I want to implement the decorator pattern in Python, and I wondered if there is a way to write a decorator that just implements the function it wants to modify, without writing boiler-plate for all the functions that are just forwarded to the decorated object. Like so:
class foo(object):
def f1(self):
print "original f1"
def f2(self):
print "original f2"
class foo_decorator(object):
def __init__(self, decoratee):
self._decoratee = decoratee
def f1(self):
print "decorated f1"
self._decoratee.f1()
def f2(self): # I would like to leave that part out
self._decoratee.f2()
I would like to have calls to foo_decorator.f2 forwarded to decoratee.f2 automatically. Is there a way to write a generic method that forwards all unimplemented function-calls to decoratee?
You could use __getattr__:
class foo(object):
def f1(self):
print "original f1"
def f2(self):
print "original f2"
class foo_decorator(object):
def __init__(self, decoratee):
self._decoratee = decoratee
def f1(self):
print "decorated f1"
self._decoratee.f1()
def __getattr__(self, name):
return getattr(self._decoratee, name)
u = foo()
v = foo_decorator(u)
v.f1()
v.f2()
As an addendum to Philipp's answer; if you need to not only decorate, but preserve the type of an object, Python allows you to subclass an instance at runtime:
class foo(object):
def f1(self):
print "original f1"
def f2(self):
print "original f2"
class foo_decorator(object):
def __new__(cls, decoratee):
cls = type('decorated',
(foo_decorator, decoratee.__class__),
decoratee.__dict__)
return object.__new__(cls)
def f1(self):
print "decorated f1"
super(foo_decorator, self).f1()
u = foo()
v = foo_decorator(u)
v.f1()
v.f2()
print 'isinstance(v, foo) ==', isinstance(v, foo)
This is a bit more involved than strictly necessary for your example, where you know the class being decorated in advance.
This might suffice:
class foo_decorator(foo):
def __init__(self, decoratee):
self.__dict__.update(decoratee.__dict__)
def f1(self):
print "decorated f1"
super(foo_decorator, self).f1()
It's arguably not the best practice, but you can add functionality to instances, as I've done to help transition my code from Django's ORM to SQLAlachemy, as follows:
def _save(self):
session.add(self)
session.commit()
setattr(Base,'save',_save)
The UML diagram in the linked Wikipedia article is wrong and so is your code.
If you follow the "decorator pattern", the decorator class is derived from the base decorated class. (In the UML diagram an inheritance arrow from the WindowDecorator to Window is missing).
with
class foo_decorator(foo):
you don't need to implement undecorated methods.
BTW: In strong typed languages there is one more reason, why the decorator must be derived from the decorated class: Otherwise you wouldnt be able to chain decorators.
In one of my projects, I also needed to do one particular thing, that is that even the underlying object should actually execute the method that was reimplemented in the decorator. It is actually quite easy to do if you know where to target it.
The use case is:
I have an object X with methods A and B.
I create a decorator class Y that overrides A.
If I instantiate Y(X) and call A, it will use the decorated A as expected.
If B calls A, then if I instantiate Y(X) and call B on the decorator, the call from within B then goes to the old A on the original object which was undesirable. I want the old B to call the new A as well.
It is possible to reach this behaviour like this:
import inspect
import six # for handling 2-3 compatibility
class MyBaseDecorator(object):
def __init__(self, decorated):
self.decorated = decorated
def __getattr__(self, attr):
value = getattr(self.decorated, attr)
if inspect.ismethod(value):
function = six.get_method_function(value)
value = function.__get__(self, type(self))
return value
class SomeObject(object):
def a(self):
pass
def b(self):
pass
class MyDecorator(MyBaseDecorator):
def a(self):
pass
decorated = MyDecorator(SomeObject())
This may not work out of the box as I typed everything else apart from the getattr method from top of my head.
The code looks up the requested attribute in the decorated object, and if it is a method (doesn't work for properties now, but the change to support them should not be too difficult), the code then pulls the actual function out of the method and using the descriptor interface invocation it "rebinds" the function as a method, but on the decorator. Then it is returned and most likely executed.
