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graph.py
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import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.cbook import get_sample_data
from matplotlib.offsetbox import AnnotationBbox, OffsetImage
from matplotlib.patches import Rectangle as pltRectangle
import pandas as pd
import sys
import configparser
import os
import logging
logging.getLogger("scapy.runtime").setLevel(logging.ERROR)
from scapy.all import rdpcap, TCP
import json
from OtherScripts.simutil import *
if (len(sys.argv)>2):
HOMEPATH=sys.argv[1]+"/" # Where to save the images
MAINPROBE=sys.argv[2] # Where the images are located
else:
print("Not enough arguments. Put: {path_to_folder} {path_to_cqiprobe}")
exit()
config = configparser.ConfigParser()
config.read(HOMEPATH + 'graph.ini')
# ----------------------------------------------------------
# graph.ini info | Configurations and env variables values
# ----------------------------------------------------------
NRTrace = config['general']['NRTrace']
TCPTrace = config['general']['TCPTrace']
flowType = config['general']['flowType']
tcpTypeId = config['general']['tcpTypeId']
resamplePeriod=int(config['general']['resamplePeriod'])
simTime = float(config['general']['simTime'])
AppStartTime = float(config['general']['AppStartTime'])
rlcBuffer = float(config['general']['rlcBuffer'])
rlcBufferPerc = int(config['general']['rlcBufferPerc'])
serverType = config['general']['serverType']
addNoise = config['general']['addNoise']
serverID = config['general']['serverID']
UENum = int(config['general']['UENum'])
SegmentSize = float(config['general']['SegmentSize'])
if config.has_option('general', 'dataRate'):
dataRate = float(config['general']['dataRate'])
else:
dataRate = 1000
thr_limit = dataRate*1.1
""" gNbNum = int(config['gNb']['gNbNum'])
gNbX = float(config['gNb']['gNbX'])
gNbY = float(config['gNb']['gNbY'])
gNbD = float(config['gNb']['gNbD'])
enableBuildings = int(config['building']['enableBuildings'])
gridWidth = int(config['building']['gridWidth'])
buildN = int(config['building']['buildN'])
buildX = float(config['building']['buildX'])
buildY = float(config['building']['buildY'])
buildDx = float(config['building']['buildDx'])
buildDy = float(config['building']['buildDy'])
buildLx = float(config['building']['buildLx'])
buildLy = float(config['building']['buildLy']) """
SUBTITLE = str(rlcBufferPerc) + '% BDP - Server: ' + serverType
test = os.listdir(HOMEPATH)
# Rewrite existing images
for item in test:
if item.endswith(".png"):
os.remove(os.path.join(HOMEPATH, item))
# Simulation Prefix
SIM_PREFIX = tcpTypeId + '-'+ serverType + '-' + str(rlcBufferPerc) + '-'
# Other stuff
RESAMPLED_DF = pd.timedelta_range("0s", f"{simTime}s", freq=f"{resamplePeriod}ms").to_frame(name="Time")
if tcpTypeId=="TcpNewReno":
points=np.array([2, 15, 21, 35, 39])
parts=np.array([[ 1, 20, "1-20"],
[20, 40, "20-40"],
[ 0, 60,"0-60"],
])
elif tcpTypeId=="TcpCubic":
tcpTypeId = "TcpCUBIC"
points=np.array([1, 2, 6, 20.5])
parts=np.array([[ 1, 22, "1-22"],
[ 0, 60,"0-60"],
])
else:
if tcpTypeId == "TcpBbr":
tcpTypeId = "TcpBBR"
points=np.array([2, 15])
parts=np.array([[ 1, 20, "1-20"],
[ 0, 60,"0-60"],
])
# Default values for matplotlib
plt.rc('lines', linewidth=2)
# ----------------------------------------------------------
# Mobility
# ----------------------------------------------------------
@info_n_time_decorator("Physical Distros", True)
def graphMobility():
fig, ax = plt.subplots()
mobFilepath="mobilityPosition.txt"
phydistroFilepath = "PhysicalDistribution.json"
title = "Physical Distribution of the objects"
# UE / gNb images
gNbicon=plt.imread(get_sample_data(f'{MAINPROBE}/images/gNb.png'))
gnbBox = OffsetImage(gNbicon, zoom = 0.25)
UEicon=plt.imread(get_sample_data(f'{MAINPROBE}/images/UE.png'))
UEbox = OffsetImage(UEicon, zoom = 0.01)
# Read buildings and gNbs positions
phyData = json.load(open(HOMEPATH + phydistroFilepath))
buildingList = phyData["Buildings"]
gnbList = phyData["gnb"]
mob = pd.read_csv(HOMEPATH + mobFilepath, sep="\t")
mob.set_index('Time', inplace=True)
ax1 = mob.plot.scatter(x='x', y='y', ax=ax, title=title, s=5, zorder=5,
color="firebrick", marker='_')
for gnb in gnbList:
gnbAnnBox = AnnotationBbox(gnbBox, [gnb["x"], gnb["y"] + 5], frameon=False)
ax.add_artist(gnbAnnBox)
for bld in buildingList:
# TODO: change the creation of the json files that there is a tree type and
# a building type, the idea is that the image is automatically chosen
# depending on the type. Maybe Inherit building class and create new models?
if phyData["istree"] or bld["ExternalWallsType"] == 0:
bldIcon = plt.imread(get_sample_data(f'{MAINPROBE}/images/tree.png'))
bldBox = OffsetImage(bldIcon, zoom=0.03)
bldAnnBox = AnnotationBbox(bldBox, [bld["xmin"], bld["ymin"]+5], frameon=False)
ax.add_artist(bldAnnBox)
else:
if bld["nroomsX"] > 0:
sep = bld["xwidth"] / bld["nroomsX"]
for xnum in range(bld["nroomsX"] + 1):
plt.vlines(x=bld["xmin"]+ xnum*sep, ymin=bld["ymin"], ymax=bld["ymax"], colors="black")
if bld["nroomsY"] > 0:
sep = bld["ywidth"] / bld["nroomsY"]
for xnum in range(bld["nroomsY"] + 1):
plt.hlines(y=bld["ymin"]+ xnum*sep, xmin=bld["xmin"], xmax=bld["xmax"], colors="black")
bldRect = pltRectangle([bld["xmin"], bld["ymin"]], bld["xwidth"], bld["ywidth"], alpha=0.9, facecolor="lightsteelblue")
plt.gca().add_patch(bldRect)
for ue in mob['UE'].unique():
UEPos=mob[mob['UE']==ue][['x','y']].iloc[-1:].values[0]+2
UEab=AnnotationBbox(UEbox,UEPos, frameon = False, zorder=5)
ax.add_artist(UEab)
plt.xlim([min(0, mob['x'].min()) , max(110,mob['x'].max()+10)])
plt.ylim([min(0, mob['y'].min()) , max(110,mob['y'].max()+10)])
ax.set_xlabel("Distance [m]")
ax.set_ylabel("Distance [m]")
fig.savefig(HOMEPATH + SIM_PREFIX +'PhyDistro.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# SINR Ctrl
# ----------------------------------------------------------
@info_n_time_decorator("SINR Control")
def graphSinrCtrl():
fig, ax = plt.subplots()
file="DlCtrlSinr.txt"
title="SINR Control"
SINR = pd.read_csv(HOMEPATH+file, sep = "\t")
SINR.set_index('Time', inplace=True)
SINR = SINR[SINR['RNTI']!=0]
SINR.groupby('RNTI')['SINR(dB)'].plot(title=title)
#plt.ylim([min(15, SINR['SINR(dB)'].min()) , max(30,SINR['SINR(dB)'].max())])
ax.set_ylabel("SINR [dB]")
ax.set_xlabel("Time [s]")
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + "SinrCtrl" +'.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# SINR Data
# ----------------------------------------------------------
@info_n_time_decorator("SINR Data")
def graphSinrData():
fig, ax = plt.subplots()
file ="DlDataSinr.txt"
title = "SINR Data"
sinr = pd.read_csv(HOMEPATH+file, sep = "\t")
sinr = sinr[sinr['RNTI']!=0]
sinr=sinr.groupby(['Time','RNTI'])['SINR(dB)'].mean().reset_index()
sinr.index=pd.to_datetime(sinr['Time'],unit='s')
sinr.rename(
columns={ "SINR(dB)":"sinr"},
inplace=True,
)
sinr=pd.DataFrame(sinr.groupby('RNTI').resample(str(resamplePeriod)+'ms').sinr.mean())
sinr=sinr.reset_index(level=0)
sinr['Time']=sinr.index
sinr['Time']=sinr['Time'].astype(np.int64)/1e9
sinr.set_index('Time', inplace=True)
sinr.groupby('RNTI')['sinr'].plot()
#plt.ylim([min(30, sinr['sinr'].min()) , max(60,sinr['sinr'].max())])
ax.set_ylabel("SINR [dB]")
ax.set_xlabel("Time [s]")
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + "SinrData" + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# CQI
# ----------------------------------------------------------
@info_n_time_decorator("CQI")
def graphCQI():
fig, ax = plt.subplots()
file="RxPacketTrace.txt"
title="CQI"
CQI = pd.read_csv(HOMEPATH+file, sep = "\t")
CQI.set_index('Time', inplace=True)
CQI = CQI[CQI['rnti']!=0]
CQI = CQI[CQI['direction']=='DL']
#print(SINR)
CQI.groupby('rnti')['CQI'].plot(title=title)
plt.ylim([0, 16])
ax.set_ylabel("CQI")
ax.set_xlabel("Time [s]")
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + title +'.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# TBLER
# ----------------------------------------------------------
@info_n_time_decorator("TBLER")
def graphTbler():
fig, ax = plt.subplots()
file="RxPacketTrace.txt"
title="BLER"
cqi = pd.read_csv(HOMEPATH+file, sep = "\t")
#CQI.set_index('Time', inplace=True)
cqi = cqi[cqi['rnti']!=0]
cqi = cqi[cqi['direction']=='DL']
cqi.index=pd.to_datetime(cqi['Time'],unit='s')
bler=pd.DataFrame(cqi.groupby('rnti').resample(str(resamplePeriod)+'ms').TBler.mean())
bler=bler.reset_index(level=0)
bler['Time']=bler.index
bler['Time']=bler['Time'].astype(np.int64)/1e9
bler=bler.set_index('Time')
for i in range(bler['rnti'].max()):
plt.semilogy(bler.index, bler[bler['rnti']==i+1].TBler, label='BLER')
# Trazar línea horizontal en y = 0.1
plt.axhline(y=0.1, color='green', linestyle='--', label='BLER = 0.1')
# Trazar línea horizontal en y = 0.5
plt.axhline(y=0.5, color='red', linestyle='--', label='BLER = 0.5')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xlabel("Time [s]")
plt.ylabel("BLER")
plt.ylim(bottom=None, top=10**0)
# ax.set_ylim([abs(min([(1e-20) ,BLER.TBler.min()*0.9])) , 1])
# plt.legend()
plt.title(title)
plt.grid(True, which="both", ls="-")
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'Tbler' + '.png', dpi=300, bbox_inches='tight')
plt.close()
return True
# ----------------------------------------------------------
# PATH LOSS
# ----------------------------------------------------------
@info_n_time_decorator("Path Loss")
def graphPathLoss():
fig, ax = plt.subplots()
file="DlPathlossTrace.txt"
title="Path Loss"
ploos = pd.read_csv(HOMEPATH+file, sep = "\t")
ploos.set_index('Time(sec)', inplace=True)
ploos = ploos.loc[ploos['IMSI']!=0]
ploos = ploos[ploos['pathLoss(dB)'] < 0]
ploos.groupby(['IMSI'])['pathLoss(dB)'].plot(title=file)
plt.suptitle(title)
plt.title(SUBTITLE)
plt.xlabel("Time [s]")
plt.ylabel("Path loss [dB]")
fig.set_size_inches(6.6, 4.8)
fig.savefig(HOMEPATH + SIM_PREFIX + "PathLoss" + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Throughput TX
# ----------------------------------------------------------
@info_n_time_decorator("Throughput TX")
def graphThrTx():
fig, ax = plt.subplots()
file="NrDlPdcpTxStats.txt"
title=tcpTypeId[3:] + " "
title=title+"Throughput TX"
TXSTAT = pd.read_csv(HOMEPATH+file, sep = "\t")
tx=TXSTAT.groupby(['time(s)','rnti'])['packetSize'].sum().reset_index()
tx.index=pd.to_datetime(tx['time(s)'],unit='s')
thrtx=pd.DataFrame(tx.groupby('rnti').resample(str(resamplePeriod)+'ms').packetSize.sum())
thrtx=thrtx.reset_index(level=0)
thrtx['InsertedDate']=thrtx.index
thrtx['deltaTime']=thrtx['InsertedDate'].astype(np.int64)/1e9
thrtx['Time']=thrtx['deltaTime']
thrtx['deltaTime']=thrtx.groupby('rnti').diff()['deltaTime']
thrtx.loc[~thrtx['deltaTime'].notnull(),'deltaTime']=thrtx.loc[~thrtx['deltaTime'].notnull(),'InsertedDate'].astype(np.int64)/1e9
thrtx['throughput']= thrtx['packetSize']*8 / thrtx['deltaTime']/1e6
thrtx=thrtx.set_index('Time')
thrtx.groupby(['rnti'])['throughput'].plot()
ax.set_xlabel("Time [s]")
ax.set_ylabel("Throughput [Mb/s]")
ax.set_ylim([0 , thrtx['throughput'].max()*1.1])
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'ThrTx' + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Throughput RX
# ----------------------------------------------------------
@info_n_time_decorator("Throughput RX")
def graphThrRx():
fig, ax = plt.subplots()
file="NrDlPdcpRxStats.txt"
title=tcpTypeId[3:] + " "
title=title + "Throughput RX"
rxStat = pd.read_csv(HOMEPATH+file, sep = "\t")
rx=rxStat.groupby(['time(s)','rnti'])['packetSize'].sum().reset_index()
rx.index=pd.to_datetime(rx['time(s)'],unit='s')
thrrx=pd.DataFrame(rx.groupby('rnti').resample(str(resamplePeriod)+'ms').packetSize.sum())
thrrx=thrrx.reset_index(level=0)
thrrx['InsertedDate']=thrrx.index
thrrx['deltaTime']=thrrx['InsertedDate'].astype(np.int64)/1e9
thrrx['Time']=thrrx['deltaTime']
thrrx['deltaTime']=thrrx.groupby('rnti').diff()['deltaTime']
thrrx.loc[~thrrx['deltaTime'].notnull(),'deltaTime']=thrrx.loc[~thrrx['deltaTime'].notnull(),'InsertedDate'].astype(np.int64)/1e9
thrrx['throughput']= thrrx['packetSize']*8 / thrrx['deltaTime']/1e6
thrrx=thrrx.set_index('Time')
ax1 = thrrx.groupby(['rnti'])['throughput'].plot( ax=ax)
ax.set_xlabel("Time [s]")
ax.set_ylabel("Throughput [Mb/s]")
ax.set_ylim([0 , max([0,thrrx['throughput'].max()*1.1])])
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'ThrRx' + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Throughput RX vs RLC Buffer | TCP Only
