#imports for runmanager - labscript from runmanager_remote import run_experiment import time import numpy as np #Imports for M-LOOP import mloop.interfaces as mli #importlib allows to import the costfunction defined in cost_model.py import importlib _module_cache = {} #avoid multiple calls of cost function for same routine #Declare your custom class that inherits from the Interface class class NNDy_Interface(mli.Interface): def __init__(self, routine_name, cost_model, hyperpars): #You must include the super command to call the parent class, Interface, constructor super(NNDy_Interface,self).__init__() #Attributes of the interface can be added here self.exp_global_par = hyperpars['globalPar'] self.input_names = hyperpars['inputPar_names'] self.routine = routine_name self.cost_model = cost_model def cost(self, parameters): module_name = self.cost_model if module_name not in _module_cache: try: module = importlib.import_module(module_name) cost_func = getattr(module, 'cost') #analysis_func = getattr(self.module, 'analysis') _module_cache[module_name] = { "cost_func": cost_func , # "analysis_func": analysis_func } except (ModuleNotFoundError, AttributeError) as e: raise ImportError(f'Failed to load cost function from "{module_name}.py": {e}') cost_model = _module_cache[module_name]["cost_func"] return cost_model(parameters) #the method that runs the experiment given a set of parameters and returns a cost def get_next_cost_dict(self,In_params_raw): #The parameters come in a dictionary and are provided in a numpy array In_params = In_params_raw['params'] #print(In_params) #optimization parameters to be send back to labscript are converted back into dictionaries if len(In_params) != len(self.input_names): raise Exception('number of optimized parameters and names do not match') In_params_dict = {} for par,name in zip(In_params, self.input_names) : In_params_dict.update({name: par}) #merge with fixed global variables global_group = In_params_dict | self.exp_global_par #Here you can include the code to run your experiment given a particular set of parameters #run_experiment runs the routine specified by routine name with global variables equal to the new set of parameters given by the optimizer #this means that the experiment parameters - In_params - are chosen among the global variablesof this labscript routine and are passed as a dictionary #print('running the experiment') results = { 'cost': np.inf, 'bad': False } try: hdf_output_file = run_experiment(self.routine, global_var = global_group) results = self.cost(hdf_output_file) except Exception as e: print(f"Exception type '{e}', considered as bad run!") results['bad'] = True #self.analysis(hdf_output_file) #print('cost is computed') uncer = 0 time.sleep(0.001) #The cost, uncertainty and bad boolean must all be returned as a dictionary cost_dict = {'cost':results['cost'], 'uncer':uncer, 'bad':results['bad']} return cost_dict