def parse_seq_ginv (seq) :
tmp_seq = parse_seq(seq)
t_begin = tmp_seq[0]
t_end = tmp_seq[-1]
ngrid = len(tmp_seq) - 1
hh = (1/t_end - 1/t_begin) / ngrid
inv_grid = np.arange(1/t_begin, 1/t_end+0.5*hh, hh)
inv_grid = 1./inv_grid
return inv_grid
def test_protect_eps(self):
array1 = parse_seq(lambda_seq, protect_eps=1e-6)
array2 = np.asarray([0.000001, 0.01, 0.02,
0.03, 0.04, 0.05,
0.07, 0.09, 0.11,
0.13, 0.15, 0.19,
0.23, 0.27, 0.31,
0.35, 0.415, 0.48,
0.545, 0.61, 0.675,
0.74, 0.805, 0.87,
0.935, 0.999999])
assert_almost_equal(array1, array2, decimal=10)
def test_normal(self):
array1 = parse_seq(lambda_seq)
array2 = np.asarray([0.0, 0.01, 0.02,
0.03, 0.04, 0.05,
0.07, 0.09, 0.11,
0.13, 0.15, 0.19,
0.23, 0.27, 0.31,
0.35, 0.415, 0.48,
0.545, 0.61, 0.675,
0.74, 0.805, 0.87,
0.935, 1.0])
assert_almost_equal(array1, array2, decimal=10)
def test_no_posi_args(self):
with self.assertRaises(TypeError):
parse_seq(lambda_seq, 1e-6)
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)