def pfa_loop_control(solution_object, args, stored_error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array):
target_species = args.target
#run detailed mechanism and retain initial conditions
species_retained = []
printout = ''
print('Threshold Species in Mech Error')
#run DRG and create new reduced solution
pfa = trim_pfa(rate_edge_data, solution_object, threshold, args.keepers, done,target_species) #Find out what to cut from the model
exclusion_list = pfa
new_solution_objects = trim(solution_object, exclusion_list, args.data_file) #Cut the exclusion list from the model.
species_retained.append(len(new_solution_objects[1].species()))
#simulated reduced solution
new_sim = helper.setup_simulations(conditions_array,new_solution_objects[1]) #Create simulation objects for reduced model for all conditions
ignition_delay_reduced = helper.simulate(new_sim) #Run simulations and process results
if (ignition_delay_detailed.all() == 0): #Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
#Calculate error
error = (abs(ignition_delay_reduced-ignition_delay_detailed)/ignition_delay_detailed)*100 #Calculate error
printout += str(threshold) + ' ' + str(len(new_solution_objects[1].species())) + ' '+ str(round(np.max(error), 2)) +'%' + '\n'
print(printout)
stored_error[0] = round(np.max(error), 2)
#Return new model.
new_solution_objects = new_solution_objects[1]
return new_solution_objects
def pfa_loop_control(solution_object, target_species, retained_species, model_file, stored_error, threshold, done, rate_edge_data, ignition_delay_detailed, conditions_array):
"""
This function handles the reduction, simulation, and comparision for a single threshold value
Parameters
----------
solution_object: object being reduced # target_species:
target_species: The target species for reduction
retained_species: A list of species to be retained even if they should be cut by the algorithm
model_file: The path to the file where the solution object was generated from
stored_error: Error from the previous reduction attempt
threshold: current threshold value
done: are we done reducing yet? Boolean
rate_edge_data: the DICs for reduction
ignition_delay_detailed: ignition delay of detailed model
conditions_array: array holding information about initial conditions
Returns
-------
Returns the reduced solution object for this threshold and updates error value
"""
# Run detailed mechanism and retain initial conditions
species_retained = []
printout = ''
print('Threshold Species in Mech Error')
# Run DRG and create new reduced solution
exclusion_list = trim_pfa(
rate_edge_data, solution_object, threshold, retained_species, done,target_species,model_file) # Find out what to cut from the model
new_solution_objects = trim(solution_object, exclusion_list, model_file) # Cut the exclusion list from the model.
species_retained.append(len(new_solution_objects[1].species()))
# Simulated reduced solution
new_sim = helper.setup_simulations(conditions_array,new_solution_objects[1]) # Create simulation objects for reduced model for all conditions
ignition_delay_reduced = helper.simulate(new_sim) # Run simulations and process results
if (ignition_delay_detailed.all() == 0): # Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
# Calculate error
error = (abs(ignition_delay_reduced-ignition_delay_detailed)/ignition_delay_detailed)*100 # Calculate error
printout += str(threshold) + ' ' + str(len(new_solution_objects[1].species())) + ' '+ str(round(np.max(error), 2)) +'%' + '\n'
print(printout)
stored_error[0] = round(np.max(error), 2)
# Return new model
new_solution_objects = new_solution_objects[1]
return new_solution_objects
def get_limbo_dic(original_model, reduced_model, limbo, final_error, args,
id_detailed, conditions_array):
dic = {}
og_excl = [
] #For information on how this is set up, refer to run_sa function.
keep = []
og_sn = []
new_sn = []
species_objex = reduced_model.species()
for sp in species_objex:
new_sn.append(sp.name)
species_objex = original_model.species()
for sp in species_objex:
og_sn.append(sp.name)
for sp in og_sn:
if sp in new_sn:
keep.append(sp)
for sp in original_model.species():
if not (sp.name in keep):
og_excl.append(sp.name)
for sp in limbo: #For all species in limbo
excluded = [sp]
for p in og_excl:
excluded.append(p) #Add that species to the list of exclusion.
new_sol_obs = trim(original_model, excluded,
"sa_trim.cti") #Remove species from the model.
new_sol = new_sol_obs[1]
#simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_sol
) #Create simulation objects for reduced model for all conditions
id_new = helper.simulate(new_sim) #Run simulations and process results
error = (abs(id_new - id_detailed) / id_detailed) * 100
error = round(np.max(error), 2)
print(sp + ": " + str(error))
error = abs(error - final_error)
dic[sp] = error #Add adjusted error to dictionary.
return dic
def drg_loop_control(solution_object, target_species, retained_species,
model_file, stored_error, threshold, done, rate_edge_data,
ignition_delay_detailed, conditions_array):
"""Handles the reduction, simulation, and comparision for a single threshold value.
Parameters
----------
solution_object:
object being reduced
target_species : list of str
List of target species
retained_species : list of str
List of species to always be retained
model_file : string
The path to the file where the solution object was generated from
stored_error: signleton float
Error of this reduced model simulation
threshold : float
current threshold value
done : bool
are we done reducing yet?
rate_edge_data :
information for calculating the DICs for reduction
ignition_delay_detailed :
ignition delay of detailed model
conditions_array :
array holding information about initial conditions
Returns
-------
Reduced solution object for this threshold and updates error value
"""
species_retained = []
printout = ''
print('Threshold Species in Mech Error')
# Run DRG and create new reduced solution
# Find out what to cut from the model
exclusion_list = trim_drg(rate_edge_data, solution_object, threshold,
retained_species, done, target_species)
# Cut the exclusion list from the model.
new_solution_objects = trim(solution_object, exclusion_list, model_file)
species_retained.append(len(new_solution_objects[1].species()))
# Simulated reduced solution
# Create simulation objects for reduced model for all conditions
new_sim = helper.setup_simulations(conditions_array,
new_solution_objects[1])
ignition_delay_reduced = helper.simulate(
new_sim) # Run simulations and process results
if ignition_delay_detailed.all() == 0: # Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
# Calculate error
error = (abs(ignition_delay_reduced - ignition_delay_detailed) /
ignition_delay_detailed) * 100 # Calculate error
printout += str(threshold) + ' ' + str(
len(new_solution_objects[1].species())) + ' ' + str(
round(np.max(error), 2)) + '%' + '\n'
print(printout)
stored_error[0] = round(np.max(error), 2)
# Return new model.
new_solution_objects = new_solution_objects[1]
return new_solution_objects
def run_sa(original_model, reduced_model, ep_star, final_error, args):
print(final_error)
if args.conditions_file:
conditions_array = readin_conditions(str(args.conditions_file))
elif not args.conditions_file:
print("Conditions file not found")
exit()
sim_array = helper.setup_simulations(
conditions_array, original_model
) #Turn conditions array into unran simulation objects for the original solution
id_detailed = helper.simulate(
sim_array) #Run simulations and process results
rate_edge_data = get_rates(
sim_array, original_model) #Get edge weight calculation data.
drgep_coeffs = make_dic_drgep(
original_model, rate_edge_data,
args.target) #Make a dictionary of overall interaction coefficients.
if (id_detailed.all() == 0): #Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
old = reduced_model
while True:
og_sn = [] #Original species names
new_sn = [] #Species names in current reduced model
keep = [] #Species retained from removals
og_excl = [
] #Species that will be excluded from the final model (Reduction will be preformed on original model)
species_objex = old.species()
for sp in species_objex:
new_sn.append(sp.name)
species_objex = original_model.species()
for sp in species_objex:
og_sn.append(sp.name)
for sp in og_sn:
if sp in new_sn:
keep.append(sp)
for sp in original_model.species():
if not (sp.name in keep):
og_excl.append(sp.name)
limbo = create_limbo(old, ep_star, drgep_coeffs,
args.keepers) #Find all the species in limbo.
if len(limbo) == 0:
return old
print("In limbo:")
print(limbo)
dic = get_limbo_dic(
original_model, old, limbo, final_error, args, id_detailed,
conditions_array
) #Calculate error for removing each limbo species.
rm = dic_lowest(dic) #Species that should be removed (Lowest error).
exclude = [rm]
for sp in og_excl: #Add to list of species that should be excluded from final model.
exclude.append(sp)
print()
print("attempting to remove " + rm)
new_sol_obs = trim(
original_model, exclude,
"sa_trim.cti") #Remove exclusion list from original model
new_sol = new_sol_obs[1]
#simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_sol
) #Create simulation objects for reduced model for all conditions
id_new = helper.simulate(new_sim) #Run simulations and process results
error = (abs(id_new - id_detailed) / id_detailed) * 100
error = round(np.max(error), 2)
print("Error of: " + str(error))
print()
if error > args.error: #If error is greater than allowed, previous reduced model was final reduction.
print("Final Solution:")
print(str(old.n_species) + " Species")
return old
else: #If error is still within allowed limit, loop through again to further reduce.
old = new_sol
def drgep_loop_control(solution_object, args, stored_error, threshold, done,
max_dic):
""" Controls the trimming and error calculation of a solution with the graph already created using the DRGEP method.
