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 [42]: c = np.zeros(4, np.int16) In [43]: d = np.zeros(4, np.int32) In [44]: np.around([1.6,np.nan,np.inf,-np.inf], out=c) Out[44]: array([2, 0, 0, 0], dtype=int16) In [45]: np.around([1.6,np.nan,np.inf,-np.inf], out=d) Out[45]: array([ 2, -2147483648, -2147483648, -2147483648], dtype=int32) Perhaps a starting point to harmonise this behaviour and get it closer to your expectations (it still would not be really nice having to define the output array first, I guess)... Cheers, Derek