Related
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.
I can't figure out how to do this, and frankly, I don't know if it's possible.
I want to write a decorator that changes the way a function is called. It's easiest to see with example code:
def my_print(*args, **kwargs):
print(args[0].upper())
#reroute_decorator('print', my_print)
def my_func():
print('normally this print function is just a print function...')
print('but since my_func is decorated with a special reroute_decorator...')
print('it is replaced with a different function, and its args sent there.')
my_func()
# NORMALLY THIS PRINT FUNCTION IS JUST A PRINT FUNCTION...
# BUT SINCE MY_FUNC IS DECORATED WITH A SPECIAL REROUTE_DECORATOR...
# IT IS REPLACED WITH A DIFFERENT FUNCTION, AND ITS ARGS SENT THERE.
Is a decorator with this kind of functionality even possible in python?
Now, I don't really need this if it's too complex, I just can't figure out how to do it in a simple way.
Is this kind of a problem trivial? Or is it really complex?
You can create a new function with an updated globals dictionary so that to that function it appears that the global was bound to the desired value.
Note that this is weaker than actual dynamic scope as any functions called by the function will see the original bindings and not the modified one.
See namespaced_function referenced in How does Python's types.FunctionType create dynamic Functions?
To elaborate on #Dan D.'s answer, you would create a new function object to replace the original, something like this:
from types import FunctionType
def reroute_decorator(**kwargs):
def actual_decorator(func):
globals = func.__globals__.copy()
globals.update(kwargs)
new_func = FunctionType(
func.__code__, globals, name=func.__name__,
argdefs=func.__defaults__, closure=func.__closure__)
new_func.__dict__.update(func.__dict__)
return new_func
return actual_decorator
The only catch here is that the updated function object is the only one that will see whatever kwargs you passed in, since they will be spoofed into globals. Additionally, any modifications you make to the module after calling the decorator function will not be visible to the decorated function, but that should not be an issue. You can go a layer deeper and create a proxy dictionary that would allow you to interact normally with the original, except for keys you explicitly defined, like print, but that's a bit out of scope here.
I've updated your print implementation to be a bit more general, and made the input to the decorator function more pythonic (less MATLABy):
def my_print(*args, **kwargs):
print(*(str(x).upper() for x in args), **kwargs)
#reroute_decorator(print=my_print)
def my_func():
print('normally this print function is just a print function...')
print('but since my_func is decorated with a special reroute_decorator...')
print('it is replaced with a different function, and its args sent there.')
Which results in:
>>> my_func()
NORMALLY THIS PRINT FUNCTION IS JUST A PRINT FUNCTION...
BUT SINCE MY_FUNC IS DECORATED WITH A SPECIAL REROUTE_DECORATOR...
IT IS REPLACED WITH A DIFFERENT FUNCTION, AND ITS ARGS SENT THERE.
I am learning about Python decorators and inner functions and have some questions about the lesson I'm learning via a YouTube video from codeacademy.com https://youtu.be/WOHsHaaJ8VQ.
When using inner functions sometimes I have to return the function with parenthesis, and sometimes without.
If I call an inner function without using decorators, I have to use parentheses when returning the inner function, otherwise it seems the inner function is returned as an object(?).
In the YouTube video from codeacademy.com as well as this one https://www.youtube.com/watch?v=FsAPt_9Bf3U, they call the inner function without parentheses and the expected result is output.
If I call an inner function using decorators, I have to not use parentheses when returning the inner function, otherwise it seems to work correctly, but throws an error along with some other weird results.
I've written some code to test different variations and output the results.
You can see the live code here: https://trinket.io/python/af1b47658f
# Test 1: The title function returns inner function wrapper without parentheses.
def title(print_name_function):
def wrapper():
print("Professor:")
print_name_function()
return wrapper # Without parentheses
def print_my_name():
print("John")
print('Test 1')
title(print_my_name)
# Results: Nothing is printed.
# Test 2: The title function returns inner function wrapper with parentheses.
def title(print_name_function):
def wrapper():
print("Professor:")
print_name_function()
return wrapper() # With parentheses
def print_my_name():
print("John")
print('Test 2')
title(print_my_name)
# Results: Professor John is printed.
# Test 3: Using a decorator while the title function returns inner function wrapper without parentheses
def title(print_name_function):
def wrapper():
print("Professor:")
print_name_function()
return wrapper # Without parentheses
#title
def print_my_name():
print("John")
print('Test 3')
print_my_name()
# Results: Professor John is printed.
# Test 4: Using a decorator while the title function returns inner function wrapper with parentheses
def title(print_name_function):
def wrapper():
print("Professor:")
print_name_function()
return wrapper() # With parentheses
#title
def print_my_name():
print("John")
print('Test 4')
print_my_name()
# Results: Professor John is printed and the following error is thrown:
'''
Traceback (most recent call last):
File "decorators.py", line 59, in <module>
print_my_name()
TypeError: 'NoneType' object is not callable.
