def main(inputPath, outputPath):
# Replicate rmgpy.chemkin.loadSpeciesDictionary here with some modifcations
# We don't need to compare against inerts, and we want to maintain the same species order
speciesList = []
with open(inputPath, 'r+b') as f:
adjlist = ''
for line in f:
if line.strip() == '' and adjlist.strip() != '':
# Finish this adjacency list
species = Species().fromAdjacencyList(adjlist)
# Try to generate the aromatic form of the molecule
aromatic = generateAromaticResonanceIsomers(
species.molecule[0])
if aromatic:
species.molecule = aromatic
speciesList.append(species)
adjlist = ''
else:
if "InChI" in line:
line = line.split()[0] + '\n'
if '//' in line:
index = line.index('//')
line = line[0:index]
adjlist += line
# Write to dictionary file
saveSpeciesDictionary(outputPath, speciesList)
def main(inputPath, outputPath):
species = []
with open(inputPath, 'r+b') as f:
for line0 in f:
line1 = line0.strip()
if line1 and line1[0] != '!':
# Parse line using regex
match = re.match(r"([^\s!]+)\s+([^\s!]+)", line1.strip())
# Get label and SMILES string
label = match.group(1)
smiles = match.group(2)
# Try to convert to aromatic form
mol = Molecule().fromSMILES(smiles)
aromatic = generateAromaticResonanceStructures(mol)
if aromatic:
spec = Species(molecule=aromatic)
else:
spec = Species(molecule=[mol])
# Save in species dictionary
spec.label = label
species.append(spec)
# Write to dictionary file
saveSpeciesDictionary(outputPath, species)
def execute(inputModelFiles, **kwargs):
try:
wd = kwargs['wd']
except KeyError:
wd = os.getcwd()
transport = kwargs['transport']
outputChemkinFile = os.path.join(wd, 'chem.inp')
outputSpeciesDictionary = os.path.join(wd, 'species_dictionary.txt')
outputTransportFile = os.path.join(wd, 'tran.dat') if transport else None
models = get_models_to_merge(inputModelFiles)
finalModel = combine_models(models)
# Save the merged model to disk
saveChemkinFile(outputChemkinFile, finalModel.species, finalModel.reactions)
saveSpeciesDictionary(outputSpeciesDictionary, finalModel.species)
if transport:
saveTransportFile(outputTransportFile, finalModel.species)
print 'Merged Chemkin file saved to {0}'.format(outputChemkinFile)
print 'Merged species dictionary saved to {0}'.format(outputSpeciesDictionary)
if transport:
print 'Merged transport file saved to {0}'.format(outputTransportFile)
def main(inputPath, outputPath):
species = []
with open(inputPath, 'r+b') as f:
xyz = ''
label = ''
lineNum = 0
specNum = 0
for line0 in f:
line1 = line0.strip()
if line1:
# The line is not blank, so add it to the xyz string
xyz += line1 + '\n'
lineNum += 1
# In openbabel xyz format, the second line is a comment/name
if lineNum == 2:
label = line1
else:
# Check if we've found a valid entry
if not xyz:
continue
lines = xyz.strip().split('\n')
# The first line is the number of atoms, which should match the number of lines minus the first two
if int(lines[0]) != len(lines) - 2:
print 'Invalid entry found'
xyz = ''
lineNum = 0
continue
# Assume that we finished reading this entry
specNum += 1
# Make sure the label is appropriate
label = label.split()[0]
label = label.strip()
if not label:
label = 'SPC({0})'.format(specNum)
# Read xyz string using openbabel
obmol = pybel.readstring('xyz', xyz)
# Convert to RMG molecule object
mol = fromOBMol(Molecule(), obmol.OBMol)
# Save in species dictionary
spec = Species(molecule=[mol])
spec.label = label
species.append(spec)
# Reset our counter
xyz = ''
lineNum = 0
# Write to dictionary file
saveSpeciesDictionary(outputPath, species)
def execute(inputModelFiles, **kwargs):
try:
wd = kwargs['wd']
except KeyError:
wd = os.getcwd()
transport = kwargs['transport']
outputChemkinFile = os.path.join(wd, 'chem.inp')
outputSpeciesDictionary = os.path.join(wd, 'species_dictionary.txt')
outputTransportFile = os.path.join(wd, 'tran.dat') if transport else None
# Load the models to merge
models = []
for chemkin, speciesPath, transportPath in inputModelFiles:
print 'Loading model #{0:d}...'.format(len(models) + 1)
model = ReactionModel()
model.species, model.reactions = loadChemkinFile(
chemkin, speciesPath, transportPath=transportPath)
models.append(model)
finalModel = ReactionModel()
for i, model in enumerate(models):
print 'Ignoring common species and reactions from model #{0:d}...'.format(
i + 1)
Nspec0 = len(finalModel.species)
Nrxn0 = len(finalModel.reactions)
finalModel = finalModel.merge(model)
Nspec = len(finalModel.species)
Nrxn = len(finalModel.reactions)
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique species from model #{0:d}.'.format(
i + 1, Nspec - Nspec0, len(model.species),
(Nspec - Nspec0) * 100. / len(model.species))
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique reactions from model #{0:d}.'.format(
i + 1, Nrxn - Nrxn0, len(model.reactions),
(Nrxn - Nrxn0) * 100. / len(model.reactions))
print 'The merged model has {0:d} species and {1:d} reactions'.format(
len(finalModel.species), len(finalModel.reactions))
# Save the merged model to disk
saveChemkinFile(outputChemkinFile, finalModel.species,
finalModel.reactions)
saveSpeciesDictionary(outputSpeciesDictionary, finalModel.species)
if transport:
saveTransportFile(outputTransportFile, finalModel.species)
print 'Merged Chemkin file saved to {0}'.format(outputChemkinFile)
print 'Merged species dictionary saved to {0}'.format(
outputSpeciesDictionary)
if transport:
print 'Merged transport file saved to {0}'.format(outputTransportFile)
def execute(inputModelFiles, **kwargs):
try:
wd = kwargs['wd']
except KeyError:
wd = os.getcwd()
transport = kwargs['transport']
outputChemkinFile = os.path.join(wd, 'chem.inp')
outputSpeciesDictionary = os.path.join(wd, 'species_dictionary.txt')
outputTransportFile = os.path.join(wd, 'tran.dat') if transport else None
# Load the models to merge
models = []
for chemkin, speciesPath, transportPath in inputModelFiles:
print 'Loading model #{0:d}...'.format(len(models)+1)
model = ReactionModel()
model.species, model.reactions = loadChemkinFile(chemkin, speciesPath, transportPath=transportPath)
models.append(model)
finalModel = ReactionModel()
for i, model in enumerate(models):
print 'Ignoring common species and reactions from model #{0:d}...'.format(i+1)
Nspec0 = len(finalModel.species)
Nrxn0 = len(finalModel.reactions)
finalModel = finalModel.merge(model)
Nspec = len(finalModel.species)
Nrxn = len(finalModel.reactions)
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique species from model #{0:d}.'.format(i+1, Nspec - Nspec0, len(model.species), (Nspec - Nspec0) * 100. / len(model.species))
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique reactions from model #{0:d}.'.format(i+1, Nrxn - Nrxn0, len(model.reactions), (Nrxn - Nrxn0) * 100. / len(model.reactions))
print 'The merged model has {0:d} species and {1:d} reactions'.format(len(finalModel.species), len(finalModel.reactions))
# Save the merged model to disk
saveChemkinFile(outputChemkinFile, finalModel.species, finalModel.reactions)
saveSpeciesDictionary(outputSpeciesDictionary, finalModel.species)
if transport:
saveTransportFile(outputTransportFile, finalModel.species)
print 'Merged Chemkin file saved to {0}'.format(outputChemkinFile)
print 'Merged species dictionary saved to {0}'.format(outputSpeciesDictionary)
if transport:
print 'Merged transport file saved to {0}'.format(outputTransportFile)
def runThermoEstimator(inputFile, library_flag):
"""
Estimate thermo for a list of species using RMG and the settings chosen inside a thermo input file.
