COMP0233: Research Software Engineering With Python


Class design

The concepts we have introduced are common between different object oriented languages. Thus, when we design our program using these concepts, we can think at an architectural level, independent of language syntax.

In Python:

In [1]:
class Particle:
    def __init__(self, position, velocity):
        self.position = position
        self.velocity = velocity
    def move(self, delta_t):
        self.position += self.velocity * delta_t

In C++:

class Particle {
    std::vector<double> position;
    std::vector<double> velocity;
    Particle(std::vector<double> position, std::vector<double> velocity);
    void move(double delta_t);

In Fortran:

type particle
    real :: position
    real :: velocity
    procedure :: init
    procedure :: move
end type particle


UML is a conventional diagrammatic notation used to describe "class structures" and other higher level aspects of software design.

Computer scientists get worked up about formal correctness of UML diagrams and learning the conventions precisely. Working programmers can still benefit from using UML to describe their designs.


We can see a YUML model for a Particle class with position and velocity data and a move() method using the YUML online UML drawing tool (example).


Here's how we can use Python code to get an image back from YUML:

In [2]:
import requests
from IPython.display import Image

def yuml(model):
    result = requests.get("http://yuml.me/diagram/boring/class/" + model)
    return Image(result.content)
In [3]:
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The representation of the Particle class defined above in UML is done with a box with three sections. The name of the class goes on the top, then the name of the member variables in the middle, and the name of the methods on the bottom. We will see later why this is useful.

Information Hiding

Sometimes, our design for a program would be broken if users start messing around with variables we don't want them to change.

Robust class design requires consideration of which subroutines are intended for users to use, and which are internal. Languages provide features to implement this: access control.

In python, we use leading underscores to control whether member variables and methods can be accessed from outside the class:

  • single leading underscore (_) is used to document it's private but people could use it if wanted (thought they shouldn't);
  • double leading underscore (__) raises errors if called.
In [4]:
class MyClass:
    def __init__(self):
        self.__private_data = 0
        self._private_data = 0
        self.public_data = 0
    def __private_method(self): pass
    def _private_method(self): pass
    def public_method(self): pass
    def called_inside(self):
        self.__private_data = 1
        self._private_data = 1

In [5]:
MyClass()._private_method() # Works, but forbidden by convention
In [6]:
MyClass().public_method() # OK

In [7]:
In [8]:
MyClass().__private_method() # Generates error
AttributeError                            Traceback (most recent call last)
Cell In[8], line 1
----> 1 MyClass().__private_method() # Generates error

AttributeError: 'MyClass' object has no attribute '__private_method'
In [9]:
print(MyClass().__private_data) # Generates error
AttributeError                            Traceback (most recent call last)
Cell In[9], line 1
----> 1 print(MyClass().__private_data) # Generates error

AttributeError: 'MyClass' object has no attribute '__private_data'

Property accessors

Python provides a mechanism to make functions appear to be variables. This can be used if you want to change the way a class is implemented without changing the interface:

In [10]:
class Person:
    def __init__(self):
        self.name = "Graham Chapman"

assert(Person().name == "Graham Chapman")


In [11]:
class Person(object):
    def __init__(self):
        self._first = "Graham"
        self._second = "Chapman"

    def name(self):
        return f"{self._first} {self._second}"

assert(Person().name == "Graham Chapman")

Making the same external code work as before.

Note that the code behaves the same way to the outside user. The implementation detail is hidden by private variables. In languages without this feature, such as C++, it is best to always make data private, and always access data through functions:

In [12]:
class Person(object):
    def __init__(self):
        self._name = "Graham Chapman"
    def name(self):  # an access function
        return self._name

assert(Person().name() == "Graham Chapman")

But in Python this is unnecessary because the @property capability.

Another way could be to create a member variable name which holds the full name. However, this could lead to inconsistent data. If we create a get_married function, then the name of the person won't change!

In [13]:
class Person(object):
    def __init__(self, first, second):
        self._first = first
        self._second = second
        self.name = f"{self._first} {self._second}"
    def get_married(self, to):
        self._second = to._second

graham = Person("Graham", "Chapman")
david = Person("David", "Sherlock")
assert(graham.name == "Graham Chapman")
assert(graham.name == "Graham Sherlock")
AssertionError                            Traceback (most recent call last)
Cell In[13], line 14
     12 assert(graham.name == "Graham Chapman")
     13 graham.get_married(david)
---> 14 assert(graham.name == "Graham Sherlock")


This type of situation could makes that the object data structure gets inconsistent with itself. Making variables being out of sync with other variables. Each piece of information should only be stored in once place! In this case, name should be calculated each time it's required as previously shown. In database design, this is called Normalisation.

UML for private/public

We prepend a +/- on public/private member variables and methods:

In [14]:
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Class Members

Class, or static members, belong to the class as a whole, and are shared between instances.

This is an object that keeps a count on how many have been created of it.

In [15]:
class Counted:
    number_created = 0
    def __init__(self):
        Counted.number_created += 1
    def howMany(cls):
        return cls.number_created

Counted.howMany()  # 0
x = Counted()
Counted.howMany()  # 1
z = [Counted() for x in range(5)]
Counted.howMany()  # 6 

The data is shared among all the objects instantiated from that class. Note that in __init__ we are not using self.number_created but the name of the class. The howMany function is not a method of a particular object. It's called on the class, not on the object. This is possible by using the @classmethod decorator.

Inheritance and Polymorphism

Object-based vs Object-Oriented

So far we have seen only object-based programming, not object-oriented programming.

Using Objects doesn't mean your code is object-oriented.

