Discussion 6: Object-Oriented Programming, Iterators and Generators

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OOP

In a previous lecture, you were introduced to the programming paradigm known as Object-Oriented Programming (OOP). OOP allows us to treat data as objects - like we do in real life.

For example, consider the class Student. Each of you as individuals is an instance of this class. So, a student Angela would be an instance of the class Student.

Details that all CS 61A students have, such as name, are called instance variables. Every student has these variables, but their values differ from student to student. A variable that is shared among all instances of Student is known as a class variable. An example would be the num_slip_days_allowed attribute; the number of slip days that students can use during the semester is not a property of any given student but rather of all of them.

All students are able to do homework, attend lecture, and go to office hours. When functions belong to a specific object, they are said to be methods. In this case, these actions would be bound methods of Student objects.

Here is a recap of what we discussed above:

• class: a template for creating objects
• instance: a single object created from a class
• instance variable: a data attribute of an object, specific to an instance
• class attribute: a data attribute of an object, shared by all instances of a class
• method: an action (function) that all instances of a class may perform

Questions

Q1: OOP WWPD - Student

Below we have defined the classes Professor and Student, implementing some of what was described above. Remember that we pass the self argument implicitly to instance methods when using dot-notation.

class Student:
    num_students = 0 # this is a class attribute
    def __init__(self, name, staff):
        self.name = name # this is an instance attribute
        self.understanding = 0
        Student.num_students += 1
        print("There are now", Student.num_students, "students")
        staff.add_student(self)

    def visit_office_hours(self, staff):
        staff.assist(self)
        print("Thanks, " + staff.name)

class Professor:
    def __init__(self, name):
        self.name = name
        self.students = {}

    def add_student(self, student):
        self.students[student.name] = student

    def assist(self, student):
        student.understanding += 1

What will the following lines output?

>>> callahan = Professor("Callahan")
>>> elle = Student("Elle", callahan)

Your Answer:

>>> elle.visit_office_hours(callahan)

Your Answer:

>>> elle.visit_office_hours(Professor("Paulette"))

Your Answer:

>>> elle.understanding

Your Answer:

>>> [name for name in callahan.students]

Your Answer:

>>> x = Student("Vivian", Professor("Stromwell")).name

Your Answer:

>>> x

Your Answer:

>>> [name for name in callahan.students]

Your Answer:

Q2: (Tutorial) Email

We would like to write three different classes (Server, Client, and Email) to simulate a system for sending and receiving email. Fill in the definitions below to finish the implementation!

Important: We suggest that you approach this problem by first filling out the Email class, then the register_client method of Server, the Client class, and lastly the send method of the Server class.

Run in 61A Code

Inheritance

Python classes can implement a useful abstraction technique known as inheritance. To illustrate this concept, consider the following Dog and Cat classes.

class Dog():
    def __init__(self, name, owner):
        self.is_alive = True
        self.name = name
        self.owner = owner
    def eat(self, thing):
        print(self.name + " ate a " + str(thing) + "!")
    def talk(self):
        print(self.name + " says woof!")

class Cat():
    def __init__(self, name, owner, lives=9):
        self.is_alive = True
        self.name = name
        self.owner = owner
        self.lives = lives
    def eat(self, thing):
        print(self.name + " ate a " + str(thing) + "!")
    def talk(self):
        print(self.name + " says meow!")

Notice that because dogs and cats share a lot of similar qualities, there is a lot of repeated code! To avoid redefining attributes and methods for similar classes, we can write a single base class from which the similar classes inherit. For example, we can write a class called Pet and redefine Dog as a subclass of Pet:

class Pet():
    def __init__(self, name, owner):
        self.is_alive = True    # It's alive!!!
        self.name = name
        self.owner = owner
    def eat(self, thing):
        print(self.name + " ate a " + str(thing) + "!")
    def talk(self):
        print(self.name)

class Dog(Pet):
    def talk(self):
        print(self.name + ' says woof!')

Inheritance represents a hierarchical relationship between two or more classes where one class is a (no relation to the Python is operator) more specific version of the other, e.g. a dog is a pet. Because Dog inherits from Pet, we didn't have to redefine __init__ or eat. However, since we want Dog to talk in a way that is unique to dogs, we did override the talk method.

We can use the super() function to refer to a class's superclass. For example, calling super() with the class definition of Dog allows us to refer to the Pet class.

Here's an example of an alternate equivalent definition of Dog that uses super() to explicitly call the __init__ method of the parent class:

class Dog(Pet):
    def __init__(self, name, owner):
        super().__init__(name, owner)
        # this is equivalent to calling Pet.__init__(self, name, owner)
    def talk(self):
        print(self.name + ' says woof!')

Keep in mind that creating the __init__ function shown above is actually not necessary, because creating a Dog instance will automatically call the _init__ method of Pet. Normally when defining an __init__ method in a subclass, we take some additional action to calling super().__init__. For example, we could add a new instance variable like the following:

def __init__(self, name, owner, has_floppy_ears):
    super().__init__(name, owner)
    self.has_floppy_ears = has_floppy_ears

Questions

Q3: Cat

Below is a skeleton for the Cat class, which inherits from the Pet class. To complete the implementation, override the __init__ and talk methods and add a new lose_life method. We have included the Pet class as well for your convenience.

