class Car(Model):
name = CarModel()
price = Price()
placa = LicensePlate()
def __repr__(self):
return '<%s %s>' % (self.name, self.price)
def __init__(self, **kwargs):
"""
Initialize the game object
kwargs -- the keywords arguments are given to the carModel
"""
model = CarModel(**kwargs)
view = CarView(model)
behaviour = CarBehaviour('human', None, model)
super(Car, self).__init__(gameObjectModel=model,
gameObjectBehaviour=behaviour,
gameObjectView=view)
logging.log(1, "Trace: Car.__init__(%s)" % kwargs)
def task_model_restore(items):
model_count = 0
for item in items:
mrange = ModelRange()
mrange.name = item['name']
mrange.year_start = item['yearStart']
mrange.year_end = item['yearEnd']
mrange.notes = item['notes']
mrange.put()
for model in item['carModels']:
carmodel = CarModel(key_name=model['modelId'])
carmodel.name = model['name']
carmodel.engine_code = model['engineCode']
carmodel.typeno = model['typeNo']
if model['imageUrl'] != None:
carmodel.imageUrl = db.Link(model['imageUrl'])
carmodel.year_from = model['yearFrom']
carmodel.year_to = model['yearTo']
carmodel.notes = model['notes']
carmodel.model_range = mrange
carmodel.put()
model_count = model_count + 1
def import_car_models(self, csv):
ranges = ModelRange.all().fetch(100)
imported = 0
lines = csv.split('\n')
for line in lines:
if len(line.strip()) == 0:
print 'Skipping line', line
continue
model = CarModel()
fields = line.strip().split(';')
model.model_range = self.get_range(ranges, fields[0].strip())
model.name = fields[1].strip()
model.engine_code = fields[2].strip()
model.typeno = fields[3].strip()
if fields[4].strip() != '':
model.image_url = db.Link(fields[4].strip())
model.year_from = int(fields[5])
model.year_to = int(fields[6])
model.notes = fields[7].strip()
model.put()
imported = imported + 1
print imported, 'models imported'
from model import CarModel
import matplotlib.pyplot as plt
# Config
n = 100 # Amount of model steps
car_count = 20 # Amount of cars in model
width = 100 # Road size
acceleration = 1
speed_limit = 5
randomization = 0.05
vision_range = speed_limit * 3
all_speeds = []
all_tracked_agent_speeds = []
model = CarModel(car_count, width, acceleration, vision_range, randomization,
speed_limit)
for i in range(n):
model.step()
total = 0
tracked_agent_total = 0
for agent in model.schedule.agents:
total += agent.speed
if agent.unique_id == round(car_count / 2):
tracked_agent_total += agent.speed
all_speeds.append(total / model.num_agents)
all_tracked_agent_speeds.append(tracked_agent_total)
print('all_speeds:', all_speeds)
plt.xlabel("Steps")
plt.ylabel("Average speed")
results_delay = []
for num, cars in enumerate(traffic_occupations, start=0):
print("Running: " + str(cars) + " cars..")
results_speed.append([])
results_delay.append([])
settings = []
for x in speed_limits:
settings.append([cars, 100, acceleration, 50, randomization, x])
for num_setting, setting in enumerate(settings, start=0):
results_speed[num].append([])
results_delay[num].append([])
all_delay = []
all_speeds = []
model = CarModel(*setting)
for i in range(n):
model.step()
# Store the results
total = 0
for agent in model.schedule.agents:
total += agent.speed
ave_speed = total / model.num_agents
all_delay.append(abs(setting[5] - ave_speed))
all_speeds.append(ave_speed)
results_speed[num][num_setting] = np.mean(all_speeds)
results_delay[num][num_setting] = np.mean(all_delay)
def plot_line(x_data, y_data, y_label):
