langspielplatte/measurement-scripts
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Adds differnt plots fpr goodput, cwnd, rtt und cqi.

Browse Source
This commit is contained in:
Lukas Prause
2023-01-20 14:59:04 +01:00
parent 16b9929dcb
commit e1b0cfa32a
1 changed files with 125 additions and 233 deletions
Download Patch File Download Diff File Expand all files Collapse all files

174
plot_single_transmission_timeline.py
View File

@@ -1,66 +1,12 @@
#!/usr/bin/env python3
import math
import multiprocessing
import os
from argparse import ArgumentParser
from math import ceil
from time import sleep
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits import axisartist
from mpl_toolkits.axes_grid1 import host_subplot
def csv_to_dataframe(csv_list, dummy):
global n
global frame_list
transmission_df = None
for csv in csv_list:
tmp_df = pd.read_csv(
"{}{}".format(args.pcap_csv_folder, csv),
dtype=dict(is_retranmission=bool, is_dup_ack=bool),
)
tmp_df["datetime"] = pd.to_datetime(tmp_df["datetime"]) - pd.Timedelta(hours=1)
tmp_df = tmp_df.set_index("datetime")
tmp_df.index = pd.to_datetime(tmp_df.index)
if transmission_df is None:
transmission_df = tmp_df
else:
transmission_df = pd.concat([transmission_df, tmp_df])
n.value += 1
frame_list.append(transmission_df)
from itertools import islice
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def plot_cdf(dataframe, column_name):
stats_df = dataframe \
.groupby(column_name) \
[column_name] \
.agg("count") \
.pipe(pd.DataFrame) \
.rename(columns={column_name: "frequency"})
# PDF
stats_df["PDF"] = stats_df["frequency"] / sum(stats_df["frequency"])
# CDF
stats_df["CDF"] = stats_df["PDF"].cumsum()
stats_df = stats_df.reset_index()
stats_df.plot(x=column_name, y=["CDF"], grid=True)
if __name__ == "__main__":
@@ -68,13 +14,6 @@ if __name__ == "__main__":
parser.add_argument("-s", "--serial_file", required=True, help="Serial csv file.")
parser.add_argument("-p", "--pcap_csv_folder", required=True, help="PCAP csv folder.")
parser.add_argument("--save", default=None, help="Location to save pdf file.")
parser.add_argument(
"-c",
"--cores",
default=1,
type=int,
help="Number of cores for multiprocessing.",
)
parser.add_argument(
"-i",
"--interval",
@@ -95,50 +34,25 @@ if __name__ == "__main__":
if filename.endswith(".csv") and "tcp" in filename:
pcap_csv_list.append(filename)
parts = chunk(pcap_csv_list, ceil(len(pcap_csv_list) / args.cores))
print("Start processing with {} jobs.".format(args.cores))
for p in parts:
process = multiprocessing.Process(target=csv_to_dataframe, args=(p, "dummy"))
jobs.append(process)
counter = 1
if len(pcap_csv_list) == 0:
print("No CSV files found.")
for j in jobs:
j.start()
pcap_csv_list.sort(key=lambda x: int(x.split("_")[-1].replace(".csv", "")))
print("Started all jobs.")
# Ensure all the processes have finished
finished_job_counter = 0
working = ["|", "/", "-", "\\", "|", "/", "-", "\\"]
w = 0
while len(jobs) != finished_job_counter:
sleep(1)
print(
"\r\t{}{}{}\t Running {} jobs ({} finished). Processed {} out of {} pcap csv files. ({}%) ".format(
working[w],
working[w],
working[w],
len(jobs),
finished_job_counter,
n.value,
len(pcap_csv_list),
round((n.value / len(pcap_csv_list)) * 100, 2),
),
end="",
for csv in pcap_csv_list:
print("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list))))
transmission_df = pd.read_csv(
"{}{}".format(args.pcap_csv_folder, csv),
dtype=dict(is_retranmission=bool, is_dup_ack=bool),
)
finished_job_counter = 0
for j in jobs:
if not j.is_alive():
finished_job_counter += 1
if (w + 1) % len(working) == 0:
w = 0
else:
w += 1
print("\r\nSorting table...")
transmission_df = pd.concat(frame_list)
frame_list = None
transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1)
transmission_df = transmission_df.set_index("datetime")
transmission_df.index = pd.to_datetime(transmission_df.index)
transmission_df = transmission_df.sort_index()
print("Calculate goodput...")
#print("Calculate goodput...")
#print(transmission_df)
@@ -158,7 +72,7 @@ if __name__ == "__main__":
cc_algo = cc_algo.upper()
transmission_direction = transmission_df["direction"].iloc[0]
transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling"])
#transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"])
# read serial csv
serial_df = pd.read_csv(args.serial_file)
@@ -184,9 +98,11 @@ if __name__ == "__main__":
twin1 = ax.twinx()
twin2 = ax.twinx()
twin3 = ax.twinx()
# Offset the right spine of twin2. The ticks and label have already been
# placed on the right by twinx above.
twin2.spines.right.set_position(("axes", 1.2))
twin2.spines.right.set_position(("axes", 1.1))
twin3.spines.right.set_position(("axes", 1.2))
# create list fo color indices
@@ -209,65 +125,41 @@ if __name__ == "__main__":
bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c]
ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=cmap.colors[c])
p1, = ax.plot(transmission_df["goodput_rolling"], "-", color="blue", label="goodput")
p2, = twin1.plot(transmission_df["downlink_cqi"], "--", color="green", label="CQI")
p3, = twin2.plot(transmission_df["ack_rtt"], "-.", color="red", label="ACK RTT")
p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
p3, = twin2.plot(transmission_df["ack_rtt"].dropna(), color="red", linestyle="dashdot", label="ACK RTT")
p1, = ax.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
p2, = twin1.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
ax.set_xlim(transmission_df["index"].min(), transmission_df["index"].max())
ax.set_ylim(0, 500)
twin1.set_ylim(0, 15)
twin2.set_ylim(0, 1)
twin2.set_ylim(0, transmission_df["ack_rtt"].max())
twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10)
ax.set_xlabel("Time")
ax.set_ylabel("Goodput")
ax.set_xlabel("arrival time")
ax.set_ylabel("Goodput [mbps]")
twin1.set_ylabel("CQI")
twin2.set_ylabel("ACK RTT")
twin2.set_ylabel("ACK RTT [s]")
twin3.set_ylabel("cwnd")
ax.yaxis.label.set_color(p1.get_color())
twin1.yaxis.label.set_color(p2.get_color())
twin2.yaxis.label.set_color(p3.get_color())
twin3.yaxis.label.set_color(p4.get_color())
tkw = dict(size=4, width=1.5)
ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
twin3.tick_params(axis='y', colors=p4.get_color(), **tkw)
ax.tick_params(axis='x', **tkw)
#ax.legend(handles=[p1, p2, p3])
if args.save:
plt.savefig("{}timeline_plot.pdf".format(args.save))
plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
else:
plt.show()
counter += 1
#goodput cdf
plt.clf()
print("Calculate and polt goodput CDF...")
plot_cdf(transmission_df, "goodput")
plt.xlabel("goodput [mbps]")
plt.ylabel("CDF")
plt.legend([cc_algo])
plt.title("{} with {}".format(transmission_direction, cc_algo))
if args.save:
plt.savefig("{}{}_cdf_plot.pdf".format(args.save, "goodput"))
else:
plt.show()
# rtt cdf
plt.clf()
print(transmission_df["ack_rtt"])
print("Calculate and polt rtt CDF...")
plot_cdf(transmission_df, "ack_rtt")
plt.xlabel("ACK RTT [s]")
plt.ylabel("CDF")
plt.xscale("log")
plt.legend([cc_algo])
plt.title("{} with {}".format(transmission_direction, cc_algo))
if args.save:
plt.savefig("{}{}_cdf_plot.pdf".format(args.save, "ack_rtt"))
else:
plt.show()