def test_newrand_bounded_rand_int(range_, n_pts):
"""Test that `bounded_rand_int` follows a uniform distribution"""
n_iter = 100
ks_pvals = []
uniform_dist = stats.uniform(loc=0, scale=range_)
# perform multiple samplings to make chance of outlier sampling negligible
for _ in range(n_iter):
# Deterministic random sampling
sample = [bounded_rand_int_wrap(range_) for _ in range(n_pts)]
res = stats.kstest(sample, uniform_dist.cdf)
ks_pvals.append(res.pvalue)
# Null hypothesis = samples come from an uniform distribution.
# Under the null hypothesis, p-values should be uniformly distributed
# and not concentrated on low values
# (this may seem counter-intuitive but is backed by multiple refs)
# So we can do two checks:
# (1) check uniformity of p-values
uniform_p_vals_dist = stats.uniform(loc=0, scale=1)
res_pvals = stats.kstest(ks_pvals, uniform_p_vals_dist.cdf)
assert res_pvals.pvalue > 0.05, (
"Null hypothesis rejected: generated random numbers are not uniform."
" Details: the (meta) p-value of the test of uniform distribution"
f" of p-values is {res_pvals.pvalue} which is not > 0.05")
# (2) (safety belt) check that 90% of p-values are above 0.05
min_10pct_pval = np.percentile(ks_pvals, q=10)
# lower 10th quantile pvalue <= 0.05 means that the test rejects the
# null hypothesis that the sample came from the uniform distribution
assert min_10pct_pval > 0.05, (
"Null hypothesis rejected: generated random numbers are not uniform. "
f"Details: lower 10th quantile p-value of {min_10pct_pval} not > 0.05."
)
def test_newrand_set_seed(seed, val):
"""Test that `set_seed` produces deterministic results"""
if seed is not None:
set_seed_wrap(seed)
x = bounded_rand_int_wrap(100)
assert x == val, f'Expected {val} but got {x} instead'
def test_newrand_bounded_rand_int_limits(range_):
"""Test that `bounded_rand_int_wrap` is defined for unsigned 32bits ints"""
with pytest.raises(OverflowError):
bounded_rand_int_wrap(range_)
