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bring ulsch and dlsch stats for gnb

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hardy
2021-10-14 12:12:20 +02:00
parent 30c5668bbe
commit 563fd66b9c
6 changed files with 201 additions and 95 deletions
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ci-scripts/stats_monitor.py
+79 -71
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@@ -1,94 +1,102 @@
"""
To create graphs and pickle from runtime statistics in L1,MAC,RRC,PDCP files
"""
import subprocess
import time
import shlex
import re
import sys
import matplotlib.pyplot as plt
import pickle
import matplotlib.pyplot as plt
import numpy as np
import os
def collect(d, node_type):
if node_type=='enb':
cmd='cat L1_stats.log MAC_stats.log PDCP_stats.log RRC_stats.log'
else: #'gnb'
cmd='cat nrL1_stats.log nrMAC_stats.log nrPDCP_stats.log nrRRC_stats.log'
process=subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE)
output = process.stdout.readlines()
for l in output:
tmp=l.decode("utf-8")
result=re.match(rf'^.*\bPHR\b ([0-9]+).+\bbler\b ([0-9]+\.[0-9]+).+\bmcsoff\b ([0-9]+).+\bmcs\b ([0-9]+)',tmp)
if result is not None:
d['PHR'].append(int(result.group(1)))
d['bler'].append(float(result.group(2)))
d['mcsoff'].append(int(result.group(3)))
d['mcs'].append(int(result.group(4)))
import yaml
def graph(d, node_type):
class StatMonitor():
def __init__(self,cfg_file):
with open(cfg_file,'r') as file:
self.d = yaml.load(file)
for node in self.d:
for metric in self.d[node]:
self.d[node][metric]=[]
figure, axis = plt.subplots(4, 1,figsize=(10, 10))
def process_gnb (self,node_type,output):
for line in output:
tmp=line.decode("utf-8")
result=re.match(r'^.*\bdlsch_rounds\b ([0-9]+)\/([0-9]+).*\bdlsch_errors\b ([0-9]+)',tmp)
if result is not None:
self.d[node_type]['dlsch_err'].append(int(result.group(3)))
percentage=float(result.group(2))/float(result.group(1))
self.d[node_type]['dlsch_err_perc_round_1'].append(percentage)
result=re.match(r'^.*\bulsch_rounds\b ([0-9]+)\/([0-9]+).*\bulsch_errors\b ([0-9]+)',tmp)
if result is not None:
self.d[node_type]['ulsch_err'].append(int(result.group(3)))
percentage=float(result.group(2))/float(result.group(1))
self.d[node_type]['ulsch_err_perc_round_1'].append(percentage)
major_ticks = np.arange(0, len(d['PHR'])+1, 1)
axis[0].set_xticks(major_ticks)
axis[0].set_xticklabels([])
axis[0].plot(d['PHR'],marker='o')
axis[0].set_xlabel('time')
axis[0].set_ylabel('PHR')
axis[0].set_title("PHR")
major_ticks = np.arange(0, len(d['bler'])+1, 1)
axis[1].set_xticks(major_ticks)
axis[1].set_xticklabels([])
axis[1].plot(d['bler'],marker='o')
axis[1].set_xlabel('time')
axis[1].set_ylabel('bler')
axis[1].set_title("bler")
major_ticks = np.arange(0, len(d['mcsoff'])+1, 1)
axis[2].set_xticks(major_ticks)
axis[2].set_xticklabels([])
axis[2].plot(d['mcsoff'],marker='o')
axis[2].set_xlabel('time')
axis[2].set_ylabel('mcsoff')
axis[2].set_title("mcsoff")
def process_enb (self,node_type,output):
for line in output:
tmp=line.decode("utf-8")
result=re.match(r'^.*\bPHR\b ([0-9]+).+\bbler\b ([0-9]+\.[0-9]+).+\bmcsoff\b ([0-9]+).+\bmcs\b ([0-9]+)',tmp)
if result is not None:
self.d[node_type]['PHR'].append(int(result.group(1)))
self.d[node_type]['bler'].append(float(result.group(2)))
self.d[node_type]['mcsoff'].append(int(result.group(3)))
self.d[node_type]['mcs'].append(int(result.group(4)))
major_ticks = np.arange(0, len(d['mcs'])+1, 1)
axis[3].set_xticks(major_ticks)
axis[3].set_xticklabels([])
axis[3].plot(d['mcs'],marker='o')
axis[3].set_xlabel('time')
axis[3].set_ylabel('mcs')
axis[3].set_title("mcs")
plt.tight_layout()
# Combine all the operations and display
plt.savefig(node_type+'_stats_monitor.png')
plt.show()
def collect(self,node_type):
if node_type=='enb':
cmd='cat L1_stats.log MAC_stats.log PDCP_stats.log RRC_stats.log'
else: #'gnb'
cmd='cat nrL1_stats.log nrMAC_stats.log nrPDCP_stats.log nrRRC_stats.log'
process=subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE)
output = process.stdout.readlines()
if node_type=='enb':
self.process_enb(node_type,output)
else: #'gnb'
self.process_gnb(node_type,output)
def graph(self,node_type):
col = 1
figure, axis = plt.subplots(len(self.d[node_type]), col ,figsize=(10, 10))
i=0
for metric in self.d[node_type]:
major_ticks = np.arange(0, len(self.d[node_type][metric])+1, 1)
axis[i].set_xticks(major_ticks)
axis[i].set_xticklabels([])
axis[i].plot(self.d[node_type][metric],marker='o')
axis[i].set_xlabel('time')
axis[i].set_ylabel(metric)
axis[i].set_title(metric)
i+=1
plt.tight_layout()
# Combine all the operations and display
plt.savefig(node_type+'_stats_monitor.png')
plt.show()
if __name__ == "__main__":
node_type = sys.argv[1]#enb or gnb
cfg_filename = sys.argv[1] #yaml file as metrics config
node = sys.argv[2]#enb or gnb
mon=StatMonitor(cfg_filename)
d={}
d['PHR']=[]
d['bler']=[]
d['mcsoff']=[]
d['mcs']=[]
cmd='ps aux | grep modem | grep -v grep'
process=subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
#collecting stats when modem process is stopped
CMD='ps aux | grep mode | grep -v grep'
process=subprocess.Popen(CMD, shell=True, stdout=subprocess.PIPE)
output = process.stdout.readlines()
while len(output)!=0 :
collect(d, node_type)
process=subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
mon.collect(node)
process=subprocess.Popen(CMD, shell=True, stdout=subprocess.PIPE)
output = process.stdout.readlines()
time.sleep(1)
print('process stopped')
with open(node_type+'_stats_monitor.pickle', 'wb') as handle:
pickle.dump(d, handle, protocol=pickle.HIGHEST_PROTOCOL)
graph(d, node_type)
print('Process stopped')
with open(node+'_stats_monitor.pickle', 'wb') as handle:
pickle.dump(mon.d, handle, protocol=pickle.HIGHEST_PROTOCOL)
mon.graph(node)