tutorial_addgene.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from saker import *\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file = \"data/addgene-plasmids-sequences.json\"\n",
    "work_dir = \"tutorial/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Parse_Addgene(data_file, columns=[\"id\", \"bacterial_resistance\", \"cloning.backbone\", \"pi\"], maxlen=8000, flank_length=48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71948 entries, 0 to 71947\n",
      "Data columns (total 5 columns):\n",
      "id                      71948 non-null int64\n",
      "bacterial_resistance    71948 non-null object\n",
      "cloning.backbone        71948 non-null object\n",
      "pi                      71948 non-null object\n",
      "seq                     71948 non-null object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n",
    "#data.columns\n",
    "#data.head()\n",
    "#data[\"pi\"].describe()\n",
    "#data[\"seq\"]\n",
    "#data[\"cloning.backbone\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GGTCGGATTCCCGGGCTGCAGGAATTCGGCACGAGGTTGCCTGCATGAGTGTGTGCTGTGTGTCAGAGTGGATTGGAGTTGAAAAAGCTTGACTGGCGTCATTCGGGAGCTGGATGGCTTGGGACATGTGCAGCCAAGACTCTGTATGGAGTGACATAGAGTGTGCTGCTCTGGTTGGTGAGGACCAGCCTCTTTGCCCAGATCTTCCTGAACTTGACCTTTCTGAACTTGATGTGAATGACTTGGATACAGACAGCTTTCTGGGTGGATTGAAGTGGTGTAGCGACCAATCGGAAATCATATCCAACCAGTACAACAATGAGCCTGCGAACATATTTGAGAAGATAGATGAAGAGAATGAGGCAAACTTGCTAGCGGTCCTCACAGAGACACTGGACAGTCTCCCCGTGGATGAAGACGGATTGCCCTCATTTGATGCACTGACAGATGGAGCCGTGACCACTGACAACGAGGCCAGTCCTTCCTCCATGCCTGACGGCACCCCTCCCCCTCAGGAGGCAGAAGAGCCGTCTCTACTTAAGAAGCTCTTACTGGCACCAGCCAACACTCAGCTCAGCTACAATGAATGCAGCGGTCTTAGCACTCAGAACCATGCAGCAAACCACACCCACAGGATCAGAACAAACCCTGCCATTGTTAAGACCGAGAATTCATGGAGCAATAAAGCGAAGAGCATTTGTCAACAGCAAAAGCCACAAAGACGTCCCTGCTCAGAGCTTCTCAAGTATCTGACCACAAACGATGACCCTCCTCACACCAAACCCACAGAAAACAGGAACAGCAGCAGAGACAAATGTGCTTCCAAAAAGAAGTCCCATACACAACCGCAGTCGCAACATGCTCAAGCCAAACCAACAACTTTATCTCTTCCTCTGACCCCAGAGTCACCAAATGACCCCAAGGGNTCCCCATTTGAGAACNAGACTATTGAGCGAACCTTAAGTGTNNNCTCTCTGGAACTGCAGNCTACTCCTCCCACACTCCTCCTCATAAGCNNCAGANACCCTTTCAGGCTTCGCCAAAGCTGAGCCTCTGCAGACGTGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTTAGCCATGCGCGTACGTAACTTGGATCCTCTAGAGCGGCCGCCGACTAGTGAGCTCGTCGAGTTTTTTTTTTTTTTTTTTTAATTTATTTATTTTCCTTTTGTATAGAATACGAACATTTTGAAGTTGTAGGTTTTAAGTGTGTCTTCATGGAAATGCTGCCATTTGAAGGGGTCGCCCTTGTTCGTTCTGTTCAGGTGCCCCCAAGTCCTCACATGTGTACATATGTACTTTCCAAACAAGTACACACACACACATACACACACGCACACCCACACACACACACGCACACACACTGTCACCAAGAGCCTCCAGGGAAAGCAAAGCTGACACCCGTGAATAAACGTGTGCTTACTGGATTATCATTCTGTCTCTTGCCTCTTCAGCAGCTGTGTTCATGTAAACCGTTGTTCTTGTTCTGGTATGGTTGTTGTTGTTATTGTTGTTGTTGTTGTTGTTATTGTTGTTTTGTTTTGTTTTATTTTTGTACAGAGAGTGTAAAGTAGGAGAAATTCCTAAGTATGACTTGCAAGAGTCTTGGGAAAGGACACGCTGTCCCATGAGGTATTGACCATCTCTCTGTCATTCCTCAGCCTGGGAACACGTTACCTGCGCAAGCTTCTCTGAGCTTCCTTCAGTAAACTATCAAAATCCAGAGAGTCATACTTGCTCTTGGTGGAAGCAGGGTCAAAATCGTCTGAGTTGGTATCTAGGTCTGCATAGTTAGACTTGAAAAATTGCTTCCGTCCACAAAAGTACAGCTCGAAGTCAGTTTCGTTCGACCTGCGTAAAGTATATCCATTCTCAAGAGCAGCGAAAGCGTCACAGGTGTAACGGTAGGTGATGAAACCATAGCTGTCTCCATCATCCCGCAGATTTACGGTGCATTCCTCAATTTCACCAAAAACTTCAAAGCGGTCTCTCAATTCTGTCCCCGTTGTGTCAGGTCGGATTTTACAACGTAAATCACACGGCGCTCTTCAATTGCTTTTCGCTTCCGCCTCCCCCCTCGGTTTGGCCCTTTCAAACTCCCGCTTCCCGGGGTCTTTTGCGGATTCATCCCTCTTGGACCCTTTCGGGCCCAAAGGCTTCAAACTGCCGAACCGGGACCTAACGGTGGTAGGGGGACCTTGAAGGTGATTTCACCTAAAAGGGAAAAATGTCCGTGGGTGGCGGGGCCGTAATGGGCTTTGATTCTAACAAAAAACAGGACTTCTAAAAGAAATCTATTGGCGTGGGAACTTGTATTTTTCCCGGCAAATGGGGGTATCGAAACACAACCTTTTTCTAATAAAAATTCTGGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN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CTCAGCTTTGGCGAAGCCTGAAAGGGTNTCTGNNGCTTATGAGGAGGAGTGTGGGAGGAGTAGNCTGCAGTTCCAGAGAGNNNACACTTAAGGTTCGCTCAATAGTCTNGTTCTCAAATGGGGANCCCTTGGGGTCATTTGGTGACTCTGGGGTCAGAGGAAGAGATAAAGTTGTTGGTTTGGCTTGAGCATGTTGCGACTGCGGTTGTGTATGGGACTTCTTTTTGGAAGCACATTTGTCTCTGCTGCTGTTCCTGTTTTCTGTGGGTTTGGTGTGAGGAGGGTCATCGTTTGTGGTCAGATACTTGAGAAGCTCTGAGCAGGGACGTCTTTGTGGCTTTTGCTGTTGACAAATGCTCTTCGCTTTATTGCTCCATGAATTCTCGGTCTTAACAATGGCAGGGTTTGTTCTGATCCTGTGGGTGTGGTTTGCTGCATGGTTCTGAGTGCTAAGACCGCTGCATTCATTGTAGCTGAGCTGAGTGTTGGCTGGTGCCAGTAAGAGCTTCTTAAGTAGAGACGGCTCTTCTGCCTCCTGAGGGGGAGGGGTGCCGTCAGGCATGGAGGAAGGACTGGCCTCGTTGTCAGTGGTCACGGCTCCATCTGTCAGTGCATCAAATGAGGGCAATCCGTCTTCATCCACGGGGAGACTGTCCAGTGTCTCTGTGAGGACCGCTAGCAAGTTTGCCTCATTCTCTTCATCTATCTTCTCAAATATGTTCGCAGGCTCATTGTTGTACTGGTTGGATATGATTTCCGATTGGTCGCTACACCACTTCAATCCACCCAGAAAGCTGTCTGTATCCAAGTCATTCACATCAAGTTCAGAAAGGTCAAGTTCAGGAAGATCTGGGCAAAGAGGCTGGTCCTCACCAACCAGAGCAGCACACTCTATGTCACTCCATACAGAGTCTTGGCTGCACATGTCCCAAGCCATCCAGCTCCCGAATGACGCCAGTCAAGCTTTTTCAACTCCAATCCACTCTGACACACAGCACACACTCATGCAGGCAACCTCGTGCCGAATTCCTGCAGCCCGGGAATCCGACC'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"seq\"][0][]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0olaF7UOZeUJm7gy8ArgHOCki7o6If23nDUyAkmpg3LmuXZl5VWa+LzMPy8wRD+Bl5vzMnJGZM6ZOndrJECQNrm7lumEP5AHvBXYF5kbE4cO9sPkg3u/+9OA4QpA06FoVttG4k5n/lZnHZuZLgT2AP4/jfTuUAP84jhAk6QndyHX3AFs0Pd68bJOkqnSrXzeszDwuM3fMzMMz85QRtnniIN6znrZhp0OQNEBaFbYjrZz3s8z8x04HM/oEuMEa97f8lOM7HaKk/tSNXHc9sF1ETIuItYEDgEtGs4OImBUR81euXDnGECTpSbrVr/NAnqTKtbrczwe79L4mQEk9Y7y5LiLOAa4FnhcRyyLikMx8DDgSuBK4Azg/M28fZVxLMnPelClTxhOeJAFd7deN+0CeJI3XGldF7qInEiBFQXsA8OaKYpGkccnMA0doX4qX9JHUR8oDebsAG0XEMuBTmXlGRDQO5E0GzhzNgbyImAXMmjZ1426ELGlAdL2wNQFKkiT1h24cyMvMJcCSHbba5tDxxCZpsHW9sO1uApxmApTUtxoH8aZPn151KJIkST2t1eJRT4iIl67psST1g17KdZ5jK6lbeinXSVIntF3YAke0eNyz7j/5C1WHIKk+apvrJGkUzHWS+krbhW1mHrqmx5LUD8x1kgZBL+W6xqXNHnzkoapCkNQH1niObatpKZl5U2fDaY+LR0nqpF7PdZ5jK6kTejXXuXiUpE5otXjUmubwJvC6DsbStrEsHnX/yceyyREf7WJUkmqsp3PdjBkz7OxJ6oSezHWS1AlrLGwz87UTFYgkVcVcJ2kQmOsk9bO2L/cTES8EtgfWabRl5lndCEqSqmKukzQIzHWS+k1bhW1EfArYhSIBLgV2B34AmAAl9Q1znaRB0Gu5zrVTJHVCu6sizwX+DrgvMw8GXgxUdmHF1avnPTzq19538j93ISJJfaInc93KlSurCkFSf+qpXNe4ZveG665fVQiS+kC7he0jmbkKeCwiNgSWA1t0L6w1W50A16sqBEn9qSdz3ZQplfU3JfWnnsp1ktQJ7Ra2N0TE04HTgBuBm4BruxaVJFXDXCdpENQi16045fSqQ5BUI22dY5uZ7y7vnhIRVwAbZuYt3Quru+476VNs+u5/rDoMST2m33KdJA3HXCepH7U1YhsR32ncz8xfZ+YtzW2S1A/MdZIGQa/lutVrpzxUVQiS+sAaC9uIWCcinglsFBHPiIhnlretgc0mIsAR4hrz4lEN9570iQ5GJKnOejXXSVIn9Wquc/EoSZ3QairyYcD7gedQnH/R8CBwQreCaiUzlwBLdthq2qFVxSCpr/RkrmtcAmP69OlVhSCpv/RkrpOkTljjiG1mfiUzpwEfzsxpTbcXZ2btE+C9J32s6hAk9YBezXWuiiypk3o1163JilPmVx2CpJpoa/Eo4NSIeB/w6vLxVcCpmfmXrkQlSdUw10kaBOY6SX2n3cv9nATsWP5t3D+5W0FNpP8+8YNVhyCpd/RtrpOkJuY6SX2n3RHb/5uZL256/N2I+Ek3ApKkCpnrJA0Cc52kvtPuiO3jEbFt40FEbAM83p2QJt49J7636hAk9Ya+znWSVOqpXOflfiR1Qrsjth8BvhcRdwMBbAW8s2tRtdBYKXTa1I2rCkFSf+qpXCdJXdJTuW711S628WoXksas3cL2B8B2wPPKx3d2J5z2eLkfSV3SU7nOy/1I6pKeynWS1AntTkW+NjP/nJm3lLc/A9d2M7Aq/Pb4g/iv4w+oOgxJ1empXOflfiR1SU/lOknqhDWO2EbEpsBmwLoR8RKK6SoAGwLrdTk2SZoQ5jpJg8BcJ6mftZqKvBtwELA58AVWJ8AHgb/vXliSNKHMdTV0wjd2a7nNkW+9cgIikWrDXCepb62xsM3MrwFfi4g5mblogmKSpAllrpM0CMx1kvrZGs+xjYi3RkSMlPwiYtuIeGV3QpOkiWGukzQI6pzrVpxyUtUhSOpxraYiPwu4OSJuBG4EVgDrANOB1wAPAEd3NcKK3H38PqyKZPqRF1cdiqTuG9hcJ2mgmOsk9a1WU5G/EhEnAK8DdgZeBDwC3AG8LTP/q/sh/jWvYyupk3o116lz/t/5M1tu889vvGICIpGq06u5rt1+3fJTjmfjw987MUFJqp2W17HNzMeBfy9vPcHr2ErqtF7MdZLUab2Y61b367axXydpzNq9ju1A+9mJs6sOQdIAiohZETF/5cqVVYciSZLU0yxs2/TTk/auOgRJAyYzl2TmvClTplQdiiRJUk9rORVZq9120t688N2XVB2GJKnLdr+k9cHMy/cu/j3Y48JPtdx26b7/OO6YJEnSyNoqbCPiKOCrwB+B04GXAEdn5re6GFtP+snJe/PiIyxupX5krlO37bn4uLa2u2y/93U5Eg0yc52kftTuVOR3ZuaDwBuAZwBvA47pWlQ97uZTZlUdgqTuMNdJGgTmOkl9p93CNsq/ewBfz8zbm9okqV+Y6yQNAnOdpL7T7jm2N0bEt4BpwMcjYgNgVffC6n03nTKLlx6+pOowJHWWuU49Za9FC1puc+mcg7oeh/pOrXPd8lO+wMaHf6jqMCT1mHYL20OAHYC7M/PhiHgWcHD3wpKkSpjrJA0Cc52kvrPGwjYiXjqkaZsIZ6o0+89TZ7HTYY7cSnVmrpM0CHo110XELGDWtKkbt/2a+08+lk2O+Gj3gpJUO61GbL9Q/l0H2BG4heIcjBcBNwCv6F5oIxtLAuym607di5cfdmnVYUgau57MdZLUYT2Z6zJzCbBkh622ObSK95fUH9a4eFRmvjYzXwvcC+yYmTMyc0eKZeHvmYgAR4hrSWbO23Dd9aoKYVj/MX+vqkOQNAa9muskqZPMdZL6WburIj8vM29tPMjM24AXdCckSaqMuU7SIDDXSeo77Ra2t0TE6RGxS3k7jWL6ioa45jRHbaUaM9dJGgTmOkl9p93C9mDgduCo8vZTXD1vRFeftmfVIUgam57KdRExKyLmr1y5sqoQJPWnnsp1ktQJbV3uJzP/F/hSeVMbrjptTzLgte+6rOpQJLWp13JdY0GVGTNmuKCKpI7ptVwnSZ3Q6nI/52fmGyPiViCHPp+ZL+paZJI0Qcx1kgZBP+a6+07+DJse8cmqw5DUA1qN2B5V/vXE0TH6zul7sork9e9aWnUokkZmrpM0CMx1kvpWq8v93Fv+/U1m/gb4H+CPTTe16Vtn7FF1CJJGYK6TNAj6Ndfdd/Knqw5BUg9o6xzbiDgM+Efgf1k9dSWBbboUlyRNOHOdpEFgrpPUj9oqbIEPAy/MzAe6GUy/u/KMPdjtEKckSz3MXCdpEJjrJPWddi/380vg4W4GIkk9wFwnaRCY6yT1nXZHbD8O/DAifgT8udGYme/rSlR97rIzdmfPQy6vOgxJf81cJ2kQmOsk9Z12R2xPBb4LXAfc2HTTGF165u4AXFz+ldQTzHWSBkFf5rp7Tzq66hAkVajdEdunZOYHuxqJJFXPXCdpEJjrJPWddkdsL4+IeRHx7Ih4ZuPW1cjWICJmRcT8Bx/pj9NDLjxzdxZ/dWbVYUjqsVwnSV3SU7ludb/uoapCkNQH2i1sD6Q8H4PV01Vu6FZQrWTmksyct+G661UVgqT+1FO5TpK6pKdy3ep+3frj3td/n/ihDkQkqY7amoqcmdO6HYhg4VdnksD+B19RdSjSQDLXSRoE5jpJ/aitEduI2D8iNijvfzIiFkfES7obmiRNLHOdpEFgrpPUj9qdivz/MvOPEfFKYFfgDOCU7oU12M4rz7c9e8FufHPBbhVHIw0Uc52kQWCuk9R32l0V+fHy757A/My8LCI+06WYJKkq5jrV1l4LL2i5zaVz95+ASFQD5jpJfafdEdt7IuJU4E3A0oh46iheqw44a8FuLPjaG6oOQ+p3E5LrImKfiDgtIs6LCH/YkiZa3/fr7jnh8KpDkDTB2k1ibwSuBHbLzD8AzwQ+0rWoJKkaY851EXFmRCyPiNuGtM+MiDsj4q6IOBogMy/KzEOBwyk6lpI0kezXSeo7bRW2mflwZi4GVkbElsBTgJ91NTJJmmDjzHULgCddkDoiJgMnArsD2wMHRsT2TZt8snxekibMoPTrlp1wcNUhSJpA7a6KvHdE/AL4FfD98u/l3QxMIzvjrDdw+lkuKiV12nhyXWZeDfx+SPNOwF2ZeXdmPgqcC8yOwueAyzPzps59AklqzX6dpH7U7lTkfwZeDvy8vPbZrsB1XYtKkqrR6Vy3GfDbpsfLyrb3lvueGxHDnggWEfMi4oaIuGHFihXjCEGS/spA9et+c9w+VYcgaQK0uyryXzLzdxExKSImZeb3IuLLXY1MLZ121m6sCkhgVcC733pl1SFJdTchuS4zjwOOa7HNfGA+wIwZM7LTMUgaaPbrJPWddgvbP0TE04CrgW9GxHLgoe6FJUmV6HSuuwfYounx5mWbJFXJfp2kvtPuVOTZwCPAB4ArgF8Cs7oVlCRVpNO57npgu4iYFhFrAwcAl4w7Skkan4Hr1/3q+H246/jZVYchqYvaGrHNzOajeF/rUizqgOO/uRurgKPe4rRkabTGk+si4hxgF2CjiFgGfCozz4iIIykuqzEZODMzbx/FPmcBs6ZPnz6aUCRpjezXSepHayxsI+KPFKdwRtnUOM8rgMzMDbsYmyRNiE7kusw8cIT2pcDSscSVmUuAJTNmzDh0LK+XpGb26yT1szUWtpm5wUQFos774tnF4lIfPvBKjjl3N44+wFFcaTjmOkmDwFwHPz9hNqsCnv+ei6sORVKHtRqxXQc4HJgO3EIxje6xiQhMkiZKr+Y6pyKrm2YvvKLlNhfPnTkBkWii9Gquk6ROaLV41NeAGcCtwB7AF7oekSRNvJ7MdZm5JDPnTZkypepQJPWHnsx1ktQJrRaP2j4z/wYgIs4A/rP7Ialb/vW83Xic4pq3qwgeD/jM/q2P2EsDwFwnaRCY60o/PWlvVgEvfLcL1Uv9otWI7V8ad5yqIqmPmeskDQJznaS+1WrE9sUR8WB5P4B1y8euniepn5jrJA0Cc52kvtVqVeTJExVIRGwDfAKYkplzJ+p9JWkic91ouHiUpE6a6FxXh77dLSfvzSqSHY5YUnUoksap1VTkcYmIMyNieUTcNqR9ZkTcGRF3RcTRAJl5d2Ye0s14NLyPLpzJhxYVK1++Z/FMDrvQVTClXuDiUZJ6jX07Sb2qq4UtsAB4UpUUEZOBE4Hdge2BAyNi+y7HIUmSpPFbgH07ST2oq4VtZl4N/H5I807AXeVRvEeBc4HZ3YxDkiRJ42ffTlKvarV4VDdsBvy26fEy4GUR8SzgX4CXRMTHM/Ozw704IuYB8wA2f+azuh3rwHrnhTN5NODPAY8Aj8dTIdcB1uby2V+vOjxJktQ7xty365V+3U2nzGIVxSURdzrM822lOqqisB1WZv4OOLyN7eYD8wF22GpadjsuSaqKi0dJqrN2+nZP7tdtY79O0ph1+xzb4dwDbNH0ePOyTZLUxMWjJNWEfTtJlauisL0e2C4ipkXE2sABwCUVxCFJkqTxs28nqXJdnYocEecAuwAbRcQy4FOZeUZEHAlcCUwGzszM20e531nArGlTN+50yGrT7hcfDrkWl+9zAntc9CFgMkv3ObbqsCRJUhd1o2/Xa/26H526F6sCVgEZ8MpDL606JElt6Gphm5kHjtC+FFg6jv0uAZbssNW0Q8e6D0mSJI1ON/p2q/t129ivkzRmVUxFliS1ISJmRcT8lStXVh2KJElST7OwlaQe5eJRkiRJ7emZy/2MRq+di6HCHhd9HJgMOQmYxNJ9P80eF36mvP/37HHhsQTBZft+pOJIJUlSr6hDv+6q0/YkA177rsv49ul7sOu7xnxGnaQuqeWIbWMUY8N116s6FEmSJI3D6n7d+lWHIqnGalnYSpIkSZLUUMupyJIkaeLst+jaltssnvOKCYhEkqThOWIrSZIkSaq1Wo7Y1mGRAa3Znou/BER5m/TE/cv2ew97LjqluD/nsCpDlCrXyHXTp0+vOhRJ6po69uu+dcYevOGQpVx+xh4ksMchLiYlVa2WI7YuHiVpEHi5H0mDwMWjJHVCLQtbSZIkSZIaLGxXYv1uAAAZ50lEQVQlSZIkSbVmYStJkiRJqjULW0mSJElSrbkqsnranotOo7FicjyxinJ5y+LvpXPfxl4Lv8mlc99SZaiSJGkM+qVft+TM3VkFzH7n5Sz+6kwSmHPwFZz/1ZmsAg44+IqKI5T6Wy1HbF0VWZIkqT+4KrKkTqhlYStJkiRJUoOFrST1qIiYFRHzV65cWXUokiRJPc3CVpJ6VGN63pQpU6oORZIkqadZ2EqSJEmSas3CVpIkSZJUa17uR31jr4Vnc+ncN7PXwnNhyKWBgmDJ3DnMWnghECyZuw97L1wCBJfM3avKsCVJGmiD1K/75oLdSGAVsIokg+JxRPkXDnvbldUGKdVULUdsvdyPJElSf/ByP5I6oZYjtpIkqXe9afHdLbc5b79tJiASSdKgqOWIrSRJkiRJDRa2kiRJkqRas7CVJEmSJNWaha0kSZIkqdYsbCVJkiRJtVbLVZEH6XpnmhizFy6lOM6z+rq3EFw0d1f2XfQ9YBIXznkN+y66hmASi+fszH6LriPK1xR/J7Fwzksq/BSSJNWP/bonO+Xru7Hqievbrv67Cvjgm6/k2HOK548+4Eo+c95urAL+4U1X8skLZvKZ/a+oNnipQrUcsfU6tpIGQUTMioj5K1eurDoUSeoar2MrqRNqWdhK0iBodPamTJlSdSiSJEk9rZZTkSVJUn/4yoX3tdzmqH03nYBIJEl15oitJEmSJKnWHLGVJEm1cPEFD7TcZvb+G01AJJKkXuOIrSRJkiSp1ixsJUmSJEm1ZmErSZIkSao1C1tJkiRJUq1Z2EqSJEmSaq2WqyJHxCxg1rSpG1cdivQkcxf9hIVzXsz+i24nmMT5c17AmxbfBUxiEpNZj+CpEaxNcXsK8BSCT+/7HD534b1MJvjwvptywoX3MzlhMsFawCSCycDb9pta7QeUJKnD7Nd1zt9fMJN/3f8KPrRwJo8HfHnOFRy2eCZ/CThz3yt448Uz+V/gktlXsPvFbwTW5vLZ32D3iw8D1oJcq+xxNN8mQU5m6b6fru6DSW2o5YhtZi7JzHkbrrte1aFIkiRpHFb369avOhRJNVbLwlaSJEmSpAYLW0mSJElSrdXyHFtJkqRWrv3aipbbvOIdrl0gSf3AEVtJkiRJUq1Z2EqSJEmSas3CVpIkSZJUaxa2kjTBImKbiDgjIhZWHYskSVI/cPEoSeqAiDgT2AtYnpkvbGqfCXyF4ir3p2fmMZl5N3CIha3UO+46/v6W20x/7yYTEIkkaSwcsZWkzlgAzGxuiIjJwInA7sD2wIERsf3EhyZJktTfLGwlqQMy82rg90OadwLuysy7M/NR4Fxg9oQHJ0mS1OeciixJ3bMZ8Numx8uAl0XEs4B/AV4SER/PzM8OfWFEzAPmAWy55ZYTEaukUbjvC79ouc2mH9puAiKRJIGFrSRNuMz8HXB4i23mA/MBZsyYkRMRlyRJUl05FVmSuuceYIumx5uXbZIkSeqgWo7YRsQsYNa0qRtXHYo04c5b9ACTgblzNuKiCx5gErD3/hux9LwHmJQUN5JJCZHw2rdM5Zqvr2BSws5vn8qPFiwnEnY6eGNuOmP5E6+JVfnE/RccsQm/OOF+Jq1Ktn3fpvzmS/cxaRVMWpVs9pFnV/0V1Mn1wHYRMY2ioD0AeHO7L27kuunTp3cpPEkT4f6vXNdym02OevkERNKb7NfVwx4X/hPFmFjjFgSTICdx2X4fYs/FXwaCy/Y7ij0Xn1Defw97Lj652GbOYey56LSifc672GvRmUBw6ZyD2WvR1yCDS+e+nb0WfgOINd5i2MdFTJfM3YvZC5dy8dw9JvT7UfVqOWKbmUsyc96G665XdSiSBEBEnANcCzwvIpZFxCGZ+RhwJHAlcAdwfmbe3u4+G7luypQp3QlaknrA6n7d+lWHIqnGajliK0m9JjMPHKF9KbB0gsORJEkaKLUcsZUkSZIkqcHCVpJ6VETMioj5K1eurDoUSZKknmZhK0k9ynNsJUmS2mNhK0mSJEmqNQtbSZIkSVKtWdhKUo/yHFtJkqT2WNhKUo/yHFtJkqT2WNhKkiRJkmrNwlaSJEmSVGsWtpIkSZKkWrOwlaQe5eJRkiRJ7bGwlaQe5eJRkiRJ7bGwlSRJkiTVmoWtJEmSJKnWLGwlSZIkSbVmYStJkiRJqjULW0nqUa6KLEmS1B4LW0nqUa6KLEmS1B4LW0mSJElSra1VdQANEbE+cBLwKHBVZn6z4pAkSZI0BvbrJE20ro7YRsSZEbE8Im4b0j4zIu6MiLsi4uiyeT9gYWYeCuzdzbgkSZI0OvbrJPWybk9FXgDMbG6IiMnAicDuwPbAgRGxPbA58Ntys8e7HJckSZJGZwH26yT1qK4Wtpl5NfD7Ic07AXdl5t2Z+ShwLjAbWEaRBLselyRJkkbHfp2kXlbFObabsfoIHhSJ72XAccAJEbEnsGSkF0fEPGAewObPfFYXw5Q0nHs/dy/EKmAVEaueuL/ph7flvi/cWdz/0Au474u3Fc+zCshyu2ST98/g/q/8CFjFJke9gvuP+0HR/r5Xcf9xV5WvySe9buMjZ7L8hMuAZOMj92L5iRcDq9j4Pfuy/KSF5faPN722P0TELGDW9OnTqw5F0gRZfsLlbW238ZG7dzmSttmvU0+avfByLp67O7MXfosguGju69ln4XchgovmvJZ9F10NBBfOeRX7LfohMInFc17OnEXXE0xi4ZwdmbvoZoJJwKTy72Qiyr9MYl0m8dQInkqwNvAU4onbWsDR+z6bL194H5MJ3rvvJsxfvJzJwCH7bczXF69gcsKb50zlgkUPMDlhv7kbcckFDzApYa83bsQV5z5AALsdsBHfOXsFk5In3eKJ+zlMG0xatbr9ee/ehF8efz+xKtnmqE357RfvY9Iq2OzDm3LvsfcAq3j2R7fgvs//CqK5X5ds+qHnc98XbyvaP/Ai7v/Sj4s3ouznxZP7bc39uGh+rtx24yN3Y/kJS4FVTf26ZOP37MPykxYV7e/enxUnnVPurz09s3hUZj4EHNzGdvOB+QA7bDWtf3qwkjREZi4BlsyYMePQqmORpNEYW79uG/t1ksasiqkh9wBbND3evGyTJElSvdivk9QTqihsrwe2i4hpEbE2cABwSQVxSJIkaXzs10nqCd2+3M85wLXA8yJiWUQckpmPAUcCVwJ3AOdn5u2j3O+siJj/4CMPdz5oSZIk/ZXu9+se6nzQkgZGV8+xzcwDR2hfCiwdx36XAEt22Gqa551JkiRNgO7367axXydpzFx+XZIkSZJUaxa2kiRJkqRaq2Vh6zm2kiRJ/cFzbCV1Qi0L28xckpnzNlx3vapDkaSuaXT2Vq5cWXUoktQ1q/t161cdiqQaq2VhK0mDoNHZmzJlStWhSJIk9TQLW0mSJElSrVnYSpIkSZJqrZaFrYtHSZIk9QcXj5LUCbUsbF08SpIkqT+4eJSkTojMrDqGMYuIVUBUHYeknvPnzFyn6iA6JSJWAL8Z5qmNgAfa3M1otq3rvnsljm7uu1fiqOu+eyWOTu17q8ycOor37GkRUd9OqaSuysyWNd9aExFIF1nUShrO41UH0EkjdVwj4obMnNHOPkazbV333StxdHPfvRJHXffdK3F0e9+SNIhqORVZkiRJkqQGC1tJkiRJUq3VfSrynwBXGpA01OKqA5gg87u0bV333StxdHPfvRJHXffdK3F0e9919TgOukgao1ovHiVJkiRJkkfFJEmSJEm1ZmErSZIkSao1C1tJkiRJUq1Z2EqSJEmSas3CVpIkSZJUaxa2altEPB4RN0fEbRFxQUSsN8w2P4iIHdrY14cj4s3l/XdGxKZtvOaDEbFOG9u1jCEijoyIW8rPc01EPH/I89Mi4qGIeP+Q97+9vL13mH1+LCIyIp5ePt4nIv6hVbzSIGvKKz+JiJsi4m/HuJ99ImL7Mbxu74g4usU2B0XECSO0ryjj/1lEfGC071/u5zkRsXANzz89It49ln13QhS+GxEbthtLRGzdyPFtbHdbi23Wi4jLyu/49og4pum5wyPi1vK/wQ8a/w9ExN9ExII2Pp40ISJi04g4NyJ+GRE3RsTSiHhuO7+BFvtd2uh3jDO+rSPikaZ8/MOIeN4Y93VVRMwYpv1P441zlHGsGxHfj4jJo8hJO0TEHm1st0tEXNrGdkeV/ebbh/QpnxkR/x4Rvyj/PqNsf35EXBsRf46IDw/Z1wfK/dwWEec0+sTl/1fbtYplEFjYajQeycwdMvOFwKPA4WPZSUQ8BXg7cF7Z9E6gZWELfBBoWdi26azMfFFm7gB8Cfi3Ic9/Ebi88aAslN8BzAB2APaLiGlNz28NvAa4p2kfF5fbdSpmqR818sqLgY8Dnx3jfvYBRlXYRsRamXlJZh7TeusRnVfmkZ2BT0TEFqPdQWb+d2bOXcMmTwcqK2yBPYCfZOaDo4hla6BlJ3IU/i0znw+8BNg5InYv28/OzL8p/xscS5G7ycxbgc0jYssOxiCNSUQEcCFwVWZum5k7UuS7Tca