JythonBook / chapter5.rst

Chapter 5: Object Oriented Jython

This chapter is going to cover the basics of object oriented programming. If you're familiar with the concepts. I'll start with covering the basic reasons for why you would want to write object oriented code in the first place, then cover all the basic syntax, and finally I'll show you a non-trivial example.

Object oriented programming is a method of programming where you package your code up into bundles of data and behaviour. In Jython, you can define a template for this bundle with a class definition With this first class written, you can then instantiate copies of your object and have them act upon each other. This helps you organize your code into smaller more manageable bundles

Throughout this chapter, I interchangably use Python and Jython - for regular object oriented programming - the two dialects of Python are so similar that there are no meaningful differences between the two languages. Enough introduction text though - let's take a look at some basic syntax to see what this is all about.

Basic Syntax

Writing a class is really simple. It is fundamentally about managing some kind of 'state' and exposing some functions to manipulate that state. In object jargon - we just call those functions 'methods'.

Let's start by creating a car class. The goal is to create an object that will manage it's own location on a two dimensional plane. We want to be able to tell it to turn, move forward, and we want to be able to interrogate the object to find out where it's current location is.

class Car(object):

    NORTH = 0
    EAST = 1
    SOUTH = 2
    WEST = 3

    def __init__(self, x=0, y=0):
        self.x = x
        self.y = y
        self.direction = 0

    def turn_right(self):
        self.direction += 1
        self.direction = self.direction % 4

    def turn_left(self):
        self.direction -= 1
        self.direction = self.direction % 4

    def move(self, distance):
        if self.direction == self.NORTH:
            self.y += distance
        elif self.direction == self.SOUTH:
            self.y -= distance
        elif self.direction == self.EAST:
            self.x += distance
            self.x -= distance

    def position(self):
        return (self.x, self.y)

We'll go over that class definition in detail but right now, let's just see how to create a car, move it around and ask the car where it is.

from car import Car

def test_car():
    c = Car()
    assert (5, 0) ==  c.position()


    assert (5, 3) == c.position()

if __name__ == '__main__':

The best way to think of a class is to think of it like a special kind of function that acts like a factory that generates object instances. For each call to the class - you are creating a new discrete copy of your object.

Once we've created the car instance, we can simply call functions that are attached to the car class and the object will manage it's own location. From the point of view of our test code - we do not need to manage the location of the car - nor do we need to manage the direction that the car is pointing in. We just tell it to move - and it does the right thing.

Let's go over the syntax in detail to see exactly what's going on here.

In Line 1, we declare that our Car object is a subclass of the root "object" class. Python, like many object oriented languages has a 'root' object that all other objects are based off of. This 'object' class defines basic behavior that all classes can reuse.

Python actually has two kinds of classes - 'newstyle' and old style. The old way of declaring classes didn't require you to type 'object' - you'll occassionally see the old-style class usage in some Python code, but it's not consider a good practice Just subclass 'object' for any of your base classes and your life will be simpler[1]_.

Lines 3 to 6 declare class attributes for the direction that any car can point to. These are class attributes so they can be shared across all object instances of the car object.

Now for the good stuff.

Line 8-11 declares the object initializer. In some languages, you might be familiar with a constructor - in Jython, we have an initializer which lets us pass values into an object at the time of creation.

In our initializer, we are setting the initial position of the car to (0, 0) on a 2 dimensional plane and then the direction of the car is initialized to pointing north. Fairly straight forward so far.

The function signature uses Python's default argument list feature so we don't have to explicitly set the initial location to (0,0), but there's a new argument introduced called 'self'. This is a reference to the current object.

Remember - your class definition is creating instances of objects. Once your object is created, it has it's own set of internal variables to manage. Your object will inevitably need to access these as well as any of the classes internal methods. Python will pass a reference to the current object as the first argument to all your instance methods.

If you're coming from some other object oriented language, you're probably familiar with the 'this' variable. Unlike C++ or Java, Python doesn't magically introduce the reference into the namespace of accessible variables, but this is consistent with Python's philosophy of making things explicit for clarity.

