[Numpy-discussion] Masking through generator arrays

Charles R Harris charlesr.harris at gmail.com
Thu May 10 15:14:46 EDT 2012


On Thu, May 10, 2012 at 12:52 PM, Scott Ransom <sransom at 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.seljebotn at 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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