def test_evaluate(self):
node_profile = NodeProfileMultiObjective(dep_times=[3, 1], label_class=LabelTime)
node_profile.update([LabelTime(departure_time=3, arrival_time_target=4)])
self.assertEquals(4, min_arrival_time_target(node_profile.evaluate(3)))
node_profile.update([LabelTime(departure_time=1, arrival_time_target=1)])
self.assertEquals(1, min_arrival_time_target(node_profile.evaluate(1)))
def test_pareto_optimality(self):
event_list_raw_data = [(0, 2, 0, 10, "trip_1", 1),
(0, 1, 2, 5, "trip_2", 1),
(1, 2, 5, 8, "trip_3", 1)]
transit_connections = list(
map(lambda el: Connection(*el), event_list_raw_data))
walk_speed = 2
source_stop = 0
target_stop = 2
transfer_margin = 0
start_time = 0
end_time = 20
walk_network = networkx.Graph()
csa_profile = MultiObjectivePseudoCSAProfiler(transit_connections,
target_stop, start_time,
end_time,
transfer_margin,
walk_network, walk_speed)
csa_profile.run()
source_profile = csa_profile.stop_profiles[source_stop]
self.assertEqual(
min_arrival_time_target(source_profile.evaluate(0, 0)), 8)
found_labels = source_profile.get_final_optimal_labels()
labels_should_be = list()
labels_should_be.append(
LabelTimeWithBoardingsCount(0,
10,
n_boardings=1,
first_leg_is_walk=False))
labels_should_be.append(
LabelTimeWithBoardingsCount(2,
8,
n_boardings=2,
first_leg_is_walk=False))
self._assert_label_sets_equal(found_labels, labels_should_be)
def test_walk_is_faster_than_by_trip(self):
event_list_raw_data = [(0, 1, 0, 10, "trip_1", 1)]
transit_connections = list(
map(lambda el: Connection(*el), event_list_raw_data))
walk_speed = 0.5
source_stop = 0
target_stop = 1
transfer_margin = 0
start_time = 0
end_time = 50
walk_network = networkx.Graph()
walk_network.add_edge(0, 1, d_walk=1)
csa_profile = MultiObjectivePseudoCSAProfiler(transit_connections,
target_stop, start_time,
end_time,
transfer_margin,
walk_network, walk_speed)
csa_profile.run()
source_profile = csa_profile.stop_profiles[source_stop]
self.assertEqual(
min_arrival_time_target(
source_profile.evaluate(0, first_leg_can_be_walk=True)), 2)
found_tuples = source_profile.get_final_optimal_labels()
self.assertEqual(len(found_tuples), 0)
def test_target_node_not_in_walk_network(self):
event_list_raw_data = [(0, 1, 0, 10, "trip_1", 1)]
transit_connections = list(
map(lambda el: Connection(*el), event_list_raw_data))
walk_speed = 2
source_stop = 0
target_stop = 1
transfer_margin = 0
start_time = 0
end_time = 50
walk_network = networkx.Graph()
csa_profile = MultiObjectivePseudoCSAProfiler(transit_connections,
target_stop, start_time,
end_time,
transfer_margin,
walk_network, walk_speed)
csa_profile.run()
source_profile = csa_profile.stop_profiles[source_stop]
self.assertEqual(
min_arrival_time_target(source_profile.evaluate(0, 0)), 10)
found_tuples = source_profile.get_final_optimal_labels()
self.assertEqual(len(found_tuples), 1)
def test_identity_profile(self):
identity_profile = NodeProfileMultiObjective(dep_times=[10])
identity_profile.update([LabelTimeWithBoardingsCount(10, 10, 0, True)])
self.assertEqual(10, min_arrival_time_target(identity_profile.evaluate(10, first_leg_can_be_walk=True)))
