[Numpy-discussion] Change in scalar upcasting rules for 1.6.x?
Travis Oliphant
travis@continuum...
Mon Feb 13 19:00:37 CST 2012
Hmmm. This seems like a regression. The scalar casting API was fairly intentional.
What is the reason for the change?
--
Travis Oliphant
(on a mobile)
512-826-7480
On Feb 13, 2012, at 6:25 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
> Hi,
>
> I recently noticed a change in the upcasting rules in numpy 1.6.0 /
> 1.6.1 and I just wanted to check it was intentional.
>
> For all versions of numpy I've tested, we have:
>
>>>> import numpy as np
>>>> Adata = np.array([127], dtype=np.int8)
>>>> Bdata = np.int16(127)
>>>> (Adata + Bdata).dtype
> dtype('int8')
>
> That is - adding an integer scalar of a larger dtype does not result
> in upcasting of the output dtype, if the data in the scalar type fits
> in the smaller.
>
> For numpy < 1.6.0 we have this:
>
>>>> Bdata = np.int16(128)
>>>> (Adata + Bdata).dtype
> dtype('int8')
>
> That is - even if the data in the scalar does not fit in the dtype of
> the array to which it is being added, there is no upcasting.
>
> For numpy >= 1.6.0 we have this:
>
>>>> Bdata = np.int16(128)
>>>> (Adata + Bdata).dtype
> dtype('int16')
>
> There is upcasting...
>
> I can see why the numpy 1.6.0 way might be preferable but it is an API
> change I suppose.
>
> Best,
>
> Matthew
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
More information about the NumPy-Discussion
mailing list