This page is meant to be a central repository of decorator code pieces, whether useful or not <wink>. It is NOT a page to discuss decorator syntax!

Feel free to add your suggestions. Please make sure example code conforms with PEP 8.

Creating Well-Behaved Decorators / "Decorator decorator"

Note: This is only one recipe. Others include inheritance from a standard decorator (link?), the functools @wraps decorator, and a factory function such as Michele Simionato's decorator module which even preserves signature information.

   1 def simple_decorator(decorator):
   2     '''This decorator can be used to turn simple functions
   3     into well-behaved decorators, so long as the decorators
   4     are fairly simple. If a decorator expects a function and
   5     returns a function (no descriptors), and if it doesn't
   6     modify function attributes or docstring, then it is
   7     eligible to use this. Simply apply @simple_decorator to
   8     your decorator and it will automatically preserve the
   9     docstring and function attributes of functions to which
  10     it is applied.'''
  11     def new_decorator(f):
  12         g = decorator(f)
  13         g.__name__ = f.__name__
  14         g.__doc__ = f.__doc__
  15         g.__dict__.update(f.__dict__)
  16         return g
  17     # Now a few lines needed to make simple_decorator itself
  18     # be a well-behaved decorator.
  19     new_decorator.__name__ = decorator.__name__
  20     new_decorator.__doc__ = decorator.__doc__
  21     new_decorator.__dict__.update(decorator.__dict__)
  22     return new_decorator
  23 
  24 #
  25 # Sample Use:
  26 #
  27 @simple_decorator
  28 def my_simple_logging_decorator(func):
  29     def you_will_never_see_this_name(*args, **kwargs):
  30         print 'calling {}'.format(func.__name__)
  31         return func(*args, **kwargs)
  32     return you_will_never_see_this_name
  33 
  34 @my_simple_logging_decorator
  35 def double(x):
  36     'Doubles a number.'
  37     return 2 * x
  38 
  39 assert double.__name__ == 'double'
  40 assert double.__doc__ == 'Doubles a number.'
  41 print double(155)

Property Definition

These decorators provide a readable way to define properties:

   1 import sys
   2 
   3 def propget(func):
   4     locals = sys._getframe(1).f_locals
   5     name = func.__name__
   6     prop = locals.get(name)
   7     if not isinstance(prop, property):
   8         prop = property(func, doc=func.__doc__)
   9     else:
  10         doc = prop.__doc__ or func.__doc__
  11         prop = property(func, prop.fset, prop.fdel, doc)
  12     return prop
  13 
  14 def propset(func):
  15     locals = sys._getframe(1).f_locals
  16     name = func.__name__
  17     prop = locals.get(name)
  18     if not isinstance(prop, property):
  19         prop = property(None, func, doc=func.__doc__)
  20     else:
  21         doc = prop.__doc__ or func.__doc__
  22         prop = property(prop.fget, func, prop.fdel, doc)
  23     return prop
  24 
  25 def propdel(func):
  26     locals = sys._getframe(1).f_locals
  27     name = func.__name__
  28     prop = locals.get(name)
  29     if not isinstance(prop, property):
  30         prop = property(None, None, func, doc=func.__doc__)
  31     else:
  32         prop = property(prop.fget, prop.fset, func, prop.__doc__)
  33     return prop
  34 
  35 # These can be used like this:
  36 
  37 class Example(object):
  38 
  39     @propget
  40     def myattr(self):
  41         return self._half * 2
  42 
  43     @propset
  44     def myattr(self, value):
  45         self._half = value / 2
  46 
  47     @propdel
  48     def myattr(self):
  49         del self._half

Here's a way that doesn't require any new decorators:

   1 class Example(object):
   2     @apply
   3     def myattr():
   4         doc = '''This is the doc string.'''
   5 
   6         def fget(self):
   7             return self._half * 2
   8 
   9         def fset(self, value):
  10             self._half = value / 2
  11 
  12         def fdel(self):
  13             del self._half
  14 
  15         return property(**locals())

Yet another property decorator:

   1 def property(function):
   2     keys = 'fget', 'fset', 'fdel'
   3     func_locals = {'doc':function.__doc__}
   4     def probe_func(frame, event, arg):
   5         if event == 'return':
   6             locals = frame.f_locals
   7             func_locals.update(dict((k, locals.get(k)) for k in keys))
   8             sys.settrace(None)
   9         return probe_func
  10     sys.settrace(probe_func)
  11     function()
  12     return property(**func_locals)
  13 
  14 #====== Example =======================================================
  15 
  16 from math import radians, degrees, pi
  17 
  18 class Angle(object):
  19     def __init__(self, rad):
  20         self._rad = rad
  21 
  22     @property
  23     def rad():
  24         '''The angle in radians'''
  25         def fget(self):
  26             return self._rad
  27         def fset(self, angle):
  28             if isinstance(angle, Angle):
  29                 angle = angle.rad
  30             self._rad = float(angle)
  31 
  32     @property
  33     def deg():
  34         '''The angle in degrees'''
  35         def fget(self):
  36             return degrees(self._rad)
  37         def fset(self, angle):
  38             if isinstance(angle, Angle):
  39                 angle = angle.deg
  40             self._rad = radians(angle)

Memoize

Here's a memoizing class.

   1 class memoized(object):
   2    '''Decorator. Caches a function's return value each time it is called.
   3    If called later with the same arguments, the cached value is returned 
   4    (not reevaluated).
   5    '''
   6    def __init__(self, func):
   7       self.func = func
   8       self.cache = {}
   9    def __call__(self, *args):
  10       try:
  11          return self.cache[args]
  12       except KeyError:
  13          value = self.func(*args)
  14          self.cache[args] = value
  15          return value
  16       except TypeError:
  17          # uncachable -- for instance, passing a list as an argument.
  18          # Better to not cache than to blow up entirely.
  19          return self.func(*args)
  20    def __repr__(self):
  21       '''Return the function's docstring.'''
  22       return self.func.__doc__
  23    def __get__(self, obj, objtype):
  24       '''Support instance methods.'''
  25       return functools.partial(self.__call__, obj)
  26 
  27 @memoized
  28 def fibonacci(n):
  29    "Return the nth fibonacci number."
  30    if n in (0, 1):
  31       return n
  32    return fibonacci(n-1) + fibonacci(n-2)
  33 
  34 print fibonacci(12)

Cached Properties

   1 #
   2 # © 2011 Christopher Arndt, MIT License
   3 #
   4 
   5 import time
   6 
   7 class cached_property(object):
   8     '''Decorator for read-only properties evaluated only once within TTL period.
   9 
  10     It can be used to created a cached property like this::
  11 
  12         import random
  13 
  14         # the class containing the property must be a new-style class
  15         class MyClass(object):
  16             # create property whose value is cached for ten minutes
  17             @cached_property(ttl=600)
  18             def randint(self):
  19                 # will only be evaluated every 10 min. at maximum.
  20                 return random.randint(0, 100)
  21 
  22     The value is cached  in the '_cache' attribute of the object instance that
  23     has the property getter method wrapped by this decorator. The '_cache'
  24     attribute value is a dictionary which has a key for every property of the
  25     object which is wrapped by this decorator. Each entry in the cache is
  26     created only when the property is accessed for the first time and is a
  27     two-element tuple with the last computed property value and the last time
  28     it was updated in seconds since the epoch.
  29 
  30     The default time-to-live (TTL) is 300 seconds (5 minutes). Set the TTL to
  31     zero for the cached value to never expire.
  32 
  33     To expire a cached property value manually just do::
  34     
  35         del instance._cache[<property name>]
  36 
  37     '''
  38     def __init__(self, ttl=300):
  39         self.ttl = ttl
  40 
  41     def __call__(self, fget, doc=None):
  42         self.fget = fget
  43         self.__doc__ = doc or fget.__doc__
  44         self.__name__ = fget.__name__
  45         self.__module__ = fget.__module__
  46         return self
  47 
  48     def __get__(self, inst, owner):
  49         now = time.time()
  50         try:
  51             value, last_update = inst._cache[self.__name__]
  52             if self.ttl > 0 and now - last_update > self.ttl:
  53                 raise AttributeError
  54         except (KeyError, AttributeError):
  55             value = self.fget(inst)
  56             try:
  57                 cache = inst._cache
  58             except AttributeError:
  59                 cache = inst._cache = {}
  60             cache[self.__name__] = (value, now)
  61         return value

Retry

Call a function which returns True/False to indicate success or failure. On failure, wait, and try the function again. On repeated failures, wait longer between each successive attempt. If the decorator runs out of attempts, then it gives up and returns False, but you could just as easily raise some exception.

   1 import time
   2 import math
   3 
   4 # Retry decorator with exponential backoff
   5 def retry(tries, delay=3, backoff=2):
   6   '''Retries a function or method until it returns True.
   7   
   8   delay sets the initial delay in seconds, and backoff sets the factor by which
   9   the delay should lengthen after each failure. backoff must be greater than 1,
  10   or else it isn't really a backoff. tries must be at least 0, and delay
  11   greater than 0.'''
  12 
  13   if backoff <= 1:
  14     raise ValueError("backoff must be greater than 1")
  15 
  16   tries = math.floor(tries)
  17   if tries < 0:
  18     raise ValueError("tries must be 0 or greater")
  19 
  20   if delay <= 0:
  21     raise ValueError("delay must be greater than 0")
  22 
  23   def deco_retry(f):
  24     def f_retry(*args, **kwargs):
  25       mtries, mdelay = tries, delay # make mutable
  26 
  27       rv = f(*args, **kwargs) # first attempt
  28       while mtries > 0:
  29         if rv is True: # Done on success
  30           return True
  31 
  32         mtries -= 1      # consume an attempt
  33         time.sleep(mdelay) # wait...
  34         mdelay *= backoff  # make future wait longer
  35 
  36         rv = f(*args, **kwargs) # Try again
  37 
  38       return False # Ran out of tries :-(
  39 
  40     return f_retry # true decorator -> decorated function
  41   return deco_retry  # @retry(arg[, ...]) -> true decorator

Pseudo-currying

   1 class curried(object):
   2   '''
   3   Decorator that returns a function that keeps returning functions
   4   until all arguments are supplied; then the original function is
   5   evaluated.
   6   '''
   7 
   8   def __init__(self, func, *a):
   9     self.func = func
  10     self.args = a
  11   
  12   def __call__(self, *a):
  13     args = self.args + a
  14     if len(args) < self.func.func_code.co_argcount:
  15       return curried(self.func, *args)
  16     else:
  17       return self.func(*args)
  18 
  19 
  20 @curried
  21 def add(a, b):
  22     return a + b
  23 
  24 add1 = add(1)
  25 
  26 print add1(2)

Creating decorator with optional arguments

   1 import functools, inspect
   2 
   3 def decorator(func):
   4     ''' Allow to use decorator either with arguments or not. '''
   5 
   6     def isFuncArg(*args, **kw):
   7         return len(args) == 1 and len(kw) == 0 and (
   8             inspect.isfunction(args[0]) or isinstance(args[0], type))
   9 
  10     if isinstance(func, type):
  11         def class_wrapper(*args, **kw):
  12             if isFuncArg(*args, **kw):
  13                 return func()(*args, **kw) # create class before usage
  14             return func(*args, **kw)
  15         class_wrapper.__name__ = func.__name__
  16         class_wrapper.__module__ = func.__module__
  17         return class_wrapper
  18 
  19     @functools.wraps(func)
  20     def func_wrapper(*args, **kw):
  21         if isFuncArg(*args, **kw):
  22             return func(*args, **kw)
  23 
  24         def functor(userFunc):
  25             return func(userFunc, *args, **kw)
  26 
  27         return functor
  28 
  29     return func_wrapper

Example:

   1 @decorator
   2 def apply(func, *args, **kw):
   3     return func(*args, **kw)
   4 
   5 @decorator
   6 class apply:
   7     def __init__(self, *args, **kw):
   8         self.args = args
   9         self.kw   = kw
  10 
  11     def __call__(self, func):
  12         return func(*self.args, **self.kw)
  13 
  14 # 
  15 # Usage in both cases:
  16 #
  17 @apply
  18 def test():
  19     return 'test'
  20 
  21 assert test == 'test'
  22 
  23 @apply(2, 3)
  24 def test(a, b):
  25     return a + b
  26 
  27 assert test is 5

Note: There is only one drawback: wrapper checks its arguments for single function or class. To avoid wrong behavior you can use keyword arguments instead of positional, e.g.:

   1 @decorator
   2 def my_property(getter, *, setter=None, deleter=None, doc=None):
   3     return property(getter, setter, deleter, doc)

Controllable DIY debug

(Other hooks could be similarly added. Docstrings and exceptions are left out for simplicity of demonstration.)

   1 import sys
   2 
   3 WHAT_TO_DEBUG = set(['io', 'core'])  # change to what you need
   4 
   5 class debug:
   6     '''Decorator which helps to control what aspects of a program to debug
   7     on per-function basis. Aspects are provided as list of arguments.
   8     It DOESN'T slowdown functions which aren't supposed to be debugged.
   9     '''
  10     def __init__(self, aspects=None):
  11         self.aspects = set(aspects)
  12 
  13     def __call__(self, f):
  14         if self.aspects & WHAT_TO_DEBUG:
  15             def newf(*args, **kwds):
  16                 print >> sys.stderr, f.func_name, args, kwds
  17                 f_result = f(*args, **kwds)
  18                 print >> sys.stderr, f.func_name, "returned", f_result
  19                 return f_result
  20             newf.__doc__ = f.__doc__
  21             return newf
  22         else:
  23             return f
  24 
  25 @debug(['io'])
  26 def prn(x):
  27     print x
  28 
  29 @debug(['core'])
  30 def mult(x, y):
  31     return x * y
  32 
  33 prn(mult(2, 2))

Easy adding methods to a class instance

Credits to John Roth.

   1 class Foo:
   2     def __init__(self):
   3         self.x = 42
   4 
   5 foo = Foo()
   6 
   7 def addto(instance):
   8     def decorator(f):
   9         import types
  10         f = types.MethodType(f, instance, instance.__class__)
  11         setattr(instance, f.func_name, f)
  12         return f
  13     return decorator
  14 
  15 @addto(foo)
  16 def print_x(self):
  17     print self.x
  18 
  19 # foo.print_x() would print "42"

Counting function calls

   1 class countcalls(object):
   2    "Decorator that keeps track of the number of times a function is called."
   3 
   4    __instances = {}
   5 
   6    def __init__(self, f):
   7       self.__f = f
   8       self.__numcalls = 0
   9       countcalls.__instances[f] = self
  10 
  11    def __call__(self, *args, **kwargs):
  12       self.__numcalls += 1
  13       return self.__f(*args, **kwargs)
  14 
  15    @staticmethod
  16    def count(f):
  17       "Return the number of times the function f was called."
  18       return countcalls.__instances[f].__numcalls
  19 
  20    @staticmethod
  21    def counts():
  22       "Return a dict of {function: # of calls} for all registered functions."
  23       return dict([(f, countcalls.count(f)) for f in countcalls.__instances])

