measurement-scripts/plot_single_transmission_paper.py

#!/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

import seaborn as sns

sns.set()
#sns.set(font_scale=1.5)

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("--fancy", action="store_true", help="Create fancy plot.")
    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 = 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 = serial_df.set_index("datetime")
        serial_df.index = pd.to_datetime(serial_df.index)
        serial_df.sort_index()

        # Select DataFrame rows between two dates
        mask = (serial_df.index >= transmission_df.index[0]) & (serial_df.index <= transmission_df.index[-1])
        serial_df = serial_df.loc[mask]

        transmission_df.index = transmission_df["arrival_time"]

        # filter active state
        for i in range(1, 5):
            serial_df["LTE_SCC{}_effective_bw".format(i)] = serial_df[
                "LTE_SCC{}_bw".format(i)
            ]

            mask = serial_df["LTE_SCC{}_state".format(i)].isin(["ACTIVE"])
            serial_df["LTE_SCC{}_effective_bw".format(i)] = serial_df[
                "LTE_SCC{}_effective_bw".format(i)
            ].where(mask, other=0)

        # filter if sc is usesd for uplink
        for i in range(1, 5):
            mask = serial_df["LTE_SCC{}_UL_Configured".format(i)].isin([False])
            serial_df["LTE_SCC{}_effective_bw".format(i)] = serial_df[
                "LTE_SCC{}_effective_bw".format(i)
            ].where(mask, other=0)

        # sum all effective bandwidth for 5G and 4G
        serial_df["SCC1_NR5G_effective_bw"] = serial_df[
            "SCC1_NR5G_bw"
        ].fillna(0)

        serial_df["lte_effective_bw_sum"] = (
                serial_df["LTE_SCC1_effective_bw"].fillna(0)
                + serial_df["LTE_SCC2_effective_bw"].fillna(0)
                + serial_df["LTE_SCC3_effective_bw"].fillna(0)
                + serial_df["LTE_SCC4_effective_bw"].fillna(0)
                + serial_df["LTE_bw"].fillna(0))
        serial_df["nr_effective_bw_sum"] = serial_df["SCC1_NR5G_effective_bw"]

        serial_df["effective_bw_sum"] = serial_df["nr_effective_bw_sum"] + serial_df[
            "lte_effective_bw_sum"]

        # transmission timeline
        scaley = 1.5
        scalex = 1.0
        fig, ax = plt.subplots(2, 1, figsize=[6.4 * scaley, 4.8 * scalex])
        fig.subplots_adjust(right=0.75)
        if not args.fancy:
            plt.title("{} with {}".format(transmission_direction, cc_algo))
            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]

        snd_plot = ax0.plot(
            transmission_df["snd_cwnd"].dropna(),
            color="lime",
            linestyle="dashed",
            label="cwnd",
        )
        srtt_plot = ax1.plot(
            transmission_df["srtt"].dropna(),
            color="red",
            linestyle="dashdot",
            label="sRTT",
        )
        goodput_plot = ax2.plot(
            transmission_df["goodput_rolling"],
            color="blue",
            linestyle="solid",
            label="goodput",
        )

        # sum all effective bandwidth for 5G and 4G
        serial_df["SCC1_NR5G_effective_bw"] = serial_df["SCC1_NR5G_bw"].fillna(0)
        serial_df["effective_bw_sum"] = (
                serial_df["SCC1_NR5G_effective_bw"]
                + serial_df["LTE_SCC1_effective_bw"]
                + serial_df["LTE_SCC2_effective_bw"]
                + serial_df["LTE_SCC3_effective_bw"]
                + serial_df["LTE_SCC4_effective_bw"]
                + serial_df["LTE_bw"]
        )
        bw_cols = [
            "SCC1_NR5G_effective_bw",
            "LTE_bw",
            "LTE_SCC1_effective_bw",
            "LTE_SCC2_effective_bw",
            "LTE_SCC3_effective_bw",
            "LTE_SCC4_effective_bw",
        ]

        serial_df["time_rel"] = serial_df["time"] - serial_df["time"].iloc[0]
        serial_df.index = serial_df["time_rel"] / 60

        ax_stacked = serial_df[bw_cols].plot.area(stacked=True, linewidth=0, ax=ax00)
        ax00.set_ylabel("bandwidth [MHz]")
        #ax.set_xlabel("time [minutes]")
        #ax00.set_xlim([0, transmission_df.index[-1]])
        ax00.xaxis.grid(False)



        ax2.spines.right.set_position(("axes", 1.1))


        ax0.set_ylim(0, 5000)
        ax1.set_ylim(0, 0.3)
        ax2.set_ylim(0, 600)
        #ax00.set_ylim(-25, 0)

        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 [MSS]")

        if args.fancy:
            legend_frame = False
            ax0.set_xlim([0, transmission_df.index[-1]])
            ax00.set_xlim([0, transmission_df.index[-1]])
            # added these three lines
            lns_ax0 = snd_plot + srtt_plot + goodput_plot
            labs_ax0 = [l.get_label() for l in lns_ax0]
            ax2.legend(lns_ax0, labs_ax0, ncols=9, fontsize=9, loc="upper right", frameon=legend_frame)
            #ax0.set_zorder(100)

            #lns_ax00 = [ax_stacked]
            #labs_ax00 = ["5G bandwidth", "4G bandwidth"]
            #ax00.legend(lns_ax00, labs_ax00, ncols=3, fontsize=9, loc="upper center", frameon=legend_frame)

            L = ax00.legend(ncols=3, fontsize=9, frameon=False)
            L.get_texts()[0].set_text("5G main")
            L.get_texts()[1].set_text("4G main")
            L.get_texts()[2].set_text("4G SCC 1")
            L.get_texts()[3].set_text("4G SCC 2")
            L.get_texts()[4].set_text("4G SCC 3")
            L.get_texts()[5].set_text("4G SCC 4")

            #ax00.set_zorder(100)
            plt.savefig("{}{}_plot.eps".format(args.save, csv.replace(".csv", "")), bbox_inches="tight")
        else:
            fig.legend(loc="lower right")
            plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")), bbox_inches="tight")
        # except Exception as e:
        #    print("Error processing file: {}".format(csv))
        #    print(str(e))
        counter += 1

        plt.close(fig)
        plt.clf()