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add boxen & ecdf plots 

Signed-off-by: Arunima Chaudhuri <arunimachaudhuri2020@gmail.com>
This commit is contained in:
Arunima Chaudhuri 2024-02-28 22:14:55 +05:30
parent 7c33fec8da
commit 575c55480f
3 changed files with 281 additions and 28 deletions
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15
DAS/visualizer.py
View File

@ -241,21 +241,6 @@ class Visualizer:
plt.savefig(filename)
plt.clf()
def plotHist(self, bandwidth):
"""Plot Bandwidth Frequency Histogram"""
plt.hist(bandwidth, bins=5)
plt.xlabel('Bandwidth')
plt.ylabel('Frequency')
plt.title('Bandwidth Histogram')
"""Create the directory if it doesn't exist already"""
histogramFolder = self.folderPath + '/histogram'
if not os.path.exists(histogramFolder):
os.makedirs(histogramFolder)
filename = os.path.join(histogramFolder, 'histogram.png')
plt.savefig(filename)
plt.clf()
def plotCandleStick(self, TX_prod, TX_avg, TX_max):
#x-axis corresponding to steps
steps = range(len(TX_prod))

268
DAS/visualizor.py
View File

@ -1,6 +1,7 @@
#!/bin/python3
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os
@ -104,9 +105,276 @@ class Visualizor:
self.plotRecvData(result, plotPath)
self.plotDupData(result, plotPath)
self.plotSamplesRepaired(result, plotPath)
self.plotBoxSamplesRepaired(result, plotPath)
self.plotBoxRowCol(result, plotPath)
self.plotBoxenMessagesRecv(result, plotPath)
self.plotBoxenMessagesSent(result, plotPath)
self.plotBoxenSamplesRecv(result, plotPath)
self.plotBoxenSamplesRepaired(result, plotPath)
self.plotBoxenRowColDist(result, plotPath)
self.plotECDFSamplesRepaired(result, plotPath)
self.plotECDFRowColDist(result, plotPath)
self.plotECDFSamplesReceived(result, plotPath)
self.plotECDFMessagesRecv(result, plotPath)
self.plotECDFMessagesSent(result, plotPath)
if self.config.saveRCdist:
self.plotRowCol(result, plotPath)
def plotECDFMessagesSent(self, result, plotPath):
"""Plots the ECDF of messages sent by all nodes using seaborn's ecdfplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+" \nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "ECDF of Messages Sent by Nodes"
conf["xlabel"] = "Number of Messages Sent"
conf["ylabel"] = "ECDF"
sns.ecdfplot(data=result.msgSentCount)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.xlim(left=0, right=max(result.msgSentCount) * 1.1)
plt.savefig(plotPath + "/ecdf_messagesSent.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/ecdf_messagesSent.png"))
def plotECDFMessagesRecv(self, result, plotPath):
"""Plots the ECDF of messages received by all nodes using seaborn's ecdfplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "ECDF of Messages Received by Nodes"
conf["xlabel"] = "Number of Messages Received"
conf["ylabel"] = "ECDF"
sns.ecdfplot(data=result.msgRecvCount)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.xlim(left=0, right=max(result.msgRecvCount) * 1.1)
plt.savefig(plotPath + "/ecdf_messagesRecv.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/ecdf_messagesRecv.png"))
def plotECDFSamplesReceived(self, result, plotPath):
"""Plots the ECDF of samples received by all nodes using seaborn's ecdfplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "ECDF of Samples Received by Nodes"
conf["xlabel"] = "Number of Samples Received"
conf["ylabel"] = "ECDF"
sns.ecdfplot(data=result.sampleRecvCount)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.xlim(left=0, right=max(result.sampleRecvCount) * 1.1)
plt.savefig(plotPath + "/ecdf_samplesReceived.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/ecdf_samplesReceived.png"))
def plotECDFRowColDist(self, result, plotPath):
"""Plots the ECDF of row col distance by all nodes using seaborn's ecdfplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "ECDF of Row-Col Distance by Nodes"
conf["xlabel"] = "Row-Col Distance"
conf["ylabel"] = "ECDF"
vector1 = result.metrics["rowDist"]
vector2 = result.metrics["columnDist"]
if len(vector1) > len(vector2):
vector2 += [np.nan] * (len(vector1) - len(vector2))
elif len(vector1) < len(vector2):
vector1 += [np.nan] * (len(vector2) - len(vector1))
n1 = int(result.numberNodes * result.class1ratio)
conf["data"] = [vector1, vector2]
sns.ecdfplot(data=conf["data"])
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.xlim(left=0, right=max(max(vector1), max(vector2)) * 1.1)
plt.savefig(plotPath + "/ecdf_rowColDist.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/ecdf_rowColDist.png"))
def plotECDFSamplesRepaired(self, result, plotPath):
"""Plots the ECDF of samples repaired by all nodes using seaborn's ecdfplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "ECDF of Samples Repaired by Nodes"
conf["xlabel"] = "Number of Samples Repaired"
conf["ylabel"] = "ECDF"
sns.ecdfplot(data=result.repairedSampleCount)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.xlim(left=0, right=max(result.repairedSampleCount) * 1.1)
plt.savefig(plotPath + "/ecdf_samplesRepaired.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/ecdf_samplesRepaired.png"))
def plotBoxenSamplesRecv(self, result, plotPath):