Alternate Counting function calls

   1 class countcalls(object):
   2    "Decorator that keeps track of the number of times a function is called."
   3 
   4    __instances = {}
   5 
   6    def __init__(self, f):
   7       self.__f = f
   8       self.__numcalls = 0
   9       countcalls.__instances[f] = self
  10 
  11    def __call__(self, *args, **kwargs):
  12       self.__numcalls += 1
  13       return self.__f(*args, **kwargs)
  14 
  15    def count(self):
  16       "Return the number of times the function f was called."
  17       return countcalls.__instances[self.__f].__numcalls
  18 
  19    @staticmethod
  20    def counts():
  21       "Return a dict of {function: # of calls} for all registered functions."
  22       return dict([(f.__name__, countcalls.__instances[f].__numcalls) for f in countcalls.__instances])
  23 
  24 #example
  25 
  26 @countcalls
  27 def f():
  28    print 'f called'
  29 
  30 @countcalls
  31 def g():
  32    print 'g called'
  33 
  34 f()
  35 f()
  36 f()
  37 print f.count() # prints 3
  38 print countcalls.counts() # same as f.counts() or g.counts()
  39 g()
  40 print g.count() # prints 1

Generating Deprecation Warnings

   1 import warnings
   2 
   3 def deprecated(func):
   4     '''This is a decorator which can be used to mark functions
   5     as deprecated. It will result in a warning being emitted
   6     when the function is used.'''
   7     def new_func(*args, **kwargs):
   8         warnings.warn("Call to deprecated function {}.".format(func.__name__),
   9                       category=DeprecationWarning)
  10         return func(*args, **kwargs)
  11     new_func.__name__ = func.__name__
  12     new_func.__doc__ = func.__doc__
  13     new_func.__dict__.update(func.__dict__)
  14     return new_func
  15 
  16 # === Examples of use ===
  17 
  18 @deprecated
  19 def some_old_function(x,y):
  20     return x + y
  21 
  22 class SomeClass:
  23     @deprecated
  24     def some_old_method(self, x,y):
  25         return x + y

Smart deprecation warnings (with valid filenames, line numbers, etc.)

   1 import warnings
   2 import functools
   3 
   4 
   5 def deprecated(func):
   6     '''This is a decorator which can be used to mark functions
   7     as deprecated. It will result in a warning being emitted
   8     when the function is used.'''
   9     
  10     @functools.wraps(func)
  11     def new_func(*args, **kwargs):
  12         warnings.warn_explicit(
  13             "Call to deprecated function {}.".format(func.__name__),
  14             category=DeprecationWarning,
  15             filename=func.func_code.co_filename,
  16             lineno=func.func_code.co_firstlineno + 1
  17         )
  18         return func(*args, **kwargs)
  19     return new_func
  20 
  21 
  22 ## Usage examples ##
  23 @deprecated
  24 def my_func():
  25     pass
  26 
  27 @other_decorators_must_be_upper
  28 @deprecated
  29 def my_func():
  30     pass

Enable/Disable Decorators

   1 def unchanged(func):
   2     "This decorator doesn't add any behavior"
   3     return func
   4 
   5 def disabled(func):
   6     "This decorator disables the provided function, and does nothing"
   7     def empty_func(*args,**kargs):
   8         pass
   9     return empty_func
  10 
  11 # define this as equivalent to unchanged, for nice symmetry with disabled
  12 enabled = unchanged
  13 
  14 #
  15 # Sample use
  16 #
  17 
  18 GLOBAL_ENABLE_FLAG = True
  19 
  20 state = enabled if GLOBAL_ENABLE_FLAG else disabled
  21 @state
  22 def special_function_foo():
  23     print "function was enabled"

Easy Dump of Function Arguments

   1 def dump_args(func):
   2     "This decorator dumps out the arguments passed to a function before calling it"
   3     argnames = func.func_code.co_varnames[:func.func_code.co_argcount]
   4     fname = func.func_name
   5     
   6     def echo_func(*args,**kwargs):
   7         print fname, ":", ', '.join(
   8             '%s=%r' % entry
   9             for entry in zip(argnames,args) + kwargs.items())
  10         return func(*args, **kwargs)
  11     
  12     return echo_func
  13 
  14 @dump_args
  15 def f1(a,b,c):
  16     print a + b + c
  17 
  18 f1(1, 2, 3)

Pre-/Post-Conditions

   1 '''
   2 Provide pre-/postconditions as function decorators.
   3 
   4 Example usage:
   5 
   6   >>> def in_ge20(inval):
   7   ...    assert inval >= 20, 'Input value < 20'
   8   ...
   9   >>> def out_lt30(retval, inval):
  10   ...    assert retval < 30, 'Return value >= 30'
  11   ...
  12   >>> @precondition(in_ge20)
  13   ... @postcondition(out_lt30)
  14   ... def inc(value):
  15   ...   return value + 1
  16   ...
  17   >>> inc(5)
  18   Traceback (most recent call last):
  19     ...
  20   AssertionError: Input value < 20
  21   >>> inc(29)
  22   Traceback (most recent call last):
  23     ...
  24   AssertionError: Return value >= 30
  25   >>> inc(20)
  26   21
  27 
  28 You can define as many pre-/postconditions for a function as you
  29 like. It is also possible to specify both types of conditions at once:
  30 
  31   >>> @conditions(in_ge20, out_lt30)
  32   ... def add1(value):
  33   ...   return value + 1
  34   ...
  35   >>> add1(5)
  36   Traceback (most recent call last):
  37     ...
  38   AssertionError: Input value < 20
  39 
  40 An interesting feature is the ability to prevent the creation of
  41 pre-/postconditions at function definition time. This makes it
  42 possible to use conditions for debugging and then switch them off for
  43 distribution.
  44 
  45   >>> debug = False
  46   >>> @precondition(in_ge20, debug)
  47   ... def dec(value):
  48   ...   return value - 1
  49   ...
  50   >>> dec(5)
  51   4
  52 '''
  53 
  54 __all__ = ['precondition', 'postcondition', 'conditions']
  55 
  56 DEFAULT_ON = True
  57 
  58 def precondition(precondition, use_conditions=DEFAULT_ON):
  59     return conditions(precondition, None, use_conditions)
  60 
  61 def postcondition(postcondition, use_conditions=DEFAULT_ON):
  62     return conditions(None, postcondition, use_conditions)
  63 
  64 class conditions(object):
  65     __slots__ = ('__precondition', '__postcondition')
  66 
  67     def __init__(self, pre, post, use_conditions=DEFAULT_ON):
  68         if not use_conditions:
  69             pre, post = None, None
  70 
  71         self.__precondition  = pre
  72         self.__postcondition = post
  73 
  74     def __call__(self, function):
  75         # combine recursive wrappers (@precondition + @postcondition == @conditions)
  76         pres  = set((self.__precondition,))
  77         posts = set((self.__postcondition,))
  78 
  79         # unwrap function, collect distinct pre-/post conditions
  80         while type(function) is FunctionWrapper:
  81             pres.add(function._pre)
  82             posts.add(function._post)
  83             function = function._func
  84 
  85         # filter out None conditions and build pairs of pre- and postconditions
  86         conditions = map(None, filter(None, pres), filter(None, posts))
  87 
  88         # add a wrapper for each pair (note that 'conditions' may be empty)
  89         for pre, post in conditions:
  90             function = FunctionWrapper(pre, post, function)
  91 
  92         return function
  93 
  94 class FunctionWrapper(object):
  95     def __init__(self, precondition, postcondition, function):
  96         self._pre  = precondition
  97         self._post = postcondition
  98         self._func = function
  99 
 100     def __call__(self, *args, **kwargs):
 101         precondition  = self._pre
 102         postcondition = self._post
 103 
 104         if precondition:
 105             precondition(*args, **kwargs)
 106         result = self._func(*args, **kwargs)
 107         if postcondition:
 108             postcondition(result, *args, **kwargs)
 109         return result
 110 
 111 def __test():
 112     import doctest
 113     doctest.testmod()
 114 
 115 if __name__ == "__main__":
 116     __test()

Profiling/Coverage Analysis

The code and examples are a bit longish, so I'll include a link instead: http://mg.pov.lt/blog/profiling.html

Line Tracing Individual Functions

I cobbled this together from the trace module. It allows you to decorate individual functions so their lines are traced. I think it works out to be a slightly smaller hammer than running the trace module and trying to pare back what it traces using exclusions.

