def fitInterpolationModels(self):
configurations = []
configurations.extend(self.network.isomers)
configurations.extend(self.network.reactants)
configurations.extend(self.network.products)
self.network.netReactions = []
Nreac = self.network.Nisom + self.network.Nreac
Nprod = Nreac + self.network.Nprod
Tmin = self.Tmin.value_si
Tmax = self.Tmax.value_si
Tdata = self.Tlist.value_si
Pmin = self.Pmin.value_si
Pmax = self.Pmax.value_si
Pdata = self.Plist.value_si
for prod in range(Nprod):
for reac in range(Nreac):
if reac == prod: continue
reaction = Reaction(
reactants = configurations[reac].species,
products = configurations[prod].species,
)
kdata = self.K[:,:,prod,reac].copy()
order = len(reaction.reactants)
kdata *= 1e6 ** (order-1)
kunits = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[order]
reaction.kinetics = self.fitInterpolationModel(Tdata, Pdata, kdata, kunits)
self.network.netReactions.append(reaction)
def fit_interpolation_models(self):
"""Fit all pressure dependent rates with interpolation models"""
configurations = []
configurations.extend(self.network.isomers)
configurations.extend(self.network.reactants)
configurations.extend(self.network.products)
self.network.net_reactions = []
n_reac = self.network.n_isom + self.network.n_reac
n_prod = n_reac + self.network.n_prod
Tdata, Pdata = self.Tlist.value_si, self.Plist.value_si
for prod in range(n_prod):
for reac in range(n_reac):
if reac == prod:
continue
reaction = Reaction(reactants=configurations[reac].species,
products=configurations[prod].species)
kdata = self.K[:, :, prod, reac].copy()
order = len(reaction.reactants)
kdata *= 1e6 ** (order - 1)
k_units = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[order]
logging.debug('Fitting master eqn data to kinetics for reaction {}.'.format(reaction))
reaction.kinetics = self.fit_interpolation_model(Tdata, Pdata, kdata, k_units)
self.network.net_reactions.append(reaction)
def fitInterpolationModels(self):
configurations = []
configurations.extend(self.network.isomers)
configurations.extend(self.network.reactants)
configurations.extend(self.network.products)
self.network.netReactions = []
Nreac = self.network.Nisom + self.network.Nreac
Nprod = Nreac + self.network.Nprod
Tmin = self.Tmin.value_si
Tmax = self.Tmax.value_si
Tdata = self.Tlist.value_si
Pmin = self.Pmin.value_si
Pmax = self.Pmax.value_si
Pdata = self.Plist.value_si
for prod in range(Nprod):
for reac in range(Nreac):
if reac == prod: continue
reaction = Reaction(
reactants = configurations[reac].species,
products = configurations[prod].species,
)
kdata = self.K[:,:,prod,reac].copy()
order = len(reaction.reactants)
kdata *= 1e6 ** (order-1)
kunits = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[order]
reaction.kinetics = self.fitInterpolationModel(Tdata, Pdata, kdata, kunits)
self.network.netReactions.append(reaction)
def compute(self):
"""
Compute the pressure-dependent rate coefficients :math:`k(T,P)` for
the loaded MEASURE calculation.
"""
# Only proceed if the input network is valid
if self.network is None or self.network.errorString != '':
raise PDepError('Attempted to run MEASURE calculation with invalid input.')
Nisom = len(self.network.isomers)
Nreac = len(self.network.reactants)
Nprod = len(self.network.products)
network = self.network
Tmin = self.Tmin.value_si
Tmax = self.Tmax.value_si
Tlist = self.Tlist.value_si
Pmin = self.Pmin.value_si
Pmax = self.Pmax.value_si
Plist = self.Plist.value_si
method = self.method
model = self.model
# Calculate the rate coefficients
K = network.calculateRateCoefficients(Tlist, Plist, method, grainCount=self.grainCount, grainSize=self.grainSize.value_si)
# Fit interpolation model
from rmgpy.reaction import Reaction
from rmgpy.measure.reaction import fitInterpolationModel
if model[0] != '':
logging.info('Fitting {0} interpolation models...'.format(model[0]))
configurations = []
configurations.extend([[isom] for isom in network.isomers])
configurations.extend([reactants for reactants in network.reactants])
configurations.extend([products for products in network.products])
for i in range(Nisom+Nreac+Nprod):
for j in range(Nisom+Nreac):
if i != j:
# Check that we have nonzero k(T,P) values
if (numpy.any(K[:,:,i,j]) and not numpy.all(K[:,:,i,j])):
raise NetworkError('Zero rate coefficient encountered while updating network {0}.'.format(network))
# Make a new net reaction
netReaction = Reaction(
reactants=configurations[j],
products=configurations[i],
kinetics=None,
reversible=(i<Nisom+Nreac),
)
network.netReactions.append(netReaction)
# Set/update the net reaction kinetics using interpolation model
netReaction.kinetics = fitInterpolationModel(netReaction, Tlist, Plist,
K[:,:,i,j],
model, Tmin, Tmax, Pmin, Pmax, errorCheck=True)
logging.info('')
def compute(self):
"""
Compute the pressure-dependent rate coefficients :math:`k(T,P)` for
the loaded MEASURE calculation.
"""
# Only proceed if the input network is valid
if self.network is None or self.network.errorString != '':
raise PDepError('Attempted to run MEASURE calculation with invalid input.')
Nisom = len(self.network.isomers)
Nreac = len(self.network.reactants)
Nprod = len(self.network.products)
network = self.network
Tmin = self.Tmin.value
Tmax = self.Tmax.value
Tlist = self.Tlist.values
Pmin = self.Pmin.value
Pmax = self.Pmax.value
Plist = self.Plist.values
method = self.method
model = self.model
# Calculate the rate coefficients
K = network.calculateRateCoefficients(Tlist, Plist, method, grainCount=self.grainCount, grainSize=self.grainSize.value)
# Fit interpolation model
from rmgpy.reaction import Reaction
from rmgpy.measure.reaction import fitInterpolationModel
if model[0] != '':
logging.info('Fitting {0} interpolation models...'.format(model[0]))
configurations = []
configurations.extend([[isom] for isom in network.isomers])
configurations.extend([reactants for reactants in network.reactants])
configurations.extend([products for products in network.products])
for i in range(Nisom+Nreac+Nprod):
for j in range(Nisom+Nreac):
if i != j:
# Check that we have nonzero k(T,P) values
if (numpy.any(K[:,:,i,j]) and not numpy.all(K[:,:,i,j])):
raise NetworkError('Zero rate coefficient encountered while updating network {0}.'.format(network))
# Make a new net reaction
netReaction = Reaction(
reactants=configurations[j],
products=configurations[i],
kinetics=None,
reversible=(i<Nisom+Nreac),
)
network.netReactions.append(netReaction)
# Set/update the net reaction kinetics using interpolation model
netReaction.kinetics = fitInterpolationModel(netReaction, Tlist, Plist,
K[:,:,i,j],
model, Tmin, Tmax, Pmin, Pmax, errorCheck=True)
logging.info('')
def getKineticsDepository(FullDatabase, family, depositoryLabel):
"""
Retrieve dictionaries of exact kinetics from NIST depository and approximated kinetics
for those same reactions from RMG.
Note: does NOT average up the database or create any rate rules from training data.
If that is desired it must be done prior to entering this function.
"""
depository = None
for tempDepository in family.depositories:
if re.search(re.escape(depositoryLabel), tempDepository.label):
depository=tempDepository
break
else:
print 'Depository {} not found in {} family.'.format(depositoryLabel, family.label)
return
exactKinetics={}
approxKinetics={}
for key, entry in depository.entries.iteritems():
try:
reaction=entry.item
template=family.getReactionTemplate(reaction)
exactKinetics[key]=entry.data
approxKinetics[key]=family.rules.estimateKinetics(template)[0]
except UndeterminableKineticsError:
# See if the reaction was written in the reverse direction
reaction = Reaction(reactants = copy.deepcopy(entry.item.products),
products = copy.deepcopy(entry.item.reactants),
kinetics = copy.deepcopy(entry.data)
)
template=family.getReactionTemplate(reaction)
# Getting thermo data erases the atomLabels, so do this after finding the template
# But we need it for setting the reverse kinetics
for spec in reaction.reactants + reaction.products:
spec.getThermoData()
reverseKinetics = reaction.generateReverseRateCoefficient()
reaction.kinetics = reverseKinetics
exactKinetics[key]=reaction.kinetics
approxKinetics[key]=family.rules.estimateKinetics(template)[0]
return exactKinetics, approxKinetics
def getKineticsDepository(FullDatabase, family, depositoryLabel):
"""
Retrieve dictionaries of exact kinetics from NIST depository and approximated kinetics
for those same reactions from RMG.
Note: does NOT average up the database or create any rate rules from training data.
If that is desired it must be done prior to entering this function.
"""
depository = None
for tempDepository in family.depositories:
if re.search(re.escape(depositoryLabel), tempDepository.label):
depository = tempDepository
break
else:
print 'Depository {} not found in {} family.'.format(
depositoryLabel, family.label)
return
exactKinetics = {}
approxKinetics = {}
for key, entry in depository.entries.iteritems():
try:
reaction = entry.item
template = family.getReactionTemplate(reaction)
exactKinetics[key] = entry.data
approxKinetics[key] = family.rules.estimateKinetics(template)[0]
except UndeterminableKineticsError:
# See if the reaction was written in the reverse direction
reaction = Reaction(reactants=copy.deepcopy(entry.item.products),
products=copy.deepcopy(entry.item.reactants),
kinetics=copy.deepcopy(entry.data))
template = family.getReactionTemplate(reaction)
# Getting thermo data erases the atomLabels, so do this after finding the template
# But we need it for setting the reverse kinetics
for spec in reaction.reactants + reaction.products:
spec.getThermoData()
reverseKinetics = reaction.generateReverseRateCoefficient()
reaction.kinetics = reverseKinetics
exactKinetics[key] = reaction.kinetics
approxKinetics[key] = family.rules.estimateKinetics(template)[0]
return exactKinetics, approxKinetics
def get_reaction_for_entry(entry, database):
"""
Return a Reaction object for a given entry that uses Species instead of
Molecules (so that we can compute the reaction thermo).
"""
reaction = Reaction(reactants=[], products=[])
for reactant in entry.item.reactants:
reactant.generate_resonance_structures()
reactant.thermo = generate_thermo_data(reactant, database)
reaction.reactants.append(reactant)
for product in entry.item.products:
product.generate_resonance_structures()
product.thermo = generate_thermo_data(product, database)
reaction.products.append(product)
reaction.kinetics = entry.data
reaction.degeneracy = entry.item.degeneracy
return reaction
def kinetics(label, kinetics):
reactants, products = label.split('_')
reactants = reactants.split('+')
products = products.split('+')
reaction = Reaction(reactants=[], products=[], reversible=True)
for in_list, out_list in [(reactants, reaction.reactants), (products, reaction.products)]:
for i, smiles in enumerate(in_list):
if smiles not in species_dict:
species = Species().fromSMILES(smiles)
species_dict[smiles] = species
species.label = '{0}({1})'.format(species.toChemkin(), len(species_dict))
out_list.append(species_dict[smiles])
reaction.kinetics = kinetics
print repr(reaction)
display(reaction)
addReactionToKineticsLibrary(reaction)
def get_rmg_reaction(self):
"""
Convert PrIMeReaction to an RMG reaction.
"""
if not all((self.reactants, self.products, self.kinetics)):
raise ConversionError(
'Missing reactants, products, or kinetics for reaction {}.'.
format(self.prime_id))
if not self._direction_matched:
self.match_direction()
reaction = Reaction()
if self.rmg_reactants is None or self.rmg_products is None:
reaction.reactants = [s.get_rmg_species() for s in self.reactants]
reaction.products = [s.get_rmg_species() for s in self.products]
else:
reaction.reactants = self.rmg_reactants
reaction.products = self.rmg_products
reaction.kinetics = self.kinetics.expression
return reaction
def getReactionForEntry(entry, database):
"""
Return a Reaction object for a given entry that uses Species instead of
Molecules (so that we can compute the reaction thermo).