# ----------------------------------------------------------
# It currently isnt working, keyerror on line mytext="("+...
@info_n_time_decorator("THR Rx vs RLC_B")
def graphThrRxRlcBuffer():
fig, ax = plt.subplots()
file="NrDlPdcpRxStats.txt"
title=tcpTypeId[3:] + " "
title=title + "Throughput RX vs RLC Buffer"
rxStat = pd.read_csv(HOMEPATH+file, sep = "\t")
rx=rxStat.groupby(['time(s)','rnti'])['packetSize'].sum().reset_index()
rx.index=pd.to_datetime(rx['time(s)'],unit='s')
thrrx=pd.DataFrame(rx.groupby('rnti').resample(str(resamplePeriod)+'ms').packetSize.sum())
thrrx=thrrx.reset_index(level=0)
thrrx['InsertedDate']=thrrx.index
thrrx['deltaTime']=thrrx['InsertedDate'].astype(np.int64)/1e9
thrrx['Time']=thrrx['deltaTime']
thrrx['deltaTime']=thrrx.groupby('rnti').diff()['deltaTime']
thrrx.loc[~thrrx['deltaTime'].notnull(),'deltaTime']=thrrx.loc[~thrrx['deltaTime'].notnull(),'InsertedDate'].astype(np.int64)/1e9
thrrx['throughput']= thrrx['packetSize']*8 / thrrx['deltaTime']/1e6
thrrx=thrrx.set_index('Time')
if flowType=='TCP':
rlcStat= pd.read_csv(HOMEPATH+"RlcBufferStat_1.0.0.2_.txt", sep = "\t")
rlcStat['direction']='UL'
rlcStat.loc[rlcStat['PacketSize'] > 1500,'direction']='DL'
rlc = rlcStat[rlcStat['direction'] =='DL']
rlc.index=pd.to_datetime(rlc['Time'],unit='s')
rlcdrop=pd.DataFrame(rlc.resample(str(resamplePeriod)+'ms').dropSize.sum())
rlcdrop['Time']=rlcdrop.index.astype(np.int64)/1e9
rlcdrop=rlcdrop.set_index('Time')
rlcdrop['state']=0
rlcdrop.loc[rlcdrop['dropSize'] > 0,'state']=1
rlcbuffer=pd.DataFrame(rlc.resample(str(resamplePeriod)+'ms').txBufferSize.max())
rlcbuffer['Time']=rlcbuffer.index.astype(np.int64)/1e9
rlcbuffer=rlcbuffer.set_index('Time')
rlcbuffer['txBufferSize']=rlcbuffer['txBufferSize']/rlcBuffer
rlcdrop['state']=rlcdrop['state']*rlcbuffer['txBufferSize']
rlcbuffer.loc[rlcdrop['state'] > 0,'txBufferSize']=0
for p in range(parts.shape[0] ):
[x, y, z] = parts[p,:]
fig, ax = plt.subplots()
ax1 = thrrx.loc[x:y].groupby(['rnti'])['throughput'].plot( ax=ax)
if p<parts.shape[0]-1:
for d in range(points.shape[0]):
if (points[d]>=int(x)) & (points[d] <= int(y)):
mytext="("+str(points[d])+","+str(round(thrrx.loc[points[d]]['throughput'],2)) +")"
ax.annotate( mytext, (points[d] , thrrx.loc[points[d]]['throughput']))
ax1 = thrrx.groupby(['rnti'])['throughput'].plot( ax=ax)
if flowType=='TCP':
ax2 = rlcdrop['state'].plot.area( secondary_y=True, ax=ax, alpha=0.2, color="red")
ax3 = rlcbuffer['txBufferSize'].plot.area( secondary_y=True, ax=ax, alpha=0.2, color="green")
ax.set_xlabel("Time [s]")
ax.set_ylabel("Throughput [Mb/s]")
ax.set_ylim([0 , max([0,thrrx['throughput'].max()*1.1])])
if flowType=='TCP':
ax2.set_ylabel("RLC Buffer [%]", loc='bottom')
ax2.set_ylim(0,4)
ax2.set_yticks([0,0.5,1])
ax2.set_yticklabels(['0','50','100' ])
ax3.set_ylim(0,rlcbuffer['txBufferSize'].max()*4)
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'ThrRx-' + z + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Throughput RX vs PER | If TCP
# ----------------------------------------------------------
@info_n_time_decorator("THR Rx vs PER")
def graphThrRxPer():
title=tcpTypeId[3:] + " Throughput vs PER"
file="tcp-per.txt"
if os.path.exists(HOMEPATH+file):
tx_ = pd.read_csv(HOMEPATH+file, sep = "\t")
else:
file = file + ".gz"
tx_ = pd.read_csv(HOMEPATH+file, compression='gzip', sep = "\t")
tx_.index=pd.to_datetime(tx_['Time'],unit='s')
tx_thr=pd.DataFrame(tx_.resample(str(resamplePeriod)+'ms').BytesTx.sum())
tx_drp=pd.DataFrame(tx_.resample(str(resamplePeriod)+'ms').BytesDroped.sum())
tx_drp['PacketsTx']=pd.DataFrame(tx_.resample(str(resamplePeriod)+'ms').PacketsTx.sum())
tx_drp['PacketsDroped']=pd.DataFrame(tx_.resample(str(resamplePeriod)+'ms').PacketsDroped.sum())
tx_thr=tx_thr.reset_index(level=0)
tx_thr['Throughput']= tx_thr['BytesTx']*8 / 0.1/1e6
tx_thr['Time']=tx_thr['Time'].astype(np.int64)/1e9
tx_thr=tx_thr.set_index('Time')
tx_drp=tx_drp.reset_index(level=0)
# tx_drp['Throughput']= tx_drp['BytesTx']*8 / 0.1/1e6
tx_drp['PER']= tx_drp['PacketsDroped']/tx_drp['PacketsTx']
tx_drp['Time']=tx_drp['Time'].astype(np.int64)/1e9
tx_drp=tx_drp.set_index('Time')
for p in range(parts.shape[0] ):
[x, y, z] = parts[p,:]
fig, ax = plt.subplots()
if p<parts.shape[0]-1:
# plt.plot(tx_thr.loc[x:y].index, tx_thr['Throughput'].loc[x:y], '-o', markevery=tx_thr.loc[x:y].index.get_indexer(points, method='nearest'))
ax1 = tx_thr['Throughput'].loc[x:y].plot(ax=ax, markevery=tx_thr.loc[x:y].index.get_indexer(points, method='nearest'))
for d in range(points.shape[0]):
if (points[d]>=int(x)) & (points[d] <= int(y)):
mytext="("+str(points[d])+","+str(round(tx_thr.loc[points[d]]['Throughput'],2)) +")"
ax.annotate( mytext, (points[d] , tx_thr.loc[points[d]]['Throughput']))
else:
ax1 = tx_thr['Throughput'].loc[x:y].plot(ax=ax)
# ax2 = tx_drp['Throughput'].plot.area( ax=ax, alpha=0.2, color="red")
ax2 = tx_drp['PER'].loc[x:y].plot.area( secondary_y=True, ax=ax, alpha=0.2, color="red")
ax.set_xlabel("Time [s]")
ax.set_ylabel("Throughput [Mb/s]")
ax.set_ylim([0 , max([thr_limit,tx_thr['Throughput'].loc[x:y].max()*1.1])])
ax2.set_ylabel("PER [%]", loc='bottom')
ax2.set_ylim(0,4)
ax2.set_yticks([0,0.5,1])
ax2.set_yticklabels(['0','50','100' ])
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'ThrDrp' +'-'+ z+ '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# RLC Buffers | If FlowType TCP
# ----------------------------------------------------------
@info_n_time_decorator('RLC Buffers')
def graphRlcBuffers():
PREFIX = "RlcBufferStat"
SUFFIX = ".txt"
buffers = []
# Find all buffers
for file in os.listdir(HOMEPATH):
if PREFIX in file and SUFFIX in file:
buffers.append(file)
if len(buffers) <= 0:
#print(red + "\tNo files found. Terminated." + clear)
return False
num_buffers = len(buffers)
if num_buffers > 1: # TCP
fig, ax = plt.subplots(num_buffers, 1, sharex='all', figsize=(7, 2.4*num_buffers))
plt.suptitle("RLC Buffers of UE(s) and Remote Host")
for buffer, index in zip(buffers, range(0, len(buffers))):
# Retrieve IP
IP = buffer.replace(PREFIX + "_", "").replace("_" + SUFFIX, "")
# Read data
data = pd.read_csv(HOMEPATH + buffer, sep="\t")
x = data['Time']
y = data['NumOfBuffers']
ax[index].plot(x, y)
ax[index].fill_between(x, y, color='#539ecd')
ax[index].tick_params('x', labelbottom=False)
ax[index].set_ylabel("Num. of packets")
ax[index].set_title("IP: " + IP)
ax[index].tick_params('x', labelbottom=True)
ax[index].set_xlabel("Time [s]")
fig.savefig(HOMEPATH + SIM_PREFIX + "RlcBuffers.png", dpi=300)
plt.close()
else: # UDP
plt.suptitle("RLC Buffers of UE(s) and Remote Host")
plt.title(SUBTITLE)
buffer = buffers.pop()
IP = buffer.replace(PREFIX + "_", "").replace("_" + SUFFIX, "")
data = pd.read_csv(HOMEPATH + buffer, sep="\t")
x = data['Time']
y = data['NumOfBuffers']
plt.plot(x, y)
plt.fill_between(x, y, color='#539ecd')
plt.ylabel("Num. of packets")
plt.xlabel("IP: " + IP)
plt.savefig(HOMEPATH + SIM_PREFIX + "RlcBuffers.png", dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Delay | Only UDP | Uses NrDlPdcpRxStats.txt
# ----------------------------------------------------------
@info_n_time_decorator("PDCP Delay")
def graphPdcpDelay():
###############
## Delay
###############
fig, ax = plt.subplots()
file="NrDlPdcpRxStats.txt"
title=tcpTypeId + " "
title=title+"Delay RX"
RXSTAT = pd.read_csv(HOMEPATH+file, sep = "\t")
rx=RXSTAT.groupby(['time(s)','rnti'])['delay(s)'].mean().reset_index()
rx.rename(
columns={ "delay(s)":"delay"},
inplace=True,
)
rx.index=pd.to_datetime(rx['time(s)'],unit='s')
ret=pd.DataFrame(rx.groupby('rnti').resample(str(resamplePeriod)+'ms').delay.mean())
ret=ret.reset_index(level=0)
ret['InsertedDate']=ret.index
ret['Time']=ret['InsertedDate'].astype(np.int64)/1e9
ret['delay'] = ret['delay'] * 1000
ret=ret.set_index('Time')
ret.groupby(['rnti'])['delay'].plot(title=title)
ax.set_xlabel("Time [s]")
ax.set_ylabel("Delay [ms]")
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'PdcpDelay' + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Delay | Only UDP | Uses delay by UDPServerHelper
# ----------------------------------------------------------
@info_n_time_decorator("UDP Delay")
def graphUdpDelay():
# We assume that there is only 1 UE
filepath = HOMEPATH + "UdpRecv_Node1.txt"
title = "Application Packet Delay"
udpstats = pd.read_csv(filepath, sep="\t")
time = udpstats["Time (s)"]
delay = udpstats["Delay"] * 1000
plt.plot(time, delay)
plt.suptitle(title)
plt.title(SUBTITLE)
plt.ylabel("Delay [ms]")
plt.xlabel("Time [s]")
plt.savefig(HOMEPATH + SIM_PREFIX + "AppDelay" + ".png", dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Retransmission | Both
# ----------------------------------------------------------
@info_n_time_decorator("RTX")
def graphRetransmissions():
filepath = HOMEPATH + "RxPacketTrace.txt"
df = pd.read_csv(filepath, sep="\t")
rtxs = df[["Time", "rv", "corrupt"]].copy()
rtxs["Time"] = pd.to_timedelta(rtxs["Time"], unit="s")
rtxs.index += 5
rtxs.loc[0] = [pd.Timedelta(0), 0, 0]
rtxs.loc[1] = [pd.Timedelta(0), 1, 0]
rtxs.loc[2] = [pd.Timedelta(0), 2, 0]
rtxs.loc[3] = [pd.Timedelta(0), 3, 0]
rtxs.loc[4] = [pd.Timedelta(0), 3, 1]
rtxs = rtxs.sort_index()
# Successful retransmissions
succ_rtx = rtxs[rtxs["corrupt"] == 0]
# Unsuccessful retransmissions
fail_rtx = rtxs[(rtxs["rv"] == 3) & (rtxs["corrupt"] == 1)]
succ_zero = succ_rtx[succ_rtx["rv"] == 0].copy().resample(f"{resamplePeriod}ms", on="Time").size().to_frame(name="Zero").copy()
succ_zero.iloc[0] -= 1
succ_once = succ_rtx[succ_rtx["rv"] == 1].copy().resample(f"{resamplePeriod}ms", on="Time").size().to_frame(name="One").copy()
succ_once.iloc[0] -= 1
succ_twice = succ_rtx[succ_rtx["rv"] == 2].copy().resample(f"{resamplePeriod}ms", on="Time").size().to_frame(name="Two").copy()
succ_twice.iloc[0] -= 1
succ_three = succ_rtx[succ_rtx["rv"] == 2].copy().resample(f"{resamplePeriod}ms", on="Time").size().to_frame(name="Three").copy()
succ_three.iloc[0] -= 1
failure = fail_rtx.resample(f"{resamplePeriod}ms", on="Time").size().to_frame(name="Fail").copy()
failure.iloc[0] -= 1
data = RESAMPLED_DF.join([succ_zero, succ_once, succ_twice, succ_three, failure]).drop("Time", axis=1).fillna(0)
data["Total"] = data.sum(axis=1)
data["S Total"] = data[["Zero", "One", "Two", "Three"]].sum(axis=1)
data[["Zero", "One", "Two", "Three"]] = 100 * data[["Zero", "One", "Two", "Three"]].div(data["S Total"], axis=0).fillna(0)
data[["S Total", "Fail"]] = 100 * data[["S Total", "Fail"]].div(data["Total"], axis=0).fillna(0)
f, (ax_succ, ax_both) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [3, 1]})
plt.subplots_adjust(hspace=0.5)
f.suptitle("Retransmissions in MAC Layer")
succ_colors = ["#019875", "#72CC50", "#BFD834", "#00AEAD"]
ax_succ.stackplot(data.index.total_seconds(), data["Zero"], data["One"], data["Two"], data["Three"], colors=succ_colors)
ax_succ.legend(["0 RTX", "1 RTX", "2 RTX", "3 RTX"], loc="lower right")
