Adds srtts to csv.

calc_bandwidth_goodput_csv.py

@@ -0,0 +1,198 @@
+#!/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(
+        "-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", "")))
+
+    concat_frame = None
+
+    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_df = transmission_df.filter(["goodput", "effective_bw_sum", "srtt"])
+        transmission_df = transmission_df.reset_index(drop=True)
+
+        if concat_frame is None:
+            concat_frame = transmission_df
+        else:
+            concat_frame = pd.concat([concat_frame, transmission_df])
+
+    concat_frame.to_csv("{}_concat_bw_gp.csv".format(args.save))
+
+

calc_gps_map_csv.py

@@ -2,6 +2,7 @@
 import multiprocessing
 import os
 from argparse import ArgumentParser
+from datetime import datetime
 from math import ceil
 from time import sleep
 
@@ -50,18 +51,10 @@ if __name__ == "__main__":
     parser.add_argument("-f", "--gps_file", required=True, help="GPS csv file.")
     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("-a", "--column", required=True, help="Column to plot")
-    parser.add_argument("-l", "--label", help="Label above the plot.")
-    parser.add_argument("--no_legend", action="store_false", default=True, help="Do not show legend.")
     parser.add_argument("--save", default=None, help="Location to save pdf file.")
-    parser.add_argument("--time_offset", default=None, type=int, help="Minutes added to GPS datetime.")
-    parser.add_argument("--no_plot", default=False, action="store_true", help="Only calculations without plotting.")
-
-    parser.add_argument(
-        "--show_providerinfo",
-        default=False,
-        help="Show providerinfo for map tiles an zoom levels.",
-    )
+    parser.add_argument("--time_offset", default=0, type=int, help="Minutes added to GPS datetime.")
+    parser.add_argument("--neg_offset", default=False, action="store_true", help="Subtract GPS time offset.")
+    parser.add_argument("--auto_offset", default=False, action="store_true", help="Calculate GPS time offset.")
     parser.add_argument(
         "-c",
         "--cores",
@@ -141,10 +134,24 @@ if __name__ == "__main__":
     # load dataframe an put it into geopandas
     df = pd.read_csv(args.gps_file)
     df["kmh"] = df["speed (knots)"].apply(lambda x: x * 1.852)
-    if args.time_offset:
+    if not args.auto_offset and args.time_offset > 0:
+        if args.neg_offset:
+            df["datetime"] = pd.to_datetime(df["datetime"]) - pd.Timedelta(minutes=args.time_offset)
+        else:
             df["datetime"] = pd.to_datetime(df["datetime"]) + pd.Timedelta(minutes=args.time_offset)
+    elif args.auto_offset:
+        gps_first = datetime.strptime(df["datetime"].iloc[0], "%Y-%m-%d %H:%M:%S.%f")
+        pcap_first = pd.to_datetime(transmission_df.first_valid_index())
+        calc_offset = gps_first - pcap_first
+        if gps_first > pcap_first:
+            time_offset = gps_first - pcap_first
+            df["datetime"] = pd.to_datetime(df["datetime"]) - time_offset
+        else:
+            time_offset = pcap_first - gps_first
+            df["datetime"] = pd.to_datetime(df["datetime"]) + time_offset
     else:
         df["datetime"] = pd.to_datetime(df["datetime"])
+
     df = df.set_index("datetime")
     df.index = pd.to_datetime(df.index)
 
@@ -180,43 +187,6 @@ if __name__ == "__main__":
     df_wm = gdf.to_crs(epsg=3857)
     #df_wm.to_csv("debug-data.csv")
     # ax2 = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')
-    if args.no_plot:
+
     df_wm.to_csv("{}gps_plot.csv".format(args.save))
     print("Saved calculations to: {}gps_plot.csv".format(args.save))
-        exit(0)
-
-    print("Start plotting...")
-
-    ax2 = df_wm.plot()
-    ax2 = df_wm.plot(args.column, cmap="hot", legend=args.no_legend, ax=ax2)
-    # ax2 = df_wm.plot.scatter(x="longitude", y="latitude", c="kmh", cmap="hot")
-    # zoom 17 is pretty
-    cx.add_basemap(ax2, source=cx.providers.OpenStreetMap.Mapnik, zoom=15)
-
-    #    gdf.plot()
-    ax2.set_axis_off()
-    ax2.set_title(args.label if args.label else args.column)
-
-    if args.show_providerinfo:
-        #####################################
-        # Identifying how many tiles
-        latlon_outline = gdf.to_crs("epsg:4326").total_bounds
-        def_zoom = cx.tile._calculate_zoom(*latlon_outline)
-        print(f"Default Zoom level {def_zoom}")
-
-        cx.howmany(*latlon_outline, def_zoom, ll=True)
-        cx.howmany(*latlon_outline, def_zoom + 1, ll=True)
-        cx.howmany(*latlon_outline, def_zoom + 2, ll=True)
-
-        # Checking out some of the other providers and tiles
-        print(cx.providers.CartoDB.Voyager)
-        print(cx.providers.Stamen.TonerLite)
-        print(cx.providers.Stamen.keys())
-        #####################################
-
-    # df.plot(x="longitude", y="latitude", kind="scatter", colormap="YlOrRd")
-
-    if args.save:
-        plt.savefig("{}gps_plot.pdf".format(args.save))
-    else:
-        plt.show()

cdf_compare.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+import multiprocessing
+import os
+import pickle
+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, folder, dummy):
+
+    global n
+    global frame_list
+
+    tmp_df = None
+
+    for csv in csv_list:
+        tmp_df = pd.read_csv(
+            "{}{}".format(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 tmp_df is None:
+            tmp_df = tmp_df
+        else:
+            tmp_df = pd.concat([tmp_df, tmp_df])
+
+        n.value += 1
+
+    tmp_df = tmp_df.filter(
+        ["srtt", "datetime", "payload_size", "congestion_control", "direction"])
+    frame_list.append(tmp_df)
+    del tmp_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, axis=None):
+    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()
+
+    if axis:
+        stats_df.plot(x=column_name, y=["CDF"], grid=True, ax=axis)
+    else:
+        stats_df.plot(x=column_name, y=["CDF"], grid=True)
+    del stats_df
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("-s", "--serials", required=True, help="Serial csv files. Comma separated.")
+    parser.add_argument("-f", "--folders", required=True, help="PCAP csv folders. Comma separated.")
+    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",
+        default=2,
+        type=int,
+        help="Time interval for rolling window.",
+    )
+
+    args = parser.parse_args()
+
+    transmission_df_list = list()
+    for f in args.folders.split(","):
+        manager = multiprocessing.Manager()
+        n = manager.Value("i", 0)
+        frame_list = manager.list()
+        jobs = []
+
+        # load all pcap csv into one dataframe
+        pcap_csv_list = list()
+        for filename in os.listdir(f):
+            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, f, "dummy"))
+            jobs.append(process)
+
+        for j in jobs:
+            j.start()
+
+        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="",
+            )
+            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 = transmission_df.sort_index()
+
+        #
+        # Don't forget to add new columns to the filter argument in the function above!
+        #
+
+        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
+        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(["srtt", "datetime", "srtt", "payload_size"])
+        transmission_df = transmission_df.drop(columns=["congestion_control", "direction"])
+
+
+        # read serial csv
+        #serial_df = pd.read_csv(args.serial_file)
+        #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()
+
+        #transmission_df = pd.merge_asof(
+        #    transmission_df,
+        #    serial_df,
+        #    tolerance=pd.Timedelta("1s"),
+        #    right_index=True,
+        #    left_index=True,
+        #)
+
+        transmission_df_list.append(dict(
+            df=transmission_df,
+            cc_algo=cc_algo,
+            transmission_direction=transmission_direction
+        ))
+        del transmission_df
+
+    # Plot sRTT CDF
+    legend = list()
+    plot_cdf(transmission_df_list[0]["df"], "srtt")
+    legend.append(transmission_df_list[0]["cc_algo"])
+    for i in range(1, len(transmission_df_list)):
+        plot_cdf(transmission_df_list[i]["df"], "srtt", axis=plt.gca())
+        legend.append(transmission_df_list[i]["cc_algo"])
+    #plt.xscale("log")
+    plt.xlim(0, 0.15)
+    plt.xlabel("sRTT [s]")
+    plt.ylabel("CDF")
+    plt.legend(legend)
+    plt.title("{}".format(transmission_df_list[0]["transmission_direction"]))
+    plt.savefig("{}{}_cdf_compare_plot.pdf".format(args.save, "srtt"))
+
+    plt.clf()
+
+    # Plot goodput CDF
+    legend = list()
+    plot_cdf(transmission_df_list[0]["df"], "goodput_rolling")
+    legend.append(transmission_df_list[0]["cc_algo"])
+    for i in range(1, len(transmission_df_list)):
+        plot_cdf(transmission_df_list[i]["df"], "goodput_rolling", axis=plt.gca())
+        legend.append(transmission_df_list[i]["cc_algo"])
+    plt.xlabel("goodput [mbps]")
+    plt.ylabel("CDF")
+    plt.legend(legend)
+    plt.title("{}".format(transmission_df_list[0]["transmission_direction"]))
+    plt.savefig("{}{}_cdf_compare_plot.pdf".format(args.save, "goodput"))

format_serial_txt_to_csv.py

@@ -0,0 +1,191 @@
+#!/usr/bin/env python3
+import csv
+import datetime
+from argparse import ArgumentParser
+import pandas as pd
+
+
+def convert_bandwidth(value):
+    try:
+        value = int(value)
+    except:
+        value = -1
+    if value == 0:
+        return 1.4
+    elif value == 1:
+        return 3
+    elif value == 2:
+        return 5
+    elif value == 3:
+        return 10
+    elif value == 4:
+        return 15
+    elif value == 5:
+        return 20
+    else:
+        return 0
+
+
+if __name__ == "__main__":
+
+    parser = ArgumentParser()
+    parser.add_argument("-f", "--file", required=True, help="Input txt file.")
+    args = parser.parse_args()
+
+    file = open(args.file, "r")
+    content = file.read()
+    file.close()
+
+    all_csv_lines = list()
+    csv_header = ["time"]
+
+    h1 = False
+    h2 = False
+    h3 = False
+
+    for line in content.split("\n"):
+        if line == "" or line == "\n":
+            break
+        raw_columns = line.split(";")
+        csv_line = list()
+        csv_line.append(raw_columns[0])
+        for i in range(1, len(raw_columns)):
+            col = raw_columns[i]
+
+            if 'AT+QNWCFG="nr5g_csi"' in col:
+                if not h1:
+                    csv_header += ["mcs_PDSCH", "ri_PDSCH", "downlink_cqi", "pmi"]
+                    h1 = True
+                tmp = raw_columns[i + 1].replace('+QNWCFG: "nr5g_csi",', "")
+                csv_line += tmp.split(",")
+            elif "AT+QENDC" in col:
+                if not h2:
+                    csv_header += [
+                        "endc_avl",
+                        "plmn_info_list_r15_avl",
+                        "endc_rstr",
+                        "5G_basic",
+                    ]
+                    h2 = True
+                tmp = raw_columns[i + 1].replace("+QENDC: ", "")
+                csv_line += tmp.split(",")
+            elif 'AT+QENG="servingcell"' in col:
+                if not h3:
+                    csv_header += [
+                        "connection_state",
+                        "is_tdd",
+                        "mcc",
+                        "mnc",
+                        "cellID",
+                        "PCID",
+                        "earfcn",
+                        "freq_band_ind",
+                        "UL_bandwidth",
+                        "DL_bandwidth",
+                        "TAC",
+                        "RSRP",
+                        "RSRQ",
+                        "RSSI",
+                        "SINR",
+                        "CQI_1-30",
+                        "tx_power",
+                        "srxlev",
+                        "MCC",
+                        "MNC",
+                        "PCID",
+                        "RSRP",
+                        "SINR",
+                        "RSRQ",
+                        "ARFCN",
+                        "band",
+                    ]
+                    h3 = True
+                if "NOCONN" in raw_columns[i + 1]:
+                    csv_line.append("NOCONN")
+                    csv_line += raw_columns[i + 2].replace('+QENG: "LTE",', "").split(",")
+                    csv_line += (
+                        raw_columns[i + 3].replace('+QENG:"NR5G-NSA",', "").split(",")
+                    )
+                elif "SEARCH" in raw_columns[i + 1]:
+                    csv_line.append("SEARCH")
+                    csv_line += [""] * 25
+                elif "OK" == raw_columns[i + 1]:
+                    csv_line.append("OK")
+                    csv_line += [""] * 25
+                else:
+                    csv_line.append("undefined")
+                    csv_line += [""] * 25
+
+
+        all_csv_lines.append(csv_line)
+
+
+
+    outputfile = open(args.file.replace("txt", "csv"), "w")
+    writer = csv.writer(outputfile, delimiter=",", lineterminator="\n", escapechar='\\')
+    writer.writerow(csv_header)
+    #print(all_csv_lines)
+    for l in all_csv_lines:
+        #print(l)
+        writer.writerow(l)
+
+    outputfile.close()
+
+    outputfile = open(args.file.replace("txt", "csv"), "r")
+    serial_df = pd.read_csv(outputfile,
+                            converters={"UL_bandwidth": convert_bandwidth, "DL_bandwidth": convert_bandwidth},
+                            )
+    serial_df = serial_df.drop(columns=["MCC", "MNC"])
+    serial_df["datetime"] = pd.to_datetime(
+        serial_df["time"].apply(lambda x: datetime.datetime.fromtimestamp(x))
+    )
+    serial_df.to_csv(args.file.replace("txt", "csv"))
+    outputfile.close()
+
+
+
+    exit()
+
+    delete_string = [
+        'AT+QNWCFG="nr5g_csi";',
+        '+QNWCFG: "nr5g_csi"',
+        'AT+QENG="servingcell";+QENG: "servingcell",',
+        "+QENG:",
+        "AT+QENDC;+QENDC:",
+    ]
+
+    for d in delete_string:
+        content = content.replace(d, ",")
+    content = (
+        content.replace(";", "")
+        .replace(" ", "")
+        .replace(",,,", ",")
+        .replace('"', "")
+        .replace("LTE,", "")
+        .replace("NR5G-NSA,", "")
+    )
+
+    header = "time,mcs,ri,cqi,pmi,conn_state,is_tdd,MCC,MNC,cellID,PCID,earfcn,freq_band_ind,UL_bandwidth,DL_bandwidth,TAC,RSRP,RSRQ,RSSI,SINR,CQI,tx_power,srxlev,MCC,MNC,PCID,RSRP,SINR,RSRQ,ARFCN,band,endc_avl,plmn_info_list_r15_avl,endc_rstr,5G_basic\n"
+    csv_path = args.file.replace("txt", "csv")
+    print("Write to: {}".format(csv_path))
+    csv_string = header
+    for csv_line in content.split("\n"):
+        if len(header.split(",")) == len(csv_line.split(",")):
+            csv_string += csv_line + "\n"
+        else:
+            # print("{} found {}".format(len(header.split(",")), len(csv_line.split(","))))
+            print("Could not interpret string: {}".format(csv_line))
+            print(
+                "Expect {} columns got {}".format(
+                    len(header.split(",")), len(csv_line.split(","))
+                )
+            )
+
+    csv_string_io = StringIO(csv_string)
+    serial_df = pd.read_csv(csv_string_io)
+    serial_df = serial_df
+    serial_df["datetime"] = pd.to_datetime(
+        serial_df["time"].apply(lambda x: datetime.datetime.fromtimestamp(x))
+    )
+    serial_df.to_csv(csv_path)
+    print(serial_df)

format_serial_txt_to_csv_EM9190.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+import datetime
+import re
+from argparse import ArgumentParser
+import pandas as pd
+
+KEY_VALUE_REGEX = r"(.+):(.+)"
+
+if __name__ == "__main__":
+
+    parser = ArgumentParser()
+    parser.add_argument("-f", "--file", required=True, help="Input txt file.")
+    args = parser.parse_args()
+
+    file = open(args.file, "r")
+    content = file.read()
+    file.close()
+
+    serial_df = None
+    p = re.compile(KEY_VALUE_REGEX)
+
+    for part in content.split(";;;"):
+        if part == "":
+            break
+        part = part.replace("\t", "\n").strip()
+        time = None
+        line_dict = dict(time=None)
+        for line in part.split("\n"):
+            if not line.startswith("!") or line == "" or line == "\n":
+                if line_dict["time"] is None:
+                    time = line
+                    line_dict["time"] = [time]
+                m = p.match(line)
+                if m:
+                    key = m.group(1).strip().replace(" ", "_")
+                    value = m.group(2).replace("MHz", "").replace("---", "").strip()
+
+                    line_dict[key] = [value]
+
+        if len(line_dict) > 1:
+            #print("line:")
+            #print(line_dict)
+            #print("serial_df:")
+            #print(serial_df)
+            if serial_df is None:
+                serial_df = pd.DataFrame.from_dict(line_dict, orient="columns",)
+            else:
+                serial_df = pd.concat([serial_df, pd.DataFrame.from_dict(line_dict, orient="columns")])
+
+    serial_df = serial_df.copy()
+    serial_df["datetime"] = pd.to_datetime(
+        serial_df["time"].apply(lambda x: datetime.datetime.fromtimestamp(int(x)))
+    )
+    serial_df.to_csv(args.file.replace("txt", "csv"))
+
+    #serial_df = serial_df.filter(["datetime", "LTE_bw", "LTE_SCC2_bw", "LTE_SCC3_bw", "LTE_SCC4_bw", "SCC1_NR5G_bw", "NR5G_dl_bw", "NR5G_ul_bw", "LTE_SCC1_bw", "NR5G_bw"])
+    #print(serial_df.to_string())

format_throughput_pcap_to_csv.py

@@ -188,7 +188,7 @@ def format_pcaps_to_csv(pcaps, dummy):
                 pcap_df = pcap_df.sort_values("arrival_time")
                 try:
                     # join tcp_trace data with pcap data
-                    merge_srtt = False
+                    merge_srtt = True
                     if merge_srtt:
                         tcp_trace_df = format_tcp_trace_to_csv(
                             pcap_number,
@@ -202,7 +202,7 @@ def format_pcaps_to_csv(pcaps, dummy):
                                     pcap_number
                                 )
                             )
-                            break
+                            continue ## break before but stoped the thread
                         merged_df = pd.merge_asof(
                             pcap_df.loc[pcap_df["src_ip"] != args.server],
                             tcp_trace_df,

measurement_main.py

@@ -23,6 +23,7 @@ GET_IPV4_SHELL_COMMAND = "ip a | grep {} | grep inet | cut -d' ' -f6 | cut -d'/'
 NR_CQI_COMMAND = b'AT+QNWCFG="nr5g_csi"\r\n'
 NR_SERVINGCELL_COMMAND = b'AT+QENG="servingcell"\r\n'
 NR_EN_DC_STATUS_COMMAND = b"AT+QENDC\r\n"
+NE_CA_COMMAND = b'AT+QCAINFO\r\n'
 NR_SERIAL_RESPOND_TIME = 0.5  # s
 CMD_TIME_EPOCH = "date +%s"
 TIMEOUT_OFFSET = 10.0
@@ -31,6 +32,8 @@ WAIT_AFTER_IPERF = 5.0
 modem_serial_obj = None
 gps_serial_obj = None
 
