def test_bus_route_type_mismatch():
bus_routes = [1,2,4]
#Assumption, user_route is not null (checked in the previous steps of the algorithm)
user_route = create_user_route()
analyzer = routes_analyzer(bus_routes, user_route)
with pytest.raises(Exception):
assert analyzer.compute_metrics()
def test_bus_route_list_mismatch():
p1 = Point(9.222919, 45.480466)
bus_routes = np.array(p1)
#Assumption, user_route is not null (checked in the previous steps of the algorithm)
user_route = create_user_route()
analyzer = routes_analyzer(bus_routes, user_route)
with pytest.raises(Exception):
assert analyzer.compute_metrics()
def test_1():
bus_routes = []
route = create_bus_route()
bus_routes.append(route)
user_route = create_user_route()
analyzer = routes_analyzer(bus_routes, user_route)
metrics = analyzer.compute_metrics()
assert len(metrics) > 0
assert metrics[0]['number_user_coordinates'] == 3
def detect_vehicle_and_km(raw_user_route: list, snapped_user_route: list):
"""
Recognize the vehicle and computes the kilometers traveled by the user
@param raw_user_route: the user raw data, used for train recognition
@param snapped_user_route: the user snapped data, used for bus recognition
@return: the type of vehicle and the kilometers traveled by the user
"""
route_dictionaries = []
try:
# STEP 1 for buses
# Retrieving initial and finhsing Point of user's trip
bus_initial_point, bus_finishing_point = find_points(
snapped_user_route)
# STEP 2-4 for buses
sliced_routes_bus = get_bus_routes(bus_initial_point,
bus_finishing_point)
sliced_routes_bus = [(x, 'BUS') for x in sliced_routes_bus]
# STEP 5
# For every route in sliced_routes compute its metrics.
bus_analyzer = routes_analyzer(sliced_routes_bus, snapped_user_route)
route_dictionaries += bus_analyzer.compute_metrics()
except Exception as message:
print(f"No bus route matches the user one: " + str(message))
print(f"Searched for bus routes {str(len(route_dictionaries))}")
try:
# STEP 1 for trains
# Retrieving initial and finhsing Point of user's trip
train_initial_point, train_finishing_point = find_points(
raw_user_route)
# STEP 2-4 for trains
sliced_routes_train = get_train_routes(train_initial_point,
train_finishing_point)
sliced_routes_train = [(x, 'TRAIN') for x in sliced_routes_train]
# STEP 5
# For every route in sliced_routes compute its metrics.
train_analyzer = routes_analyzer(sliced_routes_train, raw_user_route)
route_dictionaries += train_analyzer.compute_metrics()
except Exception as message:
print(f"No train route matches the user one: " + str(message))
print(f"Searched for train routes {str(len(route_dictionaries))}")
if len(route_dictionaries) != 0:
# STEP 6
# Search the dictionary with the maximum metrics
print("Space race between:")
for route in route_dictionaries:
print(route['route'][0])
print(route['route'][len(route['route']) - 1])
print(route['percentage_user'])
print(route['number_user_coordinates'])
print(route['percentage_poly'])
print(route['number_polygons'])
print('-------------------------------------')
evaluator = metrics_evaluator(route_dictionaries)
best_route = evaluator.evaluate()
print("winner")
print(best_route['route'][0])
print(best_route['route'][len(best_route['route']) - 1])
print(best_route['percentage_user'])
print(best_route['number_user_coordinates'])
print(best_route['percentage_poly'])
print(best_route['number_polygons'])
# STEP 7
# Calculate the distance with haversine
print(best_route['route'][0])
print(best_route['route'][len(best_route['route']) - 1])
print('route len ' + str(len(best_route['route'])))
km_travelled = compute_kilometers(best_route['route'])
vehicle = best_route['vehicle']
return vehicle, km_travelled
else:
# No match is found
return None, 0