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
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