"""
reaction = Reaction(reactants=[], products=[])
for molecule in entry.item.reactants:
molecule.makeHydrogensExplicit()
reactant = Species(molecule=[molecule], label=molecule.toSMILES())
reactant.generate_resonance_structures()
reactant.thermo = generateThermoData(reactant, database)
reaction.reactants.append(reactant)
for molecule in entry.item.products:
molecule.makeHydrogensExplicit()
product = Species(molecule=[molecule], label=molecule.toSMILES())
product.generate_resonance_structures()
product.thermo = generateThermoData(product, database)
reaction.products.append(product)
reaction.kinetics = entry.data
reaction.degeneracy = entry.item.degeneracy
return reaction
def getReactionForEntry(entry, database):
"""
Return a Reaction object for a given entry that uses Species instead of
Molecules (so that we can compute the reaction thermo).
"""
reaction = Reaction(reactants=[], products=[])
for molecule in entry.item.reactants:
molecule.makeHydrogensExplicit()
reactant = Species(molecule=[molecule], label=molecule.toSMILES())
reactant.generateResonanceIsomers()
reactant.thermo = generateThermoData(reactant, database)
reaction.reactants.append(reactant)
for molecule in entry.item.products:
molecule.makeHydrogensExplicit()
product = Species(molecule=[molecule], label=molecule.toSMILES())
product.generateResonanceIsomers()
product.thermo = generateThermoData(product, database)
reaction.products.append(product)
reaction.kinetics = entry.data
reaction.degeneracy = entry.item.degeneracy
return reaction
def loadFAMEInput(path, moleculeDict=None):
"""
Load the contents of a FAME input file into the MEASURE object. FAME
is an early version of MEASURE written in Fortran and used by RMG-Java.
This script enables importing FAME input files into MEASURE so we can
use the additional functionality that MEASURE provides. Note that it
is mostly designed to load the FAME input files generated automatically
by RMG-Java, and may not load hand-crafted FAME input files. If you
specify a `moleculeDict`, then this script will use it to associate
the species with their structures.
"""
def readMeaningfulLine(f):
line = f.readline()
while line != '':
line = line.strip()
if len(line) > 0 and line[0] != '#':
return line
else:
line = f.readline()
return ''
moleculeDict = moleculeDict or {}
logging.info('Loading file "{0}"...'.format(path))
f = open(path)
job = PressureDependenceJob(network=None)
# Read method
method = readMeaningfulLine(f).lower()
if method == 'modifiedstrongcollision':
job.method = 'modified strong collision'
elif method == 'reservoirstate':
job.method = 'reservoir state'
# Read temperatures
Tcount, Tunits, Tmin, Tmax = readMeaningfulLine(f).split()
job.Tmin = Quantity(float(Tmin), Tunits)
job.Tmax = Quantity(float(Tmax), Tunits)
job.Tcount = int(Tcount)
Tlist = []
for i in range(int(Tcount)):
Tlist.append(float(readMeaningfulLine(f)))
job.Tlist = Quantity(Tlist, Tunits)
# Read pressures
Pcount, Punits, Pmin, Pmax = readMeaningfulLine(f).split()
job.Pmin = Quantity(float(Pmin), Punits)
job.Pmax = Quantity(float(Pmax), Punits)
job.Pcount = int(Pcount)
Plist = []
for i in range(int(Pcount)):
Plist.append(float(readMeaningfulLine(f)))
job.Plist = Quantity(Plist, Punits)
# Read interpolation model
model = readMeaningfulLine(f).split()
if model[0].lower() == 'chebyshev':
job.interpolationModel = ('chebyshev', int(model[1]), int(model[2]))
elif model[0].lower() == 'pdeparrhenius':
job.interpolationModel = ('pdeparrhenius',)
# Read grain size or number of grains
job.minimumGrainCount = 0
job.maximumGrainSize = None
for i in range(2):
data = readMeaningfulLine(f).split()
if data[0].lower() == 'numgrains':
job.minimumGrainCount = int(data[1])
elif data[0].lower() == 'grainsize':
job.maximumGrainSize = (float(data[2]), data[1])
# A FAME file is almost certainly created during an RMG job, so use RMG mode
job.rmgmode = True
# Create the Network
job.network = Network()
# Read collision model
data = readMeaningfulLine(f)
assert data.lower() == 'singleexpdown'
alpha0units, alpha0 = readMeaningfulLine(f).split()
T0units, T0 = readMeaningfulLine(f).split()
n = readMeaningfulLine(f)
energyTransferModel = SingleExponentialDown(
alpha0 = Quantity(float(alpha0), alpha0units),
T0 = Quantity(float(T0), T0units),
n = float(n),
)
speciesDict = {}
# Read bath gas parameters
bathGas = Species(label='bath_gas', energyTransferModel=energyTransferModel)
molWtunits, molWt = readMeaningfulLine(f).split()
if molWtunits == 'u': molWtunits = 'amu'
bathGas.molecularWeight = Quantity(float(molWt), molWtunits)
sigmaLJunits, sigmaLJ = readMeaningfulLine(f).split()
epsilonLJunits, epsilonLJ = readMeaningfulLine(f).split()
assert epsilonLJunits == 'J'
bathGas.transportData = TransportData(
sigma = Quantity(float(sigmaLJ), sigmaLJunits),
epsilon = Quantity(float(epsilonLJ) / constants.kB, 'K'),
)
job.network.bathGas = {bathGas: 1.0}
# Read species data
Nspec = int(readMeaningfulLine(f))
for i in range(Nspec):
species = Species()
species.conformer = Conformer()
species.energyTransferModel = energyTransferModel
# Read species label
species.label = readMeaningfulLine(f)
speciesDict[species.label] = species
if species.label in moleculeDict:
species.molecule = [moleculeDict[species.label]]
# Read species E0
E0units, E0 = readMeaningfulLine(f).split()
species.conformer.E0 = Quantity(float(E0), E0units)
species.conformer.E0.units = 'kJ/mol'
# Read species thermo data
H298units, H298 = readMeaningfulLine(f).split()
S298units, S298 = readMeaningfulLine(f).split()
Cpcount, Cpunits = readMeaningfulLine(f).split()
Cpdata = []
for i in range(int(Cpcount)):
Cpdata.append(float(readMeaningfulLine(f)))
if S298units == 'J/mol*K': S298units = 'J/(mol*K)'
if Cpunits == 'J/mol*K': Cpunits = 'J/(mol*K)'
species.thermo = ThermoData(
H298 = Quantity(float(H298), H298units),
S298 = Quantity(float(S298), S298units),
Tdata = Quantity([300,400,500,600,800,1000,1500], "K"),
Cpdata = Quantity(Cpdata, Cpunits),
Cp0 = (Cpdata[0], Cpunits),
CpInf = (Cpdata[-1], Cpunits),
)
# Read species collision parameters
molWtunits, molWt = readMeaningfulLine(f).split()
if molWtunits == 'u': molWtunits = 'amu'
species.molecularWeight = Quantity(float(molWt), molWtunits)
sigmaLJunits, sigmaLJ = readMeaningfulLine(f).split()
epsilonLJunits, epsilonLJ = readMeaningfulLine(f).split()
assert epsilonLJunits == 'J'
species.transportData = TransportData(
sigma = Quantity(float(sigmaLJ), sigmaLJunits),
epsilon = Quantity(float(epsilonLJ) / constants.kB, 'K'),
)
# Read species vibrational frequencies
freqCount, freqUnits = readMeaningfulLine(f).split()
frequencies = []
for j in range(int(freqCount)):
frequencies.append(float(readMeaningfulLine(f)))
species.conformer.modes.append(HarmonicOscillator(
frequencies = Quantity(frequencies, freqUnits),
))
# Read species external rotors
rotCount, rotUnits = readMeaningfulLine(f).split()
if int(rotCount) > 0:
raise NotImplementedError('Cannot handle external rotational modes in FAME input.')
# Read species internal rotors
freqCount, freqUnits = readMeaningfulLine(f).split()
frequencies = []
for j in range(int(freqCount)):
frequencies.append(float(readMeaningfulLine(f)))
barrCount, barrUnits = readMeaningfulLine(f).split()
barriers = []
for j in range(int(barrCount)):
barriers.append(float(readMeaningfulLine(f)))
if barrUnits == 'cm^-1':
barrUnits = 'J/mol'
barriers = [barr * constants.h * constants.c * constants.Na * 100. for barr in barriers]
elif barrUnits in ['Hz', 's^-1']:
barrUnits = 'J/mol'
barriers = [barr * constants.h * constants.Na for barr in barriers]
elif barrUnits != 'J/mol':
raise Exception('Unexpected units "{0}" for hindered rotor barrier height.'.format(barrUnits))
inertia = [V0 / 2.0 / (nu * constants.c * 100.)**2 / constants.Na for nu, V0 in zip(frequencies, barriers)]
for I, V0 in zip(inertia, barriers):
species.conformer.modes.append(HinderedRotor(
inertia = Quantity(I,"kg*m^2"),
barrier = Quantity(V0,barrUnits),
symmetry = 1,
semiclassical = False,
))
# Read overall symmetry number
species.conformer.spinMultiplicity = int(readMeaningfulLine(f))
# Read isomer, reactant channel, and product channel data
Nisom = int(readMeaningfulLine(f))
Nreac = int(readMeaningfulLine(f))
Nprod = int(readMeaningfulLine(f))
for i in range(Nisom):
data = readMeaningfulLine(f).split()
assert data[0] == '1'
job.network.isomers.append(speciesDict[data[1]])
for i in range(Nreac):
data = readMeaningfulLine(f).split()
assert data[0] == '2'
job.network.reactants.append([speciesDict[data[1]], speciesDict[data[2]]])
for i in range(Nprod):
data = readMeaningfulLine(f).split()
if data[0] == '1':
job.network.products.append([speciesDict[data[1]]])
elif data[0] == '2':
job.network.products.append([speciesDict[data[1]], speciesDict[data[2]]])
# Read path reactions
Nrxn = int(readMeaningfulLine(f))
for i in range(Nrxn):
# Read and ignore reaction equation
equation = readMeaningfulLine(f)
reaction = Reaction(transitionState=TransitionState(), reversible=True)
job.network.pathReactions.append(reaction)
reaction.transitionState.conformer = Conformer()
# Read reactant and product indices
data = readMeaningfulLine(f).split()
reac = int(data[0]) - 1
prod = int(data[1]) - 1
if reac < Nisom:
reaction.reactants = [job.network.isomers[reac]]
elif reac < Nisom+Nreac:
reaction.reactants = job.network.reactants[reac-Nisom]
else:
reaction.reactants = job.network.products[reac-Nisom-Nreac]
if prod < Nisom:
reaction.products = [job.network.isomers[prod]]
elif prod < Nisom+Nreac:
reaction.products = job.network.reactants[prod-Nisom]
else:
reaction.products = job.network.products[prod-Nisom-Nreac]
# Read reaction E0
E0units, E0 = readMeaningfulLine(f).split()
reaction.transitionState.conformer.E0 = Quantity(float(E0), E0units)
reaction.transitionState.conformer.E0.units = 'kJ/mol'
# Read high-pressure limit kinetics
data = readMeaningfulLine(f)
assert data.lower() == 'arrhenius'
Aunits, A = readMeaningfulLine(f).split()
if '/' in Aunits:
index = Aunits.find('/')
Aunits = '{0}/({1})'.format(Aunits[0:index], Aunits[index+1:])
Eaunits, Ea = readMeaningfulLine(f).split()
n = readMeaningfulLine(f)
reaction.kinetics = Arrhenius(
A = Quantity(float(A), Aunits),
Ea = Quantity(float(Ea), Eaunits),
n = Quantity(float(n)),
)
reaction.kinetics.Ea.units = 'kJ/mol'
f.close()
job.network.isomers = [Configuration(isomer) for isomer in job.network.isomers]
job.network.reactants = [Configuration(*reactants) for reactants in job.network.reactants]
job.network.products = [Configuration(*products) for products in job.network.products]
return job
def getForwardReactionForFamilyEntry(self, entry, family, thermoDatabase):
"""
For a given `entry` for a reaction of the given reaction `family` (the
string label of the family), return the reaction with kinetics and
degeneracy for the "forward" direction as defined by the reaction
family. For families that are their own reverse, the direction the
kinetics is given in will be preserved. If the entry contains
functional groups for the reactants, assume that it is given in the
forward direction and do nothing. Returns the reaction in the direction
consistent with the reaction family template, and the matching template.
