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 _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 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'])