在多维numpy数组的每个索引处切片不同的范围 [英] Slicing a different range at each index of a multidimensional numpy array

问题描述

我有一个m x n numpy数组arr，对于arr的每一列，我都有要访问的给定行范围. 我有一个n x 1数组vec，它描述了该范围的开始时间.
该范围具有一定的持续时间d.

I have an m x n numpy array arr, and for each column of arr, I have a given range of rows that I want to access.
I have an n x 1 array vec that describes when this range starts.
The range has some constant duration d.

如何有效地提取感兴趣的d x n数组?
可以通过巧妙的切片来完成吗?

How can I extract this d x n array of interest efficiently?
Can this be done by clever slicing?

我最初的想法是尝试类似的事情:

My initial thought was to try something like:

arr = np.tile(np.arange(10),(4,1)).T
vec = np.array([3,4,5,4])
d = 3
vec_2 = vec+d
out = arr[vec:vec2,np.arange(n)]

但这会导致以下错误:

TypeError:只有整数标量数组可以转换为标量索引

TypeError: only integer scalar arrays can be converted to a scalar index

所需的输出将是以下数组:

The desired output would be the following array:

array([[3, 4, 5, 4],
       [4, 5, 6, 5],
       [5, 6, 7, 6],
       [6, 7, 8, 7])

我可以遍历d，但是性能对于这段代码很重要，因此我希望将其向量化.

I could loop over d, but performance is important for this piece of code so I would prefer to vectorize it.

推荐答案

In [489]: arr=np.arange(24).reshape(6,4)                                                         
In [490]: vec=np.array([0,2,1,3])                                                                

利用最近扩展的linspace来生成多个数组:

Taking advantage of the recent expansion of linspace to generate several arrays:

In [493]: x = np.linspace(vec,vec+2,3).astype(int)                                               
In [494]: x                                                                                      
Out[494]: 
array([[0, 2, 1, 3],
       [1, 3, 2, 4],
       [2, 4, 3, 5]])
In [495]: arr[x, np.arange(4)]                                                                   
Out[495]: 
array([[ 0,  9,  6, 15],
       [ 4, 13, 10, 19],
       [ 8, 17, 14, 23]])

列迭代方法:

In [498]: np.stack([arr[i:j,k] for k,(i,j) in enumerate(zip(vec,vec+3))],1)                      
Out[498]: 
array([[ 0,  9,  6, 15],
       [ 4, 13, 10, 19],
       [ 8, 17, 14, 23]])

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