trajectories_sig = np.zeros([2, nb_simul, nb_iterations]) trajectories_tau = np.zeros([2, nb_simul, nb_iterations]) for idx_simul in range(1, nb_simul + 1): print(idx_simul) Y = get_Y(path='../simulations/simulation{0}.csv'.format(idx_simul), T=100) info = pkl.load( open('N_20/python_bayesopt{0}_4.pkl'.format(idx_simul), 'rb')) r = .5 * (10**info[1][0] - 10**0.) / (10**1. - 10**0.) sigma = .5 * (10**info[1][1] - 10**0.) / (10**1. - 10**0.) tau = .5 * (10**info[1][2] - 10**0.) / (10**1. - 10**0.) trajectories_lik[0, idx_simul - 1] = get_traj( 'N_20/python_bayesopt{0}_4.dat'.format(idx_simul)) trajectories_lik_[0, idx_simul - 1] = get_traj_( 'N_20/python_bayesopt{0}_4.dat'.format(idx_simul)) trajectories_lik[1, idx_simul - 1] = np.asarray( powerpack.traj_r( 'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 0][:-1] trajectories_r[1, idx_simul - 1] = np.exp( np.asarray( powerpack.traj_r( 'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 2][:-1]) trajectories_sig[1, idx_simul - 1] = np.exp( np.asarray( powerpack.traj_r( 'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 4][:-1]) trajectories_tau[1, idx_simul - 1] = np.exp( np.asarray( powerpack.traj_r( 'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 5][:-1]) #indexes = np.asarray(get_traj_index('N_20/python_bayesopt{0}_4.dat'.format(idx_simul)))[:-1] trajectories_r[0, idx_simul - 1] = get_params( 'N_20/python_bayesopt{0}_4.dat'.format(idx_simul))[0]