def drawInstantPDR():
# Choose font
plt.rc('font', family='Times New Roman')
# Initialize number formatter
scientificformatter = FuncFormatter(formatnum)
percentageformatter = FuncFormatter(to_percent)
# Init datasets
CSNonParaDataset = initCSNonPara()
# Load datasets
(x1, y1) = LoadCSPDR(CSNonParaDataset[6])
(x2, y2) = LoadCSPDR(CSNonParaDataset[7])
(x3, y3) = LoadCSPDR(CSNonParaDataset[8])
# Calculate average PDR1
(averagePDR1, averagePDRPoints1) = getAvg((x1, y1))
# Calculate average PDR2
(averagePDR2, averagePDRPoints2) = getAvg((x2, y2))
# Calculate average PDR3
(averagePDR3, averagePDRPoints3) = getAvg((x3, y3))
# Initialize subplot
fig, ax1 = plt.subplots()
# Initialize axis
ax1.set_xlim((0, 2400000))
ax1.set_ylim((0, 1.05))
# ax1.xaxis.set_major_formatter(scientificformatter)
ax1.ticklabel_format(axis="x", style="sci", scilimits=(0, 0))
ax1.yaxis.set_major_formatter(percentageformatter)
ax1.set_xlabel(r'Time (ms)')
ax1.set_ylabel(r'Packet Delivery Ratio (%)')
# Draw lines
ax1.plot(x1, y1, color='r', label=r'Instant (M = 2)')
ax1.plot(x2, y2, color='y', label=r'Instant (M = 4)')
ax1.plot(x3, y3, color='g', label=r'Instant (M = 6)')
ax1.plot(x1,
averagePDRPoints1,
color='r',
linestyle="--",
label=r'Average (M = 2)')
ax1.plot(x2,
averagePDRPoints2,
color='y',
linestyle="--",
label=r'Average (M = 4)')
ax1.plot(x3,
averagePDRPoints3,
color='g',
linestyle="--",
label=r'Average (M = 6)')
# Choose tick pramaters
ax1.tick_params(labelsize=15)
# Draw legends
plt.legend(loc='best', ncol=2, fontsize=7)
# Draw gridlines
ax1.grid()
# Draw title
# plt.title(r'Instant Packet Deliver Ratio')
# Save subplots to files
plt.savefig("bin/PDR(Instant).pdf", format="pdf", transparent="ture")
# Show subplots
plt.show()
return
def drawEffPDR():
# Choose font
plt.rc('font', family='Times New Roman')
# x为每组柱子x轴的基准位置
labels = ['SF=7', 'SF=8', 'SF=9', 'SF=10', 'SF=11', 'SF=12']
# Init datasets
CSParaDataset = initCSPara()
TP0 = []
TP1 = []
TP2 = []
TP3 = []
TP4 = []
TP5 = []
TP6 = []
TP7 = []
if len(CSParaDataset) == SFNum * TPNum: # 8 TP Levels
# Load datasets and calculate average throughputs
for loopcount in range(SFNum * TPNum):
(averagePDR,
averagePDRPoints) = getAvg(LoadCSPDR(CSParaDataset[loopcount]))
if loopcount % TPNum == 0:
TP0.append(averagePDR)
elif loopcount % TPNum == 1:
TP1.append(averagePDR)
elif loopcount % TPNum == 2:
TP2.append(averagePDR)
elif loopcount % TPNum == 3:
TP3.append(averagePDR)
elif loopcount % TPNum == 4:
TP4.append(averagePDR)
elif loopcount % TPNum == 5:
TP5.append(averagePDR)
elif loopcount % TPNum == 6:
TP6.append(averagePDR)
else:
TP7.append(averagePDR)
datas = [TP0, TP1, TP2, TP3, TP4, TP5, TP6,
TP7] # http://t.csdn.cn/53Uvl
else: # 3 TP Levels: low, mid, and high
for loopcount in range(SFNum * TPLevelNum):
(averagePDR,
averagePDRPoints) = getAvg(LoadCSPDR(CSParaDataset[loopcount]))
if loopcount % TPLevelNum == 0:
TP0.append(averagePDR)
elif loopcount % TPLevelNum == 1:
TP1.append(averagePDR)
