def simulation(seconds, print_speed):
printer = Printer(print_speed)
print_queue = MyQueue()
wait_time_list = []
for current_second in range(seconds):
if Task.new_task():
task = Task(current_second)
print_queue.enqueue(task)
if (not printer.busy()) and (not print_queue.is_empty()):
new_task = print_queue.dequeue()
wait_time_list.append(new_task.wait_time(current_second))
printer.start_next(new_task)
printer.tick()
average_wait_time = sum(wait_time_list) / len(wait_time_list)
print(
f'Average wait {average_wait_time:6.2f}, {print_queue.size():3d} tasks remaining'
)
def list_of_depths(self):
"""
Create a linked list of all the nodes at each depth.
For example, a tree with depth D has D linked lists.
"""
queue = MyQueue()
the_dict = defaultdict(list)
level = 0
queue.push((self, level))
while (queue.is_empty() == False):
node, level = queue.pop()
the_dict[level].append(node.key)
if (node.left != None):
queue.push((node.left, level + 1))
if (node.right != None):
queue.push((node.right, level + 1))
return the_dict
def breadth_first_search(graph, start_node, destination_node):
"""
BFS. Make use of a queue, and make sure to mark nodes as
visited!
TODO: Return the path and its cost.
"""
graph.reset_visited()
q = MyQueue()
start_node.visited = True
q.push(start_node)
while (q.is_empty() == False):
node = q.pop()
if (node == destination_node):
return True
for adjacent in node.adjacents:
q.push(adjacent)
return False