The effect of this is that if b ever calls a on the original object, then when you have the object decorated and there is any method call coming from the decorator, the decorator makes sure that all methods accessed are bound to the decorator instead, therefore looking up things using the decorator and not the original object, therefore the methods specified in the decorator taking precedence.
P.S.: Yes I know it looks pretty much like inheritance, but this done in the sense of composition of multiple objects.
To complement #Alec Thomas reply. I modified his answer to follow the decorator pattern. This way you don't need to know the class you're decorating in advance.
class Decorator(object):
def __new__(cls, decoratee):
cls = type('decorated',
(cls, decoratee.__class__),
decoratee.__dict__)
return object.__new__(cls)
Then, you can use it as:
class SpecificDecorator(Decorator):
def f1(self):
print "decorated f1"
super(foo_decorator, self).f1()
class Decorated(object):
def f1(self):
print "original f1"
d = SpecificDecorator(Decorated())
d.f1()
In Python 3, Philipp's accepted answer raised RuntimeError: maximum recursion depth exceeded.
The way that worked for me:
class Foo(object):
def f1(self):
print("original f1")
def f2(self):
print("original f2")
class FooDecorator(object):
def __init__(self, decoratee):
self._decoratee = decoratee
def f1(self):
print("decorated f1")
return self._decoratee.f1()
def __getattr__(self, name):
if name in ['f1', '_decoratee']:
raise AttributeError()
return getattr(self._decoratee, name)
f = FooDecorator(Foo())
f.f1()
# decorated f1
# original f1
f.f2()
# original f2
The workaround is inspired by Ned Batchelder's blog
Related
I have 2 python classes one subclasses the other
class A:
def __init__(some params):
do something()
def method(params):
return A_new_A_instance
class B(A)
def __init__(some params):
super().__init__(some params)
def new_method(params):
a_instance=super.method(params)
return B(a)
The above works fine for some of the methods I'm using heavily.
The issue is that class A has a lot of methods some I'm using as is others I'm modifying etc. And a few I don't care about. Most of the methods in A returns another instance of A (like selecting, adding, re-ordering data) But I want to make sure that whichever A.method() I call I want return an instance of B when I do B.method().
Is there a magic way to do this for all methods of A or do I need to over them one by one?
As long as the constructor of both A and B are the same (they take the same parameters) you can use a factory function to create new instances of A and override it for B:
class A:
def __init__(self, *params):
pass
def _create_new_instance(self, *params):
return A(*params)
def method(self, *params):
# this will either call A._create_new_instance or
# B._create_new_instance depending on type(self)
return self._create_new_instance(*params)
class B(A):
def __init__(self, *params):
super().__init__(self, *params)
def _create_new_instance(self, *params):
return B(*params)
def new_method(self, *params):
new_b = self.method(*params)
do_something_new(new_b)
return new_b
assert isinstance(A().method(), A)
assert isinstance(B().method(), B)
I guess I did not word my question properly.
I was looking for a way to use the existing methods from the superclass in the subclass without knowing what they are (or not bothering to know).
The solution I came up with is follows:
have a function that queries available methods in the superclass
def get_methods(class_instance):
method_list = [attribute for attribute in dir(class_instance) if callable(getattr(class_instance, attribute))
and attribute.startswith('__') is False]
return
then you can put something like this in the __init__ to get those methods into the subclass. I'm avoiding the __ methods, since I do not care about most of them and also want to set some of them myself later on like __str__ or __add__.
methods=get_methods(super())
for method_name in methods:
method = getattr(super(), method_name)
self.__setattr__(method_name, classmethod(method))
When creating a simple object hierarchy in Python, I'd like to be able to invoke methods of the parent class from a derived class. In Perl and Java, there is a keyword for this (super). In Perl, I might do this:
package Foo;
sub frotz {
return "Bamf";
}
package Bar;
#ISA = qw(Foo);
sub frotz {
my $str = SUPER::frotz();
return uc($str);
}
In Python, it appears that I have to name the parent class explicitly from the child.
In the example above, I'd have to do something like Foo::frotz().
This doesn't seem right since this behavior makes it hard to make deep hierarchies. If children need to know what class defined an inherited method, then all sorts of information pain is created.
Is this an actual limitation in python, a gap in my understanding or both?
Use the super() function:
class Foo(Bar):
def baz(self, **kwargs):
return super().baz(**kwargs)
For Python < 3, you must explicitly opt in to using new-style classes and use:
class Foo(Bar):
def baz(self, arg):
return super(Foo, self).baz(arg)
Python also has super as well:
super(type[, object-or-type])
Return a proxy object that delegates method calls to a parent or sibling class of type.
This is useful for accessing inherited methods that have been overridden in a class.
The search order is same as that used by getattr() except that the type itself is skipped.
Example:
class A(object): # deriving from 'object' declares A as a 'new-style-class'
def foo(self):
print "foo"
class B(A):
def foo(self):
super(B, self).foo() # calls 'A.foo()'
myB = B()
myB.foo()
ImmediateParentClass.frotz(self)
will be just fine, whether the immediate parent class defined frotz itself or inherited it. super is only needed for proper support of multiple inheritance (and then it only works if every class uses it properly). In general, AnyClass.whatever is going to look up whatever in AnyClass's ancestors if AnyClass doesn't define/override it, and this holds true for "child class calling parent's method" as for any other occurrence!
Python 3 has a different and simpler syntax for calling parent method.
If Foo class inherits from Bar, then from Bar.__init__ can be invoked from Foo via super().__init__():
class Foo(Bar):
def __init__(self, *args, **kwargs):
# invoke Bar.__init__
super().__init__(*args, **kwargs)
Many answers have explained how to call a method from the parent which has been overridden in the child.
However
"how do you call a parent class's method from child class?"
could also just mean:
"how do you call inherited methods?"
You can call methods inherited from a parent class just as if they were methods of the child class, as long as they haven't been overwritten.
e.g. in python 3:
class A():
def bar(self, string):
print("Hi, I'm bar, inherited from A"+string)
class B(A):
def baz(self):
self.bar(" - called by baz in B")
B().baz() # prints out "Hi, I'm bar, inherited from A - called by baz in B"
yes, this may be fairly obvious, but I feel that without pointing this out people may leave this thread with the impression you have to jump through ridiculous hoops just to access inherited methods in python. Especially as this question rates highly in searches for "how to access a parent class's method in Python", and the OP is written from the perspective of someone new to python.
I found:
https://docs.python.org/3/tutorial/classes.html#inheritance
to be useful in understanding how you access inherited methods.
Here is an example of using super():
#New-style classes inherit from object, or from another new-style class
class Dog(object):
name = ''
moves = []
def __init__(self, name):
self.name = name
def moves_setup(self):
self.moves.append('walk')
self.moves.append('run')
def get_moves(self):
return self.moves
class Superdog(Dog):
#Let's try to append new fly ability to our Superdog
def moves_setup(self):
#Set default moves by calling method of parent class
super(Superdog, self).moves_setup()
self.moves.append('fly')
dog = Superdog('Freddy')
print dog.name # Freddy
dog.moves_setup()
print dog.get_moves() # ['walk', 'run', 'fly'].
#As you can see our Superdog has all moves defined in the base Dog class
There's a super() in Python too. It's a bit wonky, because of Python's old- and new-style classes, but is quite commonly used e.g. in constructors:
class Foo(Bar):
def __init__(self):
super(Foo, self).__init__()
self.baz = 5
I would recommend using CLASS.__bases__
something like this
class A:
def __init__(self):
print "I am Class %s"%self.__class__.__name__
for parentClass in self.__class__.__bases__:
print " I am inherited from:",parentClass.__name__
#parentClass.foo(self) <- call parents function with self as first param
class B(A):pass
class C(B):pass
a,b,c = A(),B(),C()
If you don't know how many arguments you might get, and want to pass them all through to the child as well:
class Foo(bar)
def baz(self, arg, *args, **kwargs):
# ... Do your thing
return super(Foo, self).baz(arg, *args, **kwargs)
(From: Python - Cleanest way to override __init__ where an optional kwarg must be used after the super() call?)
There is a super() in python also.
Example for how a super class method is called from a sub class method
class Dog(object):
name = ''
moves = []
def __init__(self, name):
self.name = name
def moves_setup(self,x):
self.moves.append('walk')
self.moves.append('run')
self.moves.append(x)
def get_moves(self):
return self.moves
class Superdog(Dog):
#Let's try to append new fly ability to our Superdog
def moves_setup(self):
#Set default moves by calling method of parent class
super().moves_setup("hello world")
self.moves.append('fly')
dog = Superdog('Freddy')
print (dog.name)
dog.moves_setup()
print (dog.get_moves())
This example is similar to the one explained above.However there is one difference that super doesn't have any arguments passed to it.This above code is executable in python 3.4 version.
In this example cafec_param is a base class (parent class) and abc is a child class. abc calls the AWC method in the base class.
class cafec_param:
def __init__(self,precip,pe,awc,nmonths):
self.precip = precip
self.pe = pe
self.awc = awc
self.nmonths = nmonths
def AWC(self):
if self.awc<254:
Ss = self.awc
Su = 0
self.Ss=Ss
else:
Ss = 254; Su = self.awc-254
self.Ss=Ss + Su
AWC = Ss + Su
return self.Ss
def test(self):
return self.Ss
#return self.Ss*4
class abc(cafec_param):
def rr(self):
return self.AWC()
ee=cafec_param('re',34,56,2)
dd=abc('re',34,56,2)
print(dd.rr())
print(ee.AWC())
print(ee.test())
Output
56
56
56
In Python 2, I didn't have a lot luck with super(). I used the answer from
jimifiki on this SO thread how to refer to a parent method in python?.
Then, I added my own little twist to it, which I think is an improvement in usability (Especially if you have long class names).
Define the base class in one module:
# myA.py
class A():
def foo( self ):
print "foo"
Then import the class into another modules as parent:
# myB.py
from myA import A as parent
class B( parent ):
def foo( self ):
parent.foo( self ) # calls 'A.foo()'
class department:
campus_name="attock"
def printer(self):
print(self.campus_name)
class CS_dept(department):
def overr_CS(self):
department.printer(self)
print("i am child class1")
c=CS_dept()
c.overr_CS()
If you want to call the method of any class, you can simply call Class.method on any instance of the class. If your inheritance is relatively clean, this will work on instances of a child class too:
class Foo:
def __init__(self, var):
self.var = var
def baz(self):
return self.var
class Bar(Foo):
pass
bar = Bar(1)
assert Foo.baz(bar) == 1
class a(object):
def my_hello(self):
print "hello ravi"
class b(a):
def my_hello(self):
super(b,self).my_hello()
print "hi"
obj = b()
obj.my_hello()
This is a more abstract method:
super(self.__class__,self).baz(arg)
This code:
import inspect
class Obj():
def c(self):
return 1
def b(self):
return 2
def a(self):
return 3
o = Obj()
for name, value in inspect.getmembers(o, inspect.ismethod):
print str(value())+" "+name
print:
3 a
2 b
1 c
Because of inspect.getmembers return all the members of an object in a list of (name, value) pairs sorted by name, as you can read in https://docs.python.org/2/library/inspect.html#inspect.getmembers
But I want to get that list in the same order that the members was written in the code, in other words, the output would be:
1 c
2 b
3 a
Is any way to do that?
Thanks
No. Class members are not ordered. They are gathered into a dictionary, immediately losing order. You can resort to tricks like parsing the source, but it will break easily. For starters, the source could not be available.
[edit: it seems python3 allows more flexibility in class creation, making it possible to customize the way class members are gathered, if you are on python3 only, that's probably a better approach]
If changing the code is not a problem, you can use a decorator though:
import inspect
def track_order(fn):
fn.order = track_order.idx
track_order.idx += 1
return fn
track_order.idx = 0
class Obj(object):
#track_order
def c(self):
return 1
#track_order
def b(self):
return 2
#track_order
def a(self):
return 3
o = Obj()
methods = sorted((item
for item in inspect.getmembers(o, inspect.ismethod)),
key=lambda item: item[1].order)
for name, value in methods:
print str(value())+" "+name
The decorator adds an idx attribute to all methods that pass through it.
This makes use of the fact that python has first-class functions.
$ python test.py
1 c
2 b
3 a
Note: this is the method used by Django to keep track of form and model fields order. Only, they don't need a decorator because fields' classes have the instanciation order attribute built-in (it is named creation_counter).
When creating an object, all of its attributes are contained in another specialized attribute in the object called __dict__, which as the name suggests is just a normal Python non-ordered dictionary, hence they are not guaranteed to be stored in the same fashion they were added in. When retrieving the values in __dict__ using getmembers(), Python automatically reorganizes the dictionary when printing it in order to make some logical sense.
To combat this, something must be done to turn the regular Python dictionary __dict__ into some sort of ordered one.
This can be done a number of ways, for simplicity's sake, I will assume you are using Python 3.
Using the collections package, you can obtain an OrderedDict, which is exactly the technology we require for such an issue. Prepare this ordered dictionary for use in a metaclass for the class which needs ordered members to be stored, copy over the members, and finally access this new OrderedDict when wanting to print out said members.
This can be seen in action in this Stack Overflow answer.
In cpython the code is compiled down to bytecode for the VM. And the functions have a __code__ attribute, which is a code object. The code object has a co_firstlineno attribute, which is the first line in Python source code. (Detailed in the inspect module.)
If you know your methods are all in source code, and you know you are using cpython, you could use this as a sort key. But it seems awful shaky if you don't know these things.
members = [ (name,meth) for name, meth in inspect.getmembers(o, inspect.ismethod)]
members = sorted(members, key=lambda t: t[1].__code__.co_firstlineno)
print '\n'.join(m[0] for m in members)
Hm, this is very hacky, but basically I inspect the source directly and use re to find method names. This solution is pretty brittle, though, and it doesn't deal with inheritance, but maybe it works for you. Assuming I've saved your class definition in a file named test.py:
>>> import test
>>> import re
>>> findmethods = re.compile(r" def (.+)\(")
>>> findmethods.findall(inspect.getsource(test.Obj))
['c', 'b', 'a']
>>>
def print_class_methods_by_order(class_object):
for attr in class_object.__dict__:
if callable(getattr(class_object, attr)):
print(attr)
class MyObject:
def c(self):
pass
def a(self):
pass
def b(self):
pass
output:
>>> print_class_methods_by_order(MyObject)
c
a
b
Also works with decorators:
def print_class_methods_by_order(class_object):
for attr in class_object.__dict__:
if callable(getattr(class_object, attr)):
print(attr)
def my_decorator(func):
def wrapper(*args, **kwargs):
print("my_decorator")
func(*args, **kwargs)
return wrapper
class MyObject:
#my_decorator
def c(self):
pass
#my_decorator
def a(self):
pass
#my_decorator
def b(self):
pass
output:
>>> print_class_methods_by_order(MyObject)
c
a
b
And also works with "class" decorator (not sure there is any difference compared to the previous case):
def print_class_methods_by_order(class_object):
for attr in class_object.__dict__:
if callable(getattr(class_object, attr)):
print(attr)
def decorate_all(decorator):
def decorate(cls):
for attr in cls.__dict__:
if callable(getattr(cls, attr)):
setattr(cls, attr, decorator(getattr(cls, attr), cls))
return cls
return decorate
def my_decorator(func, cls):
def wrapper(*args, **kwargs):
print("my_decorator")
fn = func(*args, **kwargs)
return fn
return wrapper
#decorate_all(my_decorator)
class MyObject:
def c(self):
pass
def a(self):
pass
def b(self):
pass
output:
>>> print_class_methods_by_order(MyObject)
c
a
b
I have a bunch of methods somewhere, already defined. I want a class to just be a collection of those methods.
def _a(self):
pass
def _b(self):
pass
#decorated()
class MyClass(object):
a = _a
b = _b
The decorator is written by someone else, and it does some analysis on the class immediately.
What I want is to stick those methods on the class, but they need to be on there before the decorator does its magic.
methods = { 'a': _a, 'b': _b }
#decorated(...)
class MyClass(object):
for name, impl in methods
# What goes here?
How can I do this. I assume it's some form of setattr(), but how do I get the target when it's the class I'm currently building?
