def do_optimization(self):
while 1:
# do the conformational search
if self.par.par['conformer_search'] == 1:
if self.scycconf == -1 and self.sconf == -1:
# conformational analysis has to be started
logging.info(
'\tStarting conformational search of {}'.format(
self.species.name))
self.species.confs = Conformers(self.species, self.par,
self.qc)
# first do the cyclic part of the molecule
if self.scycconf == -1:
# start the ring conf search
if len(self.species.cycle_chain) > 0:
# there are rings in the molecule, do a search
self.species.confs.generate_ring_conformers(
copy.deepcopy(self.species.geom))
# set the cyclic conf status to running
self.scycconf = 0
else:
# there are no rings in the molecule, continue from the current geometry
self.species.confs.cyc_conf_geoms = [
copy.deepcopy(self.species.geom)
]
# no ring conf search has to be done, set status to finished
self.scycconf = 1
if self.scycconf == 0:
# ring conf search is running, check if finished
status, self.species.confs.cyc_conf_geoms = self.species.confs.check_ring_conformers(
)
if status:
# ring conf search is finished
self.scycconf = 1
# do the open chain par of the molecule
if self.scycconf == 1:
# do open chain part if cyclic part is done
if self.sconf == -1:
# open chain part has not started yet
for geom in self.species.confs.cyc_conf_geoms:
# take all the geometries from the cyclic part
# generate the conformers for the current geometry
self.species.confs.generate_conformers(0, geom)
# set conf status to running
self.sconf = 0
if self.sconf == 0:
# conformational search is running
# check if the conformational search is done
status, geom, low_energy = self.species.confs.check_conformers(
wait=self.wait)
#print('{0} {1}'.format(status, low_energy))
if status == 1:
# cfi=open("conf_energies.txt", 'a')
# cfi.write(self.species.name)
# cfi.write(": Initial E: {0}".format(self.species.energy))
# conf search is done
# save lowest energy conformer as species geometry
self.species.geom = geom
# save lowest energy conformer energy
self.species.energy = low_energy
# cfi.write(", Final E: {0}\n".format(self.species.energy))
# cfi.close()
# set conf status to finished
self.sconf = 1
else:
# no conf search necessary, set status to finished
self.sconf = 1
if self.sconf == 1: # conf search is finished
while self.restart < self.max_restart:
# do the high level calculations
if self.par.par['high_level'] == 1:
if self.shigh == -1:
# high level calculation did not start yet
logging.info(
'\tStarting high level optimization of {}'.
format(self.species.name))
if self.species.wellorts:
# do the high level optimization of a ts
self.qc.qc_opt_ts(self.species,
self.species.geom,
high_level=1)
else:
# do the high level optimization of a well
self.qc.qc_opt(self.species,
self.species.geom,
high_level=1)
self.shigh = 0 # set the high status to running
if self.shigh == 0:
# high level calculation is running
# check if it is finished
status = self.qc.check_qc(self.job_high)
if status == 'error':
# found an error
logging.info(
'\tHigh level optimization failed for {}'.
format(self.species.name))
self.shigh = -999
if status == 'normal':
# finished successfully
err, new_geom = self.qc.get_qc_geom(
self.job_high,
self.species.natom,
wait=self.wait)
if geometry.equal_geom(self.species.bond,
self.species.geom,
new_geom, 0.1):
# geometry is as expected
err, self.species.geom = self.qc.get_qc_geom(
self.job_high, self.species.natom)
err, self.species.energy = self.qc.get_qc_energy(
self.job_high)
err, self.species.freq = self.qc.get_qc_freq(
self.job_high, self.species.natom)
err, self.species.zpe = self.qc.get_qc_zpe(
self.job_high)
self.shigh = 1
else:
# geometry diverged to other structure
logging.info(
'\tHigh level ts optimization converged to different structure for {}'
.format(self.species.name))
self.shigh = -999
else:
# no high-level calculations necessary, set status to finished
self.shigh = 1
if self.shigh == 1:
# do the HIR calculation
if self.par.par['rotor_scan'] == 1:
if self.shir == -1:
# hir not stated yet
logging.info(
'\tStarting hindered rotor calculations of {}'
.format(self.species.name))
self.species.hir = HIR(self.species, self.qc,
self.par)
self.species.hir.generate_hir_geoms(
copy.deepcopy(self.species.geom))
self.shir = 0
if self.shir == 0:
# hir is running
# check if it is done:
status = self.species.hir.check_hir(
wait=self.wait)
if status:
if len(self.species.hir.hir_energies) > 0:
# check if along the hir potential a structure was found with a lower energy
min = self.species.hir.hir_energies[0][
0]
min_rotor = -1
min_ai = -1
for rotor in range(
len(self.species.dihed)):
for ai in range(self.species.hir.
nrotation):
# use a 0.1kcal/mol cutoff for numerical noise
if self.species.hir.hir_energies[
rotor][
ai] < min - 1.6E-4:
min = self.species.hir.hir_energies[
rotor][ai]
min_rotor = rotor
min_ai = ai
if min_rotor > -1:
self.restart += 1
if self.restart < self.max_restart:
# lower energy structure found
logging.info(
'\t\tLower energy found during hindered rotor scan for {}'
.format(self.species.name))
logging.info(
'\t\tRestart number: ' +
str(self.restart))
logging.info('\t\tRotor: ' +
str(min_rotor))
logging.info(
'\t\tScan point: ' +
str(min_ai))
job = self.job_hir + str(
min_rotor) + '_' + str(
min_ai).zfill(2)
err, self.species.geom = self.qc.get_qc_geom(
job, self.species.natom)
# delete the high_level log file and the hir log files
if os.path.exists(
self.job_high +
'.log'):
# logging.info("\t\t\tRemoving file " + self.job_high + '.log')
os.remove(self.job_high +
'.log')
for rotor in range(
len(self.species.dihed)
):
for ai in range(
self.species.hir.
nrotation):
if os.path.exists(
self.job_hir +
str(rotor) +
'_' +
str(ai).zfill(
2) +
'.log'):
# logging.info("\t\t\tRemoving file " + self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
os.remove(
self.job_hir +
str(rotor) +
'_' +
str(ai).zfill(
2) +
'.log')
# set the status of high and hir back to not started
self.shigh = -1
self.shir = -1
else:
logging.info(
'\t\tLower energy found, but readched max restart for {}'
.format(self.species.name))
self.shir = 1
else:
self.shir = 1
else:
self.shir = 1
else:
# no hir calculations necessary, set status to finished
self.shir = 1
if not self.wait or self.shir == 1 or self.shigh == -999:
# break the loop if no waiting is required or
# if the hir calcs are done or
# if the high level calc failed
break
else:
time.sleep(1)
if self.shir == 1:
# finilize if no waiting is required or if the hir calcs are done
# calculate the symmetry numbers
symmetry.calculate_symmetry(self.species)
# calculate the new frequencies with the internal rotations projected out
fr_file = self.species.name
if not self.species.wellorts:
fr_file += '_well'
if self.par.par['high_level']:
fr_file += '_high'
hess = self.qc.read_qc_hess(fr_file, self.species.natom)
self.species.kinbot_freqs, self.species.reduced_freqs = frequencies.get_frequencies(
self.species, hess, self.species.geom)
# write the molpro input and read the molpro energy, if available
if self.par.par['single_point_qc'] == 'molpro':
molp = Molpro(self.species, self.par)
molp.create_molpro_input()
molp.create_molpro_submit()
status, molpro_energy = molp.get_molpro_energy()
if status:
self.species.energy = molpro_energy
# delete unnecessary files
if self.par.par['delete_intermediate_files'] == 1:
self.delete_files()
if self.wait:
if self.shir == 1 or self.shigh == -999:
return 0
time.sleep(1)
else:
return 0
def do_optimization(self):
while 1:
# do the conformational search
if self.par.par['conformer_search'] == 1:
if self.scycconf == -1 and self.sconf == -1:
# conformational analysis has to be started
logging.info('\tStarting conformational search of {}'.format(self.species.name))
self.species.confs = Conformers(self.species, self.par, self.qc)
# first do the cyclic part of the molecule
if self.scycconf == -1:
# start the ring conf search
if len(self.species.cycle_chain) > 0:
# there are rings in the molecule, do a search
self.species.confs.generate_ring_conformers(copy.deepcopy(self.species.geom))
# set the cyclic conf status to running
self.scycconf = 0
else:
# there are no rings in the molecule, continue from the current geometry
self.species.confs.cyc_conf_geoms = [copy.deepcopy(self.species.geom)]
# no ring conf search has to be done, set status to finished
self.scycconf = 1
if self.scycconf == 0:
# ring conf search is running, check if finished
status, self.species.confs.cyc_conf_geoms = self.species.confs.check_ring_conformers()
if status:
# ring conf search is finished
self.scycconf = 1
# do the open chain par of the molecule
if self.scycconf == 1:
# do open chain part if cyclic part is done
if self.sconf == -1:
# open chain part has not started yet
for geom in self.species.confs.cyc_conf_geoms:
# take all the geometries from the cyclic part
# generate the conformers for the current geometry
self.species.confs.generate_conformers(0, geom)
# set conf status to running
self.sconf = 0
if self.sconf == 0:
# conformational search is running
# check if the conformational search is done
status, geom = self.species.confs.check_conformers(wait=self.wait)
if status == 1:
# conf search is done
# save lowest energy conformer as species geometry
self.species.geom = geom
# set conf status to finished
self.sconf = 1
else:
# no conf search necessary, set status to finished
self.sconf = 1
if self.sconf == 1: # conf search is finished
while self.restart < self.max_restart:
# do the high level calculations
if self.par.par['high_level'] == 1:
if self.shigh == -1:
# high level calculation did not start yet
logging.info('\tStarting high level optimization of {}'.format(self.species.name))
if self.species.wellorts:
# do the high level optimization of a ts
self.qc.qc_opt_ts(self.species, self.species.geom, high_level=1)
else:
# do the high level optimization of a well
self.qc.qc_opt(self.species, self.species.geom, high_level=1)
self.shigh = 0 # set the high status to running
if self.shigh == 0:
# high level calculation is running
# check if it is finished
status = self.qc.check_qc(self.job_high)
if status == 'error':
# found an error
logging.info('\tHigh level optimization failed for {}'.format(self.species.name))
self.shigh = -999
if status == 'normal':
# finished successfully
err, new_geom = self.qc.get_qc_geom(self.job_high, self.species.natom, wait=self.wait)
if geometry.equal_geom(self.species.bond, self.species.geom, new_geom, 0.1):
# geometry is as expected
err, self.species.geom = self.qc.get_qc_geom(self.job_high, self.species.natom)
err, self.species.energy = self.qc.get_qc_energy(self.job_high)
err, self.species.freq = self.qc.get_qc_freq(self.job_high, self.species.natom)
err, self.species.zpe = self.qc.get_qc_zpe(self.job_high)
self.shigh = 1
else:
# geometry diverged to other structure
logging.info('\tHigh level ts optimization converged to different structure for {}'.format(self.species.name))
self.shigh = -999
else:
# no high-level calculations necessary, set status to finished
self.shigh = 1
if self.shigh == 1:
# do the HIR calculation
if self.par.par['rotor_scan'] == 1:
if self.shir == -1:
# hir not stated yet
logging.info('\tStarting hindered rotor calculations of {}'.format(self.species.name))
self.species.hir = HIR(self.species, self.qc, self.par)
self.species.hir.generate_hir_geoms(copy.deepcopy(self.species.geom))
self.shir = 0
if self.shir == 0:
# hir is running
# check if it is done:
status = self.species.hir.check_hir(wait=self.wait)
if status:
if len(self.species.hir.hir_energies) > 0:
# check if along the hir potential a structure was found with a lower energy
min = self.species.hir.hir_energies[0][0]
min_rotor = -1
min_ai = -1
for rotor in range(len(self.species.dihed)):
for ai in range(self.species.hir.nrotation):
# use a 0.1kcal/mol cutoff for numerical noise
if self.species.hir.hir_energies[rotor][ai] < min - 1.6E-4:
min = self.species.hir.hir_energies[rotor][ai]
min_rotor = rotor
min_ai = ai
if min_rotor > -1:
self.restart += 1
if self.restart < self.max_restart:
# lower energy structure found
logging.info('\t\tLower energy found during hindered rotor scan for {}'.format(self.species.name))
logging.info('\t\tRestart number: ' + str(self.restart))
logging.info('\t\tRotor: ' + str(min_rotor))
logging.info('\t\tScan point: ' + str(min_ai))
job = self.job_hir + str(min_rotor) + '_' + str(min_ai).zfill(2)
err, self.species.geom = self.qc.get_qc_geom(job, self.species.natom)
# delete the high_level log file and the hir log files
if os.path.exists(self.job_high + '.log'):
# logging.info("\t\t\tRemoving file " + self.job_high + '.log')
os.remove(self.job_high + '.log')
for rotor in range(len(self.species.dihed)):
for ai in range(self.species.hir.nrotation):
if os.path.exists(self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log'):
# logging.info("\t\t\tRemoving file " + self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
os.remove(self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
# set the status of high and hir back to not started
self.shigh = -1
self.shir = -1
else:
logging.info('\t\tLower energy found, but readched max restart for {}'.format(self.species.name))
self.shir = 1
else:
self.shir = 1
else:
self.shir = 1
else:
# no hir calculations necessary, set status to finished
self.shir = 1
if not self.wait or self.shir == 1 or self.shigh == -999:
# break the loop if no waiting is required or
# if the hir calcs are done or
# if the high level calc failed
break
else:
time.sleep(1)
if self.shir == 1:
# finilize if no waiting is required or if the hir calcs are done
# calculate the symmetry numbers
symmetry.calculate_symmetry(self.species)
# calculate the new frequencies with the internal rotations projected out
fr_file = self.species.name
if not self.species.wellorts:
fr_file += '_well'
if self.par.par['high_level']:
fr_file += '_high'
hess = self.qc.read_qc_hess(fr_file, self.species.natom)
self.species.kinbot_freqs, self.species.reduced_freqs = frequencies.get_frequencies(self.species, hess, self.species.geom)
# write the molpro input and read the molpro energy, if available
if self.par.par['single_point_qc'] == 'molpro':
molp = Molpro(self.species, self.par)
molp.create_molpro_input()
molp.create_molpro_submit()
status, molpro_energy = molp.get_molpro_energy()
if status:
self.species.energy = molpro_energy
# delete unnecessary files
delete = 1
if delete:
self.delete_files()
if self.wait:
if self.shir == 1 or self.shigh == -999:
return 0
time.sleep(1)
else:
return 0
def do_optimization(self):
while 1:
# do the conformational search
if self.par['conformer_search'] == 1:
if self.scycconf == -1 and self.sconf == -1:
logging.info('\tStarting conformational search of {}'.format(self.name))
self.species.confs = Conformers(self.species, self.par, self.qc)
# first do the cyclic part of the molecule
if self.scycconf == -1:
# start the ring conf search
if len(self.species.cycle_chain) > 0:
# there are rings in the molecule, do a search
self.species.confs.generate_ring_conformers(copy.deepcopy(self.species.geom))
# set the cyclic conf status to running
self.scycconf = 0
else:
# there are no rings in the molecule, continue from the current geometry
self.species.confs.cyc_conf_geoms = [copy.deepcopy(self.species.geom)]
# no ring conf search has to be done, set status to finished
self.scycconf = 1
if self.scycconf == 0:
# ring conf search is running, check if finished
status, self.species.confs.cyc_conf_geoms = self.species.confs.check_ring_conformers()
if status:
# ring conf search is finished
self.scycconf = 1
# first do an semi empirical optimization if requested by the user
if self.par['semi_emp_conformer_search'] == 1:
logging.info('semi empirical conformer search is starting for {}'.format(self.name))
if self.ssemi_empconf == -1:
# semi empirical part has not started yet
self.species.semi_emp_confs = Conformers(self.species, self.par, self.qc, semi_emp=1)
for geom in self.species.confs.cyc_conf_geoms:
# take all the geometries from the cyclic part
# generate the conformers for the current geometry
self.species.semi_emp_confs.generate_conformers(0, geom)
# set conf status to running
self.ssemi_empconf = 0
if self.ssemi_empconf == 0:
# semi empirical conformational search is running
# check if the conformational search is done
status, lowest_conf, geom, self.semi_emp_low_energy, self.semi_emp_conformers, self.semi_emp_energies = self.species.semi_emp_confs.check_conformers(wait=self.wait)
if status == 1:
logging.info("semi empirical lowest energy conformer for species {} is number {}".format(self.name, lowest_conf))
# set conf status to finished
self.ssemi_empconf = 1
else:
self.ssemi_empconf = 1
if self.ssemi_empconf == 1 and self.scycconf == 1:
# do open chain part if cyclic (if there were any) and semi empirical (if requested) parts are done
if self.sconf == -1:
# open chain part has not started yet
# if semi empirical conformer were searched for, start from those,
# else start from cyclic conformers
if self.par['semi_emp_conformer_search'] == 1:
self.species.confs.nconfs = 1
for i, geom in enumerate(self.semi_emp_conformers):
if (self.semi_emp_energies[i] - self.semi_emp_low_energy) * constants.AUtoKCAL < self.par['semi_emp_confomer_threshold']:
self.species.confs.generate_conformers(-999, geom)
logging.info("There are {} structures below the {} kcal/mol threshold for species {} in the semiempirical search.". \
format(i, self.par['semi_emp_confomer_threshold'], self.name))
else:
print_warning = True
for geom in self.species.confs.cyc_conf_geoms:
# take all the geometries from the cyclic part
# generate the conformers for the current geometry
self.skip_conf_check = self.species.confs.generate_conformers(0, geom, print_warning=print_warning)
print_warning = False
# set conf status to running
self.sconf = 0
if self.sconf == 0:
# conformational search is running
# check if the conformational search is done
if self.skip_conf_check == 0:
status, lowest_conf, geom, low_energy, conformers, energies = self.species.confs.check_conformers(wait=self.wait)
if status == 1:
logging.info("lowest energy conformer for species: {} is number {}".format(self.name, lowest_conf))
# save lowest energy conformer as species geometry
self.species.geom = geom
# save lowest energy conformer energy
self.species.energy = low_energy
# set conf status to finished
self.sconf = 1
elif self.skip_conf_check == 1:
self.species.geom, self.species.energy = self.species.confs.lowest_conf_info()
logging.info('Conformers are not checked for {} to speed up calculations.'.format(self.name))
logging.info('They seem to have been done in a previous run.')
