On Mon, 2014-03-03 at 23:12 +0100, Nicolas Rougier wrote: > > I never noticed this kind of cast before (1.8.0), it's just a bit surprising. > > It was convenient to write translations (for a bunch of points) such as: > > Z = np.ones((n,2),dtype=np.float32) + (300,300) > > but I can live with Z += 300,300 >
Just to note. That actually does the temporary cast anyway doing the calculation in double precision and then casting the result. If you want to make sure it stays in single precision you will need to make that an array with float32 dtype. - Sebastian > > Nicolas > > > On 03 Mar 2014, at 23:02, Benjamin Root <ben.r...@ou.edu> wrote: > > > IIRC, this is dependent on whether you are using 32bit versus 64bit numpy. > > All regular integer numbers can fit in 32 bits (is that right?), but the > > 1.1 is treated as a float32 if on a 32 bit NumPy or as float64 if on a 64 > > bit NumPy. > > > > That's my stab at it. > > > > Ben Root > > > > > > On Mon, Mar 3, 2014 at 4:06 PM, Nicolas Rougier <nicolas.roug...@inria.fr> > > wrote: > > > > Hi all, > > > > I'm using numpy 1.8.0 (osx 10.9, python 2.7.6) and I can't understand dtype > > promotion in the following case: > > > > >>> Z = np.zeros((2,2),dtype=np.float32) + 1 > > >>> print Z.dtype > > float32 > > > > >>> Z = np.zeros((2,2),dtype=np.float32) + (1,1) > > >>> print Z.dtype > > float64 > > > > > > Is this the expected behavior ? > > What it the difference between the two lines ? > > > > > > > > Nicolas > > _______________________________________________ > > 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 > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
