HackSeversteel

# for Alex. Extraction features for each XG

from collections import Counter
n_features = tags.shape[0]

xg_types = Counter([tags.iloc[i][1][:12] for i in range(n_features) if  i>0])


xg4 = [ tags.iloc[i][0] for i in range(n_features)
       if tags.iloc[i][1][:12] == xg_types.keys()[0] ]

data_min_xg4 = data_min[xg4]
data_hour_xg4 = data_hour[xg4]


xg5 = [ tags.iloc[i][0] for i in range(n_features)
       if tags.iloc[i][1][:12] == xg_types.keys()[1] ]

data_min_xg5 = data_min[xg5]
data_hour_xg5 = data_hour[xg5]


xg6 = [ tags.iloc[i][0] for i in range(n_features)
       if tags.iloc[i][1][:12] == xg_types.keys()[2] ]

data_min_xg6 = data_min[xg6]
data_hour_xg6 = data_hour[xg6]


xg7 = [ tags.iloc[i][0] for i in range(n_features)
       if tags.iloc[i][1][:12] == xg_types.keys()[3] ]


data_min_xg7 = data_min[xg7]
data_hour_xg7 = data_hour[xg7]


xg8 = [ tags.iloc[i][0] for i in range(n_features)
       if tags.iloc[i][1][:12] == xg_types.keys()[4] ]

data_min_xg8 = data_min[xg8]
data_hour_xg8 = data_hour[xg8]


xg9 = [ tags.iloc[i][0] for i in range(n_features)
       if tags.iloc[i][1][:12] == xg_types.keys()[5] ]

data_min_xg9 = data_min[xg9]
data_hour_xg9 = data_hour[xg9]




#################### STL DECOMPOSITION

import statsmodels.api as sm
import matplotlib.pyplot as plt

data_min_xg8 = data_min[xg8]
data_min_OP_E8_1 = data_min_xg8.OP_E8_1
data_min_OP_E8_1.index = pd.to_datetime(data_min.DATATIME, dayfirst = True )



data_min_OP_E8_1.fillna(method = 'pad', inplace= True)

data_min_range = pd.date_range(start = pd.datetime(2017, 4, 17), freq = 'H', end = pd.datetime(2018,2,14,14))

# i dunno how to adust freq to 'H' then i decided to use  freq = 365
fig = plt.figure(figsize = (90,90))
sm.tsa.seasonal_decompose(pd.DataFrame(data = data_min_xg8.OP_E8_1.values,
                                       index = data_min.DATATIME), freq = 365).plot()
plt.show()

print("Критерий Дики-Фуллера: p=%f" % sm.tsa.stattools.adfuller(data_min_xg8.OP_E8_1.values)[1])