On Tue, Oct 11, 2011 at 2:06 PM, Derek Homeier <
de...@astro.physik.uni-goettingen.de> wrote:
> On 11 Oct 2011, at 20:06, Matthew Brett wrote:
>
> > Have I missed a fast way of doing nice float to integer conversion?
> >
> > By nice I mean, rounding to the nearest integer, converting NaN to 0,
> > inf, -inf to the max and min of the integer range? The astype method
> > and cast functions don't do what I need here:
> >
> > In [40]: np.array([1.6, np.nan, np.inf, -np.inf]).astype(np.int16)
> > Out[40]: array([1, 0, 0, 0], dtype=int16)
> >
> > In [41]: np.cast[np.int16](np.array([1.6, np.nan, np.inf, -np.inf]))
> > Out[41]: array([1, 0, 0, 0], dtype=int16)
> >
> > Have I missed something obvious?
>
> np.[a]round comes closer to what you wish (is there consensus
> that NaN should map to 0?), but not quite there, and it's not really
> consistent either!
>
>
In a way, there is already consensus in the code. np.nan_to_num() by
default converts nans to zero, and the infinities go to very large and very
small.
>>> np.set_printoptions(precision=8)
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
Ben Root
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