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das-research
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minor plot fixes

This commit is contained in:
HajarZaiz 2023-02-26 18:36:02 +01:00
parent ceb8357034
commit 39c454d3f4
3 changed files with 16 additions and 145 deletions
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3
.gitignore vendored
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@ -1,4 +1,5 @@
*.swp
*.pyc
results/*
!results/plots.py
!results/plots.py
Frontend/

20
DAS/visualizer.py
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@ -13,9 +13,10 @@ class Visualizer:
self.execID = execID
self.folderPath = "results/"+self.execID
self.parameters = ['run', 'blockSize', 'failureRate', 'numberValidators', 'netDegree', 'chi']
self.minimumDataPoints = 2
#Store data with a unique key for each params combination
def plottingData(self):
#Store data with a unique key for each params combination
data = {}
#Loop over the xml files in the folder
for filename in os.listdir(self.folderPath):
@ -65,8 +66,8 @@ class Visualizer:
print("Getting data from the folder...")
return data
#Get the keys for all data with the same x and y labels
def similarKeys(self, data):
#Get the keys for all data with the same x and y labels
filteredKeys = {}
for key1, value1 in data.items():
subKeys1 = list(value1.keys())
@ -81,14 +82,19 @@ class Visualizer:
print("Getting filtered keys from data...")
return filteredKeys
#Title formatting for the figures
def formatLabel(self, label):
#Label formatting for the figures
result = ''.join([f" {char}" if char.isupper() else char for char in label])
return result.title()
def formatTitle(self, key):
#Title formatting for the figures
name = ''.join([f" {char}" if char.isupper() else char for char in key.split('_')[0]])
number = key.split('_')[1]
return f"{name.title()}: {number} "
#Plot and store the 2D heatmaps in subfolders
def plotHeatmaps(self):
#Plot and store the 2D heatmaps in subfolders
data = self.plottingData()
filteredKeys = self.similarKeys(data)
print("Plotting heatmaps...")
@ -103,12 +109,14 @@ class Visualizer:
for key in keys:
xlabels = np.sort(np.unique(data[key][labels[0]]))
ylabels = np.sort(np.unique(data[key][labels[1]]))
if len(xlabels) < self.minimumDataPoints or len(ylabels) < self.minimumDataPoints:
continue
hist, xedges, yedges = np.histogram2d(data[key][labels[0]], data[key][labels[1]], bins=(len(xlabels), len(ylabels)), weights=data[key]['ttas'])
hist = hist.T
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(hist, xticklabels=xlabels, yticklabels=ylabels, cmap='Purples', cbar_kws={'label': 'Time to block availability'}, linecolor='black', linewidths=0.3, annot=True, fmt=".2f", ax=ax)
plt.xlabel(labels[0])
plt.ylabel(labels[1])
plt.xlabel(self.formatLabel(labels[0]))
plt.ylabel(self.formatLabel(labels[1]))
filename = ""
title = ""
paramValueCnt = 0

