params = { 'name': 'CellFitProblem', 'maximize': False, 'normalize': True, 'model_dir': '../../../model/cells/toymodel1.json', 'mechanism_dir': '../../../model/channels/schmidthieber', 'variables': variables, 'data_dir': '../../../data/toymodels/toymodel1/ramp.csv', 'get_var_to_fit': 'get_v', 'fitnessweights': [1.0], 'errfun': 'rms', 'insert_mechanisms': True } # create problem problem = CellFitProblem(**params) dt_exp = problem.simulation_params['dt'] dts = dt_exp / 2**dt_fac channel_list = get_channel_list(problem.cell, 'soma') ion_list = get_ionlist(channel_list) # initialize errors error_weights = np.zeros([n_models, len(dts)]) error_traces = np.zeros([n_models, len(dts)]) # create pseudo random number generator seed = time() #np.savetxt(save_dir+'/seed_'+str(trial)+'.txt', np.array([seed])) prng = Random() prng.seed(seed)
params = { 'name': 'CellFitProblem', 'maximize': False, 'normalize': True, 'model_dir': '../../../model/cells/dapmodelnaka.json', 'mechanism_dir': '../../../model/channels/schmidthieber', 'variables': [], 'data_dir': '../../../data/2015_08_11d/merged/step_dap_zap.csv', 'get_var_to_fit': 'get_v', 'fitnessweights': [1.0], 'errfun': 'rms', 'insert_mechanisms': True } # create problem problem = CellFitProblem(**params) # save all information if not os.path.exists(save_dir): os.makedirs(save_dir) with open(save_dir+'/problem.json', 'w') as f: json.dump(params, f, indent=4) with open(save_dir+'/cell.json', 'w') as f: json.dump(Cell.from_modeldir(params['model_dir']).get_dict(), f, indent=4) for trial in range(0, n_trials): # get current traces v_exp = problem.data.v.values t_exp = problem.data.t.values i_exp = problem.data.i.values