'''
# Additionally, Professor John is printed before 'Test 4' is printed which seems that print_my_name() runs, then print('Test 4') runs.
In the two videos I've watched listed above about inner functions/decorators I've found...
For inner functions: the inner function was returned without using parentheses and ran correctly. Upon my testing, I have to use the parentheses for it to run correctly.
For decorators: the inner function was returned without using parentheses and ran correctly. Upon my testing, running without using parentheses works. Running with parentheses seems to work, but the output order is mixed up and an error is thrown (see test 4 in my code).
Let's break this down into two parts.
1) Let's ignore decorators for now.
You should use parentheses when you want to call some function.
Without parentheses, a function is just its name.
For example:
Here is a function, where we give it a number, and we get back that number plus 5.
def add_five(x):
return x + 5
We see that add_five, without parentheses, is just the function definition. Think of it as a recipe. It's not the actually cake, just the instructions on how to bake a cake.
>>> add_five
<function add_five at 0x10da3ce18>
Now we give it an ingredient, and it makes a cake:
>>> add_five(1)
6
Let's do a similar thing, but with better names.
>>> def make_cake(cake_type):
>>> print("Making: " + cake_type + " cake!")
>>> make_cake("carrot")
'Making: carrot cake!'
>>> make_cake
<function make_cake at 0x10da3cf28>
Ok, so when we put the function name without any parentheses, we're not actually calling the function, we're just getting the declaration of the function (which is kinda like the function's Birth Certificate, which has its memory address, in this case: 0x10da3cf28.
The same thing applies for functions that don't expect any parameters.
Without the parentheses, you're just asking, "Hey function, you exist?"
With the parentheses (and necessary parameters/variables required), you're saying, "Hey function, do something!"
Now for the second part.
2) What do Decorators do?
#SyntaxVoid has a great explanation about what you're doing. Decorators are a much more complicated thing, so I'll stick to explaining what they're doing in this specific context.
Basically, your decorator, #<Some Function Name> specifies a function to call the decorated function on.
def some_decorator(function_that_I_decorated):
print("I'm going to print this, and then call my decorated function!")
function_that_I_decorated()
#some_decorator
def my_decorated_function():
print("Did I do anything?")
Then we see the results:
>>> def some_decorator(function_that_I_decorated):
... print("I'm going to print this, and then call my decorated function!")
... function_that_I_decorated()
...
>>> #some_decorator
... def my_decorated_function():
... print("Did I do anything?")
...
I'm going to print this, and then call my decorated function!
Did I do anything?
Now here's the important part:
>>> my_decorated_function
>>> my_decorated_function()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'NoneType' object is not callable
Wait... didn't we define my_decorated_function?
Yes, we defined the function, but the decorator is reassigning that function name to something else.
Namely, my_decorator_function = some_decorator(my_decorator_function)
Now some_decorator happens to do something before calling my_decorator_function. It prints some stuff. But what is the return value of some_decorator? There's no return statement, so some_decorator returns None by default.
Therefore, my_decorator_function was created, run, and now has a new value.
Why would we want this behavior?
When we want the output to change, when running the same function with the same input(s) multiple times.
For example, maybe I want a function that returns "Go Left" every other time it's called, or "Go Right" every 5-times the function gets called.
If I want to do this with a function with more than one variable, that's easy! Just pass it in and check if num_times == whatever_int.
But life ain't so easy- sometimes other people already have functions written that are much simpler and only allow one variable, because that's more generalizable. Or maybe it's so complex it would take us a really long time to figure out how the function works (and we usually don't want to violate abstraction barriers anyways). In those situations, we need to adapt their function to our needs.
I would encourage you to read more about Currying, as that'll help you understand other uses too.
Let me use this famous quote first.
In python everything is an object.
I've been wrapping my head about two hours, until I remembered this quote. We should think original function and decorated function as objects, given an example:
def decorator(original_func):
def decorated_func():
print("Start decoration")
original_func()
print("End decoration")
return decorated_func # return decorated function as object without calling
#decorator
def func():
print("I will be decorated")
func()
The decorator_func transfers the func object to decorated_func object, and return the decorated_func as an object, so when we call func object with it's original name, we are actually calling the new function object decorated_func, which is equivalent to decorated_func().
