Python ValueError： 形状 (200,200) 和 (199,) 未对齐： 200 (dim 1) ！= 199 (dim 0) 是什么意思

我正试图通过定义函数(Black-Scholes和Crank-Nicolson)并对它们进行比较来模拟期权定价方案.带边界条件的Crank-Nicolson格式的实现如下(我包含复制的完整代码):

import numpy as np
from scipy.stats import norm
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt

def bs_call(S, K, r, sigma, T):
    d1 = (np.log(S/K) + (r + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
    d2 = d1 - sigma*np.sqrt(T)
    return S*norm.cdf(d1) - K*np.exp(-r*T)*norm.cdf(d2)

def crank_nicolson(S0, K, r, sigma, T, Smin, Smax, M, N):
    # Initialization
    dt = T/N
    ds = (Smax - Smin)/M
    S = np.linspace(Smin, Smax, M+1)
    tau = np.linspace(0, T, N+1)
    V = np.zeros((M+1, N+1))

    # Boundary conditions
    V[:, 0] = np.maximum(S-K, 0)
    V[0, :] = 0
    V[-1, :] = Smax-K*np.exp(-r*tau)

    # Tridiagonal matrix for the implicit step
    alpha = 0.25*dt*(sigma**2*S**2/ds**2 - r*S/ds)
    beta = -dt*0.5*(sigma**2*S**2/ds**2 + r)
    gamma = 0.25*dt*(sigma**2*S**2/ds**2 + r*S/ds)
    A = np.diag(alpha[2:], -1) + np.diag(1+beta[1:]) + np.diag(gamma[:-2], 1)
    B = np.diag(-alpha[2:], -1) + np.diag(1-beta[1:]) + np.diag(-gamma[:-2], 1)

    # Time loop
    for n in range(1, N+1):
        b = np.dot(B, V[1:-1, n-1])
        b[0] -= alpha[1]*V[0, n]
        b[-1] -= gamma[-2]*V[-1, n]
        V[1:-1, n] = np.linalg.solve(A, b)

    # Interpolation for S0
    i = int(round((S0-Smin)/ds))
    if i == M+1:
       i = M
    elif i == 0:
       i = 1
    a = (V[i+1, -1]-V[i, -1])/ds
    b = (V[i+1, -1]-2*V[i, -1]+V[i-1, -1])/ds**2
    return V[i, -1] + a*(S0-S[i]) + 0.5*b*(S0-S[i])**2

到目前为止，一切都很顺利.然后，我按如下方式运行一个示例(还包含用于复制的完整代码):

# Example usage
S0 = 100
K = 100
r = 0.05
sigma = 0.2
T = 1
Smin = 0
Smax = 200
M = 200
N = 1000

option_price = crank_nicolson(S0, K, r, sigma, T, Smin, Smax, M, N)

但是，我在这里收到了一个错误，消息如下:

ValueError: shapes (200,200) and (199,) not aligned: 200 (dim 1) != 199 (dim 0)

我不知道它是来自函数定义(第一部分)还是来自参数定义(第二个脚本)，以及如何解决.