ax_succ.set_title("Number of RTX needed for a successful block reception")
ax_succ.set_xlabel("Time [s]")
ax_succ.set_ylabel("Percentage [%]")
both_colors = ["#019875", "#BE0000"]
ax_both.stackplot(data.index.total_seconds(), data["S Total"], data["Fail"], colors=both_colors)
ax_both.legend(["Successful", "Failed"], loc="lower right")
ax_both.set_title("Proportion of successful and failed retransmissions")
ax_both.set_xlabel("Time [s]")
ax_both.set_ylabel("Percentage [%]")
ax_both.set_yticks((0, 50, 100))
ax_both.yaxis.grid(True)
f.savefig(HOMEPATH + SIM_PREFIX + "Rtx" + ".png", dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# RTT | Only TCP
# ----------------------------------------------------------
ret = 0
@info_n_time_decorator("RTT")
def graphTcpDelay():
###############
## Delay 2
###############
fig, ax = plt.subplots()
title=tcpTypeId[3:] + " RTT"
file="tcp-delay.txt"
if os.path.exists(HOMEPATH+file):
RXSTAT = pd.read_csv(HOMEPATH+file, sep = "\t")
else:
file = file + ".gz"
RXSTAT = pd.read_csv(HOMEPATH+file, compression='gzip', sep = "\t")
rx=RXSTAT.groupby(['Time'])['rtt'].mean().reset_index()
rx.index=pd.to_datetime(rx['Time'],unit='s')
rx = rx[(rx['Time']>=AppStartTime) & (rx['Time']<=simTime - AppStartTime)]
global ret
ret=pd.DataFrame(rx.resample(str(resamplePeriod)+'ms').rtt.mean())
ret['InsertedDate']=ret.index
ret['Time']=ret['InsertedDate'].astype(np.int64)/1e9
ret=ret.set_index('Time')
ret['rtt']=ret['rtt']*1000
for p in range(parts.shape[0] ):
[x, y, z] = parts[p,:]
fig, ax = plt.subplots()
if p<parts.shape[0]-1:
plt.plot(ret.loc[x:y].index, ret['rtt'].loc[x:y], '-o',markevery=ret.loc[x:y].index.get_indexer(points, method='nearest'))
if (points[d]>=int(x)) & (points[d] <= int(y)):
for d in range(points.shape[0]):
try:
mytext="("+str(points[d])+","+str(round(ret.loc[points[d]]['rtt'],2)) +")"
ax.annotate( mytext, (points[d] , ret.loc[points[d]]['rtt']))
except KeyError:
print(d)
else:
ret['rtt'].loc[x:y].plot()
ax.set_ylabel("RTT [ms]")
ax.set_ylim(0, ret['rtt'].loc[x:y].max()*1.1)
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + 'RTT' +'-'+ z+ '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# CWND & Inflight bytes | Only TCP | MUST GRAPH RTT BEFORE
# ----------------------------------------------------------
@info_n_time_decorator("CWND and Inflight Bytes")
def graphCWNDnInflightBytes():
###############
# Congestion Window
###############
for u in range(UENum):
# print(u)
fig, ax = plt.subplots()
file="tcp-cwnd-"+serverID+"-"+str(u)+".txt"
title=tcpTypeId + " "
title=title+"Congestion Window"
CWND.index=pd.to_datetime(CWND['Time'],unit='s')
CWND = CWND[(CWND['Time']>=AppStartTime) & (CWND['Time']<=simTime - AppStartTime)]
CWND=pd.DataFrame(CWND.resample(str(resamplePeriod)+'ms').newval.mean())
CWND['Time']=CWND.index.astype(np.int64)/1e9
CWND=CWND.set_index('Time')
CWND['newval']=CWND['newval']/1024
for p in range(parts.shape[0] ):
[x, y, z] = parts[p,:]
if (len(CWND.loc[x:y].index)>0):
fig, ax = plt.subplots()
if p<parts.shape[0]-1:
plt.plot(CWND.loc[x:y].index, CWND['newval'].loc[x:y], '-o',markevery=ret.loc[x:y].index.get_indexer(points, method='nearest'))
for d in range(points.shape[0]):
try:
if (points[d]>=int(x)) & (points[d] <= int(y)):
mytext="("+str(points[d])+","+str(round(CWND.loc[points[d]]['newval'],1)) +")"
ax.annotate( mytext, (points[d] , CWND.loc[points[d]]['newval']))
except KeyError:
print(d)
else:
CWND['newval'].loc[x:y].plot()
plt.suptitle(title)
plt.title(SUBTITLE)
ax.set_ylabel("CWD [KBytes]")
fig.savefig(HOMEPATH + SIM_PREFIX + 'cwnd-'+str(u)+'-'+ z+'.png', dpi=300)