Update
Current experiment is:
def merged(method_dict):
def _impl(cls):
for name, impl in method_dict.iteritems():
setattr(cls, name, impl)
return cls
return _impl
#decorated(...)
#merged(methods)
class MyClass(object):
c = _c
pass
The decorated decorator is reporting that it sees c, but not a or b
The problem is that as soon as your class is defined, it is immediately getting decorated, as you use the # syntax. Some alternatives are to go the way jonrsharpe and Ashwini Chaudhary suggested.
Also, another way you can do the thing, is wrapping the decorator into your custom one, where you will do your stuff and then only, manually decorate with the provided one, like this:
def your_decorator(func):
# your stuff
return decorated(func)
#your_decorator
class MyClass():
# ...
I'd go with what jonrsharpe suggested in the comments section: Instead of decorating the class using the # syntax, which decorates the class immediately after it's created, you should decorate it manually afterwards:
class MyClass:
pass
def _method_to_add_later(self):
pass
MyClass.method_to_add_later = _method_to_add_later
MyClass = decorated(...)(MyClass)
For example, may be right
def decorator(fn):
def _fn(*args, **kwargs):
print "Decorator"
fn(*args, **kwargs)
return _fn
#decorator
class MyClass(object):
def __init__(self):
print "Init"
def _a():
print "a"
def _b():
print "b"
if __name__ == '__main__':
methods = { 'a': _a, 'b': _b }
my = decorator(MyClass())
for name in methods:
setattr(my, name, methods[name])
my.a()
my.b()
Out:
Decorator
Init
a
b
I am using the python mock framework for testing (http://www.voidspace.org.uk/python/mock/) and I want to mock out a superclass and focus on testing the subclasses' added behavior.
(For those interested I have extended pymongo.collection.Collection and I want to only test my added behavior. I do not want to have to run mongodb as another process for testing purposes.)
For this discussion, A is the superclass and B is the subclass. Furthermore, I define direct and indirect superclass calls as shown below:
class A(object):
def method(self):
...
def another_method(self):
...
class B(A):
def direct_superclass_call(self):
...
A.method(self)
def indirect_superclass_call(self):
...
super(A, self).another_method()
Approach #1
Define a mock class for A called MockA and use mock.patch to substitute it for the test at runtime. This handles direct superclass calls. Then manipulate B.__bases__ to handle indirect superclass calls. (see below)
The issue that arises is that I have to write MockA and in some cases (as in the case for pymongo.collection.Collection) this can involve a lot of work to unravel all of the internal calls to mock out.
Approach #2
The desired approach is to somehow use a mock.Mock() class to handle calls on the the mock just in time, as well as defined return_value or side_effect in place in the test. In this manner, I have to do less work by avoiding the definition of MockA.
The issue that I am having is that I cannot figure out how to alter B.__bases__ so that an instance of mock.Mock() can be put in place as a superclass (I must need to somehow do some direct binding here). Thus far I have determined, that super() examines the MRO and then calls the first class that defines the method in question. I cannot figure out how to get a superclass to handle the check to it and succeed if it comes across a mock class. __getattr__ does not seem to be used in this case. I want super to to think that the method is defined at this point and then use the mock.Mock() functionality as usual.
How does super() discover what attributes are defined within the class in the MRO sequence? And is there a way for me to interject here and to somehow get it to utilize a mock.Mock() on the fly?
import mock
class A(object):
def __init__(self, value):
self.value = value
def get_value_direct(self):
return self.value
def get_value_indirect(self):
return self.value
class B(A):
def __init__(self, value):
A.__init__(self, value)
def get_value_direct(self):
return A.get_value_direct(self)
def get_value_indirect(self):
return super(B, self).get_value_indirect()
# approach 1 - use a defined MockA
class MockA(object):
def __init__(self, value):
pass
def get_value_direct(self):
return 0
def get_value_indirect(self):
return 0
B.__bases__ = (MockA, ) # - mock superclass
with mock.patch('__main__.A', MockA):
b2 = B(7)
print '\nApproach 1'
print 'expected result = 0'
print 'direct =', b2.get_value_direct()
print 'indirect =', b2.get_value_indirect()
B.__bases__ = (A, ) # - original superclass
# approach 2 - use mock module to mock out superclass
# what does XXX need to be below to use mock.Mock()?
#B.__bases__ = (XXX, )
with mock.patch('__main__.A') as mymock:
b3 = B(7)
mymock.get_value_direct.return_value = 0
mymock.get_value_indirect.return_value = 0
print '\nApproach 2'
print 'expected result = 0'
print 'direct =', b3.get_value_direct()
print 'indirect =', b3.get_value_indirect() # FAILS HERE as the old superclass is called
#B.__bases__ = (A, ) # - original superclass
is there a way for me to interject here and to somehow get it to utilize a mock.Mock() on the fly?
