I've defined this class:
class RequiredFormSet(BaseFormSet):
def __init__(self, *args, **kwargs):
super(RequiredFormSet, self).__init__(*args, **kwargs)
And overridden this method:
def total_form_count(self):
return self._total_form_count
It so happens that super(...).__init__ uses total_form_count() somewhere in that function. It's calling my function rather than the one defined in the base class.
I guess I thought because I called super() it would use its own stuff, but apparently in Python that's not true. Is this the way it works in other languages, like C#? If I call the base constructor, it will still call all the derived functions from there?
Yes, this is typical OOP behavior (polymorphism) to have subclass methods invoked by dynamic dispatch. This is part of the reason why C# requires the programmer to mark an overridable method as virtual. I'm sure you're familiar with this notion in general, and the surprise mainly comes from the fact that this is happening in a constructor.
As you have observed, this can be very dangerous in constructors because a superclass's constructor can invoke a subclass's method that may rely on properties initialized in the subclass's constructor. This problem is explicitly noted in Effective Java, and you can read more about it here: What's wrong with overridable method calls in constructors?
This is normal behavior. Note the first argument: self. That's a reference to the object the methods are being called on, so even when you call a superclass method, any overridden methods that method calls will be the subclass methods.
The only way I know of to force it to use a superclass method is with an unbound reference, ie SuperClass.overridenMethod(self, param1, param2)...
This is normal behaviour in python, C# has the same behaviour (for virtual functions), C++ has not (some people consider that as a design flaw of C++). In C++, it makes a difference if you call an overriden virtual function from the constructor or from another member function. The reason for that is that when the constructor of a superclass runs, the V-table is not complete.
Have you heard of polymorphism? If not, you have no idea what OOP is about and should look it up.
The self the base class constructor uses is of course an instance of the derived class (the same self), so when it calls self.m(), the implementation of m is dispatched dynamically. Some OO languages require explicit annotation of methods that are dispatched dynamically (virtual keyword) - although (as #Doc Brown pointed out) it doesn't work in constructors specifically in C++ - while others make it the default. Anyway, polymorphism is an essential part of OOP and although it's possible in some languages to get static dispatch, polymorphism is the only option in many languages and the generally preferred way in all others. So yes, this is the normal behaviour.
In C++, you can choose between the two behaviors based on whether total_form_count is declared virtual or not. But in Python, all methods behave like virtual: the object will always use the methods from the actual type of the object.
In C++ the constructor is a kind of special case, the calls to virtual methods result in calling the methods of the class itself (not the one at the leaf of the virtual chain).
Yes, it's a behaviour called polymorphism. Method lookup in every OOP language is done from the class of the object instance, not the one where the code resides.
Related
I'm quite new to python, and could not understand what is static method in python(for example __new__()) and what does it do. Can anyone possibly explain it? Thanks a million
Have you already read this?
https://en.wikipedia.org/wiki/Method_(computer_programming)
Especially this?
https://en.wikipedia.org/wiki/Method_(computer_programming)#Static_methods
Explanation
In OOP you define classes that you later on instantiate. A class is nothing more than a blueprint: Once you instantiate objects from a class your object will follow exactly the blueprint of your class. That means: If you define a field named "abc" in your class you will later on have a field "abc" in your object. If you define a method "foo()" in your class, you will later on have a method "foo()" to be invoked on your object.
Please note that this "on your object" is essential: You always instantiate a class and then you can invoke the method. This is the "normal" way.
A static method is different. While a normal method always requires to have an instance (where you then can invoke this method at) a static method does not. A static method exists independently from your instances (that's why it is named "static"). So a static method is associated with your class definition itself and therefore is always there and therefore can be invoked only at your class itself. It is completely independent from all instances.
That's a static method.
Python's implementation is a bit ... well ... simple. In details there are deviations from this description above. But that does not make any difference: To be in line with OOP concepts you always should use methods exactly as described above.
Example
Let's give you an example:
class FooBar:
def someMethod(self):
print("abc")
This is a regular (instance) method. You use it like this:
myObj = FooBar()
myObj.someMethod()
If you have ...
myObjB = FooBar()
myObjB.someMethod()
... you have an additional instance and therefore invoking someMethod() on this second instance will be the invocation of a second someMethod() method - defined at the second object. This is because you instantiate objects before use so all instances follow the blueprint FooBar defined. All instances therefore receive some kind of copy of someMethod().
