[Numpy-discussion] Change in scalar upcasting rules for 1.6.x?

[Matthew Brett]

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