二维numpy.take快速吗?

问题描述

numpy.take可以在 2维中应用，

numpy.take can be applied in 2 dimensions with

np.take(np.take(T,ix,axis=0), iy,axis=1 )

我测试了离散二维二维拉普拉斯算子的模版

I tested the stencil of the discret 2-dimensional Laplacian

ΔT = T[ix-1,iy] + T[ix+1, iy] + T[ix,iy-1] + T[ix,iy+1] - 4 * T[ix,iy]

具有2个take-schemes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:

with 2 take-schemes and the usual numpy.array scheme. The functions p and q are introduced for a leaner code writing and adress the axis 0 and 1 in different order. This is the code:

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:

1. 通常的numpy索引arithmeitk最快
2. take-scheme q花费了100％以上的时间(= C排序?)
3. take-scheme p花费200％以上(= Fortran排序?)

甚至没有一维 scipy手册示例表示numpy.take很快:

Not even the 1-dimensionalexample of the scipy manual indicates that numpy.take is fast:

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

有人有经验让numpy.take快速吗?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，它可以快速地进行编码，并且
也被告知执行速度很快.感谢您提供一些改进我的方法的提示！

Does anybody has experiences how to make numpy.take fast ? It would be an flexible and attractive way for lean code writing that is fast in coding and
is told to be fast in execution as well. Thank your for some hints to improve my approach !

推荐答案

可能会使用如下切片对象来清理索引版本:

The indexed version might be cleaned up with slice objects like this:

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]

这篇关于二维numpy.take快速吗?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！