[Numpy-discussion] Do we want scalar casting to behave as it does at the moment?

Nathaniel Smith njs@pobox....
Mon Nov 12 15:11:52 CST 2012


On Mon, Nov 12, 2012 at 8:54 PM, Matthew Brett <matthew.brett@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")

-n


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