def test_finalize(self):
node_profile = NodeProfileMultiObjective(label_class=LabelTimeWithBoardingsCount, dep_times=[10])
own_label = LabelTimeWithBoardingsCount(departure_time=10, arrival_time_target=20, n_boardings=0, first_leg_is_walk=False)
self.assertTrue(node_profile.update([own_label]))
neighbor_label = LabelTimeWithBoardingsCount(departure_time=15, arrival_time_target=18, n_boardings=2, first_leg_is_walk=False)
assert(len(node_profile.get_labels_for_real_connections()) == 1)
node_profile.finalize([[neighbor_label]], [3])
assert (len(node_profile.get_final_optimal_labels()) == 2)
self.assertTrue(any(map(lambda el: el.departure_time == 12, node_profile.get_final_optimal_labels())))
def test_pareto_optimality_with_transfers_only(self):
LabelClass = LabelVehLegCount
node_profile = NodeProfileMultiObjective(dep_times=[5, 6, 7], label_class=LabelClass)
pt3 = LabelClass(departure_time=5, n_vehicle_legs=0, last_leg_is_walk=False)
pt2 = LabelClass(departure_time=6, n_vehicle_legs=1, last_leg_is_walk=False)
pt1 = LabelClass(departure_time=7, n_vehicle_legs=2, last_leg_is_walk=False)
self.assertTrue(node_profile.update([pt1]))
self.assertTrue(node_profile.update([pt2]))
self.assertTrue(node_profile.update([pt3]))
node_profile.finalize()
self.assertEqual(1, len(node_profile.get_final_optimal_labels()))
def _get_analyzer(self,
labels,
start_time,
end_time,
walk_to_target_duration=float('inf')):
dep_times = list(set(map(lambda el: el.departure_time, labels)))
p = NodeProfileMultiObjective(
dep_times=dep_times,
walk_to_target_duration=walk_to_target_duration,
label_class=LabelTimeWithBoardingsCount)
for label in labels:
p.update([label])
p.finalize()
analyzer = NodeProfileAnalyzerTimeAndVehLegs.from_profile(
p, start_time, end_time)
return analyzer
def __compute_profile_stats_from_profiles(profile_data):
"""
Parameters
----------
profiles: dict
mapping from stop_I -> MultiObjectiveNodeProfile
Returns
-------
observable_name_to_data: dict
"""
profile_summary_methods, profile_observable_names = NodeProfileAnalyzerTimeAndVehLegs.all_measures_and_names_as_lists(
)
profile_summary_data = [[] for _ in range(len(profile_observable_names))]
observable_name_to_method = dict(
zip(profile_observable_names, profile_summary_methods))
observable_name_to_data = dict(
zip(profile_observable_names, profile_summary_data))
nodes = pandas.read_csv(HELSINKI_NODES_FNAME, sep=";")
for stop_I in nodes['stop_I'].values:
try:
profile = profile_data[stop_I]
except KeyError:
profile = NodeProfileMultiObjective()
profile.finalize()
profile_analyzer = NodeProfileAnalyzerTimeAndVehLegs(
profile, ANALYSIS_START_TIME_DEP, ANALYSIS_END_TIME_DEP)
for observable_name in profile_observable_names:
method = observable_name_to_method[observable_name]
observable_value = method(profile_analyzer)
if observable_value is None:
print(observable_name, stop_I)
_assert_results_are_positive_or_infs_or_nans(
numpy.array([observable_value]))
observable_name_to_data[observable_name].append(observable_value)
return observable_name_to_data
def plot_transfer_profile():
alphabetical_labels = list(labels_t_dep_dur_b)
alphabetical_labels.sort(key=lambda el: el[-1])
for label in alphabetical_labels:
print(label[-1] + " & " + str(label[0]) + " & " +
str(label[0] + label[1]) + " & " + str(label[1]) + " & " +
str(label[2]) + " \\\\")
labels = [
LabelTimeWithBoardingsCount(departure_time=label[0],
arrival_time_target=label[0] + label[1],
n_boardings=label[2],
first_leg_is_walk=False)
for label in labels_t_dep_dur_b
]
dep_times = list(set(map(lambda el: el.departure_time, labels)))
p = NodeProfileMultiObjective(dep_times=dep_times,
walk_to_target_duration=10,
label_class=LabelTimeWithBoardingsCount)
for label in labels:
p.update([label])
p.finalize()
analyzer = NodeProfileAnalyzerTimeAndVehLegs(p, 0, 20)
print(analyzer.mean_n_boardings_on_shortest_paths())
journey_letters = [label[-1] for label in labels_t_dep_dur_b[::-1]]
fig = plt.figure(figsize=(5.5, 3.5))
ax1 = plt.subplot(gs[:, :4])
fig = analyzer.plot_new_transfer_temporal_distance_profile(
format_string="%S",
duration_divider=1,
default_lw=4,
journey_letters=journey_letters,
ax=ax1,
ncol_legend=2,
legend_font_size=9)
ax1.set_xlabel("Departure time $t_{\\text{dep}}$ (min)")
ax1.set_ylabel("Temporal distance $\\tau$ (min)")
ax2 = plt.subplot(gs[:, 4:])
ax2 = analyzer.plot_temporal_distance_pdf_horizontal(use_minutes=True,
duration_divider=1,
ax=ax2,
legend_font_size=9)
ax2.set_ylabel("")
ax1.set_ylim(0, 11.5)
ax2.set_ylim(0, 11.5)
ax2.set_xlim(0, 0.3)
ax2.set_yticklabels(["" for _ in ax2.get_yticks()])
ax2.set_xlabel("Probability density $P(\\tau)$")
ax2.set_xticks([0.1, 0.2, 0.3])
for _ax, letter in zip([ax1, ax2], "AB"):
_ax.text(0.04,
0.98,
"\\textbf{" + letter + "}",
horizontalalignment="left",
verticalalignment="top",
transform=_ax.transAxes,
fontsize=15,
color="black")
fig.savefig(settings.FIGS_DIRECTORY + "schematic_transfer_profile.pdf")
plt.show()