def test_jackknife_stats_exceptions():
with pytest.raises(ValueError):
with pytest.warns(AstropyDeprecationWarning):
jackknife_stats(np.array([]), np.mean, conf_lvl=0.9)
with pytest.raises(ValueError):
jackknife_stats(np.arange(2), np.mean, confidence_level=42)
def test_jackknife_stats_conf_interval():
# Test from the first example of Ref.[3]
data = np.array([48, 42, 36, 33, 20, 16, 29, 39, 42, 38, 42, 36, 20, 15,
42, 33, 22, 20, 41, 43, 45, 34, 14, 22, 6, 7, 0, 15, 33,
34, 28, 29, 34, 41, 4, 13, 32, 38, 24, 25, 47, 27, 41, 41,
24, 28, 26, 14, 30, 28, 41, 40])
data = np.reshape(data, (-1, 2))
data = data[:, 1]
# true estimate, bias, and std_err
answer = (113.7862, -4.376391, 22.26572)
# calculate the mle of the variance (biased estimator!)
def mle_var(x): return np.sum((x - np.mean(x))*(x - np.mean(x)))/len(x)
assert_allclose(answer, jackknife_stats(data, mle_var, 0.95)[0:3],
atol=1e-4)
# test confidence interval
answer = np.array((70.14615, 157.42616))
assert_allclose(answer, jackknife_stats(data, mle_var, 0.95)[3], atol=1e-4)
def test_jackknife_stats():
# Test from the third example of Ref.[3]
data = np.array((115, 170, 142, 138, 280, 470, 480, 141, 390))
# true estimate, bias, and std_err
answer = (258.4444, 0.0, 50.25936)
assert_allclose(answer, jackknife_stats(data, np.mean)[0:3], atol=1e-4)
def test_jackknife_stats_exceptions():
with pytest.raises(ValueError):
jackknife_stats(np.arange(2), np.mean, confidence_level=42)