"""Boxen Plot of the number of samples received by all nodes"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Number of Samples Received by Nodes"
conf["xlabel"] = "Node Type"
conf["ylabel"] = "Number of Samples Received"
n1 = int(result.numberNodes * result.class1ratio)
data = [result.sampleRecvCount[1: n1], result.sampleRecvCount[n1: ]]
plt.figure(figsize=(8, 6))
sns.boxenplot(data=data, width=0.8)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.tight_layout()
plt.savefig(plotPath + "/boxen_samplesRecv.png")
plt.close()
print("Plot %s created." % (plotPath + "/boxen_samplesRecv.png"))
def plotBoxenSamplesRepaired(self, result, plotPath):
"""Boxen Plot of the number of samples repaired by all nodes"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Number of Samples Repaired by Nodes"
conf["xlabel"] = "Node Type"
conf["ylabel"] = "Number of Samples Repaired"
n1 = int(result.numberNodes * result.class1ratio)
data = [result.repairedSampleCount[1: n1], result.repairedSampleCount[n1: ]]
plt.figure(figsize=(8, 6))
sns.boxenplot(data=data, width=0.8)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.tight_layout()
plt.savefig(plotPath + "/boxen_samplesRepaired.png")
plt.close()
print("Plot %s created." % (plotPath + "/boxen_samplesRepaired.png"))
def plotBoxenRowColDist(self, result, plotPath):
"""Boxen Plot of the Row/Column distribution"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+" \nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Row/Column Distribution"
conf["xlabel"] = "Row/Column Type"
conf["ylabel"] = "Validators Subscribed"
vector1 = result.metrics["rowDist"]
vector2 = result.metrics["columnDist"]
if len(vector1) > len(vector2):
vector2 += [np.nan] * (len(vector1) - len(vector2))
elif len(vector1) < len(vector2):
vector1 += [np.nan] * (len(vector2) - len(vector1))
data = [vector1, vector2]
plt.figure(figsize=(8, 6))
sns.boxenplot(data=data, width=0.8)
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.tight_layout()
plt.savefig(plotPath + "/boxen_rowColDist.png")
plt.close()
print("Plot %s created." % (plotPath + "/boxen_rowColDist.png"))
def plotBoxenMessagesSent(self, result, plotPath):
"""Plots the number of messages sent by all nodes using seaborn's boxenplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+" \nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Number of Messages Sent by Nodes"
conf["xlabel"] = "Node Type"
conf["ylabel"] = "Number of Messages Sent"
n1 = int(result.numberNodes * result.class1ratio)
data = [result.msgSentCount[1: n1], result.msgSentCount[n1: ]]
labels = ["Class 1", "Class 2"]
sns.boxenplot(data=data, palette="Set2", ax=plt.gca())
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.savefig(plotPath + "/boxen_messagesSent.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/boxen_messagesSent.png"))
def plotBoxenMessagesRecv(self, result, plotPath):
"""Plots the number of messages received by all nodes using seaborn's boxenplot"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Number of Messages Received by Nodes"
conf["xlabel"] = "Node Type"
conf["ylabel"] = "Number of Messages Received"
n1 = int(result.numberNodes * result.class1ratio)
data = [result.msgRecvCount[1: n1], result.msgRecvCount[n1: ]]
labels = ["Class 1", "Class 2"]
sns.boxenplot(data=data, palette="Set2", ax=plt.gca())
plt.xlabel(conf["xlabel"])
plt.ylabel(conf["ylabel"])
plt.title(conf["title"])
plt.savefig(plotPath + "/boxen_messagesRecv.png", bbox_inches="tight")
print("Plot %s created." % (plotPath + "/boxen_messagesRecv.png"))
def plotBoxSamplesRepaired(self, result, plotPath):
"""Box Plot of the number of samples repaired by all nodes"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+"\nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Number of Samples Repaired by Nodes"
conf["type"] = "individual_bar"
conf["legLoc"] = 1
conf["desLoc"] = 1
conf["xlabel"] = "Node Type"
conf["ylabel"] = "Number of Samples Repaired"
n1 = int(result.numberNodes * result.class1ratio)
conf["data"] = [result.repairedSampleCount[1: n1], result.repairedSampleCount[n1: ]]
conf["path"] = plotPath + "/box_samplesRepaired.png"
plotBoxData(conf)
print("Plot %s created." % conf["path"])
def plotBoxRowCol(self, result, plotPath):
"""Box Plot of the Row/Column distribution"""
plt.clf()
conf = {}
text = str(result.shape).split("-")
conf["textBox"] = "Row Size: "+text[2]+"\nColumn Size: "+text[6]+"\nNumber of nodes: "+text[10]\
+"\nFailure rate: "+text[14]+"%"+" \nNetwork degree: "+text[32]+"\nMalicious Nodes: "+text[36]+"%"
conf["title"] = "Row/Column Distribution"
conf["xlabel"] = ""
conf["ylabel"] = "Validators Subscribed"
vector1 = result.metrics["rowDist"]
vector2 = result.metrics["columnDist"]
if len(vector1) > len(vector2):
vector2 += [np.nan] * (len(vector1) - len(vector2))
elif len(vector1) < len(vector2):
vector1 += [np.nan] * (len(vector2) - len(vector1))
n1 = int(result.numberNodes * result.class1ratio)
conf["data"] = [vector1, vector2]
conf["path"] = plotPath + "/box_rowColDist.png"
plotBoxData(conf)
print("Plot %s created." % conf["path"])
def plotRestoreRowCount(self, result, plotPath):
"""Plots the restoreRowCount for each node"""
conf = {}