   1 import sys
   2 import os
   3 import linecache
   4 
   5 def trace(f):
   6     def globaltrace(frame, why, arg):
   7         if why == "call":
   8             return localtrace
   9         return None
  10 
  11     def localtrace(frame, why, arg):
  12         if why == "line":
  13             # record the file name and line number of every trace
  14             filename = frame.f_code.co_filename
  15             lineno = frame.f_lineno
  16 
  17             bname = os.path.basename(filename)
  18             print "{}({}): {}".format(  bname, 
  19                                         lineno,
  20                                         linecache.getline(filename, lineno)),
  21         return localtrace
  22 
  23     def _f(*args, **kwds):
  24         sys.settrace(globaltrace)
  25         result = f(*args, **kwds)
  26         sys.settrace(None)
  27         return result
  28 
  29     return _f

Synchronization

Synchronize two (or more) functions on a given lock.

   1 def synchronized(lock):
   2     '''Synchronization decorator.'''
   3 
   4     def wrap(f):
   5         def new_function(*args, **kw):
   6             lock.acquire()
   7             try:
   8                 return f(*args, **kw)
   9             finally:
  10                 lock.release()
  11         return new_function
  12     return wrap
  13 
  14 # Example usage:
  15 
  16 from threading import Lock
  17 my_lock = Lock()
  18 
  19 @synchronized(my_lock)
  20 def critical1(*args):
  21     # Interesting stuff goes here.
  22     pass
  23 
  24 @synchronized(my_lock)
  25 def critical2(*args):
  26     # Other interesting stuff goes here.
  27     pass

Type Enforcement (accepts/returns)

Provides various degrees of type enforcement for function parameters and return values.

   1 '''
   2 One of three degrees of enforcement may be specified by passing
   3 the 'debug' keyword argument to the decorator:
   4     0 -- NONE:   No type-checking. Decorators disabled.
   5     1 -- MEDIUM: Print warning message to stderr. (Default)
   6     2 -- STRONG: Raise TypeError with message.
   7 If 'debug' is not passed to the decorator, the default level is used.
   8 
   9 Example usage:
  10     >>> NONE, MEDIUM, STRONG = 0, 1, 2
  11     >>>
  12     >>> @accepts(int, int, int)
  13     ... @returns(float)
  14     ... def average(x, y, z):
  15     ...     return (x + y + z) / 2
  16     ...
  17     >>> average(5.5, 10, 15.0)
  18     TypeWarning:  'average' method accepts (int, int, int), but was given
  19     (float, int, float)
  20     15.25
  21     >>> average(5, 10, 15)
  22     TypeWarning:  'average' method returns (float), but result is (int)
  23     15
  24 
  25 Needed to cast params as floats in function def (or simply divide by 2.0).
  26 
  27     >>> TYPE_CHECK = STRONG
  28     >>> @accepts(int, debug=TYPE_CHECK)
  29     ... @returns(int, debug=TYPE_CHECK)
  30     ... def fib(n):
  31     ...     if n in (0, 1): return n
  32     ...     return fib(n-1) + fib(n-2)
  33     ...
  34     >>> fib(5.3)
  35     Traceback (most recent call last):
  36       ...
  37     TypeError: 'fib' method accepts (int), but was given (float)
  38 
  39 '''
  40 import sys
  41 
  42 def accepts(*types, **kw):
  43     '''Function decorator. Checks decorated function's arguments are
  44     of the expected types.
  45 
  46     Parameters:
  47     types -- The expected types of the inputs to the decorated function.
  48              Must specify type for each parameter.
  49     kw    -- Optional specification of 'debug' level (this is the only valid
  50              keyword argument, no other should be given).
  51              debug = ( 0 | 1 | 2 )
  52 
  53     '''
  54     if not kw:
  55         # default level: MEDIUM
  56         debug = 1
  57     else:
  58         debug = kw['debug']
  59     try:
  60         def decorator(f):
  61             def newf(*args):
  62                 if debug is 0:
  63                     return f(*args)
  64                 assert len(args) == len(types)
  65                 argtypes = tuple(map(type, args))
  66                 if argtypes != types:
  67                     msg = info(f.__name__, types, argtypes, 0)
  68                     if debug is 1:
  69                         print >> sys.stderr, 'TypeWarning: ', msg
  70                     elif debug is 2:
  71                         raise TypeError, msg
  72                 return f(*args)
  73             newf.__name__ = f.__name__
  74             return newf
  75         return decorator
  76     except KeyError, key:
  77         raise KeyError, key + "is not a valid keyword argument"
  78     except TypeError, msg:
  79         raise TypeError, msg
  80 
  81 
  82 def returns(ret_type, **kw):
  83     '''Function decorator. Checks decorated function's return value
  84     is of the expected type.
  85 
  86     Parameters:
  87     ret_type -- The expected type of the decorated function's return value.
  88                 Must specify type for each parameter.
  89     kw       -- Optional specification of 'debug' level (this is the only valid
  90                 keyword argument, no other should be given).
  91                 debug=(0 | 1 | 2)
  92     '''
  93     try:
  94         if not kw:
  95             # default level: MEDIUM
  96             debug = 1
  97         else:
  98             debug = kw['debug']
  99         def decorator(f):
 100             def newf(*args):
 101                 result = f(*args)
 102                 if debug is 0:
 103                     return result
 104                 res_type = type(result)
 105                 if res_type != ret_type:
 106                     msg = info(f.__name__, (ret_type,), (res_type,), 1)
 107                     if debug is 1:
 108                         print >> sys.stderr, 'TypeWarning: ', msg
 109                     elif debug is 2:
 110                         raise TypeError, msg
 111                 return result
 112             newf.__name__ = f.__name__
 113             return newf
 114         return decorator
 115     except KeyError, key:
 116         raise KeyError, key + "is not a valid keyword argument"
 117     except TypeError, msg:
 118         raise TypeError, msg
 119 
 120 def info(fname, expected, actual, flag):
 121     '''Convenience function returns nicely formatted error/warning msg.'''
 122     format = lambda types: ', '.join([str(t).split("'")[1] for t in types])
 123     expected, actual = format(expected), format(actual)
 124     msg = "'{}' method ".format( fname )\
 125           + ("accepts", "returns")[flag] + " ({}), but ".format(expected)\
 126           + ("was given", "result is")[flag] + " ({})".format(actual)
 127     return msg

CGI method wrapper

Handles HTML boilerplate at top and bottom of pages returned from CGI methods. Works with the cgi module. Now your request handlers can just output the interesting HTML, and let the decorator deal with all the top and bottom clutter.

(Note: the exception handler eats all exceptions, which in CGI is no big loss, since the program runs in its separate subprocess. At least here, the exception contents will be written to the output page.)

