The following code does not output "sub_class radd" as expected since NumPy ignores __array_priority__ when the right-hand-side operand is a subclass of ndarray.

import numpy as np
class sub_class(np.ndarray):
    __array_priority__ = 1000
    def __add__(self, rhs):
        print "sub_class add"
    def __radd__(self, lhs):
        print "sub_class radd"
sub = sub_class((2,2))
np.float32(42.0) + sub

The problem is line 282 in number.c where a branch uses PyArray_Check() to check the right-hand-side operand m2. If PyArray_Check(m2) returns True, which is the case when m2 is a subclass of ndarray, __array_priority__ is ignored.

Now, this is not a problem when the left-hand-side operand is an ndarray since in CPython a subclass always takes precedence over its superclass. However, a NumPy scalar is not a sub-/super-class of ndarray as the example code above illustrates.

There is a very simple fix: change PyArray_Check(m2) to PyArray_CheckExact(m2). However, this fix has the side-effect that the operation will fail with NotImplementedError if the subclass doesn’t implement the operator, which is a change to the current behavior.