On Wed, Feb 25, 2015 at 1:24 PM, Jaime Fernández del Río <jaime.frio@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).