I'm in scenario where I want to refactor several classes which have identical and/or similar methods. The number of class are around ~20 and the number of similar methods are around ~15. All sorts of combinations exist within this space, which is why I'm a bit reluctant to using inheritance to solve this issue (rightfully?).
The code is part of a wrapper around another application that is controlled by a com api. The wrapper in turn is part of a package that is distributed internally at the company where I work. Therefore the interfaces of the classes have to remain the same (for backwards compatibility).
This example illustrates some very simplified versions of the classes:
class FirstCollectionLike:
def __init__(self):
self._collection = list()
def add(self, arg):
self._collection.append(arg)
def remove(self, index):
del self._collection[index]
class SecondCollectionLike:
def __init__(self):
self._collection = list()
self._resource = some_module.get_resource()
def start(self):
some_module.start(self.resource)
def add(self, arg):
self._collection.append(arg)
def remove(self, value):
self._collection.remove(value)
class SomeOtherClass:
def __init__(self):
self._some_attribute = 0
self._resource = some_module.get_resource()
def add(self, value):
self._some_attribute += value
def start(self):
some_module.start(self._resource)
Are there any design patterns I could look into that would help me solve this issue?
My initial thought was to create method classes like Add, RemoveByIndex and RemoveByName that implements __call__ like so:
class Add:
def __init__(self, owner):
self.owner = owner
def __call__(self, item):
self._collection.append(item)
class AddAndInstantiate:
def __init__(self, owner, type_to_instantiate):
self.owner = owner
self.type_to_instantiate = type_to_instantiate
def __call__(self, name):
self._collection.append(type_to_instantiate(name))
and then assign instances of those classes as instance attributes to their respective owner objects:
class RefactoredClassOne:
def __init__(self):
self.add = Add(self)
self.remove = RemoveByIndex(self)
class RefactoredClassTwo:
def __init__(self):
self.add = AddAndInstantiate(self, SomeClass)
self.remove = RemoveByName(self)
This way I could quite easily add any method I want to a class and provide some arguments to the method class if needed (like the type of the class to instantiate in the example above). The downside is that it is a bit harder to follow what is happening, and the automatic documentation generation we use (sphinx) does not work if the methods are implemented in this way.
Does this seem like a bad approach? What are the alternatives?
First, if your classes are as simple as you example suggest, I'm not sure OOP is the right tool. What your classes are doing is just renaming a couple of basic calls. This is useless abstraction and IMO a bad practice (why force me to look to into the SecondClassCollectionLike.py file to discover that .add() is 1) in fact a wrongly named append and 2) that my collection is in fact a listwith a fancy name?)
In that case I'd say that a functional approach might be better, and a workflow such as:
a = SecondClassCollectionLike()
a.add("x")
a.add("y")
a.remove(0)
a.start()
would be a lot clearer if it looked like
a = list()
a.append("x")
a.append(y)
del a[0]
somemodule.start()
If your classes are in fact more complex and you really want to keep the OOP approach, I think that this solution is probably close to your solution and what you're looking for.
The idea is to have modules which hold the logic. For example a _collection_behaviour.py module, which holds the add(), remove(), increment() or whatever. And a _runtime.py module, which holds that start(), stop(), etc. logic.
This way you could have classes which exibit behaviour from these modules:
calss MyClass():
def __init__(self):
self._collection = list()
from ._collection_behaviour import add
from ._collection_behaviour import remove
from ._runtime import start
But I do not see the point in making these functions classes which implement __call__ if that's all they do.
I want to create a subclass of a class of an existing package (whose source code I don't want to/cannot change). The objects of the class are initialized just using a string and then populated later on using all kind of add functions. A minimal example could look like this (without any add functions):
import copy
class Origin(object):
def __init__(self, name):
self.name = name
self.dummy_list = [1, 2, 'a']
self.dummy_stuff = {'a': [12, 'yt']}
def make_copy(self):
return copy.deepcopy(self)
def dummy_function(self):
return len(self.dummy_list)
I want to create a subclass in such a way that I can initialize its instances using an instance of Origin. A straightforward way would be
class BasedOnOrigin(Origin):
def __init__(self, origin_instance, new_prop):
Origin.__init__(self, origin_instance.name)
self.dummy_list = copy.deepcopy(origin_instance.dummy_list)
self.dummy_stuff = copy.deepcopy(origin_instance.dummy_stuff)
self.new_prop = new_prop
The annoying thing there is, that I need to copy all kind of things which I need to know about in advance.
Another option would be
class BasedOnOrigin2(Origin):
def __init__(self, origin_instance, new_prop):
Origin.__init__(self, origin_instance.name)
self = origin_instance.make_copy()
self.new_prop = new_prop
but the self = part looks rather non-standard and new_prop is not set, so I would need an extra function for this.
Is there a standard way of doing this?
An alternative to the above would be to add the additional functions to existing instances using e.g.
from functools import partial
def add_function(obj, func):
setattr(obj, func.__name__, partial(func, obj))
but this can be annoying if there are (i) a lot of functions to add and (ii) a lot of instances to which one wants to add functions.
but the self = part looks rather non-standard and new_prop is not set
self is just a plain local variable, so rebinding it only effects the local scope indeed.
Is there a standard way of doing this?
From what you describe it looks like your real problem is that you have instances of class created by another lib that you don't want / cannot modify and what you really want is to add new methods (and eventually override some methods) to those objects, but cannot since you can tell this lib to use your own class instead.
If the point is purely and simply "replace" the original class with your own version of it (so all instances of the original class are impacted by the change), the canonical solution is to monkeypatch the original class:
from otherlib import TheClass
def patch_the_class():
# we do this in a function to avoid
# polluting the global namespace
# add a new method
def newmethod(self):
# code here
TheClass.newmethod = newmethod
# override an existing method
# keep a reference to the original so
# we can still use it:
_original = TheClass.some_method
def mymethod(self, arg):
something = _original(self, arg)
# additional stuff here
return something
TheClass.some_method = mymethod
patch_the_class()
Just make sure this is executed before any use of the patched class and you're done.
The pro of this solution (wrt/ patching each instance individually) is a lesser cost and the assurance that no one will ever forget to patch an instance.
Now note that monkeypatches are to be considered as either a temporary workaround or a last-resort hack. If the lib you are patching is OSS, you can modify it to either improve the original class or implement some way to make the concrete class to use configurable and contribute it back.
I think the best approach is defining a function that will extend original origin instance without copying it e.g.
def exdend(*origin_instances):
def my_function_one(self):
pass
def my_function_two(self):
pass
for origin_instance in origin_instances:
setattr(origin_instance, my_function_one.__name__, partial(my_function_one, origin_instance))
setattr(origin_instance, my_function_two.__name__, partial(my_function_two, origin_instance))
return origin_instances
This question already has answers here:
What's the pythonic way to use getters and setters?
(8 answers)
Closed 4 months ago.
What advantages does the #property notation hold over the classic getter+setter? In which specific cases/situations should a programmer choose to use one over the other?
With properties:
class MyClass(object):
#property
def my_attr(self):
return self._my_attr
#my_attr.setter
def my_attr(self, value):
self._my_attr = value
Without properties:
class MyClass(object):
def get_my_attr(self):
return self._my_attr
def set_my_attr(self, value):
self._my_attr = value
Prefer properties. It's what they're there for.
The reason is that all attributes are public in Python. Starting names with an underscore or two is just a warning that the given attribute is an implementation detail that may not stay the same in future versions of the code. It doesn't prevent you from actually getting or setting that attribute. Therefore, standard attribute access is the normal, Pythonic way of, well, accessing attributes.
The advantage of properties is that they are syntactically identical to attribute access, so you can change from one to another without any changes to client code. You could even have one version of a class that uses properties (say, for code-by-contract or debugging) and one that doesn't for production, without changing the code that uses it. At the same time, you don't have to write getters and setters for everything just in case you might need to better control access later.
In Python you don't use getters or setters or properties just for the fun of it. You first just use attributes and then later, only if needed, eventually migrate to a property without having to change the code using your classes.
