Python classes have no concept of public/private, so we are told to not touch something that starts with an underscore unless we created it. But does this not require complete knowledge of all classes from which we inherit, directly or indirectly? Witness:
class Base(object):
def __init__(self):
super(Base, self).__init__()
self._foo = 0
def foo(self):
return self._foo + 1
class Sub(Base):
def __init__(self):
super(Sub, self).__init__()
self._foo = None
Sub().foo()
Expectedly, a TypeError is raised when None + 1 is evaluated. So I have to know that _foo exists in the base class. To get around this, __foo can be used instead, which solves the problem by mangling the name. This seems to be, if not elegant, an acceptable solution. However, what happens if Base inherits from a class (in a separate package) called Sub? Now __foo in my Sub overrides __foo in the grandparent Sub.
This implies that I have to know the entire inheritance chain, including all "private" objects each uses. The fact that Python is dynamically-typed makes this even harder, since there are no declarations to search for. The worst part, however, is probably the fact Base might inherit from object right now, but in some future release, it switches to inheriting from Sub. Clearly if I know Sub is inherited from, I can rename my class, however annoying that is. But I can't see into the future.
Is this not a case where a true private data type would prevent a problem? How, in Python, can I be sure that I'm not accidentally stepping on somebody's toes if those toes might spring into existence at some point in the future?
EDIT: I've apparently not made clear the primary question. I'm familiar with name mangling and the difference between a single and a double underscore. The question is: how do I deal with the fact that I might clash with classes whose existence I don't know of right now? If my parent class (which is in a package I did not write) happens to start inheriting from a class with the same name as my class, even name mangling won't help. Am I wrong in seeing this as a (corner) case that true private members would solve, but that Python has trouble with?
EDIT: As requested, the following is a full example:
File parent.py:
class Sub(object):
def __init__(self):
self.__foo = 12
def foo(self):
return self.__foo + 1
class Base(Sub):
pass
File sub.py:
import parent
class Sub(parent.Base):
def __init__(self):
super(Sub, self).__init__()
self.__foo = None
Sub().foo()
The grandparent's foo is called, but my __foo is used.
Obviously you wouldn't write code like this yourself, but parent could easily be provided by a third party, the details of which could change at any time.
Use private names (instead of protected ones), starting with a double underscore:
class Sub(Base):
def __init__(self):
super(Sub, self).__init__()
self.__foo = None
# ^^
will not conflict with _foo or __foo in Base. This is because Python replaces the double underscore with a single underscore and the name of the class; the following two lines are equivalent:
class Sub(Base):
def x(self):
self.__foo = None # .. is the same as ..
self._Sub__foo = None
(In response to the edit:) The chance that two classes in a class hierarchy not only have the same name, but that they are both using the same property name, and are both using the private mangled (__) form is so minuscule that it can be safely ignored in practice (I for one haven't heard of a single case so far).
In theory, however, you are correct in that in order to formally verify correctness of a program, one most know the entire inheritance chain. Luckily, formal verification usually requires a fixed set of libraries in any case.
This is in the spirit of the Zen of Python, which includes
practicality beats purity.
Name mangling includes the class so your Base.__foo and Sub.__foo will have different names. This was the entire reason for adding the name mangling feature to Python in the first place. One will be _Base__foo, the other _Sub__foo.
Many people prefer to use composition (has-a) instead of inheritance (is-a) for some of these very reasons.
This implies that I have to know the entire inheritance chain. . .
Yes, you should know the entire inheritance chain, or the docs for the object you are directly sub-classing should tell you what you need to know.
Subclassing is an advanced feature, and should be treated with care.
A good example of docs specifying what should be overridden in a subclass is the threading class:
This class represents an activity that is run in a separate thread of control. There are two ways to specify the activity: by passing a callable object to the constructor, or by overriding the run() method in a subclass. No other methods (except for the constructor) should be overridden in a subclass. In other words, only override the __init__() and run() methods of this class.
How often do you modify base classes in inheritance chains to introduce inheritance from a class with the same name as a subclass further down the chain???
