notebooks/Tree_build_query_times.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0332940b",
   "metadata": {},
   "source": [
    "# Tree build and query times, comparison\n",
    "\n",
    "Here, we compare the times to build and query KDTree and BallTree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d5a7877",
   "metadata": {},
   "outputs": [],
   "source": [
    "# solve issue with autocomplete\n",
    "%config Completer.use_jedi = False\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e44900e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from geotree import gtree\n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324fb7a9",
   "metadata": {},
   "source": [
    "## Build time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7595b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "million = 1000000\n",
    "list_npoints = [million // 100, \n",
    "                4 * million // 100, \n",
    "                million // 10, \n",
    "                4 * million // 10, \n",
    "                1*million, 5*million, \n",
    "                10*million]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8f520d",
   "metadata": {},
   "source": [
    "### KDTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00d704a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "40000\n",
      "100000\n",
      "400000\n",
      "1000000\n",
      "5000000\n",
      "10000000\n"
     ]
    }
   ],
   "source": [
    "build_times_kdt = []\n",
    "\n",
    "mytree = gtree()\n",
    "del mytree\n",
    "\n",
    "for npoints in list_npoints:\n",
    "    print(npoints)\n",
    "    mytree = gtree()\n",
    "    \n",
    "    lons = np.random.randint(-180, 180, npoints)\n",
    "    lats = np.random.randint(-90, 90, npoints)\n",
    "    depths = np.zeros(npoints)\n",
    "\n",
    "    # Add lons/lats/depths of the first set of points\n",
    "    mytree.add_lonlatdep(lons=lons, \n",
    "                         lats=lats, \n",
    "                         depths=depths)\n",
    "    \n",
    "    t1 = time.time()\n",
    "    mytree.create_kdt()\n",
    "    build_times_kdt.append(time.time() - t1)\n",
    "    del mytree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd933302",
   "metadata": {},
   "source": [
    "### BallTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd9d0b5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "40000\n",
      "100000\n",
      "400000\n",
      "1000000\n",
      "5000000\n",
      "10000000\n"
     ]
    }
   ],
   "source": [
    "build_times_ball = []\n",
    "\n",
    "mytree = gtree()\n",
    "del mytree\n",
    "\n",
    "for npoints in list_npoints:\n",
    "    print(npoints)\n",
    "    mytree = gtree()\n",
    "    \n",
    "    lons = np.random.randint(-180, 180, npoints)\n",
    "    lats = np.random.randint(-90, 90, npoints)\n",
    "    depths = np.zeros(npoints)\n",
    "\n",
    "    # Add lons/lats/depths of the first set of points\n",
    "    mytree.add_lonlatdep(lons=lons, \n",
    "                         lats=lats, \n",
    "                         depths=depths)\n",
    "    \n",
    "    t1 = time.time()\n",
    "    mytree.create_kdt()\n",
    "    build_times_ball.append(time.time() - t1)\n",
    "    del mytree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375bc31e",
   "metadata": {},
   "source": [
    "## Query time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b98ff5ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the first set of points, aka base.\n",
    "npoints = 1*million\n",
    "\n",
    "lons = np.random.randint(-180, 180, npoints)\n",
    "lats = np.random.randint(-90, 90, npoints)\n",
    "depths = np.zeros(npoints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6dbff0fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_queries = [million // 100, \n",
    "                4 * million // 100, \n",
    "                million // 10, \n",
    "                4 * million // 10, \n",
    "                1*million, 5*million, \n",
    "                10*million]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20461b5f",
   "metadata": {},
   "source": [
    "### KDTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b879d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mytree = gtree()\n",
    "\n",
    "# Add lons/lats/depths of the first set of points\n",
    "mytree.add_lonlatdep(lons=lons, \n",
    "                     lats=lats, \n",
    "                     depths=depths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "967462f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mytree.create_kdt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47186edc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "40000\n",
      "100000\n",
      "400000\n",
      "1000000\n",
      "5000000\n",
      "10000000\n"
     ]
    }
   ],
   "source": [
    "query_times_kdt = []\n",
    "\n",
    "for q_npoints in list_queries:\n",
    "    print(q_npoints)\n",
    "\n",
    "    q_lons = np.random.randint(-180, 180, q_npoints)\n",
    "    q_lats = np.random.randint(-90, 90, q_npoints)\n",
    "    q_depths = np.zeros(q_npoints)\n",
    "    \n",
    "    # Add lons/lats/depths of queries\n",
    "    mytree.add_lonlatdep_query(lons=q_lons, \n",
    "                               lats=q_lats, \n",
    "                               depths=q_depths)\n",
    "    \n",
    "    t1 = time.time()\n",
    "    mytree.query_kdt()\n",
    "    query_times_kdt.append(time.time() - t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca1cc34",
   "metadata": {},
   "source": [
    "### Ball tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19cfcf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "del mytree\n",
    "mytree = gtree()\n",
    "\n",
    "# Add lons/lats/depths of the first set of points\n",
    "mytree.add_lonlatdep(lons=lons, \n",
    "                     lats=lats, \n",
    "                     depths=depths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6cd585a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mytree.create_balltree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6106286b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "40000\n",
      "100000\n",
      "400000\n",
      "1000000\n",
      "5000000\n",
      "10000000\n"
     ]
    }
   ],
   "source": [
    "query_times_ball = []\n",
    "\n",
    "for q_npoints in list_queries:\n",
    "    print(q_npoints)\n",
    "    \n",
    "    q_lons = np.random.randint(-180, 180, q_npoints)\n",
    "    q_lats = np.random.randint(-90, 90, q_npoints)\n",
    "    q_depths = np.zeros(q_npoints)\n",
    "    \n",
    "    # Add lons/lats/depths of queries\n",
    "    mytree.add_lonlatdep_query(lons=q_lons, \n",
    "                               lats=q_lats, \n",
    "                               depths=q_depths)\n",
    "    \n",
    "    t1 = time.time()\n",
    "    mytree.query_balltree()\n",
    "    query_times_ball.append(time.time() - t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da114e46",
   "metadata": {},
   "source": [
    "## Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cea3e198",
   "metadata": {},
   "outputs": [
    {
     "data": {
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eEhgiIiIiIiIiEvCUwBAREREpZcaYl40xG40xe40xm4wxU4wxof6OS0REpDxTAkNERESk9L0ItLLW1gTOAc4GHvFrRCIiIuVcZX8HECz27NnDjh07OHToUKH9IiMjWbt2rY+iEn+pVKkSNWrUoE6dOoSFhfk7HBEJAsnJyYwYMYK0tDSioqIYN24cMTEx/g6rwrLW/pr/ENCitK6fmZnJrl27SE9PJzs7u9C+GlsEh9DQUOrVq0dkZKS/QxGRYJGcDCNGcEVaGkRFwbhxUMZjCyUwfCAjI4OtW7dy4oknUrVqVYwxBfZNT0+nRo0aPoxOfM1aS1ZWFnv37j3yDwklMUSkLCUnJ/Np376kZGURBaSlpvJ4374AQZ/EMMYMA84FzgNOAlKttc0K6BsC3A8MAJoB24H5wChr7X4v/R8FRgDVgZ3A0NKIOTMzk7S0NGrXrk2zZs2oUqWKxhZBzlrLwYMH+fvvvwkLCyM8PNzfIYlIRZecDPHxcOAABiA11XkPZZrE0BISH9i+fTv169cnIiKi0AGGBAdjzJGnJLVr12bXrl3+DklEKrhv77+fF7OyaIbzwd8MeDEri2/vv9+vcQWI8cBVwJ/Av0X0nQxMAn4F7gPeAgYBi1zJjTystROstTWA04AE4J/SCHjXrl3Url2bevXqERoaqrGFYIwhIiKCevXqsX37dn+HIyLBYOhQOHAg77EDB2DEiDL9tkpguBhj7jHGfGeMyTDGpJTmtTMyMqhevXppXlIqiJo1a5Kenu7vMESkAsvKymLIzp1Uy3e8GjBk505/hBRoTrbW1rXWXg1sLqiTMeZ0nKTFAmttd2ttorV2CDAEuBK4raBzrbVrgZ+BpNIIOD09nZo1a5bGpaSCqVGjBhkZGf4OQ0QqqpwcWLIErr0WNhfwkZmWVqYhKIFx1BZgAs7TlVJ1+PBhKlfWah3xVKVKlSLXLouIlNRPP/1E5zZtiC6gPcqn0QQma+1fx9i1J2CAKfmOJwIHgDuKOL8ScEqxgitAdnY2VapUKY1LSQVTuXJlDh8+7O8wRKSi2bcPXn4ZTjsNOnWCjz8uuG9U2Y4u9K9qF2vtAgBjTJn8xjW9U7zRfSEiZSEjI4OxTzxB2oQJvGEtBf2lOVC3LpofeMzOB3KA79wPWmszjDE/u9oBMMZUB3oAC4E9wBnAY0AhI77i0eeHeKP7QkRK1YYN8OKLMGMG7Nnj2V6pErg/jI2IcAp5lqFyl8Aoy2JbIiIi5d0333zDw71788Dvv1PYEOJwaCjVn3/eZ3FVAI2BHdbaTC9tm4BLjDGh1tpDODuO3A48B4QC24B3gNHeLmyMiQfiARo2bEhKSkqhgURGRhZr+WF2draWKwaRjIyMIu8hd/v27StWfwk+ukeCjLVE/ve/nLhgAfX+8x9MTk6e5sMREWzp1IlNN95IzbVraT5jBmHbtpHZoAF/9e/PtiZNoAzvl3KXwMAptrUL+BGoVUTfyTjFtRbiDCJau963McZ0sNbmFHayiIhIebF//35GjhzJpilTWADUd2vLatKEKn36wNy52LQ0TFQUlX2w1VkFEwF4S14AZLj1OeR6SHL1sV7YWpuAU+STtm3b2vbt2xfaf+3atcXaVUS7kASX8PBw2rRpc8z9U1JSKOqek+CmeyRIZGTAG2/A88/Dzz97trdoAYMGUblPH5rWqEHT3ONPPnnkHjkNp2p1WSqPCYyTc9erGmPWgPfZr/mKbd3kdnw9MBWn2Na8sg9XRESkbC1fvpyH+vbl4dRUj0qSdsAAqkycCNWrw5NPskID0ZI6ADQooC3crY+IiEj5sWULvPIKTJsG3nYx6tABBg+G666DEP+X0PR/BMXkw2JbUgIpKSkYY5g4caJH24oVK4iMjKRRo0asWrWKMWPGYIw58goNDaV+/fpcdNFFPPDAA6xatSrP+UlJSXn6F/bS4FxEgsHevXsZOHAgk6+6ig/zJS8ON2oEn3yCmTbNSV7I8doM1DPGhHlpa4KzvORQSS9ujOlijEnY422NcZDT2EJEpAx8/z3ccQdER8PYsXmTF1WrQnw8rFkDS5dC584BkbyA8jkD41gdc7EtAGNMZZzfR2UgxBgTDuQcz2BEjvrggw/o0aMHJ5xwAkuXLqVFixYsWLAAgCeeeIKTTjqJ7Oxs/v33X37++WdeffVVnn/+eYYMGcKzzz6LMYbLL7+cOXPm5LnuuHHjWLduncfxhg0b+uxnExHxh8WLFzM0Lo6HN28mNl+b7dOHylOmQGSkP0KrqL4HOgIXAF/kHnSNF84BPj+ei1trFwGL2rZtG3c81wkmGluIiBRTVhYsXAhTpsDXX3u2N20K99wDcXFQp47PwzsWFTmBUZxiWwAjyVtc6yCwAmif/+SyLLZV3gttHTjgzJ7NzMw88nPMnz+fgQMH0qJFC9577z0aNmxIeno6mZnO/zWXX3455557bp7rPP7448TGxvLcc89Rs2ZNHnjgAerXr0/Xrl3z9Js+fTqAx3Egz+/RWsv+/fupHoBPIVVsS0qb7pGKbc+ePbz00kuELF3KxziP/nMdrFWLP4YOZefFF8NPP3k9X/dHib0JDAcG45bAAOJwal8kH8/FjTFdgC4tWrQ4nssEjXnz5hEbG8upp57K0qVLady4cZ726667jrZt2+Y5NmnSJHr06MFzzz1H/fr1eeSRR2jevDnNmzfP02/GjBmsW7eOO+4ofLJuII8tRETy2LkTEhPhpZfg77892y+9FO6/H7p1g8oBniKw1pbbF7AG2FBA259AWgFts3EqhNc63hjOO+88W5Rff/21yD659u7de8x9rbV27ty5Njo62hpjbHR0tJ07d26xzi9ty5cvt4B99tlnrbXWvvzyyzYkJMRecMEFdseOHXn6jh492gL2+++/93qtnTt32po1a9rIyEi7b98+r32uuOIK69zGnjHMnDnTvvjii7Z169Y2NDTUjh49+kifN954w1566aW2evXqtmrVqvaCCy6wb731ltfvsXTpUnv11VfbyMhIGxYWZs8880z7yiuvHOuvpEjFuT+sdX4+kcLoHqm43nrrLdu8Xj2bANbme+XExFi7c2eR1yiL+wP4wQbAuKAkL6AXzkOMkcBW4F+3973y9X3BNX5YAPTHKRCeBaQAIaURT2mPK6wt3tgi0MYV1mpsUVwaV0hp0z1Sjq1ebW1cnLVVq3qMG2yVKtbecYe1Bfy9LA5fji0CYyFL2TgAeFunChWk2FZycjLx8fGkpqZirSU1NZX4+HiSk4/rIVCpeeqpp7j77rtp3749n332GXXr1i3W+XXq1KFbt27s2bOHL7/8stjff8qUKUyYMIHbbruNF154gQsvvBCAkSNHctttt1GjRg3Gjh3LhAkTiIiIoEePHrz00kt5rpGQkEDHjh3Zt28fI0aMYNKkSZx88sncddddPPzww8WOSUSkJP755x9uvvlmXu7Rg2U7duC+xiCnXj1YsAAzd27ATvcMcP2Asa5XA5wdznLf98vXdzDwEHA68BJOQfAXgOttBdjZLNDHFaCxhYhIkXJyYNEip/jmmWc6My8OHjza3qABjBoFqakwZw7km60W8LxlNcrLi8JnYHwMZANhXtr+A2w/zu/dBUho0aJFkdmjgjLhOE9xAvJVUrlPKJo3b24Be+ONN9qMjAyvfYt6SmKttc8995wF7NSpU722F/aUpHbt2nbr1q152lauXGkBO2zYMI9rde3a1daoUePIk6rNmzfbsLAw27NnT4++gwYNsiEhIfaPP/4oMPZjpSclUtp0j1QcOTk5dtasWbZJrVr2BS+zLuzNN1u7bVuxrqkZGIH9Op4ZGP4eO5TFuMJajS2KS+MKKW26R8qJPXusff55a08+2XO8ANa2aWNtUpK1Bfz9PB6agVE6vsfZZeUC94NuxbZ+OJ6LW2sXWWvjI1UgzastW7YA0Lx5c8LCCpoIU7SaNWsCTqX94urduzcNGuTd8S45ORljDLGxsezYsSPP64YbbiA9PZ2vXQVt3n77bTIzM+nXr59H3y5dupCTk8Nnn31W4p9NRKQwGzdupHPnziTGxrJi927udWvLqV0bXn8d5s+H+vX9FqOUHu1CUjSNLUREvPjzT2eb0xNPdOpY/Pnn0baQELjpJvj8c1i5EmJj4Tj+fgaCAK/QcVzKtNiWFO7RRx9lxYoVTJo0CYDnnnuuRNfJHVzkDjaK49RTT/U4tnbtWqy1tGrVqsDztm7deqQvQIcOHYrsKyJSWnJyckhMTOSxhx7i0X37GEy+Pc+7dCEkIQFOOME/AUqZsNqFpEgaW4iIuFgLy5bB88/DBx84793VqgX9+zs7ijRr5o8Iy0y5S2AYY3oB0a639YFQY8xI1/tUa+0cAGvtamPMS8C9xpgFwGKgNTAIZ3eReccZx3FXC7f5bzScnTNq1KhxTOfnrlXN3fkDICIigoSEBGJiYkocV2mIiIjggw8+oEuXLkyaNImcnBwmT55c7Ovk7tfesmXLEsWQn7UWYwxLliyhUqVKXs87/fTTj/QFmD17No0aNfLaN3/lchGR4/Hnn3/Sv39/DqSk8Dng/s8hGxmJmToVevUCY/wVogQwb+MKOPaxRSCPK3Jj0dhCRILawYMwdy5MnQpr1ni2t2oFgwZB795QrZrv4/OBcpfAwCmodUW+Y2NdX1cA7pt2DwY24Gx52hnYgVNsa5Q9zmJbgfCkJHcwMWLECNLS0oiKimLcuHEBMcgAqFq1KosWLeKGG25gypQpWGuZMmXKMZ+/a9cuFi5cSGRkJO3atSuVmE455RQ++ugjoqKiaN26dZF9AerVq1fokxIRkeOVnZ3N1KlTeXz4cB7JyGAokOefQddcg5kxw5keKhVSIGyjGujjCtDYQkSCSHIyjBgBaWnQuDGcdx58+SXs2uXZ97rrnOUjV1/tLBupwMrdT2etbW+tNQW82ufrm22tfc5a29JaG2atbWKtHWKt3een8EtdTEwMGzZsICcnhw0bNgTUIAOcgcb777/P1VdfzfPPP8+gQYOO6bxdu3bRo0cP9u7dy4gRI7w+8SiJXr16ATB8+HCys7M92rdt23bkf99yyy2EhYUxevRoDrpX7nXZs2cPmZmZpRKXiASvX3/9lXbt2jF3yBC+zMhgGEeTF7Z6dUhIgCVLlLyo4AKltlagjytAYwsRCQLJyRAf7+wUYi1s2gTvv583eVGtmrNEZN06WLwYrrmmwicvoHzOwJByJneg0bVrV1544QVycnJ48cUXj7QvWbKEdevWkZOTw7///stPP/3EwoULSU9P5+GHHy7VLcXOP/98Hn/8cUaPHs0555xDjx49aNy4MVu2bGHlypUsXryYQ4cOAXDiiSfyyiuv0L9/f1q3bk2vXr2Ijo5m+/btrF69mnfffZdff/2VZhVsXZmI+EZWVhbPPPMMTz3+OA9nZTGCfB/KV16Jee21Crd2VaQ0aGwhIhXWP//AvfeC23K+PJo1c9r79XNqXQQZJTBKKBCmepYn4eHhvPfee9x444289NJLWGupV68eAKNGjQKgSpUq1KxZk+bNm3PnnXfSt29fzjrrrFKPZdSoUZx33nlMnTqVKVOmsH//fho0aMAZZ5zB888/n6dv3759OfXUU5k4cSLTp09n9+7d1KtXj5YtWzJ27FhOUBE9ESmBn376iTvvvJPsn3/mC6CNW5uNiMA88wzcdVdQPEkRKSmNLUSkwjh0yCnGOXOmM+vSy2wuwKmB9ccfUEC9nWBgCir4JMembdu29ocfCt+Rde3atUWuicxVnCKeUjEU5/4ASElJoX379mUXkJR7ukcCV0ZGBmPHjmXihAk8lJPDaCDUvUO7ds7gpQyT42VxfxhjVlpr25bqRYOM24ORuN9//73QvsX93NDYIrhoXCGlTfdIGfrvf53P/eRk2LGj6P7R0bBhQ5mHVVy+HFtoBoaIiIgPfP3119x5552wbh1fABe4tdnwcMy4cU4BriB+qhLMAqE4uIiI+MDOnTBvnpO4+Okn731atoT1652ZGbkiImDcON/EGMA0N1VERKQM7d+/nwceeIDLLrmEzuvW8RN5kxdceCHmp59gyBAlL0RERCqiw4edQps9ejg7igwa5Jm8aNoURo6E3393CnO+9poz48IY52tCAgRgYWVf0wyMElINDBERKcqyZcuIi4sj5K+/SAHcN220oaGYxx+Hhx6Cyvo4FhERqXDWrXNmWsyZA1u2eLaHhUH37tC3L1x1Vd4HGTExSlh4oRFTCWmqp4iIFGTPnj0MHTqUxIQE7gGeBvJs2NimDWb2bDjjDP8EKCIiImVjzx54800ncfHNN977XHCBk7S47bag3EnkeCiBISIicpySk5MZMWIEaWlp1KtXj6ysLGrt3s1nwJVu/WzlypiRI2H4cKhSxV/hSgDSzE4RkXIsJweWL3eSFgsWwMGDnn0aNoRevaBPHzj9dJ+HWFEogSEiInIckpOTiY+P54Brv/bt27cTD0wE8uz7cOaZmKQkOPdc3wcpAU8zO0VEyqH16yEpCWbNgtRUz/bKlaFLF2e2xbXX6uFFKVACQ0RE5DgMHz6crgcOMB6IAjKBqm7tNiQE8+ijMGqUs9ZVREREyq/9++Gdd5zZFikp3vucdZaTtIiJgfr1fRpeRacERglpqqeIiKxatYpL09JIBKq5jrknL2jVCjNrlrPWVURERMona+E//3FmW8yfD+npnn3q1HESFn37wjnnOLuHSKlTAqOENNVTRCR47d27l9GjR/PCCy+QxtHkhbt0Y6jx449QtaqXVhEREQl4f/8Ns2c7iYvff/dsDwlxlob07essFdFMyzKnBIaIiMgxstbyxhtv8OCDD5KxZQsvAI0K6FvdWiUvREREypuMDHjvPWeJyNKlToHO/Fq2dJIWvXpB48a+jzGIKYEhIiJyDNauXcu9995LyrJl9APGA/UK6W+io30UmYiIiBwXa2HlSidp8frr8O+/nn1q1HC2Pe3bFy66SEtE/EQJDBERkULs37+fsWPHMmnSJNpkZfENcH7+TiEheZ/QRETAuHE+jFLKO9XWEhHxg23bYO5cJ3GxZo33Pldd5SQtund3Pt/Fr0L8HYDIsWrWrBnt27fPc6x9+/Y0a9bML/GISMVmrWXhwoWcdtppvPr007ySlcW35EteNGsG777rrI+NjnaexkRHQ0KCU8hL5BhZaxdZa+MjIyP9HUpQ0dhCJAhlZTlLRG68EZo0gQcf9ExenHQSPP64s03qZ5/BHXcoeREglMCQUpWSkoIxJs8rPDyc5s2b07dvX9auXVtm3zspKcnjexf0yj9YERFx9+eff9K5c2d6dO/O9Wlp/Ab0c+8QFgajR8Ovv0LXrk6yYsMGZxbGhg1KXoiUIo0tRKRUrF7tJCtOPNFJXrz3Hhw+fLQ9IgJ694bly+GPP5ztz5XMDDhaQlJCmupZuJ49e9KpUycADh48yKpVq5gxYwbvvPMOq1evJroM1oZffvnlzJkzJ8+xcePGsW7dOo/jDRs2LPXvLyLl38GDB3n66aeZMGEC52Vm8gNwTv5ON9wAkydD8+a+D1AkiGlsISLFtmuXU9Ni5kynxoU3l17qLBHp0QNq1vRtfFJsSmCUkLZRLdy5557LHXfckefYKaecwv3338+CBQt44IEHSv17Nm/enOb5/kExY8YM1q1b5xFLftZa9u/fT/Xq1Us9LhEpHxYvXsx9993Hgb/+IgHonb/DySfD1Kng+geUiPiWxhYickyys53dQ2bOdJZ5Hjrk2adJE2e2RZ8+cOqpvo5QjoOWkJR3ycnO1KaQEOdrcrK/IypQY9cWQ6GhoUeOvfzyy3Ts2JEmTZoQGhpKo0aNuOOOO9iwYUOZxZE7FTUpKYmXXnqJ0047jfDwcCZOnHikz5tvvkm7du2oUaMGERERXHjhhbz99tter/fpp5/SsWNHatWqRXh4OGeddRbTpk0rs/hFpHSlpqbSrVs3unbuzA1//cX/yJe8qFrVKci5Zo2SF1LxlaNxBWhsISJufvsNhg93alFddx3Mn583eREaCrfcAkuWQGoqjB+v5EU5pBkY5VlyMsTHw4EDzvvUVOc9+H399YEDB9ixYwfgTPNcs2YNI0aMoF69etx0001H+k2cOJGLLrqIQYMGUadOHdasWcOMGTNYtmwZq1evpm7dumUW45QpU9i5cydxcXGccMIJNG3aFICRI0cybtw4rr32WsaOHUtISAgLFy6kR48evPjii9xzzz1HrpGQkMDAgQO56KKLGDFiBNWqVWPp0qXcdddd/Pnnnzz77LNlFr+IHJ9Dhw4xadIknnjiCS48eJCfgDPyd7r5ZnjuOYiK8kOEIj4WwOMK0NhCYwsRL9LTnUTFzJnwn/9473Peec4SkZ49oU4d38Ynpc9aq9dxvM477zxblF9//dV7g7PjcGC+Smj58uUW8Po67bTT7Nq1a/P037dvn8c1Pv30UwvYp59+Os/x6Ohoe8UVV+Q5dsUVV9jo6OgC47niiiss+X6e3Bhr165tt27dmqdt5cqVFrDDhg3zuFbXrl1tjRo17N69e6211m7evNmGhYXZnj17evQdNGiQDQkJsX/88UeBseUq8P4owPLly4vVX4KP7pGiffbZZ7ZVq1a2CdjXvf0NbNXK2k8+8XeYZaIs7g/gBxsAn8kV4aVxhSeNLRzHOrbQuEJKW8DdI9nZ1i5fbm3v3tZGRHj/m1O/vrVDhli7apW/ow0KvhxbaAmJlIn4+HiWLl3K0qVLWbRoEU8//TQ7duygU6dOpKamHulXrVo1AHJyctizZw87duzg7LPPJjIykm+//bZMY+zduzcNGjTIcyw5ORljDLGxsezYsSPP64YbbiA9PZ2vv/4agLfffpvMzEz69evn0bdLly7k5OTw2WeflenPICLFs3nzZnr27Mm1//d/3LBuHeuA29w7VK8Ozz4L//0vXH21n6IUEW80ttDYQoJcaio88QS0aAFXXulsYZ47YwygcmVnZ7B334VNm5wZlGee6bdwpWxoCYmUiVNOOYUOHToceX/99ddzxRVXcNFFF/HII4/wxhtvALBs2TKeeOIJvv32WzIyMvJc499//y3TGE/1suZt7dq1WGtp1apVgedt3br1SF8gz89ZUF8R8a/Dhw/zwgsvMHr0aC5KT2c10DJ/p9tvh2eecQp7ifiYdjcrmsYWefuKBIUDB2DhQmeJyLJlzvyK/M44w1kicscdkC+BKBWPEhj+5OU/wPT0dGrUqHFs5+dfqwrO/sUJCQGxVjW/Cy+8kMjISJYtWwbA999/T8eOHWnRogUTJkzgpJNOomrVqhhjuO2228jJySnTeCIiIjyOWWsxxrBkyRIqVark9bzTTz/9SF+A2bNn06hRI69981cuFxHf+/LLL7n77rvZs3o1SUD3/B3OOANefBGuuML3wYm42NLY3czbwJ5ijC3K2bgCNLYQqZCshW++cZIWb74Je/d69qlVy3nw0LevU+PCGJ+HKf6hBEYJBcSTktzBxIgRkJbmFJkbNy5gBxngPAXNzMwEYN68eWRnZ7NkyRJOOumkI332799f5k9ICnLKKafw0UcfERUVRevWrYvsC1CvXr1Cn5SIiH9s27aNoUOH8sasWTwMDAPy/NOiZk0YOxbuvtuZdioS7MrhuAI0thCpMDZvhjlzICkJ1q3zbDcGOnZ0khZdu0J4uM9DFP9TDYwSstYustbGR0ZG+jeQmBjYsAFycpyvATzIWLp0Kfv37+e8884DOPIUwuZ7YjR+/Pgyf0JSkF69egEwfPhwsrOzPdq3bdt25H/fcssthIWFMXr0aA4ePOjRd8+ePUcGVCLiO9nZ2bz88su0bNmSHbNm8QswlnzJiz59nO3WBg1S8kLEXTkaV4DGFiLlXmYmvP02dO4MTZvCo496Ji9OOcVJpqalwUcfwa23KnkRxDRqkzLx448/MnfuXAAyMzP55ZdfSExMpEqVKjz55JMAdOvWjcmTJ9OpUyfi4+MJDQ1l6dKlrFq1inr16vkl7vPPP5/HH3+c0aNHc84559CjRw8aN27Mli1bWLlyJYsXL+aQaz/pE088kVdeeYX+/fvTunVrevXqRXR0NNu3b2f16tW8++67/PrrrzRr1swvP4tIMPr++++56667+HflSmYDXfJ3aNPGWS5yySV+iE5EjofGFhpbSAXy00/OEpHkZNi1y7O9enUnUdG3r/OZrSUi4qIEhpSJ119/nddffx2AkJAQ6taty9VXX82wYcM4//zzAbj00kt55513GDt2LI899hhVq1alQ4cOrFixgssvv9xvsY8aNYrzzjuPqVOnMmXKFPbv30+DBg0444wzeP755/P07du3L6eeeioTJ05k+vTp7N69m3r16tGyZUvGjh3LCSec4KefQiS47Nq1i+HDhzNn+nQeAYYCeZ7N1K7tPL2Jj4cC1qCLSGDT2EJjCynntm93EhZJSc5uX960b+8kLW66CVw7Com4M/mn2EnxtG3b1v7www+F9lm7dm2Rax5zFauIp1QIxbk/AFJSUmjfvn3ZBSTlXjDdIzk5OSQlJfHI0KFctnMnk4Fot3ZrDKZ/fxg/Hvz09DXQlMX9YYxZaa1tW6oXDVKlPa4AjS2CjcYVUtqO6x45fBiWLHFmW3zwAWRlefaJinKWdsbGgorUlku+HFtoBoaIiJRLP//8M/fccw87vvqKucA1+TtccAHmxRfB9WRWREREfOTXX52kxZw54G3r3/BwZ5ZF375w5ZUQotKMcmyUwBARkXJlz549jBo1ipkvvMBwaxkChLq123r1MBMmOIMiDYjED4wxYcCLwP8BDYAtwEvW2in+jEtEpEzt3g1vvOEkLr77znufiy5yPp9vvRX8vRmClEtKYIiISLlgrWXevHk89OCDXLF1K78CJ7q3h4Rg7roL88QTUKeOv8IUAWd89Q/QEfgLOAv42BizxVr7pl8jExEpTdnZsGyZk7RYuBAyMjz7NGoEvXs7S0SKsbxJxBslMEREJOD9+uuv3HPPPWxLSSEZuCp/h0svdZaLnHOO74MTycdaux94zO3Qz8aYD4FLASUwRKT8+/NPpxjnrFmwcaNne5Uq0LWrM9uiY0dtWS6lRnNrRUQkYO3bt49HHnmEdmedRZeUFP5L3uSFbdgQZs+GL75Q8kJKxBgzzBjzljHmL2OMNcZsKKRviDHmAWPMOmNMhjFmozHmOWNMoaXyjTGVgXbAqlIOX0Sk7CQnQ7NmXHHVVdCsGbz2mpO0uOIKaNECnnzSM3nRpg1MnQpbtsBbb0GnTkpeSKnS3SQiIgHHWsuCBQsYfP/9tN+0iV8B940DbaVKmEGDMKNHaw2tHK/xwC7gR6BWEX0nA4OAhcBzQGvX+zbGmA7W2pwCzpsK7AFml0bAIiJlLjnZ2Xr8wAEMQGoq9OvnvW+9ehAT48y2OPtsX0YpQUgJjBIyxnQBurRo0eKY+ltrMcaUbVBS7mgbYxFPv//+O/fddx//fPwxr+M8ts6jfXtnucjpp/shOqmATrbW/gVgjFkDVPfWyRhzOnAfsMBae5Pb8fU4CYrbgHleznsO5za+ylp7qLSC1rhCvNG4QkrNo4/CgQMFt1eqBNdd5yQtrr8eQkML7itSirSEpISstYustfGRx/Dkr3Llyhw+fNgHUUl5k5WVRaVKlfwdhkhAOHjwIKNHj6bd6adz/ccfs5K8yQvbuDG8/rpTLEzJCyklucmLY9ATMMCUfMcTgQPAHflPMMZMwSnk+X/W2h0ljzKvSpUqkZWVVVqXkwrk8OHDVNZ0fSkpa50lmTEx8PffBfd75hmnfdEi6N5dyQvxKf2F84Hw8HD27dtH7dq1/R2KBJi9e/dSo0YNf4ch4ncffvghg+69l/YbNrAGqO/WZqtUwTzwAOaxx6C614fjIr5wPpAD5Nkb0FqbYYz52dV+hDFmKk7JliuttdtLM5AaNWqwd+9e6tWrV5qXlQogPT2d8PBwf4ch5c2//8KcOTB9Ovz6a+F9o6Lg4Yd9E5eIF0pg+ED9+vVJS0sjLCyMqlWraspnkLPWkpWVxd69e/n333+Jioryd0gifpOamsr999/P3++9xzzgwvwdrr4aM3UqtGrlh+hE8mgM7LDWZnpp2wRcYowJtdYeMsZE4yw3yQTWu33uf2GtvS7/ycaYeCAeoGHDhqSkpBQaiDGGGjVqcPDgQapXr06lSpUKHVtkZ2eTnp5e9E8o5Za1lszMTDZv3szu3bv5888/j/ncffv2FXnPSQVkLTXXrqXRokU0WLaMSoc8V7hZYzBuy5Kyw8L4X69ebNP9Ivn48u+IEhg+EB4eTsOGDfnnn3/IzPQ27jkqIyNDmfMgUKlSJWrUqEFUVBRhYWH+DkfE5zIzM3nuued4ZexYHsvIoD951zTaqCjM5MnQrRso6SuBIQInIeFNhlufQ9baVJzlJsfEWpsAJAC0bdvWtm/fvshzMjMz2bVrF9u3byc7O7vQvhpbBIewsDCaNWvGsSxvdpeSksKx3HNSQezd6xTonD4d/vtfz/bq1Z0lJAMGYH79FUaMwKalYaKiqDRuHKfFxHCa76OWAOfLvyNKYPhIZGTkMX2gpKSk0KZNGx9EJCLiH59++in33X037X//nf8CddzabGgoZuhQzLBhEBHhrxBFvDkANCigLdytj0+EhYXRqFEjGjVqVGRfjS1EhJUrnaTFvHmwf79n+znnwMCBcPvtkLu8uU0biIlhhZJcEkCUwBAREZ/YtGkTQ4YMIW3+fJKBc/N36NwZM2WKs7e8SODZDJxmjAnzsoykCc7ykhLvMlLc3c1ERIq0fz+88QZMmwY//ODZXrUq9OwJAwbA+edrxqOUC9qFREREylRWVhaTJk3islNP5dr58/mavMkL27y5U8n8gw+UvJBA9j3OuOkC94PGmHDgHMDLvw6OXXF2NxMRKdSqVXDPPdC4MfTv75m8OP10eOEF2LwZXn0VLrhAyQspNzQDQ0REyswXX3zBfXfdxeW//MKPQC23Nhsejhk+HPPww6D1+RL43gSGA4OBL9yOx+HUvkg+notrBoaIHJeDB+Gtt5zZFl9/7dkeFgY9ejjLRC65RAkLKbeUwBARkVK3detWhg4dyvrZs5kNnJW/Q7dumEmToFkz3wcn4sYY0wuIdr2tD4QaY0a63qdaa+cAWGtXG2NeAu41xiwAFgOtgUHACmDe8cRhrV0ELGrbtm3c8VxHRILMunVObYtZs5ztUPM79VRniUhsLNSt6/v4REqZEhgiIlJqsrOzmTZtGi8MG8Zj6enE5Gu3p5yCeeEFuOYav8Qn4kU/4Ip8x8a6vq4A5rgdHwxswNnytDOwA3gBGGWtzSnTKEVEcmVmwoIFTuJixQrP9ipVoHt3J3HRvr1mW0iFogQGYIypDDwH9MJZ3/o2cE8Be72LiIgX3377LYMGDuSyn3/me6CGW1tORAQho0ZhBg92prGKBAhrbfti9M3GGS88V9pxaAmJiBTpjz8gIQFmzoQdOzzbTzrJSVr07QsNCto0SaR8UwLDMRzn6csZQBbwPvAUMMSfQYmIlAc7d+5k+PDh/JWQQBLOnPo8br2VkIkT4cQTfR+cSDmhJSQi4lVWFrz3njPb4tNPPdsrVYKuXZ3ERYcOEKI9GqRiUwLD0R8YYq3dDGCMGQO8YYx5SFNCRUS8y8nJYebMmUx96CFG7t5Nj/ztrVsT8uKLcNVVfolPRESk3NqwARIT4bXX4J9/PNubNoX4eLjzTme3EZEgUa4SGMaYYTi7750HnIRTXKtZAX1DgPuBAUAzYDswH2ed6n63frWApsBPbqfnFstvCqSW7k8hIlL+/fzzz9w/cCCXfvstXwHV3Npyqlcn5IknCLn3XmcdroiIiBTt8GFYvNjZSeSjj8DavO0hIdCpk7OTyLXXOrMvRIJMuUpgAOOBXeCxG583k3Eqgy/EWauaWym8jTGmg9vMitxl2nvczt2dr01ERIA9e/bw2GOP8eeLLzLDWk7J36FXL0KefhoaNfJHeCLllmpgiASxv/+GV191Zlxs2uTZ3qgR9O/vvKKifB+fSAApbwmMk621fwEYY9YA1b11MsacDtwHLLDW3uR2fD0wFbiNo9udpbu+RuJUE4ejyZHcNhGRoGatJTk5makPPMDwHTuYmq8954wzCHnlFWjXzi/xiZR3qoEhEmSys+GTT5zaFosWQU6+VevGQMeOTm2L66/XjEYRlxIlMIwx1YAmQD3gILDNWuslXVi6cpMXx6AnYIAp+Y4nAhOAO3AlMKy1u40xG4FzgD9d/drgzMLYeFwBi4hUAL/88gsPDBzIJV9+yQqgqltbds2aVBo/npABA6ByecuJS6Dw17hCRMTn/vnHqWuRkACpXlaqN2jg1LWIi4PmzX0fn0iAO+bRpjHmZOBO4Gqcf+CH5GvfCSwH3sGZ+XC4FOMsrvOBHOA794PW2gxjzM+udnczgBHGmK9xdiEZA8xUAU8RCWb79u3jiccf549Jk5iek8NJ+drtnXdS6amntFWblEg5G1eIiJRcTg4sW+bMtnj3XafWRX5XXeXMtrjxRggN9XWEIuVGkQkMY0xb4EmgA87gIgtYDfyDU4+iKlAXaAn0AG4GthtjngcmWWszyyb0QjUGdhTwvTcBlxhjQq21h1zHxuM89fkF52d8C2drVRGRoGOt5Z133uH5e+9l2NatPJOvPbtNGyq98grmwgv9Ep+Ub+V0XFHmVANDpALavh2SkpzZFn/84dlepw707evsJnLqqT4PT6Q8KjSBYYyZBcTgFLhMAN4AvrPWZhTQvxlwDRALjAMGGmN6WWs/L82gj0EEUNAAJ8OtzyEA11OdQa5XkYwx8UA8QMOGDUlJSTmeWPPYt29fqV5PKh7dI1KU47lH/v77b6ZPmsS1P/3Ep0CYW1tGtWqkDhjAlk6d4OBB0H1YLvnzb0g5HleUOdXAECnHkpNhxAhIS3O2N+3dG/78E955Bw4d8uzfrp2zk8hNN0F4uO/jFSnHipqB0REYAkw/lice1toNwHRgujHmLOBx4ArA1wONA0BBc5rD3fqUiLU2AWfgRdu2bW379u1LeikPKSkplOb1pOLRPSJFKck9cuDAAZ4aP54/nn6aeYcP09StzRoD8fGEjxtHy7p1aVmq0Yqv+flvSHkdV4iIeJec7MygOOD6p0VaGjz5pGe/yEgnsTFgAJx+um9jFKlAikpgNLfWHizJha21q4BuxpiqRXYufZuB04wxYV4GSE1wlpd4SYeKiASfRYsW8fzAgTy6eTNj87UdPv98Kk+bBuee65fYpMIpr+MKERFP1sKDDx5NXnhz4YXObItbboGICN/FJlJBhRTWWNJBRmlfowS+x/nZLnA/aIwJx9lt5Ifj/QbGmC7GmIQ9e/Yc76VERPxi/fr13NqpE+tuuIElmzfTwa0tq3ZtmDmTyt98o+SFlJpyPK4QETlqxw6YNAlOOw22bi24308/wTffQJ8+Sl6IlJJCExjl2JuABQbnOx6HU/si+Xi/gbV2kbU2PjIy8ngvJSLiU5mZmYx78kkeb9mSyUuW8DCQu7t8jjHY++6jyl9/OQOukIr6MSESWPRgRCTA5eTAZ5/BbbdBkybOzIt16wruHx0N55zjs/BEgkWxRqbGmJHGmCxjTJMC2hsbYw4ZYx4tnfA8rt/LFcNIoD4QmfveGNMrt5+1djXwEtDdGLPAGNPfGPMcMAlYAcwri/hERALd0qVLubllS9o99hhJWVk0dmvLuugiQn7+GTN1KtSq5a8QJYj4e1wRSPRgRCRA/fMPTJjg7BLSoQO8+WbewpxhYVA536r8iAgYN863cYr4QXJyMs2aNeOqq66iWbNmJCcf9zyBIhX30VoXIMVau8lbo7V2M86e7V2PN7AC9APGul4NgFpu7/vl6zsYeAg4HSeZcRvwAnC9tTbneAPRkxIRKU/+/vtv+tx4I7927MjC1FSucGs7VK8eJCdT5auv4Kyz/BajBCV/jytERDxlZ8NHHzm7hDRtCsOGObuKuLvwQpgxw1lOkpTkzLgwxvmakAAxMX4JXcRXkpOTiY+PJzU1FWstqampxMfHl3kSo6ginvm1AOYW0edX4I6ShVM4a237YvTNBp5zvcoiFm13JiIBLysri+cnT+b3xx7j6UOHaOjWlh0Sghk8mNDRo6FmTb/FKEHNr+MKEZE8Nm2C115zEhNpaZ7ttWrBHXdAXFzehH9MjBIWEnSGDh3KgXwFbA8cOMCIESOIKcP/HoqbwIig6O1HM4AaJQtHRERKy+eff84LffvywF9/8VC+tszLLiNs+nRo3dovsYm4aFwhIv51+DAsWeLMmli82Kl1kV+7ds5WqTffDFW1EZIEr4yMDBYuXEhCQgKbN2/22ifNW/KvFBU3gbERuKiIPhcBXqeCiohI2UlOTmbEiBGkpaXRpGpVhh84wJvkXSuY0aAB4S+9RNhNNzlTXUX8S+MKEfGPDRvg1VedGRfe/iFWty707u3MtlCyX4Lc2rVrSUxMZNasWezatavQvlFRUWUaS3ETGB8B9xhjbrXWvpm/0RhzG3AF8HJpBBfIjDFdgC4tWrTwdygiIiQnJ/Np376syMoiCsg5cIBKbu3ZlSrBgw8SPmoUVKvmrzBF8tO4QkR8JysL3n8fEhPhk0/AWs8+V13lJC26dXMKdIoEqYMHD/L222+TkJDAl19+6dFujCEkJITs7OwjxyIiIhhXxgVsi5vAeBqIAeYZY27FGXhsApoA1wE3ALuACaUZZCBSDQwRCSRf3HUXL2dlkTux1T15kdG+PeHTpzsV1EUCi8YVLnowIlKG/vjDqWuRlARbt3q2N2gAfftC//6g/wYlyK1evZrExETmzJnD7t27Pdqjo6Pp378/ffv2JSUl5cjs36ioKMaNG1em9S+gmAkMa+0mY8w1wFvAjeStCm6ADUAPa+3fpRWgiIgU7I8//mD0/ffzSno63lblbgMaLFum5SISkDSuOEoPRkRKWWYmLFzo1LZYvtyz3Rjo2NGZbdGlC4SG+j5GkQCxf/9+5s+fT0JCAt98841He+XKlbnhhhuIj4+nQ4cOVKrkPCqLiYkhJiaGlJQU2rdv75NYizsDA2vtD8aYU3G2PrsIZyvT3cA3wCJrbVZpBigiIp727t3Lk2PHsnXyZJ7LzqagPUTqgZIXEtA0rhCRUrV2rbNEZPZs2LnTs71xY+jXD+68E5o183l4IoHkp59+IjExkeTkZPbu3evR3rx5c+Li4ujTpw8nnHCCHyL0VOwEBoBrMLHA9QpKmuopIv6QnZ3NzJkzee2RRxi7axf/V0T/A3XrUt0nkYmUnMYVInJcDh6Et95yEhde1uoTEgKdOjk7iVx3HVQu0T+BRCqE9PR0Xn/9dRITE/nhhx882qtUqUK3bt2Ij4/nyiuvJCQkxMtV/Oe4/us1xtQGqltrN5ZSPOWGpnqKiK+tWLGCRwYNotOqVSwH3EuLHa5RA3PwIJUOHz56LDSU6s8/7/M4RUoqmMcVIlICq1Y5SYs5c2DPHs/2qCinrkXfvnDiib6PTyRAWGv54YcfSExMZN68eezfv9+jz6mnnkpcXByxsbHUr1/fD1Eem2InMIwx1YHHcYpu1Qds7nWMMRcCo4GR1tofSzFOEZGgtX79eh5++GHS33mHuYD7vK+ckBDMoEFUfuIJp7L6iBHYtDRMVBSVx42DMi6kJHK8NK4QkWLZtw/efNOpbfHdd57tlSvDDTc4tS2uvhoqVfLsIxIk9uzZw7x580hISODnn3/2aA8LC+Pmm28mLi6Oyy+/HFMOlh0XK4FhjIkEvgROB34GdgDuGyOvBi4DegIaaIiIHIf09HTGjx/P6889x9NZWdyarz37/POplJAA55zjHIiJgZgYVviwkJLI8dC4QkSO2cqVzmyLefMgPd2z/eSTndkWffpAgKzVF/EHay3ffPMNiYmJvPnmmxw4cMCjz2mnnUZcXBy9evWibt26foiy5Io7A2MEziCjj7V2tjFmNDAqt9Fae8AYswKKXJYtIiIFyMnJISkpiceGDeOmbdtYBXmKdObUrEnIM89QKS7OWdcrUn5pXCEiBduzx0lYJCbCTz95toeGQrduTm2L9u31mShB7d9//2XOnDkkJiayZs0aj/bw8HBuvfVW4uLiuOSSS8rFbAtvipvA6A58bK2dXUifVOD8kodUPqiIp4iUhS+++ILBgwdT6ccfWQScm79Dr16ETJzo7FkvUv5pXCEieVkL33zjJC3efBO8PD2mZUsnadG7N9Sr5/sYRQKEtZYvv/yShIQE3n77bTIyMjz6nHnmmcTHxxMTE0Pt2rX9EGXpKm4C40TgnSL67AMiSxZO+aEiniJSmjZs2MAjjzzCx/PnMx4YCLg/R7KtWmFefhmuvNJPEYqUCY0rXPRgRILerl0wd66TuPDy9JjwcOjRw6lt0a6dtgiXoLZjxw5mz55NYmIi69at82iPiIigZ8+exMXFccEFF5Tb2RbeFDeBkQ4U9djvJJw1rCIiUoR9+/YxYcIEJj77LDcdOsQ6wH3lrg0Pxzz2GOahh5ypsiIVi8YVLnowIkHJWvjiCydp8dZbkJnp2efMM52kxR13QAV4eixSUtZaUlJSSEhIYMGCBRw6dMijT5s2bYiPj+f222+nZs2aXq5S/hU3gfE9cL0xpoa11qN6jjGmEdAJ+KA0ghMRqahycnKYM2cOw4YNo8aWLXyIl0X+112HefFFaN7cDxGK+ITGFSLBaPt2mDULZsyA//3Psz0iAm67zUlcXHihZltIUEpOTmbEiBGkpaURGRlJWFgYW7du9ehXvXp1YmJiiIuL47zzzvNDpL5V3ATG88ASYLExJt69wRjTGkgEwoGppROeiEjF89VXXzF48GBWf/89w4BHgDD3Dk2awPPPQ/fuGrRJRadxhUiwyMmBZcuc2RYLF0JWlmefc891kha33w4V9OmxyLGYM2cOcXFxZLpmJe3evdujzwUXXEB8fDy33nor1atX93GE/lOsBIa19mNjzBhgDLAGyAIwxuwAagMGeMRa+1XphikiUv6lpaXx6KOP8vrrr3MNzv6Q7qvdbaVKmEGD4PHHoUYNP0Up4jsaV4gEgS1bICnJmW3x11+e7TVqONuAx8U5CQyRILZmzRpmzZrFpEmTyMnJ8Wg3xnD33XcTFxfH2Wef7YcI/a+4MzCw1j5hjPkCGARcBNQFLLAYmGytXVa6IQYmFdsSkWO1f/9+nnnmGZ599llqHzzIm8At+TtdeCFm2jQ45xzfByjiRxpXiFRA2dnwySfObIv333fe53fhhc5OIrfcAkH09Fgkv507d/L666+TlJTEypUri+z/4osv+iCqwFXsBAaAtXY5sLyUYylXVGxLRIqSk5PDvHnzePTRR/ln0ybuAcYCeSbF1qoFEyY4T560f70EKY0rRCqIv/+G116DV1+FtDTP9lq1oFcv5zPvzDN9Hp5IoMjKyuKjjz5i1qxZvP/++2R5W1LlRVRUVBlHFvhKlMAQEZHCffvtt9x///18++23nA+8D3hMjO3VCyZOhAZFbcIgIiISoA4fhsWLISEBlixxal3kd9llTtLi5puhalXfxygSIFatWsWsWbOYO3cu27Zt82gPDQ2la9euREdH8/LLL3PgwIEjbREREYwbN86X4QakYiUwjDHNgNOAFdba/a5jlYHHgBuB/cCz1tqFpRumiEj58PfffzNs2DDmzp1LJPASMBDIM7eiVSt4+WW48kq/xCgSKCryuMIYcw8QC5wFfGOtbe/fiERK2fr1zkyLmTNh82bP9rp1ITYW+veH1q19H59IgNixYwfz5s0jKSmJn376yWufCy64gD59+nDrrbdSp04dAM4555wju5BERUUxbtw4YmJifBl6QCruDIzRwA1AQ7djI3EGGrnmG2Mus9Z+c7zBiYiUFwcOHGDixIk8/fTTHDhwgNuBSeT9Y0l4ODz2GDz0EISG+idQkcBSkccVW4AJwPnAxX6ORaR0HDrk1LRITISlS8Fazz5XXeXUtrjxRggL82wXCQJZWVksXryYpKQkPvzwQ69LRBo3bkyvXr2IjY2ltZckX0xMjBIWXhQ3gXEx8Jm19jCAMSYEuBtYB3QETgA+BR4Abi3FOEVEApK1ljfffJOhQ4eyceNGTgVeBv4vf8dOneCFF6B5c98HKRK4Kuy4wlq7AMAYowXLUv79/ruzi0hSEniZ9k7DhtC3L/TrBypwL0Hs559/JikpiXnz5rF9+3aP9rCwMLp160afPn3o0KEDlSpV8kOU5VtxExgNgVS39+cA9YDHrbV/A38bY94DLiud8EREAtf333/P4MGD+eqrrwgHngCGAnmeNzVpAlOnQrduYIxf4hQJYH4fVxhjhuGUqDkPOAlItdY2K6BvCHA/MABoBmwH5gOjcpfAiFQYGRmwcKFT2yIlxbPdGLjmGqe2RZcuUKWKz0MUCQTbtm0jOTmZWbNm8d///tdrn4svvpjY2FhuvfVWatWq5dsAK5jiJjCq4GxtlutS13v3Lc7+BhodZ1wiIgFr8+bNDBs2jNmzZwNwDU6ti5PdO1WqBPffD2PGOHvci4g3gTCuGA/sAn4EahXRdzLOdq8LgeeA1q73bYwxHay1XqoXipQzv/7qLBGZPRt27fJsb9IE7rzTeTVr5vPwRALBoUOH+PDDD0lKSmLx4sUcPnzYo0+TJk3o3bs3sbGxtGzZ0g9RVkzFTWD8jVOMKlcnYIe1dq3bsQbA3uMNLNAZY7oAXVpompxI0Dh48CCTJk3iqaeeYv/+/TTG+dfMLfk7XnQRTJsGZ5/t+yBFypdAGFecbK39C8AYswao7q2TMeZ04D5ggbX2Jrfj64GpwG3AvDKMU6R0JSfDiBFckZYGJ54I110Hv/wC//mPZ9+QEOjc2Zltcd11UFkbGUrwsdby008/HVkisnPnTo8+4eHhdO/enT59+nDVVVdpiUgZKO5fnw+AB4wxE4EM4GpgZr4+rcg7HbRCstYuAha1bds2zt+xiEjZstby1ltvMXToUFJTU6mE88h1LFDTvWPt2jBhglNxPSTE67VEJA+/jytykxfHoCdggCn5jifiFOu8AyUwpLxITnYKbR44gAHYuNFZKpJfdLTzmda3rzPzQiQI/fPPPyQnJ5OUlMSaNWu89rn00kvp06cPPXr0IDIy0scRBpfiJjCewdnWbIjr/SacCuIAGGOigUtwHkqKiJR7P/74I4MHD+aLL74AnO0EpuEsmM+jd2949llo0MDHEYqUa+VpXHE+kAN8537QWpthjPnZ1X6EazvY3FeIMSYcyLHWHvJNuCIFSE+HQYPgwAHv7ZUrQ9euzmyLDh2cJZEiQSYzM5NFixaRlJTERx99RHZ2tkefpk2bEhsbS+/evTnllFP8EGVwKlYCw1q7zRhzJkcL7K+w1qa7damOMwj5uJTiExHxi3/++Yfhw4eTlJSEtZZaOAvlBwB55la0agWvvALt2/shSpHyrZyNKxrjLG/J9NK2CbjEGBPqlqAYiVsyBjgIrADa5z/ZGBMPxAM0bNiQFG8FE4/Dvn37Sv2aUs5YS821a2n0wQc0WL6cShkZ3rsBX735Jll16jgHXMl7CW7B8jfEWsv//vc/PvroI5YtW0Z6erpHn7CwMC6//HKuueYa2rRpQ0hICJs2bWLTpk1+iDhw+PIeKfYCNmvtQZwpn97afgF+Od6gRET8JSMjgylTpjBu3Dj27dsHQAxOtb6G7h3Dw+Gxx+ChhyA01A+RilQM5WhcEQF4S16As/wlt88hAGvtGGDMsVzYWpsAJAC0bdvWti/lhGhKSgqlfU0pJ3btgjlznC1QC5j67