Hi, I noticed a casting change running the test suite on our image reader, nibabel: https://github.com/nipy/nibabel/blob/master/nibabel/tests/test_casting.py
For this script: <pre> import numpy as np Adata = np.zeros((2,), dtype=np.uint8) Bdata = np.zeros((2,), dtype=np.int16) Bzero = np.int16(0) Bbig = np.int16(256) print np.__version__ print 'Array add', (Adata + Bdata).dtype print 'Scalar 0 add', (Adata + Bzero).dtype print 'Scalar 256 add', (Adata + Bbig).dtype </pre> 1.4.1 Array add int16 Scalar 0 add uint8 Scalar 256 add uint8 1.5.1 Array add int16 Scalar 0 add uint8 Scalar 256 add uint8 1.6.1 Array add int16 Scalar 0 add uint8 Scalar 256 add int16 1.7.0.dev-aae5b0a Array add int16 Scalar 0 add uint8 Scalar 256 add uint16 I can understand the uint8 outputs from numpy < 1.6 - the rule being not to upcast for scalars. I can understand the int16 output from 1.6.1 on the basis that the value is outside uint8 range and therefore we might prefer a type that can handle values from both uint8 and int16. Was the current change intended? It has the following odd effect: In [5]: Adata + np.int16(257) Out[5]: array([257, 257], dtype=uint16) In [7]: Adata + np.int16(-257) Out[7]: array([-257, -257], dtype=int16) In [8]: Adata - np.int16(257) Out[8]: array([65279, 65279], dtype=uint16) but I guess you can argue that there are odd effects for other choices too, Best, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion