original_docstring: r"""
  
          Quantile function for the Gamma Distribution:
  
          .. math::
              q(p) = \frac{-\ln\left ( p \right )}{\lambda}
  
          Parameters
          ----------
  
          p : numpy array or scalar
              The percentiles at which the quantile will be calculated
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          q : scalar or numpy array
              The quantiles for the Gamma distribution at each value p.
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> p = np.array([.1, .2, .3, .4, .5])
          >>> Gamma.qf(p, 3, 4)
          array([0.27551633, 0.38376105, 0.47844395, 0.57126923, 0.66851508])
          
docstring: r"""
  Quantile function for the Gamma Distribution.
  
  
  
  Parameters
  
  p : numpy array or scalar
  The percentiles at which the quantile will be calculated
  alpha : numpy array or scalar
  The shape parameter for the Gamma distribution
  beta : numpy array or scalar
  The scale parameter for the Gamma distribution
  
  Returns
  
  q : scalar or numpy array
  The quantiles for the Gamma distribution at each value p.
  
  Examples
  
  
docstring_tokens: r " " " Quantile function for the Gamma Distribution . Parameters p : numpy array or scalar The percentiles at which the quantile will be calculated alpha : numpy array or scalar The shape parameter for the Gamma distribution beta : numpy array or scalar The scale parameter for the Gamma distribution Returns q : scalar or numpy array The quantiles for the Gamma distribution at each value p . Examples
code: def qf(self, p, alpha, beta):
          r"""
  
          Quantile function for the Gamma Distribution:
  
          .. math::
              q(p) = \frac{-\ln\left ( p \right )}{\lambda}
  
          Parameters
          ----------
  
          p : numpy array or scalar
              The percentiles at which the quantile will be calculated
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          q : scalar or numpy array
              The quantiles for the Gamma distribution at each value p.
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> p = np.array([.1, .2, .3, .4, .5])
          >>> Gamma.qf(p, 3, 4)
          array([0.27551633, 0.38376105, 0.47844395, 0.57126923, 0.66851508])
          """
          return gammaincinv(alpha, p) / beta
code_tokens: def qf ( self , p , alpha , beta ) : return gammaincinv ( alpha , p ) / beta
short_docstring: r"""
  Quantile function for the Gamma Distribution:
short_docstring_tokens: r " " " Quantile function for the Gamma Distribution :
comment: r"""
  
          Quantile function for the Gamma Distribution:
  
          .. math::
              q(p) = \frac{-\ln\left ( p \right )}{\lambda}
  
          Parameters
          ----------
  
          p : numpy array or scalar
              The percentiles at which the quantile will be calculated
          alpha : numpy array or scalar
              The shape parameter for the Gamma distribution
          beta : numpy array or scalar
              The scale parameter for the Gamma distribution
  
          Returns
          -------
  
          q : scalar or numpy array
              The quantiles for the Gamma distribution at each value p.
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> p = np.array([.1, .2, .3, .4, .5])
          >>> Gamma.qf(p, 3, 4)
          array([0.27551633, 0.38376105, 0.47844395, 0.57126923, 0.66851508])
          """