778zcIzP/MN79lH7ZlI+/Bvx9h/ZblXcCizPzcdrPSTtQ5Lxxi4gXAocCOwEvBvaKiOnl00cD38nM7YDvlI8Bfg+8jyH90ojYrGyfUfbDJwMHlE+fDHy0EzHXnYWtxuoaYHrLrYb3euA/M/PxiHgTRRI5rzxKuHZEvKG8f2tEnFa2fQDYGLgmIr4NEBHzI+KG8ujVqEZGyw5aw/rAExd0joi5wM/KW8MLgOsy85HM/AtwNbBv0/NfYkhSyeIi0dfQoQQpDYANgf8BiIinRcR3ylHcWyNidmOjiHh7FDMufhIRXy9HefcGPl/mjm3L2xXlyMgTszIiYkFEnBIRPwKOjabR2IiYFRE/iogfR8S3I6LtTmdm/g64C3h2ua+pEbEoIq4vbzuX7a8pY7y5fJ8NmkdsIuL/RMR/ls/fUh6FPwbYtmz7/EjfTbmfO8q8eXtEfCsi1i2fm15+psbI+LZl+0fK+G6JiH8c4eO9heJAHcPEEuXf28pY3tS03avK7T5QxnZN+d6jGpnPzIcz83vl/UeBm4DNy8cj5nJgCas7flKVXgv8JTNPaTRk5k8y85rmjSJinYj4avlb+nFEvLZsPygiFpc57RcRcWzTa34dERu1+P3/31g9S+3z0d4IcXM+HvH3G8VstVvL3PKkg4QRManMuZ9pavtSGd93ImJq2bZDRFxXxnhhrB69vCoiPlfmxJ9HxKvK9snl52jkrsNG+AxDc1dzTvqr7zoi1gb+CXhTud2bImKnKEZQfxyjH8V+AfCjMoc9Bnwf2K98bjbFwQPKv/sAZObyzLwe+Msw+1sLWDci1gLWA/67bL8G2LVsH2yZ6c1bWzfgT+XftSgSxRHDbPMDYIcW+/mX5tc2v4bih7oM2LZ8/E3gyPL+MuDpTa97ZlM81wDbD7O/r44UD8WRr18C/9X0fhsA15ZxfAZ4f9n+QopC95kUnaf/BL5UPjcH+MIIMb6jsZ03b97++gY8Dtxc/r5WAjuW7WsBG5b3N6IoGgP4P8DPgY3K5xp5YAEwt2m/3wG2K++/DPhu03aXApPLxwcBJ5T3nwFEef9dTb/rJ7YZEnvza7csP8c65eOzgVc2PXdHeX8JsHN5/2nl59wauK1sOx54S3l/bWDd5udbfDdbA4815b/zgbeW938E7FveX6fMcW8A5pevnVR+L68e5nP+BtigvD80ljnAv1OMHmxCkU+fDewCXNq03XpN3812wA1D9wc8B1ja4v+XpwN3A9s0tb2HIpf/tvHfvGzfGVhS9f/j3rxR9DeG7QsM+Q18CDizvP/88ve0Tplr7gamlI9/A2xRbvfrMg+s6fd/G/CK8v4xzb/hIXE8QpHHfgncC2xZPjfS73d34IfAeuXjRj6+Cng5cA7wiab3SFbnt39gdf68BXhNef+fgC837aeRh/cAvl3enwd8srz/VOAGYNqQz7M2cF/T46E5aU3f9QlN220IrFXe3xVYNHR/FLP5Th/mO30Bxb9Xzyq/w2uB48vn/tC0XTQ/Lts+DXx4SNtRwJ+AFcA3hzz375T/fg7yzcpeo7FuRNxc3r8GOGOM+3k28OMRnnsB8PPM/GX5+CzgEOCvzm8DDoyIQyg6ec+hmIb40+YNMvPgkYLIzOOA4yLi7RTTbQ4B/hn4fGY+HBHN294WEV8Evk2RVH4MPB4RT6MYqd11hLdZXsYmaXiPZDGNlIh4BXBWFNO3AvjXiHg1sArYjKJweh1wQWY+AJCZvx+6w/J3+bfABU2/46c2bXJBFlPThtqcYvbIsyk6Rb9qI/43lTE+n+Ig3P+W7bsC2ze9/4ZlXP8BfDEivkkxRW5Zc66h6Ph8IiI2L5//xZDnYeTvBuBXmdnI0zcCW0fEBsBmmXkhQCPGiHgDRXHbyMdPo+i0Xj3k/Z6ZmX8c4fO/Ejin/D7vj4jvA/8XeHDIdk8BTojitI7HgecO3VFm/jdrmOFSjkacAxyXmXc3ve5E4MQozp/7JMUBRTD/qn5eSXFwi8z8WUT8htW/le9k5kqAiPgpsBXFwZxmw/3+n05xYOrasv1sYK8R3v+XTfn4TRQHvmYy8u93V+CrmflwGXNzPj4VOD8z/6WpbRWrT0P7BrA4IqZQDAh8v2z/GnBB02sWN3+e8v4bgBdFMcMOioJ/O56cszcC1jRFe03fdbMpwNeimD2TFN/Fk2TmDRQHQ4e23xERnwO+BTxEcdDgr/7tycyMiBza3qwcxZ4NTCs/1wUR8dbM/Ea5SSPf3bim/fQ7pyJrNBrnwu2Qme/NYkrYmPbDOM+VLRPMUcDrMvNFwBXj2OfZrJ4ashNFp/PXwJHAP0TEEQCZOT8zX5qZr6YYWfo5xXTsacCt5Ws2BW5pTK8pY3pkjHFJA6XseG0ETKWYQjaV4gj0DsD9tP8bn0Rx9HuHptsLmp5/aITXHU9xpP5vgMPafL/zyhz0t8AxsXohvEnAy5vef7PM/FMW5/O+i2Ik9j9iyMJ1mXk2xbTqR4ClEfG6Yd5zTd/Nn5u2exzWeAA7gM82xTg9M4c7YPlYRIy3v/CBMs4XU4xurD2GfcwHfpGZXx7h+XMpp/OVzL/qFbcDO45zH+38tkfz+2/lEuDV5f2x/H5/CLw21rzOyBqLuVLjMzV/ngDe25S7pmXmt4a8btx9zdI/A9/L4rzWWaPdZ2aekZk7ln3H/6HoO0JxILBx6sqzKQrTNdmV4sDFiixOiVtM8e9Og/kOC1tV4w6efH7uHymmADee2y4itikfv5XinISh221YPn6wTAi7jSaAePLqcbOAOwEy828zc+vM3JpilPifMvPk8jUbl3+3puh4npuZN2fmxk2vuQ94UWauKPf9XIopQJJaKIu8ycDvKI6SL8/Mv5TnmW1VbvZdYP+IeFb5mmeW7U/khyzOu/xVROxfbhMR8eI2QpjC6gXg3rGmDYcqj9h/neKAGxRH6J9YPb0c6SAits3MWzPzc8D1FCO9NG23DXB3OaPkYuBFPDn3NeIc7rsZKbY/AssiYp/yPZ4axar2VwLvLEeSiYjNGnluiDuBRk4eGss1FKPWk8sDeq+mOFVjuJjvzcxVwNso/ju3rTxHbwrw/iHtzbl8T+AXTY/Nv+oV3wWeGhHzGg0R8aLGOaNNrqE4cEVEPJfiNIY7x/PGWSws9ceIeFnZ1O5556+kmJIMI/9+/x04uMwnzfkYill9S4Hzm879nAQ0RlnfDPygHIX+n6bv4m2s7veN5ErgiCgWIyWK1aXXb94gM/8HmNxUWA+Xu4b7rofLXY1/Fw5qEddfaeo7bkkxiHJ2+dQlrP535h2sPhd4JP8FvDyKVeID+DuKPnOD+Q4LW3VARFw5XGeoPCl/uMvuLKVYQbjhq8Dp5TTnxyimBC+OiFspjtSdVm43H/h2FItH3UQx7fhnFNOV/2OE2EaK4f1RLF5wM8XI7IhTlptcVE7/uQg4PJ+8aMlIXgtc1sZ20qBat1yk42aKKWrvKKe1fhOYUeaBt1Mu5paZt1Ocp//9iPgJ5Sq4FKN1HykX+NiWosNySLnN7RRTuFr5NMX0rhuBB8bwWT5H0cnbgHL1yigWNvkpq1eRf38UCy3dQrE4yOVD9vFG4Lby+3ghxQruv6MY3b0tIj7PCN9NC28D3le+7w+BTcsRjrOBa8t9LeTJHbqGyyjOJ2OYWC6kOD/uJxSd949m5n1l2+NRLCjzAeAk4B3lf4/nM8yoeRSXPVo6TPvmwCcoTje5qfz/pTHt78imXP5BnnxAwvyrnpCZSbHg5K5RXO7ndooV4O8bsulJwKTy93gecFBm/pnxOwQ4rfydrE8x62w4jYXhfgL8K6un1w77+83MKygKtBvKfT/p8jSZ+UWKUx2+Xs76eAjYKYrFq15HcT4tFL/bz5f5aYem9pGcTtEHvKnc16kMPzr9LYoCHYbPScN919+jOI3k5iimYx8LfDYifjzCexARMyLi9BFiXVT+G7AEeE+uXsH6GOD1EfELitHYY8p9bRoRyyjy2ScjYllEbJiZP6LI0TcBt1LUcPPL12xCMaty6P9PA6exSIY0oSLiEoqFme5uuXFNRcRzgAWZ+YaqY5GksSpnxZyVma+vOpZ2RcRTKUZ9XpnFaqTSwIqIp2Xmn8r7RwPPzsyjWrys9iLipcAHMvNtVcfSTWWh/uAIp5IMFEdsVZWP0f+LemzBkKOXklQ3mXkvxWjPhlXHMgpbAkdb1EoA7FmOQN4GvIriqg99LzNvAr4XEaM69aGG/sDqSwcNNEdsJUmSJEm15oitJEmSJKnWLGwlSZIkSbVmYStJkiRJqjULW0mSJElSrVnYSpIkSZJqzcJWkiRJklRr/x/e2pPhlGkuaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize=(16, 6))\n",
    "ax1 = plt.subplot(221)\n",
    "ax1 = sns.countplot(data[\"pi\"],order=data[\"pi\"].value_counts().index)\n",
    "ax1.set(xlabel=\"P.I. (total: 3494)\", xticklabels=\"\",\n",
    "       ylabel=\"Plasmids (total: 71948)\", yscale=\"log\")\n",
    "ax2 = plt.subplot(222)\n",
    "ax2 = sns.countplot(data[\"bacterial_resistance\"],order=data[\"bacterial_resistance\"].value_counts().index)\n",
    "ax2.set(xlabel=\"Bacterial Resistance (total: 23)\", xticklabels=\"\",\n",
    "       ylabel=\"Plasmids (total: 71948)\", yscale=\"log\")\n",
    "ax3 = plt.subplot(223)\n",
    "ax3 = sns.countplot(data[\"cloning.backbone\"],order=data[\"cloning.backbone\"].value_counts().index)\n",
    "ax3.set(xlabel=\"Cloning Backbone (total: 9018)\", xticklabels=\"\",\n",
    "       ylabel=\"Plasmids (total: 71948)\", yscale=\"log\")\n",
    "ax4 = plt.subplot(224)\n",
    "ax4 = sns.countplot(data[\"seq\"],order=data[\"cloning.backbone\"].value_counts().index)\n",
    "ax4.set(xlabel=\"Cloning Backbone (total: 9018)\", xticklabels=\"\",\n",
    "       ylabel=\"Plasmids (total: 71948)\", yscale=\"log\")\n",
    "fig1.savefig(work_dir+\"fig1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp1, tmp2, tmp3 = SeparateData(data, \"pi\", 2000, [0.02,0.5])\n",
    "train, val, test = SeparateData(tmp1, \"pi\", 50, [0.6,0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = [KmerEncoding, KmerEncoding, KmerEncoding, KmerEncoding]\n",
    "params = [[1, 1], [3, 3], [1, 3], [2, 3]]\n",
    "feature_names = [\"seq_kmer_1_1\", \"seq_kmer_3_3\", \"seq_kmer_1_3\", \"seq_kmer_2_3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,enc in enumerate(encoding):\n",
    "    tmp = pd.DataFrame()\n",
    "    tmp[feature_names[i]] = train.apply(lambda df: enc(df[\"seq\"], params[i][0], params[i][1]), axis=1)\n",
    "    tmp[\"id\"] = train[\"id\"]\n",
    "    tmp[\"pi\"] = train[\"pi\"]\n",
    "    tmp.to_pickle(work_dir+feature_names[i]+\".train.pkl\")\n",
    "    \n",
    "    tmp = pd.DataFrame()\n",
    "    tmp[feature_names[i]] = val.apply(lambda df: enc(df[\"seq\"], params[i][0], params[i][1]), axis=1)\n",
    "    tmp[\"id\"] = val[\"id\"]\n",
    "    tmp[\"pi\"] = val[\"pi\"]\n",
    "    tmp.to_pickle(work_dir+feature_names[i]+\".val.pkl\")\n",
    "    \n",
    "    tmp = pd.DataFrame()\n",
    "    tmp[feature_names[i]] = test.apply(lambda df: enc(df[\"seq\"], params[i][0], params[i][1]), axis=1)\n",
    "    tmp[\"id\"] = test[\"id\"]\n",
    "    tmp[\"pi\"] = test[\"pi\"]\n",
    "    tmp.to_pickle(work_dir+feature_names[i]+\".test.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#models = [Model_CNN, Model_LSTM]\n",
    "models = [Model_CNN]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | n_batc... |  n_epoch  | n_filters | n_kern... |  n_units  |\n",
