#!/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()