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