From 61e99e6e83572098d810b473c3f1e7a536520442 Mon Sep 17 00:00:00 2001 From: Lukas Prause Date: Thu, 16 Mar 2023 15:33:13 +0100 Subject: [PATCH] Plot pcid and scid. --- plot_single_transmission.py | 190 ++++++++++++++++++------------------ 1 file changed, 95 insertions(+), 95 deletions(-) diff --git a/plot_single_transmission.py b/plot_single_transmission.py index cfd28d9..27bf807 100755 --- a/plot_single_transmission.py +++ b/plot_single_transmission.py @@ -44,123 +44,123 @@ if __name__ == "__main__": print("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list)))) - try: - transmission_df = pd.read_csv( - "{}{}".format(args.pcap_csv_folder, csv), - dtype=dict(is_retranmission=bool, is_dup_ack=bool), - ) + #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() + 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) + # srtt to [s] + transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 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 - ) + # 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 + ) - 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["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 + ) - # 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] + # 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 = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"]) + #transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling", "snd_cwnd"]) - # 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() + # 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 = pd.merge_asof( - transmission_df, - serial_df, - tolerance=pd.Timedelta("1s"), - right_index=True, - left_index=True, - ) + transmission_df = pd.merge_asof( + transmission_df, + serial_df, + tolerance=pd.Timedelta("1s"), + right_index=True, + left_index=True, + ) - # transmission timeline + # transmission timeline - scaley = 1.5 - scalex = 1.0 - fig, ax0 = plt.subplots(211, figsize=[6.4 * scaley, 4.8 * scalex]) - ax1 = ax0.twinx() - ax2 = ax0.twinx() + scaley = 1.5 + scalex = 1.0 + ax0 = plt.subplot(211) + ax1 = ax0.twinx() + ax2 = ax0.twinx() - fig, ax00 = plt.subplots(212) - ax01 = ax00.twinx() + ax00 = plt.subplot(212) + ax01 = ax00.twinx() - plt.title("{} with {}".format(transmission_direction, cc_algo)) + plt.title("{} with {}".format(transmission_direction, cc_algo)) - # create list fo color indices for lte cells - color_dict = dict() - color_list = list() - i = 0 - for cell_id in transmission_df["PCID"]: - 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 for lte cells + color_dict = dict() + color_list = list() + i = 0 + for cell_id in transmission_df["PCID"]: + if cell_id not in color_dict: + color_dict[cell_id] = i + i += 1 + color_list.append(color_dict[cell_id]) - transmission_df["lte_cell_color"] = color_list - color_dict = None - color_list = None + transmission_df["lte_cell_color"] = color_list + color_dict = None + color_list = None - cmap = matplotlib.cm.get_cmap("Set3") - unique_cells = transmission_df["lte_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["lte_cell_color"].unique() + color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) - transmission_df["index"] = transmission_df.index - for c in transmission_df["lte_cell_color"].unique(): - bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[ - c] - ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) + transmission_df["index"] = transmission_df.index + for c in transmission_df["lte_cell_color"].unique(): + bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[ + c] + ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) - # create list fo color indices for nr cells - color_dict = dict() - color_list = list() - i = 0 - for cell_id in transmission_df["PCID.1"]: - 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 for nr cells + color_dict = dict() + color_list = list() + i = 0 + for cell_id in transmission_df["PCID.1"]: + if cell_id not in color_dict: + color_dict[cell_id] = i + i += 1 + color_list.append(color_dict[cell_id]) - transmission_df["nr_cell_color"] = color_list - color_dict = None - color_list = None + transmission_df["nr_cell_color"] = color_list + color_dict = None + color_list = None - cmap = matplotlib.cm.get_cmap("Set3") - unique_cells = transmission_df["nr_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["nr_cell_color"].unique() + color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) - for c in transmission_df["nr_cell_color"].unique(): - bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c] - ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) + for c in transmission_df["nr_cell_color"].unique(): + bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c] + ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) - ax0.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd") - ax1.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT") - ax2.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput") - ax00.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI") - ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="DL_bandwidth") + ax0.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd") + ax1.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT") + ax2.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput") + ax00.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI") + ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="DL_bandwidth") - 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)) + 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