[Numpy-discussion] Fast vectorized arithmetic with ~32 significant digits under Numpy
Marten van Kerkwijk
m.h.vankerkwijk at gmail.com
Sat Dec 12 14:10:13 EST 2015
astropy `Time` indeed using two doubles internally, but is very limited in
the operations it allows: essentially only addition/subtraction, and
multiplication with/division by a normal double.
It would be great to have better support within numpy; it is a pity to have
a float128 type that does not provide the full associated precision.
All the best,
On Sat, Dec 12, 2015 at 1:02 PM, Sturla Molden <sturla.molden at gmail.com>
> "Thomas Baruchel" <baruchel at gmx.com> wrote:
> > While this is obviously the most relevant answer for many users because
> > it will allow them to use Numpy arrays exactly
> > as they would have used them with native types, the wrong thing is that
> > from some point of view "true" vectorization
> > will be lost.
> What does "true vectorization" mean anyway?
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
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