Merge remote-tracking branch 'origin/vis' into develop
# Conflicts: # .gitignore # DAS/simulator.py # study.py
This commit is contained in:
Csaba Kiraly
commit
35d1790429
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@ -3,3 +3,5 @@
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results/*
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myenv
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doc/_build
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!results/plots.py
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Frontend/
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@ -1,3 +1,4 @@
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from DAS.simulator import *
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from DAS.configuration import *
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from DAS.shape import *
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from DAS.visualizer import *
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@ -42,7 +42,6 @@ class Simulator:
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def initNetwork(self):
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"""It initializes the simulated network."""
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self.shape.netDegree = 6
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rowChannels = [[] for i in range(self.shape.blockSize)]
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columnChannels = [[] for i in range(self.shape.blockSize)]
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for v in self.validators:
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@ -0,0 +1,138 @@
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#!/bin/python3
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import os, sys
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import time
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import xml.etree.ElementTree as ET
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn as sns
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from itertools import combinations
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class Visualizer:
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def __init__(self, execID):
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self.execID = execID
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self.folderPath = "results/"+self.execID
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self.parameters = ['run', 'blockSize', 'failureRate', 'numberValidators', 'netDegree', 'chi']
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self.minimumDataPoints = 2
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def plottingData(self):
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#Store data with a unique key for each params combination
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data = {}
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#Loop over the xml files in the folder
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for filename in os.listdir(self.folderPath):
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#Loop over the xmls and store the data in variables
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if filename.endswith('.xml'):
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tree = ET.parse(os.path.join(self.folderPath, filename))
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root = tree.getroot()
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run = int(root.find('run').text)
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blockSize = int(root.find('blockSize').text)
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failureRate = int(root.find('failureRate').text)
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numberValidators = int(root.find('numberValidators').text)
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netDegree = int(root.find('netDegree').text)
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chi = int(root.find('chi').text)
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tta = int(root.find('tta').text)
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# Loop over all possible combinations of length 4 of the parameters
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for combination in combinations(self.parameters, 4):
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# Get the indices and values of the parameters in the combination
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indices = [self.parameters.index(element) for element in combination]
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selectedValues = [run, blockSize, failureRate, numberValidators, netDegree, chi]
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values = [selectedValues[index] for index in indices]
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names = [self.parameters[i] for i in indices]
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keyComponents = [f"{name}_{value}" for name, value in zip(names, values)]
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key = tuple(keyComponents[:4])
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#Get the names of the other 2 parameters that are not included in the key
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otherParams = [self.parameters[i] for i in range(6) if i not in indices]
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#Append the values of the other 2 parameters and the ttas to the lists for the key
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otherIndices = [i for i in range(len(self.parameters)) if i not in indices]
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#Initialize the dictionary for the key if it doesn't exist yet
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if key not in data:
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data[key] = {}
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#Initialize lists for the other 2 parameters and the ttas with the key
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data[key][otherParams[0]] = []
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data[key][otherParams[1]] = []
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data[key]['ttas'] = []
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if otherParams[0] in data[key]:
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data[key][otherParams[0]].append(selectedValues[otherIndices[0]])
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else:
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data[key][otherParams[0]] = [selectedValues[otherIndices[0]]]
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if otherParams[1] in data[key]:
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data[key][otherParams[1]].append(selectedValues[otherIndices[1]])
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else:
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data[key][otherParams[1]] = [selectedValues[otherIndices[1]]]
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data[key]['ttas'].append(tta)
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print("Getting data from the folder...")
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return data
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def similarKeys(self, data):
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#Get the keys for all data with the same x and y labels
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filteredKeys = {}
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for key1, value1 in data.items():
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subKeys1 = list(value1.keys())
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filteredKeys[(subKeys1[0], subKeys1[1])] = [key1]
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for key2, value2 in data.items():
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subKeys2 = list(value2.keys())
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if key1 != key2 and subKeys1[0] == subKeys2[0] and subKeys1[1] == subKeys2[1]:
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try:
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filteredKeys[(subKeys1[0], subKeys1[1])].append(key2)
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except KeyError:
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filteredKeys[(subKeys1[0], subKeys1[1])] = [key2]
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print("Getting filtered keys from data...")
