[Numpy-discussion] Priority rules between 0d array and np.scalar

[Pierre GM]

All,
That time of a month again: could anybody (and I'm thinking about you  
in particular, Travis O.) can explain me what the priority rules are  
between a 0d ndarray and a np.scalar ?

OK, I understand there are no real rules. However, the bug I was  
describing in a previous thread (www.mail-archive.com/numpy-discussion@scipy.org 
/msg13235.html) is still around:

When multiplying/adding a np.scalar and ma.masked, the result varies  
depending on the order of the arguments as well as on their dtype.   
(Keep in mind that ma.masked is a subclass 0d ndarray of value 0 and  
dtype np.float64, with a __array_priority__ of 15).

ma.masked * np.float32(1) => ma.masked
np.float32(1) * ma.masked => ma.masked
ma.masked * np.float64(1) => ma.masked
np.floa64(1) * ma.masked => 0

My understanding is that for the first 2 operations, ma.masked takes  
over because it has the higher dtype. In that case, we use the rules  
defined in MaskedArray for multiplication (either __mul__ or  
__array_wrap__).

For the 3rd and 4th operations, the two arguments have the same dtype  
and it looks like we're switching to a different priority rule.
I would have expected ma.masked to take over in both cases, because a  
MaskedArray has a higher __array_priority__ than a ndarray or a  
np.scalar. That's not the case: the fact that ma.masked is a subclass  
of ndarray is not recognized...

I hope I didn't lose anybody in my description. A ticket has recently  
been filled about the same issue:
http://scipy.org/scipy/numpy/ticket/826

Looking forward to hearing from y'all
P.