import os
import time

import pandas as pd
import numpy as np

import tsam.timeseriesaggregation as tsam



def test_accuracyIndicators():

    hoursPerPeriod = 24

    noTypicalPeriods = 8

    raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0)

    starttime = time.time()

    aggregation1 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod,
                                              clusterMethod='hierarchical')

    print('Clustering took ' + str(time.time() - starttime))

    starttime = time.time()

    aggregation2 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod,
                                              clusterMethod='hierarchical', sortValues=True)

    print('Clustering took ' + str(time.time() - starttime))

    # make sure, that the sum of the attribute specific RMSEs is smaller for the normal time series clustering than for
    # the duration curve clustering
    np.testing.assert_array_less(aggregation1.accuracyIndicators().loc[:, 'RMSE'].sum(),
                                 aggregation2.accuracyIndicators().loc[:, 'RMSE'].sum())

    # make sure, that the sum of the attribute specific duration curve RMSEs is smaller for the duration curve
    # clustering than for the normal time series clustering
    np.testing.assert_array_less(aggregation2.accuracyIndicators().loc[:, 'RMSE_duration'].sum(),
                                 aggregation1.accuracyIndicators().loc[:, 'RMSE_duration'].sum())

if __name__ == "__main__":
    test_accuracyIndicators()
Test for accuracy indicators implemented
Test for accuracy indicators implemented
