Hi, On Tue, Oct 11, 2011 at 7:32 PM, Benjamin Root <ben.root@ou.edu> wrote:
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])
Right - but - we'd still need to round, and take care of the nasty issue of thresholding:
x = np.array([np.inf, -np.inf, np.nan, -128, 128]) x array([ inf, -inf, nan, -128., 128.]) nnx = np.nan_to_num(x) nnx
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, -1.28000000e+002, 1.28000000e+002])
np.rint(nnx).astype(np.int8) array([ 0, 0, 0, -128, -128], dtype=int8)
So, I think nice_round would look something like: def nice_round(arr, out_type): in_type = arr.dtype.type mx = floor_exact(np.iinfo(out_type).max, in_type) mn = floor_exact(np.iinfo(out_type).max, in_type) nans = np.isnan(arr) out = np.rint(np.clip(arr, mn, mx)).astype(out_type) out[nans] = 0 return out with floor_exact being something like: https://github.com/matthew-brett/nibabel/blob/range-dtype-conversions/nibabe... See you, Matthew