class Nettuno:
'''Main Nettuno class containing EnsembleCollection and Optimizer objects'''
def __init__(self, optimizer, init_filename=".nettuno", log_level=0):
self.init_filename = init_filename
self.ensemble_collection = EnsembleCollection(log_level)
# self.optimizer = Optimizer()
if optimizer == "steepest_descent":
self.optimizer = SteepestDescentOptimizer(log_level)
else:
print "Unknown optimization algorithm: %s. Aborting." % optimizer
sys.exit(1)
self.read_init_file()
def read_init_file(self):
'''Reads configuration file'''
self.ensemble_collection.read_init_file(self.init_filename)
self.optimizer.read_init_file(self.init_filename)
def output_init_file(self, init_file_output):
'''Writes configuration file'''
if init_file_output == self.init_filename:
backup_name = self.init_filename + "_"+ datetime.datetime.today().isoformat("-")
shutil.copyfile(self.init_filename, backup_name)
if init_file_output == "stdout":
print "### Nettuno settings output ###\n"
print repr(self),
print "###############################"
else :
init_file = open(self.init_filename, "w")
init_file.write(repr(self))
init_file.close()
def get_ensemble_collection(self):
'''Retrieve EnsembleCollection object'''
return self.ensemble_collection
def get_optimizer(self):
'''Retrieve Optimizer object'''
return self.optimizer
def optimize(self):
'''Start optimization procedure'''
if len(self.ensemble_collection) > 0:
self.optimizer.optimize(self.ensemble_collection)
def __repr__(self):
return repr(self.optimizer) + "\n" + repr(self.ensemble_collection)
def __init__(self, optimizer, init_filename=".nettuno", log_level=0):
self.init_filename = init_filename
self.ensemble_collection = EnsembleCollection(log_level)
# self.optimizer = Optimizer()
if optimizer == "steepest_descent":
self.optimizer = SteepestDescentOptimizer(log_level)
else:
print "Unknown optimization algorithm: %s. Aborting." % optimizer
sys.exit(1)
self.read_init_file()
help="File in which to read optimization settings.")
parser.add_option("--init_file_output", dest="init_file_output", default="stdout",
help="File/stream in which to write optimization settings.")
parser.add_option("--optimizer", dest="optimizer", type='choice', choices=['steepest_descent'],
default="steepest_descent",
help="Which optimization algorithm to use")
parser.add_option("--log_level", dest="log_level", default="1",
help="How much information to output to screen")
(options, args) = parser.parse_args()
# Allocate main object
nettuno = Nettuno(options.optimizer, options.init_file, int(options.log_level))
# Add ensembles specified from command line
for target_ensemble_tuple in options.new_target_ensembles:
option_dict = EnsembleCollection.option_list_to_ensemble_option_dict(target_ensemble_tuple)
nettuno.get_ensemble_collection().add_model_ensemble(**option_dict)
for model_ensemble_tuple in options.new_model_ensembles:
option_dict = EnsembleCollection.option_list_to_ensemble_option_dict(model_ensemble_tuple)
nettuno.get_ensemble_collection().add_model_ensemble(**option_dict)
# Optionally write out settings to file or stdout
if options.init_file_output != "stdout" or options.log_level >=1:
nettuno.output_init_file(options.init_file_output)
# Call optimization
nettuno.optimize()