138
results/plots.py
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@ -1,138 +0,0 @@
import os, sys
import time
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from itertools import combinations
parameters = ['run', 'blockSize', 'failureRate', 'numberValidators', 'netDegree', 'chi']
#Title formatting for the figures
def formatTitle(key):
name = ''.join([f" {char}" if char.isupper() else char for char in key.split('_')[0]])
number = key.split('_')[1]
return f"{name.title()}: {number} "
def getLatestDirectory():
resultsFolder = os.getcwd()
#Get all folders and store their time info and sort
directories = [d for d in os.listdir(resultsFolder) if os.path.isdir(os.path.join(resultsFolder, d))]
directoriesTime = [(d, os.path.getctime(os.path.join(resultsFolder, d))) for d in directories]
directoriesTime.sort(key=lambda x: x[1], reverse=True)
#Get the path of the latest created folder
latestDirectory = directoriesTime[0][0]
folderPath = os.path.join(resultsFolder, latestDirectory)
return folderPath
def plottingData(folderPath):
#Store data with a unique key for each params combination
data = {}
plotInfo = {}
#Loop over the xml files in the folder
for filename in os.listdir(folderPath):
#Loop over the xmls and store the data in variables
if filename.endswith('.xml'):
tree = ET.parse(os.path.join(folderPath, filename))
root = tree.getroot()
run = int(root.find('run').text)
blockSize = int(root.find('blockSize').text)
failureRate = int(root.find('failureRate').text)
numberValidators = int(root.find('numberValidators').text)
netDegree = int(root.find('netDegree').text)
chi = int(root.find('chi').text)
tta = int(root.find('tta').text)
# Loop over all possible combinations of length 4 of the parameters
for combination in combinations(parameters, 4):
# Get the indices and values of the parameters in the combination
indices = [parameters.index(element) for element in combination]
selectedValues = [run, blockSize, failureRate, numberValidators, netDegree, chi]
values = [selectedValues[index] for index in indices]
names = [parameters[i] for i in indices]
keyComponents = [f"{name}_{value}" for name, value in zip(names, values)]
key = tuple(keyComponents[:4])
#Get the names of the other 2 parameters that are not included in the key
otherParams = [parameters[i] for i in range(6) if i not in indices]
#Append the values of the other 2 parameters and the ttas to the lists for the key
otherIndices = [i for i in range(len(parameters)) if i not in indices]
#Initialize the dictionary for the key if it doesn't exist yet
if key not in data:
data[key] = {}
#Initialize lists for the other 2 parameters and the ttas with the key
data[key][otherParams[0]] = []
data[key][otherParams[1]] = []
data[key]['ttas'] = []
if otherParams[0] in data[key]:
data[key][otherParams[0]].append(selectedValues[otherIndices[0]])
else:
data[key][otherParams[0]] = [selectedValues[otherIndices[0]]]
if otherParams[1] in data[key]:
data[key][otherParams[1]].append(selectedValues[otherIndices[1]])
else:
data[key][otherParams[1]] = [selectedValues[otherIndices[1]]]
data[key]['ttas'].append(tta)
return data
def similarKeys(data):
#Get the keys for all data with the same x and y labels
filteredKeys = {}
for key1, value1 in data.items():
subKeys1 = list(value1.keys())
filteredKeys[(subKeys1[0], subKeys1[1])] = [key1]
for key2, value2 in data.items():
subKeys2 = list(value2.keys())
if key1 != key2 and subKeys1[0] == subKeys2[0] and subKeys1[1] == subKeys2[1]:
try:
filteredKeys[(subKeys1[0], subKeys1[1])].append(key2)
except KeyError:
filteredKeys[(subKeys1[0], subKeys1[1])] = [key2]
return filteredKeys
def plotHeatmaps(folderPath, filteredKeys, data):
#Store the 2D heatmaps in a folder
heatmapsFolder = folderPath+'/heatmaps'
if not os.path.exists(heatmapsFolder):
os.makedirs(heatmapsFolder)
for labels, keys in filteredKeys.items():
for key in keys:
xlabels = np.sort(np.unique(data[key][labels[0]]))
ylabels = np.sort(np.unique(data[key][labels[1]]))
hist, xedges, yedges = np.histogram2d(data[key][labels[0]], data[key][labels[1]], bins=(len(xlabels), len(ylabels)), weights=data[key]['ttas'])
hist = hist.T
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(hist, xticklabels=xlabels, yticklabels=ylabels, cmap='Purples', cbar_kws={'label': 'Time to block availability'}, linecolor='black', linewidths=0.3, annot=True, fmt=".2f", ax=ax)
plt.xlabel(labels[0])
plt.ylabel(labels[1])
filename = ""
title = ""
paramValueCnt = 0
for param in parameters:
if param != labels[0] and param != labels[1]:
filename += f"{key[paramValueCnt]}"
title += formatTitle(key[paramValueCnt])
paramValueCnt += 1
title_obj = plt.title(title)
font_size = 16 * fig.get_size_inches()[0] / 10
title_obj.set_fontsize(font_size)
filename = filename + ".png"
targetFolder = os.path.join(heatmapsFolder, f"{labels[0]}Vs{labels[1]}")
if not os.path.exists(targetFolder):
os.makedirs(targetFolder)
plt.savefig(os.path.join(targetFolder, filename))
plt.close()
plt.clf()
def generateHeatmaps(folderPath):
#folderPath = getLatestDirectory()
data = plottingData(folderPath)
filteredKeys = similarKeys(data)
plotHeatmaps(folderPath, filteredKeys, data)
generateHeatmaps(sys.argv[1])