MeSH distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('ticks')\n",
    "sns.set_context('paper')\n",
    "\n",
    "matplotlib.rcParams['font.size'] = 10\n",
    "matplotlib.rcParams['axes.labelsize'] = 10\n",
    "matplotlib.rcParams['xtick.labelsize'] = 10\n",
    "matplotlib.rcParams['ytick.labelsize'] = 10\n",
    "matplotlib.rcParams['axes.titlesize'] = 10\n",
    "matplotlib.rcParams['font.family'] = 'serif'\n",
    "matplotlib.rcParams['savefig.dpi'] = 600\n",
    "matplotlib.rcParams['lines.markersize'] = 3\n",
    "matplotlib.rcParams['lines.linewidth'] = 1\n",
    "\n",
    "pd.options.display.width = 200\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = [\"pmid\", \"year\", \"TFirstP\", \"VolFirstP\", \"Pair_TFirstP\", \"Pair_VolFirstP\", \"num_mesh\", \"num_ex_mesh\", \n",
    "       \"mean_cites\", \"ncites\", \"normed_cites\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"out/pmid_novelty_all_scores_mesh_c.txt\", sep=\"\\t\", header=None,\n",
    "                names=cols,\n",
    "                 na_values=\"\\\\N\"\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>pmid</th>\n",
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       "      <th>TFirstP</th>\n",
       "      <th>VolFirstP</th>\n",
       "      <th>Pair_TFirstP</th>\n",
       "      <th>Pair_VolFirstP</th>\n",
       "      <th>num_mesh</th>\n",
       "      <th>num_ex_mesh</th>\n",
       "      <th>mean_cites</th>\n",
       "      <th>ncites</th>\n",
       "      <th>normed_cites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23199754</th>\n",
       "      <th>2012</th>\n",
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      "text/plain": [
       "                   pmid    year   TFirstP     VolFirstP  Pair_TFirstP  Pair_VolFirstP  num_mesh  num_ex_mesh  mean_cites  ncites  normed_cites\n",
       "23199754 2012 18      9   121.0  0.240273  2.402730e-01      0.123020    2.685150e-08       1.0          0.0         1.0       9            27\n",
       "21102603 2011 197    21  2386.0  0.000000  7.236510e-02      0.050997    0.000000e+00       1.0          0.0         1.0      23           118\n",
       "19005452 2008 87     10   541.0  0.000000  1.010260e-01      0.017441    0.000000e+00       1.0          0.0         1.0      14            66\n",
       "16908301 2006 17      3    71.0  0.000000  0.000000e+00      0.091700    5.286890e-37       1.0          0.0         1.0      13            45\n",
       "14811150 1951 82      2   133.0  0.000000  1.138140e-09           NaN             NaN       1.0          0.0         1.0       3            17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pmid                int64\n",
       "year              float64\n",
       "TFirstP           float64\n",
       "VolFirstP         float64\n",
       "Pair_TFirstP      float64\n",
       "Pair_VolFirstP    float64\n",
       "num_mesh          float64\n",
       "num_ex_mesh       float64\n",
       "mean_cites        float64\n",
       "ncites              int64\n",
       "normed_cites        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "    level_0  level_1  level_2  pmid    year   TFirstP     VolFirstP  Pair_TFirstP  Pair_VolFirstP  num_mesh  num_ex_mesh  mean_cites  ncites  normed_cites\n",
       "0  23199754     2012       18     9   121.0  0.240273  2.402730e-01      0.123020    2.685150e-08       1.0          0.0         1.0       9            27\n",
       "1  21102603     2011      197    21  2386.0  0.000000  7.236510e-02      0.050997    0.000000e+00       1.0          0.0         1.0      23           118\n",
       "2  19005452     2008       87    10   541.0  0.000000  1.010260e-01      0.017441    0.000000e+00       1.0          0.0         1.0      14            66\n",
       "3  16908301     2006       17     3    71.0  0.000000  0.000000e+00      0.091700    5.286890e-37       1.0          0.0         1.0      13            45\n",
       "4  14811150     1951       82     2   133.0  0.000000  1.138140e-09           NaN             NaN       1.0          0.0         1.0       3            17"
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns = [\"PMID\", \"Year\", \"AbsVal\", \"TFirstP\", \"VolFirstP\",\n",
    "           \"acc_pos_vel_min\", \"acc_pos_vel_max\", \"acc_neg_vel_max\", \"acc_neg_vel_min\",\n",
    "           \"Pair_AbsVal\", \"Pair_TFirstP\", \"Pair_VolFirstP\", \"Mesh_counts\", \"Exploded_Mesh_counts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      ],
      "text/plain": [
       "       PMID  Year  AbsVal  TFirstP  VolFirstP  acc_pos_vel_min  acc_pos_vel_max  acc_neg_vel_max  acc_neg_vel_min  Pair_AbsVal  Pair_TFirstP  Pair_VolFirstP  Mesh_counts  Exploded_Mesh_counts\n",
       "0  23199754  2012      18        9      121.0         0.240273     2.402730e-01         0.123020     2.685150e-08          1.0           0.0             1.0            9                    27\n",
       "1  21102603  2011     197       21     2386.0         0.000000     7.236510e-02         0.050997     0.000000e+00          1.0           0.0             1.0           23                   118\n",
       "2  19005452  2008      87       10      541.0         0.000000     1.010260e-01         0.017441     0.000000e+00          1.0           0.0             1.0           14                    66\n",