logging.info('Energy and geometry updated based on conf/{}_low file.'.format(self.name))
self.sconf = 1
else:
# no conf search necessary, set status to finished
self.sconf = 1
if self.sconf == 1: # conf search is finished
# if the conformers were already done in a previous run
# not clear what the purpose of these lines were
if self.par['conformer_search'] == 1:
status, lowest_conf, geom, low_energy, conformers, energies = self.species.confs.check_conformers(wait=self.wait)
while self.restart <= self.max_restart:
# do the high level calculations
if self.par['high_level'] == 1:
if self.shigh == -1:
if self.species.wellorts:
name = self.species.name
else:
name = self.species.chemid
# high level calculation did not start yet
logging.info('\tStarting high level optimization of {}'.format(name))
if self.species.wellorts:
# do the high level optimization of a ts
self.qc.qc_opt_ts(self.species, self.species.geom, high_level=1)
else:
# do the high level optimization of a well
self.qc.qc_opt(self.species, self.species.geom, high_level=1)
self.shigh = 0 # set the high status to running
if self.shigh == 0:
# high level calculation is running
# check if it is finished
status = self.qc.check_qc(self.job_high)
if status == 'error':
# found an error
logging.info('\tHigh level optimization failed for {}'.format(self.name))
self.shigh = -999
elif status == 'normal':
# finished successfully
err, new_geom = self.qc.get_qc_geom(self.job_high, self.species.natom, wait=self.wait)
temp = StationaryPoint('temp',
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=new_geom)
temp.bond_mx()
if self.species.wellorts: # for TS we need reasonable geometry agreement and normal mode correlation
if self.par['conformer_search'] == 0:
fr_file = self.fr_file_name(0) # name of the original TS file
else:
if self.skip_conf_check == 0:
fr_file = 'conf/{}_{}'.format(self.fr_file_name(0), lowest_conf)
else:
fr_file = 'conf/{}_low'.format(self.fr_file_name(0))
if self.qc.qc == 'gauss':
imagmode = reader_gauss.read_imag_mode(fr_file, self.species.natom)
fr_file = self.fr_file_name(1)
if self.qc.qc == 'gauss':
imagmode_high = reader_gauss.read_imag_mode(fr_file, self.species.natom)
# either geom is roughly same with closely matching imaginary modes, or geometry is very close
# maybe we need to do IRC at the high level as well...
same_geom = ((geometry.matrix_corr(imagmode, imagmode_high) > 0.9) and \
(geometry.equal_geom(self.species, temp, 0.3))) \
or (geometry.equal_geom(self.species, temp, 0.15))
else:
same_geom = geometry.equal_geom(self.species, temp, 0.1)
err, fr = self.qc.get_qc_freq(self.job_high, self.species.natom)
if self.species.natom == 1:
freq_ok = 1
elif len(fr) == 1 and fr[0] == 0:
freq_ok = 0
elif self.species.wellorts == 0 and fr[0] > 0.:
freq_ok = 1
elif self.species.wellorts == 1 and fr[0] < 0. and fr[1] > 0.:
freq_ok = 1
else:
freq_ok = 0
if same_geom and freq_ok:
# geometry is as expected and normal modes are the same for TS
err, self.species.geom = self.qc.get_qc_geom(self.job_high, self.species.natom)
err, self.species.energy = self.qc.get_qc_energy(self.job_high)
err, self.species.freq = self.qc.get_qc_freq(self.job_high, self.species.natom)
err, self.species.zpe = self.qc.get_qc_zpe(self.job_high)
self.shigh = 1
else:
# geometry diverged to other structure
if not same_geom:
logging.info('\tHigh level optimization converged to different structure for {}, related channels are deleted.'.format(self.name))
if not freq_ok:
logging.info('\tWrong number of imaginary frequencies for {}, related channels are deleted.'.format(self.name))
self.shigh = -999
else:
# no high-level calculations necessary, set status to finished
self.shigh = 1
if self.shigh == 1:
# do the HIR calculation
if self.par['rotor_scan'] == 1:
if self.shir == -1:
# hir not stated yet
logging.info('\tStarting hindered rotor calculations of {}'.format(self.name))
self.species.hir = HIR(self.species, self.qc, self.par)
self.species.hir.generate_hir_geoms(copy.deepcopy(self.species.geom), self.par['rigid_hir'])
self.shir = 0
if self.shir == 0:
# hir is running
# check if it is done:
status = self.species.hir.check_hir(wait=self.wait)
if status:
if len(self.species.hir.hir_energies) > 0:
# check if along the hir potential a structure was found with a lower energy
min = self.species.hir.hir_energies[0][0]
min_rotor = -1
min_ai = -1
for rotor in range(len(self.species.dihed)):
for ai in range(self.species.hir.nrotation):
# use a 0.1kcal/mol cutoff for numerical noise
if self.species.hir.hir_energies[rotor][ai] < min - 1.6E-4:
min = self.species.hir.hir_energies[rotor][ai]
min_rotor = rotor
min_ai = ai
if min_rotor > -1:
self.restart += 1
if self.restart < self.max_restart:
# lower energy structure found
logging.info('\t\tLower energy found during hindered rotor scan for {}'.format(self.name))
logging.info('\t\tRestart number: ' + str(self.restart))
logging.info('\t\tRotor: ' + str(min_rotor))
logging.info('\t\tScan point: ' + str(min_ai))
job = self.job_hir + str(min_rotor) + '_' + str(min_ai).zfill(2)
err, self.species.geom = self.qc.get_qc_geom(job, self.species.natom)
# delete the high_level log file and the hir log files
if os.path.exists(self.job_high + '.log'):
# logging.info("\t\t\tRemoving file " + self.job_high + '.log')
os.remove(self.job_high + '.log')
for rotor in range(len(self.species.dihed)):
for ai in range(self.species.hir.nrotation):
if os.path.exists(self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log'):
# logging.info("\t\t\tRemoving file " + self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
os.remove(self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
# set the status of high and hir back to not started
self.shigh = -1
self.shir = -1
else:
logging.info('\t\tLower energy found, but readched max restart for {}'.format(self.name))
self.shir = 1
else:
self.shir = 1
else:
self.shir = 1
else:
# no hir calculations necessary, set status to finished
self.shir = 1
if not self.wait or self.shir == 1 or self.shigh == -999:
# break the loop if no waiting is required or
# if the hir calcs are done or
# if the high level calc failed
break
else:
time.sleep(1)
if self.shir == 1:
# finalize if no waiting is required or if the hir calcs are done
# calculate the symmetry numbers
symmetry.calculate_symmetry(self.species)
# calculate the new frequencies with the internal rotations projected out
fr_file = self.name
if not self.species.wellorts:
fr_file += '_well'
if self.par['high_level']:
fr_file += '_high'
fr_file = self.fr_file_name(self.par['high_level'])
hess = self.qc.read_qc_hess(fr_file, self.species.natom)
self.species.kinbot_freqs, self.species.reduced_freqs = frequencies.get_frequencies(self.species, hess, self.species.geom)
# write the molpro input and read the molpro energy, if available
if self.par['L3_calc'] == 1:
if self.par['single_point_qc'] == 'molpro':
molp = Molpro(self.species, self.par)
if 'barrierless_saddle' in self.name:
key = self.par['barrierless_saddle_single_point_key']
molp.create_molpro_input(bls=1)
else:
key = self.par['single_point_key']
molp.create_molpro_input()
molp.create_molpro_submit()
status, molpro_energy = molp.get_molpro_energy(key)
# FIXME this might be wrong here:
if status:
self.species.energy = molpro_energy
self.delete_files()
if self.wait:
if self.shir == 1 or self.shigh == -999:
return 0
time.sleep(1)
else:
return 0
def testFrequencies(self):
"""
Test the frequency calculator based on the hessian
by projecting out the translations, external rotations
and internal rotations
"""
# smiles of the speciesdata
smi = 'CCCC'
# geometry of the species
geom = [[0.91879892, -0.04331223, 0.12849174],
[0.40326992, 1.40563299, 0.15982756],
[0.89480303, 2.27635373, -1.01612317],
[0.37978671, 1.82281002, -2.39283836],
[0.56484506, -0.58104398, -0.75857814],
[0.58013979, -0.60154787, 1.00923604],
[2.01620768, -0.06868878, 0.11831706],
[-0.69710354, 1.40285362, 0.16867873],
[0.71679336, 1.87619548, 1.10252902],
[0.58079101, 3.31524962, -0.84143554],
[1.99516746, 2.28446562, -1.02104071],
[-0.71762425, 1.80824531, -2.41526122],
[0.72083567, 2.50053168, -3.18429864],
[0.73195359, 0.81628395, -2.64600957]]
geom = np.array(geom)
# hessian matrix
hess = [
[
0.557479281000, 0.036110582900, -0.001644922990,
-0.094996623900, 0.033793056500, 0.001227901360,
0.004435637720, -0.010301447300, -0.000714141524,
-0.000402278745, 0.000207775781, 0.000217081305,
-0.079155177000, -0.041443873100, -0.069861527200,
-0.075582477100, -0.041043274800, 0.066112977000,
-0.304832510000, 0.000618737475, 0.001591972160,
-0.011587172100, 0.035782319500, 0.001443997110,
0.003566576500, -0.009402669700, -0.000876604471,
-0.000457712074, -0.002463678640, 0.002595980330,
0.000811779351, -0.001430576500, 0.000032881273,
0.000129362928, -0.000033252298, 0.000111660638,
0.000157802640, -0.000276891050, 0.000074700177,
0.000433511158, -0.000116808738, -0.000311955226
],
[
0.036110582900, 0.481460445000, -0.001497124110,
0.032529383300, -0.168787802000, -0.006867396700,
0.006092748400, -0.023937567300, 0.003730351660,
-0.000262084184, 0.001441647380, 0.000316178114,
-0.041989591900, -0.107986603000, -0.100880979000,
-0.041503733500, -0.111783765000, 0.104729927000,
0.001080107490, -0.048474562500, -0.000241263827,
-0.000265123753, -0.003449223290, -0.000371820596,
0.007618533730, -0.018717902300, 0.000837312337,
-0.000555734072, -0.000078154904, 0.001999374050,
0.000646690960, 0.001260483370, -0.001360417570,
0.000088413780, -0.000085577196, 0.000164034911,
-0.000076331932, -0.000238612455, -0.000622223468,
0.000486138870, -0.000622805294, 0.000064046666
],
[
-0.001644922990, -0.001497124110, 0.575851102000,
0.001964210840, -0.005551021620, -0.083609101600,
-0.009286228620, 0.027728004900, 0.001373639180,
-0.000137246872, 0.000910607647, 0.001051929890,
-0.069899107500, -0.100194460000, -0.221142958000,
0.066308541300, 0.103907569000, -0.219256026000,
0.001583703380, -0.000080359730, -0.050839140900,
-0.000325149456, 0.000352021138, 0.000321544325,
0.010807393200, -0.028918001700, -0.000253208170,
0.001475456680, 0.004079204780, -0.004016767400,
-0.000771004699, -0.000846365220, 0.001019809380,
-0.000285363620, 0.000091087635, -0.000075059137,
-0.000051358279, -0.000192607794, 0.000123367411,
0.000261076583, 0.000211445408, -0.000549130393
],
[
-0.094996623900, 0.032529383300, 0.001964210840,
0.570648686000, 0.013726467600, 0.029803019800,
-0.093894628800, -0.017540296600, 0.025240052300,
0.004430547620, 0.007089239030, -0.008470974800,
0.003985664600, -0.011160808500, -0.000344880024,
0.003352509560, -0.011319168200, -0.000141509338,
-0.012312369900, 0.035615059900, 0.001341132350,
-0.296633947000, -0.002814851200, -0.001857303680,
-0.074252517800, -0.032055118900, -0.063305094900,
0.003016262880, 0.007165737430, -0.008147262180,
-0.013171250400, -0.021050691300, 0.028389638100,
0.000515556167, 0.000833930927, -0.001531483210,
-0.000733957654, -0.000834470892, -0.003543541800,
0.000046069520, -0.000184412716, 0.000603996490
],
[
0.033793056500, -0.168787802000, -0.005551021620,
0.013726467600, 0.471256701000, 0.045550224600,
-0.019055559200, -0.110456357000, 0.036788918500,
0.003690246720, 0.008042833690, -0.005946132620,
0.007450531200, -0.016991805700, 0.000566738577,
0.007662075620, -0.017473385800, -0.003434017560,
-0.000361925575, -0.003081819410, 0.000169600942,
-0.002141608270, -0.048419727000, -0.001543381080,
-0.032001313400, -0.092648760200, -0.091748171900,
-0.012965805500, -0.019490702300, 0.027544257800,
0.000385145313, -0.001607085100, 0.002202751810,
0.000097042391, 0.000342026851, -0.000085156640,
-0.000935724242, -0.000536578498, -0.003946142400,
0.000657370855, -0.000147538049, -0.000568468443
],
[
0.001227901360, -0.006867396700, -0.083609101600,
0.029803019800, 0.045550224600, 0.530185827000, 0.024067639100,
0.038254723800, -0.133557501000, 0.009656895150,
0.018083768400, -0.030339996500, 0.011176997800,
-0.027940521700, 0.000197610447, -0.010718813500,
0.027683119400, 0.001610715360, -0.000005380242,
0.000353950668, -0.000058645732, -0.001385963250,
-0.001490240310, -0.050945021400, -0.064025490200,
-0.091980721200, -0.231554605000, -0.000068702298,
-0.003001675120, 0.001414867140, 0.000375944632,
0.002432713030, -0.002619663450, 0.000967738003,
0.000578480710, 0.002131790000, -0.001665661510,
-0.002059701750, -0.003910908180, 0.000593875240,
0.000403276118, 0.001054632250
],
[
0.004435637720, 0.006092748400, -0.009286228620,
-0.093894628800, -0.019055559200, 0.024067639100,
0.570900497000, -0.032595232800, -0.004232877500,
-0.095277421800, -0.011406581600, -0.030607826800,
0.000043159915, -0.000514838503, 0.000373962854,
-0.000738247952, 0.003637838290, -0.000237140037,
0.000536641625, 0.001192182950, -0.001206594120,
-0.013027642100, -0.020980119300, 0.028551488200,
0.003004879320, 0.005695987090, -0.009218334250,
-0.074383197800, 0.070019040800, 0.012170299900,
-0.296727831000, 0.002527681320, 0.001997681770,
-0.012251640800, -0.011694206300, -0.033682439000,
0.003373175160, 0.003467893310, 0.010833941000, 0.004006619800,
0.003613165550, 0.010476427500
],
[
-0.010301447300, -0.023937567300, 0.027728004900,
-0.017540296600, -0.110456357000, 0.038254723800,
-0.032595232800, 0.550390617000, 0.021569897200,
-0.011111359900, -0.094250354100, -0.029239287500,
-0.000711297419, 0.000972151769, 0.000156692325,
0.001860692500, -0.005293631350, -0.000606360776,
-0.000954457648, 0.002153894160, 0.000019371190,
-0.000397838529, -0.001228430010, 0.002336752460,
0.003859667870, 0.006484974130, -0.010940182100,
0.070740800200, -0.270987312000, -0.037645349500,
0.001896728730, -0.051464188100, -0.000592835086,
0.000107061175, -0.000144181197, -0.000504021609,
0.007980792090, 0.006761052700, 0.020233362800,
-0.012833812400, -0.009000668610, -0.030770768100
],
[
-0.000714141524, 0.003730351660, 0.001373639180,
0.025240052300, 0.036788918500, -0.133557501000,
-0.004232877500, 0.021569897200, 0.451154731000,
-0.032090962200, -0.028197579400, -0.158290659000,
-0.000492084585, -0.000980152816, 0.000034862639,
0.000407801351, -0.002484344690, 0.000853888771,
0.000202037882, -0.000630194064, 0.000364010457,
-0.000276661410, 0.002092844910, -0.002923760270,
0.012375619200, 0.019611685600, -0.024571878400,
0.011914527900, -0.037319661200, -0.053342229400,
0.001505776140, -0.000642112942, -0.047826423400,
0.000374606934, -0.000789448823, -0.002922212160,
-0.010438695000, -0.010818867900, -0.022572722700,
-0.003774999570, -0.001931336020, -0.007773745060
],
[
-0.000402278745, -0.000262084184, -0.000137246872,
0.004430547620, 0.003690246720, 0.009656895150,
-0.095277421800, -0.011111359900, -0.032090962200,
0.558209213000, -0.009125619780, -0.034709539800,
0.000415340383, 0.000358171193, 0.000000905828, 0.000159128422,
0.000009821173, 0.000284856041, 0.000131990037,
-0.000097828362, 0.000069311231, 0.000786266725,
0.000375904422, 0.001427105050, -0.000456009865,
-0.001763774920, 0.003115461590, 0.003579063430,
0.003594264790, 0.008763686330, -0.011547639900,
-0.011824677300, -0.033753097500, -0.305000167000,
-0.001072859680, -0.000565352953, -0.076013943900,
-0.051443542800, 0.058887756100, -0.079014088600,
0.078673338600, 0.019050222000
],
[
0.000207775781, 0.001441647380, 0.000910607647, 0.007089239030,
0.008042833690, 0.018083768400, -0.011406581600,
-0.094250354100, -0.028197579400, -0.009125619780,
0.566853551000, -0.028314924300, -0.000405813331,
-0.000316988707, 0.000087265018, 0.000073046030,
-0.000135428465, -0.000227947707, 0.000240096907,
-0.000003210597, 0.000052913364, 0.000562715807,
0.000386702658, -0.000586865840, -0.001249462100,
-0.001984311450, 0.004661147410, -0.012569365100,
-0.009659129560, -0.031620324000, 0.000388942723,
-0.000027569785, -0.000685633791, -0.001195716590,
-0.050831902500, 0.000607484566, -0.051498752000,
-0.151306241000, 0.115957274000, 0.078889494300,
-0.268209599000, -0.050727185300
],
[
0.000217081305, 0.000316178114, 0.001051929890,
-0.008470974800, -0.005946132620, -0.030339996500,
-0.030607826800, -0.029239287500, -0.158290659000,
-0.034709539800, -0.028314924300, 0.489796505000,
-0.000391829054, 0.000142067560, -0.000688267160,
0.000058546643, -0.000657571583, 0.000021899914,
-0.000169145634, 0.000141233266, -0.000156069524,
-0.000870684066, -0.001083763300, 0.001824089140,
0.000966571121, 0.002585026930, -0.002110201450,
-0.004138394740, -0.001918643950, -0.009243037780,
0.000150249840, -0.001426379210, -0.003164852780,
-0.000834451580, 0.000361231775, -0.048518990600,
0.059364188600, 0.116743826000, -0.179560342000,
0.019436209100, -0.051702861200, -0.060622006300
],
[
-0.079155177000, -0.041989591900, -0.069899107500,
0.003985664600, 0.007450531200, 0.011176997800, 0.000043159915,
-0.000711297419, -0.000492084585, 0.000415340383,
-0.000405813331, -0.000391829054, 0.082170568700,
0.043883239500, 0.078044112600, 0.003495229520, 0.005401929110,
0.009954809490, -0.011350612800, -0.016458449400,
-0.029453532500, 0.000665737135, -0.000625010895,
0.000511858440, 0.000059291070, 0.003007766240, 0.000159750828,
-0.000060170797, -0.000108422372, 0.000205866966,
0.000026998545, -0.000052468999, -0.000060908234,
-0.000223489041, 0.000140719611, -0.000038210872,
-0.000144869281, 0.000150427844, 0.000065183444,
0.000072328995, 0.000316440754, 0.000217093184
],
[
-0.041443873100, -0.107986603000, -0.100194460000,
-0.011160808500, -0.016991805700, -0.027940521700,
-0.000514838503, 0.000972151769, -0.000980152816,
0.000358171193, -0.000316988707, 0.000142067560,
0.043883239500, 0.119840850000, 0.111814261000, 0.005906654280,
0.008066891770, 0.014503839500, 0.000290968064,
-0.000150240230, 0.000002063185, 0.000111922593,
0.001514337600, 0.000276003926, 0.002702457800,
-0.005775080730, 0.000445401249, 0.000111932829,
0.000347388012, 0.000177052371, -0.000006786294,
-0.000145911482, 0.000108411307, 0.000058268378,
-0.000061770157, 0.000096427007, -0.000010851744,
0.000535119260, 0.000099909733, -0.000286456542,
0.000151661591, 0.001449697830
],
[
-0.069861527200, -0.100880979000, -0.221142958000,
-0.000344880024, 0.000566738577, 0.000197610447,
0.000373962854, 0.000156692325, 0.000034862639, 0.000000905828,
0.000087265018, -0.000688267160, 0.078044112600,
0.111814261000, 0.242817300000, -0.010493946400,
-0.013936543100, -0.022850703000, 0.001401288830,
0.000811652521, 0.000387534637, 0.000611035395,
-0.000326880526, 0.000202559149, 0.000448151560,
0.000430071969, 0.001316161050, -0.000015506148,
-0.000226147940, 0.000124908040, 0.000053325603,
0.000066745555, -0.000005338059, 0.000029804814,
-0.000043582456, -0.000002800832, 0.000036585620,
0.000161681863, 0.000229922299, -0.000283313413,
0.001319024070, -0.000620791338
],
[
-0.075582477100, -0.041503733500, 0.066308541300,
0.003352509560, 0.007662075620, -0.010718813500,
-0.000738247952, 0.001860692500, 0.000407801351,
0.000159128422, 0.000073046030, 0.000058546643, 0.003495229520,
0.005906654280, -0.010493946400, 0.078647598400,
0.043929663400, -0.073931943100, -0.010722735000,
-0.017236570000, 0.029041201600, 0.000394715293,
-0.000537710690, -0.000362268343, 0.000241854679,
-0.000788101770, 0.000060932686, 0.000467445735,
0.000365561370, -0.000302900477, 0.000423918964,
0.000300000013, -0.000134663171, 0.000006452519,
-0.000023320384, 0.000023532240, -0.000000863259,
0.000025535986, 0.000019870589, -0.000144529814,
-0.000033792868, 0.000024108483
],
[
-0.041043274800, -0.111783765000, 0.103907569000,
-0.011319168200, -0.017473385800, 0.027683119400,
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0.000074700177, -0.000622223468, 0.000123367411,
-0.003543541800, -0.003946142400, -0.003910908180,
0.010833941000, 0.020233362800, -0.022572722700,
0.058887756100, 0.115957274000, -0.179560342000,
0.000065183444, 0.000099909733, 0.000229922299, 0.000019870589,
0.000149502306, 0.000030248075, -0.000076997589,
0.000051844795, -0.000005990485, -0.000045138440,
-0.000252036680, 0.000402783060, 0.000422103376,
0.001103529870, -0.001625311350, 0.000799593785,
-0.000383102689, 0.001044358070, -0.000527224047,
0.000337657158, 0.001075125330, -0.000668728837,
-0.000105607558, 0.000069007009, -0.063986801200,
-0.127320828000, 0.199467764000, -0.002254716580,
-0.005303140270, 0.005232700240
],
[
0.000433511158, 0.000486138870, 0.000261076583, 0.000046069520,
0.000657370855, 0.000593875240, 0.004006619800,
-0.012833812400, -0.003774999570, -0.079014088600,
0.078889494300, 0.019436209100, 0.000072328995,
-0.000286456542, -0.000283313413, -0.000144529814,
-0.000107492816, -0.000123734870, -0.000230470931,
