def test_SpatialTree_shortest_path_lengths(network):
spatial_tree = spatial.SpatialTree(network)
df = DataFrame({
'u': ['link_1', 'link_2', 'link_2', 'link_1'],
'v': ['link_2', 'link_3', 'link_4', 'link_4']
})
df = spatial_tree.shortest_path_lengths(df, modes='car')
assert_semantically_equal(
df.T.to_dict(), {
0: {
'u': 'link_1',
'v': 'link_2',
'path_lengths': 153.0294
},
1: {
'u': 'link_2',
'v': 'link_3',
'path_lengths': float('nan')
},
2: {
'u': 'link_2',
'v': 'link_4',
'path_lengths': 78.443
},
3: {
'u': 'link_1',
'v': 'link_4',
'path_lengths': 231.4724
}
})
def test_SpatialTree_closest_links_in_london_finds_links_within_30_metres(
network):
spatial_tree = spatial.SpatialTree(network).modal_subtree(modes='car')
stops = GeoDataFrame({
'id': {
0: 'stop_10m_to_link_1',
1: 'stop_15m_to_link_2',
2: 'stop_20m_to_link_1'
},
'geometry': {
0: Point(-0.15186089346604492, 51.51950409732838),
1: Point(-0.15164747576623197, 51.520660715220636),
2: Point(-0.1520233977548685, 51.51952913606585)
}
})
stops.crs = {'init': 'epsg:4326'}
closest_links = spatial_tree.closest_links(stops, 30)
assert_semantically_equal(
closest_links.reset_index().groupby('id')['link_id'].apply(
list).to_dict(), {
'stop_10m_to_link_1': ['link_1'],
'stop_20m_to_link_1': ['link_1'],
'stop_15m_to_link_2': ['link_2', 'link_4']
})
def test_SpatialTree_shortest_paths(network):
spatial_tree = spatial.SpatialTree(network)
df = DataFrame({
'u': ['link_1', 'link_2', 'link_2', 'link_1'],
'v': ['link_2', 'link_3', 'link_4', 'link_4']
})
df = spatial_tree.shortest_paths(df, modes='car')
assert_semantically_equal(
df.T.to_dict(), {
0: {
'u': 'link_1',
'v': 'link_2',
'shortest_path': ['link_1', 'link_2']
},
1: {
'u': 'link_2',
'v': 'link_3',
'shortest_path': None
},
2: {
'u': 'link_2',
'v': 'link_4',
'shortest_path': ['link_2', 'link_4']
},
3: {
'u': 'link_1',
'v': 'link_4',
'shortest_path': ['link_1', 'link_2', 'link_4']
}
})
def test_SpatialTree_closest_links_in_north_canada_doesnt_find_link_within_10_metres(
):
# (out in the boonies)
n = Network('epsg:4326')
n.add_nodes({
'1': {
'x': -93.25129666354827,
'y': 73.66401680598872
},
'2': {
'x': -93.25140295754169,
'y': 73.66417415921647
}
})
n.add_link(link_id='link_1', u='1', v='2', attribs={'modes': ['car']})
spatial_tree = spatial.SpatialTree(n)
stops = GeoDataFrame(
{'geometry': {
'stop_15m_to_link_1': Point(-93.250971, 73.664114)
}})
stops.crs = {'init': 'epsg:4326'}
closest_links = spatial_tree.closest_links(stops, 10, modes='car')
closest_links = closest_links.dropna()
assert closest_links.empty
def test_SpatialTree_closest_links_in_north_canada_finds_link_within_30_metres(
):
# (out in the boonies)
n = Network('epsg:4326')
n.add_nodes({
'1': {
'x': -93.25129666354827,
'y': 73.66401680598872
},
'2': {
'x': -93.25140295754169,
'y': 73.66417415921647
}
})
n.add_link(link_id='link_1', u='1', v='2', attribs={'modes': ['car']})
spatial_tree = spatial.SpatialTree(n)
stops = GeoDataFrame(
{'geometry': {
'stop_15m_to_link_1': Point(-93.250971, 73.664114)
}})
stops.crs = {'init': 'epsg:4326'}
closest_links = spatial_tree.closest_links(stops, 30, modes='car')
assert_semantically_equal(
closest_links.reset_index().groupby('index')['link_id'].apply(
list).to_dict(), {'stop_15m_to_link_1': ['link_1']})
def test_SpatialTree_closest_links_in_indonesia_doesnt_find_link_within_10_metres(
):
# (close to equator)
n = Network('epsg:4326')
n.add_nodes({
'1': {
'x': 109.380477773586,
'y': 0.3203433505415778
},
'2': {
'x': 109.38042852136014,
'y': 0.32031507655538294
}
})
n.add_link(link_id='link_1', u='1', v='2', attribs={'modes': ['car']})
spatial_tree = spatial.SpatialTree(n)
stops = GeoDataFrame(
{'geometry': {
'stop_15m_to_link_1': Point(109.380607, 0.320333)
}})
stops.crs = {'init': 'epsg:4326'}
closest_links = spatial_tree.closest_links(stops, 10, modes='car')
closest_links = closest_links.dropna()
assert closest_links.empty
def test_SpatialTree_closest_links_in_indonesia_finds_link_within_20_metres():
# (close to equator)
n = Network('epsg:4326')
n.add_nodes({
'1': {
'x': 109.380477773586,
'y': 0.3203433505415778
},
'2': {
'x': 109.38042852136014,
'y': 0.32031507655538294
}
})
n.add_link(link_id='link_1', u='1', v='2', attribs={'modes': ['car']})
spatial_tree = spatial.SpatialTree(n)
stops = GeoDataFrame(
{'geometry': {
'stop_15m_to_link_1': Point(109.380607, 0.320333)
}})
stops.crs = {'init': 'epsg:4326'}
closest_links = spatial_tree.closest_links(stops, 20, modes='car')
closest_links = closest_links.dropna()
assert_semantically_equal(
closest_links.reset_index().groupby('index')['link_id'].apply(
list).to_dict(), {'stop_15m_to_link_1': ['link_1']})
def test_SpatialTree_roots():
spatial_tree = spatial.SpatialTree()
links = [('1', {'id': "1", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'modes': ['subway,metro', 'walk', 'car']}),
('2', {'id': "2", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'modes': ['bike', 'walk', 'piggy_back']})]
spatial_tree.add_links(links=links)
assert spatial_tree.roots() == [0]
def test_SpatialTree_closest_edges():
spatial_tree = spatial.SpatialTree()
links = [('1', {'id': "1", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'modes': ['subway,metro', 'walk', 'car']}),
('2', {'id': "2", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'modes': ['bike', 'walk', 'piggy_back']})
]
spatial_tree.add_links(links=links)
assert_semantically_equal(spatial_tree.find_closest_links(5221390301001263407, 30), ['1', '2'])
def partial_mss(network):
mss = MaxStableSet(pt_graph=network.schedule['bus_service'].graph(),
network_spatial_tree=spatial.SpatialTree(network),
modes={'car', 'bus'},
distance_threshold=10,
step_size=10)
mss.solution = {'stop_2': 'link_5_6_car',
'stop_3': 'link_7_8_car',
'stop_1': 'artificial_link===from:stop_1===to:stop_1'}
mss.artificial_stops = {
'stop_2.link:link_5_6_car': {'services': {'bus_service'}, 'routes': {'service_1_route_1', 'service_1_route_2'},
'id': 'stop_2.link:link_5_6_car', 'x': 2.0, 'y': 2.5, 'epsg': 'epsg:27700',
'name': '', 'lon': -7.557134732217642, 'lat': 49.76683094462549,
's2_id': 5205973754090230267, 'additional_attributes': set(),
'linkRefId': 'link_5_6_car', 'stop_id': 'stop_2'},
'stop_3.link:link_7_8_car': {'services': {'bus_service'}, 'routes': {'service_1_route_1', 'service_1_route_2'},
'id': 'stop_3.link:link_7_8_car', 'x': 5.5, 'y': 2.0, 'epsg': 'epsg:27700',
'name': '', 'lon': -7.55708584676138, 'lat': 49.76682879603468,
's2_id': 5205973754096513977, 'additional_attributes': set(),
'linkRefId': 'link_7_8_car', 'stop_id': 'stop_3'},
'stop_1.link:artificial_link===from:stop_1===to:stop_1': {'services': {'bus_service'},
'routes': {'service_1_route_1', 'service_1_route_2'},
'id': 'stop_1.link:artificial_link===from:stop_1===to:stop_1',
'x': 1.0, 'y': 2.5, 'epsg': 'epsg:27700', 'name': '',
'lon': -7.557148552832129, 'lat': 49.76683027967191,
's2_id': 5205973754090340691,
'additional_attributes': set(),
'linkRefId': 'artificial_link===from:stop_1===to:stop_1',
'stop_id': 'stop_1'}}
mss.artificial_links = {
'artificial_link===from:stop_1===to:stop_1': {'from': 'stop_1', 'to': 'stop_1', 'modes': {'bus'}},
'artificial_link===from:node_6===to:stop_1': {'from': 'node_6', 'to': 'stop_1', 'modes': {'bus'}},
'artificial_link===from:stop_1===to:node_5': {'from': 'stop_1', 'to': 'node_5', 'modes': {'bus'}}}
mss.pt_edges = DataFrame(
{'services': {0: {'bus_service'}, 1: {'bus_service'}, 2: {'bus_service'}, 3: {'bus_service'}},
'routes': {0: {'service_1_route_2'}, 1: {'service_1_route_2'}, 2: {'service_1_route_1'},
3: {'service_1_route_1'}},
'u': {0: 'stop_3', 1: 'stop_2', 2: 'stop_2', 3: 'stop_1'},
'v': {0: 'stop_2', 1: 'stop_1', 2: 'stop_3', 3: 'stop_2'},
'key': {0: 0, 1: 0, 2: 0, 3: 0},
'linkRefId_u': {0: 'link_7_8_car', 1: 'link_5_6_car', 2: 'link_5_6_car',
3: 'artificial_link===from:stop_1===to:stop_1'},
'linkRefId_v': {0: 'link_5_6_car', 1: 'artificial_link===from:stop_1===to:stop_1', 2: 'link_7_8_car',
3: 'link_5_6_car'},
'shortest_path': {0: ['link_7_8_car', 'link_8_7_car', 'link_7_6_car', 'link_6_5_car', 'link_5_6_car'],
1: ['link_5_6_car', 'artificial_link===from:node_6===to:stop_1',
'artificial_link===from:stop_1===to:stop_1'],
2: ['link_5_6_car', 'link_6_7_car', 'link_7_8_car'],
3: ['artificial_link===from:stop_1===to:stop_1', 'artificial_link===from:stop_1===to:node_5',
'link_5_6_car']}})
mss.unsolved_stops = {'stop_1'}
return mss
def test_SpatialTree_adds_a_link():
link, link_attrib = ('1', {
'id': "1", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'freespeed': "4.166666666666667", 'capacity': "600.0", 'permlanes': "1.0", 'oneway': "1",
'modes': ['subway,metro', 'walk', 'car'], 'attributes': {
'osm:way:access': {'name': 'osm:way:access', 'class': 'java.lang.String', 'text': 'permissive'},
'osm:way:highway': {'name': 'osm:way:highway', 'class': 'java.lang.String', 'text': 'unclassified'},
'osm:way:id': {'name': 'osm:way:id', 'class': 'java.lang.Long', 'text': '26997928'},
'osm:way:name': {'name': 'osm:way:name', 'class': 'java.lang.String', 'text': 'Brunswick Place'}
}})
spatial_tree = spatial.SpatialTree([(link, link_attrib)])
assert list(spatial_tree.edges) == [(5221390298638188544, '1'), (5764607523034234880, 5221642292959379456),
(5221642292959379456, 5221378410168713216),
(5221378410168713216, 5221390298638188544), (0, 5764607523034234880)]
for node, node_attrib in list(spatial_tree.nodes(data=True)):
assert_semantically_equal(node_attrib, link_attrib)
def test_build_graph_for_maximum_stable_set_problem_with_no_path_between_isolated_node(mocker, network):
closest_links = DataFrame({
'id': {0: 'stop_2', 1: 'stop_2', 2: 'stop_3', 3: 'stop_3', 4: 'stop_1', 5: 'stop_1',
6: 'stop_1'},
'link_id': {0: 'link_4_5_car', 1: 'link_5_6_car', 2: 'link_7_8_car', 3: 'link_8_9_car',
4: 'isolated_link', 5: 'link_1_2_bus', 6: 'link_2_3_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)
network.add_nodes({'node_iso_1': {'id': 'node_iso_1', 'x': 10, 'y': 20, 'lat': 49.8, 'lon': -7.5,
's2_id': 5205973754090365183},
'node_iso_2': {'id': 'node_iso_2', 'x': 10, 'y': 30, 'lat': 49.9, 'lon': -7.6,
's2_id': 5205973754090333257}})
network.add_link('isolated_link', u='node_iso_1', v='node_iso_2', attribs={'modes': {'car', 'bus'}})
mss = MaxStableSet(pt_graph=network.schedule['bus_service'].graph(),
network_spatial_tree=spatial.SpatialTree(network),
modes={'car', 'bus'},
distance_threshold=10,
step_size=10)
assert_semantically_equal(dict(mss.problem_graph.nodes()),
{'stop_2.link:link_4_5_car': {'id': 'stop_2', 'link_id': 'link_4_5_car', 'catchment': 10,
'coeff': 0.26666666666666666},
'stop_2.link:link_5_6_car': {'id': 'stop_2', 'link_id': 'link_5_6_car', 'catchment': 10,
'coeff': 0.26666666666666666},
'stop_3.link:link_7_8_car': {'id': 'stop_3', 'link_id': 'link_7_8_car', 'catchment': 10,
'coeff': 0.2857142857142857},
'stop_3.link:link_8_9_car': {'id': 'stop_3', 'link_id': 'link_8_9_car', 'catchment': 10,
'coeff': 0.2222222222222222},
'stop_1.link:link_1_2_bus': {'id': 'stop_1', 'link_id': 'link_1_2_bus', 'catchment': 10,
'coeff': 0.2857142857142857},
'stop_1.link:link_2_3_car': {'id': 'stop_1', 'link_id': 'link_2_3_car', 'catchment': 10,
'coeff': 0.2857142857142857}})
assert_semantically_equal(list(mss.problem_graph.edges()),
[('stop_2.link:link_4_5_car', 'stop_2.link:link_5_6_car'),
('stop_3.link:link_7_8_car', 'stop_3.link:link_8_9_car'),
('stop_1.link:link_1_2_bus', 'stop_1.link:link_2_3_car')])
def test_SpatialTree_adds_links(network):
spatial_tree = spatial.SpatialTree(network)
assert_semantically_equal(list(spatial_tree.edges(data=True)),
[('link_1', 'link_2', {
'length': 153.0294
}), ('link_1', 'link_3', {
'length': 153.0294
}), ('link_2', 'link_4', {
'length': 78.443
}), ('link_3', 'link_4', {
'length': 78.443
}), ('link_4', 'link_2', {
'length': 78.443
}), ('link_4', 'link_3', {
'length': 78.443
})])
assert_semantically_equal(dict(spatial_tree.nodes(data=True)), {
'link_1': {},
'link_2': {},
'link_3': {},
'link_4': {}
})
def test_problem_with_isolated_catchment_is_partially_viable(mocker, network):
closest_links = DataFrame({
'id': {0: 'stop_2', 1: 'stop_2', 2: 'stop_3', 3: 'stop_3', 4: 'stop_1', 5: 'stop_1'},
'link_id': {0: 'link_4_5_car', 1: 'link_5_6_car', 2: 'link_7_8_car', 3: 'link_8_9_car',
4: 'isolated_link_1', 5: 'isolated_link_2'},
}).set_index('id', drop=False)
closest_links.index.rename(name='index', inplace=True)
mocker.patch.object(spatial.SpatialTree, 'closest_links',
return_value=closest_links)
network.add_nodes({'node_iso_1': {'id': 'node_iso_1', 'x': 10, 'y': 20, 'lat': 49.8, 'lon': -7.5,
's2_id': 5205973754090365183},
'node_iso_2': {'id': 'node_iso_2', 'x': 10, 'y': 30, 'lat': 49.9, 'lon': -7.6,
's2_id': 5205973754090333257}})
network.add_link('isolated_link_1', u='node_iso_1', v='node_iso_2', attribs={'modes': {'car', 'bus'}})
network.add_link('isolated_link_2', u='node_iso_2', v='node_iso_1', attribs={'modes': {'car', 'bus'}})
mss = MaxStableSet(pt_graph=network.schedule['bus_service'].graph(),
network_spatial_tree=spatial.SpatialTree(network),
modes={'car', 'bus'},
distance_threshold=10,
step_size=10)
assert mss.is_partially_viable()
def test_SpatialTree_combines_link_list_attribs():
spatial_tree = spatial.SpatialTree()
links = [('1', {'id': "1", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'modes': ['subway,metro', 'walk', 'car']}),
('2', {'id': "2", 'from': "25508485", 'to': "21667818", 'length': 52.765151087870265,
's2_from': 5221390301001263407, 's2_to': 5221390302696205321,
'modes': ['bike', 'walk', 'piggy_back']})]
spatial_tree.add_links(links=links)
assert_semantically_equal(list(spatial_tree.edges), [(5221390298638188544, '1'), (5221390298638188544, '2'),
(5764607523034234880, 5221642292959379456),
(5221642292959379456, 5221378410168713216),
(5221378410168713216, 5221390298638188544),
(0, 5764607523034234880)])
for node, node_attrib in list(spatial_tree.nodes(data=True)):
if node not in ['1', '2']:
assert set(node_attrib['modes']) == {'subway,metro', 'car', 'bike', 'walk', 'piggy_back'}
elif node == '1':
assert set(node_attrib['modes']) == {'subway,metro', 'walk', 'car'}
elif node == '2':
assert set(node_attrib['modes']) == {'bike', 'walk', 'piggy_back'}
def test_subsetting_links_df_by_mode_that_isnt_present_throws_error(network):
spatial_tree = spatial.SpatialTree(network)
with pytest.raises(EmptySpatialTree) as e:
df = spatial_tree.modal_links_geodataframe(modes={'piggyback'})
assert 'No links found' in str(e.value)
def network_spatial_tree(network):
return spatial.SpatialTree(network)
def test_spatialtree(test_network):
return spatial.SpatialTree(test_network)
def test_subsetting_links_df_by_mode(network):
spatial_tree = spatial.SpatialTree(network)
df = spatial_tree.modal_links_geodataframe(modes={'car'})
assert set(df['link_id']) == {'link_1', 'link_2', 'link_4'}