There is indeed a lot of code with extension .py that uses getters and setters and inheritance and pointless classes everywhere where e.g. a simple tuple would do, but it's code from people writing in C++ or Java using Python.
That's not Python code.
Using properties lets you begin with normal attribute accesses and then back them up with getters and setters afterwards as necessary.
The short answer is: properties wins hands down. Always.
There is sometimes a need for getters and setters, but even then, I would "hide" them to the outside world. There are plenty of ways to do this in Python (getattr, setattr, __getattribute__, etc..., but a very concise and clean one is:
def set_email(self, value):
if '#' not in value:
raise Exception("This doesn't look like an email address.")
self._email = value
def get_email(self):
return self._email
email = property(get_email, set_email)
Here's a brief article that introduces the topic of getters and setters in Python.
[TL;DR? You can skip to the end for a code example.]
I actually prefer to use a different idiom, which is a little involved for using as a one off, but is nice if you have a more complex use case.
A bit of background first.
Properties are useful in that they allow us to handle both setting and getting values in a programmatic way but still allow attributes to be accessed as attributes. We can turn 'gets' into 'computations' (essentially) and we can turn 'sets' into 'events'. So let's say we have the following class, which I've coded with Java-like getters and setters.
class Example(object):
def __init__(self, x=None, y=None):
self.x = x
self.y = y
def getX(self):
return self.x or self.defaultX()
def getY(self):
return self.y or self.defaultY()
def setX(self, x):
self.x = x
def setY(self, y):
self.y = y
def defaultX(self):
return someDefaultComputationForX()
def defaultY(self):
return someDefaultComputationForY()
You may be wondering why I didn't call defaultX and defaultY in the object's __init__ method. The reason is that for our case I want to assume that the someDefaultComputation methods return values that vary over time, say a timestamp, and whenever x (or y) is not set (where, for the purpose of this example, "not set" means "set to None") I want the value of x's (or y's) default computation.
So this is lame for a number of reasons describe above. I'll rewrite it using properties:
class Example(object):
def __init__(self, x=None, y=None):
self._x = x
self._y = y
#property
def x(self):
return self.x or self.defaultX()
#x.setter
def x(self, value):
self._x = value
#property
def y(self):
return self.y or self.defaultY()
#y.setter
def y(self, value):
self._y = value
# default{XY} as before.
What have we gained? We've gained the ability to refer to these attributes as attributes even though, behind the scenes, we end up running methods.
Of course the real power of properties is that we generally want these methods to do something in addition to just getting and setting values (otherwise there is no point in using properties). I did this in my getter example. We are basically running a function body to pick up a default whenever the value isn't set. This is a very common pattern.
But what are we losing, and what can't we do?
The main annoyance, in my view, is that if you define a getter (as we do here) you also have to define a setter.[1] That's extra noise that clutters the code.
Another annoyance is that we still have to initialize the x and y values in __init__. (Well, of course we could add them using setattr() but that is more extra code.)
Third, unlike in the Java-like example, getters cannot accept other parameters. Now I can hear you saying already, well, if it's taking parameters it's not a getter! In an official sense, that is true. But in a practical sense there is no reason we shouldn't be able to parameterize an named attribute -- like x -- and set its value for some specific parameters.
It'd be nice if we could do something like:
e.x[a,b,c] = 10
e.x[d,e,f] = 20
for example. The closest we can get is to override the assignment to imply some special semantics:
e.x = [a,b,c,10]
e.x = [d,e,f,30]
and of course ensure that our setter knows how to extract the first three values as a key to a dictionary and set its value to a number or something.
But even if we did that we still couldn't support it with properties because there is no way to get the value because we can't pass parameters at all to the getter. So we've had to return everything, introducing an asymmetry.
The Java-style getter/setter does let us handle this, but we're back to needing getter/setters.
In my mind what we really want is something that capture the following requirements:
Users define just one method for a given attribute and can indicate there
whether the attribute is read-only or read-write. Properties fail this test
if the attribute writable.
There is no need for the user to define an extra variable underlying the function, so we don't need the __init__ or setattr in the code. The variable just exists by the fact we've created this new-style attribute.
