def rpath_ref_idx(ts_dct, scn_vals, coord_name, scn_prefix, ene_info1,
ene_info2):
""" Get the reference energy along a reaction path
"""
# Set up the filesystem
zma_fs = autofile.fs.zmatrix(scn_prefix)
zma_path = zma_fs[-1].path([0])
scn_fs = autofile.fs.scan(zma_path)
ene_info1 = ene_info1[1][0][1]
ene_info2 = ene_info2[0]
ioprinter.debug_message('mod_eneinf1', ene_info1)
ioprinter.debug_message('mod_eneinf2', ene_info2)
mod_ene_info1 = tinfo.modify_orb_label(sinfo.from_dct(ts_dct), ene_info1)
mod_ene_info2 = tinfo.modify_orb_label(sinfo.from_dct(ts_dct), ene_info2)
ene1, ene2, ref_val = None, None, None
for val in reversed(scn_vals):
locs = [[coord_name], [val]]
path = scn_fs[-1].path(locs)
hs_fs = autofile.fs.high_spin(path)
if hs_fs[-1].file.energy.exists(mod_ene_info1[1:4]):
ene1 = hs_fs[-1].file.energy.read(mod_ene_info1[1:4])
if hs_fs[-1].file.energy.exists(mod_ene_info2[1:4]):
ene2 = hs_fs[-1].file.energy.read(mod_ene_info2[1:4])
if ene1 is not None and ene2 is not None:
ref_val = val
break
if ref_val is not None:
scn_idx = scn_vals.index(ref_val)
return scn_idx, ene1, ene2
def set_rpath_filesys(ts_dct, level):
""" Gets filesystem objects for reading many calculations
"""
# Set the spc_info
spc_info = sinfo.from_dct(ts_dct)
# Set some path stuff
save_path = ts_dct['rxn_fs'][3]
run_path = ts_dct['rxn_fs'][2]
# Set theory filesystem used throughout
thy_save_fs = autofile.fs.theory(save_path)
thy_run_fs = autofile.fs.theory(run_path)
levelp = tinfo.modify_orb_label(level[1], spc_info)
# Get the save fileystem path
save_path = thy_save_fs[-1].path(levelp[1:4])
run_path = thy_run_fs[-1].path(levelp[1:4])
thy_save_fs[-1].create(levelp[1:4])
thy_run_fs[-1].create(levelp[1:4])
thy_save_path = thy_save_fs[-1].path(levelp[1:4])
thy_run_path = thy_run_fs[-1].path(levelp[1:4])
ts_save_fs = autofile.fs.transition_state(thy_save_path)
ts_save_fs[0].create()
ts_save_path = ts_save_fs[0].path()
ts_run_fs = autofile.fs.transition_state(thy_run_path)
ts_run_fs[0].create()
ts_run_path = ts_run_fs[0].path()
return ts_run_path, ts_save_path, thy_run_path, thy_save_path
def thermo_paths(spc_dct, spc_queue, spc_mods, run_prefix):
""" Set up the path for saving the pf input and output.
Placed in a MESSPF, NASA dirs high in run filesys.
"""
thm_paths = []
for spc_name in spc_queue:
thm_path = {}
for idx, mod in enumerate(spc_mods):
spc_info = sinfo.from_dct(spc_dct[spc_name])
spc_formula = automol.inchi.formula_string(spc_info[0])
thm_prefix = [spc_formula, automol.inchi.inchi_key(spc_info[0])]
print('thm_prefix test:', thm_prefix)
thm_path[mod] = (job_path(run_prefix,
'MESS',
'PF',
thm_prefix,
locs_idx=idx),
job_path(run_prefix,
'THERM',
'NASA',
thm_prefix,
locs_idx=idx))
thm_paths.append(thm_path)
return thm_paths
def from_dct(reacs, prods, spc_dct, rxn_mul='low', sort=False):
""" prepare rxn info and reverse the reactants and products
if reaction is endothermic
"""
# Build the tuples of the reacs+prods infos
rxn_ichs, rxn_chgs, rxn_muls = tuple(), tuple(), tuple()
for side in (reacs, prods):
ichs, chgs, muls = tuple(), tuple(), tuple()
for rct in side:
spc_info = spc.from_dct(spc_dct[rct])
ichs += (spc.value(spc_info, par.SPC.INCHI), )
chgs += (spc.value(spc_info, par.SPC.CHARGE), )
muls += (spc.value(spc_info, par.SPC.MULT), )
rxn_ichs += (ichs, )
rxn_chgs += (chgs, )
rxn_muls += (muls, )
# Determine the multiplicity of the full reaction
_, ts_mul_low, ts_mul_high = rxn_chg_mult(rxn_muls, rxn_chgs)
ts_mul = ts_mul_low if rxn_mul == 'low' else ts_mul_high
rxn_info = (rxn_ichs, rxn_chgs, rxn_muls, ts_mul)
if sort:
rxn_info = sort(rxn_info)
return rxn_info
def set_etrans_well(rxn_lst, spc_dct):
""" Build info object for reference well on PES
"""
well_dct = None
_, (reacs, prods) = rxn_lst[0]
if len(reacs) == 1:
well_dct = spc_dct[reacs[0]]
elif len(prods) == 1:
well_dct = spc_dct[prods[0]]
else:
rct1_dct = spc_dct[reacs[0]]
rct2_dct = spc_dct[reacs[1]]
rct1_count = automol.geom.count(
automol.inchi.geometry(rct1_dct['inchi']))
rct2_count = automol.geom.count(
automol.inchi.geometry(rct2_dct['inchi']))
if rct1_count > rct2_count:
well_dct = rct1_dct
else:
well_dct = rct2_dct
well_info = sinfo.from_dct(well_dct)
return well_info
def from_dct(reacs, prods, spc_dct, rxn_mul='low'):
""" Construct a full reaction info object using the names of the
reactants and products, which are used to read a species dictionary for
the required physical information.
:param reacs: names of the reactants
:type reacs: tuple(str)
:param prods: names of the products
:type prods: tuple(str)
:param spc_dct:
:type spc_dct: dict[]
:param rxn_mul: multiplicity of reaction to store in object
:type rxn_mul: str
"""
# Build the tuples of the reacs+prods infos
rxn_ichs, rxn_chgs, rxn_muls = tuple(), tuple(), tuple()
for side in (reacs, prods):
ichs, chgs, muls = tuple(), tuple(), tuple()
for rct in side:
spc_info = spc.from_dct(spc_dct[rct])
ichs += (spc.value(spc_info, par.SPC.INCHI),)
chgs += (spc.value(spc_info, par.SPC.CHARGE),)
muls += (spc.value(spc_info, par.SPC.MULT),)
rxn_ichs += (ichs,)
rxn_chgs += (chgs,)
rxn_muls += (muls,)
# Determine the multiplicity of the full reaction
fake_rxn_info = (rxn_ichs, rxn_chgs, rxn_muls, ())
ts_mul = ts_mult(fake_rxn_info, rxn_mul=rxn_mul)
rxn_info = (rxn_ichs, rxn_chgs, rxn_muls, ts_mul)
return rxn_info
def instability_transformation(spc_dct,
spc_name,
thy_info,
save_prefix,
zma_locs=(0, )):
""" see if a species and unstable and handle task management
"""
spc_info = sinfo.from_dct(spc_dct[spc_name])
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
_, cnf_save_fs = build_fs('',
save_prefix,
'CONFORMER',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:])
# Check if any locs exist first?
ini_loc_info = min_energy_conformer_locators(cnf_save_fs, mod_thy_info)
_, min_cnf_path = ini_loc_info
zma_save_fs = autofile.fs.zmatrix(min_cnf_path)
# Check if the instability files exist
if zma_save_fs[-1].file.instability.exists(zma_locs):
instab_trans = zma_save_fs[-1].file.instability.read(zma_locs)
zma = zma_save_fs[-1].file.zmatrix.read(zma_locs)
_instab = (instab_trans, zma)
path = zma_save_fs[-1].file.zmatrix.path(zma_locs)
else:
_instab = None
path = None
return _instab, path
def conformer_list_from_models(spc_name, thy_dct, print_keyword_dct,
save_prefix, run_prefix, spc_dct_i,
spc_mod_dct_i):
""" Create a list of conformers based on the species name
and model.dat info
"""
# conformer range
cnf_range = _set_conf_range(print_keyword_dct)
hbond_cutoffs = spc_dct_i['hbond_cutoffs']
# thy_info build
thy_info = spc_mod_dct_i['vib']['geolvl'][1][1]
spc_info = sinfo.from_dct(spc_dct_i)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
sort_info_lst = _set_sort_info_lst(print_keyword_dct['sort'], thy_dct,
spc_info)
zrxn = spc_dct_i.get('zrxn', None)
_root = filesys.root_locs(spc_dct_i,
name=spc_name,
saddle=(zrxn is not None))
_, cnf_save_fs = filesys.build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=mod_thy_info[1:],
**_root)
rng_cnf_locs_lst, rng_cnf_locs_path = filesys.mincnf.conformer_locators(
cnf_save_fs,
mod_thy_info,
cnf_range=cnf_range,
sort_info_lst=sort_info_lst,
hbond_cutoffs=hbond_cutoffs,
print_enes=True)
return cnf_save_fs, rng_cnf_locs_lst, rng_cnf_locs_path, mod_thy_info
def set_model_filesys(spc_dct_i, level,
run_prefix, save_prefix, saddle, name=None, rings='all'):
""" Gets filesystem objects for reading many calculations
"""
# Set the spc_info
if saddle:
rxn_info = spc_dct_i['rxn_info']
spc_info = rinfo.ts_info(rxn_info)
else:
spc_info = sinfo.from_dct(spc_dct_i)
print('level', level)
levelp = tinfo.modify_orb_label(level, spc_info)
_root = root_locs(spc_dct_i, saddle=saddle, name=name)
cnf_run_fs, cnf_save_fs = build_fs(
run_prefix, save_prefix, 'CONFORMER',
thy_locs=levelp[1:],
**_root)
if rings == 'min':
min_rngs_locs, min_rngs_path = mincnf.min_energy_conformer_locators(
cnf_save_fs, levelp)
cnf_run_fs[-1].create(min_rngs_locs)
else:
min_rngs_locs, min_rngs_path = mincnf.conformer_locators(
cnf_save_fs, levelp, cnf_range='r100')
for min_locs in min_rngs_locs:
cnf_run_fs[-1].create(min_locs)
print('model filesys', min_rngs_locs, min_rngs_path)
# Create run fs if that directory has been deleted to run the jobs
return [cnf_save_fs, min_rngs_path, min_rngs_locs, '', cnf_run_fs]
def build_rotors(spc_dct_i, pf_filesystems, spc_mod_dct_i,
read_potentials=True):
""" Add more rotor info
"""
run_prefix = pf_filesystems['run_prefix']
spc_info = sinfo.from_dct(spc_dct_i)
spc_fml = automol.inchi.formula_string(spc_info[0])
if spc_fml is None:
spc_fml = 'TS'
run_path = job_path(run_prefix, 'PROJROT', 'FREQ', spc_fml, locs_idx=None)
# Set up tors level filesystem and model and level
tors_model = spc_mod_dct_i['tors']['mod']
tors_ene_info = spc_mod_dct_i['tors']['enelvl'][1][1]
mod_tors_ene_info = tinfo.modify_orb_label(
tors_ene_info, sinfo.from_dct(spc_dct_i))
rotors = None
if pf_filesystems['tors'] is not None:
[cnf_fs, cnf_save_path, min_cnf_locs, _, _] = pf_filesystems['tors']
# Build the rotors
ref_ene = filesys.read.energy(cnf_fs, min_cnf_locs, mod_tors_ene_info)
zma_fs = fs.zmatrix(cnf_fs[-1].path(min_cnf_locs))
if (
zma_fs[-1].file.torsions.exists([0]) and
zma_fs[-1].file.zmatrix.exists([0]) and
tors_model != 'rigid'
):
rotors = automol.rotor.from_data(
zma=zma_fs[-1].file.zmatrix.read([0]),
tors_inf_dct=zma_fs[-1].file.torsions.read([0]),
tors_names=spc_dct_i.get('tors_names', None),
multi=bool('1d' in tors_model))
# Read the potential grids
if read_potentials and rotors is not None:
rotors = _read_potentials(
rotors, spc_dct_i, run_path, cnf_save_path,
ref_ene, mod_tors_ene_info,
tors_model)
return rotors
def set_model_filesys(spc_dct_i,
level,
run_prefix,
save_prefix,
saddle,
name=None,
cnf_range='min',
spc_locs=None,
nprocs=1):
""" Gets filesystem objects for reading many calculations
"""
# Set the spc_info
if saddle:
rxn_info = spc_dct_i['rxn_info']
spc_info = rinfo.ts_info(rxn_info)
else:
spc_info = sinfo.from_dct(spc_dct_i)
levelp = tinfo.modify_orb_label(level, spc_info)
_root = root_locs(spc_dct_i, saddle=saddle, name=name)
cnf_run_fs, cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=levelp[1:],
**_root)
hbond_cutoffs = spc_dct_i['hbond_cutoffs']
if cnf_range == 'specified':
min_rngs_locs = spc_locs
min_rngs_path = cnf_save_fs[-1].path(min_rngs_locs)
cnf_run_fs[-1].create(min_rngs_locs)
elif cnf_range == 'min':
min_rngs_locs, min_rngs_path = min_energy_conformer_locators(
cnf_save_fs, levelp, hbond_cutoffs=hbond_cutoffs)
cnf_run_fs[-1].create(min_rngs_locs)
else:
min_rngs_locs_lst, min_rngs_path_lst = conformer_locators(
cnf_save_fs,
levelp,
cnf_range=cnf_range,
hbond_cutoffs=hbond_cutoffs,
nprocs=nprocs)
for min_locs in min_rngs_locs_lst:
cnf_run_fs[-1].create(min_locs)
min_rngs_locs = min_rngs_locs_lst[0]
min_rngs_path = min_rngs_path_lst[0]
ioprinter.warning_message('Only returning first location in this list')
# Create run fs if that directory has been deleted to run the jobs
return [cnf_save_fs, min_rngs_path, min_rngs_locs, '', cnf_run_fs]
def electronic_energy(spc_dct_i, pf_filesystems, spc_model_dct_i, conf=None):
""" get high level energy at low level optimized geometry
"""
ioprinter.info_message('- Calculating electronic energy')
# spc_dct_i = spc_dct[spc_name]
rxn_info = spc_dct_i.get('rxn_info', None)
if rxn_info is not None:
spc_info = rinfo.ts_info(rxn_info)
else:
spc_info = sinfo.from_dct(spc_dct_i)
# Get the harmonic filesys information
if conf:
cnf_path = conf[1]
else:
[_, cnf_path, _, _, _] = pf_filesystems['harm']
# Get the electronic energy levels
ene_levels = tuple(val[1] for key, val in spc_model_dct_i['ene'].items()
if 'lvl' in key)
print('ene levels', ene_levels)
# Read the energies from the filesystem
e_elec = None
if os.path.exists(cnf_path):
e_elec = 0.0
# ioprinter.info_message('lvls', ene_levels)
for (coeff, level) in ene_levels:
# Build SP filesys
mod_thy_info = tinfo.modify_orb_label(level, spc_info)
sp_save_fs = autofile.fs.single_point(cnf_path)
sp_save_fs[-1].create(mod_thy_info[1:4])
# Read the energy
sp_path = sp_save_fs[-1].path(mod_thy_info[1:4])
if os.path.exists(sp_path):
ioprinter.reading('Energy', sp_path)
ene = sp_save_fs[-1].file.energy.read(mod_thy_info[1:4])
e_elec += (coeff * ene)
else:
ioprinter.warning_message('No energy at path')
e_elec = None
break
else:
ioprinter.warning_message('No conformer to calculate the energy')
return e_elec
def thermo_paths(spc_dct, spc_locs_dct, spc_mods, run_prefix):
""" Set up the path for saving the pf input and output.
Placed in a MESSPF, NASA dirs high in run filesys.
"""
thm_path_dct = {}
for spc_name in spc_locs_dct:
spc_thm_path_dct = {}
spc_info = sinfo.from_dct(spc_dct[spc_name])
spc_formula = automol.inchi.formula_string(spc_info[0])
thm_prefix = [spc_formula, automol.inchi.inchi_key(spc_info[0])]
spc_locs_lst = spc_locs_dct[spc_name]
for sidx, spc_locs in enumerate(spc_locs_lst, start=1):
spc_mod_thm_path_dct = {}
for midx, mod in enumerate(spc_mods):
idx = sidx * 10 + midx
spc_mod_thm_path_dct[mod] = (job_path(run_prefix,
'MESS',
'PF',
thm_prefix,
locs_idx=idx),
job_path(run_prefix,
'THERM',
'NASA',
thm_prefix,
locs_idx=idx))
spc_mod_thm_path_dct['mod_total'] = (job_path(run_prefix,
'MESS',
'PF',
thm_prefix,
locs_idx=sidx),
job_path(run_prefix,
'THERM',
'NASA',
thm_prefix,
locs_idx=sidx))
spc_thm_path_dct[tuple(spc_locs)] = spc_mod_thm_path_dct
spc_thm_path_dct['spc_total'] = (job_path(run_prefix,
'MESS',
'PF',
thm_prefix,
locs_idx=0),
job_path(run_prefix,
'THERM',
'NASA',
thm_prefix,
locs_idx=0))
thm_path_dct[spc_name] = spc_thm_path_dct
return thm_path_dct
def geom_init(spc_dct, spc_name, thy_dct, es_keyword_dct, run_prefix,
save_prefix):
""" Execute the task for a species used to seed the
filesystem with a reliable initial conformer.
