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

[Reggie Dugard]

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:

class MyClass(numpy.ndarray):
    def __new__(self, ...):
        # Some stuff here
        H, edges = numpy.histogramdd(...)
        result = H.view(MyClass)
        result.edges = edges
        return result

    def __array_finalize__(self, obj):
        self.edges = getattr(obj, 'edges', [])

If you could show me the error of my ways, it would help me in *my*
attempt to subclass ndarray.

> The __array_finalize__ method is intended for "passing-on" attributes to 
> sub-classes from parent classes during operations where __new__ and 
> __init__ are not called (but a new instance is still created). It was 
> not intended to be used in all circumstances.

Thanks,

-Reggie