Any default code for the attribute executes in the method body itself.
We can set the attribute as an attribute and reference it as an attribute.
We can parameterize the attribute.
In terms of code, we want a way to write:
def x(self, *args):
return defaultX()
and be able to then do:
print e.x -> The default at time T0
e.x = 1
print e.x -> 1
e.x = None
print e.x -> The default at time T1
and so forth.
We also want a way to do this for the special case of a parameterizable attribute, but still allow the default assign case to work. You'll see how I tackled this below.
Now to the point (yay! the point!). The solution I came up for for this is as follows.
We create a new object to replace the notion of a property. The object is intended to store the value of a variable set to it, but also maintains a handle on code that knows how to calculate a default. Its job is to store the set value or to run the method if that value is not set.
Let's call it an UberProperty.
class UberProperty(object):
def __init__(self, method):
self.method = method
self.value = None
self.isSet = False
def setValue(self, value):
self.value = value
self.isSet = True
def clearValue(self):
self.value = None
self.isSet = False
I assume method here is a class method, value is the value of the UberProperty, and I have added isSet because None may be a real value and this allows us a clean way to declare there really is "no value". Another way is a sentinel of some sort.
This basically gives us an object that can do what we want, but how do we actually put it on our class? Well, properties use decorators; why can't we? Let's see how it might look (from here on I'm going to stick to using just a single 'attribute', x).
class Example(object):
#uberProperty
def x(self):
return defaultX()
This doesn't actually work yet, of course. We have to implement uberProperty and
make sure it handles both gets and sets.
Let's start with gets.
My first attempt was to simply create a new UberProperty object and return it:
def uberProperty(f):
return UberProperty(f)
I quickly discovered, of course, that this doens't work: Python never binds the callable to the object and I need the object in order to call the function. Even creating the decorator in the class doesn't work, as although now we have the class, we still don't have an object to work with.
So we're going to need to be able to do more here. We do know that a method need only be represented the one time, so let's go ahead and keep our decorator, but modify UberProperty to only store the method reference:
class UberProperty(object):
def __init__(self, method):
self.method = method
It is also not callable, so at the moment nothing is working.
How do we complete the picture? Well, what do we end up with when we create the example class using our new decorator:
class Example(object):
#uberProperty
def x(self):
return defaultX()
print Example.x <__main__.UberProperty object at 0x10e1fb8d0>
print Example().x <__main__.UberProperty object at 0x10e1fb8d0>
in both cases we get back the UberProperty which of course is not a callable, so this isn't of much use.
What we need is some way to dynamically bind the UberProperty instance created by the decorator after the class has been created to an object of the class before that object has been returned to that user for use. Um, yeah, that's an __init__ call, dude.
Let's write up what we want our find result to be first. We're binding an UberProperty to an instance, so an obvious thing to return would be a BoundUberProperty. This is where we'll actually maintain state for the x attribute.
class BoundUberProperty(object):
def __init__(self, obj, uberProperty):
self.obj = obj
self.uberProperty = uberProperty
self.isSet = False
def setValue(self, value):
self.value = value
self.isSet = True
def getValue(self):
return self.value if self.isSet else self.uberProperty.method(self.obj)
def clearValue(self):
del self.value
self.isSet = False
Now we the representation; how do get these on to an object? There are a few approaches, but the easiest one to explain just uses the __init__ method to do that mapping. By the time __init__ is called our decorators have run, so just need to look through the object's __dict__ and update any attributes where the value of the attribute is of type UberProperty.
Now, uber-properties are cool and we'll probably want to use them a lot, so it makes sense to just create a base class that does this for all subclasses. I think you know what the base class is going to be called.
class UberObject(object):
def __init__(self):
for k in dir(self):
v = getattr(self, k)
if isinstance(v, UberProperty):
v = BoundUberProperty(self, v)
setattr(self, k, v)
We add this, change our example to inherit from UberObject, and ...
e = Example()
print e.x -> <__main__.BoundUberProperty object at 0x104604c90>
After modifying x to be:
#uberProperty
def x(self):
return *datetime.datetime.now()*
We can run a simple test:
print e.x.getValue()
print e.x.getValue()
e.x.setValue(datetime.date(2013, 5, 31))
print e.x.getValue()
e.x.clearValue()
print e.x.getValue()
And we get the output we wanted:
2013-05-31 00:05:13.985813
2013-05-31 00:05:13.986290
2013-05-31
2013-05-31 00:05:13.986310
(Gee, I'm working late.)
