def refine_tasks(from_task, to_task, err) :
jdata = json.load(open(os.path.join(from_task, 'in.json')))
equi_conf = get_task_file_abspath(from_task, jdata['equi_conf'])
model = get_task_file_abspath(from_task, jdata['model'])
create_path(to_task)
copied_conf = os.path.join(os.path.abspath(to_task), 'conf.lmp')
shutil.copyfile(equi_conf, copied_conf)
jdata['equi_conf'] = 'conf.lmp'
linked_model = os.path.join(os.path.abspath(to_task), 'graph.pb')
shutil.copyfile(model, linked_model)
jdata['model'] = 'graph.pb'
jdata['orig_task'] = from_task
jdata['refine_error'] = err
cwd = os.getcwd()
os.chdir(to_task)
with open('in.json', 'w') as fp:
json.dump(jdata, fp, indent=4)
os.chdir(cwd)
from_name = os.path.join(from_task, '00.angle_on')
to_name = os.path.join(to_task, '00.angle_on')
_refine_tasks(from_name, to_name, err, 'angle_on')
from_name = os.path.join(from_task, '01.deep_on')
to_name = os.path.join(to_task, '01.deep_on')
_refine_tasks(from_name, to_name, err, 'deep_on')
from_name = os.path.join(from_task, '02.bond_angle_off')
to_name = os.path.join(to_task, '02.bond_angle_off')
_refine_tasks(from_name, to_name, err, 'bond_angle_off')
def _setup_dpdt (task_path, jdata) :
name_0 = jdata['phase_i']['name']
name_1 = jdata['phase_ii']['name']
conf_0 = os.path.join(task_path, '../', jdata['phase_i']['equi_conf'])
conf_1 = os.path.join(task_path, '../', jdata['phase_ii']['equi_conf'])
conf_0 = os.path.abspath(conf_0)
conf_1 = os.path.abspath(conf_1)
model = os.path.join(task_path, '../', jdata['model'])
if model:
model = os.path.abspath(model)
task_abs_dir = create_path(task_path)
conf_0_name = 'conf.%s.lmp' % '0'
conf_1_name = 'conf.%s.lmp' % '1'
# conf_0_name = 'conf.%s.lmp' % name_0
# conf_1_name = 'conf.%s.lmp' % name_1
copied_conf_0 = os.path.join(os.path.abspath(task_path), conf_0_name)
copied_conf_1 = os.path.join(os.path.abspath(task_path), conf_1_name)
shutil.copyfile(conf_0, copied_conf_0)
shutil.copyfile(conf_1, copied_conf_1)
if model:
linked_model = os.path.join(os.path.abspath(task_path), 'graph.pb')
shutil.copyfile(model, linked_model)
with open(os.path.join(os.path.abspath(task_path), 'in.json'), 'w') as fp:
json.dump(jdata, fp, indent=4)
def make_tasks(iter_name, jdata, if_meam=None):
if not if_meam:
if_meam = jdata['if_meam']
equi_conf = os.path.abspath(jdata['equi_conf'])
if jdata.get('model', None):
model = os.path.abspath(jdata['model'])
else:
model = None
meam_model = jdata.get('meam_model', None)
create_path(iter_name)
copied_conf = os.path.join(os.path.abspath(iter_name), 'conf.lmp')
shutil.copyfile(equi_conf, copied_conf)
# jdata['equi_conf'] = copied_conf
if model:
copied_model = os.path.join(os.path.abspath(iter_name), 'graph.pb')
shutil.copyfile(model, copied_model)
# jdata['model'] = copied_model
cwd = os.getcwd()
os.chdir(iter_name)
with open('in.json', 'w') as fp:
json.dump(jdata, fp, indent=4)
os.chdir(cwd)
subtask_name = os.path.join(iter_name, '00.soft_on')
_make_tasks(subtask_name,
jdata,
'soft_on',
if_meam=if_meam,
meam_model=meam_model)
subtask_name = os.path.join(iter_name, '01.deep_on')
_make_tasks(subtask_name,
jdata,
'deep_on',
if_meam=if_meam,
meam_model=meam_model)
subtask_name = os.path.join(iter_name, '02.soft_off')
_make_tasks(subtask_name,
jdata,
'soft_off',
if_meam=if_meam,
meam_model=meam_model)
def make_tasks(iter_name, jdata) :
equi_conf = os.path.abspath(jdata['equi_conf'])
model = os.path.abspath(jdata['model'])
create_path(iter_name)
copied_conf = os.path.join(os.path.abspath(iter_name), 'conf.lmp')
shutil.copyfile(equi_conf, copied_conf)
jdata['equi_conf'] = 'conf.lmp'
linked_model = os.path.join(os.path.abspath(iter_name), 'graph.pb')
shutil.copyfile(model, linked_model)
jdata['model'] = 'graph.pb'
cwd = os.getcwd()
os.chdir(iter_name)
with open('in.json', 'w') as fp:
json.dump(jdata, fp, indent=4)
os.chdir(cwd)
subtask_name = os.path.join(iter_name, '00.angle_on')
_make_tasks(subtask_name, jdata, 'angle_on')
subtask_name = os.path.join(iter_name, '01.deep_on')
_make_tasks(subtask_name, jdata, 'deep_on')
subtask_name = os.path.join(iter_name, '02.bond_angle_off')
_make_tasks(subtask_name, jdata, 'bond_angle_off')
def make_task(iter_name,
jdata,
ens=None,
temp=None,
pres=None,
avg_posi=None,
npt_conf=None,
if_meam=None):
equi_conf = jdata['equi_conf']
equi_conf = os.path.abspath(equi_conf)
if npt_conf is not None:
npt_conf = os.path.abspath(npt_conf)
model = jdata['model']
meam_model = jdata.get('meam_model', None)
model = os.path.abspath(model)
model_mass_map = jdata['model_mass_map']
nsteps = jdata['nsteps']
dt = jdata['dt']
stat_freq = jdata['stat_freq']
dump_freq = jdata['dump_freq']
tau_t = jdata['tau_t']
tau_p = jdata['tau_p']
if ens is None:
ens = jdata['ens']
elif 'ens' in jdata:
print('ens = %s overrides the ens in json data' % ens)
jdata['ens'] = ens
if temp is None:
temp = jdata['temp']
elif 'temp' in jdata:
print('T = %f overrides the temp in json data' % temp)
jdata['temp'] = temp
if 'npt' in ens:
if pres == None:
pres = jdata['pres']
elif 'pres' in jdata:
print('P = %f overrides the pres in json data' % pres)
jdata['pres'] = pres
if if_meam is None:
if_meam = jdata.get('if_meam', False)
create_path(iter_name)
cwd = os.getcwd()
os.chdir(iter_name)
with open('in.json', 'w') as fp:
json.dump(jdata, fp, indent=4)
if npt_conf is None:
os.symlink(os.path.realpath(equi_conf), 'conf.lmp')
else:
open('conf.lmp', 'w').write(npt_equi_conf(npt_conf))
os.symlink(os.path.realpath(model), 'graph.pb')
lmp_str \
= _gen_lammps_input('conf.lmp',
model_mass_map,
'graph.pb',
nsteps,
dt,
ens,
temp,
pres,
tau_t = tau_t,
tau_p = tau_p,
prt_freq = stat_freq,
dump_freq = dump_freq,
dump_ave_posi = avg_posi,
if_meam = if_meam,
meam_model = meam_model)
with open('in.lammps', 'w') as fp:
fp.write(lmp_str)
os.chdir(cwd)
def _make_tasks_onephase(temp,
pres,
task_path,
jdata,
ens = 'npt',
conf_file = 'conf.lmp',
graph_file = 'graph.pb',
if_meam=False,
meam_model=None):
# assume that model and conf.lmp exist in the current dir
assert os.path.isfile(conf_file), (conf_file, os.getcwd())
# assert(os.path.isfile(graph_file))
conf_file = os.path.abspath(conf_file)
if graph_file:
graph_abs_file = os.path.abspath(graph_file)
mass_map = jdata['mass_map']
# MD simulation protocol
nsteps = jdata['nsteps']
timestep = jdata['timestep']
thermo_freq = jdata['thermo_freq']
tau_t = jdata['tau_t']
tau_p = jdata['tau_p']
cwd = os.getcwd()
if not os.path.isdir(task_path):
create_path(task_path)
os.chdir(task_path)
if not os.path.exists('conf.lmp'):
os.symlink(os.path.relpath(conf_file), 'conf.lmp')
if graph_file:
if not os.path.exists('graph.pb'):
os.symlink(os.path.relpath(graph_abs_file), 'graph.pb')
if if_meam:
relative_link_file(meam_model['library_abs_path'], './')
relative_link_file(meam_model['potential_abs_path'], './')