      "-------------------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.8636  \u001b[0m | \u001b[0m 15.38   \u001b[0m | \u001b[0m 12.26   \u001b[0m | \u001b[0m 104.3   \u001b[0m | \u001b[0m 3.398   \u001b[0m | \u001b[0m 40.17   \u001b[0m |\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m 1.0     \u001b[0m | \u001b[95m 6.821   \u001b[0m | \u001b[95m 37.18   \u001b[0m | \u001b[95m 61.37   \u001b[0m | \u001b[95m 14.88   \u001b[0m | \u001b[95m 15.77   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 20.71   \u001b[0m | \u001b[0m 50.55   \u001b[0m | \u001b[0m 112.3   \u001b[0m | \u001b[0m 14.26   \u001b[0m | \u001b[0m 4.965   \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 30.54   \u001b[0m | \u001b[0m 93.51   \u001b[0m | \u001b[0m 18.77   \u001b[0m | \u001b[0m 10.48   \u001b[0m | \u001b[0m 2.799   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 9.711   \u001b[0m | \u001b[0m 88.64   \u001b[0m | \u001b[0m 122.9   \u001b[0m | \u001b[0m 9.902   \u001b[0m | \u001b[0m 36.28   \u001b[0m |\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.5     \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 100.0   \u001b[0m | \u001b[0m 128.0   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.0     \u001b[0m |\n",
      "=====================================================================================\n",
      "{'target': 1.0, 'params': {'n_units': 15.774498123823463, 'n_filters': 61.37221963199319, 'n_kernel_size': 14.884226003633353, 'n_epoch': 37.178890924446726, 'n_batch_size': 6.8207100966128}}\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 16049, 61)         4636      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_7 (MaxPooling1 (None, 2, 61)             0         \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 122)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_13 (Batc (None, 122)               488       \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 16)                1968      \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_14 (Batc (None, 16)                64        \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 7,190\n",
      "Trainable params: 6,914\n",
      "Non-trainable params: 276\n",
      "_________________________________________________________________\n",
      "None\n",
      "|   iter    |  target   | n_batc... |  n_epoch  | n_filters | n_kern... |  n_units  |\n",
      "-------------------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 15.38   \u001b[0m | \u001b[0m 12.26   \u001b[0m | \u001b[0m 104.3   \u001b[0m | \u001b[0m 3.398   \u001b[0m | \u001b[0m 40.17   \u001b[0m |\n",
      "| \u001b[0m 2       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 6.821   \u001b[0m | \u001b[0m 37.18   \u001b[0m | \u001b[0m 61.37   \u001b[0m | \u001b[0m 14.88   \u001b[0m | \u001b[0m 15.77   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 20.71   \u001b[0m | \u001b[0m 50.55   \u001b[0m | \u001b[0m 112.3   \u001b[0m | \u001b[0m 14.26   \u001b[0m | \u001b[0m 4.965   \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 30.54   \u001b[0m | \u001b[0m 93.51   \u001b[0m | \u001b[0m 18.77   \u001b[0m | \u001b[0m 10.48   \u001b[0m | \u001b[0m 2.799   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 9.711   \u001b[0m | \u001b[0m 88.64   \u001b[0m | \u001b[0m 122.9   \u001b[0m | \u001b[0m 9.902   \u001b[0m | \u001b[0m 36.28   \u001b[0m |\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.8636  \u001b[0m | \u001b[0m 7.065   \u001b[0m | \u001b[0m 2.547   \u001b[0m | \u001b[0m 2.805   \u001b[0m | \u001b[0m 4.979   \u001b[0m | \u001b[0m 63.12   \u001b[0m |\n",
      "=====================================================================================\n",
      "{'target': 1.0, 'params': {'n_units': 40.16784585461846, 'n_filters': 104.25196913566545, 'n_kernel_size': 3.3976277826841494, 'n_epoch': 12.257244304508239, 'n_batch_size': 15.379177234672872}}\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 16049, 104)        39104     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_14 (MaxPooling (None, 2, 104)            0         \n",
      "_________________________________________________________________\n",
      "flatten_14 (Flatten)         (None, 208)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_27 (Batc (None, 208)               832       \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 40)                8360      \n",
      "_________________________________________________________________\n",
      "activation_27 (Activation)   (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_28 (Batc (None, 40)                160       \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 2)                 82        \n",
      "_________________________________________________________________\n",
      "activation_28 (Activation)   (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 48,538\n",
      "Trainable params: 48,042\n",
      "Non-trainable params: 496\n",
      "_________________________________________________________________\n",
      "None\n",
      "|   iter    |  target   | n_batc... |  n_epoch  | n_filters | n_kern... |  n_units  |\n",
      "-------------------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 15.38   \u001b[0m | \u001b[0m 12.26   \u001b[0m | \u001b[0m 104.3   \u001b[0m | \u001b[0m 3.398   \u001b[0m | \u001b[0m 40.17   \u001b[0m |\n",
      "| \u001b[0m 2       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 6.821   \u001b[0m | \u001b[0m 37.18   \u001b[0m | \u001b[0m 61.37   \u001b[0m | \u001b[0m 14.88   \u001b[0m | \u001b[0m 15.77   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 20.71   \u001b[0m | \u001b[0m 50.55   \u001b[0m | \u001b[0m 112.3   \u001b[0m | \u001b[0m 14.26   \u001b[0m | \u001b[0m 4.965   \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 30.54   \u001b[0m | \u001b[0m 93.51   \u001b[0m | \u001b[0m 18.77   \u001b[0m | \u001b[0m 10.48   \u001b[0m | \u001b[0m 2.799   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 9.711   \u001b[0m | \u001b[0m 88.64   \u001b[0m | \u001b[0m 122.9   \u001b[0m | \u001b[0m 9.902   \u001b[0m | \u001b[0m 36.28   \u001b[0m |\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.5     \u001b[0m | \u001b[0m 7.065   \u001b[0m | \u001b[0m 2.547   \u001b[0m | \u001b[0m 2.805   \u001b[0m | \u001b[0m 4.979   \u001b[0m | \u001b[0m 63.12   \u001b[0m |\n",
      "=====================================================================================\n",
      "{'target': 1.0, 'params': {'n_units': 40.16784585461846, 'n_filters': 104.25196913566545, 'n_kernel_size': 3.3976277826841494, 'n_epoch': 12.257244304508239, 'n_batch_size': 15.379177234672872}}\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 16049, 104)        1664      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_21 (MaxPooling (None, 2, 104)            0         \n",
      "_________________________________________________________________\n",
      "flatten_21 (Flatten)         (None, 208)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_41 (Batc (None, 208)               832       \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 40)                8360      \n",
      "_________________________________________________________________\n",
      "activation_41 (Activation)   (None, 40)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_42 (Batc (None, 40)                160       \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 2)                 82        \n",
      "_________________________________________________________________\n",
      "activation_42 (Activation)   (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 11,098\n",
      "Trainable params: 10,602\n",
      "Non-trainable params: 496\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | n_batc... |  n_epoch  | n_filters | n_kern... |  n_units  |\n",
      "-------------------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.9091  \u001b[0m | \u001b[0m 15.38   \u001b[0m | \u001b[0m 12.26   \u001b[0m | \u001b[0m 104.3   \u001b[0m | \u001b[0m 3.398   \u001b[0m | \u001b[0m 40.17   \u001b[0m |\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m 1.0     \u001b[0m | \u001b[95m 6.821   \u001b[0m | \u001b[95m 37.18   \u001b[0m | \u001b[95m 61.37   \u001b[0m | \u001b[95m 14.88   \u001b[0m | \u001b[95m 15.77   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 20.71   \u001b[0m | \u001b[0m 50.55   \u001b[0m | \u001b[0m 112.3   \u001b[0m | \u001b[0m 14.26   \u001b[0m | \u001b[0m 4.965   \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 30.54   \u001b[0m | \u001b[0m 93.51   \u001b[0m | \u001b[0m 18.77   \u001b[0m | \u001b[0m 10.48   \u001b[0m | \u001b[0m 2.799   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 9.711   \u001b[0m | \u001b[0m 88.64   \u001b[0m | \u001b[0m 122.9   \u001b[0m | \u001b[0m 9.902   \u001b[0m | \u001b[0m 36.28   \u001b[0m |\n",
      "| \u001b[0m 6       \u001b[0m | \u001b[0m 0.5     \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 100.0   \u001b[0m | \u001b[0m 128.0   \u001b[0m | \u001b[0m 1.0     \u001b[0m | \u001b[0m 1.0     \u001b[0m |\n",
      "=====================================================================================\n",
      "{'target': 1.0, 'params': {'n_units': 15.774498123823463, 'n_filters': 61.37221963199319, 'n_kernel_size': 14.884226003633353, 'n_epoch': 37.178890924446726, 'n_batch_size': 6.8207100966128}}\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 16049, 61)         22936     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_28 (MaxPooling (None, 2, 61)             0         \n",
      "_________________________________________________________________\n",
      "flatten_28 (Flatten)         (None, 122)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_55 (Batc (None, 122)               488       \n",
      "_________________________________________________________________\n",
      "dense_55 (Dense)             (None, 16)                1968      \n",
      "_________________________________________________________________\n",
      "activation_55 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_56 (Batc (None, 16)                64        \n",
      "_________________________________________________________________\n",
      "dense_56 (Dense)             (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "activation_56 (Activation)   (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 25,490\n",
      "Trainable params: 25,214\n",
      "Non-trainable params: 276\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "ensembledModels = []\n",
    "preds_val = []\n",
    "preds_test = []\n",
    "for feat in feature_names:\n",
    "    X_train, y_train, X_val, y_val, X_test, y_test, seqLength, seqWidth, numClasses = GetEncodedMat(work_dir, feat, \"pi\")\n",
    "    for mod in models:\n",
    "        tmpModel = mod(work_dir, feat, \"pi\")\n",
    "        ensembledModels.append(tmpModel)\n",
    "        preds_val.append(tmpModel.predict(X_val))\n",
    "        preds_test.append(tmpModel.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, array([1., 0., 0., 0.]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train, X_val, y_val, X_test, y_test, seqLength, seqWidth, numClasses = GetEncodedMat(work_dir, feature_names[0], \"pi\")\n",
    "error, best = GetBestEnsemble(len(ensembledModels), 100, 5, preds_val, y_val)\n",
    "error, best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score = 1\n",
    "total = 0.0\n",
    "for i in range(len(ensembledModels)):\n",
    "    total += best[i]*preds_test[i]\n",
    "total = np.argmax(total, axis=1)\n",
    "test_score = np.sum(np.not_equal(total, np.argmax(y_test, axis=1))) / y_test.shape[0]\n",
    "test_score = 1 - test_score\n",
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getseq(seq, maxlen, flank_length):\n",
    "    if len(seq) > maxlen:\n",
    "        seq = seq[:maxlen]\n",
    "    else:\n",
    "        seq += \"N\"*(maxlen-len(seq)) \n",
    "    revcomp = seq.translate(str.maketrans(\"ACGTN\", \"TGCAN\"))[::-1]\n",
    "    return seq + \"N\"*flank_length + revcomp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'getseq' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-7a62a99ede2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmyData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTACTTCGCGTTATGCAGGCTTTACTAGTAGCGGCCGCTGCAGTCCGTTTCTTTAAAACC'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmyData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"seq\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgetseq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m48\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0menc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmyData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmyData\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0menc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"seq\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'getseq' is not defined"
     ]
    }
   ],
   "source": [
    "myData = pd.DataFrame()\n",
    "seq = 'GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTACTTCGCGTTATGCAGGCTTTACTAGTAGCGGCCGCTGCAGTCCGTTTCTTTAAAACC'\n",
    "myData[\"seq\"] = [getseq(seq, 8000, 48)]\n",
    "for i,enc in enumerate(encoding):\n",
    "    myData[feature_names[i]] = myData.apply(lambda df: enc(df[\"seq\"], params[i][0], params[i][1]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'myData' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-1eaecce9b4e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmyData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'myData' is not defined"
     ]
    }
   ],
   "source": [
    "myData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'myData' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-afbb125f6936>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmyData\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq_kmer_1_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'myData' is not defined"
     ]
    }
   ],
   "source": [
    "myData.seq_kmer_1_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9984629 , 0.00153709]], dtype=float32)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = 0.0\n",
    "for i in range(len(ensembledModels)):\n",
    "    X_myData = []\n",
    "    for j in myData[feature_names[i]]:\n",
    "        X_myData.append(j)\n",
    "    X_myData = np.array(X_myData)\n",
    "    pred = ensembledModels[i].predict(X_myData)\n",
    "    total += best[i]*pred\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeMutations(seq, mut_length):\n",
    "    mut_seq = []\n",
    "    for i in range(len(seq)+1-mut_length):\n",
    "        mut_seq.append(seq[:i] + 'N'*mut_length + seq[i+mut_length:])\n",
    "    return mut_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>seqs</th>\n",
       "      <th>seq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>NCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GNAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GNAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCNAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCNAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GCANAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCANAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCAANAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAANAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GCAAANAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAANAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GCAAAANAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAANAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GCAAAAANACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAANACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GCAAAAAANCGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAANCGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GCAAAAAAANGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAANGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GCAAAAAAACNGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACNGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GCAAAAAAACGNGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGNGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GCAAAAAAACGGNCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGNCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GCAAAAAAACGGGNAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGNAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GCAAAAAAACGGGCNAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCNAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GCAAAAAAACGGGCANGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCANGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GCAAAAAAACGGGCAANGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAANGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GCAAAAAAACGGGCAAGNTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGNTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGNGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGNGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTNTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTNTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGNCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGNCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTNACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTNACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCNCCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCNCCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCANCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCANCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACNACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACNACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCNCCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCNCCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCANCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCANCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACNCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACNCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCNTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCNTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCNGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCNGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "      <td>GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  seqs  \\\n",
       "0    NCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "1    GNAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "2    GCNAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "3    GCANAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "4    GCAANAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "5    GCAAANAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "6    GCAAAANAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "7    GCAAAAANACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "8    GCAAAAAANCGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "9    GCAAAAAAANGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "10   GCAAAAAAACNGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "11   GCAAAAAAACGNGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "12   GCAAAAAAACGGNCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "13   GCAAAAAAACGGGNAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "14   GCAAAAAAACGGGCNAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "15   GCAAAAAAACGGGCANGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "16   GCAAAAAAACGGGCAANGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "17   GCAAAAAAACGGGCAAGNTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "18   GCAAAAAAACGGGCAAGGNGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "19   GCAAAAAAACGGGCAAGGTNTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "20   GCAAAAAAACGGGCAAGGTGNCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "21   GCAAAAAAACGGGCAAGGTGTNACCACCCTGCCCTTGAAAAGATTA...   \n",
       "22   GCAAAAAAACGGGCAAGGTGTCNCCACCCTGCCCTTGAAAAGATTA...   \n",
       "23   GCAAAAAAACGGGCAAGGTGTCANCACCCTGCCCTTGAAAAGATTA...   \n",
       "24   GCAAAAAAACGGGCAAGGTGTCACNACCCTGCCCTTGAAAAGATTA...   \n",
       "25   GCAAAAAAACGGGCAAGGTGTCACCNCCCTGCCCTTGAAAAGATTA...   \n",
       "26   GCAAAAAAACGGGCAAGGTGTCACCANCCTGCCCTTGAAAAGATTA...   \n",
       "27   GCAAAAAAACGGGCAAGGTGTCACCACNCTGCCCTTGAAAAGATTA...   \n",
       "28   GCAAAAAAACGGGCAAGGTGTCACCACCNTGCCCTTGAAAAGATTA...   \n",
       "29   GCAAAAAAACGGGCAAGGTGTCACCACCCNGCCCTTGAAAAGATTA...   \n",
       "..                                                 ...   \n",
       "73   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "74   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "75   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "76   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "77   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "78   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "79   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "80   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "81   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "82   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "83   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "84   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "85   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "86   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "87   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "88   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "89   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "90   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "91   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "92   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "93   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "94   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "95   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "96   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "97   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "98   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "99   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "100  GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "101  GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "102  GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...   \n",
       "\n",
       "                                                   seq  \n",
       "0    NCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "1    GNAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "2    GCNAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "3    GCANAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "4    GCAANAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "5    GCAAANAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "6    GCAAAANAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "7    GCAAAAANACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "8    GCAAAAAANCGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "9    GCAAAAAAANGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "10   GCAAAAAAACNGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "11   GCAAAAAAACGNGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "12   GCAAAAAAACGGNCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "13   GCAAAAAAACGGGNAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "14   GCAAAAAAACGGGCNAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "15   GCAAAAAAACGGGCANGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "16   GCAAAAAAACGGGCAANGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "17   GCAAAAAAACGGGCAAGNTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "18   GCAAAAAAACGGGCAAGGNGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "19   GCAAAAAAACGGGCAAGGTNTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "20   GCAAAAAAACGGGCAAGGTGNCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "21   GCAAAAAAACGGGCAAGGTGTNACCACCCTGCCCTTGAAAAGATTA...  \n",
