def run(Ns, step, dt, acc, c):
args = (m, omega, acc, x0, c)
v = harmonic_potential(x, t, args=args)
sch = Schrodinger(x, psi_x, v, k, hbar=hbar, m=m, t=0, args=args)
for i in range(Ns):
sch.evolve_t(N_steps=step, dt=dt)
return sch.expectation_x()
def run(acc):
t = 0
psi_x = gauss_init(x, k_initial, x0=0, d=d)
V_x = gaussbox(x, w, L, x0=xb, A=Amp, a=acc)
sch = Schrodinger(x, psi_x, V_x, k, hbar=hbar, m=m, t=t)
t_list = []
x_list = []
for i in range(Ns):
sch.evolve_t(N_steps=step, dt=dt)
t_list.append(sch.t)
x_list.append(sch.expectation_x())
dat = list(zip(t_list, x_list))
# np.savetxt("GBox_{}.txt".format(a), dat)
return dat
lim1=((0, x_length), (0, max(np.real(psi_x)))),
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')
# a = Animate(sch, V_x, step, dt, lim1=((0, x_length), (0, max(np.real(psi_x)))),
# lim2=((ks[0], ks[N-1]), (0, 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())