def mutate_and_relax(candidate, name, iteration, cnt_max, **kwargs):
print_output("__%s__" % name)
found = False
cnt = 0
while found is False and cnt < cnt_max:
candidate_backup = Structure(candidate)
candidate.mutate(**kwargs)
print_output("%s after mutation: " % name + str(candidate))
run_util.str_info(candidate)
if not candidate.is_geometry_valid():
print_output(" The geometry of %s is invalid." % name)
cnt += 1
# Rebuild the structure
candidate = candidate_backup
continue
if candidate not in blacklist:
name = "generation_%d_%s" % (iteration, name)
run_util.optimize(candidate, energy_function, params, name)
run_util.check_for_kill()
candidate.send_to_blacklist(blacklist)
print_output(str(candidate)+":, energy: "+str(float(
candidate))+", is temporary added to the population")
run_util.relax_info(candidate)
found = True
population.append(candidate)
else:
print_output("Geomerty of "+str(candidate)+" is fine, but already "
"known.")
cnt += 1
candidate = candidate_backup
if cnt == cnt_max:
raise Exception("The allowed number of trials for generating"
" a unique child has been exceeded.")
def mutate_and_relax(candidate, name, iteration, cnt_max, **kwargs):
print_output("__%s__" % name)
found = False
cnt = 0
while found is False and cnt < cnt_max:
candidate_backup = Structure(candidate)
candidate.mutate(**kwargs)
print_output("%s after mutation: " % name + str(candidate))
run_util.str_info(candidate)
if not candidate.is_geometry_valid():
print_output(" The geometry of %s is invalid." % name)
cnt += 1
# Rebuild the structure
candidate = candidate_backup
continue
if candidate not in blacklist:
name = "generation_%d_%s" % (iteration, name)
run_util.optimize(candidate, energy_function, params, name)
run_util.check_for_kill()
candidate.send_to_blacklist(blacklist)
print_output(
str(candidate) + ":, energy: " + str(float(candidate)) +
", is temporary added to the population")
run_util.relax_info(candidate)
found = True
population.append(candidate)
else:
print_output("Geomerty of " + str(candidate) +
" is fine, but already "
"known.")
cnt += 1
candidate = candidate_backup
if cnt == cnt_max:
raise Exception("The allowed number of trials for generating"
" a unique child has been exceeded.")
def mutate_and_relax(candidate, name, prob_for_mut_torsions,
prob_for_mut_cistrans, max_mutations_torsions,
max_mutations_cistrans, iteration, cnt_max):
print_output("__%s__" % name)
found = False
cnt = 0
while found is False and cnt < cnt_max:
mut1, mut2 = np.random.rand(), np.random.rand()
candidate_backup = Structure(candidate)
if mut1 < prob_for_mut_torsions:
candidate.mutation_tor(max_mutations_torsions)
print_output("%s after mutation in torsions: " % name + str(
candidate))
if mut2 < prob_for_mut_cistrans:
candidate.mutation_cistrans(max_mutations_cistrans)
print_output("%s after mutation in cistrans: " % name + str(
candidate))
if not candidate.is_geometry_valid():
print_output(" The geometry of %s is invalid." % name)
cnt += 1
candidate = candidate_backup
continue
if candidate not in blacklist:
name = "generation_%d_%s" % (iteration, name)
candidate.perform_ff(p_file)
candidate.send_to_blacklist(black_dir, blacklist)
print_output(str(candidate)+":, energy: "+str(float(
candidate))+", is temporary added to the population")
found = True
population.append(candidate)
else:
print_output("Geomerty fine, but the structure already known")
cnt += 1
candidate = candidate_backup
if cnt == cnt_max:
raise Exception("The allowed number of trials for generating a"
" unique child has been exceeded.")
prob_for_mut_torsions = float(p_dict['prob_for_mut_torsions'])
max_mutations_cistrans = int(p_dict['max_mutations_cistrans'])
max_mutations_torsions = int(p_dict['max_mutations_torsions'])
iter_limit_conv = int(p_dict['iter_limit_conv'])
energy_diff_conv = float(p_dict['energy_diff_conv'])
energy_wanted = float(p_dict['energy_wanted'])
cnt_max = 100
mol = MoleculeDescription(p_file)
mol.get_mol_parameters()
mol.create_template_sdf()
population, blacklist = [], []
min_energy = []
print_output("___Initialization___")
cnt = 0
while len(population) < popsize and cnt < cnt_max:
print_output("New trial")
str3d = Structure(mol)
str3d.generate_random_structure(cistrans1, cistrans2)
if not str3d.is_geometry_valid():
print_output("The geometry of "+str(str3d)+" is invalid.")
