import pandas as pd
import numpy as np
import import_funcs
#reload(import_funcs)

# Import the BGE hourly electricity data and weather data using import_funcs.py class
old_apt = import_funcs.BGEdata("old apt")
new_apt = import_funcs.BGEdata("new apt")
weather = import_funcs.weather()

# Merge into one Pandas dataframe
old_apt_and_weather = pd.merge(weather,old_apt,left_index=True,right_index=True)
new_apt_and_weather = pd.merge(weather,new_apt,left_index=True,right_index=True)
#new_apt_and_weather = new_apt

new_apt.to_csv('New_Apt_thru_Jan_2015.csv')

# Pretty colors
gray_light = '#bbbbbb'
gray_med = '#737373'
red_orange = '#ff3700'

fig = plt.figure(figsize=[10,3])
plot_south = old_apt_and_weather['USAGE'].plot(color=gray_light,linewidth=.6)
plot_north = new_apt_and_weather['USAGE'].plot(color=red_orange,grid='off',linewidth=.6)
xlim([min(old_apt_and_weather.index),max(new_apt_and_weather.index)])
title('Time Series, Hourly Electricity Consumption')
legend(['Old Apt','New Apt'],loc='upper left')
ylabel('Usage (kWh)')
xlabel('')

fig.savefig('plots/New-Old-TS.png')

fig = plt.figure(figsize=[14,3])
plot_north = new_apt_and_weather['USAGE'].plot(color=red_orange,grid='off',linewidth=.6)
#xlim([min(old_apt_and_weather.index),max(new_apt_and_weather.index)])
title('Time Series, Hourly Electricity Consumption')
#legend(['Old Apt','New Apt'],loc='upper left')
ylabel('Usage (kWh)')
xlabel('')

#fig.savefig('plots/New-TS.png')

fig = plt.figure(figsize=[8,6])
plot_old = plot(old_apt_and_weather['tempF'],old_apt_and_weather['USAGE'],'x',color=gray_light)
plot_new = plot(new_apt_and_weather['tempF'],new_apt_and_weather['USAGE'],'x',color=red_orange)
legend(['Old Apt','New Apt'],loc='upper left')
title('Electricity Usage and Outdoor Temperature')
ylabel('Usage (kWh)')
xlabel('Outdoor Temperature (deg F)')

fig.savefig('plots/New-Old-Elec-Temp.png')



# Histogram
def elec_hist(data,date_range,hist_title):

    fig = plt.figure()
    elec_pdf = data.ix[date_range].hist(weights=np.ones_like(data.ix[date_range])/data.ix[date_range].size,bins=20,grid=False)
    #xlim(xmax=)
    #ylim(ymax=.5)
    ylabel('Proportion')
    xlabel('Usage (kWh)')
    title(hist_title)
    elec_pdf.text(0.1,.14,'N = %d hours'%size(data.ix[date_range]))
    
# Cumulative Distribution Function
def elec_cdf(data,date_range,cdf_title):

    fig = plt.figure()
    elec_cdf = data.ix[date_range].hist(normed=1,cumulative=True,histtype='step',bins=200,grid=False)
    #xlim(xmax=6)
    #ylim(ymax=1)
    ylabel('Proportion of Hours (x <= X)')
    xlabel('Usage (kWh)')
    title(cdf_title)
    #fig.text(0,1,'N_feb = %d hours \n N_mar = %d hours \n N_new = %d hours'%[size(old_apt_and_weather['USAGE'].ix['feb 2014']),size(old_apt_and_weather['USAGE'].ix['mar 2014']),size(new_apt_and_weather['USAGE'])])
    #legend(['St. Paul South - Feb','St. Paul South - March','St. Paul North'],'lower right')

