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__closure__ attribute of function object always be 'None' when defining func inside exec()

EDIT2：
A minimal demonstration is:
code = """\
a=1
def f1():
print(a)
print(f1.__closure__)
f1()
"""
def foo():
exec(code)
foo()
Which gives:
None
Traceback (most recent call last):
File "D:/workfiles/test_eval_rec.py", line 221, in <module>
foo()
File "D:/workfiles//test_eval_rec.py", line 219, in foo
exec(code)
File "<string>", line 5, in <module>
File "<string>", line 3, in f1
NameError: name 'a' is not defined
It can be seen that the __closure__ attribute of function defined inside code str passed to exec() is None, making calling the function fails.
Why does this happen and how can I define a function successfully?
I find several questions that may be related.
Closure lost during callback defined in exec()
Using exec() with recursive functions
Why exec() works differently when invoked inside of function and how to avoid it
Why are closures broken within exec?
NameError: name 'self' is not defined IN EXEC/EVAL
These questions are all related to "defining a function insdie exec()". I think the fourth question here is closest to the essence of these problems. The common cause of these problems is that when defining a function in exec(), the __closure__ attribute of the function object can not be set correctly and will always be None. However, many existing answers to this question didn't realize this point.
Why these questions are caused by wrong __closure__:
When defining a function, __closure__ attribute is set to a dict that contains all local symbols (at the place where the keyword def is used) that is used inside the newly defined funtion. When calling a function, local symbol tables will be retrived from the __closure__ attribute. Since the __closure__ is set to None, the local symbol tables can not be retrived as expected, making the function call fail.
These answers work by making None a correct __closure__ attribute:
Existing solutions to the questions listed above solve these problems by getting the function definition rid of the usage of local symbol, i.e, they make the local symbols used(variable, function definition) global by passing globals() as locals of exec or by using keyword global explicitly in the code string.
Why existing solution unsatisfying:
These solutions I think is just an escape of the core problem of setting __closure__ correctly when define a functioni inside exec(). And as symbols used in the function definition is made global, these solutions will produce redundant global symbol which I don't want.
Original Questions：
(You May ignore this session, I have figured something out, and what I currently want to ask is described as the session EDIT2. The original question can be viewed as a sepecial case of the question described in session EDIT2)
original title of this question is: Wrapping class function to new function with exec() raise NameError that ‘self’ is not defined
I want to wrap an existing member function to a new class function. However, exec() function failed with a NameError that ‘self’ is not defined.
I did some experiment with the following codes. I called globals() and locals() in the execed string, it seems that the locals() is different in the function definition scope when exec() is executed. "self" is in the locals() when in exec(), however, in the function definition scope inside the exec(), "self" is not in the locals().
class test_wrapper_function():
def __init__(self):
# first wrapper
def temp_func():
print("locals() inside the function definition without exec:")
print(locals())
return self.func()
print("locals() outside the function definition without exec:")
print(locals())
self.wrappered_func1 = temp_func
# third wrapper using eval
define_function_str = '''def temp_func():
print("locals() inside the function definition:")
print(locals())
print("globals() inside the function definition:")
print(globals())
return self.func()
print("locals() outside the function definition:")
print(locals())
print("globals() outside the function definition:")
print(globals())
self.wrappered_func2 = temp_func'''
exec(define_function_str)
# call locals() here, it will contains temp_func
def func(self):
print("hi!")
t = test_wrapper_function()
print("**********************************************")
t.wrappered_func1()
t.wrappered_func2()
I have read this link. In the exec(), memeber function, attribute of "self" can be accessed without problem, while in the function difinition in the exec(), "self" is not available any more. Why does this happen?
Why I want to do this:
I am building a PyQt program. I want to create several similar slots(). These slots can be generated by calling one member function with different arguments. I decided to generate these slots using exec() function of python. I also searched with the keyword "nested name scope in python exec", I found this question may be related, but there is no useful answer.
To be more specific. I want to define a family of slots like func_X (X can be 'a', 'b', 'c'...), each do something like self.do_something_on(X). Here, do_something is a member function of my QWidget. So I use a for loop to create these slots function. I used codes like this:
class MyWidget():
def __init__(self):
self.create_slots_family()
def do_something(self, character):
# in fact, this function is much more complex. Do some simplification.
print(character)
def create_slots_i(self, character):
# want to define a function like this:
# if character is 'C', define self.func_C such that self.func_C() works like self.do_something(C)
create_slot_command_str = "self.func_" + character + " = lambda:self.do_something('" + character + "')"
print(create_slot_command_str)
exec(create_slot_command_str)
def create_slots_family(self):
for c in ["A", "B", "C", "D"]:
self.create_slots_i(c)
my_widget = MyWidget()
my_widget.func_A()
Note that, as far as I know, the Qt slots should not accept any parameter, so I have to wrap self.do_something(character) to be a series function self.func_A, self.func_C and so on for all the possible characters.
So the above is what I want to do orignially.
EDIT1：
(You May ignore this session, I have figured something out, and what I currently want to ask is described as the session EDIT2. This simplified version of original question can also be viewed as a sepecial case of the question described in session EDIT2)
As #Mad Physicist suggested. I provide a simplified version here, deleting some codes used for experiments.
class test_wrapper_function():
def __init__(self):
define_function_str = '''\
def temp_func():
return self.func()
self.wrappered_func2 = temp_func'''
exec(define_function_str)
def func(self):
print("hi!")
t = test_wrapper_function()
t.wrappered_func2()
I expected this to print a "hi". However, I got the following exception:
Traceback (most recent call last):
File "D:/workfiles/test_eval_class4.py", line 12, in <module>
t.wrappered_func2()
File "<string>", line 2, in temp_func
NameError: name 'self' is not defined