(In practice Python will use optimizations internally, so there actually is only one piece of code that implements your someMethod() in memory, but forget about this for now. To a programmer it appears as that every instance of a class will have a copy of the method someMethod(). And that's the level of abstraction that is relevant to us as this is the "surface" we work on. Deep within the implementation of a programming or script language things might be a bit different but this is not very relevant.)
Let's have a look at a static method:
class FooBar:
#staticmethod
def someStaticMethod():
print("abc")
Such static methods can be invoked like this:
FooBar.someStaticMethod()
As you can see: No instance. You directly invoke this method in the context of the class itself. While regular methods work on the particular instance itself - they typically modify data within this instance itself - a class method does not. It could modify static (!) data, but typically it does not anyway.
Consider a static method a special case. It is rarely needed. What you typically want if you write code is not to implement a static method. Only in very specific situations it makes sense to implement a static method.
The self parameter
Please note that a standard "instance" method always must have self as a first argument. This is a python specific. In the real world Python will (of course!) store your method only once in memory, even if you instantiate thousands of objects of your class. Consider this an optimization. If you then invoke your method on one of your thousands of instances, always the same single piece of code is called. But for it to distinguish on which particular object the code of the method should work on your instance is passed to this internally stored piece of code as the very first argument. This is the self argument. It is some kind of implicit argument and always needed for regular (instance) methods. (Not: static methods - there you don't need an instance to invoke them).
As this argument is implicit and always needed most programming languages hide it to the programmer (and handle this internally - under the hood - in the correct way). It does not really make much sense to expose this special argument anyway.
Unfortunately Python does not follow this principle. Python does not hide this argument which is implicitly required. (Python's incorporation of OOP concepts is a bit ... simple.) Therefore you see self almost everywhere in methods. In your mind you can ignore it, but you need to write it explicitly if you define your own classes. (Which is something you should do in order to structure your programs in a good way.)
The static method __new__()
Python is quite special. While regular programming languages follow a strict and immutable concept of how to create instances of particular classes, Python is a bit different here. This behavior can be changed. This behavior is implemented in __new__(). So if you do this ...
myObj = FooBar()
... Python implicitly invokes FooBar.__new__() which in turn invokes a constructor-like (instance) method named __init__() that you could (!) define in your class (as an instance method) and then returns the fully initialized instance. This instance is then what is stored in myObj in this example her.
You could modify this behavior if you want. But this would requires a very very very particularly unusual use case. You will likely never have anything to do with __new__() itself in your entire work with Python. My advice: If you're somehow new to Python just ignore it.
Related: Python object conversion
I recently learned that Python allows you to change an instance's class like so:
class Robe:
pass
class Dress:
pass
r = Robe()
r.__class__ = Dress
I'm trying to figure out whether there is a case where 'transmuting' an object like this can be useful. I've messed around with this in IDLE, and one thing I've noticed is that assigning a different class doesn't call the new class's __init__ method, though this can be done explicitly if needed.
Virtually every use case I can think of would be better served by composition, but I'm a coding newb so what do I know. ;)
There is rarely a good reason to do this for unrelated classes, like Robe and Dress in your example. Without a bit of work, it's hard to ensure that the object you get in the end is in a sane state.
However, it can be useful when inheriting from a base class, if you want to use a non-standard factory function or constructor to build the base object. Here's an example:
class Base(object):
pass
def base_factory():
return Base() # in real code, this would probably be something opaque
def Derived(Base):
def __new__(cls):
self = base_factory() # get an instance of Base
self.__class__ = Derived # and turn it into an instance of Derived
return self
In this example, the Derived class's __new__ method wants to construct its object using the base_factory method which returns an instance of the Base class. Often this sort of factory is in a library somewhere, and you can't know for certain how it's making the object (you can't just call Base() or super(Derived, cls).__new__(cls) yourself to get the same result).
The instance's __class__ attribute is rewritten so that the result of calling Derived.__new__ will be an instance of the Derived class, which ensures that it will have the Derived.__init__ method called on it (if such a method exists).
I remember using this technique ages ago to “upgrade” existing objects after recognizing what kind of data they hold. It was a part of an experimental XMPP client. XMPP uses many short XML messages (“stanzas”) for communication.
When the application received a stanza, it was parsed into a DOM tree. Then the application needed to recognize what kind of stanza it is (a presence stanza, message, automated query etc.). If, for example, it was recognized as a message stanza, the DOM object was “upgraded” to a subclass that provided methods like “get_author”, “get_body” etc.