s5ER3Np9+4+CEzKk4r+N2Tz5s3MnTuXWbNm8euvv3rtc/nllxMbG8vNN99MzZo1vfYJZr68R1SBR0QEJ+u+YMECHn74YdavXw9AS+Bl4Kr8nTt1ghdegObNfRyliPjRAZyCot6Eu/UpERUHl1KTkwMrVjg7iSxYAJle8m6NG8P27ZCVdfRYRASMG+e7OEX8KCMjg/fee49Zs2bx8ccfk5PjuYlUdHT0kSUiJ598speriD8UWmXOGPOCMeaEkl7cGNPNGNOzpOeLiPjCzz//zJVXXsnNN9/M+vXrCQeeAFaTL3nRpAm88w588IGSFyIlUM7HFZuBesaYMC9tTXCWl5S4voW1dpG1Nl7F36TE/vnHKSR96qlw1VXw+ut5kxcREU4xzq++gr//hpkzIToaa4xTrDMhAWJi/Be/SBmz1vLtt99y11130ahRI2677TaWLFmSJ3lRrVo1YmNjWb58OX/99RePP/64khcBpqgZGDHAncaY2UCStfbboi5ojInE2UpsAHA2cP9xRykiUkqSk5MZMWIEaWlpNGnShFNOOYWUlBSstQBcA7wSEsJJ7pn4SpXg/vthzBioUcMvcYtUEOV5XPE90BG4ADhSGMBVnPMc4HP/hCVBLTsbPv7YmW2xaJHzPr/zznMKcvbsCe5T32NiICaGFRV8eYDIpk2bmDNnDklJSfzvf//z2qd9+/b06dOHm266ierVve6mLQGiqATGyTg7BcYD8caYjcB/gB+ALcC/ONMm6+Jsc3YRThXuMGAtcL21dknZhC4iUjzJycnEx8dzwFV5/e+//+bvv/8GnOp8zwM3gzP9NtdFF8G0aXD22b4OV6QiKs/jijeB4cBg3BIYQBxO7Yvk47m4lpBIsaSmwmuvOS/X51gekZFOgqJ/f2jTxvfxifjZwYMHeffdd0lKSuLTTz/1ukTkpJNOok+fPvTq1YuTTjrJD1FKSRSawLDW/gvca4x5GhgI9MHZB70nTqFidwbIBj7DWTb+gbXW804REfGT4cOHH0le5KoE3AM8VakSEe5PrmrXdqbi9u8PIYWuthORYxSI4wpjTC8g2vW2PhBqjBnpep9qrZ3jin21MeYlV/wLgMVAa2AQzu4i844nDmvtImBR27Zt447nOlKBHTrkzLJITIRPPgGb/z8Z4LLLnM+tm292loyIBBFrLV9//TVJSUm8+eab7N2716NP9erVueWWW4iNjaVdu3aEaIxX7hxTEU9r7UZgBDDCGHM60A6IwnlCchDYBqwCvrDWet4pIiJ+tnLlStLS0uiJsx1qFLAVZ0uBZpB32m3v3vDss9CgoHp9InI8Amxc0Q+4It+xsa6vK4A5bscHAxtwZpB0BnYALwCj9NBGysxvvzm7iMyaBdu2ebbXqwexsU7iolUr38cn4mcbN248skTk999/92g3xnDVVVcRGxtL9+7dqVatmh+ilNJSkm1UA2lLMxGRQm3atInhw4cze/ZsegKJQO7HVqP8nVu1gldeAa0FFvEZf48rrLXti9E3G2dX5edKOw4tIZE8Dh50ikbPmOHsKJKfMXD11U7SomtXbectQefAgQMsXLiQpKQkPvvssyO1zNydfPLJR5aIREdHe7mKlEfaRrWENNAQCWz79+/n2Wef5dlnnz2ybOQpjiYv3FnAjBsHDz2kQaCI+IWWkAgAq1Y5S0TmzoXduz3bGzeGO+90XlqzL0HGWst//vMfkpKSmD9/Punp6R59atSowa233kqfPn245JJLMMb4IVIpS0pglJAGGiKBKScnhzlz5jB8+HA2b9585PjFOPPTvTHGwPDhPolPREQkj/R0eOMNZ7bFd995tleqBNdf78y2uPZaqKzhuwSX1NRUZs+ezaxZs/jzzz892o0xdOjQgT59+nDjjTcSofovFZr+AopIhbFixQqGDBnCjz/+eORYIyChdm2u//ffgk+MKii1ISIiUgasdZIViYlO8mL/fs8+J53kJC369HFmXogEkf379/POO+8wa9Ysli1b5rXPqaeeSp8+fbjjjjto2rSpjyMUf1ECQ0TKvT/++IOhQ4eycOHCI8dCgVHVqzM0K4sqhSUvIiJg3LiyD1JEpBBamhokdu1ylofMmAGrV3u2h4ZCt24QFwdXXqldsCSo5OTk8MUXXzBr1izeeust9u3b59EnMjLyyBKRiy66SEtEgpASGCJSbu3evZuxY8fywgsvkJWVdeR49ypVSKhenbr5Exc33QTt2sGUKZCW5sy8GDcOYmJ8G7iISD5amlqBWesU4pwxA95+GzIzPfu0bu0kLXr1cnYVEQki69evP7JEZP369R7tISEhXH311fTp04euXbtStWpVP0QpgUIJDBEpd7Kyspg+fTpjxoxh586dR463BOY3asRZW7aAe/Li9NNh6lS46irn/eDBPo1XRESC0NatkJTkJC7++MOzvWpVuPVWJ3Fx8cXOziIiQWLfvn28/fbbJCUlscLbTjtAq1atjiwRadKkiY8jlEClBIaIlBvWWhYvXsxDDz3EunXrjhyvCUxr1Ihbt20jZMuWoyfUqgVjx8LAgSp6JiIiZS87Gz75xKltsWgRHD7s2ee885zaFj17QmSk72MU8ZOcnBxWrFhBUlISb7/99pFd4tzVqlWLnj17EhsbywUXXKAlIuLhuEb0xpjaQHVr7cZSikdExKvVq1czZMgQPv300yPHDPBQ3bo8cfgw4e6JC2NgwAAneaGpuCLlRjCPK1QDo5xLS4PXXnNeG73cvjVrOssV4+KgTRvfxyfiR3/++SezZs1i9uzZpKamerSHhIRw7bXXEhsbyw033EB4eLgfopTyotgJDGNMdeBxIAaoD9jc6xhjLgRGAyOttT8WeBERkWO0detWHnvsMV599VVycnKOHL8qIoK5derQ6O+/855w2WXOcpFzzvFtoCJSIhpXOFQDoxzKynJmWSQmwscfO7Uu8rv0Uidp0aOHUzRaJEjs3buXt956i1mzZvHFF1947XPaaacdWSLSqFEjH0co5VWxEhjGmEjgS+B04GdgB9Darctq4DKgJ1ChBxoiUrYyMjKYPHky48ePz1OFurExvHPKKVz022/gPvWwSROYONFZT6zphiLlgsYVUi4kJ8OIEUeLP993H2zb5tS32LbNs3/duhAb6ywTad3as12kgsrJyWHZsmXMmjWLd955h4MHD3r0qVOnDj179qRPnz6cd955WiIixVbcGRgjcAYZfay1s40xo4FRuY3W2gPGmBXA/5VijCISRKy1vPnmmzz66KN5phmGAi+dcgp9N22i0m+/HT0hLAwefhgefRSqVfN9wCJyPDSukMCWnAzx8UcT5qmp8NBD3vt26ODMtuja1flsEgkSv//++5ElIhu9LKGqVKkS1113HX369OH6668nTP99yHEobgKjO/CxtXZ2IX1SgfNLHpLvGWPuAWKBs4BvrLXt/RuRSHD65ptveOCBB/jmm2/yHB/YtCkTs7Op9vvveU/o1g2eew5OOsmHUYpIKaqQ4wqpQB5+OO9sv/waN4a+faFfP30WSVDZs2cP8+fPJykpia+++sprnzPOOIO+ffsSExNDw4YNfRyhVFTFTWCcCLxTRJ99QHkrqbwFmIAzQLrYz7GIBJ3U1FQeffRR3njjjTzHL6xVizebNCH6l1/ynnDaafD8887TLhEpzyrquKLYVMQzgGRkwDvvwPTp4F4gOr/334frrtMuVxI0srOz+eyzz0hKSmLhwoVkZGR49Klbty4xMTHExsbSpk0bLRGRUlfcv7jpQIMi+pyEs4a13LDWLgAwxkT5OxaRYJKens5TTz3FpEmTyMzMPHK8bpUqvHPOOVz+888Y9+RFZCQ8/jjcfTdUqeKHiEWklFXIcUVJqIhnAFi3DhISYNYs2LWr8L7R0dCli2/iEvGT5ORkRowYQWpqKjVr1iQkJITdu3d79KtcuTKdO3cmNjaWzp07Exoa6vtgJWgUN4HxPXC9MaaGtTY9f6MxphHQCfigOBc1xgwDzgXOwxmopFprmxXQNwS4HxgANAO2A/OBUdba/cX5viLiH9nZ2bz22muMHDmSbW4F0AwwtW1bBm7YQOXvvz96gjHOuuInn4T69X0fsIiUlTIZV4gcs8xMZ7ZFQgKsWOHZbgyEhEB29tFjEREwbpzvYhTxg5kzZzJw4EAOHToEOLuK5HfOOecQGxvL7bffToMGReWiRUpHcRMYzwNLgMXGmHj3BmNMayARCAemFvO644FdOBXGaxXRdzIwCFgIPIdTrXwQ0MYY08Fae2SfRWNMGFDYY9qD1trsQtpFpJR9+umnDBkyhNWrV+c53rtVK140hho//JD3hEsugRdegHPP9WGUIuIjZTWuECncb785SYukJNi507M9OtpJnN95JyxblncXknHjICbG5yGL+MJvv/3G9OnTmTJlSp7t63OFhIQwaNAgYmNjOUdb1osfFCuBYa392BgzBhgDrAGyAIwxO4DaOA9QH7HWeq/kUrCTrbV/ua61BqjurZMx5nTgPmCBtfYmt+PrcQY3twHz3E55FWdf+YJcCaQUM1YRKYF169bx0EMP8eGHH+Y53qZRI95q0YKT8+8R3rgxPPss9OypbVFFKqgyHFeIeMrMhIULndoWKSme7ZUqOctCBgyAq6923oOTrFDCQiqwQ4cO8d577zFt2jSWLVtWaF9rLZMnT/ZRZCKeil11yFr7hDHmC5xZDxcBdQELLAYmW2sLv+u9X/OvY+zaE2cwMyXf8UScIpx34JbAsNbe4TomIn6yc+dOxowZwyuvvEK22xTcyKpVeeuyy+jw9dcY9+RFaKizRd2wYVDday5TRCqQshhXiOTx+++QmAgzZ8IOL+VUoqKOzrZo3Nj38Yn4yYYNG0hMTOTVV19l69atx3ROVJRKBop/lahssrV2ObC8lGM5FucDOcB3+eLJMMb8TAm3WTPGVMb5XVQGQowx4UCOtfbQ8YUrErwOHTrEiy++yNixY/MUfDLG8FyHDtz3559U/uSTvCd17epsi3ryyb4NVkT8yo/jCqmoDh2Cd991Zlt4e6IcEuLMtoiPh2uuOTrbQqSCy87OZsmSJUybNo3Fixdjrc3THhISwvXXX0/Lli156aWXOOC2jXBERATjVP9F/Ky87fvUGNhhrc300rYJuMQYE1qCxMNIYLTb+4PACqC9t86udbrxAA0bNiTF2zTEEtq3b1+pXk8qnkC/R6y1fPnll0ybNo3NmzfnabuhdWteqFyZqKVL8xzfHxXFH/fey7/nnw8bNzovKbFAv0fEv3R/BCZto1pK/vjj6GyL7ds925s2hf79oV8/aNLE9/GJ+MmWLVt49dVXSUhIYKOXcVajRo2Ii4ujf//+NG3aFICzzz6bESNGkJaWRlRUFOPGjSNGy6nEz0qcwDDOpr4nUECRTGttWkmvXYgIwFvyAiDDrU+xEhjW2jE462+PtX8CkADQtm1b2759++J8u0KlpKRQmteTiieQ75Eff/yRIUOGsCJfJfezmzfnrXPOocWiRZisrKMNNWvC449T7Z57OFvbopaaQL5HxP8C9f7w07giYGgb1eNw6BC8955TlPPTTz3bQ0Kgc2entsW112q2hQSNnJwcli1bxrRp03jvvfc4fPiwR5+OHTsycOBArr/+eqrkG4vFxMQQExMTsJ8bEpyKncAwxvQAHgXOBAr6BLAlufYxOEDB+8WHu/URER/atGkTI0aMYPbs2XmmItaOjOT1zp3puHw5ZsGCoycY46w1Hj8etO2WSFDz87hCyrO//nJmW7z2GrhtyX3EiSc6sy3uvNOZeSESJHbs2EFSUhLTp0/njz/+8GivV68ed955J/Hx8ZysZbtSzhRrMGCMuQdnt4/DwJc4yzY8U3llZzNwmjEmzMsykiY4y0t8UrdCUz1FYP/+/UycOJFnnnkmzxrJypUrM+Gmmxj0559UmTcv70kXXwxTp0Lbtj6OVkQCTQCMK6S8ycqC9993alvkW44IOLMtrrvOmW1x3XVQWXkvCQ7WWv7zn/8wbdo03nrrLQ4d8vwn0eWXX87AgQPp3r07YWFhfohS5PgV96/6A8A24BJr7foyiKco3wMdgQuAI9sWuIpungN87qtANNVTgllOTg5z585l+PDhbNq0KU9br44dmVqjBrXmzwf3wlCNGsEzzzhb0WlbVBFx+HtcIeXF+vVHZ1t42y2hceOjtS20S4IEkT179jB37lymTZvGmjVrPNojIyOJjY1lwIABnHbaaX6IUKR0FTeB0QRI9OMg401gODAYtwQGEIdT+yLZDzGJBJXPP/+cIUOGsHLlyjzHzz3jDF5v145T582DvXuPNoSGwpAhMHw41Kjh42hFJMD5e1whgSwrCxYtcmpbfPJJ3qQ4OMnw665zdhLp3FmzLSSorFy5kmnTpjFv3rw8s2BzXXDBBQwcOJBbb72ViIgIP0QoUjaK+5d+I1Dq842MMb2AaNfb+kCoMWak632qtXYOgLV2tTHmJeBeY8wCnD3iW+PsHb8CyDdXvexoCYkEmz///JOhQ4eywL2WBc5OPEk9e3LNRx9hpk3Le1KXLjBpEui/ExHxrkzGFVLObdgAM2Y4sy22bPFsb9TImWnRvz9ER3u2i1RQ+/fv54033mDatGn88MMPHu3VqlXj9ttvZ+DAgZx77rl+iFCk7BU3gZEEDDTG1LDWppdiHP2AK/IdG+v6ugKY43Z8MLABZxvTzsAO4AVglLU2pxRjKpSWkEiw2L17N08++SRTp04ly20HkfDwcMbdeSf3bdhAlSlT8p7UsiVMmeJUexcRKVgSZTOukPLm8GH44AOntsXHH3ufbXHNNU5ti+uv12wLCSpr1qxh+vTpzJ49m73us1xdzjjjDO666y5iYmKIjIz0Q4QivlPcv/5PA+cBnxpjhgI/lsaAw1rbvhh9s4HnXC8RKSNZWVkkJCQwevRodu7cmaftzltuYVL9+kQmJjrb1+WqUQNGj4b77nOWjoiIFK5MxhVSjqSmwquvOq/Nmz3bTzjh6GyLZs18Hp6Iv2RkZPDOO+8wbdo0vvzyS4/2sLAwbrnlFgYOHMjFF1+MUX0xCRLFSmBYa7NdSzjeApYBBf3HYq21So2LlEPWWhYvXsxDDz3EunXr8rRdcvHFzLrmGlokJHgONPv2dbZFPeEEH0YrIuWZxhVHBdXS1MOHYfFiZ7bFkiXeZ1t07OjUtujSBapU8U+cIn7w+++/k5CQwMyZMz0eIAGccsopDBw4kNjYWOrWreuHCEX8q7jbqHYF3sbZp309zramQbndWVANNCRorF69mgcffJCl+bama9asGQkDBtBh0SLMmDF5T7rwQmdb1Asu8F2gIlIhaFxxVFAsTd240alt8eqrkG8HKwAaNoQ774S4ODjpJN/HJ+InWVlZLFq0iFdeeYVPP/3Uo71y5cp069aNgQMHcuWVV2q2hQS14j7NGAMcADpbaz3nMgWRoBhoSNDYunUro0aNYsaMGeTkHC0lU6NGDcbdfz93bdpE5eHD8z4lO+EEePppuOMOCAnxQ9QiUgGMQeOKii0725ltkZDgfM3xUq7s6qud2RZdu2q2hQSVtLQ0ZsyYwYwZM9jipWBtVFQU8fHx9OvXjxM0w1UEKH4CoyUwW4MMkYohIyODKVOmMH78eNLTjy47DwkJYWC/fkxo2pQazz0He/YcPalKFXjgARgxAmrW9EPUIlKBaFxRUf39tzPTYsYM53/n16CBs/QwLg5OPtn38Yn4SXZ2Nh9//DHTpk3jww8/zPPgCJxldJ07d2bgwIFce+21VKpUyU+RigSm4iYwdgCHiuwlIgHNWsubb77Jo48+Smpqap62jh07Mu2mmzhpyhRYuzbviZ07O9uinnqq74IVkYpM44qKJDsbPvrImW3xwQfeZ1v83/85O4l07apizxJU/vnnH1577TUSEhI8xl4AJ5xwAv3796d///5Ea3tgkQIVN4HxDnCtMaaKtTaryN4iEnC++eYbHnjgAb755ps8x1u3bs3LDz1E+0WLnMGlu1NOgcmTnQSGiEjp0biiIti0CV57zZltkZbm2V6/vjPbon9/5/NEJEhYa1m+fDnTpk1j4cKFHD7sWeKnQ4cODBw4kBtuuIEqWkIlUqTiJjBGAhcCbxljBltrN5R+SOWDinhKeZOamsqwYcN4/fXX8xyvV68e44YPp//27YTcfTdkZh5trF4dRo2C++/XkzIRKQsaV5RX2dnwySfOTiIffOC8z+/KK52E+I03QliYz0MU8Zddu3Yxa9Yspk2bxm+//ebRXrduXfr27Ut8fDynKKknUizFTWCsBqrgDDa6GGN2A3u89LPW2gq9oFFFPKW8SE9PZ8KECUyaNImMjIwjx0NDQ7l/0CBGt2xJtTFjPCvCx8bCU09Bo0a+DVhEgonGFeXN5s1HZ1t4mQZPvXrQp49T20LLDSVIJCcnM3z4cDZu3EhERAQZGRlke0nqtWvXjoEDB3LTTTcRHh7uh0hFyr/iJjBCcLY3c58f6G0fH+3tI+Jn2dnZzJw5k5EjR7J169Y8bTfffDOTevWi6bPPwsSJeU88/3x44QVne1QRkbJVYccVxpjKwHNAL5yf823gHmttZqEnBqKcHFi61Jlt8f773mdbtG/v7CTSvbtmW0hQmTFjBnfffTdZWc4quP379+dpr1mzJr1792bAgAGcccYZ/ghRpEIpVgLDWtusjOIQkVL06aefMmTIEFavXp3neNu2bXlh1Cgu+uADZ0qv+7aoDRs6My5iY7Utqoj4RAUfVwwHrgDOALKA94GngCH+DKpY/vnHmW2RmAgbNni216njzLaIj4eWLX0dnYjfWGv5/vvvmT59OjNnzsS6j6dcQkNDefnll7ntttuoVq2aH6IUqZiKOwNDRALYunXrePjhh/nggw/yHG/SpAkTnnyS2/fuJaR3b9i9+2hj5coweDA89pi2RRURKT39gSHW2s0AxpgxwBvGmIestV625wgQOTnw6adHZ1t4KTrI5Zc7tS26dwdNg5cgkp6eTnJyMtOnT+fnn38utG9WVhb9+vXzTWAiQUQJjBJSEU8JJDt37uTxxx/nlVdeyVPhOiIigkcffZSHzz2X8EcegV9+yXvitdc6u4u0auXjiEVEAoMxZhhwLnAecBKQWtDMEGNMCHA/MABoBmwH5gOjrLX73frVApoCP7md/iOQe9xL8Qg/27oVZs50Zlv89Zdne506zgy9uDho3dr38Yn40cqVK5k+fTrz5s3zWCJSkKioqDKOSiQ4FZrAMMb0dv3PhdbadLf3RbLWzj6uyAKcinhKIDh06BAvvfQSTzzxBLvdZlUYY+jTpw9PDRhAw2eecXYScXfyyTBlirMtqil3S8tFpJwK0HHFeGAXRxMMhZkMDAIW4tS3aO1638YY08FtZkUN11f3gqS787X5X04OLFvmzLZ4913vsy0uu8xZInLzzZptIUFl3759vP7660yfPp2VK1d6tIeHh3PrrbfSrFkznn32WQ4cOHCkLSIignHjxvkyXJGgUdQMjCTAAt8A6W7vC2NcfSp0AkPEn6y1vPvuuwwdOpQ//vgjT1v79u2ZPG4c53z0kVNUzW3nEapVc5aKDB6sImsi4g9JBN644mRr7V8Axpg1QHWvQRhzOnAfsMBae5Pb8fXAVOA2YJ7rcLrraySww/W/a+Vr859t247OtvjzT8/2WrWc2Rbx8XDaaT4PT8SffvrpJ6ZPn05ycjL79u3zaD/ttNMYMGAAvXr1onbt2gCccsopjBgxgrS0NKKiohg3bhwxMTG+Dl0kKBSVwLgTZ9CwxfW+b9mGIyIFSU5OZsSIEaSmphIWFkZmZt5C9i1atODZZ56ha2Ym5rbbYOPGvBfo1QsmTIDGjX0YtYhIHgE3rshNXhyDnjjJlCn5jicCE4A7cCUwrLW7jTEbgXOA3AxBG5xZGPn+OJeh5GQYMYIr0tKgaVOIiXESFgsXgmvHhDwuvdSpbXHzzVC1qs/CFPG3/fv388YbbzB9+nS+//57j/awsDB69OjBgAEDuPTSSzH5Zq/GxMQoYSHiI4UmMKy1Sa7pnScBq6y1s3wTloi4S05OJi4ujoMHDwLkSV7UqlWL0aNHc0+7dlR58EH4/PO8J593nrMt6sUX+zJkEREP5XxccT6QA3znftBam2GM+dnV7m4GMMIY8zXOLiRjgJk+K+CZnOzMoDhwwNmDNi3N2Wkqv8hI6N3b6astHiXIrFq1iunTpzN37lz27t3r0d6qVasjsy3q1q3rhwhFJL9jKeI5E3gcWFXGsYiIF9u3b+euu+46krxwV6NGDf787jvqTJ4MDz7orGfOVb++M+OiTx9tiyoigaS8jisaAzustZle2jYBlxhjQq21h1zHxgP1gF+AEOAtnK1VPRhj4oF4gIYNG5KSknLcwV70wAOEu63Jz2/P6aez+frr2d6+PTnh4bBjB5TC95XyZ9++faVyz5UXGRkZpKSksGjRIn799VeP9ipVqnD55ZfTpUsXzjrrLIwxHtvSB5tgu0ek+Hx5jxxLAkMV/rzQLiRS1tLT05k0aRITJ070ugazEtA7PZ06F14I//57tKFyZbjvPqdwZ61aPotXROQYlddxRQTgLXkBkOHW5xCAtfYwToHPQUVd2FqbACQAtG3b1rZv3/54Y3USEgVZtYrIM88kEqcKqQS3lJQUSuWeC3Br1qwhISGB2bNns2fPHo/2U089lfj4eGJjY6lXr54fIgxcwXKPSMn58h7RNqolpF1IpKxkZGTwyiuvMH78eHa4DUB74jzOiwK24YyQoyBv8qJjR2d3EW1xJyJS2g4ADQpoC3frUyKl/mAkKgpSvezWGh0NZ55ZOt9DJMAdPHiQt956i+nTp/PVV195tFepUoXu3bszYMAA2rdv71HbQkQCjxIYIgHi8OHDzJ49mzFjxrAxXwHOu2vV4pndu6nmen9C/pObN4fJk6FLF22LKiJSNjYDpxljwrwsI2mCs7zkkJfzjkmpPxgZNw7i4sB9+WFEhHNcpIJbu3Yt06dPZ/bs2fzr/qDHpUWLFsTHx9OnTx/q16/vhwhFpKSONYFRyxgTVZwLW2vTShCPSNCx1vLOO+/w2GOPsW7dujxt0dHRPDlqFDGPPup9zrUxzmD0gQcgPNxbDxGRQFQexxXfAx2BC4Avcg8aY8Jxdhv53PtpfpK7I8KIEdi0NExUlPN5oZ0SpILKyMjgnXfeYfr06XzxxRce7ZUrV6Zbt24MGDCAK6+8khDVBxMpl441gXG/63WsbDGuLRKUrLV8+umnDB8+nB9++CFPW4P69XnxjjvofuAAlYYOhZ07C77QsGFlHKmISKkrj+OKN3GKcA7GLYEBxOHUvkg+nouXSW2tmBiIiWGF1q9LBfa///2PhIQEZs2axU4v46WTTjqJ+Ph4+vbtS8OGDf0QoYiUpmMdDOzF2btcRErBt99+y7Bhw1i+fHme4+dUr87z559Pu9RUQiZPLvpCUcV6gCkiEigCZlxhjOkFRLve1gdCjTEjXe9TrbVzAKy1q40xLwH3GmMWAItxamAOAlYA844nDtXWEjl2mZmZLFiwgISEBK87H1SqVImuXbsyYMAAOnTooNkWIhXIsSYwJltrnyjTSESCwC+//MLIkSN59913jxyrA9xRuTJD6tcnessWyJfUcDrVgb174fDho8e0lllEyq9AGlf0A67Id2ys6+sKYI7b8cHABpwtTzsDO4AXgFHWWrd9rEWkLPz+++8kJCSQlJSUp9B5rujoaOLi4rjzzjtp1KiRHyIUkbLm7+mYIkFhw4YNjB49mjlz5mCtJQy4HugNdDaGSocPw5YteU+qWRN69IBeveCyy+D117WWWUSklFlr2xejbzbwnOtVqrQ9u4h3hw4d4t1332X69OksW7bMo71SpUp06dKFAQMGcPXVV1OpUiU/RCkivqIERglpoCHHYuvWrYwbN45p06ZxOCuLy4A7gB5ArdxO1h49oXJl6NQJ7rjD2VHEvTCn1jKLiFRYWkIikteff/5JYmIiM2fOZNu2bR7tTZs2PTLbokmTJn6IUET8QQmMEtJAQwqzZ88eJk6cyOTJk2m6fz9jgBiOLrL2cNFFzkyLW26BevV8FqeIiIhIoMjKyuL9999n+vTpLF261KM9JCSEzp07M2DAAK699lrNthAJQkpgiJSigwcP8uKLL/La+PFcs3s3K4DzCup88snOTIs77gDN5BEREZEgtX79ehITE3nttdfYunWrR3uTJk3o378//fr1o2nTpn6IUEQCRZEJDGutyvaKFCErK4s506axctQort+9m9UU8B9XnTpw221O0uKii8AYH0cqIuJfGld4p6WpEmyysrL44IMPmD59Op988gnWfUktYIyhU6dOxMfH06lTJypX1nNXEdEMDJHjkpOVxYrRo/n3xRfpkZ7OnV762LAwTJcuTtLiuusgNNTncYqISGDT0lQJFqmpqcyYMYNXX32VLfkLmAONGzemX79+9O/fnyhtFy8i+SiBIVJc1mL/+1/Wjx1Ljfff50r3rU3d5Fx2GSG9e2Nuvhlq1fJtjCIiIiIB4vDhw3z44YckJCSwZMkSr7MtrrnmGgYMGMD111+v2RYiUiD9dRA5Vn//DfPmsX/6dKr99RfNvXTZWb8+Ne65h9A+fQiJLrBkp4iIiEiFt3HjxiOzLTZt2uTRfsIJJ3DnnXcSFxdHs2bNfB+giJQ7SmCIFGbvXnjnHZg7F7t8OcZaquXrsg34vW1bznzmGeq2b6+6FiIiUmyqgSEVRXZ2NkuWLGH69OksXryYnJwcjz5XX301AwcOpEuXLlSpUsUPUYpIeaUEhkh+WVnwyScwZw689x5kZADgnpY4ALxnDP927kz3V17h0hNP9EuoIiJSMagGhpR3mzZt4tVXX2XGjBls3LjRo71BgwZHZls0b+5tHquISNGUwBABsBa+/x7mzoU33oDt2z265ACfAXOB0FtvZdj48foAFhERkaCVnZ3NJ598wvTp0/nggw/Izs726PN///d/DBgwgK5duxKqQuYicpyUwJDgtn49JCc7sy1++81rl59xkhavA+d16cK4ceM488wzfRikiIiISODYsmXLkdkWqampHu316tWjb9++xMXFccopp/ghQhGpqJTAKCGtVS3H/v0X5s93Zlt8+aXXLpuAZGAOsAa44ooreGv8eC655BIfBioiIiISGHJycli6dCnTp0/n/fff9zrb4sorryQ+Pp5u3boRFhbmhyhFpKJTAqOEtFa1nMnMhMWLnZkWH34Ihw55dNlnDG9Zy1wgBWfJSJs2bfjoqafo2LEjRsU5RUSkjOjBiASqrVu38tprr5GYmMj69es92uvWrUufPn2Ij4/n1FNP9UOEIhJMlMCQiiU5GUaMgLQ0aNoUYmNh2zZnxsW//3p0zwkJYUVYGNMPHuR9aznoOn7qqacyduxYbr75ZkJCQnz7M4iISNDRgxEJJDk5OSxbtozp06fz7rvvcvjwYY8+l19+OQMGDKB79+6Eh4f7IUoRCUZKYEjFkZwM8fFw4IDzPi0Nxo712vXfU05h2r59TN6yhe0HDx453qRJE8aMGUOfPn2oXFn/eYiIiEjw2LZtG0lJSSQkJPDnn396tNeuXZvY2Fji4+Np3bq1HyIUkWCnf6FJxTF8+NHkhTfNmpF62WU8uno1b/z8c56mOnXqMHz4cO6++26qVq1atnGKiIiI+FlycjIjRowgLS2N+vXrc9JJJ/Hjjz+SlZXl0ffSSy9lwIAB3HzzzRoniYhfKYEhFcOaNc6MiwL8b8YMBs+fz0dz5uQ5Xq1aNYYMGcKDDz5IZGRkWUcpIiIi4nfJycnExcVx0DULddu2bWzbti1Pn1q1atG7d2/i4+M5/fTT/RGmiIgHJTCkfMvMhHHjYMKEArtsj4igVf/+eY6FhoZy1113MXz4cBo0aFDWUYqIiIj4XU5ODsuXLyc+Pv5I8iK/iy++mAEDBtCjRw8iIiJ8HKGISOGUwJDy68svIS4O1q07csgC7nuF7Afud1tWEhISQu/evRkzZgzR0dE+C1VERETEX7Zu3UpSUhKJiYlea1vkMsbw1Vdf+TAyEZHiUQJDyp89e+DRR2HatDyHt59yCpP/+ov47GyigDRgOPC6q7179+6MHTuW0047zccBi4iIFE7bqEppy8nJYenSpSQmJvLee+953Ukkv6ioKB9EJiJSckpgSPny7rtwzz2wefPRYzVqsOm++2g1ZQr7srN5Kt8p4eHhrFixggsuuMCXkYqIiBwzbaMqpWXz5s3MnDmTGTNmsGHDBo/2WrVqccEFF/D555+TkZFx5HhERATjxo3zYaQiIsUX4u8ARI7J5s1w003QrVue5MWOSy6h13nnceL48ewrYAeSzMxMJS9ERESkwsrOzmbx4sXceOONREVFMXLkSI/kRbt27Zg9ezabN2/m448/ZsaMGURHR2OMITo6moSEBGJiYvzzA4iIHCPNwJDAlpMDr74KDz/sLB1xyahViyfq1eOpY1inqemQIiIiUhFt3LiR1157jVdffZWNGzd6tNepU4fY2Fj69+/vsYQ2JiaGmJgYUlJSaN++vY8iFhE5PkGfwDDGhAEvAv8HNAC2AC9Za6f4My4B/vc/iI+Hzz/Pc/ityEjid+9m9+7dR44ZY2jbti2rV6/WdEgRERGpsA4fPszixYtJTExk8eLF5OTkePRp3749cXFxdO/enfDwcD9EKSJSNrSExEni/AN0BGoCPYBhxphb/RpVMDt0yNka9eyz8yQv/qpUiSuBW/bsYbfrWGhoKPHx8axbt47vvvtO0yFFRESkQkpNTWXUqFFER0fTtWtXPvjggzzJi3r16vHQQw+xbt06li9fzu23367khYhUOEE/A8Naux94zO3Qz8aYD4FLgTf9E1UQ++476N8fVq8+cugw8AwwNjub3LkVNWvW5O677+b+++/nhBNOONI3dzqkiIiISHmXlZXFBx98QEJCAh9//DHWWo8+//d//0d8fDxdu3YlLCzMD1GKiPhOQCQwjDHDgHOB84CTgFRrbbMC+oYA9wMDgGbAdmA+MMqVjDjeWCoD7XD+zSy+sm8fjBwJU6eC24fz90B/YJXrfaNGjXjggQcYMGAANWvW9EekIiIiImXqr7/+YsaMGcycOZN//vnHo71hw4b07duX/v37c/LJJ/shQhER/wiIBAYwHtgF/AjUKqLvZGAQsBB4Dmjtet/GGNPBWntkLp2rvkWVQq510Fqbne/YVGAPMLs4P4AchyVLYOBASEs7cmg/MBJ4AcgGWrZsydChQ4mJidHTBREREalwDh06xHvvvUdCQgKffvqpR7sxho4dOxIfH0+XLl2oUqWwIa6ISMUUKAmMk621fwEYY9YA1b11MsacDtwHLLDW3uR2fD1O4uE2YJ7bKa8Cha0nuBJIcbvOczizL66y1h4q0U8ix277duzgwZh58/Ic/gi4C9gAXHTRRTzyyCPccMMNhISoZIuIiFRMxpguQJcWLVr4OxTxsd9//53ExESSkpLYvn27R3ujRo3o168f/fr1o1mzZr4PUEQkgAREAiM3eXEMegIGmJLveCIwAbgDtwSGtfYO17EiGWOm4OxEcpW1dscxxiMlYS3ZSUlkDRpE+L59Rw7vAAYDyUCnTp2Y9cgjXHbZZRhj/BSoiIiIb1hrFwGL2rZtG+fvWKTsZWZmsnDhQhISEli+fLlHuzGGTp06ERcXR+fOnalcOSCG7CIiflfe/hqeD+QA37kftNZmGGN+drUXmzFmKnAVcKW11jP1LaXmwC+/sOOmm4j63/+o5HZ8DjC0UiWuvv12Vj38MGeeeaa/QhQREREpE+vWrSMxMZFZs2axc+dOj/YTTzyRfv36ceeddxIVFeWHCEVEAlt5S2A0BnZYazO9tG0CLjHGhBZn+YcxJhpnWUomsN7taf8X1trrCjgnHogHp4hSSkrKsf8ERdi3b1+pXi9Q7N21iwNPPUWXH37A/eN4A3BflSqE3XADU3r0oGHDhuzcubNC/g5KS0W9R6T06B6Rwuj+EPGtgwcP8s4775CQkMAXX3zh0R4SEsL1119PfHw81157LZUqVfJyFRERgfKXwIjASTR4k+HW55gTGNbaVJxlKcfMWpsAJAC0bdvWtm/fvjinFyolJYXSvJ6/paam8sajj3L1/Pmc67ZXeTaQEB7O7iFDSBoyhLp16/ovyHKmot0jUvp0j0hhdH+I+MaaNWtITExkzpw5/Pvvvx7t0dHR9O/fn759+9KkSRM/RCgiUv6UtwTGAaBBAW3hbn3Ez1atWsXk8eNpPX8+D1qb50ZbW6UKawYPJnbMGCIiIvwWo4iIiEhpOnDgAPPnzycxMZGvvvrKo71SpUp07dqVuLg4rr76as22EBEppvKWwNgMnGaMCfOyjKQJzvISn+weomrhnqy1fP755zz99NNkLFlCAuD+28k0hnW33MLpM2fSumpVf4UpIiIiUqr++9//kpiYyNy5c9mzZ49He/Pmzenfvz99+vShUaNGfohQRKRiKG8JjO+BjsAFwJFFhMaYcOAc4HNfBaJq4Ufl5OTw7rvv8vTTT/P7d9/xLNAvX59dZ51F7fnzObtlS3+EKCIiIlKq9u3bxxtvvEFiYiLfffedR3uVKlW48cYbiY+P56qrrtJ28CIipaC8JTDeBIbj7LbpXgUpDqf2RbIfYgpamZmZzJkzh2effZbffvuNHsD7QEO3PoerV6fylCnUufNO0HaoIiISJIwx9wCxwFnAN9ba9v6NSEoqOTmZESNGkJaWRlRUFHFxcWzcuJHk5GT2uW0Hn6tFixbEx8cTGxtLgwYFrXwWEZGSCIgEhjGmFxDtelsfCDXGjHS9T7XWzgGw1q42xrwE3GuMWQAsBloDg4AVwDwfxhy0S0j27NnDtGnTmDJlCv/88w8nAu8BN+TvePPNVJ46FTRVUkREgs8WYALOFu8X+zkWKaHk5GTi4+M5cMApsZaamsrIkSM9+oWGhnLTTTcRHx/PFVdcgdFDGxGRMhEQCQycFQdX5Ds21vV1BTDH7fhgnN0344HOwA7gBWCUtTYHHwnGJSRbtmxhypQpTJs2jb1792KAu3FGZzXcOzZpAi+9BF27+iVOERERf7PWLgAwxkQV1VcCU1ZWFg888MCR5IU3rVq1Ii4ujt69e1OvXj0fRiciEpwCIoFRnGmV1tps4DnXS3zgt99+49lnn2X27NkcOuTUSD0NSAQuyd/57rvhqaegZk0fRykiInKUMWYYcC5wHnASzozOZgX0DQHuBwYAzYDtwHychyP7fRGvBAZrLV9//TXJycnMnz+fHTt2FNj3888/p127dpptISLiQwGRwJDA9O233/LMM8+wcOFCrLUAhOIUIRnm+t9HtGoFiYnQrp3vAxUREfE0HtgF/AjUKqLvZJzlqAtxHpDkLk9tY4zp4D7D0xgTBlQp5FoHXQ9bpBxZt24dycnJJCcns379+iL7R0dHc9lll/kgMhERcacERglV1BoY1lo++ugjnn76aVasWJGn7RJgbtWqnHTw4NGDVarA8OEwbBiEhfk2WBERkYKdbK39C8AYswao7q2TMeZ04D5ggbX2Jrfj64GpwG3krbH1KhBTyPe9Ekg5rsjFJzZv3swbb7xBcnIyP/74o9c+tWvXJj09ncOHDx85FhERwbhx43wVpoiIuNF+TiVkrV1krY2PjIz0dyilIisri7lz53L22WfTqVOnPMmLGsAHUVH8B/ImLy6+GH76CcaMUfJCREQCSm7y4hj0BAwwJd/xROAAcEe+695hrTWFvFKON3YpO3v37iUpKYmrr76apk2b8uCDD3okLyIjI+nfvz/Lly9nx44dJCUlER0djTGG6OhoEhISiIkpLIclIiJlRTMwgtz+/ft59dVXmTRpEqmpqXnaKleuzLPt2nHPr79SJS3taEP16jBhAtx1F2hPcxERKd/OB3KA79wPWmszjDE/u9qLzRhTGWecVRkIMcaEAznW2kPHF64U16FDh1iyZAnJycn8f3t3HidFfed//PUB5BiIEAUPVBDxZ0TNShSIZxhRPPEBC4pKjYlGYT3igaKoGEVdNgsewYg++I1H3Ac0iicrETVGMqNZj8jGqKgb12NAxAsUEDnk+OwfVQM9PT3T0z3T3TUz7+fjUY+ZrvpW1aedj91fPlXfb82fP58NGzbUatO+fXuGDRtGWVkZJ598Mh2SLswEQaCChYhITKiA0UqtWLGCGTNmMGPGDFauXFljW+fOnblyzBgmfvopJQsW1Nxx2DC45x7Ya68CRisiIpI3PYEV7r4xzbZPgSPMrH0OhYfrgRuTXq8nfLJaaWpDMxtH+HQ1dt11VyoqKrI8Vf3Wrl3b5MeMu61bt7J48WL+9Kc/UVlZyZo1a2q1MTP69+/Psccey+DBg+nSJRxl9MorrxQ63KJqjfkh2VGOSCaFzBEVMHLUXOfAqKqq4o477uC+++5jffJwEKB79+5cesklXNG1K51vvBFWr96+cZdd4K674PTTQbNti4hIy1ECpCteAGxIapNVAcPdJwOTG9i2HCgHGDBggJeWlmZzqowqKipo6mPG1TvvvMPs2bOZM2cOS5PvHk1y8MEHEwQBZ511FnvuuWeBI4yf1pQfkhvliGRSyBxRASNH7j4fmD9gwICxxY6lId58802mTZvG3Llz2bKl5uToffr0YcKECZx71FF0uuwySK2e/fKXcOutsNNOhQtYRESkMNYBu9SxrWNSm7xqrhdG4mDZsmU89NBDJBIJ3nzzzbRtevXqtW0oyIEHHljgCEVEpKmogNGCuTuVlZVMnTqVZ599ttb2/v37M3HiRE4bPpx206fDoEGwMekiVN++UF4OQ4YULmgREZHCWg4cYGYd0gwj2YNweEne561obhdGim3VqlU8/vjjzJ49m8rKym2Pe0/2wx/+kNGjRxMEAUceeSRtNG+XiEizpwJGC7RlyxbmzZvH1KlTef3112ttHzJkCBMnTmTo0KHYokVw2GHw1lvbG7RtCxMmwI03QqdOBYxcRESk4F4HjgcGAS9Vr4wm3ewPvFicsCTVxo0befrpp0kkEjz99NNs3Fh75E/Hjh059dRTKSsr48QTT6R9+/ZFiFRERPJFBYwWZMOGDcyaNYvbbruN999/v8a2Nm3aMGrUKK6++moGDBgAa9fCFVfA734HW7dub3jooXDffdC/f2GDFxERKY65wHXA5SQVMICxhHNfJAoRhIaQpLd161ZefPFFEokEjz76KKuT5+eKtGnThiFDhhAEASNHjmTHHXcsQqQiIlIIKmDkKE4djdWrVzNz5kymT5/O559/XmNbhw4dOOecc5gwYQLbYn32WbjgAkh+bGqnTnDLLXDZZdBOaSEiIs2bmZ0N9I5e9gDam9n10esl7j4LwN3fNrO7gV+Z2RPAAqAfcCnhU0PmFCJeDSGp6a233mL27Nk89NBDLFu2LG2bQw45hCAIOPPMM+nZs2eBIxQRkWLQv1RzFIeOxvLly5k+fTozZ87k22+/rbGtW7duXHTRRVx66aXsuuuu4cqvvoLx4yGRcjFp6FCYORP22adAkYuIiOTdecDglHW3RD8rgVlJ6y8HqggfZXoKsAK4C7jB3ZNuU5R8Wrp0KXPmzCGRSLB48eK0bfr06UMQBIwZM4Z+/foVOEIRESk2FTCaoX/84x/ceuutzJo1i++/rzmv2B577MH48eMZN24cP/jBD8KV7mHR4vLLYeXK7Y132gl++1s4+2w9GlVERFoUdy/Nou0W4PZoKYo43dlZSF9//TWPPfYYs2fP5qWXXkrbZuedd+aMM84gCAIOP/xwTH0WEZFWSwWMZuTVV19l2rRpzJs3r9Zs2/vvvz9XX301QRDUnLDq44/hwgvhuedqHiwIwuJFjx4FiFxERETqE4c7Owtl/fr1/OEPfyCRSLBgwQI2bdpUq02nTp0YPnw4ZWVlHH/88eywww5FiFREROJGBYyYc3eeeeYZpk6dyosv1p4I/YgjjmDixIkMGzas5uPBNm8OJ+j89a9hXdLj63v1CoeLnHRSAaIXERERCZ+QVlFRQSKR4PHHH2fNmjW12rRp04ahQ4cSBAEjRozYfiepiIhIRAWMmNq0aRNz585l2rRpvP3227W2Dxs2jIkTJ3LUUUfV3vnNN+H882HRou3rzMIJOm+5Bbp0yWPkIiIikq2WOITE3XnjjTdIJBI8/PDDLF++PG27gQMHEgQBZ5xxBrvttluBoxQRkeZEBYyYSCQSTJo0iaVLl9KtWzfMjK+//rpGm3bt2hEEARMmTOCggw6qfZD16+Hmm+HWW2HLlu3rf/xjuPde+OlP8/wuREREJBctaQjJxx9/vG0yzvfeey9tm3333XfbZJz77bdfgSMUEZHmSgWMHDXllZJEIsHYsWNZv349AN98802N7Z07d2bcuHGMHz+evfbaK/1B/vxnGDcOPvhg+7oOHeCGG+Cqq0BjR0VERCRPVqxYwSOPPEIikeDll19O26ZHjx6ceeaZBEHAoEGDNBmniIhkTQWMHDXllZJJkyZtK14ka9OmDTfddBMXXXQRO+20U/qdv/kmLFDcf3/N9YMHQ3k56KqGiIiI5MG6det46qmnSCQSPPvss2zevLlWm86dOzNixAjKyso47rjjaNdOXU8REcmdvkViYOnSpWnXuzvXX399+p3c4bHH4JJL4Isvtq/v2jUcQnLeeZA8qaeIiIhII23evJmFCxcye/ZsnnzySdauXVurTdu2bTnhhBMIgoDhw4fTuXPnIkQqIiItkQoYMdCrVy+WLFmSdn1ay5bBxRfDU0/VXD9qFNx1F+y+ex6iFBERkXyJ8ySe7s6iRYu2Tcb5RfKFkySHHXYYQRAwevRodtlllwJHKSIirYEKGDEwZcoUxo0bx7qkx52WlJQwZcqUmg23bg0fgXrNNfDtt9vX9+wJd98NI0YUJmARERFpUnGcxPPDDz8kkUiQSCR4//3307b50Y9+tG0yzr59+xY4QhERaW1UwIiBIAgAtj2FpFevXkyZMmXbegDefRfGjoXUibEuuAD+/d/DoSMiIiIijfDll18yd+5cEokEr732Wto2u+2227bJOA899FBNxikiIgWjAkZMBEFAEARUVFRQWlq6fcPGjWGBYsoU2LRp+/r99w8n6Tz66ILHKiIiIi3Hd999x7x580gkEvzxj39kS/Kj2CNdunRh5MiRlJWVccwxx2gyThERKQp9+8TZyy/D+edD8jPUd9ghHEJy3XXQsWPxYhMREZFma9OmTTz//PMkEgnmzZtXYxhrtXbt2nHSSScRBAGnnnoqJSUlRYhURERkOxUwcpTXybbWrAkLFPfcEz5tpNphh8G998JBBzX9OUVERKRoCjGJp7vz2muvkUgkmDt3Ll999VXadkceeSRBEHD66afTvXv3vMUjIiKSLRUwctTkk20lEjBpEoOXLIG2bSH59s0uXeA3v4ELLwy3iYiISIuSj0k8E4kEkyZNYsmSJXTt2pUOHTrw5Zdfpm3br18/ysrKGDNmDHvvvXdThSAiItKkVMCIg0QinKBz/XoMahYvTjklvBOjrkeqioiIiKR44IEHuOCCC9gUzZ+1evXqWm169uzJWWedRRAE9O/fX5NxiohI7KmAEQeTJsH69bXXd+8O8+eDOhQiIiKShcmTJ28rXiQzM84991yCIGDw4MG01Z2dIiLSjKiAEQdLl6Zfv3KlihciIiKStWXLltW57f777y9gJCIiIk2nTbEDEOoeHqJhIyIiIpKDXnX0IepaLyIi0hyogBEHU6ZA6qPJSkrC9SIiItLimdmpZlaebq6KXEyZMqXWY09LSkqYor6FiIg0YypgxEEQQHk59O6Nm0Hv3uHrICh2ZCIiIlIA7j7f3cd17dq1SY4XBAHl5eX07t0bM6N3796Ul5cTqG8hIiLNmAoYcREEUFVF5cKFUFWl4oWIiIg0ShAEVFVVsXDhQqqqqlS8EBGRZk8FDBERERERERGJPRUwRERERERERCT2VMDIUVNPtiUiIiIiIiIidVMBI0dNPdmWiIiIiIiIiNRNBQwRERERERERiT0VMEREREREREQk9lTAEBEREREREZHYM3cvdgzNmpl9BSypY3NXoK5ZPuva1h1Y0QSh5VN97ysux8/1GNns15C2mdooR4p3/HznSLHyA5QjTXX8XI7RWj9Dert7jyY+ZquUh34F6DOhqY6vz4TiUo4oR+qT7/xoqnMoRxoufd/C3bXkaQHKs90GLCp23I15X3E5fq7HyGa/hrTN1EY50nJzpFj5oRwpbo7oM0RLPhd9JhT3+PpMUI4oR+K75Ds/lCPxyRENIcmv+Tlui7t8x94Ux8/1GNns15C2mdooR4p3/HzniPKjfi01R/QZIvnUkv/e+kzQZ0ImyhHlSH0KEbtyJAY5oiEkMWNmi9x9QLHjkPhSjkgmyhGpj/Kj9dHfXOqj/JBMlCOSSSFzRHdgxE95sQOQ2FOOSCbKEamP8qP10d9c6qP8kEyUI5JJwXJEd2CIiIiIiIiISOzpDgwRERERERERiT0VMJoxM+tkZh+Y2apixyLxYmYPmtn3ZrY2aRlY7LgkXszsVDP7W5Qfn5nZVcWOSeIj5fNjrZltMrO3ih2X5Jf6FlIX9S0kE/UrpD5N1a9ol4/gpGBuBpYRPndXJNU97n55sYOQeDKzE4GZwM+BSqAE6FXUoCRW3L1L8uuok/FwkcKRwlHfQuqjvoWkpX6FZNJU/QrdgdFMmdkhwMnAb4odi4g0S7cA/+ruL7j7Zndf4+6Lix2UxJOZDQIOAB4sciiSR+pbiEgjqF8hDdaYfoUKGHlkZtea2aNm9pGZuZlV1dO2jZmNN7P/MbMNZvaJmd1uZp3TtG0H3AtcDGzM3zuQfMtXjkR+bmZfm9k7Znalmen/92YoHzkSvT4UKDGzd83sCzN70sx0paQZyvPnSLXzgGfcfXmTBi9ZU99CMlHfQuqjfoVkEvt+hbtrydMCOLASeB74Gqiqp+2dUfsngLHAHcAmYCHQJqXtROD30e+lwKpiv1ctscuRQ4AeQFvgcGApML7Y71dLPHIE2DNq9xawN+Ftnv8feL3Y71dLPHIkZZ8SYDUwvNjvVYv6FlqKmiPqW7SARf0KLcXIkZR9GtWvKPp/oJa8APsk/b64rj8+cCCwFXg8Zf0lUUKMSVrXN/rC2Dl6rU5GM17ykSN17H8R8Gqx36+WeOQI0DVad37Suu7Rup7Ffs9aip8jKdvPAT4D2hX7vWpR30JLcXKkjv3Vt2iGi/oVWoqRIynbG9Wv0G1feeTuHzWw6VmAAdNT1t8LrAPKktYdTVj9fsfMPiesdu1oZp+b2RGNi1gKLU85ks7W7CKTuMhHjrj7amAJ4ZfLttW5RynFVIDPkfOB/3D3zTkFKE1KfQvJRH0LqY/6FZJJ3PsVKmDEw0DCL4G/Jq909w3A36Pt1eYCfYD+0XI+8G30+6J8BypFk02OYGajzWxHCw0ArgEeL1CsUhxZ5QjhTOGXmdleZtaRcPKtRa45DlqybHMEM/sRcATwQAHik6alvoVkor6F1Ef9CsmkKP0KFTDioSewwt3TTZr1KdDdzNoDuPt6d/+8eiEcl+TR6+8LGLMUVoNzJPIrwtuBvwUSwD3A7XmPUoop2xyZBjwD/C3avgcwKu9RSjFlmyMQTrL1kru/n/fopKmpbyGZqG8h9VG/QjIpSr+iXa47SpMqoe4ZvzcktanViXD3CqBbXqKSOMkqR9z9Z4UISmIl2xzZSjhp38T8hyYxkfV3jbtfne+gJG/Ut5BM1LeQ+qhfIZkUpV+hOzDiYR3QoY5tHZPaSOulHJFMlCOSiXKkddHfWzJRjkh9lB+SSVFyRAWMeFhOeItNugTYg/DWHN3C2bopRyQT5YhkohxpXfT3lkyUI1If5YdkUpQcUQEjHl4n/FsMSl4ZTYDTH02gJcoRyUw5IpkoR1oX/b0lE+WI1Ef5IZkUJUdUwIiHuYSPGro8Zf1YwnFDiUIHJLGjHJFMlCOSiXKkddHfWzJRjkh9lB+SSVFyRJN45pGZnQ30jl72ANqb2fXR6yXuPgvA3d82s7uBX5nZE8ACoB9wKVAJzCls5FIoyhHJRDkimShHWhf9vSUT5YjUR/khmcQ9R8zd83FcAcysAhhcx+ZKdy9NatuWsHo1DtgbWEFY1brB3dfmM04pHuWIZKIckUyUI62L/t6SiXJE6qP8kEziniMqYIiIiIiIiIhI7GkODBERERERERGJPRUwRERERERERCT2VMAQERERERERkdhTAUNEREREREREYk8FDBERERERERGJPRUwRERERERERCT2VMAQERERERERkdhTAUNEREREREREYk8FDBFpMcysysyqih2HiIiICICZ7W1mbmYPFjsWkZZABQwRaRQz62RmG8zsjqR15Wa2xszapbQtjb7Ek5fvzWyZmT1qZocX/h3UZmYPRrHtXexYREREWpJs+g1J283MRpnZf5rZ8qjvsNLMXjSzy8ysY+HegYgUU9oPCRGRLBwJdAAWJq07FnjR3TfXsc8S4MHo987AYcBpwEgzO83dn8wxlmNz3E9EREQKI6t+g5l1Ax4BhgKrgQVAFbATcAIwHbjYzE5x9//NZ+A5+hToRxi7iDSSChgi0lhDgC3AixDeKgnsA9xdzz5V7j45eYWZ3QTcANwO5FTAcPcPc9lPRERECqbB/QYzawM8ChwHPAcE7r4yaXs74GbgWuB5M/uJu3+T7zeQDXffBPxPseMQaSk0hEREsmJmPzCzfasX4HjgPWCX6PXoqOnHSe06NeDQ1R2XPmbWI+l8o6NbRFeb2Xoze9vMrjWzDmliqzUHhpmdEw0HOcfMjjGzCjP7NrpV9Wkz65fS3oFfJL2H6qEuVUlt9olud/0giunrKK6ZZrZzA96riIhIq9DIfsMYwuLFR8DI5OIFgLtvdvfrgLlAb+DGlHNXRN/r6eLa1j9Is21PM5thZh+Z2cZouMpTZjYwTdvJ0XFKzWyMmb1mZmur+w31zYFhZiVRn+bvZvZdtN8rZnZWmrZmZr8ws5fN7KtoGM4nZvacmZ2R7j2KtES6A0NEsjUK+H2a9am3bT6R9PsxQEWG41rS7w5gZv9GeFVlBTAHWAucBPwbcIKZDY2ubDTEMGA48AwwEzgAOBkYaGYHuPuKqN1NwAjgYOBOYFW0flUU0+7A68COhLexPg50BPoAZwMzgBodLBERkVasMf2GsdHr29x9XT3nuBk4AzjPzK7Kom9Qi5kdAvyRcIjKc1Fc3Qn7Bn8xs3929wVpdr2ScJjLfODPQNcM5+lGOIzmJ8DfgAcILy6fAMwxswPd/fqkXaYQ9ok+JhxSsxrYHRgInE5YxBFp8VTAEJFs/ZnwixLgCGA84dCP96J1/wG8BtyTtM87DTjuxdHPj9x9hYUTel4LfAIMcvfPAczsWsIhJsOAqwiLGQ0xAjjB3V+oXmFmvwGuAX4JTANw98nR7awHA9PdvSrlOKcRdmoud/c7kzeYWWdgawPjERERaQ1y6jdEw0MOi17/qb4TuPu7ZrYc6ElYEPhrLoFG53wE6AIc4+6VSdt6El7AuN/M9nb3jSm7DwEOd/c3Gni66VGsE919WtJ5OgLzgOvM7DF3/3u06V8I59M4KLWYY2bdG3hOkWZPBQwRyYq7LyGchJOoyLAJuMPdvzOz/YAS4FF3f6yew+xtZpOj3zsDPwWOJvzH/4Ro/S+jn/9aXbyIzr/ZzK4kvHvifBpewHg4uXgRKScsYAxq4DGSrU9d4e7f5XAcERGRFivXfoOZ7QK0j15+0oBTfUJYwNiTHAsYwClAX8I7PiqTN7j7cjObRlh4OJbwLsxk5Q0tXkTDTcuARcnFi+g8G8xsIuGdGGOAvydt3kQ4fwgp+6xIXSfSUqmAISKNMQR4Pekf7oOjn5V1tK+WPE51M/AV4S2at7v7y9H6Q6KfC1P2xd3fN7NlhPNldHP3VQ2IdVGaddUdoh82YP9qTxEWTe42sxMIby/9L+Bdd087zlZERESA7PoNlmZdfarbN+aRqtWPc++ddKEl2f+LfvajdgEjm6LJQKAt4HWcZ4ek81RLAJcQ3p3yKOF/s1fcXU83kVZFBQwRaTAzKwVKq18C/wT8d9KX78mEVwbOqJ40K/VpI5FKdy9Nsz5Z9djRz+rY/hnQK2q3KsOxSNcmupsDwk5Eg7j7EjMbBEwGTgRGRps+MbPb3P13DT2WiIhIS9bIfsNK4HvCuzD2ovacGan2jH5+1YiQqyfiPr3eVuEQk1Sfp1mX6TwDo6Uh5xkPfEh4h+o10bLZzBYAV7r7B1mcX6TZUgFDRLJRSsoM36T/8r0h6ffJOZ6r+orCboRf2Kl2T2lXMO7+HmFnqx3hXBnHEV4VudPMvnP3+wsdk4iISAyVkmO/IbrI8BrhENPjqKeAET1RrCfhUNTkYRxbo+3t3H1zym7d0hyquk8x3N2fqut8dcjmLszq8/zW3a9o0MHdtxBOLn5nNLzmKOBMwmLLgdGkn6nzcoi0OHqMqog0mLtPdndzdwPuADYAHaPXB0TNLqhuE63PVXUHpDR1Q/TYtT2Bjxs4fCRb1eNL670zI3p823+7+1Sg+pFnI/IQj4iISLPTBP2G8ujnFVb/I9mrn9bxfMp8EN9EP/dKs8+ANOtejX4eXc+5msJfCYsrOZ3H3b909yfcfTThUNu+wEFNGJ9IbKmAISK5OgZ4NanaXxr9rGii4z8Q/bzezHpUrzSztsBthJ9f+brTofoxqL1SN5jZIDPbNc0+1evqe8ybiIhIa5VLv+Eh4AVgX+AxM6sxZ5WZtTWzmwknu1wHTEzZv3peirEp+x3L9gsPyf6T8K7Pi83s5HQBmdnhZlZST8wZufuXhHNaDDCzX0d3dKaep6+Z9Yl+72Bmx1o07jWpzQ6ET0YD9T+kldAQEhHJWtSBOJjwuevVSoHP3f0fTXEOd385mu37amCxmT0GfAecRHiV4S/ArU1xrjReIHxE673RedcCq9x9BmEn6WIzqwQ+ILy60xc4FdhIODu5iIiIRHLtN7j7FjM7DXiUcL6Mj8zsacKnmuxE+KSOPoTfv4G7v5lyiN8Tfp9fa2YHA+8C+xH2JZ4ERqWcb5OZjSScoPtpM3uZ8Ckg6wjv4hgI7EM4jLWxBYNfEU4KejNwtpn9BfiCcChMv+hcZwEfA50IHyVbFQ2rWUI4WenQqO1T0fBWkRZPBQwRycVgwjsgKpLW/Yymu/sCAHefaGZvEH7J/5xwVu4PCW8Vvd3dv2/K8yWd97noUa1jCSfNak/YWZhBeDWoA+Gz7A8h7FR8CjwcxbQ4HzGJiIg0Yzn3G9x9lZkdD5xG2Bc4DujO9mGerwC/cPdac2S4+5dmNpjwgsfPojgWEf7Dvw8pBYxon7eiYscVwDDgXMLhHp8RDm+9EWj0Y0vdfU0U2zjCiyOjCIsSXxDO9zEeeD5q/h3h3SXHEPY/RgDfEvaJLmT7XasiLZ7pqX8iIiIiItKcmNmPCe/GXAf8LF0BQ0RaHs2BISIiIiIizYq7vw2MJrwb4wUz613kkESkAHQHhoiIiIiINEtmNhz4CfChu88qdjwikl8qYIiIiIiIiIhI7GkIiYiIiIiIiIjEngoYIiIiIiIiIhJ7KmCIiIiIiIiISOypgCEiIiIiIiIisacChoiIiIiIiIjEngoYIiIiIiIiIhJ7/wd9uDjmmAwrGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# --- Build time\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(list_npoints, build_times_kdt, \n",
    "         marker=\"o\", c=\"k\", lw=3, \n",
    "         label=\"KDTree\")\n",
    "plt.plot(list_npoints, build_times_ball, \n",
    "         marker=\"o\", c=\"r\", lw=3, \n",
    "         label=\"BallTree\")\n",
    "\n",
    "plt.legend(fontsize=18)\n",
    "plt.xscale(\"log\"); plt.yscale(\"log\")\n",
    "plt.xticks(size=18); plt.yticks(size=18)\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel(\"#Points\", size=20)\n",
    "plt.ylabel(\"Time (sec)\", size=20)\n",
    "plt.title(\"Build time\", size=24)\n",
    "\n",
    "# --- Query time\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "plt.plot(list_queries, query_times_kdt, \n",
    "         marker=\"o\", c=\"k\", lw=3, \n",
    "         label=\"KDTree\")\n",
    "plt.plot(list_queries, query_times_ball, \n",
    "         marker=\"o\", c=\"r\", lw=3, \n",
    "         label=\"BallTree\")\n",
    "\n",
    "plt.legend(fontsize=18)\n",
    "plt.xscale(\"log\"); plt.yscale(\"log\")\n",
    "plt.xticks(size=18); plt.yticks(size=18)\n",
    "plt.grid()\n",
    "\n",
    "plt.xlabel(\"#Queries\", size=20)\n",
    "plt.ylabel(\"Time (sec)\", size=20)\n",
    "plt.title(\"Query time\", size=24)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}