def _test_disp_corr_matrix(ph3):
supercell_phonon = get_supercell_phonon(ph3)
uu = DispCorrMatrix(supercell_phonon)
uu.run(300.0)
sqrt_masses = np.repeat(np.sqrt(ph3.supercell.masses), 3)
uu_inv = mass_inv(uu.psi_matrix, sqrt_masses)
np.testing.assert_allclose(uu.upsilon_matrix, uu_inv, atol=1e-8, rtol=0)
rd = RandomDisplacements(ph3.supercell, ph3.primitive, ph3.fc2)
rd.run_correlation_matrix(300)
rd_uu_inv = np.transpose(rd.uu_inv, axes=[0, 2, 1,
3]).reshape(uu_inv.shape)
np.testing.assert_allclose(uu.upsilon_matrix, rd_uu_inv, atol=1e-8, rtol=0)
def test_NaCl(self):
"""Test by fixed random numbers of np.random.normal
randn_ii and randn_ij were created by
np.random.seed(seed=100)
randn_ii = np.random.normal(size=(N_ii, 1, num_band))
randn_ij = np.random.normal(size=(N_ij, 2, 1, num_band)).
numpy v1.16.4 (py37h6b0580a_0) on macOS installed from conda-forge
was used.
"""
phonon = self._get_phonon_NaCl()
rd = RandomDisplacements(phonon.supercell,
phonon.primitive,
phonon.force_constants,
cutoff_frequency=0.01)
num_band = len(phonon.primitive) * 3
N = int(np.rint(phonon.supercell.volume / phonon.primitive.volume))
N_ij = N - len(rd.qpoints)
N_ii = N - N_ij * 2
shape_ii = (N_ii, 1, num_band)
randn_ii = np.fromstring(randn_ii_str.replace('\n', ' '),
dtype=float,
sep=' ').reshape(shape_ii)
shape_ij = (N_ij, 2, 1, num_band)
randn_ij = np.zeros(shape_ij, dtype=float)
randn_ij[:, 0, 0, :] = np.fromstring(randn_ij_str_1.replace('\n', ' '),
dtype=float,
sep=' ').reshape(N_ij, num_band)
randn_ij[:, 1, 0, :] = np.fromstring(randn_ij_str_2.replace('\n', ' '),
dtype=float,
sep=' ').reshape(N_ij, num_band)
rd.run(500, randn=(randn_ii, randn_ij))
data = np.fromstring(disp_str.replace('\n', ' '), dtype=float, sep=' ')
np.testing.assert_allclose(data, rd.u.ravel(), atol=1e-5)
def test_random_displacements(ph_nacl):
"""Test by fixed random numbers of np.random.normal
randn_ii and randn_ij were created by
np.random.seed(seed=100)
randn_ii = np.random.normal(size=(N_ii, 1, num_band))
randn_ij = np.random.normal(size=(N_ij, 2, 1, num_band)).
numpy v1.16.4 (py37h6b0580a_0) on macOS installed from conda-forge
was used.