I could of course just make a new class to represent a parsed message, make new object of that class and copy the relevant data from the original XML DOM object. There were two benefits of changing object's class in-place, though. Firstly, XMPP is a very extensible standard, and it was useful to still have an easy access to the original DOM object in case some other part of the code found something useful there, or while debugging. Secondly, profiling the code told me that creating a new object and explicitly copying data is much slower than just reusing the object that would be quickly destroyed anyway—the difference was enough to matter in XMPP, which uses many short messages.
I don't think any of these reasons justifies the use of this technique in production code, unless maybe you really need the (not that big) speedup in CPython. It's just a hack which I found useful to make code a bit shorter and faster in the experimental application. Note also that this technique will easily break JIT engines in non-CPython implementations, making the code much slower!
I have a python class and ran pylint against it. One message it gave was:
Warning: Method could be a function
Is this telling me that it would be better to move this method out of the class because it doesn't use any instance variables?
In C# I would make this a static method. What's the most pythonic thing to do here?
Moving it to a function is common, if it doesn't touch the class at all.
If it manipulates class attributes, use the classmethod decorator:
#classmethod
def spam(cls, ...):
# cls is the class, you can use it to get class attributes
classmethod and staticmethod (which is the same as the former, except that the method doesn't get a reference to the class from its first parameter) have been introduced quite recently.
It means that some Python programmers are used to avoid static and class methods.
Some hardcore Python programmers will tell you that these decorators just complicate things; some other people (usually former C# or Java programmers) will tell you that using a function isn't object-oriented enough.
I think it's just a matter of preference.
Python has the idea of metaclasses that, if I understand correctly, allow you to modify an object of a class at the moment of construction. You are not modifying the class, but instead the object that is to be created then initialized.
Python (at least as of 3.0 I believe) also has the idea of class decorators. Again if I understand correctly, class decorators allow the modifying of the class definition at the moment it is being declared.
Now I believe there is an equivalent feature or features to the class decorator in Ruby, but I'm currently unaware of something equivalent to metaclasses. I'm sure you can easily pump any Ruby object through some functions and do what you will to it, but is there a feature in the language that sets that up like metaclasses do?
So again, Does Ruby have something similar to Python's metaclasses?
Edit I was off on the metaclasses for Python. A metaclass and a class decorator do very similar things it appears. They both modify the class when it is defined but in different manners. Hopefully a Python guru will come in and explain better on these features in Python.
But a class or the parent of a class can implement a __new__(cls[,..]) function that does customize the construction of the object before it is initialized with __init__(self[,..]).
Edit This question is mostly for discussion and learning about how the two languages compare in these features. I'm familiar with Python but not Ruby and was curious. Hopefully anyone else who has the same question about the two languages will find this post helpful and enlightening.
Ruby doesn't have metaclasses. There are some constructs in Ruby which some people sometimes wrongly call metaclasses but they aren't (which is a source of endless confusion).
However, there's a lot of ways to achieve the same results in Ruby that you would do with metaclasses. But without telling us what exactly you want to do, there's no telling what those mechanisms might be.
In short:
Ruby doesn't have metaclasses
Ruby doesn't have any one construct that corresponds to Python's metaclasses
Everything that Python can do with metaclasses can also be done in Ruby
But there is no single construct, you will use different constructs depending on what exactly you want to do
Any one of those constructs probably has other features as well that do not correspond to metaclasses (although they probably correspond to something else in Python)
While you can do anything in Ruby that you can do with metaclasses in Python, it might not necessarily be straightforward
Although often there will be a more Rubyish solution that is elegant
Last but not least: while you can do anything in Ruby that you can do with metaclasses in Python, doing it might not necessarily be The Ruby Way
So, what are metaclasses exactly? Well, they are classes of classes. So, let's take a step back: what are classes exactly?
Classes …
are factories for objects
define the behavior of objects
define on a metaphysical level what it means to be an instance of the class
For example, the Array class produces array objects, defines the behavior of arrays and defines what "array-ness" means.
Back to metaclasses.
