The problem is that these sorts of things take a while to emerge. The original system was more consistent than I think you give it credit. What you are seeing is that most people get NumPy from distributions and are relying on us to keep things consistent.
The scalar coercion rules were deterministic and based on the idea that a scalar does not determine the output dtype unless it is of a different kind. The new code changes that unfortunately. Another thing I noticed is that I thought that int16 <op> scalar float would produce float32 originally. This seems to have changed, but I need to check on an older version of NumPy. Changing the scalar coercion rules is an unfortunate substantial change in semantics and should not have happened in the 1.X series. I understand you did not get a lot of feedback and spent a lot of time on the code which we all appreciate. I worked to stay true to the Numeric casting rules incorporating the changes to prevent scalar upcasting due to the absence of single precision Numeric literals in Python. We will need to look in detail at what has changed. I will write a test to do that. Thanks, Travis -- Travis Oliphant (on a mobile) 512-826-7480 On Feb 13, 2012, at 7:58 PM, Mark Wiebe <mwwi...@gmail.com> wrote: > On Mon, Feb 13, 2012 at 5:00 PM, Travis Oliphant <tra...@continuum.io> wrote: > Hmmm. This seems like a regression. The scalar casting API was fairly > intentional. > > What is the reason for the change? > > In order to make 1.6 ABI-compatible with 1.5, I basically had to rewrite this > subsystem. There were virtually no tests in the test suite specifying what > the expected behavior should be, and there were clear inconsistencies where > for example "a+b" could result in a different type than "b+a". I recall there > being some bugs in the tracker related to this as well, but I don't remember > those details. > > This change felt like an obvious extension of an existing behavior for > eliminating overflow, where the promotion changed unsigned -> signed based on > the value of the scalar. This change introduced minimal upcasting only in a > set of cases where an overflow was guaranteed to happen without that > upcasting. > > During the 1.6 beta period, I signaled that this subsystem had changed, as > the bullet point starting "The ufunc uses a more consistent algorithm for > loop selection.": > > http://mail.scipy.org/pipermail/numpy-discussion/2011-March/055156.html > > The behavior Matthew has observed is a direct result of how I designed the > minimization function mentioned in that bullet point, and the algorithm for > it is documented in the 'Notes' section of the result_type page: > > http://docs.scipy.org/doc/numpy/reference/generated/numpy.result_type.html > > Hopefully that explains it well enough. I made the change intentionally and > carefully, tested its impact on SciPy and other projects, and advocated for > it during the release cycle. > > Cheers, > Mark > > -- > Travis Oliphant > (on a mobile) > 512-826-7480 > > > On Feb 13, 2012, at 6:25 PM, Matthew Brett <matthew.br...@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 > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion
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