On Tue, Oct 11, 2011 at 2:06 PM, Derek Homeier <derek@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