d1f932a01a3a2d73167dea9be55ffae747d1b66b,astroML/stats/tests/test_stats.py,,test_sigmaG,#,90

Before Change

def test_sigmaG():
    np.random.seed(0)
    a = np.random.random((20, 40, 60))
    for axis in [None, 0, 1, 2]:
        yield (check_sigmaG, a, axis)


//---------------------------------------------------------------------------
// Check that median_sigmaG() is a good approximation of mean_sigma()
// for normally-distributed data.
def check_median_sigmaG_approx(a, axis, keepdims, atol=0.15):
    med, sigmaG = median_sigmaG(a, axis=axis, keepdims=keepdims)
    mu, sigma = mean_sigma(a, axis=axis, ddof=1, keepdims=keepdims)

After Change

    factor = 1. / (2 * np.sqrt(2) * erfinv(0.5))

    sigmaG1 = sigmaG(a, axis=axis)
    q25, q75 = np.percentile(a, [25, 75], axis=axis)
    sigmaG2 = factor * (q75 - q25)

    assert_array_almost_equal(sigmaG1, sigmaG2)

Instances