When we want to assign the initial x,y position, we just need to assign values on to the name 'x', and 'y' on the object. Binding the values of x and y to self makes the position values accessible to any code that has access to self - namely the other methods of the object. One minor detail here - in Python, you can technically name the arguments however you want. There's nothing stopping you from calling the first argument 'this' instead of 'self', but the community standard is to use 'self' [2].

Line 13 to 19 declare two methods to turn the vehicle in different directions. Notice how the direction is never directly manipulated by the caller of the Car object. We just asked the car to turn, and the car changed it's own internal 'direction' state.

Line 21 to 29 define where the car should move to when we move the car forward. The internal direction variable informs the car how it should manipulate the x and y position. Notice how the caller of the car object never needs to know precisely what direction the car is pointing in. The caller only needs to tell the object to turn and move forward. The particular details of how that message is used is abstracted away.

That's not too bad for a couple dozen lines of code.

This concept of hiding internal details is called encapsulation. This is a core concept in object oriented programming. As you can see from even this simple example - it allows you to structure your code so that you can provide a simplified interface to the users of your code.

Having a simplified interface means that we could have all kinds of behaviour happening behind the function calls to turn and move - but the caller can ignore all those details and concentrate on using the car instead of managing the car.

As long as the method signatures don't change, the caller really doesn't need to care about any of that. We can easily add persistence to this class - so we can save and load the car's state to disk.

First, pull in the pickle module - pickle will let us convert python objects into byte strings that can be restored to full objects later.

import pickle

Now, just add two new methods to load and save the state of the object.

def save(self):
    state = (self.direction, self.x, self.y)
    pickle.dump(state, open('mycar.pickle','wb'))

def load(self):
    state = pickle.load(open('mycar.pickle','rb'))
    (self.direction, self.x, self.y) = state

Simply add calls to save() at the end of the turn and move methods, and the object will automatically save all the relevant internal values to disk.

People who use the car object don't even need to know that it's saving to disk, because the Car object handles it behind the scenes.

def turn_right(self):
    self.direction += 1
    self.direction = self.direction % 4

def turn_left(self):
    self.direction -= 1
    self.direction = self.direction % 4

def move(self, distance):
    if self.direction == self.NORTH:
        self.y += distance
    elif self.direction == self.SOUTH:
        self.y -= distance
    elif self.direction == self.EAST:
        self.x += distance
        self.x -= distance

Now, when you call the turn, or move methods, the car will automatically save itself to disk. If you want to reconstruct the car object's state from a previously saved pickle file, you can simply call the load() method.

Object Attribute Lookups

If you've beeen paying attention, you're probably wondering how the NORTH, SOUTH, EAST and WEST variables got bound to self. We never actually assigned them to the self variable during object initialization - so what's going on when we call move()? How is Jythpon actually resolving the value of those four variables?

Now seems like a good time to show how Jython resolves name lookups.

The direction names actually got bound to the Car class. The Jython object system does a little bit of magic when you try accessing any name against an object, it first searches for anything that was bound to 'self'. If python can't resolve any attribute on self with that name, it goes up the object graph to the class definition. The direction attributes NORTH, SOUTH, EAST, WEST were bound to the class definition - so the name resolution succeeds and we get the value of the class attribute.

An very short example will help clarify this

>>> class Foobar(object):
...   def __init__(self):
...     self.somevar = 42
...   class_attr = 99
>>> obj = Foobar()
>>> obj.somevar
>>> obj.class_attr
>>> obj.not_there
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Foobar' object has no attribute 'not_there'

So the key difference here is what you bind a value to. The values you bind to self are available only to a single object. Values you bind to the class definition are available to all instances of the class. The sharing of class attributes among all instances is a critical distinction because mutating a class attribute will affect all instances. This may cause unintended side effects if you're not paying attention as a variable may change value on you when you aren't expecting it to.

>>> other = Foobar()
>>> other.somevar
>>> other.class_attr
>>> # obj and other will have different values for somevar
>>> obj.somevar = 77
>>> obj.somevar
>>> other.somevar
>>> # Now show that we have the same copy of class_attr
>>> other.class_attr = 66
>>> other.class_attr
>>> obj.class_attr

I think it's important to stress just how transparent Python's object system really is. Object attributes are just stored in a plain python dictionary. You can directly access this dictionary by looking at the __dict__ attribute.

>>> obj = Foobar()
>>> obj.__dict__
{'somevar': 42}

Notice that there are no references to the methods of the class, or the class attribute. I'll reiterate it again - Python is going to just go up your inheritance graph - and go to the class definition to look for the methods of Foobar and the class attributes of foobar.

The same trick can be used to inspect all the attributes of the class, just look into the __dict__ attribute of the class definition and you'll find your class attributes and all the methods that are attached to your class definition

>>> Foobar.__dict__
{'__module__': '__main__',
    'class_attr': 99,
    '__dict__': <attribute '__dict__' of 'Foobar' objects>,
    '__init__': <function __init__ at 1>}

This transparency can be leveraged with dynamic programming techniques using closures and binding new functions into your class definition at runtime. We'll revisit this later in the chapter when we look at generating function dynamically and finally with a short introduction to metaprogramming.

Inheritance and Overloading

In the car example, we subclass from the root object type. You can also subclass your own classes to specialize the behaviour of your objects. You may want to do this if you notice that your code naturally has a structure where you have many different classes that all share some common behaviour.

With objects, you can write one class, and then reuse it using inheritance to automatically gain access to the pre-existing behavior and attributes of the parent class. Your 'base' objects will inherit behaviour from the root 'object' class, but any subsequent subclasses will inherit from your own classes.

Let's take a simple example of using some animal classes to see how this works. Define a module "animals.py" with the following code:

class Animal(object):
def sound(self):
return "I don't make any sounds"
class Goat(Animal):
def sound(self):
return "Bleeattt!"
class Rabbit(Animal):
def jump(self):
return "hippity hop hippity hop"
class Jackalope(Goat, Rabbit):

Now you should be able to explore that module with the jython interpreter:

>>> from animals import *
>>> animal = Animal()
>>> goat = Goat()
>>> rabbit = Rabbit()
>>> jack = Jackalope()
>>> animal.sound()
"I don't make any sounds"
>>> animal.jump()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Animal' object has no attribute 'jump'
>>> rabbit.sound()
"I don't make any sounds"
>>> rabbit.jump()
'hippity hop hippity hop'
>>> goat.sound()
>>> goat.jump()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Goat' object has no attribute 'jump'
>>> jack.jump()
'hippity hop hippity hop'
>>> jack.sound()

Inheritance is a very simple concept, when you declare your class, you simply specify which parent classes you would like to reuse. Your new class can then automatically access all the methods and attributes of the super class. Notice how the goat couldn't jump and the rabbit couldn't make any sound, but the Jackalope had access to methods from both the rabbit and the goat.

With single inheritance - when your class simply inherits from one parent class - the rules for resolving where to find an attribute or a method are very straight forward. Jython just looks up to the parent if the current object doesn't have a matching attribute.

It's important to point out now that the Rabbit class is a type of Animal - the Python runtime can tell you that programmatically by using the isinstance function

>>> isinstance(bunny, Rabbit)
>>> isinstance(bunny, Animal)
>>> isinstance(bunny, Goat)

For many classes, you may want to extend the behavior of the parent class instead of just completley overriding it. For this, you'll want to use the super(). Let's specialize the Rabbit class like this.

class EasterBunny(Rabbit):
    def sound(self):
        orig = super(EasterBunny, self).sound()
        return "%s - but I have eggs!" % orig

If you now try making this rabbit speak, it will extend the original sound() method from the base Rabbit class

>>> bunny = EasterBunny()
>>> bunny.sound()
"I don't make any sounds - but I have eggs!"

That wasn't so bad. For these examples, I only demonstrated that inherited methods can be invoked, but you can do exactly the same thing with attributes that are bound to the self.

For multiple inheritance, things get very tricky. In fact, the rules for resolving how attributes are looked up would easily fill an entire chapter (look up "The Python 2.3 Method Resolution Order" on Google if you don't believe me). There's not enough space in this chapter to properly cover the topic which should be a good indication to you that you really don't want to use multiple inheritance.

More advanced abstraction

Abstraction using plain classes is wonderful and all, but it's even better if your code seems to naturally fit into the syntax of the language. Python supports a variety of underscore methods - methods that start and end with double "_" signs that let you overload the behaviour of your objects. This means that your objects will seem to integrate more tightly with the language itself.

