How can I decorate the last function in a class inheritance?
If I decorate a superclass function, the subclass function overrides the decorator.
I'd like to find out if there is a neat way to automatically decorate the top function in the MRO.
def wrapper(f):
def _wrap(*args, **kwargs):
print("In wrapper")
return f(*args, **kwargs)
return _wrap
class A:
#wrapper
def f(self):
print("In class A")
class B(A):
def f(self):
print("In class B")
if __name__ == '__main__':
a = A()
b = B()
print("Calling A:")
a.f()
print("Calling B:")
b.f()
Here is the output. As expected, B.f() does not call the wrapper, though I'd like it to.
Calling A:
In wrapper
In class A
Calling B:
In class B
Here is what I have tried thus far. A metaclass that holds all the decorators and injects them during class instantiation.
from abc import ABCMeta
class WrapperMetaClass(ABCMeta):
def __init__(cls, *args, **kwargs):
wrappers_dict = getattr(cls, "_wrappers")
for attr_name in dir(cls):
if attr_name not in wrappers_dict:
continue
else:
wrapper = wrappers_dict[attr_name]
attr = getattr(cls, attr_name)
if not hasattr(attr, '__call__'):
raise Exception("What you're trying to wrap is not a function!")
attr = wrapper(attr)
setattr(cls, attr_name, attr)
super().__init__(*args, **kwargs)
This works:
class A(metaclass=WrapperMetaClass):
_wrappers = {
"f": wrapper
}
def f(self):
print("In class A")
class B(A):
def f(self):
print("In class B")
The output is what I wanted.
Calling A:
In wrapper
In class A
Calling B:
In wrapper
In class B
However, this runs into a different issue. If B does not override f, the metaclass wraps A.f() twice. This makes sense, as both A and B inherit WrapperMetaClass, so A.f() is wrapped first, and then B.f() is wrapped again.
class A(metaclass=WrapperMetaClass):
_wrappers = {
"f": wrapper
}
def f(self):
print("In class A")
class B(A):
pass
The output becomes:
Calling A:
In wrapper
In class A
Calling B:
In wrapper
In wrapper
In class A
I have no idea what else I could do.
Yes, I remember facing this once or twice - and you are on the right track.
But first things first: if the logic in your "wrapper" is something that
could be put in a method in the base class, then breaking-up the methods
in smaller-tasks, and have a "method slot" system is preferable to this,
as user 2357112 supports monica puts in the comments. If you find out you really need or prefer decorators, the full code is bellow
class A:
def do_things(self):
create_connection() # <- this is the code you'd are putting in the wrapper in the other approach
do_thing_1()
class B(A):
def do_things(self):
# here we have to do thing_1 and thing_2, but
# the connection is created in the superclass method...
# this could be the origin of your question
# Refactor to:
class A:
def do_things(self):
create_connection()
self.internal_do_things()
def internal_do_things(self):
do_thing_1()
class B(A):
def internal_do_things(self):
super().internal_do_things()
do_thing_2()
So, classical inheritance and OO solves this
If you need the decorators anway:
The thing to do is to have the decorator itself, the "wrapper", get
a way to "know" if it already was called in an outer method (i.e. a method
in a subclass which calls super()), and just act as a transparent
wrapper in this case.
It gets a bit further complicated when we want a robust solution:
a wrapper that can work for different methods in the same class,
and does not get confused if they are called concurrently
(in different threads, or a method calling another method,
not super(), which should trigger the wrapper).
And in the end, the mechanisms for that are complicated enough that
they should not get in the way of your actual wrapper - so,
ideally they should be built as a decorator themselves, which will
decorate your wrapper.
[hours later]
So, sorry if it does not look "neat" - it turns out implementing what is described above is a bit more complex than I thought initially - we need an intermediate decorator level (called meta_wrapper_applier in the code), so that the metaclass can re-wrap the methods each time they are redeclared.
