python修饰器各种实用方法

python修饰器各种实用方法

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.

Contents

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.

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   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:

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   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:

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   1 class Example(object):
   2     @apply  # doesn't exist in Python 3
   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())
  16     #myattr = myattr()  # works in Python 2 and 3

Yet another property decorator:

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

Memoize

Here's a memoizing class.

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   1 import collections
   2 import functools
   3 
   4 class memoized(object):
   5    '''Decorator. Caches a function's return value each time it is called.
   6    If called later with the same arguments, the cached value is returned
   7    (not reevaluated).
   8    '''
   9    def __init__(self, func):
  10       self.func = func
  11       self.cache = {}
  12    def __call__(self, *args):
  13       if not isinstance(args, collections.Hashable):
  14          # uncacheable. a list, for instance.
  15          # better to not cache than blow up.
  16          return self.func(*args)
  17       if args in self.cache:
  18          return self.cache[args]
  19       else:
  20          value = self.func(*args)
  21          self.cache[args] = value
  22          return value
  23    def __repr__(self):
  24       '''Return the function's docstring.'''
  25       return self.func.__doc__
  26    def __get__(self, obj, objtype):
  27       '''Support instance methods.'''
  28       return functools.partial(self.__call__, obj)
  29 
  30 @memoized
  31 def fibonacci(n):
  32    "Return the nth fibonacci number."
  33    if n in (0, 1):
  34       return n
  35    return fibonacci(n-1) + fibonacci(n-2)
  36 
  37 print fibonacci(12)

Alternate memoize as nested functions

Here's a memoizing function that works on functions, methods, or classes, and exposes the cache publicly.

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   1 # note that this decorator ignores **kwargs
   2 def memoize(obj):
   3     cache = obj.cache = {}
   4 
   5     @functools.wraps(obj)
   6     def memoizer(*args, **kwargs):
   7         if args not in cache:
   8             cache[args] = obj(*args, **kwargs)
   9         return cache[args]
  10     return memoizer

Here's a modified version that also respects kwargs.

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   1 def memoize(obj):
   2     cache = obj.cache = {}
   3 
   4     @functools.wraps(obj)
   5     def memoizer(*args, **kwargs):
   6         key = str(args) + str(kwargs)
   7         if key not in cache:
   8             cache[key] = obj(*args, **kwargs)
   9         return cache[key]
  10     return memoizer

Alternate memoize as dict subclass

This is an idea that interests me, but it only seems to work on functions:

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   1 class memoize(dict):
   2     def __init__(self, func):
   3         self.func = func
   4 
   5     def __call__(self, *args):
   6         return self[args]
   7 
   8     def __missing__(self, key):
   9         result = self[key] = self.func(*key)
  10         return result
  11 
  12 #
  13 # Sample use
  14 #
  15 
  16 >>> @memoize
  17 ... def foo(a, b):
  18 ...     return a * b
  19 >>> foo(2, 4)
  20 8
  21 >>> foo
  22 {(2, 4): 8}
  23 >>> foo('hi', 3)
  24 'hihihi'
  25 >>> foo
  26 {(2, 4): 8, ('hi', 3): 'hihihi'}

Alternate memoize that stores cache between executions

Additional information and documentation for this decorator is available on Github.

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   1 import pickle
   2 import collections
   3 import functools
   4 import inspect
   5 import os.path
   6 import re
   7 import unicodedata
   8 
   9 class Memorize(object):
  10     '''
  11     A function decorated with @Memorize caches its return
  12     value every time it is called. If the function is called
  13     later with the same arguments, the cached value is
  14     returned (the function is not reevaluated). The cache is
  15     stored as a .cache file in the current directory for reuse
  16     in future executions. If the Python file containing the
  17     decorated function has been updated since the last run,
  18     the current cache is deleted and a new cache is created
  19     (in case the behavior of the function has changed).
  20     '''
  21     def __init__(self, func):
  22         self.func = func
  23         self.set_parent_file() # Sets self.parent_filepath and self.parent_filename
  24         self.__name__ = self.func.__name__
  25         self.set_cache_filename()
  26         if self.cache_exists():
  27             self.read_cache() # Sets self.timestamp and self.cache
  28             if not self.is_safe_cache():
  29                 self.cache = {}
  30         else:
  31             self.cache = {}
  32 
  33     def __call__(self, *args):
  34         if not isinstance(args, collections.Hashable):
  35             return self.func(*args)
  36         if args in self.cache:
  37             return self.cache[args]
  38         else:
  39             value = self.func(*args)
  40             self.cache[args] = value
  41             self.save_cache()
  42             return value
  43 
  44     def set_parent_file(self):
  45         """
  46         Sets self.parent_file to the absolute path of the
  47         file containing the memoized function.
  48         """
  49         rel_parent_file = inspect.stack()[-1].filename
  50         self.parent_filepath = os.path.abspath(rel_parent_file)
  51         self.parent_filename = _filename_from_path(rel_parent_file)
  52 
  53     def set_cache_filename(self):
  54         """
  55         Sets self.cache_filename to an os-compliant
  56         version of "file_function.cache"
  57         """
  58         filename = _slugify(self.parent_filename.replace('.py', ''))
  59         funcname = _slugify(self.__name__)
  60         self.cache_filename = filename+'_'+funcname+'.cache'
  61 
  62     def get_last_update(self):
  63         """
  64         Returns the time that the parent file was last
  65         updated.
  66         """
  67         last_update = os.path.getmtime(self.parent_filepath)
  68         return last_update
  69 
  70     def is_safe_cache(self):
  71         """
  72         Returns True if the file containing the memoized
  73         function has not been updated since the cache was
  74         last saved.
  75         """
  76         if self.get_last_update() > self.timestamp:
  77             return False
  78         return True
  79
  80    def read_cache(self):
  81        """
  82        Read a pickled dictionary into self.timestamp and
  83        self.cache. See self.save_cache.
  84        """
  85        with open(self.cache_filename, 'rb') as f:
  86            data = pickle.loads(f.read())
  87            self.timestamp = data['timestamp']
  88            self.cache = data['cache']
  89
  90    def save_cache(self):
  91        """
  92        Pickle the file's timestamp and the function's cache
  93        in a dictionary object.
  94        """
  95        with open(self.cache_filename, 'wb+') as f:
  96            out = dict()
  97            out['timestamp'] = self.get_last_update()
  98            out['cache'] = self.cache
  99            f.write(pickle.dumps(out))
 100
 101    def cache_exists(self):
 102        '''
 103        Returns True if a matching cache exists in the current directory.
 104        '''
 105        if os.path.isfile(self.cache_filename):
 106            return True
 107        return False
 108
 109    def __repr__(self):
 110        """ Return the function's docstring. """
 111        return self.func.__doc__
 112
 113    def __get__(self, obj, objtype):
 114        """ Support instance methods. """
 115        return functools.partial(self.__call__, obj)
 116
 117def _slugify(value):
 118    """
 119    Normalizes string, converts to lowercase, removes
 120    non-alpha characters, and converts spaces to
 121    hyphens. From
 122    http://stackoverflow.com/questions/295135/turn-a-string-into-a-valid-filename-in-python
 123    """
 124    value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')
 125    value = re.sub(r'[^ws-]', '', value.decode('utf-8', 'ignore'))
 126    value = value.strip().lower()
 127    value = re.sub(r'[-s]+', '-', value)
 128    return value
 129
 130def _filename_from_path(filepath):
 131    return filepath.split('/')[-1]

Cached Properties

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   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 create 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.

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   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

(FYI you can use functools.partial() to emulate currying (which works even for keyword arguments))

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   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

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   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:

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   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.:

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   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.)

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   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.

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   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

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   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

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   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

Toggle line numbers

   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.)

Toggle line numbers

   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

Ignoring Deprecation Warnings

Toggle line numbers

   1 import warnings
   2 
   3 def ignore_deprecation_warnings(func):
   4     '''This is a decorator which can be used to ignore deprecation warnings
   5     occurring in a function.'''
   6     def new_func(*args, **kwargs):
   7         with warnings.catch_warnings():
   8             warnings.filterwarnings("ignore", category=DeprecationWarning)
   9             return func(*args, **kwargs)
  10     new_func.__name__ = func.__name__
  11     new_func.__doc__ = func.__doc__
  12     new_func.__dict__.update(func.__dict__)
  13     return new_func
  14 
  15 # === Examples of use ===
  16 
  17 @ignore_deprecation_warnings
  18 def some_function_raising_deprecation_warning():
  19     warnings.warn("This is a deprecationg warning.",
  20                   category=DeprecationWarning)
  21 
  22 class SomeClass:
  23     @ignore_deprecation_warnings
  24     def some_method_raising_deprecation_warning():
  25         warnings.warn("This is a deprecationg warning.",
  26                       category=DeprecationWarning)

Enable/Disable Decorators

Toggle line numbers

   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

Toggle line numbers

   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

Toggle line numbers

   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
  94class 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
 111def __test():
 112    import doctest
 113    doctest.testmod()
 114
 115if __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.

Toggle line numbers

   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.

Toggle line numbers

   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.

Toggle line numbers

   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  #!python
   6 -- MEDIUM: Print warning message to stderr. (Default)
   7     2 -- STRONG: Raise TypeError with message.
   8 If 'debug' is not passed to the decorator, the default level is used.
   9 
  10 Example usage:
  11     >>> NONE, MEDIUM, STRONG = 0, 1, 2
  12     >>>
  13     >>> @accepts(int, int, int)
  14     ... @returns(float)
  15     ... def average(x, y, z):
  16     ...     return (x + y + z) / 2
  17     ...
  18     >>> average(5.5, 10, 15.0)
  19     TypeWarning:  'average' method accepts (int, int, int), but was given
  20     (float, int, float)
  21     15.25
  22     >>> average(5, 10, 15)
  23     TypeWarning:  'average' method returns (float), but result is (int)
  24     15
  25 
  26 Needed to cast params as floats in function def (or simply divide by 2.0).
  27 
  28     >>> TYPE_CHECK = STRONG
  29     >>> @accepts(int, debug=TYPE_CHECK)
  30     ... @returns(int, debug=TYPE_CHECK)
  31     ... def fib(n):
  32     ...     if n in (0, 1): return n
  33     ...     return fib(n-1) + fib(n-2)
  34     ...
  35     >>> fib(5.3)
  36     Traceback (most recent call last):
  37       ...
  38     TypeError: 'fib' method accepts (int), but was given (float)
  39 
  40 '''
  41 import sys
  42 
  43 def accepts(*types, **kw):
  44     '''Function decorator. Checks decorated function's arguments are
  45     of the expected types.
  46 
  47     Parameters:
  48     types -- The expected types of the inputs to the decorated function.
  49              Must specify type for each parameter.
  50     kw    -- Optional specification of 'debug' level (this is the only valid
  51              keyword argument, no other should be given).
  52              debug = ( 0 | 1 | 2 )
  53 
  54     '''
  55     if not kw:
  56         # default level: MEDIUM
  57         debug = 1
  58     else:
  59         debug = kw['debug']
  60     try:
  61         def decorator(f):
  62             def newf(*args):
  63                 if debug is 0:
  64                     return f(*args)
  65                 assert len(args) == len(types)
  66                 argtypes = tuple(map(type, args))
  67                 if argtypes != types:
  68                     msg = info(f.__name__, types, argtypes, 0)
  69                     if debug is 1:
  70                         print >> sys.stderr, 'TypeWarning: ', msg
  71                     elif debug is 2:
  72                         raise TypeError, msg
  73                 return f(*args)
  74             newf.__name__ = f.__name__
  75             return newf
  76         return decorator
  77     except KeyError, key:
  78         raise KeyError, key + "is not a valid keyword argument"
  79     except TypeError, msg:
  80        raise TypeError, msg
  81
  82
  83def returns(ret_type, **kw):
  84    '''Function decorator. Checks decorated function's return value
  85    is of the expected type.
  86  87    Parameters:
  88    ret_type -- The expected type of the decorated function's return value.
  89                Must specify type for each parameter.
  90    kw       -- Optional specification of 'debug' level (this is the only valid
  91                keyword argument, no other should be given).
  92                debug=(0 | 1 | 2)
  93    '''
  94    try:
  95        if not kw:
  96            # default level: MEDIUM
  97            debug = 1
  98        else:
  99            debug = kw['debug']
 100        def decorator(f):
 101            def newf(*args):
 102                result = f(*args)
 103                if debug is 0:
 104                    return result
 105                res_type = type(result)
 106                if res_type != ret_type:
 107                    msg = info(f.__name__, (ret_type,), (res_type,), 1)
 108                    if debug is 1:
 109                        print >> sys.stderr, 'TypeWarning: ', msg
 110                    elif debug is 2:
 111                        raise TypeError, msg
 112                return result
 113            newf.__name__ = f.__name__
 114            return newf
 115        return decorator
 116    except KeyError, key:
 117        raise KeyError, key + "is not a valid keyword argument"
 118    except TypeError, msg:
 119        raise TypeError, msg
 120
 121def info(fname, expected, actual, flag):
 122    '''Convenience function returns nicely formatted error/warning msg.'''
 123    format = lambda types: ', '.join([str(t).split("'")[1] for t in types])
 124    expected, actual = format(expected), format(actual)
 125    msg = "'{}' method ".format( fname )
 126          + ("accepts", "returns")[flag] + " ({}), but ".format(expected)
 127          + ("was given", "result is")[flag] + " ({})".format(actual)
 128    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.)

