atc_sectors_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of French ACC/UAC control sectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of Air Traffic Control en-route sectors of Metropolitan France"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### *Refer to tables_building.ipynb to generate sectors and volumes files (sectors.csv and volumes.geojson)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Load tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>acc</th>\n",
       "      <th>control_sector</th>\n",
       "      <th>volumes</th>\n",
       "      <th>elementary_sectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>LFRR</td>\n",
       "      <td>GAIU</td>\n",
       "      <td>['GI', 'GU', 'AI', 'AU']</td>\n",
       "      <td>['GU', 'AI', 'GI', 'AU']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>LFFF</td>\n",
       "      <td>TENB</td>\n",
       "      <td>['TB 1', 'TB 2', 'TE 1', 'TE 2', 'TN 1', 'TN 2']</td>\n",
       "      <td>['TN', 'TB', 'TE']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>LFRR</td>\n",
       "      <td>ZSI</td>\n",
       "      <td>['ZI', 'ZS']</td>\n",
       "      <td>['ZI', 'ZS']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>LFMM</td>\n",
       "      <td>DZH</td>\n",
       "      <td>['DH', 'ZH']</td>\n",
       "      <td>['DH', 'ZH']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      acc control_sector                                           volumes  \\\n",
       "72   LFRR           GAIU                          ['GI', 'GU', 'AI', 'AU']   \n",
       "270  LFFF           TENB  ['TB 1', 'TB 2', 'TE 1', 'TE 2', 'TN 1', 'TN 2']   \n",
       "136  LFRR            ZSI                                      ['ZI', 'ZS']   \n",
       "163  LFMM            DZH                                      ['DH', 'ZH']   \n",
       "\n",
       "           elementary_sectors  \n",
       "72   ['GU', 'AI', 'GI', 'AU']  \n",
       "270        ['TN', 'TB', 'TE']  \n",
       "136              ['ZI', 'ZS']  \n",
       "163              ['DH', 'ZH']  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('sectors.csv')\n",
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>volume</th>\n",
       "      <th>elementary_sector</th>\n",
       "      <th>acc</th>\n",
       "      <th>level_min</th>\n",
       "      <th>level_max</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LM</td>\n",
       "      <td>LM</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>0</td>\n",
       "      <td>195</td>\n",
       "      <td>POLYGON ((-0.25 47.167, 0.261 47.167, 1.467 47...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>X4</td>\n",
       "      <td>X4</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>365</td>\n",
       "      <td>999</td>\n",
       "      <td>POLYGON ((0.657 45.032, 1.098 45.193, 2.281 44...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>HR</td>\n",
       "      <td>HR</td>\n",
       "      <td>LFEE</td>\n",
       "      <td>375</td>\n",
       "      <td>999</td>\n",
       "      <td>POLYGON ((3.61 50.495, 3.588184441755715 50.37...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L1</td>\n",
       "      <td>L1</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>195</td>\n",
       "      <td>295</td>\n",
       "      <td>POLYGON ((1.467 47.167, 1.814 46.882, 2.353 46...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    volume elementary_sector   acc  level_min  level_max  \\\n",
       "10      LM                LM  LFBB          0        195   \n",
       "34      X4                X4  LFBB        365        999   \n",
       "200     HR                HR  LFEE        375        999   \n",
       "5       L1                L1  LFBB        195        295   \n",
       "\n",
       "                                              geometry  \n",
       "10   POLYGON ((-0.25 47.167, 0.261 47.167, 1.467 47...  \n",
       "34   POLYGON ((0.657 45.032, 1.098 45.193, 2.281 44...  \n",
       "200  POLYGON ((3.61 50.495, 3.588184441755715 50.37...  \n",
       "5    POLYGON ((1.467 47.167, 1.814 46.882, 2.353 46...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "gdf = gpd.read_file('volumes.geojson')\n",
    "gdf.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ac8acc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zM7uAzVSOrZnHGDrpax58cw0H7doFU0GsIFSSOjVimD6iO/3PCYppHkwV9fbKDPo+tYg3ln9Pfr4ddDblYwWhElWLieT5W7pwY2pTt6OYKuzwyRx+985arnv+SzbutQl5TNlZQahkUZERPH5tB+7vaxewmcq18vsfGfzvpTz64QaO27ULpgysIASAiPCbS9vwt6va2wVsplLl5SsvLtlB//GLmLd+n127YErFCkIA3dq9Oc8N60xMlH3spnLtPXyKu19LZ8SraXbtgvGbfTMF2ID2jZl2VzcS4uwCNlP5Pt+UySUTFvHcwm3k5Nm1C6ZkVhBc0DWlLm+N7kmjhDi3o5gwcConnyc+3sSgiUtYvuOQ23FMELOC4JI2jeJ5596etGpQ0+0oJkx8u/8YN7zwFQ+9tYZDx0+7HccEIb8LgohEisgqEZnjPO4nIiudSW+WiojX02hEZJyIbBWRzSJyWaHlA5xlW0Xkd+V/K6HnrNrVmDW6B6nN67gdxYSRt9Iz6PvUQmausGsXzM+VZg9hDD+fJvM5YJiqdsIzJ/IjRTcQkXPxTLvZDhgA/NcpLJHAs8DlwLnATU7bsFO7egzTRnTjknMbuh3FhJEfT+Tw8NtrueGFr9i0z65dMB5+FQQRSQIGAZMLLVYgwblfC9jjZdMrgTdUNVtVdwBbga7ObauqblfV08AbTtuwFBcdyXPDOnNT12ZuRzFhJu27Hxg8cSn/mLuRE6ft2oVw5+8ewtPAWKDwaQojgI9EJAPPnMqPe9muCbCr0OMMZ1lxy88gIqNEJE1E0rKysvyMG3qiIiN47Or2DO+V4nYUE2Zy85UXFm3nkvGL+XTDfrfjGBf5LAgiMhjIVNX0IqseAAaqahIwBRjvbXMvy7SE5WcuVJ2kqqmqmpqYWLUnthcRxg5oQ0p9GynVBN7uH08ycmoaI6emsfvHk27HMS7wZw+hFzBERHbi6drpKyIfAh1VdZnTZibQ08u2GUDhgXyS8HQtFbc87MVFR/LoVe3djmHC2Kcb9tP/qUW8sMiuXQg3PguCqo5T1SRVTcZzgHg+nv7+WiJyttPsEn5+wLnA+8BQEYkVkRSgNbAcWAG0FpEUEYlxnvf9cr+bKqJnq/pc2znJ7RgmjJ3MyeMfczcxeOJSm3chjJTpOgRVzQVGAm+LyBo8xxAeAhCRISLyV6fdeuBNYAPwMfBLVc1ztr8PmIenkLzptDWO3w86hzrVo92OYcLc5v1HGTrpa+6fsYr9R065HcdUMgmlwa9SU1M1LS3N7RgBMys9g9++tcbtGMYAUCMmkvv7tebOXik2HleIEZF0VU311c7+VYPYtZ2b0KNFPbdjGAPA8dOebqTLn1nM0i0H3I5jKoEVhCAmIjx6dXv7a8wElW1Zx7nlpWXcOz3dzkaqYuybJsi1SKzJfRfb5Dom+Hy0dh/9n1rEswu2kp2b53YcUwGsIISAuy9qYYPgmaB0MiePJ+dt5rIJi1mwOdPtOKacrCCEgNioSB67uoPbMYwp1s6DJ7hzygpGTrUJeUKZFYQQ0TWlLkMvaOq7oTEu+nTDfvqPX8SET7/lVI51I4UaKwgh5HeXt6V+zRi3YxhTouzcfJ75fAv9xy/iE5vXOaRYQQghtavH8IfBYTlKuAlBGT+cZNRr6dz5ygp2HDjudhzjBysIIWZIx7Po3bq+2zGM8dvCzVlcNmExT87bZENsBzkrCCFGRPj7Ve2JtWsTTAg5nZfPswu20f+pRXy0dq91IwUp+1YJQc3r1WBM/9ZuxzCm1PYcPsW901dy60vL2Zp51O04pggrCCFqZO8WtGkY73YMY8pk6dYDDHh6CY99tJFj2daNFCysIISo6MgIHrumA+JtqiFjQkBuvjJp8Xb6PbWQ91bvtm6kIGAFIYR1aV6HYd1sHmYT2vYfyWbMG6u5cdLXbNp3xO04Yc0KQogbO6AtifGxbscwptyW7zjEoIlL+csH6zl8MsftOGHJ74IgIpEiskpE5jiPl4jIaue2R0Te9bLNxYXarBaRUyJylbPuFRHZUWhdp4p7W+EjIS7ark0wVUZevjLli530e2ohs9IzyM+3bqRAKs0ewhgKTZOpqr1VtZOqdgK+At4puoGqLijUpi9wAvikUJOHCtar6uqyvQVzxXmN7doEU6UcOHaa3761hhte+IoNe6wbKVD8KggikgQMAiZ7WReP58v+jD2EIq4D5qqqjXxVwUSEv11p8yaYqiftux8Y/O8l/OWD9Rw5Zd1Ilc3fb5CngbFAvpd1VwOfq6qvMj4UmFFk2aMi8o2ITBARrx3hIjJKRNJEJC0rK8vPuOEnuX4NfmXzJpgqKF9hyhc76fuvRcxelWFnI1UinwVBRAYDmaqaXkyTmzjzi77oczQGOgDzCi0eB7QFLgDqAg9721ZVJ6lqqqqmJiYm+oob1kZd1IIWiTXcjmFMpThwLJsHZq7hxklfs3mfXdRWGfzZQ+gFDBGRncAbQF8RmQYgIvWArsCHPp7jBmC2qv60z6eqe9UjG5jiPI8ph9ioSB69yuZNMFXb8h2HGDhxCY9+uMEuaqtgPguCqo5T1SRVTcbT7TNfVW9xVl8PzFHVUz6e5oy9CGevARER4CpgXSmzGy96tKzHtZ2T3I5hTKXKy1deXLKDfk8t5P01e6wbqYKU9yjkGccFRCRVRCYXepwMNAUWFdl2uoisBdYC9YG/lzOLcfzfwLbUrh7tdgxjKt3+I9ncP2MVwyYvs7GRKoCEUmVNTU3VtLQ0t2OEhJkrvufht9e6HcOYgImKEO7qncL9fVtTIzbK7ThBRUTSVTXVVzs7T7GKur5LU1Kb13E7hjEBk5uvvLBoO/3H2xDbZWUFoYqKiBAeu6YDURE2+p0JL3udIbZve3k527OOuR0npFhBqMLObhjPyD4t3I5hjCuWbPEMsf3kvE2cPJ3ndpyQYAWhiru/b2uS6lRzO4Yxrvhpprbxi5i3fp91I/lgBaGKqxYTyd+ubO92DGNctfvHk9z9WjrDX1nBdwePux0naFlBCAMXt23A5e0buR3DGNct2JzFJRMWM/7TbzmVY91IRVlBCBN/uqIdNWIi3Y5hjOtO5+Yz8fMtXDJhEZ9v3O92nKBiBSFMNKoVx4OXtnE7hjFBY9ehk9z1ahojXl3BrkM2CDNYQQgrt/dMpn2TBLdjGBNUPtuYSf/xi5j4+Zaw70ayghBGIiOEx67ugNilCcb8THZuPuM//ZYBTy9m4eZMt+O4xgpCmDkvqTa3dW/udgxjgtLOgye4Y8oKRr+Wzu4fT7odJ+CsIIShBy9rQ4N4r/MRGWOAj9fvo99TC3l2wVZO53qbF6xqsoIQhhLiovnjFee6HcOYoHYqJ58n521mwDOLWbrlgNtxAsIKQpga1KExF51tM9AZ48v2rOPc8tIyfjl9JXsPV+1uJCsIYUpE+NuV7YmNsl8BY/zx4dq99HtqES8s2lZlu5H8/jYQkUgRWSUic5zHS0RktXPbIyLvFrNdXqF27xdaniIiy0Rki4jMFJGY8r8dUxrN6lXnt3ZtgjF+O3E6j3/M3cTAiUv4clvV60YqzZ+HY4CNBQ9UtbeqdlLVTsBXwDvFbHeyoJ2qDim0/Alggqq2Bn4A7ipldlMBRvZpwbxf9+Ga85sQaUNlG+OXrZnHuPnFZdw/YxX7j/iaQTh0+FUQRCQJGARM9rIuHugLeN1DKOb5xNlmlrPoVTzzKhsXtGkUz/gbO7Hwt7/gjp7JxEVbN5Ix/nh/zR76PbWIyUu2k5MX+t1I/v7PfxoYC3h7x1cDn6vqkWK2jRORNBH5WkQKvvTrAT+qaq7zOANo4m1jERnlbJ+WlZXlZ1xTFk3rVufPQ9rxxcN9ub9vK2pVs3mZjfHlWHYuf/9wI4MnLmXZ9oNuxykXnwVBRAYDmaqaXkyTm4AZJTxFM2cuz5uBp0WkJeCtb8LrQOWqOklVU1U1NTHRzooJhHo1Y/nNpW348nd9eWTQOTRKiHM7kjFBb/P+o9w46Wt+M3M1mUdDsxvJnz2EXsAQEdkJvAH0FZFpACJSD+gKfFjcxqq6x/m5HVgInA8cAGqLSMFM2EnAnrK9BVNZasRGMaJ3CxaPvZh/XnceLRJruB3JmKD3zqrd9PvXIl75Yge5IdaN5LMgqOo4VU1S1WRgKDBfVW9xVl8PzFFVr+VQROqISKxzvz6e4rJBPdMWLQCuc5reDrxXrndiKk1MVAQ3pDblswcu4vlbutCxaW23IxkT1I5m5/LnDzZwxX++IDOEDjqX9+jhUIp0F4lIqogUHHw+B0gTkTV4CsDjqrrBWfcw8BsR2YrnmMJL5cxiKllEhDCgfSPevbcnM0Z2p49d2GZMiTbuPcLbK3e7HcNvEkpzjKampmpaWprbMUwh63Yf5vlF2/ho7V7yQ+dXyZiAObthTeb9ug/i4jDDIpLuHMstkZ1faMqlfZNa/Ofmzsx/8Bfc3K0ZMXblszE/8+3+Y6zfU9xJmMHF/veaCpFcvwaPXd2BpQ9fzD2/aEl8bJTvjYwJE7NXhUa3kRUEU6EaxMfx8IC2fDGuLw8PaEv9mjbMtjHvr9kTEmccWUEwlSIhLpp7ftGSpQ9fzKNXt6d5vepuRzLGNVlHs/liW/BftGYFwVSquOhIhnVrzue/uYhnhnbi7IY13Y5kjCtmr8xwO4JPVhBMQERFRnBlpyZ8PKYPL9zahfOSarkdyZiAmrd+P8ezc303dJEVBBNQERHCZe0a8d4vezF1eFe6ptR1O5IxAXEyJ4956/e5HaNEVhCMK0SEPmcn8ubdPXjz7h42e5sJC8F+tpEVBOO6ril1eXV4Vz6470IGtGvkdpxKZUOLh7cvth4I6vkT7GRxEzQ6JNXi+Vu7sGX/Uf67cBvvr9lDXghf/lwjJpJ2Z9WifZNadEhKoEOTWqTUr8kLi7fxz483ux3PuCBf4f3VexjZp4XbUbyyoStM0Pr+4AmeW7SNt9MzOB3k53DXjI3i3LM8X/odmniKQIv6NYgoZha6aV9/xx/eW0cI/fczFeScxgnMHdM7oK/p79AVVhBM0Nt3+BSTFm/n9eXfcSrH/cJQMzaKdgVf/kmeL/+UesV/+RfnvdW7efDNNeSG8F6QKZuPf92bto0SAvZ6VhBMlXPwWDYvf7GDqV9+x9EAnb4XHxdF+7P+98XfoUktmtetXuov/+LM37Sfe6atJDvX/UJnAufui1ow7vJzAvZ6VhBMlXX4ZA6vfbWTl5bu4IcTORX2vAlxUT996Rf8bFaBX/7FWbb9IHe9msaxID9H3VScRglxfPG7vkRW8u9WASsIpso7cTqX15d9z4tLtrP/SHaptq1VLZr2TRJ++uIv+PJ3a4jitRmHue3lZRVa4Exwmz6iG71a1Q/Ia1V4QRCRSCAN2K2qg0VkCRDvrG4ALFfVq4ps0wl4DkgA8oBHVXWms+4V4CLgsNP8DlVdXVIGKwjGm+zcPGalZ/D8om3sOnTyjPW1q0f/7K/+Dk1qkVSnmqvj03uzNfMot0xezr4gPi3RVJxrOyfx1A0dA/JalVEQfgOkAgmqOrjIureB91R1apHlZwOqqltE5CwgHThHVX90CsIcVZ3lVwCsIJiS5ebl8/6aPcxdt4/WDWr+VASC8cu/OLsOneDWl5ax8+AJt6OYSlYjJpK0Ry6hWkxkpb9WhU6QIyJJwCBgspd18UBf4N2i61T1W1Xd4tzfA2QCdkmqqRRRkRFc0zmJF29LZeyAtlzeoTFNXewGKoumdavz5ugetG0U77uxCWnHT+fxyYbgGsrC38smnwbGAt5Ohbga+FxVS5wSSES6AjHAtkKLHxWRb0Rkgoh4HThfREaJSJqIpGVlZfkZ15jQ1SA+jpmjetC5WW23o5hKFmxDWfgsCCIyGMhU1fRimtwEzPDxHI2B14A7VbWgqIwD2gIXAHWBh71tq6qTVDVVVVMTE23nwoSHWtWjmTaiG71bB+ago3HHki0HyDpauhMiKpM/ewi9gCEishN4A+grItMARKQe0BX4sLiNRSTBWf+Iqn5dsFxV96pHNjDFeR5jjKN6TBSTb0+t8uM7hbO8fOWDNXvcjvETnwVBVcepapKqJgNDgfmqeouz+no8B4a9nhYhIjHAbGCqqr71io8eAAANrElEQVRVZF1j56cAVwHryvwujKmiYqMi+c/N53NdlyS3o5hKEkzdRuUdenEoRbqLRCRVRAoOPt8A9AHuEJHVzq2Ts266iKwF1gL1gb+XM4sxVVJUZAT/vPY8hvdKcTuKqQRrdx9ma+ZRt2MAdmGaMSFDVZn4+VYmfPat21FMBfvlxS156LK2lfb8FXraqTHGfSLCmP6t+dMV57odxVSwd1ftIT8IBjm0gmBMiLmzVwr/ur4jARoGxwTA7h9PsnznIbdjWEEwJhRd1yWJ/w7rQkyk/ReuKt4NgoPL9ttkTIga0L4RL99xAdUDMPSBqXwfrt3LqZw8VzNYQTAmhF3Yuj7TRnQjIc5mww11R0/l8vnGTFczWEEwJsR1blaHN0f3IDHe6+gvJoTMXpXh6utbQTCmCmjbKIG37u5BUp1qbkcx5bBwcxaHjp927fWtIBhTRSTXr8Gs0T1p1aCm21FMGeXmK3O+cW8oCysIxlQhjWrF8ebdPTgvqZbbUUwZvbPSvbONrCAYU8XUrRHD9BHd6JZS1+0opgxW7/qRHQeOu/LaVhCMqYLi46J5dXhX+rVt4HYUUwZuDXhnBcGYKiouOpLnb+3ClZ3OcjuKKaV3V+3GjXHmrCAYU4VFR0Yw4YZO3NK9mdtRTCl8f+gEK7//IeCvawXBmCouIkL425XtufcXLd2OYkrBjYPLVhCMCQMiwtgBbRl3eeUNsWwq1pxv9pKdG9ihLPwuCCISKSKrRGSO83hJoUlv9ojIu8Vsd7uIbHFutxda3kVE1orIVhGZ6MycZoypRHdf1JJ/XNMB+98W/A6fzGHBpqyAvmZp9hDGABsLHqhqb1XtpKqdgK+Ad4puICJ1gT8B3fDMmfwnEanjrH4OGAW0dm4DyvQOjDGlclPXZkwcej5RNn520Av0CKh+FQQRSQIGAZO9rIsH+gLe9hAuAz5V1UOq+gPwKTDAmU85QVW/Us+h9Kl45lU2xgTAFR3P4sXbU4mLtl7jYDZ/UyaHT+QE7PX8/W14GhgL5HtZdzXwuaoe8bKuCbCr0OMMZ1kT537R5WcQkVEikiYiaVlZgd19MqYqu7hNA6YO70Z8rI2UGqxO5+UzZ23ghrLwWRBEZDCQqarpxTS5CZhR3OZelmkJy89cqDpJVVNVNTUxMdFXXGNMKXRNqcuMUd2pVyPG7SimGIHsNvJnD6EXMEREdgJvAH1FZBqAiNTDc2zgw2K2zQCaFnqcBOxxlid5WW6MCbD2TWrx5ugeNK4V53YU48WKnT+w69CJgLyWz4KgquNUNUlVk4GhwHxVvcVZfT0wR1VPFbP5POBSEanjHEy+FJinqnuBoyLS3Tm76DbgvfK+GWNM2bRMrMlbo3uQUr+G21FMIdWiI7nv4lYBOyusvEeUhlKku0hEUkVkMoCqHgL+Bqxwbn91lgHcg+cg9VZgGzC3nFmMMeWQVKc6b97dg3MaJ7gdxQDnNk5gzv0X8tvL2pBUp3pAXlPcGC+jrFJTUzUtLc3tGMZUaYdP5nDXKytI+y7wQycYj7suTGHsgDbERlXMfNkikq6qqb7a2TlnxpifqVUtmql3daV36/puRwk79WvG8MqdF/CHwedWWDEoDSsIxpgzVI+JYvLtqQzs0MjtKGGjz9mJzB3Th1+0cW/IcjsB2RjjVWxUJP++qTPxsWuZmbbL9wamTKIjhYcHtGV4rxQiXL563AqCMaZYkRHC49d2IKFaFC8u2eF2nCqnRf0aTLzpfNo3CY4pT60gGGNKJCL838BzqFUtmn998q3bcaqM67sk8ech7agRRFeKB08SY0zQEhHu69ua+Lho/vT+erfjhLT42Cgeu6YDV3QMvpnsrCAYY/x2e89k4uOieGjWN+Tlh84p68Gic7PaPDP0fJrWDcx1BaVlBcEYUyrXdE6iZmwU981Yxelcb+NdmqJE4L6LWzGmX2uiIoP35M7gTWaMCVqXtmvEK3dcQPWYwJ8rH2oaJsQyfUQ3Hry0TVAXA7CCYIwpo56t6vP6yO7Urh7tdpSg1f+chswd04eeLUPjIj8rCMaYMuvUtDYzR/WgQXys21GCSkxUBH+9sh0v3taFuiE0tLgVBGNMubRpFM+s0T1pWrea21GCQqsGNXnvl724rUcyoTZVvBUEY0y5NatXnVmje3J2w5puR3HVTV2b8v59vUJ2xFgrCMaYCtEwIY6Zo3rQMSk4rroNpPi4KJ69uTP/uOY8qseE7smbVhCMMRWmTo0Ypo/sTo8W9dyOEjBdmtdh7pjeDDqvsdtRys3vgiAikSKySkTmOI9FRB4VkW9FZKOI3O9lm4tFZHWh2ykRucpZ94qI7Ci0rlPFvS1jjFtqxkYx5c4LuOTchm5HqVQicH/fVswc1T1gE9hUttLs24wBNgIFnWN34Jkvua2q5ovIGWO2quoCoBOAiNTFMzvaJ4WaPKSqs8qQ2xgTxOKiI/nvsM6MnfUNswM4SXygNEqIY8KNnejRsmrtCfm1hyAiScAgPFNeFrgHz5SY+QCqmunjaa4D5qpqYGaLNsa4Kjoygqeu78htPZq7HaVCXXJuQ+aO6V3ligH432X0NDAWKHydekvgRhFJE5G5ItLax3OcMf8y8KiIfCMiE0TE64nMIjLKeY20rKwsP+MaY4JBRITwlyHt+FXfVm5HKbeCawsm3dqFOiF0bUFp+CwIIjIYyFTV9CKrYoFTzjydLwIvl/AcjYEOwLxCi8cBbYELgLrAw962VdVJqpqqqqmJiYm+4hpjgoyI8OClbXhk0DluRymz1iF8bUFp+HMMoRcwREQGAnFAgohMAzKAt502s4EpJTzHDcBsVc0pWKCqe5272SIyBfhtacMbY0LHiN4tiI+LYtw7awmlgVJv7taMPww6l2phMG6Tzz0EVR2nqkmqmoyn22e+qt4CvAv0dZpdBJQ0c8ZNFOkucvYaEE+5vQpYV+r0xpiQcuMFzfjPzZ2Jjgz+v7IT4qL477DOPHZ1h7AoBlC+4a8fB6aLyAPAMWAEgIikAqNVteBxMp6zkRYV2X66iCQCAqwGRpcjizEmRAzs0JgasVHc/Voap3KCc/js1OZ1eHpopypzOqm/RDV09t1SU1M1LS3N7RjGmAqQ8cMJfjie47uhC85pHB/0Q1WXhoikO8d7SxS611gbY0JaUp3qJNVxO4UprOqUQGOMMeViBcEYYwxgBcEYY4zDCoIxxhjACoIxxhiHFQRjjDGAFQRjjDEOKwjGGGOAELtSWUSygO/czuGoDxxwO0Q5WH53WX53hVv+5qrqc7jokCoIwURE0vy5FDxYWX53WX53WX7vrMvIGGMMYAXBGGOMwwpC2U1yO0A5WX53WX53WX4v7BiCMcYYwPYQjDHGOKwglEBE4kRkuYisEZH1IvIXL21iRWSmiGwVkWXODHFBwc/8d4hIloisdm4j3MhaHBGJFJFVIjLHy7qg/ewL+Mgf1J89gIjsFJG1Tr4zZqcSj4nOv8E3ItLZjZzF8SP/L0TkcKF/gz+6kbM4IlJbRGaJyCYR2SgiPYqsr9DP3ybIKVk20FdVj4lINLBUROaq6teF2twF/KCqrURkKPAEcKMbYb3wJz/ATFW9z4V8/hgDbAQSvKwL5s++QEn5Ibg/+wIXq2px57xfDrR2bt2A55yfwaSk/ABLVHVwwNKUzjPAx6p6nYjEAEXn9KzQz9/2EEqgHsech9HOrehBlyuBV537s4B+IhIUM4j7mT9oiUgSMAiYXEyToP3swa/8VcGVwFTnd+1roLaINHY7VFUgIglAH+AlAFU9rao/FmlWoZ+/FQQfnF3+1UAm8KmqLivSpAmwC0BVc4HDQL3ApiyeH/kBrnV2N2eJSNMARyzJ08BYoLiZ2IP6s8d3fgjez76AAp+ISLqIjPKy/qd/A0eGsyxY+MoP0MPpVp0rIu0CGc6HFkAWMMXpdpwsIjWKtKnQz98Kgg+qmqeqnYAkoKuItC/SxNtfpEHzV7gf+T8AklX1POAz/vcXt6tEZDCQqarpJTXzsiwoPns/8wflZ19EL1XtjKdr4pci0qfI+qD9N3D4yr8Sz7AOHYF/A+8GOmAJooDOwHOqej5wHPhdkTYV+vlbQfCTs6u2EBhQZFUG0BRARKKAWsChgIbzQ3H5VfWgqmY7D18EugQ4WnF6AUNEZCfwBtBXRKYVaRPMn73P/EH82f9EVfc4PzOB2UDXIk1++jdwJAF7ApPON1/5VfVIQbeqqn4ERItI/YAH9S4DyCi0Vz8LT4Eo2qbCPn8rCCUQkUQRqe3crwb0BzYVafY+cLtz/zpgvgbJxR3+5C/S3zgEzwFQ16nqOFVNUtVkYCiez/WWIs2C9rP3J3+wfvYFRKSGiMQX3AcuBdYVafY+cJtztkt34LCq7g1wVK/8yS8ijQqOO4lIVzzfiQcDndUbVd0H7BKRNs6ifsCGIs0q9PO3s4xK1hh4VUQi8fyivKmqc0Tkr0Caqr6P54DPayKyFc9fp0Pdi3sGf/LfLyJDgFw8+e9wLa0fQuiz9yrEPvuGwGzn+zIKeF1VPxaR0QCq+jzwETAQ2AqcAO50Kas3/uS/DrhHRHKBk8DQYPmjwvErYLpzhtF24M7K/PztSmVjjDGAdRkZY4xxWEEwxhgDWEEwxhjjsIJgjDEGsIJgjDHGYQXBGGMMYAXBGGOMwwqCMcYYAP4fAU1L5i5/jAwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac8a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.query('volume==\"KD 3\"').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Number of Area Control Centers (ACC), sectors and airspace volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ACCs over Metropolitan France: 5\n",
      "Number of control sectors over Metropolitan France: 333\n",
      "Number of elementary sectors over Metropolitan France: 168\n",
      "Number of airspace volumes over Metropolitan France: 224\n"
     ]
    }
   ],
   "source": [
    "print('Number of ACCs over Metropolitan France: {}'.format(gdf['acc'].nunique()))\n",
    "print('Number of control sectors over Metropolitan France: {}'.format(df['control_sector'].nunique()))\n",
    "print('Number of elementary sectors over Metropolitan France: {}'.format(gdf['elementary_sector'].nunique()))\n",
    "print('Number of airspace volumes over Metropolitan France: {}'.format(gdf['volume'].count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Number of volumes and sectors per ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>nb_volumes</th>\n",
