请将以下零散的代码块修改为一个完整的Python代码并将最后的结果输出改为psi随x的变化图像：function square_poten_well(x::Vector, N::Int) L = 2 V0 = -1 mat_V = zeros(N, N) for (i, xx) in enumerate(x) if abs(xx) <= L/2 mat_V[i, i] = V0 end end return mat_V end φ(k, x::Vector, N::Int) = [exp(1.0imkx[i]) for i in 1:N] function Green_func(k, x::Vector, xp::Vector, N::Int) G = ones(ComplexF64, N, N) for i in 1:N G[i, :] = [-1.0im / k * exp(1.0imkabs(x[i]-xp[j]) ) for j in 1:N] end return G end function change_of_var(node, weight, a, b, N) nop = [(b-a) * node[i] / 2.0 + (a+b) / 2.0 for i in 1:N] wp = [(b-a) / 2.0 * weight[i] for i in 1:N] return nop, wp end const N = 298 #节点的个数 const a = -1.5 #积分下限 const b = 1.5 #积分上限 const k_vec = 0.5:1:5.5 # 波数k的取值

以下是将零散的代码块修改为一个完整的Python代码并将最后的结果输出改为psi随x的变化图像的代码：

import numpy as np
import matplotlib.pyplot as plt

def square_poten_well(x, N):
    L = 2
    V0 = -1
    mat_V = np.zeros((N, N))
    for i, xx in enumerate(x):
        if abs(xx) <= L/2:
            mat_V[i, i] = V0
    return mat_V

def phi(k, x, N):
    return [np.exp(1.0j*k*x[i]) for i in range(N)]

def Green_func(k, x, xp, N):
    G = np.ones((N, N), dtype=np.complex128)
    for i in range(N):
        for j in range(N):
            G[i, j] = -1.0j / k * np.exp(1.0j * k * abs(x[i] - xp[j]))
    return G

def change_of_var(node, weight, a, b, N):
    nop = [(b-a) * node[i] / 2.0 + (a+b) / 2.0 for i in range(N)]
    wp = [(b-a) / 2.0 * weight[i] for i in range(N)]
    return nop, wp

N = 298 # 节点的个数
a = -1.5 # 积分下限
b = 1.5 # 积分上限
k_vec = np.arange(0.5, 6.0) # 波数k的取值

x = np.linspace(a, b, N)
dx = (b - a) / (N - 1)

psi = np.zeros((len(k_vec), N))
for i, k in enumerate(k_vec):
    V = square_poten_well(x, N)
    phi_k = phi(k, x, N)
    G = Green_func(k, x, x, N)
    node, weight = np.polynomial.legendre.leggauss(N)
    node = np.flip(node, axis=0)
    weight = np.flip(weight, axis=0)
    xp, wp = change_of_var(node, weight, a, b, N)
    m = np.matmul(np.matmul(np.diag(phi_k), G), np.diag(phi_k.conj())) * dx
    psi_k = np.linalg.solve(V - k**2 * np.eye(N), np.matmul(m, phi_k))
    psi[i] = np.abs(psi_k)**2

fig, ax = plt.subplots()
for i, k in enumerate(k_vec):
    ax.plot(x, psi[i], label=f'k={k:.1f}')
ax.set_xlabel('x')
ax.set_ylabel('$|\psi|^2$')
ax.legend()
plt.show()

输出结果为psi随x的变化图像，其中不同颜色的曲线对应不同的波数k值。