代码如下：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from matplotlib.colors import LinearSegmentedColormap, Normalize
import numpy as np

def make_cube(matrix: np.ndarray)->None:
    
    fig = plt.figure(figsize=(28, 25), dpi=360)
    ax = fig.add_subplot(111, projection='3d')
    cube_size = 15

    gap = 2 * cube_size

    center_x = (2.5 * cube_size + (6 - 1) * gap) / 2
    center_y = (2.5 * cube_size + (6 - 1) * gap) / 2
    center_z = (2.5 * cube_size + (5 - 1) * gap) / 2
    normalized_matrix = (matrix - matrix.min()) / (matrix.max() - matrix.min()) * 254  

    colors = [(1, 1, 1), (0, 0, 0)]  
    cmap_name = 'custom_cmap'
    n_colors = 256
    cmap = LinearSegmentedColormap.from_list(cmap_name, colors, N=n_colors)
    norm = Normalize(vmin=0, vmax=255)  # 定义数值范围
    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
    sm.set_array([])

    for i in range(5):
        for j in range(6):
            for k in range(6):
                x = center_x - (2.5 * cube_size + (6 - 1) * gap) + 0.7* j * (cube_size + gap)
                y = center_y - (2.5 * cube_size + (6 - 1) * gap) + 0.7* i * (cube_size + gap)
                z = center_z - (2.5 * cube_size + (5 - 1) * gap) + k * (cube_size + 1.8*gap)
                vertices = np.array([
                    [x, y, z],
                    [x + cube_size, y, z],
                    [x + cube_size, y + cube_size, z],
                    [x, y + cube_size, z],
                    [x, y, z + cube_size],
                    [x + cube_size, y, z + cube_size],
                    [x + cube_size, y + cube_size, z + cube_size],
                    [x, y + cube_size, z + cube_size]
                ])

                faces = [
                    [vertices[0], vertices[1], vertices[2], vertices[3]],
                    [vertices[4], vertices[5], vertices[6], vertices[7]],
                    [vertices[0], vertices[1], vertices[5], vertices[4]],
                    [vertices[1], vertices[2], vertices[6], vertices[5]],
                    [vertices[2], vertices[3], vertices[7], vertices[6]],
                    [vertices[3], vertices[0], vertices[4], vertices[7]]
                ]

                value = normalized_matrix[i, j, k]

                color = cmap(norm(value))

                collection = Poly3DCollection(faces, facecolors=color, linewidths=1.5, edgecolors='black')
                ax.add_collection3d(collection)

    ax.set_xlim(center_x - (2.5 * cube_size + (6 - 1) * gap) - 0.5, center_x + (2.5 * cube_size + (6 - 1) * gap) - 100)
    ax.set_ylim(center_y - (2.5 * cube_size + (6 - 1) * gap) - 0.5, center_y + (2.5 * cube_size + (6 - 1) * gap) - 100)
    ax.set_zlim(center_z - (2.5 * cube_size + (5 - 1) * gap) - 0.5, center_z + (2.5 * cube_size + (5 - 1) * gap) - 60)

    ax.view_init(elev=15, azim=-50)  

    ax.axis('off')

    fig.patch.set_facecolor('none')

    cbar = fig.colorbar(sm, shrink=0.4)

    cbar.set_ticks([0, 255])
    cbar.set_ticklabels(['{:.0f}'.format(matrix.min()), '{:.0f}'.format(matrix.max())])

效果如图：

# Example usage:
 matrix = np.random.randint(0, 512, size=(5, 6, 6))  # 生成一个5x6x6的随机矩阵
 make_cube(matrix)