new_contents: import os
  import time
  
  import pandas as pd
  import numpy as np
  
  import tsam.timeseriesaggregation as tsam
  
  
  
  def test_k_maxoids():
  
      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=8, hoursPerPeriod=24,
                                                clusterMethod='k_means', rescaleClusterPeriods=False)
  
      predictedPeriods1 = aggregation1.predictOriginalData()
  
      print('Clustering took ' + str(time.time() - starttime))
  
      starttime = time.time()
  
      aggregation2 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=8, hoursPerPeriod=24,
                                                clusterMethod ='k_maxoids', rescaleClusterPeriods=False)
  
      predictedPeriods2 = aggregation2.predictOriginalData()
  
      print('Clustering took ' + str(time.time() - starttime))
  
      # make sure that the maximum values of the time series predicted by k-maxoids are bigger than those predicted by
      # k-means
      np.testing.assert_array_less(predictedPeriods1.max(), predictedPeriods2.max())
  
      # make sure that the minimum values of the time series predicted by k-maxoids are smaller than those predicted by
      # k-means except for those of GHI since the minimum value of 0 during night time is found by both algorithms
      np.testing.assert_array_less(predictedPeriods2.min()[1:], predictedPeriods1.min()[1:])
  
  if __name__ == "__main__":
      test_k_maxoids()
subject: test for k-maxoids added