Using Exec
You've already covered most of the problems and workarounds with exec, but I feel that there is still value in adding a summary.
The key issue is that exec only knows about globals and locals, but not about free variables and the non-local namespace. That is why the docs say
If exec gets two separate objects as globals and locals, the code will be executed as if it were embedded in a class definition.
There is no way to make it run as though it were in a method body. However, as you've already noted, you can make exec create a closure and use that instead of the internal namespace by adding a method body to your snippet. However, there are still a couple of subtle restrictions there.
Your example of what you are trying to do showcases the issues perfectly, so I will use a modified version of that. The goal is to make a method that binds to self and has a variable argument in the exec string.
class Test:
def create_slots_i(self, c):
create_slot_command_str = f"self.func_{c} = lambda: self.do_something('{c}')"
exec(create_slot_command_str)
def do_something(self, c):
print(f'I did {c}!')
There are different ways of getting exec to "see" variables: literals, globals, and internal closures.
Literals. This works robustly, but only for simple types that can be easily instantiated from a string. The usage of c above is a perfect example. This will not help you with a complex object like self:
>>> t = Test()
>>> t.create_slots_i('a')
>>> t.func_a()
...
NameError: name 'self' is not defined
This happens exactly because exec has no concept of free variables. Since self is passed to it via the default locals(), it does not bind the reference to a closure.
globals. You can pass in a name self to exec via globals. There are a couple of ways of doing this, each with its own issues. Remember that globals are accessed by a function through its __globals__ (look at the table under "Callable types") attribute. Normally __globals__ refers to the __dict__ of the module in which a function is defined. In exec, this is the case by default as well, since that's what globals() returns.
Add to globals: You can create a global variable named self, which will make your problem go away, sort of:
>>> self = t
>>> t.func_a()
I did a!
But of course this is a house of cards that falls apart as soon as you delete, self, modify it, or try to run this on multiple instances:
>>> del self
>>> t.func_a()
...
NameError: name 'self' is not defined
Copy globals. A much more versatile solution, on the surface of it, is to copy globals() when you run exec in create_slots_i:
def create_slots_i(self, c):
create_slot_command_str = f"self.func_{c} = lambda: self.do_something('{c}')"
g = globals().copy()
g['self'] = self
exec(create_slot_command_str, g)
This appears to work normally, and for a very limited set of cases, it actually does:
>>> t = Test()
>>> t.create_slots_i('a')
>>> t.func_a()
I did a!
But now, your function's __globals__ attribute is no longer bound to the module you created it in. If it uses any other global values, especially ones that might change, you will not be able to see the changes. For limited functionality, this is OK, but in the general case, it can be a severe handicap.
Internal Closures. This is the solution you already hit upon, where you create a closure within the exec string to let it know that you have a free variable by artificial means. For example:
class Test:
def create_slots_i(self, c):
create_slot_command_str = f"""def make_func(self):
def func_{c}():
self.do_something('{c}')
return func_{c}
self.func_{c} = make_func(self)"""
g = globals().copy()
g['self'] = self
exec(create_slot_command_str, g)
def do_something(self, c):
print(f'I did {c}!')
This approach works completely:
>>> t = Test()
>>> t.create_slots_i('a')
>>> t.func_a()
I did a!
The only real drawbacks here are security, which is always a problem with exec, and the sheer awkwardness of this monstrosity.
A Better Way
Since you are already creating closures, there is really no need to use exec at all. In fact, the only thing you are really doing is creating methods so that self.func_... will bind the method for you, since you need a function with the signature of your slot and access to self. You can write a simple method that will generate functions that you can assign to your slots directly. The advantage of doing it this way is that (a) you avoid calling exec entirely, and (b) you don't need to have a bunch of similarly named auto-generated methods polluting your class namespace. The slot generator would look something like this:
def create_slots_i(self, c):
def slot_func():
self.do_something(c) # This is a real closure now
slot_func.__name__ = f'func_{c}'
return slot_func
Since you will not be referring to these function objects anywhere except your slots, __name__ is the only way to get the "name" under which they were stored. That is the same thing that def does for you under the hood.
You can now assign slots directly:
some_widget.some_signal.connect(self.create_slots_i('a'))
Note
I originally had a more complex approach in mind for you, since I thought you cared about generating bound methods, instead of just setting __name__. In case you have a sufficiently complex scenario where it still applies, here is my original blurb:
A quick recap of the descriptor protocol: when you bind a function with the dot operator, e.g., t.func_a, python looks at the class for descriptors with that name. If your class has a data descriptor (like property, but not functions), then that descriptor will shadow anything you may have placed in the instance __dict__. However, if you have a non-data descriptor (one a __get__ method but without a __set__ method, like a function object), then it will only be bound if an instance attribute does not shadow it. Once this decision has been made, actually invoking the descriptor protocol involves calling type(t).func_a.__get__(t). That's how a bound method knows about self.
Now you can return a bound method from within your generator:
def create_slots_i(self, c):
def slot_func(self):
self.do_something(c) # This is a closure on `c`, but not on `self` until you bind it
slot_func.__name__ = f'func_{c}'
return slot_func.__get__(self)

