This is a snippet for registers for an emulator I'm working on:
class registers(object):
def __init__(self):
self._AF = registerpair()
def _get_AF(self):
return self._AF.getval()
def _set_AF(self, val):
self._AF.setval(val)
AF = property(_get_AF, _set_AF)
The registerpair() class has an increment() method. I would like to know if there is any way I could do the following:
r = registers()
r.AF.increment()
rather than having to do:
r._AF.increment()
As is, no. You have set the fget method to return a getval() for your registerpair() class.
Since the property is for the _AF attribute which is a registerpair() instance, I believe it would be more reasonable to change your fget (and fset for that matter) to actually return it, and maybe create an auxiliary function to actually get the value with getval() or access it directly.
So if your _get_AF looked something like:
def _get_AF(self):
return self._AF
you can then call r.AF.increment() just fine. Then you could move the getval() call to another function in your class:
def getAFval(self):
self._AF.getval()
Or just make direct calls like r.AF.getval() which seems like the most clear way to do things.
You are effectively modifying the interface to the registerpair class using this wrapper class, and in doing so hiding the original interface. As such in your new interface the property() in Python refers to the values stored in the registerpair, not to the registerpair itself, as it reimplements the getval() and setval() interface of the registerpair.
So a couple of suggestions, firstly if this wrapper class is just reimplementing the interface to the registerpair, should you not just inherit from the registerpair, that way the original interface would be available?
Alternatively you could implement the remainder of the registerpair interface, using for example a method such as registers.increment_AF():
class registers(object):
def __init__(self):
self._AF = registerpair()
def _get_AF(self):
return self._AF.getval()
def _set_AF(self, val):
self._AF.setval(val)
AF = property(_get_AF, _set_AF)
def increment_AF(self):
self._AF.increment()
If I understand you correctly
You can call r._AF.increment() which references registerpair() object but since self._AF is a private method you cannot use
r.AF.increment()
for further information..check this
https://www.python.org/dev/peps/pep-0008/
an extract from this site
_single_leading_underscore : weak "internal use" indicator. E.g. from M import * does not import objects whose name starts with an underscore.
single_trailing_underscore_ : used by convention to avoid conflicts with Python keyword, e.g.
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.
In Python (I'm talking 2 here, but would be interested to know about 3 too) is there a way to define in advance a list of all instance variables (member fields) you want available i.e. make it an error to use one you've not defined somewhere?
Something like
class MyClass(object):
var somefield
def __init__ (self):
self.somefield = 4
self.banana = 25 # error!
A bit like you do in Java, C++, PHP, etc
Edit:
The reason I wanted this kind of thing was to spot early on using variables that hadn't been setup initially. It seems that a linter will actually pick these errors up without any extra plumbing so perhaps my question is moot...
Why yes, you can.
class MyClass(object):
__slots__ = ['somefield']
def __init__ (self):
self.somefield = 4
self.banana = 25 # error!
But mind the caveats.
You can use the answer posted above, but for a more "pythonic" approach, try the method listed at (link to code.activestate.com)
For future reference, and until I can figure out how to link to the website, here's the code:
def frozen(set):
"""Raise an error when trying to set an undeclared name, or when calling
from a method other than Frozen.__init__ or the __init__ method of
a class derived from Frozen"""
def set_attr(self,name,value):
import sys
if hasattr(self,name): #If attribute already exists, simply set it
set(self,name,value)
return
elif sys._getframe(1).f_code.co_name is '__init__': #Allow __setattr__ calls in __init__ calls of proper object types
for k,v in sys._getframe(1).f_locals.items():
if k=="self" and isinstance(v, self.__class__):
set(self,name,value)
return
raise AttributeError("You cannot add attributes to %s" % self)
return set_attr
I have two classes (let's call them Working and ReturnStatement) which I can't modify, but I want to extend both of them with logging. The trick is that the Working's method returns a ReturnStatement object, so the new MutantWorking object also returns ReturnStatement unless I can cast it to MutantReturnStatement. Saying with code:
# these classes can't be changed
class ReturnStatement(object):
def act(self):
print "I'm a ReturnStatement."
class Working(object):
def do(self):
print "I am Working."
return ReturnStatement()
# these classes should wrap the original ones
class MutantReturnStatement(ReturnStatement):
def act(self):
print "I'm wrapping ReturnStatement."
return ReturnStatement().act()
class MutantWorking(Working):
def do(self):
print "I am wrapping Working."
# !!! this is not working, I'd need that casting working !!!
return (MutantReturnStatement) Working().do()
rs = MutantWorking().do() #I can use MutantWorking just like Working
print "--" # just to separate output
rs.act() #this must be MutantReturnState.act(), I need the overloaded method
The expected result:
I am wrapping Working.
I am Working.
--
I'm wrapping ReturnStatement.
I'm a ReturnStatement.
Is it possible to solve the problem? I'm also curious if the problem can be solved in PHP, too. Unless I get a working solution I can't accept the answer, so please write working code to get accepted.
There is no casting as the other answers already explained. You can make subclasses or make modified new types with the extra functionality using decorators.
Here's a complete example (credit to How to make a chain of function decorators?). You do not need to modify your original classes. In my example the original class is called Working.
# decorator for logging
def logging(func):
def wrapper(*args, **kwargs):
print func.__name__, args, kwargs
res = func(*args, **kwargs)
return res
return wrapper
# this is some example class you do not want to/can not modify
class Working:
def Do(c):
print("I am working")
def pr(c,printit): # other example method
print(printit)
def bla(c): # other example method
c.pr("saybla")
# this is how to make a new class with some methods logged:
class MutantWorking(Working):
pr=logging(Working.pr)
bla=logging(Working.bla)
Do=logging(Working.Do)
h=MutantWorking()
h.bla()
h.pr("Working")
h.Do()
this will print
h.bla()
bla (<__main__.MutantWorking instance at 0xb776b78c>,) {}
pr (<__main__.MutantWorking instance at 0xb776b78c>, 'saybla') {}
saybla
pr (<__main__.MutantWorking instance at 0xb776b78c>, 'Working') {}
Working
Do (<__main__.MutantWorking instance at 0xb776b78c>,) {}
I am working
In addition, I would like to understand why you can not modify a class. Did you try? Because, as an alternative to making a subclass, if you feel dynamic you can almost always modify an old class in place:
Working.Do=logging(Working.Do)
ReturnStatement.Act=logging(ReturnStatement.Act)
Update: Apply logging to all methods of a class
As you now specifically asked for this. You can loop over all members and apply logging to them all. But you need to define a rule for what kind of members to modify. The example below excludes any method with __ in its name .
import types
def hasmethod(obj, name):
return hasattr(obj, name) and type(getattr(obj, name)) == types.MethodType
def loggify(theclass):
for x in filter(lambda x:"__" not in x, dir(theclass)):
if hasmethod(theclass,x):
print(x)
setattr(theclass,x,logging(getattr(theclass,x)))
return theclass
With this all you have to do to make a new logged version of a class is:
#loggify
class loggedWorker(Working): pass
Or modify an existing class in place:
loggify(Working)
There is no "casting" in Python.
Any subclass of a class is considered an instance of its parents. Desired behavior can be achieved by proper calling the superclass methods, and by overriding class attributes.
update: with the advent of static type checking, there is "type casting" - check bellow.
What you can do on your example, is to have to have a subclass initializer that receives the superclass and copies its relevant attributes - so, your MutantReturnstatement could be written thus:
class MutantReturnStatement(ReturnStatement):
def __init__(self, previous_object=None):
if previous_object:
self.attribute = previous_object.attribute
# repeat for relevant attributes
def act(self):
print "I'm wrapping ReturnStatement."
return ReturnStatement().act()
And then change your MutantWorking class to:
class MutantWorking(Working):
def do(self):
print "I am wrapping Working."
return MutantReturnStatement(Working().do())
There are Pythonic ways for not having a lot of self.attr = other.attr lines on the __init__method if there are lots (like, more than 3 :-) ) attributes you want to copy -
the laziest of which wiuld be simply to copy the other instance's __dict__ attribute.
Alternatively, if you know what you are doing, you could also simply change the __class__ attribute of your target object to the desired class - but that can be misleading and carry you to subtle errors (the __init__ method of the subclass would not be called, would not work on non-python defined classes, and other possible problems), I don't recomment this approach - this is not "casting", it is use of introspection to bruteforce an object change and is only included for keeping the answer complete:
class MutantWorking(Working):
def do(self):
print "I am wrapping Working."
result = Working.do(self)
result.__class__ = MutantReturnStatement
return result
Again - this should work, but don't do it - use the former method.
By the way, I am not too experienced with other OO languages, that allow casting - but is casting to a subclass even allowed in any language? Does it make sense? I think casting s only allowed to parentclasses.
update: When one works with type hinting and static analysis in the ways describd in PEP 484, sometimes the static analysis tool can't figure out what is going on. So, there is the typing.cast call: it does absolutely nothing in runtime, just return the same object that was passed to it, but the tools then "learn" that the returned object is of the passed type, and won't complain about it. It will remove typing errors in the helper tool, but I can't emphasise enough it does not have any effect in runtime:
In [18]: from typing import cast
In [19]: cast(int, 3.4)
Out[19]: 3.4
No direct way.
You may define MutantReturnStatement's init like this:
def __init__(self, retStatement):
self.retStatement = retStatement
and then use it like this:
class MutantWorking(Working):
def do(self):
print "I am wrapping Working."
