import pandas as pd
import numpy as np
import import_funcs
import statsmodels.api as sm

# Import the BGE hourly electricity data and weather data using import_funcs.py class
elec_data = import_funcs.BGEdata()
weather = import_funcs.weather()

# Merge into one Pandas dataframe
elec_and_weather = pd.merge(weather,elec_data,left_index=True,right_index=True)
elec_and_weather[:5]

fig = plt.figure()

plot1=elec_and_weather['tempF']['2014-02-01 00:00:00':'2014-02-07 23:00:00'].plot(color='b',label='Temp',grid=False)
plot1.set_ylabel('Ambient Temp. (deg F)')
legend(['Temp'],loc='upper left')
plot1.set_ylim([15,65])
plot1.set_xlabel('')

plot2=elec_and_weather['USAGE']['2014-02-01 00:00:00':'2014-02-07 23:00:00'].plot(color='g',secondary_y=True,label='Elec',mark_right=False,grid=False)
ylabel('Elec. Usage (kWh)')
legend(['Elec.'],loc='upper right')
plot2.set_ylim([1,2.8])
plot2.set_xlabel('')

fig.savefig('Elec_and_Temp_TS.png')

fig = plt.figure()
plot_scatter = plot(elec_and_weather['tempF'],elec_and_weather['USAGE'],'gx')
xlabel('Outdoor Temperature (deg F)')
ylabel('Hourly Electricity Use (kWh)')
ylim(ymin=0)
title('Hourly Electricity Consumption \n Jan. 18 through March 2014')

fig.savefig('Elec_and_Temp_Scatter.png')

# Linear regression on elec. vs. outdoor T (deg F)
model = sm.OLS(elec_and_weather['USAGE'],sm.add_constant(elec_and_weather['tempF']))
res = model.fit()
print res.summary()

fig = plt.figure()

plot_scatter = plot(elec_and_weather['tempF'],elec_and_weather['USAGE'],'gx')
plot_model = plot(elec_and_weather['tempF'],res.fittedvalues,'b',label='OLS $R^2$=%.2f' % res.rsquared)
xlabel('Outdoor Temperature (deg F)')
ylabel('Hourly Electricity Use (kWh)')
ylim(ymin=0)
title('Hourly Electricity Consumption \n Jan. 18 through March 2014')
legend()


fig.savefig('Elec_And_Temp_OLS.png')

fig = plt.figure()

plot1=elec_and_weather['wspdm']['2014-02-01 00:00:00':'2014-02-07 23:00:00'].plot(color='b',label='Wind',grid=False)
plot1.set_ylabel('Mean Wind Speed (kph)')
legend(loc='upper left')
#plot1.set_ylim([15,65])
plot1.set_xlabel('')

plot2=elec_and_weather['USAGE']['2014-02-01 00:00:00':'2014-02-07 23:00:00'].plot(color='g',secondary_y=True,label='Elec',mark_right=False,grid=False)
ylabel('Elec. Usage (kWh)')
legend(['Elec.'],loc='upper right')
plot2.set_ylim([1,2.8])
plot2.set_xlabel('')

#fig.savefig('Elec_and_Temp_TS.png')

fig = plt.figure()
plot_scatter = plot(elec_and_weather['wspdm'],elec_and_weather['USAGE'],'gx')
xlabel('Wind Speed (km/h)')
ylabel('Elec. Usage (kWh)')
#ylim(ymin=0)
#title('Hourly Electricity Consumption \n Jan. 18 through March 2014')

#fig.savefig('Elec_and_Temp_Scatter.png')

elec_and_weather['wspdm'].hist()

fig = plt.figure()

plot1=elec_and_weather['hum']['2014-02-01 00:00:00':'2014-02-07 23:00:00'].plot(color='b',label='Hum.',grid=False)
plot1.set_ylabel('Relative Humidity (%)')
legend(loc='lower right')
#plot1.set_ylim([15,65])
plot1.set_xlabel('')

plot2=elec_and_weather['USAGE']['2014-02-01 00:00:00':'2014-02-07 23:00:00'].plot(color='g',secondary_y=True,label='Elec',mark_right=False,grid=False)
ylabel('Elec. Usage (kWh)')
legend(['Elec.'],loc='upper right')
plot2.set_ylim([1,2.8])
plot2.set_xlabel('')

#fig.savefig('Elec_and_Temp_TS.png')

fig = plt.figure()
plot_scatter = plot(elec_and_weather['hum'],elec_and_weather['USAGE'],'gx')
xlabel('Relative Humidity (%)')
ylabel('Elec. Usage (kWh)')
#ylim(ymin=0)
#title('Hourly Electricity Consumption \n Jan. 18 through March 2014')

#fig.savefig('Elec_and_Temp_Scatter.png')

elec_and_weather['hum'].hist()