{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow v2.15.0\n",
      "Numpy v1.26.4\n"
     ]
    }
   ],
   "source": [
    "print(\"TensorFlow v\" + tf.__version__)\n",
    "print(\"Numpy v\" + np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    142246.000000\n",
       "mean        553.636679\n",
       "std         641.728770\n",
       "min           3.000000\n",
       "25%         248.000000\n",
       "50%         411.000000\n",
       "75%         654.000000\n",
       "max       35375.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "141865 [246, 255, 246, 247, 248, 245, 245, 502, 580, 486]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RERERERERERERERE5HcMZIiIiIiIiIiIiIiKiHtQvw5l33nkHo0ePVtqJJSQk4Ndff1Xur6+vx4oVK+Dn5wd3d3csXrwYhYWFzc6RnZ2NRYsWwWAwIDAwEE888QRMJlNPfypERERERERERERERDTA9MtwJjw8HC+++CISExNx8OBBzJs3D1dddRVSUlIAAI888gh+/PFHfPXVV9i2bRvy8vJw7bXXKo83m81YtGgRGhsbsXv3bnz00UdYs2YNnn766d76lIiIiIiIiIiIiIiIaIAQRFEUe/sinMHX1xf//Oc/cd111yEgIADr1q3DddddBwBIS0vD8OHDsWfPHkydOhW//vorLr/8cuTl5SEoKAgA8O677+JPf/oTiouLodPp7HrOqqoqeHl5obKyEp6ent32uRERERERERERERERUd9nb27QLytnmjKbzfj8889RW1uLhIQEJCYmwmg0Yv78+coxw4YNQ2RkJPbs2QMA2LNnD0aNGqUEMwCwcOFCVFVVKdU3bWloaEBVVVWzGxERERERERERERERkSP6bThz7NgxuLu7Q6/X47777sO3336L+Ph4FBQUQKfTwdvbu9nxQUFBKCgoAAAUFBQ0C2bk++X72vPCCy/Ay8tLuUVERDj3kyIiIiIiIiIi6mcaTRYs/yQRq3ed6e1LISIi6jf6bTgzdOhQHDlyBPv27cPy5cuxdOlSpKamdutzrly5EpWVlcotJyenW5+PiIiIiIiIiKivO5hZhl+TC/DahpO4QLrnExERdTtNb19AZ+l0OsTFxQEAJkyYgAMHDuCNN97AjTfeiMbGRlRUVDSrniksLERwcDAAIDg4GPv37292vsLCQuW+9uj1euj1eid/JkRERERERERE/VdmaR0AoKrehOKaBgR6uPTyFREREfV9/bZypiWLxYKGhgZMmDABWq0WmzZtUu47ceIEsrOzkZCQAABISEjAsWPHUFRUpByzYcMGeHp6Ij4+vsevnYiIiIiIiHrWj0l5mPHSZiTnVvb2pRD1e1lltcqf0wtrevFKiIiI+o9+WTmzcuVKXHrppYiMjER1dTXWrVuHrVu3Yv369fDy8sJdd92FRx99FL6+vvD09MSDDz6IhIQETJ06FQCwYMECxMfHY8mSJXj55ZdRUFCAp556CitWrGBlDBERERER0QDwzaGzOFt+Dp/uy8IL147u7csh6teyrZUzAHCqqAbT4vx78WqIiIj6h34ZzhQVFeH2229Hfn4+vLy8MHr0aKxfvx4XX3wxAOC1116DSqXC4sWL0dDQgIULF2LVqlXK49VqNX766ScsX74cCQkJcHNzw9KlS/Hcc8/11qdEREREREREPehs+TkAwK700l6+EqL+L6tZOFPdi1dCRETUfwgiJ7V1WlVVFby8vFBZWQlPT8/evhwiIiIiIiKygyiKiH96Pc4ZzQCAHU/ORYSvoZeviqh/EkURo579HTUNJgDAlBhffPGHhF6+KiIiot5jb25wwcycISIiIiIiIrJHaW2jEswAwK70kl68GqL+ray2UQlmACC9iDNniIiI7MFwhoiIiIiIiAYUuaWZbCfDGaJOy7S2NPN100EQpPCztKahl6+KiIio72M4Q0RERERERANKTpm0mGzQqQEAezJKYbGw4zdRZ2SX1QIABge6I9zHFQBwitUzREREHWI4Q0RERERERAOKXDkzd1ggDDo1SmsbkVbAIeZEnZFlrZyJ8jNgSKAHAIYzRERE9mA4Q0RERERERANKTrm0mDzI3w2TY3wBALsz2NqMqDOylXDGDXFB7gCA9EKGnURERB1hOENEREREREQDilw5E+FjwIw4fwCcO0PUWVll5ytnBrNyhoiIyG6a3r4AIiIiIiIiop501rqYHO7jCm+DFwBg/5kyNJos0Gm4h5HIEUpbM183WERpdhPDGSIioo7xXScRERERERENGBaLiLMV1soZXwOGBXvAz02HukYzks5W9O7FEfUztQ0mlNQ0AAAi/QyIDZTamhVXN6CirrE3L42IiJr4aHcmvk4829uXQS0wnCEiIiIiIqIBo7imAY0mC1QCEOzlApVKQEKsHwBg5ym2NiNyRLa1Cs3boIWXqxbueg3CvF0BAOmsniEi6hPSi2rwzA8peOJ/SaiqN/b25VATDGeIiIiIiIhowDhbLi0mh3i5QquW/ks83Tp3ZncGwxkiR2SV1gIAonwNysfirNUzbG1GRNQ37LLO1bOIQPLZyl6+GmqK4QwRERERERENGGfLpZZm4T6uysdmWMOZw9kVqG0w9cp1EfVH8ryZSD835WOD5XCmsPfCmY/3ZmHZ6v1KyzUiooFsZ/r5zSdJDGf6FIYzRERERERENGDkWNswhfuc3+kf4WtAhK8rTBYR+8+U9dalEfU7Wdafp6aVM4OD5MqZ6l65pnqjGS/9moatJ4rxwi9pvXINRER9hclswd7Tpcrfk3Iqeu9iqBWGM0RERERERDRgyJUzEb6uzT4uV8803V1KRLZlWytnovyatjXzANB7M2e2nihCjbUC7utDZ5GYVd4r10FE1Bccy61Edf35quCjZyt672KoFYYzRERERERENGDklLeunAGAabFSOLOL4QyR3bLKrDNnmrQ1k2fO5FfWo7oXBk//kJQHAHDTqQEAz/6QArNF7PHrICLqC3ZnSFUzMwf7QxCAvMp6FFXX9/JVkYzhDBEREREREQ0YSuWMT/PKmWmxfgCAtIJqzqkgsoPRbEFehbTA17RyxstViyBPPYCer56paTBh0/EiAMDbt46Hh16DY7mV+PJgTo9eBxFRX7HzlLTpZEF8EOICpPD8aI5z5s7UNpggigy/u4LhDBEREREREQ0IZouIvAopnAn3bV454+eux/AQTwDnd5kSUftyy8/BbBHholUh0EPf7L7B1tZmp3o4nNmQWoAGkwWD/N0we0gAHr54CADgn+tPoLKu56t4iKjvaDCZUVbb2NuX0aPONZqV1o7T4/wxOtwbgPNam/3xs8OY88pWJQAixzGcISIiIiIiogGhsKoeRrMIjUpAsKdLq/tnxEnVM7u4yEDUoawyqUVgpK8BgiA0u09ubdbTlTM/JuUDAC4fEwpBEHB7QhQGB7qjrLYRr2082aPXQkR9hyiKuHdtIqb+v01IL6ru7cvpMQezytBotiDEywUx/m4YG+EFADhytuuVM+cazdiZXoKs0jr4e+i6fL6BiuEMERERERERDQg51sXkUG9XqFVCq/unxVnnzmQwnCHqSFapNG8m0tet1X2Dg6Rw5lRhzy2CVtQ1YvvJYgDAlWNCAABatQrPXjkCAPDx3iykFVT12PUQUd+xK70U204Wo9Fswa/HCnr7cnrMrnSpEnh6nD8EQWhWOdPVdmQ700vQYLIg3McVQ4M8unqpAxbDGSIiIiIiIhoQ5Hkz4S3mzcgmR/tCqxZwtvwcskvrevLSiPqdLOvPSNN5M7LeaGv2a3IBTBYRw0M8ERd4fqFwepw/Lh0ZDLNFxDPfp3A+AtEAI4oiXm9SObcjfeBswNhl/VynWyuDh4V4QKsWUFFnRHZZ197nbEwtBADMHx7UqnqS7MdwhoiIiIiIiAYEOZyJ8Gm9mAwAbnoNxkX4AJB2hBJR++RwJrrNcEaqnDlbfg51jaYeuZ4fk/IAAFdYq2aa+r9Fw6HXqLDvTBl+OprfI9dDRH3DrvRSHMwqh8ZaMXs4uxw1DT3zutSbKuoakZwntS+bFitVBus1asRb5+sldaG1mcUiYlNaEQApnKHOYzhDREREREREA0JOubSY3F7lDCDtsgfY2oyoI9ll1rZmfq3bmvm46eDvLs0gyCiq7fZrKaqqx57TUvueK0aHtro/3MeA5XNiAQD/75fjPRYYEVHvalo1syQhCpG+BhjNIvZZXy8uZHsySiGKUlge1GTOntLaLKei0+dOOluBkpoGeOg1mBzj28UrHdgYzhAREREREdGAcNYazkT4tl05A5xv/bE7vQQWC9sfEbVFFEWlJU5UOz9PcdbqmVM9MHz752P5EEVgXKR3uz/f982ORbiPK/Ir6/H2lvRuvyYi6n0700twMKsceo0Ky2fHYsZgaQPGjlP9cwOGKIr48kAOknM7rnqRN5nIm05kYyK8AUgBS2dtPC61NJs9NAA6DeOFruBXj4iIiIiIiAaEnDLbM2cAadHCTadGeZ0Rxzk8fMARRRHPfJ/cbD4BtVZU3YB6owVqlYCwdn6eenLujNLSrI2qGZmLVo2nFsUDAN7ffgbpPTgPx9lMZgue/F8S3tuW0duXQtRnSVUzpwAAt0yJRKCnC2Zag4r+2rr0QGY5nvz6KJZ8sA+V54w2j92dLlUHtQpnwr0AAMm5VTCZLZ26jo2pbGnmLAxniIiIiIiI6IJnMltQUFUPwHbljFatwpRBUvXMrn66eEOdd6KwGh/tycLrG0+hoq6xty+nz5LnzYR6u0CrbntpaXCQtXKmsHtDkJyyOhzKroAgAJePbj1vpqmFI4Iwc7A/Gs0W3LP2ICrrbC9u9lX7z5Thy4Nn8c/1J1A7AGZnEHXGzvQSJDapmgGk2SsqAUgvqkF+5blevkLHHbVWu5TXGbFqa/sVgHkV53C6pBYqAZgyqHnbsUEB7nDTqXHOaO5UeJ5TVocThdVQqwTMGRrg8OOpuX4ZzrzwwguYNGkSPDw8EBgYiKuvvhonTpxodsycOXMgCEKz23333dfsmOzsbCxatAgGgwGBgYF44oknYDLxlxoREREREdGFJr+yHmaLCJ1GhQB3vc1jE6zhzP4zZT1xadSHpOadr5Y6ZkfbmIEqq1SaIxPl23rejKyn2pr9dDQfADA1xg+BTeYqtEUQBLx6w1iEerngTEkt7l+XCGMnd473pgOZ5QAAk0XEoezyXr4aor6nraoZAPAyaJWZK/2xtdmJgvOvp6t3ZiLH2l6yJXlzyZgIb3i6aJvdp1YJGGWtnjnaidZmckuzSdE+8DboHH48Ndcvw5lt27ZhxYoV2Lt3LzZs2ACj0YgFCxagtrb5kLl77rkH+fn5yu3ll19W7jObzVi0aBEaGxuxe/dufPTRR1izZg2efvrpnv50iIiIiIiIqJvlWOfNhHu7QqUSbB47yTrc9kBmOefODDApTcKZo2cZzrRHrpyJ9Gu/Ck1ua5ZdVod6o7nbruUHa0uzK8e239KsqQAPPf67dBIMOjV2pZfiuR9Tu+3ausvBrPPBMUNk56hpMGHev7biz18f7e1LISfYcap11YxspnXuzM7+GM4USuGMh4sGjWYL/rn+RJvHyeHM9Fj/Nu8fYw2okjrxe04OZ9jSzDn6ZTjz22+/YdmyZRgxYgTGjBmDNWvWIDs7G4mJic2OMxgMCA4OVm6enp7Kfb///jtSU1PxySefYOzYsbj00kvx/PPP4+2330ZjI0uXiYiIiIiILiRnrfNm2puP0dSIUE+4atWoPGfskXkZ1HekNgtnKnrsOT/ceaZfBYFZ1t3a0TbCGX93HbwNWogikFHcPT9H6UXVOJ5fBY1KwCUjgu1+XHyoJ16/cSwEAfh4bxbW7snsluvrDmaLiMPZFcrf951mOOMMSTkVOF1ci28O5XZ6Dgf1DVLVjDQ37NYpUa0q6mZYZ7DsSi/p1tfdynNGvLHxFAqtLVW7ymwRcdIazrxw7SgIghROH8mpaHacKIrYldH2vBnZmAhvANL3vSOq6o3Ka85FDGecol+GMy1VVkopn69v8x56n376Kfz9/TFy5EisXLkSdXXnS7327NmDUaNGISjo/DfSwoULUVVVhZSUlDafp6GhAVVVVc1uRERERERE1HvW7cvGkg/2dTgf5Ky1csbWvBmZVq3C+ChvAMD+TC58NmW2iBfsLBZRFJGa36StWQ9Vzqz89hie+ykVv6UU9MjzdaSirhHmDhYss61tzSJttDUTBAGDra3N0rsp5PwxSWppNnOwP3zcHGuvs2BEMJ5cOAwA8LcfU7HjVHGnryO9qBqL39mNvadLO30Oe6UVVKGmwQSNtQLwSE5Ft1YmDRTZ1sCx0WxRwkfqn3acKsGh7AroNSrcN3tQq/vHRfrATadGaW1js9d8Z3tr8ym8tvEk/u/bY045n1SFaIFeo8KlI0OweHw4AOAfP6dCFM+/Zp8qqkFxdQNctOffy7Q02trW7ERBtUOvH9tOFMNkEREX6I4Y//Zf/8l+/T6csVgsePjhhzF9+nSMHDlS+fgtt9yCTz75BFu2bMHKlSvx8ccf47bbblPuLygoaBbMAFD+XlDQ9huiF154AV5eXsotIiKiGz4jIiIiIiIiste/N5/CjlMl+O5wrs3jzpZLlTPhdlTOAMCkaGtrM7YMaubRL49gwt839lhVSU/KrTiHynNGaFQCBAHIq6xHcXVDtz6nyWzBcWu1Tl9oT5WYVYbxz2/A335se9OqTF68jrJROQMAcdbWZqcKnR/OiKKIHx1sadbSfbMH4drxYTBbRNz/6aFOh0hr92QhMascH+3O7NTjHXHQOm8mIdYPAR56NJotrXbOk+OymwQy3fH9Sj2jo6oZANBpVJhqnS23M717WpuJoojfU6X2X5vSitqdDeOIEwXS74rBQe5QqwQ8vmAoXLQqHMgsx/om4b7c0mxStC/0GnWb5wrzdoW/uw4mi9isnWdH5JZmFw0P7OynQS30+3BmxYoVSE5Oxueff97s4/feey8WLlyIUaNG4dZbb8XatWvx7bffIiMjo9PPtXLlSlRWViq3nJycrl4+ERERERERdVJJTQPyK6V2IVtP2t71rsyc8em4cgYAJsvhTGZZsx2pA9mejFJ8fyQPZouIX471jSoPZ5JbmsUFuiM2QKr4OJZb0a3PebqkFo3WFkqH+8Bg95+O5sMiAp/tz243mKo8Z0RFnREAENlBJZpcOXOqqNrmcZ2RkleF0yW10GtUuDje/pZmTQmCgBeuHYWJUT6orjfh7o8OdKoyTG4N1BNtEA9Yq/kmR/tiinU+Vl8I9vq77NKm4Yzzv1+pZ3RUNSOb0c1zZzKKa5TZXKIIfLovu8vnTCuQvi+HBkljO4K9XHDvTOlzfPHXNDSapN8lyryZdlqaAdJr32jr3Bl7N1sYzRZsSSsCAFzMlmZO06/DmQceeAA//fQTtmzZgvDwcJvHTpkyBQCQnp4OAAgODkZhYWGzY+S/Bwe3/Utdr9fD09Oz2Y2IiIiIiIh6R9PdnnsySm225pArZyLsrJwZF+kDjUpAfmW98tiBzGIR8Y9fzg9O35PR/wYpd0RubzMi1Aujw6SWL0e7ubXZ8SYtdVLyqhxqL/PNobMY9ex6HHRi67291lkCRrOILw60vZgoL2L7u+vhptfYPN/gIDmccX5oIVfNXDQ8EO4dXIcteo0a7y6ZgDBvV2SW1uH+Tw/B6MDMkUaTBcfzpUXTzJJaZYG0O4iiqFTOTIj2UcKZfWe6v53aha5p5cxJzhpzmqNnK/BDUl67N2dUlDT11mZp3fe2qW1XzchmDg4AILUu7Y62gBtSpRDDx6AFAHxxILvLz3PCGs4MC/ZQPnbv7Fj4u+uRWVqHT/ZmwWS2KK/j02PbD2eA863N7J07czCzHFX1Jvi66TAu0qcTnwG1pV+GM6Io4oEHHsC3336LzZs3IyYmpsPHHDlyBAAQEhICAEhISMCxY8dQVFSkHLNhwwZ4enoiPj6+W66biIiIiIiInCc59/zCeYPJ0u68hwaTGQXWgbz2Vs646tQYaV2gP8C5M/juSC6Sc6vgqpVapBzLrURVvbGXr8q55LAvPtRTWbTq7rkz8qI+AJgsYrPv6Y58sjcL1fUm/JbsnCqmirpGpBWcD4s+3Zfd5mD0rDJp3kxHLc0AYLC1rVlWaR0aTM5bABVFET8fk+bNXDG6cy3NmvJ31+O/SyfCTafG7oxS/C/xrN2PTSuoUqqfTBYRZ0pqu3w97cmtOIeCqnpoVALGRnhjirU1U2JWebeGQgNB87ZmrJxxhne3ZeDKt3bhj58dbvd2w3t7YOlgxpW9iqrrlTlx98xsv2oGAGID3BDi5YJGk6VbKs82Wdt/PTx/CMK8XVFeZ8TPR/O7dE45nBnaJJxx12vw2IIhAIA3N5/CjvQS1DSY4G3QIj7UdlHBmAhvAPZvQpBbms0dGgi1deYVdV2/DGdWrFiBTz75BOvWrYOHhwcKCgpQUFCAc+ek3UwZGRl4/vnnkZiYiMzMTPzwww+4/fbbMWvWLIwePRoAsGDBAsTHx2PJkiVISkrC+vXr8dRTT2HFihXQ6/W9+ekRERERERGRHeSFbL1G+q/t1hNttzbLr6iHKAIuWhX83e0fGj455nxrs4Gs3mjGP9efAAA8eFEcBvm7wSIC+0/b/3WxWBetHalI6GlyW7MRoZ4YJbd7ya3s1rZ2cuWMvNB1yM7WZrUNJmVBLbPUOTvP950pgygC0X4G+LrpkF9Zj01pRa2Ok1v1RHXQ0gwAgjz18NBrYLaIyCxx3g759KIanC0/B51GhdlDA5xyzuEhnrhnlrSgu8uBORQtd52f7MaF/cQs6ftjRKgnDDoN4gLc4WPQot5owTEHgj1qrrLOiMpz58Pm08W1bQaTZL93t2XgxV/TAADjI70xLdav1U2vUSG/sl6pWuyq7Seln9tRYV4I9mq/agaQ2nrNsLb9cvbcmdKaBiRaX8svjg/CLVMiAQBr92Z1+pz1RjMyS6Xgt2nlDABcPyEcQ4LcUVFnxONfJgEAEgb5dRigjLH+njtdUtvs+78toigq4czF8Zw340z9Mpx55513UFlZiTlz5iAkJES5ffHFFwAAnU6HjRs3YsGCBRg2bBgee+wxLF68GD/++KNyDrVajZ9++glqtRoJCQm47bbbcPvtt+O5557rrU+LiIiIiIiIHJCcJy1G3jxZWvjYeqL1QjLQfN6MINi/23NSNOc5AMAHO88gv7IeYd6uuHN6DBJipd36uzPsb6X07vYMzH1lK8Y/twEr1h3Ct4fPorzW8dke3aWyzojcCmnD5/AQT8SHeEKtElBc3aBUXXUHuVJlvnW48qGsCrsedzCrHCbrbvOsUudUasiVZzMHB+CGiREAgI/3tF5MlJ8vys+tw3MKgoC4IOfPnZGD2CkxvjDoOt/SrCU5kD2YWW53KJfUYtd5d86dkYPiidbXJpVKUK55oL9OdYX8O8LPTQeDTo1GswVZTm63NZA0DWYemT8E39w/HevumdrqJocju53UJnOL9T3AHDsDW3nuzA4nz53ZnFYEUZRC1FBvV9w4KQI6tQpJORV2z3dp6VRhDSyi1CYtwKN5UYFGrcJfLhsOACi1/l61NW9G5uumQ4Sv1Oq1oypReYaOTq1SWsKRc/TLcEYUxTZvy5YtAwBERERg27ZtKC0tRX19PU6dOoWXX3651YyYqKgo/PLLL6irq0NxcTFeeeUVaDTO+6VORERERERE3aOyzoicMmkx/d5Zg6BVC8gsrUNmGy2FHJ03I5sYJfVUzyiuRUlN28PRL3QlNQ14Z2sGAOCJhUPholU3CWfsW9ASRRFfHZTaRFU3mPDz0Xw88kUSJvx9A65/dzfe3ZaB9G4YGO+IlHxpYSrcxxVerlq46tQYEiTtTu6uuTNltY0orJK+r26yBoyHsu0LBZq28Msqq3NKW6A91rBt6iA/3DolEoIg7SjPKG4eNiiVM3a0NQOAwYHWcKbQeaHF1pPyIqxzd3CPjfCGRiWgoKpeCes6IlfOJFhbjHVnSyx53syk6PPzHqbESM/LuTOdJ7c0i/IzIE75fmVrs854Z2vzYOah+YPbPXaaUrnS9e9dk9mCHSel0NbucMb6/Mfzq1Bc7bzf8XKFyfzhQQCktomXjZLmm7cVeNtDDvKHBnu0uclk9pAAzBx8PpCxJ5wBgNHW6pmkDkIjeYZOQqxfh7PGyDH9MpwhIiIiIiKigU2umon0NSDU2xUTo6Td421Vz8gDh+2dNyPzcdNhiHXXvzOHrvcnr288iZoGE0aHe+HKMdJsj6nWRei0gmqU2hFaZRTX4ExJLXRqFdbdPQUr5sZiWLAHLCJwILMcL/6ahvmvbsf/fXus11oJNW1pJhsd1r1zZ+SWZlF+BiQM8oNGJaCousGuUKBpONNosiC/i9U95bWNSLPOM5gyyBcRvgZcNEwKPlouJsoL2ZF2hzNSyJXupIqS2gaTUiUy10ktzWQGnUb5HpBbiNlS02BCujW8um5COIDua2tWec6IE9ZzT7C+3gHNq33MTprdMdDIgWOkr0H5fnVmmDhQvLM1Ay/9Zl8wA5wPRw6cKevyTKojORWoqpdmrYyNsG9YvZ+7Xvl5d6SVoS31RrNSiSOHMwCwJCEaAPBDUl6nqkbleTPDgtueIyMIAv5y2XDoNCoMC/ZAtJ2vz2Os89VatmdsSQmc4oNsHkeOYzhDRERERERE/Y48b2ZkmLRQIe+U3dLG3BmlcsbXscoZoGlrM/tmgVxI0ouq8dn+HADA/102HCpr/3p/d73S836fHa2U1qdIizrT4/wwLc4fTywcht8enoWdf5qL564agdlDAiAI0gD65Z8eQr3ReYPj7SWHM/EhXsrHRsmLVp1sQ9MROZwZHuwJF61aGd58KNv28zWdN+PhIu1gzuriEHr533FwoDv83aWWOfJi4teJZ1HbYAIgLTzKbd7smTkDQGlrlppf5ZSZQ7vSS2A0i4j0NSDGv+PWao6SW4bZM2sqObcSogiEerlgWpwUWmaW1nV5obktUlWVNBOoaVuj4SGe8HDRoKbBpHwfk2OUwNHXgMHW79eT3die7kLUNJh59OKOgxkAGBIkvd6cM5pxuIPXvY7IrQ5nDg5waFi9s1ub7TldirpGM4I89cr7E0CauxMf4okGkwVfJeY4fF45mB3aYt5MU8NDPLH5sdn4/N6pdrdwlefO2KoQLa1pUOahyS04yXkYzhAREREREVG/k6xUOkgL6HOtu/z3ni5ttbh/trxzlTPA+V3p9izUXmhe+CUNZouIBfFBmGKtlpHJ1TP2tDb7PVUKZxaMCG728XAfA25PiMZHd07GO7eOh06jwobUQtz6332oqOvZeTTyQOr4ppUz1nDmWG6l3fNHHHE8X1psGx4iPef4SGm396EOKjYOZkkVEhG+rphgbb2XWdq1+RhyJc7UJv/OM+P8Ee1nQHWDCd8fyQMg/SyJIuCu18DXTWfXuUeEekKrFnCmpBaL39ndqk2ao7Y2aV3kyAwpe8ktw+QWYrbIu81Hh3sj2NMFHnoNzBYRmSXOn1dysMW8GZlaJSghMlubdY5cXRnha1CqJdnWzH4tg5k/XtRxMANI1R7T5DaZXaxcUebNDHGsmm6WdX7KzvRip7zOb0w939Ks6euTIAi4PSEKAPDJ3myHW1HKlY22whlA+r3qbbDvtRkARoZ5QSUABVX1KGynArPpDJ0QL8c3uZBtDGeIiIiIiIio35ErZ0ZZW08NDnRHqJcLGkwW7DndfIEyx1o5E+7gzBngfDiTkleJGmv1wECwO70Em9KKoFEJ+POlw1rdryyoZdheDC6orEdSTgUEAbjIxo7bS0aG4JO7psDTRYPErHJc9+4e5Nk586Or6o1mpeVW07ZmQ4M9oFOrUFFnVKqvnEmunBkWIi22jYv0BgAczrYdCihBSowfov2kypGs0q5VzrQVzqhUAm6bKi0mrt2TCVEUm7V/sjcYCfRwwb9vHgcvVy2Onq3Eojd34GPr+RwliiK2nXBsroSj5JZhJwqrUXnOaPNYebf5mAhvCIJwvuqiGxb25bBInoXV1JQYOZwZeCGyM5yfOeOmtDU7XVzba20W+5PErLJOBTOyGcrcmc6HM0XV9UixbtiY5WA4MyHKB3qNCoVVDTjVxWopURSx6bgUEjVtaSa7amwYPF00yC6rw7aTrat821NW26jMxJFnoTmLm16jfM+31dqsut6I747kAmj7c6KuYzhDRERERERE/Up1vRFnrG2c5MV0QRAw2zocfFuT1mb1RrOyqBHRicqZEC9XhPu4wiJ2XNFwobBYRPz95+MAgNumRmFQgHurY6YM8oNKkBYw29ttCwAbUgsASFUhgR4uNp93cowvvrpvGoI9XZBeVINrV+1W+ux3p1OFNTBZRHgbtAjxOn+Neo1aCU6c3drMaLYogVB8i8qZlLwqm63dmgYp8lyBzC6EMy3nzTR1/YQIuGhVSCuoxsGsciWcibJznoHskpEhWP/wLMyI80e90YK/fp+CO9YcQFG1Y7Ny0otqkFtxDjqNCgmD7Bt47agADz2i/QwQRSitfNpzxLqYKc9tOD+vxLnft40mi/JcLStngOYVfo7uyB/oTGaLMucp0teAMG9XuGrVaDRbkFXm/Aqo3rTzVAkufnUbZv9zS5u3i1/dplSg2EsOIxaNCnE4mAGgtANMOluJ6nrbYWh75N/5o8O9mrX8s4eLVq38/HS1tVlKXhUKqurhqlUjIdav1f2uOjWunxgBAPh4b1ar+9uTViAFTxG+rnDXa7p0jW2Rq0TlsFkURSTlVODPXx/FlP+3CbvSpd85C0YwnOkODGeIiIiIiIioX5HnKoR6ucDP/fxCjLyTfmuTxSW54sFNp4a3Qdup55vswAyKC8E3h3ORml8FDxdNu4ttXq5ajLRWLe2xUT2jtDSzc4jw0GAPfHP/NMQFuqOgqh7Xv7sb+053b6um1HxpQWpEqGerahC5MuuYjX78nXG6uBaNZgvc9RqEeUsVXeE+rgjw0MNkEXEst+3nazpvZsogX0T5y5UznV9Elqst5PkPTXkZtLhqTBgAYO2eLKVCJ9LBcAYAgr1csPbOyXj68njoNCpsPVGMha9tx2/JBXafQ144njrID646tcPXYC85AEm00dqspKZBWdQfKYczSuWMc+eVpORVosFkgY9Bi9iA1nN2RoZ5waBTo6LOiJNFbMfliPzKepgtInQaFQI99FCpzldAXUitzYqq6vHHzw/jVFENskrr2rydKqrBe9syHDqvXD0ptxZ1VLiPAdF+BpgtIvad7tzvWKXVoYNVM7KZ1rkzO0/ZX83Slg3W33ezhvjDRdv265NcjbjlRJHSTq8j8iaFoUGeHRzZOWMivAEA+8+U4ZO9Wbj83ztx1du78PmBHNQ1mhEb4IZXbxijtJEl52I4Q0RERERERP2KMm8mrPlCwfQ4f2jVAjJL65TKGnneTIQDbZhammTdVbt/ALQMMpkteH3jSQDAA3PjbM4VSehg7kzlOaMS3LScN2NLqLcr/ndfAiZG+aCq3oQlH+7v8jwCW+SwT65gacqeYcmdIe+EHhbsAZV1eLUgCBhvbW3WXpVW03kz0qKmtFCfWVrb6XkJbbU0a2qJdU7Cb8n5SLRWksjP6yiVSsCdM2Lw04MzEB/iifI6I+77JBHPfJ9s1/XLQ787uwhrL7l1mK1A9qi1mio2wA2eLlLwK7ccOuXkgERuaTYhyrfN1zGtWqXMH+rsAvdAJQebET6uys+iXAHl7JCtt1gsIh77KglltY0YHuKJr5cn4Ovl05rd3lsyAQCQmFWOukb7WnhW1xuVILmtShF7TbO2NttlxwyzlkxmC3ZYwxm5etZRM61zZ/aeLkODqf2qxY5sPC6FMxfZaP8V4++GmYP9IYrAJ3ZWz8jhzLAO5s10lvx7bn9mGZ76LhkpeVXQaVS4ZlwYvvxDAjY+OhvXjg/vlucmhjNEREREREQDkiiKOJRt/yJMX9Jy3ozMXa9RBmPL1TNdmTcjk895JKeiSws3/cHG44U4W34OPgYtlk6LtnlsQgdzZ7aeKILJImJwoDti/B1bzPc26PDJ3VNw0bBANJoseG/7aYce7wh5VkF8aOtwZpS1IiI5t9Kp7aJSW8ybkcmtzdprpyWHXVNjpK99mLcr1CoB9UYLiqzt+xzVUTgzMswL4yO9YTSLSM6VrjvK1/HKmaaGBHnguxXTcd/sWAgC8NGeLJsVWABQ02BSwpLO7tK318QmP/ONprbnjiTlWOfNWBc2gfPhTGZpnVNfK+TPe1J063kzsikDKER2JnneTGST7+kh3Tg7qDd8uOsMdpwqgYtWhX/fPBYTonwxIcqn2W1BfBDCfVxhNIt2zy46kFkGs0VElJ9BqQDsDHnuzK5OhPCHcypQVW+Ct0GLsdYKEEcNC/aAv7se54zmDueotSe/8hxS8qogCMC8Dl6fbk+IBgB8cTDHZgtLmdx2cmg3hTNDrZ8/AMQFuuOvl8dj38qL8NqNYzE5pu1AmJyH4QwREREREdEAtPVkMa5dtRs3v78Pxn429FgOZ0aGtV5MP9/aTNpJK1fOhHdi3owsNsANfm46NJgsynNfqD7clQkAuGVKZLttWWSTon2hUQk4W36uzfYsv6dYW5p1sk+9i1aNFfPiAJwPM5zNYhFx3Hrutlq2DA50h4tWheoGE850Ya5LS8fzpcW24S2qdcZHyeFMRZuVJC2DFJ1GpSyKZpY4fn1N583IcxfaIi8myjrT1qwlnUaFP186DLdb2/y8vvGUzeqZ3eklMJqlhWBHwz5HxQa4wcegRYPJgpS8tn/m5cqZMU0WhIM89fDQa2C2iEr1XleJoohEayXVRBvhzGRrYLfvTGmnq6gGIjmciWpSDSa3NUvv4oD4viAlrxIv/3YCAPDUonjEBba9wC8IgtLea8dJ+0KS3dZZJNO6UDUDSFWYgiBVKjk6h0reiDFrcADUqs6FCIIgYNEoqbrzuR9Tca7R8WB1o3X2zvhIn1btIVuaNywQYd6uqKgz4sekPJvHWiyiEhJ2V+WMTqPCt/dPw48PzMCGR2bhrhkx8LFRNUvOxXCGiIiIiIhoAJLbRCXlVChtrPqDukYTMoqlBbORbSymz7G2Ndl7uhTnGs04W9b1yhlBEJRF0f1nbA8I78+Scyux/0wZNCoBS6ZGd3i8m16jLEy3rHqoN5qVRbOFDrQ0a2lYsAcEASiubkBJTecqQ2zJLqtDbaMZOo0Kg9pY8NeoVUpo48y5M2nWQKhlODMqzAsalYDi6gZlXpKspsGktBCaMuh8kBJlDUo6M3fG1ryZpi4dFQw/62KdVi0gxKvzP08tLZ8TB51ahf2ZZTarZ7o6V8IRgiBgQpT0NT7YxtwZURSRZP1+kIdpy49z9tyZMyW1KK1thE6jUuY8tWVMhBf0GhVKahpx2knB0EAgB8sRTSpn5LZmp4trYepnmxeaOtdoxh8/O4xGswUXxwfh1imRNo+fESf9bO1Mt2/2yh5rWJwQ69+l6/Rx02GEtXJRDnzspbQ6HNq114VHFwxFsKcLzpTU4pXfTzj8+I2pckuzjqv61CoBt06V/i3W7c+2eezZ8nOoazRDp1YhuhtD6QhfA0aFe7FKphcwnCEiIiIiIhqAmg4cX7U1Q9mR39cdz6+CRQQCPPQI9HRpdf/gQHeEebuiwWTB3tOlzWbOdIXc2szWDIr+brW1auayUSEI9mr9tW3LtNi2587syShFbaMZwZ4urdrPOcKg0yDGuqP9eDdUz8gtzYYFe0CjbnuJRL7+JGulRFeV1jSgqLoBggAMDWq+E9pFq1YWKVu2NjtobSEkz5uRNZ0746iOWprJ9Bo1bpwUAQCI8DF0eod6W4K9XHDzZOnc7VXPiKKIrWlS2Denk3MlHCW3EDuY1fpn/mz5OZTVNkKjEloFbHJrs3QntcQ6aK2aGRPuBb2m/Wo2vUaNcdaZRZw7Y7+22pqFebvCoFOj0WxBZidCz77i7z+nIqO4FoEeery0eHSHC+/TYs9XsBRW2a5gKa9tVCoaEzp4/bDH9FjHW5sVVdcrr+Gzuhjaerlq8cLiUQCkNnCO/K6vbTApwfLFNubNNHXdhHCoVQIOZ1fglI3XCnk+WWygO7Tt/I6i/o3/qkRERERERA76aHcmnv0hBWYnzqDoSRaLiBTr7IhJ0T4QReCRL46gss7Yy1fWMXnmRXsL/oIgYLbS2qxIqT7oSuUMcL7l08HMMqfOHukriqsblPYqd0yPtvtxTefONF1UX59SAEBqadbVnbjy4ndqnvPDmdR8KaQc0ca8GZlcGeGsyhm5pVmUrwFuek2r+8dZ584czq5o9vG91gV3ed6MTK6c6c5wBgDunBGDmYP9cc+sQQ4/T0eWz4mDTtN+9cypohrkVdZDr1HZda3OIFfLHcwsbxUYyUHd8BDPVu3/4gKdWzlz0LpILM/BsUVubbb/TP8I2/uCLOvPTdNwRqUSMDhQbm3WP+fO/J5SgE/3SVUZ/7phDHztaFPl46bDaOvv1h2nbIckUvs8aUNEgIftNl72mNZk7oy9bfm2WatmRod7ddhKzB5zhwbi+gnhEEXgia+S7G5vtuNUMRrNFkT5GZSf/44Eergos2m+PJjT7nEnCrq3pRn1PoYzREREREREDqg3mvH3n1OxZndmvx28nFVWh+oGE/QaFf67dBKi/QzIr6zHX7491udnFSjzZmwspsttj9anFKK0thFA12bOAEB8iCfcdGpU1Ztw4gIZEt3Up/uy0Gi2YFyktxIO2GN8pA90GhWKqhuQUSwtcpotIjYet86bie98SzPZ8BBpUao7KmfkwCc+xFY44w1AqrJxRosjeSf0sOC2n/P83JnmlTN7lRZCzcMJef5KZoljO/zL7Jw3I/N31+Pju6bg5sm2WyN1RrCXC26xnve1jSdbvQ7JLfKmDvKDq872LCRnGRnmBZ1GhdLaxlbzY4620dJMJlfOnLRzUT8xq9zmnA25rdokG/NmZFOt/477zpT1+dfyvqCyzoiqehMAIMK3eYAvz2ZxVsjWkwqr6vGnr48CAO6ZGYOZg+2vKplhnTuz85Tt1ma7M5wzb0Y2KdoHOrUKeZX1dlcrnW9p5rxquqcuj0ewpwsyS+vwz/X2tTfbkCq9Pl00zLHNCDdMlCoGvzmU2+7svzTr+42hDGcuWAxniIiIiIiIHHA8vwpGs7TotSfD/vYbfcnRJru+vVy1eOOmcdCoBPx8LB9fJZ7t3YvrQLJ1MX2EjVZZ0+L8oVULKLC2ZfF00cDLVdul59WoVcqi+YXW2qzBZMYne6Ud1ndMj3HosS5aNSZYwxx5/sDh7HKU1DTCw0XTbDZKZ8Vbg7jUbmxrFm8j7Bvk7wY3nRrnjGakF3d9oTa1nXkzsvHW1lSpeVWoN0o7t5vPm2lZOSOFM1mltQ4tyMvVFR3Nm+kp982OhU6jwoHMcmXhV+asuRKO0GvUGGMNX+TWYrKknAoAUGYuNSWHM1mldWgw2d55v+VEERa/sxsXvbIN3xw62+rfr7SmQZkfM96O0HRcpA+0agH5lfWtZhZRa3JLM393PQy65lVsQ5TZQf0rjLdYRDz2ZRLK64wYEeqJxxcOdejx5+fOlNqsEpUr3Lo6b0Zm0GmUtnz2tDYzmS3Yccr5rwtN25ut3n2mw004ZouILdbweH68YyHR3KEBCPDQo7S2EZuOF7V5jFw5w3DmwsVwhoiIiIiIyAFHm7Q2armA2F/I1Sdya7AxEd545OIhAIBnf0hptUu8r6g3mpXe7LYGY7vrNcqMGKDrVTMy+Zz9tWKqPT8l5aOkpgHBni64dKTjlS7yzmk5rPxdHow8LNApPfLlECOjuFYJK5yhuPr87Jf2qlgAqcWR/P121AmtzdKsbc3kiqCWwrxdEeChh8kiKs8nz5uJ9DUgzLv5Dv8IX1cIAlDbaEZJTaPd16G0SeuhNmEdaVo983qT6pmaBpMSiPbUvBmZ3ErsYJNA1mwRlaBsjLWqqqkgTz08XDQwW8QOX0u/O5wLAKhuMOHRL5PwwLrDqKg7/28oh0JDgtzhbei4LZWrTq1UevWXOWK9SQ5n5NaATckh26kuVM6Ioog/fnYY81/dhrc2n+pwjou9Pt6bhVHPrMewv/7a+vb0b9iZXgIXrQpv3DTO5pyitoyP8oarVo2Smgalsq6loup6nCqqgSAAU50QwMtmxNk/d+ZwTgWq6k3wMWjb/DnsiqbtzZ78n+32Zoezy1FW2whPl+bvO+yhUauweHw4gLZbmzWYzMprCNuaXbgYzhARERERETlA3jENAEdyKlDXaOq9i+kkeWFxVJOWPPfNjsWUGF/UNZrx0OeH0WjqevsmZztRUA2TRYSvmw6hHQysn9tkEbdlu5rOkhdeDmReOC2DRFHEh7vOAACWJER1KkyZFieHM9JO6/PzZrre0gwAgj1d4GPQwmwRu7RQ2pLcJi3Gz63N2S9NyRUSXZ07YzRbkF4kfQ7tVc4IgqBUz8itzc4HKa0X//QaNUK9pO/xLAfmziht0vpIOAMAy+e0rp7ZlV4Co1lEtJ9BaeHWU+RWYk0rZzKKa1DXaIZBp25zvoQgnJ9XYqslVoPJjM3W3fKLx4cr1YsLX9+uVAQkWp/XnnkzMrlF3YUWIncHOZxpOm9GJv/bni6p6XQ7w1+OFeCHpDykF9Xgld9PYtqLm3Hv2oPYcqKoSzPrPt2bheoGE+qNllY3+Xf3c1eOtHv+SVN6jVp5ndmZ3nZrM7lqJj7E067Q0F7y3Jk9p0s7/PrIrQ5nDg6AWtW1uWZtadre7OX1aa3uzyiuwT9+TsU9aw8CAOZ2cjPC9ROlcGbriaJW4V16UQ3MFhGeLhoEe9p+z0P9F8MZIiIiIiIiB8iDoAHAZBFxILO8/YP7IItFREqutCg9qkn1iVol4LUbx8LLVYujZyvx2saTvXWJ7UrOOz+8vaO+7k3bnDircmZcpDe0agGFVQ3IKbswWgYdyCxHSl4V9BqVUrXgqNHh3jDo1CivM+LHo3nIKq2DTqPC7CHOaTUjCIISZDhz7ow9Lc1ko5TKmYouPWdGcQ0azRa46zUI92k/NJRbWB3KksMZaTG0vSqXaH/pe9zeWQ2OzpvpKUGeratnumOuhL3kf4fTxbUorWkAIIXygFS9196i8Pmqi/ZbYu1KL0F1gwlBnnr887rR+Ob+aRgU4IbCqgYs+WA/nv0hBbut1WgTo+yfAzWlF+bOWCwiiqrqO2zj1tfI4UxEG+FMmLcrDDo1jGbR7p+rphpMZrz0m7Sov2hUCCZF+8BsEfF7aiHuWH0As17egn9vcrya5lyjGaesAe8PD0zHzj/NbXVLfGo+bpgU4fA1y2ZYZ9TsONV2BcseJ8+bkY0J94K7XoOKOqMyD6w9W9Kk14W5w7qn1WHT9mbyjMF6oxnfH8nFje/twUX/2ob3d5xBeZ0RYd6ueGBuXKeeJzbAHZOifWARgf+1aCsrtzQbFtzxex7qv2xvDSEiIiIiIiJFVb1RGXo+b1ggNqcVYXdGidMWodtzrtGMbSeLMGNwANw72OHfkczSWlQ3mKDXqJTd3bJQb1e8cO0o3P/pIby7LQMzB/tjmpP6yTtDsjVUstXSTBYX6I4wb1fkVpxDhI1FcEe4aNUYFeaFQ9kVeGvLqTaHyKvVKiwcEYRAj97f5XokpwLV9UbMiPNvd2FntbVq5trxYfBx69wOaK1ahUnRvth2shgv/SotRs6M8++wGsUR8SGe2J1R6tS5M/K57Aln5MHvx/Or0WiyQKfp3F5XuaXZsGAPm4tt8nyjQ9nSv2F782ZkUX5u2JVeanfljDxvZmiQB/z6wLyZppbPicW6/dk4kFmOXeml2GbdIT+7B+fNyLwNOgwJcsfJwhokZpVjwYhgJaAbE97+69DgIHmYfPvhzG/JUoXZwhHBUKkEjA73xs8PzsQLvx7H2j1ZWLM7UznWkXZJE6N9odOokF1Wh2O5lUqbs+70xP+O4utD0sKyj0GLIE8XBHjoEeTpgkAPPaL93XDlmFC4aB1rsdXdssukn5e2KmdUKqkCKulsJU4VVjtchfLxnixkl9Uh0EOPf14/GgadBqcKq/HZ/hx8fegscivO4V8bTmLV1gxsemw2Qr3t+z2VklcJs0VEkKe+2/5tZw6Wfu/LgUTLfzd5vpiz3x9o1CpMHeSLjceLsCujpFl1b1NFVfVIza+CIACzBnff68LcoYG4YWI4vjx4FivWHYLJbEF5nREAoBKAecOCcMuUCMweEtil6p3rJ0bgQGY5vjqYg/vnxCq/GzhvZmBg5QwREREREZGdkq0tjcK8XXHFmBAAwN4emDvzp6+P4r5PDuGyN3YgMatrrWrkRd74UE9o2mjBcdmoENw4MQKiCPxhbaKyQ9YRju7WFkURH+/JxKu/n7DZPkaelTMytONwRhAEPDgvDsOCPZzWXgsAJsdIi+NfHjyLZ39MbXX763fJuO/jxF5ve7ZuXzauXbULSz7Yj9s+2Ke00moqp6xOaUG2bFpMl55P3kGdVyntAl8wIqhL52tJrpxxZjiTYq3EaitkaynS1wAvVy0azZYuDQiXK3/aa2kmGxXmBY1KQElNA747nNvuvBlZtJ9jlTO22qT1tqbVMyu/PYq8ynroNapea7+mzJ2xVjHJc4DkVndtkYfJn2rj5w6QhplvsM5muqTJ65OrTo3nrhqJNXdMQoCHFJoFeeptVlm15K7X4DLr7Kh1+7LtflxnpRdV45vD53f8l9cZkVZQjR2nSvC/xLNYtTUDT/7vKG58bw/yK/tWxaGttmZA05DNsXaKFXWNeHPTKQDAYwuGwKDTKOd7+op47PvLRXjtxjEI9XLBOaPZrhkrMrlyqztDt8GB7gjy1KPBZMHBFtXBZ8vrkFVaB7VKwKRuqLqbbsfcma0npaqZ0WFe3R4uP3V5PEK8XFBc3YDyOiNCvVzwyPwh2PXnefjv0omYNyyoy23VFo0KgZtOjczSumbtCNMYzgwIDGeIiIiIiIjslGRdlBsb4Y2EQdICwrHcSlSeM3bbc+aU1eGno3kApIWk69/dg1fWn4Cxkz3w5YBjlI3qk2eujMfkGF9UN5iw9MP9+Plovl3n3p1egnmvbMUt7+9DTYP9s3g+2p2Jv36fgjc3p2PV1ow2j2k0WZRdpLauvambJkfit4dn2b0j2R53To/GzZMjcPnokDZvrlo1DmVX4JdjBU57TkeIoojXN57EX749Boso7e7dlV6KS9/Yjhd+PY7aJv8uH+/NgkWUhjB3dfGn6Q5qQQAuGu7ccEaubjmeX+WU4Kuu0aQMWh5hZ9gnV88c7cLcmVQ7wxkXrRojrJ/ze9tPA7AdpET5SbNY7K2c6ahNWm9bPicWeo1KaR+YEOvXa1UXckuxA5llaDCZlYDN1hDywYHSz1NWaV2brb72nSlDeZ0RPgZtm23l5gwNxPqHZ+EPswbhxWtHO9zS6GZruPVDUh6q67vv9xMAvLP1NEQRuDg+CIf+ejF+e3gmPrpzMv553Wg8sXAobk+IgrdBi6Szlbji3zv7zCwco9mCvAopTI7yayecCZRDNscC2Tc3paOq3oRhwR64bkLr9mIuWjWuGReOK8eGATg/W8geR5u8D+kugiBgRpy1tVmLuTPyho3R1hZkziaHMwcypaqdtmzrwVaHni5avH/7RCyZGoUPl03Ejj/Nw0PzByPEy3nvK9z0GlwxJhQA8MXBHOXj59uaMZy5kDGcISIiIiIislOSsmPVC8FeLhjk7waL2L2Dlz/YeQYWUVqYvXZcGCwi8NaWdFy7aneb1RAdkRd2bLUGM+g0WHvnZFwyIhiNZgse+OwQ1ljbX7Wl3mjG8z+l4pb/7sPpklrsOV2KP3x80K75A9tPFuO5n1KVv7+x6VSbC1WniqrRaLbAw0WDCF/nLYo4KtDTBS9cOxpv3TK+zdu9swYBAF76La3H5y+YLSL+77tkvL5R2rH94Lw4bHl8Di4aFgijWcR7207jon9tw09H81DbYMLn+6Vd9XdMj+7yc8eHesLTRVqomxjlA38n72aODXCHVi2gut6Es+Vd332fVlANUQQCPPRKhUJHnDF3Rt4JPSyk48W2cdZ5J/LnaytIibaGM2dKajsMr/rqvJmmgjxdcMuU8zOQ5nRz60hb5JZiybmVOJxdAaNZhI9Ba7OaJchTDw8XDcwWEaeLWwdmckuzBfHBbVYwAoCvmw4rLxuOucMcX4CeHOOL2AA31DWa8f2RPIcfb6+csjp8dyQXALBibhx83XQYFuyJ2UMCcP3ECKyYG4fnrhqJH1bMwLBgD5TUNOKW9/di7Z7MXq8uzK+oh9kiQq9RIaCd16vzs4Ps/12bWVKLj/dmAgD+ctlwm1UVE6zBnyPhjDz3brSNtnrOILc229li7sz5lmbdE+wODnRHgIce9UYLDmWf/7rUG8345tBZXP/ubvx8TNowMqeHWh2ODPPC81ePdEqVTHuunyiFeL8cy0d1vRGVdUYUWOcRDWE4c0FjOENERERERGQnZdaAdcdqgnVxQh7a7GwVdY344oC0i/KBuYPx6o1j8fYt4+HlqsWx3Epc/u8dDi1yWSyiMgS9o+oTF60ab986HkumRkEUgWd/TMXLv6W1eq7UvCpc9dYufLBTCm+uGhsKg06NXemlePTLJJgt7V9belE1Vqw7BIsIXDchHFeNDYXZIuLhLw632u3dtKVZXx6M+4fZgxDooUd2WR0+3pPVY89bbzRj+SeJWLcvG4IAPH/VCDy2YCii/NzwwbJJ+O/tExHh64qCqno8sO4wFr6+HVX1JkT7GTDXCbuP1SoBM629/y8dGdLl87Wk06iUagRntDaTfw7saWkm62rlTElNA4qrGyAI0qyXjoxvMQTeVjgj7/yvrjehos52pURfnjfT1PLZUvWMSkCnAgpnCfdxRaCHHkaziI/3Sj/TYyK8bb4OCYJwfmG/RYhusYhKO8FLRjmv5WLL55erZ9bty+62IOQ/20/DbBExI87fZiVHpJ8B39w/DVeMCYXJIuLp71Pw5P+OtlsZ0RPklmYRvgao2llwH2xtT3e6pMbuatWXfkuD0Sxi9pAAzOogVBwf6Q1A+h6p7ODnFpDeE2RZWxeODvO263o6S65gScmrQklNAwCpMlOunJGrh51NEARMl99bpZfiZGE1/vZjCqb8v0149MskHMgsh1ol4PaEKJvVa/3N+EhvxAW6o95owY9J+UgrkH5HhXm7wtNF28tXR92J4QwREREREZEdiqrrkVdZD0E4X3Uit3LqzFwWe3yyNwvnjGbEh3hiepy0WLFodAjWPzwLMwf7o95owdPfp2Dp6gOoqGvs8HyZpbWoaTBBr1Ep7VpsUasEPHfVCDy+YAgAYNXWDDz+1VEYzRaYLSLe25aBq9/ehROF1fB31+GDpRPxxk3j8N6SCdCqBfx8NB9/+zGlzYXB8tpG3PXRQVTXmzAp2gf/uGYknr96JMJ9XJFTdg7PfJ/S7PjkXGmhYmSY/YvpvcGg0+Ax69frzU2n7Pp3sVhEfH8kF78lFzRrO2avirpG3Pbfffg9tRA6jQrv3DoeSxKimx0zPz4IGx6ZjYfnD4ZOo1KqMZZNi253YdJRz1wZj1euH4PbE6Kccr6W5FZgx50QzqRawxm5dZg95BkPJwqr7VpIbUm+7ihfA9zsaAckL9wCUvhiqz2fi1aNEC8XAMCZDlqbbbPOa0jopp3vzhLo6YLP7p2KNXdMVtq29QZBEJTqGbnixZ55H8rcmRYzig5ll6OougEeek23VR8AwOLx4dBpVEjNr+pSK772FFXXKy2Y7p8b2+HxBp0Gb940Fn+5bBhUAvBV4lnc8N4e5FX0zhyajubNAEColysMOjWMZlEJRWw5mFmGX5MLoBKkqpmO+LnrEeMvfW83rRJpj/zvGOPvBi9D9y7YB3jolddcef5LZmkd8ivroVOrlKqf7jDNGgz9Z8dpLHhtO1bvykTlOSPCvF3x+IIh2P3neXjuqpFO+93VFwiCgBut1TNfHsxRZptx3syFj+EMERERERGRHY7mSIsicQHuSp91eQZEWkE1Sq07S52l3mjGmt3SLu17Zw1qtks72MsFH90xGc9cEQ+9RoXtJ4vxyu8nOjznMWv1SXyoZ7utdFoSBAEPzBuMlxePhlol4OtDZ3H3Rwdxy/t78cKvaWg0WzB/eBB+e3iWMmdk5uAA/OuGsRAEYO2eLPx7c3qzczaaLFj+aSKySusQ7uOKd2+bAL1GDU8XLV6/cSxUAvDN4Vx8b22XAwDJeR23Y+srrpsQgWHBHqiqN7X63Nvy4m9peOjzI7jvk0SMe24Dbv9wPz7anYmz5R0vBuZVnMP