Refactor plots.

plot_single_transmission.py

 import pandas as pd
 import matplotlib.pyplot as plt
 
+# Using seaborn's style
+#plt.style.use('seaborn')
+
+tex_fonts = {
+    "pgf.texsystem": "lualatex",
+    # "legend.fontsize": "x-large",
+    # "figure.figsize": (15, 5),
+    "axes.labelsize": 15,  # "small",
+    # "axes.titlesize": "x-large",
+    "xtick.labelsize": 15,  # "small",
+    "ytick.labelsize": 15,  # "small",
+    "legend.fontsize": 15,
+    "axes.formatter.use_mathtext": True,
+    "mathtext.fontset": "dejavusans",
+}
+
+#plt.rcParams.update(tex_fonts)
+
 
 if __name__ == "__main__":
     parser = ArgumentParser()
         )
         transmission_df = transmission_df.rename(columns={"PCID": "lte_pcid", "PCID.1": "nr_pcid"})
 
+        transmission_df.index = transmission_df["arrival_time"]
+
         # transmission timeline
         scaley = 1.5
         scalex = 1.0
         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)
+        fig.suptitle("{} with {}".format(transmission_direction, cc_algo))
         ax0 = ax[0]
         ax1 = ax0.twinx()
         ax2 = ax0.twinx()
-        ax2.spines.right.set_position(("axes", 3))
+        #ax2.spines.right.set_position(("axes", 1.22))
 
         ax00 = ax[1]
         ax01 = ax00.twinx()
 
-        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
-
-        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')
+        lte_handovers = transmission_df["lte_pcid"].diff().dropna()
+        for index, value in lte_handovers.items():
+            if value > 0:
+                ax00.axvline(index, ymin=0, ymax=1, color="skyblue", label="4G Handover")
+
+        nr_handovers = transmission_df["nr_pcid"].diff().dropna()
+        for index, value in nr_handovers.items():
+            if value > 0:
+                ax00.axvline(index, ymin=0, ymax=1, color="greenyellow", label="5G Handover")
 
         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")
+        ax01.plot(transmission_df["DL_bandwidth"].dropna(), color="peru", linestyle="dotted", label="bandwidth")
 
         ax2.spines.right.set_position(("axes", 1.1))
 
         ax0.set_ylabel("cwnd")
         ax01.set_ylabel("Bandwidth [MHz]")
 
+        fig.legend(loc="lower right")
+
         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.close(fig)
         plt.clf()