Introduction

Python decorators are an interesting example of why syntactic sugar matters. In principle, their introduction in Python 2.4 changed nothing, since they do not provide any new functionality which was not already present in the language. In practice, their introduction has significantly changed the way we structure our programs in Python. I believe the change is for the best, and that decorators are a great idea since:

• decorators help reducing boilerplate code;
• decorators help separation of concerns;
• decorators enhance readability and maintenability;
• decorators are explicit.

Still, as of now, writing custom decorators correctly requires some experience and it is not as easy as it could be. For instance, typical implementations of decorators involve nested functions, and we all know that flat is better than nested.

The aim of the decorator module it to simplify the usage of decorators for the average programmer, and to popularize decorators by showing various non-trivial examples. Of course, as all techniques, decorators can be abused (I have seen that) and you should not try to solve every problem with a decorator, just because you can.

You may find the source code for all the examples discussed here in the documentation.py file, which contains this documentation in the form of doctests.

Definitions

Technically speaking, any Python object which can be called with one argument can be used as a decorator. However, this definition is somewhat too large to be really useful. It is more convenient to split the generic class of decorators in two subclasses:

• signature-preserving decorators, i.e. callable objects taking a function as input and returning a function with the same signature as output;
• signature-changing decorators, i.e. decorators that change the signature of their input function, or decorators returning non-callable objects.

Signature-changing decorators have their use: for instance the builtin classes staticmethod and classmethod are in this group, since they take functions and return descriptor objects which are not functions, nor callables.

However, signature-preserving decorators are more common and easier to reason about; in particular signature-preserving decorators can be composed together whereas other decorators in general cannot.

Writing signature-preserving decorators from scratch is not that obvious, especially if one wants to define proper decorators that can accept functions with any signature. A simple example will clarify the issue.

Statement of the problem

A very common use case for decorators is the memoization of functions. A memoize decorator works by caching the result of the function call in a dictionary, so that the next time the function is called with the same input parameters the result is retrieved from the cache and not recomputed. There are many implementations of memoize in http://www.python.org/moin/PythonDecoratorLibrary, but they do not preserve the signature. A simple implementation could be the following (notice that in general it is impossible to memoize correctly something that depends on non-hashable arguments):

def memoize_uw(func):
    func.cache = {}
    def memoize(*args, **kw):
        if kw: # frozenset is used to ensure hashability
            key = args, frozenset(kw.iteritems())
        else:
            key = args
        cache = func.cache
        if key in cache:
            return cache[key]
        else:
            cache[key] = result = func(*args, **kw)
            return result
    return functools.update_wrapper(memoize, func)

Here we used the functools.update_wrapper utility, which has been added in Python 2.5 expressly to simplify the definition of decorators (in older versions of Python you need to copy the function attributes __name__, __doc__, __module__ and __dict__ from the original function to the decorated function by hand).

The implementation above works in the sense that the decorator can accept functions with generic signatures; unfortunately this implementation does not define a signature-preserving decorator, since in general memoize_uw returns a function with a different signature from the original function.

Consider for instance the following case:

>>> @memoize_uw
... def f1(x):
...     time.sleep(1) # simulate some long computation
...     return x

Here the original function takes a single argument named x, but the decorated function takes any number of arguments and keyword arguments:

>>> from inspect import getargspec
>>> print(getargspec(f1))
ArgSpec(args=[], varargs='args', keywords='kw', defaults=None)

This means that introspection tools such as pydoc will give wrong informations about the signature of f1. This is pretty bad: pydoc will tell you that the function accepts a generic signature *args, **kw, but when you try to call the function with more than an argument, you will get an error:

>>> f1(0, 1)
Traceback (most recent call last):
   ...
TypeError: f1() takes exactly 1 positional argument (2 given)

The solution

The solution is to provide a generic factory of generators, which hides the complexity of making signature-preserving decorators from the application programmer. The decorator function in the decorator module is such a factory:

>>> from decorator import decorator

decorator takes two arguments, a caller function describing the functionality of the decorator and a function to be decorated; it returns the decorated function. The caller function must have signature (f, *args, **kw) and it must call the original function f with arguments args and kw, implementing the wanted capability, i.e. memoization in this case:

def _memoize(func, *args, **kw):
    if kw: # frozenset is used to ensure hashability
        key = args, frozenset(kw.iteritems())
    else:
        key = args
    cache = func.cache # attributed added by memoize
    if key in cache:
        return cache[key]
    else:
        cache[key] = result = func(*args, **kw)
        return result

At this point you can define your decorator as follows:

def memoize(f):
    f.cache = {}
    return decorator(_memoize, f)

The difference with respect to the memoize_uw approach, which is based on nested functions, is that the decorator module forces you to lift the inner function at the outer level (flat is better than nested). Moreover, you are forced to pass explicitly the function you want to decorate to the caller function.

Here is a test of usage:

>>> @memoize
... def heavy_computation():
...     time.sleep(2)
...     return "done"

>>> print(heavy_computation()) # the first time it will take 2 seconds
done

>>> print(heavy_computation()) # the second time it will be instantaneous
done

The signature of heavy_computation is the one you would expect:

>>> print(getargspec(heavy_computation))
ArgSpec(args=[], varargs=None, keywords=None, defaults=None)

A trace decorator

As an additional example, here is how you can define a trivial trace decorator, which prints a message everytime the traced function is called:

def _trace(f, *args, **kw):
    kwstr = ', '.join('%r: %r' % (k, kw[k]) for k in sorted(kw))
    print("calling %s with args %s, {%s}" % (f.__name__, args, kwstr))
    return f(*args, **kw)

def trace(f):
    return decorator(_trace, f)

Here is an example of usage:

>>> @trace
... def f1(x):
...     pass

It is immediate to verify that f1 works

>>> f1(0)
calling f1 with args (0,), {}

and it that it has the correct signature:

>>> print(getargspec(f1))
ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)

The same decorator works with functions of any signature:

>>> @trace
... def f(x, y=1, z=2, *args, **kw):
...     pass

>>> f(0, 3)
calling f with args (0, 3, 2), {}

>>> print(getargspec(f))
ArgSpec(args=['x', 'y', 'z'], varargs='args', keywords='kw', defaults=(1, 2))

Function annotations

Python 3 introduced the concept of function annotations,i.e. the ability to annotate the signature of a function with additional information, stored in a dictionary named __annotations__. The decorator module, starting from release 3.3, is able to understand and to preserve the annotations. Here is an example:

>>> @trace
... def f(x: 'the first argument', y: 'default argument'=1, z=2,
...       *args: 'varargs', **kw: 'kwargs'):
...     pass

In order to introspect functions with annotations, one needs the utility inspect.getfullargspec, new in Python 3:

>>> from inspect import getfullargspec
>>> argspec = getfullargspec(f)
>>> argspec.args
['x', 'y', 'z']
>>> argspec.varargs
'args'
>>> argspec.varkw
'kw'
>>> argspec.defaults
(1, 2)
>>> argspec.kwonlyargs
[]
>>> argspec.kwonlydefaults

You can also check that the __annotations__ dictionary is preserved:

>>> f.__annotations__ == f.__wrapped__.__annotations__
True

Depending on the version of the decorator module, the two dictionaries can be the same object or not: you cannot rely on object identity, but you can rely on the content being the same.

decorator is a decorator

It may be annoying to write a caller function (like the _trace function above) and then a trivial wrapper (def trace(f): return decorator(_trace, f)) every time. For this reason, the decorator module provides an easy shortcut to convert the caller function into a signature-preserving decorator: you can just call decorator with a single argument. In our example you can just write trace = decorator(_trace). The decorator function can also be used as a signature-changing decorator, just as classmethod and staticmethod. However, classmethod and staticmethod return generic objects which are not callable, while decorator returns signature-preserving decorators, i.e. functions of a single argument. For instance, you can write directly

>>> @decorator
... def trace(f, *args, **kw):
...     kwstr = ', '.join('%r: %r' % (k, kw[k]) for k in sorted(kw))
...     print("calling %s with args %s, {%s}" % (f.__name__, args, kwstr))
...     return f(*args, **kw)

and now trace will be a decorator. Actually trace is a partial object which can be used as a decorator:

>>> trace
<function trace at 0x...>

Here is an example of usage:

>>> @trace
... def func(): pass

>>> func()
calling func with args (), {}

If you are using an old Python version (Python 2.4) the decorator module provides a poor man replacement for functools.partial.

