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Add an automated function call to each method

Is it possible to create a "constructor".. or rather "Initializer" to each function, instead of having to manually write it at the top of each function in class?
So, each time a function in a class is called, the other assigned function (unknown to caller) is always called first (called pre_check in below example).
An example using super(), but I then have to manually copy it inside each function.
class Helper():
def pre_check(self):
print("Helper fcn")
class Parent(Helper):
def __init__(self):
print("Initializer")
def foo(self):
super().pre_check() # <---- new code
# ... existing code here ...
def bar(self):
super().pre_check() # <---- new code
# ... existing code here ...
def many_more_functions(self):
super().pre_check() # <---- new code
# ... existing code here ...
m = Parent()
m.foo()
m.bar()
Note how __init__ in Parent is not supposed to run pre_check.

You can use a decorator for the class that will in turn decorate all public methods defined in the class:
def addhelper(helpmethod):
def deco(cls):
def decomethod(method):
def inner(self, *args, **kwargs):
helpmethod(self)
return method(self, *args, **kwargs)
# copy signature, doc and names from the original method
inner.__signature__ = inspect.signature(method)
inner.__doc__ = method.__doc__
inner.__name__ = method.__name__
inner.__qualname__ = method.__qualname__
return inner
# search all methods declared in cls with a name not starting with _
for name, meth in inspect.getmembers(
cls,lambda x: inspect.isfunction(x)
and not x.__name__.startswith('_')
and x.__qualname__.startswith(cls.__name__)):
# replace each method with its decoration
setattr(cls, name, decomethod(meth))
return cls
return deco
class Helper():
def pre_check(self):
print("Helper fcn")
#addhelper(Helper.pre_check)
class Parent(Helper):
def __init__(self):
print("Initializer")
def foo(self):
# super().pre_check() # <----
print('in foo')
def bar(self):
# super().pre_check() # <----
print('in bar')
def many_more_functions(self):
# super().pre_check() # <----
print('in many_more_functions')
We can now use it:
>>> p = Parent()
Initializer
>>> p.foo()
Helper fcn
in foo
>>> p.bar()
Helper fcn
in bar
>>> p.many_more_functions()
Helper fcn
in many_more_functions

Use __init_subclass__ to change subclasses as they are created. You can wrap the methods of subclasses:
class Helper():
def __init_subclass__(cls):
for field, value in cls.__dict__.items():
# add additional checks as desired, e.g. exclude __special_methods__
if inspect.isfunction(value) and not getattr(value, 'checked', False):
setattr(cls, field, cls._check(value)) # wrap method
#classmethod
def _check(cls, fcn):
"""Create a wrapper to inspect the arguments passed to methods"""
#functools.wraps(fcn)
def checked_fcn(*args, **kwargs):
print(fcn, "got", args, kwargs)
return fcn(*args, **kwargs)
return checked_fcn
class Parent(Helper):
def __init__(self):
print("Initializer")
def foo(self):
print("Foo")
Note that this will wrap all methods, including special methods such as __init__:
>>> Parent().foo()
<function Parent.__init__ at 0x1029b2378> got (<__main__.Parent object at 0x102c09080>,) {}
Initializer
<function Parent.foo at 0x1029b2158> got (<__main__.Parent object at 0x102c09080>,) {}
Foo
You can extend the check in __init_subclass__ with arbitrary rules to filter out functions. For example, field[:2] == field[-2:] == "__" excludes special methods.

You can use metaclass and define a decorator for each method in the instance of that metaclass
Code :
def decorate(f):
def do_something(self, a):
if (f(self, a) > 18) :
return ("Eligible to vote")
else :
return ("Not eligible to vote")
return do_something
class Meta(type):
def __new__(cls, name, bases, namespace, **kwds):
namespace = {k: v if k.startswith('__') else decorate(v) for k, v in namespace.items()}
return type.__new__(cls, name, bases, namespace)
class MetaInstance(metaclass=Meta):
def foo1(self, val):
return val + 15
def foo2(self, val):
return val + 9
obj1 = MetaInstance()
print(obj1.foo1(5))
print(obj1.foo2(2))

Is there a use for the code cls().__init__() in a classmethod or elsewhere?

I have seen cls().__init__() used in a classmethod, but it seems that the code could have used a simple cls() instead. As in:
class SomeCrazyClass:
#classmethod
def newclass(cls):
return cls().__init__()
#classmethod
def newclass2(cls):
return cls()
Is this just a poor coding style choice or is there a practical use of cls().__init__() in some situation?

