%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt, mpld3
from matplotlib.animation import FuncAnimation
from JSAnimation import IPython_display
import scipy, scipy.linalg, scipy.sparse, scipy.sparse.linalg, scipy.fftpack
plt.rcParams['figure.figsize'] = 12, 8
mpld3.enable_notebook()

C:\Users\Michael\Anaconda\lib\site-packages\numpy\oldnumeric\__init__.py:11: ModuleDeprecationWarning: The oldnumeric module will be dropped in Numpy 1.9
  warnings.warn(_msg, ModuleDeprecationWarning)

Initial wave packet:

x_0 = -10.
k = 5.
a = 1

def wavepacket(x, t=0):
    return (np.pi * a**2 / 2)**(-1./4) \
        * np.sqrt(a**2 / (a**2 + 2j*t)) \
        * np.exp(1j*(k*x - k**2 * t / 2)) \
        * np.exp(-(x - x_0 - k*t)**2 / (a**2 + 2j*t))

(Discretized) Hamiltonian for given potential:

def hamiltonian(V):
    diag = V(xs) + 1. / dx**2
    offdiag = -0.5 / dx**2 * np.ones(x_steps)
    return scipy.sparse.dia_matrix(([diag, offdiag, offdiag], [0, -1, 1]), shape=(x_steps, x_steps))

Video recording:

def display_video(next, V=None):
    fig, ax1 = plt.subplots()
    if V:
        ax2 = ax1.twinx()
        ax2.set_aspect('equal')
        ax2.plot(xs, V(xs), '--', color='gray')
        for tl in ax2.get_yticklabels(): tl.set_color('gray')
    line_real, = ax1.plot([], [], 'r-', label="Re \Psi")
    line_imag, = ax1.plot([], [], 'g-', label="Im \Psi")
    line_abs_sqr, = ax1.plot([], [], 'b-', lw=2, label="|\Psi|^2")
    ax1.set_xlim(x_min, x_max)
    ax1.set_ylim(-1, 1)
    ax1.set_xlabel("x")
    ax1.axvline(0, ls="--", color="gray")
    ax1.legend()

    def update(t):
        psi = next(dt, t)
        line_real.set_data(xs, psi.real)
        line_imag.set_data(xs, psi.imag)
        line_abs_sqr.set_data(xs, np.abs(psi)**2)

    anim = FuncAnimation(fig, update, ts, interval=30, blit=True)
    #anim.save(filename, fps=1/dt)
    #anim.save(filename, fps=1/dt, writer="ffmpeg", codec="libx264")
    #anim.save(filename, fps=1/dt, writer="ffmpeg", codec="libvpx")
    #plt.close(fig)
    return anim

1. "No" Wall

Discretization parameters:

x_min = -50
x_max = 50
x_steps = 5000
xs, dx = np.linspace(x_min, x_max, x_steps, retstep=True)

t_min = 0
t_max = 15
t_steps = 100 # 1000
ts, dt = np.linspace(t_min, t_max, t_steps, retstep=True)

V_free = np.vectorize(lambda x: 0)
H_free = hamiltonian(V_free)

Exact Solution:

display_video(lambda dt, t: wavepacket(xs, t))

– +

Once Loop Reflect