Plot pcid and scid.

plot_single_transmission.py

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+#!/usr/bin/env python3
+import math
+import multiprocessing
+import os
+from argparse import ArgumentParser
+
+import matplotlib
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    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(
+        "-i",
+        "--interval",
+        default=10,
+        type=int,
+        help="Time interval for rolling window.",
+    )
+
+    args = parser.parse_args()
+    manager = multiprocessing.Manager()
+    n = manager.Value("i", 0)
+    frame_list = manager.list()
+    jobs = []
+
+    # load all pcap csv into one dataframe
+    pcap_csv_list = list()
+    for filename in os.listdir(args.pcap_csv_folder):
+        if filename.endswith(".csv") and "tcp" in filename:
+            pcap_csv_list.append(filename)
+
+    counter = 1
+    if len(pcap_csv_list) == 0:
+        print("No CSV files found.")
+
+    pcap_csv_list.sort(key=lambda x: int(x.split("_")[-1].replace(".csv", "")))
+
+    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))))
+
+        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()
+
+            # 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
+            )
+
+            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]
+
+            #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()
+
+            transmission_df = pd.merge_asof(
+                transmission_df,
+                serial_df,
+                tolerance=pd.Timedelta("1s"),
+                right_index=True,
+                left_index=True,
+            )
+
+            # transmission timeline
+
+            scaley = 1.5
+            scalex = 1.0
+            ax0 = plt.subplots(211, figsize=[6.4 * scaley, 4.8 * scalex])
+            ax1 = ax0.twinx()
+            ax2 = ax0.twinx()
+
+            ax00 = plt.subplots(212)
+            ax01 = ax00.twinx()
+
+            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])
+
+            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)
+
+            # 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
+
+            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)
+
+            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])
+            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")
+
+            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()