Adds differnt plots fpr goodput, cwnd, rtt und cqi.
This commit is contained in:
Lukas Prause
@@ -1,66 +1,12 @@
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#!/usr/bin/env python3
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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 multiprocessing
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import os
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import os
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from argparse import ArgumentParser
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from argparse import ArgumentParser
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from math import ceil
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from time import sleep
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import matplotlib
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import matplotlib
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import pandas as pd
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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from mpl_toolkits import axisartist
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from mpl_toolkits.axes_grid1 import host_subplot
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def csv_to_dataframe(csv_list, dummy):
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global n
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global frame_list
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transmission_df = None
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for csv in csv_list:
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tmp_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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tmp_df["datetime"] = pd.to_datetime(tmp_df["datetime"]) - pd.Timedelta(hours=1)
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tmp_df = tmp_df.set_index("datetime")
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tmp_df.index = pd.to_datetime(tmp_df.index)
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if transmission_df is None:
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transmission_df = tmp_df
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else:
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transmission_df = pd.concat([transmission_df, tmp_df])
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n.value += 1
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frame_list.append(transmission_df)
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from itertools import islice
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def chunk(it, size):
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it = iter(it)
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return iter(lambda: tuple(islice(it, size)), ())
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def plot_cdf(dataframe, column_name):
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stats_df = dataframe \
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.groupby(column_name) \
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[column_name] \
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.agg("count") \
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.pipe(pd.DataFrame) \
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.rename(columns={column_name: "frequency"})
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# PDF
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stats_df["PDF"] = stats_df["frequency"] / sum(stats_df["frequency"])
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# CDF
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stats_df["CDF"] = stats_df["PDF"].cumsum()
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stats_df = stats_df.reset_index()
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stats_df.plot(x=column_name, y=["CDF"], grid=True)
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if __name__ == "__main__":
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if __name__ == "__main__":
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@@ -68,13 +14,6 @@ if __name__ == "__main__":
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parser.add_argument("-s", "--serial_file", required=True, help="Serial csv file.")
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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("-p", "--pcap_csv_folder", required=True, help="PCAP csv folder.")
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parser.add_argument("--save", default=None, help="Location to save pdf file.")
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parser.add_argument("--save", default=None, help="Location to save pdf file.")
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parser.add_argument(
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"-c",
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"--cores",
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default=1,
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type=int,
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help="Number of cores for multiprocessing.",
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)
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parser.add_argument(
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parser.add_argument(
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"-i",
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"-i",
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"--interval",
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"--interval",
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@@ -95,179 +34,132 @@ if __name__ == "__main__":
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if filename.endswith(".csv") and "tcp" in filename:
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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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pcap_csv_list.append(filename)
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parts = chunk(pcap_csv_list, ceil(len(pcap_csv_list) / args.cores))
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counter = 1
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print("Start processing with {} jobs.".format(args.cores))
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if len(pcap_csv_list) == 0:
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for p in parts:
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print("No CSV files found.")
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process = multiprocessing.Process(target=csv_to_dataframe, args=(p, "dummy"))
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jobs.append(process)
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for j in jobs:
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pcap_csv_list.sort(key=lambda x: int(x.split("_")[-1].replace(".csv", "")))
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j.start()
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print("Started all jobs.")
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for csv in pcap_csv_list:
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# Ensure all the processes have finished
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finished_job_counter = 0
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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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working = ["|", "/", "-", "\\", "|", "/", "-", "\\"]
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transmission_df = pd.read_csv(
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w = 0
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"{}{}".format(args.pcap_csv_folder, csv),
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while len(jobs) != finished_job_counter:
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dtype=dict(is_retranmission=bool, is_dup_ack=bool),
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sleep(1)
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print(
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"\r\t{}{}{}\t Running {} jobs ({} finished). Processed {} out of {} pcap csv files. ({}%) ".format(
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working[w],
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working[w],
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working[w],
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len(jobs),
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finished_job_counter,
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n.value,
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len(pcap_csv_list),
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round((n.value / len(pcap_csv_list)) * 100, 2),
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),
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end="",
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)
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)
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finished_job_counter = 0
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transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1)
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for j in jobs:
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transmission_df = transmission_df.set_index("datetime")
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if not j.is_alive():
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transmission_df.index = pd.to_datetime(transmission_df.index)
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finished_job_counter += 1
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transmission_df = transmission_df.sort_index()
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if (w + 1) % len(working) == 0:
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w = 0
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#print("Calculate goodput...")
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#print(transmission_df)
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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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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 timeline
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scaley = 1.5
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scalex = 1.0
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fig, ax = plt.subplots(figsize=[6.4 * scaley, 4.8 * scalex])
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plt.title("{} with {}".format(transmission_direction, cc_algo))
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fig.subplots_adjust(right=0.75)
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twin1 = ax.twinx()
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twin2 = ax.twinx()
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twin3 = ax.twinx()
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# Offset the right spine of twin2. The ticks and label have already been
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# placed on the right by twinx above.
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twin2.spines.right.set_position(("axes", 1.1))
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twin3.spines.right.set_position(("axes", 1.2))
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# create list fo color indices
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transmission_df["index"] = transmission_df.index
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color_dict = dict()
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color_list = list()
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i = 0
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for cell_id in transmission_df["cellID"]:
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if cell_id not in color_dict:
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color_dict[cell_id] = i
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i += 1
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color_list.append(color_dict[cell_id])
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transmission_df["cell_color"] = color_list
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color_dict = None
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color_list = None
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cmap = matplotlib.cm.get_cmap("Set3")
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for c in transmission_df["cell_color"].unique():
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bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c]
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ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=cmap.colors[c])
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p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
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p3, = twin2.plot(transmission_df["ack_rtt"].dropna(), color="red", linestyle="dashdot", label="ACK RTT")
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p1, = ax.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
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p2, = twin1.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
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ax.set_xlim(transmission_df["index"].min(), transmission_df["index"].max())
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ax.set_ylim(0, 500)
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twin1.set_ylim(0, 15)
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twin2.set_ylim(0, transmission_df["ack_rtt"].max())
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twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10)
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ax.set_xlabel("arrival time")
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ax.set_ylabel("Goodput [mbps]")
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twin1.set_ylabel("CQI")
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twin2.set_ylabel("ACK RTT [s]")
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twin3.set_ylabel("cwnd")
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ax.yaxis.label.set_color(p1.get_color())
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twin1.yaxis.label.set_color(p2.get_color())
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twin2.yaxis.label.set_color(p3.get_color())
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twin3.yaxis.label.set_color(p4.get_color())
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tkw = dict(size=4, width=1.5)
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ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
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twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
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twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
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twin3.tick_params(axis='y', colors=p4.get_color(), **tkw)
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ax.tick_params(axis='x', **tkw)
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#ax.legend(handles=[p1, p2, p3])
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if args.save:
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plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
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else:
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else:
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w += 1
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plt.show()
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print("\r\nSorting table...")
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counter += 1
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transmission_df = pd.concat(frame_list)
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plt.clf()
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frame_list = None
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transmission_df = transmission_df.sort_index()
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print("Calculate goodput...")
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#print(transmission_df)
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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"])
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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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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 timeline
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scaley = 1.5
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scalex = 1.0
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fig, ax = plt.subplots(figsize=[6.4 * scaley, 4.8 * scalex])
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plt.title("{} with {}".format(transmission_direction, cc_algo))
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fig.subplots_adjust(right=0.75)
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twin1 = ax.twinx()
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twin2 = ax.twinx()
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# Offset the right spine of twin2. The ticks and label have already been
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# placed on the right by twinx above.
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twin2.spines.right.set_position(("axes", 1.2))
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# create list fo color indices
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transmission_df["index"] = transmission_df.index
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color_dict = dict()
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color_list = list()
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i = 0
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for cell_id in transmission_df["cellID"]:
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if cell_id not in color_dict:
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color_dict[cell_id] = i
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i += 1
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color_list.append(color_dict[cell_id])
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transmission_df["cell_color"] = color_list
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color_dict = None
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color_list = None
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cmap = matplotlib.cm.get_cmap("Set3")
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for c in transmission_df["cell_color"].unique():
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bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c]
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ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=cmap.colors[c])
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p1, = ax.plot(transmission_df["goodput_rolling"], "-", color="blue", label="goodput")
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p2, = twin1.plot(transmission_df["downlink_cqi"], "--", color="green", label="CQI")
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p3, = twin2.plot(transmission_df["ack_rtt"], "-.", color="red", label="ACK RTT")
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ax.set_xlim(transmission_df["index"].min(), transmission_df["index"].max())
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ax.set_ylim(0, 500)
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twin1.set_ylim(0, 15)
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twin2.set_ylim(0, 1)
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ax.set_xlabel("Time")
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ax.set_ylabel("Goodput")
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twin1.set_ylabel("CQI")
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twin2.set_ylabel("ACK RTT")
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ax.yaxis.label.set_color(p1.get_color())
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twin1.yaxis.label.set_color(p2.get_color())
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twin2.yaxis.label.set_color(p3.get_color())
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tkw = dict(size=4, width=1.5)
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ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
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twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
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twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
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ax.tick_params(axis='x', **tkw)
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#ax.legend(handles=[p1, p2, p3])
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if args.save:
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plt.savefig("{}timeline_plot.pdf".format(args.save))
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else:
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plt.show()
|
|
||||||
|
|
||||||
#goodput cdf
|
|
||||||
plt.clf()
|
|
||||||
|
|
||||||
print("Calculate and polt goodput CDF...")
|
|
||||||
plot_cdf(transmission_df, "goodput")
|
|
||||||
plt.xlabel("goodput [mbps]")
|
|
||||||
plt.ylabel("CDF")
|
|
||||||
plt.legend([cc_algo])
|
|
||||||
plt.title("{} with {}".format(transmission_direction, cc_algo))
|
|
||||||
|
|
||||||
if args.save:
|
|
||||||
plt.savefig("{}{}_cdf_plot.pdf".format(args.save, "goodput"))
|
|
||||||
else:
|
|
||||||
plt.show()
|
|
||||||
|
|
||||||
# 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]")
|
|
||||||
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"))
|
|
||||||
else:
|
|
||||||
plt.show()
|
|
||||||
Reference in New Issue
Block a user