def execute(self,
output_file,
plot,
file_format='pdf',
print_summary=True,
species_list=None,
thermo_library=None,
kinetics_library=None):
"""Execute an ExplorerJob"""
logging.info('Exploring network...')
rmg = RMG()
rmg.species_constraints = {
'allowed':
['input species', 'seed mechanisms', 'reaction libraries'],
'maximumRadicalElectrons': self.maximum_radical_electrons,
'explicitlyAllowedMolecules': []
}
rmgpy.rmg.input.rmg = rmg
reaction_model = CoreEdgeReactionModel()
reaction_model.pressure_dependence = self.pdepjob
reaction_model.pressure_dependence.rmgmode = True
if output_file:
reaction_model.pressure_dependence.output_file = os.path.dirname(
output_file)
kinetics_database = get_db('kinetics')
thermo_database = get_db('thermo')
thermo_database.libraries['thermojobs'] = thermo_library
thermo_database.library_order.insert(0, 'thermojobs')
kinetics_database.libraries['kineticsjobs'] = kinetics_library
kinetics_database.library_order.insert(
0, ('kineticsjobs', 'Reaction Library'))
self.job_rxns = [rxn for rxn in reaction_model.core.reactions]
if output_file is not None:
if not os.path.exists(
os.path.join(
reaction_model.pressure_dependence.output_file,
'pdep')):
os.mkdir(
os.path.join(
reaction_model.pressure_dependence.output_file,
'pdep'))
else:
shutil.rmtree(
os.path.join(
reaction_model.pressure_dependence.output_file,
'pdep'))
os.mkdir(
os.path.join(
reaction_model.pressure_dependence.output_file,
'pdep'))
# get the molecular formula for the network
mmol = None
for spc in self.source:
if mmol:
mmol = mmol.merge(spc.molecule[0])
else:
mmol = spc.molecule[0].copy(deep=True)
form = mmol.get_formula()
for spec in list(self.bath_gas.keys()) + self.source:
nspec, is_new = reaction_model.make_new_species(spec,
reactive=False)
flags = np.array([
s.molecule[0].get_formula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(nspec,
react_edge=False,
unimolecular_react=flags,
bimolecular_react=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
reaction_model.add_seed_mechanism_to_core('kineticsjobs')
for lib in kinetics_database.library_order:
if lib[0] != 'kineticsjobs':
reaction_model.add_reaction_library_to_edge(lib[0])
for spc in reaction_model.core.species:
for i, item in enumerate(self.source):
if spc.is_isomorphic(item):
self.source[i] = spc
# react initial species
if len(self.source) == 1:
flags = np.array([
s.molecule[0].get_formula() == form
for s in reaction_model.core.species
])
biflags = np.zeros((len(reaction_model.core.species),
len(reaction_model.core.species)))
elif len(self.source) == 2:
flags = np.array([False for s in reaction_model.core.species])
biflags = np.array(
[[False for i in range(len(reaction_model.core.species))]
for j in range(len(reaction_model.core.species))])
biflags[reaction_model.core.species.index(self.source[0]),
reaction_model.core.species.index(self.source[1])] = True
else:
raise ValueError(
"Reactant channels with greater than 2 reactants not supported"
)
reaction_model.enlarge(react_edge=True,
unimolecular_react=flags,
bimolecular_react=biflags)
# find the networks we're interested in
networks = []
for nwk in reaction_model.network_list:
if set(nwk.source) == set(self.source):
self.source = nwk.source
networks.append(nwk)
if len(networks) == 0:
raise ValueError(
'Did not generate a network with the requested source. This usually means no unimolecular '
'reactions were generated for the source. Note that library reactions that are not '
'properly flagged as elementary_high_p can replace RMG generated reactions that would '
'otherwise be part of networks.')
