2013/11/26 Olivier Grisel <olivier.gri...@ensta.org>
> 2013/11/26 Peter Prettenhofer <peter.prettenho...@gmail.com>:
> > Hi all,
> >
> > I'm currently modifying our tree code so that it runs on both fortran
> and c
> > continuous arrays. After some benchmarking I got aware of the following
> > numpy behavior that was contrary to what I was expecting::
> >
> > >>> X = # some feature matrix
> > >>> X = np.asfortranarray(X)
> > >>> X.flags.f_contiguous
> > True
> > >>> # so far so good
> > >>> X_train = X[:1000]
> > >>> X_train.flags.f_contiguous
> > False
> > >>> X_train.flags.c_contiguous
> > False
> > >>> # damn - seems like a view is neither c nor fortran continuous
>
> Only if you slice the rows of a fortran aligned 2D array, this is
> expected. If you slices the rows of a C-contiguous 2D array or the
> columns of a F-contiguous 2D array it stays contiguous.
>
Actually, now that I think about it, it totally makes sense -- next time I
think before I write ;-)
thanks guys!
>
> >>> import numpy as np
> >>> a_c = np.arange(12).reshape(3, 4)
> >>> a_f = np.asfortranarray(a_c)
> >>> a_c.flags
> C_CONTIGUOUS : True
> F_CONTIGUOUS : False
> OWNDATA : False
> WRITEABLE : True
> ALIGNED : True
> UPDATEIFCOPY : False
> >>> a_f.flags
> C_CONTIGUOUS : False
> F_CONTIGUOUS : True
> OWNDATA : True
> WRITEABLE : True
> ALIGNED : True
> UPDATEIFCOPY : False
> >>> a_c[1:].flags
> C_CONTIGUOUS : True
> F_CONTIGUOUS : False
> OWNDATA : False
> WRITEABLE : True
> ALIGNED : True
> UPDATEIFCOPY : False
> >>> a_f[:, 1:].flags
> C_CONTIGUOUS : False
> F_CONTIGUOUS : True
> OWNDATA : False
> WRITEABLE : True
> ALIGNED : True
> UPDATEIFCOPY : False
>
>
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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--
Peter Prettenhofer
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