The NumPy Fortran-ordering quiz
Charles R Harris
charlesr.harris at gmail.com
Tue Oct 17 21:53:52 CDT 2006
On 10/17/06, Charles R Harris <charlesr.harris at gmail.com> wrote:
>
>
>
> On 10/17/06, Travis Oliphant <oliphant.travis at ieee.org> wrote:
> >
> > Charles R Harris wrote:
> > >
> > >
> > > On 10/17/06, *Lisandro Dalcin* <dalcinl at gmail.com
> > > <mailto:dalcinl at gmail.com>> wrote:
> > >
> > > I was surprised by this
> > >
> > > In [14]: array([[1,2,3],[4,5,6]]).reshape((3,2),order='F')
> > > Out[14]:
> > > array([[1, 5],
> > > [4, 3],
> > > [2, 6]])
> > >
> > >
> > > This one still looks wrong.
> > >
> > > In [15]: array([1,2,3,4,5,6]).reshape((3,2),order='F')
> > > Out[15]:
> > > array([[1, 2],
> > > [3, 4],
> > > [5, 6]])
> > >
> > >
> > >
> > > This one is fixed,
> > >
> > > In [3]: array([[1,2,3,4,5,6]]).reshape((3,2),order='F')
> > > Out[3]:
> > > array([[1, 4],
> > > [2, 5],
> > > [3, 6]])
> > >
> > > I also don't understand why a copy is returned if 'F' just fiddles
> > > with the indices and strides; the underlying data should be the same,
> > > just the view changes. FWIW, I think both examples should be returning
> >
> > > views.
> >
> > You are right, it doesn't need to. My check is not general enough.
> >
> > It can be challenging to come up with a general way to differentiate the
> > view-vs-copy situation and I struggled with it. In this case, it's the
> > fact that while self->nd > 1, the other dimensions are only of shape 1
> > and so don't really matter. If you could come up with some kind of
> > striding check that would distinguish the two cases, I would appreciate
> > it.
>
>
> I suppose the problem is mostly in discontiguous arrays. Hmmm..., this
> isn't too different that reshaping the transpose.
>
> a.reshape((m,n),order='F' ~ a.reshape((n,m)).T.reshape(m,n)
>
Erm, brainfart. Make that a.reshape((m,n),order='F') ~ a.reshape((n,m)).T
That's how I think of it, anyway. Actual layout in memory is not affected
until something like ascontiguosarray is called.
Chuck
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