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()
