parameters: (self, lambd)
return_statement: return -_log(1.0 - self.random())/lambd
docstring: Exponential distribution.
  
          lambd is 1.0 divided by the desired mean.  It should be
          nonzero.  (The parameter would be called "lambda", but that is
          a reserved word in Python.)  Returned values range from 0 to
          positive infinity if lambd is positive, and from negative
          infinity to 0 if lambd is negative.
docstring_summary: Exponential distribution.
docstring_tokens: Exponential distribution .
function: def expovariate(self, lambd):
          """Exponential distribution.
  
          lambd is 1.0 divided by the desired mean.  It should be
          nonzero.  (The parameter would be called "lambda", but that is
          a reserved word in Python.)  Returned values range from 0 to
          positive infinity if lambd is positive, and from negative
          infinity to 0 if lambd is negative.
  
          """
          # lambd: rate lambd = 1/mean
          # ('lambda' is a Python reserved word)
  
          # we use 1-random() instead of random() to preclude the
          # possibility of taking the log of zero.
          return -_log(1.0 - self.random())/lambd
function_tokens: def expovariate ( self , lambd ) : # lambd: rate lambd = 1/mean # ('lambda' is a Python reserved word) # we use 1-random() instead of random() to preclude the # possibility of taking the log of zero. return - _log ( 1.0 - self . random ( ) ) / lambd