for network in networks:
network.bath_gas = self.bath_gas
self.networks = networks
# determine T and P combinations
if self.pdepjob.Tlist:
t_list = self.pdepjob.Tlist.value_si
else:
t_list = np.linspace(self.pdepjob.Tmin.value_si,
self.pdepjob.Tmax.value_si,
self.pdepjob.Tcount)
if self.pdepjob.Plist:
p_list = self.pdepjob.Plist.value_si
else:
p_list = np.linspace(self.pdepjob.Pmin.value_si,
self.pdepjob.Pmax.value_si,
self.pdepjob.Pcount)
# generate the network
forbidden_structures = get_db('forbidden')
incomplete = True
checked_species = []
while incomplete:
incomplete = False
for temperature in t_list:
for pressure in p_list:
for network in self.networks:
# compute the characteristic rate coefficient by summing all rate coefficients
# from the reactant channel
for spc in reaction_model.edge.species:
if spc in checked_species:
continue
if forbidden_structures.is_molecule_forbidden(
spc.molecule[0]):
reaction_model.remove_species_from_edge(
reaction_model.reaction_systems, spc)
reaction_model.remove_empty_pdep_networks()
else:
checked_species.append(spc)
kchar = 0.0
for rxn in network.net_reactions: # reaction_model.core.reactions+reaction_model.edge.reactions
if (set(rxn.reactants) == set(self.source) and
rxn.products[0].molecule[0].get_formula()
== form):
kchar += rxn.kinetics.get_rate_coefficient(
T=temperature, P=pressure)
elif (set(rxn.products) == set(self.source) and
rxn.reactants[0].molecule[0].get_formula()
== form):
kchar += rxn.generate_reverse_rate_coefficient(
network_kinetics=True
).get_rate_coefficient(T=temperature,
P=pressure)
if network.get_leak_coefficient(
T=temperature,
P=pressure) > self.explore_tol * kchar:
incomplete = True
spc = network.get_maximum_leak_species(
T=temperature, P=pressure)
logging.info(
'adding new isomer {0} to network'.format(spc))
flags = np.array([
s.molecule[0].get_formula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(
(network, spc),
react_edge=False,
unimolecular_react=flags,
bimolecular_react=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
flags = np.array([
s.molecule[0].get_formula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(
react_edge=True,
unimolecular_react=flags,
bimolecular_react=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
for network in self.networks:
rm_rxns = []
for rxn in network.path_reactions: # remove reactions with forbidden species
for r in rxn.reactants + rxn.products:
if forbidden_structures.is_molecule_forbidden(
r.molecule[0]):
rm_rxns.append(rxn)
for rxn in rm_rxns:
logging.info('Removing forbidden reaction: {0}'.format(rxn))
network.path_reactions.remove(rxn)
# clean up output files
if output_file is not None:
path = os.path.join(
reaction_model.pressure_dependence.output_file, 'pdep')
for name in os.listdir(path):
if name.endswith('.py') and '_' in name:
if name.split('_')[-1].split('.')[0] != str(
len(network.isomers)):
os.remove(os.path.join(path, name))
else:
os.rename(
os.path.join(path, name),
os.path.join(
path, 'network_full{}.py'.format(
self.networks.index(network))))
warns = []
for rxn in self.job_rxns:
if rxn not in network.path_reactions:
warns.append(
'Reaction {0} in the input file was not explored during network expansion and was '
'not included in the full network. This is likely because your explore_tol value is '
'too high.'.format(rxn))
# reduction process
for network in self.networks:
if self.energy_tol != np.inf or self.flux_tol != 0.0:
rxn_set = None
product_set = None
for temperature in t_list:
if self.energy_tol != np.inf:
rxns = network.get_energy_filtered_reactions(
temperature, self.energy_tol)
if rxn_set is not None:
rxn_set &= set(rxns)
else:
rxn_set = set(rxns)
for pressure in p_list:
if self.flux_tol != 0.0:
products = network.get_rate_filtered_products(
temperature, pressure, self.flux_tol)
products = [tuple(x) for x in products]
if product_set is not None:
product_set &= set(products)
else:
product_set = set(products)
if rxn_set:
logging.info('removing reactions during reduction:')
for rxn in rxn_set:
logging.info(rxn)
rxn_set = list(rxn_set)
if product_set:
logging.info('removing products during reduction:')
for prod in product_set:
logging.info([x.label for x in prod])
product_set = list(product_set)
network.remove_reactions(reaction_model,
rxns=rxn_set,
prods=product_set)
for rxn in self.job_rxns:
if rxn not in network.path_reactions:
warns.append(
'Reaction {0} in the input file was not included in the reduced model.'
