Hi Jens,
I don't have enough knowledge about the internal memory layout, but the
documentation ndarray.view
<http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.view.html>
says
that:
Views that change the dtype size (bytes per entry) should normally be
avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
In your case, creating a *copy *of the slice and then calling *view *works.
>>>a
array([[ 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j],
[ 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j]])
>>> a.view(float)
array([[ 1., 0., 1., 0., 1., 0., 1., 0.],
[ 1., 0., 1., 0., 1., 0., 1., 0.]])
>>> b=a[:,:2].copy()
>>> b.view(float)
array([[ 1., 0., 1., 0.],
[ 1., 0., 1., 0.]])
>>> c=a[:,:2]
>>> c.view(float)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array
Hope it helps :)
Cheers,
N.Maniteja.
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