def get_error(solution_object, args):
"""Get error from comparison between ignition delay for detailed and
skeletal models. Uses tau array from detailed simulations, and makes
new array of ignition delays from the reduced mechanism.
Parameters
----------
solution_object : obj
Cantera solution object
args : obj
Terminal arguments
"""
args.initial_sim = False
print 'triggered'
if args.tau_array:
detailed_tau_array = args.tau_array
error_array = []
for index, initial_condition in enumerate(args.initial_temperature_array):
tau_detailed = detailed_tau_array[index]
args.Temp = initial_condition
try:
solution_result = autoignition_loop_control(solution_object, args)
tau_skeletal = solution_result.tau
error_array.append(float((abs((tau_detailed-tau_skeletal)/tau_detailed))*100.0))
except Exception:
tau_skeletal = 0.0
error_array.append(0.0)
print np.max(error_array)
print 'Error: %s%%' %"{0:.2f}"''.format(max(error_array))
else:
tau_detailed = args.tau
solution_result = autoignition_loop_control(solution_object, args)
tau_skeletal = solution_result.tau
error = float((abs((tau_detailed-tau_skeletal)/tau_detailed))*100.0)
print 'Error: %s%%' %"{0:.2f}"''.format(max(error))
def readin(args='none', **argv):
"""Main function for pyMARS
Parameters
----------
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
write_ai_times:
write autoignition times for each inital condition
run_drg:
Run DRG model reduction
run_drgep:
Run drgep model.
error:
Maximum ammount of error allowed.
keepers: list of strings
The string names of the species that should be kept in the model no matter what.
targets: list of strings
The string names of the species that should be used as target species.
Returns
-------
Converted mechanism file
Trimmed Solution Object
Trimmed Mechanism file
Examples
--------
readin(file='gri30.cti', plot='y', species='OH, H')
"""
class args():
#package from terminal use case
if args is not 'none':
plot = args.plot
points = args.points
writecsv = True
writehdf5 = True
data_file = args.file
thermo_file = args.thermo
transport_file = args.transport
run_drg = args.run_drg
conditions_file = args.conditions
convert = args.convert
error = args.error
run_drgep = args.run_drgep
write_ai_times = args.write_ai_times
target = 0
if args.species is None:
keepers = []
else:
keepers = [str(item) for item in args.species.split(',')]
#strip spaces
for i, sp in enumerate(keepers):
keepers[i] = sp.strip()
if args.target is None:
target = []
else:
target = [str(item) for item in args.target.split(',')]
#strip spaces
for i, sp in enumerate(target):
target[i] = sp.strip()
file_extension = os.path.splitext(args.data_file)[1]
if file_extension == ".cti" or file_extension == ".xml": #If the file is a Cantera file.
print("\nThis is a Cantera xml or cti file\n")
solution_object = ct.Solution(args.data_file)
#runs simulation once with additional features on
if args.plot is True or args.writecsv is True or args.points is True or args.writehdf5 is True or args.write_ai_times is True:
if os.path.exists('mass_fractions.hdf5'):
os.system('rm mass_fractions.hdf5')
if args.write_ai_times is True:
if os.path.exists('autoignition_times.txt'):
os.system('rm autoignition_times.txt')
print 'running simulation\n'
sim_result = autoignition_loop_control(solution_object, args, True)
if args.run_drg is True:
run_drg(args, solution_object)
if args.convert is True:
soln2ck.write(solution_object)
if args.run_drgep is True: #If the user wants to run drgep and specifies it as a command line argument.
run_drgep(args, solution_object)
elif file_extension == ".inp" or file_extension == ".dat" or file_extension == ".txt":
print("\n\nThis is a Chemkin file")
#convert file to cti
converted_file_name = convert(args.data_file, args.thermo_file,
args.transport_file)
else:
print("\n\nFile type not supported")
def readin(args='none', **argv):
"""Main function for pyMARS
Parameters
----------
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
run_drg:
Run DRG model reduction
Returns
-------
Converted mechanism file
Trimmed Solution Object
Trimmed Mechanism file
Examples
--------
readin(file='gri30.cti', plot='y', species='OH, H')
"""
class args():
#package from terminal use case
if args is not 'none':
plot = args.plot
points = args.points
writecsv = args.writecsv
writehdf5 = args.writehdf5
data_file = args.file
thermo_file = args.thermo
transport_file = args.transport
run_drg = args.run_drg
threshold_values = args.thresholds
conditions_file = args.conditions
convert = args.convert
if args.species is None:
exclusion_list = []
else:
exclusion_list = [
str(item) for item in args.species.split(',')
]
#strip spaces
for i, sp in enumerate(exclusion_list):
exclusion_list[i] = sp.strip()
file_extension = os.path.splitext(args.data_file)[1]
if file_extension == ".cti" or file_extension == ".xml":
print("\nThis is an Cantera xml or cti file\n")
solution_object = ct.Solution(args.data_file)
if args.plot is True or args.writecsv is True or args.points is True or args.writehdf5 is True:
print 'running sim'
sim_result = autoignition_loop_control(solution_object, args)
if args.run_drg is True:
new_solution_objects = drg_loop_control(solution_object, args)
os.system('rm production_rates.hdf5')
os.system('rm mass_fractions.hdf5')
drg_trimmed_file = soln2cti.write(new_solution_objects[1])
try:
os.system('rm production_rates.hdf5')
except Exception:
pass
if args.convert is True:
soln2ck.write(solution_object)
elif file_extension == ".inp" or file_extension == ".dat" or file_extension == ".txt":
print("\n\nThis is a Chemkin file")
#convert file to cti
converted_file_name = convert(args.data_file, args.thermo_file,
args.transport_file)
else:
print("\n\nFile type not supported")
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 readin(args='none', **argv):
"""Main function for pyMARS
Parameters
----------
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
run_drg:
Run DRG model reduction
Returns
-------
Converted mechanism file
Trimmed Solution Object
Trimmed Mechanism file
Examples
--------
readin(file='gri30.cti', plot='y', species='OH, H')
"""
class args():
#package from terminal use case
if args is not 'none':
plot = args.plot
points = args.points
writecsv = args.writecsv
writehdf5 = args.writehdf5
data_file= args.file
thermo_file = args.thermo
transport_file = args.transport
run_drg = args.run_drg
threshold_values = args.thresholds
conditions_file = args.conditions
convert = args.convert
if args.species is None:
exclusion_list = []
else:
exclusion_list = [str(item) for item in args.species.split(',')]
#strip spaces
for i, sp in enumerate(exclusion_list):
exclusion_list[i]=sp.strip()
file_extension= os.path.splitext(args.data_file)[1]
if file_extension == ".cti" or file_extension == ".xml":
print("\nThis is an Cantera xml or cti file\n")
solution_object = ct.Solution(args.data_file)
if args.plot is True or args.writecsv is True or args.points is True or args.writehdf5 is True:
print 'running sim'
sim_result = autoignition_loop_control(solution_object, args)
if args.run_drg is True:
new_solution_objects = drg_loop_control(solution_object, args)
os.system('rm production_rates.hdf5')
os.system('rm mass_fractions.hdf5')
drg_trimmed_file = soln2cti.write(new_solution_objects[1])
try:
os.system('rm production_rates.hdf5')
except Exception:
pass
if args.convert is True:
soln2ck.write(solution_object)
elif file_extension == ".inp" or file_extension == ".dat" or file_extension == ".txt":
print("\n\nThis is a Chemkin file")
#convert file to cti
converted_file_name = convert(args.data_file, args.thermo_file, args.transport_file)
else:
print("\n\nFile type not supported")
def run_drgep(args, solution_object):
"""
Function to run the drgep method for model reduction
Parameters
----------
solution_object: ct._Solutuion object
An object that represents the solution to be trimmed.
args: args object
An object that contains the following:
file:
Input mechanism file (ex. file='gri30.cti')
species:
Species to eliminate (ex. species='H, OH')
thermo:
Thermo data file if Chemkin format (ex. thermo= 'thermo.dat')
transport:
Transport data file if Chemkin format
plot:
plot ignition curve (ex. plot='y')
points:
print ignition point and sample range (ex. points='y')
writecsv:
write data to csv (ex. writecsv='y')
writehdf5:
write data to hdf5 (ex. writehdf5='y')
run_drg:
Run DRG model reduction
run_drgep:
Run drgep model.
error:
Maximum ammount of error allowed.
keepers: list of strings
The string names of the species that should be kept in the model no matter what.
targets: list of strings
The string names of the species that should be used as target species.
"""
#Set up variables
if len(args.target
) == 0: #If the target species are not specified, puke and die.
print "Please specify a target species."
exit()
done = [
] #Singleton to hold wether or not any more species can be cut from the simulation.
done.append(False)
threshold = .1 #Starting threshold value
threshold_i = .1
error = [
10.0
] #Singleton to hold the error value of the previously ran simulation.
try:
os.system('rm mass_fractions.hdf5')
except Exception:
pass
#Find overall interaction coefficients.
detailed_result = autoignition_loop_control(
solution_object, args
) #The simulation needs to be ran to make the mass_fractions file which has the info to calucalte edge weights I think?
detailed_result.test.close()
ignition_delay_detailed = np.array(detailed_result.tau_array)
rate_edge_data = get_rates(
'mass_fractions.hdf5',
solution_object) #Get edge weight calculation data.
max_dic = make_dic_drgep(
solution_object, rate_edge_data,
args.target) #Make a dictionary of overall interaction coefficients.
print "Testing for starting threshold value"
drgep_loop_control(
solution_object, args, error, threshold, done,
max_dic) #Trim the solution at that threshold and find the error.
while error[
0] != 0: #While the error for trimming with that threshold value is greater than allowed.
threshold = threshold / 10 #Reduce the starting threshold value and try again.
threshold_i = threshold_i / 10
drgep_loop_control(solution_object, args, error, threshold, done,
max_dic)
print("Starting with a threshold value of " + str(threshold))
sol_new = solution_object
past = 0 #An integer representing the error introduced in the past simulation.
done[0] = False
while not done[0] and error[
0] < args.error: #Run the simulation until nothing else can be cut.
sol_new = drgep_loop_control(
solution_object, args, error, threshold, done,
max_dic) #Trim at this threshold value and calculate error.
if args.error > error[
0]: #If a new max species cut without exceeding what is allowed is reached, save that threshold.
max_t = threshold
#if (past == error[0]): #If error wasn't increased, increase the threshold at a higher rate.
# threshold = threshold + (threshold_i * 4)
past = error[0]
#if (threshold >= .01):
# threshold_i = .01
threshold = threshold + threshold_i
print "\nGreatest result: "
sol_new = drgep_loop_control(solution_object, args, error, max_t, done,
max_dic)
if os.path.exsists("production_rates.hdf5"):
os.system('rm production_rates.hdf5')
if os.path.exsists('mass_fractions.hdf5'):
os.system('rm mass_fractions.hdf5')
drgep_trimmed_file = soln2cti.write(
sol_new
) #Write the solution object with the greatest error that isn't over the allowed ammount.
try:
os.system('rm production_rates.hdf5')
except Exception:
pass
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