def __init__(self,
reaction,
Tmin=None,
Tmax=None,
Tlist=None,
Tcount=0,
sensitivity_conditions=None):
self.usedTST = False
self.Tmin = Tmin if Tmin is not None else (298, 'K')
self.Tmax = Tmax if Tmax is not None else (2500, 'K')
self.Tcount = Tcount if Tcount > 3 else 50
if Tlist is not None:
self.Tlist = Tlist
self.Tmin = (min(self.Tlist.value_si), 'K')
self.Tmax = (max(self.Tlist.value_si), 'K')
self.Tcount = len(self.Tlist.value_si)
else:
self.Tlist = (1 / np.linspace(1 / self.Tmax.value_si,
1 / self.Tmin.value_si, self.Tcount),
'K')
self.reaction = reaction
self.k_units = None
if sensitivity_conditions is not None:
self.sensitivity_conditions = [
quantity.Quantity(condition)
for condition in sensitivity_conditions
]
else:
self.sensitivity_conditions = None
self.arkane_species = ArkaneSpecies(
species=self.reaction.transition_state)
def test_loading_different_versions_of_yaml(self):
"""Test loading a YAML file generated by RMG v 2.4.1 and by a more recent version"""
arkane_spc_v_241 = ArkaneSpecies.__new__(ArkaneSpecies)
arkane_spc_v_241.load_yaml(
path=os.path.join(self.data_path, 'vinoxy_v_2.4.1.yml'))
self.assertIsInstance(arkane_spc_v_241,
ArkaneSpecies) # checks make_object
self.assertEqual(arkane_spc_v_241.conformer.spin_multiplicity, 2)
arkane_current = ArkaneSpecies.__new__(ArkaneSpecies)
arkane_current.load_yaml(
path=os.path.join(self.data_path, 'vinoxy_current.yml'))
self.assertIsInstance(arkane_current,
ArkaneSpecies) # checks make_object
self.assertEqual(arkane_current.conformer.spin_multiplicity, 2)
def test_load_existing_yaml(self):
"""
Test that existing Arkane YAML files can still be loaded
"""
# Load in YAML file
arkane_spc = ArkaneSpecies.__new__(ArkaneSpecies)
arkane_spc.load_yaml(path=os.path.join(self.load_path, 'C2H6.yml'))
self.assertIsInstance(arkane_spc, ArkaneSpecies) # checks make_object
self.assertIsInstance(arkane_spc.molecular_weight, ScalarQuantity)
self.assertIsInstance(arkane_spc.thermo, NASA)
self.assertNotEqual(arkane_spc.author, '')
self.assertEqual(arkane_spc.inchi, 'InChI=1S/C2H6/c1-2/h1-2H3')
self.assertEqual(arkane_spc.inchi_key, 'OTMSDBZUPAUEDD-UHFFFAOYSA-N')
self.assertEqual(arkane_spc.smiles, 'CC')
self.assertTrue('8 H u0 p0 c0 {2,S}' in arkane_spc.adjacency_list)
self.assertEqual(arkane_spc.label, 'C2H6')
self.assertEqual(arkane_spc.frequency_scale_factor,
0.99) # checks float conversion
self.assertFalse(arkane_spc.use_bond_corrections)
self.assertAlmostEqual(
arkane_spc.conformer.modes[2].frequencies.value_si[0], 818.91718,
4) # HarmonicOsc.
self.assertIsInstance(arkane_spc.energy_transfer_model,
SingleExponentialDown)
self.assertFalse(arkane_spc.is_ts)
self.assertTrue(arkane_spc.use_hindered_rotors)
self.assertTrue('C 7.54e-14 1.193e-13 5.52e-14' in arkane_spc.xyz)
self.assertIsInstance(arkane_spc.chemkin_thermo_string, str)
def __init__(self, reaction,
Tmin=None,
Tmax=None,
Tlist=None,
Tcount=0,
sensitivity_conditions=None):
self.usedTST = False
if Tmin is not None:
self.Tmin = quantity.Quantity(Tmin)
else:
self.Tmin = None
if Tmax is not None:
self.Tmax = quantity.Quantity(Tmax)
else:
self.Tmax = None
self.Tcount = Tcount
if Tlist is not None:
self.Tlist = quantity.Quantity(Tlist)
self.Tmin = quantity.Quantity(numpy.min(self.Tlist.value_si), "K")
self.Tmax = quantity.Quantity(numpy.max(self.Tlist.value_si), "K")
self.Tcount = len(self.Tlist.value_si)
else:
if Tmin and Tmax is not None:
if self.Tcount <= 3.:
self.Tcount = 50
stepsize = (self.Tmax.value_si - self.Tmin.value_si) / self.Tcount
self.Tlist = quantity.Quantity(numpy.arange(self.Tmin.value_si,
self.Tmax.value_si + stepsize, stepsize), 'K')
else:
self.Tlist = None
self.reaction = reaction
self.kunits = None
if sensitivity_conditions is not None:
self.sensitivity_conditions = [quantity.Quantity(condition) for condition in sensitivity_conditions]
else:
self.sensitivity_conditions = None
self.arkane_species = ArkaneSpecies(species=self.reaction.transitionState)
def test_create_and_load_yaml(self):
"""
Test properly loading the ArkaneSpecies object and respective sub-objects
"""
# Create YAML file by running Arkane
job_list = self.arkane.load_input_file(self.dump_input_path)
for job in job_list:
job.execute(output_directory=self.dump_path)
# Load in newly created YAML file
arkane_spc_old = job_list[0].arkane_species
arkane_spc = ArkaneSpecies.__new__(ArkaneSpecies)
arkane_spc.load_yaml(
path=os.path.join(self.dump_path, 'species', arkane_spc_old.label +
'.yml'))
self.assertIsInstance(arkane_spc, ArkaneSpecies) # checks make_object
self.assertIsInstance(arkane_spc.molecular_weight, ScalarQuantity)
self.assertIsInstance(arkane_spc.thermo, NASA)
self.assertNotEqual(arkane_spc.author, '')
self.assertEqual(arkane_spc.inchi, 'InChI=1S/C2H6/c1-2/h1-2H3')
self.assertEqual(arkane_spc.inchi_key, 'OTMSDBZUPAUEDD-UHFFFAOYSA-N')
self.assertEqual(arkane_spc.smiles, 'CC')
self.assertTrue('8 H u0 p0 c0 {2,S}' in arkane_spc.adjacency_list)
self.assertEqual(arkane_spc.label, 'C2H6')
self.assertEqual(arkane_spc.frequency_scale_factor,
0.99 * 1.014) # checks float conversion
self.assertFalse(arkane_spc.use_bond_corrections)
self.assertAlmostEqual(
arkane_spc.conformer.modes[2].frequencies.value_si[0], 830.38202,
4) # HarmonicOsc.
self.assertIsInstance(arkane_spc.energy_transfer_model,
SingleExponentialDown)
self.assertFalse(arkane_spc.is_ts)
self.assertEqual(arkane_spc.level_of_theory, 'cbs-qb3')
self.assertIsInstance(arkane_spc.thermo_data, ThermoData)
self.assertTrue(arkane_spc.use_hindered_rotors)
self.assertIsInstance(arkane_spc.chemkin_thermo_string, str)
expected_xyz = """8
C2H6
C 0.00075400 0.00119300 0.00055200
H 0.00074000 0.00117100 1.09413800
H 1.04376600 0.00117100 -0.32820200
H -0.44760300 0.94289500 -0.32825300
C -0.76014200 -1.20389600 -0.55748300
H -0.76012800 -1.20387400 -1.65106900
H -0.31178500 -2.14559800 -0.22867800
H -1.80315400 -1.20387400 -0.22872900"""
self.assertEqual(arkane_spc.xyz, expected_xyz)
def __init__(self, reaction,
Tmin=None,
Tmax=None,
Tlist=None,
Tcount=0,
sensitivity_conditions=None):
self.usedTST = False
if Tmin is not None:
self.Tmin = quantity.Quantity(Tmin)
else:
self.Tmin = None
if Tmax is not None:
self.Tmax = quantity.Quantity(Tmax)
else:
self.Tmax = None
self.Tcount = Tcount
if Tlist is not None:
self.Tlist = quantity.Quantity(Tlist)
self.Tmin = quantity.Quantity(numpy.min(self.Tlist.value_si), "K")
self.Tmax = quantity.Quantity(numpy.max(self.Tlist.value_si), "K")
self.Tcount = len(self.Tlist.value_si)
else:
if Tmin and Tmax is not None:
if self.Tcount <= 3.:
self.Tcount = 50
stepsize = (self.Tmax.value_si-self.Tmin.value_si) / self.Tcount
self.Tlist = quantity.Quantity(numpy.arange(self.Tmin.value_si, self.Tmax.value_si+stepsize, stepsize),"K")
else:
self.Tlist = None
self.reaction = reaction
self.kunits = None
if sensitivity_conditions is not None:
self.sensitivity_conditions = [quantity.Quantity(condition) for condition in sensitivity_conditions]
else:
self.sensitivity_conditions = None
self.arkane_species = ArkaneSpecies(species=self.reaction.transitionState)
def test_create_and_load_yaml(self):
"""
Test properly loading the ArkaneSpecies object and respective sub-objects
"""
# Create YAML file by running Arkane
jobList = self.arkane.loadInputFile(self.dump_input_path)
for job in jobList:
job.execute(output_directory=self.dump_path)
# Load in newly created YAML file
arkane_spc_old = jobList[0].arkane_species
arkane_spc = ArkaneSpecies.__new__(ArkaneSpecies)
arkane_spc.load_yaml(
path=os.path.join(self.dump_path, 'species', arkane_spc_old.label +
'.yml'))
self.assertIsInstance(arkane_spc, ArkaneSpecies) # checks make_object
self.assertIsInstance(arkane_spc.molecular_weight, ScalarQuantity)
self.assertIsInstance(arkane_spc.thermo, NASA)
self.assertNotEqual(arkane_spc.author, '')
self.assertEqual(arkane_spc.inchi, 'InChI=1S/C2H6/c1-2/h1-2H3')
self.assertEqual(arkane_spc.inchi_key, 'OTMSDBZUPAUEDD-UHFFFAOYSA-N')
self.assertEqual(arkane_spc.smiles, 'CC')
self.assertTrue('8 H u0 p0 c0 {2,S}' in arkane_spc.adjacency_list)
self.assertEqual(arkane_spc.label, 'C2H6')
self.assertEqual(arkane_spc.frequency_scale_factor,
0.99 * 1.014) # checks float conversion
self.assertFalse(arkane_spc.use_bond_corrections)
self.assertAlmostEqual(
arkane_spc.conformer.modes[2].frequencies.value_si[0], 830.38202,
4) # HarmonicOsc.
self.assertIsInstance(arkane_spc.energy_transfer_model,
SingleExponentialDown)
self.assertFalse(arkane_spc.is_ts)
self.assertEqual(arkane_spc.level_of_theory, 'cbs-qb3')
self.assertIsInstance(arkane_spc.thermo_data, ThermoData)
self.assertTrue(arkane_spc.use_hindered_rotors)
self.assertTrue('C 7.54e-14 1.193e-13 5.52e-14' in arkane_spc.xyz)
self.assertIsInstance(arkane_spc.chemkin_thermo_string, str)
class KineticsJob(object):
"""
A representation of an Arkane kinetics job. This job is used to compute
and save the high-pressure-limit kinetics information for a single reaction.
`usedTST` - a boolean representing if TST was used to calculate the kinetics
if kinetics is already given in the input, then it is False.
"""
def __init__(self, reaction,
Tmin=None,
Tmax=None,
Tlist=None,
Tcount=0,
sensitivity_conditions=None):
self.usedTST = False
if Tmin is not None:
self.Tmin = quantity.Quantity(Tmin)
else:
self.Tmin = None
if Tmax is not None:
self.Tmax = quantity.Quantity(Tmax)
else:
self.Tmax = None
self.Tcount = Tcount
if Tlist is not None:
self.Tlist = quantity.Quantity(Tlist)
self.Tmin = quantity.Quantity(numpy.min(self.Tlist.value_si), "K")
self.Tmax = quantity.Quantity(numpy.max(self.Tlist.value_si), "K")
self.Tcount = len(self.Tlist.value_si)
else:
if Tmin and Tmax is not None:
if self.Tcount <= 3.:
self.Tcount = 50
stepsize = (self.Tmax.value_si-self.Tmin.value_si) / self.Tcount
self.Tlist = quantity.Quantity(numpy.arange(self.Tmin.value_si, self.Tmax.value_si+stepsize, stepsize),"K")
else:
self.Tlist = None
self.reaction = reaction
self.kunits = None
if sensitivity_conditions is not None:
self.sensitivity_conditions = [quantity.Quantity(condition) for condition in sensitivity_conditions]
else:
self.sensitivity_conditions = None
self.arkane_species = ArkaneSpecies(species=self.reaction.transitionState)
@property
def Tmin(self):
"""The minimum temperature at which the computed k(T) values are valid, or ``None`` if not defined."""
return self._Tmin
@Tmin.setter
def Tmin(self, value):
self._Tmin = quantity.Temperature(value)
@property
def Tmax(self):
"""The maximum temperature at which the computed k(T) values are valid, or ``None`` if not defined."""
return self._Tmax
@Tmax.setter
def Tmax(self, value):
self._Tmax = quantity.Temperature(value)
@property
def Tlist(self):
"""The temperatures at which the k(T) values are computed."""
return self._Tlist
@Tlist.setter
def Tlist(self, value):
self._Tlist = quantity.Temperature(value)
def execute(self, outputFile=None, plot=True):
"""
Execute the kinetics job, saving the results to the given `outputFile` on disk.
"""
if self.Tlist is not None:
self.generateKinetics(self.Tlist.value_si)
else:
self.generateKinetics()
if outputFile is not None:
self.save(outputFile)
if plot:
self.plot(os.path.dirname(outputFile))
self.draw(os.path.dirname(outputFile))
if self.sensitivity_conditions is not None:
logging.info('\n\nRunning sensitivity analysis...')
sa(self, os.path.dirname(outputFile))
logging.debug('Finished kinetics job for reaction {0}.'.format(self.reaction))
logging.debug(repr(self.reaction))
def generateKinetics(self,Tlist=None):
"""
Generate the kinetics data for the reaction and fit it to a modified Arrhenius model.
"""
if isinstance(self.reaction.kinetics, Arrhenius):
return None
self.usedTST=True
kineticsClass = 'Arrhenius'
tunneling = self.reaction.transitionState.tunneling
if isinstance(tunneling, Wigner) and tunneling.frequency is None:
tunneling.frequency = (self.reaction.transitionState.frequency.value_si,"cm^-1")
elif isinstance(tunneling, Eckart) and tunneling.frequency is None:
tunneling.frequency = (self.reaction.transitionState.frequency.value_si,"cm^-1")
tunneling.E0_reac = (sum([reactant.conformer.E0.value_si for reactant in self.reaction.reactants])*0.001,"kJ/mol")
tunneling.E0_TS = (self.reaction.transitionState.conformer.E0.value_si*0.001,"kJ/mol")
tunneling.E0_prod = (sum([product.conformer.E0.value_si for product in self.reaction.products])*0.001,"kJ/mol")
elif tunneling is not None:
if tunneling.frequency is not None:
# Frequency was given by the user
pass
else:
raise ValueError('Unknown tunneling model {0!r} for reaction {1}.'.format(tunneling, self.reaction))
logging.debug('Generating {0} kinetics model for {1}...'.format(kineticsClass, self.reaction))
if Tlist is None:
Tlist = 1000.0/numpy.arange(0.4, 3.35, 0.05)
klist = numpy.zeros_like(Tlist)
for i in range(Tlist.shape[0]):
klist[i] = self.reaction.calculateTSTRateCoefficient(Tlist[i])
order = len(self.reaction.reactants)
klist *= 1e6 ** (order-1)
self.kunits = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[order]
self.Kequnits = {2:'mol^2/cm^6', 1:'mol/cm^3', 0:' ', -1:'cm^3/mol', -2:'cm^6/mol^2'}[len(self.reaction.products)-len(self.reaction.reactants)]
self.krunits = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[len(self.reaction.products)]
self.reaction.kinetics = Arrhenius().fitToData(Tlist, klist, kunits=self.kunits)
self.reaction.elementary_high_p = True
def save(self, outputFile):
"""
Save the results of the kinetics job to the file located
at `path` on disk.
"""
reaction = self.reaction
ks = []
k0s = []
k0revs = []
krevs = []
logging.info('Saving kinetics for {0}...'.format(reaction))
order = len(self.reaction.reactants)
factor = 1e6 ** (order-1)
f = open(outputFile, 'a')
if self.usedTST:
# If TST is not used, eg. it was given in 'reaction', then this will throw an error.
f.write('# ======= =========== =========== =========== ===============\n')
f.write('# Temp. k (TST) Tunneling k (TST+T) Units\n')
f.write('# ======= =========== =========== =========== ===============\n')
if self.Tlist is None:
Tlist = numpy.array([300,400,500,600,800,1000,1500,2000])
else:
Tlist =self.Tlist.value_si
for T in Tlist:
tunneling = reaction.transitionState.tunneling
reaction.transitionState.tunneling = None
try:
k0 = reaction.calculateTSTRateCoefficient(T) * factor
except SpeciesError:
k0 = 0
reaction.transitionState.tunneling = tunneling
try:
k = reaction.calculateTSTRateCoefficient(T) * factor
kappa = k / k0
except (SpeciesError,ZeroDivisionError):
k = reaction.getRateCoefficient(T)
kappa = 0
logging.info("The species in reaction {} do not have adequate information for TST, using default kinetics values.".format(reaction))
tunneling = reaction.transitionState.tunneling
ks.append(k)
k0s.append(k0)
f.write('# {0:4g} K {1:11.3e} {2:11g} {3:11.3e} {4}\n'.format(T, k0, kappa, k, self.kunits))
f.write('# ======= =========== =========== =========== ===============\n')
f.write('\n\n')
f.write('# ======= ============ =========== ============ ============= =========\n')
f.write('# Temp. Kc (eq) Units krev (TST) krev (TST+T) Units\n')
f.write('# ======= ============ =========== ============ ============= =========\n')
# Initialize Object for Converting Units
if self.Kequnits != ' ':
keq_unit_converter = quantity.Units(self.Kequnits).getConversionFactorFromSI()
else:
keq_unit_converter = 1
for n,T in enumerate(Tlist):
k = ks[n]
k0 = k0s[n]
Keq = keq_unit_converter * reaction.getEquilibriumConstant(T) # getEquilibriumConstant returns SI units
k0rev = k0/Keq
krev = k/Keq
k0revs.append(k0rev)
krevs.append(krev)
f.write('# {0:4g} K {1:11.3e} {2} {3:11.3e} {4:11.3e} {5}\n'.format(T, Keq, self.Kequnits, k0rev, krev, self.krunits))
f.write('# ======= ============ =========== ============ ============= =========\n')
f.write('\n\n')
kinetics0rev = Arrhenius().fitToData(Tlist, numpy.array(k0revs), kunits=self.krunits)
kineticsrev = Arrhenius().fitToData(Tlist, numpy.array(krevs), kunits=self.krunits)
f.write('# krev (TST) = {0} \n'.format(kinetics0rev))
f.write('# krev (TST+T) = {0} \n\n'.format(kineticsrev))
# Reaction path degeneracy is INCLUDED in the kinetics itself!
string = 'kinetics(label={0!r}, kinetics={1!r})'.format(reaction.label, reaction.kinetics)
f.write('{0}\n\n'.format(prettify(string)))
f.close()
# Also save the result to chem.inp
f = open(os.path.join(os.path.dirname(outputFile), 'chem.inp'), 'a')
reaction = self.reaction
kinetics = reaction.kinetics
string = ''
if reaction.kinetics.comment:
for line in reaction.kinetics.comment.split("\n"):
string += "! {0}\n".format(line)
string += '{0!s:51} {1:9.3e} {2:9.3f} {3:9.3f}\n'.format(
reaction,
kinetics.A.value_si * factor,
kinetics.n.value_si,
kinetics.Ea.value_si / 4184.,
)
f.write('{0}\n'.format(string))
f.close()
# We're saving a YAML file for TSs iff structures of the respective reactant/s and product/s are known
if all ([spc.molecule is not None and len(spc.molecule)
for spc in self.reaction.reactants + self.reaction.products]):
self.arkane_species.update_species_attributes(self.reaction.transitionState)
self.arkane_species.reaction_label = reaction.label
self.arkane_species.reactants = [{'label': spc.label, 'adjacency_list': spc.molecule[0].toAdjacencyList()}
for spc in self.reaction.reactants]
self.arkane_species.products = [{'label': spc.label, 'adjacency_list': spc.molecule[0].toAdjacencyList()}
for spc in self.reaction.products]
self.arkane_species.save_yaml(path=os.path.dirname(outputFile))
def plot(self, outputDirectory):
"""
Plot both the raw kinetics data and the Arrhenius fit versus
temperature. The plot is saved to the file ``kinetics.pdf`` in the
output directory. The plot is not generated if ``matplotlib`` is not
installed.
"""
# Skip this step if matplotlib is not installed
try:
import matplotlib.pyplot as plt
except ImportError:
return
if self.Tlist is not None:
t_list = [t for t in self.Tlist.value_si]
else:
t_list = 1000.0/numpy.arange(0.4, 3.35, 0.05)
klist = numpy.zeros_like(t_list)
klist2 = numpy.zeros_like(t_list)
for i in xrange(len(t_list)):
klist[i] = self.reaction.calculateTSTRateCoefficient(t_list[i])
klist2[i] = self.reaction.kinetics.getRateCoefficient(t_list[i])
order = len(self.reaction.reactants)
klist *= 1e6 ** (order-1)
klist2 *= 1e6 ** (order-1)
t_list = [1000.0 / t for t in t_list]
plt.semilogy(t_list, klist, 'ob', label='TST calculation')
plt.semilogy(t_list, klist2, '-k', label='Fitted rate')
plt.legend()
reaction_str = '{0} {1} {2}'.format(
' + '.join([reactant.label for reactant in self.reaction.reactants]),
'<=>', ' + '.join([product.label for product in self.reaction.products]))
plt.title(reaction_str)
plt.xlabel('1000 / Temperature (1000/K)')
plt.ylabel('Rate coefficient ({0})'.format(self.kunits))
plot_path = os.path.join(outputDirectory, 'plots')
if not os.path.exists(plot_path):
os.mkdir(plot_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
filename = ''.join(c for c in reaction_str if c in valid_chars) + '.pdf'
plt.savefig(os.path.join(plot_path, filename))
plt.close()
def draw(self, outputDirectory, format='pdf'):
"""
Generate a PDF drawing of the reaction.
This requires that Cairo and its Python wrapper be available; if not,
the drawing is not generated.
You may also generate different formats of drawings, by changing format to
one of the following: `pdf`, `svg`, `png`.
"""
drawing_path = os.path.join(outputDirectory, 'paths')
if not os.path.exists(drawing_path):
os.mkdir(drawing_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
reaction_str = '{0} {1} {2}'.format(
' + '.join([reactant.label for reactant in self.reaction.reactants]),
'<=>', ' + '.join([product.label for product in self.reaction.products]))
filename = ''.join(c for c in reaction_str if c in valid_chars) + '.pdf'
path = os.path.join(drawing_path, filename)
KineticsDrawer().draw(self.reaction, format=format, path=path)
def __init__(self, species, thermo_class):
self.species = species
self.thermo_class = thermo_class
self.arkane_species = ArkaneSpecies(species=species)
class ThermoJob(object):
"""
A representation of an Arkane thermodynamics job. This job is used to
compute and save the thermodynamics information for a single species.
"""
def __init__(self, species, thermo_class):
self.species = species
self.thermo_class = thermo_class
self.arkane_species = ArkaneSpecies(species=species)
def execute(self, output_directory=None, plot=False):
"""
Execute the thermodynamics job, saving the results within
the `output_directory`.
If `plot` is true, then plots of the raw and fitted values for heat
capacity, entropy, enthalpy, gibbs free energy, and hindered rotors
will be saved.
"""
self.generate_thermo()
if output_directory is not None:
try:
self.write_output(output_directory)
except Exception as e:
logging.warning("Could not write output file due to error: "
"{0} for species {1}".format(
e, self.species.label))
try:
self.arkane_species.chemkin_thermo_string = self.write_chemkin(
output_directory)
except Exception as e:
logging.warning("Could not write chemkin output due to error: "
"{0} for species {1}".format(
e, self.species.label))
if self.species.molecule is None or len(
self.species.molecule) == 0:
logging.debug(
"Not generating a YAML file for species {0}, since its structure wasn't"
" specified".format(self.species.label))
else:
# We're saving a YAML file for species iff Thermo is called and they're structure is known
self.arkane_species.update_species_attributes(self.species)
self.arkane_species.save_yaml(path=output_directory)
if plot:
try:
self.plot(output_directory)
except Exception as e:
logging.warning("Could not create plots due to error: "
"{0} for species {1}".format(
e, self.species.label))
def generate_thermo(self):
"""
Generate the thermodynamic data for the species and fit it to the
desired heat capacity model (as specified in the `thermo_class`
attribute).
