CODEWITHSUNDEEP
pythonnumpy
Dschoni
Dschoni

Reputation: 3872

Padding large numpy arrays

Another numpy array handling question: I have an approx. 2000³ entries numpy array with fixed size (that I know), containing integers. I want to pad the array with another integer, so that it's surrounded in all dimensions. This integer is fixed for the whole padding process.

example (2D)
1----->000
       010
       000

I have two ideas, leading to that result:

  1. Creating a larger numpy array, containing the padded values and "slicing" the old area in the padded:

    padded=np.zeros((z+2,x+2,y+2))
padded[1:z+1,1:x+1,1:y+1]=olddata

  2. Using np.insert or hstack,vstack,dstack to add the values:

    padded=np.insert(data,0,0,axis=0)
padded=np.insert(data,x+1,0,axis=0) etc.

The Problem is, that all these methods are not in-place and allocate a new array (1.) or copy the old one (2.). Is there a way to do the padding in-place? I know, that since numpy 1.7. there's the numpy.pad module. But that also seems to use some kind of allocation and overriding (like in my 1. way).

Upvotes: 2

Views: 2197

Answers (1)

Janne Karila
Janne Karila

Reputation: 25207

You can't add padding in-place because there is no room for it in the memory layout. You can go the other way though: allocate the padded array first, and use a view into it when accessing unpadded data.

padded = np.empty((2002,2002,2002))
padded[0] = 0
padded[-1] = 0
padded[:,0] = 0
padded[:,-1] = 0
padded[:,:,0] = 0
padded[:,:,-1] = 0

unpadded = padded[1:-1, 1:-1, 1:-1]

Upvotes: 1

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