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| Original file line number | Diff line number | Diff line change |
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| # This file compiles the code to analyze the gathered metrics from each of the | ||
| # folders in the $ARMIARMAPATH/examples folder, providing a global overview of the | ||
| # Peer distributions and client types over the dayly analysis | ||
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| import os, sys | ||
| import json | ||
| import pandas as pd | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
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| def mainexecution(): | ||
| projectsFolder = sys.argv[1] | ||
| outFolder = sys.argv[2] | ||
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| outJson = outFolder + '/' + 'client-distribution.json' | ||
| # So far, the generated panda will just contain the client count | ||
| p = {'date': [], 'Lighthouse': [], 'Teku': [], 'Nimbus': [], 'Prysm': [], 'Lodestar': [], 'Unknown': []} | ||
| # Concatenation of the Json values | ||
| j = [] | ||
| print(projectsFolder) | ||
| for root, dirs, _ in os.walk(projectsFolder): | ||
| print(dirs) | ||
| dirs.sort() | ||
| # We just need to access the folders inside examples | ||
| for d in dirs: | ||
| fold = os.path.join(root, d) | ||
| f = fold + '/metrics/custom-metrics.json' | ||
| if os.path.exists(f): | ||
| # Load the readed values from the json into the panda | ||
| poblatePandaCustomMetrics(p, j, f) | ||
| break | ||
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| # Export the Concatenated json to the given folder | ||
| with open(outJson, 'w', encoding='utf-8') as f: | ||
| json.dump(j, f, ensure_ascii=False, indent=4) | ||
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| # After filling the dict with all the info from the JSONs, generate the panda | ||
| df = pd.DataFrame (p, columns = ['date', 'Lighthouse', 'Teku', 'Nimbus', 'Prysm', 'Lodestar', 'Unknown']) | ||
| df = df.sort_values(by="date") | ||
| print(df) | ||
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| outputFigsFolder = outFolder + '/' + 'plots' | ||
| clientList = ['Lighthouse', 'Teku', 'Nimbus', 'Prysm', 'Lodestar', 'Unknown'] | ||
| figSize = (10,6) | ||
| titleSize = 24 | ||
| textSize = 20 | ||
| labelSize = 20 | ||
| # Plot the images | ||
| plotStackedChart(df, opts={ | ||
| 'figSize': figSize, | ||
| 'figTitle': 'Client-Distribution.png', | ||
| 'figtitle': 'client-distribution.png', | ||
| 'outputPath': outputFigsFolder, | ||
| 'align': 'center', | ||
| 'grid': 'y', | ||
| 'textSize': textSize, | ||
| 'title': "Evolution of the projected client distribution", | ||
| 'ylabel': 'Client concentration (%)', | ||
| 'xlabel': None, | ||
| 'yticks': np.arange(0, 110, 10), | ||
| 'legendLabels': clientList, | ||
| 'titleSize': titleSize, | ||
| 'labelSize': labelSize, | ||
| 'lengendPosition': 1, | ||
| 'legendSize': labelSize, | ||
| 'tickRotation': 90, | ||
| 'show': True}) | ||
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| def poblatePandaCustomMetrics(p, j, jsonFile): | ||
| # Read the Json | ||
| jsf = open(jsonFile) | ||
| jsonValues = json.load(jsf) | ||
| jsf.close() | ||
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| # Add readed Json to the previous ones (concatenate them) | ||
| j.append(jsonValues) | ||
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| # Compose the date of the crawling day | ||
| cday = str(jsonValues['StartTime']['Year']) + '-' + str(jsonValues['StartTime']['Month']) + '-' + str(jsonValues['StartTime']['Day']) | ||
| p['date'].append(cday) | ||
| ps = jsonValues['PeerStore']['Total'] | ||
| # Get percentages for each of the clients | ||
| crwlig = jsonValues['PeerStore']['ConnectionSucceed']['Lighthouse']['Total'] | ||
| crwtek = jsonValues['PeerStore']['ConnectionSucceed']['Teku']['Total'] | ||
| crwnim = jsonValues['PeerStore']['ConnectionSucceed']['Nimbus']['Total'] | ||
| crwlod = jsonValues['PeerStore']['ConnectionSucceed']['Lodestar']['Total'] | ||
| crwunk = jsonValues['PeerStore']['ConnectionSucceed']['Unknown']['Total'] | ||
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| prysPort = jsonValues['PeerStore']['Port13000'] | ||
| res = ps - prysPort | ||
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| noPrysm = crwlig + crwtek + crwnim + crwlod + crwunk | ||
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| estimlig = (res*crwlig)/noPrysm | ||
| estimtek = (res*crwtek)/noPrysm | ||
| estimnim = (res*crwnim)/noPrysm | ||
| estimlod = (res*crwlod)/noPrysm | ||
| estimunk = (res*crwunk)/noPrysm | ||
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| lig = round((estimlig*100)/ps, 2) | ||
| tek = round((estimtek*100)/ps, 2) | ||
| nim = round((estimnim*100)/ps, 2) | ||
| pry = round((prysPort*100)/ps, 2) | ||
| lod = round((estimlod*100)/ps, 2) | ||
| unk = round((estimunk*100)/ps, 2) | ||
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| p['Lighthouse'].append(lig) | ||
| p['Teku'].append(tek) | ||
| p['Nimbus'].append(nim) | ||
| p['Prysm'].append(pry) | ||
| p['Lodestar'].append(lod) | ||
| p['Unknown'].append(unk) | ||
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| def plotStackedChart(p, opts): | ||
| outputFile = str(opts['outputPath']) + '/' + opts['figTitle'] | ||
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| ax = p.plot.area(figsize = opts['figSize'], x='date', stacked=True) | ||
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| # labels | ||
| if opts['ylabel'] is not None: | ||
| plt.ylabel(opts['ylabel'], fontsize=opts['labelSize']) | ||
| if opts['xlabel'] is not None: | ||
| plt.xlabel(opts['xlabel'], fontsize=opts['labelSize']) | ||
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| handles,legends = ax.get_legend_handles_labels() | ||
| ax.legend(handles=handles, labels=opts['legendLabels'], loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=6) | ||
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| if opts['grid'] != None: | ||
| ax.grid(which='major', axis=opts['grid'], linestyle='--') | ||
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| plt.yticks(opts['yticks']) | ||
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| plt.title(opts['title'], fontsize = opts['titleSize']) | ||
| plt.tight_layout() | ||
| plt.savefig(outputFile) | ||
| if opts['show'] is True: | ||
| plt.show() | ||
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| if __name__ == '__main__': | ||
| mainexecution() |