The difference between cls().__init__() and cls() is that former calls the __init__ on instance twice and hence will return None and the latter will return the actual instance.
But an imaginary scenario to of calling __init__ again can be used in lazy initialization of a class or may be some other use-cases as well.
For example in the below code the instance variables are loaded only on the first access of an attribute:
def init(cls, real_init):
def wrapped(self, *args, **kwargs):
cls.__init__ = real_init
return wrapped
class A(object):
def __new__(cls, *args, **kwargs):
cls.__init__ = init(cls, cls.__init__)
instance = object.__new__(cls)
return instance
def __getattr__(self, attr):
expected_attrs = ('a', 'b')
if attr in expected_attrs:
self.__init__(range(10000), range(1000))
return object.__getattribute__(self, attr)
def __init__(self, a, b):
print('inside __init__')
self.a = sum(a)
self.b = sum(b)
Demo:
>>> a = A()
>>> a.__dict__
{}
>>> a.a, a.b
inside __init__
(49995000, 499500)
>>> a.__dict__
{'a': 49995000, 'b': 499500}
>>> a = A()
>>> a.__init__(range(10**5), range(10**4))
inside __init__
>>> a.a, a.b
(4999950000, 49995000)
We can now also return a value from __init__ now which is usually not possible.

Python functools.wraps equivalent for classes

When defining a decorator using a class, how do I automatically transfer over__name__, __module__ and __doc__? Normally, I would use the #wraps decorator from functools. Here's what I did instead for a class (this is not entirely my code):
class memoized:
"""Decorator that caches a function's return value each time it is called.
If called later with the same arguments, the cached value is returned, and
not re-evaluated.
"""
def __init__(self, func):
super().__init__()
self.func = func
self.cache = {}
def __call__(self, *args):
try:
return self.cache[args]
except KeyError:
value = self.func(*args)
self.cache[args] = value
return value
except TypeError:
# uncacheable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(*args)
def __repr__(self):
return self.func.__repr__()
def __get__(self, obj, objtype):
return functools.partial(self.__call__, obj)
__doc__ = property(lambda self:self.func.__doc__)
__module__ = property(lambda self:self.func.__module__)
__name__ = property(lambda self:self.func.__name__)
Is there a standard decorator to automate the creation of name module and doc? Also, to automate the get method (I assume that's for creating bound methods?) Are there any missing methods?

Everyone seems to have missed the obvious solution. Using functools.update_wrapper:
>>> import functools
>>> class memoized(object):
"""Decorator that caches a function's return value each time it is called.
If called later with the same arguments, the cached value is returned, and
not re-evaluated.
"""
def __init__(self, func):
self.func = func
self.cache = {}
functools.update_wrapper(self, func) ## TA-DA! ##
def __call__(self, *args):
pass # Not needed for this demo.
>>> #memoized
def fibonacci(n):
"""fibonacci docstring"""
pass # Not needed for this demo.
>>> fibonacci
<__main__.memoized object at 0x0156DE30>
>>> fibonacci.__name__
'fibonacci'
>>> fibonacci.__doc__
'fibonacci docstring'

I'm not aware of such things in stdlib, but we can create our own if we need to.
Something like this can work :
from functools import WRAPPER_ASSIGNMENTS
def class_wraps(cls):
"""Update a wrapper class `cls` to look like the wrapped."""
class Wrapper(cls):
"""New wrapper that will extend the wrapper `cls` to make it look like `wrapped`.
wrapped: Original function or class that is beign decorated.
assigned: A list of attribute to assign to the the wrapper, by default they are:
['__doc__', '__name__', '__module__', '__annotations__'].
"""
def __init__(self, wrapped, assigned=WRAPPER_ASSIGNMENTS):
self.__wrapped = wrapped
for attr in assigned:
setattr(self, attr, getattr(wrapped, attr))
super().__init__(wrapped)
def __repr__(self):
return repr(self.__wrapped)
return Wrapper
Usage:
#class_wraps
class memoized:
"""Decorator that caches a function's return value each time it is called.
If called later with the same arguments, the cached value is returned, and
not re-evaluated.
"""
def __init__(self, func):
super().__init__()
self.func = func
self.cache = {}
def __call__(self, *args):
try:
return self.cache[args]
except KeyError:
value = self.func(*args)
self.cache[args] = value
return value
except TypeError:
# uncacheable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(*args)
def __get__(self, obj, objtype):
return functools.partial(self.__call__, obj)
#memoized
def fibonacci(n):
"""fibonacci docstring"""
if n in (0, 1):
return n
return fibonacci(n-1) + fibonacci(n-2)
print(fibonacci)
print("__doc__: ", fibonacci.__doc__)
print("__name__: ", fibonacci.__name__)
Output:
<function fibonacci at 0x14627c0>
__doc__: fibonacci docstring
__name__: fibonacci
EDIT:
And if you are wondering why this wasn't included in the stdlib is because you can
wrap your class decorator in a function decorator and use functools.wraps like this:
def wrapper(f):
memoize = memoized(f)
#functools.wraps(f)
def helper(*args, **kws):
return memoize(*args, **kws)
return helper
#wrapper
def fibonacci(n):
"""fibonacci docstring"""
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)

