"""

Creates a dictionary with user names as keys and specie objects as values

=========================== =======================================================================

Input Description

=========================== =======================================================================

smiles_dictionary A dictionary with user names as keys and SMILES strings as values

===================================================================================================

=========================== =======================================================================

Output Description

=========================== =======================================================================

user_species_dictionary A dictionary with user names as keys and species objects as values

===================================================================================================

"""

user_species_dictionary = {}

for (user_name, smiles_string) in smiles_dictionary.iteritems():

user_species_dictionary[user_name] = Species(label=user_name).fromSMILES(smiles_string)

"""Creates a mol_fraction dictionary for run_cantera_job()

=========================== =======================================================================

Input Description

=========================== =======================================================================

exp_file String relative path of .json file with mol_per_mass data

===================================================================================================

=========================== =======================================================================

Output Description

=========================== =======================================================================

mol_fractions A dictionary with user names as keys and mol fractions as values

===================================================================================================

"""

with open(exp_file, 'r') as exp_data_file:

exp_data = json.load(exp_data_file)

initial_mol_per_mass = 0

for species in exp_data:

if species != 'Time':

initial_mol_per_mass += exp_data[species][0]

mol_fractions = {}

for species in exp_data:

if species != 'Time':

mol_fractions[species] = (exp_data[species][0]) / initial_mol_per_mass

if mol_fractions[species] < 1e-8:

del mol_fractions[species]

"""

Creates a mol_fraction dictionary for cantera simulation

=========================== =======================================================================

Input Description

=========================== =======================================================================

mol_fractions A dictionary with user names as keys and mol fractions as values

user_species_dictionary A dictionary with user names as keys and species objects as values

===================================================================================================

=========================== =======================================================================

Output Description

=========================== =======================================================================

cantera_mol_fractions A dictionary with species objects as keys and mol fractions as values

===================================================================================================

"""

cantera_mol_fractions = {}

for (user_name, mol_frac) in mol_fractions.iteritems():

cantera_mol_fractions[user_species_dictionary[user_name]] = mol_frac

specie_initial_mol_frac,

final_time,

temp_initial,

initial_p,

chemkin_file='',

species_dictionary_file='',

transport_file=None,

reactor_type='IdealGasConstPressureTemperatureReactor',

time_units='s',

temp_units='K',

p_units='atm',

species_list=None,

reaction_list=None,

):

"""General function for running Cantera jobs from chemkin files with common defaults

=========================== =======================================================================

Input (Required) Description

=========================== =======================================================================

smiles_dictionary A dictionary with user names as keys and SMILES strings as values

specie_initial_mol_frac A dictionary with user specie names as keys and mol fractions as values

final_time Termination time for the simulation

temp_initial Initial temperature for the simulation

initial_p Initial pressure for the simulation

=========================== =======================================================================

Inputs with Defaults Description

=========================== =======================================================================

chemkin_file String relative path of the chem.inp or chem_annotated.inp file

species_dictionary_file String relative path of species_dictionary file

reactor_type String with Cantera reactor type

time_units Default is s (min and h are also supported)

temp_units Default is K (C is also supported)

p_units Default is atm (bar and Pa are also supported)

=========================== =======================================================================

Optional Inputs Description

=========================== =======================================================================

transport_file String relative path of trans.dat file

species_list Output from loadChemkinFile for faster simulation (otherwise generated)

reaction_list Output from loadChemkinFile for faster simulation (otherwise generated)

===================================================================================================

=========================== =======================================================================

Output Description

=========================== =======================================================================

all_data Cantera Simulation Data Object [time, [temp, pressure, spc1, spc2,..]]

===================================================================================================

"""

logging.info('Running a cantera job using the chemkin file {}'.format(chemkin_file))

logging.debug('loading chemkin and species dictionary file')

cwd = os.getcwd()

if chemkin_file == '':

chemkin_file = os.path.join(cwd, 'chem_annotated.inp')

if species_dictionary_file == '':

species_dictionary_file = os.path.join(cwd, 'species_dictionary.txt')

user_species_dictionary = create_species_from_smiles(smiles_dictionary)

specie_initial_mol_frac = set_species_mol_fractions(specie_initial_mol_frac, user_species_dictionary)

if (not species_list) or (not reaction_list):

(species_list, reaction_list) = loadChemkinFile(chemkin_file, species_dictionary_file)

name_dictionary = getRMGSpeciesFromUserSpecies(user_species_dictionary.values(), species_list)

mol_fractions = {}

for (user_name, chemkin_name) in name_dictionary.iteritems():

try:

mol_fractions[chemkin_name] = specie_initial_mol_frac[user_name]

except KeyError:

logging.debug('{} initial mol fractions set to 0'.format(user_name))

if temp_units == 'C':

temp_initial += 273.0

temp_initial = ([temp_initial], 'K')

initial_p = ([initial_p], p_units)

job = Cantera(speciesList=species_list, reactionList=reaction_list, outputDirectory='')

job.loadChemkinModel(chemkin_file, transportFile=transport_file)

job.generateConditions([reactor_type], ([final_time], time_units), [mol_fractions], temp_initial, initial_p)

logging.debug('Starting Cantera Simulation')

all_data = job.simulate()

all_data = all_data[0]

logging.info('Cantera Simulation Complete')

logging.debug('Setting labels to user defined species labels')

species_index = {}

for i in range(len(species_list)):

species_index[species_list[i]] = i+2

user_index = {}

for (user_name, specie) in user_species_dictionary.iteritems():

try:

user_index[species_index[name_dictionary[specie]]] = user_name

except KeyError:

logging.info('{0} is not in the model for {1}'.format(user_name, chemkin_file))

for (indices, user_label) in user_index.iteritems():

try:

all_data[1][indices].label = user_label

except KeyError:

pass

temp_conversions = {'K:C': -273, 'C:K': 273, 'K:K': 0, 'C:C': 0}

if temp_units:

conversion_key = '{0}:{1}'.format(cantera_data[1][0].units, temp_units)

try:

cantera_data[1][0].data += temp_conversions[conversion_key]

cantera_data[1][0].units = temp_units

except KeyError:

raise Exception('{} units not currently supported'.format(temp_units))

time_conversions = {'s:h': float(1.0/3600.0), 's:m': float(1.0/60.0), 's:s': 1.0, 'h:s': 3600.0, 'h:m': 60.0,

'h:h': 1.0, 'm:h': float(1.0/60.0), 'm:m': 1.0, 'm:s': 60.0}

if time_units:

conversion_key = '{0}:{1}'.format(cantera_data[0].units, time_units)

try:

cantera_data[0].data *= time_conversions[conversion_key]

cantera_data[0].units = time_units

except KeyError:

raise Exception('{} units not currently supported'.format(time_units))

pressure_conversions = {'Pa:bar': 1.0e-5, 'Pa:atm': float(1.0/101325.0), 'Pa:Pa': 1.0, 'atm:Pa': 101325.0,

'atm:bar': 1.01325, 'atm:atm': 1, 'bar:Pa': 1.0e5, 'bar:atm': float(1/1.01325),

'bar:bar': 1.0}

if pressure_units:

conversion_key = '{0}:{1}'.format(cantera_data[1][1].units, pressure_units)

try:

cantera_data[1][1].data *= pressure_conversions[conversion_key]

cantera_data[1][1].units = pressure_units

except KeyError:

raise Exception('{} units not currently supported'.format(pressure_units))

if comp_units:

if (not cantera_data[1][2].units) and (comp_units == 'moles_per_mass'):

for i in range(len(cantera_data[1][2].data)):

mass = 0.0

for j in range(len(cantera_data[1])-2):

mass += cantera_data[1][j+2].data[i]*cantera_data[1][j+2].species.molecule[0].getMolecularWeight()

for j in range(len(cantera_data[1])-2):

cantera_data[1][j+2].data[i] /= mass

for j in range(len(cantera_data[1])-2):

cantera_data[1][j+2].units = 'moles_per_mass'

"""Calculate the percent of the model from experimental data for the species specified"""

with open(exp_file, 'r') as exp_data_file:

exp_data = json.load(exp_data_file)

times = exp_data['Time']

times = times[1:]

time_index = {}

for t in times:

time_index[t] = 0

for (index, cantera_times) in enumerate(cantera_data[0].data):

if (abs(t - cantera_times)) < (abs(t - cantera_data[0].data[time_index[t]])):

time_index[t] = index

percent_errors = {}

for species in species_to_compare:

percent_errors[species] = 0

for column in cantera_data[1]:

if column.label == species:

for t in range(len(times)):

percent_errors[species] += abs(

(exp_data[species][t + 1] - column.data[time_index[times[t]]]) * 100) / (

exp_data[species][t + 1])

percent_errors[species] /= float(len(times))

print 'The average percent error for {0} is {1}%'.format(species, percent_errors[species])

total_percent_error = 0

for error in percent_errors.values():

total_percent_error += error

total_percent_error /= float(len(percent_errors))

