Switch from time steps to miliseconds (#30)
Switch from time steps to miliseconds
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Leo
GitHub
parent
57b0ee89f7
commit
7719f84a10
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@ -7,22 +7,23 @@ from dicttoxml import dicttoxml
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class Result:
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"""This class stores and process/store the results of a simulation."""
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def __init__(self, shape):
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def __init__(self, shape, execID):
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"""It initializes the instance with a specific shape."""
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self.shape = shape
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self.execID = execID
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self.blockAvailable = -1
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self.tta = -1
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self.missingVector = []
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self.metrics = {}
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def populate(self, shape, missingVector):
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def populate(self, shape, config, missingVector):
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"""It populates part of the result data inside a vector."""
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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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self.tta = len(missingVector) * (config.stepDuration)
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else:
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self.blockAvailable = 0
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self.tta = -1
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@ -35,8 +36,8 @@ class Result:
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"""It dumps the results of the simulation in an XML file."""
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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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if not os.path.exists("results/"+self.execID):
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os.makedirs("results/"+self.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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@ -44,6 +45,6 @@ class Result:
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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+"/"+str(self.shape)+".xml"
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filePath = "results/"+self.execID+"/"+str(self.shape)+".xml"
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with open(filePath, "w") as f:
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f.write(xmlPretty)
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@ -13,12 +13,13 @@ from DAS.validator import *
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class Simulator:
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"""This class implements the main DAS simulator."""
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def __init__(self, shape, config):
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def __init__(self, shape, config, execID):
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"""It initializes the simulation with a set of parameters (shape)."""
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self.shape = shape
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self.config = config
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self.format = {"entity": "Simulator"}
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self.result = Result(self.shape)
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self.execID = execID
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self.result = Result(self.shape, self.execID)
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self.validators = []
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self.logger = []
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self.logLevel = config.logLevel
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@ -192,14 +193,14 @@ class Simulator:
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# log TX and RX statistics
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trafficStats = self.glob.getTrafficStats(self.validators)
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self.logger.debug("step %d: %s" %
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self.logger.debug("step %d: %s" %
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(steps, trafficStats), extra=self.format)
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for i in range(0,self.shape.numberNodes):
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self.validators[i].updateStats()
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trafficStatsVector.append(trafficStats)
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missingSamples, sampleProgress, nodeProgress, validatorProgress = self.glob.getProgress(self.validators)
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self.logger.debug("step %d, arrived %0.02f %%, ready %0.02f %%, validated %0.02f %%"
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self.logger.debug("step %d, arrived %0.02f %%, ready %0.02f %%, validated %0.02f %%"
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% (steps, sampleProgress*100, nodeProgress*100, validatorProgress*100), extra=self.format)
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cnS = "samples received"
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@ -231,7 +232,7 @@ class Simulator:
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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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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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missingVector.append(missingSamples)
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break
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else:
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@ -240,6 +241,6 @@ class Simulator:
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progress = pd.DataFrame(progressVector)
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if self.config.saveProgress:
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self.result.addMetric("progress", progress.to_dict(orient='list'))
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self.result.populate(self.shape, missingVector)
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self.result.populate(self.shape, self.config, missingVector)
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return self.result
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@ -36,7 +36,7 @@ class Visualizer:
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bwUplinkProd = int(root.find('bwUplinkProd').text)
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bwUplink1 = int(root.find('bwUplink1').text)
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bwUplink2 = int(root.find('bwUplink2').text)
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tta = int(root.find('tta').text)
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tta = float(root.find('tta').text)
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# Loop over all possible combinations of of the parameters minus two
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for combination in combinations(self.parameters, len(self.parameters)-2):
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@ -120,7 +120,7 @@ class Visualizer:
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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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sns.heatmap(hist, xticklabels=xlabels, yticklabels=ylabels, cmap='Purples', cbar_kws={'label': 'Time to block availability (ms)'}, 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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@ -131,6 +131,8 @@ class Visualizer:
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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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if (paramValueCnt+1) % 5 == 0:
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title += "\n"
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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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@ -27,14 +27,14 @@ logLevel = logging.INFO
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# number of parallel workers. -1: all cores; 1: sequential
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# for more details, see joblib.Parallel
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numJobs = 3
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numJobs = -1
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# distribute rows/columns evenly between validators (True)
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# or generate it using local randomness (False)
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evenLineDistribution = True
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# Number of simulation runs with the same parameters for statistical relevance
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runs = range(10)
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runs = range(2)
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# Number of validators
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numberNodes = range(256, 513, 128)
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@ -49,14 +49,14 @@ blockSizes = range(32,65,16)
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netDegrees = range(6, 9, 2)
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# number of rows and columns a validator is interested in
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chis = range(1, 5, 2)
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chis = range(2, 5, 2)
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# ratio of class1 nodes (see below for parameters per class)
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class1ratios = np.arange(0, 1, .2)
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class1ratios = [0.8, 0.9]
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# Number of validators per beacon node
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validatorsPerNode1 = [1]
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validatorsPerNode2 = [2, 4, 8, 16, 32]
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validatorsPerNode2 = [500]
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# Set uplink bandwidth. In segments (~560 bytes) per timestep (50ms?)
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# 1 Mbps ~= 1e6 / 20 / 8 / 560 ~= 11
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@ -64,8 +64,11 @@ bwUplinksProd = [2200]
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bwUplinks1 = [110]
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bwUplinks2 = [2200]
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# Step duration in miliseconds (Classic RTT is about 100ms)
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stepDuration = 50
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# Set to True if you want your run to be deterministic, False if not
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deterministic = False
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deterministic = True
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# If your run is deterministic you can decide the random seed. This is ignore otherwise.
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randomSeed = "DAS"
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