-0.000007975487, -0.000144934523, 0.000030532015,
0.000080159392, 0.000006043879, -0.000061535147,
-0.000164903323, 0.000164720405, 0.000063586822,
-0.001026058750, -0.002821565820, 0.000650645014,
-0.000305373851, 0.000738835582, -0.011228983200,
0.032915002300, 0.007154649910, 0.003504547540,
-0.011175418200, -0.002254716580, 0.081871766900,
-0.087120674300, -0.018952145900
],
[
-0.000116808738, -0.000622805294, 0.000211445408,
-0.000184412716, -0.000147538049, 0.000403276118,
0.003613165550, -0.009000668610, -0.001931336020,
0.078673338600, -0.268209599000, -0.051702861200,
0.000316440754, 0.000151661591, 0.001319024070,
-0.000033792868, 0.000330197117, 0.000225358158,
-0.000053324956, 0.000015565803, -0.000084799847,
-0.000057961271, 0.000038523433, 0.000025108980,
-0.000019191454, 0.000129961184, -0.000158353984,
-0.001218387310, 0.000957816966, -0.001619506420,
-0.000611794096, 0.000327669725, 0.000048572694,
-0.001439226010, 0.000556604861, 0.000544568335,
0.008252628830, -0.020016956200, -0.005303140270,
-0.087120674300, 0.295489566000, 0.058022644000
],
[
-0.000311955226, 0.000064046666, -0.000549130393,
0.000603996490, -0.000568468443, 0.001054632250,
0.010476427500, -0.030770768100, -0.007773745060,
0.019050222000, -0.050727185300, -0.060622006300,
0.000217093184, 0.001449697830, -0.000620791338,
0.000024108483, 0.000160952609, 0.000436607680,
-0.000048396325, 0.000051313421, -0.000045791452,
0.000021980223, 0.000034002544, -0.000161425910,
-0.000113295827, 0.000240416270, 0.000346357198,
-0.002453862220, -0.001611656380, -0.005414335160,
0.000067580103, 0.000648275660, 0.001417002250, 0.000095977887,
-0.000132316779, -0.000404681882, -0.008677730390,
0.023139046100, 0.005232700240, -0.018952145900,
0.058022644000, 0.067104607900
]
]
hess = np.array(hess)
st_pt = StationaryPoint(smi, 0, 1, smiles=smi)
st_pt.characterize()
natom = st_pt.natom
freq, reduced_freqs = frequencies.get_frequencies(st_pt, hess, geom)
# check the number of frequencies when neglecting internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(
smi, 3 * natom - 6, len(freq))
self.assertEqual(len(freq), 3 * natom - 6, warn)
# check the number of freauencies when including internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(
smi, 3 * natom - 6 - 3, len(reduced_freqs))
self.assertEqual(len(reduced_freqs), 3 * natom - 6 - 3, warn)
# check if the frequencies are as expected
exps = [
114.00308362581993, 217.27775429467988, 258.3569949257438,
324.95225959788496, 444.19934314923285, 775.6200599866132,
809.834778642176, 851.31690919327, 978.7594097713534,
1001.6985870040352, 1016.7427610310635, 1110.2892683008433,
1182.5149554411457, 1226.4874456219302, 1318.400582167711,
1338.054870101307, 1396.7770994771845, 1417.5721206176297,
1452.5015242293932, 1460.9939823348343, 1533.7500760993864,
1534.471579819868, 1544.1270899676583, 1549.977599644375,
1552.1602547884133, 1556.1540474667054, 3018.3546194275327,
3020.597688051164, 3036.615370205335, 3039.4844797216215,
3056.030309062557, 3060.954764774267, 3108.0034857725796,
3110.0837061265997, 3114.029978361547, 3121.6966999165047
]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, freq[i])
self.assertAlmostEqual(exp, freq[i], places=2, msg=warn)
# check if the reduced frequencies are as expected
exps = [
323.2231139328416, 443.06424130022316, 670.1999625377555,
765.4302138965138, 805.4817609384712, 923.6453995768678,
998.4208652995756, 1010.1550075636384, 1026.2590199716992,
1035.9563299669474, 1110.728140649036, 1237.3899533374413,
1297.0701335112522, 1319.639259548847, 1338.4248295874343,
1402.476769356911, 1417.8484347981505, 1476.7358681560938,
1487.8976160436305, 1525.2008164401302, 1544.0032443689747,
1547.12404969892, 1549.9807205743023, 2426.2794467384574,
2507.323795982035, 2864.813636413937, 3024.7798090881265,
3034.7484086579275, 3037.1066584298032, 3052.278595042064,
3107.809971415559, 3113.170533468414, 3113.8054919074493
]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, reduced_freqs[i])
self.assertAlmostEqual(exp, reduced_freqs[i], places=2, msg=warn)
def testFrequencies(self):
"""
Test the frequency calculator based on the hessian
by projecting out the translations, external rotations
and internal rotations
"""
# smiles of the speciesdata
smi = 'CCCC'
# geometry of the species
geom = [[ 0.91879892, -0.04331223, 0.12849174],
[ 0.40326992, 1.40563299, 0.15982756],
[ 0.89480303, 2.27635373, -1.01612317],
[ 0.37978671, 1.82281002, -2.39283836],
[ 0.56484506, -0.58104398, -0.75857814],
[ 0.58013979, -0.60154787, 1.00923604],
[ 2.01620768, -0.06868878, 0.11831706],
[-0.69710354, 1.40285362, 0.16867873],
[ 0.71679336, 1.87619548, 1.10252902],
[ 0.58079101, 3.31524962, -0.84143554],
[ 1.99516746, 2.28446562, -1.02104071],
[-0.71762425, 1.80824531, -2.41526122],
[ 0.72083567, 2.50053168, -3.18429864],
[ 0.73195359, 0.81628395, -2.64600957]]
geom = np.array(geom)
# hessian matrix
hess = [[ 0.557479281000, 0.036110582900, -0.001644922990, -0.094996623900, 0.033793056500, 0.001227901360, 0.004435637720, -0.010301447300, -0.000714141524, -0.000402278745, 0.000207775781, 0.000217081305, -0.079155177000, -0.041443873100, -0.069861527200, -0.075582477100, -0.041043274800, 0.066112977000, -0.304832510000, 0.000618737475, 0.001591972160, -0.011587172100, 0.035782319500, 0.001443997110, 0.003566576500, -0.009402669700, -0.000876604471, -0.000457712074, -0.002463678640, 0.002595980330, 0.000811779351, -0.001430576500, 0.000032881273, 0.000129362928, -0.000033252298, 0.000111660638, 0.000157802640, -0.000276891050, 0.000074700177, 0.000433511158, -0.000116808738, -0.000311955226],
[ 0.036110582900, 0.481460445000, -0.001497124110, 0.032529383300, -0.168787802000, -0.006867396700, 0.006092748400, -0.023937567300, 0.003730351660, -0.000262084184, 0.001441647380, 0.000316178114, -0.041989591900, -0.107986603000, -0.100880979000, -0.041503733500, -0.111783765000, 0.104729927000, 0.001080107490, -0.048474562500, -0.000241263827, -0.000265123753, -0.003449223290, -0.000371820596, 0.007618533730, -0.018717902300, 0.000837312337, -0.000555734072, -0.000078154904, 0.001999374050, 0.000646690960, 0.001260483370, -0.001360417570, 0.000088413780, -0.000085577196, 0.000164034911, -0.000076331932, -0.000238612455, -0.000622223468, 0.000486138870, -0.000622805294, 0.000064046666],
[-0.001644922990, -0.001497124110, 0.575851102000, 0.001964210840, -0.005551021620, -0.083609101600, -0.009286228620, 0.027728004900, 0.001373639180, -0.000137246872, 0.000910607647, 0.001051929890, -0.069899107500, -0.100194460000, -0.221142958000, 0.066308541300, 0.103907569000, -0.219256026000, 0.001583703380, -0.000080359730, -0.050839140900, -0.000325149456, 0.000352021138, 0.000321544325, 0.010807393200, -0.028918001700, -0.000253208170, 0.001475456680, 0.004079204780, -0.004016767400, -0.000771004699, -0.000846365220, 0.001019809380, -0.000285363620, 0.000091087635, -0.000075059137, -0.000051358279, -0.000192607794, 0.000123367411, 0.000261076583, 0.000211445408, -0.000549130393],
