[Numpy-discussion] Regression: in-place operations (possibly intentional)
Charles R Harris
Fri Sep 21 20:29:12 CDT 2012
On Fri, Sep 21, 2012 at 5:51 PM, Eric Firing <email@example.com> wrote:
> On 2012/09/21 12:20 PM, Nathaniel Smith wrote:
> > On Fri, Sep 21, 2012 at 10:04 PM, Chris Barker <firstname.lastname@example.org>
> >> On Fri, Sep 21, 2012 at 10:03 AM, Nathaniel Smith <email@example.com>
> >>> You're right of course. What I meant is that
> >>> a += b
> >>> should produce the same result as
> >>> a[...] = a + b
> >>> If we change the casting rule for the first one but not the second,
> >>> then these will produce different results if a is integer and b is
> >> I certainly agree that we would want that, however, numpy still needs
> >> to deal tih pyton symantics, which means that wile (at the numpy
> >> level) we can control what "a[...] =" means, and we can control what
> >> "a + b" produces, we can't change what "a + b" means depending on the
> >> context of the left hand side.
> >> that means we need to do the casting at the assignment stage, which I
> >> gues is your point -- so:
> >> a_int += a_float
> >> should do the addition with the "regular" casting rules, then cast to
> >> an int after doing that.
> >> not sure the implimentation details.
> > Yes, that seems to be what happens.
> > In : a = np.arange(3)
> > In : a *= 1.5
> > In : a
> > Out: array([0, 1, 3])
> > But still, the question is, can and should we tighten up the
> > assignment casting rules to same_kind or similar?
> An example of where tighter casting seems undesirable is the case of
> functions that return integer values with floating point dtype, such as
> rint(). It seems natural to do something like
> In : ind = np.empty((3,), dtype=int)
> In : rint(np.arange(3, dtype=float) / 3, out=ind)
> Out: array([0, 0, 1])
> where one is generating integer indices based on some manipulation of
> floating point numbers. This works in 1.6 but fails in 1.7.
In : rint(arange(3, dtype=float)/3, out=ind, casting='unsafe')
Out: array([0, 0, 1])
I'm not sure how to make this backward compatible though.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion