Revert changes

plot_transmission_timeline.py

 
     print("Calculate and polt CDF...")
     # Get the frequency, PDF and CDF for each value in the series
-    # copy column
-    transmission_df["gp_frequency"] = transmission_df.loc[:, "goodput"]
-    print(transmission_df)
+
     # Frequency
-    transmission_df = transmission_df.groupby("gp_frequency")["gp_frequency"].agg("count").pipe(pd.DataFrame)
+    stats_df = transmission_df \
+        .groupby("goodput") \
+        ["goodput"] \
+        .agg("count") \
+        .pipe(pd.DataFrame) \
+        .rename(columns={"goodput": "frequency"})
 
     # PDF
-    transmission_df["pdf"] = transmission_df["gp_frequency"] / sum(transmission_df["gp_frequency"])
+    stats_df["pdf"] = stats_df["frequency"] / sum(stats_df["frequency"])
 
     # CDF
-    transmission_df["cdf"] = transmission_df["pdf"].cumsum()
-    print(transmission_df)
-    transmission_df = transmission_df.reset_index()
+    stats_df["cdf"] = stats_df["pdf"].cumsum()
+    stats_df = stats_df.reset_index()
 
-    transmission_df.plot(x="goodput", y=["cdf"], grid=True)
+    stats_df.plot(x="goodput", y=["cdf"], grid=True)
 
     if args.save:
         plt.savefig("{}cdf_plot.pdf".format(args.save))