def test_estimated_arrival_time_from_destination(self):
origin = Stop("NMC", "New Mills Central", 100, 200)
destination = Stop("MAN", "Manchester", 200, 300)
train = Train("ID", origin, destination, 1, False)
arrival_time = train.estimated_arrival_time()
self.assertEqual(arrival_time, 300)
def test_scheduled_departure_time_from_origin(self):
origin = Stop("NMC", "New Mills Central", 100, 200)
destination = Stop("MAN", "Manchester", 200, 300)
train = Train("ID", origin, destination, 1, False)
departure_time = train.scheduled_departure_time()
self.assertEqual(departure_time, 100)
def main():
num_rows = count_num_rows()
num_iter = (num_rows + CHUNK_SIZE - 1) // CHUNK_SIZE
for index_chunk in range(num_iter):
train = Train(index_chunk)
train.cv()
merge_mul_df()
def test_sort_departures(self):
fake_darwin_service = {}
departures = [
Train("123", Stop("NMC", "New Mills Central", 100, 100),
Stop("MAN", "Manchester", 200, 200), 1),
Train("321", Stop("NMC", "New Mills Central", 50, 50),
Stop("MAN", "Manchester", 100, 100), 1)
]
app = TrainApp(fake_darwin_service)
sorted_departures = app.sort_departures(departures)
self.assertEqual(sorted_departures[0].id, '321')
def build_train(self, service_data, from_crs, to_crs):
station_list = StationList()
scheduled_departure_time = time_to_integer(service_data['std'])
estimated_departure_time = time_to_integer(
self.calculate_estimated_time(service_data['etd'],
service_data['std']))
origin = Stop(from_crs, station_list.get_station_name(from_crs),
scheduled_departure_time, estimated_departure_time)
calling_points = self.extract_calling_points(service_data)
destination = self.extract_destination(calling_points, to_crs)
scheduled_time = destination['st']
estimated_time = destination['et']
estimated_time = self.calculate_estimated_time(scheduled_time,
estimated_time)
destination = Stop(to_crs, station_list.get_station_name(to_crs),
time_to_integer(scheduled_time),
time_to_integer(estimated_time))
platform = self.extract_platform(service_data)
is_cancelled = self.extract_cancelled(service_data['isCancelled'])
is_delayed = self.extract_delayed(service_data['std'])
train = Train(service_data['serviceID'], origin, destination, platform,
is_cancelled, is_delayed)
return train
def add_train(self, index: int, line, direction: int, nb_trains: int,
start_minute: int):
l: Line = line
train = Train(self.config, l, l.all_stations[index], direction,
nb_trains, start_minute)
l.add_train(train)
nb_trains += 1
self.trains.append(train)
return nb_trains
def plot_train_val(patterns, fontsize=15):
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
dummy_trainer = Train(None, None, None, None)
for pattern in patterns:
for cpt_fn in glob.glob(pattern):
cpt = torch.load(cpt_fn)
name = cpt_fn.split('.')[0].split('/')[-1]
l = cpt['train_losses']
dummy_trainer.all_losses = l
plt.plot(dummy_trainer.smooth_loss(), label=name)
plt.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))
plt.title('training loss (RMSE)', fontsize=fontsize)
plt.grid()
plt.show()
for pattern in patterns:
for cpt_fn in glob.glob(pattern):
cpt = torch.load(cpt_fn)
name = cpt_fn.split('.')[0].split('/')[-1]
l = cpt['val_losses']
dummy_trainer.val_losses = l
plt.plot(dummy_trainer.smooth_valloss(), label=name)
plt.legend(loc='center right', bbox_to_anchor=(1.5, 0.5))
plt.title('validation loss (RMSE)', fontsize=fontsize)
plt.grid()
plt.show()
def test_is_cancelled(self):
origin = Stop("NMC", "New Mills Central", 100, 200)
destination = Stop("MAN", "Manchester", 200, 300)
cancelled_train = Train("ID", origin, destination, 1, True)
non_cancelled_train = Train("ID", origin, destination, 1, False)
self.assertTrue(cancelled_train.is_cancelled())
self.assertFalse(non_cancelled_train.is_cancelled())
def get_train_val_curves(pattern):
dummy_trainer = Train(None, None, None, None)
tr_curves = []
val_curves = []
name = ""
for cpt_fn in glob.glob(pattern):
cpt = torch.load(cpt_fn)
name = cpt_fn.split('.')[0].split('/')[-1]
dummy_trainer.all_losses = cpt['train_losses']
dummy_trainer.val_losses = cpt['val_losses']
tr_curves.append(dummy_trainer.smooth_loss())
val_curves.append(dummy_trainer.smooth_valloss())
return tr_curves, val_curves, name