'adapt self.evaluations depending on the current measurement value
and return ``sigma_fac in (1.0, self.alphasigma)``'

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