# meam_library_basename = os.path.basename(meam_model['library'])
# meam_potential_basename = os.path.basename(meam_model['potential'])
# os.symlink(os.path.join('../../../', meam_library_basename), meam_library_basename)
# os.symlink(os.path.join('../../../', meam_potential_basename), meam_potential_basename)
# input for NPT MD
lmp_str \
= _gen_lammps_input('conf.lmp',
mass_map,
graph_file,
nsteps,
timestep,
ens,
temp,
pres,
tau_t = tau_t,
tau_p = tau_p,
thermo_freq = thermo_freq,
if_meam=if_meam,
meam_model=meam_model)
with open('thermo.out', 'w') as fp :
fp.write('%.16e %.16e' % (temp, pres))
with open('in.lammps', 'w') as fp :
fp.write(lmp_str)
os.chdir(cwd)
def make_task(iter_name,
jdata,
ens=None,
temp=None,
pres=None,
if_dump_avg_posi=None,
npt_dir=None):
# jfile_path = os.path.abspath(jfile)
# with open(jfile, 'r') as f:
# jdata = json.load(f)
equi_args = [
Argument("equi_conf", str),
Argument("mass_map", list, alias=['model_mass_map']),
Argument("model", str),
Argument("nsteps", int),
Argument("timestep", float, alias=['dt']),
Argument("ens", str),
Argument("temp", int),
Argument("pres", int),
Argument("tau_t", float),
Argument("tau_p", float),
Argument("thermo_freq", int, alias=['stat_freq']),
Argument("dump_freq", int),
Argument("stat_skip", int),
Argument("stat_bsize", int),
Argument("if_dump_avg_posi", bool, optional=True, default=False),
Argument("is_water",
bool,
optional=True,
default=False,
alias=['if_water']),
Argument("if_meam", bool, optional=True, default=False),
Argument("meam_model", list, optional=True, default=False),
]
equi_format = Argument("equi", dict, equi_args)
equi_kwargs_settings = create_dict_not_empty_key(
ens=ens,
temp=temp,
pres=pres,
if_dump_avg_posi=if_dump_avg_posi,
npt_dir=npt_dir)
equi_pre_settings = jdata.copy()
equi_pre_settings.update(equi_kwargs_settings)
equi_settings = equi_format.normalize_value(equi_pre_settings)
task_abs_dir = create_path(iter_name)
if npt_dir is not None:
npt_avg_conf_lmp = npt_equi_conf(npt_dir)
with open(os.path.join(task_abs_dir, 'npt_avg.lmp'), 'w') as f:
f.write(npt_avg_conf_lmp)
equi_settings['equi_conf'] = 'npt_avg.lmp'
else:
equi_conf = equi_settings['equi_conf']
relative_link_file(equi_conf, task_abs_dir)
equi_settings['equi_conf'] = os.path.basename(equi_conf)
model = equi_settings['model']
if model:
relative_link_file(model, task_abs_dir)
equi_settings['model'] = os.path.basename(model)
if_meam = equi_settings.get('if_meam', None)
meam_model = equi_settings.get('meam_model', None)
if if_meam:
relative_link_file(meam_model['library'], task_abs_dir)
relative_link_file(meam_model['potential'], task_abs_dir)
with open(os.path.join(task_abs_dir, 'jdata.json'), 'w') as f:
json.dump(jdata, f, indent=4)
with open(os.path.join(task_abs_dir, 'equi_kwargs_setting.json'),
'w') as f:
json.dump(equi_kwargs_settings, f, indent=4)
with open(os.path.join(task_abs_dir, 'equi_settings.json'), 'w') as f:
json.dump(equi_settings, f, indent=4)
lmp_str = gen_equi_lammps_input(
nsteps=equi_settings['nsteps'],
thermo_freq=equi_settings['thermo_freq'],
dump_freq=equi_settings['dump_freq'],
mass_map=equi_settings['mass_map'],
temp=equi_settings['temp'],
tau_t=equi_settings['tau_t'],
tau_p=equi_settings['tau_p'],
equi_conf=equi_settings['equi_conf'],
model=equi_settings['model'],
timestep=equi_settings['timestep'],
if_dump_avg_posi=equi_settings['if_dump_avg_posi'],
ens=equi_settings['ens'],
pres=equi_settings['pres'],
if_meam=equi_settings['if_meam'],
meam_model=equi_settings['meam_model'])
with open(os.path.join(task_abs_dir, 'in.lammps'), 'w') as fp:
fp.write(lmp_str)
return equi_settings
def _make_tasks(iter_name, jdata, step, if_meam=False, meam_model=None):
if step == 'soft_on':
all_lambda = parse_seq(jdata['lambda_soft_on'])
elif step == 'deep_on':
all_lambda = parse_seq(jdata['lambda_deep_on'])
elif step == 'soft_off':
all_lambda = parse_seq(jdata['lambda_soft_off'])
else:
raise RuntimeError('unknow step')
equi_conf = jdata['equi_conf']
equi_conf = os.path.abspath(equi_conf)
# mass_map = jdata['mass_map']
mass_map = get_first_matched_key_from_dict(jdata,
['mass_map', 'model_mass_map'])
model = jdata.get('model', None)
if model:
model = os.path.abspath(model)
soft_param = jdata['soft_param']
nsteps = jdata['nsteps']
# timestep = jdata['timestep']
timestep = get_first_matched_key_from_dict(jdata, ['timestep', 'dt'])
# thermo_freq = jdata['thermo_freq']
thermo_freq = get_first_matched_key_from_dict(jdata,
['thermo_freq', 'stat_freq'])
copies = None
if 'copies' in jdata:
copies = jdata['copies']
temp = jdata['temp']
sparam = jdata.get('soft_param', {})
if sparam:
if 'sigma_oo' in sparam:
sparam['sigma_0_0'] = sparam['sigma_oo']
sparam['sigma_0_1'] = sparam['sigma_oh']
sparam['sigma_1_1'] = sparam['sigma_hh']
# element_num=sparam.get('element_num', 1)
element_num = len(mass_map)
sigma_key_index = filter(lambda t: t[0] <= t[1],
((i, j) for i in range(element_num)
for j in range(element_num)))
sigma_key_name_list = [
'sigma_' + str(t[0]) + '_' + str(t[1]) for t in sigma_key_index
]
for sigma_key_name in sigma_key_name_list:
assert sparam.get(
sigma_key_name, None
), 'there must be key-value for {sigma_key_name} in soft_param'.format(
sigma_key_name=sigma_key_name)
job_abs_dir = create_path(iter_name)
if meam_model:
relative_link_file(os.path.abspath(meam_model['library']), job_abs_dir)
relative_link_file(os.path.abspath(meam_model['potential']),
job_abs_dir)
# os.symlink(os.path.join('..', 'conf.lmp'), 'conf.lmp')
# os.symlink(os.path.join('..', 'graph.pb'), 'graph.pb')
cwd = os.getcwd()
os.chdir(iter_name)
os.symlink(os.path.join('..', 'in.json'), 'in.json')
os.symlink(os.path.join('..', 'conf.lmp'), 'conf.lmp')
os.symlink(os.path.join('..', 'graph.pb'), 'graph.pb')
os.chdir(cwd)
# print(9898, meam_model)
for idx, ii in enumerate(all_lambda):
work_path = os.path.join(iter_name, 'task.%06d' % idx)
create_path(work_path)
os.chdir(work_path)
os.symlink(os.path.join('..', 'conf.lmp'), 'conf.lmp')
os.symlink(os.path.join('..', 'graph.pb'), 'graph.pb')
if meam_model:
meam_library_basename = os.path.basename(meam_model['library'])
meam_potential_basename = os.path.basename(meam_model['potential'])
os.symlink(os.path.join('..', meam_library_basename),
meam_library_basename)
os.symlink(os.path.join('..', meam_potential_basename),
meam_potential_basename)
lmp_str \
= _gen_lammps_input_ideal(step,
'conf.lmp',
mass_map,
ii,
soft_param,
'graph.pb',
nsteps,
timestep,
'nvt',
temp,
thermo_freq = thermo_freq,
copies = copies,
if_meam = if_meam,
meam_model=meam_model)
with open('in.lammps', 'w') as fp:
fp.write(lmp_str)
with open('lambda.out', 'w') as fp:
fp.write(str(ii))
os.chdir(cwd)
def make_tasks(iter_name, jdata, if_meam=None):
ti_settings = jdata.copy()
if if_meam is None:
if_meam = jdata.get('if_meam', None)
equi_conf = jdata['equi_conf']
equi_conf = os.path.abspath(equi_conf)
copies = None
if 'copies' in jdata :
copies = jdata['copies']
model = jdata['model']
meam_model = jdata.get('meam_model', None)
