def standard_deviation(cls, month):
stats = cls.objects.filter(month_id=month)
total = stats.values_list("rating", flat=True)
N = stats.count()
mean = cls.mean(month)
sum_diff = sum([(mean-float(each))**2 for each in total])
sd = cmath.sqrt(sum_diff / N-1)
return sd.imag * 100
def calcrating(cls, comp, month):
stats = CompanyStats.objects.filter(
company_id=comp, month_id=month).select_related("param")
rating = float(sum([
each.param.associated_value * each.value for each in stats]) / sum(
stats.values_list("param__associated_value", flat=True)))
return cls.objects.get_or_create(month_id=month, company_id=comp, defaults={
"month_id": month, "company_id": comp, "rating": rating})
def mean(cls, month):
stats = cls.objects.filter(month_id=month)
total = sum(stats.values_list("rating", flat=True))
N = stats.count()
return total / N