Turns out there's a straightforward solution using functools.wraps itself:
import functools
def dec(cls):
#functools.wraps(cls, updated=())
class D(cls):
decorated = 1
return D
#dec
class C:
"""doc"""
print(f'{C.__name__=} {C.__doc__=} {C.__wrapped__=}')
$ python3 t.py
C.__name__='C' C.__doc__='doc' C.__wrapped__=<class '__main__.C'>
Note that updated=() is needed to prevent an attempt to update the class's __dict__ (this output is without updated=()):
$ python t.py
Traceback (most recent call last):
File "t.py", line 26, in <module>
class C:
File "t.py", line 20, in dec
class D(cls):
File "/usr/lib/python3.8/functools.py", line 57, in update_wrapper
getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
AttributeError: 'mappingproxy' object has no attribute 'update'

I needed something that would wrap both classes and functions and wrote this:
def wrap_is_timeout(base):
'''Adds `.is_timeout=True` attribute to objects returned by `base()`.
When `base` is class, it returns a subclass with same name and adds read-only property.
Otherwise, it returns a function that sets `.is_timeout` attribute on result of `base()` call.
Wrappers make best effort to be transparent.
'''
if inspect.isclass(base):
class wrapped(base):
is_timeout = property(lambda _: True)
for k in functools.WRAPPER_ASSIGNMENTS:
v = getattr(base, k, _MISSING)
if v is not _MISSING:
try:
setattr(wrapped, k, v)
except AttributeError:
pass
return wrapped
#functools.wraps(base)
def fun(*args, **kwargs):
ex = base(*args, **kwargs)
ex.is_timeout = True
return ex
return fun

All we really need to do is modify the behavior of the decorator so that it is "hygienic", i.e. it is attribute-preserving.
#!/usr/bin/python3
def hygienic(decorator):
def new_decorator(original):
wrapped = decorator(original)
wrapped.__name__ = original.__name__
wrapped.__doc__ = original.__doc__
wrapped.__module__ = original.__module__
return wrapped
return new_decorator
This is ALL you need. In general. It doesn't preserve the signature, but if you really want that you can use a library to do that. I also went ahead and rewrote the memoization code so that it works on keyword arguments as well. Also there was a bug where failure to convert it to a hashable tuple would make it not work in 100% of cases.
Demo of rewritten memoized decorator with #hygienic modifying its behavior. memoized is now a function that wraps the original class, though you can (like the other answer) write a wrapping class instead, or even better, something which detects if it's a class and if so wraps the __init__ method.
#hygienic
class memoized:
def __init__(self, func):
self.func = func
self.cache = {}
def __call__(self, *args, **kw):
try:
key = (tuple(args), frozenset(kw.items()))
if not key in self.cache:
self.cache[key] = self.func(*args,**kw)
return self.cache[key]
except TypeError:
# uncacheable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.func(*args,**kw)
In action:
#memoized
def f(a, b=5, *args, keyword=10):
"""Intact docstring!"""
print('f was called!')
return {'a':a, 'b':b, 'args':args, 'keyword':10}
x=f(0)
#OUTPUT: f was called!
print(x)
#OUTPUT: {'a': 0, 'b': 5, 'keyword': 10, 'args': ()}
y=f(0)
#NO OUTPUT - MEANS MEMOIZATION IS WORKING
print(y)
#OUTPUT: {'a': 0, 'b': 5, 'keyword': 10, 'args': ()}
print(f.__name__)
#OUTPUT: 'f'
print(f.__doc__)
#OUTPUT: 'Intact docstring!'

Another solution using inheritance:
import functools
import types
class CallableClassDecorator:
"""Base class that extracts attributes and assigns them to self.
By default the extracted attributes are:
['__doc__', '__name__', '__module__'].
"""
def __init__(self, wrapped, assigned=functools.WRAPPER_ASSIGNMENTS):
for attr in assigned:
setattr(self, attr, getattr(wrapped, attr))
super().__init__()
def __get__(self, obj, objtype):
return types.MethodType(self.__call__, obj)
And, usage:
class memoized(CallableClassDecorator):
"""Decorator that caches a function's return value each time it is called.
If called later with the same arguments, the cached value is returned, and
not re-evaluated.
"""
def __init__(self, function):
super().__init__(function)
self.function = function
self.cache = {}
def __call__(self, *args):
try:
return self.cache[args]
except KeyError:
value = self.function(*args)
self.cache[args] = value
return value
except TypeError:
# uncacheable -- for instance, passing a list as an argument.
# Better to not cache than to blow up entirely.
return self.function(*args)

Better way to log method calls in Python?

We can code out some sort of logging decorator to echo function/method calls like the following:
def log(fn):
...
#log
def foo():
...
class Foo(object):
#log
def foo(self):
...
#log
def bar(self, a, b):
...
#log
def foobar(self, x, y, z):
...
But what if we are like to log method calls without putting that many #log in front of each meth definition? Is there some way to just put one decorator above a class definition to make all its method calls decorated/logged? Or are there some other better and interesting ways to do that instead of decorator?

This might be overkill, but there is a trace function facility that will inform you of a great deal of activity within your program:
import sys
def trace(frame, event, arg):
if event == "call":
filename = frame.f_code.co_filename
if filename == "path/to/myfile.py":
lineno = frame.f_lineno
# Here I'm printing the file and line number,
# but you can examine the frame, locals, etc too.
print "%s # %s" % (filename, lineno)
return trace
sys.settrace(trace)
call_my_function()
sys.settrace(None)

I'm not sure what your use case is for this, but generally, I would think more about what exactly is the problem that you're trying to solve.
That said, here's an example that might do what you want but without a decorator:
#!/usr/bin/env python
import inspect
class Foo(object):
def foo(self):
pass
def bar(self, a, b):
pass
def foobar(self, x, y, z):
pass
def __getattribute__(self, name):
returned = object.__getattribute__(self, name)
if inspect.isfunction(returned) or inspect.ismethod(returned):
print 'called ', returned.__name__
return returned
if __name__ == '__main__':
a = Foo()
a.foo()
a.bar(1, 2)
a.foobar(1, 2, 3)
Output:
called foo
called bar
called foobar

