Full-Pipeline-Rebuild.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from api import *\n",
    "from collections import defaultdict\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query and Download\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download traffic flow data for stations that report for the full 3 year peroid only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python download_reports.py all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download traffic for the days of focus for the sattelite data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 331/331 [01:05<00:00,  5.02it/s]\n",
      "100%|██████████| 52/52 [00:04<00:00, 11.23it/s]\n",
      "100%|██████████| 231/231 [00:16<00:00, 14.41it/s]\n",
      "100%|██████████| 70/70 [00:21<00:00,  3.24it/s]\n",
      "100%|██████████| 71/71 [00:04<00:00, 14.35it/s]\n",
      "100%|██████████| 38/38 [00:03<00:00, 11.21it/s]\n",
      "100%|██████████| 69/69 [00:04<00:00, 14.24it/s]\n",
      "100%|██████████| 47/47 [00:04<00:00, 11.34it/s]\n",
      "100%|██████████| 102/102 [00:06<00:00, 14.57it/s]\n",
      "100%|██████████| 37/37 [00:18<00:00,  1.96it/s]\n",
      "100%|██████████| 150/150 [00:30<00:00,  4.91it/s]\n",
      "100%|██████████| 87/87 [00:07<00:00, 11.24it/s]\n"
     ]
    }
   ],
   "source": [
    "# Birmingham\n",
    "start_date, end_date = '15062021', '15062021'\n",
    "max_lat, max_long  = 52.50, -1.67\n",
    "min_lat, min_long = 52.42, -1.75\n",
    "clean_birmingham_report_df, birmingham_report_df = download_clean_pipeline(start_date, end_date, max_lat, max_long, min_lat, min_long, 40)\n",
    "# Manchester\n",
    "start_date, end_date = '19042019', '19042019'\n",
    "max_lat, max_long  = 53.51,  -2.31\n",
    "min_lat, min_long = 53.44,  -2.39\n",
    "clean_manc_report_df,manc_report_df = download_clean_pipeline(start_date, end_date, max_lat, max_long, min_lat, min_long, 40)\n",
    "\n",
    "# Cambridge\n",
    "start_date, end_date = '02112021', '02112021'\n",
    "max_lat, max_long  = 52.25,   0.11\n",
    "min_lat, min_long = 52.19,   0.02\n",
    "clean_cam_report_df, cam_report_df = download_clean_pipeline(start_date, end_date, max_lat, max_long, min_lat, min_long, 40)\n",
    "\n",
    "# Thorpe\n",
    "start_date, end_date = '08032022', '08032022'\n",
    "max_lat, max_long  = 51.43,  -0.50\n",
    "min_lat, min_long = 51.38,  -0.57\n",
    "clean_thorpe_report_df, thorpe_report_df = download_clean_pipeline(start_date, end_date, max_lat, max_long, min_lat, min_long, 40)\n",
    "\n",
    "\n",
    "# Epping\n",
    "start_date, end_date = '08032022', '08032022'\n",
    "max_lat, max_long  = 51.72,   0.15\n",
    "min_lat, min_long = 51.62,   0.09\n",
    "clean_epping_report_df, epping_report_df = download_clean_pipeline(start_date, end_date, max_lat, max_long, min_lat, min_long, 40)\n",
    "\n",
    "# Bristol\n",
    "start_date, end_date = '08082020', '08082020'\n",
    "max_lat, max_long  = 51.60, -2.52\n",
    "min_lat, min_long = 51.52, -2.59\n",
    "clean_bristol_df, bristol_report_df = download_clean_pipeline(start_date, end_date, max_lat, max_long, min_lat, min_long, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export these reports\n",
    "clean_birmingham_report_df.reset_index(drop=True).to_feather('sattelite_time_traffic_reports/clean_birmingham_report_df')\n",
    "clean_manc_report_df.reset_index(drop=True).to_feather('sattelite_time_traffic_reports/clean_manc_report_df')\n",
    "clean_cam_report_df.reset_index(drop=True).to_feather('sattelite_time_traffic_reports/clean_cam_report_df')\n",
    "clean_thorpe_report_df.reset_index(drop=True).to_feather('sattelite_time_traffic_reports/clean_thorpe_report_df')\n",
    "clean_epping_report_df.reset_index(drop=True).to_feather('sattelite_time_traffic_reports/clean_epping_report_df')\n",
    "clean_bristol_df.reset_index(drop=True).to_feather('sattelite_time_traffic_reports/clean_bristol_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import these reports\n",
    "clean_birmingham_report_df = pd.read_feather('sattelite_time_traffic_reports/clean_birmingham_report_df')\n",
    "clean_manc_report_df = pd.read_feather('sattelite_time_traffic_reports/clean_manc_report_df')\n",
    "clean_cam_report_df = pd.read_feather('sattelite_time_traffic_reports/clean_cam_report_df')\n",
    "clean_thorpe_report_df = pd.read_feather('sattelite_time_traffic_reports/clean_thorpe_report_df')\n",
    "clean_epping_report_df = pd.read_feather('sattelite_time_traffic_reports/clean_epping_report_df')\n",
    "clean_bristol_df = pd.read_feather('sattelite_time_traffic_reports/clean_bristol_df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEMP QUERY DATA AVALIABILITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = datetime.datetime(2019,4,19,11,44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_name</th>\n",
       "      <th>site_id</th>\n",
       "      <th>report_date</th>\n",
       "      <th>time_period_ending</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>0-520cm</th>\n",
       "      <th>521-660cm</th>\n",
       "      <th>661-1160cm</th>\n",
       "      <th>1160+cm</th>\n",
       "      <th>avg_mph</th>\n",
       "      <th>total_volume</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>0-520cm_normalised</th>\n",
       "      <th>521-660cm_normalised</th>\n",
       "      <th>661-1160cm_normalised</th>\n",
       "      <th>1160+cm_normalised</th>\n",
       "      <th>total_volume_normalised</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3886</th>\n",
       "      <td>M60/9144A</td>\n",
       "      <td>17580</td>\n",
       "      <td>2019-04-19T00:00:00</td>\n",
       "      <td>11:44:00</td>\n",
       "      <td>46</td>\n",
       "      <td>1153</td>\n",
       "      <td>126</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>1337</td>\n",
       "      <td>2019-04-19 11:44:00</td>\n",
       "      <td>2.517811</td>\n",
       "      <td>2.959628</td>\n",
       "      <td>2.308766</td>\n",
       "      <td>1.476082</td>\n",
       "      <td>2.512027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      site_name site_id          report_date time_period_ending time_interval  \\\n",
       "3886  M60/9144A   17580  2019-04-19T00:00:00           11:44:00            46   \n",
       "\n",
       "      0-520cm  521-660cm  661-1160cm  1160+cm avg_mph  total_volume  \\\n",
       "3886     1153        126          31       27      57          1337   \n",
       "\n",
       "               timestamp  0-520cm_normalised  521-660cm_normalised  \\\n",
       "3886 2019-04-19 11:44:00            2.517811              2.959628   \n",
       "\n",
       "      661-1160cm_normalised  1160+cm_normalised  total_volume_normalised  \n",
       "3886               2.308766            1.476082                 2.512027  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_manc_report_df[(clean_manc_report_df.timestamp==time) & (clean_manc_report_df.site_id=='17580')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site_name</th>\n",
       "      <th>site_id</th>\n",
       "      <th>report_date</th>\n",
       "      <th>time_period_ending</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>0-520cm</th>\n",
       "      <th>521-660cm</th>\n",
       "      <th>661-1160cm</th>\n",
       "      <th>1160+cm</th>\n",
       "      <th>avg_mph</th>\n",
       "      <th>total_volume</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>0-520cm_normalised</th>\n",
       "      <th>521-660cm_normalised</th>\n",
       "      <th>661-1160cm_normalised</th>\n",
       "      <th>1160+cm_normalised</th>\n",
       "      <th>total_volume_normalised</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>M60/9144B</td>\n",
       "      <td>17555</td>\n",
       "      <td>2019-04-19T00:00:00</td>\n",
       "      <td>11:44:00</td>\n",
       "      <td>46</td>\n",
       "      <td>813</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>897</td>\n",
       "      <td>2019-04-19 11:44:00</td>\n",
       "      <td>1.851321</td>\n",
       "      <td>1.393681</td>\n",
       "      <td>2.192651</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>1.817552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      site_name site_id          report_date time_period_ending time_interval  \\\n",
       "3310  M60/9144B   17555  2019-04-19T00:00:00           11:44:00            46   \n",
       "\n",
       "      0-520cm  521-660cm  661-1160cm  1160+cm avg_mph  total_volume  \\\n",
       "3310      813         34          23       27      59           897   \n",
       "\n",
       "               timestamp  0-520cm_normalised  521-660cm_normalised  \\\n",
       "3310 2019-04-19 11:44:00            1.851321              1.393681   \n",
       "\n",
       "      661-1160cm_normalised  1160+cm_normalised  total_volume_normalised  \n",
       "3310               2.192651            1.384615                 1.817552  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_manc_report_df[(clean_manc_report_df.timestamp==time) & (clean_manc_report_df.site_id=='17555')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get predictions of the traffic count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "road_lengths = pd.read_csv('road_lengths.csv') \n",
    "traffic_data = copy.copy(road_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_train_report_df = pd.concat([clean_birmingham_report_df, clean_manc_report_df, clean_cam_report_df, clean_thorpe_report_df, clean_epping_report_df, clean_bristol_df])\n",
    "\n",
    "\n",
    "sites = []\n",
    "for x in list(traffic_data.sites):\n",
    "    sites = sites + x.split(', ')\n",
    "    \n",
    "filtered_train_report = clean_train_report_df[['site_name','site_id','report_date','time_period_ending','time_interval','0-520cm','521-660cm','661-1160cm','1160+cm','avg_mph','total_volume','timestamp']][clean_train_report_df.site_id.isin(sites)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a particular row x in road_lengths get the correspoiding traffic data\n",
    "def area_report(x):\n",
    "    sites = x.sites.split(', ')\n",
    "    day, month, year = x.ddmmyy.split('/')\n",
    "    hour, minute  = x.time.split(':')\n",
    "    \n",
    "    # Get the nearest of the 15 minute intervals to compare to\n",
    "    min_mod_15 = round((int(minute)+1)/15)\n",
    "    nearest_minute_interval = 15*min_mod_15 - 1\n",
    "    time = datetime.datetime(int(year),int(month),int(day), int(hour), int(nearest_minute_interval))\n",
    "    x_report = filtered_train_report[(filtered_train_report.site_id.isin(sites)) & (filtered_train_report.timestamp == time)]\n",
    "    return x_report\n",
    "\n",
    "# Add the reports to the dataframe\n",
    "traffic_data['report'] = traffic_data.apply(area_report, axis=1)\n",
    "# Extract values from the report\n",
    "traffic_data['0-520cm'] = traffic_data.apply(lambda x: x.report['0-520cm'].sum(), axis=1)\n",
    "traffic_data['521-660cm'] = traffic_data.apply(lambda x: x.report['521-660cm'].sum(), axis=1)\n",
    "traffic_data['661-1160cm'] = traffic_data.apply(lambda x: x.report['661-1160cm'].sum(), axis=1)\n",
    "traffic_data['1160+cm'] = traffic_data.apply(lambda x: x.report['1160+cm'].sum(), axis=1)\n",
    "traffic_data['total_volume'] = traffic_data.apply(lambda x: x.report['total_volume'].sum(), axis=1)\n",
    "traffic_data['avg_mph'] = traffic_data.apply(lambda x: x.report.avg_mph.astype(int).mean(), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_traffic_from_detector(traffic_data, threshold):\n",
