From e2625998c693059d40dac3a2fcb4f6d7770d069a Mon Sep 17 00:00:00 2001 From: Lukas Prause Date: Thu, 9 Mar 2023 14:45:13 +0100 Subject: [PATCH] Adds 'some' error handling. --- plot_single_transmission_timeline.py | 198 ++++++++++++++------------- 1 file changed, 101 insertions(+), 97 deletions(-) diff --git a/plot_single_transmission_timeline.py b/plot_single_transmission_timeline.py index b42f083..980a774 100755 --- a/plot_single_transmission_timeline.py +++ b/plot_single_transmission_timeline.py @@ -44,125 +44,129 @@ if __name__ == "__main__": 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)))) - 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) + 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() - # 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 - ) + # srtt to [s] + transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 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 - ) + # 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 + ) - # 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["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 + ) - #transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"]) + # 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] - # 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 = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"]) - transmission_df = pd.merge_asof( - transmission_df, - serial_df, - tolerance=pd.Timedelta("1s"), - right_index=True, - left_index=True, - ) + # 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 timeline + transmission_df = pd.merge_asof( + transmission_df, + serial_df, + tolerance=pd.Timedelta("1s"), + right_index=True, + left_index=True, + ) - scaley = 1.5 - scalex = 1.0 - fig, ax = plt.subplots(figsize=[6.4 * scaley, 4.8 * scalex]) - plt.title("{} with {}".format(transmission_direction, cc_algo)) - fig.subplots_adjust(right=0.75) + # transmission timeline - 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", 1.1)) - twin3.spines.right.set_position(("axes", 1.2)) + scaley = 1.5 + scalex = 1.0 + fig, ax = plt.subplots(figsize=[6.4 * scaley, 4.8 * scalex]) + plt.title("{} with {}".format(transmission_direction, cc_algo)) + fig.subplots_adjust(right=0.75) + + 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", 1.1)) + twin3.spines.right.set_position(("axes", 1.2)) - # create list fo color indices - transmission_df["index"] = transmission_df.index - color_dict = dict() - color_list = list() - i = 0 - for cell_id in transmission_df["cellID"]: - if cell_id not in color_dict: - color_dict[cell_id] = i - i += 1 - color_list.append(color_dict[cell_id]) + # create list fo color indices + transmission_df["index"] = transmission_df.index + color_dict = dict() + color_list = list() + i = 0 + for cell_id in transmission_df["cellID"]: + if cell_id not in color_dict: + color_dict[cell_id] = i + i += 1 + color_list.append(color_dict[cell_id]) - transmission_df["cell_color"] = color_list - color_dict = None - color_list = None + transmission_df["cell_color"] = color_list + color_dict = None + color_list = None - cmap = matplotlib.cm.get_cmap("Set3") - unique_cells = transmission_df["cell_color"].unique() - color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) + cmap = matplotlib.cm.get_cmap("Set3") + unique_cells = transmission_df["cell_color"].unique() + color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) - for c in transmission_df["cell_color"].unique(): - bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c] - ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=color_list[c]) + for c in transmission_df["cell_color"].unique(): + bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c] + ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=color_list[c]) - p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd") - p3, = twin2.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT") - 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") + p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd") + p3, = twin2.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT") + 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, transmission_df["ack_rtt"].max()) - twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10) + 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, transmission_df["ack_rtt"].max()) + twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10) - ax.set_xlabel("arrival time") - ax.set_ylabel("Goodput [mbps]") - twin1.set_ylabel("CQI") - twin2.set_ylabel("sRTT [s]") - twin3.set_ylabel("cwnd") + ax.set_xlabel("arrival time") + ax.set_ylabel("Goodput [mbps]") + twin1.set_ylabel("CQI") + twin2.set_ylabel("sRTT [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()) + 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) + 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("{}{}_plot.pdf".format(args.save, csv.replace(".csv", ""))) + #ax.legend(handles=[p1, p2, p3]) + 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() \ No newline at end of file