Paper plots.
This commit is contained in:
Lukas Prause
306
plot_single_treansmission_paper.py
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306
plot_single_treansmission_paper.py
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#!/usr/bin/env python3
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import math
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import multiprocessing
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import os
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from argparse import ArgumentParser
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import matplotlib
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.set()
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#sns.set(font_scale=1.5)
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tex_fonts = {
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"pgf.texsystem": "lualatex",
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# "legend.fontsize": "x-large",
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# "figure.figsize": (15, 5),
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"axes.labelsize": 15, # "small",
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# "axes.titlesize": "x-large",
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"xtick.labelsize": 15, # "small",
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"ytick.labelsize": 15, # "small",
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"legend.fontsize": 15,
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"axes.formatter.use_mathtext": True,
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"mathtext.fontset": "dejavusans",
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}
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# plt.rcParams.update(tex_fonts)
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def convert_cellid(value):
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if isinstance(value, str):
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try:
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r = int(value.split(" ")[-1].replace("(", "").replace(")", ""))
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return r
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except Exception as e:
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return -1
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else:
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return int(-1)
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("-s", "--serial_file", required=True, help="Serial csv file.")
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parser.add_argument(
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"-p", "--pcap_csv_folder", required=True, help="PCAP csv folder."
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)
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parser.add_argument("--save", required=True, help="Location to save pdf file.")
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parser.add_argument("--fancy", action="store_true", help="Create fancy plot.")
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parser.add_argument(
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"-i",
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"--interval",
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default=10,
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type=int,
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help="Time interval for rolling window.",
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)
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args = parser.parse_args()
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pcap_csv_list = list()
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for filename in os.listdir(args.pcap_csv_folder):
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if filename.endswith(".csv") and "tcp" in filename:
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pcap_csv_list.append(filename)
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counter = 1
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if len(pcap_csv_list) == 0:
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print("No CSV files found.")
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pcap_csv_list.sort(key=lambda x: int(x.split("_")[-1].replace(".csv", "")))
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for csv in pcap_csv_list:
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print(
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"\rProcessing {} out of {} CSVs.\t({}%)\t".format(
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counter, len(pcap_csv_list), math.floor(counter / len(pcap_csv_list))
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)
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)
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# try:
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transmission_df = pd.read_csv(
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"{}{}".format(args.pcap_csv_folder, csv),
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dtype=dict(is_retranmission=bool, is_dup_ack=bool),
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)
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transmission_df["datetime"] = pd.to_datetime(
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transmission_df["datetime"]
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) - pd.Timedelta(hours=1)
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transmission_df = transmission_df.set_index("datetime")
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transmission_df.index = pd.to_datetime(transmission_df.index)
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transmission_df = transmission_df.sort_index()
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# srtt to [s]
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transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 10 ** 6)
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# key for columns and level for index
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transmission_df["goodput"] = (
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transmission_df["payload_size"]
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.groupby(pd.Grouper(level="datetime", freq="{}s".format(args.interval)))
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.transform("sum")
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)
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transmission_df["goodput"] = transmission_df["goodput"].apply(
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lambda x: ((x * 8) / args.interval) / 10 ** 6
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)
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transmission_df["goodput_rolling"] = (
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transmission_df["payload_size"].rolling("{}s".format(args.interval)).sum()
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)
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transmission_df["goodput_rolling"] = transmission_df["goodput_rolling"].apply(
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lambda x: ((x * 8) / args.interval) / 10 ** 6
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)
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# set meta values and remove all not needed columns
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cc_algo = transmission_df["congestion_control"].iloc[0]
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cc_algo = cc_algo.upper()
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transmission_direction = transmission_df["direction"].iloc[0]
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# transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"])
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# read serial csv
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serial_df = pd.read_csv(
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args.serial_file, converters={"Cell_ID": convert_cellid}
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)
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serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(
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hours=1
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)
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serial_df = serial_df.set_index("datetime")
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serial_df.index = pd.to_datetime(serial_df.index)
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serial_df.sort_index()
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# print(serial_df["Cell_ID"])
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# serial_df["Cell_ID"] = serial_df["Cell_ID"].apply(
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# lambda x: int(x.split(" ")[-1].replace("(", "").replace(")", "")))
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transmission_df = pd.merge_asof(
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transmission_df,
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serial_df,
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tolerance=pd.Timedelta("1s"),
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right_index=True,
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left_index=True,
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)
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transmission_df.index = transmission_df["arrival_time"]
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# replace 0 in RSRQ with Nan
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transmission_df["NR5G_RSRQ_(dB)"] = transmission_df["NR5G_RSRQ_(dB)"].replace(
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0, np.NaN
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)
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transmission_df["RSRQ_(dB)"] = transmission_df["RSRQ_(dB)"].replace(0, np.NaN)
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# filter active state
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for i in range(1, 5):
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transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
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"LTE_SCC{}_bw".format(i)
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]
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mask = transmission_df["LTE_SCC{}_state".format(i)].isin(["ACTIVE"])
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transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
