On Fri, Nov 16, 2012 at 9:53 PM, Gökhan Sever <gokhanse...@gmail.com> wrote:
> Thanks for the explanations.
>
> For either case, I was expecting to get float32 as a resulting data type.
> Since, float32 is large enough to contain the result. I am wondering if
> changing casting rule this way, requires a lot of modification in the NumPy
> code. Maybe as an alternative to the current casting mechanism?
>
> I like the way that NumPy can convert to float64. As if these data-types are
> continuation of each other. But just the conversation might happen too early
> --at least in my opinion, as demonstrated in my example.
>
> For instance comparing this example to IDL surprises me:
>
> I16 np.float32(5555)*5e38
> O16 2.7774999999999998e+42
>
> I17 (np.float32(5555)*5e38).dtype
> O17 dtype('float64')In this case, what's going on is that 5e38 is a Python float object, and Python float objects have double-precision, i.e., they're equivalent to np.float64's. So you're multiplying a float32 and a float64. I think most people will agree that in this situation it's better to use float64 for the output? -n _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
