[Numpy-discussion] Request for clarification from Travis

[Travis E. Oliphant]

Charles R Harris wrote:
> Hi Travis,
>
> Could you expand on your thinking  concerning NotImplemented? The 
> relevant code is:
>
>     /*
>      * FAIL with NotImplemented if the other object has
>      * the __r<op>__ method and has __array_priority__ as
>      * an attribute (signalling it can handle ndarray's)
>      * and is not already an ndarray
>      */
>     if ((arg_types[1] == PyArray_OBJECT) &&                         \
>         (loop->ufunc->nin==2) && (loop->ufunc->nout == 1)) {
>         PyObject *_obj = PyTuple_GET_ITEM(args, 1);
>         if (!PyArray_CheckExact(_obj) &&                        \
>             PyObject_HasAttrString(_obj, "__array_priority__") && \
>             _has_reflected_op(_obj, loop->ufunc->name)) {
>             loop->notimplemented = 1;
>             return nargs;
>         }
>     }
>
> And the check is made after the needed promotions have been determined 
> and stored in arg_types. Consequently if a type is promoted to an 
> object array, as happens for the shift operations with floating 
> scalars, the rest of the conditions will be checked and pass because 
> numpy scalar types aren't arrays. I wonder if that is what you 
> intended here? 
I'm not sure, I don't understand what problem you are trying to solve.

> The reason I ask is that this determination should be associated with 
> the standard slot functions when the call is made and I don't want to 
> worry about type promotions. Further, I wonder why you want this 
> return before the attempt to run the function with the current 
> arguments is made. So it would be very helpful if you could clarify 
> what you aim to achieve here.
This is to ensure that object arrays get a chance to run their own code 
if they have the required right-hand special name (e.g. __radd__).

The problem is that NumPy is very aggressive and tries to handle 
everything so if I let the code get past this point, it would run 
successfully as an object array which is not what is necessarily wanted

-Travis