Note that I have used getValue, setValue, and clearValue here. This is because I haven't yet linked in the means to have these automatically returned.
But I think this is a good place to stop for now, because I'm getting tired. You can also see that the core functionality we wanted is in place; the rest is window dressing. Important usability window dressing, but that can wait until I have a change to update the post.
I'll finish up the example in the next posting by addressing these things:
We need to make sure UberObject's __init__ is always called by subclasses.
So we either force it be called somewhere or we prevent it from being implemented.
We'll see how to do this with a metaclass.
We need to make sure we handle the common case where someone 'aliases'
a function to something else, such as:
class Example(object):
#uberProperty
def x(self):
...
y = x
We need e.x to return e.x.getValue() by default.
What we'll actually see is this is one area where the model fails.
It turns out we'll always need to use a function call to get the value.
But we can make it look like a regular function call and avoid having to use e.x.getValue(). (Doing this one is obvious, if you haven't already fixed it out.)
We need to support setting e.x directly, as in e.x = <newvalue>. We can do this in the parent class too, but we'll need to update our __init__ code to handle it.
Finally, we'll add parameterized attributes. It should be pretty obvious how we'll do this, too.
Here's the code as it exists up to now:
import datetime
class UberObject(object):
def uberSetter(self, value):
print 'setting'
def uberGetter(self):
return self
def __init__(self):
for k in dir(self):
v = getattr(self, k)
if isinstance(v, UberProperty):
v = BoundUberProperty(self, v)
setattr(self, k, v)
class UberProperty(object):
def __init__(self, method):
self.method = method
class BoundUberProperty(object):
def __init__(self, obj, uberProperty):
self.obj = obj
self.uberProperty = uberProperty
self.isSet = False
def setValue(self, value):
self.value = value
self.isSet = True
def getValue(self):
return self.value if self.isSet else self.uberProperty.method(self.obj)
def clearValue(self):
del self.value
self.isSet = False
def uberProperty(f):
return UberProperty(f)
class Example(UberObject):
#uberProperty
def x(self):
return datetime.datetime.now()
[1] I may be behind on whether this is still the case.
I think both have their place. One issue with using #property is that it is hard to extend the behaviour of getters or setters in subclasses using standard class mechanisms. The problem is that the actual getter/setter functions are hidden in the property.
You can actually get hold of the functions, e.g. with
class C(object):
_p = 1
#property
def p(self):
return self._p
#p.setter
def p(self, val):
self._p = val
you can access the getter and setter functions as C.p.fget and C.p.fset, but you can't easily use the normal method inheritance (e.g. super) facilities to extend them. After some digging into the intricacies of super, you can indeed use super in this way:
# Using super():
class D(C):
# Cannot use super(D,D) here to define the property
# since D is not yet defined in this scope.
#property
def p(self):
return super(D,D).p.fget(self)
#p.setter
def p(self, val):
print 'Implement extra functionality here for D'
super(D,D).p.fset(self, val)
# Using a direct reference to C
class E(C):
p = C.p
#p.setter
def p(self, val):
print 'Implement extra functionality here for E'
C.p.fset(self, val)
Using super() is, however, quite clunky, since the property has to be redefined, and you have to use the slightly counter-intuitive super(cls,cls) mechanism to get an unbound copy of p.
Using properties is to me more intuitive and fits better into most code.
Comparing
o.x = 5
ox = o.x
vs.
o.setX(5)
ox = o.getX()
is to me quite obvious which is easier to read. Also properties allows for private variables much easier.
I feel like properties are about letting you get the overhead of writing getters and setters only when you actually need them.
Java Programming culture strongly advise to never give access to properties, and instead, go through getters and setters, and only those which are actually needed.