.format(rxn))
self.networks = networks
for p, network in enumerate(self.networks):
self.pdepjob.network = network
if len(self.networks) > 1:
root, file_name = os.path.split(output_file)
s1, s2 = file_name.split(".")
ind = str(self.networks.index(network))
stot = os.path.join(root, s1 + "{}.".format(ind) + s2)
else:
stot = output_file
self.pdepjob.execute(stot,
plot,
file_format='pdf',
print_summary=True)
if os.path.isfile('network.pdf'):
os.rename('network.pdf', 'network' + str(p) + '.pdf')
if warns:
logging.info('\nOUTPUT WARNINGS:\n')
for w in warns:
logging.warning(w)
def execute(self,
outputFile,
plot,
format='pdf',
print_summary=True,
speciesList=None,
thermoLibrary=None,
kineticsLibrary=None):
logging.info('Exploring network...')
rmg = RMG()
rmg.speciesConstraints = {
'allowed':
['input species', 'seed mechanisms', 'reaction libraries'],
'maximumRadicalElectrons': self.maximumRadicalElectrons,
'explicitlyAllowedMolecules': []
}
rmgpy.rmg.input.rmg = rmg
reaction_model = CoreEdgeReactionModel()
reaction_model.pressureDependence = self.pdepjob
reaction_model.pressureDependence.rmgmode = True
if outputFile:
reaction_model.pressureDependence.outputFile = os.path.dirname(
outputFile)
kineticsDatabase = getDB('kinetics')
thermoDatabase = getDB('thermo')
thermoDatabase.libraries['thermojobs'] = thermoLibrary
thermoDatabase.libraryOrder.insert(0, 'thermojobs')
kineticsDatabase.libraries['kineticsjobs'] = kineticsLibrary
kineticsDatabase.libraryOrder.insert(
0, ('kineticsjobs', 'Reaction Library'))
jobRxns = [rxn for rxn in reaction_model.core.reactions]
self.jobRxns = jobRxns
if outputFile is not None:
if not os.path.exists(
os.path.join(reaction_model.pressureDependence.outputFile,
'pdep')):
os.mkdir(
os.path.join(reaction_model.pressureDependence.outputFile,
'pdep'))
else:
shutil.rmtree(
os.path.join(reaction_model.pressureDependence.outputFile,
'pdep'))
os.mkdir(
os.path.join(reaction_model.pressureDependence.outputFile,
'pdep'))
# get the molecular formula for the network
mmol = None
for spc in self.source:
if mmol:
mmol.merge(spc.molecule[0])
else:
mmol = spc.molecule[0]
form = mmol.getFormula()
for spec in self.bathGas.keys() + self.source:
nspec, isNew = reaction_model.makeNewSpecies(spec, reactive=False)
flags = np.array([
s.molecule[0].getFormula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(nspec,
reactEdge=False,
unimolecularReact=flags,
bimolecularReact=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
reaction_model.addSeedMechanismToCore('kineticsjobs')
for lib in kineticsDatabase.libraryOrder:
if lib[0] != 'kineticsjobs':
reaction_model.addReactionLibraryToEdge(lib[0])
for spc in reaction_model.core.species:
for i, item in enumerate(self.source):
if spc.isIsomorphic(item):
self.source[i] = spc
# react initial species
flags = np.array([
s.molecule[0].getFormula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(reactEdge=True,
unimolecularReact=flags,
bimolecularReact=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
# find the network we're interested in
for nwk in reaction_model.networkList:
if set(nwk.source) == set(self.source):
self.source = nwk.source
network = nwk
break
else:
raise ValueError(
'Did not generate a network with the requested source. This usually means no unimolecular'
'reactions were generated for the source. Note that library reactions that are not'
' properly flagged as elementary_high_p can replace RMG generated reactions that would'
' otherwise be part of networks.')