With the underscore methods, you can give you objects behaviour for logical and mathematical operations. You can even make your objects behave more like standard builtin types like lists, sets or dictionaries.

from __future__ import with_statement from contextlib import closing

with closing(open('simplefile','w')) as fout:
with closing(open('simplefile','r')) as fin:
print fin.readlines()

The above snippet of code just opens a file, writes a little bit of text and then we just read the contents out. Not terriblly exciting. Most objects in Python are serializable to strings using the pickle module. We can leverage pickle to write out full blown objects to disk. Let's see the functional version of this:

from __future__ import with_statement
from contextlib import closing
from pickle import dumps, loads

def write_object(fout, obj):
    data = dumps(obj)
    fout.write("%020d" % len(data))

def read_object(fin):
    length = int(fin.read(20))
    obj = loads(fin.read(length))
    return obj

class Simple(object):
    def __init__(self, value):
        self.value = value
    def __unicode__(self):
        return "Simple[%s]" % self.value

with closing(open('simplefile','wb')) as fout:
    for i in range(10):
        obj = Simple(i)
        write_object(fout, obj)

print "Loading objects from disk!"
print '=' * 20

with closing(open('simplefile','rb')) as fin:
    for i in range(10):
        print read_object(fin)

This should output something like this:

Loading objects from disk!

So now we're doing something interesting. Let's look at exactly what happening here.

First, you'll notice that the Simple object is rendering a nice - the Simple object can render itself using the __unicode__ method. This is clearly an improvement over the earlier rendering of the object with angle brackets and a hex code.

The write_object function is fairly straight forward, we're just converting our objects into strings using the pickle module, computing the length of the string and then writing the length and the actual serialized object to disk.

This is fine, but the read side is a bit clunky. We don't really know when to stop reading. We can fix this using the iteration protocol. Which bring us to one of my favourite reasons to use objects at all in Python.


In Python, we have 'duck typing'. If it sounds like a duck, quacks like a duck and looks like a duck - well - it's a duck. This is in stark contrast to more rigid languagse like C# or Java which have formal interface definitions. One of the nice benefits of having duck typing is that Python has the notion of object 'protocols'.

If you happen to implement the right methods - python will recognize your object as a certain type of 'thing'.

Iterators are objects that look like lists that let you read the next object. Implementing an iterator protocol is straight forward - just implement a next() method and a __iter__ method and you're ready to rock and roll. Let's see this in action:

class PickleStream(object):
    This stream can be used to stream objects off of a raw file stream
    def __init__(self, file):
        self.file = file

    def write(self, obj):
        data = dumps(obj)
        length = len(data)
        self.file.write("%020d" % length)

    def __iter__(self):
        return self

    def next(self):
        data = self.file.read(20)
        if len(data) == 0:
            raise StopIteration
        length = int(data)well
        return loads(self.file.read(length))

    def close(self):

The above class will let you wrap a simple file object and you can now send it raw python objects to write to a file, or you can read objects out as if the stream was just a list of objects. Writing and reading becomes much simpler

with closing(PickleStream(open('simplefile','wb'))) as stream:
    for i in range(10):
        obj = Simple(i)

with closing(PickleStream(open('simplefile','rb'))) as stream:
    for obj in stream:
        print obj

Abstracting out the details of serialization into the PickleStream lets us 'forget' about the details of how we are writing to disk. All we care about is that the object will do the right thing when we call the write() method.

The iteration protocol can be used for much more advanced uses, but even with this example, it should be obvious how useful it is. While you could implement the reading behaviour with a read() mo loethod, just using the stream as something you can loop over makes the code much easier to understand.

An aside a common problem that everyone seems to have

One particular snag that seems to catch every python programmer is when you use default values in a method signature.

>>> class Tricky(object):
...   def mutate(self, x=[]):
...     x.append(1)
...     return x
>>> obj = Tricky()
>>> obj.mutate()
>>> obj.mutate()
[1, 1]
>>> obj.mutate()
[1, 1, 1]

What's happening here is that the instance method 'mutate' is an object. The method object stores the default value for 'x' in an attribute inside the method object. So when you go and mutate the list, you're actually changing the value of an attribute of the method itself. Remember - this happens because when you invoke the mutate method, you're just accessing a callable attribute on the Tricky object.

