I am instantiating a class A (which I am importing from somebody
else, so I can't modify it) into my class X.
Is there a way I can intercept or wrap calls to methods in A?
I.e., in the code below can I call
x.a.p1()
and get the output
X.pre
A.p1
X.post
Many TIA!
class A:
# in my real application, this is an imported class
# that I cannot modify
def p1(self): print 'A.p1'
class X:
def __init__(self):
self.a=A()
def pre(self): print 'X.pre'
def post(self): print 'X.post'
x=X()
x.a.p1()
Here is the solution I and my colleagues came up with:
from types import MethodType
class PrePostCaller:
def __init__(self, other):
self.other = other
def pre(self): print 'pre'
def post(self): print 'post'
def __getattr__(self, name):
if hasattr(self.other, name):
func = getattr(self.other, name)
return lambda *args, **kwargs: self._wrap(func, args, kwargs)
raise AttributeError(name)
def _wrap(self, func, args, kwargs):
self.pre()
if type(func) == MethodType:
result = func( *args, **kwargs)
else:
result = func(self.other, *args, **kwargs)
self.post()
return result
#Examples of use
class Foo:
def stuff(self):
print 'stuff'
a = PrePostCaller(Foo())
a.stuff()
a = PrePostCaller([1,2,3])
print a.count()
Gives:
pre
stuff
post
pre
post
0
So when creating an instance of your object, wrap it with the PrePostCaller object. After that you continue using the object as if it was an instance of the wrapped object. With this solution you can do the wrapping on a per instance basis.
You could just modify the A instance and replace the p1 function with a wrapper function:
def wrapped(pre, post, f):
def wrapper(*args, **kwargs):
pre()
retval = f(*args, **kwargs)
post()
return retval
return wrapper
class Y:
def __init__(self):
self.a=A()
self.a.p1 = wrapped(self.pre, self.post, self.a.p1)
def pre(self): print 'X.pre'
def post(self): print 'X.post'
The no-whistles-or-bells solution would be to write a wrapper class for class A that does just that.
As others have mentioned, the wrapper/decorator solution is probably be the easiest one. I don't recommend modifyng the wrapped class itself, for the same reasons that you point out.
If you have many external classes you can write a code generator to generate the wrapper classes for you. Since you are doing this in Python you can probably even implement the generator as a part of the program, generating the wrappers at startup, or something.
I've just recently read about decorators in python, I'm not understanding them yet but it seems to me that they can be a solution to your problem. see Bruce Eckel intro to decorators at:
http://www.artima.com/weblogs/viewpost.jsp?thread=240808
He has a few more posts on that topic there.
Edit: Three days later I stumble upon this article, which shows how to do a similar task without decorators, what's the problems with it and then introduces decorators and develop a quite full solution:
http://wordaligned.org/articles/echo
Here's what I've received from Steven D'Aprano on comp.lang.python.
# Define two decorator factories.
def precall(pre):
def decorator(f):
def newf(*args, **kwargs):
pre()
return f(*args, **kwargs)
return newf
return decorator
def postcall(post):
def decorator(f):
def newf(*args, **kwargs):
x = f(*args, **kwargs)
post()
return x
return newf
return decorator
Now you can monkey patch class A if you want. It's probably not a great
idea to do this in production code, as it will effect class A everywhere.
[this is ok for my application, as it is basically a protocol converter and there's exactly one instance of each class being processed.]
class A:
# in my real application, this is an imported class
# that I cannot modify
def p1(self): print 'A.p1'
class X:
def __init__(self):
self.a=A()
A.p1 = precall(self.pre)(postcall(self.post)(A.p1))
def pre(self): print 'X.pre'
def post(self): print 'X.post'
x=X()
x.a.p1()
Gives the desired result.
X.pre
A.p1
X.post
Related
I am not new to python but I am far from being an expert (or intermediate). Right now, I play around with objects and their behavior (like setattr, monkey-patch, etc.). During this, I stumbled upon a problem where I do not have any idea on how this might work.
Imagine following code:
class MyCalculator():
def __init__(self):
pass
def addition(self, a, b):
return a + b
def substraction(self, a, b):
return a - b
import inspect
class Changing():
def __init__(self):
pass
def listUserMethods(self, myObject):
object_names = [object_name for object_name in inspect.getmembers(myObject) if (inspect.ismethod(object_name[1]))]
return object_names
def setMethodAttribute(self, myMethod):
pass
if __name__=="__main__":
myCalc = MyCalculator()
change = Changing()
Now, I would like that setMethodAttribute() will change the code of the method I provide itself. Like, inserting a print() statement before the rest of the original method is executed. E.g. printing the input parameter before executing the addition, etc.
In my case, this does not need to be done during runtime (even if this is very interesting to know). I could imagine, that using inheritance or something similar could be a way. Perhaps somebody has a great idea?
Thanks for the help!
The answer really depends what you are after.
Wrapping a method of a class (before runtime)
This is very typical use case of decorators (the #something above a function definition).
def with_printing(func):
def wrapper(*args, **kwargs):
print("Before calling method")
ret = func(*args, **kwargs)
print("After calling method")
return ret
return wrapper
class MyCalculator:
#with_printing
def addition(self, a, b):
print("calling addition")
return a + b
If you want to keep the docstring of the original method, you would use the functools.wraps().
Example output
mycalc = MyCalculator()
print(mycalc.addition(2, 3))
would print
Before calling method
calling addition
After calling method
5
Wrapping a method of an object instance (runtime)
Here is one implementation which changes the method of an object. Note that this changes the method of an instance and not every instance of that class.
class MyCalculator:
def addition(self, a, b):
print("calling addition")
return a + b
class Changing:
def set_method_attribute(self, obj, method_name):
method = getattr(obj, method_name)
def wrapper(*args, **kwargs):
print("Before calling method")
ret = method(*args, **kwargs)
print("After calling method")
return ret
setattr(obj, method_name, wrapper)
Example usage
# Create two calculator objects for testing
mycalc = MyCalculator()
mycalc2 = MyCalculator()
change = Changing()
# Before change
print(mycalc.addition(2, 3))
print("----")
# After change
change.set_method_attribute(mycalc, "addition")
print(mycalc.addition(2, 3))
print("----")
# The another calculator object remains unchanged
print(mycalc2.addition(2, 3))
will print
calling addition
5
----
Before calling method
calling addition
After calling method
5
----
calling addition
5
I recreated some of the code in a new way. Would you mind taking a look on it and telling me if this is a "good" way?
import inspect
class InBetween():
def __init__(self):
object_names = [object_name for object_name in inspect.getmembers(self) if (inspect.ismethod(object_name[1]) and (object_name[0] != 'with_print'))]
for name in object_names:
method = getattr(self, name[0])
wrapper = self.with_print(method)
setattr(self, name[0], wrapper)
def with_print(self, method):
def wrapper(*args, **kwargs):
print("before")
ret = method(*args, **kwargs)
print("after")
return ret
return wrapper
class MyCalculator(InBetween):
def __init__(self):
super().__init__()
def addition(self, a, b):
return a + b
def substraction(self, a, b):
return a - b
def multiply(self, a, b):
return a * b
if __name__=="__main__":
myCalc = MyCalculator()
print(myCalc.addition(2,5))
print(myCalc.multiply(2,5))
The basic idea is, that every class wich will inherit "InBetween" can add via super() the wrapper to each method. Without doing this manually in the script. My next idea is, to replace the print statements by logging etc. At the end, if I call the parent "init" infromation will be easily logged, and if not, "nothing" happens.
Love to hear other opinions on that!
Thank you all!
I'm trying to guard the use of certain methods in a class against misuse. I think a guard decorator could work as below.
For example, we have class Hello. It has an attribute allowed and two methods allowed_function and disallowed_function. The guard decorator would manage what functions can and can't be called.
class Hello:
def __init__(self):
self.allowed = True
def guard_func(self):
return(self.allowed)
#guard(guard_func)
def allowed_function(self):
print "I'm allowed!"
#guard(not guard_func)
def disallowed_function(self):
print "I'm not allowed!"
How should I go about this in Python?
Here's an implementation of guard for Python 3 (it looks like you might be using 2; I highly recommend upgrading).
import functools
class NotAllowed(Exception):
pass
def guard(condition):
def decorator(func):
#functools.wraps(func)
def wrapper(self, *args, **kwargs):
if not condition(self):
raise NotAllowed(f"Not allowed to call {func}")
return func(self, *args, **kwargs)
return wrapper
return decorator
class Hello:
def __init__(self):
self.allowed = True
def guard_func(self):
return self.allowed
#guard(guard_func)
def allowed_function(self):
print("I'm allowed!")
#guard(lambda self: not self.guard_func())
def disallowed_function(self):
print("I'm not allowed!")
h = Hello()
h.allowed_function() # will print the message
h.disallowed_function() # will raise a `NotAllowed` exception
Basically, you need two levels of indirection here. You have to write a function which returns the actual decorator function, so that you can parameterize it based on the condition function you pass in when you actually use the decorator. You also need to be careful about how self gets passed around.
functools.wraps is not necessary, but highly advised: https://docs.python.org/3.8/library/functools.html#functools.wraps
Everything above should work as advertised on Python 2, you just need to replace the f-string with a string format.
The implementation of the guard decorator by #JoshKarpel seems just fine.
However, I would suggest changing your design slightly:
class Hello:
def __init__(self):
self.group1_enable = True
self.group2_enable = False
def group1(self):
return(self.group1_enable)
def group2(self):
return(self.group2_enable)
#guard(group1)
def allowed_function(self):
print "I'm allowed!"
#guard(group2)
def disallowed_function(self):
print "I'm not allowed!"
This design allows groups of functions to be independently enabled and disabled.
I have a test framework that requires test cases to be defined using the following class patterns:
class TestBase:
def __init__(self, params):
self.name = str(self.__class__)
print('initializing test: {} with params: {}'.format(self.name, params))
class TestCase1(TestBase):
def run(self):
print('running test: ' + self.name)
When I create and run a test, I get the following:
>>> test1 = TestCase1('test 1 params')
initializing test: <class '__main__.TestCase1'> with params: test 1 params
>>> test1.run()
running test: <class '__main__.TestCase1'>
The test framework searches for and loads all TestCase classes it can find, instantiates each one, then calls the run method for each test.
load_test(TestCase1(test_params1))
load_test(TestCase2(test_params2))
...
load_test(TestCaseN(test_params3))
...
for test in loaded_tests:
test.run()
However, I now have some test cases for which I don't want the __init__ method called until the time that the run method is called, but I have little control over the framework structure or methods. How can I delay the call to __init__ without redefining the __init__ or run methods?
Update
The speculations that this originated as an XY problem are correct. A coworker asked me this question a while back when I was maintaining said test framework. I inquired further about what he was really trying to achieve and we figured out a simpler workaround that didn't involve changing the framework or introducing metaclasses, etc.
However, I still think this is a question worth investigating: if I wanted to create new objects with "lazy" initialization ("lazy" as in lazy evaluation generators such as range, etc.) what would be the best way of accomplishing it? My best attempt so far is listed below, I'm interested in knowing if there's anything simpler or less verbose.
First Solution：use property.the elegant way of setter/getter in python.
class Bars(object):
def __init__(self):
self._foo = None
#property
def foo(self):
if not self._foo:
print("lazy initialization")
self._foo = [1,2,3]
return self._foo
if __name__ == "__main__":
f = Bars()
print(f.foo)
print(f.foo)
Second Solution:the proxy solution,and always implement by decorator.
In short, Proxy is a wrapper that wraps the object you need. Proxy could provide additional functionality to the object that it wraps and doesn't change the object's code. It's a surrogate which provide the abitity of control access to a object.there is the code come form user Cyclone.
class LazyProperty:
def __init__(self, method):
self.method = method
self.method_name = method.__name__
def __get__(self, obj, cls):
if not obj:
return None
value = self.method(obj)
print('value {}'.format(value))
setattr(obj, self.method_name, value)
return value
class test:
def __init__(self):
self._resource = None
#LazyProperty
def resource(self):
print("lazy")
self._resource = tuple(range(5))
return self._resource
if __name__ == '__main__':
t = test()
print(t.resource)
print(t.resource)
print(t.resource)
To be used for true one-time calculated lazy properties. I like it because it avoids sticking extra attributes on objects, and once activated does not waste time checking for attribute presence
Metaclass option
You can intercept the call to __init__ using a metaclass. Create the object with __new__ and overwrite the __getattribute__ method to check if __init__ has been called or not and call it if it hasn't.
class DelayInit(type):
def __call__(cls, *args, **kwargs):
def init_before_get(obj, attr):
if not object.__getattribute__(obj, '_initialized'):
obj.__init__(*args, **kwargs)
obj._initialized = True
return object.__getattribute__(obj, attr)
cls.__getattribute__ = init_before_get
new_obj = cls.__new__(cls, *args, **kwargs)
new_obj._initialized = False
return new_obj
class TestDelayed(TestCase1, metaclass=DelayInit):
pass
In the example below, you'll see that the init print won't occur until the run method is executed.
