[Numpy-discussion] Structured array dtype
Stéfan van der Walt
Sat Aug 31 00:51:36 CDT 2013
On Fri, 30 Aug 2013 17:26:51 +0200, Nicolas Rougier wrote:
> >>> Z = np.zeros(10, [('a', np.float32, 3), ('b', np.float32, 4)])
> >>> Z['a'].dtype
> >>> Z.dtype['a']
> dtype(('<f4', (3,)))
> Does that mean that dtype['a'] is the dtype of field 'a' when in Z, while Z['a'].dtype is the dtype of field 'a' when "extracted" or my way of thinking is totally wrong ?
Apologies if this is a duplicate response; I'm sending it offline.
In case 1, you are indexing into the array, and querying its dtype. In case
two, you are indexing into a dtype.
I.e., in case two, you are doing this:
In : dtype = np.dtype([('a', float, 3), ('b', int)])
In : dtype['a']
Out: dtype(('<f8', (3,)))
> What bothers me the most is that I cannot do:
> >>> Z['a'].view(Z.dtype['a'])
> ValueError: new type not compatible with array.
That's quite a tricky operation to perform, since it has to take into account
the underlying strides of the old array as well as calculate a shape for the
new array. It should be possible to make it work using something similar to
`np.lib.stride_tricks.as_strided`, but my quick attempt failed because of the
In : class Foo:
__array_interface__ = Z.__array_interface__
In : f = Foo()
In : np.asarray(f)
This does not seem right.
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