Lab 7: Iterators and Generators, Object-Oriented Programming

Due by 11:59pm on Tuesday, July 20.

Starter Files

Download lab07.zip. Inside the archive, you will find starter files for the questions in this lab, along with a copy of the Ok autograder.

Topics

Consult this section if you need a refresher on the material for this lab. It's okay to skip directly to the questions and refer back here should you get stuck.

Iterators

An iterable is any object that can be iterated through, or gone through one element at a time. One construct that we've used to iterate through an iterable is a for loop:

for elem in iterable:
    # do something

for loops work on any object that is iterable. We previously described it as working with any sequence -- all sequences are iterable, but there are other objects that are also iterable! We define an iterable as an object on which calling the built-in function iter function returns an iterator. An iterator is another type of object that allows us to iterate through an iterable by keeping track of which element is next in the sequence.

To illustrate this, consider the following block of code, which does the exact same thing as a the for statement above:

iterator = iter(iterable)
try:
    while True:
        elem = next(iterator)
        # do something
except StopIteration:
    pass

Here's a breakdown of what's happening:

  • First, the built-in iter function is called on the iterable to create a corresponding iterator.
  • To get the next element in the sequence, the built-in next function is called on this iterator.
  • When next is called but there are no elements left in the iterator, a StopIteration error is raised. In the for loop construct, this exception is caught and execution can continue.

Calling iter on an iterable multiple times returns a new iterator each time with distinct states (otherwise, you'd never be able to iterate through a iterable more than once). You can also call iter on the iterator itself, which will just return the same iterator without changing its state. However, note that you cannot call next directly on an iterable.

Let's see the iter and next functions in action with an iterable we're already familiar with -- a list.

>>> lst = [1, 2, 3, 4]
>>> next(lst)             # Calling next on an iterable
TypeError: 'list' object is not an iterator
>>> list_iter = iter(lst) # Creates an iterator for the list
>>> list_iter
<list_iterator object ...>
>>> next(list_iter)       # Calling next on an iterator
1
>>> next(list_iter)       # Calling next on the same iterator
2
>>> next(iter(list_iter)) # Calling iter on an iterator returns itself
3
>>> list_iter2 = iter(lst)
>>> next(list_iter2)      # Second iterator has new state
1
>>> next(list_iter)       # First iterator is unaffected by second iterator
4
>>> next(list_iter)       # No elements left!
StopIteration
>>> lst                   # Original iterable is unaffected
[1, 2, 3, 4]

Since you can call iter on iterators, this tells us that that they are also iterables! Note that while all iterators are iterables, the converse is not true - that is, not all iterables are iterators. You can use iterators wherever you can use iterables, but note that since iterators keep their state, they're only good to iterate through an iterable once:

>>> list_iter = iter([4, 3, 2, 1])
>>> for e in list_iter:
...     print(e)
4
3
2
1
>>> for e in list_iter:
...     print(e)

Analogy: An iterable is like a book (one can flip through the pages) and an iterator for a book would be a bookmark (saves the position and can locate the next page). Calling iter on a book gives you a new bookmark independent of other bookmarks, but calling iter on a bookmark gives you the bookmark itself, without changing its position at all. Calling next on the bookmark moves it to the next page, but does not change the pages in the book. Calling next on the book wouldn't make sense semantically. We can also have multiple bookmarks, all independent of each other.

Iterable Uses

We know that lists are one type of built-in iterable objects. You may have also encountered the range(start, end) function, which creates an iterable of ascending integers from start (inclusive) to end (exclusive).

>>> for x in range(2, 6):
...     print(x)
...
2
3
4
5

Ranges are useful for many things, including performing some operations for a particular number of iterations or iterating through the indices of a list.

There are also some built-in functions that take in iterables and return useful results:

  • map(f, iterable) - Creates an iterable over f(x) for x in iterable. In some cases, computing a list of the values in this iterable will give us the same result as [func(x) for x in iterable]. However, it's important to keep in mind that iterators can potentially have infinite values because they are evaluated lazily, while lists cannot have infinite elements.
  • filter(f, iterable) - Creates iterator over x for each x in iterable if f(x)
  • zip(iterables*) - Creates an iterable over co-indexed tuples with elements from each of the iterables
  • reversed(iterable) - Creates iterator over all the elements in the input iterable in reverse order
  • list(iterable) - Creates a list containing all the elements in the input iterable
  • tuple(iterable) - Creates a tuple containing all the elements in the input iterable
  • sorted(iterable) - Creates a sorted list containing all the elements in the input iterable
  • reduce(f, iterable) - Must be imported with functools. Apply function of two arguments f cumulatively to the items of iterable, from left to right, so as to reduce the sequence to a single value.

Generators

We can create our own custom iterators by writing a generator function, which returns a special type of iterator called a generator. Generator functions have yield statements within the body of the function instead of return statements. Calling a generator function will return a generator object and will not execute the body of the function.

For example, let's consider the following generator function:

def countdown(n):
    print("Beginning countdown!")
    while n >= 0:
        yield n
        n -= 1
    print("Blastoff!")

Calling countdown(k) will return a generator object that counts down from k to 0. Since generators are iterators, we can call iter on the resulting object, which will simply return the same object. Note that the body is not executed at this point; nothing is printed and no numbers are output.

>>> c = countdown(5)
>>> c
<generator object countdown ...>
>>> c is iter(c)
True

So how is the counting done? Again, since generators are iterators, we call next on them to get the next element! The first time next is called, execution begins at the first line of the function body and continues until the yield statement is reached. The result of evaluating the expression in the yield statement is returned. The following interactive session continues from the one above.

>>> next(c)
Beginning countdown!
5

Unlike functions we've seen before in this course, generator functions can remember their state. On any consecutive calls to next, execution picks up from the line after the yield statement that was previously executed. Like the first call to next, execution will continue until the next yield statement is reached. Note that because of this, Beginning countdown! doesn't get printed again.

>>> next(c)
4
>>> next(c)
3

The next 3 calls to next will continue to yield consecutive descending integers until 0. On the following call, a StopIteration error will be raised because there are no more values to yield (i.e. the end of the function body was reached before hitting a yield statement).

>>> next(c)
2
>>> next(c)
1
>>> next(c)
0
>>> next(c)
Blastoff!
StopIteration

Separate calls to countdown will create distinct generator objects with their own state. Usually, generators shouldn't restart. If you'd like to reset the sequence, create another generator object by calling the generator function again.

>>> c1, c2 = countdown(5), countdown(5)
>>> c1 is c2
False
>>> next(c1)
5
>>> next(c2)
5

Here is a summary of the above:

  • A generator function has a yield statement and returns a generator object.
  • Calling the iter function on a generator object returns the same object without modifying its current state.
  • The body of a generator function is not evaluated until next is called on a resulting generator object. Calling the next function on a generator object computes and returns the next object in its sequence. If the sequence is exhausted, StopIteration is raised.
  • A generator "remembers" its state for the next next call. Therefore,

    • the first next call works like this:

      1. Enter the function and run until the line with yield.
      2. Return the value in the yield statement, but remember the state of the function for future next calls.
    • And subsequent next calls work like this:

      1. Re-enter the function, start at the line after the yield statement that was previously executed, and run until the next yield statement.
      2. Return the value in the yield statement, but remember the state of the function for future next calls.
  • Calling a generator function returns a brand new generator object (like calling iter on an iterable object).
  • A generator should not restart unless it's defined that way. To start over from the first element in a generator, just call the generator function again to create a new generator.

Another useful tool for generators is the yield from statement (introduced in Python 3.3). yield from will yield all values from an iterator or iterable.

>>> def gen_list(lst):
...     yield from lst
...
>>> g = gen_list([1, 2, 3, 4])
>>> next(g)
1
>>> next(g)
2
>>> next(g)
3
>>> next(g)
4
>>> next(g)
StopIteration

Object-Oriented Programming

Minilecture Video: OOP

Object-oriented programming (OOP) is a style of programming that allows you to think of code in terms of "objects." Here's an example of a Car class:

class Car:
    num_wheels = 4

    def __init__(self, color):
        self.wheels = Car.num_wheels
        self.color = color

    def drive(self):
        if self.wheels <= Car.num_wheels:
            return self.color + ' car cannot drive!'
        return self.color + ' car goes vroom!'

    def pop_tire(self):
        if self.wheels > 0:
            self.wheels -= 1

Here's some terminology:

  • class: a blueprint for how to build a certain type of object. The Car class (shown above) describes the behavior and data that all Car objects have.
  • instance: a particular occurrence of a class. In Python, we create instances of a class like this:

    >>> my_car = Car('red')

    my_car is an instance of the Car class.

