def plotSensitivity(outputDirectory, reactionSystemIndex, sensitiveSpeciesList, number=10, fileformat='.png'):
"""
A function for plotting the top reaction thermo sensitivities (the number is
inputted as the variable `number`) in bar plot format.
To be called after running a simulation on a particular reactionSystem.
"""
for species in sensitiveSpeciesList:
csvFile = os.path.join(
outputDirectory,
'solver',
'sensitivity_{0}_SPC_{1}.csv'.format(
reactionSystemIndex + 1, species.index
)
)
reactionPlotFile = os.path.join(
outputDirectory,
'solver',
'sensitivity_{0}_SPC_{1}_reactions'.format(
reactionSystemIndex + 1, species.index
) + fileformat
)
thermoPlotFile = os.path.join(
outputDirectory,
'solver',
'sensitivity_{0}_SPC_{1}_thermo'.format(
reactionSystemIndex + 1, species.index
) + fileformat
)
ReactionSensitivityPlot(csvFile=csvFile, numReactions=number).barplot(reactionPlotFile)
ThermoSensitivityPlot(csvFile=csvFile, numSpecies=number).barplot(thermoPlotFile)
def plotSensitivity(outputDirectory, reactionSystemIndex,
sensitiveSpeciesList):
"""
A function for plotting the top 10 reaction and top 10 thermo sensitivities in bar plot format.
To be called after running a simulation on a particular reactionSystem.
"""
for species in sensitiveSpeciesList:
csvFile = os.path.join(
outputDirectory, 'solver',
'sensitivity_{0}_SPC_{1}.csv'.format(reactionSystemIndex + 1,
species.index))
reactionPlotFile = os.path.join(
outputDirectory, 'solver',
'sensitivity_{0}_SPC_{1}_reactions.png'.format(
reactionSystemIndex + 1, species.index))
thermoPlotFile = os.path.join(
outputDirectory, 'solver',
'sensitivity_{0}_SPC_{1}_thermo.png'.format(
reactionSystemIndex + 1, species.index))
ReactionSensitivityPlot(csvFile=csvFile,
numReactions=10).barplot(reactionPlotFile)
ThermoSensitivityPlot(csvFile=csvFile,
numSpecies=10).barplot(thermoPlotFile)
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 local_analysis(self,
sensitive_species,
reaction_system_index=0,
correlated=False,
number=10,
fileformat='.png'):
"""
Conduct local uncertainty analysis on the reaction model.
sensitive_species is a list of sensitive Species objects
number is the number of highest contributing uncertain parameters desired to be plotted
fileformat can be either .png, .pdf, or .svg
"""
output = {}
for sens_species in sensitive_species:
csvfile_path = os.path.join(
self.output_directory, 'solver',
'sensitivity_{0}_SPC_{1}.csv'.format(reaction_system_index + 1,
sens_species.index))
time, data_list = parse_csv_data(csvfile_path)
# Assign uncertainties
thermo_data_list = []
reaction_data_list = []
for data in data_list:
if data.species:
for species in self.species_list:
if species.to_chemkin() == data.species:
index = self.species_list.index(species)
break
else:
raise Exception(
'Chemkin name {} of species in the CSV file does not match anything in the '
'species list.'.format(data.species))
data.uncertainty = self.thermo_input_uncertainties[index]
thermo_data_list.append(data)
if data.reaction:
rxn_index = int(data.index) - 1
data.uncertainty = self.kinetic_input_uncertainties[
rxn_index]
reaction_data_list.append(data)
if correlated:
correlated_thermo_data = {}
correlated_reaction_data = {}
for data in thermo_data_list:
for label, dpG in data.uncertainty.items():
if label in correlated_thermo_data:
# Unpack the labels and partial uncertainties
correlated_thermo_data[label].data[-1] += data.data[
-1] * dpG # Multiply the sensitivity with the partial uncertainty
else:
correlated_thermo_data[label] = GenericData(
data=[data.data[-1] * dpG],
uncertainty=1,
label=label,
species='dummy')
for data in reaction_data_list:
for label, dplnk in data.uncertainty.items():
if label in correlated_reaction_data:
correlated_reaction_data[label].data[
-1] += data.data[-1] * dplnk
else:
correlated_reaction_data[label] = GenericData(
data=[data.data[-1] * dplnk],
uncertainty=1,
label=label,
reaction='dummy')
thermo_data_list = list(correlated_thermo_data.values())
reaction_data_list = list(correlated_reaction_data.values())
# Compute total variance
total_variance = 0.0
for data in thermo_data_list:
total_variance += (data.data[-1] * data.uncertainty)**2
for data in reaction_data_list:
total_variance += (data.data[-1] * data.uncertainty)**2
if not correlated:
# Add the reaction index to the data label of the reaction uncertainties
# data.index stores the physical index of the reaction + 1, so we convert it to the RMG index here
for data in reaction_data_list:
data.label = 'k' + str(self.reaction_list[
data.index - 1].index) + ': ' + data.label.split()[-1]
if correlated:
folder = os.path.join(self.output_directory, 'correlated')
else:
folder = os.path.join(self.output_directory, 'uncorrelated')
if not os.path.exists(folder):
try:
os.makedirs(folder)
except OSError as e:
raise OSError(
'Uncertainty output directory could not be created: {0!s}'
.format(e))
r_path = os.path.join(
folder, 'kineticsLocalUncertainty_{0}'.format(
sens_species.to_chemkin()) + fileformat)
t_path = os.path.join(
folder, 'thermoLocalUncertainty_{0}'.format(
sens_species.to_chemkin()) + fileformat)
reaction_uncertainty = ReactionSensitivityPlot(
x_var=time, y_var=reaction_data_list,
num_reactions=number).uncertainty_plot(total_variance,
filename=r_path)
thermo_uncertainty = ThermoSensitivityPlot(
x_var=time, y_var=thermo_data_list,
num_species=number).uncertainty_plot(total_variance,
filename=t_path)
output[sens_species] = (total_variance, reaction_uncertainty,
thermo_uncertainty)
return output
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())))
