Python Online Test

Your company is analyzing malware which targets numerical record files.

The malware uses a sliding window over the array of numbers in a file, and tries to match the following pattern:

Tl, -, -, X, -, -, -, Tr

The entire window is moved so that 'X' passes through all the values and is compared to the numbers at the 'Tl' and 'Tr' locations, which are positioned at a constant offset to 'X'.

The malware has the following rules:

• If the value at the 'Tl' or 'Tr' position of the pattern is bigger or equal to the value at the 'X’ position, the malware replaces the value at 'X' with 0.
• If the value at the 'Tl' or 'Tr' offset is out of bounds, then the value at 'X' is only compared to the other existing value.
• The record is processed in two stages: first, all the positions that should be set to 0 are located, using the original values for comparison. Only after all positions have been identified do they get set to 0.

For example, if the values in a record file are the following:

[1, 2, 0, 5, 0, 2, 4, 3, 3, 3]

The expected values after the malware runs are:

[1, 0, 0, 5, 0, 0, 0, 3, 3, 0]

In this example, both '2's, the '4' and the last '3' were replaced by 0.

Implement the simulate function so that the malware behavior is replicated for further study.

A turn-based strategy game has a grid with water and land. The grid contains a True value where it's water and False where it's land.

The player controls a boat unit with a particular movement pattern. It can only move to fixed destinations from its current position as shown in the image below:

The boat can only move in a direct path through water to the possible destinations, so a destination will become unreachable if there is land in the way. 

Implement the can_travel_to function, that checks whether a destination is reachable by the boat. It should return True for destinations that are reachable according to the pattern above, and False for unreachable or out of bounds destinations which are outside the grid. 

For example, consider the following code:

gameMatrix = [
    [False, True,  True,  False, False, False],
    [True,  True,  True,  False, False, False],
    [True,  True,  True,  True,  True,  True],
    [False, True,  True,  False, True,  True],
    [False, True,  True,  True,  False, True],
    [False, False, False, False, False, False],
]

print(can_travel_to(gameMatrix, 3, 2, 2, 2)) # True, Valid move
print(can_travel_to(gameMatrix, 3, 2, 3, 4)) # False, Can't travel through land
print(can_travel_to(gameMatrix, 3, 2, 6, 2)) # False, Out of bounds

The following image shows valid and invalid destinations when the boat is in the position (3, 2):

A user interface contains two types of user input controls: TextInput, which accepts all characters and NumericInput, which accepts only digits.

Implement the class TextInput that contains:

• Method add(self, character) - adds the given character to the current value
• Method get_value(self) - returns the current value

Implement the class NumericInput that:

• Inherits TextInput
• Overrides the add method so that each non-numeric character is ignored

For example, the following code should output "10":

input = NumericInput()
input.add("1")
input.add("a")
input.add("0")
print(input.get_value())

Implement function count_numbers that accepts a sorted list of unique integers and, efficiently with respect to time used, counts the number of list elements that are less than the parameter less_than.

For example, count_numbers([1, 3, 5, 7], 4) should return 2 because there are two list elements less than 4.

Implement the function find_roots to find the roots of the quadratic equation: ax² + bx + c = 0. The function should return a tuple containing roots in any order. If the equation has only one solution, the function should return that solution as both elements of the tuple. The equation will always have at least one solution.

The roots of the quadratic equation can be found with the following formula:

For example, find_roots(2, 10, 8) should return (-1, -4) or (-4, -1) as the roots of the equation 2x² + 10x + 8 = 0 are -1 and -4.

Binary search tree (BST) is a binary tree where the value of each node is larger or equal to the values in all the nodes in that node's left subtree and is smaller than the values in all the nodes in that node's right subtree.

Write a function that, efficiently with respect to time used, checks if a given binary search tree contains a given value.

For example, for the following tree:

• n1 (Value: 1, Left: null, Right: null)
• n2 (Value: 2, Left: n1, Right: n3)
• n3 (Value: 3, Left: null, Right: null)

Call to contains(n2, 3) should return True since a tree with root at n2 contains number 3.

As a part of the route planner, the route_exists method is used as a quick filter if the destination is reachable, before using more computationally intensive procedures for finding the optimal route.

The roads on the map are rasterized and produce a matrix of boolean values - True if the road is present or False if it is not. The roads in the matrix are connected only if the road is immediately left, right, below or above it.

Finish the route_exists method so that it returns True if the destination is reachable or False if it is not. The from_row and from_column parameters are the starting row and column in the map_matrix. The to_row and to_column are the destination row and column in the map_matrix. The map_matrix parameter is the above mentioned matrix produced from the map.

For example, for the given rasterized map, the code below should return True since the destination is reachable:

map_matrix = [
    [True, False, False],
    [True, True, False],
    [False, True, True]
];

route_exists(0, 0, 2, 2, map_matrix)

A TrainComposition is built by attaching and detaching wagons from the left and the right sides, efficiently with respect to time used.

