Merge branch 'vis' of https://github.com/status-im/das-research into vis
This commit is contained in:
HajarZaiz
commit
ceb8357034
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@ -1,2 +1,4 @@
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*.swp
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*.pyc
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results/*
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!results/plots.py
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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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@ -33,6 +33,7 @@ class Configuration:
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self.numberRuns = int(config.get("Advanced", "numberRuns"))
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self.deterministic = config.get("Advanced", "deterministic")
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self.dumpXML = config.get("Advanced", "dumpXML")
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if self.nvStop < (self.blockSizeStart*4):
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print("ERROR: The number of validators cannot be lower than the block size * 4")
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|
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@ -1,17 +1,50 @@
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#!/bin/python3
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import os
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from xml.dom import minidom
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from dicttoxml import dicttoxml
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class Result:
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config = []
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shape = []
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missingVector = []
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blockAvailable = -1
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tta = -1
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def __init__(self, config):
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self.config = config
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def __init__(self, shape):
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self.shape = shape
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self.blockAvailable = -1
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self.tta = -1
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self.missingVector = []
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def addMissing(self, missingVector):
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def populate(self, shape, missingVector):
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self.shape = shape
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self.missingVector = missingVector
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missingSamples = missingVector[-1]
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if missingSamples == 0:
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self.blockAvailable = 1
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self.tta = len(missingVector)
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else:
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self.blockAvailable = 0
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self.tta = -1
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def dump(self, execID):
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if not os.path.exists("results"):
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os.makedirs("results")
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if not os.path.exists("results/"+execID):
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os.makedirs("results/"+execID)
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resd1 = self.shape.__dict__
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resd2 = self.__dict__.copy()
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resd2.pop("shape")
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resd1.update(resd2)
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resXml = dicttoxml(resd1)
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xmlstr = minidom.parseString(resXml)
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xmlPretty = xmlstr.toprettyxml()
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filePath = "results/"+execID+"/nbv-"+str(self.shape.numberValidators)+\
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"-bs-"+str(self.shape.blockSize)+\
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"-nd-"+str(self.shape.netDegree)+\
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"-fr-"+str(self.shape.failureRate)+\
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"-chi-"+str(self.shape.chi)+\
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"-r-"+str(self.shape.run)+".xml"
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with open(filePath, "w") as f:
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f.write(xmlPretty)
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@ -1,16 +1,18 @@
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#!/bin/python3
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class Shape:
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run = 0
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numberValidators = 0
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failureRate = 0
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blockSize = 0
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failureRate = 0
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netDegree = 0
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chi = 0
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def __init__(self, blockSize, numberValidators, failureRate, chi, netDegree):
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def __init__(self, blockSize, numberValidators, failureRate, chi, netDegree, run):
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self.run = run
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self.numberValidators = numberValidators
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self.failureRate = failureRate
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self.blockSize = blockSize
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self.failureRate = failureRate
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self.netDegree = netDegree
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self.chi = chi
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@ -42,7 +42,6 @@ class Simulator:
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self.validators.append(val)
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def initNetwork(self):
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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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@ -87,6 +86,7 @@ class Simulator:
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def resetShape(self, shape):
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self.shape = shape
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self.result = Result(self.shape)
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for val in self.validators:
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val.shape.failureRate = shape.failureRate
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val.shape.chi = shape.chi
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@ -120,19 +120,16 @@ class Simulator:
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missingRate = missingSamples*100/expected
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self.logger.debug("step %d, missing %d of %d (%0.02f %%)" % (steps, missingSamples, expected, missingRate), extra=self.format)
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if missingSamples == oldMissingSamples:
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#self.logger.info("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
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missingVector.append(missingSamples)
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break
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elif missingSamples == 0:
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#self.logger.info("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
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missingVector.append(missingSamples)
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break
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else:
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steps += 1
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self.result.addMissing(missingVector)
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if missingSamples == 0:
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self.result.blockAvailable = 1
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||||
self.logger.debug("The entire block is available at step %d, with failure rate %d !" % (steps, self.shape.failureRate), extra=self.format)
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return self.result
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else:
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self.result.blockAvailable = 0
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self.logger.debug("The block cannot be recovered, failure rate %d!" % self.shape.failureRate, extra=self.format)
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return self.result
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self.result.populate(self.shape, missingVector)
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||||
return self.result
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||||
|
|
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|
|
@ -0,0 +1,130 @@
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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
|
||||
import matplotlib.pyplot as plt
|
||||
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:
|
||||
|
||||
def __init__(self, execID):
|
||||
self.execID = execID
|
||||
self.folderPath = "results/"+self.execID
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||||
self.parameters = ['run', 'blockSize', 'failureRate', 'numberValidators', 'netDegree', 'chi']
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||||
|
||||
#Store data with a unique key for each params combination
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||||
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()
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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
|
||||
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]
|
||||
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:
|
||||
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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||||
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||||
if otherParams[0] in data[key]:
|
||||
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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#Get the keys for all data with the same x and y labels
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def similarKeys(self, data):
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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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#Title formatting for the figures
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def formatTitle(self, key):
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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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#Plot and store the 2D heatmaps in subfolders
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def plotHeatmaps(self):
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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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||||
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)
|
||||
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()
|
||||
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@ -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;
|
||||
}
|
||||
|
|
@ -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>
|
||||
|
|
@ -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";
|
||||
});
|
||||
});
|
||||
|
|
@ -25,3 +25,4 @@ chiStep = 2
|
|||
|
||||
deterministic = 0
|
||||
numberRuns = 2
|
||||
dumpXML = 1
|
||||
|
|
|
|||
|
|
@ -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])
|
||||
45
study.py
45
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 chi in range(config.chiStart, config.chiStop+1, config.chiStep):
|
||||
for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
|
||||
for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
|
||||
for netDegree in range(config.netDegreeStart, config.netDegreeStop+1, config.netDegreeStep):
|
||||
for nv in range(config.nvStart, config.nvStop+1, config.nvStep):
|
||||
for blockSize in range(config.blockSizeStart, config.blockSizeStop+1, config.blockSizeStep):
|
||||
for fr in range(config.failureRateStart, config.failureRateStop+1, config.failureRateStep):
|
||||
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)
|
||||
sim.resetShape(shape)
|
||||
sim.initValidators()
|
||||
sim.initNetwork()
|
||||
result = sim.run()
|
||||
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)
|
||||
results.append(result)
|
||||
simCnt += 1
|
||||
# Network Degree has to be an even number
|
||||
if netDegree % 2 == 0:
|
||||
shape = Shape(blockSize, nv, fr, chi, netDegree, run)
|
||||
sim.resetShape(shape)
|
||||
sim.initValidators()
|
||||
sim.initNetwork()
|
||||
result = sim.run()
|
||||
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()
|
||||
|
|
|
|||
Loading…
Reference in New Issue