I hope the comments in the code and variable names are enough to understand the idea:
from abc import ABCMeta
from functools import wraps
import threading
class WrapperMetaClass(ABCMeta):
def __init__(cls, name, bases, ns, **kw):
super().__init__(name, bases, ns, **kw)
# Get the wrapped methods for all the superclasses
super_wrappers = {}
for supercls in cls.__mro__[::-1]:
super_wrappers.update(supercls.__dict__.get("_wrappers", {}))
# unconditionally install a wrappers dict for each subclass:
sub_wrappers = cls._wrappers = {}
for attrname, attr in ns.items():
if attrname in super_wrappers:
# Applies the wrapper in the baseclass to the subclass method:
setattr(cls, attrname, super_wrappers[attrname]._run_once_wrapper(attr))
elif hasattr(attr, "_run_once_wrapper"):
# Store the wrapper information in the cls for use of the subclasses:
sub_wrappers[attrname] = attr
def run_once_method_decorator(original_wrapper):
re_entering_stacks = {}
# This is the callable used to place a wrapper on the original
# method and on each overriden method.
# All methods with the same name in the subclasses will share the same original wrapper and the
# "re_entering_stacks" data structure.
def meta_wrapper_applier(raw_method):
wrapped_method_in_subclass = None
#wraps(original_wrapper)
def meta_wrapper(*args, **kw):
nonlocal wrapped_method_in_subclass
# uses a plain list to keep track of re-entering the same-named method
# in each thread:
re_entering_stack = re_entering_stacks.setdefault(threading.current_thread(), [])
re_entering = bool(re_entering_stack)
try:
re_entering_stack.append(1)
if re_entering:
result = raw_method(*args, **kw)
else:
if wrapped_method_in_subclass is None:
# Applies the original decorator lazily, and caches the result:
wrapped_method_in_subclass = original_wrapper(raw_method)
result = wrapped_method_in_subclass(*args, **kw)
finally:
re_entering_stack.pop()
return result
# registry = original_wrapper.__dict__.setdefault("_run_once_registry", {})
meta_wrapper._run_once_wrapper = meta_wrapper_applier
return meta_wrapper
return meta_wrapper_applier
# From here on, example code only;
#run_once_method_decorator
def wrapper(f):
#wraps(f)
def _wrap(*args, **kwargs):
print("Entering wrapper")
result = f(*args, **kwargs)
print("Leaving wrapper\n")
return result
return _wrap
#run_once_method_decorator
def other_wrapper(f):
#wraps(f)
def _wrap(*args, **kwargs):
print("Entering other wrapper")
result = f(*args, **kwargs)
print("Leaving other wrapper\n")
return result
return _wrap
class A(metaclass=WrapperMetaClass):
#wrapper
def f(self):
print("In class A")
def g(self):
print("g in A")
class B(A):
def f(self):
print("In class B")
super().f()
#other_wrapper
def g(self):
print("g in B")
super().g()
class C(B):
def g(self):
print("g in C")
super().g()
if __name__ == '__main__':
a = A()
b = B()
print("Calling A:")
a.f()
a.g()
print("Calling B:")
b.f()
b.g()
print("Calling C:")
C().g()
Output:
Calling A:
Entering wrapper
In class A
Leaving wrapper
g in A
Calling B:
Entering wrapper
In class B
In class A
Leaving wrapper
Entering other wrapper
g in B
g in A
Leaving other wrapper
Calling C:
Entering other wrapper
g in C
g in B
g in A
Leaving other wrapper
I am currently trying to wrap my head around metaclasses for a particular problem:
- a container class needs to expose some functions from its elements
- the container class cannot be modified afterwards
Metaclasses seem to be a good way to accomplish the above (e.g. __slots__ can be modified in __new__.
Example:
import inspect
class MyMeta(type):
def __new__(cls, name, bases, attrs):
...
# modify slots ?
return super().__new__(cls, name, bases, attrs)
def __call__(cls, *args, **kwargs):
name = args[0]
elements = args[1]
tmp_class = super().__call__(*args, **kwargs)
ds_methods = [m for m, _ in inspect.getmembers(tmp_class, predicate=inspect.ismethod)]
e_methods = [m for m, _ in inspect.getmembers(elements[0], predicate=inspect.ismethod) if m not in ds_methods]
attrs = {m:f for m, f in inspect.getmembers(cls, predicate=inspect.ismethod)}
# for testing map to print
new_attr = {m: print for m in e_methods}
attrs.update(new_attr)
.... # call __new__?
# this does not contain the new methods
newclass = cls.__new__(cls, cls.__name__, [object], attrs)
class Container(metaclass=MyMeta):
__slots__ = ['_name', '_elements']
def __init__(self, name, elements):
self._name = name
self._elements = elements
In short: the only way I found to modify slots is in __new__ and the only way to intercept creation arguments is in __call__.
There is probably a much simpler way to accomplish this (the above does not work), I would be thankful for any pointers to help me better understand metaclasses.
I have a subclass that adds graphics capabilities to a superclass that implements the algorithms. So, in addition to a few extra initialization functions, this subclass will only need to refresh the graphics after the execution of each algorithm-computing function in the superclass.
Classes:
class graph(algorithms):
... #initialization and refresh decorators
#refreshgraph
def algorithm1(self, *args, **kwargs):
return algorithms.algorithm1(self, *args, **kwargs)
#refreshgraph
def algorithm2(self, *args, **kwargs):
return algorithms.algorithm2(self, *args, **kwargs)
... #and so on
Is there an pythonic way to automatically decorate all the non-private methods defined in the superclass, such that if I add a new algorithm there I don't need to explicitly mention it in my subclass? I would also like to be able to explicitly exclude some of the superclass' methods.
The subclass always gets all the methods from the parent class(es) by default. If you wish to make emulate the behavior other languages use for privacy (eg the 'private' or 'protected' modifiers in C#) you have two options:
1) Python convention (and it's just a convention) is that methods with a single leading underscore in their names are not designed for access from outside the defining class.
2) Names with a double leading underscore are mangled in the bytecode so they aren't visible to other code under their own names. ParentClass.__private is visible inside ParentClass, but can only be accessed from outside ParentClass as ParentClass._ParentClass__private. (Great explanations here). Nothing in Python is truly private ;)
To override an inherited method just define the same name in a derived class. To call the parent class method inside the derived class you can do it as you did in your example, or using super:
def algorithm2(self, *args, **kwargs):
super(graph, self).algorithm2(self, *args, **kwargs)
# do other derived stuff here....
self.refresh()
This is ugly, but I think it does what you want, but without inheritance:
class DoAfter(object):
def __init__(self, obj, func):
self.obj = obj
self.func = func
def __getattribute__(self, attr, *a, **kw):
obj = object.__getattribute__(self, 'obj')
if attr in dir(obj):
x = getattr(obj, attr)
if callable(x):
def b(*a, **kw):
retval = x(*a, **kw)
self.func()
return retval
return b
else:
return x
else:
return object.__getattribute__(self, attr)
Use it like this:
>>> class A(object):
... def __init__(self):
... self.a = 1
...
... def boo(self, c):
... self.a += c
... return self.a
>>> def do_something():
... print 'a'
>>> a = A()
>>> print a.boo(1)
2
>>> print a.boo(2)
4
>>> b = DoAfter(a, do_something)
>>> print b.boo(1)
a
5
>>> print b.boo(2)
a
7
A increments a counter each time A.boo is called. DoAfter wraps A, so that any method in the instance a can be called as if it were a member of b. Note that every method is wrapped this way, so do_something() is called whenever a method is accessed.
This is barely tested, not recommended, and probably a bad idea. But, I think it does what you asked for.
EDIT: to do this with inheritance:
class graph(algorithms):
def refreshgraph(self):
print 'refreshgraph'
def __getattribute__(self, attr):
if attr in dir(algorithms):
x = algorithms.__getattribute__(self, attr)
if callable(x):
def wrapped(*a, **kw):
retval = x(*a, **kw)
self.refreshgraph()
return retval
return wrapped
else:
return x
else:
return object.__getattribute__(self, attr)
I have python class trees, each made up of an abstract base class and many deriving concrete classes. I want all concrete classes to be accessible through a base-class method, and I do not want to specify anything during child-class creation.
This is what my imagined solution looks like:
class BaseClassA(object):
# <some magic code around here>
#classmethod
def getConcreteClasses(cls):
# <some magic related code here>
class ConcreteClassA1(BaseClassA):
# no magic-related code here
class ConcreteClassA2(BaseClassA):
# no magic-related code here
As much as possible, I'd prefer to write the "magic" once as a sort of design pattern. I want to be able to apply it to different class trees in different scenarios (i.e. add a similar tree with "BaseClassB" and its concrete classes).
Thanks Internet!
you can use meta classes for that:
class AutoRegister(type):
def __new__(mcs, name, bases, classdict):
new_cls = type.__new__(mcs, name, bases, classdict)
#print mcs, name, bases, classdict
for b in bases:
if hasattr(b, 'register_subclass'):
b.register_subclass(new_cls)
return new_cls
class AbstractClassA(object):
__metaclass__ = AutoRegister
_subclasses = []
#classmethod
def register_subclass(klass, cls):
klass._subclasses.append(cls)
#classmethod
def get_concrete_classes(klass):
return klass._subclasses
class ConcreteClassA1(AbstractClassA):
pass
class ConcreteClassA2(AbstractClassA):
pass
class ConcreteClassA3(ConcreteClassA2):
pass
print AbstractClassA.get_concrete_classes()
I'm personnaly very wary of this kind of magic. Don't put too much of this in your code.
Here is a simple solution using modern python's (3.6+) __init__subclass__ defined in PEP 487. It allows you to avoid using a meta-class.
class BaseClassA(object):
_subclasses = []
#classmethod
def get_concrete_classes(cls):
return list(cls._subclasses)
def __init_subclass__(cls):
BaseClassA._subclasses.append(cls)
class ConcreteClassA1(BaseClassA):
pass # no magic-related code here
class ConcreteClassA2(BaseClassA):
pass # no magic-related code here
print(BaseClassA.get_concrete_classes())
You should know that part of the answer you're looking for is built-in. New-style classes automatically keep a weak reference to all of their child classes which can be accessed with the __subclasses__ method:
#classmethod
def getConcreteClasses(cls):
return cls.__subclasses__()
This won't return sub-sub-classes. If you need those, you can create a recursive generator to get them all:
#classmethod
def getConcreteClasses(cls):
for c in cls.__subclasses__():
yield c
for c2 in c.getConcreteClasses():
yield c2
Another way to do this, with a decorator, if your subclasses are either not defining __init__ or are calling their parent's __init__:
def lister(cls):
cls.classes = list()
cls._init = cls.__init__
def init(self, *args, **kwargs):
cls = self.__class__
if cls not in cls.classes:
cls.classes.append(cls)
cls._init(self, *args, **kwargs)
cls.__init__ = init
#classmethod
def getclasses(cls):
return cls.classes
cls.getclasses = getclasses
return cls
#lister
class A(object): pass
class B(A): pass
class C(A):
def __init__(self):
super(C, self).__init__()
b = B()
c = C()
c2 = C()
print 'Classes:', c.getclasses()
It will work whether or not the base class defines __init__.
This question already has answers here:
Creating a singleton in Python
(38 answers)
Closed 4 years ago.
There seem to be many ways to define singletons in Python. Is there a consensus opinion on Stack Overflow?
I don't really see the need, as a module with functions (and not a class) would serve well as a singleton. All its variables would be bound to the module, which could not be instantiated repeatedly anyway.
If you do wish to use a class, there is no way of creating private classes or private constructors in Python, so you can't protect against multiple instantiations, other than just via convention in use of your API. I would still just put methods in a module, and consider the module as the singleton.
Here's my own implementation of singletons. All you have to do is decorate the class; to get the singleton, you then have to use the Instance method. Here's an example:
#Singleton
class Foo:
def __init__(self):
print 'Foo created'
f = Foo() # Error, this isn't how you get the instance of a singleton
f = Foo.instance() # Good. Being explicit is in line with the Python Zen
g = Foo.instance() # Returns already created instance
print f is g # True
And here's the code:
class Singleton:
"""
A non-thread-safe helper class to ease implementing singletons.
This should be used as a decorator -- not a metaclass -- to the
class that should be a singleton.
The decorated class can define one `__init__` function that
takes only the `self` argument. Also, the decorated class cannot be
inherited from. Other than that, there are no restrictions that apply
to the decorated class.
To get the singleton instance, use the `instance` method. Trying
to use `__call__` will result in a `TypeError` being raised.
"""
def __init__(self, decorated):
self._decorated = decorated
def instance(self):
"""
Returns the singleton instance. Upon its first call, it creates a
new instance of the decorated class and calls its `__init__` method.
On all subsequent calls, the already created instance is returned.
"""
try:
return self._instance
except AttributeError:
self._instance = self._decorated()
return self._instance
def __call__(self):
raise TypeError('Singletons must be accessed through `instance()`.')
def __instancecheck__(self, inst):
return isinstance(inst, self._decorated)
You can override the __new__ method like this:
class Singleton(object):
_instance = None
def __new__(cls, *args, **kwargs):
if not cls._instance:
cls._instance = super(Singleton, cls).__new__(
cls, *args, **kwargs)
return cls._instance
if __name__ == '__main__':
s1 = Singleton()
s2 = Singleton()
if (id(s1) == id(s2)):
print "Same"
else:
print "Different"
A slightly different approach to implement the singleton in Python is the borg pattern by Alex Martelli (Google employee and Python genius).
class Borg:
__shared_state = {}
def __init__(self):
self.__dict__ = self.__shared_state
So instead of forcing all instances to have the same identity, they share state.
The module approach works well. If I absolutely need a singleton I prefer the Metaclass approach.
class Singleton(type):
def __init__(cls, name, bases, dict):
super(Singleton, cls).__init__(name, bases, dict)
cls.instance = None
def __call__(cls,*args,**kw):
if cls.instance is None:
cls.instance = super(Singleton, cls).__call__(*args, **kw)
return cls.instance
class MyClass(object):
__metaclass__ = Singleton
See this implementation from PEP318, implementing the singleton pattern with a decorator:
def singleton(cls):
instances = {}
def getinstance():
if cls not in instances:
instances[cls] = cls()
return instances[cls]
return getinstance
#singleton
class MyClass:
...
The Python documentation does cover this:
class Singleton(object):
def __new__(cls, *args, **kwds):
it = cls.__dict__.get("__it__")
if it is not None:
return it
cls.__it__ = it = object.__new__(cls)
it.init(*args, **kwds)
return it
def init(self, *args, **kwds):
pass
I would probably rewrite it to look more like this:
class Singleton(object):
"""Use to create a singleton"""
def __new__(cls, *args, **kwds):
"""
>>> s = Singleton()
>>> p = Singleton()
>>> id(s) == id(p)
True
"""
it_id = "__it__"
# getattr will dip into base classes, so __dict__ must be used
it = cls.__dict__.get(it_id, None)
if it is not None:
return it
it = object.__new__(cls)
setattr(cls, it_id, it)
it.init(*args, **kwds)
return it
def init(self, *args, **kwds):
pass
class A(Singleton):
pass
class B(Singleton):
pass
class C(A):
pass
assert A() is A()
assert B() is B()
assert C() is C()
assert A() is not B()
assert C() is not B()
assert C() is not A()
It should be relatively clean to extend this:
class Bus(Singleton):
def init(self, label=None, *args, **kwds):
self.label = label
self.channels = [Channel("system"), Channel("app")]
...
As the accepted answer says, the most idiomatic way is to just use a module.
With that in mind, here's a proof of concept:
def singleton(cls):
obj = cls()
# Always return the same object
cls.__new__ = staticmethod(lambda cls: obj)
# Disable __init__
try:
del cls.__init__
except AttributeError:
pass
return cls
See the Python data model for more details on __new__.
Example:
#singleton
class Duck(object):
pass
if Duck() is Duck():
print "It works!"
else:
print "It doesn't work!"
Notes:
You have to use new-style classes (derive from object) for this.
The singleton is initialized when it is defined, rather than the first time it's used.
This is just a toy example. I've never actually used this in production code, and don't plan to.
I'm very unsure about this, but my project uses 'convention singletons' (not enforced singletons), that is, if I have a class called DataController, I define this in the same module:
_data_controller = None
def GetDataController():
global _data_controller
if _data_controller is None:
_data_controller = DataController()
return _data_controller
It is not elegant, since it's a full six lines. But all my singletons use this pattern, and it's at least very explicit (which is pythonic).
The one time I wrote a singleton in Python I used a class where all the member functions had the classmethod decorator.
class Foo:
x = 1
#classmethod
def increment(cls, y=1):
cls.x += y
Creating a singleton decorator (aka an annotation) is an elegant way if you want to decorate (annotate) classes going forward. Then you just put #singleton before your class definition.
def singleton(cls):
instances = {}
def getinstance():
if cls not in instances:
instances[cls] = cls()
return instances[cls]
return getinstance
#singleton
class MyClass:
...
There are also some interesting articles on the Google Testing blog, discussing why singleton are/may be bad and are an anti-pattern:
Singletons are Pathological Liars
Where Have All the Singletons Gone?
Root Cause of Singletons
I think that forcing a class or an instance to be a singleton is overkill. Personally, I like to define a normal instantiable class, a semi-private reference, and a simple factory function.
class NothingSpecial:
pass
_the_one_and_only = None
def TheOneAndOnly():
global _the_one_and_only
if not _the_one_and_only:
_the_one_and_only = NothingSpecial()
return _the_one_and_only
Or if there is no issue with instantiating when the module is first imported:
class NothingSpecial:
pass
THE_ONE_AND_ONLY = NothingSpecial()
That way you can write tests against fresh instances without side effects, and there is no need for sprinkling the module with global statements, and if needed you can derive variants in the future.
The Singleton Pattern implemented with Python courtesy of ActiveState.
It looks like the trick is to put the class that's supposed to only have one instance inside of another class.
class Singleton(object[,...]):
staticVar1 = None
staticVar2 = None
def __init__(self):
if self.__class__.staticVar1==None :
# create class instance variable for instantiation of class
# assign class instance variable values to class static variables
else:
# assign class static variable values to class instance variables
class Singeltone(type):
instances = dict()
def __call__(cls, *args, **kwargs):
if cls.__name__ not in Singeltone.instances:
Singeltone.instances[cls.__name__] = type.__call__(cls, *args, **kwargs)
return Singeltone.instances[cls.__name__]
class Test(object):
__metaclass__ = Singeltone
inst0 = Test()
inst1 = Test()
print(id(inst1) == id(inst0))
OK, singleton could be good or evil, I know. This is my implementation, and I simply extend a classic approach to introduce a cache inside and produce many instances of a different type or, many instances of same type, but with different arguments.
I called it Singleton_group, because it groups similar instances together and prevent that an object of the same class, with same arguments, could be created:
# Peppelinux's cached singleton
class Singleton_group(object):
__instances_args_dict = {}
def __new__(cls, *args, **kwargs):
if not cls.__instances_args_dict.get((cls.__name__, args, str(kwargs))):
cls.__instances_args_dict[(cls.__name__, args, str(kwargs))] = super(Singleton_group, cls).__new__(cls, *args, **kwargs)
return cls.__instances_args_dict.get((cls.__name__, args, str(kwargs)))
# It's a dummy real world use example:
class test(Singleton_group):
def __init__(self, salute):
self.salute = salute
a = test('bye')
b = test('hi')
c = test('bye')
d = test('hi')
e = test('goodbye')
f = test('goodbye')
id(a)
3070148780L
id(b)
3070148908L
id(c)
3070148780L
b == d
True
b._Singleton_group__instances_args_dict
{('test', ('bye',), '{}'): <__main__.test object at 0xb6fec0ac>,
('test', ('goodbye',), '{}'): <__main__.test object at 0xb6fec32c>,
('test', ('hi',), '{}'): <__main__.test object at 0xb6fec12c>}
Every object carries the singleton cache... This could be evil, but it works great for some :)
My simple solution which is based on the default value of function parameters.
def getSystemContext(contextObjList=[]):
if len( contextObjList ) == 0:
contextObjList.append( Context() )
pass
return contextObjList[0]
class Context(object):
# Anything you want here
Being relatively new to Python I'm not sure what the most common idiom is, but the simplest thing I can think of is just using a module instead of a class. What would have been instance methods on your class become just functions in the module and any data just becomes variables in the module instead of members of the class. I suspect this is the pythonic approach to solving the type of problem that people use singletons for.
If you really want a singleton class, there's a reasonable implementation described on the first hit on Google for "Python singleton", specifically:
class Singleton:
__single = None
def __init__( self ):
if Singleton.__single:
raise Singleton.__single
Singleton.__single = self
That seems to do the trick.
Singleton's half brother
I completely agree with staale and I leave here a sample of creating a singleton half brother:
class void:pass
a = void();
a.__class__ = Singleton
a will report now as being of the same class as singleton even if it does not look like it. So singletons using complicated classes end up depending on we don't mess much with them.
Being so, we can have the same effect and use simpler things like a variable or a module. Still, if we want use classes for clarity and because in Python a class is an object, so we already have the object (not and instance, but it will do just like).
class Singleton:
def __new__(cls): raise AssertionError # Singletons can't have instances
There we have a nice assertion error if we try to create an instance, and we can store on derivations static members and make changes to them at runtime (I love Python). This object is as good as other about half brothers (you still can create them if you wish), however it will tend to run faster due to simplicity.
In cases where you don't want the metaclass-based solution above, and you don't like the simple function decorator-based approach (e.g. because in that case static methods on the singleton class won't work), this compromise works:
class singleton(object):
"""Singleton decorator."""
def __init__(self, cls):
self.__dict__['cls'] = cls
instances = {}
def __call__(self):
if self.cls not in self.instances:
self.instances[self.cls] = self.cls()
return self.instances[self.cls]
def __getattr__(self, attr):
return getattr(self.__dict__['cls'], attr)
def __setattr__(self, attr, value):
return setattr(self.__dict__['cls'], attr, value)