Pls fix.

plot_single_transmission.py

     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("--save", required=True, help="Location to save pdf file.")
     parser.add_argument(
         "-i",
         "--interval",
     )
 
     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:
             right_index=True,
             left_index=True,
         )
+        transmission_df = transmission_df.rename(columns={"PCID": "lte_pcid", "PCID.1": "nr_pcid"})
 
         # transmission timeline
-
         scaley = 1.5
         scalex = 1.0
-        ax0 = plt.subplot(211)
+        plt.title("{} with {}".format(transmission_direction, cc_algo))
+        fig, ax = plt.subplots(2, 1, figsize=[6.4 * scaley, 4.8 * scalex])
+        fig.subplots_adjust(right=0.75)
+        ax0 = ax[0]
         ax1 = ax0.twinx()
         ax2 = ax0.twinx()
+        ax2.spines.right.set_position(("axes", 3))
 
-        ax00 = plt.subplot(212)
+        ax00 = ax[1]
         ax01 = ax00.twinx()
 
-        plt.title("{} with {}".format(transmission_direction, cc_algo))
+        transmission_df["lte_handovers"] = transmission_df["lte_pcid"].diff()
+
+
+        lte_handovers = transmission_df[transmission_df.lte_pcid.diff() != 0].index.values
+        nr_handovers = transmission_df[transmission_df.nr_pcid.diff() != 0].index.values
 
-        # 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)
-
-        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])
-
-        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)
-
-        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])
+        print(transmission_df["lte_handovers"])
+        print(len(transmission_df["lte_handovers"]))
+        continue
+
+        # Plot vertical lines
+        for item in lte_handovers[1::]:
+            ax00.axvline(item, ymin=0, ymax=1, color='red')
+        for item in nr_handovers[1::]:
+            ax00.axvline(item, ymin=0, ymax=1, color='red')
 
         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")
         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", "")))
+        ax2.spines.right.set_position(("axes", 1.1))
+
+        ax0.set_ylim(0, 5000)
+        ax1.set_ylim(0, 0.3)
+        ax2.set_ylim(0, 500)
+        ax00.set_ylim(0, 16)
+        ax01.set_ylim(0, 21)
+
+        ax00.set_xlabel("arrival time")
+        ax2.set_ylabel("Goodput [mbps]")
+        ax00.set_ylabel("CQI")
+        ax1.set_ylabel("sRTT [s]")
+        ax0.set_ylabel("cwnd")
+        ax01.set_ylabel("Bandwidth [MHz]")
+
+        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()
+        plt.clf()