cnt += 1
continue
if str3d not in blacklist:
name = "initial_%d" % (len(population))
str3d.perform_ff(p_file)
str3d.send_to_blacklist(black_dir, blacklist)
population.append(str3d)
print_output(str(str3d)+", energy: "+str(float(str3d)) +
# Set defaults for parameters not defined in the parameter file.
params = set_default(params, dict_default)
energy_function = run_util.detect_energy_function(params)
cnt_max = 200
population, blacklist = [], []
min_energy = []
if opt == "simple":
mol = MoleculeDescription(p_file)
# Assign the permanent attributes to the molecule.
mol.get_parameters()
mol.create_template_sdf()
# Check for potential degree of freedom related parameters.
linked_params = run_util.find_linked_params(mol, params)
print_output("Number of atoms: " + str(mol.atoms))
print_output("Number of bonds: " + str(mol.bonds))
for dof in mol.dof_names:
print_output("Number of identified " + str(dof) + ": " +
str(len(getattr(mol, dof))))
print_output("Identified " + str(dof) + ": " + str(getattr(mol, dof)))
print_output("___Initialization___")
cnt = 0
# Generate sensible and unique 3d structures.
while len(population) < params['popsize'] and cnt < cnt_max:
print_output("New trial")
str3d = Structure(mol)
str3d.generate_structure()
if not str3d.is_geometry_valid():
print_output("The geometry of " + str(str3d) + " is invalid.")
# Set defaults for parameters not defined in the parameter file.
params = set_default(params, dict_default)
energy_function = run_util.detect_energy_function(params)
cnt_max = 200
population, blacklist = [], []
min_energy = []
if opt == "simple":
mol = MoleculeDescription(p_file)
# Assign the permanent attributes to the molecule.
mol.get_parameters()
mol.create_template_sdf()
# Check for potential degree of freedom related parameters.
linked_params = run_util.find_linked_params(mol, params)
print_output("Number of atoms: "+str(mol.atoms))
print_output("Number of bonds: "+str(mol.bonds))
for dof in mol.dof_names:
print_output("Number of identified "+str(dof)+": " +
str(len(getattr(mol, dof))))
print_output("Identified "+str(dof)+": "+str(getattr(mol, dof)))
print_output("___Initialization___")
cnt = 0
# Generate sensible and unique 3d structures.
while len(population) < params['popsize'] and cnt < cnt_max:
print_output("New trial")
str3d = Structure(mol)
str3d.generate_structure()
if not str3d.is_geometry_valid():
print_output("The geometry of "+str(str3d)+" is invalid.")
fitness_sum_limit = str(p_dict['fitness_sum_limit'])
prob_for_crossing = float(p_dict['prob_for_crossing'])
prob_for_mut_cistrans = float(p_dict['prob_for_mut_cistrans'])
prob_for_mut_torsions = float(p_dict['prob_for_mut_torsions'])
max_mutations_cistrans = int(p_dict['max_mutations_cistrans'])
max_mutations_torsions = int(p_dict['max_mutations_torsions'])
aims_call = str(p_dict['aims_call'])
iter_limit_conv = int(p_dict['iter_limit_conv'])
energy_diff_conv = float(p_dict['energy_diff_conv'])
energy_wanted = float(p_dict['energy_wanted'])
cnt_max = 100
array = np.array
population, blacklist = [], []
min_energy = []
print_output(" \n ___Restart will be performed___")
with open("backup_mol.dat", 'r') as inf:
mol = eval(inf.readline())
inf.close()
with open("backup_population.dat", 'r') as inf:
for line in inf:
population.append(eval(line))
inf.close()
with open("backup_blacklist.dat", 'r') as inf:
for line in inf:
blacklist.append(eval(line))
inf.close()
with open("backup_min_energy.dat", 'r') as inf:
for line in inf:
min_energy.append(eval(line))
inf.close()
