01_results_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "import joblib\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import rdMolDescriptors\n",
    "from mapchiral import mapchiral\n",
    "\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import tmap as tm\n",
    "from faerun import Faerun\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = \"Arial\"\n",
    "plt.rcParams['font.family'] = \"sans-serif\"\n",
    "plt.rcParams.update({'font.size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Single Sequence Runs: Generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generations_plot(query, second_query, order=['0', '1', '2']):\n",
    "    csv_files = [filename for filename in os.listdir('results/generations/') if f'{query}_MAP4' in filename and second_query not in filename and f'{query}_{query}' not in filename and filename.endswith('.csv')]\n",
    "    csv_files = sorted(csv_files, key=lambda x: order.index(x.split('_')[3].split('.')[0]))\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "    colors = ['#648FFF', '#DC267F', '#FFB000']\n",
    "    for i, csv_file in enumerate(csv_files):\n",
    "        df = pd.read_csv(f'results/generations/{csv_file}')\n",
    "        ax.plot(df['generation'], df['best_score'], label=csv_file.split('_')[3].split('.')[0], color=colors[i], linewidth=2)\n",
    "\n",
    "    ax.set_xlabel('Generation')\n",
    "    ax.set_ylabel('Best Score')\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_ylim(0, 1)\n",
    "    ax.set_xticks([10**i for i in range(1, 5)])\n",
    "\n",
    "    plt.savefig(f'plots/{query}_MAP4_generations.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EB9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot('EB9', 'Oncocin', order=['0', '2', '1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oncocin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot('Oncocin', 'EB9', order=['1', '0', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gramicidin S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot('GramicidinS', 'PolymyxinB2', order=['0', '1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polymyxin B2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ywYs2yaWWlU6n44YbbrB1piulbIsrcnJymDx5MsHBwYSHhzNnzhwqKyvrrPvVV18xZMgQ9Ho9MTExLF++3KX3EuemYzAYzjyvnJppvR/LJqhfzeu8b2rtq7uiF5oOvgCU7Dri8t+HEHVxqWX1xhtvNMmbTp8+nS5dupCZmUlWVhaTJk3i6aefZu7cuQ71Dh06xDXXXMOLL77IrbfeyoEDB7jiiiuIj49n6tSpTRKLqJtGAz27wA+HrVPdr/oEuoTB1ZdAQMdx8Puz1oo/3AB9n4aufwJfa5+Dxs+HgEu6UvrNEaqyTeQt24b/RdF0uLS7dLiLc+byeFYrV64kISEBrVZLjx49WLZsmUvfnKmpqSQmJvLEE0+g1WqJi4tjwYIFPP/887XqvvDCC0yePJnbbrsNjUZD//792b17N8OHD3c1bHEOeto9mLA3FT76Hj7/GYi4CvwjazYefBAOP+awr/2ln/Gt78n52wbSrn4W88FMhDgXLiWrlStX8tRTT/GXv/yFjRs38uCDD/LSSy/xxBNPOH2MpKQkQkNDHe7LSkhIIC0tjcLCQoe6e/bsoXv37sycOZPw8HD69OlDYmIinTp1oj5ms5mioiKHRZybS+JrLgWr/X4S8PKBHg85bihwnEEn8MaB+J71CI4qrcC0+ZdmiFS0C8oFvXv3Vnv37nUo27t3r4qNjXX6GG+//baKiYlxKEtNTVWASk9Pdyi/4IILlF6vV1u2bFEVFRXqq6++UoGBgWrdunX1Hn/hwoUK649YDovRaHQ6RlGjolKpzDyl7nteqT8/rdT8189ssFiUMh5UajPWZVt07X1zipRx7U8q/6WdKrXnQpXac6FKm/Rii8YvWjej0ej059OlllVmZiYXXXSRQ9lFF11EXl5ePXvUptPpKClxnCe9ej0w0LE/w9/fn+uuu45rr70WHx8fRo4cyaxZs1i7dm29x58/fz5Go9G2pKenOx2bqM3HGzqHWheAXKO1DwuNBoIuhOAz9ziUZUCV2XHfiECCbhxIyF0j8OtlvWwsP5wjwySLc+JSsoqPj2fTpk0OZZs2bSI+Pt7pY/Tt25e8vDyys7NtZcnJyURHR2MwGBzqJiQkYDY7fgCqqqoa7CPz9/cnKCjIYRHnrzpZKSC7wG6DNhbbljoeyanm17Oj9YVFUX44B1Upk6oK17j0a+Cjjz7K9OnTuf7664mLi+PIkSP873//Y/369U4fIz4+nuHDhzNnzhxWrVpFbm4uS5YsYfbs2bXq3n333YwdO5Z33nmHm2++ma+//po1a9awZs0aV8IWTaA6WQGs/wZCA60zeI3TxtKxesO+P4KvXUX/COi1ELTd8IsLtxVnTH0Fjc6Pjo9fj/6qPi0RvmgDXGpZTZ48mW3btuHv78/evXsJCQlh586dTJgwwaU3Xb9+PZWVlcTGxjJkyBDGjRvHggXWKaP0er0tGV1xxRV8+OGHrFy5EoPBwO23386TTz7JpEmTXHo/cf6i7PrKf02DXUnw9UHY9mtczYb83ZC9pWZJewOSHgbA78LODsdTp8sxvra7JUIXbYTLE0Z89tlnDBw4kLCwMD755BP8/Py48sormyu+8ybPBjYNcwUsfsc627M9nVce/4gaTIRvPWOz+wbD+FyU0pC/8kvK9qVjPpCJKrGOf9V9zyN4Gzo0b/Ci1Wq2ZwNfeOEFbrzxRk6ePAlY70SfNm0ab7311rlHKzyCvy8suQ3+dSssngX/vBnCguC0JYx/nEjlweOneCjtFHmXnYJxp6DTddYdKwqhcB8aLy/CHhxDl7f+SIehsbbjFjyX6I7TER7IpZZVjx49WLduHQMHDrSV/fTTT9x0002tdtIIaVk1n1IzJO6Hjbtqym4cCVcPBI7+F/bfay2MuBqGfgRe1lmQircmkT1nnW0fvz6d8O0STOhDY9D4+eATGSgjNbQTzTZETGBgIEajES+vmgaZxWIhNDS01g2drYUkq+Z35CSssBsdpmcXePDqw/gk9qop7DQRhnxoW00b+xwVx+q+5cUruAPR6+/ANya0zu2i7Wi2y8CEhATefvtth7J3332X3r17ux6laDNiIyHQbmKcwxlw8FQ8BPatKcz+BCw147RH/GtCrTvcq1kKSyne/mtzhSs8lEtt7ccee4xJkybxyiuv0K1bN9LS0ti7dy+ffPJJc8UnPICXF/x5HKzcBJYz7fQDxzQMuOxj+KybtUBVwekjEGi9VaHDkFi6bv8LYJ0hp3h7MlV5xZTuso5/VZV3usXPQ7RuLv8amJKSwnvvvcfJkyeJiYlhxowZxMXFNb6jm8hlYMsxV8CDL0HFmfs9E7rC5LDFxBYushYM3ghR19e7f/mRU6Rf8wIA/hdHEzip5mkJjbeGDpfFyaVhG9NsfVb2lFIUFBQQGtq6/3gkWbWs1Z/BN3bzng7SreWujtNt64Ud/4pv/xXodLVvV6gqKOHY0IYfiu/4xPV46eymfvPxosOgrnjpA847dtHyXPl8On0ZuGzZMnx9fZk7dy6pqalcffXVHD9+nFGjRrF582ZJBAKAScMg6TgUFFvXk0rGUmbRE+BlLQjOeZY1H1zImOvvpFOI475ewR3w69mR8sM59R4/5+FNtcp8u4cS8/F9aHxa78Ql4vw51cH+n//8hxdffJHYWOv9MQ888AAxMTH88ssvdOnShUWLFjVnjMKDhOhh+Z9g2e3Wh6BLlYFNBUsd6nTyOcjelNr7ajQaot65nY7/mULHxyfbFv11/Rt8z4pj+WQ/tAFLiUy02pY5dRnYv39/XnnlFYYMGUJxcTGhoaFs27aNK664grS0NEaOHMmxY8daIFzXyWWg+2TmwXe/WvuwtFXHmGiyftn9VjqSr4K+4s5rGjmAndNfHab8t2yHsvz/fG59svqM0L+NIeTOEU0RumghTd5nFRQUZBvE7ssvv2Ts2LEUFRUREGDtJ9DpdJw+3Tp/vZFk1UoohdqiRWMpA+BpUyoP3tLjvA5p3yFfTTs6Hr/4jgTNGIRvdEg9e4rWosnvs/L29rZN6PDtt98yYMAAW6I6efIkHTrIs12iERoNmg4xttVLeBKV+w2Un/t08349Ioj9+e8OZSWJKRS+sou0MStJn/gi6de/hPG9H875PUTr4VSyGjx4MGvXrqWiooL333+f8ePH27Zt2LDB4fEbIerVv2ac/ZGBL6HZNQK2R1nvvzpHXh38CJ17VZ3byg/nUJ6cRe7SbdKf1QY4dRm4a9currrqKvz9/dFqtfzyyy+Eh4czffp0PvzwQ7Zs2cKYMWNaIl6XyWVgK6IUxi19MFjOeo60/wsQe+/5HbrKQsXvuZi2HMD4lnU8eGWuhCrrn3fQjEFotH749+2M/pq+DnNeCvdp8lsX/vCHP5CcnMwPP/zA6NGjCQ+3DqTm7+/Ppk2bWm2iEq2MRkN67Ifs/PEduvgdZKDuzG0IFec/qYfG2wu/+I6EPTiGsAetf485j36Iad1eAIre/7GmskXR4dLugPV2CS9/3/N+f9H8zvmmUE8hLavWRSm4cyX0DviCv3U+8yUXPw8Smn7y2tOfHyLrvvcdfjGsS9g/xhF869Amf3/RuGa5KVSIpqDRQLAOSirsxttvgpZVXXRjetP18zlUnjRiTjpJ3rJtddbLW7qNitRTeJ+ZgNVL64f+2r74RMqXW2siyUq0OG0AlJntEkGlsf7K58m3SzC+XYLpMKgbGh8vSr+zjmiqKqoo+fKwrV7RBz857Hc68TBd3vpjs8UlXCfJSrQ4rT9kW+xaVqVpULAHNN4Q1M82SF9TM9x8KYabL7Wtl/2cTsb01+qsa/7lRLPEIM6dS+NZPfnkk3WWV0/2IIQztP5QquxaVnlfw84h8NUg+Mifiv/pqNjWg+zUnTTnjF0BA2LolvggnV+52bZUU2WVMl1YK9NoB3tubi7JyckAjB8/nm3btjnM22c0Gpk5cybFxcXNG+k5kg721uetHdaZcZZGX0BH3/rvsfrp9BQ2V6xn4S3W5wxbQuaf36H061QAfHuEo/H1RuPthUbvT/Dsy9CN6tkygbQTTdrB7ufnx9SpU8nNzQVg1KhRDtv9/f256667ziNc0d6MGQDpp+Ctok1cEvAOPpoyQBFMKiE+6UT7HQSsM+dkFcCxbLggqmVi8w6tGfK04kiuw7bcEwXovpBk5S6NJqugoCBycqxDdvTu3ZtDhw41e1CibesSDv+YCdAPeNxWrhR8c1AR9bsfXlTSwcv6K+Hja2HlPdbLx+YWfOtQyg9lU5llRFVYoLIKVW69HKzMaZ1XD+2FSx3shw4doqqqCm9va5t869atREREMGjQoGYJTrQvGg2M6KeB9CCoyCfAq+aWhgf+C95e1mVUf5g2snli8O8bRcyH9ziUnZjxKuZ9J6CiCmWxoPFyqatXNBGX/tc/+ugjoqKs7fHHHnuMG264gZEjR/LKK680S3CinfKx3u8U6GtyKK6yQHklfLbXOiu0xdIy4dhPC1a6+3csZRVYyipQLRWAAFxMVkuWLOGxxx7DYrHw3HPPsXHjRr7++msef/zxxne2k5OTw+TJkwkODiY8PJw5c+bYRnWoz8GDB9FqtSQmJrr0XsID+Vo7Wjtocpl4aTmxkRXEdqzAm5pl5cYK7npWkV3Q/OF4aWtupTg5+x2OXrSUoxctJW3MSsqP5jawp2hKLl0G/v7779xxxx3s27ePkpISrrrqKnx8fMjOzm58ZzvTp0+nS5cuZGZmkpWVxaRJk3j66aeZO3dunfVLSkqYOXMmpaWlLr2P8FABUVB0AI2qYtIpfyZV93nH1q7649aplHTuhbcXdPCHsCAvvDqNh9BhTRZOyH2jHG4grVaZaST/yR2E/PVyl47nHdxB7o4/By4lK61WS05ODh999BHDhw/Hx8eH/fv3ExZW9/xvdUlNTSUxMZGMjAy0Wi1xcXEsWLCAhx9+uN5kde+993L99ddz8OBBV8IVnipsFORsd6rqIN16sH9aJxtIWQK9FoJ/R/ANhYgrrR1idfHyBx99g+8R0K8LHZ+4HtP/fkFVWij/NQtLkXUQwdM7DnF6h+s/OoXcPwrDLUNc3s/TeIdoG6/kJJeS1Z/+9CcuvvhiCgoK2LBhAz/99BPjxo3j//7v/5w+RlJSEqGhoba+L7BOnpqWlkZhYSHBwcEO9d966y1SU1N57bXXWLJkSaPHN5vNmM1m23r1CKfCg8TeD6dT4HRq3duryqCwkQH1flvs3HtpvK2JrVfDNzYHXncRgddZpwarMpVxfMRTqNIK596jDgXPf0XB81+d8/4ewdebHgeb7oZxl5LVokWLGD16NAEBAQwdOpT09HRefvllbrjhBqePYTKZ0Ol0DmVarTX7FhcXOySrQ4cO8Y9//INdu3bZfoFszPLly1m82Mk/VNE6+QbCxa83WMVSbiI95UdKyqqoqIQd+yA+YCcTQxr/QnOgquDIM40mK3vegQF0+u9MTm9LdrmT3bR2r2vxCRuXnw0cPXo0+/btY+PGjUyYMIHhw4e7tL9Op6OkpMShrHo9MDDQVlZWVsb06dN55pln6Nq1q9PHnz9/Pg899JBtvaioiJiYmAb2EJ7Iyy+QbhfW9BXtKYAPD13JzyXXEeWXTPdIGBO5+cyU9fU8pJH9sfXfinywVIKX8x8H7bA4tMNcn9w3aMrFGFd/1z5GLvVu4ls8lAuys7PVZZddpnx9fZVOp1PJyclKr9er3bt3O32Mw4cPK0BlZWXZyt5//30VHR3tUG/nzp0qICBAGQwG2wIonU6n7rnnHqffz2g0KkAZjUan9xGe5+Pvlfrz0zXLXSud2GnXVUptxrqUFzZvgKJOrnw+XUp9c+bMoV+/fhQWFuLr60ufPn2YN29evR3jdYmPj2f48OHMmTMHk8nE0aNHWbJkCbNnz3aoN2LECEpLSyksLLQtAFu2bOHFF190JWzRDlwxwPFG0Spnrs687bojKlvn7EyihkvJ6osvvuA///kPWq3WNob1ww8/TFJSUiN7Olq/fj2VlZXExsYyZMgQxo0bZxu5Qa/Xs2bNGpeOJ0SAH1w1EHp0rimzNDYGrv2vgN+OBfOpZolNNA2X+qz8/PwoLS1Fq9XaRl4wmUwOfU3OiIyMZN26dXVua2j0BtW2R2AWTcC+m8RiAa+Gfpfxs7vlxnQQ0t6EeOevEkTLcqllNWnSJG655RZSUlLQaDTk5ORw7733cu211zZXfEK4xP52qkYvBbvd4bheltHk8Yim41KyWrFiBXq9nl69elFYWEjnzp0pKSlhxYoVzRWfEC6xb1k12hAPuhCu+LVmPa3h2yWEe7l0GajX61m3bh2nTp3i2LFjREdH07lz58Z3FKKF2A+I4FQnu3+k3c4BTR6PaDrndCNEREQEvr6+rFmzhu+//76pYxLinHnZXQY6db+mX0jN6/JTUCXPn7ZWTiWrEydOMHr0aAIDA7n99tv59ttvGTJkCCtWrGD48OFs3LixueMUwikOLStnf48JthuPLXN9k8Yjmo5Tyer+++/HYDDw3nvvUV5ezrXXXsuyZcvIzc1l1apV0mclWg37PqvvD1nHeq9ejtU3OEjw4JrXRfubNT5x7pyakTksLIzjx4+j1+spKCggLCyM0tJS/P39qaqqIiwszHbTZmsjE0a0L6s+gR9qj+ZiM2+6471YAJz6HHZfWbPeZzn0nNcs8QlHrnw+nWpZlZeXo9dbb6ALCQkhKCgIf3/rgNje3t5y/5NoNWI7Nbx93c46CnUXAHadXSfebsqQRBNx6tdAzVljAXmdNQa1JCvRWowZAJ1DocDu3uL0U/DlL9bXdXa6a7vBJe/CTzPPVGoHDxl7IKeSVVVVFV9//bUtKVVWVjqsV1XJZJCidfDygr7da5dXJ6t6v1ajZ8DBB8CcA6rhIbaFeziVrEpLS2vNF2i/fnbLS4jWxtvLet9VgxcBmjMfByVfvq2RU8nKIrN4CA9X/X3acLI68yChRVpWrZFMgCbaheq2f4MjMVS3rJCWVWskyUq0C9W/CUnLynNJshLtQvVloFMtK+mzapUkWYl2ofoy0KmWlfwa2CpJshLtgq2DvcFK1clKWlatkSQr0S5Uj8bQ4A/b1ZeBljIZk70VkmQl2gWNKx3sANs6Qd7XzRqTcI0kK9EuVP+hN5istN1qXlcVw4n3mzMk4SJJVqJdcKrPKmEFhI+pWa+qf/IS0fIkWYl2oTpZ5Zvg3+vgSGYdlXQ94OLXatarylokNuEcSVaiXfD3rXl9OANWrIXk43VU9O5Q8zpzLeybDUoeN2sNJFmJdmH8YAjSOpa9vr2OPiyfQPDyq1lPex1yE5s7POEESVaiXbgsAZ66E261GxDUWALmirMqenew9l3ZKz3R7PGJxrklWeXk5DB58mSCg4MJDw9nzpw5VFbWfdfwSy+9RK9evQgMDKRnz568+OKLLRytaEtG9IWBF9Ssp9c1Y3yPB2GA3RyCFum7ag3ckqymT5+OXq8nMzOTPXv2sGPHDp5++ula9TZv3sz8+fNZvXo1RUVFrF69mn/84x9s2LDBDVGLtsJ+PsGiknoqedvNIVhR2JzhCCe1eLJKTU0lMTGRJ554Aq1WS1xcHAsWLOD555+vVTczM5N58+YxdOhQNBoNw4YN4/LLL2fnzroG0hbCORfF1bx+fTskp9VRyb6jPfkRKDvZ7HGJhrV4skpKSiI0NJSoqChbWUJCAmlpabVmyLn33nt55JFHbOs5OTns3LmTSy65pN7jm81mioqKHBYh7OnsGk3llfDsZjCe/XSNvo/jes6nzR2WaESLJyuTyYROp3Mo02qtP9MUF9d/E15WVhbjx4/nkksu4aabbqq33vLlyzEYDLYlJiamaQIXbUavaNDbJawqC2Tln1UpsBd0HFezrs7uiRctrcWTlU6no6TEsaOgej0wMLDOfb777jsGDx5Mr169+PDDD/HxqX805vnz52M0Gm1Lenp60wUv2gRdAKyY7djRnlrXVV7UtJrXcq+V27V4surbty95eXlkZ9dMj5ucnEx0dDQGg6FW/ddff50xY8YwZ84c3n33Xdt8hfXx9/cnKCjIYRHibP6+jnMM1vnMoMbu4yHDxrhdiyer+Ph4hg8fzpw5czCZTBw9epQlS5Ywe/bsWnU3bNjAPffcw8aNG/nb3/7W0qGKNi4mouZ1ZV25yH4UBklWbueWWxfWr19PZWUlsbGxDBkyhHHjxrFgwQIA9Ho9a9asAWDx4sVUVlYyZcoU9Hq9bbn77rvdEbZoY3ztcpEkq9bPqam4mlpkZCTr1q2rc5t9J/v+/ftbKiTRDnnb5aKqurqkJFm1KvK4jWi3fKRl5VEkWYl2y8fur1+SVesnyUq0W/aXgd8kwemzHwGUZNWqSLIS7ZZ9BzvAurOf4rJPVsh9Vu4myUq0WyGB0CmkZv1E7tk17D4eh/4JXw+HClNLhCbqIMlKtFteGnjU7smt8rNHKfILd1zP3wXZHzV7XKJukqxEu+bvC4YzI4iaK6zzCtqWoEtQFzziuEOltKzcxS33WQnRmvidGZ893wR3PWu/RQOsYIiuL3/uOMtaZJEHmt1FWlai3QvWN7y9CrvZJmT0BbeRlpVo96YOhy17oNRc93ZltEtWlvKWCUrUIslKtHtxneGv19W//Y23ama7KUtajvHgGr4sX0Zq1QS6doQZo8FPPknNTi4DhWiExbfm/oYAjZFIrwMM1SzieA58fRD2pboxuHZEkpUQjbjwomH8UHIrhZWdsSjr1M46r5qhRU31TTohmpQ0XoVoxNAEL0hYbV3ZHg1lGRg61PRdVciTOC1CWlZCuMLLOlKtl6rpja9zeBnR5CRZCeGKM8lKY5esysqtN5GK5iWXgUK4wsv6y6CXpYSrg54EQKXAx8fhsgshLFADEWPAMMCNQbZNkqyEcEV1y4oqbgyb67iterJULz+4Kg0CIls2tjZOLgOFcEX4qMbrWMrBlNz8sbQz0rISwhUJKyDyGii3jidTUAzvJ1o3je28njjLB9aV8lNQXmB9rfEGX5kS7nxJshLCFRovCB9tW600wt4z91ldzFHiOJOsfpzuuF/UVBhc9yQpwjlyGSjEefC2+wSZ6Fp/xcz1UHqi+QNqw6RlJcR5sE9WqZbruSp+Phh/riksOgBlZ5JUhRE6RLdofG2JJCshzoN9sjqU6ceKsmUO26/0+SuDfJ4DYPdn7/Jr1QzbtiIVjZkQGhPXGaYMd3yv9sgtp5+Tk8PkyZMJDg4mPDycOXPmUFl59piyVp988gn9+vVDp9PRp08ftmzZ0sLRClE/Xx/rEH0AJWY4ctJxKTqtbHUv81nGbP/+tuV+v86Qv7vWPmcvn+2F5OPuOb/WxC3Javr06ej1ejIzM9mzZw87duzg6aefrlUvJSWFKVOmsGTJEoxGI4sXL2batGlkZGS4IWohavP3hTEX19/q2VX8x3r39fUyc7F2k1PvUyQPS6NRSqnGqzWd1NRU4uPjycjIICoqCoAPPviAhx9+mOPHHb8+Hn30Ufbs2cOnn35qKxs/fjyXXnopixcvdur9ioqKMBgMGI1GgoLk52PRPCwNfYpOvIcm78uadXMumixrklLd70P1f77O3b45CG9/bn1965Uwom8TBduKuPL5bPE+q6SkJEJDQ22JCiAhIYG0tDQKCwsJDg52qNuvXz+H/RMSEvjll1/qPb7ZbMZsrnluy2g0Atb/FCHcwnCtdalW9Cv8fqZFVWQEU91/m+ZSKC+rqdYW/4SrP5fOtJlaPFmZTCZ0Op1DmVZrnV6kuLjYIVnVV7e4uLje4y9fvrzOVldMTMx5RC1Ec3nnzNKwt5o/ELcymUwYDIYG67R4stLpdJSUOF6AV68HBgY6Vffsevbmz5/PQw89ZFu3WCzk5+cTFhaGRqOpd7+2qKioiJiYGNLT09vMJbCck2dw9pyUUphMJocrrfq0eLLq27cveXl5ZGdnExlpfdAzOTmZ6OjoWpm1b9++7N2716EsOTmZQYMG1Xt8f39//P39HcrsW2vtUVBQUJv5EFSTc/IMzpxTYy2qai3+a2B8fDzDhw9nzpw5mEwmjh49ypIlS5g9e3aturNmzSIxMZG1a9dSWVnJ2rVrSUxMZNasWS0dthDCzdxy68L69euprKwkNjaWIUOGMG7cOBYsWACAXq9nzZo1APTu3ZvNmzezbNkyQkJC+Ne//sWGDRvo2bOnO8IWQriRW+5gj4yMZN26uh/qPLvzfOzYsYwdO7Ylwmpz/P39WbhwYa3LYk8m5+QZmuOcWvw+KyGEOBft/GkjIYSnkGQlhPAIkqyEEB5BkpUQwiNIshJCeARJVu3Y5s2buf32290dRpMwm81MmzaNkSNHMnToUL777jt3h3ReKisrmTlzJsOHD2f8+PHk5+e7O6Qmk52dTUhI44MOnk2SVTs1b9485s2b59TT7p7gzTffpGfPnuzcuZPVq1fz4IMPujuk87J+/XoiIyP55ptvmDFjBs8884y7Q2oyjzzyCOXl5S7vJ8Mat1ODBw9m7NixrF692t2hNImbbrrJ9qB6VVWVx99gOWPGDKZOnQpAWlpam3lm8IsvviA8PJyIiAiX95WWVTs1ZcqUNjUKRWBgIHq9ntzcXG699VaWLFni7pDOm4+PD9deey3PPfccV199tbvDOW/l5eUsXbqURYsWndP+kqxEm5GSksIVV1zBP//5T0aMGOHucJrExx9/zO7du5k2bZq7QzlvK1as4O6770av15/T/pKsRJuQkZHBpEmTeOWVV5g0aZK7wzlvr7zyCs8++yxgbTV6eXn+R3XHjh288MILjB49mqysLCZMmODS/tJnJdqEpUuXYjKZeOSRRwCIiIio92F5TzB9+nRmzZrFhg0bsFgsvPzyy+4O6bzt3LnT9rp79+6uz1SlhMfLyclRPXr0UF9++aWtLDs7W1133XXKYDCosLAw9cADD6iKigr3BemitnZObe18lGr5c/L8tmU7t2vXLoYNG8aRI0ccyp2d7qw1amvn1NbOB9x0Tk2S8oRbvPnmm6pr167q/fffV4DtGy4lJUUBKiMjw1b3/fffV127dnVTpM5ra+fU1s5HKfedkyQrD3by5ElbE9v+j2bz5s0qNDTUoe7+/fsVoAoKClo4Ste0tXNqa+ejlPvOSS4DPVinTp3w8an9G0lj0521Zm3tnNra+YD7zkmSVRvkynRnnqKtnVNbOx9o/nOSZNUG2U93Vq2+6c48RVs7p7Z2PtD85yTJqg1yZbozT9HWzqmtnQ80/zlJsmqjGpruzFO1tXNqa+cDzXtOMruNEMIjSMtKCOERJFkJITyCJCshhEeQZCWE8AiSrIQQHkGSlRDCI0iyEkJ4BElWol1ISUlxdwjiPEmyEi5LS0vjnnvuITY2Fp1OR2hoKOPGjeOzzz5zd2h1euGFF7jzzjtt6xdeeCFr1qxxY0TiXEiyEi45ePAg/fv3x2w2s3XrVoqKikhNTeWWW25h8uTJbN261d0h1nLq1CmHyVyTkpK4+eab3RiROCfnPSKWaFcuu+wydeONN9a57c0331SbNm1SSin12WefqcGDByuDwaASEhLUO++8Y6t32223qbvuuktNmDBB6fV6FRsbq1auXGnbXlRUpO677z4VHR2tIiIi1PTp01VWVpZSSqmjR48qQD300EMqODhY3XvvvcpsNqv/+7//U71791Z6vV5FRESo+++/X1ksFvXmm28qX19f5eXlpQwGg1JKqW7duqk33nhDKaVUSUmJmjt3roqOjlbBwcFq1KhRas+ePbZYAPXss8+qnj17Kr1ery677DK1f//+JvwfFc6SZCWclp6ergD1+eefN1jv559/Vh06dFAbNmxQlZWVateuXSo8PFxt27ZNKWVNVn5+furTTz9VFRUV6qWXXlLe3t7qxIkTSimlpk6dqq6++mqVnZ2tTCaTuuOOO9SwYcOUxWKxJas77rhDmc1mVVBQoB5//HF14YUXqszMTKWUUrt371Y+Pj5qx44dSimlFi5cqEaNGmWLzz5Z3Xbbbap///4qJSVFmc1m9cwzz6jAwEB1/PhxpZQ1WQ0bNkydPHlSFRYWqiuvvFKNHTu2Kf9bhZMkWQmnffvttwpQv/32m61sx44dymAwKIPBoPR6verZs6e655571IwZMxz2nT9/vpowYYJSypog7D/w5eXlClBfffWVys7OVoA6dOiQbfvp06eVj4+P+vHHH23J6ptvvrFtz8/PV1lZWcpisajMzEz1+eefq5CQELV69WqlVP3JqrS0VPn5+amPP/7YIdbBgwer5cuXK6Wsyeq9996zbVu1apWKjY091/9CcR5k3kDhtM6dOwPWCUV79uwJwJgxYygsLATgzTffZNGiRRw7dowvvviC4OBg275VVVX06NHDtt6pUyfba19fXwAsFgvHjh0DYMiQIQ7v7ePjw9GjRwkLCwMgKirKtu306dPcf//9fPXVV0RHRzNw4ECUUlgslgbPp6CggPLycuLi4hzKY2NjbXHUFWtjxxXNQ5KVcFq3bt0YPHgwr776Kpdffnm99aKjo/njH//ISy+9ZCs7efKkQyd3Q/sCHDp0yCFJJCcnExcXR1ZWFgAajca27Y477iA0NJSTJ08SEBCAxWIhJCSk0feKjIwkICCAI0eO0Lt3b1v5kSNHmDhxYqP7i5YlvwYKl7z++uts3bqVO++8k8OHD6OUwmQy8fbbb7Nw4UKioqKYPXs27777Lp9++ikWi4WUlBRGjhzJk08+2ejxo6KiuPbaa3nggQfIy8ujoqKCpUuXMnjwYFsL7mxGo5GAgAC8vb0xmUzMnTuXoqIiysvLAQgICKCoqKhWsvTy8uJPf/oTf//730lNTaW8vJyVK1eSlJTEzJkzz/v/SjQtSVbCJX379iUpKQmtVsuECRMwGAxER0ezatUqHnnkEb788kuGDBnCe++9x9///ndCQkIYNWoUkyZNYsWKFU69x9tvv01wcDADBgwgPDycjz/+mO3btzu0tOw999xz/Pzzz4SEhNCrVy+KiooYN24cBw4cAGDixInk5eVhMBhqJbx///vfjB07ljFjxhAWFsYHH3zA9u3bbZe5ovWQkUKFEB5BWlZCCI8gyUoI4REkWQkhPIIkKyGER5BkJYTwCJKshBAeQZKVEMIjSLISQngESVZCCI8gyUoI4REkWQkhPIIkKyGER/h/dRhnwWKR3isAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot('PolymyxinB2', 'GramicidinS', order=['1', '0', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cathelicidin BF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot('CathelicidinBF', 'CirculinD', order=['0', '1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Circulin D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot('CirculinD', 'CathelicidinBF', order=['0', '1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MXFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generations_plot_mxfp(query):\n",
    "    csv_files = [filename for filename in os.listdir('results/generations/') if f'{query}_MXFP' in filename and filename.endswith('.csv')]\n",
    "    csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "\n",
    "    colors = ['#648FFF', '#DC267F', '#FFB000']\n",
    "\n",
    "    for i, csv_file in enumerate(csv_files):\n",
    "        df = pd.read_csv(f'results/generations/{csv_file}')\n",
    "        ax.plot(df['generation'], df['best_score'], label=csv_file.split('_')[3].split('.')[0], color=colors[i], linewidth=2)\n",
    "\n",
    "    ax.set_xlabel('Generation')\n",
    "    ax.set_ylabel('Best Score')\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_ylim(0, 600)\n",
    "    ax.set_xticks([10**i for i in range(1, 5)])\n",
    "\n",
    "    plt.savefig(f'plots/{query}_MXFP_generations.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cyclosporin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot_mxfp('Cyclosporin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valinomycin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations_plot_mxfp('Valinomycin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Single Sequence Runs: Summarized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv_files = [filename for filename in os.listdir('results/generations/') if filename.split('_')[2] == 'MAP4' and filename.endswith('.csv')]\n",
    "csv_files = [filename for filename in csv_files if 'aConotoxinGIA' not in filename and 'EpothiloneA' not in filename and 'aCyclodextrin' not in filename and 'EB9Oncocin' not in filename and 'GramicidinPolymyxin' not in filename and 'CathelicidinCirculin' not in filename]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "csv_files = sorted(csv_files, key=lambda x: x.split('_')[1])\n",
    "\n",
    "csv_files_sorted = []\n",
    "sorting_order = ['PolymyxinB2', 'GramicidinS', 'EB9', 'Oncocin', 'CathelicidinBF', 'CirculinD']\n",
    "\n",
    "for order in sorting_order:\n",
    "    csv_files_sorted += [filename for filename in csv_files if order in filename]\n",
    "\n",
    "runs = np.zeros(len(csv_files_sorted))\n",
    "for i, csv_file in enumerate(csv_files_sorted):\n",
    "    df = pd.read_csv(f'results/generations/{csv_file}')\n",
    "    if len(df) == 10000:\n",
    "        runs[i] = 10000\n",
    "    else:\n",
    "        runs[i] = len(df) - 4\n",
    "    \n",
    "runs = runs.reshape((6, 3))\n",
    "n_groups = runs.shape[0]\n",
    "n_bars = runs.shape[1]\n",
    "group_positions = np.arange(n_groups)\n",
    "bar_width = 0.2\n",
    "colors = np.array([['#DC267F' if value == 10000 else '#648FFF' for value in row] for row in runs])\n",
    "\n",
    "plt.figure(figsize=(3, 2.25))\n",
    "for i in range(n_bars):\n",
    "    plt.bar(group_positions + i*bar_width , runs[:, i], color=colors[:, i], width=bar_width-0.02)\n",
    "\n",
    "plt.ylabel('Generation')\n",
    "plt.xticks(group_positions + bar_width, ['1', '2', '3', '4', '5', '6'], weight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/AllQueries_generations.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Single Sequence Runs: Number of Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7877.,  6362.,  6934.],\n",
       "       [ 4119.,  4142.,  5438.],\n",
       "       [20998., 20377.,  7160.],\n",
       "       [55462., 39835., 46591.],\n",
       "       [88738., 31301., 86265.],\n",
       "       [73535., 37526., 33738.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files = [filename for filename in os.listdir('results/generations/') if filename.split('_')[2] == 'MAP4' and filename.endswith('.csv')]\n",
    "csv_files = [filename for filename in csv_files if 'aConotoxinGIA' not in filename and 'EpothiloneA' not in filename and 'aCyclodextrin' not in filename and 'EB9Oncocin' not in filename and 'GramicidinPolymyxin' not in filename and 'CathelicidinCirculin' not in filename]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "csv_files = sorted(csv_files, key=lambda x: x.split('_')[1])\n",
    "\n",
    "csv_files_sorted = []\n",
    "sorting_order = ['PolymyxinB2', 'GramicidinS', 'EB9', 'Oncocin', 'CathelicidinBF', 'CirculinD']\n",
    "\n",
    "for order in sorting_order:\n",
    "    csv_files_sorted += [filename for filename in csv_files if order in filename]\n",
    "\n",
    "runs = np.zeros(len(csv_files_sorted))\n",
    "for i, csv_file in enumerate(csv_files_sorted):\n",
    "    df = pd.read_csv(f'results/{csv_file}')\n",
    "    df = df.dropna()\n",
    "    runs[i] = len(df)\n",
    "\n",
    "runs = runs.reshape((6, 3))\n",
    "runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Single Sequence Runs: Percent of Sequences below Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files = [filename for filename in os.listdir('results/generations/') if filename.split('_')[2] == 'MAP4' and filename.endswith('.csv')]\n",
    "csv_files = [filename for filename in csv_files if 'aConotoxinGIA' not in filename and 'EpothiloneA' not in filename and 'aCyclodextrin' not in filename and 'EB9Oncocin' not in filename and 'GramicidinPolymyxin' not in filename and 'CathelicidinCirculin' not in filename]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "csv_files = sorted(csv_files, key=lambda x: x.split('_')[1])\n",
    "\n",
    "csv_files_sorted = []\n",
    "sorting_order = ['PolymyxinB2', 'GramicidinS', 'EB9', 'Oncocin', 'CathelicidinBF', 'CirculinD']\n",
    "\n",
    "for order in sorting_order:\n",
    "    csv_files_sorted += [filename for filename in csv_files if order in filename]\n",
    "\n",
    "runs = np.zeros(len(csv_files_sorted))\n",
    "for i, csv_file in enumerate(csv_files_sorted):\n",
    "    df = pd.read_csv(f'results/{csv_file}')\n",
    "    df = df.dropna()\n",
    "    df_half = df[df['dist'] < 0.5]\n",
    "    runs[i] = len(df_half)/len(df)\n",
    "\n",
    "runs = runs.reshape((6, 3))\n",
    "runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Single Sequence Runs: Number of Sequences without Stereochemistry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4851.,  4792.,  5123.],\n",
       "       [ 3384.,  2958.,  4505.],\n",
       "       [16705., 16333.,  5720.],\n",
       "       [32698., 27829., 22023.],\n",
       "       [57367., 20831., 63374.],\n",
       "       [43550., 23368., 26092.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files = [filename for filename in os.listdir('results/generations/') if filename.split('_')[2] == 'MAP4' and filename.endswith('.csv')]\n",
    "csv_files = [filename for filename in csv_files if 'aConotoxinGIA' not in filename and 'EpothiloneA' not in filename and 'aCyclodextrin' not in filename and 'EB9Oncocin' not in filename and 'GramicidinPolymyxin' not in filename and 'CathelicidinCirculin' not in filename]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "csv_files = sorted(csv_files, key=lambda x: x.split('_')[1])\n",
    "\n",
    "csv_files_sorted = []\n",
    "sorting_order = ['PolymyxinB2', 'GramicidinS', 'EB9', 'Oncocin', 'CathelicidinBF', 'CirculinD']\n",
    "\n",
    "for order in sorting_order:\n",
    "    csv_files_sorted += [filename for filename in csv_files if order in filename]\n",
    "\n",
    "runs = np.zeros(len(csv_files_sorted))\n",
    "for i, csv_file in enumerate(csv_files_sorted):\n",
    "    df = pd.read_csv(f'results/{csv_file}')\n",
    "    df = df.dropna()\n",
    "    df['smiles'] = df['smiles'].apply(lambda x: Chem.MolToSmiles(Chem.MolFromSmiles(x), isomericSmiles=False))\n",
    "    df_flat = df.drop_duplicates(subset='smiles')\n",
    "    runs[i] = len(df_flat)\n",
    "\n",
    "runs = runs.reshape((6, 3))\n",
    "runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12904556, 0.66548414, 0.57212571],\n",
       "       [0.63061466, 0.09263016, 0.76293008],\n",
       "       [0.31559413, 0.41119207, 0.2798951 ],\n",
       "       [0.52232552, 0.69603651, 0.65499705],\n",
       "       [0.80900169, 0.80044165, 0.8332597 ],\n",
       "       [0.58362801, 0.60621363, 0.5331136 ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files = [filename for filename in os.listdir('results/generations/') if filename.split('_')[2] == 'MAP4' and filename.endswith('.csv')]\n",
    "csv_files = [filename for filename in csv_files if 'aConotoxinGIA' not in filename and 'EpothiloneA' not in filename and 'aCyclodextrin' not in filename and 'EB9Oncocin' not in filename and 'GramicidinPolymyxin' not in filename and 'CathelicidinCirculin' not in filename]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "csv_files = sorted(csv_files, key=lambda x: x.split('_')[1])\n",
    "\n",
    "csv_files_sorted = []\n",
    "sorting_order = ['PolymyxinB2', 'GramicidinS', 'EB9', 'Oncocin', 'CathelicidinBF', 'CirculinD']\n",
    "\n",
    "for order in sorting_order:\n",
    "    csv_files_sorted += [filename for filename in csv_files if order in filename]\n",
    "\n",
    "runs = np.zeros(len(csv_files_sorted))\n",
    "for i, csv_file in enumerate(csv_files_sorted):\n",
    "    df = pd.read_csv(f'results/{csv_file}')\n",
    "    df = df.dropna()\n",
    "    df['smiles'] = df['smiles'].apply(lambda x: Chem.MolToSmiles(Chem.MolFromSmiles(x), isomericSmiles=False))\n",
    "    df_flat = df.drop_duplicates(subset='smiles')\n",
    "    df_flat_half = df_flat[df_flat['dist'] < 0.5]\n",
    "    runs[i] = len(df_flat_half)/len(df_flat)\n",
    "\n",
    "runs = runs.reshape((6, 3))\n",
    "runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New sequences per generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newmols_plot(query, second_query, c1='#648FFF', c2='#DC267F', c3='#FFB000', order=['0', '1', '2']): \n",
    "    csv_files = [filename for filename in os.listdir('results/') if f'{query}_MAP4' in filename and second_query not in filename and f'{query}_{query}' not in filename and filename.endswith('.csv')]\n",
    "    csv_files = sorted(csv_files, key=lambda x: order.index(x.split('_')[3].split('.')[0]))\n",
    "\n",
    "    # Read data\n",
    "    df = pd.DataFrame()\n",
    "    for i, csv_file in enumerate(csv_files):\n",
    "        df_run = pd.read_csv(f'results/{csv_file}')\n",
    "        df_run['run'] = i\n",
    "        df = pd.concat([df, df_run])\n",
    "\n",
    "    df['generation'] = df['generation'] + 1\n",
    "    df['generation'].fillna(0, inplace=True)\n",
    "\n",
    "    df_run0 = df[df['run'] == 0]\n",
    "    df_run1 = df[df['run'] == 1]\n",
    "    df_run2 = df[df['run'] == 2]\n",
    "\n",
    "    # Group by generation and count molecules\n",
    "    run0_counts = df_run0.groupby('generation').size()\n",
    "    run1_counts = df_run1.groupby('generation').size()\n",
    "    run2_counts = df_run2.groupby('generation').size()\n",
    "\n",
    "    # Calculate cumulative counts\n",
    "    run0_cumulative_counts = run0_counts.cumsum()\n",
    "    run1_cumulative_counts = run1_counts.cumsum()\n",
    "    run2_cumulative_counts = run2_counts.cumsum()\n",
    "\n",
    "    # Plot data\n",
    "    fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "\n",
    "    ax.plot(run0_cumulative_counts.index, run0_cumulative_counts.values, label='Run 1', color=c1, linewidth=2)\n",
    "    ax.plot(run1_cumulative_counts.index, run1_cumulative_counts.values, label='Run 2', color=c2, linewidth=2)\n",
    "    ax.plot(run2_cumulative_counts.index, run2_cumulative_counts.values, label='Run 3', color=c3, linewidth=2)\n",
    "\n",
    "    ax.set_xlabel('Generation')\n",
    "    ax.set_ylabel('Cumulative Molecules')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'plots/{query}_newmols_cumulative.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EB9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot('EB9', 'Oncocin', order=['0', '2', '1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Onconcin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot('Oncocin', 'EB9', order=['1', '0', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gramicidin S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot('GramicidinS', 'PolymyxinB2', order=['0', '1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polymyxin B2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot('PolymyxinB2', 'GramicidinS', order=['1', '0', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cathelicidin BF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot('CathelicidinBF', 'CirculinD', order=['0', '1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Circulin D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AzKNpNMCeU0Ditdp1I/sBE55lIyoyev40Gzp0KObOnYuysjIAXN+zefPm1ZvvjGHqUlYC/zusm4QmPwdMZGNLM9X0uiNas2YNhg8fDpFIBGdnZxQUFCA4OBgHDx40VHxMO1coB778DXiQzy3zeUD0cODZYOPGxZgWvRKRj48PUlJScOrUKeTl5aFr164IDQ2FmRn7gc/UdzsL+OYQN8Y0wLWWnhMBhHQ1aliMCWpWIsrIyNBZDggI0I5BlJWVBYBLUgwDcGMHHbsC/HimdhwhF3vgzbFsREWmYc1KRF27dtV28ah5yFZ3mcfjsS4eDACgXAlsOarbe/5JH2DOKMDW2nhxMaatWYmoZ8+euH//PqZOnYrp06fDw8PD0HEx7VBxKdddI7uwdl1Ef2DcMwBrfM88SrMuj6SkJCQkJIDP52Ps2LGYP38+kpKSIBaL4evryyZYZJCZD6zeXZuEbK2At8YDEwayJMQ0Te/phJRKJQ4cOIAtW7bg5s2bmDZtGl5//XU2MFondjsT+OJX4GF1nzEXe66ltKuDUcNiWonJTScEAJaWlnj11VcRHx+P33//HUeOHEFQUNBjfXhVVRWGDBmCGTNmaNdduHABYWFhsLW1hZ+fHzZv3qyzz/bt2xEQEACBQIB+/frh3LlzOsdbsmQJ3NzcIBQKMW7cOJ2pjvLy8jB+/Hg4ODjA2dkZCxYsgFqtfqzYGU56DrD+l9ok5OcGvPsKS0KMfh7rpjk+Ph6vvfYaBgwYAAsLC6xfv/6xPnzlypX4448/tMvFxcWIiIhAdHQ0SkpKsHnzZixcuBAXL14EAJw4cQLz58/H9u3bUVJSgqioKIwdOxbl5dzz4Q8//BBxcXG4fPkysrKyYG1tjdmzZ2uPHxkZCVtbW2RnZ+PixYuIj49HbGzsY8XOcG2DPv+Za7AIACG+wOJJgL3AqGEx7RE10+3bt2nZsmXk7e1Nrq6utGjRIrp+/Xpzd68nISGBgoKCaPLkyTR9+nQiItq0aRN169ZNZ7s33niDoqOjiYgoKiqKYmJidMp79OhBW7ZsISIisVhMO3fu1JZJpVLi8XiUlpZGqampBICysrK05bt37yYfH59mxyyTyQgAyWQyvb5rR5RTSLTwG6LZsdxr7T4iZaWxo2IMoS2u+2bdET377LPo2bMnbt68iQ0bNiA7OxuffvopQkJCHiv55eXlYdasWfjhhx9gY1M7+IxEIkHPnj11tg0KCkJSUlKT5TKZDJmZmTrlbm5uEIlESE5OhkQigaOjIzw9PXX2zcjIQElJyWN9j86qQMaNqFgzmJm/O9dGyILNsME8pmZdOufOnYO9vT2uXr2Ka9euYcGCBfW2SU9Pr79jAzQaDaZNm4ZFixbhqaee0ilTKBQQCHTv621sbFBaWtpkuUKhAIBH7t9QGcD1mXNwcKgXq1KphFKp1C7L5fJmfceOrLQCiP2Je1QPcLOu/ms8YMX6PTMt0KxEtHXr1lb7wNWrV8PKygrz58+vVyYQCOrdnZSXl2tnCREIBNr6oLrlzs7O2iTTULlQKIRGo2mwDECjs5CsXr1aO48bA6irgK8P1g5w7y4CFk4ABGwcIaaFmpWIpk+f3mofuGPHDmRnZ2vvQGqSwc8//4y1a9ciLi5OZ/uUlBTtT8CQkBBIJJJ65RERERCJRPDy8oJEItFuL5VKUVRUhJCQEGg0GhQWFiI3Nxdubm7afcViMezt7RuMdenSpTqDvsnlcnh7d85pJTQEfBfP9R8DAHsbNqwr04oMVvvUTNOnT9dWVhcUFJCDgwPFxsaSSqWixMREEgqFlJiYSERE8fHx2mWVSkWxsbEkEomosLCQiIjee+89CgkJofT0dJLL5RQZGUnh4eHazxo0aBBNmTKF5HI5paenU3BwMC1fvrzZsXbmyuoDp2srpueuJ0rPMXZETFtpi+vepKoXnZyccOzYMbz11lv44IMP4OLigvXr12Po0KEAgGHDhuGrr77C3LlzkZmZieDgYBw5cgSOjo4AgA8++ACVlZV47rnnoFAoMHToUOzdu1d7/P379+PNN9+En58f+Hw+oqOj8f777xvlu7Ynl/4Cjlzi3vN4wOxRgJ+7cWNqKU2FClVF5dAoHnKvUiU0pUpQuQoapRqkqgJUau17Uqm5V93luu8rq7jbRiKAwP1Fdd/MmuW6f8H9rVcOAjTE7f63Y9UsUyPr65Wj/mdTI+s9Nk2DzXMBbXDmG6Z3y+rOrDO2rM7IAz7eC6iq231OCQeGPW3cmJpSJatA5f0iVGYUQZ1ZjKqCUqjzS1FVUIqqgjJUFZRCU6ps+kCdiPvGKAjCuzVYZnJDxdZVUFAAZ2fn1oyFMTElpdygZjVJaGAQ8Hxvo4akg6o0qLxbAOWNHChvZEOZkgNVWj40JRVtH4w5H+DxwOOBu22s+xfcX97f19dMWsprqLz2Pa+BY9XbBw0cu6YcDcTE41Xvxi2bCS3b5jw1Qq9EpFarsXz5cmzYsAFqtRrXr19HZGQkfv31V9Yjv4OpUHKtpou4VhHwcwOmPV/7b8cYNGVKVJy/i4rzd7nEc0sKKq/U6xh8W0uYOdvCzMUWZo4C8O2swBdagm9b/dfGAjxLc/AszKv/mnHvLarfN1hmBh7r2dsieiWiFStWIDExEfv27UNkZCTc3NwgFovx1ltv6dTFMO2buoobWTGzgFt2sgP+ORboYoQaxSrFQ5QdkaD0dwkqLt6vnYOoEWZuQnTp6oQuPo7cX7EDzN3suOTjLADfmjV4MkV6XVo7d+7E6dOn4eXlBR6PB4FAgK1bt2pHa2TaP6p+TJ9SPSinwApYML5t+48REZRXH0C26zLKjqaAlA13TDb3coBliAcsgz1hGeIJyyAPmIlYe4L2SK9EVFpaCldXVwC1IzXa2NiwKac7kF/OAeducu/NzbiuG+6ObfPZRITyU3dQ/PVJKK9m1is397KHzXPdYDM4AFZPe8PMkfWu7Sj0SkQDBgzAypUr8eGHH2orwdavX4/Q0FCDBMe0rZPJ3ASIAFefOXskNxV0W6i4koGitcfw8OoDnfV8B2vYju4Juwm9YRHsUVv52hAiQPMQqCoHqh5Wv1dyf+u+rynTKBt5X4nq59rVj8ibeN/c7RrdB/rv1/UNwP2llp52k6FXIoqNjcXw4cOxbds2KBQKBAUFQaFQID4+3lDxMW3kwi1gZ2Lt8ivhQN+Gn+a2qipZBQpWHUbpwes667t0c4HD6wNhOyoYfH4JUHYHeBAPKKWAKh9Q5gPKvOr3eUClDKgqBaiTjJ3uOtLYEbQqvRLRE088AYlEgoMHD+L+/fsQi8UYPXp0o321mPbhxj1ga1zt/5tH9AWGt0FboYorGch7+wDU2TIAhC4uhbB+qgL2oyzRxe0meGVbgMQ7gJp1Nu7o9EpE8+fPx+zZs/HKK68YKh6mjd3O4jqyVmm45cEh3AyshkRVGpT87w/Itv8Km8BUCIbfhtUTGTCzrm7/UwUgu7lH4wMWTkAXEWBuC5gLAXMBwLcGzCwBvhX3qnlvZgXwG3tfs9wF1Y1wUN3opv5yc943ezvofwxz28c9/SZJr0SUl5eHAQMG4Mknn0RMTAymTp3aaIdRxvTdlQJf/FLbYLFPABBlwLZCmtKHKD8tQeUfG2DtfgyiZVlN7MEHbLoCggDAthv311oMWDgDFq6ApQtg4Qjw2ASf7Z3eXTxkMhl++OEHbN++HdevX8ekSZMwa9YsDB482FAxmoyO1MWjQAZ8tAsofcgth/gC88a0fluhyqwSlMXfgvLSKVha/wxhn6sws3lYf0MLZ8BxIODQFxAGA8IQQOAH8Fm7H2Nri+u+RX3NEhISMHv2bGRkZHSKCRY7SiJSqYGP9wAZ1fPRB3oBb01ovREWqUqD8j/uQP7DJVD2UTiEn4FNYP2B8zTmPcD3nwy4vQSIQgEeawZiikyyr1lpaSn27duH7777DhcuXMDo0aOxceNGQ8TGGAARsCOhNgm5OgD/HNM6SaiquBzyA1ch33UJVJIJp7FHIXzphu7na7qgymo0+H3eBt91gHH7jDAmQ6/LLyoqCr/88gu8vb0xe/Zs7Nu3j3V8bWcSk4Dz1Q0WLbsA80YDNi0cYVGVlo+STadReugGqFINu/5X4BgTDzPr2h7u1MUX6PZP8Hxfh7mFU8s+kOlw9EpE5ubm+P333zFokIEfqzAGcTsL2Heqdnn6C4BXC/4/UlVcjuIvT0K26xKg1sDcsRiuU36CddfaRonUxQm84LXg+UxnP72YRumViLZv326oOBgDK1JwHVlrHtOP6AuEBj7+8UqPSJC/6hA0RdxQv1b+9+A+fW/tI3gA8J4BXvBawJLdNTOP1qxEZGdnB7lcDj6f32gT+85QWd1eqdTAV78Biuq5A570BiY8+3jH0pQ+RP6KQyj9rbolNI9gP+QynEYeBY9XfQ0IAoCnNgIuQ1sePNMpNCsRHT58GABw/PhxgwbDtL6a3vT387hlZztgTgRg9hi/ktQ5MuTM2QnVbe5gZnYKeMw9BkunOt0zXEcB/XYBXVj7Mqb5mpWIauqEDhw40OD00tHR0QgPD2/dyJhWcexPrh8ZwFVO/3MsYGut3zGICIqfrqHw4zjt6IeCp+/Cdeqv4KOkdsOAt4EnVwN8kxoKnWkHmrxisrKykJCQAAD49ttvERoairpNj2QyGX766SfDRcg8thv3gP2na5dnvgiI9ayuqXxQhPwPDqLibE07IILjhCtwGHAYvJreaZYeQJ/tgOsLrRE20wk1mYicnZ2xYcMG5OfnQ6lU4oMPPtApt7KywvLlyw0WIPN48kqATUdqJ3oYHaZfb3rSaFCy5RyK1x/XDkzGM6+Ex4JTsHatk93cxwNPf8v192KYx9RkIrK0tMTFi9wgNSNGjMDRo0cNHhTTMuoq4H+HgPLqZjy9/YExzzR/f02pErmL96P8RCq3gqeBbf98uExOBF9zu3bDHv8FAv/DGiUyLabXj/mGklDNIPpPP23ic8x0Ij+eqW057S4CXh8B8JuZK9T5CuT84weoJNmw9M6GbZ9k2PVPBb9LMVD96B9mAqDPDsBzgkHiZzofvRLR4cOHMXfuXGRlZenUE3Xp0gUPHzbQkZFpc5dvcxXUAPdkbPYowLqZM8WUn0tH/vs/wNb/JNyWXIWFS2H9jez7AH22AXY9Wy1mhtErEb3zzjuYOHEiRCIRkpKS8Oqrr2LVqlWYNWuWoeJj9FCk4PqR1YgMB3xdm95Po6xEyYY94Eu/gHfMNfCtVLob8C25EQE9JwNekeypGNPq9Op9b2NjA7lcjrt37yImJgYnTpxASkoKIiMjcf369aYP0M6Zcu97jQb47Efgr+ox50MDgZhRTVffPLx4HpXH3oZtj3PgmWt0C52HAOLXAM+JrF1QJ2Zyve9dXFzA5/Ph6+uLmze5npNBQUHIzKw/4wLTtg5erE1CIttmDHCmKobyyGJYVO6AVUjtdD1EloDva+B1WwLYtqAPCMPoQa/2tb169dI+vndzc8Phw4dx4sQJWFvr2UKOaVV/ZQIHL3DveTyuXkjQWI96jRqU9jk0B31hia3gd+GSkKbSElXOC8CLyALv6U0sCTFtSq87ojVr1mDSpEmIiYnBypUrMW7cOGg0Gqxdu9ZQ8TFNUJQD39ZpLzT2GW6gswYVnAQlzQWv9KZ2dFVSm6GicAysp30Dvo1bm8TMMH+n1x3Rk08+CYlEAl9fX0yYMAEZGRm4efMmFi1apNeHJiUl4YUXXoCjoyPc3d0RHR2NggJufuMLFy4gLCwMtra28PPzw+bNm3X23b59OwICAiAQCNCvXz+cO3dOW1ZVVYUlS5bAzc0NQqEQ48aNQ05OjrY8Ly8P48ePh4ODA5ydnbFgwQKo1Q3PItoeaIibfaOkjFvu4Q1ENDTFXFUFkDQPODMEvNKb2tWKK70gL9sL65gfwWNJiDGiZiWiU6dONfhKTU2FVCrFqVOnmj5ItYqKCowaNQoDBw6EVCqFRCJBYWEhZs6cieLiYkRERCA6OholJSXYvHkzFi5cqG1QeeLECcyfPx/bt29HSUkJoqKiMHbsWJSXc93KP/zwQ8TFxeHy5cvIysqCtbU1Zs+erf3syMhI2NraIjs7GxcvXkR8fDxiY2P1OV8m5cBp4Po97r3QGpg1Eqg36a7iJnAqDLj3tXbVwwwvZG36B/iD98A++uVHT1rIMG2BmoHH4z3yxefzm3MYIiK6desWjRw5ktRqtXbdL7/8QnZ2drRp0ybq1q2bzvZvvPEGRUdHExFRVFQUxcTE6JT36NGDtmzZQkREYrGYdu7cqS2TSqXE4/EoLS2NUlNTCQBlZWVpy3fv3k0+Pj7Njl0mkxEAkslkzd7HUE4mE82O5V4x64iu3/3bBhoN0b3NRL/ZEP0Mop9BVXvNKW/6KLo7aA09vJFV/6AM04C2uO6bVUek0Wia3qiZunfvjiNHjuis279/P/r27QuJRIKePXUbygUFBWl/nkkkErz++uv1ypOSkiCTyZCZmamzv5ubG0QiEZKTk8Hj8eDo6AhPT0+dfTMyMlBSUgIHB4d6sSqVSiiVtcOdyuWmMdHfg3xg14na5WnPAyFd62xQKQeS3gCydmlXqaQukO6cBJ5jL4j3TYW5O3scz5gOvSqrMzIyGi3z8fHR+8OJCO+//z5+++03nDp1Cp9//jkEAoHONjY2NigtLQUAKBSKRssVCgUAPHL/hsoAbkKAhhLR6tWrsXLlSr2/lyE9VAH/O8z1JwOAoU8Bg+vm7oc5wNkXAUXtoPWy831R+NsIWA8KhtunE8EXNLOpNcO0Eb0SUdeuXcHj8bTdO+rWLeg7QqNcLsfMmTNx5coVnDp1Cj179oRAIEBJSYnOduXl5doprQUCgbY+qG65s7OzNsk0VC4UCqHRaBosA9DolNlLly7VqYiXy+Xw9vbW63u2tv1/ALnF3HsfV2Dyc3UK5RLgwmig/B4AoKrCEvn7x6DsejDsXx8Ap7dfAO9xRkRjGAPTKxHdvXtXZzk/Px9r1qzBuHHj9PrQtLQ0REREwMfHB5cvX9bOBBISEoK4uDidbVNSUhASEqItl0gk9cojIiIgEong5eUFiUSi3V4qlaKoqAghISHQaDQoLCxEbm4u3NzctPuKxeJGZ6u1tLSEpaXp3D1cvwucrG7AbmEOzBlVZ0LEgpPAhbHaeeIri+yR8+1rUFd4wG39eNiOCDJO0AzTHC2tZCopKSF/f/9mb19UVEQ+Pj40Y8YMqqqq0ikrKCggBwcHio2NJZVKRYmJiSQUCikxMZGIiOLj47XLKpWKYmNjSSQSUWFhIRERvffeexQSEkLp6ekkl8spMjKSwsPDtccfNGgQTZkyheRyOaWnp1NwcDAtX7682bEbs7JaUU60+H+1FdTHk+oUZv9C9KultlL64QYPuttvEd17PpaUt3PbPFamY2mL675VEpFIJGr29p9++ikBIBsbGxIIBDovIqJLly7RwIEDSSgUkr+/P23dulVn/x07dlD37t1JIBBQ//796fz589oylUpF7777Lnl5eZGdnR2NGzeOcnNr/yFKpVKaNGkSOTk5kYuLCy1evFjn6V1TjJWINBqirw/WJqF1P3HriIgo4zvS/GKmTUKlq7tRWvBSyozaQurC0jaNk+mY2uK616vT66pVq3SWVSoVfv/9d7i5ueHQoUOteaNmkozV6fVkMvB9IvdeYAWseA1wEAC49z/u6Vg1xdWeyNszDnavhMH5PyPBa605pJlOzeQ6vf59Fg8zMzMMGDAAy5Yta9WgmFoZecDuk7XL0cOrk9Ddb4Dkudr1sjOhKPh1FFxWjoFdZL+2D5RhWqBFiYgxrEo18O3vuo/q+wSgXhIqPjEQRYeHw2UVS0JM+6RXIqqsrMSePXtw7969eo0c/z6oPtNyP54Bcoq49z4u1Y/qM74DJc9DTcOJ4uPPoujIMDh/8BJLQky7pVcimjp1Ko4fP46QkBDw63Rq4vF4LBG1sqt3gPir3HtzM2DmCKBL7j7Q1ZnaaXyKjw9E0dHhcFk1miUhpl3TKxHFxcUhOTkZXbt2NVA4DADky4Btx2qXXxkMiNW/ga68Cl71CPayM6EoThwJ9y9fgeD57kaKlGFah16JyMPDA46OjoaKhQFXH7TxcO1UQH27AUM8EkDnJ4FH3JAl8otPo/jMRHjunAarno0NPsQw7YdeieiTTz7BK6+8gnnz5tXrmzV48ODWjKvTOnAauJfLvXexB6Y/8xdw7mXwiBvQXnG1J4rPvgrP72fAwk/PaVsZxkTplYjOnz+PuLi4et0weDye3n3NmPqupenWC80bdg9W58PBq+K6bZSlBKLo7Ax4fv86ung5GC9QhmllevWA/Oqrr3D48GGo1WpoNBrtiyWhliuQcaMt1nj12WJ43ngBPDV3e6TMdkPRxTnw+j6GJSGmw9HrjsjKygovvPCCzhMzpuX+Xi8UFlCGgdIR4FfeAQCo8pxQcGoJPDe/ATNHwSOOxDDtk14ZZfHixfj3v/+N4uJiQ8XTKR04DdytrhfysK/AdLMXYVZ5CQBQVWqDosvvwH3jP1kSYjosvfqa+fn54f79+w2OcdwZfp4Zos9N8l3gi1+491ZmSnz8RARsKrmOZVUVlii6+j6c//su6zfGGI3J9TXbtm2bQYLorErK6tYLEd7zi9YmIY3SAkWX3oHzapaEmI5Prys8PDzcUHF0OhoCthwFSiu45X94L4Nb1V6uTGWOwj8WwOnj91gSYjoFva5yPz+/RqeeSU9Pb5WAOou4K8DN6iHAX3b6CP3M/09bVhD/GpxWfwC+jYWRomOYtqVXIlqxYoXOcn5+PrZs2aIzdxjTtPQc4Oez3PsXbD/DKLv3tGUFR8fA4d01rGKa6VT0qqxuSFpaGqZOnaqdBLEja41Ku4cqYNVOrj/Zi8K1mOz8jrasMG4EbGb+D9b9fFsrZIZpsbaorG5xg6CuXbvi9u3brRFLp7DnJJeEXrL7r04SKkoYCutpX7EkxHRKev00+/vU0iqVCvv370dgYGCrBtVRXUsDTkuAYcJYjHeqHTal8NjzsJr8NWwG+BsxOoYxHr0S0ZAhQ3SW+Xw+nnzySXz99dcN78BolSuB7xOAQbbfYopz7VxpBYdfhPXkzyEIZ8mc6bz0SkStOfV0Z/PTGaA7bxeinedo1xUdGwKrSesgGN7DiJExjPE1u46IiJCWlqazbs+ePZ2iRXVL3ckGiu/8ilnOr4HH454NlJx6BmaD1sD2hSeNHB3DGF+zElFZWRkGDRqEJUuWaNfl5eVhxowZGDJkCMrKygwWYHtXqQaOJ57GGy6TwedzSVt2vg803h/CfkqokaNjGNPQrET04YcfwsLCAt988412naurK+7fv4/KykqsXr3aYAG2d8fPZyLKeizM+bUDmyl5/4HoreeNHBnDmI5mJaL9+/dj06ZNcHV11Vnv6uqKb775Bnv37jVIcO1ddgHB994M2JhzoxWU//UESnMWw2XluEZbqDNMZ9SsRJSXl4eAgIAGy3r37g2pVNqqQXUEREDKsa/QXZgAAFDLhChOehNusVPB62Jm5OgYxrQ0KxHZ2dmhsLCwwbKioiLY2Ni0alAdwbUrqRhivli7XHjqVbh/EcP6jzFMA5qViIYNG4Yvv/yywbKvvvoKAwYMaNWg2ruKh1XwvTQe5hbckIuya/3huHw16z/GMI1oVjuiZcuWoU+fPsjPz8eUKVPg7u6OnJwc7NmzB1u2bKnX4tqU5eXlYc6cOThx4gTMzc0xbdo0fPLJJzA3b73hNh5sW4pAzxQAQGWJA6xe3oEu3qJWOz7DdDTNuiMKDAxEXFwcTpw4geeeew6BgYEYPHgwTp06hSNHjqBv376GjrPVREZGwtbWFtnZ2bh48SLi4+MRGxvbaseXXr+GAKd12mWZ4zpY9mStphnmUfTufZ+eno78/Hx4eHjAx8fHUHEZxJ07d9CtWzdkZWXB09MTANco85133sH9+/eb3L+pXsikIRR/3QeO4msAgNz0UXBbeLhVvwPDtDWTGyoWAPz9/eHv3z47Z0okEjg6OmqTEAAEBQUhIyMDJSUl9SaN1Nf9PV+ia3USqpTZQzh1W4uOxzCdRacah1ShUEAg0K0wrnniV1paWi8RKZVKKJVK7bJcLm/02JUVD+H+cBVQ/QAxm/cBfN1dG92eYZhanWqCMoFAgPLycp11NctCobDe9qtXr4a9vb325e3t3eixVXI5SuS9QRoe5FmB8H51QavGzjAdWadKRCEhISgsLERubq52XUpKCsRiMezt7ettv3TpUshkMu3rwYMHjR5b4OYK97fikOtxEuVP/Q98s051ahmmRTrVT7Nu3bph0KBBWLBgATZu3IiCggL897//xaxZsxrc3tLSEpaWlnp9hvszz7VGqAzTqXS6/23v378farUafn5+CAsLw8iRI/H+++8bOyyG6dQ61R0RALi5uWHfvn3GDoNhmDo63R0RwzCmp9PdEbVETdvPRz3GZ5iOpuZ6b+HMY4/EEpEeFAoFADzyMT7DdFQKhaLBp8utocUTLHYmGo0G2dnZEAqFjQ5sJpfL4e3tjQcPHhisOXxnxs6v4f39HBMRFAoFPD09wecbpjaH3RHpgc/nQywWN2tbOzs79g/FgNj5Nby659hQd0I1WGU1wzBGxxIRwzBGxxJRK7O0tMTy5cv1bpHNNA87v4ZnjHPMKqsZhjE6dkfEMIzRsUTEMIzRsUTUSvLy8jB+/Hg4ODjA2dkZCxYsgFqtNnZYJm3Pnj0wNzeHra2t9vXaa68BAC5cuICwsDDY2trCz88Pmzdv1tl3+/btCAgIgEAgQL9+/XDu3DltWVVVFZYsWQI3NzcIhUKMGzcOOTk5bfrdjC0/Px8BAQE4ceKEdp0hz2mLr39iWsWQIUMoKiqKysrKKC0tjYKDg2nNmjXGDsukLV68mGbMmFFvfVFRETk6OtKGDRuosrKSEhISSCgU0oULF4iI6Pjx4yQUCun06dOkUqnos88+I2dnZyorKyMiohUrVlCvXr0oIyODZDIZRUZGUkRERJt+N2M6ffo0PfHEEwSAjh8/TkSGP6ctvf5ZImoFqampBICysrK063bv3k0+Pj5GjMr0DR48mDZs2FBv/aZNm6hbt24669544w2Kjo4mIqKoqCiKiYnRKe/Rowdt2bKFiIjEYjHt3LlTWyaVSonH41FaWlprfwWTs23bNvLx8aHdu3frJCJDntPWuP7ZT7NW0NSg/Ex9Go0Gf/75Jw4dOgRfX1+IxWLMmTMHxcXFkEgk6Nmzp872QUFBSEpKAoBHlstkMmRmZuqUu7m5QSQSITk52fBfzMhGjBiBtLQ0REZG6qw35DltjeufJaJW0NSg/Ex9+fn5ePrppzFp0iTcvHkTZ8+eRWpqKqZNm9bo+aw5l48qr+mY/Kj9OzJ3d/cGJws15Dltjeuf9TVrBfoOys9w/0etO0Owj48P1qxZg7CwMMycObPB81lzLhs7387Oztp/EI/avzMSCAT17k5a65xqNJoWX//sjqgV6DsoPwMkJyfj3//+t84YN0qlEnw+H/3794dEItHZPiUlBSEhIQC4891YuUgkgpeXl065VCpFUVGRdv/O6FHnrKnyps5pq1z/+lSEMY0bNGgQTZkyheRyOaWnp1NwcDAtX77c2GGZrAcPHpBAIKCPP/6YKisr6f79+/TMM8/QrFmzqKCggBwcHCg2NpZUKhUlJiaSUCikxMREIiKKj4/XLqtUKoqNjSWRSESFhYVERPTee+9RSEgIpaenk1wup8jISAoPDzfitzUO1KmsNvQ5ben1zxJRK5FKpTRp0iRycnIiFxcXWrx4ManVamOHZdJOnDhBAwYMIKFQSC4uLjR//nyqqKggIqJLly7RwIEDSSgUkr+/P23dulVn3x07dlD37t1JIBBQ//796fz589oylUpF7777Lnl5eZGdnR2NGzeOcnNz2/KrmYS6iYjIsOe0pdc/62vGMIzRsToihmGMjiUihmGMjiUihmGMjiUihmGMjiUihmGMjiUihmGMjiUihmGMjiUipkNLTU01dghMM7BExDRbRkYG5s6dCz8/PwgEAjg6OmLkyJE4duyYsUNr0Jdffok5c+Zol4ODg7Fz504jRsQ0hiUipllu3LiBXr16QalU4siRI5DL5bhz5w6mTZuG8ePH48iRI8YOsZ78/HydTrUSiQRRUVFGjIhp1OP1YmE6m4EDB9LkyZMbLNu2bRv99NNPRER07NgxCg0NJXt7ewoKCqLvv/9eu9306dPpH//4B40ePZpsbW3Jz8+PPv/8c225XC6nf/7znyQWi8nFxYUiIyNJKpUSEdHdu3cJAC1atIgcHBxo3rx5pFQq6e2336YePXqQra0tubi40JtvvkkajYa2bdtGXbp0IT6fT/b29kRE5Ovrq+1fVV5eTkuWLCGxWEwODg4UHh5OFy9e1MYCgNavX0+BgYFka2tLAwcOpOTk5FY8o0xdLBExTXrw4AEBoISEhEdud+3aNbK2tqYDBw6QWq2mM2fOkLOzM/3+++9ExCUiCwsLiouLo8rKSvrmm2/IzMyMMjMziYho0qRJ9OKLL1Jubi4pFAqKiYmhAQMGkEaj0SaimJgYUiqVVFxcTB9//DEFBwdTdnY2ERGdPXuWzM3NKT4+noiIli9frtNDvG4imj59OvXq1YtSU1NJqVTSunXrSCgU0v3794mIS0QDBgygnJwcKikpoeHDh9OIESNa87QydbBExDTp3LlzBID++usv7br4+Hiyt7cne3t7srW1pcDAQJo7dy5NmTJFZ9+lS5fS6NGjiYj7x1/3H7NKpSIAdPLkScrNzSUAdOvWLW15WVkZmZub0+XLl7WJ6PTp09ryoqIikkqlpNFoKDs7mxISEkgkEtH27duJqPFEVFFRQRYWFnTo0CGdWENDQ2n16tVExCWiXbt2acs2btxIfn5+j3sKmSawERqZJnl4eAAAsrKyEBgYCAAYNmyYdsS/bdu2YcWKFbh37x4SExPh4OCg3beqqgpPPPGEdtnd3V37vkuXLgC48avv3bsHAAgLC9P5bHNzc9y9exdOTk4AoDMucllZGd58802cPHkSYrEYffr0ARFBo9E88vsUFxdDpVLB399fZ72fn582joZibeq4zONjiYhpkq+vL0JDQ/Htt99i6NChjW4nFosxY8YMfPPNN9p1OTk5OhXGj9oXAG7duqWTAFJSUuDv7w+pVAoA4PF42rKYmBg4OjoiJycHVlZW0Gg0EIlETX6Wm5sbrKyskJaWhh49emjXp6WlYcyYMU3uz7Q+9tSMaZYtW7bgyJEjmDNnDm7fvg0igkKhwI4dO7B8+XJ4enpi1qxZ+OGHHxAXFweNRoPU1FQMHjwYn3zySZPH9/T0xEsvvYS33noLhYWFqKysxEcffYTQ0NBGZ4KQyWSwsrKCmZkZFAoFlixZArlcDpVKBQCwsrKCXC6vlwj5fD5ef/11LFu2DHfu3IFKpcLnn38OiUSCqVOntvhcMfpjiYhplpoxjW1sbDB69GjY29tDLBZj48aNePfdd3H8+HGEhYVh165dWLZsGUQiEcLDwzF27Fj83//9X7M+Y8eOHXBwcEDv3r3h7OyMQ4cO4ejRozp3SHV98cUXuHbtGkQiEbp37w65XI6RI0fi+vXrAIAxY8agsLAQ9vb29ZLZ2rVrMWLECAwbNgxOTk7Ys2cPjh49qv3pybQtNkIjwzBGx+6IGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxOpaIGIYxuv8PfpE2t+KuvzUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot('CirculinD', 'CathelicidinBF', order=['0', '1', '2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MXFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newmols_plot_mxfp(query, c1='#648FFF', c2='#DC267F', c3='#FFB000'): \n",
    "    csv_files = [filename for filename in os.listdir('results/') if f'{query}_MXFP' in filename and filename.endswith('.csv')]\n",
    "    csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[3].split('.')[0]))\n",
    "\n",
    "    # Read data\n",
    "    df = pd.DataFrame()\n",
    "    for i, csv_file in enumerate(csv_files):\n",
    "        df_run = pd.read_csv(f'results/{csv_file}')\n",
    "        df_run['run'] = i\n",
    "        df = pd.concat([df, df_run])\n",
    "\n",
    "    df['generation'] = df['generation'] + 1\n",
    "    df['generation'].fillna(0, inplace=True)\n",
    "\n",
    "    df_run0 = df[df['run'] == 0]\n",
    "    df_run1 = df[df['run'] == 1]\n",
    "    df_run2 = df[df['run'] == 2]\n",
    "\n",
    "    # Group by generation and count molecules\n",
    "    run0_counts = df_run0.groupby('generation').size()\n",
    "    run1_counts = df_run1.groupby('generation').size()\n",
    "    run2_counts = df_run2.groupby('generation').size()\n",
    "\n",
    "    # Calculate cumulative counts\n",
    "    run0_cumulative_counts = run0_counts.cumsum()\n",
    "    run1_cumulative_counts = run1_counts.cumsum()\n",
    "    run2_cumulative_counts = run2_counts.cumsum()\n",
    "\n",
    "    # Plot data\n",
    "    fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "\n",
    "    ax.plot(run0_cumulative_counts.index, run0_cumulative_counts.values, label='Run 1', color=c1, linewidth=2)\n",
    "    ax.plot(run1_cumulative_counts.index, run1_cumulative_counts.values, label='Run 2', color=c2, linewidth=2)\n",
    "    ax.plot(run2_cumulative_counts.index, run2_cumulative_counts.values, label='Run 3', color=c3, linewidth=2)\n",
    "\n",
    "    ax.set_xlabel('Generation')\n",
    "    ax.set_ylabel('Cumulative Molecules')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'plots/{query}_newmols_cumulative.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cyclosporin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot_mxfp('Cyclosporin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valinomycin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newmols_plot_mxfp('Valinomycin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duplicates in parallel runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polymyxin Random + Self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/markus/miniconda3/envs/pdga2/lib/python3.7/site-packages/ipykernel_launcher.py:36: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "/home/markus/miniconda3/envs/pdga2/lib/python3.7/site-packages/ipykernel_launcher.py:37: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_run0 = pd.read_csv('results/20240131_PolymyxinB2_MAP4_1.csv')\n",
    "df_run1 = pd.read_csv('results/20240131_PolymyxinB2_MAP4_2.csv')\n",
    "df_run2 = pd.read_csv('results/20240131_PolymyxinB2_MAP4_0.csv')\n",
    "df_self = pd.read_csv('results/20240523_PolymyxinB2_PolymyxinB2_MAP4_0.csv')\n",
    "\n",
    "df_run0 = df_run0.dropna()\n",
    "df_run1 = df_run1.dropna()\n",
    "df_run2 = df_run2.dropna()\n",
    "df_self = df_self.dropna()\n",
    "\n",
    "df_run0 = df_run0['smiles']\n",
    "df_run1 = df_run1['smiles']\n",
    "df_run2 = df_run2['smiles']\n",
    "df_self = df_self['smiles']\n",
    "\n",
    "smiles_sets = [set(df_run0), set(df_run1), set(df_run2), set(df_self)]\n",
    "overlap_matrix = [[0] * len(smiles_sets) for _ in range(len(smiles_sets))]\n",
    "\n",
    "for i in range(len(smiles_sets)):\n",
    "    for j in range(len(smiles_sets)):\n",
    "        overlap_matrix[i][j] = len(smiles_sets[i] & smiles_sets[j])\n",
    "\n",
    "overlap_df = pd.DataFrame(overlap_matrix, index=['Run 1', 'Run 2', 'Run 3', 'Self'], columns=['Run 1', 'Run 2', 'Run 3', 'Self'])\n",
    "\n",
    "# Heatmap\n",
    "fig, ax = plt.subplots(figsize=(5, 2.25))\n",
    "\n",
    "colors = ['#648FFF', '#785EF0', '#DC267F', '#FE6100', '#FFB000']\n",
    "cmap = LinearSegmentedColormap.from_list('ibm_cb', colors)\n",
    "\n",
    "ax.matshow(overlap_df, cmap=cmap)\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        ax.text(j, i, overlap_matrix[i][j], ha='center', va='center', color='white')\n",
    "\n",
    "ax.set_xticklabels([''] + ['Run 1', 'Run 2', 'Run 3', 'Self'])\n",
    "ax.set_yticklabels([''] + ['Run 1', 'Run 2', 'Run 3', 'Self'])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'plots/PolymyxinB2_PolymyxinB2_randself_heatmap.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jaccard and Levenshtein analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def levenshtein_distance(seq1, seq2):\n",
    "    blocks1 = seq1.split('-')\n",
    "    blocks2 = seq2.split('-')\n",
    "    len1 = len(blocks1)\n",
    "    len2 = len(blocks2)\n",
    "\n",
    "    dp = [[0] * (len2 + 1) for _ in range(len1 + 1)]\n",
    "    \n",
    "    for i in range(len1 + 1):\n",
    "        dp[i][0] = i\n",
    "    for j in range(len2 + 1):\n",
    "        dp[0][j] = j\n",
    "    \n",
    "    for i in range(1, len1 + 1):\n",
    "        for j in range(1, len2 + 1):\n",
    "            if blocks1[i - 1] == blocks2[j - 1]:\n",
    "                dp[i][j] = dp[i - 1][j - 1]\n",
    "            else:\n",
    "                dp[i][j] = min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) + 1\n",
    "    \n",
    "    if \"c\" in seq1 and \"c\" in seq2:\n",
    "        return dp[len1][len2]\n",
    "    elif \"c\" in seq1 and \"c\" not in seq2:\n",
    "        return dp[len1][len2] - 1\n",
    "    elif \"c\" not in seq1 and \"c\" in seq2:\n",
    "        return dp[len1][len2] - 1\n",
    "    else:\n",
    "        return dp[len1][len2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_run0 = pd.read_csv('results/20240131_PolymyxinB2_MAP4_1.csv')\n",
    "df_run1 = pd.read_csv('results/20240131_PolymyxinB2_MAP4_2.csv')\n",
    "df_run2 = pd.read_csv('results/20240131_PolymyxinB2_MAP4_0.csv')\n",
    "df_self = pd.read_csv('results/20240523_PolymyxinB2_PolymyxinB2_MAP4_0.csv')\n",
    "\n",
    "df_run0 = df_run0.dropna()\n",
    "df_run1 = df_run1.dropna()\n",
    "df_run2 = df_run2.dropna()\n",
    "df_self = df_self.dropna()\n",
    "\n",
    "df_run0 = df_run0[df_run0['dist'] < 0.5]\n",
    "df_run1 = df_run1[df_run1['dist'] < 0.5]\n",
    "df_run2 = df_run2[df_run2['dist'] < 0.5]\n",
    "df_self = df_self[df_self['dist'] < 0.5]\n",
    "\n",
    "polymyxin_sequence = 'T011-BB045-BB012-BB045-b000-BB045-BB030-BB003-BB045-BB045-BB012'\n",
    "\n",
    "df_run0['levenshtein'] = df_run0['sequence'].apply(lambda x: levenshtein_distance(x, polymyxin_sequence))\n",
    "df_run1['levenshtein'] = df_run1['sequence'].apply(lambda x: levenshtein_distance(x, polymyxin_sequence))\n",
    "df_run2['levenshtein'] = df_run2['sequence'].apply(lambda x: levenshtein_distance(x, polymyxin_sequence))\n",
    "df_self['levenshtein'] = df_self['sequence'].apply(lambda x: levenshtein_distance(x, polymyxin_sequence))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Levenshtein plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "\n",
    "ax.bar(0, df_run0['levenshtein'].mean(), yerr=df_run0['levenshtein'].std(), color='#648FFF', label='Run 1', capsize=3)\n",
    "ax.bar(1, df_run1['levenshtein'].mean(), yerr=df_run1['levenshtein'].std(), color='#FFB000', label='Run 2', capsize=3)\n",
    "ax.bar(2, df_run2['levenshtein'].mean(), yerr=df_run2['levenshtein'].std(), color='#DC267F', label='Run 3', capsize=3)\n",
    "ax.bar(3, df_self['levenshtein'].mean(), yerr=df_self['levenshtein'].std(), color='#DADADA', label='Self', capsize=3)\n",
    "\n",
    "ax.set_xticks(range(4))\n",
    "ax.set_xticklabels(['Run 1', 'Run 2', 'Run 3', 'Self'])\n",
    "ax.set_ylabel('$d_L$')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'plots/PolymyxinB2_randself_levenshtein.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jaccard plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASIAAADXCAYAAAC+llyoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZgElEQVR4nO3df1RUdd4H8DcIz8j8YCbkd6CCUkKIeMwH86DWcXf9USqu2vizdcV0Jas5urWxLSGQ8shJcTc0szIt6SnBtcdcO27sNg+HTisuVmuDPUqRkMOPFR1mEB0duc8fHiYnGEWY4XvV9+uce87c7/3e73y+4ry99w736iNJkgQiIoF8RRdARMQgIiLhGEREJByDiIiEYxARkXAMIiISjkFERMIxiIhIOD/RBYjU0dEBs9kMjUYDHx8f0eUQ3VEkSYLNZkNkZCR8fW98zHNXB5HZbEZ0dLToMojuaPX19YiKirphH9kFUXNzM1asWAGj0Qg/Pz8sXrwYr7zyCvz8upY6bdo0fPrppy7bSktLMXXq1B69l0ajAXDtDyowMNAzEyAiAIDVakV0dLTzc3YjsgsivV6Pe++9F2azGY2NjZg5cyYKCwvx3HPPden7z3/+E4cPH8akSZN69V6dp2OBgYEMIiIv6cllDx853fRaU1ODuLg4nDlzBpGRkQCADz74AM8//zxOnz7t0re2thbDhw+HxWLpUeJ2x2q1QqvVorW1lUFE5GG38vmS1bdmJpMJQUFBzhACgISEBNTV1cFisbj0PXr0KDQaDfR6PUJCQpCYmIidO3fecHy73Q6r1eqyEJF4sgoim80GlUrl0qZUKgEAbW1tLu12ux0PPfQQ1q9fD7PZjM2bN+PZZ59FSUmJ2/Hz8/Oh1WqdCy9UE8mDrE7N9u/fjyeffBJnz551th0/fhxJSUmwWCzQarU33P+pp55CU1MTSktLu91ut9tht9ud650X03hqRuR5t3JqJquL1YmJiWhpaUFTUxPCwsIAANXV1YiKiuoSQjt37oRGo8G8efOcbXa7HQEBAW7HVygUUCgU3imebmsNDQ1oaGhwuz0iIgIRERH9WNFdRpKZ1NRUaf78+ZLVapW+++476YEHHpCys7O79Nu8ebMUGhoqHTt2TLp69ap08OBBKSAgQCovL+/xe7W2tkoApNbWVg/OgG5H2dnZEgC3S3d/B+nGbuXzJbsgamxslObOnSsNGjRICgkJkdauXSs5HA5JkiRJpVJJe/bskSRJkjo6OqS8vDxpyJAhUkBAgPTAAw9IJSUlt/ReDCLqZDabpaqqKqmiosIZPhUVFVJVVZVUVVUlmc1m0SXedm7l8yWra0T9jV/f009duHABarUawLUvSH765Qn13G379T0R3Z0YREQkHIOIiIRjEBGRcLL6PSKi3vj2/nUeG6u947Lz9XfJ66H0/Q+PjDvs/9Z5ZJw7FY+IiEg4BhERCccgIiLhGEREJByDiIiE47dmRACaHTY0O9pwqeOKs636UiMG+voDAEL91Aj1692TQOnmGETUK3faYzP+21KFV8/9r0vb/B/edr5+OmgSng1+uJ+runswiPrJnfbBff3115GTk+N2e3Z2NtatW9d/BfXRAt0YTFbf73Z7qJ+6H6u5+zCI+smd9sFduXIlZs6ciYsXLyI1NRUAUFFR4Xww3e0UqgAQ6qfhqZdADKJ+cqd9cDuP4C5cuOBsS05O5mMzqFcYRP2EH1wi9/j1PREJxyAiIuEYREQkHIOIiIRjEBGRcAwiIhKOQUREwskuiJqbm5GWlgadTofg4GAYDAY4HI4b7vP1119DqVTCaDT2T5FE5FGyCyK9Xg+1Wg2z2YzKykqUlZWhsLDQbf/29nYsWLAAFy9e7McqiciTZBVENTU1MBqNKCgogFKpRGxsLLKyslBUVOR2n4yMDMyePbsfqyQiT5NVEJlMJgQFBSEyMtLZlpCQgLq6Olgsli7933nnHdTU1CA7O7sfqyQiT5PVvWY2m63LvVdKpRLAtf+HXKfTOdu/+eYbvPjii/jss88wYMCAHo1vt9tht9ud61arte9FE8nQ7fbYGVkFkUqlQnt7u0tb57pG8+MjGi5dugS9Xo8tW7Zg8ODBPR4/Pz//ho/iILpT3G6PnZHVqVliYiJaWlrQ1NTkbKuurkZUVBS0Wq2z7ejRozh58iTS09Oh0+mcR0qPPfYYMjIy3I6fmZmJ1tZW51JfX++1uRCJtHLlSlRVVaGiosLZVlFRgaqqKlRVVWHlypUCq+tKVkdEcXFxSE1NhcFgwI4dO3D27Fnk5eUhPT3dpd+ECRO6fEvm4+ODgwcP4uGHH3Y7vkKhgEKh8EbpRLJyuz12RlZHRABQWloKh8OBmJgYpKSkYOrUqcjKygIAqNVqFBcXC66QiDxNVkdEABAWFoaSkpJut7W1tbndT5Ikb5VERF4muyMiIrr7MIiISDgGEREJxyAiIuEYREQkHIOIiIRjEBGRcAwiIhKOQUREwjGIiEg4BhERCccgIiLhGEREJJzs7r6Xqye3eGacKz8+qRZPFQH+Hnw80huGHnb8Hx/Pveml614fVAMDPTc0ZvGJCncLHhERkXAMIiISjkFERMIxiIhIOAYREQnHICIi4RhERCQcg4iIhGMQEZFwDCIiEk52QdTc3Iy0tDTodDoEBwfDYDDA4XB06dfR0YF169YhOjoaarUaI0eOxN69ewVUTER9Jbsg0uv1UKvVMJvNqKysRFlZGQoLC7v027p1K9555x0YjUa0tbUhPz8fCxYswLfffiugaiLqC1kFUU1NDYxGIwoKCqBUKhEbG4usrCwUFRV16fvUU0/h+PHjGDZsGOx2O/79739DpVJBqVQKqJyI+kJWd9+bTCYEBQUhMjLS2ZaQkIC6ujpYLBbodDpnu6+vL1QqFf76179i2rRpkCQJhYWFiIiIcDu+3W6H3f7j7e9Wq9Ur8yCiWyOrIyKbzQaVSuXS1nmE09bW1u0+kyZNgt1uxyeffII//OEP+OCDD9yOn5+fD61W61yio6M9VzwR9ZqsgkilUqG9vd2lrXNdo9F0u49CoYCfnx8mT56MJUuW4L333nM7fmZmJlpbW51LfX2954onol6T1alZYmIiWlpa0NTUhLCwMABAdXU1oqKioNVqXfquXbsWALBp0yZnm91uR1BQkNvxFQoFFAoPPomMyAtOnTrlsbGu/4e9pqbGY9dQ4+LiPDJOJ1kdEcXFxSE1NRUGgwE2mw21tbXIy8tDenp6l74TJ07E9u3bUV5ejo6ODnz00Ud4//338eSTTwqonIj6QlZBBAClpaVwOByIiYlBSkoKpk6diqysLACAWq1GcXExAGDWrFl49dVXsXz5ctxzzz3Izc3Fn//8Z4wfP15k+UTUC7I6NQOAsLAwlJSUdLvtpxesly1bhmXLlvVHWUTkRbI7IiKiuw+DiIiEYxARkXAMIiISrk9BlJubi/3796OmpsZT9RDRXahP35oNHDgQ+/fvR25uLurr6zFs2DAkJSVh5MiRePDBB5GSkoIBAwZ4qlYiukP1OIjGjx+P5ORkJCcnY9SoUUhKSsLzzz/v3N7R0YGTJ0/i66+/xvHjx7Fx40ZUV1fjtddew89+9jOvFE9Ed4YeB9GsWbPw1Vdf4Y9//CNOnjwJABg+fDhGjRqF5ORkjB49GklJSZg7dy7mzp0LAGhsbMSjjz6Kqqoq71RPRHeEHgfR7373O+frqqoqzJo1C6NHj4a/vz+Ki4vx+9//Hj4+PggJCUFjYyMAIDw8HMuXL/d81UR0R+nVNaIVK1Zg69atmDVrlrPt0KFDWLFiBZYuXerSd9WqVX0qkIjufL361uzEiRNISkpyaZs+fTq2bduGI0eOeKQwIrp79OqIKCUlBdu3b8fGjRtd2keOHIkvvvjCI4XdadpbG9BubYDjykVnW8uZL+HnHwAAUAZGQKl1/3RJuWk4BzScBy5e/rHty1og4D+uvY64B4hw/0QWIhe9CqJt27Zh3LhxaGpqwpo1a5CYmIjLly/jlVde6fKERbrmxOev44vDOS5tB19Ndb4ePSUbY6au6+eqeu/1w0DOTx6GmZr54+tsPbBuQf/WRLevXgVRfHw8jhw5gtWrVyM5ORn+/v7o6OiAn58f3nrrLU/XeEeIf2glhjww0+12ZeDtczQEACunADP/0/32iHv6rxa6/fX6FxpHjBiBsrIy1NXV4csvv4Svry/GjBlzw4fX382U2tvr1OtmIoJ46kWe0+fnEQ0ePBiDBw/2RC1EdJfiTa9EJByDiIiEYxARkXAMIiISjkFERMIxiIhIOAYREQnHICIi4WQXRM3NzUhLS4NOp0NwcDAMBgMcDke3fbdv3477778fGo0G9913H7Zt29bP1RKRJ8guiPR6PdRqNcxmMyorK1FWVobCwsIu/T788ENkZmZi9+7dsFqt2L17N1588UXs27dPQNVE1BeyCqKamhoYjUYUFBRAqVQiNjYWWVlZKCoq6tLXbDbjhRdewLhx4+Dj44OHHnoIjzzyCMrLywVUTkR90ed7zTzJZDIhKCgIkZGRzraEhATU1dXBYrFAp9M52zMyMlz2bW5uRnl5OTZv3ux2fLvdDrvd7ly3Wq2eK56Iek1WR0Q2m63L84yUSiUAoK2tze1+jY2NmDZtGsaMGYOFCxe67Zefnw+tVutcoqOjPVM4EfWJrIJIpVKhvb3dpa1zXaPRdLvPP/7xD4wdOxb3338/Dhw4AD8/9wd5mZmZaG1tdS719fWeK55IRpqbm2EymXDixAln24kTJ2AymWAymdDc3Cywuq5kdWqWmJiIlpYWNDU1ISwsDABQXV2NqKgoaLXaLv137tyJp59+Grm5uVi7du1Nx1coFFAoFB6vm0hu3n///S7XVhcs+PGRmatXr8YzzzzT32W5JasgiouLQ2pqKgwGA3bs2IGzZ88iLy8P6enpXfru27cPq1atwoEDBzBlyhQB1RLJ1/z58zF58mS320NCQvqxmpuTVRABQGlpKVavXo2YmBj4+vriiSeeQFZWFgBArVbj9ddfx6JFi5CTkwOHw4E5c+a47L948WJs375dROlEshEaGorQ0FDRZfSY7IIoLCwMJSUl3W67/oL1v/71r/4qiYi8TFYXq4no7sQgIiLhGEREJByDiIiEYxARkXAMIiISjkFERMIxiIhIOAYREQnHICIi4RhERCQcg4iIhGMQEZFwDCIiEo5BRETCMYiISDgGEREJxyAiIuEYREQkHIOIiIRjEBGRcAwiIhKOQUREwskuiJqbm5GWlgadTofg4GAYDAY4HI4b7rNv3z7Exsb2U4VE5GmyCyK9Xg+1Wg2z2YzKykqUlZWhsLCw275XrlxBQUEB5s+fj46Ojn6ulIg8RVZBVFNTA6PRiIKCAiiVSsTGxiIrKwtFRUXd9v/FL36BTz/9FC+88EI/V0pEniSrIDKZTAgKCkJkZKSzLSEhAXV1dbBYLF36v/vuu/j4448xbNiwfqySiDzNT3QB17PZbFCpVC5tSqUSwLX/916n07lsi4qKuqXx7XY77Ha7c91qtfauUCLyKFkdEalUKrS3t7u0da5rNJo+j5+fnw+tVutcoqOj+zwmEfWdrIIoMTERLS0taGpqcrZVV1cjKioKWq22z+NnZmaitbXVudTX1/d5TCLqO1kFUVxcHFJTU2EwGGCz2VBbW4u8vDykp6d7ZHyFQoHAwECXhYjEk1UQAUBpaSkcDgdiYmKQkpKCqVOnIisrCwCgVqtRXFwsuEIi8jRZXawGgLCwMJSUlHS7ra2trdv2pUuXYunSpV6sioi8SXZHRER092EQEZFwDCIiEo5BRETCMYiISDgGEREJxyAiIuEYREQkHIOIiIRjEBGRcAwiIhKOQUREwjGIiEg4BhERCccgIiLhGEREJByDiIiEYxARkXAMIiISjkFERMIxiIhIOAYREQnHICIi4RhERCSc7IKoubkZaWlp0Ol0CA4OhsFggMPh6LbvoUOHMHLkSKhUKsTHx+PgwYP9XC0ReYLsgkiv10OtVsNsNqOyshJlZWUoLCzs0u/UqVOYM2cO8vLy0NraipycHDz++OM4c+aMgKqJqC9kFUQ1NTUwGo0oKCiAUqlEbGwssrKyUFRU1KXv7t27MWHCBKSlpcHPzw+PP/44Jk2ahB07dgionIj6wk90AdczmUwICgpCZGSksy0hIQF1dXWwWCzQ6XQufUeOHOmyf0JCAr766iu349vtdtjtdud6a2srAMBqtd60tsuXejoLcXowjWvavVqG5/RwQrar9pt3Eqwnf8c6tbW1ebESz+jJfDr7SJJ0076yCiKbzQaVSuXSplQqAVz74VwfRO763uiHmJ+fj5ycnC7t0dHRfahaPt7JFF2Bp2lFF+A52v8SXYEwNpsNWu2Nf5ayCiKVSoX2dtd/rjvXNRpNj/r+tN/1MjMzsWbNGud6R0cHzp07h0GDBsHHx6ev5feY1WpFdHQ06uvrERgY2G/v6y2cj7yJmo8kSbDZbC5nOO7IKogSExPR0tKCpqYmhIWFAQCqq6sRFRXVJVETExNx7Ngxl7bq6mo8+OCDbsdXKBRQKBQubdcfZfW3wMDAO+IveifOR95EzOdmR0KdZHWxOi4uDqmpqTAYDLDZbKitrUVeXh7S09O79F2yZAmMRiP27t0Lh8OBvXv3wmg0YsmSJQIqJ6K+kFUQAUBpaSkcDgdiYmKQkpKCqVOnIisrCwCgVqtRXFwMABgxYgQ+/PBDbNiwAffccw9yc3Oxb98+3HfffSLLJ6JekNWpGQCEhYWhpKSk220/vRA9ZcoUTJkypT/K8iiFQoHs7Owup4m3K85H3m6H+fhIPflujYjIi2R3akZEdx8GEREJxyAiIuEYRL0wdOhQDBw4EGq1Gmq1GiqVCoGBgZg4cSK++OILr7//5s2b8fDDD3tsPBHzkSQJeXl5iImJQWBgIJKSklBaWtrncUXM5dKlS3j66acRHh4OjUaDcePG4e9//7tX3ut658+fR0ZGBqKjo6FSqRAREYFf/epX+OGHH2667/fffw8fHx98//33AIDy8nIMHjwYGo0Gr732mpcr74ZEt2zIkCHS22+/7dLW2NgozZgxQxoyZIh09epVr7xvW1ubtGbNGgmANGnSJI+NK2I+hYWFUkxMjFRdXS11dHRIBw4ckAYOHCgdOXKkT+OKmMuaNWuksWPHSg0NDdLVq1elrVu3SiqVSrLZbB5/r+v9/Oc/l+bNmyc1NDRIknRtnnq9XhoxYoR05cqVG+5bW1srAZBqa2slSZKk5cuXSzNnzvRqvTfCIyIPCQsLw4oVK3D69GmcO3cOAODj4wOj0ejss2vXLgwdOhQAYDQaMXToUKxfvx733nsvgoKCMGfOnBveTDhq1Cg0NDRg1apV3pwKAO/P5/z583jppZcQHx8PHx8fzJgxA/Hx8fjss89uu7kUFBTAaDQiPDwcFy9eREtLC3Q6Hfz9/T0+l+tVVFRg9uzZCA8Pd85zy5YtSElJwfnz52Gz2bB69WpER0cjNDQU8+fPR1NTU5dx5s2bh127duHQoUNQq9UuN4b3FwaRh9TX1+PVV1/F2LFjERwc3KN9Tp8+jTNnzqCmpgaVlZX4/PPPsW3bNrf9jUYj3nvvPYSGhnqqbLe8PZ+cnBwsXbrUuX7ixAmYTCaMGTPGE+W78PZcBgwYAKVSiR07dkCj0eDll1/Gli1bvP57OwsWLMBvfvMbZGRkYO/evTh9+jTCw8Oxa9cuhISEYNmyZTh16hSqqqrw3XffITAwELNnz+5yN3xJSQkWLVqERYsWoa2tTcjvGzGIeikjIwM6nQ4qlQr+/v6YNGkSEhMT8fHHH9/SOC+99BICAgIwfPhwPPLIIzh58qTbvlFRUX0t2y0R8+l08uRJTJ8+HYsXL8bEiRN7OwUnUXN54oknYLfbsXv3bixatMgrR3fXe+ONN7B161bU1dVhxYoVGDp0KIYPH47i4mI0NzejtLQUf/rTnxAaGgq1Wo0tW7bg6NGjXe7RlAMGUS9t27YNFosF586dw4YNG3Du3DlMnz4dgwYNuqVxOg+rAcDf3x8dHR2eLrVHRM3no48+wrhx4/DLX/4Sb775Zq9q/ylRcxk4cCD8/f0xf/58TJ48GXv37u1V/T3l6+uLxYsX4+DBgzh//jxMJhPmzZuHJUuW4O233wYApKSkQKfTQafTITIyEn5+fqitrfVqXb3BIOojhUKB5557DqtWrUJaWprLg9l8fX1x+fJl5/rZs2dFlHhL+nM+eXl5WLhwIYqKirBp0yaPP4qlv+ai1+u7PM7YbrcjKCio12PezOHDh6FWq12ueSUkJCA/Px+jR492Xuf55ptvYLFYnEtVVRUee+wxr9XVWwwiD3n55ZeRlJSEBQsW4OLFiwCA+Ph450283377Ld566y3BVfact+ezefNmbNq0CeXl5Vi4cKGnyu6Wt+cyfvx4bNy4EcePH4fD4cCbb76Jo0ePYvHixZ6aQhcTJ05EWFgYfv3rX+P48eO4cuUKbDYbiouLcerUKcybNw+PPvoonn32WbS0tODKlStYv349xo4dC4vF4rW6eotB5CEDBgzAnj17cObMGfz2t78FcO0U4dixYwgKCoJer+/2cSZy5c35SJKE3NxcXLhwARMmTHD+zo9arcaGDRs8OQ0A3v/ZPPPMMzAYDJgxYwZCQkKwZ88e/O1vf8OwYcM8NYUuAgICUFFRgfDwcMyYMQNarRbR0dHYs2cPPvnkE8THx+Pdd9+FTqdDcnIygoOD8Ze//AWHDx92OeWUC970SkTC8YiIiIRjEBGRcAwiIhKOQUREwjGIiEg4BhERCccgIiLhGEREJByDiIiEYxARkXAMIiISjkFERML9P1UkKbEpHPujAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 300x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "\n",
    "ax.bar(0, df_run0['dist'].mean(), yerr=df_run0['dist'].std(), color='#648FFF', label='Run 1', capsize=3)\n",
    "ax.bar(1, df_run1['dist'].mean(), yerr=df_run1['dist'].std(), color='#FFB000', label='Run 2', capsize=3)\n",
    "ax.bar(2, df_run2['dist'].mean(), yerr=df_run2['dist'].std(), color='#DC267F', label='Run 3', capsize=3)\n",
    "ax.bar(3, df_self['dist'].mean(), yerr=df_self['dist'].std(), color='#DADADA', label='Self', capsize=3)\n",
    "\n",
    "ax.set_xticks(range(4))\n",
    "ax.set_xticklabels(['Run 1', 'Run 2', 'Run 3', 'Self'])\n",
    "ax.set_ylabel('$d_J$')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'plots/PolymyxinB2_randself_jaccard.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample EB9 to Oncocin run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "eb9_to_oncocin = pd.read_csv('results/20240206_EB9_Oncocin_MAP4_0.csv')\n",
    "eb9_to_oncocin = eb9_to_oncocin.sort_values(by=['generation', 'dist'])\n",
    "\n",
    "oncocin_to_eb9 = pd.read_csv('results/20240206_Oncocin_EB9_MAP4_0.csv')\n",
    "oncocin_to_eb9 = oncocin_to_eb9.sort_values(by=['generation', 'dist'])\n",
    "\n",
    "eb9oncocin = pd.read_csv('results/20240503_EB9Oncocin_MAP4_COMBO_0.csv')\n",
    "eb9oncocin = eb9oncocin.sort_values(by=['generation', 'dist'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample for the best sequence at each generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "eb9_to_oncocin_best = eb9_to_oncocin.groupby('generation').head(1)\n",
    "eb9_to_oncocin_best = eb9_to_oncocin_best.head(-4)\n",
    "eb9_to_oncocin_best = eb9_to_oncocin_best[['smiles', 'generation']]\n",
    "\n",
    "oncocin_to_eb9_best = oncocin_to_eb9.groupby('generation').head(1)\n",
    "oncocin_to_eb9_best = oncocin_to_eb9_best.head(-4)\n",
    "oncocin_to_eb9_best = oncocin_to_eb9_best[['smiles', 'generation']]\n",
    "\n",
    "eb9oncocin_best = eb9oncocin.groupby('generation').head(1)\n",
    "eb9oncocin_best = eb9oncocin_best.head(-4)\n",
    "eb9oncocin_best = eb9oncocin_best[['smiles', 'generation']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace best of generation 0 with polymyxin B2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "eb9_smiles = 'N[C@@H](CCCC[NH3+])C(=O)N(CCCC[NH3+])CC(=O)N[C@@H](CC(C)C)C(=O)N(CC(C)C)CC(=O)N[C@@H](CCCC[NH3+])C(=O)N(CC(C)C)CC(=O)N[C@@H](CC(C)C)C(=O)N(CCCC[NH3+])CC(=O)N[C@@H](CC(C)C)C(=O)N(CC(C)C)CC(=O)N[C@@H](CC(C)C)C(=O)O'\n",
    "eb9_to_oncocin_best['smiles'].iloc[0] = eb9_smiles\n",
    "\n",
    "oncocin_smiles = 'N[C@@H](CCCNC(N)=N)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](CCCNC(N)=N)C(=O)N[C@@H](CCCNC(N)=N)C(=O)N1CCC[C@@H]1C(=O)N1CCC[C@@H]1C(=O)N[C@@H](CCCNC(N)=N)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](CCCNC(N)=N)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N1CCC[C@@H]1C(=O)N1CCC[C@@H]1C(=O)N[C@@H](CCCC[NH3+])C(=O)N[C@@H](CC(O)=O)C(=O)N[C@@H](C(C)C)C(=O)O'\n",
    "oncocin_to_eb9_best['smiles'].iloc[0] = oncocin_smiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate MAP4C fingerprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "eb9_to_oncocin_best['map4c'] = eb9_to_oncocin_best['smiles'].progress_apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "oncocin_to_eb9_best['map4c'] = oncocin_to_eb9_best['smiles'].progress_apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "eb9oncocin_best['map4c'] = eb9oncocin_best['smiles'].progress_apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine distances towards EB9 and oncocin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "eb9_to_oncocin_best['dist_eb9'] = eb9_to_oncocin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, eb9_to_oncocin_best['map4c'].iloc[0]))\n",
    "eb9_to_oncocin_best['dist_oncocin'] = eb9_to_oncocin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, eb9_to_oncocin_best['map4c'].iloc[-1]))\n",
    "\n",
    "oncocin_to_eb9_best['dist_oncocin'] = oncocin_to_eb9_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, oncocin_to_eb9_best['map4c'].iloc[0]))\n",
    "oncocin_to_eb9_best['dist_eb9'] = oncocin_to_eb9_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, oncocin_to_eb9_best['map4c'].iloc[-1]))\n",
    "\n",
    "eb9oncocin_best['dist_eb9'] = eb9oncocin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, eb9_to_oncocin_best['map4c'].iloc[0]))\n",
    "eb9oncocin_best['dist_oncocin'] = eb9oncocin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, oncocin_to_eb9_best['map4c'].iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pareto Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eb9_to_oncocin_best['set'], eb9_to_oncocin_best['color'] = 'EB9 -> Oncocin', '#648FFF'\n",
    "oncocin_to_eb9_best['set'], oncocin_to_eb9_best['color'] = 'Oncocin -> EB9', '#DC267F'\n",
    "eb9oncocin_best['set'], eb9oncocin_best['color'] = 'EB9 + Oncocin', '#FFB000'\n",
    "\n",
    "eb9_oncocin = pd.concat([eb9_to_oncocin_best, oncocin_to_eb9_best, eb9oncocin_best])\n",
    "eb9_oncocin = eb9_oncocin.sample(frac=1, random_state=0).reset_index(drop=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "ax.scatter(eb9_oncocin['dist_eb9'], eb9_oncocin['dist_oncocin'], c=eb9_oncocin['color'], s=1)\n",
    "\n",
    "ax.set_xlabel('$d_J$ EB9')\n",
    "ax.set_ylabel('$d_J$ Oncocin')\n",
    "\n",
    "# Add legend \n",
    "for set_name, color in zip(eb9_oncocin['set'].unique(), eb9_oncocin['color'].unique()):\n",
    "    ax.scatter([], [], c=color, label=set_name)\n",
    "\n",
    "#ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.savefig('plots/Oncocin_EB9_MAP4_pareto.png', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample Polymyxin B2 to Gramicidin S run "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "polymyxin_to_gramicidin = pd.read_csv('results/20240206_PolymyxinB2_GramicidinS_MAP4_2.csv')\n",
    "gramicidin_to_polymyxin = pd.read_csv('results/20240206_GramicidinS_PolymyxinB2_MAP4_0.csv')\n",
    "\n",
    "csv_files = [filename for filename in os.listdir('results/') if f'GramicidinPolymyxin_MAP4_COMBO' in filename and filename.endswith('.csv')]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[4].split('.')[0]))\n",
    "gramicidinpolymyxin = pd.DataFrame()\n",
    "for i, csv_file in enumerate(csv_files):\n",
    "    df_run = pd.read_csv(f'results/{csv_file}')\n",
    "    df_run['run'] = i\n",
    "    gramicidinpolymyxin = pd.concat([gramicidinpolymyxin, df_run])\n",
    "\n",
    "polymyxin_to_gramicidin = polymyxin_to_gramicidin.sort_values(by=['generation', 'dist'])\n",
    "gramicidin_to_polymyxin = gramicidin_to_polymyxin.sort_values(by=['generation', 'dist'])\n",
