Dear Peter Yes the f[t] or f[:,:,:] might give a marginal increase, but then i need to do further operations using the indices, in which case this wouldnt help
Dear Wojciech np.flat() works if u dont care about the indices and only the matrix/array values matter. but if the <i,j,k> matters, flatten wouldnt work On Wed, Aug 6, 2014 at 1:34 PM, Wojciech Giel <wojtekg...@gmail.com> wrote: > You might check numpy it is really powerful tool for working with multi > dimensional arrays: > > ex. > >>> a = arange(81).reshape(3,3,3,3) > >>> a > > array([[[[ 0, 1, 2], > [ 3, 4, 5], > [ 6, 7, 8]], > > [[ 9, 10, 11], > [12, 13, 14], > [15, 16, 17]], > > [[18, 19, 20], > [21, 22, 23], > [24, 25, 26]]], > > > [[[27, 28, 29], > [30, 31, 32], > [33, 34, 35]], > > [[36, 37, 38], > [39, 40, 41], > [42, 43, 44]], > > [[45, 46, 47], > [48, 49, 50], > [51, 52, 53]]], > > > [[[54, 55, 56], > [57, 58, 59], > [60, 61, 62]], > > [[63, 64, 65], > [66, 67, 68], > [69, 70, 71]], > > [[72, 73, 74], > [75, 76, 77], > [78, 79, 80]]]]) > > >>> f = a.flat > >>> for i in f: > ... print(i) > 0 > 1 > 2 > .. > 98 > 99 > > cheers > Wojciech > > > > > On 05/08/14 21:06, Frank Miles wrote: > >> I need to evaluate a complicated function over a multidimensional space >> as part of an optimization problem. This is a somewhat general problem >> in which the number of dimensions and the function being evaluated can >> vary from problem to problem. >> >> I've got a working version (with loads of conditionals, and it only works >> to #dimensions <= 10), but I'd like something simpler and clearer and >> less hard-coded. >> >> I've web-searched for some plausible method, but haven't found anything >> "nice". Any recommendations where I should look, or what technique should >> be used? >> >> TIA! >> > > -- > https://mail.python.org/mailman/listinfo/python-list >
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