def read_json_network(network_path: str, epsg: str):
"""
Reads Network graph from JSON file.
:param network_path: path to json or geojson network file
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
logging.info(f'Reading Network from {network_path}')
with open(network_path) as json_file:
json_data = json.load(json_file)
for node, data in json_data['nodes'].items():
try:
del data['geometry']
except KeyError:
pass
for link, data in json_data['links'].items():
try:
data['geometry'] = spatial.decode_polyline_to_shapely_linestring(data['geometry'])
except KeyError:
pass
try:
data['modes'] = set(data['modes'].split(','))
except KeyError:
pass
n = core.Network(epsg=epsg)
n.add_nodes(json_data['nodes'])
n.add_links(json_data['links'])
n.change_log = change_log.ChangeLog()
return n
def read_geojson_network(nodes_path: str, links_path: str, epsg: str):
"""
Reads Network graph from JSON file.
:param nodes_path: path to geojson network nodes file
:param links_path: path to geojson network links file
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
logging.info(f'Reading Network nodes from {nodes_path}')
nodes = gpd.read_file(nodes_path)
nodes = nodes.drop('geometry', axis=1)
nodes['id'] = nodes['id'].astype(int).astype(str)
nodes = nodes.set_index('id', drop=False)
if 'index' in nodes.columns:
nodes = nodes.drop('index', axis=1)
logging.info(f'Reading Network links from {links_path}')
links = gpd.read_file(links_path).to_crs(epsg)
links['modes'] = links['modes'].apply(lambda x: set(x.split(',')))
links['id'] = links['id'].astype(int).astype(str)
links = links.set_index('id', drop=False)
if 'index' in links.columns:
links = links.drop('index', axis=1)
n = core.Network(epsg=epsg)
n.add_nodes(nodes.T.to_dict())
n.add_links(links.T.to_dict())
n.change_log = change_log.ChangeLog()
return n
def schedule_graph():
graph = DiGraph(
name='Schedule Graph',
routes={'4': {'ordered_stops': ['4', '5'], 'route_short_name': 'route4', 'mode': 'rail',
'trips': {'trip_id': ['route4_05:40:00'], 'trip_departure_time': ['05:40:00'],
'vehicle_id': ['veh_0_bus']},
'arrival_offsets': ['00:00:00', '00:03:00'],
'departure_offsets': ['00:00:00', '00:05:00'], 'route_long_name': '', 'id': '4',
'route': ['4', '5'], 'await_departure': []},
'3': {'ordered_stops': ['3', '4'], 'route_short_name': 'route3', 'mode': 'rail',
'trips': {'trip_id': ['route3_04:40:00'], 'trip_departure_time': ['04:40:00'],
'vehicle_id': ['veh_1_bus']},
'arrival_offsets': ['00:00:00', '00:02:00'],
'departure_offsets': ['00:00:00', '00:02:00'], 'route_long_name': '', 'id': '3',
'route': ['3', '4'], 'await_departure': []},
'1': {'ordered_stops': ['0', '1'], 'route_short_name': 'route1', 'mode': 'bus',
'trips': {'trip_id': ['route1_04:40:00'], 'trip_departure_time': ['04:40:00'],
'vehicle_id': ['veh_2_bus']},
'arrival_offsets': ['00:00:00', '00:02:00'],
'departure_offsets': ['00:00:00', '00:02:00'], 'route_long_name': '', 'id': '1',
'route': ['0', '1'], 'await_departure': []},
'2': {'ordered_stops': ['1', '2'], 'route_short_name': 'route2', 'mode': 'bus',
'trips': {'trip_id': ['route2_05:40:00'], 'trip_departure_time': ['05:40:00'],
'vehicle_id': ['veh_3_bus']},
'arrival_offsets': ['00:00:00', '00:03:00'],
'departure_offsets': ['00:00:00', '00:05:00'], 'route_long_name': '', 'id': '2',
'route': ['1', '2'], 'await_departure': []}},
services={'service2': {'id': 'service2', 'name': 'route3'},
'service1': {'id': 'service1', 'name': 'route1'}},
route_to_service_map={'1': 'service1', '2': 'service1', '3': 'service2', '4': 'service2'},
