Robert Kern wrote: > On Mon, Apr 12, 2010 at 17:26, Travis Oliphant <oliph...@enthought.com> wrote: > >> On Apr 11, 2010, at 2:56 PM, Anne Archibald wrote: >> >> 2010/4/10 Stéfan van der Walt <ste...@sun.ac.za>: >> >> On 10 April 2010 19:45, Pauli Virtanen <p...@iki.fi> wrote: >> >> Another addition to ufuncs that should be though about is specifying the >> >> Python-side interface to generalized ufuncs. >> >> This is an interesting idea; what do you have in mind? >> >> I can see two different kinds of answer to this question: one is a >> tool like vectorize/frompyfunc that allows construction of generalized >> ufuncs from python functions, and the other is thinking out what >> methods and support functions generalized ufuncs need. >> >> The former would be very handy for prototyping gufunc-based libraries >> before delving into the templated C required to make them actually >> efficient. >> >> The latter is more essential in the long run: it'd be nice to have a >> reduce-like function, but obviously only when the arity and dimensions >> work out right (which I think means (shape1,shape2)->(shape2) ). This >> could be applied along an axis or over a whole array. reduceat and the >> other, more sophisticated, schemes might also be worth supporting. At >> a more elementary level, gufunc objects should have good introspection >> - docstrings, shape specification accessible from python, named formal >> arguments, et cetera. (So should ufuncs, for that matter.) >> >> We should collect all of these proposals into a NEP. To clarify what I >> mean by "group-by" behavior. >> Suppose I have an array of floats and an array of integers. Each element >> in the array of integers represents a region in the float array of a certain >> "kind". The reduction should take place over like-kind values: >> Example: >> add.reduceby(array=[1,2,3,4,5,6,7,8,9], by=[0,1,0,1,2,0,0,2,2]) >> results in the calculations: >> 1 + 3 + 6 + 7 >> 2 + 4 >> 5 + 8 + 9 >> and therefore the output (notice the two arrays --- perhaps a structured >> array should be returned instead...) >> [0,1,2], >> [17, 6, 22] >> >> The real value is when you have tabular data and you want to do reductions >> in one field based on values in another field. This happens all the time >> in relational algebra and would be a relatively straightforward thing to >> support in ufuncs. >> > > I might suggest a simplification where the by array must be an array > of non-negative ints such that they are indices into the output. For > example (note that I replace 2 with 3 and have no 2s in the by array): > > add.reduceby(array=[1,2,3,4,5,6,7,8,9], by=[0,1,0,1,3,0,0,3,3]) == > [17, 6, 0, 22] > > This basically generalizes bincount() to other binary ufuncs. > >
A bit more generalization of `by` gives behavior like matlab's accumarray (http://www.mathworks.com/access/helpdesk/help/techdoc/ref/accumarray.html), which I partly cloned here: [This would be a link to the scipy cookbook, but scipy.org is not responding.] Warren _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