# !!! this is not working, I'd need that casting working !!!
return MutantReturnStatement(Working().do())
And you should get rid from inheriting ReturnStatement in your wrapper, like this
class MutantReturnStatement(object):
def act(self):
print "I'm wrapping ReturnStatement."
return self.retStatement.act()
You don't need casting here. You just need
class MutantWorking(Working):
def do(self):
print "I am wrapping Working."
Working().do()
return MutantReturnStatement()
This will obviously give the correct return and desired printout.
What you do is not a casting, it is a type conversion. Still, you could write something like
def cast_to(mytype: Type[any], obj: any):
if isinstance(obj, mytype):
return obj
else:
return mytype(obj)
class MutantReturnStatement(ReturnStatement):
def __init__(self, *args, **kwargs):
if isinstance(args[0], Working):
pass
# your custom logic here
# for the type conversion.
Usage:
cast_to(MutantReturnStatement, Working()).act()
# or simply
MutantReturnStatement(Working()).act()
(Note that in your example MutantReturnStatement does not have .do() member function.)
Here's my idea: Start with a simple object:
class dynamicObject(object):
pass
And to be able to add pre written methods to it on the fly:
def someMethod(self):
pass
So that I can do this:
someObject = dyncamicObject()
someObject._someMethod = someMethod
someObject._someMethod()
Problem is, it wants me to specify the self part of _someMethod() so that it looks like this:
someObject._someMethod(someObject)
This seems kind of odd since isn't self implied when a method is "attached" to an object?
I'm new to the Python way of thinking and am trying to get away from the same thought process for languages like C# so the idea here it to be able to create an object for validation by picking and choosing what validation methods I want to add to it rather than making some kind of object hierarchy. I figured that Python's "self" idea would work in my favor as I thought the object would implicitly know to send itself into the method attached to it.
One thing to note, the method is NOT attached to the object in any way (Completely different files) so maybe that is the issue? Maybe by defining the method on it's own, self is actually the method in question and therefore can't be implied as the object?
Although below I've tried to answer the literal question, I think
Muhammad Alkarouri's answer better addresses how the problem should actually be solved.
Add the method to the class, dynamicObject, rather than the object, someObject:
class dynamicObject(object):
pass
def someMethod(self):
print('Hi there!')
someObject=dynamicObject()
dynamicObject.someMethod=someMethod
someObject.someMethod()
# Hi there!
When you say someObject.someMethod=someMethod, then someObject.__dict__ gets the key-value pair ('someMethod',someMethod).
When you say dynamicObject.someMethod=someMethod, then someMethod is added to dynamicObject's __dict__. You need someMethod defined in the class for
someObject.someMethod to act like a method call. For more information about this, see Raymond Hettinger's essay on descriptors -- after all, a method is nothing more than a descriptor! -- and Shalabh Chaturvedi's essay on attribute lookup.
There is an alternative way:
import types
someObject.someMethod=types.MethodType(someMethod,someObject,type(someObject))
but this is really an abomination since you are defining 'someMethod' as a key in someObject.__dict__, which is not the right place for methods. In fact, you do not get a class method at all, just a curried function. This is more than a mere technicality. Subclasses of dynamicObject would fail to inherit the someMethod function.
To achieve what you want (create an object for validation by picking and choosing what validation methods I want to add to it), a better way is:
class DynamicObject(object):
def __init__(self, verify_method = None):
self.verifier = verify_method
def verify(self):
self.verifier(self)
def verify1(self):
print "verify1"
def verify2(self):
print "verify2"
obj1 = DynamicObject()
obj1.verifier = verify1
obj2 = DynamicObject(verify2)
#equivalent to
#obj2 = DynamicObject()
#obj2.verify = verify2
obj1.verify()
obj2.verify()
Why don't you use setattr? I found this way much more explicit.
class dynamicObject(object):
pass
def method():
print "Hi"
someObject = dynamicObject()
setattr(someObject,"method", method)
someObject.method()
Sometimes it is annoying to need to write a regular function and add it afterwards when the method is very simple. In that case, lambdas can come to the rescue:
class Square:
pass
Square.getX = lambda self: self.x
Square.getY = lambda self: self.y
Square.calculateArea = lambda self: self.getX() * self.getY()
Hope this helps.
If you just want to wrap another class, and not have to deal with assigning a new method to any instance, you can just make the method in question a staticmethod of the class:
class wrapperClass(object):
#staticmethod
def foo():
print("yay!")
obj = wrapperClass()
obj.foo() // Yay!
And you can then give any other class the .foo method with multiple inheritance.
class fooDict(dict, wrapperClass):
"""Normal dict with foo method"""
foo_dict = fooDict()
foo_dict.setdefault('A', 10)
print(foo_dict) // {'A': 10}
foo_dict.foo() // Yay!