def test_time_dist_fixed_add_with_weight():
random.seed(0)
time_dist = TimeDist('fixed', 1, unit='minutes', weight=0.1) \
+ TimeDist('fixed', 2, unit='minutes', weight=0.9)
counter = Counter([time_dist.random_variate() for i in range(1_000)])
assert counter[timedelta(minutes=1)] == 114
assert counter[timedelta(minutes=2)] == 886
def test_time_dist_fixed_add_another():
time_dist = TimeDist('fixed', 1, unit='minutes', weight=1.0) \
+ TimeDist('fixed', 2, unit='minutes', weight=1.0) \
+ TimeDist('fixed', 3, unit='minutes', weight=1.0)
assert time_dist.random_variate() in (timedelta(minutes=1),
timedelta(minutes=2),
timedelta(minutes=3))
def test_simulation_helper_skip_load_agents():
restaurants, customers, sessions, couriers = skip_load_agents(
date(2020, 4, 29), 'Winnipeg - NW')
for restaurant in restaurants:
restaurant.wait = TimeDist('norm', 5, 2)
simulation = Simulation(restaurants=restaurants,
customers=customers,
sessions=sessions,
couriers=couriers,
assigner=MipAssigner(),
metrics=[DeliveryTime()])
simulation.run(verbose=0, until=timedelta(2))
simulation.reporting.get_results()
assert True
def test_customer_customer_session_started():
env = Env()
customer = Customer('test_customer_id', 0, 0, TimeDist('fixed', 10))
env.register(customer)
input_data = {
'restaurant_id': 'test_restaurant_id',
'customer_id': 'test_customer_id'
}
env.publish('customer_session_started', input_data)
for topic, output_data in env.run():
if topic == 'order_placed':
assert env.now == timedelta(minutes=0)
assert input_data == output_data
return
assert False
def test_mock_restaurant():
env = Env()
restaurant = Restaurant('test_id', 0, 0, timedelta(0), timedelta(1),
TimeDist('fixed', 10, unit='minutes'))
env.register(restaurant)
input_data = {
'order_id': '1',
'restaurant_id': restaurant.id,
'customer_id': '3',
'courier_id': '4'
}
env.publish('courier_at_restaurant', input_data)
for topic, output_data in env.run():
if topic == 'restaurant_order_ready':
assert env.now == timedelta(minutes=10)
assert input_data == output_data
return
assert False
def __init__(self, wait: Optional[TimeDist] = None, **kwargs) -> None:
super().__init__(**kwargs)
self.wait = wait or TimeDist('fixed', 0)
def test_time_dist_fixed():
time_dist = TimeDist('fixed', 1, unit='minutes')
assert time_dist.random_variate() == timedelta(minutes=1)
def test_time_dist_fixed_add_most_likely_estimate():
time_dist = TimeDist('fixed', 1, unit='minutes', weight=1.0) \
+ TimeDist('fixed', 2, unit='minutes', weight=1.0)
assert time_dist.most_likely_estimate() == timedelta(seconds=90)
def test_time_dist_fixed_most_likely_estimate():
time_dist = TimeDist('fixed', 1, unit='minutes')
assert time_dist.most_likely_estimate() == timedelta(minutes=1)
def __init__(self,
average_speed: float = 10.0,
noise: Optional[TimeDist] = None) -> None:
self.average_speed = average_speed # average speed m/s
self.noise = noise or TimeDist('fixed', 0.0) # default
query_job = client.query(query_string, job_config=job_config)
return query_job.result().to_dataframe()
'''
dfrestaurants = query(qrestaurants)
restaurants = query(qrestaurants).values.tolist()
restaurants = [Restaurant(id = restaurants[i][0], lat = restaurants[i][1], lng = restaurants[i][2]) for i in range(len(restaurants))]
lat = dfrestaurants['address_latitude'].mean()
lng = dfrestaurants['address_longitude'].mean()
'''
num_customers = 279
radius = 500 # meters
num_couriers = 5
num_restaurants = 5
time_dist = TimeDist('norm', 16.0, 1.0, unit='hours')
restaurants = list([])
for i in range(num_restaurants):
lat, lng = random_lat_lng(51.5076, -0.0994, radius)
restaurants.append(Restaurant(f'Restaurant {i}', lat, lng))
customers = list([])
for j in range(num_customers):
lat, lng = random_lat_lng(lat, lng, radius)
customers.append(Customer(f'Customer {j}', lat, lng))
sessions = list([])
for customer in customers:
restaurant = random.choice(restaurants)
timestamp = time_dist.random_variate()