To understand object-oriented programming, we need to introduce polymorphism and inheritance.


  • Inheritance is a mechanism that allows related classes to share code.
  • Inheritance allows a program to reflect the ontology) of kinds of thing in a program.

Ontology and inheritance

  • A bird is a kind of animal
  • An eagle is a kind of bird
  • A starling is also a kind of bird
  • All animals can be born and die
  • Only birds can fly (Ish.)
  • Only eagles hunt
  • Only starlings flock

Inheritance in python

In [16]:
class Animal:
    def beBorn(self):
        print("I exist")
    def die(self): 

class Bird(Animal):
    def fly(self): 

class Eagle(Bird):
    def hunt(self): 
        print("I'm gonna eatcha!")

class Starling(Bird):
    def flew(self): 
        print("I'm flying away!")

I exist
I'm gonna eatcha!

Inheritance terminology

Here are two equivalents definition, one coming from C++ and another from Java:

  • A derived class derives from a base class.
  • A subclass inherits from a superclass.

These are different terms for the same thing. So, we can say:

  • Eagle is a subclass of the Animal superclass.
  • Animal is the base class of the Eagle derived class.

Another equivalent definition is using the synonym child / parent for derived / base class:

  • A child class extends a parent class.

Inheritance and constructors

To use implicitly constructors from a superclass, we can use super as shown below.

In [17]:
class Animal:
    def __init__(self, age):
        self.age = age

class Person(Animal):
    def __init__(self, age, name):
        self.name = name

Read Raymond Hettinger's article about super to see various real examples.

Inheritance UML diagrams

UML shows inheritance with an open triangular arrow pointing from subclass to superclass.

In [18]:
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Aggregation vs Inheritance

If one object has or owns one or more objects, this is not inheritance.

For example, the boids example we saw few weeks ago, could be organised as an overall Model, which it owns several Boids, and each Boid owns two 2-vectors, one for position and one for velocity.

Aggregation in UML

The Boids situation can be represented thus:

In [19]:
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The open diamond indicates Aggregation, the closed diamond composition. (A given boid might belong to multiple models, a given position vector is forever part of the corresponding Boid.)

The asterisk represents cardinality, a model may contain multiple Boids. This is a one to many relationship). Many to many relationship) is shown with * on both sides.

Refactoring to inheritance

Smell: Repeated code between two classes which are both ontologically subtypes of something


In [20]:
class Person:
    def __init__(self, age, job): 
        self.age = age
        self.job = job
    def birthday(self): 
        self.age += 1

class Pet:
    def __init__(self, age, owner): 
        self.age = age
        self.owner = owner
    def birthday(self): 
        self.age += 1


In [21]:
class Animal:
    def __init__(self, age): 
        self.age = age
    def birthday(self): 
        self.age += 1

class Person(Animal):
    def __init__(self, age, job):
        self.job = job
class Pet(Animal):
    def __init__(self, age, owner):
        self.owner = owner


In [22]:
class Dog:
    def noise(self):
        return "Bark"

class Cat:
    def noise(self):
        return "Miaow"

class Pig:
    def noise(self):
        return "Oink"

class Cow:
    def noise(self):
        return "Moo"

animals = [Dog(), Dog(), Cat(), Pig(), Cow(), Cat()]
for animal in animals:

This will print "Bark Bark Miaow Oink Moo Miaow"

If two classes support the same method, but it does different things for the two classes, then if an object is of an unknown class, calling the method will invoke the version for whatever class the instance is an instance of.

Polymorphism and Inheritance

Often, polymorphism uses multiple derived classes with a common base class. However, duck typing in Python means that all that is required is that the types support a common Concept (Such as iterable, or container, or, in this case, the Noisy concept.)

A common base class is used where there is a likely default that you want several of the derived classes to have.

In [23]:
class Animal:
    def noise(self):
        return "I don't make a noise."

class Dog(Animal):
    def noise(self):
        return "Bark"

class Worm(Animal):

class Poodle(Dog):

animals = [Dog(), Worm(), Pig(), Cow(), Poodle()]
for animal in animals:
I don't make a noise.

Undefined Functions and Polymorphism

In the above example, we put in a dummy noise for Animals that don't know what type they are.

Instead, we can explicitly deliberately leave this undefined, and we get a crash if we access an undefined method.

In [24]:
class Animal:

class Worm(Animal):
In [25]:
Worm().noise() # Generates error
AttributeError                            Traceback (most recent call last)
Cell In[25], line 1
----> 1 Worm().noise() # Generates error

AttributeError: 'Worm' object has no attribute 'noise'

Refactoring to Polymorphism

Smell: a function uses a big set of if statements or a case statement to decide what to do:


In [26]:
class Animal:
    def __init__(self, animal_kind): 
        self.animal_kind = animal_kind
    def noise(self): 
        if self.animal_kind == "Dog":
            return "Bark"
        elif self.animal_kind == "Cat":
            return "Miaow"
        elif self.animal_kind == "Cow":
            return "Moo"

which is better replaced by the code above.

Interfaces and concepts

In C++, it is common to define classes which declare dummy methods, called "virtual" methods, which specify the methods which derived classes must implement. Classes which define these methods, but which cannot be instantiated into actual objects, are called "abstract base" classes or "interfaces".

Python's Duck Typing approach means explicitly declaring these is unnesssary: any class concept which implements appropriately named methods will do. These as user-defined concepts, just as "iterable" or "container" are built-in Python concepts. A class is said to "implement an interface" or "satisfy a concept".

Interfaces in UML

Interfaces implementation (a common ancestor that doesn't do anything but defines methods to share) in UML is indicated thus:

In [27]: