def prep_vertical_ip(path):
# Given a path to the outfile of a finished run, this preps the files for a corresponding vertical IP run
# Returns a list of the PATH(s) to the jobscript(s) to start the vertical IP calculations(s)
home = os.getcwd()
path = convert_to_absolute_path(path)
results = manager_io.read_outfile(path)
if not results['finished']:
raise Exception('This calculation does not appear to be complete! Aborting...')
infile_dict = manager_io.read_infile(path)
if infile_dict['spinmult'] == 1:
new_spin = [2]
else:
new_spin = [infile_dict['spinmult'] - 1, infile_dict['spinmult'] + 1]
base = os.path.split(path)[0]
if infile_dict['run_type'] == 'minimize':
optimxyz = os.path.join(base, 'scr', 'optim.xyz')
else:
optimxyz = os.path.join(base, 'scr', 'xyz.xyz')
extract_optimized_geo(optimxyz)
ipname = results['name'] + '_vertIP'
vertip_base_path = os.path.join(base, ipname)
if os.path.isdir(vertip_base_path):
return ['Directory for vertIP single point already exists']
os.mkdir(vertip_base_path)
os.chdir(vertip_base_path)
jobscripts = []
for calc in new_spin:
if calc < 7:
name = results['name'] + '_vertIP_' + str(calc)
PATH = os.path.join(vertip_base_path, str(calc))
if os.path.isdir(PATH):
jobscripts.append('File for vert IP spin ' + str(calc) + 'already exists')
else:
os.mkdir(PATH)
os.chdir(PATH)
shutil.copyfile(os.path.join(base, 'scr', 'optimized.xyz'), os.path.join(PATH, name + '.xyz'))
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['charge'], local_infile_dict['guess'] = infile_dict['charge'] + 1, False
local_infile_dict['run_type'], local_infile_dict['spinmult'] = 'energy', calc
local_infile_dict['name'] = name
local_infile_dict['levelshifta'], local_infile_dict['levelshiftb'] = 0.25, 0.25
local_infile_dict['machine'] = machine
manager_io.write_input(local_infile_dict)
manager_io.write_jobscript(name,machine=machine)
jobscripts.append(os.path.join(PATH, name + '_jobscript'))
os.chdir(home)
return jobscripts
def resub_scf(outfile_path):
"""Resubmits a job that's having trouble converging the scf with different level shifts (1.0 and 0.1).
Parameters
----------
outfile_path : str
The name of an output file.
Returns
-------
Resub_flag : bool
True if resubmitted.
"""
# Resubmits a job that's having trouble converging the scf with different level shifts (1.0 and 0.1)
history = resub_history()
history.read(outfile_path)
resubbed_before = False
if 'SCF convergence error, level shifts adjusted to aid convergence' in history.notes:
resubbed_before = True
history.status = os.path.split(outfile_path)[
-1] + ' has been submitted with levels shifted and is still encountering an scf error'
history.save()
if 'Needs clean resub' in history.notes:
resubbed_before = True
history.status = os.path.split(outfile_path)[
-1] + ' job recovery has failed - requesting resub_scf() after clean resubmission round'
history.save()
if not resubbed_before:
save_run(outfile_path, rewrite_inscr=False)
history = resub_history()
history.read(outfile_path)
history.resub_number += 1
history.status = 'Level shifts adjusted to assist convergence'
history.needs_resub = True
history.notes.append('SCF convergence error, level shifts adjusted to aid convergence')
history.save()
machine=tools.get_machine()
root = outfile_path.rsplit('.', 1)[0]
name = os.path.split(root)[-1]
directory = os.path.split(outfile_path)[0]
infile_dict = manager_io.read_infile(outfile_path)
home = os.getcwd()
if len(directory) > 0: # if the string is blank, then we're already in the correct directory
os.chdir(directory)
infile_dict['levelshifta'], infile_dict['levelshiftb'] = 1.0, 0.1
infile_dict['machine'] = machine
manager_io.write_input(infile_dict)
manager_io.write_jobscript(name, machine=machine)
os.chdir(home)
tools.qsub(root + '_jobscript')
return True
else:
return False
def resub_thermo(outfile_path):
# Similar to simple resub, but specific for addressing thermo gradient errors
# Checks for the existance of an ultratight version of this run. If it exists, uses the most up to date version for the new thermo run
save_run(outfile_path, rewrite_inscr=False)
history = resub_history()
history.read(outfile_path)
history.resub_number += 1
history.status = 'Normal'
history.notes.append(
'Resubmitting thermo, possibly with a better initial geo')
history.needs_resub = False
history.save()
name = os.path.split(outfile_path)[-1]
