d|z  |          d|z  z  S r  r  r  s      r3   r~   zhalfgennorm_gen._isf+  s!    s4x++c$h77r5   c                 f    d|z  t          j        |          z
  t          j        d|z            z   S r  r<  r  s     r3   r   zhalfgennorm_gen._entropy+  s,    4x"&,,&CH)=)===r5   Nr  r   r5   r3   r  r  +  s        " "FH H H- - -= = =. . .7 7 7/ / /8 8 8> > > > >r5   r  halfgennormc                   L     e Zd ZdZd Zd Z fdZd Zd Zd Z	d Z
d	 Z xZS )
crystalball_gena  
    Crystalball distribution

    %(before_notes)s

    Notes
    -----
    The probability density function for `crystalball` is:

    .. math::

        f(x, \beta, m) =  \begin{cases}
                            N \exp(-x^2 / 2),  &\text{for } x > -\beta\\
                            N A (B - x)^{-m}  &\text{for } x \le -\beta
                          \end{cases}

    where :math:`A = (m / |\beta|)^m  \exp(-\beta^2 / 2)`,
    :math:`B = m/|\beta| - |\beta|` and :math:`N` is a normalisation constant.

    `crystalball` takes :math:`\beta > 0` and :math:`m > 1` as shape
    parameters.  :math:`\beta` defines the point where the pdf changes
    from a power-law to a Gaussian distribution.  :math:`m` is the power
    of the power-law tail.

    %(after_notes)s

    .. versionadded:: 0.19.0

    References
    ----------
    .. [1] "Crystal Ball Function",
           https://en.wikipedia.org/wiki/Crystal_Ball_function

    %(example)s
    c                     |dk    |dk    z  S )z@
        Shape parameter bounds are m > 1 and beta > 0.
        r   r   r   )rB   rk  r  s      r3   r`   zcrystalball_gen._argcheck$,  s     A$(##r5   c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )Nrk  Fr   r  r  r   re   )rB   ibetaims      r3   rh   zcrystalball_gen._shape_info*,  s>    651bf+~FFUQK@@r{r5   c                 J    t                                          |d          S )N)r   r  rJ  r  r  s     r3   r  zcrystalball_gen._fitstart/,  s     ww  H 555r5   c                     d||z  |dz
  z  t          j        |dz   dz            z  t          t          |          z  z   z  }d }d }|t	          || k    |||f||          z  S )a`  
        Return PDF of the crystalball function.

                                            --
                                           | exp(-x**2 / 2),  for x > -beta
        crystalball.pdf(x, beta, m) =  N * |
                                           | A * (B - x)**(-m), for x <= -beta
                                            --
        r   r   rR   r   c                 8    t          j        | dz   dz            S r  r9  rn   rk  r  s      r3   rhsz!crystalball_gen._pdf.<locals>.rhs@,  s    61a4%!)$$$r5   c                 j    ||z  |z  t          j        |dz   dz            z  ||z  |z
  | z
  | z  z  S r   r9  r  s      r3   lhsz!crystalball_gen._pdf.<locals>.lhsC,  sG    tVaK"&$'C"8"88tVd]Q&1"-. /r5   r0  rM   r   r   r   r   rB   rn   rk  r  r  r  r  s          r3   ro   zcrystalball_gen._pdf3,  s     1T6QqS>BFD!G8c>$:$::401 2	% 	% 	%	/ 	/ 	/ :a4%i!T1EEEEEr5   c                     d||z  |dz
  z  t          j        |dz   dz            z  t          t          |          z  z   z  }d }d }t          j        |          t          || k    |||f||          z   S )zH
        Return the log of the PDF of the crystalball function.
        r   r   rR   r   c                     | dz   dz  S r  r   r  s      r3   r  z$crystalball_gen._logpdf.<locals>.rhsP,  s    qD57Nr5   c                     |t          j        ||z            z  |dz  dz  z
  |t          j        ||z  |z
  | z
            z  z
  S r  r.  r  s      r3   r  z$crystalball_gen._logpdf.<locals>.lhsS,  sG    RVAdF^^#dAgai/!BF1T6D=1;L4M4M2MMMr5   r0  )rM   r   r   r   r   r   r  s          r3   r   zcrystalball_gen._logpdfI,  s     1T6QqS>BFD!G8c>$:$::401 2	 	 		N 	N 	N vayy:a4%i!T1MMMMMr5   c                     d||z  |dz
  z  t          j        |dz   dz            z  t          t          |          z  z   z  }d }d }|t	          || k    |||f||          z  S )z8
        Return CDF of the crystalball function
        r   r   rR   r   c                     ||z  t          j        |dz   dz            z  |dz
  z  t          t          |           t          |           z
  z  z   S NrR   r   r   rM   r   r   r   r  s      r3   r  z!crystalball_gen._cdf.<locals>.rhs_,  sT    tVrvtQwhn5551=9Q<<)TE2B2B#BCD Er5   c                 |    ||z  |z  t          j        |dz   dz            z  ||z  |z
  | z
  | dz   z  z  |dz
  z  S r	  r9  r  s      r3   r  z!crystalball_gen._cdf.<locals>.lhsc,  sW    tVaK"&$'C"8"88tVd]Q&1"Q$/034Q38 9r5   r0  r  r  s          r3   rr   zcrystalball_gen._cdfX,  s     1T6QqS>BFD!G8c>$:$::401 2	E 	E 	E	9 	9 	9 :a4%i!T1EEEEEr5   c                    d||z  |dz
  z  t          j        |dz   dz            z  t          t          |          z  z   z  }|||z  z  t          j        |dz   dz            z  |dz
  z  }d }d }t	          ||k     |||f||          S )Nr   r   rR   r   c                     t          j        |dz   dz            }||z  |z  |dz
  z  }d|t          t          |          z  z   z  }||z  |z
  |dz
  ||z  | z  z  |z  | z  |z  dd|z
  z  z  z
  S r  r
  r  rk  r  eb2r  r  s         r3   ppf_lessz&crystalball_gen._ppf.<locals>.ppf_lessn,  s    &$'!$$C43!A#&A1{Yt__445AdFTM!eaf^+C/1!3q!A#w?@ Ar5   c                     t          j        |dz   dz            }||z  |z  |dz
  z  }d|t          t          |          z  z   z  }t	          t          |           dt          z  | |z  |z
  z  z             S r  )rM   r   r   r   r   r  s         r3   ppf_greaterz)crystalball_gen._ppf.<locals>.ppf_greateru,  sy    &$'!$$C43!A#&A1{Yt__445AYu--;1q0IIJJJr5   r0  r  )rB   r  rk  r  r  pbetar  r  s           r3   r{   zcrystalball_gen._ppfi,  s    1T6QqS>BFD!G8c>$:$::401 2QtVrvtQwhqj111QU;	A 	A 	A	K 	K 	K !e)aq\X+NNNNr5   c           	         d||z  |dz
  z  t          j        |dz   dz            z  t          t          |          z  z   z  }d }|t	          |dz   |k     |||ft          j        |t           j        g          t           j                  z  S )zR
        Returns the n-th non-central moment of the crystalball function.
        r   r   rR   r   c                    ||z  |z  t          j        |dz   dz            z  }||z  |z
  }d| dz
  dz  z  t          j        | dz   dz            z  dd| z  t          j        | dz   dz  |dz  dz            z  z   z  }t          j        |j                  }t          | dz             D ]B}|t          j        | |          || |z
  z  z  d|z  z  ||z
  dz
  z  ||z  | |z   dz   z  z  z  }C||z  |z   S )z
            Returns n-th moment. Defined only if n+1 < m
            Function cannot broadcast due to the loop over n
            rR   r   r   r   r  )	rM   r   rt   r  r  r  rw  r  binom)r_   rk  r  r+  r,  r  r  r  s           r3   r/  z*crystalball_gen._munp.<locals>.n_th_moment,  s    
 4!bfdAgX^444A$A!Sy>BHac1W$5$552'BK1aq1$E$EEEGC(39%%C1q5\\ 0 0AQqS1R!G;q1uqyI4A26A:./ 0s7S= r5   rR  )rM   r   r   r   r   rL  rV  rf   )rB   r_   rk  r  r  r/  s         r3   r   zcrystalball_gen._munp},  s     1T6QqS>BFD!G8c>$:$::401 2	! 	! 	! :a!eai!T1 l;
|LLL f& & & 	&r5   )r   r   r   r   r`   rh   r  ro   r   rr   r{   r   r  r  s   @r3   r  r   ,  s        " "F$ $ $  
6 6 6 6 6F F F,N N NF F F"O O O(& & & & & & &r5   r  crystalballzA Crystalball Function)r   longnamec                 >    t          j        d| dz  dz            dz  S )a  
    Utility function for the argus distribution used in the pdf, sf and
    moment calculation.
    Note that for all x > 0:
    gammainc(1.5, x**2/2) = 2 * (_norm_cdf(x) - x * _norm_pdf(x) - 0.5).
    This can be verified directly by noting that the cdf of Gamma(1.5) can
    be written as erf(sqrt(x)) - 2*sqrt(x)*exp(-x)/sqrt(Pi).
    We use gammainc instead of the usual definition because it is more precise
    for small chi.
    r  rR   r  )r  s    r3   
_argus_phir  ,  s#     ;sCF1H%%))r5   c                   F    e Zd ZdZd Zd Zd Zd Zd ZddZ	dd	Z
d
 ZdS )	argus_gena  
    Argus distribution

    %(before_notes)s

    Notes
    -----
    The probability density function for `argus` is:

    .. math::

        f(x, \chi) = \frac{\chi^3}{\sqrt{2\pi} \Psi(\chi)} x \sqrt{1-x^2}
                     \exp(-\chi^2 (1 - x^2)/2)

    for :math:`0 < x < 1` and :math:`\chi > 0`, where

    .. math::

        \Psi(\chi) = \Phi(\chi) - \chi \phi(\chi) - 1/2

    with :math:`\Phi` and :math:`\phi` being the CDF and PDF of a standard
    normal distribution, respectively.

    `argus` takes :math:`\chi` as shape a parameter. Details about sampling
    from the ARGUS distribution can be found in [2]_.

    %(after_notes)s

    References
    ----------
    .. [1] "ARGUS distribution",
           https://en.wikipedia.org/wiki/ARGUS_distribution
    .. [2] Christoph Baumgarten "Random variate generation by fast numerical
           inversion in the varying parameter case." Research in Statistics,
           vol. 1, 2023, doi:10.1080/27684520.2023.2279060.

    .. versionadded:: 0.19.0

    %(example)s
    c                 @    t          dddt          j        fd          gS )Nr  Fr   r  re   rg   s    r3   rh   zargus_gen._shape_info,      5%!RVnEEFFr5   c                 p   t          j        d          5  d||z  z
  }dt          j        |          z  t          z
  t          j        t	          |                    z
  }|t          j        |          z   dt          j        | |z            z  z   |dz  |z  dz  z
  cd d d            S # 1 swxY w Y   d S )Nr5  r6  r   r  r   rR   )rM   r8  r   r   r  r  )rB   rn   r  ri  r+  s        r3   r   zargus_gen._logpdf,  s    [))) 	G 	Gac	A"&++.
31H1HHArvayy=3rx1~~#55Q
QF	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	G 	Gs   BB++B/2B/c                 R    t          j        |                     ||                    S rK   r  rB   rn   r  s      r3   ro   zargus_gen._pdf,  s     vdll1c**+++r5   c                 4    d|                      ||          z
  S r  r  r!  s      r3   rr   zargus_gen._cdf,  s    TXXa%%%%r5   c                 v    t          |t          j        d|dz  z
            z            t          |          z  S r-  )r  rM   r   r!  s      r3   rv   zargus_gen._sf,  s2    #AqD 1 1122Z__DDr5   Nc                   	
 t          j        |          }|j        dk    r|                     |||          }nt	          |j        |          \  }	t          t          j        |                    }t          j        |          }t          j	        |gdgdgg          

j
        st          	
fdt          t          |           d          D                       }|                     
d         ||          }|                    |          ||<   
                                 
j
        |dk    r|d         }|S )	Nr   )r  r   r  r  r  c              3   `   K   | ](}|         sj         |         nt          d           V  )d S rK   r  r  s     r3   ru  z!argus_gen._rvs.<locals>.<genexpr>,  r  r5   r   r   )rM   r   r   r  r   rw  r  r  r  r  r  r  r  r  rN  r  )rB   r  r   r   r  r  r  r  r  r  r  s            @@r3   r   zargus_gen._rvs,  sb   joo8q==""340< # > >CC #39d33GCRWS\\**J(4..CC5"/&0\N4 4 4B k  ; ; ; ; ;%*CII:q%9%9; ; ; ; ;$$RUz2> % @ @99S>>C k  2::b'C
r5   c                    t          t          j        |                    }t          t          j        |                    }t          j        |          }d}||z  }|dk    r| dz  }	||k     r||z
  }
|                    |
          }|                    |
          }|dz  }t          j        |          |	|z  k    }t          j        |          }|dk    r,t          j	        d||         z
            }|||||z   <   ||z  }||k     nN|dk    rt          j
        | dz            }||k     r||z
  }
|                    |
          }|                    |
          }dt          j        |d|z
  z  |z             z  |z  }|dz  |z   dk    }t          j        |          }|dk    r,t          j	        d||         z             }|||||z   <   ||z  }||k     n}||k     rZ||z
  }
|                    d|
          }||dz  k    }t          j        |          }|dk    r||         ||||z   <   ||z  }||k     Zt          j	        dd|z  |z  z
            }t          j        ||          S )	Nr   r   rR   r  rK  r   g?r  )r  rM   r  r  r  r  r  r   r  r   r   r  rN  )rB   r  r  r   r  r  rn   r  r  r  r  r  r  r  r  r  r  echir  s                      r3   r  zargus_gen._rvs_scalar,  s   h r}Z0011  HQKK	Sy#::	Aa--	M ((a(00 ((a(00H&))q1u,VF^^
>>'!ai-00C<?AiZ!789+I a-- CZZ64%!)$$Da--	M ((a(00 ((a(00tq1u~1222T9 Q$(a-VF^^
>>'!ai-00C<?AiZ!789+I a-- a--	M //!/<<tax-VF^^
>><=fIAiZ!789+I a-- AEDL())Az!V$$$r5   c                    t          j        |t                    }t          |          }t          j        t           j        dz            |z  t          j        d|dz  dz            z  |z  }t          j        |          }|dk    }||         }dd|dz  z  z
  |t          |          z  ||         z  z   ||<   ||          }g d}t          j
        ||          || <   |||dz  z
  d d fS )	Nr  r*  r   rR   r   g?r  )	g_1g־r   gWBar   gp|RH?r   gE'卡?r   g?)rM   r   rL  r  r   r   rt   r  r   r   r  )rB   r  r  r  rA  rV	  r  coefs           r3   r   zargus_gen._statsb-  s     jE***ooGBE!Gs"RVAsAvax%8%883>mC  SyIAqDL1y||#3c$i#??D	JKKKZa((TE
#1*dD((r5   r   )r   r   r   r   rh   r   ro   rr   rv   r   r  r   r   r5   r3   r  r  ,  s        ' 'PG G GG G G, , ,& & &E E E   0c% c% c% c%J) ) ) ) )r5   r  arguszAn Argus Function)r   r  r   r   c                   ^     e Zd ZdZej        Zdd fd
Zd Zd Zd Z	d Z
d	 Z fd
Z xZS )rv_histograma3  
    Generates a distribution given by a histogram.
    This is useful to generate a template distribution from a binned
    datasample.

    As a subclass of the `rv_continuous` class, `rv_histogram` inherits from it
    a collection of generic methods (see `rv_continuous` for the full list),
    and implements them based on the properties of the provided binned
    datasample.

    Parameters
    ----------
    histogram : tuple of array_like
        Tuple containing two array_like objects.
        The first containing the content of n bins,
        the second containing the (n+1) bin boundaries.
        In particular, the return value of `numpy.histogram` is accepted.

    density : bool, optional
        If False, assumes the histogram is proportional to counts per bin;
        otherwise, assumes it is proportional to a density.
        For constant bin widths, these are equivalent, but the distinction
        is important when bin widths vary (see Notes).
        If None (default), sets ``density=True`` for backwards compatibility,
        but warns if the bin widths are variable. Set `density` explicitly
        to silence the warning.

        .. versionadded:: 1.10.0

    Notes
    -----
    When a histogram has unequal bin widths, there is a distinction between
    histograms that are proportional to counts per bin and histograms that are
    proportional to probability density over a bin. If `numpy.histogram` is
    called with its default ``density=False``, the resulting histogram is the
    number of counts per bin, so ``density=False`` should be passed to
    `rv_histogram`. If `numpy.histogram` is called with ``density=True``, the
    resulting histogram is in terms of probability density, so ``density=True``
    should be passed to `rv_histogram`. To avoid warnings, always pass
    ``density`` explicitly when the input histogram has unequal bin widths.

    There are no additional shape parameters except for the loc and scale.
    The pdf is defined as a stepwise function from the provided histogram.
    The cdf is a linear interpolation of the pdf.

    .. versionadded:: 0.19.0

    Examples
    --------

    Create a scipy.stats distribution from a numpy histogram

    >>> import scipy.stats
    >>> import numpy as np
    >>> data = scipy.stats.norm.rvs(size=100000, loc=0, scale=1.5,
    ...                             random_state=123)
    >>> hist = np.histogram(data, bins=100)
    >>> hist_dist = scipy.stats.rv_histogram(hist, density=False)

    Behaves like an ordinary scipy rv_continuous distribution

    >>> hist_dist.pdf(1.0)
    0.20538577847618705
    >>> hist_dist.cdf(2.0)
    0.90818568543056499

    PDF is zero above (below) the highest (lowest) bin of the histogram,
    defined by the max (min) of the original dataset

    >>> hist_dist.pdf(np.max(data))
    0.0
    >>> hist_dist.cdf(np.max(data))
    1.0
    >>> hist_dist.pdf(np.min(data))
    7.7591907244498314e-05
    >>> hist_dist.cdf(np.min(data))
    0.0

    PDF and CDF follow the histogram

    >>> import matplotlib.pyplot as plt
    >>> X = np.linspace(-5.0, 5.0, 100)
    >>> fig, ax = plt.subplots()
    >>> ax.set_title("PDF from Template")
    >>> ax.hist(data, density=True, bins=100)
    >>> ax.plot(X, hist_dist.pdf(X), label='PDF')
    >>> ax.plot(X, hist_dist.cdf(X), label='CDF')
    >>> ax.legend()
    >>> fig.show()

    N)densityc                8   || _         || _        t          |          dk    rt          d          t	          j        |d                   | _        t	          j        |d                   | _        t          | j                  dz   t          | j                  k    rt          d          | j        dd         | j        dd         z
  | _        t	          j	        | j        | j        d                    }|#|r!d}t          j        |t          d	           d
}n|s| j        | j        z  | _        | j        t          t	          j        | j        | j        z                      z  | _        t	          j        | j        | j        z            | _        t	          j        d| j        dg          | _        t	          j        d| j        g          | _        | j        d         x|d<   | _        | j        d         x|d<   | _         t)                      j        |i | dS )a5  
        Create a new distribution using the given histogram

        Parameters
        ----------
        histogram : tuple of array_like
            Tuple containing two array_like objects.
            The first containing the content of n bins,
            the second containing the (n+1) bin boundaries.
            In particular, the return value of np.histogram is accepted.
        density : bool, optional
            If False, assumes the histogram is proportional to counts per bin;
            otherwise, assumes it is proportional to a density.
            For constant bin widths, these are equivalent.
            If None (default), sets ``density=True`` for backward
            compatibility, but warns if the bin widths are variable. Set
            `density` explicitly to silence the warning.
        rR   z)Expected length 2 for parameter histogramr   r   zbNumber of elements in histogram content and histogram boundaries do not match, expected n and n+1.Nr  zjBin widths are not constant. Assuming `density=True`.Specify `density` explicitly to silence this warning.r  Tr   r   r   )
_histogram_densityr  r   rM   r   _hpdf_hbins_hbin_widthsallcloser  r  r  rL  r  cumsum_hcdfhstackr   r   r>   rM  )rB   	histogramr-  rD   r
  	bins_varyr
  r  s          r3   rM  zrv_histogram.__init__-  s   & $y>>QHIIIZ	!--
j1..tz??Q#dk"2"222 3 4 4 4 !KOdk#2#.>>D$5t7H7KLLL	?y?OGM'>a@@@@GG 	8d&77DJZ%tzD<M/M(N(N"O"OO
YtzD,==>>
YTZ566
YTZ011
#{1~-sdf#{2.sdf$)&)))))r5   c                 P    | j         t          j        | j        |d                   S )z&
        PDF of the histogram
        r  )side)r1  rM   searchsortedr2  r   s     r3   ro   zrv_histogram._pdf.  s$     z"/$+qwGGGHHr5   c                 B    t          j        || j        | j                  S )z3
        CDF calculated from the histogram
        )rM   interpr2  r6  r   s     r3   rr   zrv_histogram._cdf
.  s     yDK444r5   c                 B    t          j        || j        | j                  S )zC
        Percentile function calculated from the histogram
        )rM   r>  r6  r2  r   s     r3   r{   zrv_histogram._ppf.  s     yDJ444r5   c                     | j         dd         |dz   z  | j         dd         |dz   z  z
  |dz   z  }t          j        | j        dd         |z            S )z$Compute the n-th non-central moment.r   Nr  )r2  rM   r  r1  )rB   r_   	integralss      r3   r   zrv_histogram._munp.  s\    [_qs+dk#2#.>1.EE!A#N	vdj2&2333r5   c                     t          | j        dd         dk    | j        dd         ft          j        d          }t          j        | j        dd         |z  | j        z             S )zCompute entropy of distributionr   r  r   )r   r1  rM   r   r  r3  )rB   r  s     r3   r   zrv_histogram._entropy.  si    AbD)C/*QrT*,  tz!B$'#-0AABBBBr5   c                 p    t                                                      }| j        |d<   | j        |d<   |S )zF
        Set the histogram as additional constructor argument
        r8  r-  )r>   _updated_ctor_paramr/  r0  )rB   dctr  s     r3   rD  z rv_histogram._updated_ctor_param#.  s6     gg))++?KI
r5   )r   r   r   r   r   r  rM  ro   rr   r{   r   r   rD  r  r  s   @r3   r,  r,  v-  s        Z Zv "/M15 .* .* .* .* .* .* .*`I I I5 5 55 5 54 4 4
C C C        r5   r,  c                   @     e Zd ZdZd Zd Z fdZd Zd Zd Z	 xZ
S )studentized_range_genuO  A studentized range continuous random variable.

    %(before_notes)s

    See Also
    --------
    t: Student's t distribution

    Notes
    -----
    The probability density function for `studentized_range` is:

    .. math::

         f(x; k, \nu) = \frac{k(k-1)\nu^{\nu/2}}{\Gamma(\nu/2)
                        2^{\nu/2-1}} \int_{0}^{\infty} \int_{-\infty}^{\infty}
                        s^{\nu} e^{-\nu s^2/2} \phi(z) \phi(sx + z)
                        [\Phi(sx + z) - \Phi(z)]^{k-2} \,dz \,ds

    for :math:`x ≥ 0`, :math:`k > 1`, and :math:`\nu > 0`.

    `studentized_range` takes ``k`` for :math:`k` and ``df`` for :math:`\nu`
    as shape parameters.

    When :math:`\nu` exceeds 100,000, an asymptotic approximation (infinite
    degrees of freedom) is used to compute the cumulative distribution
    function [4]_ and probability distribution function.

    %(after_notes)s

    References
    ----------

    .. [1] "Studentized range distribution",
           https://en.wikipedia.org/wiki/Studentized_range_distribution
    .. [2] Batista, Ben Dêivide, et al. "Externally Studentized Normal Midrange
           Distribution." Ciência e Agrotecnologia, vol. 41, no. 4, 2017, pp.
           378-389., doi:10.1590/1413-70542017414047716.
    .. [3] Harter, H. Leon. "Tables of Range and Studentized Range." The Annals
           of Mathematical Statistics, vol. 31, no. 4, 1960, pp. 1122-1147.
           JSTOR, www.jstor.org/stable/2237810. Accessed 18 Feb. 2021.
    .. [4] Lund, R. E., and J. R. Lund. "Algorithm AS 190: Probabilities and
           Upper Quantiles for the Studentized Range." Journal of the Royal
           Statistical Society. Series C (Applied Statistics), vol. 32, no. 2,
           1983, pp. 204-210. JSTOR, www.jstor.org/stable/2347300. Accessed 18
           Feb. 2021.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.stats import studentized_range
    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots(1, 1)

    Display the probability density function (``pdf``):

    >>> k, df = 3, 10
    >>> x = np.linspace(studentized_range.ppf(0.01, k, df),
    ...                 studentized_range.ppf(0.99, k, df), 100)
    >>> ax.plot(x, studentized_range.pdf(x, k, df),
    ...         'r-', lw=5, alpha=0.6, label='studentized_range pdf')

    Alternatively, the distribution object can be called (as a function)
    to fix the shape, location and scale parameters. This returns a "frozen"
    RV object holding the given parameters fixed.

    Freeze the distribution and display the frozen ``pdf``:

    >>> rv = studentized_range(k, df)
    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

    Check accuracy of ``cdf`` and ``ppf``:

    >>> vals = studentized_range.ppf([0.001, 0.5, 0.999], k, df)
    >>> np.allclose([0.001, 0.5, 0.999], studentized_range.cdf(vals, k, df))
    True

    Rather than using (``studentized_range.rvs``) to generate random variates,
    which is very slow for this distribution, we can approximate the inverse
    CDF using an interpolator, and then perform inverse transform sampling
    with this approximate inverse CDF.

    This distribution has an infinite but thin right tail, so we focus our
    attention on the leftmost 99.9 percent.

    >>> a, b = studentized_range.ppf([0, .999], k, df)
    >>> a, b
    0, 7.41058083802274

    >>> from scipy.interpolate import interp1d
    >>> rng = np.random.default_rng()
    >>> xs = np.linspace(a, b, 50)
    >>> cdf = studentized_range.cdf(xs, k, df)
    # Create an interpolant of the inverse CDF
    >>> ppf = interp1d(cdf, xs, fill_value='extrapolate')
    # Perform inverse transform sampling using the interpolant
    >>> r = ppf(rng.uniform(size=1000))

    And compare the histogram:

    >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
    >>> ax.legend(loc='best', frameon=False)
    >>> plt.show()

    c                     |dk    |dk    z  S rK  r   )rB   r  r  s      r3   r`   zstudentized_range_gen._argcheck.  s    A"q&!!r5   c                     t          dddt          j        fd          }t          dddt          j        fd          }||gS )Nr  Fr   r  r  r   re   )rB   r1  r  s      r3   rh   z!studentized_range_gen._shape_info.  s>    UQK@@uq"&k>BBCyr5   c                 J    t                                          |d          S )N)rR   r   rJ  r  r  s     r3   r  zstudentized_range_gen._fitstart.  s     ww  F 333r5   c                     d|                                  \  fd}t          j        |dd          }t          j         ||||          t          j                  d         S )N_studentized_range_momentc                    t          j        ||          }| |||g}t          j        |t                    j                            t
          j                  }t          j	        t           |          }t          j
         t          j
        fdt          j
        f	
fg}t          dd          }t          j        |||          d         S )Nr   r  -q=rZ  rY  rangesopts)r   _studentized_range_pdf_logconstrM   r[  rL  r\  r]  r^  r   r_  rf   dictr   nquad)rQ  r  r  	log_constargusr_datarf  rQ  rR  r  r  cython_symbols            r3   _single_momentz3studentized_range_gen._munp.<locals>._single_moment.  s    >q"EEIaY'CxU++2::6?KKH".v}hOOCw'!RVr2h?FuU333D?3vDAAA!DDr5   r  r   r  r   )r   rM   
frompyfuncr   rV  )	rB   rQ  r  r  rZ  ufuncr  r  rY  s	         @@@r3   r   zstudentized_range_gen._munp.  s    3""$$B
	E 
	E 
	E 
	E 
	E 
	E 
	E na33z%%1b//<<<R@@r5   c                     d }t          j        |dd          }t          j         ||||          t           j                  d         S )Nc                 Z   |dk     rd}t          j        ||          }| |||g}t          j        |t                    j                            t
          j                  }t          j         t          j        fdt          j        fg}n\d}| |g}t          j        |t                    j                            t
          j                  }t          j         t          j        fg}t          j
        t           ||          }t          dd          }	t          j        |||	          d         S )	N順 _studentized_range_pdfr   !_studentized_range_pdf_asymptoticr  rN  rO  rP  )r   rS  rM   r[  rL  r\  r]  r^  rf   r   r_  rT  r   rU  
rz   r  r  rY  rV  rW  rX  rQ  rf  rR  s
             r3   _single_pdfz/studentized_range_gen._pdf.<locals>._single_pdf.  s     F{{ 8"B1bII	!R+8C//6>>vOOF7BF+a[9 !D!f8C//6>>vOOF7BF+,".v}hOOCuU333D?3vDAAA!DDr5   r  r   r  r   )rM   r[  r   rV  )rB   rn   r  r  rc  r\  s         r3   ro   zstudentized_range_gen._pdf.  sQ    	E 	E 	E( k1a00z%%1b//<<<R@@r5   c           	          d }t          j        |dd          }t          j        t          j         ||||          t           j                  d         dd          S )Nc                 Z   |dk     rd}t          j        ||          }| |||g}t          j        |t                    j                            t
          j                  }t          j         t          j        fdt          j        fg}n\d}| |g}t          j        |t                    j                            t
          j                  }t          j         t          j        fg}t          j
        t           ||          }t          dd          }	t          j        |||	          d         S )	Nr_  _studentized_range_cdfr   !_studentized_range_cdf_asymptoticr  rN  rO  rP  )r   _studentized_range_cdf_logconstrM   r[  rL  r\  r]  r^  rf   r   r_  rT  r   rU  rb  s
             r3   _single_cdfz/studentized_range_gen._cdf.<locals>._single_cdf.  s    
 F{{ 8"B1bII	!R+8C//6>>vOOF7BF+a[9 !D!f8C//6>>vOOF7BF+,".v}hOOCuU333D?3vDAAA!DDr5   r  r   r  r   r   )rM   r[  r6	  r   rV  )rB   rn   r  r  ri  r\  s         r3   rr   zstudentized_range_gen._cdf.  sa    	E 	E 	E, k1a00 wrz%%1b//DDDRH!QOOOr5   )r   r   r   r   r`   rh   r  r   ro   rr   r  r  s   @r3   rG  rG  -.  s        h hT" " "  
4 4 4 4 4A A A*A A A2P P P P P P Pr5   rG  studentized_range)r   r   r   c                   h     e Zd ZdZd Zd Zd Zd Zd Zd Z	 e
e           fd            Z xZS )	rel_breitwigner_gena  A relativistic Breit-Wigner random variable.

    %(before_notes)s

    See Also
    --------
    cauchy: Cauchy distribution, also known as the Breit-Wigner distribution.

    Notes
    -----

    The probability density function for `rel_breitwigner` is

    .. math::

        f(x, \rho) = \frac{k}{(x^2 - \rho^2)^2 + \rho^2}

    where

    .. math::
        k = \frac{2\sqrt{2}\rho^2\sqrt{\rho^2 + 1}}
            {\pi\sqrt{\rho^2 + \rho\sqrt{\rho^2 + 1}}}

    The relativistic Breit-Wigner distribution is used in high energy physics
    to model resonances [1]_. It gives the uncertainty in the invariant mass,
    :math:`M` [2]_, of a resonance with characteristic mass :math:`M_0` and
    decay-width :math:`\Gamma`, where :math:`M`, :math:`M_0` and :math:`\Gamma`
    are expressed in natural units. In SciPy's parametrization, the shape
    parameter :math:`\rho` is equal to :math:`M_0/\Gamma` and takes values in
    :math:`(0, \infty)`.

    Equivalently, the relativistic Breit-Wigner distribution is said to give
    the uncertainty in the center-of-mass energy :math:`E_{\text{cm}}`. In
    natural units, the speed of light :math:`c` is equal to 1 and the invariant
    mass :math:`M` is equal to the rest energy :math:`Mc^2`. In the
    center-of-mass frame, the rest energy is equal to the total energy [3]_.

    %(after_notes)s

    :math:`\rho = M/\Gamma` and :math:`\Gamma` is the scale parameter. For
    example, if one seeks to model the :math:`Z^0` boson with :math:`M_0
    \approx 91.1876 \text{ GeV}` and :math:`\Gamma \approx 2.4952\text{ GeV}`
    [4]_ one can set ``rho=91.1876/2.4952`` and ``scale=2.4952``.

    To ensure a physically meaningful result when using the `fit` method, one
    should set ``floc=0`` to fix the location parameter to 0.

    References
    ----------
    .. [1] Relativistic Breit-Wigner distribution, Wikipedia,
           https://en.wikipedia.org/wiki/Relativistic_Breit-Wigner_distribution
    .. [2] Invariant mass, Wikipedia,
           https://en.wikipedia.org/wiki/Invariant_mass
    .. [3] Center-of-momentum frame, Wikipedia,
           https://en.wikipedia.org/wiki/Center-of-momentum_frame
    .. [4] M. Tanabashi et al. (Particle Data Group) Phys. Rev. D 98, 030001 -
           Published 17 August 2018

    %(example)s

    c                     |dk    S r  r   rB   rhos     r3   r`   zrel_breitwigner_gen._argcheck2/  s    Qwr5   c                 @    t          dddt          j        fd          gS )Nro  Fr   r  re   rg   s    r3   rh   zrel_breitwigner_gen._shape_info5/  r  r5   c           
      0   t          j        ddd|dz  z  z   z  dt          j        dd|dz  z  z             z   z            dz  t           j        z  }t          j        d          5  |||z
  ||z   z  |z  dz  dz   z  cd d d            S # 1 swxY w Y   d S )NrR   r   r5  rn  )rM   r   r   r8  )rB   rn   ro  r  s       r3   ro   zrel_breitwigner_gen._pdf8/  s    GQsAvX!bga!CF(l&;&;";<
 
 [h''' 	: 	:!c'AG,S014q89	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	: 	:s   'BBBc           
      |   t          j        ddt          j        dd|dz  z  z             z   z            t           j        z  }t          j        dd|z  z             t          j        |t          j        | |dz   z            z            z  }|dz  t          j        |          z  }t          j        |d d          S )NrR   r   r  r
  )rM   r   r   r  imagr6	  )rB   rn   ro  r  r  s        r3   rr   zrel_breitwigner_gen._cdf@/  s    GAq271qax<000122258GBCK  i"'3$b/222334 	 Q(wvtQ'''r5   c                 $   |dk    rxt          j        ddd|dz  z  z   z  dt          j        dd|dz  z  z             z   z            t           j        z  |z  }|t           j        dz  t          j        |          z   z  S |dk    rt          j        dd|dz  z  z   ddt          j        dd|dz  z  z             z   z  z            |z  }d|dz  z
  t          j        dd|z  z
            z  }d|z  t          j        |          z  S t           j        S )Nr   rR   r
  r  )rM   r   r   r  r  rf   )rB   r_   ro  r  r  s        r3   r   zrel_breitwigner_gen._munpK/  s   66Q36\"a"'!aQh,*?*?&?@ A a")C..01166QsAvX!q271qax<+@+@'@"AB A #(lbgb2c6k&:&::Fq5276??**6Mr5   c                 6    d d t           j        t           j        fS rK   r  rn  s     r3   r   zrel_breitwigner_gen._stats\/  s     T2626))r5   c                    t          | |||          \  }}}}t          |t                    }|r!|                                dk    r	|j        }d}||r t                      j        |g|R i |S |7t          j        ||z
  g d          \  }}	}
|
|z
  }|	|z  }|s|g}d|vr||d<   n!t          j	        ||z
            }||z  }|s|g} t                      j        |g|R i |S )Nr   F)r  r   g      ?r-   )
r  r<   r(   r=   rA   r>   r@   rM   quantilerH  )rB   rC   rD   r2   r  r   r   rE   r  r  r  scale_0rho_0M_0r  s                 r3   r@   zrel_breitwigner_gen.fitb/  sD    !<$d!
 !
av dL11 	!  ""a'' ' <8<577;t3d333d333> Kt5F5F5FGGMCcCiG'ME wd"" 'W)D4K((C&LE wuww{4/$///$///r5   )r   r   r   r   r`   rh   ro   rr   r   r   r   r   r@   r  r  s   @r3   rl  rl  .  s        < <z  G G G: : :	( 	( 	(  "* * * M** 0  0  0  0 +* 0  0  0  0  0r5   rl  rel_breitwignerrK   (I  r  collections.abcr   	functoolsr   r   r\  numpyrM   numpy.polynomialr   scipy.interpolater   scipy._lib.doccerr	   r
   r   scipy._lib._ccallbackr   scipyr   r   scipy.specialspecialrt   scipy.special._ufuncsr  rk   scipy._lib._utilr   r   rR  r   _tukeylambda_statsr   r  r   r  _distn_infrastructurer   r   r   r   r   r   r   r   r   _ksstatsr   r   r    
_constantsr!   r"   r#   r$   r%   r&   r'   _censored_datar(   scipy.optimizer)   scipy.stats._warnings_errorsr*   scipy.statsr  r4   rH   rW   rY   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r  r  r  r  r0  r2  rF  r   rH  rT   r\  r`  rb  rk  r  r  r  r  r  rZ  r\  rm  ro  r  r  r  r  r  r  r  r  r  r  r  r  r2  r4  rJ  rO  rf  rj  rl  r|  r~  r  r  r  r  r  r  r  r  r  r  r  r  r7  r9  rE  rG  r`  rb  r  r  r  r  r  r  r  r  r   r  r  r1  r3  r>  r@  r  r  r  r  r  r  r  r  r  r  r  r  r	  r  r  r+  r-  r6  r8  rM  rO  rn  rt  ro  r  r  r  r  r  r  r  r%  r'  r8  r:  r=  rJ  r]  r  rr  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r  r-  r/  r<  r>  rz  r|  r  r  r  r  r  r  r  r  r  r	  r	  r  r1	  rJ	  rL	  r[	  rh	  rs	  ru	  r	  r	  r	  r	  r	  r	  r	  r	  r	  r	  r	  r	  r
  r
  r
  r 
  r"
  rC
  rE
  rQ
  rS
  r]
  r_
  ri
  rk
  r
  r
  r
  r
  r
  r
  _method_namesr
  r  setattrr
  r
  r
  r
  r
  r
  r  r  r=  r?  rk  rt  r  r  r  r  r  r  r  ro  rU  r  r  r  r  r  r  r  r  r  r  r*  r,  rG  rf   rj  rl  r{  listglobalsrw	  itemspairs_distn_names_distn_gen_names__all__r   r5   r3   <module>r     sz#  
  $ $ $ $ $ $ , , , , , , , ,      ' ' ' ' ' ' % % % % % %7 7 7 7 7 7 7 7 7 7 3 2 2 2 2 2                   # # # # # # # # # 4 4 4 4 4 4 4 4      B B B B B B B BJ J J J J J J J J J J J J J J J J J J J J J 2 1 1 1 1 1 1 1 1 1? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ( ( ( ( ( ( & & & & & & 1 1 1 1 1 1      9 9 9&  *   .W! W! W! W! W! W! W! W!t 		C3W---Z) Z) Z) Z) Z) Z) Z) Z)| 		!sc8885 5 5 5 5M 5 5 5p MCk222	 bgag$$+ + +( ( (            k k k k k} k k k\ xV3' 3' 3' 3' 3' 3' 3' 3'l 		Cg&&&( ( ( ( ( ( ( (V 
rufQh"%'	9	9	9*' *' *' *' *'- *' *' *'Z +s
3
3
3

 
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
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	 	 	 	 	X 	 	 	    o! o! o! o! o!} o! o! o!d x#6***i i i i iM i i iX MCk222	4# 4# 4# 4# 4#= 4# 4# 4#n <#:666~" ~" ~" ~" ~"} ~" ~" ~"B x#F###T, T, T, T, T, T, T, T,n 
c	)	)	)O O O O Ox O O Od x#F###2" 2" 2" 2" 2" 2" 2" 2"j 
	"	"	"O1 O1 O1 O1 O1m O1 O1 O1d g%   V1 V1 V1 V1 V1} V1 V1 V1r x#F###4# 4# 4# 4# 4# 4# 4# 4#n 
rufH	5	5	5J6 J6 J6 J6 J6 J6 J6 J6Z 
	"	"	"E E E E E= E E EP <Z(((j( j( j( j( j( j( j( j(Z 		Cg&&&K! K! K! K! K!M K! K! K!\ M{+++	& & &&E4 E4 E4 E4 E4M E4 E4 E4P MCk222	23 23 23 23 23= 23 23 23j <#J///J J J J Jm J J JZ o-888-. -. -. -. -.] -. -. -.` ^c555
k@ k@ k@ k@ k@M k@ k@ k@\ 
ECc; ; ; ; ;= ; ; ;| <#J///^F ^F ^F ^F ^Fm ^F ^F ^FB o-888h! h! h! h! h!= h! h! h!V ('-?@@@ D4 D4 D4 D4 D4m D4 D4 D4N o-888Q? Q? Q? Q? Q?m Q? Q? Q?h o=111u u u u uM u u up MCk222	=; =; =; =; =;= =; =; =;@ <#J///R" R" R" R" R"] R" R" R"j ^...
) ) )dM M M M M M M M` 		Cg&&&40 40 40 40 40 40 40 40n 
c	)	)	)^ ^ ^ ^ ^= ^ ^ ^B <#J////% /% /% /% /%- /% /% /%d &%2CDDDe e e e e e e eP "!777-> -> -> -> ->= -> -> ->` <#J///* * *| | | | |= | | |~ <Z(((I I I I I= I I IX <Z(((W W W W W] W W Wt ^c555
y y y y y} y y yx  #N;;;P P P P P= P P Pf <#J///+ + + + +M + + +\ M{+++	5 5 5 5 5] 5 5 5p ^cS|<<<
S S S S S= S S Sl <#J///M! M! M! M! M!= M! M! M!` <#J///z z z z zm z z zz	 o-888P2 P2 P2 P2 P2} P2 P2 P2f  ^444@2 @2 @2 @2 @2] @2 @2 @2F ^al333
]
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@ M{+++	56 56 56 56 56M 56 56 56p MC3[999	Z Z Z Z ZM Z Z Zz M{+++	E E E E E- E E EP +9
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%]) ]) ]) ]) ])] ]) ]) ])@ ,+1EFFF .* .* .*bb. b. b. b. b.} b. b. b.J x#F###b. b. b. b. b. b. b. b.J 
c	)	)	)q7 q7 q7 q7 q7= q7 q7 q7h <Z(((o o o o o= o o od <Z(((^ ^ ^ ^ ^] ^ ^ ^@ ^c555
  C! C! C! C! C!- C! C! C!L +)
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c	)	)	)LI LI LI LI LI LI LI LI^ 
	"	"	"JI JI JI JI JI JI JI JIZ 
c	)	)	)Y$ Y$ Y$ Y$ Y$ Y$ Y$ Y$x 		wa# a# a# a# a#= a# a# a#H <#J///   X
 X
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v x#F###Y Y Y Y Ym Y Y Yx g%   s s s s sM s s sl 
EsOOON N N N Nm N N Nb g5[! [! [! [! [! [! [! [!| 
c	)	)	)6! 6! 6! 6! 6! 6! 6! 6!r 		Cg&&&AD AD AD AD AD= AD AD ADH <Z(((W4 W4 W4 W4 W4= W4 W4 W4t <#:66640 40 40 40 40} 40 40 40n  #N;;;31 31 31 31 31M 31 31 31l M{+++	:3 :3 :3 :3 :3 :3 :3 :3z 		DCg...v v v v v= v v vr <#J///a? a? a? a? a?] a? a? a?P ^...
^...
<& <& <& <& <&} <& <& <&~ x#F###|& |& |& |& |&M |& |& |&| M{+++	29 29 29 29 29 29 29 29j "!Co>>>9& 9& 9& 9& 9&} 9& 9& 9&x  $#NCCC@' @' @' @' @'] @' @' @'F ^...
YF YF YF YF YF= YF YF YFx <Z(((gE gE gE gE gEM gE gE gEV2* * * * * * * * MC3[999		C3W---  	, 	, 	, 	, 	, 	, 	, 	, 
 . .AGE1))!,,----F F F F F F F FR 
cSx	0	0	0<4 <4 <4 <4 <4] <4 <4 <4~ ^c555
0 0 0
9 9 9% % %Pg7 g7 g7 g7 g7M g7 g7 g7T M{A666	z z z z zm z z zz	 o-888/. /. /. /. /.m /. /. /.d o=111
 
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t( t( t( t( t(- t( t( t(n +s
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T T T T T- T T Tn +9
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%=> => => => =>m => => =>@ o666U& U& U& U& U&m U& U& U&p o=;STTT* * *G) G) G) G) G) G) G) G)T 		w)<sKKKt t t t t= t t tn@P @P @P @P @PM @P @P @PF *)/Ba,.F4 4 4 O0 O0 O0 O0 O0- O0 O0 O0d &%2CDDD 	WWYY^^##%%&&!7!7}!M!M 
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