network.bathGas = self.bathGas
self.network = network
# determine T and P combinations
if self.pdepjob.Tlist:
Tlist = self.pdepjob.Tlist.value_si
else:
Tlist = np.linspace(self.pdepjob.Tmin.value_si,
self.pdepjob.Tmax.value_si,
self.pdepjob.Tcount)
if self.pdepjob.Plist:
Plist = self.pdepjob.Plist.value_si
else:
Plist = np.linspace(self.pdepjob.Pmin.value_si,
self.pdepjob.Pmax.value_si,
self.pdepjob.Pcount)
# generate the network
forbiddenStructures = getDB('forbidden')
incomplete = True
while incomplete:
incomplete = False
for T in Tlist:
for P in Plist:
if network.getLeakCoefficient(T=T, P=P) > self.explore_tol:
incomplete = True
spc = network.getMaximumLeakSpecies(T=T, P=P)
if forbiddenStructures.isMoleculeForbidden(
spc.molecule[0]):
reaction_model.removeSpeciesFromEdge(
reaction_model.reactionSystems, spc)
reaction_model.removeEmptyPdepNetworks()
logging.error(spc.label)
else:
logging.info(
'adding new isomer {0} to network'.format(spc))
flags = np.array([
s.molecule[0].getFormula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(
(network, spc),
reactEdge=False,
unimolecularReact=flags,
bimolecularReact=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
flags = np.array([
s.molecule[0].getFormula() == form
for s in reaction_model.core.species
])
reaction_model.enlarge(
reactEdge=True,
unimolecularReact=flags,
bimolecularReact=np.zeros(
(len(reaction_model.core.species),
len(reaction_model.core.species))))
rmRxns = []
for rxn in network.pathReactions: # remove reactions with forbidden species
for r in rxn.reactants + rxn.products:
if forbiddenStructures.isMoleculeForbidden(r.molecule[0]):
rmRxns.append(rxn)
for rxn in rmRxns:
logging.info('Removing forbidden reaction: {0}'.format(rxn))
network.pathReactions.remove(rxn)
# clean up output files
if outputFile is not None:
path = os.path.join(reaction_model.pressureDependence.outputFile,
'pdep')
for name in os.listdir(path):
if name.endswith('.py') and '_' in name:
if name.split('_')[-1].split('.')[0] != str(
len(network.isomers)):
os.remove(os.path.join(path, name))
else:
os.rename(os.path.join(path, name),
os.path.join(path, 'network_full.py'))
warns = []
for rxn in jobRxns:
if rxn not in network.pathReactions:
warns.append(
'Reaction {0} in the input file was not explored during network expansion and was not included in the full network. This is likely because your explore_tol value is too high.'
.format(rxn))
# reduction process
if self.energy_tol != np.inf or self.flux_tol != 0.0:
rxnSet = None
for T in Tlist:
if self.energy_tol != np.inf:
rxns = network.get_energy_filtered_reactions(
T, self.energy_tol)
if rxnSet is not None:
rxnSet &= set(rxns)
else:
rxnSet = set(rxns)
for P in Plist:
if self.flux_tol != 0.0:
rxns = network.get_rate_filtered_reactions(
T, P, self.flux_tol)
if rxnSet is not None:
rxnSet &= set(rxns)
else:
rxnSet = set(rxns)
logging.info('removing reactions during reduction:')
for rxn in rxnSet:
logging.info(rxn)
network.remove_reactions(reaction_model, list(rxnSet))
for rxn in jobRxns:
if rxn not in network.pathReactions:
warns.append(
'Reaction {0} in the input file was not included in the reduced model.'
.format(rxn))
self.network = network
self.pdepjob.network = network
self.pdepjob.execute(outputFile,
plot,
format='pdf',
print_summary=True)
if warns != []:
logging.info('\nOUTPUT WARNINGS:\n')
for w in warns:
logging.warning(w)
def execute(self, outputFile, plot, format='pdf', print_summary=True, speciesList=None, thermoLibrary=None, kineticsLibrary=None):
logging.info('Exploring network...')
rmg = RMG()
rmg.speciesConstraints = {'allowed' : ['input species', 'seed mechanisms', 'reaction libraries'], 'maximumRadicalElectrons' : self.maximumRadicalElectrons, 'explicitlyAllowedMolecules': []}
rmgpy.rmg.input.rmg = rmg
reaction_model = CoreEdgeReactionModel()
reaction_model.pressureDependence = self.pdepjob
reaction_model.pressureDependence.rmgmode = True
if outputFile:
reaction_model.pressureDependence.outputFile = os.path.dirname(outputFile)
kineticsDatabase = getDB('kinetics')
thermoDatabase = getDB('thermo')
thermoDatabase.libraries['thermojobs'] = thermoLibrary
thermoDatabase.libraryOrder.insert(0,'thermojobs')
kineticsDatabase.libraries['kineticsjobs'] = kineticsLibrary
kineticsDatabase.libraryOrder.insert(0,('kineticsjobs','Reaction Library'))
jobRxns = [rxn for rxn in reaction_model.core.reactions]
self.jobRxns = jobRxns
if outputFile is not None:
if not os.path.exists(os.path.join(reaction_model.pressureDependence.outputFile,'pdep')):
os.mkdir(os.path.join(reaction_model.pressureDependence.outputFile,'pdep'))
else:
shutil.rmtree(os.path.join(reaction_model.pressureDependence.outputFile,'pdep'))
os.mkdir(os.path.join(reaction_model.pressureDependence.outputFile,'pdep'))
# get the molecular formula for the network
mmol = None
for spc in self.source:
if mmol:
mmol = mmol.merge(spc.molecule[0])
else:
mmol = spc.molecule[0].copy(deep=True)
form = mmol.getFormula()
for spec in self.bathGas.keys()+self.source:
nspec,isNew = reaction_model.makeNewSpecies(spec,reactive=False)
flags = np.array([s.molecule[0].getFormula()==form for s in reaction_model.core.species])
reaction_model.enlarge(nspec,reactEdge=False,unimolecularReact=flags,
bimolecularReact=np.zeros((len(reaction_model.core.species),len(reaction_model.core.species))))
reaction_model.addSeedMechanismToCore('kineticsjobs')
for lib in kineticsDatabase.libraryOrder:
if lib[0] != 'kineticsjobs':
reaction_model.addReactionLibraryToEdge(lib[0])
for spc in reaction_model.core.species:
for i,item in enumerate(self.source):
if spc.isIsomorphic(item):
self.source[i] = spc
# react initial species
if len(self.source) == 1:
flags = np.array([s.molecule[0].getFormula()==form for s in reaction_model.core.species])
biflags = np.zeros((len(reaction_model.core.species),len(reaction_model.core.species)))
elif len(self.source) == 2:
flags = np.array([False for s in reaction_model.core.species])
biflags = np.array([[False for i in xrange(len(reaction_model.core.species))] for j in xrange(len(reaction_model.core.species))])
biflags[reaction_model.core.species.index(self.source[0]),reaction_model.core.species.index(self.source[1])] = True
else:
raise ValueError("Reactant channels with greater than 2 reactants not supported")
reaction_model.enlarge(reactEdge=True,unimolecularReact=flags,
bimolecularReact=biflags)
# find the networks we're interested in
networks = []
for nwk in reaction_model.networkList:
if set(nwk.source) == set(self.source):
self.source = nwk.source
networks.append(nwk)
if len(networks) == 0:
raise ValueError('Did not generate a network with the requested source. This usually means no unimolecular'
'reactions were generated for the source. Note that library reactions that are not'
' properly flagged as elementary_high_p can replace RMG generated reactions that would'
' otherwise be part of networks.')
