Merge branch 'vis' of https://github.com/status-im/das-research into vis

.gitignore

@@ -1,2 +1,4 @@
 *.swp
 *.pyc
+results/*
+!results/plots.py

DAS/__init__.py

@@ -1,3 +1,4 @@
 from DAS.simulator import *
 from DAS.configuration import *
 from DAS.shape import *
+from DAS.visualizer import *

DAS/configuration.py

@@ -33,6 +33,7 @@ class Configuration:
 
         self.numberRuns = int(config.get("Advanced", "numberRuns"))
         self.deterministic = config.get("Advanced", "deterministic")
+        self.dumpXML = config.get("Advanced", "dumpXML")
 
         if self.nvStop < (self.blockSizeStart*4):
             print("ERROR: The number of validators cannot be lower than the block size * 4")

DAS/results.py

@@ -1,17 +1,50 @@
 #!/bin/python3
 
+import os
+from xml.dom import minidom
+from dicttoxml import dicttoxml
 
 class Result:
 
-    config = []
+    shape = []
     missingVector = []
     blockAvailable = -1
+    tta = -1
 
-    def __init__(self, config):
+    def __init__(self, shape):
-        self.config = config
+        self.shape = shape
         self.blockAvailable = -1
+        self.tta = -1
         self.missingVector = []
 
-
+    def populate(self, shape, missingVector):
-    def addMissing(self, missingVector):
+        self.shape = shape
         self.missingVector = missingVector
+        missingSamples = missingVector[-1]
+        if missingSamples == 0:
+            self.blockAvailable = 1
+            self.tta = len(missingVector)
+        else:
+            self.blockAvailable = 0
+            self.tta = -1
+
+    def dump(self, execID):
+        if not os.path.exists("results"):
+            os.makedirs("results")
+        if not os.path.exists("results/"+execID):
+            os.makedirs("results/"+execID)
+        resd1 = self.shape.__dict__
+        resd2 = self.__dict__.copy()
+        resd2.pop("shape")
+        resd1.update(resd2)
+        resXml = dicttoxml(resd1)
+        xmlstr = minidom.parseString(resXml)
+        xmlPretty = xmlstr.toprettyxml()
+        filePath = "results/"+execID+"/nbv-"+str(self.shape.numberValidators)+\
+                "-bs-"+str(self.shape.blockSize)+\
+                "-nd-"+str(self.shape.netDegree)+\
+                "-fr-"+str(self.shape.failureRate)+\
+                "-chi-"+str(self.shape.chi)+\
+                "-r-"+str(self.shape.run)+".xml"
+        with open(filePath, "w") as f:
+            f.write(xmlPretty)

DAS/shape.py

@@ -1,16 +1,18 @@
 #!/bin/python3
 
 class Shape:
+    run = 0
     numberValidators = 0
-    failureRate = 0
     blockSize = 0
+    failureRate = 0
     netDegree = 0
     chi = 0
 
-    def __init__(self, blockSize, numberValidators, failureRate, chi, netDegree):
+    def __init__(self, blockSize, numberValidators, failureRate, chi, netDegree, run):
+        self.run = run
         self.numberValidators = numberValidators
-        self.failureRate = failureRate
         self.blockSize = blockSize
+        self.failureRate = failureRate
         self.netDegree = netDegree
         self.chi = chi
 

DAS/simulator.py

@@ -42,7 +42,6 @@ class Simulator:
             self.validators.append(val)
 
     def initNetwork(self):
-        self.shape.netDegree = 6
         rowChannels = [[] for i in range(self.shape.blockSize)]
         columnChannels = [[] for i in range(self.shape.blockSize)]
         for v in self.validators:
@@ -87,6 +86,7 @@ class Simulator:
 
     def resetShape(self, shape):
         self.shape = shape
+        self.result = Result(self.shape)
         for val in self.validators:
             val.shape.failureRate = shape.failureRate
             val.shape.chi = shape.chi
@@ -120,19 +120,16 @@ class Simulator:
             missingRate = missingSamples*100/expected
             self.logger.debug("step %d, missing %d of %d (%0.02f %%)" % (steps, missingSamples, expected, missingRate), extra=self.format)
             if missingSamples == oldMissingSamples:
+                #self.logger.info("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
+                missingVector.append(missingSamples)
                 break
             elif missingSamples == 0:
+                #self.logger.info("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
+                missingVector.append(missingSamples)
                 break
             else:
                 steps += 1
 
-        self.result.addMissing(missingVector)
+        self.result.populate(self.shape, missingVector)
-        if missingSamples == 0:
+        return self.result
-            self.result.blockAvailable = 1
-            self.logger.debug("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
-            return self.result
-        else:
-            self.result.blockAvailable = 0
-            self.logger.debug("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
-            return self.result
 