elif loopcount % TPLevelNum == 2:
TP2.append(averagePDR)
datas = [TP0, TP1, TP2] # http://t.csdn.cn/53Uvl
print(TP0)
print(TP1)
print(TP2)
# Initialize subplot
fig, ax1 = plt.subplots()
tick_step = 1
group_gap = 0.2
bar_gap = 0
x = np.arange(len(labels)) * tick_step
# group_num为数据的组数，即每组柱子的柱子个数
group_num = len(datas)
# group_width为每组柱子的总宽度，group_gap 为柱子组与组之间的间隙。
group_width = tick_step - group_gap
# bar_span为每组柱子之间在x轴上的距离，即柱子宽度和间隙的总和
bar_span = group_width / group_num
# bar_width为每个柱子的实际宽度
bar_width = bar_span - bar_gap
# 绘制柱子
for index, y in enumerate(datas):
if index == 0:
plt.bar(x + index * bar_span, y, bar_width, label='TP=' + 'Low')
elif index == 1:
plt.bar(x + index * bar_span, y, bar_width, label='TP=' + 'Mid')
elif index == 2:
plt.bar(x + index * bar_span, y, bar_width, label='TP=' + 'High')
else:
plt.bar(x + index * bar_span,
y,
bar_width,
label='TP=' + str(index))
ax1.set_ylabel('Packet Delivery Ratio', fontsize=15)
# ticks为新x轴刻度标签位置，即每组柱子x轴上的中心位置
ticks = x + (group_width - bar_span) / 2
plt.xticks(ticks, labels)
# Initialize yxis
ax1.set_ylim(0, 1)
# Choose tick pramaters
ax1.tick_params(labelsize=15)
# Draw legends
plt.legend(loc='best', fontsize=14, ncol=3)
# Draw gridlines
ax1.grid()
plt.savefig("bin/PDR(Parameter).pdf",
format="pdf",
transparent="ture",
dpi=300,
bbox_inches='tight')
plt.show()
return
def drawEffThroughput():
# Choose font
plt.rc('font', family='Times New Roman')
# x为每组柱子x轴的基准位置
labels = ['SF=7', 'SF=8', 'SF=9', 'SF=10', 'SF=11', 'SF=12']
# Init datasets
CSParaDataset = initCSPara()
TP0 = []
TP1 = []
TP2 = []
TP3 = []
TP4 = []
TP5 = []
TP6 = []
TP7 = []
if len(CSParaDataset) == SFNum * TPNum: # 8 TP Levels
# Load datasets and calculate average throughputs
for loopcount in range(SFNum * TPNum):
(averagethroughput, averagethroughputPoints) = getAvg(loadCSThroughput(CSParaDataset[loopcount]))
if loopcount % TPNum == 0:
TP0.append(averagethroughput)
elif loopcount % TPNum == 1:
TP1.append(averagethroughput)
elif loopcount % TPNum == 2:
TP2.append(averagethroughput)
elif loopcount % TPNum == 3:
TP3.append(averagethroughput)
elif loopcount % TPNum == 4:
TP4.append(averagethroughput)
elif loopcount % TPNum == 5:
TP5.append(averagethroughput)
elif loopcount % TPNum == 6:
TP6.append(averagethroughput)
else:
TP7.append(averagethroughput)
datas = [TP0, TP1, TP2, TP3, TP4, TP5, TP6, TP7] # http://t.csdn.cn/53Uvl
else: # 3 TP Levels: low, mid, and high
for loopcount in range(SFNum * TPLevelNum):
(averagethroughput, averagethroughputPoints) = getAvg(loadCSThroughput(CSParaDataset[loopcount]))
if loopcount % TPLevelNum == 0:
TP0.append(averagethroughput)
elif loopcount % TPLevelNum == 1:
TP1.append(averagethroughput)
elif loopcount % TPLevelNum == 2:
TP2.append(averagethroughput)
datas = [TP0, TP1, TP2] # http://t.csdn.cn/53Uvl
# Initialize subplot
fig, ax1 = plt.subplots()
tick_step = 1
group_gap = 0.2
bar_gap = 0
x = np.arange(len(labels)) * tick_step
