Adds rolling window for goodput

plot_transmission_timeline.py

@@ -150,17 +150,21 @@ if __name__ == "__main__":
 
     # 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["payload_size"].rolling("{}s".format(args.interval)).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"])
+    transmission_df = transmission_df.filter(["goodput", "datetime", "ack_rtt", "goodput_rolling"])
 
     # read serial csv
     serial_df = pd.read_csv(args.serial_file)
@@ -193,7 +197,7 @@ if __name__ == "__main__":
 
     plt.subplots_adjust()
 
-    host.plot(transmission_df["goodput"], "-", color="blue", label="goodput")
+    host.plot(transmission_df["goodput_rolling"], "-", color="blue", label="goodput")
     host.set_xlabel("datetime")
     host.set_ylabel("goodput [Mbps]")