Choosing and iterating specific sub-arrays in multidimensional arrays in Python

Question

This is a question that comes from the post here Iterating and selecting a specific array from a multidimensional array in Python

In that post, user @Cleb solved what it was my original problem: how to perform a sum through columns in a 3d array:

 import numpy as np
 arra = np.arange(16).reshape(2, 2, 4)

which gives

 array([[[0, 1, 2, 3],  
     [4, 5, 6, 7]],  
     [[8, 9, 10, 11],  
     [12, 13, 14, 15]]])

and the problem was how to perform the sum of columns in each matrix, i. e., 0 + 4, 1 + 5, ... , 8 + 12, ..., 11 + 15. It was solved by @Cleb.

Then I wondered how to do it in the case of a sum of 0 + 8, 1 + 9, ..., 4 + 12, ..., 7 + 15, (odd and even columns) which was also solved by @Cleb.

But then I wondered if there are a general idea (which can be modified in each specific case). Imagine you can add the first and the last rows and the center rows, in columns, separately, i. e., 0 + 12, 1 + 13, ..., 3 + 15, 4 + 8, 5 + 9, ..., 7 + 11.

Is there a general way? Thank you.

Community · Accepted Answer · 2017-05-23 11:54:35Z

1

Depending on the how exactly arra is defined, you can shift your values appropriately using np.roll:

arra_mod = np.roll(arra, arra.shape[2])

arra_mod then looks as follows:

array([[[12, 13, 14, 15],
        [ 0,  1,  2,  3]],

       [[ 4,  5,  6,  7],
        [ 8,  9, 10, 11]]])

Now you can simply use the command from your previous question to get your desired output:

map(sum, arra_mod)

which gives you the desired output:

[array([12, 14, 16, 18]), array([12, 14, 16, 18])]

You can also use a list comprehension

[sum(ai) for ai in arra_mod]

which gives you the same output.

If you prefer one-liner, you can therefore simply do:

map(sum, np.roll(arra, arra.shape[2]))

edited May 23, 2017 at 11:54

CommunityBot

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answered Mar 3, 2016 at 18:30

Cleb

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