"""
if self.thermo_class.lower() not in ['wilhoit', 'nasa']:
raise InputError('Unknown thermodynamic model "{0}".'.format(
self.thermo_class))
species = self.species
logging.debug('Generating {0} thermo model for {1}...'.format(
self.thermo_class, species))
if species.thermo is not None:
logging.info(
"Thermo already generated for species {}. Skipping thermo generation."
.format(species))
return None
Tlist = np.arange(10.0, 3001.0, 10.0, np.float64)
Cplist = np.zeros_like(Tlist)
H298 = 0.0
S298 = 0.0
conformer = self.species.conformer
for i in range(Tlist.shape[0]):
Cplist[i] += conformer.get_heat_capacity(Tlist[i])
H298 += conformer.get_enthalpy(298.) + conformer.E0.value_si
S298 += conformer.get_entropy(298.)
if not any([
isinstance(mode, (LinearRotor, NonlinearRotor))
for mode in conformer.modes
]):
# Monatomic species
n_freq = 0
n_rotors = 0
Cp0 = 2.5 * constants.R
CpInf = 2.5 * constants.R
else:
# Polyatomic species
linear = True if isinstance(conformer.modes[1],
LinearRotor) else False
n_freq = len(conformer.modes[2].frequencies.value)
n_rotors = len(conformer.modes[3:])
Cp0 = (3.5 if linear else 4.0) * constants.R
CpInf = Cp0 + (n_freq + 0.5 * n_rotors) * constants.R
wilhoit = Wilhoit()
if n_freq == 0 and n_rotors == 0:
wilhoit.Cp0 = (Cplist[0], "J/(mol*K)")
wilhoit.CpInf = (Cplist[0], "J/(mol*K)")
wilhoit.B = (500., "K")
wilhoit.H0 = (0.0, "J/mol")
wilhoit.S0 = (0.0, "J/(mol*K)")
wilhoit.H0 = (H298 - wilhoit.get_enthalpy(298.15), "J/mol")
wilhoit.S0 = (S298 - wilhoit.get_entropy(298.15), "J/(mol*K)")
else:
wilhoit.fit_to_data(Tlist,
Cplist,
Cp0,
CpInf,
H298,
S298,
B0=500.0)
if self.thermo_class.lower() == 'nasa':
species.thermo = wilhoit.to_nasa(Tmin=10.0,
Tmax=3000.0,
Tint=500.0)
else:
species.thermo = wilhoit
def write_output(self, output_directory):
"""
Save the results of the thermodynamics job to the `output.py` file located
in `output_directory`.
"""
species = self.species
output_file = os.path.join(output_directory, 'output.py')
logging.info('Saving thermo for {0}...'.format(species.label))
with open(output_file, 'a') as f:
f.write('# Thermodynamics for {0}:\n'.format(species.label))
H298 = species.get_thermo_data().get_enthalpy(298) / 4184.
S298 = species.get_thermo_data().get_entropy(298) / 4.184
f.write(
'# Enthalpy of formation (298 K) = {0:9.3f} kcal/mol\n'.
format(H298))
f.write(
'# Entropy of formation (298 K) = {0:9.3f} cal/(mol*K)\n'.
format(S298))
f.write(
'# =========== =========== =========== =========== ===========\n'
)
f.write(
'# Temperature Heat cap. Enthalpy Entropy Free energy\n'
)
f.write(
'# (K) (cal/mol*K) (kcal/mol) (cal/mol*K) (kcal/mol)\n'
)
f.write(
'# =========== =========== =========== =========== ===========\n'
)
for T in [300, 400, 500, 600, 800, 1000, 1500, 2000, 2400]:
try:
Cp = species.get_thermo_data().get_heat_capacity(T) / 4.184
H = species.get_thermo_data().get_enthalpy(T) / 4184.
S = species.get_thermo_data().get_entropy(T) / 4.184
G = species.get_thermo_data().get_free_energy(T) / 4184.
f.write(
'# {0:11g} {1:11.3f} {2:11.3f} {3:11.3f} {4:11.3f}\n'
.format(T, Cp, H, S, G))
except ValueError:
logging.debug(
"Valid thermo for {0} is outside range for temperature {1}"
.format(species, T))
f.write(
'# =========== =========== =========== =========== ===========\n'
)
thermo_string = 'thermo(label={0!r}, thermo={1!r})'.format(
species.label, species.get_thermo_data())
f.write('{0}\n\n'.format(prettify(thermo_string)))
def write_chemkin(self, output_directory):
"""
Appends the thermo block to `chem.inp` and species name to
`species_dictionary.txt` within the `outut_directory` specified
"""
species = self.species
with open(os.path.join(output_directory, 'chem.inp'), 'a') as f:
if isinstance(species, Species):
if species.molecule and isinstance(species.molecule[0],
Molecule):
element_counts = get_element_count(species.molecule[0])
else:
try:
element_counts = species.props['element_counts']
except KeyError:
element_counts = self.element_count_from_conformer()
else:
element_counts = {'C': 0, 'H': 0}
chemkin_thermo_string = write_thermo_entry(
species, element_counts=element_counts, verbose=True)
f.write('{0}\n'.format(chemkin_thermo_string))
# write species dictionary
if isinstance(species, Species):
if species.molecule and isinstance(species.molecule[0], Molecule):
spec_dict_path = os.path.join(output_directory,
'species_dictionary.txt')
is_species_in_dict = False
if os.path.isfile(spec_dict_path):
with open(spec_dict_path, 'r') as f:
# check whether the species dictionary contains this species, in which case do not re-append
for line in f.readlines():
if species.label == line.strip():
is_species_in_dict = True
break
if not is_species_in_dict:
with open(spec_dict_path, 'a') as f:
f.write(species.molecule[0].to_adjacency_list(
remove_h=False, label=species.label))
f.write('\n')
return chemkin_thermo_string
def element_count_from_conformer(self):
"""
Get an element count in a dictionary form (e.g., {'C': 3, 'H': 8}) from the species.conformer attribute.
Returns:
dict: Element count, keys are element symbols,
values are number of occurrences of the element in the molecule.
"""
element_counts = dict()
for number in self.species.conformer.number.value_si:
symbol = symbol_by_number[number]
if symbol in element_counts:
element_counts[symbol] += 1
else:
element_counts[symbol] = 1
return element_counts
def plot(self, output_directory):
"""
Plot the heat capacity, enthapy, entropy, and Gibbs free energy of the
fitted thermodynamics model, along with the same values from the
statistical mechanics model that the thermodynamics model was fitted
to. The plot is saved to the file ``thermo.pdf`` in the output
directory. The plot is not generated if ``matplotlib`` is not installed.
"""
# Skip this step if matplotlib is not installed
try:
import matplotlib.pyplot as plt
except ImportError:
return
Tlist = np.arange(10.0, 2501.0, 10.0)
Cplist = np.zeros_like(Tlist)
Cplist1 = np.zeros_like(Tlist)
Hlist = np.zeros_like(Tlist)
Hlist1 = np.zeros_like(Tlist)
Slist = np.zeros_like(Tlist)
Slist1 = np.zeros_like(Tlist)
Glist = np.zeros_like(Tlist)
Glist1 = np.zeros_like(Tlist)
conformer = self.species.conformer
thermo = self.species.get_thermo_data()
for i in range(Tlist.shape[0]):
try:
Cplist[i] = conformer.get_heat_capacity(Tlist[i])
Slist[i] = conformer.get_entropy(Tlist[i])
Hlist[i] = (conformer.get_enthalpy(Tlist[i]) +
conformer.E0.value_si) * 0.001
Glist[i] = Hlist[i] - Tlist[i] * Slist[i] * 0.001
Cplist1[i] = thermo.get_heat_capacity(Tlist[i])
Slist1[i] = thermo.get_entropy(Tlist[i])
Hlist1[i] = thermo.get_enthalpy(Tlist[i]) * 0.001
Glist1[i] = thermo.get_free_energy(Tlist[i]) * 0.001
except (ValueError, AttributeError):
continue
fig = plt.figure(figsize=(10, 8))
fig.suptitle('{0}'.format(self.species.label))
plt.subplot(2, 2, 1)
plt.plot(Tlist, Cplist / 4.184, '-r', Tlist, Cplist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Heat capacity (cal/mol*K)')
plt.legend(['statmech', 'fitted'], loc=4)
plt.subplot(2, 2, 2)
plt.plot(Tlist, Slist / 4.184, '-r', Tlist, Slist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Entropy (cal/mol*K)')
plt.subplot(2, 2, 3)
plt.plot(Tlist, Hlist / 4.184, '-r', Tlist, Hlist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Enthalpy (kcal/mol)')
plt.subplot(2, 2, 4)
plt.plot(Tlist, Glist / 4.184, '-r', Tlist, Glist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Gibbs free energy (kcal/mol)')
fig.subplots_adjust(left=0.10,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.20)
plot_path = os.path.join(output_directory, 'plots')
if not os.path.exists(plot_path):
os.mkdir(plot_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
filename = ''.join(
c for c in self.species.label if c in valid_chars) + '.pdf'
plt.savefig(os.path.join(plot_path, filename))
plt.close()
class KineticsJob(object):
"""
A representation of an Arkane kinetics job. This job is used to compute
and save the high-pressure-limit kinetics information for a single reaction.
`usedTST` - a boolean representing if TST was used to calculate the kinetics
if kinetics is already given in the input, then it is False.
`three_params` - a boolean representing if the modified three-parameter Arrhenius equation is used to calculate
high pressure kinetic rate coefficients. If it is False, the classical two-parameter Arrhenius
equation is used.
"""
def __init__(self,
reaction,
Tmin=None,
Tmax=None,
Tlist=None,
Tcount=0,
sensitivity_conditions=None,
three_params=True):
self.usedTST = False
self.Tmin = Tmin if Tmin is not None else (298, 'K')
self.Tmax = Tmax if Tmax is not None else (2500, 'K')
self.Tcount = Tcount if Tcount > 3 else 50
self.three_params = three_params
if Tlist is not None:
self.Tlist = Tlist
self.Tmin = (min(self.Tlist.value_si), 'K')
self.Tmax = (max(self.Tlist.value_si), 'K')
self.Tcount = len(self.Tlist.value_si)
else:
self.Tlist = (1 / np.linspace(1 / self.Tmax.value_si,
1 / self.Tmin.value_si, self.Tcount),
'K')
self.reaction = reaction
self.k_units = None
if sensitivity_conditions is not None:
self.sensitivity_conditions = [
quantity.Quantity(condition)
for condition in sensitivity_conditions
]
else:
self.sensitivity_conditions = None
self.arkane_species = ArkaneSpecies(
species=self.reaction.transition_state)
@property
def Tmin(self):
"""The minimum temperature at which the computed k(T) values are valid, or ``None`` if not defined."""
return self._Tmin
@Tmin.setter
def Tmin(self, value):
self._Tmin = quantity.Temperature(value)
@property
def Tmax(self):
"""The maximum temperature at which the computed k(T) values are valid, or ``None`` if not defined."""
return self._Tmax
@Tmax.setter
def Tmax(self, value):
self._Tmax = quantity.Temperature(value)
@property
def Tlist(self):
"""The temperatures at which the k(T) values are computed."""
return self._Tlist
@Tlist.setter
def Tlist(self, value):
self._Tlist = quantity.Temperature(value)
def execute(self, output_directory=None, plot=True):
"""
Execute the kinetics job, saving the results within
the `output_directory`.
If `plot` is True, then plots of the raw and fitted values for the kinetics
will be saved.
"""
self.generate_kinetics()
if output_directory is not None:
try:
self.write_output(output_directory)
except Exception as e:
logging.warning(
"Could not write kinetics output file due to error: "
"{0} in reaction {1}".format(e, self.reaction.label))
try:
self.write_chemkin(output_directory)
except Exception as e:
logging.warning(
"Could not write kinetics chemkin output due to error: "
"{0} in reaction {1}".format(e, self.reaction.label))
if plot:
try:
self.plot(output_directory)
except Exception as e:
logging.warning("Could not plot kinetics due to error: "
"{0} in reaction {1}".format(
e, self.reaction.label))
try:
self.draw(output_directory)
except Exception as e:
logging.warning(
"Could not draw reaction {1} due to error: {0}".format(
e, self.reaction.label))
if self.sensitivity_conditions is not None:
logging.info('\n\nRunning sensitivity analysis...')
SensAnalysis(self, output_directory)
logging.debug('Finished kinetics job for reaction {0}.'.format(
self.reaction))
logging.debug(repr(self.reaction))
def generate_kinetics(self):
"""
Generate the kinetics data for the reaction and fit it to a modified Arrhenius model.
"""
if isinstance(self.reaction.kinetics, Arrhenius):
return None
self.usedTST = True
kinetics_class = 'Arrhenius'
tunneling = self.reaction.transition_state.tunneling
if isinstance(tunneling, Wigner) and tunneling.frequency is None:
tunneling.frequency = (
self.reaction.transition_state.frequency.value_si, "cm^-1")
elif isinstance(tunneling, Eckart) and tunneling.frequency is None:
tunneling.frequency = (
self.reaction.transition_state.frequency.value_si, "cm^-1")
tunneling.E0_reac = (sum([
reactant.conformer.E0.value_si
for reactant in self.reaction.reactants
]) * 0.001, "kJ/mol")
tunneling.E0_TS = (
self.reaction.transition_state.conformer.E0.value_si * 0.001,
"kJ/mol")
tunneling.E0_prod = (sum([
product.conformer.E0.value_si
for product in self.reaction.products
]) * 0.001, "kJ/mol")
elif tunneling is not None:
if tunneling.frequency is not None:
# Frequency was given by the user
pass
else:
raise ValueError(
'Unknown tunneling model {0!r} for reaction {1}.'.format(
tunneling, self.reaction))
logging.debug('Generating {0} kinetics model for {1}...'.format(
kinetics_class, self.reaction))
klist = np.zeros_like(self.Tlist.value_si)
for i, t in enumerate(self.Tlist.value_si):
klist[i] = self.reaction.calculate_tst_rate_coefficient(t)
order = len(self.reaction.reactants)
klist *= 1e6**(order - 1)
self.k_units = {
1: 's^-1',
2: 'cm^3/(mol*s)',
3: 'cm^6/(mol^2*s)'
}[order]
self.K_eq_units = {
2: 'mol^2/cm^6',
1: 'mol/cm^3',
0: ' ',
-1: 'cm^3/mol',
-2: 'cm^6/mol^2'
}[len(self.reaction.products) - len(self.reaction.reactants)]
self.k_r_units = {
1: 's^-1',
2: 'cm^3/(mol*s)',
3: 'cm^6/(mol^2*s)'
}[len(self.reaction.products)]
self.reaction.kinetics = Arrhenius().fit_to_data(
self.Tlist.value_si,
klist,
kunits=self.k_units,
three_params=self.three_params)
self.reaction.elementary_high_p = True
def write_output(self, output_directory):
"""
Save the results of the kinetics job to the `output.py` file located
in `output_directory`.
"""
reaction = self.reaction
ks, k0s, k0_revs, k_revs = [], [], [], []
logging.info('Saving kinetics for {0}...'.format(reaction))
order = len(self.reaction.reactants)
factor = 1e6**(order - 1)
f = open(os.path.join(output_directory, 'output.py'), 'a')
if self.usedTST:
# If TST is not used, eg. it was given in 'reaction', then this will throw an error.
f.write(
'# ======= =========== =========== =========== ===============\n'
)
f.write('# Temp. k (TST) Tunneling k (TST+T) Units\n')
f.write(
'# ======= =========== =========== =========== ===============\n'
)
if self.Tlist is None:
t_list = np.array([300, 400, 500, 600, 800, 1000, 1500, 2000])
else:
t_list = self.Tlist.value_si
for T in t_list:
tunneling = reaction.transition_state.tunneling
reaction.transition_state.tunneling = None
try:
k0 = reaction.calculate_tst_rate_coefficient(T) * factor
except SpeciesError:
k0 = 0
reaction.transition_state.tunneling = tunneling
try:
k = reaction.calculate_tst_rate_coefficient(T) * factor
kappa = k / k0
except (SpeciesError, ZeroDivisionError):
k = reaction.get_rate_coefficient(T)
kappa = 0
logging.info(
"The species in reaction {0} do not have adequate information for TST, "
"using default kinetics values.".format(reaction))
tunneling = reaction.transition_state.tunneling
ks.append(k)
k0s.append(k0)
f.write(
'# {0:4g} K {1:11.3e} {2:11g} {3:11.3e} {4}\n'.format(
T, k0, kappa, k, self.k_units))
f.write(
'# ======= =========== =========== =========== ===============\n'
)
f.write('\n\n')
f.write(
'# ======= ============ =========== ============ ============= =========\n'
)
f.write(
'# Temp. Kc (eq) Units k_rev (TST) k_rev (TST+T) Units\n'
)
f.write(
'# ======= ============ =========== ============ ============= =========\n'
)
# Initialize Object for Converting Units
if self.K_eq_units != ' ':
keq_unit_converter = quantity.Units(
self.K_eq_units).get_conversion_factor_from_si()
else:
keq_unit_converter = 1
for n, T in enumerate(t_list):
k = ks[n]
k0 = k0s[n]
K_eq = keq_unit_converter * reaction.get_equilibrium_constant(
T) # returns SI units
k0_rev = k0 / K_eq
k_rev = k / K_eq
k0_revs.append(k0_rev)
k_revs.append(k_rev)
f.write(
'# {0:4g} K {1:11.3e} {2} {3:11.3e} {4:11.3e} {5}\n'
.format(T, K_eq, self.K_eq_units, k0_rev, k_rev,
self.k_r_units))
f.write(
'# ======= ============ =========== ============ ============= =========\n'
)
f.write('\n\n')
kinetics_0_rev = Arrhenius().fit_to_data(
t_list,
np.array(k0_revs),
kunits=self.k_r_units,
three_params=self.three_params)
kinetics_rev = Arrhenius().fit_to_data(
t_list,
np.array(k_revs),
kunits=self.k_r_units,
three_params=self.three_params)
f.write('# k_rev (TST) = {0} \n'.format(kinetics_0_rev))
f.write('# k_rev (TST+T) = {0} \n\n'.format(kinetics_rev))
if self.three_params:
f.write(
'# kinetics fitted using the modified three-parameter Arrhenius equation '
'k = A * (T/T0)^n * exp(-Ea/RT) \n')
else:
f.write(
'# kinetics fitted using the two-parameter Arrhenius equation k = A * exp(-Ea/RT) \n'
)
# Reaction path degeneracy is INCLUDED in the kinetics itself!
rxn_str = 'kinetics(label={0!r}, kinetics={1!r})'.format(
reaction.label, reaction.kinetics)
f.write('{0}\n\n'.format(prettify(rxn_str)))
f.close()
def write_chemkin(self, output_directory):
"""
Appends the kinetics rates to `chem.inp` in `outut_directory`
"""
# obtain a unit conversion factor
order = len(self.reaction.reactants)
factor = 1e6**(order - 1)
reaction = self.reaction
kinetics = reaction.kinetics
rxn_str = ''
if reaction.kinetics.comment:
for line in reaction.kinetics.comment.split("\n"):
rxn_str += "! {0}\n".format(line)
rxn_str += '{0!s:51} {1:9.3e} {2:9.3f} {3:9.3f}\n'.format(
reaction,
kinetics.A.value_si * factor,
kinetics.n.value_si,
kinetics.Ea.value_si / 4184.,
)
with open(os.path.join(output_directory, 'chem.inp'), 'a') as f:
f.write('{0}\n'.format(rxn_str))
def save_yaml(self, output_directory):
"""
Save a YAML file for TSs if structures of the respective reactant/s and product/s are known
"""
if all([
spc.molecule is not None and len(spc.molecule)
for spc in self.reaction.reactants + self.reaction.products
]):
self.arkane_species.update_species_attributes(
self.reaction.transition_state)
self.arkane_species.reaction_label = self.reaction.label
self.arkane_species.reactants = [{
'label':
spc.label,
'adjacency_list':
spc.molecule[0].to_adjacency_list()
} for spc in self.reaction.reactants]
self.arkane_species.products = [{
'label':
spc.label,
'adjacency_list':
spc.molecule[0].to_adjacency_list()
} for spc in self.reaction.products]
self.arkane_species.save_yaml(path=output_directory)
def plot(self, output_directory):
"""
Plot both the raw kinetics data and the Arrhenius fit versus
temperature. The plot is saved to the file ``kinetics.pdf`` in the
output directory. The plot is not generated if ``matplotlib`` is not
installed.
"""
import matplotlib.pyplot as plt
f, ax = plt.subplots()
if self.Tlist is not None:
t_list = [t for t in self.Tlist.value_si]
else:
t_list = 1000.0 / np.arange(0.4, 3.35, 0.05)
klist = np.zeros_like(t_list)
klist2 = np.zeros_like(t_list)
for i in range(len(t_list)):
klist[i] = self.reaction.calculate_tst_rate_coefficient(t_list[i])
klist2[i] = self.reaction.kinetics.get_rate_coefficient(t_list[i])
order = len(self.reaction.reactants)
klist *= 1e6**(order - 1)
klist2 *= 1e6**(order - 1)
t_list = [1000.0 / t for t in t_list]
plt.semilogy(t_list, klist, 'ob', label='TST calculation')
plt.semilogy(t_list, klist2, '-k', label='Fitted rate')
plt.legend()
reaction_str = '{0} {1} {2}'.format(
' + '.join([
reactant.label for reactant in self.reaction.reactants
]), '<=>',
' + '.join([product.label for product in self.reaction.products]))
plt.title(reaction_str)
plt.xlabel('1000 / Temperature (K^-1)')
plt.ylabel('Rate coefficient ({0})'.format(self.k_units))
plot_path = os.path.join(output_directory, 'plots')
if not os.path.exists(plot_path):
os.mkdir(plot_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
filename = ''.join(c
for c in reaction_str if c in valid_chars) + '.pdf'
plt.savefig(os.path.join(plot_path, filename))
plt.close()
def draw(self, output_directory, file_format='pdf'):
"""
Generate a PDF drawing of the reaction.
This requires that Cairo and its Python wrapper be available; if not,
the drawing is not generated.
You may also generate different formats of drawings, by changing format to
one of the following: `pdf`, `svg`, `png`.
"""
drawing_path = os.path.join(output_directory, 'paths')
if not os.path.exists(drawing_path):
os.mkdir(drawing_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
reaction_str = '{0} {1} {2}'.format(
' + '.join([
reactant.label for reactant in self.reaction.reactants
]), '<=>',
' + '.join([product.label for product in self.reaction.products]))
filename = ''.join(c
for c in reaction_str if c in valid_chars) + '.pdf'
path = os.path.join(drawing_path, filename)
KineticsDrawer().draw(self.reaction,
file_format=file_format,
path=path)
class ThermoJob(object):
"""
A representation of an Arkane thermodynamics job. This job is used to
compute and save the thermodynamics information for a single species.
"""
def __init__(self, species, thermoClass):
self.species = species
self.thermoClass = thermoClass
self.arkane_species = ArkaneSpecies(species=species)
def execute(self, outputFile=None, plot=False):
"""
Execute the thermodynamics job, saving the results to the
given `outputFile` on disk.
"""
self.generateThermo()
if outputFile is not None:
self.arkane_species.chemkin_thermo_string = self.save(outputFile)
if self.species.molecule is None or len(
self.species.molecule) == 0:
logging.debug(
"Not generating database YAML file for species {0}, since its structure wasn't"
" specified".format(self.species.label))
else:
self.arkane_species.update_species_attributes(self.species)
self.arkane_species.save_yaml(path=os.path.dirname(outputFile))
if plot:
self.plot(os.path.dirname(outputFile))
def generateThermo(self):
"""
Generate the thermodynamic data for the species and fit it to the
desired heat capacity model (as specified in the `thermoClass`
attribute).
"""
if self.thermoClass.lower() not in ['wilhoit', 'nasa']:
raise Exception('Unknown thermodynamic model "{0}".'.format(
self.thermoClass))
species = self.species
logging.debug('Generating {0} thermo model for {1}...'.format(
self.thermoClass, species))
if species.thermo is not None:
logging.info(
"Thermo already generated for species {}. Skipping thermo generation."
.format(species))
return None
Tlist = np.arange(10.0, 3001.0, 10.0, np.float64)
Cplist = np.zeros_like(Tlist)
H298 = 0.0
S298 = 0.0
conformer = self.species.conformer
for i in range(Tlist.shape[0]):
Cplist[i] += conformer.getHeatCapacity(Tlist[i])
H298 += conformer.getEnthalpy(298.) + conformer.E0.value_si
S298 += conformer.getEntropy(298.)
if not any([
isinstance(mode, (LinearRotor, NonlinearRotor))
for mode in conformer.modes
]):
# Monatomic species
linear = False
Nfreq = 0
Nrotors = 0
Cp0 = 2.5 * constants.R
CpInf = 2.5 * constants.R
else:
# Polyatomic species
linear = True if isinstance(conformer.modes[1],
LinearRotor) else False
Nfreq = len(conformer.modes[2].frequencies.value)
Nrotors = len(conformer.modes[3:])
Cp0 = (3.5 if linear else 4.0) * constants.R
CpInf = Cp0 + (Nfreq + 0.5 * Nrotors) * constants.R
wilhoit = Wilhoit()
if Nfreq == 0 and Nrotors == 0:
wilhoit.Cp0 = (Cplist[0], "J/(mol*K)")
wilhoit.CpInf = (Cplist[0], "J/(mol*K)")
wilhoit.B = (500., "K")
wilhoit.H0 = (0.0, "J/mol")
wilhoit.S0 = (0.0, "J/(mol*K)")
wilhoit.H0 = (H298 - wilhoit.getEnthalpy(298.15), "J/mol")
wilhoit.S0 = (S298 - wilhoit.getEntropy(298.15), "J/(mol*K)")
else:
wilhoit.fitToData(Tlist, Cplist, Cp0, CpInf, H298, S298, B0=500.0)
if self.thermoClass.lower() == 'nasa':
species.thermo = wilhoit.toNASA(Tmin=10.0, Tmax=3000.0, Tint=500.0)
else:
species.thermo = wilhoit
def save(self, outputFile):
"""
Save the results of the thermodynamics job to the file located
at `path` on disk.
"""
species = self.species
logging.info('Saving thermo for {0}...'.format(species.label))
f = open(outputFile, 'a')
f.write('# Thermodynamics for {0}:\n'.format(species.label))
H298 = species.getThermoData().getEnthalpy(298) / 4184.
S298 = species.getThermoData().getEntropy(298) / 4.184
f.write(
'# Enthalpy of formation (298 K) = {0:9.3f} kcal/mol\n'.format(
H298))
f.write('# Entropy of formation (298 K) = {0:9.3f} cal/(mol*K)\n'.
format(S298))
f.write(
'# =========== =========== =========== =========== ===========\n'
)
f.write(
'# Temperature Heat cap. Enthalpy Entropy Free energy\n'
)
f.write(
'# (K) (cal/mol*K) (kcal/mol) (cal/mol*K) (kcal/mol)\n'
)
f.write(
'# =========== =========== =========== =========== ===========\n'
)
for T in [300, 400, 500, 600, 800, 1000, 1500, 2000, 2400]:
try:
Cp = species.getThermoData().getHeatCapacity(T) / 4.184
H = species.getThermoData().getEnthalpy(T) / 4184.
S = species.getThermoData().getEntropy(T) / 4.184
G = species.getThermoData().getFreeEnergy(T) / 4184.
f.write(
'# {0:11g} {1:11.3f} {2:11.3f} {3:11.3f} {4:11.3f}\n'.
format(T, Cp, H, S, G))
except ValueError:
logging.debug(
"Valid thermo for {0} is outside range for temperature {1}"
.format(species, T))
f.write(
'# =========== =========== =========== =========== ===========\n'
)
thermo_string = 'thermo(label={0!r}, thermo={1!r})'.format(
species.label, species.getThermoData())
f.write('{0}\n\n'.format(prettify(thermo_string)))
f.close()
# write chemkin file
f = open(os.path.join(os.path.dirname(outputFile), 'chem.inp'), 'a')
if isinstance(species, Species):
if species.molecule and isinstance(species.molecule[0], Molecule):
elementCounts = retrieveElementCount(species.molecule[0])
else:
try:
elementCounts = species.props['elementCounts']
except KeyError:
elementCounts = {'C': 0, 'H': 0}
else:
elementCounts = {'C': 0, 'H': 0}
chemkin_thermo_string = writeThermoEntry(species,
elementCounts=elementCounts,
verbose=True)
f.write('{0}\n'.format(chemkin_thermo_string))
f.close()
# write species dictionary
if isinstance(species, Species):
if species.molecule and isinstance(species.molecule[0], Molecule):
with open(
os.path.join(os.path.dirname(outputFile),
'species_dictionary.txt'), 'a') as f:
f.write(species.molecule[0].toAdjacencyList(
removeH=False, label=species.label))
f.write('\n')
return chemkin_thermo_string
def plot(self, outputDirectory):
"""
Plot the heat capacity, enthapy, entropy, and Gibbs free energy of the
fitted thermodynamics model, along with the same values from the
statistical mechanics model that the thermodynamics model was fitted
to. The plot is saved to the file ``thermo.pdf`` in the output
directory. The plot is not generated if ``matplotlib`` is not installed.
"""
# Skip this step if matplotlib is not installed
try:
import matplotlib.pyplot as plt
except ImportError:
return
Tlist = np.arange(10.0, 2501.0, 10.0)
Cplist = np.zeros_like(Tlist)
Cplist1 = np.zeros_like(Tlist)
Hlist = np.zeros_like(Tlist)
Hlist1 = np.zeros_like(Tlist)
Slist = np.zeros_like(Tlist)
Slist1 = np.zeros_like(Tlist)
Glist = np.zeros_like(Tlist)
Glist1 = np.zeros_like(Tlist)
conformer = self.species.conformer
thermo = self.species.getThermoData()
for i in range(Tlist.shape[0]):
try:
Cplist[i] = conformer.getHeatCapacity(Tlist[i])
Slist[i] = conformer.getEntropy(Tlist[i])
Hlist[i] = (conformer.getEnthalpy(Tlist[i]) +
conformer.E0.value_si) * 0.001
Glist[i] = Hlist[i] - Tlist[i] * Slist[i] * 0.001
Cplist1[i] = thermo.getHeatCapacity(Tlist[i])
Slist1[i] = thermo.getEntropy(Tlist[i])
Hlist1[i] = thermo.getEnthalpy(Tlist[i]) * 0.001
Glist1[i] = thermo.getFreeEnergy(Tlist[i]) * 0.001
except (ValueError, AttributeError):
continue
fig = plt.figure(figsize=(10, 8))
fig.suptitle('{0}'.format(self.species.label))
plt.subplot(2, 2, 1)
plt.plot(Tlist, Cplist / 4.184, '-r', Tlist, Cplist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Heat capacity (cal/mol*K)')
plt.legend(['statmech', 'fitted'], loc=4)
plt.subplot(2, 2, 2)
plt.plot(Tlist, Slist / 4.184, '-r', Tlist, Slist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Entropy (cal/mol*K)')
plt.subplot(2, 2, 3)
plt.plot(Tlist, Hlist / 4.184, '-r', Tlist, Hlist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Enthalpy (kcal/mol)')
plt.subplot(2, 2, 4)
plt.plot(Tlist, Glist / 4.184, '-r', Tlist, Glist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Gibbs free energy (kcal/mol)')
fig.subplots_adjust(left=0.10,
bottom=0.08,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.20)
plot_path = os.path.join(outputDirectory, 'plots')
if not os.path.exists(plot_path):
os.mkdir(plot_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
filename = ''.join(
c for c in self.species.label if c in valid_chars) + '.pdf'
plt.savefig(os.path.join(plot_path, filename))
plt.close()
def __init__(self, species, thermoClass):
self.species = species
self.thermoClass = thermoClass
self.arkane_species = ArkaneSpecies(species=species)
class ThermoJob(object):
"""
A representation of an Arkane thermodynamics job. This job is used to
compute and save the thermodynamics information for a single species.
"""
def __init__(self, species, thermoClass):
self.species = species
self.thermoClass = thermoClass
self.arkane_species = ArkaneSpecies(species=species)
def execute(self, outputFile=None, plot=False):
"""
Execute the thermodynamics job, saving the results to the
given `outputFile` on disk.
"""
self.generateThermo()
if outputFile is not None:
self.arkane_species.chemkin_thermo_string = self.save(outputFile)
if self.species.molecule is None or len(self.species.molecule) == 0:
logging.debug("Not generating a YAML file for species {0}, since its structure wasn't"
" specified".format(self.species.label))
else:
# We're saving a YAML file for species iff Thermo is called and they're structure is known
self.arkane_species.update_species_attributes(self.species)
self.arkane_species.save_yaml(path=os.path.dirname(outputFile))
if plot:
self.plot(os.path.dirname(outputFile))
def generateThermo(self):
"""
Generate the thermodynamic data for the species and fit it to the
desired heat capacity model (as specified in the `thermoClass`
attribute).
"""
if self.thermoClass.lower() not in ['wilhoit', 'nasa']:
raise Exception('Unknown thermodynamic model "{0}".'.format(self.thermoClass))
species = self.species
logging.debug('Generating {0} thermo model for {1}...'.format(self.thermoClass, species))
if species.thermo is not None:
logging.info("Thermo already generated for species {}. Skipping thermo generation.".format(species))
return None
Tlist = np.arange(10.0, 3001.0, 10.0, np.float64)
Cplist = np.zeros_like(Tlist)
H298 = 0.0
S298 = 0.0
conformer = self.species.conformer
for i in range(Tlist.shape[0]):
Cplist[i] += conformer.getHeatCapacity(Tlist[i])
H298 += conformer.getEnthalpy(298.) + conformer.E0.value_si
S298 += conformer.getEntropy(298.)
if not any([isinstance(mode, (LinearRotor, NonlinearRotor)) for mode in conformer.modes]):
# Monatomic species
linear = False
Nfreq = 0
Nrotors = 0
Cp0 = 2.5 * constants.R
CpInf = 2.5 * constants.R
else:
# Polyatomic species
linear = True if isinstance(conformer.modes[1], LinearRotor) else False
Nfreq = len(conformer.modes[2].frequencies.value)
Nrotors = len(conformer.modes[3:])
Cp0 = (3.5 if linear else 4.0) * constants.R
CpInf = Cp0 + (Nfreq + 0.5 * Nrotors) * constants.R
wilhoit = Wilhoit()
if Nfreq == 0 and Nrotors == 0:
wilhoit.Cp0 = (Cplist[0],"J/(mol*K)")
wilhoit.CpInf = (Cplist[0],"J/(mol*K)")
wilhoit.B = (500.,"K")
wilhoit.H0 = (0.0,"J/mol")
wilhoit.S0 = (0.0,"J/(mol*K)")
wilhoit.H0 = (H298 -wilhoit.getEnthalpy(298.15), "J/mol")
wilhoit.S0 = (S298 - wilhoit.getEntropy(298.15),"J/(mol*K)")
else:
wilhoit.fitToData(Tlist, Cplist, Cp0, CpInf, H298, S298, B0=500.0)
if self.thermoClass.lower() == 'nasa':
species.thermo = wilhoit.toNASA(Tmin=10.0, Tmax=3000.0, Tint=500.0)
else:
species.thermo = wilhoit
def save(self, outputFile):
"""
Save the results of the thermodynamics job to the file located
at `path` on disk.
"""
species = self.species
logging.info('Saving thermo for {0}...'.format(species.label))
f = open(outputFile, 'a')
f.write('# Thermodynamics for {0}:\n'.format(species.label))
H298 = species.getThermoData().getEnthalpy(298) / 4184.
S298 = species.getThermoData().getEntropy(298) / 4.184
f.write('# Enthalpy of formation (298 K) = {0:9.3f} kcal/mol\n'.format(H298))
f.write('# Entropy of formation (298 K) = {0:9.3f} cal/(mol*K)\n'.format(S298))
f.write('# =========== =========== =========== =========== ===========\n')
f.write('# Temperature Heat cap. Enthalpy Entropy Free energy\n')
f.write('# (K) (cal/mol*K) (kcal/mol) (cal/mol*K) (kcal/mol)\n')
f.write('# =========== =========== =========== =========== ===========\n')
for T in [300,400,500,600,800,1000,1500,2000,2400]:
try:
Cp = species.getThermoData().getHeatCapacity(T) / 4.184
H = species.getThermoData().getEnthalpy(T) / 4184.
S = species.getThermoData().getEntropy(T) / 4.184
G = species.getThermoData().getFreeEnergy(T) / 4184.
f.write('# {0:11g} {1:11.3f} {2:11.3f} {3:11.3f} {4:11.3f}\n'.format(T, Cp, H, S, G))
except ValueError:
logging.debug("Valid thermo for {0} is outside range for temperature {1}".format(species,T))
f.write('# =========== =========== =========== =========== ===========\n')
thermo_string = 'thermo(label={0!r}, thermo={1!r})'.format(species.label, species.getThermoData())
f.write('{0}\n\n'.format(prettify(thermo_string)))
f.close()
# write chemkin file
f = open(os.path.join(os.path.dirname(outputFile), 'chem.inp'), 'a')
if isinstance(species, Species):
if species.molecule and isinstance(species.molecule[0], Molecule):
elementCounts = retrieveElementCount(species.molecule[0])
else:
try:
elementCounts = species.props['elementCounts']
except KeyError:
elementCounts = {'C': 0, 'H': 0}
else:
elementCounts = {'C': 0, 'H': 0}
chemkin_thermo_string = writeThermoEntry(species, elementCounts=elementCounts, verbose=True)
f.write('{0}\n'.format(chemkin_thermo_string))
f.close()
# write species dictionary
if isinstance(species, Species):
if species.molecule and isinstance(species.molecule[0], Molecule):
with open(os.path.join(os.path.dirname(outputFile), 'species_dictionary.txt'), 'a') as f:
f.write(species.molecule[0].toAdjacencyList(removeH=False, label=species.label))
f.write('\n')
return chemkin_thermo_string
def plot(self, outputDirectory):
"""
Plot the heat capacity, enthapy, entropy, and Gibbs free energy of the
fitted thermodynamics model, along with the same values from the
statistical mechanics model that the thermodynamics model was fitted
to. The plot is saved to the file ``thermo.pdf`` in the output
directory. The plot is not generated if ``matplotlib`` is not installed.
"""
# Skip this step if matplotlib is not installed
try:
import matplotlib.pyplot as plt
except ImportError:
return
Tlist = np.arange(10.0, 2501.0, 10.0)
Cplist = np.zeros_like(Tlist)
Cplist1 = np.zeros_like(Tlist)
Hlist = np.zeros_like(Tlist)
Hlist1 = np.zeros_like(Tlist)
Slist = np.zeros_like(Tlist)
Slist1 = np.zeros_like(Tlist)
Glist = np.zeros_like(Tlist)
Glist1 = np.zeros_like(Tlist)
conformer = self.species.conformer
thermo = self.species.getThermoData()
for i in range(Tlist.shape[0]):
try:
Cplist[i] = conformer.getHeatCapacity(Tlist[i])
Slist[i] = conformer.getEntropy(Tlist[i])
Hlist[i] = (conformer.getEnthalpy(Tlist[i]) + conformer.E0.value_si) * 0.001
Glist[i] = Hlist[i] - Tlist[i] * Slist[i] * 0.001
Cplist1[i] = thermo.getHeatCapacity(Tlist[i])
Slist1[i] = thermo.getEntropy(Tlist[i])
Hlist1[i] = thermo.getEnthalpy(Tlist[i]) * 0.001
Glist1[i] = thermo.getFreeEnergy(Tlist[i]) * 0.001
except (ValueError,AttributeError):
continue
fig = plt.figure(figsize=(10,8))
fig.suptitle('{0}'.format(self.species.label))
plt.subplot(2,2,1)
plt.plot(Tlist, Cplist / 4.184, '-r', Tlist, Cplist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Heat capacity (cal/mol*K)')
plt.legend(['statmech', 'fitted'], loc=4)
plt.subplot(2,2,2)
plt.plot(Tlist, Slist / 4.184, '-r', Tlist, Slist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Entropy (cal/mol*K)')
plt.subplot(2,2,3)
plt.plot(Tlist, Hlist / 4.184, '-r', Tlist, Hlist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Enthalpy (kcal/mol)')
plt.subplot(2,2,4)
plt.plot(Tlist, Glist / 4.184, '-r', Tlist, Glist1 / 4.184, '-b')
plt.xlabel('Temperature (K)')
plt.ylabel('Gibbs free energy (kcal/mol)')
fig.subplots_adjust(left=0.10, bottom=0.08, right=0.95, top=0.95, wspace=0.35, hspace=0.20)
plot_path = os.path.join(outputDirectory, 'plots')
if not os.path.exists(plot_path):
os.mkdir(plot_path)
valid_chars = "-_.()<=> %s%s" % (string.ascii_letters, string.digits)
filename = ''.join(c for c in self.species.label if c in valid_chars) + '.pdf'
plt.savefig(os.path.join(plot_path, filename))
plt.close()