   1 class CGImethod(object):
   2     def __init__(self, title):
   3         self.title = title
   4     
   5     def __call__(self, fn):
   6         def wrapped_fn(*args):
   7             print "Content-Type: text/html\n\n"
   8             print "<HTML>"
   9             print "<HEAD><TITLE>{}</TITLE></HEAD>".format(self.title)
  10             print "<BODY>"
  11             try:
  12                 fn(*args)
  13             except Exception, e:
  14                 print
  15                 print e
  16             print
  17             print "</BODY></HTML>"
  18 
  19         return wrapped_fn
  20 
  21 @CGImethod("Hello with Decorator")
  22 def say_hello():
  23     print '<h1>Hello from CGI-Land</h1>'

State Machine Implementaion

A much improved version of decorators for implementing state machines, too long to show here, is at State Machine via Decorators

This example uses Decorators to facilitate the implementation of a state machine in Python. Decorators are used to specify which methods are the event handlers for the class. In this example, actions are associated with the transitions, but it is possible with a little consideration to associate actions with states instead.

The example defines a class, MyMachine that is a state machine. Multiple instances of the class may be instantiated with each maintaining its own state. A class also may have multiple states. Here I've used gstate and tstate.

The code in the imported statedefn file gets a bit hairy, but you may not need to delve into it for your application.

   1 # State Machine example Program
   2 
   3 from statedefn import *
   4 
   5 class MyMachine(object):
   6 
   7     # Create Statedefn object for each state you need to keep track of.
   8     # the name passed to the constructor becomes a StateVar member of the current class.
   9     # i.e. if my_obj is a MyMachine object, my_obj.gstate maintains the current gstate
  10     gstate = StateTable("gstate")
  11     tstate = StateTable("turtle")
  12 
  13     def __init__(self, name):
  14         # must call init method of class's StateTable object. to initialize state variable
  15         self.gstate.initialize(self)
  16         self.tstate.initialize(self)
  17         self.mname = name
  18         self.a_count = 0
  19         self.b_count = 0
  20         self.c_count = 0
  21 
  22     # Decorate the Event Handler virtual functions -note gstate parameter
  23     @event_handler(gstate)
  24     def event_a(self): pass
  25     
  26     @event_handler(gstate)
  27     def event_b(self): pass
  28     
  29     @event_handler(gstate)
  30     def event_c(self, val): pass
  31 
  32     @event_handler(tstate)
  33     def toggle(self): pass
  34 
  35 
  36     # define methods to handle events.
  37     def _event_a_hdlr1(self):
  38         print "State 1, event A"
  39         self.a_count += 1
  40     def _event_b_hdlr1(self):
  41         print "State 1, event B"
  42         self.b_count += 1
  43     def _event_c_hdlr1(self, val):
  44         print "State 1, event C"
  45         self.c_count += 3*val
  46 
  47     def _event_a_hdlr2(self):
  48         print "State 2, event A"
  49         self.a_count += 10
  50         # here we brute force the tstate to on, leave & enter functions called if state changes.
  51         # turtle is object's state variable for tstate, comes from constructor argument
  52         self.turtle.set_state(self, self._t_on)
  53     def _event_b_hdlr2(self):
  54         print "State 2, event B"
  55         self.b_count += 10
  56     def _event_c_hdlr2(self, val):
  57         print "State 2, event C"
  58         self.c_count += 2*val
  59 
  60     def _event_a_hdlr3(self):
  61         self.a_count += 100
  62         print "State 3, event A"
  63     def _event_b_hdlr3(self):
  64         print "State 3, event B"
  65         self.b_count += 100
  66         # we decide here we want to go to state 2, overrrides spec in state table below.
  67         # transition to next_state is made after the method exits.
  68         self.gstate.next_state = self._state2 
  69     def _event_c_hdlr3(self, val):
  70         print "State 3, event C"
  71         self.c_count += 5*val
  72     
  73     # Associate the handlers with a state. The first argument is a list of methods.
  74     # One method for each event_handler decorated function of gstate. Order of methods
  75     # in the list correspond to order in which the Event Handlers were declared.
  76     # Second arg is the name of the state.  Third argument is to be come a list of the
  77     # next states. 
  78     # The first state created becomes the initial state.
  79     _state1 = gstate.state("One",  (_event_a_hdlr1, _event_b_hdlr1, _event_c_hdlr1), 
  80                                       ("Two", "Three", None))
  81     _state2 = gstate.state("Two",  (_event_a_hdlr2, _event_b_hdlr2, _event_c_hdlr2),
  82                                      ("Three",        None,          "One"))
  83     _state3 = gstate.state("Three",(_event_a_hdlr3, _event_b_hdlr3, _event_c_hdlr3),
  84                                  (None,         "One",         "Two"))
  85 
  86 
  87     # Declare a function that will be called when entering a new gstate.
  88     # Can also declare a leave function using @on_leave_function(gstate)
  89     @on_enter_function(gstate)
  90     def _enter_gstate(self):
  91         print "entering state ", self.gstate.name() , "of ", self.mname
  92     @on_leave_function(tstate)
  93     def _leave_tstate(self):
  94         print "leaving state ", self.turtle.name() , "of ", self.mname
  95 
  96 
  97     def _toggle_on(self):
  98         print "Toggle On"
  99 
 100     def _toggle_off(self):
 101         print "Toggle Off"
 102 
 103     _t_off = tstate.state("Off", [_toggle_on],
 104                          ["On"])
 105     _t_on =  tstate.state("On", [_toggle_off],
 106                           ["Off"])
 107 
 108 
 109 def main():
 110     big_machine = MyMachine("big")
 111     lil_machine = MyMachine("lil")
 112 
 113     big_machine.event_a()
 114     lil_machine.event_a()
 115     big_machine.event_a()
 116     lil_machine.event_a()
 117     big_machine.event_b()
 118     lil_machine.event_b()
 119     big_machine.event_c(4)
 120     lil_machine.event_c(2)
 121     big_machine.event_c(1)
 122     lil_machine.event_c(3)
 123     big_machine.event_b()
 124     lil_machine.event_b()
 125     big_machine.event_a()
 126     lil_machine.event_a()
 127     big_machine.event_a()
 128 
 129     big_machine.toggle()
 130     big_machine.toggle()
 131     big_machine.toggle()
 132 
 133     lil_machine.event_a()
 134     big_machine.event_b()
 135     lil_machine.event_b()
 136     big_machine.event_c(3)
 137     big_machine.event_a()
 138     lil_machine.event_c(2)
 139     lil_machine.event_a()
 140     big_machine.event_b()
 141     lil_machine.event_b()
 142     big_machine.event_c(7)
 143     lil_machine.event_c(1)
 144 
 145     print "Event A count ", big_machine.a_count
 146     print "Event B count ", big_machine.b_count
 147     print "Event C count ", big_machine.c_count
 148     print "LilMachine C count ", lil_machine.c_count
 149 
 150 main()

