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plot_conv (1) (1).py
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plot_conv (1) (1).py
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import numpy as np
from scipy import optimize
from scipy.optimize import fsolve
from matplotlib import pyplot as plt
import string
from pylab import *
#import plot_settings as ps
import cvxpy as cvx
#import unNormalizedMUB as umub
import unNormalizedMUB as nmub
reload(nmub)
#ps.set_mode("tiny")
#omega(hvac,dc,bk)
omega=np.array([1.5*5,0.1/3,0.3]) #0.3$/KWh
#omega=np.array([0,1.0, 0.3*1e-3]) #set baseline1
#wear and tear cost
omega_wat=np.array([0.5,0.5,0.5])
#sla weight
omega_sla=np.array([0.5,0.5,0.5])
hvac= nmub.HVAC(25,omega[0])
Tnhat=[2.0,4.0,5.0,2.0,5.0]
hvac.setTn(Tnhat)
bk=nmub.bkGen(omega[2])
rho=100
Q=300 #Wh ( 1MWh)
n=3 #number of offices
I=3 # number of DCs
nmub.omega_wat=omega_wat
nmub.Q=Q
nmub.n=n
nmub.I=I
nmub.rho=rho
#hvac.userComfCost()
#hvac.enReduc()
#hvac.optimizeOfficei(1,5)
#initialize the datacenter with services rate of each tenant per s and max server
#nmub.DC(mu,S,omega)
dc =nmub.DC([2.5,4.,6.5,3.5],[2000,2000,2000],omega[1],omega_sla,omega_wat)
#arrival rate
dc.setlamb([1100.0,1100.0,1100.0])
#dc.setSwitchSever([200,100,100,200,100])
#dc.minServer()
#dc.solx()
#def __init__(self,sigmai,sigman,e_i,e_n,e_z):
mub=nmub.operator([1,2,3],[1,2,3],100,[1,2,3],[1,2,3],200,omega)
#mub.calv()
# def updateEn(self,sigman,en_):
runTime = 2
#
#
#
#
bk_totalcost= np.zeros(runTime)
T=np.ones((3,runTime))
s=np.ones((3,runTime))
energy = np.ones((7,runTime))
sigma = np.ones((7,runTime))
cost = np.ones((7,runTime))
Tnhat1=np.zeros(runTime)
Totalcost=np.zeros(runTime)
for i in range(runTime):
pre=sum(mub.sigmai)+sum(mub.sigman)+mub.sigmaz
#print('pre',pre)
hvac.updateEn(mub.sigman,mub.e_n)
# def updateEi(self,sigmai,ei_):
#print ("Ti",hvac.Tn)
energy[0][i] = hvac.e[0]
energy[1][i] = hvac.e[1]
energy[2][i] = hvac.e[2]
T[0][i]=hvac.Tn[0]
T[1][i]=hvac.Tn[1]
T[2][i]=hvac.Tn[2]
#def updateEi(self,sigmai,ei_):
#dc.updateEi(mub.sigmai,mub.e_i)
dc.updateEi2(mub.sigmai,mub.e_i)
s[0][i]=dc.s[0]
s[1][i]=dc.s[1]
s[2][i]=dc.s[2]
energy[3][i] = dc.e[0]
energy[4][i] = dc.e[1]
energy[5][i] = dc.e[2]
#print(sum(hvac.e)+sum(dc.e)+bk.ez)
# def updateEz(self,sigmaz,ez_):
bk.updateEz(mub.sigmaz,mub.e_z)
energy[6][i] = bk.ez
#def updateehat(self, ei,en,ez):
mub.updateehat(dc.e,hvac.e,bk.ez)
#mub.updateehat2(dc.e,hvac.e,bk.ez)
mub.tempei=dc.e
mub.tempen=hvac.e
mub.tempez=bk.ez
#mub.update_e()
# def updateSigma(self, ei,en,ez):
mub.updateSigma(dc.e,hvac.e,bk.ez)
sigma[0][i] = mub.sigman[0]
sigma[1][i] = mub.sigman[1]
sigma[2][i] = mub.sigman[2]
sigma[3][i] = mub.sigmai[0]
sigma[4][i] = mub.sigmai[1]
sigma[5][i] = mub.sigmai[2]
sigma[6][i] = mub.sigmaz
cost[0][i]=hvac.userComfCosti(0)
cost[1][i]=hvac.userComfCosti(1)
cost[2][i]=hvac.userComfCosti(2)
cost[3][i]=dc.dcCosti(0)
cost[4][i]=dc.dcCosti(1)
cost[5][i]=dc.dcCosti(2)
cost[6][i]=bk.bgCost()
curr=sum(mub.sigmai)+sum(mub.sigman)+mub.sigmaz
Totalcost[i]=cost[0][i]+cost[1][i]+cost[2][i]+cost[3][i]+cost[4][i]+cost[5][i]+cost[6][i]
#
#x=np.arange(runTime)
#plt.plot(Totalcost)
#ax.plot(x, y, color=next(color_cycler), label = current_label, marker = next(marker_cycler), markevery=stride)
def plot_lines(datas, numb_of_line, ls,markerstyle, title, labels, xlb='', ylb=''):
fig=plt.gcf()
stride=20
for line in range(numb_of_line):
plt.plot(datas[line], ls=ls[line],marker = markerstyle[line], markersize=4, label=labels[line],markevery=stride)
plt.legend(loc=1)
#plt.xlim([0,50])
#plt.ylim(ylim)
plt.title(title)
plt.xlabel=xlb
plt.ylabel=ylb
plt.show()
name=ylb+'.pdf'
#fig.savefig(name)
#####################################################################
def sub_energy(energy):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
ax.set_xlabel('common xlabel')
ax.set_ylabel('common ylabel')
axarr0=subplot(3,1,1)
axarr0.plot(energy[0],ls='-',marker='',label='Office 1')
