From 7a35d5014d04789e720f40de88191a787f7b4cca Mon Sep 17 00:00:00 2001 From: Lukas Prause Date: Thu, 16 Mar 2023 14:51:18 +0100 Subject: [PATCH] Plot pcid and scid. --- plot_single_transmission.py | 164 ++++++++++++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100755 plot_single_transmission.py diff --git a/plot_single_transmission.py b/plot_single_transmission.py new file mode 100755 index 0000000..3d67752 --- /dev/null +++ b/plot_single_transmission.py @@ -0,0 +1,164 @@ +#!/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 + + +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", default=None, 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() + 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(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 timeline + + scaley = 1.5 + scalex = 1.0 + ax0 = plt.subplots(211, figsize=[6.4 * scaley, 4.8 * scalex]) + ax1 = ax0.twinx() + ax2 = ax0.twinx() + + ax00 = plt.subplots(212) + ax01 = ax00.twinx() + + plt.title("{} with {}".format(transmission_direction, cc_algo)) + + # create list fo color indices for lte cells + color_dict = dict() + color_list = list() + i = 0 + for cell_id in transmission_df["PCID"]: + if cell_id not in color_dict: + color_dict[cell_id] = i + i += 1 + color_list.append(color_dict[cell_id]) + + transmission_df["lte_cell_color"] = color_list + color_dict = None + color_list = None + + cmap = matplotlib.cm.get_cmap("Set3") + unique_cells = transmission_df["lte_cell_color"].unique() + color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) + + # create list fo color indices for nr cells + color_dict = dict() + color_list = list() + i = 0 + for cell_id in transmission_df["PCID.1"]: + if cell_id not in color_dict: + color_dict[cell_id] = i + i += 1 + color_list.append(color_dict[cell_id]) + + transmission_df["nr_cell_color"] = color_list + color_dict = None + color_list = None + + cmap = matplotlib.cm.get_cmap("Set3") + unique_cells = transmission_df["nr_cell_color"].unique() + color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) + + transmission_df["index"] = transmission_df.index + for c in transmission_df["lte_cell_color"].unique(): + bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[c] + ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) + for c in transmission_df["nr_cell_color"].unique(): + bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c] + ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) + + 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="DL_bandwidth") + + if args.save: + 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.clf() \ No newline at end of file