It's a bit verbose to always write these obvious pieces of code, and notice that 70% of the time they are never replaced by some non-trivial logic.
In Python, people actually care for that kind of overhead, so that you can embrace the following practice :
Do not use getters and setters at first, when if they not needed
Use #property to implement them without changing the syntax of the rest of your code.
I would prefer to use neither in most cases. The problem with properties is that they make the class less transparent. Especially, this is an issue if you were to raise an exception from a setter. For example, if you have an Account.email property:
class Account(object):
#property
def email(self):
return self._email
#email.setter
def email(self, value):
if '#' not in value:
raise ValueError('Invalid email address.')
self._email = value
then the user of the class does not expect that assigning a value to the property could cause an exception:
a = Account()
a.email = 'badaddress'
--> ValueError: Invalid email address.
As a result, the exception may go unhandled, and either propagate too high in the call chain to be handled properly, or result in a very unhelpful traceback being presented to the program user (which is sadly too common in the world of python and java).
I would also avoid using getters and setters:
because defining them for all properties in advance is very time consuming,
makes the amount of code unnecessarily longer, which makes understanding and maintaining the code more difficult,
if you were define them for properties only as needed, the interface of the class would change, hurting all users of the class
Instead of properties and getters/setters I prefer doing the complex logic in well defined places such as in a validation method:
class Account(object):
...
def validate(self):
if '#' not in self.email:
raise ValueError('Invalid email address.')
or a similiar Account.save method.
Note that I am not trying to say that there are no cases when properties are useful, only that you may be better off if you can make your classes simple and transparent enough that you don't need them.
I am surprised that nobody has mentioned that properties are bound methods of a descriptor class, Adam Donohue and NeilenMarais get at exactly this idea in their posts -- that getters and setters are functions and can be used to:
validate
alter data
duck type (coerce type to another type)
This presents a smart way to hide implementation details and code cruft like regular expression, type casts, try .. except blocks, assertions or computed values.
In general doing CRUD on an object may often be fairly mundane but consider the example of data that will be persisted to a relational database. ORM's can hide implementation details of particular SQL vernaculars in the methods bound to fget, fset, fdel defined in a property class that will manage the awful if .. elif .. else ladders that are so ugly in OO code -- exposing the simple and elegant self.variable = something and obviate the details for the developer using the ORM.
If one thinks of properties only as some dreary vestige of a Bondage and Discipline language (i.e. Java) they are missing the point of descriptors.
In complex projects I prefer using read-only properties (or getters) with explicit setter function:
class MyClass(object):
...
#property
def my_attr(self):
...
def set_my_attr(self, value):
...
In long living projects debugging and refactoring takes more time than writing the code itself. There are several downsides for using #property.setter that makes debugging even harder:
1) python allows creating new attributes for an existing object. This makes a following misprint very hard to track:
my_object.my_atttr = 4.
If your object is a complicated algorithm then you will spend quite some time trying to find out why it doesn't converge (notice an extra 't' in the line above)
2) setter sometimes might evolve to a complicated and slow method (e.g. hitting a database). It would be quite hard for another developer to figure out why the following function is very slow. He might spend a lot of time on profiling do_something() method, while my_object.my_attr = 4. is actually the cause of slowdown:
def slow_function(my_object):
my_object.my_attr = 4.
my_object.do_something()
Both #property and traditional getters and setters have their advantages. It depends on your use case.
Advantages of #property
You don't have to change the interface while changing the implementation of data access. When your project is small, you probably want to use direct attribute access to access a class member. For example, let's say you have an object foo of type Foo, which has a member num. Then you can simply get this member with num = foo.num. As your project grows, you may feel like there needs to be some checks or debugs on the simple attribute access. Then you can do that with a #property within the class. The data access interface remains the same so that there is no need to modify client code.
Cited from PEP-8:
For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax.
Using #property for data access in Python is regarded as Pythonic:
It can strengthen your self-identification as a Python (not Java) programmer.
It can help your job interview if your interviewer thinks Java-style getters and setters are anti-patterns.
