[Numpy-discussion] Priority rules between numpy scalars and 0d arrays

[Pierre GM]

All,
Sorry to bring back this subject, but I still haven't got any proper answers:

* What are the priority rules between numpy scalars and 0d arrays ?

When multiplying a numpy scalar by a 0d array, shouldn't the __mul__ or 
__rmul__ methods of the array be called ?
Should the result be a numpy scalar, or a 0d array (possibly recasted to the 
higher dtype) ?

The problem occurs with numpy.ma.masked, defined as a 0d, np.float64 
MaskedArray, which has the __mul__ and __rmul__ of a MaskedArray.

np.float(1)*ma.masked gives ma.masked, as it should
np.float(64)* ma.masked gives 0, when ma.masked should have been obtained:
that leads me to think that ma.masked.__rmul__ isn't called. Why ? Are 0d 
arrays that special beasts ?