numpy.take可以应用于2维
np.take(np.take(T,ix,axis=0), iy,axis=1 )
我测试了离散二维拉普拉斯算子的模版
ΔT = T[ix-1,iy] + T[ix+1, iy] + T[ix,iy-1] + T[ix,iy+1] - 4 * T[ix,iy]
有2个方案和通常的numpy.array方案。引入函数p和q来进行更精简的代码编写，并以不同的顺序处理轴0和1。这是代码：
nx = 300; ny= 300
T = np.arange(nx*ny).reshape(nx, ny)
ix = np.linspace(1,nx-2,nx-2,dtype=int)
iy = np.linspace(1,ny-2,ny-2,dtype=int)
#------------------------------------------------------------
def p(Φ,kx,ky):
return np.take(np.take(Φ,ky,axis=1), kx,axis=0 )
#------------------------------------------------------------
def q(Φ,kx,ky):
return np.take(np.take(Φ,kx,axis=0), ky,axis=1 )
#------------------------------------------------------------
%timeit ΔT_n = T[0:nx-2,1:ny-1] + T[2:nx,1:ny-1] + T[1:nx-1,0:ny-2] + T[1:nx-1,2:ny] - 4.0 * T[1:nx-1,1:ny-1]
%timeit ΔT_t = p(T,ix-1,iy) + p(T,ix+1,iy) + p(T,ix,iy-1) + p(T,ix,iy+1) - 4.0 * p(T,ix,iy)
%timeit ΔT_t = q(T,ix-1,iy) + q(T,ix+1,iy) + q(T,ix,iy-1) + q(T,ix,iy+1) - 4.0 * q(T,ix,iy)
.
1000 loops, best of 3: 944 µs per loop
100 loops, best of 3: 3.11 ms per loop
100 loops, best of 3: 2.02 ms per loop
结果似乎很明显：
通常的numpy索引arithmeitk最快
take-scheme q需要100％更长的时间（= C排序？）
take-scheme p花费200％以上的时间（= Fortran-order？）
甚至不是一维的
example of the scipy manual表示numpy.take是快速的：
a = np.array([4, 3, 5, 7, 6, 8])
indices = [0, 1, 4]
%timeit np.take(a, indices)
%timeit a[indices]
.
The slowest run took 6.58 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 4.32 µs per loop
The slowest run took 7.34 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 3.87 µs per loop
有人有经验如何快速制作numpy.take吗？对于精简代码编写而言，这将是一种灵活且有吸引力的方法，可以快速地进行编码和
is told to be fast in execution也是如此。感谢您提供一些改进我的方法的提示！
最佳答案
可以使用如下切片对象清除索引版本：
T[0:nx-2,1:ny-1] + T[2:nx,1:ny-1] + T[1:nx-1,0:ny-2] + T[1:nx-1,2:ny] - 4.0 * T[1:nx-1,1:ny-1]
sy1 = slice(1,ny-1)
sx1 = slice(1,nx-1)
sy2 = slice(2,ny)
sy_2 = slice(0,ny-2)
T[0:nx-2,sy1] + T[2:nx,sy1] + T[sx1,xy_2] + T[sx1,sy2] - 4.0 * T[sx1,sy1]
关于python - 二维numpy.take快速吗？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/45290102/