Numba JIT可以有效地实现这一点。下面是一个实现：

@njit
def fastImpl(x_dist, y_dist):
    res_array = np.empty(x_dist.shape)
    for z in range(res_array.shape[0]):
        for y in range(res_array.shape[1]):
            for x in range(res_array.shape[2]):
                xDist = x_dist[z,y,x]
                yDist = y_dist[z,y,x]
                if xDist > 0.0 and yDist <= (1.0 + xDist):
                    res_array[z,y,x] = (1.0 - yDist) * xDist
    return res_array

以下是随机输入矩阵的性能结果：

Original implementation: 494 µs ± 6.23 µs per loop (mean ± std. dev. of 7 runs, 500 loops each)
New implementation: 37.8 µs ± 236 ns per loop (mean ± std. dev. of 7 runs, 500 loops each)

新的实现大约快13倍（不考虑编译/预热时间）。