Paper plots.

plot_single_treansmission_paper.py

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+#!/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["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)
+
+        # filter active state
+        for i in range(1, 5):
+            transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
+                "LTE_SCC{}_bw".format(i)
+            ]
+
+            mask = transmission_df["LTE_SCC{}_state".format(i)].isin(["ACTIVE"])
+            transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_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 = transmission_df["LTE_SCC{}_UL_Configured".format(i)].isin([False])
+            transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
+                "LTE_SCC{}_effective_bw".format(i)
+            ].where(mask, other=0)
+
+        # sum all effective bandwidth for 5G and 4G
+        transmission_df["SCC1_NR5G_effective_bw"] = transmission_df[
+            "SCC1_NR5G_bw"
+        ].fillna(0)
+
+        transmission_df["lte_effective_bw_sum"] = (
+                transmission_df["LTE_SCC1_effective_bw"].fillna(0)
+                + transmission_df["LTE_SCC2_effective_bw"].fillna(0)
+                + transmission_df["LTE_SCC3_effective_bw"].fillna(0)
+                + transmission_df["LTE_SCC4_effective_bw"].fillna(0)
+                + transmission_df["LTE_bw"].fillna(0))
+        transmission_df["nr_effective_bw_sum"] = transmission_df["SCC1_NR5G_effective_bw"]
+
+        transmission_df["effective_bw_sum"] = transmission_df["nr_effective_bw_sum"] + transmission_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",
+        )
+
+        # filter active state
+        for i in range(1, 5):
+            transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df["LTE_SCC{}_bw".format(i)]
+
+            mask = transmission_df["LTE_SCC{}_state".format(i)].isin(["ACTIVE"])
+            transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_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 = transmission_df["LTE_SCC{}_UL_Configured".format(i)].isin([False])
+            transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
+                "LTE_SCC{}_effective_bw".format(i)
+            ].where(mask, other=0)
+
+        # sum all effective bandwidth for 5G and 4G
+        transmission_df["SCC1_NR5G_effective_bw"] = transmission_df["SCC1_NR5G_bw"].fillna(0)
+        transmission_df["effective_bw_sum"] = (
+                transmission_df["SCC1_NR5G_effective_bw"]
+                + transmission_df["LTE_SCC1_effective_bw"]
+                + transmission_df["LTE_SCC2_effective_bw"]
+                + transmission_df["LTE_SCC3_effective_bw"]
+                + transmission_df["LTE_SCC4_effective_bw"]
+                + transmission_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",
+        ]
+
+        ax_stacked = transmission_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 = [l.get_label() for l in lns_ax00]
+            ax00.legend(lns_ax00, labs_ax00, ncols=3, fontsize=9, loc="upper center", frameon=legend_frame)
+            #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()