Changes for new modem.
This commit is contained in:
Lukas Prause
@@ -1,5 +1,4 @@
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#!/usr/bin/env python3
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import csv
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import datetime
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import re
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from argparse import ArgumentParser
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@@ -7,7 +6,6 @@ import pandas as pd
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KEY_VALUE_REGEX = r"(.+):(.+)"
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if __name__ == "__main__":
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parser = ArgumentParser()
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@@ -18,54 +16,42 @@ if __name__ == "__main__":
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content = file.read()
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file.close()
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header = ["time"]
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csv_lines = list()
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serial_df = None
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p = re.compile(KEY_VALUE_REGEX)
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for part in content.split(";;;"):
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if part == "":
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break
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part = part.replace("\t", "\n").strip()
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csv_line = list()
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time = None
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line_dict = dict(time=None)
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for line in part.split("\n"):
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if not line.startswith("!") or line == "" or line == "\n":
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if time is None:
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if line_dict["time"] is None:
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time = line
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csv_line.append(time)
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line_dict["time"] = [time]
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m = p.match(line)
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if m:
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key = m.group(1).strip().replace(" ", "_")
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value = m.group(2).replace("MHz", "").strip()
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value = m.group(2).replace("MHz", "").replace("---", "").strip()
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if key not in header:
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header.append(key)
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line_dict[key] = [value]
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csv_line.append(value)
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if len(csv_line) > 1:
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#print(csv_line)
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csv_lines.append(csv_line)
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if len(line_dict) > 1:
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#print("line:")
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#print(line_dict)
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#print("serial_df:")
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#print(serial_df)
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if serial_df is None:
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serial_df = pd.DataFrame.from_dict(line_dict, orient="columns",)
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else:
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serial_df = pd.concat([serial_df, pd.DataFrame.from_dict(line_dict, orient="columns")])
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outputfile = open(args.file.replace("txt", "csv"), "w")
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writer = csv.writer(outputfile, delimiter=",", lineterminator="\n", escapechar='\\')
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writer.writerow(header)
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#print(all_csv_lines)
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for l in csv_lines:
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#print(l)
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writer.writerow(l)
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outputfile.close()
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outputfile = open(args.file.replace("txt", "csv"), "r")
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serial_df = pd.read_csv(outputfile)
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serial_df = serial_df.copy()
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serial_df["datetime"] = pd.to_datetime(
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serial_df["time"].apply(lambda x: datetime.datetime.fromtimestamp(x))
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serial_df["time"].apply(lambda x: datetime.datetime.fromtimestamp(int(x)))
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)
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serial_df.to_csv(args.file.replace("txt", "csv"))
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outputfile.close()
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#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"])
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#print(serial_df.to_string())
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177
plot_single_transmission_EM9190.py
Executable file
177
plot_single_transmission_EM9190.py
Executable file
@@ -0,0 +1,177 @@
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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 pandas as pd
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import matplotlib.pyplot as plt
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# Using seaborn's style
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#plt.style.use('seaborn')
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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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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("-p", "--pcap_csv_folder", required=True, help="PCAP csv folder.")
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parser.add_argument("--save", required=True, help="Location to save pdf file.")
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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("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list))))
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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(transmission_df["datetime"]) - 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"] = transmission_df["payload_size"].groupby(pd.Grouper(level="datetime", freq="{}s".format(args.interval))).transform("sum")
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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"] = transmission_df["payload_size"].rolling("{}s".format(args.interval)).sum()
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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(args.serial_file)
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serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)
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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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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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# sum bandwidth
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# columns: 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
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transmission_df["bw_sum"] = transmission_df["LTE_bw"] + transmission_df["LTE_SCC2_bw"] \
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+ transmission_df["LTE_SCC3_bw"] + transmission_df["LTE_SCC4_bw"] \
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+ transmission_df["SCC1_NR5G_bw"] + transmission_df["NR5G_dl_bw"] \
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+ transmission_df["LTE_SCC1_bw"]
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transmission_df.index = transmission_df["arrival_time"]
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# transmission timeline
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scaley = 1.5
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scalex = 1.0
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plt.title("{} with {}".format(transmission_direction, cc_algo))
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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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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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ax01 = ax00.twinx()
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ax02 = ax00.twinx()
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# Plot vertical lines
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lte_handovers = transmission_df["lte_pcid"].diff().dropna()
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for index, value in lte_handovers.items():
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if value > 0:
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ax00.axvline(index, ymin=0, ymax=1, color="skyblue", label="4G Handover")
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nr_handovers = transmission_df["nr_pcid"].diff().dropna()
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for index, value in nr_handovers.items():
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if value > 0:
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ax00.axvline(index, ymin=0, ymax=1, color="greenyellow", label="5G Handover")
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ax0.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
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ax1.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
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ax2.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
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ax00.plot(transmission_df["NR5G_RSRQ_(dB)"].dropna(), color="magenta", linestyle="dotted", label="NR RSRQ")
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ax01.plot(transmission_df["bw_sum"].dropna(), color="peru", linestyle="dotted", label="bandwidth")
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ax02.plot(transmission_df["RSRQ_(dB)"].dropna(), color="magenta", linestyle="dotted", label="LTE RSRQ")
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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, 500)
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ax00.set_ylim(-25, -5)
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ax01.set_ylim(0, 200)
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ax02.set_ylim(-25, -5)
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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("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")
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ax01.set_ylabel("Bandwidth [MHz]")
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fig.legend(loc="lower right")
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plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
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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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