Time matrix.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "from tqdm.notebook import tqdm\n",
    "from datetime import datetime\n",
    "from random import choice\n",
    "from glob import glob\n",
    "## gmplot - dependency to create geoplot\n",
    "import gmplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution = 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = glob(\"input/feed/bus_movements_2019_07_07.db\")\n",
    "db = sqlite3.connect(files[0])\n",
    "speed_data = db.execute(\"Select speed, timestamp, route_id, trip_id, lat, lng from vehicle_feed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_radian = lambda x : x/57.29577951\n",
    "\n",
    "def haversine_dist(lat1, lon1, lat2, lon2):\n",
    "    lat1 = to_radian(lat1)\n",
    "    lon = to_radian(lon1) - to_radian(lon2)\n",
    "    lat2 = to_radian(lat2)\n",
    "    a = np.sin((lat1-lat2) / 2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(lon / 2)**2\n",
    "    c = 2*np.arcsin(np.sqrt(a))\n",
    "\n",
    "    return 6373.0 * c * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n"
    }
   ],
   "source": [
    "tree = {}\n",
    "count = 0\n",
    "for e in tqdm(speed_data):\n",
    "    if int(e[2]) not in tree:\n",
    "        tree[int(e[2])]  = {}\n",
    "    if e[3] not in tree[int(e[2])]:\n",
    "        tree[int(e[2])][e[3]]  = {}\n",
    "    \n",
    "    if int(e[1]) not in tree[int(e[2])][e[3]]:\n",
    "        tree[int(e[2])][e[3]][int(e[1])] = np.array([e[0], e[-2], e[-1]]).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stops_data = dict()\n",
    "i = 0\n",
    "for e in open(\"input/static/stops.txt\").read().split(\"\\n\")[1:-1]:\n",
    "    split = e.split(\",\")\n",
    "    stops_data[int(split[0])] = (float(split[-2]), float(split[-1]), split[0], split[1], i)\n",
    "    i+=1\n",
    "# stops_data = np.array([e.split(\",\")[-2:] for e in  open(\"input/stops.txt\").read().split(\"\\n\")[1:-1]]).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "trips_data = dict()\n",
    "for e in open(\"input/static/stop_times.txt\").read().split(\"\\n\")[1:-1]:\n",
    "    split = e.split(\",\")\n",
    "    trip_id = split[0]\n",
    "    if trip_id not in trips_data:\n",
    "        trips_data[trip_id] = set()\n",
    "    trips_data[trip_id].add(int(split[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "routes_data = dict()\n",
    "for e in open(\"input/static/trips.txt\").read().split(\"\\n\")[1:-1]:\n",
    "    split = e.split(\",\")\n",
    "    route_id = int(split[0])\n",
    "    trip_id = split[2]\n",
    "    if route_id not in routes_data:\n",
    "        routes_data[route_id] = set()\n",
    "        routes_data[route_id] |= trips_data[trip_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = np.zeros([100 * 100, 144])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stops_used = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_keys = list(tree.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [
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     "metadata": {}
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n"
    },
    {
     "output_type": "error",
     "ename": "AssertionError",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-3c66888f86cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdistances\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mleast_far\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mroute_stops\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mleast_far\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstops_visited\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m                     \u001b[0;32massert\u001b[0m \u001b[0mstops_order\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mroute_stops\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mleast_far\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m                     \u001b[0mstops_order\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for route_id in tqdm(tree_keys[2:]):\n",
    "    route_stops = list(routes_data[route_id])\n",
    "    for j in tree[route_id]:\n",
    "        if j in [\"2806\", \"12272\"]:\n",
    "            continue\n",
    "        stops_order = []\n",
    "        stops_visited = set()\n",
    "        for ts in tree[route_id][j]:\n",
    "            k = tree[route_id][j][ts]\n",
    "            distances = [haversine_dist(k[1], k[2], stops_data[e][0], stops_data[e][1]) for e in route_stops]\n",
    "            least_far = np.argsort(distances)[0]\n",
    "            if distances[least_far] < 50:\n",
    "                if route_stops[least_far] in stops_visited:\n",
    "                    assert stops_order[-1][0] == route_stops[least_far]\n",
    "                    stops_order[-1][1].append(ts)\n",
    "                else:\n",
    "                    stops_visited.add(route_stops[least_far])\n",
    "                    stops_order.append((route_stops[least_far], [ts]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys_sorted = sorted(tree[route_id][j].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd = []\n",
    "for e in keys_sorted:\n",
    "    cd.append(tree[route_id][j][e][1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd = np.array(cd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmap3 = gmplot.GoogleMapPlotter(lat = cd[0][0], lng = cd[0][1], zoom=15)\n",
    "gmap3.marker(cd[0][0], cd[0][1], color=\"green\")\n",
    "gmap3.marker(cd[-1][0], cd[-1][1], color=\"red\")\n",
    "\n",
    "gmap3.plot(cd[:, 0], cd[:, 1],  \n",
    "           'cornflowerblue', edge_width = 2.5) \n",
    "gmap3.draw( \"output/map.html\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'4618'"
     },
     "metadata": {},
     "execution_count": 89
    }
   ],
   "source": [
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1073"
     },
     "metadata": {},
     "execution_count": 90
    }
   ],
   "source": [
    "route_stops[least_far]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(28.6631, 77.3205, '2916', 'Ram Prastha Temple', 2738)"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "stops_data[2916]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_distances = sorted(distances, key=lambda e:e[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = [haversine_dist(lat1, lon1, e[0], e[1]) for e in stops_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
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117.897531 145.624372 \nL 117.970695 102.859177 \nL 118.043859 147.22456 \nL 118.190188 59.932046 \nL 118.409681 181.32758 \nL 118.482845 104.417717 \nL 118.55601 163.264423 \nL 118.629174 35.94375 \nL 118.702338 91.540541 \nL 118.775503 46.151064 \nL 119.06816 155.913133 \nL 119.141324 58.792478 \nL 119.360817 170.496946 \nL 119.507146 82.738568 \nL 119.58031 101.62528 \nL 119.653475 93.635887 \nL 119.799803 107.694226 \nL 119.872968 140.050945 \nL 120.092461 66.684392 \nL 120.165625 102.109026 \nL 120.238789 78.084264 \nL 120.311954 165.444969 \nL 120.385118 70.857661 \nL 120.458282 192.823716 \nL 120.531447 34.108239 \nL 120.604611 67.845808 \nL 120.677775 112.305794 \nL 120.75094 99.171926 \nL 120.897268 119.020705 \nL 120.970433 182.977182 \nL 121.043597 81.909205 \nL 121.116761 96.10545 \nL 121.189926 100.029993 \nL 121.26309 69.607045 \nL 121.336254 131.386934 \nL 121.409419 103.348255 \nL 121.482583 139.806836 \nL 121.555747 89.842583 \nL 121.628912 118.285646 \nL 121.77524 64.228964 \nL 121.848405 124.605921 \nL 121.921569 87.956312 \nL 121.994733 107.684032 \nL 122.067898 73.350964 \nL 122.141062 119.114645 \nL 122.214226 66.543789 \nL 122.287391 93.204601 \nL 122.360555 91.536417 \nL 122.433719 138.730714 \nL 122.506884 99.442706 \nL 122.580048 138.455864 \nL 122.653212 93.092723 \nL 122.726377 111.300306 \nL 122.799541 211.245804 \nL 122.872705 105.715402 \nL 122.94587 115.793042 \nL 123.019034 67.054701 \nL 123.165363 158.916822 \nL 123.238527 117.946907 \nL 123.384856 144.767671 \nL 123.531184 58.642007 \nL 123.604349 178.899798 \nL 123.677513 117.776979 \nL 123.750677 99.486549 \nL 123.823842 150.529615 \nL 123.97017 71.823536 \nL 124.043335 95.371719 \nL 124.189663 55.573488 \nL 124.262828 96.422917 \nL 124.335992 84.30973 \nL 124.482321 122.130002 \nL 124.555485 89.476268 \nL 124.701814 158.989044 \nL 124.774978 141.683298 \nL 124.848142 214.49941 \nL 124.921307 61.249735 \nL 124.994471 161.285159 \nL 125.067635 82.946921 \nL 125.1408 170.273861 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123.241956 \nL 129.603824 98.331811 \nL 129.676989 122.790732 \nL 129.750153 43.376571 \nL 129.896482 113.505539 \nL 129.969646 99.892808 \nL 130.04281 110.126988 \nL 130.115975 86.17582 \nL 130.189139 123.570689 \nL 130.262303 84.522454 \nL 130.335468 92.857914 \nL 130.408632 78.475156 \nL 130.481796 89.022354 \nL 130.554961 48.660723 \nL 130.774454 138.642416 \nL 130.847618 99.51479 \nL 130.920782 165.168444 \nL 131.067111 40.766507 \nL 131.140275 62.779068 \nL 131.21344 93.792921 \nL 131.286604 74.722722 \nL 131.359768 132.059884 \nL 131.579261 59.686797 \nL 131.652426 173.476925 \nL 131.72559 77.903461 \nL 131.798754 84.455684 \nL 131.871919 154.167161 \nL 132.018247 40.015427 \nL 132.091412 113.994282 \nL 132.164576 113.335187 \nL 132.384069 64.077547 \nL 132.457233 94.06985 \nL 132.530398 92.959487 \nL 132.603562 55.852219 \nL 132.749891 118.031149 \nL 132.823055 128.149963 \nL 132.896219 90.660414 \nL 132.969384 134.371221 \nL 133.042548 95.808323 \nL 133.115712 103.168223 \nL 133.188877 89.789495 \nL 133.262041 91.558098 \nL 133.40837 153.633616 \nL 133.481534 96.805074 \nL 133.554698 135.737876 \nL 133.627863 96.328841 \nL 133.701027 180.814593 \nL 133.774191 40.219612 \nL 133.847356 87.428756 \nL 133.92052 104.681034 \nL 134.140013 58.732416 \nL 134.213177 56.395359 \nL 134.286342 167.814737 \nL 134.359506 110.154735 \nL 134.43267 127.955847 \nL 134.505835 121.19146 \nL 134.578999 89.388972 \nL 134.652163 118.923852 \nL 134.798492 94.615786 \nL 134.871656 100.975331 \nL 134.944821 126.340456 \nL 135.091149 78.337649 \nL 135.237478 100.174051 \nL 135.310642 83.257132 \nL 135.383807 184.379346 \nL 135.456971 76.996587 \nL 135.530135 79.351297 \nL 135.6033 138.415863 \nL 135.749628 75.141384 \nL 135.822793 107.831653 \nL 135.895957 88.503667 \nL 135.969122 154.744127 \nL 136.11545 44.243491 \nL 136.188615 92.657081 \nL 136.261779 71.181708 \nL 136.334943 95.942505 \nL 136.408108 55.597061 \nL 136.481272 75.080771 \nL 136.554436 63.807239 \nL 136.627601 81.617317 \nL 136.700765 61.312044 \nL 136.773929 146.11924 \nL 136.847094 123.016037 \nL 136.920258 67.403859 \nL 137.066587 147.157324 \nL 137.212915 60.296578 \nL 137.28608 192.698271 \nL 137.359244 101.090845 \nL 137.432408 120.409332 \nL 137.505573 96.062347 \nL 137.651901 163.924164 \nL 137.725066 158.937048 \nL 137.79823 60.146003 \nL 137.944559 224.856251 \nL 138.017723 52.114648 \nL 138.090887 125.237975 \nL 138.164052 114.012964 \nL 138.31038 69.081489 \nL 138.383545 161.501892 \nL 138.456709 97.554933 \nL 138.603038 166.803249 \nL 138.676202 116.252483 \nL 138.749366 175.876977 \nL 138.822531 79.310875 \nL 138.895695 91.948663 \nL 138.968859 61.131346 \nL 139.042024 81.817897 \nL 139.115188 142.192305 \nL 139.188352 54.712441 \nL 139.334681 129.126784 \nL 139.48101 88.698094 \nL 139.554174 124.871826 \nL 139.627338 118.989862 \nL 139.700503 85.594578 \nL 139.773667 130.258714 \nL 139.846831 104.188992 \nL 139.919996 161.050183 \nL 140.066324 58.847436 \nL 140.212653 123.330395 \nL 140.285817 119.499412 \nL 140.358982 164.298902 \nL 140.50531 75.753683 \nL 140.578475 93.937966 \nL 140.651639 58.782206 \nL 140.724803 75.946726 \nL 140.797968 127.816116 \nL 140.871132 58.963091 \nL 141.017461 126.469441 \nL 141.090625 43.366646 \nL 141.163789 166.816607 \nL 141.236954 86.842384 \nL 141.310118 107.377617 \nL 141.383282 67.666964 \nL 141.456447 99.360436 \nL 141.529611 199.253989 \nL 141.67594 65.799472 \nL 141.822268 134.868262 \nL 142.041761 51.383822 \nL 142.18809 62.566101 \nL 142.334419 99.384963 \nL 142.407583 77.042219 \nL 142.627076 124.293778 \nL 142.846569 38.773596 \nL 142.919733 146.987585 \nL 143.066062 58.247361 \nL 143.139226 108.485935 \nL 143.212391 104.367817 \nL 143.285555 112.28329 \nL 143.358719 182.293381 \nL 143.651377 59.767805 \nL 143.797705 122.249809 \nL 143.87087 85.939727 \nL 144.017198 168.034527 \nL 144.090363 167.06711 \nL 144.236691 67.199475 \nL 144.309856 67.270206 \nL 144.38302 88.747652 \nL 144.456184 49.815976 \nL 144.602513 88.144759 \nL 144.675677 33.950109 \nL 144.822006 126.645145 \nL 144.968335 72.717893 \nL 145.114663 218.547501 \nL 145.187828 197.552294 \nL 145.260992 90.464748 \nL 145.407321 170.536461 \nL 145.480485 96.657522 \nL 145.553649 167.111555 \nL 145.626814 65.183905 \nL 145.699978 118.223459 \nL 145.773142 104.665171 \nL 145.992635 57.571174 \nL 146.0658 82.924568 \nL 146.138964 42.500576 \nL 146.212128 140.146066 \nL 146.285293 86.395128 \nL 146.358457 148.555422 \nL 146.431622 67.143267 \nL 146.504786 86.608004 \nL 146.57795 69.650713 \nL 146.651115 121.005761 \nL 146.724279 52.078076 \nL 146.797443 138.049666 \nL 146.870608 65.127126 \nL 146.943772 74.98325 \nL 147.016936 128.127635 \nL 147.090101 92.093099 \nL 147.163265 148.629401 \nL 147.236429 67.591308 \nL 147.309594 88.46153 \nL 147.382758 76.811861 \nL 147.455922 119.553598 \nL 147.529087 95.482761 \nL 147.602251 105.620465 \nL 147.675415 111.219534 \nL 147.74858 94.227793 \nL 147.821744 144.634611 \nL 147.894908 100.862601 \nL 147.968073 111.830531 \nL 148.041237 105.341998 \nL 148.114401 120.028946 \nL 148.187566 158.998188 \nL 148.26073 40.893389 \nL 148.333894 224.4942 \nL 148.407059 67.627238 \nL 148.480223 88.914028 \nL 148.553387 45.599477 \nL 148.699716 153.365792 \nL 148.919209 59.173503 \nL 148.992373 61.148932 \nL 149.065538 55.114543 \nL 149.138702 127.375082 \nL 149.211866 95.958825 \nL 149.285031 94.641665 \nL 149.358195 94.960798 \nL 149.431359 141.652366 \nL 149.577688 90.395659 \nL 149.650852 86.838869 \nL 149.724017 169.271646 \nL 149.797181 89.026243 \nL 149.870345 119.180554 \nL 149.94351 176.244019 \nL 150.089838 52.652591 \nL 150.236167 120.193301 \nL 150.309331 96.734303 \nL 150.382496 98.030805 \nL 150.45566 163.069294 \nL 150.601989 121.700086 \nL 150.675153 118.542987 \nL 150.748317 142.265317 \nL 150.894646 37.785831 \nL 150.96781 138.400669 \nL 151.040975 118.144579 \nL 151.114139 106.178776 \nL 151.187303 57.175287 \nL 151.260468 74.327048 \nL 151.406796 162.520805 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44.280504 \nL 155.211342 138.831256 \nL 155.284506 58.219476 \nL 155.35767 125.826304 \nL 155.430835 101.817111 \nL 155.503999 136.892663 \nL 155.577163 86.596661 \nL 155.650328 87.928264 \nL 155.723492 86.955003 \nL 155.796656 103.079346 \nL 155.869821 86.246428 \nL 155.942985 115.790804 \nL 156.089314 49.948932 \nL 156.308807 177.698131 \nL 156.381971 118.783986 \nL 156.455135 140.981755 \nL 156.5283 120.125718 \nL 156.601464 64.090283 \nL 156.747793 113.747253 \nL 156.820957 71.385542 \nL 156.894122 118.950433 \nL 156.967286 108.50637 \nL 157.04045 75.097615 \nL 157.113615 81.530425 \nL 157.186779 100.382351 \nL 157.259943 90.426071 \nL 157.479436 160.728905 \nL 157.625765 69.460929 \nL 157.772094 157.233837 \nL 157.845258 96.192373 \nL 157.918422 120.851051 \nL 157.991587 92.865137 \nL 158.064751 124.597412 \nL 158.137915 119.83935 \nL 158.284244 75.485177 \nL 158.357408 100.346811 \nL 158.430573 212.66645 \nL 158.503737 72.143476 \nL 158.576901 113.193605 \nL 158.72323 87.822002 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162.235118 139.251415 \nL 162.381447 34.867928 \nL 162.454611 76.805851 \nL 162.527775 125.664053 \nL 162.60094 98.270354 \nL 162.674104 174.743076 \nL 162.747268 73.812386 \nL 162.820433 80.852915 \nL 162.893597 166.153229 \nL 163.039926 62.64962 \nL 163.11309 109.666753 \nL 163.186254 60.875978 \nL 163.259419 62.799779 \nL 163.552076 212.689739 \nL 163.62524 209.479443 \nL 163.698405 46.532151 \nL 163.771569 104.369636 \nL 163.844733 165.864674 \nL 163.917898 89.863147 \nL 163.991062 102.468978 \nL 164.064226 68.844211 \nL 164.137391 83.754791 \nL 164.210555 99.225884 \nL 164.283719 148.671324 \nL 164.356884 74.82469 \nL 164.430048 85.343924 \nL 164.503212 192.168057 \nL 164.576377 77.163946 \nL 164.649541 94.007128 \nL 164.79587 57.25843 \nL 164.869034 100.904557 \nL 164.942198 84.463129 \nL 165.015363 72.769023 \nL 165.161691 122.557006 \nL 165.234856 96.938954 \nL 165.30802 133.46264 \nL 165.381184 114.648217 \nL 165.454349 118.827474 \nL 165.527513 76.265051 \nL 165.673842 124.768859 \nL 165.747006 102.26286 \nL 165.82017 140.718826 \nL 165.893335 131.141769 \nL 165.966499 146.347005 \nL 166.039663 123.481518 \nL 166.112828 46.418911 \nL 166.185992 156.135106 \nL 166.259156 123.942564 \nL 166.405485 87.952782 \nL 166.478649 36.50763 \nL 166.551814 131.93346 \nL 166.624978 100.659158 \nL 166.771307 90.836147 \nL 166.844471 40.695868 \nL 166.917635 70.10905 \nL 166.9908 52.407701 \nL 167.063964 100.971907 \nL 167.137128 57.933753 \nL 167.210293 75.588361 \nL 167.283457 79.54323 \nL 167.356622 54.936279 \nL 167.429786 122.034192 \nL 167.50295 90.240694 \nL 167.722443 46.006161 \nL 167.795608 100.861645 \nL 167.868772 86.784868 \nL 167.941936 123.239864 \nL 168.015101 58.620494 \nL 168.088265 113.361246 \nL 168.161429 106.870638 \nL 168.234594 83.303297 \nL 168.380922 183.690526 \nL 168.454087 57.786379 \nL 168.527251 96.060717 \nL 168.600415 95.421802 \nL 168.67358 187.297874 \nL 168.746744 57.410214 \nL 168.819908 103.886061 \nL 168.893073 119.135151 \nL 169.039401 87.684761 \nL 169.112566 116.959145 \nL 169.18573 94.584331 \nL 169.258894 95.646828 \nL 169.405223 166.877526 \nL 169.478387 113.392236 \nL 169.551552 138.761586 \nL 169.69788 153.791722 \nL 169.771045 90.383342 \nL 169.844209 119.384315 \nL 169.990538 80.090912 \nL 170.063702 112.578123 \nL 170.210031 46.877906 \nL 170.283195 141.785668 \nL 170.502688 60.564005 \nL 170.575852 52.572965 \nL 170.649017 116.88318 \nL 170.722181 87.24556 \nL 170.795345 135.272288 \nL 170.86851 93.430336 \nL 170.941674 147.311419 \nL 171.014838 107.132764 \nL 171.088003 107.941193 \nL 171.234331 94.783076 \nL 171.307496 94.759306 \nL 171.453824 57.698314 \nL 171.600153 101.16817 \nL 171.673317 82.483207 \nL 171.819646 134.573283 \nL 171.89281 104.298617 \nL 171.965975 108.931574 \nL 172.112303 153.805846 \nL 172.404961 113.868174 \nL 172.478125 102.238476 \nL 172.551289 65.106453 \nL 172.697618 133.185935 \nL 172.843947 42.746435 \nL 172.917111 114.566923 \nL 172.990275 78.182359 \nL 173.06344 60.303991 \nL 173.136604 192.857417 \nL 173.282933 53.111901 \nL 173.429261 111.742888 \nL 173.57559 83.583292 \nL 173.648754 85.056316 \nL 173.721919 94.87422 \nL 173.795083 207.917088 \nL 173.941412 47.670246 \nL 174.08774 184.838327 \nL 174.380398 49.670273 \nL 174.453562 88.250102 \nL 174.526726 73.989738 \nL 174.599891 36.441722 \nL 174.746219 166.427194 \nL 174.965712 78.879906 \nL 175.038877 137.467465 \nL 175.112041 57.107995 \nL 175.185205 88.578827 \nL 175.25837 83.633928 \nL 175.331534 107.141302 \nL 175.404698 100.829642 \nL 175.551027 74.246879 \nL 175.624191 92.128411 \nL 175.697356 162.30567 \nL 175.77052 146.091704 \nL 175.843684 47.613027 \nL 175.916849 93.564177 \nL 175.990013 64.819918 \nL 176.136342 180.457898 \nL 176.28267 98.732044 \nL 176.355835 114.904101 \nL 176.428999 58.707369 \nL 176.502163 118.479487 \nL 176.575328 93.275587 \nL 176.648492 85.943674 \nL 176.794821 123.094517 \nL 176.941149 55.810554 \nL 177.014314 116.94111 \nL 177.087478 99.923742 \nL 177.160642 140.918454 \nL 177.233807 94.158827 \nL 177.306971 149.978312 \nL 177.4533 67.792557 \nL 177.526464 112.053295 \nL 177.599628 111.205663 \nL 177.672793 76.280018 \nL 177.745957 83.999883 \nL 177.819122 116.810886 \nL 177.892286 66.073059 \nL 177.96545 98.729066 \nL 178.038615 68.132567 \nL 178.111779 127.346817 \nL 178.331272 40.478963 \nL 178.550765 105.578876 \nL 178.623929 91.961678 \nL 178.697094 70.427907 \nL 178.770258 117.811937 \nL 178.843422 42.504983 \nL 178.916587 80.404691 \nL 178.989751 61.184231 \nL 179.13608 108.49993 \nL 179.209244 107.392904 \nL 179.282408 61.576467 \nL 179.355573 101.945352 \nL 179.428737 89.984392 \nL 179.575066 79.966077 \nL 179.64823 179.183411 \nL 179.721394 134.884319 \nL 179.867723 34.016707 \nL 179.940887 117.597078 \nL 180.014052 102.291446 \nL 180.087216 106.668447 \nL 180.16038 129.757663 \nL 180.379873 63.621738 \nL 180.599366 156.101195 \nL 180.672531 118.991837 \nL 180.745695 142.075465 \nL 180.818859 100.715717 \nL 180.892024 118.330937 \nL 181.038352 94.549311 \nL 181.111517 150.88868 \nL 181.33101 82.250052 \nL 181.404174 112.550946 \nL 181.477338 61.286002 \nL 181.623667 133.148846 \nL 181.84316 58.289085 \nL 181.989489 191.960873 \nL 182.135817 77.140904 \nL 182.208982 121.865104 \nL 182.282146 55.849127 \nL 182.35531 92.618852 \nL 182.501639 46.122884 \nL 182.574803 152.162253 \nL 182.647968 66.799313 \nL 182.721132 95.053061 \nL 182.794296 163.987393 \nL 182.940625 84.586204 \nL 183.013789 127.458346 \nL 183.086954 53.812076 \nL 183.160118 156.036282 \nL 183.233282 111.511181 \nL 183.306447 117.728853 \nL 183.379611 59.142693 \nL 183.452775 89.482468 \nL 183.52594 57.804902 \nL 183.599104 163.127224 \nL 183.745433 71.086766 \nL 183.891761 131.748173 \nL 183.964926 93.196623 \nL 184.03809 194.896161 \nL 184.184419 42.229179 \nL 184.330747 76.856862 \nL 184.403912 134.774502 \nL 184.623405 42.719264 \nL 184.696569 120.920657 \nL 184.769733 75.519681 \nL 184.842898 94.679922 \nL 184.916062 106.808564 \nL 184.989226 138.983839 \nL 185.062391 52.264647 \nL 185.135555 75.479101 \nL 185.281884 93.987476 \nL 185.355048 135.142045 \nL 185.428212 116.987751 \nL 185.501377 121.977023 \nL 185.574541 76.129104 \nL 185.647705 90.589626 \nL 185.72087 123.266428 \nL 185.794034 119.469028 \nL 185.867198 51.357965 \nL 185.940363 180.757107 \nL 186.013527 68.915835 \nL 186.086691 94.302705 \nL 186.159856 47.409292 \nL 186.23302 156.80832 \nL 186.379349 66.885825 \nL 186.452513 129.319748 \nL 186.598842 41.850469 \nL 186.74517 135.669331 \nL 186.818335 120.021728 \nL 186.964663 55.322267 \nL 187.037828 117.48174 \nL 187.110992 33.043572 \nL 187.184156 73.173302 \nL 187.403649 125.338214 \nL 187.549978 73.14201 \nL 187.696307 133.952716 \nL 187.842635 102.974654 \nL 187.9158 108.896578 \nL 187.988964 63.094094 \nL 188.135293 150.172161 \nL 188.281622 74.382473 \nL 188.354786 131.502985 \nL 188.42795 76.391589 \nL 188.501115 84.502828 \nL 188.574279 105.20529 \nL 188.720608 70.506074 \nL 188.866936 97.928082 \nL 188.940101 65.219434 \nL 189.013265 105.453547 \nL 189.086429 102.345183 \nL 189.305922 47.363476 \nL 189.379087 71.288483 \nL 189.452251 43.820451 \nL 189.525415 95.03082 \nL 189.671744 55.487265 \nL 189.744908 122.69776 \nL 189.818073 118.465554 \nL 190.037566 54.399504 \nL 190.11073 165.910615 \nL 190.183894 125.269076 \nL 190.330223 54.813328 \nL 190.403387 114.288802 \nL 190.476552 106.542315 \nL 190.62288 75.46257 \nL 190.696045 122.867087 \nL 190.769209 87.086963 \nL 190.842373 111.768469 \nL 190.915538 100.458024 \nL 190.988702 43.073381 \nL 191.135031 113.588831 \nL 191.208195 120.463664 \nL 191.281359 45.961752 \nL 191.354524 61.362406 \nL 191.427688 65.295523 \nL 191.500852 109.677579 \nL 191.574017 44.063949 \nL 191.720345 202.612047 \nL 191.79351 91.290319 \nL 191.866674 95.937551 \nL 191.939838 68.452005 \nL 192.086167 124.636509 \nL 192.159331 99.579169 \nL 192.232496 109.741604 \nL 192.30566 55.927958 \nL 192.525153 165.438424 \nL 192.598317 72.867385 \nL 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215.42559 83.867527 \nL 215.571919 105.546588 \nL 215.645083 74.052722 \nL 215.718247 106.808472 \nL 215.864576 73.009877 \nL 216.010905 108.746762 \nL 216.084069 100.571036 \nL 216.230398 168.532096 \nL 216.376726 71.231526 \nL 216.449891 81.348411 \nL 216.523055 33.53713 \nL 216.742548 219.372911 \nL 216.962041 46.505733 \nL 217.035205 56.495138 \nL 217.10837 136.990733 \nL 217.181534 93.704699 \nL 217.254698 96.415041 \nL 217.327863 102.462467 \nL 217.401027 162.868064 \nL 217.474191 76.137819 \nL 217.547356 120.381953 \nL 217.840013 53.486571 \nL 217.913177 122.527265 \nL 217.986342 48.656447 \nL 218.13267 115.724214 \nL 218.205835 99.149821 \nL 218.278999 121.734089 \nL 218.352163 106.080969 \nL 218.425328 109.205603 \nL 218.498492 107.585439 \nL 218.571656 191.950981 \nL 218.644821 164.404498 \nL 218.791149 100.4795 \nL 218.864314 158.892718 \nL 218.937478 97.859297 \nL 219.010642 176.434947 \nL 219.083807 165.187437 \nL 219.156971 112.846547 \nL 219.230135 155.78482 \nL 219.449628 83.001296 \nL 219.522793 91.748839 \nL 219.595957 67.277867 \nL 219.669122 84.918155 \nL 219.81545 225.354801 \nL 219.888615 70.201714 \nL 219.961779 94.642068 \nL 220.034943 78.521012 \nL 220.108108 123.792208 \nL 220.254436 87.937768 \nL 220.327601 126.497916 \nL 220.400765 58.607794 \nL 220.620258 158.97341 \nL 220.693422 93.64048 \nL 220.766587 94.050179 \nL 220.839751 123.422688 \nL 220.912915 38.88062 \nL 220.98608 53.772117 \nL 221.132408 144.237156 \nL 221.351901 75.119949 \nL 221.571394 108.169155 \nL 221.644559 71.435834 \nL 221.717723 116.835619 \nL 221.790887 96.863953 \nL 221.864052 96.629673 \nL 221.937216 155.664139 \nL 222.229873 70.653377 \nL 222.303038 115.815762 \nL 222.376202 56.761536 \nL 222.522531 109.19945 \nL 222.595695 55.480738 \nL 222.668859 55.86356 \nL 222.815188 150.945497 \nL 222.888352 54.697777 \nL 223.107845 148.399112 \nL 223.18101 104.172351 \nL 223.254174 111.978661 \nL 223.473667 76.211126 \nL 223.619996 185.362962 \nL 223.69316 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231.375415 60.549689 \nL 231.594908 118.418682 \nL 231.668073 122.142933 \nL 231.741237 88.749567 \nL 231.814401 113.105211 \nL 231.887566 58.301522 \nL 231.96073 113.82342 \nL 232.033894 63.797712 \nL 232.107059 122.322821 \nL 232.180223 96.643819 \nL 232.253387 68.670241 \nL 232.399716 123.192829 \nL 232.546045 119.474622 \nL 232.619209 80.748642 \nL 232.692373 109.688938 \nL 232.838702 45.124441 \nL 232.911866 162.120817 \nL 232.985031 55.901643 \nL 233.058195 97.648771 \nL 233.131359 130.217845 \nL 233.204524 57.279076 \nL 233.277688 117.709347 \nL 233.350852 96.585549 \nL 233.424017 138.86509 \nL 233.497181 95.725615 \nL 233.570345 102.739481 \nL 233.64351 86.02635 \nL 233.789838 156.433511 \nL 233.863003 124.24224 \nL 233.936167 126.29519 \nL 234.009331 88.50713 \nL 234.082496 105.130495 \nL 234.228824 48.680492 \nL 234.448317 202.387166 \nL 234.521482 197.545992 \nL 234.66781 44.11393 \nL 234.740975 105.79122 \nL 234.814139 80.738621 \nL 234.887303 80.412943 \nL 234.960468 79.046653 \nL 235.033632 60.819063 \nL 235.106796 92.066263 \nL 235.179961 66.114284 \nL 235.326289 111.774817 \nL 235.472618 56.581729 \nL 235.545782 90.704606 \nL 235.618947 58.50553 \nL 235.692111 62.216912 \nL 235.765275 58.232195 \nL 235.984768 168.769747 \nL 236.057933 110.766112 \nL 236.131097 190.899044 \nL 236.277426 70.189279 \nL 236.423754 144.055927 \nL 236.496919 87.734175 \nL 236.570083 127.394529 \nL 236.716412 95.462143 \nL 236.789576 104.662452 \nL 236.86274 45.524732 \nL 236.935905 129.340506 \nL 237.009069 104.826816 \nL 237.228562 136.97521 \nL 237.521219 45.464704 \nL 237.594384 114.45707 \nL 237.667548 91.517793 \nL 237.740712 63.816148 \nL 237.813877 115.009977 \nL 237.887041 92.739852 \nL 237.960205 117.551217 \nL 238.106534 88.953859 \nL 238.179698 86.954007 \nL 238.326027 130.660833 \nL 238.472356 67.672443 \nL 238.54552 129.11924 \nL 238.618684 64.797854 \nL 238.691849 85.841987 \nL 238.911342 118.2113 \nL 238.984506 88.796096 \nL 239.05767 163.11418 \nL 239.130835 68.268591 \nL 239.203999 114.684873 \nL 239.277163 160.819776 \nL 239.423492 91.755532 \nL 239.569821 131.744509 \nL 239.642985 56.402635 \nL 239.716149 92.460435 \nL 239.789314 83.110256 \nL 239.862478 124.5947 \nL 239.935642 119.632103 \nL 240.008807 114.898926 \nL 240.081971 85.471113 \nL 240.155135 129.302858 \nL 240.2283 93.338615 \nL 240.301464 101.797858 \nL 240.374628 102.759194 \nL 240.447793 48.160005 \nL 240.594122 180.548223 \nL 240.74045 99.150171 \nL 240.813615 129.918529 \nL 240.886779 58.718909 \nL 241.106272 171.625972 \nL 241.179436 186.505447 \nL 241.325765 91.887688 \nL 241.398929 115.089069 \nL 241.472094 84.690138 \nL 241.618422 116.551414 \nL 241.691587 82.934456 \nL 241.91108 151.033493 \nL 241.984244 105.577848 \nL 242.057408 172.133199 \nL 242.130573 166.675227 \nL 242.276901 43.390295 \nL 242.350066 45.339222 \nL 242.42323 50.545751 \nL 242.496394 107.980967 \nL 242.715887 42.882148 \nL 242.789052 125.57932 \nL 242.862216 67.208184 \nL 243.008545 125.715496 \nL 243.081709 79.645387 \nL 243.228038 119.551446 \nL 243.374366 53.695192 \nL 243.447531 138.203286 \nL 243.520695 123.316769 \nL 243.593859 88.587153 \nL 243.667024 106.606481 \nL 243.813352 88.95228 \nL 243.886517 197.947265 \nL 243.959681 161.481272 \nL 244.032845 74.228371 \nL 244.10601 102.216098 \nL 244.179174 91.44182 \nL 244.252338 212.688898 \nL 244.325503 209.535633 \nL 244.398667 76.461089 \nL 244.471831 88.343362 \nL 244.61816 124.37902 \nL 244.837653 56.458682 \nL 244.910817 133.90715 \nL 244.983982 83.420332 \nL 245.057146 87.640186 \nL 245.13031 114.390771 \nL 245.203475 84.016027 \nL 245.276639 104.393791 \nL 245.349803 59.354268 \nL 245.422968 63.858362 \nL 245.642461 119.333966 \nL 245.788789 63.606653 \nL 245.861954 107.582564 \nL 245.935118 69.199725 \nL 246.081447 122.665446 \nL 246.30094 62.482455 \nL 246.374104 164.079017 \nL 246.447268 93.508571 \nL 246.593597 144.997366 \nL 246.666761 56.507063 \nL 246.739926 192.1934 \nL 246.886254 42.50043 \nL 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290.85802 166.601762 \nL 290.931184 48.800244 \nL 291.004349 125.167489 \nL 291.077513 125.085281 \nL 291.150677 49.16672 \nL 291.223842 110.299682 \nL 291.297006 65.350693 \nL 291.37017 149.309827 \nL 291.443335 117.728262 \nL 291.516499 181.312613 \nL 291.662828 101.560464 \nL 291.735992 79.21254 \nL 291.809156 142.483392 \nL 291.882321 68.801645 \nL 291.955485 91.170894 \nL 292.028649 70.294444 \nL 292.101814 112.890738 \nL 292.174978 48.403532 \nL 292.248142 65.4225 \nL 292.321307 69.413365 \nL 292.394471 35.485697 \nL 292.467635 69.494059 \nL 292.613964 191.833767 \nL 292.760293 99.413073 \nL 292.833457 136.19911 \nL 292.906622 122.890642 \nL 292.979786 104.397343 \nL 293.05295 120.935229 \nL 293.126115 61.219894 \nL 293.199279 158.764811 \nL 293.418772 38.461969 \nL 293.565101 153.852146 \nL 293.638265 79.637105 \nL 293.784594 185.81461 \nL 293.857758 59.1003 \nL 293.930922 64.719939 \nL 294.150415 146.539912 \nL 294.22358 85.20511 \nL 294.296744 201.6363 \nL 294.369908 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[(24, 891, 2.0105548598760845),\n (3137, 3162, 2.36703212660892),\n (2128, 3733, 2.4664311231056617),\n (2299, 2523, 2.472864219542169),\n (751, 814, 2.4738626505492096),\n (2234, 2824, 2.514644506868477),\n (201, 3215, 2.515723740306358),\n (6, 3910, 2.5162722374378585),\n (2118, 3219, 2.5620123210445023),\n (3491, 3927, 2.6979214721388045),\n (741, 2795, 2.948388097908186),\n (3725, 4116, 3.219059537581496),\n (1845, 3756, 3.2237793610856404),\n (323, 3262, 3.231562027035296),\n (3646, 3966, 3.266288794642316),\n (1709, 2703, 