[FIXED] Swapping the dimensions of a numpy array

Issue

I would like to do the following:

for i in dimension1:
  for j in dimension2:
    for k in dimension3:
      for l in dimension4:
        B[k,l,i,j] = A[i,j,k,l]

without the use of loops. In the end both A and B contain the same information but indexed differently.

I must point out that the dimension 1,2,3 and 4 can be the same or different. So a numpy.reshape() seems difficult.

Solution

Please note: Jaime's answer is better. NumPy provides np.transpose precisely for this purpose.

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Or use np.einsum; this is perhaps a perversion of its intended purpose, but the syntax is quite nice:

In [195]: A = np.random.random((2,4,3,5))

In [196]: B = np.einsum('klij->ijkl', A)

In [197]: A.shape
Out[197]: (2, 4, 3, 5)

In [198]: B.shape
Out[198]: (3, 5, 2, 4)

In [199]: import itertools as IT    
In [200]: all(B[k,l,i,j] == A[i,j,k,l] for i,j,k,l in IT.product(*map(range, A.shape)))
Out[200]: True

Answered By - unutbu