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Using "new" style classes (I'm in python 3.2) is there a way to split a class over multiple files? I've got a large class (which really should be a single class from an object-oriented design perspective, considering coupling, etc, but it'd be nice to split over a few files just for ease of editing the class.
If your problem really is just working with a large class in an editor, the first solution I'd actually look for is a better way to break down the problem. The second solution would be a better editor, preferably one with code folding.
That said, there are a couple of ways you might break up a class into multiple files. Python lets you use a folder as a module by putting an __init__.py in it, which can then import things from other files. We'll use this capability in each solution. Make a folder called, say, bigclass first.
In the folder put the various .py files that will eventually comprise your class. Each should contain functions and variable definitions for the eventual class, not classes. In __init__.py in the same folder write the following to join them all together.
class Bigclass(object):
from classdef1 import foo, bar, baz, quux
from classdef2 import thing1, thing2
from classdef3 import magic, moremagic
# unfortunately, "from classdefn import *" is an error or warning
num = 42 # add more members here if you like
This has the advantage that you end up with a single class derived directly from object, which will look nice in your inheritance graphs.
You could use multiple inheritance to combine the various parts of your class. In your individual modules you would write a class definition for Bigclass with parts of the class. Then in your __init__.py write:
import classdef1, classdef2, classdef3
class Bigclass(classdef1.Bigclass, classdef2.Bigclass, classdef3.Bigclass):
num = 42 # add more members if desired
If the multiple inheritance becomes an issue, you can use single inheritance: just have each class inherit from another one in chain fashion. Assuming you don't define anything in more than one class, the order doesn't matter. For example, classdef2.py would be like:
import classdef1
class Bigclass(classdef1.Bigclass):
# more member defs here
classdef3 would import Bigclass from classdef2 and add to it, and so on. Your __init__.py would just import the last one:
from classdef42 import Bigclass
I'd generally prefer #1 because it's more explicit about what members you're importing from which files but any of these solutions could work for you.
To use the class in any of these scenarios you can just import it, using the folder name as the module name: from bigclass import Bigclass
You can do this with decorators like so:
class Car(object):
def start(self):
print 'Car has started'
def extends(klass):
def decorator(func):
setattr(klass, func.__name__, func)
return func
return decorator
#this can go in a different module/file
#extends(Car)
def do_start(self):
self.start()
#so can this
car = Car()
car.do_start()
#=> Car has started
Class definitions containing hundreds of lines do occur "in the wild" (I have seen some in popular open-source Python-based frameworks), but I believe that if you ponder what the methods are doing, it will be possible to reduce the length of most classes to a manageable point. Some examples:
Look for places where mostly the same code occurs more than once. Break that code out into its own method and call it from each place with arguments.
"Private" methods that do not use any of the object state can be brought out of the class as stand-alone functions.
Methods that should be called only under certain conditions may indicate a need to place those methods in a subclass.
To directly address your question, it is possible to split up the definition of a class. One way is to "monkey-patch" the class by defining it and then adding outside functions to it as methods. Another is to use the built-in type function to create the class "by hand", supplying its name, any base classes, and its methods and attributes in a dictionary. But I do not recommend doing this just because the definition would be long otherwise. That sort of cure is worse than the disease in my opinion.
I've previously toyed around with something similar. My usecase was a class hierarchy of nodes in an abstract syntax tree, and then I wanted to put all e.g. prettyprinting functions in a separate prettyprint.py file but still have them as methods in the classes.
One thing I tried was to use a decorator that puts the decorated function as an attribute on a specified class. In my case this would mean that prettyprint.py contains lots of def prettyprint(self) all decorated with different #inclass(...)
A problem with this is that one must make sure that the sub files are always imported, and that they depend on the main class, which makes for a circular dependency, which may be messy.
def inclass(kls):
"""
Decorator that adds the decorated function
as a method in specified class
"""
def _(func):
setattr(kls,func.__name__, func)
return func
return _
## exampe usage
class C:
def __init__(self, d):
self.d = d
# this would be in a separate file.
#inclass(C)
def meth(self, a):
"""Some method"""
print "attribute: %s - argument: %s" % (self.d, a)
i = C(10)
print i.meth.__doc__
i.meth(20)
I've not used it, but this package called partial claims to add support for partial classes.
It seems like there's a few other ways you could implement this yourself as well.
You could implement separate parts of the class as mixins in seperate files, then import them all somewhere and subclass them.
Alternatively, you could implement each of the methods of your class somewhere then in a central file import them and assign them as attributes on a class, to create the whole object. Like so:
a.py:
def AFunc( self, something ):
# Do something
pass
b.py:
def BFunc( self, something ):
# Do something else
pass
c.py:
import a, b
class C:
AFunc = a.AFunc
BFunc = b.BFunc
You could even go so far as to automate this process if you really wanted - loop through all the functions provided by modules a and b and then add them as attributes on C. Though that might be total overkill.
There might be other (possibly better) ways to go about it, but those are the 2 that popped into mind.
I would like to add that the pythonic way of doing this is through multiple inheritance, not necessarily using mixins. Instance attributes can be added using super().__init__(*args, **kwargs) in __init__ calls to pass arguments to baseclasses (see ‘super considered super’ presentation by Raymond Hettinger 1). This also enables dependency injection and kind of forces you to think about organization of base classes (it works best if only one baseclass sets an attribute in __init__ and all classes using the attribute inherit from it).
This does usually require you having control over the base classes (or they being written for this pattern).
Another option is using descriptors returning functions through __get__ to add functionality to classes in a decoupled way.
You could also look at __init_subclass__ to add e.g. methods to classes during class generation (i think added in python 3.6, but check)
First I'd like to say that something this complicated it probably not a good idea just to make finding your place in the class easier - it would be best to add comments, highlight sections etc. However, I see two ways you could do this:
Write the class in several files, then read them in as text, concatenate them and exec the resulting string.
Create a separate class in each file, then inherit them all into a master class as mixins. However, if you're subclassing another class already this could lead to MRO problems. You could get around this by creating a metaclass for your master class which manually resolves the MRO, but this could get messy.
The easiest would be the first option.
First off, I don't see how splitting the class into multiple files makes editing any easier. A decent IDE should be able to find any method easily whether in one file or multiple; if you're not using a decent IDE, splitting the class means the maintainer has to guess which file a given method is in, which sounds harder rather than easier.
More fundamentally, this class - so large that you want a special language feature just to support its weight - sounds fundamentally broken. How many lines of code are we talking about? Almost certainly, it would be a better idea to do one of:
Refactor duplicated code into fewer, more general primitives
Define a base class and extend it with subclasses as Karoly Horvath suggests in comments (this is the closest thing to the 'partial classes' that you're asking for that I would endorse)
Define a few separate classes to encapsulate different parts of this
class's functionality, and compose this class of instances of those
smaller ones.
I met the same situation - I want to slipt my class to 2 files.
the reason is that - I want part 1 for GUI layout, only layout
and another file keeps all function.
like c#'s Partial class. one for XAML and another one for functions.
What i have is for each instance of a class a different method.So what i want is when i make a new intance of that class to be somehow able to choose which method this instance will call.I am a complete newbie in python and i wonder if i could have a list of methods and each instance to call a specific one from this list.Is that even possible?
I did some search and found the http://docs.python.org/2/library/inspect.html (inspect module) but i got confused.
In python, functions are first class objects. So, you can directly pass a function as argument. If you have defined functions f(x), g(x), h(x), you can create a class method
def set_function(self, external_function):
self.F = external_function
You can then use object.F(x), as if it had been defined inside the class.
However, object belonging to the same class having different methods is bad design. If objects of the same class have different behavior, they should probably belong to different classes to begin with. A better approach would be to subclass the original class, define the different functions inside the subclasses, and then instantiate the corresponding objects.
Using "new" style classes (I'm in python 3.2) is there a way to split a class over multiple files? I've got a large class (which really should be a single class from an object-oriented design perspective, considering coupling, etc, but it'd be nice to split over a few files just for ease of editing the class.
If your problem really is just working with a large class in an editor, the first solution I'd actually look for is a better way to break down the problem. The second solution would be a better editor, preferably one with code folding.
That said, there are a couple of ways you might break up a class into multiple files. Python lets you use a folder as a module by putting an __init__.py in it, which can then import things from other files. We'll use this capability in each solution. Make a folder called, say, bigclass first.
In the folder put the various .py files that will eventually comprise your class. Each should contain functions and variable definitions for the eventual class, not classes. In __init__.py in the same folder write the following to join them all together.
class Bigclass(object):
from classdef1 import foo, bar, baz, quux
from classdef2 import thing1, thing2
from classdef3 import magic, moremagic
# unfortunately, "from classdefn import *" is an error or warning
num = 42 # add more members here if you like
This has the advantage that you end up with a single class derived directly from object, which will look nice in your inheritance graphs.
You could use multiple inheritance to combine the various parts of your class. In your individual modules you would write a class definition for Bigclass with parts of the class. Then in your __init__.py write:
import classdef1, classdef2, classdef3
class Bigclass(classdef1.Bigclass, classdef2.Bigclass, classdef3.Bigclass):
num = 42 # add more members if desired
If the multiple inheritance becomes an issue, you can use single inheritance: just have each class inherit from another one in chain fashion. Assuming you don't define anything in more than one class, the order doesn't matter. For example, classdef2.py would be like:
import classdef1
class Bigclass(classdef1.Bigclass):
# more member defs here
classdef3 would import Bigclass from classdef2 and add to it, and so on. Your __init__.py would just import the last one:
from classdef42 import Bigclass
I'd generally prefer #1 because it's more explicit about what members you're importing from which files but any of these solutions could work for you.
To use the class in any of these scenarios you can just import it, using the folder name as the module name: from bigclass import Bigclass
You can do this with decorators like so:
class Car(object):
def start(self):
print 'Car has started'
def extends(klass):
def decorator(func):
setattr(klass, func.__name__, func)
return func
return decorator
#this can go in a different module/file
#extends(Car)
def do_start(self):
self.start()
#so can this
car = Car()
car.do_start()
#=> Car has started
Class definitions containing hundreds of lines do occur "in the wild" (I have seen some in popular open-source Python-based frameworks), but I believe that if you ponder what the methods are doing, it will be possible to reduce the length of most classes to a manageable point. Some examples:
Look for places where mostly the same code occurs more than once. Break that code out into its own method and call it from each place with arguments.
"Private" methods that do not use any of the object state can be brought out of the class as stand-alone functions.
Methods that should be called only under certain conditions may indicate a need to place those methods in a subclass.
To directly address your question, it is possible to split up the definition of a class. One way is to "monkey-patch" the class by defining it and then adding outside functions to it as methods. Another is to use the built-in type function to create the class "by hand", supplying its name, any base classes, and its methods and attributes in a dictionary. But I do not recommend doing this just because the definition would be long otherwise. That sort of cure is worse than the disease in my opinion.
I've previously toyed around with something similar. My usecase was a class hierarchy of nodes in an abstract syntax tree, and then I wanted to put all e.g. prettyprinting functions in a separate prettyprint.py file but still have them as methods in the classes.
One thing I tried was to use a decorator that puts the decorated function as an attribute on a specified class. In my case this would mean that prettyprint.py contains lots of def prettyprint(self) all decorated with different #inclass(...)
A problem with this is that one must make sure that the sub files are always imported, and that they depend on the main class, which makes for a circular dependency, which may be messy.
def inclass(kls):
"""
Decorator that adds the decorated function
as a method in specified class
"""
def _(func):
setattr(kls,func.__name__, func)
return func
return _
## exampe usage
class C:
def __init__(self, d):
self.d = d
# this would be in a separate file.
#inclass(C)
def meth(self, a):
"""Some method"""
print "attribute: %s - argument: %s" % (self.d, a)
i = C(10)
print i.meth.__doc__
i.meth(20)
I've not used it, but this package called partial claims to add support for partial classes.
It seems like there's a few other ways you could implement this yourself as well.
You could implement separate parts of the class as mixins in seperate files, then import them all somewhere and subclass them.
Alternatively, you could implement each of the methods of your class somewhere then in a central file import them and assign them as attributes on a class, to create the whole object. Like so:
a.py:
def AFunc( self, something ):
# Do something
pass
b.py:
def BFunc( self, something ):
# Do something else
pass
c.py:
import a, b
class C:
AFunc = a.AFunc
BFunc = b.BFunc
You could even go so far as to automate this process if you really wanted - loop through all the functions provided by modules a and b and then add them as attributes on C. Though that might be total overkill.
There might be other (possibly better) ways to go about it, but those are the 2 that popped into mind.
I would like to add that the pythonic way of doing this is through multiple inheritance, not necessarily using mixins. Instance attributes can be added using super().__init__(*args, **kwargs) in __init__ calls to pass arguments to baseclasses (see ‘super considered super’ presentation by Raymond Hettinger 1). This also enables dependency injection and kind of forces you to think about organization of base classes (it works best if only one baseclass sets an attribute in __init__ and all classes using the attribute inherit from it).
This does usually require you having control over the base classes (or they being written for this pattern).
Another option is using descriptors returning functions through __get__ to add functionality to classes in a decoupled way.
You could also look at __init_subclass__ to add e.g. methods to classes during class generation (i think added in python 3.6, but check)
First I'd like to say that something this complicated it probably not a good idea just to make finding your place in the class easier - it would be best to add comments, highlight sections etc. However, I see two ways you could do this:
Write the class in several files, then read them in as text, concatenate them and exec the resulting string.
Create a separate class in each file, then inherit them all into a master class as mixins. However, if you're subclassing another class already this could lead to MRO problems. You could get around this by creating a metaclass for your master class which manually resolves the MRO, but this could get messy.
The easiest would be the first option.
First off, I don't see how splitting the class into multiple files makes editing any easier. A decent IDE should be able to find any method easily whether in one file or multiple; if you're not using a decent IDE, splitting the class means the maintainer has to guess which file a given method is in, which sounds harder rather than easier.
More fundamentally, this class - so large that you want a special language feature just to support its weight - sounds fundamentally broken. How many lines of code are we talking about? Almost certainly, it would be a better idea to do one of:
Refactor duplicated code into fewer, more general primitives
Define a base class and extend it with subclasses as Karoly Horvath suggests in comments (this is the closest thing to the 'partial classes' that you're asking for that I would endorse)
Define a few separate classes to encapsulate different parts of this
class's functionality, and compose this class of instances of those
smaller ones.
I met the same situation - I want to slipt my class to 2 files.
the reason is that - I want part 1 for GUI layout, only layout
and another file keeps all function.
like c#'s Partial class. one for XAML and another one for functions.
I've been reading lots of previous SO discussions of factory functions, etc. and still don't know what the best (pythonic) approach is to this particular situation. I'll admit up front that i am imposing a somewhat artificial constraint on the problem in that i want my solution to work without modifying the module i am trying to extend: i could make modifications to it, but let's assume that it must remain as-is because i'm trying to understand best practice in this situation.
I'm working with the http://pypi.python.org/pypi/icalendar module, which handles parsing from and serializing to the Icalendar spec (hereafter ical). It parses the text into a hierarchy of dictionary-like "component" objects, where every "component" is an instance of a trivial derived class implementing the different valid ical types (VCALENDAR, VEVENT, etc.) and they are all spit out by a recursive factory from the common parent class:
class Component(...):
#classmethod
def from_ical(cls, ...)
I have created a 'CalendarFile' class that extends the ical 'Calendar' class, including in it generator function of its own:
class CalendarFile(Calendar):
#classmethod
def from_file(cls, ics):
which opens a file (ics) and passes it on:
instance = cls.from_ical(f.read())
It initializes and modifies some other things in instance and then returns it. The problem is that instance ends up being a Calendar object instead of a CalendarFile object, in spite of cls being CalendarFile. Short of going into the factory function of the ical module and fiddling around in there, is there any way to essentially "recast" that object as a 'CalendarFile'?
The alternatives (again without modifying the original module) that I have considered are:make the CalendarFile class a has-a Calendar class (each instance creates its own internal instance of a Calendar object), but that seems methodically stilted.
fiddle with the returned object to give it the methods it needs (i know there's a term for creating a customized object but it escapes me).
make the additional methods into functions and just have them work with instances of Calendar.
or perhaps the answer is that i shouldn't be trying to subclass from a module in the first place, and this type of code belongs in the module itself.