Why this phenomena happen:
Actually the answer of the question 4 listed above can answer this question.
When call exec() on one code string, the code string is first compiled. I suppose that during compiling, the provided globals and locals is not considered. The symbol in the exec()ed code str is compiled to be in the globals. So the function defined in the code str will be considered using global variables, and thus __closure__ is set to None.
Refer to this answer for more information about what the func exec does.
How to deal with this phenomena:
Imitating the solutions provided in the previous questions, for the minimal demostration the question, it can also be modified this way to work:
a=1 # moving out of the variable 'code'
code = """\
def f1():
print(a)
print(f1.__closure__)
f1()
"""
def foo():
exec(code)
foo()
Although the __closure__ is still None, the exception can be avoided because now only the global symbol is needed and __closure__ should also be None if correctly set. You can read the part The reason why the solutions work in the question body for more information.
This was originally added in Revision 4 of the question.

TL;DR
To set correct __closure__ attribute of function defined in the code string passed to exec() function. Just wrap the total code string with a function definition.
I provide an example here to demonstrate all possible situations. Suppose you want to define a function named foo inside a code string used by exec(). The foo use function, variables that defined inside and outside the code string:
def f1():
outside_local_variable = "this is local variable defined outside code str"
def outside_local_function():
print("this is function defined outside code str")
code = """\
local_variable = "this is local variable defined inside code str"
def local_function():
print("this is function defined inside code str")
def foo():
print(local_variable)
local_function()
print(outside_local_variable)
outside_local_function()
foo()
"""
exec(code)
f1()
It can be wrapper like this:
def f1():
outside_local_variable = "this is local variable defined outside code str"
def outside_local_function():
print("this is function defined outside code str")
code = """\
def closure_helper_func(outside_local_variable, outside_local_function):
local_variable = "this is local variable defined inside code str"
def local_function():
print("this is function defined inside code str")
def foo():
print(local_variable)
local_function()
print(outside_local_variable)
outside_local_function()
foo()
closure_helper_func(outside_local_variable, outside_local_function)
"""
exec(code)
f1()
Detailed explanation:
Why the __closure__ attribute is not corretly set:
please refer to The community wiki answer.
How to set the __closure__ attribute to what's expected:
Just wrap the whole code str with a helper function definition and call the helper function once, then during compiling, the variables are considered to be local, and will be stored in the __closure__ attribute.
For the minimal demonstration in the question, it can be modified to following:
code = """\
def closure_helper_func():
a=1
def f1():
print(a)
print(f1.__closure__)
f1()
closure_helper_func()
"""
def foo():
exec(code)
foo()
This output as expected
(<cell at 0x0000019CE6239A98: int object at 0x00007FFF42BFA1A0>,)
1
The example above provide a way to add symbols that defined in the code str to the __closure__ For example, in the minimal demo, a=1 is a defined inside the code str. But what if one want to add the local symbols defined outside the code str? For example, in the code snippet in EDIT1 session, the self symbol needs to be added to the __closure__, and the symbol is provided in the locals() when exec() is called. Just add the name of these symbols to the arguments of helper function and you can handle this situation.
The following shows how to fix the problem in EDIT1 session.
class test_wrapper_function():
def __init__(self):
define_function_str = '''\
def closure_helper_func(self):
def temp_func():
return self.func()
self.wrappered_func2 = temp_func
closure_helper_func(self)
'''
exec(define_function_str)
def func(self):
print("hi!")
t = test_wrapper_function()
t.wrappered_func2()
The following shows how to fix the codes in the session "Why I want to do this"
class MyWidget():
def __init__(self):
self.create_slots_family()
def do_something(self, character):
# in fact, this function is much more complex. Do some simplification.
print(character)
def create_slots_i(self, character):
# want to define a function like this:
# if character is 'C', define self.func_C such that self.func_C() works like self.do_something(C)
# create_slot_command_str = "self.func_" + character + " = lambda:self.do_something('" + character + "')"
create_slot_command_str = """
def closure_helper_func(self):
self.func_""" + character + " = lambda:self.do_something('" + character + """')
closure_helper_func(self)
"""
# print(create_slot_command_str)
exec(create_slot_command_str)
def create_slots_family(self):
for c in ["A", "B", "C", "D"]:
self.create_slots_i(c)
my_widget = MyWidget()
my_widget.func_A()
This solution seems to be too tricky. However, I can not find a more elegant way to declare that some variables should be local symbol during compiling.

Why do we need to specify self in __init__ constructor while the instance have not been created yet?

While executing the following code:
class Test():
def __init__(self):
self.hi_there()
self.a = 5
def hi_there(self):
print(self.a)
new_object = Test()
new_object.hi_there()
I have received an error:
Traceback (most recent call last):
File "/root/a.py", line 241, in <module>
new_object = Test()
File "/root/a.py", line 233, in __init__
self.hello()
File "/root/a.py", line 238, in hello
print(self.a)
AttributeError: 'Test' object has no attribute 'a'
Why do we need to specify the self inside the function while the object is not initialized yet? The possibility to call hi_there() function means that the object is already set, but how come if other variables attributed to this instances haven't been initialized yet?
What is the self inside the __init__ function if it's not a "full" object yet?
Clearly this part of code works:
class Test():
def __init__(self):
#self.hi_there()
self.a = 5
self.hi_there()
def hi_there(self):
print(self.a)
new_object = Test()
new_object.hi_there()
I come from C++ world, there you have to declare the variables before you assign them.
I fully understand your the use of self. Although I don't understand what is the use of self inside__init__() if the self object is not fully initialized.