Less flippantly, yes, you have to know the code you are working with. You certainly have to know the public names being used, after all. Python being python, discovering the public names in use by your ancestor classes takes pretty much the same effort as discovering the private ones.
In years of Python programming, I have never found this to be much of an issue in practice. When you're naming instance variables, you should have a pretty good idea whether (a) a name is generic enough that it's likely to be used in other contexts and (b) the class you're writing is likely to be involved in an inheritance hierarchy with other unknown classes. In such cases, you think a bit more carefully about the names you're using; self.value isn't a great idea for an attribute name, and neither is something like Adaptor a great class name.
In contrast, I have run into difficulties with the overuse of double-underscore names a number of times. Python being Python, even "private" names tend to be accessed by code defined outside the class. You might think that it would always be bad practice to let an external function access "private" attributes, but what about things like getattr and hasattr? The invocation of them can be in the class's own code, so the class is still controlling all access to the private attributes, but they still don't work without you doing the name-mangling manually. If Python had actually-enforced private variables you couldn't use functions like those on them at all. These days I tend to reserve double-underscore names for cases when I'm writing something very generic like a decorator, metaclass, or mixin that needs to add a "secret attribute" to the instances of the (unknown) classes it's applied to.
And of course there's the standard dynamic language argument: the reality is that you have to test your code thoroughly to have much justification in making the claim "my software works". Such testing will be very unlikely to miss the bugs caused by accidentally clashing names. If you are not doing that testing, then many more uncaught bugs will be introduced by other means than by accidental name clashes.
In summation, the lack of private variables is just not that big a deal in idiomatic Python code in practice, and the addition of true private variables would cause more frequent problems in other ways IMHO.
Mangling happens with double underscores. Single underscores are more of a "please don't".
You don't need to know all the details of all parent classes (note that deep inheritance is usually best avoided), because you can still dir() and help() and any other form of introspection you can come up with.
As noted, you can use name mangling. However, you can stick with a single underscore (or none!) if you document your code adequately - you should not have so many private variables that this proves to be a problem. Just say if a method relies on a private variable, and add either the variable, or the name of the method to the class docstring to alert users.
Further, if you create unit tests, you should create tests that check invariants on members, and accordingly these should be able to show up such name clashes.
If you really want to have "private" variables, and for whatever reason name-mangling doesn't meet your needs, you can factor your private state into another object:
class Foo(object):
class Stateholder(object): pass
def __init__(self):
self._state = Stateholder()
self.state.private = 1
Related
I'm coming from the Java world and reading Bruce Eckels' Python 3 Patterns, Recipes and Idioms.
While reading about classes, it goes on to say that in Python there is no need to declare instance variables. You just use them in the constructor, and boom, they are there.
So for example:
class Simple:
def __init__(self, s):
print("inside the simple constructor")
self.s = s
def show(self):
print(self.s)
def showMsg(self, msg):
print(msg + ':', self.show())
If that’s true, then any object of class Simple can just change the value of variable s outside of the class.
For example:
if __name__ == "__main__":
x = Simple("constructor argument")
x.s = "test15" # this changes the value
x.show()
x.showMsg("A message")
In Java, we have been taught about public/private/protected variables. Those keywords make sense because at times you want variables in a class to which no one outside the class has access to.
Why is that not required in Python?
It's cultural. In Python, you don't write to other classes' instance or class variables. In Java, nothing prevents you from doing the same if you really want to - after all, you can always edit the source of the class itself to achieve the same effect. Python drops that pretence of security and encourages programmers to be responsible. In practice, this works very nicely.
If you want to emulate private variables for some reason, you can always use the __ prefix from PEP 8. Python mangles the names of variables like __foo so that they're not easily visible to code outside the namespace that contains them (although you can get around it if you're determined enough, just like you can get around Java's protections if you work at it).
By the same convention, the _ prefix means _variable should be used internally in the class (or module) only, even if you're not technically prevented from accessing it from somewhere else. You don't play around with another class's variables that look like __foo or _bar.
Private variables in Python is more or less a hack: the interpreter intentionally renames the variable.
class A:
def __init__(self):
self.__var = 123
def printVar(self):
print self.__var
Now, if you try to access __var outside the class definition, it will fail:
>>> x = A()
>>> x.__var # this will return error: "A has no attribute __var"
>>> x.printVar() # this gives back 123
But you can easily get away with this:
>>> x.__dict__ # this will show everything that is contained in object x
# which in this case is something like {'_A__var' : 123}
>>> x._A__var = 456 # you now know the masked name of private variables
>>> x.printVar() # this gives back 456
You probably know that methods in OOP are invoked like this: x.printVar() => A.printVar(x). If A.printVar() can access some field in x, this field can also be accessed outside A.printVar()... After all, functions are created for reusability, and there isn't any special power given to the statements inside.
As correctly mentioned by many of the comments above, let's not forget the main goal of Access Modifiers: To help users of code understand what is supposed to change and what is supposed not to. When you see a private field you don't mess around with it. So it's mostly syntactic sugar which is easily achieved in Python by the _ and __.
Python does not have any private variables like C++ or Java does. You could access any member variable at any time if wanted, too. However, you don't need private variables in Python, because in Python it is not bad to expose your classes' member variables. If you have the need to encapsulate a member variable, you can do this by using "#property" later on without breaking existing client code.
In Python, the single underscore "_" is used to indicate that a method or variable is not considered as part of the public API of a class and that this part of the API could change between different versions. You can use these methods and variables, but your code could break, if you use a newer version of this class.
The double underscore "__" does not mean a "private variable". You use it to define variables which are "class local" and which can not be easily overridden by subclasses. It mangles the variables name.
For example:
class A(object):
def __init__(self):
self.__foobar = None # Will be automatically mangled to self._A__foobar
class B(A):
def __init__(self):
self.__foobar = 1 # Will be automatically mangled to self._B__foobar
self.__foobar's name is automatically mangled to self._A__foobar in class A. In class B it is mangled to self._B__foobar. So every subclass can define its own variable __foobar without overriding its parents variable(s). But nothing prevents you from accessing variables beginning with double underscores. However, name mangling prevents you from calling this variables /methods incidentally.
I strongly recommend you watch Raymond Hettinger's Python's class development toolkit from PyCon 2013, which gives a good example why and how you should use #property and "__"-instance variables.
If you have exposed public variables and you have the need to encapsulate them, then you can use #property. Therefore you can start with the simplest solution possible. You can leave member variables public unless you have a concrete reason to not do so. Here is an example:
class Distance:
def __init__(self, meter):
self.meter = meter
d = Distance(1.0)
print(d.meter)
# prints 1.0
class Distance:
def __init__(self, meter):
# Customer request: Distances must be stored in millimeters.
# Public available internals must be changed.
# This would break client code in C++.
# This is why you never expose public variables in C++ or Java.
# However, this is Python.
self.millimeter = meter * 1000
# In Python we have #property to the rescue.
#property
def meter(self):
return self.millimeter *0.001
#meter.setter
def meter(self, value):
self.millimeter = value * 1000
d = Distance(1.0)
print(d.meter)
# prints 1.0
There is a variation of private variables in the underscore convention.
In [5]: class Test(object):
...: def __private_method(self):
...: return "Boo"
...: def public_method(self):
...: return self.__private_method()
...:
In [6]: x = Test()
In [7]: x.public_method()
Out[7]: 'Boo'
In [8]: x.__private_method()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-fa17ce05d8bc> in <module>()
----> 1 x.__private_method()
AttributeError: 'Test' object has no attribute '__private_method'
There are some subtle differences, but for the sake of programming pattern ideological purity, it's good enough.
There are examples out there of #private decorators that more closely implement the concept, but your mileage may vary. Arguably, one could also write a class definition that uses meta.
