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import pandas as pd
import numpy as np
import AutoForecast as autof
import Bench.TS_datasets as tsds
#import SignalDecomposition_Perf as tsperf


%matplotlib inline  

import pandas as pd
import numpy as np
import sys
import datetime as dt

from sqlalchemy import *
#from sqlalchemy import desc, nullsfirst
import sqlalchemy


    

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sqlalchemy.sql.expression.func.AAA


    

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b1 = tsds.load_airline_passengers()
df = b1.mPastData


def convert_double_to_datetime(x):
    ratio = (x - int(x))
    fulldate = dt.datetime(int(x), 1, 1, 0, 0, 0)
    year_length = dt.datetime(int(x) + 1, 1, 1, 0, 0, 0) - fulldate
    fulldate = fulldate + dt.timedelta(days = int(year_length.days*ratio))
    return fulldate

lDateCol = 'time';
df[lDateCol] = df[lDateCol].apply(lambda x : convert_double_to_datetime(x))
    
lAutoF = autof.cAutoForecast()
lAutoF.mOptions.mDebugCycles = True
lAutoF

lAutoF.train(df , 'time' , 'AirPassengers' , 12)
lAutoF.getModelInfo()


    

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lAutoF.generateCode()


    

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import TS_CodeGen_Objects as tscodegen
lCodeGenerator = tscodegen.cDecompositionCodeGenObject();
lSQL = lCodeGenerator.testGeneration(lAutoF);


    

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lCodeGenerator.mEngine.table_names()


    

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sqlalchemy.types.Interval?


    

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