Doing a lot by doing very little
Decorators have something in common with previous metaprogramming abstractions introduced to Python: they do not actually do anything you could not do without them. As described in the article, Metaclass programming in Python, it was possible even in Python 1.5 to manipulate Python class creation without the “metaclass” hook.
Decorators are similar in their ultimate banality. All a decorator does is modify the function or method that is defined immediately after the decorator. This was always possible, but the capability was particularly motivated by the introduction of the
staticmethod() built-in functions in Python 2.2. In the older style, you would use a
classmethod() call, for example, as follows:
Listing 1. Typical “old style” classmethod
class C: def foo(cls, y): print "classmethod", cls, y foo = classmethod(foo)
classmethod() is a built-in, there is nothing unique about it; you could also have “rolled your own” method transforming function. For example:
Listing 2. Typical “old style” method transform
def enhanced(meth): def new(self, y): print "I am enhanced" return meth(self, y) return new class C: def bar(self, x): print "some method says:", x bar = enhanced(bar)
All a decorator does is let you avoid repeating the method name, and put the decorator near the first mention of the method in its definition. For example:
Listing 3. Typical “old style” classmethod
class C: @classmethod def foo(cls, y): print "classmethod", cls, y @enhanced def bar(self, x): print "some method says:", x
Decorators work for regular functions too, in the same manner as for methods in classes. It is surprising just how much easier such a simple, and strictly-speaking unnecessary, change in syntax winds up making things work better, and makes reasoning about programs easier. Decorators can be chained together by listing more than one prior to a function of method definition; good sense urges avoiding chaining too many decorators together, but several are sometimes sensible:
Listing 4. Chained decorators
@synchronized @logging def myfunc(arg1, arg2, ...): #...do something #decorators are equivalent to ending with: # myfunc = synchronized(logging(myfunc)) #Nested in that declaration order
Being simply syntax sugar, decorators let you shoot yourself in the foot if you are so inclined. A decorator is just a function that takes at least one argument — it is up to the programmer of the decorator to make sure that what it returns is still a meaningful function or method that does enough of what the original function did for the connection to be useful. For example, a couple of syntactic misuses are:
Listing 5. Bad decorator that does not even return function
>>> def spamdef(fn): ... print "spam, spam, spam" ... >>> @spamdef ... def useful(a, b): ... print a2 + b2 ... spam, spam, spam >>> useful(3, 4) Traceback (most recent call last): File "<stdin>", line 1, in ? TypeError: 'NoneType' object is not callable
A decorator might return a function, but one that is not meaningfully associated with the undecorated function:
Listing 6. Decorator whose function ignores passed-in function
>>> def spamrun(fn): ... def sayspam(args): ... print "spam, spam, spam" ... return sayspam ... >>> @spamrun ... def useful(a, b): ... print a*2 + b2 ... >>> useful(3,4) spam, spam, spam
Finally, a better behaved decorator will in some way enhance or modify the action of the undecorated function:
Listing 7. Decorator that modifies behavior of undecorated func
>>> def addspam(fn): ... def new(args): ... print "spam, spam, spam" ... return fn(args) ... return new ... >>> @addspam ... def useful(a, b): ... print a2 + b2 ... >>> useful(3,4) spam, spam, spam 25
You might quibble over just how useful
useful() is, or whether
addspam() is really such a good enhancement, but at least the mechanisms follow the pattern you will typically see in useful decorators.
