On 05/01/07, Charles R Harris <[EMAIL PROTECTED]> wrote: > On 1/5/07, Tim Hochberg <[EMAIL PROTECTED]> wrote: > > Matt Knox wrote: > > > Basically, I'd like to be able to do accumulate operations with custom > functions. numpy.vectorize does not seem to provide an accumulate method > with the functions it returns.

If I understand correctly, you want to be able to write a python function that combines two scalars and returns another, then apply it (via vectorize or some such) like a ufunc's accumulate, or like python's reduce (for lists; for some reason the analogous function for iterators doesn't seem to exist). Have you tried using reduce? > > Note that if you are looking for speed, numpy.vectorize is probably not > > what you are looking for even if it did work for this sort of stuff. In fact, anything that goes through python code for the "combine two scalars" will be slow. The slowness of looping in python is not because python's looping constructs are slow, it's because executing python code is slow. So vectorize is kind of a cheat - it doesn't actually run fast, but it is convenient. [text deleted] > I think what he needs is something like a linear prediction code or a IIP > filter. The place to look would be in scipy, either in signal processing or > statistics (ARMA). I don't know that it is there, but it might (should) be. I don't think so. As he said in his original post, he's looking for a general function to produce this from any function which combines two scalars to produce another, whether it's been implemented in signal processing or not. It would be fairly straightforward to add this ability to the functions returned from vectorize(); it's implemented in python, IIRC, so one would just turn it into a class (SoftUfunc?) (with a __call__) and add an accumulate() function. It won't avoid python looping, but neither does vectorize, and in any case it can't be fast (even if the provided function were implemented in C calling back and forth through python would probably be a slowdown). And *convenience* is actually one of the biggest advantages of ufuncs, fancy indexing, and all that numpy whatnot. That said, it might also be worth looking at whether numexpr can do this sort of thing - it's supposed to take a numpy expression and evaluate it efficiently in C (avoiding intermediate arrays and so on). Finally, if you're careful, you can in fact abuse slicing and augmented assignment to do this efficiently: In [9]: a = ones(10) In [10]: a[1:]+=a[:-1] In [11]: a Out[11]: array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]) Unfortunately, this cannot really be adapted to anything but the operators with augmented assignment and things with an output argument - which includes ufuncs, but not, alas, vectorize. Really it would be nice if what vectorize() returned were effectively a ufunc, supporting all the various operations we might want from a ufunc (albeit inefficiently). This should not be difficult, but I am not up to writing it this evening. A. M. Archibald _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion