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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