There is no magic. By the time __init__ is called, the object is created and its methods defined, but you have the chance to set all the instance attributes and do all other initialization. If you look at execution in __init__:
def __init__(self):
self.hi_there()
self.a = 5
def hi_there(self):
print(self.a)
the first thing that happens in __init__ is that hi_there is called. The method already exists, so the function call works, and we drop into hi_there(), which does print(self.a). But this is the problem: self.a isn't set yet, since this only happens in the second line of __init__, but we called hi_there from the first line of __init__. Execution hasn't reached the line where you set self.a = 5, so there's no way that the method call self.hi_there() issued before this assignment can use self.a. This is why you get the AttributeError.

Actually, the object has already been created when __init__ is called. That's why you need self as a parameter. And because of the way Python works internally, you don't have access to the objects without self (Bear in mind that it doesn't need to be called self, you can call it anything you want as long as it is a valid name. The instance is always the first parameter of a method, whatever it's name is.).
The truth is that __init__ doesn't create the object, it just initializes it. There is a class method called __new__, which is in charge of creating the instance and returning it. That's where the object is created.
Now, when does the object get it's a attribute. That's in __init__, but you do have access to it's methods inside of __init__. I'm not completely knowledable about how the creation of the objects works, but methods are already set once you get to that point. That doesn't happen with values, so they are not available until you define them yourself in __init__.
Basically Python creates the object, gives it it's methods, and then gives you the instance so you can initialize it's attributes.
EDIT
Another thing I forgot to mention. Just like you define __init__, you can define __new__ yourself. It's not very common, but you do it when you need to modify the actual object's creation. I've only seen it when defining metaclasses (What are metaclasses in Python?). Another method you can define in that case is __call__, giving you even more control.

Not sure what you meant here, but I guess the first code sample should call an hello() function instead of the hi_there() function.
Someone corrects me if I'm wrong, but in Python, defining a class, or a function is dynamic. By this I mean, defining a class or a function happens at runtime: these are regular statements that are executed just like others.
This language feature allows powerful thing such as decorating the behavior of a function to enrich it with extra functionality (see decorators).
Therefore, when you create an instance of the Test class, you try to call the hello() function before you have set explicitly the value of a. Therefore, the Test class is not YET aware of its a attribute. It has to be read sequentially.

Why is this code not throwing a 'not defined' error?

I created some test code, but I can't really understand why it works.
Shouldn't moo be defined before we can use it?
#!/usr/bin/python3
class Test():
def __init__(self):
self.printer = None
def foo(self):
self.printer = self.moo
self.printer()
def moo(self):
print("Y u printing?")
test = Test()
test.foo()
Output:
$ python test.py
Y u printing?
I know that the rule is define earlier, not higher, but in this case it's neither of those.

There's really nothing to be confused about here.
We have a function that says "when you call foo with a self parameter, look up moo in self's namespace, assign that value to printer in self's namespace, look up printer in self's namespace, and call that value".1
Unless/until you call that function, it doesn't matter whether or not anyone anywhere has an attribute named moo.
When you do call that method, whatever you pass as the self had better have a moo attribute or you're going to get an AttributeError. But this is no different from looking up an attribute on any object. If you write def spam(n): return n.bit_length() as a global function, when you call that function, whatever you pass as the n had better have a bit_length attribute or you're going to get an AttributeError.
So, we're calling it as test.foo(), so we're passing test as self. If you know how attribute lookup works (and there are already plenty of questions and answers on SO about that), you can trace this through. Slightly oversimplified:
Does test.__dict__ have a 'moo'? No.
Does type(test).__dict__ have a 'moo'? Yes. So we're done.
Again, this is the same way we check if 3 has a bit_length() method; there's no extra magic here.
That's really all there is to it.
In particular, notice that test.__dict__ does not have a 'moo'. Methods don't get created at construction time (__new__) any more than they get created at initialization time (__init__). The instance doesn't have any methods in it, because it doesn't have to; they can be looked up on the type.2
Sure, we could get into descriptors, and method resolution order, and object.__getattribute__, and how class and def statements are compiled and executed, and special method lookup to see if there's a custom __getattribute__, and so on, but you don't need any of that to understand this question.
1. If you're confused by this, it's probably because you're thinking in terms of semi-OO languages like C++ and its descendants, where a class has to specify all of its instances' attributes and methods, so the compiler can look at this->moo(), work out that this has a static type ofFoo, work out thatmoois the third method defined onFoo, and compile it into something likethis->vptr2`. If that's what you're expecting, forget all of it. In Python, methods are just attributes, and attributes are just looked up, by name, on demand.
2. If you're going to ask "then why is a bound method not the same thing as a function?", the answer is descriptors. Briefly: when an attribute is found on the type, Python calls the value's __get__ method, passing it the instance, and function objects' __get__ methods return method objects. So, if you want to refer specifically to bound method objects, then they get created every time a method is looked up. In particular, the bound method object does not exist yet when we call foo; it gets created by looking up self.moo inside foo.

While all that #scharette says is likely true (I don't know enough of Python internals to agree with confidence :) ), I'd like to propose an alternative explanation as to why one can instantiate Test and call foo():
The method's body is not executed until you actually call it. It does not matter if foo() contains references to undefined attributes, it will be parsed fine. As long as you create moo before you call foo, you're ok.
Try entering a truncated Test class in your interpreter:
class Test():
def __init__(self):
self.printer = None
def foo(self):
self.printer = self.moo
self.printer()
No moo, so we get this:
>>> test = Test()
>>> test.foo()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 5, in foo
Let's add moo to the class now:
>>> def moo(self):
... print("Y u printing?")
...
>>> Test.moo = moo
>>> test1 = Test()
>>> test1.foo()
Y u printing?
>>>
Alternatively, you can add moo directly to the instance:
>>> def moo():
... print("Y u printing?")
...
>>> test.moo = moo
>>> test.foo()
Y u printing?
The only difference is that the instance's moo does not take a self (see here for explanation).

Python: Multiple ways to initialize a class

I have a class A which can be 'initialized' in two different ways. So, I provide a 'factory-like' interface for it based on the second answer in this post.
class A(object):
#staticmethod
def from_method_1(<method_1_parameters>):
a = A()
# set parameters of 'a' using <method_1_parameters>
return a
#staticmethod
def from_method_2(<method_2_parameters>):
a = A()
# set parameters of 'a' using <method_2_parameters>
return a
The two methods are different enough that I can't just plug their parameters into the class's __init__. So, class A should be initialized using:
a = A.from_method_1(<method_1_parameters>)
or
a = A.from_method_2(<method_2_parameters>)
However, it is still possible to call the 'default init' for A:
a = A() # just an empty 'A' object
Is there any way to prevent this? I can't just raise NotImplementedError from __init__ because the two 'factory methods' use it too.
Or do I need to use a completely different approach altogether.

Has been a very long time since this question was asked but I think it's interesting enough to be revived.
When I first saw your problem the private constructor concept just popped out my mind. It's a concept important in other OOP languages, but as Python doesn't enforces privacy I didn't really thought about it since Python became my main language.
Therefore, I became curious and I found this "Private Constructor in Python" question. It covers pretty much all about this topic and I think the second answer can be helpful in here.
Basically it uses name mangling to declare a pseudo-private class attribute (there isn't such thing as private variables in Python) and assign the class object to it. Therefore you'll have an as-private-as-Python allows variable to use to check if your initialization was made from an class method or from an outside call. I made the following example based on this mechanism:
class A(object):
__obj = object()
def __init__(self, obj=None):
assert(obj == A.__obj), \
'A object must be created using A.from_method_1 or A.from_method_2'
#classmethod
def from_method_1(cls):
a = A(cls.__obj)
print('Created from method 1!')
return a
#classmethod
def from_method_2(cls):
a = A(cls.__obj)
print('Created from method 2!')
return a
Tests:
>>> A()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "t.py", line 6, in __init__
'A object must be created using A.from_method_1 or A.from_method_2'
AssertionError: A object must be created using A.from_method_1 or A.from_method_2
>>> A.from_method_1()
Created from method 1!
<t.A object at 0x7f3f7f2ca450>
>>> A.from_method_2()
Created from method 2!
<t.A object at 0x7f3f7f2ca350>
However, as this solution is a workaround with name mangling, it does have one flaw if you know how to look for it:
>>> A(A._A__obj)
<t.A object at 0x7f3f7f2ca450>

"self" inside plain function?

I've got a bunch of functions (outside of any class) where I've set attributes on them, like funcname.fields = 'xxx'. I was hoping I could then access these variables from inside the function with self.fields, but of course it tells me:
global name 'self' is not defined
So... what can I do? Is there some magic variable I can access? Like __this__.fields?
A few people have asked "why?". You will probably disagree with my reasoning, but I have a set of functions that all must share the same signature (accept only one argument). For the most part, this one argument is enough to do the required computation. However, in a few limited cases, some additional information is needed. Rather than forcing every function to accept a long list of mostly unused variables, I've decided to just set them on the function so that they can easily be ignored.
Although, it occurs to me now that you could just use **kwargs as the last argument if you don't care about the additional args. Oh well...
Edit: Actually, some of the functions I didn't write, and would rather not modify to accept the extra args. By "passing in" the additional args as attributes, my code can work both with my custom functions that take advantage of the extra args, and with third party code that don't require the extra args.
Thanks for the speedy answers :)

self isn't a keyword in python, its just a normal variable name. When creating instance methods, you can name the first parameter whatever you want, self is just a convention.
You should almost always prefer passing arguments to functions over setting properties for input, but if you must, you can do so using the actual functions name to access variables within it:
def a:
if a.foo:
#blah
a.foo = false
a()
see python function attributes - uses and abuses for when this comes in handy. :D

def foo():
print(foo.fields)
foo.fields=[1,2,3]
foo()
# [1, 2, 3]
There is nothing wrong with adding attributes to functions. Many memoizers use this to cache results in the function itself.
For example, notice the use of func.cache:
from decorator import decorator
#decorator
def memoize(func, *args, **kw):
# Author: Michele Simoniato
# Source: http://pypi.python.org/pypi/decorator
if not hasattr(func, 'cache'):
func.cache = {}
if kw: # frozenset is used to ensure hashability
key = args, frozenset(kw.iteritems())
else:
key = args
cache = func.cache # attribute added by memoize
if key in cache:
return cache[key]
else:
cache[key] = result = func(*args, **kw)
return result

You can't do that "function accessing its own attributes" correctly for all situations - see for details here how can python function access its own attributes? - but here is a quick demonstration:
>>> def f(): return f.x
...
>>> f.x = 7
>>> f()
7
>>> g = f
>>> g()
7
>>> del f
>>> g()
Traceback (most recent call last):
File "<interactive input>", line 1, in <module>
File "<interactive input>", line 1, in f
NameError: global name 'f' is not defined
Basically most methods directly or indirectly rely on accessing the function object through lookup by name in globals; and if original function name is deleted, this stops working. There are other kludgey ways of accomplishing this, like defining class, or factory - but thanks to your explanation it is clear you don't really need that.
Just do the mentioned keyword catch-all argument, like so:
def fn1(oneArg):
// do the due
def fn2(oneArg, **kw):
if 'option1' in kw:
print 'called with option1=', kw['option1']
//do the rest
fn2(42)
fn2(42, option1='something')
Not sure what you mean in your comment of handling TypeError - that won't arise when using **kw. This approach works very well for some python system functions - check min(), max(), sort(). Recently sorted(dct,key=dct.get,reverse=True) came very handy to me in CodeGolf challenge :)

Example:
>>> def x(): pass
>>> x
<function x at 0x100451050>
>>> x.hello = "World"
>>> x.hello
"World"
You can set attributes on functions, as these are just plain objects, but I actually never saw something like this in real code.
Plus. self is not a keyword, just another variable name, which happens to be the particular instance of the class. self is passed implicitly, but received explicitly.

if you want globally set parameters for a callable 'thing' you could always create a class and implement the __call__ method?

There is no special way, within a function's body, to refer to the function object whose code is executing. Simplest is just to use funcname.field (with funcname being the function's name within the namespace it's in, which you indicate is the case -- it would be harder otherwise).

This isn't something you should do. I can't think of any way to do what you're asking except some walking around on the call stack and some weird introspection -- which isn't something that should happen in production code.
That said, I think this actually does what you asked:
import inspect
_code_to_func = dict()
def enable_function_self(f):
_code_to_func[f.func_code] = f
return f
def get_function_self():
f = inspect.currentframe()
code_obj = f.f_back.f_code
return _code_to_func[code_obj]
#enable_function_self
def foo():
me = get_function_self()
print me
foo()

While I agree with the the rest that this is probably not good design, the question did intrigue me. Here's my first solution, which I may update once I get decorators working. As it stands, it relies pretty heavily on being able to read the stack, which may not be possible in all implementations (something about sys._getframe() not necessarily being present...)
import sys, inspect
def cute():
this = sys.modules[__name__].__dict__.get(inspect.stack()[0][3])
print "My face is..." + this.face
cute.face = "very cute"
cute()
What do you think? :3

You could use the following (hideously ugly) code:
class Generic_Object(object):
pass
def foo(a1, a2, self=Generic_Object()):
self.args=(a1,a2)
print "len(self.args):", len(self.args)
return None
... as you can see it would allow you to use "self" as you described. You can't use an "object()" directly because you can't "monkey patch(*)" values into an object() instance. However, normal subclasses of object (such as the Generic_Object() I've shown here) can be "monkey patched"
If you wanted to always call your function with a reference to some object as the first argument that would be possible. You could put the defaulted argument first, followed by a *args and optional **kwargs parameters (through which any other arguments or dictionaries of options could be passed during calls to this function).
This is, as I said hideously ugly. Please don't ever publish any code like this or share it with anyone in the Python community. I'm only showing it here as a sort of strange educational exercise.
An instance method is like a function in Python. However, it exists within the namespace of a class (thus it must be accessed via an instance ... myobject.foo() for example) and it is called with a reference to "self" (analagous to the "this" pointer in C++) as the first argument. Also there's a method resolution process which causes the interpreter to search the namespace of the instance, then it's class, and then each of the parent classes and so on ... up through the inheritance tree.
An unbound function is called with whatever arguments you pass to it. There can't bee any sort of automatically pre-pended object/instance reference to the argument list. Thus, writing a function with an initial argument named "self" is meaningless. (It's legal because Python doesn't place any special meaning on the name "self." But meaningless because callers to your function would have to manually supply some sort of object reference to the argument list and it's not at all clear what that should be. Just some bizarre "Generic_Object" which then floats around in the global variable space?).
I hope that clarifies things a bit. It sounds like you're suffering from some very fundamental misconceptions about how Python and other object-oriented systems work.
("Monkey patching" is a term used to describe the direct manipulation of an objects attributes -- or "instance variables" by code that is not part of the class hierarchy of which the object is an instance).

As another alternative, you can make the functions into bound class methods like so:
class _FooImpl(object):
a = "Hello "
#classmethod
def foo(cls, param):
return cls.a + param
foo = _FooImpl.foo
# later...
print foo("World") # yes, Hello World
# and if you have to change an attribute:
foo.im_self.a = "Goodbye "
If you want functions to share attribute namespaecs, you just make them part of the same class. If not, give each its own class.

What exactly are you hoping "self" would point to, if the function is defined outside of any class? If your function needs some global information to execute properly, you need to send this information to the function in the form of an argument.
If you want your function to be context aware, you need to declare it within the scope of an object.