parameters: (self, alpha, beta)
return_statement: return alpha * (-_log(u)) ** (1.0/beta)
docstring: Weibull distribution.
  
          alpha is the scale parameter and beta is the shape parameter.
docstring_summary: Weibull distribution.
docstring_tokens: Weibull distribution .
function: def weibullvariate(self, alpha, beta):
          """Weibull distribution.
  
          alpha is the scale parameter and beta is the shape parameter.
  
          """
          # Jain, pg. 499; bug fix courtesy Bill Arms
  
          u = 1.0 - self.random()
          return alpha * (-_log(u)) ** (1.0/beta)
function_tokens: def weibullvariate ( self , alpha , beta ) : # Jain, pg. 499; bug fix courtesy Bill Arms u = 1.0 - self . random ( ) return alpha * ( - _log ( u ) ) ** ( 1.0 / beta )