Hello all,
I'm writing up a general function to allocate aligned numpy arrays
(I'll post it shortly, as Anne suggested that such a function would be
useful).
However, I've run into trouble with using ndarray.view() in odd corner-
cases:
In : numpy.__version__
Out: '1.1.0.dev5077'
In : a = numpy.ones((3,8),dtype=numpy.uint8)
In : a.view(numpy.uint16)
Out:
array([[257, 257, 257, 257],
[257, 257, 257, 257],
[257, 257, 257, 257]], dtype=uint16)
In : a = numpy.ones((3,9),dtype=numpy.uint8)
In : a[:,1:].view(numpy.uint16)
ValueError: new type not compatible with array.
In : a[:,:-1].view(numpy.uint16)
ValueError: new type not compatible with array.
In : numpy.array(a[:,:-1]).view(numpy.uint16)
Out:
array([[257, 257, 257, 257],
[257, 257, 257, 257],
[257, 257, 257, 257]], dtype=uint16)
It seems like 'view' won't create a view where the stride length is
not a whole multiple of the dtype's itemsize. However, since strides
are measured in bytes (right?), this shouldn't be a problem.
Is this a minor bug? Or am I woefully misunderstanding the issue
involved here?
Thanks,
Zach
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