

def test_mean_masked_features_distance(features, n_channels, n_features_per_channel):
    shift = 10.0
    f0 = mean(features)
    f1 = (mean(features) + shift)
    m0 = m1 = np.zeros(d_expected)
    m0[(n_channels // 2)] = 1
    d_expected = (np.sqrt(n_features_per_channel) * shift)
    d_computed = get_mean_masked_features_distance(f0, f1, m0, m1, n_features_per_channel)
    ac(d_expected, d_computed)

dataset/ETHPy150Open kwikteam/phy/phy/stats/tests/test_clusters.py test_mean_masked_features_distance/VarMisuse@44/57 `n_channels`->`d_expected`