    "    # Initially set the column names so they agree with the names in the pickle files\n",
    "    detections = pd.DataFrame(\n",
    "       {\n",
    "           \"filename\": [],\n",
    "           \"small_car_detections\": [],\n",
    "           \"bus_detections\": [],\n",
    "           \"pickup_truck_detections\": [],\n",
    "           \"utility_truck_detections\": [],\n",
    "           \"truck_detections\": [],\n",
    "           \"cargo_truck_detections\": [],\n",
    "           \"truck_w_box_detections\": [],\n",
    "           \"truck_tractor_detections\": [],\n",
    "           \"trailer_detections\": [],\n",
    "           \"truck_w_flatbed_detections\": [],\n",
    "           \"truck_w_liquid_detections\": [],\n",
    "           \"all_vehicle_detections\": [],\n",
    "\n",
    "       }\n",
    "    )\n",
    "\n",
    "    # Import the predictions the pickle files\n",
    "    pickle_folder = '/rds/general/user/jrb21/home/small-object-detection-benchmark/runs/highways/XVIEW_SAHI_PO19/pickles'\n",
    "    prediction_lists = defaultdict()\n",
    "\n",
    "    for i, file in enumerate(os.listdir(pickle_folder)):\n",
    "        with (open(os.path.join(pickle_folder, file), \"rb\")) as f:\n",
    "            prediction_list = pickle.load(f)\n",
    "        filename = file.split('.')[0]\n",
    "        prediction_lists[filename] = prediction_list\n",
    "        counts = defaultdict(int)\n",
    "        for pred in prediction_list:\n",
    "            if pred.score.value > threshold:\n",
    "                counts[pred.category.name] +=1\n",
    "\n",
    "        detections.loc[i] = [filename, \n",
    "                     counts[\"Small Car\"],\n",
    "                     counts[\"Bus\"],\n",
    "                     counts[\"Pickup Truck\"],\n",
    "                     counts[\"Utility Truck\"],\n",
    "                     counts[\"Truck\"],\n",
    "                     counts[\"Cargo Truck\"],\n",
    "                     counts[\"Truck w/Box\"],\n",
    "                     counts[\"Truck Tractor\"],\n",
    "                     counts[\"Trailer\"],\n",
    "                     counts[\"Truck w/Flatbed\"],\n",
    "                     counts[\"Truck w/Liquid\"],\n",
    "                     sum([x for x in counts.values()])]\n",
    "        \n",
    "    # combine the reports    \n",
    "    def detection_report(x):\n",
    "        detections_report = detections[detections.filename == x.filename]\n",
    "        return detections_report\n",
    "    \n",
    "    traffic_data['det_report'] = traffic_data.apply(detection_report, axis=1)\n",
    "    \n",
    "    # get the detection numbers\n",
    "    traffic_data['small_car_detections'] = traffic_data.apply(lambda x: x.det_report.small_car_detections.sum(), axis=1)\n",
    "    traffic_data['bus_detections'] = traffic_data.apply(lambda x: x.det_report.bus_detections.sum(), axis=1)\n",
    "    traffic_data['pickup_truck_detections'] = traffic_data.apply(lambda x: x.det_report.pickup_truck_detections.sum(), axis=1)\n",
    "    traffic_data['utility_truck_detections'] = traffic_data.apply(lambda x: x.det_report.utility_truck_detections.sum(), axis=1)\n",
    "    traffic_data['truck_detections'] = traffic_data.apply(lambda x: x.det_report.truck_detections.sum(), axis=1)\n",
    "    traffic_data['cargo_truck_detections'] = traffic_data.apply(lambda x: x.det_report.cargo_truck_detections.sum(), axis=1)\n",
    "    traffic_data['truck_w_box_detections'] = traffic_data.apply(lambda x: x.det_report.truck_w_box_detections.sum(), axis=1)\n",
    "    traffic_data['truck_tractor_detections'] = traffic_data.apply(lambda x: x.det_report.truck_tractor_detections.sum(), axis=1)\n",
    "    traffic_data['trailer_detections'] = traffic_data.apply(lambda x: x.det_report.trailer_detections.sum(), axis=1)\n",
    "    traffic_data['truck_w_flatbed_detections'] = traffic_data.apply(lambda x: x.det_report.truck_w_flatbed_detections.sum(), axis=1)\n",
    "    traffic_data['truck_w_liquid_detections'] = traffic_data.apply(lambda x: x.det_report.truck_w_liquid_detections.sum(), axis=1)\n",
    "    traffic_data['all_vehicle_detections'] = traffic_data.apply(lambda x: x.det_report.all_vehicle_detections.sum(), axis=1)\n",
    "\n",
    "    # Predict the traffic count from these detections via raodlength and avg speed\n",
    "    traffic_data['length_milles'] = traffic_data.length.apply(lambda x: x/1000 *5/8)\n",
    "    traffic_data['time_to_cross_hour'] = traffic_data.apply(lambda x : x.length_milles/x.avg_mph * 1/2 , axis=1)\n",
    "    traffic_data['time_to_cross_min'] = traffic_data.apply(lambda x : x.time_to_cross_hour * 60 , axis=1)\n",
    "\n",
    "    detection_cols = ['small_car_detections',\n",
    "     'bus_detections',\n",
    "     'pickup_truck_detections',\n",
    "     'utility_truck_detections',\n",
    "     'truck_detections',\n",
    "     'cargo_truck_detections',\n",
    "     'truck_w_box_detections',\n",
    "     'truck_tractor_detections',\n",
    "     'trailer_detections',\n",
    "     'truck_w_flatbed_detections',\n",
    "     'truck_w_liquid_detections',\n",
    "     'all_vehicle_detections']\n",
    "\n",
    "    for col in detection_cols:\n",
    "        pred_col = col.replace('detections', 'pred')\n",
    "        traffic_data[pred_col] = traffic_data[col] * 15 / traffic_data['time_to_cross_min']\n",
    "        \n",
    "    return traffic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ddmmyy</th>\n",
       "      <th>time</th>\n",
       "      <th>area</th>\n",
       "      <th>focus</th>\n",
       "      <th>length</th>\n",
       "      <th>sites</th>\n",
       "      <th>filename</th>\n",
       "      <th>report</th>\n",
       "      <th>0-520cm</th>\n",
       "      <th>521-660cm</th>\n",
       "      <th>...</th>\n",
       "      <th>pickup_truck_pred</th>\n",
       "      <th>utility_truck_pred</th>\n",
       "      <th>truck_pred</th>\n",
       "      <th>cargo_truck_pred</th>\n",
       "      <th>truck_w_box_pred</th>\n",
       "      <th>truck_tractor_pred</th>\n",
       "      <th>trailer_pred</th>\n",
       "      <th>truck_w_flatbed_pred</th>\n",
       "      <th>truck_w_liquid_pred</th>\n",
       "      <th>all_vehicle_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>A452</td>\n",
       "      <td>2542.41</td>\n",
       "      <td>9092, 9093</td>\n",
       "      <td>A452_birmingham</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>321</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.668842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.834421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>488.198206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>M6</td>\n",
       "      <td>5549.01</td>\n",
       "      <td>18212, 19227</td>\n",
       "      <td>M6_birmingham</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>366</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.029171</td>\n",
       "      <td>16.147024</td>\n",
       "      <td>96.882147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.147024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>758.910148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>M42</td>\n",
       "      <td>7347.07</td>\n",
       "      <td>10560, 10337, 10398</td>\n",
       "      <td>M42_birmingham</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>916</td>\n",
       "      <td>321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.998250</td>\n",
       "      <td>39.199300</td>\n",
       "      <td>137.197549</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1378.508712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>A45</td>\n",
       "      <td>4559.31</td>\n",
       "      <td>9490, 9491</td>\n",
       "      <td>A45_birmingham</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>395</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.226297</td>\n",
       "      <td>28.425354</td>\n",
       "      <td>18.950236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.475118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>577.982195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M25</td>\n",
       "      <td>8613.81</td>\n",
       "      <td>1910, 6134</td>\n",
       "      <td>M25_thorpe</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1853</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.298122</td>\n",
       "      <td>11.702139</td>\n",
       "      <td>87.766041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2147.342465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M25_2</td>\n",
       "      <td>6190.41</td>\n",
       "      <td>5107, 4648</td>\n",
       "      <td>M25_2_thorpe</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1845</td>\n",
       "      <td>229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.546385</td>\n",
       "      <td>7.883161</td>\n",
       "      <td>55.182128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.883161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1860.426046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M3</td>\n",
       "      <td>9400.57</td>\n",
       "      <td>14547, 14484, 14496, 14463, 14433</td>\n",
       "      <td>M3_thorpe</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>438</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.547701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.765668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.531337</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>524.223531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M3_2</td>\n",
       "      <td>4360.48</td>\n",
       "      <td>14437, 14470</td>\n",
       "      <td>M3_2_thorpe</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1091</td>\n",
       "      <td>222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.197189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1329.211463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>M11</td>\n",
       "      <td>5005.66</td>\n",
       "      <td>2166, 3092, 4178, 9676</td>\n",
       "      <td>M11_cambridge</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>731</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.593296</td>\n",
       "      <td>8.510366</td>\n",
       "      <td>59.572564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>629.767104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>M11_2</td>\n",
       "      <td>3308.62</td>\n",
       "      <td>3092, 4178</td>\n",
       "      <td>M11_2_cambridge</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>574</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.123000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.707667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.353833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>475.968833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>A14</td>\n",
       "      <td>4662.76</td>\n",
       "      <td>18577, 18594</td>\n",
       "      <td>A14_cambridge</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>665</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.263844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.894835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>261.476036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>A14_2</td>\n",
       "      <td>5764.49</td>\n",
       "      <td>18841, 18628, 18676</td>\n",
       "      <td>A14_2_cambridge</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>877</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.282255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.641128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.272569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M25</td>\n",
       "      <td>8327.00</td>\n",
       "      <td>1548, 4695</td>\n",
       "      <td>M25_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>559</td>\n",
       "      <td>129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183.163204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.901885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>762.195268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M25_2</td>\n",
       "      <td>3690.04</td>\n",
       "      <td>10431, 10564</td>\n",
       "      <td>M25_2_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>901</td>\n",
       "      <td>307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.663776</td>\n",
       "      <td>13.333189</td>\n",
       "      <td>26.666378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.333189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>799.991328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M11</td>\n",
       "      <td>7286.01</td>\n",
       "      <td>3798, 10340, 10519, 6776</td>\n",
       "      <td>M11_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>724</td>\n",
       "      <td>154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.101129</td>\n",
       "      <td>6.505618</td>\n",
       "      <td>32.528092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>852.235997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M11_2</td>\n",
       "      <td>7575.47</td>\n",
       "      <td>5439, 4660</td>\n",
       "      <td>M11_2_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>933</td>\n",
       "      <td>147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.006264</td>\n",
       "      <td>19.800752</td>\n",
       "      <td>105.604009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.800752</td>\n",
       "      <td>6.600251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1122.042593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>08/08/2020</td>\n",
       "      <td>11:08</td>\n",
       "      <td>bristol</td>\n",
       "      <td>M4</td>\n",
       "      <td>8535.34</td>\n",
       "      <td>6041, 5311</td>\n",
       "      <td>M4_bristol</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1471</td>\n",
       "      <td>226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.336146</td>\n",
       "      <td>3.233615</td>\n",
       "      <td>29.102531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.233615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1587.704766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>08/08/2020</td>\n",
       "      <td>11:08</td>\n",
       "      <td>bristol</td>\n",
       "      <td>M4_2</td>\n",
       "      <td>4997.57</td>\n",
       "      <td>5610, 1897, 5994</td>\n",
       "      <td>M4_2_bristol</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1284</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.138049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.853537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2200.429409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>08/08/2020</td>\n",
       "      <td>11:08</td>\n",
       "      <td>bristol</td>\n",
       "      <td>M5</td>\n",
       "      <td>6741.38</td>\n",
       "      <td>3434, 3267</td>\n",
       "      <td>M5_bristol</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1678</td>\n",
       "      <td>245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.767261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1781.831020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M602</td>\n",
       "      <td>5552.47</td>\n",
       "      <td>5509, 1993</td>\n",
       "      <td>M602_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>734</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.868808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.644801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>501.398477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M60_1</td>\n",
       "      <td>2418.93</td>\n",
       "      <td>17666, 17597</td>\n",
       "      <td>M60_1_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>2499</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.429740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.685948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1364.074198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M60_2</td>\n",
       "      <td>2210.23</td>\n",
       "      <td>17644, 17559</td>\n",
       "      <td>M60_2_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>2815</td>\n",
       "      <td>221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.346919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>141.885686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.538767</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3749.835990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M60_3</td>\n",
       "      <td>1825.46</td>\n",
       "      <td>17637, 17619, 17691</td>\n",
       "      <td>M60_3_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1886</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218.246360</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.499191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2788.703487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M60_4</td>\n",
       "      <td>3381.43</td>\n",
       "      <td>17608, 17571</td>\n",
       "      <td>M60_4_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>2113</td>\n",
       "      <td>108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.695369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.539074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.539074</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>789.961643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M60_5</td>\n",
       "      <td>2498.54</td>\n",
       "      <td>17542, 17627</td>\n",
       "      <td>M60_5_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1746</td>\n",
       "      <td>104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.056097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>883.716090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19/04/2019</td>\n",
       "      <td>11:48</td>\n",
       "      <td>manchester</td>\n",
       "      <td>M60_6</td>\n",
       "      <td>3179.29</td>\n",
       "      <td>17580, 17555</td>\n",
       "      <td>M60_6_manchester</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1966</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.566721</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.594453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>992.422837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ddmmyy   time        area  focus   length  \\\n",
       "0   15/06/2021  11:09  birmingham   A452  2542.41   \n",
       "1   15/06/2021  11:09  birmingham     M6  5549.01   \n",
       "2   15/06/2021  11:09  birmingham    M42  7347.07   \n",
       "3   15/06/2021  11:09  birmingham    A45  4559.31   \n",
       "4   08/03/2022  11:12      thorpe    M25  8613.81   \n",
       "5   08/03/2022  11:12      thorpe  M25_2  6190.41   \n",
       "6   08/03/2022  11:12      thorpe     M3  9400.57   \n",
       "7   08/03/2022  11:12      thorpe   M3_2  4360.48   \n",
       "8   02/11/2021  11:26   cambridge    M11  5005.66   \n",
       "9   02/11/2021  11:26   cambridge  M11_2  3308.62   \n",
       "10  02/11/2021  11:26   cambridge    A14  4662.76   \n",
       "11  02/11/2021  11:26   cambridge  A14_2  5764.49   \n",
       "12  08/03/2022  11:13      epping    M25  8327.00   \n",
       "13  08/03/2022  11:13      epping  M25_2  3690.04   \n",
       "14  08/03/2022  11:13      epping    M11  7286.01   \n",
       "15  08/03/2022  11:13      epping  M11_2  7575.47   \n",
       "16  08/08/2020  11:08     bristol     M4  8535.34   \n",
       "17  08/08/2020  11:08     bristol   M4_2  4997.57   \n",
       "18  08/08/2020  11:08     bristol     M5  6741.38   \n",
       "19  19/04/2019  11:48  manchester   M602  5552.47   \n",
       "20  19/04/2019  11:48  manchester  M60_1  2418.93   \n",
       "21  19/04/2019  11:48  manchester  M60_2  2210.23   \n",
       "22  19/04/2019  11:48  manchester  M60_3  1825.46   \n",
       "23  19/04/2019  11:48  manchester  M60_4  3381.43   \n",
       "24  19/04/2019  11:48  manchester  M60_5  2498.54   \n",
       "25  19/04/2019  11:48  manchester  M60_6  3179.29   \n",
       "\n",
       "                                sites          filename  \\\n",
       "0                          9092, 9093   A452_birmingham   \n",
       "1                        18212, 19227     M6_birmingham   \n",
       "2                 10560, 10337, 10398    M42_birmingham   \n",
       "3                          9490, 9491    A45_birmingham   \n",
       "4                          1910, 6134        M25_thorpe   \n",
       "5                          5107, 4648      M25_2_thorpe   \n",
       "6   14547, 14484, 14496, 14463, 14433         M3_thorpe   \n",
       "7                        14437, 14470       M3_2_thorpe   \n",
       "8              2166, 3092, 4178, 9676     M11_cambridge   \n",
       "9                          3092, 4178   M11_2_cambridge   \n",
       "10                       18577, 18594     A14_cambridge   \n",
       "11                18841, 18628, 18676   A14_2_cambridge   \n",
       "12                         1548, 4695        M25_epping   \n",
       "13                       10431, 10564      M25_2_epping   \n",
       "14           3798, 10340, 10519, 6776        M11_epping   \n",
       "15                         5439, 4660      M11_2_epping   \n",
       "16                         6041, 5311        M4_bristol   \n",
       "17                   5610, 1897, 5994      M4_2_bristol   \n",
       "18                         3434, 3267        M5_bristol   \n",
       "19                         5509, 1993   M602_manchester   \n",
       "20                       17666, 17597  M60_1_manchester   \n",
       "21                       17644, 17559  M60_2_manchester   \n",
       "22                17637, 17619, 17691  M60_3_manchester   \n",
       "23                       17608, 17571  M60_4_manchester   \n",
       "24                       17542, 17627  M60_5_manchester   \n",
       "25                       17580, 17555  M60_6_manchester   \n",
       "\n",
       "                                               report  0-520cm  521-660cm  \\\n",
       "0        site_name site_id          report_date ti...      321         44   \n",
       "1        site_name site_id          report_date ti...      366         66   \n",
       "2         site_name site_id          report_date t...      916        321   \n",
       "3        site_name site_id          report_date ti...      395         28   \n",
       "4         site_name site_id          report_date t...     1853        250   \n",
       "5         site_name site_id          report_date t...     1845        229   \n",
       "6         site_name site_id          report_date t...      438        136   \n",
       "7        site_name site_id          report_date ti...     1091        222   \n",
       "8        site_name site_id          report_date ti...      731         61   \n",
       "9        site_name site_id          report_date ti...      574         56   \n",
       "10        site_name site_id          report_date t...      665         40   \n",
       "11        site_name site_id          report_date t...      877         51   \n",
       "12        site_name site_id          report_date t...      559        129   \n",
       "13        site_name site_id          report_date t...      901        307   \n",
       "14        site_name site_id          report_date t...      724        154   \n",
       "15        site_name site_id          report_date t...      933        147   \n",
       "16       site_name site_id          report_date ti...     1471        226   \n",
       "17       site_name site_id          report_date ti...     1284         58   \n",
       "18       site_name site_id          report_date ti...     1678        245   \n",
       "19        site_name site_id          report_date t...      734         94   \n",
       "20        site_name site_id          report_date t...     2499         90   \n",
       "21        site_name site_id          report_date t...     2815        221   \n",
       "22        site_name site_id          report_date t...     1886        153   \n",
       "23        site_name site_id          report_date t...     2113        108   \n",
       "24        site_name site_id          report_date t...     1746        104   \n",
       "25        site_name site_id          report_date t...     1966        160   \n",
       "\n",
       "    ...  pickup_truck_pred  utility_truck_pred  truck_pred  cargo_truck_pred  \\\n",
       "0   ...                0.0                 0.0   33.668842          0.000000   \n",
       "1   ...                0.0                 0.0  113.029171         16.147024   \n",
       "2   ...                0.0                 0.0   97.998250         39.199300   \n",
       "3   ...                0.0                 0.0  104.226297         28.425354   \n",
       "4   ...                0.0                 0.0  263.298122         11.702139   \n",
       "5   ...                0.0                 0.0  165.546385          7.883161   \n",
       "6   ...                0.0                 0.0   90.547701          0.000000   \n",
       "7   ...                0.0                 0.0   42.197189          0.000000   \n",
       "8   ...                0.0                 0.0   76.593296          8.510366   \n",
       "9   ...                0.0                 0.0   92.123000          0.000000   \n",
       "10  ...                0.0                 0.0   76.263844          0.000000   \n",
       "11  ...                0.0                 0.0   89.282255          0.000000   \n",
       "12  ...                0.0                 0.0  183.163204          0.000000   \n",
       "13  ...                0.0                 0.0  266.663776         13.333189   \n",
       "14  ...                0.0                 0.0  117.101129          6.505618   \n",
       "15  ...                0.0                 0.0  165.006264         19.800752   \n",
       "16  ...                0.0                 0.0   32.336146          3.233615   \n",
       "17  ...                0.0                 0.0   37.138049          0.000000   \n",
       "18  ...                0.0                 0.0   20.767261          0.000000   \n",
       "19  ...                0.0                 0.0   51.868808          0.000000   \n",
       "20  ...                0.0                 0.0   93.429740          0.000000   \n",
       "21  ...                0.0                 0.0  101.346919          0.000000   \n",
       "22  ...                0.0                 0.0  218.246360          0.000000   \n",
       "23  ...                0.0                 0.0   62.695369          0.000000   \n",
       "24  ...                0.0                 0.0   96.056097          0.000000   \n",
       "25  ...                0.0                 0.0   87.566721          0.000000   \n",
       "\n",
       "   truck_w_box_pred  truck_tractor_pred  trailer_pred  truck_w_flatbed_pred  \\\n",
       "0         16.834421                 0.0      0.000000              0.000000   \n",
       "1         96.882147                 0.0     16.147024              0.000000   \n",
       "2        137.197549                 0.0      0.000000              0.000000   \n",
       "3         18.950236                 0.0      9.475118              0.000000   \n",
       "4         87.766041                 0.0      0.000000              0.000000   \n",
       "5         55.182128                 0.0      7.883161              0.000000   \n",
       "6          4.765668                 0.0      9.531337              0.000000   \n",
       "7          0.000000                 0.0      0.000000              0.000000   \n",
       "8         59.572564                 0.0      0.000000              0.000000   \n",
       "9         30.707667                 0.0     15.353833              0.000000   \n",
       "10        10.894835                 0.0      0.000000              0.000000   \n",
       "11        44.641128                 0.0      0.000000              0.000000   \n",
       "12        70.901885                 0.0      0.000000              0.000000   \n",
       "13        26.666378                 0.0     13.333189              0.000000   \n",
       "14        32.528092                 0.0      0.000000              0.000000   \n",
       "15       105.604009                 0.0     19.800752              6.600251   \n",
       "16        29.102531                 0.0      3.233615              0.000000   \n",
       "17        27.853537                 0.0      0.000000              0.000000   \n",
       "18         0.000000                 0.0      0.000000              0.000000   \n",
       "19         8.644801                 0.0      0.000000              0.000000   \n",
       "20        18.685948                 0.0      0.000000              0.000000   \n",
       "21       141.885686                 0.0     40.538767              0.000000   \n",
       "22        48.499191                 0.0      0.000000              0.000000   \n",
       "23        12.539074                 0.0     12.539074              0.000000   \n",
       "24         0.000000                 0.0      0.000000              0.000000   \n",
       "25        14.594453                 0.0      0.000000              0.000000   \n",
       "\n",
       "    truck_w_liquid_pred  all_vehicle_pred  \n",
       "0                   0.0        488.198206  \n",
       "1                   0.0        758.910148  \n",
       "2                   0.0       1378.508712  \n",
       "3                   0.0        577.982195  \n",
       "4                   0.0       2147.342465  \n",
       "5                   0.0       1860.426046  \n",
       "6                   0.0        524.223531  \n",
       "7                   0.0       1329.211463  \n",
       "8                   0.0        629.767104  \n",
       "9                   0.0        475.968833  \n",
       "10                  0.0        261.476036  \n",
       "11                  0.0        339.272569  \n",
       "12                  0.0        762.195268  \n",
       "13                  0.0        799.991328  \n",
       "14                  0.0        852.235997  \n",
       "15                  0.0       1122.042593  \n",
       "16                  0.0       1587.704766  \n",
       "17                  0.0       2200.429409  \n",
       "18                  0.0       1781.831020  \n",
       "19                  0.0        501.398477  \n",
       "20                  0.0       1364.074198  \n",
       "21                  0.0       3749.835990  \n",
       "22                  0.0       2788.703487  \n",
       "23                  0.0        789.961643  \n",
       "24                  0.0        883.716090  \n",
       "25                  0.0        992.422837  \n",
       "\n",
       "[26 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_traffic_from_detector(traffic_data, 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the object detectors results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initially set the column names so they agree with the names in the pickle files\n",
    "detections = pd.DataFrame(\n",
    "   {\n",
    "       \"filename\": [],\n",
    "       \"small_car_detections\": [],\n",
    "       \"bus_detections\": [],\n",
    "       \"pickup_truck_detections\": [],\n",
    "       \"utility_truck_detections\": [],\n",
    "       \"truck_detections\": [],\n",
    "       \"cargo_truck_detections\": [],\n",
    "       \"truck_w_box_detections\": [],\n",
    "       \"truck_tractor_detections\": [],\n",
    "       \"trailer_detections\": [],\n",
    "       \"truck_w_flatbed_detections\": [],\n",
    "       \"truck_w_liquid_detections\": [],\n",
    "       \"all_vehicle_detections\": [],\n",
    "       \n",
    "   }\n",
    ")\n",
    "\n",
    "# Import the predictions the pickle files\n",
    "pickle_folder = '/rds/general/user/jrb21/home/small-object-detection-benchmark/runs/highways/XVIEW_SAHI_PO17/pickles'\n",
    "prediction_lists = defaultdict()\n",
    "\n",
    "for i, file in enumerate(os.listdir(pickle_folder)):\n",
    "    with (open(os.path.join(pickle_folder, file), \"rb\")) as f:\n",
    "        prediction_list = pickle.load(f)\n",
    "    filename = file.split('.')[0]\n",
    "    prediction_lists[filename] = prediction_list\n",
    "    counts = defaultdict(int)\n",
    "    for pred in prediction_list:\n",
    "        # if pred.score.value > 0.2:\n",
    "            # print('true')\n",
    "        counts[pred.category.name] +=1\n",
    "        \n",
    "    detections.loc[i] = [filename, \n",
    "                 counts[\"Small Car\"],\n",
    "                 counts[\"Bus\"],\n",
    "                 counts[\"Pickup Truck\"],\n",
    "                 counts[\"Utility Truck\"],\n",
    "                 counts[\"Truck\"],\n",
    "                 counts[\"Cargo Truck\"],\n",
    "                 counts[\"Truck w/Box\"],\n",
    "                 counts[\"Truck Tractor\"],\n",
    "                 counts[\"Trailer\"],\n",
    "                 counts[\"Truck w/Flatbed\"],\n",
    "                 counts[\"Truck w/Liquid\"],\n",
    "                 sum([x for x in counts.values()])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detection_report(x):\n",
    "    detections_report = detections[detections.filename == x.filename]\n",
    "    return detections_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_data['det_report'] = traffic_data.apply(detection_report, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "traffic_data['small_car_detections'] = traffic_data.apply(lambda x: x.det_report.small_car_detections.sum(), axis=1)\n",
    "traffic_data['bus_detections'] = traffic_data.apply(lambda x: x.det_report.bus_detections.sum(), axis=1)\n",
    "traffic_data['pickup_truck_detections'] = traffic_data.apply(lambda x: x.det_report.pickup_truck_detections.sum(), axis=1)\n",
    "traffic_data['utility_truck_detections'] = traffic_data.apply(lambda x: x.det_report.utility_truck_detections.sum(), axis=1)\n",
    "traffic_data['truck_detections'] = traffic_data.apply(lambda x: x.det_report.truck_detections.sum(), axis=1)\n",
    "traffic_data['cargo_truck_detections'] = traffic_data.apply(lambda x: x.det_report.cargo_truck_detections.sum(), axis=1)\n",
    "traffic_data['truck_w_box_detections'] = traffic_data.apply(lambda x: x.det_report.truck_w_box_detections.sum(), axis=1)\n",
    "traffic_data['truck_tractor_detections'] = traffic_data.apply(lambda x: x.det_report.truck_tractor_detections.sum(), axis=1)\n",
    "traffic_data['trailer_detections'] = traffic_data.apply(lambda x: x.det_report.trailer_detections.sum(), axis=1)\n",
    "traffic_data['truck_w_flatbed_detections'] = traffic_data.apply(lambda x: x.det_report.truck_w_flatbed_detections.sum(), axis=1)\n",
    "traffic_data['truck_w_liquid_detections'] = traffic_data.apply(lambda x: x.det_report.truck_w_liquid_detections.sum(), axis=1)\n",
    "traffic_data['all_vehicle_detections'] = traffic_data.apply(lambda x: x.det_report.all_vehicle_detections.sum(), axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate a predicted count fromthe detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ddmmyy</th>\n",
       "      <th>time</th>\n",
       "      <th>area</th>\n",
       "      <th>focus</th>\n",
       "      <th>length</th>\n",
       "      <th>sites</th>\n",
       "      <th>filename</th>\n",
       "      <th>report</th>\n",
       "      <th>0-520cm</th>\n",
       "      <th>521-660cm</th>\n",
       "      <th>661-1160cm</th>\n",
       "      <th>1160+cm</th>\n",
       "      <th>total_volume</th>\n",
       "      <th>avg_mph</th>\n",
       "      <th>det_report</th>\n",
       "      <th>small_car_detections</th>\n",
       "      <th>bus_detections</th>\n",
       "      <th>pickup_truck_detections</th>\n",
       "      <th>utility_truck_detections</th>\n",
       "      <th>truck_detections</th>\n",
       "      <th>cargo_truck_detections</th>\n",
       "      <th>truck_w_box_detections</th>\n",
       "      <th>truck_tractor_detections</th>\n",
       "      <th>trailer_detections</th>\n",
       "      <th>truck_w_flatbed_detections</th>\n",
       "      <th>truck_w_liquid_detections</th>\n",
       "      <th>all_vehicle_detections</th>\n",
       "      <th>length_milles</th>\n",
       "      <th>time_to_cross_hour</th>\n",
       "      <th>time_to_cross_min</th>\n",
       "      <th>small_car_pred</th>\n",
       "      <th>bus_pred</th>\n",
       "      <th>pickup_truck_pred</th>\n",
       "      <th>utility_truck_pred</th>\n",
       "      <th>truck_pred</th>\n",
       "      <th>cargo_truck_pred</th>\n",
       "      <th>truck_w_box_pred</th>\n",
       "      <th>truck_tractor_pred</th>\n",
       "      <th>trailer_pred</th>\n",
       "      <th>truck_w_flatbed_pred</th>\n",
       "      <th>truck_w_liquid_pred</th>\n",
       "      <th>all_vehicle_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>A452</td>\n",
       "      <td>2542.41</td>\n",
       "      <td>9092, 9093</td>\n",
       "      <td>A452_birmingham</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>321</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>433</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>filename  small_car_detections  bus_...</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1.589006</td>\n",
       "      <td>0.014851</td>\n",
       "      <td>0.891032</td>\n",
       "      <td>420.860522</td>\n",
       "      <td>16.834421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.668842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.834421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>488.198206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>M6</td>\n",
       "      <td>5549.01</td>\n",
       "      <td>18212, 19227</td>\n",
       "      <td>M6_birmingham</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>366</td>\n",
       "      <td>66</td>\n",
       "      <td>44</td>\n",
       "      <td>113</td>\n",
       "      <td>589</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>filename  small_car_detections  bus_de...</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>3.468131</td>\n",
       "      <td>0.030965</td>\n",
       "      <td>1.857927</td>\n",
       "      <td>492.484245</td>\n",
       "      <td>24.220537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.029171</td>\n",
       "      <td>16.147024</td>\n",
       "      <td>96.882147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.147024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>758.910148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>M42</td>\n",
       "      <td>7347.07</td>\n",
       "      <td>10560, 10337, 10398</td>\n",
       "      <td>M42_birmingham</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>916</td>\n",
       "      <td>321</td>\n",
       "      <td>247</td>\n",
       "      <td>337</td>\n",
       "      <td>1821</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>filename  small_car_detections  bus_...</td>\n",
       "      <td>151</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>4.591919</td>\n",
       "      <td>0.038266</td>\n",
       "      <td>2.295959</td>\n",
       "      <td>986.515713</td>\n",
       "      <td>117.597900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.998250</td>\n",
       "      <td>39.199300</td>\n",
       "      <td>137.197549</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1378.508712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15/06/2021</td>\n",
       "      <td>11:09</td>\n",
       "      <td>birmingham</td>\n",
       "      <td>A45</td>\n",
       "      <td>4559.31</td>\n",
       "      <td>9490, 9491</td>\n",
       "      <td>A45_birmingham</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>395</td>\n",
       "      <td>28</td>\n",
       "      <td>66</td>\n",
       "      <td>35</td>\n",
       "      <td>524</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>filename  small_car_detections  bus_d...</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>2.849569</td>\n",
       "      <td>0.026385</td>\n",
       "      <td>1.583094</td>\n",
       "      <td>397.954954</td>\n",
       "      <td>18.950236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.226297</td>\n",
       "      <td>18.950236</td>\n",
       "      <td>18.950236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.475118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>568.507077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M25</td>\n",
       "      <td>8613.81</td>\n",
       "      <td>1910, 6134</td>\n",
       "      <td>M25_thorpe</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1853</td>\n",
       "      <td>250</td>\n",
       "      <td>241</td>\n",
       "      <td>209</td>\n",
       "      <td>2553</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>filename  small_car_detections  bus_dete...</td>\n",
       "      <td>274</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>367</td>\n",
       "      <td>5.383631</td>\n",
       "      <td>0.042727</td>\n",
       "      <td>2.563634</td>\n",
       "      <td>1603.193012</td>\n",
       "      <td>181.383151</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.298122</td>\n",
       "      <td>11.702139</td>\n",
       "      <td>87.766041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2147.342465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M25_2</td>\n",
       "      <td>6190.41</td>\n",
       "      <td>5107, 4648</td>\n",
       "      <td>M25_2_thorpe</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>1845</td>\n",
       "      <td>229</td>\n",
       "      <td>209</td>\n",
       "      <td>223</td>\n",
       "      <td>2506</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>filename  small_car_detections  bus_de...</td>\n",
       "      <td>199</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>3.869006</td>\n",
       "      <td>0.031713</td>\n",
       "      <td>1.902790</td>\n",
       "      <td>1568.749081</td>\n",
       "      <td>47.298967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.546385</td>\n",
       "      <td>7.883161</td>\n",
       "      <td>55.182128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.883161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1852.542885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M3</td>\n",
       "      <td>9400.57</td>\n",
       "      <td>14547, 14484, 14496, 14463, 14433</td>\n",
       "      <td>M3_thorpe</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>438</td>\n",
       "      <td>136</td>\n",
       "      <td>80</td>\n",
       "      <td>39</td>\n",
       "      <td>693</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>filename  small_car_detections  bus_detect...</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>5.875356</td>\n",
       "      <td>0.052459</td>\n",
       "      <td>3.147512</td>\n",
       "      <td>419.378825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.547701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.765668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.531337</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>524.223531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:12</td>\n",
       "      <td>thorpe</td>\n",
       "      <td>M3_2</td>\n",
       "      <td>4360.48</td>\n",
       "      <td>14437, 14470</td>\n",
       "      <td>M3_2_thorpe</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1091</td>\n",
       "      <td>222</td>\n",
       "      <td>132</td>\n",
       "      <td>159</td>\n",
       "      <td>1604</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>filename  small_car_detections  bus_det...</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>126</td>\n",
       "      <td>2.725300</td>\n",
       "      <td>0.023698</td>\n",
       "      <td>1.421896</td>\n",
       "      <td>1287.014274</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.197189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1329.211463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>M11</td>\n",
       "      <td>5005.66</td>\n",
       "      <td>2166, 3092, 4178, 9676</td>\n",
       "      <td>M11_cambridge</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>731</td>\n",
       "      <td>61</td>\n",
       "      <td>59</td>\n",
       "      <td>130</td>\n",
       "      <td>981</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>filename  small_car_detections  bus_d...</td>\n",
       "      <td>47</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>3.128537</td>\n",
       "      <td>0.029376</td>\n",
       "      <td>1.762556</td>\n",
       "      <td>399.987214</td>\n",
       "      <td>85.103663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.593296</td>\n",
       "      <td>8.510366</td>\n",
       "      <td>59.572564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>629.767104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>M11_2</td>\n",
       "      <td>3308.62</td>\n",
       "      <td>3092, 4178</td>\n",
       "      <td>M11_2_cambridge</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>574</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "      <td>127</td>\n",
       "      <td>807</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>filename  small_car_detections  bus...</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2.067887</td>\n",
       "      <td>0.016283</td>\n",
       "      <td>0.976955</td>\n",
       "      <td>307.076666</td>\n",
       "      <td>30.707667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.123000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.707667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.353833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>475.968833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>A14</td>\n",
       "      <td>4662.76</td>\n",
       "      <td>18577, 18594</td>\n",
       "      <td>A14_cambridge</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>665</td>\n",
       "      <td>40</td>\n",
       "      <td>91</td>\n",
       "      <td>165</td>\n",
       "      <td>961</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>filename  small_car_detections  bus_d...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2.914225</td>\n",
       "      <td>0.022947</td>\n",
       "      <td>1.376799</td>\n",
       "      <td>174.317357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.263844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.894835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>261.476036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>02/11/2021</td>\n",
       "      <td>11:26</td>\n",
       "      <td>cambridge</td>\n",
       "      <td>A14_2</td>\n",
       "      <td>5764.49</td>\n",
       "      <td>18841, 18628, 18676</td>\n",
       "      <td>A14_2_cambridge</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>877</td>\n",
       "      <td>51</td>\n",
       "      <td>104</td>\n",
       "      <td>266</td>\n",
       "      <td>1298</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>filename  small_car_detections  bus_...</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>3.602806</td>\n",
       "      <td>0.028001</td>\n",
       "      <td>1.680065</td>\n",
       "      <td>205.349187</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.282255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.641128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>339.272569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M25</td>\n",
       "      <td>8327.00</td>\n",
       "      <td>1548, 4695</td>\n",
       "      <td>M25_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>559</td>\n",
       "      <td>129</td>\n",
       "      <td>95</td>\n",
       "      <td>228</td>\n",
       "      <td>1011</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>filename  small_car_detections  bus_detec...</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>5.204375</td>\n",
       "      <td>0.042312</td>\n",
       "      <td>2.538720</td>\n",
       "      <td>508.130179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183.163204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.901885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>762.195268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M25_2</td>\n",
       "      <td>3690.04</td>\n",
       "      <td>10431, 10564</td>\n",
       "      <td>M25_2_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>901</td>\n",
       "      <td>307</td>\n",
       "      <td>200</td>\n",
       "      <td>359</td>\n",
       "      <td>1767</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>filename  small_car_detections  bus_det...</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>2.306275</td>\n",
       "      <td>0.018750</td>\n",
       "      <td>1.125012</td>\n",
       "      <td>479.994797</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.663776</td>\n",
       "      <td>13.333189</td>\n",
       "      <td>26.666378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.333189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>799.991328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M11</td>\n",
       "      <td>7286.01</td>\n",
       "      <td>3798, 10340, 10519, 6776</td>\n",
       "      <td>M11_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>724</td>\n",
       "      <td>154</td>\n",
       "      <td>124</td>\n",
       "      <td>71</td>\n",
       "      <td>1073</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>filename  small_car_detections  bus_detec...</td>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>4.553756</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>2.305699</td>\n",
       "      <td>650.561830</td>\n",
       "      <td>45.539328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.101129</td>\n",
       "      <td>6.505618</td>\n",
       "      <td>32.528092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>852.235997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>08/03/2022</td>\n",
       "      <td>11:13</td>\n",
       "      <td>epping</td>\n",
       "      <td>M11_2</td>\n",
       "      <td>7575.47</td>\n",
       "      <td>5439, 4660</td>\n",
       "      <td>M11_2_epping</td>\n",
       "      <td>site_name site_id          report_date t...</td>\n",
       "      <td>933</td>\n",
       "      <td>147</td>\n",
       "      <td>120</td>\n",
       "      <td>199</td>\n",
       "      <td>1399</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>filename  small_car_detections  bus_det...</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>4.734669</td>\n",
       "      <td>0.037877</td>\n",
       "      <td>2.272641</td>\n",
       "      <td>778.829564</td>\n",
       "      <td>26.401002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.006264</td>\n",
       "      <td>19.800752</td>\n",
       "      <td>105.604009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.800752</td>\n",
       "      <td>6.600251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1122.042593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>08/08/2020</td>\n",
       "      <td>11:08</td>\n",
       "      <td>bristol</td>\n",
       "      <td>M4</td>\n",
       "      <td>8535.34</td>\n",
       "      <td>6041, 5311</td>\n",
       "      <td>M4_bristol</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1471</td>\n",
       "      <td>226</td>\n",
       "      <td>80</td>\n",
       "      <td>59</td>\n",
       "      <td>1836</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>filename  small_car_detections  bus_dete...</td>\n",
       "      <td>455</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>491</td>\n",
       "      <td>5.334587</td>\n",
       "      <td>0.077313</td>\n",
       "      <td>4.638772</td>\n",
       "      <td>1471.294641</td>\n",
       "      <td>48.504219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.336146</td>\n",
       "      <td>3.233615</td>\n",
       "      <td>29.102531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.233615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1587.704766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>08/08/2020</td>\n",
       "      <td>11:08</td>\n",
       "      <td>bristol</td>\n",
       "      <td>M4_2</td>\n",
       "      <td>4997.57</td>\n",
       "      <td>5610, 1897, 5994</td>\n",
       "      <td>M4_2_bristol</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1284</td>\n",
       "      <td>58</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1421</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>filename  small_car_detections  bus_det...</td>\n",
       "      <td>225</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "      <td>3.123481</td>\n",
       "      <td>0.026927</td>\n",
       "      <td>1.615594</td>\n",
       "      <td>2089.015261</td>\n",
       "      <td>37.138049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.138049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.853537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2191.144896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>08/08/2020</td>\n",
       "      <td>11:08</td>\n",
       "      <td>bristol</td>\n",
       "      <td>M5</td>\n",
       "      <td>6741.38</td>\n",
       "      <td>3434, 3267</td>\n",
       "      <td>M5_bristol</td>\n",
       "      <td>site_name site_id          report_date ti...</td>\n",
       "      <td>1678</td>\n",
       "      <td>245</td>\n",
       "      <td>113</td>\n",
       "      <td>86</td>\n",
       "      <td>2122</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>filename  small_car_detections  bus_dete...</td>\n",
       "      <td>419</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>429</td>\n",
       "      <td>4.213363</td>\n",
       "      <td>0.060191</td>\n",
       "      <td>3.611454</td>\n",
       "      <td>1740.296497</td>\n",
       "      <td>20.767261</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.767261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1781.831020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ddmmyy   time        area  focus   length  \\\n",
       "0   15/06/2021  11:09  birmingham   A452  2542.41   \n",
       "1   15/06/2021  11:09  birmingham     M6  5549.01   \n",
       "2   15/06/2021  11:09  birmingham    M42  7347.07   \n",
       "3   15/06/2021  11:09  birmingham    A45  4559.31   \n",
       "4   08/03/2022  11:12      thorpe    M25  8613.81   \n",
       "5   08/03/2022  11:12      thorpe  M25_2  6190.41   \n",
       "6   08/03/2022  11:12      thorpe     M3  9400.57   \n",
       "7   08/03/2022  11:12      thorpe   M3_2  4360.48   \n",
       "8   02/11/2021  11:26   cambridge    M11  5005.66   \n",
       "9   02/11/2021  11:26   cambridge  M11_2  3308.62   \n",
       "10  02/11/2021  11:26   cambridge    A14  4662.76   \n",
       "11  02/11/2021  11:26   cambridge  A14_2  5764.49   \n",
       "12  08/03/2022  11:13      epping    M25  8327.00   \n",
       "13  08/03/2022  11:13      epping  M25_2  3690.04   \n",
       "14  08/03/2022  11:13      epping    M11  7286.01   \n",
       "15  08/03/2022  11:13      epping  M11_2  7575.47   \n",
       "16  08/08/2020  11:08     bristol     M4  8535.34   \n",
       "17  08/08/2020  11:08     bristol   M4_2  4997.57   \n",
       "18  08/08/2020  11:08     bristol     M5  6741.38   \n",
       "\n",
       "                                sites         filename  \\\n",
       "0                          9092, 9093  A452_birmingham   \n",
       "1                        18212, 19227    M6_birmingham   \n",
       "2                 10560, 10337, 10398   M42_birmingham   \n",
       "3                          9490, 9491   A45_birmingham   \n",
       "4                          1910, 6134       M25_thorpe   \n",
       "5                          5107, 4648     M25_2_thorpe   \n",
       "6   14547, 14484, 14496, 14463, 14433        M3_thorpe   \n",
       "7                        14437, 14470      M3_2_thorpe   \n",
       "8              2166, 3092, 4178, 9676    M11_cambridge   \n",
       "9                          3092, 4178  M11_2_cambridge   \n",
       "10                       18577, 18594    A14_cambridge   \n",
       "11                18841, 18628, 18676  A14_2_cambridge   \n",
       "12                         1548, 4695       M25_epping   \n",
       "13                       10431, 10564     M25_2_epping   \n",
       "14           3798, 10340, 10519, 6776       M11_epping   \n",
       "15                         5439, 4660     M11_2_epping   \n",
       "16                         6041, 5311       M4_bristol   \n",
       "17                   5610, 1897, 5994     M4_2_bristol   \n",
       "18                         3434, 3267       M5_bristol   \n",
       "\n",
       "                                               report  0-520cm  521-660cm  \\\n",
       "0        site_name site_id          report_date ti...      321         44   \n",
       "1        site_name site_id          report_date ti...      366         66   \n",
       "2         site_name site_id          report_date t...      916        321   \n",
       "3        site_name site_id          report_date ti...      395         28   \n",
       "4         site_name site_id          report_date t...     1853        250   \n",
       "5         site_name site_id          report_date t...     1845        229   \n",
       "6         site_name site_id          report_date t...      438        136   \n",
       "7        site_name site_id          report_date ti...     1091        222   \n",
       "8        site_name site_id          report_date ti...      731         61   \n",
       "9        site_name site_id          report_date ti...      574         56   \n",
       "10        site_name site_id          report_date t...      665         40   \n",
       "11        site_name site_id          report_date t...      877         51   \n",
       "12        site_name site_id          report_date t...      559        129   \n",
       "13        site_name site_id          report_date t...      901        307   \n",
       "14        site_name site_id          report_date t...      724        154   \n",
       "15        site_name site_id          report_date t...      933        147   \n",
       "16       site_name site_id          report_date ti...     1471        226   \n",
       "17       site_name site_id          report_date ti...     1284         58   \n",
       "18       site_name site_id          report_date ti...     1678        245   \n",
       "\n",
       "    661-1160cm  1160+cm  total_volume    avg_mph  \\\n",
       "0           46       22           433  53.500000   \n",
       "1           44      113           589  56.000000   \n",
       "2          247      337          1821  60.000000   \n",
       "3           66       35           524  54.000000   \n",
       "4          241      209          2553  63.000000   \n",
       "5          209      223          2506  61.000000   \n",
       "6           80       39           693  56.000000   \n",
       "7          132      159          1604  57.500000   \n",
       "8           59      130           981  53.250000   \n",
       "9           50      127           807  63.500000   \n",
       "10          91      165           961  63.500000   \n",
       "11         104      266          1298  64.333333   \n",
       "12          95      228          1011  61.500000   \n",
       "13         200      359          1767  61.500000   \n",
       "14         124       71          1073  59.250000   \n",
       "15         120      199          1399  62.500000   \n",
       "16          80       59          1836  34.500000   \n",
       "17          40       39          1421  58.000000   \n",
       "18         113       86          2122  35.000000   \n",
       "\n",
       "                                           det_report  small_car_detections  \\\n",
       "0             filename  small_car_detections  bus_...                    25   \n",
       "1           filename  small_car_detections  bus_de...                    61   \n",
       "2             filename  small_car_detections  bus_...                   151   \n",
       "3            filename  small_car_detections  bus_d...                    42   \n",
       "4         filename  small_car_detections  bus_dete...                   274   \n",
       "5           filename  small_car_detections  bus_de...                   199   \n",
       "6       filename  small_car_detections  bus_detect...                    88   \n",
       "7          filename  small_car_detections  bus_det...                   122   \n",
       "8            filename  small_car_detections  bus_d...                    47   \n",
       "9              filename  small_car_detections  bus...                    20   \n",
       "10           filename  small_car_detections  bus_d...                    16   \n",
       "11            filename  small_car_detections  bus_...                    23   \n",
       "12       filename  small_car_detections  bus_detec...                    86   \n",
       "13         filename  small_car_detections  bus_det...                    36   \n",
       "14       filename  small_car_detections  bus_detec...                   100   \n",
       "15         filename  small_car_detections  bus_det...                   118   \n",
       "16        filename  small_car_detections  bus_dete...                   455   \n",
       "17         filename  small_car_detections  bus_det...                   225   \n",
       "18        filename  small_car_detections  bus_dete...                   419   \n",
       "\n",
       "    bus_detections  pickup_truck_detections  utility_truck_detections  \\\n",
       "0                1                        0                         0   \n",
       "1                3                        0                         0   \n",
       "2               18                        0                         0   \n",
       "3                2                        0                         0   \n",
       "4               31                        0                         0   \n",
       "5                6                        0                         0   \n",
       "6                0                        0                         0   \n",
       "7                0                        0                         0   \n",
       "8               10                        0                         0   \n",
       "9                2                        0                         0   \n",
       "10               0                        0                         0   \n",
       "11               0                        0                         0   \n",
       "12               0                        0                         0   \n",
       "13               0                        0                         0   \n",
       "14               7                        0                         0   \n",
       "15               4                        0                         0   \n",
       "16              15                        0                         0   \n",
       "17               4                        0                         0   \n",
       "18               5                        0                         0   \n",
       "\n",
       "    truck_detections  cargo_truck_detections  truck_w_box_detections  \\\n",
       "0                  2                       0                       1   \n",
       "1                 14                       2                      12   \n",
       "2                 15                       6                      21   \n",
       "3                 11                       2                       2   \n",
       "4                 45                       2                      15   \n",
       "5                 21                       1                       7   \n",
       "6                 19                       0                       1   \n",
       "7                  4                       0                       0   \n",
       "8                  9                       1                       7   \n",
       "9                  6                       0                       2   \n",
       "10                 7                       0                       1   \n",
       "11                10                       0                       5   \n",
       "12                31                       0                      12   \n",
       "13                20                       1                       2   \n",
       "14                18                       1                       5   \n",
       "15                25                       3                      16   \n",
       "16                10                       1                       9   \n",
       "17                 4                       0                       3   \n",
       "18                 5                       0                       0   \n",
       "\n",
       "    truck_tractor_detections  trailer_detections  truck_w_flatbed_detections  \\\n",
       "0                          0                   0                           0   \n",
       "1                          0                   2                           0   \n",
       "2                          0                   0                           0   \n",
       "3                          0                   1                           0   \n",
       "4                          0                   0                           0   \n",
       "5                          0                   1                           0   \n",
       "6                          0                   2                           0   \n",
       "7                          0                   0                           0   \n",
       "8                          0                   0                           0   \n",
       "9                          0                   1                           0   \n",
       "10                         0                   0                           0   \n",
       "11                         0                   0                           0   \n",
       "12                         0                   0                           0   \n",
       "13                         0                   1                           0   \n",
       "14                         0                   0                           0   \n",
       "15                         0                   3                           1   \n",
       "16                         0                   1                           0   \n",
       "17                         0                   0                           0   \n",
       "18                         0                   0                           0   \n",
       "\n",
       "    truck_w_liquid_detections  all_vehicle_detections  length_milles  \\\n",
       "0                           0                      29       1.589006   \n",
       "1                           0                      94       3.468131   \n",
       "2                           0                     211       4.591919   \n",
       "3                           0                      60       2.849569   \n",
       "4                           0                     367       5.383631   \n",
       "5                           0                     235       3.869006   \n",
       "6                           0                     110       5.875356   \n",
       "7                           0                     126       2.725300   \n",
       "8                           0                      74       3.128537   \n",
       "9                           0                      31       2.067887   \n",
       "10                          0                      24       2.914225   \n",
       "11                          0                      38       3.602806   \n",
       "12                          0                     129       5.204375   \n",
       "13                          0                      60       2.306275   \n",
       "14                          0                     131       4.553756   \n",
       "15                          0                     170       4.734669   \n",
       "16                          0                     491       5.334587   \n",
       "17                          0                     236       3.123481   \n",
       "18                          0                     429       4.213363   \n",
       "\n",
       "    time_to_cross_hour  time_to_cross_min  small_car_pred    bus_pred  \\\n",
       "0             0.014851           0.891032      420.860522   16.834421   \n",
       "1             0.030965           1.857927      492.484245   24.220537   \n",
       "2             0.038266           2.295959      986.515713  117.597900   \n",
       "3             0.026385           1.583094      397.954954   18.950236   \n",
       "4             0.042727           2.563634     1603.193012  181.383151   \n",
       "5             0.031713           1.902790     1568.749081   47.298967   \n",
       "6             0.052459           3.147512      419.378825    0.000000   \n",
       "7             0.023698           1.421896     1287.014274    0.000000   \n",
       "8             0.029376           1.762556      399.987214   85.103663   \n",
       "9             0.016283           0.976955      307.076666   30.707667   \n",
       "10            0.022947           1.376799      174.317357    0.000000   \n",
       "11            0.028001           1.680065      205.349187    0.000000   \n",
       "12            0.042312           2.538720      508.130179    0.000000   \n",
       "13            0.018750           1.125012      479.994797    0.000000   \n",
       "14            0.038428           2.305699      650.561830   45.539328   \n",
       "15            0.037877           2.272641      778.829564   26.401002   \n",
       "16            0.077313           4.638772     1471.294641   48.504219   \n",
       "17            0.026927           1.615594     2089.015261   37.138049   \n",
       "18            0.060191           3.611454     1740.296497   20.767261   \n",
       "\n",
       "    pickup_truck_pred  utility_truck_pred  truck_pred  cargo_truck_pred  \\\n",
       "0                 0.0                 0.0   33.668842          0.000000   \n",
       "1                 0.0                 0.0  113.029171         16.147024   \n",
       "2                 0.0                 0.0   97.998250         39.199300   \n",
       "3                 0.0                 0.0  104.226297         18.950236   \n",
       "4                 0.0                 0.0  263.298122         11.702139   \n",
       "5                 0.0                 0.0  165.546385          7.883161   \n",
       "6                 0.0                 0.0   90.547701          0.000000   \n",
       "7                 0.0                 0.0   42.197189          0.000000   \n",
       "8                 0.0                 0.0   76.593296          8.510366   \n",
       "9                 0.0                 0.0   92.123000          0.000000   \n",
       "10                0.0                 0.0   76.263844          0.000000   \n",
       "11                0.0                 0.0   89.282255          0.000000   \n",
       "12                0.0                 0.0  183.163204          0.000000   \n",
       "13                0.0                 0.0  266.663776         13.333189   \n",
       "14                0.0                 0.0  117.101129          6.505618   \n",
       "15                0.0                 0.0  165.006264         19.800752   \n",
       "16                0.0                 0.0   32.336146          3.233615   \n",
       "17                0.0                 0.0   37.138049          0.000000   \n",
       "18                0.0                 0.0   20.767261          0.000000   \n",
       "\n",
       "    truck_w_box_pred  truck_tractor_pred  trailer_pred  truck_w_flatbed_pred  \\\n",
       "0          16.834421                 0.0      0.000000              0.000000   \n",
       "1          96.882147                 0.0     16.147024              0.000000   \n",
       "2         137.197549                 0.0      0.000000              0.000000   \n",
       "3          18.950236                 0.0      9.475118              0.000000   \n",
       "4          87.766041                 0.0      0.000000              0.000000   \n",
       "5          55.182128                 0.0      7.883161              0.000000   \n",
       "6           4.765668                 0.0      9.531337              0.000000   \n",
       "7           0.000000                 0.0      0.000000              0.000000   \n",
       "8          59.572564                 0.0      0.000000              0.000000   \n",
       "9          30.707667                 0.0     15.353833              0.000000   \n",
       "10         10.894835                 0.0      0.000000              0.000000   \n",
       "11         44.641128                 0.0      0.000000              0.000000   \n",
       "12         70.901885                 0.0      0.000000              0.000000   \n",
       "13         26.666378                 0.0     13.333189              0.000000   \n",
       "14         32.528092                 0.0      0.000000              0.000000   \n",
       "15        105.604009                 0.0     19.800752              6.600251   \n",
       "16         29.102531                 0.0      3.233615              0.000000   \n",
       "17         27.853537                 0.0      0.000000              0.000000   \n",
       "18          0.000000                 0.0      0.000000              0.000000   \n",
       "\n",
       "    truck_w_liquid_pred  all_vehicle_pred  \n",
       "0                   0.0        488.198206  \n",
       "1                   0.0        758.910148  \n",
       "2                   0.0       1378.508712  \n",
       "3                   0.0        568.507077  \n",
       "4                   0.0       2147.342465  \n",
       "5                   0.0       1852.542885  \n",
       "6                   0.0        524.223531  \n",
       "7                   0.0       1329.211463  \n",
       "8                   0.0        629.767104  \n",
       "9                   0.0        475.968833  \n",
       "10                  0.0        261.476036  \n",
       "11                  0.0        339.272569  \n",
       "12                  0.0        762.195268  \n",
       "13                  0.0        799.991328  \n",
       "14                  0.0        852.235997  \n",
       "15                  0.0       1122.042593  \n",
       "16                  0.0       1587.704766  \n",
       "17                  0.0       2191.144896  \n",
       "18                  0.0       1781.831020  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_data['length_milles'] = traffic_data.length.apply(lambda x: x/1000 *5/8)\n",
    "traffic_data['time_to_cross_hour'] = traffic_data.apply(lambda x : x.length_milles/x.avg_mph * 1/2 , axis=1)\n",
    "traffic_data['time_to_cross_min'] = traffic_data.apply(lambda x : x.time_to_cross_hour * 60 , axis=1)\n",
    "\n",
    "detection_cols = ['small_car_detections',\n",
    " 'bus_detections',\n",
    " 'pickup_truck_detections',\n",
    " 'utility_truck_detections',\n",
    " 'truck_detections',\n",
    " 'cargo_truck_detections',\n",
    " 'truck_w_box_detections',\n",
    " 'truck_tractor_detections',\n",
    " 'trailer_detections',\n",
    " 'truck_w_flatbed_detections',\n",
    " 'truck_w_liquid_detections',\n",
    " 'all_vehicle_detections']\n",
    "\n",
    "for col in detection_cols:\n",
    "    pred_col = col.replace('detections', 'pred')\n",
    "    traffic_data[pred_col] = traffic_data[col] * 15 / traffic_data['time_to_cross_min']\n",
    "    \n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "\n",
    "traffic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot predicted vs total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.scatterplot(x='all_vehicle_pred', y='total_volume', hue='area', data=traffic_data, ax=ax)\n",
    "for i in range(traffic_data.shape[0]):\n",
    "     plt.text(traffic_data.all_vehicle_pred[i]+10, traffic_data.total_volume[i]+10, traffic_data.filename[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regional Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import shape,mapping, Point, Polygon, MultiPolygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_region(x):\n",
    "    for row in regions_gdf.iterrows():\n",
    "        name = row[1].nuts118nm\n",
    "        region = row[1].geometry\n",
    "        x_name = 'other'\n",
    "        if region.contains(Point(x.Longitude, x.Latitude)):\n",
    "            x_name = name\n",
    "            return x_name\n",
    "    return x_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter down sites onto our regions of focus\n",
    "regions_gdf = geopandas.read_file(\"NUTS_Level_1_(January_2018)_Boundaries.geojson\")\n",
    "\n",
    "sites_df = pd.DataFrame(data = sites)\n",
    "sites_df['region'] = sites_df.apply(get_region, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "focus_regions = ['South East (England)', \n",
    "                 'East of England', \n",
    "                 'West Midlands (England)', \n",
    "                 'North West (England)', \n",
    "                 'South West (England)']\n",
    "\n",
    "focus_sites_df = sites_df[sites_df.region.isin(focus_regions)]\n",
    "focus_sites_df = focus_sites_df.reset_index(drop=True)\n",
    "focus_sites_df['road_type'] = focus_sites_df.Description.apply(lambda x : x.split('/')[0][0])\n",
    "focus_sites_motorway_df = focus_sites_df[focus_sites_df.road_type == 'M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get quality filter these sites down to quality > 90%\n",
    "start_date, end_date = '19032019', '08042022'\n",
    "quality_sites = get_quality_area(focus_sites_motorway_df, start_date = start_date, end_date = end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "quality_sites = pd.read_feather('quality_sites_motorway')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SE_sites_df = quality_sites[quality_sites.region == 'South East (England)']\n",
    "EE_sites_df = quality_sites[quality_sites.region == 'East of England']\n",
    "WM_sites_df = quality_sites[quality_sites.region == 'West Midlands (England)']\n",
    "NW_sites_df = quality_sites[quality_sites.region == 'North West (England)']\n",
    "SW_sites_df = quality_sites[quality_sites.region == 'South West (England)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 51/489 [21:05<3:01:06, 24.81s/it]"
     ]
    }
   ],
   "source": [
    "start_date, end_date = '19032019', '08042022'\n",
    "\n",
    "SE_report_df = get_reports_from_sites_df(SE_sites_df, start_date, end_date)\n",
    "SE_report_df.to_feather('SE_report_df')\n",
    "del SE_report_df\n",
    "\n",
    "EE_report_df = get_reports_from_sites_df(EE_sites_df, start_date, end_date)\n",
    "EE_report_df.to_feather('EE_report_df')\n",
    "del EE_report_df\n",
    "\n",
    "WM_report_df = get_reports_from_sites_df(WM_sites_df, start_date, end_date)\n",
    "WM_report_df.to_feather('WM_report_df')\n",
    "del WM_report_df\n",
    "\n",
    "NW_report_df = get_reports_from_sites_df(NW_sites_df, start_date, end_date)\n",
    "NW_report_df.to_feather('NW_report_df')\n",
    "del NW_report_df\n",
    "\n",
    "SW_report_df = get_reports_from_sites_df(SW_sites_df, start_date, end_date)\n",
    "SW_report_df.to_feather('SW_report_df')\n",
    "del SW_report_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:sklearn-env]",
   "language": "python",
   "name": "conda-env-sklearn-env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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