+MODEM_MODEL = None
+
 
 class ProcessHandler:
     def __init__(self):
@@ -219,7 +222,8 @@ def raise_receive_window():
 
 
 def monitor_serial(ser, output_file):
-    run_cmds = [NR_CQI_COMMAND, NR_SERVINGCELL_COMMAND, NR_EN_DC_STATUS_COMMAND]
+    #run_cmds = [NR_CQI_COMMAND, NR_SERVINGCELL_COMMAND, NR_EN_DC_STATUS_COMMAND, NE_CA_COMMAND]
+    run_cmds = [b"at!gstatus?\r\n", b"AT!NRINFO?\r\n"]
     try:
         while ser.is_open:
             response = subprocess.check_output(CMD_TIME_EPOCH, shell=True).decode(
@@ -229,13 +233,13 @@ def monitor_serial(ser, output_file):
                 ser.write(cmd)
                 sleep(NR_SERIAL_RESPOND_TIME)
                 response += ser.read(ser.inWaiting()).decode("utf-8")
-            response = (
-                response.replace("\n", ";")
-                .replace("\r", "")
-                .replace(";;OK", ";")
-                .replace(";;", ";")
-            )
-            write_to_file(output_file, response + "\n")
+            #response = (
+            #    response.replace("\n", ";")
+            #    .replace("\r", "")
+            #    .replace(";;OK", ";")
+            #    .replace(";;", ";")
+            #)
+            write_to_file(output_file, response + ";;;\n")
     except:
         if not ser.is_open:
             print_message("Serial port is closed. Exit monitoring thread.")
@@ -319,11 +323,20 @@ def monitor_gps(ser, output_file):
 
 def connect_moden(provider="telekom"):
     print_message("Connect modem with provider {} ...".format(provider))
+    if MODEM_MODEL == "EM9191":
+        os.system("/root/connection_mbim.py -l {}".format(provider))
+    else:
         os.system("/root/connect-modem.py -l {}".format(provider))
     print_message("...done")
 
 
 def reconnect_modem(provider="telekom", hard=False):
+    #TODO
+    os.system("/root/connection_mbim.py -s")
+    sleep(2)
+    os.system("/root/connection_mbim.py -l {}".format(provider))
+
+    return
     global modem_serial_obj
     print_message("Reonnect modem with provider {} ...".format(provider))
     if hard:
@@ -1200,6 +1213,7 @@ if __name__ == "__main__":
         default=None,
         help="Start in client mode and set the server IPv4 address.",
     )
+    parser.add_argument("--modem", default="EM9191", help="Modem model name.")
     parser.add_argument(
         "--prefix", default=now.strftime("%Y-%m-%d"), help="Prefix on filename."
     )
@@ -1302,6 +1316,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     disable_tso(args.interface)
+    MODEM_MODEL = args.modem
 
     if args.server:
         asyncio.run(start_server(args))