Advantages of traditional getters and setters
Traditional getters and setters allow for more complicated data access than simple attribute access. For example, when you are setting a class member, sometimes you need a flag indicating where you would like to force this operation even if something doesn't look perfect. While it is not obvious how to augment a direct member access like foo.num = num, You can easily augment your traditional setter with an additional force parameter:
def Foo:
def set_num(self, num, force=False):
...
Traditional getters and setters make it explicit that a class member access is through a method. This means:
What you get as the result may not be the same as what is exactly stored within that class.
Even if the access looks like a simple attribute access, the performance can vary greatly from that.
Unless your class users expect a #property hiding behind every attribute access statement, making such things explicit can help minimize your class users surprises.
As mentioned by #NeilenMarais and in this post, extending traditional getters and setters in subclasses is easier than extending properties.
Traditional getters and setters have been widely used for a long time in different languages. If you have people from different backgrounds in your team, they look more familiar than #property. Also, as your project grows, if you may need to migrate from Python to another language that doesn't have #property, using traditional getters and setters would make the migration smoother.
Caveats
Neither #property nor traditional getters and setters makes the class member private, even if you use double underscore before its name:
class Foo:
def __init__(self):
self.__num = 0
#property
def num(self):
return self.__num
#num.setter
def num(self, num):
self.__num = num
def get_num(self):
return self.__num
def set_num(self, num):
self.__num = num
foo = Foo()
print(foo.num) # output: 0
print(foo.get_num()) # output: 0
print(foo._Foo__num) # output: 0
Here is an excerpts from "Effective Python: 90 Specific Ways to Write Better Python" (Amazing book. I highly recommend it).
Things to Remember
✦ Define new class interfaces using simple public attributes and avoid
defining setter and getter methods.
✦ Use #property to define special behavior when attributes are
accessed on your objects, if necessary.
✦ Follow the rule of least surprise and avoid odd side effects in your
#property methods.
✦ Ensure that #property methods are fast; for slow or complex
work—especially involving I/O or causing side effects—use normal
methods instead.
One advanced but common use of #property is transitioning what was
once a simple numerical attribute into an on-the-fly calculation. This
is extremely helpful because it lets you migrate all existing usage of
a class to have new behaviors without requiring any of the call sites
to be rewritten (which is especially important if there’s calling code
that you don’t control). #property also provides an important stopgap
for improving interfaces over time.
I especially like #property because it lets you make incremental
progress toward a better data model over time.
#property is a tool to
help you address problems you’ll come across in real-world code. Don’t
overuse it. When you find yourself repeatedly extending #property
methods, it’s probably time to refactor your class instead of further
paving over your code’s poor design.
✦ Use #property to give existing instance attributes
new functionality.
✦ Make incremental progress toward better data
models by using #property.
✦ Consider refactoring a class and all call
sites when you find yourself using #property too heavily.
I was wondering if there is anything wrong (from a OOP point of view) in doing something like this:
class Foobar:
foobars = {}
def __init__(self, name, something):
self.name = name
self.something = something
Foobar.foobars[name] = self
Foobar('first', 42)
Foobar('second', 77)
for name in Foobar.foobars:
print name, Foobar.foobars[name]
EDIT: this is the actual piece of code I'm using right now
from threading import Event
class Task:
ADDED, WAITING_FOR_DEPS, READY, IN_EXECUTION, DONE = range(5)
tasks = {}
def __init__(self, name, dep_names, job, ins, outs, uptodate, where):
self.name = name
self.dep_names = [dep_names] if isinstance(dep_names, str) else dep_names
self.job = job
self.where = where
self.done = Event()
self.status = Task.ADDED
self.jobs = []
# other stuff...
Task.tasks[name] = self
def set_done(self):
self.done.set()
self.status = Task.DONE
def wait_for_deps(self):
self.status = Task.WAITING_FOR_DEPS
for dep_name in self.dep_names:
Task.tasks[dep_name].done.wait()
self.status = Task.READY
def add_jobs_to_queues(self):
jobs = self.jobs
# a lot of stuff I trimmed here
for w in self.where: Queue.queues[w].put(jobs)
self.status = Task.IN_EXECUTION
def wait_for_jobs(self):
for j in self.jobs: j.wait()
#[...]
As you can see I need to access the dictionary with all the instances in
the wait_for_deps method. Would it make more sense to have a global variable
instead of a class field? I could be using a wrong approach here, maybe that
stuff shouldn't even be in a method, but it made sense to me (I'm new to OOP)
Yes. It's bad. It conflates the instance with the collection of instances.
Collections are one thing.
The instances which are collected are unrelated.
Also, class-level variables which get updated confuse some of us. Yes, we can eventually reason on what's going on, but the Standard Expectation™ is that state change applies to objects, not classes.
class Foobar_Collection( dict ):
def __init__( self, *arg, **kw ):
super( Foobar_Collection, self ).__init__( *arg, **kw ):
def foobar( self, *arg, **kw ):
fb= Foobar( *arg, **kw )
self[fb.name]= fb
return fb
class Foobar( object ):
def __init__( self, name, something )
self.name= name
self.something= something
fc= Foobar_Collection()
fc.foobar( 'first', 42 )
fc.foobar( 'second', 77 )
for name in fc:
print name, fc[name]
That's more typical.
In your example, the wait_for_deps is simply a method of the task collection, not the individual task. You don't need globals.
You need to refactor.
I don't suppose that there's anything wrong with this, but I don't really see how this would be sensible. Why would you need to keep a global variable (in the class, of all places) that holds references to all the instances? The client could just as easily implement this himself if he just kept a list of his instances. All in all, it seems a little hackish and unnecessary, so I'd recommend that you don't do it.
If you're more specific about what you're trying to do, perhaps we can find a better solution.
This is NOT cohesive, as well as not very functional, you want to strive to get your objects as far from the 'data-bucket' mindset as possible. The static object collection is not going to really gain you anything, you need to think WHY do you need all the objects in the collection and think about creating a second class whose responsibility is to manage and be queried for all the Foobars in the system.
Why would you want to do this?
There are several problems with this code. The first is that you have to take care of deleting instances -- there will always be a reference to each Foobar instance left in Foobar.foobars, so the garbage collector will never garbage collect them. The second problem is that it won't work with copy and pickle.
But apart from the technical problems, it feels like a wrong design. The purpose of object instances is hiding state, and you make them see each other.
From a OOP point of view there's nothing wrong with it. A class is an instance of a metaclass, and any instance can hold any kind of data in it.
However, from an efficiency point of view, if you don't eventualy clean up the foobars dict on a long running Python program, you are having potential memory leak.
No one has mentioned the potential problem this might have if you later derive a subclass from Foobar which could happen if the base class __init__() function is called from the derived class's __init__(). Specifically whether you want all the subclass instances to be sored in the same place as those of the base class -- which of course depend on why you're doing this.
It's a solvable problem but something to consider, and perhaps to code for, up front in the base class.
I needed multiple Jinja environments in an app engine application:
class JinjaEnv(object):
""" Jinja environment / loader instance per env_name """
_env_lock = threading.Lock()
with _env_lock:
_jinja_envs = dict() # instances of this class
def __init__(self, env_name):
self.jinja_loader = ..... # init jinja loader
self.client_cache = memcache.Client()
self.jinja_bcc = MemcachedBytecodeCache(self.client_cache, prefix='jinja2/bcc_%s/' % env_name, timeout=3600)
self.jinja_env = self.jinja_loader(self.jinja_bcc, env_name)
#classmethod
def get_env(cls, env_name):
with cls._env_lock:
if env_name not in cls._jinja_envs:
cls._jinja_envs[env_name] = JinjaEnv(env_name) # new env
return cls._jinja_envs[env_name].jinja_env
#classmethod
def flush_env(cls, env_name):
with cls._env_lock:
if env_name not in cls._jinja_envs:
self = cls._jinja_envs[env_name] = JinjaEnv(env_name) # new env
else:
self = cls._jinja_envs[env_name]
self.client_cache.flush_all()
self.jinja_env = self.jinja_loader(self.jinja_bcc, env_name)
return self.jinja_env
Used like:
template = JinjaEnv.get_env('example_env').get_template('example_template')