[Numpy-discussion] Do we want scalar casting to behave as it does at the moment?
Thu Jan 17 19:24:18 CST 2013
I'd like to echo what Chris is saying. It was a big annoyance with Numeric to make it so hard to preserve the array type in ordinary expressions.
On Jan 17, 2013, at 8:04 PM, Chris Barker - NOAA Federal wrote:
> On Thu, Jan 17, 2013 at 6:26 AM, Matthew Brett <email@example.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.
> Christopher Barker, Ph.D.
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