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plot_MUB (1).py
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plot_MUB (1).py
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# -*- coding: utf-8 -*-
"""
Filename: plot_colo.py
Authors: Nguyen H. Tran
LastModified: 11/2014
"""
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['legend.fancybox'] = True
import plot_settings as ps
from pylab import *
reload(ps)
num_days = 1
scale = 100
markers = ['o','x', 's','v','d','^','<','>','+','*','x']
linestyles = ['-','--']#,'-.']
linewidth = 1.5
start_iter = 0
runTime = 100
maxtimeslot=100
### Traces
def plot_traces(Lamb_array):
ps.set_mode("small")
fig, ax = plt.subplots()
x = np.arange(np.size(Lamb_array[0])) + 1
name = ['MSR', 'FIU', 'Syn.1','Syn.2','Syn.3']
for i in range(len(name)):
y = Lamb_array[i]
if i < len(linestyles):
ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = name[i], ls = linestyles[i])
else:
ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = name[i], ls = linestyles[-1], marker = markers[i])
fig.tight_layout()
ax.set_xlabel(r'Time (h)')
ax.set_ylabel(r"$\lambda_i$")
ax.set_ylim([0., 1])
ax.legend(loc="upper left")
fig.tight_layout()
plt.savefig('traces.pdf')
plt.show()
def plot_lines(datas, numb_of_line, ls,markerstyle, title, labels, xlb, ylb):
ps.set_mode("small")
fig=plt.gcf()
stride=20
for line in range(numb_of_line):
plt.plot(datas[line], ls=ls[line],marker = markerstyle[line], markersize=5, label=labels[line],markevery=stride)
plt.legend(loc=1)
#plt.xlim([0,50])
#plt.ylim(0,20)
plt.title(title)
plt.xlabel=xlb
plt.ylabel=ylb
plt.show()
name=ylb+'.pdf'
fig.savefig(name)
#####################################################################
def sub_energy(energy):
ps.set_mode("small")
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(2,1,1)
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,runTime])
axarr0.set_ylim([0,200])
axarr0.legend(loc=1, ncol=1, shadow=True, fancybox=False)
#xticks([]), yticks([0,20,40,60,80])
axarr1=subplot(2,1,2)
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.plot(energy[6],ls='-',marker='',label='BK')
axarr1.set_xlim([0,runTime])
axarr1.set_ylim([-5,300])
axarr1.legend(loc=1, ncol=1, shadow=True, fancybox=False)
#xticks([]), yticks([0,20,40,60,80])
# 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(DANE,baseline1,baseline2,opt):
#
fig=plt.figure(figsize=(6,3.5))
stride=5
plt.plot(DANE, marker = '^', markersize=5, label='DANE',markevery=stride)
plt.plot(baseline1-0.3, ls='--',marker = '+', markersize=5, label='Baseline 1',markevery=stride)
plt.plot(baseline1, ls='--',marker = 'o', markersize=5, label='Baseline 2',markevery=stride)
#optimal value
plt.plot(np.ones(runTime)*opt,ls='--', markersize=5, label='Optimal')
plt.xlim([0,50])
#plt.ylim([0,10])
#plt.ylim([35,60])
plt.legend(loc=1)
plt.xlabel('Iterations')
plt.ylabel('Total cost')
plt.show()
fig.savefig('plot/totalcost_conv.pdf')
#########################################################
def totalCost_trace(DANE,baseline1,baseline2):
#
fig=plt.figure(figsize=(6,3.5))
stride=5
plt.plot(DANE, marker = '^', markersize=5, label='DANE',markevery=stride)
plt.plot(baseline1-0.3, ls='--',marker = '+', markersize=5, label='Baseline 1',markevery=stride)
plt.plot(baseline1, ls='--',marker = 'o', markersize=5, label='Baseline 2',markevery=stride)
#optimal value
#plt.plot(np.ones(runTime)*opt,ls='--', markersize=5, label='Optimal')
plt.xlim([0,50])
#plt.ylim([0,10])
#plt.ylim([35,60])
plt.legend(loc=1)
plt.xlabel('Time slots')
plt.ylabel('Total cost')
plt.show()
fig.savefig('plot/totalcost_trace.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')
### Alpha comparison
def plot_alpha(nu_SWO, price_convg_list_alpha, alpha_prov_pay_list, alpha_list):
ps.set_mode("small")
fig, (ax1, ax2) = plt.subplots(1, 2)
x_range = range(len(price_convg_list_alpha) + 1)
stride = 10.
for i in x_range:
if i != max(x_range):
x = range(len(price_convg_list_alpha[i]))
y = price_convg_list_alpha[i]
current_label = r"$\alpha=${}".format(alpha_list[i])
else:
x = range(len(nu_SWO))
y = nu_SWO
current_label = r'$\nu^{*}$'
# if i < len(linestyles):
# ax1.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[i])
# else:
ax1.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label =current_label, ls = linestyles[0], marker = markers[i], markevery=stride)
# ax1.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, marker = markers[i])
y_max = max([i[-1] for i in price_convg_list_alpha])
ax1.set_ylim(0.5*y_max, 2.5*y_max )
ax1.set_xlabel('iterations')
ax1.set_ylabel(r'$g(\Theta^{ne})$')
# Legend
leg = ax1.legend(loc="upper center", ncol=2, fancybox=True, shadow=True, bbox_to_anchor=(0.5, 1.10))
# leg.get_frame().set_facecolor('0.9') # set the frame face color to light gray
# leg.get_frame().set_alpha(0.99)
# for t in leg.get_texts():
# t.set_fontsize('small') # the legend text fontsize
# for l in leg.get_lines():
# l.set_linewidth(1.0) # the legend line width
x = alpha_list
y = alpha_prov_pay_list
ax2.plot(x,y, lw=linewidth, alpha=0.6)
ax2.set_xlabel(r'$\alpha$')
ax2.set_ylabel(r'$\sum\limits_{i} R_i$')
fig.tight_layout()
plt.savefig('alpha.pdf')
plt.show()
#def plot_provider_payment_alpha(alpha_prov_pay_list, alpha_list):
# ps.set_mode("small")
# fig, ax = plt.subplots()
#
# x = alpha_list
# y = alpha_prov_pay_list
# ax.plot(x,y, lw=linewidth, alpha=0.6)
#
# ax.set_xlabel(r'$\alpha$')
# ax.set_ylabel(r'$\sum\limits_{i} R_i$')
# ax.legend(loc="best")
# plt.savefig('total_payment_alpha.pdf')
# plt.show()
### Convergence
def plot_price_convg(provider, k):
ps.set_mode("small")
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
x = np.arange(k)[start_iter:]
y = provider.price_convg_list[start_iter:]
ax1.plot(x,y, lw=linewidth, alpha=0.6, marker = markers[1])
y_max = provider.price_convg_list[-1]
ax1.set_ylim(0.5*y_max, 1.5*y_max )
ax1.set_xlabel('iterations')
ax1.set_ylabel(r'$g(\Theta^{ne})$')
y = provider.price_convg_SWO_list[start_iter:]
ax2.plot(x,y, lw=linewidth, alpha=0.6, marker = markers[2])
y_max = provider.price_convg_SWO_list[-1]
ax2.set_ylim(0.5*y_max, 1.5*y_max )
ax2.set_xlabel('iterations')
ax2.set_ylabel(r'$\nu^*$')
ax2.legend(loc="upper left")
fig.tight_layout()
plt.savefig('price_convg.pdf')
plt.show()
#def plot_price_convg_SWO(provider, k):
# ps.set_mode("small")
# fig, ax = plt.subplots()
# x = np.arange(k)[start_iter:]
# y = provider.price_convg_SWO_list[start_iter:]
# ax.plot(x,y, lw=linewidth, alpha=0.6, marker = markers[1])
#
# y_max = provider.price_convg_SWO_list[-1]
# ax.set_ylim(0., 1.5*y_max )
# ax.set_xlabel('iterations')
# ax.set_ylabel(r'$\nu^*$')
# ax.legend(loc="best")
# plt.show()
def plot_bid_convg(tenant_list, k):
ps.set_mode("small")
fig, ax = plt.subplots()
x = np.arange(k)[start_iter:]
stride = max( int(len(x)/10), 1 )
for i in range(len(tenant_list)):
y = tenant_list[i].bid_convg_list[start_iter:]
current_label = r'Tenant {}'.format(i+1)
# if i < len(linestyles):
# ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[i])
# else:
ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[0], marker = markers[i], markevery=stride)
ax.set_xlabel('iterations')
ax.set_ylabel(r'$\theta_i$')
ax.legend(loc="upper left")
fig.tight_layout()
plt.savefig('bid_convg.pdf')
plt.show()
def plot_reward_convg(tenant_list, k):
ps.set_mode("small")
fig, ax = plt.subplots()
x = np.arange(k)[start_iter:]
stride = max( int(len(x)/10), 1 )
for i in range(len(tenant_list)):
y = tenant_list[i].reward_convg_list[start_iter:]
current_label = r'Tenant {}'.format(i+1)
# if i < len(linestyles):
# ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[i])
# else:
ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[0], marker = markers[i], markevery=stride)
y_max = max([i.reward_convg_list[-1] for i in tenant_list])
ax.set_ylim(0., 1.5*y_max )
ax.set_xlabel('iterations')
ax.set_ylabel(r'$R_i$')
ax.legend(loc="upper left")
fig.tight_layout()
plt.savefig('reward_convg.pdf')
plt.show()
def plot_e_convg_SWO(tenant_list, k):
ps.set_mode("small")
fig, ax = plt.subplots()
x = np.arange(k)[start_iter:]
stride = max( int(len(x)/10), 1 )
for i in range(len(tenant_list)):
y = (np.array(tenant_list[i].e_convg_SWO_list))[start_iter:]*scale
current_label = r'Tenant {}'.format(i+1)
# if i < len(linestyles):
# ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[i])
# else:
ax.plot(x,y, lw=linewidth, color=ps.colors[i], alpha=0.6, label = current_label, ls = linestyles[0], marker = markers[i], markevery=stride)
ax.set_xlabel('iterations')
ax.set_ylabel(r'$\Delta e_i$')
ax.legend(loc="upper left")
fig.tight_layout()
plt.savefig('e_convg_SWO.pdf')
plt.show()
### Hours comparison
def plot_tenant_reward(tenant_reward_hours_array, num_hours, num_tenants,scheme=1):
ps.set_mode("small")
fig, ax = plt.subplots()
width = 0.5
patterns = ["|||", "\\" ,"///" , "...", "---" , "xxx" , "+++" , "o", "O", "*" ]
ind = np.arange(num_hours) + 1
name = ['Tenant 1', 'Tenant 2', 'Tenant 3','Tenant 4','Tenant 5']
bottom = np.zeros(num_hours)
for i in range(num_tenants):
ax.bar(ind, tenant_reward_hours_array[:,i], width=width, color= 'white', align='center', edgecolor='black', \
label=name[i], hatch=patterns[i], bottom=bottom)
bottom += tenant_reward_hours_array[:,i]
ax.set_xlabel(r'Time (h)')
ax.set_ylabel(r'$R_i$')
ax.set_xlim([-0.5, num_hours + 1])
if scheme == 1:
# ax.set_title('EPM')
plt.savefig('tenant_reward_EPM.pdf')
elif scheme ==2:
# ax.set_title('SWO')
plt.savefig('tenant_reward_SWO.pdf')
else:
ax.legend(loc="best")
# ax.set_title('RAND')
plt.savefig('tenant_reward_RAND.pdf')
plt.show()
def plot_tenant_e(tenant_e_hours_list, num_hours, num_tenants, scheme=1):
ps.set_mode("small")
fig, ax = plt.subplots()
width = 0.5
patterns = ["|||", "\\" ,"///" , "...", "---" , "xxx" , "+++" , "o", "O", "*" ]
index = np.arange(num_hours) + 1
name = ['Tenant 1', 'Tenant 2', 'Tenant 3','Tenant 4','Tenant 5']
bottom = np.zeros(num_hours)
for i in range(num_tenants):
ax.bar(index, tenant_e_hours_list[:,i]*scale, width=width, color= 'white', align='center', edgecolor='black', \
label=name[i], hatch=patterns[i], bottom=bottom)
bottom += tenant_e_hours_list[:,i]*scale
ax.set_xlabel(r'Time (h)')
ax.set_ylabel(r'$\Delta e_i$')
ax.set_xlim([-0.5, num_hours+1])
if scheme == 1:
# ax.set_title('EPM')
plt.savefig('tenant_e_EPM.pdf')
elif scheme ==2:
# ax.set_title('SWO')
plt.savefig('tenant_e_SWO.pdf')
else:
ax.legend(loc="best")
# ax.set_title('RAND')
plt.savefig('tenant_e_RAND.pdf')
plt.show()
def plot_tenant_cost(tenant_cost_hours_array, num_hours, num_tenants, scheme=1):
ps.set_mode("small")
fig, ax = plt.subplots()
width = 0.5
patterns = ["|||", "\\" ,"///" , "...", "---" , "xxx" , "+++" , "o", "O", "*" ]
ind = np.arange(num_hours) + 1
name = ['Tenant 1', 'Tenant 2', 'Tenant 3','Tenant 4','Tenant 5']
bottom = np.zeros(num_hours)
for i in range(num_tenants):
ax.bar(ind, tenant_cost_hours_array[:,i], width=width, color= 'white', align='center', edgecolor='black', \
label=name[i], hatch=patterns[i], bottom=bottom)
bottom += tenant_cost_hours_array[:,i]
ax.set_xlabel(r'Time (h)')
ax.set_ylabel(r'$C_i(m_i)$')
ax.set_xlim([-0.5, num_hours + 1])
if scheme == 1:
# ax.set_title('EPM')
plt.savefig('tenant_cost_EPM.pdf')
elif scheme ==2:
# ax.set_title('SWO')
plt.savefig('tenant_cost_SWO.pdf')
else:
ax.legend(loc="best")
# ax.set_title('RAND')
plt.savefig('tenant_cost_RAND.pdf')
plt.show()
def plot_sum_cost(sum_cost_all_array, num_hours, num_tenants):
ps.set_mode("small")
fig, ax = plt.subplots()
index = np.arange(num_hours)+1
bar_width = 0.25
space = 0.01
opacity = 0.99
error_config = {'ecolor': '0.3'}
patterns = ["|||", "\\" ,"///" , "...", "---" , "xxx" , "+++" , "o", "O", "*" ]
alg1 = ax.bar(index, sum_cost_all_array[0,:]*scale, bar_width,
alpha=opacity,
color= 'white',
error_kw=error_config, hatch=patterns[1],
label='EPM', align='center')
alg2 = ax.bar(index + bar_width + space, sum_cost_all_array[1,:]*scale, bar_width,
alpha=opacity,
color= 'white',
error_kw=error_config, hatch=patterns[2],
label='SWO', align='center')
alg3 = ax.bar(index + 2*(bar_width + space), sum_cost_all_array[2,:]*scale, bar_width,
alpha=opacity,
color= 'white',
error_kw=error_config, hatch=patterns[3],
label='RAND',align='center')
# autolabel(alg1)
# autolabel(alg2, 0.01)
# autolabel(alg3, 0.03)
plt.xticks(index + bar_width, index, rotation='0', fontsize=8)
ax.set_xlim(0,len(index) + 2)
y_max = sum_cost_all_array.max()*scale
ax.set_ylim(0., 1.1*y_max )
ax.set_ylabel(r"$\sum\nolimits_{i} C_i(m_i)$")
ax.set_xlabel(r'Time (h)')
ax.legend(loc="lower right")
fig.tight_layout()
plt.savefig("sum_cost.pdf")
def plot_tenant_util(tenant_reward_hours_array, tenant_cost_hours_array, num_hours, num_tenants, scheme=1):
ps.set_mode("small")
fig, ax = plt.subplots()
width = 0.5
patterns = ["|||", "\\" ,"///" , "...", "---" , "xxx" , "+++" , "o", "O", "*" ]
ind = np.arange(num_hours) + 1
name = ['Tenant 1', 'Tenant 2', 'Tenant 3','Tenant 4','Tenant 5']
bottom = np.zeros(num_hours)
y = tenant_reward_hours_array - tenant_cost_hours_array
print " Tenants' utility is: {} ".format(y)
for i in range(num_tenants):
ax.bar(ind, y[:,i], width=width, color= 'white', align='center', edgecolor='black', \
label=name[i], hatch=patterns[i], bottom=bottom)
bottom += y[:,i]
ax.set_xlabel(r'Time (h)')
ax.set_ylabel("Tenants' utility")
ax.set_xlim([-0.5, num_hours + 1])
if scheme == 1:
# ax.set_title('EPM')
plt.savefig('tenant_util_EPM.pdf')
elif scheme ==2:
# ax.set_title('SWO')
plt.savefig('tenant_util_SWO.pdf')
else:
ax.legend(loc="best")
# ax.set_title('RAND')
plt.savefig('tenant_util_RAND.pdf')
plt.show()