# $\chi^2$ test for the variance in Python [closed]

I am looking for a function in Python testing the hypothesis that the variance of a Gaussian sample is equal to a given value, to validate my own function.

I could find the $$\chi^2$$ test for categorical variance, and the Levene and Bartlett tests to compare sample variances, but not this simple test. Anybody aware of such a function in Python?

import numpy as np
from scipy.stats import chi2

def var_test(x, va0, direction = "two-tailed", alpha = 0.05):
n = len(x)
Q = (n - 1) * np.var(x) / va0
if direction == "lower":
q = chi2.ppf(alpha, n - 1)
if Q <= q:
return "H_0 rejected"
else:
return "H_0 not rejected"
elif direction == "upper":
q = chi2.ppf(1 - alpha, n - 1)
if Q >= q:
return "H_0 rejected"
else:
return "H_0 not rejected"
else:
q1 = chi2.ppf(alpha / 2, n - 1)
q2 = chi2.ppf(1 - (alpha / 2), n - 1)
if Q <= q1 or Q >= q2:
return "H_0 rejected"
else:
return "H_0 not rejected"

n = 25

x = np.random.normal(0, 3, n)

var_test(x, va0 = 1)