axarr0.plot(energy[1],ls='-',marker='+',label='Office 2')
axarr0.plot(energy[2],ls='-',marker='.',label='Office 3')
axarr0.set_xlim([0,30])
axarr0.legend(loc="right", ncol=1, shadow=True, fancybox=False)
xticks([]), yticks([0,20,40,60])
axarr1=subplot(3,1,2)
axarr1.plot(energy[3],ls='--',marker='+',label='DC 1')
axarr1.plot(energy[4],ls='--',marker='.',label='DC 2')
axarr1.plot(energy[5],ls='--',marker='',label='DC 3')
axarr1.set_xlim([0,30])
axarr1.legend(loc="right", ncol=1, shadow=True, fancybox=False)
xticks([]), yticks([55,60,65,70])
axarr2=subplot(3,1,3)
axarr2.plot(energy[6],ls='-',marker='.',label='Backup')
axarr2.legend(loc="right", ncol=1, shadow=True, fancybox=False)
axarr2.set_ylim([75,80])
axarr2.set_xlim([0,30])
yticks([75,77,79])
fig.text(0.5, 0.04, 'Iterations', ha='center', va='center')
fig.text(0.06, 0.5, 'Energy (Kwh)', ha='center', va='center', rotation='vertical')
plt.show()
#fig.savefig('energy_conv.pdf')
#############################################################
def totalCost(data,opt):
#
fig=plt.figure(figsize=(6,3.5))
plt.plot(data, marker = '^', markersize=3, label='Total cost')
#optimal value
plt.plot(np.ones(runTime)*opt,ls='--', markersize=4, label='Optimal')
plt.xlim([0,runTime])
#plt.ylim([35,60])
plt.legend(loc=1)
plt.xlabel('Iterations')
plt.ylabel('Total cost')
plt.show()
#fig.savefig('totalcost_conv.pdf')
#########################################################
def costConv(cost):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
ax.set_xlabel('common xlabel')
ax.set_ylabel('common ylabel')
axarr0=subplot(3,1,1)
axarr0.plot(cost[0],ls='-',marker='',label='Office 1')
axarr0.plot(cost[1],ls='-',marker='+',label='Office 2')
axarr0.plot(cost[2],ls='-',marker='.',label='Office 3')
axarr0.set_xlim([0,30])
axarr0.legend(loc="right", ncol=1, shadow=True, fancybox=False)
xticks([]), yticks([])
axarr1=subplot(3,1,2)
axarr1.plot(cost[3],ls='--',marker='+',label='DC 1')
axarr1.plot(cost[4],ls='--',marker='.',label='DC 2')
axarr1.plot(cost[5],ls='--',marker='',label='DC 3')
axarr1.set_xlim([0,30])
axarr1.set_ylim([55,70])
axarr1.legend(loc="right", ncol=1, shadow=True, fancybox=False)
xticks([]), yticks([])
axarr2=subplot(3,1,3)
axarr2.plot(cost[6],ls='-',marker='.',color='black',label='Backup')
axarr2.legend(loc="right", ncol=1, shadow=True, fancybox=False)
axarr2.set_xlim([0,30])
yticks([75,77,79])
fig.text(0.5, 0.04, 'Iterations', ha='center', va='center')
fig.text(0.06, 0.5, 'Energy (Kwh)', ha='center', va='center', rotation='vertical')
fig.savefig('energy_conv.pdf')
#plot convergence of totalcost
#totalCost(Totalcost,57.9)
#print(Totalcost)
#plot converge of energy
#sub_energy(energy)
#a.plot(energy[0])
#xticks([]), yticks([])
##text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)
#
#subplot(2,1,2)
#xticks([]), yticks([])
#text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)
#
labels = ['Office 1','Office 2','Office 3','DC 1','DC 2','DC 3','BK']
ls=['-','-','-','--','--','--','-.']
plot_lines(energy,7,ls,['+','o','','d','+','',''],'energy',labels,'Iterations','Energy(KWh)')
#def plot_lines(datas, numb_of_line, ls,markerstyle, title, labels, xlb='', ylb=''):
plot_lines(sigma,7,ls,['+','o','','d','+','',''],'sigma',labels,'Iterations','Energy(KWh)')
# labels = ['Office 1','Office 2','Office 3','DC 1','DC 2','DC 3','BK']
plot_lines(cost,7,ls,['+','o','','d','+','',''],'cost',labels,'Iterations','Energy(KWh)')
# labels = ['Office 1','Office 2','Office 3']
# plot_lines(T,3,"+",labels,"Temperature")
# labels = ['DC 1','DC 2','DC 3']
# plot_lines(s,3,"+",labels,"s")
# plt.plot(Totalcost, lw='0.5',marker = '^', markersize=3, label='Total cost')
# plt.plot(np.ones(runTime)*45.1,ls='--', markersize=4, label='Optimal')
# plt.xlim([0,runTime])
# plt.ylim([30,60])
# plt.legend(loc=1)
# plt.xlabel('Iterations')
# plt.ylabel('Total cost')
#fig1=plt.gcf()
#plt.figure(figsize=(9, 4.9))
#plt.show()
#plt.draw()
#
#fig1.savefig('totalcost_conv.pdf')
#plt.savefig('common_labels.png', dpi=300)