from __future__ import absolute_import

import pandas as pd
import numpy as np


import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds



def pickleModel(iModel):
    import pickle
    output = pickle.dumps(iModel)
    lReloadedObject = pickle.loads(output)
    output2 = pickle.dumps(lReloadedObject)    
    assert(iModel.to_json() == lReloadedObject.to_json())
    return lReloadedObject;

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.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
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");

lEngine2 = pickleModel(lEngine)

dfapp_in = df.copy();
dfapp_in.tail()

#H = 12
dfapp_out = lEngine2.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));

print("\n\n<ModelInfo>")
print(lEngine2.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")