As mentioned earlier, you can indicate that a variable or method is private by prefixing it with an underscore. If you don't feel like this is enough, you can always use the property decorator. Here's an example:
class Foo:
def __init__(self, bar):
self._bar = bar
#property
def bar(self):
"""Getter for '_bar'."""
return self._bar
This way, someone or something that references bar is actually referencing the return value of the bar function rather than the variable itself, and therefore it can be accessed but not changed. However, if someone really wanted to, they could simply use _bar and assign a new value to it. There is no surefire way to prevent someone from accessing variables and methods that you wish to hide, as has been said repeatedly. However, using property is the clearest message you can send that a variable is not to be edited. property can also be used for more complex getter/setter/deleter access paths, as explained here: https://docs.python.org/3/library/functions.html#property
Python has limited support for private identifiers, through a feature that automatically prepends the class name to any identifiers starting with two underscores. This is transparent to the programmer, for the most part, but the net effect is that any variables named this way can be used as private variables.
See here for more on that.
In general, Python's implementation of object orientation is a bit primitive compared to other languages. But I enjoy this, actually. It's a very conceptually simple implementation and fits well with the dynamic style of the language.
The only time I ever use private variables is when I need to do other things when writing to or reading from the variable and as such I need to force the use of a setter and/or getter.
Again this goes to culture, as already stated. I've been working on projects where reading and writing other classes variables was free-for-all. When one implementation became deprecated it took a lot longer to identify all code paths that used that function. When use of setters and getters was forced, a debug statement could easily be written to identify that the deprecated method had been called and the code path that calls it.
When you are on a project where anyone can write an extension, notifying users about deprecated methods that are to disappear in a few releases hence is vital to keep module breakage at a minimum upon upgrades.
So my answer is; if you and your colleagues maintain a simple code set then protecting class variables is not always necessary. If you are writing an extensible system then it becomes imperative when changes to the core is made that needs to be caught by all extensions using the code.
"In java, we have been taught about public/private/protected variables"
"Why is that not required in python?"
For the same reason, it's not required in Java.
You're free to use -- or not use private and protected.
As a Python and Java programmer, I've found that private and protected are very, very important design concepts. But as a practical matter, in tens of thousands of lines of Java and Python, I've never actually used private or protected.
Why not?
Here's my question "protected from whom?"
Other programmers on my team? They have the source. What does protected mean when they can change it?
Other programmers on other teams? They work for the same company. They can -- with a phone call -- get the source.
Clients? It's work-for-hire programming (generally). The clients (generally) own the code.
So, who -- precisely -- am I protecting it from?
In Python 3, if you just want to "encapsulate" the class attributes, like in Java, you can just do the same thing like this:
class Simple:
def __init__(self, str):
print("inside the simple constructor")
self.__s = str
def show(self):
print(self.__s)
def showMsg(self, msg):
print(msg + ':', self.show())
To instantiate this do:
ss = Simple("lol")
ss.show()
Note that: print(ss.__s) will throw an error.
In practice, Python 3 will obfuscate the global attribute name. It is turning this like a "private" attribute, like in Java. The attribute's name is still global, but in an inaccessible way, like a private attribute in other languages.
But don't be afraid of it. It doesn't matter. It does the job too. ;)
Private and protected concepts are very important. But Python is just a tool for prototyping and rapid development with restricted resources available for development, and that is why some of the protection levels are not so strictly followed in Python. You can use "__" in a class member. It works properly, but it does not look good enough. Each access to such field contains these characters.
Also, you can notice that the Python OOP concept is not perfect. Smalltalk or Ruby are much closer to a pure OOP concept. Even C# or Java are closer.
Python is a very good tool. But it is a simplified OOP language. Syntactically and conceptually simplified. The main goal of Python's existence is to bring to developers the possibility to write easy readable code with a high abstraction level in a very fast manner.
Here's how I handle Python 3 class fields:
class MyClass:
def __init__(self, public_read_variable, private_variable):
self.public_read_variable_ = public_read_variable
self.__private_variable = private_variable
I access the __private_variable with two underscores only inside MyClass methods.
