[Numpy-tickets] [NumPy] #826: Error in basic arithmetic involving masked objects and scalar types

NumPy numpy-tickets@scipy....
Wed Jan 21 12:23:10 CST 2009


#826: Error in basic arithmetic involving masked objects and scalar types
------------------------+---------------------------------------------------
 Reporter:  dharland    |        Owner:  somebody
     Type:  defect      |       Status:  new     
 Priority:  normal      |    Milestone:  1.3.0   
Component:  numpy.core  |      Version:  none    
 Severity:  major       |   Resolution:          
 Keywords:  ma          |  
------------------------+---------------------------------------------------
Comment (by DarrenDale):

 This issue is causing problems for me as well. Here is a script
 illustrating the problem:

 {{{
 import numpy as np

 class MyArray(np.ndarray):

     __array_priority__ = 20

     def __new__(cls):
         return np.asarray(1.0, 'float64').view(cls).copy()

     def __repr__(self):
         return 'my_array'

     __str__ = __repr__

     def __mul__(self, other):
         return super(MyArray, self).__mul__(other)

     def __rmul__(self, other):
         return super(MyArray, self).__rmul__(other)

 mine = MyArray()
 print type(np.float32(1)*mine)
 print type(mine*np.float32(1))
 print type(mine*np.float64(1))
 print type(1*mine)
 print type(mine*1)

 print type(np.float64(1)*mine)
 }}}

 the first 5 print statements yield a !MyArray, the final one yields a
 numpy.float64. If I initialize my subclass to have a dtype of float128,
 the final test yields a !MyArray again.

 I can understand the need to cast the !MyArray instance to the higher
 dtype, but I dont understand why that has any bearing on the results type,
 which should respect `__array_priority__` if I understand
 `__array_priority__`'s purpose correctly.

-- 
Ticket URL: <http://scipy.org/scipy/numpy/ticket/826#comment:5>
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