如何使用numpy实现三维双线性插值？

import numpy as np

def trilinear_interpolation(data, x, y, z):
    """
    三维双线性插值函数
    :param data: 三维数据集，形状为 (x_size, y_size, z_size)
    :param x: 需要插值的点的 x 坐标
    :param y: 需要插值的点的 y 坐标
    :param z: 需要插值的点的 z 坐标
    :return: 插值点的值
    """
    x_size, y_size, z_size = data.shape
    x1 = int(np.floor(x))
    x2 = int(np.ceil(x))
    y1 = int(np.floor(y))
    y2 = int(np.ceil(y))
    z1 = int(np.floor(z))
    z2 = int(np.ceil(z))

    # 边界处理
    x1 = max(0, min(x1, x_size - 2))
    x2 = max(0, min(x2, x_size - 1))
    y1 = max(0, min(y1, y_size - 2))
    y2 = max(0, min(y2, y_size - 1))
    z1 = max(0, min(z1, z_size - 2))
    z2 = max(0, min(z2, z_size - 1))

    # 计算权重
    wx = x - x1
    wy = y - y1
    wz = z - z1

    # 双线性插值
    v1 = (1 - wx) * data[x1, y1, z1] + wx * data[x2, y1, z1]
    v2 = (1 - wx) * data[x1, y2, z1] + wx * data[x2, y2, z1]
    v3 = (1 - wx) * data[x1, y1, z2] + wx * data[x2, y1, z2]
    v4 = (1 - wx) * data[x1, y2, z2] + wx * data[x2, y2, z2]

    v5 = (1 - wy) * v1 + wy * v2
    v6 = (1 - wy) * v3 + wy * v4

    result = (1 - wz) * v5 + wz * v6

    return result

# 示例数据
data = np.array([
    [[1, 2], [3, 4]],
    [[5, 6], [7, 8]]
])

# 需要插值的点
x, y, z = 1.5, 1.5, 0.5

# 执行插值
result = trilinear_interpolation(data, x, y, z)
print("插值结果:", result)