
On Wed, Jan 26, 2011 at 8:29 PM, Joshua Holbrook <[hidden email]> wrote:
>>
>> The only disadvantage I see, is that choosing the axes to operate on
>> in a program or function requires string manipulation.
>
>
> One possibility would be for the Python exposure to accept lists or tuples
> of integers. The subscript 'ii' could be [(0,0)], and 'ij,jk>ik' could be
> [(0,1), (1,2), (0,2)]. Internally it would convert this directly to a
> Cstring to pass to the API function.
> Mark
>
What if you made objects i, j, etc. such that i*j = (0, 1) and
etcetera? Maybe you could generate them with something like (i, j, k)
= einstein((1, 2, 3)) .
Feel free to disregard me since I haven't really thought too hard
about things and might not even really understand what the problem is
*anyway*. I'm just trying to help brainstorm. :)
No worries. :) The problem is that someone will probably want to dynamically generate the axes to process in a loop, rather than having them hardcoded beforehand. For example, generalizing the diag function as follows. Within Python, creating lists and tuples is probably more natural.
Mark
>>> def diagij(x, i, j): ... ss = "" ... so = "" ... # should error check i, j ... fill = ord('b')
... for k in range(x.ndim): ... if k in [i, j]: ... ss += 'a' ... else: ... ss += chr(fill) ... so += chr(fill)
... fill += 1 ... ss += '>' + so + 'a' ... return np.einsum(ss, x) ... >>> x = np.arange(3*3*3).reshape(3,3,3)
>>> diagij(x, 0, 1) array([[ 0, 12, 24], [ 1, 13, 25], [ 2, 14, 26]])
>>> [np.diag(x[:,:,i]) for i in range(3)] [array([ 0, 12, 24]), array([ 1, 13, 25]), array([ 2, 14, 26])]
>>> diagij(x, 1, 2) array([[ 0, 4, 8], [ 9, 13, 17], [18, 22, 26]])
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