"""Calculate the percent of the model from experimental data for the species specified"""

with open(exp_file, 'r') as exp_data_file:

exp_data = json.load(exp_data_file)

exp_conv = []

for t in range(len(exp_data['Time'])):

exp_conv += [(exp_data[conv_spec][0] - exp_data[conv_spec][t])/(exp_data[conv_spec][0])]

time_index = {}

for t in range(len(exp_conv)):

time_index[t] = 0

conversion = exp_conv[t]

for column in cantera_data[1]:

if column.label == conv_spec:

best_conv = (column.data[0]-column.data[time_index[t]])/(column.data[0])

for i in range(len(column.data)):

test_conv = (column.data[0]-column.data[i])/(column.data[0])

if (abs(test_conv - conversion)) < (abs(best_conv - conversion)):

time_index[t] = i

best_conv = test_conv

percent_errors = {}

for species in species_to_compare:

percent_errors[species] = 0

for column in cantera_data[1]:

if column.label == species:

for t in range(1, len(exp_conv)):

percent_errors[species] += \

abs((exp_data[species][t] - column.data[time_index[t]])/exp_data[species][t])*100.0

percent_errors[species] /= float(len(exp_conv) - 1.0)

print 'The average percent error for {0} is {1}%'.format(species, percent_errors[species])

total_percent_error = 0

for error in percent_errors.values():

total_percent_error += error

if conv_spec in species_to_compare:

total_percent_error -= percent_errors[conv_spec]

total_percent_error /= float(len(percent_errors) - 1)

else:

total_percent_error /= float(len(percent_errors))

"""Calculate the MSE of the model from experimental data for the species specified"""

with open(exp_file, 'r') as exp_data_file:

exp_data = json.load(exp_data_file)

times = exp_data['Time']

times = times[1:]

time_index = {}

for t in times:

time_index[t] = 0

for (index, cantera_times) in enumerate(cantera_data[0].data):

if (abs(t - cantera_times)) < (abs(t - cantera_data[0].data[time_index[t]])):

time_index[t] = index

mean_square_errors = {}

for species in species_to_compare:

mean_square_errors[species] = 0

for column in cantera_data[1]:

if column.label == species:

for t in range(len(times)):

mean_square_errors[species] += (exp_data[species][t + 1] - column.data[time_index[times[t]]]) ** 2.0

mean_square_errors[species] /= float(len(times))

print 'The mean square error for {0} is {1} {2}'.format(species, mean_square_errors[species],

cantera_data[1][2].units)

total_mse = 0

for error in mean_square_errors.values():

total_mse += error

total_mse /= float(len(mean_square_errors))

"""Creates a publishable quality plot of species concentrations versus time"""

color_tup = ('b', 'g', 'darkorange', 'r', '#33EECC', 'k', 'm')

color_cycle = itertools.cycle(color_tup)

font = {'family': 'normal',

'weight': 'bold',

'size': 14,

}

matplotlib.rc('font', **font) # Use a bold font

matplotlib.rc('axes', linewidth=3) # Increase weight of figure border

times = cantera_data[0].data

profiles = {}

colors = {}

for species in species_to_plot:

for column in cantera_data[1]:

if column.label == species:

profiles[species] = column.data

colors[species] = color_cycle.next()

plt.figure(edgecolor='black', figsize=(10, 7))

plt.xlabel('Time [{}]'.format(cantera_data[0].units), fontsize=18, weight='bold')

plt.xlim([0, times[-1] * 1.05])

if not cantera_data[1][2].units:

plt.ylabel('Mol Fraction [-]', fontsize=18, weight='bold')

elif cantera_data[1][2].units == 'moles_per_mass':

plt.ylabel('Moles per Mass [mol/kg]', fontsize=18, weight='bold')

for (label, profile) in profiles.iteritems():

plt.plot(times, profile, linewidth=3.0, color=colors[label])

if exp_file:

with open(exp_file, 'r') as exp_data_file:

exp_data = json.load(exp_data_file)

if err_bar_file:

with open(err_bar_file, 'r') as err_file:

err_data = json.load(err_file)

for species in species_to_plot:

for i in range(len(exp_data['Time'])):

plt.plot(exp_data['Time'][i], exp_data[species][i], 'o', color=colors[species])

plt.errorbar(exp_data['Time'][i], exp_data[species][i], err_data[species][i], 0,

color=colors[species], markersize=0, capsize=5)

else:

for species in species_to_plot:

for i in range(len(exp_data['Time'])):

plt.plot(exp_data['Time'][i], exp_data[species][i], 'o', color=colors[species])

plt.legend(profiles.keys(), loc='lower center', bbox_to_anchor=(0.5, 1), ncol=min(len(profiles), 3))

plt.gca().tick_params(direction='in', width=3, length=10) # Place ticks on the inside of the plot

if not logscale:

current_yticks = plt.yticks()[0] # Get the current tick values

plt.gca().set_ylim(top=current_yticks[-1]) # Set the top most tick flush with the top of the figure

plt.yticks(current_yticks) # Set the yticks to the same values as before (previous step changes these)

plt.gca().set_ylim(bottom=0) # Set the lowest concentration at zero

else:

plt.yscale('log')

finals = []

for concentrations in profiles.values():

finals += [np.log10(concentrations[-1])]

low = float(round(min(finals)) - 1)

if 10.0**low < 0.0001:

plt.gca().set_ylim(bottom=0.0001)

else:

plt.gca().set_ylim(bottom=10.0**low)

t_index = 0

t_best = cantera_data[0].data[t_index]

for i in range(len(cantera_data[0].data)):

if (abs(cantera_data[0].data[i] - time)) < (abs(t_best - time)):

t_index = i

t_best = cantera_data[0].data[i]

logscale=False):

"""Creates a publishable quality plot of species concentrations versus time"""

color_tup = ('b', 'g', 'darkorange', 'r', '#33EECC', 'k', 'm')

color_cycle = itertools.cycle(color_tup)

font = {'family': 'normal',

'weight': 'bold',

'size': 14,

}

matplotlib.rc('font', **font) # Use a bold font

matplotlib.rc('axes', linewidth=3) # Increase weight of figure border

profiles = {}

colors = {}

for species in species_to_plot:

for column in cantera_data[1]:

if column.label == species:

profiles[species] = column.data

colors[species] = color_cycle.next()

conversions = []

for i in range(len(cantera_data[1]) - 2):

if cantera_data[1][i + 2].label == conversion_spec:

x_init = cantera_data[1][i + 2].data[0]

for j in range(len(cantera_data[1][i + 2].data)):

conversions += [(x_init - cantera_data[1][i + 2].data[j]) / x_init]

plt.figure(edgecolor='black', figsize=(10, 7))

plt.xlabel('{} Conversion [-]'.format(conversion_spec), fontsize=18, weight='bold')

plt.xlim([0, conversions[-1] * 1.05])

if not cantera_data[1][2].units:

plt.ylabel('Mol Fraction [-]', fontsize=18, weight='bold')

elif cantera_data[1][2].units == 'moles_per_mass':

plt.ylabel('Moles per Mass [mol/kg]', fontsize=18, weight='bold')

for (label, profile) in profiles.iteritems():

plt.plot(conversions, profile, linewidth=3.0, color=colors[label])

if exp_file:

with open(exp_file, 'r') as exp_data_file:

exp_data = json.load(exp_data_file)

plt.xlim(

[0, ((exp_data[conversion_spec][0] - exp_data[conversion_spec][-1])/exp_data[conversion_spec][0])*1.05]

)

if err_bar_file:

with open(err_bar_file, 'r') as err_file:

err_data = json.load(err_file)

for species in species_to_plot:

for i in range(len(exp_data['Time'])):

conv = (exp_data[conversion_spec][0]-exp_data[conversion_spec][i])/(exp_data[conversion_spec][0])

plt.plot(conv, exp_data[species][i], 'o', color=colors[species])

plt.errorbar(conv, exp_data[species][i], err_data[species][i], 0,

color=colors[species], markersize=0, capsize=5)

else:

for species in species_to_plot:

for i in range(len(exp_data['Time'])):

conv = (exp_data[conversion_spec][0]-exp_data[conversion_spec][i])/(exp_data[conversion_spec][0])

plt.plot(conv, exp_data[species][i], 'o', color=colors[species])

plt.legend(profiles.keys(), loc='lower center', bbox_to_anchor=(0.5, 1), ncol=min(len(profiles), 3))

plt.gca().tick_params(direction='in', width=3, length=10) # Place ticks on the inside of the plot

if not logscale:

current_yticks = plt.yticks()[0] # Get the current tick values

plt.gca().set_ylim(top=current_yticks[-1]) # Set the top most tick flush with the top of the figure

plt.yticks(current_yticks) # Set the yticks to the same values as before (previous step changes these)

plt.gca().set_ylim(bottom=0) # Set the lowest concentration at zero

else:

plt.yscale('log')

finals = []

for concentrations in profiles.values():

finals += [np.log10(concentrations[-1])]

low = float(round(min(finals)) - 1)

if 10.0**low < 0.0001:

plt.gca().set_ylim(bottom=0.0001)

else:

plt.gca().set_ylim(bottom=10.0**low)