Again i'm trying to understand what the "best" approach is and also learn if i'm missing any alternatives. Thanks.
Normally, I would expect an alternative constructor defined as a classmethod to simply call the class's standard constructor, transforming the arguments that it receives into valid arguments to the standard constructor.
>>> class Toy(object):
... def __init__(self, x):
... self.x = abs(x)
... def __repr__(self):
... return 'Toy({})'.format(self.x)
... #classmethod
... def from_string(cls, s):
... return cls(int(s))
...
>>> Toy.from_string('5')
Toy(5)
In most cases, I would strongly recommend something like this approach; this is the gold standard for alternative constructors.
But this is a special case.
I've now looked over the source, and I think the best way to add a new class is to edit the module directly; otherwise, scrap inheritance and take option one (your "has-a" option). The different classes are all slightly differentiated versions of the same container class -- they shouldn't really even be separate classes. But if you want to add a new class in the idiom of the code as it it is written, you have to add a new class to the module itself. Furthermore, from_iter is deceptively named; it's not really a constructor at all. I think it should be a standalone function. It builds a whole tree of components linked together, and the code that builds the individual components is buried in a chain of calls to various factory functions that also should be standalone functions but aren't. IMO much of that code ought to live in __init__ where it would be useful to you for subclassing, but it doesn't.
Indeed, none of the subclasses of Component even add any methods. By adding methods to your subclass of Calendar, you're completely disregarding the actual idiom of the code. I don't like its idiom very much but by disregarding that idiom, you're making it even worse. If you don't want to modify the original module, then forget about inheritance here and give your object a has-a relationship to Calendar objects. Don't modify __class__; establish your own OO structure that follows standard OO practices.
Say I have a class, which has a number of subclasses.
I can instantiate the class. I can then set its __class__ attribute to one of the subclasses. I have effectively changed the class type to the type of its subclass, on a live object. I can call methods on it which invoke the subclass's version of those methods.
So, how dangerous is doing this? It seems weird, but is it wrong to do such a thing? Despite the ability to change type at run-time, is this a feature of the language that should completely be avoided? Why or why not?
(Depending on responses, I'll post a more-specific question about what I would like to do, and if there are better alternatives).
Here's a list of things I can think of that make this dangerous, in rough order from worst to least bad:
It's likely to be confusing to someone reading or debugging your code.
You won't have gotten the right __init__ method, so you probably won't have all of the instance variables initialized properly (or even at all).
The differences between 2.x and 3.x are significant enough that it may be painful to port.
There are some edge cases with classmethods, hand-coded descriptors, hooks to the method resolution order, etc., and they're different between classic and new-style classes (and, again, between 2.x and 3.x).
If you use __slots__, all of the classes must have identical slots. (And if you have the compatible but different slots, it may appear to work at first but do horrible things…)
Special method definitions in new-style classes may not change. (In fact, this will work in practice with all current Python implementations, but it's not documented to work, so…)
If you use __new__, things will not work the way you naively expected.
If the classes have different metaclasses, things will get even more confusing.
Meanwhile, in many cases where you'd think this is necessary, there are better options:
Use a factory to create an instance of the appropriate class dynamically, instead of creating a base instance and then munging it into a derived one.
Use __new__ or other mechanisms to hook the construction.
Redesign things so you have a single class with some data-driven behavior, instead of abusing inheritance.
As a very most common specific case of the last one, just put all of the "variable methods" into classes whose instances are kept as a data member of the "parent", rather than into subclasses. Instead of changing self.__class__ = OtherSubclass, just do self.member = OtherSubclass(self). If you really need methods to magically change, automatic forwarding (e.g., via __getattr__) is a much more common and pythonic idiom than changing classes on the fly.
Assigning the __class__ attribute is useful if you have a long time running application and you need to replace an old version of some object by a newer version of the same class without loss of data, e.g. after some reload(mymodule) and without reload of unchanged modules. Other example is if you implement persistency - something similar to pickle.load.
All other usage is discouraged, especially if you can write the complete code before starting the application.