Now it is easy to see why return decorated_func() is wrong, if we return decorated_func() in definition of decorator, we are return None, because the default return value of a function is None, and None is not callable as traceback says, so we can't use func() to call func.
Additionally, although the following two codes are equivalent, decorator help us to simplify our code, and without changing the original function object, also don't need to create a new function object mannually
#decorator
def func():
print("I will be decorated")
func()
def func():
print("I will be decorated")
dec_func = decorator(func)
dec_func()
Is it possible to forward-declare a function in Python? I want to sort a list using my own cmp function before it is declared.
print "\n".join([str(bla) for bla in sorted(mylist, cmp = cmp_configs)])
I've put the definition of cmp_configs method after the invocation. It fails with this error:
NameError: name 'cmp_configs' is not defined
Is there any way to "declare" cmp_configs method before it's used?
Sometimes, it is difficult to reorganize code to avoid this problem. For instance, when implementing some forms of recursion:
def spam():
if end_condition():
return end_result()
else:
return eggs()
def eggs():
if end_condition():
return end_result()
else:
return spam()
Where end_condition and end_result have been previously defined.
Is the only solution to reorganize the code and always put definitions before invocations?
Wrap the invocation into a function of its own so that
foo()
def foo():
print "Hi!"
will break, but
def bar():
foo()
def foo():
print "Hi!"
bar()
will work properly.
The general rule in Python is that a function should be defined before its usage, which does not necessarily mean it needs to be higher in the code.
If you kick-start your script through the following:
if __name__=="__main__":
main()
then you probably do not have to worry about things like "forward declaration". You see, the interpreter would go loading up all your functions and then start your main() function. Of course, make sure you have all the imports correct too ;-)
Come to think of it, I've never heard such a thing as "forward declaration" in python... but then again, I might be wrong ;-)
If you don't want to define a function before it's used, and defining it afterwards is impossible, what about defining it in some other module?
Technically you still define it first, but it's clean.
You could create a recursion like the following:
def foo():
bar()
def bar():
foo()
Python's functions are anonymous just like values are anonymous, yet they can be bound to a name.
In the above code, foo() does not call a function with the name foo, it calls a function that happens to be bound to the name foo at the point the call is made. It is possible to redefine foo somewhere else, and bar would then call the new function.
Your problem cannot be solved because it's like asking to get a variable which has not been declared.
I apologize for reviving this thread, but there was a strategy not discussed here which may be applicable.
Using reflection it is possible to do something akin to forward declaration. For instance lets say you have a section of code that looks like this:
# We want to call a function called 'foo', but it hasn't been defined yet.
function_name = 'foo'
# Calling at this point would produce an error
# Here is the definition
def foo():
bar()
# Note that at this point the function is defined
# Time for some reflection...
globals()[function_name]()
So in this way we have determined what function we want to call before it is actually defined, effectively a forward declaration. In python the statement globals()[function_name]() is the same as foo() if function_name = 'foo' for the reasons discussed above, since python must lookup each function before calling it. If one were to use the timeit module to see how these two statements compare, they have the exact same computational cost.
Of course the example here is very useless, but if one were to have a complex structure which needed to execute a function, but must be declared before (or structurally it makes little sense to have it afterwards), one can just store a string and try to call the function later.
If the call to cmp_configs is inside its own function definition, you should be fine. I'll give an example.
def a():
b() # b() hasn't been defined yet, but that's fine because at this point, we're not
# actually calling it. We're just defining what should happen when a() is called.
a() # This call fails, because b() hasn't been defined yet,
# and thus trying to run a() fails.
def b():
print "hi"
a() # This call succeeds because everything has been defined.
In general, putting your code inside functions (such as main()) will resolve your problem; just call main() at the end of the file.
There is no such thing in python like forward declaration. You just have to make sure that your function is declared before it is needed.
Note that the body of a function isn't interpreted until the function is executed.
Consider the following example:
def a():
b() # won't be resolved until a is invoked.
def b():
print "hello"
a() # here b is already defined so this line won't fail.
You can think that a body of a function is just another script that will be interpreted once you call the function.
Sometimes an algorithm is easiest to understand top-down, starting with the overall structure and drilling down into the details.
You can do so without forward declarations:
def main():
make_omelet()
eat()
def make_omelet():
break_eggs()
whisk()
fry()
def break_eggs():
for egg in carton:
break(egg)
# ...
main()
# declare a fake function (prototype) with no body
def foo(): pass
def bar():
# use the prototype however you see fit
print(foo(), "world!")
# define the actual function (overwriting the prototype)
def foo():
return "Hello,"
bar()
Output:
Hello, world!
No, I don't believe there is any way to forward-declare a function in Python.
Imagine you are the Python interpreter. When you get to the line
print "\n".join([str(bla) for bla in sorted(mylist, cmp = cmp_configs)])
either you know what cmp_configs is or you don't. In order to proceed, you have to
know cmp_configs. It doesn't matter if there is recursion.
You can't forward-declare a function in Python. If you have logic executing before you've defined functions, you've probably got a problem anyways. Put your action in an if __name__ == '__main__' at the end of your script (by executing a function you name "main" if it's non-trivial) and your code will be more modular and you'll be able to use it as a module if you ever need to.
Also, replace that list comprehension with a generator express (i.e., print "\n".join(str(bla) for bla in sorted(mylist, cmp=cmp_configs)))
Also, don't use cmp, which is deprecated. Use key and provide a less-than function.
Import the file itself. Assuming the file is called test.py:
import test
if __name__=='__main__':
test.func()
else:
def func():
print('Func worked')
TL;DR: Python does not need forward declarations. Simply put your function calls inside function def definitions, and you'll be fine.
def foo(count):
print("foo "+str(count))
if(count>0):
bar(count-1)
def bar(count):
print("bar "+str(count))
if(count>0):
foo(count-1)
foo(3)
print("Finished.")
recursive function definitions, perfectly successfully gives:
foo 3
bar 2
foo 1
bar 0
Finished.
However,
bug(13)
def bug(count):
print("bug never runs "+str(count))
print("Does not print this.")
breaks at the top-level invocation of a function that hasn't been defined yet, and gives:
Traceback (most recent call last):
File "./test1.py", line 1, in <module>
bug(13)
NameError: name 'bug' is not defined
Python is an interpreted language, like Lisp. It has no type checking, only run-time function invocations, which succeed if the function name has been bound and fail if it's unbound.
Critically, a function def definition does not execute any of the funcalls inside its lines, it simply declares what the function body is going to consist of. Again, it doesn't even do type checking. So we can do this:
def uncalled():
wild_eyed_undefined_function()
print("I'm not invoked!")
print("Only run this one line.")
and it runs perfectly fine (!), with output
Only run this one line.
The key is the difference between definitions and invocations.
The interpreter executes everything that comes in at the top level, which means it tries to invoke it. If it's not inside a definition.
Your code is running into trouble because you attempted to invoke a function, at the top level in this case, before it was bound.
The solution is to put your non-top-level function invocations inside a function definition, then call that function sometime much later.
The business about "if __ main __" is an idiom based on this principle, but you have to understand why, instead of simply blindly following it.
There are certainly much more advanced topics concerning lambda functions and rebinding function names dynamically, but these are not what the OP was asking for. In addition, they can be solved using these same principles: (1) defs define a function, they do not invoke their lines; (2) you get in trouble when you invoke a function symbol that's unbound.
Python does not support forward declarations, but common workaround for this is use of the the following condition at the end of your script/code:
if __name__ == '__main__': main()
With this it will read entire file first and then evaluate condition and call main() function which will be able to call any forward declared function as it already read the entire file first. This condition leverages special variable __name__ which returns __main__ value whenever we run Python code from current file (when code was imported as a module, then __name__ returns module name).
"just reorganize my code so that I don't have this problem." Correct. Easy to do. Always works.
You can always provide the function prior to it's reference.
"However, there are cases when this is probably unavoidable, for instance when implementing some forms of recursion"
Can't see how that's even remotely possible. Please provide an example of a place where you cannot define the function prior to it's use.
Now wait a minute. When your module reaches the print statement in your example, before cmp_configs has been defined, what exactly is it that you expect it to do?
If your posting of a question using print is really trying to represent something like this:
fn = lambda mylist:"\n".join([str(bla)
for bla in sorted(mylist, cmp = cmp_configs)])
then there is no requirement to define cmp_configs before executing this statement, just define it later in the code and all will be well.
Now if you are trying to reference cmp_configs as a default value of an argument to the lambda, then this is a different story:
fn = lambda mylist,cmp_configs=cmp_configs : \
"\n".join([str(bla) for bla in sorted(mylist, cmp = cmp_configs)])
Now you need a cmp_configs variable defined before you reach this line.
[EDIT - this next part turns out not to be correct, since the default argument value will get assigned when the function is compiled, and that value will be used even if you change the value of cmp_configs later.]
