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()