Note that the returned reaction will have its kinetics and degeneracy
set appropriately.
In order to reverse the reactions that are given in the reverse of the
direction the family is defined, we need to compute the thermodynamics
of the reactants and products. For this reason you must also pass
the `thermoDatabase` to use to generate the thermo data.
"""
def generateThermoData(species, thermoDatabase):
thermoData = [thermoDatabase.getThermoData(species)]
thermoData.sort(key=lambda x: x.getEnthalpy(298))
return thermoData[0]
def matchSpeciesToMolecules(species, molecules):
if len(species) == len(molecules) == 1:
return species[0].isIsomorphic(molecules[0])
elif len(species) == len(molecules) == 2:
if species[0].isIsomorphic(
molecules[0]) and species[1].isIsomorphic(
molecules[1]):
return True
elif species[0].isIsomorphic(
molecules[1]) and species[1].isIsomorphic(
molecules[0]):
return True
return False
reaction = None
template = None
# Get the indicated reaction family
try:
groups = self.families[family].groups
except KeyError:
raise ValueError(
'Invalid value "{0}" for family parameter.'.format(family))
if all([(isinstance(reactant, Group)
or isinstance(reactant, LogicNode))
for reactant in entry.item.reactants]):
# The entry is a rate rule, containing functional groups only
# By convention, these are always given in the forward direction and
# have kinetics defined on a per-site basis
reaction = Reaction(
reactants=entry.item.reactants[:],
products=[],
kinetics=entry.data,
degeneracy=1,
)
template = [
groups.entries[label] for label in entry.label.split(';')
]
elif (all([
isinstance(reactant, Molecule)
for reactant in entry.item.reactants
]) and all(
[isinstance(product, Molecule)
for product in entry.item.products])):
# The entry is a real reaction, containing molecules
# These could be defined for either the forward or reverse direction
# and could have a reaction-path degeneracy
reaction = Reaction(reactants=[], products=[])
for molecule in entry.item.reactants:
reactant = Species(molecule=[molecule])
reactant.generateResonanceIsomers()
reactant.thermo = generateThermoData(reactant, thermoDatabase)
reaction.reactants.append(reactant)
for molecule in entry.item.products:
product = Species(molecule=[molecule])
product.generateResonanceIsomers()
product.thermo = generateThermoData(product, thermoDatabase)
reaction.products.append(product)
# Generate all possible reactions involving the reactant species
generatedReactions = self.generateReactionsFromFamilies(
[reactant.molecule for reactant in reaction.reactants], [],
only_families=[family])
# Remove from that set any reactions that don't produce the desired reactants and products
forward = []
reverse = []
for rxn in generatedReactions:
if matchSpeciesToMolecules(
reaction.reactants,
rxn.reactants) and matchSpeciesToMolecules(
reaction.products, rxn.products):
forward.append(rxn)
if matchSpeciesToMolecules(
reaction.reactants,
rxn.products) and matchSpeciesToMolecules(
reaction.products, rxn.reactants):
reverse.append(rxn)
# We should now know whether the reaction is given in the forward or
# reverse direction
if len(forward) == 1 and len(reverse) == 0:
# The reaction is in the forward direction, so use as-is
reaction = forward[0]
template = reaction.template
# Don't forget to overwrite the estimated kinetics from the database with the kinetics for this entry
reaction.kinetics = entry.data
elif len(reverse) == 1 and len(forward) == 0:
# The reaction is in the reverse direction
# First fit Arrhenius kinetics in that direction
Tdata = 1000.0 / numpy.arange(0.5, 3.301, 0.1, numpy.float64)
kdata = numpy.zeros_like(Tdata)
for i in range(Tdata.shape[0]):
kdata[i] = entry.data.getRateCoefficient(
Tdata[i]) / reaction.getEquilibriumConstant(Tdata[i])
kunits = 'm^3/(mol*s)' if len(
reverse[0].reactants) == 2 else 's^-1'
kinetics = Arrhenius().fitToData(Tdata, kdata, kunits, T0=1.0)
kinetics.Tmin = entry.data.Tmin
kinetics.Tmax = entry.data.Tmax
kinetics.Pmin = entry.data.Pmin
kinetics.Pmax = entry.data.Pmax
# Now flip the direction
reaction = reverse[0]
reaction.kinetics = kinetics
template = reaction.template
elif len(reverse) > 0 and len(forward) > 0:
print 'FAIL: Multiple reactions found for {0!r}.'.format(
entry.label)
elif len(reverse) == 0 and len(forward) == 0:
print 'FAIL: No reactions found for "%s".' % (entry.label)
else:
print 'FAIL: Unable to estimate kinetics for {0!r}.'.format(
entry.label)
assert reaction is not None
assert template is not None
return reaction, template
def loadFAMEInput(path, moleculeDict=None):
"""
Load the contents of a FAME input file into the MEASURE object. FAME
is an early version of MEASURE written in Fortran and used by RMG-Java.
This script enables importing FAME input files into MEASURE so we can
use the additional functionality that MEASURE provides. Note that it
is mostly designed to load the FAME input files generated automatically
by RMG-Java, and may not load hand-crafted FAME input files. If you
specify a `moleculeDict`, then this script will use it to associate
the species with their structures.
"""
def readMeaningfulLine(f):
line = f.readline()
while line != '':
line = line.strip()
if len(line) > 0 and line[0] != '#':
return line
else:
line = f.readline()
return ''
moleculeDict = moleculeDict or {}
logging.info('Loading file "{0}"...'.format(path))
f = open(path)
job = PressureDependenceJob(network=None)
# Read method
method = readMeaningfulLine(f).lower()
if method == 'modifiedstrongcollision':
job.method = 'modified strong collision'
elif method == 'reservoirstate':
job.method = 'reservoir state'
# Read temperatures
Tcount, Tunits, Tmin, Tmax = readMeaningfulLine(f).split()
job.Tmin = Quantity(float(Tmin), Tunits)
job.Tmax = Quantity(float(Tmax), Tunits)
job.Tcount = int(Tcount)
Tlist = []
for i in range(int(Tcount)):
Tlist.append(float(readMeaningfulLine(f)))
job.Tlist = Quantity(Tlist, Tunits)
# Read pressures
Pcount, Punits, Pmin, Pmax = readMeaningfulLine(f).split()
job.Pmin = Quantity(float(Pmin), Punits)
job.Pmax = Quantity(float(Pmax), Punits)
job.Pcount = int(Pcount)
Plist = []
for i in range(int(Pcount)):
Plist.append(float(readMeaningfulLine(f)))
job.Plist = Quantity(Plist, Punits)
# Read interpolation model
model = readMeaningfulLine(f).split()
if model[0].lower() == 'chebyshev':
job.model = ['chebyshev', int(model[1]), int(model[2])]
elif model[0].lower() == 'pdeparrhenius':
job.model = ['pdeparrhenius']
# Read grain size or number of grains
job.grainCount = 0
job.grainSize = Quantity(0.0, "J/mol")
for i in range(2):
data = readMeaningfulLine(f).split()
if data[0].lower() == 'numgrains':
job.grainCount = int(data[1])
elif data[0].lower() == 'grainsize':
job.grainSize = Quantity(float(data[2]), data[1])
# Create the Network
job.network = Network()
# Read collision model
data = readMeaningfulLine(f)
assert data.lower() == 'singleexpdown'
alpha0units, alpha0 = readMeaningfulLine(f).split()
T0units, T0 = readMeaningfulLine(f).split()
n = readMeaningfulLine(f)
energyTransferModel = SingleExponentialDown(
alpha0 = Quantity(float(alpha0), alpha0units),
T0 = Quantity(float(T0), T0units),
n = float(n),
)
speciesDict = {}
# Read bath gas parameters
bathGas = Species(label='bath_gas', energyTransferModel=energyTransferModel)
molWtunits, molWt = readMeaningfulLine(f).split()
if molWtunits == 'u': molWtunits = 'amu'
bathGas.molecularWeight = Quantity(float(molWt), molWtunits)
sigmaLJunits, sigmaLJ = readMeaningfulLine(f).split()
epsilonLJunits, epsilonLJ = readMeaningfulLine(f).split()
assert epsilonLJunits == 'J'
bathGas.lennardJones = LennardJones(
sigma = Quantity(float(sigmaLJ), sigmaLJunits),
epsilon = Quantity(float(epsilonLJ) / constants.kB, 'K'),
)
job.network.bathGas = {bathGas: 1.0}
# Read species data
Nspec = int(readMeaningfulLine(f))
for i in range(Nspec):
species = Species()
species.conformer = Conformer()
# Read species label
species.label = readMeaningfulLine(f)
speciesDict[species.label] = species
if species.label in moleculeDict:
species.molecule = [moleculeDict[species.label]]
# Read species E0
E0units, E0 = readMeaningfulLine(f).split()
species.conformer.E0 = Quantity(float(E0), E0units)
species.conformer.E0.units = 'kJ/mol'
# Read species thermo data
H298units, H298 = readMeaningfulLine(f).split()
S298units, S298 = readMeaningfulLine(f).split()
Cpcount, Cpunits = readMeaningfulLine(f).split()
Cpdata = []
for i in range(int(Cpcount)):
Cpdata.append(float(readMeaningfulLine(f)))
if S298units == 'J/mol*K': S298units = 'J/(mol*K)'
if Cpunits == 'J/mol*K': Cpunits = 'J/(mol*K)'
species.thermo = ThermoData(
H298 = Quantity(float(H298), H298units),
S298 = Quantity(float(S298), S298units),
Tdata = Quantity([300,400,500,600,800,1000,1500], "K"),
Cpdata = Quantity(Cpdata, Cpunits),
)
# Read species collision parameters
molWtunits, molWt = readMeaningfulLine(f).split()
if molWtunits == 'u': molWtunits = 'amu'
species.molecularWeight = Quantity(float(molWt), molWtunits)
sigmaLJunits, sigmaLJ = readMeaningfulLine(f).split()
epsilonLJunits, epsilonLJ = readMeaningfulLine(f).split()
assert epsilonLJunits == 'J'
species.lennardJones = LennardJones(
sigma = Quantity(float(sigmaLJ), sigmaLJunits),
epsilon = Quantity(float(epsilonLJ) / constants.kB, 'K'),
)
# Read species vibrational frequencies
freqCount, freqUnits = readMeaningfulLine(f).split()
frequencies = []
for j in range(int(freqCount)):
frequencies.append(float(readMeaningfulLine(f)))
species.conformer.modes.append(HarmonicOscillator(
frequencies = Quantity(frequencies, freqUnits),
))
# Read species external rotors
rotCount, rotUnits = readMeaningfulLine(f).split()
if int(rotCount) > 0:
raise NotImplementedError('Cannot handle external rotational modes in FAME input.')
# Read species internal rotors
freqCount, freqUnits = readMeaningfulLine(f).split()
frequencies = []
for j in range(int(freqCount)):
frequencies.append(float(readMeaningfulLine(f)))
barrCount, barrUnits = readMeaningfulLine(f).split()
barriers = []
for j in range(int(barrCount)):
barriers.append(float(readMeaningfulLine(f)))
if barrUnits == 'cm^-1':
barrUnits = 'J/mol'
barriers = [barr * constants.h * constants.c * constants.Na * 100. for barr in barriers]
elif barrUnits in ['Hz', 's^-1']:
barrUnits = 'J/mol'
barriers = [barr * constants.h * constants.Na for barr in barriers]
elif barrUnits != 'J/mol':
raise Exception('Unexpected units "{0}" for hindered rotor barrier height.'.format(barrUnits))
inertia = [V0 / 2.0 / (nu * constants.c * 100.)**2 / constants.Na for nu, V0 in zip(frequencies, barriers)]
for I, V0 in zip(inertia, barriers):
species.conformer.modes.append(HinderedRotor(
inertia = Quantity(I,"kg*m^2"),
barrier = Quantity(V0,barrUnits),
symmetry = 1,
))
# Read overall symmetry number
species.conformer.spinMultiplicity = int(readMeaningfulLine(f))
# Read isomer, reactant channel, and product channel data
Nisom = int(readMeaningfulLine(f))
Nreac = int(readMeaningfulLine(f))
Nprod = int(readMeaningfulLine(f))
for i in range(Nisom):
data = readMeaningfulLine(f).split()
assert data[0] == '1'
job.network.isomers.append(speciesDict[data[1]])
for i in range(Nreac):
data = readMeaningfulLine(f).split()
assert data[0] == '2'
job.network.reactants.append([speciesDict[data[1]], speciesDict[data[2]]])
for i in range(Nprod):
data = readMeaningfulLine(f).split()
if data[0] == '1':
job.network.products.append([speciesDict[data[1]]])
elif data[0] == '2':
job.network.products.append([speciesDict[data[1]], speciesDict[data[2]]])
# Read path reactions
Nrxn = int(readMeaningfulLine(f))
for i in range(Nrxn):
# Read and ignore reaction equation
equation = readMeaningfulLine(f)
reaction = Reaction(transitionState=TransitionState(), reversible=True)
job.network.pathReactions.append(reaction)
reaction.transitionState.conformer = Conformer()
# Read reactant and product indices
data = readMeaningfulLine(f).split()
reac = int(data[0]) - 1
prod = int(data[1]) - 1
if reac < Nisom:
reaction.reactants = [job.network.isomers[reac]]
elif reac < Nisom+Nreac:
reaction.reactants = job.network.reactants[reac-Nisom]
else:
reaction.reactants = job.network.products[reac-Nisom-Nreac]
if prod < Nisom:
reaction.products = [job.network.isomers[prod]]
elif prod < Nisom+Nreac:
reaction.products = job.network.reactants[prod-Nisom]
else:
reaction.products = job.network.products[prod-Nisom-Nreac]
# Read reaction E0
E0units, E0 = readMeaningfulLine(f).split()
reaction.transitionState.conformer.E0 = Quantity(float(E0), E0units)
reaction.transitionState.conformer.E0.units = 'kJ/mol'
# Read high-pressure limit kinetics
data = readMeaningfulLine(f)
assert data.lower() == 'arrhenius'
Aunits, A = readMeaningfulLine(f).split()
if '/' in Aunits:
index = Aunits.find('/')
Aunits = '{0}/({1})'.format(Aunits[0:index], Aunits[index+1:])
Eaunits, Ea = readMeaningfulLine(f).split()
n = readMeaningfulLine(f)
reaction.kinetics = Arrhenius(
A = Quantity(float(A), Aunits),
Ea = Quantity(float(Ea), Eaunits),
n = Quantity(float(n)),
)
reaction.kinetics.Ea.units = 'kJ/mol'
f.close()
job.network.isomers = [Configuration(isomer) for isomer in job.network.isomers]
job.network.reactants = [Configuration(*reactants) for reactants in job.network.reactants]
job.network.products = [Configuration(*products) for products in job.network.products]
return job
def getForwardReactionForFamilyEntry(self, entry, family, thermoDatabase):
"""
For a given `entry` for a reaction of the given reaction `family` (the
string label of the family), return the reaction with kinetics and
degeneracy for the "forward" direction as defined by the reaction
family. For families that are their own reverse, the direction the
kinetics is given in will be preserved. If the entry contains
functional groups for the reactants, assume that it is given in the
forward direction and do nothing. Returns the reaction in the direction
consistent with the reaction family template, and the matching template.
Note that the returned reaction will have its kinetics and degeneracy
set appropriately.
In order to reverse the reactions that are given in the reverse of the
direction the family is defined, we need to compute the thermodynamics
of the reactants and products. For this reason you must also pass
the `thermoDatabase` to use to generate the thermo data.
"""
def generateThermoData(species, thermoDatabase):
thermoData = [thermoDatabase.getThermoData(species)]
thermoData.sort(key=lambda x: x.getEnthalpy(298))
return thermoData[0]
def matchSpeciesToMolecules(species, molecules):
if len(species) == len(molecules) == 1:
return species[0].isIsomorphic(molecules[0])
elif len(species) == len(molecules) == 2:
if species[0].isIsomorphic(molecules[0]) and species[1].isIsomorphic(molecules[1]):
return True
elif species[0].isIsomorphic(molecules[1]) and species[1].isIsomorphic(molecules[0]):
return True
return False
reaction = None; template = None
# Get the indicated reaction family
try:
groups = self.families[family].groups
except KeyError:
raise ValueError('Invalid value "{0}" for family parameter.'.format(family))
if all([(isinstance(reactant, Group) or isinstance(reactant, LogicNode)) for reactant in entry.item.reactants]):
# The entry is a rate rule, containing functional groups only
# By convention, these are always given in the forward direction and
# have kinetics defined on a per-site basis
reaction = Reaction(
reactants = entry.item.reactants[:],
products = [],
kinetics = entry.data,
degeneracy = 1,
)
template = [groups.entries[label] for label in entry.label.split(';')]
elif (all([isinstance(reactant, Molecule) for reactant in entry.item.reactants]) and
all([isinstance(product, Molecule) for product in entry.item.products])):
# The entry is a real reaction, containing molecules
# These could be defined for either the forward or reverse direction
# and could have a reaction-path degeneracy
reaction = Reaction(reactants=[], products=[])
for molecule in entry.item.reactants:
reactant = Species(molecule=[molecule])
reactant.generateResonanceIsomers()
reactant.thermo = generateThermoData(reactant, thermoDatabase)
reaction.reactants.append(reactant)
for molecule in entry.item.products:
product = Species(molecule=[molecule])
product.generateResonanceIsomers()
product.thermo = generateThermoData(product, thermoDatabase)
reaction.products.append(product)
# Generate all possible reactions involving the reactant species
generatedReactions = self.generateReactionsFromFamilies([reactant.molecule for reactant in reaction.reactants], [], only_families=[family])
