CODEWITHSUNDEEP
pythonarraysnumpy
Tacratis
Tacratis

Reputation: 1055

expand numpy array in n dimensions

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

Upvotes: 2

Views: 443

Answers (3)

user2357112
user2357112

Reputation: 280281

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

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

Upvotes: 2

Divakar
Divakar

Reputation: 221514

You can use slice notation -

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

Upvotes: 2

Alec
Alec

Reputation: 9536

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

This article might help

Upvotes: 3

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