name = name.rsplit('.', 1)[0]
directory = os.path.split(outfile_path)[0]
parent_name = name.rsplit('_', 1)[0]
parent_directory = os.path.split(os.path.split(outfile_path)[0])[0]
ultratight_dir = os.path.join(parent_directory,
parent_name + '_ultratight')
infile_dict = manager_io.read_infile(outfile_path)
if os.path.exists(ultratight_dir):
if os.path.exists(os.path.join(ultratight_dir, 'scr', 'optim.xyz')):
tools.extract_optimized_geo(
os.path.join(ultratight_dir, 'scr', 'optim.xyz'))
shutil.copy(os.path.join(ultratight_dir, 'scr', 'optimized.xyz'),
outfile_path.rsplit('.', 1)[0] + '.xyz')
else:
raise Exception(
'Unable to identify the ultratight geometry for run: ' +
outfile_path)
if infile_dict['spinmult'] == 1 and os.path.exists(
os.path.join(ultratight_dir, 'scr', 'c0')):
shutil.copy(os.path.join(ultratight_dir, 'scr', 'c0'),
os.path.join(directory, 'c0'))
elif infile_dict['spinmult'] != 1 and os.path.exists(
os.path.join(ultratight_dir, 'scr', 'ca0')) and os.path.exists(
os.path.join(ultratight_dir, 'scr', 'cb0')):
shutil.copy(os.path.join(ultratight_dir, 'scr', 'ca0'),
os.path.join(directory, 'ca0'))
shutil.copy(os.path.join(ultratight_dir, 'scr', 'cb0'),
os.path.join(directory, 'cb0'))
else:
raise Exception(
'Unable to find wavefunction files for ultratight geometry for run: '
+ outfile_path)
else:
raise Exception(
'An ultratight run does not exist for this thermo file. Consider calling simple_resub() or resub_tighter() instead of resub_thermo()'
)
jobscript = outfile_path.rsplit('.', 1)[0] + '_jobscript'
tools.qsub(jobscript)
return True
def read_run(outfile_PATH):
# Evaluates all aspects of a run using the outfile and derivative files
results = manager_io.read_outfile(outfile_PATH, long_output=True)
infile_dict = manager_io.read_infile(outfile_PATH)
results['levela'], results['levelb'] = infile_dict['levelshifta'], infile_dict['levelshiftb']
results['method'], results['hfx'] = infile_dict['method'], infile_dict['hfx']
results['constraints'] = infile_dict['constraints']
mullpop_path = os.path.join(os.path.split(outfile_PATH)[0], 'scr', 'mullpop')
if os.path.exists(mullpop_path):
mullpops = manager_io.read_mullpop(outfile_PATH)
metal_types = ['Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Mo', 'Tc', 'Ru', 'Rh']
metals = [i for i in mullpops if i.split()[0] in metal_types]
if len(metals) > 1:
results['metal_spin'] = np.nan
elif len(metals) == 0:
pass
else:
results['metal_spin'] = float(metals[0].split()[-1])
else:
results['metal_spin'] = np.nan
optim_path = os.path.join(os.path.split(outfile_PATH)[0], 'scr', 'optim.xyz')
check_geo = False
if os.path.isfile(optim_path):
fil = open(optim_path, 'r')
lines = fil.readlines()
fil.close()
if len(lines) > 0:
check_geo = True # Only apply geo check if an optimized geometry exists
if check_geo:
tools.extract_optimized_geo(optim_path)
optimized_path = os.path.join(os.path.split(optim_path)[0], 'optimized.xyz')
mol = mol3D()
mol.readfromxyz(optimized_path)
IsOct, flag_list, oct_check = mol.IsOct(dict_check=mol.dict_oct_check_st,
silent=True)
if IsOct:
IsOct = True
else:
IsOct = False
results['Is_Oct'] = IsOct
results['Flag_list'] = flag_list
results['Oct_check_details'] = oct_check
else:
results['Is_Oct'] = None
results['Flag_list'] = None
results['Oct_check_details'] = None
return results
def resub_spin(outfile_path):
# resubmits a spin contaminated job with blyp to help convergence to a non-spin contaminated solution
history = resub_history()
history.read(outfile_path)
resubbed_before = False
if 'Spin contaminated, lowering HFX to aid convergence' in history.notes:
resubbed_before = True
history.status = os.path.split(
outfile_path
)[-1] + ' has been submitted with lower HFX and still converges to a spin contaminated solution'
history.save()
if 'Needs clean resub' in history.notes:
resubbed_before = True
history.status = os.path.split(
outfile_path
)[-1] + ' job recovery has failed - requesting resub_spin() after clean resubmission round'
history.save()
if 'HFXresampling' in outfile_path:
resubbed_before = True
history.status = os.path.split(
outfile_path
)[-1] + ' is spin contaminated, but submitting with lower HFX does not make sense for HFX resampling jobs'
history.save()
if not resubbed_before:
save_run(outfile_path, rewrite_inscr=False)
history = resub_history()
history.read(outfile_path)
history.resub_number += 1
