Hi Dan 2009/4/29 Dan Goodman <dg.gm...@thesamovar.net>: > I have a slightly strange idea for something I would like to do with > numpy which I guess probably doesn't exist, but I may be wrong. What I > want to do, essentially, is to have two arrays of equal size, say X and > Y, of pointers to doubles say. Then I want to do (in C notation) *x += > *y for each x, y in X, Y. I'd like to write this in Python as X+=Y where > X and Y understand that they consist of arrays of pointers to doubles > and that it should do the arithmetic on the values pointed to. Now I can > do this in C easily enough, and integrate it with numpy via weave or > whatever (it only took a few lines using scipy.weave.inline), but it > would be really nice if something along these lines were already in > numpy or an extension of it already. Anyone know of anything like that > or had any experience with similar situations?
That's an interesting idea; I'm just glad I don't have to deal with all those segfaults :-) If you are not concerned about speed, you can define an array of objects, and tell those objects how to handle the different operations. But, since you are doing simulations, I guess you want it to be fast, so I would look at defining a "pointer" data-type. All this is much more work than simply writing the loop in Cython or ctypes, though! I wonder if it wouldn't be safer to abstract your "pointers" to an object that is less hazardous to use, say a list. Then you have slots = [obj1, obj2, obj3, ..., obj10] links = array([slots[0], slots[6], slots[20]], dtype=object) # at least this won't segfault links = array([slots[0], slots[1], slots[3]], dtype=object) # and this should work fine Regards Stéfan _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
