I was trying to perform an action before and after the execution of some methods in my class.
First I thought about using a decorator to extend the functionality, something like this:
def decorator(f):
def wrap(*args, **kwargs):
print("before")
f(*args, **kwargs)
print("after")
return wrap
class Foo(object):
#decorator
def do(self):
print("This is do")
a = Foo()
a.do()
This outputs what I want:
before
This is do
after
But then I realized that if I wanted to inherit from Foo to extend do that wouldn't work, at least the way I'm doing it:
class Bar(Foo):
def do(self):
super(Bar, self).do()
print("This is the new implementation of do")
b = Bar()
b.do()
This will be the output and it's not okay. "after" should be the last thing to be printed:
before
This is do
after
This is the new implementation of do
Maybe there is another way to decorate do in Bar so it does what I want but I don't know how (if there is I would like to know it) and tbh decorating do every time doesn't seem like a good way to go.
So finally I came up with what I think is a nice solution. Defining __getattribute__so it returns a wrapper for do:
class Foo(object):
def __getattribute__(self, name):
attribute = super(Foo, self).__getattribute__(name)
if name == "do":
def wrap(*args, **kwargs):
print("before")
attribute(*args, **kwargs)
print("after")
return wrap
else:
return attribute
def do(self):
print("This is do")
class Bar(Foo):
def __init__(self):
super(Bar, self).__init__()
def do(self):
super(Bar, self).do()
print("This is the new implementation of do")
a = Bar()
a.do()
Is this a good solution? Any downsides? I'm missing something that could create a problem in the future? Other workarounds?
Thx!!
You can access the wrapped function in python3 with __wrapped__, since it's a custom decorator you need a slight modification with the functools.wraps decorator.
from functools import wraps
def decorator(f):
#wraps(f)
def wrap(*args, **kwargs):
print("before")
f(*args, **kwargs)
print("after")
return wrap
Reapply the decorator to the new do() and strip it from the old one
class Bar(Foo):
#decorator
def do(self):
super(Bar, self).do.__wrapped__(super)
print("This is the new implementation of do")
you get:
before
This is do
This is the new implementation of do
after
You could place the implementation of do() in a separate method that will be the one to override in subclasses and have the main do() method with pre/post processing only in the base class:
class Foo:
def _do(self):
# do your stuff
def do(self):
self.preProcessing()
self._do()
self.postProcessing()
class Bar(Foo):
def _do(self):
super()._do()
# do more stuff
Related
In the code below I created a decorator to my class Class methods. I noticed that this decorator is called even without creating a class instance!
And without calling these methods in the class!
Is there any explanation for that?
decorator :
def deco(class_name):
def inner_function(method):
print("method is = {} and class is: {}".format(method.__name__,class_name.__name__))
return method
return inner_function
class_deco
class class_deco :
def __init__(self):
pass
Class:
class Class :
def __init__(self):
pass
#deco(class_deco)
def f1(self):
pass
#deco(class_deco)
def f2(self):
pass
When I run the script :
if __name__ == "__main__":
pass
I get this result:
method is = f1 and class is: class_deco
method is = f2 and class is: class_deco
Decorators are just syntatic sugar for the following
#deco(class_deco)
def f1(self):
pass
is same as -
f1 = deco(class_deco)(f1)
So this code runs as soon as the module is imported just like any other name declaration would and f1 name is replaced with decorated f1 as above.
As already explained, the #decorator syntax is only syntactic sugar, so this:
#somedecorator
def foo():
pass
is stryctly equivalent to
def foo():
pass
foo = somedecorator(foo)
In you case, you ARE explicitely calling the decorator function:
#deco(class_deco)
def f1(self):
pass
which is equivalent to:
def f1(self):
pass
_effective_decorator = deco(class_deco)
f1 = _effective_decorator(f1)
which is why your inner_function is indeed executed at import time.
Decorators that take additional params needs one more level of nesting, so technically your decorator should look like:
def deco(cls):
def real_deco(func):
def inner_function(*args, **kw):
print("method is = {} and class is: {}".format(func.__name__,cls.__name__))
return func(*args, **kw)
return inner_function
return real_deco
return inner_function
BUT if the point is to get the name of the class the method really belongs to, this is still broken - you should get the class from the instance on which the method is called, not try to hard-code it in the decorator call (which will never work as intended since the real class doesn't exist when you're applying the decorator to the function). So the proper implementation would look something like:
def deco(func):
# we're only supposed to use this on methods...
def wrapper(self, *args, **kw):
print("class {} - method {}".format(type(self).__name__, func.__name__))
return wrapper
class Class:
#deco
def f1(self):
pass
NB: this won't handle classmethods nor staticmethods, of course.
Here is a demo showing just two possible ways a decorator could be constructed:
def Deco(*deco_params):
print('In Deco', deco_params)
def deco(func):
print('In deco(func)')
def inner(*args, **kwargs):
print('In inner(*args, **kwargs)')
return func(*args, **kwargs)
return inner
return deco
def deco(method):
print('In deco(method)')
def inner_function(*args, **kwargs):
print("method is = {} called".format(method.__name__))
return method(*args, **kwargs)
return inner_function
class Class :
def __init__(self):
pass
#deco
def f1(self):
pass
#Deco(42)
def f2(self):
pass
if __name__ == "__main__":
print('Now in Main')
c = Class()
c.f1()
c.f2()
Output:
In deco(method)
In Deco (42,)
In deco(func)
Now in Main
method is = f1 called
In inner(*args, **kwargs)
I am trying to implement a simple logging feature within my app.
class messages(object):
# Implement decorator here
def on(self):
def wrapper():
# Do something here
return wrapper
def off(self):
def wrapper():
# Do something here
return wrapper
class website(object):
#messages.on #This line can be switched on/off
def login(self):
# Do a whole bunch of stuff here
self.response = "[+] Login Succeeded!"
website = website()
website.login() # prints self.response based on #messages.on/off
But i am not sure what i need to apply in my decorator. I have tried creating instances and adding params but mostly receive TypeError. I am fairly new to decorators. Any help would be appreciated and i'd love to remember this for next time.
If you just want Dog to bark (like in the example), there is no need for enabling a decorator
class Dog(object):
def __init__(self):
self.sound = "Woof!"
def bark(self):
return self.sound
If you want to enable logging for some functions in class, here is a code that does that with explanation in comments
from functools import wraps
class Utilities(object):
#staticmethod # no default first argument in logger function
def logger(f): # accepts a function
#wraps(f) # good practice https://docs.python.org/2/library/functools.html#functools.wraps
def wrapper(self, *args, **kwargs): # explicit self, which means this decorator better be used inside classes only
print("Before:", self.sound)
result = f(self, *args, **kwargs)
print("After:", self.sound)
return result
return wrapper
class Dog(object):
def __init__(self):
self.sound = "Woof!"
#Utilities.logger
def run(self):
"""Perform running movement"""
print("Running")
Example:
>>> dog = Dog()
>>> dog.run()
Before: Woof!
Running
After: Woof!
Though after all there is no need to store decorators functionality in the Utilities class - it is better to have a separate module (file) named utils and put decorator there as a standalone function
Below is the sample decorator that you may use:
class Utilities:
#staticmethod
def add_logger(func):
def wrapped_func(*args, **kwargs):
# Sample logic, feel free to update this
try:
func_response = func(*args, **kwargs)
except:
print 'I am error handled by logger'
func_response = None
return func_response
return wrapped_func
Let's define your class now:
class Dog(object):
#Utilities.add_logger
def bark(self):
print 'In bark'
#Utilities.add_logger
def my_error_function(self):
print 'In my_error_function'
raise Exception # <--- Raises Exception
Now, let's check how it works:
>>> dog = Dog()
>>> dog.bark()
In bark
>>> dog.my_error_function()
In my_error_function
I am error handled by logger # Content from logger in case of exception
Note: You shouldn't really be creating a class here to store utility function. Create a separate utility file and write such functions over there.
Without class, your decorator should be like (let's say in utility.py):
def add_logger(func):
def wrapped_func(*args, **kwargs):
# Sample logic, feel free to update this
try:
func_response = func(*args, **kwargs)
except:
print 'I am error handled by logger'
func_response = None
return func_response
return wrapped_func
For using it, simply do:
import utility
class Dog(object):
#utility.add_logger
def bark(self):
print 'In bark'
Suppose I have some python code, e.g. some class defined somewhere, which cannot be modified
class MyClass(object):
def __init__(self, arg1, arg2):
do_something...
def foo(self):
do_something
Now I want to add a trace capability, e.g. some mechanism from outside that traces each and every method call for the above class. I want to be able to print out when e.g, __init__ has been called, or foo or even the __del__ method of MyClass.
Is this possible to do, and how is this done best?
Create a proxy class that wraps the original class and then delegates the work after printing a trace:
class MyClassProxy(object):
def __init__(*args, **kwds):
print 'Initializing'
self.o = MyClass(*args, **kwds)
def foo(self):
print 'Fooing'
return self.o.foo()
You can create a trace decorator and attach it to all the methods of the class instance or class definition as shown in decorate_methods function.
import functools
import inspect
import types
class TestClass(object):
def func1(self):
pass
def func2(self, a, b):
pass
def trace(func):
#functools.wraps(func)
def decorator(*args, **kwargs):
print "TRACE:", func.__name__, args, kwargs
return func(*args, **kwargs)
return decorator
def decorate_methods(obj, decorator):
for name, func in inspect.getmembers(obj):
if isinstance(func, types.MethodType):
setattr(obj, name, decorator(func))
# Apply the decorator to a class instance
test1 = TestClass()
decorate_methods(test1, trace)
test1.func1()
test1.func2('bar1', b='bar2')
# Apply the decorator to the class definition
decorate_methods(TestClass, trace)
test2 = TestClass()
test2.func1()
test2.func2('bar1', b='bar2')
The output of the script will be:
TRACE: func1 () {}
TRACE: func2 ('bar1',) {'b': 'bar2'}
TRACE: func1 (<__main__.TestClass object at 0x7f5a8d888150>,) {}
TRACE: func2 (<__main__.TestClass object at 0x7f5a8d888150>, 'bar1') {'b': 'bar2'}
Use decorator as shown below:
def call_trace(orig_func):
def decorated_func(*args, **kwargs):
print "========>In function: " + orig_func.__name__ + "<========"
orig_func(*args, **kwargs)
return decorated_func
Apply this decorator to trace the function. It prints function name before entering the function.
Ex:
#call_trace
def foo(self):
do_something
Hope it helps.
[Update]: You can use metaclass, only thing you got to change is to add "metaclass" parameter to your class as shown below. As you can see, below code applies "call_trace" decorator to every function in the class "ExBase".
I tried this out yesterday, it worked fine. I am also new to python.:)
def call_trace(orig_func):
def inner_func(*args, **kwargs):
print ("function name:" + str(orig_func.__name__))
orig_func(*args, **kwargs)
return inner_func
class ExMeta(type):
def __new__(cls, name, bases, attrs):
for attr in attrs:
if hasattr(attrs[attr], '__call__'):
attrs[attr] = call_trace(attrs[attr])
return type.__new__(cls, name, bases, attrs)
class ExBase(metaclass=ExMeta):
x = "x"
y = "y"
def __init__(self):
self.__name = "name"
def getname(self):
return self.__name
b = ExBase()
b.getname()
Get the code for OnlinePythonTutor from github.com/pgbovine/OnlinePythonTutor/tree/master/v3.
You don't need to bother with all the JS stuff. Extract the files into some directory. You can run your scripts using python /path/to/my/OnlinePythonTutor-master/v3/generate_json_trace my_script.py
This will give you basically everything your program is doing in a step by step manner. It will probably be overkill so if you want look into the source code and the underlying source in bdb http://docs.python.org/2/library/bdb.html. The docs for bdb are horrible so I'm having trouble figuring out what exactly is going on but I think this is a pretty cool problem, good luck.
I have come across this singleton implementation here: http://blog.amir.rachum.com/post/21850841339/implementing-the-singleton-pattern-in-python in the first reply.
def singleton(cls):
return cls()
#singleton
class Foo(object):
def bar(self):
pass
if __name__ == '__main__':
print id(Foo)
print id(Foo)
But I don't understand the inner workings, the decorator returns a class instance, but why the same instance every time ?
You can rewrite that code to
class Foo(object):
pass
Foo = singleton(Foo)
# which is
Foo = Foo()
So here the name of the class is replaced by an instantiation of it. A bit cheesy in my opinion, especially since you can still create new objects of the same class by using Foo.__class__ and you are messing with the naming schema.
The singleton does that by holding internal state. This state here would probably be an instance of the class. The decorator can be something arbitrary.
Have a look at this:
http://hairysun.com/downloads/DecoratorHandout.pdf
class Decorator(object):
# in __init__ set up state
def __call__(self, function):
#functools.wraps(function)
def wrapper(*args, **kw): # 1.
print "before func"
result = function(*args, **kw) # 2.
print "after func"
return result
return wrapper # 3.
>>> decorator2 = Decorator()
>>> #decorator2
... def nothing(): pass
The decorator is essentially a function that
Defines a function
That calls the function that you passed in
Returns the newly 'wrapped' function to be called later
The surrounding class (here: the decorator) could do something like this:
class Singleton(object):
def __init__(self):
self.instance = None
def __call__(self, function):
#functools.wraps(function)
def wrapper(*args, **kw):
if self.instance is None:
self.instance = function(*args, **kw)
return self.instance
return wrapper
I did not run the code, but I assume this is in general how it works. If there is no instance available create one. If one is available, don't create a new one - return the single old one instead. One might probably want to check for other properties of the callable before using this in production.
Can you create a decorator inside a class that will see the classes methods and variables?
The decorator here doesnt see: self.longcondition()
class Foo:
def __init__(self, name):
self.name = name
# decorator that will see the self.longcondition ???
class canRun(object):
def __init__(self, f):
self.f = f
def __call__(self, *args):
if self.longcondition(): # <-------- ???
self.f(*args)
# this is supposed to be a very long condition :)
def longcondition(self):
return isinstance(self.name, str)
#canRun # <------
def run(self, times):
for i in xrange(times):
print "%s. run... %s" % (i, self.name)
There's no real need to implement this decorator as a class, and there's no need to implement it inside the definition of the Foo class. The following will suffice:
def canRun(meth):
def decorated_meth(self, *args, **kwargs):
if self.longcondition():
print 'Can run'
return meth(self, *args, **kwargs)
else:
print 'Cannot run'
return None
return decorated_meth
Using that decorator seems to work:
>>> Foo('hello').run(5)
Can run
0. run... hello
1. run... hello
2. run... hello
3. run... hello
4. run... hello
>>> Foo(123).run(5)
Cannot run
You can have it be a class but you need to use the descriptor protocol
import types
class canRun(object):
def __init__(self, f):
self.f = f
self.o = object # <-- What the hell is this about?
def __call__(self, *args):
if self.longcondition():
self.f(*args)
def __get__(self, instance, owner):
return types.MethodType(self, instance)
You always need to use a descriptor when you want to decorate class methods with a class instance using the __call__ method. The reason for this is that there will only be one self passed in which refers to the instance of the decorating class and not the instance of the decorated method.
My previous answer was made in haste. If you're wanting to write a decorator you should really use wraps from the functools module. It takes care of the hard stuff for you.
A proper way to define the canRun decorator is:
from functools import wraps
def canRun(f):
#wraps(f)
def wrapper(instance, *args):
if instance.longcondition():
return f(instance, *args)
return wrapper
The canRun function should be defined outside of the class.