class Employee:
    def __init__(self, name, salary, department, position):
        self.name = name
        self.salary = salary
        self.department = department
        self.position = position

    def calculate_emp_salary(self, hours_worked):
        if hours_worked > 50:
            overtime_hours = hours_worked - 50
            overtime_pay = overtime_hours * (self.salary / 50)
            total_salary = self.salary + overtime_pay
        else:
            total_salary = self.salary
        return total_salary

    def assign_department(self, new_department):
        self.department = new_department

    def print_employee_details(self):
        print("Employee Details:")
        print("Name:", self.name)
        print("Salary:", self.salary)
        print("Department:", self.department)
        print("Position:", self.position)


# Пример использования класса Employee
employee1 = Employee("John Doe", 50000, "Marketing", "Manager")
employee1.print_employee_details()

print("Calculating salary for John Doe...")
total_salary = employee1.calculate_emp_salary(55)
print("Total Salary:", total_salary)

print("Assigning new department for John Doe...")
employee1.assign_department("Sales")
employee1.print_employee_details()


...


import numpy as np

class ArrayTransformer:
    def __init__(self, p1, p2, p3):
        self.p1 = p1
        self.p2 = p2
        self.p3 = p3

    def __call__(self, array):
        if np.random.uniform() < self.p1:
            array = np.flip(array, axis=1)
        elif np.random.uniform() < self.p2:
            array = np.flip(array, axis=0)

        if np.random.uniform() < self.p3:
            mean_value = np.mean(array)
            array[array > mean_value] = 1
            array[array <= mean_value] = 0

        self.array = array  # сохраняем массив для доступа через __getitem__
        return array

    def __getitem__(self, index):
        return self.array[:, :, index]

# Пример использования класса ArrayTransformer
p1 = 0.2
p2 = 0.3
p3 = 0.4

transformer = ArrayTransformer(p1, p2, p3)
input_array = np.random.rand(20, 20, 10)

output_array = transformer(input_array)

# Доступ к слоям массива по третьему измерению
index = int(input())
print(transformer[index])