[Numpy-discussion] field names on numpy arrays

[josef.pktd at gmail.com]

On Wed, Jun 3, 2009 at 7:56 PM,  <josef.pktd at gmail.com> wrote:
> On Wed, Jun 3, 2009 at 7:33 PM, Pierre GM <pgmdevlist at gmail.com> wrote:
>>
>> On Jun 3, 2009, at 7:23 PM, Robert Kern wrote:
>>
>>> On Wed, Jun 3, 2009 at 18:20, Pierre GM <pgmdevlist at gmail.com> wrote:
>>>>
>>>>
>>>> Or, as all fields have the same dtype:
>>>>
>>>>  >>> a_array.view(dtype=('f',len(a_array.dtype)))
>>>> array([[ 0.,  1.,  2.,  3.,  4.],
>>>>        [ 1.,  2.,  3.,  4.,  5.]], dtype=float32)
>>>>
>>>> Ain't it fun ?
>>>
>>> Ah, yes, there is that niggle, too.
>>
>>
>>
>> Except that I always get bitten by that:
>>
>>  >>> backandforth =
>> a_array.view(dtype=('f',len(a_array.dtype))).view(a_array.dtype)
>>  >>> backandforth
>> array([[(0.0, 1.0, 2.0, 3.0, 4.0)],
>>        [(1.0, 2.0, 3.0, 4.0, 5.0)]],
>>       dtype=[('a', '<f4'), ('b', '<f4'), ('c', '<f4'), ('d', '<f4'),
>> ('e', '<f4')])
>>  >>> backandforth.shape
>> (2,1)
>>
>> We gained a dimension !
>>
>
> I looked at the archives to my first discovery of views, for sorting
> rows proposed by Pierre. In this case reshape was not necessary.
>
>>>> np.sort(np.array([[4.0, 1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0, 5.0]]).view(dt),0).view(float)
> array([[ 1.,  2.,  3.,  4.,  5.],
>       [ 4.,  1.,  2.,  3.,  4.]])
>
>>>> dt
> [('a', '<f8'), ('b', '<f8'), ('c', '<f8'), ('d', '<f8'), ('e', '<f8')]
>
> looking closer, the extra dimension helps to maintain shape:
>
> direct construction of structured array
>
>>>> np.array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)],dt)
> array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)],
>      dtype=[('a', '<f8'), ('b', '<f8'), ('c', '<f8'), ('d', '<f8'),
> ('e', '<f8')])
>>>> np.array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)],dt).shape
> (2,)
>
> structured view on existing array is 2d
>>>> np.array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)]).view(dt).shape
> (2, 1)
>
> view on view returns original shape,
>>>> np.array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)]).view(dt).view(float).shape
> (2, 5)
>
> But sorting in between the two views also preserved original shape.
> This was the source about my initial confusion about the necessity of
> reshape.
>

here is a minimal example for 2d structured array:

>>> dt = dtype=[('a', '<f8'), ('b', '<f8'), ('c', '<f8'), ('d', '<f8'), ('e', '<f8')]
>>> ys = np.array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)],dt)
>>> ys.shape
(2,)
>>> ys.view(float)
array([ 0.,  1.,  2.,  3.,  4.,  1.,  2.,  3.,  4.,  5.])
>>> ys = ys.reshape((len(ys),1))
>>> ys.shape
(2, 1)
>>> ys.view(float)
array([[ 0.,  1.,  2.,  3.,  4.],
       [ 1.,  2.,  3.,  4.,  5.]])


Josef