def _hess_freqs(geo, geo_save_fs, save_path, locs, run_prefix, overwrite):
""" Calculate harmonic frequencies using Hessian
"""
if _json_database(save_path):
exists = geo_save_fs[-1].json.harmonic_frequencies.exists(locs)
else:
exists = geo_save_fs[-1].file.harmonic_frequencies.exists(locs)
if not exists:
ioprinter.info_message(
'No harmonic frequencies found in save filesys...')
_run = True
elif overwrite:
ioprinter.info_message(
'User specified to overwrite frequencies with new run...')
_run = True
else:
_run = False
if _run:
# Read the Hessian from the filesystem
if _json_database(save_path):
hess = geo_save_fs[-1].json.hessian.read(locs)
else:
hess = geo_save_fs[-1].file.hessian.read(locs)
# Calculate and save the harmonic frequencies
ioprinter.info_message(
" - Calculating harmonic frequencies from Hessian...")
script_str = autorun.SCRIPT_DCT['projrot']
fml_str = automol.geom.formula_string(geo)
vib_path = job_path(run_prefix, 'PROJROT', 'FREQ', fml_str)
rt_freqs, _, rt_imags, _ = autorun.projrot.frequencies(
script_str, vib_path, [geo], [[]], [hess])
rt_imags = tuple(-1 * imag_freq for imag_freq in rt_imags)
freqs = sorted(rt_imags + rt_freqs)
ioprinter.frequencies(freqs)
ioprinter.geometry(geo)
if _json_database(save_path):
geo_save_fs[-1].json.harmonic_frequencies.write(freqs, locs)
else:
geo_save_fs[-1].file.harmonic_frequencies.write(freqs, locs)
ioprinter.save_frequencies(save_path)
else:
ioprinter.existing_path('Harmonic frequencies', save_path)
freqs = geo_save_fs[-1].file.harmonic_frequencies.read(locs)
ioprinter.frequencies(freqs)
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 saddle_point_hessian(opt_ret, ts_info, method_dct, run_fs, run_prefix,
overwrite):
""" run things for checking Hessian
"""
mod_thy_info = tinfo.modify_orb_label(tinfo.from_dct(method_dct), ts_info)
script_str, kwargs = qchem_params(method_dct)
# Obtain geometry from optimization
opt_inf_obj, _, opt_out_str = opt_ret
opt_prog = opt_inf_obj.prog
geo = elstruct.reader.opt_geometry(opt_prog, opt_out_str)
# Run a Hessian
hess_success, hess_ret = es_runner.execute_job(
job='hessian',
script_str=script_str,
run_fs=run_fs,
geo=geo,
spc_info=ts_info,
thy_info=mod_thy_info,
overwrite=overwrite,
**kwargs,
)
# If successful, Read the geom and energy from the optimization
if hess_success:
hess_inf_obj, _, hess_out_str = hess_ret
hess = elstruct.reader.hessian(hess_inf_obj.prog, hess_out_str)
fml_str = automol.geom.formula_string(geo)
vib_path = job_path(run_prefix, 'PROJROT', 'FREQ', fml_str)
run_fs[-1].create(['hessian'])
script_str = autorun.SCRIPT_DCT['projrot']
freqs, _, imags, _ = autorun.projrot.frequencies(
script_str, vib_path, [geo], [[]], [hess])
else:
freqs, imags = [], []
return hess_ret, freqs, imags
def run(pes_rlst, ktp_tsk_lst, spc_dct, glob_dct, thy_dct, pes_mod_dct,
spc_mod_dct, run_prefix, save_prefix):
""" main driver for generation of full set of rate constants on a single PES
"""
# --------------------------------------- #
# LOOP OVER ALL OF THE SUBPES in PES_RLST #
# --------------------------------------- #
for pes_inf, rxn_lst in pes_rlst.items():