       "      <th>nb_elementary_sectors</th>\n",
       "      <th>nb_control_sectors</th>\n",
       "      <th>total_sectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LFFF</th>\n",
       "      <td>54</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LFEE</th>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LFBB</th>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>58</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LFRR</th>\n",
       "      <td>42</td>\n",
       "      <td>31</td>\n",
       "      <td>84</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LFMM</th>\n",
       "      <td>59</td>\n",
       "      <td>47</td>\n",
       "      <td>101</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nb_volumes  nb_elementary_sectors  nb_control_sectors  total_sectors\n",
       "LFFF          54                     29                  38             67\n",
       "LFEE          30                     23                  52             75\n",
       "LFBB          39                     38                  58             96\n",
       "LFRR          42                     31                  84            115\n",
       "LFMM          59                     47                 101            148"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_cs = df['acc'].value_counts()\n",
    "s_vol = gdf['acc'].value_counts()\n",
    "s_es = gdf.groupby('acc')['elementary_sector'].nunique()\n",
    "df_s = pd.concat([s_vol.rename('nb_volumes'), s_es.rename('nb_elementary_sectors'), s_cs.rename('nb_control_sectors')], axis=1)\n",
    "df_s['total_sectors'] = df_s['nb_elementary_sectors'] + df_s['nb_control_sectors']\n",
    "df_s.sort_values(['total_sectors'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Smallest and largest airspace volumes (horizontal surface) and elementary sectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Largest and smallest volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "      <th>elementary_sector</th>\n",
       "      <th>acc</th>\n",
       "      <th>level_min</th>\n",
       "      <th>level_max</th>\n",
       "      <th>geometry</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AU</td>\n",
       "      <td>AU</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>365</td>\n",
       "      <td>999</td>\n",
       "      <td>POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>AS 3</td>\n",
       "      <td>AS</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>195</td>\n",
       "      <td>345</td>\n",
       "      <td>POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>AI</td>\n",
       "      <td>AI</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>345</td>\n",
       "      <td>365</td>\n",
       "      <td>POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>AS 2</td>\n",
       "      <td>AS</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>55</td>\n",
       "      <td>195</td>\n",
       "      <td>POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume elementary_sector   acc  level_min  level_max  \\\n",
       "43     AU                AU  LFRR        365        999   \n",
       "42   AS 3                AS  LFRR        195        345   \n",
       "39     AI                AI  LFRR        345        365   \n",
       "41   AS 2                AS  LFRR         55        195   \n",
       "\n",
       "                                             geometry    surface  \n",
       "43  POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...  12.835058  \n",
       "42  POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...  12.835058  \n",
       "39  POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...  12.835058  \n",
       "41  POLYGON ((-8.75 47.25, -8 47.25, -5.5 47.25, -...  12.835058  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf[\"surface\"] = gdf['geometry'].area\n",
    "gdf.sort_values(['surface'], ascending=False, inplace=True)\n",
    "gdf.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>volume</th>\n",
       "      <th>elementary_sector</th>\n",
       "      <th>acc</th>\n",
       "      <th>level_min</th>\n",
       "      <th>level_max</th>\n",
       "      <th>geometry</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>UK 1</td>\n",
       "      <td>UK</td>\n",
       "      <td>LFFF</td>\n",
       "      <td>195</td>\n",
       "      <td>245</td>\n",
       "      <td>POLYGON ((-0.25 50, 0.444 50.274, 0.833 49.937...</td>\n",
       "      <td>0.426362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>TP 3</td>\n",
       "      <td>TP</td>\n",
       "      <td>LFFF</td>\n",
       "      <td>245</td>\n",
       "      <td>265</td>\n",
       "      <td>POLYGON ((0.444 50.274, 0.821 50.421, 1.388 49...</td>\n",
       "      <td>0.325937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>DO 3</td>\n",
       "      <td>DO</td>\n",
       "      <td>LFFF</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>POLYGON ((1.875 48.502, 2.92 48.502, 2.793 48....</td>\n",
       "      <td>0.212400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>E 1</td>\n",
       "      <td>E</td>\n",
       "      <td>LFEE</td>\n",
       "      <td>125</td>\n",
       "      <td>145</td>\n",
       "      <td>POLYGON ((6.212 49.507, 6.186320428094206 49.4...</td>\n",
       "      <td>0.026361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    volume elementary_sector   acc  level_min  level_max  \\\n",
       "183   UK 1                UK  LFFF        195        245   \n",
       "178   TP 3                TP  LFFF        245        265   \n",
       "151   DO 3                DO  LFFF          0        125   \n",
       "194    E 1                 E  LFEE        125        145   \n",
       "\n",
       "                                              geometry   surface  \n",
       "183  POLYGON ((-0.25 50, 0.444 50.274, 0.833 49.937...  0.426362  \n",
       "178  POLYGON ((0.444 50.274, 0.821 50.421, 1.388 49...  0.325937  \n",
       "151  POLYGON ((1.875 48.502, 2.92 48.502, 2.793 48....  0.212400  \n",
       "194  POLYGON ((6.212 49.507, 6.186320428094206 49.4...  0.026361  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.tail(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Largest and smallest elementary sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "      <th>acc</th>\n",
       "      <th>level_min</th>\n",
       "      <th>level_max</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elementary_sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>AI</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>345</td>\n",
       "      <td>365</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>AS 3</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>195</td>\n",
       "      <td>345</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>AU</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>365</td>\n",
       "      <td>999</td>\n",
       "      <td>12.835058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>ID 2</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>195</td>\n",
       "      <td>255</td>\n",
       "      <td>9.885116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  volume   acc  level_min  level_max    surface\n",
       "elementary_sector                                              \n",
       "AI                    AI  LFRR        345        365  12.835058\n",
       "AS                  AS 3  LFRR        195        345  12.835058\n",
       "AU                    AU  LFRR        365        999  12.835058\n",
       "ID                  ID 2  LFRR        195        255   9.885116"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.groupby('elementary_sector').max().nlargest(4, 'surface')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "      <th>acc</th>\n",
       "      <th>level_min</th>\n",
       "      <th>level_max</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elementary_sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DS</th>\n",
       "      <td>DS</td>\n",