  • data attributes: a variable that belongs to the instance (also called instance variables). Think of a data attribute as a quality of the object: cars have wheels and color, so we have given our Car instance self.wheels and self.color attributes. We can access attributes using dot notation:

    >>> my_car.color
    'red'
    >>> my_car.wheels
    4
  • method: Methods are just like normal functions, except that they are bound to an instance. Think of a method as a "verb" of the class: cars can drive and also pop their tires, so we have given our Car instance the methods drive and pop_tire. We call methods using dot notation:

    >>> my_car = Car('red')
    >>> my_car.drive()
    'red car goes vroom!'
  • constructor: As with data abstraction, constructors build an instance of the class. The constructor for car objects is Car(color). When Python calls that constructor, it immediately calls the __init__ method. That's where we initialize the data attributes:

    def __init__(self, color):
        self.wheels = Car.num_wheels
        self.color = color

    The constructor takes in one argument, color. As you can see, this constructor also creates the self.wheels and self.color attributes.

  • self: in Python, self is the first parameter for many methods (in this class, we will only use methods whose first parameter is self). When a method is called, self is bound to an instance of the class. For example:

    >>> my_car = Car('red')
    >>> car.drive()

    Notice that the drive method takes in self as an argument, but it looks like we didn't pass one in! This is because the dot notation implicitly passes in car as self for us.


Required Questions

Iterators and Generators

Generators also allow us to represent infinite sequences, such as the sequence of natural numbers (1, 2, ...) shown in the function below!

Relevant Topics: Iterators and Generators

def naturals():
    """A generator function that yields the infinite sequence of natural
    numbers, starting at 1.

    >>> m = naturals()
    >>> type(m)
    <class 'generator'>
    >>> [next(m) for _ in range(10)]
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    """
    i = 1
    while True:
        yield i
        i += 1

Q1: Scale

Write a generator function scale(it, multiplier) which yields the elements of the iterable it, multiplied by multiplier.

As an extra challenge, try writing this function using a yield from statement! A yield from statement yields the values from an iterator one at a time.

def scale(it, multiplier):
    """Yield elements of the iterable it multiplied by a number multiplier.

    >>> m = scale([1, 5, 2], 5)
    >>> type(m)
    <class 'generator'>
    >>> list(m)
    [5, 25, 10]

    >>> m = scale(naturals(), 2)
    >>> [next(m) for _ in range(5)]
    [2, 4, 6, 8, 10]
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q scale

Q2: Hailstone

Write a generator function that outputs the hailstone sequence starting at number n.

Here's a quick reminder of how the hailstone sequence is defined:

  1. Pick a positive integer n as the start.
  2. If n is even, divide it by 2.
  3. If n is odd, multiply it by 3 and add 1.
  4. Continue this process until n is 1.

Note: It is highly encouraged (though not required) to try writing a solution using recursion for some extra practice. Since hailstone returns a generator, you can yield from a call to hailstone!

def hailstone(n):
    """Yields the elements of the hailstone sequence starting at n.

    >>> for num in hailstone(10):
    ...     print(num)
    ...
    10
    5
    16
    8
    4
    2
    1
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q hailstone

WWPD: Objects

Q3: The Car class

Note: These questions use inheritance. For an overview of inheritance, see the inheritance portion of Composing Programs

Below is the definition of a Car class that we will be using in the following WWPD questions. Note: This definition can also be found in car.py.

class Car:
    num_wheels = 4
    gas = 30
    headlights = 2
    size = 'Tiny'

    def __init__(self, make, model):
        self.make = make
        self.model = model
        self.color = 'No color yet. You need to paint me.'
        self.wheels = Car.num_wheels
        self.gas = Car.gas

    def paint(self, color):
        self.color = color
        return self.make + ' ' + self.model + ' is now ' + color

    def drive(self):
        if self.wheels < Car.num_wheels or self.gas <= 0:
            return 'Cannot drive!'
        self.gas -= 10
        return self.make + ' ' + self.model + ' goes vroom!'

    def pop_tire(self):
        if self.wheels > 0:
            self.wheels -= 1

    def fill_gas(self):
        self.gas += 20
        return 'Gas level: ' + str(self.gas)

Use Ok to test your knowledge with the following What would Python Display questions.

python3 ok -q wwpd-car -u

If an error occurs, type Error. If nothing is displayed, type Nothing.

