On Sat, Jan 1, 2011 at 19:42, Enzo Michelangeli <enzom...@gmail.com> wrote: > Is there any way, not involving compilation of C code, to define ndarrays > where some rows or columns share the same data buffers? For example, > something built by a hypothetical variant of the np.repeat() function, such > that, if a = array([2,3]), calling: > > b = np.aliasedrepeat(x, [1, 2], axis=0) > > would return in b: > > array([[2, 3], > [2, 3], > [2, 3]]) > > ...with the understanding that the three rows would actually share the same > data, so setting e.g.: > > b[0,1] = 5 > > ...would change b into: > > array([[2, 5], > [2, 5], > [2, 5]]) > > In other words, something with a behaviour similar to a list of lists: > >>>> a = [2,3] >>>> b = [a,a,a] >>>> b > [[2, 3], [2, 3], [2, 3]] >>>> b[0][1] = 5 >>>> b > [[2, 5], [2, 5], [2, 5]] > > This would save memory (and time spent in unnecessary copying) in some > applications with large arrays, and would allow to cope with the current > inability of weave.blitz to understand broadcasting rules, e.g. for > calculating outer products (I mentioned this in a previous thread).
See numpy.lib.stride_tricks for tools to do this, specifically the as_strided() function. See numpy.broadcast_arrays() for the latter functionality. http://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_arrays.html -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