:param spc_dct:
:type spc_dct:
:param spc_name: name of species
:type spc_name: str
:param thy_dct:
:type thy_dct:
:param es_keyword_dct: keyword-val pairs for electronic structure task
:type es_keyword_dct: dict[str:str]
:param run_prefix: root-path to the run-filesystem
:type run_prefix: str
:param save_prefix: root-path to the save-filesystem
:type save_prefix: str
"""
spc_dct_i = spc_dct[spc_name]
spc_info = sinfo.from_dct(spc_dct_i)
# Get the theory info
method_dct = thy_dct.get(es_keyword_dct['runlvl'])
ini_method_dct = thy_dct.get(es_keyword_dct['inplvl'])
thy_info = tinfo.from_dct(method_dct)
ini_thy_info = tinfo.from_dct(ini_method_dct)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
mod_ini_thy_info = tinfo.modify_orb_label(ini_thy_info, spc_info)
# Set the filesystem objects
_, ini_cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
spc_locs=spc_info,
thy_locs=mod_ini_thy_info[1:])
cnf_run_fs, cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:])
# Get a reference geometry if one not found
success = conformer.initial_conformer(spc_dct_i, spc_info, ini_method_dct,
method_dct, ini_cnf_save_fs,
cnf_run_fs, cnf_save_fs,
es_keyword_dct)
return success
def root_locs(spc_dct_i, saddle=False, name=None):
""" Set the root locatores for the species and TS
"""
if not saddle:
spc_info = sinfo.from_dct(spc_dct_i)
rxn_info = None
ts_info = None
else:
spc_info = None
rxn_info = rinfo.sort(spc_dct_i['rxn_info'])
ts_num = int(name.split('_')[-1])
ts_info = (ts_num, )
return {'spc_locs': spc_info, 'rxn_locs': rxn_info, 'ts_locs': ts_info}
def set_bath(spc_dct, etrans_dct):
""" Build nfo object for the bath
"""
# Try to obtain bath set by the user, otherwise use N2
bath_name = etrans_dct.get('bath', None)
bath_dct = spc_dct.get(bath_name, None)
if bath_dct is not None:
bath_info = sinfo.from_dct(bath_dct)
ioprinter.info_message(f' - Using bath {bath_name} input by user')
else:
bath_info = ['InChI=1S/Ar', 0, 1]
ioprinter.info_message(' - No bath provided, using Argon as bath')
return bath_info
def geom_init(spc_dct, spc_name, thy_dct, es_keyword_dct, run_prefix,
save_prefix):
""" Find the initial geometry
"""
spc_dct_i = spc_dct[spc_name]
spc_info = sinfo.from_dct(spc_dct_i)
# Get the theory info
method_dct = thy_dct.get(es_keyword_dct['runlvl'])
ini_method_dct = thy_dct.get(es_keyword_dct['inplvl'])
thy_info = tinfo.from_dct(method_dct)
ini_thy_info = tinfo.from_dct(ini_method_dct)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
mod_ini_thy_info = tinfo.modify_orb_label(ini_thy_info, spc_info)
# Set the filesystem objects
_, ini_cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
spc_locs=spc_info,
thy_locs=mod_ini_thy_info[1:])
cnf_run_fs, cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:])
# _, ini_cnf_save_fs = build_fs(
# run_prefix, save_prefix, 'CONFORMER',
# spc_locs=spc_info, thy_locs=mod_ini_thy_info[1:])
# cnf_run_fs, cnf_save_fs = build_fs(
# run_prefix, save_prefix, 'CONFORMER',
# spc_locs=spc_info, thy_locs=mod_thy_info[1:])
_, instab_save_fs = build_fs(run_prefix,
save_prefix,
'INSTAB',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:])
# Get a reference geometry if one not found
success = conformer.initial_conformer(spc_dct_i, spc_info, ini_method_dct,
method_dct, ini_cnf_save_fs,
cnf_run_fs, cnf_save_fs,
instab_save_fs, es_keyword_dct)
return success
def conformer_list_from_models(print_keyword_dct, save_prefix, run_prefix,
spc_dct_i, spc_mod_dct_i):
""" Create a list of conformers based on the species name
and model.dat info
"""
# conformer range
cnf_range = _set_conf_range(print_keyword_dct)
# thy_info build
thy_info = spc_mod_dct_i['geo'][1]
spc_info = sinfo.from_dct(spc_dct_i)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
_root = filesys.root_locs(spc_dct_i, saddle=False)
_, cnf_save_fs = filesys.build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=mod_thy_info[1:],
**_root)
rng_cnf_locs_lst, rng_cnf_locs_path = filesys.mincnf.conformer_locators(
cnf_save_fs, mod_thy_info, cnf_range=cnf_range)
return cnf_save_fs, rng_cnf_locs_lst, rng_cnf_locs_path
def root_locs(spc_dct_i, saddle=False, name=None):
""" Set the root locatores for the species and TS
"""
if not saddle:
spc_info = sinfo.from_dct(spc_dct_i)
rxn_info = None
ts_info = None
else:
spc_info = None
rxn_info = rinfo.sort(spc_dct_i['rxn_info'])
ts_num = int(name.split('_')[-1])
ts_info = (ts_num, )
# if 'ts_locs' in spc_dct_i:
# ts_info = spc_dct_i['ts_locs']
# else:
# ts_num = int(name.split('_')[-1])
# ts_info = (ts_num,)
# print('TEST ts locs:', ts_info)
# ts_info = (0,) # may be more complicated
# ts_info = ()
return {'spc_locs': spc_info, 'rxn_locs': rxn_info, 'ts_locs': ts_info}
def energy(spc_name, spc_dct_i, spc_mod_dct_i, proc_keyword_dct, thy_dct, locs,
locs_path, cnf_fs, run_prefix, save_prefix):
""" collect energy
"""
saddle = 'ts_' in spc_name
_ene = None
if spc_mod_dct_i:
pf_filesystems = filesys.models.pf_filesys(spc_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
name=spc_name,
saddle=saddle)
_ene = ene.electronic_energy(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
conf=(locs, locs_path, cnf_fs))
else:
spc_info = sinfo.from_dct(spc_dct_i)
thy_info = tinfo.from_dct(thy_dct.get(proc_keyword_dct['proplvl']))
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
sp_save_fs = autofile.fs.single_point(locs_path)
# Read the energy
sp_path = sp_save_fs[-1].path(mod_thy_info[1:4])
if os.path.exists(sp_path):
if sp_save_fs[-1].file.energy.exists(mod_thy_info[1:4]):
ioprinter.reading('Energy', sp_path)
_ene = sp_save_fs[-1].file.energy.read(mod_thy_info[1:4])
if _ene is not None:
miss_data = None
else:
miss_data = (spc_name + '_'.join(locs), mod_thy_info, 'energy')
return [locs_path, _ene], miss_data
def _get_prop_fs(spc_model_dct_i,
spc_dct_i,
prop,
sort_info_lst,
run_prefix,
save_prefix,
saddle=False,
name=None):
""" Get filesystem info for a property in the spc model dct
"""
if saddle:
rxn_info = spc_dct_i['rxn_info']
spc_info = rinfo.ts_info(rxn_info)
else:
spc_info = sinfo.from_dct(spc_dct_i)
level = spc_model_dct_i[prop]['geolvl'][1][1]
levelp = tinfo.modify_orb_label(level, spc_info)
mod_info_lst = []
if sort_info_lst is not None:
for info in sort_info_lst[:2]:
if info is not None:
mod_info_lst.append(tinfo.modify_orb_label(info, spc_info))
else:
mod_info_lst.append(info)
for info in sort_info_lst[2:]:
mod_info_lst.append(info)
_root = root_locs(spc_dct_i, saddle=saddle, name=name)
cnf_run_fs, cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=levelp[1:],
**_root)
return cnf_run_fs, cnf_save_fs, levelp, mod_info_lst
def instability_transformation(spc_dct, spc_name, thy_info, save_prefix,
zma_locs=(0,)):
""" see if a species and unstable and handle task management
"""
spc_info = sinfo.from_dct(spc_dct[spc_name])
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
_, zma_save_fs = build_fs(
'', save_prefix, 'ZMATRIX',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:],
instab_locs=())
# Check if the instability files exist
if zma_save_fs[-1].file.reaction.exists(zma_locs):
zrxn = zma_save_fs[-1].file.reaction.read(zma_locs)
zma = zma_save_fs[-1].file.zmatrix.read(zma_locs)
_instab = (zrxn, zma)
path = zma_save_fs[-1].file.zmatrix.path(zma_locs)
else:
_instab = None
path = None
return _instab, path
def mol_data(spc_name,
spc_dct,
pes_mod_dct_i,
spc_mod_dct_i,
chn_basis_ene_dct,
run_prefix,
save_prefix,
calc_chn_ene=True,
zrxn=None,
spc_locs=None):
""" Reads all required data from the SAVE filesystem for a molecule.
Stores data into an info dictionary.
All of the data that is read is determined by the models that
are described in the pes and spc model dictionaries.
:param spc_dct:
:type spc_dct:
:param pes_mod_dct_i: keyword dict of specific PES model
:type pes_mod_dct_i: dict[]
:param spc_mod_dct_i: keyword dict of specific species model
:type spc_mod_dct_i: dict[]
:param run_prefix: root-path to the run-filesystem
:type run_prefix: str
:param save_prefix: root-path to the save-filesystem
:type save_prefix: str
:rtype: dict[]
"""
spc_dct_i = spc_dct[spc_name]
ene_chnlvl = None
ene_reflvl = None
zpe = None
hf0k = None
hf0k_trs = None
hf0k = None
# Initialize all of the elements of the inf dct
geom, sym_factor, freqs, imag, elec_levels = None, None, None, None, None
allr_str, mdhr_dat = '', ''
xmat, rovib_coups, rot_dists = None, None, None
# Set up all the filesystem objects using models and levels
pf_filesystems = filesys.models.pf_filesys(spc_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
zrxn is not None,
name=spc_name,
spc_locs=spc_locs)
# Obtain rotation partition function information
ioprinter.info_message('Obtaining info for rotation partition function...',
newline=1)
geom = rot.read_geom(pf_filesystems)
if typ.nonrigid_rotations(spc_mod_dct_i):
rovib_coups, rot_dists = rot.read_rotational_values(pf_filesystems)
# Obtain vibration partition function information
ioprinter.info_message(
'Preparing internal rotor info building partition functions...',
newline=1)
rotors = tors.build_rotors(spc_dct_i, pf_filesystems, spc_mod_dct_i)
ioprinter.info_message(
'Obtaining the vibrational frequencies and zpves...', newline=1)
freqs, imag, zpe, _, tors_strs, _, _, _ = vib.full_vib_analysis(
spc_dct_i, pf_filesystems, spc_mod_dct_i, run_prefix, zrxn=zrxn)
allr_str = tors_strs[0]
# ioprinter.info_message('zpe in mol_data test:', zpe)
if typ.anharm_vib(spc_mod_dct_i):
xmat = vib.read_anharmon_matrix(pf_filesystems)
# Obtain symmetry factor
ioprinter.info_message('Determining the symmetry factor...', newline=1)
zma = None
if zrxn:
[_, cnf_save_path, _, _, _] = pf_filesystems['harm']
# Build the rotors
if cnf_save_path:
zma_fs = autofile.fs.zmatrix(cnf_save_path)
zma = zma_fs[-1].file.zmatrix.read([0])
sym_factor = symm.symmetry_factor(pf_filesystems,
spc_mod_dct_i,
spc_dct_i,
rotors,
grxn=zrxn,
zma=zma)
# Obtain electronic energy levels
elec_levels = spc_dct_i['elec_levels']
# Obtain energy levels
ioprinter.info_message('Obtaining the electronic energy + zpve...',
newline=1)
if calc_chn_ene:
chn_ene = ene.read_energy(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
run_prefix,
read_ene=True,
read_zpe=False,
saddle=zrxn is not None)
ene_chnlvl = chn_ene + zpe
zma = None
# Determine info about the basis species used in thermochem calcs
hf0k, hf0k_trs, chn_basis_ene_dct, _ = basis.enthalpy_calculation(
spc_dct,
spc_name,
ene_chnlvl,
chn_basis_ene_dct,
pes_mod_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
zrxn=zrxn)
ene_reflvl = None
# Build the energy transfer section strings
if zrxn is None:
ioprinter.info_message('Determining energy transfer parameters...',
newline=1)
well_info = sinfo.from_dct(spc_dct_i)
# ioprinter.debug_message('well_inf', well_info)
# bath_info = ['InChI=1S/N2/c1-2', 0, 1] # how to do...
bath_info = ['InChI=1S/Ar', 0, 1] # how to do...
etrans_dct = etrans.build_etrans_dct(spc_dct_i)
edown_str, collid_freq_str = etrans.make_energy_transfer_strs(
well_info, bath_info, etrans_dct)
else:
edown_str, collid_freq_str = None, None
# Create info dictionary
keys = [
'geom', 'sym_factor', 'freqs', 'imag', 'elec_levels', 'mess_hr_str',
'mdhr_dat', 'xmat', 'rovib_coups', 'rot_dists', 'ene_chnlvl',
'ene_reflvl', 'zpe_chnlvl', 'ene_tsref', 'edown_str', 'collid_freq_str'
]
vals = [
geom, sym_factor, freqs, imag, elec_levels, allr_str, mdhr_dat, xmat,
rovib_coups, rot_dists, hf0k, ene_reflvl, zpe, hf0k_trs, edown_str,
collid_freq_str
]
inf_dct = dict(zip(keys, vals))
return inf_dct, chn_basis_ene_dct
def tau_tsk(job, spc_dct, spc_name, thy_dct, es_keyword_dct, run_prefix,
save_prefix):
""" Energies, gradients, and hessians,
for set of arbitrarily sampled torsional coordinates
with all other coordinates optimized
"""
spc_dct_i = spc_dct[spc_name]
# Set the spc_info
spc_info = sinfo.from_dct(spc_dct_i)
# Get es options
overwrite = es_keyword_dct['overwrite']
retryfail = es_keyword_dct['retryfail']
# scan_increment = spc_dct_i['hind_inc']
nsamp_par = spc_dct_i['tau_nsamp']
# Modify the theory
method_dct = thy_dct.get(es_keyword_dct['runlvl'])
ini_method_dct = thy_dct.get(es_keyword_dct['inplvl'])
thy_info = tinfo.from_dct(method_dct)
ini_thy_info = tinfo.from_dct(ini_method_dct)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
mod_ini_thy_info = tinfo.modify_orb_label(ini_thy_info, spc_info)
# Script
script_str, kwargs = qchem_params(method_dct, elstruct.Job.OPTIMIZATION)
# Set the filesystem objects for thy info
_, ini_cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
spc_locs=spc_info,
thy_locs=mod_ini_thy_info[1:])
ini_loc_info = filesys.mincnf.min_energy_conformer_locators(
ini_cnf_save_fs, mod_ini_thy_info)
ini_min_cnf_locs, ini_min_cnf_path = ini_loc_info
ini_min_rid, ini_min_cid = ini_min_cnf_locs
ini_zma_save_fs = autofile.fs.zmatrix(ini_min_cnf_path)
geo = ini_cnf_save_fs[-1].file.geometry.read(ini_min_cnf_locs)
zma = ini_zma_save_fs[-1].file.zmatrix.read((0, ))
ini_sp_save_fs = autofile.fs.single_point(ini_min_cnf_path)
if ini_sp_save_fs[-1].file.energy.exists(mod_ini_thy_info[1:4]):
ref_ene = ini_sp_save_fs[-1].file.energy.read(mod_ini_thy_info[1:4])
else:
ref_ene = ini_cnf_save_fs[-1].file.energy.read(ini_min_cnf_locs)
# Get the tors names
ini_zma_save_fs = autofile.fs.zmatrix(ini_min_cnf_path)
if ini_zma_save_fs[-1].file.torsions.exists([0]):
tors_dct = ini_zma_save_fs[-1].file.torsions.read([0])
torsions = automol.rotor.from_data(zma, tors_dct)
else:
torsions = ()
saddle = bool('ts_' in spc_name)
# Run the task if any torsions exist
if torsions and not saddle:
# Set up tau filesystem objects
tau_run_fs, tau_save_fs = build_fs(run_prefix,
save_prefix,
'TAU',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:])
# db_style = 'jsondb'
db_style = 'directory'
if db_style == 'jsondb':
tau_save_fs[-1].root.create()
tau_save_fs[-1].json_create()
for locs in tau_save_fs[-1].existing():
if tau_save_fs[-1].file.geometry.exists(locs):
geol = tau_save_fs[-1].file.geometry.read(locs)
tau_save_fs[-1].json.geometry.write(geol, locs)
if tau_save_fs[-1].file.energy.exists(locs):
enel = tau_save_fs[-1].file.energy.read(locs)
tau_save_fs[-1].json.energy.write(enel, locs)
if tau_save_fs[-1].file.geometry_info.exists(locs):
geo_infl = tau_save_fs[-1].file.geometry_info.read(locs)
tau_save_fs[-1].json.geometry_info.write(geo_infl, locs)
if tau_save_fs[-1].file.geometry_input.exists(locs):
inp_strl = tau_save_fs[-1].file.geometry_input.read(locs)
tau_save_fs[-1].json.geometry_input.write(inp_strl, locs)
if tau_save_fs[-1].file.gradient_input.exists(locs):
inp_strl = tau_save_fs[-1].file.gradient_input.read(locs)
tau_save_fs[-1].json.gradient_input.write(inp_strl, locs)
if tau_save_fs[-1].file.hessian_input.exists(locs):
inp_strl = tau_save_fs[-1].file.hessian_input.read(locs)
tau_save_fs[-1].json.hessian_input.write(inp_strl, locs)
if tau_save_fs[-1].file.gradient_info.exists(locs):
inf_objl = tau_save_fs[-1].file.gradient_info.read(locs)
tau_save_fs[-1].json.gradient_info.write(inf_objl, locs)
if tau_save_fs[-1].file.hessian_info.exists(locs):
inf_objl = tau_save_fs[-1].file.hessian_info.read(locs)
tau_save_fs[-1].json.hessian_info.write(inf_objl, locs)
if tau_save_fs[-1].file.gradient.exists(locs):
gradl = tau_save_fs[-1].file.gradient.read(locs)
tau_save_fs[-1].json.gradient.write(gradl, locs)
if tau_save_fs[-1].file.hessian.exists(locs):
hessl = tau_save_fs[-1].file.hessian.read(locs)
tau_save_fs[-1].json.energy.hessian(hessl, locs)
if tau_save_fs[-1].file.zmatrix.exists(locs):
zmatl = tau_save_fs[-1].file.zmatrix.read(locs)
tau_save_fs[-1].json.zmatrix.write(zmatl, locs)
if tau_save_fs[-1].file.harmonic_frequencies.exists(locs):
hfreql = tau_save_fs[-1].file.harmonic_frequencies.read(
locs)
tau_save_fs[-1].json.harmonic_frequencies.write(
hfreql, locs)
save_path = tau_save_fs[-1].path(locs)
sp_save_fs = autofile.fs.single_point(save_path)
sp_save_locs = sp_save_fs[-1].existing()
save_path = tau_save_fs[-1].root.path()
jsp_save_fs = autofile.fs.single_point(save_path,
json_layer=locs)
for sp_locs in sp_save_locs:
if sp_save_fs[-1].file.energy.exists(sp_locs):
enel = sp_save_fs[-1].file.energy.read(sp_locs)
jsp_save_fs[-1].json.energy.write(enel, sp_locs)
if sp_save_fs[-1].file.input.exists(sp_locs):
inp_strl = sp_save_fs[-1].file.input.read(sp_locs)
jsp_save_fs[-1].json.input.write(inp_strl, sp_locs)
if sp_save_fs[-1].file.info.exists(sp_locs):
inf_objl = sp_save_fs[-1].file.info.read(sp_locs)
jsp_save_fs[-1].json.info.write(inf_objl, sp_locs)
if job == 'samp':
# Set up the script
script_str, kwargs = qchem_params(method_dct,
elstruct.Job.OPTIMIZATION)
tors_names = automol.rotor.names(torsions, flat=True)
# Run sampling
tau.tau_sampling(zma,
ref_ene,
spc_info,
tors_names,
nsamp_par,
mod_ini_thy_info,
tau_run_fs,
tau_save_fs,
script_str,
overwrite,
saddle=saddle,
**kwargs)
elif job in ('energy', 'grad'):
# Set up the run scripts
script_str, kwargs = qchem_params(method_dct)
# Run the job over all the conformers requested by the user
for locs in tau_save_fs[-1].existing():
geo_run_path = tau_run_fs[-1].path(locs)
if db_style == 'jsondb':
geo_save_path = tau_save_fs[-1].root.path()
geo = tau_save_fs[-1].json.geometry.read(locs)
elif db_style == 'directory':
geo_save_path = tau_save_fs[-1].path(locs)
geo = tau_save_fs[-1].file.geometry.read(locs)
tau_run_fs[-1].create(locs)
zma = None
ES_TSKS[job](zma,
geo,
spc_info,
mod_thy_info,
tau_save_fs,
geo_run_path,
geo_save_path,
locs,
script_str,
overwrite,
retryfail=retryfail,
**kwargs)
ioprinter.obj('vspace')
elif job == 'hess':
# Add the hessian max
hessmax = es_keyword_dct['hessmax']
# Set up the run scripts
script_str, kwargs = qchem_params(method_dct)
# Run the job over all the conformers requested by the user
hess_cnt = 0
for locs in tau_save_fs.existing():
ioprinter.info_message('HESS Number {}'.format(hess_cnt + 1),
newline=1)
geo_run_path = tau_run_fs[-1].path(locs)
if db_style == 'directory':
geo_save_path = tau_save_fs[-1].path(locs)
if tau_save_fs[-1].file.hessian.exists(locs):
ioprinter.existing_path('Hessian', geo_save_path)
hess_cnt += 1
continue
geo = tau_save_fs[-1].file.geometry.read(locs)
elif db_style == 'jsondb':
geo_save_path = tau_save_fs[-1].root.path()
if tau_save_fs[-1].json.hessian.exists(locs):
ioprinter.existing_path('Hessian', geo_save_path)
hess_cnt += 1
continue
geo = tau_save_fs[-1].json.geometry.read(locs)
zma = None
tau_run_fs[-1].create(locs)
ES_TSKS[job](zma,
geo,
spc_info,
mod_thy_info,
tau_save_fs,
geo_run_path,
geo_save_path,
locs,
script_str,
overwrite,
retryfail=retryfail,
**kwargs)
hess_cnt += 1
if hess_cnt == hessmax:
break
else:
ioprinter.info_message('No torsional modes in the species')
def hr_tsk(job, spc_dct, spc_name, thy_dct, es_keyword_dct, run_prefix,
save_prefix):
""" run a scan over the specified torsional coordinates
"""
spc_dct_i = spc_dct[spc_name]
saddle = bool('ts_' in spc_name)
# Set the spc_info
if not saddle:
spc_info = sinfo.from_dct(spc_dct_i)
else:
spc_info = rinfo.ts_info(spc_dct_i['rxn_info'])