There may be better approaches, but you can always write your own super() and inject it into the module that contains the class you're mocking. Have it return whatever it should based on what's calling it.
You can either just define super() in the current namespace (in which case the redefinition only applies to the current module after the definition), or you can import __builtin__ and apply the redefinition to __builtin__.super, in which case it will apply globally in the Python session.
You can capture the original super function (if you need to call it from your implementation) using a default argument:
def super(type, obj=None, super=super):
# inside the function, super refers to the built-in
I played around with mocking out super() as suggested by kindall. Unfortunately, after a great deal of effort it became quite complicated to handle complex inheritance cases.
After some work I realized that super() accesses the __dict__ of classes directly when resolving attributes through the MRO (it does not do a getattr type of call). The solution is to extend a mock.MagicMock() object and wrap it with a class to accomplish this. The wrapped class can then be placed in the __bases__ variable of a subclass.
The wrapped object reflects all defined attributes of the target class to the __dict__ of the wrapping class so that super() calls resolve to the properly patched in attributes within the internal MagicMock().
The following code is the solution that I have found to work thus far. Note that I actually implement this within a context handler. Also, care has to be taken to patch in the proper namespaces if importing from other modules.
This is a simple example illustrating the approach:
from mock import MagicMock
import inspect
class _WrappedMagicMock(MagicMock):
def __init__(self, *args, **kwds):
object.__setattr__(self, '_mockclass_wrapper', None)
super(_WrappedMagicMock, self).__init__(*args, **kwds)
def wrap(self, cls):
# get defined attribtues of spec class that need to be preset
base_attrs = dir(type('Dummy', (object,), {}))
attrs = inspect.getmembers(self._spec_class)
new_attrs = [a[0] for a in attrs if a[0] not in base_attrs]
# pre set mocks for attributes in the target mock class
for name in new_attrs:
setattr(cls, name, getattr(self, name))
# eat up any attempts to initialize the target mock class
setattr(cls, '__init__', lambda *args, **kwds: None)
object.__setattr__(self, '_mockclass_wrapper', cls)
def unwrap(self):
object.__setattr__(self, '_mockclass_wrapper', None)
def __setattr__(self, name, value):
super(_WrappedMagicMock, self).__setattr__(name, value)
# be sure to reflect to changes wrapper class if activated
if self._mockclass_wrapper is not None:
setattr(self._mockclass_wrapper, name, value)
def _get_child_mock(self, **kwds):
# when created children mocks need only be MagicMocks
return MagicMock(**kwds)
class A(object):
x = 1
def __init__(self, value):
self.value = value
def get_value_direct(self):
return self.value
def get_value_indirect(self):
return self.value
class B(A):
def __init__(self, value):
super(B, self).__init__(value)
def f(self):
return 2
def get_value_direct(self):
return A.get_value_direct(self)
def get_value_indirect(self):
return super(B, self).get_value_indirect()
# nominal behavior
b = B(3)
assert b.get_value_direct() == 3
assert b.get_value_indirect() == 3
assert b.f() == 2
assert b.x == 1
# using mock class
MockClass = type('MockClassWrapper', (), {})
mock = _WrappedMagicMock(A)
mock.wrap(MockClass)
# patch the mock in
B.__bases__ = (MockClass, )
A = MockClass
# set values within the mock
mock.x = 0
mock.get_value_direct.return_value = 0
mock.get_value_indirect.return_value = 0
# mocked behavior
b = B(7)
assert b.get_value_direct() == 0
assert b.get_value_indirect() == 0
assert b.f() == 2
assert b.x == 0