plot_gps_csv.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+
+from argparse import ArgumentParser
+
+import numpy as np
+import pandas as pd
+import geopandas as gpd
+import contextily as cx
+import matplotlib.pyplot as plt
+
+import seaborn as sns
+
+sns.set()
+#sns.set(font_scale=1.5)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("-f", "--file", required=True, help="Messfahrt csv")
+    parser.add_argument("-a", "--column", required=True, help="Column to plot")
+    parser.add_argument("-l", "--label", help="Label above the plot.")
+    parser.add_argument("--no_legend", action="store_false", default=True, help="Do not show legend.")
+    parser.add_argument("--save", default=None, help="Location to save pdf file.")
+
+    parser.add_argument(
+        "--show_providerinfo",
+        default=False,
+        help="Show providerinfo for map tiles an zoom levels.",
+    )
+
+    args = parser.parse_args()
+
+    df = pd.read_csv(args.file)
+
+    gdf = gpd.GeoDataFrame(
+        df,
+        geometry=gpd.points_from_xy(df["longitude"], df["latitude"]),
+        crs="EPSG:4326",
+    )
+
+    gdf["srtt"] = gdf["srtt"].apply(lambda x: x / 10 ** 6)
+    gdf["is_retranmission"] = gdf["is_retranmission"].replace(True, np.NaN).dropna().astype(float)
+
+    print("Start plotting...")
+
+    df_wm = gdf.to_crs(epsg=3857)
+    ax2 = df_wm.plot()
+    ax2 = df_wm.plot(column=args.column, cmap="hot", legend=args.no_legend, ax=ax2, legend_kwds={"label": args.label},)
+    # ax2 = df_wm.plot.scatter(x="longitude", y="latitude", c="kmh", cmap="hot")
+    # zoom 17 is pretty
+    cx.add_basemap(ax2, source=cx.providers.OpenStreetMap.Mapnik, zoom=17)
+
+    #    gdf.plot()
+    ax2.set_axis_off()
+    if not args.no_legend:
+        ax2.set_title(args.label if args.label else args.column)
+    else:
+        fig = ax2.figure
+        cb_ax = fig.axes[0]
+        cb_ax.set_label(args.label)
+        cb_ax.tick_params(labelsize=30)
+
+    if args.show_providerinfo:
+        #####################################
+        # Identifying how many tiles
+        latlon_outline = gdf.to_crs("epsg:4326").total_bounds
+        def_zoom = cx.tile._calculate_zoom(*latlon_outline)
+        print(f"Default Zoom level {def_zoom}")
+
+        cx.howmany(*latlon_outline, def_zoom, ll=True)
+        cx.howmany(*latlon_outline, def_zoom + 1, ll=True)
+        cx.howmany(*latlon_outline, def_zoom + 2, ll=True)
+
+        # Checking out some of the other providers and tiles
+        print(cx.providers.CartoDB.Voyager)
+        print(cx.providers.Stamen.TonerLite)
+        print(cx.providers.Stamen.keys())
+        #####################################
+
+    # df.plot(x="longitude", y="latitude", kind="scatter", colormap="YlOrRd")
+
+    if args.save:
+        plt.savefig("{}gps_plot.eps".format(args.save), bbox_inches="tight")
+    else:
+        plt.show()

plot_single_transmission.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+import math
+import multiprocessing
+import os
+from argparse import ArgumentParser
+
+import matplotlib
+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)
+
+
+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)
+        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()
+
+        transmission_df = pd.merge_asof(
+            transmission_df,
+            serial_df,
+            tolerance=pd.Timedelta("1s"),
+            right_index=True,
+            left_index=True,
+        )
+        transmission_df = transmission_df.rename(columns={"PCID": "lte_pcid", "PCID.1": "nr_pcid"})
+
+        transmission_df.index = transmission_df["arrival_time"]
+
+        # 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()
+
+        # Plot vertical lines
+        lte_handovers = transmission_df["lte_pcid"].diff().dropna()
+        for index, value in lte_handovers.items():
+            if value > 0:
+                ax00.axvline(index, ymin=0, ymax=1, color="skyblue", label="4G Handover")
+
+        nr_handovers = transmission_df["nr_pcid"].diff().dropna()
+        for index, value in nr_handovers.items():
+            if value > 0:
+                ax00.axvline(index, ymin=0, ymax=1, color="greenyellow", label="5G Handover")
+
+        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")
+        ax00.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
+        ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="bandwidth")
+
+        ax2.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(0, 16)
+        ax01.set_ylim(0, 21)
+
+        ax00.set_xlabel("arrival time [s]")
+        ax2.set_ylabel("Goodput [mbps]")
+        ax00.set_ylabel("CQI")
+        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()

plot_single_transmission_EM9190.py

@@ -0,0 +1,343 @@
+#!/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]
+        ax01 = ax00.twinx()
+        ax02 = ax00.twinx()
+
+        # Plot vertical lines
+        first = True
+        lte_handovers = transmission_df["Cell_ID"].dropna().diff()
+        lte_hanover_plot = None
+        for index, value in lte_handovers.items():
+            if value > 0:
+                if first:
+                    lte_hanover_plot = 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()
+        )
+
+        nr_hanover_plot = None
+        for index, value in nr_handovers.items():
+            if value > 0:
+                if first:
+                    nr_hanover_plot = ax00.axvline(
+                        index, ymin=0, ymax=1, color="greenyellow", label="5G Handover"
+                    )
+                    first = False
+                else:
+                    ax00.axvline(index, ymin=0, ymax=1, color="greenyellow")
+
+        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",
+        )
+        # ax2.plot(transmission_df["goodput"], color="blue", linestyle="solid", label="goodput")
+
+        eff_bw_plot = ax01.plot(
+            transmission_df["effective_bw_sum"].dropna(),
+            color="peru",
+            linestyle="solid",
+            label="bandwidth",
+        )
+        lte_eff_bw_plot = ax01.plot(
+            transmission_df["lte_effective_bw_sum"].dropna(),
+            color="lightsteelblue",
+            linestyle="solid",
+            label="4G bandwidth",
+            alpha=0.5,
+        )
+        nr_eff_bw_plot = ax01.plot(
+            transmission_df["nr_effective_bw_sum"].dropna(),
+            color="cornflowerblue",
+            linestyle="solid",
+            label="5G bandwidth",
+            alpha=0.5,
+        )
+
+        # 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"]
+        #               )
+
+        lte_rsrq_plot = ax02.plot(
+            transmission_df["RSRQ_(dB)"].dropna(),
+            color="purple",
+            linestyle="dotted",
+            label="LTE RSRQ",
+        )
+        nr_rsrq_plot = 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, 600)
+        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 [MSS]")
+        ax01.set_ylabel("Bandwidth [MHz]")
+
+        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 = eff_bw_plot + lte_eff_bw_plot + nr_eff_bw_plot + lte_rsrq_plot + nr_rsrq_plot
+            if lte_hanover_plot:
+                lns_ax00.append(lte_hanover_plot)
+            if nr_hanover_plot:
+                lns_ax00.append(nr_hanover_plot)
+            labs_ax00 = [l.get_label() for l in lns_ax00]
+            ax02.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()