    

elec_hist(old_apt_and_weather['USAGE'],'feb 2014','Old Apartment \n February 2014')

elec_hist(new_apt_and_weather['USAGE'],'june 2014','New Apartment \n June 2014')

fig = plt.figure()
CDF_old = old_apt_and_weather['USAGE'].hist(normed=1,cumulative=True,histtype='step',bins=200,grid=False)
CDF_new = new_apt_and_weather['USAGE'].hist(normed=1,cumulative=True,histtype='step',bins=200,grid=False)
xlim(xmax=max(max(old_apt_and_weather['USAGE']),max(new_apt_and_weather['USAGE'])))
ylim(ymax=1)
ylabel('Proportion of Hours (x <= X)')
xlabel('Usage (kWh)')
title('Elec. Usage Cumulative Distributions')
#fig.text(0,1,'N_feb = %d hours \n N_mar = %d hours \n N_new = %d hours'%[size(old_apt_and_weather['USAGE'].ix['feb 2014']),size(old_apt_and_weather['USAGE'].ix['mar 2014']),size(new_apt_and_weather['USAGE'])])
legend(['Old Apt','New Apt'],'lower right')

#fig.savefig('ElecCDF - Feb_March2014.png')

# Define a function to calculate average weekday and weekend

def avg_day(df,start_date,end_date):
    # df is pandas df, has 'USAGE' and 'tempF' fields, indexed by pandas datetime
    # date_range is string for .ix call
    # returns average_day
    # average_day[True
    
    df_dates = df.ix[start_date:end_date]
    
    # First groupby weekday/weekend and hour of day
    weekday_weekend_hour = df_dates.groupby([(df_dates.index.dayofweek==5)|(df_dates.index.dayofweek==6),df_dates.index.hour])

    # Calculate an average weekday and average weekend by hour (electricity and outdoor temp)
    average_day = pd.DataFrame([weekday_weekend_hour['USAGE'].mean(),weekday_weekend_hour['tempF'].mean()])
    
    average_weekend = average_day[True]
    average_weekday = average_day[False]

    return average_weekday, average_weekend


weekday_oldapt_march, weekend_oldapt_march = avg_day(old_apt_and_weather,'1-mar-2014','31-mar-2014')
weekday_newapt_june, weekend_newapt_june = avg_day(new_apt_and_weather,'1-jun-2014','30-jun-2014')

fig = plt.figure(figsize=[12,6])

weekend_linestyle = 'dashed'
linewidth = 2

plot1 = weekend_oldapt_march.ix['USAGE'].plot(linestyle=weekend_linestyle,marker='',color=gray_light,linewidth=linewidth)
plot2 = weekday_oldapt_march.ix['USAGE'].plot(marker='',color=gray_light,linewidth=linewidth)
plot3 = weekend_newapt_june.ix['USAGE'].plot(linestyle=weekend_linestyle,marker='',color=red_orange,linewidth=linewidth)
plot4 = weekday_newapt_june.ix['USAGE'].plot(marker='',color=red_orange,grid='off',linewidth=linewidth)
ylim(ymin=0,ymax=3)
xlim(xmax=23)
xticks(np.arange(0,23,2))
xlabel('Hour of Day')

ylabel('Electricity Usage (kWh)')
title('Average Day of Electricity Consumption')
legend(['Old Apt, Weekend in March, (Daily Total = %.2f kWh)' % round(sum(weekend_oldapt_march.ix['USAGE']),1),
    'Old Apt, Weekday in March, (Daily Total = %.2f kWh)' % round(sum(weekday_oldapt_march.ix['USAGE']),1),
    'New Apt, Weekend in June, (Daily Total = %.2f kWh)' % round(sum(weekend_newapt_june.ix['USAGE']),1),
    'New Apt, Weekday in June, (Daily Total = %.2f kWh)' % round(sum(weekday_newapt_june.ix['USAGE']),1)],
    'upper right')

fig.savefig('plots/Average_Days_New_and_Old_Apts.png')

print 'Total Daily Usage'
print 'Weekday @ Old Apartment in March:',round(sum(weekday_oldapt_march.ix['USAGE']),1)
print 'Weekend @ Old Apartment in March:',round(sum(weekend_oldapt_march.ix['USAGE']),1)
print 'Weekday @ New Apartment in June:',round(sum(weekday_newapt_june.ix['USAGE']),1)
print 'Weekend @ New Apartment in June:',round(sum(weekend_newapt_june.ix['USAGE']),1)


a=pd.DataFrame(old_apt_and_weather['USAGE'].groupby(old_apt_and_weather.index.date).sum(),index=pd.to_datetime(old_apt_and_weather.index.date))
a = a.groupby(a.index.month).mean().plot(marker='o')