Hi Nathaniel,
thanks for your reply, it works fine and suffice for my purpose.
cheers,
Chao
On Sat, Mar 16, 2013 at 5:49 PM, Nathaniel Smith njs@pobox.com wrote:
On 16 Mar 2013 16:41, "Chao YUE" chaoyuejoy@gmail.com wrote:
Dear all,
Is there some way to index the numpy array by specifying arbitrary axis
and arbitrary slice, while
not knowing the actual shape of the data? For example, I have a 3-dim data, data.shape = (3,4,5) Is there a way to retrieve data[:,0,:] by using something like
np.retrieve_data(data,axis=2,slice=0),
by this way you don't have to know the actual shape of the array. for for 4-dim data, np.retrieve_data(data,axis=2,slice=0) will actually
be data[:,0,:,:]
I don't know of anything quite like that, but it's easy to fake it:
def retrieve_data(a, ax, idx): full_idx = [slice(None)] * a.ndim full_idx[ax] = idx return a[tuple(full_idx)]
Or for the specific case where you do know the axis in advance, you just don't know how many trailing axes there are, use a[:, :, 0, ...] and the ... will expand to represent the appropriate number of :'s.
-n
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