# Modify the theory
method_dct = thy_dct.get(es_keyword_dct['runlvl'])
ini_method_dct = thy_dct.get(es_keyword_dct['inplvl'])
thy_info = tinfo.from_dct(method_dct)
ini_thy_info = tinfo.from_dct(ini_method_dct)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
mod_ini_thy_info = tinfo.modify_orb_label(ini_thy_info, spc_info)
# Set the filesystem objects
_root = root_locs(spc_dct_i, saddle=saddle, name=spc_name)
ini_cnf_run_fs, ini_cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=mod_ini_thy_info[1:],
**_root)
cnf_run_fs, cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=mod_thy_info[1:],
**_root)
instab_save_fs = ()
ini_loc_info = filesys.mincnf.min_energy_conformer_locators(
ini_cnf_save_fs, mod_ini_thy_info)
ini_min_locs, ini_cnf_save_path = ini_loc_info
# ini_min_rng_locs, ini_min_cnf_locs = ini_min_cnf_locs
# ini_min_rng_path, ini_min_cnf_path = ini_min_cnf_path
# Create run fs if that directory has been deleted to run the jobs
ini_cnf_run_fs[-1].create(ini_min_locs)
ini_cnf_run_path = ini_cnf_run_fs[-1].path(ini_min_locs)
# Get options from the dct or es options lst
overwrite = es_keyword_dct['overwrite']
retryfail = es_keyword_dct['retryfail']
tors_model = es_keyword_dct['tors_model']
# Read zma, geo, and torsions
ini_zma_save_fs = autofile.fs.zmatrix(ini_cnf_save_path)
geo = ini_cnf_save_fs[-1].file.geometry.read(ini_min_locs)
zma = ini_zma_save_fs[-1].file.zmatrix.read((0, ))
if ini_zma_save_fs[-1].file.torsions.exists([0]):
tors_dct = ini_zma_save_fs[-1].file.torsions.read([0])
torsions = automol.rotor.from_data(
zma,
tors_dct,
)
else:
torsions = ()
# Run the task if any torsions exist
if any(torsions):
scn = 'SCAN' if 'fa' not in tors_model else 'CSCAN'
ini_scn_run_fs, ini_scn_save_fs = build_fs(ini_cnf_run_path,
ini_cnf_save_path,
scn,
zma_locs=(0, ))
if job == 'scan':
increment = spc_dct_i.get('hind_inc', 30.0 * phycon.DEG2RAD)
hr.hindered_rotor_scans(zma,
spc_info,
mod_thy_info,
instab_save_fs,
ini_scn_run_fs,
ini_scn_save_fs,
torsions,
tors_model,
method_dct,
overwrite,
saddle=saddle,
increment=increment,
retryfail=retryfail)
# elif job == 'reopt':
# # pull stuff from dcts
# two_stage = saddle
# rxn_class = spc_dct_i['class'] if saddle else ''
# mc_nsamp = spc_dct_i['mc_nsamp']
# ethresh = es_keyword_dct['hrthresh']
# # Read and print the potential
# sp_fs = autofile.fs.single_point(ini_cnf_save_path)
# ref_ene = sp_fs[-1].file.energy.read(mod_ini_thy_info[1:4])
# # ref_ene = ini_cnf_save_fs[-1].file.energy.read(ini_min_cnf_locs)
# tors_pots, tors_zmas, tors_paths = {}, {}, {}
# for tors_names, tors_grids in zip(run_tors_names, run_tors_grids):
# constraint_dct = automol.zmat.build_constraint_dct(
# zma, const_names, tors_names)
# pot, _, _, _, zmas, paths = filesys.read.potential(
# tors_names, tors_grids,
# ini_cnf_save_path,
# mod_ini_thy_info, ref_ene,
# constraint_dct,
# read_zma=True)
# tors_pots[tors_names] = pot
# tors_zmas[tors_names] = zmas
# tors_paths[tors_names] = paths
# # Check for new minimum conformer
# new_min_zma = __.check_hr_pot(
# tors_pots, tors_zmas, tors_paths, emax=ethresh)
# if new_min_zma is not None:
# ioprinter.info_message(
# 'Finding new low energy conformer...', newline=1)
# conformer.single_conformer(
# zma, spc_info, mod_thy_info,
# ini_cnf_run_fs, ini_cnf_save_fs,
# script_str, overwrite,
# retryfail=retryfail, rxn=rxn, **kwargs)
elif job in ('energy', 'grad', 'hess', 'vpt2'):
# Script (add energy script call)
script_str, kwargs = qchem_params(method_dct)
run_tors_names = automol.rotor.names(torsions, flat=True)
for tors_names in run_tors_names:
# Set the constraint dct and filesys for the scan
const_names = automol.zmat.set_constraint_names(
zma, run_tors_names, tors_model)
constraint_dct = automol.zmat.build_constraint_dct(
zma, const_names, tors_names)
# get the scn_locs, maybe get a function?
scn_locs = ()
for locs in scn_locs:
geo_run_path = ini_scn_run_fs[-1].path(locs)
geo_save_path = ini_scn_save_fs[-1].path(locs)
geo = ini_scn_save_fs[-1].file.geometry.read(locs)
zma = ini_scn_save_fs[-1].file.zmatrix.read(locs)
ini_scn_run_fs[-1].create(locs)
ES_TSKS[job](zma,
geo,
spc_info,
mod_thy_info,
ini_scn_save_fs,
geo_run_path,
geo_save_path,
locs,
script_str,
overwrite,
retryfail=retryfail,
**kwargs)
ioprinter.obj('vspace')
else:
ioprinter.info_message('No torsional modes in the species')
def rpath_tsk(job, spc_dct, spc_name, thy_dct, es_keyword_dct, run_prefix,
save_prefix):
""" run a scan over the specified torsional coordinates
"""
# Get dct for specific species task is run for
spc_dct_i = spc_dct[spc_name]
# Set up coordinate name
rxn_coord = es_keyword_dct.get('rxn_coord')
if rxn_coord == 'auto':
coord_name = ['Rn'] # grab from zrxn object
else:
# coord_name =
coord_name = ['IRC']
# Set the spc_info
spc_info = sinfo.from_dct(spc_dct_i)
# Modify the theory
method_dct = thy_dct.get(es_keyword_dct['runlvl'])
ini_method_dct = thy_dct.get(es_keyword_dct['inplvl'])
thy_info = tinfo.from_dct(method_dct)
ini_thy_info = tinfo.from_dct(ini_method_dct)
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
mod_ini_thy_info = tinfo.modify_orb_label(ini_thy_info, spc_info)
# Get options from the dct or es options lst
overwrite = es_keyword_dct['overwrite']
# retryfail = es_keyword_dct['retryfail']
# Set up the script
script_str, kwargs = qchem_params(method_dct, elstruct.Job.OPTIMIZATION)
# Set the filesystem objects
rxn_info = spc_dct_i['rxn_info']
fs_rxn_info = rinfo.sort(rxn_info)
# New filesystem objects
if coord_name == 'irc':
_root = root_locs(spc_dct_i, saddle=True)
# ini_cnf_run_fs, ini_cnf_save_fs = build_fs(
# run_prefix, save_prefix, 'CONFORMER',
# thy_locs=mod_ini_thy_info[1:],
# **_root)
# cnf_run_fs, cnf_save_fs = build_fs(
# run_prefix, save_prefix, 'CONFORMER',
# thy_locs=mod_thy_info[1:],
# **_root)
ini_cnf_run_fs, ini_cnf_save_fs = build_fs(
run_prefix,
save_prefix,
'CONFORMER',
thy_locs=mod_ini_thy_info[1:],
**_root)
cnf_run_fs, cnf_save_fs = build_fs(run_prefix,
save_prefix,
'CONFORMER',
thy_locs=mod_thy_info[1:],
**_root)
ini_loc_info = filesys.mincnf.min_energy_conformer_locators(
ini_cnf_save_fs, mod_ini_thy_info)
ini_min_locs, ini_pfx_save_path = ini_loc_info
# ini_min_rng_locs, ini_min_cnf_locs = ini_min_cnf_locs
# ini_min_rng_path, ini_min_cnf_path = ini_min_cnf_path
ini_cnf_run_fs[-1].create(ini_min_locs)
ini_pfx_run_path = ini_cnf_run_fs[-1].path(ini_min_locs)
else:
ts_info = (ts_num, )
ini_ts_run_fs, ini_ts_save_fs = build_fs(run_prefix,
save_prefix,
'TS',
thy_locs=mod_ini_thy_info[1:],
**_root)
ini_pfx_run_path = ini_ts_run_fs.path(ts_info)
ini_pfx_save_path = ini_ts_save_fs.path(ts_info)
# Get options from the dct or es options lst
overwrite = es_keyword_dct['overwrite']
ini_scn_run_fs, ini_scn_save_fs = build_fs(ini_pfx_run_path,
ini_pfx_save_path,
'SCAN',
zma_locs=(0, ))
ini_zma_save_fs = autofile.fs.zmatrix(ini_cnf_save_path)
geo = ini_cnf_save_fs[-1].file.geometry.read(ini_min_locs)
zma = ini_zma_save_fs[-1].file.zmatrix.read((0, ))
# Run job
if job == 'scan':
if rcoord == 'auto':
pass
elif rcoord == 'irc':
rpath.irc_scan(geo, spc_info, coord_name, mod_ini_thy_info,
ini_method_dct, ini_scn_save_fs, ini_cnf_run_path,
overwrite)
elif job in ('energy', 'grad', 'hess'):
# Script
script_str, kwargs = qchem_params(method_dct)
# Need to put in something with the IRC idxs
for locs in ini_scn_save_fs[-1].existing():
geo_run_path = ini_scn_run_fs[-1].path(locs)
geo_save_path = ini_scn_save_fs[-1].path(locs)
geo = ini_scn_save_fs[-1].file.geometry.read(locs)
zma = None
ini_scn_run_fs[-1].create(locs)
ES_TSKS[job](zma, geo, spc_info, mod_thy_info, ini_scn_save_fs,
geo_run_path, geo_save_path, locs, script_str,
overwrite, **kwargs)
ioprinter.obj('vspace')
elif job == 'infene':
pass
def run_tsk(tsk, spc_dct, spc_name, thy_dct, proc_keyword_dct, pes_mod_dct_i,
spc_mod_dct_i, run_prefix, save_prefix):
""" run a proc tess task
for generating a list of conformer or tau sampling geometries
"""
# Print the head of the task
ioprinter.output_task_header(tsk)
ioprinter.obj('line_dash')
ioprinter.output_keyword_list(proc_keyword_dct, thy_dct)
# Setup csv data dictionary for specific task
csv_data = util.set_csv_data(tsk)
chn_basis_ene_dct = {}
spc_array = []
# print species
ioprinter.obj('line_dash')
ioprinter.info_message("Species: ", spc_name)
# Heat of formation basis molecules and coefficients
# is not conformer specific
if 'coeffs' in tsk:
thy_info = spc_mod_dct_i['geo'][1]
filelabel = 'coeffs'
filelabel += '_{}'.format(pes_mod_dct_i['thermfit']['ref_scheme'])
filelabel += '.csv'
label = spc_name
basis_dct, _ = basis.prepare_refs(
pes_mod_dct_i['thermfit']['ref_scheme'], spc_dct, (spc_name, ))
# Get the basis info for the spc of interest
spc_basis, coeff_basis = basis_dct[spc_name]
coeff_array = []
for spc_i in spc_basis:
if spc_i not in spc_array:
spc_array.append(spc_i)
for spc_i in spc_array:
if spc_i in spc_basis:
coeff_array.append(coeff_basis[spc_basis.index(spc_i)])
else:
coeff_array.append(0)
csv_data[label] = [*coeff_array]
else:
# unpack spc and level info
spc_dct_i = spc_dct[spc_name]
if proc_keyword_dct['geolvl']:
thy_info = tinfo.from_dct(thy_dct.get(proc_keyword_dct['geolvl']))
else:
thy_info = spc_mod_dct_i['geo'][1]
# Loop over conformers
if proc_keyword_dct['geolvl']:
_, rng_cnf_locs_lst, rng_cnf_locs_path = util.conformer_list(
proc_keyword_dct, save_prefix, run_prefix, spc_dct_i, thy_dct)
spc_mod_dct_i, pf_models = None, None
else:
ret = util.conformer_list_from_models(proc_keyword_dct,
save_prefix, run_prefix,
spc_dct_i, thy_dct,
spc_mod_dct_i, pf_models)
_, rng_cnf_locs_lst, rng_cnf_locs_path = ret
for locs, locs_path in zip(rng_cnf_locs_lst, rng_cnf_locs_path):
label = spc_name + '_' + '_'.join(locs)
_, cnf_fs = filesys.build_fs(run_prefix, save_prefix, 'CONFORMER')
if 'freq' in tsk:
filelabel = 'freq'
if spc_mod_dct_i:
filelabel += '_m{}'.format(spc_mod_dct_i['harm'][0])
else:
filelabel += '_{}'.format(proc_keyword_dct['geolvl'])
filelabel += '.csv'
if pf_models:
pf_filesystems = filesys.models.pf_filesys(spc_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
saddle=False)
ret = vib.full_vib_analysis(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
run_prefix,
zrxn=None)
freqs, _, tors_zpe, sfactor, torsfreqs, all_freqs = ret
csv_data['tfreq'][label] = torsfreqs
csv_data['allfreq'][label] = all_freqs
csv_data['scalefactor'][label] = [sfactor]
else:
es_model = util.freq_es_levels(proc_keyword_dct)
spc_mod_dct_i = parser.model.pf_level_info(
es_model, thy_dct)
try:
freqs, _, zpe = vib.read_locs_harmonic_freqs(
cnf_fs, locs, run_prefix, zrxn=None)
except:
freqs = []
zpe = 0
tors_zpe = 0.0
spc_data = []
zpe = tors_zpe + (sum(freqs) / 2.0) * phycon.WAVEN2EH
if freqs and proc_keyword_dct['scale'] is not None:
freqs, zpe = vib.scale_frequencies(freqs,
tors_zpe,
spc_mod_dct_i,
scale_method='3c')
spc_data = [locs_path, zpe, *freqs]
csv_data['freq'][label] = spc_data
elif 'geo' in tsk:
filelabel = 'geo'
if spc_mod_dct_i:
filelabel += '_{}'.format(spc_mod_dct_i['harm'])
else:
filelabel += '_{}'.format(proc_keyword_dct['geolvl'])
filelabel += '.txt'
if cnf_fs[-1].file.geometry.exists(locs):
geo = cnf_fs[-1].file.geometry.read(locs)
energy = cnf_fs[-1].file.energy.read(locs)
comment = 'energy: {0:>15.10f}'.format(energy)
xyz_str = automol.geom.xyz_string(geo, comment=comment)
else:
xyz_str = '\t -- Missing --'
spc_data = '\n\nSPC: {}\tConf: {}\tPath: {}\n'.format(
spc_name, locs, locs_path) + xyz_str
csv_data[label] = spc_data
elif 'zma' in tsk:
filelabel = 'zmat'
if spc_mod_dct_i:
filelabel += '_{}'.format(spc_mod_dct_i['harm'])
else:
filelabel += '_{}'.format(proc_keyword_dct['geolvl'])
filelabel += '.txt'
geo = cnf_fs[-1].file.geometry.read(locs)
zma = automol.geom.zmatrix(geo)
energy = cnf_fs[-1].file.energy.read(locs)
comment = 'energy: {0:>15.10f}\n'.format(energy)
zma_str = automol.zmat.string(zma)
spc_data = '\n\nSPC: {}\tConf: {}\tPath: {}\n'.format(
spc_name, locs, locs_path) + comment + zma_str
csv_data[label] = spc_data
elif 'ene' in tsk:
filelabel = 'ene'
if spc_mod_dct_i:
filelabel += '_{}'.format(spc_mod_dct_i['harm'])
filelabel += '_{}'.format(spc_mod_dct_i['ene'])
else:
filelabel += '_{}'.format(proc_keyword_dct['geolvl'])
filelabel += '_{}'.format(proc_keyword_dct['proplvl'])
filelabel += '.csv'
energy = None
if spc_mod_dct_i:
pf_filesystems = filesys.models.pf_filesys(spc_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
saddle=False)
energy = ene.electronic_energy(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
conf=(locs, locs_path,
cnf_fs))
else:
spc_info = sinfo.from_dct(spc_dct_i)
thy_info = tinfo.from_dct(
thy_dct.get(proc_keyword_dct['proplvl']))
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
sp_save_fs = autofile.fs.single_point(locs_path)
sp_save_fs[-1].create(mod_thy_info[1:4])
# Read the energy
sp_path = sp_save_fs[-1].path(mod_thy_info[1:4])
if os.path.exists(sp_path):
if sp_save_fs[-1].file.energy.exists(
mod_thy_info[1:4]):
ioprinter.reading('Energy', sp_path)
energy = sp_save_fs[-1].file.energy.read(
mod_thy_info[1:4])
csv_data[label] = [locs_path, energy]
elif 'enthalpy' in tsk:
filelabel = 'enthalpy'
if spc_mod_dct_i:
filelabel += '_{}'.format(spc_mod_dct_i['harm'])
filelabel += '_{}'.format(spc_mod_dct_i['ene'])
else:
filelabel += '_{}'.format(proc_keyword_dct['geolvl'])
filelabel += '_{}'.format(proc_keyword_dct['proplvl'])
filelabel = '.csv'
energy = None
pf_filesystems = filesys.models.pf_filesys(spc_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
saddle=False)
ene_abs = ene.read_energy(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
run_prefix,
conf=(locs, locs_path, cnf_fs),
read_ene=True,
read_zpe=True,
saddle=False)
hf0k, _, chn_basis_ene_dct, hbasis = basis.enthalpy_calculation(
spc_dct,
spc_name,
ene_abs,
chn_basis_ene_dct,
pes_mod_dct_i,
spc_mod_dct_i,
run_prefix,
save_prefix,
pforktp='pf',
zrxn=None)
spc_basis, coeff_basis = hbasis[spc_name]
coeff_array = []
for spc_i in spc_basis:
if spc_i not in spc_array:
spc_array.append(spc_i)
for spc_i in spc_array:
if spc_i in spc_basis:
coeff_array.append(coeff_basis[spc_basis.index(spc_i)])
else:
coeff_array.append(0)
csv_data[label] = [locs_path, ene_abs, hf0k, *coeff_array]
util.write_csv_data(tsk, csv_data, filelabel, spc_array)
def run(spc_rlst, therm_tsk_lst, pes_mod_dct, spc_mod_dct, spc_dct, run_prefix,
save_prefix):
""" main driver for thermo run
"""
# Print Header fo
ioprinter.info_message('Calculating Thermochem:')
ioprinter.runlst(('SPC', 0, 0), spc_rlst)
# ------------------------------------------------ #
# PREPARE INFORMATION TO PASS TO THERMDRIVER TASKS #
# ------------------------------------------------ #
# Build a list of the species to calculate thermochem for loops below
spc_mods = list(spc_mod_dct.keys()) # hack
split_spc_lst = split_unstable_spc(spc_rlst, spc_dct,
spc_mod_dct[spc_mods[0]], save_prefix)
spc_queue = parser.rlst.spc_queue('spc', tuple(split_spc_lst.values())[0])
# Build the paths [(messpf, nasa)], models and levels for each spc
thm_paths = thermo_paths(spc_dct, spc_queue, spc_mods, run_prefix)
# ----------------------------------- #
# RUN THE REQUESTED THERMDRIVER TASKS #
# ----------------------------------- #
# Write and Run MESSPF inputs to generate the partition functions
write_messpf_tsk = parser.run.extract_task('write_mess', therm_tsk_lst)
if write_messpf_tsk is not None:
ioprinter.messpf('write_header')
spc_mods, pes_mod = parser.models.extract_models(write_messpf_tsk)
for idx, spc_name in enumerate(spc_queue):
print('write test {}'.format(spc_name))
for spc_mod in spc_mods:
messpf_inp_str = thmroutines.qt.make_messpf_str(
pes_mod_dct[pes_mod]['therm_temps'], spc_dct, spc_name,
pes_mod_dct[pes_mod], spc_mod_dct[spc_mod], run_prefix,
save_prefix)
ioprinter.messpf('input_string')
ioprinter.info_message(messpf_inp_str)
autorun.write_input(thm_paths[idx][spc_mod][0],
messpf_inp_str,
input_name='pf.inp')
# Run the MESSPF files that have been written
run_messpf_tsk = parser.run.extract_task('run_mess', therm_tsk_lst)
if run_messpf_tsk is not None:
spc_mod, pes_mod = parser.models.extract_models(run_messpf_tsk)
spc_mods = parser.models.split_model(spc_mod[0])
ioprinter.messpf('run_header')
for idx, spc_name in enumerate(spc_queue):
_spc_mods, coeffs, operators = spc_mods
