def METHOD_NAME():
    cands = np.array((10, 25, 50, 75, 100))
    x = np.arange(128, dtype=float)
    y = np.zeros_like(x)
    for c, h in zip(cands, (10, 15, 25, 30, 35)):
        y += gauss_gen(x, c, h, 3)
    for j in range(1, len(cands) + 2):
        out = feature.filter_n_largest(y, cands, j)
        assert len(out) == np.min([len(cands), j])
    assert_raises(ValueError, feature.filter_n_largest, y, cands, 0)
    assert_raises(ValueError, feature.filter_n_largest, y, cands, -1)