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# fmt: off
import itertools
import numpy as np
import pytest
from ase.build import bulk
from ase.build.supercells import (
all_score_funcs,
find_optimal_cell_shape,
make_supercell,
)
from ase.geometry.cell import cell_to_cellpar
sq2 = np.sqrt(2.0)
@pytest.fixture()
def rng():
return np.random.RandomState(seed=42)
@pytest.fixture(
params=[
bulk("NaCl", crystalstructure="rocksalt", a=4.0),
bulk("NaCl", crystalstructure="rocksalt", a=4.0, cubic=True),
bulk("Au", crystalstructure="fcc", a=4.0),
]
)
def prim(request):
return request.param
@pytest.fixture(
params=[
3 * np.diag([1, 1, 1]),
4 * np.array([[1, 1, 0], [0, 1, 1], [1, 0, 1]]),
3 * np.diag([1, 2, 1]),
]
)
def P(request):
return request.param
@pytest.fixture(params=["cell-major", "atom-major"])
def order(request):
return request.param
def test_make_supercell(prim, P, order):
n = int(round(np.linalg.det(P)))
expected = n * len(prim)
sc = make_supercell(prim, P, order=order)
assert len(sc) == expected
if order == "cell-major":
symbols_expected = list(prim.symbols) * n
elif order == "atom-major":
symbols_expected = [s for s in prim.symbols for _ in range(n)]
assert list(sc.symbols) == symbols_expected
def test_make_supercells_arrays(prim, P, order, rng):
reps = int(round(np.linalg.det(P)))
tags = list(range(len(prim)))
momenta = rng.random((len(prim), 3))
prim.set_tags(tags)
prim.set_momenta(momenta)
sc = make_supercell(prim, P, order=order)
assert reps * len(prim) == len(sc.get_tags())
if order == "cell-major":
assert all(sc.get_tags() == np.tile(tags, reps))
assert np.allclose(sc[: len(prim)].get_momenta(), prim.get_momenta())
assert np.allclose(sc.get_momenta(), np.tile(momenta, (reps, 1)))
elif order == "atom-major":
assert all(sc.get_tags() == np.repeat(tags, reps))
assert np.allclose(sc[::reps].get_momenta(), prim.get_momenta())
assert np.allclose(sc.get_momenta(), np.repeat(momenta, reps, axis=0))
@pytest.mark.parametrize(
"rep",
[
(1, 1, 1),
(1, 2, 1),
(4, 5, 6),
(40, 19, 42),
],
)
def test_make_supercell_vs_repeat(prim, rep):
P = np.diag(rep)
at1 = prim * rep
at1.wrap()
at2 = make_supercell(prim, P, wrap=True)
assert np.allclose(at1.positions, at2.positions)
assert all(at1.symbols == at2.symbols)
at1 = prim * rep
at2 = make_supercell(prim, P, wrap=False)
assert np.allclose(at1.positions, at2.positions)
assert all(at1.symbols == at2.symbols)
@pytest.mark.parametrize('score_func', all_score_funcs.values())
@pytest.mark.parametrize(
'cell, target_shape', (
([[1, 0, 0], [0, 1, 0], [0, 0, 1]], 'sc'),
([[0, 1, 1], [1, 0, 1], [1, 1, 0]], 'fcc'),
)
)
def test_cell_metric_ideal(target_shape, cell, score_func):
"""Test cell with the ideal shape.
Test if `get_deviation_from_optimal_cell_shape` returns perfect scores
(0.0) for the ideal cells.
Test also cell vectors with permutation and elongation.
"""
cell = np.asarray(cell)
indices_permuted = itertools.permutations(range(3))
elongations = range(1, 4)
for perm, factor in itertools.product(indices_permuted, elongations):
permuted_cell = np.array([cell[i] * factor for i in perm])
cell_metric = score_func(permuted_cell, target_shape=target_shape)
assert np.isclose(cell_metric, 0.0)
@pytest.mark.parametrize(
'cell, target_shape', (
([[1, 0, 0], [0, 1, 0], [0, 0, 2]], 'sc'),
([[0, 1, 1], [1, 0, 1], [2, 2, 0]], 'fcc'),
)
)
def test_cell_metric_twice_larger_lattice_vector(cell, target_shape):
"""Test cell with a twice larger lattice vector than the others.
Test if score function gives a correct value for
the cells that have a lattice vector twice longer than the others.
