Quick question hopefully somewhat related to this:
Does numexpr fully support float32 arrays ?
-Sebastian
On Wednesday 04 October 2006 09:32, Tim Hochberg wrote:
> Ivan Vilata i Balaguer wrote:
> > It seemed that discontiguous arrays worked OK in Numexpr since r1977 or
> > so, but I have come across some alignment or striding problems which can
> > be seen with the following code::
> >
> > import numpy
> > import numexpr
> >
> > array_length = 10
> > array_descr = [('c1', numpy.int32), ('c2', numpy.uint16)]
> >
> > array = numpy.empty((array_length,), dtype=array_descr)
> > for i in xrange(array_length):
> > array['c1'][i] = i
> > array['c2'][i] = 0xaaaa
> >
> > print numexpr.evaluate('c1', {'c1': array['c1']})
> > print numexpr.evaluate('c1', {'c1': array['c1'].copy()})
> >
> > Im my computer, Pentium IV with NumPy 1.0rc1 and Numexpr r2239
> > (unmodified) this gives the following result::
> >
> > [ 0 109226 -1431699456 2 240298 -1431699456
> > 4 371370 8 633514]
> > [0 1 2 3 4 5 6 7 8 9]
> >
> > The test works right when ``evaluate()`` is used with 'c2' instead of
> > 'c1', and also when 'c2' also measures 32 bits and fields are aligned.
> > Maybe the ``memsteps`` value is not getting used somewhere. Any ideas
> > on this?
>
> I suspect that there are some assumptions that the element separation
> is an integral multiple of the element size. I certainly didn't have
> record arrays in mind when I was working on the striding stuff, so it
> wouldn't surprise me. This should be fixed: preferably to do the right
> thing and at a minimum to cleanly raise an exception rather than
> spitting out garbage. I don't know that I'll have time to mess with it
> soon though.
>
> -tim
>
>
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