User guide

NumPy User Guide, Release 1.9.0
>>> c = C(’hello’)
Cls in __new__: <class ’C’>
Args in __new__: (’hello’,)
type(self) in __init__: <class ’C’>
Args in __init__: (’hello’,)
When we call C(’hello’), the __new__ method gets its own class as first argument, and the passed argument,
which is the string ’hello’. After python calls __new__, it usually (see below) calls our __init__ method, with
the output of __new__ as the first argument (now a class instance), and the passed arguments following.
As you can see, the object can be initialized in the __new__ method or the __init__ method, or both, and in fact
ndarray does not have an __init__ method, because all the initialization is done in the __new__ method.
Why use __new__ rather than just the usual __init__? Because in some cases, as for ndarray, we want to be able
to return an object of some other class. Consider the following:
class D(C):
def __new__(cls,
*
args):
print ’D cls is:’, cls
print ’D args in __new__:’, args
return C.__new__(C,
*
args)
def __init__(self,
*
args):
# we never get here
print ’In D __init__’
meaning that:
>>> obj = D(’hello’)
D cls is: <class ’D’>
D args in __new__: (’hello’,)
Cls in __new__: <class ’C’>
Args in __new__: (’hello’,)
>>> type(obj)
<class ’C’>
The definition of C is the same as before, but for D, the __new__ method returns an instance of class C rather than D.
Note that the __init__ method of D does not get called. In general, when the __new__ method returns an object
of class other than the class in which it is defined, the __init__ method of that class is not called.
This is how subclasses of the ndarray class are able to return views that preserve the class type. When taking a view,
the standard ndarray machinery creates the new ndarray object with something like:
obj = ndarray.__new__(subtype, shape, ...
where subdtype is the subclass. Thus the returned view is of the same class as the subclass, rather than being of
class ndarray.
That solves the problem of returning views of the same type, but now we have a new problem. The machinery of
ndarray can set the class this way, in its standard methods for taking views, but the ndarray __new__ method knows
nothing of what we have done in our own __new__ method in order to set attributes, and so on. (Aside - why not
call obj = subdtype.__new__(... then? Because we may not have a __new__ method with the same call
signature).
The role of __array_finalize__
__array_finalize__ is the mechanism that numpy provides to allow subclasses to handle the various ways that
new instances get created.
2.8. Subclassing ndarray 37