Extract sub rows with varying sizes from a big 2D NumPy Array

Question

I have a NumPy Array with size say 3*10, I would like to extract sub rows with varying sizes from each row. The sub rows are centered in the middle pixel with varying pixel sizes. Then I take the average number of each subrow. I have a pseudo example below:

import numpy as np
arr = np.arange(1,31).reshape((3,10))
pixel_size = np.array([2,3,1])
## the subrow centers in the middle of the array, index 5
mask = [[5-2:5+2],[5-3:5+3],[5-1:5+1]] ## index for each row
### submatrix = arr[;,mask]
submatrix = [[3,4,5,6],[12,13,14,15,16,17],[24,25]]
## output = np.mean(submatrix, axis=1) output is the average number of each row in the submatrix
output = [4.5,14.5,24.5]

If I have over 10 millions of rows, how can I handle this situation fast.

This question is a bit unintelligible. A clearer way to ask is to show some input and expected output. — Matt Messersmith, Aug 10 '18 at 02:33
Recent similar question https://stackoverflow.com/q/51736715/901925 — hpaulj, Aug 10 '18 at 04:45

score 3 · Accepted Answer · answered Aug 10 '18 at 02:51

You can do it using list comprehensions and index slicing:

import numpy as np

arr = np.arange(1,31).reshape((3,10))
pixel_size = np.array([2,3,1])

middle_ind = int(arr.shape[1]/2.)
print middle_ind
sub_arr = [arr[i,middle_ind - pixel_size[i]:middle_ind + pixel_size[i]] for i in range(len(pixel_size))]
print('sub_arr: ', sub_arr)
output = [np.mean(item) for item in sub_arr]
print('output: ', output)

> sub_arr: [array([4, 5, 6, 7]), array([13, 14, 15, 16, 17, 18]), array([25, 26])]
> output: [5.5, 15.5, 25.5]

Your submatrix is a list not an array so it's more difficult to vectorize operations. You might want to think about restructuring your code to take advantage of matrix operations.

Extract sub rows with varying sizes from a big 2D NumPy Array

1 Answers1