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Silence NPY002 on intentional legacy np.random calls

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The RNG migration was reverted to keep np.random.seed() behavior, so add
per-line noqa: NPY002 on the three legacy np.random calls instead of the
Generator API.
This commit is contained in:
Fabian Joswig 2026-06-18 10:56:13 +02:00
commit 3ca4b18edb
2 changed files with 3 additions and 3 deletions
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2
pyerrors/fits.py
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@ -946,6 +946,6 @@ def _construct_prior_obs(i_prior, i_n):
return i_prior return i_prior
elif isinstance(i_prior, str): elif isinstance(i_prior, str):
loc_val, loc_dval = _extract_val_and_dval(i_prior) loc_val, loc_dval = _extract_val_and_dval(i_prior)
return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}") return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}") # noqa: NPY002
else: else:
raise TypeError("Prior entries need to be 'Obs' or 'str'.") raise TypeError("Prior entries need to be 'Obs' or 'str'.")

4
pyerrors/misc.py
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@ -120,7 +120,7 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
return Obs([np.zeros(samples) + value], [name]) return Obs([np.zeros(samples) + value], [name])
else: else:
for _ in range(100): for _ in range(100):
deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)] deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)] # noqa: NPY002
deltas -= np.mean(deltas) deltas -= np.mean(deltas)
deltas *= dvalue / np.sqrt(np.var(deltas) / samples) / np.sqrt(1 + 3 / samples) deltas *= dvalue / np.sqrt(np.var(deltas) / samples) / np.sqrt(1 + 3 / samples)
deltas += value deltas += value
@ -163,7 +163,7 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
raise Exception('All integrated autocorrelations have to be >= 0.5.') raise Exception('All integrated autocorrelations have to be >= 0.5.')
a = (2 * tau - 1) / (2 * tau + 1) a = (2 * tau - 1) / (2 * tau + 1)
rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples) rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples) # noqa: NPY002
# Normalize samples such that sample variance matches input # Normalize samples such that sample variance matches input
norm = np.array([np.var(o, ddof=1) / samples for o in rand.T]) norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])