Hi, > > This seems like a rather common operation - I know I've needed it on > > at least two occasions - is it worth creating some sort of C > > implementation? What is the appropriate generalization? > > Some sort of indirect addressing infrastructure. But it looks like this > could be tricky to make safe, it would need to do bounds checking at the > least and would probably work best with a contiguous array as the target. I > could see some sort of low-level function called argassign(target, indirect > index, source) that could be used to build more complicated things in > python.
This looks somehow like the behaviour of builtin map. One could do map(fn, index) with appropriate fn. But iirc this is not faster than a for loop if fn is not a builtin function. An infrastructure like you imagine might use a similar syntax (with underlying C funcs). The main point is, how to tell it which operation to perform (add, multiply, average, whatever). Implementing a bunch of functions add_argassign, ... whatever_argassign contradicts my understanding of "generalized". ;) Maybe it would be simpler to just have functions which handle the index arrays in advance. An example will show it best: index = array([1, 2, 4, 2, 3, 1]) # 1 and 2 occur twice data = array([1, 1, 1, 1, 1, 1]) newindex, newdata = filter_and_add(index, data) # the kind of function I mean print newindex --> array([1, 2, 4, 3]) # duplicates have been removed print newdata --> array([2, 2, 1, 1]) # corresponding entries have been added a[newindex] += newdata Johannes ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
