def test_tubeutils(self):
"""
Unit test for helper functions in WhensMyTrain
"""
self.assertEqual(get_line_code('Central'), 'C')
self.assertEqual(get_line_code('Circle'), 'O')
self.assertEqual(get_line_name('C'), 'Central')
self.assertEqual(get_line_name('O'), 'Circle')
for (line_code, line_name) in LINE_NAMES.keys():
self.assertEqual(line_name, get_line_name(get_line_code(line_name)))
self.assertEqual(line_code, get_line_code(get_line_name(line_code)))
def import_network_data_to_graph():
"""
Import data from a file describing the edges of the Tube network and turn it into a graph object which we pickle and save
"""
database = WMTDatabase("whensmytrain.geodata.db")
# Adapted from https://github.com/smly/hubigraph/blob/fa23adc07c87dd2a310a20d04f428f819d43cbdb/test/LondonUnderground.txt
# which is a CSV of all edges in the network
reader = csv.reader(open('./sourcedata/tube-connections.csv'))
reader.next()
# First we organise our data so that each station knows which lines it is on, and which stations it connects to
stations_neighbours = {}
interchanges_by_foot = []
for (station1, station2, line) in reader:
if line in ("National Rail", "East London"):
continue
if line == "Walk":
interchanges_by_foot.append((station1, station2))
else:
# When a line splits into two branches, we don't want people being able to travel from one branch to another without
# changing. So for these special cases, we mark the transitions as being in a particular direction in the CSV, with the
# direction coming after a colon (e.g. "Leytonstone:Northbound","Wanstead","Central" and "Snaresbrook","Leytonstone:Southbound","Central"
# Effectively the Central Line station has become two nodes, and now you cannot go directly from Snaresbrook to Wanstead.
direction = station1.partition(':')[2] # Blank for most
station1 = station1.partition(':')[0] # So station name becomes just e.g. Leytonstone
station_data = stations_neighbours.get(station1, [])
if (station2, direction, line) not in station_data:
station_data += [(station2, direction, line)]
stations_neighbours[station1] = station_data
# Sanity-check our data and make sure it matches database
canonical_data = database.get_rows("SELECT * FROM locations")
canonical_station_names = unique_values([canonical['name'] for canonical in canonical_data])
for station in sorted(stations_neighbours.keys()):
if station not in canonical_station_names:
print "Error! %s is not in the canonical database of station names" % station
for (neighbour, direction, line) in stations_neighbours[station]:
line_code = get_line_code(line)
if not database.get_value("SELECT name FROM locations WHERE name=? AND line=?", (station, line_code)):
print "Error! %s is mistakenly labelled as being on the %s line in list of nodes" % (station, line)
for station in sorted(canonical_station_names):
if station not in stations_neighbours.keys():
print "Error! %s is not in the list of station nodes" % station
continue
database_lines = database.get_rows("SELECT line FROM locations WHERE name=?", (station,))
for row in database_lines:
if row['line'] not in [get_line_code(line) for (neighbour, direction, line) in stations_neighbours[station]]:
print "Error! %s is not shown as being on the %s line in the list of nodes" % (station, row['line'])
# Produce versions of the graphs for unique lines
graphs = {}
lines = unique_values([line for station in stations_neighbours.values() for (neighbour, direction, line) in station])
for line in lines:
this_line_only = {}
for (station_name, neighbours) in stations_neighbours.items():
neighbours_for_this_line = [neighbour for neighbour in neighbours if neighbour[2] == line]
if neighbours_for_this_line:
this_line_only[station_name] = neighbours_for_this_line
graphs[get_line_code(line)] = create_graph_from_dict(this_line_only, database, interchanges_by_foot)
graphs['All'] = create_graph_from_dict(stations_neighbours, database, interchanges_by_foot)
pickle.dump(graphs, open("./db/whensmytrain.network.gr", "w"))
