Todo: Refactor

plot_gps_new.py

@@ -10,188 +10,41 @@ import geopandas as gpd
 import contextily as cx
 import matplotlib.pyplot as plt
 
-
-def csv_to_dataframe(csv_list, dummy):
-
-    global n
-    global frame_list
-
-    transmission_df = None
-
-    for csv in csv_list:
-        tmp_df = pd.read_csv(
-            "{}{}".format(args.pcap_csv_folder, csv),
-            dtype=dict(is_retranmission=bool, is_dup_ack=bool),
-        )
-        #tmp_df["datetime"] = pd.to_datetime(tmp_df["datetime"]) - pd.Timedelta(hours=1)
-        tmp_df["datetime"] = pd.to_datetime(tmp_df["datetime"])
-        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
-        else:
-            transmission_df = pd.concat([transmission_df, tmp_df])
-
-        n.value += 1
-
-    frame_list.append(transmission_df)
-
-
-from itertools import islice
-
-
-def chunk(it, size):
-    it = iter(it)
-    return iter(lambda: tuple(islice(it, size)), ())
-
-
 if __name__ == "__main__":
     parser = ArgumentParser()
-    parser.add_argument("-f", "--gps_file", required=True, help="GPS csv file.")
+    parser.add_argument("-f", "--file", required=True, help="Messfahrt csv")
-    parser.add_argument("-s", "--serial_file", required=True, help="Serial csv file.")
-    parser.add_argument("-p", "--pcap_csv_folder", required=True, help="PCAP csv folder.")
     parser.add_argument("-a", "--column", required=True, help="Column to plot")
     parser.add_argument("-l", "--label", help="Label above the plot.")
     parser.add_argument("--no_legend", action="store_false", default=True, help="Do not show legend.")
     parser.add_argument("--save", default=None, help="Location to save pdf file.")
     parser.add_argument("--time_offset", default=None, type=int, help="Minutes added to GPS datetime.")
-    parser.add_argument("--no_plot", default=False, action="store_true", help="Only calculations without plotting.")
 
     parser.add_argument(
         "--show_providerinfo",
         default=False,
         help="Show providerinfo for map tiles an zoom levels.",
     )
-    parser.add_argument(
-        "-c",
-        "--cores",
-        default=1,
-        type=int,
-        help="Number of cores for multiprocessing.",
-    )
-    parser.add_argument(
-        "-i",
-        "--interval",
-        default=10,
-        type=int,
-        help="Time interval for rolling window.",
-    )
 
     args = parser.parse_args()
-    manager = multiprocessing.Manager()
-    n = manager.Value("i", 0)
-    frame_list = manager.list()
-    jobs = []
 
-    # load all pcap csv into one dataframe
+    df = pd.read_csv(args.file)
-    pcap_csv_list = list()
-    for filename in os.listdir(args.pcap_csv_folder):
-        if filename.endswith(".csv") and "tcp" in filename:
-            pcap_csv_list.append(filename)
-
-    parts = chunk(pcap_csv_list, ceil(len(pcap_csv_list) / args.cores))
-    print("Start processing with {} jobs.".format(args.cores))
-    for p in parts:
-        process = multiprocessing.Process(target=csv_to_dataframe, args=(p, "dummy"))
-        jobs.append(process)
-
-    for j in jobs:
-        j.start()
-
-    print("Started all jobs.")
-    # Ensure all of the processes have finished
-    finished_job_counter = 0
-    working = ["|", "/", "-", "\\", "|", "/", "-", "\\"]
-    w = 0
-    while len(jobs) != finished_job_counter:
-        sleep(1)
-        print(
-            "\r\t{}{}{}\t Running {} jobs ({} finished). Processed {} out of {} pcap csv files.    ({}%)    ".format(
-                working[w],
-                working[w],
-                working[w],
-                len(jobs),
-                finished_job_counter,
-                n.value,
-                len(pcap_csv_list),
-                round((n.value / len(pcap_csv_list)) * 100, 2),
-            ),
-            end="",
-        )
-        finished_job_counter = 0
-        for j in jobs:
-            if not j.is_alive():
-                finished_job_counter += 1
-        if (w + 1) % len(working) == 0:
-            w = 0
-        else:
-            w += 1
-    print("\r\nSorting table...")
-
-    transmission_df = pd.concat(frame_list)
-    frame_list = None
-    transmission_df = transmission_df.sort_index()
-
-    print("Calculate goodput...")
-    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
-    )
-
-    # load dataframe an put it into geopandas
-    df = pd.read_csv(args.gps_file)
-    df["kmh"] = df["speed (knots)"].apply(lambda x: x * 1.852)
-    if args.time_offset:
-        df["datetime"] = pd.to_datetime(df["datetime"]) + pd.Timedelta(minutes=args.time_offset)
-    else:
-        df["datetime"] = pd.to_datetime(df["datetime"])
-    df = df.set_index("datetime")
-    df.index = pd.to_datetime(df.index)
 
     gdf = gpd.GeoDataFrame(
         df,
         geometry=gpd.points_from_xy(df["longitude"], df["latitude"]),
         crs="EPSG:4326",
     )
-    gdf = pd.merge_asof(
-        gdf,
-        transmission_df,
-        tolerance=pd.Timedelta("10s"),
-        right_index=True,
-        left_index=True,
-    )
 
-    # read serial csv
+    gdf["srtt"] = gdf["srtt"].apply(lambda x: x / 10 ** 6)
-    serial_df = pd.read_csv(args.serial_file)
-    #serial_df["datetime"] = pd.to_datetime(serial_df["datetime"]) - pd.Timedelta(hours=1)
-    serial_df["datetime"] = pd.to_datetime(serial_df["datetime"])
-    serial_df = serial_df.set_index("datetime")
-    serial_df.index = pd.to_datetime(serial_df.index)
-
-    gdf = pd.merge_asof(
-        gdf,
-        serial_df,
-        tolerance=pd.Timedelta("1s"),
-        right_index=True,
-        left_index=True,
-    )
-
-    # format to needed format and add basemap as background
-    df_wm = gdf.to_crs(epsg=3857)
-    #df_wm.to_csv("debug-data.csv")
-    # ax2 = df_wm.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')
-    if args.no_plot:
-        df_wm.to_csv("{}gps_plot.csv".format(args.save))
-        print("Saved calculations to: {}gps_plot.csv".format(args.save))
-        exit(0)
 
     print("Start plotting...")
 
+    df_wm = gdf.to_crs(epsg=3857)
     ax2 = df_wm.plot()
     ax2 = df_wm.plot(args.column, cmap="hot", legend=args.no_legend, ax=ax2)
     # ax2 = df_wm.plot.scatter(x="longitude", y="latitude", c="kmh", cmap="hot")
     # zoom 17 is pretty
-    cx.add_basemap(ax2, source=cx.providers.OpenStreetMap.Mapnik, zoom=15)
+    cx.add_basemap(ax2, source=cx.providers.OpenStreetMap.Mapnik, zoom=17)
 
     #    gdf.plot()
     ax2.set_axis_off()