I do read access of the public_read_variable_ with one underscore
outside the class, but never modify the variable:
my_class = MyClass("public", "private")
print(my_class.public_read_variable_) # OK
my_class.public_read_variable_ = 'another value' # NOT OK, don't do that.
So I’m new to Python but I have a background in C# and JavaScript. Python feels like a mix of the two in terms of features. JavaScript also struggles in this area and the way around it here, is to create a closure. This prevents access to data you don’t want to expose by returning a different object.
def print_msg(msg):
# This is the outer enclosing function
def printer():
# This is the nested function
print(msg)
return printer # returns the nested function
# Now let's try calling this function.
# Output: Hello
another = print_msg("Hello")
another()
https://www.programiz.com/python-programming/closure
https://developer.mozilla.org/en-US/docs/Web/JavaScript/Closures#emulating_private_methods_with_closures
About sources (to change the access rights and thus bypass language encapsulation like Java or C++):
You don't always have the sources and even if you do, the sources are managed by a system that only allows certain programmers to access a source (in a professional context). Often, every programmer is responsible for certain classes and therefore knows what he can and cannot do. The source manager also locks the sources being modified and of course, manages the access rights of programmers.
So I trust more in software than in human, by experience. So convention is good, but multiple protections are better, like access management (real private variable) + sources management.
I have been thinking about private class attributes and methods (named members in further reading) since I have started to develop a package that I want to publish. The thought behind it were never to make it impossible to overwrite these members, but to have a warning for those who touch them. I came up with a few solutions that might help. The first solution is used in one of my favorite Python books, Fluent Python.
Upsides of technique 1:
It is unlikely to be overwritten by accident.
It is easily understood and implemented.
Its easier to handle than leading double underscore for instance attributes.
*In the book the hash-symbol was used, but you could use integer converted to strings as well. In Python it is forbidden to use klass.1
class Technique1:
def __init__(self, name, value):
setattr(self, f'private#{name}', value)
setattr(self, f'1{name}', value)
Downsides of technique 1:
Methods are not easily protected with this technique though. It is possible.
Attribute lookups are just possible via getattr
Still no warning to the user
Another solution I came across was to write __setattr__. Pros:
It is easily implemented and understood
It works with methods
Lookup is not affected
The user gets a warning or error
class Demonstration:
def __init__(self):
self.a = 1
def method(self):
return None
def __setattr__(self, name, value):
if not getattr(self, name, None):
super().__setattr__(name, value)
else:
raise ValueError(f'Already reserved name: {name}')
d = Demonstration()
#d.a = 2
d.method = None
Cons:
You can still overwrite the class
To have variables not just constants, you need to map allowed input.
Subclasses can still overwrite methods
To prevent subclasses from overwriting methods you can use __init_subclass__:
class Demonstration:
__protected = ['method']
def method(self):
return None
def __init_subclass__(cls):
protected_methods = Demonstration.__protected
subclass_methods = dir(cls)
for i in protected_methods:
p = getattr(Demonstration,i)
j = getattr(cls, i)
if not p is j:
raise ValueError(f'Protected method "{i}" was touched')
You see, there are ways to protect your class members, but it isn't any guarantee that users don't overwrite them anyway. This should just give you some ideas. In the end, you could also use a meta class, but this might open up new dangers to encounter. The techniques used here are also very simple minded and you should definitely take a look at the documentation, you can find useful feature to this technique and customize them to your need.
I am writing some code that is an upside down triangle of inheritance. I have a base Linux class that has a CLIENT attr which holds a connection. I have several APIs that are logically separated (kvm, yum, gdb, dhcp, etc..) that use CLIENT but I only want the user to need to create a single instance of Linux class but be able to call all the methods from the Parent classes. While maintaining the nice logical code separation among the parents:
class Linux(
SSHClient,
yum.Yum,
kvm.Kvm,
ldap.Ldap,
lshw.Lshw,
packet_capture.Tcpdump,
tc.TrafficControl,
networking.Networking,
gdb.Gdb,
dhcp.Dhcp,
httputil.Http,
scp.Scp,
fileutils.FileUtils):
I made a little example:
class Dad(object):
def __init__(self):
raise NotImplementedError("Create Baby instead")
def dadCallBaby(self):
print('sup {}'.format(self.babyName))
class Mom(object):
def __init__(self):
raise NotImplementedError("Create Baby instead")
def momCallBaby(self):
print('goochi goo {}'.format(self.babyName))
class Baby(Mom, Dad):
def __init__(self, name):
self.babyName = name
def greeting(self):
self.momCallBaby()
self.dadCallBaby()
x=Baby('Joe')
x.greeting()
What is doing this called? Is this Duck Typing? And is there a better option?
There's really no such thing as "child-only attributes".
The attribute babyName is just stored in each object's namespace, and looked up there. Python doesn't care that it happened to be stored by Baby.__init__. And in fact, you can write store the same attribute on a Mom that isn't a Baby and it will work the same way:
class NotABaby(Mom):
def __init__(self): pass
mom = NotABaby()
mom.babyName = 'Me?'
mom.momCallBaby()
Also, it's hard to suggest a better way to do what you're doing, because what you're doing is inherently confusing and probably shouldn't be done.
Inheritance normally means subtyping—that is, Baby should only be a subclass of Mom if every Baby instance is usable as a Mom.1
But a baby is not a mom and a dad.2 A baby has a mom and a dad. And the way to represent that is by giving Baby attributes for its mom and dad:
class Baby(object):
def __init__(self, mom, dad, name):
self.mom, self.dad, self.name = mom, dad, name
def greeting(self):
self.mom.momCallBaby(self.name)
self.dad.dadCallBaby(self.name)
Notice that, e.g., this means that the same woman can be the mom of two babies. Since that's also true of the real-life thing you're modeling here, that's a sign that you're modeling things correctly.
Your "real" example is a little less clear, but I suspect the same thing is going on there.
The only reason you want to use inheritance, as far as I can tell, is:
I only want the user to need to create a single instance of Linux class
You don't need, or want, inheritance for that:
class Linux(object):
def __init__(self):
self.ssh_client = SSHClient()
self.yum = yum.Yum()
# etc.
… but be able to call all the methods from the Parent classes
If yum.Yum, ldap.Ldap and dhcp.Dhcp both have methods named lookup, which one would be called by Linux.lookup?
What you probably want is to just leave the attributes as public attributes, and use them explicitly:
system = Linux()
print(system.yum.lookup(package))
print(system.ldap.lookup(name))
print(system.dhcp.lookup(reservation))
Or you'll want to provide a "Linux API" that wraps all the underlying APIs:
def lookup_package(self, package):
return self.yum.lookup(package)
def lookup_ldap_name(self, name):
return self.ldap.lookup(name)
def lookup_reservation(self, reservation):
return self.dhcp.lookup(reservation)
If you really do want to just forward every method of all of your different components, and you're sure that none of them conflict with each other, and there are way too many to write out manually, you can always do it programmatically, by iterating all of the classes, iterating inspect.getmembers of each one, filtering out the ones that start with _ or aren't unbound methods, creating a proxy function, and setattr-ing it onto Linux.
Or, alternatively (probably not as good an idea in this case, but very commonly useful in cases that aren't that different), you can proxy dynamically, at method lookup time, by implementing a __getattr__ method (and, often, a __dir__ method).
I think one of these two kinds of proxying may be what you're really after here.
1. There are some cases where you want to inherit for reasons other than subtyping. For example, you inherit a mixin class to get implementations for a bunch of methods. The question of whether your class is usable wherever that mixin's instances are usable doesn't really make sense, because the mixin isn't usable anywhere (except as a base class). But the subtyping is still the standard that you're bending there.