>>> deneros_car = Car('Tesla', 'Model S')
>>> deneros_car.model
______
'Model S'
>>> deneros_car.gas = 10 >>> deneros_car.drive()
______
'Tesla Model S goes vroom!'
>>> deneros_car.drive()
______
'Cannot drive!'
>>> deneros_car.fill_gas()
______
'Gas level: 20'
>>> deneros_car.gas
______
20
>>> Car.gas
______
30
>>> deneros_car = Car('Tesla', 'Model S')
>>> deneros_car.wheels = 2
>>> deneros_car.wheels
______
2
>>> Car.num_wheels
______
4
>>> deneros_car.drive()
______
'Cannot drive!'
>>> Car.drive()
______
Error (TypeError)
>>> Car.drive(deneros_car)
______
'Cannot drive!'

For the following, we reference the MonsterTruck class below. Note: The MonsterTruck class can also be found in car.py.

 class MonsterTruck(Car):
     size = 'Monster'

     def rev(self):
         print('Vroom! This Monster Truck is huge!')

     def drive(self):
         self.rev()
         return Car.drive(self)
>>> deneros_car = MonsterTruck('Monster', 'Batmobile')
>>> deneros_car.drive()
______
Vroom! This Monster Truck is huge! 'Monster Batmobile goes vroom!'
>>> Car.drive(deneros_car)
______
'Monster Batmobile goes vroom!'
>>> MonsterTruck.drive(deneros_car)
______
Vroom! This Monster Truck is huge! 'Monster Batmobile goes vroom!'
>>> Car.rev(deneros_car)
______
Error (AttributeError)

Magic: The Lambda-ing

In the next part of this lab, we will be implementing a card game! This game is inspired by the similarly named Magic: The Gathering.

You can start the game by typing:

python3 cardgame.py

This game doesn't work yet. If we run this right now, the code will error, since we haven't implemented anything yet. When it's working, you can exit the game and return to the command line with Ctrl-C or Ctrl-D.

This game uses several different files.

  • Code for all the questions in this lab can be found in classes.py.
  • Some utility for the game can be found in cardgame.py, but you won't need to open or read this file. This file doesn't actually mutate any instances directly - instead, it calls methods of the different classes, maintaining a strict abstraction barrier.
  • If you want to modify your game later to add your own custom cards and decks, you can look in cards.py to see all the standard cards and the default deck; here, you can add more cards and change what decks you and your opponent use. If you're familiar with the original game, you may notice the cards were not created with balance in mind, so feel free to modify the stats and add/remove cards as desired.

Rules of the Game This game is a little involved, though not nearly as much as its namesake. Here's how it goes:

There are two players. Each player has a hand of cards and a deck, and at the start of each round, each player draws a random card from their deck. If a player's deck is empty when they try to draw, they will automatically lose the game. Cards have a name, an attack statistic, and a defense statistic. Each round, each player chooses one card to play from their own hands. The card with the higher power wins the round. Each played card's power value is calculated as follows:

(player card's attack) - (opponent card's defense) / 2

For example, let's say Player 1 plays a card with 2000 attack/1000 defense and Player 2 plays a card with 1500 attack/3000 defense. Their cards' powers are calculated as:

P1: 2000 - 3000/2 = 2000 - 1500 = 500
P2: 1500 - 1000/2 = 1500 - 500 = 1000

So Player 2 would win this round.

The first player to win 8 rounds wins the match!

However, there are a few effects we can add (in the optional questions section) to make this game a bit more interesting. Cards are split into Tutor, TA, and Professor types, and each type has a different effect when they're played. All effects are applied before power is calculated during that round:

  • A Tutor card will cause the opponent to discard and re-draw the first 3 cards in their hand.
  • A TA card will swap the opponent card's attack and defense.
  • A Professor card will add the opponent card's attack and defense to all cards in their deck and then remove all cards in the opponent's deck that share its attack or defense!

These are a lot of rules to remember, so refer back here if you need to review them, and let's start making the game!

Q4: Making Cards

To play a card game, we're going to need to have cards, so let's make some! We're gonna implement the basics of the Card class first.

First, implement the Card class constructor in classes.py. This constructor takes three arguments:

  • the name of the card, a string
  • the attack stat of the card, an integer
  • the defense stat of the card, an integer

Each Card instance should keep track of these values using instance attributes called name, attack, and defense.

You should also implement the power method in Card, which takes in another card as an input and calculates the current card's power. Check the Rules section if you want a refresher on how power is calculated.

class Card:
    cardtype = 'Staff'

    def __init__(self, name, attack, defense):
        """
        Create a Card object with a name, attack,
        and defense.
        >>> staff_member = Card('staff', 400, 300)
        >>> staff_member.name
        'staff'
        >>> staff_member.attack
        400
        >>> staff_member.defense
        300
        >>> other_staff = Card('other', 300, 500)
        >>> other_staff.attack
        300
        >>> other_staff.defense
        500
        """
        "*** YOUR CODE HERE ***"

    def power(self, opponent_card):
        """
        Calculate power as:
        (player card's attack) - (opponent card's defense)/2
        >>> staff_member = Card('staff', 400, 300)
        >>> other_staff = Card('other', 300, 500)
        >>> staff_member.power(other_staff)
        150.0
        >>> other_staff.power(staff_member)
        150.0
        >>> third_card = Card('third', 200, 400)
        >>> staff_member.power(third_card)
        200.0
        >>> third_card.power(staff_member)
        50.0
        """
        "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q Card.__init__
python3 ok -q Card.power

Q5: Making a Player

Now that we have cards, we can make a deck, but we still need players to actually use them. We'll now fill in the implementation of the Player class.

A Player instance has three instance attributes:

  • name is the player's name. When you play the game, you can enter your name, which will be converted into a string to be passed to the constructor.
  • deck is an instance of the Deck class. You can draw from it using its .draw() method.
  • hand is a list of Card instances. Each player should start with 5 cards in their hand, drawn from their deck. Each card in the hand can be selected by its index in the list during the game. When a player draws a new card from the deck, it is added to the end of this list.

Complete the implementation of the constructor for Player so that self.hand is set to a list of 5 cards drawn from the player's deck.

Next, implement the draw and play methods in the Player class. The draw method draws a card from the deck and adds it to the player's hand. The play method removes and returns a card from the player's hand at the given index.

Call deck.draw() when implementing Player.__init__ and Player.draw. Don't worry about how this function works - leave it all to the abstraction!

class Player:
    def __init__(self, deck, name):
        """Initialize a Player object.
        A Player starts the game by drawing 5 cards from their deck. Each turn,
        a Player draws another card from the deck and chooses one to play.
        >>> test_card = Card('test', 100, 100)
        >>> test_deck = Deck([test_card.copy() for _ in range(6)])
        >>> test_player = Player(test_deck, 'tester')
        >>> len(test_deck.cards)
        1
        >>> len(test_player.hand)
        5
        """
        self.deck = deck
        self.name = name
        "*** YOUR CODE HERE ***"

    def draw(self):
        """Draw a card from the player's deck and add it to their hand.
        >>> test_card = Card('test', 100, 100)
        >>> test_deck = Deck([test_card.copy() for _ in range(6)])
        >>> test_player = Player(test_deck, 'tester')
        >>> test_player.draw()
        >>> len(test_deck.cards)
        0
        >>> len(test_player.hand)
        6
        """
        assert not self.deck.is_empty(), 'Deck is empty!'
        "*** YOUR CODE HERE ***"

    def play(self, card_index):
        """Remove and return a card from the player's hand at the given index.
        >>> from cards import *
        >>> test_player = Player(standard_deck, 'tester')
        >>> ta1, ta2 = TACard("ta_1", 300, 400), TACard("ta_2", 500, 600)
        >>> tutor1, tutor2 = TutorCard("t1", 200, 500), TutorCard("t2", 600, 400)
        >>> test_player.hand = [ta1, ta2, tutor1, tutor2]
        >>> test_player.play(0) is ta1
        True
        >>> test_player.play(2) is tutor2
        True
        >>> len(test_player.hand)
        2
        """
        "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q Player.__init__
python3 ok -q Player.draw
python3 ok -q Player.play

After you complete this problem, you'll be able to play a working version of the game! Type

python3 cardgame.py

to start a game of Magic: The Lambda-ing!

This version doesn't have the effects for different cards, yet - to get those working, try out the optional questions below.

Submit

Make sure to submit this assignment by running:

python3 ok --submit

Optional Questions

The following code-writing questions will all be in classes.py.

For the following sections, do not overwrite any lines already provided in the code. Additionally, make sure to uncomment any calls to print once you have implemented each method. These are used to display information to the user, and changing them may cause you to fail tests that you would otherwise pass.

Q6: Tutors: Flummox

To really make this card game interesting, our cards should have effects! We'll do this with the effect function for cards, which takes in the opponent card, the current player, and the opponent player.

Implement the effect method for Tutors, which causes the opponent to discard the first 3 cards in their hand and then draw 3 new cards. Assume there at least 3 cards in the opponent's hand and at least 3 cards in the opponent's deck.

Remember to uncomment the call to print once you're done!

class TutorCard(Card):
    cardtype = 'Tutor'

    def effect(self, opponent_card, player, opponent):
        """
        Discard the first 3 cards in the opponent's hand and have
        them draw the same number of cards from their deck.
        >>> from cards import *
        >>> player1, player2 = Player(player_deck, 'p1'), Player(opponent_deck, 'p2')
        >>> opponent_card = Card('other', 500, 500)
        >>> tutor_test = TutorCard('Tutor', 500, 500)
        >>> initial_deck_length = len(player2.deck.cards)
        >>> tutor_test.effect(opponent_card, player1, player2)
        p2 discarded and re-drew 3 cards!
        >>> len(player2.hand)
        5
        >>> len(player2.deck.cards) == initial_deck_length - 3
        True
        """
        "*** YOUR CODE HERE ***"
        #Uncomment the line below when you've finished implementing this method!
        #print('{} discarded and re-drew 3 cards!'.format(opponent.name))

Use Ok to test your code:

python3 ok -q TutorCard.effect

Q7: TAs: Shift

Let's add an effect for TAs now! Implement the effect method for TAs, which swaps the attack and defense of the opponent's card.

class TACard(Card):
    cardtype = 'TA'

    def effect(self, opponent_card, player, opponent):
        """
        Swap the attack and defense of an opponent's card.
        >>> from cards import *
        >>> player1, player2 = Player(player_deck, 'p1'), Player(opponent_deck, 'p2')
        >>> opponent_card = Card('other', 300, 600)
        >>> ta_test = TACard('TA', 500, 500)
        >>> ta_test.effect(opponent_card, player1, player2)
        >>> opponent_card.attack
        600
        >>> opponent_card.defense
        300
        """
        "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q TACard.effect

Q8: The Professor Arrives

A new challenger has appeared! Implement the effect method for the Professor, who adds the opponent card's attack and defense to all cards in the player's deck and then removes all cards in the opponent's deck that have the same attack or defense as the opponent's card.

Note: You might run into trouble when you mutate a list as you're iterating through it. Try iterating through a copy instead! You can use slicing to copy a list:

  >>> lst = [1, 2, 3, 4]
  >>> copy = lst[:]
  >>> copy
  [1, 2, 3, 4]
  >>> copy is lst
  False
class ProfessorCard(Card):
    cardtype = 'Professor'

    def effect(self, opponent_card, player, opponent):
        """
        Adds the attack and defense of the opponent's card to
        all cards in the player's deck, then removes all cards
        in the opponent's deck that share an attack or defense
        stat with the opponent's card.
        >>> test_card = Card('card', 300, 300)
        >>> professor_test = ProfessorCard('Professor', 500, 500)
        >>> opponent_card = test_card.copy()
        >>> test_deck = Deck([test_card.copy() for _ in range(8)])
        >>> player1, player2 = Player(test_deck.copy(), 'p1'), Player(test_deck.copy(), 'p2')
        >>> professor_test.effect(opponent_card, player1, player2)
        3 cards were discarded from p2's deck!
        >>> [(card.attack, card.defense) for card in player1.deck.cards]
        [(600, 600), (600, 600), (600, 600)]
        >>> len(player2.deck.cards)
        0
        """
        orig_opponent_deck_length = len(opponent.deck.cards)
        "*** YOUR CODE HERE ***"
        discarded = orig_opponent_deck_length - len(opponent.deck.cards)
        if discarded:
            #Uncomment the line below when you've finished implementing this method!
            #print('{} cards were discarded from {}\'s deck!'.format(discarded, opponent.name))
            return

Use Ok to test your code:

python3 ok -q ProfessorCard.effect

After you complete this problem, we'll have a fully functional game of Magic: The Lambda-ing! This doesn't have to be the end, though - we encourage you to get creative with more card types, effects, and even adding more custom cards to your deck!