# Run MESSPF for all requested models, combine the PFS at the end
ioprinter.message('Run MESSPF: {}'.format(spc_name), newline=1)
_pfs = []
for spc_mod in _spc_mods:
autorun.run_script(autorun.SCRIPT_DCT['messpf'],
thm_paths[idx][spc_mod][0])
_pfs.append(
pfrunner.mess.read_messpf(thm_paths[idx][spc_mod][0]))
final_pf = pfrunner.mess.combine_pfs(_pfs, coeffs, operators)
# need to clean thm path build
tot_idx = len(spc_mods)
spc_info = sinfo.from_dct(spc_dct[spc_name])
spc_fml = automol.inchi.formula_string(spc_info[0])
thm_prefix = [spc_fml, automol.inchi.inchi_key(spc_info[0])]
thm_paths[idx]['final'] = (job_path(run_prefix,
'MESS',
'PF',
thm_prefix,
locs_idx=tot_idx),
job_path(run_prefix,
'THERM',
'NASA',
thm_prefix,
locs_idx=tot_idx))
pfrunner.mess.write_mess_output(fstring(
spc_dct[spc_name]['inchi']),
final_pf,
thm_paths[idx]['final'][0],
filename='pf.dat')
# Use MESS partition functions to compute thermo quantities
run_fit_tsk = parser.run.extract_task('run_fits', therm_tsk_lst)
if run_fit_tsk is not None:
spc_mods, pes_mod = parser.models.extract_models(run_fit_tsk)
pes_mod_dct_i = pes_mod_dct[pes_mod]
ioprinter.nasa('header')
chn_basis_ene_dct = {}
for idx, spc_name in enumerate(spc_queue):
# Take species model and add it to the chn_basis_ene dct
spc_mod = spc_mods[0]
spc_mod_dct_i = spc_mod_dct[spc_mod]
if spc_mod not in chn_basis_ene_dct:
chn_basis_ene_dct[spc_mod] = {}
# Get the reference scheme and energies (ref in different place)
ref_scheme = pes_mod_dct_i['therm_fit']['ref_scheme']
ref_enes = pes_mod_dct_i['therm_fit']['ref_enes']
# Determine info about the basis species used in thermochem calcs
basis_dct, uniref_dct = thmroutines.basis.prepare_refs(
ref_scheme, spc_dct, [[spc_name, None]], run_prefix,
save_prefix)
# Get the basis info for the spc of interest
spc_basis, coeff_basis = basis_dct[spc_name]
# Get the energies for the spc and its basis
ene_basis = []
energy_missing = False
for spc_basis_i in spc_basis:
if spc_basis_i in chn_basis_ene_dct[spc_mod]:
ioprinter.message(
'Energy already found for basis species: ' +
spc_basis_i)
ene_basis.append(chn_basis_ene_dct[spc_mod][spc_basis_i])
else:
ioprinter.message(
'Energy will be determined for basis species: ' +
spc_basis_i)
energy_missing = True
if not energy_missing:
pf_filesystems = filesys.models.pf_filesys(spc_dct[spc_name],
spc_mod_dct_i,
run_prefix,
save_prefix,
saddle=False)
ene_spc = ene.read_energy(spc_dct[spc_name],
pf_filesystems,
spc_mod_dct_i,
run_prefix,
read_ene=True,
read_zpe=True,
saddle=False)
else:
ene_spc, ene_basis = thmroutines.basis.basis_energy(
spc_name, spc_basis, uniref_dct, spc_dct, spc_mod_dct_i,
run_prefix, save_prefix)
for spc_basis_i, ene_basis_i in zip(spc_basis, ene_basis):
chn_basis_ene_dct[spc_mod][spc_basis_i] = ene_basis_i
# Calculate and store the 0 K Enthalpy
hf0k = thmroutines.heatform.calc_hform_0k(ene_spc,
ene_basis,
spc_basis,
coeff_basis,
ref_set=ref_enes)
spc_dct[spc_name]['Hfs'] = [hf0k]
# Write the NASA polynomials in CHEMKIN format
ckin_nasa_str = ''
ckin_path = output_path('CKIN')
for idx, spc_name in enumerate(spc_queue):
ioprinter.nasa('calculate', spc_name)
# Write the header describing the models used in thermo calcs
ckin_nasa_str += writer.ckin.model_header(spc_mods, spc_mod_dct)
# Build and write the NASA polynomial in CHEMKIN-format string
# Call dies if you haven't run "write mess" task
ckin_nasa_str += thmroutines.nasapoly.build_polynomial(
spc_name, spc_dct, thm_paths[idx]['final'][0],
thm_paths[idx]['final'][1])
ckin_nasa_str += '\n\n'
print(ckin_nasa_str)
nasa7_params_all = chemkin_io.parser.thermo.create_spc_nasa7_dct(
ckin_nasa_str)
# print('ckin_nasa_str test', ckin_nasa_str)
ioprinter.info_message(
'SPECIES H(0 K) H(298 K) S(298 K) Cp(300 K) Cp(500 K) Cp(1000 K) Cp(1500 K)\n'
)
ioprinter.info_message(
' kcal/mol kcal/mol cal/(mol K) ... \n')
for spc_name in nasa7_params_all:
nasa7_params = nasa7_params_all[spc_name]
whitespace = 18 - len(spc_name)
h0 = spc_dct[spc_name]['Hfs'][0]
h298 = mechanalyzer.calculator.thermo.enthalpy(
nasa7_params, 298.15) / 1000.
s298 = mechanalyzer.calculator.thermo.entropy(nasa7_params, 298.15)
cp300 = mechanalyzer.calculator.thermo.heat_capacity(
nasa7_params, 300)
cp500 = mechanalyzer.calculator.thermo.heat_capacity(
nasa7_params, 500)
cp1000 = mechanalyzer.calculator.thermo.heat_capacity(
nasa7_params, 1000)
cp1500 = mechanalyzer.calculator.thermo.heat_capacity(
nasa7_params, 1500)
whitespace = whitespace * ' '
ioprinter.info_message(
'{}{}{:>7.2f}{:>9.2f}{:>9.2f}{:>9.2f}{:>9.2f}{:>9.2f}{:>9.2f}'.
format(spc_name, whitespace, h0, h298, s298, cp300, cp500,
cp1000, cp1500))
# Write all of the NASA polynomial strings
writer.ckin.write_nasa_file(ckin_nasa_str, ckin_path)
def tau_data(spc_dct_i, spc_mod_dct_i, run_prefix, save_prefix, saddle=False):
""" Read the filesystem to get information for TAU
"""
# Set up model and basic thy objects
spc_info = sinfo.from_dct(spc_dct_i)
thy_info = spc_mod_dct_i['vib']['geolvl'][1][1]
mod_thy_info = tinfo.modify_orb_label(thy_info, spc_info)
vib_model = spc_mod_dct_i['vib']['mod']
# Set up reference conformer filesys
pf_filesystems = filesys.models.pf_filesys(spc_dct_i, spc_mod_dct_i,
run_prefix, save_prefix, saddle)
[harm_save_fs, _, harm_min_locs, _, _] = pf_filesystems['harm']
# Obtain all values from initial reference conformer
rotors = tors.build_rotors(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
read_potentials=False)
vib_info = vib.full_vib_analysis(spc_dct_i,
pf_filesystems,
spc_mod_dct_i,
run_prefix,
zrxn=None)
freqs, _, zpe, _, tors_strs, _, harm_freqs, _ = vib_info
harm_zpve = 0.5 * sum(harm_freqs) * phycon.WAVEN2EH
ioprinter.info_message('Determining the symmetry factor...', newline=1)
sym_factor = symm.symmetry_factor(
pf_filesystems,
spc_mod_dct_i,
spc_dct_i,
rotors,
)
zpe_chnlvl = zpe * phycon.EH2KCAL
ref_ene = harm_zpve * phycon.EH2KCAL
ref_geom = [harm_save_fs[-1].file.geometry.read(harm_min_locs)]
ref_grad = [harm_save_fs[-1].file.gradient.read(harm_min_locs)]
ref_hessian = [harm_save_fs[-1].file.hessian.read(harm_min_locs)]
min_cnf_ene = filesys.read.energy(harm_save_fs, harm_min_locs,
mod_thy_info)
# Set up the TAU filesystem objects, get locs, and read info
_, tau_save_fs = filesys.build_fs(run_prefix,
save_prefix,
'TAU',
spc_locs=spc_info,
thy_locs=mod_thy_info[1:])
db_style = 'jsondb'
vib_model = spc_mod_dct_i['vib']['mod']
if vib_model == 'tau':
if db_style == 'directory':
tau_locs = [
locs for locs in tau_save_fs[-1].existing()
if tau_save_fs[-1].file.hessian.exists(locs)
]
elif db_style == 'jsondb':
tau_locs = [
locs for locs in tau_save_fs[-1].json_existing()
if tau_save_fs[-1].json.hessian.exists(locs)
]
else:
if db_style == 'directory':
tau_locs = tau_save_fs[-1].existing()
elif db_style == 'jsondb':
tau_locs = tau_save_fs[-1].json_existing()
ioprinter.info_message(
'Reading data for the Monte Carlo samples from db.json'
f'at path {tau_save_fs[0].path()}')
samp_geoms, samp_enes, samp_grads, samp_hessians = [], [], [], []
tot_locs = len(tau_locs)
for idx, locs in enumerate(tau_locs):
if db_style == 'directory':
geo = tau_save_fs[-1].file.geometry.read(locs)
elif db_style == 'jsondb':
geo = tau_save_fs[-1].json.geometry.read(locs)
# geo_str = autofile.data_types.swrite.geometry(geo)
samp_geoms.append(geo)
if db_style == 'directory':
tau_ene = tau_save_fs[-1].file.energy.read(locs)
elif db_style == 'jsondb':
tau_ene = tau_save_fs[-1].json.energy.read(locs)
rel_ene = (tau_ene - min_cnf_ene) * phycon.EH2KCAL
# ene_str = autofile.data_types.swrite.energy(rel_ene)
samp_enes.append(rel_ene)
if vib_model == 'tau':
if db_style == 'directory':
grad = tau_save_fs[-1].file.gradient.read(locs)
elif db_style == 'jsondb':
grad = tau_save_fs[-1].json.gradient.read(locs)
# grad_str = autofile.data_types.swrite.gradient(grad)
samp_grads.append(grad)
if db_style == 'directory':
hess = tau_save_fs[-1].file.hessian.read(locs)
elif db_style == 'jsondb':
hess = tau_save_fs[-1].json.hessian.read(locs)
# hess_str = autofile.data_types.swrite.hessian(hess)
samp_hessians.append(hess)
# Print progress message (every 150 geoms read)
if idx % 149 == 0:
print(f'Read {idx+1}/{tot_locs} samples...')
# Determine the successful conformer ratio
inf_obj = tau_save_fs[0].file.info.read()
excluded_volume_factor = len(samp_geoms) / inf_obj.nsamp
print('excluded volume factor test:', excluded_volume_factor,
len(samp_geoms), inf_obj.nsamp)
# Create info dictionary
keys = [
'geom', 'sym_factor', 'elec_levels', 'freqs', 'flux_mode_str',
'samp_geoms', 'samp_enes', 'samp_grads', 'samp_hessians', 'ref_geom',
'ref_grad', 'ref_hessian', 'zpe_chnlvl', 'ref_ene',
'excluded_volume_factor'
]
vals = [
ref_geom[0], sym_factor, spc_dct_i['elec_levels'], freqs, tors_strs[2],
samp_geoms, samp_enes, samp_grads, samp_hessians, ref_geom, ref_grad,
ref_hessian, zpe_chnlvl, ref_ene, excluded_volume_factor
]
inf_dct = dict(zip(keys, vals))
return inf_dct
def run(pes_rlst, spc_rlst,
therm_tsk_lst,
pes_mod_dct, spc_mod_dct,
spc_dct,
run_prefix, save_prefix):
""" Executes all thermochemistry tasks.
:param pes_rlst: species from PESs to run
[(PES formula, PES idx, SUP-PES idx)
(CHANNEL idx, (REACS, PRODS))
:type pes_rlst: tuple(dict[str: dict])
:param spc_rlst: lst of species to run
:type spc_rlst: tuple(dict[str: dict])
:param es_tsk_lst: list of the electronic structure tasks
tuple(tuple(obj, tsk, keyword_dict))
:type es_tsk_lst: tuple(tuple(str, str, dict))
:param spc_dct: species information
dict[spc_name: spc_information]
:type spc_dct: dict[str:dict]
:param glob_dct: global information for all species
dict[spc_name: spc_information]
:type glob_dct: dict[str: dict]
:param thy_dct: all of the theory information
dict[thy name: inf]
:type thy_dct: dict[str:dict]
:param run_prefix: root-path to the run-filesystem
:type run_prefix: str
:param save_prefix: root-path to the save-filesystem
:type save_prefix: str
"""
# Print Header
ioprinter.info_message('Calculating Thermochem:')
ioprinter.runlst(('SPC', 0, 0), spc_rlst)
# ------------------------------------------------ #
# PREPARE INFORMATION TO PASS TO THERMDRIVER TASKS #
# ------------------------------------------------ #
# Build a list of the species to calculate thermochem for loops below
spc_mods = list(spc_mod_dct.keys()) # hack
spc_mod_dct_i = spc_mod_dct[spc_mods[0]]
split_rlst = split_unstable_full(
pes_rlst, spc_rlst, spc_dct, spc_mod_dct_i, save_prefix)
spc_queue = parser.rlst.spc_queue(
tuple(split_rlst.values())[0], 'SPC')
# Build the paths [(messpf, nasa)], models and levels for each spc
thm_paths = thermo_paths(spc_dct, spc_queue, spc_mods, run_prefix)
# ----------------------------------- #
# RUN THE REQUESTED THERMDRIVER TASKS #
# ----------------------------------- #
# Write and Run MESSPF inputs to generate the partition functions
write_messpf_tsk = parser.run.extract_task('write_mess', therm_tsk_lst)
if write_messpf_tsk is not None:
ioprinter.messpf('write_header')
spc_mods, pes_mod = parser.models.extract_models(write_messpf_tsk)
for idx, spc_name in enumerate(spc_queue):
print('write test {}'.format(spc_name))
for spc_mod in spc_mods:
messpf_inp_str = thmroutines.qt.make_messpf_str(
pes_mod_dct[pes_mod]['therm_temps'],
spc_dct, spc_name,
pes_mod_dct[pes_mod], spc_mod_dct[spc_mod],
run_prefix, save_prefix)
ioprinter.messpf('input_string')
ioprinter.info_message(messpf_inp_str)
autorun.write_input(
thm_paths[idx][spc_mod][0], messpf_inp_str,
input_name='pf.inp')
# Run the MESSPF files that have been written
run_messpf_tsk = parser.run.extract_task('run_mess', therm_tsk_lst)
if run_messpf_tsk is not None:
spc_mod, pes_mod = parser.models.extract_models(run_messpf_tsk)
spc_mods = parser.models.split_model(spc_mod[0])
ioprinter.messpf('run_header')
for idx, spc_name in enumerate(spc_queue):
_spc_mods, coeffs, operators = spc_mods
# Run MESSPF for all requested models, combine the PFS at the end
ioprinter.message('Run MESSPF: {}'.format(spc_name), newline=1)
_pfs = []
for spc_mod in _spc_mods:
autorun.run_script(
autorun.SCRIPT_DCT['messpf'],
thm_paths[idx][spc_mod][0])
_pfs.append(
reader.mess.messpf(thm_paths[idx][spc_mod][0]))
final_pf = thermfit.pf.combine(_pfs, coeffs, operators)
# need to clean thm path build
tdx = len(spc_mods)
spc_info = sinfo.from_dct(spc_dct[spc_name])
spc_fml = automol.inchi.formula_string(spc_info[0])
thm_prefix = [spc_fml, automol.inchi.inchi_key(spc_info[0])]
thm_paths[idx]['final'] = (
job_path(run_prefix, 'MESS', 'PF', thm_prefix, locs_idx=tdx),
job_path(run_prefix, 'THERM', 'NASA', thm_prefix, locs_idx=tdx)
)
writer.mess.output(
fstring(spc_dct[spc_name]['inchi']),
final_pf, thm_paths[idx]['final'][0],
filename='pf.dat')
# Use MESS partition functions to compute thermo quantities
run_fit_tsk = parser.run.extract_task('run_fits', therm_tsk_lst)
if run_fit_tsk is not None:
spc_mods, pes_mod = parser.models.extract_models(run_fit_tsk)
pes_mod_dct_i = pes_mod_dct[pes_mod]
ioprinter.nasa('header')
chn_basis_ene_dct = {}
for idx, spc_name in enumerate(spc_queue):
# Take species model and add it to the chn_basis_ene dct
spc_mod = spc_mods[0]
spc_mod_dct_i = spc_mod_dct[spc_mod]
if spc_mod not in chn_basis_ene_dct:
chn_basis_ene_dct[spc_mod] = {}
# Get the reference scheme and energies (ref in different place)
ref_scheme = pes_mod_dct_i['therm_fit']['ref_scheme']
ref_enes = pes_mod_dct_i['therm_fit']['ref_enes']
# Determine info about the basis species used in thermochem calcs
basis_dct, uniref_dct = thermfit.prepare_refs(
ref_scheme, spc_dct, (spc_name,))
# Get the basis info for the spc of interest
spc_basis, coeff_basis = basis_dct[spc_name]
# Get the energies for the spc and its basis
ene_basis = []
energy_missing = False
for spc_basis_i in spc_basis:
if spc_basis_i in chn_basis_ene_dct[spc_mod]:
ioprinter.message(
'Energy already found for basis species: '
+ spc_basis_i)
ene_basis.append(chn_basis_ene_dct[spc_mod][spc_basis_i])
else:
ioprinter.message(
'Energy will be determined for basis species: '
+ spc_basis_i)
energy_missing = True
if not energy_missing:
pf_filesystems = filesys.models.pf_filesys(
spc_dct[spc_name], spc_mod_dct_i,
run_prefix, save_prefix, saddle=False)
ene_spc = ene.read_energy(
spc_dct[spc_name], pf_filesystems, spc_mod_dct_i,
run_prefix, read_ene=True, read_zpe=True, saddle=False)
else:
ene_spc, ene_basis = thmroutines.basis.basis_energy(
spc_name, spc_basis, uniref_dct, spc_dct,
spc_mod_dct_i,
run_prefix, save_prefix)
for spc_basis_i, ene_basis_i in zip(spc_basis, ene_basis):
chn_basis_ene_dct[spc_mod][spc_basis_i] = ene_basis_i
# Calculate and store the 0 K Enthalpy
hf0k = thermfit.heatform.calc_hform_0k(
ene_spc, ene_basis, spc_basis, coeff_basis, ref_set=ref_enes)
spc_dct[spc_name]['Hfs'] = [hf0k]
# Write the NASA polynomials in CHEMKIN format
ckin_nasa_str = ''
ckin_path = output_path('CKIN')
for idx, spc_name in enumerate(spc_queue):
ioprinter.nasa('calculate', spc_name)
# Write the header describing the models used in thermo calcs
ckin_nasa_str += writer.ckin.model_header(spc_mods, spc_mod_dct)
# Build and write the NASA polynomial in CHEMKIN-format string
# Call dies if you haven't run "write mess" task
ckin_nasa_str += thmroutines.nasapoly.build_polynomial(
spc_name, spc_dct,
thm_paths[idx]['final'][0], thm_paths[idx]['final'][1])
ckin_nasa_str += '\n\n'
print('CKIN NASA STR\n')
print(ckin_nasa_str)
nasa7_params_all = chemkin_io.parser.thermo.create_spc_nasa7_dct(
ckin_nasa_str)
ioprinter.info_message(
'SPECIES\t\tH(0 K)[kcal/mol]\tH(298 K)[kcal/mol]\t' +
'S(298 K)[cal/mol K]\n')
for spc_name in nasa7_params_all:
nasa7_params = nasa7_params_all[spc_name]
ht0 = spc_dct[spc_name]['Hfs'][0]
ht298 = mechanalyzer.calculator.thermo.enthalpy(
nasa7_params, 298.15)
st298 = mechanalyzer.calculator.thermo.entropy(
nasa7_params, 298.15)
ioprinter.info_message(
'{}\t{:3.2f}\t{:3.2f}\t{:3.2f}'.format(
spc_name, ht0, ht298/1000., st298))
# Write all of the NASA polynomial strings
writer.ckin.write_nasa_file(ckin_nasa_str, ckin_path)