lim2=((ks[0], ks[N - 1]), (0, 30))) a.make_fig() t_list = [] norm_x = [] expec_x = [] expec_xs = [] expec_k = [] for i in range(Ns): if i != 0: sch.evolve_t(step, dt) t_list.append(sch.t) norm_x.append(sch.norm_x() - 1) expec_x.append(sch.expectation_x()) expec_xs.append(np.sqrt(sch.expectation_x_square() - expec_x[i]**2)) expec_k.append(sch.expectation_k()) # x_pos_list = [x_pos(j, x0, k_initial, hbar=hbar, m=m) for j in t_list] # xdiff = [np.abs(expec_x[n] - x_pos_list[n]) for n in range(len(expec_x))] # popt1, pcov = curve_fit(func, t_list, x_pos_list) # print("Expected x :", popt1) # # popt2, pcov = curve_fit(func, t_list, expec_x) # print("Calculated x :", popt2) # # plt.plot(t_list, norm_x, linestyle='none', marker='x') # plt.title('Normalistaion of wavefunction over time') # plt.xlabel('Time') # plt.ylabel('Normalisation-1')
# a.make_fig() t_list = [] norm_x = [] expec_x = [] expec_xs = [] expec_k = [] for i in range(int(Ns / 2)): if i != 0: sch.evolve_t(step, dt) t_list.append(sch.t) norm_x.append(sch.norm_x() - 1) expec_x.append(sch.expectation_x()) expec_xs.append( np.sqrt(sch.expectation_x_square() - sch.expectation_x()**2)) expec_k.append(sch.expectation_k()) sch.momentum_kick(-2 * k_initial) for i in range(int(Ns / 2)): sch.evolve_t(step, dt) t_list.append(sch.t) norm_x.append(sch.norm_x() - 1) expec_x.append(sch.expectation_x()) expec_xs.append( np.sqrt(sch.expectation_x_square() - sch.expectation_x()**2)) expec_k.append(sch.expectation_k()) x_pos_list = [x_pos(j, x0, k_initial, hbar=hbar, m=m) for j in t_list] # xdiff = [np.abs(expec_x[n] - x_pos_list[n]) for n in range(len(expec_x))]