def test_mutations(seed):
    from ase.ga.startgenerator import StartGenerator
    from ase.ga.utilities import closest_distances_generator
    from ase.ga.standardmutations import RattleMutation, PermutationMutation
    import numpy as np
    from ase.build import fcc111
    from ase.constraints import FixAtoms

    # set up the random number generator
    rng = np.random.RandomState(seed)

    # first create two random starting candidates
    slab = fcc111('Au', size=(4, 4, 2), vacuum=10.0, orthogonal=True)
    slab.set_constraint(FixAtoms(mask=slab.positions[:, 2] <= 10.))

    pos = slab.get_positions()
    cell = slab.get_cell()
    p0 = np.array([0., 0., max(pos[:, 2]) + 2.])
    v1 = cell[0, :] * 0.8
    v2 = cell[1, :] * 0.8
    v3 = cell[2, :]
    v3[2] = 3.

    blmin = closest_distances_generator(atom_numbers=[47, 79],
                                        ratio_of_covalent_radii=0.7)

    atom_numbers = 2 * [47] + 2 * [79]
    n_top = len(atom_numbers)
    sg = StartGenerator(slab=slab,
                        blocks=atom_numbers,
                        blmin=blmin,
                        box_to_place_in=[p0, [v1, v2, v3]],
                        rng=rng)

    c1 = sg.get_new_candidate()
    c1.info['confid'] = 1

    # first verify that the rattle mutation works
    rmut = RattleMutation(blmin, n_top, rattle_strength=0.8, rattle_prop=0.4,
                          rng=rng)

    c2, desc = rmut.get_new_individual([c1])

    assert np.all(c1.numbers == c2.numbers)

    top1 = c1[-n_top:]
    top2 = c2[-n_top:]
    slab2 = c2[0:(len(c1) - n_top)]

    assert len(slab) == len(slab2)
    assert np.all(slab.get_positions() == slab2.get_positions())

    dp = np.sum((top2.get_positions() - top1.get_positions())**2, axis=1)**0.5

    # check that all displacements are smaller than the rattle strength we
    # cannot check if 40 % of the structures have been rattled since it is
    # probabilistic and because the probability will be lower if two atoms
    # get too close
    for p in dp:
        assert p < 0.8 * 3**0.5

    # now we check the permutation mutation

    mmut = PermutationMutation(n_top, probability=0.5, rng=rng)

    c3, desc = mmut.get_new_individual([c1])
    assert np.all(c1.numbers == c3.numbers)

    top1 = c1[-n_top:]
    top2 = c3[-n_top:]
    slab2 = c3[0:(len(c1) - n_top)]

    assert len(slab) == len(slab2)
    assert np.all(slab.get_positions() == slab2.get_positions())
    dp = np.sum((top2.get_positions() - top1.get_positions())**2, axis=1)**0.5

    # verify that two positions have been changed
    assert len(dp[dp > 0]) == 2