# ---------------------------------------------- #
# PREPARE INFORMATION TO PASS TO KTPDRIVER TASKS #
# ---------------------------------------------- #
# Set objects
pes_formula, pes_idx, subpes_idx = pes_inf
label_dct = None
# Print PES Channels that are being run
ioprinter.runlst(pes_inf, rxn_lst)
# Set paths where files will be written and read
mess_path = job_path(run_prefix,
'MESS',
'RATE',
pes_formula,
locs_idx=subpes_idx)
# --------------------------------- #
# RUN THE REQUESTED KTPDRIVER TASKS #
# --------------------------------- #
# Write the MESS file
write_rate_tsk = parser.run.extract_task('write_mess', ktp_tsk_lst)
if write_rate_tsk is not None:
# Get all the info for the task
tsk_key_dct = write_rate_tsk[-1]
pes_mod = tsk_key_dct['kin_model']
spc_mod = tsk_key_dct['spc_model']
spc_dct, rxn_lst, label_dct = _process(pes_idx, rxn_lst,
ktp_tsk_lst, spc_mod_dct,
spc_mod, thy_dct, spc_dct,
glob_dct, run_prefix,
save_prefix)
ioprinter.messpf('write_header')
mess_inp_str, dats = ktproutines.rates.make_messrate_str(
pes_idx, rxn_lst, pes_mod, spc_mod, spc_dct, thy_dct,
pes_mod_dct, spc_mod_dct, label_dct, mess_path, run_prefix,
save_prefix)
autorun.write_input(mess_path,
mess_inp_str,
aux_dct=dats,
input_name='mess.inp')
# Run mess to produce rates (currently nothing from tsk lst keys used)
run_rate_tsk = parser.run.extract_task('run_mess', ktp_tsk_lst)
if run_rate_tsk is not None:
ioprinter.obj('vspace')
ioprinter.obj('line_dash')
ioprinter.running('MESS for the input file', mess_path)
autorun.run_script(autorun.SCRIPT_DCT['messrate'], mess_path)
# Fit rate output to modified Arrhenius forms, print in ChemKin format
run_fit_tsk = parser.run.extract_task('run_fits', ktp_tsk_lst)
if run_fit_tsk is not None:
# Get all the info for the task
tsk_key_dct = run_fit_tsk[-1]
spc_mod = tsk_key_dct['spc_model']
pes_mod = tsk_key_dct['kin_model']
ratefit_dct = pes_mod_dct[pes_mod]['rate_fit']
if label_dct is None:
spc_dct, rxn_lst, label_dct = _process(pes_idx, rxn_lst,
ktp_tsk_lst,
spc_mod_dct, spc_mod,
thy_dct, spc_dct,
run_prefix, save_prefix)
ioprinter.obj('vspace')
ioprinter.obj('line_dash')
ioprinter.info_message(
'Fitting Rate Constants for PES to Functional Forms',
newline=1)
# Read and fit rates; write to ckin string
ratefit_dct = pes_mod_dct[pes_mod]['rate_fit']
ckin_dct = ratefit.fit.fit_ktp_dct(
mess_path=mess_path,
inp_fit_method=ratefit_dct['fit_method'],
pdep_dct=ratefit_dct['pdep_fit'],
arrfit_dct=ratefit_dct['arrfit_fit'],
chebfit_dct=ratefit_dct['chebfit_fit'],
troefit_dct=ratefit_dct['troefit_fit'],
label_dct=label_dct,
fit_temps=pes_mod_dct[pes_mod]['rate_temps'],
fit_pressures=pes_mod_dct[pes_mod]['pressures'],
fit_tunit=pes_mod_dct[pes_mod]['temp_unit'],
fit_punit=pes_mod_dct[pes_mod]['pressure_unit'])
# Write the header part
ckin_dct.update(
{'header': writer.ckin.model_header((spc_mod, ), spc_mod_dct)})
ckin_path = output_path('CKIN')
writer.ckin.write_rxn_file(ckin_dct, pes_formula, ckin_path)
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 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)
def run(pes_rlst, ktp_tsk_lst, spc_dct, glob_dct, pes_mod_dct, spc_mod_dct,
run_prefix, save_prefix):
""" Executes all kinetics 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
"""
# --------------------------------------- #
# LOOP OVER ALL OF THE SUBPES in PES_RLST #
# --------------------------------------- #
for pes_inf, rxn_lst in pes_rlst.items():
# ---------------------------------------------- #
# PREPARE INFORMATION TO PASS TO KTPDRIVER TASKS #
# ---------------------------------------------- #
# Set objects
pes_formula, pes_idx, subpes_idx = pes_inf
label_dct = None
# Print PES Channels that are being run
ioprinter.runlst(pes_inf, rxn_lst)
# Set paths where files will be written and read
mess_path = job_path(run_prefix,
'MESS',
'RATE',
pes_formula,
locs_idx=subpes_idx)
# --------------------------------- #
# RUN THE REQUESTED KTPDRIVER TASKS #
# --------------------------------- #
# Write the MESS file
write_rate_tsk = parser.run.extract_task('write_mess', ktp_tsk_lst)
if write_rate_tsk is not None:
# Get all the info for the task
tsk_key_dct = write_rate_tsk[-1]
pes_mod = tsk_key_dct['kin_model']
spc_mod = tsk_key_dct['spc_model']
spc_dct, rxn_lst, instab_chnls, label_dct = _process(
pes_idx, rxn_lst, ktp_tsk_lst, spc_mod_dct, spc_mod, spc_dct,
glob_dct, run_prefix, save_prefix)
ioprinter.messpf('write_header')
# Doesn't give full string
mess_inp_str, dats = ktproutines.rates.make_messrate_str(
pes_idx,
rxn_lst,
pes_mod,
spc_mod,
spc_dct,
pes_mod_dct,
spc_mod_dct,
instab_chnls,
label_dct,
mess_path,
run_prefix,
save_prefix,
make_lump_well_inp=tsk_key_dct['lump_wells'])
autorun.write_input(mess_path,
mess_inp_str,
aux_dct=dats,
input_name='mess.inp')
# Run mess to produce rates (currently nothing from tsk lst keys used)
run_rate_tsk = parser.run.extract_task('run_mess', ktp_tsk_lst)
if run_rate_tsk is not None:
ioprinter.obj('vspace')
ioprinter.obj('line_dash')
ioprinter.running('MESS for the input file', mess_path)
autorun.run_script(autorun.SCRIPT_DCT['messrate'], mess_path)
# Fit rate output to modified Arrhenius forms, print in ChemKin format
run_fit_tsk = parser.run.extract_task('run_fits', ktp_tsk_lst)
if run_fit_tsk is not None:
# Get all the info for the task
tsk_key_dct = run_fit_tsk[-1]
spc_mod = tsk_key_dct['spc_model']
pes_mod = tsk_key_dct['kin_model']
ratefit_dct = pes_mod_dct[pes_mod]['rate_fit']
if label_dct is None:
spc_dct, rxn_lst, _, label_dct = _process(
pes_idx, rxn_lst, ktp_tsk_lst, spc_mod_dct, spc_mod,
spc_dct, glob_dct, run_prefix, save_prefix)
ioprinter.obj('vspace')
ioprinter.obj('line_dash')
ioprinter.info_message(
'Fitting Rate Constants for PES to Functional Forms',
newline=1)
# Read and fit rates; write to ckin string
ratefit_dct = pes_mod_dct[pes_mod]['rate_fit']
ckin_dct = ratefit.fit.fit_ktp_dct(
mess_path=mess_path,
inp_fit_method=ratefit_dct['fit_method'],
pdep_dct=ratefit_dct['pdep_fit'],
arrfit_dct=ratefit_dct['arrfit_fit'],
chebfit_dct=ratefit_dct['chebfit_fit'],
troefit_dct=ratefit_dct['troefit_fit'],
label_dct=label_dct,
fit_temps=pes_mod_dct[pes_mod]['rate_temps'],
fit_pressures=pes_mod_dct[pes_mod]['pressures'],
fit_tunit=pes_mod_dct[pes_mod]['temp_unit'],
fit_punit=pes_mod_dct[pes_mod]['pressure_unit'])
# Write the header part
ckin_dct.update(
{'header': writer.ckin.model_header((spc_mod, ), spc_mod_dct)})
ckin_path = output_path('CKIN')
writer.ckin.write_rxn_file(ckin_dct, pes_formula, ckin_path)