Adds fancy legend.

plot_single_transmission_EM9190.py

 import matplotlib.pyplot as plt
 
 import seaborn as sns
+
 sns.set()
 
 tex_fonts = {
     "mathtext.fontset": "dejavusans",
 }
 
+
 # plt.rcParams.update(tex_fonts)
 
 
         transmission_df = transmission_df.sort_index()
 
         # 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
         transmission_df["goodput"] = (
             .transform("sum")
         )
         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["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
         ].fillna(0)
 
         transmission_df["lte_effective_bw_sum"] = (
-            transmission_df["LTE_SCC1_effective_bw"].fillna(0)
-            + transmission_df["LTE_SCC2_effective_bw"].fillna(0)
-            + transmission_df["LTE_SCC3_effective_bw"].fillna(0)
-            + transmission_df["LTE_SCC4_effective_bw"].fillna(0)
-            + transmission_df["LTE_bw"].fillna(0))
+                transmission_df["LTE_SCC1_effective_bw"].fillna(0)
+                + transmission_df["LTE_SCC2_effective_bw"].fillna(0)
+                + transmission_df["LTE_SCC3_effective_bw"].fillna(0)
+                + transmission_df["LTE_SCC4_effective_bw"].fillna(0)
+                + transmission_df["LTE_bw"].fillna(0))
         transmission_df["nr_effective_bw_sum"] = transmission_df["SCC1_NR5G_effective_bw"]
 
-        transmission_df["effective_bw_sum"] = transmission_df["nr_effective_bw_sum"] + transmission_df["lte_effective_bw_sum"]
+        transmission_df["effective_bw_sum"] = transmission_df["nr_effective_bw_sum"] + transmission_df[
+            "lte_effective_bw_sum"]
 
         # transmission timeline
         scaley = 1.5
         ax01.set_ylabel("Bandwidth [MHz]")
 
         if args.fancy:
+            ax0.set_xlim([0, transmission_df.index[-1]])
+            ax00.set_xlim([0, transmission_df.index[-1]])
             # added these three lines
             lns_ax0 = snd_plot + srtt_plot + goodput_plot
             labs_ax0 = [l.get_label() for l in lns_ax0]
             ax0.legend(lns_ax0, labs_ax0, ncols=4, fontsize=12, loc="upper center")
+
             lns_ax00 = eff_bw_plot + lte_eff_bw_plot + nr_eff_bw_plot + lte_rsrq_plot + nr_rsrq_plot
             if lte_hanover_plot:
                 lns_ax00.append(lte_hanover_plot)
             if nr_hanover_plot:
                 lns_ax00.append(nr_hanover_plot)
-            labs_ax00 = [l.get_label() for l in lns_ax0]
+            labs_ax00 = [l.get_label() for l in labs_ax00]
             ax00.legend(lns_ax00, labs_ax00, ncols=4, fontsize=12, loc="upper center")
             plt.savefig("{}{}_plot.eps".format(args.save, csv.replace(".csv", "")), bbox_inches="tight")
         else:
             fig.legend(loc="lower right")
-            plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")),  bbox_inches="tight")
+            plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")), bbox_inches="tight")
         # except Exception as e:
         #    print("Error processing file: {}".format(csv))
         #    print(str(e))