def check_events(ai_settings, screen, stats, sb, play_button, ship, aliens,
bullets):
for event in pygame.event.get():
if event.type == pygame.QUIT:
fd.write_data(stats.high_score)
sys.exit()
elif event.type == pygame.KEYDOWN:
check_keydown_events(event, ai_settings, screen, ship, bullets)
elif event.type == pygame.KEYUP:
check_keyup_events(event, ship)
elif event.type == pygame.MOUSEBUTTONDOWN:
mouse_x, mouse_y = pygame.mouse.get_pos()
check_play_button(ai_settings, screen, stats, sb, play_button,
ship, aliens, bullets, mouse_x,
mouse_y) #又想抽死自己 play
def delete_employee(id):
#0 load in the database
database = data.read_data()
#1 find the index of the employee that we want to delete
employee_to_delete = data.find_employee_by_id(id)
#2 remove that index from the database list
for index,e in enumerate(database):
if e['id'] == employee_to_delete['id']:
# update the entry in the databse
database[index] = employee_to_delete
break # break out of the loop because each id is unique
del database[index]
#3 write the whole database back to the file
data.write_data(database)
return redirect(url_for('display_employees'))
def process_edit_employee(id):
#1 - read in the database
database = data.read_data()
#2 - update the entry in the database
employee_to_update = data.find_employee_by_id(id)
employee_to_update['first_name'] = request.form.get('firstname')
employee_to_update['last_name'] = request.form.get('lastname')
employee_to_update['salary'] = request.form.get('salary')
employee_to_update['title'] = request.form.get('title')
#find the index to replace
for index,e in enumerate(database):
if e['id'] == employee_to_update['id']:
# update the entry in the databse
database[index] = employee_to_update
break # break out of the loop because each id is unique
#3 - overwrite the file with our current version of database
data.write_data(database)
return redirect(url_for('display_employees'))
def write(data, olution):
name = file_name + '_' + str(evaluate(data, solution)) + output_extension
path = join(output_solve_folder, name)
write_data(path, solution)
# init bodies
data.read_data(bodies)
# init gui
slider = gui.Slider(buttons)
start_stop = gui.StartStop(buttons)
clock = pygame.time.Clock()
running = True
while running:
for event in pygame.event.get():
buttons.update(event)
if event.type == pygame.QUIT:
running = False
data.write_data()
screen.fill(BG_COLOR)
# determining how fast is the system drawn based on the position of the slider.
dt = slider.calc_value()
if start_stop.on and bodies.size > 0:
# first we calculate new positions on all bodies
bodies.new_state(dt)
# then we move them
bodies.update(WINDOW_SIZE)
buttons.draw(screen)
bodies.draw(screen)
pygame.display.flip()
def load(args, client):
data = load_data(args["input"])
return write_data(client, data)
def evolutionary_algorithm():
best_timetable = None
data = dt.load_data(input_file)
neighbour = mutation.neighbour
for i in range(num_runs):
chromosome = dt.generate_chromosome(data)
for j in range(max_generations):
new_chromosome = neighbour(deepcopy(chromosome))
ft = cost_function(chromosome)
if ft == 0:
break
ftn = cost_function(new_chromosome)
if ftn <= ft:
chromosome = new_chromosome
if j % 200 == 0:
print('Iteration', j, 'cost', cost_function(chromosome))
print('Run', i + 1, 'cost', cost_function(chromosome), 'chromosome', chromosome)
if best_timetable is None or cost_function2(chromosome) <= cost_function2(best_timetable):
best_timetable = deepcopy(chromosome)
chromosome = best_timetable
neighbour2 = mutation.neighbour2
for j in range(3 * max_generations):
new_chromosome = neighbour2(deepcopy(chromosome))
ft = cost_function2(chromosome)