And now the imported statedefn.py

   1 #
   2 # Support for State Machines.  ref - Design Patterns by GoF
   3 #  Many of the methods in these classes get called behind the scenes. 
   4 #
   5 #  Notable exceptions are methods of the StateVar class. 
   6 #
   7 #  See example programs for how this module is intended to be used.
   8 #
   9 class StateMachineError(Exception):
  10     def __init__(self, args = None):
  11        self.args = args
  12 
  13 class StateVar(object):
  14     def __init__(self, initial_state):
  15         self._current_state = initial_state
  16         self.next_state = initial_state            # publicly settable in an event handling routine.
  17     
  18     def set_state(self, owner, new_state):
  19         '''
  20         Forces a state change to new_state
  21         '''
  22         self.next_state = new_state
  23         self.__to_next_state(owner)
  24 
  25     def __to_next_state(self, owner):
  26         '''
  27         The low-level state change function which calls leave state & enter state functions as 
  28         needed.
  29 
  30         LeaveState and EnterState functions are called as needed when state transitions.
  31         '''
  32         if self.next_state is not self._current_state:
  33             if hasattr(self._current_state, "leave"):
  34                 self._current_state.leave(owner)
  35             elif hasattr(self, "leave"):
  36                 self.leave(owner)
  37             self._current_state =  self.next_state
  38             if hasattr(self._current_state, "enter"):
  39                 self._current_state.enter(owner)
  40             elif hasattr(self, "enter"):
  41                 self.enter(owner)
  42 
  43     def __fctn(self, func_name):
  44         ''' 
  45         Returns the owning class's method for handling an event for the current state.
  46         This method not for public consumption.
  47         '''
  48         vf = self._current_state.get_fe(func_name)
  49         return vf
  50 
  51     def name(self):
  52         '''
  53         Returns the current state name.
  54         '''
  55         return self._current_state.name
  56 
  57 class STState(object):
  58     def __init__(self, state_name):
  59         self.name = state_name
  60         self.fctn_dict = {}
  61 
  62     def set_events(self, event_list, event_hdlr_list, next_states):
  63         dictionary = self.fctn_dict
  64         if not next_states:
  65             def set_row(event, method):
  66                 dictionary[event] = [method, None]
  67             map(set_row, event_list, event_hdlr_list)
  68         else:
  69             def set_row2(event, method, next_state):
  70                 dictionary[event] = [method, next_state]
  71             map(set_row2, event_list, event_hdlr_list, next_states)
  72         self.fctn_dict = dictionary
  73 
  74     def get_fe(self, fctn_name):
  75         return self.fctn_dict[fctn_name]
  76 
  77     def map_next_states(self, state_dict):
  78         ''' Changes second dict value from name of state to actual state.'''
  79         for de in self.fctn_dict.values():
  80             next_state_name = de[1]
  81             if next_state_name:
  82                 if next_state_name in state_dict:
  83                     de[1] = state_dict[next_state_name]
  84                 else:
  85                     raise StateMachineError('Invalid Name for next state: {}'.format(next_state_name))
  86 
  87 
  88 class StateTable(object):
  89     '''
  90     Magical class to define a state machine, with the help of several decorator functions
  91     which follow.
  92     '''
  93     def __init__(self, declname):
  94         self.machine_var = declname
  95         self._initial_state = None
  96         self._state_list = {}
  97         self._event_list = []
  98         self.need_initialize = 1
  99     
 100     def initialize(self, parent):
 101         '''
 102         Initializes the parent class's state variable for this StateTable class. 
 103         Must call this method in the parent' object's __init__ method.  You can have
 104         Multiple state machines within a parent class. Call this method for each
 105         '''
 106         statevar= StateVar(self._initial_state)
 107         setattr(parent, self.machine_var, statevar)
 108         if hasattr(self, "enter"):
 109             statevar.enter = self.enter
 110         if hasattr(self, "leave"):
 111             statevar.leave = self.leave
 112         #Magic happens here - in the 'next state' table, translate names into state objects.
 113         if  self.need_initialize:
 114             for xstate in list(self._state_list.values()):
 115                 xstate.map_next_states(self._state_list)
 116             self.need_initialize = 0
 117 
 118     def def_state(self, event_hdlr_list, name):
 119         '''
 120         This is used to define a state. the event handler list is a list of functions that
 121         are called for corresponding events. name is the name of the state.
 122         '''
 123         state_table_row = STState(name)
 124         if len(event_hdlr_list) != len(self._event_list):
 125             raise StateMachineError('Mismatch between number of event handlers and the methods specified for the state.')
 126 
 127         state_table_row.set_events(self._event_list, event_hdlr_list, None)
 128         
 129         if self._initial_state is None:
 130             self._initial_state = state_table_row
 131         self._state_list[name] = state_table_row
 132         return state_table_row
 133 
 134     def state(self, name, event_hdlr_list, next_states):
 135         state_table_row = STState(name)
 136         if len(event_hdlr_list) != len(self._event_list):
 137             raise StateMachineError('Mismatch between number of event handlers and the methods specified for the state.')
 138         if next_states is not None and len(next_states) != len(self._event_list):
 139             raise StateMachineError('Mismatch between number of event handlers and the next states specified for the state.')
 140         
 141         state_table_row.set_events(self._event_list, event_hdlr_list, next_states)
 142         
 143         if self._initial_state is None:
 144             self._initial_state = state_table_row
 145         self._state_list[name] = state_table_row
 146         return state_table_row
 147 
 148     def __add_ev_hdlr(self, func_name):
 149         '''
 150         Informs the class of an event handler to be added. We just need the name here. The
 151         function name will later be associated with one of the functions in a list when a state is defined.
 152         '''
 153         self._event_list.append(func_name)
 154 
 155 # Decorator functions ... 
 156 def event_handler(state_class):
 157     '''
 158     Declare a method that handles a type of event.
 159     '''
 160     def wrapper(func):
 161         state_class._StateTable__add_ev_hdlr(func.__name__)
 162         def obj_call(self, *args, **keywords):
 163             state_var = getattr(self, state_class.machine_var)
 164             funky, next_state = state_var._StateVar__fctn(func.__name__)
 165             if next_state is not None:
 166                 state_var.next_state = next_state
 167             rv = funky(self, *args, **keywords)
 168             state_var._StateVar__to_next_state(self)
 169             return rv
 170         return obj_call
 171     return wrapper
 172 
 173 def on_enter_function(state_class):
 174     '''
 175     Declare that this method should be called whenever a new state is entered.
 176     '''
 177     def wrapper(func):
 178         state_class.enter = func
 179         return func
 180     return wrapper
 181 
 182 def on_leave_function(state_class):
 183     '''
 184     Declares that this method should be called whenever leaving a state.
 185     '''
 186     def wrapper(func):
 187         state_class.leave = func
 188         return func
 189     return wrapper

C++/Java-keyword-like function decorators

@abstractMethod, @deprecatedMethod, @privateMethod, @protectedMethod, @raises, @parameterTypes, @returnType

The annotations provide run-time type checking and an alternative way to document code.

The code and documentation are long, so I offer a link: http://fightingquaker.com/pyanno/

Different Decorator Forms

There are operational differences between:

This example demonstrates the operational differences between the three using a skit taken from Episode 22: Bruces.

   1 from sys import stdout,stderr
   2 from pdb import set_trace as bp
   3 
   4 class DecoTrace(object):
   5     '''
   6     Decorator class with no arguments
   7 
   8     This can only be used for functions or methods where the instance
   9     is not necessary
  10     
  11     '''
  12 
  13     def __init__(self, f):
  14         self.f = f
  15 
  16     def _showargs(self, *fargs, **kw):
  17         print >> stderr, 'T: enter {} with args={}, kw={}'.format(self.f.__name__, str(fargs), str(kw))
  18 
  19     def _aftercall(self, status):
  20         print >> stderr, 'T: exit {} with status={}'.format(self.f.__name__, str(status))
  21 
  22     def __call__(self, *fargs, **kw):
  23         '''Pass *just* function arguments to wrapped function.'''
  24         self._showargs(*fargs, **kw)
  25         ret=self.f(*fargs, **kw)
  26         self._aftercall(ret)
  27         return ret
  28 
  29     def __repr__(self):
  30         return self.f.func_name
  31 
  32 
  33 class DecoTraceWithArgs(object):
  34     '''decorator class with ARGUMENTS
  35 
  36        This can be used for unbounded functions and methods.  If this wraps a
  37        class instance, then extract it and pass to the wrapped method as the
  38        first arg.
  39     '''
  40     
  41     def __init__(self, *dec_args, **dec_kw):
  42         '''The decorator arguments are passed here.  Save them for runtime.'''
  43         self.dec_args = dec_args
  44         self.dec_kw = dec_kw
  45 
  46         self.label = dec_kw.get('label', 'T')
  47         self.fid = dec_kw.get('stream', stderr)
  48 
  49     def _showargs(self, *fargs, **kw):
  50 
  51         print >> self.fid, \
  52               '{}: enter {} with args={}, kw={}'.format(self.label, self.f.__name__, str(fargs), str(kw))
  53         print >> self.fid, \
  54               '{}:   passing decorator args={}, kw={}'.format(self.label, str(self.dec_args), str(self.dec_kw))
  55 
  56     def _aftercall(self, status):
  57         print >> self.fid, '{}: exit {} with status={}'.format(self.label, self.f.__name__, str(status))
  58     def _showinstance(self, instance):
  59         print >> self.fid, '{}: instance={}'.format(self.label, instance)
  60         
  61     def __call__(self, f):
  62         def wrapper(*fargs, **kw):
  63             '''
  64               Combine decorator arguments and function arguments and pass to wrapped
  65               class instance-aware function/method.
  66 
  67               Note: the first argument cannot be "self" because we get a parse error
  68               "takes at least 1 argument" unless the instance is actually included in
  69               the argument list, which is redundant.  If this wraps a class instance,
  70               the "self" will be the first argument.
  71             '''
  72 
  73             self._showargs(*fargs, **kw)
  74 
  75             # merge decorator keywords into the kw argument list
  76             kw.update(self.dec_kw)
  77 
  78             # Does this wrap a class instance?
  79             if fargs and getattr(fargs[0], '__class__', None):
  80 
  81                 # pull out the instance and combine function and
  82                 # decorator args
  83                 instance, fargs = fargs[0], fargs[1:]+self.dec_args
  84                 self._showinstance(instance)
  85 
  86                 # call the method
  87                 ret=f(instance, *fargs, **kw)
  88             else:
  89                 # just send in the give args and kw
  90                 ret=f(*(fargs + self.dec_args), **kw)
  91 
  92             self._aftercall(ret)
  93             return ret
  94 
  95         # Save wrapped function reference
  96         self.f = f
  97         wrapper.__name__ = f.__name__
  98         wrapper.__dict__.update(f.__dict__)
  99         wrapper.__doc__ = f.__doc__
 100         return wrapper
 101 
 102 
 103 @DecoTrace
 104 def FirstBruce(*fargs, **kwargs):
 105     'Simple function using simple decorator.'
 106     if fargs and fargs[0]:
 107         print fargs[0]
 108 
 109 @DecoTraceWithArgs(name="Second Bruce", standardline="G'day, Bruce!")
 110 def SecondBruce(*fargs, **kwargs):
 111     'Simple function using decorator with arguments.'
 112     print '{}:'.format(kwargs.get('name', 'Unknown Bruce'))
 113     
 114     if fargs and fargs[0]:
 115         print fargs[0]
 116     else:
 117         print kwargs.get('standardline', None)
 118 
 119 class Bruce(object):
 120     'Simple class.'
 121     
 122     def __init__(self, id):
 123         self.id = id
 124 
 125     def __str__(self):
 126         return self.id
 127 
 128     def __repr__(self):
 129         return 'Bruce'
 130 
 131     @DecoTraceWithArgs(label="Trace a class", standardline="How are yer Bruce?", stream=stdout)
 132     def talk(self, *fargs, **kwargs):
 133         'Simple function using decorator with arguments.'
 134         
 135         print '{}:'.format(self)
 136         if fargs and fargs[0]:
 137             print fargs[0]
 138         else:
 139             print kwargs.get('standardline', None)
 140 
 141 ThirdBruce = Bruce('Third Bruce')
 142 
 143 SecondBruce()
 144 FirstBruce("First Bruce: Oh, Hello Bruce!")
 145 ThirdBruce.talk()
 146 FirstBruce("First Bruce: Bit crook, Bruce.")
 147 SecondBruce("Where's Bruce?")
 148 FirstBruce("First Bruce: He's not here, Bruce")
 149 ThirdBruce.talk("Blimey, s'hot in here, Bruce.")
 150 FirstBruce("First Bruce: S'hot enough to boil a monkey's bum!")
 151 SecondBruce("That's a strange expression, Bruce.")
 152 FirstBruce("First Bruce: Well Bruce, I heard the Prime Minister use it. S'hot enough to boil a monkey's bum in 'ere, your Majesty,' he said and she smiled quietly to herself.")
 153 ThirdBruce.talk("She's a good Sheila, Bruce and not at all stuck up.")

Unimplemented function replacement

Allows you to test unimplemented code in a development environment by specifying a default argument as an argument to the decorator (or you can leave it off to specify None to be returned.

   1 # Annotation wrapper annotation method
   2 def unimplemented(defaultval):
   3     if(type(defaultval) == type(unimplemented)):
   4         return lambda: None
   5     else:
   6         # Actual annotation
   7         def unimp_wrapper(func):        
   8             # What we replace the function with
   9             def wrapper(*arg):
  10                 return defaultval
  11             return wrapper
  12         return unimp_wrapper

Redirects stdout printing to python standard logging.

   1 class LogPrinter:
   2     '''LogPrinter class which serves to emulates a file object and logs
   3        whatever it gets sent to a Logger object at the INFO level.'''
   4     def __init__(self):
   5         '''Grabs the specific logger to use for logprinting.'''
   6         self.ilogger = logging.getLogger('logprinter')
   7         il = self.ilogger
   8         logging.basicConfig()
   9         il.setLevel(logging.INFO)
  10     
  11     def write(self, text):
  12         '''Logs written output to a specific logger'''
  13         self.ilogger.info(text)
  14 
  15 def logprintinfo(func):
  16     '''Wraps a method so that any calls made to print get logged instead'''
  17     def pwrapper(*arg):
  18         stdobak = sys.stdout
  19         lpinstance = LogPrinter()
  20         sys.stdout = lpinstance
  21         try:
  22             return func(*arg)
  23         finally:
  24             sys.stdout = stdobak
  25     return pwrapper

Access control

This example prevents users from getting access to places where they are not authorised to go

   1 class LoginCheck:
   2     '''
   3     This class checks whether a user
   4     has logged in properly via 
   5     the global "check_function". If so,
   6     the requested routine is called.
   7     Otherwise, an alternative page is
   8     displayed via the global "alt_function"
   9     '''
  10     def __init__(self, f):
  11         self._f = f
  12         
  13     def __call__(self, *args):
  14         Status = check_function()
  15         if Status is 1:
  16             return self._f(*args)
  17         else:
  18             return alt_function()
  19 
  20 def check_function():
  21     return test
  22 
  23 def alt_function():
  24     return 'Sorry - this is the forced behaviour'
  25 
  26 @LoginCheck
  27 def display_members_page():
  28     print 'This is the members page'

Example:

   1 test = 0
   2 DisplayMembersPage()
   3 # Displays "Sorry - this is the forced behaviour"
   4 
   5 test = 1
   6 DisplayMembersPage()
   7 # Displays "This is the members page"

