Hi,
I'm a bit lost with the following example (numpy 1.7.1, osx 10.8):
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 ?
What bothers me the most is that I cannot do:
Z['a'].view(Z.dtype['a'])
ValueError: new type not compatible with array.
Nicolas
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
Thanks for the explanation. In fact, since the 4 components of 'b' are contiguous in memory, I wanted to find a way to express that fact in the dtype.
Z = np.zeros(10, [('a', np.float32, 3), ('b', np.float32, 4)]) Z['b'].strides (28,4)
Z = np.zeros((10,4), np.float32) Z.strides (16,4)
Z = np.zeros(10, (np.float32,4)) Z.strides (16,4)
Nicolas
On Aug 31, 2013, at 7:51 AM, Stéfan van der Walt stefan@sun.ac.za wrote:
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
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Nicolas Rougier -
Stéfan van der Walt