[Numpy-discussion] Bitwise operations and unsigned types
Charles R Harris
Fri Apr 6 08:50:05 CDT 2012
On Fri, Apr 6, 2012 at 3:57 AM, Nathaniel Smith <email@example.com> wrote:
> On Fri, Apr 6, 2012 at 7:19 AM, Travis Oliphant <firstname.lastname@example.org>
> > That is an interesting point of view. I could see that point of view.
> > But, was this discussed as a bug prior to this change occurring?
> > I just heard from a very heavy user of NumPy that they are nervous about
> > upgrading because of little changes like this one. I don't know if this
> > particular issue would affect them or not, but I will re-iterate my view
> > that we should be very careful of these kinds of changes.
> I agree -- these changes make me very nervous as well, especially
> since I haven't seen any short, simple description of what changed or
> what the rules actually are now (comparable to the old "scalars do not
> affect the type of arrays").
> But, I also want to speak up in favor in one respect, since real world
> data points are always good. I had some code that did
> def do_something(a):
> a = np.asarray(a)
> a -= np.mean(a)
> If someone happens to pass in an integer array, then this is totally
> broken -- np.mean(a) may be non-integral, and in 1.6, numpy silently
> discards the fractional part and performs the subtraction anyway,
> In : a
> Out: array([0, 1, 2, 3])
> In : a -= 1.5
> In : a
> Out: array([-1, 0, 0, 1])
> The bug was discovered when Skipper tried running my code against
> numpy master, and it errored out on the -=. So Mark's changes did
> catch one real bug that would have silently caused completely wrong
> numerical results!
> Yes, these things are trade offs between correctness and convenience. I
don't mind new warnings/errors so much, they may break old code but they
don't lead to wrong results. It's the unexpected and unnoticed successes
that are scary.
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