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任意维度的numpy数组切片

[英]numpy array slicing with arbitrary dimension

Say I create an array of arbitrary dimension (n). 假设我创建了一个任意维度的数组(n)。

#assign the dimension

>>> n=22

#create the numpy array

>>> TheArray=zeros([2]*n)

>>> shape(TheArray)

(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)

Have some code (skipped in this example) to populate the values of the array. 有一些代码(在本例中跳过)来填充数组的值。

Now, try to access some values of the array 现在,尝试访问数组的某些值

>>> TheArray[0:2,0:2,0:2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

array([[[ 0.,  0.],
        [ 0.,  0.]],

       [[ 0.,  0.],
        [ 0.,  0.]]])

How to make the 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 part of the syntax generalized to n? 如何将0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0部分语法推广到n?

One way would be to use numpy.s_ : 一种方法是使用numpy.s_

In [55]: m = arange(2**6).reshape([2]*6)

In [56]: m.shape
Out[56]: (2, 2, 2, 2, 2, 2)

In [57]: m[:2,:2,:2,0,0,0]
Out[57]: 
array([[[ 0,  8],
        [16, 24]],

       [[32, 40],
        [48, 56]]])

In [58]: m[s_[:2, :2, :2] + (0,)*(n-3)]
Out[58]: 
array([[[ 0,  8],
        [16, 24]],

       [[32, 40],
        [48, 56]]])

And I guess you could get rid of the hardcoded -3.. 而且我猜你可以摆脱硬编码-3 ..

In [69]: m[(s_[:2, :2, :2] + (0,)*m.ndim)[:m.ndim]]
Out[69]: 
array([[[ 0,  8],
        [16, 24]],

       [[32, 40],
        [48, 56]]])

but to be honest, I'd probably just wrap this up in a function if I needed it. 但说实话,如果需要的话,我可能只是把它包装成一个函数。

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