Related
I'll start by saying, I have a suspicion this is a solution that could be solved with a functional programming approach, but I don't know nearly enough of the concepts (but have been trying).
I've based my current solution on:
https://pythonconquerstheuniverse.wordpress.com/2012/04/29/python-decorators/
http://www.brianholdefehr.com/decorators-and-functional-python
https://github.com/mitsuhiko/click/blob/master/click/decorators.py
In the interest of learning (that's all this is!) how to build decorators, I decided to make a simple cache decorator.
But I get stuck in a loop where, I try to encapsulate the function I'm wrapping in a class, but every time I call the function I've wrapped, I call __call__ in wrapping class and so on ad infinitum.
I think I could have a nest of closures between the chain decorators, but I don't know how to collect all my variables in one scope.
I appreciate I could put all my arguments in a single decorator call, but my intention here is to learn how to chain decorators and store state between them.
Can anyone suggest a way (or ammend my way) to store state between chained decorators?
My intended design was:
# main.py
import http.client
from cache import cache
#cache.keys('domain', 'url')
#cache.lifetime(3600)
def make_http_request(domain,url='/'):
conn = httplib.HTTPConnection(domain)
conn.request("GET",url)
return conn.getresponse()
if __name__ == '__main__':
print(make_http_request('http://example.com/'))
with cache.py looking like
import hashlib
import os
import inspect
__author__ = 'drews'
def expand(path):
return os.path.abspath(os.path.expanduser(path))
class CacheManager():
"""Decorator to take the result and store it in a a file. If the result is needed again, then the file result is returned"""
def __init__(self, function, function_arg_name):
self.lifetime = 3600
self.cache_keys = None
self.cache_path = '~/.decorator_cache/'
self.f = function
self.arg_names = function_arg_name
def __call__(self, *args, **kwargs):
if len(args) > 0:
arg_names = self.arg_names
if 'self' in arg_names:
arg_names.remove('self')
key_args = dict(zip(arg_names, args))
key_args.update(kwargs)
else:
key_args = kwargs
self._initialise(cache_path=expand(self.cache_path))
key = self._build_key(key_args)
if self.key_exists(key):
result = self.get_key(key)
else:
result = self.f()
self.set_key(key, result)
return result
def _build_key(self, key_elements):
m = hashlib.md5()
for key in self.cache_keys:
m.update(key_elements[key].encode('utf-8'))
return m.hexdigest()
def _initialise(self, cache_path):
def initialise_path(path):
if not os.path.isdir(path):
(head, tail) = os.path.split(path)
if not os.path.isdir(head):
initialise_path(head)
os.mkdir(path)
initialise_path(cache_path)
def key_exists(self, key):
path = os.path.join(expand(self.cache_path), key)
return os.path.exists(path)
class CacheDefinitionDecorator(object):
def __init__(self, *args, **kwargs):
self.d_args = args
class CacheKeyDefinitionDecorator(CacheDefinitionDecorator):
def __call__(self, func, *args, **kwargs):
if not isinstance(func, CacheManager):
func = CacheManager(func,inspect.getargspec(func)[0])
func.cache_keys = self.d_args
return func
class CacheLifetimeDefintionDecorator(CacheDefinitionDecorator):
def __call__(self, func, *args, **kwargs):
if not isinstance(func, CacheManager):
func = CacheManager(func,inspect.getargspec(func)[0])
func.lifetime = self.d_args[0]
return func
class CacheStruct(object):
def __init__(self, **kwargs):
for item in kwargs:
setattr(self, item, kwargs[item])
cache = CacheStruct(
keys=CacheKeyDefinitionDecorator,
lifetime=CacheLifetimeDefintionDecorator
)
How do I pass self.key below into the decorator?
class CacheMix(object):
def __init__(self, *args, **kwargs):
super(CacheMix, self).__init__(*args, **kwargs)
key_func = Constructor(
memoize_for_request=True,
params={'updated_at': self.key}
)
#cache_response(key_func=key_func)
def list(self, *args, **kwargs):
pass
class ListView(CacheMix, generics.ListCreateAPIView):
key = 'test_key'
I get the error:
'self' is not defined
Here's an example of doing it with a class decorator as I tried to describe to you in the comments. I filled-in a few undefined references in your question and used a super-simplified version of your cache_response function decorator, but hopefully this will convey the idea concretely enough for you to be able adapt it to your real code.
import inspect
import types
class Constructor(object):
def __init__(self, memoize_for_request=True, params=None):
self.memoize_for_request = memoize_for_request
self.params = params
def __call__(self):
def key_func():
print('key_func called with params:')
for k, v in self.params.items():
print(' {}: {!r}'.format(k, v))
key_func()
def cache_response(key_func):
def decorator(fn):
def decorated(*args, **kwargs):
key_func()
fn(*args, **kwargs)
return decorated
return decorator
def example_class_decorator(cls):
key_func = Constructor( # define key_func here using cls.key
memoize_for_request=True,
params={'updated_at': cls.key} # use decorated class's attribute
)
# create and apply cache_response decorator to marked methods
# (in Python 3 use types.FunctionType instead of types.UnboundMethodType)
decorator = cache_response(key_func)
for name, fn in inspect.getmembers(cls):
if isinstance(fn, types.UnboundMethodType) and hasattr(fn, 'marked'):
setattr(cls, name, decorator(fn))
return cls
def decorate_me(fn):
setattr(fn, 'marked', 1)
return fn
class CacheMix(object):
def __init__(self, *args, **kwargs):
super(CacheMix, self).__init__(*args, **kwargs)
#decorate_me
def list(self, *args, **kwargs):
classname = self.__class__.__name__
print('list() method of {} object called'.format(classname))
#example_class_decorator
class ListView(CacheMix):
key = 'test_key'
listview = ListView()
listview.list()
Output:
key_func called with params:
updated_at: 'test_key'
list() method of ListView object called
I just found out that if you write the decorator function like so:
def decorator(the_func):
#wraps(the_func)
def wrapper(*args, **kwargs):
the_func(*args, **kwargs)
return wrapper
and decorate any method which takes self as an argument, self will appear in args. Therefore you can do this:
from functools import wraps
class myClass:
def __init__(self):
self.myValue = "Hello"
def decorator(the_func):
#wraps(the_func)
def wrapper(*args, **kwargs):
print(args[0].myValue)
the_func(*args, **kwargs)
return wrapper
#decorator
def myFunction(self):
print("World")
Call it like you normally would
foo = myClass()
foo.myFunction()
and you should get
Hello
World
I want to be able to call a method according to some standard format:
outputs = obj.meth(in_0, in_1, ...)
, where outputs is a tuple of arrays, and each input is an array.
However, in most instances, I only return one array, and don't want to be forced to return a tuple of length 1 just for the sake of the standard format. (My actual formatting problem is more complicated but lets stick with this explanation for now.)
I want to be able to define a class like:
class _SomeClass(object):
def __init__(self):
self._amount_to_add = 1
#single_return_format
def add_one(self, x):
return x+self._amount_to_add
And then be able to call it as follows:
obj = _SomeClass()
assert obj.add_one(3) == 4
assert obj.add_one.standard_format(3)==(4, )
Question is: how do I define the decorator to allow this behaviour?
I tried:
def single_return_format(fcn):
fcn.standard_format = lambda *args: (fcn(*args), )
return fcn
, but it fails on the line with the second assert with:
TypeError: add_one() takes exactly 2 arguments (1 given)
Because the add_one requires "self" as an argument, and the the object has not even been created yet at the time the decorator modifies the function.
So Stack, how can I do this?
Notes:
1) I know I could do this with base-classes and inheritance instead, but that becomes a problem when you have more than one method in the class that you want to decorate this way.
2) The actual problem comes from using theano - the standard format is outputs, updates = fcn(*inputs), but most functions don't return any updates, so you want to be able to define those functions in a natural way, but still have the option of calling them according to this standard interface.
That's indeed a problem, because the way the "bound" method is retrieved from the function doesn't consider this way.
I see two ways:
You could just wrap the function:
def single_return_format(fcn):
# TODO Do some functools.wraps here...
return lambda *args, **kwargs: (fcn(*args, **kwargs), )
No fooling around with .standard_format, but a mere replacement of the function. So the function can define itself as returning one value, but can only be called as returning the tuple.
If this is not what you want, you can define a class for decorating methods which overrides __get__ and does the wrapping in a "live fashion". Of course, it can as well redefine __call__ so that it is usable for (standalone, non-method) functions as well.
To get exactly what you want you'd have to write a non-data descriptor and a set of wrapper classes for your functions. The reason for this is that the process of getting functions from objects as methods is highly optimised and it's not possible to hijack this mechanism. Instead you have to write your own classes that simulate this mechanism -- which will slow down your code if you are making lots of small method calls.
The very best way I can think to get the desired functionality is not to use any of the methods that you describe, but rather write a wrapper function that you use when needed to call a normal function in the standard format. eg.
def vectorise(method, *args, **kwargs):
return tuple(method(arg, **kwargs) for arg in args)
obj = _SomeClass()
result = vectorise(obj.add_one, 1, 2, 3)
Indeed, this is how numpy takes functions that operate on one argument and turns them into a function that works on arrays.
import numpy
def add_one(x):
return x + 1
arr = numpy.vectorize(add_one)([1, 2, 3])
If you really, really want to use non-data descriptors then following will work. Be warned these method calls are considerably slower. On my computer a normal method call takes 188 nanoseconds versus 1.53 microseconds for a "simple" method call -- a ten-fold difference. And vectorise call takes half the time a standard_form call does. The vast majority of that time is the lookup of the methods. The actual method calls are quite fast.
class simple_form:
"""Allows a simple function to be called in a standard way."""
def __init__(self, func):
self.func = func
def __get__(self, instance, owner):
if instance is None:
return self.func
return SimpleFormMethod(self.func, instance)
class MethodBase:
"""Provides support for getting the string representation of methods."""
def __init__(self, func, instance):
self.func = func
self.instance = instance
def _format(self):
return "<bound {method_class} {obj_class}.{func} of {obj}>".format(
method_class=self.__class__.__name__,
obj_class=self.instance.__class__.__name__,
func=self.func.__name__,
obj=self.instance)
def __str__(self):
return self._format()
def __repr__(self):
return self._format()
class SimpleFormMethod(MethodBase):
def __call__(self, *args, **kwargs):
return self.func(self.instance, *args, **kwargs)
#property
def standard_form(self):
return StandardFormMethod(self.func, self.instance)
class StandardFormMethod(MethodBase):
def __call__(self, *args, **kwargs):
return tuple(self.func(self.instance, arg, **kwargs) for arg in args)
class Number(object):
def __init__(self, value):
self.value = value
def add_to(self, *values):
return tuple(val + self.value for val in values)
#simple_form
def divide_into(self, value):
return value / self.value
num = Number(2)
print("normal method access:", num.add_to, sep="\n")
print("simple form method access:", num.divide_into, sep="\n")
print("standard form method access:", num.divide_into.standard_form, sep="\n")
print("access to underlying function:", Number.divide_into, sep="\n")
print("simple example usage:", num.divide_into(3))
print("standard example usage:", num.divide_into.standard_form(*range(3)))
Dunes gave the correct answer. I've stripped it down to bare bones so that it solves the problem in the question. The stripped-down code is here:
class single_return_format(object):
def __init__(self, func):
self._func = func
def __get__(self, instance, owner):
return SimpleFormMethod(instance, self._func)
class SimpleFormMethod(object):
def __init__(self, instance, func):
self._instance = instance
self._func = func
def __call__(self, *args, **kwargs):
return self._func(self._instance, *args, **kwargs)
#property
def standard_format(self):
return lambda *args, **kwargs: (self._func(self._instance, *args, **kwargs), )
class _SomeClass(object):
def __init__(self):
self._amount_to_add = 1
#single_return_format
def add_one(self, x):
return x+self._amount_to_add
obj = _SomeClass()
assert obj.add_one(3) == 4
assert obj.add_one.standard_format(3) == (4, )
I understand from this question that if I want to have a set which is thread-safe I have to implement the thread-safety part on my own.
Therefore I could come up with:
from threading import Lock
class LockedSet(set):
"""A set where add() and remove() are thread-safe"""
def __init__(self, *args, **kwargs):
# Create a lock
self._lock = Lock()
# Call the original __init__
super(LockedSet, self).__init__(*args, **kwargs)
def add(self, elem):
self._lock.acquire()
try:
super(LockedSet, self).add(elem)
finally:
self._lock.release()
def remove(self, elem):
self._lock.acquire()
try:
super(LockedSet, self).remove(elem)
finally:
self._lock.release()
So, of course only add() and remove() are thread-safe in this implementation. The other methods are not because they were not overwritten in the subclass.
Now, the pattern is pretty simple: acquire lock, call original method, release lock.
If I follow the logic above, I would have to overwrite all methods exposed by set in essentially the same way, e.g.:
(pseudo-code)
def <method>(<args>):
1. acquire lock
2. try:
3. call original method passing <args>
4. finally:
5. release lock
(/pseudo-code)
This is not only tedious but also prone to errors. So, any ideas/suggestions on how to approach this in a better way?
You can use Python's metaprogramming facilities to accomplish this. (Note: written quickly and not thoroughly tested.) I prefer to use a class decorator.
I also think you may need to lock more than add and remove to make a set thread-safe, but I'm not sure. I'll ignore that problem and just concentrate on your question.
Also consider whether delegation (proxying) is a better fit than subclassing. Wrapping objects is the usual approach in Python.
Finally, there is no "magic wand" of metaprogramming that will magically add fine-grained locking to any mutable Python collection. The safest thing to do is to lock any method or attribute access using RLock, but this is very coarse-grained and slow and probably still not a guarantee that your object will be thread-safe in all cases. (For example, you may have a collection that manipulates another non-threadsafe object accessible to other threads.) You really do need to examine each and every data structure and think about what operations are atomic or require locks and which methods might call other methods using the same lock (i.e., deadlock itself).
That said, here are some techniques at your disposal in increasing order of abstraction:
Delegation
class LockProxy(object):
def __init__(self, obj):
self.__obj = obj
self.__lock = RLock()
# RLock because object methods may call own methods
def __getattr__(self, name):
def wrapped(*a, **k):
with self.__lock:
getattr(self.__obj, name)(*a, **k)
return wrapped
lockedset = LockProxy(set([1,2,3]))
Context manager
class LockedSet(set):
"""A set where add(), remove(), and 'in' operator are thread-safe"""
def __init__(self, *args, **kwargs):
self._lock = Lock()
super(LockedSet, self).__init__(*args, **kwargs)
def add(self, elem):
with self._lock:
super(LockedSet, self).add(elem)
def remove(self, elem):
with self._lock:
super(LockedSet, self).remove(elem)
def __contains__(self, elem):
with self._lock:
super(LockedSet, self).__contains__(elem)
Decorator
def locked_method(method):
"""Method decorator. Requires a lock object at self._lock"""
def newmethod(self, *args, **kwargs):
with self._lock:
return method(self, *args, **kwargs)
return newmethod
class DecoratorLockedSet(set):
def __init__(self, *args, **kwargs):
self._lock = Lock()
super(DecoratorLockedSet, self).__init__(*args, **kwargs)
#locked_method
def add(self, *args, **kwargs):
return super(DecoratorLockedSet, self).add(elem)
#locked_method
def remove(self, *args, **kwargs):
return super(DecoratorLockedSet, self).remove(elem)
Class Decorator
I think this is the cleanest and easiest-to-understand of the abstract methods, so I've expanded it to allow one to specify the methods to lock and a lock object factory.
def lock_class(methodnames, lockfactory):
return lambda cls: make_threadsafe(cls, methodnames, lockfactory)
def lock_method(method):
if getattr(method, '__is_locked', False):
raise TypeError("Method %r is already locked!" % method)
def locked_method(self, *arg, **kwarg):
with self._lock:
return method(self, *arg, **kwarg)
locked_method.__name__ = '%s(%s)' % ('lock_method', method.__name__)
locked_method.__is_locked = True
return locked_method
def make_threadsafe(cls, methodnames, lockfactory):
init = cls.__init__
def newinit(self, *arg, **kwarg):
init(self, *arg, **kwarg)
self._lock = lockfactory()
cls.__init__ = newinit
for methodname in methodnames:
oldmethod = getattr(cls, methodname)
newmethod = lock_method(oldmethod)
setattr(cls, methodname, newmethod)
return cls
#lock_class(['add','remove'], Lock)
class ClassDecoratorLockedSet(set):
#lock_method # if you double-lock a method, a TypeError is raised
def frobnify(self):
pass
Override Attribute access with __getattribute__
class AttrLockedSet(set):
def __init__(self, *args, **kwargs):
self._lock = Lock()
super(AttrLockedSet, self).__init__(*args, **kwargs)
def __getattribute__(self, name):
if name in ['add','remove']:
# note: makes a new callable object "lockedmethod" on every call
# best to add a layer of memoization
lock = self._lock
def lockedmethod(*args, **kwargs):
with lock:
return super(AttrLockedSet, self).__getattribute__(name)(*args, **kwargs)
return lockedmethod
else:
return super(AttrLockedSet, self).__getattribute__(name)
Dynamically-added wrapper methods with __new__
class NewLockedSet(set):
def __new__(cls, *args, **kwargs):
# modify the class by adding new unbound methods
# you could also attach a single __getattribute__ like above
for membername in ['add', 'remove']:
def scoper(membername=membername):
# You can also return the function or use a class
def lockedmethod(self, *args, **kwargs):
with self._lock:
m = getattr(super(NewLockedSet, self), membername)
return m(*args, **kwargs)
lockedmethod.__name__ = membername
setattr(cls, membername, lockedmethod)
self = super(NewLockedSet, cls).__new__(cls, *args, **kwargs)
self._lock = Lock()
return self
Dynamically-added wrapper methods with __metaclass__
def _lockname(classname):
return '_%s__%s' % (classname, 'lock')
class LockedClass(type):
def __new__(mcls, name, bases, dict_):
# we'll bind these after we add the methods
cls = None
def lockmethodfactory(methodname, lockattr):
def lockedmethod(self, *args, **kwargs):
with getattr(self, lockattr):
m = getattr(super(cls, self), methodname)
return m(*args,**kwargs)
lockedmethod.__name__ = methodname
return lockedmethod
lockattr = _lockname(name)
for methodname in ['add','remove']:
dict_[methodname] = lockmethodfactory(methodname, lockattr)
cls = type.__new__(mcls, name, bases, dict_)
return cls
def __call__(self, *args, **kwargs):
#self is a class--i.e. an "instance" of the LockedClass type
instance = super(LockedClass, self).__call__(*args, **kwargs)
setattr(instance, _lockname(self.__name__), Lock())
return instance
class MetaLockedSet(set):
__metaclass__ = LockedClass
Dynamically-created Metaclasses
def LockedClassMetaFactory(wrapmethods):
class LockedClass(type):
def __new__(mcls, name, bases, dict_):
# we'll bind these after we add the methods
cls = None
def lockmethodfactory(methodname, lockattr):
def lockedmethod(self, *args, **kwargs):
with getattr(self, lockattr):
m = getattr(super(cls, self), methodname)
return m(*args,**kwargs)
lockedmethod.__name__ = methodname
return lockedmethod
lockattr = _lockname(name)
for methodname in wrapmethods:
dict_[methodname] = lockmethodfactory(methodname, lockattr)
cls = type.__new__(mcls, name, bases, dict_)
return cls
def __call__(self, *args, **kwargs):
#self is a class--i.e. an "instance" of the LockedClass type
instance = super(LockedClass, self).__call__(*args, **kwargs)
setattr(instance, _lockname(self.__name__), Lock())
return instance
return LockedClass
class MetaFactoryLockedSet(set):
__metaclass__ = LockedClassMetaFactory(['add','remove'])
I'll bet using a simple, explicit try...finally doesn't look so bad now, right?
Exercise for the reader: let the caller pass in their own Lock() object (dependency injection) using any of these methods.
This is my first attempt to play with decorators (although my code doesn't actually use the #decorate syntax), and I don't have much experience with multi-threading/multiprocessing. With that disclaimer, though, here's an attempt I made:
from multiprocessing import Lock
def decorate_all(obj):
lock = Lock()
#you'll want to make this more robust:
fnc_names = [fnctn for fnctn in dir(obj) if '__' not in fnctn]
for name in fnc_names:
print 'decorating ' + name
fnc = getattr(obj, name)
setattr(obj, name, decorate(fnc, lock))
return obj
def decorate(fnctn, lock):
def decorated(*args):
print 'acquiring lock'
lock.acquire()
try:
print 'calling decorated function'
return fnctn(*args)
finally:
print 'releasing lock'
lock.release()
return decorated
def thread_safe(superclass):
lock = Lock()
class Thread_Safe(superclass):
def __init__(self, *args, **kwargs):
super(Thread_Safe, self).__init__(*args, **kwargs)
return decorate_all(Thread_Safe)
>>> thread_safe_set = thread_safe(set)
decorating add
decorating clear
decorating copy
decorating difference
decorating difference_update
decorating discard
decorating intersection
decorating intersection_update
decorating isdisjoint
decorating issubset
decorating issuperset
decorating pop
decorating remove
decorating symmetric_difference
decorating symmetric_difference_update
decorating union
decorating update
>>> s = thread_safe_set()
>>> s.add(1)
acquiring lock
calling decorated function
releasing lock
>>> s.add(4)
acquiring lock
calling decorated function
releasing lock
>>> s.pop()
acquiring lock
calling decorated function
releasing lock
1
>>> s.pop()
acquiring lock
calling decorated function
releasing lock
4
>>>
[Indeed, see the comments, it is not true]
If you are running CPython you can see from the set source code that it doesn't release the GIL (http://hg.python.org/cpython/file/db20367b20de/Objects/setobject.c) so all its operations should be atomic.
If it is all what you need and you are sure to run your code on CPython you can just use it directly.
You can implement your own context manager:
class LockableSet:
def __enter__(self):
self.lock()
return self
def __exit__(self, exc_type, exc_value, traceback):
#Do what you want with the error
self.unlock()
with LockableSet() as s:
s.whatever()
raise Exception()
No matter what, the object's __exit__ method will be called at the end. More detailed informations are available here (python official docs).
Another use for this could be a lock decorator for methods, like this:
def lock(func):
def safe_func(self, *args, **kwargs):
with self:
func(self, *args, **kwargs)
return safe_func
I have been trying to create a decorator that can be used with both functions and methods in python. This on it's own is not that hard, but when creating a decorator that takes arguments, it seems to be.
class methods(object):
def __init__(self, *_methods):
self.methods = _methods
def __call__(self, func):
def inner(request, *args, **kwargs):
print request
return func(request, *args, **kwargs)
return inner
def __get__(self, obj, type=None):
if obj is None:
return self
new_func = self.func.__get__(obj, type)
return self.__class__(new_func)
The above code wraps the function/method correctly, but in the case of a method, the request argument is the instance it is operating on, not the first non-self argument.
Is there a way to tell if the decorator is being applied to a function instead of a method, and deal accordingly?
To expand on the __get__ approach. This can be generalized into a decorator decorator.
class _MethodDecoratorAdaptor(object):
def __init__(self, decorator, func):
self.decorator = decorator
self.func = func
def __call__(self, *args, **kwargs):
return self.decorator(self.func)(*args, **kwargs)
def __get__(self, instance, owner):
return self.decorator(self.func.__get__(instance, owner))
def auto_adapt_to_methods(decorator):
"""Allows you to use the same decorator on methods and functions,
hiding the self argument from the decorator."""
def adapt(func):
return _MethodDecoratorAdaptor(decorator, func)
return adapt
In this way you can just make your decorator automatically adapt to the conditions it is used in.
def allowed(*allowed_methods):
#auto_adapt_to_methods
def wrapper(func):
def wrapped(request):
if request not in allowed_methods:
raise ValueError("Invalid method %s" % request)
return func(request)
return wrapped
return wrapper
Notice that the wrapper function is called on all function calls, so don't do anything expensive there.
Usage of the decorator:
class Foo(object):
#allowed('GET', 'POST')
def do(self, request):
print "Request %s on %s" % (request, self)
#allowed('GET')
def do(request):
print "Plain request %s" % request
Foo().do('GET') # Works
Foo().do('POST') # Raises
The decorator is always applied to a function object -- have the decorator print the type of its argument and you'll be able to confirm that; and it should generally return a function object, too (which is already a decorator with the proper __get__!-) although there are exceptions to the latter.
I.e, in the code:
class X(object):
#deco
def f(self): pass
deco(f) is called within the class body, and, while you're still there, f is a function, not an instance of a method type. (The method is manufactured and returned in f's __get__ when later f is accessed as an attribute of X or an instance thereof).
Maybe you can better explain one toy use you'd want for your decorator, so we can be of more help...?
Edit: this goes for decorators with arguments, too, i.e.
class X(object):
#deco(23)
def f(self): pass
then it's deco(23)(f) that's called in the class body, f is still a function object when passed as the argument to whatever callable deco(23) returns, and that callable should still return a function object (generally -- with exceptions;-).
Since you're already defining a __get__ to use your decorator on the Bound Method, you could pass a flag telling it if it's being used on a method or function.
class methods(object):
def __init__(self, *_methods, called_on_method=False):
self.methods = _methods
self.called_on_method
def __call__(self, func):
if self.called_on_method:
def inner(self, request, *args, **kwargs):
print request
return func(request, *args, **kwargs)
else:
def inner(request, *args, **kwargs):
print request
return func(request, *args, **kwargs)
return inner
def __get__(self, obj, type=None):
if obj is None:
return self
new_func = self.func.__get__(obj, type)
return self.__class__(new_func, called_on_method=True)
Here is a general way I found to detect whether a decorated callable is a function or method:
import functools
class decorator(object):
def __init__(self, func):
self._func = func
self._obj = None
self._wrapped = None
def __call__(self, *args, **kwargs):
if not self._wrapped:
if self._obj:
self._wrapped = self._wrap_method(self._func)
self._wrapped = functools.partial(self._wrapped, self._obj)
else:
self._wrapped = self._wrap_function(self._func)
return self._wrapped(*args, **kwargs)
def __get__(self, obj, type=None):
self._obj = obj
return self
def _wrap_method(self, method):
#functools.wraps(method)
def inner(self, *args, **kwargs):
print('Method called on {}:'.format(type(self).__name__))
return method(self, *args, **kwargs)
return inner
def _wrap_function(self, function):
#functools.wraps(function)
def inner(*args, **kwargs):
print('Function called:')
return function(*args, **kwargs)
return inner
Example usage:
class Foo(object):
#decorator
def foo(self, foo, bar):
print(foo, bar)
#decorator
def foo(foo, bar):
print(foo, bar)
foo(12, bar=42) # Function called: 12 42
foo(12, 42) # Function called: 12 42
obj = Foo()
obj.foo(12, bar=42) # Method called on Foo: 12 42
obj.foo(12, 42) # Method called on Foo: 12 42
A partial (specific) solution I have come up with relies on exception handling. I am attempting to create a decorator to only allow certain HttpRequest methods, but make it work with both functions that are views, and methods that are views.
So, this class will do what I want:
class methods(object):
def __init__(self, *_methods):
self.methods = _methods
def __call__(self, func):
#wraps(func)
def inner(*args, **kwargs):
try:
if args[0].method in self.methods:
return func(*args, **kwargs)
except AttributeError:
if args[1].method in self.methods:
return func(*args, **kwargs)
return HttpResponseMethodNotAllowed(self.methods)
return inner
Here are the two use cases: decorating a function:
#methods("GET")
def view_func(request, *args, **kwargs):
pass
and decorating methods of a class:
class ViewContainer(object):
# ...
#methods("GET", "PUT")
def object(self, request, pk, *args, **kwargs):
# stuff that needs a reference to self...
pass
Is there a better solution than to use exception handling?