>>> new_test = TestDelayed('delayed test params')
>>> new_test.run()
initializing test: <class '__main__.TestDelayed'> with params: delayed test params
running test: <class '__main__.TestDelayed'>
Decorator option
You could also use a decorator that has a similar pattern to the metaclass above:
def delayinit(cls):
def init_before_get(obj, attr):
if not object.__getattribute__(obj, '_initialized'):
obj.__init__(*obj._init_args, **obj._init_kwargs)
obj._initialized = True
return object.__getattribute__(obj, attr)
cls.__getattribute__ = init_before_get
def construct(*args, **kwargs):
obj = cls.__new__(cls, *args, **kwargs)
obj._init_args = args
obj._init_kwargs = kwargs
obj._initialized = False
return obj
return construct
#delayinit
class TestDelayed(TestCase1):
pass
This will behave identically to the example above.
In Python, there is no way that you can avoid calling __init__ when you instantiate a class cls. If calling cls(args) returns an instance of cls, then the language guarantees that cls.__init__ will have been called.
So the only way to achieve something similar to what you are asking is to introduce another class that will postpone the calling of __init__ in the original class until an attribute of the instantiated class is being accessed.
Here is one way:
def delay_init(cls):
class Delay(cls):
def __init__(self, *arg, **kwarg):
self._arg = arg
self._kwarg = kwarg
def __getattribute__(self, name):
self.__class__ = cls
arg = self._arg
kwarg = self._kwarg
del self._arg
del self._kwarg
self.__init__(*arg, **kwarg)
return getattr(self, name)
return Delay
This wrapper function works by catching any attempt to access an attribute of the instantiated class. When such an attempt is made, it changes the instance's __class__ to the original class, calls the original __init__ method with the arguments that were used when the instance was created, and then returns the proper attribute. This function can be used as decorator for your TestCase1 class:
class TestBase:
def __init__(self, params):
self.name = str(self.__class__)
print('initializing test: {} with params: {}'.format(self.name, params))
class TestCase1(TestBase):
def run(self):
print('running test: ' + self.name)
>>> t1 = TestCase1("No delay")
initializing test: <class '__main__.TestCase1'> with params: No delay
>>> t2 = delay_init(TestCase1)("Delayed init")
>>> t1.run()
running test: <class '__main__.TestCase1'>
>>> t2.run()
initializing test: <class '__main__.TestCase1'> with params: Delayed init
running test: <class '__main__.TestCase1'>
>>>
Be careful where you apply this function though. If you decorate TestBase with delay_init, it will not work, because it will turn the TestCase1 instances into TestBase instances.
In my answer I'd like to focus on cases when one wants to instantiate a class whose initialiser (dunder init) has side effects. For instance, pysftp.Connection, creates an SSH connection, which may be undesired until it's actually used.
In a great blog series about conceiving of wrapt package (nit-picky decorator implementaion), the author describes Transparent object proxy. This code can be customised for the subject in question.
class LazyObject:
_factory = None
'''Callable responsible for creation of target object'''
_object = None
'''Target object created lazily'''
def __init__(self, factory):
self._factory = factory
def __getattr__(self, name):
if not self._object:
self._object = self._factory()
return getattr(self._object, name)
Then it can be used as:
obj = LazyObject(lambda: dict(foo = 'bar'))
obj.keys() # dict_keys(['foo'])
But len(obj), obj['foo'] and other language constructs which invoke Python object protocols (dunder methods, like __len__ and __getitem__) will not work. However, for many cases, which are limited to regular methods, this is a solution.
To proxy object protocol implementations, it's possible to use neither __getattr__, nor __getattribute__ (to do it in a generic way). The latter's documentation notes:
This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup.
As a complete solution is demanded, there are examples of manual implementations like werkzeug's LocalProxy and django's SimpleLazyObject. However a clever workaround is possible.
Luckily there's a dedicated package (based on wrapt) for the exact use case, lazy-object-proxy which is described in this blog post.
from lazy_object_proxy import Proxy
obj = Proxy(labmda: dict(foo = 'bar'))
obj.keys() # dict_keys(['foo'])
len(len(obj)) # 1
obj['foo'] # 'bar'
One alternative would be to write a wrapper that takes a class as input and returns a class with delayed initialization until any member is accessed. This could for example be done as this:
def lazy_init(cls):
class LazyInit(cls):
def __init__(self, *args, **kwargs):
self.args = args
self.kwargs = kwargs
self._initialized = False
def __getattr__(self, attr):
if not self.__dict__['_initialized']:
cls.__init__(self,
*self.__dict__['args'], **self.__dict__['kwargs'])
self._initialized = True
return self.__dict__[attr]
return LazyInit
This could then be used as such
load_test(lazy_init(TestCase1)(test_params1))
load_test(lazy_init(TestCase2)(test_params2))
...
load_test(lazy_init(TestCaseN)(test_params3))
...
for test in loaded_tests:
test.run()
Answering your original question (and the problem I think you are actually trying to solve), "How can I delay the init call until an attribute is accessed?": don't call init until you access the attribute.
Said another way: you can make the class initialization simultaneous with the attribute call. What you seem to actually want is 1) create a collection of TestCase# classes along with their associated parameters; 2) run each test case.
Probably your original problem came from thinking you had to initialize all your TestCase classes in order to create a list of them that you could iterate over. But in fact you can store class objects in lists, dicts etc. That means you can do whatever method you have for finding all TestCase classes and store those class objects in a dict with their relevant parameters. Then just iterate that dict and call each class with its run() method.
It might look like:
tests = {TestCase1: 'test 1 params', TestCase2: 'test 2 params', TestCase3: 'test 3 params'}
for test_case, param in tests.items():
test_case(param).run()
Overridding __new__
You could do this by overriding __new__ method and replacing __init__ method with a custom function.
def init(cls, real_init):
def wrapped(self, *args, **kwargs):
# This will run during the first call to `__init__`
# made after `__new__`. Here we re-assign the original
# __init__ back to class and assign a custom function
# to `instances.__init__`.
cls.__init__ = real_init
def new_init():
if new_init.called is False:
real_init(self, *args, **kwargs)
new_init.called = True
new_init.called = False
self.__init__ = new_init
return wrapped
class DelayInitMixin(object):
def __new__(cls, *args, **kwargs):
cls.__init__ = init(cls, cls.__init__)
return object.__new__(cls)
class A(DelayInitMixin):
def __init__(self, a, b):
print('inside __init__')
self.a = sum(a)
self.b = sum(b)
def __getattribute__(self, attr):
init = object.__getattribute__(self, '__init__')
if not init.called:
init()
return object.__getattribute__(self, attr)
def run(self):
pass
def fun(self):
pass
Demo:
>>> a = A(range(1000), range(10000))
>>> a.run()
inside __init__
>>> a.a, a.b
(499500, 49995000)
>>> a.run(), a.__init__()
(None, None)
>>> b = A(range(100), range(10000))
>>> b.a, b.b
inside __init__
(4950, 49995000)
>>> b.run(), b.__init__()
(None, None)
Using cached properties
The idea is to do the heavy calculation only once by caching results. This approach will lead to much more readable code if the whole point of delaying initialization is improving performance.
Django comes with a nice decorator called #cached_property. I tend to use it a lot in both code and unit-tests for caching results of heavy properties.
A cached_property is a non-data descriptor. Hence once the key is set in instance's dictionary, the access to property would always get the value from there.
class cached_property(object):
"""
Decorator that converts a method with a single self argument into a
property cached on the instance.
Optional ``name`` argument allows you to make cached properties of other
methods. (e.g. url = cached_property(get_absolute_url, name='url') )
"""
def __init__(self, func, name=None):
self.func = func
self.__doc__ = getattr(func, '__doc__')
self.name = name or func.__name__
def __get__(self, instance, cls=None):
if instance is None:
return self
res = instance.__dict__[self.name] = self.func(instance)
return res
Usage:
class A:
#cached_property
def a(self):
print('calculating a')
return sum(range(1000))
#cached_property
def b(self):
print('calculating b')
return sum(range(10000))
Demo:
>>> a = A()
>>> a.a
calculating a
499500
>>> a.b
calculating b
49995000
>>> a.a, a.b
(499500, 49995000)
I think you can use a wrapper class to hold the real class you want to instance, and use call __init__ yourself in your code, like(Python 3 code):
class Wrapper:
def __init__(self, cls):
self.cls = cls
self.instance = None
def your_method(self, *args, **kwargs):
if not self.instance:
self.instnace = cls()
return self.instance(*args, **kwargs)
class YourClass:
def __init__(self):
print("calling __init__")
but it's a dump way, but without any trick.
I want to create a class that behaves like collections.defaultdict, without having the usage code specify the factory. EG:
instead of
class Config(collections.defaultdict):
pass
this:
Config = functools.partial(collections.defaultdict, list)
This almost works, but
isinstance(Config(), Config)
fails. I am betting this clue means there are more devious problems deeper in also. So is there a way to actually achieve this?
I also tried:
class Config(Object):
__init__ = functools.partial(collections.defaultdict, list)
I don't think there's a standard method to do it, but if you need it often, you can just put together your own small function:
import functools
import collections
def partialclass(cls, *args, **kwds):
class NewCls(cls):
__init__ = functools.partialmethod(cls.__init__, *args, **kwds)
return NewCls
if __name__ == '__main__':
Config = partialclass(collections.defaultdict, list)
assert isinstance(Config(), Config)
I had a similar problem but also required instances of my partially applied class to be pickle-able. I thought I would share what I ended up with.
I adapted fjarri's answer by peeking at Python's own collections.namedtuple. The below function creates a named subclass that can be pickled.
from functools import partialmethod
import sys
def partialclass(name, cls, *args, **kwds):
new_cls = type(name, (cls,), {
'__init__': partialmethod(cls.__init__, *args, **kwds)
})
# The following is copied nearly ad verbatim from `namedtuple's` source.
"""
# For pickling to work, the __module__ variable needs to be set to the frame
# where the named tuple is created. Bypass this step in enviroments where
# sys._getframe is not defined (Jython for example) or sys._getframe is not
# defined for arguments greater than 0 (IronPython).
"""
try:
new_cls.__module__ = sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
pass
return new_cls
At least in Python 3.8.5 it just works with functools.partial:
import functools
class Test:
def __init__(self, foo):
self.foo = foo
PartialClass = functools.partial(Test, 1)
instance = PartialClass()
instance.foo
If you actually need working explicit type checks via isinstance, you can simply create a not too trivial subclass:
class Config(collections.defaultdict):
def __init__(self): # no arguments here
# call the defaultdict init with the list factory
super(Config, self).__init__(list)
You'll have no-argument construction with the list factory and
isinstance(Config(), Config)
will work as well.
Could use *args and **kwargs:
class Foo:
def __init__(self, a, b):
self.a = a
self.b = b
def printy(self):
print("a:", self.a, ", b:", self.b)
class Bar(Foo):
def __init__(self, *args, **kwargs):
return super().__init__(*args, b=123, **kwargs)
if __name__=="__main__":
bar = Bar(1)
bar.printy() # Prints: "a: 1 , b: 123"
I have a decorator
def deco(func):
def inner(params):
#< DO STUFF WITH func >
return inner
And a base class
class GenericClass:
def __init__(self,value):
self.value = value
def method(self,params):
print 'NOT IMPLEMENTED YET'
def other_method(self):
print 'GOOD TO GO'
I would like to be able to decorate the "method" method on classes which are child of GenericClass, for exemple to check input/output or handle exceptions (the method "method" will be overrided)
what I want to do is something like...
class ChildClass(GenericClass):
#deco
def method(self,params):
#< NEW METHOD >
I am not an expert python developper and all the doc at that level is quite confusing (i.e. metaclasses, subtleties in decorators, __call__ method etc etc) and I didn't found the solution on SO.
Got it. You are basically asking how to write a decorator which can be applied to both functions and methods. It's possible:
def deco(func):
def inner(*args):
print('DECORATED: args={}'.format(args))
func(*args)
return inner
class Class:
#deco
def method(self, param): print('PARAM is {}'.format(param))
#deco
def func(a, b, c): print('{} {} {}'.format(a, b, c))
Class().method('X')
func(1, 2, 3)
OUTPUT:
DECORATED: args=(<__main__.Class instance at 0x7f740c6297a0>, 'X')
PARAM is X
DECORATED: args=(1, 2, 3)
1 2 3
P.S.
One year later I found one useful post (which was asked 8 years ago) here: Using the same decorator (with arguments) with functions and methods. The approach described there will be useful if you are care of actual parameters of the decorated function.
I figured it out. The trick is that module-level functions (except for closures, I guess, which you probably don't want to decorate anyway) have a simple name while methods at least have two parts in their qualified name.
Forget about inspect.ismethod - for some reason it just won't work in this case, although it should be the obvious choice, possibly a bug.
def can(*fargs):
def wrapper(func):
if len(func.__qualname__.split('.')) > 1:
def calling(self, *args, **kwargs):
self, thing = args[0], args[1]
do_stuff(thing)
func(*args, **kwargs)
else:
def calling(*args, **kwargs):
thing = args[0]
do_stuff(thing)
func(*args, **kwargs)
return calling
return wrapper
class C:
#can(2, 3)
def test(self, x):
print(7, ismethod(self.test), x)
#can()
def test(x):
print(8, ismethod(test), x)
c = C()
c.test(12)
test(8)