    "gramicidinpolymyxin = gramicidinpolymyxin.sort_values(by=['generation', 'dist'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample for the best sequence at each generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "polymyxin_to_gramicidin_best = polymyxin_to_gramicidin.groupby('generation').head(1)\n",
    "polymyxin_to_gramicidin_best = polymyxin_to_gramicidin_best.head(-4)\n",
    "polymyxin_to_gramicidin_best = polymyxin_to_gramicidin_best[['smiles', 'generation']]\n",
    "\n",
    "gramicidin_to_polymyxin_best = gramicidin_to_polymyxin.groupby('generation').head(1)\n",
    "gramicidin_to_polymyxin_best = gramicidin_to_polymyxin_best.head(-4)\n",
    "gramicidin_to_polymyxin_best = gramicidin_to_polymyxin_best[['smiles', 'generation']]\n",
    "\n",
    "gramicidinpolymyxin_best = gramicidinpolymyxin.groupby('generation').head(1)\n",
    "gramicidinpolymyxin_best = gramicidinpolymyxin_best.head(-4)\n",
    "gramicidinpolymyxin_best = gramicidinpolymyxin_best[['smiles', 'generation', 'run']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace best of generation 0 with polymyxin B2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "polymyxin_smiles = 'CC(C)CCCCC(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H]([C@H](C)O)C(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H](CCN8)C(=O)N[C@@H](CC[NH3+])C(=O)N[C@H](CC1=CC=CC=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H]([C@H](C)O)C(=O)8'\n",
    "polymyxin_to_gramicidin_best['smiles'].iloc[0] = polymyxin_smiles\n",
    "\n",
    "gramicidin_smiles = 'N8[C@@H](CC(C)C)C(=O)N[C@H](CC1=CC=CC=C1)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC[NH3+])C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](CC1=CC=CC=C1)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC[NH3+])C(=O)8'\n",
    "gramicidin_to_polymyxin_best['smiles'].iloc[0] = gramicidin_smiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate MAP4C fingerprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "polymyxin_to_gramicidin_best['map4c'] = polymyxin_to_gramicidin_best['smiles'].apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "gramicidin_to_polymyxin_best['map4c'] = gramicidin_to_polymyxin_best['smiles'].apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "gramicidinpolymyxin_best['map4c'] = gramicidinpolymyxin_best['smiles'].apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine distances towards polymyxin B2 and gramicidin S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "polymyxin_to_gramicidin_best['dist_polymyxin'] = polymyxin_to_gramicidin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, polymyxin_to_gramicidin_best['map4c'].iloc[0]))\n",
    "polymyxin_to_gramicidin_best['dist_gramicidin'] = polymyxin_to_gramicidin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, polymyxin_to_gramicidin_best['map4c'].iloc[-1]))\n",
    "\n",
    "gramicidin_to_polymyxin_best['dist_gramicidin'] = gramicidin_to_polymyxin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, gramicidin_to_polymyxin_best['map4c'].iloc[0]))\n",
    "gramicidin_to_polymyxin_best['dist_polymyxin'] = gramicidin_to_polymyxin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, gramicidin_to_polymyxin_best['map4c'].iloc[-1]))\n",
    "\n",
    "gramicidinpolymyxin_best['dist_polymyxin'] = gramicidinpolymyxin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, polymyxin_to_gramicidin_best['map4c'].iloc[0]))\n",
    "gramicidinpolymyxin_best['dist_gramicidin'] = gramicidinpolymyxin_best['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, gramicidin_to_polymyxin_best['map4c'].iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pareto plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_files = [filename for filename in os.listdir('results/') if f'PolymyxinB2_GramicidinS_MAP4' in filename and filename.endswith('.csv')]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[4].split('.')[0]))\n",
    "polymyxin_to_gramicidin = pd.DataFrame()\n",
    "for i, csv_file in enumerate(csv_files):\n",
    "    df_run = pd.read_csv(f'results/{csv_file}')\n",
    "    df_run['run'] = i\n",
    "    polymyxin_to_gramicidin = pd.concat([polymyxin_to_gramicidin, df_run])\n",
    "\n",
    "csv_files = [filename for filename in os.listdir('results/') if f'GramicidinS_PolymyxinB2_MAP4' in filename and filename.endswith('.csv')]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[4].split('.')[0]))\n",
    "gramicidin_to_polymyxin = pd.DataFrame()\n",
    "for i, csv_file in enumerate(csv_files):\n",
    "    df_run = pd.read_csv(f'results/{csv_file}')\n",
    "    df_run['run'] = i\n",
    "    gramicidin_to_polymyxin = pd.concat([gramicidin_to_polymyxin, df_run])\n",
    "\n",
    "csv_files = [filename for filename in os.listdir('results/') if f'GramicidinPolymyxin_MAP4_COMBO' in filename and filename.endswith('.csv')]\n",
    "csv_files = sorted(csv_files, key=lambda x: int(x.split('_')[4].split('.')[0]))\n",
    "gramicidinpolymyxin = pd.DataFrame()\n",
    "for i, csv_file in enumerate(csv_files):\n",
    "    df_run = pd.read_csv(f'results/{csv_file}')\n",
    "    df_run['run'] = i\n",
    "    gramicidinpolymyxin = pd.concat([gramicidinpolymyxin, df_run])\n",
    "\n",
    "polymyxin_to_gramicidin = polymyxin_to_gramicidin.sort_values(by=['generation', 'dist'])\n",
    "gramicidin_to_polymyxin = gramicidin_to_polymyxin.sort_values(by=['generation', 'dist'])\n",
    "gramicidinpolymyxin = gramicidinpolymyxin.sort_values(by=['generation', 'dist'])\n",
    "\n",
    "polymyxin_to_gramicidin_best = polymyxin_to_gramicidin.groupby('generation').head(1)\n",
    "polymyxin_to_gramicidin_best = polymyxin_to_gramicidin_best.head(-4)\n",
    "polymyxin_to_gramicidin_best = polymyxin_to_gramicidin_best[['smiles', 'generation', 'run']]\n",
    "\n",
    "gramicidin_to_polymyxin_best = gramicidin_to_polymyxin.groupby('generation').head(1)\n",
    "gramicidin_to_polymyxin_best = gramicidin_to_polymyxin_best.head(-4)\n",
    "gramicidin_to_polymyxin_best = gramicidin_to_polymyxin_best[['smiles', 'generation', 'run']]\n",
    "\n",
    "gramicidinpolymyxin_best = gramicidinpolymyxin.groupby('generation').head(1)\n",
    "gramicidinpolymyxin_best = gramicidinpolymyxin_best.head(-4)\n",
    "gramicidinpolymyxin_best = gramicidinpolymyxin_best[['smiles', 'generation', 'run']]\n",
    "\n",
    "polymyxin_smiles = 'CC(C)CCCCC(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H]([C@H](C)O)C(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H](CCN8)C(=O)N[C@@H](CC[NH3+])C(=O)N[C@H](CC1=CC=CC=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H](CC[NH3+])C(=O)N[C@@H]([C@H](C)O)C(=O)8'\n",
    "polymyxin_map4c = mapchiral.encode(Chem.MolFromSmiles(polymyxin_smiles), max_radius=2, n_permutations=2048)\n",
    "polymyxin_to_gramicidin_best['smiles'].iloc[0] = polymyxin_smiles\n",
    "\n",
    "gramicidin_smiles = 'N8[C@@H](CC(C)C)C(=O)N[C@H](CC1=CC=CC=C1)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC[NH3+])C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](CC1=CC=CC=C1)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC[NH3+])C(=O)8'\n",
    "gramicidin_map4c = mapchiral.encode(Chem.MolFromSmiles(gramicidin_smiles), max_radius=2, n_permutations=2048)\n",
    "gramicidin_to_polymyxin_best['smiles'].iloc[0] = gramicidin_smiles\n",
    "\n",
    "polymyxin_to_gramicidin_best['map4c'] = polymyxin_to_gramicidin_best['smiles'].apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "gramicidin_to_polymyxin_best['map4c'] = gramicidin_to_polymyxin_best['smiles'].apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "gramicidinpolymyxin_best['map4c'] = gramicidinpolymyxin_best['smiles'].apply(lambda x: mapchiral.encode(Chem.MolFromSmiles(x), max_radius=2, n_permutations=2048))\n",
    "\n",
    "polymyxin_to_gramicidin_best['set'], polymyxin_to_gramicidin_best['color'] = ' Polymyxin B2 -> Gramicidin S', '#648FFF'\n",
    "gramicidin_to_polymyxin_best['set'], gramicidin_to_polymyxin_best['color'] = 'Gramicidin S -> Polymyxin B2', '#DC267F'\n",
    "gramicidinpolymyxin_best['set'], gramicidinpolymyxin_best['color'] = 'Polymyxin B2 + Gramicidin S', '#FFB000'\n",
    "\n",
    "polymyxin_gramicidin = pd.concat([polymyxin_to_gramicidin_best, gramicidin_to_polymyxin_best, gramicidinpolymyxin_best])\n",
    "polymyxin_gramicidin['dist_polymyxin'] = polymyxin_gramicidin['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, polymyxin_map4c))\n",
    "polymyxin_gramicidin['dist_gramicidin'] = polymyxin_gramicidin['map4c'].apply(lambda x: 1 - mapchiral.jaccard_similarity(x, gramicidin_map4c))\n",
    "\n",
    "polymyxin_gramicidin = polymyxin_gramicidin.sample(frac=1, random_state=0).reset_index(drop=True)\n",
    "polymyxin_gramicidin = polymyxin_gramicidin.sort_values(by='generation')\n",
    "polymyxin_gramicidin.drop_duplicates(subset='smiles', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 2.25))\n",
    "ax.scatter(polymyxin_gramicidin['dist_polymyxin'], polymyxin_gramicidin['dist_gramicidin'], c=polymyxin_gramicidin['color'], s=1)\n",
    "\n",
    "ax.set_xlabel('$d_J$ Polymyxin B2')\n",
    "ax.set_ylabel('$d_J$ Gramicidin S')\n",
    "\n",
    "# Add legend \n",
    "for set_name, color in zip(polymyxin_gramicidin['set'].unique(), polymyxin_gramicidin['color'].unique()):\n",
    "    ax.scatter([], [], c=color, label=set_name)\n",
    "\n",
    "plt.savefig('plots/GramicidinS_PolymyxinB2_MAP4_pareto.png', dpi=1200, bbox_inches='tight')"
   ]
  }
 ],
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