Python Visualizer.update方法代码示例

# 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()

# 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()

# 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: