fixed_p = np.ndarray(shape=(len(initial_params), len(prior_set.params))) for idx, item in enumerate(initial_params): for i, param in enumerate(prior_set.params): fixed_p[idx, i] = item[param.name] database.create_array( database.root, "fixed_params", title="True value for each parameter in given simulation", atom=tb.Atom.from_dtype(fixed_p.dtype), shape=fixed_p.shape, obj=fixed_p) database.flush() print("Parameter space initialization data saved to disk") print(database) database.close() # RUN INFERENCE -------------------------------------------------------------------------------------------- working_path = "/home/szabolcs/parameter_inference/exp/inference_w" if __name__ == '__main__': run_protocol_simulations(model=exp_model, target_traces=target_traces, noise_std=noise_std, param_set=prior_set, working_path=working_path) runningTime = (time.time() - startTime) / 60 print("\n\nThe script was running for %f minutes" % runningTime)
"/Users/Dani/TDK/parameter_estim/stim_protocol2/zap/%i/stim.txt" % item) working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/combining2/zaps/%i" % item modell = partial(model, stype='custom', custom_stim=stim) # Generate synthetic data for each fixed params and given repetition target_traces = more_w_trace(sigma=noise_std, model=modell, params=fixed_params, rep=noise_rep) if __name__ == '__main__': run_protocol_simulations(model=modell, target_traces=target_traces, noise_std=noise_std, param_set=prior_set, fixed_params=fixed_params, working_path=working_path) for item in duration: print( "\n\n---------------------------------------- Running %i ms impulse protocol" % item) # Stimulus path stim = np.loadtxt( "/Users/Dani/TDK/parameter_estim/stim_protocol2/steps/%i/stim.txt" % item) working_path = "/Users/Dani/TDK/parameter_estim/stim_protocol2/combining2/steps/%i" % item modell = partial(model, stype='custom', custom_stim=stim)