@ZacDiggum

• (1) may be a bug. np.sin(scalar) + af_array is perhaps calling the __add__ on np.ndarray but not on af.Array. I will see if I can figure this out.
• (2) There is an issue for this: #56. The problem is arrayfire library expects the data in the fortran format. For this reason it is not a trivial thing to implement but I am looking into it.
• (3) Adding new methods is fine, but I don't like the idea of renaming attributes. The goal of the wrapper is to be consistent with other arrayfire language functions when it comes to naming things. You could always have a child class that inherits from af.Array in your code that does the renaming.

@FilipeMaia any thoughts ?

• (1) np.sin(scalar) + af_array definitely calls __add__ on numpy. Probably then numpy tries to convert the second argument to a numpy array by calling __array__ on it. The exact same issue exists in afnumpy. I don't know of a way around this while still ensuring that np.array(af_array) works.
• (3) I would actually avoid adding multiple names for the same function. It makes the namespace more busy and confusing.

BTW about (1) the issue only happens with numpy. functions, not the ones in math. or cmath. which return normal python scalars.

@FilipeMaia Any idea why numpy's __add__ takes precedence over arrayfires __radd__ ?

@pavanky Why would your class have precedence over numpy's one? The numpy argument is first so its __add__ is always the first to be called. Only in case that it fails is __radd__ called on the other argument.

@FilipeMaia yeah I just found that out myself. May be a simple solution would be to have the user set a flag which chooses between __array__ or not ?

@pavanky That could be an option. Another possibility might be to raise a warning if __array__ is being called by someone other than numpy.array, although this might require some ugly stack introspection.

@FilipeMaia This will still not solve the problem. I am guessing np.ndarray.__add__ calls __array__ which would result in no warning at all.

You mean __add__ calls numpy.array. Yes you're probably right, so we would need to be clever about it...

@FilipeMaia I am leaning towards not supporting __array__ by default and only enabling it when a user manually asks for it. We could have a method to_ndarray which users can call conversion to numpy.

Sure, that's cleaner and also a reasonable solution.

@pavanky I found a better solution! Setting the __array_priority__ attribute in Array. I tested this on afnumpy and it seems to work.
Check http://stackoverflow.com/questions/35866738/avoid-numpy-distributing-an-operation-for-overloaded-operator

@FilipeMaia can you send in a patch ? You understand the situation better than I do here.

@pavanky #71 should solve the first issue.

@ZacDiggum since (1) is now fixed and (2) has a different issue associated with it, can you close the issue and open a new one for adding new methods (or atleast rename this issue to reflect the unresolved ones).