diff --git a/plot_gps_new.py b/plot_gps_new.py
index f516920..e9e5719 100755
--- a/plot_gps_new.py
+++ b/plot_gps_new.py
@@ -14,7 +14,6 @@ if __name__ == "__main__":
     parser.add_argument("-l", "--label", help="Label above the plot.")
     parser.add_argument("--no_legend", action="store_false", default=True, help="Do not show legend.")
     parser.add_argument("--save", default=None, help="Location to save pdf file.")
-    parser.add_argument("--time_offset", default=None, type=int, help="Minutes added to GPS datetime.")
 
     parser.add_argument(
         "--show_providerinfo",
diff --git a/plot_single_transmission_timeline.py b/plot_single_transmission_timeline.py
index 72a0ac9..dfdbe84 100755
--- a/plot_single_transmission_timeline.py
+++ b/plot_single_transmission_timeline.py
@@ -53,9 +53,8 @@ if __name__ == "__main__":
         transmission_df.index = pd.to_datetime(transmission_df.index)
         transmission_df = transmission_df.sort_index()
 
-        #print("Calculate goodput...")
-
-        #print(transmission_df)
+        # 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")
@@ -131,8 +130,8 @@ if __name__ == "__main__":
             ax.axvspan(bounds.min(), bounds.max(), alpha=0.3, color=color_list[c])
 
         p4, = twin3.plot(transmission_df["snd_cwnd"].dropna(), color="lime", linestyle="dashed", label="cwnd")
-        p3, = twin2.plot(transmission_df["ack_rtt"].dropna(), color="red", linestyle="dashdot", label="ACK RTT")
-        p1, = ax.plot(transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
+        p3, = twin2.plot(transmission_df["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
+        p1, = ax.plot(transmission_df["arrival_time"], transmission_df["goodput_rolling"], color="blue", linestyle="solid", label="goodput")
         p2, = twin1.plot(transmission_df["downlink_cqi"].dropna(), color="magenta", linestyle="dotted", label="CQI")
 
         ax.set_xlim(transmission_df["index"].min(), transmission_df["index"].max())
@@ -144,7 +143,7 @@ if __name__ == "__main__":
         ax.set_xlabel("arrival time")
         ax.set_ylabel("Goodput [mbps]")
         twin1.set_ylabel("CQI")
-        twin2.set_ylabel("ACK RTT [s]")
+        twin2.set_ylabel("sRTT [s]")
         twin3.set_ylabel("cwnd")
 
         ax.yaxis.label.set_color(p1.get_color())
@@ -163,6 +162,30 @@ if __name__ == "__main__":
 
         if args.save:
             plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
+
+
+        # plot correlations
+        corr_pairs = [
+            ["goodput_rolling", "RSRQ"],
+            ["goodput_rolling", "RSRP"],
+            ["goodput_rolling", "RSSI"],
+            ["goodput_rolling", "SINR"],
+            ["goodput_rolling", "downlink_cqi"],
+        ]
+
+        for pair in corr_pairs:
+            # spearman and pearson
+            sp = transmission_df[pair[0]].corr(transmission_df[pair[1]], method="spearman")
+            pe = transmission_df[pair[0]].corr(transmission_df[pair[1]], method="pearson")
+            title = "{}/{} spearman: {} pearson: {}".format(pair[0], pair[1], round(sp, 4), round(pe, 4))
+            transmission_df.plot.scatter(x=pair[0], y=pair[1], c="DarkBlue", title=title)
+
+            if args.save:
+                plt.savefig("{}{}_corr_{}_and_{}.pdf".format(args.save, csv.replace(".csv", ""), pair[0], pair[1]))
+
+            plt.clf()
+
+
         counter += 1
 
         plt.clf()
\ No newline at end of file