On Wed, Feb 25, 2015 at 2:48 PM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> I am not really sure what the behavior of __array__ should be. The link
> to the subclassing docs I gave before indicates that it should be possible
> to write to it if it is writeable (and probably pandas sh
On Wed, Feb 25, 2015 at 1:56 PM, Stephan Hoyer wrote:
>
>
> On Wed, Feb 25, 2015 at 1:24 PM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> 1. When converting these objects to arrays using PyArray_Converter, if
>> the arrays returned by any of the array interfaces is not C contiguo
On Wed, Feb 25, 2015 at 1:24 PM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> 1. When converting these objects to arrays using PyArray_Converter, if
> the arrays returned by any of the array interfaces is not C contiguous,
> aligned, and writeable, a copy that is will be made. Proper a
An issue was raised yesterday in github, regarding np.may_share_memory when
run on a class exposing an array using the __array__ method. You can check
the details here:
https://github.com/numpy/numpy/issues/5604
Looking into it, I found out that NumPy doesn't really treat objects
exposing __array
