old_contents: import os
  import time
  
  import pandas as pd
  import numpy as np
  
  import tsam.timeseriesaggregation as tsam
  
  
  
  def test_preprocess():
  
      raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0)
  
      raw_wind = raw.loc[:, 'Wind'].to_frame()
  
      aggregation_wind = tsam.TimeSeriesAggregation(raw_wind, noTypicalPeriods = 8, hoursPerPeriod = 24,
                                              clusterMethod = 'hierarchical')
  
      aggregation_wind._preProcessTimeSeries()
  
      test = aggregation_wind.normalizedPeriodlyProfiles
  
      orig = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','results','preprocessed_wind.csv'), index_col = [0], header = [0,1])
  
      np.testing.assert_array_almost_equal(test.values, orig.values,decimal=4)
  
      
  if __name__ == "__main__":
      test_preprocess()
new_contents: import os
  import time
  
  import pandas as pd
  import numpy as np
  
  import tsam.timeseriesaggregation as tsam
  
  
  
  def test_preprocess():
  
      raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0)
  
      raw_wind = raw.loc[:, 'Wind'].to_frame()
  
      aggregation_wind = tsam.TimeSeriesAggregation(raw_wind, noTypicalPeriods = 8, hoursPerPeriod = 24,
                                              clusterMethod = 'hierarchical')
  
      aggregation_wind._preProcessTimeSeries()
  
      test = aggregation_wind.normalizedPeriodlyProfiles
  
      orig = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','results','preprocessed_wind.csv'), index_col = [0], header = [0,1])
  
      np.testing.assert_array_almost_equal(test.values, orig.values,decimal=15)
  
      
  if __name__ == "__main__":
      test_preprocess()
subject: increase decimal precision in testing