import pandas as pd
import statsmodels.api as sm
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family' : 'meiryo'}
matplotlib.rc('font', **font)
plt.rcParams["figure.figsize"] = [10,10]
df=pd.read_csv("./data/tokyo-hoge.csv")
df["TIMESTAMP"]=pd.to_datetime(df["DATE"])
print(df["TIMESTAMP"])
df_sum=df.groupby("TIMESTAMP")["POWER"].sum().reset_index()
df_sum=df_sum.sort_values("TIMESTAMP")
df_sum= df_sum.set_index(['TIMESTAMP'])
print(df_sum)
df_sum.plot(alpha=0.6)
plt.title("timeplot")
plt.savefig("./timelog/plot.png")
res = sm.tsa.seasonal_decompose(df_sum)
print(res)
res.plot()
plt.title("trend")
plt.savefig("./timelog/trend.png")