[Numpy-discussion] Do we want scalar casting to behave as it does at the moment?
Mon Nov 12 20:21:21 CST 2012
2012/11/12 Nathaniel Smith <firstname.lastname@example.org>
> On Mon, Nov 12, 2012 at 8:54 PM, Matthew Brett <email@example.com>
> > 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
> > Specifically, as of 1.6.0:
> > In : arr = np.array([1.], dtype=np.float32)
> > In : (arr + (2**16-1)).dtype
> > Out: dtype('float32')
> > In : (arr + (2**16)).dtype
> > Out: dtype('float64')
> > In : arr = np.array([1.], dtype=np.int8)
> > In : (arr + 127).dtype
> > Out: dtype('int8')
> > In : (arr + 128).dtype
> > Out: dtype('int16')
> > There's discussion about the changes here:
> > http://mail.scipy.org/pipermail/numpy-discussion/2011-March/055156.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(, 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,
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.
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