[Numpy-discussion] Possible bug in scalar * array

Todd Miller jmiller at stsci.edu
Tue Oct 21 10:33:11 CDT 2003


On Tue, 2003-10-21 at 10:07, Nadav Horesh wrote:
> As I underoustand the range checking (from the results --- not from the source code), it checks if the range of the scalar exceeds the range of the array elements type. Don't see any significant execution time penalty with that. However there might be a place for a flag-controlled behavior:
> * State 1: stay with the current "saturated" over/underflow whatever the scalar is. This is consistent with what numarray does now with scalars in range.
> * State 2: Raise exception as suggested.
> * State 3: Use the normal wrap-around on integer overflow, thus make a*2 give the same results as a+a in the following example:
> 
> >>> a = array((100,200,128), type=UInt8)
> >>> a+a
> array([200, 144,   0], type=UInt8)
> >>> a*2
> array([200, 255, 255], type=UInt8)
> 
>   Nadav

This sounds like an attempt to turn a bug fix into a coherent plan. :)

We could implement what you're describing here a lot like we handle IEEE
floating point,  but I'm wondering if we should.  I think state 3 is
marginally portable, so I'm not sure we should support it,  but if we
did, what would we use it for? 

Similarly,  if we support both states 1 and 2,  is anyone going to be
sufficiently on the ball to know the difference and set their error
handling appropriately?  Or, are 99.9% of the people going to just use
whatever the default is?  If the latter is the case, we should just
implement "the default" and keep life simple.

I'm not opposed to this if there are valid uses for it,  but we should
know those reasons before implementing, and I don't.

Thanks for the ideas,
Todd

> 
> -----Original Message-----
> From:	Todd Miller [mailto:jmiller at stsci.edu]
> Sent:	Mon 20-Oct-03 21:36
> To:	Edward C. Jones
> Cc:	numpy-discussion
> Subject:	Re: [Numpy-discussion] Possible bug in scalar * array
> I tracked down the problem to some (relatively) new overflow checking
> code which detects the overflow of the scalar -1 as it is assigned to an
> array pseudo buffer of type UInt8.  This error was mishandled, and hence
> was transformed into an invalid shape tuple (you gotta smile :-)).  The
> *2nd* call is where the exception shows up because of caching logic.
> 
> I talked this over with Perry and we concluded that it's probably a good
> thing to trap the out of range scalar values before using them.  Thus,
> we're proposing to fix the error handling,  but to make the calls in
> question raise an overflow exception on the first call.  We are
> interested in hearing other opinions however.  Comments?
> 
> Regards,
> Todd
> 
> On Sat, 2003-10-18 at 18:18, Edward C. Jones wrote:
> > #! /usr/bin/env python
> > 
> > # Python 2.3.2, numarray 0.7
> > import numarray
> > 
> > def fun2(code, scale):
> >      arr = numarray.ones((4,4), code)
> >      arr2 = scale * arr
> >      # Bug appears at second multiply.
> >      arr3 = scale * arr
> > 
> > # These calls fail when "scale" is too big for "code":
> > 
> > #   File 
> > "/usr/local/lib/python2.3/site-packages/numarray/numarraycore.py", line 
> > 653, in __rmul__
> > #    def __rmul__(self, operand): return ufunc.multiply(operand, self)
> > # ValueError: invalid shape tuple
> > 
> > #fun2('Int16', 100000)
> > fun2('UInt8' , -1)
> > 
> > 
> > 
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> -- 
> Todd Miller 			
> Space Telescope Science Institute
> 3700 San Martin Drive
> Baltimore MD, 21030
> (410) 338 - 4576
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-- 
Todd Miller 			
Space Telescope Science Institute
3700 San Martin Drive
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(410) 338 - 4576





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