def main():
model_path, sens_path, software, output = parse_arguments()
chemkin_path = os.path.join(model_path, 'chem_annotated.inp')
spc_dict_path = os.path.join(model_path, 'species_dictionary.txt')
spc_dict = load_spc_dict(spc_dict_path)
# Get species info from flux diagrams
sensitivities = find_sensitivity_results(sens_path)
print('Find sensitivities:\n' + '\n'.join(sensitivities))
spc_info = get_spc_info_from_sensitivities(sensitivities)
# The results usually contains a lot of S(XX), better to convert them using aliases
spc_aliases = get_species_aliases(chemkin_path, key='chemkin')
# Get smiles, adjacency list and other information from speceis dictionary
spc_info = expand_spc_info_by_spc_dict(spc_info, spc_dict)
# Generate ARC input
arc_input = {'species': []}
for label, spc in spc_info.items():
spc.update({'label': spc_aliases[label]})
arc_input['species'].append(spc)
output = output or os.curdir
output = os.path.join(output, 'input_sens.yml')
actual_output_path = save_yaml_file(output, arc_input, overwrite=False)
print(f'Saved to {actual_output_path}.')
def main():
model_path, flux_path, software, output = parse_arguments()
chemkin_path = os.path.join(model_path, 'chem_annotated.inp')
spc_dict_path = os.path.join(model_path, 'species_dictionary.txt')
spc_dict = load_spc_dict(spc_dict_path)
# Get species info from flux diagrams
flux_diagrams = find_flux_diagrams(flux_path)
print('Find fluxdiagrams:\n' + '\n'.join(flux_diagrams))
spc_info = get_spc_info_from_flux_diagrams(flux_diagrams)
# These packages use rmg label instead of Chemkin label
if software in ['rmg']:
spc_aliases = get_species_aliases(chemkin_path, key='rmg')
else:
spc_aliases = {'label': label for label in spc_info.keys()}
# Get smiles, adjacency list and other information from speceis dictionary
spc_info = expand_spc_info_by_spc_dict(spc_info, spc_dict, spc_aliases)
# Generate ARC input
arc_input = {'species': []}
for label, spc in spc_info.items():
spc.update({'label': spc_aliases[label]})
arc_input['species'].append(spc)
output = os.curdir or output
output = os.path.join(output, 'input_flux.yml')
actual_output_path = save_yaml_file(output, arc_input, overwrite=False)
print(f'Saved to {actual_output_path}.')
def main():
inputs, output, resonance = parse_arguments()
arc_input = combine_arc_species_inputs(*inputs, resonance)
output = output or os.curdir
output = os.path.join(output, 'input_merged.yml')
actual_output_path = save_yaml_file(output, arc_input, overwrite=False)
print(f'Saved to {actual_output_path}.')
def main():
model_path, output = parse_arguments()
chemkin_path = os.path.join(model_path, 'chem_annotated.inp')
spc_dict_path = os.path.join(model_path, 'species_dictionary.txt')
spc_dict = load_spc_dict(spc_dict_path)
spc_aliases = get_species_aliases(chemkin_path, key='rmg')
spc_info = {label: {'label': label} for label in spc_dict.keys()}
# Get smiles, adjacency list and other information from speceis dictionary
spc_info = expand_spc_info_by_spc_dict(spc_info, spc_dict)
# Generate ARC input
arc_input = {'species': []}
for label, spc in spc_info.items():
spc.update({'label': spc_aliases[label]})
arc_input['species'].append(spc)
if not output:
output = os.path.join(model_path, 'input_spc_dict.yml')
actual_output_path = save_yaml_file(output, arc_input, overwrite=False)
print(f'Saved to {actual_output_path}.')
def main():
input_file, libraries_path, filter_spc_dict, output = parse_arguments()
# Get species info in the input file
arc_input_species = read_yaml_file(input_file)['species']
spc_info = {spc['label']: spc for spc in arc_input_species}
print(f'Starting with {len(spc_info)} species...')
if filter_spc_dict:
# Load filtered species dictionary
filter_spc_dict = load_spc_dict(filter_spc_dict)
# Clean work
clean = []
for label, spc in spc_info.items():
dict_label, _ = find_species_from_spc_dict(spc, filter_spc_dict)
if not dict_label: # cannot find species
clean.append(label)
else:
print(
f'Warning: species {label} is cleaned out due to belonging '
f'to filtered species dictionary')
spc_info = {
label: spc
for label, spc in spc_info.items() if label in clean
}
if libraries_path:
# Load thermo libraries
libraries = read_yaml_file(libraries_path)
thermo_db = load_thermo_database(libraries=libraries['built-in_libs'])
for t_lib in libraries['external_libs']:
load_thermo_lib_by_path(t_lib, thermo_db)
# Clean work
clean = []
for label, spc in spc_info.items():
try:
thermo_data = thermo_db.get_all_thermo_data(
species_from_spc_info(spc))
except:
print(f'Warning: Cannot generate thermo for {label}.')
continue
if len(thermo_data) <= 1: # Only GAV availabel
clean.append(label)
else:
print(
f'Warning: species {label} is cleaned out due to existing '
f'in thermo libraries')
spc_info = {
label: spc
for label, spc in spc_info.items() if label in clean
}
# Make sure there is no duplicates in the spc_info
# Iteratively using combine function can help filter out duplicates
cleaned_info = {}
cleaned_spc_dict = {}
for label, spc in spc_info.items():
cleaned_info = combine_spc_info(spc_info1=cleaned_info,
spc_info2={label: spc},
spc_dict=cleaned_spc_dict)
# Convert species info to ARC input
print(f'Eventually, {len(cleaned_info)} species are left...')
arc_input1, arc_input2 = {'species': []}, {'species': []}
for spc in cleaned_info.values():
if ('multiplicity' in spc) and (spc['multiplicity'] < 3):
# closed_shell species and multiplicity 1 species
arc_input1['species'].append(spc)
else:
# other species
arc_input2['species'].append(spc)
output = output or os.curdir
output1 = os.path.join(output, 'input_cleaned_mul12.yml')
output2 = os.path.join(output, 'input_cleaned_mul3p.yml')
actual_output_path1 = save_yaml_file(output1, arc_input1, overwrite=False)
actual_output_path2 = save_yaml_file(output2, arc_input2, overwrite=False)
print(
f'Species with mulitplicity < 3 are saved to {actual_output_path1}.\n'
f'Species with mulitplicity >= 3 are saved to {actual_output_path2}.')