def test_msd_bootstrap_d(self):
"""
Test msd_bootstrap very few particles.
"""
ordinate1 = np.random.randn(10, 10, 3)
ordinate2 = np.random.randn(1, 1, 3)
ordinate3 = np.random.randn(1, 1, 3)
ordinate4 = np.random.randn(1, 1, 3)
ordinate5 = np.random.randn(1, 1, 3)
to_resample = [
ordinate1,
ordinate2,
ordinate3,
ordinate4,
ordinate5,
]
boot = diffusion.MSDBootstrap(np.linspace(100, 600, 5, dtype=int),
to_resample,
progress=False,
n_resamples=1,
max_resamples=10,
sub_sample_dt=2)
assert_equal(boot.dt.size, 1)
assert_equal(len(boot.distributions), 1)
assert_equal(boot.msd_observed.size, 1)
assert_equal(boot.msd_sampled.size, 1)
def test_msd_bootstrap_c(self):
"""
Test msd_bootstrap for to go over the limit point.
"""
ordinate1 = np.random.randn(5, 500, 3)
ordinate2 = np.random.randn(5, 450, 3)
ordinate3 = np.random.randn(5, 400, 3)
ordinate4 = np.random.randn(5, 100, 3)
ordinate5 = np.random.randn(5, 10, 3)
to_resample = [
ordinate1,
ordinate2,
ordinate3,
ordinate4,
ordinate5,
]
boot = diffusion.MSDBootstrap(np.linspace(100, 600, 5, dtype=int),
to_resample,
progress=False,
n_resamples=1,
max_resamples=10,
sub_sample_dt=2)
assert_equal(boot.dt.size, 3)
assert_equal(len(boot.distributions), 3)
assert_equal(boot.msd_observed.size, 3)
assert_equal(boot.msd_sampled.size, 3)
def test_msd_bootstrap_b(self):
"""
Test msd_bootstrap for initial uniform with progress.
"""
ordinate = np.random.uniform(size=(100, 50, 3))
to_resample = [np.array(ordinate) for i in range(1, 6)]
boot = diffusion.MSDBootstrap(np.linspace(100, 600, 5, dtype=int),
to_resample,
progress=True,
n_resamples=1,
max_resamples=1000)
assert_equal(boot.dt.size, 5)
assert_equal(len(boot.distributions), 5)
assert_equal(boot.msd_observed.size, 5)
assert_equal(boot.msd_sampled.size, 5)
def __init__(self,
trajectory,
parser_params,
bootstrap_params=None,
dtype='Xdatcar'): # pragma: no cover
if bootstrap_params is None:
bootstrap_params = {}
if dtype is 'Xdatcar':
try:
from pymatgen.io.vasp import Xdatcar
except ModuleNotFoundError:
raise ModuleNotFoundError(
"To use the Xdatcar file parsing, pymatgen must be installed."
)
if isinstance(trajectory, list):
trajectory_list = (Xdatcar(f) for f in trajectory)
structures = _flatten_list(
[x.structures for x in trajectory_list])
else:
xd = Xdatcar(trajectory)
structures = xd.structures
u = PymatgenParser(structures, **parser_params)
self.first_structure = structures[0]
dt = u.delta_t
disp_3d = u.disp_3d
elif dtype is 'IdenticalXdatcar':
try:
from pymatgen.io.vasp import Xdatcar
except ModuleNotFoundError:
raise ModuleNotFoundError(
"To use the Xdatcar file parsing, pymatgen must be installed."
)
u = [
PymatgenParser(Xdatcar(f).structures, **parser_params)
for f in trajectory
]
joint_disp_3d = []
for i in range(len(u[0].disp_3d)):
disp = np.zeros(
(u[0].disp_3d[i].shape[0] * len(u),
u[0].disp_3d[i].shape[1], u[0].disp_3d[i].shape[2]))
disp[:u[0].disp_3d[i].shape[0]] = u[0].disp_3d[i]
for j in range(1, len(u)):
disp[u[0].disp_3d[i].shape[0] *
j:u[0].disp_3d[i].shape[0] *
(j + 1)] = u[j].disp_3d[i]
joint_disp_3d.append(disp)
dt = u[0].delta_t
disp_3d = joint_disp_3d
elif dtype is 'structures':
u = PymatgenParser(trajectory, **parser_params)
self.first_structure = trajectory[0]
dt = u.delta_t
disp_3d = u.disp_3d
elif dtype == 'universe':
u = MDAnalysisParser(trajectory, **parser_params)
dt = u.delta_t
disp_3d = u.disp_3d
else:
try:
import MDAnalysis as mda
except ModuleNotFoundError:
raise ModuleNotFoundError(
"To use the MDAnalysis from file parsing, MDAnalysis must be installed."
)
universe = mda.Universe(*trajectory, format=dtype)
u = MDAnalysisParser(universe, **parser_params)
dt = u.delta_t
disp_3d = u.disp_3d
self._diff = diffusion.MSDBootstrap(dt, disp_3d, **bootstrap_params)
self.dt = self._diff.dt
self.msd_observed = self._diff.msd_observed
self.distributions = self._diff.distributions