# Full descriptions of these models can be found in the docstrings for each module.

#TOU_pricing.py contains functions that determine the season, period (peak, off-peak, intermediate), 
#and cost of electricity for each hour of a timestampd dataframe containing hourly residential 
#electricity consumption.
import TOU_pricing


#storage_logic.py contains functions that implement a control algorithm for the purchase of electricity
#based on available storage and period of the day (peak, off-peak). 
import storage_logic

#calculations.py contains functions which return key metrics for analyzing the model output.
import calculations

import pandas as pd
import numpy as np
import time


# Reload modules during development
reload(TOU_pricing)
reload(storage_logic)
reload(calculations)

def inverter_efficiency(direction):
    if direction == 'charging':
        eff = .85
    elif direction == 'discharging':
        eff = .85
    return eff

def battery_efficiency(direction):
    if direction == 'charging':
        eff = .85
    elif direction == 'discharging':
        eff = .85
    return eff

battery_sizes = [1., 2., 5., 7.5, 10., 15., 20., 25., 30.]
max_dod = .2

R_output = TOU_pricing.main('R',False)
R_annual_cost = np.sum(R_output['USAGE'] * R_output['cost'])
print R_annual_cost

save_results = False

t0 = time.time()

for i, bat_size in enumerate(battery_sizes):

    system_param = {
        'Inverter Cost' : 1500., # ballpark
        'Storage Cost' : 200. / 1.2, # cost per kilowatt-hour based on link above
        'Storage Size' : bat_size,  # storage size in kilowatt-hours
        'Max Charge Rate' : bat_size / 8.,
        'Max DOD' : max_dod, # DOD
        'Bat Depleted' : max_dod * bat_size,
        'Inverter Efficiency' : inverter_efficiency,
        'Battery Efficiency' : battery_efficiency,
    }

    results = storage_logic.main(TOU_pricing.main('EV', False), system_param)
    filename = 'results/storage_size/output_%s_kWh.csv'%bat_size
    if save_results == True: results.to_csv(filename)
    
    metrics = calculations.calc_metrics(results, system_param)

    if i == 0: all_metrics = pd.DataFrame(index=battery_sizes, columns=metrics.keys()) 
        
    all_metrics['Total kWh Purchased'][bat_size] = metrics['Total kWh Purchased']
    all_metrics['% Purchased During Peak'][bat_size] = metrics['% Purchased During Peak']
    all_metrics['% Purchased During Off-Peak'][bat_size] =  metrics['% Purchased During Off-Peak']
    all_metrics['Hours on Battery Only'][bat_size] = metrics['Hours on Battery Only']
    all_metrics['% Peak Demand Battery'][bat_size] = metrics['% Peak Demand Battery']
    all_metrics['Hours Battery Full'][bat_size] = metrics['Hours Battery Full']
    all_metrics['Hours Battery Depleted'][bat_size] = metrics['Hours Battery Depleted']
    all_metrics['Annual System Eff'][bat_size] = metrics['Annual System Eff']
    all_metrics['Annual Var Cost'][bat_size] = metrics['Annual Var Cost']
    all_metrics['Initial Cost'][bat_size] = metrics['Initial Cost']
    all_metrics['Peak kWh Shaved'][bat_size] = metrics['Peak kWh Shaved']
    all_metrics['% Peak kWh Shaved'][bat_size] = metrics['% Peak kWh Shaved']
    all_metrics['PBP'][bat_size] = metrics['PBP']
    all_metrics['Annual Savings'][bat_size] = metrics['Annual Savings']
    all_metrics['% Annual Cost Savings'][bat_size] = metrics['% Annual Cost Savings']

if save_results == True: all_metrics.to_csv('results/storage_size/all_metrics_storage_size.csv')    
    
t1 = time.time()
print 'Time for model run:',round(t1-t0,1),'s'



# We now have a Pandas DataFrame containing metrics that describe the system performance
# indexed by battery size.
all_metrics.head()

save_fig = True

a = 6.
fig = plt.figure(figsize=[1.3*a,a])
ax = subplot(111)

# turn off square border around plot
ax.spines["top"].set_visible(False)  
ax.spines["bottom"].set_visible(False)  
ax.spines["right"].set_visible(False)  
ax.spines["left"].set_visible(False)

# ensure only ticks on bottom and left, not top and right (unnecessary)
#ax.get_xaxis().tick_bottom()  
#ax.get_yaxis().tick_left()  

all_metrics['PBP'].plot(grid='off',color='#FF3300',linewidth=2,marker='.',markersize=14)
title('Payback Period in Years',fontsize=16)
xlabel('Storage Size (kWh)',fontsize=14)
xticks(fontsize=14)
yticks(fontsize=14)

# turn off tick marks
plt.tick_params(axis="both", which="both", bottom="off", top="off",
                labelbottom="on", left="off", right="off", labelleft="on")  

ylabel('Years',fontsize=14)

if save_fig == True: fig.savefig('results/storage_size/PBP.png',bbox_inches='tight')


save_fig = True

a = 6.
fig = plt.figure(figsize=[1.3*a,a])
ax1 = plt.subplot(111)


# Ticks
ticks_left = np.array(range(10,30,2))/100.
ticks_right_ymin = min(ticks_left) * R_annual_cost
ticks_right_ymax = max(ticks_left) * R_annual_cost
ticks_right = np.array(range(80,200,20))

# turn off square border around plot
ax1.spines["top"].set_visible(False)  
ax1.spines["bottom"].set_visible(False)  
ax1.spines["right"].set_visible(False)  
ax1.spines["left"].set_visible(False)

title('Annual Electricity Bill Cost Savings',fontsize=16)
xlabel('Storage Size (kWh)',fontsize=14)
xticks(fontsize=14)

ax1.plot(all_metrics.index, all_metrics['% Annual Cost Savings'],color='#FF3300',linewidth=2,marker='.',markersize=14)
ax1.set_ylabel('% Annual Savings', fontsize=14)

ax2 = ax1.twinx()
ax2.set_ylim(min(all_metrics['Annual Savings']), max(all_metrics['Annual Savings']))
ax2.set_yticklabels(ticks_right, fontsize=14)
ax2.set_ylabel('Annual Savings (USD)', fontsize=14)


# turn off ticks
ax1.tick_params(axis="both", which="both", bottom="off", top="off",
               labelbottom="on", left="off", right="off", labelleft="on",labelsize=14) 
ax2.tick_params(axis="y", which="both", bottom="off", top="off",
               labelbottom="on", left="off", right="off", labelleft="off",labelsize=14) 



if save_fig == True: fig.savefig('results/storage_size/cost_savings.png',bbox_inches='tight')

save_fig = True

a = 6.
fig = plt.figure(figsize=[1.3*a,a])
ax1 = plt.subplot(111)

# Ticks

ticks_right = np.array(range(0,2000,200))

# turn off square border around plot
ax1.spines["top"].set_visible(False)  
ax1.spines["bottom"].set_visible(False)  
ax1.spines["right"].set_visible(False)  
ax1.spines["left"].set_visible(False)

title('Demand Shaved From Peak Hours',fontsize=16)
xlabel('Storage Size (kWh)',fontsize=14)
xticks(fontsize=14)

ax1.plot(all_metrics.index, all_metrics['% Peak kWh Shaved'],color='#FF3300',linewidth=2,marker='.',markersize=14)
ax1.set_ylabel('% Peak Demand Shaved', fontsize=14)
ax1.set_ylim(0,1.1)

ax2 = ax1.twinx()
ax2.set_yticks(ticks_right)
ax2.set_yticklabels(ticks_right, fontsize=14)
ax2.set_ylim(0,1.1*max(all_metrics['Peak kWh Shaved']))
ax2.set_ylabel('Peak Demand Shaved (kWh)', fontsize=14)

# turn off ticks
ax1.tick_params(axis="both", which="both", bottom="off", top="off",
               labelbottom="on", left="off", right="off", labelleft="on",labelsize=14) 
ax2.tick_params(axis="y", which="both", bottom="off", top="off",
               labelbottom="on", left="off", right="off", labelleft="off",labelsize=14) 

if save_fig == True: fig.savefig('results/storage_size/peak_shaved_stor_cap.png',bbox_inches='tight')

save_fig = True

a = 6.
fig = plt.figure(figsize=[1.3*a,a])
ax1 = plt.subplot(111)

# Ticks
ticks_right = np.array(range(0,2000,200))

# turn off square border around plot
ax1.spines["top"].set_visible(False)  
ax1.spines["bottom"].set_visible(False)  
ax1.spines["right"].set_visible(False)  
ax1.spines["left"].set_visible(False)

title('Annual System Efficiency',fontsize=16)
xlabel('Storage Size (kWh)',fontsize=14)
xticks(fontsize=14)

ax1.plot(all_metrics.index, all_metrics['Total kWh Purchased'],color='#FF3300',linewidth=2,marker='.',markersize=14)
ax1.set_ylabel('Total kWh Purchased', fontsize=14, color='#FF3300')
#ax1.set_ylim(0,1.1)

ax2 = ax1.twinx()
ax2.plot(all_metrics.index, all_metrics['Annual System Eff'],color='#00CCFF',linewidth=2,marker='.',markersize=14)
#ax2.set_yticks([0,.25,.5,1])
#ax2.set_yticklabels(, fontsize=14)
ax2.set_ylim(0,1)
ax2.set_ylabel('System Efficiency', fontsize=14, color='#00CCFF')

# turn off ticks
ax1.tick_params(axis="both", which="both", bottom="off", top="off",
               labelbottom="on", left="off", right="off", labelleft="on",labelsize=14) 
ax2.tick_params(axis="y", which="both", bottom="off", top="off",
               labelbottom="on", left="off", right="off", labelleft="off",labelsize=14) 

if save_fig == True: fig.savefig('results/storage_size/sys_eff_stor_cap.png',bbox_inches='tight')