Plot pcid and scid.
This commit is contained in:
Lukas Prause
@@ -44,123 +44,123 @@ if __name__ == "__main__":
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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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#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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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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# 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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# 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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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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# 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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#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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# 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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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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# transmission timeline
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scaley = 1.5
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scalex = 1.0
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fig, ax0 = plt.subplots(211, figsize=[6.4 * scaley, 4.8 * scalex])
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ax1 = ax0.twinx()
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ax2 = ax0.twinx()
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scaley = 1.5
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scalex = 1.0
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ax0 = plt.subplot(211)
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ax1 = ax0.twinx()
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ax2 = ax0.twinx()
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fig, ax00 = plt.subplots(212)
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ax01 = ax00.twinx()
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ax00 = plt.subplot(212)
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ax01 = ax00.twinx()
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plt.title("{} with {}".format(transmission_direction, cc_algo))
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plt.title("{} with {}".format(transmission_direction, cc_algo))
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# create list fo color indices for lte cells
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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["PCID"]:
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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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# create list fo color indices for lte cells
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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["PCID"]:
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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["lte_cell_color"] = color_list
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color_dict = None
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color_list = None
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transmission_df["lte_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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unique_cells = transmission_df["lte_cell_color"].unique()
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color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
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cmap = matplotlib.cm.get_cmap("Set3")
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unique_cells = transmission_df["lte_cell_color"].unique()
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color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
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transmission_df["index"] = transmission_df.index
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for c in transmission_df["lte_cell_color"].unique():
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bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[
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c]
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ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
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transmission_df["index"] = transmission_df.index
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for c in transmission_df["lte_cell_color"].unique():
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bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[
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c]
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ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
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# create list fo color indices for nr cells
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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["PCID.1"]:
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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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# create list fo color indices for nr cells
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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["PCID.1"]:
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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["nr_cell_color"] = color_list
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color_dict = None
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color_list = None
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transmission_df["nr_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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unique_cells = transmission_df["nr_cell_color"].unique()
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color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
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cmap = matplotlib.cm.get_cmap("Set3")
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unique_cells = transmission_df["nr_cell_color"].unique()
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color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
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for c in transmission_df["nr_cell_color"].unique():
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bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c]
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ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
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for c in transmission_df["nr_cell_color"].unique():
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bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c]
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ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
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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["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
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ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="DL_bandwidth")
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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["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
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ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="DL_bandwidth")
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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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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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if args.save:
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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.clf()
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