for network in networks:
network.bathGas = self.bathGas
self.networks = networks
# determine T and P combinations
if self.pdepjob.Tlist:
Tlist = self.pdepjob.Tlist.value_si
else:
Tlist = np.linspace(self.pdepjob.Tmin.value_si,self.pdepjob.Tmax.value_si,self.pdepjob.Tcount)
if self.pdepjob.Plist:
Plist = self.pdepjob.Plist.value_si
else:
Plist = np.linspace(self.pdepjob.Pmin.value_si,self.pdepjob.Pmax.value_si,self.pdepjob.Pcount)
# generate the network
forbiddenStructures = getDB('forbidden')
incomplete = True
while incomplete:
incomplete = False
for T in Tlist:
for P in Plist:
for network in self.networks:
kchar = 0.0 #compute the characteristic rate coefficient by summing all rate coefficients from the reactant channel
for rxn in network.netReactions:#reaction_model.core.reactions+reaction_model.edge.reactions:
if set(rxn.reactants) == set(self.source) and rxn.products[0].molecule[0].getFormula() == form:
kchar += rxn.kinetics.getRateCoefficient(T=T,P=P)
elif set(rxn.products) == set(self.source) and rxn.reactants[0].molecule[0].getFormula() == form:
kchar += rxn.generateReverseRateCoefficient(network_kinetics=True).getRateCoefficient(T=T,P=P)
if network.getLeakCoefficient(T=T,P=P) > self.explore_tol*kchar:
incomplete = True
spc = network.getMaximumLeakSpecies(T=T,P=P)
if forbiddenStructures.isMoleculeForbidden(spc.molecule[0]):
reaction_model.removeSpeciesFromEdge(reaction_model.reactionSystems,spc)
reaction_model.removeEmptyPdepNetworks()
logging.error(spc.label)
else:
logging.info('adding new isomer {0} to network'.format(spc))
flags = np.array([s.molecule[0].getFormula()==form for s in reaction_model.core.species])
reaction_model.enlarge((network,spc),reactEdge=False,unimolecularReact=flags,
bimolecularReact=np.zeros((len(reaction_model.core.species),len(reaction_model.core.species))))
flags = np.array([s.molecule[0].getFormula()==form for s in reaction_model.core.species])
reaction_model.enlarge(reactEdge=True,unimolecularReact=flags,
bimolecularReact=np.zeros((len(reaction_model.core.species),len(reaction_model.core.species))))
for network in self.networks:
rmRxns = []
for rxn in network.pathReactions: # remove reactions with forbidden species
for r in rxn.reactants+rxn.products:
if forbiddenStructures.isMoleculeForbidden(r.molecule[0]):
rmRxns.append(rxn)
for rxn in rmRxns:
logging.info('Removing forbidden reaction: {0}'.format(rxn))
network.pathReactions.remove(rxn)
# clean up output files
if outputFile is not None:
path = os.path.join(reaction_model.pressureDependence.outputFile,'pdep')
for name in os.listdir(path):
if name.endswith('.py') and '_' in name:
if name.split('_')[-1].split('.')[0] != str(len(network.isomers)):
os.remove(os.path.join(path,name))
else:
os.rename(os.path.join(path,name),os.path.join(path,'network_full{}.py'.format(self.networks.index(network))))
warns = []
for rxn in jobRxns:
if rxn not in network.pathReactions:
warns.append('Reaction {0} in the input file was not explored during network expansion and was not included in the full network. This is likely because your explore_tol value is too high.'.format(rxn))
# reduction process
for network in self.networks:
if self.energy_tol != np.inf or self.flux_tol != 0.0:
rxnSet = None
for T in Tlist:
if self.energy_tol != np.inf:
rxns = network.get_energy_filtered_reactions(T,self.energy_tol)
if rxnSet is not None:
rxnSet &= set(rxns)
else:
rxnSet = set(rxns)
for P in Plist:
if self.flux_tol != 0.0:
rxns = network.get_rate_filtered_reactions(T,P,self.flux_tol)
if rxnSet is not None:
rxnSet &= set(rxns)
else:
rxnSet = set(rxns)
logging.info('removing reactions during reduction:')
for rxn in rxnSet:
logging.info(rxn)
network.remove_reactions(reaction_model,list(rxnSet))
for rxn in jobRxns:
if rxn not in network.pathReactions:
warns.append('Reaction {0} in the input file was not included in the reduced model.'.format(rxn))
self.networks = networks
for p,network in enumerate(self.networks):
self.pdepjob.network = network
if len(self.networks) > 1:
s1,s2 = outputFile.split(".")
ind = str(self.networks.index(network))
stot = s1+"{}.".format(ind)+s2
else:
stot = outputFile
self.pdepjob.execute(stot, plot, format='pdf', print_summary=True)
if os.path.isfile('network.pdf'):
os.rename('network.pdf','network'+str(p)+'.pdf')
if warns != []:
logging.info('\nOUTPUT WARNINGS:\n')
for w in warns:
logging.warning(w)