"""
rmg = RMG()
rmg.loadThermoInput(inputFile)
rmg.database = RMGDatabase()
path = os.path.join(settings['database.directory'])
# forbidden structure loading
rmg.database.loadThermo(os.path.join(path, 'thermo'),
rmg.thermoLibraries,
depository=False)
if rmg.solvent:
rmg.database.loadSolvation(os.path.join(path, 'solvation'))
Species.solventData = rmg.database.solvation.getSolventData(
rmg.solvent)
Species.solventName = rmg.solvent
for species in rmg.initialSpecies:
submit(species)
if library_flag:
library = ThermoLibrary(name='Thermo Estimation Library')
for species in rmg.initialSpecies:
library.loadEntry(
index=len(library.entries) + 1,
label=species.label,
molecule=species.molecule[0].toAdjacencyList(),
thermo=species.getThermoData().toThermoData(),
shortDesc=species.getThermoData().comment,
)
library.save(os.path.join(rmg.outputDirectory, 'ThermoLibrary.py'))
# Save the thermo data to chemkin format output files and dictionary, with no reactions
saveChemkinFile(os.path.join(rmg.outputDirectory, 'chem_annotated.inp'),
species=rmg.initialSpecies,
reactions=[])
saveSpeciesDictionary(os.path.join(rmg.outputDirectory,
'species_dictionary.txt'),
species=rmg.initialSpecies)
def main(inputPath, outputPath):
species = []
with open(inputPath, 'r+b') as f:
for line0 in f:
line1 = line0.strip()
if line1 and line1[0] != '!':
# Parse line using regex
match = re.match(r"([^\s!]+)\s+([^\s!]+)", line1.strip())
# Get label and SMILES string
label = match.group(1)
smiles = match.group(2)
# Save in species dictionary
spec = Species().fromSMILES(smiles)
spec.label = label
species.append(spec)
# Write to dictionary file
saveSpeciesDictionary(outputPath, species)
def runThermoEstimator(inputFile):
"""
Estimate thermo for a list of species using RMG and the settings chosen inside a thermo input file.
"""
rmg = RMG()
rmg.loadThermoInput(inputFile)
rmg.database = RMGDatabase()
path = os.path.join(settings['database.directory'])
# forbidden structure loading
rmg.database.loadThermo(os.path.join(path, 'thermo'), rmg.thermoLibraries, depository=False)
if rmg.solvent:
rmg.database.loadSolvation(os.path.join(path, 'solvation'))
Species.solventData = rmg.database.solvation.getSolventData(rmg.solvent)
Species.solventName = rmg.solvent
for species in rmg.initialSpecies:
submit(species)
# library = ThermoLibrary(name='Thermo Estimation Library')
# for spc in rmg.initialSpecies:
# library.loadEntry(
# index = len(library.entries) + 1,
# label = species.label,
# molecule = species.molecule[0].toAdjacencyList(),
# thermo = species.getThermoData().toThermoData(),
# shortDesc = species.getThermoData().comment,
# )
# library.save(os.path.join(rmg.outputDirectory,'ThermoLibrary.py'))
# Save the thermo data to chemkin format output files and dictionary, with no reactions
saveChemkinFile(os.path.join(rmg.outputDirectory, 'chem_annotated.inp'), species=rmg.initialSpecies, reactions=[])
saveSpeciesDictionary(os.path.join(rmg.outputDirectory, 'species_dictionary.txt'), species=rmg.initialSpecies)
for chemkin, speciesPath, transportPath in inputModelFiles:
print 'Loading model #{0:d}...'.format(len(models)+1)
model = ReactionModel()
model.species, model.reactions = loadChemkinFile(chemkin, speciesPath, transportPath=transportPath)
models.append(model)
allSpecies = []
speciesIndices = [[] for i in range(len(models))]
for i, model in enumerate(models):
speciesIndices[i] = []
for j, species in enumerate(model.species):
for index, species0 in enumerate(allSpecies):
if species0.isIsomorphic(species):
speciesIndices[i].append(index)
break;
else:
allSpecies.append(species)
speciesIndices[i].append(allSpecies.index(species))
# Reassign species names and labels according to the list of all species in all models
# We must retain the original thermochemistry
for i, model in enumerate(models):
for j, species in enumerate(model.species):
index = speciesIndices[i][j]
species.label = allSpecies[index].label
species.index = allSpecies[index].index
# Resave the models
saveChemkinFile('chem{0}.inp'.format(i+1), model.species, model.reactions)
saveSpeciesDictionary('species_dictionary{0}.txt'.format(i+1), model.species)
print 'Saving of new models with consistent names is complete!'
