Add script to analyze the global overview of the client distribution

Author: cortze

src/analyzer/total-overview-analysis.py

@@ -0,0 +1,100 @@
+# 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
+
+import os, sys
+import json
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def mainexecution():
+    projectsFolder = sys.argv[1]
+
+    # So far, the generated panda will just contain the client count
+    p = {'date': [], 'Lighthouse': [], 'Teku': [], 'Nimbus': [], 'Prysm': [], 'Lodestar': [], 'Unknown': []}
+    print(projectsFolder)
+    for root, dirs, _ in os.walk(projectsFolder):
+        print(dirs)
+        # 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, f)
+        break
+    # 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'])
+    print(df)
+
+    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': 'CrawlSummary.png',                                          
+            'align': 'center',
+            'textSize': textSize,                                                  
+            'title': "Evolution of the observed client distribution",           
+            'ylabel': 'Client concentration (%)',                                                         
+            'xlabel': None,
+            'legendLabels': clientList,                    
+            'titleSize': titleSize,                                                        
+            'labelSize': labelSize,                                                        
+            'lengendPosition': 1,                                                   
+            'legendSize': labelSize,                                                     
+            'tickRotation': 90,                                                     
+            'show': True})
+
+def poblatePandaCustomMetrics(p, jsonFile):
+    print('poblating panda')
+    # Read the Json
+    jsf = open(jsonFile)
+    jsonValues = json.load(jsf)
+    jsf.close()
+
+    # 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']['ConnectionSucceed']['Total']
+    # Get percentages for each of the clients
+    lig = (100 * int(jsonValues['PeerStore']['ConnectionSucceed']['Lighthouse']['Total'])) / ps
+    tek = (100 * int(jsonValues['PeerStore']['ConnectionSucceed']['Teku']['Total'])) / ps
+    nim = (100 * int(jsonValues['PeerStore']['ConnectionSucceed']['Nimbus']['Total'])) / ps
+    pry = (100 * int(jsonValues['PeerStore']['ConnectionSucceed']['Prysm']['Total'])) / ps
+    lod = (100 * int(jsonValues['PeerStore']['ConnectionSucceed']['Lodestar']['Total'])) / ps
+    unk = (100 * int(jsonValues['PeerStore']['ConnectionSucceed']['Unknown']['Total'])) / ps
+
+    p['Lighthouse'].append(lig)
+    p['Teku'].append(tek)
+    p['Nimbus'].append(nim)
+    p['Prysm'].append(pry)
+    p['Lodestar'].append(lod)
+    p['Unknown'].append(unk)
+
+
+def plotStackedChart(p, opts):
+    
+    ax = p.plot.area(figsize = opts['figSize'], x='date', stacked=True)
+
+    # 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'])
+    
+    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)
+
+    plt.title(opts['title'], fontsize = opts['titleSize'])
+    plt.tight_layout()
+    #plt.savefig(outputFile)
+    if opts['show'] is True:
+        plt.show()
+
+if __name__ == '__main__':
+    mainexecution()