priority = [2,6,1,7,10,8,5] time = [30,200,20,70,120,60,150] import numpy as np # # define number of tasks: don't hardwire this but rather use the len() command n_tasks = len(priority) max_time = 8*60 n = 0 n_list= np.array (np.linspace (1, n_tasks, n_tasks)) print ( f"TASK PRIORITY TIME TO COMPLETE") for x in range (0, n_tasks): print (f" {n_list [n]} {priority[n]} {time[n]}") n = n +1 # you need to add an appropriate for loop here and modify the next print statement so that you can print out all tasks

test_list = [ 4, 5, 3, 7, 11, 2] max_value = max(test_list) # use function max() to find maximum value in list max_index = test_list.index (max_value) # use method list.index () to find the index where this max occurs print (f"{max_index + 1}. {max_value}") # print out results in formatted statement

test_list = [ 4, 5, 3, 7, 11, 2] time_list = [30, 45, 20, 120, 90, 70 ] max_value = max(test_list) max_index = test_list.index (max_value) task_time = time_list [max_index] # input the time it takes to complete this task print (f"{max_index + 1}. {max_value}, time = {task_time}") # print out values as in Step #2 plus the time it takes to complete task

max_time = 6 * 60 # this is not the maximum time available for your example but rather for testing time_used = 3 * 60 # # Following 6 statements are from Step 3 test_list = [ 4, 5, 3, 7, 11, 2] time_list = [30, 45, 20, 120, 90, 70 ] max_value = max(test_list) max_index = test_list.index (max_value) task_time = time_list [max_index] if (time_used <= max_time) : time_used = time_used + task_time print (f"Task number {max_index + 1} is complete, current time used = {time_used}") else: print ("There is not enough time to complete this task")

# Input problem specific data and print it out from Step 1 priority = [ 2, 6, 1, 7, 10, 8, 5] time = [30,200,20,70,120,60, 150] print ( f"TASK PRIORITY TIME TO COMPLETE") # # Initialize any variables before loop n_tasks = len(priority) max_time = 8*60 time_used = 0 # # add for loop to go through all tasks # for x in range (0, n_tasks): max_value = max(priority) max_index = priority.index (max_value) task_time = time [max_index] priority[max_index] = 0 if (time_used + task_time <= max_time): time_used = time_used + task_time print (f" {max_index + 1} {max_value} {task_time}") else: print (f" Task {max_index +1} will not be completed in desired time") print (f"*Total time of all tasks completed in day 1 = {time_used}*") # # find highest priority left and task number it corresponds to from Step 3/4 # # zero out this entry in the priority list so you won't find it again # # add conditional to check to see if task can be completed as in Step 4 # # Print out the total time used for all tasks in day 1

# Input problem specific data and print it out from Step 1 priority = [ 2, 6, 1, 7, 10, 8, 5] time = [30,200,20,70,120,60, 150] n_tasks = len(priority) time_used = 0 max_time = 8*60 print ( f"TASK PRIORITY TIME TO COMPLETE") # # Initialize any variables before loop # # add for loop to go through all tasks # for x in range (0, n_tasks): max_value = max(priority) max_index = priority.index (max_value) task_time = time [max_index] priority[max_index] = 0 if (time_used + task_time <= max_time): time_used = time_used + task_time print (f" {max_index + 1} {max_value} {task_time}") if (time_used == max_time): break else: print (f" Task {max_index +1} will not be completed in desired time") print (f"*Total time of all tasks completed in day 1 = {time_used}*") # # find highest priority left and task number it corresponds to from Step 3/4 # # zero out this entry in the priority list so you won't find it again # # add conditional to check to see if task can be completed as in Step 4 # if this conditional is satisfied then add another conditional to check if time_used = max_time; if so break out of loop # # Print out the total time used for all tasks in day 1

# Input problem specific data old_priority = [ 2, 6, 1, 7, 10, 8, 5] time = [30,200,20,70,120,60, 150] u_time = 0 # n_tasks = n_tasks = len(priority) # Create new priority list containing ratios import numpy as np ratio_priority = (np.array(old_priority)/ np.array(time)) n = 0 for x in range (0, n_tasks): m_value = max(ratio_priority) m_index = np.where(ratio_priority == n) task_tim = np.where(ratio_priority == n) ratio_priority[max] = 0 n = n + 1 if (u_time + task_tim <= m_time): u_time = u_time + task_tim print (f" {m_index + 1} {m_value} {task_tim}") if (u_time == m_time): break else: print (f" Task {m_index +1} will not be completed in desired time") print (f"*Total time of all tasks completed in day 1 = {time_used}*") # initialize the list which contains the new priorities which are original priority divided by # time it takes to complete this task # add for loop to create this list # use method .append() to create elements of the new priority list # # Remainder of code should be the same; this is because we called the new list which contained the ratios the same as # before.