Hi Nicolas 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 dtype('float32')
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 [18]: dtype = np.dtype([('a', float, 3), ('b', int)]) In [19]: dtype['a'] Out[19]: 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 following: In [13]: class Foo: __array_interface__ = Z.__array_interface__ ....: In [14]: f = Foo() In [15]: np.asarray(f) Out[15]: array([, , , , , , , , , ], dtype='|V28') This does not seem right. Stéfan