> > I think it would be quite complicated. One fundamental "limitation" of > numpy is that it views a contiguous chunk of memory. You can't have one > numpy array which is the union of two memory blocks with a hole in > between, so if you slice every 1000 items, the underlying memory of the > array still needs to 'view' the whole thing. I think it is not possible > to support what you want with one numpy array.
Yes, I see the problem in getting the same kind of reuse of objects using simple indexing. For my specific case, I will just allocate a new array as containing a copy of every 100th element and return this array. It will basically give me the same result as the original recarray is for read-only purposes only. This will be very simple implement for the specific cases I have > > I think the simple solution really is to go 64 bits, that's exactly the > kind of things it is used for. If your machine is relatively recent, it > supports 64 bits addressing. > The machine is new and shiny with loads of processing power and many TB of HDD storage. I am however bound to 32 bits Win XP OS as there are some other costum made third-party and very expensive applications running on that machine (which generate the large files I analyze), which can only run on 32 bits, oh well.... Cheers, Kim > cheers, > > David > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