Events rising and handling

Please see the code and examples here: http://pypi.python.org/pypi/Decovent

Singleton

   1 import functools
   2 
   3 def singleton(cls):
   4     ''' Use class as singleton. '''
   5 
   6     cls.__new_original__ = cls.__new__
   7 
   8     @functools.wraps(cls.__new__)
   9     def singleton_new(cls, *args, **kw):
  10         it =  cls.__dict__.get('__it__')
  11         if it is not None:
  12             return it
  13 
  14         cls.__it__ = it = cls.__new_original__(cls, *args, **kw)
  15         it.__init_original__(*args, **kw)
  16         return it
  17 
  18     cls.__new__ = singleton_new
  19     cls.__init_original__ = cls.__init__
  20     cls.__init__ = object.__init__
  21 
  22     return cls
  23 
  24 #
  25 # Sample use:
  26 #
  27 
  28 @singleton
  29 class Foo:
  30     def __new__(cls):
  31         cls.x = 10
  32         return object.__new__(cls)
  33 
  34     def __init__(self):
  35         assert self.x == 10
  36         self.x = 15
  37 
  38 assert Foo().x == 15
  39 Foo().x = 20
  40 assert Foo().x == 20

Asynchronous Call

   1 from Queue import Queue
   2 from threading import Thread
   3         
   4 class asynchronous(object):  
   5     def __init__(self, func):    
   6         self.func = func
   7         
   8         def threaded(*args, **kwargs):
   9             self.queue.put(self.func(*args, **kwargs))
  10         
  11         self.threaded = threaded
  12         
  13     def __call__(self, *args, **kwargs):
  14         return self.func(*args, **kwargs)
  15         
  16     def start(self, *args, **kwargs):
  17         self.queue = Queue()
  18         thread = Thread(target=self.threaded, args=args, kwargs=kwargs);
  19         thread.start();
  20         return asynchronous.Result(self.queue, thread)
  21         
  22     class NotYetDoneException(Exception):
  23         def __init__(self, message):
  24             self.message = message
  25 
  26     class Result(object):
  27         def __init__(self, queue, thread):
  28             self.queue = queue
  29             self.thread = thread
  30                         
  31         def is_done(self):
  32             return not self.thread.is_alive()
  33             
  34         def get_result(self):
  35             if not self.is_done():
  36                 raise asynchronous.NotYetDoneException('the call has not yet completed its task')
  37         
  38             if not hasattr(self, 'result'):
  39                 self.result = self.queue.get()
  40                 
  41             return self.result
  42 
  43 if __name__ == '__main__':
  44     # sample usage
  45     import time
  46 
  47     @asynchronous
  48     def long_process(num):
  49         time.sleep(10)
  50         return num * num
  51 
  52     result = long_process.start(12)
  53 
  54     for i in range(20):
  55         print i
  56         time.sleep(1)
  57         
  58         if result.is_done():
  59             print "result {0}".format(result.get_result())
  60             
  61     
  62     result2 = long_process.start(13)
  63 
  64     try:
  65         print "result2 {0}".format(result2.get_result())
  66         
  67     except asynchronous.NotYetDoneException as ex:
  68         print ex.message

Class method decorator using instance

When decorating a class method, the decorator receives an function not yet bound to an instance.

The decorator can't to do anything on the instance invocating it, unless it actually is a descriptor.

   1 from functools import wraps
   2 
   3 def decorate(f):
   4     '''
   5     Class method decorator specific to the instance.
   6 
   7     It uses a descriptor to delay the definition of the 
   8     method wrapper.
   9     '''
  10     class descript(object):
  11         def __init__(self, f):
  12             self.f = f
  13         
  14         def __get__(self, instance, klass):
  15             if instance is None:
  16                 # Class method was requested
  17                 return self.make_unbound(klass)
  18             return self.make_bound(instance)
  19         
  20         def make_unbound(self, klass):
  21             @wraps(self.f)
  22             def wrapper(*args, **kwargs):
  23                 '''This documentation will vanish :)'''
  24                 raise TypeError(
  25                     'unbound method {}() must be called with {} instance '
  26                     'as first argument (got nothing instead)'.format(
  27                         self.f.__name__, 
  28                         klass.__name__)
  29                 )
  30             return wrapper
  31         
  32         def make_bound(self, instance):
  33             @wraps(self.f)
  34             def wrapper(*args, **kwargs):
  35                 '''This documentation will disapear :)'''
  36                 print "Called the decorated method {} of {}".format(self.f.__name__, instance)
  37                 return self.f(instance, *args, **kwargs)
  38             # This instance does not need the descriptor anymore,
  39             # let it find the wrapper directly next time:
  40             setattr(instance, self.f.__name__, wrapper)
  41             return wrapper
  42 
  43     return descript(f)

This implementation replaces the descriptor by the actual decorated function ASAP to avoid overhead, but you could keep it to do even more (counting calls, etc...)

Another Retrying Decorator

Here's another decorator for causing a function to be retried a certain number of times. This decorator is superior IMHO because it should work with any old function that raises an exception on failure.

Features:

GIST: https://gist.github.com/2570004

   1 #
   2 # Copyright 2012 by Jeff Laughlin Consulting LLC
   3 # 
   4 # Permission is hereby granted, free of charge, to any person obtaining a copy
   5 # of this software and associated documentation files (the "Software"), to deal
   6 # in the Software without restriction, including without limitation the rights
   7 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
   8 # copies of the Software, and to permit persons to whom the Software is
   9 # furnished to do so, subject to the following conditions:
  10 # 
  11 # The above copyright notice and this permission notice shall be included in
  12 # all copies or substantial portions of the Software.
  13 # 
  14 # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  15 # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  16 # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  17 # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  18 # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  19 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  20 # SOFTWARE.
  21  
  22 
  23 import sys
  24 from time import sleep
  25 
  26 
  27 def example_exc_handler(tries_remaining, exception, delay):
  28     """Example exception handler; prints a warning to stderr.
  29 
  30     tries_remaining: The number of tries remaining.
  31     exception: The exception instance which was raised.
  32     """
  33     print >> sys.stderr, "Caught '%s', %d tries remaining, sleeping for %s seconds" % (exception, tries_remaining, delay)
  34 
  35 
  36 def retries(max_tries, delay=1, backoff=2, exceptions=(Exception,), hook=None):
  37     """Function decorator implementing retrying logic.
  38 
  39     delay: Sleep this many seconds * backoff * try number after failure
  40     backoff: Multiply delay by this factor after each failure
  41     exceptions: A tuple of exception classes; default (Exception,)
  42     hook: A function with the signature myhook(tries_remaining, exception);
  43           default None
  44 
  45     The decorator will call the function up to max_tries times if it raises
  46     an exception.
  47 
  48     By default it catches instances of the Exception class and subclasses.
  49     This will recover after all but the most fatal errors. You may specify a
  50     custom tuple of exception classes with the 'exceptions' argument; the
  51     function will only be retried if it raises one of the specified
  52     exceptions.
  53 
  54     Additionally you may specify a hook function which will be called prior
  55     to retrying with the number of remaining tries and the exception instance;
  56     see given example. This is primarily intended to give the opportunity to
  57     log the failure. Hook is not called after failure if no retries remain.
  58     """
  59     def dec(func):
  60         def f2(*args, **kwargs):
  61             mydelay = delay
  62             tries = range(max_tries)
  63             tries.reverse()
  64             for tries_remaining in tries:
  65                 try:
  66                    return func(*args, **kwargs)
  67                 except exceptions as e:
  68                     if tries_remaining > 0:
  69                         if hook is not None:
  70                             hook(tries_remaining, e, mydelay)
  71                         sleep(mydelay)
  72                         mydelay = mydelay * backoff
  73                     else:
  74                         raise
  75                 else:
  76                     break
  77         return f2
  78     return dec


CategoryDocumentation

PythonDecoratorLibrary (last edited 2012-05-16 23:26:33 by Igor)