Parameters
----------
solution_object : obj
Cantera solution object
args : obj
function arguments object
stored_error: float singleton
The error introduced by the last simulation (to be replaced with this simulation).
done: singleton
a singleton boolean value that represnts wether or not more species can be excluded from the graph or not.
max_dic: dictionary
a dictionary keyed by species name that represents the species importance to the model.
Returns
-------
new_solution_objects : obj
Cantera solution object That has been reduced.
"""
target_species = args.target
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
#run detailed mechanism and retain initial conditions
detailed_result = autoignition_loop_control(solution_object,
args) #Run simulation
detailed_result.test.close()
ignition_delay_detailed = np.array(detailed_result.tau_array)
species_retained = []
printout = ''
print 'Threshold Species in Mech Error'
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
#run DRGEP and create new reduced solution
drgep = trim_drgep(max_dic, solution_object, threshold, args.keepers,
done) #Find out what to cut from the model
exclusion_list = drgep
new_solution_objects = trim(
solution_object, exclusion_list,
args.data_file) #Cut the exclusion list from the model.
species_retained.append(len(new_solution_objects[1].species()))
#simulated reduced solution
reduced_result = autoignition_loop_control(
new_solution_objects[1], args) #Run simulation on reduced model
if (reduced_result == 0):
stored_error[0] = 100
error = 100
printout += str(threshold) + ' ' + str(
len(new_solution_objects[1].species())) + ' ' + str(
round(np.max(error), 2)) + '%' + '\n'
print printout
return new_solution_objects
reduced_result.test.close()
ignition_delay_reduced = np.array(reduced_result.tau_array)
#Calculate and print error.
error = (abs(ignition_delay_reduced - ignition_delay_detailed) /
ignition_delay_detailed) * 100 #Calculate error
printout += str(threshold) + ' ' + str(
len(new_solution_objects[1].species())) + ' ' + str(
round(np.max(error), 2)) + '%' + '\n'
print printout
stored_error[0] = round(np.max(error), 2)
#Return new model
new_solution_objects = new_solution_objects[1]
return new_solution_objects
def drgep_loop_control(solution_object, target_species, retained_species,
model_file, stored_error, threshold, done, max_dic,
ignition_delay_detailed, conditions_array):
"""
This function handles the reduction, simulation, and comparision for a single threshold value.
Parameters
----------
solution_object: object being reduced
target_species: An array of the target species for reduction
retained_species: An array of species that should not be removed from the model
model_file: The path to the file holding the original model
stored_error: past error
threshold: current threshold value
done: are we done reducing yet? Boolean
max_dic: OIC dictionary for DRGEP
ignition_delay_detailed: ignition delay of detailed model
conditions_array: array holding information about initial conditions
Returns
-------
Returns the reduced solution object for this threshold and updates error value
"""
# Run detailed mechanism and retain initial conditions
species_retained = []
printout = ''
print('Threshold Species in Mech Error')
# Run DRGEP and create new reduced solution
exclusion_list = trim_drgep(max_dic, solution_object, threshold,
retained_species,
done) # Find out what to cut from the model
new_solution_objects = trim(
solution_object, exclusion_list,
model_file) # Cut the exclusion list from the model.
species_retained.append(len(new_solution_objects[1].species()))
# Simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_solution_objects[1]
) # Create simulation objects for reduced model for all conditions
ignition_delay_reduced = helper.simulate(
new_sim) # Run simulations and process results
if ignition_delay_detailed.all() == 0: # Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
# Calculate and print error.
error = (abs(ignition_delay_reduced - ignition_delay_detailed) /
ignition_delay_detailed) * 100 # Calculate error
printout += str(threshold) + ' ' + str(
len(new_solution_objects[1].species())) + ' ' + str(
round(np.max(error), 2)) + '%' + '\n'
print(printout)
stored_error[0] = round(np.max(error), 2)
# Return new model
new_solution_objects = new_solution_objects[1]
return new_solution_objects
def get_limbo_dic(original_model, reduced_model, limbo, final_error,
id_detailed, conditions_array):
"""
Creates a dictionary of all of the species in limbo and their errors for sensitivity analysis.
Parameters
----------
original_model: The original version of the model being reduced
reduced_model: The model produced by the previous reduction
limbo: A list of the species in limbo
final_error: Error percentage between the reduced and origanal models
id_detailed: The ignition delays for each simulation of the original model
conditions_array: An array holding the initial conditions for simulations
Returns
-------
A dictionary with species error to be used for sensitivity anaylsis.
"""
dic = {}
# For information on how this is set up, refer to run_sa function.
og_excl = []
keep = []
og_sn = []
new_sn = []
species_objex = reduced_model.species()
for sp in species_objex:
new_sn.append(sp.name)
species_objex = original_model.species()
for sp in species_objex:
og_sn.append(sp.name)
for sp in og_sn:
if sp in new_sn:
keep.append(sp)
for sp in original_model.species():
if not (sp.name in keep):
og_excl.append(sp.name)
for sp in limbo: # For all species in limbo
excluded = [sp]
for p in og_excl:
excluded.append(p) # Add that species to the list of exclusion.
# Remove species from the model.
new_sol_obs = trim(original_model, excluded, "sa_trim.cti")
new_sol = new_sol_obs[1]
# Simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_sol
) # Create simulation objects for reduced model for all conditions
id_new = helper.simulate(
new_sim) # Run simulations and process results
error = (abs(id_new - id_detailed) / id_detailed) * 100
error = round(np.max(error), 2)
print(sp + ": " + str(error))
error = abs(error - final_error)
dic[sp] = error # Add adjusted error to dictionary.
return dic
def run_sa(original_model, reduced_model, ep_star, final_error,
conditions_file, target, keepers, error_limit):
"""
Runs a sensitivity analysis on a resulting reduced model.
Parameters
----------
original_model: The original version of the model being reduced
reduced_model: The model produced by the previous reduction
ep_star: The epsilon star value for the sensitivity analysis
final_error: Error percentage between the reduced and origanal models
conditions_file: The file holding the initial conditions for simulations
target: The target species for the reduction
keepers: A list of species that should be retained no matter what
error_limit: The maximum allowed error between the reduced and original models
Returns
-------
The model after the sensitivity analysis has been preformed on it.
"""
if conditions_file:
conditions_array = readin_conditions(str(conditions_file))
elif not conditions_file:
print("Conditions file not found")
exit()
# Turn conditions array into unran simulation objects for the original solution
sim_array = helper.setup_simulations(conditions_array, original_model)
id_detailed = helper.simulate(
sim_array) # Run simulations and process results
rate_edge_data = get_rates(
sim_array, original_model) # Get edge weight calculation data.
# Make a dictionary of overall interaction coefficients.
drgep_coeffs = make_dic_drgep(original_model, rate_edge_data, target)
if (id_detailed.all() == 0): # Ensure that ignition occured
print("Original model did not ignite. Check initial conditions.")
exit()
old = reduced_model
while True:
og_sn = [] # Original species names
new_sn = [] # Species names in current reduced model
keep = [] # Species retained from removals
og_excl = [
] # Species that will be excluded from the final model (Reduction will be preformed on original model)
species_objex = old.species()
for sp in species_objex:
new_sn.append(sp.name)
species_objex = original_model.species()
for sp in species_objex:
og_sn.append(sp.name)
for sp in og_sn:
if sp in new_sn:
keep.append(sp)
for sp in original_model.species():
if not (sp.name in keep):
og_excl.append(sp.name)
# Find all the species in limbo.
limbo = create_limbo(old, ep_star, drgep_coeffs, keepers)
if len(limbo) == 0:
return old
print("In limbo:")
print(limbo)
# Calculate error for removing each limbo species.
dic = get_limbo_dic(original_model, old, limbo, final_error,
id_detailed, conditions_array)
rm = dic_lowest(dic) # Species that should be removed (Lowest error).
exclude = [rm]
for sp in og_excl: # Add to list of species that should be excluded from final model.
exclude.append(sp)
print()
print("attempting to remove " + rm)
# Remove exclusion list from original model
new_sol_obs = trim(original_model, exclude, "sa_trim.cti")
new_sol = new_sol_obs[1]
# Simulated reduced solution
new_sim = helper.setup_simulations(
conditions_array, new_sol
) # Create simulation objects for reduced model for all conditions
id_new = helper.simulate(
new_sim) # Run simulations and process results
error = (abs(id_new - id_detailed) / id_detailed) * 100
error = round(np.max(error), 2)
print("Error of: " + str(error))
print()
# If error is greater than allowed, previous reduced model was final reduction.
if error > error_limit:
print("Final Solution:")
print(str(old.n_species) + " Species")
return old
else: # If error is still within allowed limit, loop through again to further reduce.
old = new_sol
def drg_loop_control(solution_object, args):
""" Controls repeated use of drg Function
Parameters
----------
solution_object : obj
Cantera solution object
args : obj
function arguments object
Returns
-------
new_solution_objects : obj
Cantera solution object with skeletal mechanism
"""
#get user input
target_species = raw_input('\nEnter target starting species: ').split(',')
#run detailed mechanism and retain initial conditions
args.multiple_conditions = True
detailed_result = autoignition_loop_control(solution_object, args)
detailed_result.test.close()
ignition_delay_detailed = np.array(detailed_result.tau_array)
#--------------------------
#get-rate data called here
#--------------------------
rate_edge_data = get_rates('mass_fractions.hdf5', solution_object)
if args.threshold_values is None:
try:
threshold = float(raw_input('Enter threshold value: '))
except ValueError:
print 'try again'
threshold = float(raw_input('Enter threshold value: '))
#run DRG and create new reduced solution
drg = make_graph(solution_object, 'production_rates.hdf5', threshold)
exclusion_list = graph_search(solution_object, drg, target_species)
new_solution_objects = trim(solution_object, exclusion_list, args.data_file)
#simulate reduced solution
reduced_result = autoignition_loop_control(new_solution_objects[1], args)
reduced_result.test.close()
ignition_delay_reduced = np.array(reduced_result.tau_array)
error = (abs(ignition_delay_reduced-ignition_delay_detailed)/ignition_delay_detailed)*100
print 'Error index: %s' %error
#get_error()
n_species_retained = len(new_solution_objects[1].species())
print 'Number of species in reduced model: %s' %n_species_retained
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
else:
threshold_values = genfromtxt(args.threshold_values, delimiter=',')
species_retained = []
printout = ''
print 'Threshold Species in Mech Error'
print 'flag'
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
if threshold_values.size > 1:
for threshold in threshold_values:
#run DRG and create new reduced solution
drg = make_graph(solution_object, threshold, rate_edge_data, target_species)
#exclusion_list = graph_search(solution_object, drg, target_species)
exclusion_list = drg
new_solution_objects = trim(solution_object, exclusion_list, args.data_file)
species_retained.append(len(new_solution_objects[1].species()))
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
#simulated reduced solution
reduced_result = autoignition_loop_control(new_solution_objects[1], args)
reduced_result.test.close()
ignition_delay_reduced = np.array(reduced_result.tau_array)
error = (abs(ignition_delay_reduced-ignition_delay_detailed)/ignition_delay_detailed)*100
printout += str(threshold) + ' ' + str(len(new_solution_objects[1].species())) + ' '+ str(round(np.max(error), 2))+'%' + '\n'
print printout
else:
#run DRG and create new reduced solution
drg = make_graph(solution_object, threshold_values, rate_edge_data, target_species)
#exclusion_list = graph_search(solution_object, drg, target_species)
exclusion_list = drg
new_solution_objects = trim(solution_object, exclusion_list, args.data_file)
species_retained.append(len(new_solution_objects[1].species()))
#simulated reduced solution
reduced_result = autoignition_loop_control(new_solution_objects[1], args)
reduced_result.test.close()
ignition_delay_reduced = np.array(reduced_result.tau_array)
error = (abs(ignition_delay_reduced-ignition_delay_detailed)/ignition_delay_detailed)*100
printout += str(threshold_values) + ' ' + str(len(new_solution_objects[1].species())) + ' '+ str(round(np.max(error), 2)) +'%' + '\n'
print printout
# print 'Detailed soln ign delay:'
# print ignition_delay_detailed
# print 'Reduced soln ign delay:'
# print ignition_delay_reduced
return new_solution_objects