3.5181832665067305),\n (3113, 4117, 3.5647113229837015),\n (1507, 2628, 3.577971737558319),\n (2516, 3053, 3.6705844969393553),\n (896, 1998, 3.7331668434540304),\n (556, 1646, 3.7381343928125195),\n (880, 1633, 3.742369047137203),\n (603, 707, 3.743656427989036),\n (1780, 2345, 3.745246795432413),\n (249, 2787, 3.7818143302549845),\n (3902, 3957, 3.817679840964198),\n (2192, 2587, 3.821035568295031),\n (1098, 1401, 4.027986136212571),\n (2985, 3628, 4.130021490316059),\n (18, 369, 4.387590965976641),\n (1634, 3220, 4.399379310519507),\n (182, 1119, 4.7868650207483165),\n (2052, 2883, 4.880730445257108),\n (1309, 3049, 4.8814453603199315),\n (1385, 3025, 4.883457145240654),\n (268, 1419, 4.883489219793591),\n (775, 1865, 4.8837951139229),\n (2441, 3664, 4.884678681969669),\n (1028, 3171, 4.894112662703732),\n (635, 1992, 4.903943168376789),\n (3188, 4144, 4.946903517550801),\n (740, 930, 4.947537711679765),\n (2272, 3110, 4.968603944891811),\n (156, 1322, 5.074828153710823),\n (96, 2228, 5.189714837184639),\n (1854, 2912, 5.1971642452817015),\n (1770, 2242, 5.217799450443494),\n (720, 3395, 5.222473300196961),\n (1299, 3977, 5.235545412648354),\n (212, 1444, 5.237972389254633),\n (2334, 3261, 5.336415423983705),\n (36, 2753, 5.561491661536189),\n (1418, 2722, 5.561491661536189),\n (712, 1118, 5.561491661889962),\n (1759, 2653, 5.561491662243734),\n (3386, 3744, 5.561491662243734),\n (2451, 3871, 5.6517642107939725),\n (472, 988, 5.66690478116016),\n (3552, 3986, 5.803462100313432),\n (638, 675, 5.803754230273941),\n (2305, 2816, 5.803863997658949),\n (68, 2892, 5.803950950104525),\n (2323, 3015, 5.836433352444859),\n (1916, 2448, 5.9834481646479905),\n (3319, 4057, 6.046815851999112),\n (431, 1585, 6.101186834425158),\n (3637, 4048, 6.103837438152013),\n (635, 1986, 6.105141547450704),\n (1641, 2426, 6.153862291576467),\n (356, 2336, 6.175030904221429),\n (943, 2960, 6.266292258470111),\n (434, 1503, 6.426443970146718),\n (106, 1896, 6.443839377648046),\n (742, 1713, 6.457488793682685),\n (703, 3115, 6.690556235215197),\n (804, 2132, 6.715206657726775),\n (830, 855, 6.717391000094073),\n (475, 1080, 6.7807993580311425),\n (791, 846, 6.78266693341044),\n (387, 2351, 6.868789193924695),\n (3290, 3494, 6.920624602934207),\n (3426, 4114, 7.10910717270417),\n (1888, 1988, 7.33853952982331),\n (412, 2360, 7.377874838480181),\n (1637, 1962, 7.399552739256184),\n (1664, 2510, 7.399686238333423),\n (1276, 2737, 7.400218057167508),\n (159, 2576, 7.400628488455601),\n (1080, 2286, 7.400871709402022),\n (168, 2387, 7.401385086263026),\n (1952, 4154, 7.423241810729857),\n (1465, 2936, 7.486533884220036),\n (4002, 4097, 7.488678532304204),\n (884, 1150, 7.532326180678296),\n (893, 2702, 7.5347706598326685),\n (3697, 4150, 7.5542254157344),\n (726, 813, 7.598862363580475),\n (3187, 3944, 7.614045827766771),\n (268, 3033, 7.614279068313855),\n (2754, 3513, 7.715635224883401)]"
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "sorted_distances[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_distances = sorted(distances, key=lambda e:e[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ind in range(len(sorted_distances)):\n",
    "    if sorted_distances[ind][-1]>30:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "choose = sorted_distances[ind]\n",
    "gmap3 = gmplot.GoogleMapPlotter(lat = stops_data[choose[1]][0], lng = stops_data[choose[1]][1], zoom=50)\n",
    "\n",
    "gmap3.marker(stops_data[choose[1]][0], stops_data[choose[1]][1])\n",
    "gmap3.marker(stops_data[choose[2]][0], stops_data[choose[2]][1])\n",
    "\n",
    "gmap3.draw( \"output/stops.html\" )\n",
    "ind += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(28.6803, 77.0893, '21', 'Udyog Nagar')\n"
    }
   ],
   "source": [
    "print(stops_data[21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(261, 1892, 2501, 3.745246795432413)"
     },
     "metadata": {},
     "execution_count": 135
    }
   ],
   "source": [
    "choose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(28.73081667, 77.03966667, '1892', 'Karala School')"
     },
     "metadata": {},
     "execution_count": 138
    }
   ],
   "source": [
    "stops_data[choose[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(28.730783, 77.03966700000001, '2501', 'Karala Pathsala Jain Mandir')"
     },
     "metadata": {},
     "execution_count": 139
    }
   ],
   "source": [
    "stops_data[choose[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}