# model = os.path.abspath(model)
# mass_map = jdata['mass_map']
mass_map = get_first_matched_key_from_dict(jdata, ['model_mass_map', 'mass_map'])
nsteps = jdata['nsteps']
# timestep = jdata['timestep']
timestep = get_first_matched_key_from_dict(jdata, ['timestep', 'dt'])
# thermo_freq = jdata['thermo_freq']
thermo_freq = get_first_matched_key_from_dict(jdata, ['thermo_freq', 'stat_freq'])
# thermos = jdata['thermos']
ens = jdata['ens']
path = jdata['path']
if 'nvt' in ens :
if path == 't' :
temp_seq = get_first_matched_key_from_dict(jdata, ['temp_seq', 'temps'])
temp_list = parse_seq(temp_seq)
tau_t = jdata['tau_t']
ntasks = len(temp_list)
else :
raise RuntimeError('supported path of nvt ens is \'t\'')
elif 'npt' in ens :
if path == 't' :
temp_seq = get_first_matched_key_from_dict(jdata, ['temp_seq', 'temps'])
temp_list = parse_seq(temp_seq)
pres = get_first_matched_key_from_dict(jdata, ['pres', 'press'])
ntasks = len(temp_list)
elif path == 't-ginv' :
temp_seq = get_first_matched_key_from_dict(jdata, ['temp_seq', 'temps'])
temp_list = parse_seq_ginv(temp_seq)
pres = get_first_matched_key_from_dict(jdata, ['pres', 'press'])
ntasks = len(temp_list)
elif path == 'p' :
temp = get_first_matched_key_from_dict(jdata, ['temp', 'temps'])
pres_seq = get_first_matched_key_from_dict(jdata, ['pres_seq', 'press'])
pres_list = parse_seq(pres_seq)
ntasks = len(pres_list)
else :
raise RuntimeError('supported path of npt ens are \'t\' or \'p\'')
tau_t = jdata['tau_t']
tau_p = jdata['tau_p']
else :
raise RuntimeError('invalid ens')
job_abs_dir = create_path(iter_name)
# dct1 = link_file_in_dict(
# dct=jdata,
# key_list=["equi_conf", "model"],
# target_dir=job_abs_dir
# )
# ti_settings.update(dct1)
# meam_model = jdata.get('meam_model', None)
# dct2 = link_file_in_dict(
# dct=meam_model,
# key_list=["library", "potential"],
# target_dir=job_abs_dir
# )
# if meam_model:
# ti_settings['meam_model'].update(dct2)
# link_file_dict = {}
# link_file_dict.update(dct1)
# link_file_dict.update(dct2)
# copied_conf = os.path.join(os.path.abspath(iter_name), 'conf.lmp')
# shutil.copyfile(equi_conf, copied_conf)
# jdata['equi_conf'] = 'conf.lmp'
# linked_model = os.path.join(os.path.abspath(iter_name), 'graph.pb')
# shutil.copyfile(model, linked_model)
# jdata['model'] = 'graph.pb'
# cwd = os.getcwd()
# os.chdir(iter_name)
relative_link_file(equi_conf, job_abs_dir)
if model:
relative_link_file(model, job_abs_dir)
if if_meam:
relative_link_file(meam_model['library'], job_abs_dir)
relative_link_file(meam_model['potential'], job_abs_dir)
with open(os.path.join(job_abs_dir,
'ti_settings.json'), 'w') as fp:
json.dump(ti_settings, fp, indent=4)
with open(os.path.join(job_abs_dir, 'ti_settings.json'),'w') as f:
json.dump(ti_settings, f, indent=4)
for ii in range(ntasks) :
task_dir = os.path.join(job_abs_dir, 'task.%06d' % ii)
task_abs_dir = create_path(task_dir)
# os.chdir(work_path)
relative_link_file(equi_conf, task_abs_dir)
if model:
relative_link_file(model, task_abs_dir)
if if_meam:
relative_link_file(meam_model['library'], task_abs_dir)
relative_link_file(meam_model['potential'], task_abs_dir)
# for file in list(link_file_dict.values()):
# file_path = os.path.join(job_abs_dir, file)
# relative_link_file(file_path, task_abs_dir)
# os.symlink()
# os.symlink(os.path.relpath(copied_conf), 'conf.lmp')
# os.symlink(os.path.relpath(linked_model), 'graph.pb')
if 'nvt' in ens and path == 't' :
lmp_str \
= _gen_lammps_input(os.path.basename(equi_conf),
