2012/11/12 Matthew Brett <matthew.br...@gmail.com> > Hi, > > On Mon, Nov 12, 2012 at 8:15 PM, Benjamin Root <ben.r...@ou.edu> wrote: > > > > > > On Monday, November 12, 2012, Olivier Delalleau wrote: > >> > >> 2012/11/12 Nathaniel Smith <n...@pobox.com> > >>> > >>> On Mon, Nov 12, 2012 at 8:54 PM, Matthew Brett < > matthew.br...@gmail.com> > >>> wrote: > >>> > Hi, > >>> > > >>> > I wanted to check that everyone knows about and is happy with the > >>> > scalar casting changes from 1.6.0. > >>> > > >>> > Specifically, the rules for (array, scalar) casting have changed such > >>> > that the resulting dtype depends on the _value_ of the scalar. > >>> > > >>> > Mark W has documented these changes here: > >>> > > >>> > http://docs.scipy.org/doc/numpy/reference/ufuncs.html#casting-rules > >>> > > >>> > > http://docs.scipy.org/doc/numpy/reference/generated/numpy.result_type.html > >>> > > >>> > > http://docs.scipy.org/doc/numpy/reference/generated/numpy.promote_types.html > >>> > > >>> > Specifically, as of 1.6.0: > >>> > > >>> > In [19]: arr = np.array([1.], dtype=np.float32) > >>> > > >>> > In [20]: (arr + (2**16-1)).dtype > >>> > Out[20]: dtype('float32') > >>> > > >>> > In [21]: (arr + (2**16)).dtype > >>> > Out[21]: dtype('float64') > >>> > > >>> > In [25]: arr = np.array([1.], dtype=np.int8) > >>> > > >>> > In [26]: (arr + 127).dtype > >>> > Out[26]: dtype('int8') > >>> > > >>> > In [27]: (arr + 128).dtype > >>> > Out[27]: dtype('int16') > >>> > > >>> > There's discussion about the changes here: > >>> > > >>> > > >>> > > http://mail.scipy.org/pipermail/numpy-discussion/2011-September/058563.html > >>> > > http://mail.scipy.org/pipermail/numpy-discussion/2011-March/055156.html > >>> > > >>> > > http://mail.scipy.org/pipermail/numpy-discussion/2012-February/060381.html > >>> > > >>> > It seems to me that this change is hard to explain, and does what you > >>> > want only some of the time, making it a false friend. > >>> > >>> The old behaviour was that in these cases, the scalar was always cast > >>> to the type of the array, right? So > >>> np.array([1], dtype=np.int8) + 256 > >>> returned 1? Is that the behaviour you prefer? > >>> > >>> I agree that the 1.6 behaviour is surprising and somewhat > >>> inconsistent. There are many places where you can get an overflow in > >>> numpy, and in all the other cases we just let the overflow happen. And > >>> in fact you can still get an overflow with arr + scalar operations, so > >>> this doesn't really fix anything. > >>> > >>> I find the specific handling of unsigned -> signed and float32 -> > >>> float64 upcasting confusing as well. (Sure, 2**16 isn't exactly > >>> representable as a float32, but it doesn't *overflow*, it just gives > >>> you 2.0**16... if I'm using float32 then I presumably don't care that > >>> much about exact representability, so it's surprising that numpy is > >>> working to enforce it, and definitely a separate decision from what to > >>> do about overflow.) > >>> > >>> None of those threads seem to really get into the question of what the > >>> best behaviour here *is*, though. > >>> > >>> Possibly the most defensible choice is to treat ufunc(arr, scalar) > >>> operations as performing an implicit cast of the scalar to arr's > >>> dtype, and using the standard implicit casting rules -- which I think > >>> means, raising an error if !can_cast(scalar, arr.dtype, > >>> casting="safe") > >> > >> > >> I like this suggestion. It may break some existing code, but I think > it'd > >> be for the best. The current behavior can be very confusing. > >> > >> -=- Olivier > > > > > > > > "break some existing code" > > > > I really should set up an email filter for this phrase and have it send > back > > an email automatically: "Are you nuts?!" > > Well, hold on though, I was asking earlier in the thread what we > thought the behavior should be in 2.0 or maybe better put, sometime in > the future. > > If we know what we think the best answer is, and we think the best > answer is worth shooting for, then we can try to think of sensible > ways of getting there. > > I guess that's what Nathaniel and Olivier were thinking of but they > can correct me if I'm wrong... > > Cheers, > > Matthew >
This is indeed what I had in mind, thanks. I definitely agree a (long) period with a deprecation warning would be needed if this is changed. -=- Olivier
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