Plot pcid and scid.

plot_single_transmission.py

@@ -44,123 +44,123 @@ if __name__ == "__main__":
 
         print("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list))))
 
-        try:
-            transmission_df = pd.read_csv(
-                "{}{}".format(args.pcap_csv_folder, csv),
-                dtype=dict(is_retranmission=bool, is_dup_ack=bool),
-            )
-
-            transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1)
-            transmission_df = transmission_df.set_index("datetime")
-            transmission_df.index = pd.to_datetime(transmission_df.index)
-            transmission_df = transmission_df.sort_index()
-
-            # 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")
-            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", "goodput_rolling", "snd_cwnd"])
-
-            # read serial csv
-            serial_df = pd.read_csv(args.serial_file)
-            serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)
-            serial_df = serial_df.set_index("datetime")
-            serial_df.index = pd.to_datetime(serial_df.index)
-            serial_df.sort_index()
-
-            transmission_df = pd.merge_asof(
-                transmission_df,
-                serial_df,
-                tolerance=pd.Timedelta("1s"),
-                right_index=True,
-                left_index=True,
-            )
-
-            # transmission timeline
-
-            scaley = 1.5
-            scalex = 1.0
-            fig, ax0 = plt.subplots(211, figsize=[6.4 * scaley, 4.8 * scalex])
-            ax1 = ax0.twinx()
-            ax2 = ax0.twinx()
-
-            fig, ax00 = plt.subplots(212)
-            ax01 = ax00.twinx()
-
-            plt.title("{} with {}".format(transmission_direction, cc_algo))
-
-            # create list fo color indices for lte cells
-            color_dict = dict()
-            color_list = list()
-            i = 0
-            for cell_id in transmission_df["PCID"]:
-                if cell_id not in color_dict:
-                    color_dict[cell_id] = i
-                    i += 1
-                color_list.append(color_dict[cell_id])
-
-            transmission_df["lte_cell_color"] = color_list
-            color_dict = None
-            color_list = None
-
-            cmap = matplotlib.cm.get_cmap("Set3")
-            unique_cells = transmission_df["lte_cell_color"].unique()
-            color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
-
-            transmission_df["index"] = transmission_df.index
-            for c in transmission_df["lte_cell_color"].unique():
-                bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[
-                    c]
-                ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
-
-            # create list fo color indices for nr cells
-            color_dict = dict()
-            color_list = list()
-            i = 0
-            for cell_id in transmission_df["PCID.1"]:
-                if cell_id not in color_dict:
-                    color_dict[cell_id] = i
-                    i += 1
-                color_list.append(color_dict[cell_id])
-
-            transmission_df["nr_cell_color"] = color_list
-            color_dict = None
-            color_list = None
-
-            cmap = matplotlib.cm.get_cmap("Set3")
-            unique_cells = transmission_df["nr_cell_color"].unique()
-            color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
-
-            for c in transmission_df["nr_cell_color"].unique():
-                bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c]
-                ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
-
-            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")
-
-            if args.save:
-                plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
-        except Exception as e:
-            print("Error processing file: {}".format(csv))
-            print(str(e))
+        #try:
+        transmission_df = pd.read_csv(
+            "{}{}".format(args.pcap_csv_folder, csv),
+            dtype=dict(is_retranmission=bool, is_dup_ack=bool),
+        )
+
+        transmission_df["datetime"] = pd.to_datetime(transmission_df["datetime"]) - pd.Timedelta(hours=1)
+        transmission_df = transmission_df.set_index("datetime")
+        transmission_df.index = pd.to_datetime(transmission_df.index)
+        transmission_df = transmission_df.sort_index()
+
+        # 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")
+        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", "goodput_rolling", "snd_cwnd"])
+
+        # read serial csv
+        serial_df = pd.read_csv(args.serial_file)
+        serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)
+        serial_df = serial_df.set_index("datetime")
+        serial_df.index = pd.to_datetime(serial_df.index)
+        serial_df.sort_index()
+
+        transmission_df = pd.merge_asof(
+            transmission_df,
+            serial_df,
+            tolerance=pd.Timedelta("1s"),
+            right_index=True,
+            left_index=True,
+        )
+
+        # transmission timeline
+
+        scaley = 1.5
+        scalex = 1.0
+        ax0 = plt.subplot(211)
+        ax1 = ax0.twinx()
+        ax2 = ax0.twinx()
+
+        ax00 = plt.subplot(212)
+        ax01 = ax00.twinx()
+
+        plt.title("{} with {}".format(transmission_direction, cc_algo))
+
+        # create list fo color indices for lte cells
+        color_dict = dict()
+        color_list = list()
+        i = 0
+        for cell_id in transmission_df["PCID"]:
+            if cell_id not in color_dict:
+                color_dict[cell_id] = i
+                i += 1
+            color_list.append(color_dict[cell_id])
+
+        transmission_df["lte_cell_color"] = color_list
+        color_dict = None
+        color_list = None
+
+        cmap = matplotlib.cm.get_cmap("Set3")
+        unique_cells = transmission_df["lte_cell_color"].unique()
+        color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
+
+        transmission_df["index"] = transmission_df.index
+        for c in transmission_df["lte_cell_color"].unique():
+            bounds = transmission_df[["index", "lte_cell_color"]].groupby("lte_cell_color").agg(["min", "max"]).loc[
+                c]
+            ax0.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
+
+        # create list fo color indices for nr cells
+        color_dict = dict()
+        color_list = list()
+        i = 0
+        for cell_id in transmission_df["PCID.1"]:
+            if cell_id not in color_dict:
+                color_dict[cell_id] = i
+                i += 1
+            color_list.append(color_dict[cell_id])
+
+        transmission_df["nr_cell_color"] = color_list
+        color_dict = None
+        color_list = None
+
+        cmap = matplotlib.cm.get_cmap("Set3")
+        unique_cells = transmission_df["nr_cell_color"].unique()
+        color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
+
+        for c in transmission_df["nr_cell_color"].unique():
+            bounds = transmission_df[["index", "nr_cell_color"]].groupby("nr_cell_color").agg(["min", "max"]).loc[c]
+            ax00.axvspan(bounds.min(), bounds.max(), alpha=0.1, color=color_list[c])
+
+        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")
+
+        if args.save:
+            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.clf()