On Thu, May 10, 2012 at 12:52 PM, Scott Ransom <sran...@nrao.edu> wrote:
> On 05/10/2012 02:23 PM, Chris Barker wrote: > > On Thu, May 10, 2012 at 2:38 AM, Dag Sverre Seljebotn > > <d.s.seljeb...@astro.uio.no> wrote: > >> What would serve me? I use NumPy as a glorified "double*". > > > >> all I want is my glorified > >> "double*". I'm probably not a representative user.) > > > > Actually, I think you are representative of a LOT of users -- it > > turns, out, whether Jim Huginin originally was thinking this way or > > not, but numpy arrays are really powerful because the provide BOTH and > > nifty, full featured array object in Python, AND a wrapper around a > > generic "double*" (actually char*, that could be any type). > > > > This is are really widely used feature, and has become even more so > > with Cython's numpy support. > > > > That is one of my concerns about the "bit pattern" idea -- we've then > > created a new binary type that no other standard software understands > > -- that looks like a a lot of work to me to deal with, or even worse, > > ripe for weird, non-obvious errors in code that access that good-old > > char*. > > > > So I'm happier with a mask implementation -- more memory, yes, but it > > seems more robust an easy to deal with with outside code. > > > > But either way, Dag's key point is right on -- in Cython (or any other > > code) -- we need to make sure ti's easy to get a regular old pointer > > to a regular old C array, and get something else by accident. > > > > -Chris > > Agreed. (As someone who has been heavily using Numpy since the early > days of numeric, and who wrote and maintains a suite of scientific > software that uses Numpy and its C-API in exactly this way.) > > Note that I wasn't aware that the proposed mask implementation might (or > would?) change this behavior... (and hopefully I haven't just > misinterpreted these last few emails. If so, I apologize.). > > I haven't seen a change in this behavior, otherwise most of current numpy would break. Chuck
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