Fortunately, Python being so type-accommodating as it is, does not care what you define as cmp_configs, so you could just preface with this statement:
cmp_configs = None
And the compiler will be happy. Just be sure to declare the real cmp_configs before you ever invoke fn.
Python technically has support for forward declaration.
if you define a function/class then set the body to pass, it will have an empty entry in the global table.
you can then "redefine" the function/class later on to implement the function/class.
unlike c/c++ forward declaration though, this does not work from outside the scope (i.e. another file) as they have their own "global" namespace
example:
def foo(): pass
foo()
def foo(): print("FOOOOO")
foo()
foo is declared both times
however the first time foo is called it does not do anything as the body is just pass
but the second time foo is called. it executes the new body of print("FOOOOO")
but again. note that this does not fix circular dependancies. this is because files have their own name and have their own definitions of functions
example 2:
class bar: pass
print(bar)
this prints <class '__main__.bar'> but if it was declared in another file it would be <class 'otherfile.foo'>
i know this post is old, but i though that this answer would be useful to anyone who keeps finding this post after the many years it has been posted for
One way is to create a handler function. Define the handler early on, and put the handler below all the methods you need to call.
Then when you invoke the handler method to call your functions, they will always be available.
The handler could take an argument nameOfMethodToCall. Then uses a bunch of if statements to call the right method.
This would solve your issue.
def foo():
print("foo")
#take input
nextAction=input('What would you like to do next?:')
return nextAction
def bar():
print("bar")
nextAction=input('What would you like to do next?:')
return nextAction
def handler(action):
if(action=="foo"):
nextAction = foo()
elif(action=="bar"):
nextAction = bar()
else:
print("You entered invalid input, defaulting to bar")
nextAction = "bar"
return nextAction
nextAction=input('What would you like to do next?:')
while 1:
nextAction = handler(nextAction)
I am having trouble in understanding the flow of control in a Python program having a decorator.
def executor(func):
def innerExecutor():
#print("inside executor")
print("------------------------")
func()
print("------------------------")
return innerExecutor
#executor
def multiply():
n1 = 10
n2 = 20
print("multiplication = ", (n1 * n2))
multiply()
What trouble I am having with is: When the executor() function is called, and it returns the reference of innerExecutor(), where is this reference stored? When is the control actually passed to the inner function? I am very new in Python.
The syntax
#some_decorator
def somefunc():
...
is equivalent to:
somefunc = some_decorator(somefunc)
This is possible in Python because functions are first-class objects. They can be passed as arguments to, and returned from, other functions - so-called higher-order functions.
In your example, the final call to multiply() actually runs innerExector(), a function created by the decorated definition of multiply(). To understand what's going on here, take a closer look at the definition of executor:
def executor(func):
def innerExecutor():
#print("inside executor")
print("------------------------")
func()
print("------------------------")
return innerExecutor
When the code:
#executor
def multiply():
...
is run, the function executor is called with the function multiply as its argument. executor defines the function innerExecutor(), and returns it, but does not execute it. This instance of innerExecutor() is called in place of all future references to multiply().
But wait - usually, we don't want to completely replace the function being decorated. innerExecutor() needs to call the decorated function at some point. Recall that a decorated function definition:
#executor
def multiply():
...
is equivalent to:
def multiply():
...
multiply = executor(multiply)
Passing the function to be decorated as an argument to the decorator, allows the definition of innerExecutor() to call it. But wait - innerExecutor() isn't called until much later, after executor() has returned and it's argument func has gone out of scope. So why does it work?
Thanks to another feature of Python called a closure. What this means is the inner function can refer to local variables defined in the enclosing scope - including arguments passed into the outer function executor - even after exiting the enclosing scope. The magical thing about closures is that the interpreter detects the dependency of the inner function definition on a local variable of the outer function, and keeps it available even after executor() returns. This link goes into more detail about closures.
Finally, the call to multiply() in the last line actually calls the instance of innerExecutor() that was created when the decorated definition was run, which in turn calls the original undecorated version of multiply().
Every time a decorator is used to decorate a function definition, it creates a new instance of the inner function, which replaces the undecorated function. So if you decorate 5 different functions, 5 different instances of innerExecutor() are created. Later, when any of those decorated functions are called, it will instead call instead the appropriate instance of innerExecutor(), which in turn calls the corresponding undecorated function.
it returns the reference of innerExecutor(), where is this reference stored?
When is the control actually passed to the inner function?
It is not stored in anywhere, it is called as below:
innerExecutor(multiply)
That's why your decorator method needs to return its own reference, otherwise, it would be equivalent to this:
None(multiply) # TypeError: 'NoneType' object is not callable
Here's the documentation of aboves behavior