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Plot pcid and scid.

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This commit is contained in:
Lukas Prause
2023-03-16 15:33:13 +01:00
parent 7d2d047903
commit 61e99e6e83
1 changed files with 95 additions and 95 deletions
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190
plot_single_transmission.py
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@@ -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)))) print("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list))))
try: #try:
transmission_df = pd.read_csv( transmission_df = pd.read_csv(
"{}{}".format(args.pcap_csv_folder, csv), "{}{}".format(args.pcap_csv_folder, csv),
dtype=dict(is_retranmission=bool, is_dup_ack=bool), dtype=dict(is_retranmission=bool, is_dup_ack=bool),
) )
transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1) transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1)
transmission_df = transmission_df.set_index("datetime") transmission_df = transmission_df.set_index("datetime")
transmission_df.index = pd.to_datetime(transmission_df.index) transmission_df.index = pd.to_datetime(transmission_df.index)
transmission_df = transmission_df.sort_index() transmission_df = transmission_df.sort_index()
# srtt to [s] # srtt to [s]
transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 10**6) transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 10**6)
# key for columns and level for 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["payload_size"].groupby(pd.Grouper(level="datetime", freq="{}s".format(args.interval))).transform("sum")
transmission_df["goodput"] = transmission_df["goodput"].apply( transmission_df["goodput"] = transmission_df["goodput"].apply(
lambda x: ((x * 8) / args.interval) / 10**6 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["payload_size"].rolling("{}s".format(args.interval)).sum()
transmission_df["goodput_rolling"] = transmission_df["goodput_rolling"].apply( transmission_df["goodput_rolling"] = transmission_df["goodput_rolling"].apply(
lambda x: ((x * 8) / args.interval) / 10 ** 6 lambda x: ((x * 8) / args.interval) / 10 ** 6
) )
# set meta values and remove all not needed columns # set meta values and remove all not needed columns
cc_algo = transmission_df["congestion_control"].iloc[0] cc_algo = transmission_df["congestion_control"].iloc[0]
cc_algo = cc_algo.upper() cc_algo = cc_algo.upper()
transmission_direction = transmission_df["direction"].iloc[0] 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 # read serial csv
serial_df = pd.read_csv(args.serial_file) serial_df = pd.read_csv(args.serial_file)
serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1) serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)
serial_df = serial_df.set_index("datetime") serial_df = serial_df.set_index("datetime")
serial_df.index = pd.to_datetime(serial_df.index) serial_df.index = pd.to_datetime(serial_df.index)
serial_df.sort_index() serial_df.sort_index()
transmission_df = pd.merge_asof( transmission_df = pd.merge_asof(
transmission_df, transmission_df,
serial_df, serial_df,
tolerance=pd.Timedelta("1s"), tolerance=pd.Timedelta("1s"),
right_index=True, right_index=True,
left_index=True, left_index=True,
) )
# transmission timeline # transmission timeline
scaley = 1.5 scaley = 1.5
scalex = 1.0 scalex = 1.0
fig, ax0 = plt.subplots(211, figsize=[6.4 * scaley, 4.8 * scalex]) ax0 = plt.subplot(211)
ax1 = ax0.twinx() ax1 = ax0.twinx()
ax2 = ax0.twinx() ax2 = ax0.twinx()
fig, ax00 = plt.subplots(212) ax00 = plt.subplot(212)
ax01 = ax00.twinx() 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 # create list fo color indices for lte cells
color_dict = dict() color_dict = dict()
color_list = list() color_list = list()
i = 0 i = 0
for cell_id in transmission_df["PCID"]: for cell_id in transmission_df["PCID"]:
if cell_id not in color_dict: if cell_id not in color_dict:
color_dict[cell_id] = i color_dict[cell_id] = i
i += 1 i += 1
color_list.append(color_dict[cell_id]) color_list.append(color_dict[cell_id])
transmission_df["lte_cell_color"] = color_list transmission_df["lte_cell_color"] = color_list
color_dict = None color_dict = None
color_list = None color_list = None
cmap = matplotlib.cm.get_cmap("Set3") cmap = matplotlib.cm.get_cmap("Set3")
unique_cells = transmission_df["lte_cell_color"].unique() unique_cells = transmission_df["lte_cell_color"].unique()
color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
transmission_df["index"] = transmission_df.index transmission_df["index"] = transmission_df.index
for c in transmission_df["lte_cell_color"].unique(): for c in transmission_df["lte_cell_color"].unique():
bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[ bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[
c] c]
ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c]) ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
# create list fo color indices for nr cells # create list fo color indices for nr cells
color_dict = dict() color_dict = dict()
color_list = list() color_list = list()
i = 0 i = 0
for cell_id in transmission_df["PCID.1"]: for cell_id in transmission_df["PCID.1"]:
if cell_id not in color_dict: if cell_id not in color_dict:
color_dict[cell_id] = i color_dict[cell_id] = i
i += 1 i += 1
color_list.append(color_dict[cell_id]) color_list.append(color_dict[cell_id])
transmission_df["nr_cell_color"] = color_list transmission_df["nr_cell_color"] = color_list
color_dict = None color_dict = None
color_list = None color_list = None
cmap = matplotlib.cm.get_cmap("Set3") cmap = matplotlib.cm.get_cmap("Set3")
unique_cells = transmission_df["nr_cell_color"].unique() unique_cells = transmission_df["nr_cell_color"].unique()
color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1) color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
for c in transmission_df["nr_cell_color"].unique(): 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] 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]) 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") 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") ax1.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
ax2.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput") 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") 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") ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="DL_bandwidth")
if args.save: if args.save:
plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", ""))) plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
except Exception as e: #except Exception as e:
print("Error processing file: {}".format(csv)) # print("Error processing file: {}".format(csv))
print(str(e)) # print(str(e))
counter += 1 counter += 1
plt.clf() plt.clf()