[Numpy-discussion] some typestrings not recognized anymore
Sun Jun 3 15:20:08 CDT 2012
On Sun, Jun 3, 2012 at 4:49 PM, Nathaniel Smith <firstname.lastname@example.org> wrote:
> On Sun, Jun 3, 2012 at 3:28 PM, Ralf Gommers
> <email@example.com> wrote:
> > Hi,
> > Just ran into this:
> >>>> np.__version__
> > '1.5.1'
> >>>> np.empty((1,), dtype='>h2') # works in 1.6.2 too
> > array(, dtype=int16)
> >>>> np.__version__
> > '1.7.0.dev-fd78546'
> >>>> np.empty((1,), dtype='>h2')
> > Traceback (most recent call last):
> > File "<stdin>", line 1, in <module>
> > TypeError: data type ">h2" not understood
> For reference the problem seems to be that in 1.6 and earlier, "h"
> plus a number was allowed, and the number was ignored:
> >>> np.__version__
> >>> np.dtype("h2")
> >>> np.dtype("h4")
> >>> np.dtype("h100")
> In current master, the number is disallowed -- all of those give
> TypeErrors. Presumably because "h" already means the same as "i2", so
> adding a second number on their is weird.
> Other typecodes with an "intrinsic size" seem to have the same problem
> -- "q", "l", etc.
> Obviously "h2" should be allowed in 1.7, seeing as disallowing it
> breaks scipy. And the behavior for "h100" is clearly broken and should
> be disallowed in the long run. So I guess we need to do two things:
> 1) Re-enable the use of typecode + size specifier even in cases where
> the typcode has an intrinsic size
> 2) Issue a deprecation warning for cases where the intrinsic size and
> the specified size don't match (like "h100"), and then turn that into
> an error in 1.8.
> Does that sound correct?
Seems correct as far as I can tell. Your approach to fixing the issue
> I guess the other option would be to
> deprecate *all* use of size specifiers with these typecodes (i.e.,
> deprecate "h2" as well, where the size specifier is merely redundant),
> but I'm not sure removing that feature is really worth it.
Either way would be OK I think. Using "h2" is redundant, but I can see how
someone could prefer writing it like that for clarity. It's not like 'h'
--> np.int16 is obvious.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion