Zero pad ndarray along axis

Question

I want to pad:

[[1, 2]
 [3, 4]]

to

[[0, 0, 0]
 [0, 1, 2]
 [0, 3, 4]]

I have no problem in doing so when input is 2D by vstack & hsrack

However, when I add 1 dim to represent the dim of number, such as:

img = np.random.randint(-10, 10, size=(2, 2, 2))

I cannot do so if I would not like to use for loop.
Is there any way to avoid for but only numpy’s stack to make it?

ps: for this example I should get (2,3,3) array by each 2 img instance is pad by zero at 1st column & 1st row.

Thanks

Asked By: chang jc

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Source

Answer 1

You can use np.pad and remove the last row/column:

import numpy as np

a = np.array([[1, 2], [3, 4]])
result = np.pad(a, 1, mode='constant', constant_values=0)[:-1, :-1]
print(result)

Output:

[[0 0 0]
 [0 1 2]
 [0 3 4]]

Answered By: gmds

Answer 2

You can use np.pad, which allows you to specify the number of values padded to the edges of each axis.

So for the first example 2D array you could do:

a = np.array([[1, 2],[3, 4]])
np.pad(a,((1, 0), (1, 0)), mode = 'constant')

array([[0, 0, 0],
       [0, 1, 2],
       [0, 3, 4]])

So here each tuple is representing the side which to pad with zeros along each axis, i.e. ((before_1, after_1), … (before_N, after_N)).

And for a 3D array the same applies, but in this case we must specify that we only want to zero pad the two last dimensions:

img = np.random.randint(-10, 10, size=(2, 2, 2))
np.pad(img, ((0,0), (1,0), (1,0)), 'constant')

array([[[ 0,  0,  0],
        [ 0, -3, -2],
        [ 0,  9, -5]],

       [[ 0,  0,  0],
        [ 0,  1, -9],
        [ 0, -1, -3]]])

Answered By: yatu

Answer 3

If you want to pad only the column

import numpy as np

max_length =20
a = np.random.randint(1,size=(1, 10))
print(a.shape)
print(a.shape[1])
a =np.pad(a, [(0, 0), (0,  max_length - a.shape[1])], mode='constant')
print(a.shape)

Output

(1, 10)
10
(1, 20)

Answered By: Alex Punnen

Zero pad ndarray along axis

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