Runtime binding of methods

One interesting feature in Python is that instance methods are actually just attributes hanging off of the class defintion - the functions are just attributes like any other variable, except that they happen to be 'callable'.

It's even possible to create and bind in functions to a class definition at runtime using the new module to create instance methods. In the following example, you can see that it's possible to define a class with nothing in it, and then bind methods to the class definition at runtime.

>>> def some_func(self, x, y):
...   print "I'm in object: %s" % self
...   return x * y
>>> import new
>>> class Foo(object): pass
>>> f = Foo()
>>> f
<__main__.Foo object at 0x1>
>>> Foo.mymethod = new.instancemethod(some_func, f, Foo)
>>> f.mymethod(6,3)
I'm in object: <__main__.Foo object at 0x1>

When you invoke the 'mymethod' method, the same attribute lookup machinery is being invoked. Python looks up the name against the 'self' object. When it can't find anything there, it goes to the class definition. When it finds it there, the instancemethod object is returned. The function is then caled with two arguments and you get to see the final result.

This kind of dynamism in Jython is extremely powerful. You can write code that generates functions at program runtime and then bind those functions to objects. You can do all of this because in Jython, classes are what are known as 'first class objects'. The class definition itself is an actual object - just like any other object. Manipulating classes is as easy as manipulating any other object.

Closures and Passing Objects

Python supports the notion of nested scopes - this can be used by to preserve some state information inside of another function. This technique isn't all that common outside of dynamic languages, so you may have never seen this before. Let's look at a simple example

def adder(x):
    def inner(y):
        return x + y
    return inner

>>> func = adder(5)
>>> func
<function inner at 0x7adf0>
>>> func(8)

This is pretty cool - we can actually create functions from templates of other functions. If you can think of a way to parameterize the behavior of a function, it becomes possible to create new functions dynamically. You can think of currying as yet another way of creating templates - this time you are creating a template for new functions.

This is a tremendously powerful tool once you gain some experience with it. Remember - everything in python is an object - even functions are first class objects in Python so you can pass those in as arguments as well. A practical use of this is to partially construct new functions from 'base' functions with some basic known behavior.

Let's take the previous adder closure and convert it to a more general form

def arith(math_func, x):
    def inner(y):
        return math_func(x, y)
    return inner

def adder(x, y):
    return x + y

>>> func = arith(adder, 91)
>>> func(5)

This technique is called currying - you're now creating new function objects based on previous functions. The most common use for this is to create decorators. In Python, you can define special kinds of objects that wrap up your methods and add extra behavior. Some decorators are builtin already like 'property', 'classmethod' and 'staticmethod'. Once you have a decorator, you can sprinkle it on to of another function to add new behavior.

Decorator syntax looks something like this

@decorator_func_name(arg1, arg2, arg3, ...)
def some_functions(x, y, z, ...):
    # Do something useful here

Suppose we have some method that requires intensive computational resoures to run, but the results do not vary much over time. Wouldn't it be nice if we could cache the results so that the computation wouldn't have to run each and every time?

Here's our class with a slow computation method

import time
class Foobar(object):
    def slow_compute(self, *args, **kwargs):
        return args, kwargs, 42

Now let's cache the value using a decorator function. Our strategy is that for any function named X with some argument list, we want to create a unique name and save the final computed value to that name. We want our cached value to have a human readable name, we we want to reuse the original function name, as well as the arguments that were passed in the first time.

Let's get to some code!

import hashlib
def cache(func):
    This decorator will add a _cache_functionName_HEXDIGEST
    attribute after the first invocation of an instance method to
    store cached values.
    # Obtain the function's name
    func_name = func.func_name
    # Compute a unique value for the unnamed and named arguments
    arghash = hashlib.sha1(str(args) + str(kwargs)).hexdigest()
    cache_name = '_cache_%s_%s' % (func_name, arghash)
    def inner(self, *args, **kwargs):
        if hasattr(self, cache_name):
            # If we have a cached value, just use it
            print "Fetching cached value from : %s" % cache_name
            return getattr(self, cache_name)
        result = func(self, *args, **kwargs)
        setattr(self, cache_name, result)
        return result
    return inner

There are only two new tricks that are in this code.

  1. I'm using the hashlib module to convert the arguments to the function into a unique single string.
  2. The use of getattr, hasattr and setattr to manipulate the cached value on the instance object.

Now, if we want to cache the slow method, we just throw on a @cache line above the method declaration.

def slow_compute(self, *args, **kwargs):
    return args, kwargs, 42

Fantastic - we can reuse this cache decorator for any method we want now. Let's suppose now that we want our cache to invalidate itself after every 3 calls. This practical use of currying is only a slight modification to the original caching code.

import hashlib
def cache(loop_iter):
    def function_closure(func):
        func_name = func.func_name
        def closure(self, loop_iter, *args, **kwargs):
            arghash = hashlib.sha1(str(args) + str(kwargs)).hexdigest()
            cache_name = '_cache_%s_%s' % (func_name, arghash)
            counter_name = '_counter_%s_%s' % (func_name, arghash)
            if hasattr(self, cache_name):
                # If we have a cached value, just use it
                print "Fetching cached value from : %s" % cache_name
                loop_iter -= 1
                setattr(self, counter_name, loop_iter)
                result = getattr(self, cache_name)
                if loop_iter == 0:
                    delattr(self, counter_name)
                    delattr(self, cache_name)
                    print "Cleared cached value"
                return result
            result = func(self, *args, **kwargs)
            setattr(self, cache_name, result)
            setattr(self, counter_name, loop_iter)
            return result
        return closure
    return function_closure

Now we're free to use @cache for any slow method and caching will come in for free - including automatic invalidation of the cached value. Just use it like this

def slow_compute(self, *args, **kwargs):
    # TODO: stuff goes here...

Review - and a taste of how we could fit all of this together

Now - I'm going to ask you to use your imagination a litte. We've covered quite a bit of ground really quickly.

We can :

  • look up attributes in an object (use the __dict__ attribute).
  • check if an object belongs to a particular class hierarchy (use the isinstance function).
  • build functions out of other functions using currying.and even bind those functions to arbitrary names

This is fantastic - we now have all the basic building blocks we need to generate complex methods based on the attributes of our class. Imagine a simplified addressbook application with a simple contact.

class Contact(object):
    first_name = str
    last_name = str
    date_of_birth = datetime.Date

Assuming we know how to save and load to a database, we can use the function generation techniques to automatically generate load() and save() methods and bind them into our Contact class. We can use our introspection techniques to determine what attributes need to be saved to our database. We could even grow special methods onto our Contact class so that we could iterate over all of the class attributes and magically grow 'searchby_first_name' and 'searchby_last_name' methods.

See how powerful this can be? We can write extremly minimal code, and we could code generate all of our required specialized behavior for saving, loading and searching for records in a database. Since we do all of that programmatically - we can dramatically reduce the amount of code that we have to write by hand and by doing so - we can redue the chance that we introduce bugs into our system.

We're going to do exactly that in a later chapter. Build a simple database abstraction layer to demonstrate how to create your own object system that will automatically know how to read and write to a database.

[1]New style classes provide a large number of useful features that simply aren't available to old-style classes. If you end up mixing old and new style classes together, you'll usually get unexpected behaviour that will surprise you - and not in the good way. It'll surprise you in the kind of way that will keep you up late at night wondering why your code doesn't work and you'll curse the fact that both styles of classes exist at all.
[2]One of Python's strengths is legibility - of your code and other code. Community standards help the legibility of code tremendously.
Tip: Filter by directory path e.g. /media app.js to search for public/media/app.js.
Tip: Use camelCasing e.g. ProjME to search for ProjectModifiedEvent.java.
Tip: Filter by extension type e.g. /repo .js to search for all .js files in the /repo directory.
Tip: Separate your search with spaces e.g. /ssh pom.xml to search for src/ssh/pom.xml.
Tip: Use ↑ and ↓ arrow keys to navigate and return to view the file.
Tip: You can also navigate files with Ctrl+j (next) and Ctrl+k (previous) and view the file with Ctrl+o.
Tip: You can also navigate files with Alt+j (next) and Alt+k (previous) and view the file with Alt+o.