history.status = 'HFX altered to assist convergence'
history.needs_resub = True
history.notes.append(
'Spin contaminated, lowering HFX to aid convergence')
history.save()
root = outfile_path.rsplit('.', 1)[0]
name = os.path.split(root)[-1]
directory = os.path.split(outfile_path)[0]
infile_dict = manager_io.read_infile(outfile_path)
home = os.getcwd()
if len(
directory
) > 0: # if the string is blank, then we're already in the correct directory
os.chdir(directory)
infile_dict['method'] = 'blyp'
infile_dict['machine'] = machine
manager_io.write_input(infile_dict)
manager_io.write_jobscript(name, machine=machine)
os.chdir(home)
tools.qsub(root + '_jobscript')
return True
else:
return False
def clean_resub(outfile_path):
# Resubmits a job with default parameters, useful for undoing level shift or hfx alterations
save_run(outfile_path)
history = resub_history()
history.read(outfile_path)
history.resub_number += 1
history.status = 'Normal'
history.notes.append('Needs clean resub')
history.needs_resub = False
history.save()
root = outfile_path.rsplit('.', 1)[0]
name = os.path.split(root)[-1]
directory = os.path.split(outfile_path)[0]
infile_dict = manager_io.read_infile(outfile_path)
home = os.getcwd()
if len(
directory
) > 0: # if the string is blank, then we're already in the correct directory
os.chdir(directory)
if os.path.isfile('inscr/optimized.xyz'):
coordinates = 'inscr/optimized.xyz' # Should trigger for optimization runs
elif os.path.isfile(name + '.xyz'):
coordinates = name + '.xyz' # Should trigger for single point runs
else:
raise ValueError(
'No coordinates idenfied for clean in resubmission in directory ' +
os.getcwd())
configure_dict = manager_io.read_configure('in_place', outfile_path)
infile_dict['coordinates'] = coordinates
infile_dict['method'] = configure_dict['method']
infile_dict['levelshifta'], infile_dict['levelshiftb'] = configure_dict[
'levela'], configure_dict['levelb']
infile_dict['dispersion'] = configure_dict['dispersion']
infile_dict['constraints'] = False
infile_dict['machine'] = machine
if infile_dict['spinmult'] == 1:
infile_dict['guess'] = 'inscr/c0'
manager_io.write_input(infile_dict)
else:
infile_dict['guess'] = 'inscr/ca0 inscr/cb0'
manager_io.write_input(infile_dict)
manager_io.write_jobscript(name,
custom_line='# -fin inscr/',
machine=machine)
os.chdir(home)
tools.qsub(root + '_jobscript')
return True
def resub_oscillating_scf(outfile_path):
# Resubmits a job that's having trouble converging the scf with different level shifts (1.0 and 0.1)
history = resub_history()
history.read(outfile_path)
resubbed_before = False
if 'SCF convergence error, precision and grid adjusted to aid convergence' in history.notes:
resubbed_before = True
history.status = os.path.split(
outfile_path
)[-1] + ' has been submitted with higher precision and grid and is still encountering an scf error'
history.save()
if 'Needs clean resub' in history.notes:
resubbed_before = True
history.status = os.path.split(
outfile_path
)[-1] + ' job recovery has failed - requesting resub_oscillating_scf() after clean resubmission round'
history.save()
if not resubbed_before:
save_run(outfile_path, rewrite_inscr=False)
history = resub_history()
history.read(outfile_path)
history.resub_number += 1
history.status = 'precision and grid adjusted to assist convergence'
history.notes.append(
'SCF convergence error, precision and grid adjusted to aid convergence'
)
history.save()
root = outfile_path.rsplit('.', 1)[0]
name = os.path.split(root)[-1]
directory = os.path.split(outfile_path)[0]
infile_dict = manager_io.read_infile(outfile_path)
home = os.getcwd()
if len(
directory
) > 0: # if the string is blank, then we're already in the correct directory
os.chdir(directory)
infile_dict['precision'], infile_dict['dftgrid'], infile_dict[
'dynamicgrid'] = "double", 5, "no"
infile_dict['machine'] = machine
manager_io.write_input(infile_dict)
manager_io.write_jobscript(name, machine=machine)
os.chdir(home)
tools.qsub(root + '_jobscript')
return True
else:
return False
def prep_thermo(path):
# Given a path to the outfile of a finished run, this preps the files for a thermo calculation
# Uses the wavefunction from the previous calculation as an initial guess
# Returns a list of the PATH(s) to the jobscript(s) to start the solvent sp calculations(s)
home = os.getcwd()
path = convert_to_absolute_path(path)
results = manager_io.read_outfile(path)
