[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
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