def plot(self, data):
"""
Plots data from the simulations from this cantera job.
Takes data in the format of a list of tuples containing (time, [list of temperature, pressure, and species data])
3 plots will be created for each condition:
- T vs. time
- P vs. time
- Maximum 10 species mole fractions vs time
"""
for i, conditionData in enumerate(data):
time, dataList = conditionData
# In RMG, any species with an index of -1 is an inert and should not be plotted
inertList = [
species for species in self.speciesList if species.index == -1
]
TData = dataList[0]
PData = dataList[1]
speciesData = [
data for data in dataList if data.species not in inertList
]
# plot
GenericPlot(xVar=time,
yVar=TData).plot('{0}_temperature.png'.format(i + 1))
GenericPlot(xVar=time,
yVar=PData).plot('{0}_pressure.png'.format(i + 1))
SimulationPlot(xVar=time, yVar=speciesData,
ylabel='Mole Fraction').plot(
os.path.join(
self.outputDirectory,
'{0}_mole_fractions.png'.format(i + 1)))
def plot(self, data, topSpecies=10, topSensitiveReactions=10):
"""
Plots data from the simulations from this cantera job.
Takes data in the format of a list of tuples containing (time, [list of temperature, pressure, and species data])
3 plots will be created for each condition:
- T vs. time
- P vs. time
- Maximum species mole fractions (the number of species plotted is based on the `topSpecies` argument)
Reaction sensitivity plots will also be plotted automatically if there were sensitivities evaluated.
The number of reactions to be plotted is defined by the `topSensitiveReactions` argument.
"""
numCtReactions = len(self.model.reactions())
for i, conditionData in enumerate(data):
time, dataList, reactionSensitivityData = conditionData
# In RMG, any species with an index of -1 is an inert and should not be plotted
inertList = [species for species in self.speciesList if species.index == -1 ]
TData = dataList[0]
PData = dataList[1]
speciesData = [data for data in dataList if data.species not in inertList]
# plot
GenericPlot(xVar=time, yVar=TData).plot(os.path.join(self.outputDirectory,'{0}_temperature.png'.format(i+1)))
GenericPlot(xVar=time, yVar=PData).plot(os.path.join(self.outputDirectory,'{0}_pressure.png'.format(i+1)))
SimulationPlot(xVar=time, yVar=speciesData, numSpecies=topSpecies, ylabel='Mole Fraction').plot(os.path.join(self.outputDirectory,'{0}_mole_fractions.png'.format(i+1)))
for j, species in enumerate(self.sensitiveSpecies):
ReactionSensitivityPlot(xVar=time, yVar=reactionSensitivityData[j*numCtReactions:(j+1)*numCtReactions], numReactions=topSensitiveReactions).barplot(os.path.join(self.outputDirectory,'{0}_{1}_sensitivity.png'.format(i+1,species.toChemkin())))
def update(self, reaction_system):
"""
Saves a png with filename referring to:
- reaction system
- number of core species
"""
csv_file = os.path.join(
self.output_directory, 'solver',
'simulation_{0}_{1:d}.csv'.format(self.reaction_sys_index + 1,
len(self.core_species)))
png_file = os.path.join(
self.output_directory, 'solver',
'simulation_{0}_{1:d}.png'.format(self.reaction_sys_index + 1,
len(self.core_species)))
SimulationPlot(csv_file=csv_file, num_species=10,
ylabel='Moles').plot(png_file)
def plot_profiles(data, topSpecies=10):
"""
data is tuple:
(time data, [temperature data, density data, species1 data, species2 data, ...])
"""
time, data_list = data
temperature = data_list[0]
density = data_list[1]
species_data = [d for d in data_list[2:]
if d.index != -1] # remove unwanted species
# plot
GenericPlot(xVar=time, yVar=temperature).plot('temperature.png')
GenericPlot(xVar=time, yVar=density).plot('density.png')
SimulationPlot(xVar=time,
yVar=species_data,
numSpecies=topSpecies,
ylabel='Mole Fraction').plot('mole_fractions.png')
def compare(self, tol, plot=False):
"""
Compare the old and new model
'tol': average error acceptable between old and new model for variables
`plot`: if set to True, it will comparison plots of the two models comparing their species.
Returns a list of variables failed in a list of tuples in the format:
(CanteraCondition, variable label, variableOld, variableNew)
"""
# Ignore Inerts
inertList = ['[Ar]','[He]','[N#N]','[Ne]']
oldConditionData, newConditionData = self.runSimulations()
conditionsBroken=[]
variablesFailed=[]
print ''
print '{0} Comparison'.format(self)
print '================'
# Check the species profile observables
if 'species' in self.observables:
oldSpeciesDict = getRMGSpeciesFromUserSpecies(self.observables['species'], self.oldSim.speciesList)
newSpeciesDict = getRMGSpeciesFromUserSpecies(self.observables['species'], self.newSim.speciesList)
# Check state variable observables
implementedVariables = ['temperature','pressure']
if 'variable' in self.observables:
for item in self.observables['variable']:
if item.lower() not in implementedVariables:
print 'Observable variable {0} not yet implemented'.format(item)
failHeader='\nThe following observables did not match:\n'
failHeaderPrinted=False
for i in range(len(oldConditionData)):
timeOld, dataListOld = oldConditionData[i]
timeNew, dataListNew = newConditionData[i]
# Compare species observables
if 'species' in self.observables:
smilesList=[] #This is to make sure we don't have species with duplicate smiles
multiplicityList=['','(S)','(D)','(T)','(Q)'] #list ot add multiplcity
for species in self.observables['species']:
smiles=species.molecule[0].toSMILES() #For purpose of naming the plot only
if smiles in smilesList: smiles=smiles+multiplicityList[species.molecule[0].multiplicity]
smilesList.append(smiles)
fail = False
oldRmgSpecies = oldSpeciesDict[species]
newRmgSpecies = newSpeciesDict[species]
if oldRmgSpecies:
variableOld = next((data for data in dataListOld if data.species == oldRmgSpecies), None)
else:
print 'No RMG species found for observable species {0} in old model.'.format(smiles)
fail = True
if newRmgSpecies:
variableNew = next((data for data in dataListNew if data.species == newRmgSpecies), None)
else:
print 'No RMG species found for observable species {0} in new model.'.format(smiles)
fail = True
if fail is False:
if not curvesSimilar(timeOld.data, variableOld.data, timeNew.data, variableNew.data, tol):
fail = True
# Try plotting only when species are found in both models
if plot:
oldSpeciesPlot = SimulationPlot(xVar=timeOld, yVar=variableOld)
newSpeciesPlot = SimulationPlot(xVar=timeNew, yVar=variableNew)
oldSpeciesPlot.comparePlot(newSpeciesPlot,
title='Observable Species {0} Comparison'.format(smiles),
ylabel='Mole Fraction',
filename='condition_{0}_species_{1}.png'.format(i+1,smiles))
# Append to failed variables or conditions if this test failed
if fail:
if not failHeaderPrinted:
print failHeader
failHeaderPrinted=True
if i not in conditionsBroken: conditionsBroken.append(i)
print "Observable species {0} varied by more than {1:.3f} on average between old model {2} and \
new model {3} in condition {4:d}.".format(smiles,
tol,
variableOld.label,
variableNew.label,
i+1)
variablesFailed.append((self.conditions[i], smiles, variableOld, variableNew))
# Compare state variable observables
if 'variable' in self.observables:
for varName in self.observables['variable']:
variableOld = next((data for data in dataListOld if data.label == varName), None)
variableNew = next((data for data in dataListNew if data.label == varName), None)
if not curvesSimilar(timeOld.data, variableOld.data, timeNew.data, variableNew.data, 0.05):
if i not in conditionsBroken: conditionsBroken.append(i)
if not failHeaderPrinted:
failHeaderPrinted=True
print failHeader
print "Observable variable {0} varied by more than {1:.3f} on average between old model and \
new model in condition {2:d}.".format(variableOld.label, i+1)
variablesFailed.append((self.conditions[i], tol, varName, variableOld, variableNew))
if plot:
oldVarPlot = GenericPlot(xVar=timeOld, yVar=variableOld)
newVarPlot = GenericPlot(xVar=timeNew, yVar=variableNew)
oldVarPlot.comparePlot(newSpeciesPlot,
title='Observable Variable {0} Comparison'.format(varName),
filename='condition_{0}_variable_{1}.png'.format(i+1, varName))
# Compare ignition delay observables
if 'ignitionDelay' in self.observables:
print 'Ignition delay observable comparison not implemented yet.'
if failHeaderPrinted:
print ''
print 'The following reaction conditions were had some discrepancies:'
print ''
for index in conditionsBroken:
print "Condition {0:d}:".format(index+1)
print str(self.conditions[index])
print ''
return variablesFailed
else:
print ''
print 'All Observables varied by less than {0:.3f} on average between old model and \
new model in all conditions!'.format(tol)
print ''
def compare(self, tol, plot=False):
"""
Compare the old and new model
'tol': average error acceptable between old and new model for variables
`plot`: if set to True, it will comparison plots of the two models comparing their species.
Returns a list of variables failed in a list of tuples in the format:
(CanteraCondition, variable label, variable_old, variable_new)
"""
# Ignore Inerts
inert_list = ['[Ar]', '[He]', '[N#N]', '[Ne]']
old_condition_data, new_condition_data = self.run_simulations()
conditions_broken = []
variables_failed = []
print('')
print('{0} Comparison'.format(self))
print('================')
# Check the species profile observables
if 'species' in self.observables:
old_species_dict = get_rmg_species_from_user_species(self.observables['species'], self.old_sim.species_list)
new_species_dict = get_rmg_species_from_user_species(self.observables['species'], self.new_sim.species_list)
# Check state variable observables
implemented_variables = ['temperature', 'pressure']
if 'variable' in self.observables:
for item in self.observables['variable']:
if item.lower() not in implemented_variables:
print('Observable variable {0} not yet implemented'.format(item))
fail_header = '\nThe following observables did not match:\n'
fail_header_printed = False
for i in range(len(old_condition_data)):
time_old, data_list_old, reaction_sensitivity_data_old, thermodynamic_sensitivity_data_old = old_condition_data[i]
time_new, data_list_new, reaction_sensitivity_data_new, thermodynamic_sensitivity_data_new = new_condition_data[i]
# Compare species observables
if 'species' in self.observables:
smiles_list = [] # This is to make sure we don't have species with duplicate smiles
multiplicity_list = ['', '(S)', '(D)', '(T)', '(Q)'] # list ot add multiplcity
for species in self.observables['species']:
smiles = species.molecule[0].to_smiles() # For purpose of naming the plot only
if smiles in smiles_list: smiles = smiles + multiplicity_list[species.molecule[0].multiplicity]
smiles_list.append(smiles)
fail = False
old_rmg_species = old_species_dict[species]
new_rmg_species = new_species_dict[species]
if old_rmg_species:
variable_old = next((data for data in data_list_old if data.species == old_rmg_species), None)
else:
print('No RMG species found for observable species {0} in old model.'.format(smiles))
fail = True
if new_rmg_species:
variable_new = next((data for data in data_list_new if data.species == new_rmg_species), None)
else:
print('No RMG species found for observable species {0} in new model.'.format(smiles))
fail = True
if fail is False:
if not curves_similar(time_old.data, variable_old.data, time_new.data, variable_new.data, tol):
fail = True
# Try plotting only when species are found in both models
if plot:
old_species_plot = SimulationPlot(x_var=time_old, y_var=variable_old)
new_species_plot = SimulationPlot(x_var=time_new, y_var=variable_new)
old_species_plot.compare_plot(new_species_plot,
title='Observable Species {0} Comparison'.format(smiles),
ylabel='Mole Fraction',
filename='condition_{0}_species_{1}.png'.format(i + 1, smiles))
# Append to failed variables or conditions if this test failed
if fail:
if not fail_header_printed:
print(fail_header)
fail_header_printed = True
if i not in conditions_broken:
conditions_broken.append(i)
print("Observable species {0} varied by more than {1:.3f} on average between old model {2} and "
"new model {3} in condition {4:d}.".format(smiles, tol, variable_old.label,
variable_new.label, i + 1))
variables_failed.append((self.conditions[i], smiles, variable_old, variable_new))
# Compare state variable observables
if 'variable' in self.observables:
for varName in self.observables['variable']:
variable_old = next((data for data in data_list_old if data.label == varName), None)
variable_new = next((data for data in data_list_new if data.label == varName), None)
if not curves_similar(time_old.data, variable_old.data, time_new.data, variable_new.data, 0.05):
if i not in conditions_broken:
conditions_broken.append(i)
if not fail_header_printed:
fail_header_printed = True
print(fail_header)
print("Observable variable {0} varied by more than {1:.3f} on average between old model and "
"new model in condition {2:d}.".format(variable_old.label, tol, i + 1))
variables_failed.append((self.conditions[i], varName, variable_old, variable_new))
if plot:
old_var_plot = GenericPlot(x_var=time_old, y_var=variable_old)
new_var_plot = GenericPlot(x_var=time_new, y_var=variable_new)
old_var_plot.compare_plot(new_var_plot,
title='Observable Variable {0} Comparison'.format(varName),
filename='condition_{0}_variable_{1}.png'.format(i + 1, varName))
# Compare ignition delay observables
if 'ignitionDelay' in self.observables:
print('Ignition delay observable comparison not implemented yet.')
if fail_header_printed:
print('')
print('The following reaction conditions were had some discrepancies:')
print('')
for index in conditions_broken:
print("Condition {0:d}:".format(index + 1))
print(str(self.conditions[index]))
print('')
return variables_failed
else:
print('')
print('All Observables varied by less than {0:.3f} on average between old model and '
'new model in all conditions!'.format(tol))
print('')
def plot(self,
data,
top_species=10,
top_sensitive_reactions=10,
top_sensitive_species=10):
"""
Plots data from the simulations from this cantera job.
Takes data in the format of a list of tuples containing (time, [list of temperature, pressure, and species data])
3 plots will be created for each condition:
- T vs. time
- P vs. time
- Maximum species mole fractions (the number of species plotted is based on the `top_species` argument)
Reaction sensitivity plots will also be plotted automatically if there were sensitivities evaluated.
The number of reactions to be plotted is defined by the `top_sensitive_reactions` argument.
"""
num_ct_reactions = len(self.model.reactions())
num_ct_species = len(self.model.species())
for i, condition_data in enumerate(data):
time, data_list, reaction_sensitivity_data, thermodynamic_sensitivity_data = condition_data
# In RMG, any species with an index of -1 is an inert and should not be plotted
inert_list = [
species for species in self.species_list if species.index == -1
]
T_data = data_list[0]
P_data = data_list[1]
species_data = [
data for data in data_list if data.species not in inert_list
]
# plot
GenericPlot(x_var=time, y_var=T_data).plot(
os.path.join(self.output_directory,
'{0}_temperature.png'.format(i + 1)))
GenericPlot(x_var=time, y_var=P_data).plot(
os.path.join(self.output_directory,
'{0}_pressure.png'.format(i + 1)))
SimulationPlot(x_var=time,
y_var=species_data,
num_species=top_species,
ylabel='Mole Fraction').plot(
os.path.join(
self.output_directory,
'{0}_mole_fractions.png'.format(i + 1)))
for j, species in enumerate(self.sensitive_species):
ReactionSensitivityPlot(
x_var=time,
y_var=reaction_sensitivity_data[j *
num_ct_reactions:(j + 1) *
num_ct_reactions],
num_reactions=top_sensitive_reactions).barplot(
os.path.join(
self.output_directory,
'{0}_{1}_reaction_sensitivity.png'.format(
i + 1, species.to_chemkin())))
if self.thermo_SA:
ThermoSensitivityPlot(
x_var=time,
y_var=thermodynamic_sensitivity_data[j *
num_ct_species:
(j + 1) *
num_ct_species] *
4184000,
xlabel='dln(c)/d(H_i) [(kcal/mol)^-1]',
num_species=top_sensitive_species).barplot(
os.path.join(
self.output_directory,
'{0}_{1}_thermo_sensitivity.png'.format(
i + 1, species.to_chemkin())))
def compare(self, plot=False):
"""
Compare the old and new model
`plot`: if set to True, it will comparison plots of the two models comparing their species.