26
smallConf.py
View File

@ -41,33 +41,33 @@ logLevel = logging.INFO
numJobs = -1
# Number of simulation runs with the same parameters for statistical relevance
runs = [1]
runs = range(3)
# Number of validators
numberNodes = [1024]
numberNodes = range(128, 513, 128)
# select failure model between: "random, sequential, MEP, MEP+1, DEP, DEP+1, MREP, MREP-1"
failureModels = ["random"]
# Percentage of block not released by producer
failureRates = [0]
failureRates = range(40, 81, 20)
# Percentage of nodes that are considered malicious
maliciousNodes = [0]
maliciousNodes = range(40,41,20)
# Parameter to determine whether to randomly assign malicious nodes or not
# If True, the malicious nodes will be assigned randomly; if False, a predefined pattern may be used
randomizeMaliciousNodes = True
# Per-topic mesh neighborhood size
netDegrees = [8]
netDegrees = range(8, 9, 2)
# ratio of class1 nodes (see below for parameters per class)
class1ratios = [0.8]
# Number of validators per beacon node
validatorsPerNode1 = [1]
validatorsPerNode2 = [1]
validatorsPerNode1 = [10]
validatorsPerNode2 = [50]
# Set uplink bandwidth in megabits/second
bwUplinksProd = [200]
@ -98,12 +98,12 @@ diagnostics = False
# True to save git diff and git commit
saveGit = False
blockSizeR = [128]
blockSizeC = [64]
blockSizeRK = [64]
blockSizeCK = [64]
chiR = [2]
chiC = [2]
blockSizeR = range(64, 113, 128)
blockSizeC = range(32, 113, 128)
blockSizeRK = range(32, 65, 128)
blockSizeCK = range(32, 65, 128)
chiR = range(2, 3, 2)
chiC = range(2, 3, 2)
def nextShape():
for blckSizeR, blckSizeRK, blckSizeC, blckSizeCK, run, fm, fr, mn, class1ratio, chR, chC, vpn1, vpn2, nn, netDegree, bwUplinkProd, bwUplink1, bwUplink2 in itertools.product(