为什么我会收到这个错误：ValueError: setting an array element with a sequence？

如何解决为什么我会收到这个错误：ValueError: setting an array element with a sequence？

我目前正在开展一个项目，研究恒星形成区域如何被银河中心的尘埃遮挡，需要模拟恒星形成区域以进行进一步测试。我使用多元高斯概率函数制作了我的恒星形成区域，这需要三个输入，即“x”、“mu”和“cov”。基本上，我想生成多个恒星形成区域并将它们加在一起，每个恒星形成区域都有自己的“x”、“mu”和“cov”。在我的例子中，'x' 和 'mu' 需要是一个列向量，'cov' 需要是一个 3x3 矩阵。目前，我的代码在这一行给了我一个错误：grid[i,j,k] += pdf_multivariate_gauss(vect,mu_in,cov_in)，我不知道如何解决它。任何帮助将不胜感激。

完整代码：

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal
from scipy import linalg

def pdf_multivariate_gauss(x,mu,cov):
    '''
    Calculate the multivariate normal density (pdf)

    Keyword arguments:
        x = numpy array of a "d x 1" sample vector
        mu = numpy array of a "d x 1" mean vector
        cov = "numpy array of a d x d" covariance matrix
    '''
    'mu must be a column vector'
    'x must be a column vector'
    'covariance matrix must be square'
    'cov_mat and mu_vec must have the same dimensions'
    'mu and x must have the same dimensions'
    part1 = 1 / ((2*np.pi)**(len(mu)/2))*(linalg.det(cov))         # Defining the two halves of the equation separately
    part2 = -0.5*((x-mu).T).dot(linalg.inv(cov)).dot(x-mu)   # .linalg is the package with matrix operations in
    return np.sqrt(part1) * np.exp(part2)                          # Multiplying the two halves together

n = 100                           # Defines the for loop boundaries,will have to be much bigger for real thing
n_core = 5          
vect = np.zeros(3)                # A dx1 array of vector values,each run of loop will produce 1 vector
grid = np.zeros([100,100,100])    # Defining space to plot the Gaussian inside 
c_dens = np.zeros([100,100])      # Defining a space to plot column density

for i in range(n):
    x = 0 + i*0.1 + 0.5*0.1       # Will have to code values for dx,dy and dz rather than hard coding them
    for j in range(n):            # Can reduce size of grid to make it run faster
        y = 0 + j*0.1 + 0.5*0.1   # To generalise into 3D just add another vect[2] = z,have to make grid() 3D too
        for k in range(n):
            z = 0 + k*0.1 + 0.5*0.1
            vect[0] = x
            vect[1] = y
            vect[2] = z
            for l in range(n_core):
                mu = np.random.uniform(0,10,(n_core,3,1))  # Controls the 'position' of the gaussian core
                cov = np.random.uniform(0,3)) # Contols the shape of the gaussian core,off axis elements to zero for circle     
                mu_in = mu[l,:,:]
                cov_in = cov[l,:]
                grid[i,cov_in)
        c_dens[i,j] = np.sum(grid[i,:])*0.1       # Column density map is summation of i and j components of grid multiplied by dz
    
print("grid",grid)

plt.clf()
im = plt.imshow(image,aspect='auto',cmap='magma',origin='upper')
plt.colorbar()
plt.show()