DAS/visualizer.py

@@ -0,0 +1,130 @@
+#!/bin/python3
+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
+
+class Visualizer:
+
+    def __init__(self, execID):
+        self.execID = execID
+        self.folderPath = "results/"+self.execID
+        self.parameters = ['run', 'blockSize', 'failureRate', 'numberValidators', 'netDegree', 'chi']
+
+    #Store data with a unique key for each params combination
+    def plottingData(self):
+        data = {}
+        #Loop over the xml files in the folder
+        for filename in os.listdir(self.folderPath):
+            #Loop over the xmls and store the data in variables
+            if filename.endswith('.xml'):
+                tree = ET.parse(os.path.join(self.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(self.parameters, 4):
+                    # Get the indices and values of the parameters in the combination
+                    indices = [self.parameters.index(element) for element in combination]
+                    selectedValues = [run, blockSize, failureRate, numberValidators, netDegree, chi]
+                    values = [selectedValues[index] for index in indices]
+                    names = [self.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 = [self.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(self.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)
+        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):
+        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]
+        print("Getting filtered keys from data...")
+        return filteredKeys
+
+    #Title formatting for the figures
+    def formatTitle(self, 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} "
+
+    #Plot and store the 2D heatmaps in subfolders
+    def plotHeatmaps(self):
+        data = self.plottingData()
+        filteredKeys = self.similarKeys(data)
+        print("Plotting heatmaps...")
+        
+        #Create the directory if it doesn't exist already
+        heatmapsFolder = self.folderPath + '/heatmaps'
+        if not os.path.exists(heatmapsFolder):
+            os.makedirs(heatmapsFolder)
+
+        #Plot
+        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 self.parameters:
+                    if param != labels[0] and param != labels[1]:
+                        filename += f"{key[paramValueCnt]}"
+                        formattedTitle = self.formatTitle(key[paramValueCnt])
+                        title += formattedTitle
+                        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()

Frontend/css/style.css

@@ -0,0 +1,85 @@
+*{
+    text-decoration: none;
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+    list-style: none;
+}
+
+body{
+    display: flex;
+}
+
+.navbar{
+    width: 7vw;
+    height: 100vh;
+    padding-left: 5px;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    background-color: #7f5a83;
+    background-image: linear-gradient(280deg, #3f305e 0%, #00060a 74%);
+}
+
+#logo{
+    margin-top: 25px;
+    width: 3vw;
+    opacity: 0.9;
+}
+
+.navbar-bar{
+    color: aliceblue;
+    margin-top: 15vh;
+}
+
+.navbar-bar li{
+    margin-top: 10px;
+    height: 10vh;
+    width: 7vw;
+    text-align: center;
+    line-height: 10vh;
+}
+
+.navbar li:hover, .navbar li:focus, .navbar li:active{
+    background: #eee5fdea;
+    border-top-left-radius: 50%;
+    border-bottom-left-radius: 50%;
+    cursor: pointer;
+    transition: all 0.5s ease-in-out;
+}
+
+.navbar li:hover .fa-solid{
+    color: #160f25;
+}
+
+.fa-solid{
+    color: #b9aecf;
+    opacity: 0.7;
+    cursor: pointer;
+    transition: all 0.5s ease-in-out;
+}
+
+.fa-solid:hover{
+    opacity: 1;
+    color: #160f25;
+}
+
+.content{
+    width: 93vw;
+    height: 100vh;
+    background-color: #eee5fdea;
+}
+
+.p1, .p2, .p3, .p4{
+    width: 93vw;
+    height: 100vh;
+}
+
+.p2, .p3, .p4{
+  display: none;   
+}
+
+.plot1{
+    margin: auto;
+    display: block;
+}

Frontend/index.html

@@ -0,0 +1,32 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <link rel="stylesheet" href="css/style.css">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.2.0/css/all.min.css" integrity="sha512-xh6O/CkQoPOWDdYTDqeRdPCVd1SpvCA9XXcUnZS2FmJNp1coAFzvtCN9BmamE+4aHK8yyUHUSCcJHgXloTyT2A==" crossorigin="anonymous" referrerpolicy="no-referrer" />    <title>DAS Dashboard</title>
+</head>
+<body>
+    <nav class="navbar">
+        <div class="company-logo">
+            <img src="Imgs/logo.png" id="logo">
+        </div>
+        <ul class="navbar-bar">
+            <li class = "sec1"><i class="fa-solid fa-chart-line fa-xl"></i></li>
+            <li class = sec2><i class="fa-solid fa-layer-group fa-xl"></i></li>
+            <li class = "sec3"><i class="fa-solid fa-chart-simple fa-xl"></i></li>
+            <li class = "sec4"><i class="fa-solid fa-table-list fa-xl"></i></li>
+        </ul>
+    </nav>
+    <div class="content">
+        <div class="p1">
+            <img class = "plot1" src="Plots/plot1.png">
+        </div>
+        <div class="p2">Text2</div>
+        <div class="p3">Text3</div>
+        <div class="p4">Text4</div>
+    </div>
+    <script src="script.js"></script>
+</body>
+</html>

