CODEWITHSUNDEEP
pythonarraysnumpymatrix3d
Chris Parry
Chris Parry

Reputation: 3047

Efficient transformations of 3D numpy arrays

I have some 3D numpy arrays that need to be transformed in various ways. E.g.:

x.shape = (4, 17, 17)

This array is 1 sample of 4 planes, each of size 17x17. What is the most efficient way to transform each plane: flipud, fliplr, and rot90? Is there a better way than using a for loop? Thanks!

for p in range(4):
    x[p, :, :] = np.fliplr(x[p, :, :])

Upvotes: 0

Views: 875

Answers (1)

hpaulj
hpaulj

Reputation: 231335

Look at the code of these functions:

def fliplr(...):
   ....
   return m[:, ::-1]

In other words it returns a view with reverse slicing on the 2nd dimension

Your x[p, :, :] = np.fliplr(x[p, :, :] applies that reverse slicing to the last dimension, so the equivalent for the whole array should be

x[:, :, ::-1]

flipping the 2nd axis would be

x[:, ::-1, :]

etc.

np.rot90 has 4 case (k); for k=1 it is

return fliplr(m).swapaxes(0, 1)

in other words m[:, ::-1].swapaxes(0,1)

To work on your planes you would do something like

m[:, :,::-1].swapaxes(1,2)

or you could do the swapaxes/transpose first

m.transpose(0,2,1)[:, :, ::-1]

Does that give you enough tools to transform the plane's in what ever way you want?

As I discussed in another recent question, https://stackoverflow.com/a/41291462/901925, the flip... returns a view, but the rot90, with both flip and swap, will, most likely return a copy. Either way, numpy will be giving you the most efficient version.

Upvotes: 1

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