original_docstring: r"""
  
          Surival (or Reliability) function for the Gamma Distribution:
  
          .. math::
              R(x) = 1 - \frac{\gamma \left ( \alpha, \beta x \right )
              }{\Gamma \left ( \alpha \right )}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The values at which the function 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
          -------
  
          sf : scalar or numpy array
              The value(s) for the survival function at each x
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.sf(x, 3, 2)
          array([0.67667642, 0.23810331, 0.0619688 , 0.01375397, 0.0027694 ])
          
docstring: r"""
  Surival (or Reliability) function for the Gamma Distribution.
  
  
  
  Parameters
  
  x : numpy array or scalar
  The values at which the function 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
  
  sf : scalar or numpy array
  The value(s) for the survival function at each x
  
  Examples
  
  
docstring_tokens: r " " " Surival ( or Reliability ) function for the Gamma Distribution . Parameters x : numpy array or scalar The values at which the function 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 sf : scalar or numpy array The value ( s ) for the survival function at each x Examples
code: def sf(self, x, alpha, beta):
          r"""
  
          Surival (or Reliability) function for the Gamma Distribution:
  
          .. math::
              R(x) = 1 - \frac{\gamma \left ( \alpha, \beta x \right )
              }{\Gamma \left ( \alpha \right )}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The values at which the function 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
          -------
  
          sf : scalar or numpy array
              The value(s) for the survival function at each x
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.sf(x, 3, 2)
          array([0.67667642, 0.23810331, 0.0619688 , 0.01375397, 0.0027694 ])
          """
          return 1 - self.ff(x, alpha, beta)
code_tokens: def sf ( self , x , alpha , beta ) : return 1 - self . ff ( x , alpha , beta )
short_docstring: r"""
  Surival (or Reliability) function for the Gamma Distribution:
short_docstring_tokens: r " " " Surival ( or Reliability ) function for the Gamma Distribution :
comment: r"""
  
          Surival (or Reliability) function for the Gamma Distribution:
  
          .. math::
              R(x) = 1 - \frac{\gamma \left ( \alpha, \beta x \right )
              }{\Gamma \left ( \alpha \right )}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The values at which the function 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
          -------
  
          sf : scalar or numpy array
              The value(s) for the survival function at each x
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.sf(x, 3, 2)
          array([0.67667642, 0.23810331, 0.0619688 , 0.01375397, 0.0027694 ])
          """