def test_replicas(self):
"""Test that replication works."""
for num_replicas in range(1, 10):
box, points = freud.data.UnitCell.fcc().generate_system(
num_replicas=num_replicas)
assert box == freud.box.Box.cube(num_replicas)
test_points = np.array([[0, 0.5, 0.5], [0.5, 0, 0.5],
[0.5, 0.5, 0], [0.0, 0.0, 0.0]])
test_points = test_points[np.newaxis, np.newaxis, np.newaxis, ...]
test_points = np.tile(
test_points, [num_replicas, num_replicas, num_replicas, 1, 1])
test_points[...,
0] += np.arange(num_replicas)[:, np.newaxis,
np.newaxis, np.newaxis]
test_points[...,
1] += np.arange(num_replicas)[np.newaxis, :,
np.newaxis, np.newaxis]
test_points[..., 2] += np.arange(num_replicas)[np.newaxis,
np.newaxis, :,
np.newaxis]
test_points = (test_points - (num_replicas * 0.5)).reshape(-1, 3)
npt.assert_allclose(
sort_rounded_xyz_array(points),
sort_rounded_xyz_array(box.wrap(test_points)),
)
def test_voronoi_tess_2d(self):
# Test that the voronoi polytope works for a 2D system
L = 10 # Box length
box = freud.box.Box.square(L)
vor = freud.locality.Voronoi()
# Make a regular grid
points = np.array([
[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[1, 0, 0],
[1, 1, 0],
[1, 2, 0],
[2, 0, 0],
[2, 1, 0],
[2, 2, 0],
]).astype(np.float64)
vor.compute((box, points))
center_polytope = sort_rounded_xyz_array(vor.polytopes[4])
expected_polytope = sort_rounded_xyz_array([[1.5, 1.5,
0], [0.5, 1.5, 0],
[0.5, 0.5, 0],
[1.5, 0.5, 0]])
npt.assert_almost_equal(center_polytope, expected_polytope)
# Verify the cell areas
npt.assert_almost_equal(vor.volumes[4], 1)
npt.assert_almost_equal(np.sum(vor.volumes), box.volume)
# Verify the neighbor list weights
npt.assert_almost_equal(
vor.nlist.weights[vor.nlist.query_point_indices == 4], 1)
# Verify the neighbor distances
wrapped_distances = np.linalg.norm(
box.wrap(points[vor.nlist.point_indices] -
points[vor.nlist.query_point_indices]),
axis=-1,
)
npt.assert_allclose(wrapped_distances, vor.nlist.distances)
# Double the points (still inside the box) and test again
points *= 2
vor.compute((box, points))
center_polytope = sort_rounded_xyz_array(vor.polytopes[4])
expected_polytope = sort_rounded_xyz_array([[3, 3, 0], [1, 3, 0],
[1, 1, 0], [3, 1, 0]])
npt.assert_almost_equal(center_polytope, expected_polytope)
npt.assert_almost_equal(vor.volumes[4], 4)
npt.assert_almost_equal(np.sum(vor.volumes), box.volume)
npt.assert_almost_equal(
vor.nlist.weights[vor.nlist.query_point_indices == 4], 2)
# Verify the neighbor distances
wrapped_distances = np.linalg.norm(
box.wrap(points[vor.nlist.point_indices] -
points[vor.nlist.query_point_indices]),
axis=-1,
)
npt.assert_allclose(wrapped_distances, vor.nlist.distances)
def test_voronoi_tess_3d(self):
# Test that the voronoi polytope works for a 3D system
L = 10 # Box length
box = freud.box.Box.cube(L)
vor = freud.locality.Voronoi()
# Make a regular grid
points = np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [1, 0,
0], [1, 1, 0],
[1, 2, 0], [2, 0, 0], [2, 1, 0], [2, 2,
0], [0, 0, 1],
[0, 1, 1], [0, 2, 1], [1, 0, 1], [1, 1,
1], [1, 2, 1],
[2, 0, 1], [2, 1, 1], [2, 2, 1], [0, 0,
2], [0, 1, 2],
[0, 2, 2], [1, 0, 2], [1, 1, 2], [1, 2, 2],
[2, 0, 2], [2, 1, 2], [2, 2, 2]]).astype(np.float64)
vor.compute((box, points))
center_polytope = sort_rounded_xyz_array(vor.polytopes[13])
expected_polytope = sort_rounded_xyz_array([[1.5, 1.5, 1.5],
[1.5, 0.5, 1.5],
[1.5, 0.5, 0.5],
[1.5, 1.5, 0.5],
[0.5, 0.5, 0.5],
[0.5, 0.5, 1.5],
[0.5, 1.5, 0.5],
[0.5, 1.5, 1.5]])
npt.assert_almost_equal(center_polytope, expected_polytope)
# Verify the cell volumes
npt.assert_almost_equal(vor.volumes[13], 1)
npt.assert_almost_equal(np.sum(vor.volumes), box.volume)
# Verify the neighbor list weights
npt.assert_almost_equal(
vor.nlist.weights[vor.nlist.query_point_indices == 13], 1)
# Double the points (still inside the box) and test again
points *= 2
vor.compute((box, points))
center_polytope = sort_rounded_xyz_array(vor.polytopes[13])
expected_polytope = sort_rounded_xyz_array([[3, 3, 3], [3, 1, 3],
[3, 1, 1], [3, 3, 1],
[1, 1, 1], [1, 1, 3],
[1, 3, 1], [1, 3, 3]])
npt.assert_almost_equal(center_polytope, expected_polytope)
npt.assert_almost_equal(vor.volumes[13], 8)
npt.assert_almost_equal(np.sum(vor.volumes), box.volume)
npt.assert_almost_equal(
vor.nlist.weights[vor.nlist.query_point_indices == 13], 4)