Introduction to high-level abstraction
Most of what metaclasses are used for, in my experience, is modifying the methods contained in a class once it is instantiated. Decorators do not currently let you modify class instantiation per se, but they can massage the methods that are attached to the class. This does not let you add or remove methods or class attributes dynamically during instantiation, but it does let the methods change their behavior depending on conditions in the environment at runtime. Now technically, a decorator applies when a
class statement is run, which for top-level classes is closer to “compile time” than to “runtime.” But arranging runtime determination of decorators is as simple as creating a class factory. For example:
Listing 8. Robust, but deeply nested, decorator
def argsayer(what): def whatsayer(meth): def new(self, args, **kws): print what return meth(self, args, **kws) return new return whatsayer def FooMaker(word): class Foo(object): @argsayer(word) def say(self): pass return Foo() foo1 = FooMaker('this') foo2 = FooMaker('that') print type(foo1),; foo1.say() #prints: <class '__main.Foo'> this print type(foo2),; foo2.say() #prints: <class '__main.Foo'> that
@arg_sayer() example goes through a lot of contortions to obtain a rather limited result, but it is worthwhile for the several things it illustrates:
Foo.say()method has different behaviors for different instances. In the example, the difference only amounts to a data value that could easily be varied by other means; but in principle, the decorator could have completely rewritten the method based on runtime decisions.
- The undecorated
Foo.say()method in this case is a simple placeholder, with the entire behavior determined by the decorator. However, in other cases, the decorator might combine the undecorated method behavior with some new capabilities.
- As already observed, the modification of
Foo.say()is determined strictly at runtime, via the use of the
FooMaker()class factory. Probably more typical is using decorators on top-level defined classes, which depend only on conditions available at compile-time (which are often adequate).
- The decorator is parameterized. Or rather
arg_sayer()itself is not really a decorator at all; rather, the function returned by
what_sayer(), is a decorator function that uses a closure to encapsulate its data. Parameterized decorators are common, but they wind up needed functions nested three-levels deep.
Marching into metaclass territory
As mentioned in the last section, decorators could not completely replace the metaclass hook since they only modify methods rather than add or delete methods. This is actually not quite true. A decorator, being a Python function, can do absolutely anything other Python code can. By decorating the
.__new__() method of a class, even a placeholder version of it, you can, in fact, change what methods attach to a class. I have not seen this pattern “in the wild,” but I think it has a certain explicitness, perhaps even as an improvement on the
Listing 9. A decorator to add and remove methods
def flaz(self): return 'flaz' #Silly utility method def flam(self): return 'flam' #Another silly method def changemethods(new): "Warning: Only decorate the new() method with this decorator" if new.name != 'new': return new #Return an unchanged method def new(cls, args, **kws): cls.flaz = flaz cls.flam = flam if hasattr(cls, 'say'): del cls.say return super(cls.class, cls).new(cls, args, **kws) return new class Foo(object): @changemethods def __new(): pass def say(self): print "Hi me:", self foo = Foo() print foo.flaz() #prints: flaz foo.say() #AttributeError: 'Foo' object has no attribute 'say'
In the sample
change_methods() decorator, some fixed methods are added and removed, fairly pointlessly. A more realistic case would use some patterns from the previous section. For example, a parameterized decorator could accept a data structure indicating methods to be added or removed; or perhaps some feature of the environment like a database query could make this decision. This manipulation of attached methods could also be wrapped in a function factory as before, deferring the final decision until runtime. These latter techniques might even be more versatile than
_metaclass_ assignment. For example, you might call an enhanced
change_methods() like this:
Listing 10. Enhanced changemethods()
class Foo(object): @changemethods(add=(foo, bar, baz), remove=(fliz, flam)) def __new(): pass
Changing a call model
The most typical examples you will see discussed for decorators can probably be described as making a function or method “do something extra” while it does its basic job. For example, on places like the Python Cookbook Web site (see resources on the right for a link), you might see decorators to add capabilities like tracing, logging, memorization/caching, thread locking, and output redirection. Related to these modifications — but in a slightly different spirit — are “before” and “after” modifications. One interesting possibility for before/after decoration is checking types of arguments to a function and the return value from a function. Presumably such a
type_check() decorator would raise an exception or take some corrective action if the types are not as expected.
In somewhat the same vein as before/after decorators, I got to thinking about the “elementwise” application of functions that is characteristic of the R programming language, and of NumPy
NumPy. In these languages, numeric functions generally apply to each element in a sequence of elements, but also to an individual number.
map() function, list-comprehensions, and more recently generator-comprehensions, let you do elementwise application. But these require minor workarounds to get R-like behavior: the type of sequence returned by
map() is always a list; and the call will fail if you pass it a single element rather than a sequence. For example:
Listing 11. map() call that will fail
>>> from math import sqrt >>> map(sqrt, (4, 16, 25)) [2.0, 4.0, 5.0] >>> map(sqrt, 144) TypeError: argument 2 to map() must support iteration
It is not hard to create a decorator that “enhances” a regular numerical function:
Listing 12. Converting a function to an elementwise function
def elementwise(fn): def newfn(arg): if hasattr(arg,'getitem'): #is a Sequence return type(arg)(map(fn, arg)) else: return fn(arg) return newfn @elementwise def compute(x): return x**3 ‑ 1 print compute(5) #prints: 124 print compute([1,2,3]) #prints: 0, 7, 26print compute((1,2,3)) #prints: (0, 7, 26)
It is not hard, of course, to simply write a
compute() function that builds in the different return types; the decorator only takes a few lines, after all. But in what might be described as a nod to aspect-oriented programming, this example lets us separate concerns that operate at different levels. We might write a variety of numeric computation functions and wish to turn them each into elementwise call models without thinking about the details of argument type testing and return value type coercion.
elementwise() decorator works equally well for any function that might operate on either an individual thing or on a sequence of things (while preserving the sequence type). As an exercise, you might try working out how to allow the same decorated call to also accept and return iterators (hint: it is easy if you just iterate a completed elementwise computation, it is less straightforward to do lazily if and only if an iterator object is passed in).
Most good decorators you will encounter employ much of this paradigm of combining orthogonal concerns. Traditional object-oriented programming, especially in languages like Python that allow multiple inheritance, attempt to modularize concerns with an inheritance hierarchy. However, merely getting some methods from one ancestor, and other methods from other ancestors requires a conception in which concerns are much more separated than they are in aspect-oriented thinking. Taking best advantage of generators involves thinking about issues somewhat differently than does mix-and-matching methods: each method might be made to work in different ways depending on concerns that are outside of the “heart” of the method itself.
Decorating your decorators
Before I end this installment, I want to point you to a really wonderful Python module called decorator
decorator written by my sometimes co-author Michele Simionato. This module makes developing decorators much nicer. Having a certain reflexive elegance, the main component of the decorator
decorator module is a decorator called
decorator(). A function decorated with
@decorator can be written in a simpler manner than one without it (see resources on the right for related reading).
Michele has produced quite good documentation of his module, so I will not attempt to reproduce it; but I would like to point out the basic problems it solves. There are two main benefits to the decorator
decorator module. On the one hand, it lets you write decorators with fewer levels of nesting than you would otherwise need (“flat is better than nested”); but more interesting possibly is the fact that it makes decorated functions actually match their undecorated version in metadata, which my examples have not. For example, recalling the somewhat silly “tracing” decorator
addspam() that I used above:
Listing 13. How a naive decorator corrupts metadata
>>> def useful(a, b): return a2 + b2 >>> useful.name 'useful' >>> from inspect import getargspec >>> getargspec(useful) (['a', 'b'], None, None, None) >>> @addspam ... def useful(a, b): return a2 + b2 >>> useful.name 'new' >>> getargspec(useful) (, 'args', None, None)
While the decorated function does its enhanced job, a closer look shows it is not quite right, especially to code-analysis tools or IDEs that care about these sorts of details. Using decorator
decorator, we can improve matters:
Listing 14. Smarter use of decorator
>>> from decorator import decorator >>> @decorator ... def addspam(f, args, **kws): ... print "spam, spam, spam" ... return f(args, kws) >>> @addspam ... def useful(a, b): return a2 + b**2 >>> useful.name 'useful' >>> getargspec(useful) (['a', 'b'], None, None, None)
This looks better both to write the decorator in the first place, and in its behavior-preserving metadata. Of course, reading the full incantations that Michele used to develop the module brings you back into brain-melting territory; we can leave that for cosmologists like Dr. Simionato.