(self)
adapt self.evaluations depending on the current measurement value
        and return ``sigma_fac in (1.0, self.alphasigma)``
adapt self.evaluations depending on the current measurement value
        and return ``sigma_fac in (1.0, self.alphasigma)``
adapt self . evaluations depending on the current measurement value and return sigma_fac in ( 1 . 0 self . alphasigma )
def treat(self):
        """adapt self.evaluations depending on the current measurement value
        and return ``sigma_fac in (1.0, self.alphasigma)``

        """
        if self.noiseS > 0:
            self.evaluations = min((self.evaluations * self.alphaevals, self.maxevals))
            return self.alphasigma
        else:
            self.evaluations = max((self.evaluations * self.alphaevalsdown, self.minevals))
            return 1.0
def treat ( self ) : if self . noiseS > 0 : self . evaluations = min ( ( self . evaluations * self . alphaevals , self . maxevals ) ) return self . alphasigma else : self . evaluations = max ( ( self . evaluations * self . alphaevalsdown , self . minevals ) ) return 1.0