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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