old_contents: import pandas as pd
  import numpy as np
  import ForecastEngine as autof
  import datetime
  
  #get_ipython().magic('matplotlib inline')
  
  # http://stackoverflow.com/questions/32693048/forecast-with-r
  trainfile = "https://raw.githubusercontent.com/antoinecarme/TimeSeriesData/master/misc/r_wikipedia-googletrends-model.csv";
  df = pd.read_csv(trainfile, sep=r';', engine='python', skiprows=0);
  df['date'] = df['date'].apply(lambda x : datetime.datetime.strptime(x, "%m/%d/%Y"))
  
  print(df.head());
  
  lDateVar = 'date'
  lSignalVar = 'dengue_index'
  
  lEngine = autof.cForecastEngine()
  lEngine
  
  H = 5;
  
  #lEngine.mOptions.enable_slow_mode();
  lEngine.mOptions.mDebugPerformance = True;
  lEngine.train(df , lDateVar , lSignalVar, H);
  lEngine.getModelInfo();
  print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
  
  lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
  
  lEngine.standrdPlots("outputs/sof_example2");
  
  dfapp_in = df.copy();
  dfapp_in.tail()
  
  #H = 12
  dfapp_out = lEngine.forecast(dfapp_in, H);
  #dfapp_out.to_csv("outputs/sof_example2_apply_out.csv")
  dfapp_out.tail(2 * H)
  print("Forecast Columns " , dfapp_out.columns);
  Forecast_DF = dfapp_out[[lDateVar , lSignalVar,
                           lSignalVar + '_Forecast' ,
                           lSignalVar + '_Forecast_Lower_Bound',
                           lSignalVar + '_Forecast_Upper_Bound']]
  print(Forecast_DF.info())
  print("Forecasts\n" , Forecast_DF.tail(2*H));
  
  print("\n\n<ModelInfo>")
  print(lEngine.to_json());
  print("</ModelInfo>\n\n")
  print("\n\n<Forecast>")
  print(Forecast_DF.to_json(date_format='iso'))
  print("</Forecast>\n\n")
  
  lEngine.standrdPlots(name = "outputs/sof_example2")
  
new_contents: import pandas as pd
  import numpy as np
  import ForecastEngine as autof
  import datetime
  
  #get_ipython().magic('matplotlib inline')
  
  # http://stackoverflow.com/questions/32693048/forecast-with-r
  trainfile = "https://raw.githubusercontent.com/antoinecarme/TimeSeriesData/master/misc/r_wikipedia-googletrends-model.csv";
  df = pd.read_csv(trainfile, sep=r';', engine='python', skiprows=0);
  df['date'] = df['date'].apply(lambda x : datetime.datetime.strptime(x, "%m/%d/%Y"))
  
  print(df.head());
  
  lDateVar = 'date'
  lSignalVar = 'dengue_index'
  
  lEngine = autof.cForecastEngine()
  lEngine
  
  H = 5;
  
  #lEngine.mOptions.enable_slow_mode();
  lEngine.mOptions.mDebugPerformance = True;
  lEngine.train(df , lDateVar , lSignalVar, H);
  lEngine.getModelInfo();
  print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
  
  lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
  
  lEngine.standrdPlots("outputs/sof_example2");
  
  dfapp_in = df.copy();
  dfapp_in.tail()
  
  #H = 12
  dfapp_out = lEngine.forecast(dfapp_in, H);
  #dfapp_out.to_csv("outputs/sof_example2_apply_out.csv")
  dfapp_out.tail(2 * H)
  print("Forecast Columns " , dfapp_out.columns);
  Forecast_DF = dfapp_out[[lDateVar , lSignalVar,
                           lSignalVar + '_Forecast' ,
                           lSignalVar + '_Forecast_Lower_Bound',
                           lSignalVar + '_Forecast_Upper_Bound']]
  print(Forecast_DF.info())
  print("Forecasts\n" , Forecast_DF.tail(2*H));
  
  print("\n\n<ModelInfo>")
  print(lEngine.to_json());
  print("</ModelInfo>\n\n")
  print("\n\n<Forecast>")
  print(Forecast_DF.to_json(date_format='iso'))
  print("</Forecast>\n\n")
  
  
subject: Remove an extra plot.