def __init__(self): """Constructor""" super(GraupnerBrunelEvaluator, self).__init__() # Graupner-Brunel model parameters and boundaries, # from (Graupner and Brunel, 2012) self.graup_params = [('tau_ca', 1e-3, 100e-3), ('C_pre', 0.1, 20.0), ('C_post', 0.1, 50.0), ('gamma_d', 5.0, 5000.0), ('gamma_p', 5.0, 2500.0), ('sigma', 0.35, 70.7), ('tau', 2.5, 2500.0), ('D', 0.0, 50e-3), ('b', 1.0, 100.0)] self.params = [ bpop.parameters.Parameter(param_name, bounds=(min_bound, max_bound)) for param_name, min_bound, max_bound in self.graup_params ] self.param_names = [param.name for param in self.params] self.protocols, self.sg, self.stdev, self.stderr = \ stdputil.load_neviansakmann() self.objectives = [ bpop.objectives.Objective(protocol.prot_id) for protocol in self.protocols ]
def analyse(): """Generate plot""" cp = pickle.load(open(cp_filename, "r")) results = (cp["population"], cp["halloffame"], cp["history"], cp["logbook"]) _, hof, hst, log = results best_ind = hof[0] best_ind_dict = evaluator.get_param_dict(best_ind) print("Best Individual") for attribute, value in best_ind_dict.items(): print("\t{} : {}".format(attribute, value)) good_solutions = [ evaluator.get_param_dict(ind) for ind in hst.genealogy_history.itervalues() if np.all(np.array(ind.fitness.values) < 1) ] # model_sg = evaluator.compute_synaptic_gain_with_lists(best_ind) # Load data protocols, sg, _, stderr = stdputil.load_neviansakmann() dt = np.array([float(p.prot_id[:3]) for p in protocols]) plt.rcParams["lines.linewidth"] = 2 # plot_epspamp_discrete(dt, model_sg, sg, stderr) plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr) plot_calcium_transients(protocols, best_ind_dict) plot_log(log) plt.show()
def __init__(self): """Constructor""" super(GraupnerBrunelEvaluator, self).__init__() # Graupner-Brunel model parameters and boundaries, # from (Graupner and Brunel, 2012) self.graup_params = [('tau_ca', 1e-3, 100e-3), ('C_pre', 0.1, 20.0), ('C_post', 0.1, 50.0), ('gamma_d', 5.0, 5000.0), ('gamma_p', 5.0, 2500.0), ('sigma', 0.35, 70.7), ('tau', 2.5, 2500.0), ('D', 0.0, 50e-3), ('b', 1.0, 100.0)] self.params = [bpop.parameters.Parameter (param_name, bounds=(min_bound, max_bound)) for param_name, min_bound, max_bound in self. graup_params] self.param_names = [param.name for param in self.params] self.protocols, self.sg, self.stdev, self.stderr = \ stdputil.load_neviansakmann() self.objectives = [bpop.objectives.Objective(protocol.prot_id) for protocol in self.protocols]
def analyse(): """Generate plot""" cp = pickle.load(open(cp_filename, "r")) results = (cp["population"], cp["halloffame"], cp["history"], cp["logbook"]) _, hof, hst, log = results best_ind = hof[0] best_ind_dict = evaluator.get_param_dict(best_ind) print("Best Individual") for attribute, value in best_ind_dict.iteritems(): print("\t{} : {}".format(attribute, value)) good_solutions = [ evaluator.get_param_dict(ind) for ind in hst.genealogy_history.itervalues() if np.all(np.array(ind.fitness.values) < 1) ] # model_sg = evaluator.compute_synaptic_gain_with_lists(best_ind) # Load data protocols, sg, _, stderr = stdputil.load_neviansakmann() dt = np.array([float(p.prot_id[:3]) for p in protocols]) plt.rcParams["lines.linewidth"] = 2 # plot_epspamp_discrete(dt, model_sg, sg, stderr) plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr) plot_calcium_transients(protocols, best_ind_dict) plot_log(log) plt.show()