def test_artificially_filling_in_solution_for_partial_pt_routing_problem_results_in_correct_solution_and_routed_path(
mocker, test_network, test_spatialtree):
df = DataFrame({
'index_left': {4: 4611, 5: 2836, 6: 1620, 7: 1619, 8: 4612, 9: 4611,
10: 1929, 11: 17, 12: 18, 13: 2291, 14: 17, 15: 2804, 16: 3361},
'link_id': {4: '5221390698590575489_5221390721979501095', 5: '52213908340665748775221390828301496736',
6: '5221390721979501095_5221390721979501095', 7: '5221390721979501095_5221390721985855617',
8: '5221390698590575489_5221390698590575489', 9: '5221390698590575489_5221390721979501095',
10: '5221390698557344687_5221390698590575489', 11: '5221390688502743083_5221390698590575489',
12: '5221390688502743083_5221390688502743083', 13: '5221390319100521975_5221390688502743083',
14: '5221390688502743083_5221390698590575489', 15: '5221390319261602009_5221390688502743083',
16: '5221390688613602227_5221390688502743083'},
'id': {4: '5221390721979501095', 5: '5221390721979501095', 6: '5221390721979501095', 7: '5221390721979501095',
8: '5221390698590575489', 9: '5221390698590575489', 10: '5221390698590575489',
11: '5221390698590575489', 12: '5221390688502743083', 13: '5221390688502743083',
14: '5221390688502743083', 15: '5221390688502743083', 16: '5221390688502743083'},
'catchment': {4: 5, 5: 5, 6: 5, 7: 5, 8: 5, 9: 5,
10: 5, 11: 5, 12: 5, 13: 5, 14: 5, 15: 5, 16: 5},
'problem_nodes': {
4: '5221390721979501095.link:5221390698590575489_5221390721979501095',
5: '5221390721979501095.link:52213908340665748775221390828301496736',
6: '5221390721979501095.link:5221390721979501095_5221390721979501095',
7: '5221390721979501095.link:5221390721979501095_5221390721985855617',
8: '5221390698590575489.link:5221390698590575489_5221390698590575489',
9: '5221390698590575489.link:5221390698590575489_5221390721979501095',
10: '5221390698590575489.link:5221390698557344687_5221390698590575489',
11: '5221390698590575489.link:5221390688502743083_5221390698590575489',
12: '5221390688502743083.link:5221390688502743083_5221390688502743083',
13: '5221390688502743083.link:5221390319100521975_5221390688502743083',
14: '5221390688502743083.link:5221390688502743083_5221390698590575489',
15: '5221390688502743083.link:5221390319261602009_5221390688502743083',
16: '5221390688502743083.link:5221390688613602227_5221390688502743083'}}).set_index('id', drop=False)
df.index.rename(name='index', inplace=True)
mocker.patch.object(spatial.SpatialTree, 'closest_links',
return_value=df)
mss = MaxStableSet(pt_graph=test_network.schedule.route('40230_1').graph(),
network_spatial_tree=test_spatialtree,
modes={'car', 'bus'},
distance_threshold=5,
step_size=5)
assert not mss.all_stops_have_nearest_links()
mss.solve()
assert mss.unsolved_stops == {'5221390319100521975'}
mss.route_edges()
mss.fill_in_solution_artificially()
art_link = 'artificial_link===from:5221390319100521975===to:5221390319100521975'
assert mss.solution['5221390319100521975'] == art_link
art_stop = '5221390319100521975.link:artificial_link===from:5221390319100521975===to:5221390319100521975'
assert art_stop in mss.artificial_stops
assert mss.artificial_stops[art_stop]['linkRefId'] == art_link
assert mss.artificial_stops[art_stop]['stop_id'] == '5221390319100521975'
assert mss.artificial_stops[art_stop]['id'] == art_stop
assert mss.routed_path(
['5221390319100521975', '5221390688502743083', '5221390698590575489', '5221390721979501095']) == [
'artificial_link===from:5221390319100521975===to:5221390319100521975',
'artificial_link===from:5221390319100521975===to:5221390688502743083',
'5221390688502743083_5221390698590575489', '5221390698590575489_5221390721979501095']
def test_snapping_disconnected_partial_pt_route_results_in_all_stops_with_link_references_and_routes_between_viable_catchments(
mocker, test_network, test_spatialtree):
df = DataFrame({
'index_left': {0: 2291, 1: 2290, 2: 2292, 3: 5178, 4: 4611, 5: 2836, 6: 1620, 7: 1619, 8: 4612, 9: 4611,
10: 1929, 11: 17, 12: 18, 13: 2291, 14: 17, 15: 2804, 16: 3361},
'link_id': {0: '5221390319100521975_5221390688502743083', 1: '5221390319100521975_5221390319062365867',
2: '5221390319100521975_5221390319100521975', 3: '5221390319091334983_5221390319100521975',
4: '5221390698590575489_5221390721979501095', 5: '52213908340665748775221390828301496736',
6: '5221390721979501095_5221390721979501095', 7: '5221390721979501095_5221390721985855617',
12: '5221390688502743083_5221390688502743083', 13: '5221390319100521975_5221390688502743083',
14: '5221390688502743083_5221390698590575489', 15: '5221390319261602009_5221390688502743083',
16: '5221390688613602227_5221390688502743083'},
'id': {0: '5221390319100521975', 1: '5221390319100521975', 2: '5221390319100521975', 3: '5221390319100521975',
4: '5221390721979501095', 5: '5221390721979501095', 6: '5221390721979501095', 7: '5221390721979501095',
12: '5221390688502743083', 13: '5221390688502743083',
14: '5221390688502743083', 15: '5221390688502743083', 16: '5221390688502743083'},
'catchment': {0: 5, 1: 5, 2: 5, 3: 5, 4: 5, 5: 5, 6: 5, 7: 5, 12: 5, 13: 5, 14: 5, 15: 5, 16: 5},
'problem_nodes': {
0: '5221390319100521975.link:5221390319100521975_5221390688502743083',
1: '5221390319100521975.link:5221390319100521975_5221390319062365867',
2: '5221390319100521975.link:5221390319100521975_5221390319100521975',
3: '5221390319100521975.link:5221390319091334983_5221390319100521975',
4: '5221390721979501095.link:5221390698590575489_5221390721979501095',
5: '5221390721979501095.link:52213908340665748775221390828301496736',
6: '5221390721979501095.link:5221390721979501095_5221390721979501095',
7: '5221390721979501095.link:5221390721979501095_5221390721985855617',
12: '5221390688502743083.link:5221390688502743083_5221390688502743083',
13: '5221390688502743083.link:5221390319100521975_5221390688502743083',
14: '5221390688502743083.link:5221390688502743083_5221390698590575489',
15: '5221390688502743083.link:5221390319261602009_5221390688502743083',
16: '5221390688502743083.link:5221390688613602227_5221390688502743083'}}).set_index('id', drop=False)
df.index.rename(name='index', inplace=True)
mocker.patch.object(spatial.SpatialTree, 'closest_links',
return_value=df)
mss = MaxStableSet(pt_graph=test_network.schedule.route('40230_1').graph(),
network_spatial_tree=test_spatialtree,
modes={'car', 'bus'},
distance_threshold=5,
step_size=5)
assert not mss.all_stops_have_nearest_links()
mss.solve()
assert mss.unsolved_stops == {'5221390698590575489', '5221390721979501095'}
mss.route_edges()
assert set(mss.pt_edges[mss.pt_edges['shortest_path'].isna()]['u']) == {'5221390698590575489',
'5221390688502743083'}
assert set(mss.pt_edges[mss.pt_edges['shortest_path'].isna()]['v']) == {'5221390698590575489',
'5221390721979501095'}
def test_detects_stops_that_lack_nearest_links(mocker, network, network_spatial_tree):
closest_links = DataFrame({
'id': {0: 'stop_2', 1: 'stop_2', 2: 'stop_3', 3: 'stop_3'},
'link_id': {0: 'link_4_5_car', 1: 'link_5_6_car', 2: 'link_7_8_car', 3: 'link_8_9_car'},
}).set_index('id', drop=False)
closest_links.index.rename(name='index', inplace=True)
mocker.patch.object(spatial.SpatialTree, 'closest_links',
return_value=closest_links)
mss = MaxStableSet(pt_graph=network.schedule['bus_service'].graph(),
network_spatial_tree=network_spatial_tree,
modes={'car', 'bus'},
step_size=10,
distance_threshold=10
)
assert not mss.all_stops_have_nearest_links()