       "3  16908301  2006      17        3       71.0         0.000000     0.000000e+00         0.091700     5.286890e-37          1.0           0.0             1.0           13                    45\n",
       "4  14811150  1951      82        2      133.0         0.000000     1.138140e-09              NaN              NaN          1.0           0.0             1.0            3                    17"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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L7biTkpIUGRmpjh07WmeUzyz2AgUKZOn7zW+//aYePXpo+PDhun79ultxuvMd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\nrG0kJydryJAhio+PNyRmKMO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/LTMS16hRw9A+jVodNCufn/7+/ob0VaBAgUw/x959911D\n+rqT2F7z/P39NWjQILfaa9OmTYbbPf28zq3re/HixfNsVVgAAAAAANzRv3//TFcNnD9/vq5fv+5W\nP2knfbUUeuUHZrPZblWztEwmk2HjNZLUvn37DMcEv/32W12+fDnL7WWU/7jrrrtcDzSNGjVqqHTp\n0oa1l5m0E4J2797drfYyG9f19DxYVq1atUqHDx+W5HgMvHr16mrZsqWhfRqV3546dap1BVNH+e0O\nHTq4PDmiI//3f//n8HHL6/XJJ5/o1q1bhvV3J7C9d6RAgQIaPny4W+01a9ZMPj4+dm3b8vTz+o8/\n/nC6zcj8tpeXV67d5wEAADwPhYEAAAC4rfj5+emDDz5QwYIFJTkueIuLi9OwYcOsq3I5M2HCBGsS\nzzZR3apVqxyK3jlL/88++6xhRYklS5bUM8884zBxaEmUL126VFeuXDGkv5yQUYGLyWRS165dDe3P\n19c33aqUaf9/ypQp2W533rx56R5LW+DYvHnz7AfsxODBgw1ry5mDBw9q69at6WZttfDz89NTTz1l\naJ9p20v73nz55ZfZau/PP//UL7/8kuFqgS+++KIKFSrketA2goKCHK56Cvc1bdpUoaGhma4auGLF\nCpf7WL16tf78809ruyaTSc8995yhA/ruWrhwoS5duiQp/UCyyWRSnTp1VLVqVcP6K168uBo0aOD0\nmnn+/HmtX78+y+2dP3/e6ba4uDjXgnSgcOHChg5KZ4e7N01IUnh4eIbbM7oB5XaRkJCQbsXatIz+\njiBJTZo0UdGiRd1qI7PPz0KFCqlDhw5u9ZFW69atrTcLOZpV+ffff9cPP/xgaJ93Astr+MQTT6hE\niRJutVWxYkXrvwHSHteW98iTXbhwwem2a9euGdpXflnhAAAAAACA7KhWrZpatmyZo6sGps1LmUwm\ntWrVytCcsTvWrl1rl4OX0helNWnSxLD+vL299dhjjznNbyckJGjx4sVZbi+j/LbR+Y977rnH4SS6\nOcXSV+fOna3FP64KCwtLN85vy9PzYFk1bdq0XB0Dl6T77rvP7fP9/PnzWrx4cYbH3gsvvOBWH2mF\nhYVZJwNzdG/IxYsXtWjRIkP7vBNYXs/69evr3nvvdastPz8/1axZM91nmOXzJqPCOk+Qm9d38tsA\nAMAZCgMBAABgZTabDf/JC6GhoRo6dKjTQh6z2az9+/fr448/dtrGDz/8oEWLFtklrStXrqzRo0fn\nWNxZ0bZtW0Pbc3Rjue3rlpiYqNWrVxvap1F27dplXQHO0QCgt7e3oasFWjh7DyzH1pYtW7K0IqXF\n8ePHdeTIEYfFZ5Z2O3Xq5HK8jtSoUUN33323tf2c4GyAxXIetm7dWgEBAYb2+dhjjzl83PLarlmz\nJlurky1ZssRhWxZeXl6KiIjIfqAZePLJJw1tD/+T06sGOlot8KWXXnKprZzyzTffZHjOd+zY0fA+\nnZ2XFsuXL89yW7du3XIa/4EDB7IVV2Yeeugh1apVS7Vr11atWrVUsWJFQ9u3sN0fHx8fNW3a1O02\ng4KCFBgYmK59i1OnTrndR363cuVKXb16VZLj7wglSpTQQw89ZHi/BQsW1COPPOLWd+CFCxc6fNzy\n+dm4cWPDPz8LFCiQ7ianrMaFzBn1fdRS4G7LclzHxMR49MQClhXa07L8u81IoaGh1mt77dq1DV85\nFAAAAACAnJKVHPe8efNcXjUw7cSXRq/A567Mxp3at29veJ8Z5bfNZnO289vOGJ3frl27tjX/UatW\nrVwr7jQiD+bj46OqVas6LSC6E/LbO3bs0IkTJyQ5HwN/9NFHc6TvFi1auJVn/Pbbb61jsY5WOgwN\nDVXlypXdjjOtzMbvKQx0nZH5bVtpC60zKq7L7xzlty3H3sGDBw3t695777XLb9eqVcvQ9gEAgOei\nMBAAAABWJpPJ8J+88uKLL6phw4YOZ0O0JOFmz56t7du3p3vu2bNn9frrr9vNKFewYEG9//778vX1\nzZX4bWO1qFixomrWrGlo+yEhIdbku7P3a82aNYb2aZSoqCiHj1ve81q1alkLI4wUEhLicFUdi9TU\nVKexObJq1apM/yYnBnfatm2bYzeQJyQkaO3atRleAxo2bGh4v0FBQdaVmpytFrpjx44st7du3TqH\n+2A5xh566CEVK1bMzajt1apVS8HBwZJyrmjzTtWiRQuFhIRkuGrgX3/9pZUrV2a77TVr1qRbLfDZ\nZ591e4UoI+3atUsxMTGS5PTcb9CggeH9ph3os7B8Fv/888+6efNmltoqUqSI05sBli5dqvj4eLfj\ntXj77bf19ddfW3/69etnWNtp2c666u/vb0ibFStWdPg+m81mjx5czaply5Y5fNzyWjdq1CjHrrFh\nYWEuPzcrn59GznqelXYt59jOnTuzNfHBncz2/QsICFD9+vUNabdSpUp2v9ue40lJSbp8+bIh/eSF\nIrkJZuQAACAASURBVEWK2P1u+1l99OhR7d2717C+unTpYnd9/+STTwxrGwAAAACAnBQSEqLmzZtn\nmOOOi4vTF198ke22Ha0W2KJFC1WvXt2Q2N11+vRp7dq1y+kkm1LOjDs5ym/bvv7Hjh3TyZMns9RW\n2vyH9L/c208//WTN3xth8ODBdvmPMWPGGNa2Ldv3o0qVKunyV67KaKK+CxcuGNJHfrZ06VKHj1uO\nvdq1azs8noxw//33u/X8qKioDFc6zM38tu21Ijo6Wnv27MmRvm93LVu2NKSdzK4Pnjx2lfaeFNtj\n7/Lly9kqIs9Mq1at7K7vX3/9tWFtAwAAz0ZhIAAAwB3Okpg1mUyKiIjQ0aNHDfvJaObK3DBp0iQV\nL17cboDGdma6lJQUvfbaa4qNjbU+JzU1VcOGDbNbZcZkMumVV15xWtyQ0ywx1K1bN0fab9CggdOV\n6sxmsw4cOJDloo3cYhkky+jG+fDw8Bzrv169ek4HHs1mszZu3JjltrZt25buMduBtKpVq1oLEY30\n4IMPGt6mxS+//GI9Zpy9TvXq1cuRvv8fe/cdFsX19QH8u3QQpSiiKKICCqggiigWVMQSe0/UxKgx\nFtT4syYqGjVK1KixYtTY0MSINfYKxhp7FyxgwQaC0kHavn/4zmbLzO7s7mwBz+d5eJTd2XvP7N6Z\nWc6de2/dunWVDnjkOzAwMTERr169AsC9D7roZAaAxo0bl+pVf4yZqtmNNV01MDIyUmHlN2NbLZDt\nvCQ/+FwX5xp3d3eYm5vL1Cf9/hYXF+PKlSu8ynJ2dpb5Xbqc9PR0TJ48Wa1VQY2NkN8z2D5L5v0v\nzYOH+MjIyMD169cN9h2hadOmaN68OYKCgiT/litXjtdrL1y4IJlZVt/Xz8aNG8PMzAwA+7Gq7veb\nTx3z/b1OnTowNTUVpEz5c6C80nxsc+0b8534+++//yRu+iKEEEIIIYQQQlTR1aqBq1atUqsufYuN\njVV4TDr/Z2VlpdWEXVxsbW1RrVo1hfqk8e13UpbfLiwsxMSJEzVe7dHQRCKRTvPb8iuLsa3OVVbw\n6QPXVY4YAPz9/RXy23wnwXzw4IHKvlVd5ebd3NwkxxjXe0f5bX6k3z9nZ2c4OjoKUu6nnN+OiIjA\n48eP9RwVIYQQQj41NDCQEEIIIYSUWZUqVcLPP/8s+Z3tJufU1FRMnTpV8vvy5ctx48YNmYFZzZo1\nw/Dhw/UUNTcvLy+dlMvWUSb9HhUVFeH69es6qVtTd+/elSSHuToW6tWrp7P6la2ABQD3799Henq6\nynJyc3Nx9+5dpTMn+vn5aR6oErpqTwBw7tw5hcek97F69eo6GYAEAJ6enkqf5zsb5OXLl1VuUxo/\nm09dhw4d4OnpyTqjMvP7kydP1Fo1kG21wP79+8PJyUnY4LV07tw5pStg6mqwsJmZmWRlWi58V4Ji\n6zBm4mc6ywcMGFBqO9dUnb/Uoayj3tgG+wvtwoULKC4uBsD9HUGXkz14eXlh48aNMj/MSrCqqLp+\n2tra8i5LXZaWlqhVq5bSbdjiI8p5eHgIVpaqGzByc3MFq0vfuM7vwMdj4MWLF+jduzfrTYCEEEII\nIYQQQsinpF69emjTpo3SVQMzMjLUWjXw9u3bOHPmjMxqgW3btjWqvgquvBQTr5+fn2CTM8lj+hS4\n8O13UpXfvnPnDvr06YMbN25oHKsh6Su/DZTuPJgqd+7ckfQxc7U7b29vndVfsWJFhfx2w4YNeb2W\nT/5Yl/333t7eSif2pfw2f8y5SZ/57dLcd9W4cWOZCdkB2fx2eno6vvjiC+zZs8dgMRJCCCGk7KOB\ngYQQQgghpExr3bo1vvzyS4UksHRHy/nz57F+/Xr8+++/WL9+vUxHmr29PRYsWKDvsFnVrVtXJ+Xy\n6diLi4vTSd2aun37tspthOyA4lO2/Ko6d+7cUVnOw4cPVQ5e0FXHq52dHapUqQKAe+ZETXF9Psxx\nV7t2bUHrk1ahQgXWx5njnRnApcr9+/dVbqOrz0ZXxzr5iO+qgXytWbNG5hgyNzc3isHk0nJzc5GQ\nkKB0G10fl8punHjw4AGvctq3by+5uYNtYKdIJMKdO3fQvXt3TJ48GXfv3tUucD0T8jNQtkJdQUGB\nYPUYI7ZrkHR7EYlEcHd312dIvKm6fgrZCc+G6yYn6ZuTiHqEbGuqVp4szce2j48P3NzcWG9qZB5L\nS0vD6NGjMWDAAMTExKCkpMSAERNCCCGEEEIIIYajLMctvWog38FTq1evVnjMmFYLBD4OllLWl2WI\nfifgY+7i4cOHvMpp0aIFypcvD0Axv8089vTpUwwYMAAjR47EpUuXtIha//SV3wY+rrBYVvHJwdap\nU0cPkahPVW7eyclJ6fGkLa57A5gYEhISyvRqk7ogZH7b1tZW6fOlOb/t4OCAwMBA1nuSmPx2Tk4O\npk+fjm7dumH//v2len8JIYQQYpxoYCAhhBBCCCnzpk6dijp16ijcaCpt+fLlmDhxouQGU2bbefPm\nwdnZWZ/hcmIGcQmNz8ozfDu19IVtoKL8Tf8uLi46q79atWoqt+EzsIzPIDVdrQwEAM7OzkoH62ii\npKQEDx8+VNpBq8t9YutUkN7H3NxcvHr1SmU5bJ+N9D45ODio7JjUlLGcc8qqTp06wcPDQ5BVA48e\nPSo5PzKv79u3r9F9hnFxcZLjgOuY1/dxCag/YLd69ero1q2bzI0SDOnONbFYjIMHD6Jv377o3bs3\nNm/ezOu4NzR7e3vByrK0tJT5XfpzL+uDaVRNZuDk5ARzc3M9RcNfcXGxyusnn+8f2mArX7rtpKWl\nISUlRacxlDVC3uhibW2t9PnSfGyLRCKMHDlS5ndp0uf9GzduICwsDG3btsXSpUtx7949vcZKCCGE\nEEIIIYQYmq+vL1q2bCnIqoF37tzBP//8I7NaYHBwsE5X9VJXcnIy3r17B8D48tsA8PTpU8kkoMqU\nK1cOgwcPZs1vA7IT4P3zzz/4+uuv0bFjR0RGRqqc+M8Y2NnZCVaWfH5bHp/3u7Ri61/WZx+4NuLi\n4ljz20zbNnR+u6SkxOgmQzZ2Qua3rayslD5fmvPbgOygfeZczpA+7z969AhTp05FcHAw5s2bh6tX\nrwp+vwYhhBBCPk00MJAQQgghhJR5FhYWWLp0qSTZyCThpAdJFBcX4/379zIdX/369UNoaKjB4pan\nq0FI5cqVk3TWcN2MbmwDKpKSkpQ+b29vr9Ob/vkM0nzx4oUg21StWpVXTJrQRZtKTk6WzHBnTB20\n0l6/fq1ym5cvX3J2XgG6G6gL6O5YJx+JRCKMGjVK6TZ8Vw2MjIyUaSdmZmYYMWKE1jEKTdU5EwBq\n1Kihs/pVHZfJycm8y/rhhx/g4uIi04nG1bkmEolw//59LFiwACEhIejbty8iIyMRHx+vwV7ono2N\njWBlmZmZCVZWaZOUlKT0/G1sA3cZKSkpKq+fuo5dqO835D98vpfwxayYWlb17t0bbdq0kRnozTUA\nXCQSISUlBevWrUOfPn3Qrl07RERE4N9//y3TN4YRQgghhBBCCCEMZSv6MX2dmzZtUrlqYGlYLZBP\nftuQE1IWFRUhNTWVV1kjR45EvXr1eOc/nj9/jhUrVqBLly7o2rUrfv31V9y8edMoB5EI2bf1qee3\nlXFwcDDK90csFqu8n4Dy26WPkMd1Wc9vN23aFIMGDZKcxwHuCU5FIhEyMjKwbds2fPnll2jVqhVm\nzZqF06dP00qChBBCCNGY8f2VQAghhBBCyhxj6Jzw8PDA1KlTMXfuXEmyjS0u5rFatWph+vTp+g5T\nKV0OFrKzs0NmZqbC48z7ZGyrw7x580bpijpCzkrJxsbGBmZmZiguLuZsS3wGU/J5Xx0cHDSKkQ8h\nb1Zn8Bl0t3DhQixatEjwugF+55u3b9+qLCMtLY3zeZFIVOo+FyKrS5cuWL16NZ4+fSpzDEuvOJeY\nmIhDhw6hS5curGUcO3ZMsroX87revXvrdNCopt68eaNymz59+uisfrZZPqVnsy4uLsa7d+/g6Oio\nsix7e3usWbMG3377Ld6+fatw84R0hxsg2+l29+5d3L17FytWrICLiwtCQkLQtm1bBAYGGsUKckIO\nDFR2jSzrlA00FYlEgs5wKyQ+109df7/h897wiZP8R8jj2sSk7M/xt3jxYowYMQI3btxQuHlCfuVb\n6XP/q1evEBUVhaioKFSoUAGtWrVCSEgIgoODUb58eQPsCSGEEEIIIYQQolv+/v5o3rw5Lly4wJrj\nBiAZdMA1md3du3dx+vRpmRx3ixYt4Ovrq7f94INPPuq7777TWU6UzypWKSkpvAY9WVhYYPXq1fjm\nm2+QmJjImt+W/lf6uYSEBDx+/Bhr165FxYoV0aZNG4SEhKBFixYqV+LSB8pvC0NVH7ix5rdTUlJQ\nVFTE2W8OUH67NBLy/pRPIb/9ww8/4O3btzhx4oTSwYHM48xzaWlpiI6ORnR0NKytrdG8eXO0bdsW\nbdu2RcWKFfW/I4QQQggplcr+ty1CCCGEEGJQTELLGBL4AwcOREhIiMqBQ+bm5liyZIlRdKJIs7a2\n1lnZ5cuXV/q+ZGRk6KxuTaSnp7M+zuyDPgZWKatDLBZzxigtKytL5Ta67OARspOOoeqzYRLeJSUl\nOvmR7vTmOv+8f/9e6T7k5ORIVpvhOi50+bnQioG6x6waqOy8JxaLERkZyfm8/GqBpqamGDlypKBx\nCoXPcamrY5K5aULb41Ja3bp1sWPHDvj6+ko6meU70uQHCkrfYCESifD69Wts27YN33zzDVq0aIEZ\nM2bg4sWLvG7y0BVjnOW3tMnOzkZRUREA7vO3sQ4S4vO9Qdffb/i8N3ziJP8xhkHHpYmtrS02b96M\nnj17ykzmIn8OB2TP78B/5/+srCwcOnQIkyZNQlBQEEaNGoWDBw8iLy/PkLtGCCGEEEIIIYQILiws\njPM5PqsGrlq1SuExY1stEDB8vxMgbH67SpUq2L59O4KDg2VyH+rkP969e4fdu3djzJgxaNasGSZM\nmIBTp06hsLBQvTdXQJQHE4aq9k75bW583ht1jlVCx7W6zM3NsWLFCgwfPhxmZmacfZgA9/k9Pz8f\np06dQnh4OFq1aoXBgwcjOjra6O7XIYQQQojxoYGBhBBCCCFEZ8aOHYu4uDiZn7Fjxxo0pvnz56Ny\n5coAuGcbdHJyQs2aNfUYleGpSup++PBBT5Hwk5+fz/mcSCTSS5JaiPdM2X7wrUcbuhiwy2efpBPd\n+vxhqPpsDP25EP3o1q0b3NzcACjO1sj8npiYiMOHDyu89vjx43jw4IHM9j179oSLi4seIldfWTgu\n5VWtWhU7duxAeHg4HBwcFG6gAJQPEpR+PisrC7t378bQoUMRGhqKqKgoGkBSSvFpR8Z6/jaGaw+f\n8vnESYg2LCws8PPPP2Pjxo1wd3dXGCCozk1yxcXFOH36NCZPnoyWLVti4cKFSlcVJYQQQgghhBBC\nSpOAgAAEBgbK5LQB2Qmz0tPTsW3bNoXX3rt3T2G1wGbNmsHf318vsaujLOa3K1SogLVr12LJkiVw\ncXFRyHEA/PPbHz58wJEjRzBmzBi0bt0akZGRNLlXKaasLemrD1wTxpCb51O+sd3zQMqmSZMmYceO\nHfDz89Mqvy0Wi3H58mXMmjULrVq1wsyZM/HkyRND7hohhBBCjBgNDCSEEEIIIZ8UBwcHLFy4UPK7\ndLKN8fr1a8ydO1fvsRmSqkS5IWeYZFNQUKD0eVNTU53HwPWeMW1KVYwAv/fVxKR0/dnGZ7+lk936\n/gFUd/rw+Vz00caIbpmYmGi8amBpWi0QMP7jkrl5QRODBg1CbGwspk2bBjc3N9YONvn9A7g72V6/\nfo2IiAi0bdsWu3fv1igmYjh82rqxnr/5xK7rVSX5lM8nTkKEEBQUhIMHD2LlypVo1KgR5zmc7/k9\nNzcXmzZtQrt27fDLL7/QAHBCCCGEEEIIIWWCsglZmTzppk2bFP4OXrlypcL2xrhaIGD8+W1A88FG\nnTt3xokTJxAREQEvLy+t89vv37/HihUr0LZtW/z+++8oLi7WKC5iOKrau7H2G1N+mxBZ9erVw19/\n/YVNmzahVatWMDU1VZnfVnb+LywsxM6dO9G5c2dMnz6dBoATQgghRIFuv20TQgghhBBihIKCglC3\nbl3Ex8fLDOxgEm9isRj79u1DcHAwPvvsMwNGqj+qBkIZ2+yDFhYWShP3+ujo4nrPmCSthYWFyjL4\nvK8lJSVG28nDhmu/mWNLJBIhIiICvXr10nNk/PH5XKgztWzo3r07IiMj8eLFC0kbBWSvBwkJCTh8\n+DA6d+4M4ONqgcz1g9muW7ducHV1NeSuKMXnuDx16pTRrnioipWVFQYPHozBgwfj1q1bOHDgAI4e\nPYq0tDQAsoP/pW+eYJ5jez4jIwMzZszAvn37sHz5cjg6Oupvh4jG+Fx7i4qK9BCJ+owhdj7l84mT\nECGFhoYiNDQUSUlJOHjwIA4fPozHjx8DYD+HM7jO78XFxdiwYQOOHDmC5cuXo0GDBvrbGUIIIYQQ\nQgghRGCBgYEICAjA1atXWXPcwMdVA7du3YoRI0YAYF8tsEmTJggICDDYfijDJ78dFRWFJk2a6Dky\nYZiamqJXr17o1asXHj16hAMHDuDw4cN4+fIlAM3y2/n5+Vi8eDH279+P1atXG3X/BZFlDH3gmqD8\nNiHsmjVrhmbNmiE1NVWS375z5w4A7nO4quf27NmDmJgYLFy4EK1bt9bj3hBCCCHEmJWeu0sJIYQQ\nQggRyObNm2UGBUp3oDC/i8Vi/Pjjj3j9+rUhQtQ7VQMDLS0t9RQJP1ZWVpzPicVivaxwqKoOZTGq\ns40uZy5UtlKapqytrVVuY2wrUMoz9OdC9MfU1BQjRoxQa9VAttUCR40apdM4tcXnuCwrbdrPzw/h\n4eE4d+4coqKiMHDgQDg5OamcaZMh/9yVK1fw+eefIykpySD7Q9TD5/uKsV6D+Fx7dB07n/L5xEmI\nLri6umL06NE4cOAADh48iDFjxsDT05PzZglV5/dXr15h8ODBOH36tN73hRBCCCGEEEIIEZKylf7Y\nVg1ctWqVWmUY2qeU3/b09MTEiRNx8uRJ7Ny5E9988w1cXV01zm8/fPgQ/fv3x+3btw2yP0R9xtAH\nrgljyM1TfpsYs0qVKmHIkCGIjo7GqVOnMGXKFPj6+rKuFgioPr+np6cjLCwM0dHRBtkfQgghhBgf\nGhhICCGEEEI+KfHx8Vi6dKnCzaPSvzOysrIwZcoUnQye0kR+fr7Oys7MzFQYICnN3t5eZ3Vrgise\nZh+ysrJ0HkN2djbncyKRiNd7VqFCBZXbZGZmqhWXOnJzcwUvk89+f/jwQfB6hVSuXDmYmZkBUBw4\nzNBlG8vJydFZ2URRr169JCvlsa0iC0CyauDJkycRHx8v83yXLl3g5uam/8DVUBaOS000adIEM2fO\nxNmzZ7F161YMGjQIlStX5t3BxjyelJSEUaNGSW6cIcbL1tZWsuor1/lb2fXbkPgcp7qOnc93DgcH\nB53GoK2yeC4jitzd3TF27FjJIMFx48ahbt26rDfJKTu/5+XlYcKECXj48KGhdoUQQgghhBBCCNFa\nUFAQ/P39ZXLagGyfZ3p6OrZt24a4uDjExsbKrC7YuHFjNG3aVO9x8/Wp5rfr16+PyZMn48SJE9i1\naxeGDx+OGjVqqJ3ffv/+PcLCwpCSkmKQ/SDqUdUHXprz27ruv+eT3za2ex7klcVzGVHk4uKCYcOG\nSQYJTp06Fb6+vjAxMVErv11cXIw5c+bgwoULhtoVQgghhBgRGhhICCGEEEI+Gfn5+Zg4caJktjjp\nRJr84EDmsWvXrmHNmjWGDFtCl4n+jIwM1seZ96Jy5co6q1sTVatWVTpgk2t/hJKTk4OioiIA3Kvu\nMQONlHF2dla5zfv379ULTg26GIDGZ791/floSyQSoVKlSpzPi8XiUve5EG5mZmYYOXKkykHgkZGR\nWL16tcxjJiYmRr9aIPDxnKmKsR+X2goICEB4eDjOnDmDLVu2oHfv3rCxsWG9gYIh3SYSExOxaNEi\nvcdN1FelShXO58RisdG2dT7Xz/T0dJ3GwOfGCT7nE0Oia+inx93dHWFhYfj7779x8OBBjBgxAs7O\nzgoTv3DdPJGfn49JkybpP3BCCCGEEEIIIURAfFb827hxIxYvXizzmEgkMurVAgHKbwNAvXr1MGnS\nJBw/fhzR0dH48ssvYW9vzzu/nZaWhvDwcL3HTdRn6D5wTVWuXFnlpKu6jp1PfptPHt6QKL/96XFx\nccHQoUOxY8cOnDx5EhMmTICbm5vMfUwAd367uLgYU6ZMoclNCSGEEEIDAwkhhBBCyKcjIiICiYmJ\nAP4b8DZ48GD069ePdXsm0RYZGYlbt27pM1RWukoEZ2dnSxLl2gxy0ydXV1elz2dkZKCgoEBn9b95\n80blNtWrV1e5TbVq1VRu8/r1a14xaUIXg02dnJxgaWkJgLvjJy0tTfB6hVatWjXW44HZp1evXums\nbmOd7bMs6927t+TmAraOFbFYjMePHyMuLk5mMHmnTp1Qq1Ytg8SsDlXnTKB0HJdCCQwMxPz583Hm\nzBl8//33klUEua6BzHO7d+/mdf4nhuXq6qr0/G2sM2NXrlxZ5fUzOTlZpzHwad98zifqYiZbEIIu\nVkMmpYe7uzsmTJiA2NhYLFu2DD4+Ppznd/lr/OHDh/UdLiGEEEIIIYQQIpiWLVvCz8+Pc9VAsViM\n9PR0nD9/XuZv5YYNGyIoKMggMfNF+W1ZDRo0wIwZM3DmzBn89NNPkgEkXPkP5rmzZ8/i9u3bBoiY\nqENVe09PT5dMgmxMRCKRyn5vym+rRvntT5uLiwtGjBiBY8eO4ffff0dgYKDMAHCuwd/v3r3Dtm3b\nDBEyIYQQQowIDQwkhBBCCCGfhBMnTiA6OlomWebt7Y3Jkydj+vTpqFWrlsKqgcDHJHZRUREmT55s\n8BnadHUje1JSkspt6tSpo5O6NeXj46PwmHTyUywW4+XLlzqrn0/Z9erVU7kNn/f1+fPnvGLSxNu3\nbzkHH2jKxMQEXl5eSmezfPHihaB16oKnp6fCY9L7lJGRobMBfG/fvtVJuYSbubk5hg8frrTdMjMz\nSv8eFhamj/C05uXlBVNTUwDcA45Kw3EpNFtbWwwZMgQnTpzAqFGjYGZmprRjrbCwEAcPHjREqEQN\nbN8RpL19+9Yob5wwMTFB3bp1lZ6HdPndhqt86ePByclJ6Yq6XExMlKeghZzMQderKpLSwcTEBB07\ndsSePXuwcOFC2NvbK5zf5e3bt0+PERJCCCGEEEIIIcLjk68ujTnuypUrS3JSlN/+j4WFBfr27Ysj\nR45g2rRpsLa2Vpn/2Lt3rx4jJJrg0weuy0llteHj48M5aZ9YLNb5caoqv21qagovLy+1y2X617hQ\nfpvoQosWLbBlyxasXbtW5STeYrGY8tuEEEIIoYGBhBBCCCGk7Hvz5g3Cw8NlBv1ZW1tj6dKlMDc3\nh5WVFZYsWQJzc3MAUBgcCHzsUJo9e7beY5f24MEDnZQbFxenchtVN9nrm5+fn8ptHj16pLP62cqW\n70ytX7++ynLq1KkDMzMzhddL4/P5aCIrK0uy6p2yQQia8PX1ZX2c6fhhVu40ZnzavK4+m/j4eJ2U\nS5Tr168fnJ2dAbAfj9IzK4tEInTs2BHu7u56jVFT1tbW8PDwUHqsl4bjUlcsLS0xfvx4LFu2TOUA\nyosXL+ozNKIBtu8I8jdOJCQk6DMk3lRdPx8/fqzT+h89esR5/hOJRGjQoIFG5TLfsbkIOfmGsX62\nxHC6d++OnTt3ct5AyBxfV69eRXFxsSFCJIQQQgghhBBCBNG6dWvUq1ePc3CYdI4b+LjyXKtWrfQa\no6Z8fX0pv83BxMQEgwcPxubNm2FtbQ2AO/9B+W3jx6cP/OHDh3qIRH2qcvNpaWk6HfjGdW8AE4OH\nhwcsLS3VLpfpy+ci5ESylN8m8oKDg7F7927Url2bdXJT5vfExESafJgQQgj5xNHAQEIIIYQQYlBR\nUVH46aefZH6EXCFNLBZjypQpyMjIkPwuEokwc+ZM1KxZU7Kdj48PJkyYoNCpxGwvFotx8OBB7N+/\nX7DY1KWrwUI3b95UeEw6oWhmZgZ/f3+d1K0pHx8fVKxYEQD34I179+7prP779++zPi5947ydnZ3K\ncqysrNCgQQOlsyeyfT5C0OXgs5YtWyo8Jr2Pr169wvv373VWPwDk5ubi2rVrCj9820XTpk1VbnPj\nxg1tw2RFAwMNw8LCQumqgfKDf0ePHq2v0ATBdlwyxGIx7t69q/MYkpOTWY9LXa6Mqo7Q0FCMHDlS\n6Tn56dOn+g+MqCUoKEjloHuu67ihsd2IJd0ec3Jy8OzZM53UnZ+fjydPnijdRtMbxWxsbJQ+/+7d\nO43KZaPLiSFI6eXq6orFixez/q3HyMvLQ3Jysr5DI4QQQgghhBBCBDVmzBhe24lEIt7bGgOu/DaT\nt9VHvu/9+/es+W1dT+bFl5+fH8LDw5UOoExKStJjREQTfPrAdTVxqbaU9UMxdHms3r9/n/M9E4lE\nOstvC9nnTfltwsbBwQGrV69W2fdFfZiEEELIp035dBaEEEIIIYTo2IkTJ3DlyhXJ7yKRCJ06dUKN\nGjUEKX/t2rW4cuWKpGNIJBKhc+fO6N27t8K2w4YNw7lz53DhwgXJ9tJxicVizJ07F40aNUL16tUF\niY8Ppu5r167ppPyLFy8qXR3G398fVlZWOqlbG23btsWuXbs4E5+XL1/WWd2XL1/mrBcAQkJCeJfV\nsmVLhQFm0rO7PX36FG/evEGVKlU0C5aDrtoT8HFQho2NDfLy8hSOJcaVK1fQoUMHncVw4MAB/Pjj\njwqPd+3aFYsXL1b5ejc3N1SvXh0vX77k3IcLFy5gxIgRgsQr7fr160rbF9Gdzz//HGvXrkVazsZ2\n+wAAIABJREFUWhrr584cm6GhoahTp46BotRMu3btsGHDBoXHmf1MSEjAu3fv4OjoqLMYFi5ciMOH\nDys8/tNPP/G67v/xxx/IzMyU/N64cWMEBgYKGuM333yD33//HYWFhaxtQMgBTEQ3bG1t0aRJE87v\nN8DH6zjbd0Eh/PLLL9i6davC4xMnTsSQIUOUvpbP9fPy5ctwc3MTKlwJZrU06XrlB0S3bdtWo7Id\nHByUPi/ULLYFBQWcqx4S4xYbGyszMULVqlXRs2dPQeto1qwZ/P39cePGDc7j6927d3BxcRG0XkII\nIYQQQgghRJ9CQkLg7e2NuLg4zr5O4OPgo9atWxsqTLWFhITgp59+AvDffkj3peXk5ODevXuoV6+e\nzmLYuHEj1q9fr/D4yJEjMWHCBJWv//vvv/Hq1SvJ73Xq1EG7du0EjbFPnz5YuXIlkpOTWd+nkpIS\nvH//XmW+jhiWIfvA//jjDyxcuFDh8S+++ALTp09X+loPDw/UqFEDSUlJnPm3S5cuoXnz5oLFy3jy\n5Anevn3LWS+gXv+9NBsbG1hYWHD2G6WkpGhULhtlgxuJ8bp69arMPU+2trb46quvBK2jVq1a6NSp\nEw4ePMjZRqgPkxBCCPm00YqBhBBCCCHEKOgiwXnr1i2sWrVKpuxq1aphzpw5nK9ZsGAB7O3tZWKS\nX6ll8uTJKCkpETxeNtJ1P336VPAZAO/duyeZHZIrSd61a1dB6xQK1w39TEL+zp07SE9PF7zeuLg4\nyQ3sbDfOm5qaqnUjc+fOnVVuc/ToUTWjVO3QoUM661iwsLBA165dlc5KGhsbq5O6GSdPnoRIJJL8\nAP8NPOarc+fOSlcOu3r1quAJ9jt37khWg1L2/hHdkF81UL4NMcLCwgwRnlYaNWokWSmX7fomFotx\n5swZndVfVFSEs2fPKrynZmZmCA0N5VXGhg0bsHz5csnP8ePHBY+zXLly8PPz4zz+9HX9J9pR9R3h\n/PnzOqv7zJkzKCwslPwUFBSgsLAQwcHBKl9rYWGBLl26KD3/nz59WsBo//PPP/+wPs7cONS8eXM4\nOztrVLajo6PSmWyFmsX2/PnzyMvLA0DXUG2Ymprqvc7jx4/LnN83btyok3pU3XRE53hCCCGEEEII\nIWWB9EqAbDnu0rZaIABUqVIFzZs3N2i/06lTp7Tqd9q1a5dM/mPXrl06ibNZs2ZK3yfKfxg/Vfnt\nW7duITs7Wyd1nz17VuP8NvAxdq72JxaLOfPQ2mIrV/q8V7NmTfj7+2tcfuXKlVkfF4vFkn5dbT18\n+JD6iAVgYqL/W+L//fdfmfP7smXLdFKPqvx2cXGxTuolhBBCSOlAAwMJIYQQQkiZlJOTg0mTJkmS\nX2KxGGZmZliyZAlsbW05X1e5cmXMnz9fYcAXc1M0k2xftWqV7neCxYEDBwQtb8eOHQqPSSfJLS0t\n1RpIpU+NGjVC3bp1ZWa6lE6SFxcXs65Mpa39+/ezPs7EERISotaN87Vq1ZIMQuFauXHnzp0ax8sm\nPj4ejx49kpSvCwMHDmR9nDmOTpw4gfz8fJ3UnZycjIsXLyo8bm1tzbvjCgB69Oih8Jh8GxO641bo\nY5yob8CAAahYsaJkJl3pH+DjjKJeXl4GjlIzAwcOVHrM7927V2d1nzhxAllZWZLfmXNeQECAWrMT\nS994kZaWpotQOTt4AaB8+fI6qZMIq2PHjpLVL9m+I6SmpuLff/8VvN6XL19Krq/SatSogdq1a/Mq\nY8CAAayPM9fPs2fPyqycKYSSkhIcOXJE6YQBgwYN0rh8ExMT1tW2mX2SXilOG8eOHROknE+djY0N\n53OFhYW8y4mPj8fp06dlfi5duqT0NUwbNMT5HaBzPCGEEEIIIYSQsiE0NFTSf8aW4/by8tJ45SxD\nUtXvxNV/J4Tbt28jMTFR4XFXV1d4e3vzLsfQ+W2A8h+lgao+8MLCQp3kQnNzc1lXI7SxsUHTpk15\nldG3b1+Ym5sDULzPAgAePHiAhIQEgSL+z8GDB1kfZ+rWJr8NAG5ubgr9a8w+PX78WJABt7qYKPhT\nJFR+++nTpwr5bVUDW5k2kZubiw8fPvCuiy9V5/cKFSoIXichhBBCSg8aGEgIIYQQQsqk2bNn48WL\nFwD+S/iOGzcOfn5+Kl/brl07fPHFF0pXClu7di2uXbsmeNxcmHqjo6Mlq9VpKzk5GX///TfnYDSR\nSIQ+ffoYdQJx3LhxnM+JxWL88ccfgtaXn5+PPXv2KMysKv1/TWZZHTZsmMJj0p0kiYmJgnbwLF++\nXLCyuHh5eaF9+/acnVY5OTmIjo7WSd0bNmxAUVGR5Hcmhp49e8LCwoJ3Oe7u7mjTpg3roE3mmIyK\nipKsTqSt5ORk7Ny5U2crORJ+LC0tMWzYMJkOeumf0jaTsrTPP/9cMnBZ/twlFotx5coVwQboyFu/\nfj3r41988YVG5TED9XUhJyeHtT6RSIRatWrppE4iLAsLC4wYMULpQNht27YJXq98mUy76d69O+8y\nfHx8EBISovSmj7/++kuYgP/fkSNHkJqaKlMXc14AgPr166Nt27Za1eHl5aWwSinj+fPnSElJ0ar8\nN2/e4OjRo3QNFUC5cuVYHxeLxSgoKOBdzm+//YZRo0bJ/Pz888+8Xvvu3TvJ33JCkj+/y6/67erq\nKnidhBBCCCGEEEKIIYwePRoAWHPcYWFhBo5OM+3atUO9evU482ZJSUk4deqUTupeu3atzO9MDJ9/\n/rla5TDxisViPHjwQCcDR5TlP6pWrapWPxkxHH33gQMfJ4/Mzc2VqUckEqFjx46SwX6qVKpUifM+\nC0ZUVJTWsUq7desW7t69K5PTlm73zs7O6N+/v1Z1yE8YKr1/Hz58wO3bt7UqPzc3F9HR0ZTfFgBX\nfhuAWvntnTt3KuS3J0+ezPv1N27c4L0tX2z9l9Jq1qwpeJ2EEEIIKT3MDB0AIYQQUhb98ccfePfu\nncLjjo6OWs9ERco2ajvC+Pvvv3HgwAGZ5G/Tpk0xcuRI3mVMmzYNV65cQUJCgqQcJvktEolQXFyM\nKVOmYP/+/UpXIBSCdAdXTk4OFi5ciMWLFytsp277iYiIwIcPHziT5BYWFqwD1oxJaGgoGjdujGvX\nril8TmKxGImJiTh48CC6du0qSH1btmxBRkaGzHsG/PcZ9ejRA3Xr1lW73A4dOsDT0xOPHj1SKJv5\nfdGiRWjVqpXSWe74+OeffxAbG6vQsSC/P5mZmVi5cqVW55+JEyfi9OnTKCoqUmhnYrEY69atQ+/e\nvQU9hp4/f846uM7CwkKtcwAjLCxMMvuffBsDPq5os2LFCnz//fdaxx4REYG8vDyFNlAalLXr18CB\nA2FlZaXweIUKFeDj42OAiIRhaWmJ8ePHY/r06ZLrmfxNFEuWLOEcxKepQ4cO4f79+wrHpYeHBzp1\n6qRx+3n9+jUePnyIOnXqCBrvs2fPODtfGzduLGhdRDvK2s7AgQPx559/4vnz56zXoJiYGMTFxak1\no7cy6enpCpMHAB9Xq/3yyy/VKmvSpEk4c+YMiouLWWPfsmULBg4cKMj1s7i4GL/99htnmxeJRGp1\ndnPx9/dXOuPxiRMntLpeLF26VO2ViMvatUsodnZ2Co8xbS8jI4N3OVlZWQrtSp0JR06fPq32saPK\ns2fPFB5jroO+vr4wM+PfXULth2iD2g8hhBCArgdEO9R+iKao7Xw6OnXqhDlz5shMoggA5ubmaN++\nvUZlcrWfzMxMjcrTxNSpU/H1118DAGtf3fLly9GmTRuYmpoKVuf169cRExOjkOfQ9rgpKCjAhQsX\ntJ6QS56y/Ich89t0/lGPqj7wuLg4nDp1Cu3atROkvsLCQkRFRbFOlDp8+HC1yho9ejT279+PzMxM\n1vz2vn378O2336J69eq8y1TWfmJiYlhfw7xf48eP13pArL+/v9Lnjx07hoYNG2pc/vr165Gamloq\n+4iNjXx+W3pAdkZGBlauXAlA9bknKysLgOz9M+rkt//55x80a9aM9/Z8yJ/f5QfAqnNMEX7o2kW0\nQe2HaIPaD9EEDQwkhBBCdODPP//E48ePFR738PCgL2ZEKWo72ktKSsJPP/0kkwRzcHDAokWL1CrH\n0tISS5YsQb9+/WQGNUkPoHj9+jVmzZqFpUuXCroP0qSTv2ZmZigqKsKhQ4cQEBCgsMqSOu1n+/bt\nOHbsGGtymdnHYcOGoVq1agLvkfDmz5+Pnj17sg5yZAbUtWzZEvb29lrV8/z5c6xdu5ZztcBKlSph\n2rRpGpUtEokwY8YMDBkyRCZ26fb26tUrhIeHa9Xenj59ivDwcM5OBenHMjMzsWrVKq3OP7Vq1cK4\nceOwdOlS1kFIaWlpmD9/Pu/VY1QpKSnB1KlTJYPrgP/ac79+/SQrpanD19cX3bt3Z11dU3qARtOm\nTdGmTRuNY9+2bRvnMVkalLXrl7W1damMm4/evXvj8OHDOHfunMJxKRaLce7cOezduxe9evUSpL7k\n5GTMnTtX5vhh6vvuu+8AaNd+IiMjsWzZMkFiBYCHDx/iyZMnnMdix44dBauLaE9V24mIiMBXX30F\nAArXILFYjNmzZ2P79u0wMTHROpaIiAjJ5AFM+SKRCF988QXrQCtl3N3dERYWhhUrVrBeP9+9e4dF\nixZh7ty5Wse9YcMGhYkJpOvr378/mjZtqnU9rVu3Zr3eM3Vt3boVAwYM0OizOHHihGRSECsrK96D\n7MvatUsotWvX5nxOnZXLnzx5Ivk/054qVaqk8nXMZ7dx40b0799fsFnsS0pKcOrUKc5BsJ06dVKr\nPGo/RBvUfgghhAB0PSDaofZDNEVt59Oi7mp2qnC1H332JTZt2hT9+/eXrKwln99+9OgR1qxZg7Fj\nxwpSX15ensLEkEx93377LesEg+pYs2aNoAMDMzMz8e+//3LmPwyZ36bzj/pU9YH//PPPaNasmdIV\n0viKjIyUmTSRaeft27dXmi9k4+joiOnTp+P7779nzW8XFBRg9uzZ+P3333mXydV+nJ2dkZKSwvr+\nAEDLli0F6e8KCgqCubk552S4e/bswZgxYzSazO/mzZvYtGmT2vltwk6+vUq/jyUlJVi1ahUA1ece\nbfPb0dHR+Pbbb+Ho6KjuLnA6fvy4wmPSK3sS4dG1i2iD2g/RBrUfognt73ohhBBCCCHESBQXF2PS\npEnIyckB8F8SLCIiApUrV1a7PC8vL0yaNIlz4JxYLMaRI0ewZ88eQeJXRXog4Ny5c7Fr1y6Nytmz\nZ4/C4ElAdpCbu7s7Ro0apVmgelazZk3MmDGDddVD4OMNzJMmTUJxcbHGdeTk5GD8+PHIy8sDAIWB\nBWZmZli0aJFas8TJa9asGfr06aOwH/LtbcaMGQozvPLx7NkzDBkyBKmpqZLyy5cvL1OXLgwfPhxB\nQUGsgw2YWSE3btyodT1isRjTp0/HzZs3FfanWrVqGD9+vMZlf//996hUqZLMqqHS+1NSUoIJEyZI\nVhZU1/bt2zF//nxJ3Pr4XMinjbkuyp9vmLY9Z84cXLt2Tet60tPTMXLkSJkZo5njKDQ0VOOZqaVj\nPXbsGE6ePKl1rIxffvmFtR4ACAwMFGx1OaIfAQEBGDlyJOc16Pbt24IMTo+Ojsb+/fsVztvOzs4a\nrVYLACNHjkRgYCBn7Dt37sS+ffu0ivvff/+VDD5kSP/fw8NDkBVxgY/f13x8fBS+QzGePXuGiIgI\ntcu9cuWKzIqG3377rfbBfuLkV8aV/pwKCgqQlJSksoyXL1/i1atXCo/7+vryjuP169dYsmQJ7+1V\n+f333yXfg6XbIfBxpmehBsQTQgghhBBCCCFEt3744Qd4enpy5rcjIyNx9OhRrespKCjAmDFjWHMh\nDRo0wJdffqlx2Uysd+7cQVRUlDZhyliyZImkD1E+/1GjRg2EhIQIVhfRPVV94C9fvsQPP/yg9QCy\nf/75B+vXr1co38bGRib3qo4ePXqgW7dunPnt8+fPSwZoaePt27ec+e3KlStj/vz5WtcBAOXKlUPb\ntm0VVillZGZmavRZJCQkYNSoUcjPzwcAjBgxQpB4P2Xy+W1N5Ofn4+7duwrHhDr57dzcXMyePVvr\nWBiHDx+WxCR/fjc1NaVBIoQQQgihgYGEEEIIIaTsWLFiBW7fvg3gv86OQYMGaTXT4pAhQ9CyZUuF\n5BrwXwJ73rx5eP78uVax8zF+/HhUrFhRspJceHg4xo4dizdv3vB6/fv37zFjxgxMnz5dUgbbIDQr\nKyssWbIElpaWOtsXofXr1w9Dhw6V2ScAMp0LY8eORW5urtplv3//HsOHD0dcXBwAxUGBIpEI06dP\nR1BQkNb7ER4eDnd3d87BgQCwe/dufPnll4iPj+dVZlFREdatW4eePXsiOTlZUnbjxo3Rrl07rWNW\nxcTEBMuXL0etWrU4O2kXLVqE5cuXa9xxlZ2djYkTJ2Lfvn0Kq5JZWFhg2bJlksF2mnB0dMQvv/wi\ns4qR/ODA/Px8jB49GgsXLkR2djavcl+8eIFRo0Zhzpw5kngBYOrUqRrHSggflStXRmRkJKytrRWO\nO6Y9Dx8+XKsBd0+ePMHgwYNZz1XVq1cXZDAWcxxOmjQJMTExWpe3YMECnD17lrVTzdzcXONVYYlh\n/e9//0OHDh0UVuKVXqluwYIFKCkp0aj87du3Y/bs2QrXHxMTEyxatEjt1QIZpqamWLlyJdzc3Div\nnzNmzMDu3bs1Kv/cuXMICwuTTJwg//2mUqVKWLt2LWxsbDQqn83gwYMl/2ebAGHbtm2YM2cOr+to\nYWEhli9fjqFDh6KgoAAA0L17dwQEBAgW76fK29tbMss42yQFly5dUllGdHQ06+N8Ph/pNhEVFYVl\ny5ZpfXPTkSNHsHLlSpljSLqu8ePHa/VdkRBCCCGEEEIIIfpjY2OD3377TdJnKo2ZzHHSpEnYvn27\nxnWkpKRg+PDhuHDhgkLez87ODsuWLYOZmZnG5TOxisViLFiwAH/99ZdWZQHA5s2bJSspMuUzMTN9\nmdL9XKR0UNUHfvLkSUyaNAkfPnzQqPxTp07hu+++k5lgl2kzM2fOhKurq8axz58/H/7+/py5+VWr\nVmH16tUalw9AkteXz29bW1sjMjJSo8mjuajKb586dQrjx49HWloar/K2bt2Kvn37IiMjA8DH3Gm3\nbt0Ei/dT5eTkBDc3NwCaT8K7f/9+yYTR0tTNb584cQLTpk2T9GFo6vLly/jxxx85z+9ff/01atSo\noVUdhBBCCCn9tPsLlRBCCCGEGJXXr1/j3r17nM+zrdrAJJDFYjH27t2LvXv36iw+Xbp06ZJkJjtm\nn+rUqSPIAJsFCxage/fueP/+vaR86eRyXl6epINJ204gZcqXL4/Fixdj+PDhKC4uhkgkwsmTJ/HP\nP/+gefPmMisyScvOzsa0adNw5MgR5Ofnc64UKBaLYWpqisWLF6Nu3bqCxKztTbzq+P7771FQUIA/\n//xToXMBAGJjY9GnTx/MmjWL9yC+48ePIyIiAm/evOFMtE6ZMgUDBw4UZB+sra2xbt06fP7550hN\nTZWsTiddn0gkws2bN9GrVy80b94c7du3R8OGDVG9enXY2NggKysLqampeP78OWJjYxETEyMpi2Fv\nb4+ff/4ZkZGRgsStSoUKFbBlyxYMHToUiYmJrJ/PmjVrcPHiRXz//ffw9/fnVW5xcTGOHTuGX375\nBa9fv1bonDU1NcWcOXNQv359rfchKCgIs2bNkszsx7ZyoFgsxqZNm7Bz50507doVLVu2hJeXF5yc\nnGBqaoq0tDSkpqbi7t27OHHiBC5duoTCwkKZQUihoaHo168fZs6cqXXMpHTQ53lSWv369bFu3TqM\nGjVKstKu9HGZn5+PsWPHokePHhg/fjxcXFx4lZudnY0///wTkZGR+PDhg8LKYHZ2dli5ciVsbW21\n3gfmuCkoKEBYWBh69uyJsWPHonr16mqVExcXhwULFuDSpUuc5/offvgBXl5eWsdMDGPJkiWYMGEC\nTp06xXoN2rx5M+7cuYOZM2fy/pyfPn2KRYsWISYmRuH6www0CgwM1CpuOzs7REVF4euvv8azZ88U\nYi8pKcGMGTNw6dIlTJkyBU5OTirLzM7OxurVqxEVFSUzGFL6mlapUiVs3ryZ93HPV/fu3bFx40Y8\nfPhQ5joqvV/bt2/HiRMn0LVrVwQHB8PNzQ2Ojo4QiUR4+/YtXr58iZiYGBw9ehQpKSmSMtzc3DBr\n1iylfwvpgj7P4fqqy9LSEp07d8bOnTsV/m5gvut89tlnksGD8q5cuYJNmzYpvNbb25vXdzL54/O3\n337DxYsXMW3aNDRs2FCtfUlLS8OKFStYByoy7a5Dhw6CfZcnhBBCCCGEEEI+RYbIcVerVk3S75Sa\nmsqaN5szZw5Onz6NKVOmwMPDg1e5Hz58wN69e/Hrr78iIyNDIe9nZWWFpUuXCpY3Y3Jbs2fPRmxs\nLCZPngxPT0+1ykhKSsLixYtx7NgxhUnvmP8PGTIErVu31irWspgHKy1U9YEfPnwYjx49wqxZs9Ck\nSRNeZSYnJ2PVqlUKOUCm/H79+qFnz55axW1hYYH169dj+PDhuHXrFmvsK1euxM2bNxEeHi4ZzKUJ\n6fy2jY0N1q5dK0j/sLSAgAAEBwfjzJkznPnt48eP4+LFi+jSpQtat24NDw8PODg4wMLCAqmpqXj9\n+jXOnDmDI0eO4Pnz55IyHBwcsHjxYsmKn/pSVo/rXr16YdmyZRoNDExISMDSpUsVXluxYkVeE5LL\nt/G9e/fi1q1bmDZtGlq1aqVWLNnZ2Vi/fj02bdqEwsJCmeeYdtewYUNMmDBBrXIJIYQQUjaJxPSX\nFCGEECK4Ll264PHjxwqPe3h44NChQwaIiJQW2radvXv3arWajaYzZmlLetYskUiEqKgozqT1vn37\nMGPGDIXHpW9slh5AxbZPJ0+eRNWqVVnLf/XqFUJDQxVeV1JSIhMnQ35Qnbzq1avj2LFjrHXJW7Vq\nFVatWsU62EgkEklWrNu6dSsiIiIUOnaUfbVnG2DGYF5nZmaGefPmqZ3k5/pMGMwgRrZ94vqMACAi\nIgI9evRQKxYAWL16NVavXi1pE2z7Wr9+fXTu3BkBAQGoWbMmypcvD7FYjIyMDCQmJuLy5cs4ePAg\nEhISWG+IBj52ZsyaNQt9+/ZVO0ZVHj16hOHDhyMlJYX185IfaKOM/GstLS2xceNGNG7cGNOmTcPe\nvXslnaR8ymB+Z9qjOt69e4fx48fj6tWrCqsfMfEBQL169dChQwc0atQItWrVgr29PczMzJCdnY33\n798jPj4e169fx+HDh5GcnMxahqWlJRYvXoz27durHacymzdvxqJFixTaF9dxpYz8axs0aICtW7fC\n0tISXl5enMeGsrJ79eolyEps6hLyu8+MGTOwb98+zueZcwrw33unbJZbdc7DusB1XWGIxWKUlJSw\nXl+Y39muL4xevXph3rx5Gsd39+5djBs3Dm/evOE8Lk1NTdGqVSsEBwfD19cXNWrUkAzsy8zMRGpq\nKm7fvo0rV67g6NGjyMvLYz0eXFxc8Pvvv6N27doyMfBtPyEhITKTHLCdn0UiEQICAtCqVSv4+Pig\ndu3aKF++vCTenJwcpKenIyEhAXFxcTh16hTu3LnDut/MY+PHj8eoUaN4vZ9XrlzB119/zfp5s32X\nkL4mcrXjMWPGICwsjPU5Hx8fQevStj3pmzrnHuZmoOjoaKXXoKCgIISGhsLf31/S1gsLC5GVlYWX\nL1/ixo0bOHPmDM6fP896TRaJRJgwYQJGjBgh2H6mpaVh3LhxuHHjBmfs5ubm6NChA0JCQuDj44Nq\n1arBwsICBQUFSElJQXx8PM6cOYNDhw4hJyeHs727u7sjMjJSZ7PLxsXFoX///pKB8WwxSP/ORXo7\nR0dHbN26Fe7u7rh8+TIGDx7M6xrKVQdb++E61tjO4Xy+6wYGBmLTpk2s9X/99de4evWqYHVpch2M\nj49Hnz59FL4fMvV6enoiLCwMTZs2hYODA/Lz85GYmIiDBw/ijz/+kLlJgYlv2bJl6NixI2t9zHdS\nBtf379q1a6N9+/aoV68e6tSpA3t7e9ja2sLMzAx5eXnIysrCs2fP8PDhQ5w5cwYXLlxQ2tZat26N\nlStXwtzcXK33B6C8D9EOtR9CCCEAXQ+Idqj9EE1R29GP1atXK52YkS3Hray/TCQS4e7duzqJlS8f\nHx+ZFcXkSQ+SkX6MTw5fWZ5ElaSkJISFheHx48eceTORSIQmTZogJCQEDRs2RI0aNWBvbw+RSISs\nrCy8e/cO9+7dw9WrV3HkyBGkp6ez5hLs7OywZs0aNGrUSKNYv/rqK1y5ckXyO1f+o379+mjTpg3q\n1asHDw8P2NnZwdbWFiKRCLm5ucjKysKTJ08QHx+PmJgYXLt2TZIzki+LGeA1d+5cXjEq689QlZtS\nlXPmOv8AUJh4V9O6tGlLpQGfPnBfX1989tlnaNy4Mdzc3FChQgWUlJQgMzMTycnJuHnzJi5cuICY\nmBgUFRWxHi8DBgzArFmzBIs7Ly8PkydPRkxMDOdxamJigtatWyM0NBS+vr6oXr06rK2tUVRUhLS0\nNHz++ed4/fo1a/kmJiaS2J2dnbFixQr4+voKFr+0N2/eoEePHqwDh6X3S538NjNxcJMmTfDy5Uu0\na9dOoz7isWPHYuzYsazPtW/fnnUScUCzezmU5ZyV9fNqUpcm18C3b9+iY8eOyMvL47wHonLlyti1\naxecnJxQVFSE58+f49ixY9i0aZNkQlVAdrLoYcOGsZbF3OcjHbM06X7SDh06oH79+vD29oaDgwNs\nbW1hYWGB/Px85OTk4Pnz53j06BHOnz+PM2fOcPa5MvcWbNiwAeXLl1fr/SH80Xdnog1qP0Qb1H6I\nJmjFQEIIIYSQMshQA/w0pU68YrFYpsNMWXlcg+WUJUylOxq4yuV6ji2pqItZ3b766iuDktLiAAAg\nAElEQVSYmJggIiJC0gkn39nGFp/8/6W3t7Ozwy+//ILg4GC141H1mbC9b6o+I/n41DFmzBj4+fkh\nPDwcycnJrAM57969qzKBLJ18li+jdu3aWLRoEerVq6dRjKp4enrir7/+wpgxYxAXFyeT4GV7H5WR\njt3e3h6RkZFqd1qqO+CNi6OjI7Zs2YLly5djw4YNKC4uZt23e/fuKaz4w9bG2T4jkUiEGjVqICIi\nAo0bN9Y4Vi5DhgxB5cqVER4ejry8PNYOLLbf2Ui/tnnz5li+fDksLS1Vvq60nePVVVJSwuucIv1/\nZQNb9T27pjxl1xUG1/WFz/4puxmDj/r162Pv3r2YPn06YmNjZc7LzDFWUlKC06dP4/Tp0yrL47oB\npGnTpli4cCGcnZ01jrVTp07YuXMnsrOzASiem5n368qVKzI3WPCJma08e3t7zJo1C5999hnvslR9\n3squiVyfs7LPX+i6tG1PxszExARz5sxBo0aNEBERgczMTNZr0MWLF3Hx4kXJ67i+Y7G1dRsbG0yf\nPl3wSQMqVqyIrVu3YunSpdiyZQvr9bOoqAiHDh2S6Qjge+1kHu/ZsydmzZoFa2trQeOX5u3tjSVL\nlmDixImS8zNbh7+qax0Tu5OTEzZs2AB3d3de9Wv6nUbTY43ru66yY62kpITzGNWkLk2ug15eXhgx\nYgTWrFkjUw9T16NHjzhnIWa7mahPnz6cgwKBjwNyL126JLm5h+v8/uTJE6xdu5a1TmXHqXyZ5ubm\n+OabbzB+/Pgy/72KEEIIIYQQQoj+8e03k/6/sj5NY/jbVVmOEjBcjtvV1RU7d+7EvHnzsHv3bgBg\nzfldvnwZly9fVoiNK3cmn5vw9vbGL7/8wjsHxSYkJASJiYl49+6dTJzysfLpw2SLWbpMkUgEa2tr\nTJw4EV9++SXvsnSR3+bz+bK9lvLbivj0gd++fRu3b99WWRZbWzc3N0dYWBhGjx4taNzW1tZYvXo1\nNm7ciJUrVyI/P1/hOBWLxYiNjUVsbKxMjMx2qu7vEIk+TgIWEREBR0dHQeOXVqVKFfz222/45ptv\nJP3E2uS3bW1tsWrVKt4rPWp6PSguLtZbzllZP6+6dWl6DXRycsL06dMRHh7OuU1KSgrrSqps+e0W\nLVpg6NChnGX5+/vD3d0diYmJktexlfn69Wts3ryZtU518tsmJibo27cvwsPDYWFhwRkXIYQQQj4t\n3NPxEEIIIYSQUotJmpW2H33so77fQ10ZNGgQNm3aBBcXF85Eoap9YhKJwcHB+PvvvzUaFKisfEO+\nby1btsThw4cxbNgwWFtbsyaUmf3n+pGPRyQSwc7ODhMmTMDff/+ts0GBjKpVqyI6OhrffvstLCws\nFGLiep/YtmES1rt27WIdFKjqPRfyszExMcGECROwd+9eNG/eXOn7Lf0j/5h8XCKRCFZWVhg3bhwO\nHDigk0GBjM6dO2Pv3r0ICAhgjZ/vZyMSiWBhYYExY8Zg3bp1khXNuLY35DnHEIzlfCIUIc+RQu8f\nM2h49erVcHd3Z23Xqs6Z8scl85izszOWLFmCzZs3azUoEACmTp2Kc+fO4eeff0bjxo1VnrPVjZl5\nnZmZGQYNGoTjx4+rNShQmj4/X2NrT8auR48eOHLkCPr06QNzc3OV7Uf+d662HhwcjEOHDulkJWHg\n48qhU6ZMwa5du9C0aVNexylb7Gzb169fH1u2bMHPP/+s00GBjA4dOmDDhg1wcnLS6hrarFkz7Nu3\nD3Xq1FG5vRDtXZ/HmjEc12PHjsXAgQPVvibIb9ejRw/8+OOPSuvq3r07YmJi8Pvvv6NTp06S775s\n7YPrXK7sOJV+XYsWLbB//37873//0/hGGkIIIYQQQgghhI9PKV9nyH20srLCvHnzsG3bNvj6+grS\n78Q8Zmdnh5kzZ2L37t1aDQoEgKFDh+LMmTNYsWIFgoODYWpqKmh+Wzruzp0748iRI2oNCpSm79yU\nofNgpYkQfeDS2zOP+fn5Ye/evYIPCpQ2bNgwHDhwAO3bt4eJiYlauXllatasieXLl+O3337T6aBA\nhr+/P7Zv3w43Nzet8tve3t7Ys2cPmjVrpnJ7YzrO9FmfNvr27Yv//e9/Srfhk99u1aoVfv31V6Xt\nsEWLFjh06BC2b9+OPn36oFy5cirP05rmt319ffHXX39h7ty5NCiQEEIIITJoxUBCCCGEkDKmtN/c\nyCd+bfZR1+Xrsix5gYGBOHz4MP766y9s3rxZssIGG+lkOvNvUFAQhg8fjqCgIK1jMcZ2Z2NjgylT\npuDbb7/Fjh07cODAASQkJMhsw5VQlu9k8PHxQc+ePdG3b1+93DDPMDMzw8SJE/H5559j3bp1OHjw\nIHJzcyXPs8Uv/1kEBARg6NChCAkJUdjWxMQEZmYf/ywsLCxUGofQPD09sWHDBty/fx/bt2/H8ePH\nkZmZKXleWbJf/vPx9vZG79690bVrV9jb2wseKxs3Nzds3boVsbGx2LRpk8zqZFydFdIxW1lZoWfP\nnhg6dChq1KihsK2m77kuPitDKC3nYWOIQciyQ0JCEBISgpiYGOzcuRPnz5+XOTfwPS5NTU3RvHlz\n9O7dG+3atYO5ublgMVpaWqJnz57o2bMnUlJScO7cOZw9exYXLlyQOYeoilc+ZgCoXbs2unfvjm7d\nusHFxUXjGPXZ5oSuyxiOF31wdHTEvHnzMG7cOPzxxx84dOgQXr16JbMNn+8IlpaW+Oyzz9C/f3/4\n+/vrPG7g40pumzdvxp07d7B9+3acPHkSWVlZKuMGZGO3srJC69at0b9/fzRv3lzncctr2rQpjhw5\ngnXr1mHHjh0qvwNIt81atWrhu+++Q6dOnVi343stVPbdR1n9QlBVnjFcB01NTTFr1iwEBgZi7dq1\niI+PlzzH5/jw9PREWFgY6+fEpUWLFmjRogVycnJw8eJFnDt3DufOncPLly8VtlXnu6KjoyM6d+6M\n7t27o0GDBrzjIYQQQgghhBBCNGUMf9sLiRksoex5bcoWQuPGjbFjxw5cvnwZO3bsQExMDPLz8yXP\nq5NLaNy4MXr37o1OnTrBxsZGkPiAj/mW9u3bo3379khPT8eFCxdw9uxZnDt3DqmpqQrbqxNz1apV\n0a1bN3Tv3l2rQYyGyDkLVacxHCv6IFQfuKmpKUJCQvDFF1/oLUdcvXp1rFixAk+ePMEff/yBo0eP\nIi0tTWXcXKpUqYKjR48KHaZKdevWxf79+7FlyxZERUXJHL+q8ttVqlTB6NGj0bdvX5iYmChsp2lf\nr3xZymLQVmnIbwPAqFGjsGPHDoX+H4b8ZyV9fFSrVg3ffPMNBgwYwLu+hg0bomHDhpg1axYuX74s\nyW/LH59sdXPFAXxcWbJjx47o2rUr60BSQgghhBAAEIk/lelSCCGEED3q0qULHj9+rPC4h4cHDh06\nZICISGlBbefTtmrVKqxatUqmc435v0gkQlxcnNLXt2vXDi9evFB4XCQSwd7eHlWqVIG7uzuaNGmC\n1q1bo2rVqjrZD2OWmJiIa9eu4datW3j27BlevnyJrKws5OXlQSQSwdraGnZ2dqhWrRpq1qyJhg0b\nIjAwUKsBIkLKzc3FhQsXcPHiRTx48ADPnz9HVlYWCgoKYGVlhXLlysHFxQW1a9eGn58fgoODeX/O\nhj7/FBUV4ebNm7h+/Tru37+PpKQkJCcnIycnR7J/FSpUgK2tLSpVqgRvb2/Ur18fDRo0gKurq87j\nUyUpKQlnzpzBtWvXkJCQIIkd+NhBV6FCBbi5ucHT0xNNmzZFs2bNYGVlZeCohWPo9kN0Izs7G1eu\nXMGNGzfw8OFDvHjxAmlpacjLy0NRURHKlSuH8uXLo3z58qhatSrq1auH+vXrw9fXFw4ODrzrEar9\nPH78GI8ePUJCQgIePXqEV69eISsrCzk5OcjOzkZhYSFsbGxga2uLcuXKoXLlyqhbty68vb1Rr149\n1K5dm3ddxDgIee6Jj4/H1atXcefOHTx//hyvXr1CdnY2Pnz4AHNzc9jZ2aFChQqoUqUKfH194evr\ni0aNGims9qpvhYWFuHHjBq5du4b4+HgkJSUhJSVFcv00NzeHtbU1nJyc4Orqijp16qBRo0YICAjQ\n62QHyhQUFODcuXO4dOkS7t+/j5cvX+L9+/coKCiApaUl7OzsUKtWLTRo0ADt2rWDr6+vIPXStUs9\nN2/exKVLl3D79m08efIEmZmZyMnJQVFRkeRaULNmTTRo0ADNmzdnXaVaU69evcLDhw/x+PFjJCQk\n4OnTp8jKykJ2djays7ORn58PS0tL2NrawtbWFvb29vD09IS3t7fkO6OqG2PURe2HaIPaDyGEEICu\nB0Q71H6IpqjtEG2Utvbz4cMHXL16FdevX8eDBw+QlJSEt2/fIi8vD4WFhbC2tpbkNJydnSU5BF9f\nXzg7O+s93mfPnuHRo0d4/PgxHj9+jBcvXiAzMxPZ2dnIycnBhw8fYGVlhfLly8PW1haOjo6oU6cO\nvL294ePjAy8vL73HrI7S1n5KG2V94GZmZqhQoQLs7Ozg5OSE+vXrw8/PD40aNVKrL0cXxGIxbt++\njWvXruHevXt4/vw53rx5I2nzZmZmsLa2luS75RlD+ykpKcGlS5dw/vx5SR/3u3fvJH0LFSpUgKur\nKxo0aIDg4GBBJm0m/HGde0xNTWFpaYnCwkLY2tqifPnyks8pMDAQLVq0ECyG1NRUPHjwQHJ+f/r0\nKdLT0yX9l7m5uTA3N5fkt+3s7ODu7g5vb294eXnB19eXVgc0ELp2EW1Q+yHaoPZDNFE2lhIghBBC\nCCGEcA4ycnd3pz8K/1/t2rVRu3Zt9OvXz9ChaMTGxgahoaEIDQ01dCiCMzMzQ0BAAAICAgwdikZc\nXV0xaNAgDBo0yNChECIYW1tbtG3bFm3btjV0KLx4eHjAw8PD0GGQUsrLy8vob55hY25ujsDAQAQG\nBho6FI1ZWFhIViwlxouZ7dgQXFxc4OLigjZt2hikfkIIIYQQQgghhJQ+lpaWaNGihaCDO3TJzc0N\nbm5uZbIPkOheae0DF4lE8PPzg5+fn9LtuG6ONwYmJiYICgqiAX+lTK1atfR2D02lSpVQqVKlUnM9\nIoQQQkjpJOw0uYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjRKVoxUA8SExPRs2dP\n1uXMGVu3bkWTJk30GNWnJTU1FadPn8bt27fx+PFjvHz5EtnZ2cjPz4eVlRVsbW3h4uICT09PNGjQ\nAG3atIGTk5Ohw1aK9on2iRBCCCGEEEIIIYQQQkozsViMBw8e4M6dO3j48CESExORkpKC1NRU5OXl\noaCgAGZmZrC0tISDgwMqVaoEV1dXST7V398fFhYWht4NpeRzxE+fPmXdLiUlBTt37iwVOeKymPem\nfaJ9MpSyuE+EEEIIIYQQQgghhBBCCNEfGhioB+Hh4SgsLIRIJDJYDJcvX8bgwYMNVr+DgwMuXryo\n1zrFYjFOnjyJqKgoXL16FWKxWPKc9GeRm5uL3NxcJCcn48aNG4iOjoZIJEKjRo3w9ddfIzQ0FCYm\nxrG4Ju0T7RMhhBBCCCGEEEIIIYSUZu/evUNsbCxiY2Nx6dIlZGVlyTwv35dSWFiIwsJCZGdn4/nz\n57h+/brkOQsLCzRv3hzdunVDhw4dYG5urpd9UEVZjlj6/9IyMzMxc+ZMo80Rl8W8N+0T7ZOhlMV9\nIoQQQgghhBBCCCGEEEKIYdDAQB3btm0brl+/DpFIpNDZyzymzwGDhhycqE/Xr1/H7Nmz8fDhQwAf\n95vZd7FYzNnxLv3+XPs/9u48ysr6vh/457ILAgq4IIIaIMGMwagc1DTWNdJoa40Rq2LUeGyqrYpL\n0hQlKqlxRbTBBVJrxAXrikerrWuQqHGNObgixAUQRAIooDMCM/f3h787zjDbXefOc+f1OucemOc+\n9/v9vodHMvl++DzPK6/EK6+8EiNHjoyLLrooxowZU/qFt0Kmr8gEAAAAAJActbW18fjjj8cDDzwQ\nzzzzTGzatCkiGu+fRrS+h5rR8PyNGzfG3LlzY+7cubHNNtvEKaecEieccEJZGwTb2iNuTea8jrZH\nXIn73jJ9Rab2VYmZAAAAAACA8nELwRJatmxZTJs2rcM142WKSqV+NZyvPbNdddVVMWHChFi4cGGz\nxbTMseZeDdeeObZw4cL40Y9+FJdffnnU1dW1WxaZZCp3JgAAAACAQqxfvz5mzpwZBx10UJx99tkx\nd+7cqK2tbbERprU91Ob2Uht+5i9/+UtcccUV8bd/+7fx8ssvt3vWbPeI2xojouPsEVfivrdMMskE\nAAAAAABUEk8MLKELL7wwPv/88/piDaVVU1MTZ5xxRjzzzDNNiuuZYtouu+wSRx99dOyzzz6x0047\nRZ8+feKzzz6L999/P1544YW455574v33329UoM/8esstt8Q777wT1113XfTu3VsmmSo6EwAAAABA\nIW688ca4+eabY926dY0aYDIaNrhsueWWcfDBB8e3v/3tGD16dAwaNCj69esXvXr1ik8//TRWr14d\nCxcujGeffTaefvrpWLFiRaPPN2yo+eCDD+Kkk06KiRMnxk9+8pN2yZrLHvHs2bNjyZIlTcbo3r17\n1NbWdpg94krc95ZJJpkAAAAAAIBKozGwRObMmVNf3OmoTYFt3Zk2STZs2BCnnXZaPP/8880W1Hr3\n7h3/9m//Fsccc0yTz/bp0yeqqqqiqqoqTjnllPjv//7vuOKKK6K6urrJPyh47rnn4rTTToubbrop\nevToIZNMFZkJAAAAAKBQs2bNivXr1zd6IlZE40a+rbfeOs4888w48sgjY4sttmh2nP79+0f//v1j\nl112iUMPPTQ2bdoUd911V9x4442xatWq+rEajl9XVxfTpk2Ljz/+OCZPnlzSnLnuEd93333NjrPT\nTjvFj370ow6xR1yJ+94yySQTAAAAAABQibqUewGVaNWqVXH55Zc3ultjR2zCS6fT7fJqDz/96U/r\nC2oN50yn0zFw4MCYPXt2swW15hx77LFx++23x1ZbbdXsPyh46aWX4txzzy1JjoZkap1MAAAAAADJ\n0FxT4F/91V/FQw89FMcdd1yLTYHN6datW0yYMCEefPDB2H333Vt8EmFExB133BHTp08vTogWVOIe\nsUytk6l0ZGpdR8kEAAAAAAB0HBoDS2DKlCnx6aef1n/dng1yucg0LLbXq1RuueWWeOyxxxr9g4KI\nL7/vvXr1ihkzZsSoUaNyGrOqqipuvPHG+jtqNiyspdPpePLJJ+Pmm28ubpAGZMqOTAAAAAAAydBw\nH/U73/lOzJgxIwYOHJj3eAMGDIhbb701xo4d22h/NiIaNczccMMN8cwzzxQjQhOVuEcsU3ZkKj6Z\nslPuTAAAAAAAQMeiMbDInnjiiUYFnoiO+cTAVCoVTz31VLz11lvt8nruuedKkmPhwoUxderUZu8I\nnEql4uyzz45vfetbeY29xx57xBlnnNGkqTPzZztt2rR455138l57S2TKjUwAAAAAAB1bw33UHXfc\nMX79619Ht27dCh63R48eMW3atBg0aFCTeTJfp9PpmDx5cmzYsKHg+RqqxD1imXIjU/HIlBt1JAAA\nAAAAIENjYBGtW7cupkyZ0qTAs++++8bgwYMjomlRupw64lMMczVlypSora2NiMZ3AI6I+PrXvx4n\nn3xyQeOfeuqp8bWvfS0iGt9xMyKitrY2fvnLXxY0fnNkyp1MAAAAAAAdW2Yf9Wc/+1n06dOnaOMO\nHDgwLrzwwiY1j4Zfr1ixIm677baizRlRmXvEMuVOpuKQKXfqSAAAAAAAQITGwKK69NJLY+XKlY2O\n9erVq77w0pGaAivBI488Ei+//HJENG1yTKVScdZZZxU8R5cuXeLMM89sNH7D4t0rr7wS//u//1vw\nPBky5UcmAJLkpJNOiqqqqmZf119/faufbelzu+22WzutHgAAAHKX2eccNWpUHHrooUUf/3vf+17s\nvPPOEdHyUwPvuOOOos1XiXvEMuVHpsLJlB91JAAAAAAAIEJjYNE899xzMWfOnPqCc6bwMnHixNhx\nxx3LvLrKNGPGjEYF/oa/32GHHeLggw8uyjzjxo2L7bbbrskcDddRLDLlTyYAkqKurq7FV8RXf+en\nUqlGv2/ps7W1tfV3HwcAAICOKpVKxeGHH16ysSdMmNDqUwOXL19e36hTqErcI5YpfzIVRqb8qSMB\nAAAAAAAaA4uguro6LrzwwkZNgRERo0ePjpNOOqmcS6tYv/vd7+Kdd96JiGj2TphHH3100ebq0qVL\n/PCHP2zxjpvvvPNOzJ07t+B5ZCqMTAAkSTqdLtoLAAAAkmLcuHElG3ufffZp85w//OEPBc9TiXvE\nMhVGpvzJVBh1JAAAAAAAoFu5F1AJpk2bFkuXLm10J8bu3bvHr371q2bvzkjh7rrrrlbfP/TQQ4s6\n36GHHho33HBDi+/ffffdccABBxQ0h0yFkwmApCj2z4h+5gSac9BBB8WyZcvaZa6WGpUXLVoUo0aN\navFzQ4YMiSeffLJUywIK0Np/ux1Fr169yr0EANrQ8OfEvn37xtChQ0s218iRI6N///6xdu3a+qaY\nzb322msFz1OJe8QyFU6m/MhUOHUkAABoqj1rZPlSIwMAAIpFY2CB/vSnP8Udd9zR6GmBqVQqfvKT\nn8SIESPKvLrKtHr16njmmWca/QP4hr8fOnRoDB8+vKhzjho1KnbYYYdYvnx5o39QkPn9vHnzYvXq\n1TFgwIC8xpepOGQCIAluu+22ci8BACArLTVVAEC2Gu6fjhw5suTzDRs2LF577bUmN9DJ/G/aBx98\nUND4lbhHLFNxyJQ7mYpDHQkAAAAAADq3LuVeQJJt3Lgxzj///Cb/QGr48OFx2mmnlWlVle///u//\nYtOmTRHR+G7DmabMsWPHlmTesWPHNpkvo7a2Nh599NG8x5apeGQCAICvpFKpdnkVsgagY8r899le\nf48U++8eAMpv2rRpMWPGjPrX5MmTSz5n//79W31/9erVBY1fiXvEMhWPTLmRqXjUkQAAoKly71/b\n4wYAANqLJwYW4Prrr4933323/v+kpdPp6NKlS1xyySXRvXv3Mq+ucs2bN6/V9/faa6+SzLvXXnvF\nAw880OL78+bNi+OOOy6vsWUqHpkAAOBLTz31VLvNdfjhh8eiRYuaHB8xYkQ8/PDD7bYOoHjeeuut\nci+hTS393QNAx/Cd73yn3edsqzGwurq6oPErcY9YpuKRKTcyFY86EgAANNaeNTIAAIBy88TAPL39\n9ttx0003NWoKTKVScfzxx8e3v/3tMq+ucm3YsCFeeOGFVu+Y881vfrMkc7c0biqVinQ6HS+88EJs\n3Lgx53FlKi6ZAAAAAAA6p8yTulrSp0+fvMeuxD1imYpLpuzJVFzqSAAAAAAA0HlpDMxDXV1dXHDB\nBVFbW9vo+ODBg+O8884r06o6hzfeeKP+jr7pdDoiolGBrWvXrjF8+PCSzD1y5Mjo2rVrozkza4j4\n8k7Db7zxRs7jylRcMgEAAAAAdE7r169v9nhmP3XQoEF5j12Je8QyFZdM2ZOpuNSRAAAAAACg89IY\nmIebb765UfEk87TAKVOmxBZbbFHGlVW+119/vdX3hw4dGt26dSvJ3D169Igdd9yx1XPaWl8+n5Ep\nNzIBAAAAAHROa9asafG9VCoVI0eOzHvsStwjlqm4ZMqeTMWljgQAAAAAAJ2XxsAcvf/++3Hdddc1\nuttiKpWKI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gACTcyy+/3OJrTQeSffr0KXrwOHv27HYHp+Uu4IYOHdpuAffaa6+V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\nw83GJUuWxJw5c+Kss86qur1rr702fvzjH3c6V7a/Gbj//vv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AKBALAwEAAAAAAAAAAAAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa236c3eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist([\"Mesh_counts\", \"Exploded_Mesh_counts\"], bins=range(-1,150), grid=False,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mesh_counts</th>\n",
       "      <th>Exploded_Mesh_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.234942e+07</td>\n",
       "      <td>2.234942e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.579647e+00</td>\n",
       "      <td>4.558257e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.978129e+00</td>\n",
       "      <td>2.285986e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>3.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>4.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>6.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.030000e+02</td>\n",
       "      <td>3.110000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Mesh_counts  Exploded_Mesh_counts\n",
       "count  2.234942e+07          2.234942e+07\n",
       "mean   9.579647e+00          4.558257e+01\n",
       "std    4.978129e+00          2.285986e+01\n",
       "min    1.000000e+00          1.000000e+00\n",
       "25%    6.000000e+00          3.000000e+01\n",
       "50%    9.000000e+00          4.400000e+01\n",
       "75%    1.300000e+01          6.000000e+01\n",
       "max    1.030000e+02          3.110000e+02"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Mesh_counts\", \"Exploded_Mesh_counts\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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