示例#1
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 def _load_dataset(self, dataset_name):
     if dataset_name == "bc-data":
         self._dataset = bc.load()
     elif dataset_name == "total-load":
         self._dataset = ul.total_experiment_load()[1]['Load']
     else:
         raise RuntimeError("Invalid dataset name: %s" % dataset_name)
 def _load_dataset(self, dataset_name):
     if dataset_name == "bc-data":
         self._dataset = bc.load()
     elif dataset_name == "total-load":
         self._dataset = ul.total_experiment_load()[1]['Load']
     else:
         raise RuntimeError("Invalid dataset name: %s" % dataset_name)
 def _prepare_dataset(self):
     """Return datasets for the experiment periods containing the total load
     for all users in the experiment."""
     load = bc.load()
     data = pd.DataFrame({'Load': load, 'Temperature': 0})
     test_period_starts = "2010-03-15 00:00:00"
     (train, test) = (data[:test_period_starts], data[test_period_starts:])
     return (train, test)
示例#4
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 def _prepare_dataset(self):
     """Return datasets for the experiment periods containing the total load
     for all users in the experiment."""
     load = bc.load()
     data = pd.DataFrame({'Load': load, 'Temperature': 0})
     test_period_starts = "2010-03-15 00:00:00"
     (train, test) = (data[:test_period_starts], data[test_period_starts:])
     return (train, test)
示例#5
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def _show_smoother():
    import sg.data.bchydro as bchydro
    import matplotlib.pyplot as plt
    all_timeseries = bchydro.load()
    week = all_timeseries.data[0:24*7]
    sm = BSplineSmoother(week, smoothness=0)
    t = np.linspace(0, 1, 1000)
    y = sm.splev(t)
    plt.figure()
    plt.plot(t, y)
    plt.title("Show smoother")
def setUpModule():
    np.seterr(all='raise')
    global bchydro_timeseries
    bchydro_timeseries = bchydro.load()
def setUpModule():
    np.seterr(all='raise')
    global bchydro_timeseries
    bchydro_timeseries = bchydro.load()
# Short demonstration of the utilities to load BCHydro data
import sys
import os
from datetime import timedelta as dt

import matplotlib.pyplot as plt

import sg.data.bchydro as bc

if __name__ == "__main__":
    # Option 1: load the entire dataset as a timeseries
    timeseries = bc.load()
    filtered = [x if x > 10 else 4000 for x in timeseries]
    plt.plot(filtered, '-')
    plt.title("The entire BC Hydro dataset")
    # Option 2: load the using the Dataset class
    dataset = bc.Dataset(period=dt(days=30), step_length=dt(days=7))
    plt.figure()
    plt.plot(dataset.get_random_period(), '-')
    plt.title("A randomly selected 30-day period from the BC Hydro dataset")
    plt.show()