infile_dict = manager_io.read_infile(path)
base = os.path.split(path)[0]
if infile_dict['run_type'] == 'minimize':
optimxyz = os.path.join(base, 'scr', 'optim.xyz')
else:
optimxyz = os.path.join(base, 'scr', 'xyz.xyz')
extract_optimized_geo(optimxyz)
# Now, start generating the new directory
name = results['name'] + '_thermo'
PATH = os.path.join(base, name)
if os.path.isdir(PATH):
return ['Thermo Calculation Directory already exists']
os.mkdir(PATH)
os.chdir(PATH)
shutil.copyfile(os.path.join(base, 'scr', 'optimized.xyz'),
os.path.join(PATH, name + '.xyz'))
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['guess'] = True
local_infile_dict['run_type'] = 'frequencies'
local_infile_dict['name'] = name
manager_io.write_input(local_infile_dict)
if infile_dict['spinmult'] == 1:
shutil.copyfile(os.path.join(base, 'scr', 'c0'),
os.path.join(PATH, 'c0'))
manager_io.write_jobscript(name, custom_line='# -fin c0')
if infile_dict['spinmult'] != 1:
shutil.copyfile(os.path.join(base, 'scr', 'ca0'),
os.path.join(PATH, 'ca0'))
shutil.copyfile(os.path.join(base, 'scr', 'cb0'),
os.path.join(PATH, 'cb0'))
manager_io.write_jobscript(
name, custom_line=['# -fin ca0\n', '# -fin cb0\n'])
os.chdir(home)
return [os.path.join(PATH, name + '_jobscript')]
def resub_bad_geo(outfile_path, home_directory):
"""Resubmits a job that's converged to a bad geometry with additional contraints.
Parameters
----------
outfile_path : str
The name of an output file.
home_directory : str
Path to the base directory of the run.
Returns
-------
Resub_flag : bool
True if resubmitted.
"""
# Resubmits a job that's converged to a bad geometry with additional contraints
history = resub_history()
history.read(outfile_path)
resubbed_before = False
if 'Bad geometry detected, adding constraints and trying again' in history.notes:
resubbed_before = True
history.status = os.path.split(outfile_path)[
-1] + " has been submitted with additional constraints and still isn't a good geometry"
history.save()
if 'Needs clean resub' in history.notes:
resubbed_before = True
history.status = os.path.split(outfile_path)[
-1] + ' job recovery has failed - requesting resub_bad_geo after clean resubmission round'
history.save()
if not resubbed_before:
save_run(outfile_path, rewrite_inscr=True)
history = resub_history()
history.read(outfile_path)
history.resub_number += 1
history.status = 'Constraints added to help convergence'
history.needs_resub = True
history.notes.append('Bad geometry detected, adding constraints and trying again')
history.save()
machine=tools.get_machine()
root = outfile_path.rsplit('.', 1)[0]
name = os.path.split(root)[-1]
directory = os.path.split(outfile_path)[0]
infile_dict = manager_io.read_infile(outfile_path)
if infile_dict['constraints']:
raise Exception(
'resub.py does not currently support the use of external atom constraints. These will be overwritten by clean_resub() during job recovery')
goal_geo = manager_io.read_configure(home_directory, outfile_path)['geo_check']
if not goal_geo:
raise Exception(
'Goal geometry not specified, job ' + outfile_path + ' should not have been labelled bad geo!')
else:
metal_index, bonded_atom_indices = moltools.get_metal_and_bonded_atoms(outfile_path, goal_geo)
# convert indexes from zero-indexed to one-indexed
metal_index += 1
bonded_atom_indices = [index + 1 for index in bonded_atom_indices]
# Convert to TeraChem input syntax
constraints = ['bond ' + str(metal_index) + '_' + str(index) + '\n' for index in bonded_atom_indices]
home = os.getcwd()
if len(directory) > 0: # if the string is blank, then we're already in the correct directory
os.chdir(directory)
infile_dict['constraints'] = constraints
infile_dict['machine'] = machine
manager_io.write_input(infile_dict)
manager_io.write_jobscript(name, machine=machine)
os.chdir(home)
tools.qsub(root + '_jobscript')
return True
else:
return False
def prep_ligand_breakown(outfile_path):
# Given a path to the outfile of a finished run, this preps the files for rigid ligand dissociation energies of all ligands
# Returns a list of the PATH(s) to the jobscript(s) to start the rigid ligand calculations
home = os.getcwd()
outfile_path = tools.convert_to_absolute_path(outfile_path)
results = manager_io.read_outfile(outfile_path)
if not results['finished']:
raise Exception(
'This calculation does not appear to be complete! Aborting...')
infile_dict = manager_io.read_infile(outfile_path)
charge = int(infile_dict['charge'])
spinmult = int(infile_dict['spinmult'])
base = os.path.split(outfile_path)[0]
name = os.path.split(outfile_path)[-1][:-4]
breakdown_folder = os.path.join(base, name + '_dissociation')
if os.path.isdir(breakdown_folder):
return ['Ligand dissociation directory already exists']
optimxyz = os.path.join(base, 'scr', 'optim.xyz')
tools.extract_optimized_geo(optimxyz)
mol = mol3D()
mol.readfromxyz(os.path.join(base, 'scr', 'optimized.xyz'))
ligand_idxs, _, _ = ligand_breakdown(mol, silent=True)
ligand_syms = []
for ii in ligand_idxs:
ligand_syms.append([mol.getAtom(i).symbol() for i in ii])
ligand_names = name_ligands(ligand_syms)
if not os.path.isdir(breakdown_folder):
os.mkdir(breakdown_folder)
os.chdir(breakdown_folder)
jobscripts = []
for ligand in zip(ligand_names, ligand_idxs):
# Assign charges to use during the breakdown for special cases...oxygen, hydroxide, peroxide, and acac
# All other ligands are currently assigned charge 0
ligand_charges = {'O1': -2, 'H1O1': -1, 'H1O2': -1, 'C5H7O2': -1}
if ligand[0] in list(ligand_charges.keys()):
ligand_charge = ligand_charges[ligand[0]]
else:
ligand_charge = 0
metal_charge = charge - ligand_charge
# Assign spin, which always remains with the metal except for when an O2 leaves
if spinmult == 1: # If the whole complex is restricted, it's components must be restricted as well
ligand_spin, metal_spin = 1, 1
else:
ligand_spinmults = {'O2': 3}
if ligand[0] in list(ligand_spinmults.keys()):
ligand_spin = ligand_spinmults[ligand[0]]
else:
ligand_spin = 1
metal_spin = spinmult - ligand_spin + 1 # Derived from spinmult = (2S+1) where S=1/2 per electron
# Create the necessary files for the metal complex single point
local_name = name + '_rm_' + ligand[0]
if os.path.isdir('rm_' + ligand[0]):
pass
else:
os.mkdir('rm_' + ligand[0])
os.chdir('rm_' + ligand[0])
local_mol = mol3D()
local_mol.copymol3D(mol)
local_mol.deleteatoms(ligand[1])
local_mol.writexyz(local_name + '.xyz')
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['name'] = local_name
local_infile_dict['charge'], local_infile_dict[
'spinmult'] = metal_charge, metal_spin
local_infile_dict['run_type'] = 'energy'
local_infile_dict['constraints'], local_infile_dict[
'convergence_thresholds'] = False, False
manager_io.write_input(local_infile_dict)
manager_io.write_jobscript(local_name,
time_limit='12:00:00',
sleep=True)
jobscripts.append(local_name + '.in')
os.chdir('..')
# Create the necessary files for the dissociated ligand single point
local_name = name + '_kp_' + ligand[0]
if os.path.isdir('kp_' + ligand[0]):
pass
else:
os.mkdir('kp_' + ligand[0])
os.chdir('kp_' + ligand[0])
local_mol = mol3D()
local_mol.copymol3D(mol)
deletion_indices = list(
set(range(local_mol.natoms)) - set(ligand[1]))
local_mol.deleteatoms(deletion_indices)
local_mol.writexyz(local_name + '.xyz')
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['name'] = local_name
local_infile_dict['charge'], local_infile_dict[
'spinmult'] = ligand_charge, ligand_spin
local_infile_dict['run_type'] = 'energy'
local_infile_dict['constraints'], local_infile_dict[
'convergence_thresholds'] = False, False
manager_io.write_input(local_infile_dict)
manager_io.write_jobscript(local_name,
time_limit='12:00:00',
sleep=True)
jobscripts.append(local_name + '.in')
os.chdir('..')
os.chdir(home)
return jobscripts
def prep_hfx_resample(path, hfx_values=[0, 5, 10, 15, 20, 25, 30]):
# Given a path to the outfile of a finished run, this preps the files for hfx resampling
# Uses the wavefunction from the gas phase calculation as an initial guess
# Returns a list of the PATH(s) to the jobscript(s) to start the resampling calculations(s)
home = os.getcwd()
path = convert_to_absolute_path(path)
base = os.path.split(path)[0]
results = manager_io.read_outfile(path)
if not results['finished']:
raise Exception('This calculation does not appear to be complete! Aborting...')