plt.close()
###############
# inflight Bytes
###############
for u in range(UENum):
fig, ax = plt.subplots()
file="tcp-inflight-"+serverID+"-"+str(u)+".txt"
title=tcpTypeId + " "
title=title + "inflight Bytes"
inflight = pd.read_csv(HOMEPATH+file, sep = "\t")
# INF.loc[INF['oldval']>0]['oldval']= INF.loc[INF['oldval']>0]['oldval']/SegmentSize
# INF.loc[INF['newval']>0]['newval']= INF.loc[INF['newval']>0]['newval']/SegmentSize
inflight['oldval']= inflight['oldval'] / SegmentSize
inflight['newval']= inflight['newval'] / SegmentSize
inflight.set_index('Time', inplace=True)
if (len(inflight.index)>0):
inflight['newval'].plot(title=title)
plt.suptitle(title)
plt.title(SUBTITLE)
fig.savefig(HOMEPATH + SIM_PREFIX + "InflightBytes-UE" + str(u) + '.png', dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# RTT 2 | Only TCP
# ----------------------------------------------------------
@info_n_time_decorator("RTT 2")
def get_RTT(pcap_filename):
# Read the pcap file
packets = rdpcap(pcap_filename)
# Dictionary to store packet timestamps, rtt's and ack's received
packet_timestamps = {}
rtt_dict = {}
ack_received = []
# Iterate over each packet
for packet in packets:
if packet.haslayer(TCP):
seq_num = packet[TCP].seq
ack_num = packet[TCP].ack
timestamp = packet.time
TCP_payload = len(packet[TCP].payload)
# Cases to skip
case_1 = (seq_num == 0) # SYN
case_2 = (seq_num == 1 and ack_num == 1 and TCP_payload == 0) # SYN, ACK
case_3 = (ack_num in ack_received) # DUP ACK
skip_packets = (case_1 or case_2 or case_3)
if skip_packets:
continue
# Add timestamp of sending packet
if seq_num not in packet_timestamps:
packet_timestamps[seq_num] = timestamp
rtt_dict[seq_num] = 0
# Get Sequence Number from ACK
recovered_seq_num = ack_num-1448
# Check if a packet is ACK and the SEQ exists
if (seq_num == 1) and (recovered_seq_num in packet_timestamps):
rtt = timestamp - packet_timestamps[recovered_seq_num]
rtt_dict[recovered_seq_num] = rtt*1000
ack_received.append(ack_num)
new_rtt_dict = {int((key-1)/1448 + 1) : value for key, value in rtt_dict.items()}
n_packet_array = list(new_rtt_dict.keys())
rtt_array = list(new_rtt_dict.values())
fig, ax = plt.subplots()
plt.plot(n_packet_array, rtt_array)
plt.xlabel('Packet Number')
plt.ylabel('RTT [ms]')
plt.title('RTT 2')
plt.grid(True)
fig.savefig(HOMEPATH + SIM_PREFIX + 'RTT_2.png', dpi=300)
return True
# ----------------------------------------------------------
# GoodPut | Only UDP
# ----------------------------------------------------------
@info_n_time_decorator("GoodPut")
def graphGoodPut():
timeCol = "Time (s)"
# In this test we only have 1 UE, with NodeId=1
filepath = HOMEPATH + "UdpRecv_Node1.txt"
df = pd.read_csv(filepath, sep="\t")
df[timeCol] = pd.to_timedelta(df[timeCol], unit="s")
df.index += 1
df.loc[0] = [pd.Timedelta(0), 0, 0, 0, 0]
df.sort_index(inplace=True)
resampled = df.copy().resample(f"{resamplePeriod}ms", on=timeCol)
new_df = resampled['Packet Size'].sum().to_frame(name='Bytes').copy()
new_df = RESAMPLED_DF.join([new_df]).drop('Time', axis=1).fillna(0)
# THR = bits / ( ( time in a period in secs ) * bits in a megabit )
new_df["GoodPut (Mbps)"] = (new_df["Bytes"] * 8 / ( (resamplePeriod * 1e-3) * 1e6))
plt.plot(new_df.index.total_seconds(), new_df["GoodPut (Mbps)"])
plt.suptitle("Throughput in Application layer")
plt.title(SUBTITLE)
plt.ylabel("Throughput [Mb/s]")
plt.xlabel("Time [s]")
plt.xlim([0, simTime])
plt.ylim([0, new_df["GoodPut (Mbps)"].max()+5])
plt.savefig(HOMEPATH + SIM_PREFIX + "GoodPut.png", dpi=300)
plt.close()
return True
# ----------------------------------------------------------
# Phy Throughput | Both