Metaclasses …
are factories for classes
define the behavior of classes
define on a metaphysical level what it means to be a class
In Ruby, those three responsibilities are split across three different places:
the Class class creates classes and defines a little bit of the behavior
the individual class's eigenclass defines a little bit of the behavior of the class
the concept of "classness" is hardwired into the interpreter, which also implements the bulk of the behavior (for example, you cannot inherit from Class to create a new kind of class that looks up methods differently, or something like that – the method lookup algorithm is hardwired into the interpreter)
So, those three things together play the role of metaclasses, but neither one of those is a metaclass (each one only implements a small part of what a metaclass does), nor is the sum of those the metaclass (because they do much more than that).
Unfortunately, some people call eigenclasses of classes metaclasses. (Until recently, I was one of those misguided souls, until I finally saw the light.) Other people call all eigenclasses metaclasses. (Unfortunately, one of those people is the author of one the most popular tutorials on Ruby metaprogramming and the Ruby object model.) Some popular libraries add a metaclass method to Object that returns the object's eigenclass (e.g. ActiveSupport, Facets, metaid). Some people call all virtual classes (i.e. eigenclasses and include classes) metaclasses. Some people call Class the metaclass. Even within the Ruby source code itself, the word "metaclass" is used to refer to things that are not metaclasses.
Your updated question looks quite different now. If I understand you correctly, you want to hook into object allocation and initialization, which has absolutely nothing whatsoever to do with metaclasses. (But you still don't write what it is that you actually want to do, so I might still be off.)
In some object-oriented languages, objects are created by constructors. However, Ruby doesn't have constructors. Constructors are just factory methods (with stupid restrictions); there is no reason to have them in a well-designed language, if you can just use a (more powerful) factory method instead.
Object construction in Ruby works like this: object construction is split into two phases, allocation and initialization. Allocation is done by a public class method called allocate, which is defined as an instance method of class Class and is generally never overriden. (In fact, I don't think you actually can override it.) It just allocates the memory space for the object and sets up a few pointers, however, the object is not really usable at this point.
That's where the initializer comes in: it is an instance method called initialize, which sets up the object's internal state and brings it into a consistent, fully defined state which can be used by other objects.
So, in order to fully create a new object, what you need to do is this:
x = X.allocate
x.initialize
[Note: Objective-C programmers may recognize this.]
However, because it is too easy to forget to call initialize and as a general rule an object should be fully valid after construction, there is a convenience factory method called Class#new, which does all that work for you and looks something like this:
class Class
def new(*args, &block)
obj = allocate
obj.initialize(*args, &block)
return obj
end
end
[Note: actually, initialize is private, so reflection has to be used to circumvent the access restrictions like this: obj.send(:initialize, *args, &block)]
That, by the way, is the reason why to construct an object you call a public class method Foo.new but you implement a private instance method Foo#initialize, which seems to trip up a lot of newcomers.
However, none of this is in any way baked into the language. The fact that the primary factory method for any class is usually called new is just a convention (and sometimes I wish it were different, because it looks similar to constructors in Java, but is completely different). In other languages, the constructor must have a specific name. In Java, it must have the same name as the class, which means that a) there can be only one constructor and b) anonymous classes can't have constructors because they don't have names. In Python, the factory method must be called __new__, which again means there can be only one. (In both Java and Python, you can of course have different factory methods, but calling them looks different from calling the default, while in Ruby (and Smalltalk from whence this pattern originated) it looks just the same.)
In Ruby, there can be as many factory methods as you like, with any name you like, and a factory method can have many different names. (For collection classes, for example, the factory method is often aliased to [], which allows you to write List[1, 2, 3] instead of List.new(1, 2, 3) which ends looking more like an array, thus emphasizing the collection-ish nature of lists.)
In short:
the standardized factory method is Foo.new, but it can be anything
Foo.new calls allocate to allocate memory for an empty object foo
Foo.new then calls foo.initialize, i.e. the Foo#initialize instance method
all three of those are just methods like any other, which you can undefine, redefine, override, wrap, alias and whatnot
well, except allocate which needs to allocate memory inside the Ruby runtime which you can't really do from Ruby
In Python, __new__ roughly corresponds to both new and allocate in Ruby, and __init__ exactly corresponds to initialize in Ruby. The main difference is that in Ruby, new calls initialize whereas in Python, the runtime automatically calls __init__ after __new__.
For example, here is a class which only allows a maximum of 2 instances created:
class Foo
def self.new(*args, &block)
#instances ||= 0
raise 'Too many instances!' if #instances >= 2
obj = allocate
obj.send(:initialize, *args, &block)
#instances += 1
return obj
end
attr_reader :name
def initialize(name)
#name = name
end
end
one = Foo.new('#1')
two = Foo.new('#2')
puts two.name # => #2
three = Foo.new('#3') # => RuntimeError: Too many instances!
I have some Python code that creates a Calendar object based on parsed VEvent objects from and iCalendar file.
The calendar object just has a method that adds events as they get parsed.
Now I want to create a factory function that creates a calendar from a file object, path, or URL.
I've been using the iCalendar python module, which implements a factory function as a class method directly on the Class that it returns an instance of:
cal = icalendar.Calendar.from_string(data)
From what little I know about Java, this is a common pattern in Java code, though I seem to find more references to a factory method being on a different class than the class you actually want to instantiate instances from.
The question is, is this also considered Pythonic ? Or is it considered more pythonic to just create a module-level method as the factory function ?
[Note. Be very cautious about separating "Calendar" a collection of events, and "Event" - a single event on a calendar. In your question, it seems like there could be some confusion.]
There are many variations on the Factory design pattern.
A stand-alone convenience function (e.g., calendarMaker(data))
A separate class (e.g., CalendarParser) which builds your target class (Calendar).
A class-level method (e.g. Calendar.from_string) method.
These have different purposes. All are Pythonic, the questions are "what do you mean?" and "what's likely to change?" Meaning is everything; change is important.
Convenience functions are Pythonic. Languages like Java can't have free-floating functions; you must wrap a lonely function in a class. Python allows you to have a lonely function without the overhead of a class. A function is relevant when your constructor has no state changes or alternate strategies or any memory of previous actions.
Sometimes folks will define a class and then provide a convenience function that makes an instance of the class, sets the usual parameters for state and strategy and any other configuration, and then calls the single relevant method of the class. This gives you both the statefulness of class plus the flexibility of a stand-alone function.
The class-level method pattern is used, but it has limitations. One, it's forced to rely on class-level variables. Since these can be confusing, a complex constructor as a static method runs into problems when you need to add features (like statefulness or alternative strategies.) Be sure you're never going to expand the static method.
Two, it's more-or-less irrelevant to the rest of the class methods and attributes. This kind of from_string is just one of many alternative encodings for your Calendar objects. You might have a from_xml, from_JSON, from_YAML and on and on. None of this has the least relevance to what a Calendar IS or what it DOES. These methods are all about how a Calendar is encoded for transmission.
What you'll see in the mature Python libraries is that factories are separate from the things they create. Encoding (as strings, XML, JSON, YAML) is subject to a great deal of more-or-less random change. The essential thing, however, rarely changes.
Separate the two concerns. Keep encoding and representation as far away from state and behavior as you can.
It's pythonic not to think about esoteric difference in some pattern you read somewhere and now want to use everywhere, like the factory pattern.
Most of the time you would think of a #staticmethod as a solution it's probably better to use a module function, except when you stuff multiple classes in one module and each has a different implementation of the same interface, then it's better to use a #staticmethod
Ultimately weather you create your instances by a #staticmethod or by module function makes little difference.
I'd probably use the initializer ( __init__ ) of a class because one of the more accepted "patterns" in python is that the factory for a class is the class initialization.
IMHO a module-level method is a cleaner solution. It hides behind the Python module system that gives it a unique namespace prefix, something the "factory pattern" is commonly used for.
The factory pattern has its own strengths and weaknesses. However, choosing one way to create instances usually has little pragmatic effect on your code.
A staticmethod rarely has value, but a classmethod may be useful. It depends on what you want the class and the factory function to actually do.
A factory function in a module would always make an instance of the 'right' type (where 'right' in your case is the 'Calendar' class always, but you might also make it dependant on the contents of what it is creating the instance out of.)
Use a classmethod if you wish to make it dependant not on the data, but on the class you call it on. A classmethod is like a staticmethod in that you can call it on the class, without an instance, but it receives the class it was called on as first argument. This allows you to actually create an instance of that class, which may be a subclass of the original class. An example of a classmethod is dict.fromkeys(), which creates a dict from a list of keys and a single value (defaulting to None.) Because it's a classmethod, when you subclass dict you get the 'fromkeys' method entirely for free. Here's an example of how one could write dict.fromkeys() oneself:
class dict_with_fromkeys(dict):
#classmethod
def fromkeys(cls, keys, value=None):
self = cls()
for key in keys:
self[key] = value
return self