# Remove from that set any reactions that don't produce the desired reactants and products
forward = []; reverse = []
for rxn in generatedReactions:
if matchSpeciesToMolecules(reaction.reactants, rxn.reactants) and matchSpeciesToMolecules(reaction.products, rxn.products):
forward.append(rxn)
if matchSpeciesToMolecules(reaction.reactants, rxn.products) and matchSpeciesToMolecules(reaction.products, rxn.reactants):
reverse.append(rxn)
# We should now know whether the reaction is given in the forward or
# reverse direction
if len(forward) == 1 and len(reverse) == 0:
# The reaction is in the forward direction, so use as-is
reaction = forward[0]
template = reaction.template
# Don't forget to overwrite the estimated kinetics from the database with the kinetics for this entry
reaction.kinetics = entry.data
elif len(reverse) == 1 and len(forward) == 0:
# The reaction is in the reverse direction
# First fit Arrhenius kinetics in that direction
Tdata = 1000.0 / numpy.arange(0.5, 3.301, 0.1, numpy.float64)
kdata = numpy.zeros_like(Tdata)
for i in range(Tdata.shape[0]):
kdata[i] = entry.data.getRateCoefficient(Tdata[i]) / reaction.getEquilibriumConstant(Tdata[i])
kunits = 'm^3/(mol*s)' if len(reverse[0].reactants) == 2 else 's^-1'
kinetics = Arrhenius().fitToData(Tdata, kdata, kunits, T0=1.0)
kinetics.Tmin = entry.data.Tmin
kinetics.Tmax = entry.data.Tmax
kinetics.Pmin = entry.data.Pmin
kinetics.Pmax = entry.data.Pmax
# Now flip the direction
reaction = reverse[0]
reaction.kinetics = kinetics
template = reaction.template
elif len(reverse) > 0 and len(forward) > 0:
print 'FAIL: Multiple reactions found for {0!r}.'.format(entry.label)
elif len(reverse) == 0 and len(forward) == 0:
print 'FAIL: No reactions found for "%s".' % (entry.label)
else:
print 'FAIL: Unable to estimate kinetics for {0!r}.'.format(entry.label)
assert reaction is not None
assert template is not None
return reaction, template
def get_forward_reaction_for_family_entry(self, entry, family,
thermo_database):
"""
For a given `entry` for a reaction of the given reaction `family` (the
string label of the family), return the reaction with kinetics and
degeneracy for the "forward" direction as defined by the reaction
family. For families that are their own reverse, the direction the
kinetics is given in will be preserved. If the entry contains
functional groups for the reactants, assume that it is given in the
forward direction and do nothing. Returns the reaction in the direction
consistent with the reaction family template, and the matching template.
Note that the returned reaction will have its kinetics and degeneracy
set appropriately.
In order to reverse the reactions that are given in the reverse of the
direction the family is defined, we need to compute the thermodynamics
of the reactants and products. For this reason you must also pass
the `thermo_database` to use to generate the thermo data.
"""
reaction = None
template = None
# Get the indicated reaction family
try:
groups = self.families[family].groups
except KeyError:
raise ValueError(
'Invalid value "{0}" for family parameter.'.format(family))
if all([(isinstance(reactant, Group)
or isinstance(reactant, LogicNode))
for reactant in entry.item.reactants]):
# The entry is a rate rule, containing functional groups only
# By convention, these are always given in the forward direction and
# have kinetics defined on a per-site basis
reaction = Reaction(
reactants=entry.item.reactants[:],
products=[],
specific_collider=entry.item.specific_collider,
kinetics=entry.data,
degeneracy=1,
)
template = [
groups.entries[label] for label in entry.label.split(';')
]
elif (all([
isinstance(reactant, Molecule)
for reactant in entry.item.reactants
]) and all(
[isinstance(product, Molecule)
for product in entry.item.products])):
# The entry is a real reaction, containing molecules
# These could be defined for either the forward or reverse direction
# and could have a reaction-path degeneracy
reaction = Reaction(reactants=[], products=[])
for molecule in entry.item.reactants:
reactant = Species(molecule=[molecule])
reactant.generate_resonance_structures()
reactant.thermo = thermo_database.get_thermo_data(reactant)
reaction.reactants.append(reactant)
for molecule in entry.item.products:
product = Species(molecule=[molecule])
product.generate_resonance_structures()
product.thermo = thermo_database.get_thermo_data(product)
reaction.products.append(product)
# Generate all possible reactions involving the reactant species
generated_reactions = self.generate_reactions_from_families(
[reactant.molecule for reactant in reaction.reactants], [],
only_families=[family])
# Remove from that set any reactions that don't produce the desired reactants and products
forward = []
reverse = []
for rxn in generated_reactions:
if (same_species_lists(reaction.reactants, rxn.reactants) and
same_species_lists(reaction.products, rxn.products)):
forward.append(rxn)
if (same_species_lists(reaction.reactants, rxn.products) and
same_species_lists(reaction.products, rxn.reactants)):
reverse.append(rxn)
# We should now know whether the reaction is given in the forward or
# reverse direction
if len(forward) == 1 and len(reverse) == 0:
# The reaction is in the forward direction, so use as-is
reaction = forward[0]
template = reaction.template
# Don't forget to overwrite the estimated kinetics from the database with the kinetics for this entry
reaction.kinetics = entry.data
elif len(reverse) == 1 and len(forward) == 0:
# The reaction is in the reverse direction
# First fit Arrhenius kinetics in that direction
T_data = 1000.0 / np.arange(0.5, 3.301, 0.1, np.float64)
k_data = np.zeros_like(T_data)
for i in range(T_data.shape[0]):
k_data[i] = entry.data.get_rate_coefficient(
T_data[i]) / reaction.get_equilibrium_constant(
T_data[i])
try:
k_units = ('s^-1', 'm^3/(mol*s)',
'm^6/(mol^2*s)')[len(reverse[0].reactants) - 1]
except IndexError:
raise NotImplementedError(
'Cannot reverse reactions with {} products'.format(
len(reverse[0].reactants)))
kinetics = Arrhenius().fit_to_data(T_data,
k_data,
k_units,
T0=1.0)
kinetics.Tmin = entry.data.Tmin
kinetics.Tmax = entry.data.Tmax
kinetics.Pmin = entry.data.Pmin
kinetics.Pmax = entry.data.Pmax
# Now flip the direction
reaction = reverse[0]
reaction.kinetics = kinetics
template = reaction.template
elif len(reverse) > 0 and len(forward) > 0:
print('FAIL: Multiple reactions found for {0!r}.'.format(
entry.label))
elif len(reverse) == 0 and len(forward) == 0:
print('FAIL: No reactions found for "%s".' % (entry.label))
else:
print('FAIL: Unable to estimate kinetics for {0!r}.'.format(
entry.label))
assert reaction is not None
assert template is not None
return reaction, template