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return filteredKeys
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def formatLabel(self, label):
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#Label formatting for the figures
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result = ''.join([f" {char}" if char.isupper() else char for char in label])
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return result.title()
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def formatTitle(self, key):
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#Title formatting for the figures
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name = ''.join([f" {char}" if char.isupper() else char for char in key.split('_')[0]])
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number = key.split('_')[1]
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return f"{name.title()}: {number} "
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def plotHeatmaps(self):
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#Plot and store the 2D heatmaps in subfolders
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data = self.plottingData()
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filteredKeys = self.similarKeys(data)
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print("Plotting heatmaps...")
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#Create the directory if it doesn't exist already
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heatmapsFolder = self.folderPath + '/heatmaps'
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if not os.path.exists(heatmapsFolder):
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os.makedirs(heatmapsFolder)
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#Plot
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for labels, keys in filteredKeys.items():
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for key in keys:
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xlabels = np.sort(np.unique(data[key][labels[0]]))
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ylabels = np.sort(np.unique(data[key][labels[1]]))
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if len(xlabels) < self.minimumDataPoints or len(ylabels) < self.minimumDataPoints:
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continue
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hist, xedges, yedges = np.histogram2d(data[key][labels[0]], data[key][labels[1]], bins=(len(xlabels), len(ylabels)), weights=data[key]['ttas'])
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hist = hist.T
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fig, ax = plt.subplots(figsize=(10, 6))
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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)
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plt.xlabel(self.formatLabel(labels[0]))
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plt.ylabel(self.formatLabel(labels[1]))
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filename = ""
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title = ""
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paramValueCnt = 0
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for param in self.parameters:
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if param != labels[0] and param != labels[1]:
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filename += f"{key[paramValueCnt]}"
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formattedTitle = self.formatTitle(key[paramValueCnt])
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title += formattedTitle
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paramValueCnt += 1
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title_obj = plt.title(title)
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font_size = 16 * fig.get_size_inches()[0] / 10
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title_obj.set_fontsize(font_size)
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filename = filename + ".png"
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targetFolder = os.path.join(heatmapsFolder, f"{labels[0]}Vs{labels[1]}")
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if not os.path.exists(targetFolder):
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os.makedirs(targetFolder)
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plt.savefig(os.path.join(targetFolder, filename))
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plt.close()
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plt.clf()
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32
study.py
32
study.py
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@ -23,23 +23,25 @@ def study():
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start = time.time()
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for run in range(config.numberRuns):
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for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
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for chi in range(config.chiStart, config.chiStop+1, config.chiStep):
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for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
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for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
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for netDegree in range(config.netDegreeStart, config.netDegreeStop+1, config.netDegreeStep):
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for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
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for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
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for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
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for netDegree in range(config.netDegreeStart, config.netDegreeStop+1, config.netDegreeStep):
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for chi in range(config.chiStart, config.chiStop+1, config.chiStep):
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if not config.deterministic:
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random.seed(datetime.now())
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shape = Shape(blockSize, nv, fr, chi, netDegree, run)
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sim.resetShape(shape)
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sim.initValidators()
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sim.initNetwork()
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result = sim.run()
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sim.logger.info("Shape: %s ... Block Available: %d in %d steps" % (str(sim.shape.__dict__), result.blockAvailable, len(result.missingVector)), extra=sim.format)
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results.append(copy.deepcopy(result))
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simCnt += 1
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# Network Degree has to be an even number
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if netDegree % 2 == 0:
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shape = Shape(blockSize, nv, fr, chi, netDegree, run)
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sim.resetShape(shape)
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sim.initValidators()
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sim.initNetwork()
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result = sim.run()
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sim.logger.info("Shape: %s ... Block Available: %d in %d steps" % (str(sim.shape.__dict__), result.blockAvailable, len(result.missingVector)), extra=sim.format)
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results.append(copy.deepcopy(result))
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simCnt += 1
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end = time.time()
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sim.logger.info("A total of %d simulations ran in %d seconds" % (simCnt, end-start), extra=sim.format)
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@ -49,6 +51,10 @@ def study():
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res.dump(execID)
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sim.logger.info("Results dumped into results/%s/" % (execID), extra=sim.format)
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visualization = 1
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if visualization:
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vis = Visualizer(execID)
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vis.plotHeatmaps()
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study()
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