       "22   GCAAAAAAACGGGCAAGGTGTCNCCACCCTGCCCTTGAAAAGATTA...  \n",
       "23   GCAAAAAAACGGGCAAGGTGTCANCACCCTGCCCTTGAAAAGATTA...  \n",
       "24   GCAAAAAAACGGGCAAGGTGTCACNACCCTGCCCTTGAAAAGATTA...  \n",
       "25   GCAAAAAAACGGGCAAGGTGTCACCNCCCTGCCCTTGAAAAGATTA...  \n",
       "26   GCAAAAAAACGGGCAAGGTGTCACCANCCTGCCCTTGAAAAGATTA...  \n",
       "27   GCAAAAAAACGGGCAAGGTGTCACCACNCTGCCCTTGAAAAGATTA...  \n",
       "28   GCAAAAAAACGGGCAAGGTGTCACCACCNTGCCCTTGAAAAGATTA...  \n",
       "29   GCAAAAAAACGGGCAAGGTGTCACCACCCNGCCCTTGAAAAGATTA...  \n",
       "..                                                 ...  \n",
       "73   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "74   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "75   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "76   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "77   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "78   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "79   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "80   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "81   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "82   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "83   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "84   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "85   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "86   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "87   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "88   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "89   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "90   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "91   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "92   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "93   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "94   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "95   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "96   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "97   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "98   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "99   GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "100  GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "101  GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "102  GCAAAAAAACGGGCAAGGTGTCACCACCCTGCCCTTGAAAAGATTA...  \n",
       "\n",
       "[103 rows x 2 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData_mut = pd.DataFrame()\n",
    "myData_mut[\"seqs\"] = MakeMutations(seq, 1)\n",
    "myData_mut[\"seq\"] = myData_mut.apply(lambda df: getseq(df[\"seqs\"], 8000, 48), axis=1)\n",
    "myData_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,enc in enumerate(encoding):\n",
    "    myData_mut[feature_names[i]] = myData_mut.apply(lambda df: enc(df[\"seq\"], params[i][0], params[i][1]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.9869734e-01, 1.3027311e-03],\n",
       "       [9.9791569e-01, 2.0842617e-03],\n",
       "       [9.9826729e-01, 1.7326962e-03],\n",
       "       [9.9799854e-01, 2.0014553e-03],\n",
       "       [9.9868160e-01, 1.3183909e-03],\n",
       "       [9.9889565e-01, 1.1043367e-03],\n",
       "       [9.9827194e-01, 1.7280326e-03],\n",
       "       [9.9869019e-01, 1.3098238e-03],\n",
       "       [9.9782407e-01, 2.1759078e-03],\n",
       "       [9.9750453e-01, 2.4954812e-03],\n",
       "       [9.9827015e-01, 1.7297986e-03],\n",
       "       [9.9840683e-01, 1.5931429e-03],\n",
       "       [9.9839443e-01, 1.6055983e-03],\n",
       "       [9.9736625e-01, 2.6338277e-03],\n",
       "       [9.9871540e-01, 1.2845687e-03],\n",
       "       [9.9831736e-01, 1.6826782e-03],\n",
       "       [9.9818534e-01, 1.8146434e-03],\n",
       "       [9.9832743e-01, 1.6725686e-03],\n",
       "       [9.9834716e-01, 1.6528851e-03],\n",
       "       [9.9933892e-01, 6.6106708e-04],\n",
       "       [9.9656492e-01, 3.4351235e-03],\n",
       "       [9.9860209e-01, 1.3979132e-03],\n",
       "       [9.9906284e-01, 9.3722716e-04],\n",
       "       [9.9886608e-01, 1.1339480e-03],\n",
       "       [9.9816787e-01, 1.8321397e-03],\n",
       "       [9.9847621e-01, 1.5238349e-03],\n",
       "       [9.9937397e-01, 6.2604493e-04],\n",
       "       [9.9866414e-01, 1.3358396e-03],\n",
       "       [9.9857938e-01, 1.4206059e-03],\n",
       "       [9.9892944e-01, 1.0706022e-03],\n",
       "       [9.9891734e-01, 1.0827268e-03],\n",
       "       [9.9827039e-01, 1.7295830e-03],\n",
       "       [9.9907744e-01, 9.2250091e-04],\n",
       "       [9.9871600e-01, 1.2839760e-03],\n",
       "       [9.9865913e-01, 1.3408137e-03],\n",
       "       [9.9858594e-01, 1.4140677e-03],\n",
       "       [9.9748588e-01, 2.5141493e-03],\n",
       "       [9.9885690e-01, 1.1430448e-03],\n",
       "       [9.9868315e-01, 1.3167884e-03],\n",
       "       [9.9746120e-01, 2.5387867e-03],\n",
       "       [9.9925071e-01, 7.4929581e-04],\n",
       "       [9.9788851e-01, 2.1115078e-03],\n",
       "       [9.9631786e-01, 3.6820949e-03],\n",
       "       [9.9828917e-01, 1.7107828e-03],\n",
       "       [9.9751955e-01, 2.4804377e-03],\n",
       "       [9.9814916e-01, 1.8508892e-03],\n",
       "       [9.9857950e-01, 1.4204733e-03],\n",
       "       [9.9872988e-01, 1.2701312e-03],\n",
       "       [9.9898142e-01, 1.0185300e-03],\n",
       "       [9.9847919e-01, 1.5208329e-03],\n",
       "       [9.9867743e-01, 1.3225253e-03],\n",
       "       [9.9754637e-01, 2.4535917e-03],\n",
       "       [9.9459273e-01, 5.4072277e-03],\n",
       "       [9.9894685e-01, 1.0531313e-03],\n",
       "       [9.9766964e-01, 2.3303486e-03],\n",
       "       [9.9851471e-01, 1.4852653e-03],\n",
       "       [9.9925011e-01, 7.4985472e-04],\n",
       "       [9.9902499e-01, 9.7503175e-04],\n",
       "       [9.9829870e-01, 1.7013395e-03],\n",
       "       [9.9851710e-01, 1.4828776e-03],\n",
       "       [9.9810058e-01, 1.8994831e-03],\n",
       "       [9.9707961e-01, 2.9203892e-03],\n",
       "       [9.9804014e-01, 1.9598277e-03],\n",
       "       [9.9784434e-01, 2.1556695e-03],\n",
       "       [9.9758661e-01, 2.4134505e-03],\n",
       "       [9.9486887e-01, 5.1311576e-03],\n",
       "       [9.9912685e-01, 8.7320630e-04],\n",
       "       [9.9799961e-01, 2.0003249e-03],\n",
       "       [9.9786013e-01, 2.1399173e-03],\n",
       "       [9.9824333e-01, 1.7567087e-03],\n",
       "       [9.9921358e-01, 7.8637712e-04],\n",
       "       [9.9708301e-01, 2.9169463e-03],\n",
       "       [9.9883848e-01, 1.1615665e-03],\n",
       "       [9.9898881e-01, 1.0111235e-03],\n",
       "       [9.9894983e-01, 1.0502498e-03],\n",
       "       [9.9866951e-01, 1.3304815e-03],\n",
       "       [9.9884784e-01, 1.1521027e-03],\n",
       "       [9.9614125e-01, 3.8587456e-03],\n",
       "       [9.9816626e-01, 1.8337213e-03],\n",
       "       [9.9895465e-01, 1.0453062e-03],\n",
       "       [9.9876714e-01, 1.2329222e-03],\n",
       "       [9.9920207e-01, 7.9791364e-04],\n",
       "       [9.9663866e-01, 3.3613704e-03],\n",
       "       [9.9845350e-01, 1.5464401e-03],\n",
       "       [9.9868125e-01, 1.3187614e-03],\n",
       "       [9.9894148e-01, 1.0585267e-03],\n",
       "       [9.9808002e-01, 1.9199499e-03],\n",
       "       [9.9758232e-01, 2.4176189e-03],\n",
       "       [9.9699724e-01, 3.0028205e-03],\n",
       "       [9.9782580e-01, 2.1741651e-03],\n",
       "       [9.9831355e-01, 1.6865098e-03],\n",
       "       [9.9767178e-01, 2.3282266e-03],\n",
       "       [9.9843663e-01, 1.5633866e-03],\n",
       "       [9.9797028e-01, 2.0297146e-03],\n",
       "       [9.9868387e-01, 1.3160801e-03],\n",
       "       [9.9560767e-01, 4.3923738e-03],\n",
       "       [9.9876761e-01, 1.2323780e-03],\n",
       "       [9.9914205e-01, 8.5795100e-04],\n",
       "       [9.9810708e-01, 1.8929166e-03],\n",
       "       [9.9808645e-01, 1.9135807e-03],\n",
       "       [9.9856734e-01, 1.4326429e-03],\n",
       "       [9.9871743e-01, 1.2825954e-03],\n",
       "       [9.9751246e-01, 2.4875673e-03]], dtype=float32)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_mut = 0.0\n",
    "for i in range(len(ensembledModels)):\n",
    "    X_myData_mut = []\n",
    "    for j in myData_mut[feature_names[i]]:\n",
    "        X_myData_mut.append(j)\n",
    "    X_myData_mut = np.array(X_myData_mut)\n",
    "    pred = ensembledModels[i].predict(X_myData_mut)\n",
    "    total_mut += best[i]*pred\n",
    "total_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99977654, 0.99923897, 0.99870646, 0.9985361 , 0.99916446,\n",
       "       0.9993193 , 0.99949706, 0.9992211 , 0.99827206, 0.9986712 ,\n",
       "       0.9993716 , 0.99863225, 0.9991574 , 0.9983954 , 0.99871624,\n",
       "       0.9980792 , 0.99741566, 0.9983304 , 0.9972771 , 0.9980107 ,\n",
       "       0.99932563, 0.999361  , 0.99949443, 0.9999312 ], dtype=float32)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_mut.transpose()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(16, 6))\n",
    "ax1 = sns.lineplot(data=total_mut.transpose()[0])\n",
    "#ax1.set(xlabel=\"P.I. (total: 3494)\", xticklabels=\"\",\n",
    "       #ylabel=\"Plasmids (total: 71948)\", yscale=\"log\")\n",
    "\n",
    "fig2.savefig(work_dir+\"fig2.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(-2.39549521e-02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7589711 , 0.4286198 , 3.0841786 , 0.58154187, 0.97894764],\n",
       "       [0.50214905, 1.75664331, 0.73340576, 1.55889282, 2.2227555 ],\n",
       "       [1.57780161, 0.83497634, 1.21642264, 1.96814028, 0.4365716 ],\n",
       "       [1.15036145, 1.40859052, 0.34515262, 1.57075545, 2.30413146],\n",
       "       [0.41997377, 1.38800478, 0.73538966, 0.39523278, 0.84436997],\n",
       "       [1.22529732, 0.7010728 , 1.82370431, 0.47381028, 1.2324594 ],\n",
       "       [0.47253919, 0.63250528, 0.86558832, 0.5788142 , 1.4637065 ],\n",
       "       [1.36209069, 1.83132545, 0.6021505 , 0.50987723, 0.72466359],\n",
       "       [0.86891448, 1.7759153 , 1.64977057, 0.77067516, 1.84397401],\n",
       "       [0.86384666, 0.33800392, 0.89410843, 1.58091257, 1.41181933],\n",
       "       [0.71531167, 0.38157492, 0.59107401, 1.13283667, 1.83128586],\n",
       "       [0.41014753, 0.34821993, 0.60009502, 0.58925356, 0.69639253],\n",
       "       [0.66567605, 0.6610164 , 1.35234204, 0.83840708, 0.9914236 ],\n",
       "       [2.4300918 , 1.28029152, 1.57277604, 1.11629147, 0.92267323],\n",
       "       [1.2703346 , 1.63744645, 1.13107769, 0.55452264, 0.70995483],\n",
       "       [2.55537297, 0.79222877, 1.07473274, 0.63552364, 1.58335899],\n",
       "       [0.88920714, 0.69181289, 2.44225613, 0.55122587, 1.09944317],\n",
       "       [1.7216851 , 0.89448721, 0.98171061, 1.52124134, 1.84210544],\n",
       "       [1.77696451, 1.33389213, 0.49156035, 1.55402192, 0.81517916],\n",
       "       [0.74970904, 0.74956822, 0.4797639 , 1.80094006, 0.54495157],\n",
       "       [1.03070121, 0.49044437, 2.75152587, 1.22649903, 0.64787569],\n",
       "       [1.43477142, 1.60911613, 0.5893359 , 2.90851328, 2.99083694],\n",
       "       [2.43298157, 1.41874047, 0.87141675, 0.76640276, 1.86640645],\n",
       "       [0.79351397, 1.73477321, 0.53847588, 2.32793325, 2.28561432],\n",
       "       [0.91168831, 1.46168829, 2.49596724, 1.22942481, 1.40105176],\n",
       "       [0.65225836, 0.7888378 , 2.40859016, 0.98705487, 0.67742869],\n",
       "       [1.11393045, 2.10374537, 2.37364983, 1.56751697, 2.25394172],\n",
       "       [2.4120396 , 1.89104831, 0.76951231, 0.71363969, 0.58521399],\n",
       "       [1.15260779, 0.52596142, 0.80197233, 1.80048046, 0.62568736],\n",
       "       [0.71599447, 1.02229663, 1.13015536, 0.6464742 , 0.71798608],\n",
       "       [2.21070935, 0.60103935, 0.711859  , 1.7280961 , 0.58819075],\n",
       "       [0.80483076, 1.34662781, 0.39071257, 0.37244713, 0.34221676],\n",
       "       [1.48463199, 1.35830409, 0.48712179, 0.79559062, 0.41502226],\n",
       "       [2.1522264 , 0.87217687, 1.80835903, 1.1019656 , 1.31099115],\n",
       "       [0.53247296, 1.0104529 , 2.07031939, 2.12672533, 1.66534121],\n",
       "       [1.95654964, 2.32169427, 0.76593913, 0.60066236, 1.59971812],\n",
       "       [2.35647127, 0.99922865, 2.032603  , 1.27492386, 0.39813966],\n",
       "       [0.46551641, 0.81916081, 1.49854706, 1.95516286, 0.45795247],\n",
       "       [2.58840067, 1.50666043, 1.49205813, 0.99646699, 0.80077571],\n",
       "       [0.70172571, 1.13233413, 0.73182245, 0.57035769, 0.84367546],\n",
       "       [1.73623815, 0.97114927, 1.94589477, 1.41920117, 0.66768467],\n",
       "       [0.58617786, 0.49928668, 0.54514768, 1.13649884, 1.313216  ],\n",
       "       [0.66289646, 1.48315249, 1.98781308, 0.36129063, 1.663664  ],\n",
       "       [1.17752197, 0.57690117, 1.30296556, 2.83246894, 0.45242316],\n",
       "       [0.42240309, 2.16821786, 0.46214877, 1.16645247, 2.29855281],\n",
       "       [1.61539372, 0.4518589 , 1.57954326, 0.68149511, 2.12551017],\n",
       "       [2.16002473, 0.73541285, 1.04211742, 2.18062086, 1.04775655],\n",
       "       [0.51246698, 0.70090166, 0.70376361, 1.56111507, 0.84697726],\n",
       "       [1.52678218, 0.4316127 , 0.74605003, 2.98010023, 1.15869295],\n",
       "       [1.24178465, 0.95598582, 0.67550467, 1.51047318, 0.68016221],\n",
       "       [2.19905252, 0.98193774, 0.53110239, 1.30172507, 0.52224351],\n",
       "       [0.49067871, 0.85421717, 0.45137099, 1.39466543, 1.12567966],\n",
       "       [1.56766838, 0.56517747, 1.46857331, 2.08233312, 1.29586171],\n",
       "       [0.97332555, 1.55505108, 1.93724716, 0.67698768, 0.72747084],\n",
       "       [0.85990949, 1.20257328, 1.00338906, 0.43187939, 2.40653883],\n",
       "       [0.97498986, 2.42832626, 0.52207862, 0.50040948, 0.50487233],\n",
       "       [1.38182583, 1.05955871, 0.83413799, 0.51335498, 0.38739947],\n",
       "       [1.7135728 , 1.54376377, 0.51960873, 1.26355614, 0.81220711],\n",
       "       [1.54006222, 0.83499208, 0.4693678 , 1.61725656, 0.62412927],\n",
       "       [0.52663821, 0.44705203, 0.48298507, 1.14903631, 0.59759693],\n",
       "       [0.9613778 , 0.46643782, 0.71780943, 0.43660737, 2.32384649]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(100000**(ensembledModels[0].get_layer(\"conv1d_1\").get_weights()[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         7.32414871e-02]], dtype=float32)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensembledModels[0].get_layer(\"conv1d_1\").get_weights()[0][0]"
   ]
  }
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