"""
cb2 = np.cbrt(2.0)
# cell_length
# (ai / a0) - 1.0
# sqrt((1./cb2 - 1.)**2 + (1./cb2 - 1.)**2 + (2./cb2 - 1.)**2)
dia1 = (1. / cb2 - 1.) ** 2
dia2 = (2. / cb2 - 1.) ** 2
ref_score_length = np.sqrt(2. * dia1 + dia2)
# cell_shape
# (a0 / ai) - 1.0
dia1 = (1. / cb2 ** 2 - 1.) ** 2
dia2 = (4. / cb2 ** 2 - 1.) ** 2
ang1 = (1. / cb2 ** 2 / 2. - 0.5) ** 2
ang2 = (1. / cb2 ** 2 - 0.5) ** 2
ref_score_shape = {}
ref_score_shape['sc'] = 2. * dia1 + dia2
ref_score_shape['fcc'] = 2. * dia1 + dia2 + 2. * ang1 + 4. * ang2
ref_scores = [ref_score_length, ref_score_shape[target_shape]]
for score_func, ref_score in zip(all_score_funcs.values(), ref_scores):
score = score_func(cell, target_shape)
assert np.isclose(score, ref_score)
@pytest.mark.parametrize('score_func', all_score_funcs.values())
@pytest.mark.parametrize('target_shape', ['sc', 'fcc'])
def test_multiple_cells(target_shape, score_func):
"""Test if multiple cells can be evaluated at one time."""
cells = np.array([
[[1, 0, 0], [0, 1, 0], [0, 0, 2]],
[[0, 1, 1], [1, 0, 1], [2, 2, 0]],
])
metrics_separate = []
for i in range(cells.shape[0]):
metric = score_func(cells[i], target_shape)
metrics_separate.append(metric)
metrics_together = score_func(cells, target_shape)
np.testing.assert_allclose(metrics_separate, metrics_together)
@pytest.mark.parametrize('score_func', all_score_funcs.values())
@pytest.mark.parametrize(
'cell, target_shape', (
([[-1, 0, 0], [0, -1, 0], [0, 0, -1]], 'sc'),
([[0, -1, -1], [-1, 0, -1], [-1, -1, 0]], 'fcc'),
)
)
def test_cell_metric_negative_determinant(cell, target_shape, score_func):
"""Test cell with negative determinant.
Test if `get_deviation_from_optimal_cell_shape` works for the cells with
negative determinants.
"""
cell_metric = score_func(cell, target_shape)
assert np.isclose(cell_metric, 0.0)
@pytest.mark.parametrize('score_key', all_score_funcs.keys())
@pytest.mark.parametrize('cell, target_shape, target_size, ref_cellpar', [
(np.diag([1.0, 2.0, 4.0]), 'sc', 8, [4.0, 4.0, 4.0, 90., 90., 90.]),
([[0, 1, 1], [1, 0, 1], [1, 1, 0]], 'sc', 4, [2., 2., 2., 90., 90., 90.]),
(np.eye(3), 'fcc', 2, [sq2, sq2, sq2, 60.0, 60.0, 60.0])
])
def test_find_optimal_cell_shape(
cell, target_shape, target_size, ref_cellpar, score_key):
"""Test `find_optimal_cell_shape`.
We test from sc to sc; from sc to fcc; and from fcc to sc."""
sc_matrix = find_optimal_cell_shape(cell, target_size, target_shape,
score_key=score_key,
lower_limit=-1, upper_limit=1)
score_func = all_score_funcs[score_key]
cell_metric = score_func(
sc_matrix @ cell,
target_shape,
)
sc = np.dot(sc_matrix, cell)
cellpar = cell_to_cellpar(sc)
assert np.isclose(cell_metric, 0.0)
assert np.allclose(cellpar, ref_cellpar)
@pytest.mark.parametrize('score_key', all_score_funcs.keys())
@pytest.mark.parametrize('cell, target_shape, target_size, sc_matrix_ref', [
([[1, 0, 0], [0, 1, 0], [0, 0, 1]], 'fcc', 2,
[[0, 1, 1], [1, 0, 1], [1, 1, 0]]),
([[0, 1, 1], [1, 0, 1], [1, 1, 0]], 'sc', 4,
[[-1, 1, 1], [1, -1, 1], [1, 1, -1]]),
])
def test_ideal_orientation(cell, target_shape,
target_size, sc_matrix_ref, score_key) -> None:
"""Test if the ideal orientation is selected among candidates."""
sc_matrix = find_optimal_cell_shape(cell, target_size, target_shape,
score_key=score_key)
np.testing.assert_array_equal(sc_matrix, sc_matrix_ref)
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