def __call__(self, distribution):
    f = self.fit_vm(distribution)
    if ((self.fit_exit_code != 0) or (f[(- 1)] > self.worst_fit)):
        return self.null_result
    results = {}
    (p, s, g) = f
    p = (self.value_scale[1] * (p + self.value_scale[0]))
    s = (self.selectivity_scale[1] * (s + self.selectivity_scale[0]))
    results[''] = Pref(preference=p, selectivity=s, goodness_of_fit=g)
    results['Modes'] = Pref(number=1)
    return results
def __call__(self, distribution):
    f = self.fit_vm(distribution)
    if ((self.fit_exit_code != 0) or (f[(- 1)] > self.worst_fit)):
        return self.null_result
    results = {}
    (p, s, g) = f
    p = (self.value_scale[1] * (p + self.value_scale[0]))
    s = (self.selectivity_scale[1] * (s + self.selectivity_scale[0]))
    results[] = Pref(preference=p, selectivity=s, goodness_of_fit=g)
    results['Modes'] = Pref(number=1)
    return results