It can be done many different ways. I will show how to make it through meta-class, class decorator and inheritance.
by changing meta class
import functools
class Logger(type):
#staticmethod
def _decorator(fun):
#functools.wraps(fun)
def wrapper(*args, **kwargs):
print(fun.__name__, args, kwargs)
return fun(*args, **kwargs)
return wrapper
def __new__(mcs, name, bases, attrs):
for key in attrs.keys():
if callable(attrs[key]):
# if attrs[key] is callable, then we can easily wrap it with decorator
# and substitute in the future attrs
# only for extra clarity (though it is wider type than function)
fun = attrs[key]
attrs[key] = Logger._decorator(fun)
# and then invoke __new__ in type metaclass
return super().__new__(mcs, name, bases, attrs)
class A(metaclass=Logger):
def __init__(self):
self.some_val = "some_val"
def method_first(self, a, b):
print(a, self.some_val)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A()
# __init__ (<__main__.A object at 0x7f852a52a2b0>,) {}
b.method_first(5, b="Here should be 5")
# method_first (<__main__.A object at 0x7f852a52a2b0>, 5) {'b': 'Here should be 5'}
# 5 some_val
b.method_first(6, b="Here should be 6")
# method_first (<__main__.A object at 0x7f852a52a2b0>, 6) {'b': 'Here should be 6'}
# 6 some_val
b.another_method(7)
# another_method (<__main__.A object at 0x7f852a52a2b0>, 7) {}
# 7
b.static_method(7)
# 7
Also, I will show two approaches how to make it without changing meta information of class (through class decorator and class inheritance). The first approach through class decorator put_decorator_on_all_methods accepts decorator to wrap all member callable objects of class.
def logger(f):
#functools.wraps(f)
def wrapper(*args, **kwargs):
print(f.__name__, args, kwargs)
return f(*args, **kwargs)
return wrapper
def put_decorator_on_all_methods(decorator, cls=None):
if cls is None:
return lambda cls: put_decorator_on_all_methods(decorator, cls)
class Decoratable(cls):
def __init__(self, *args, **kargs):
super().__init__(*args, **kargs)
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if callable(value):
return decorator(value)
return value
return Decoratable
#put_decorator_on_all_methods(logger)
class A:
def method(self, a, b):
print(a)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A()
b.method(5, b="Here should be 5")
# >>> method (5,) {'b': 'Here should be 5'}
# >>> 5
b.method(6, b="Here should be 6")
# >>> method (6,) {'b': 'Here should be 6'}
# >>> 6
b.another_method(7)
# >>> another_method (7,) {}
# >>> 7
b.static_method(8)
# >>> static_method (8,) {}
# >>> 8
And, recently, I've come across on the same problem, but I couldn't put decorator on class or change it in any other way, except I was allowed to add such behavior through inheritance only (I am not sure that this is the best choice if you can change codebase as you wish though).
Here class Logger forces all callable members of subclasses to write information about their invocations, see code below.
class Logger:
def _decorator(self, f):
#functools.wraps(f)
def wrapper(*args, **kwargs):
print(f.__name__, args, kwargs)
return f(*args, **kwargs)
return wrapper
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if callable(value):
decorator = object.__getattribute__(self, '_decorator')
return decorator(value)
return value
class A(Logger):
def method(self, a, b):
print(a)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A()
b.method(5, b="Here should be 5")
# >>> method (5,) {'b': 'Here should be 5'}
# >>> 5
b.method(6, b="Here should be 6")
# >>> method (6,) {'b': 'Here should be 6'}
# >>> 6
b.another_method(7)
# >>> another_method (7,) {}
# >>> 7
b.static_method(7)
# >>> static_method (7,) {}
# >>> 7
Or more abstractly, you can instantiate base class based on some decorator.
def decorator(f):
#functools.wraps(f)
def wrapper(*args, **kwargs):
print(f.__name__, args, kwargs)
return f(*args, **kwargs)
return wrapper
class Decoratable:
def __init__(self, dec):
self._decorator = dec
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if callable(value):
decorator = object.__getattribute__(self, '_decorator')
return decorator(value)
return value
class A(Decoratable):
def __init__(self, dec):
super().__init__(dec)
def method(self, a, b):
print(a)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A(decorator)
b.method(5, b="Here should be 5")
# >>> method (5,) {'b': 'Here should be 5'}
# >>> 5
b.method(6, b="Here should be 6")
# >>> method (6,) {'b': 'Here should be 6'}
# >>> 6
b.another_method(7)
# >>> another_method (7,) {}
# >>> 7
b.static_method(7)
# >>> static_method (7,) {}
# >>> 7

See Attaching a decorator to all functions within a class
However, as the accepted answer to that question points out, it generally isn't a good idea.
If you decide to go the aspect oriented programming route, I suggest starting here: Any AOP support library for Python?

Well, If you do not want to explicitly decorate all your functions, you can get all the functions/methods of a given module and apply your decorator automatically. not the easiest thing but not infeasible in python :)
You can also try an aspect oriented programming framework.
my2c

Attaching a decorator to all functions within a class

I don't really need to do this, but was just wondering, is there a way to bind a decorator to all functions within a class generically, rather than explicitly stating it for every function.
I suppose it then becomes a kind of aspect, rather than a decorator and it does feel a bit odd, but was thinking for something like timing or auth it'd be pretty neat.

The cleanest way to do this, or to do other modifications to a class definition, is to define a metaclass.
Alternatively, just apply your decorator at the end of the class definition using inspect:
import inspect
class Something:
def foo(self):
pass
for name, fn in inspect.getmembers(Something, inspect.isfunction):
setattr(Something, name, decorator(fn))
In practice of course you'll want to apply your decorator more selectively. As soon as you want to decorate all but one method you'll discover that it is easier and more flexible just to use the decorator syntax in the traditional way.

Everytime you think of changing class definition, you can either use the class decorator or metaclass. e.g. using metaclass
import types
class DecoMeta(type):
def __new__(cls, name, bases, attrs):
for attr_name, attr_value in attrs.iteritems():
if isinstance(attr_value, types.FunctionType):
attrs[attr_name] = cls.deco(attr_value)
return super(DecoMeta, cls).__new__(cls, name, bases, attrs)
#classmethod
def deco(cls, func):
def wrapper(*args, **kwargs):
print "before",func.func_name
result = func(*args, **kwargs)
print "after",func.func_name
return result
return wrapper
class MyKlass(object):
__metaclass__ = DecoMeta
def func1(self):
pass
MyKlass().func1()
Output:
before func1
after func1
Note: it will not decorate staticmethod and classmethod

Following code works for python2.x and 3.x
import inspect
def decorator_for_func(orig_func):
def decorator(*args, **kwargs):
print("Decorating wrapper called for method %s" % orig_func.__name__)
result = orig_func(*args, **kwargs)
return result
return decorator
def decorator_for_class(cls):
for name, method in inspect.getmembers(cls):
if (not inspect.ismethod(method) and not inspect.isfunction(method)) or inspect.isbuiltin(method):
continue
print("Decorating function %s" % name)
setattr(cls, name, decorator_for_func(method))
return cls
#decorator_for_class
class decorated_class:
def method1(self, arg, **kwargs):
print("Method 1 called with arg %s" % arg)
def method2(self, arg):
print("Method 2 called with arg %s" % arg)
d=decorated_class()
d.method1(1, a=10)
d.method2(2)

Update for Python 3:
import types
class DecoMeta(type):
def __new__(cls, name, bases, attrs):
for attr_name, attr_value in attrs.items():
if isinstance(attr_value, types.FunctionType):
attrs[attr_name] = cls.deco(attr_value)
return super().__new__(cls, name, bases, attrs)
#classmethod
def deco(cls, func):
def wrapper(*args, **kwargs):
print("before",func.__name__)
result = func(*args, **kwargs)
print("after",func.__name__)
return result
return wrapper
(and thanks to Duncan for this)

Of course that the metaclasses are the most pythonic way to go when you want to modify the way that python creates the objects. Which can be done by overriding the __new__ method of your class. But there are some points around this problem (specially for python 3.X) that I'd like to mention:
types.FunctionType doesn't protect the special methods from being decorated, as they are function types. As a more general way you can just decorate the objects which their names are not started with double underscore (__). One other benefit of this method is that it also covers those objects that exist in namespace and starts with __ but are not function like __qualname__, __module__ , etc.
The namespace argument in __new__'s header doesn't contain class attributes within the __init__. The reason is that the __new__ executes before the __init__ (initializing).
It's not necessary to use a classmethod as the decorator, as in most of the times you import your decorator from another module.
If your class is contain a global item (out side of the __init__) for refusing of being decorated alongside checking if the name is not started with __ you can check the type with types.FunctionType to be sure that you're not decorating a non-function object.
Here is a sample metacalss that you can use:
class TheMeta(type):
def __new__(cls, name, bases, namespace, **kwds):
# if your decorator is a class method of the metaclass use
# `my_decorator = cls.my_decorator` in order to invoke the decorator.
namespace = {k: v if k.startswith('__') else my_decorator(v) for k, v in namespace.items()}
return type.__new__(cls, name, bases, namespace)
Demo:
def my_decorator(func):
def wrapper(self, arg):
# You can also use *args instead of (self, arg) and pass the *args
# to the function in following call.
return "the value {} gets modified!!".format(func(self, arg))
return wrapper
class TheMeta(type):
def __new__(cls, name, bases, namespace, **kwds):
# my_decorator = cls.my_decorator (if the decorator is a classmethod)
namespace = {k: v if k.startswith('__') else my_decorator(v) for k, v in namespace.items()}
return type.__new__(cls, name, bases, namespace)
class MyClass(metaclass=TheMeta):
# a = 10
def __init__(self, *args, **kwargs):
self.item = args[0]
self.value = kwargs['value']
def __getattr__(self, attr):
return "This class hasn't provide the attribute {}.".format(attr)
def myfunction_1(self, arg):
return arg ** 2
def myfunction_2(self, arg):
return arg ** 3
myinstance = MyClass(1, 2, value=100)
print(myinstance.myfunction_1(5))
print(myinstance.myfunction_2(2))
print(myinstance.item)
print(myinstance.p)
Output:
the value 25 gets modified!!
the value 8 gets modified!!
1
This class hasn't provide the attribute p. # special method is not decorated.
For checking the 3rd item from the aforementioned notes you can uncomment the line a = 10 and do print(myinstance.a) and see the result then change the dictionary comprehension in __new__ as follows then see the result again:
namespace = {k: v if k.startswith('__') and not isinstance(v, types.FunctionType)\
else my_decorator(v) for k, v in namespace.items()}

I will repeat my answer here, for a similar issue
It can be done many different ways. I will show how to make it through meta-class, class decorator and inheritance.
by changing meta class
import functools
class Logger(type):
#staticmethod
def _decorator(fun):
#functools.wraps(fun)
def wrapper(*args, **kwargs):
print(fun.__name__, args, kwargs)
return fun(*args, **kwargs)
return wrapper
def __new__(mcs, name, bases, attrs):
for key in attrs.keys():
if callable(attrs[key]):
# if attrs[key] is callable, then we can easily wrap it with decorator
# and substitute in the future attrs
# only for extra clarity (though it is wider type than function)
fun = attrs[key]
attrs[key] = Logger._decorator(fun)
# and then invoke __new__ in type metaclass
return super().__new__(mcs, name, bases, attrs)
class A(metaclass=Logger):
def __init__(self):
self.some_val = "some_val"
def method_first(self, a, b):
print(a, self.some_val)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A()
# __init__ (<__main__.A object at 0x7f852a52a2b0>,) {}
b.method_first(5, b="Here should be 5")
# method_first (<__main__.A object at 0x7f852a52a2b0>, 5) {'b': 'Here should be 5'}
# 5 some_val
b.method_first(6, b="Here should be 6")
# method_first (<__main__.A object at 0x7f852a52a2b0>, 6) {'b': 'Here should be 6'}
# 6 some_val
b.another_method(7)
# another_method (<__main__.A object at 0x7f852a52a2b0>, 7) {}
# 7
b.static_method(7)
# 7
Also, will show two approaches how to make it without changing meta information of class (through class decorator and class inheritance). The first approach through class decorator put_decorator_on_all_methods accepts decorator to wrap all member callable objects of class.
def logger(f):
#functools.wraps(f)
def wrapper(*args, **kwargs):
print(f.__name__, args, kwargs)
return f(*args, **kwargs)
return wrapper
def put_decorator_on_all_methods(decorator, cls=None):
if cls is None:
return lambda cls: put_decorator_on_all_methods(decorator, cls)
class Decoratable(cls):
def __init__(self, *args, **kargs):
super().__init__(*args, **kargs)
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if callable(value):
return decorator(value)
return value
return Decoratable
#put_decorator_on_all_methods(logger)
class A:
def method(self, a, b):
print(a)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A()
b.method(5, b="Here should be 5")
# >>> method (5,) {'b': 'Here should be 5'}
# >>> 5
b.method(6, b="Here should be 6")
# >>> method (6,) {'b': 'Here should be 6'}
# >>> 6
b.another_method(7)
# >>> another_method (7,) {}
# >>> 7
b.static_method(8)
# >>> static_method (8,) {}
# >>> 8
And, recently, I've come across on the same problem, but I couldn't put decorator on class or change it in any other way, except I was allowed to add such behavior through inheritance only (I am not sure that this is the best choice if you can change codebase as you wish though).
Here class Logger forces all callable members of subclasses to write information about their invocations, see code below.
class Logger:
def _decorator(self, f):
#functools.wraps(f)
def wrapper(*args, **kwargs):
print(f.__name__, args, kwargs)
return f(*args, **kwargs)
return wrapper
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if callable(value):
decorator = object.__getattribute__(self, '_decorator')
return decorator(value)
return value
class A(Logger):
def method(self, a, b):
print(a)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A()
b.method(5, b="Here should be 5")
# >>> method (5,) {'b': 'Here should be 5'}
# >>> 5
b.method(6, b="Here should be 6")
# >>> method (6,) {'b': 'Here should be 6'}
# >>> 6
b.another_method(7)
# >>> another_method (7,) {}
# >>> 7
b.static_method(7)
# >>> static_method (7,) {}
# >>> 7
Or more abstractly, you can instantiate base class based on some decorator.
def decorator(f):
#functools.wraps(f)
def wrapper(*args, **kwargs):
print(f.__name__, args, kwargs)
return f(*args, **kwargs)
return wrapper
class Decoratable:
def __init__(self, dec):
self._decorator = dec
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if callable(value):
decorator = object.__getattribute__(self, '_decorator')
return decorator(value)
return value
class A(Decoratable):
def __init__(self, dec):
super().__init__(dec)
def method(self, a, b):
print(a)
def another_method(self, c):
print(c)
#staticmethod
def static_method(d):
print(d)
b = A(decorator)
b.method(5, b="Here should be 5")
# >>> method (5,) {'b': 'Here should be 5'}
# >>> 5
b.method(6, b="Here should be 6")
# >>> method (6,) {'b': 'Here should be 6'}
# >>> 6
b.another_method(7)
# >>> another_method (7,) {}
# >>> 7
b.static_method(7)
# >>> static_method (7,) {}
# >>> 7

There's another slightly similar thing you might want to do in some cases. Sometimes you want to trigger the attachment for something like debugging and not on all the classes but for every method of an object you might want a record of what it's doing.
def start_debugging():
import functools
import datetime
filename = "debug-{date:%Y-%m-%d_%H_%M_%S}.txt".format(date=datetime.datetime.now())
debug_file = open(filename, "a")
debug_file.write("\nDebug.\n")
def debug(func):
#functools.wraps(func)
def wrapper_debug(*args, **kwargs):
args_repr = [repr(a) for a in args] # 1
kwargs_repr = [f"{k}={v!r}" for k, v in kwargs.items()] # 2
signature = ", ".join(args_repr + kwargs_repr) # 3
debug_file.write(f"Calling {func.__name__}({signature})\n")
value = func(*args, **kwargs)
debug_file.write(f"{func.__name__!r} returned {value!r}\n") # 4
debug_file.flush()
return value
return wrapper_debug
for obj in (self):
for attr in dir(obj):
if attr.startswith('_'):
continue
fn = getattr(obj, attr)
if not isinstance(fn, types.FunctionType) and \
not isinstance(fn, types.MethodType):
continue
setattr(obj, attr, debug(fn))
This function will go through some objects (only self currently) and replace all functions and methods that do not start with _ with a debugging decorator.
The method used for this of just iterating the dir(self) is not addressed above but totally works. And can be called externally from the object and much more arbitrarily.

In Python 3 you could also write a simple function that overwrites/applies a decorator to certain methods like so:
from functools import wraps
from types import MethodType
def logged(func):
#wraps(func)
def wrapper(*args, **kwargs):
res = func(*args, **kwargs)
print("logging:", func.__name__, res)
return res
return wrapper
class Test:
def foo(self):
return 42
...
def aspectize(cls, decorator):
for name, func in cls.__dict__.items():
if not name.startswith("__"):
setattr(cls, name, MethodType(decorator(func), cls)) # MethodType is key
aspectize(Test, logged)
t = Test()
t.foo() # printing "logging: foo 42"; returning 42

I came to this question from:
How to decorate all functions of a class without typing it over and over for each method?
And I want add a one note:
Answers with class decorators or repalcing class methods like this one:
https://stackoverflow.com/a/6307868/11277611
Will not work with staticmethod.
You will get TypeError, unexpected argument because your method will get self/cls as first argument.
Probably:
Decorated class doesn't know about decorators of self methods and can't be distincted even with inspect.ismethod.
I come to such quickfix:
I'm not checked it closely but it passes my (no so comprehensive) tests.
Using dynamically decorators is already a bad approach, so, it must be okay as temporary solution.
TLD:TD Add try/exception to use with staticmethod
def log_sent_data(function):
#functools_wraps(function)
def decorator(*args, **kwargs):
# Quickfix
self, *args = args
try: # If method has self/cls/descriptor
result = function(self, *args, **kwargs)
except TypeError:
if args: # If method is static but has positional args
result = function(*args, **kwargs)
else: # If method is static and doesn't has positional args
result = function(**kwargs)
# End of quickfix
return result
return decorator

Combining information from various answers, here's a DecorateMethods metaclass:
class DecorateMethods(type):
""" Decorate all methods of the class with the decorator provided """
def __new__(cls, name, bases, attrs, **kwargs):
try:
decorator = kwargs['decorator']
except KeyError:
raise ValueError('Please provide the "decorator" argument, eg. '
'MyClass(..., metaclass=DecorateMethods, decorator=my_decorator)')
exclude = kwargs.get('exclude', [])
for attr_name, attr_value in attrs.items():
if isinstance(attr_value, types.FunctionType) and \
attr_name not in exclude and \
not attr_name.startswith('__'):
attrs[attr_name] = decorator(attr_value)
return super(DecorateMethods, cls).__new__(cls, name, bases, attrs)
Used as:
class MyClass(metaclass=DecorateMethods, decorator=my_decorator, exclude=["METHOD_TO_BE_EXCLUDED"]):
...
It works nicely with unittests, as opposed to function-based solutions.
Credit to answers in 1, 2 and other answers in this question.

You could override the __getattr__ method. It's not actually attaching a decorator, but it lets you return a decorated method. You'd probably want to do something like this:
class Eggs(object):
def __getattr__(self, attr):
return decorate(getattr(self, `_` + attr))
There's some ugly recursion hiding in there that you'll want to protect against, but that's a start.