[-0.094996623900, 0.032529383300, 0.001964210840, 0.570648686000, 0.013726467600, 0.029803019800, -0.093894628800, -0.017540296600, 0.025240052300, 0.004430547620, 0.007089239030, -0.008470974800, 0.003985664600, -0.011160808500, -0.000344880024, 0.003352509560, -0.011319168200, -0.000141509338, -0.012312369900, 0.035615059900, 0.001341132350, -0.296633947000, -0.002814851200, -0.001857303680, -0.074252517800, -0.032055118900, -0.063305094900, 0.003016262880, 0.007165737430, -0.008147262180, -0.013171250400, -0.021050691300, 0.028389638100, 0.000515556167, 0.000833930927, -0.001531483210, -0.000733957654, -0.000834470892, -0.003543541800, 0.000046069520, -0.000184412716, 0.000603996490],
[ 0.033793056500, -0.168787802000, -0.005551021620, 0.013726467600, 0.471256701000, 0.045550224600, -0.019055559200, -0.110456357000, 0.036788918500, 0.003690246720, 0.008042833690, -0.005946132620, 0.007450531200, -0.016991805700, 0.000566738577, 0.007662075620, -0.017473385800, -0.003434017560, -0.000361925575, -0.003081819410, 0.000169600942, -0.002141608270, -0.048419727000, -0.001543381080, -0.032001313400, -0.092648760200, -0.091748171900, -0.012965805500, -0.019490702300, 0.027544257800, 0.000385145313, -0.001607085100, 0.002202751810, 0.000097042391, 0.000342026851, -0.000085156640, -0.000935724242, -0.000536578498, -0.003946142400, 0.000657370855, -0.000147538049, -0.000568468443],
[ 0.001227901360, -0.006867396700, -0.083609101600, 0.029803019800, 0.045550224600, 0.530185827000, 0.024067639100, 0.038254723800, -0.133557501000, 0.009656895150, 0.018083768400, -0.030339996500, 0.011176997800, -0.027940521700, 0.000197610447, -0.010718813500, 0.027683119400, 0.001610715360, -0.000005380242, 0.000353950668, -0.000058645732, -0.001385963250, -0.001490240310, -0.050945021400, -0.064025490200, -0.091980721200, -0.231554605000, -0.000068702298, -0.003001675120, 0.001414867140, 0.000375944632, 0.002432713030, -0.002619663450, 0.000967738003, 0.000578480710, 0.002131790000, -0.001665661510, -0.002059701750, -0.003910908180, 0.000593875240, 0.000403276118, 0.001054632250],
[ 0.004435637720, 0.006092748400, -0.009286228620, -0.093894628800, -0.019055559200, 0.024067639100, 0.570900497000, -0.032595232800, -0.004232877500, -0.095277421800, -0.011406581600, -0.030607826800, 0.000043159915, -0.000514838503, 0.000373962854, -0.000738247952, 0.003637838290, -0.000237140037, 0.000536641625, 0.001192182950, -0.001206594120, -0.013027642100, -0.020980119300, 0.028551488200, 0.003004879320, 0.005695987090, -0.009218334250, -0.074383197800, 0.070019040800, 0.012170299900, -0.296727831000, 0.002527681320, 0.001997681770, -0.012251640800, -0.011694206300, -0.033682439000, 0.003373175160, 0.003467893310, 0.010833941000, 0.004006619800, 0.003613165550, 0.010476427500],
[-0.010301447300, -0.023937567300, 0.027728004900, -0.017540296600, -0.110456357000, 0.038254723800, -0.032595232800, 0.550390617000, 0.021569897200, -0.011111359900, -0.094250354100, -0.029239287500, -0.000711297419, 0.000972151769, 0.000156692325, 0.001860692500, -0.005293631350, -0.000606360776, -0.000954457648, 0.002153894160, 0.000019371190, -0.000397838529, -0.001228430010, 0.002336752460, 0.003859667870, 0.006484974130, -0.010940182100, 0.070740800200, -0.270987312000, -0.037645349500, 0.001896728730, -0.051464188100, -0.000592835086, 0.000107061175, -0.000144181197, -0.000504021609, 0.007980792090, 0.006761052700, 0.020233362800, -0.012833812400, -0.009000668610, -0.030770768100],
[-0.000714141524, 0.003730351660, 0.001373639180, 0.025240052300, 0.036788918500, -0.133557501000, -0.004232877500, 0.021569897200, 0.451154731000, -0.032090962200, -0.028197579400, -0.158290659000, -0.000492084585, -0.000980152816, 0.000034862639, 0.000407801351, -0.002484344690, 0.000853888771, 0.000202037882, -0.000630194064, 0.000364010457, -0.000276661410, 0.002092844910, -0.002923760270, 0.012375619200, 0.019611685600, -0.024571878400, 0.011914527900, -0.037319661200, -0.053342229400, 0.001505776140, -0.000642112942, -0.047826423400, 0.000374606934, -0.000789448823, -0.002922212160, -0.010438695000, -0.010818867900, -0.022572722700, -0.003774999570, -0.001931336020, -0.007773745060],
[-0.000402278745, -0.000262084184, -0.000137246872, 0.004430547620, 0.003690246720, 0.009656895150, -0.095277421800, -0.011111359900, -0.032090962200, 0.558209213000, -0.009125619780, -0.034709539800, 0.000415340383, 0.000358171193, 0.000000905828, 0.000159128422, 0.000009821173, 0.000284856041, 0.000131990037, -0.000097828362, 0.000069311231, 0.000786266725, 0.000375904422, 0.001427105050, -0.000456009865, -0.001763774920, 0.003115461590, 0.003579063430, 0.003594264790, 0.008763686330, -0.011547639900, -0.011824677300, -0.033753097500, -0.305000167000, -0.001072859680, -0.000565352953, -0.076013943900, -0.051443542800, 0.058887756100, -0.079014088600, 0.078673338600, 0.019050222000],
[ 0.000207775781, 0.001441647380, 0.000910607647, 0.007089239030, 0.008042833690, 0.018083768400, -0.011406581600, -0.094250354100, -0.028197579400, -0.009125619780, 0.566853551000, -0.028314924300, -0.000405813331, -0.000316988707, 0.000087265018, 0.000073046030, -0.000135428465, -0.000227947707, 0.000240096907, -0.000003210597, 0.000052913364, 0.000562715807, 0.000386702658, -0.000586865840, -0.001249462100, -0.001984311450, 0.004661147410, -0.012569365100, -0.009659129560, -0.031620324000, 0.000388942723, -0.000027569785, -0.000685633791, -0.001195716590, -0.050831902500, 0.000607484566, -0.051498752000, -0.151306241000, 0.115957274000, 0.078889494300, -0.268209599000, -0.050727185300],
[ 0.000217081305, 0.000316178114, 0.001051929890, -0.008470974800, -0.005946132620, -0.030339996500, -0.030607826800, -0.029239287500, -0.158290659000, -0.034709539800, -0.028314924300, 0.489796505000, -0.000391829054, 0.000142067560, -0.000688267160, 0.000058546643, -0.000657571583, 0.000021899914, -0.000169145634, 0.000141233266, -0.000156069524, -0.000870684066, -0.001083763300, 0.001824089140, 0.000966571121, 0.002585026930, -0.002110201450, -0.004138394740, -0.001918643950, -0.009243037780, 0.000150249840, -0.001426379210, -0.003164852780, -0.000834451580, 0.000361231775, -0.048518990600, 0.059364188600, 0.116743826000, -0.179560342000, 0.019436209100, -0.051702861200, -0.060622006300],
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[ 0.000111660638, 0.000164034911, -0.000075059137, -0.001531483210, -0.000085156640, 0.002131790000, -0.033682439000, -0.000504021609, -0.002922212160, -0.000565352953, 0.000607484566, -0.048518990600, -0.000038210872, 0.000096427007, -0.000002800832, 0.000023532240, 0.000059096657, 0.000014479678, 0.000148869356, -0.000192239409, 0.000044588785, 0.001199897110, 0.000196170553, -0.000139936846, 0.000355884856, -0.000225290437, 0.000462709809, 0.000670043314, 0.000252036708, 0.001289783720, -0.002131478270, -0.002056008930, -0.005580464400, 0.003297591000, 0.001623164620, 0.053631786900, 0.024986835900, -0.000480266327, 0.000069007009, 0.007154649910, 0.000544568335, -0.000404681882],
[ 0.000157802640, -0.000076331932, -0.000051358279, -0.000733957654, -0.000935724242, -0.001665661510, 0.003373175160, 0.007980792090, -0.010438695000, -0.076013943900, -0.051498752000, 0.059364188600, -0.000144869281, -0.000010851744, 0.000036585620, -0.000000863259, -0.000026671850, -0.000018292858, 0.000006913742, -0.000015838536, 0.000030151342, 0.000425109666, 0.000041609570, -0.000326542275, 0.000467119865, 0.000183020208, -0.000439580294, 0.000236669508, 0.000174692057, 0.000768879885, 0.000410888095, 0.000508508927, 0.000418020512, -0.010801126300, -0.022680777200, 0.024986835900, 0.079112534200, 0.058103695900, -0.063986801200, 0.003504547540, 0.008252628830, -0.008677730390],
[-0.000276891050, -0.000238612455, -0.000192607794, -0.000834470892, -0.000536578498, -0.002059701750, 0.003467893310, 0.006761052700, -0.010818867900, -0.051443542800, -0.151306241000, 0.116743826000, 0.000150427844, 0.000535119260, 0.000161681863, 0.000025535986, 0.000148561268, 0.000237094498, 0.000060554518, 0.000041274254, 0.000025660583, -0.000023343949, -0.000164615301, 0.000011716933, 0.000078313296, 0.000300248641, -0.000527497416, 0.000359711857, 0.000878659823, 0.000408824903, 0.000459443519, 0.000184012806, 0.000671917929, 0.001048090670, -0.000201125886, -0.000480266327, 0.058103695900, 0.163615200000, -0.127320828000, -0.011175418200, -0.020016956200, 0.023139046100],
[ 0.000074700177, -0.000622223468, 0.000123367411, -0.003543541800, -0.003946142400, -0.003910908180, 0.010833941000, 0.020233362800, -0.022572722700, 0.058887756100, 0.115957274000, -0.179560342000, 0.000065183444, 0.000099909733, 0.000229922299, 0.000019870589, 0.000149502306, 0.000030248075, -0.000076997589, 0.000051844795, -0.000005990485, -0.000045138440, -0.000252036680, 0.000402783060, 0.000422103376, 0.001103529870, -0.001625311350, 0.000799593785, -0.000383102689, 0.001044358070, -0.000527224047, 0.000337657158, 0.001075125330, -0.000668728837, -0.000105607558, 0.000069007009, -0.063986801200, -0.127320828000, 0.199467764000, -0.002254716580, -0.005303140270, 0.005232700240],
[ 0.000433511158, 0.000486138870, 0.000261076583, 0.000046069520, 0.000657370855, 0.000593875240, 0.004006619800, -0.012833812400, -0.003774999570, -0.079014088600, 0.078889494300, 0.019436209100, 0.000072328995, -0.000286456542, -0.000283313413, -0.000144529814, -0.000107492816, -0.000123734870, -0.000230470931, -0.000007975487, -0.000144934523, 0.000030532015, 0.000080159392, 0.000006043879, -0.000061535147, -0.000164903323, 0.000164720405, 0.000063586822, -0.001026058750, -0.002821565820, 0.000650645014, -0.000305373851, 0.000738835582, -0.011228983200, 0.032915002300, 0.007154649910, 0.003504547540, -0.011175418200, -0.002254716580, 0.081871766900, -0.087120674300, -0.018952145900],
[-0.000116808738, -0.000622805294, 0.000211445408, -0.000184412716, -0.000147538049, 0.000403276118, 0.003613165550, -0.009000668610, -0.001931336020, 0.078673338600, -0.268209599000, -0.051702861200, 0.000316440754, 0.000151661591, 0.001319024070, -0.000033792868, 0.000330197117, 0.000225358158, -0.000053324956, 0.000015565803, -0.000084799847, -0.000057961271, 0.000038523433, 0.000025108980, -0.000019191454, 0.000129961184, -0.000158353984, -0.001218387310, 0.000957816966, -0.001619506420, -0.000611794096, 0.000327669725, 0.000048572694, -0.001439226010, 0.000556604861, 0.000544568335, 0.008252628830, -0.020016956200, -0.005303140270, -0.087120674300, 0.295489566000, 0.058022644000],
[-0.000311955226, 0.000064046666, -0.000549130393, 0.000603996490, -0.000568468443, 0.001054632250, 0.010476427500, -0.030770768100, -0.007773745060, 0.019050222000, -0.050727185300, -0.060622006300, 0.000217093184, 0.001449697830, -0.000620791338, 0.000024108483, 0.000160952609, 0.000436607680, -0.000048396325, 0.000051313421, -0.000045791452, 0.000021980223, 0.000034002544, -0.000161425910, -0.000113295827, 0.000240416270, 0.000346357198, -0.002453862220, -0.001611656380, -0.005414335160, 0.000067580103, 0.000648275660, 0.001417002250, 0.000095977887, -0.000132316779, -0.000404681882, -0.008677730390, 0.023139046100, 0.005232700240, -0.018952145900, 0.058022644000, 0.067104607900]]
hess = np.array(hess)
st_pt = StationaryPoint(smi, 0, 1, smiles=smi)
st_pt.characterize()
natom = st_pt.natom
freq, reduced_freqs = frequencies.get_frequencies(st_pt, hess, geom)
# check the number of frequencies when neglecting internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(smi, 3 * natom - 6, len(freq))
self.assertEqual(len(freq), 3 * natom - 6, warn)
# check the number of freauencies when including internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(smi, 3 * natom - 6 - 3, len(reduced_freqs))
self.assertEqual(len(reduced_freqs), 3 * natom - 6 - 3, warn)
# check if the frequencies are as expected
exps = [114.00308362581993, 217.27775429467988, 258.3569949257438, 324.95225959788496, 444.19934314923285, 775.6200599866132, 809.834778642176, 851.31690919327, 978.7594097713534, 1001.6985870040352, 1016.7427610310635, 1110.2892683008433, 1182.5149554411457, 1226.4874456219302, 1318.400582167711, 1338.054870101307, 1396.7770994771845, 1417.5721206176297, 1452.5015242293932, 1460.9939823348343, 1533.7500760993864, 1534.471579819868, 1544.1270899676583, 1549.977599644375, 1552.1602547884133, 1556.1540474667054, 3018.3546194275327, 3020.597688051164, 3036.615370205335, 3039.4844797216215, 3056.030309062557, 3060.954764774267, 3108.0034857725796, 3110.0837061265997, 3114.029978361547, 3121.6966999165047]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, freq[i])
self.assertAlmostEqual(exp, freq[i], places=2, msg=warn)
# check if the reduced frequencies are as expected
exps = [323.2231139328416, 443.06424130022316, 670.1999625377555, 765.4302138965138, 805.4817609384712, 923.6453995768678, 998.4208652995756, 1010.1550075636384, 1026.2590199716992, 1035.9563299669474, 1110.728140649036, 1237.3899533374413, 1297.0701335112522, 1319.639259548847, 1338.4248295874343, 1402.476769356911, 1417.8484347981505, 1476.7358681560938, 1487.8976160436305, 1525.2008164401302, 1544.0032443689747, 1547.12404969892, 1549.9807205743023, 2426.2794467384574, 2507.323795982035, 2864.813636413937, 3024.7798090881265, 3034.7484086579275, 3037.1066584298032, 3052.278595042064, 3107.809971415559, 3113.170533468414, 3113.8054919074493]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, reduced_freqs[i])
self.assertAlmostEqual(exp, reduced_freqs[i], places=2, msg=warn)