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Namespace Links
The prior section introduced the special __class__ and __bases__ instance and class attributes, without really explaining why you might care about them. In short, these attributes allow you to inspect inheritance hierarchies within your own code. For example, they can be used to display a class tree, as in the following example:
# classtree.py
"""
Climb inheritance trees using namespace links,
displaying higher superclasses with indentation
"""
def classtree(cls, indent):
print('.' * indent + cls.__name__) # Print class name here
for supercls in cls.__bases__: # Recur to all superclasses
classtree(supercls, indent+3) # May visit super > once
def instancetree(inst):
print('Tree of %s' % inst) # Show instance
classtree(inst.__class__, 3) # Climb to its class
def selftest():
class A: pass
class B(A): pass
class C(A): pass
class D(B,C): pass
class E: pass
class F(D,E): pass
instancetree(B())
instancetree(F())
if __name__ == '__main__': selftest()
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The classtree function in this script is recursive—it prints a class’s name using __name__, then climbs up to the superclasses by calling itself. This allows the function to traverse arbitrarily shaped class trees; the recursion climbs to the top, and stops at root superclasses that have empty __bases__ attributes. When using recursion, each active level of a function gets its own copy of the local scope; here, this means that cls and indent are different at each classtree level.
Most of this file is self-test code. When run standalone in Python 3.0, it builds an empty class tree, makes two instances from it, and prints their class tree structures:
C:\misc> c:\python26\python classtree.py
Tree of <__main__.B instance at 0x02557328>
...B
......A
Tree of <__main__.F instance at 0x02557328>
...F
......D
.........B
............A
.........C
............A
......E
When run under Python 3.0, the tree includes the implied object superclasses that are automatically added above standalone classes, because all classes are “new style” in 3.0 (more on this change in Chapter 31):
C:\misc> c:\python30\python classtree.py
Tree of <__main__.B object at 0x02810650>
...B
......A
.........object
Tree of <__main__.F object at 0x02810650>
...F
......D
.........B
............A
...............object
.........C
............A
...............object
......E
.........object
Here, indentation marked by periods is used to denote class tree height. Of course, we could improve on this output format, and perhaps even sketch it in a GUI display. Even as is, though, we can import these functions anywhere we want a quick class tree display:
C:\misc> c:\python30\python
>>> class Emp: pass
...
>>> class Person(Emp): pass
>>> bob = Person()
>>> import classtree
>>> classtree.instancetree(bob)
Tree of <__main__.Person object at 0x028203B0>
...Person
......Emp
.........object
Regardless of whether you will ever code or use such tools, this example demonstrates one of the many ways that you can make use of special attributes that expose interpreter internals. You’ll see another when we code the lister.py general-purpose class display tools in the section Multiple Inheritance: “Mix-in” Classes—there, we will extend this technique to also display attributes in each object in a class tree. And in the last part of this book, we’ll revisit such tools in the context of Python tool building at large, to code tools that implement attribute privacy, argument validation, and more. While not for every Python programmer, access to internals enables powerful development tools.
[67] As you can see, the contents of attribute dictionaries and dir call results may change over time. For example, because Python now allows built-in types to be subclassed like classes, the contents of dir results for built-in types have expanded to include operator overloading methods, just like our dir results here for user-defined classes under Python 3.0. In general, attribute names with leading and trailing double underscores are interpreter-specific. Type subclasses will be discussed further in Chapter 31.
