In my experience writing ndarray-like objects, you likely want to implement __array__ instead of __array_interface__. The former gives you full control to create the ndarray yourself. On Fri, Mar 13, 2015 at 7:22 AM, Daniel Smith <dgasmith@icloud.com> wrote:

Greetings everyone, I have a new project that deals with core and disk tensors wrapped into a single object so that the expressions are transparent to the user after the tensor is formed. I would like to add __array_interface__ to the core tensor and provide a reasonable error message if someone tries to call the __array_interface__ for a disk tensor. I may be missing something, but I do not see an obvious way to do this in the python layer.

Currently I do something like:

if ttype == “Core": self.__array_interface__ = self.tensor.ndarray_interface() else: self.__array_interface__ = {'typestr’: 'Only Core tensor types are supported.’}

Which provides at least a readable error message if it is not a core tensor: TypeError: data type "Only Core tensor types are supported." not understood

A easy solution I see is to change numpy C side __array_interface__ error message to throw custom strings.

In numpy/core/src/multiarray/ctors.c:2100 we have the __array_interface__ conversion:

if (!PyDict_Check(iface)) { Py_DECREF(iface); PyErr_SetString(PyExc_ValueError, "Invalid __array_interface__ value, must be a dict"); return NULL; }

It could simply be changed to:

if (!PyDict_Check(iface)) { if (PyString_Check(iface)){ PyErr_SetString(PyExc_ValueError, iface); } else{ PyErr_SetString(PyExc_ValueError, "Invalid __array_interface__ value, must be a dict”); } Py_DECREF(iface); return NULL; }

Thoughts?

Cheers, -Daniel Smith _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion