[Numpy-discussion] Possible bug in scalar * array
nadavh at visionsense.com
Tue Oct 21 20:26:25 CDT 2003
To my opinion state 2 is surplus: Consider a large loop where an integer array s are multiplied by a wide range of scalars, and, at some point an exception is raised; It is not easy to track down what happened, especially when the scalars are not ordered (say, read from a data file). I can not find any justification for state 2 singularity (A*n is ok, A*(n+1) is not).
I suspect that state 3 is the fastest (it is up to you to judge), it is also consistent with the behavior of the __add__ operator. Why the __add__ operator should have the risk of being nonportable and __mul__ should not?
Which state should be the default?
There are two way to use the system: interactive and in a script. To my opinion the default should be the state that fits more the interactive mode --- slower and with a lot of checking. Maybe it is better to add a section to the documentation, how to tune the package for maximum performance: those who are interested in a high performance computing should be ready to do some extra work (read the manual, at least).
From: Todd Miller [mailto:jmiller at stsci.edu]
Sent: Tue 21-Oct-03 17:12
To: Nadav Horesh
Cc: Edward C. Jones; numpy-discussion
Subject: RE: [Numpy-discussion] Possible bug in scalar * array
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)
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,
> -----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?
> 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)
> > -------------------------------------------------------
> > This SF.net email sponsored by: Enterprise Linux Forum Conference & Expo
> > The Event For Linux Datacenter Solutions & Strategies in The Enterprise
> > Linux in the Boardroom; in the Front Office; & in the Server Room
> > http://www.enterpriselinuxforum.com
> > _______________________________________________
> > Numpy-discussion mailing list
> > Numpy-discussion at lists.sourceforge.net
> > https://lists.sourceforge.net/lists/listinfo/numpy-discussion
> Todd Miller
> Space Telescope Science Institute
> 3700 San Martin Drive
> Baltimore MD, 21030
> (410) 338 - 4576
> This SF.net email is sponsored by OSDN developer relations
> Here's your chance to show off your extensive product knowledge
> We want to know what you know. Tell us and you have a chance to win $100
> Numpy-discussion mailing list
> Numpy-discussion at lists.sourceforge.net
Space Telescope Science Institute
3700 San Martin Drive
Baltimore MD, 21030
(410) 338 - 4576
This SF.net email is sponsored by OSDN developer relations
Here's your chance to show off your extensive product knowledge
We want to know what you know. Tell us and you have a chance to win $100
Numpy-discussion mailing list
Numpy-discussion at lists.sourceforge.net
More information about the Numpy-discussion