[Numpy-discussion] Objects exposing the array interface

[Stephan Hoyer]

On Wed, Feb 25, 2015 at 1:24 PM, Jaime Fernández del Río <
jaime.frio at 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 arrays and
> subclasses are passed unchanged. This is the source of the error reported
> above.
>


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 arrays and
subclasses are passed unchanged. This is the source of the error reported
above.

I'm not entirely sure I understand this -- how is PyArray_Convert used in
numpy? For example, if I pass a non-contiguous array to your class Foo,
np.asarray does not do a copy:

In [25]: orig = np.zeros((3, 4))[:2, :3]

In [26]: orig.flags
Out[26]:
  C_CONTIGUOUS : False
  F_CONTIGUOUS : False
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

In [27]: subclass = Foo(orig)

In [28]: np.asarray(subclass)
Out[28]:
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

In [29]: np.asarray(subclass)[:] = 1

In [30]: np.asarray(subclass)
Out[30]:
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])


But yes, this is probably a bug.

2. When converting these objects using PyArray_OutputConverter, as well as
> in similar code in the ufucn machinery, anything other than a proper array
> or subclass raises an error. This means that, contrary to what the docs on
> subclassing say, see below, you cannot use an object exposing the array
> interface as an output parameter to a ufunc
>

Here it might be a good idea to distinguish between objects that define
__array__ vs __array_interface__/__array_struct__. A class that defines
__array__ might not be very ndarray-like at all, but rather be something
that can be *converted* to an ndarray. For example, objects in pandas
define __array__, but updating the return value of df.__array__() in-place
will not necessarily update the DataFrame (e.g., if the frame had
inhomogeneous dtypes).
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20150225/70a1c845/attachment.html>