On arbitrary classes, this is extremely unlikely to work, and is very fragile even if it does. It's basically the same thing as pulling the underlying function objects out of the methods of one class, and calling them on objects which are not instances of the original class. Whether or not that will work depends on internal implementation details, and is a form of very tight coupling.
That said, changing the __class__ of objects amongst a set of classes that were particularly designed to be used this way could be perfectly fine. I've been aware that you can do this for a long time, but I've never yet found a use for this technique where a better solution didn't spring to mind at the same time. So if you think you have a use case, go for it. Just be clear in your comments/documentation what is going on. In particular it means that the implementation of all the classes involved have to respect all of their invariants/assumptions/etc, rather than being able to consider each class in isolation, so you'd want to make sure that anyone who works on any of the code involved is aware of this!
Well, not discounting the problems cautioned about at the start. But it can be useful in certain cases.
First of all, the reason I am looking this post up is because I did just this and __slots__ doesn't like it. (yes, my code is a valid use case for slots, this is pure memory optimization) and I was trying to get around a slots issue.
I first saw this in Alex Martelli's Python Cookbook (1st ed). In the 3rd ed, it's recipe 8.19 "Implementing Stateful Objects or State Machine Problems". A fairly knowledgeable source, Python-wise.
Suppose you have an ActiveEnemy object that has different behavior from an InactiveEnemy and you need to switch back and forth quickly between them. Maybe even a DeadEnemy.
If InactiveEnemy was a subclass or a sibling, you could switch class attributes. More exactly, the exact ancestry matters less than the methods and attributes being consistent to code calling it. Think Java interface or, as several people have mentioned, your classes need to be designed with this use in mind.
Now, you still have to manage state transition rules and all sorts of other things. And, yes, if your client code is not expecting this behavior and your instances switch behavior, things will hit the fan.
But I've used this quite successfully on Python 2.x and never had any unusual problems with it. Best done with a common parent and small behavioral differences on subclasses with the same method signatures.
No problems, until my __slots__ issue that's blocking it just now. But slots are a pain in the neck in general.
I would not do this to patch live code. I would also privilege using a factory method to create instances.
But to manage very specific conditions known in advance? Like a state machine that the clients are expected to understand thoroughly? Then it is pretty darn close to magic, with all the risk that comes with it. It's quite elegant.
Python 3 concerns? Test it to see if it works but the Cookbook uses Python 3 print(x) syntax in its example, FWIW.
The other answers have done a good job of discussing the question of why just changing __class__ is likely not an optimal decision.
Below is one example of a way to avoid changing __class__ after instance creation, using __new__. I'm not recommending it, just showing how it could be done, for the sake of completeness. However it is probably best to do this using a boring old factory rather than shoe-horning inheritance into a job for which it was not intended.
class ChildDispatcher:
_subclasses = dict()
def __new__(cls, *args, dispatch_arg, **kwargs):
# dispatch to a registered child class
subcls = cls.getsubcls(dispatch_arg)
return super(ChildDispatcher, subcls).__new__(subcls)
def __init_subclass__(subcls, **kwargs):
super(ChildDispatcher, subcls).__init_subclass__(**kwargs)
# add __new__ contructor to child class based on default first dispatch argument
def __new__(cls, *args, dispatch_arg = subcls.__qualname__, **kwargs):
return super(ChildDispatcher,cls).__new__(cls, *args, **kwargs)
subcls.__new__ = __new__
ChildDispatcher.register_subclass(subcls)
#classmethod
def getsubcls(cls, key):
name = cls.__qualname__
if cls is not ChildDispatcher:
raise AttributeError(f"type object {name!r} has no attribute 'getsubcls'")
try:
return ChildDispatcher._subclasses[key]
except KeyError:
raise KeyError(f"No child class key {key!r} in the "
f"{cls.__qualname__} subclasses registry")
#classmethod
def register_subclass(cls, subcls):
name = subcls.__qualname__
if cls is not ChildDispatcher:
raise AttributeError(f"type object {name!r} has no attribute "
f"'register_subclass'")
if name not in ChildDispatcher._subclasses:
ChildDispatcher._subclasses[name] = subcls
else:
raise KeyError(f"{name} subclass already exists")
class Child(ChildDispatcher): pass
c1 = ChildDispatcher(dispatch_arg = "Child")
assert isinstance(c1, Child)
c2 = Child()
assert isinstance(c2, Child)
How "dangerous" it is depends primarily on what the subclass would have done when initializing the object. It's entirely possible that it would not be properly initialized, having only run the base class's __init__(), and something would fail later because of, say, an uninitialized instance attribute.
