On Fri, Jan 4, 2013 at 12:39 AM, Peter Cock <p.j.a.c...@googlemail.com> wrote: >> Since I've actually used NumPy arrays with specific low memory >> types, I thought I should comment about my use case if case it >> is helpful: >> >> I've only used the low precision types like np.uint8 (unsigned) where >> I needed to limit my memory usage. In this case, the topology of a >> graph allowing multiple edges held as an integer adjacency matrix, A. >> I would calculate things like A^n for paths of length n, and also make >> changes to A directly (e.g. adding edges). So an overflow was always >> possible, and neither the old behaviour (type preserving but wrapping >> on overflow giving data corruption) nor the current behaviour (type >> promotion overriding my deliberate memory management) are nice. >> My preferences here would be for an exception, so I knew right away. >> >> The other use case which comes to mind is dealing with low level >> libraries and/or file formats, and here automagic type promotion >> would probably be unwelcome. > > Regards, > > Peter
Elsewhere on the thread, Nathaniel Smith <n...@pobox.com> wrote: > > To be clear: we're only talking here about the case where you have a mix of > a narrow dtype in an array and a scalar value that cannot be represented in > that narrow dtype. If both sides are arrays then we continue to upcast as > normal. So my impression is that this means very little in practical terms, > because this is a rare and historically poorly supported situation. > > But if this is something you're running into in practice then you may have a > better idea than us about the practical effects. Do you have any examples > where this has come up that you can share? > > -n Clarification appreciated - on closer inspection for my adjacency matrix example I would not fall over the issue in https://github.com/numpy/numpy/issues/2878 >>> import numpy as np >>> np.__version__ '1.6.1' >>> A = np.zeros((100,100), np.uint8) # Matrix could be very big >>> A[3,4] = 255 # Max value, setting up next step in example >>> A[3,4] 255 >>> A[3,4] += 1 # Silently overflows on NumPy 1.6 >>> A[3,4] 0 To trigger the contentious behaviour I'd have to do something like this: >>> A = np.zeros((100,100), np.uint8) >>> B = A + 256 >>> B array([[256, 256, 256, ..., 256, 256, 256], [256, 256, 256, ..., 256, 256, 256], [256, 256, 256, ..., 256, 256, 256], ..., [256, 256, 256, ..., 256, 256, 256], [256, 256, 256, ..., 256, 256, 256], [256, 256, 256, ..., 256, 256, 256]], dtype=uint16) I wasn't doing anything like that in my code though, just simple matrix multiplication and in situ element modification, for example A[i,j] += 1 to add an edge. I still agree that for https://github.com/numpy/numpy/issues/2878 an exception sounds sensible. Peter _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