Toggle line numbers

   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

"
   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.

Toggle line numbers

   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
 109def 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
 150main()

And now the imported statedefn.py

Toggle line numbers

   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
  88class 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 ...
 156def 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
 173def 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
 182def 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:

Decorator with no arguments
Decorator with arguments
Decorator with wrapped class instance awareness

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

Toggle line numbers

   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
 104def 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!")
 110def 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
 119class 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
 141ThirdBruce = Bruce('Third Bruce')
 142
 143SecondBruce()
 144FirstBruce("First Bruce: Oh, Hello Bruce!")
 145ThirdBruce.talk()
 146FirstBruce("First Bruce: Bit crook, Bruce.")
 147SecondBruce("Where's Bruce?")
 148FirstBruce("First Bruce: He's not here, Bruce")
 149ThirdBruce.talk("Blimey, s'hot in here, Bruce.")
 150FirstBruce("First Bruce: S'hot enough to boil a monkey's bum!")
 151SecondBruce("That's a strange expression, Bruce.")
 152FirstBruce("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.")
 153ThirdBruce.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.

Toggle line numbers

   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.

Toggle line numbers

   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, **kwargs):
  18         stdobak = sys.stdout
  19         lpinstance = LogPrinter()
  20         sys.stdout = lpinstance
  21         try:
  22             return func(*arg, **kwargs)
  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

Toggle line numbers

   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:

Toggle line numbers

   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

Toggle line numbers

   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

The Sublime Singleton

Toggle line numbers

   1 def singleton(cls):
   2     instance = cls()
   3     instance.__call__ = lambda: instance
   4     return instance
   5 
   6 #
   7 # Sample use
   8 #
   9 
  10 @singleton
  11 class Highlander:
  12     x = 100
  13     # Of course you can have any attributes or methods you like.
  14 
  15 Highlander() is Highlander() is Highlander #=> True
  16 id(Highlander()) == id(Highlander) #=> True
  17 Highlander().x == Highlander.x == 100 #=> True
  18 Highlander.x = 50
  19 Highlander().x == Highlander.x == 50 #=> True

Asynchronous Call

Toggle line numbers

   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.

Toggle line numbers

   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:

Works with any function that signals failure by raising an exception (I.E. just about any function)
Supports retry delay and backoff
User can specify which exceptions are caught for retrying. E.g. networking code might be expected to raise SocketError in the event of communications difficulties, while any other exception likely indicates a bug in the code.
Hook for custom logging

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

Toggle line numbers

   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

Logging decorator with specified logger (or default)

This decorator will log entry and exit points of your funtion using the specified logger or it defaults to your function's module name logger.

In the current form it uses the logging.INFO level, but I can easily customized to use what ever level. Same for the entry and exit messages.

Toggle line numbers

   1 import functools, logging
   2 
   3 
   4 log = logging.getLogger(__name__)
   5 log.setLevel(logging.DEBUG)
   6 
   7 class log_with(object):
   8     '''Logging decorator that allows you to log with a
   9 specific logger.
  10 '''
  11     # Customize these messages
  12     ENTRY_MESSAGE = 'Entering {}'
  13     EXIT_MESSAGE = 'Exiting {}'
  14 
  15     def __init__(self, logger=None):
  16         self.logger = logger
  17 
  18     def __call__(self, func):
  19         '''Returns a wrapper that wraps func.
  20 The wrapper will log the entry and exit points of the function
  21 with logging.INFO level.
  22 '''
  23         # set logger if it was not set earlier
  24         if not self.logger:
  25             logging.basicConfig()
  26             self.logger = logging.getLogger(func.__module__)
  27 
  28         @functools.wraps(func)
  29         def wrapper(*args, **kwds):
  30             self.logger.info(self.ENTRY_MESSAGE.format(func.__name__))  # logging level .info(). Set to .debug() if you want to
  31             f_result = func(*args, **kwds)
  32             self.logger.info(self.EXIT_MESSAGE.format(func.__name__))   # logging level .info(). Set to .debug() if you want to
  33             return f_result
  34         return wrapper

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   1 # Sample use and output:
   2 
   3 if __name__ == '__main__':
   4     logging.basicConfig()
   5     log = logging.getLogger('custom_log')
   6     log.setLevel(logging.DEBUG)
   7     log.info('ciao')
   8 
   9     @log_with(log)     # user specified logger
  10     def foo():
  11         print 'this is foo'
  12     foo()
  13 
  14     @log_with()        # using default logger
  15     def foo2():
  16         print 'this is foo2'
  17     foo2()

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   1 # output
   2 >>> ================================ RESTART ================================
   3 >>>
   4 INFO:custom_log:ciao
   5 INFO:custom_log:Entering foo # uses the correct logger
   6 this is foo
   7 INFO:custom_log:Exiting foo
   8 INFO:__main__:Entering foo2  # uses the correct logger
   9 this is foo2
  10 INFO:__main__:Exiting foo2

Lazy Thunkify

This decorator will cause any function to, instead of running its code, start a thread to run the code, returning a thunk (function with no args) that wait for the function's completion and returns the value (or raises the exception).

Useful if you have Computation A that takes x seconds and then uses Computation B, which takes y seconds. Instead of x+y seconds you only need max(x,y) seconds.

