def _default_rcut(th):
"""
Look for the first minimum in the partial g(r)
"""
from atooms.system.particle import distinct_species
from atooms.postprocessing.partial import Partial
from atooms.postprocessing import RadialDistributionFunction
from .helpers import ifabsmm
ids = distinct_species(th[0].particle)
gr = Partial(RadialDistributionFunction, ids, th, dr=0.1)
gr.do(update=False)
rcut = {}
for isp in ids:
for jsp in ids:
# First find absolute maximum
_, m = ifabsmm(list(gr.partial[(isp, jsp)].grid),
list(gr.partial[(isp, jsp)].value))
# Then look for first minimum after the maximum
for i in range(len(gr.partial[(isp, jsp)].grid)):
if gr.partial[(isp, jsp)].grid[i] >= m[0]:
delta = gr.partial[(isp, jsp)].value[i+1] - gr.partial[(isp, jsp)].value[i]
if delta >= 0:
rcut[(isp, jsp)] = gr.partial[(isp, jsp)].grid[i]
break
return rcut
def gr(input_file,
dr=0.04,
grandcanonical=False,
ndim=-1,
rmax=-1.0,
*input_files,
**global_args):
"""Radial distribution function"""
global_args = _compat(global_args)
if global_args['legacy']:
backend = pp.RadialDistributionFunctionLegacy
else:
backend = pp.RadialDistributionFunction
for th in _get_trajectories([input_file] + list(input_files), global_args):
th._grandcanonical = grandcanonical
cf = backend(th,
dr=dr,
rmax=rmax,
norigins=global_args['norigins'],
ndim=ndim)
if global_args['filter'] is not None:
cf = pp.Filter(cf, global_args['filter'])
cf.do(update=global_args['update'])
ids = distinct_species(th[0].particle)
if len(ids) > 1 and not global_args['no_partial']:
cf = Partial(backend,
ids,
th,
dr=dr,
rmax=rmax,
norigins=global_args['norigins'],
ndim=ndim)
cf.do(update=global_args['update'])
def qt(input_file,
tmax=-1.0,
tmax_fraction=0.75,
tsamples=60,
func='logx',
*input_files,
**global_args):
"""Collective overlap correlation function"""
global_args = _compat(global_args)
func = _func_db[func]
for th in _get_trajectories([input_file] + list(input_files), global_args):
if tmax > 0:
t_grid = [0.0] + func(th.timestep, tmax, tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(th.timestep, tmax_fraction * th.total_time,
tsamples)
else:
t_grid = None
pp.CollectiveOverlap(
th, t_grid,
norigins=global_args['norigins']).do(update=global_args['update'])
ids = distinct_species(th[0].particle)
if len(ids) > 1:
Partial(pp.CollectiveOverlap,
ids,
th,
t_grid,
norigins=global_args['norigins']).do(
update=global_args['update'])
def alpha2(input_file,
tmax=-1.0,
tmax_fraction=0.75,
tsamples=60,
func='logx',
*input_files,
**global_args):
"""Non-Gaussian parameter"""
global_args = _compat(global_args)
func = _func_db[func]
for th in _get_trajectories([input_file] + list(input_files), global_args):
if tmax > 0:
t_grid = [0.0] + func(th.timestep, tmax, tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(th.timestep, tmax_fraction * th.total_time,
tsamples)
else:
t_grid = None
pp.NonGaussianParameter(
th, t_grid,
norigins=global_args['norigins']).do(update=global_args['update'])
ids = distinct_species(th[0].particle)
if len(ids) > 1:
Partial(pp.NonGaussianParameter,
ids,
th,
t_grid,
norigins=global_args['norigins']).do(
update=global_args['update'])
def vacf(input_file,
tmax=-1.0,
tmax_fraction=0.10,
tsamples=30,
func='linear',
*input_files,
**global_args):
"""Velocity autocorrelation function"""
global_args = _compat(global_args)
func = _func_db[func]
for th in _get_trajectories([input_file] + list(input_files), global_args):
if tmax > 0:
t_grid = [0.0] + func(th.timestep, min(th.total_time, tmax),
tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(th.timestep, tmax_fraction * th.total_time,
tsamples)
else:
t_grid = None
pp.VelocityAutocorrelation(
th, t_grid,
norigins=global_args['norigins']).do(update=global_args['update'])
ids = distinct_species(th[0].particle)
if len(ids) > 1:
Partial(pp.VelocityAutocorrelation,
ids,
th,
t_grid,
norigins=global_args['norigins']).do(
update=global_args['update'])
def test_gr_partial_2(self):
from atooms.postprocessing.partial import Partial
f = os.path.join(self.reference_path, 'kalj-small.xyz')
ts = trajectory.TrajectoryXYZ(f)
ref = {}
ref[('A', 'A')] = numpy.array([0., 0.00675382, 0.27087136, 1.51486318])
ref[('B', 'B')] = numpy.array(
[0.31065645, 0.51329066, 0.67485665, 0.78039485])
ref[('A', 'B')] = numpy.array(
[4.25950671, 3.86572027, 2.70020052, 1.78935426])
gr = Partial(postprocessing.RadialDistributionFunction, ['A', 'B'], ts)
gr.compute()
for ab in [('A', 'A'), ('A', 'B'), ('B', 'B')]:
self.assertLess(deviation(gr.partial[ab].value[21:25], ref[ab]),
4e-2)
def fskt(input_file,
tmax=-1.0,
tmax_fraction=0.75,
tsamples=60,
kmin=7.0,
kmax=8.0,
ksamples=1,
dk=0.1,
nk=8,
kgrid=None,
func='logx',
total=False,
fix_cm=False,
lookup_mb=64.0,
*input_files,
**global_args):
"""Self intermediate scattering function"""
global_args = _compat(global_args)
func = _func_db[func]
if global_args['legacy']:
backend = pp.SelfIntermediateScatteringLegacy
else:
backend = pp.SelfIntermediateScattering
for th in _get_trajectories([input_file] + list(input_files), global_args):
if tmax > 0:
t_grid = [0.0] + func(th.timestep, tmax, tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(th.timestep, tmax_fraction * th.total_time,
tsamples)
else:
t_grid = None
if kgrid is not None:
k_grid = [float(_) for _ in kgrid.split(',')]
else:
k_grid = linear_grid(kmin, kmax, ksamples)
if total:
backend(th,
k_grid,
t_grid,
nk,
dk=dk,
norigins=global_args['norigins'],
fix_cm=fix_cm,
lookup_mb=lookup_mb).do(update=global_args['update'])
ids = distinct_species(th[0].particle)
if len(ids) > 1:
Partial(backend,
ids,
th,
k_grid,
t_grid,
nk,
dk=dk,
norigins=global_args['norigins'],
fix_cm=fix_cm,
lookup_mb=lookup_mb).do(update=global_args['update'])
def sk(input_file,
nk=20,
dk=0.1,
kmin=-1.0,
kmax=15.0,
ksamples=30,
kgrid=None,
weight=None,
weight_trajectory=None,
weight_fluctuations=False,
*input_files,
**global_args):
"""
Structure factor
"""
from atooms.trajectory import TrajectoryXYZ
global_args = _compat(global_args)
if global_args['fast']:
backend = pp.StructureFactorFast
else:
backend = pp.StructureFactorLegacy
if kgrid is not None:
kgrid = [float(_) for _ in kgrid.split(',')]
for th in _get_trajectories([input_file] + list(input_files), global_args):
cf = backend(th,
kgrid=kgrid,
norigins=global_args['norigins'],
kmin=kmin,
kmax=kmax,
nk=nk,
dk=dk,
ksamples=ksamples)
if global_args['filter'] is not None:
cf = pp.Filter(cf, global_args['filter'])
if weight_trajectory is not None:
weight_trajectory = TrajectoryXYZ(weight_trajectory)
cf.add_weight(trajectory=weight_trajectory,
field=weight,
fluctuations=weight_fluctuations)
cf.do(update=global_args['update'])
ids = distinct_species(th[0].particle)
if len(ids) > 1 and not global_args['no_partial']:
cf = Partial(backend,
ids,
th,
kgrid=kgrid,
norigins=global_args['norigins'],
kmin=kmin,
kmax=kmax,
nk=nk,
dk=dk,
ksamples=ksamples)
cf.add_weight(trajectory=weight_trajectory,
field=weight,
fluctuations=weight_fluctuations)
cf.do(update=global_args['update'])
def fkt(input_file,
tmax=-1.0,
tmax_fraction=0.75,
tsamples=60,
kmin=7.0,
kmax=7.0,
ksamples=1,
dk=0.1,
nk=100,
kgrid=None,
func='logx',
fix_cm=False,
*input_files,
**global_args):
"""Total intermediate scattering function"""
global_args = _compat(global_args)
func = _func_db[func]
for th in _get_trajectories([input_file] + list(input_files), global_args):
if tmax > 0:
t_grid = [0.0] + func(th.timestep, tmax, tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(th.timestep, tmax_fraction * th.total_time,
tsamples)
else:
t_grid = None
if kgrid is not None:
k_grid = [float(_) for _ in kgrid.split(',')]
else:
k_grid = linear_grid(kmin, kmax, ksamples)
ids = distinct_species(th[0].particle)
if len(ids) > 1:
Partial(pp.IntermediateScattering,
ids,
th,
k_grid,
t_grid,
norigins=global_args['norigins'],
nk=nk,
dk=dk,
fix_cm=fix_cm).do(update=global_args['update'])
def chi4qs(input_file,
tsamples=60,
a=0.3,
tmax=-1.0,
func='logx',
tmax_fraction=0.75,
total=False,
*input_files,
**global_args):
"""Dynamic susceptibility of self overlap"""
global_args = _compat(global_args)
func = _func_db[func]
if global_args['fast']:
backend = pp.Chi4SelfOverlapOpti
else:
backend = pp.Chi4SelfOverlap
for th in _get_trajectories([input_file] + list(input_files), global_args):
if tmax > 0:
t_grid = [0.0] + func(th.timestep, min(th.total_time, tmax),
tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(th.timestep, tmax_fraction * th.total_time,
tsamples)
else:
t_grid = None
if total:
backend(th, t_grid, a=a, norigins=global_args['norigins']).do(
update=global_args['update'])
ids = distinct_species(th[0].particle)
if not total and len(ids) > 1:
Partial(backend,
ids,
th,
t_grid,
a=a,
norigins=global_args['norigins']).do(
update=global_args['update'])
def msd(input_file,
tmax=-1.0,
tmax_fraction=0.75,
tsamples=30,
sigma=1.0,
func='linear',
rmsd_max=-1.0,
fix_cm=False,
*input_files,
**global_args):
"""Mean square displacement"""
func = _func_db[func]
global_args = _compat(global_args)
for th in _get_trajectories([input_file] + list(input_files), global_args):
dt = th.timestep
if tmax > 0:
t_grid = [0.0] + func(dt, min(th.total_time, tmax), tsamples)
elif tmax_fraction > 0:
t_grid = [0.0] + func(dt, tmax_fraction * th.total_time, tsamples)
else:
t_grid = None
ids = distinct_species(th[0].particle)
pp.MeanSquareDisplacement(
th,
tgrid=t_grid,
norigins=global_args['norigins'],
sigma=sigma,
rmax=rmsd_max,
fix_cm=fix_cm).do(update=global_args['update'])
if len(ids) > 1:
Partial(pp.MeanSquareDisplacement,
ids,
th,
tgrid=t_grid,
norigins=global_args['norigins'],
sigma=sigma,
rmax=rmsd_max).do(update=global_args['update'])