# Load the models to merge
models = []
for chemkin, speciesDict in zip(args.files[0::2], args.files[1::2]):
print 'Loading model #{0:d}...'.format(len(models)+1)
model = ReactionModel()
model.species, model.reactions = loadChemkinFile(chemkin, speciesDict)
models.append(model)
finalModel = ReactionModel()
for i, model in enumerate(models):
print 'Ignoring common species and reactions from model #{0:d}...'.format(i+1)
Nspec0 = len(finalModel.species)
Nrxn0 = len(finalModel.reactions)
finalModel = finalModel.merge(model)
Nspec = len(finalModel.species)
Nrxn = len(finalModel.reactions)
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique species from model #{0:d}.'.format(i+1, Nspec - Nspec0, len(model.species), (Nspec - Nspec0) * 100. / len(model.species))
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique reactions from model #{0:d}.'.format(i+1, Nrxn - Nrxn0, len(model.reactions), (Nrxn - Nrxn0) * 100. / len(model.reactions))
print 'The merged model has {0:d} species and {1:d} reactions'.format(len(finalModel.species), len(finalModel.reactions))
# Save the merged model to disk
saveChemkinFile(outputChemkinFile, finalModel.species, finalModel.reactions)
saveSpeciesDictionary(outputSpeciesDictionary, finalModel.species)
print 'Merged Chemkin file saved to {0}'.format(outputChemkinFile)
print 'Merged species dictionary saved to {0}'.format(outputSpeciesDictionary)
for chemkin, speciesPath, transportPath in inputModelFiles:
print 'Loading model #{0:d}...'.format(len(models)+1)
model = ReactionModel()
model.species, model.reactions = loadChemkinFile(chemkin, speciesPath, transportPath=transportPath)
models.append(model)
allSpecies = []
speciesIndices = [[] for i in range(len(models))]
for i, model in enumerate(models):
speciesIndices[i] = []
for j, species in enumerate(model.species):
for index, species0 in enumerate(allSpecies):
if species0.isIsomorphic(species):
speciesIndices[i].append(index)
break;
else:
allSpecies.append(species)
speciesIndices[i].append(allSpecies.index(species))
# Reassign species names and labels according to the list of all species in all models
# We must retain the original thermochemistry
for i, model in enumerate(models):
for j, species in enumerate(model.species):
index = speciesIndices[i][j]
species.label = allSpecies[index].label
species.index = allSpecies[index].index
# Resave the models
saveChemkinFile('chem{0}.inp'.format(i+1), model.species, model.reactions)
saveSpeciesDictionary('species_dictionary{0}.txt'.format(i+1), model.species)
print 'Saving of new models with consistent names is complete!'
reaction.kinetics = entry.data
reactionList.append(reaction)
speciesList = []
index = 0
for spec in kineticLibrary.getSpecies().values():
index = index + 1
species = Species(molecule = spec.molecule)
species.generateThermoData(database)
species.index = index
speciesList.append(species)
for reaction in reactionList:
for reactant in reaction.reactants:
for spec in speciesList:
if reactant.isIsomorphic(spec):
reactant.index = spec.index
spec.label = reactant.label
for product in reaction.products:
for spec in speciesList:
if product.isIsomorphic(spec):
product.index = spec.index
spec.label = product.label
print 'Saving the chem.inp and species_dictionary.txt to local directory'
saveChemkinFile('chem.inp', speciesList, reactionList)
saveSpeciesDictionary('species_dictionary.txt', speciesList)
reaction.kinetics = entry.data
reactionList.append(reaction)
speciesList = []
index = 0
speciesDict = kineticLibrary.getSpecies(
os.path.join(settings['database.directory'], 'kinetics', 'libraries',
libraryName, 'dictionary.txt'))
for spec in speciesDict.values():
index = index + 1
species = Species(molecule=spec.molecule)
species.generateThermoData(database)
species.index = index
speciesList.append(species)
for reaction in reactionList:
for reactant in reaction.reactants:
for spec in speciesList:
if reactant.isIsomorphic(spec):
reactant.index = spec.index
spec.label = reactant.label
for product in reaction.products:
for spec in speciesList:
if product.isIsomorphic(spec):
product.index = spec.index
spec.label = product.label
print 'Saving the chem.inp and species_dictionary.txt to local directory'
saveChemkinFile('chem.inp', speciesList, reactionList)
saveSpeciesDictionary('species_dictionary.txt', speciesList)
for i, model in enumerate(models):
print 'Ignoring common species and reactions from model #{0:d}...'.format(
i + 1)
Nspec0 = len(finalModel.species)
Nrxn0 = len(finalModel.reactions)
finalModel = finalModel.merge(model)
Nspec = len(finalModel.species)
Nrxn = len(finalModel.reactions)
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique species from model #{0:d}.'.format(
i + 1, Nspec - Nspec0, len(model.species),
(Nspec - Nspec0) * 100. / len(model.species))
print 'Added {1:d} out of {2:d} ({3:.1f}%) unique reactions from model #{0:d}.'.format(
i + 1, Nrxn - Nrxn0, len(model.reactions),
(Nrxn - Nrxn0) * 100. / len(model.reactions))
print 'The merged model has {0:d} species and {1:d} reactions'.format(
len(finalModel.species), len(finalModel.reactions))
# Save the merged model to disk
saveChemkinFile(outputChemkinFile, finalModel.species,
finalModel.reactions)
saveSpeciesDictionary(outputSpeciesDictionary, finalModel.species)
if transport:
saveTransportFile(outputTransportFile, finalModel.species)
print 'Merged Chemkin file saved to {0}'.format(outputChemkinFile)
print 'Merged species dictionary saved to {0}'.format(
outputSpeciesDictionary)
if transport:
print 'Merged transport file saved to {0}'.format(outputTransportFile)
)
reactionList.append(library_reaction)
speciesList = []
index = 0
speciesDict = kineticLibrary.getSpecies(
os.path.join(settings["database.directory"], "kinetics", "libraries", libraryName, "dictionary.txt")
)
for spec in speciesDict.values():
index = index + 1
species = Species(molecule=spec.molecule)
species.getThermoData()
species.index = index
speciesList.append(species)
for reaction in reactionList:
for reactant in reaction.reactants:
for spec in speciesList:
if reactant.isIsomorphic(spec):
reactant.index = spec.index
spec.label = reactant.label
for product in reaction.products:
for spec in speciesList:
if product.isIsomorphic(spec):
product.index = spec.index
spec.label = product.label
print "Saving the chem.inp and species_dictionary.txt to local directory"
saveChemkinFile("chem.inp", speciesList, reactionList)
saveSpeciesDictionary("species_dictionary.txt", speciesList)