Toggle line numbers

   1 import threading, sys, functools, traceback
   2 
   3 def lazy_thunkify(f):
   4     """Make a function immediately return a function of no args which, when called,
   5     waits for the result, which will start being processed in another thread."""
   6 
   7     @functools.wraps(f)
   8     def lazy_thunked(*args, **kwargs):
   9         wait_event = threading.Event()
  10 
  11         result = [None]
  12         exc = [False, None]
  13 
  14         def worker_func():
  15             try:
  16                 func_result = f(*args, **kwargs)
  17                 result[0] = func_result
  18             except Exception, e:
  19                 exc[0] = True
  20                 exc[1] = sys.exc_info()
  21                 print "Lazy thunk has thrown an exception (will be raised on thunk()):
%s" % (
  22                     traceback.format_exc())
  23             finally:
  24                 wait_event.set()
  25 
  26         def thunk():
  27             wait_event.wait()
  28             if exc[0]:
  29                 raise exc[1][0], exc[1][1], exc[1][2]
  30 
  31             return result[0]
  32 
  33         threading.Thread(target=worker_func).start()
  34 
  35         return thunk
  36 
  37     return lazy_thunked

Example:

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   1 @lazy_thunkify
   2 def slow_double(i):
   3     print "Multiplying..."
   4     time.sleep(5)
   5     print "Done multiplying!"
   6     return i*2
   7 
   8 
   9 def maybe_multiply(x):
  10     double_thunk = slow_double(x)
  11     print "Thinking..."
  12     time.sleep(3)
  13     time.sleep(3)
  14     time.sleep(1)
  15     if x == 3:
  16         print "Using it!"
  17         res = double_thunk()
  18     else:
  19         print "Not using it."
  20         res = None
  21     return res
  22 
  23 #both take 7 seconds
  24 maybe_multiply(10)
  25 maybe_multiply(3)

Aggregative decorators for generator functions

This could be a whole family of decorators. The aim is applying an aggregation function to the iterated outcome of a generator-functions.

Two interesting aggregators could be sum and average:

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   1 import functools as ft import operator as op
   2 
   3 def summed(f):
   4   return lambda *xs : sum(f(*xs))
   5 
   6 def averaged(f):
   7   def aux(acc, x):
   8     return (acc[0] + x, acc[1] + 1)
   9 
  10   def out(*xs):
  11     s, n = ft.reduce(aux, f(*xs), (0, 0))
  12     return s / n if n > 0 else 0
  13 
  14   return out

Examples for the two proposed decorators:

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   1 @averaged
   2 def producer2():
   3     yield 10
   4     yield 5
   5     yield 2.5
   6     yield 7.5
   7 
   8 assert producer2() == (10 + 5 + 2.5 + 7.5) / 4
   9 
  10 @summed
  11 def producer1():
  12     yield 10
  13     yield 5
  14     yield 2.5
  15     yield 7.5
  16 
  17 assert producer1() == (10 + 5 + 2.5 + 7.5)

Function Timeout

Ever had a function take forever in weird edge cases? In one case, a function was extracting URIs from a long string using regular expressions, and sometimes it was running into a bug in the Python regexp engine and would take minutes rather than milliseconds. The best solution was to install a timeout using an alarm signal and simply abort processing. This can conveniently be wrapped in a decorator:

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   1 import signal
   2 import functools
   3 
   4 class TimeoutError(Exception): pass
   5 
   6 def timeout(seconds, error_message = 'Function call timed out'):
   7     def decorated(func):
   8         def _handle_timeout(signum, frame):
   9             raise TimeoutError(error_message)
  10 
  11         def wrapper(*args, **kwargs):
  12             signal.signal(signal.SIGALRM, _handle_timeout)
  13             signal.alarm(seconds)
  14             try:
  15                 result = func(*args, **kwargs)
  16             finally:
  17                 signal.alarm(0)
  18             return result
  19 
  20         return functools.wraps(func)(wrapper)
  21 
  22     return decorated

Example:

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   1 import time
   2 
   3 @timeout(1, 'Function slow; aborted')
   4 def slow_function():
   5     time.sleep(5)

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原文地址：https://www.cnblogs.com/UnGeek/p/5421687.html