17+7BwaxyeLpo8MldU3BJO5UrLlo1Hp4/BBsfmY3LR4dg3rBApZWKMwR6uOC6CeF2f285quncma6Sq2/iHQhnQrxcMCzYA2aLiLV7Mh1+zrR8abGto3kzsjBvqWIDAKbGdLyIL1fP2Jo7I4oiNqcVAejdahR7jY/06bD6oCdMjJYWouVKwLERHb8OxQXKw+SbhzNywHPR8EDoNd03R8fHTYfLRkqVOZ9ZWxh2RBRFWGxUOzb1wc4zaDRZMC7SGwl2zi4SBAH3zorF2junwNugxdGzlbjyrZ1KZWRPyiqTfk5shTMqlXB+7kyh7bkzoiji7z8fBwDcMDHC7kV1R1qbNW2t2hNatjaTq4THRXrDVdd937szrOFMo8kCtUrAwhFBWHPHJGx/ci4emDcYQZ4u3fbcvema8WHQqAQcyanAj0lS6zaGMxc+hjNERERERER2SGrR0gyQdr3Kg1r3nnbu3JnvDueipKYBoV4uWDS69WK7SiXgjukxWHXreADAz0fzO2y7csy667ajlmZtuWFSBN6/fQJctCpsO1mMfWfKYNCp8eK1o/D+7RNazRi5ckwonrk8HgDw6oaT+HSfFDSJoohnfkjB3tNlcNOp8cHSSc1aK02M9sUfLxoMAHjq22TklNXBZLYoC/L9IZxRqwRl1/TaPZnILGl/sfw/2zPwH+vA9zBvVzSaLdh+shjP/JCCGS9twSWvb8fLv6Vhza4zePm3NDz2ZRKWfLAPC1/bjnHP/Y5pL27GqaIaBHu64Kv7ptk1QyTSz4C3bhmPD5dNsquCo68YHuKctmYmswVp+XLljP3fT4IgYPkcqULgw11nUNfoWJWT/D1sbzgjCAIuGi4FKBfHB3V4vDx3JrOk/VAvJa8KhVUNMOjUmNJH5830RROjmn+tHKucOd/WTBRF/GoNZ9oLUZ3plilSFdsPSXmtWkW21GiyYOnqA5j1zy2tAqWWKuuM+MTatvGBuXEOt5qcMdgfPz4wA8NDPFFS04i7PjqAgsp6h87RVTl2VM4AwOAgOWSzPXfmp6P5OJJTAYNOjUcvHmL3dTgUzlh/h/dUOy85JNlxqqRZSzO5ari7hHq74o2bxuL/LhuOPX+eh/eWTMScoYHdNu+lr/B31yuv+fszpfeUwxjOXPD6ZTjzwgsvYNKkSfDw8EBgYCCuvvpqnDjRfJdYfX09VqxYAT8/P7i7u2Px4sUoLCxsdkx2djYWLVoEg8GAwMBAPPHEEzCZHC8hJyIiIiKiC9/5RZHmi7ndMXfGYhHx/g5pwf7OGTHQ2qhEmD0kAP7uOpTXGbEz3fY1yJUznQ045g0Lwrp7piLUywVTYnzx60MzcdPkyHYX5pZNj8GD8+IAAE99l4xfj+Vjze5MfLZfmovy71vGtbkr9IG5cZgY5YPqBhMe/uIIThbWoN5ogZtOjZhebG/kiFlDAjB7SACMZhEvr09r85ivE8/i//0i3bfy0mHY+ae52PjobKy8dBgmR/tCJUhVWau2ZuDZH1OxamsGvj50FjtOleBEYTXKra21RoZ54uv7p13wO2zl+TA5ZedQ1cFCsy1fJZ5Fg0n6forqYGG2pUWjQhDpa0B5nRGf7c9x6LHHC6QFb0cW2/56eTx+/uMMzLcjnJFbf9mqnNlirZqZHucPF2337Xy/0AwP8YDBWikQ5u3aKoxuizxzJrO0Fg0mabZKcm4VcivOwVWrxuweqAiaFO2DuEB31DWa8d2RPJvHvrrhJLafLMbZ8nO4/YP9yLXRbuyjPZmobTRjWLAH5nWyAivC14Av/zAVgwPdUVjVgHs/PohzjT03g8aetmbA+ZDtZFH7gVW90YyXfpNey/8wKxaBDlR2yOHMkZwKmxssRFFsskmkZzYpTI7xhU6jQkFVPdKLas7Pm+mBlohXjQ3DPbMGOfS1vBDc0KKa9UL/vU79NJzZtm0bVqxYgb1792LDhg0wGo1YsGABamvPvwF55JFH8OOPP+Krr77Ctm3bkJeXh2uvvVa532w2Y9GiRWhsbMTu3bvx0UcfYc2aNXj66ad741MiIiIiIqI+TBRFHG2jcgaA0s5lz2nnzZ3ZnFaEjOJaeLhocJN1qHN7NGoVLhsl7cD+Man9xTeLRVSGoHelJcr4SB/s+vM8fPGHBLvmQDx68RDcPDkSogg89PkRPP9TKgDgL5cOx7xhbS84a9QqvHbjWHjoNUjMKsfjXyUBkKoc+lOP+b9cNhwqAfjlWAESs5pXVm1OK8STXx8FANwzMwZ/mB0LQRAQF+iOP8yOxZf3JeDQXy/G6zeOxdVjQ3HpyGAsmxaNJxYOxSvXj8HaOydj/cOzcOTpi/HjAzMQZmMeyYXC26BDqHWuitwizFEbUgvxf98eAwDcPXOQw99PGrUK982Wqmfe335aWXTvSKPJgvQix9qaAdKcDnure6Ktbc0ybczG2HxCCmc6u6A+UGnUKoyzzgCyd2E80EMPTxcNLCJwulhar/o1WWpVNGdoQLe2hZIJgoCbrb9D1u3LbnP+FyBtLnhvewYAIMhTj4Kqeiz5YB/KalvPzKptMOHDXWcAAPd3omqmKQ8XLT5YOgk+1hZnj/8vqd1rdLZs689JpF8HlTPW9nTpNipn1u7JxNnycwjy1OOeWTEOXUdcgDs8XTQ4ZzTbfF0rqKpHcXUD1CoB8SE9E864aNWYbJ239OGuMyitbYSLVoWxLd4HkfPMHhKgtLPUqAQM8u94PiD1b10OZ+bNm4eLLroIWVlZdj8mLy9PeVxn/Pbbb1i2bBlGjBiBMWPGYM2aNcjOzkZiYiIAoLKyEh988AFeffVVzJs3DxMmTMDq1auxe/du7N27FwDw+++/IzU1FZ988gnGjh2LSy+9FM8//zzefvttNDZ2PLCRiIiIiIgGjuyyOlTUGaFTqzAsuPnC6pRBflAJ0uKbs9qy/MdaNXPLlEhlvo0tV44JBQD8nlKIemPbi8VnSmtR02CCi1aFuICu/WffkcU4QRDw96tHYuGIIDSaLbCIwA0Tw3H3TNsLWBG+Bvz9mpEAzrexGhFm/6J2XzA02EPZBfv3n48ri46JWWW4/9NDMFtEXDsuDCsvbXtwtLdBh6vHheH1m8bhndsm4NkrR2DF3DhcNyEcs4YEYGiwB7wNui4tjvY3crDRmbkz+8+U4YF1h2ARgesnhOPh+YM7dQ2LJ4Qh0ENawP72UG7HDwCQUVwDo1mEh16DcJ/uCdI6qpwprWnAEevMirlDGc446uqxYQCAy0eH2nW8IAhNWmJVQxRFZd7MJdZZMD1h8fgw6DQqHM+vUipAmyqvbcSjXyRBFIGbJ0fguxXTEerlgtPFtbhjzQHUNjTvMPPZ/mxU1BkR7WfAolFdb80W6WfAu7dNgFYt4Oej+Xhj06kun7MjlXVGVNVLn1eET0dtzaTfl6dLatqsbPn1WD5e2yBd82MLhsKgc6xVpEolYLzS2qz99qhJ1rl3Q4M8eiTYk82wzp358uBZAMCkaKmahrqHRq3CdRPCAQCxAe78Wg8AXf4X3rp1K7Zu3dqsaqUj586dUx7nDJWV0guUr6+U5iYmJsJoNGL+/PnKMcOGDUNkZCT27NkDANizZw9GjRqFoKDzO7UWLlyIqqoqpKSktPk8DQ0NqKqqanYjIiIiIqILn7ygNTzUs9V/lL1ctUqbsD2nu97a7EhOBfafKYNWLeCOafbtwB0f6YNQLxfUNJiw1bozviV54PLwEM9uG9jeHrVKwBs3jcNNkyJw06QI/P3qUXYFCleNDcM148KUv490YD5IX/HoxUNg0KlxOLsCPx/Lx8nCaty55iDqjRbMHRqAl64b3a+qgXpbfKgUzqTmOfb/8eP5VbjrowNoMFkwf3ggXrjWvu/Btug1atw7axAA4J1tGTB1MOsJANIKpOsdFuLRbWFalLUCoLzOiMq61m3ftp4ohigCI0I9Eew1sFoFOcP1EyOQ9vwlSqWiPZrOnTlVVIPTJbXQqVU9WrnkbdApIcpn+7Kb3SeKIlZ+cwwFVfUYFOCGv14ejxAvV6y9azK8DVok5VRg+aeH0GiSvscbTGal5eZ9s2OdNgNkyiA//P1qKYx/feMpm1WgzpBVJq1hBnjoOww6wrxd4aZTw2gWmwWfZouIl39Lw/JPD+Gc0YxZQwKweHx4p65nQqQ1nMmuaPeYnm5pJpPnzpgt0uaC7p43Q1I727lDA3D/3NjevhTqAf0+frNYLHj44Ycxffp0jBwpvZAXFBRAp9PB29u72bFBQUEoKChQjmkazMj3y/e15YUXXoCXl5dyi4iIaPM4IiIiIiK6sCRZd5u3nDcjU+bOpNtubVZYVY/1KQXtVrcAUqskALhyTJjdC6gqlYArrNUzPyblt3nMMWvANLqT82a6ykWrxouLR+PFxaMd2gn63FUjEO1ngE6tsmvYfV8T6OmCP8ySFlhe+CUNt3+wH5XnjBgX6Y23bx1vc54QtaZUzhTYH87klNVh6Yf7UV1vwsQoH/z75vFdDihvnhwJb4MWWaV1+CW57TWEpo7nO97SzFFueg0CrO1w5MXnpjZb581cxJZmnebonB65Jdapomr8ekz6Ppkx2B8eLlqnX5stt0yRWpv9kJTXbF7TFwdy8FtKAbRqAW/eNE6p+ogL9MDqZZPgqlVj+8liPPG/JFgsIr45lIvCqgYEe7rgmvFhbT5XZ904KRJ3z5A2JDz+VZLye7c7yPNm7Jk5JbebBICT1tZmlXVG3LnmAFZtlVrB3T0jBh8undjpsEqeO5OY2X7ljNJaNdy7U8/RWfEhnvBz0yl/74l5MwOdv7seq++YjKvGOvdnjPqmXnkXKFfZuLh0fafGihUrkJycjM8//7zL5+rIypUrUVlZqdxychwb/kdERERERP2TvCgyup1FEXvmzlTUNWLxO7vxh48TMeefW7F2T2arkCa7tE6ZSeBo33o5nNl4vBA1LdrQAMAxa+XMyF4KZzrLw0WLHx+cgU2PzUaEg8Pb+4p7ZsUgyFOP3IpzKKiqR1ygOz5cOsnh9jckLRQCQFpBtV0VKyU1Dbj9w/0oqm7A0CAPfLB0klNaArnpNbhzuvQzumpLeodzMuRwtGVbRGeLsbY2azl3xmi2YPvJYgDAXIYzPWZwk8qZ31J6vqWZbGKUD+IC3XHOaMb3R6SqlIziGvztR2kG2OMLhrb63TAu0gfvLpkAjUrA90fy8NxPqXh3mxRG3DNrEPQa57fWWnnZcMwdGoAGkwX3rD3otFahLcnhTKSdv1Pk9nSnCmtwoqAaV769E9tOFsNFq8IbN43FU5fHdynwHRPhDbVKQF5lPfIqzrW632IRcdTa1qy99yHdRaUSMN1aPeOh12BkaP9qL0rU1/VKOPPrr78CAMLDO1fuJ3vggQfw008/YcuWLc3OFRwcjMbGRlRUVDQ7vrCwEMHBwcoxhYWFre6X72uLXq+Hp6dnsxsREREREV3YTGaLEmyMbaedyKRoX2hUAs6Wn0NOWeth3BaLiEe+OIKz5dKiS0FVPZ7+PqVVSPPBztOwiNJAWEcXcUeEemKQvxsaTBZsTG3+fx2LRUSKtQ3UqHaqf/oyDxdtvw1mAGmo+xMLhwEAQrxcsPbOyfBpshOZ7Bfpa4CbTo1GkwVnSmy3V69pMOGO1QdwpqQWYd6u+OjOyfAyOK9iYWlCNNx0aqQVVCtVKS1ZLCL+/lOqEtzKQ+W7i9zaLKvF1+ZAZhmqG0zwc9P1+M77gWyIdVH/TGktjudXQa0ScPHwoA4e5XyCIOCWyVL1zLp92Wg0WfDw50dwzmjG9Dg/3DNzUJuPmz0kAK9cPwYAsGZ3JrJK6+Bj0OLmyd3TSUatEvDmzeMwJMgdRdUNuGftQZxrbL/StLPk39P2/l6R29P9kJSLa1btQlZpHcK8XfH18mlOqW5w02swPET6XjmUXd7q/jOltai2zoyTr6UnLRghfc/OGRbY421RiS50Dm/TufPOO9v8+FNPPdWqjVhLDQ0NyMjIwIEDByAIAmbPnu3o0wOQemI++OCD+Pbbb7F161bExDTfUTZhwgRotVps2rQJixcvBgCcOHEC2dnZSEhIAAAkJCTgH//4B4qKihAYKO0a2bBhAzw9PREfH9+p6yIiIiIiogvPqaIa1BstcNdrMMi/7UURN70GYyO8cTCrHLszSnCjb2Sz+1dtTceWE8XQa1T4/N6pSM6txNtbMpSQ5u0t6bh7xiBl4K48z8IRgiDg8jGheHOT1K//6iazWs6U1qLGurATF9DzCzskDeUO8NBjeIgHAj0476OzVCoBw0I8kZhVjtT8KmVHe0tGswX3fZyIY7mV8HXTYe1dk50+Z8XLoMVtCVF4b9tpvLUlHfOGBTabJ9NosuCJ/yUplQorLx3WrW3NACDav+3KmS3W8GjO0EDOOOpBgR56eLpolOHzUwf59lowe+34MLz0WxqO51fhDx8fxLHcSvgYtPjX9WNtfk9cPS4MpbWNeP4nqcrmzukx3Vr15+GixQdLJ+Gqt3fhWG4lXvz1OP521UinPkdnK2cyiqXQc3qcH/5983j4OvHfckKkD5Jzq3AwsxyXjw5tdp/c4m1kqFevhCOLRoXA924dRvTDuW9EfZ3Dr6Zr1qxpNbxOFEV8//33dj1eLvX19fXFypUrHX16AFIrs3Xr1uH777+Hh4eHMiPGy8sLrq6u8PLywl133YVHH30Uvr6+8PT0xIMPPoiEhARMnToVALBgwQLEx8djyZIlePnll1FQUICnnnoKK1asgF6v79R1ERERERHRhUduaTYqzMvmAta0WD9rOFOKGyedD2d2pZfg1Q0nAQDPXzUS4yJ9MC7SBzdMisCXB3KUkOYfvxwHIFXATOtkT/crx4TgzU2nsP1UMSrqGuFtkBaOkq2VP/Ehntz12ksEQcDsIQG9fRkXhOEhHko4096u9dW7zmBnegkMOjVWL5uE2G4KJe+aEYPVuzJxOLsCe0+XKfMYahpMuO/jROxML4FGJeDl60bj2k4OC3eEXDmTWdq8ckau7OnJQfQk/dwPCfLAwSypGuKSkSG9di3eBh0WjQrBN4dzseWE1OLupcWj7Qot75oRA1EUcSi7HMumR3fzlUoVLW/cNBZLPtiPT/ZlY0lCtDL3xRmUcMbPvnBmeLAnBAEQRWnzxJMLhzr9d+n4KB98tCerzcqZo2d7p6WZTBAETLO2NiMi53I4nImMjGwWzmRlZUEQBISEhECrbb88WBAEuLi4ICQkBNOmTcPy5csRGhra7vG2vPPOOwCAOXPmNPv46tWrsWzZMgDAa6+9BpVKhcWLF6OhoQELFy7EqlWrlGPVajV++uknLF++HAkJCXBzc8PSpUvx3HPPdeqaiIiIiKh3FVXX42BmOS4dGdxqMxFd+DamFmJwkDuirPMWnOmI3Oe9nZZmsqmxfnhzczr2ZJRCFEUIgoCCynr88bPDsIjADRPDccOk861g9Bo1liRESyHNwbNYtSUdBVX1eOiiwZ3+Ho4L9MDwEE8cz6/Cb8kFuMnaxkaedzGqn82bIWpLfIj0fZxqbdXXUlFVPd7YeAoA8OwVIzAmwrvbriXQwwU3TozAx3uzsGprOhJi/VBc3YA71uxHcm4VDDo13rltQo8Fc9HW18CsJuFMVmktMoproVEJmDmEC6w9bXCQOw5mlUMQgIUjer6lWVO3TInEN4dzlT8vGGH//Ju722l91l1mDg7A/OGB2Hi8CC/+mob/Lp1o1+PMFhEbUgsxLtIbQZ6tgyej2YK8CmmWTZSdlTPBXi5497YJMOjUmDm4e36WJ0b7AgBS8qpQ12hqVp2UZN0kMqaD9yFE1P84HM5kZmY2+7tKJSXFv//+e4+1A+to0B4AuLi44O2338bbb7/d7jFRUVH45ZdfnHlpRERERNRL/u/bZGxILcQbN411Sv9v6j8OZ5fj7rUHEebtii2Pz4FO49zdrHLlzNgOdqyOj/SBTqNCUXUDMoprEelrwP2fJqK0thHxIZ54rp22LHqNGkumRuHGiREoq23scuulK8aE4Hh+FX5IylPCmaPWypmWA5+J+iN5NsPx/Oo273/x1zTUNpoxJsIb103o/mqVe2cNwrr92dhxqgTfH8nFv34/ieyyOvi56bD6jkk9uttdrgQoqWlEdb0RHi5apWpmUrQvPF2cN3OH7CO3spsY5dPrLQ0nRPngmnFhKK9rxF8X9f2W/n++dDi2nCjGxuOF2JNRqlSm2fLP9Sfw7rYMhHi54PsHprf6mudVnIPZIkKvUSHAw/7OOQsdCLI6I9TLBcGeLiioqsfRs5WYOkj6XBtNFmVmHOdFEV14uvy/llmzZmHWrFlwc3P+DjUiIiIiInuYzBbsSi8BAGw/WdLLV0M9Te7FnltxDt8dyXXqueuNZqQVSAvAozvYfe+iVWNilA8AYE9GCV749TgOZVfAw0WDd24bDxet2ubjdRqVU2ZiXGHtVb/ndCmKquphsYhKhUFvtUQhcqZhwZ5QCUBJTQOKquub3ZeYVaZUBvztyhE9Ml8lwteAq8ZKP3cPfX4E2WV1iPQ14Ovl03r8Z87TRQs/6xyMLOvcGbY06103TIzAg/Pi8MK1o3v7UiAIAl67cSzW3DEZrjrbv5P6grhAd9xi3WTwj19SYbHY3qy9O6ME723PAADkV9bj3rWJqDeamx3TdN5MX6q0FgQBE6zvIRKzzrc2O1lYjUaTBV6uWqVtIRFdOLoczmzduhVbtmxBVFSUM66HiIiIiMhhyXlVqGuU/vO970xpL18N9bQThTXKn9/dmgFzB4s3jkjJq4LZIsLfXYdQO4KTBOtO13e3ncbqXZkAgH9dP6Zb2q21J8LXgHGR3hBF4Jdj+ThTWouaBhNctCrEBnBTHfV/rjq1Mvi+afWM2SLimR9SAEhtBMd2Yzuzlu6fEwt5nXdkmCe+Xj5NucaeJi/gZpXWoabBhH2nywAA84YznOkNLlo1Hlsw1KkzUwaSh+YPhrteg+TcKpsbMMprG/HoF0kQRal9nJerFkdyKvCnr48268DTNJzpa9oKZ45YN6CMDvfqU2ESETkHJ0ESERERUb+3v0kgc7b8HHIrzvXi1VBPO1V4fnH2dEktfksucNq55aqcMeHedi2KTIuTwhn5e/C+2bEO9fR3livHSLv4f0jKU+bNxId4On2AMVFvibe2ijqef37uzBcHcpCcWwUPFw2evGRYj15PXKAHnr9qJO6eEYPP701wqF2Ss8lzZzJLa7HzVAkazRZE+RkwqJfCIqKu8HfXY/mcWABSy7KWlTCANP5g5TfHUFBVj0EBbnjtxrF459bx0KgEfH8kD29vSVeOlcOZiD4czhzKLleqhOTWqmxpRnRh6pZ35lVVVcjNzUV2dnaHNyIiIiKirpJ3Bcv2s3pmwBBFESet4Yw8aPntLel2zam0h7woYm9rotHh3jBYW8VMHeSLxxcMccp1OGrRqBAIAnAou0IJq0Zx3gxdQOQ5HnLLvoq6RvxzfRoA4JH5Q+Dv3vPhyG1To/DU5fFw1zs83tep5IqdrNJabGnS0oy77qm/umtGDEK9XJBfWY8Pdp5pdf8XB3LwW0oBtGoBb940DgadBtPi/PG3q0YAAF75/SR+PZYPAMjpw5Uz8aGecNGqUFFnxOkSqSr4qHWDxehw/g4nuhA5LZzZsGEDrrnmGgQEBMDHxweRkZGIiYmxeRs0aJCznp6IiIiIBiizRcT+TCmcmTrIF0DrsGagE0URRrPFqefcdrIYd645gDMltU49r6MKqxpQVW+CWiXg+atGwqBTIzW/CltPFjvl/PKiyJgI+xZFtGoVHp4/GDMH++PNm8f1WqVKoKcLpsZIVTy/pVjDGe66pQtIfGjzyplXN5xEeZ0RQ4LcsSRhYLddl9uanSmpxeYTnDdD/Z+LVo0nLhkKAHhnawZKahqU+zKKa/C3H1MBAI8vGIqRTTYi3DolCsumRQMAHv0yCcm5lcospr44v0WrVimbQRKzylHXaFI2oPRkm0Yi6jlO+Z/CH//4R1xyySX44YcfUFpaClEU7b4REREREXVFWkEVqutNcNdrcMf0GADA/jMMZ2TV9Ubc+N5ezHxpC04X13T8ADsYzRb85Ztj2JxWhIc+PwyTk4MfR8iLFtF+BgR6uuDWKdLg4FVNWph0VuU5I05bwydHhnrfOysWH981BYEeHc+o6U5XWgeUy1g5QxcSua1ZRnENDmeX45O9WQCAZ68YAe0Ab98ntzU7lF2B4uoGGHRqTI7x7eWrIuqaq8aEYVSYF2oaTHh940kAQKPJgoc+P4xzRjOmx/nhnpmtN4E/tWg4Zg0JwDmjGXd/dBCZ1t/rfbFyBgAmNpk7k5xbBYsIBHu6INCzd99TEFH36HKt7bp16/DWW28BAFxcXHD11VdjwoQJ8PX1hUo1sN8QEREREVH3k4OYCVE+mDrID4IgzR0pqqq/4P4j+98dp2GyiLh35iCoVB23p6k3mnHv2kSlsuihz4/g6+XToNN07X3690fylJkqR89W4r3tp7FiblyXztlZcjgzJMgDAHD3zEH4aHcWDmSWY/+Zsi4tSMqzWiJ9DfB103X9YnvYJSOC8dfvkmGyiHDRqhAbwHkTdOEI9NDD102HstpGrPj0ECwicNmoYEyL8+/tS+t1cjhjts6smDnYH3qNujcviajLVCoBf7lsOG5+fy8+25+DZdOi8dXBs0jOrYKPQYt/XT+2zfdGGrUK/755HK5dtQsZxeerfcN9+mY4M6FJODM4UHpvY2/1LhH1P10OZ9577z0AQEREBDZv3ozY2NguXxQRERERkb3kFmZTBvnCy1WL4cGeSM2vwv7MMlw+OrSDRwM7ThVj5TfH8PLi0X16Ue90cQ3+/vNxAMCx3Eq8esMYm4ttZouIR744gj2nS+Gu10CtEqTHbTiJP1/a+UHZZouIVVulqpTJMb7Yf6YMr288ifnDgzA02KPT5+2sluFMkKcLrpsYjnX7svHWlnSsjZnc6XMnKfNm+ueiiI+bDrOGBGBzWhHiQzx7rcUaUXcQBAHxIZ7YmV6CvMp6uGhV+L9F8b19WX2Cl0ELb4MWFXVGAGxpRheOhFg/zB8ehI3HC7Hi08M4WSS9B3hp8WgEe7W/IcfLVYsPlk7C1at2oaLOiEAPPVx1fTOwHB8phTMZxbXYZm3R6kj1LhH1L11+d3706FEIgoBnnnmGwQwRERER9ShRPD9vZoq1QkKulLB37sy/N6XjbPk5fLo/u3su0kk2W4c6A8DPR/Ox9MP9qKo3tnmsKIp4+vtk/JpcAJ1ahf8smYCXFo8CALy3PQO7M0o6fR2/pxTgdHEtPF00+HDZJMwfHgijWcTjXyU5fa6NPU4USq3a5HAGAO6bFQuVAGw/WaxUv3RGUk4FAGBMP14UuWN6NNQqwa6gkqi/GR5y/uf+/jlxCPN27cWr6Vui/M5Xys0dynCGLhwrLxsGtUrAicJqiCJwy5RILBgR3OHjov3d8M6tE+Cu12DO0IAeuNLO8XHTKZWuO9Ol92v9+X0IEdnW5XDGaJT+Qzhu3LguXwwRERERkSPSi2pQVtsIF60Ko8K8AQBTB0nhjD1zZ/IrzynhzqGs8m67TmeQw5krxoTCXa/B3tNluOHdPSisqm917OsbT+HTfdkQBOD1m8ZiWpw/LhkZgpsmRUAUgUe/SEJ5baPD1yCKIt62Vs0smx4Dd70G/++aUfB00eBYbiXe25bRtU/SQRaLiHRr5czQYHfl45F+Blw5Rgoj5CofRx3OLscB6/fGmH48hHfm4ACceP4S3Dkjprcvhcjp5J/NCF9X3Dur9ayJgSzaOux8VJjXBdfikwa22AB3Zb5cbIAb/upAxVxCrB8OPjUfLy0e3V2X5xRyazPZqH5awUtEHetyOBMdHQ0AqKlxznBRIiIiIiJ77bMGMOMjfZQ5KpOipXDmRGE1yjoIIH5Kylf+nF9Zr8xR6Wuq641K2PTYxUPw+b1T4e+uR1pBNa5dtRsZxeffi3+8NwtvbDoFAHjuqpG4bFSIct/TV8RjkL8bCqrqsfKbYxBF0aHr2H6qBMm5VTDo1LhjWjQAINDTBc9eOQIA8MamU0grqOrKp+qQ3IpzqG00Q6sWmu0SB4Dlc6QZOL+lFCDd2vbEHoezy7Fs9X5cs2o3yuuMCPLUY1RY/14UYTszulBdOjIEz189Eh/fOQUu2r7Zoqi3TIv1AwBcNyG8l6+EyPlWXjocTy0ajrV3TXG4PZmLVg1B6HhuX29qGs4M8neDl6u2F6+GiLpTl9+lX3vttQCATZs2dfliiIiIiIgcIYczU2L8lI/5uesxOFCqopArH9rz49G8Zn9PdKB6pry2EY98cQSbjhfa/ZjO2nGqBCaLiEH+boj2d8PIMC98e/80xPi7IbfiHK57ZzcOZZfj56P5ePr7ZADAQxcNxpKpUc3OY9Bp8MZN46BVC/gtpQCfH8hx6Dre3iJVodwyORI+bjrl49eMC+uV9manrKHLIH93aFsEEEODPbAgPgiiCLyz9XSH52oaymw9UQy1SsB1E8Lx1R+m9dm+9EQDnVolYMnUKET7u3V88ABz/YQI7HhyLm5PiOr4YKJ+xlWnxt0zB12wrQybhjP9uXqXiDrW5XDmscceQ2RkJF5//XWkpaU545qIiIiIiDokiiL2nykFcH7OjMyeuTOZJbU4erYSapWAK6wtsBxpbbZufza+PZyLez9OxI9JeR0/oAvklmZNhzpH+Brwv/sSMCbcC+V1Rtzy/l488sURiCJw65RIPDx/cJvnGhXuhccXDAUAPPdjarOqG1sOZJZh/5ky6NQq3D2zefsgQRDw/64ZBS9XLZJzq/Du1p5pb3aiwDpvJtijzfvvnytVz3x3JBc5ZXXKx0VRRFltI9IKqrAxtbBVKHP9hHBsfmw2Xrl+DCKtrYGIiPoTlUpAhK+hz1cIEFFrg/zd4W2QqmVGs6UZ0QWty+GMl5cX1q9fj6CgIEybNg2rVq1CeXnf7tdNRERERP1fVmkdCqsaoFOrMC7Su9l9UwZJlTT7rOFNW+RAZVqsHxbEBwFwrHJml3VIq9ki4uEvjnRbQGOxiNh6onU4A0hVQuvumYo5QwNQb7Sg0WzBZaOC8dxVI20uyN0zcxCmx/nhnNGMhz4/jEZTx5UuctXM4gnhCPZqPb9Aam8m9X1/c3PPtDc7ZZ03MyTQvc37x0Z4Y0acP8wWEXd/dBDXrNqF6S9uxtCnfsP45zfgktd34O61B1uFMv+8fkyrNmlEREREPUGlEnDjpAj4u+txsfU9KhFdmDRdPcGgQdKuubq6OlRUVODBBx/EH//4R/j7+8NgsL3LTBAEZGT07NBQIiIiIrowyDNYxkR4tZo1MMVaOZOaX4WqeiM8XVr36pZbml05JlRpH5GaX4XaBhPc9LbfJtcbzThoDXJmDQnA9pPFePiLIwCgVOE4y9HcSpTUNMJDr8HEaN9W97vpNXj/9ol4Y+MpVNcb8ZdFw6FW2d4prVIJ+Nf1Y3HJG9uRnFuFf/1+AisvG97u8cm5ldh6ohgqAbhvdvtDt68eG4afjxZg4/FCPP5VEr69f3qrdmPOdNLa1qy9yhkAuH9uLHaml+BEYeu5M75uOgR66DEu0hv3zY5lIENERER9wspLh2Plpe2/NyOiC0OXw5nMzMxmfxdFEaIooqioqMPHsryWiIiIiDprr7Uqpum8GVmQpwui/QzILK3DwcwyzBvWfNdhWkEVThbWQKdWYcGIYHi5ahHi5YL8ynokna3AtFh/m899MLMcjSYLgjz1WL1sEv709VH8L/FstwQ0m60zbWYO8YdO03bQoVWr8PjCoQ6dN9jLBS8tHo0/fJyI97afRlW9EU8sHAbfJrNkZO9Y25RdMSbUZoAhtTcbiQOZZUjOrcL7O07j/jlxDl2XvcwWEacKrW3NgtoPZ6bF+uPd28ajqLoBgR56BHq6INBDjwAPPfQazpIhIiIiIqLe0eVwZunSpc64DiIiIiIih8jzZFrOm5FNjvFFZmkd9p1pHc7ILcjmDA2Al6tUVTMhygc/Hc3HoazyDsOZXRlSS7Ppcf5QqwS8tHg0AOB/iWfx0OeHATgvoNlsbWk2d2hgB0c6buGIYNw/Jxartmbgs/05+DW5AE8sHIqbJkUq1TcZxTX4JTkfALB8TmyH5wz0dMHTl8fjsa+S8NbmdFw7ru02aF2VU1aHBpMFeo0Kkb62K/YvGRni9OcnIiIiIiLqii6HM6tXr3bGdRARERER2e1seR1yK85BrRKUlmQtTYnxw5cHzyohjkwURfyYJIUNTQMUOZyxZ+6MPG9mujXEkQMaAcBXTgxoCqvqkZxbBUEA5nRDOAMAT14yDHOHBeKv3yUjraAa//dtMj7fn4Pnrx6JsRHeeHdrBkQRmD88CMOCPe0657Xjw/Dpviwcyq7A//vlON68eZzTr1tuUxYX6N5hGzciIiIiIqK+pvsaQBMRERERdRN53szIMK9258NMGSRV1CTnVqK2waR8POlsJbLL6uCqVeOi4ecDDznkScwqh8UitvvclXVGHMutBCBVzsjkgOb6CeGwiMBDnx/GJ3uzbJ6rI1vSpKqZ0eHeCPDQd/o8HZkU7YufHpyBZ6+Ih4deg2O5lbhm1S48+sURfHs4FwCwYm7HVTMyQRDw3FUjIQjAD0l5yr+XM52yhjNDbbQ0IyIiIiIi6qsYzhARERFRj6s8Z0RRdX2nHy8v9k9tp6UZAIT7GBDm7QqTRcSh7PPVMHJLs4vjg2DQnQ92hod4wlWrRlW9CRnFNe2ed8/pEogiEBvg1qpdl6pFQPPUd8m47t3dSLaGOY7abA1nLhrWPVUzTWnUKiybHoPNj8/B4vHhEEXgm8O5MFlETI/zw7jItiuU2jMyzAs3TYoEADzzQwrMXQip2nLCOm9mMMMZIiIiIiLqhxjOEBEREVGPqmkw4fJ/78BFr2xDWW1jp86x74zteTOyKdb75TDHbBHx01EpnGnZckyrVmFMhBcA2Gxttiu9FEDzqpmm5IDmL5cNg0GnxqHsClz51k48/X0yKuuMHX1qigaTGTut7dPm9UA4Iwvw0ONfN4zB/+5LwPAQT+g1Kjx68ZBOneuJhUPh5arF8fwqrNuX5dTrVCpngt2del4iIiIiIqKe0OWZM2vXru3S42+//fauXgIRERER9SP/3nwKOWXnAAB7MkqxaLRjw9qLqupxpqQWggBMjLYdzkyO8cU3h3OVuTMHMstQWNUATxcNZg1pHa5MiPLB3tNlSMwqx02TI9s8pzJvpp1wBpACmntnxeKKMaH4x8/H8dPRfKzdk4Wfj+bjT5cOw3Xjw6HqYE7KvtNlqGs0I9BDjxGh9s16caaJ0b745Y8zUG+0wFWn7tQ5fN10eGzBEDz9fQpe+f0kFo0Oha+brsvXZjRblOqmwYGsnCEiIiIiov6ny+HMsmXLIAidG8ApCALDGSIiIqIB5HRxDT7ceUb5+4HMMofDGblqJj7EE16uWpvHThnkBwA4klOBeqMZP1hbml0yMhh6TevAQZk7k9125UxexTmcLqmFSgCmWs9tS4iXK966ZTxumVyCp39IQXpRDZ7831F8tj8bL1w7CsOC2w9d5JZm84YFdvr9dlcJgtDpYEZ2y+RIrNuXjbSCavzr9xP4xzWjunxdWaW1MJpFuOnUCPN27fL5iIiIiIiIeppT2pqJotjpGxERERENDKIo4rmfUmE0i0r1RGcGxe+3s6UZAET7GRDgoUej2YLErHL8eiwfAHDlmLA2jx9vnatyuri2zZZrctXMqHDvDoOhpqbF+ePXh2bi/y4bDjedGoezK3D9u3twPL+qzeNFUVTCmbk92NKsO2jUKjx75QgAwLr92Z2ev9PUSeu8mbggjw4rkIiIiIiIiPqiLoczZ86c6fCWnJyMb775Btdddx0AYPr06UhJScHp06c79Zzbt2/HFVdcgdDQUAiCgO+++67Z/XI1T9PbJZdc0uyYsrIy3HrrrfD09IS3tzfuuusu1NS0P/iViIiIiLpm0/EibD1RDK1awKpbxwMAjhdUoare/jksALDvjDTzZUpMx5UrgiAoc2de3XAS5XVG+LvrMHVQ28GOt0GHuEBphsmhNubO7M6wzpuJ7fi5W9KqVbhn1iBsemwOJkT5oLrehKUf7kdOWV2rYzOKa5FdVgedWoUZNtqn9RdTB/nhijGhEEXg2R9SurxJ60SBNG9mSCDnzRARERERUf/U5XAmKiqqw1t8fDyuvvpqfPnll/j888+xZ88ePPjgg4iMbLuPd0dqa2sxZswYvP322+0ec8kllyA/P1+5ffbZZ83uv/XWW5GSkoINGzbgp59+wvbt23Hvvfd26nqIiIiIyLZ6oxnP/ZQKALhrxiBMHeSHSF8DRBFIbCMEaU9ZbaNSNWFP5QwAJZyRn+eyUSHQqNt/Gzwhsu3WZqIoKpUzXQlMgr1c8OHSSRga5IGi6gbc/uF+lNQ0NDtmc1qhdO2DfOGm73In4j7hL5cNg6tWjYNZ5fjuSG6XznWqSApnhgZz3gwREREREfVPTmlr5ogbbrgBS5cuxZYtW/Dee+916hyXXnop/v73v+Oaa65p9xi9Xo/g4GDl5uPjo9x3/Phx/Pbbb/jvf/+LKVOmYMaMGfj3v/+Nzz//HHl5eZ26JiIiIiJq3393nEZ2WR2CPPV4cF4cAGBStBSaHHCgtZnc0mxIkLvdg+WntJgNc+WYUJvHK3NnWoRG6UU1KKpugF6jwvgon7YeajcvgxZr75qMMG9XnCmpxR2rD6CmwaTc33TezIUixMsVD1j/7V/4JQ3JuZU4UVDd6pZeVAOzxXZljVw5MziI4QwREREREfVPPR7OAFJAI4oi1qxZ023PsXXrVgQGBmLo0KFYvnw5SktLlfv27NkDb29vTJw4UfnY/PnzoVKpsG/fvnbP2dDQgKqqqmY3IiIiIrItr+Ic3t6SAQD4y2XDlUqQyTFSwHEg0/Fwxt6qGQCIC3CHj0GaDxPq5aLMlWmPHLwk5VSg0WRRPi5XzUyK9oWLVm3387cnyNMFH981Gb5uOhzLrcR9HyeiwWRG5TkjDmRKwdCFFM4AwN0zYxDlZ0BRdQMu//dOLHx9e6vb/Fe34W8/prR7jgaTGZmlUiu4oQxniIiIiIion+qVcCYoKAgAcOLEiW45/yWXXIK1a9di06ZNeOmll7Bt2zZceumlMJvNAICCggIEBjb/j65Go4Gvry8KCgraPe8LL7wALy8v5RYREdEt109ERER0Ifl/vxzHOaMZk6N9m1WtyJUzSTmVqDea7TqXHJDYM29GplIJmGqtnrliTGiHA+RjA9zgbdCiwWRBav75zTg706XNPtPiHJ83055BAe5YvWwSDDo1dqaX4LEvk7DtZDHMFhGxAW6I8nNz2nP1BXqNGi9eOxrhPq7wd9e1eQOAT/ZmIaO47XmQp4trYbaI8HDRIMhT35OXT0RERERE5DS90sA6OzsbAGA0Ojb81V433XST8udRo0Zh9OjRiI2NxdatW3HRRRd1+rwrV67Eo48+qvy9qqqKAQ0RERGRDXsySvHT0XyoBODZK0dAEM4HIzH+bvB316GkphFHz1Z2WA1zpqQWJwqroVEJmDU4wKHrWHnpcAwOdMc9swZ1eKwgCJgQ6YNNaUVIzCrH2AhvmMwW7DsthTNdmTfTljER3nhvyQTcueYAfjqaj+0niwFceFUzsoRYP+z807x277/7o4PYeLwQr/5+Em/fOr7V/ScLrfNmgjyafT8RERERERH1Jz1eOWM0GvHyyy8DAOLi4nrkOQcNGgR/f3+kp6cDAIKDg1FUVNTsGJPJhLKyMgQHB7d7Hr1eD09Pz2Y3IiIiImqbyWxR2lPdOiUK8aHN3zsJgnB+7owdrc1+S5YqnBNi/eBlbVNmr0g/Ax5dMBQeLvY9Tm5tdsg6d+ZobiWqG0zwdNFgRKiXQ89tj5mDA/CvG8ZCEICqemn2zLxhQU5/nv7g8YVDIAjAz8fykZxb2ep+OZzhvBkiIiIiIurPuhzOZGdnd3jLzMzE4cOH8f7772PixInYs2cPBEFoVuHSnc6ePYvS0lKEhIQAABISElBRUYHExETlmM2bN8NisWDKlCk9ck1EREREfUVBZT2uensXVm1Nd+p5P9mbhbSCangbtHj04iFtHiOHM/IsGVt+S84HAFwysv3NNM4ywRrOHMwqgyiK2G1tpzYt1h/qDtqiddaVY0LxzOXxAABfNx0mRtuejXOhGhbsiaus7e9eXt+6DfLJQqnd2dAg9x69LiIiIiIiImfqcluzmJgYhx8jiiISEhLwyCOPdOo5a2pqlCoYADhz5gyOHDkCX19f+Pr64m9/+xsWL16M4OBgZGRk4Mknn0RcXBwWLlwIABg+fDguueQS3HPPPXj33XdhNBrxwAMP4KabbkJoaGh7T0tERER0QXpz8ykk5VQgo6gGd88YBJ2m8/t3TGYLtpwoxmf7s7H1hFSp/PiCofBx07V5vNzK7FBWOcwWsd3gI7fiHJLOVkIQgAXx3R/OjAn3hkYloLCqAbkV57DTGs5Md+K8mbYsmx6DmAB3BLjroVX3ynjIPuGRi4coLd72ni5VZgYB5ytnhrByhoiIiIiI+rEu/49PFEWHbj4+Pli5ciU2btwIvb5zAzwPHjyIcePGYdy4cQCARx99FOPGjcPTTz8NtVqNo0eP4sorr8SQIUNw1113YcKECdixY0ez5/v0008xbNgwXHTRRbjsssswY8YM/Oc//+nql4OIiIioXzlbXoevDuYAAGoaTHa1F2tLbsU5vLrhJGa8tAX3rD2IzWlFsIjA5aNDcPPkyHYfNzzEE+56DaobTDieX9XuceutLc0mRfsiwKP7h8C76tQYYW3Dtiu9BIeyKgAA0508b6Yts4cEtGoBN9BE+bnhpsnSbMeXf0uDKIoAgHONZmSX1QEAhgQznCEiIiIiov6ry5Uzq1ev7vAYlUoFDw8PxMTEYOTIkVCr1V16zjlz5ij/QWvL+vXrOzyHr68v1q1b16XrICIiIurvVm3NgNF8/n3V5rQihwKI/WfK8M7WdGw9WQz57Zmvmw7XTwjHjZMiMCjAdusptUrA+CgfbD9ZjAOZZRgZ1vY8F3nezCUjur9qRjY+ygdJZyvxn+2n0Wi2IMTLBTH+bj32/APdH+cNxv8Sz+JQdgU2pxXhouFByCiugShK32P+7t0f0hEREREREXWXLoczS5cudcZ1EBEREVEPa1o1c+f0GHy46ww2pxXhr9a5Jx3JKavDbf/dh0azBQAwLdYPN0+OxIIRQdBr7N+MMzn6fDhzx/TWLXOLqutxIEuq6OmJeTOyCVE+WL0rExnFtQCkqhlB6J55M9RaoKcLlk2LwbvbMvDP9Scwd2ggThRILc0GB3LeDBERERER9W8Dt5E1ERER0QD39hapamZ6nB8euXgwtGoBZ0pqcaak1q7H/3g0D41mC0aGeWLzY7Ox7p6puGJMqEPBDABMjpHmiew/U95mdfTvKYUQRWBMhDdCvV0dOndXTIjyafb37p43Q60tnx0LDxcN0gqq8ePRPJwsksKZoWxpRkRERERE/RzDGSIiIqIBqGnVzMPzh8DDRYvJMb4ApNZm9vjhSB4A4LYpUR22L7NldLgXdGoVSmoakFla1+r+9Sk939IMAEK8XBHWJAyaHtv982aoOS+DFvfNjgUAvLrhJFLzpLlEg4MYzhARERERUf/W5bZmLSUmJmLjxo1ITk5GWZnUfsLX1xcjR47E/PnzMWHCBGc/JRERERE56O0tGTBZRMyI88ekaCmUmTs0ELvSS7E5rRB3zWjdXqypU4XVSCuohlYtdLnVmIv2/7d33+FRVVsfx78zKZNCCukJLSC999BBioACKoqCqKhYLhdQwYoNudbXdrGg6BWxYi8IqEjvNfQWWkJoSSAhndQ57x8xIzGdlCHw+zzPPCTn7LLO5HiIs1h7O9Cunhdbos6xJTKhwL4uielZbDgSD8DQalzSLF/HBrU5mXieJgG1CPB0qfb5Be7qEcrcdZEci0/n2F/Ju2ZKzoiIiIiISA1XacmZ3bt3c//997N58+Zi2zz11FOEhYXx4Ycf0qZNm8qaWkRERETK4cKqmYcGNrEdH9AikBcX7WdzZAIpGdl4uDgVO8aCnXlVM32a+OPt5lzhmLqE+rAl6hyboxK4pUs92/El+2LJsRo0D/Ig9IKkTXUZ2jqIBTtPcX37kGqfW/K4WxyZdHVjnl+wz3asaaD2nBERERERkZqtUpY1W7p0KV27dmXz5s0YhoFhGDg6OhIYGEhgYCCOjo624xs3bqRr164sW7asMqYWERERkXIqqmoGoKGfOw393MnONVh76Gyx/Q3DYMGu0wAMb1c5SYsufy2ptiUqocBx25JmdqiaAbi2TTDrn+zPv/s1tsv8kmdMWH3bEnMBHpZKSQiKiIiIiIjYU4WTM2fPnmXUqFFkZmZiMpm499572bRpE2lpaZw6dYpTp06Rnp7O5s2bue+++3BwcCAzM5NRo0YRHx9fGdcgIiIiImV0PKHoqpl8/ZsHACXvO7PnZDKRZ9NwcTIzqGVgpcTVqUFtTCY4Fp9OXHIGAKmZOaz+K0k0tHVwpcxzMUK8XTGbTXabX8Di6MBjg5sBFEgoioiIiIiI1FQVTs68/fbbJCUl4ezszKJFi/joo4/o0qULjo5/r5jm4OBA586d+fDDD1m0aBFOTk4kJSXx9ttvV3R6ERERESmH91ceLrJqJl9+cmZFxBmsVqPIMRbsylvSbEDzQNwtlbNKrqeLEy2CPAHY/Ff1zPIDcWTlWGnk565lrIQbOtThl4k9eflGLY8sIiIiIiI1X4WTM4sWLcJkMjFp0iQGDx5cavtrrrmGyZMnYxgGixYtquj0IiIiIlJGeVUzJwB4uIiqGcirSqhlceRsaia7TyYVOm+1Giz8a7+ZylrSLF/Xv5Y22xyZl5xZvCdvSbPBrYMwmVS5ItC+njdebsXvhSQiIiIiIlJTVDg5ExkZCcCIESPK3Ce/7dGjRys6vYiIiIiUUX7VTO8mfnQuZmkoZ0czvZv4AUUvbRYefY5TSRnUsjjSr5l/pcaXX8mzOTKBjOxcVkTkzT/UTvvNiIiIiIiIiFSVCidnMjLy1gR3d3cvc5/8tpmZmRWdXkRERERKkZqZw7xN0baqmYcGFF01k+/qEvadWfBX1cw1rQJxcXKo1Di7NKwNQERsCgt2niI9K5c63q60qeNVqfOIiIiIiIiI2FuFFwkPCgoiOjqa7du306lTpzL12b59OwCBgZWzgayIiIiIFLb7RBLzNkfz646TpGXlAnl7yhRXNZMvvyJm98kk4pIzCPB0ASAn18pvu08DMKKSlzQDCPBwIdTXjaj4dN788yAAg1tpSTMRERERERG5/FS4cqZ3794YhsGrr75KcnJyqe1TUlL4v//7P0wmE717967o9CIiIiJygdTMHL7adIxh765h+Htr+XpzNGlZuTTyd+eZ61rw3m0dSh0jwMOFdnXzqlVWRpyxHd9wNJ6zqVnUdnOiZ2O/Kok/f2mzmOS86uyhbbSkmYiIiIiIiFx+KpyceeCBB4C8vWf69OnD1q1bi227detW+vbty5EjRwr0FREREZGK2xqVQPeXl/H0z3vYczIZZwcz17cP4Zv7u7Fsal/u7d0IN+eyFU7nL2227ECs7divO/KWNLu2TTBODhX+NbJIXRr+XdXj72GhU/3aVTKPiIiIiIiIiD1VeFmznj178u9//5v333+f3bt3ExYWRqtWrQgLCyMgIACTyURsbCybNm1i7969tn7//ve/6dmzZ0WnFxEREREgO9fKkz/tJiUzh4Z+7owNq8/IjnXxcXe+qPEGNA9k5tJDrD10lsycvCXR/tgbA8DwKljSLF/XC5Zcu6ZlIGazljQTERERERGRy0+FkzMA7777Lm5ubrz11ltYrVb27NlTIBEDYBgGAGazmUcffZRXX321MqYWEREREeCz9VEcjkvFx92ZX/7dEy83pwqN1yrEE38PC2dSMtkcmUBGtpWUjBwCPS0FEiiVrYGvG3W8XTmZeJ7r2gRX2TwiIiIiIiIi9lQp61GYTCZee+01duzYwYQJE2jSpAmGYRR4NWnShAkTJrBjxw7bnjMiIiIiUnFxKRnMXHoIgMcHN6twYgbAbDZxdTN/AJYfiOPXnXlLmg1rG1Kl1Swmk4kPbu/I26Pb06OK9rURERERERERsbdKqZzJ17p1a2bNmgVAVlYW586dA6B27do4O1/ckhoiIiIiZWW1Gry2OIJ6Pq6MDWtg73CqzWt/RJCamUPbul7c0rlepY3bv3kg3209wZ97Y0lIywKqdkmzfG3retO2rneVzyMiIiIiIiJiL5WanLmQs7MzgYGBVTW8iIiISCFbj51j9qojAIR4udo2ta9pft5+gv/7PYKpg5pyS5eSky3bos/xQ/gJAJ4f0apSq1p6NfHDycHEycTzANT3caNdXa9KG19ERERERETkSlXuZc1+//13OnbsSMeOHZk3b165+s6bN8/Wd+nSpeWdWkRERKREm47G275+7IednEnJtGM0FychLYvn5u8lJjmDx3/cxdebo4tta7UaPP9r3j5/N3WsS8f6tSs1lloWR7o18rV9P7xdsJamFREREREREakE5UrOGIbBlClT2LlzJ/7+/tx2223lmmzMmDH4+fmxY8cOHnnkkXL1FRERESnN5qgEAJwcTJxNzeKxH3ZiGIadoyqfd5YdIiUjB3dnBwCm/bS72ATNd1uPs+tEErUsjjwxtFmVxHN1s7+rj0a0q1Mlc4iIiIiIiIhcacqVnFm+fDkHDx7EbDbz3//+t9yTmUwmZs6ciYODA3v27GHVqlXlHkNERESkKNm5VsKP5e139+Yt7bE4mlkZcYZP10fZN7ByOHomlS83HgPgwzs6c0/PhkBegmbepoIJmqTz2by2OAKAhwc2IcDDpUpiGtI6CA+LI10b+tAsyKNK5hARERERERG50pQrOfPjjz8CMGjQIFq2bHlRE7Zs2ZLBgwcD8MMPP1zUGCIiIiL/tOdkEulZuXi7OTGsTTBPX9cCgFd+O8D+08kl9k3OyOaFhfuYsWAv2bnW6gi3SP/3xwFyrAb9mwfQq4kfzw5rYUvQPPVzwQTNf5ccJCEti8YBtRjXI7TKYgrxdmXtE/35/J6uVTaHiIiIiIiIyJWmXMmZzZs3YzKZGD58eIUmHTZsGIZhsHHjxgqNIyIiIpJvU2TekmZdQ30wm03c0a0BA5oHkJVr5cGvt5ORnVtkv82RCQyduYY5ayOZuy6KGQv2VmfYNpuOxrN4byxmE0wb2hzIqzp+dlgLxvcqmKCJiEnhi78qbJ4f3gonh3JvI1guXm5OuDg5VOkcIiIiIiIiIleScv2f/LFjeR8CNGtWsTXNmzZtCkBUVNRF9V+9ejXDhw8nJCQEk8nEL7/8UuC8YRg899xzBAcH4+rqysCBAzl06FCBNgkJCYwdOxZPT0+8vb0ZP348qampFxWPiIiI2N+mo/EAdG3oA+QlNv7v5rb41bJwKC6Vl3/bX6B9Vo6VV38/wK0fbeBk4nmCPF0wmeDLjdF8viGqWmO3Wg1bfKO71qdJ4N/Lh5lMJp65rmCCZvxnW8i1GgxpFUSvJn7VGquIiIiIiIiIVFy5kjNJSUkA+Pj4VGjS/P7JySUvMVKctLQ02rVrx6xZs4o8/9prr/HOO+8we/ZsNm3ahLu7O4MHDyYjI8PWZuzYsezdu5clS5awcOFCVq9ezf33339R8YiIiIh95VoNtkbl7TfTrZGv7bhfLQtv3tIOgM83HGPZ/lgADsamcMOsdcxedQTDgFGd6rJkah8eH5xXsTJjwT5WHzxTbfEv2HWKnSeScHd2YMrApoXO/zNBc+LceSyOZtvSbSIiIiIiIiJSs5QrOePp6QlAYmJihSbN7+/hcXGbyg4dOpQXX3yRG2+8sdA5wzCYOXMmzzzzDNdffz1t27bl888/59SpU7YKm/379/PHH3/w8ccfExYWRq9evXj33Xf55ptvOHXq1MVeloiIiNjJ/tPJpGTm4GFxpEWwZ4FzfZv625Iaj/2wi1krDjPs3bXsO51MbTcnZt/ekddHtcPDxYl/9W3EyI51yLUaTJy3jcNxVV9Vm5Gdy2t/RAAwod9V+HtYimyXn6B5oE8jHMwmHhvcjHo+blUen4iIiIiIiIhUvnIlZ/z9/QHYt29fhSbdvz9v2Y6AgIAKjVOUyMhIYmJiGDhwoO2Yl5cXYWFhbNiwAYANGzbg7e1N586dbW0GDhyI2Wxm06ZNxY6dmZlJcnJygZeIiIjYX/5+M51Da+NgNhU6//iQZrQI9iQhLYvXF0eQlWOlXzN/Fj/chyGtg23tTCYTr4xsQ+cGtUnJyOHez7aQmJ5VpbHPXRfFycTzBHu5ML5XoxLbmkwmpl3bgt3PX8O9vUtuKyIiIiIiIiKXrnIlZ7p27YphGCxYsKBCk86fPx+TyUSXLl0qNE5RYmJiAAgMDCxwPDAw0HYuJiamUGLI0dERHx8fW5uivPLKK3h5edle9erVq+ToRURE5GL8vd+Mb5HnLY4OvDO6Pe7ODrg4mXnh+lbMvasLAZ4uRbadfUcn6ni7EhWfzoQvt5Gda62SuONTM3l/xWEAHr2mGa7ODmXq5+bsWCXxiIiIiIiIiEj1KFdyZujQoQD8+eefrF279qImXL16NX/++WeB8WqKadOmkZSUZHsdP37c3iGJiIhc8axWgy1ReZUzYY2K3xevSaAHyx/tx7on+nNH91BMpsIVNvn8aln4eFxn3J0d2HA0nufm78UwjEqP/e1lh0jJzKFViCc3dqhT6eOLiIiIiIiIyKWpXP/s8qabbiI0NJSoqChGjRrF6tWradKkSZn7Hzx4kFtuuQWTyURoaCg333xzuQMuTVBQEACxsbEEB/+9TElsbCzt27e3tYmLiyvQLycnh4SEBFv/olgsFiyWoteBFxEREfs4FJfKufRsXJ0caFPHq8S2gUVUyhSnRbAnb4/uwH1fbOXrzdE0DazF3T0bliu2lIxstkUnYi0isZOSkcNXm6IBePraFpiLWI5NRERERERERC5P5UrOODk58cYbb3DzzTcTFxdHp06deOGFF7j33ntxd3cvtl9qaioff/wxzz33HKmpqZhMJt58800cHSt/SY6GDRsSFBTEsmXLbMmY5ORkNm3axIQJEwDo3r07iYmJhIeH06lTJwCWL1+O1WolLCys0mMSERGRqrM5Mm9Js04NauPkUK6i4FINbBnIk0Oa88rvB3hh4T66NfKlRbBnmfoahsH4z7ay+a/9cIozoHkAPRr7VUa4IiIiIiIiIlJDlDs7MnLkSGbMmMH06dNJS0tj6tSpPPvss/Tu3ZtOnToREBCAu7s7aWlpxMbGsm3bNtasWUNaWpptOZAZM2Zwww03XHTQqampHD582PZ9ZGQkO3bswMfHh/r16/Pwww/z4osv0qRJExo2bMizzz5LSEiIbc4WLVowZMgQ7rvvPmbPnk12djaTJk1i9OjRhISEXHRcIiIiUv02/pX86Nqw+CXNKuL+Po3YFn2OxXtjefm3/Xx+T9cSl0TL9/ueGDZHJuDsaKZpYK0i23hYnJg+vFVlhywiIiIiIiIilziTcZELqM+dO5fJkyeTnp6eN1AJH1LkT+Hm5sZ7773HXXfddTFT2qxcuZKrr7660PFx48bx6aefYhgG06dP56OPPiIxMZFevXrx/vvv07RpU1vbhIQEJk2axIIFCzCbzdx0002888471KpV9IcnRUlOTsbLy4ukpCQ8Pcv2r2hFRESk8hiGQdeXl3EmJZNv7+9GWCPfKpknOj6dAW+tJDvX4NO7u9CvWUCJ7bNyrAx8axXRCek8OKAJUwc1LbG9iIiIiIiIiFweypo3uOjkDMCpU6d46623+Pzzzzl79myx7fz8/Bg3bhxTpky5rCpTlJwREREp2fGEdF5bHMG/+jaiVUjJ+8FcjKNnUun/5iqcHc3smn4NLk4OlT5HvhcX7uPjtZE0DazFbw/2xrGEJdTmrI3khYX78PewsPLRfrhbKn8pVxERERERERG59JQ1b1ChTwpCQkJ44403eOONN9i7dy87d+4kPj6elJQUPDw88PX1pV27drRqpeU6RERErkRvLzvEgp2nOJuSydf3d6v08fP3c2lfz7tKEzMAk/s34fvwExyMTeX78BOM6Vq/yHaJ6Vm8s+wQAI8MaqrEjIiIiIiIiIgUUmmfFrRq1UpJGBEREbHJzMll8Z4YADYcjefEuXTq1nar1Dk2/ZWc6VZF+81cyMvNiQcHNOGFhft488+DjGgXUmTi5b3lh0k6n02zQA9Gda5X5XGJiIiIiIiISM1T/HocIiIiIhWwMuIMKZk5tu9/3nay0ufIr5zp2rBq9pr5pzu6NaCBrxtnUzP5cNWRQuej49P5bEMUANOubY6Dufg9+URERERERETkyqXkjIiIiFSJBTtPAVDPxxWAn7afpAJb3RVyPCGdk4nncTSb6NjAu9LGLYmzo5knhzQH4KM1R4lJyihw/v/+OEB2rkHvJn70axZQLTGJiIiIiIiISM2j5IyIiIhUurTMHJbujwXgtZva4ebsQOTZNLZFn6u0OfKrZtrU9cLNufr2dRnSOojODWqTkW3ljT8jbMfDj51j0e7TmEzw1LUtqi0eEREREREREal5lJwRERGRSrd0fywZ2VZCfd3o1siHIa2DAPghvPKWNtsUGQ9AWDUtaZbPZDLx9HV5yZcft51g76kkDMPgpUX7ALilUz1aBHtWa0wiIiIiIiIiUrMoOSMiIiKVLn9Js+HtQjCZTNzcsS4AC3edIiM7t1LmyK+cCWvoUynjlUeH+rUZ3i4Ew4CXf9vPb7tj2BadiKuTA1OvaVrt8YiIiIiIiIhIzaLkjIiIiFSqpPRsVh08A+QlZwC6NfKljrcrKRk5LNkXW+oYB2NT6P/mSiZ+tY2TiecLnY9NziAqPh2zCTqF1q7cCyijxwc3w9nBzLrD8Tz50y4A7u/TiEBPF7vEIyIiIiIiIiI1h5IzIiIiUqkW740hO9egeZAHTQM9ADCbTdzYoQ6QtxRYSaxWg8d/2MXRM2ks2n2aAW+uZNaKw2Tm/F1xs/Fo3pJmLUM88XRxqqIrKVk9Hzfu7hkKQEpGDgEeFh7o28gusYiIiIiIiIhIzaLkjIiIiFSqXy9Y0uxCIzvmJWdWHzxDXHJGsf2/2hzNjuOJ1LI40jXUh4xsK68vjmDIzDWsjIgDLlzSrHr3m/mnf1/dmNpuecmhR65pipuzo13jEREREREREZGaQckZERERqTRnUjJZf+QsAMPbFkzONPKvRcf63lgN+GXHySL7xyVn8NrvBwB49JqmfPtAN94e3R5/DwuRZ9O4a+4WHvhiK2sO5c3R1Q77zVzIy9WJz+8J47Wb2zKqUz27xiIiIiIiIiIiNYeSMyIiIlJpftt9GqsB7ep5U9/XrdD5mzrVBeDH8JMYhlHo/IwF+0jJzKFdXS/u6B6KyWTi+vZ1WP5IX+7t1RAHs4nFe2OJTkgHoGuofZMzAG3qenFL53qYzSZ7hyIiIiIiIiIiNYSSMyIiIjVQTFIGx+LT7B1GIQv+WtJsxD+WNMs3rG0Izo5mImJT2HsqucC55QdiWbT7NA5mEy+PbIPDBckODxcnnhnWkt8e7E3YX9Uy7ep6UdvduYquRERERERERESk6mhhdBERkRomJ9fKyPfXcS49mz+n9KGeT+EKFXs4mXiercfOYTLBdW2Ci2zj5erEoJaBLNp1mh+3naB1HS8A0rNyePaXvQDc0zOUViFeRfZvFuTBN/d3Y1NkAg393KvmQkREREREREREqpgqZ0RERGqYHccTOZWUwfnsXD5ZF2nvcGwW/lU10zXUhyAvl2Lb3dwxb2mzX3ecIjvXCsDbSw9xMvE8dbxdeXhg0xLnMZlMdGvkS6Bn8XOIiIiIiIiIiFzKlJwRERGpYVYfPGP7+tstx0lKz67S+QzDYMfxRL7fepyEtKxi2y3Y9deSZu2LXtIsX+8mfvh7WIhPy2JlxBn2nkri47V5Sab/XN8Kd4sKe0VERERERETk8qZPP0RERGqYVYfOAuBoNpGelcuXm44x8erGlT5PckY283ec4utN0ew7nbc/jLebE48Pbs6tXeoV2BPm6JlU9pxMxtFsYmjropc0y+foYOaG9iH8b00k3289TmxKJrlWg2vbBDGgRWClX4eIiIiIiIiIyKVGlTMiIiI1yLm0LHadSATgkWuaAfDp+igyc3IrZXzDMNgefY7Hf9hJ2EvLePaXPew7nYyzo5l6Pq4kpmfz1M+7ufH9dew8nmjrt2DnaQB6NfHDx9251Hlu6pS3tNmf+2LZeTyRWhZHpg9vVSnXICIiIiIiIiJyqVPljIiISA2y5vBZDAOaB3lwb++GfLY+ipjkDOZvP8UtXepVaOzNkQk8N38PB2JSbMeaBNTitrD63NihDrUsjny+4Rj/XXKQXSeSuOH9dYzuUo/HBjfn150nARjetuQlzfI1D/KkVYgne0/lVeQ8PqSZ9pARERERERERkSuGKmdERERqkPz9Zvo09cfJwcw9vUIB+GjNUaxW46LHTc7I5t9fhXMgJgWLo5mRHevww7+68+eUPtzdsyHebs44Opi5p1dDlj3al5Ed6mAY8PXm4/R9bQVHzqTh7GjmmlZlX5bs1r+SSe3qeTM2rMFFxy4iIiIiIiIiUtOockZERKSGMAyDNYf+Ss408QdgTNf6vLvsMIfjUll5MI7+zS9uz5aZSw5xNjWLRv7u/DyhJ15uTsW2DfBw4a1b2zO6a/0ClTb9mwXg4VJ8v3+6PawBvu4Wejb2LbB/jYiIiIiIiIjI5U6VMyIiIjVERGwKscmZuDiZ6RxaGwAPFyduC6sPwIerjl7UuAdjU/hsQxQAzw9vVWJi5kJdG/qwcHIvpg9vSa/Gfjw8qEm55jWbTVzXNhhvt9L3qBERERERERERuZwoOSMiIlJD5C9p1r2RLy5ODrbjd/UMxdFsYlNkAjuPJ5ZrTMMweP7XveRaDa5pGUifpv7l6u/oYObung358t4wmgd5lquviIiIiIiIiMiVSskZERGRGmLVBfvNXCjYy5UR7UOAvL1nyuP3PTGsPxKPxdHMs8NaVk6gIiIiIiIiIiJSIiVnREREaoD0rBy2RJ4DCidnAO7r3QiA33efJjo+vUxjns/K5aVF+wF4oO9V1PNxq6RoRURERERERESkJJdlcub555/HZDIVeDVv3tx2PiMjg4kTJ+Lr60utWrW46aabiI2NtWPEIiIiJdt0NIGsXCt1vF1p5Ode6HyLYE/6NPXHasAn6yLLNOYHq45wMvE8dbxdmdD3qsoOWUREREREREREinFZJmcAWrVqxenTp22vtWvX2s5NmTKFBQsW8P3337Nq1SpOnTrFyJEj7RitiIhIyS5c0sxkMhXZ5v6/qme+3XKcc2lZJY53PCGd2auOAPD0dS1wdXYosb2IiIiIiIiIiFQeR3sHUFUcHR0JCgoqdDwpKYk5c+Ywb948+vfvD8DcuXNp0aIFGzdupFu3btUdqoiISKlWH8pLzvQtYkmzfD0b+9Iy2JN9p5P5atMxJvVvUmzbFxbuIyvHSo+rfBnauvDflyIiIiIiIiIiUnUu28qZQ4cOERISQqNGjRg7dizR0dEAhIeHk52dzcCBA21tmzdvTv369dmwYUOJY2ZmZpKcnFzgJSIicjHiUjL4dks0E+dtY9aKwyW2PZ6QztEzaTiYTfRo7FtsO5PJxAN986pnPlkXxVebjhGTlFGo3eqDZ/hzXywOZhPPj2hVbCWOiIiIiIiIiIhUjcuyciYsLIxPP/2UZs2acfr0aWbMmEHv3r3Zs2cPMTExODs74+3tXaBPYGAgMTExJY77yiuvMGPGjCqMXEREqlqu1WDH8XO0q+uNo0PZ/42CYRhsi06kWZAHtSzl/+vTMAz2nU5m2f44lu2PZeeJJNu5RbtOU8/HjRHtQorsm18107G+N54uTiXOc22bYGYuPUTk2TSe/nkPT7OHViGeDGgRyMAWATQL8uD5BXsBuLN7A5oGepT7WkREREREREREpGIuy+TM0KFDbV+3bduWsLAwGjRowHfffYerq+tFjztt2jSmTp1q+z45OZl69epVKFYREak+hmHw0DfbWbjrNANbBPLRHZ0wm8tWNfLa4gg+WHmERn7ufHN/NwI8XcrU73xWLm/+GcGi3ac5/Y8qlrZ1vfCvZWHZgTie/nk3Het7U7e2W6ExVufvN9Ok+CXN8jk5mPn2gW58v/UEy/bHsv14IntPJbP3VDLvLDtELYsjqZk5+Lo78/DApmW6BhERERERERERqVyXZXLmn7y9vWnatCmHDx9m0KBBZGVlkZiYWKB6JjY2tsg9ai5ksViwWCxVHK2IiFSVT9dHsXDXaQCW7o/ltcURPDm0ean9fgw/wQcrjwBw9Gwaoz/aWKYEzbm0LMZ/toVt0YkAuDiZ6dXYn4EtAujfPIAATxdycq2M+nAD26MTmfLtDr6+r1uBip7sXCvrD8cD0KeE/WYuFODhwsSrGzPx6sacTc1kxYE4lh+IY/XBM6Rm5gDwxJDmeLmWXIUjIiIiIiIiIiJV47Ldc+ZCqampHDlyhODgYDp16oSTkxPLli2znY+IiCA6Opru3bvbMUoREalK4ccSeGnRfgAGtwoEYPaqI/wYfqLUftN+2g3AbWH1qePtakvQxCYX3s8l34lz6dw8ez3bohPxdHHk/bEd2fHcNXw8rjOju9a3JXYcHcy8fWsHalkc2RJ1jvf/SgLl23E8kZTMHHzcnWlTx6vc1+1Xy8KozvX44PZObHtuEJ/f05XZt3dkVOe65R5LREREREREREQqx2WZnHn00UdZtWoVUVFRrF+/nhtvvBEHBwfGjBmDl5cX48ePZ+rUqaxYsYLw8HDuvvtuunfvTrdu3ewduojIJS0rx0p2rtXeYZTb2dRMJn61nRyrwXVtg5l9eycmXn0VANN+2s3WqIQi+504l879n4eTlWtlSKsgXry+Nd/c382WoBlTTILmQEwyN32wniNn0gj2cuGHCT24tk0wLk4ORc5T39eN/1zfCoC3lx0i/Ng527lVEXlLmvVq7FfmJdiKY3F0oE9Tf4a0DsZkqthYIiIiIiIiIiJy8S7L5MyJEycYM2YMzZo145ZbbsHX15eNGzfi75+3HMx///tfhg0bxk033USfPn0ICgrip59+snPUIiKXtjMpmfR5bQXXvbOGc2lZ9g6nzHKtefvMxCRncJW/O/93U1tMJhOPDGrG4FaBZOVaeeCLcI4npBfol5qZw72fbSU+LYtWIZ68dWs7zGYT9XzcSkzQbDwaz6jZG4hNzqRJQC1+nNCDpoEepcZ5Y4c6jGgXQq7V4OFvt5OSkQ3A6kN/7TdTxiXNRERERERERETk0mcyDMOwdxA1VXJyMl5eXiQlJeHp6WnvcEREqtRD32xn/o5TAHRr5MMX48Nwcih7jj8jO5czKZnEJmcQ948/PV2ceHJo82IrSyrijcURvLfiMG7ODsyf2JMmFyRK0rNyuPmDDew7nUzzIA9+mNCDWhZHcq0GD3wRztL9sfh7WPh1Uk+CvVwLjHs8IZ3RH23kZOJ5Gvq588393dh27BwPfbuDrBwrXUJr8/GdXfByK/u+LskZ2Vz79hpOnDvPjR3q8OywlnR6cQmGAZufGlDqHjciIiIiIiIiImJfZc0bKDlTAUrOiMiVYvXBM9z5yWbMJnBxciA9K5cxXevz8o2tS10eKzE9i8lfb2fNobMltvtX36t4cmjzygyb5QdiuefTrQC8Pbo917evU6jNqcTzXD9rHWdSMhnQPICP7uzMa4sP8OGqo1gczXz7QHfa1/MucvwLEzSBnhbiUjIxjLw9bd4e3eGikk1boxK45cMNWP8aZ/HeWJoHefDHw33KPZaIiIiIiIiIiFSvsuYNLstlzUREpPKcz8rlmV/2ADCuRyjvjumAyQRfb47m0/VRJfY9mXiem2dvsCVmLI5m6vm40rlBba5tE8RdPUK5u2coAB+tPsL26HMljFY+xxPSmfLtzry4uzcoMjEDEOLtyv/u7IzF0cyyA3GM/XgjH646CsDro9oVm5gBCixxFpucl5gZG1af98d2uugqoM6hPkzu3wSAxXtjAeirJc1ERERERERERC4rjvYOQERELm3vLj9EdEI6QZ4uPHJNM2pZHHlqaAte+m0/LyzcR0M/d/o1CyjU70BMMuM+2UxscibBXi7MGdeFFsEeRVbaJKRlMX/HKR79fieLHuxd4eXNMrJzmfBVOEnns2lfz5unr2tZYvv29bx57ea2PPTNDjYeTQDgwf6NGdEupNS58hM0LyzcR1gjX+7pGVpqNVFpJvdvzNrDZwk/lpesUnJGREREREREROTyosoZEREpVkRMCh+tzqsimXF9K2pZ8nL69/ZuyKhOdbEaMHnedg7HpRTot/FoPKNmbyA2OZMmAbX4cUIPWoZ4Fpu0eH54K/xqWThyJo3/Lj14UbGmZGTz2+7TPPLdTnq8upw9J5Op7ebErLEdcXYs/a+769vX4eGBeRUrw9uF8PDApmWeu56PGx/d2ZnxvRpWODED4OhgZuat7fF2cyLYy4VOobUrPKaIiIiIiIiIiFw6tOdMBWjPGbkU/BB+gn2nknlscDNcnSt/M3W5clmtBqM+3ED4sXNc0zKQj+7sXOB8Zk4ud3y8mc1RCdT3cWP+xJ7Udnfm992neejbHWTlWOkSWpv/3dkZbzfnUuf7c28M938RjtkEP07oQYf6pSckjieks3R/LMsPxLHxaDzZuX//lVbbzYlZt3WkR2O/cl13TFIGgZ6WSkmyVNS5tCzMJhNebk72DkVERERERERERMqgrHkDLWsmUoOtPniGR7/P21Mj6Xw2b4xqe0l8oFxdEtOzuPXDjbhbHHj1prY0DfSwd0iXla+3RBN+7Bzuzg48P6JVofMWRwc+uL0jN7y/juiEdP71ZThDWgfxn4X7MAy4pmUg74zpUOYlyq5pFcQN7UP4pQzLmyWkZfHML7v5bXdMgeMN/dwZ0DyAAS0C6RxaGyeH8heIBnm5lLtPVantXnpSS0REREREREREah5VzlSAKmfEnuJTMxny9hrOpGTajr0ysg1juta3Y1TV6/2Vh3ntjwgAnB3NPDGkOXf3CMVsvnISVKXJybUSn5ZFXHImsckZxKXk/RmflknzIE9GtA/B06VwVUZcSgYD3lxFSkYOzw1ryT29GhY7x8HYFEa+v57UzBzbsdvC6vPC9a1xKOfPIjE9i0H/Xc2ZlEwe6NuIaUNbFGqzIiKOx3/YxZmUTMwm6BLqw8AWgfRvEcBV/rXKNZ+IiIiIiIiIiEhlKmveQMmZClByRuzFMAzu+3wrS/fH0TigFte1CebtZYdwdjTz47960Kaul71DrHLZuVb6vLaC00kZNPJz5+jZNAB6NvbljVHtCPZytXOEVc8wDA7FpRIdn05sSgZxyZnE/fVn/vdnUzOxlvCUd3VyYES7EMaE1addXS9b5dXkr7ezYOcp2tTx4peJPUtNsqw4EMf4z7ZgNWDqoKZM7t/4oqu4luyL5b7Pt2I2wQ8TetDxr+XNzmfl8vJv+/li4zEAGgfUYuat7Wld5/K/30VEREREREREpGZQcqYaKDkj9vLFxmM8+8senB3M/DKxJy2CPbjv83CW7o+lbm1XFk7uVaY9PmqyBTtPMfnr7fjVcmbtE/35futxXvptPxnZVjxdHHnpxjYMbxdSqXMahsGGI/FsiTqHQdGPztpuztzUqS61LFW7amTU2TSeX7CXlRFnSm1rNoG/h4UADxcCPS0EeLrg6eLE0v2xHI5LtbVrEezJbV3rUdvdmUnztmM2wa+TepU5+bE1KoGsXCs9rirfHi9FmfLtDn7efpKr/N1Z9GBvDsam8PC3Ozh6Ji8Jd1ePUJ4c2rzMS6aJiIiIiIiIiIhUByVnqoGSM1em2OQMXli4j26NfLm9W4Nqn/9QbArD3l1LZo6VZ4e1ZPxfy00lnc9m+LtriU5Ip3/zAD6+s/NlvbzXyPfXsS06kYcGNGHKoKYAHDmTypRvd7DrRBIAN7QPYcb1rfFyrdhm6oZhsPbwWWYuPUT4sXOltg/0tPDUtS0Y0S6k0vcAOp+Vy/srD/PhqqNk5VpxcjDRPMjTlnQJ8LAQeOGfnhZ83S1FVr4YhsHWY+f4elM0C3efJivHWuD8+F4NeXZYy0qNv6wuXN6sc4Pa7DieSI7VINDTwus3t6NPU3+7xCUiIiIiIiIiIlISJWeqgZIzV56k89nc+uEGDsSkAPDoNU2Z1L9Jtc2fkZ3LDbPWcSAmhT5N/fn0ri4FEjB7TiYx8oP1ZOVYeWxwMyZe3bjaYqtOO44ncsOsdTg5mFj3ZH8CPP7ewD0718o7yw4xa8VhrAYEebrwzLAWXNcmuNyJkqKSMhZHM0NbB1HLpejKmDWHznIsPh2Abo18+M/1rWka6HGRV1owlj/3xfKfBfs4mXgegD5N/Xl+eEsaVcI+K4npWfy07SRfb47mUFwqdWu7svjhPrhXcQVQSfKXN8t3XZtgXrqx9WVfFSYiIiIiIiIiIjWXkjPVQMmZK0tGdi53zNnElqhzuDs7kJaVC1RvguY/C/bxybpIfN2d+f3h3gWSEvm+3RLNEz/uxmyCL8aH0bNxxZeYKqt9p5L5YmMUfZr4M7hVUJVV7jz0zXbm7zjFyI51eOuW9kW2CT92jqnf7bAlSnpc5ct/rm9F44DSEyXFJWXGhjXgX30bEeBZ+H3Pl5Gdy/9WH2XWysNkZFtxNJu4q0coDw1sgodLwQqe2OQMdp9IYtfJJI7Fp1HbzRn/vypeAj3/XobsXHo2z/+6l1UH85Ywq+PtyrPDWjK4VWClV+YYhsGBmBQCPCz41rJU6tgX46VF+1i46zSPDW7GjR3qVPr1ioiIiIiIiIiIVCYlZ6qBkjNXjpxcK//6chtL98fi4eLIdw90Z/mBOF5fHAFUT4JmZUQcd83dAsCccZ0Z0CKw2LaP/7CT77aewNfdmUUP9ibIq/hkQmXItRr8b81R3vwzguzcvEdK8yAPHhrQpNKTNDFJGfT6v+XkWA0WTi55P5SM7FxmrzrCByuPkJmTlygZ36shkwc0KbQnTK7VYMfxRJbtj2Xp/lgOxubtxVLWpMw/nTiXzgsL97F4bywAAR4WJvS7inPp2ew5mcTuk0mcScks17U7O5i5v08jJl7dGFdn7bUiIiIiIiIiIiJyqVFyphooOXNlMAyDJ37cxXdbT+DsaOaLe7oS1sgXgFkrDtsSNI8MasrkAVWToDmbmsmQmWs4m5rJnd0b8J/rW5fYPiM7l5Hvr2ff6WQ61vfm03u64ulSsX1XinPiXDpTv9vJ5sgEALqE1ubA6RRSMnMAaBbowUMDmzCkkpI0byyO4L0Vh+kSWpvv/9WjTH2i49P5z8K9LN0fB+TtCfPMdS25unkAaw6eYdmBOFYciCM+LcvW52KTMv+0MiKO53/dS9RfFTwXMpugSYAHret40TigFikZ2cQmZxKXkkHcX3+eS88GoG9Tf54f0YqGfu4XHYuIiIiIiIiIiIhULSVnqoGSM1eG1/44wPsrj2A2wezbO3FNq6AC56s6QbPvVDLPzd/D1mPnaBpYi18n9cLFqfSqiWPxaQx7dy0pGTn41bIwbWhzRnasvGWhDMPg5+0nmT5/LymZObg5OzB9eEtu6VyP5PM5zFkXydy1kQWSNA8OaEKbYipdzOa85bpKii8jO5cery4nIS2LD8Z2ZGib4HLFvPxALM//uo/ohLxEidkE1guegB4ujvRrFsCA5gH0a+ZfaXubZObk8vGaSFZGxFHfx502dTxpU9eblsGepVbAZGTncj4rl9ru2mdFRERERERERETkUqfkTDVQcuby98naSP6zcB8Ar45sw+iu9Yts9/7Kw7z2R+UmaPadSuadZYf4Y28MAC5OZn7+d09aBJf9Xgs/do7HftjJ0TNpQF5Vy4wRrWkZUrH7NTE9i6d/3sOi3acB6Fjfm//e2p4GvgWrOpLSs/lkXSSfXJCkKUlYQx8+vKNTsUmR/P106ni7suqxfjg6mMsde0Z2Lh+tPsqsFYfJzLES6uvGgBaBDGgRQJdQH5wuYkwRERERERERERERUHKmWig5c3mbv+MkD32zA4DHBjdj4tWNS2x/YYLm/j6NmDqoaZkqXP5p76kk3ll2yLZXickEw9qG8NCAJjQOqFXu8bJyrMxZG8k7yw5xPjsXswnu7B7KlEFN8XItfqmzjOxczqRkEpucQdw//lx76CxxKZk4mk08PLAJ/+p7VYmJkvwkzdebo0krJkmTkWMl12rQJKAWn93TlRBv1wLnDcNg6NtrOBCTwlPXNuf+PleV+724UGJ6FikZOdTzcavQOCIiIiIiIiIiIiL5lJypBkrO1GwZ2blsOBrPyXPniUvJ5ExKhm2/j9jkTM6mZmIYcFePUKYPb1mm5cAuTNDU93Fj+vCWDGgRWKZ4dp9I4r0VBZMyw9uGMLl/Y5oEelz8hf7lVOJ5Xlq031bt4lfLmQn9GmM28Xfy5YLrTzqfXeJ4jfzdmXlre9rW9a5wbAAHYpK565MtxCRnEOTpwufju9L0gutef/gst328CVcnBzZOG4CXW9XsoSMiIiIiIiIiIiJysZScqQZKztRMh+NS+WZzND9uO2HbbL04t3Suy6sj25ZrI/vfdp/mPwv2EZOcAcCA5gFMH96K+r6FKzTSMnNYsPMUX2+OZueJJODvpMyDAxrTOKDiSZl/WnvoLM/9use21FlJLI5mAjwtBHq4EOBpIcDDhUBPF+rUdmVQi8BS90spr5OJ5xn3yWYOx6Xi6eLIx+O60LWhDwD3fraVpftjuaNbA164oXWlzisiIiIiIiIiIiJSGZScqQZKztQcGdm5LN4bw1ebotkcmWA7HuTpQus6XgTaEg8WWxIiyMsFv1qWi5ovLTOHd5YfYs6aSHKsBs6OZib0vYoJ/a7CxcmBPSeTmLc5mvnbT5KWlQuAk4OJ69oEM6l/1SRlLpSVY+XT9ZGsjDhDbTdn/D0sBHq6FHwfPFzwdHUsU8VQZUpMz2L8Z1sJP3YOZ0cz747pQPMgD/q9sRLDgGWP9OUq//Iv7yYiIiIiIiIiIiJS1ZScqQZKzlSfqLNpbDwaz/B2IbhbHMvc70xKJrNXHeHHbSdI/KtKxmyC/s0DuS2sHn2bBuBQjqqY8jocl8L0X/ey7nA8APV8XKnt5syuv6pkABr6uTOmaz1u6lgX34tMBl1uMrJzmTRvO0v3x2I2QasQL3afTKJvU38+u6ervcMTERERERERERERKZKSM9VAyZmqZxgGX2w8xsu/7Scj20qorxtv3dqejvVrl9p3yb5YnvxxF/FpWQCEeLkwumt9RnWuS7CXaym9K49hGPy2O4YXFv691Jmzg5nBrYO4rWt9ujXyqfbqlJogJ9fKs/P38PXm47Zjn93Tlb5N/e0YlYiIiIiIiIiIiEjxlJypBkrOVK245Awe+2EXqw6eAfL2P8nMseJgNjHx6sZM7t8YJwdzoX5pmTm8sHAf32zJ+1C/eZAHjw9pVuVVMqVJy8zh2y3HMZlgRLsQVcmUgWEYzFx6iLeXHaJ5kAe/P9RbiSwRERERERERERG5ZCk5Uw2UnKk6f+w5zbSfdnMuPRuLo5knhzZnZIe6TP91D7/sOAVAu7pe/PfW9jS6YP+R8GPnmPrdDo7Fp2Mywf29GzH1mqZYHCt343qpXntOJhHs5aKEloiIiIiIiIiIiFzSypo3KFx2cIWZNWsWoaGhuLi4EBYWxubNm+0d0hUtJSObR77byb++3Ma59GxaBnuycHIv7u7ZEC83J2aO7sC7Yzrg6eLIzhNJXPfOWr7ceIzsXCtv/RnBqNnrORafToiXC/Pu7ca0a1soMXMZaF3HS4kZERERERERERERuWxc0ZUz3377LXfeeSezZ88mLCyMmTNn8v333xMREUFAQECp/VU5c/EMwyAlM4e45AzikjOJTckgJimTrzYd48S585hMMKHvVTw8sCnOjoVziKeTzvPo9ztZdzgeAF93Z9veMjd2qMPzI1rh5epUrdckIiIiIiIiIiIiIlc2LWtWBmFhYXTp0oX33nsPAKvVSr169Zg8eTJPPvlkqf2VnCnaTR+sJy0zp9jz57NziU3OICPbWuT5urVd+e+t7ekS6lPiPFarwdz1UfzfHwfIyrHi5erESze2ZljbkArFLyIiIiIiIiIiIiJyMcqaN3CsxpguKVlZWYSHhzNt2jTbMbPZzMCBA9mwYUORfTIzM8nMzLR9n5ycXOVx1kSHYlNIzig+OXMhDxdHAj1dCPCwEOjpQiM/d+7qGYqHS+lVL2azifG9GtKniR9/7otlZMc6BHu5VjR8EREREREREREREZEqdcUmZ86ePUtubi6BgYEFjgcGBnLgwIEi+7zyyivMmDGjOsKr0T68ozO51uILsixOZgI8LAR4uODqXPH9YJoEetAk0KPC44iIiIiIiIiIiIiIVIcrNjlzMaZNm8bUqVNt3ycnJ1OvXj07RnRp6n6Vr71DEBERERERERERERG5ZF2xyRk/Pz8cHByIjY0tcDw2NpagoKAi+1gsFiwWS3WEJyIiIiIiIiIiIiIilymzvQOwF2dnZzp16sSyZctsx6xWK8uWLaN79+52jExERERERERERERERC5nV2zlDMDUqVMZN24cnTt3pmvXrsycOZO0tDTuvvtue4cmIiIiIiIiIiIiIiKXqSs6OXPrrbdy5swZnnvuOWJiYmjfvj1//PEHgYGB9g5NREREREREREREREQuUybDMAx7B1FTJScn4+XlRVJSEp6envYOR0RERERERERERERE7KiseYMrds8ZERERERERERERERERe1ByRkREREREREREREREpBpd0XvOVFT+inDJycl2jkREREREREREREREROwtP19Q2o4ySs5UQEpKCgD16tWzcyQiIiIiIiIiIiIiInKpSElJwcvLq9jzJqO09I0Uy2q1curUKTw8PDCZTPYOp9IlJydTr149jh8/XuLGRSKXKt3DUtPpHpaaTvew1HS6h6Wm0z0sNZ3uYanpdA9LTad7+OIYhkFKSgohISGYzcXvLKPKmQowm83UrVvX3mFUOU9PT/3HJzWa7mGp6XQPS02ne1hqOt3DUtPpHpaaTvew1HS6h6Wm0z1cfiVVzOQrPm0jIiIiIiIiIiIiIiIilU7JGRERERERERERERERkWqk5IwUy2KxMH36dCwWi71DEbkouoelptM9LDWd7mGp6XQPS02ne1hqOt3DUtPpHpaaTvdw1TIZhmHYOwgREREREREREREREZErhSpnREREREREREREREREqpGSMyIiIiIiIiIiIiIiItVIyRkREREREREREREREZFqpOSMiIiIiIiIiIiIiIhINVJyRoo0a9YsQkNDcXFxISwsjM2bN9s7JJEivfLKK3Tp0gUPDw8CAgK44YYbiIiIKNCmX79+mEymAq9//etfdopYpKDnn3++0P3ZvHlz2/mMjAwmTpyIr68vtWrV4qabbiI2NtaOEYsUFBoaWugeNplMTJw4EdAzWC49q1evZvjw4YSEhGAymfjll18KnDcMg+eee47g4GBcXV0ZOHAghw4dKtAmISGBsWPH4unpibe3N+PHjyc1NbUar0KuZCXdw9nZ2TzxxBO0adMGd3d3QkJCuPPOOzl16lSBMYp6dr/66qvVfCVypSrtOXzXXXcVuj+HDBlSoI2ew2JPpd3DRf1ubDKZeP31121t9BwWeyrLZ2ll+SwiOjqa6667Djc3NwICAnjsscfIycmpzkup8ZSckUK+/fZbpk6dyvTp09m2bRvt2rVj8ODBxMXF2Ts0kUJWrVrFxIkT2bhxI0uWLCE7O5trrrmGtLS0Au3uu+8+Tp8+bXu99tprdopYpLBWrVoVuD/Xrl1rOzdlyhQWLFjA999/z6pVqzh16hQjR460Y7QiBW3ZsqXA/btkyRIARo0aZWujZ7BcStLS0mjXrh2zZs0q8vxrr73GO++8w+zZs9m0aRPu7u4MHjyYjIwMW5uxY8eyd+9elixZwsKFC1m9ejX3339/dV2CXOFKuofT09PZtm0bzz77LNu2beOnn34iIiKCESNGFGr7n//8p8CzefLkydURvkipz2GAIUOGFLg/v/766wLn9RwWeyrtHr7w3j19+jSffPIJJpOJm266qUA7PYfFXsryWVppn0Xk5uZy3XXXkZWVxfr16/nss8/49NNPee655+xxSTWXIfIPXbt2NSZOnGj7Pjc31wgJCTFeeeUVO0YlUjZxcXEGYKxatcp2rG/fvsZDDz1kv6BESjB9+nSjXbt2RZ5LTEw0nJycjO+//952bP/+/QZgbNiwoZoiFCmfhx56yLjqqqsMq9VqGIaewXJpA4yff/7Z9r3VajWCgoKM119/3XYsMTHRsFgsxtdff20YhmHs27fPAIwtW7bY2vz++++GyWQyTp48WW2xixhG4Xu4KJs3bzYA49ixY7ZjDRo0MP773/9WbXAiZVDUPTxu3Djj+uuvL7aPnsNyKSnLc/j66683+vfvX+CYnsNyKfnnZ2ll+Szit99+M8xmsxETE2Nr88EHHxienp5GZmZm9V5ADabKGSkgKyuL8PBwBg4caDtmNpsZOHAgGzZssGNkImWTlJQEgI+PT4HjX331FX5+frRu3Zpp06aRnp5uj/BEinTo0CFCQkJo1KgRY8eOJTo6GoDw8HCys7MLPJObN29O/fr19UyWS1JWVhZffvkl99xzDyaTyXZcz2CpKSIjI4mJiSnw3PXy8iIsLMz23N2wYQPe3t507tzZ1mbgwIGYzWY2bdpU7TGLlCYpKQmTyYS3t3eB46+++iq+vr506NCB119/XcuQyCVl5cqVBAQE0KxZMyZMmEB8fLztnJ7DUpPExsayaNEixo8fX+icnsNyqfjnZ2ll+Sxiw4YNtGnThsDAQFubwYMHk5yczN69e6sx+prN0d4ByKXl7Nmz5ObmFvgPCyAwMJADBw7YKSqRsrFarTz88MP07NmT1q1b247fdtttNGjQgJCQEHbt2sUTTzxBREQEP/30kx2jFckTFhbGp59+SrNmzTh9+jQzZsygd+/e7Nmzh5iYGJydnQt9mBIYGEhMTIx9AhYpwS+//EJiYiJ33XWX7ZiewVKT5D9bi/pdOP9cTEwMAQEBBc47Ojri4+OjZ7NccjIyMnjiiScYM2YMnp6etuMPPvggHTt2xMfHh/Xr1zNt2jROnz7NW2+9ZcdoRfIMGTKEkSNH0rBhQ44cOcJTTz3F0KFD2bBhAw4ODnoOS43y2Wef4eHhUWhpaj2H5VJR1GdpZfksIiYmpsjfmfPPSdkoOSMil42JEyeyZ8+eAvt1AAXWHm7Tpg3BwcEMGDCAI0eOcNVVV1V3mCIFDB061PZ127ZtCQsLo0GDBnz33Xe4urraMTKR8pszZw5Dhw4lJCTEdkzPYBER+8jOzuaWW27BMAw++OCDAuemTp1q+7pt27Y4OzvzwAMP8Morr2CxWKo7VJECRo8ebfu6TZs2tG3blquuuoqVK1cyYMAAO0YmUn6ffPIJY8eOxcXFpcBxPYflUlHcZ2lSPbSsmRTg5+eHg4MDsbGxBY7HxsYSFBRkp6hESjdp0iQWLlzIihUrqFu3boltw8LCADh8+HB1hCZSLt7e3jRt2pTDhw8TFBREVlYWiYmJBdromSyXomPHjrF06VLuvffeEtvpGSyXsvxna0m/CwcFBREXF1fgfE5ODgkJCXo2yyUjPzFz7NgxlixZUqBqpihhYWHk5OQQFRVVPQGKlEOjRo3w8/Oz/e6g57DUFGvWrCEiIqLU349Bz2Gxj+I+SyvLZxFBQUFF/s6cf07KRskZKcDZ2ZlOnTqxbNky2zGr1cqyZcvo3r27HSMTKZphGEyaNImff/6Z5cuX07Bhw1L77NixA4Dg4OAqjk6k/FJTUzly5AjBwcF06tQJJyenAs/kiIgIoqOj9UyWS87cuXMJCAjguuuuK7GdnsFyKWvYsCFBQUEFnrvJycls2rTJ9tzt3r07iYmJhIeH29osX74cq9VqSz6K2FN+YubQoUMsXboUX1/fUvvs2LEDs9lcaKkokUvBiRMniI+Pt/3uoOew1BRz5syhU6dOtGvXrtS2eg5LdSrts7SyfBbRvXt3du/eXSBZnv8PQlq2bFk9F3IZ0LJmUsjUqVMZN24cnTt3pmvXrsycOZO0tDTuvvtue4cmUsjEiROZN28e8+fPx8PDw7aupZeXF66urhw5coR58+Zx7bXX4uvry65du5gyZQp9+vShbdu2do5eBB599FGGDx9OgwYNOHXqFNOnT8fBwYExY8bg5eXF+PHjmTp1Kj4+Pnh6ejJ58mS6d+9Ot27d7B26iI3VamXu3LmMGzcOR8e/f73UM1guRampqQUqtyIjI9mxYwc+Pj7Ur1+fhx9+mBdffJEmTZrQsGFDnn32WUJCQrjhhhsAaNGiBUOGDOG+++5j9uzZZGdnM2nSJEaPHl1gST+RqlLSPRwcHMzNN9/Mtm3bWLhwIbm5ubbfj318fHB2dmbDhg1s2rSJq6++Gg8PDzZs2MCUKVO4/fbbqV27tr0uS64gJd3DPj4+zJgxg5tuuomgoCCOHDnC448/TuPGjRk8eDCg57DYX2m/S0DeP+74/vvvefPNNwv113NY7K20z9LK8lnENddcQ8uWLbnjjjt47bXXiImJ4ZlnnmHixIlamq88DJEivPvuu0b9+vUNZ2dno2vXrsbGjRvtHZJIkYAiX3PnzjUMwzCio6ONPn36GD4+PobFYjEaN25sPPbYY0ZSUpJ9Axf5y6233moEBwcbzs7ORp06dYxbb73VOHz4sO38+fPnjX//+99G7dq1DTc3N+PGG280Tp8+bceIRQpbvHixARgREREFjusZLJeiFStWFPm7w7hx4wzDMAyr1Wo8++yzRmBgoGGxWIwBAwYUurfj4+ONMWPGGLVq1TI8PT2Nu+++20hJSbHD1ciVqKR7ODIystjfj1esWGEYhmGEh4cbYWFhhpeXl+Hi4mK0aNHCePnll42MjAz7XphcMUq6h9PT041rrrnG8Pf3N5ycnIwGDRoY9913nxETE1NgDD2HxZ5K+13CMAzjww8/NFxdXY3ExMRC/fUcFnsr7bM0wyjbZxFRUVHG0KFDDVdXV8PPz8945JFHjOzs7Gq+mprNZBiGUYW5HxEREREREREREREREbmA9pwRERERERERERERERGpRkrOiIiIiIiIiIiIiIiIVCMlZ0RERERERERERERERKqRkjMiIiIiIiIiIiIiIiLVSMkZERERERERERERERGRaqTkjIiIiIiIiIiIiIiISDVSckZERERERERERERERKQaKTkjIiIiIiIiIiIiIiJSjZScERERERERqeH69euHyWSiX79+9g7lkmMymTCZTDz//PP2DkVERERExMbR3gGIiIiIiAikpaXxxRdf8Ouvv7Jz507i4+MxDANPT09CQ0Np06YN3bt3Z8iQIdSrV8/e4YqIiIiIiEgFKDkjIiIiImJnGzZsYPTo0URHRxc6d/bsWc6ePcvWrVuZO3cugYGBxMTE2CFKkUtHv379WLVqFX379mXlypX2DkdEREREpNyUnBERERERsaODBw8yePBgUlJSABgxYgQ333wzTZs2xdnZmbNnz7Jz506WLFnCihUr7BytiIiIiIiIVAYlZ0RERERE7Ojpp5+2JWbmzp3LXXfdVajNoEGDePTRRzlz5gzfffddNUcoIiIiIiIilc1s7wBERERERK5Uubm5LFq0CIDOnTsXmZi5kL+/PxMnTqyGyERERERERKQqKTkjIiIiImInZ86c4fz58wA0bty4UsbMyMjgvffeY8CAAQQFBeHs7ExAQAADBw5kzpw55OTklDrGxo0bGTVqFEFBQbi4uNCwYUPuv/9+IiIigLz9PkwmE/369SvU99NPP8VkMmEymYiKiip2jqioKFu7Tz/9tMR4fvnlF0aNGkX9+vVxcXHB29ubzp07M2PGDM6dO1dsv7vuuguTyURoaCgAiYmJPPfcc7Rq1Qp3d3e8vb3p06cPX331VWlvCQApKSm8+eab9O/f3/beenp60qFDByZPnsy6deuK7Zubm8tnn33GsGHDCAkJwWKx4OvrS69evXjrrbds90FVi4mJ4emnn6Zz5874+PhgsVioV68et9xyC0uXLi22X1E/ryVLljB8+HCCgoKwWCw0bNiQCRMmcOLEiVLjiI+P5/HHH6dZs2a4uroSGBjIoEGD+Pnnn4Hi76P8n+mqVasAWLVqla1d/iv/512cLVu2MGbMGOrWrYvFYqFOnTrccccd7N+/v9S4RUREREQqjSEiIiIiInYRHx9vAAZgtGvXrsLj7dixw2jQoIFtzKJeXbp0MWJiYood46233jLMZnORfd3d3Y1FixYZffv2NQCjb9++hfrPnTvX1j4yMrLYeSIjI23t5s6dW2SbhIQEo3///iVeT0BAgLFhw4Yi+48bN84AjAYNGhgHDhwwQkNDix1n4sSJJb21xpIlSww/P78SYynuf6+OHTtmtGvXrsR+jRs3NiIiIkqMoSQl/Uzyffnll4a7u3uJcYwfP97Izs4u1PefP68nn3yy2DH8/f2Nffv2FRvHrl27jMDAwGL733///cXeR/k/05JeDRo0KDBf/vHp06cbs2bNMhwdHYvs5+bmZqxataq8b72IiIiIyEVR5YyIiIiIiJ34+PjQoEEDAHbu3Mn//d//YbVaL2qsw4cP07dvX44dO4anpyfTpk3j559/ZuvWrSxevJiJEyfi6OjIli1buP7668nOzi40xs8//8zUqVOxWq14eXnx8ssvs379etavX8+LL76Ig4MDY8eO5dSpUxW67rLIzMxk4MCBLF++HAcHB+644w6+/vprNm7cyJo1a3jppZfw9fUlLi6Oa6+9lmPHjhU7Vnp6OsOHDyc+Pp5nnnmGlStXsnXrVv73v/9Rt25dAGbNmsXixYuL7L9ixQqGDh3K2bNncXBw4K677uLnn38mPDycdevW8b///Y+RI0fi5ORUqG98fDy9evVi586dWCwWJk2axPfff8+WLVtYsWIF06ZNw83NjcOHDzN06FCSkpIq5w38h++++4477riDtLQ0GjVqxFtvvcUff/xBeHg4P/74I9deey0Ac+bM4fHHHy9xrP/973+8+uqr9O3bl3nz5rF161aWLl3KnXfeCeRVhN1zzz1F9k1MTGTIkCHExsYCcMcdd/D777+zdetWvvnmG7p3785HH33E7Nmzi+z/0ksvsXv3bjp37gzkLQe4e/fuAq8///yzyL6LFy9m8uTJtGrVik8++YQtW7awevVqpkyZgtlsJj09nTvuuIOsrKzS31ARERERkYqyd3ZIRERERORK9sYbbxT41/uhoaHGgw8+aHzzzTfG0aNHyzxOjx49DMDo0KGDcebMmSLb/P7777aqmI8++qjAuczMTCMkJMQADC8vryIrH3bv3m14enraYq3KypmnnnrKAAxvb29j69atRY4RFRVlBAcHG4Bx2223FTp/YZWFl5eXsWfPnkJtDh06ZLi4uBiAMWLEiELnz58/b3tf3NzcjBUrVhR7TdHR0YWO3XbbbbZqjuJ+ntu2bbNVtDz11FPFjl+Skipnzpw5Y3h5eRmAcc899xRZGWMYf7/nZrPZOHDgQIFzF/68AOO+++4zrFZroTHuvfdeW5tt27YVOv/www/bzs+cObPQ+ZycHOP6668vMFdR91FZKoXyXTjWtddea2RmZhZq8+KLL9ra/PTTT6WOKSIiIiJSUaqcERERERGxoylTphSoMoiKiuKdd95h9OjRNGrUiKCgIEaPHs2CBQswDKPIMdasWcP69esB+Oyzz/Dz8yuy3ZAhQ7j55psBCu3zMn/+fFtFzLPPPkuLFi0K9W/dujVPP/10ua+xvFJTU5k1axYAL7zwAp06dSqyXYMGDXj22WcB+P7770lLSyt2zBdeeIFWrVoVOt64cWNuuOEGANauXVvo/Oeff257X15++eUi99nJV69evQLfR0VF8e233wLw3nvv0bBhwyL7dejQgYkTJwKFfy6V4YMPPiApKYk6derw/vvv4+joWGS7GTNmUKdOHaxWK59//nmx4wUHB/Puu+9iMpkKnXv00UdtX69Zs6bAuczMTNv1denShYceeqhQfwcHBz788ENcXFzKcmnl4uLiwty5c3F2di507sEHH7Qd/2fcIiIiIiJVQckZERERERE7MpvNzJkzhz///JMhQ4YU+uA8NjaWb7/9lhEjRtC1a1eOHDlSaIxff/0VgGbNmtGmTZsS5+vTpw+Qtyl6Tk6O7Xj+ZvAmk4lx48YV2//uu+8u8kP5yrRq1Srb8l75yaTi5F9PdnY24eHhRbYxmUzcdtttxY6Rn/xJSEggMTGxwLmFCxcC4O7uzn333Vem+PMtWrSI3Nxc3NzcGDp0aIlt86/j1KlTREdHl2ue0uTfH8OGDcNisRTbztHRke7duwOwYcOGYtvdfPPNxY7TrFkzatWqBcDRo0cLnNu6davt/b399tuLHT8wMJDBgwcXe/5iDRo0iICAgCLPeXh40KRJE6Bw3CIiIiIiVaHofzIlIiIiIiLVatCgQQwaNIjk5GTWrVvHli1b2Lp1K6tXr7YlKrZu3Urv3r0JDw8nODjY1nfr1q0ARERElDlxkp2dTUJCgu3D6t27dwPQsGHDYitvAPz9/QkNDSUyMvKirrMs8q8HKHCdpYmJiSnyuJ+fH76+vsX28/HxsX2dkpKCt7e37fvt27cDeQkcNze3MscCf19Henp6sdUqRYmJiaF+/frlmqs4ubm57NixA4APP/yQDz/8sMwxFKd58+Yl9q1duzapqamkpKQUOL5nzx7b18VVQ+Xr3Lkz8+fPL0OkZVda3Pn3wT/jFhERERGpCqqcERERERG5hHh6ejJ06FCee+45fv31V2JjY/nkk0+oXbs2AKdPn7Yt5ZUvLi7uouZKT0+3fZ2QkABQbGXBhQIDAy9qvrKqjOu5UGlJFbP57/8tys3NLXDu7NmzQPmSRPkq+zouRkJCQoEKqcqIoazv5z/fy3Pnztm+9vf3L3GM0s5fjIuNW0RERESkKqhyRkRERETkEmaxWLj77rsJCQlhyJAhAPz000989NFHhT5MbteuHV9++WWZx65Tp06hY1W9ZFlZXPjh+LZt23BycipTv7p161ZVSBcl/zr8/PxYsWJFmfsVtzdNRWIAuPfee4vc56UoRe3LIiIiIiIilUfJGRERERGRGmDw4MHUq1eP48ePc+7cOeLj423VBflLdqWmptK6deuLGj+/Mic2NrbUtiW1ubAKxWq1FtsuLS2t2HMXLkHm7+9v16SLn58fJ06c4PTp0+Xum38dKSkptGjRAgcHh8oOr1QXLtlmGMZF3x+VIf8eAzhz5gxNmzYttu2ZM2eqIyQREREREbvRsmYiIiIiIjVESEiI7esLK1w6dOgA5G1kXtJeISVp06YNAJGRkcTHxxfb7syZM0RFRRV73sPDw/b1hctY/dPBgweLPZd/PQDr1q0rtl116NixI5C3f0x5lxvLv47MzMwC++hUJ2dnZ1q1agXY/73MjwMgPDy8xLalvV+XQoWXiIiIiEhFKDkjIiIiIlIDpKens2/fPiBvX5oLq0tGjBgB5FVGvP322xc1/sCBA21jfP7558W2+/TTTzEMo9jzFy7JVdIH7F9//XWJseTvD/LOO++UOF9VGz58OJD3/n/00Ufl7pufRJg5c2Zlh1Zm+ffHgQMHWLx4sd3i6Ny5M15eXgAlLr8XGxtbapwuLi5AXuJLRERERKQmUnJGRERERMROUlNTCQsLY+HChSUuAWa1Wpk8eTIpKSlA3oftF1YOXHPNNXTt2hWA119/ne+++67EeXfv3s2CBQsKHLvhhhtsm96/8MILREREFOq3b98+XnrppRLHbt26tW0prffee6/ID8+/++47vv/++2LH8Pb2ZtKkSQCsX7+eKVOmlPj+xMbG8vHHH5cY18W6/fbbbXvzPP3006xatarYtidOnCjwfbNmzRg1ahQA33zzDW+99VaJc0VGRpaYtLpYDz30ELVq1QLg7rvvZu/evSW2X7RoEbt27ar0OFxcXLjzzjsB2LJlS5GJRKvVygMPPEBGRkaJY+Xfq0ePHrVr8k5ERERE5GIpOSMiIiIiYkebN29m+PDh1K9fn0mTJvHVV1+xdu1adu7cyapVq5g5cybt27fnk08+AcDLy4sXXnih0Djz5s3Dx8eH3Nxcbr31VkaMGMFXX33F5s2bCQ8P5/fff+fll1+me/futG3btlCSwdnZmXfffRfIW46sW7duvPrqq2zcuJENGzbwyiuv0KNHDwAaN25c7PU4OjrywAMPALBnzx769+/P/Pnz2b59O3/88Qfjx49nzJgxtrGK85///IewsDAA3n77bTp27MisWbNYt24dO3bsYMWKFbz33nvccMMN1K9fn9mzZ5fxHS8fFxcXvvjiCxwdHUlPT2fgwIHcc889/Prrr2zbto0NGzYwd+5cRo0axVVXXVWo/wcffECjRo0AeOSRR+jbty9z5sxh48aNbN++naVLl/Lmm28yaNAgGjduzI8//ljp1xAYGMhnn32GyWTi9OnTdO7cmQkTJtiuYdOmTfz444888cQTXHXVVQwbNozo6OhKjwPg+eefJygoCICHH36YO++8k8WLF7Nt2za+++47evfuzfz5823JRih6CbP8+ycuLo6pU6cSHh7O4cOHOXz4MMeOHauS2EVEREREKpUhIiIiIiJ2cf78eSMoKMgAyvRq0qSJsXXr1mLHi4iIMFq3bl2msWbMmFHkGK+//rphMpmK7OPm5mYsXLjQ6Nu3rwEYffv2LXKMtLQ0o1u3bsXO3a9fP2PPnj227+fOnVvkOMnJycbIkSPLdD1XX311of7jxo0zAKNBgwYl/hzmzp1rGycyMrLINn/88YdRu3btUuMoyunTp43evXuX6TruvvvuEmMtTmk/E8MwjF9//dXw8fEpNQaz2WwsX768QN/IyMhSf175GjRoYADGuHHjijy/Y8cOw9/fv9j577rrLmPOnDm272NiYgqNkZKSYjRq1KjI/v/8eecfnz59eolxl+U9FBERERGpLKqcERERERGxExcXF06ePMm6deuYMWMGQ4cOpVGjRri7u+Pg4ICnpyfNmzfn1ltvZd68eezZs4dOnToVO17Tpk3ZsWMH8+bN46abbqJ+/fq4urri7OxMcHAw/fr145lnniE8PJznnnuuyDEeffRR1q5dy8iRIwkICMBisdCgQQPuuecetm7dynXXXVfqdbm5ubF8+XJeeukl2rRpg6urK56ennTp0oX33nuPpUuX4u7uXuo4Hh4e/Pjjj6xZs4Z7772XZs2a4eHhgaOjIz4+PnTp0oWJEyfy22+/sWTJklLHq4jBgwdz9OhRXn75ZXr06IGvr6/tZ9SxY0cefvhhNm/eXGTfoKAgVq9ezcKFCxk7diyNGjXCzc0NJycn/P396dGjB4888girVq2yVUhVheHDhxMZGckbb7xB//79CQwMxMnJCVdXVxo2bMiwYcN46623iIqK4uqrr66yONq1a8e+fft45JFHaNKkCRaLBT8/P66++mrmzZvH3LlzSU5OtrXP36fmQrVq1WL9+vU89NBDtGjRwrZHkYiIiIhITWEyDC3QKyIiIiIiZdevXz9WrVpF3759Wblypb3DkcvQvffey5w5c6hbty7Hjx+3dzgiIiIiIpVOlTMiIiIiIiJyyTh//jzz588HoFu3bnaORkRERESkaig5IyIiIiIiItXmyJEjFLeAQ25uLhMmTODs2bMAjBs3rjpDExERERGpNo72DkBERERERESuHC+88AKbN29m9OjRhIWFERAQwPnz59m1axf/+9//2LZtGwADBw4s0x5HIiIiIiI1kZIzIiIiIiIiUq3279/P9OnTiz3fs2dPvvnmG0wmUzVGJSIiIiJSfZScERERERERkWozbdo0mjZtytKlS4mKiuLMmTNkZ2fj6+tL586dufXWWxk9ejRms1bhFhEREZHLl8kobrFfERERERERERERERERqXT6p0giIiIiIiIiIiIiIiLVSMkZERERERERERERERGRaqTkjIiIiIiIiIiIiIiISDVSckZERERERERERERERKQaKTkjIiIiIiIiIiIiIiJSjZScERERERERERERERERqUZKzoiIiIiIiIiIiIiIiFQjJWdERERERERERERERESq0f8DG1vOwjT+IDgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1727\n",
      "CPU times: user 2.36 s, sys: 74.2 ms, total: 2.43 s\n",
      "Wall time: 2.05 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    141865.000000\n",
       "mean        477.118225\n",
       "std         462.470394\n",
       "min           2.000000\n",
       "25%         225.000000\n",
       "50%         375.000000\n",
       "75%         577.000000\n",
       "max       35213.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "from Bio import SeqIO\n",
    "\n",
    "fn = \"dataset/Train/train_sequences.fasta\"\n",
    "sequences = SeqIO.parse(fn, \"fasta\")\n",
    "l = []\n",
    "for seq in sequences:\n",
    "    l.append(len(seq.seq))\n",
    "display(pd.Series(l).describe())\n",
    "print()\n",
    "\n",
    "fn = \"dataset/Test (Targets)/testsuperset.fasta\"\n",
    "\n",
    "sequences = SeqIO.parse(fn, \"fasta\")\n",
    "list_seq_id_test = [seq.id for seq in sequences]\n",
    "sequences = SeqIO.parse(fn, \"fasta\")\n",
    "list_seq_len = [len(seq.seq) for seq in sequences]\n",
    "print(len(list_seq_len), list_seq_len[:10])\n",
    "sr = pd.Series(list_seq_len).value_counts().sort_index()\n",
    "fig = plt.figure(figsize=(20, 3))\n",
    "plt.plot(sr.head(1500))\n",
    "plt.title(\"Test data\", fontsize=20)\n",
    "plt.xlabel(\"Sequence length\", fontsize=20)\n",
    "plt.ylabel(\"Count\", fontsize=20)\n",
    "plt.show()\n",
    "fig = plt.figure(figsize=(20, 3))\n",
    "plt.plot(sr.head(200))\n",
    "plt.title(\"Test data\", fontsize=20)\n",
    "plt.xlabel(\"Sequence length\", fontsize=20)\n",
    "plt.ylabel(\"Count\", fontsize=20)\n",
    "plt.show()\n",
    "print((np.array(list_seq_len) < 60).sum())\n",
    "pd.Series(list_seq_len).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.809\n",
      "2.151\n",
      "17 ['Vaccinia virus (strain Copenhagen)', 'Danio rerio', 'Homo sapiens', 'Mus musculus', 'Drosophila melanogaster', 'Homo sapiens', 'Escherichia coli (strain K12)', 'Homo sapiens', 'Candida albicans (strain SC5314 / ATCC MYA-2876)', 'Caenorhabditis elegans', 'Homo sapiens', 'Drosophila melanogaster', 'Schizosaccharomyces pombe (strain 972 / ATCC 24843)', 'Schizosaccharomyces pombe (strain 972 / ATCC 24843)', 'Homo sapiens', 'Rattus norvegicus', 'Danio rerio', 'Gallus gallus', 'Homo sapiens', 'Rattus norvegicus', 'Mus musculus', 'Drosophila melanogaster', 'Homo sapiens', 'Vaccinia virus (strain Western Reserve)', 'Dictyostelium discoideum', 'Homo sapiens', 'Mus musculus', 'Homo sapiens', 'Escherichia coli (strain K12)', 'Arabidopsis thaliana']\n",
      "142246 ['UNG', 'wnt11', 'VENTX', 'Arrdc4', 'Gsc', 'CD80', 'yagF', 'LAMA3', 'BST1', 'glp-1', 'RASL10B', 'CG12262', 'gle1', 'gsa1', 'WDR45B', 'Mrs2', 'myo9aa', 'ANKRD1', 'ARMC5', 'Cyp2c7', 'Acsl1', 'IntS2', 'CAPNS1', 'VACWR052', 'msp', 'SOAT2', 'Renbp', 'ARHGAP8', 'ebgR', 'LAZ5']\n",
      "(142246, 13)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    142246.000000\n",
       "mean        553.636679\n",
       "std         641.728770\n",
       "min           3.000000\n",
       "25%         248.000000\n",
       "50%         411.000000\n",
       "75%         654.000000\n",
       "max       35375.000000\n",
       "Name: length, dtype: float64"
      ]
     },
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "organism\n",
       "Homo sapiens                                                                                                         25125\n",
       "Arabidopsis thaliana                                                                                                 14461\n",
       "Mus musculus                                                                                                         14384\n",
       "Danio rerio                                                                                                          12671\n",
       "Drosophila melanogaster                                                                                              12020\n",
       "Rattus norvegicus                                                                                                     8817\n",
       "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)                                                                 5469\n",
       "Trypanosoma brucei brucei (strain 927/4 GUTat10.1)                                                                    5209\n",
       "Caenorhabditis elegans                                                                                                4915\n",
       "Schizosaccharomyces pombe (strain 972 / ATCC 24843)                                                                   4634\n",
       "Escherichia coli (strain K12)                                                                                         3402\n",
       "Mycobacterium tuberculosis (strain ATCC 25618 / H37Rv)                                                                2062\n",
       "Dictyostelium discoideum                                                                                              1706\n",
       "Candida albicans (strain SC5314 / ATCC MYA-2876)                                                                      1491\n",
       "Oryza sativa subsp. japonica                                                                                          1311\n",
       "Gallus gallus                                                                                                         1298\n",
       "Plasmodium falciparum (isolate 3D7)                                                                                   1044\n",
       "Bos taurus                                                                                                            1026\n",
       "Emericella nidulans (strain FGSC A4 / ATCC 38163 / CBS 112.46 / NRRL 194 / M139)                                       921\n",
       "Pseudomonas aeruginosa (strain ATCC 15692 / DSM 22644 / CIP 104116 / JCM 14847 / LMG 12228 / 1C / PRS 101 / PAO1)      915\n",
       "Xenopus laevis                                                                                                         685\n",
       "Sus scrofa                                                                                                             511\n",
       "Oncorhynchus mykiss                                                                                                    498\n",
       "Candida glabrata (strain ATCC 2001 / CBS 138 / JCM 3761 / NBRC 0622 / NRRL Y-65)                                       495\n",
       "Bacillus subtilis (strain 168)                                                                                         396\n",
       "Neosartorya fumigata (strain ATCC MYA-4609 / Af293 / CBS 101355 / FGSC A1100)                                          309\n",
       "Gossypium hirsutum                                                                                                     267\n",
       "Zea mays                                                                                                               260\n",
       "Salmo salar                                                                                                            248\n",
       "Desulfovibrio vulgaris (strain ATCC 29579 / DSM 644 / NCIMB 8303 / VKM B-1760 / Hildenborough)                         227\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dbase\n",
       "sp    83883\n",
       "tr    58363\n",
       "Name: count, dtype: int64"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(142246, 13)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>length</th>\n",
       "      <th>description</th>\n",
       "      <th>dbase</th>\n",
       "      <th>sequence</th>\n",
       "      <th>in test</th>\n",
       "      <th>fragment</th>\n",
       "      <th>organism</th>\n",
       "      <th>taxonomyID</th>\n",
       "      <th>gene name</th>\n",
       "      <th>gene name lower</th>\n",
       "      <th>sequence version</th>\n",
       "      <th>protein existence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P20536</th>\n",
       "      <td>0</td>\n",
       "      <td>218</td>\n",
       "      <td>UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia v...</td>\n",
       "      <td>sp</td>\n",
       "      <td>MNSVTVSHAPYTITYHDDWEPVMSQLVEFYNEVASWLLRDETSPIP...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Vaccinia virus (strain Copenhagen)</td>\n",
       "      <td>10249</td>\n",
       "      <td>UNG</td>\n",
       "      <td>ung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O73864</th>\n",
       "      <td>1</td>\n",
       "      <td>354</td>\n",
       "      <td>WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7...</td>\n",
       "      <td>sp</td>\n",
       "      <td>MTEYRNFLLLFITSLSVIYPCTGISWLGLTINGSSVGWNQTHHCKL...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>wnt11</td>\n",
       "      <td>wnt11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O95231</th>\n",
       "      <td>2</td>\n",
       "      <td>258</td>\n",
       "      <td>VENTX_HUMAN Homeobox protein VENTX OS=Homo sap...</td>\n",
       "      <td>sp</td>\n",
       "      <td>MRLSSSPPRGPQQLSSFGSVDWLSQSSCSGPTHTPRPADFSLGSLP...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>VENTX</td>\n",
       "      <td>ventx</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A0B4J1F4</th>\n",
       "      <td>3</td>\n",
       "      <td>415</td>\n",
       "      <td>ARRD4_MOUSE Arrestin domain-containing protein...</td>\n",
       "      <td>sp</td>\n",
       "      <td>MGGEAGADGPRGRVKSLGLVFEDESKGCYSSGETVAGHVLLEAAEP...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Mus musculus</td>\n",
       "      <td>10090</td>\n",
       "      <td>Arrdc4</td>\n",
       "      <td>arrdc4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P54366</th>\n",
       "      <td>4</td>\n",
       "      <td>415</td>\n",
       "      <td>GSC_DROME Homeobox protein goosecoid OS=Drosop...</td>\n",
       "      <td>sp</td>\n",
       "      <td>MVETNSPPAGYTLKRSPSDLGEQQQPPRQISRSPGNTAAYHLTTAM...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Drosophila melanogaster</td>\n",
       "      <td>7227</td>\n",
       "      <td>Gsc</td>\n",
       "      <td>gsc</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A286YAI0</th>\n",
       "      <td>142241</td>\n",
       "      <td>450</td>\n",
       "      <td>A0A286YAI0_DANRE Macrophage receptor with coll...</td>\n",
       "      <td>tr</td>\n",
       "      <td>METEVDDFPGKASIFSQVNPLYSNNMKLCEAERYDFQHSEPKTMKS...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>marco</td>\n",
       "      <td>marco</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A1D5NUC4</th>\n",
       "      <td>142242</td>\n",
       "      <td>643</td>\n",
       "      <td>A0A1D5NUC4_CHICK Mitogen-activated protein kin...</td>\n",
       "      <td>tr</td>\n",
       "      <td>MSAAASAEMIETPPVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWR...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Gallus gallus</td>\n",
       "      <td>9031</td>\n",
       "      <td>MAP3K7</td>\n",
       "      <td>map3k7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q5RGB0</th>\n",
       "      <td>142243</td>\n",
       "      <td>448</td>\n",
       "      <td>Q5RGB0_DANRE Potassium channel, subfamily K, m...</td>\n",
       "      <td>tr</td>\n",
       "      <td>MADKGPILTSVIIFYLSIGAAIFQILEEPNLNSAVDDYKNKTNNLL...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>kcnk5b</td>\n",
       "      <td>kcnk5b</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A2R8QMZ5</th>\n",
       "      <td>142244</td>\n",
       "      <td>459</td>\n",
       "      <td>A0A2R8QMZ5_DANRE Myocyte enhancer factor 2aa O...</td>\n",
       "      <td>tr</td>\n",
       "      <td>MGRKKIQITRIMDERNRQVTFTKRKFGLMKKAYELSVLCDCEIALI...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>mef2aa</td>\n",
       "      <td>mef2aa</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A8I6GHU0</th>\n",
       "      <td>142245</td>\n",
       "      <td>138</td>\n",
       "      <td>A0A8I6GHU0_RAT U6 snRNA-associated Sm-like pro...</td>\n",
       "      <td>tr</td>\n",
       "      <td>HCISSLKLTAFFKRSFLLSPEKHLVLLRDGRTLIGFLRSIDQFANL...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Rattus norvegicus</td>\n",
       "      <td>10116</td>\n",
       "      <td>Lsm1</td>\n",
       "      <td>lsm1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142246 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  length                                        description  \\\n",
       "id                                                                              \n",
       "P20536           0     218  UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia v...   \n",
       "O73864           1     354  WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7...   \n",
       "O95231           2     258  VENTX_HUMAN Homeobox protein VENTX OS=Homo sap...   \n",
       "A0A0B4J1F4       3     415  ARRD4_MOUSE Arrestin domain-containing protein...   \n",
       "P54366           4     415  GSC_DROME Homeobox protein goosecoid OS=Drosop...   \n",
       "...            ...     ...                                                ...   \n",
       "A0A286YAI0  142241     450  A0A286YAI0_DANRE Macrophage receptor with coll...   \n",
       "A0A1D5NUC4  142242     643  A0A1D5NUC4_CHICK Mitogen-activated protein kin...   \n",
       "Q5RGB0      142243     448  Q5RGB0_DANRE Potassium channel, subfamily K, m...   \n",
       "A0A2R8QMZ5  142244     459  A0A2R8QMZ5_DANRE Myocyte enhancer factor 2aa O...   \n",
       "A0A8I6GHU0  142245     138  A0A8I6GHU0_RAT U6 snRNA-associated Sm-like pro...   \n",
       "\n",
       "           dbase                                           sequence  in test  \\\n",
       "id                                                                             \n",
       "P20536        sp  MNSVTVSHAPYTITYHDDWEPVMSQLVEFYNEVASWLLRDETSPIP...    False   \n",
       "O73864        sp  MTEYRNFLLLFITSLSVIYPCTGISWLGLTINGSSVGWNQTHHCKL...     True   \n",
       "O95231        sp  MRLSSSPPRGPQQLSSFGSVDWLSQSSCSGPTHTPRPADFSLGSLP...     True   \n",
       "A0A0B4J1F4    sp  MGGEAGADGPRGRVKSLGLVFEDESKGCYSSGETVAGHVLLEAAEP...     True   \n",
       "P54366        sp  MVETNSPPAGYTLKRSPSDLGEQQQPPRQISRSPGNTAAYHLTTAM...     True   \n",
       "...          ...                                                ...      ...   \n",
       "A0A286YAI0    tr  METEVDDFPGKASIFSQVNPLYSNNMKLCEAERYDFQHSEPKTMKS...    False   \n",
       "A0A1D5NUC4    tr  MSAAASAEMIETPPVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWR...    False   \n",
       "Q5RGB0        tr  MADKGPILTSVIIFYLSIGAAIFQILEEPNLNSAVDDYKNKTNNLL...    False   \n",
       "A0A2R8QMZ5    tr  MGRKKIQITRIMDERNRQVTFTKRKFGLMKKAYELSVLCDCEIALI...    False   \n",
       "A0A8I6GHU0    tr  HCISSLKLTAFFKRSFLLSPEKHLVLLRDGRTLIGFLRSIDQFANL...    False   \n",
       "\n",
       "            fragment                            organism  taxonomyID  \\\n",
       "id                                                                     \n",
       "P20536         False  Vaccinia virus (strain Copenhagen)       10249   \n",
       "O73864         False                         Danio rerio        7955   \n",
       "O95231         False                        Homo sapiens        9606   \n",
       "A0A0B4J1F4     False                        Mus musculus       10090   \n",
       "P54366         False             Drosophila melanogaster        7227   \n",
       "...              ...                                 ...         ...   \n",
       "A0A286YAI0     False                         Danio rerio        7955   \n",
       "A0A1D5NUC4     False                       Gallus gallus        9031   \n",
       "Q5RGB0         False                         Danio rerio        7955   \n",
       "A0A2R8QMZ5     False                         Danio rerio        7955   \n",
       "A0A8I6GHU0     False                   Rattus norvegicus       10116   \n",
       "\n",
       "           gene name gene name lower  sequence version  protein existence  \n",
       "id                                                                         \n",
       "P20536           UNG             ung                 1                  1  \n",
       "O73864         wnt11           wnt11                 1                  2  \n",
       "O95231         VENTX           ventx                 1                  1  \n",
       "A0A0B4J1F4    Arrdc4          arrdc4                 1                  1  \n",
       "P54366           Gsc             gsc                 2                  2  \n",
       "...              ...             ...               ...                ...  \n",
       "A0A286YAI0     marco           marco                 1                  4  \n",
       "A0A1D5NUC4    MAP3K7          map3k7                 1                  3  \n",
       "Q5RGB0        kcnk5b          kcnk5b                 1                  3  \n",
       "A0A2R8QMZ5    mef2aa          mef2aa                 1                  4  \n",
       "A0A8I6GHU0      Lsm1            lsm1                 1                  3  \n",
       "\n",
       "[142246 rows x 13 columns]"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fragment\n",
      "False    134009\n",
      "True       8237\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gene name lower\n",
       "ank2      98\n",
       "dscam1    79\n",
       "polg      71\n",
       "gh        53\n",
       "neb       41\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organism\n",
      "Homo sapiens               67\n",
      "Drosophila melanogaster    23\n",
      "Rattus norvegicus           7\n",
      "Mus musculus                1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    78493.000000\n",
       "mean         1.812213\n",
       "std          1.965690\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max         98.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "import time\n",
    "\n",
    "t0 = time.time()\n",
    "fn = \"dataset/Train/train_sequences.fasta\"\n",
    "sequences = SeqIO.parse(fn, \"fasta\")\n",
    "l1 = []\n",
    "l2 = []\n",
    "l3 = []\n",
    "l4 = []\n",
    "l5 = []\n",
    "for seq in sequences:\n",
    "    #     df_seq.loc[IX, 'id'] =\n",
    "    l1.append(seq.id)\n",
    "    #     df_seq.loc[IX, 'length'] =\n",
    "    l2.append(len(seq.seq))\n",
    "    #     df_seq.loc[IX, 'description'] =\n",
    "    l3.append(seq.description.split(\"|\")[-1])\n",
    "    #     df_seq.loc[IX, 'dbase'] =\n",
    "    l4.append(seq.description.split(\" \")[1].split(\"|\")[0])\n",
    "    #     df_seq.loc[IX, 'sequence'] =\n",
    "    l5.append(str(seq.seq))\n",
    "#     IX += 1\n",
    "\n",
    "print(\"%.3f\" % (time.time() - t0))\n",
    "\n",
    "df_seq = pd.DataFrame(data=[l1, l2, l3, l4, l5]).T\n",
    "print(\"%.3f\" % (time.time() - t0))\n",
    "df_seq.columns = [\"id\", \"length\", \"description\", \"dbase\", \"sequence\"]\n",
    "df_seq[\"length\"] = df_seq[\"length\"].astype(int)\n",
    "df_seq = df_seq.reset_index()\n",
    "df_seq = df_seq.set_index(\"id\")\n",
    "\n",
    "# ------------------------ Fragment -------------------------------------------------\n",
    "\n",
    "df_seq[\"in test\"] = df_seq.index.isin(list_seq_id_test)\n",
    "\n",
    "\n",
    "# ------------------------ Fragment -------------------------------------------------\n",
    "\n",
    "l = [\"Fragment\".lower() in t.lower() for t in l3]\n",
    "df_seq[\"fragment\"] = l\n",
    "\n",
    "\n",
    "# ------- organism - extracted from \"OX\" (if no - from the second term) --------------------------------\n",
    "l = []\n",
    "for t in l3:\n",
    "    if \"OS=\" in t:\n",
    "        s = t.split(\"OS=\")[1].split(\"OX=\")[0]\n",
    "        if s[-1] == \" \":\n",
    "            s = s[:-1]\n",
    "    elif \"HUMAN\" in t:  # Manually checked that only for humans it can happen\n",
    "        s = \"Homo sapiens\"\n",
    "    else:\n",
    "        # that cannot happen - manually checked\n",
    "        print(\"Problem !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\")\n",
    "        s = t.split(\"_\")[1].split(\" \")[0]\n",
    "    l.append(s)\n",
    "df_seq[\"organism\"] = l\n",
    "print(len(s), l[:30])\n",
    "\n",
    "# ------- organism symbol ------------ ----------------\n",
    "# Fails on : 'CAB85509.1 OS=Arabidopsis thaliana OX=3702 GN=F8F6_100 PE=4 SV=1'\n",
    "# df_seq['organism symbol'] = [t.split(' ')[0].split('_')[1] if '_' in t else 'None' for t in l3 ]\n",
    "# print(len(s), l[:30])\n",
    "\n",
    "\n",
    "# ------- organism taxon id ---------------- ----------------\n",
    "fn = \"dataset/Train/train_taxonomy.tsv\"\n",
    "df = pd.read_csv(fn, sep=\"\\t\", index_col=0)\n",
    "df_seq = df_seq.join(df, how=\"left\")\n",
    "\n",
    "\n",
    "# ---------------- gene ------------------------------------------------------------------------------------------------\n",
    "l = []\n",
    "ll = []\n",
    "for t in l3:\n",
    "    if \"GN=\" in t:\n",
    "        s = t.split(\"GN=\")[-1].split(\" \")[0]\n",
    "    else:\n",
    "        s = t.split(\"_\")[0]\n",
    "    l.append(s)\n",
    "    ll.append(s.lower())\n",
    "print(len(l), l[:30])\n",
    "df_seq[\"gene name\"] = l\n",
    "df_seq[\"gene name lower\"] = ll\n",
    "\n",
    "\n",
    "# ---------------- PE SV ------------------------------------------------------------------------------------------------\n",
    "l = [int(t.split(\"SV=\")[-1]) if \"SV=\" in t else 0 for t in l3]\n",
    "df_seq[\"sequence version\"] = l\n",
    "\n",
    "l = [int(t.split(\"PE=\")[1].split(\" \")[0]) if \"PE=\" in t else 0 for t in l3]\n",
    "df_seq[\"protein existence\"] = l\n",
    "\n",
    "\n",
    "# ---------------- Some analysis ------------------------------------------------------------------------------------------------\n",
    "\n",
    "print(df_seq.shape)\n",
    "display(df_seq[\"length\"].describe())\n",
    "print()\n",
    "display(df_seq[\"organism\"].value_counts().head(30))\n",
    "print()\n",
    "display(df_seq[\"dbase\"].value_counts().head(30))\n",
    "print()\n",
    "print(df_seq.shape)\n",
    "display(df_seq)\n",
    "\n",
    "print(df_seq[\"fragment\"].value_counts())\n",
    "df_seq.groupby(\"fragment\")[\"length\"].mean()\n",
    "\n",
    "# ------------------ Brief Look on frequent genes ----------------------------\n",
    "print()\n",
    "v = df_seq[\"gene name lower\"].value_counts()\n",
    "display(v.head(5))\n",
    "m = df_seq[\"gene name lower\"] == \"ank2\"\n",
    "print(df_seq[m][\"organism\"].value_counts())\n",
    "fig = plt.figure(figsize=(20, 3))\n",
    "ax = plt.plot(v.iloc[0:75], \"*-\")\n",
    "plt.xticks(rotation=90, fontsize=15)\n",
    "plt.show()\n",
    "v.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A0A8I6GHU0_RAT U6 snRNA-associated Sm-like protein LSm1 OS=Rattus norvegicus OX=10116 GN=Lsm1 PE=3 SV=1'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq.loc[\"A0A8I6GHU0\", \"description\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "protein existence\n",
       "1    85719\n",
       "2    25663\n",
       "3    15401\n",
       "4    15290\n",
       "5      170\n",
       "0        3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq[\"protein existence\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sequence version\n",
       "1     106753\n",
       "2      25025\n",
