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   def __call__(self, *a):
  12     args = self.args + a
  13     if len(args) < self.func.func_code.co_argcount:
  14       return curried(self.func, *args)
  15     else:
  16       return self.func(*args)
  17 
  18 
  19 @curried
  20 def add(a, b):
  21     return a+b
  22 
  23 add1 = add(1)
  24 
  25 print add1(2)

   1 import sys, sets
   2 
   3 WHAT_TO_DEBUG = sets.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 = sets.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))

   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 new
  10         f = new.instancemethod(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"

   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])

   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 mySimpleLoggingDecorator( func ):
  29     def logged_func( *args, **kwargs ):
  30         print 'calling %s' % func.__name__
  31         return func( *args, **kwargs )
  32     return logged_func
  33 
  34 @mySimpleLoggingDecorator
  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)

   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 emptyFunc(*args,**kargs):
   8         pass
   9     return emptyFunc
  10 
  11 # define this as equivalent to unchanged, for nice symmetry with disabled
  12 enabled = unchanged
  13 
  14 #
  15 # Sample use
  16 #
  17 
  18 globalEnableFlag = int(True)
  19 
  20 @(disabled, enabled)[globalEnableFlag]
  21 def specialFunctionFoo():
  22     print "function was enabled"

   1 def dumpArgs(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     def echoFunc(*args,**kwargs):
   6         print fname,":", ', '.join('%s=%r' % entry 
   7                                    for entry in zip(argnames,args) + kwargs.items())
   8         return func(*args, **kwargs)
   9     return echoFunc
  10     
  11 @dumpArgs
  12 def f1(a,b,c):
  13     print a+b+c
  14     
  15 f1(1,2,3)

   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()