plot_single_transmission_paper.py

@@ -0,0 +1,269 @@
+#!/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]
+
+        serial_df["arrival_time"] = (serial_df["time"] - serial_df["time"].iloc[0]) * 60
+        serial_df.index = serial_df["arrival_time"]
+
+        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["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",
+        ]
+
+
+        # 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="darkorange",
+            linestyle="dashed",
+            label="cwnd",
+        )
+        srtt_plot = ax1.plot(
+            transmission_df["srtt"].dropna(),
+            color="maroon",
+            linestyle="dotted",
+            label="sRTT",
+        )
+        goodput_plot = ax2.plot(
+            transmission_df["goodput_rolling"],
+            color="blue",
+            linestyle="solid",
+            label="goodput",
+        )
+
+        serial_df["time_rel"] = serial_df["time"] - serial_df["time"].iloc[0]
+        serial_df.index = serial_df["time_rel"]
+
+        ax_stacked = serial_df[bw_cols].plot.area(stacked=True, linewidth=0, ax=ax00)
+        ax00.set_ylabel("bandwidth [MHz]")
+        ax00.set_ylim(0, 200)
+        #ax.set_xlabel("time [minutes]")
+        #ax00.set_xlim([0, transmission_df.index[-1]])
+        ax00.xaxis.grid(True)
+
+
+
+        ax2.spines.right.set_position(("axes", 1.1))
+
+
+        ax0.set_ylim(0, 5000) #2500
+        ax1.set_ylim(0, 2) #0.3
+        ax2.set_ylim(0, 500)
+        #ax00.set_ylim(-25, 0)
+
+        ax00.set_xlabel("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, 60])
+            ax00.set_xlim([0, 60])
+            # 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")
+            #serial_df.to_csv("{}{}_plot.csv".format(args.save, csv.replace(".csv", "")))
+        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()

plot_single_transmission_timeline.py

@@ -2,7 +2,6 @@
 import math
 import multiprocessing
 import os
-import pickle
 from argparse import ArgumentParser
 
 import matplotlib
@@ -44,10 +43,13 @@ if __name__ == "__main__":
     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)
@@ -100,10 +102,12 @@ if __name__ == "__main__":
             twin1 = ax.twinx()
             twin2 = ax.twinx()
             twin3 = ax.twinx()
+            twin4 = 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", 1.1))
             twin3.spines.right.set_position(("axes", 1.2))
+            twin4.spines.right.set_position(("axes", 1.3))
 
 
             # create list fo color indices
@@ -131,61 +135,45 @@ if __name__ == "__main__":
 
             p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
             p3, = twin2.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
-        p1, = ax.plot(transmission_df["arrival_time"], transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
+            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")
+            p5, = twin4.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="DL_bandwidth")
 
             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, transmission_df["ack_rtt"].max())
+            twin2.set_ylim(0, 0.2) #twin2.set_ylim(0, transmission_df["ack_rtt"].max())
             twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10)
+            twin4.set_ylim(0, 21)
 
             ax.set_xlabel("arrival time")
             ax.set_ylabel("Goodput [mbps]")
             twin1.set_ylabel("CQI")
             twin2.set_ylabel("sRTT [s]")
             twin3.set_ylabel("cwnd")
+            twin4.set_ylabel("DL_bandwidth")
 
             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())
+            twin4.yaxis.label.set_color(p5.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)
+            twin4.tick_params(axis='y', colors=p5.get_color(), **tkw)
             ax.tick_params(axis='x', **tkw)
 