       "      <td>LFFF</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>0.770265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QI</th>\n",
       "      <td>QI</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>345</td>\n",
       "      <td>365</td>\n",
       "      <td>0.804880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QS</th>\n",
       "      <td>QS</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>295</td>\n",
       "      <td>345</td>\n",
       "      <td>0.804880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QU</th>\n",
       "      <td>QU</td>\n",
       "      <td>LFRR</td>\n",
       "      <td>365</td>\n",
       "      <td>999</td>\n",
       "      <td>0.804880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  volume   acc  level_min  level_max   surface\n",
       "elementary_sector                                             \n",
       "DS                    DS  LFFF          0        175  0.770265\n",
       "QI                    QI  LFRR        345        365  0.804880\n",
       "QS                    QS  LFRR        295        345  0.804880\n",
       "QU                    QU  LFRR        365        999  0.804880"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.groupby('elementary_sector').max().nsmallest(4, 'surface')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display largest and smallest elementary sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c252940>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0e9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "france = world[world.name == \"France\"].copy()\n",
    "france.geometry = france.geometry.intersection(Polygon([(-10,41),(-10,52),(10,52),(10,41)]))\n",
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "smalls = list(gdf.groupby('elementary_sector').max().nsmallest(4, 'surface').index)\n",
    "bigs = list(gdf.groupby('elementary_sector').max().nlargest(4, 'surface').index)\n",
    "gdf.query('elementary_sector == @smalls').plot(ax=ax, color='green', alpha=0.6)\n",
    "gdf.query('elementary_sector == @bigs').plot(ax=ax, color='orange', alpha=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Flight Levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### All flight levels used as level max in Metropolitan France"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([999, 345, 365, 195,  55, 255, 355, 245, 145, 385, 295, 285, 375,\n",
       "       305, 225, 315, 335, 265, 175, 250, 125])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.level_max.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Flight levels used per ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LFBB :  [195, 265, 295, 345, 365, 385, 999]\n",
      "LFEE :  [145, 195, 225, 265, 285, 315, 345, 355, 365, 375, 385, 999]\n",
      "LFFF :  [125, 175, 195, 245, 250, 265, 285, 295, 305, 315, 345, 355, 385, 999]\n",
      "LFMM :  [175, 195, 245, 305, 335, 355, 375, 999]\n",
      "LFRR :  [55, 145, 195, 245, 255, 305, 345, 355, 365, 999]\n"
     ]
    }
   ],
   "source": [
    "for key, group in gdf.groupby('acc'):\n",
    "    print(key, ': ', sorted(group['level_max'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Identify complex elementary sectors composed of most different volumes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WS    5\n",
       "AR    4\n",
       "UK    4\n",
       "Name: elementary_sector, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['elementary_sector'].value_counts().nlargest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e0d27f0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e0d19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "complex = list(gdf['elementary_sector'].value_counts().nlargest(3).index)\n",
    "gdf.query('elementary_sector == @complex').plot(ax=ax, color='red', alpha=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Display airspace volumes, ACCs and sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48 volumes at FL200, 40 volumes at FL350\n"
     ]
    }
   ],
   "source": [
    "gdf_low = gdf.query('level_min <= 200 <= level_max')\n",
    "gdf_high = gdf.query('level_min <= 350 <= level_max')\n",
    "print('{} volumes at FL200, {} volumes at FL350'.format(gdf_low['volume'].count(),\n",
    "                                                        gdf_high['volume'].count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display all volumes at FL200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b1c0978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eJy0fS1O3ctuI7/cua8P3nXwKHgqQ6ugfc18tyd+xioXA6PMCdjlVl6ykYpmPeSscBKoktkAUzdoPDM2bGBnuxW6zU15eygc/Uora4UDfVUFhfw0OrwOr1YrFYqE71M1f3fLnrLl9PZ//4udxu3NbXvdCwhTwaURKSVj2Dwt1G616G+16Oyk5chaIpdXJXBdvAJvVitPhJBaN4vP5Rh1nUSyUFJdQXFxMOBSmrb0NVVUJBIKUlZbh888O0T6BsayAHR87QNQ/MOa4SEWI1HfcVHyjhGRd16jjon1t9NceJlC/fNQxi48X86IQGcXWx8JXZKd6rYeihQqu4iSGM4zklG0TX5YlKO5fh7+lFr3VAoqA/DNHVLtrePBDX6a+pZ6PXHkvd//VvXz8Ex8ftTG0yeiYAj6FRI0YbXobrfopwY4ZYz+Cnk737sgUWje9+PP8DAwOjCngJxCIkxOeIKcs5XDCWC303e7hnSu3ZXx/VfPitHyjjZrv1RDffnzUcc0DL7JGqUUYI1+a7oidBYsraOxozdhcl99C+VIPpUts+Mo1LP5BdBFmqLzRUI3oyWLX/JR2XYytsQRjcPx9OhwOVi1axd+W1bDrd7u544e38Vdf/zzXX5/7WunnM6aA5whVqrTrHUNirQ2JddgY+5F5LBSpcGjLsRxaOLPk+fM4dvwYjN3A/QzEaf+dNVTksf+P2ugqO5j1prpD4+iDR1j0w6XEX2gecUxioIfw4gPk160ZdT8rLFU0ckrAhQUKK10UVDrIK7PgKQBHnobFlcSwxTCIcSIUArkRbAAkBOOLCR5fDc0upJ55qOUEXo+Xa9ZexZr5q3n5e6/wX//8X6y+bDU1C2qorq6mpqaG4uJiczJ0FEwBnwC61Ok0uoaFesi77tF7MEZpCjARvGE/6XjOLrUZx+P1oKoq6XQam802ZccJXWLHrxZgSQoUFZSkRCQNREJHJDVkUoNkGrL9riTE31fAWzfvQLdN4ntR4OgDh6jJX47605HL3LaEfkfAugJFO3V5Gh4NbVGCdHWcwnkRPpCfj82rgiOBrkSB2NnmTiIMMs4pGHbKQhfjaqhC7x2tFUY2CILBIPdceRehUIhYZ5y+xhBb+1/lia5mOgc6sfiseIu8BOcVMK96HtU1Q+JeU1NDIBC4YAXeFPBxMKRByAhxXG8dDoe00a53oMmpFdfYwdlRHCpXCAR+v5+BwUEKpzBPuO6aelRP59iDJDhUJ46kC0fSiSPpwKE6sCft2JJWbEkr1qQFa1IZuhEk4dglPTQuGXmiMmuEoOmeQ1QWLUY81ofd48Xq9WJ1u7G4XChOB735R1CcDjSRQgZTDNQ0kZSnnugUcuhJZ4hHLaO4fSNKYx5GMvfHF0KhoKCQAqCKKtaxFgDD0FHVFGpKJaWqxFvi9Bzs48XQezR2NtKXCGPPs+Mt9lFQWUhlTeVJca+ursbrzX33o9mCKeBnMWgMnoxXH9eGRDsppzvXWlL/h/ZpPubU4/fnMTg4MKUCnhECVGcS1Zl9BcFccvyaI8xbup68LUsxUmfKYZT+Uy50N9h7ivAsLyXqa0eXKQw0DDkdN/mzJiURWYdJJouiWHC5XLhcp1rCVVPDJjYCEl3XUVUVNZUipapE62J07W5ld2gHDR2NRPUY9qADX7GPT/3lp7jsPKqqeEELeEImaNPahwV7SKwHjJmfOPQkvfQ0ZT5JNVfIy/PT1taKnI0Tk1OAYthw7b2KwbrRJ25bgFDawjK7DSM1xg1FCtIHDByUnnoJiWJTEHaBsAmEdehfrIBVIiwgrRIsEhQDaZGgSAyhIRUdKQwMoQ/dDISGQRpdaugyhV3zZTUpOXMILBYrbrcV9/DagRJgIQuH35ekNY1oNMoTr/739KzGnUYuGAHXpHZyccwJwe7RZ2I12/jIFvuUH8OQBslkkkQiSTKZIJlMYrPZcDqdJ39sNntOZdZus2Oz2ojH49NSmXAmcSfL6H/qEtqPjH+J9ZCkz2fnioVejBFauo2GQCDTEpnONAothn9O/X0pjJx17nIUYB8sQx+cbn871whsViuHWw9Tc+tiLrvsspk2KKectwLepXdzXB9aGNOmtdNhdGDMkvog49H2VmbF+TPhhFAnE0kSyQSJRIJkIkkqlcLhcOB0OXG5XAQCQTQtTSKRGFrpmEyi6/oZgu50DP3rcDqwKJYJ2ePP8zMwMHDeCriUEDh+MUcfr0RXM7/9DURSbD6kc+3yPIiPnVc+HSTUPlTnIEWeNWgdczs/u7Wjjd/3bOapx5+eaVNyznkr4D+M/oi4nIKSoVOMzbBx5I3GrLczpERVkyQSp0Q6kUygqikcDvtQDNHpIj8/H5fLhcPhHHdRjK7rJNXk0A0gmSTcPyTsalLFarWeFHaH04nT6cDpdGG328YMj+T582jvaKe87PzrjC4lOF69mbpXJ3ZzSqo6v9sb5v1rC7HEc3cTnyiGTNOl7SJ//hKU1jymeN5+SohEIzy69T959IXHsFgm5nTMZs5bAZ+ruHq8GONcKGpKJRaLkxz2qBOJU0LtdLpwuZwE84OUO8txOB0TLtlqsVjwuD3neMsSSSqVOinsyWSC/v5TXrvD4TjTcx/+sSgWvD4viYYEmq5NqgLhbEFKcODCpwfwpr14/RYmk70vJby0p4erVhfjUccI8Qmw+e0oAQPp0KDXTiqcnvQKzZEIxetwlRbiG1yEHpk7IRVNS/P8W7/kT7/1F+dtqdu5fwWdZ/TvGz8z4tDBQ3i8HtxuN8FAkLKyMpxOZ85ra4+GQOCwO3DYHeT58854Tzf004Q9SX//AMlkJ6qqYrFYcDqdCCGIRCIEA+f0wJ6VSAkFegmBVACv6sKbsOJJCNxRHXckjT19StQkDVg3rOL3uycjdIKt+3vYuLSAIvpBShSHDcuCFKn8bhJ5bUT9TRiWM0swOJL5lO65EbUx9yKbSPWiugYo8s6VkIpk39H9WNc7ueHGG2bamCnDFPBZhAAOvTJ+9onNbqOiogK3a/YVAbIoY3ntadRkkqSanDMFjMrUatY1FlLUeSIclx7+GRmp2GirWYA1qqPVjbxQJ1N2He5j6fwAC4s13nLsRh2MsHKpBdV9ZMTxqjNEy6VPkr9wHZ6dq0j3p0YcN1EMmaYrPTdCKp1dnTzf8mv+57FfzLQpU8qcEPABY4D/jv0cn+LFL/z4FC8+4cOv+PEJLz7Fh1d4sYo5cTqj4o3mEekdvU7GCRwOB8mkOisFfDSGvHY7DrsdP/6ZNmdcSlJVrGsqoaQ9DmQ2l5J0FPId5x3s1oIsq00RnKSAA4RI0qC8iaapoMGOLZIVy9finH8YXYz8tBYq3kPo5j0UtV+C60AtqZ4cCrmY/SGVWCzGf7z2Q/7luX+d0lW/s4E5oXg6Bh16Jx3j3PE9intY0P2ozP5GB2eTPprZOKfDiarO7CKU85XCdDnrW+ZRejyKIPPCY8f9q/mqcR0RfShFL6rAaufQexJIpyXJhEEsrhONpolEUuOGqxde6ae79NWzUpcFBw72EmitYtlFaRL2UW4SAnoq3oLyt8jvXYvvwCrUttz1Pp2tIRVd13hh94t89GsfZ/78+TNtzpQzJwQ8U2JGnBhxOvXumTZlQjS/0ZvROIfDQSweG3/gBUTZIidXfDaNza1P6Nadr5Ww7lgVFS0xBDEyLaJlCCsvB27gB6nlZywSGRQWGg+OnEliAQIOkFJgGKDroGmSdNoglTLQdINFN7iJLt4xqhX9g3HeegXWrF2PUv4uxmhhHQGhor2ErtlLsHclvj3rSXXnxiOffSEVycGmwwzWxLjzrjtn2php4bwS8LmMU3PS/M4xMhEOh8NBKDzzaWazAXfAwvv/zIFY9h4poWEXVVltn6cXsa61hqrGEx535rnbqqOA77pu5+10wTmbDUrL0GtjeNlCSCwWsFjAbocTy2ocTguVSwzGSyaVwN693ZS0LaZmXT9J69jlF8KF7xF+33sUtV+Kc89i0gM5EPJZFFLp6e3l54ee4hevPDNjNkw3poDPEmztbjIVD4fTgarOvRBRLlEskus+mUfeFYfRrNGsK+L59HzWti+kuiGGIjP3uE/Q5l/J1+T76ddGibEKULwOjEj235Oa1DnyrGD5h9ZT53pn3PFdPYP0braybuNqtIL9Yw8W0FOxHVG2g9Lm67HsK0WLTz60MhtCKu+07OELD30Rp9M5I8efCUwBnyX0vJP5Emq73U46ncaQxrSlDs4eJJtuCVBzxzFU55GsS6Z69DzWdi6m5mgCi5Gdxw0ghYVXAzfwb6kVY9bV2NDVMiHxPoGuS959Ps6qmy6mLrgjg/EGu3aEmF+5kuIVraSVsWvRS8WgY8HLWKrslB+8Be2gC6lPzHvWpYVIuoh+tYidUTc2rYCrnW1jNIabCiTvtrzLp9Y/MK1HnWkyFnAhhAXYBbRJKW8VQlwP/CNDz31R4H4pZYbTcCano0iFQ69mvvzjRB62qqq4nK7xN5iTSPwFNgJlNgLFVnyFCp6gJLAijJq/M+s4tyvtZ922dSxpKkBURtGVdrKtCa7ag/yL+w62pQvH1P15hor4f69naeHIvPviAMuvuoSj5W+N1SrzJC3H++nsDrDmoiJSvnrGu0Hp1hTHVz+Pe8E8inZfi9o6tjfeEV9EV7SUvoifrpCHzh4nXb0WDHnmcdrXOfloWcO0ibiqpug3BgkEAtN0xNlBNh74XwOH4GQO2KPAbVLKQ0KIPwP+D3B/bs27MPD2+1GjLVlt43DMPQFXLJJAiYNgqZW8QiveQnAFJc48HZsvjeJWwRnHcMRIWyIgzvUIsxVup+Zh3Y4NLP8vP9aYYCiH24EtbyHGpTb0+YMZiXmnbzlf5f2ENMeY4wRQ9avNpI3cxYIPbu1n8YaLaat9B02MH+5QVY23X9dYXLsK/8Kjo6Ybnk7c20rL1T+lqO0SHLsWo0XPPI6U8EbL+/nVlnkZPbRs3uMEFk6biCcScfzz8sYfeJ6RkYALIeYBtwDfBL44/LLklJjnAedfAetpIn44++n7EwI+e5CsvNpHYbUFV0DHmadj8aSweFSkI4Zui6NZo4wmlLlLcBvCajhYv+siVj6Wh21wBMUZMFB+p6LgwJa3AOMSO/r8CLqlnbMbg8VdpXzWuCWjUqTV6RjpxsyyibLhyO4BqmJrGVh/gESG6Y1H6vvxt1ew7KI0qjOzJ7yeirewlLxD2aFb0A84kbrEMBReOHgrW3efO1k7FtMp4vFEgnlLK6f4KLOPTD3wfwG+Apxe2PjTwG+FEAmGGu5dMtKGQogHgAcAqqqyyxC4MJAc+UNH1ls5nA7i8TiGlDPeqX3JxR7WfCKMGjg14WYM/+RamDOhqmcpV/7TQryNGcrGgET5vYqCHZtvAYmPHz1Dp7ps5WBk9hmXNbdM2TkfOxyhNL4U7fL3SIvMMkgGoyo7XpWsXrsKS/lBZAaVvXVritZVz+OumYdvx/v5+S9vYf8YNc3HYvMeJ1Iu5P3lXQSJYx+jHcTedAlpXVDmiFMsomOOPZuuaBcrL145IRvnMuMKuBDiVqBbSrlbCHHNaW99AbhZSrlDCPFl4HsMifoZSCkfAx4D2Lhx49yo5zqNuFUvXUezb96Q58+jr6+PfXv34vF68Pl8+Hw+PG7PtPUHLF3o4IoHdFIVe2bFsim76uCq5zYx7zkx8YYREQNFcSONU15uk7UEMsy4s7/bMKU3rc5jcVYu2kB96fYsthLs3xumuG0R1etCpKw9GW0V97YSveYJKgYV6o/fTjI+sTr1r+518ure+UNNkP2SskKDYr9GkSdNoStFkT3Jrt4Av915YmVxAQIoLzaoLtaoyEtR4UlQao1RQGxEb76uo56PrP74hOyby2TigV8OfEgIcTPgBPxCiBeApVLKE9PjTwO/myIbz2vEBJs3OJ1Oli9bjqZrRCNRIpEIx1qOoaoqHq8X/7Cguz3unHe/8RVYuO5zDsTS90gLbVb01nHH/NzzvQ/hrJv8rUTgQJ4WpnhPFma0nV9IYs3hSR9/PA5u6afqI9W005zVdt09MUKbXazdtBwt/wCZxEMUi8HyO35E9dXPs/tHX+Ptl1ZOvKuNgHBEEI5YOIgFGH0+QQJt3Qpt3XaGGlBBy/4TAAAgAElEQVR4gSLsVklVqUFVYZpKv0qZK04xgxw8fpja2tqJ2TWHGVfApZRfBb4KMOyBfwm4HegUQtRKKeuB9zM0wWmSJR07x073Gg+rxUogEDg5+67pGpFIhEgkQnNLM6lUCq/Xe9JDd7snLuhWB1z/J368Fx9Cs8Ry0I08N+QNFHLnP96EvTk35QViFh9OIzTU8QbYmc5MwMvd/Sz722YGtxfS/pobqU9N5FfYNGxdHmRJ9q3pNN1g11uDVM9fSeGy42jKYEbbufNDXPnlL7Hs5kt49fuf53jDzFSSTGmCo60WjrZaGPIn84jH/CQHvkF8UJA3w+1Wp5sJ5YFLKTUhxGeAZ4UQBhAGPpVTyy4ArIaVw1ubc7tPi5VgIHiyVKumaUSiw4LePFFBl1x+bx7lNzaRstdnnXs9lRT3VPDBh6/F2pkb8X7lw07+s+Iw8wwPH9CCLFAdxNOcPa85IkX+YwwWvwO1UPVxN56OBcR3FdP2so90NLNmAlIYeMs0vFUarjIVe3EKa0ESAnF0T5SUsx/VEiEuYEHrxTQdmFgP1+aWAbq7Clh1cTGqJ/Ps33nl+/nC5V9gd+Au/vfgTUQTM7+UJJ5IUFPcT17BzNsy3WR1xlLKLcCW4d+fB57PvUkXDu5eH0bG/QwnhtV6pqCntTSRSJRoJEJzbzOpdAqf13dS0F1u1xmCvuoaL8s+0o3qPZJpGHjaqGxbyI0PX4wSyk0Eft+1Tn64rg4BtClJfmLvADv4eYQSSw1CW0xrZD6DqZE77vhcB0/+rlniDMx7D+ZB8W0Ci8wsVGaINHI4fXK0wrUnvh214l1czbUkYhP7ZuLJNDteS7F8+Wqc8+swxOifozVuo/SVYrTXj6JpPazhYWprf8YftG+w5b1FM9osOBlPcNH1xxiK9l5YXHi3rFnEwLvTP/Vns9rIDwbJD54u6EMeek9vD1paw+vzsnCVnxu+ZMe5uJ4sWjtOGwsbV3D9d1YhormZMjy+3MY/3dCAFOfeUHU02vUjII6AHxYoZbjlYkLxGtpiBQgUJGB1vjXyzoVEH0McJ0paxJm3TuPIG5PZi+DgwRD57fNZvDGGam87410lLSh9swL5cgvpeP0Z77m0Nm7mk6y/6Eaea/1rmjsmlqkyGRLxOMn4m1z3ofOvRV8mmAI+Y0gOb84++yTXDAl6PvnBfAA8+ZKlt3ehLdhPQ3QQ5ZCFmpoaXK7ZU1/CNbCKvLfX8e4GA7tqxZ4Ce1JiSxjYVbAnDOxJiTVhZBQj7i+18K1PdJDMYJGMAPqMDvroANdWKjxeCqhFatUotuwWY+WCfs97lFVdRMexzEsxjESoP87OVwXrNq5CL3wXDEnxnkpsv+1CC9WNuW1p/Hd8Lv9l9s97kF/uv4G4Ornek4MDAyQSCdyeoa5TI/WyNHSd7u4WBvoe5jv/9zKuv/66SR1zrmIK+Azh0J0s/8DoCw/sg1ac4eltwupe3Q2bdmAoSaAKCfT29FJXV0dpWSklxSUz+aQMDK0IPGqU8psrx58zlxLswoJdWrFJC3ZpwSGt2AwFu2HFbijYdIV2d5g+ZWICGDOixHiHjYGxRW4qWSTLubjlKl6qeIq4deJCbhiw++0wl/rXUvh2A6njdRnPdyjorI1/i9qlP2Nz+v/j9QMLsy0zc5J4vJug57NIuZgDewUe30Y83vUIpQKPx42maXR3PM37rzvE9//92xQXF0/sQOcBpoDPEKpFhWvOfawuVQq4yljAYs2JMs0Jepvdv6WBU5OBAigqKsTv99HU3Ex/fz811TU4HBNLfcwFQkB54GX8tmvZMRAd8xMSAtLopIU+YTHJyCYEPveeqTvACLijhWzc9idUv3I19roAQsBSzyaaLznI72ueJGzLLNf7dMp1N6ubIsimN0hN8ANza8f4oPhj/Ov+lRf2bpjQPoTwc9lll/GDRx5G0zQOHTrE7t272bbtf9j+VgjSKs8+/Wmuu+4vJ7T/8wlTwGcJJ4U7Nf3CPR4Oh4MltUvo6uri0KFDVMyroLCwcMasFAJ83j9wrW0t20MuVGNm82KWufPAMg1NRAzBynfvYtnmO8jbPh+RHk5THP4ilJiFBZtX8TnbN/n3+/5A0r0ZjfHtyjPsbOoAy6G9SCnJxd1uteVnvCA3TGhXDoeduvoBYGgSftWqVaxatYr775+0WecdpoDPMGWnCXeuF9xkizJGxQohoLS0hLy8PJqamugP91NdPX9Gew5aHHu5oqiC/aFaetKTiwFPhmLv1BXhtGCjUFax5rf3M+/5K7D0jl1MC0CkFVI9FeyJ38KyyiRO71Y0hicnpQW7qAFjPolkIeFBN4aRRNZ/h3F7vGVBMLmTBeWDNHZk3//UbrfT3Dwb1vbOfuaEgNukwnLl/OpvJxCsMopZlHLMuHCfoMAo48g4a09cLidLly2lo6ODAwcPUlVVdTKjZUawtrGqMExb/+XUJSa3KGoizLP7wDqpNJAzCMgyioxKClMFBJNe/EkFIRVcemFG4n2CJXEfbxt9vNfiRBEfYEGZTjJloTMM2lnlUFpxEl/0NT7U9I/Y1IGcncvFFdtp7Lghq22SiQRNTU3cceuqnNlxPjMnBNyDnbtS55eAz0aK9NKMak4rQlBRXk7gNG+8qqoKq3V6J11PIJQ4FcGXCTqu4a3+6e0VWuPrn/DErg0n5UYtBeli8lU/wYQN6yirNxPrt+J9bQk0ZJYNtLjTB8Nze4aEo+1jfzdHB608M/9B7mz7VxyxrqzOYzSWak+giA+cUyt8NEJ9fRw/fpxFi0p4/MdfyIkN5zsXWjsXkzEo0PLJJmjp8XhYvnw5VpuVAwcPMDCY2bLsqUAI8Hi2UGqf+lxkv3RzVWgJn3l9I+vfrsYiswsjOfGxXn0ft3XdxmWdK1jSV0RR1DGqeAOggHrvaxlHORY0eLBkeWc5FlV4qvzzxP25cZZc6WOsWTj+zUAaBsdajtHe3k5tbS333FmOxTI7nkpnO3PCAzeZHuzSRpAKwmSen64oClWVlQQCAZqbmikoyKe8omLmgkISrFhxCDsOYccubJz4n0VasGLFIq0oUkFIgaakSYgEgwwSMgYwRlkz75Uu1gxUsvRgHiX7TvN83itlfdv9HLjnOeLWvjFN8xBkRfIi5vcHsBrZ+07pynrsV26EN8Yv+GFPW1jg9nIklt1S+86Y4OdFf8pHrI/jCx3O2sazWR/czB4+dsZrVgusWS0oyIfI4ADPPvsM+Xk+/uSTt+FyOrn1ZlO8M8UUcJMzKJbVhEX2C4z8Ph/Lly+jvr4eCVTMkIgviFbiG3CP+r42/HMKBxa8BCkiKCR2u8DqlGBLk7ak8MUky/f7KNsHyig1we0HvKzu/ghHP/MSve4j57zvl8WsTG6ist+LIif30Jv84O9w7byPTJbHLtX9HCH7WimhBPzMfz/3WZ8mr3ty6ZELEj/Faf8oeXkKl12icPmlgg3rBG634MUXX+STn/wkX/7yl/niFx+YtjLI5xOmgJucQZGsoG6C15HVaqW2tpa6unrEcJx8upmUBkhBSoWUKhgqYWrn4gM+/IfHz3Cx9NhZ/I+34Pns27SUbgMgKCtYmdhAxYAbkWEceDx0XwjjtgaU/1k07tjFIR+Mfi8bk8EUPOH8MPdVeChsm/gkbenKNTz5FZXyKt9JgdZ1nb/7u6/z4x//mGeeeYYrrrhiwvu/0DEF3OQMirXSIe2aICdEvL6+DoGgvLwsd8bNANZY5u3ulLSg4vsXkXdPGfkLdEojDsQUTDMlrngZz2vzoWvs2PuSRi9MoklNQoOfWm7lo/PdlLa8lNW2is3Ggjv/mvKr7j7Ds+7p6eG+++5D13V2795NSUnJxA00MScxTc4koPtRmFxut81mpbZ2CaFQiI6OzhxZNjNYo9n1KxUIfL+oIm/H0ikRbwBp1Uh/eOe444IhB8XOzFMPRyKlw8+06zi24PaMt3GXVrPuyz+h4up7zhDvbdu2sWHDBjZt2sRLL71kincOMAXc5Ays0kohk+9darNZWbKklr6+Xjo7566IWyMTW+XZu0Ultnc+U7WGX12yG9aMH99eYsl+Ic3Z6BKeSl7C0dr7xh1bdvltrH/wcbzzFp/x+ve//33uuOMOfvCDH/Ctb30Lq9V8+M8F5qdocg7Fsppu0TDp/dhsNmprl1BXV4cQYs55XDahoGiZN9Y9m75tKkZqPr6LjpFRR4hsEJC86xUc796OGKPh8pKYn9fJvi7K2Ujg2ehqPn5RIeuVkVeeFqy8gqL115/zeiqV4gtf+AL19fXU1NRM2haTU5gCbnIORUY55GhNjt1uY8mS2pMiPpcqxzmFlZFbKmROeFcSI1VJ4Io2ZI57GWmFrThu6IAXR58sXtzqg3k5POiCy1h61cfGH3caiUQCl8tlivcUYIZQTM6hSMutyNrtdmqXLKGzq4vu7sl7g9OFe5JzAScY2K8SerUcMZnZ4VGIXf97pH/0OH1VkxuHkrvLvNCffV34ZDKJ0zl76smfT5gCbnIOft2LbaL5Z6PgsNtZUltLZ2cnPT29Od33CSRkvGw7ExwTWGwzGpHDKXpfLEGM0Yk9UzRh41BgPa0NQQ5tbaNz07ZRx1qkoNY1+Tj4CQp82d+ETnjgJrnHFHCTcxAoFMmFOd+vw+GgdkktHR3t9PbmTsQt0ok/cRWJhk/SO5A7oXDmuKt8rClN96+LETJ7G0+I9lPz7uYfqj/Oj4JrafGUYKQ1jtk2k1o6eiGvJenclRfItQcejUZ55plnePfddydr2gWJGQM3OQOJoD88j4BYSLsr9xeV0+EYmtisrwMhKCwYf1n4aHiMKvT+9TS1FZPWcl9Iy6FNQQ738TSdzxdQekc/Uoy9QEgTNo4EVrHPs4B3bT7UszJatq9YxY0730QIOFzzJKuaPodQz7W5tscPebmxfyIeeDKZPMMDHxwc5IUXXuCZZ57hlVdeYcOGDRw6dIi3336bysrRu1SZnIsp4CYnMaSF/lAFybiOz1UFU/TU63Q6WFJbS1390IrNgvz8jLcVWPGr6+nrWsqhPjdT2WrHnp6iFMBOjWRLAY7qcwU8rdg4mje6aJ9Op9+HrF6IaG4gQTe9l+yh6LVzu+AsavDAOib9UTltFjyO7CUjkUhgGAY//elPeeaZZ9iyZQtXXnkld999Nz/84Q/Jz8/n4Ycf5p577uG1117D4Zh8mOlCwRRwEwB0aSPcW0YqOTQhVtC7nKvSf0u/t4FeZz3dlkNoJHJ2PKfTSe3iWurr6xEI8vPHrinulIVYI5toaZ1Hizo9f7b21NTt2zitlklasXEkbzX7PAt4z+YdU7TPpn7lWpY2D6V8Nrp+jbxWR5Hnfj73lg6SUkZOZZQIktKOtFpJaAqDmoXBlGBQFURUg9TwHGmBzz6heiU2m436+nqeffZZ7rnnHp544gkCgcAZY77yla+wY8cOPv/5z/Poo49mfYwLlYyvBCGEBdgFtEkpbxVD3+Q3gHsAHXhUSvlvU2OmyVSi6w5CvcWkU6eyGRRpxd9fg7+/hireh8Qg7utiwNtEn6ueHuth4mJycWyXy8ni2sUcqa9HCAie1RhCwYY3tZxoaBlHOwM5naDMBLuauw41Z5NMKjQFNwx72t4J96DcUV3Ncq8PIxoBAU3O3444rjKDPg1Or4dl3l6sRuJkDRUpQVVcRBU/aauPxLbDWAqqseRXoeRXIiyjZ+oYiQG0lt1sWHUx0Wh0zMU7Qggef/xxNm3axOOPP879Zv+0jMjGlflr4BBwYkr7fqASWCqlNIQQcyfB1+Qkmu6mr7sAfZwFKwIFT6QMT6SMci4DQHX2M+hvJuQ+Qq+tnrDSzFAuSOa4XS4WL66l/kj9UEw8rwxPagWx/hpauwOktZl7SLQlp07Aj4pSng4snfR+DEUhtHwNgbcn3xUoGY1x1FZGra0FhaGbuRDglAmcegL0LtJNR0k3vTW0gVBQ8sqxFMwf/qlGyStHxkOoB18m3bgdDA31wO9wbvoIVK0f8/h+v5/nnnuOa665hjVr1rBu3bpJn9P5TkZXhxBiHnAL8E3gi8Mv/ylwn5TSAJBSTkNXV5NcktZ89HXnYegTWyXoSAYoSq6liLUsATRLgmjgGGF3I72OOnos9RiMH4fwu4pYWbyG3/9PK/OX3IrfP/GJzVxii2dXByWrfccmt0DodLatWMnNO9/MSU/LSLif5uIF1Mgj41d2lAZGfytGfyvphjeHXlOsYOicfiOXyUESrz9GunIdzk0fQXGNPqO6YsUKHnnkEe6880527dpFwSQmuS8EMnVv/gX4CnB6PtJC4MNCiDuAHuCvpJTnFkM2mZWk0kFC3R4MI3deplV3EehbQqBvCTXchCHSxH2dhH0NhJxH6bYcQhVDz/J2mYczuZKB0Hyae3wYUmHBihhHjxyhttY9K/KGrfEcL38/DVssd017u3w+jOpFKE25ufz6uvtwVCyhIl2X/cbG6KtNteN7iPe14L7xwTFF/N5772XHjh187GMf48UXXzTrhI/BuAIuhLgV6JZS7hZCXHPaWw4gKaXcKIS4E/gxcOUI2z8APABQVTX5IkkmkyeZKiDc7UTmsAv5SCjShnewEu9gJZVcg0SSdPfxjqWF/d0G8qzmBh63B6fThaalmbIUmCzIthJhdvvObdf1+pVrWDos4Bp5aKIAm9GBRUxs4rmtrRvrvKWUpCbfled0jHiIxJZHcN/wIEIZPfXz29/+Nh6Ph0gkgt+fu4VI5xuZeOCXAx8SQtwMOAG/EOJnQCvw7PCY54GfjLSxlPIx4DGAjRs3Tq1imIxLQi0m3J2bJeLZIhC44oVEjAhShkYck9bS2GwzY9/ZWKO5C3Ocs+9IbgV8x/z55Ns+RFNDFcePuZBSgJR4fAbBYAq/L47PPYjbHsZp6cYuOrAafSjizKcMKSGurKKl9yL+96187r/+tyz2vJ5TWy1ly8cUbxiqGx4IBEzxHodxBVxK+VXgqwDDHviXpJQfF0J8G7iOIc/7aqB+Cu00yQHxeBn9fTO/+DYxxmN2OjV7BFwtduBqzV3q5OlYcuyBG4rCkYLltL96miALQSxqIRZ1MfREUwCcVlBKSlxuicer43GncLlS9IU89HSfWqzzsy038VcfaKPA1pgTO23zN+JYc9u4444cOcLixYvHHXehM5mr+dvAXUKId4H/C3w6NyaZ5BopIRKtmBXiDRDXR57YNAwDKSUWy+xYntC7aOoKMClSYs9xCKvzOoOSdQreIpHZhKYQJBIKvT02Wlo8HD4cPEO8ARJJhcffup+k7s2JjfYVN2YU0zYFPDOyulKklFuALcO/9zOUmWIyi5ESIpF5RAdmT/Qqqo/sfabTaWy22SHeAO35GlO5sNupQyqHpztYKBn82lDc3pqUBDsUPG1gaxXIVki0SSIdcihJJAu6uu08dfAv+KOV3zkn5JItyR0/x33tXyAcnjHHNTU1UVhYOKljXQjMDpfMZEqQCAYGKmeVeCNAHyVXfDbFvwFalSiGbeouEYc+dd+L5hT01Bg0X2Fw5CM6R7+k0/bPBpGfG5Rdn/05HawPsLnjU5O2S+9rIvbK9zASY68suu+++3jiiSfYs2fPpI95PmMK+HmKIRXCfZXEI1OXCjchxnh8nk3xbwBNGgwsyU3oYCQcE8y/nwzSImj4lEb+ouxT817etpD9gzdO2gajv434S9/FiI08kQ1D+eDf//73ueuuuwiFRh93oWMK+HmIIa2Ee+eRnMKFKBNFjvEXN9s8cICeytxXOTyBYxLt2iaDYReEv2QwRir2yAjB03+4io7U8snbEO0huevpMcd8+MMf5vbbb+djH/sYhjHLHJFZging5xm6Yaevpxw1OfvEG2CM9o3DMfDZJeDHPckp27cjPXOiFCkA6xcFIksFSGsKv9x/d05s0Fr3oXWNnbz28MMPE4/H+frXv56TY55vmAJ+HqHpLvq6S0irs1O8AYwxaqVo6TTWWSbgvTJOKn9qypvOpIADdCw3KPnj7CWgucVFVM/NBKP6zrMMV+MYEZvNxtNPP82PfvQjfvOb3+TkmOcTpoCfJ6R1L33dBWgzLArjoY/jgdtnmYADhJZMzapQu5rbJscTwXZ9go9+epCy8sxtkQjqI5fl5Ph6qAWteeeYY0pLS3nyySf5xCc+QXe3WXLpdGZPzpbJhEmlA4S6vTmtazJV6Ix+g0nPQg8coLNEUjoF+7WnZuhJSUquSyS5NtxPXiwCNbDqzwWDcS979ubx6ksu0umxfbvuaCkExhySMere/8VauQ5hHbnbTzQa5dvf/jaXXnqpuTLzLEwBn+OoqQJC01DXJFdoYnQ70+k0NuvsE/Bj1ihryH3vH3tyej1wh2FwWyTOpnAIR+rs2L7E745w9WURrrjURt2RAl76nYdYTJBODf3I0z6BmJq7ptdGPESq7lUcK87NcOns7OSWW25h/fr1PProo2PWFL8QMT+NOUxSLSLUnX2PwplEG9UDl6TT2uyaxJQSZ4efgXcVnowbbCywsDARwTrGU0Q22JNTV2vldIo1nbsGItT2h7Do4980LCLN8tpOltee/c6QiEsphrJBj5RCpDMnNqbeexHbwstRnKcKnh46dIibb76ZT3/603zta18zqxKOgCngc5R4opT+3qlLcZsq0nLksIGmaVgslllxkdr7XaQOOmncFaU/dGrByf4GcDosbFqUx1qPQXl8EGUS9tqm2ANfnkrzofAAZYP9iDEmCjNHDkn48CkbtTehvPcsqIOT37Omktr/G5wXfRSArVu3cs899/Dd736XT3ziE5Pe//mKKeBzDCkhFq9gcI6ubUiN4r2m02ls9pnzvkVawXI4j9ZdSdpbYkBsxHFJVef1AyFeBwoCDi6p9rLKkiR4TkhifGyJKfDAz45vTyGGBFbeidj7JGKU+jbZkDqyFduSa3mn/jh33303Tz75JO973/smb+h5zJwQcKHYsfvXZrWNlGmkHkNqMQw9hjQSOelYMpPMxrom2aKO4oHPZPxbpBX6nnXTfLgvq+36+lVe2KvyArCwwsumUjtL9RiuDMIUALZY7romOwyD2yNxNo4Y3546DCmIzf8Q6Y46rELDwtCPK9WJ0LKt5ChJv/M2aUchRUVFpnhnwNwQcGHBYp9c3qmUBlJPIPVhQT/tX3LyeDm1DNU1mTf7lsZnSUqOLG4ztYhnSLxdNB+eXBigoS1KQxsoimDNgjw25CvUjBEvl8DBy6sndUyAEk3nzizi27lEImhsifLeO8c52QV5mMqiYjZ5diNG+b5HQkgPljfLuPijtQwODnLgwAFWrFiRY6vPL+aEgOcCIRSE1QNWD6dHjqWUSCM55K2fJe5jtYeaTgypMBCeRyI2exfoZEpyFgn4KfHOXajBMCR7jvazB3A7rWxamMdat05pInJGvLzxjtXsL554fnmpVLk73MPivmSO4tvZs3t3J23HRn5qOd4Dftc6lrIz4/Qdh3oLxCXy963cfdfd/OIXvzAFfBwuGAEfDSEEwuICiws45eVLKUGmhgRdOyXsUo8hjdw9+o6HYVgJh8pRE3NfvAHmby0nb9+5WdX79u3D7XaPWwPa8Bi8fesBBl2Ta7Qg0gqhZ92T9rzHIp7UeO1AiNeA4nwnF8/3sFIkURb4+f2lFRPa50IjwuWR31M9+GsUKVHdl+LUrgR1+rOR0umx/yYPHFMI1K6hVN037r4UsQjROFQ4TLbG+MuFt/OfT/0E/bqOoQG6hIEUYkMhSuXUFRiba1zwAj4aQggQDiyKA2z5Z7wnjfSwlx4f/jc6LO65jT3qhp1wbympWbw0Plu0egVt/7kxfGejn7xgAC0xnrtm4eIjqzn0QAOthb0TNEIQes5N0xSK99l0h5L8OpTk18AivFj6VLTCzBpGCCSrtV4uGXiOsvibp79B3NhOXNmO070Gt3E9JHOXnz0ebu/49r9Z7+ADyxbji4/RcFmCvfuK0xvZM6/Dxb3BK4j98ggu56njKB4rmAJ+ElPAJ4BQbAglgGI7cymalDpSj5/hrQ/9Hs96AlU3nPR1F6GN4+XMNbTBUWqBZxFC0XthyfcWEvgTL+8tbM7SAEH4WQ9Nh6ZPvM/m6DtduP90Kwu/sJbQZcWjjrMi2ZQ6xkX9/4+genDMfSblPpJiH3b3IrzyJkhkW2owe1yuTL4vweb6IDcvLseeaB9xhI3rED3KWVtBMBgkHA7hKis/+bpsiU7C4vMPU8BziBAWhNWHYvWd8Xq2E6ia7qGvOx99hsqNTiXaKBk06XQaaxZZKDIJRY+UcNmHPWy76EBmcVZN0P/czIr3CeIRlXcf2sGymxYQe2AJuuvUpejG4LLkIdaHf4pbG1n0RiMljxLi37G4K/DzQUSikDHqh00KtyezIl+6Lni1uZL3V8awpM5s5CCEH2tDDSMZGcwP0tLcQvnpAn48itQNhMUs4wSmgE8LmU2gxjH0KFpKI9TpRNfOL8/7BOlRBFybQC1wATif9nJdxwa23roXzTrGZ6YJBp730nhw7E4w082hFxsp3N9D0YPrsSx2cWViNyv7f4pdn9zEqi7bCPMfKK58/NyGkqwgRwtIT5KZBz5ELAnbe5dyeXDPGTnjjvhNkBz5b8Lj8aLrOolk8lQYJW0g2+MIM4wCmAI+o5w5gTqEHagKSrRUmlRSJZVQURNJUgmVtDp9k6dThR6TnO0u6/qJZsYTXFn6/7d3p9FxnXWex7//e2tTLVKVVNol24pteU2cxIoJxImdhOzBhAa605mwQ+jpAwdOQ9Pk9JneGGZgmGk43dOQk+luSOjQcJhOgElDGtKxHUiTBMd4txPvtixrX0pVpdqfeVElW5ZLVqlUm6znc46OS1W37n18q/Srp577LC9buL2vi1c/uI/xLBc3JSmMPOuquPCeJLEJNh96lo3OlzHn0O0uFyk1zCjfRhxuPLINa+QaZuiKP2emOcMIrFUAACAASURBVLdRqH0jcMC5gWstuwCFKauRkzOvjXnFZhQd4IAO8IokIljtNqx2G66ai80xqWTqQqjHJiLEIulwT5Vhaa58iBKytXVMNp/MZxR98k24+RsbOPSJo5zzX+zaJsl0m/eJQ+VvNpnOW2Py0EPDbLr5JayWRNGaOgCUChJQ3wOblRrjUcxQfr1gppI8pvd665yBd8UNtMd+i63v7bP+n30+H6dPT2tGOT0Om4sxP+TCowN8ATFMA4erCofrYo1dKUUinkgH+kQ0HfDhCPForJh5kJ8Z1lMrVB/w5IBi1VdWsMa5EkQhpqBcir+L7Jr3vgvJ7TbZti3ALbe8iM1W6m9VcZIMYDL/ADesQjqB5xbkrx+z0ND2XqoGZ3+ey315M4o6FUQpVRHz5pSbDvAFTkSw2qxYbdZLa+upFNHQBMGRAKGx8cpoU59hPbV82r9nPgakgpMfXQpG4YOrN/CtnvKHuKPK4MH7w9y29SWqHNnnWikFi6ovyH4i9ghr72znzZd7SM5hIRGPzUn1SS+5NMpnbUYJxWEoCjl2w7ya6QC/ShmGQZXHRZXHhV81MTEeJjQaIDQ6TjJZpjBPTtbYLhUr8mr0zsNWtq5eyo7zp4t2jCux2QzuvSfKHe98CZez/E05hirASgyGyVBwGGVRrLqzkbO7xhgfDOf01NuqVmAM5x74dXV1vPnWm4yOjOJ2u3G73cgbp2i4Z3W+pb9q5BzgImICu4BzSqkHp9z/t8BHlFL6qkKFEhGc1S6c1S787U1MjIcu1MxL2n4+Q4AXtAaehQjcdK6Ffc5+hiPzG8E5F6YpvPOdCe6++yWqq+c2UVZRJea/RFzUFFTmtUyoCK1dTsZOVHH+rSv/P6+pbsR3bm7vOafTyfUbriccDjMeDDI8MsKz/+U5nnzsBTZv3nzhZ926dRjG4upeOJca+GeAw8CFNY1EpIuCLayklUI6zN04q92kUikmxkOERsbTYZ4qbpirePYmlHg8jstV3M9/FYRHG6/lbyZeL/zSOtMYAlu3wr33vYzPV5gFDwpGAQmT+V4xHYld2s0xpZJ4OsC3pJXuPaMEBi9vIjIQusKNwNy/AYoILpcLl8sFjY18Ye1KPun5KGf7z3H8P07xg3/8a1ZtuZ5Hv/JpxLJ4QjynABeRNuAB4MvAH2XuM4GvAY8A7ylWAbXiMQwDV40HV40nHeaBEMHRAOGxYFHCPD4yc4BbrcVvzbMdN3lg7Ur+tfsKw7rnQQRueYfwwIO/xu8/W5RjzJfLvKsg0yqPR7L3U0+YYZo22miN+ji1e4CJQPTCY2+vXYHtTIGa76IpaqJCjdHG+vY21kabqAvVkvjKHoxNDRh3tiyKwT65/tV8A/gCMHWI4aeAnyilzuurwQufYRi4vB5c3nSYh8eC6TAPBFEFWCxZYlZOfP3yPs7j40FCoRBLly6d9zFyseaEn92+Xs4XeLGDm24y2bbtdZqaThR0v4VkM67BPvG2guzLNC0krnBhPGkPseQdblTQz8ldfVSlrCw7b6cYfSWVUgSD4yxbtgzCCVI7epA6O7KxMBdrK9msAS4iDwL9Sqk3RGRr5r4W4P3A1hye/xjwGMCSJUvmU1atRAzDwO2rxu2rJpVMEQ4ECY5kwjyP2psog1P/U1DRS58biUQ4ceI4HR0d2Gwlmk0vAg/b1vH14KsFaUrZcK2Vh35nN21tR+a/syISqvDEHi7Q1PeCmZx90JVSKXCFWb7Vi3+sDrU9lVff8dmEwiHsNjvWqQseJyuuE21R5FIDvwXYJiL3Aw7SbeAHgShwLFP7dorIMaXUiulPVko9CTwJ0NXVtTjO6lXEMKeGeZLQWJDQaIBwIJRbmCvo+2crE9O+OicSCY4ePUZLSws1NcWfeGkq87Tw/nVr+eHZK08QdSWdS/zc2bycDfdux6zw8EaBjz9AxQvTpKD6LDiSdsLe3HqdpFSS/up+xh6y03a6jtrfqIIGeSAwjqe6+pL7lA7wNKXU48DjAJka+Oen9kLJ3B/MFt7a1cUwTTy1NXhqa0gmkoTHxgmOBpgYD83YrBp6vYrhly9d+zGVSnHs2DF8Pi/19eX5mrv0zRpWNNdybGxui4te0+rjztYVNE94kQkhOboUs61IhSwQV+B3weamMM0XBuM/HcS63AJdc3tmVEU5vqSH3iUu2g7XUJP/5+clxgMBmpqnjcwcL8J6oxVI9wPX8mJaTDx1Xjx1XpKJJKHRdJhHxkMXYiLV6+Dsty/9Q1JKcerUKaxWK62t8x8NmLcEvDu5im/IqyRz+CbR3ljDXctW0hb2IRG50PwSH26g9Esp5M56rouJZ2qJ1QZwbfFhtoKS/OdbSZ4RUuNxjAMJzJsMknm0yYQI8eaaEDWramj/rRvnqfw/WJKpFKFwGLf70hlAU7/qxdhQhzTOv8tkJZtTgCuldgA7styv+4AvYqbFpNrvpdrvJRlPEBobZ/x8iH3//fI+1z09PUSjMVat6iz7UGjpgUfWXst3u/fNuE2z38Ndy1eyNFyHMSGXtZvHhr3MPB1TeRnBZlLPbgKVIjkUJfBsL4jg3OTFdl0VVM1tGL8ok+DPMotoJBSuqJuALf+BSWPGGGMbx/BfW0vra1XY++cW5Ilkku6zZ3G7XJjT+38nUiS+dxRzcxOyogbx5Tb17UKja+BaQZlWC9V+H9V+H3XPJeh9KUjPi0FG9k4wODDI8PAwq1evrpgBF42HXVy7tIH9w/2X3O/3Obl75UqWR+oxJowZL3hGB6tQinlNxFUUCTuWH72HRHhaDVkpwq+NEH5tBEujHedtPswmhZLZu/fFjyrUlNWh7D0GLJt/UQdtwwzdJjQG62n+lQVrDms2DA8Pc7b7LD6vj2uWL8++0UCE5HOn0rf9DowVNcjKaqSjGnHkOfNlhdEBrhWNo87Csvd7WfZ+Lz//l5fY94FDvH3FlrKsPj8jBfeGlvOWOUw0mcBX7eCuVSvpjDZiRmb/kElMGBBtAkdlDdhx7H6I2CwjHhN9UQI/7AVDqOryYjivHGqhlwcu+d3cGytIgEO6aa3X1c/AvSad+xrxvJV9u2g0yukzp4nHE6xYviI9sCcXgxFSgxF4tQ8MQZa6SGyMYbTXYfW3IlIZFYq50gGuFd2hQ4d49D8/zLfufB5Hf+VNQKQG4CPrN9DXMMG6eDNmdG61MxVYjVRQgBuhBuK/rCXni5YpxcTrI3M+jgzEcagqIlK46QmSKknUp/BM+8qjlKK3r5e+vj6ampppbGjIrwnOgMTSCCHHKyT298N+ELsTe8tK7K2d2FpWYlYtnBZhHeBaUQUCAe6/736+9rWv0fxKMyP9hV34uVD8Q04avXV5PTc5ugxj5qUtS8726weJJXJvT1ZKEYvGsNpsGMbcQtE5bCdSV9z5ZYLBIKdPn8Zmt7NmzVrs+YwZsAuJ1gmC9tdJpoYvmQhRRcNETu4lcnIvAJa6FuwtndhbO7HWL0EqpLkvGx3gWlFZrVaW1l/DgeePs6Lm9nIXZ0aJYIp8W0UTIy1USqOQZWg1sTeuHHDxeJxgMEQoFCQYDDERDmNaLCQScSwWC3a7fcqPA7vdjsNux7RcfoZs/xHDss1CosArCQEkkgm6u88RGBujfUk7Pq8v5+cql5CqixF3jhCTM8ST3enrGDl0mkkM9ZAY6iG0fwditWNrWYG9dRX2lpWYrtKOWZiNDnCtqKqqqvh/L/2I37/9w7QO7GVF06qy9z7JJhFMYctzkYDYUC2V0FlNKTB3bL0ko5RShMMThIJBgqEQoWCQZCqJy+XG7XbR0tKCy+XCNA2UgngsRjQaJRKNEo1GGRkZIRqNEI1GEZFp4W7HPu7A9ZKbwO0XZyecr7gjxfnQEH3Hz1Prq2XdunWzLrenfELSO0HcMUhUnSKpMr1lFPmsOXFxv/Eo0dMHiZ4+CIDF24C9dRW21k5sDUsRs7wRqgNcK7rq6mp+sPNpHnnnh1HdH2Rly+rKC3EFpgH5TPsSHayMjoT2s7cQPhwnFAwRDAUJBUOEw2Fsdjtut4ua6mpaWppxOLJfhxABm92GzW7Dg+eyxxOJBNHIxXAfHw8yODhI9ESUQ6+eo35by4VgdzguhrzNZs/59Y5GI/z78C9J3pRg6XuXIW4nIRXGmrBgjVuwRkxsE4JjVLDHQ8RtvURSJ1Eq03WlyLMjJ0b7SYz2Ezr4S8Rixda0PN123tqJxVNb3INnoQNcKwm32833X3qaR+7+MKmTj7CqdW3FhbhhCqk8WgJiYyYq7kKspV1lRylFeGKCUCjI+FiY3v+6Fk/0IC6XC7fLTXNLCy6XM//FoqexWCxY3BZc7ss/sK5NXsuAI8lw3QjRaJSJ8AQjI6NEo1Hi8RhWqy1ruNvtdgzDRKkUvb299PX109zcTEPmImVURYkSTSeVBagCfOBrtVK7/5V8ZqYtGJWIE+0+QrQ7PZWCWV13oe3c1nQNYil+w5oOcK1knE4n33/xaR697yOk3nofq1uunfNFs2LKvyiCCq5CfLsLWZzLxBOX1q5D4TB2mw2X24X98H3ctGxTunZdhlNqmAYN2wX3B5YS8F46uEcpRSyWrrVHp9TeJ2+bpokIOJ0u1q5dm9PEZkaqApYInCYZGCIc+DXhI7/GcHlpeN8Xin5MHeBaSTkcDp554Tt84F0fJXkgyZrma7FYKuNtKCr/xtLU6HKMIgb48RPHCQQCF9qum5qb0yMQTRPiLizP306qqoSrK2UhCM4fREk94iZouTgaJ9127sBuz9Z0o4jH4yQSSaqqcr+SYFZggF+iROWrjL8cbVGx2+380/Pf5rMf+xy/ePaHrHN10enYgNdRi9vtLt9An3nkX3KkHUtH4YoylVKKsbExNmzYgGlc3hzieONdxILlDe9JEkvhfiqKfZuPofpc+pYLVquNub7kRqL8k1UlEgnOnTuHy+XC7/eXpQw6wLWysNlsfPO7f0vi2wl2797Nzu07eeVnB+n/TZA1zhtZVXUDXnstHo+ndIGezH9MfGzYT7Fm24hEItistqzhbYQbib/ioxgLJeRLFNh+HKTxhhqGukJF6WJYzgBXSjEwMEBPTw9VVVXEYjEd4NriZLFY2LRpE5s2bYI/gWQyyZ49e9i5fScvvLCf3tcCdNqvZ43zRrw2Px6Pu2iLP6i4It+pBWND1bNvlKfwRBin05n1MeuvHiA+h0E7pWT8Noy/20bgQTths7AXeCU+t4m4CiUYDHLmzBkM06CzsxOLxcKhQ4dQeXZBnS8d4FpFMU2TjRs3snHjRvh8OtD379/Pzh07+fnPnuX8r0dZbl3PWmcXtfZ63G4Pdvv8A10p6O/px91Wn1ebfHTIhlIGIoVvygiHw1RlCXDL4Briv63kyWzTw+2rnwLHe3wM+2ZuUhEBu8WOFUFUEkml0u3IqSQkE6hkHJWIo5IxVCJGWEzsDe0w0J25dlFc8Xic7u5uAoEA7e1t+Hy1iMiFRU3i8XjpVpWaQge4VtFM0+T666/n+uuvh8+mF4M4dOgQO7bv4MUXfkz3K8NcI+tY5+mi1tqAx+PJ9DvO/RgTExMcGtjD9t4fUnOogXvW/B7t/g7cHje5XtRMJQUVWo64C79gcjgcpqnp0gULlAJj+5Zid3suCEmB/V+CNL6thsHrgiRVErvFhlUMzEQcNREgGRoGlcr5/xNTKfoMsDW34ktI0YJcKUVffx+953vx+/2sX7/+km6ZIoLT6Uz3t9cBrmlXZhgG69evZ/369fDp9B/YkSNH2LljJ9t/9jxnfzXEktQq1ldvos7ShMfjxm53ZA30ZDJJ98BZfjT697zrLzfzi089x/j4ON99+rt88+m/Z63r7dy09Hb8tX7MHGrlaqwTChzgCgiHJy5rQrGf2UyictdPzsp4LUzDCTuxm0aI2E6jgPm2jsdS8YtBHgcZ6C5EUYH0PD5nzpzBZrOxevXqGQdAuVwuQqEQXq+3YMfOleSzSG2+urq61K5du0p2PG3xUUpx9OhRdu7Yyav/9gand/bTEl/OdTU347c043Z7cDjsDA8P8+8Dz2F/YJwvff3PaWxsvGw/r7zyCt/51j8xuDvOvWsfps2/DLd75lp5w+27sa3/VkH/P9FYlCNHjrDhug0Xy5YysX3nkyQHFkL9OwtT4B0pArXHC95n3W7YqEkKZnAMFRrLax9KKU6ePEkwGKS9vR2v13vF9u2RkREGBwdZuXLlhfuMKg8Nv/t4XscHEJE3lFKzLlqna+DaVUVE6OzspLOzk0889gmUUpw4cYKdO3fyygsvcnJnH9VjjYSuOc9f/tsXufnmm2fcz+bNm9m8eTNDQ0M8/dTT/N13n+S66lvZuGQLdXV1mNPmwYgPNxZ8ebVstW/HofuIL9TwhnRvn18KvrXrGF1xBGUUrs90NBWjXwCPA/E4cJpVOJRgjYRhuC+nfYyMDBOJRFi/fn1OC49MNqGUg66Ba4vO+fPnaWhomPMQc6UUL7/8Mk898T1G9yruXft7tNYtzQwtF1ztIXwPfbZg5VRAT885AFpbMuuHxtxYnvwgqdACDvApzAYboa5zxGz51ZZzpqApHIfxKy9inUqlOHjwIMuWLcPjuXw+mKy7Voq9e/deMopU18A1rUiam5vzep6IsGXLFrZs2cLg4CDf+fZTPPfME2zwbWVj+2ZSqpbqvtsxG7fntL9kMkksHiMWm/yJMzYKAwNxzveEOXVqlLFR4Y//+BM47DEiseM4fvNuYldJeAMk+2M4B5uItRQ5wAVGql34QmNXHCU5MDCAw+HIObyhvBcydYBrWh78fj+f/+PP8bnPK7Zv387TT/xvAi8Y3HPu3dz4h29R3X7mQijHYzGisRiBAAz2x+npmeD06TGGBpOYZh1VjmY87hXU+Ttob1tKW1sbN3e109bWRk3NxfmnU8kYkRvHmHAMMnFwgNj58TKegcIwvFbGmmZYP63AoskYsaal2HqyX/1NJhOcP3+elWvXIzX1pBwOUmJipJJIIgGJOBKPoGIRVCJ+Sbv4ZICX+kKmbkLRtALp7+/nO99+ipd+/K+42vZhSh2Oqiaq3e3U1nbQlgnn9vZ22tvbqampmdfgj/hQmImDA4QPDDDx5iAqVuHzg2SRvDNCyFO4niOzUoqmuKCGehGHC5weUjY7CYuFPYcOEJckyztXzLobAWyGDYsYWDA4d+Ysp48f5/Zbb8VIxDFMGw33fTLvYubahKIDXNMKbPJvqpQj81Q8SeT4COH9/YQPDhDvy2Fp9zJLNFjobj5OdXMCi6N0OWQggDC113k8HufgwYM5z4Y4XTSa6S20Id1byGp3sW7rx/MuY8HbwEXEBHYB55RSD4rIM0AXEAdeBz6plCr/DDOaVmblGFItVpOq1X6qVvupez/EBzO184P9TBwZQsUrrHZuCjt3v0p/b7pniL/DT+OqWnwddtz1McwiNiWnLizTc9HkqMp850632WykUini8XhJJ2ObSxv4Z4DDwOSkD88Aj2Zufw/4OFDYTrCapuXF6ndi3bKU6i1LUfEkE0eHmTiQDvR4f2kXnshmwDlxIbwBBk8OMngyvQyaYRg0dDbQsMqHb4kFZ10Mo8hX6ywWC263m7GxMWpr576yjohgsViIxyowwEWkDXgA+DLwRwBKqZ9Oefx1oK0YBdQ0bX7EauJcW49zbT11rCU+ECJ8cICJA/1MvDmMSpS2dq5cJi+/8ssZH0+lUvQe6aX3SC8AFpuFpjVN1K+sxttuUOWLIUVYCKTWV8vw8HBeAR6NRkkmk9gdxZqTMrtcP9e+AXwBLl8oT0SswAdI19A1Tatw1noXNVtd1GxdRiqaYOLNoXTb+f5+kmORoh//0OgpYrHcZxNMxBJ07+2me2/6d4fbQfPaRuqWu/G2g80TL0izldfr5czZMyQSiTlPaNbf34+/zl+w5etyNWspReRBoF8p9YaIbM2yyTeBl5VSWT9SReQx4DGAJUuWzKOomqYVmmG34LquEdd1jSilOPelXxane6KCuM/kTKCXfXv3zWtXkWCEk6+f5uTr6d/ddR6a1tRTt9xJTWsKqzO/bxSmxcTj8TA6Ojqn+b2TySRDQ0OsWbMmr+PORy4fM7cA20TkfsABVIvIPymlHhWRPwfqgRn7yyilngSehHQvlAKUWdO0IhCR9DwlBaScJkNmiL2HD9D/Rm5D2ecqODTOsV+Nc+xX6d+9LV4a19RT1+GgujmBac994FNtbS2Dg4NzCvDh4WHcbjd2e2mbTyCHAFdKPQ48DpCpgX8+E94fB+4B7lRKXT1DwzRNmx9DCNcojvac5NCuQyU//GjPKKM9o0D6Q8nf4adhVS31nVZc9VfuKFdTU8Pp06dz7k2ilKK/v5/29vaClH2u5nNt9wngNPDrTPvTs0qpvypIqTRNW3CSNRbOTQzw2717CIfKM7nTdEopBk4MMHBiAHlB6HpkLQ1rZp7E1jRNampqGB0Zpb6hftb9j4+Pg2JOQ+8LaU4BrpTaAezI3NbD8DVtsXOYjNgjHHjrEN27SjiiMg9KKX7zzEE2PLSG1o3JGS981tbW0tfXl1OA9/f309DQUJa+/6DnQtE0ba5EiPiE4/1nOPgf+0mmFlYL6t4fHSYWXEnHbWTtjlhdXc3JkyeJxWJXHJUZjUYJBoN0dHQUs7hXpANc07ScpKotnI+P8Nu9exgPBMpdnHk5/OJREvHldN51eQQahoHP56Onp4e2trYZuxQODAxk5oUvbdfBqXSAa5o2M5tJwBnj4IkjnNp1qtylKajB4yN03pW9maSlpYVz586xf/9+6urqaGxsvKSXSSqZYnBwsCxdB6fSAa5p2qUEYl6Tk8Pn2Pf6XhKJ+a5cufDYbDY6OjqIxWL09/dz+PBhPB4PjY2NuN1uhoaHcLvK03VwKh3gmqZdcF7G+M2JVxkZuvLKNYuFzWajra2N5uZmhgaHOHnyJBaLhUQiwdKlS8tdPB3gmqZdtPfgvkUV3kaOs2QZhoWm5hYam5sZGhomEBijxls745rMUuzZtzJ0gGuatugYpskdn34vq++4odxFmZfZl1zWNE27ihimyXv+20cXfHiDDnBN0xaRqym8QQe4pmmLxNUW3qADXNO0ReBqDG/QAa5p2iKw+WP3XXXhDTrANU1bBKpqXOUuQlHoANc0TVugdIBrmqYtUHogj6ZpFcs0DaxVVqwOGxa7BavDis1hxWKzYLFnfqwWLDbz4n1WA9NqXvixWE0aO2ef23sh0gGuaVpZ3frBd7DutnZEQAQQhVDY5XOrmqsLur9KoQNc07SyWXfHGq67vRVYWItCVArdBq5pWlm0rm1hy6PXlrsYC5qugWuaVnSm1UJzZyP1y/zUtnvxNrqpX+JGRNe850MHuKZpBVHdWE3zykb8S2rxttRQXeekqtqK3WFgGNmCWof3fOkA1zQtPwI3PHg9G+5agdNjXOHCow7qYtEBrmnanNiddm559G2svKkZi2UynAvba0TLjQ5wTdNyUre0jlsffRsty92IKHTNuvxyDnARMYFdwDml1IMi0gF8H6gFdgMfUErFilNMTdPKZeXbV7Dpd67F65+MC13brhRzqYF/BjgMTPaI/yrwdaXU90XkCeBjwLcKXD5N08pADGHTe7tYf3sHjiod2JUqpwAXkTbgAeDLwB+JiAB3AI9kNnkK+At0gGvaglblcXDPp++g4/p6TCOFrm1Xtlxr4N8AvgB4Mr/XAaNKqUTm926gtcBl0zStxN71uVtJRYPo9u2FYdaRmCLyINCvlHpj6t1ZNs36US0ij4nILhHZNTAwkGcxNU0rBcn2l61VrFyG0t8CbBORU6QvWt5BukbuFZHJGnwb0JPtyUqpJ5VSXUqprvr6q3NGME3TtHKYNcCVUo8rpdqUUsuAh4GXlFL/CdgOvC+z2YeAHxetlJqmadpl5jOZ1Z+QvqB5jHSb+D8UpkiapmlaLuY0kEcptQPYkbl9AthU+CJpmqZpudDTyWqapi1QOsA1TdMWKB3gmqZpC5QOcE3TtAVKB7imadoCpQNc0zRtgdIBrmmatkDpANc0TVugdIBrmqYtUDrANU3TFii9JqamaReIacGwWMtdjIIT4+qsq+oA1zTtAnfHDeUugjYHV+fHkqZp2iKgA1zTNG2B0gGuaZq2QOkA1zRNW6B0gGuapi1QOsA1TdMWKB3gmqZpC5QOcE3TtAVKB7imadoCJUqp0h1MZAA4XbIDXpkfGCx3IWahyzh/lV4+0GUshEovH8ytjEuVUvWzbVTSAK8kIrJLKdVV7nJciS7j/FV6+UCXsRAqvXxQnDLqJhRN07QFSge4pmnaArWYA/zJchcgB7qM81fp5QNdxkKo9PJBEcq4aNvANU3TFrrFXAPXNE1b0BZNgIvID0RkT+bnlIjsmWG7UyKyP7PdrhKX8S9E5NyUct4/w3b3isibInJMRL5YwvJ9TUSOiMg+EXlORLwzbFfyczjbORERe+Y9cExEXhORZaUo15Tjt4vIdhE5LCIHReQzWbbZKiJjU17/PytxGa/4ukna32TO4T4RubHE5Vs15dzsEZGAiHx22jYlP4ci8o8i0i8iB6bcVysivxCRo5l/fTM890OZbY6KyIfmfHCl1KL7Af4X8GczPHYK8JepXH8BfH6WbUzgOHANYAP2AmtLVL67AUvm9leBr1bCOczlnAB/CDyRuf0w8IMSv7bNwI2Z2x7grSxl3Ao8X473Xi6vG3A/8DNAgJuB18pYVhPoJd1fuqznELgNuBE4MOW+/wF8MXP7i9n+VoBa4ETmX1/mtm8ux140NfBJIiLA7wL/XO6y5GkTcEwpdUIpFQO+D7y7FAdWSv1cKZXI/Poq0FaK4+Ygl3PybuCpzO3/C9yZeS+UhFLqvFJqd+b2OHAYaC3V8Qvk3cDTKu1VwCsizWUqy53AcaVU2QcGKqVeBoan3T31/fYU8FCWp94D/EIpNayUGgF+Adw7XIRA0wAAAzJJREFUl2MvugAHbgX6lFJHZ3hcAT8XkTdE5LESlmvSpzJfT/9xhq9drcDZKb93U54g+Cjp2lg2pT6HuZyTC9tkPoTGgLoSlO0ymeabG4DXsjz8dhHZKyI/E5F1JS3Y7K9bpbz3IP0taqZKWDnP4aRGpdR5SH94Aw1Ztpn3+byqFjUWkReBpiwP/alS6seZ27/PlWvftyilekSkAfiFiBzJfMIWvYzAt4Avkf5D+hLppp6PTt9FlucWrCtRLudQRP4USADPzLCbop7DLHI5J0U9b7kSETfwL8BnlVKBaQ/vJt0kEMxc//gRsLKExZvtdauUc2gDtgGPZ3m43OdwLuZ9Pq+qAFdKvfNKj4uIBfgdYOMV9tGT+bdfRJ4j/fW8YOEzWxknicj/AZ7P8lA30D7l9zagpwBFA3I6hx8CHgTuVJmGvCz7KOo5zCKXczK5TXfmfVDD5V97i0pErKTD+xml1LPTH58a6Eqpn4rIN0XEr5QqyRwfObxuRX3vzcF9wG6lVN/0B8p9DqfoE5FmpdT5TDNTf5Ztukm32U9qA3bM5SCLrQnlncARpVR3tgdFxCUinsnbpC/aHci2bTFMa098zwzH/g2wUkQ6MjWRh4GflKh89wJ/AmxTSoVn2KYc5zCXc/ITYPIq//uAl2b6ACqGTHv7PwCHlVJ/PcM2TZPt8iKyifTf51CJypfL6/YT4IOZ3ig3A2OTzQQlNuO36HKew2mmvt8+BPw4yzb/BtwtIr5Mc+ndmftyV8qrteX+Ab4D/MG0+1qAn2ZuX0O6B8Ne4CDpZoNSlu+7wH5gX+YN0Dy9jJnf7yfdi+F4KcsIHCPdZrcn8/PE9PKV6xxmOyfAX5H+sAFwAD/M/B9eB64p8Wu7mfTX431Tzt/9wB9MvieBT2XO2V7SF4nfUcLyZX3dppVPgL/LnOP9QFcpz2GmDE7SgVwz5b6ynkPSHybngTjpWvXHSF9f+XfgaObf2sy2XcDfT3nuRzPvyWPAR+Z6bD0SU9M0bYFabE0omqZpVw0d4JqmaQuUDnBN07QFSge4pmnaAqUDXNM0bYHSAa5pmrZA6QDXNE1boHSAa5qmLVD/H++uMBoQAdZ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1ae710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shapely.geometry