CODEWITHSUNDEEP
pythonarraysperformancenumpy
Aleksandr Motsjonov
Aleksandr Motsjonov

Reputation: 1328

How to slice looped array with numpy

I am not sure what is right terminology here. Please correct. I have a grid (2D array) that is looped. By this I mean first row is the next after last row. Same for columns.

I want to slice subset of big grid having this looped rule in mind. So, having grid:

[[ 0  1  2  3  4  5  6  7  8  9]
 [10 11 12 13 14 15 16 17 18 19]
 [20 21 22 23 24 25 26 27 28 29]
 [30 31 32 33 34 35 36 37 38 39]
 [40 41 42 43 44 45 46 47 48 49]
 [50 51 52 53 54 55 56 57 58 59]
 [60 61 62 63 64 65 66 67 68 69]
 [70 71 72 73 74 75 76 77 78 79]
 [80 81 82 83 84 85 86 87 88 89]
 [90 91 92 93 94 95 96 97 98 99]]

And I want subset with size 3 by 3 centered in the middle (5,5), I would get:

[[44 45 46]
 [54 55 56]
 [64 65 66]]

But if I want it to be centered in (0,0) I would get:

[[99 90 91]
 [ 9  0  1]
 [19 10 11]]

In my current solution I've combined np.roll with slicing. It's working, but I am looking for more performant solution.

My current solution:

def get_centered_section(arr, center, side_size):
    if side_size % 2 is 0:
        raise "size shuold be odd number"
    half_side_size = int((side_size - 1) / 2)
    w, h = arr.shape
    x, y = center

    ystart = y - half_side_size
    if ystart < 0:
        arr = np.roll(arr, abs(ystart), 0)
        ystart = 0
    elif ystart + side_size >= h:
        overflow = ystart + side_size - h
        ystart -= overflow
        arr = np.roll(arr, -overflow, 0)

    xstart = x - half_side_size
    if xstart < 0:
        arr = np.roll(arr, abs(xstart), 1)
        xstart = 0
    elif xstart + side_size >= w:
        overflow = xstart + side_size - w
        xstart -= overflow
        arr = np.roll(arr, -overflow, 1)

    return arr[ystart:ystart+side_size,xstart:xstart+side_size]

test_a1 = np.reshape(np.arange(10*10), (10, 10))
get_centered_section(test_a1, (0, 0), 3)

Maybe there is a way to cache my way out. My specific usage will require going through each cell getting this kinda slice.

Upvotes: 1

Views: 136

Answers (1)

Divakar
Divakar

Reputation: 221714

One approach would involve padding with wrap around using np.pad and then slicing, like so -

def get_centered_section(a, center, side_size):
    ext_size = (side_size[0]-1)/2, (side_size[1]-1)//2
    a_pad = np.lib.pad(a, ([ext_size[0]],[ext_size[1]]), 'wrap')
    return a_pad[center[0]:center[0]+side_size[0], \
                 center[1]:center[1]+side_size[1]]

Few sample runs -

In [94]: a
Out[94]: 
array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

In [95]: get_centered_section(a, center = (0,0), side_size = (3,3))
Out[95]: 
array([[99, 90, 91],
       [ 9,  0,  1],
       [19, 10, 11]])

In [97]: get_centered_section(a, center = (5,5), side_size = (5,5))
Out[97]: 
array([[33, 34, 35, 36, 37],
       [43, 44, 45, 46, 47],
       [53, 54, 55, 56, 57],
       [63, 64, 65, 66, 67],
       [73, 74, 75, 76, 77]])

In [98]: get_centered_section(a, center = (7,2), side_size = (3,5))
Out[98]: 
array([[60, 61, 62, 63, 64],
       [70, 71, 72, 73, 74],
       [80, 81, 82, 83, 84]])

Upvotes: 2

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