Deleting Zeros from Numpy Array in Python

Question

I have a question regarding the deletion from arrays. I have a 3-D numpy array (coordinates are z,y,x) with an estimated 900^3 size. Only a few values are nonzero, but they have a nontrivial spatial distribution. I want to delete all 2-D slices in that array which have only zeros in it. In other words, I want an array with minimum size which still has all relevant data in it.

My attempt looks like:

    while np.all(a[0]==0):
        a=np.delete(a,0,0)
    while np.all(a[a.shape[0]-1]==0) and a.shape[0]>1:
        a=np.delete(a,-1,0)

and seems to work for the z-direction. How can I do something equal in the other two directions? And is there maybe another way of doing it better?

Another Idea was

tmp=np.delete(tmp,np.all(tmp==0,axis=1),1)

but that one seems to work only from the start and leaves out the zeros at the end.

Is it maybe possible, to rotate an array in 3D space?

Answer 1

You can use transpose to rearrange your axes, but it sounds like you might really be looking for a sparse array

Answer 2

This is a fun problem, here is what I came up with:

for ax in range(3):
    all_but_ax = [i for i in range(3) if i != ax]
    a = delete(a, where(apply_over_axes(sum, abs(a), all_but_ax).ravel() == 0), 
                  ax)

So you some the abs(a) over all but the current axis == current 2D slice, and check if it is zero, which means it is empty. The where gives the indices for the delete.