Decorators make magic easy
A look at the newest Python facility for metaprogramming
This content is part # of # in the series: Charming Python
This content is part of the series:Charming Python
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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 Michele Simionato and I pointed out in earlier Charming Python installments, 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 a**2 + b**2 ... 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 + b**2 ... >>> 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 a**2 + b**2 ... >>> 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
Listing 8. Robust, but deeply nested, decorator
def arg_sayer(what): def what_sayer(meth): def new(self, *args, **kws): print what return meth(self, *args, **kws) return new return what_sayer def FooMaker(word): class Foo(object): @arg_sayer(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
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 change_methods(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): @change_methods 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
_metaclass_ assignment. For example, you might call an
change_methods() like this:
Listing 10. Enhanced change_methods()
class Foo(object): @change_methods(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 Related topics 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
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. In these languages, numeric functions
generally apply to each element in a sequence of elements, but also to an
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, 26] print 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
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 module is a decorator called
decorator(). A function decorated with
be written in a simpler manner than one without it (see Related topics 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 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
addspam() that I used above:
Listing 13. How a naive decorator corrupts metadata
>>> def useful(a, b): return a**2 + b**2 >>> useful.__name__ 'useful' >>> from inspect import getargspec >>> getargspec(useful) (['a', 'b'], None, None, None) >>> @addspam ... def useful(a, b): return a**2 + b**2 >>> 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, 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 a**2 + 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.
- The ASPN online Python Cookbook is a good source for examples of decorator usage, as well as other esoteric Python examples.
- Michele Simionato covers the
decoratormodule for Python 2.4, as well as a few changes pertaining to 2.5, in his online Python documentation.
- For background reading, check out "Metaclass programming in Python", a two-part series on developerWorks by David and Michele.
- Also see the introduction to "Statistical programming with the R programming language", a three-part series on developerWorks by David with Brad Huntting.
- Wikipedia's article on aspect-oriented programming is a good place to start if you are unfamiliar with this concept.
- With IBM trial software, available for download directly from developerWorks, build your next development project on Linux.