[Numpy-discussion] Please help with subclassing numpy.ndarray

[Travis Oliphant]

Reggie Dugard wrote:

>On Wed, 2007-02-07 at 14:36 -0700, Travis Oliphant wrote:
>  
>
>>Sturla Molden wrote:
>>
>>    
>>
>>>>def __new__(cls,...)
>>>>    ...
>>>>   (H, edges) = numpy.histogramdd(..)
>>>>   cls.__defaultedges = edges
>>>>
>>>>def __array_finalize__(self, obj):
>>>>   if  not hasattr(self, 'edges'):
>>>>       self.edges = self.__defaultedges
>>>>   
>>>>
>>>>        
>>>>
>>>So in order to get an instance attribute, one has to temporarily define it
>>>as a class attribute? 
>>>
>>>      
>>>
>>No, you don't *have* to do it this way for all instance attributes.  
>>
>>In this example, the user was trying to keep the edges computed during 
>>the __new__ method as an attribute.   What are the possibilities?
>>
>>1) Use the __new__ method to create the object in full and then store 
>>the edges in some kind of global (or class global) variable. 
>>
>>This solution because it uses global variables has all of the thread 
>>problems global variables bring.
>>
>>2) Create a "dummy" arrayobject in the __new__ method and fill it in 
>>(i.e. using setstate or resize) during the __init__ method where the 
>>instance attribute is actually set.
>>
>>    
>>
>I'm probably missing something obvious here, but why can't you just
>attach the attribute to the actual object in the __new__ method before
>returning it.  For example:
>  
>
Good point.  I guess I thought the OP had tried that already.  It turns 
out it works fine, too.

The __array_finalize__ is useful if you want the attribute to be carried 
around when arrays are created automatically internally (after math 
operations for example).


-Travis