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.
-Travis
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
