original_docstring: r"""
  
          Conditional survival function for the Gamma Distribution:
  
          .. math::
              R(x) = e^{-\lambda x}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The value(s) at which the function will be calculated
          X : numpy array or scalar
              The value(s) at which each value(s) in x was known to have survived
          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
          -------
  
          cs : scalar or numpy array
              the conditional survival probability.
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.cs(x, 5, 3, 4)
          array([2.59402488e-02, 6.39048747e-04, 1.51519143e-05, 3.48776510e-07,
                 7.79933496e-09])
          
docstring: r"""
  Conditional survival function for the Gamma Distribution.
  
  
  
  Parameters
  
  x : numpy array or scalar
  The value(s) at which the function will be calculated
  X : numpy array or scalar
  The value(s) at which each value(s) in x was known to have survived
  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
  
  cs : scalar or numpy array
  the conditional survival probability.
  
  Examples
  
  
docstring_tokens: r " " " Conditional survival function for the Gamma Distribution . Parameters x : numpy array or scalar The value ( s ) at which the function will be calculated X : numpy array or scalar The value ( s ) at which each value ( s ) in x was known to have survived 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 cs : scalar or numpy array the conditional survival probability . Examples
code: def cs(self, x, X, alpha, beta):
          r"""
  
          Conditional survival function for the Gamma Distribution:
  
          .. math::
              R(x) = e^{-\lambda x}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The value(s) at which the function will be calculated
          X : numpy array or scalar
              The value(s) at which each value(s) in x was known to have survived
          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
          -------
  
          cs : scalar or numpy array
              the conditional survival probability.
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.cs(x, 5, 3, 4)
          array([2.59402488e-02, 6.39048747e-04, 1.51519143e-05, 3.48776510e-07,
                 7.79933496e-09])
          """
          return self.sf(x + X, alpha, beta) / self.sf(X, alpha, beta)
code_tokens: def cs ( self , x , X , alpha , beta ) : return self . sf ( x + X , alpha , beta ) / self . sf ( X , alpha , beta )
short_docstring: r"""
  Conditional survival function for the Gamma Distribution:
short_docstring_tokens: r " " " Conditional survival function for the Gamma Distribution :
comment: r"""
  
          Conditional survival function for the Gamma Distribution:
  
          .. math::
              R(x) = e^{-\lambda x}
  
          Parameters
          ----------
  
          x : numpy array or scalar
              The value(s) at which the function will be calculated
          X : numpy array or scalar
              The value(s) at which each value(s) in x was known to have survived
          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
          -------
  
          cs : scalar or numpy array
              the conditional survival probability.
  
          Examples
          --------
          >>> import numpy as np
          >>> from surpyval import Gamma
          >>> x = np.array([1, 2, 3, 4, 5])
          >>> Gamma.cs(x, 5, 3, 4)
          array([2.59402488e-02, 6.39048747e-04, 1.51519143e-05, 3.48776510e-07,
                 7.79933496e-09])
          """