       "3       7747\n",
       "4       1973\n",
       "5        332\n",
       "8        246\n",
       "6        107\n",
       "9         31\n",
       "7         24\n",
       "10         5\n",
       "0          3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq[\"sequence version\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fragment\n",
       "False    134009\n",
       "True       8237\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq[\"fragment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "in test\n",
       "True     73653\n",
       "False    68593\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq[\"in test\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25125\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gene name lower\n",
       "ank2        67\n",
       "rnaseh2b    21\n",
       "smarca4     20\n",
       "pax6        18\n",
       "pign        18\n",
       "ddx3x       17\n",
       "nfib        15\n",
       "dnajc7      14\n",
       "tpm1        14\n",
       "psen1       14\n",
       "atxn2       13\n",
       "fhl1        13\n",
       "bin1        13\n",
       "elp4        13\n",
       "tmbim1      13\n",
       "chd4        13\n",
       "racgap1     13\n",
       "pabpc1      13\n",
       "enpep       12\n",
       "epb41       12\n",
       "Name: gene name lower, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = df_seq[\"organism\"] == \"Homo sapiens\"\n",
    "print(m.sum())\n",
    "df_seq[m][\"gene name lower\"].nunique()\n",
    "df_seq[m].groupby(\"gene name lower\")[\"gene name lower\"].count().sort_values(\n",
    "    ascending=False\n",
    ").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gene name lower\n",
       "ank2      98\n",
       "dscam1    79\n",
       "polg      71\n",
       "gh        53\n",
       "neb       41\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organism\n",
      "Homo sapiens               67\n",
      "Drosophila melanogaster    23\n",
      "Rattus norvegicus           7\n",
      "Mus musculus                1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    78493.000000\n",
       "mean         1.812213\n",
       "std          1.965690\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max         98.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print()\n",
    "v = df_seq[\"gene name lower\"].value_counts()\n",
    "display(v.head(5))\n",
    "m = df_seq[\"gene name lower\"] == \"ank2\"\n",
    "print(df_seq[m][\"organism\"].value_counts())\n",
    "fig = plt.figure(figsize=(20, 3))\n",
    "ax = plt.plot(v.iloc[0:75], \"*-\")\n",
    "plt.xticks(rotation=90, fontsize=15)\n",
    "plt.show()\n",
    "v.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55090 9782\n",
      "1 78493\n",
      "2 23403\n",
      "3 13621\n",
      "4 8350\n",
      "5 5411\n",
      "6 3631\n",
      "7 2462\n",
      "8 1734\n",
      "9 1218\n",
      "10 904\n",
      "11 646\n",
      "12 491\n",
      "13 354\n",
      "14 285\n",
      "15 214\n",
      "16 174\n",
      "17 135\n",
      "18 105\n",
      "19 83\n"
     ]
    }
   ],
   "source": [
    "print((v == 1).sum(), (v == 2).sum())\n",
    "for t in range(1, 20):\n",
    "    print(t, (v >= t).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_colwidth\", 200)\n",
    "pd.set_option(\"display.max_rows\", 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'length', 'description', 'dbase', 'sequence', 'in test',\n",
       "       'fragment', 'organism', 'taxonomyID', 'gene name', 'gene name lower',\n",
       "       'sequence version', 'protein existence'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98\n",
      "67\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>length</th>\n",
       "      <th>description</th>\n",
       "      <th>dbase</th>\n",
       "      <th>organism</th>\n",
       "      <th>taxonomyID</th>\n",
       "      <th>gene name</th>\n",
       "      <th>gene name lower</th>\n",
       "      <th>in test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q01484</th>\n",
       "      <td>15692</td>\n",
       "      <td>3957</td>\n",
       "      <td>ANK2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=4</td>\n",
       "      <td>sp</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHT4</th>\n",
       "      <td>84840</td>\n",
       "      <td>3870</td>\n",
       "      <td>A0A5F9ZHT4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGS7</th>\n",
       "      <td>85060</td>\n",
       "      <td>1827</td>\n",
       "      <td>A0A5F9ZGS7_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGS5</th>\n",
       "      <td>88666</td>\n",
       "      <td>1871</td>\n",
       "      <td>A0A5F9ZGS5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH35</th>\n",
       "      <td>90486</td>\n",
       "      <td>1758</td>\n",
       "      <td>A0A5F9ZH35_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH39</th>\n",
       "      <td>91029</td>\n",
       "      <td>1887</td>\n",
       "      <td>A0A5F9ZH39_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH10</th>\n",
       "      <td>93730</td>\n",
       "      <td>1005</td>\n",
       "      <td>A0A5F9ZH10_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGX3</th>\n",
       "      <td>95675</td>\n",
       "      <td>3972</td>\n",
       "      <td>A0A5F9ZGX3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHE2</th>\n",
       "      <td>96688</td>\n",
       "      <td>1806</td>\n",
       "      <td>A0A5F9ZHE2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGY1</th>\n",
       "      <td>97660</td>\n",
       "      <td>1887</td>\n",
       "      <td>A0A5F9ZGY1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5K1VW73</th>\n",
       "      <td>98875</td>\n",
       "      <td>683</td>\n",
       "      <td>A0A5K1VW73_HUMAN Ankyrin-2 (Fragment) OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=2</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHA1</th>\n",
       "      <td>99292</td>\n",
       "      <td>1849</td>\n",
       "      <td>A0A5F9ZHA1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H0Y8Y2</th>\n",
       "      <td>99650</td>\n",
       "      <td>1062</td>\n",
       "      <td>H0Y8Y2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=2</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI81</th>\n",
       "      <td>99657</td>\n",
       "      <td>1915</td>\n",
       "      <td>A0A5F9ZI81_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHN5</th>\n",
       "      <td>99795</td>\n",
       "      <td>3903</td>\n",
       "      <td>A0A5F9ZHN5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D6RHE1</th>\n",
       "      <td>100207</td>\n",
       "      <td>3961</td>\n",
       "      <td>D6RHE1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=2</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I6L894</th>\n",
       "      <td>101357</td>\n",
       "      <td>3924</td>\n",
       "      <td>I6L894_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI16</th>\n",
       "      <td>103347</td>\n",
       "      <td>1055</td>\n",
       "      <td>A0A5F9ZI16_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH34</th>\n",
       "      <td>103931</td>\n",
       "      <td>1689</td>\n",
       "      <td>A0A5F9ZH34_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH99</th>\n",
       "      <td>105141</td>\n",
       "      <td>1060</td>\n",
       "      <td>A0A5F9ZH99_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHL3</th>\n",
       "      <td>105434</td>\n",
       "      <td>1858</td>\n",
       "      <td>A0A5F9ZHL3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGY3</th>\n",
       "      <td>106016</td>\n",
       "      <td>3934</td>\n",
       "      <td>A0A5F9ZGY3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHD2</th>\n",
       "      <td>106036</td>\n",
       "      <td>3936</td>\n",
       "      <td>A0A5F9ZHD2_HUMAN Ankyrin 2, neuronal, isoform CRA_b OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHC9</th>\n",
       "      <td>106551</td>\n",
       "      <td>1910</td>\n",
       "      <td>A0A5F9ZHC9_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGZ1</th>\n",
       "      <td>107354</td>\n",
       "      <td>1943</td>\n",
       "      <td>A0A5F9ZGZ1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI18</th>\n",
       "      <td>107838</td>\n",
       "      <td>1879</td>\n",
       "      <td>A0A5F9ZI18_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHN0</th>\n",
       "      <td>108158</td>\n",
       "      <td>1777</td>\n",
       "      <td>A0A5F9ZHN0_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHJ6</th>\n",
       "      <td>108307</td>\n",
       "      <td>1851</td>\n",
       "      <td>A0A5F9ZHJ6_HUMAN Ankyrin 2, neuronal, isoform CRA_a OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHQ5</th>\n",
       "      <td>108755</td>\n",
       "      <td>1894</td>\n",
       "      <td>A0A5F9ZHQ5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI15</th>\n",
       "      <td>110180</td>\n",
       "      <td>1768</td>\n",
       "      <td>A0A5F9ZI15_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHV4</th>\n",
       "      <td>111306</td>\n",
       "      <td>1001</td>\n",
       "      <td>A0A5F9ZHV4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHE4</th>\n",
       "      <td>111731</td>\n",
       "      <td>3964</td>\n",
       "      <td>A0A5F9ZHE4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHJ4</th>\n",
       "      <td>112091</td>\n",
       "      <td>1818</td>\n",
       "      <td>A0A5F9ZHJ4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHQ3</th>\n",
       "      <td>112182</td>\n",
       "      <td>1838</td>\n",
       "      <td>A0A5F9ZHQ3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH17</th>\n",
       "      <td>112299</td>\n",
       "      <td>1879</td>\n",
       "      <td>A0A5F9ZH17_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHT8</th>\n",
       "      <td>112688</td>\n",
       "      <td>3891</td>\n",
       "      <td>A0A5F9ZHT8_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHL9</th>\n",
       "      <td>113412</td>\n",
       "      <td>1817</td>\n",
       "      <td>A0A5F9ZHL9_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHR2</th>\n",
       "      <td>113555</td>\n",
       "      <td>1810</td>\n",
       "      <td>A0A5F9ZHR2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI65</th>\n",
       "      <td>114214</td>\n",
       "      <td>3879</td>\n",
       "      <td>A0A5F9ZI65_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI56</th>\n",
       "      <td>114353</td>\n",
       "      <td>1882</td>\n",
       "      <td>A0A5F9ZI56_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHF7</th>\n",
       "      <td>114470</td>\n",
       "      <td>3862</td>\n",
       "      <td>A0A5F9ZHF7_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHG3</th>\n",
       "      <td>115194</td>\n",
       "      <td>1891</td>\n",
       "      <td>A0A5F9ZHG3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH70</th>\n",
       "      <td>115711</td>\n",
       "      <td>1931</td>\n",
       "      <td>A0A5F9ZH70_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH03</th>\n",
       "      <td>117109</td>\n",
       "      <td>1785</td>\n",
       "      <td>A0A5F9ZH03_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH38</th>\n",
       "      <td>118757</td>\n",
       "      <td>1739</td>\n",
       "      <td>A0A5F9ZH38_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI08</th>\n",
       "      <td>121021</td>\n",
       "      <td>1797</td>\n",
       "      <td>A0A5F9ZI08_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI69</th>\n",
       "      <td>121771</td>\n",
       "      <td>1839</td>\n",
       "      <td>A0A5F9ZI69_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHL5</th>\n",
       "      <td>121871</td>\n",
       "      <td>1872</td>\n",
       "      <td>A0A5F9ZHL5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI46</th>\n",
       "      <td>122046</td>\n",
       "      <td>3882</td>\n",
       "      <td>A0A5F9ZI46_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H0Y931</th>\n",
       "      <td>124025</td>\n",
       "      <td>966</td>\n",
       "      <td>H0Y931_HUMAN Ankyrin-2 (Fragment) OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHR8</th>\n",
       "      <td>124376</td>\n",
       "      <td>3939</td>\n",
       "      <td>A0A5F9ZHR8_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI30</th>\n",
       "      <td>124607</td>\n",
       "      <td>3820</td>\n",
       "      <td>A0A5F9ZI30_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH18</th>\n",
       "      <td>124729</td>\n",
       "      <td>1109</td>\n",
       "      <td>A0A5F9ZH18_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGX8</th>\n",
       "      <td>125498</td>\n",
       "      <td>1830</td>\n",
       "      <td>A0A5F9ZGX8_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E9PCH6</th>\n",
       "      <td>125936</td>\n",
       "      <td>1048</td>\n",
       "      <td>E9PCH6_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI73</th>\n",
       "      <td>126065</td>\n",
       "      <td>1822</td>\n",
       "      <td>A0A5F9ZI73_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH30</th>\n",
       "      <td>126246</td>\n",
       "      <td>3995</td>\n",
       "      <td>A0A5F9ZH30_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGY4</th>\n",
       "      <td>126809</td>\n",
       "      <td>3848</td>\n",
       "      <td>A0A5F9ZGY4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH58</th>\n",
       "      <td>126922</td>\n",
       "      <td>1770</td>\n",
       "      <td>A0A5F9ZH58_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZGZ5</th>\n",
       "      <td>127368</td>\n",
       "      <td>3874</td>\n",
       "      <td>A0A5F9ZGZ5_HUMAN Ankyrin-2 (Fragment) OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHS1</th>\n",
       "      <td>127903</td>\n",
       "      <td>3984</td>\n",
       "      <td>A0A5F9ZHS1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI40</th>\n",
       "      <td>134834</td>\n",
       "      <td>3915</td>\n",
       "      <td>A0A5F9ZI40_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI36</th>\n",
       "      <td>136141</td>\n",
       "      <td>1846</td>\n",
       "      <td>A0A5F9ZI36_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHY6</th>\n",
       "      <td>136974</td>\n",
       "      <td>1763</td>\n",
       "      <td>A0A5F9ZHY6_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZI53</th>\n",
       "      <td>137919</td>\n",
       "      <td>1755</td>\n",
       "      <td>A0A5F9ZI53_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZHM6</th>\n",
       "      <td>139164</td>\n",
       "      <td>1708</td>\n",
       "      <td>A0A5F9ZHM6_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A5F9ZH19</th>\n",
       "      <td>142096</td>\n",
       "      <td>3895</td>\n",
       "      <td>A0A5F9ZH19_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>ANK2</td>\n",
       "      <td>ank2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  length  \\\n",
       "id                           \n",
       "Q01484       15692    3957   \n",
       "A0A5F9ZHT4   84840    3870   \n",
       "A0A5F9ZGS7   85060    1827   \n",
       "A0A5F9ZGS5   88666    1871   \n",
       "A0A5F9ZH35   90486    1758   \n",
       "A0A5F9ZH39   91029    1887   \n",
       "A0A5F9ZH10   93730    1005   \n",
       "A0A5F9ZGX3   95675    3972   \n",
       "A0A5F9ZHE2   96688    1806   \n",
       "A0A5F9ZGY1   97660    1887   \n",
       "A0A5K1VW73   98875     683   \n",
       "A0A5F9ZHA1   99292    1849   \n",
       "H0Y8Y2       99650    1062   \n",
       "A0A5F9ZI81   99657    1915   \n",
       "A0A5F9ZHN5   99795    3903   \n",
       "D6RHE1      100207    3961   \n",
       "I6L894      101357    3924   \n",
       "A0A5F9ZI16  103347    1055   \n",
       "A0A5F9ZH34  103931    1689   \n",
       "A0A5F9ZH99  105141    1060   \n",
       "A0A5F9ZHL3  105434    1858   \n",
       "A0A5F9ZGY3  106016    3934   \n",
       "A0A5F9ZHD2  106036    3936   \n",
       "A0A5F9ZHC9  106551    1910   \n",
       "A0A5F9ZGZ1  107354    1943   \n",
       "A0A5F9ZI18  107838    1879   \n",
       "A0A5F9ZHN0  108158    1777   \n",
       "A0A5F9ZHJ6  108307    1851   \n",
       "A0A5F9ZHQ5  108755    1894   \n",
       "A0A5F9ZI15  110180    1768   \n",
       "A0A5F9ZHV4  111306    1001   \n",
       "A0A5F9ZHE4  111731    3964   \n",
       "A0A5F9ZHJ4  112091    1818   \n",
       "A0A5F9ZHQ3  112182    1838   \n",
       "A0A5F9ZH17  112299    1879   \n",
       "A0A5F9ZHT8  112688    3891   \n",
       "A0A5F9ZHL9  113412    1817   \n",
       "A0A5F9ZHR2  113555    1810   \n",
       "A0A5F9ZI65  114214    3879   \n",
       "A0A5F9ZI56  114353    1882   \n",
       "A0A5F9ZHF7  114470    3862   \n",
       "A0A5F9ZHG3  115194    1891   \n",
       "A0A5F9ZH70  115711    1931   \n",
       "A0A5F9ZH03  117109    1785   \n",
       "A0A5F9ZH38  118757    1739   \n",
       "A0A5F9ZI08  121021    1797   \n",
       "A0A5F9ZI69  121771    1839   \n",
       "A0A5F9ZHL5  121871    1872   \n",
       "A0A5F9ZI46  122046    3882   \n",
       "H0Y931      124025     966   \n",
       "A0A5F9ZHR8  124376    3939   \n",
       "A0A5F9ZI30  124607    3820   \n",
       "A0A5F9ZH18  124729    1109   \n",
       "A0A5F9ZGX8  125498    1830   \n",
       "E9PCH6      125936    1048   \n",
       "A0A5F9ZI73  126065    1822   \n",
       "A0A5F9ZH30  126246    3995   \n",
       "A0A5F9ZGY4  126809    3848   \n",
       "A0A5F9ZH58  126922    1770   \n",
       "A0A5F9ZGZ5  127368    3874   \n",
       "A0A5F9ZHS1  127903    3984   \n",
       "A0A5F9ZI40  134834    3915   \n",
       "A0A5F9ZI36  136141    1846   \n",
       "A0A5F9ZHY6  136974    1763   \n",
       "A0A5F9ZI53  137919    1755   \n",
       "A0A5F9ZHM6  139164    1708   \n",
       "A0A5F9ZH19  142096    3895   \n",
       "\n",
       "                                                                                              description  \\\n",
       "id                                                                                                          \n",
       "Q01484                                     ANK2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=4   \n",
       "A0A5F9ZHT4                           A0A5F9ZHT4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGS7                           A0A5F9ZGS7_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGS5                           A0A5F9ZGS5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH35                           A0A5F9ZH35_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH39                           A0A5F9ZH39_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH10                           A0A5F9ZH10_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGX3                           A0A5F9ZGX3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHE2                           A0A5F9ZHE2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGY1                           A0A5F9ZGY1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5K1VW73                A0A5K1VW73_HUMAN Ankyrin-2 (Fragment) OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=2   \n",
       "A0A5F9ZHA1                           A0A5F9ZHA1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "H0Y8Y2                                   H0Y8Y2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=2   \n",
       "A0A5F9ZI81                           A0A5F9ZI81_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHN5                           A0A5F9ZHN5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "D6RHE1                                   D6RHE1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=2   \n",
       "I6L894                                   I6L894_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI16                           A0A5F9ZI16_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH34                           A0A5F9ZH34_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH99                           A0A5F9ZH99_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHL3                           A0A5F9ZHL3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGY3                           A0A5F9ZGY3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHD2  A0A5F9ZHD2_HUMAN Ankyrin 2, neuronal, isoform CRA_b OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHC9                           A0A5F9ZHC9_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGZ1                           A0A5F9ZGZ1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI18                           A0A5F9ZI18_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHN0                           A0A5F9ZHN0_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHJ6  A0A5F9ZHJ6_HUMAN Ankyrin 2, neuronal, isoform CRA_a OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHQ5                           A0A5F9ZHQ5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI15                           A0A5F9ZI15_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHV4                           A0A5F9ZHV4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHE4                           A0A5F9ZHE4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHJ4                           A0A5F9ZHJ4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHQ3                           A0A5F9ZHQ3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH17                           A0A5F9ZH17_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHT8                           A0A5F9ZHT8_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHL9                           A0A5F9ZHL9_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHR2                           A0A5F9ZHR2_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI65                           A0A5F9ZI65_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI56                           A0A5F9ZI56_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHF7                           A0A5F9ZHF7_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHG3                           A0A5F9ZHG3_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH70                           A0A5F9ZH70_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH03                           A0A5F9ZH03_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH38                           A0A5F9ZH38_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI08                           A0A5F9ZI08_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI69                           A0A5F9ZI69_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHL5                           A0A5F9ZHL5_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI46                           A0A5F9ZI46_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "H0Y931                        H0Y931_HUMAN Ankyrin-2 (Fragment) OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHR8                           A0A5F9ZHR8_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI30                           A0A5F9ZI30_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH18                           A0A5F9ZH18_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGX8                           A0A5F9ZGX8_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "E9PCH6                                   E9PCH6_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI73                           A0A5F9ZI73_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH30                           A0A5F9ZH30_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGY4                           A0A5F9ZGY4_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH58                           A0A5F9ZH58_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZGZ5                A0A5F9ZGZ5_HUMAN Ankyrin-2 (Fragment) OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHS1                           A0A5F9ZHS1_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI40                           A0A5F9ZI40_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI36                           A0A5F9ZI36_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHY6                           A0A5F9ZHY6_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZI53                           A0A5F9ZI53_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZHM6                           A0A5F9ZHM6_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "A0A5F9ZH19                           A0A5F9ZH19_HUMAN Ankyrin-2 OS=Homo sapiens OX=9606 GN=ANK2 PE=1 SV=1   \n",
       "\n",
       "           dbase      organism  taxonomyID gene name gene name lower  in test  \n",
       "id                                                                             \n",
       "Q01484        sp  Homo sapiens        9606      ANK2            ank2     True  \n",
       "A0A5F9ZHT4    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGS7    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGS5    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH35    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH39    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH10    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGX3    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHE2    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGY1    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5K1VW73    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHA1    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "H0Y8Y2        tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI81    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHN5    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "D6RHE1        tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "I6L894        tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI16    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH34    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH99    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHL3    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGY3    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHD2    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHC9    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGZ1    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI18    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHN0    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHJ6    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHQ5    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI15    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHV4    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHE4    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHJ4    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHQ3    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH17    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHT8    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHL9    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHR2    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI65    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI56    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHF7    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHG3    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH70    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH03    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH38    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI08    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI69    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHL5    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI46    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "H0Y931        tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHR8    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI30    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH18    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGX8    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "E9PCH6        tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI73    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH30    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGY4    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH58    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZGZ5    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHS1    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI40    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI36    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHY6    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZI53    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZHM6    tr  Homo sapiens        9606      ANK2            ank2    False  \n",
       "A0A5F9ZH19    tr  Homo sapiens        9606      ANK2            ank2    False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = df_seq['gene name lower'] == 'ank2'\n",
    "print(m.sum())\n",
    "m2 = df_seq['organism'] == 'Homo sapiens'\n",
    "print((m & m2).sum())\n",
    "dict(df_seq[m & m2]['description'])\n",
    "df_seq[m & m2][['index', 'length', 'description', 'dbase',  'organism',\n",
    "                'taxonomyID', 'gene name', 'gene name lower', 'in test']].head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1863\n"
     ]
    }
   ],
   "source": [
    "m = df_seq['length'] < 60\n",
    "print(m.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['P84814', 'P83318', 'P86300', 'P83277', 'P84866', 'P86010', 'P86168', 'P86707', 'P86569', 'P84761', 'C0HJB2', 'P84700', 'C0HLM4', 'P83281', 'B3EWG7', 'P85002', 'P84834', 'P83308', 'P86587', 'P83276', 'P83319', 'P41495', 'P84701', 'C0HLT7', 'P84678', 'P86299', 'P84774', 'P83661', 'P0DJJ6', 'P86705', 'Q9T2K9', 'P86563', 'P84831', 'P86489', 'P86012', 'P86582', 'P83279', 'P86492', 'P86557', 'P84773', 'P84822', 'P84677', 'C0HKA9', 'P86575', 'P86494', 'P85069', 'P86298', 'P86487', 'P85003', 'P82858', 'P84825', 'P83222', 'P86302', 'P84683', 'P86594', 'P83275', 'P83316', 'C0HKA8', 'P84760', 'P83320', 'P83274', 'Q47505', 'P84829', 'Q9T2L1', 'P84759', 'P85067', 'P84833', 'Q9T2L0', 'P86301', 'P86922', 'P86482', 'P0DKJ0', 'C4MRC6', 'A0A0U1RNE4', 'Q7LZJ8', 'A0A1Y7AKU3', 'Q3YNA4', 'A0A0R4IAH1', 'A0A0J9YIY0', 'A0A1L1SSW0']\n",
      "['OPIP1', 'FAR3', 'TLP3', 'FAR4', 'PLEUR', 'CUTI1', 'COLUA', 'AMP3', 'TRP1', 'ACI', 'LAC', 'CONO', 'CARP', 'FAR8', 'PERO', 'CYCLF', 'TY2', 'FARP', 'TRP1', 'FAR3', 'FAR4', 'TMOF', 'CONO', 'BRK', 'SC31', 'TLP2', 'BRK', 'C125', 'VM1B', 'AMP1', 'CB22', 'TRP1', 'TY1', 'PE12', 'CUTI2', 'TRP1', 'FAR6', 'PE13', 'TRP1', 'BRK', 'TY4', 'SC29', 'BRKP5', 'TRP1', 'PE14', 'OJP3', 'TLP1', 'PE11', 'CYCLE', 'URIC', 'BRK1', 'NUC34', 'TLP5', 'SC40', 'TRP1', 'FAR2', 'FAR1', 'BRKP4', 'ALVOL', 'FAR5', 'FAR1', 'mccA', 'SKSP4', 'CB2C', 'JELL3', 'OJP1', 'TY3', 'CB2B', 'TLP4', 'LECT', 'PE15', 'miPEP160b', 'myh7', 'cpt1aa', 'Q7LZJ8', 'rtn4r', 'Orc3', 'cnp', 'mbnl2', 'Ifi47']\n"
     ]
    }
   ],
   "source": [
    "m = df_seq['length'] < 10\n",
    "print(df_seq[m].index.tolist())\n",
    "print(df_seq[m]['gene name'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count length < 60 :  1863\n"
     ]
    }
   ],
   "source": [
    "sr = df_seq[\"length\"].value_counts().sort_index()\n",
    "\n",
    "fig = plt.figure(figsize=(20, 3))\n",
    "plt.plot(sr.head(1500))\n",
    "plt.title(\"Train data - first 1500\", fontsize=20)\n",
    "plt.xlabel(\"Sequence length\", fontsize=20)\n",
    "plt.ylabel(\"Count\", fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure(figsize=(20, 3))\n",
    "plt.plot(sr.head(150))\n",
    "plt.title(\"Train data - first 150 \", fontsize=20)\n",
    "plt.xlabel(\"Sequence length\", fontsize=20)\n",
    "plt.ylabel(\"Count\", fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "m = df_seq[\"length\"] < 60\n",
    "print(\"count length < 60 : \", m.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 1\n",
      "{'P84761': 'Macrocybe gigantea'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th>P84761</th>\n",
       "      <td>10118</td>\n",
       "      <td>3</td>\n",
       "      <td>ACI_MACGN Angiotensin-1-converting enzyme inhibitory peptide OS=Macrocybe gigantea OX=1491104 PE=1 SV=1</td>\n",
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       "        index  length  \\\n",
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       "P84761  10118       3   \n",
       "\n",
       "                                                                                                    description  \\\n",
       "id                                                                                                                \n",
       "P84761  ACI_MACGN Angiotensin-1-converting enzyme inhibitory peptide OS=Macrocybe gigantea OX=1491104 PE=1 SV=1   \n",
       "\n",
       "       dbase sequence  in test  fragment            organism  taxonomyID  \\\n",
       "id                                                                         \n",
       "P84761    sp      GEP    False     False  Macrocybe gigantea     1491104   \n",
       "\n",
       "       gene name gene name lower  sequence version  protein existence  \n",
       "id                                                                     \n",
       "P84761       ACI             aci                 1                  1  "
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      "\n",
      "5 2\n",
      "{'P83308': 'Gallus gallus', 'P0DKJ0': 'Arabidopsis thaliana'}\n"
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       "                                                                                            description  \\\n",
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       "       dbase sequence  in test  fragment              organism  taxonomyID  \\\n",
       "id                                                                           \n",
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       "\n",
       "        gene name gene name lower  sequence version  protein existence  \n",
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      "\n",
      "6 4\n",
      "{'P41495': 'Sarcophaga bullata', 'P86012': 'Colletotrichum kahawae', 'P85003': 'Annona cherimola', 'P84833': 'Ascaphus truei'}\n"
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       "        index  length  \\\n",
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       "\n",
       "                                                                                  description  \\\n",
       "id                                                                                              \n",
       "P41495  TMOF_SARBU Trypsin-modulating oostatic factor OS=Sarcophaga bullata OX=7385 PE=1 SV=1   \n",
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       "P85003                      CYCLE_ANNCH Cyclopeptide E OS=Annona cherimola OX=49314 PE=1 SV=1   \n",
       "P84833                          TY3_ASCTR Tryptophyllin-3 OS=Ascaphus truei OX=8439 PE=1 SV=1   \n",
       "\n",
       "       dbase sequence  in test  fragment                organism  taxonomyID  \\\n",
       "id                                                                             \n",
       "P41495    sp   NPTNLH    False     False      Sarcophaga bullata        7385   \n",
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       "\n",
       "       gene name gene name lower  sequence version  protein existence  \n",
       "id                                                                     \n",
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      "\n",
      "7 15\n",
      "{'P86010': 'Colletotrichum kahawae', 'P86168': 'Colletotrichum dematium', 'C0HLM4': 'Rhizopus azygosporus', 'P84831': 'Ascaphus truei', 'P86489': 'Litoria peronii', 'P86492': 'Litoria peronii', 'P86494': 'Litoria peronii', 'P86298': 'Phoneutria nigriventer', 'P86487': 'Litoria peronii', 'P83274': 'Macrobrachium rosenbergii', 'Q47505': 'Escherichia coli', 'P85067': 'Sepia officinalis', 'P86482': 'Litoria peronii', 'A0A0U1RNE4': 'Danio rerio', 'A0A1Y7AKU3': 'Danio rerio'}\n"
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       "      <td>CARP_RHIAZ Rhizopuspepsin (Fragment) OS=Rhizopus azygosporus OX=86630 PE=1 SV=1</td>\n",
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       "      <td>TY1_ASCTR Tryptophyllin-1 OS=Ascaphus truei OX=8439 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
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       "      <td>False</td>\n",
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       "      <td>Ascaphus truei</td>\n",
       "      <td>8439</td>\n",
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       "      <th>P86489</th>\n",
       "      <td>38244</td>\n",
       "      <td>7</td>\n",
       "      <td>PE12_LITPE Peroniin-1.2 OS=Litoria peronii OX=317363 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>QPWIPFV</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Litoria peronii</td>\n",
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       "      <th>P86492</th>\n",
       "      <td>39847</td>\n",
       "      <td>7</td>\n",
       "      <td>PE13_LITPE Peroniin-1.3 OS=Litoria peronii OX=317363 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>QPWLPFV</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Litoria peronii</td>\n",
       "      <td>317363</td>\n",
       "      <td>PE13</td>\n",
       "      <td>pe13</td>\n",
       "      <td>1</td>\n",
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       "      <th>P86494</th>\n",
       "      <td>44686</td>\n",
       "      <td>7</td>\n",
       "      <td>PE14_LITPE Peroniin-1.4 OS=Litoria peronii OX=317363 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>QTWLPFV</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Litoria peronii</td>\n",
       "      <td>317363</td>\n",
       "      <td>PE14</td>\n",
       "      <td>pe14</td>\n",
       "      <td>1</td>\n",
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       "      <th>P86298</th>\n",
       "      <td>45877</td>\n",
       "      <td>7</td>\n",
       "      <td>TLP1_PHONI Tachykinin-like peptide-I OS=Phoneutria nigriventer OX=6918 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>QKKDKKD</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Phoneutria nigriventer</td>\n",
       "      <td>6918</td>\n",
       "      <td>TLP1</td>\n",
       "      <td>tlp1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P86487</th>\n",
       "      <td>47188</td>\n",
       "      <td>7</td>\n",
       "      <td>PE11_LITPE Peroniin-1.1 OS=Litoria peronii OX=317363 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>QPWLPFG</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Litoria peronii</td>\n",
       "      <td>317363</td>\n",
       "      <td>PE11</td>\n",
       "      <td>pe11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P83274</th>\n",
       "      <td>66744</td>\n",
       "      <td>7</td>\n",
       "      <td>FAR1_MACRS FMRFamide-like neuropeptide FLP1 OS=Macrobrachium rosenbergii OX=79674 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>DRNFLRF</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Macrobrachium rosenbergii</td>\n",
       "      <td>79674</td>\n",
       "      <td>FAR1</td>\n",
       "      <td>far1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q47505</th>\n",
       "      <td>68961</td>\n",
       "      <td>7</td>\n",
       "      <td>MCCC7_ECOLX Microcin C7 OS=Escherichia coli OX=562 GN=mccA PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>MRTGNAN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Escherichia coli</td>\n",
       "      <td>562</td>\n",
       "      <td>mccA</td>\n",
       "      <td>mcca</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P85067</th>\n",
       "      <td>76966</td>\n",
       "      <td>7</td>\n",
       "      <td>OJP1_SEPOF Ovarian jelly-peptide 1 OS=Sepia officinalis OX=6610 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>DQVKIVL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sepia officinalis</td>\n",
       "      <td>6610</td>\n",
       "      <td>OJP1</td>\n",
       "      <td>ojp1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P86482</th>\n",
       "      <td>82007</td>\n",
       "      <td>7</td>\n",
       "      <td>PE15_LITPE Peroniin-1.5 OS=Litoria peronii OX=317363 PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>QPWLPFR</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Litoria peronii</td>\n",
       "      <td>317363</td>\n",
       "      <td>PE15</td>\n",
       "      <td>pe15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A0U1RNE4</th>\n",
       "      <td>93704</td>\n",
       "      <td>7</td>\n",
       "      <td>A0A0U1RNE4_DANRE Carnitine palmitoyltransferase 1Aa (liver) (Fragment) OS=Danio rerio OX=7955 GN=cpt1aa PE=4 SV=2</td>\n",
       "      <td>tr</td>\n",
       "      <td>MAEAHQA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>cpt1aa</td>\n",
       "      <td>cpt1aa</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A0A1Y7AKU3</th>\n",
       "      <td>112897</td>\n",
       "      <td>7</td>\n",
       "      <td>A0A1Y7AKU3_DANRE Reticulon 4 receptor (Fragment) OS=Danio rerio OX=7955 GN=rtn4r PE=4 SV=1</td>\n",
       "      <td>tr</td>\n",
       "      <td>MKTLIVE</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>rtn4r</td>\n",
       "      <td>rtn4r</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  length  \\\n",
       "id                           \n",
       "P86010        6376       7   \n",
       "P86168        7091       7   \n",
       "C0HLM4       12329       7   \n",
       "P84831       36219       7   \n",
       "P86489       38244       7   \n",
       "P86492       39847       7   \n",
       "P86494       44686       7   \n",
       "P86298       45877       7   \n",
       "P86487       47188       7   \n",
       "P83274       66744       7   \n",
       "Q47505       68961       7   \n",
       "P85067       76966       7   \n",
       "P86482       82007       7   \n",
       "A0A0U1RNE4   93704       7   \n",
       "A0A1Y7AKU3  112897       7   \n",
       "\n",
       "                                                                                                                  description  \\\n",
       "id                                                                                                                              \n",
       "P86010                                         CUTI1_COLKA Cutinase 1 (Fragment) OS=Colletotrichum kahawae OX=34407 PE=1 SV=1   \n",
       "P86168                                     COLUA_COLDE Colutellin-A (Fragments) OS=Colletotrichum dematium OX=34405 PE=1 SV=1   \n",
       "C0HLM4                                        CARP_RHIAZ Rhizopuspepsin (Fragment) OS=Rhizopus azygosporus OX=86630 PE=1 SV=1   \n",
       "P84831                                                          TY1_ASCTR Tryptophyllin-1 OS=Ascaphus truei OX=8439 PE=1 SV=1   \n",
       "P86489                                                         PE12_LITPE Peroniin-1.2 OS=Litoria peronii OX=317363 PE=1 SV=1   \n",
       "P86492                                                         PE13_LITPE Peroniin-1.3 OS=Litoria peronii OX=317363 PE=1 SV=1   \n",
       "P86494                                                         PE14_LITPE Peroniin-1.4 OS=Litoria peronii OX=317363 PE=1 SV=1   \n",
       "P86298                                       TLP1_PHONI Tachykinin-like peptide-I OS=Phoneutria nigriventer OX=6918 PE=1 SV=1   \n",
       "P86487                                                         PE11_LITPE Peroniin-1.1 OS=Litoria peronii OX=317363 PE=1 SV=1   \n",
       "P83274                            FAR1_MACRS FMRFamide-like neuropeptide FLP1 OS=Macrobrachium rosenbergii OX=79674 PE=1 SV=1   \n",
       "Q47505                                                   MCCC7_ECOLX Microcin C7 OS=Escherichia coli OX=562 GN=mccA PE=1 SV=1   \n",
       "P85067                                              OJP1_SEPOF Ovarian jelly-peptide 1 OS=Sepia officinalis OX=6610 PE=1 SV=1   \n",
       "P86482                                                         PE15_LITPE Peroniin-1.5 OS=Litoria peronii OX=317363 PE=1 SV=1   \n",
       "A0A0U1RNE4  A0A0U1RNE4_DANRE Carnitine palmitoyltransferase 1Aa (liver) (Fragment) OS=Danio rerio OX=7955 GN=cpt1aa PE=4 SV=2   \n",
       "A0A1Y7AKU3                         A0A1Y7AKU3_DANRE Reticulon 4 receptor (Fragment) OS=Danio rerio OX=7955 GN=rtn4r PE=4 SV=1   \n",
       "\n",
       "           dbase sequence  in test  fragment                   organism  \\\n",
       "id                                                                        \n",
       "P86010        sp  VIYIFAR    False      True     Colletotrichum kahawae   \n",
       "P86168        sp  VISIIPV    False      True    Colletotrichum dematium   \n",
       "C0HLM4        sp  AGVGTVP    False      True       Rhizopus azygosporus   \n",
       "P84831        sp  GPIPWQR    False     False             Ascaphus truei   \n",
       "P86489        sp  QPWIPFV    False     False            Litoria peronii   \n",
       "P86492        sp  QPWLPFV    False     False            Litoria peronii   \n",
       "P86494        sp  QTWLPFV    False     False            Litoria peronii   \n",
       "P86298        sp  QKKDKKD    False     False     Phoneutria nigriventer   \n",
       "P86487        sp  QPWLPFG    False     False            Litoria peronii   \n",
       "P83274        sp  DRNFLRF    False     False  Macrobrachium rosenbergii   \n",
       "Q47505        sp  MRTGNAN    False     False           Escherichia coli   \n",
       "P85067        sp  DQVKIVL    False     False          Sepia officinalis   \n",
       "P86482        sp  QPWLPFR    False     False            Litoria peronii   \n",
       "A0A0U1RNE4    tr  MAEAHQA    False      True                Danio rerio   \n",
       "A0A1Y7AKU3    tr  MKTLIVE    False      True                Danio rerio   \n",
       "\n",
       "            taxonomyID gene name gene name lower  sequence version  \\\n",
       "id                                                                   \n",
       "P86010           34407     CUTI1           cuti1                 1   \n",
       "P86168           34405     COLUA           colua                 1   \n",
       "C0HLM4           86630      CARP            carp                 1   \n",
       "P84831            8439       TY1             ty1                 1   \n",
       "P86489          317363      PE12            pe12                 1   \n",
       "P86492          317363      PE13            pe13                 1   \n",
       "P86494          317363      PE14            pe14                 1   \n",
       "P86298            6918      TLP1            tlp1                 1   \n",
       "P86487          317363      PE11            pe11                 1   \n",
       "P83274           79674      FAR1            far1                 1   \n",
       "Q47505             562      mccA            mcca                 1   \n",
       "P85067            6610      OJP1            ojp1                 1   \n",
       "P86482          317363      PE15            pe15                 1   \n",
       "A0A0U1RNE4        7955    cpt1aa          cpt1aa                 2   \n",
       "A0A1Y7AKU3        7955     rtn4r           rtn4r                 1   \n",
       "\n",
       "            protein existence  \n",
       "id                             \n",
       "P86010                      1  \n",
       "P86168                      1  \n",
       "C0HLM4                      1  \n",
       "P84831                      1  \n",
       "P86489                      1  \n",
       "P86492                      1  \n",
       "P86494                      1  \n",
       "P86298                      1  \n",
       "P86487                      1  \n",
       "P83274                      1  \n",
       "Q47505                      1  \n",
       "P85067                      1  \n",
       "P86482                      1  \n",
       "A0A0U1RNE4                  4  \n",
       "A0A1Y7AKU3                  4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for k in range(8):\n",
    "    m = df_seq[\"length\"] == k\n",
    "    if m.sum() == 0:\n",
    "        continue\n",
    "    print(k, m.sum())\n",
    "    #     print(dict(df_seq2[m]['description']))\n",
    "    print(dict(df_seq[m][\"organism\"]))\n",
    "    display(df_seq[m])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in ./.venv/lib/python3.10/site-packages (1.12.0)\n",
      "Requirement already satisfied: numpy<1.29.0,>=1.22.4 in ./.venv/lib/python3.10/site-packages (from scipy) (1.26.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sequence version</th>\n",
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       "  <tbody>\n",
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       "      <td>0.2</td>\n",
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       "      <td>-0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sequence version</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein existence</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in test</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fragment</th>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index  length  taxonomyID  sequence version  \\\n",
       "index                1.0     0.0        -0.1              -0.2   \n",
       "length               0.0     1.0        -0.0               0.1   \n",
       "taxonomyID          -0.1    -0.0         1.0              -0.1   \n",
       "sequence version    -0.2     0.1        -0.1               1.0   \n",
       "protein existence    0.4     0.0         0.1              -0.1   \n",
       "in test             -0.7    -0.0         0.0               0.2   \n",
       "fragment             0.2    -0.1        -0.1              -0.0   \n",
       "\n",
       "                   protein existence  in test  fragment  \n",
       "index                            0.4     -0.7       0.2  \n",
       "length                           0.0     -0.0      -0.1  \n",
       "taxonomyID                       0.1      0.0      -0.1  \n",
       "sequence version                -0.1      0.2      -0.0  \n",
       "protein existence                1.0     -0.4       0.1  \n",
       "in test                         -0.4      1.0      -0.2  \n",
       "fragment                         0.1     -0.2       1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>index</th>\n",
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       "      <th>fragment</th>\n",
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       "  <tbody>\n",
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       "      <td>-0.7</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>protein existence</th>\n",
       "      <td>0.4</td>\n",
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       "      <td>0.1</td>\n",
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       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fragment</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                   index  protein existence  in test  fragment\n",
       "index                1.0                0.4     -0.7       0.2\n",
       "protein existence    0.4                1.0     -0.4       0.1\n",
       "in test             -0.7               -0.4      1.0      -0.2\n",
       "fragment             0.2                0.1     -0.2       1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "RuntimeError",
     "evalue": "clustermap requires scipy to be available",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[27], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m display(newdf\u001b[38;5;241m.\u001b[39mcorr()\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m      8\u001b[0m display(newdf[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprotein existence\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      9\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124min test\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfragment\u001b[39m\u001b[38;5;124m'\u001b[39m]]\u001b[38;5;241m.\u001b[39mcorr()\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m---> 10\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclustermap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnewdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/Desktop/cafa/.venv/lib/python3.10/site-packages/seaborn/matrix.py:1250\u001b[0m, in \u001b[0;36mclustermap\u001b[0;34m(data, pivot_kws, method, metric, z_score, standard_scale, figsize, cbar_kws, row_cluster, col_cluster, row_linkage, col_linkage, row_colors, col_colors, mask, dendrogram_ratio, colors_ratio, cbar_pos, tree_kws, **kwargs)\u001b[0m\n\u001b[1;32m   1157\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1158\u001b[0m \u001b[38;5;124;03mPlot a matrix dataset as a hierarchically-clustered heatmap.\u001b[39;00m\n\u001b[1;32m   1159\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1247\u001b[0m \n\u001b[1;32m   1248\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _no_scipy:\n\u001b[0;32m-> 1250\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclustermap requires scipy to be available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1252\u001b[0m plotter \u001b[38;5;241m=\u001b[39m ClusterGrid(data, pivot_kws\u001b[38;5;241m=\u001b[39mpivot_kws, figsize\u001b[38;5;241m=\u001b[39mfigsize,\n\u001b[1;32m   1253\u001b[0m                       row_colors\u001b[38;5;241m=\u001b[39mrow_colors, col_colors\u001b[38;5;241m=\u001b[39mcol_colors,\n\u001b[1;32m   1254\u001b[0m                       z_score\u001b[38;5;241m=\u001b[39mz_score, standard_scale\u001b[38;5;241m=\u001b[39mstandard_scale,\n\u001b[1;32m   1255\u001b[0m                       mask\u001b[38;5;241m=\u001b[39mmask, dendrogram_ratio\u001b[38;5;241m=\u001b[39mdendrogram_ratio,\n\u001b[1;32m   1256\u001b[0m                       colors_ratio\u001b[38;5;241m=\u001b[39mcolors_ratio, cbar_pos\u001b[38;5;241m=\u001b[39mcbar_pos)\n\u001b[1;32m   1258\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plotter\u001b[38;5;241m.\u001b[39mplot(metric\u001b[38;5;241m=\u001b[39mmetric, method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m   1259\u001b[0m                     colorbar_kws\u001b[38;5;241m=\u001b[39mcbar_kws,\n\u001b[1;32m   1260\u001b[0m                     row_cluster\u001b[38;5;241m=\u001b[39mrow_cluster, col_cluster\u001b[38;5;241m=\u001b[39mcol_cluster,\n\u001b[1;32m   1261\u001b[0m                     row_linkage\u001b[38;5;241m=\u001b[39mrow_linkage, col_linkage\u001b[38;5;241m=\u001b[39mcol_linkage,\n\u001b[1;32m   1262\u001b[0m                     tree_kws\u001b[38;5;241m=\u001b[39mtree_kws, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: clustermap requires scipy to be available"
     ]
    }
   ],
   "source": [
    "numerics = [\"bool\", \"float\", \"int16\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\"]\n",
    "df.select_dtypes(include=numerics)\n",
    "newdf = pd.concat(\n",
    "    [df_seq.select_dtypes(\"number\"), df_seq.select_dtypes(\"bool\")], axis=1\n",
    ")\n",
    "newdf\n",
    "display(newdf.corr().round(1))\n",
    "display(newdf[[\"index\", \"protein existence\", \"in test\", \"fragment\"]].corr().round(1))\n",
    "sns.clustermap(newdf.corr().abs())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5363863, 3)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EntryID</th>\n",
       "      <th>term</th>\n",
       "      <th>aspect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0A009IHW8</td>\n",
       "      <td>GO:0008152</td>\n",
       "      <td>BPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0A009IHW8</td>\n",
       "      <td>GO:0034655</td>\n",
       "      <td>BPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0A009IHW8</td>\n",
       "      <td>GO:0072523</td>\n",
       "      <td>BPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0A009IHW8</td>\n",
       "      <td>GO:0044270</td>\n",
       "      <td>BPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0A009IHW8</td>\n",
       "      <td>GO:0006753</td>\n",
       "      <td>BPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363858</th>\n",
       "      <td>X5L565</td>\n",
       "      <td>GO:0050649</td>\n",
       "      <td>MFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363859</th>\n",
       "      <td>X5L565</td>\n",
       "      <td>GO:0016491</td>\n",
       "      <td>MFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363860</th>\n",
       "      <td>X5M5N0</td>\n",
       "      <td>GO:0005515</td>\n",
       "      <td>MFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363861</th>\n",
       "      <td>X5M5N0</td>\n",
       "      <td>GO:0005488</td>\n",
       "      <td>MFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5363862</th>\n",
       "      <td>X5M5N0</td>\n",
       "      <td>GO:0003674</td>\n",
       "      <td>MFO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5363863 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            EntryID        term aspect\n",
       "0        A0A009IHW8  GO:0008152    BPO\n",
       "1        A0A009IHW8  GO:0034655    BPO\n",
       "2        A0A009IHW8  GO:0072523    BPO\n",
       "3        A0A009IHW8  GO:0044270    BPO\n",
       "4        A0A009IHW8  GO:0006753    BPO\n",
       "...             ...         ...    ...\n",
       "5363858      X5L565  GO:0050649    MFO\n",
       "5363859      X5L565  GO:0016491    MFO\n",
       "5363860      X5M5N0  GO:0005515    MFO\n",
       "5363861      X5M5N0  GO:0005488    MFO\n",
       "5363862      X5M5N0  GO:0003674    MFO\n",
       "\n",
       "[5363863 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(142246,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EntryID\n",
       "A0A009IHW8    49\n",
       "A0A021WW32    77\n",
       "Name: Terms Count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(142246, 14)\n"
     ]
    },
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       "    <tr>\n",
       "      <th>P20536</th>\n",
       "      <td>0</td>\n",
       "      <td>218</td>\n",
       "      <td>UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>MNSVTVSHAPYTITYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPDKFFIQLKQPLRNKRVCVCGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFE...</td>\n",
       "      <td>False</td>\n",
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       "      <td>Vaccinia virus (strain Copenhagen)</td>\n",
       "      <td>10249</td>\n",
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       "      <td>33</td>\n",
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       "      <th>O73864</th>\n",
       "      <td>1</td>\n",
       "      <td>354</td>\n",
       "      <td>WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7955 GN=wnt11 PE=2 SV=1</td>\n",
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       "      <td>MTEYRNFLLLFITSLSVIYPCTGISWLGLTINGSSVGWNQTHHCKLLDGLVPDQQQLCKRNLELMHSIVRAARLTKSACTSSFSDMRWNWSSIESAPHFTPDLAKGTREAAFVVSLAAAVVSHAIARACASGDLPSCSCAAMPSEQAAPDFRWGGCGDNLRYYGLQMGSAFSDAPMRNRRSGPQDFRLMQLHNNAV...</td>\n",
       "      <td>True</td>\n",
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       "      <td>170</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  length  \\\n",
       "id                      \n",
       "P20536      0     218   \n",
       "O73864      1     354   \n",
       "\n",
       "                                                                                             description  \\\n",
       "id                                                                                                         \n",
       "P20536  UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1   \n",
       "O73864                              WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7955 GN=wnt11 PE=2 SV=1   \n",
       "\n",
       "       dbase  \\\n",
       "id             \n",
       "P20536    sp   \n",
       "O73864    sp   \n",
       "\n",
       "                                                                                                                                                                                                       sequence  \\\n",
       "id                                                                                                                                                                                                                \n",
       "P20536  MNSVTVSHAPYTITYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPDKFFIQLKQPLRNKRVCVCGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFE...   \n",
       "O73864  MTEYRNFLLLFITSLSVIYPCTGISWLGLTINGSSVGWNQTHHCKLLDGLVPDQQQLCKRNLELMHSIVRAARLTKSACTSSFSDMRWNWSSIESAPHFTPDLAKGTREAAFVVSLAAAVVSHAIARACASGDLPSCSCAAMPSEQAAPDFRWGGCGDNLRYYGLQMGSAFSDAPMRNRRSGPQDFRLMQLHNNAV...   \n",
       "\n",
       "        in test  fragment                            organism  taxonomyID  \\\n",
       "id                                                                          \n",
       "P20536    False     False  Vaccinia virus (strain Copenhagen)       10249   \n",
       "O73864     True     False                         Danio rerio        7955   \n",
       "\n",
       "       gene name gene name lower  sequence version  protein existence  \\\n",
       "id                                                                      \n",
       "P20536       UNG             ung                 1                  1   \n",
       "O73864     wnt11           wnt11                 1                  2   \n",
       "\n",
       "        Terms Count  \n",
       "id                   \n",
       "P20536           33  \n",
       "O73864          170  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <td>-0.1</td>\n",
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       "      <td>-0.1</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein existence</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.1</td>\n",
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       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terms Count</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>0.1</td>\n",
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       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in test</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fragment</th>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index  length  taxonomyID  sequence version  \\\n",
       "index                1.0     0.0        -0.1              -0.2   \n",
       "length               0.0     1.0        -0.0               0.1   \n",
       "taxonomyID          -0.1    -0.0         1.0              -0.1   \n",
       "sequence version    -0.2     0.1        -0.1               1.0   \n",
       "protein existence    0.4     0.0         0.1              -0.1   \n",
       "Terms Count         -0.2     0.1        -0.0               0.1   \n",
       "in test             -0.7    -0.0         0.0               0.2   \n",
       "fragment             0.2    -0.1        -0.1              -0.0   \n",
       "\n",
       "                   protein existence  Terms Count  in test  fragment  \n",
       "index                            0.4         -0.2     -0.7       0.2  \n",
       "length                           0.0          0.1     -0.0      -0.1  \n",
       "taxonomyID                       0.1         -0.0      0.0      -0.1  \n",
       "sequence version                -0.1          0.1      0.2      -0.0  \n",
       "protein existence                1.0         -0.2     -0.4       0.1  \n",
       "Terms Count                     -0.2          1.0      0.3      -0.1  \n",
       "in test                         -0.4          0.3      1.0      -0.2  \n",
       "fragment                         0.1         -0.1     -0.2       1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "RuntimeError",
     "evalue": "clustermap requires scipy to be available",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 22\u001b[0m\n\u001b[1;32m     20\u001b[0m display(newdf\u001b[38;5;241m.\u001b[39mcorr()\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# display(newdf[['index', 'protein existence' ,'in test','fragment']].corr().round(1))\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclustermap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnewdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     23\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/Desktop/cafa/.venv/lib/python3.10/site-packages/seaborn/matrix.py:1250\u001b[0m, in \u001b[0;36mclustermap\u001b[0;34m(data, pivot_kws, method, metric, z_score, standard_scale, figsize, cbar_kws, row_cluster, col_cluster, row_linkage, col_linkage, row_colors, col_colors, mask, dendrogram_ratio, colors_ratio, cbar_pos, tree_kws, **kwargs)\u001b[0m\n\u001b[1;32m   1157\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1158\u001b[0m \u001b[38;5;124;03mPlot a matrix dataset as a hierarchically-clustered heatmap.\u001b[39;00m\n\u001b[1;32m   1159\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1247\u001b[0m \n\u001b[1;32m   1248\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _no_scipy:\n\u001b[0;32m-> 1250\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclustermap requires scipy to be available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1252\u001b[0m plotter \u001b[38;5;241m=\u001b[39m ClusterGrid(data, pivot_kws\u001b[38;5;241m=\u001b[39mpivot_kws, figsize\u001b[38;5;241m=\u001b[39mfigsize,\n\u001b[1;32m   1253\u001b[0m                       row_colors\u001b[38;5;241m=\u001b[39mrow_colors, col_colors\u001b[38;5;241m=\u001b[39mcol_colors,\n\u001b[1;32m   1254\u001b[0m                       z_score\u001b[38;5;241m=\u001b[39mz_score, standard_scale\u001b[38;5;241m=\u001b[39mstandard_scale,\n\u001b[1;32m   1255\u001b[0m                       mask\u001b[38;5;241m=\u001b[39mmask, dendrogram_ratio\u001b[38;5;241m=\u001b[39mdendrogram_ratio,\n\u001b[1;32m   1256\u001b[0m                       colors_ratio\u001b[38;5;241m=\u001b[39mcolors_ratio, cbar_pos\u001b[38;5;241m=\u001b[39mcbar_pos)\n\u001b[1;32m   1258\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plotter\u001b[38;5;241m.\u001b[39mplot(metric\u001b[38;5;241m=\u001b[39mmetric, method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m   1259\u001b[0m                     colorbar_kws\u001b[38;5;241m=\u001b[39mcbar_kws,\n\u001b[1;32m   1260\u001b[0m                     row_cluster\u001b[38;5;241m=\u001b[39mrow_cluster, col_cluster\u001b[38;5;241m=\u001b[39mcol_cluster,\n\u001b[1;32m   1261\u001b[0m                     row_linkage\u001b[38;5;241m=\u001b[39mrow_linkage, col_linkage\u001b[38;5;241m=\u001b[39mcol_linkage,\n\u001b[1;32m   1262\u001b[0m                     tree_kws\u001b[38;5;241m=\u001b[39mtree_kws, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: clustermap requires scipy to be available"
     ]
    }
   ],
   "source": [
    "fn = \"dataset/Train/train_terms.tsv\"\n",
    "df = pd.read_csv(fn, sep=\"\\t\")\n",
    "print(df.shape)\n",
    "display(df)\n",
    "sr2 = df.groupby(\"EntryID\")[\"term\"].apply(lambda x: len(list(x)))\n",
    "sr2.name = \"Terms Count\"\n",
    "print(sr2.shape)\n",
    "display(sr2.head(2))\n",
    "\n",
    "df_seq2 = df_seq.join(sr2, how=\"left\")\n",
    "print(df_seq2.shape)\n",
    "display(df_seq2.head(2))\n",
    "\n",
    "# df_seq.select_dtypes('number').corr()\n",
    "# numerics = ['bool','float',  'int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "df.select_dtypes(include=numerics)\n",
    "newdf = pd.concat(\n",
    "    [df_seq2.select_dtypes(\"number\"), df_seq2.select_dtypes(\"bool\")], axis=1\n",
    ")\n",
    "newdf\n",
    "display(newdf.corr().round(1))\n",
    "# display(newdf[['index', 'protein existence' ,'in test','fragment']].corr().round(1))\n",
    "sns.clustermap(newdf.corr().abs())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aspect\n",
       "BPO    3497732\n",
       "CCO    1196017\n",
       "MFO     670114\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"aspect\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BPO' 'CCO' 'MFO']\n",
      "(92210,)\n",
      "(142246, 15)\n",
      "(92912,)\n",
      "(142246, 16)\n",
      "(78637,)\n",
      "(142246, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>length</th>\n",
       "      <th>taxonomyID</th>\n",
       "      <th>sequence version</th>\n",
       "      <th>protein existence</th>\n",
       "      <th>Terms Count</th>\n",
       "      <th>Terms Count BPO</th>\n",
       "      <th>Terms Count CCO</th>\n",
       "      <th>Terms Count MFO</th>\n",
       "      <th>in test</th>\n",
       "      <th>fragment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taxonomyID</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sequence version</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein existence</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terms Count</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terms Count BPO</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terms Count CCO</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terms Count MFO</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in test</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fragment</th>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index  length  taxonomyID  sequence version  \\\n",
       "index                1.0     0.0        -0.1              -0.2   \n",
       "length               0.0     1.0        -0.0               0.1   \n",
       "taxonomyID          -0.1    -0.0         1.0              -0.1   \n",
       "sequence version    -0.2     0.1        -0.1               1.0   \n",
       "protein existence    0.4     0.0         0.1              -0.1   \n",
       "Terms Count         -0.2     0.1        -0.0               0.1   \n",
       "Terms Count BPO     -0.1     0.1        -0.1               0.1   \n",
       "Terms Count CCO     -0.1     0.1         0.0               0.1   \n",
       "Terms Count MFO     -0.3     0.0        -0.0               0.1   \n",
       "in test             -0.7    -0.0         0.0               0.2   \n",
       "fragment             0.2    -0.1        -0.1              -0.0   \n",
       "\n",
       "                   protein existence  Terms Count  Terms Count BPO  \\\n",
       "index                            0.4         -0.2             -0.1   \n",
       "length                           0.0          0.1              0.1   \n",
       "taxonomyID                       0.1         -0.0             -0.1   \n",
       "sequence version                -0.1          0.1              0.1   \n",
       "protein existence                1.0         -0.2             -0.1   \n",
       "Terms Count                     -0.2          1.0              1.0   \n",
       "Terms Count BPO                 -0.1          1.0              1.0   \n",
       "Terms Count CCO                 -0.2          0.5              0.3   \n",
       "Terms Count MFO                 -0.3          0.5              0.4   \n",
       "in test                         -0.4          0.3              0.2   \n",
       "fragment                         0.1         -0.1             -0.1   \n",
       "\n",
       "                   Terms Count CCO  Terms Count MFO  in test  fragment  \n",
       "index                         -0.1             -0.3     -0.7       0.2  \n",
       "length                         0.1              0.0     -0.0      -0.1  \n",
       "taxonomyID                     0.0             -0.0      0.0      -0.1  \n",
       "sequence version               0.1              0.1      0.2      -0.0  \n",
       "protein existence             -0.2             -0.3     -0.4       0.1  \n",
       "Terms Count                    0.5              0.5      0.3      -0.1  \n",
       "Terms Count BPO                0.3              0.4      0.2      -0.1  \n",
       "Terms Count CCO                1.0              0.2      0.3      -0.0  \n",
       "Terms Count MFO                0.2              1.0      0.3      -0.1  \n",
       "in test                        0.3              0.3      1.0      -0.2  \n",
       "fragment                      -0.0             -0.1     -0.2       1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "RuntimeError",
     "evalue": "clustermap requires scipy to be available",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 24\u001b[0m\n\u001b[1;32m     22\u001b[0m display(newdf\u001b[38;5;241m.\u001b[39mcorr()\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# display(newdf[['index', 'protein existence' ,'in test','fragment']].corr().round(1))\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclustermap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnewdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/Desktop/cafa/.venv/lib/python3.10/site-packages/seaborn/matrix.py:1250\u001b[0m, in \u001b[0;36mclustermap\u001b[0;34m(data, pivot_kws, method, metric, z_score, standard_scale, figsize, cbar_kws, row_cluster, col_cluster, row_linkage, col_linkage, row_colors, col_colors, mask, dendrogram_ratio, colors_ratio, cbar_pos, tree_kws, **kwargs)\u001b[0m\n\u001b[1;32m   1157\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1158\u001b[0m \u001b[38;5;124;03mPlot a matrix dataset as a hierarchically-clustered heatmap.\u001b[39;00m\n\u001b[1;32m   1159\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1247\u001b[0m \n\u001b[1;32m   1248\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _no_scipy:\n\u001b[0;32m-> 1250\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclustermap requires scipy to be available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1252\u001b[0m plotter \u001b[38;5;241m=\u001b[39m ClusterGrid(data, pivot_kws\u001b[38;5;241m=\u001b[39mpivot_kws, figsize\u001b[38;5;241m=\u001b[39mfigsize,\n\u001b[1;32m   1253\u001b[0m                       row_colors\u001b[38;5;241m=\u001b[39mrow_colors, col_colors\u001b[38;5;241m=\u001b[39mcol_colors,\n\u001b[1;32m   1254\u001b[0m                       z_score\u001b[38;5;241m=\u001b[39mz_score, standard_scale\u001b[38;5;241m=\u001b[39mstandard_scale,\n\u001b[1;32m   1255\u001b[0m                       mask\u001b[38;5;241m=\u001b[39mmask, dendrogram_ratio\u001b[38;5;241m=\u001b[39mdendrogram_ratio,\n\u001b[1;32m   1256\u001b[0m                       colors_ratio\u001b[38;5;241m=\u001b[39mcolors_ratio, cbar_pos\u001b[38;5;241m=\u001b[39mcbar_pos)\n\u001b[1;32m   1258\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plotter\u001b[38;5;241m.\u001b[39mplot(metric\u001b[38;5;241m=\u001b[39mmetric, method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m   1259\u001b[0m                     colorbar_kws\u001b[38;5;241m=\u001b[39mcbar_kws,\n\u001b[1;32m   1260\u001b[0m                     row_cluster\u001b[38;5;241m=\u001b[39mrow_cluster, col_cluster\u001b[38;5;241m=\u001b[39mcol_cluster,\n\u001b[1;32m   1261\u001b[0m                     row_linkage\u001b[38;5;241m=\u001b[39mrow_linkage, col_linkage\u001b[38;5;241m=\u001b[39mcol_linkage,\n\u001b[1;32m   1262\u001b[0m                     tree_kws\u001b[38;5;241m=\u001b[39mtree_kws, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: clustermap requires scipy to be available"
     ]
    }
   ],
   "source": [
    "print(df[\"aspect\"].unique())\n",
    "for a in [\"BPO\", \"CCO\", \"MFO\"]:\n",
    "    m = df[\"aspect\"] == a\n",
    "    sr2 = df[m].groupby(\"EntryID\")[\"term\"].apply(lambda x: len(list(x)))\n",
    "    sr2.name = \"Terms Count \" + a\n",
    "    sr2 = sr2.fillna(0)\n",
    "    print(sr2.shape)\n",
    "    #     display(sr2.head(2))\n",
    "\n",
    "    df_seq2 = df_seq2.join(sr2, how=\"left\")\n",
    "    df_seq2[sr2.name] = df_seq2[sr2.name].fillna(0)  # df_seq.join(sr2, how = 'left')\n",
    "    print(df_seq2.shape)\n",
    "#     display(df_seq2.head(2))\n",
    "\n",
    "# df_seq.select_dtypes('number').corr()\n",
    "# numerics = ['bool','float',  'int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "df.select_dtypes(include=numerics)\n",
    "newdf = pd.concat(\n",
    "    [df_seq2.select_dtypes(\"number\"), df_seq2.select_dtypes(\"bool\")], axis=1\n",
    ")\n",
    "newdf\n",
    "display(newdf.corr().round(1))\n",
    "# display(newdf[['index', 'protein existence' ,'in test','fragment']].corr().round(1))\n",
    "sns.clustermap(newdf.corr().abs())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>length</th>\n",
       "      <th>taxonomyID</th>\n",
       "      <th>sequence version</th>\n",
       "      <th>protein existence</th>\n",
       "      <th>Terms Count</th>\n",
       "      <th>Terms Count BPO</th>\n",
       "      <th>Terms Count CCO</th>\n",
       "      <th>Terms Count MFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "      <td>1.422460e+05</td>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>71122.500000</td>\n",
       "      <td>553.636679</td>\n",
       "      <td>7.630008e+04</td>\n",
       "      <td>1.354716</td>\n",
       "      <td>1.724182</td>\n",
       "      <td>37.708357</td>\n",
       "      <td>24.589317</td>\n",
       "      <td>8.408089</td>\n",
       "      <td>4.710951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41063.027533</td>\n",
       "      <td>641.728770</td>\n",
       "      <td>1.739173e+05</td>\n",
       "      <td>0.746035</td>\n",
       "      <td>1.037118</td>\n",
       "      <td>42.524701</td>\n",
       "      <td>36.016803</td>\n",
       "      <td>9.108287</td>\n",
       "      <td>6.391059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35561.250000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>7.227000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>71122.500000</td>\n",
       "      <td>411.000000</td>\n",
       "      <td>9.606000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>106683.750000</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>3.994700e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>142245.000000</td>\n",
       "      <td>35375.000000</td>\n",
       "      <td>2.902295e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>815.000000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index         length    taxonomyID  sequence version  \\\n",
       "count  142246.000000  142246.000000  1.422460e+05     142246.000000   \n",
       "mean    71122.500000     553.636679  7.630008e+04          1.354716   \n",
       "std     41063.027533     641.728770  1.739173e+05          0.746035   \n",
       "min         0.000000       3.000000  2.400000e+01          0.000000   \n",
       "25%     35561.250000     248.000000  7.227000e+03          1.000000   \n",
       "50%     71122.500000     411.000000  9.606000e+03          1.000000   \n",
       "75%    106683.750000     654.000000  3.994700e+04          1.000000   \n",
       "max    142245.000000   35375.000000  2.902295e+06         10.000000   \n",
       "\n",
       "       protein existence    Terms Count  Terms Count BPO  Terms Count CCO  \\\n",
       "count      142246.000000  142246.000000    142246.000000    142246.000000   \n",
       "mean            1.724182      37.708357        24.589317         8.408089   \n",
       "std             1.037118      42.524701        36.016803         9.108287   \n",
       "min             0.000000       2.000000         0.000000         0.000000   \n",
       "25%             1.000000      10.000000         0.000000         0.000000   \n",
       "50%             1.000000      24.000000        13.000000         6.000000   \n",
       "75%             2.000000      50.000000        34.000000        13.000000   \n",
       "max             5.000000     815.000000       708.000000       102.000000   \n",
       "\n",
       "       Terms Count MFO  \n",
       "count    142246.000000  \n",
       "mean          4.710951  \n",
       "std           6.391059  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           3.000000  \n",
       "75%           7.000000  \n",
       "max          83.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seq2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142243 142246 ['Uracil-DNA glycosylase ', 'Protein Wnt-11 ', 'Homeobox protein VENTX ', 'Arrestin domain-containing protein 4 ', 'Homeobox protein goosecoid ', 'T-lymphocyte activation antigen CD80 ', 'D-xylonate dehydratase YagF ', 'Laminin subunit alpha-3 ', 'GPI inositol-deacylase ', 'Protein glp-1 ']\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>length</th>\n",
       "      <th>description</th>\n",
       "      <th>dbase</th>\n",
       "      <th>sequence</th>\n",
       "      <th>in test</th>\n",
       "      <th>fragment</th>\n",
       "      <th>organism</th>\n",
       "      <th>taxonomyID</th>\n",
       "      <th>gene name</th>\n",
       "      <th>gene name lower</th>\n",
       "      <th>sequence version</th>\n",
       "      <th>protein existence</th>\n",
       "      <th>Terms Count</th>\n",
       "      <th>Terms Count BPO</th>\n",
       "      <th>Terms Count CCO</th>\n",
       "      <th>Terms Count MFO</th>\n",
       "      <th>Description Cleaned</th>\n",
       "      <th>len description cleaned</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P20536</th>\n",
       "      <td>0</td>\n",
       "      <td>218</td>\n",
       "      <td>UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>MNSVTVSHAPYTITYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPDKFFIQLKQPLRNKRVCVCGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFE...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Vaccinia virus (strain Copenhagen)</td>\n",
       "      <td>10249</td>\n",
       "      <td>UNG</td>\n",
       "      <td>ung</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Uracil-DNA glycosylase</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O73864</th>\n",
       "      <td>1</td>\n",
       "      <td>354</td>\n",
       "      <td>WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7955 GN=wnt11 PE=2 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>MTEYRNFLLLFITSLSVIYPCTGISWLGLTINGSSVGWNQTHHCKLLDGLVPDQQQLCKRNLELMHSIVRAARLTKSACTSSFSDMRWNWSSIESAPHFTPDLAKGTREAAFVVSLAAAVVSHAIARACASGDLPSCSCAAMPSEQAAPDFRWGGCGDNLRYYGLQMGSAFSDAPMRNRRSGPQDFRLMQLHNNAV...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Danio rerio</td>\n",
       "      <td>7955</td>\n",
       "      <td>wnt11</td>\n",
       "      <td>wnt11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Protein Wnt-11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O95231</th>\n",
       "      <td>2</td>\n",
       "      <td>258</td>\n",
       "      <td>VENTX_HUMAN Homeobox protein VENTX OS=Homo sapiens OX=9606 GN=VENTX PE=1 SV=1</td>\n",
       "      <td>sp</td>\n",
       "      <td>MRLSSSPPRGPQQLSSFGSVDWLSQSSCSGPTHTPRPADFSLGSLPGPGQTSGAREPPQAVSIKEAAGSSNLPAPERTMAGLSKEPNTLRAPRVRTAFTMEQVRTLEGVFQHHQYLSPLERKRLAREMQLSEVQIKTWFQNRRMKHKRQMQDPQLHSPFSGSLHAPPAFYSTSSGLANGLQLLCPWAPLSGPQALM...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>9606</td>\n",
       "      <td>VENTX</td>\n",
       "      <td>ventx</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Homeobox protein VENTX</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  length  \\\n",
       "id                      \n",
       "P20536      0     218   \n",
       "O73864      1     354   \n",
       "O95231      2     258   \n",
       "\n",
       "                                                                                             description  \\\n",
       "id                                                                                                         \n",
       "P20536  UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1   \n",
       "O73864                              WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7955 GN=wnt11 PE=2 SV=1   \n",
       "O95231                     VENTX_HUMAN Homeobox protein VENTX OS=Homo sapiens OX=9606 GN=VENTX PE=1 SV=1   \n",
       "\n",
       "       dbase  \\\n",
       "id             \n",
       "P20536    sp   \n",
       "O73864    sp   \n",
       "O95231    sp   \n",
       "\n",
       "                                                                                                                                                                                                       sequence  \\\n",
       "id                                                                                                                                                                                                                \n",
       "P20536  MNSVTVSHAPYTITYHDDWEPVMSQLVEFYNEVASWLLRDETSPIPDKFFIQLKQPLRNKRVCVCGIDPYPKDGTGVPFESPNFTKKSIKEIASSISRLTGVIDYKGYNLNIIDGVIPWNYYLSCKLGETKSHAIYWDKISKLLLQHITKHVSVLYCLGKTDFSNIRAKLESPVTTIVGYHPAARDRQFEKDRSFE...   \n",
       "O73864  MTEYRNFLLLFITSLSVIYPCTGISWLGLTINGSSVGWNQTHHCKLLDGLVPDQQQLCKRNLELMHSIVRAARLTKSACTSSFSDMRWNWSSIESAPHFTPDLAKGTREAAFVVSLAAAVVSHAIARACASGDLPSCSCAAMPSEQAAPDFRWGGCGDNLRYYGLQMGSAFSDAPMRNRRSGPQDFRLMQLHNNAV...   \n",
       "O95231  MRLSSSPPRGPQQLSSFGSVDWLSQSSCSGPTHTPRPADFSLGSLPGPGQTSGAREPPQAVSIKEAAGSSNLPAPERTMAGLSKEPNTLRAPRVRTAFTMEQVRTLEGVFQHHQYLSPLERKRLAREMQLSEVQIKTWFQNRRMKHKRQMQDPQLHSPFSGSLHAPPAFYSTSSGLANGLQLLCPWAPLSGPQALM...   \n",
       "\n",
       "        in test  fragment                            organism  taxonomyID  \\\n",
       "id                                                                          \n",
       "P20536    False     False  Vaccinia virus (strain Copenhagen)       10249   \n",
       "O73864     True     False                         Danio rerio        7955   \n",
       "O95231     True     False                        Homo sapiens        9606   \n",
       "\n",
       "       gene name gene name lower  sequence version  protein existence  \\\n",
       "id                                                                      \n",
       "P20536       UNG             ung                 1                  1   \n",
       "O73864     wnt11           wnt11                 1                  2   \n",
       "O95231     VENTX           ventx                 1                  1   \n",
       "\n",
       "        Terms Count  Terms Count BPO  Terms Count CCO  Terms Count MFO  \\\n",
       "id                                                                       \n",
       "P20536           33             30.0              0.0              3.0   \n",
       "O73864          170            160.0              5.0              5.0   \n",
       "O95231           62             34.0             13.0             15.0   \n",
       "\n",
       "            Description Cleaned  len description cleaned  \n",
       "id                                                        \n",
       "P20536  Uracil-DNA glycosylase                        23  \n",
       "O73864          Protein Wnt-11                        15  \n",
       "O95231  Homeobox protein VENTX                        23  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = []\n",
    "i = 0\n",
    "for t in l3:\n",
    "    if \"OX=\" in t:\n",
    "        s1 = t.split(\"OS=\")[0]\n",
    "        p = s1.find(\" \")\n",
    "        s = s1[(p + 1) :]\n",
    "        l.append(s)\n",
    "        i += 1\n",
    "    else:\n",
    "        l.append(\"\")\n",
    "print(i, len(l), l[:10])\n",
    "df_seq2[\"Description Cleaned\"] = l\n",
    "l = [len(t) for t in l]\n",
    "df_seq2[\"len description cleaned\"] = l\n",
    "display(df_seq2.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.995028\n",
      "CPU times: user 1.22 s, sys: 168 ms, total: 1.39 s\n",
      "Wall time: 1.48 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(df_seq2.memory_usage().sum() / 1e6)\n",
    "df_seq2.to_csv(\"df_train_eda.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'length', 'description', 'dbase', 'sequence', 'in test',\n",
       "       'fragment', 'organism', 'taxonomyID', 'gene name', 'gene name lower',\n",
       "       'sequence version', 'protein existence', 'Terms Count',\n",
       "       'Terms Count BPO', 'Terms Count CCO', 'Terms Count MFO',\n",
       "       'Description Cleaned', 'len description cleaned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_seq2\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence version</th>\n",
       "      <th>protein existence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142246.000000</td>\n",
       "      <td>142246.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.354716</td>\n",
       "      <td>1.724182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.746035</td>\n",
       "      <td>1.037118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sequence version  protein existence\n",
       "count     142246.000000      142246.000000\n",
       "mean           1.354716           1.724182\n",
       "std            0.746035           1.037118\n",
       "min            0.000000           0.000000\n",
       "25%            1.000000           1.000000\n",
       "50%            1.000000           1.000000\n",
       "75%            1.000000           2.000000\n",
       "max           10.000000           5.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence version</th>\n",
       "      <th>protein existence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142241</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142242</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142243</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142244</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142245</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142246 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sequence version  protein existence\n",
       "0                      1                  1\n",
       "1                      1                  2\n",
       "2                      1                  1\n",
       "3                      1                  1\n",
       "4                      2                  2\n",
       "...                  ...                ...\n",
       "142241                 1                  4\n",
       "142242                 1                  3\n",
       "142243                 1                  3\n",
       "142244                 1                  4\n",
       "142245                 1                  3\n",
       "\n",
       "[142246 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame()\n",
    "d[\"sequence version\"] = df[\"sequence version\"].values\n",
    "d[\"protein existence\"] = df[\"protein existence\"].values\n",
    "display(d.describe())\n",
    "d.to_csv(\"train_features01_SV_PE.csv\")\n",
    "np.save(\"train_features01_SV_PE\", d.values)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene name</th>\n",
       "      <th>gene name lower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142246</td>\n",
       "      <td>142246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>97788</td>\n",
       "      <td>78493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Dscam1</td>\n",
       "      <td>ank2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>79</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene name gene name lower\n",
       "count     142246          142246\n",
       "unique     97788           78493\n",
       "top       Dscam1            ank2\n",
       "freq          79              98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene name</th>\n",
       "      <th>gene name lower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UNG</td>\n",
       "      <td>ung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wnt11</td>\n",
       "      <td>wnt11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VENTX</td>\n",
       "      <td>ventx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arrdc4</td>\n",
       "      <td>arrdc4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gsc</td>\n",
       "      <td>gsc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142241</th>\n",
       "      <td>marco</td>\n",
       "      <td>marco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142242</th>\n",
       "      <td>MAP3K7</td>\n",
       "      <td>map3k7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142243</th>\n",
       "      <td>kcnk5b</td>\n",
       "      <td>kcnk5b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142244</th>\n",
       "      <td>mef2aa</td>\n",
       "      <td>mef2aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142245</th>\n",
       "      <td>Lsm1</td>\n",
       "      <td>lsm1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142246 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gene name gene name lower\n",
       "0            UNG             ung\n",
       "1          wnt11           wnt11\n",
       "2          VENTX           ventx\n",
       "3         Arrdc4          arrdc4\n",
       "4            Gsc             gsc\n",
       "...          ...             ...\n",
       "142241     marco           marco\n",
       "142242    MAP3K7          map3k7\n",
       "142243    kcnk5b          kcnk5b\n",
       "142244    mef2aa          mef2aa\n",
       "142245      Lsm1            lsm1\n",
       "\n",
       "[142246 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame()\n",
    "l = [\"gene name\", \"gene name lower\"]\n",
    "for col in l:\n",
    "    d[col] = df[col].values\n",
    "display(d.describe())\n",
    "d.to_csv(\"train_features02_genes.csv\")\n",
    "np.save(\"train_features02_genes\", d.values)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "P20536                      UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1\n",
       "O73864                                                  WNT11_DANRE Protein Wnt-11 OS=Danio rerio OX=7955 GN=wnt11 PE=2 SV=1\n",
       "O95231                                         VENTX_HUMAN Homeobox protein VENTX OS=Homo sapiens OX=9606 GN=VENTX PE=1 SV=1\n",
       "A0A0B4J1F4                     ARRD4_MOUSE Arrestin domain-containing protein 4 OS=Mus musculus OX=10090 GN=Arrdc4 PE=1 SV=1\n",
       "P54366                              GSC_DROME Homeobox protein goosecoid OS=Drosophila melanogaster OX=7227 GN=Gsc PE=2 SV=2\n",
       "                                                                   ...                                                      \n",
       "A0A286YAI0         A0A286YAI0_DANRE Macrophage receptor with collagenous structure OS=Danio rerio OX=7955 GN=marco PE=4 SV=1\n",
       "A0A1D5NUC4    A0A1D5NUC4_CHICK Mitogen-activated protein kinase kinase kinase 7 OS=Gallus gallus OX=9031 GN=MAP3K7 PE=3 SV=1\n",
       "Q5RGB0                     Q5RGB0_DANRE Potassium channel, subfamily K, member 5b OS=Danio rerio OX=7955 GN=kcnk5b PE=3 SV=1\n",
       "A0A2R8QMZ5                           A0A2R8QMZ5_DANRE Myocyte enhancer factor 2aa OS=Danio rerio OX=7955 GN=mef2aa PE=4 SV=1\n",
       "A0A8I6GHU0           A0A8I6GHU0_RAT U6 snRNA-associated Sm-like protein LSm1 OS=Rattus norvegicus OX=10116 GN=Lsm1 PE=3 SV=1\n",
       "Name: description, Length: 142246, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"description\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description Cleaned</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142246</td>\n",
       "      <td>142246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>84530</td>\n",
       "      <td>142246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Uncharacterized protein</td>\n",
       "      <td>UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Description Cleaned  \\\n",
       "count                     142246   \n",
       "unique                     84530   \n",
       "top     Uncharacterized protein    \n",
       "freq                         538   \n",
       "\n",
       "                                                                                             Description  \n",
       "count                                                                                             142246  \n",
       "unique                                                                                            142246  \n",
       "top     UNG_VACCC Uracil-DNA glycosylase OS=Vaccinia virus (strain Copenhagen) OX=10249 GN=UNG PE=1 SV=1  \n",
       "freq                                                                                                   1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = pd.DataFrame()\n",
    "l = ['Description Cleaned', 'description']\n",
    "for col in l:\n",
    "    d[col] = df[col].values\n",
    "d.columns = ['Description Cleaned', 'Description']\n",
    "display(d.describe())\n",
    "d.to_csv('train_features03_description.csv')\n",
    "np.save('train_features03_description', d.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dbase</th>\n",
       "      <th>fragment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142246</td>\n",
       "      <td>142246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>sp</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>83883</td>\n",
       "      <td>134009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dbase fragment\n",
       "count   142246   142246\n",
       "unique       2        2\n",
       "top         sp    False\n",
       "freq     83883   134009"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = pd.DataFrame()\n",
    "l = ['dbase', 'fragment']\n",
    "for col in l:\n",
    "    d[col] = df[col].values\n",
    "display(d.describe())\n",
    "d.to_csv('train_features04_dbase_fragment.csv')\n",
    "np.save('train_features04_dbase_fragment', d.values)"
   ]
  }
 ],
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