ftn = cost_function2(new_chromosome)
if ftn <= ft:
chromosome = new_chromosome
if j % 200 == 0:
print('Iteration', j, 'cost', cost_function2(chromosome))
print('Run', 'cost', cost_function2(chromosome), 'chromosome', chromosome)
dt.write_data(chromosome[0], output_file)
professor_hard = True
classroom_hard = True
group_hard = True
allowed_classrooms = True
# Check hard constraints
for single_class in chromosome[0]:
if single_class['Assigned_classroom'] not in single_class['Classroom']:
allowed_classrooms = False
for profesor in chromosome[1]:
for i in range(len(chromosome[1][profesor])):
if chromosome[1][profesor][i] > 1:
professor_hard = False
for Classroom in chromosome[2]:
for i in range(len(chromosome[2][Classroom])):
if chromosome[2][Classroom][i] > 1:
classroom_hard = False
for group in chromosome[3]:
for i in range(len(chromosome[3][group])):
if chromosome[3][group][i] > 1:
group_hard = False
print('Are hard restrictions for professors satisfied:', professor_hard)
print('Are hard restrictions for classrooms satisfied:', classroom_hard)
print('Are hard restrictions for groups satisfied:', group_hard)
print('Are hard restrictions for allowed classrooms satisfied:', allowed_classrooms)
# Check preferred order statistics
subjects_cost = 0
for single_class in chromosome[4]:
subject_cost = 0
for lab in chromosome[4][single_class]['L']:
for practice in chromosome[4][single_class]['V']:
for group in lab[1]:
if group in practice[1] and lab[0] < practice[0]:
subject_cost += 1
for lecture in chromosome[4][single_class]['P']:
for group in lab[1]:
if group in lecture[1] and lab[0] < lecture[0]:
subject_cost += 1
for practice in chromosome[4][single_class]['V']:
for lecture in chromosome[4][single_class]['P']:
for group in practice[1]:
if group in lecture[1] and practice[0] < lecture[0]:
subject_cost += 1
subjects_cost += subject_cost
print('Subject cost for subject', single_class, 'is:', subject_cost)
print('Total subject cost:', subjects_cost)
# Check group statistics
total_group_cost = 0
total_group_load = 0
max_group_cost = 0
for group in chromosome[3]:
group_cost = 0
group_load = 0
for day in range(5):
last_seen = 0
found = False
current_load = 0
for hour in range(12):
time = day * 12 + hour
if chromosome[3][group][time] >= 1:
current_load += 1
if not found:
found = True
else:
group_cost += (time - last_seen - 1)
last_seen = time
if current_load > 6:
group_load += 1
print('Group cost for group', group, 'is:', group_cost, ', number of hard days:', group_load)
if max_group_cost < group_cost:
max_group_cost = group_cost
total_group_cost += group_cost
total_group_load += group_load
print('Maximum group cost is:', max_group_cost)
print('Average group cost is:', total_group_cost / len(chromosome[3]))
print('Total group load is:', total_group_load)
# Check professor statistics
total_prof_cost = 0
total_prof_load = 0
free_hour = True
max_prof_cost = 0
for prof in chromosome[1]:
prof_cost = 0
prof_load = 0
for day in range(5):
last_seen = 0
found = False
current_load = 0
for hour in range(12):
time = day * 12 + hour
if chromosome[1][prof][time] >= 1:
if time == 59:
free_hour = False
current_load += 1
if not found:
found = True
else:
prof_cost += (time - last_seen - 1)
last_seen = time
if current_load > 6:
prof_load += 1
print('Prof cost for prof', prof, 'is:', prof_cost, ', number of hard days:', prof_load)
if max_prof_cost < prof_cost:
max_prof_cost = prof_cost
total_prof_cost += prof_cost
total_prof_load += prof_load
print('Max prof cost is:', max_prof_cost)
print('Average prof cost is:', total_prof_cost / len(chromosome[1]))
print('Total prof load is:', total_prof_load)
print('Free hour:', free_hour, ', 59')
