102 lines
3.7 KiB
Python
Executable File
102 lines
3.7 KiB
Python
Executable File
"""
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To create graphs and pickle from runtime statistics in L1,MAC,RRC,PDCP files
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"""
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import subprocess
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import time
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import shlex
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import re
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import sys
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import pickle
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import matplotlib.pyplot as plt
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import numpy as np
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import yaml
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class StatMonitor():
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def __init__(self,):
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with open('stats_monitor_conf.yaml','r') as file:
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self.d = yaml.load(file)
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for node in self.d:
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for metric in self.d[node]:
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self.d[node][metric]=[]
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def process_gnb (self,node_type,output):
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for line in output:
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tmp=line.decode("utf-8")
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result=re.match(r'^.*\bdlsch_rounds\b ([0-9]+)\/([0-9]+).*\bdlsch_errors\b ([0-9]+)',tmp)
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if result is not None:
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self.d[node_type]['dlsch_err'].append(int(result.group(3)))
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percentage=float(result.group(2))/float(result.group(1))
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self.d[node_type]['dlsch_err_perc_round_1'].append(percentage)
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result=re.match(r'^.*\bulsch_rounds\b ([0-9]+)\/([0-9]+).*\bulsch_errors\b ([0-9]+)',tmp)
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if result is not None:
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self.d[node_type]['ulsch_err'].append(int(result.group(3)))
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percentage=float(result.group(2))/float(result.group(1))
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self.d[node_type]['ulsch_err_perc_round_1'].append(percentage)
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def process_enb (self,node_type,output):
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for line in output:
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tmp=line.decode("utf-8")
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result=re.match(r'^.*\bPHR\b ([0-9]+).+\bbler\b ([0-9]+\.[0-9]+).+\bmcsoff\b ([0-9]+).+\bmcs\b ([0-9]+)',tmp)
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if result is not None:
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self.d[node_type]['PHR'].append(int(result.group(1)))
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self.d[node_type]['bler'].append(float(result.group(2)))
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self.d[node_type]['mcsoff'].append(int(result.group(3)))
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self.d[node_type]['mcs'].append(int(result.group(4)))
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def collect(self,node_type):
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if node_type=='enb':
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cmd='cat L1_stats.log MAC_stats.log PDCP_stats.log RRC_stats.log'
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else: #'gnb'
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cmd='cat nrL1_stats.log nrMAC_stats.log nrPDCP_stats.log nrRRC_stats.log'
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process=subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE)
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output = process.stdout.readlines()
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if node_type=='enb':
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self.process_enb(node_type,output)
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else: #'gnb'
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self.process_gnb(node_type,output)
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def graph(self,node_type):
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col = 1
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figure, axis = plt.subplots(len(self.d[node_type]), col ,figsize=(10, 10))
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i=0
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for metric in self.d[node_type]:
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major_ticks = np.arange(0, len(self.d[node_type][metric])+1, 1)
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axis[i].set_xticks(major_ticks)
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axis[i].set_xticklabels([])
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axis[i].plot(self.d[node_type][metric],marker='o')
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axis[i].set_xlabel('time')
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axis[i].set_ylabel(metric)
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axis[i].set_title(metric)
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i+=1
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plt.tight_layout()
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# Combine all the operations and display
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plt.savefig(node_type+'_stats_monitor.png')
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plt.show()
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if __name__ == "__main__":
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node = sys.argv[1]#enb or gnb
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mon=StatMonitor()
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#collecting stats when modem process is stopped
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CMD='ps aux | grep mode | grep -v grep'
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process=subprocess.Popen(CMD, shell=True, stdout=subprocess.PIPE)
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output = process.stdout.readlines()
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while len(output)!=0 :
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mon.collect(node)
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process=subprocess.Popen(CMD, shell=True, stdout=subprocess.PIPE)
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output = process.stdout.readlines()
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time.sleep(1)
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print('Process stopped')
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with open(node+'_stats_monitor.pickle', 'wb') as handle:
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pickle.dump(mon.d, handle, protocol=pickle.HIGHEST_PROTOCOL)
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mon.graph(node)
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