             #ax.legend(handles=[p1, p2, p3])
 
             if args.save:
                 plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
-
-
-        # plot correlations
-        corr_pairs = [
-            ["goodput_rolling", "RSRQ"],
-            ["goodput_rolling", "RSRP"],
-            ["goodput_rolling", "RSSI"],
-            ["goodput_rolling", "SINR"],
-            ["goodput_rolling", "downlink_cqi"],
-        ]
-
-        for pair in corr_pairs:
-            # spearman and pearson
-            sp = transmission_df[pair[0]].corr(transmission_df[pair[1]], method="spearman")
-            pe = transmission_df[pair[0]].corr(transmission_df[pair[1]], method="pearson")
-            title = "{}/{} spearman: {} pearson: {}".format(pair[0], pair[1], round(sp, 4), round(pe, 4))
-            transmission_df.plot.scatter(x=pair[0], y=pair[1], c="DarkBlue", title=title)
-
-            if args.save:
-                plt.savefig("{}{}_corr_{}_and_{}.pdf".format(args.save, csv.replace(".csv", ""), pair[0], pair[1]))
-
-            plt.clf()
-
-
+        except Exception as e:
+            print("Error processing file: {}".format(csv))
+            print(str(e))
         counter += 1
 
         plt.clf()

plot_stacked_bandwidth.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+
+from argparse import ArgumentParser
+
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+plt_params = {
+    "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(plt_params)
+
+import seaborn as sns
+sns.set()
+sns.set(font_scale=1.5)
+
+plt.rcParams["figure.figsize"] = (10, 3)
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("-f", "--file", required=True, help="Serial CSV")
+    parser.add_argument("--save", default=None, help="Location to save pdf file.")
+
+    args = parser.parse_args()
+
+    df = pd.read_csv(args.file)
+    df["time_rel"] = df["time"] - df["time"].iloc[0]
+    df.index = df["time_rel"] / 60
+
+    # filter active state
+    for i in range(1, 5):
+        df["LTE_SCC{}_effective_bw".format(i)] = df["LTE_SCC{}_bw".format(i)]
+
+        mask = df["LTE_SCC{}_state".format(i)].isin(["ACTIVE"])
+        df["LTE_SCC{}_effective_bw".format(i)] = 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 = df["LTE_SCC{}_UL_Configured".format(i)].isin([False])
+        df["LTE_SCC{}_effective_bw".format(i)] = df[
+            "LTE_SCC{}_effective_bw".format(i)
+        ].where(mask, other=0)
+
+    # sum all effective bandwidth for 5G and 4G
+    df["SCC1_NR5G_effective_bw"] = df["SCC1_NR5G_bw"].fillna(0)
+    df["effective_bw_sum"] = (
+        df["SCC1_NR5G_effective_bw"]
+        + df["LTE_SCC1_effective_bw"]
+        + df["LTE_SCC2_effective_bw"]
+        + df["LTE_SCC3_effective_bw"]
+        + df["LTE_SCC4_effective_bw"]
+        + 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 = df[bw_cols].plot.area(stacked=True, linewidth=0)
+    ax.set_ylabel("bandwidth [MHz]")
+    ax.set_xlabel("time [minutes]")
+    ax.set_xlim([0, df.index[-1]])
+    ax.xaxis.grid(False)
+
+
+    L = plt.legend(ncols=2, fontsize=12, 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")
+
+    if args.save:
+        plt.savefig("{}-used_bandwidth.eps".format(args.save), bbox_inches="tight")
+    else:
+        plt.show()

plot_transmission_timeline.py

@@ -144,6 +144,9 @@ if __name__ == "__main__":
 
     #print(transmission_df)
 
+    # 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(
@@ -160,7 +163,7 @@ 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", "srtt", "goodput_rolling"])
 
     # read serial csv
     serial_df = pd.read_csv(args.serial_file)
@@ -217,7 +220,7 @@ if __name__ == "__main__":
 
     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")
+    p3, = twin2.plot(transmission_df["srtt"], "-.", color="red", label="sRTT")
 
     ax.set_xlim(transmission_df["index"].min(), transmission_df["index"].max())
     ax.set_ylim(0, 500)
@@ -227,7 +230,7 @@ if __name__ == "__main__":
     ax.set_xlabel("Time")
     ax.set_ylabel("Goodput")
     twin1.set_ylabel("CQI")
-    twin2.set_ylabel("ACK RTT")
+    twin2.set_ylabel("sRTT")
 
     ax.yaxis.label.set_color(p1.get_color())
     twin1.yaxis.label.set_color(p2.get_color())
@@ -264,16 +267,15 @@ if __name__ == "__main__":
     # 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]")
+    plot_cdf(transmission_df, "srtt")
+    plt.xlabel("sRTT [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"))
+        plt.savefig("{}{}_cdf_plot.pdf".format(args.save, "srtt"))
     else:
         plt.show()