I am trying to perform non-consectuitive slicing of a multidimensional array like this (Matlab peudo code)
A = B(:,:,[1,3],[2,4,6]) %A and B are two 4D matrices
But when I try to write this code in Python:
A = B[:,:,np.array([0,2]),np.array([1,3,5])] #A and B are two 4D arrays
it gives an error: IndexError: shape mismatch: indexing arrays could not be broadcast...
It should be noted that slicing for one dimension each time works fine!
In numpy, if you use more than one fancy index (i.e. array) to index different dimension of the same array at the same time, they must broadcast. This is designed such that indexing can be more powerful. For your situation, the simplest way to solve the problem is indexing twice:
B[:, :, [0,2]] [..., [1,3,5]]
where ... stands for as many : as possible.
Indexing twice this way would generate some extra data moving time. If you want to index only once, make sure they broadcast (i.e. put fancy indices on different dimension):
B[:, :, np.array([0,2])[:,None], [1,3,5]]
which will result in a X by Y by 2 by 3 array. On the other hand, you can also do
B[:, :, [0,2], np.array([1,3,5])[:,None]]
which will result in a X by Y by 3 by 2 array. The [1,3,5] axis is transposed before the [0,2] axis.
Yon don't have to use np.array([0,2]) if you don't need to do fancy operation with it. Simply [0,2] is fine.
np.array([0,2])[:,None] is equivalent to [[0],[2]], where the point of [:,None] is to create an extra dimension such that the shape becomes (2,1). Shape (2,) and (3,) cannot broadcast, while shape (2,1) and (3,) can, which becomes (2,3).
AandBin your question, as well as your expected outputnp.array([0,2])andnp.array([1,3])which can be broadcast together, It wouldn't do what it does in matlab, and the indices are off, but it shouldn't give you that error. Please come up with a proper minimal reproducible example and run it to check that it reproduces your problem.np.ix_. One relevant duplicate, another one. The top-voted answer to the former shows how to use it. Also don't forget that while MATLAB indices are 1-based, numpy indices are 0-based.