Adds 'some' error handling.

plot_single_transmission_timeline.py

@@ -44,125 +44,129 @@ if __name__ == "__main__":
     for csv in pcap_csv_list:
 
         print("\rProcessing {} out of {} CSVs.\t({}%)\t".format(counter, len(pcap_csv_list), math.floor(counter/len(pcap_csv_list))))
-        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, ax = plt.subplots(figsize=[6.4 * scaley, 4.8 * scalex])
-        plt.title("{} with {}".format(transmission_direction, cc_algo))
-        fig.subplots_adjust(right=0.75)
-
-        twin1 = ax.twinx()
-        twin2 = ax.twinx()
-        twin3 = ax.twinx()
-        # Offset the right spine of twin2.  The ticks and label have already been
-        # placed on the right by twinx above.
-        twin2.spines.right.set_position(("axes", 1.1))
-        twin3.spines.right.set_position(("axes", 1.2))
-
-
-        # create list fo color indices
-        transmission_df["index"] = transmission_df.index
-        color_dict = dict()
-        color_list = list()
-        i = 0
-        for cell_id in transmission_df["cellID"]:
-            if cell_id not in color_dict:
-                color_dict[cell_id] = i
-                i += 1
-            color_list.append(color_dict[cell_id])
-
-        transmission_df["cell_color"] = color_list
-        color_dict = None
-        color_list = None
-
-        cmap = matplotlib.cm.get_cmap("Set3")
-        unique_cells = transmission_df["cell_color"].unique()
-        color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
-
-        for c in transmission_df["cell_color"].unique():
-            bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c]
-            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["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
-        p1, = ax.plot(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())
-        ax.set_ylim(0, 500)
-        twin1.set_ylim(0, 15)
-        twin2.set_ylim(0, transmission_df["ack_rtt"].max())
-        twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10)
-
-        ax.set_xlabel("arrival time")
-        ax.set_ylabel("Goodput [mbps]")
-        twin1.set_ylabel("CQI")
-        twin2.set_ylabel("sRTT [s]")
-        twin3.set_ylabel("cwnd")
-
-        ax.yaxis.label.set_color(p1.get_color())
-        twin1.yaxis.label.set_color(p2.get_color())
-        twin2.yaxis.label.set_color(p3.get_color())
-        twin3.yaxis.label.set_color(p4.get_color())
-
-        tkw = dict(size=4, width=1.5)
-        ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
-        twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
-        twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
-        twin3.tick_params(axis='y', colors=p4.get_color(), **tkw)
-        ax.tick_params(axis='x', **tkw)
-
-        #ax.legend(handles=[p1, p2, p3])
-
-        if args.save:
-            plt.savefig("{}{}_plot.pdf".format(args.save, csv.replace(".csv", "")))
 
+        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, ax = plt.subplots(figsize=[6.4 * scaley, 4.8 * scalex])
+            plt.title("{} with {}".format(transmission_direction, cc_algo))
+            fig.subplots_adjust(right=0.75)
+
+            twin1 = ax.twinx()
+            twin2 = ax.twinx()
+            twin3 = ax.twinx()
+            # Offset the right spine of twin2.  The ticks and label have already been
+            # placed on the right by twinx above.
+            twin2.spines.right.set_position(("axes", 1.1))
+            twin3.spines.right.set_position(("axes", 1.2))
+
+
+            # create list fo color indices
+            transmission_df["index"] = transmission_df.index
+            color_dict = dict()
+            color_list = list()
+            i = 0
+            for cell_id in transmission_df["cellID"]:
+                if cell_id not in color_dict:
+                    color_dict[cell_id] = i
+                    i += 1
+                color_list.append(color_dict[cell_id])
+
+            transmission_df["cell_color"] = color_list
+            color_dict = None
+            color_list = None
+
+            cmap = matplotlib.cm.get_cmap("Set3")
+            unique_cells = transmission_df["cell_color"].unique()
+            color_list = cmap.colors * (round(len(unique_cells) / len(cmap.colors)) + 1)
+
+            for c in transmission_df["cell_color"].unique():
+                bounds = transmission_df[["index", "cell_color"]].groupby("cell_color").agg(["min", "max"]).loc[c]
+                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["srtt"].dropna(), color="red", linestyle="dashdot", label="sRTT")
+            p1, = ax.plot(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())
+            ax.set_ylim(0, 500)
+            twin1.set_ylim(0, 15)
+            twin2.set_ylim(0, transmission_df["ack_rtt"].max())
+            twin3.set_ylim(0, transmission_df["snd_cwnd"].max() + 10)
+
+            ax.set_xlabel("arrival time")
+            ax.set_ylabel("Goodput [mbps]")
+            twin1.set_ylabel("CQI")
+            twin2.set_ylabel("sRTT [s]")
+            twin3.set_ylabel("cwnd")
+
+            ax.yaxis.label.set_color(p1.get_color())
+            twin1.yaxis.label.set_color(p2.get_color())
+            twin2.yaxis.label.set_color(p3.get_color())
+            twin3.yaxis.label.set_color(p4.get_color())
+
+            tkw = dict(size=4, width=1.5)
+            ax.tick_params(axis='y', colors=p1.get_color(), **tkw)
+            twin1.tick_params(axis='y', colors=p2.get_color(), **tkw)
+            twin2.tick_params(axis='y', colors=p3.get_color(), **tkw)
+            twin3.tick_params(axis='y', colors=p4.get_color(), **tkw)
+            ax.tick_params(axis='x', **tkw)
+
+            #ax.legend(handles=[p1, p2, p3])
+
+            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()