On Sat, Oct 6, 2012 at 12:17 PM, Ralf Gommers <ralf.gomm...@gmail.com>wrote:
> > > On Fri, Oct 5, 2012 at 5:17 PM, Dan Goodman <dg.gm...@thesamovar.net>wrote: > >> Hi, >> >> numpy.set_printoptions(precision=...) doesn't affect single floats, even >> if they are numpy floats rather than Python floats. Is this a bug or is >> there some reason for this behaviour? I ask because I have a class that >> derives from numpy.float64 and adds some extra information, and I'd like >> to be able to control the precision. I could fix it to use the precision >> set by numpy.set_printoptions, but then it would be inconsistent with >> how numpy itself handles precision. Thoughts? >> > > Do you mean scalars or arrays? For me set_printoptions only affects arrays > and not scalars. Both float32 and float64 arrays work as advertised: > > In [28]: np.set_printoptions(precision=4) > > In [29]: np.array([np.float32(1.234567891011011101111012345679)]) > Out[29]: array([ 1.2346], dtype=float32) > > In [30]: np.array([np.float64(1.234567891011011101111012345679)]) > Out[30]: array([ 1.2346]) > > In [31]: np.set_printoptions(precision=8) > > In [32]: np.array([np.float32(1.234567891011011101111012345679)]) > Out[32]: array([ 1.23456788], dtype=float32) > > In [33]: np.array([np.float64(1.234567891011011101111012345679)]) > Out[33]: array([ 1.23456789]) > > > But for scalars it doesn't work: > > In [34]: np.float32(1.234567891011011101111012345679) > Out[34]: 1.2345679 > > In [35]: np.float64(1.234567891011011101111012345679) > Out[35]: 1.2345678910110112 > > In [36]: np.set_printoptions(precision=4) > > In [37]: np.float32(1.234567891011011101111012345679) > Out[37]: 1.2345679 > > In [38]: np.float64(1.234567891011011101111012345679) > Out[38]: 1.2345678910110112 > > > Ralf > It also does not affect zero-dimensional (i.e. scalar) arrays (e.g. array(1.2345)): In [1]: x = array(1./3) In [2]: x Out[2]: array(0.3333333333333333) In [3]: set_printoptions(precision=3) In [4]: x Out[4]: array(0.3333333333333333) In [5]: type(x) Out[5]: numpy.ndarray `y` is a 1-d array, so this works as expected: In [6]: y = array([1./3]) In [7]: y Out[7]: array([ 0.333]) Warren > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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