# Check the state of the calculation and ensure than hfx resampling is valid
infile_dict = manager_io.read_infile(path)
if infile_dict['method'] != 'b3lyp':
raise Exception('HFX resampling may not behave well for methods other than b3lyp!')
if not infile_dict['hfx']:
infile_dict['hfx'] = 20
if infile_dict['hfx'] not in hfx_values:
raise Exception('HFX resampling list does not contain the original hfx value!')
# Now, start generating the base directory to hold all the hfx resampling values
name = results['name'] + '_HFXresampling'
hfx_path = os.path.join(base, name)
if not os.path.isdir(hfx_path):
os.mkdir(hfx_path)
os.chdir(hfx_path)
# Make the directory for the original calculation
subname = name + '_' + str(infile_dict['hfx'])
PATH = os.path.join(hfx_path, subname)
if not os.path.isdir(PATH):
os.mkdir(PATH)
os.chdir(PATH)
if not os.path.exists(os.path.join(PATH, subname + '.out')):
shutil.copyfile(path, subname + '.out')
shutil.copyfile(path.rsplit('.', 1)[0] + '_jobscript', subname + '_jobscript')
shutil.copyfile(path.rsplit('.', 1)[0] + '.in', subname + '.in')
shutil.copytree(os.path.join(os.path.split(path)[0], 'scr'), 'scr')
if os.path.exists(path.rsplit('.', 1)[0] + '.xyz'):
shutil.copyfile(path.rsplit('.', 1)[0] + '.xyz', subname + '.xyz')
# Find the hfx resampling values that we're ready to generate
hfx_values_to_generate = []
existing_resampled_values = glob.glob(os.path.join(hfx_path, name + '_*'))
for existing in existing_resampled_values:
hfx = int(existing.rsplit('_', 1)[1])
subname = name + '_' + str(hfx)
outfile_path = os.path.join(existing, subname + '.out')
if os.path.exists(outfile_path):
if manager_io.read_outfile(outfile_path)['finished']:
hfx_values_to_generate.append(hfx - 5)
hfx_values_to_generate.append(hfx + 5)
hfx_values_to_generate = list(set(hfx_values_to_generate))
hfx_values_to_generate = [i for i in hfx_values_to_generate if i in hfx_values]
# Now generate the additional hfx resampling values
jobscripts = []
for hfx in hfx_values_to_generate:
subname = name + '_' + str(hfx)
if os.path.exists(os.path.join(hfx_path, subname)): # skip over values that we've already done
continue
os.mkdir(os.path.join(hfx_path, subname))
os.chdir(os.path.join(hfx_path, subname))
higher_hfx = subname.rsplit('_', 1)[0] + '_' + str(int(subname.rsplit('_', 1)[1]) + 5)
lower_hfx = subname.rsplit('_', 1)[0] + '_' + str(int(subname.rsplit('_', 1)[1]) - 5)
if os.path.exists(os.path.join(hfx_path, higher_hfx)):
source_dir = os.path.join(hfx_path, higher_hfx)
else:
source_dir = os.path.join(hfx_path, lower_hfx)
if infile_dict['run_type'] == 'minimize':
optimxyz = os.path.join(source_dir, 'scr', 'optim.xyz')
else:
optimxyz = os.path.join(source_dir, 'scr', 'xyz.xyz')
extract_optimized_geo(optimxyz)
shutil.copy(os.path.join(source_dir, 'scr', 'optimized.xyz'), subname + '.xyz')
if infile_dict['spinmult'] == 1:
shutil.copy(os.path.join(source_dir, 'scr', 'c0'), 'c0')
manager_io.write_jobscript(subname, custom_line='# -fin c0', machine=machine)
elif infile_dict['spinmult'] != 1:
shutil.copyfile(os.path.join(source_dir, 'scr', 'ca0'), os.path.join('ca0'))
shutil.copyfile(os.path.join(source_dir, 'scr', 'cb0'), os.path.join('cb0'))
manager_io.write_jobscript(subname, custom_line=['# -fin ca0\n', '# -fin cb0\n'],
machine=machine)
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['guess'] = True
local_infile_dict['hfx'] = hfx / 100.
local_infile_dict['name'] = subname
local_infile_dict['machine'] = machine
manager_io.write_input(local_infile_dict)
jobscripts.append(os.path.join(os.getcwd(), subname + '_jobscript'))
os.chdir(home)
return jobscripts
def prep_ultratight(path):
# Given a path to the outfile of a finished run, this preps a run with tighter convergence criteria
# Uses the wavefunction and geometry from the previous calculation as an initial guess
# Returns a list of the PATH(s) to the jobscript(s) to start the solvent sp calculations(s)
home = os.getcwd()
path = convert_to_absolute_path(path)
results = manager_io.read_outfile(path)
if not results['finished']:
raise Exception('This calculation does not appear to be complete! Aborting...')
infile_dict = manager_io.read_infile(path)
base = os.path.split(path)[0]
if infile_dict['run_type'] == 'minimize':
optimxyz = os.path.join(base, 'scr', 'optim.xyz')
else:
optimxyz = os.path.join(base, 'scr', 'xyz.xyz')
extract_optimized_geo(optimxyz)
# Now, start generating the new directory
name = results['name'] + '_ultratight'
PATH = os.path.join(base, name)
if not os.path.isdir(PATH): # First time that ultratight has been run, create necessary files
os.mkdir(PATH)
os.chdir(PATH)
if os.path.exists(name + '.in') or os.path.exists(name + '.out') or os.path.exists(name + '_jobscript'):
raise Exception('This tightened convergence run appears to already exist. Aborting...')
shutil.copyfile(os.path.join(base, 'scr', 'optimized.xyz'), os.path.join(PATH, name + '.xyz'))
if infile_dict['spinmult'] == 1:
shutil.copyfile(os.path.join(base, 'scr', 'c0'), os.path.join(PATH, 'c0'))
manager_io.write_jobscript(name, custom_line='# -fin c0', machine=machine)
elif infile_dict['spinmult'] != 1:
shutil.copyfile(os.path.join(base, 'scr', 'ca0'), os.path.join(PATH, 'ca0'))
shutil.copyfile(os.path.join(base, 'scr', 'cb0'), os.path.join(PATH, 'cb0'))
manager_io.write_jobscript(name, custom_line=['# -fin ca0\n', '# -fin cb0\n'], machine=machine)
criteria = ['2.25e-04', '1.5e-04', '0.9e-03', '0.6e-03', '0.5e-06', '1.5e-05']
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['guess'] = True
local_infile_dict['convergence_thresholds'] = criteria
local_infile_dict['name'] = name
local_infile_dict['machine'] = machine
manager_io.write_input(local_infile_dict)
# Make an empty .out file to prevent the resubmission module from mistakenly submitting this job twice
f = open(name + '.out', 'w')
f.close()
os.chdir(home)
return [os.path.join(PATH, name + '_jobscript')]
else: # This has been run before, further tighten the convergence criteria
os.chdir(PATH)
infile_dict = manager_io.read_infile(os.path.join(PATH, name + '.out'))
criteria = [str(float(i) / 2.) for i in infile_dict['convergence_thresholds']]
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['guess'] = True
local_infile_dict['convergence_thresholds'] = criteria
local_infile_dict['name'] = name
local_infile_dict['machine'] = machine
manager_io.write_input(local_infile_dict)
extract_optimized_geo(os.path.join(PATH, 'scr', 'optim.xyz'))
shutil.copy(os.path.join(PATH, 'scr', 'optimized.xyz'), os.path.join(PATH, name + '.xyz'))
os.chdir(home)
return [os.path.join(PATH, name + '_jobscript')]
def prep_functionals_sp(path, functionalsSP):
home = os.getcwd()
path = convert_to_absolute_path(path)
results = manager_io.read_outfile(path)
if not results['finished']:
raise Exception('This calculation does not appear to be complete! Aborting...')
infile_dict = manager_io.read_infile(path)
base = os.path.split(path)[0]
if infile_dict['run_type'] == 'minimize':
optimxyz = os.path.join(base, 'scr', 'optim.xyz')
else:
optimxyz = os.path.join(base, 'scr', 'xyz.xyz')
extract_optimized_geo(optimxyz)
# Now, start generating the new directory
funcname = results['name'] + '_functionalsSP'
functional_base_path = os.path.join(base, funcname)
if os.path.isdir(functional_base_path):
# print('Directory for functional single point already exists')
pass
else:
os.mkdir(functional_base_path)
os.chdir(functional_base_path)
jobscripts = []
for func in functionalsSP:
PATH = os.path.join(functional_base_path, str(func))
if os.path.isdir(PATH):
continue
ensure_dir(PATH)
name = results['name'] + "_functional_" + str(func)
shutil.copyfile(os.path.join(base, 'scr', 'optimized.xyz'), os.path.join(PATH, name + '.xyz'))
guess = False
os.chdir(PATH)
if infile_dict['spinmult'] == 1:
if os.path.isfile(os.path.join(base, 'scr', 'c0')):
shutil.copyfile(os.path.join(base, 'scr', 'c0'), os.path.join(PATH, 'c0'))
manager_io.write_jobscript(name, custom_line='# -fin c0', machine=machine)
guess = True
else:
if os.path.isfile(os.path.join(base, 'scr', 'ca0')) and os.path.isfile(os.path.join(base, 'scr', 'cb0')):
shutil.copyfile(os.path.join(base, 'scr', 'ca0'), os.path.join(PATH, 'ca0'))
shutil.copyfile(os.path.join(base, 'scr', 'cb0'), os.path.join(PATH, 'cb0'))
manager_io.write_jobscript(name, custom_line=['# -fin ca0\n', '# -fin cb0\n'], machine=machine)
guess = True
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['guess'] = guess
local_infile_dict['run_type'] = 'energy'
local_infile_dict['name'] = name
local_infile_dict['levelshifta'], local_infile_dict['levelshiftb'] = 0.25, 0.25
local_infile_dict['method'] = func
local_infile_dict['machine'] = machine
manager_io.write_input(local_infile_dict)
fil = open('configure', 'w')
fil.write('method:' + func)
fil.close()
os.chdir(home)
jobscripts.append(os.path.join(PATH, name + '_jobscript'))
os.chdir(home)
return jobscripts
def prep_solvent_sp(path, solvents=[78.9]):
# Given a path to the outfile of a finished run, this preps the files for a single point solvent run
# Uses the wavefunction from the gas phase calculation as an initial guess
# Returns a list of the PATH(s) to the jobscript(s) to start the solvent sp calculations(s)
home = os.getcwd()
path = convert_to_absolute_path(path)
results = manager_io.read_outfile(path)
if not results['finished']:
raise Exception('This calculation does not appear to be complete! Aborting...')
infile_dict = manager_io.read_infile(path)
base = os.path.split(path)[0]
if infile_dict['run_type'] == 'minimize':
optimxyz = os.path.join(base, 'scr', 'optim.xyz')
else:
optimxyz = os.path.join(base, 'scr', 'xyz.xyz')
extract_optimized_geo(optimxyz)
# Now, start generating the new directory
solname = results['name'] + '_solvent'
solvent_base_path = os.path.join(base, solname)
# print(solvent_base_path)
if os.path.isdir(solvent_base_path):
# print('Directory for solvent single point already exists')
pass
else:
os.mkdir(solvent_base_path)
os.chdir(solvent_base_path)
jobscripts = []
for sol_val in solvents:
PATH = os.path.join(solvent_base_path, str(sol_val).replace('.', '_'))
if os.path.isdir(PATH):
continue
ensure_dir(PATH)
name = results['name'] + "_solvent_" + str(sol_val)
shutil.copyfile(os.path.join(base, 'scr', 'optimized.xyz'), os.path.join(PATH, name + '.xyz'))
guess = False
os.chdir(PATH)
if infile_dict['spinmult'] == 1:
if os.path.isfile(os.path.join(base, 'scr', 'c0')):
shutil.copyfile(os.path.join(base, 'scr', 'c0'), os.path.join(PATH, 'c0'))
manager_io.write_jobscript(name, custom_line='# -fin c0', machine=machine)
guess = True
else:
if os.path.isfile(os.path.join(base, 'scr', 'ca0')) and os.path.isfile(os.path.join(base, 'scr', 'cb0')):
shutil.copyfile(os.path.join(base, 'scr', 'ca0'), os.path.join(PATH, 'ca0'))
shutil.copyfile(os.path.join(base, 'scr', 'cb0'), os.path.join(PATH, 'cb0'))
manager_io.write_jobscript(name, custom_line=['# -fin ca0\n', '# -fin cb0\n'], machine=machine)
guess = True
local_infile_dict = copy.copy(infile_dict)
local_infile_dict['solvent'], local_infile_dict['guess'] = sol_val, guess
local_infile_dict['run_type'] = 'energy'
local_infile_dict['name'] = name
local_infile_dict['levelshifta'], local_infile_dict['levelshiftb'] = 0.25, 0.25
local_infile_dict['machine'] = machine
manager_io.write_input(local_infile_dict)
os.chdir(home)
jobscripts.append(os.path.join(PATH, name + '_jobscript'))
os.chdir(home)
return jobscripts