mass_map,
model,
nsteps,
timestep,
ens,
temp_list[ii],
pres=pres,
tau_t = tau_t,
thermo_freq = thermo_freq,
copies = copies,
if_meam=if_meam,
meam_model=meam_model
)
thermo_out = temp_list[ii]
# with open('thermo.out', 'w') as fp :
# fp.write('%f' % temps[ii])
elif 'npt' in ens and (path == 't' or path == 't-ginv'):
lmp_str \
= _gen_lammps_input(os.path.basename(equi_conf),
mass_map,
model,
nsteps,
timestep,
ens,
temp_list[ii],
pres,
tau_t = tau_t,
tau_p = tau_p,
thermo_freq = thermo_freq,
copies = copies,
if_meam=if_meam,
meam_model=meam_model
)
thermo_out = temp_list[ii]
# with open('thermo.out', 'w') as fp :
# fp.write('%f' % (temps[ii]))
elif 'npt' in ens and path == 'p' :
lmp_str \
= _gen_lammps_input(os.path.basename(equi_conf),
mass_map,
model,
nsteps,
timestep,
ens,
temp,
pres_list[ii],
tau_t = tau_t,
tau_p = tau_p,
thermo_freq = thermo_freq,
copies = copies,
if_meam=if_meam,
meam_model=meam_model
)
thermo_out = pres_list[ii]
else:
raise RuntimeError('invalid ens or path setting' )
with open(os.path.join(task_abs_dir, 'thermo.out'), 'w') as fp:
fp.write('%f' % (thermo_out))
with open(os.path.join(task_abs_dir, 'in.lammps'), 'w') as fp:
fp.write(lmp_str)
def _make_tasks(iter_name, jdata, step) :
if step == 'angle_on' :
all_lambda = parse_seq(jdata['lambda_angle_on'])
protect_eps = jdata['protect_eps']
if all_lambda[0] == 0 :
all_lambda[0] += protect_eps
elif step == 'deep_on' :
all_lambda = parse_seq(jdata['lambda_deep_on'])
protect_eps = jdata['protect_eps']
if all_lambda[0] == 0 :
all_lambda[0] += protect_eps
elif step == 'bond_angle_off' :
all_lambda = parse_seq(jdata['lambda_bond_angle_off'])
protect_eps = jdata['protect_eps']
if all_lambda[-1] == 1 :
all_lambda[-1] -= protect_eps
equi_conf = jdata['equi_conf']
equi_conf = os.path.abspath(equi_conf)
model = jdata['model']
model = os.path.abspath(model)
# mass_map = jdata['mass_map']
mass_map = get_first_matched_key_from_dict(jdata, ['mass_map', 'model_mass_map'])
nsteps = jdata['nsteps']
# dt = jdata['dt']
# timestep = jdata['timestep']
timestep = get_first_matched_key_from_dict(jdata, ['timestep', 'dt'])
bparam = jdata['bond_param']
sparam = jdata['soft_param']
# stat_freq = jdata['stat_freq']
# thermo_freq = jdata['thermo_freq']
thermo_freq = get_first_matched_key_from_dict(jdata, ['thermo_freq', 'stat_freq'])
ens = jdata['ens']
temp = jdata['temp']
pres = jdata['pres']
tau_t = jdata['tau_t']
tau_p = jdata['tau_p']
copies = None
if 'copies' in jdata :
copies = jdata['copies']
create_path(iter_name)
cwd = os.getcwd()
os.chdir(iter_name)
os.symlink(os.path.join('..', 'in.json'), 'in.json')
os.symlink(os.path.join('..', 'conf.lmp'), 'orig.lmp')
os.symlink(os.path.join('..', 'graph.pb'), 'graph.pb')
with open('orig.lmp', 'r') as f:
lines = water.add_bonds(f.read().split('\n'))
with open('conf.lmp', 'w') as c:
c.write('\n'.join(lines))
os.chdir(cwd)
for idx in range(len(all_lambda)) :
work_path = os.path.join(iter_name, 'task.%06d' % idx)
create_path(work_path)
os.chdir(work_path)
os.symlink(os.path.join('..', 'conf.lmp'), 'conf.lmp')
os.symlink(os.path.join('..', 'graph.pb'), 'graph.pb')
lmp_str \
= _gen_lammps_input(step,
'conf.lmp',
mass_map,
all_lambda[idx],
'graph.pb',
bparam,
sparam,
nsteps,
timestep,
ens,
temp,
pres,
tau_t = tau_t,
tau_p = tau_p,
prt_freq = thermo_freq,
copies = copies)
with open('in.lammps', 'w') as fp :
fp.write(lmp_str)
with open('lambda.out', 'w') as fp :
fp.write(str(all_lambda[idx]))
os.chdir(cwd)