On Thu, Jan 17, 2013 at 6:26 AM, Matthew Brett <matthew.br...@gmail.com> wrote:
> I am starting to wonder if we should aim for making > > * scalar and array casting rules the same; > * Python int / float scalars become int32 / 64 or float64; aren't they already? I'm not sure what you are proposing. > This has the benefit of being very easy to understand and explain. It > makes dtypes predictable in the sense they don't depend on value. That is key -- I don't think casting should ever depend on value. > Those wanting to maintain - say - float32 will need to cast scalars to > float32. > > Maybe the use-cases motivating the scalar casting rules - maintaining > float32 precision in particular - can be dealt with by careful casting > of scalars, throwing the burden onto the memory-conscious to maintain > their dtypes. IIRC this is how it worked "back in the day" (the Numeric day? -- and I'm pretty sure that in the long run it worked out badly. the core problem is that there are only python literals for a couple types, and it was oh so easy to do things like: my_arr = np,zeros(shape, dtype-float32) another_array = my_array * 4.0 and you'd suddenly get a float64 array. (of course, we already know all that..) I suppose this has the up side of being safe, and having scalar and array casting rules be the same is of course appealing, but you use a particular size dtype for a reason,and it's a real pain to maintain it. Casual users will use the defaults that match the Python types anyway. So in the in the spirit of "practicality beats purity" -- I"d like accidental upcasting to be hard to do. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
