Adds differnt plots fpr goodput, cwnd, rtt und cqi.

plot_single_transmission_timeline.py

@@ -1,66 +1,12 @@
 #!/usr/bin/env python3
+import math
 import multiprocessing
 import os
 from argparse import ArgumentParser
-from math import ceil
-from time import sleep
 
 import matplotlib
 import pandas as pd
 import matplotlib.pyplot as plt
-from mpl_toolkits import axisartist
-from mpl_toolkits.axes_grid1 import host_subplot
-
-
-
-def csv_to_dataframe(csv_list, dummy):
-
-    global n
-    global frame_list
-
-    transmission_df = None
-
-    for csv in csv_list:
-        tmp_df = pd.read_csv(
-            "{}{}".format(args.pcap_csv_folder, csv),
-            dtype=dict(is_retranmission=bool, is_dup_ack=bool),
-        )
-        tmp_df["datetime"] = pd.to_datetime(tmp_df["datetime"]) - pd.Timedelta(hours=1)
-        tmp_df = tmp_df.set_index("datetime")
-        tmp_df.index = pd.to_datetime(tmp_df.index)
-        if transmission_df is None:
-            transmission_df = tmp_df
-        else:
-            transmission_df = pd.concat([transmission_df, tmp_df])
-
-        n.value += 1
-
-    frame_list.append(transmission_df)
-
-
-from itertools import islice
-
-def chunk(it, size):
-    it = iter(it)
-    return iter(lambda: tuple(islice(it, size)), ())
-
-
-def plot_cdf(dataframe, column_name):
-    stats_df = dataframe \
-        .groupby(column_name) \
-        [column_name] \
-        .agg("count") \
-        .pipe(pd.DataFrame) \
-        .rename(columns={column_name: "frequency"})
-
-    # PDF
-    stats_df["PDF"] = stats_df["frequency"] / sum(stats_df["frequency"])
-
-    # CDF
-    stats_df["CDF"] = stats_df["PDF"].cumsum()
-    stats_df = stats_df.reset_index()
-
-    stats_df.plot(x=column_name, y=["CDF"], grid=True)
 
 
 if __name__ == "__main__":
@@ -68,13 +14,6 @@ if __name__ == "__main__":
     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(
-        "-c",
-        "--cores",
-        default=1,
-        type=int,
-        help="Number of cores for multiprocessing.",
-    )
     parser.add_argument(
         "-i",
         "--interval",
@@ -95,50 +34,25 @@ if __name__ == "__main__":
         if filename.endswith(".csv") and "tcp" in filename:
             pcap_csv_list.append(filename)
 
-    parts = chunk(pcap_csv_list, ceil(len(pcap_csv_list) / args.cores))
+    counter = 1
-    print("Start processing with {} jobs.".format(args.cores))
+    if len(pcap_csv_list) == 0:
-    for p in parts:
+        print("No CSV files found.")
-        process = multiprocessing.Process(target=csv_to_dataframe, args=(p, "dummy"))
-        jobs.append(process)
 
-    for j in jobs:
+    pcap_csv_list.sort(key=lambda x: int(x.split("_")[-1].replace(".csv", "")))
-        j.start()
 
-    print("Started all jobs.")
+    for csv in pcap_csv_list:
-    # Ensure all the processes have finished
+
-    finished_job_counter = 0
+        print("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list))))
-    working = ["|", "/", "-", "\\", "|", "/", "-", "\\"]
+        transmission_df = pd.read_csv(
-    w = 0
+            "{}{}".format(args.pcap_csv_folder, csv),
-    while len(jobs) != finished_job_counter:
+            dtype=dict(is_retranmission=bool, is_dup_ack=bool),
-        sleep(1)
-        print(
-            "\r\t{}{}{}\t Running {} jobs ({} finished). Processed {} out of {} pcap csv files.    ({}%)    ".format(
-                working[w],
-                working[w],
-                working[w],
-                len(jobs),
-                finished_job_counter,
-                n.value,
-                len(pcap_csv_list),
-                round((n.value / len(pcap_csv_list)) * 100, 2),
-            ),
-            end="",
         )
-        finished_job_counter = 0
+        transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1)
-        for j in jobs:
+        transmission_df = transmission_df.set_index("datetime")
-            if not j.is_alive():
+        transmission_df.index = pd.to_datetime(transmission_df.index)
-                finished_job_counter += 1
-        if (w + 1) % len(working) == 0:
-            w = 0
-        else:
-            w += 1
-    print("\r\nSorting table...")
-
-    transmission_df = pd.concat(frame_list)
-    frame_list = None
         transmission_df = transmission_df.sort_index()
 
-    print("Calculate goodput...")
+        #print("Calculate goodput...")
 
         #print(transmission_df)
 
@@ -158,7 +72,7 @@ if __name__ == "__main__":
         cc_algo = cc_algo.upper()
         transmission_direction = transmission_df["direction"].iloc[0]
 
-    transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling"])
+        #transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"])
 
         # read serial csv
         serial_df = pd.read_csv(args.serial_file)
@@ -184,9 +98,11 @@ if __name__ == "__main__":
 
         twin1 = ax.twinx()
         twin2 = ax.twinx()
+        twin3 = ax.twinx()
         # Offset the right spine of twin2.  The ticks and label have already been
         # placed on the right by twinx above.
-    twin2.spines.right.set_position(("axes", 1.2))
+        twin2.spines.right.set_position(("axes", 1.1))
+        twin3.spines.right.set_position(("axes", 1.2))
 
 
         # create list fo color indices
@@ -209,65 +125,41 @@ if __name__ == "__main__":
             bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c]
             ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=cmap.colors[c])
 
-    p1, = ax.plot(transmission_df["goodput_rolling"], "-", color="blue", label="goodput")
+        p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
-    p2, = twin1.plot(transmission_df["downlink_cqi"], "--", color="green", label="CQI")
+        p3, = twin2.plot(transmission_df["ack_rtt"].dropna(), color="red", linestyle="dashdot", label="ACK RTT")
-    p3, = twin2.plot(transmission_df["ack_rtt"], "-.", color="red", label="ACK RTT")
+        p1, = ax.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
+        p2, = twin1.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
 
         ax.set_xlim(transmission_df["index"].min(), transmission_df["index"].max())
         ax.set_ylim(0, 500)
         twin1.set_ylim(0, 15)
-    twin2.set_ylim(0, 1)
+        twin2.set_ylim(0, transmission_df["ack_rtt"].max())
+        twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10)
 
-    ax.set_xlabel("Time")
+        ax.set_xlabel("arrival time")
-    ax.set_ylabel("Goodput")
+        ax.set_ylabel("Goodput [mbps]")
         twin1.set_ylabel("CQI")
-    twin2.set_ylabel("ACK RTT")
+        twin2.set_ylabel("ACK RTT [s]")
+        twin3.set_ylabel("cwnd")
 
         ax.yaxis.label.set_color(p1.get_color())
         twin1.yaxis.label.set_color(p2.get_color())
         twin2.yaxis.label.set_color(p3.get_color())
+        twin3.yaxis.label.set_color(p4.get_color())
 
         tkw = dict(size=4, width=1.5)
         ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
         twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
         twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
+        twin3.tick_params(axis='y', colors=p4.get_color(), **tkw)
         ax.tick_params(axis='x', **tkw)
 
         #ax.legend(handles=[p1, p2, p3])
 
         if args.save:
-        plt.savefig("{}timeline_plot.pdf".format(args.save))
+            plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
         else:
             plt.show()
+        counter += 1
 
-    #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()