service_to_route_map={'service1': ['1', '2'], 'service2': ['3', '4']},
crs='epsg:27700'
)
nodes = {'4': {'services': {'service2'}, 'routes': {'3', '4'}, 'id': '4', 'x': 529350.7866124967,
'y': 182388.0201078112, 'epsg': 'epsg:27700', 'name': '', 'lat': 51.52560003323918,
'lon': -0.13682698708848137, 's2_id': 5221390668558830581,
'additional_attributes': {'linkRefId'}, 'linkRefId': '4'},
'5': {'services': {'service2'}, 'routes': {'4'}, 'id': '5', 'x': 529350.7866124967,
'y': 182388.0201078112, 'epsg': 'epsg:27700', 'name': '', 'lat': 51.52560003323918,
'lon': -0.13682698708848137, 's2_id': 5221390668558830581,
'additional_attributes': {'linkRefId'}, 'linkRefId': '5'},
'3': {'services': {'service2'}, 'routes': {'3'}, 'id': '3', 'x': 529455.7452394223,
'y': 182401.37630677427, 'epsg': 'epsg:27700', 'name': '', 'lat': 51.525696033239186,
'lon': -0.13530998708775874, 's2_id': 5221390668020036699,
'additional_attributes': {'linkRefId'}, 'linkRefId': '3'},
'1': {'services': {'service1'}, 'routes': {'2', '1'}, 'id': '1', 'x': 529350.7866124967,
'y': 182388.0201078112, 'epsg': 'epsg:27700', 'name': '', 'lat': 51.52560003323918,
'lon': -0.13682698708848137, 's2_id': 5221390668558830581,
'additional_attributes': {'linkRefId'}, 'linkRefId': '1'},
'2': {'services': {'service1'}, 'routes': {'2'}, 'id': '2', 'x': 529350.7866124967,
'y': 182388.0201078112, 'epsg': 'epsg:27700', 'name': '', 'lat': 51.52560003323918,
'lon': -0.13682698708848137, 's2_id': 5221390668558830581,
'additional_attributes': {'linkRefId'}, 'linkRefId': '2'},
'0': {'services': {'service1'}, 'routes': {'1'}, 'id': '0', 'x': 529455.7452394223,
'y': 182401.37630677427, 'epsg': 'epsg:27700', 'name': '', 'lat': 51.525696033239186,
'lon': -0.13530998708775874, 's2_id': 5221390668020036699,
'additional_attributes': {'linkRefId'}, 'linkRefId': '0'}}
edges = [('4', '5', {'services': {'service2'}, 'routes': {'4'}, 'modes': {'rail'}}),
('3', '4', {'services': {'service2'}, 'routes': {'3'}, 'modes': {'rail'}}),
('1', '2', {'services': {'service1'}, 'routes': {'2'}, 'modes': {'bus'}}),
('0', '1', {'services': {'service1'}, 'routes': {'1'}, 'modes': {'bus'}})]
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
set_node_attributes(graph, nodes)
graph.graph['change_log'] = change_log.ChangeLog()
return graph
def read_csv(path_to_network_nodes: str, path_to_network_links: str, epsg: str):
"""
Reads CSV data into a genet.Network object
:param path_to_network_nodes: CSV file describing nodes. Should at least include columns:
- id: unique ID for the node
- x: spatial coordinate in given epsg
- y: spatial coordinate in given epsg
:param path_to_network_links: CSV file describing links.
Should at least include columns:
- from - source Node ID
- to - target Node ID
Optional columns, but strongly encouraged
- id - unique ID for link
- length - link length in metres
- freespeed - meter/seconds speed
- capacity - vehicles/hour
- permlanes - number of lanes
- modes - set of modes
:param epsg: projection for the network, e.g. 'epsg:27700'
:return: genet.Network object
"""
logging.info(f'Reading nodes from {path_to_network_nodes}')
df_nodes = pd.read_csv(path_to_network_nodes)
if {'index', 'id'}.issubset(set(df_nodes.columns)):
df_nodes = df_nodes.drop('index', axis=1)
elif 'id' not in df_nodes.columns:
raise NetworkSchemaError('Expected `id` column in the nodes.csv is missing. This need to be the IDs to which '
'links.csv refers to in `from` and `to` columns.')
df_nodes['id'] = df_nodes['id'].astype(int).astype(str)
df_nodes = df_nodes.set_index('id', drop=False)
try:
df_nodes = df_nodes.drop('geometry', axis=1)
except KeyError:
pass
logging.info(f'Reading links from {path_to_network_nodes}')
df_links = pd.read_csv(path_to_network_links)
if {'index', 'id'}.issubset(set(df_links.columns)):
df_links = df_links.drop('index', axis=1)
elif 'id' not in df_links.columns:
if 'index' in df_links.columns:
if not df_links['index'].duplicated().any():
df_links['id'] = df_links['index']
else:
df_links = df_links.drop('index', axis=1)
else:
df_links['id'] = range(len(df_links))
df_links['id'] = df_links['id'].astype(int).astype(str)
df_links['from'] = df_links['from'].astype(int).astype(str)
df_links['to'] = df_links['to'].astype(int).astype(str)
df_links = df_links.set_index('id', drop=False)
# recover encoded geometry
try:
df_links['geometry'] = df_links['geometry'].apply(lambda x: spatial.decode_polyline_to_shapely_linestring(x))
except KeyError:
pass
df_links['attributes'] = _literal_eval_col(df_links['attributes'])
df_links['modes'] = _literal_eval_col(df_links['modes'])
n = core.Network(epsg=epsg)
n.add_nodes(df_nodes.T.to_dict())
n.add_links(df_links.T.to_dict())
n.change_log = change_log.ChangeLog()
return n
def correct_schedule_graph_data_from_test_gtfs():
return {'name': 'Schedule graph', 'crs': {'init': 'epsg:4326'},
'route_to_service_map': {'1001_0': '1001', '1002_0': '1002'},
'service_to_route_map': {'1001': ['1001_0'], '1002': ['1002_0']}, 'change_log': change_log.ChangeLog(),
'routes': {'1001_0': {'arrival_offsets': ['00:00:00', '00:02:00'], 'route_color': 'CE312D',
'ordered_stops': ['BSE', 'BSN'], 'mode': 'bus', 'route_type': 3,
'departure_offsets': ['00:00:00', '00:02:00'],
'route_long_name': 'Bus Test Route', 'route_short_name': 'BTR',
'trips': {'trip_id': ['BT1'], 'trip_departure_time': ['03:21:00'],
'vehicle_id': ['veh_0']}, 'service_id': '1001', 'id': '1001_0'},
'1002_0': {'arrival_offsets': ['00:00:00', '00:02:00'], 'route_color': 'CE312D',
'ordered_stops': ['RSN', 'RSE'], 'mode': 'rail', 'route_type': 2,
'departure_offsets': ['00:00:00', '00:02:00'],
'route_long_name': 'Rail Test Route', 'route_short_name': 'RTR',
'trips': {'trip_id': ['RT1'], 'trip_departure_time': ['03:21:00'],
'vehicle_id': ['veh_1']}, 'service_id': '1002',
'id': '1002_0'}},
'services': {'1001': {'id': '1001', 'name': 'BTR'}, '1002': {'id': '1002', 'name': 'RTR'}}}
def gtfs_db_to_schedule_graph(stop_times_db, stops_db, trips_db, routes_db,
services):
def get_time(time):
# return time as datetime.datetime, account for 24 in %H
time_list = time.split(':')
if int(time_list[0]) >= 24:
days = int(time_list[0]) // 24
time_list[0] = int(time_list[0]) % 24
if time_list[0] < 10:
time_list[0] = '0{}'.format(time_list[0])
else:
time_list[0] = str(time_list[0])
return datetime.strptime(':'.join(time_list),
'%H:%M:%S') + timedelta(days=days)
else:
return datetime.strptime(time, '%H:%M:%S')
def timedelta_to_hms(td):
return str(td).split('days')[-1].strip(' ')
def generate_stop_sequence(group):
group = group.sort_values(by='stop_sequence')
# remove stops that are loopy (consecutively duplicated)
unique_stops_mask = group['stop_id'].shift() != group['stop_id']
if not unique_stops_mask.all():
logging.warning(
'Your GTFS has (a) looooop edge(s)! A zero link between a node and itself, edge affected '
'\nThis edge will not be considered for computation, the stop will be deleted and the '
f'schedule will be changed. Affected stops: {group[~unique_stops_mask]["stop_id"].to_list()}'
)
group = group.loc[unique_stops_mask]
flattened = group.iloc[0, :][list(
set(group.columns) - {
'trip_id', 'stop_sequence', 'stop_id', 'arrival_time',
'departure_time'
})]
departure_time = group.iloc[0, :]['arrival_time']
flattened['trip_departure_time'] = departure_time.strftime("%H:%M:%S")
flattened['ordered_stops'] = group['stop_id'].to_list()
flattened['stops_str'] = ','.join(group['stop_id'].to_list())
flattened['arrival_offsets'] = [
timedelta_to_hms(t - departure_time) for t in group['arrival_time']
]
flattened['departure_offsets'] = [
timedelta_to_hms(t - departure_time)
for t in group['departure_time']
]
return flattened
def generate_trips(group):
flattened = group.iloc[0, :][list(
set(group.columns) - {
'route_id', 'stops_str', 'trip_id', 'vehicle_id',
'trip_departure_time'
})]
trip_id = group['trip_id'].to_list()
trip_departure_time = group['trip_departure_time'].to_list()
vehicle_id = group['vehicle_id'].to_list()
flattened['trips'] = {
'trip_id': trip_id,
'trip_departure_time': trip_departure_time,
'vehicle_id': vehicle_id
}
return flattened
def generate_routes(group):
service_id = group.iloc[0, :]['service_id']
group['route_id'] = [f'{service_id}_{i}' for i in range(len(group))]
return group
trips_db = trips_db[trips_db['service_id'].isin(services)]
df = trips_db[['route_id', 'trip_id']].merge(routes_db[[
'route_id', 'route_type', 'route_short_name', 'route_long_name',
'route_color'
]],
on='route_id',
how='left')
df['mode'] = df['route_type'].apply(lambda x: get_mode(x))
df = df.merge(stop_times_db[[
'trip_id', 'stop_id', 'arrival_time', 'departure_time', 'stop_sequence'
]],
on='trip_id',
how='left')
df['arrival_time'] = df['arrival_time'].apply(lambda x: get_time(x))
df['departure_time'] = df['departure_time'].apply(lambda x: get_time(x))
df = df.groupby('trip_id').apply(generate_stop_sequence).reset_index()
# drop stop sequences that are single stops
df = df[df['ordered_stops'].str.len() > 1]
df['vehicle_id'] = [f'veh_{i}' for i in range(len(df))]
df = df.groupby(['route_id',
'stops_str']).apply(generate_trips).reset_index()
df = df.drop('stops_str', axis=1)
df['service_id'] = df['route_id'].astype(str)
df = df.groupby(['service_id']).apply(generate_routes)
g = nx.DiGraph(name='Schedule graph')
g.graph['crs'] = {'init': 'epsg:4326'}
g.graph['route_to_service_map'] = df.set_index(
'route_id')['service_id'].T.to_dict()
g.graph['service_to_route_map'] = df.groupby(
'service_id')['route_id'].apply(list).to_dict()
g.graph['change_log'] = change_log.ChangeLog()
df['id'] = df['route_id']
g.graph['routes'] = df.set_index('route_id').T.to_dict()
df['id'] = df['service_id']
df = df.rename(columns={'route_short_name': 'name'})
g.graph['services'] = df[['service_id', 'id', 'name'
]].groupby('service_id').first().T.to_dict()
# finally nodes
stops = pd.DataFrame({
col: np.repeat(df[col].values, df['ordered_stops'].str.len())
for col in {'route_id', 'service_id'}
}).assign(stop_id=np.concatenate(df['ordered_stops'].values))
stop_groups = stops.groupby('stop_id')
stops = set(stop_groups.groups)
g.add_nodes_from(stops)
stops_db = stops_db.rename(columns={
'stop_lat': 'lat',
'stop_lon': 'lon',
'stop_name': 'name'
})
stops_db['id'] = stops_db['stop_id']
stops_db['x'] = stops_db['lon']
stops_db['y'] = stops_db['lat']
stops_db['epsg'] = 'epsg:4326'
stops_db['s2_id'] = stops_db.apply(lambda x: spatial.generate_index_s2(
lat=float(x['lat']), lng=float(x['lon'])),
axis=1)
nx.set_node_attributes(
g, stops_db[stops_db['stop_id'].isin(stops)].set_index(
'stop_id').T.to_dict())
nx.set_node_attributes(
g,
pd.DataFrame(
stop_groups['route_id'].apply(set)).rename(columns={
'route_id': 'routes'
}).T.to_dict())
nx.set_node_attributes(
g,
pd.DataFrame(stop_groups['service_id'].apply(set)).rename(
columns={
'service_id': 'services'
}).T.to_dict())
# and edges
df['ordered_stops'] = df['ordered_stops'].apply(
lambda x: list(zip(x[:-1], x[1:])))
stop_cols = np.concatenate(df['ordered_stops'].values)
edges = pd.DataFrame({
col: np.repeat(df[col].values, df['ordered_stops'].str.len())
for col in {'route_id', 'service_id'}
}).assign(from_stop=stop_cols[:, 0], to_stop=stop_cols[:, 1])
edge_groups = edges.groupby(['from_stop', 'to_stop'])
g.add_edges_from(edge_groups.groups)
nx.set_edge_attributes(
g,
pd.DataFrame(
edge_groups['route_id'].apply(set)).rename(columns={
'route_id': 'routes'
}).T.to_dict())
nx.set_edge_attributes(
g,
pd.DataFrame(edge_groups['service_id'].apply(set)).rename(
columns={
'service_id': 'services'
}).T.to_dict())
return g