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"LTE_SCC{}_effective_bw".format(i)
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].where(mask, other=0)
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# filter if sc is usesd for uplink
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for i in range(1, 5):
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mask = transmission_df["LTE_SCC{}_UL_Configured".format(i)].isin([False])
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transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
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"LTE_SCC{}_effective_bw".format(i)
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].where(mask, other=0)
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# sum all effective bandwidth for 5G and 4G
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transmission_df["SCC1_NR5G_effective_bw"] = transmission_df[
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"SCC1_NR5G_bw"
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].fillna(0)
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transmission_df["lte_effective_bw_sum"] = (
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transmission_df["LTE_SCC1_effective_bw"].fillna(0)
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+ transmission_df["LTE_SCC2_effective_bw"].fillna(0)
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+ transmission_df["LTE_SCC3_effective_bw"].fillna(0)
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+ transmission_df["LTE_SCC4_effective_bw"].fillna(0)
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+ transmission_df["LTE_bw"].fillna(0))
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transmission_df["nr_effective_bw_sum"] = transmission_df["SCC1_NR5G_effective_bw"]
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transmission_df["effective_bw_sum"] = transmission_df["nr_effective_bw_sum"] + transmission_df[
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"lte_effective_bw_sum"]
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# transmission timeline
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scaley = 1.5
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scalex = 1.0
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fig, ax = plt.subplots(2, 1, figsize=[6.4 * scaley, 4.8 * scalex])
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fig.subplots_adjust(right=0.75)
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if not args.fancy:
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plt.title("{} with {}".format(transmission_direction, cc_algo))
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fig.suptitle("{} with {}".format(transmission_direction, cc_algo))
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ax0 = ax[0]
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ax1 = ax0.twinx()
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ax2 = ax0.twinx()
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# ax2.spines.right.set_position(("axes", 1.22))
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ax00 = ax[1]
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snd_plot = ax0.plot(
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transmission_df["snd_cwnd"].dropna(),
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color="lime",
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linestyle="dashed",
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label="cwnd",
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)
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srtt_plot = ax1.plot(
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transmission_df["srtt"].dropna(),
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color="red",
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linestyle="dashdot",
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label="sRTT",
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)
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goodput_plot = ax2.plot(
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transmission_df["goodput_rolling"],
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color="blue",
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linestyle="solid",
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label="goodput",
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)
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# filter active state
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for i in range(1, 5):
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transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df["LTE_SCC{}_bw".format(i)]
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mask = transmission_df["LTE_SCC{}_state".format(i)].isin(["ACTIVE"])
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transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
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"LTE_SCC{}_effective_bw".format(i)
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].where(mask, other=0)
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# filter if sc is usesd for uplink
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for i in range(1, 5):
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mask = transmission_df["LTE_SCC{}_UL_Configured".format(i)].isin([False])
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transmission_df["LTE_SCC{}_effective_bw".format(i)] = transmission_df[
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"LTE_SCC{}_effective_bw".format(i)
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].where(mask, other=0)
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# sum all effective bandwidth for 5G and 4G
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transmission_df["SCC1_NR5G_effective_bw"] = transmission_df["SCC1_NR5G_bw"].fillna(0)
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transmission_df["effective_bw_sum"] = (
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transmission_df["SCC1_NR5G_effective_bw"]
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+ transmission_df["LTE_SCC1_effective_bw"]
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+ transmission_df["LTE_SCC2_effective_bw"]
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+ transmission_df["LTE_SCC3_effective_bw"]
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+ transmission_df["LTE_SCC4_effective_bw"]
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+ transmission_df["LTE_bw"]
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)
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bw_cols = [
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"SCC1_NR5G_effective_bw",
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"LTE_bw",
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"LTE_SCC1_effective_bw",
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"LTE_SCC2_effective_bw",
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"LTE_SCC3_effective_bw",
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"LTE_SCC4_effective_bw",
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]
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ax_stacked = transmission_df[bw_cols].plot.area(stacked=True, linewidth=0, ax=ax00)
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ax00.set_ylabel("bandwidth [MHz]")
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#ax.set_xlabel("time [minutes]")
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ax00.set_xlim([0, transmission_df.index[-1]])
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ax00.xaxis.grid(False)
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ax2.spines.right.set_position(("axes", 1.1))
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ax0.set_ylim(0, 5000)
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ax1.set_ylim(0, 0.3)
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ax2.set_ylim(0, 600)
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#ax00.set_ylim(-25, 0)
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ax00.set_xlabel("arrival time [s]")
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ax2.set_ylabel("Goodput [mbps]")
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#ax00.set_ylabel("LTE/NR RSRQ [dB]")
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# ax02.set_ylabel("LTE RSRQ [dB]")
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ax1.set_ylabel("sRTT [s]")
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ax0.set_ylabel("cwnd [MSS]")
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if args.fancy:
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legend_frame = False
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ax0.set_xlim([0, transmission_df.index[-1]])
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ax00.set_xlim([0, transmission_df.index[-1]])
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# added these three lines
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lns_ax0 = snd_plot + srtt_plot + goodput_plot
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labs_ax0 = [l.get_label() for l in lns_ax0]
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ax2.legend(lns_ax0, labs_ax0, ncols=9, fontsize=9, loc="upper right", frameon=legend_frame)
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#ax0.set_zorder(100)
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lns_ax00 = [ax_stacked]
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labs_ax00 = [l.get_label() for l in lns_ax00]
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ax00.legend(lns_ax00, labs_ax00, ncols=3, fontsize=9, loc="upper center", frameon=legend_frame)
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#ax00.set_zorder(100)
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plt.savefig("{}{}_plot.eps".format(args.save, csv.replace(".csv", "")), bbox_inches="tight")
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else:
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fig.legend(loc="lower right")
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plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")), bbox_inches="tight")
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# except Exception as e:
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# print("Error processing file: {}".format(csv))
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# print(str(e))
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counter += 1
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plt.close(fig)
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plt.clf()
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