import Polygon\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "france = world[world.name == \"France\"].copy()\n",
    "france.geometry = france.geometry.intersection(Polygon([(-10,41),(-10,52),(10,52),(10,41)]))\n",
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "custom_colors = [np.random.random(3) for i in range(gdf_low['volume'].count())]\n",
    "gdf_low.plot(ax=ax, color=custom_colors, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display all volumes at FL350"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba947f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "custom_colors = [np.random.random(3) for i in range(gdf_high['volume'].count())]\n",
    "gdf_high.plot(ax=ax, color=custom_colors, alpha=0.8)\n",
    "plt.savefig('fl350_volumes.svg', format=\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display the ACCs at level FL350"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b27c048>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2ac208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option('mode.chained_assignment', None)\n",
    "gdf_high['geometry'] = gdf_high.buffer(0.01)\n",
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "custom_colors = [np.random.random(3) for i in range(5)]\n",
    "gdf_high.dissolve(by='acc').plot(ax=ax, color=custom_colors, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display elementary sectors' volumes of LFBB ACC at level FL190 (FIR Bordeaux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b44edd8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b3af4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf_vlow = gdf.query('level_min <= 190 <= level_max')\n",
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "custom_colors = [np.random.random(3) for i in range(gdf_vlow['volume'].count())]\n",
    "gdf_vlow.query('acc == \"LFBB\"').dissolve(by='elementary_sector').plot(ax=ax, color=custom_colors, alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <br> Sectors' centroids and cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e79d940>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e8b4ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))\n",
    "fr_cities = cities.cx[-10:10, 41:52]\n",
    "gdf['geometry'].centroid.plot(ax=ax)\n",
    "fr_cities.plot(ax=ax, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test which volumes/sectors are above a given city (ex: Toulouse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>volume</th>\n",
       "      <th>elementary_sector</th>\n",
       "      <th>acc</th>\n",
       "      <th>level_min</th>\n",
       "      <th>level_max</th>\n",
       "      <th>geometry</th>\n",
       "      <th>surface</th>\n",
       "      <th>centroids</th>\n",
       "      <th>above_toulouse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TZ</td>\n",
       "      <td>TZ</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>0</td>\n",
       "      <td>195</td>\n",
       "      <td>POLYGON ((0.239 45.206, 0.58 44.5, 1.271 44.5,...</td>\n",
       "      <td>6.449720</td>\n",
       "      <td>POINT (-0.1279949205688178 43.76232898351121)</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>N2</td>\n",
       "      <td>N2</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>295</td>\n",
       "      <td>345</td>\n",
       "      <td>POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...</td>\n",
       "      <td>1.029352</td>\n",
       "      <td>POINT (1.194875090027859 43.13795924870804)</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>N1</td>\n",
       "      <td>N1</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>195</td>\n",
       "      <td>295</td>\n",
       "      <td>POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...</td>\n",
       "      <td>1.029352</td>\n",
       "      <td>POINT (1.194875090027859 43.13795924870804)</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>N4</td>\n",
       "      <td>N4</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>365</td>\n",
       "      <td>999</td>\n",
       "      <td>POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...</td>\n",
       "      <td>1.029352</td>\n",
       "      <td>POINT (1.194875090027859 43.13795924870804)</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>N3</td>\n",
       "      <td>N3</td>\n",
       "      <td>LFBB</td>\n",
       "      <td>345</td>\n",
       "      <td>365</td>\n",
       "      <td>POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...</td>\n",
       "      <td>1.029352</td>\n",
       "      <td>POINT (1.194875090027859 43.13795924870804)</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume elementary_sector   acc  level_min  level_max  \\\n",
       "30     TZ                TZ  LFBB          0        195   \n",
       "12     N2                N2  LFBB        295        345   \n",
       "11     N1                N1  LFBB        195        295   \n",
       "14     N4                N4  LFBB        365        999   \n",
       "13     N3                N3  LFBB        345        365   \n",
       "\n",
       "                                             geometry   surface  \\\n",
       "30  POLYGON ((0.239 45.206, 0.58 44.5, 1.271 44.5,...  6.449720   \n",
       "12  POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...  1.029352   \n",
       "11  POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...  1.029352   \n",
       "14  POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...  1.029352   \n",
       "13  POLYGON ((0.647 43.492, 1.472 43.919, 1.697 42...  1.029352   \n",
       "\n",
       "                                        centroids  above_toulouse  \n",
       "30  POINT (-0.1279949205688178 43.76232898351121)            True  \n",
       "12    POINT (1.194875090027859 43.13795924870804)            True  \n",
       "11    POINT (1.194875090027859 43.13795924870804)            True  \n",
       "14    POINT (1.194875090027859 43.13795924870804)            True  \n",
       "13    POINT (1.194875090027859 43.13795924870804)            True  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import Point\n",
    "toulouse = Point(1.433, 43,60)\n",
    "gdf['above_toulouse'] = gdf['geometry'].map(lambda x: True if x.contains(toulouse) else False)\n",
    "gdf_a = gdf.query('above_toulouse == True')\n",
    "gdf_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ede28d0>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f0384a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = france.plot(color='white', edgecolor='black', linewidth=1.0)\n",
    "custom_colors = [np.random.random(3) for i in range(gdf_a['volume'].count())]\n",
    "gdf_a.query('above_toulouse == True').plot(ax=ax, color=custom_colors, alpha=0.3)\n",
    "df_t = pd.DataFrame({'geometry':[toulouse]})\n",
    "gdf_t = gpd.GeoDataFrame(df_t, geometry='geometry')\n",
    "gdf_t.plot(ax=ax, color='red')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}