blocking

Sometimes one has to deal with blocking resources, such as stdin, and sometimes it is best to have back a "busy" message than to block everything. This behavior can be implemented with a suitable family of decorators, where the parameter is the busy message:

def blocking(not_avail):
    def blocking(f, *args, **kw):
        if not hasattr(f, "thread"): # no thread running
            def set_result(): f.result = f(*args, **kw)
            f.thread = threading.Thread(None, set_result)
            f.thread.start()
            return not_avail
        elif f.thread.isAlive():
            return not_avail
        else: # the thread is ended, return the stored result
            del f.thread
            return f.result
    return decorator(blocking)

Functions decorated with blocking will return a busy message if the resource is unavailable, and the intended result if the resource is available. For instance:

>>> @blocking("Please wait ...")
... def read_data():
...     time.sleep(3) # simulate a blocking resource
...     return "some data"

>>> print(read_data()) # data is not available yet
Please wait ...

>>> time.sleep(1)
>>> print(read_data()) # data is not available yet
Please wait ...

>>> time.sleep(1)
>>> print(read_data()) # data is not available yet
Please wait ...

>>> time.sleep(1.1) # after 3.1 seconds, data is available
>>> print(read_data())
some data

async

We have just seen an examples of a simple decorator factory, implemented as a function returning a decorator. For more complex situations, it is more convenient to implement decorator factories as classes returning callable objects that can be converted into decorators.

As an example, here will I show a decorator which is able to convert a blocking function into an asynchronous function. The function, when called, is executed in a separate thread. Moreover, it is possible to set three callbacks on_success, on_failure and on_closing, to specify how to manage the function call (of course the code here is just an example, it is not a recommended way of doing multi-threaded programming). The implementation is the following:

def on_success(result): # default implementation
    "Called on the result of the function"
    return result

def on_failure(exc_info): # default implementation
    "Called if the function fails"
    pass

def on_closing(): # default implementation
    "Called at the end, both in case of success and failure"
    pass

class Async(object):
    """
    A decorator converting blocking functions into asynchronous
    functions, by using threads or processes. Examples:

    async_with_threads =  Async(threading.Thread)
    async_with_processes =  Async(multiprocessing.Process)
    """

    def __init__(self, threadfactory, on_success=on_success,
                 on_failure=on_failure, on_closing=on_closing):
        self.threadfactory = threadfactory
        self.on_success = on_success
        self.on_failure = on_failure
        self.on_closing = on_closing

    def __call__(self, func, *args, **kw):
        try:
            counter = func.counter
        except AttributeError: # instantiate the counter at the first call
            counter = func.counter = itertools.count(1)
        name = '%s-%s' % (func.__name__, next(counter))
        def func_wrapper():
            try:
                result = func(*args, **kw)
            except:
                self.on_failure(sys.exc_info())
            else:
                return self.on_success(result)
            finally:
                self.on_closing()
        thread = self.threadfactory(None, func_wrapper, name)
        thread.start()
        return thread

The decorated function returns the current execution thread, which can be stored and checked later, for instance to verify that the thread .isAlive().

Here is an example of usage. Suppose one wants to write some data to an external resource which can be accessed by a single user at once (for instance a printer). Then the access to the writing function must be locked. Here is a minimalistic example:

>>> async = decorator(Async(threading.Thread))

>>> datalist = [] # for simplicity the written data are stored into a list.

>>> @async
... def write(data):
...     # append data to the datalist by locking
...     with threading.Lock():
...         time.sleep(1) # emulate some long running operation
...         datalist.append(data)
...     # other operations not requiring a lock here

Each call to write will create a new writer thread, but there will be no synchronization problems since write is locked.

>>> write("data1")
<Thread(write-1, started...)>

>>> time.sleep(.1) # wait a bit, so we are sure data2 is written after data1

>>> write("data2")
<Thread(write-2, started...)>

>>> time.sleep(2) # wait for the writers to complete

>>> print(datalist)
['data1', 'data2']

contextmanager

For a long time Python had in its standard library a contextmanager decorator, able to convert generator functions into _GeneratorContextManager factories. For instance if you write

>>> from contextlib import contextmanager
>>> @contextmanager
... def before_after(before, after):
...     print(before)
...     yield
...     print(after)

then before_after is a factory function returning _GeneratorContextManager objects which can be used with the with statement:

>>> ba = before_after('BEFORE', 'AFTER')
>>> type(ba)
<class 'contextlib._GeneratorContextManager'>
>>> with ba:
...     print('hello')
BEFORE
hello
AFTER

Basically, it is as if the content of the with block was executed in the place of the yield expression in the generator function. In Python 3.2 _GeneratorContextManager objects were enhanced with a __call__ method, so that they can be used as decorators as in this example:

>>> @ba
... def hello():
...     print('hello')
...
>>> hello()
BEFORE
hello
AFTER

The ba decorator is basically inserting a with ba: block inside the function. However there two issues: the first is that _GeneratorContextManager objects are callable only in Python 3.2, so the previous example will break in older versions of Python; the second is that _GeneratorContextManager objects do not preserve the signature of the decorated functions: the decorated hello function here will have a generic signature hello(*args, **kwargs) but will break when called with more than zero arguments. For such reasons the decorator module, starting with release 3.4, offers a decorator.contextmanager decorator that solves both problems and works even in Python 2.5. The usage is the same and factories decorated with decorator.contextmanager will returns instances of ContextManager, a subclass of contextlib._GeneratorContextManager with a __call__ method acting as a signature-preserving decorator.

The FunctionMaker class

You may wonder about how the functionality of the decorator module is implemented. The basic building block is a FunctionMaker class which is able to generate on the fly functions with a given name and signature from a function template passed as a string. Generally speaking, you should not need to resort to FunctionMaker when writing ordinary decorators, but it is handy in some circumstances. You will see an example shortly, in the implementation of a cool decorator utility (decorator_apply).

FunctionMaker provides a .create classmethod which takes as input the name, signature, and body of the function we want to generate as well as the execution environment were the function is generated by exec. Here is an example:

>>> def f(*args, **kw): # a function with a generic signature
...     print(args, kw)

>>> f1 = FunctionMaker.create('f1(a, b)', 'f(a, b)', dict(f=f))
>>> f1(1,2)
(1, 2) {}

It is important to notice that the function body is interpolated before being executed, so be careful with the % sign!

FunctionMaker.create also accepts keyword arguments and such arguments are attached to the resulting function. This is useful if you want to set some function attributes, for instance the docstring __doc__.

For debugging/introspection purposes it may be useful to see the source code of the generated function; to do that, just pass the flag addsource=True and a __source__ attribute will be added to the generated function:

>>> f1 = FunctionMaker.create(
...     'f1(a, b)', 'f(a, b)', dict(f=f), addsource=True)
>>> print(f1.__source__)
def f1(a, b):
    f(a, b)
<BLANKLINE>

FunctionMaker.create can take as first argument a string, as in the examples before, or a function. This is the most common usage, since typically you want to decorate a pre-existing function. A framework author may want to use directly FunctionMaker.create instead of decorator, since it gives you direct access to the body of the generated function. For instance, suppose you want to instrument the __init__ methods of a set of classes, by preserving their signature (such use case is not made up; this is done in SQAlchemy and in other frameworks). When the first argument of FunctionMaker.create is a function, a FunctionMaker object is instantiated internally, with attributes args, varargs, keywords and defaults which are the the return values of the standard library function inspect.getargspec. For each argument in the args (which is a list of strings containing the names of the mandatory arguments) an attribute arg0, arg1, ..., argN is also generated. Finally, there is a signature attribute, a string with the signature of the original function.

Notice that while I do not have plans to change or remove the functionality provided in the FunctionMaker class, I do not guarantee that it will stay unchanged forever. For instance, right now I am using the traditional string interpolation syntax for function templates, but Python 2.6 and Python 3.0 provide a newer interpolation syntax and I may use the new syntax in the future. On the other hand, the functionality provided by decorator has been there from version 0.1 and it is guaranteed to stay there forever.

Getting the source code

Internally FunctionMaker.create uses exec to generate the decorated function. Therefore inspect.getsource will not work for decorated functions. That means that the usual '??' trick in IPython will give you the (right on the spot) message Dynamically generated function. No source code available. In the past I have considered this acceptable, since inspect.getsource does not really work even with regular decorators. In that case inspect.getsource gives you the wrapper source code which is probably not what you want:

def identity_dec(func):
    def wrapper(*args, **kw):
        return func(*args, **kw)
    return wrapper

@identity_dec
def example(): pass

>>> print(inspect.getsource(example))
    def wrapper(*args, **kw):
        return func(*args, **kw)
<BLANKLINE>

(see bug report 1764286 for an explanation of what is happening). Unfortunately the bug is still there, even in Python 2.7 and 3.1. There is however a workaround. The decorator module adds an attribute .__wrapped__ to the decorated function, containing a reference to the original function. The easy way to get the source code is to call inspect.getsource on the undecorated function:

>>> print(inspect.getsource(factorial.__wrapped__))
@tail_recursive
def factorial(n, acc=1):
    "The good old factorial"
    if n == 0: return acc
    return factorial(n-1, n*acc)
<BLANKLINE>

Dealing with third party decorators

Sometimes you find on the net some cool decorator that you would like to include in your code. However, more often than not the cool decorator is not signature-preserving. Therefore you may want an easy way to upgrade third party decorators to signature-preserving decorators without having to rewrite them in terms of decorator. You can use a FunctionMaker to implement that functionality as follows:

def decorator_apply(dec, func):
    """
    Decorate a function by preserving the signature even if dec
    is not a signature-preserving decorator.
    """
    return FunctionMaker.create(
        func, 'return decorated(%(signature)s)',
        dict(decorated=dec(func)), __wrapped__=func)

decorator_apply sets the attribute .__wrapped__ of the generated function to the original function, so that you can get the right source code.

Notice that I am not providing this functionality in the decorator module directly since I think it is best to rewrite the decorator rather than adding an additional level of indirection. However, practicality beats purity, so you can add decorator_apply to your toolbox and use it if you need to.

In order to give an example of usage of decorator_apply, I will show a pretty slick decorator that converts a tail-recursive function in an iterative function. I have shamelessly stolen the basic idea from Kay Schluehr's recipe in the Python Cookbook, http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/496691.

class TailRecursive(object):
    """
    tail_recursive decorator based on Kay Schluehr's recipe
    http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/496691
    with improvements by me and George Sakkis.
    """

    def __init__(self, func):
        self.func = func
        self.firstcall = True
        self.CONTINUE = object() # sentinel

    def __call__(self, *args, **kwd):
        CONTINUE = self.CONTINUE
        if self.firstcall:
            func = self.func
            self.firstcall = False
            try:
                while True:
                    result = func(*args, **kwd)
                    if result is CONTINUE: # update arguments
                        args, kwd = self.argskwd
                    else: # last call
                        return result
            finally:
                self.firstcall = True
        else: # return the arguments of the tail call
            self.argskwd = args, kwd
            return CONTINUE

Here the decorator is implemented as a class returning callable objects.

def tail_recursive(func):
    return decorator_apply(TailRecursive, func)

Here is how you apply the upgraded decorator to the good old factorial:

@tail_recursive
def factorial(n, acc=1):
    "The good old factorial"
    if n == 0: return acc
    return factorial(n-1, n*acc)

>>> print(factorial(4))
24

This decorator is pretty impressive, and should give you some food for your mind ;) Notice that there is no recursion limit now, and you can easily compute factorial(1001) or larger without filling the stack frame. Notice also that the decorator will not work on functions which are not tail recursive, such as the following

def fact(n): # this is not tail-recursive
    if n == 0: return 1
    return n * fact(n-1)

(reminder: a function is tail recursive if it either returns a value without making a recursive call, or returns directly the result of a recursive call).

Caveats and limitations

The first thing you should be aware of, it the fact that decorators have a performance penalty. The worse case is shown by the following example:

$ cat performance.sh
python3 -m timeit -s "
from decorator import decorator

@decorator
def do_nothing(func, *args, **kw):
    return func(*args, **kw)

@do_nothing
def f():
    pass
" "f()"

python3 -m timeit -s "
def f():
    pass
" "f()"

On my MacBook, using the do_nothing decorator instead of the plain function is more than three times slower:

$ bash performance.sh
1000000 loops, best of 3: 0.669 usec per loop
1000000 loops, best of 3: 0.181 usec per loop

It should be noted that a real life function would probably do something more useful than f here, and therefore in real life the performance penalty could be completely negligible. As always, the only way to know if there is a penalty in your specific use case is to measure it.

You should be aware that decorators will make your tracebacks longer and more difficult to understand. Consider this example:

>>> @trace
... def f():
...     1/0

Calling f() will give you a ZeroDivisionError, but since the function is decorated the traceback will be longer:

>>> f()
Traceback (most recent call last):
  ...
     File "<string>", line 2, in f
     File "<doctest __main__[22]>", line 4, in trace
       return f(*args, **kw)
     File "<doctest __main__[51]>", line 3, in f
       1/0
ZeroDivisionError: ...

You see here the inner call to the decorator trace, which calls f(*args, **kw), and a reference to File "<string>", line 2, in f. This latter reference is due to the fact that internally the decorator module uses exec to generate the decorated function. Notice that exec is not responsibile for the performance penalty, since is the called only once at function decoration time, and not every time the decorated function is called.

At present, there is no clean way to avoid exec. A clean solution would require to change the CPython implementation of functions and add an hook to make it possible to change their signature directly. That could happen in future versions of Python (see PEP 362) and then the decorator module would become obsolete. However, at present, even in Python 3.2 it is impossible to change the function signature directly, therefore the decorator module is still useful. Actually, this is one of the main reasons why I keep maintaining the module and releasing new versions.

In the present implementation, decorators generated by decorator can only be used on user-defined Python functions or methods, not on generic callable objects, nor on built-in functions, due to limitations of the inspect module in the standard library.

There is a restriction on the names of the arguments: for instance, if try to call an argument _call_ or _func_ you will get a NameError:

>>> @trace
... def f(_func_): print(f)
...
Traceback (most recent call last):
  ...
NameError: _func_ is overridden in
def f(_func_):
    return _call_(_func_, _func_)

Finally, the implementation is such that the decorated function contains a copy of the original function dictionary (vars(decorated_f) is not vars(f)):

>>> def f(): pass # the original function
>>> f.attr1 = "something" # setting an attribute
>>> f.attr2 = "something else" # setting another attribute

>>> traced_f = trace(f) # the decorated function

>>> traced_f.attr1
'something'
>>> traced_f.attr2 = "something different" # setting attr
>>> f.attr2 # the original attribute did not change
'something else'

Compatibility notes

Version 3.3 is the first version of the decorator module to fully support Python 3, including function annotations. Version 3.2 was the first version to support Python 3 via the 2to3 conversion tool invoked in the build process by the distribute project, the Python 3-compatible replacement of easy_install. The hard work (for me) has been converting the documentation and the doctests. This has been possible only after that docutils and pygments have been ported to Python 3.

Version 3 of the decorator module do not contain any backward incompatible change, apart from the removal of the functions get_info and new_wrapper, which have been deprecated for years. get_info has been removed since it was little used and since it had to be changed anyway to work with Python 3.0; new_wrapper has been removed since it was useless: its major use case (converting signature changing decorators to signature preserving decorators) has been subsumed by decorator_apply, whereas the other use case can be managed with the FunctionMaker.

There are a few changes in the documentation: I removed the decorator_factory example, which was confusing some of my users, and I removed the part about exotic signatures in the Python 3 documentation, since Python 3 does not support them.

Finally decorator cannot be used as a class decorator and the functionality introduced in version 2.3 has been removed. That means that in order to define decorator factories with classes you need to define the __call__ method explicitly (no magic anymore). All these changes should not cause any trouble, since they were all rarely used features. Should you have any trouble, you can always downgrade to the 2.3 version.

The examples shown here have been tested with Python 2.6. Python 2.4 is also supported - of course the examples requiring the with statement will not work there. Python 2.5 works fine, but if you run the examples in the interactive interpreter you will notice a few differences since getargspec returns an ArgSpec namedtuple instead of a regular tuple. That means that running the file documentation.py under Python 2.5 will print a few errors, but they are not serious.

LICENCE

Copyright (c) 2005-2012, Michele Simionato All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in bytecode form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

If you use this software and you are happy with it, consider sending me a note, just to gratify my ego. On the other hand, if you use this software and you are unhappy with it, send me a patch!