When you do this:

a = np.array([[np.array([[2, 2]]), np.array([[3, 3]])]])

the final call to np.array actually concatenates the inner two, so you get one array at the end:

>>> a
array([[[[2, 2]],

        [[3, 3]]]])

>>> a.shape
(1, 2, 1, 2)

But to mimic a cell array you want to basically have an array of arrays. You can acheive this by setting dtype=object, but you must create the array and set the elements separately to avoid the automatic merging.

three = array([[array([[2, 2, 2]]), array([[3, 3]])]])
two = np.empty(three.shape, dtype=object)
two[0,0,0] = np.array([[2,2]])
two[0,1,0] = np.array([[3,3]])

Then:

sio.savemat('two.mat', {'two': two})

to see what they look like:

>>> two
array([[[array([[2, 2]])],
        [array([[3, 3]])]]
 
], dtype=object)

>>> two.shape
(1, 2, 1)

Note that I may have gotten confused about your desired shape, since you have so many nested brackets, so you might have to reshape some of this, but the idea should hold regardless.

例如：

npose = 5
nsmile = 2

poseSmile_cell = np.empty((npose,nsmile),dtype=object)

for
 
 i in range(5):
    for k in range(2):
        poseSmile_cell[i,k] = np.zeros((4,4))


print poseSmile_cell.shape

參考文獻：