energy[4][t] = dc.e[1]
energy[5][t] = dc.e[2]
energy[6][t] = bk.ez
cost[0][t]=hvac.userComfCosti(0)
cost[1][t]=hvac.userComfCosti(1)
cost[2][t]=hvac.userComfCosti(2)
cost[3][t]=dc.dcCosti(0)
cost[4][t]=dc.dcCosti(1)
cost[5][t]=dc.dcCosti(2)
cost[6][t]=bk.bgCost()
totalcost[t]=hvac.userComfCost()+dc.dcCost()+bk.bgCost()
################################################
plt.plot(totalcost)
nmub.saveFile('baseline1.p',totalcost)
def plot_lines(datas, numb_of_line, markerstyle, labels, title ):
for line in range(numb_of_line):
plt.plot(datas[line], marker = markerstyle, markersize=4, label=labels[line])
plt.legend(loc=1)
plt.title(title)
plt.show()
#labels = ['Office 1','Office 2','Office 3','DC 1','DC 2','DC 3','BK']
#plot_lines(energy,7,".",labels,"Energy")
#plot_lines(sigma,7,"+",labels,"Sigma")
#labels = ['Office 1','Office 2','Office 3','DC 1','DC 2','DC 3','BK']
#plot_lines(cost,7,"+",labels,"Cost")
#labels = ['Office 1','Office 2','Office 3']
energy[5][t] = dc.e[2]
energy[6][t] = bk.ez
cost[0][t]=hvac.userComfCosti(0)
cost[1][t]=hvac.userComfCosti(1)
cost[2][t]=hvac.userComfCosti(2)
cost[3][t]=dc.dcCosti(0)
cost[4][t]=dc.dcCosti(1)
cost[5][t]=dc.dcCosti(2)
cost[6][t]=bk.bgCost()
totalcost[t]=hvac.userComfCost()+dc.dcCost()+bk.bgCost()
################################################
nmub.saveFile('DANE.p',totalcost)
plt.plot(totalcost)
labels = ['Office 1','Office 2','Office 3','DC 1','DC 2','DC 3','BK']
def totalCost(data,opt):
#
fig=plt.figure(figsize=(6,3.5))
plt.plot(data, color='black',marker = '^', markersize=3, label='Total cost')
#optimal value
plt.plot(np.ones(runTime)*opt,color='black',ls='--', markersize=4, label='Optimal')
plt.xlim([0,runTime])
plt.ylim([35,60])
plt.legend(loc=1)
plt.xlabel('Iterations')
plt.ylabel('Total cost')
