onefile_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I looked at a particular file group.perf-flavtag.8324358.Akt4EMTo._001543.root\n",
    "\n",
    "The number of jets passing all cuts (JVT, pT>20GeV, |eta|<2.5 and electron veto) is 43603. I put the IP3D, SV1 and MV2C10 ROC curves in the attached pdf file. The rejections for 70% b-jet efficiency are 92, 119 and 292 respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandautils as pup\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# dan_df = pup.root2panda('../data/Dan/user.dguest.410000.rnnip_dl1_full.v4_Akt4EMTo/user.dguest.8512444.Akt4EMTo._00001*.root',\n",
    "#                     'bTag_AntiKt4EMTopoJets',\n",
    "#                     branches = ['jet_eta', 'jet_pt', 'jet_JVT', 'jet_aliveAfterOR', 'jet_trk_ip3d_z0', 'jet_sv1_llr', \n",
    "#                                 'jet_LabDr_HadF', 'jet_mv2c10', 'jet_ip3d_llr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v47_df = pup.root2panda('../data/v47_btag_ntuples/group.perf-flavtag.8324358.Akt4EMTo._001543.root', \n",
    "                    'bTag_AntiKt4EMTopoJets',\n",
    "                    branches = ['jet_eta', 'jet_pt', 'jet_JVT', 'jet_aliveAfterOR', 'jet_trk_ip3d_z0', 'jet_sv1_llr', \n",
    "                                'jet_LabDr_HadF', 'jet_mv2c10', 'jet_ip3d_llr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def flatten(trk):\n",
    "    ''' turn from event-flat to jet-flat'''\n",
    "    newdf = pd.DataFrame()\n",
    "    for key in trk.keys():\n",
    "        newdf[key] = pup.flatten(trk[key])\n",
    "    return newdf\n",
    "\n",
    "def applycuts(trk):\n",
    "    # -- ftag cuts\n",
    "    cuts = (abs(trk['jet_eta']) < 2.5) & \\\n",
    "           (trk['jet_pt'] > 20e3) & \\\n",
    "           ((trk['jet_JVT'] > 0.59) | (trk['jet_pt'] > 60e3) | (abs(trk['jet_eta']) > 2.4))  & \\\n",
    "           (trk['jet_aliveAfterOR'] == 1)\n",
    "\n",
    "#     #-- remove jets with 0 tracks\n",
    "#     try:\n",
    "#         z0 = trk['jet_ipmp_z0']\n",
    "#     except KeyError:\n",
    "#         z0 = trk['jet_trk_ip3d_z0']\n",
    "#     notracks = []\n",
    "#     for i in xrange(len(z0)):\n",
    "#         notracks.append(len(z0[i]) == 0)\n",
    "#     hastracks = -np.array(notracks)\n",
    "\n",
    "#     # -- apply all\n",
    "#     trk = trk[(cuts & hastracks) == 1]\n",
    "    trk = trk[cuts]\n",
    "    trk = trk.reset_index(drop=True)\n",
    "    return trk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# dan_df = applycuts(flatten(dan_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v47_df = applycuts(flatten(v47_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44348\n"
     ]
    }
   ],
   "source": [
    "# -- Number of jets that pass the cuts:\n",
    "print v47_df.shape[0]\n",
    "#print dan_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare MV2c10 in v47 vs Dan's ntuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr_mv2_v47, tpr_mv2_v47, _ = roc_curve(\n",
    "    v47_df['jet_LabDr_HadF'][(v47_df['jet_LabDr_HadF'].values == 0) | (v47_df['jet_LabDr_HadF'].values == 5)], \n",
    "    v47_df['jet_mv2c10'][(v47_df['jet_LabDr_HadF'].values == 0) | (v47_df['jet_LabDr_HadF'].values == 5)],\n",
    "    pos_label=5)\n",
    "\n",
    "fpr_ip3d_v47, tpr_ip3d_v47, _ = roc_curve(\n",
    "    v47_df['jet_LabDr_HadF'][(v47_df['jet_LabDr_HadF'].values == 0) | (v47_df['jet_LabDr_HadF'].values == 5)], \n",
    "    v47_df['jet_ip3d_llr'][(v47_df['jet_LabDr_HadF'].values == 0) | (v47_df['jet_LabDr_HadF'].values == 5)],\n",
    "    pos_label=5)\n",
    "\n",
    "fpr_sv1_v47, tpr_sv1_v47, _ = roc_curve(\n",
    "    v47_df['jet_LabDr_HadF'][(v47_df['jet_LabDr_HadF'].values == 0) | (v47_df['jet_LabDr_HadF'].values == 5)], \n",
    "    v47_df['jet_sv1_llr'][(v47_df['jet_LabDr_HadF'].values == 0) | (v47_df['jet_LabDr_HadF'].values == 5)],\n",
    "    pos_label=5)\n",
    "\n",
    "# fpr_mv2_dan, tpr_mv2_dan, _ = roc_curve(\n",
    "#     dan_df['jet_LabDr_HadF'][(dan_df['jet_LabDr_HadF'].values == 0) | (dan_df['jet_LabDr_HadF'].values == 5)], \n",
    "#     dan_df['jet_mv2c10'][(dan_df['jet_LabDr_HadF'].values == 0) | (dan_df['jet_LabDr_HadF'].values == 5)],\n",
    "#     pos_label=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VlvQUWr58+X6z64wYMYJvv/2WTz/9lE6dOlGzZk0KCgrYvXs3AJ07d+bee+8t9ZnDSCP1\nktJOPBEefxzatIHf/AZuuSXZPRIREalYZnbAkfqyjOIf7BqFSbyZUbduXS688MJS2zrniq63fv16\n1q5dG3NZs2YNO3fu3O/8atWqMXbsWN555x06d+5Mo0aN2LFjB5mZmeTk5PDAAw/Qv3//Az7HgZZY\n6taty6xZs3jooYc49dRTqVKlCjVr1iQnJ4cXX3yRd955h0gkvdJgC+I3wcrGzJzi5l9ubm65RqVG\njIA5c7y1eMobQ4lNcQyG4uhfOsbQzAIZdRZJBeX9PEfbJ+QNB+n1K4qIiIiISCWkkfo4aKQ+OTRS\nLyISPhqpl3SikXoREREREakwSuolaTQfs3+KYTAUx2Aojv4phiISLyX1IiIiIiIhp5r6OKimPjlU\nUy8iEj6qqZd0opp6ERERERGpMErqJWlUO+qfYhgMxTEYiqN/iqGIxEtJvYiIiIhIyKmmPg6qqU+O\nESNg1Cjo1evnfbVrwxVXQBnemC0iIkmgmnpJJ6lcU6+kPg5K6pPj66/hmWf23ffSS7BqFRx6aHL6\nJCIiB6akXtJJKif1Kr+RpClv7WizZvDss/sutWtXTN/CQvW3wVAcg6E4+qcYiki8lNSLiIiIiISc\nym/ioPKb1HHoobB0qcpvRERSlcpvJJ2o/EZERERERCqMknpJGtWO+qcYBkNxDIbi6J9iKCLxCkVS\nb2Znm9nbZvaTme00s/+a2Xtmdn6Mtq3MbLKZbTCz7Wb2uZkNNLNSn9XMepvZHDPbYmZ5ZjbNzDpV\n7FOJiIiI7K9du3ZEIhFGjx6d7K6UWXZ2NpFIhOnTpye7K5VWyif1ZvYI8AHQAvgHMAx4F6gPtC3R\ntgswA/gdMAF4BqgGPAGMLeX6w4CRQEPgRWAMcAow0cyuD/6JpFC7du2S3YXQUwyDoTgGQ3H0TzGU\n4izAl7BMmzaNIUOG0KFDB4477jjq1KlDjRo1OOaYY7jkkkuYMmWK73uYWaB9lvLJSHYHDsTM+gO3\nAKOAAc65/BLHM4r9XA8YAewB2jnn5kX33w1MBbqZWXfn3N+LndMKuBlYCpzunNsU3f8o8CkwzMwm\nOedWVNxTioiIiPzsmGOO4aSTTiIzMzOwaz788MO8//77gJd816tXj4KCAlauXMkPP/zAhAkT6NWr\nF6NHjyYSiW/MV1+ITq6UHak3s+rAX4AVxEjoAUrs64Y3ej+2MKGPttkF3BndvLbEJa6Jrv9SmNBH\nz1kBDAeqA319PoqUQrWj/imGwVAcg6E4+qcYCsDo0aP56quv6NKlS2DXPO+883j++ef58ssv2bFj\nBxs3bmTHjh0sWrSI3r17A/Daa68xfPjwwO4piZWyST3wP3hJ+luAM7NOZjYkWh9/Zoz2HaLr92Ic\nmwHsAHLMrFqJc1wp5xT+Hap9XL2XhNi40XujrIiIiJRu0KBBDBgwgGbNmlGt2s+pUJMmTRg5ciSt\nW7cGYOzYmNXKEgKpnNSfHl3vAuYDE4EH8erjZ5lZrpnVL9a+aXS9uOSFnHMFwHd45UbHAZhZbaAx\nsNU5tzrG/ZdG1yf6fA4pRRC1o02bwttv++9LWKn+NhiKYzAUR/8UQ4HYX5TNzc0lEolw7LHHAjBx\n4kTat2/PIYccQp06dWjVqhVvvPFG3Pds2bIlAFu3bi21zWuvvcaZZ55JnTp1OPTQQzn77LOZPHly\n3PeUYKVyUt8gur4VKMD78msd4NfA+0AbYHyx9pl4o+6biG0TYNF2FFsfqD1AVnk7LonTtWuyeyAi\nIlIxYn3p1Mx48skn6dKlCzNnziQSibBr1y5mz55Nr169uPHGG8t9H+ccs2fPBuD000+P2eaGG27g\n8ssvZ86cOezatQszIzc3lwsuuICnn3663PeU4KVyUl/Ytz3Ahc65Wc657c65hcBFwEqgrZmdkbQe\nii9B1I6aQWX+Xo7qb4OhOAZDcfRPMZSDWbNmDUOGDKF3796sWrWK9evXs3btWgYPHgzA8OHDyzxi\nv2nTJubMmUP37t2ZPXs2jRs3ZujQofu1e+2113j22WcxM2699VbWr1/P+vXrWbVqFVdccQW33nor\n69atC/IxJQ6pPPtNXnT9mXPu++IHnHM7zOxfwJXAb4FP2H8kvqTC/YXX3VRi/8Ha76NPnz5kZ2cD\nkJWVRfPmzYv+bFr4f8raPvB2IT/XM4Nly3LJzU3+82g7vNvz589Pqf5ou/Juz58/P6X6E9S2BGf7\n9u2cc845jBw5smhfVlYWjz76KOvWrWP06NHcc889XHbZZTHP//e//02bNm322Ve7dm0GDhzI//7v\n/9KgQYN9jjnnuOeeewDo3bs3Dz/8cNGxBg0aMGrUKH788Uf+7//+L6hHDI3S/nuSl+eljsuXL09s\nh5xzKbngzTqzF3i3lOOPRo//v+j2mOh2jxhtM4BtePX5VYvtX4lX2tMoxjk50etNj3HMSWq4+27n\n7rkn2b0QEZHS6L+Z5de2bVtnZm706NFF+6ZNm+bMzEUiEZebmxvzvCVLlhS1mT9/fsw2c+bMcY0a\nNXJHHHGEq1q1qotEIs7M3GmnneY+/PDD/drPmzev6JqLFy+Oec3ifZs+fXocTxwe5f08R9snJHdO\n5fKbD/Fq5E+22G8y+FV0/V2x9gDnxWjbBqgJzHLO7SlxDyvlnMK31U4tT6clsSp7+Y2IiFQuVatW\n5ayzzop57IQTTqBRo0Y455g3b17MNqeffjqrVq3ixx9/ZOfOncybN48LL7yQTz/9lPPOO2+/l1AV\nXqdhw4Y0adIk5jVzcnKoUqWKj6eSIKRs+Y1z7nszmwhcCAwEniw8ZmbnAOcCG/l5Oso3gYeBHmb2\njHPu02jbGsCfo22eK3Gb54HLgTvM7B/OubzoOdnA9cBOvLfNSgXIzc0t+pNVvMxg795g+hNGQcRQ\nFMegKI7+KYax2b2p85ZSd09yR5Lq169PRkbp6duRRx7JTz/9VKYa90gkwqmnnso//vEPLrnkEiZM\nmMBNN93E+eefX9Rm7dq1ADRu3LjU61SvXp369euzenWsyQQlUVI2qY+6HvgN8LiZdcKb2vJY4A94\nX6C9yjm3BcA5tyX6Bto3gVwzG4uX9F+INy3leOfcuOIXd859bGaP471V9gszmwBUA7rjzXpzoytR\nzy+pRSP1IiLpL9mJdGVwww03MGHCBJYsWcIPP/zAUUcdlewuSTmlcvkNzrn/Ai2BvwJNgD/hldK8\nA5zlnHu7RPt3gLZ4L5vqCtyAV0d/E9CjlHvcgle//xPQH/gjsADo7Jx7NvinkkJBjEZFIpV7pF4j\nesFQHIOhOPqnGMrBrFu3jvz8/FKP//jjjwAcfvjh5bpu8ZH4VcXe6lj4xdnC68aye/du1q1bF3MK\nTkmcVB+pxzm3Di+Z/1MZ288COpXzHqOB0QdtKClHI/UiIlKZ7Nmzh1mzZu03gw3A0qVLWbVqFWZG\nixYtynXd7777rujn4jPgFF5n9erVLFmyJGZd/axZsygoKFBSn2QpPVIv6S2Iqc4qe1Kv6eKCoTgG\nQ3H0TzGUg3HO8eCDD8Y8Vri/SZMm/PrXvy7aX1BQcMBr7t27l8cffxyAY489tmjKboDmzZtzwgkn\n4JzbZzrL4v156KGHin6W5FFSL6FW2ctvRESkcqlVqxYffvghV155ZdGXWPPy8hgyZAgjR47EzPZ7\ngdTMmTPp0KED48aNKzoHID8/n1mzZtGxY0fef/99zIz77rtvv3sWXu/ll1/mtttuY9Mm71U/q1ev\npl+/fkybNo1atWpVzANLmaV8+Y2kryBqRyv7SL3qb4OhOAZDcfRPMZSDadCgAYMGDWLQoEGMGjWK\nzMxMNm3ahHMOM+P666+nR499v0ZoZuTm5hb9JahWrVrUrFmTTZs2FdXnV6tWjQceeICePXvud8+e\nPXvy8ccfM3z4cB555BGGDRtGvXr1yMvLw8x46qmnGDZsGN9/r7lFkkkj9RJqlT2pFxGR9GNmB6xP\n/9Of/sQ///lP2rZtC3hJek5ODmPGjOHpp5/er33Lli0ZPXo0V1xxBaeccgq1a9dm8+bN1KlTh5Yt\nWzJ48GAWLFjAzTffXOo9n3nmGcaMGcMZZ5xBzZo1MTPat2/PpEmTuOGGGw7aZ6l4pvqn8jMzp7j5\nF8R8zI8+Cj/9BI89FkyfwkZzWgdDcQyG4uhfOsbQzFRrHYDc3Fw6dOhAdnY2y5YtS3Z3Kq3yfp6j\n7RPy245G6iXUIhGN1IuIiIgoqZekUU29f+k2opcsimMwFEf/FEMRiZeSegm1yp7Ui4iIiICSekmi\noOapr8xTWmpO62AojsFQHP1TDKU0+hKqHIymtJRQU029iIhUBm3btmVvZR7FkoPS7Ddx0Ow3qeNv\nf4OBAyEzE+6/H668Mtk9EhGR4jT7jaQTzX4jUkH69YMlS6BnT1ixItm9EREREUkOJfWSNEHUjkYi\n0Lgx1Kvnvz9hpPrbYCiOwVAc/VMMRSReSupFREREREJONfVxUE196rnvPsjP99YiIpI6VFMv6UQ1\n9SIiIiIiUmGU1EvSqHbUP8UwGIpjMBRH/xRDEYmXknoRERERkZBTTX0cVFOfelRTLyKSmlRTL+lE\nNfUiIiIiIlJhlNRL0qh21D/FMBiKYzAUR/8UQxGJl5J6EREREZGQU019HFRTn3pUUy8ikppUU5/a\nVq5cyfTp05k7dy5z585l/vz57Nixg4YNG7Jq1apkdy/lpHJNfUYibiIiIiIiqWfYsGE8/fTT++03\nS0geKgFS+Y0kjWpH/VMMg6E4BkNx9E8xlESLRCKccMIJ9OjRg8cee4ybb7452V2SOGmkXkRERKSS\nGjZsGI8//njR9qhRo5LXGfFFI/WSNO3atUt2F0JPMQyG4hgMxdE/xVAK7d69m6eeeopWrVqRlZVF\n1apVadiwIaeeeio33HADs2fPBqB///5EIhEuueSSA17vwQcfJBKJ0KJFi332RyJKBdOFRupFRERE\nUkh+fj7nnHMOM2bMALzEOzMzk40bN7J27VoWLFjA+vXrOfPMM+nVqxcvvfQSkydPZsuWLdStWzfm\nNd944w0AevXqlbDnkMTSr2eSNEHXjr73XqCXCwXV3wZDcQyG4uifYigAr7/+OjNmzKB27dqMGTOG\n7du3s379enbt2sWKFSv461//SvPmzQFo27YtRx55JDt27ODtt9+Oeb0vv/yShQsXEolEuOyyyxL5\nKJJASuolLXTqBHPnetNaioiIhFlhac0VV1xBz549qVatGuDNSHPUUUdx3XXXMWTIkKJ93bt3B7xf\nBmIpHKVv3bo1jRs3rujuS5IoqZekCbJ2tGVLqFIlsMuFhupvg6E4BkNx9E8xFIDMzEwAfvzxxzK1\n79mzJwBTp05l7dq1+x0fO3bsPu0kPSmpFxEREUkh559/PgDvvPMOXbp04e2332bDhg2ltm/RogVN\nmzYlPz+fcePG7XPsk08+YdmyZVSrVu2gX6aVcFNSL0mj2lH/FMNgKI7BUBz9UwxLYZY6SwK0adOG\n++67j4yMDCZOnEjXrl2pX78+zZo149Zbb2Xp0qX7nVNYK19YalOocPvcc88lKyur4jsvSaOkXkRE\nRFKbc6mzJMidd97J4sWLefDBBzn33HPJzMzkm2++4bHHHuPkk0/m1Vdf3ad9YWnN7NmzWbFiBQB7\n9+4tGrlX6U36M5fAD2i6MDOnuKWejAzYudNbi4hIajAz9N9M//bu3cvMmTO55557imbGWbZsGYcf\nfnhRm9/+9rf85z//4cEHH2TIkCFMnTqV3//+99SpU4c1a9ZQo0aNg95n1KhR9OvXj0aNGpW5pr8y\nKe/nOdo+IX/i0Ui9iIiISIqLRCK0bduWSZMmkZGRwfbt2/nPf/6zT5vC0fjCkpvCdZcuXcqU0Eu4\nKamXpFHtqH+KYTAUx2Aojv4phgKwZ8+eUo9VrVqVKlWq4Jxj9+7d+xzr0aMHZsaCBQuYP38+EyZM\nAFR6U1koqZe0UaMG6P+3REQk7C6//HL69evH+++/z5YtW4r2L1++nN69e7Nr1y5q1apF69at9zmv\nUaNGtG/fHuccV111FXl5edSvX59zzjmn1Hvl5+ezbt26omXr1q0AOOdYv3590f68vLyKeVgJjGrq\n46Ca+tTfpM+ZAAAgAElEQVQ0axYMGAALFya7JyIiUkg19eV30UUX8c477xRtZ2Zmsnv3bnbs2AFA\nRkYGI0eOpFevXvud+/LLL3PVVVcVbV977bUMHz681Hvl5ubSoUOHg/bpmGOO4bvvvivPY6Ql1dSL\nJEC9esnugYiIiH8PPfQQjzzyCOeffz4nnHAC+fn5OOc44YQT6NevH/PmzYuZ0AN07dqV6tWrY2aY\n2UFLbyw6TWdh+wMtkto0Uh8HjdQHIzc3N9C3Jy5cCD16VK6R+qBjWFkpjsFQHP1LxxhqpF7SiUbq\nRURERESkwmikPg4aqU9NlXGkXkQk1WmkXtKJRupFRERERKTCKKmXpNF8zP4phsFQHIOhOPqnGIpI\nvDKS3QGRoEQisHw5nHfe/sdOOw3+/OeEd0lEREQkIVRTHwfV1KemvXth2jQo+SK+ZcvghRfg88+T\n0y8RkcpMNfWSTlK5pl5JfRyU1IfL55/DFVcoqRcRSQYl9ZJOUjmpV029JI1qR/1TDIOhOAZDcfRP\nMRSReCmpFxEREREJOZXfxEHlN+Gi8hsRkeRR+Y2kE5XfxMnMlpvZ3lKWVaWc08rMJpvZBjPbbmaf\nm9lAMyv1Wc2st5nNMbMtZpZnZtPMrFPFPZmIiIiISHBSOqmPygOGxlgeLdnQzLoAM4DfAROAZ4Bq\nwBPA2FgXN7NhwEigIfAiMAY4BZhoZtcH9xhSkmpH/VMMg6E4BkNx9E8xFJF4hWGe+jzn3H0Ha2Rm\n9YARwB6gnXNuXnT/3cBUoJuZdXfO/b3YOa2Am4GlwOnOuU3R/Y8CnwLDzGySc25F0A8liVOzJnz5\nJdSvD3ffDX/6U7J7JCIiIhKslK6pN7PlwF7n3HFlaNsP+Bsw2jnXt8Sx9sCHwAznXLti+18B/gj0\ndc6NLnHOvcBdwH3OuaEljqmmPmTy8uDJJ2HbNnh0v7/xiIhIRVFNvaSTVK6pD8NIfQ0z+yNwNLAN\n+BwvOd9bol2H6Pq9GNeYAewAcsysmnNud7FzXCnnTMFL6tvjlftIiGVlQe3aXlIvIiKJZZaQnEak\nUkv1mnoHNAJeAf6MVxs/FVhiZm1KtG0aXS/e7yLOFQDf4f0ScxyAmdUGGgNbnXOrY9x7aXR9os9n\nkFKodtQ/xTAYimMwFEf/0jGGzrmEL9OmTUvKfdNpUQxLX1JVqif1I/FG0xsCtfC+wPoCkA1MMbNf\nF2ubifdLwKZSrrUJsGg7iq0P1B4gK56Oi4iIiIgkSkrX1Jcm+kXWwcA/nHMXR/ctBo4HmjjnlsU4\n5yMgB8hxzn1iZo2BlcBK59zRMdpXBXYBu5xzNUscc2GMW2X36KOwZo1q6kVERCQxVFN/cM/jJfWt\ni+0rORJfUuH+vGLti+8/WPt99OnTh+zsbACysrJo3rw57dq1A37+86m2U2sbUqs/2ta2trWtbW1r\nO72258+fT16elzouX76cRArrSH0msBHY6ZyrFd03BugJ9HTOjS3RPgMvic8A6jjn9kT3rwSOAI50\nzv1U4pwc4CNgpnOubYljGqkPQG5ubtE/hER49FH45z+hY0dvesv+/RN26wqT6BimK8UxGIqjf4ph\nMBRH/xTDYOiNsgd3ZnRdvMzmw+j6vBjt2wA1gVmFCX2xc6yUc86Prqf66KekkI4d4Xe/g40b4brr\nkt0bERERkeCk7Ei9mZ0E/OCc21ZifzbwAV79/O3OuYei++sC3wL1gLOcc59G99fAS8zPBHo458YV\nu1bhaPy3eC+fyit2j0/xfhE4yTn3fYk+aKQ+xPbsgVq1vLWIiIhIRUnkSH0qJ/VD8ermpwPfA1vw\nEvlOQHXgXeAi51x+sXO6AG8CO4GxeCU6F+JNSzneOdc9xn2G4b1VdiUwAagGdAcOAW50zj0b4xwl\n9SGmpF5EREQSQeU3nqnARLxE/jLgJrwvxs4ArnDOdS6e0AM4594B2kbbdAVuwJvB5iagR6ybOOdu\nAfoCPwH98d4wuwDoHCuhl+AUfsFE4qcYBkNxDIbi6J9iGAzF0T/FMHxSdvYb59wMvOS8vOfNwhvN\nL885o4HR5b2XiIiIiEgqSNnym1Sm8ptwU/mNiIiIJILKb0Qq2N698MYbMGVKsnsiIiIi4p+Sekma\nZNXrZWTAgAE/z1lfUJCUbgRCNY/BUByDoTj6pxgGQ3H0TzEMn5StqRepKGbw3HPez+PGHbitiIiI\nSBiopj4OqqlPH1WqwO7d3lpEREQkSKqpFxERERGRMlNSL0mjej3/FMNgKI7BUBz9UwyDoTj6pxiG\nj5J6EREREZGQU019HFRTnz6qVIFbb4WuXeH005PdGxEREUknqqkXSZDnnoPPPoM330x2T0RERETi\np6RekiYV6vUGDIAOHZLdi/ilQgzTgeIYDMXRP8UwGIqjf4ph+CipFxEREREJOdXUx0E19enl4Ydh\nwwZvLSIiIhIU1dSLiIiIiEiZKamXpFG9nn+KYTAUx2Aojv4phsFQHP1TDMNHSb1UeoccAo884k1v\ned11ye6NiIiISPmppj4OqqlPPwUFMH48vPUWjBuX7N6IiIhIOlBNvUiCVakCEf1rEBERkZBSGiNJ\no3o9/xTDYCiOwVAc/VMMg6E4+qcYho+SehERERGRkFNNfRxUU5+exo2DN99UTb2IiIgEQzX1IkkQ\nicCMGXD//cnuiYiIiEj5KKmXpEm1er2OHeG22+Ddd5Pdk7JLtRiGleIYDMXRP8UwGIqjf4ph+Cip\nF4mqVQvOPDPZvRAREREpP9XUx0E19elr9mwYNMhbi4iIiPihmnoRERERESkzJfWSNKrX808xDIbi\nGAzF0T/FMBiKo3+KYfgoqRcpJhKBb76Bm29Odk9EREREyk419XFQTX362rMH/v53+H//D378Mdm9\nERERkTBTTb1IklStCh06JLsXIiIiIuWjpF6SRvV6/imGwVAcg6E4+qcYBkNx9E8xDB8l9SIiIiIi\nIaea+jiopj69/fgjnHaaaupFRETEn0TW1Gck4iYiYVK9OqxZAw0bxj5+yCHw9ddgCfknKiIiInJw\nKr+RpEnVer3DDoO1a+GLL2Iv33wDqfKHmlSNYdgojsFQHP1TDIOhOPqnGIaPRupFYjjkkNKPaYRe\nREREUo1q6uOgmvrKLRKB/HxvLSIiIlIazVMvIiIiIiJlpqRekibM9XpvvOEtX3+d3H6EOYapRHEM\nhuLon2IYDMXRP8UwfFRTL1JO118PkybB8uVw/PEwZkyyeyQiIiKVnWrq46CaegF47TWYPNlbi4iI\niJSkmnoRERERESkzJfWSNKrX808xDIbiGAzF0T/FMBiKo3+KYfgoqRcRERERCTnV1MdBNfUCXj19\np06lH69aFb79Fo46KnF9EhERkdSRyJp6zX4jEqeOHWHv3tKPN2sGW7cmrj8iIiJSean8RpImHer1\nzA68VLR0iGEqUByDoTj6pxgGQ3H0TzEMHyX1IiIiIiIhp5r6OKimXsqiWTN46y1vLSIiIpWP5qkX\nSQOrV8Oddya7FyIiIlIZKKmXpEn3er2//Q1WrKjYe6R7DBNFcQyG4uifYhgMxdE/xTB8QpXUm9kf\nzWxvdLmylDatzGyymW0ws+1m9rmZDTSzUp/VzHqb2Rwz22JmeWY2zcwOMFmhyME1aAA1aiS7FyIi\nIlIZhKam3syOAhbg/SJSB7jKOfdyiTZdgAnAduDvwAbgQqAp8KZz7tIY1x0G3Az8ALwJVAd6AIcC\nNzrnhsc4RzX1clD//jfcdpu3FhERkcpHNfUlmJkBI4G1wPOltKkHjAD2AO2cc/2dc0OA5sDHQDcz\n617inFZ4Cf1S4NfOucHOuRuAlni/EAwzs2Mq6LFERERERAIRiqQe+BPQHuiLNwofSzegPjDWOTev\ncKdzbhdQ+HXFa0ucc010/Rfn3KZi56wAhuON2vf13XuJKd3r9apXh7lz4ZhjvCU7G2bPDvYe6R7D\nRFEcg6E4+qcYBkNx9E8xDJ+UT+rNrBnwEPCkc+5AhQwdouv3YhybAewAcsysWolzXCnnTImu25ev\nxyKe006Db7+FGTO8pVkzWLky2b0SERGRdJTSNfVmlgHMBmoDzZ1zu8xsKHA3JWrqzWwuXtlMS+fc\nZzGutRBoBvzSObfIzGoDW4AtzrnMGO3rA2uA1c65I0ocU029lNull0LXrtC9+8HbioiISPglsqY+\nIxE38eFuvJr4s6JlNAeSiTfqvqmU45sAi7aj2PpA7QGyytZVkQOrUgUKCpLdCxEREUlHKVt+Y2Zn\nAP8LPOqc+yTZ/ZHgVbZ6vYpI6itbDCuK4hgMxdE/xTAYiqN/imH4pORIfbTs5hXgG+Ce0pqV2C45\nEl9S4f68Yu2L7z9Y+3306dOH7OxsALKysmjevDnt2rUDfv6HoO0DbxdKlf5U9HYk0o6CgtTpj7Z/\n3p4/f35K9UfblXd7/vz5KdWfsG4XSpX+aLvybM+fP5+8PC91XL58OYmUkjX1ZpaFN6VkWTzlnLvJ\nzMYAPYGezrmxJa6XgZfEZwB1nHN7ovtXAkcARzrnfipxTg7wETDTOde2xDHV1Eu59e0LrVtDv37J\n7omIiIgkgmrqYSfwEl6NfEktgd8AM/FG8mdF93+Il9SfB4wtcU4boCYwvTChL3bO5dFzRpU45/zo\nempcTyBSgmrqRUREpKJEkt2BWJxzO6MvjxpQcgEmRpuNju4bH91+E1gH9DCzloXXMrMawJ+jm8+V\nuFXhi6zuiP51oPCcbOB6vF8uRgb5bPKzkn8mTXeqqU9dimMwFEf/FMNgKI7+KYbhk5JJfTycc1uA\n/kAVINfMRpjZI8B84ExgvHNuXIlzPgYeB44HvjCzJ8xsOPAfvFlvbnHOfZ/I55D0VaUKTJkCs2Yd\nvK2IiIhIeaRkTf2BmNk9eFNd9i8+T32x462AO4AcoAawBHgZeLq0Qngz6403Mn8yUADMw5t1Z3Ip\n7VVTL+U2cyY8/DBkZcGYMcnujYiIiFS0RNbUhy6pTwVK6iVeY8bAe+8pqRcREakMEpnUp035jYSP\n6vX8UwyDoTgGQ3H0TzEMhuLon2IYPkrqRRJMf+QRERGRoKn8Jg4qv5F4TZ4MF1wA774L559/8PYi\nIiISXiq/EUlTHTtCjx6wcWOyeyIiIiLpREm9JI3q9fxTDIOhOAZDcfRPMQyG4uifYhg+SupFRERE\nREJONfVxUE29+NGtG6xbBxoEERERSW+qqRdJYwMGwIIFye6FiIiIpBMl9ZI0lbVe77jj4JBDgrlW\nZY1h0BTHYCiO/imGwVAc/VMMwychSb2ZZZnZ1WZ2WCLuJyIiIiJSmVRYTb2ZfQR8B0yNLt8DNzrn\nnqqQGyaQaurFj6VL4bTT4Jpr4Nhj4eqrk90jERERqQiJrKmvyKS+AXA20AFoB9QCPnbOdauQGyaQ\nknrxY/du+OtfYe1aePVVWLky2T0SERGRipAWX5R1zq1xzr3hnOvvnGsCtAfGVtT9JHwqa71etWpw\n881w/fX+r1VZYxg0xTEYiqN/imEwFEf/FMPwqbCk3sxOM7NuZlYTwDm3GEjIbyoiIiIiIpVJRZbf\nvARUB/4HmAF8CxznnLu0Qm6YQCq/kSCsXAlHHQVbtkCdOsnujYiIiAQtLcpvgHnAQKAJMAFYBwyp\nwPuJhEqjRt568eLk9kNERETCryKT+ueAtoBzzo11zg1zzn1XgfeTkKns9XoZGfCb3/i7RmWPYVAU\nx2Aojv4phsFQHP1TDMMn7qTezLLNLKe04865vc65t5xzW+K9h4iIiIiIHFzcNfVm9grwjXPuLyX2\n5wD/dc59H0D/UpJq6iUoLVpAdjYcfri3nZMDffoks0ciIiISlFDMU29m64EWzrkVZpbhnMuP7q+F\nV3pzh3MuLWfgVlIvQZk27eea+m++gc8+8/aJiIhI+IXli7I18L78CtC/cKdzbjvwV+BeH9eWSkD1\netC+vfdG2auvhs6dy3++YhgMxTEYiqN/imEwFEf/FMPw8ZPULwGi83dweIlji/CmshQRERERkQrm\np/zmMWCBc26UmfV3zo0odiwL+ME5VzegfqYUld9IRfj3v+Hcc+Gii2DMmGT3RkRERPwKS/nNcKC3\nmbUCTjGz4h3+JfCpr56JVDKtWsFbb8HHHye7JyIiIhI2cSf1zrllwP8DjgWeAJ4zs9+ZWUvgz8Ad\nwXRR0pXq9fYVicAJJ5TvHMUwGIpjMBRH/xTDYCiO/imG4ZPh52Tn3FxgLoCZ3QR0ARoAVzvn9J5M\nkTjs3g1ffeW9nKpJE7CE/NFOREREwizumvrKTDX1UlE2bICzz4Zdu2DZMpg7F045Jdm9EhERkXik\nTE29mV1kZm+b2SVmVj0RHRKpzA491Jur/quv4Je/9EbtRURERA7mgEm9c+5t4H+BU4BZZvaKmZ1r\nZn6+YCsCqF4vCIphMBTHYCiO/imGwVAc/VMMw+egyblzbpFz7m7nXEu8l0p1BD4zs2fMLKfCeyhS\niQ0dCn37wsCBsHdvsnsjIiIiqSqumnozqwJ0AHoCzYHJwGvOua+C7V5qUk29JMLMmbB0qffz1VfD\nxo1Qu3Zy+yQiIiJll8iaet9flDWzGkAnoAdwNDABeMM594P/7qUmJfWSaLVrw5o1SupFRETCJGW+\nKFsWzrmdzrkJzrlLgHOAdcDfzCzXzK4xs0N99zIVffHFvkvhkKqUmer1ymfBAu+jtnr1z/sUw2Ao\njsFQHP1TDIOhOPqnGIaPr3nqS3LObQJeBl42s0Z4o/f/NLM84HXgH8657UHeM2n++Md9t7/6yquP\nqFs3Of2RtNamDQwYANu2QePGXmmOiIiISKGEzFNvZifg1d+f5py7sMJvWMFilt9kZcHy5d5apILM\nng2DBnlrERERSW2JLL8JdKS+NM65pcB9ibiXiIiIiEhlo/nmg1KtGpx2GjRteuDl/feT3dOUoXq9\n8qtWDebP//njdNRRubz8crJ7FX76LAZDcfRPMQyG4uifYhg+FTZSb2Y9nXOvV9T1U87nn8PmzQdu\nc/fd8N13iemPpKXf/Aa+/BLy873tu++GRYuS2ycRERFJvgqrqTezqc65DhVy8SSLe0rLq6+GFi28\ntUgAHnkE1q3z1iIiIpJaQl9Tb2Y1gV9VxLVD75VXYO7cn7cHDIDf/jZ5/ZHQe+892LDB+7lrVzj/\n/OT2R0RERBKvTDX1ZvaFmRVEl70HWQqAbcBhFdv1EBo4EPr2hTPP9JaVK2HatGT3KmlUr+dfdnYu\nf/qT93HasQMmTkx2j8JJn8VgKI7+KYbBUBz9UwzDp6wj9WcC04F84JsytK8BXBpvp9LWySd7S6El\nS5LXF0kLDRrApdF/abt3w8KFye2PiIiIJEeZa+rN7HTgOudc3zK2X+CcO8VP51JV3DX1JQ0Z4r0i\ntF07rwSnfXv/15RK69lnvequiy7yXpcwYABYQqr4REREJJZE1tSXeUpL59xc4PByXPu/5e9OJXPx\nxfDrX3s19o89luzeSMidfTa0bevV11977c8z5IiIiEj6K+889beUo+3l5bx25XPGGfDww3DVVZUy\nA1O9nn/FY9i0qfdxevhhqFIleX0KI30Wg6E4+qcYBkNx9E8xDJ9yzX7jnCvzjNjOubXl7054PDf3\nOQByjsqheaPm/i5WtWqlTOpFREREJBgVNk99OjMzd83Ea/hm/Tf8ot4veOWiV/xdcPp07y1C06cH\n00Gp9KpWhe3bvbWIiIgkR0rW1Mu+nrvgOa78zZXk7w1ghD0jQyP1IiIiIhI3JfU+VK1SlT179/i/\nUEaGN2f9E0/s+2KqNKd6Pf9Ki2F+vvemWSkbfRaDoTj6pxgGQ3H0TzEMHyX1PmREMoIZqT/pJG+y\n8SlTYPhw/9eTSu+II+Cjj5LdCxEREUkU1dTHoXCe+onfTOSFT19gUs9JwVx41CjIzfXWIj507Qo9\ne3prERERSQ7V1EeZ2cNm9qGZ/WBm281sg5l9bmZ/NrOGpZzTyswmR9tuj7YfaGalPquZ9TazOWa2\nxczyzGyamXU6WP8yIhksXLOQm967iTun3knB3gI/j+uZMAHWr/d/HRERERGpNFI6qQcGATWBfwFP\nAq8Cu4DbgQVm1qR4YzPrAswAfgdMAJ4BqgFPAGNj3cDMhgEjgYbAi8AY4BRgopldf6DO5RyVw01n\n3sTRmUfz+MePk7czL97n9Jx7LmzbBl9/7e86IaF6Pf8Uw2AojsFQHP1TDIOhOPqnGIZPueapT4K6\nzrndJXea2Z/xEvvbgCuj++oBI4A9QDvn3Lzo/ruBqUA3M+vunPt7seu0Am4GlgKnO+c2Rfc/CnwK\nDDOzSc65FbE6l1Uji4FnDgTgwX8/6L++/ogjoFUrf9cQERERkUonlDX1ZnYq8BnwL+fc+dF9/YC/\nAaOdc31LtG8PfAjMcM61K7b/FeCPQF/n3OgS59wL3AXc55wbWuKYKxm3Ix8/kk+u+oRf1PuFv4dr\n2xY2b4bMzH33Z2bC229DJNX/uCKpoGNHOPpoeP75ZPdERESk8kpkTX2qj9SXpnN0nVtsX4fo+r0Y\n7WcAO4AcM6tWbPS/A+BKOWcKXlLfHhh6sA5VjVRlT0EA01uOHAnff7///t//3punsFo1//eQtNe6\nNXz5ZbJ7ISIiIokSimFfM7vFzIaa2RNmNhO4G29U/vFizZpG14tLnu+cKwC+w/sl5rjoNWsDjYGt\nzrnVMW67NLo+sSx9zIhksCN/B3sK9hQtcZXjHHcctGu3/xKJwJ49Py8h/AtLSarX86+0GDZuDGZp\n8TFJCH0Wg6E4+qcYBkNx9E8xDJ+wjNQPxvsia6GPgLHOueJD45l4o+6bSrnGJsCi7Si2PlB7gKyy\ndPAX9X7Bqc+fut/+7wd9zxF1jyjLJQ6sYUPIinaloADuuAPuv9//dSUtHXYYvPEG1KunVx+IiIhU\nBqGqqTezw4GzgIeAE4A+zrkx0WOLgeOBJs65ZTHO/QjIAXKcc5+YWWNgJbDSOXd0jPZV8Wba2eWc\nq1ni2H419bEc//TxvP/H9zn+0OPL+aQH8cwzsHixtxYpxWuvweTJ3lpEREQSTzX1pXDOrQX+YWbz\n8MpsHsObghL2H4kvqXB/4byTm0rsP1j7ffTp04fs7GwAsrKyaN68Oe3atQP2/ZPV24veZsNXGwC4\nuefN1K9Vv+h4yfZl3l6yBGbPpt3WrVCnjv/raTstt6Edy5bB7bfn8stfQq9eqdU/bWtb29rWtrbT\nbXv+/Pnk5Xmp4/Lly0mkUI3UF2dmnwG/Bho751ab2RigJ9DTOTe2RNsMvCQ+A6hTWLZjZiuBI4Aj\nnXM/lTgnB6/MZ6Zzrm2JY2UaqX9g5gN8vc6bc37WD7O4o/Ud9PtNv7iedx+ffgqdO8PLL8N55/m/\nXpLk5uYW/UOQ+Bwohl9/DQ8+6K1zcuDppxPbtzDRZzEYiqN/imEwFEf/FMNg6I2yZdMYr4Z+a3T7\nw+g6VpbbBu8lVrNK1OF/iDe6H+uc86PrqfF28PbWt/PqRa/y6kWv0u6YdgT2C1TLlnDqqfoWpBxQ\ns2bwyitwxRX6qIiIiKS7lB2pj74tdk3hC6GK7Y8A9wP/C7zvnDsvur8u8C1QDzjLOfdpdH8NvMT8\nTKCHc25csWsVjsZ/i/fyqbzo/my8l0/VBE5yzn1fog9lGqkv7uqJV/PRDx8xp/8calWtVa5zY+rc\nGZYsgUMO8bbvv9+b9lKkhOeeg3vvhWOP/XnfwIHQo0fy+iQiIlIZJHKkPpWT+kHAg8BMYDmwHm8G\nnLbAscAKoL1zbnmxc7oAbwI7gbHARuBCvGkpxzvnuse4zzC8t8quBCYA1YDuwCHAjc65Z2OcU+6k\nfu22tZw0/CQWXrswmNlw/vvfn+ezf+IJOOssL1MTKWH7dvj885+3X30V6taFhx9OXp9EREQqA5Xf\neD7Am4v+cOAi4BbgD8Bq4A7glOIJPYBz7h28pH8G0BW4AW8Gm5uAmOOSzrlbgL7AT0B/vDfMLgA6\nx0ro43V47cOpVqUaH/3wEdO+m8a076axedfm+C945JFeoXRODhxxhDdqP22al8GFROEXTCR+ZYlh\nrVo/f1RycuCYY2DFCli23xxRlZc+i8FQHP1TDIOhOPqnGIZPys5+45z7ErgxjvNmAZ3Kec5oYHR5\n71VenU/szPC53qThSzcsZXDOYAadOcj/hc84A0aMgPHjvVH7nj39X1PSVvPmMHYsXHUVTI37GyMi\nIiKSSlK2/CaVxVN+U9Lgfw2mcd3GDG41OKBeAZdfDuec461FDmD6dLj7bm8tIiIiFUPz1FcCGZEM\nnpj9BK8vfH2/Y3/p8BfOOyGOqSozMuCuu+DJJ/fdX60aTJrkvWZURERERNKOkvokub317Vz6y0v3\n2z/s42EsWrcovqR+2DCI9aKDiy6C9etTLqnXHLj+KYbBUByDoTj6pxgGQ3H0TzEMHyX1SZJZI5OW\njVvut79h7YbMXjmbrbu3UqdanfJd9LDDYifuu3Z5Sb1IMZ9+Cnv2QNWqye6JiIiI+KWa+jgEUVNf\nmg+Xfcjlb1/O611fp112u2AuesopcNtt0KtXMNeT0Fu9Gho18t42e9JJye6NiIhIetKUlpXY2ced\nzYmHnciWXVuCewPtKafAtm3eIgI0bAhNm0JeHhQUJLs3IiIi4peS+hSUnZVN13Fdeevrt4K54NFH\nw5Ah8ItfBHO9gGgOXP/8xPC446BDB7j99uD6E1b6LAZDcfRPMQyG4uifYhg+SupT0Kg/jKLHr3qw\nbU9AI+sPPeTVW2ikXoqZPBkeeUQfCxERkXSgmvo4VGRNfaFu47pRtUpV3uj6RjAX3LMHatb03jxU\n3FFHwdtvB3MPCZ3nn/dmQW3aFP71L6hdO9k9EhERSR+ap174n+P+hylLpwR3wapVYeFC2Lr1533b\ntsEFFwR3Dwmd3r3htNPgvPNg0yYl9SIiImGl8psUdVitw1iwZgEvf/ZycBc96SQvgytcWrSAnTvh\nnb93vMgAACAASURBVHeCu0c5qF7PP78xrFnT+yhUqwZ33AEffRRMv8JGn8VgKI7+KYbBUBz9UwzD\nR0l9imqf3Z6LT7qY5//zfMXdpE4duPZaeH3/t9pK5fLss7B2Lbz7brJ7IiIiIvFQTX0cElFTDzDn\nv3O4dPyl3Pa72/Y7dnGzi2lQu4H/m/z97963Jfv3994827Ch/2tKKD3wAHz4obeIiIiIf6qpFwBO\nOPQELjjxAub/NH+f/VO/m0rNjJr0bt7b/01OPx1++1t48kmv7v7KK/1fU0LptNO8EhwREREJH5Xf\npLBDax7KXzv+lecveH6fJeeoHOb/NJ93F7/Lpp2b/N3kuOPguecgJwc+/zyYjpeR6vX8CzKGp53m\nfVE2wR+DlKDPYjAUR/8Uw2Aojv4phuGjpD6E2me3Z/GGxVz77rWM+3JcMBdt0QKeeSaYa0ko1akD\nbdrAZZcluyciIiJSXqqpj0OiauoP5uqJV3NYrcP4c4c/EzGfv59t3QqHH+4VVFep4g3bVqkSTEcl\nNBYtgnPOgbFj993fvDnUqpWcPomIiIRVImvqldTHIVWS+r/N+xuD3hvE9D7Tadm4pb+LOefNWb9x\nIyxYAP/3f3DGGcF0VEJj/Xr+P3v3HV1FtT1w/Hty03uBhFBD70V6UZoUEQX1+SzAEywPC4qKBZ9i\nb9g7/ECe5aGCIs9C9wnGAogI0nsJoYaEFNLLvfP74ySUkADJndy5N9mftWZN7tSTnclae+buOYe/\n/Q0KCk4v27sXnn8exo+3rl1CCCGEJ3JlUi/lNx7sjs530Da6LZuSNpFTmOPcwZTS/RmuWgXNm+vs\nropJvZ7zzI5hVBTEx+vLoGS67jqd2K9de3pKSzP1tJaTa9EcEkfnSQzNIXF0nsTQ80jvNx7u0gaX\nMvnHyeQU5jCh+wRzDhoUBP/7H1x5pTnHEx6tc2eYORNWrNCfk5L00/y33rK2XUIIIYQ4TcpvKsFd\nym9K3L/kfgBeGfwK/t7+zh/wlVd0Fyh33w2tW0OtWs4fU1Qb772nn+Z/9pkekVYIIYQQZZPyG1Eh\n3ep144stX/D9zu/NOWCHDnDwIIwdq0ckEuIM7drBb7/pccuEEEII4R4kqa8GxnQYw6Amg9h6fKs5\nBxw2DH79Fe6/H3btgh9+0C/Smkzq9ZxnRQwHDNCVWWvXuvzUVUauRXNIHJ0nMTSHxNF5EkPPI0l9\nNTGkyRBe/PVFTuafNO+gXbuCt7fuFSclxbzjCo93ySUwY4bVrRBCCCFECamprwR3q6kvoZ5V7Lx3\nJy2iWph74Oho/dS+a1cYOtTcYwuPdPgwtGmju7ocOhRatrS6RUIIIYT7kZp6USmNwxvz+6HfzT/w\n5MmwZYueC4Eep+zOO2HOHPjPf6xujRBCCCEkqa9GetbvWTVJ/UMP6YT+4EF48EHIzTXlsFKv5zyr\nYujrC6++qiuzli2D/fstaYZp5Fo0h8TReRJDc0gcnScx9DyS1FcjLaNacjTraNUcvHVrmDpVP5Y9\nWkXnEB5n1Ciw2+HPP61uiRBCCFGzSU19JbhrTf0327/h9u9vp3lU81PLFIr3hr1Ht3rdzDlJcDBM\nnChdXYpTbr5Z94Tz/vtwxRVWt0YIIYRwH66sqZekvhLcNakvsBew4dgGzmzbm7+/SZ8GfZjYY6I5\nJ3nzTT206Hffgc1mzjGFR0tK0lVZHTvqOejyHCGEEKKmkxdlRaX42nzpXq87Per3ODV1jOnI0UwT\ny2X69oUff4T69Z0+lNTrOc8dYhgToxP6J5/UX+T4+8O8eVa3qmLcIY7VgcTReRJDc0gcnScx9DyS\n1Fdz3l7erDy4EofhMOeAXbtCaiocOwb5+eYcU3i8yZOhoEBPt90Gv/wCCxbAjh1Wt0wIIYSoGaT8\nphLctfymLPEJ8Qz4dAC779tNs8hm5hzUbteDUi1fDgMHmnNMUW18/jnMnQvHj0NEBCxdanWLhBBC\nCGtITb2b86SkHqDFey3oENOBbnW7MflSk/qaHzQIAgNh9mwICzPnmKJa+eEHPTBVdra+VIQQQoia\nRmrqhammD59Ot7rdmP7ndPMO+uKLsH49HDhQ6UNIvZ7z3DmGvXvr+bFj1rbjYrhzHD2JxNF5EkNz\nSBydJzH0PJLU1wCXN7mccZ3GcTjzMFGvRnEy/6TzB+3RAxo1gp49YfVq548nqp3gYOjQAdq0geho\n08YsE0IIIUQZpPymEjyt/KZEflE+QS8FsfPenTSNbOr8AYuK4OqroX17GDNGZ3BCnKGwUE+xsbo3\n1I4d9bvWQgghRE0g5TeiSvh5+9EovBEbjm0w54De3vC3v8GaNbr7EyFK8fHR9fTjx+uXZydMsLpF\nQgghRPUkSX0NMyBuAI+veJyx344154B33AFTpuguThwV6zZT6vWc5ykxfO01mDoVtm3TQx2MGFHh\ny6VKeUoc3Z3E0XkSQ3NIHJ0nMfQ83lY3QLjWiwNfZM3hNfzjm3+wJ3WPOd1c9u+v52vW6Kf3AAEB\n0K6d88cW1UanTrBsma7a6tcP8vKkVxwhhBDCLFJTXwmeWlNfIq8oj0H/GcTOEztJfiTZnIMOH647\nJi/x11+QnKw7KheiFKXgm2/gmmusbokQQghRdaSmXlQpf29/Fty8gCJHkXkHXbQI1q49PdntMG+e\neccX1crw4fq+TwghhBDmkKS+BityFLHuyDoOpFe+r/ly3X67rrVYt67s6fhxqdczgafGMC4OtmwB\nd/nCy1Pj6G4kjs6TGJpD4ug8iaHnkZr6GirQJ5BL6lzCuO/GkVeUx+77dpt7glGj4JFHdLcnpWVk\n6Hr7Bx4w95zCYwwerEtvEhP1cAdCCCGEcI7U1FeCp9fUn+lo5lEumXEJxx524bCfP/4IL78My5e7\n7pzC7YSGwi23wPvvW90SIYQQompITb1wmQCfADILMtmctNmFJw2AvXtddz7hlt54A1atsroVQggh\nRPUgSX0NF+IbwpCmQxj131GuO2mzZnDggNTrmcCTY9ili9TUVzcSR+dJDM0hcXSexNDzSFJfw9m8\nbMy+dja7T+wm9o1YFu1aVPUnjYmp+nMItxcRAZs363u83FyrWyOEEEJ4Nqmpr4TqVFNfIi03jfuW\n3EdKTgqdYztzS8dbaFWrVdWdUCn3eUwrLJOSAk2bwl13wZNPQnCw1S0SQgghzOPKmnpJ6iuhOib1\nAGsOrWH5/uUs3LWQyxpexpgOY/Cx+dAyqiVKmXw9KgWbNp29rHlz8Pc39zzC7X3yCTz2GHz6KQwd\nanVrhBBCCPPIi7LCEj3q9+Dxyx5nXKdxLN6zmFH/HUXH/+vIrhO7zD9Z377Ejxihu74cNQoGDoQP\nPjD/PNVcdah5HDcORoyAq6+2rg3VIY7uQOLoPImhOSSOzpMYeh63TeqVUpFKqTuUUt8opfYopXKU\nUulKqV+VUrepch4dK6V6K6UWK6VSi/fZqJS6XylV7u+qlBqrlPpDKZVZfI6flFLDq+63c2/ju4xn\n892b2Xz3ZtrWbsvQz4bS8v2WvLn6TfNO8vPP8PHHuqh682Y9WFVhoXnHFx5lxgw9tzKxF0IIITyZ\n25bfKKXuAqYBR4CfgESgDnAdEAbMNwzj76X2GQnMB3KAL4FUYATQEvjaMIwbyjjP68Ak4CDwNeAH\n3AREAvcZhnHO4+PqWn5TlpScFE7knGDetnks2r2I1bevrpoTTZ4MX3wBN9547rouXeDmm6vmvMJt\nbNwIvXrBPffowamkFEcIIYSnk5p6QCk1AAg0DGNRqeUxwB9AA+B6wzD+W7w8FNgDhAB9DMNYX7zc\nD1gB9AJuNgzjyzOO1Rv4rXi/boZhZBQvbwSsA4KAVoZhHCjVhhqT1JdYvm85t31/GwceOHDhjStj\nyxZYuvTc5fv2wS+/wJtvQng4dO9eNecXlnM4YPp0+O03fTmsXw8+Pla3SgghhKg8qakHDMP4qXRC\nX7w8Cfi/4o/9zlh1PVALmFuS0Bdvnw9MKf54d6nD3VU8f7EkoS/e5wDwAfqp/a3O/B7VRZOIJgAU\n2guxO+ymHPOser127eDhh8+dHnsMGjSA117Tj3GLikw5d3VRnWoevbxgwgR45BGd1K+uoi+FylKd\n4mgliaPzJIbmkDg6T2Loedw2qb+AolJzgIHF8zIe9/ILkAv0Ukr5ltrHKGefJcXzAU60s9qIDIgk\nPS+dgBcDaPxOY9eduGFDWLIEfvgBatXSveNs2+a68wuX69wZrr8errgCcnKsbo0QQgjhGdy2/KY8\nSilv4C+gLTDUMIz/FS9fC3QBuhiG8VcZ+20BWgNtDcPYoZQKAjKBTMMwwsrYvhZwHEgyDCO21Loa\nV35TIrsgm7CpYfSs35NvbvyG2kG1XduAli2hSROd6ItqLTZWl+DExl54WyGEEMIdSfnN+U1FJ/SL\nShL6YmHop+4ZZe6ll6vi7Thjfr7tAcIr39TqJ8g3iI13beRw5mEmLp3IhEUT2Jbswifnb7+ta+/z\n8lx3TmGJoiLYscPqVgghhBCewaOSeqXURHRPNduBf1jcnBqrbXRbpg+fzqUNLuWvY38xc93MSh2n\nUvV6A4urrI4erdQ5q5vqXPPYrBl89hkkJlb9uapzHF1J4ug8iaE5JI7Okxh6Hm+rG3CxlFL3Am8D\nW4HLDcNIL7VJ6SfxpZUsL9kvo9TyC21/lnHjxhEXFwdAeHg4nTp1on///sDpf4Tq/Nkffyb0n8DR\nrKPMWzSPphlNuWH4DcQEx1z08UpU6Px+fsQHBcH339P//vvdJh7y2fzPjz7an2efhYceimfChKo9\n34YNGyz/feWzfAbYsGGDW7XHUz+XcJf2yOea83nDhg2kp+vUMSEhAVfyiJp6pdQDwJvAZnRCn1LG\nNp8Bo4BRhmHMLbXOG53EewPBhmEUFi8/BMQC9QzDOFZqn17ASuBXwzD6lVpXY2vqS9t6fCt3LbqL\nY1nH6F6vO28PfZsAnwCCfYOr7qSXXgq5uboMJypKd5siqqUpU2DlSvjqq7OXh4WBr681bRJCCCEu\nltTUn0EpNRmd0P8FDCgroS+2vHh+RRnr+gIBwKqShP6MfVQ5+wwrnq+ocKNrkLbRbfn11l/54MoP\n+GHvD7T+oDWN3m5UtSedNg0OH9Y943xwzthgohq58krYuhXatDk9NWkCd9114X2FEEKImsStk3ql\n1JPAy8Cf6Cf0qefZ/GsgBbhJKdXljGP4Ay8Uf5xeap+S/u6fUEqFn7FPHDAByAM+duJXqDGGNB1C\n8iPJHHnoCKm5qRzMOHjBfUp/TXrROnSAY8fgiSdg0iQICjo9lX6kW81VOoYeondvOH4ckpNPT3Pn\nwqefQu3akJVlznmqexxdReLoPImhOSSOzpMYeh63TeqVUmOBZwE7etTXB5RSz5SaxpZsbxhGJvBP\nwAbEK6U+VEq9CmwAegLzDMM4K+MzDGM1+luApsAmpdRbSqkP0DcR4cDDhmEkVv1vW3342nyJ8I9g\n5cGVVX+yJ56A9HSd9R0/DmPGwB13VP15haWGD4fMTD0CrXSCJIQQQmhuW1OvlHoaeBrdTWV5tUjx\nhmEMLLVfb+AJoBfgD+wGPgLeLa8QvvjmYALQBn0TsR54zTCMxeVsLzX153H1nKsJ9g1mzt/muPbE\nR49C3bowYADMnw8REa49v3AppeCdd2DiRKtbIoQQQpTNlTX1bpvUuzNJ6s9v1vpZzN8+nyWjLRgg\nav16/SLtrFkwapTrzy9cZtIk/Wc+edLqlgghhBBlkxdlhUdrFNaIIkfRBberknq9zp1h2DB47TUY\nOxbS0sw/hxupyTWPU6boMpwpU5w/Vk2Oo5kkjs6TGJpD4ug8iaHn8Zh+6oXn8Pf2Z3PSZu5ccCcv\nD3qZyIBI1zbgjTdg1Sp47DG47TaIjNS1Gv/6FzRt6tq2iCoTGQlffKH/xCVjkfn66vu54CrsUVUI\nIYRwR1J+UwlSfnN+uYW5fLX1K56Kf4r/3vBfutTtcuGdqkJ8POzdq3+eNg2io3W3KdHR1rRHmM4w\nYM4cPWwBwDPPwMKF0LGjpc0SQgghAKmpd3uS1F+cLjO7MPOqmdYl9Wf680+49lp46y24/nqrWyOq\nSOfOcMst8MADVrdECCGEkJp6UY08+/OzPBP/TJnrXFqv17UrdOumu0t5/HHXnbeKSc3j2Tp3hpkz\n4fffK7afxNEcEkfnSQzNIXF0nsTQ80hSL6rM20PfZlCTQbz3x3tWN0WbOhVGj9ZP60W19Nxz0Lgx\n3HtvtX9HWgghhDiLlN9UgpTfXLy03DSiX4/mlUGvMKnXJKubo0crCgqCF17QL86KaicxEdq101/I\nPPaY1a0RQghRk0lNvZuTpL5i3l3zLk/+9CQdYjrQrnY7pl813doGzZoFDz4InTqdXnbzzXDPPda1\nSZjq3nvh22/h0CGrWyKEEKImk5p6Ua3c0+0eFo9azL3d7mXZ3mWnlltWr3frrfDDD/DSS3oaPBgm\nTIBdu6xpjxOk5rFsDz8Mhw/DyJEXt73E0RwSR+dJDM0hcXSexNDzSFIvqpy3lzd9Gvahb6O+JGYk\n8sBSi7smsdmgVy+47DI9Pf44xMZCv366j0Th8eLiYNMm+P572LnT6tYIIYQQVU/KbypBym8qb87m\nOcxYN4P4cfFWN+VsiYnQqBG88go8+qjVrREmadwYWraEpUutbokQQoiaSGrq3Zwk9ZW35tAaev67\nJwtvXnhqWf3Q+nSs4wajBT3yiB6iNDFRP80XHm/hQrj6aj3v3Fl/ISOEEEK4itTUi2qre73ujG4/\nmml/TuP5/zzPq6teZehnQ3lr9VscyzpmbePGjoUjR3SXKR7yaFdqHs9vyBA91tjDD8PAgVBQUPZ2\nEkdzSBydJzE0h8TReRJDzyNJvXAppRSfXfcZi0YtYuqgqSwZvYSxHccy/c/p5Q5S5TLt2sHcufoN\nyzFj9ONd4dF8fWHePP1n3bED9u2zukVCCCFE1ZDym0qQ8hvzvfzry0xdOZW2tdsCEO4fzsJRC/FS\nFtx35uXB3XfrpD452fXnF1WibVuIiIDly8HPz+rWCCGEqAmkpt7NSVJvvgJ7AeuOrMNAx7XPR31Y\nMnoJg5sMxuZlQX17UhLUqQPTpukEX3i8vXuhWTN4+mn9J42JsbpFQgghqjupqRc1wpn1er42X3o1\n6EXvBr3p3aA34zuP59ovr+WN1W9Y07iYGP3i7MSJsG2bNW24CFLzePGaNoUPP4TZs6F9e1iw4PQ6\niaM5JI7OkxiaQ+LoPImh55GkXrilGVfPYFzHcbzwywvYHXZrGvHEE3DppXDttXpwqkcegcJCa9oi\nTHHHHfoebeRIPd13H6SkWN0qIYQQwnlSflMJUn7jGsnZyUS/Hs3u+3bTNKIpSrnk26uzHT8OX3+t\nB6WaOhVCQuA//4GuXV3fFmGqL7/UZTj9++s/sZc84hBCCGEyqal3c5LUu87l/7mcXw/8yu2X3M7d\n3e6mQ0wH6xpz+DCMHw+LF8O33+plvr4wdKhkhB7qhx9g2DC46SY9DR4M/v5Wt0oIIUR1ITX1oka4\nmHq95bcs55sbv2Fr8laGzB7CxCUTWbrHoj7k69WD+fPhxhvho4/09Pe/w623wrp1ljRJah6dM2QI\nxMdDQkI8t98ON9+sE31ROXI9Ok9iaA6Jo/Mkhp5Hknrh9oa3GM6yMct4qt9THM06yvgF49mftp8i\nR5HrG+Pvrzs9/+47PX35pS7KHjwYDh50fXuE0y67DF58Ud+vhYbC9OmQmWl1q4QQQoiKkfKbSpDy\nG+skZiTS56M+ZORl8NLlL3Fv93utbhLk5uq+EuvXh3/8A5o31yU5wuOsXQvdu0NQkP7ypWVLq1sk\nhBDCk0n5jRDlaBjWkIMPHmRcp3H8cuAXq5ujBQTArFn65dlFi+CKK2DUKP1yrfAo3brpVyciIqBV\nK/2ljBBCCOEJJKkXlnGmXq9n/Z7M2zaPtYfXmtcgZwwbBh98oJP6xYthzhx46qkqP63UPJrjzDjW\nrQsbN8Jtt+ka+/37rWuXp5Hr0XkSQ3NIHJ0nMfQ8ktQLjzSq/Sgua3gZ3Wd156ovriI5O9nqJmle\nXjrB//BD+OkneVrvoSIj4d//hrg43be9EEII4e6kpr4SpKbePWQXZLN8/3JGzh3J2I5j+eSaT6xu\n0mmrVkGfPnpE2nfesbo1opJ++EG/HvH003r8sdq1rW6REEIITyI19UJchCDfIEa0HMH8G+azZM8S\nhswewqGTh6xulta7N7z5Jvz4owxZ6sGGDIE33tC19bNnW90aIYQQonyS1AvLmFWvN7z5cOb8bQ7J\nOcnsOrHLlGOa4sordd+ItWvDo4/Cq6+a3lei1Dya43xxnDQJRo6Ehx6C3393XZs8kVyPzpMYmkPi\n6DyJoeeRpF54PD9vPwY2Hki4fzh3LryT+IR4TuaftLpZuj/ExETd8bnNBpMnQ1gY5OVZ3TJRQQ88\nAG3bQq9esMuN7huFEEKIElJTXwlSU++e1h9dz50L7+TPI39SK7AWVzS7go9GfISPzcfqpmlJSVCn\nDnToAOvX60RfeIysLD0EwbFjsGkTtG9vdYuEEEK4O1fW1EtSXwmS1Lu3hPQEVh1cxej/jmZ85/HM\nuHqG1U06betWXW+/ezdER1vdGlFBRUW6L/sNG+C33/S70EIIIUR55EVZUSNUVb1eXHgco9qPYtqV\n05i5fibT1k6rkvNUStu2EB6uB6r69ls9HTtW6cNJzaM5LjaO3t56pNkePeDSS2Hz5qptl6eR69F5\nEkNzSBydJzH0PJLUi2rr7m53c8cldzBh8QQchsPq5py2cqVO7j/5BKZM0dORI1a3SlwkLy9Ytkw/\npb/xRqtbI4QQQmhSflMJUn7jOQrsBfi94Mfcv83lxnZumIEtXw433aS7vdy3T9fcBwRY3SpxEU6c\ngFq1ICdH/mRCCCHKJuU3QpjE1+bLjW1v5Kb5N3Es6xgF9gL3emp/+eX6KX2dOtCqFVx7LRQWWt0q\ncRGiosDPD0aNArvd6tYIIYSo6SSpF5ZxVb3eJ9d8QlRAFLFvxOL3gh8hL4eQU5jjknNfFB8fOHoU\n1qyBn38GX1+4/35wXPjmQ2oezVHZOC5dCosXQ7t25rbHU8n16DyJoTkkjs6TGHoeSepFtefv7U/K\noykYTxukTU6jyFHE48sfJzU31eqmna1TJ8jNhS++gHff1V1eJiRY3SpxHv376/ecd+yAL7+0ujVC\nCCFqMqmprwSpqfds87fNZ8w3Y8gryuOmdjcx529zrG7SufLydMa4Zg1ccgn88gsEB1vdKlGOhx/W\nif2kSTBwoNWtEUII4S6kn3o3J0m957M77CzctZBrvryG9ePXc0nsJVY36VyGoUc56tRJf541C26/\n3do2iTKtWwcvvwwnT8IPP1jdGiGEEO5CXpQVNYKV9Xo2LxsjW42kX6N+dJ7ZmSs/v5J5W+dRaHej\nl1SVgo4d9Yuz994Ld9wBiYlnbSI1j+ZwNo5dusD48bBnD2zbZk6bPJFcj86TGJpD4ug8iaHnkaRe\n1Gg/jf2JZWOWEe4fzg1f30Ct12pZ3aRzeXvDe+/pEY8efRS++cbqFokydOmi321u2xaSk61ujRBC\niJpGym8qQcpvqqciRxE+z/sw46oZjO8y3urmnOunn+Drr+F//4Ndu6xujShDQQFERMB118Hs2Va3\nRgghhNWkpt7NSVJffd254E4W7l5Iy6iWAOQV5XFNq2t4tM+jFres2L590LSprrVv397q1ogyrFql\n33H+6y/91F4IIUTNJTX1okZwx3q914e8zuxrZzOl7xSm9J1C17pdmfzjZCJfiaT3v3vz+abPrW1g\n48bQtSt06ABXX038/fdDdra1baoGzLwWe/TQY4oNGmTaIT2GO/5PexqJoTkkjs6TGHoeSeqFOEOI\nXwgDGw88Nb077F0SH0jk6xu+plOdToz5ZgyTlk2yroFKwW+/6U7R4+J0rX1wMEydelGDVYmqZ7PB\np5/q/usnTIBDh6xukRBCiJpAym8qQcpvaq6P//qYiUsnck/Xe3hl8CtWN0f71790Ul+3LowcCR98\noJN/YamlS+GBB2DvXl1rL38SIYSoeaSm3s1JUl9z2R12vt72NTfNv4kQ3xA+HvkxXep2oV5IPXxs\nPtY1bN8++P13GD0aPv8cRoyQwarchFLw/vv6qb0QQoiaRWrqRY3gifV6Ni8bN7a7kWMPHePShpcy\n7rtxNH6nMdGvR5Oc7fp+DE/FsEkTGDUKnntOPx7u29flbfFkVXktvvQSPPOM7ryouvPE/2l3IzE0\nh8TReRJDzyNJvRCVEBMcw+LRi8n8VybHHz5Ow7CGRL8ejf8L/ry28jXrGvbkk7Bype565a679PCm\nOTnWtUcwYgT06QMDB0JqqtWtEUIIUV1J+U0lSPmNKK3QXkhGfgafbviUh//3MNvu2Ubr2q2ta9Dn\nn8O0abp/RYAZM/SQp8Iy4eHw4IPw9NNWt0QIIYSrSE19MaXU9UA/oBPQEQgGPjcM4x/n2ac3MAXo\nCfgDu4GPgPcMwyizexCl1FhgAtAasAN/Aa8bhrGonO0lqRfl6jGrB38c/oP+cf1pH92e0e1H06N+\nD2sak5UFL7+sR0J65BG47z5r2iF47jmd0A8dqj/n5+tuL594wtp2CSGEqDpSU3/aFHSy3QEo6Riu\n3GxaKTUS+AW4FJgPvAf4Am8Bc8vZ53XgYyAGmAl8BrQHFiil5NW2KlRd6/WW37KcpaOX0rdhXzYc\n20DPf/ek84zOTP1tKjmF5pbCXDCGwcHw6KNw660wc6ap565OXHEtPvTQ6R5xHngAOnWCKVMgKanK\nT+0y1fV/2pUkhuaQODpPYuh53D2pfwBobhhGGHD3+TZUSoUCHwKFQH/DMP5pGMZk9FP+1cD1Sqkb\nS+3TG5gE7AE6GIbxkGEY9wJdgFTgdaVUI7N/KVG9BfsGM7TZUJ4d8Cy/3PoLG+7cQNe6XfnXZUCe\n0AAAIABJREFU8n8R9FIQTd9tyow/Z1BgL3BNg8LC9Eu0W7ZASAg8+ywUFbnm3OKUoCD9lP6KK/T0\n5pt6qIEbb4TERKtbJ4QQwtO5dfnNmZRS/YEVwGeGYdxSxvrbgFnAp4Zh3Fpq3QBgOfCLYRj9z1j+\nH2AMcKthGJ+W2udZ4EngOcMwnim1TspvRIXZHXZ2p+7mmfhnWLpnKZP7TGZsp7HUDanrmgbk58Pr\nr+vHwz166P7su3RxzblFmdatg3799KDAEyfCO+9Y3SIhhBBmkvKbyhlYPF9axrpfgFygl1LKt9Q+\nRjn7LCmeDzCthaJGs3nZaFWrFXOvn8urg1/l3T/epcV7LXj/j/f5fuf3nMg5UbUN8PPTBdwrVuge\ncbp2hQYN4Pvv5VGxRbp0gcxMfa/17ru6T/sFC+SLFCGEEBVXnZL6lsXzXaVXGIZhB/YD3kATAKVU\nEFAXyDIMo6yq1j3F8xbmN1VAza7XG99lPDsm7OCaVtewaPci7ltyH30+6kNWQVaFjlOpGA4YAJs2\nwZ490KgR3HGHnl93HWRkgKPM98mrNSuvRaV0vX12ti7LGTECfHxg/nyd8HtSgl+T/6fNIjE0h8TR\neRJDz1Odkvow9FP3jHLWZwCqeDvOmJ9ve4BwU1onRClh/mF8dt1nLBm9hMWjFrPzxE5CXg7hzdVv\nYnfYq74BTZvCb7/pNzXnz4dvvtH9Lvr4wMiR8MYbsOuce2RRRQIDYckSncj37w/XXw+hofrPcc89\nerkQQghRnupUU78LaIp+sXZfGetXAr2AXoZhrFFK1UX3qHPIMIyGZWzvA+QD+YZhBJRaJzX1wnQF\n9gIeXPog0/6cBkCjsEa8Nvg1/t72765rREaGTu7Xr4ePP4Zu3XS5jrBEYSF88gncfbceJPill6Bn\nT6tbJYQQ4mK5sqbe2xUncZHST+JLK1mefsb2Zy6/0PZnGTduHHFxcQCEh4fTqVMn+vfvD5z+yko+\ny+eKfv5g+AcM9xlOSm4Kf/j8wQ1f30DXL7ry/IDnuWLQFVXfnrAw4uPiIC6O/iNHwpAhxCsFLVvS\nf8kSaNzYreJV3T/7+EDz5vF89hnMmtWfXr0gODieMWNg+nTr2yef5bN8ls/y+ezPGzZsID1dp44J\nCQm4UnV6Uv8ZMAoYZRjG3FLrvNFJvDcQbBhGYfHyQ0AsUM8wjGOl9ukFrAR+NQyjX6l18qTeBPHx\n8af+EUTZfk74mf6f9mdg44Esv2X5OeurPIZ5ebBmDdxwAxw/Dn36wJNPQu/eunvMasJTrsWDB3VX\nmG+/DYcOQb16VrfobJ4SR3cmMTSHxNF5EkNzSO83lVOS8VxRxrq+QACwqiShP2MfVc4+w4rnUnsg\nLNMvrh+/3fobK/avwO8FP1JyUlzbAH9/3ediUhJs364Lv6+7Thd7Dx4MGze6tj01XIMG8NZb0Lgx\nXHqp7sRICCGEgOr1pD4E2AuEAn0Mw1hXvNy/eL+ewE2GYXx1xj4lT+P3At0Mw0gvXh4HrEPfCLQy\nDCOx1LnkSb1wqePZx4l5PQYfLx8W3LyAPg37EOwbbF2DduzQI9UuWACffQZXX60TfeES+/bp95xB\n95bj5QXPPQdt2oDNZm3bhBBCnObKJ/VundQrpa4Brin+WAcYAuwDfitelmwYxiNnbD8S+BrIA+YC\nacAIdLeU8wzDOGtE2eJ9XkePKnsImA/4AjcCEcB9hmFMK2MfSeqFy21O2syt393KuqPrAAjyCWJK\n3ymM7zKeyIBIaxo1c6YeNcnLC+67D55+Wj/NF1Xu6FFYuRJSU3VCf+QIPPYY3H47NGmiu8oUQghh\nLUnqiymlngaeRndVedaq4nmCYRhNSu3TG3gC3dONP7Ab+Ah4t7xMXCk1FpgAtAHswHrgNcMwFpez\nvST1JpB6vcpxGA6SspKYvWk2kz+cDI3hzi53Mn34dJQVmVxODjz1lO4Cs04dOHxYJ/kepDpci1On\n6nr75GQ9qFV8PAS7+Muc6hBHq0kMzSFxdJ7E0BzS+00xwzCeBZ6t4D6rgOEV3OdT4NOK7COEVbyU\nF7EhsTza51E65HbgL7+/eHzF4+xO3c2QJkMAiA2JpWlEU2KCY2gW2axqGxQYqIdEnTwZoqN1/Ue9\netCxoy7LufNOeWzsAo89pqdff9XdX4aEwI8/wuWXW90yIYQQruDWT+rdlTypF+5m0a5F/HzgZxSK\nrclbOZx5mCJHEVuOb+GJy57ghYEvuKYhhYWwfz/8+afuNefdd/XymTPhllvAz8817ajh8vP1+GHL\nlsGXX8JVV0lVlBBCWEHKb9ycJPXCU6w6uIo+H/Xh/4b/H3d2vdP1DcjPh7FjdWYJcOIERFpU/1/D\n5OfDuHEwt7iD36IieYlWCCFcTbq0FDVCyaANovIuFMPeDXrzWJ/HuGvRXYz7dhx5RXmuaVgJPz+d\nVWZng6+vfny8/Nz+9q1WHa9FPz+YMwccDv3Z2xt++61qu8GsjnF0NYmhOSSOzpMYeh5J6oWo5l4e\n9DILb17Ipxs/JeDFAMKmhvHlli9d24jAQN39ZW4uDBoELVuC3e7aNtRQSkFBAbRvD5ddBrGxuhpK\nCCFE9SLlN5Ug5TfCExmGwcn8k0xbO43HVzxOZEAkqbmpdIntwkcjP6JWYC3qhtSt+obs2qWT+iZN\n4LXX9GBWwiWys+Hxx0+/6uDlBX//O7zwAjSr4vephRCiJpKaejcnSb3wdJn5mRTYC1i4ayGPr3ic\nAnsBKTkpfDzyY8Z1Glf1DVi+XPdpv3Il1K2rX6yNja368wpAf2Fy8iTMmweffqrDP3my7hZTCCGE\neaSmXtQIUq/nvMrGMMQvhKjAKMZ2GsvhSYdJfiSZp/o+xa3f3coN825gc9Jmcxta2uWX6wLv3bvB\nMHRi/9hjlpXk1LRrMSAAYmLg3nth7Vr48EN45RU9Su3o0frJ/fbtFT9uTYtjVZAYmkPi6DyJoeeR\npF4IAcCzA55l9rWzWb5/OR3+rwNxb8fRdWZXbv3uVpKzk6vmpM2a6aFQX3pJZ5W9esGWLbqrFuEy\nd9wB69bB/fdDVBRMnw5t2kC/frBokdWtE0IIcTGk/KYSpPxGVHd7U/eSkJ7Akj1LeGP1GwAMbjKY\nUe1HMajJIOqH1jf/pCtWwJQpsHq1/hwervtg/O03XYMvA1i51KZN8Oqr8Pnn+jWIJk2kS0whhKgo\nqal3c5LUi5pm0a5FLN2zlAW7FnAg4wA3tbuJ94a9R63AWlVzwkOHID0dRo2CzcWlQJMmwYsvgr9/\n1ZxTnMNu1yU5Bw7oz1deqb9QadfO2nYJIYSnkJp6USNIvZ7zXBXD4S2G896V75HwQAI/jPmBuVvm\nUvu12rzz+zs4DIf5J6xfX2eOmzbp5P6ZZ+Df/9bF4A8/DF99BXv26Hp8E8i1WDabDRIS9EBW06bB\nxo26a8xu3fTrEKVJHJ0nMTSHxNF5EkPPI0m9EKJCBjcdTN4TeUzqOYkHlj3AO7+/Q6G9sOpOGBam\ne8pJTYUvvtBJ/qRJ0Ly57pNRKejTB1JSqq4NNZyvL9x9t/4CZflynei3aAE33ggZGVa3TgghBEj5\nTaVI+Y0Q2r/X/5s7FtwBwJTLppBblMu1ra6le73u+Nh8qvbkDodO9Feu1N24HDqkC8BHjara8woA\nZs2Cf/5T/3zNNfDNN9a2Rwgh3JHU1Ls5SeqFOO1Y1jHeXaNHM5q7ZS770/cD8GDPB3lt8GvYvFz0\nduXLL+uRlSZO1INa+fq65rw13I8/wuDB+ucXX9R/AiGEEJrU1IsaQer1nOcOMawTXIeXLn+Jly5/\niX3378PxlIO3hr7FW7+/xXVfuXC02H/9C557Tg+XGham60QukjvE0VMNGqS/MLn6anjiiXhuuw1m\nz9ZfnIiKk2vRHBJH50kMPY8k9UIIUymleKDnA6y+fTXf7/yea7+8lgU7F7jm5E8+qYdH7dxZZ5vj\nxrnmvDVcRAR8/73ukdTLS4e9QQP9cq0MOSCEEK4h5TeVIOU3Qlyc+IR43vvjPf67/b8A+Hj5MKLl\nCO7rfh/94vpV7ck//RRuvVX3kLN+PVxySdWeT5ylpBrK11cn/EOHWt0iIYRwPampd3OS1AtRMYX2\nQnKLcpm9cTYz189kU9ImAIY1G8aUvlPo3aB31ZzYbtcF3z/9pLvFfPhhCAqqmnOJcxQUwIQJ+qXa\nbdugdWurWySEEK4lNfWiRpB6Ped5Sgx9bD6E+oUyofsENt61kdRHU/niui/YeWInfT7qw9u/v101\nJ7bZ4IcfdDctL74IwcE6uU9PP6uPe0+Jo7srHUdfX/jwQ/3ucps24OMDw4bpnklzc61po7uTa9Ec\nEkfnSQw9jyT1QgiXiwiI4Ob2N7Pz3p18MvITHlz2IP4v+HPbd7dxw7wbmL1xNtuSt5lzMm9vmDlT\nPzaeMwdeekkXgV9yCfzxhznnEOf1zjt6AKvNmyEmRj+9DwyEe+4xbfwwIYSo8aT8phKk/EYIc2Xm\nZ/LNjm8ochSxYv8KEtITWHlwJcOaDWNg44GMaj+KuiF1zTvhyZNwww2wbJl+ofbf/4aGDc07vrig\nGTNg8mT9Yu0TT+h7rNhYiIvTAwcLIUR1IDX1bk6SeiGqXkJ6Ap9s+IT3/niP1NxUnur7FI/0eYRg\n32DzThIfr9/mXL0a+vXT3bcMGmTe8cV5FRXBfffpEWoTEuDIEX2/NWUKPPWULtcRQghPJjX1okaQ\nej3nVecYxoXH8Uz/Zzjx6AmWjVnGwt0LCXk5hG4fduONVW+QXZDt/En694dVq4j/6iuoW1e/VNu2\nLWRlOX/sGqii16O3N0yfDkuWwPbtkJGh6+3ffVeX6fzjH/D773rw4JqiOv9Pu5LE0XkSQ88jSb0Q\nwu0NaTqEtf9cy7Z7tjGx+0TmbJlD8MvBtJvWjqd/epotx7c4d4LatXU2efQoZGdDSAh07QrXXANz\n55rzS4iLcvPNejCrd9+FxETo1Uu/7zxpkl4uhBCibFJ+UwlSfiOE9U7mn+SrrV/xzpp32HJ8C68O\nepW7ut5FiF+Icwc2DNiyRffBuHq1fsvz/ff1253CEgsX6gGDt2zR1VH33w9XXWV1q4QQ4sKkpt7N\nSVIvhHv5dMOnPPTDQ5zIPUHvBr15uNfDjGg5ApuXzfmDz5gBd92lpxdegKgo548pKuX77/UXJ3Pm\n6N5Jb75Zj1rr7W11y4QQomxSUy9qBKnXc57EUBvbaSwpj6aw7Z5t1A+tz3VfXYfP8z5c++W1fPTX\nR3y34zvsDnu5+583juPH687Wv/wSatXSo9Tu22f+L1ENVPX1OGKErpLKyIDnntN/Eh8fuPtu+Owz\nyMys0tO7hPxPm0Pi6DyJoeeRpF4IUW20rt2aL6//EvtTdlaMXUHb2m2Zv30+13x5Dd7Pe9PivRbM\nWj/rvAn+OZSCO+6AEyfgu+/gwAFo2hSef75mvcHpRkJD4cEH9Z/k7bf1QFaPPqqXDxumn94fPWp1\nK4UQwrWk/KYSpPxGCM9TaC/kv9v/y5hvxlDkKKJ/XH8ahjVkVLtR9G7Qu2K1+D//rHvOueIKPVJt\n585V1m5x8Vavhs8/hzVr4M8/YfhwePhh/acSQggrSE29m5OkXgjPZRgGO0/sZO3htczfPp+1R9Zy\nJPMIrWu1JjoomiYRTbij8x30qNfj/DX5Bw7AvffqtzjbtoVZs6BnT9f9IuK81q+HDz6Ajz7S44oN\nGwaPPKK/ZBFCCFeRpN7NSVJvjvj4ePrLIzSnSAzN8cWCL4hoFcGmpE2sPrSa73Z+B0DXul1pGNaQ\ndrXb4e/tj5+3HzFBMfSL60f90Pp6Z4dDl+I88wwsXQpDh1r3i1jMHa/HpCSYPRv+9z9Yu1Yn+SNH\n6qoqd+SOMfREEkfnSQzN4cqkXvoMEELUeHVD6tK/eX+GNR8G6Kf5m5I2sS9tHxuObUApRUZ+BjuP\n7Dy1vF10O/o06MMldS7h1in/wjcmRpfjbNwIHTpY/BuJEjExugTnoYfg1Vfh2mshPBxGj4ZGjeCS\nS6B1a6hXz+qWCiGEc+RJfSXIk3oharaT+Sf588if/HrgV2b9NYtDJw9RK7AWz68P5/b5+/njuu7s\nnTCaoKg69GnYhzrBdaxusiiWnw8ff6yHIdi1C5KTdalO27Y64X/8cQgIsLqVQojqQspv3Jwk9UKI\nMyVlJbHl+BZ2pOzAd8XP/PNf8wBo+3IDtuUfpHF4Y34a+xONwhtZ3FJRlv379RhjCxbo3kofewza\ntAE/P4iL01+8+Ptb3UohhCeSpN7NSVJvDqnXc57E0Bymx7GoSL+dGRRE/tBB/O/EH/yU9hdFoUHU\nbdCG0NoNaNiwPbENWlM7thkh0fUJD4027/wW8fTr0TB0/X18PKSl6af4qamwfbvuQWfKFLjsMt03\nflXV5Ht6DN2FxNF5EkNzSE29EEJ4Mm9vnQnOn49fRgZXRUUz+ER3UpMSyFt/lNyU37CdXIpvVi5B\nuQ6C8yDHG4pCgvGNqo1fVAwqLEwXf585L2tZyTw4GLxk6BFnKAW33KKnM6Wnw8svw6BB+k9bVASN\nG8Ntt8E11+jSHXd98VYIUXPIk/pKkCf1QggzFRYVsOPAOj748SW27PoNR3o6XQKa0M6vAaF5Bo0I\noyFhRBf64ZuVo7PMjIyz53l5EBJSsRuB0nM/P6tD4RGSkvQ4ZL//Dt9+q+v0hw7V3WZ27apfvhVC\nCJDyG7cnSb0Qoqo4DAd/HvmT/Wn7OXTyELlFuSzavYijmUc5kHGAQJ9AmkU2o0VUC1pFtaJhWENa\n1WpF1+hOBOQWlp3wn29e8nN6un4MXdEbgTNvHkJDa9y3BQ6HftH2f/+DefPgr7/08i5ddA8799wj\n90pC1GSS1Ls5SerNIfV6zpMYmsNT4lhoL2R7ynb2pe3jzyN/ciTzCLtO7GJj0kayC7JpU7sNsSGx\ndIrpRMOwhtQLrUfn2M7UC6mHj83n/Ac3DMjNrdiNQKl5fFYW/cv6tqAi3x74+3t0LUthoX7xdt48\n/TR/7VpdqtOxox54uFMn6NtX/6pl8ZRr0d1JHJ0nMTSH1NQLIYQ4h4/Nhw4xHegQ04FrWl1z1rqs\ngiz+OPwHiRmJHMw4yI6UHXy84WN2pOygwF5Ak4gmtK7dmksbXEqzyGYE+wbTqU4nagXWQimlE+nA\nQD3FxlaugcuX68z1fDcASUmwc2f5Nw1Q8RuBM+ehoWA7z0jAVczHB1q0gCee0JPdDlu26OEL/vhD\n95m/axdcein06aO37dVL95UvhBDOkCf1lSBP6oUQniQpK4kDGQfYmbKTVQdXcSTrCOuPric1N5Wc\nwhzqh9YnNjiWbnW7EegTyBXNrqB9THtqB9bWCb8r5eVVrnyoZJ6ZCUFBzpURBQZW6bcFe/bAunWw\nYgUcP67r8hs31sl9ly7QvTv07KmroYQQnk3Kb9ycJPVCiOrAMAySc5I5kH6AxIxEdqfuZsvxLexL\n28fW5K0UOYroH9ef5pHNaRTWiPqh9Wka2ZSGYQ2JCohyfcJ/MRwOndhXonzo1LyoqOI3AqWXVSAj\nz8nRdfmbN8OSJbB4sX7CP3AgtGqlk/3OnaFZM/D1rcLYCSFMJ0m9m5Ok3hxSr+c8iaE5JI7nMgyD\nnSd2suX4Fg6kH2DT8U0cPnmYY1nH2Jq8FW8vb126g6Jvo740Dm9M/t58uvfpTuPwxrSp3YYQvxCr\nf43Kyc8/nfxX5FuDkvnJk/rdgEqUEcVv20a/K4ax52gQa/5Q/PorJCToyiY/P+jRQ5fq1K0Lo0bp\nJ/ziXPI/7TyJoTmkpl4IIYSllFK0qtWKVrValbm+0F5IUnYSe1L3sD9tP4kZiWw+sZnEbYms2L+C\ntLw0gn2DiQuPI9w/nKiAKFpEtaB+aH1aRLWgZVRLYkNi8fd2w6Fa/fwgOlpPlWEYkJV14RuAAwfO\nXXb8OCo3l+b5+TQPC2NMyY3AZeHk+4dxvCCcg3+EsSclnP97JoxUI5y4DmFENgknIi6MTv3Dqd82\njOB6YfJYX4gaRp7UV4I8qRdCiPMrKe3ZmbKT1NxU9qXto8BewO7U3exL28fu1N0cOnmINrXbnCrn\n6RDTgQFxA2hVq5XnPuU3S2Hhud8UlLoBsKdlkL4/nZMHMzDS0nGkZeCdlU6wI4Nw0rHbfCkKCsMr\nIhzvqDC8a4WjwitQRiQDmgnhNCm/cXOS1AshhPNO5p9k94ndbEvexoGMA+w6sYv4hHgOnjyIn82P\nYc2HER0YTZ3gOnSO7Uyj8EY0CG1AZECke9bzu4nCQvhphUHCthy2/57BgY3pZB3OINSRTse4DNrV\nT6d9wwwaR6RjyzrPuwY5Obo3ocp2TxoeLp30ixpPkno3J0m9OaRez3kSQ3NIHM1hVhwdhoMD6Qf4\nKeEn9qXtIyE9gdTcVA6dPMTOEzspsBfQIqoFzSKbUS+kHvVD6xMXHkfTiKa0iGpxuptOD1SV12JC\nAqxcqQfKWrUKdu/WD+O7dYM6daBNG12v36gRNGgAkaFF+OSedO6lYy+vyo1uXDKv5IBm8j/tPImh\nOaSmXgghRI3lpbxoHNGYxhFlvwV6JPMIG49t5ETuCbYnbyctN40dKTtYf3Q9O0/sBCAuPI5mkc2I\nCYrBz+ZHXHgcMcExhPmF0T6mPU0imrhnPX8ViovT0+jR+nN2Nmzbpt/rPXgQNm2C2bNh61Z9A1BU\n5I3NFknLlpHUrw8NG0KHDnoArUaN9HAGPucb08wwdBelF7oROHas/BuDrCx951HR3ogSE+HoUb3M\nwwc0E+JiyZP6SpAn9UII4b5yCnNIzEjkQPoBEtITSM5JJiE9gUJHIQnpCWxP3k5yTjIA4f7h1A+t\nT+tarQn0CaRDTAcahzcmLjyO6KBoagXWws+7ZpaQOByn3+c9ckQPmrV1q07+t2/X+XZMDISEQFSU\nfrofHQ1NmujuN+vV0730xMY6kVPb7RfuovRC3xo4HM6NchwaKoMGiEqT8hs3J0m9EEJ4NsMwyCzI\n5FjWMY5lHeNI5hH2pO4hMSORTUmbOJl/krS8NFJyUvDx8iEmOAZfmy9tarehTlAdGoQ1oHZgbeLC\n42gY1pDYkFhC/UKt/rVcqrAQUlJ0zp2SogcK3rgR0tLgxAmd+B8+DAEBOuGPitJJf2ysfurft69O\n/OvUqeIH6SUDmlWkbKh0F6WBgZUf5dgFA5oJ9yVJvZuTpN4cUq/nPImhOSSO5qiOcTQMg6yCLJKy\nkziQfoD0vHQOZx7mSOYRkrKTSMxIJDEjkT2pewCoF1KPcP9wIgIiCPcPp13tdsSGxBIVEEWATwCd\nYzvTILQBNi9bmeerbjE0DD1q7tGjOvFPTtY///or7N2ry34yM6F2bZ3416mjc+DoaGjbFlq2hFq1\n9BP/4OCLz4tNjaPDcXFdlJ7v24PCworfCJT++by1TuarbteiVaSm3sWUUvWB54ArgEjgKPAt8Kxh\nGOlWtk0IIYR1lFKE+IUQ4hdCs8hm5W5nGAbZhdmk5aaRnpdOel46J3JPsClpEztTdpKWl8aW41s4\nkXuCpKwkQvxCiPCPoFlkM9rUbkNUQBRh/mEk7U3Cr6kfYf5h1A+tT4hviMe+9As6CY+J0dOZJk06\n/XNhoS6rT0zUif/hw/rn+HiYOVMvO3pU3yBERek8Ny4OIiP14FsREXpe8g1ArVom/xJeXroEJzRU\n33lURkHBhb8hSEg4/82Bn59zZUQVuSsSHqnGP6lXSjUFVgG10Yn8DqAHMADYCfQxDCO11D7ypF4I\nIUSlFDmKSMtNIyE9gQMZBziYcZCUnBQSTyZyMv8kh04e4mDGQXKLcim0FxLsG0z3et2pH1qfUL9Q\nfG2+xIXHUSe4Dpc3vpwg3yCrfyWXKMlt09Jg3z5d3nPokM6XDx7U3wiUPPkPC9PJfu3aOqcNCdGf\nIyP1uthYfaMRGQlBQae3cdsqGcPQbzZXtPehM+d5efrGxJkyIhnQrMKk/MaFlFLLgMHAfYZhfHDG\n8jeAB4EZhmHcXWofSeqFEEJUucz8TA5kHGB/2n4OZx4mMz+T/en7KbAXsPn4ZrYnb6dhWEPqh9an\ndlBtagXUIiowiqiAKBpHNKZRWCMaRzSuUT395OaeTv5TUvQ8K0vX+e/bp78ZSEvT3w6kpuru+Eu2\nKSw8nfwHBur3AWrX1rls06b6HYBatXRuHBSkt4uI8JAH4YWF+v2AypQPlcx9fCrfPWlYmL5zqmED\nmklS7yLFT+l3A/sNw2haal0wcAwwgBjDMHLOWCdJvQmkXs95EkNzSBzNIXF0XkVjmFWQxZ7UPRw+\neZiUnBRO5J7Q85wTbEvZxuakzeQW5eLv7U+d4DrEBMUQ7h9OgE8A/t7+2B123cOPzQ9fmy92w050\nUDQB3gEE+gQS6BNIgE8AAd4B2LxshPiG4O/tj5+3H342P/y9/U9N5b0nYIXKXosFBTrBL0n2s7P1\nzcDevfpB9+HDel1mpr4JSE4+nfvm5+ucNSTk9AvAoaH6G4HY2NM3AiX5bUm+GxGhe910t1z3nBga\nhr5jquyYBRkZOqAhIZV7r6Bk7u9ZN6hSU+86A4rnP5ReYRhGllJqJfopfk9ghSsbVhNs2LBBEgAn\nSQzNIXE0h8TReRWNYbBvMJ3qdKJTnU7lbmMYBul56ad6+snIzyCvKI+T+SfJLsgGIN+eT35RPmk5\naeQU5pyasguzySvKI7cwl6TsJBQKh+E4tX2+Pf/UepuX7VSC72fzw89b3yiU/BzkE3TWTULJtoE+\ngWftc+aNgkIRGRBJsG8wwb7Bp7Yt2c/X5ou3l/c57x1U9lr09S37HYCLUVR0+kH4kSP6m4CTJ/U8\nIUH/nJ2t12dm6jw3Le10ZYyPj076bbbTJULBwfrbgoAA/c1BcLDeJihI57b+/rrU3t9ft93PT89L\nti/Zp/R2F/ONwjkxVOr0QevWrXiAQHdReqFvC44ehR07yr9pUMq5UY5DQnSQq6GantSbSzyOAAAP\n6ElEQVS3LJ7vKmf9bnRS3xxJ6k2Xni7vIDtLYmgOiaM5JI7Oq4oYKqWICIggIiCC1rVbm3580DcO\nRY4i8oryTk0F9gLy7fkU2AvIK8o7faNQkH3qZiCvKI+sgizyi/LJyM8gP7t4uV2vS8pKwkt5kVuU\nS1ZBFjmFOeQW5nIk8wgGBjZlw27Y8fbyxsfLBx+bDz5ePuT9mMebxpunPvvYfPC1+Z61TXnzsrbz\n9vLG28sbm7Kd/tnr9M8l5z/1c6QPPrW8CbUXUt8v9KztzpxKjuGFDXuRjYI8b9LTbOTl2MjLtZGb\n5U1Bvo38PC/ycm3kZHmRk20jLV1RkK/Iy9PfEOTl6W8Z8vP1PCdHP1TPydE3EPn5esrN1Xl1SfLv\n53d68vU9ezp0KJ3Fi/XPPj7lb+ftfe7k43N6/dn72fD1jSiewD8MfGqd3ufM/Uo++/iU+hajrAHN\nSs937Sp/XWbm6QHNKltGFBDglrVWNT2pDyueZ5SzvmR5uAvaIoQQQngkpZROgm0+hPiFuPTchmFQ\n6Cik0F54aj41bSoTb5141vICe8FZ25xvXnrbIkeRvmlx5GF32E99thv20+uNorO2TctLw6ZsKKVO\nLTtzKrQXYjfsp45X+ueUnBRd1lR84+IwHNgdem4EGngFeeGlvLApGzYvG17Kq9zJT9kIUF5EFn9W\n6CmvKB+7w4GXTxh2vMjDRp7hhTK8yF52lENDfgBDb4vDhmF4gWEDhxeG4YXhUGB4YRgKirwwChU4\nvHAYCsOu1zscXjjsCkcZnx12LxwOdWpu2L2wF6kzluuflVL6d/HSc5uXwstL4W3zwttWvE4pvLy8\n8PLyxaai8bLFYKul8IrW23p5KWxeCpsyCCksIKwwn9DCPEIL8ggpzCckLZ+QY0kEFxwgpCCPkPxc\ngvNzCcrPK57nEpSXS2BeLjbDQY5/ANn+geQGBJAbEEhuQCA5AYHkBQSSG6g/F4QEuvR/oaYn9cJC\nCQkJVjfB40kMzSFxNIfE0XkSw4pTSuFr88XXdrpnlhNHT9AovJGFrapahmGck+jbDTuGYeAwHKem\nkm3KmwrthRjodwRLjlOy37Orn+WJG5446xwOw0GRo+jUzwb6fIZhnPVz6XXlfbY77BgYF9jHwG53\nUFhkUGR3UGQ3KCpyYHcYFBYaFNr1z3a7gyK7HbvDwOEwsNsNvbzkc/G85OcCh8Fxw+CY4YvD4YPD\nCCr+vQwMhz5vyVTy2Sj+bCssIjgvn+C8AoLzCgjKyyckr4Dg/AJC81IITi0gJL+AgOwCl14XNf1F\n2deAh4CHDMN4q4z17wP3AHcbhjHjjOU1N2hCCCGEEOKiyYuyrrGjeN6ynPXNi+dn1dy76o8jhBBC\nCCHExajpT+qbAHuA/UCzM/upVEqFoEeWNYBowzByrWmlEEIIIYQQ5+dmvaK6lmEY+9DdWTYGJpRa\n/SwQCMyWhF4IIYQQQrizGv2kHk49rV8FRAPfoUtyegD9gZ1Ab8Mw0ixroBBCCCGEEBdQY57UK6Xq\nK6U+UkodUUrlKaX2K6XeAlKBrsAn6GR+EvrJ/dtAz7ISeqXU5Uqpb5RSx4qPdVgptVQpNaycc/dW\nSi1WSqUqpXKUUhuVUvcrpTwu/uXFUSlV4W4/LzaOSqk4pZTjPNMc835D13A2jkqpcReIiUMpVVTO\nvtXierQihnItlnsMpZS6USn1U/H/cY5Saq9S6iulVM/z7CfX4uljVCiGci2WewyllPqnUmqNUiqr\neFqrlLpTqfI7Fpdr8axjVCiG1elaVEpdr5R6Tyn1q1LqZHH7Z1fyWBX+Wzh7HdaIJ/VKqabop/G1\ngW85/TR+APppfB/DMFIv8livAg8DB4ElQAr6KX9n4EfDMB4rtf1IYD6QA3yJvokYgX4592vDMG5w\n9vdzFaviqJSKA/YBG4rPW9oWwzD+W6lfygJmxFEp1REYWc7qvsBAYKFhGCNK7VctrkerYijXYrnH\nmQXchv4//rZ43hx9bXkDtxiG8XmpfeRaPPs4FYqhXIvlHudz4GYgCfgefX0NAVqjy2nHlrGPXItn\nH6dCMaxO16JSagPQAcgEDgOtgM8Mw7ilgsep8N/ClOvQKO53szpPwDLAAUwotfyN4uXTL/I4/yze\n/iPAu4z13qU+hwLHgVyg8xnL/YCVxce60er4eEAc40q2tzoG7hTH8xx/dfFxriq1vNpcjxbGUK7F\nc4/RqHjbI0CtUuv6F6/7//buPVaOqg7g+PdnINJi5SE0IGqglRgfIIESrIXyUIhKFFFDTARfMShR\nIyoGwz+o0RBAISpKEOuj0URRAYMPBAsEkgbUBBCioEiBQCRShPISpL3HP87ZdO/c3b17t2NnZ/f7\nSTbTPWd2ztzf/Lp7ZubMzD8q5ebitsfQXJy7jBPLvPcAu3eV70junM4AJ5qLtcdwYnKx/H9bXv59\nZPm71v6/t0Vdedh4ALfDBlre6wux1L0IeIq8R7Z4nuW8sAR8Az06on0+8+HS9vd71B1d6m5oOkYt\niOMkfWHUEscByz+gLP8Bypm4rrqJyMeGY2guzp13RVnOFX3qnwA2VcrMxW2Pobk4d961ZTmn9ah7\nfalbVyk3F7c9hhOTi5W/6yhG6NSPsi3qysNWjRUb0dFlek21IqX0FHkPaGeg77jP4lhgD+ByIEXE\n8RFxZhnr1O+zx5Tp1T3qbiTvka2MiB3naXscNBnHjn3KmL6zyvSABf4N46CuOPZzapmuSeXboMuk\n5GOTMewwF7e6E3gYOCwiXtJdERGryT9iv698xlycbZQYdpiLW+1Vpvf2qNtQpodX8spcnG3YGPZ6\nztEk5GIdRtkWteThNHTqOw+W+luf+r+X6f596jsOLdPnyOPGrgLOAS4E1kfEDRGxx7Btp5S2UI5W\nA8vmaXscNBnHjmOBi4Evl+ntEXFdRLx8iPUfF3XFcY6IWAScDGwGvruQtluWj03GsMNcLFJKzwLv\nJB99+ktEfCcizomIy8inoK8BPjps29OYiyPGsMNc3GpjmfbKm05ZNa/MxdmGjeHyHvWTkIt1GGVb\n1JKH09Cp36VMN/Wp75TPd2X40jL9HLAFOJx89ORA8hfuauBnPdpO87QdQ7Q9DpqM49PAl8gX0e5a\nXkcC15NPj62LiMXD/BFjoK449nJSWf7VKaWH+rQ9CfnYZAzNxd7+TL6D2E7AR4AzgfeQL4T/YUpp\nY2V+c3GuhcbQXJzrV2X6mYjYrVNYjm5+sfO2shxzcbZRYjhJuViHUbZFLXk4DZ36unRi9TzwjpTS\n+pTSMymlO8kXljwIHDnEEJJpt+A4ppQeSSl9IaV0W0rpifK6iXw1/i3AK8k/gtOuM2zkkkbXot0G\nxtBcnKuchl9HPjp3KflI0mLgEPIp/B9HxLnNreH4GyWG5mJPPyGf2VhOPuNxSUR8nXxW+HDydTKQ\nxyertwXH0FwcH9PQqe/s9ezSp75T/vg8y+nU35pSeqC7IuUnzv6uvD20q6qzZ7WtbY+DJuPYUzkl\n1RkiccR884+JuuI4S0S8FlhJPqr3mwFtT0I+NhnDnqY8F08mx+3ylNIZKaX7UkrPppRuJe+oPwR8\nNiL2q7RtLm41Sgx7muZcTCnNAG8HPg88ArwfOIV8C8GV5OFNiXyzhu62zcVixBj2W1Ybc7EOo2yL\nWvJwGjr1d5Xpq/rUd8Y09Rv7VF1Ov4B2yhd1ld3dr+1yZGY/8hHrXhekjJsm4zhI55T0zkPO37S6\n4lg1zMWdk5KPTcZwkGnNxRVlen21ouyo/5H8W3NQV5W5ONsoMRxkWnORlNLmlNJ5KaUDU0qLUkq7\np5TeRT7CvD+wMaV0f9dHzMWKEWI4SNtysQ6jbIta8nAaOvWdL8ljI2Y/CS0ilgCryOPBbp5nOevI\ne6evqS6neF2ZbugqW1emb+kx/2pyx3V9Sun5edoeB03GcZDOMJ1x/8LtqCuO3Z/biXwkZTOwZsCs\nk5KPTcZwkGnNxf+W6dI+9XtW5gNzsWqUGA4yrbk4yHvJ91qvPt3UXBxevxgO0rZcrMMo26KePFzI\nvTfb+iLfImgG+ESl/IJS/u2ush3ITxBb1mM5V5b5T6+UH1fKHwWWdJUvIZ+iehY4pKt8J/KTxmaA\nk5qOTwvieDCV+4WX8jeV2G4B3tB0fLZ3HLvmOaV87pfztDsx+dhgDM3FShyBt5V5/wm8tFL31lL3\nNLCbuVhrDM3F3r8vL+5RdhB5KMlGYK9Knbm47TGcqFzsWv+jGHCf+nliOPS2qDMPGw/adtowy8j3\nAJ4BriDfQvG68v6vlS/KfUv5hh7L2Qe4v9RfC5wP/Jx8dO85Kk9ZK585gXzK5EnyBVDnkU/NzAA/\nbTo2bYgjcAP5AtrLyLe+vJC8VztTvizOajo2TcSxa56byjzHD9H2RORjUzE0F/v+n7681G0i38Hl\nXLY+fXIL8Elzsd4Ymot943gL+UjpRWUZV5Y8exw4ok/b5uI2xHCScpF8a9kflFenY35PV9n5Q8Zw\n6G1RZx42HsDtuKFeBnyP/Bju58jDOy4AdqnM19lI9/ZZzh7AN4D7ynL+BfwCWDGg7TcCvwb+DTwD\n3A58ih57tuP+aiKO5CetXVXaepK8J3sf+RTgqqZj0nAcX13q7x82nyYlH5uIobnYO47koZwfIz9U\nZRP5h+lhcqf0zeZi/TE0F/vG8QzgT8BjJSb3AN+kcgbEXKwvhpOUi8DZbN0Z6X7NVOM1KIYL2RZ1\n5mGUhUiSJElqqWm4UFaSJEmaaHbqJUmSpJazUy9JkiS1nJ16SZIkqeXs1EuSJEktZ6dekiRJajk7\n9ZIkSVLL2amXJEmSWs5OvSRJktRyduolSZKklrNTL0lakIhYFhFfi4jbIuLpiHg0ItZGxL5Nr5sk\nTSs79ZKkoUXEocAa4A7g3cBS4GDgbmDvBldNkqZapJSaXgdJUktExPuADSml9U2viyRpKzv1kqSh\nRcR+wKfJR+r3Bh4BLk0pbW50xSRpytmplyQNJSIWARcCpyV/PCRprDimXpI0rBOAH83XoY+Ij0fE\nExFx2HZaL0maenbqJUnDWgr8p1oYEYsjYmVX0doy3x+214pJ0rSzUy9JGtbVwFcjYlVE7BoRS8rd\ncC4GHuqa72jgRofoSNL245h6SdLQImIV8BXgEOAx4Frg7JTSg13zXEQ+qn8lsBr4VkrpjgZWV5Km\nhp16SVKtIuIu4IMppZsj4njg1JTSCU2vlyRNMoffSJJqExGvAF6QUrq5FO0F7NngKknSVLBTL0mq\n0wrgpq73xwG/bWhdJGlq7ND0CkiSJsom4HGAiNgfOAD4UKNrJElTwCP1kqQ6XQfMRMQHgNOBY1JK\nzzS8TpI08bxQVpIkSWo5j9RLkiRJLWenXpIkSWo5O/WSJElSy9mplyRJklrOTr0kSZLUcnbqJUmS\npJazUy9JkiS1nJ16SZIkqeXs1EuSJEkt9z8mInlAAD+uewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aabf390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11.69, 8.27), dpi=100)\n",
    "matplotlib.rcParams.update({'font.size' : 20})\n",
    "\n",
    "_ = plt.plot(tpr_mv2_v47, 1 / fpr_mv2_v47, label='mv2c10')\n",
    "_ = plt.plot(tpr_ip3d_v47, 1 / fpr_ip3d_v47, label='ip3d')\n",
    "_ = plt.plot(tpr_sv1_v47, 1 / fpr_sv1_v47, label='sv1')\n",
    "\n",
    "# _ = plt.plot(tpr_mv2_dan, 1 / fpr_mv2_dan, color='red', label='Dan')\n",
    "\n",
    "#plt.title('MV2c10 ROC Curve')\n",
    "plt.xlabel(r'$\\varepsilon_b$')\n",
    "plt.ylabel(r'$1/\\varepsilon_u$')\n",
    "plt.xlim(xmin=0.6)\n",
    "#plt.yscale('log')\n",
    "plt.ylim(ymax=700)\n",
    "plt.grid(which='both')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333.94029850746267"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/fpr_mv2_v47[np.argmin(abs(tpr_mv2_v47 - 0.7))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.074074074074076"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/fpr_ip3d_v47[np.argmin(abs(tpr_ip3d_v47 - 0.7))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.94054054054055"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/fpr_sv1_v47[np.argmin(abs(tpr_sv1_v47 - 0.7))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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