Looks like there are some errors in copying your code to question.
But I suspect there’s a known problem with indexing:
In [73]: a=np.zeros((2,3,4)); b=np.ones((3,4)); I=np.array([0,1])
Make I 2 elements. Indexing b gives the expected (3,2) shape. 3 rows from the slice, 2 columns from I indexing
In [74]: b[:,I].shape
Out[74]: (3, 2)
But with 3d a we get the transpose.
In [75]: a[0,:,I].shape
Out[75]: (2, 3)
and assignment would produce an error
In [76]: b[:,I]=a[0,:,I]
...
ValueError: array is not broadcastable to correct shape
It’s putting the 2 element dimension defined by I first, and the 3 element from : second. It’s a case of mixed advanced indexing that has been discussed earlier – and there’s a bug issue as well. (I’ll have to look those up).
You are probably using a newer numpy (or scipy) and getting a different error message.
It’s documented that indexing with two arrays or lists, and slice in the middle, puts the slice at the end, e.g.
In [86]: a[[[0],[0],[1],[1]],:,[0,1]].shape
Out[86]: (4, 2, 3)
The same thing is happening with a[0,:,[0,1]]. But there’s a good argument that it shouldn’t be this way.
As to a fix, you could transpose a value, or change the indexing
In [88]: b[:,I]=a[0:1,:,I]
In [90]: b[:,I]=a[0,:,I].T
In [91]: b
Out[91]:
array([[ 0., 0., 1., 1.],
[ 0., 0., 1., 1.],
[ 0., 0., 1., 1.]])
In [92]: b[:,I]=a[0][:,I]