[Numpy-discussion] Contiguity of result of astype changed - intentional?

[Travis Oliphant]

On Sep 12, 2012, at 1:36 PM, Matthew Brett wrote:

> Hi,
> 
> We hit a subtle behavior change for the ``astype`` array method
> between 1.6.1 and 1.7.0 beta.
> 
> In 1.6.1:
> 
> 
> In [18]: a = np.arange(24).reshape((2, 3, 4)).transpose((1, 2, 0))
> 
> In [19]: a.flags
> Out[19]:
>  C_CONTIGUOUS : False
>  F_CONTIGUOUS : False
>  OWNDATA : False
>  WRITEABLE : True
>  ALIGNED : True
>  UPDATEIFCOPY : False
> 
> In [20]: b = a.astype(float)
> 
> In [21]: b.flags
> Out[21]:
>  C_CONTIGUOUS : True
>  F_CONTIGUOUS : False
>  OWNDATA : True
>  WRITEABLE : True
>  ALIGNED : True
>  UPDATEIFCOPY : False
> 
> In [22]: b.strides
> Out[22]: (64, 16, 8)
> 
> So - ``a.astype(float)`` here has made a new C-contiguous array,
> somewhat as implied by the 'copy' explanation in the docstring.  In
> 1.7.0 beta, ``a`` is the same but:
> 
> In [22]: b.flags
> Out[22]:
>  C_CONTIGUOUS : False
>  F_CONTIGUOUS : False
>  OWNDATA : True
>  WRITEABLE : True
>  ALIGNED : True
>  UPDATEIFCOPY : False
> 
> In [23]: b.strides
> Out[23]: (32, 8, 96)
> 
> Is this intended?  Is there a performance reason to keep the same
> strides in 1.7.0?

I believe that this could be because in 1.7.0, NumPy was changed so that copying does not always default to "C-order" but to "Keep-order".    So, in 1.7.0, the strides of b is governed by the strides of a, while in 1.6.1, the strides of b is C-order (because of the copy).  

-Travis