[Numpy-discussion] Do we want scalar casting to behave as it does at the moment?
Sun Jan 6 20:01:07 CST 2013
On Mon, Jan 7, 2013 at 1:43 AM, Olivier Delalleau <email@example.com> wrote:
> 2013/1/5 Nathaniel Smith <firstname.lastname@example.org>:
>> On Fri, Jan 4, 2013 at 5:25 PM, Andrew Collette
>> <email@example.com> wrote:
>>> I agree the current behavior is confusing. Regardless of the details
>>> of what to do, I suppose my main objection is that, to me, it's really
>>> unexpected that adding a number to an array could result in an
>> I think the main objection to the 1.5 behaviour was that it violated
>> "Errors should never pass silently." (from 'import this'). Granted
>> there are tons of places where numpy violates this but this is the one
>> we're thinking about right now...
>> Okay, here's another idea I'll throw out, maybe it's a good compromise:
>> 1) We go back to the 1.5 behaviour.
>> 2) If this produces a rollover/overflow/etc., we signal that using the
>> standard mechanisms (whatever is configured via np.seterr). So by
>> default things like
>> np.maximum(np.array([1, 2, 3], dtype=uint8), 256)
>> would succeed (and produce [1, 2, 3] with dtype uint8), but also issue
>> a warning that 256 had rolled over to become 0. Alternatively those
>> who want to be paranoid could call np.seterr(overflow="raise") and
>> then it would be an error.
> That'd work for me as well. Although I'm not sure about the name
> "overflow", it sounds generic enough that it may be associated to many
> different situations. If I want to have an error but only for this
> very specific scenario (an "unsafe" cast in a mixed scalar/array
> operation), would that be possible?
I suggested "overflow" because that's how we signal rollover in
general right now:
In : np.int8(100) * np.int8(2)
overflow encountered in byte_scalars
Two caveats on this: One, right now this is only implemented for
scalars, not arrays -- which is bug #593 -- and two, I actually agree
(?) that integer rollover and float overflow are different things we
should probably add a new category to np.seterr() for integer rollover
But the proposal here is that we not add a specific category for
"unsafe cast" (which we would then have to define!), but instead just
signal it using the standard mechanisms for the particular kind of
corruption that happened. (Which right now is overflow, and might
become something else later.)
> Also, do we all agree that "float32 array + float64 scalar" should
> cast the scalar to float32 (thus resulting in a float32 array as
> output) without warning, even if the scalar can't be represented
> exactly in float32?
I guess for consistency, if this proposal is adopted then a float64
which ends up getting cast to 'inf' or 0.0 should trigger an overflow
or underflow warning respectively... e.g.:
In : np.float64(1e300)
In : np.float32(_12)
...but otherwise I think yes we agree.
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