reordering of numpy arrays

python numpy

Check out rollaxis, a function which shifts the axes around, allowing you to reorder your array in a single command. If im has shape i, j, k

rollaxis(im, 2)

should return an array with shape k, i, j.

After this, you can flatten your array, ravel is a clear function for this purpose. Putting this all together, you have a nice one-liner:

new_im_vec = ravel(rollaxis(im, 2))

python numpy

new_im = im.swapaxes(0,2).swapaxes(1,2) # First swap i and k, then i and jnew_im_vec = new_im.flatten() # Vectorize

This should be much faster because swapaxes returns a view on the array, rather than copying elements over.

And of course if you want to skip new_im, you can do it in one line, and still only flatten is doing any copying.

new_im_vec = im.swapaxes(0,2).swapaxes(1,2).flatten()

python numpy

With einops:

x = einops.rearrange(x, 'height width color -> color height width')

Pros:

you see how axes were ordered in input
you see how axes are ordered in output
you don't need to think about steps you need to take (and e.g. no need to remember which direction axes are rolled)

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