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from __future__ import print_function
from lammps import lammps
import numpy as np
class LAMMPSFix(object):
def __init__(self, ptr, group_name="all"):
self.lmp = lammps(ptr=ptr)
self.group_name = group_name
class LAMMPSFixMove(LAMMPSFix):
def __init__(self, ptr, group_name="all"):
super(LAMMPSFixMove, self).__init__(ptr, group_name)
def init(self):
pass
def initial_integrate(self, vflag):
pass
def final_integrate(self):
pass
def initial_integrate_respa(self, vflag, ilevel, iloop):
pass
def final_integrate_respa(self, ilevel, iloop):
pass
def reset_dt(self):
pass
class NVE(LAMMPSFixMove):
""" Python implementation of fix/nve """
def __init__(self, ptr, group_name="all"):
super(NVE, self).__init__(ptr)
assert(self.group_name == "all")
def init(self):
dt = self.lmp.extract_global("dt")
ftm2v = self.lmp.extract_global("ftm2v")
self.ntypes = self.lmp.extract_global("ntypes")
self.dtv = dt
self.dtf = 0.5 * dt * ftm2v
def initial_integrate(self, vflag):
mass = self.lmp.numpy.extract_atom("mass")
atype = self.lmp.numpy.extract_atom("type")
x = self.lmp.numpy.extract_atom("x")
v = self.lmp.numpy.extract_atom("v")
f = self.lmp.numpy.extract_atom("f")
for i in range(x.shape[0]):
dtfm = self.dtf / mass[int(atype[i])]
v[i,:]+= dtfm * f[i,:]
x[i,:] += self.dtv * v[i,:]
def final_integrate(self):
mass = self.lmp.numpy.extract_atom("mass")
atype = self.lmp.numpy.extract_atom("type")
v = self.lmp.numpy.extract_atom("v")
f = self.lmp.numpy.extract_atom("f")
for i in range(v.shape[0]):
dtfm = self.dtf / mass[int(atype[i])]
v[i,:] += dtfm * f[i,:]
class NVE_Opt(LAMMPSFixMove):
""" Performance-optimized Python implementation of fix/nve """
def __init__(self, ptr, group_name="all"):
super(NVE_Opt, self).__init__(ptr)
assert(self.group_name == "all")
def init(self):
dt = self.lmp.extract_global("dt")
ftm2v = self.lmp.extract_global("ftm2v")
self.ntypes = self.lmp.extract_global("ntypes")
self.dtv = dt
self.dtf = 0.5 * dt * ftm2v
def initial_integrate(self, vflag):
mass = self.lmp.numpy.extract_atom("mass")
atype = self.lmp.numpy.extract_atom("type")
x = self.lmp.numpy.extract_atom("x")
v = self.lmp.numpy.extract_atom("v")
f = self.lmp.numpy.extract_atom("f")
dtf = self.dtf
dtv = self.dtv
dtfm = dtf / np.take(mass, atype)
for d in range(x.shape[1]):
v[:,d] += dtfm * f[:,d]
x[:,d] += dtv * v[:,d]
def final_integrate(self):
mass = self.lmp.numpy.extract_atom("mass")
atype = self.lmp.numpy.extract_atom("type")
v = self.lmp.numpy.extract_atom("v")
f = self.lmp.numpy.extract_atom("f")
dtf = self.dtf
dtfm = dtf / np.take(mass, atype)
for d in range(v.shape[1]):
v[:,d] += dtfm * f[:,d]
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