|
- #!/usr/bin/env python3
- import math
- import multiprocessing
- import os
- from argparse import ArgumentParser
-
- import matplotlib
- import numpy as np
- import pandas as pd
- import matplotlib.pyplot as plt
-
- # Using seaborn's style
- #plt.style.use('seaborn')
-
- tex_fonts = {
- "pgf.texsystem": "lualatex",
- # "legend.fontsize": "x-large",
- # "figure.figsize": (15, 5),
- "axes.labelsize": 15, # "small",
- # "axes.titlesize": "x-large",
- "xtick.labelsize": 15, # "small",
- "ytick.labelsize": 15, # "small",
- "legend.fontsize": 15,
- "axes.formatter.use_mathtext": True,
- "mathtext.fontset": "dejavusans",
- }
-
- #plt.rcParams.update(tex_fonts)
-
-
- def convert_cellid(value):
- if isinstance(value, str):
- try:
- r = int(value.split(" ")[-1].replace("(", "").replace(")", ""))
- return r
- except Exception as e:
- return -1
- else:
- return int(-1)
-
-
- if __name__ == "__main__":
- parser = ArgumentParser()
- 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", required=True, help="Location to save pdf file.")
- parser.add_argument(
- "-i",
- "--interval",
- default=10,
- type=int,
- help="Time interval for rolling window.",
- )
-
- args = parser.parse_args()
-
- pcap_csv_list = list()
- for filename in os.listdir(args.pcap_csv_folder):
- if filename.endswith(".csv") and "tcp" in filename:
- pcap_csv_list.append(filename)
-
- counter = 1
- if len(pcap_csv_list) == 0:
- print("No CSV files found.")
-
- pcap_csv_list.sort(key=lambda x: int(x.split("_")[-1].replace(".csv", "")))
-
- 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))))
-
- #try:
- transmission_df = pd.read_csv(
- "{}{}".format(args.pcap_csv_folder, csv),
- dtype=dict(is_retranmission=bool, is_dup_ack=bool),
- )
-
- 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()
-
- # srtt to [s]
- transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 10**6)
-
- # key for columns and level for index
- transmission_df["goodput"] = transmission_df["payload_size"].groupby(pd.Grouper(level="datetime", freq="{}s".format(args.interval))).transform("sum")
- transmission_df["goodput"] = transmission_df["goodput"].apply(
- lambda x: ((x * 8) / args.interval) / 10**6
- )
-
- transmission_df["goodput_rolling"] = transmission_df["payload_size"].rolling("{}s".format(args.interval)).sum()
- transmission_df["goodput_rolling"] = transmission_df["goodput_rolling"].apply(
- lambda x: ((x * 8) / args.interval) / 10 ** 6
- )
-
- # set meta values and remove all not needed columns
- cc_algo = transmission_df["congestion_control"].iloc[0]
- cc_algo = cc_algo.upper()
- transmission_direction = transmission_df["direction"].iloc[0]
-
- #transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"])
-
- # read serial csv
- serial_df = pd.read_csv(args.serial_file, converters={"Cell_ID": convert_cellid})
- serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)
- serial_df = serial_df.set_index("datetime")
- serial_df.index = pd.to_datetime(serial_df.index)
- serial_df.sort_index()
-
- #print(serial_df["Cell_ID"])
-
- #serial_df["Cell_ID"] = serial_df["Cell_ID"].apply(
- # lambda x: int(x.split(" ")[-1].replace("(", "").replace(")", "")))
-
- transmission_df = pd.merge_asof(
- transmission_df,
- serial_df,
- tolerance=pd.Timedelta("1s"),
- right_index=True,
- left_index=True,
- )
-
- transmission_df.index = transmission_df["arrival_time"]
-
- # replace 0 in RSRQ with Nan
- transmission_df["NR5G_RSRQ_(dB)"] = transmission_df["NR5G_RSRQ_(dB)"].replace(0, np.NaN)
- transmission_df["RSRQ_(dB)"] = transmission_df["RSRQ_(dB)"].replace(0, np.NaN)
- # stacked plot for bandwidth
- transmission_df["lte_bw_sum"] = transmission_df["bw_sum"] - transmission_df["NR5G_dl_bw"]
- transmission_df["nr_bw_sum"] = transmission_df["NR5G_dl_bw"]
-
-
- # transmission timeline
- scaley = 1.5
- scalex = 1.0
- plt.title("{} with {}".format(transmission_direction, cc_algo))
- fig, ax = plt.subplots(2, 1, figsize=[6.4 * scaley, 4.8 * scalex])
- fig.subplots_adjust(right=0.75)
- fig.suptitle("{} with {}".format(transmission_direction, cc_algo))
- ax0 = ax[0]
- ax1 = ax0.twinx()
- ax2 = ax0.twinx()
- #ax2.spines.right.set_position(("axes", 1.22))
-
- ax00 = ax[1]
- ax01 = ax00.twinx()
- ax02 = ax00.twinx()
-
- # Plot vertical lines
- first = True
- lte_handovers = transmission_df["Cell_ID"].dropna().diff()
- for index, value in lte_handovers.items():
- if value > 0:
- if first:
- ax00.axvline(index, ymin=0, ymax=1, color="skyblue", label="4G Handover")
- first = False
- else:
- ax00.axvline(index, ymin=0, ymax=1, color="skyblue")
-
- first = True
- nr_handovers = transmission_df["NR5G_Cell_ID"].replace(0, np.NaN).dropna().diff()
- for index, value in nr_handovers.items():
- if value > 0:
- if first:
- ax00.axvline(index, ymin=0, ymax=1, color="greenyellow", label="5G Handover")
- first = False
- else:
- ax00.axvline(index, ymin=0, ymax=1, color="greenyellow")
-
-
- ax0.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
- ax1.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
- ax2.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
- #ax2.plot(transmission_df["goodput"], color="blue", linestyle="solid", label="goodput")
-
- ax01.plot(transmission_df["bw_sum"].dropna(), color="peru", linestyle="solid", label="bandwidth")
- ax01.plot(transmission_df["lte_bw_sum"].dropna(), color="lightsteelblue", linestyle="solid", label="4G bandwidth", alpha=.5)
- ax01.plot(transmission_df["nr_bw_sum"].dropna(), color="cornflowerblue", linestyle="solid",label="5G bandwidth", alpha=.5)
-
- #ax01.stackplot(transmission_df["arrival_time"].to_list(),
- # [transmission_df["lte_bw_sum"].to_list(), transmission_df["nr_bw_sum"].to_list()],
- # colors=["lightsteelblue", "cornflowerblue"],
- # labels=["4G bandwidth", "5G bandwidth"]
- # )
-
- ax02.plot(transmission_df["RSRQ_(dB)"].dropna(), color="purple", linestyle="dotted", label="LTE RSRQ")
- ax00.plot(transmission_df["NR5G_RSRQ_(dB)"].dropna(), color="magenta", linestyle="dotted", label="NR RSRQ")
-
- ax2.spines.right.set_position(("axes", 1.1))
- ax02.spines.right.set_position(("axes", 1.1))
-
- ax0.set_ylim(0, 5000)
- ax1.set_ylim(0, 0.3)
- ax2.set_ylim(0, 500)
- ax00.set_ylim(-25, 0)
- ax01.set_ylim(0, 250)
- # second dB axis
- ax02.set_ylim(-25, 0)
- ax02.set_axis_off()
-
- ax00.set_xlabel("arrival time [s]")
-
- ax2.set_ylabel("Goodput [mbps]")
- ax00.set_ylabel("LTE/NR RSRQ [dB]")
- #ax02.set_ylabel("LTE RSRQ [dB]")
- ax1.set_ylabel("sRTT [s]")
- ax0.set_ylabel("cwnd")
- ax01.set_ylabel("Bandwidth [MHz]")
-
- fig.legend(loc="lower right")
-
- plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
- #except Exception as e:
- # print("Error processing file: {}".format(csv))
- # print(str(e))
- counter += 1
-
- plt.close(fig)
- plt.clf()
|