Frontend/script.js

@@ -0,0 +1,33 @@
+let section1 = document.querySelector('.sec1');
+let section2 = document.querySelector('.sec2');
+let section3 = document.querySelector('.sec3');
+let section4 = document.querySelector('.sec4');
+let sections = [section1, section2, section3, section4];
+
+let icon1 = document.querySelector(".sec1 i");
+let icon2 = document.querySelector(".sec2 i");
+let icon3 = document.querySelector(".sec3 i");
+let icon4 = document.querySelector(".sec4 i");
+let icons = [icon1, icon2, icon3, icon4];
+
+let par1 = document.querySelector(".p1");
+let par2 = document.querySelector(".p2");
+let par3 = document.querySelector(".p3");
+let par4 = document.querySelector(".p4");
+let paragraphs = [par1, par2, par3, par4];
+
+section1.style.cssText = "background: #eee5fdea; border-top-left-radius: 50%; border-bottom-left-radius: 50%; cursor: pointer; transition: all 0.5s ease-in-out;"
+icon1.style.cssText = "opacity: 1; color: #160f25;"
+
+sections.forEach(section =>{
+    section.addEventListener("click", function(){
+        sections.forEach(s =>{
+            s.style.cssText = "background: none; border-top-left-radius: 0%; border-bottom-left-radius: 0%;"
+            icons[sections.indexOf(s)].style.cssText = "color: #b9aecf; opacity: 0.7;"
+            paragraphs[sections.indexOf(s)].style.display = "none";
+        });
+        section.style.cssText = "background: #eee5fdea; border-top-left-radius: 50%; border-bottom-left-radius: 50%; cursor: pointer; transition: all 0.5s ease-in-out;"
+        icons[sections.indexOf(section)].style.cssText = "opacity: 1; color: #160f25;"
+        paragraphs[sections.indexOf(section)].style.display = "block";
+    });
+});

config.das

@@ -25,3 +25,4 @@ chiStep  = 2
 
 deterministic = 0
 numberRuns = 2
+dumpXML = 1

results/plots.py

@@ -0,0 +1,138 @@
+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])

study.py

@@ -1,6 +1,6 @@
 #! /bin/python3
 
-import time, sys
+import time, sys, random, copy
 from DAS import *
 
 
@@ -10,36 +10,51 @@ def study():
         exit(1)
 
     config = Configuration(sys.argv[1])
-    sim = Simulator(config)
+    shape = Shape(0, 0, 0, 0, 0, 0)
+    sim = Simulator(shape)
     sim.initLogger()
     results = []
     simCnt = 0
 
+    now = datetime.now()
+    execID = now.strftime("%Y-%m-%d_%H-%M-%S_")+str(random.randint(100,999))
+
     sim.logger.info("Starting simulations:", extra=sim.format)
     start = time.time()
 
     for run in range(config.numberRuns):
-        for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
+        for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
-            for chi in range(config.chiStart, config.chiStop+1, config.chiStep):
+            for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
-                for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
+                for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
-                    for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
+                    for netDegree in range(config.netDegreeStart, config.netDegreeStop+1, config.netDegreeStep):
-                        for netDegree in range(config.netDegreeStart, config.netDegreeStop+1, config.netDegreeStep):
+                        for chi in range(config.chiStart, config.chiStop+1, config.chiStep):
 
                             if not config.deterministic:
                                 random.seed(datetime.now())
 
-                            shape = Shape(blockSize, nv, fr, chi, netDegree)
+                            # Network Degree has to be an even number
-                            sim.resetShape(shape)
+                            if netDegree % 2 == 0:
-                            sim.initValidators()
+                                shape = Shape(blockSize, nv, fr, chi, netDegree, run)
-                            sim.initNetwork()
+                                sim.resetShape(shape)
-                            result = sim.run()
+                                sim.initValidators()
-                            sim.logger.info("Run %d, FR: %d %%, Chi: %d, BlockSize: %d, Nb.Val: %d, netDegree: %d ... Block Available: %d" % (run, fr, chi, blockSize, nv, netDegree, result.blockAvailable), extra=sim.format)
+                                sim.initNetwork()
-                            results.append(result)
+                                result = sim.run()
-                            simCnt += 1
+                                sim.logger.info("Shape: %s ... Block Available: %d" % (str(sim.shape.__dict__), result.blockAvailable), extra=sim.format)
+                                results.append(copy.deepcopy(result))
+                                simCnt += 1
 
     end = time.time()
     sim.logger.info("A total of %d simulations ran in %d seconds" % (simCnt, end-start), extra=sim.format)
 
+    if config.dumpXML:
+        for res in results:
+            res.dump(execID)
+        sim.logger.info("Results dumped into results/%s/" % (execID), extra=sim.format)
+
+    visualization = 1
+    if visualization:
+        vis = Visualizer(execID)
+        vis.plotHeatmaps()
 
 
 study()