267            else prediction
268        )
269
270    # demo of prediction of the last <display> data points, which is based on the <lookback> values before the final 100 points271    demo = None
272    demo_levels = scalar.transform(
273        levels[-display - lookback : -display].reshape(-1, 1)

 29
 30def map_palette(station):
 31    """
 32    Function that returns the colour of a given station to use on the map, depending on the relationship between latest level and typical range. 33
 34    Args:
 35        station (MonitoringStation): The station.

261    Function that plots the prediction made by predictor.
262
263    Args:
264        date (2-tuple): List of datetime objects of actual and demo data, list of datatime objects of future predicted data.265        data (3-tuple): Lists of water levels of actual data, demo data, predicted data.
266
267    Returns:

295
296def plot_water_level_with_fit(station, dates, levels, p):
297    """
298    Function that makes a graph of the water level over time for a given station with a least-square fit polynomial with a degree of p.299
300    Args:
301        station (MonitoringStation): The desired station to graph.

 8def run():
 9    stations = build_station_list()
10
11    # date, data, demo, prediction = predict('Cam', dataset_size=1000, lookback=2000, iteration=100, display=300, use_pretrained=True, batch_size=256, epoch=20)12    date, data = predict(
13        stations[5].name,
14        dataset_size=1000,