2. If it is, call Child Protective Services. And also call Professor X, because that shouldn't be physically possible.
I have a large program with multiple classes.
class Dog(object):
def __init__(self):
self.food = 10
def print_dog_food(self):
print(self.food)
class Cat(object):
def __init__(self):
self.food = 5
def print_cat_food(self):
print(self.food)
They are inherited by other classes:
class AllAnimals(Cat, Dog):
def __init__(self):
Cat.__init__(self)
Dog.__init__(self)
Some variables will be having similar names. I am afraid of accidentally overriding an existing variable and creating nasty bugs:
# Wrong result.
AllAnimals().print_cat_food() # prints 10
AllAnimals().print_dog_food() # prints 10
Keeping track of all those variables to avoid accidents seems impractical, so I was thinking of using mangling:
def __init__(self):
self.__food = ......
and get what i expect:
# Correct result.
AllAnimals().print_cat_food() # prints 5
AllAnimals().print_dog_food() # prints 10
Mangling solves my problem, but reading this highly upvoted answer stating that:
If you want to use it eventually, you can, but it is neither usual nor
recommended.
and the "This is culture" section of that answer makes me sceptical.
Questions:
- Should I use mangling as in the example?
- If not, what are my alternatives to avoid accidental overriding of variables?
Using mangling to produce 'private' attributes is neither usual nor recommended, because there is no such thing as truly private attributes in Python.
Using mangling for what it was designed to do, reduce the likelyhood of subclasses clashing on attribute names, is absolutely pythonic and fine.
From the documentation:
Names in this category, when used within the context of a class definition, are re-written to use a mangled form to help avoid name clashes between “private” attributes of base and derived classes.
Emphasis mine.
However, the use-case is pretty rare; there are not many situations where you want the attribute to not be accidentally overridden. But if you are building base classes that are going to be widely subclassed (in a large project or as part of a framework or library intended for a wide range of uses), then using double-underscore names for implementation details is perfectly fine.
It is unusual, because what you're doing here is unusual. However, given your requirements, I would say that using name-mangled variables is a perfectly appropriate thing to do.
I've a class that has some callbacks and its own interface, something like:
class Service:
def __init__(self):
connect("service_resolved", self.service_resolved)
def service_resolved(self, a,b c):
''' This function is called when it's triggered
service resolved signal and has a lot of parameters'''
the connect function is for example the gtkwidget.connect, but I want that this connection is something more general, so I've decided to use a "twisted like" approach:
class MyService(Service):
def my_on_service_resolved(self, little_param):
''' it's a decorated version of srvice_resolved '''
def service_resolved(self,a,b,c):
super(MyService,self).service_resolved(a,b,c)
little_param = "something that's obtained from a,b,c"
self.my_on_service_resolved(little_param)
So I can use MyService by overriding my_on_service_resolved.
The problem is the "attributes" pollution. In the real implementation, Service has some attributes that can accidentally be overriden in MyService and those who subclass MyService.
How can I avoid attribute pollution?
What I've thought is a "wrapper" like approach but I don't know if it's a good solution:
class WrapperService():
def __init__(self):
self._service = service_resolved
# how to override self._service.service_resolved callback?
def my_on_service_resolved(self,param):
'''
'''
Avoiding accidental clashes with derived classes is the reason the "double-leading-underscore" naming approach exists: if you name an attribute in class Service __foo, the Python compiler will internally "mangle" that name to _Service__foo, making accidental clashes unlikely. Alas, not impossible: a subclass might also be named Service (and live in another module) and also have its own __foo attribute... which would be named-mangled exactly the same way, resulting in a conflict again. Ameliorations include naming base classes BaseService and the like, exploiting the fact that a derived class is very unlikely to be named Basewhatever. But, in the end, there's no alternative to clearly documenting a convention such as this (at least if subclasses are going to be written by programmers with little Python experience).
I don't think the "accidental clash" issue is enough to force you to forego subclassing entirely (in favor of using wrapping exclusively), which is basically the only way to avoid accidental name clashes for certain. The approach you call "twisted-like" (and is actually a case of the Template Method design pattern) is quite viable, and a mix of convention for naming, and documentation of your design choices, in real-life practice proves sufficient to avoid its "clash" risks.
