Memory management

cdf_compare.py

     global n
     global frame_list
 
-    transmission_df = None
+    tmp_df = None
 
     for csv in csv_list:
         tmp_df = pd.read_csv(
         tmp_df["datetime"] = pd.to_datetime(tmp_df["datetime"]) - pd.Timedelta(hours=1)
         tmp_df = tmp_df.set_index("datetime")
         tmp_df.index = pd.to_datetime(tmp_df.index)
-        if transmission_df is None:
-            transmission_df = tmp_df
+        if tmp_df is None:
+            tmp_df = tmp_df
         else:
-            transmission_df = pd.concat([transmission_df, tmp_df])
+            tmp_df = pd.concat([tmp_df, tmp_df])
 
         n.value += 1
 
-    frame_list.append(transmission_df)
+    tmp_df = tmp_df.filter(
+        ["srtt", "datetime", "srtt", "payload_size", "congestion_control", "direction"])
+    frame_list.append(tmp_df)
+    del tmp_df
 
 
 from itertools import islice
         frame_list = None
         transmission_df = transmission_df.sort_index()
 
-        print("Calculate goodput...")
+        #
+        # Don't forget to add new columns to the filter argument in the function above!
+        #
 
         transmission_df["srtt"] = transmission_df["srtt"].apply(lambda x: x / 10 ** 6)
 
         cc_algo = cc_algo.upper()
         transmission_direction = transmission_df["direction"].iloc[0]
 
+        transmission_df = transmission_df.filter(["srtt", "datetime", "srtt", "payload_size"])
+
+
         # read serial csv
         #serial_df = pd.read_csv(args.serial_file)
         #serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)