For example, if we start by attaching wagon 7 from the left followed by attaching wagon 13, again from the left, we get a composition of two wagons (13 and 7 from left to right). Now the first wagon that can be detached from the right is 7 and the first that can be detached from the left is 13.

Implement a TrainComposition that models this problem.

Write a function that, when passed a list and a target sum, returns, efficiently with respect to time used, two distinct zero-based indices of any two of the numbers, whose sum is equal to the target sum. If there are no two numbers, the function should return None.

For example, find_two_sum([3, 1, 5, 7, 5, 9], 10) should return a single tuple containing any of the following pairs of indices:

• 0 and 3 (or 3 and 0) as 3 + 7 = 10
• 1 and 5 (or 5 and 1) as 1 + 9 = 10
• 2 and 4 (or 4 and 2) as 5 + 5 = 10

Implement the IceCreamMachine's scoops method so that it returns all combinations of one ingredient and one topping. If there are no ingredients or toppings, the method should return an empty list.

For example, IceCreamMachine(["vanilla", "chocolate"], ["chocolate sauce"]).scoops() should return [['vanilla', 'chocolate sauce'], ['chocolate', 'chocolate sauce']].

As part of a data processing pipeline, complete the implementation of the pipeline method:

• The pipeline method should accept a variable number of functions, and it should return a new function that accepts one parameter arg.
• The returned function should call the first function in pipeline with the parameter arg, and call the second function with the result of the first function.
• The returned function should continue calling each function in pipeline in order, following the same pattern, and return the value from the last function.

For example, calling pipeline(lambda x: x * 3, lambda x: x + 1, lambda x: x / 2), and then calling the returned function with 3 should return 5.0.

Implement the unique_names method. When passed two lists of names, it will return a list containing the names that appear in either or both lists. The returned list should have no duplicates.

For example, calling unique_names(['Ava', 'Emma', 'Olivia'], ['Olivia', 'Sophia', 'Emma']) should return a list containing Ava, Emma, Olivia, and Sophia in any order.

A playlist is considered a repeating playlist if any of the songs contain a reference to a previous song in the playlist. Otherwise, the playlist will end with the last song which points to None.

Implement a function is_in_repeating_playlist that, efficiently with respect to time used, returns true if a playlist is repeating or false if it is not.

For example, the following code prints "True" as both songs point to each other.

first = Song("Hello")
second = Song("Eye of the tiger")
    
first.next_song(second)
second.next_song(first)
    
print(first.is_in_repeating_playlist())

The LeagueTable class tracks the score of each player in a league. After each game, the player records their score with the record_result function. 

The player's rank in the league is calculated using the following logic:

1. The player with the highest score is ranked first (rank 1). The player with the lowest score is ranked last.
2. If two players are tied on score, then the player who has played the fewest games is ranked higher.
3. If two players are tied on score and number of games played, then the player who was first in the list of players is ranked higher.

Implement the player_rank function that returns the player at the given rank.

For example:

table = LeagueTable(['Mike', 'Chris', 'Arnold'])
table.record_result('Mike', 2)
table.record_result('Mike', 3)
table.record_result('Arnold', 5)
table.record_result('Chris', 5)
print(table.player_rank(1))

All players have the same score. However, Arnold and Chris have played fewer games than Mike, and as Chris is before Arnold in the list of players, he is ranked first. Therefore, the code above should display "Chris".

Implement a group_by_owners function that:

• Accepts a dictionary containing the file owner name for each file name.
• Returns a dictionary containing a list of file names for each owner name, in any order.

For example, for dictionary {'Input.txt': 'Randy', 'Code.py': 'Stan', 'Output.txt': 'Randy'} the group_by_owners function should return {'Randy': ['Input.txt', 'Output.txt'], 'Stan': ['Code.py']}.

You are working on a calculator application where users can input a mathematical expression such as: (3 + 5) * (2 - 4).

To correctly evaluate this expression, the application needs to validate the expression and ensure that the parentheses are balanced. The balanced parentheses are crucial for determining the order of operations and evaluating the expression correctly.

You have used an AI assistant to generate the following static function to check if the parentheses in an expression are balanced:

def is_balanced(parentheses):
    stack = []
    opening = set({'(', '[', '{'})
    closing = set({')', ']', '}'})
    pairs = dict({
        ')': '(',
        ']': '[',
        '}': '{'
    })

    for c in parentheses:
        if c in opening:
            stack.append(c)
        elif c in closing:
            if not stack or stack[-1] != pairs[c]:
                return False
            stack.pop()

    return len(stack) == 0

What are correct statements for the given function?

A university's Office of Admission keeps track of student majors. Each student can have a single major. Below is an example of how their system stores students and majors, and how it manipulates them:

students = [("Allen Anderson", "Computer Science"),
            ("Edgar Einstein", "Engineering"),
            ("Farrah Finn", "Fine Arts")]
     
def add_new_student(students, name, major):
    students.append((name, major))

def update_student(students, index, name, major):
    students[index] = (name, major)

def find_students_by_name(students, name):
    return [student for student in students if name in student[0]]

def get_all_majors(students):
    return [student[1] for student in students]

What can be concluded from the code snippet above?