content: import pandas as pd
  import numpy as np
  
  # from memory_profiler import profile
  # from memprof import *
  
  import pyaf.ForecastEngine as autof
  import pyaf.Bench.TS_datasets as tsds
  
  #get_ipython().magic('matplotlib inline')
  
  
  # @memprof
  def test_ozone_debug_perf():
      b1 = tsds.load_ozone()
      df = b1.mPastData
  
      # df.tail(10)
      # df[:-10].tail()
      # df[:-10:-1]
      # df.describe()
  
      lEngine = autof.cForecastEngine()
      lEngine
  
      H = b1.mHorizon;
      lEngine.mOptions.mDebugPerformance = True;
      lEngine.mOptions.mEnableCycles = False;
      lEngine.mOptions.mEnableTimeBasedTrends = False;
      lEngine.mOptions.mEnableARModels = False;
      lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
      lEngine.getModelInfo();
      print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
      
      lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
      
      lEngine.standardPlots("outputs/my_ozone");
      
      dfapp_in = df.copy();
      dfapp_in.tail()
  
      dfapp_out = lEngine.forecast(dfapp_in, H);
      #dfapp_out.to_csv("outputs/ozone_apply_out.csv")
      dfapp_out.tail(2 * H)
      print("Forecast Columns " , dfapp_out.columns);
      Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
      print(Forecast_DF.info())
      print("Forecasts\n" , Forecast_DF.tail(H).values);
      
      print("\n\n<ModelInfo>")
      print(lEngine.to_json());
      print("</ModelInfo>\n\n")
      print("\n\n<Forecast>")
      print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
      print("</Forecast>\n\n")
  
  
  
  
  test_ozone_debug_perf();
  