def test_pbc_diff(self):
self.assertArrayAlmostEqual(pbc_diff([0.1, 0.1, 0.1], [0.3, 0.5, 0.9]),
[-0.2, -0.4, 0.2])
self.assertArrayAlmostEqual(
pbc_diff([0.9, 0.1, 1.01], [0.3, 0.5, 0.9]), [-0.4, -0.4, 0.11])
self.assertArrayAlmostEqual(
pbc_diff([0.1, 0.6, 1.01], [0.6, 0.1, 0.9]), [-0.5, 0.5, 0.11])
self.assertArrayAlmostEqual(
pbc_diff([100.1, 0.2, 0.3], [0123123.4, 0.5, 502312.6]),
[-0.3, -0.3, -0.3])
def test_pbc_diff(self):
self.assertTrue(np.allclose(pbc_diff([0.1, 0.1, 0.1], [0.3, 0.5, 0.9]),
[-0.2, -0.4, 0.2]))
self.assertTrue(np.allclose(pbc_diff([0.9, 0.1, 1.01],
[0.3, 0.5, 0.9]),
[-0.4, -0.4, 0.11]))
self.assertTrue(np.allclose(pbc_diff([0.1, 0.6, 1.01],
[0.6, 0.1, 0.9]),
[-0.5, 0.5, 0.11]))
self.assertTrue(np.allclose(pbc_diff([100.1, 0.2, 0.3],
[0123123.4, 0.5, 502312.6]),
[-0.3, -0.3, -0.3]))
def test_pbc_diff(self):
self.assertArrayAlmostEqual(pbc_diff([0.1, 0.1, 0.1], [0.3, 0.5, 0.9]),
[-0.2, -0.4, 0.2])
self.assertArrayAlmostEqual(pbc_diff([0.9, 0.1, 1.01],
[0.3, 0.5, 0.9]),
[-0.4, -0.4, 0.11])
self.assertArrayAlmostEqual(pbc_diff([0.1, 0.6, 1.01],
[0.6, 0.1, 0.9]),
[-0.5, 0.5, 0.11])
self.assertArrayAlmostEqual(pbc_diff([100.1, 0.2, 0.3],
[0123123.4, 0.5, 502312.6]),
[-0.3, -0.3, -0.3])
def get_matching_coord(coord):
for op in self.symmetry_operations:
c = op.operate(coord)
for k in coord_to_species.keys():
if np.allclose(pbc_diff(c, k), (0, 0, 0), atol=self._site_tolerance):
return tuple(k)
return False
def get_matching_coord(coord):
for op in self.symmetry_operations:
c = op.operate(coord)
for k in coord_to_species.keys():
if np.allclose(pbc_diff(c, k), (0, 0, 0),
atol=self._site_tolerance):
return tuple(k)
return False
def should_stop(self, old_centroids, centroids, iterations):
if iterations > self.max_iterations:
warnings.warn("Max iterations %d reached!" % self.max_iterations)
return True
if old_centroids is None:
return False
for c1, c2 in zip(old_centroids, centroids):
if not np.allclose(pbc_diff(c1, c2), [0, 0, 0]):
return False
return True
def from_vaspruns(cls, vaspruns, specie, min_obs=30, weighted=True):
"""
Convenient constructor that takes in a list of Vasprun objects to
perform diffusion analysis.
Args:
vaspruns:
list of Vasprun objects (must be ordered in sequence of run).
E.g., you may have performed sequential VASP runs to obtain
sufficient statistics.
specie:
Specie to calculate diffusivity for as a String. E.g., "Li".
min_obs:
Minimum number of observations to have before including in
the MSD vs dt calculation. E.g. If a structure has 10
diffusing atoms, and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
weighted:
Uses a weighted least squares to fit the MSD vs dt. Weights are
proportional to 1/dt, since the number of observations are
also proportional to 1/dt (and hence the variance is
proportional to dt)
"""
structure = vaspruns[0].initial_structure
step_skip = vaspruns[0].ionic_step_skip or 1
p = []
final_structure = vaspruns[0].initial_structure
for vr in vaspruns:
#check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
p.extend([np.array(s['structure'].frac_coords)[:, None]
for s in vr.ionic_steps])
p = np.concatenate(p, axis=1)
dp = p[:, 1:] - p[:, :-1]
dp = np.concatenate([np.zeros_like(dp[:, (0,)]), dp], axis=1)
dp = dp - np.round(dp)
f_disp = np.cumsum(dp, axis=1)
disp = structure.lattice.get_cartesian_coords(f_disp)
temperature = vaspruns[0].parameters['TEEND']
time_step = vaspruns[0].parameters['POTIM']
return cls(structure, disp, specie, temperature,
time_step, step_skip=step_skip, min_obs=min_obs,
weighted=weighted)
def from_vaspruns(cls, vaspruns, specie, min_obs=30, weighted=True):
"""
Convenient constructor that takes in a list of Vasprun objects to
perform diffusion analysis.
Args:
vaspruns ([Vasprun]): List of Vaspruns (must be ordered in
sequence of MD simulation). E.g., you may have performed
sequential VASP runs to obtain sufficient statistics.
specie (Element/Specie): Specie to calculate diffusivity for as a
String. E.g., "Li".
min_obs (int): Minimum number of observations to have before
including in the MSD vs dt calculation. E.g. If a structure
has 10 diffusing atoms, and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
weighted (bool): Uses a weighted least squares to fit the
MSD vs dt. Weights are proportional to 1/dt, since the
number of observations are also proportional to 1/dt (and
hence the variance is proportional to dt)
"""
step_skip = vaspruns[0].ionic_step_skip or 1
final_structure = vaspruns[0].initial_structure
structures = []
for vr in vaspruns:
#check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
structures.extend([s['structure'] for s in vr.ionic_steps])
temperature = vaspruns[0].parameters['TEEND']
time_step = vaspruns[0].parameters['POTIM']
return cls.from_structures(structures,
specie,
temperature,
time_step,
step_skip=step_skip,
min_obs=min_obs,
weighted=weighted)
def get_structure(vaspruns):
for i, vr in enumerate(vaspruns):
if i == 0:
skipstep = vr.ionic_skipstep or 1
final_material = vr.initial_material
temp = vr.parameters['TEEND']
steptime = vr.parameters['POTIM']
yield skipstep, temp, steptime
fdist = pbc_diff(vr.initial_material.frac_coords, final_material.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structure do not ''match.')
final_material = vr.final_material
assert (vr.ionic_skipstep or 1) == skipstep
for s in vr.ionic_steps:
yield s['material']
def get_structures(vaspruns):
for i, vr in enumerate(vaspruns):
if i == 0:
step_skip = vr.ionic_step_skip or 1
final_structure = vr.initial_structure
temperature = vr.parameters['TEEND']
time_step = vr.parameters['POTIM']
yield step_skip, temperature, time_step
# check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
for s in vr.ionic_steps:
yield s['structure']
def test_cluster(self):
lattice = Lattice.cubic(4)
pts = []
initial = [[0, 0, 0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.5, 0, 0]]
for c in initial:
for i in range(100):
pts.append(np.array(c) + np.random.randn(3) * 0.01 +
np.random.randint(3))
pts = np.array(pts)
k = KmeansPBC(lattice)
centroids, labels, ss = k.cluster(pts, 4)
for c1 in centroids:
found = False
for c2 in centroids:
if np.allclose(pbc_diff(c1, c2), [0, 0, 0], atol=0.1):
found = True
break
self.assertTrue(found)
def get_structures(vaspruns):
for i, vr in enumerate(vaspruns):
if i == 0:
step_skip = vr.ionic_step_skip or 1
final_structure = vr.initial_structure
temperature = vr.parameters['TEEND']
time_step = vr.parameters['POTIM']
yield step_skip, temperature, time_step
# check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
for s in vr.ionic_steps:
yield s['structure']
def is_periodic_image(self, other, tolerance=1e-8, check_lattice=True):
"""
Returns True if sites are periodic images of each other.
Args:
other:
Other site
tolerance:
Tolerance to compare fractional coordinates
check_lattice:
Whether to check if the two sites have the same lattice.
"""
if check_lattice and self._lattice != other._lattice:
return False
if self._species != other._species:
return False
frac_diff = pbc_diff(self._fcoords, other._fcoords)
return np.allclose(frac_diff, [0, 0, 0], atol=tolerance)
def from_vaspruns(cls, vaspruns, specie, min_obs=30, weighted=True):
"""
Convenient constructor that takes in a list of Vasprun objects to
perform diffusion analysis.
Args:
vaspruns ([Vasprun]): List of Vaspruns (must be ordered in
sequence of MD simulation). E.g., you may have performed
sequential VASP runs to obtain sufficient statistics.
specie (Element/Specie): Specie to calculate diffusivity for as a
String. E.g., "Li".
min_obs (int): Minimum number of observations to have before
including in the MSD vs dt calculation. E.g. If a structure
has 10 diffusing atoms, and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
weighted (bool): Uses a weighted least squares to fit the
MSD vs dt. Weights are proportional to 1/dt, since the
number of observations are also proportional to 1/dt (and
hence the variance is proportional to dt)
"""
step_skip = vaspruns[0].ionic_step_skip or 1
final_structure = vaspruns[0].initial_structure
structures = []
for vr in vaspruns:
#check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
structures.extend([s['structure'] for s in vr.ionic_steps])
temperature = vaspruns[0].parameters['TEEND']
time_step = vaspruns[0].parameters['POTIM']
return cls.from_structures(structures, specie, temperature,
time_step, step_skip=step_skip, min_obs=min_obs,
weighted=weighted)
def is_periodic_image(self, other, tolerance=1e-8, check_lattice=True):
"""
Returns True if sites are periodic images of each other.
Args:
other:
Other site
tolerance:
Tolerance to compare fractional coordinates
check_lattice:
Whether to check if the two sites have the same lattice.
"""
if check_lattice and self._lattice != other._lattice:
return False
if self._species != other._species:
return False
frac_diff = pbc_diff(self._fcoords, other._fcoords)
return np.allclose(frac_diff, [0, 0, 0], atol=tolerance)
def from_vaspruns(cls,
vaspruns,
specie,
smoothed="max",
min_obs=30,
avg_nsteps=1000,
initial_disp=None,
initial_structure=None):
"""
Convenient constructor that takes in a list of Vasprun objects to
perform diffusion analysis.
Args:
vaspruns ([Vasprun]): List of Vaspruns (must be ordered in
sequence of MD simulation). E.g., you may have performed
sequential VASP runs to obtain sufficient statistics.
specie (Element/Specie): Specie to calculate diffusivity for as a
String. E.g., "Li".
min_obs (int): Minimum number of observations to have before
including in the MSD vs dt calculation. E.g. If a structure
has 10 diffusing atoms, and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
smoothed (str): Whether to smooth the MSD, and what mode to smooth.
Supported modes are:
i. "max", which tries to use the maximum #
of data points for each time origin, subject to a
minimum # of observations given by min_obs, and then
weights the observations based on the variance
accordingly. This is the default.
ii. "constant", in which each timestep is averaged over
the same number of observations given by min_obs.
iii. None / False / any other false-like quantity. No
smoothing.
min_obs (int): Used with smoothed="max". Minimum number of
observations to have before including in the MSD vs dt
calculation. E.g. If a structure has 10 diffusing atoms,
and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
Only applies in smoothed="max".
avg_nsteps (int): Used with smoothed="constant". Determines the
number of time steps to average over to get the msd for each
timestep. Default of 1000 is usually pretty good.
initial_disp (np.ndarray): Sometimes, you need to iteratively
compute estimates of the diffusivity. This supplies an
initial displacement that will be added on to the initial
displacements. Note that this makes sense only when
smoothed=False.
initial_structure (Structure): Like initial_disp, this is used
for iterative computations of estimates of the diffusivity. You
typically need to supply both variables. This stipulates the
initial strcture from which the current set of displacements
are computed.
"""
step_skip = vaspruns[0].ionic_step_skip or 1
final_structure = vaspruns[0].initial_structure
structures = []
for vr in vaspruns:
#check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
structures.extend([s['structure'] for s in vr.ionic_steps])
temperature = vaspruns[0].parameters['TEEND']
time_step = vaspruns[0].parameters['POTIM']
return cls.from_structures(structures=structures,
specie=specie,
temperature=temperature,
time_step=time_step,
step_skip=step_skip,
smoothed=smoothed,
min_obs=min_obs,
avg_nsteps=avg_nsteps,
initial_disp=initial_disp,
initial_structure=initial_structure)
def from_vaspruns(cls, vaspruns, specie, min_obs=30, weighted=True):
"""
Convenient constructor that takes in a list of Vasprun objects to
perform diffusion analysis.
Args:
vaspruns:
list of Vasprun objects (must be ordered in sequence of run).