Returns a list of variables failed in a list of tuples in the format:
(CanteraCondition, variable label, variableOld, variableNew)
"""
# Ignore Inerts
inertList = ['[Ar]','[He]','[N#N]','[Ne]']
oldConditionData, newConditionData = self.runSimulations()
conditionsBroken=[]
variablesFailed=[]
print ''
print '{0} Comparison'.format(self)
print '================'
# Check the species profile observables
if 'species' in self.observables:
oldSpeciesDict = getRMGSpeciesFromSMILES(self.observables['species'], self.oldSim.speciesList)
newSpeciesDict = getRMGSpeciesFromSMILES(self.observables['species'], self.newSim.speciesList)
# Check state variable observables
implementedVariables = ['temperature','pressure']
if 'variable' in self.observables:
for item in self.observables['variable']:
if item.lower() not in implementedVariables:
print 'Observable variable {0} not yet implemented'.format(item)
print ''
print 'The following observables did not match:'
print ''
for i in range(len(oldConditionData)):
timeOld, dataListOld = oldConditionData[i]
timeNew, dataListNew = newConditionData[i]
# Compare species observables
if 'species' in self.observables:
for smiles in self.observables['species']:
fail = False
oldRmgSpecies = oldSpeciesDict[smiles]
newRmgSpecies = newSpeciesDict[smiles]
if oldRmgSpecies:
variableOld = next((data for data in dataListOld if data.species == oldRmgSpecies), None)
else:
print 'No RMG species found for observable species {0} in old model.'.format(smiles)
fail = True
if newRmgSpecies:
variableNew = next((data for data in dataListNew if data.species == newRmgSpecies), None)
else:
print 'No RMG species found for observable species {0} in new model.'.format(smiles)
fail = True
if fail is False:
if not curvesSimilar(timeOld.data, variableOld.data, timeNew.data, variableNew.data, 0.05):
fail = True
# Try plotting only when species are found in both models
if plot:
oldSpeciesPlot = SimulationPlot(xVar=timeOld, yVar=variableOld)
newSpeciesPlot = SimulationPlot(xVar=timeNew, yVar=variableNew)
oldSpeciesPlot.comparePlot(newSpeciesPlot,
title='Observable Species {0} Comparison'.format(smiles),
ylabel='Mole Fraction',
filename='condition_{0}_species_{1}.png'.format(i+1,smiles))
# Append to failed variables or conditions if this test failed
if fail:
if i not in conditionsBroken: conditionsBroken.append(i)
print "Observable species {0} does not match between old model {1} and \
new model {2} in condition {3:d}.".format(smiles,
variableOld.label,
variableNew.label,
i+1)
variablesFailed.append((self.conditions[i], smiles, variableOld, variableNew))
# Compare state variable observables
if 'variable' in self.observables:
for varName in self.observables['variable']:
variableOld = next((data for data in dataListOld if data.label == varName), None)
variableNew = next((data for data in dataListNew if data.label == varName), None)
if not curvesSimilar(timeOld.data, variableOld.data, timeNew.data, variableNew.data, 0.05):
if i not in conditionsBroken: conditionsBroken.append(i)
print "Observable variable {0} does not match between old model and \
new model in condition {1:d}.".format(variableOld.label, i+1)
variablesFailed.append((self.conditions[i], varName, variableOld, variableNew))
if plot:
oldVarPlot = GenericPlot(xVar=timeOld, yVar=variableOld)
newVarPlot = GenericPlot(xVar=timeNew, yVar=variableNew)
oldVarPlot.comparePlot(newSpeciesPlot,
title='Observable Variable {0} Comparison'.format(varName),
filename='condition_{0}_variable_{1}.png'.format(i+1, varName))
# Compare ignition delay observables
if 'ignitionDelay' in self.observables:
print 'Ignition delay observable comparison not implemented yet.'
print ''
print 'The following reaction conditions were broken:'
print ''
for index in conditionsBroken:
print "Condition {0:d}:".format(index+1)
print str(self.conditions[index])
print ''
return variablesFailed