本文整理汇总了Python中visualizer.Visualizer.update方法的典型用法代码示例。如果您正苦于以下问题:Python Visualizer.update方法的具体用法?Python Visualizer.update怎么用?Python Visualizer.update使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类visualizer.Visualizer
的用法示例。
在下文中一共展示了Visualizer.update方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: PhysPopulation
# 需要导入模块: from visualizer import Visualizer [as 别名]
# 或者: from visualizer.Visualizer import update [as 别名]
class PhysPopulation(AnnPopulation):
"""Population class for the physical EvoFab system. Is used to
wrap up a significant amount of information important for the
execution of the GA within members
"""
def __init__(self, random_seed, printer_runtime, size, mutation_rate, mutation_range, crossover_rate, replacement_number, num_input, num_hidden, num_output, serial_port, sensor_serial_port, conveyor_port, z_port, camera, outputfolder, crop=True, is_visual=True, dump_to_files=False):
super(PhysPopulation, self).__init__(random_seed, printer_runtime, size, mutation_rate, mutation_range, crossover_rate, replacement_number, num_input, num_hidden, num_output, outputfolder, is_visual=is_visual, dump_to_files=dump_to_files)
self.genotype_factory = PhysGenotypeFactory(self)
#TODO: should probably test that sensor and controller serial ports are valid
self.controller = EvoController(serial_port)
self.sense = EvoArray(sensor_serial_port)
self.camera = EvoCamera(camera, crop)
self.conveyor = EvoConveyor(conveyor_port)
self.visualizer = Visualizer([self.sense.getNext() for x in range(10)])
self.visualizer.update(self.sense.getNext())
self.z_axis = EvoZAxis(z_port)
listener = threading.Thread(target=kbdListener)
listener.start()
示例2: main
# 需要导入模块: from visualizer import Visualizer [as 别名]
# 或者: from visualizer.Visualizer import update [as 别名]
def main():
# get settings from command line arguments
settings = CommandLineParser().parse_args()
# create problem
problem = GrayScottProblem(settings.size, coefficients=settings.coefficients)
# create visualizer
visualizer = Visualizer(problem.problem_size(), \
export=settings.export, \
keepalive=settings.keepalive, \
show=(not settings.noshow))
# create step generator
stop_point_generator = TimeStepper(settings.timesteps, \
settings.outputs, mode='linear')
# evolution loop
stop_point = next(stop_point_generator)
for step in range(settings.timesteps + 1):
# print progress message
if settings.verbose == True:
progress = 100 * step / settings.timesteps
print('{:3.0f}% finished'.format(progress), end='\r')
# trigger visualization
if step == stop_point:
visualizer.update(problem.v)
try:
stop_point = next(stop_point_generator)
except StopIteration:
pass
# evolve problem
problem.evolve()
else:
if settings.verbose == True:
print('\nEvolution finished')
visualizer.close()
示例3: Driver
# 需要导入模块: from visualizer import Visualizer [as 别名]
# 或者: from visualizer.Visualizer import update [as 别名]
#.........这里部分代码省略.........
#ratio = random.random()/10.0
#disaster.create_disaster(ratio)
#print 'DISASTER killed', ratio, 'in:', time.time() - start
death_from_disaster = random.randint(1,20)
disaster.create_disaster(death_from_disaster)
print 'DISASTER killed', death_from_disaster, 'people and', (death_from_disaster+10)*2500.0, 'food in:', time.time() - start
start = time.time()
# If enough food, expand facility. Assume 3 month build time
if food.remaining_food > 2500*10 and (facility.personnel_capacity - population.num_people()) <= 10:
facility.start_pod_construction(cur_sim_time, 3)
facility.add_pod(cur_sim_time)
# Adding newborns
born_count = 0
for add_count in range (people_born.get(cur_sim_time % 9, 0)):
if population.num_people() < facility.personnel_capacity:
population.add_person(Person(cur_sim_time, population.get_rand_death_time(cur_sim_time), random.random()))
born_count += 1
print 'added', born_count, 'people in', time.time()-start
# Removing the dead
start = time.time()
people_to_kill = len(population.death_dict.get(cur_sim_time, []))
population.remove_dead(cur_sim_time)
print 'removed', people_to_kill, 'people in:', time.time() - start
# Calculating total kcal
start = time.time()
total_kcal = population.kcal_requirements(cur_sim_time)
print 'completed total kcal in:', time.time() - start
# Food consumption
food_delta = food.update_food(total_kcal)
print 'produced food = ', food.produced_food, '; remaining food = ', food.remaining_food
# if not enough food
if food_delta < 0:
# people who are unfed will die, starting with oldest people
while (food_delta < 0):
food_delta = food_delta + population.people[0].kcal_requirements(cur_sim_time)
population.remove_person(0)
population.generate_death_dict()
# Calculating how many newborns to be created in 9 months time
num_people = population.num_people()
num_adults = population.num_adults(cur_sim_time)
# newborns based on number of adults. Average US birthrate in 2014: 0.01342 (indexmundi.com)
people_born[cur_sim_time % 9] = random.randint(np.rint(num_adults*0.01),np.rint(num_adults*0.020))
print 'total people:', num_people, 'and total adults:', num_adults, 'and total kcal:', total_kcal
print 'total capacity:', facility.personnel_capacity
print('-'*100)
# Record results of the iteration.
self._write_results(population=num_people,
food=food.produced_food,
kcals=total_kcal,
facility_crop=facility.crop_area,
facility_personnel=facility.personnel_capacity,
air=air.oxygen_consumed(),
power=power.power_consumed())
# If the visualization option has been selected, plot the results
# every 10 timesteps.
if self.vis and cur_sim_time % 10 == 0: