On Fri, Mar 21, 2008 at 12:35 AM, David Cournapeau <[EMAIL PROTECTED]> wrote: > numpy 1.0.5 is on the way, and I was wondering about numpy's future. I > myself have some ideas about what could be done; has there been any > discussion behind what is on 1.1 trac's roadmap ?
MaskedArray, although derived from ndarray, doesn't always play nice with the rest of numpy as evidenced by the need to recreate many of the numpy "library" functions specifically for MaskedArrays. There are many surprises, ones_like returns a MaskedArray when given one, but empty_like and zeros_like do not, and functions like unique include masked values in the results, etc. Some of these issues might be considered bugs (and perhaps already fixed), while others result more from a lack of overall design for "numpy" working with multiple array types. Maybe I'm missing something because I'm still relatively new to numpy, so please correct me if I'm wrong. I'm also thinking obliquely about sparse arrays (and masked spare arrays?). It would be great, in my opinion, to move towards a design that allows multiple array types to work more cohesively in the numpy ecosystem. Precisely, a design that makes it easier to write functions that work on basic ndarrays, masked arrays (and sparse arrays?) not by special casing each container, but by expressing their operations using universal primitives (of course this isn't always possible, but where it is possible). What does this mean? Some functions might work better as methods so that the different array-like containers can special case them (dot, for instance, could be a candidate). Or perhaps this escapes the intent of numpy and what I'm really providing is an argument for why masked or sparse arrays shouldn't be in numpy and this work (to make functions more container agnostic) should be carried out in scipy, OR there should be "three" library stacks for each dense, masked dense, and sparse arrays. Alex P.S. Oh yeah, David's ideas about finding accelerator libraries dynamically sounds great. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
