def bisect(low, high, error, targets, reactionModel, rmg, reactionSystemIndex,
orig_observable):
"""
Bisect method in log space.
Interrupt iterations when two consecutive, successful iterations differ less than a
threshold value.
"""
THRESHOLD = 0.05
importantReactions = None
final_devs = None
old_trial = low
while True:
midpoint = (low + high) / 2.0
reduced_observable, newImportantReactions = evaluate(
midpoint, targets, reactionModel, rmg, reactionSystemIndex)
devs = computeDeviation(orig_observable, reduced_observable, targets)
if isInvalid(devs, error):
high = midpoint
else:
if len(newImportantReactions) == 0:
logging.error(
'Model reduction resulted in a model with 0 reactions.')
logging.error(
'Perhaps change reactor conditions to allow for more adequate reduction. Exiting...'
)
break
low = midpoint
importantReactions = newImportantReactions
final_devs = devs
writeModel(rmg,
chemkin_name='chem_reduced_{}.inp'.format(
len(importantReactions)))
if np.abs((midpoint - old_trial) / old_trial) < THRESHOLD:
break
old_trial = low
if not importantReactions:
logging.error("Could not find a good guess...")
importantReactions = []
logging.info('Final deviations: '.format())
for dev, target in zip(final_devs, targets):
logging.info('Final deviation for {}: {:.2f}%'.format(
target, dev * 100))
return low, importantReactions
def main():
args = parseCommandLineArguments()
level = INFO
if args.debug: level = 0
elif args.verbose: level = DEBUG
elif args.quiet: level = WARNING
initializeLog(level)
inputFile, reductionFile, chemkinFile, spcDict = args.requiredFiles[-4:]
for f in [inputFile, reductionFile, chemkinFile, spcDict]:
assert os.path.isfile(f), 'Could not find {}'.format(f)
inputDirectory = os.path.abspath(os.path.dirname(inputFile))
output_directory = inputDirectory
rmg, targets, error = load(inputFile, reductionFile, chemkinFile, spcDict)
logger.info('Allowed error in target observables: {0:.0f}%'.format(error *
100))
reactionModel = rmg.reactionModel
initialize(rmg.outputDirectory, reactionModel.core.reactions)
atol, rtol = rmg.absoluteTolerance, rmg.relativeTolerance
index = 0
reactionSystem = rmg.reactionSystems[index]
#compute original target observables
observables = computeObservables(targets, reactionModel, reactionSystem, \
rmg.absoluteTolerance, rmg.relativeTolerance)
logger.info('Observables of original model:')
for target, observable in zip(targets, observables):
logger.info('{}: {:.2f}%'.format(target, observable * 100))
# optimize reduction tolerance
tol, importantReactions = optimize(targets, reactionModel, rmg, index,
error, observables)
logger.info('Optimized tolerance: {:.0E}'.format(10**tol))
logger.info('Number of reactions in optimized reduced model : {}'.format(
len(importantReactions)))
# plug the important reactions into the RMG object and write:
rmg.reactionModel.core.reactions = importantReactions
writeModel(rmg)
def bisect(low, high, error, targets, reactionModel, rmg, reactionSystemIndex, orig_observable):
"""
Bisect method in log space.
Interrupt iterations when two consecutive, successful iterations differ less than a
threshold value.
"""
THRESHOLD = 0.05
importantReactions = None
final_devs = None
old_trial = low
while True:
midpoint = (low + high) / 2.0
reduced_observable, newImportantReactions = evaluate(midpoint, targets, reactionModel, rmg, reactionSystemIndex)
devs = computeDeviation(orig_observable, reduced_observable, targets)
if isInvalid(devs, error):
high = midpoint
else:
if len(newImportantReactions) == 0:
logging.error('Model reduction resulted in a model with 0 reactions.')
logging.error('Perhaps change reactor conditions to allow for more adequate reduction. Exiting...')
break
low = midpoint
importantReactions = newImportantReactions
final_devs = devs
writeModel(rmg, chemkin_name='chem_reduced_{}.inp'.format(len(importantReactions)))
if np.abs((midpoint - old_trial) / old_trial) < THRESHOLD:
break
old_trial = low
if not importantReactions:
logging.error("Could not find a good guess...")
importantReactions = []
logging.info('Final deviations: '.format())
for dev, target in zip(final_devs, targets):
logging.info('Final deviation for {}: {:.2f}%'.format(target, dev * 100))
return low, importantReactions
def main():
args = parseCommandLineArguments()
level = INFO
if args.debug: level = 0
elif args.verbose: level = DEBUG
elif args.quiet: level = WARNING
initializeLog(level)
inputFile, reductionFile, chemkinFile, spcDict = args.requiredFiles[-4:]
for f in [inputFile, reductionFile, chemkinFile, spcDict]:
assert os.path.isfile(f), 'Could not find {}'.format(f)
inputDirectory = os.path.abspath(os.path.dirname(inputFile))
output_directory = inputDirectory
rmg, targets, error = load(inputFile, reductionFile, chemkinFile, spcDict)
logger.info('Allowed error in target observables: {0:.0f}%'.format(error * 100))
reactionModel = rmg.reactionModel
initialize(rmg.outputDirectory, reactionModel.core.reactions)
atol, rtol = rmg.absoluteTolerance, rmg.relativeTolerance
index = 0
reactionSystem = rmg.reactionSystems[index]
#compute original target observables
observables = computeObservables(targets, reactionModel, reactionSystem, \
rmg.absoluteTolerance, rmg.relativeTolerance)
logger.info('Observables of original model:')
for target, observable in zip(targets, observables):
logger.info('{}: {:.2f}%'.format(target, observable * 100))
# optimize reduction tolerance
tol, importantReactions = optimize(targets, reactionModel, rmg, index, error, observables)
logger.info('Optimized tolerance: {:.0E}'.format(10**tol))
logger.info('Number of reactions in optimized reduced model : {}'.format(len(importantReactions)))
# plug the important reactions into the RMG object and write:
rmg.reactionModel.core.reactions = importantReactions
writeModel(rmg)