Even without that, it seems like bad practice for most use cases. Easier to just instantiate the desired class in the first place.
Here's an example of one way you could do the same thing without changing __class__. Quoting #unutbu in the comments to the question:
Suppose you were modeling cellular automata. Suppose each cell could be in one of say 5 Stages. You could define 5 classes Stage1, Stage2, etc. Suppose each Stage class has multiple methods.
class Stage1(object):
…
class Stage2(object):
…
…
class Cell(object):
def __init__(self):
self.current_stage = Stage1()
def goToStage2(self):
self.current_stage = Stage2()
def __getattr__(self, attr):
return getattr(self.current_stage, attr)
If you allow changing __class__ you could instantly give a cell all the methods of a new stage (same names, but different behavior).
Same for changing current_stage, but this is a perfectly normal and pythonic thing to do, that won't confuse anyone.
Plus, it allows you to not change certain special methods you don't want changed, just by overriding them in Cell.
Plus, it works for data members, class methods, static methods, etc., in ways every intermediate Python programmer already understands.
If you refuse to change __class__, then you might have to include a stage attribute, and use a lot of if statements, or reassign a lot of attributes pointing to different stage's functions
Yes, I've used a stage attribute, but that's not a downside—it's the obvious visible way to keep track of what the current stage is, better for debugging and for readability.
And there's not a single if statement or any attribute reassignment except for the stage attribute.
And this is just one of multiple different ways of doing this without changing __class__.
In the comments I proposed modeling cellular automata as a possible use case for dynamic __class__s. Let's try to flesh out the idea a bit:
Using dynamic __class__:
class Stage(object):
def __init__(self, x, y):
self.x = x
self.y = y
class Stage1(Stage):
def step(self):
if ...:
self.__class__ = Stage2
class Stage2(Stage):
def step(self):
if ...:
self.__class__ = Stage3
cells = [Stage1(x,y) for x in range(rows) for y in range(cols)]
def step(cells):
for cell in cells:
cell.step()
yield cells
For lack of a better term, I'm going to call this
The traditional way: (mainly abarnert's code)
class Stage1(object):
def step(self, cell):
...
if ...:
cell.goToStage2()
class Stage2(object):
def step(self, cell):
...
if ...:
cell.goToStage3()
class Cell(object):
def __init__(self, x, y):
self.x = x
self.y = y
self.current_stage = Stage1()
def goToStage2(self):
self.current_stage = Stage2()
def __getattr__(self, attr):
return getattr(self.current_stage, attr)
cells = [Cell(x,y) for x in range(rows) for y in range(cols)]
def step(cells):
for cell in cells:
cell.step(cell)
yield cells
Comparison:
The traditional way creates a list of Cell instances each with a
current stage attribute.
The dynamic __class__ way creates a list of instances which are
subclasses of Stage. There is no need for a current stage
attribute since __class__ already serves this purpose.
The traditional way uses goToStage2, goToStage3, ... methods to
switch stages.
The dynamic __class__ way requires no such methods. You just
reassign __class__.
The traditional way uses the special method __getattr__ to delegate
some method calls to the appropriate stage instance held in the
self.current_stage attribute.
The dynamic __class__ way does not require any such delegation. The
instances in cells are already the objects you want.
The traditional way needs to pass the cell as an argument to
Stage.step. This is so cell.goToStageN can be called.
The dynamic __class__ way does not need to pass anything. The
object we are dealing with has everything we need.
Conclusion:
Both ways can be made to work. To the extent that I can envision how these two implementations would pan-out, it seems to me the dynamic __class__ implementation will be
simpler (no Cell class),
more elegant (no ugly goToStage2 methods, no brain-teasers like why
you need to write cell.step(cell) instead of cell.step()),
and easier to understand (no __getattr__, no additional level of
indirection)