I'm interested in hearing some discussion about class attributes in Python. For example, what is a good use case for class attributes? For the most part, I can not come up with a case where a class attribute is preferable to using a module level attribute. If this is true, then why have them around?
The problem I have with them, is that it is almost too easy to clobber a class attribute value by mistake, and then your "global" value has turned into a local instance attribute.
Feel free to comment on how you would handle the following situations:
Constant values used by a class and/or sub-classes. This may include "magic number" dictionary keys or list indexes that will never change, but possible need one-time initialization.
Default class attribute, that in rare occasions updated for a special instance of the class.
Global data structure used to represent an internal state of a class shared between all instances.
A class that initializes a number of default attributes, not influenced by constructor arguments.
Some Related Posts:
Difference Between Class and Instance Attributes
#4:
I never use class attributes to initialize default instance attributes (the ones you normally put in __init__). For example:
class Obj(object):
def __init__(self):
self.users = 0
and never:
class Obj(object):
users = 0
Why? Because it's inconsistent: it doesn't do what you want when you assign anything but an invariant object:
class Obj(object):
users = []
causes the users list to be shared across all objects, which in this case isn't wanted. It's confusing to split these into class attributes and assignments in __init__ depending on their type, so I always put them all in __init__, which I find clearer anyway.
As for the rest, I generally put class-specific values inside the class. This isn't so much because globals are "evil"--they're not so big a deal as in some languages, because they're still scoped to the module, unless the module itself is too big--but if external code wants to access them, it's handy to have all of the relevant values in one place. For example, in module.py:
class Obj(object):
class Exception(Exception): pass
...
and then:
from module import Obj
try:
o = Obj()
o.go()
except o.Exception:
print "error"
Aside from allowing subclasses to change the value (which isn't always wanted anyway), it means I don't have to laboriously import exception names and a bunch of other stuff needed to use Obj. "from module import Obj, ObjException, ..." gets tiresome quickly.
what is a good use case for class attributes
Case 0. Class methods are just class attributes. This is not just a technical similarity - you can access and modify class methods at runtime by assigning callables to them.
Case 1. A module can easily define several classes. It's reasonable to encapsulate everything about class A into A... and everything about class B into B.... For example,
# module xxx
class X:
MAX_THREADS = 100
...
# main program
from xxx import X
if nthreads < X.MAX_THREADS: ...
Case 2. This class has lots of default attributes which can be modified in an instance. Here the ability to leave attribute to be a 'global default' is a feature, not bug.
class NiceDiff:
"""Formats time difference given in seconds into a form '15 minutes ago'."""
magic = .249
pattern = 'in {0}', 'right now', '{0} ago'
divisions = 1
# there are more default attributes
One creates instance of NiceDiff to use the existing or slightly modified formatting, but a localizer to a different language subclasses the class to implement some functions in a fundamentally different way and redefine constants:
class Разница(NiceDiff): # NiceDiff localized to Russian
'''Из разницы во времени, типа -300, делает конкретно '5 минут назад'.'''
pattern = 'через {0}', 'прям щас', '{0} назад'
Your cases:
constants -- yes, I put them to class. It's strange to say self.CONSTANT = ..., so I don't see a big risk for clobbering them.
Default attribute -- mixed, as above may go to class, but may also go to __init__ depending on the semantics.
Global data structure --- goes to class if used only by the class, but may also go to module, in either case must be very well-documented.
Class attributes are often used to allow overriding defaults in subclasses. For example, BaseHTTPRequestHandler has class constants sys_version and server_version, the latter defaulting to "BaseHTTP/" + __version__. SimpleHTTPRequestHandler overrides server_version to "SimpleHTTP/" + __version__.
Encapsulation is a good principle: when an attribute is inside the class it pertains to instead of being in the global scope, this gives additional information to people reading the code.
In your situations 1-4, I would thus avoid globals as much as I can, and prefer using class attributes, which allow one to benefit from encapsulation.