On Mar 21, 2007, at 6:58 AM, Anne Archibald wrote: > Vectorizing apply is what you're looking for, by the sound of it: > In [13]: a = array([lambda x: x**2, lambda x: x**3]) > > In [14]: b = arange(5) > > In [15]: va = vectorize(lambda f, x: f(x)) > > In [16]: va(a[:,newaxis],b[newaxis,:]) > Out[16]: > array([[ 0, 1, 4, 9, 16], > [ 0, 1, 8, 27, 64]]) > > Once in a while it would also be nice to vectorize methods, either > over self or not over self, but the same trick (vectorize an anonymous > function wrapper) should work fine. Varadic functions do give you > headaches; I don't think even frompyfunc will allow you to vectorize > only some of the arguments of a function and leave the others > unchanged. > > Anne
Thanks for the info. I tried this, and found that it benchmarks about half of just iterating over the methods in the list and calling them. I think the reason is that two python frames are actually being run -- one for the vectorized apply, and one for whatever I'm calling. So what I'm thinking is that I could accomplish this if I implemented the apply call as a C-level ufunc. I'll try hacking about with this idea. Thanks Anne! _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