"""
ph = ph_nacl
rd = RandomDisplacements(ph.supercell,
ph.primitive,
ph.force_constants,
cutoff_frequency=0.01)
num_band = len(ph.primitive) * 3
N = len(ph.supercell) // len(ph.primitive)
# N = N_ii + N_ij * 2
# len(rd.qpoints) = N_ii + N_ij
N_ij = N - len(rd.qpoints)
N_ii = N - N_ij * 2
shape_ii = (N_ii, 1, num_band)
randn_ii = np.fromstring(randn_ii_str.replace('\n', ' '),
dtype=float, sep=' ').reshape(shape_ii)
shape_ij = (N_ij, 2, 1, num_band)
randn_ij = np.zeros(shape_ij, dtype=float)
randn_ij[:, 0, 0, :] = np.fromstring(
randn_ij_str_1.replace('\n', ' '),
dtype=float, sep=' ').reshape(N_ij, num_band)
randn_ij[:, 1, 0, :] = np.fromstring(
randn_ij_str_2.replace('\n', ' '),
dtype=float, sep=' ').reshape(N_ij, num_band)
rd.run(500, randn=(randn_ii, randn_ij))
# for line in rd.u.ravel().reshape(-1, 6):
# print(("%.7f, " * 6) % tuple(line))
data = np.array(disp_ref)
np.testing.assert_allclose(data, rd.u.ravel(), atol=1e-5)
rd.run_d2f()
np.testing.assert_allclose(rd.force_constants, ph.force_constants,
atol=1e-5, rtol=1e-5)
rd = RandomDisplacements(ph.supercell,
ph.primitive,
ph.force_constants)
rd.run_correlation_matrix(500)
shape = (len(ph.supercell) * 3, len(ph.supercell) * 3)
uu = np.transpose(rd.uu, axes=[0, 2, 1, 3]).reshape(shape)
uu_inv = np.transpose(rd.uu_inv, axes=[0, 2, 1, 3]).reshape(shape)
sqrt_masses = np.repeat(np.sqrt(ph.supercell.masses), 3)
uu_bare = mass_sand(uu, sqrt_masses)
uu_inv_bare = np.linalg.pinv(uu_bare)
_uu_inv = mass_sand(uu_inv_bare, sqrt_masses)
np.testing.assert_allclose(_uu_inv, uu_inv,
atol=1e-5, rtol=1e-5)
def test_NaCl(self):
phonon = self._get_phonon_NaCl()
np.random.seed(19680801)
rd = RandomDisplacements(phonon.dynamical_matrix,
cutoff_frequency=0.01)
def test_random_displacements_all_atoms_TiPN3(ph_tipn3):
"""Test by fixed random numbers of np.random.normal
randn_ii and randn_ij were created by
np.random.seed(seed=100)
randn_ii = np.random.normal(size=(N_ii, 1, num_band))
randn_ij = np.random.normal(size=(N_ij, 2, 1, num_band)).
Precalculated eigenvectors are used because those depend on
eigensolvers such as openblas, mkl, blis, or netlib. Eigenvalues
are expected to be equivalent over the eigensolvers. So the
eigenvalues calculated on the fly are used.
"""
ph = ph_tipn3
rd = RandomDisplacements(ph.supercell,
ph.primitive,
ph.force_constants)
num_band = len(ph.primitive) * 3
N = len(ph.supercell) // len(ph.primitive)
# N = N_ii + N_ij * 2
# len(rd.qpoints) = N_ii + N_ij
N_ij = N - len(rd.qpoints)
N_ii = N - N_ij * 2
shape_ii = (N_ii, 1, num_band)
shape_ij = (N_ij, 2, 1, num_band)
randn_ii = np.reshape(randn_ii_TiPN3, shape_ii)
randn_ij = np.reshape(randn_ij_TiPN3, shape_ij)
eigvecs_ii = np.reshape(
np.loadtxt(os.path.join(current_dir, "eigvecs_ii_TiPN3.txt")),
(N_ii, num_band, num_band))
eigvecs_ij = np.reshape(
np.loadtxt(os.path.join(current_dir, "eigvecs_ij_TiPN3.txt"),
dtype=complex),
(N_ij, num_band, num_band))
rd._eigvecs_ii = eigvecs_ii
rd._eigvecs_ij = eigvecs_ij
rd.run(500, randn=(randn_ii, randn_ij))
# for line in rd.u.ravel().reshape(-1, 6):
# print(("%.7f, " * 6) % tuple(line))
data = np.array(disp_ref_TiPN3)
np.testing.assert_allclose(data, rd.u.ravel(), atol=1e-5)
rd.run_d2f()
np.testing.assert_allclose(rd.force_constants, ph.force_constants,
atol=1e-5, rtol=1e-5)
rd.run_correlation_matrix(500)
shape = (len(ph.supercell) * 3, len(ph.supercell) * 3)
uu = np.transpose(rd.uu, axes=[0, 2, 1, 3]).reshape(shape)
uu_inv = np.transpose(rd.uu_inv, axes=[0, 2, 1, 3]).reshape(shape)
sqrt_masses = np.repeat(np.sqrt(ph.supercell.masses), 3)
uu_bare = _mass_sand(uu, sqrt_masses)
uu_inv_bare = np.linalg.pinv(uu_bare)
_uu_inv = _mass_sand(uu_inv_bare, sqrt_masses)
np.testing.assert_allclose(_uu_inv, uu_inv,
atol=1e-5, rtol=1e-5)