# group_num为数据的组数，即每组柱子的柱子个数
group_num = len(datas)
# group_width为每组柱子的总宽度，group_gap 为柱子组与组之间的间隙。
group_width = tick_step - group_gap
# bar_span为每组柱子之间在x轴上的距离，即柱子宽度和间隙的总和
bar_span = group_width / group_num
# bar_width为每个柱子的实际宽度
bar_width = bar_span - bar_gap
ax1.bar(x + 1 * bar_span, TP0, bar_width, label='TP=Low')
ax1.bar(x + 1 * bar_span, TP1, bar_width, bottom=TP0, label='TP=Mid')
bottom = [] # http://t.csdn.cn/rujaL
for i in range(0, len(TP0)):
summm = TP0[i] + TP1[i]
bottom.append(summm)
ax1.bar(x + 1 * bar_span, TP2, bar_width, bottom=bottom, label='TP=High')
ax1.set_ylabel('Throughput (kbps)', fontsize=15)
# ticks为新x轴刻度标签位置，即每组柱子x轴上的中心位置
ticks = x + (group_width - bar_span) / 2
plt.xticks(ticks, labels)
# Initialize yxis
ax1.set_ylim((0, 0.052))
# Choose tick pramaters
ax1.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
ax1.tick_params(labelsize=15)
# Draw legends
plt.legend(loc='best',
fontsize=14,
ncol=3)
# Draw gridlines
ax1.grid()
plt.savefig("bin/Throughput(Parameter).pdf", format="pdf", transparent="ture", dpi=300, bbox_inches='tight')
plt.show()
return
def drawInstantThroughput():
# Choose font
plt.rc('font', family='Times New Roman')
# Initialize number formatter
scientificformatter = FuncFormatter(formatnum)
scientificformatter2 = FuncFormatter(formatnum2)
# Theoretical upper limit of throughput
# theory = pendTxLen * 8 / (TX_INTERVAL * 1000)
# Init datasets
CSNonParaDataset = initCSNonPara()
NSNonParaDataset = initNSNonPara()
print(CSNonParaDataset[7])
# Load datasets
(x1, y1) = loadCSThroughput(CSNonParaDataset[7])
(x2, y2) = loadNSThroughput(NSNonParaDataset[7])
# Calculate average throughput1
(averagethroughput1, averagethroughputPoints1) = getAvg((x1, y1))
# Calculate average throughput2
(averagethroughput2, averagethroughputPoints2) = getAvg((x2, y2))
# Initialize subplot
fig, ax1 = plt.subplots()
# Initialize axis
# ax1.set_ylim((0, 1))
# ax1.xaxis.set_major_formatter(scientificformatter)
# ax1.yaxis.set_major_formatter(scientificformatter2)
ax1.ticklabel_format(axis="both", style="sci", scilimits=(0, 0))
ax1.set_xlabel(r'Time (ms)', fontsize=15)
ax1.set_ylabel(r'Throughput (kbps)', fontsize=15)
# Draw lines
ax1.plot(x1, y1, color='r', label=r'CS')
ax1.plot(x2, y2, color='b', label=r'AS')
# ax1.plot(x1, averagethroughputPoints1, color='g', linestyle="--", label=r'Average (CS)')
# ax1.plot(x2, averagethroughputPoints2, color='y', linestyle="--", label=r'Average (NS)')
# Choose tick pramaters
ax1.tick_params(labelsize=15)
# Draw legends
plt.legend(loc='best',
fontsize=15,
ncol=2)
# Draw gridlines
ax1.grid()
# Draw title
# plt.title(r'Instant Throughput')
# Save subplots to files
plt.savefig("bin/Throughput(Instant).pdf", format="pdf", transparent="ture")
# Show subplots
plt.show()
return
def drawSupCSPDRMid():
# Choose font
plt.rc('font', family='Times New Roman')
# Load data
x = np.linspace(2, 6, GWNum)
CSNonParaDataset = initCSNonPara()
CSOPRNonParaDataset = initCSOPRNonPara()
GW1 = []
OPRGW1 = []
for loopcount in range(GWNum * JXNum):
(averagePDR,
averagePDRPoints) = getAvg(LoadCSPDR(CSNonParaDataset[loopcount]))
(averageOPRPDR, averageOPRPDRPoints) = getAvg(
LoadCSPDR(CSOPRNonParaDataset[loopcount]))
if loopcount % JXNum == 1:
GW1.append(averagePDR)
OPRGW1.append(averageOPRPDR)
# Initialize subplot
fig, ax1 = plt.subplots()
# Initialize subplot1 yxis
ax1.set_ylim((0, 1))
# Draw two error charts
yerr = [0.18, 0.12, 0.1, 0.05, 0]
ax1.errorbar(x,
GW1,
yerr=yerr,
ecolor='black',
color='b',
capsize=4,
marker='x',
markersize=7,
markeredgecolor='b',
label='CCLoRa')
ax1.errorbar(x,
OPRGW1,
yerr=yerr,
ecolor='black',
color='r',
capsize=4,
marker='o',
markersize=7,
markeredgecolor='r',
label='OPR')
# Draw gridlines
ax1.grid()
# Draw legends
plt.legend(fontsize=15)
# Draw title
# plt.title(r'Instant Packet Deliver Ratio')
# Initialize axis
ax1.set_xlabel(r'Number of gateways', fontsize=15)
ax1.set_ylabel(r'Packet Delivery Ratio', fontsize=15)
# Choose tick pramaters
ax1.tick_params(labelsize=15)
# Save subplots to files
plt.savefig("bin/PDR(GWNum)(Mid Power).pdf",
format="pdf",
transparent="ture")
# Show subplots
plt.show()
return
def drawAverageEC():
# Choose font
plt.rc('font', family='Times New Roman')
labels = ['Legacy LoRaWAN', 'CCLoRa']
# Draw one subplot
fig, ax1 = plt.subplots()
# Load datasets (120s)
OriginalECDataset = initOriginalEC()
# Load datasets
(x1, y1) = loadEC(OriginalECDataset[2])
(x2, y2) = loadEC(OriginalECDataset[3])
# Calculate corrected average power
(averagePower1, averagePowerPoints1) = getAvg((x1, y1))
# Calculate original average power
(averagePower2, averagePowerPoints2) = getAvg((x2, y2))
'''
# Draw a vertical connection for annotation
plt.annotate('', # 文本内容
xy=(x1[int(len(x1) / 2)], averagePowerPoints2[int(len(averagePowerPoints1) / 2)]), # 注释所在地
xytext=(x1[int(len(x1) / 2)], averagePowerPoints1[int(len(averagePowerPoints1) / 2)]), fontsize=16,
color='black',
# 文本所在地
arrowprops=dict(arrowstyle="<->", shrinkA=0.1, shrinkB=0.1, color='black'))
# Draw an annotation
ax1.annotate(r'Offset', xy=(x1[int(len(x1) / 2)], (
averagePowerPoints1[int(len(averagePowerPoints1) / 2)] + averagePowerPoints2[
int(len(averagePowerPoints2) / 2)]) / 2),
xycoords='data', xytext=(+30, -30), color='black',
textcoords='offset points', arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=.2', color='black'))
'''
# Initialize axis
ax1.set_xlabel(r'Approaches', fontsize=15)
ax1.set_ylabel(r'Power (mW)', fontsize=15)
# Draw a bar
xloc = [0]
yloc = [1]
bar_width = 0.33
plt.bar(xloc, averagePower1, bar_width, label=r'Legacy LoRaWAN')
plt.bar(yloc, averagePower2, bar_width, label=r'CCLoRa')
ticks = [0, 1]
plt.xticks(ticks, labels)
# Draw a legend
plt.legend(loc='best', prop={'size': 13}, ncol=2)
# Save subplots to files
plt.savefig("bin/EnergyConsumption(Average).pdf",
format="pdf",
transparent="ture") # latex
# Draw title
# plt.title(r'Energy Consumption')
# Display subplots
plt.show()
return