expand numpy array in n dimensions

Question

I am trying to 'expand' an array (generate a new array with proportionally more elements in all dimensions). I have an array with known numbers (let's call it X) and I want to make it j times bigger (in each dimension).

So far I generated a new array of zeros with more elements, then I used broadcasting to insert the original numbers in the new array (at fixed intervals).

Finally, I used linspace to fill the gaps, but this part is actually not directly relevant to the question.

The code I used (for n=3) is:

import numpy as np
new_shape = (np.array(X.shape) - 1 ) * ratio + 1
new_array = np.zeros(shape=new_shape)
new_array[::ratio,::ratio,::ratio] = X

My problem is that this is not general, I would have to modify the third line based on ndim. Is there a way to use such broadcasting for any number of dimensions in my array?

Edit: to be more precise, the third line would have to be:

new_array[::ratio,::ratio] = X

if ndim=2 or

new_array[::ratio,::ratio,::ratio,::ratio] = X

if ndim=4

etc. etc. I want to avoid having to write code for each case of ndim

p.s. If there is a better tool to do the entire process (such as 'inner-padding' that I am not aware of, I will be happy to learn about it).

Thank you

Alec · Accepted Answer · 2019-03-25 18:36:36Z

3

array = array[..., np.newaxis] will add another dimension

This article might help

edited Mar 25, 2019 at 18:36

answered Mar 25, 2019 at 18:33

Alec

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Divakar · Accepted Answer · 2019-03-25 18:38:03Z

2

You can use slice notation -

slicer = tuple(slice(None,None,ratio) for i in range(X.ndim))
new_array[slicer] = X

answered Mar 25, 2019 at 18:38

Divakar

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user2357112 · Accepted Answer · 2019-03-25 18:39:02Z

2

Build the slicing tuple manually. ::ratio is equivalent to slice(None, None, ratio):

new_array[(slice(None, None, ratio),)*new_array.ndim] = ...

answered Mar 25, 2019 at 18:39

user2357112

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