E.g., you may have performed sequential VASP runs to obtain
sufficient statistics.
specie:
Specie to calculate diffusivity for as a String. E.g., "Li".
min_obs:
Minimum number of observations to have before including in
the MSD vs dt calculation. E.g. If a structure has 10
diffusing atoms, and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
weighted:
Uses a weighted least squares to fit the MSD vs dt. Weights are
proportional to 1/dt, since the number of observations are
also proportional to 1/dt (and hence the variance is
proportional to dt)
"""
structure = vaspruns[0].initial_structure
step_skip = vaspruns[0].ionic_step_skip or 1
p = []
final_structure = vaspruns[0].initial_structure
for vr in vaspruns:
#check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
p.extend([
np.array(s['structure'].frac_coords)[:, None]
for s in vr.ionic_steps
])
p = np.concatenate(p, axis=1)
dp = p[:, 1:] - p[:, :-1]
dp = np.concatenate([np.zeros_like(dp[:, (0, )]), dp], axis=1)
dp = dp - np.round(dp)
f_disp = np.cumsum(dp, axis=1)
disp = structure.lattice.get_cartesian_coords(f_disp)
temperature = vaspruns[0].parameters['TEEND']
time_step = vaspruns[0].parameters['POTIM']
return cls(structure,
disp,
specie,
temperature,
time_step,
step_skip=step_skip,
min_obs=min_obs,
weighted=weighted)
def from_vaspruns(cls, vaspruns, specie, smoothed="max", min_obs=30,
avg_nsteps=1000, initial_disp=None,
initial_structure=None):
"""
Convenient constructor that takes in a list of Vasprun objects to
perform diffusion analysis.
Args:
vaspruns ([Vasprun]): List of Vaspruns (must be ordered in
sequence of MD simulation). E.g., you may have performed
sequential VASP runs to obtain sufficient statistics.
specie (Element/Specie): Specie to calculate diffusivity for as a
String. E.g., "Li".
min_obs (int): Minimum number of observations to have before
including in the MSD vs dt calculation. E.g. If a structure
has 10 diffusing atoms, and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
smoothed (str): Whether to smooth the MSD, and what mode to smooth.
Supported modes are:
i. "max", which tries to use the maximum #
of data points for each time origin, subject to a
minimum # of observations given by min_obs, and then
weights the observations based on the variance
accordingly. This is the default.
ii. "constant", in which each timestep is averaged over
the same number of observations given by min_obs.
iii. None / False / any other false-like quantity. No
smoothing.
min_obs (int): Used with smoothed="max". Minimum number of
observations to have before including in the MSD vs dt
calculation. E.g. If a structure has 10 diffusing atoms,
and min_obs = 30, the MSD vs dt will be
calculated up to dt = total_run_time / 3, so that each
diffusing atom is measured at least 3 uncorrelated times.
Only applies in smoothed="max".
avg_nsteps (int): Used with smoothed="constant". Determines the
number of time steps to average over to get the msd for each
timestep. Default of 1000 is usually pretty good.
initial_disp (np.ndarray): Sometimes, you need to iteratively
compute estimates of the diffusivity. This supplies an
initial displacement that will be added on to the initial
displacements. Note that this makes sense only when
smoothed=False.
initial_structure (Structure): Like initial_disp, this is used
for iterative computations of estimates of the diffusivity. You
typically need to supply both variables. This stipulates the
initial strcture from which the current set of displacements
are computed.
"""
step_skip = vaspruns[0].ionic_step_skip or 1
final_structure = vaspruns[0].initial_structure
structures = []
for vr in vaspruns:
#check that the runs are continuous
fdist = pbc_diff(vr.initial_structure.frac_coords,
final_structure.frac_coords)
if np.any(fdist > 0.001):
raise ValueError('initial and final structures do not '
'match.')
final_structure = vr.final_structure
assert (vr.ionic_step_skip or 1) == step_skip
structures.extend([s['structure'] for s in vr.ionic_steps])
temperature = vaspruns[0].parameters['TEEND']
time_step = vaspruns[0].parameters['POTIM']
return cls.from_structures(structures=structures, specie=specie,
temperature=temperature, time_step=time_step, step_skip=step_skip,
smoothed=smoothed, min_obs=min_obs, avg_nsteps=avg_nsteps,
initial_disp=initial_disp, initial_structure=initial_structure)