Hint: You can call the __init__ method of Pet to set a cat's name and owner.

Run in 61A Code

Q4: (Tutorial) NoisyCat

More cats! Fill in this implemention of a class called NoisyCat, which is just like a normal Cat. However, NoisyCat talks a lot -- twice as much as a regular Cat! If you'd like to test your code, feel free to copy over your solution to the Cat class above. Run in 61A Code

Iterators

An iterable is a data type which contains a collection of values which can be processed one by one sequentially. Some examples of iterables we've seen include lists, tuples, strings, and dictionaries. In general, any object that can be iterated over in a for loop can be considered an iterable.

While an iterable contains values that can be iterated over, we need another type of object called an iterator to actually retrieve values contained in an iterable. Calling the iter function on an iterable will create an iterator over that iterable. Each iterator keeps track of its position within the iterable. Calling the next function on an iterator will give the current value in the iterable and move the iterator's position to the next value.

In this way, the relationship between an iterable and an iterator is analogous to the relationship between a book and a bookmark - an iterable contains the data that is being iterated over, and an iterator keeps track of your position within that data.

Once an iterator has returned all the values in an iterable, subsequent calls to next on that iterable will result in a StopIteration exception. In order to be able to access the values in the iterable a second time, you would have to create a second iterator. Check out the example below:

>>> a = [1, 2]
>>> a_iter = iter(a)
>>> next(a_iter)
1
>>> next(a_iter)
2
>>> next(a_iter)
StopIteration

Iterables can be used in for loops and as arguments to functions that require a sequence (e.g. map and zip). For example:

>>> for n in range(2):
...     print(n)
...
0
1

This works because the for loop implicitly creates an iterator using the __iter__ method. Python then repeatedly calls next repeatedly on the iterator, until it raises StopIteration. In other words, the loop above is (basically) equivalent to:

range_iterator = iter(range(2))
is_done = False
while not is_done:
    try:
        val = next(range_iterator)
        print(val)
    except StopIteration:
        is_done = True

One important application of iterables and iterators is the for loop. We've seen how we can use for loops to iterate over iterables like lists and dictionaries.

This only works because the for loop implicitly creates an iterator using the built-in iter function. Python then calls next repeatedly on the iterator, until it raises StopIteration.

Most iterators are also iterables - that is, calling iter on them will return an iterator. This means that we can use them inside for loops. However, calling iter on most iterators will not create a new iterator - instead, it will simply return the same iterator.

We can also iterate over iterables in a list comprehension or pass in an iterable to the built-in function list in order to put the items of an iterable into a list.

In addition to the sequences we've learned, Python has some built-in ways to create iterables and iterators. Here are a few useful ones:

• range(start, end) returns an iterable containing numbers from start to end-1. If start is not provided, it defaults to 0. Check out the docs for more details.
• map(f, iterable) returns a new iterator containing the values resulting from applying f to each value in iterable. Check out the docs for more details and other uses of map, such as passing in multiple iterables.
• filter(f, iterable) returns a new iterator containing only the values in iterable for which f(value) returns True. Check out the docs for more details.

Questions

Q5: Iterators WWPD

What would Python display?

>>> s = [[1, 2]]
>>> i = iter(s)
>>> j = iter(next(i))
>>> next(j)

Your Answer:

>>> s.append(3)
>>> next(i)

Your Answer:

>>> next(j)

Your Answer:

>>> next(i)

Your Answer:

Generators

A generator function is a special kind of Python function that uses a yield statement instead of a return statement to report values. When a generator function is called, it returns a generator object, which is a type of iterator. Below, you can see a function that returns an iterator over the natural numbers.

>>> def gen_naturals():
...     current = 0
...     while True:
...         yield current
...         current += 1
>>> gen = gen_naturals()
>>> gen
<generator object gen at ...>
>>> next(gen)
0
>>> next(gen)
1

The yield statement is similar to a return statement. However, while a return statement closes the current frame after the function exits, a yield statement causes the frame to be saved until the next time next is called, which allows the generator to automatically keep track of the iteration state.

Once next is called again, execution resumes where it last stopped and continues until the next yield statement or the end of the function. A generator function can have multiple yield statements.

Including a yield statement in a function automatically tells Python that this function will create a generator. When we call the function, it returns a generator object instead of executing the body. When the generator's next method is called, the body is executed until the next yield statement is executed.

When yield from is called on an iterator, it will yield every value from that iterator. It's similar to doing the following:

for x in an_iterator:
    yield x

Questions

Q6: Filter-Iter

Implement a generator function called filter_iter(iterable, fn) that only yields elements of iterable for which fn returns True. Run in 61A Code

Q7: (Tutorial) Merge

Write a generator function merge that takes in two infinite generators a and b that are in increasing order without duplicates and returns a generator that has all the elements of both generators, in increasing order, without duplicates. Run in 61A Code