Figure 2, Extended Data Fig. 4, Extended Data Figure 5, Extended Data Figure 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f26a048c",
   "metadata": {},
   "source": [
    "# Figure 2, Extended Data Fig. 4, Extended Data Figure 5, Extended Data Figure 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9739bac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from uncertainties import ufloat\n",
    "import uncertainties\n",
    "from tqdm.auto import tqdm\n",
    "from matplotlib.lines import Line2D\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b18568e",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 4 A-C\n",
    "### ATP Yield per molecule of glucose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0324a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4a\n",
    "#ATP yield per molecule of glucose for E. coli\n",
    "\n",
    "#ATP yield for ecoli \n",
    "Y_Glyc = 2\n",
    "Y_Pta_acka = 10\n",
    "Y_Resp = 20\n",
    "\n",
    "#make three dataframes with fermentation and respiration yield values\n",
    "d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "d2 = {'yield': [Y_Pta_acka, -1], 'pathway': ['Pta-AckA', ' ']}\n",
    "d3 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', '    ']}\n",
    "df = pd.DataFrame(data=d)\n",
    "df2 = pd.DataFrame(data=d2)\n",
    "df3 = pd.DataFrame(data=d3)\n",
    "\n",
    "#generate figure\n",
    "fig, ax = plt.subplots(figsize = (7,7))\n",
    "sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "sns.set_context(\"talk\")\n",
    "plt.ylim(0,32)\n",
    "\n",
    "plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "plt.stem(df2['pathway'], df2['yield'], 'green', markerfmt = \"go\")\n",
    "plt.stem(df3['pathway'], df3['yield'], 'blue', markerfmt = \"bo\")\n",
    "\n",
    "plt.ylabel('ATP yield per molecule of glucose',fontsize=20)\n",
    "plt.yticks(fontsize=17)\n",
    "plt.xticks(fontsize=17)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/edf4a_ecoli_yield.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ceb986c1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def yieldPlot(Y_Glyc, Y_Resp):\n",
    "    \n",
    "    '''Takes the ATP yield per molecule values for fermentation and respiration and generates a stem plot'''\n",
    "    \n",
    "    #make two dataframes with fermentation and respiration yield values\n",
    "    d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "    d2 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', ' ']}\n",
    "    df = pd.DataFrame(data=d)\n",
    "    df2 = pd.DataFrame(data=d2)\n",
    "\n",
    "    #generate figure\n",
    "    fig, ax = plt.subplots(figsize = (7,7))\n",
    "    sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "    sns.set_context(\"talk\")\n",
    "    plt.ylim(0,32)\n",
    "\n",
    "    plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "    plt.stem(df2['pathway'], df2['yield'], 'darkblue', markerfmt = \"bo\")\n",
    "\n",
    "    plt.ylabel('ATP yield per molecule of glucose',fontsize=20)\n",
    "    plt.yticks(fontsize=17)\n",
    "    plt.xticks(fontsize=17)\n",
    "    \n",
    "    return(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ca05da9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4b\n",
    "#ATP yield per molecule of glucose for S. cerevisiae\n",
    "yeast_yield = yieldPlot(2, 16)\n",
    "\n",
    "#save figure\n",
    "yeast_yield.savefig('./Figures/edf4b_scerevisiae_yield.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80681121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xYvanX+93/frd9eTRkeORZYMGDfK0QYPBwP79+/McSETkUVa69N3rkqmpOa/j5nZ3PXl05FiWHh4e+ZlDROSxEBJy967XB7lzB15+OX/yiHXkWJa7du3KzxwiIo+FYsXuPh5i7m5Y3dzzaLHapAQiInLXvVk8P/vMyM2bt4CCFC5swGg08MYb/z8ujw6LHx2ZNWuW2XUKFChA4cKFKV26NPXq1aNEiRIPHdAW9OiIiOSHw4cTqVPnJ8CdTz6py9//7qYjSgdltUdHZs2aZXqfZXb9+texggUL8vrrrzN48ODc5BUReWwULQpwBICePf1VlI8wi8ty06ZNvPbaayQnJ/PKK69Qt25dSpYsSUpKCv/zP//DggULSEtL4/333+f69essX76cadOmUalSJYKCgmz5exAREbEpi8ty6dKlJCcns379ekr/5Z7nmjVr0rJlSzp37swvv/zCe++9x0svvcRLL73EkiVLVJYiIvJIs3hSgu3bt9O+ffssRXlP6dKlCQoKYsuWLQC4uroSGBjI77//bp2kIiIidmJxWd66dYsbN248cJ2MjAzS7psQ0dXVlfT09LynExERcQAWl+XTTz/Njh07OHv2bLbj586dY/v27fj5+ZmW/frrr/j6+j58ShERETuy+Jrla6+9xsCBAwkJCaFv3774+/vj4+NDSkoKv/76q+ma5tChQwF45513+Pnnnxk2bJjNwouIiOQHi8uyYcOGTJs2jQ8//DDTYyRw93GRokWLMnnyZJo3b86lS5dYu3YttWvXJjQ01Ba5RURE8k2uZvBp3bo1TZs25YcffuDIkSPEx8fj4eFhuhu2cOHCALi7u7NmzRpq1qyZqVRFREQeRbme7s7V1ZWgoKAHPg7i4eFBrVq1HiqYiIiIo9D7LEVERMxQWYqIiJihshQRETFDZSkiImJGjmU5dOhQ1q9fb/r6woULpKSk5EcmERERh5JjWf7444+cOnXK9HXLli1ZvHhxfmQSERFxKDk+OuLq6sq+ffsICQnBy8sLo9FIenq6RUeXHh4eVg0pIiJiTzmWZYsWLVi3bh2tW7cG7r7ced68ecybN++BGzQYDERHR1s3pYiIiB3lWJZjx47Fx8eH6Oho0tPTOXjwIGXKlNHE6CIi8sTJsSwLFy7Mm2++afq6Ro0adOnShbCwsHwJJiIi4igsnu5uyZIlOqoUEZEnksVl2aBBAwAOHjxIeHg4x44d48aNG3h5eVGtWjU6duxIQECAzYKKiIjYS64mUp8yZQrz58/HaDQCd0/VxsbGcujQIVavXs3gwYMZMWKETYKKiIjYi8Uz+ERERDBv3jyqVq3KnDlzOHjwIIcOHeLw4cMsXLgQPz8/5s6dy44dO2yZV0REJN9ZXJZLliyhRIkSLFmyhGbNmpmepXRxceGFF15g4cKF+Pj4sHTpUpuFFRERsQeLy/LYsWMEBgZSrFixbMe9vb0JDAwkJibGauFEREQcgdUnUr9165a1NykiImJXFpeln58fu3fvJiEhIdvxuLg4du3ahZ+fn7WyiYiIOASLy7Jfv35cuXKFAQMGcODAAW7fvg1ASkoKUVFRhIaGcu3aNfr06WOzsCIiIvZg8aMj7dq148iRIyxatIj+/fvj5OSEi4sLaWlpABiNRl555RXat29vs7AiIiL2kKvnLEePHk3Lli1Zu3YtR48eJTU1FXd3d9NUeJqUQEREHke5KkuAgIAAlaKIiDxRrH43rIiIyONGZSkiImKGylJERMQMlaWIiIgZdi/LjIwMVq5cSceOHXn22WcJDAzknXfe4erVq6Z10tLSmDBhAk2aNKF27dr07t2bw4cP2zG1iIg8SfJclpcvX+b48eMApgkK8mLOnDl8+OGHNGrUiNmzZzNs2DD+/e9/0717d1JSUgAYNWoU4eHhhIWFMXXqVABCQ0OJjY3N835FREQslatHR9LS0pg1axZr164lPj4eg8FAdHQ0CxcuZO/evfzrX/+icuXKFm/v9u3bzJ07l+7duzN69GjT8ipVqtCjRw8iIiLw8/MjMjKS6dOn06ZNGwAaNWpE69atmTNnDp9++mlufgsiIiK5ZvGRZWpqKr169WL+/PkUKlSIcuXKmV4CnZaWxoEDB+jduzfnzp2zeOdJSUl06dKFDh06ZFpetWpVAC5dusSePXtwdnamRYsWpnFXV1cCAwP5/vvvLd6XiIhIXllcll9++SXR0dG899577Nq1K1PBvf7660yYMIHExES++OILi3fu7e3N+++/T7169TItj4yMBKBatWr88ccf+Pr64uLikmmdihUrEhcXR3x8vMX7ExERyQuLy3Lr1q00adKEPn36YDAYMBgMmcaDg4Np3rw5+/fvf6hAJ0+eZNKkSVSvXp1WrVqRnJyMu7t7lvXuLbt3XVNERMRWLC7Ly5cv8/TTTz9wnUqVKnHlypU8h4mJiaFfv344Ozsze/ZsChYsiNFozFLM93vQmIiIiDVYXJbe3t788ccfD1zn+PHjeHt75ynI9u3b6dWrF4UKFWLZsmWUL18eAE9Pz2yPHu8t8/T0zNP+RERELGVxWQYGBrJ7926ioqKyHd+2bRt79uyhadOmuQ6xaNEihg8fTtWqVfn222+pWLGiaaxSpUpcuHCB9PT0TJ85ffo0Pj4+FC1aNNf7ExERyQ2LyzIsLIwSJUrw97//naFDh7J3714AZs6cyZAhQ/jnP/9J8eLFGTZsWK4CrFixggkTJhAYGMjSpUspXrx4pvEmTZqQnp7Orl27TMvS0tLYvXs3jRs3ztW+RERE8sLi5yx9fHxYuXIlH3zwAVFRUabHRmbPng1A/fr1GTduHKVKlbJ45xcvXuSTTz6hdOnSDBgwgKNHj2YaL1WqFPXq1aNx48a8/fbbXLt2jTJlyrBw4UJSUlIYMmSIxfsSERHJq1xNSlCmTBnmzp3LlStXiI6OJikpCTc3N/z8/Chbtmyud75z507S09O5ePEivXv3zjI+aNAgRo4cyfTp05k0aRIzZswgPT2dWrVqsWjRolxNgCAiIpJXBuO9Q8QniJeXFwAJCQl2zSEij7fY2EQqVZoHwKlTg6hYUfdYOCpzvZDjkeWSJUvyvNN+/frl+bMiIiKOJsey/OSTTzAYDOT2wNNgMKgsRUTksZJjWWqCchERkbtyLMvOnTvnZw4RERGHZfeXP4uIiDg6ix8dadCggUXrGQyGh55MXURExJFYXJYeHh7ZLk9LSyMhIYGMjAyqV69OuXLlrBZORETEEVhclvdPN/dXycnJfPnll4SHhzNt2jSrBBMREXEUVrlm6enpyVtvvUXVqlX57LPPrLFJERERh2HVG3yeffZZDh48aM1NioiI2J1VyzImJkYvYxYRkceOxdcsd+7cme1yo9HI9evX+f7779m3bx9BQUFWCyciIuIILC7LYcOGPfCo0Wg0UrJkSUaOHGmVYCIiIo7CKmXp4uJC5cqVadasGc7OzlYLJyIi4ggsLsvhw4fbMoeIiIjDytUNPpcvX+bjjz/mu+++y7S8TZs2fPTRRyQnJ1s1nIiIiCOwuCzPnTtHSEgIy5Yt4+jRo6blN27cICMjg+XLl9OlSxcuX75sk6AiIiL2YnFZzpgxg7i4OCZPnsw///lP0/LChQsTGRnJtGnTuHDhgmbwERGRx47FZXngwAHatm3LSy+9lO1427ZtCQoKIioqymrhREREHIHFZZmYmEixYsUeuE7p0qVJSUl56FAiIiKOxOKyLF++PD/++CO3b9/OdjwjI4P9+/dTtmxZq4UTERFxBBaXZXBwMMePH+ett97iypUrmcauXbvGu+++y9GjR+nUqZPVQ4qIiNiTxc9Z9u/fn3//+99ERESwdetWypQpg4eHB6mpqfz5559kZGTQqFEjBgwYYMu8IiIi+c7isnRycmL+/PmsWbOGLVu2cOzYMS5fvoybmxt169alY8eOhISE4ORk1bnZRURE7M7isrwnJCSEkJAQW2QRERFxSLkuS4A//viDmJgYEhMT6d27NxcuXKBo0aK4u7tbO5+IiIjd5eqc6YkTJ+jWrRvt27dn1KhRjB8/HoC1a9fSrFkzIiIibBJSRETEniwuy7Nnz9K7d2+io6Np3749zz//vGmsbNmyZGRkMHLkSA4ePGiToCIiIvZicVlOnz6dtLQ0Vq1axWeffUa9evVMY8HBwXz77be4uroyd+5cmwQVERGxF4vLct++fbRt25aaNWtmO161alXatGlDdHS01cKJiIg4AovLMiUlBW9v7weuU6RIEb2mS0REHjsWl2W5cuX45Zdfchw3Go0cOHCAcuXKWSWYiIiIo7C4LDt27Mjhw4eZOnUqd+7cyTSWnp7Op59+SkxMDO3atbN6SBEREXuy+DnLV199lX379jF37lxWrlxJoUKFAOjbty/Hjx8nISGB2rVrM3DgQJuFFRERsQeLjyydnZ1ZsGABI0eOxNvbmytXrmA0Gvn5559xc3Nj2LBhLFmyBBcXF1vmFRERyXe5msGnYMGCDBw4kIEDB3L9+nWSk5Nxd3fHw8PDVvlERETsLk/T3QG4ubnh5uZmzSwiIiIOKceyDAsLy9MGDQYDM2fOzHMgERERR5NjWe7YsSNPGzQYDHkOIyIi4ohyLMudO3fmZw4RERGHlWNZ+vr65mcOERERh5XrG3wOHjxIeHg4x44d48aNG3h5eVGtWjU6duxIQECALTKKiIjYVa7KcsqUKcyfPx+j0QhA4cKFiY2N5dChQ6xevZrBgwczYsQImwQVERGxF4snJYiIiGDevHlUrVqVOXPmcPDgQQ4dOsThw4dZuHAhfn5+zJ07N883BomIiDgqi8tyyZIllChRgiVLltCsWTPTRAQuLi688MILLFy4EB8fH5YuXWqzsCIiIvZgcVkeO3aMwMBAihUrlu24t7c3gYGBxMTEWC2ciIiII7C4LC1169atPH82JSWFli1bsmLFikzLw8PD8fPzy/IrNDT0IdOKiIiYZ/ENPn5+fuzevZuEhAS8vLyyjMfFxbFr1y78/PzyFCQ+Pp7XXnuNc+fOZRmLiYmhbNmyTJkyJdNyT0/PPO1LREQkNyw+suzXrx9XrlxhwIABHDhwgNu3bwN3jwajoqIIDQ3l2rVr9OnTJ1cBjEYjW7duJTg4mJMnT2a7TnR0NP7+/tSpUyfTrypVquRqXyIiInlh8ZFlu3btOHLkCIsWLaJ///44OTnh4uJCWloacLf0XnnlFdq3b5+rAOfPn+fNN9+kc+fO9OrViy5dumQaNxqNHD16lGbNmuVquyIiItaSq+csR48eTcuWLVm7di1Hjx4lNTUVd3d3atSoQZcuXfI0KUGxYsXYtm0b5cqVy/YUbGxsLKmpqcTExNCuXTtOnz5NqVKl6Nu3L6GhoZqLVkREbC7XM/gEBARYdaYed3d33N3dcxy/d3ftmTNneOONN3BzcyMyMpKJEydy7do1Ro4cabUsIiIi2cl1Wd68eZPz58+Tnp6e4zo1atR4qFD3a9CgAV9++SXPPfecqVRfeOEF0tPTWbRoEa+++ire3t5W25+IiMhfWVyW8fHxjB071qIZeqz5rKWPjw8tWrTIsrxFixaEh4dz4sQJGjRoYLX9iYiI/JXFZfnJJ5+wfft2KlSoQM2aNSlUqJAtc5kcOHCAs2fP0rVr10zL791YlNMkCSIiItZicVn++9//5tlnn2X58uU4OVl9LoMc/fTTT8yePRt/f3+qV69uWr5582ZKly5NxYoV8y2LiIg8mSxuvfT0dOrWrZuvRQnQq1cvSpQowWuvvcbGjRuJiorijTfe4Pvvv2fMmDE4Ozvnax4REXnyWHxk2bhxY3755RdbZsmWj48PK1asYOrUqUyaNImkpCT8/PyYPXs2LVu2zPc8IiLy5DEY772c0oxLly7Rs2dP6tSpQ2hoKGXLlsXFxSXbde+9kcRR3ZuuLyEhwa45ROTxFhubSKVK8wA4dWoQFSsWtXMiyYm5XrD4yLJo0aL4+/uzdetWtm7dmuN6BoOB6OjoXIUUERFxZLm6GzYyMhJXV1eqVKlC4cKFbZlLRETEYVhclpGRkVStWpVvvvlGb/sQEZEnisW3tt68eZOmTZuqKEVE5IljcVnWrVuXo0eP2jKLiIiIQ7K4LEePHs3//M//MGHCBP78809bZhIREXEoFl+znDBhAt7e3nz99dd8/fXXFCxYMNubfAwGA/v377dqSBEREXuyuCxjY2MBKFOmjK2yiIiIOCSLy3LXrl22zCEiIuKw8neiVxERkUeQylJERMQMlaWIiIgZKksREREzVJYiIiJmWFyW27Zt49KlS7bMIiIi4pAsLssPPviADz74wJZZREREHFKuJlKvXLmyLbOIiIg4JIvLsmvXrmzcuJHff//dlnlEREQcjsUz+BQpUgSA4OBgKlSogK+vL66urlnWMxgMzJw503oJRURE7Mzisvziiy9M/33q1ClOnTqV7XoGg+HhU4mIiDgQi8ty586dtswhIiLisCwuS19fX1vmEBERcVgWl+U9J06cYN26dRw9epTExETWrFnD7t27SUxMpGPHjjg5aZ4DERF5vOSqLOfOncv06dO5c+cO8P/XJw8cOMDixYuJjIxk+vTpODs7Wz+piIiIneRqBp+pU6fyzDPPsGjRIl555RXTWI8ePXjhhRfYvXs333zzjU2CioiI2IvFZblo0SLKly/P119/TcOGDXF3dzeNVahQgblz51K5cmXWrVtnk6AiIiL2YnFZHjt2jJYtW+Li4pLteIECBWjatClnzpyxWjgRERFHYHFZFihQgNTU1Aeuk5iYSIECBR46lIiIiCOxuCz9/f3ZtWsXSUlJ2Y5fvXqVnTt3UqtWLauFExERcQQWl+XgwYO5du0avXv3JjIykqtXrwJw/vx5vvvuO3r37k1SUlKmG39EREQeBwaj0Wi0dOXVq1fz0UcfcevWLQCMRqPp8REnJydGjRpFaGioTYJak5eXFwAJCQl2zSEij7fY2EQqVZoHwKlTg6hYsaidE0lOzPVCrp6zfPnll2natCkbNmzgt99+Izk5GTc3N/z8/OjYsSMVKlR42LwiIiIOJ9cz+JQqVYrBgwfbIouIiIhDynVZnjlzhq1bt3L06FGSk5MpVqwYderUoU2bNhQvXtwWGUVEROwqV2U5a9YsvvrqK27fvp1p+aZNm5gyZQrvvvsuXbt2tWpAERERe7O4LNetW8esWbMoV64cQ4YM4ZlnnsHHx4fk5GQOHTrEl19+yfvvv0+pUqVo3LixLTOLiIjkK4vLcvHixZQuXZpVq1bh7e1tWu7t7U2FChVo1KgRnTt35quvvlJZiojIY8Xi5yxPnz5Nq1atMhXl/UqUKEFQUBC//fab1cKJiIg4AovLskSJEmafS0xJSaFYsWIPm0lERMShWFyWoaGhfPfdd2zfvj3b8V9++YVt27YxYMAAq4UTERFxBBZfsyxUqBDVq1fn9ddfp27dutSvX59SpUpx8+ZNjhw5QmRkJB4eHpw5c4ZPP/3U9DmDwcCYMWNsEl5ERCQ/WDzdXY0aNfK2A4OBmJiYPH3WVjTdnYjkB0139+iw2nR3S5YssUYeERGRR47FZdmgQQNb5hAREXFYFt/gIyIi8qRSWYqIiJihshQRETHDocoyJSWFli1bsmLFikzL09LSmDBhAk2aNKF27dr07t2bw4cP2ymliIg8aRymLOPj4xk0aBDnzp3LMjZq1CjCw8MJCwtj6tSpwN1JEmJjY/M5pYiIPInsXpZGo5GtW7cSHBzMyZMns4wfPnyYyMhIPvroI7p3707Lli1ZsGABnp6ezJkzxw6JRUTkSZPjoyPr16/P80aDg4MtXvf8+fO8+eabdO7cmV69etGlS5dM43v27MHZ2ZkWLVqYlrm6uhIYGEhkZGSeM4qIiFgqx7IcM2YMBoPB9LXRaMzy9T33L4fclWWxYsXYtm0b5cqVy/YU7B9//IGvry8uLi6ZllesWJG4uDji4+M1ebuIiNhUjmX59ttvZ/o6IyODBQsWkJKSQnBwMM8++yxeXl6kpqZy5MgRwsPDKVasGCNGjMhVAHd3d9zd3XMcT05Oznb83jK96URERGwtx7Ls379/pq+/+uorUlNTWb58OTVr1sw01q5dO0JCQujevTv//e9/adOmjdUC/vWI9q8eNCYiImINFt/gs3LlSlq3bp2lKO+pUqUKbdq0eahrndnx9PQkJSUly/J7yzw9Pa26PxERkb+yuCwTExMpXLiw2fVu3LjxUIH+qlKlSly4cIH09PRMy0+fPo2Pjw9Fi2oWfxERsS2Ly7J69ers2LGDy5cvZzseGxvLtm3b8Pf3t1o4gCZNmpCens6uXbtMy9LS0ti9ezeNGze26r5ERESyY/FbRwYNGkRYWBg9evSgX79+1KxZE3d3d5KTk/nPf/7D0qVLuXHjBmFhYVYNWK9ePRo3bszbb7/NtWvXKFOmDAsXLiQlJYUhQ4ZYdV8iIiLZsbgsW7VqxUcffcSkSZOYMGFClsdIvL29mT59OgEBAVYPOX36dCZNmsSMGTNIT0+nVq1aLFq0iMqVK1t9XyIiIn9lMN7/wKQFkpOT+f777zl27BhJSUkUKVKEmjVr0qxZM9zc3GyV06rMvRFbRMQaYmMTqVRpHgCnTg2iYkXdY+GozPWCxUeW93h6etKhQwc6dOjwMLlEREQeGTmW5c6dO/O80ZYtW+b5syIiIo4mx7IcNmxYrh/4vzeBQExMzEMHExERcRRWLUsREZHHUY5lOXz48PzMISIi4rByfYMP3H0TSExMDImJifTu3ZsLFy5QtGjRB06ILiIi8qjK1cufT5w4Qbdu3Wjfvj2jRo1i/PjxAKxdu5ZmzZoRERFhk5AiIiL2ZHFZnj17lt69exMdHU379u15/vnnTWNly5YlIyODkSNHcvDgQZsEFRERsReLy3L69OmkpaWxatUqPvvsM+rVq2caCw4O5ttvv8XV1ZW5c+faJKiIiIi9WFyW+/bto23btjm+oqtq1aq0adOG6Ohoq4UTERFxBBaXZUpKCt7e3g9cp0iRIiQnJz90KBEREUdicVmWK1eOX375Jcdxo9HIgQMHKFeunFWCiYiIOAqLy7Jjx44cPnyYqVOncufOnUxj6enpfPrpp8TExNCuXTurhxQREbEni5+zfPXVV9m3bx9z585l5cqVFCpUCIC+ffty/PhxEhISqF27NgMHDrRZWBEREXuw+MjS2dmZBQsWMHLkSLy9vbly5QpGo5Gff/4ZNzc3hg0bxpIlS3BxcbFlXhERkXyXqxl8ChYsyMCBAxk4cCDXr18nOTkZd3d3PDw8bJVPRETE7vI03R2Am5vbI/OyZxERkYeRY1l27tyZHj160L17d9PXljAYDKxdu9Y66URERBxAjmUZExPDlStXMn1tCb3WS0REHjc5luXRo0cf+LWIiMiTwuK7YX/88Udb5hAREXFYFpflK6+8QrNmzZgyZQrHjx+3ZSYRERGHYnFZhoWFUbhwYebNm0fHjh3p3Lkzixcv5urVq7bMJyIiYncGo9FozM0Hjhw5woYNG/juu++4evUqBQsWpGHDhgQHB9OqVSvTzD6OzMvLC4CEhAS75hCRx1tsbCKVKs0D4NSpQVSsWNTOiSQn5noh12V5T0ZGBnv37mXr1q388MMPXLt2DTc3N1q3bk1wcDDPPfdcXjPbnMpSRPKDyvLRYa4XLD4Nm+WDTk40bdqUESNGMGzYMMqUKUNqairr1q0jNDSUF198kU2bNuV18yIiIg4jTzP4xMfHs3XrVrZs2cKhQ4cwGo0UL16cV155hbZt2xITE8PXX3/NW2+9xfnz5xk6dKi1c4uIiOQbi0/DXr9+ne3bt7Nlyxb27dvH7du3cXV1pWXLlnTq1InGjRvj5PT/B6rJycm0aNGCAgUK8NNPP9nsN5AXOg0rIvlBp2EfHeZ6weIjy0aNGpGWlgZAvXr16NSpE23bts1xEnVPT098fX25du1a7hKLiIg4GIvL0sfHh06dOhEcHEzZsmUt+sy7775LyZIl8xxORETEEVhcltu3b8/1xuvXr5/rz4iIiDiaPN8NKyIi8qRQWYqIiJihshQRETFDZSkiImKGylJERMQMs3fD/uc//yEqKoqEhARKlSpFUFAQ1apVy49sIiIiDiHHsszIyGDMmDGm+V3vTfQza9YsBg4cyBtvvJE/CUVEROwsx7L85ptv2LhxIyVLlqRLly6UKlWK06dPs2bNGubNm8ff/vY32rRpk59ZRURE7CLHsty0aRMlS5Zkw4YNFCtWzLS8b9++dOrUiTVr1qgsRUTkiZDjDT4nT54kKCgoU1EC+Pr60qJFC2JiYmweTkRExBHkWJbXr1+naNHsZ8j39fUlMTHRZqFEREQcSY5leefOnUyv3LpfgQIFuHPnjs1CiYiIOBI9ZykiImKGylJERMSMB05KcODAAWbNmpVl+f79+wGYPXu26fnLewwGA8OGDbNiRBEREfsyGP/adv+nRo0aedugwWCTO2UDAwO5cOFCluXLly8nICAgV9vy8vICICEhwQrJRESyFxubSKVK8wA4dWoQFStmf9Ok2J+5XsjxyPLTTz+1RZ48SUhI4MKFC4wYMYLnn38+01jVqlXtlEpERJ4UOZZl586d8zPHA907Ug0KCqJKlSp2TiMiIk+aHG/w6devH+vXr8/HKDn77bffcHV1pWLFivaOIiIiT6Acy/LAgQOcO3cuP7PkKCYmBi8vL958803q16/PM888w+DBgzl16pS9o4mIyBPgkXh0JDo6mitXrlClShW+/PJLxo0bx8mTJ+nZsycXL160dzwREXnMmX2fpSP4+OOPKVCgALVr1wYgICCAunXr0q5dOxYuXMg777xj54QiIvI4e2BZGgyG/MrxQHXr1s2yrHz58lSpUkUTuouIiM09sCxnzZqV7aQED2IwGIiOjn6oUPdLSkoiMjKSWrVqZXn2My0tjQoVKlhtXyIiItl5YFl6eHjg6emZX1my5eLiwrhx42jevDkzZswwLT9y5Ahnzpyhf//+dkwnIiJPggeWZf/+/QkLC8uvLNlydXVlyJAhzJgxg3HjxtGqVStOnz7NzJkz8fPz4+WXX7ZrPhERefw9Ejf4vPbaa/j4+LB8+XLCw8Px8PAgKCiIESNG4OzsbO94IiLymLN6WR4/fpxq1apZdZsGg4Hu3bvTvXt3q25XRETEElYpy+vXr7NlyxbWrFnDkSNHrHqDj4iIiL09cG7Yp59++oEfPnz4MKtXryYiIoIbN25gNBopUqSI1UOKiIjYU67fOpKQkMCGDRtYs2YNJ06cwGg04uTkRMOGDenSpQutW7e2WVgRERF7sPg07I8//sjq1avZsWMHt27dMr30+bnnnmPChAmUKVPGZiFFRETs6YFleenSJdauXUt4eDjnz5/HaDRSvHhx2rRpQ/v27enZsyeVKlVSUYqIyGMtx7IcOnQoe/fu5fbt23h6ehIcHEy7du1o1KgRTk6PxPzrIiIiVpFjWX7//fcULlyYv//97wwaNAgXF5f8zCUiIuIwcjxEbNy4Menp6cyaNYsmTZrwxhtvsGPHDtLT0/Mzn4iIiN3leGQ5f/58rl69ysaNG1m/fj0RERFs3boVd3d3goKCeOmll/Izp4iIiN088OKjj48Pr776Khs3bmTDhg30798fV1dX1q1bx6BBg0xvGDl06FB+5RUREcl3BuO9Z0AslJGRwd69e9mwYQM7d+4kLS0Ng8GAr68vHTp0oEOHDlSuXNlWea3Cy8sLuPvMqIiIrcTGJlKp0jwATp0aRMWKRe2cSHJirhdyXZb3S01NZevWrWzYsIGDBw+aJihw9OnuVJYikh9Ulo8Oc73wUHPDuru7ExISQkhICBcuXGD9+vVs2rTpYTYpIiLicKz2wORTTz3Fa6+9xtatW621SREREYeg2QVERETMUFmKiIiYobIUERExQ2UpIiJihspSRETEDJWliIiIGSpLERERM1SWIiIiZqgsRUREzHio6e5EbCY+HtasgYsXoXRpCAmBYsXsnUpEnlAqS3EsRiOMHQuTJ0OBAnD9Ori5weuvw8iRMG4cGAz2TikiTxiVpTiWsWNh6lRIS/v/Zampd/936tS7//vRR/mfS0SeaLpmKY4jPv7uEeX169mPX79+d1yvVhORfKayFMexZs3dU68PUqAArF6dP3lERP6PylIcx8WLOR9V3nP9+t31RETykcpSHEfp0ndv5nkQN7e764mI5COVpTiOkBC4c+fB69y5Ay+/nD95RET+j8pSHEexYncfD8np6NLN7e64l1e+xhIR0aMj4ljGjQPA+NlnGG/ewkAGFHbDYMyAN94wjYuI5CcdWYpjMRjgo4+4/fMhrnpUJt6tPHc++BD+/PPu85WakEBE7EBHluKYinqR7Hr3Rh7Pnr116lVE7EpHliIiImaoLEVERMxQWYqIiJihshQRETFDZSkiImKGylJERMQMlaWIiIgZKksREREzVJYiIiJmqCxFRETMUFmKiIiYobIUEREx45EpywMHDtCjRw/q1KlDkyZNmDp1Kunp6faOJSIiT4BHoix//fVXBgwYQIkSJZg+fTr9+vVj4cKFjB8/3t7RRETkCfBIvKJr+vTpVKxYkRkzZmAwGGjWrBmurq588sknDB48mLJly9o7ooiIPMYcvizT09PZv38/gwcPxnDfi3/btm3L+PHjiYqKonfv3vmey3jrFrcuXc73/T4pbl+8jNHpDgC3Ll7E6JRh50SPL+dSJTE4O9s7xmOrIBmULJhKxsULpBdMsXecx5at/xw7fFmePXuWW7duUalSpUzLfXx8cHd35+TJk7neZlJSEkajEa+HeKFwRmoqZOgHeL5oGGDvBI83Jyec3N3tneKxlJFhpJBTKskZRuo00mUjm3rIP8eJiYmZDsj+yuHLMjk5GQAPD48sY+7u7qSk5P5fak5OTmQ8ZNHph4uImOPkZMDJM+vPLnE8BoMBJ6ecb+Nx+LK8V2o5Nf6D/iWQk9u3bz9UJhERebI4/N2wRYoUAcj2CDI1NTXbI04RERFrcviyLF++PAULFiQ2NjbT8qtXr5KamkqVKlXsE0xERJ4YDl+WLi4uNGjQgO3bt3Pnzh3T8q1bt+Lk5MQLL7xgx3QiIvIkcPiyBBg2bBjHjx/ntddeIyoqivnz5zNx4kRCQkKoUKGCveOJiMhjzmA0Go32DmGJqKgoPv/8c06cOIG3tzedOnVi+PDhOOv5MBERsbFHpixFRETs5ZE4DSsiImJPKksREREzVJYiIiJmqCxFRETMUFmKiDwCdC+mfaksJVtnz55l4sSJvPTSS9StW5dnn32WTp06MWvWLNPk9gBjxoyhUaNG+Z5v//79+Pn5sWfPHqttc+3atfj5+fHHH39YbZtiW2PGjMHPzy/Lr9q1axMUFMTHH3/M9evX7ZLNz8+PyZMnW2VbBw4cYNCgQaavz507h5+fHytWrLDK9sU8h59IXfLfjh07GDVqFE899RQ9evSgWrVq3Llzh4MHD/L111+zZcsWVqxY8VCvOHNEzZs3Z9WqVXqZ+CPGy8uLOXPmZFqWkJBAVFQUS5YsIS4ujilTpuR7rlWrVlGqVCmrbGvFihWZXkdYsmRJVq1aRbly5ayyfTFPZSmZxMbGMmrUKOrVq8cXX3yBi4uLaaxRo0a0b9+eLl26MGPGDMaOHWvHpNbn7e2Nt7e3vWNILhUsWJA6depkWd68eXPi4+PZsmUL48aNwz2fX6uXXSZrcXFxsen2JSudhpVM5s+fT0ZGBp988kmmorynSpUqDB48ONujylWrVuHn58d///vfTMt//fVX/Pz8iIqKAiAuLo53332Xxo0bU6dOHbp27cquXbsyfeann36ib9++1K9fn4CAAIYNG5bj6dHk5GRq167NRx99lGWsffv2/OMf/wAgJiaGV199lfr161OnTh169OjB7t27Tev+9TRseno648ePp3nz5tSqVYuWLVsyZcoU0tPTH/AdFEdSpEgRDAaD6VV+J0+eJCwsjICAAOrUqUP//v05cuRIps/89NNP9OzZk7p161K3bl1CQ0P5z3/+YxqfOXMmjRo1Yu/evbRv355nnnmGjh078t1332Xazv2nYe+dNl28eDEdOnQgICCABQsWAPDLL78waNAgnnvuOWrWrEnTpk356KOPuHHjBgB9+/YlIiKC8+fP4+fnx9q1a7M9DZuSksKkSZNo3bo1/v7+tGnThsWLF2e61tm3b1/GjBnD4sWLadmyJbVq1aJTp06mv5uSM5WlZLJ9+3YaNmxIyZIlc1xn2LBhvP7661mWv/TSS7i5ubF+/fpMy8PDwylVqhSNGzfmxo0b9OzZk6ioKMLCwvjiiy+oWLEiYWFh/PjjjwBs2rSJ/v37U6RIESZNmsS//vUvTp48Sffu3Tl16lSW/Xp6etKmTRs2b96cqcgOHz7M8ePHCQkJISUlhVdffZU7d+4wefJkZs+eTdGiRR9YwuPHj2fjxo28/vrrLFy4kB49erBgwQJmz55tybdS8tHt27dNv27dusWVK1dYtWoV69at48UXX8TNzY0zZ87QvXt3zpw5w7hx45g8eTIZGRn06dOH6Oho4O61+qFDh1KqVClmzZrF1KlTuXHjBgMGDCAhIcG0v+TkZEaMGEGnTp2YOXMm5cqV45///Cc7dux4YM7JkyfTvXt3Jk6cSPPmzTl27Bj9+/fH1dWVzz77jHnz5tG2bVuWLVvG/PnzAfjggw944YUXKFGiBKtWraJ58+ZZtnvz5k169erFmjVr6Nu3L19++SXNmzdn4sSJ/Otf/8q07o4dO9i4cSOjRo1i9uzZODk5ERYWRlxc3EP9f/C402lYMUlKSiIhIYFKlSplGcvuhdkFC2b+4+Ph4UHbtm3ZvHkzo0ePxtnZmZs3bxIREUGvXr0oUKAA69atIzY2lpUrV/Lss88C8Pzzz3Pu3Dl+/PFHnnvuOSZOnEi9evUylVLDhg1p3bo106dP5/PPP8+S5eWXX2b9+vVERUURFBQE3C3pMmXK0KhRI44cOUJcXBwhISE0a9YMgGeeeYZZs2Zx69atbL8fP//8M7Vq1aJLly4ANGjQgMKFC+f76Tx5sKtXr1KzZs0sy0uUKEGfPn0YPnw4ALNmzcJgMPD1119TrFgxAJo1a0b79u2ZMmUKCxYs4MiRI9y4cYN+/fpRt25dACpXrszKlStJTU01nVG5efMm77zzDj169ACgadOmpuJs1apVjllbtGhBnz59TF9v2LCBBg0aMG3aNNPfpxdeeIF9+/axf/9+hg8fTtWqVfHy8sp06vWvNy2tXbuWY8eOsXjxYho2bAhA48aNcXZ2Zu7cufTt25eqVasCkJaWxvz5802XHNzc3OjTpw979+6lY8eOln/jnzAqSzG5/xVo97t9+3a2P4x27tyZZVm3bt0IDw8nKiqKVq1aERkZSXJyMiEhIQAcPHiQEiVKmIoSwMnJiVWrVgHwxx9/cOXKFYYNG5Zpu8WLF6dJkybs378/24wBAQFUrlyZdevWERQURFpaGhEREfTr1w8nJyeqVauGj48P7733Hnv27KFJkyY0bdqUt99+O8fvR8OGDVm+fDk9e/akRYsWNG/ePNMPOnEMXl5epqOw69evs2TJEvbu3cvIkSMJDg42rbdv3z4CAgLw9PQ0/ePPYDAQGBjIsmXLSE9Pp06dOhQuXJihQ4fSpk0bGjduTKNGjXjrrbcy7dNgMGTatsFgoHXr1sycOZOkpCTTS+v/qnr16pm+7tSpE506dSI9PZ3ff/+dM2fO8Pvvv3Pt2jXc3Nws/h7s378fHx8fU1HeExwczNy5c9m/f7+pLCtVqpTp2nzp0qUBTKd9JXsqSzEpVqwY7u7unDt3LtPyggULsmbNGtPX27dvz3L34T116tShevXqrFu3jlatWrF27VoaNGhgumsvPj4eHx+fHDPcO9VVokSJLGMlSpTI9NjKX4WEhDBt2jTi4uLYu3cvKSkppqNCNzc3vvnmG7744gu+//57Nm7cSMGCBWnZsiX/+te/sr2xZ8yYMZQuXZoNGzYwefJkJk+eTNWqVXn77bdp3LhxjjkkfxUsWBB/f3/T1w0aNODvf/87Y8aMwc3NjdatWwN3/+zt3Lkz23/43Rt/6qmnWLZsGXPmzGHTpk2sWrUKV1dXOnTowHvvvYerqytwt6Dv/fc9xYsXByAxMTHHsry3zj33rouvX7+emzdv8tRTT+Hv74+rq2uunqtMTEzM9u/Vvb9H9/+9KVy4cKZ17l3PzcjIsHh/TyKVpWQSFBRkOhr09PQ0Lb//h9Ffb+D5q5CQED777DNiY2P56aefmDhxomnM09Mz22uEv/32G3fu3DGd5rpy5UqWdS5fvvzAx1U6d+7MtGnTiIyMZPfu3TRs2DDTYyAVKlRg4sSJZGRkEB0dzbZt21iwYAEeHh588sknWbbn4uLC4MGDGTx4MJcuXeKHH35gzpw5DB8+nH379mX5oSOOwWAw8PHHH9OuXTvef/99AgIC8Pb2xtPTk/r16zN48OBsP3fv1GytWrWYOXMmt27d4vDhw2zcuJFVq1ZRpkwZ0xmPpKQk7ty5Q4ECBUyfv3btGgaDIVd3VH/88cds2rSJCRMm0LRpUzw8PABMZ2IsVbRoUX7//fcsyy9fvpzp9yZ5pxt8JJMhQ4YAd4+qbt68mWXcaDRy4sSJB26jU6dOGAwGxo4di4eHBy+++KJpLCAggEuXLmW6A9FoNPLOO+8we/ZsKlWqRIkSJdi8eXOmbcbFxfHDDz/QoEGDHPfr7e1NixYtWL9+Pfv27cv0A+fHH3+kYcOGREdH4+TkRK1atXjzzTepVq0aFy5cyLKtjIwMOnfuzIQJEwAoVaoUISEh9O7dm+vXr5OYmPjA74HYV/HixRk1ahQJCQl89tlnwN0jzuPHj+Pn54e/v7/p15YtW1i2bBnOzs6sX7+e559/nri4OJydnQkICGDcuHEUKVIk05+TO3fuZLqD22g0sm3bNurUqZOra9o///wz9erVo127dqaivHjxIr///numIz0npwf/qH7uuee4evWq6Sa5ezZs2ABA/fr1Lc4k2dORpWRSuXJlPv/8c0aNGkXHjh0JCQnhb3/7GwaDgZiYGNavX8/vv/9O8+bNc/wXtJeXF61bt2bz5s306tWLQoUKmca6du3K8uXLCQsL4/XXX6dMmTKsXbuWP/74g7Fjx+Lk5MTIkSMZPXo0w4YN4+WXXyY1NZUvv/wSo9GY5VrmX3Xr1o0BAwbg5eVlutEH7h4ZOzs7M3LkSMLCwihevDj79u3j6NGjjB8/Pst2nJycqFevHsuWLcPb25vatWtz8eJFFi9eTN26dU3XecRxde3alTVr1rBu3Tq6devG8OHDTX8++vTpQ5EiRYiIiODbb7/lH//4BwaDgfr165Oens5rr73GwIEDcXd3Z+vWrSQnJ9O2bdtM23/vvfe4evUqTz31FCtXruTkyZMsWrQoVxlr167N5s2bWbZsGdWrV+fUqVPMmTOH9PT0TNcQixYtytWrV4mKiuLpp5/Osp3OnTvzzTff8M9//pOwsDAqV67Mv//9bxYtWsTLL79M5cqV8/ZNFBOVpWTRrFkz0/WazZs3m/7yli5dmvr16/Phhx+a7hTMSWBgIJs3b85yOsnd3Z1ly5YxZcoUJk2aRHp6On5+fsyfP5969eoBd29K8PDwMJ3ydHNzo0GDBkyfPp0qVao8cL8NGzY0XWO6/zlRDw8PFi1axNSpUxk/fjxJSUlUqFCBDz74gJdffjnbbb311lu4u7uzevVqZs6ciaenJ4GBgbz55puWfBvFzgwGAx988AFdu3blww8/JDw8nBUrVvD555/z7rvvcuvWLSpUqMCHH35ouqvV19eXBQsWMGPGDN59911u3LhB1apV+fzzz7Ncpx4/fjyfffYZly5d4umnn2bBggUPPPORnTFjxpCRkcHs2bNJS0ujTJkydOnShQIFCjBr1iyuXLlCiRIl6NatG/v27TM9ttWuXbtM23F1dWXp0qVMnTqVr776isTERCpUqMDo0aPp16/fw30jBQCDUbPzig28+eabnD59OtONQflh7969DBgwgC1btpju/hOxppkzZzJr1ix+/fXXTGdN5PGmI0uxqi+//JKzZ8+yefNmZs2alW/73bVrF7/++itr1qyhRYsWKkoRsSqVpVjVnj17OHbsGEOHDs10zdDW/vzzTxYvXkzNmjX58MMP822/IvJk0GlYERERM/ToiIiIiBkqSxERETNUliIiImaoLEVERMxQWYqIiJihshQRETHjfwHAwudpoU84MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exended Data Fig. 4c\n",
    "#ATP yield per molecule of glucose for mammalian cells \n",
    "mammalian_yield = yieldPlot(2, 24)\n",
    "\n",
    "#save figure\n",
    "mammalian_yield.savefig('./Figures/edf4c_mammalian_yield.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f102fb4",
   "metadata": {},
   "source": [
    "## Extednded Data Fig. 4, D to F\n",
    "### Proteome Occupany of Respiration, Glycolysis, and Total ATP-Producing Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "678ea0e1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0564840640555226\n",
      "0.08070300291611598\n",
      "0.11203320246301975\n",
      "0.1244051523108865\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4d\n",
    "#Proteome Occupany of E. coli \n",
    "\n",
    "#parameter estimates for e. coli proteome occupany\n",
    "total_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_atp_total.csv')\n",
    "ferm_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_specific_activity_fermentation_no_ETC_occupancy.csv')\n",
    "pta_acka_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_specific_activity_fermentation.csv')\n",
    "resp_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_specific_activity_respiration.csv')\n",
    "\n",
    "#calculate mean and std for fermentation and respiration \n",
    "ferm_mean = ferm_proteomics['value'].mean()\n",
    "ferm_std = ferm_proteomics['value'].std()\n",
    "pta_acka_mean = pta_acka_proteomics.loc[pta_acka_proteomics['estimate']=='fermentation', 'value'].mean()\n",
    "pta_acka_std = pta_acka_proteomics.loc[pta_acka_proteomics['estimate']=='fermentation', 'value'].std()\n",
    "resp_mean = resp_proteomics.loc[resp_proteomics['estimate']=='respiration', 'value'].mean()\n",
    "resp_std = resp_proteomics.loc[resp_proteomics['estimate']=='respiration', 'value'].std()\n",
    "total_mean = total_proteomics['value'].mean()\n",
    "total_std = total_proteomics['value'].std()\n",
    "\n",
    "#generate figure\n",
    "fig, ax = plt.subplots(figsize = (7,7))\n",
    "sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "#create a dataset\n",
    "frac = [ferm_mean, pta_acka_mean, resp_mean, total_mean]\n",
    "frac_std = [ferm_std, pta_acka_std, resp_std, total_std]\n",
    "label_list = ['$\\phi_{glyc}$', '$\\phi_{pta-acka}$', '$\\phi_{resp}$', '$\\phi_{total}$']\n",
    "x_pos = np.arange(len(frac))\n",
    "    \n",
    "#create bars with different colors\n",
    "ax.bar(x_pos, frac, color=['crimson', 'green', 'darkblue', 'purple'], yerr = frac_std)\n",
    "\n",
    "#create names on the x-axis\n",
    "plt.xticks(x_pos, label_list)\n",
    "plt.ylim(0,0.32)\n",
    "\n",
    "print(ferm_mean)\n",
    "print(pta_acka_mean)\n",
    "print(resp_mean)\n",
    "print(total_mean)\n",
    "\n",
    "#crate y-axis label\n",
    "plt.ylabel('Fraction of the Proteome Occupied')\n",
    "\n",
    "#save fig\n",
    "fig.savefig('./Figures/edf4d_ecoli_proteome_fraction_occupied.svg',format='svg', dpi=1200)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "186be07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def proteomeSpace(ferm_proteomics, resp_proteomics, total_proteomics):\n",
    "    \n",
    "    '''Takes proteome sum .csv for an organism and calculates mean and std for \n",
    "    respiration, fermentation, and total ATP-producing space. Returns a bar graph.'''\n",
    "    \n",
    "    #read proteome occupancy summary .csv\n",
    "    #proteomics = pd.read_csv(csv)\n",
    "    \n",
    "    #calculate mean and std for fermentation and respiration \n",
    "    ferm_mean = ferm_proteomics.loc[ferm_proteomics['estimate']=='fermentation', 'value'].mean()\n",
    "    ferm_std = ferm_proteomics.loc[ferm_proteomics['estimate']=='fermentation', 'value'].std()\n",
    "    resp_mean = resp_proteomics.loc[resp_proteomics['estimate']=='respiration', 'value'].mean()\n",
    "    resp_std = resp_proteomics.loc[resp_proteomics['estimate']=='respiration', 'value'].std()\n",
    "    total_mean = total_proteomics['value'].mean()\n",
    "    total_std = total_proteomics['value'].std()\n",
    "    \n",
    "    print(ferm_mean)\n",
    "    print(resp_mean)\n",
    "    print(total_mean)\n",
    "    \n",
    "    #generate figure 2D\n",
    "    fig, ax = plt.subplots(figsize = (7,7))\n",
    "    sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "    sns.set_context(\"talk\")\n",
    "\n",
    "    #create a dataset\n",
    "    frac = [ferm_mean, resp_mean, total_mean]\n",
    "    frac_std = [ferm_std, resp_std, total_std]\n",
    "    label_list = ['$\\phi_{glyc}$', '$\\phi_{resp}$', '$\\phi_{total}$']\n",
    "    x_pos = np.arange(len(frac))\n",
    "    \n",
    "    #create bars with different colors\n",
    "    ax.bar(x_pos, frac, color=['crimson', 'darkblue', 'purple'], yerr = frac_std)\n",
    "\n",
    "    #create names on the x-axis\n",
    "    plt.xticks(x_pos, label_list)\n",
    "    plt.ylim(0,0.32)\n",
    "\n",
    "    #crate y-axis label\n",
    "    plt.ylabel('Fraction of the Proteome Occupied')\n",
    "    \n",
    "    return(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3bb9e8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09128256610067087\n",
      "0.11676296447919751\n",
      "0.16757971719697087\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4e\n",
    "#Proteome Occupany of S. cerevisiae\n",
    "\n",
    "#parameter estimates for proteome occupany\n",
    "total_proteomics = pd.read_csv('./Data/2024_02_25_yeast_atp_total_core.csv')\n",
    "resp_proteomics = pd.read_csv('./Data/2024_02_25_yeast_specific_activity_resp_core.csv')\n",
    "ferm_proteomics = pd.read_csv('./Data/2024_02_25_yeast_specific_activity_fermentation.csv')\n",
    "\n",
    "#S. cerevisiae Proteome Occupancy\n",
    "fig = proteomeSpace(ferm_proteomics, resp_proteomics, total_proteomics)\n",
    "\n",
    "#save fig\n",
    "fig.savefig('./Figures/edf4e_scerevisiae_proteome_fraction_occupied.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba8458c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.043132361598303776\n",
      "0.04043458383462101\n",
      "0.08356694543292485\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4f\n",
    "#Proteome Occupany of Mammalian Cells \n",
    "\n",
    "#parameter estimates for proteome occupany\n",
    "total_proteomics = pd.read_csv('./Data/2024_02_26_mammalian_atp_total_core.csv')\n",
    "resp_proteomics = pd.read_csv('./Data/2024_02_26_mammalian_specific_actvity_resp_core.csv')\n",
    "ferm_proteomics = pd.read_csv('./Data/2024_02_26_mammalian_specific_actvity_ferm.csv')\n",
    "\n",
    "#mammalian cell proteome occupany\n",
    "fig = proteomeSpace(ferm_proteomics, resp_proteomics, total_proteomics)\n",
    "\n",
    "#save fig\n",
    "fig.savefig('./Figures/edf4f_mammalian_proteome_fraction_occupied.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532974ca",
   "metadata": {},
   "source": [
    "## Extended Data Fig. 4, J to O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "001a6707",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#ATP production ratios for E. coli using molecular weight and stoich data for Extended Data Fig. 4\n",
    "\n",
    "#Read in the E. coli inclusion list  \n",
    "ecoli_inclusion = pd.read_csv('./Data/ecoli_inclusion.csv')\n",
    "ecoli_inclusion = ecoli_inclusion[['EC number', 'pathway', 'Gene name', 'Accession', 'MW', 'meta_pathway']]\n",
    "\n",
    "#Read in the molecular weight list for each EC number\n",
    "ecoli_mw = pd.read_csv('./Data/ecoli_mw.csv')\n",
    "ecoli_mw = ecoli_mw[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "ecoli_inclusion = pd.merge(ecoli_inclusion, ecoli_mw, how = 'left', on = 'EC number')\n",
    "\n",
    "#save as extended data table \n",
    "ecoli_inclusion.to_csv('./Data/ecoli_stoich_mass.csv')\n",
    "\n",
    "#Fermentation Mass\n",
    "ecoli_inclusion_ferm = ecoli_inclusion.loc[ecoli_inclusion['meta_pathway'] == 'fermentation']\n",
    "ecoli_inclusion_ferm = ecoli_inclusion_ferm[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "ferm_mass = ecoli_inclusion_ferm[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "ferm_mass.drop_duplicates(inplace = True)\n",
    "ferm_mass_pathway = ferm_mass.sum()[2]\n",
    "\n",
    "#Respiration Mass\n",
    "ecoli_inclusion_resp = ecoli_inclusion.loc[ecoli_inclusion['meta_pathway'] == 'respiration']\n",
    "ecoli_inclusion_resp = ecoli_inclusion_resp[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "ecoli_inclusion_resp.loc[ecoli_inclusion_resp['Gene name'] == 'ndh', 'pathway_stoich'] = 10 \n",
    "ecoli_inclusion_resp['MW*Subunit_Stoich*Pathway_Stoich'] = ecoli_inclusion_resp['pathway_stoich'] * ecoli_inclusion_resp['MW*Subunit_Stoich']\n",
    "resp_mass = ecoli_inclusion_resp[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "resp_mass.drop_duplicates(inplace = True)\n",
    "resp_mass_pathway = resp_mass.sum()[2]\n",
    "\n",
    "#Glycolysis Mass\n",
    "ecoli_inclusion_glycolysis = ecoli_inclusion.loc[ecoli_inclusion['pathway'] == 'glycolysis']\n",
    "ecoli_inclusion_glycolysis = ecoli_inclusion_glycolysis[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "glycolysis_mass = ecoli_inclusion_glycolysis[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "glycolysis_mass.drop_duplicates(inplace = True)\n",
    "glycolysis_mass_pathway = glycolysis_mass.sum()[2]\n",
    "\n",
    "#ETC Mass\n",
    "ecoli_inclusion_etc = ecoli_inclusion.loc[ecoli_inclusion['pathway'] == 'etc']\n",
    "ecoli_inclusion_etc = ecoli_inclusion_etc[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "ecoli_inclusion_etc.loc[(ecoli_inclusion_etc['EC number'] == '7.1.1.2') | (ecoli_inclusion_etc['Gene name'] == 'ndh'), 'pathway_stoich'] = 4\n",
    "ecoli_inclusion_etc.loc[(ecoli_inclusion_etc['EC number'] == '7.1.1.3') | (ecoli_inclusion_etc['EC number'] == '7.1.1.7.1')| (ecoli_inclusion_etc['EC number'] == '7.1.1.7.2'), 'pathway_stoich'] = 2\n",
    "ecoli_inclusion_etc['MW*Subunit_Stoich*Pathway_Stoich'] = ecoli_inclusion_etc['pathway_stoich'] * ecoli_inclusion_etc['MW*Subunit_Stoich']\n",
    "etc_mass = ecoli_inclusion_etc[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "etc_mass.drop_duplicates(inplace = True)\n",
    "etc_mass_pathway = etc_mass.sum()[2]\n",
    "\n",
    "#ATP Mass\n",
    "ecoli_inclusion_atp = ecoli_inclusion.loc[ecoli_inclusion['pathway'] == 'atp']\n",
    "ecoli_inclusion_atp = ecoli_inclusion_atp[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "ecoli_inclusion_atp['pathway_stoich'] = 3.2\n",
    "ecoli_inclusion_atp['MW*Subunit_Stoich*Pathway_Stoich'] = ecoli_inclusion_atp['pathway_stoich'] * ecoli_inclusion_atp['MW*Subunit_Stoich']\n",
    "atp_mass = ecoli_inclusion_atp[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "atp_mass.drop_duplicates(inplace = True)\n",
    "atp_mass_pathway = atp_mass.sum()[2]\n",
    "\n",
    "#Calculating the total mass of each pathway\n",
    "fermentation_mass_pathway_total = ferm_mass_pathway\n",
    "pta_acka_mass_pathway_total = ferm_mass_pathway + etc_mass_pathway + atp_mass_pathway\n",
    "resp_mass_pathway_total = resp_mass_pathway + glycolysis_mass_pathway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0423b6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.669701\n",
      "6.4147718\n",
      "15.755932866666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4j\n",
    "#Cumulative molecular weight of pathway for E. coli\n",
    "\n",
    "#ATP yield for ecoli \n",
    "Y_Glyc = fermentation_mass_pathway_total / 1e6\n",
    "Y_Pta_acka = pta_acka_mass_pathway_total / 1e6\n",
    "Y_Resp = resp_mass_pathway_total / 1e6\n",
    "\n",
    "#make three dataframes with fermentation and respiration yield values\n",
    "d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "d2 = {'yield': [Y_Pta_acka, -1], 'pathway': ['Pta-AckA', ' ']}\n",
    "d3 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', '    ']}\n",
    "df = pd.DataFrame(data=d)\n",
    "df2 = pd.DataFrame(data=d2)\n",
    "df3 = pd.DataFrame(data=d3)\n",
    "\n",
    "#generate figure\n",
    "fig, ax = plt.subplots(figsize = (7,7))\n",
    "sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "sns.set_context(\"talk\")\n",
    "plt.ylim(0,20)\n",
    "\n",
    "plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "plt.stem(df2['pathway'], df2['yield'], 'green', markerfmt = \"go\")\n",
    "plt.stem(df3['pathway'], df3['yield'], 'blue', markerfmt = \"bo\")\n",
    "\n",
    "plt.ylabel('Cummulative MW of Pathway (MDa)',fontsize=20)\n",
    "plt.yticks(fontsize=17)\n",
    "plt.xticks(fontsize=17)\n",
    "\n",
    "print(Y_Glyc)\n",
    "print(Y_Pta_acka)\n",
    "print(Y_Resp)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/edf4j_ecoli_MW_pathway.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07061d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.197819250272953\n",
      "1.5589019082487081\n",
      "1.269363113517201\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exended Data Fig. 4m\n",
    "#ATP yield / molecular weight of pathway for E. coli\n",
    "\n",
    "#ATP yield for ecoli \n",
    "y_fermentation = 2\n",
    "y_pta_acka = 10\n",
    "y_resp = 20\n",
    "\n",
    "#ATP yield for ecoli \n",
    "Y_Glyc = y_fermentation / (fermentation_mass_pathway_total / 1e6)\n",
    "Y_Pta_acka =  y_pta_acka / (pta_acka_mass_pathway_total / 1e6)\n",
    "Y_Resp =  y_resp / (resp_mass_pathway_total / 1e6)\n",
    "\n",
    "#make three dataframes with fermentation and respiration yield values\n",
    "d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "d2 = {'yield': [Y_Pta_acka, -1], 'pathway': ['Pta-AckA', ' ']}\n",
    "d3 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', '    ']}\n",
    "df = pd.DataFrame(data=d)\n",
    "df2 = pd.DataFrame(data=d2)\n",
    "df3 = pd.DataFrame(data=d3)\n",
    "\n",
    "#generate figure\n",
    "fig, ax = plt.subplots(figsize = (7,7))\n",
    "sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "sns.set_context(\"talk\")\n",
    "plt.ylim(0,2.1)\n",
    "\n",
    "plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "plt.stem(df2['pathway'], df2['yield'], 'green', markerfmt = \"go\")\n",
    "plt.stem(df3['pathway'], df3['yield'], 'blue', markerfmt = \"bo\")\n",
    "\n",
    "plt.ylabel(r'$ Yield$ / $MW$', fontsize=23)\n",
    "plt.yticks(fontsize=17)\n",
    "plt.xticks(fontsize=17)\n",
    "\n",
    "print(Y_Glyc)\n",
    "print(Y_Pta_acka)\n",
    "print(Y_Resp)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/edf4m_ecoli_ratio.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "495cb3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ATP production ratios for S. cerevisiae using molecular weight and stoich data for Extended Data Fig. 4\n",
    "\n",
    "#read in the S. cerevisiae inclusion list  \n",
    "yeast_inclusion = pd.read_csv('./Data/yeast_inclusion.csv')\n",
    "#Read in the molecular weight list for each EC number\n",
    "yeast_mw = pd.read_csv('./Data/yeast_mw.csv')\n",
    "yeast_mw = yeast_mw[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "yeast_inclusion = pd.merge(yeast_inclusion, yeast_mw, how = 'left', on = 'EC number')\n",
    "yeast_inclusion.drop(columns = {'Unnamed: 0'}, inplace = True)\n",
    "yeast_inclusion.drop_duplicates(inplace = True)\n",
    "\n",
    "#Extended Data Table\n",
    "yeast_inclusion.to_csv('./Data/yeast_stoich_mass.csv')\n",
    "\n",
    "yeast_inclusion_ferm = yeast_inclusion.loc[yeast_inclusion['meta_pathway'] == 'fermentation']\n",
    "yeast_inclusion_ferm = yeast_inclusion_ferm[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "ferm_mass = yeast_inclusion_ferm[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "ferm_mass.drop_duplicates(inplace = True)\n",
    "ferm_mass_pathway = ferm_mass.sum()[2]\n",
    "\n",
    "yeast_inclusion_resp = yeast_inclusion.loc[yeast_inclusion['meta_pathway'] == 'respiration']\n",
    "yeast_inclusion_resp = yeast_inclusion_resp[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "resp_mass = yeast_inclusion_resp[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "resp_mass.drop_duplicates(inplace = True)\n",
    "resp_mass_pathway = resp_mass.sum()[2]\n",
    "\n",
    "yeast_inclusion_glycolysis = yeast_inclusion.loc[yeast_inclusion['pathway'] == 'glycolysis']\n",
    "yeast_inclusion_glycolysis = yeast_inclusion_glycolysis[['EC number', 'pathway', 'Gene name', 'meta_pathway', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']].drop_duplicates()\n",
    "glycolysis_mass = yeast_inclusion_glycolysis[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "glycolysis_mass.drop_duplicates(inplace = True)\n",
    "glycolysis_mass_pathway = glycolysis_mass.sum()[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "836abc04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pathway Yields\n",
    "Y_ferm = 2\n",
    "Y_resp = 16\n",
    "\n",
    "#Mass * Pathway Stoichometry for Fermentation / Respiration in S. cereivisae\n",
    "mass_pathway = (1/ferm_mass_pathway * Y_ferm) / ((1/ (resp_mass_pathway + glycolysis_mass_pathway)) * Y_resp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2d27cea",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0777580247619045\n",
      "20.77689273476191\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4k\n",
    "#Cumulative molecular weight of pathway for S. cerevisiae\n",
    "def yieldPlot(Y_Glyc, Y_Resp):\n",
    "    \n",
    "    '''Takes the ATP yield per molecule values for fermentation and respiration and generates a stem plot'''\n",
    "    \n",
    "    #make two dataframes with fermentation and respiration yield values\n",
    "    d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "    d2 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', ' ']}\n",
    "    df = pd.DataFrame(data=d)\n",
    "    df2 = pd.DataFrame(data=d2)\n",
    "\n",
    "    #generate figure\n",
    "    fig, ax = plt.subplots(figsize = (7,7))\n",
    "    sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "    sns.set_context(\"talk\")\n",
    "    plt.ylim(0,25)\n",
    "\n",
    "    plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "    plt.stem(df2['pathway'], df2['yield'], 'darkblue', markerfmt = \"bo\")\n",
    "\n",
    "    plt.ylabel('Cummulative MW of Pathway (MDa)',fontsize=20)\n",
    "    plt.yticks(fontsize=17)\n",
    "    plt.xticks(fontsize=17)\n",
    "    \n",
    "    return(fig)\n",
    "\n",
    "#Exended Data Fig. 4b\n",
    "#ATP yield per molecule of glucose for S. cerevisiae\n",
    "print(ferm_mass_pathway / 1e6)\n",
    "print((resp_mass_pathway + glycolysis_mass_pathway) / 1e6)\n",
    "yeast_yield = yieldPlot(ferm_mass_pathway / 1e6, (resp_mass_pathway + glycolysis_mass_pathway)/ 1e6)\n",
    "\n",
    "\n",
    "#save figure\n",
    "yeast_yield.savefig('./Figures/edf4k_scerevisiae_MW.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "614294cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8557041135850831\n",
      "0.7700862782638489\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4n\n",
    "#ATP yield / molecular weight of pathway for S. cerevisiae\n",
    "\n",
    "ferm_ratio = Y_ferm / (ferm_mass_pathway / 1e6)\n",
    "resp_ratio = Y_resp / ((resp_mass_pathway + glycolysis_mass_pathway) / 1e6)\n",
    "\n",
    "def yieldPlot(Y_Glyc, Y_Resp):\n",
    "    \n",
    "    '''Takes the ATP yield per molecule values for fermentation and respiration and generates a stem plot'''\n",
    "    \n",
    "    #make two dataframes with fermentation and respiration yield values\n",
    "    d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "    d2 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', ' ']}\n",
    "    df = pd.DataFrame(data=d)\n",
    "    df2 = pd.DataFrame(data=d2)\n",
    "\n",
    "    #generate figure\n",
    "    fig, ax = plt.subplots(figsize = (7,7))\n",
    "    sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "    sns.set_context(\"talk\")\n",
    "    plt.ylim(0,2.7)\n",
    "\n",
    "    plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "    plt.stem(df2['pathway'], df2['yield'], 'darkblue', markerfmt = \"bo\")\n",
    "\n",
    "    \n",
    "    plt.ylabel(r'$ Yield$ / $MW$', fontsize=23)\n",
    "    plt.yticks(fontsize=17)\n",
    "    plt.xticks(fontsize=17)\n",
    "    \n",
    "    return(fig)\n",
    "\n",
    "#Exended Data Fig. 4b\n",
    "#ATP yield per molecule of glucose for S. cerevisiae\n",
    "print(ferm_ratio)\n",
    "print(resp_ratio)\n",
    "\n",
    "yeast_yield = yieldPlot(ferm_ratio, resp_ratio)\n",
    "\n",
    "\n",
    "#save figure\n",
    "yeast_yield.savefig('./Figures/edf4n_scerevisiae_ratio.svg',format='svg', dpi=1200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d889d451",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ATP production ratios for mammalian cells using molecular weight and stoich data for Extended Data Fig. 4\n",
    "\n",
    "#Read in the mammalian inclusion list  \n",
    "mammalian_inclusion = pd.read_csv('./Data/mammalian_inclusion.csv')\n",
    "mammalian_inclusion = mammalian_inclusion[['EC number', 'pathway', 'Gene name', 'MW', 'meta_pathway']]\n",
    "\n",
    "#Read in the molecular weight list for each EC number\n",
    "mammalian_mw = pd.read_csv('./Data/mammalian_mw.csv')\n",
    "mammalian_mw = mammalian_mw[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "mammalian_inclusion = pd.merge(mammalian_inclusion, mammalian_mw, how = 'left', on = 'EC number')\n",
    "\n",
    "#Extended Data Table\n",
    "mammalian_inclusion.to_csv('./Data/mammalian_mass_stoich.csv')\n",
    "\n",
    "mammalian_inclusion_ferm = mammalian_inclusion.loc[mammalian_inclusion['meta_pathway'] == 'fermentation']\n",
    "ferm_mass = mammalian_inclusion_ferm[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "ferm_mass.drop_duplicates(inplace = True)\n",
    "ferm_mass_pathway = ferm_mass.sum()[2]\n",
    "\n",
    "mammalian_inclusion_resp = mammalian_inclusion.loc[mammalian_inclusion['meta_pathway'] == 'respiration']\n",
    "resp_mass = mammalian_inclusion_resp[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "resp_mass.drop_duplicates(inplace = True)\n",
    "resp_mass_pathway = resp_mass.sum()[2]\n",
    "\n",
    "mammalian_inclusion_glycolysis = mammalian_inclusion.loc[mammalian_inclusion['pathway'] == 'glycolysis']\n",
    "glycolysis_mass = mammalian_inclusion_glycolysis[['EC number', 'MW*Subunit_Stoich', 'MW*Subunit_Stoich*Pathway_Stoich', 'pathway_stoich']]\n",
    "glycolysis_mass.drop_duplicates(inplace = True)\n",
    "glycolysis_mass_pathway = glycolysis_mass.sum()[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb647e96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.067968\n",
      "29.424359833333337\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4l\n",
    "#Cumulative molecular weight of pathway for mammalian cells\n",
    "\n",
    "def yieldPlot(Y_Glyc, Y_Resp):\n",
    "    \n",
    "    '''Takes the ATP yield per molecule values for fermentation and respiration and generates a stem plot'''\n",
    "    \n",
    "    #make two dataframes with fermentation and respiration yield values\n",
    "    d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "    d2 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', ' ']}\n",
    "    df = pd.DataFrame(data=d)\n",
    "    df2 = pd.DataFrame(data=d2)\n",
    "\n",
    "    #generate figure\n",
    "    fig, ax = plt.subplots(figsize = (7,7))\n",
    "    sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "    sns.set_context(\"talk\")\n",
    "    plt.ylim(0,33)\n",
    "\n",
    "    plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "    plt.stem(df2['pathway'], df2['yield'], 'darkblue', markerfmt = \"bo\")\n",
    "\n",
    "    plt.ylabel('Cummulative MW of Pathway (MDa)',fontsize=20)\n",
    "    plt.yticks(fontsize=17)\n",
    "    plt.xticks(fontsize=17)\n",
    "    \n",
    "    return(fig)\n",
    "\n",
    "#Exended Data Fig. 4b\n",
    "#ATP yield per molecule of glucose for S. cerevisiae\n",
    "print(ferm_mass_pathway / 1e6)\n",
    "print((resp_mass_pathway + glycolysis_mass_pathway)/ 1e6)\n",
    "yeast_yield = yieldPlot(ferm_mass_pathway / 1e6, (resp_mass_pathway + glycolysis_mass_pathway) / 1e6)\n",
    "\n",
    "\n",
    "#save figure\n",
    "yeast_yield.savefig('./Figures/sf4l_mammalian_mw.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "781120d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8727152873494337\n",
      "0.8156507103618152\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4o\n",
    "#ATP yield / molecular weight of pathway for mammalian cells\n",
    "\n",
    "Y_ferm = 2\n",
    "Y_resp = 24\n",
    "\n",
    "ferm_ratio =  Y_ferm / (ferm_mass_pathway / 1e6)\n",
    "resp_ratio =  Y_resp / ((resp_mass_pathway + glycolysis_mass_pathway) /  1e6)\n",
    "def yieldPlot(Y_Glyc, Y_Resp):\n",
    "    \n",
    "    '''Takes the ATP yield per molecule values for fermentation and respiration and generates a stem plot'''\n",
    "    \n",
    "    #make two dataframes with fermentation and respiration yield values\n",
    "    d = {'yield': [-1, Y_Glyc, -1], 'pathway': ['', 'Glycolysis', '   ']}\n",
    "    d2 = {'yield': [Y_Resp, -1], 'pathway': ['Respiration', ' ']}\n",
    "    df = pd.DataFrame(data=d)\n",
    "    df2 = pd.DataFrame(data=d2)\n",
    "\n",
    "    #generate figure\n",
    "    fig, ax = plt.subplots(figsize = (7,7))\n",
    "    sns.set_style(\"white\", {\"axes.edgecolor\": \"black\"})\n",
    "    sns.set_context(\"talk\")\n",
    "    plt.ylim(0,2.7)\n",
    "\n",
    "    plt.stem(df['pathway'], df['yield'], 'crimson', markerfmt = \"ro\")\n",
    "    plt.stem(df2['pathway'], df2['yield'], 'darkblue', markerfmt = \"bo\")\n",
    "\n",
    "    \n",
    "    plt.ylabel(r'$ Yield$ / $MW$', fontsize=23)\n",
    "    plt.yticks(fontsize=17)\n",
    "    plt.xticks(fontsize=17)\n",
    "    \n",
    "    \n",
    "    return(fig)\n",
    "\n",
    "#Exended Data Fig. 4b\n",
    "#ATP yield per molecule of glucose for S. cerevisiae\n",
    "print(ferm_ratio)\n",
    "print(resp_ratio)\n",
    "yeast_yield = yieldPlot(ferm_ratio, resp_ratio)\n",
    "\n",
    "\n",
    "#save figure\n",
    "yeast_yield.savefig('./Figures/sf4o_mammalian_ratio.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e106fc7",
   "metadata": {},
   "source": [
    "## Fig. 2 A to C, Supplemental Fig. 4, G to I, Supplemental Fig. 6\n",
    "### Glucose consumption and ATP production rate (umol mgprotein-1 min-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "beee3908",
   "metadata": {},
   "outputs": [],
   "source": [
    "def warburgRates(rates, anaerobic_proteomics, ferm_proteomics, resp_proteomics, gly_proteomics, Y_Anaerobic, Y_Ferm, Y_Resp): \n",
    "    \n",
    "    '''For E. coli, performs bootstrapping to resample the five parameter estimates (i.e. fraction of \n",
    "    the proteome occupied by pathway, specific activity, and ATP yield) with replacement (N=10,000). \n",
    "    After each round of sampling, the specific activity of each pathway is calculated. The sampled \n",
    "    parameter values for proteome occupied by pathway, specific activity, and ATP yield from each \n",
    "    round of bootstrapping are used in the linear program described in the function above. \n",
    "    All 10,000 results are stored in a data frame, which in the output.'''\n",
    "\n",
    "    results_final = []\n",
    "    \n",
    "    for j in tqdm(range(10000)):\n",
    "    \n",
    "        #random choice with replacement for each paramter \n",
    "        sample_activity_respiration = rates.loc[rates['pathway'] == 'Respiration', 'glucose uptake rate  (mmol gprotein-1 hr-1)'].unique() / 60\n",
    "        #print(sample_activity_respiration)\n",
    "        sample_activity_fermentation = rates.loc[rates['pathway'] == 'Fermentation', 'glucose uptake rate  (mmol gprotein-1 hr-1)'].unique() / 60\n",
    "        #print(sample_activity_fermentation)\n",
    "        sample_activity_anaerobic = rates.loc[rates['pathway'] == 'Fermentation', 'glucose uptake rate  (mmol gprotein-1 hr-1)'].unique() / 60\n",
    "        #print(sample_activity_anaerobic)\n",
    "        sample_mass_respiraition = resp_proteomics.loc[(resp_proteomics['estimate'] == 'respiration'), 'value'].unique()\n",
    "        #print(sample_mass_respiraition)\n",
    "        sample_mass_fermentation = ferm_proteomics.loc[(ferm_proteomics['estimate'] == 'fermentation'), 'value'].unique()\n",
    "        #print(sample_mass_respiraition)\n",
    "        sample_mass_anaerobic = anaerobic_proteomics.loc[(anaerobic_proteomics['estimate'] == 'fermentation'), 'value'].unique()\n",
    "        #print(sample_mass_anaerobic)\n",
    "        sample_mass_glycolysis = gly_proteomics.loc[(gly_proteomics['estimate'] == 'fermentation'), 'value'].unique()\n",
    "           \n",
    "        sample_size_activity_respiration = len(sample_activity_respiration)\n",
    "        sample_size_activity_fermentation = len(sample_activity_fermentation)\n",
    "        sample_size_activity_anaerobic = len(sample_activity_anaerobic)\n",
    "        sample_size_mass_respiraition = len(sample_mass_respiraition)\n",
    "        sample_size_mass_fermentation = len(sample_mass_fermentation)\n",
    "        sample_size_mass_anaerobic = len(sample_mass_anaerobic)\n",
    "        sample_size_mass_glycolysis= len(sample_mass_glycolysis)\n",
    "    \n",
    "        sample_specific_activity_fermentation = np.random.choice(sample_activity_fermentation, size=sample_size_activity_fermentation,replace=True)\n",
    "        sample_specific_activity_respiration = np.random.choice(sample_activity_respiration, size=sample_size_activity_respiration, replace=True)\n",
    "        sample_specific_activity_anaerobic = np.random.choice(sample_activity_anaerobic, size=sample_size_activity_anaerobic, replace=True)\n",
    "        \n",
    "        sample_fraction_fermentation = np.random.choice(sample_mass_fermentation, size=sample_size_mass_fermentation, replace=True)\n",
    "        sample_fraction_respiration = np.random.choice(sample_mass_respiraition, size=sample_size_mass_respiraition, replace=True)\n",
    "        sample_fraction_anaerobic = np.random.choice(sample_mass_anaerobic, size=sample_size_mass_anaerobic, replace=True)\n",
    "        sample_fraction_glycolysis = np.random.choice(sample_mass_glycolysis, size=sample_size_mass_glycolysis, replace=True)\n",
    "        \n",
    "        V_Ferm =  sample_specific_activity_fermentation.mean() / sample_fraction_fermentation.mean() \n",
    "        V_Resp =  sample_specific_activity_respiration.mean() / sample_fraction_respiration.mean() \n",
    "        V_Anaerobic = sample_specific_activity_anaerobic.mean() / sample_fraction_anaerobic.mean() \n",
    "        V_Correction = sample_specific_activity_fermentation.mean() / sample_fraction_glycolysis.mean()\n",
    "        #print(sample_fraction_anaerobic.mean())\n",
    "    \n",
    "        rate_ratio = V_Resp / V_Anaerobic\n",
    "        \n",
    "        V_Resp =  sample_specific_activity_respiration.mean() / (sample_fraction_respiration.mean() + sample_fraction_glycolysis.mean() * rate_ratio)\n",
    "\n",
    "        #ATP yield\n",
    "        Y_Ferm = Y_Ferm\n",
    "        Y_Resp = Y_Resp\n",
    "        Y_Anaerobic = Y_Anaerobic\n",
    "        \n",
    "        #ATP Production Rate \n",
    "        V_Ferm_ATP = V_Ferm * Y_Ferm\n",
    "        V_Resp_ATP = V_Resp * Y_Resp\n",
    "        V_Anaerobic_ATP = V_Anaerobic * Y_Anaerobic\n",
    "        #print(V_Anaerobic_ATP)\n",
    "        \n",
    "        results = [V_Anaerobic, V_Ferm, V_Resp, V_Anaerobic_ATP, V_Ferm_ATP, V_Resp_ATP]\n",
    "        \n",
    "        #Append the outputs for each bootstrap iteration\n",
    "        results_final.append(results)\n",
    "    \n",
    "    #print(len(results_final))\n",
    "    #Convert the array into a dataframe\n",
    "    df = pd.DataFrame({'V_Anaerobic' : [], 'V_Ferm' : [], 'V_Resp' : [], 'V_Anaerobic_ATP' : [], 'V_Ferm_ATP' : [], 'V_Resp_ATP' : []})\n",
    "\n",
    "    for i in range(len(results_final)):\n",
    "        df_temp_vanaerobic = pd.DataFrame({'V_Anaerobic' : [results_final[i][0]]})\n",
    "        df_temp_vferm = pd.DataFrame({'V_Ferm' : [results_final[i][1]]})\n",
    "        df_temp_vresp = pd.DataFrame({'V_Resp' : [results_final[i][2]]})\n",
    "        df_temp_vanaerobicatp = pd.DataFrame({'V_Anaerobic_ATP' : [results_final[i][3]]})\n",
    "        df_temp_vfermatp = pd.DataFrame({'V_Ferm_ATP' : [results_final[i][4]]})\n",
    "        df_temp_vrespatp = pd.DataFrame({'V_Resp_ATP' : [results_final[i][5]]})\n",
    "        out = df_temp_vanaerobic.join([df_temp_vferm, df_temp_vresp, df_temp_vanaerobicatp, df_temp_vfermatp, df_temp_vrespatp])\n",
    "        \n",
    "        df = pd.concat([df, out])\n",
    "        \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2271c00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def activityPlot(data, graph_type):\n",
    "    \n",
    "    'For E. coli, plots the results of the warburgRate function'\n",
    "\n",
    "    data2 = data.melt()\n",
    "    data2['label'] = 'none'\n",
    "    data2.loc[data2['variable'].str.contains('Ferm'), 'label'] = 'Fermentation'\n",
    "    data2.loc[data2['variable'].str.contains('Resp'), 'label'] = 'Respiration'\n",
    "    \n",
    "\n",
    "    if graph_type == 'glucose': \n",
    "    \n",
    "        data2 = data2.loc[~(data2['variable'].str.contains('ATP'))] \n",
    "        \n",
    "        print(data2.loc[(data2['variable'].str.contains('Ana')), 'value'].mean())\n",
    "        print(data2.loc[(data2['variable'].str.contains('Ferm')), 'value'].mean())\n",
    "        print(data2.loc[(data2['variable'].str.contains('Resp')), 'value'].mean())\n",
    "        \n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "        # plot a bar chart\n",
    "        colors = [\"crimson\", \"green\", 'darkblue']\n",
    "        customPalette = sns.set_palette(sns.color_palette(colors))\n",
    "        #sns.barplot(x=\"variable\", y=\"value\", data=data, estimator=np.mean, ci=95, capsize=0, zorder=2, hue = 'label', dodge = False,  palette=customPalette)\n",
    "        summarystats = data2.groupby('variable').mean()\n",
    "        summarystats.reset_index(inplace = True)\n",
    "\n",
    "        yerr = warburgCI(data)\n",
    "        yerr_subset = yerr.loc[yerr.index.isin(summarystats['variable'])]\n",
    "        yerr_subset.reset_index(inplace = True)\n",
    "        yerr_subset.rename(columns = {'index':'variable'}, inplace = True)\n",
    "        yerr_subset = pd.merge(yerr_subset, summarystats, on = 'variable')\n",
    "        yerr_subset['lb'] = yerr_subset['value'] - yerr_subset['conf95_low']\n",
    "        yerr_subset['ub'] = yerr_subset['conf95_high'] - yerr_subset['value']\n",
    "        yerr2 = np.array([yerr_subset['lb'],yerr_subset['ub']])\n",
    "        #print(yerr2)\n",
    "\n",
    "        sns.barplot(x=\"variable\", y=\"value\", data=data2, hue = 'label', palette = customPalette, dodge = False)\n",
    "        plt.errorbar(x=summarystats['variable'], y=summarystats['value'], yerr = yerr2, fmt = 'none', color = 'black', capsize = 7)\n",
    "        \n",
    "        #axis format\n",
    "        ax.set_ylabel('', fontsize=16)\n",
    "        ax.set_xlabel('', fontsize=16)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "        ax.set_ylabel(\"Glucose consumption rate\" r'$ \\left(\\frac{\\mu mol}{mg_{pathway} \\cdot min^{}}\\right)$', fontsize=23)\n",
    "\n",
    "        ax.legend(fontsize=13).set_title('')\n",
    "        ax.set(yscale=\"log\")\n",
    "\n",
    "        ax.legend_.remove()\n",
    "        ax.grid(b=True, which='minor', linestyle='-', linewidth='0.3')\n",
    "        ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "        #ax.set_ylim(bottom=0)\n",
    "        ax.set_ylim((.2/60,700/60))\n",
    "\n",
    "    if graph_type == 'ATP': \n",
    "\n",
    "        data2 = data2.loc[(data2['variable'].str.contains('ATP'))] \n",
    "        \n",
    "        print(data2.loc[(data2['variable'].str.contains('Ana')), 'value'].mean())\n",
    "        print(data2.loc[(data2['variable'].str.contains('Ferm')), 'value'].mean())\n",
    "        print(data2.loc[(data2['variable'].str.contains('Resp')), 'value'].mean())\n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "    \n",
    "        # plot a bar chart\n",
    "        colors = [\"crimson\", \"green\", 'darkblue']\n",
    "        customPalette = sns.set_palette(sns.color_palette(colors))\n",
    "        #sns.barplot(x=\"variable\", y=\"value\", data=data, estimator=np.mean, ci=\"sd\", capsize=0.2, hue = 'label', dodge = False,  palette=customPalette)\n",
    "        summarystats = data2.groupby('variable').mean()\n",
    "        summarystats.reset_index(inplace = True)\n",
    "\n",
    "        yerr = warburgCI(data)\n",
    "        \n",
    "        yerr_subset = yerr.loc[yerr.index.isin(summarystats['variable'])]\n",
    "        yerr_subset.reset_index(inplace = True)\n",
    "        yerr_subset.rename(columns = {'index':'variable'}, inplace = True)\n",
    "        yerr_subset = pd.merge(yerr_subset, summarystats, on = 'variable')\n",
    "        yerr_subset['lb'] = yerr_subset['value'] - yerr_subset['conf95_low']\n",
    "        yerr_subset['ub'] = yerr_subset['conf95_high'] - yerr_subset['value']\n",
    "        yerr2 = np.array([yerr_subset['lb'],yerr_subset['ub']])\n",
    "        \n",
    "        sns.barplot(x=\"variable\", y=\"value\", data=data2, hue = 'label', palette = customPalette, dodge = False)\n",
    "        plt.errorbar(x=summarystats['variable'], y=summarystats['value'], yerr = yerr2, fmt = 'none', color = 'black', capsize = 5)\n",
    "        \n",
    "        #axis format\n",
    "        ax.set_ylabel('', fontsize=16)\n",
    "        ax.set_xlabel('', fontsize=16)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "        ax.set_ylabel(\"ATP Production Rate\" r'$ \\left(\\frac{\\mu mol}{mg_{pathway} \\cdot min^{}}\\right)$', fontsize=23)\n",
    "\n",
    "        ax.legend(fontsize=13).set_title('')\n",
    "        #ax.set(yscale=\"log\")\n",
    "\n",
    "        ax.legend_.remove()\n",
    "        ax.grid(b=True, which='minor', linestyle='-', linewidth='0.2')\n",
    "        ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "        #ax.set_ylim(bottom=0)\n",
    "        ax.set_ylim((0,yerr['conf95_high'][4] * 1.15))\n",
    "\n",
    "    return(fig)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7342fe29",
   "metadata": {},
   "outputs": [],
   "source": [
    "def warburgCI(df): \n",
    "    \n",
    "    '''For E. coli, calculates the 95 confidence interval for each output of the model'''\n",
    "\n",
    "    df_sub = df[['V_Anaerobic', 'V_Ferm', 'V_Resp', 'V_Anaerobic_ATP', 'V_Ferm_ATP', 'V_Resp_ATP']]\n",
    "    \n",
    "    conf95_high = []\n",
    "    conf95_low = []\n",
    "\n",
    "    for column in list(df_sub.columns): \n",
    "        df_temp = df_sub[[column]]\n",
    "        sample = np.array(df_temp[column])\n",
    "        #mean, sigma = np.mean(sample), np.std(sample)\n",
    "        #conf_int = stats.norm.interval(0.95, loc=mean, scale=sigma)\n",
    "        #confidence95 = mean - conf_int[0]\n",
    "        sample_count = len(sample)\n",
    "        sample_high = sample_count * .975\n",
    "        sample_low = sample_count * .025\n",
    "        sample = np.sort(sample)\n",
    "        #print(type(sample[int(sample_high) - 1]))\n",
    "        conf95_high = np.append(conf95_high, sample[int(sample_high) - 1])\n",
    "        conf95_low = np.append(conf95_low, sample[int(sample_low) - 1])\n",
    "        \n",
    "    df = pd.DataFrame({'conf95_low': conf95_low, 'conf95_high': conf95_high}, \n",
    "                      index = ['V_Anaerobic', 'V_Ferm', 'V_Resp', 'V_Anaerobic_ATP', 'V_Ferm_ATP', 'V_Resp_ATP'])\n",
    "    #print(df)\n",
    "    return(df)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "874fa43b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a49e1e067ca94ebf846b1f59a7c2a952",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Specific acitivity estimate inputs for E. coli\n",
    "\n",
    "#parameters estimates for specfic activity\n",
    "rates = pd.read_csv('./Data/ecoli_cellular_rates.csv')\n",
    "\n",
    "#parameters estimates for specfic activity proteome\n",
    "total_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_atp_total.csv')\n",
    "ferm_anaerobic_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_specific_activity_fermentation_no_ETC_occupancy.csv')\n",
    "ferm_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_specific_activity_fermentation.csv')\n",
    "resp_proteomics = pd.read_csv('./Data/2024_02_22_ecoli_specific_activity_respiration.csv')\n",
    "\n",
    "#Calculating specific activities\n",
    "ecoli_anaerobic = warburgRates(rates, ferm_anaerobic_proteomics, ferm_proteomics, resp_proteomics, ferm_proteomics, 2, 10, 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "60e1012a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.213013142537706\n",
      "2.947269128091007\n",
      "0.7440057017325268\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4g\n",
    "#Glucose consumption specific activities for E. coli \n",
    "fig = activityPlot(ecoli_anaerobic, 'glucose')\n",
    "\n",
    "fig.savefig('./Figures/edf4g_ecoli_glucose_specific_activity.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edc536dd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.426026285075412\n",
      "29.472691280909878\n",
      "14.880114034650639\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fig. 2a\n",
    "#ATP production specific activities for E. coli \n",
    "fig = activityPlot(ecoli_anaerobic, 'ATP')\n",
    "\n",
    "fig.savefig('./Figures/fig2a_ecoli_atp_specific_activity.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33770dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def warburgRates(rates, ferm_proteomics, resp_proteomics, gly_proteomics, Y_Ferm, Y_Resp): \n",
    "    \n",
    "    '''For yeast and mammlian cells, performs bootstrapping to resample the five parameter estimates (i.e. fraction of \n",
    "    the proteome occupied by pathway, specific activity, and ATP yield) with replacement (N=10,000). \n",
    "    After each round of sampling, the specific activity of each pathway is calculated. The sampled \n",
    "    parameter values for proteome occupied by pathway, specific activity, and ATP yield from each \n",
    "    round of bootstrapping are used in the linear program described in the function above. \n",
    "    All 10,000 results are stored in a data frame, which is the output.'''\n",
    "\n",
    "    results_final = []\n",
    "    \n",
    "    for j in tqdm(range(10000)):\n",
    "    \n",
    "        #random choice with replacement for each paramter \n",
    "        sample_activity_respiration = rates.loc[rates['pathway'] == 'Respiration', 'glucose uptake rate  (mmol gprotein-1 hr-1)'].unique() / 60\n",
    "        #print(sample_activity_respiration)\n",
    "        sample_activity_fermentation = rates.loc[rates['pathway'] == 'Fermentation', 'glucose uptake rate  (mmol gprotein-1 hr-1)'].unique() / 60\n",
    "        #print(sample_activity_fermentation)\n",
    "        sample_mass_respiraition = resp_proteomics.loc[(resp_proteomics['estimate'] == 'respiration'), 'value'].unique()\n",
    "        #print(sample_mass_respiraition)\n",
    "        sample_mass_fermentation = ferm_proteomics.loc[(ferm_proteomics['estimate'] == 'fermentation'), 'value'].unique()\n",
    "        #print(sample_mass_respiraition)\n",
    "        sample_mass_glycolysis = gly_proteomics.loc[(gly_proteomics['estimate'] == 'fermentation'), 'value'].unique()\n",
    "        \n",
    "        sample_size_activity_respiration = len(sample_activity_respiration)\n",
    "        sample_size_activity_fermentation = len(sample_activity_fermentation)\n",
    "        sample_size_mass_respiraition = len(sample_mass_respiraition)\n",
    "        sample_size_mass_fermentation = len(sample_mass_fermentation)\n",
    "        sample_size_mass_glycolysis= len(sample_mass_glycolysis)\n",
    "    \n",
    "        sample_specific_activity_fermentation = np.random.choice(sample_activity_fermentation, size=sample_size_activity_fermentation,replace=True)\n",
    "        sample_specific_activity_respiration = np.random.choice(sample_activity_respiration, size=sample_size_activity_respiration, replace=True)\n",
    "        sample_fraction_fermentation = np.random.choice(sample_mass_fermentation, size=sample_size_mass_fermentation, replace=True)\n",
    "        sample_fraction_respiration = np.random.choice(sample_mass_respiraition, size=sample_size_mass_respiraition, replace=True)\n",
    "        sample_fraction_glycolysis = np.random.choice(sample_mass_glycolysis, size=sample_size_mass_glycolysis, replace=True)\n",
    "        \n",
    "        V_Ferm =  sample_specific_activity_fermentation.mean() / sample_fraction_fermentation.mean() \n",
    "        V_Resp =  sample_specific_activity_respiration.mean() / sample_fraction_respiration.mean() \n",
    "    \n",
    "        rate_ratio = V_Resp / V_Ferm\n",
    "        \n",
    "        V_Resp =  sample_specific_activity_respiration.mean() / (sample_fraction_respiration.mean() + sample_fraction_glycolysis.mean() * rate_ratio)\n",
    "\n",
    "        #ATP yield\n",
    "        Y_Ferm = Y_Ferm\n",
    "        Y_Resp = Y_Resp\n",
    "        \n",
    "        #ATP Production Rate \n",
    "        V_Ferm_ATP = V_Ferm * Y_Ferm\n",
    "        V_Resp_ATP = V_Resp * Y_Resp\n",
    "        \n",
    "        results = [V_Ferm, V_Resp, V_Ferm_ATP, V_Resp_ATP]\n",
    "        \n",
    "        #Append the outputs for each bootstrap iteration\n",
    "        results_final.append(results)\n",
    "    \n",
    "    #print(len(results_final))\n",
    "    #Convert the array into a dataframe\n",
    "    df = pd.DataFrame({'V_Ferm' : [], 'V_Resp' : [], 'V_Ferm_ATP' : [], 'V_Resp_ATP' : []})\n",
    "\n",
    "    for i in range(len(results_final)):\n",
    "        df_temp_vferm = pd.DataFrame({'V_Ferm' : [results_final[i][0]]})\n",
    "        df_temp_vresp = pd.DataFrame({'V_Resp' : [results_final[i][1]]})\n",
    "        df_temp_vfermatp = pd.DataFrame({'V_Ferm_ATP' : [results_final[i][2]]})\n",
    "        df_temp_vrespatp = pd.DataFrame({'V_Resp_ATP' : [results_final[i][3]]})\n",
    "        out = df_temp_vferm.join([df_temp_vresp, df_temp_vfermatp, df_temp_vrespatp])\n",
    "        \n",
    "        df = pd.concat([df, out])\n",
    "        \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0de45e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def warburgCI(df): \n",
    "    \n",
    "    '''For yeast and mammlian cells, calculates the 95 confidence interval for each output of the model'''\n",
    "\n",
    "    df_sub = df[['V_Ferm', 'V_Resp', 'V_Ferm_ATP', 'V_Resp_ATP']]\n",
    "    \n",
    "    conf95_high = []\n",
    "    conf95_low = []\n",
    "\n",
    "    for column in list(df_sub.columns): \n",
    "        df_temp = df_sub[[column]]\n",
    "        sample = np.array(df_temp[column])\n",
    "        #mean, sigma = np.mean(sample), np.std(sample)\n",
    "        #conf_int = stats.norm.interval(0.95, loc=mean, scale=sigma)\n",
    "        #confidence95 = mean - conf_int[0]\n",
    "        sample_count = len(sample)\n",
    "        sample_high = sample_count * .975\n",
    "        sample_low = sample_count * .025\n",
    "        sample = np.sort(sample)\n",
    "        #print(type(sample[int(sample_high) - 1]))\n",
    "        conf95_high = np.append(conf95_high, sample[int(sample_high) - 1])\n",
    "        conf95_low = np.append(conf95_low, sample[int(sample_low) - 1])\n",
    "        \n",
    "    df = pd.DataFrame({'conf95_low': conf95_low, 'conf95_high': conf95_high}, \n",
    "                      index = ['V_Ferm', 'V_Resp', 'V_Ferm_ATP', 'V_Resp_ATP'])\n",
    "    \n",
    "    return(df)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca17af6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def activityPlot(data, graph_type):\n",
    "    \n",
    "    'For yeast and mammalian cells, plots the results of the warburgRate function'\n",
    "\n",
    "    data2 = data.melt()\n",
    "    data2['label'] = 'none'\n",
    "    data2.loc[data2['variable'].str.contains('Ferm'), 'label'] = 'Fermentation'\n",
    "    data2.loc[data2['variable'].str.contains('Resp'), 'label'] = 'Respiration'\n",
    "\n",
    "    if graph_type == 'glucose': \n",
    "    \n",
    "        data2 = data2.loc[~(data2['variable'].str.contains('ATP'))] \n",
    "        \n",
    "        print(data2.loc[(data2['variable'].str.contains('Ferm')), 'value'].mean())\n",
    "        print(data2.loc[(data2['variable'].str.contains('Resp')), 'value'].mean())\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "\n",
    "        # plot a bar chart\n",
    "        colors = [\"crimson\", \"darkblue\"]\n",
    "        customPalette = sns.set_palette(sns.color_palette(colors))\n",
    "        #sns.barplot(x=\"variable\", y=\"value\", data=data, estimator=np.mean, ci=95, capsize=0, zorder=2, hue = 'label', dodge = False,  palette=customPalette)\n",
    "        summarystats = data2.groupby('variable').mean()\n",
    "        summarystats.reset_index(inplace = True)\n",
    "\n",
    "        yerr = warburgCI(data)\n",
    "        yerr_subset = yerr.loc[yerr.index.isin(summarystats['variable'])]\n",
    "        yerr_subset.reset_index(inplace = True)\n",
    "        yerr_subset.rename(columns = {'index':'variable'}, inplace = True)\n",
    "        yerr_subset = pd.merge(yerr_subset, summarystats, on = 'variable')\n",
    "        yerr_subset['lb'] = yerr_subset['value'] - yerr_subset['conf95_low']\n",
    "        yerr_subset['ub'] = yerr_subset['conf95_high'] - yerr_subset['value']\n",
    "        yerr2 = np.array([yerr_subset['lb'],yerr_subset['ub']])\n",
    "\n",
    "        sns.barplot(x=\"variable\", y=\"value\", data=data2, hue = 'label', palette = customPalette, dodge = False)\n",
    "        plt.errorbar(x=summarystats['variable'], y=summarystats['value'], yerr = yerr2, fmt = 'none', color = 'black', capsize = 7)\n",
    "        \n",
    "        #axis format\n",
    "        ax.set_ylabel('', fontsize=16)\n",
    "        ax.set_xlabel('', fontsize=16)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "        ax.set_ylabel(\"Glucose consumption rate\" r'$ \\left(\\frac{\\mu mol}{mg_{pathway} \\cdot min^{}}\\right)$', fontsize=23)\n",
    "\n",
    "        ax.legend(fontsize=13).set_title('')\n",
    "        ax.set(yscale=\"log\")\n",
    "\n",
    "        ax.legend_.remove()\n",
    "        ax.grid(b=True, which='minor', linestyle='-', linewidth='0.3')\n",
    "        ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "        #ax.set_ylim(bottom=0)\n",
    "        ax.set_ylim((.2/ 60,700/ 60))\n",
    "\n",
    "    if graph_type == 'ATP': \n",
    "\n",
    "        data2 = data2.loc[(data2['variable'].str.contains('ATP'))] \n",
    "        \n",
    "        print(data2.loc[(data2['variable'].str.contains('Ferm')), 'value'].mean())\n",
    "        print(data2.loc[(data2['variable'].str.contains('Resp')), 'value'].mean())\n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "    \n",
    "        # plot a bar chart\n",
    "        colors = [\"crimson\", \"darkblue\"]\n",
    "        customPalette = sns.set_palette(sns.color_palette(colors))\n",
    "        #sns.barplot(x=\"variable\", y=\"value\", data=data, estimator=np.mean, ci=\"sd\", capsize=0.2, hue = 'label', dodge = False,  palette=customPalette)\n",
    "        summarystats = data2.groupby('variable').mean()\n",
    "        summarystats.reset_index(inplace = True)\n",
    "\n",
    "        yerr = warburgCI(data)\n",
    "        #print(yerr)\n",
    "        yerr_subset = yerr.loc[yerr.index.isin(summarystats['variable'])]\n",
    "        yerr_subset.reset_index(inplace = True)\n",
    "        yerr_subset.rename(columns = {'index':'variable'}, inplace = True)\n",
    "        yerr_subset = pd.merge(yerr_subset, summarystats, on = 'variable')\n",
    "        yerr_subset['lb'] = yerr_subset['value'] - yerr_subset['conf95_low']\n",
    "        yerr_subset['ub'] = yerr_subset['conf95_high'] - yerr_subset['value']\n",
    "        yerr2 = np.array([yerr_subset['lb'],yerr_subset['ub']])\n",
    "        \n",
    "        sns.barplot(x=\"variable\", y=\"value\", data=data2, hue = 'label', palette = customPalette, dodge = False)\n",
    "        plt.errorbar(x=summarystats['variable'], y=summarystats['value'], yerr = yerr2, fmt = 'none', color = 'black', capsize = 5)\n",
    "        \n",
    "        #axis format\n",
    "        ax.set_ylabel('', fontsize=16)\n",
    "        ax.set_xlabel('', fontsize=16)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "\n",
    "        ax.set_ylabel(\"ATP Production Rate\" r'$ \\left(\\frac{\\mu mol}{mg_{pathway} \\cdot min^{}}\\right)$', fontsize=23)\n",
    "\n",
    "        ax.legend(fontsize=13).set_title('')\n",
    "        #ax.set(yscale=\"log\")\n",
    "\n",
    "        ax.legend_.remove()\n",
    "        ax.grid(b=True, which='minor', linestyle='-', linewidth='0.2')\n",
    "        ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "        #ax.set_ylim(bottom=0)\n",
    "        ax.set_ylim((0,yerr['conf95_high'][2] * 1.15))\n",
    "\n",
    "    return(fig)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "33b8343c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specific acitivity estimate inputs for S. cerevisiae\n",
    "\n",
    "#parameter estimates for proteome occupany\n",
    "resp_proteomics_core = pd.read_csv('./Data/2024_02_25_yeast_specific_activity_resp_core.csv')\n",
    "resp_proteomics_partial_mito = pd.read_csv('./Data/2024_02_25_yeast_specific_activity_resp_partial_mito.csv')\n",
    "resp_proteomics_full_mito = pd.read_csv('./Data/2024_02_25_yeast_specific_activity_resp_full_mito.csv')\n",
    "ferm_proteomics = pd.read_csv('./Data/2024_02_25_yeast_specific_activity_fermentation.csv')\n",
    "\n",
    "#rates\n",
    "rates = pd.read_csv('./Data/yeast_cellular_rates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39df95f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4ba4005e0c642d79ed8db502c0752a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "caefc04046c0434689e4212e5abc41bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "284b5a4b347c492ba079e452895ea6f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculating specific activities\n",
    "yeast_core = warburgRates(rates, ferm_proteomics, resp_proteomics_core, ferm_proteomics, 2, 16)\n",
    "\n",
    "yeast_partial_mito = warburgRates(rates, ferm_proteomics, resp_proteomics_partial_mito, ferm_proteomics, 2, 16)\n",
    "\n",
    "yeast_full_mito = warburgRates(rates, ferm_proteomics, resp_proteomics_full_mito, ferm_proteomics, 2, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "45e77ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.600174365320493\n",
      "0.2697097177175229\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zs2aSFBcbG4vjx49j5MiRmD9/PpYuXQoTk6fejJiIiIjqqTqnArlcjoMHD+LGjRuwtbVFu3btJDsFu1rDhg2V+5x07twZ69evx5IlSyQbj4iIiPRLnYPM3LlzERMTg0aNGqG8vBwymQyCIKBJkybK068jIyPVWpylpSXkcjkaN26MvLw82NvbIyMjQ61jEBERkf6qc5A5ceIEwsPDMWPGDIiiiD///BMpKSlITk5Gamoqdu3apfYgM3fuXJSUlGD06NGYNGkS2rVrx7OXiIiISKnOQaZBgwZ48cUXAfz1roybmxvc3NwQHBwsWXGtWrWCpaUlXnnlFVhbWyM5OZn7xxAREZFSnTfEGzx4sKQHRNY2ZlRUFO7du4fevXvjX//6F5o3b67RGoiIiEh31TnIREZG4vz58zhw4ICU9dQQExMDS0tLjB49GosXL8adO3c0NjYRERHpvjoHmZycHBgbG2Pu3LkYOHAgFi5ciH379iE1NVWy91YaNmyIiRMnIjY2Ft7e3pg6dSrmzJkjyVhERESkf+r8jszs2bORkZGBkJAQlJWVIT4+Hjt37gQAmJqawt3dHfv27VNrcRMnTsT9+/dRXFwMuVwOQRBw48YNtY5BRERE+qvOQeb69ev49NNPa7zcW1xcjJSUFCQlJdV6oOTzUCgUsLS0xIcffohOnTqp/f5ERESk3+ocZFxcXB45GNLa2hpdu3ZF165d1V4YAPz3v/9FUlISvv76azx8+BDh4eHKlVNEREREdX5HJiwsDLt375ayFpXatm2L+fPnY/z48Vi2bBlGjhyp8RqIiIhIN9V5RiY+Ph7Xrl1DZGQkpk+fDjc3NynrAgD4+PjA3NwcNjY2sLGxgZ2dHRo3biz5uERERKQf6hxk0tLScPfuXcTFxeH48eOwt7eHp6cnvL294enpCS8vLzg4OKi1uEuXLsHY2Fit9yQiIqL6o85B5tChQ6isrMT169eRnJysPJ7gm2++QWlpKQRBQEpKilqLKykpwdmzZ2FjYwMvLy80atRIrffXZ+Xl5Wq5T/WhnET1hUKhgEwm03YZRKQhdQ4ywF/LrD09PeHp6am8Jooi0tPTkZqaqvbi3nnnHVhbW0MulyMlJQU2Njbw9vbGypUr1T4WERER6Z+nCjKqCIKA1q1bo3Xr1uqop4bCwkLs2bNH+XNGRgauXr2q9nH0kYWFhbZLINJJRkZGfD6IDEidVy1FR0fj/PnzKCkpeaStpKQEoiiqrajqe/n4+KCsrEx53dXVVdJDKomIiEi/1HlGZunSpVAoFBAEAc7OzvDy8oKHhwc8PT2RlpaGI0eO4Ntvv1VLUV26dIGXlxdeeOEFTJ8+HXPmzJFkxoeIiIj0W52DzIULF5CamqrcxTc5ORlxcXHKc5YaNmyotqJOnTqFq1ev4sqVK7h8+TLeeecdlJeXw8fHB97e3oiIiFDbWERERKS/6hxkzM3N0bFjR3Ts2FF57cGDB9ixYwc2bNiAVatWqa0oKysrdO/eHd27d1deKywsxOXLl3HlyhW1jUNERET6rc7vyKjSqFEjTJ48Ga+++ir279+vrpqUli9frvzezs4O/fr142wMERERKT1XkKnWo0cP/Pjjj+q4FYD/veybkJDwSNvSpUvVNg4RERHptzoHmTVr1uDkyZO4c+fOI22FhYWPHCj5PL766isMGjQIubm52Lx5M86dO6dcLfXzzz+rbRwiIiLSb3V+R2bbtm14+PAhBEFA48aNlRvjmZqaYu/evZg9e7baipoyZQpefvllhIWF4c6dOzh+/DhSU1NhamqKzp07q20cIiIi0m91DjLnzp1DZmYmrl69qly5tGfPHhQVFQEAFi5ciH379sHLywve3t4ICgp6rsLatm2LzZs3Kw+nVCgUyMvLg729/XPdl4iIiOqPp9rZ18XFBS4uLjVCyq1bt5CcnIykpCQkJSXhwIED2Lx583MHGQBwc3NDfn4+zM3N0bBhQ7UfSklERET67bmPKHB2doazszMCAwOV127fvv28twUAREVFYf/+/SgoKECzZs2U+8iEhYWp5f5ERESk39SyaumfHB0d1XKfI0eO4Pjx43Bzc8Nnn32G7OxsSQ6nJCIiIv0kSZBRl4YNG8LMzAwA0LlzZ6xfv16tZzoRERGRftPpIGNpaQm5XI7GjRsjLy8PdnZ2yMjI0HZZREREpCOe+x0ZKc2dOxclJSUIDQ3FpEmT0K5dO+XZTkREREQ6HWQ8PT0BAEFBQWjUqBGSkpJ4RAEREREp6XSQycvLQ3R0NO7duwcfHx+89dZbyndmiIiIiHT6HZmIiAjcuXMHrq6u+PnnnxESEoKcnBxtl0VEREQ6QqdnZIqLi7FixQoAwNtvv40ff/wRn3zyCb7++mstV0ZERES6QKdnZOzs7FBcXKz8uX///ioPrSQiIiLDpNNBxtjYGO+88w4uX74MhUKBP/74A02bNtV2WURERKQjdDrIdO/eHU2aNMG0adPg4+OD4OBgtG7dGqdOnUJhYaG2yyMiIiIt0+l3ZCZNmqT8Pi8vD5cvX8aVK1ewdetWJCUlISEhQYvVERERkbbpRJBZt24dvLy84OXlVetHR/b29vD394e/v7+Gq3s+MpkMixYtwrFjx2BmZoYJEyZg4sSJ2i6LiIioXtCJILN582blS71NmzZVhprqLwcHBy1X+OxWrFiBixcvYvPmzcjJycHs2bPh6OiIwYMHa7s0IiIivacTQebbb7/F22+/jRdeeAGurq7IyclBdHQ0Hjx4AABwdXVFaGgoxo4dCxMTnSi5TkpLS7Fnzx6sX78e3t7e8Pb2xjvvvIPo6GgGGSIiIjXQiVQwb948DBw4ELNmzVJeKykpwYYNG7B3717069cPGzduxIkTJ/DNN9/oze6+qampkMlk8PPzU17z8/PDunXrUFVVpVehjIiISBfpxKqlS5cuoVevXjWuWVlZ4d1338WgQYNgZmaGffv2ITMzExs3btRSlU8vLy8PNjY2MDc3V15r2rQpKisrueqKiIhIDXQiyDRp0gRJSUkq2wYOHIjDhw+jefPmmDBhAmJjYzVc3bMrKyt7ZPao+meZTKaNkoiIiOoVnQgyw4cPx1dffYVff/31kbacnBzcu3cPAODh4YGsrCxNl/fMzM3NHwks1T9bWlpqoyQiIqJ6RSde0pg8eTJSU1MxYcIEvPzyyxg4cCDs7e2Rnp6OL7/8Ej4+PgD+CgGmpqZarrbuHBwc8ODBA8hkMuVMTF5eHszMzGBjY6Pl6oiIiPSfTgQZExMTrF27Fvv378d///tfxMXFQRAEiKKItm3bYsGCBQD+epfGxcVFy9XWnYeHB0xNTXHx4kV0794dAHD+/Hl4eXnxRV8iIiI10Kl/m7766qt49dVXkZ2djZycHNjZ2cHV1RWCIAAA+vbti27dummkltzcXAQFBSEiIgITJkx4pL2qqgrR0dHYs2cPsrKyYG9vjxEjRiA8PFw5a2RpaYmQkBAsWLAAy5YtQ15eHr755hssWrRII38DERFRfadTQaaak5MTnJycHrneqVMnjYxfUlKCiIiIGidv/9PChQuxe/du+Pn5YcCAAbhw4QKioqKQlpaGqKgoZb8PPvgA8+fPx5tvvgkrKytMnToVQUFBmvgziIiI6j2dCDJyuRwHDx7EjRs3YGtri3bt2sHT0xP29vYaryU7OxsRERG1rqICgAsXLmD37t0IDAzE6tWrlR+DzZkzBwcPHsSPP/6I/v37A/hrVmb58uVYvny5pv4EIiIig6ETQWbu3LmIiYlBo0aNUF5eDplMBkEQ0KRJE+UxBZGRkZLXsWXLFkRFRaG8vBw9evSo9VDKHTt2AACmTZum/NhLEATMmDEDMTEx2Lt3rzLIEBERkXR0Yvn1iRMnEB4ejt9++w2///47YmNj8dlnn2Ho0KGorKzErl27NFLHtm3b4OTkhOjoaAwbNqzWfomJibC1tYW7u3uN6w4ODnB1dcW5c+ekLpWIiIigIzMyDRo0wIsvvgjgr5kNNzc3uLm5ITg4WKN1LFiwAD179oSxsTEyMjJU9pHJZMjJyUHHjh1Vtjs5OSE9PR2FhYWws7OTsFoiIiLSiRmZwYMHIz4+XttloHfv3jA2Nn5sn/v37wMAGjZsqLK9+vrDhw/VWhsRERE9SieCTGRkJM6fP48DBw5ou5QnqqqqAoBaD66svl5RUaGxmoiIiAyVTny0lJOTA2NjY8ydOxdfffUVevXqBU9PT3h7e6Nt27ZPnCXRJAsLCwBAZWWlynZNHUFQXl6ulvvoy0niRHWlUCh4lhmRAdGJIDN79mxkZGQgJCQEZWVliI+Px86dOwEApqamcHd3x759+7Rc5V+sra1hZGRU6x4z1R8p1fbRExEREamPTgSZ69ev49NPP63xcm9xcTFSUlKQlJSE5ORkLVZXk5mZGRwdHWs9vDIrKwu2trZo3LixpHVUzwwRUU1GRkZ8PogMiE4EGRcXF1hZWdW4Zm1tja5du6Jr165aqqp2fn5+iImJQXp6Olq1aqW8npubi5s3b6Jfv37aK46IiMiA6MTLvmFhYdi9e7e2y6izkJAQAMDKlSuhUCgAAKIo4osvvoAoiggNDdVidURERIZDJ2Zk4uPjce3aNURGRmL69Olwc3PTdkmP1bNnTwQFBeHo0aMIDQ1F9+7dcfHiRSQmJiIwMJAzMkRERBqiE0EmLS0Nd+/eRVxcHI4fPw57e3vlqiVPT094eXnBwcFB22XWsGLFCrRp0wYHDhzA1q1b4ejoiMjISEycOFF5bIGUuGqJSDWuWiIyLIIoiqKmBvPw8EBKSorKtsrKSly/fh3JyclISUlBcnIy0tLSUFpaCkEQav09Q1P9EnFOTo5a7mdmZoaH55NwJewDtdyPSJt8/rsUDf28GGSI6pFmzZoB+N+GtP+k0RmZx2UmU1NTeHp6wtPTs0b/9PR0pKamaqI8vcJVGUSqcdUSkWHRaJB52kAiCAJat26N1q1bS1QRERER6bMnrlpKTU3Fli1bNFDK0zl79iy2b9+u7TKIiIhIi54YZNq3b49GjRph1qxZePDggSZqeqyKigosX74cx44dw7hx47RdDhEREWlRnfaRGTFiBIKCgjBy5Ejs3btXeXCiph09ehRDhgxB8+bNsXDhQq3UQERERLqjzu/I9O/fH61atcK8efPw1Vdf4c0338TQoUNha2srZX0oLS3FkSNH8M0338DU1BSff/45OnToIOmY+oDLr4lU4/JrIsPyVC/7urq6Ijo6GgcOHMDq1avx+eefo2/fvujfvz969eqltr1eCgoKkJCQgOPHj+Onn36CmZkZ/vWvf+HNN9+EiYlObH1DREREOuCZUsHw4cMxZMgQHDp0CJs3b8aHH34IQRDg7OyM9u3bo127dnB2dsYLL7wABwcHNGzYEObm5jA3N4eRkREqKipQUVGBkpIS3L17F3l5ecjIyMD169eRkpKC9PR0iKKIJk2aYPLkyRgzZgysra3V/bfrNS4vJVKNy6+JDMszT2+YmJhgxIgRGDFiBJKTkxEbG4sTJ04gLi4OcXFxT727bfUeMw0bNkRQUBCGDBmCXr16cQaGiIiIaqWWlFC9kd2sWbOQn5+PxMREJCcnIzMzE5mZmcjLy0NZWRnKysogl8thYWEBCwsL2NjYoEWLFnBxcUGbNm3g5+eHdu3aaWSLfyIiItJ/ap/uaNq0KQYNGoRBgwap+9ZERERENdRp+TURERGRLmKQISIiIr3FIENERER6S9IlQQ8fPsS+ffuQkJCA27dvo1evXnj//fexYcMGeHp6olevXlIOX69xQzwi1bghHpFhkSzInDt3DtOnT8e9e/cgiiIEQYCHhwcA4MiRI1i5ciWmTJmCiIgIqUogIiKiek6SIHPr1i1MmjQJJSUlGDRoEPr164c5c+Yo2wcOHIibN29i3bp16NKlC1588UUpyqjXuOEXkWrcEI/IsEjyjsz69etRUlKCRYsWYdWqVQgJCanRPm3aNKxevRqiKGLr1q1SlEBEREQGQJIgEx8fj7Zt22LkyJG19unXrx88PT1x7do1KUogIiIiAyBJkMnPz0fr1q2f2K9FixbIy8uTogQiIiIyAJIEGRsbG2RlZT2xX2ZmJmxsbKQogYiIiAyAJEHGz88PKSkpOH/+fK19EhISkJqais6dO0tRAhERERkASYLM22+/DQCYMmUK9u7dW2N2pqKiAkePHsXMmTMhCALGjx8vRQlERERkACRZft2pUyfMnTsXS5YswccffwwAEAQBR48exeHDhwEAoigiMjISXbt2laKEeo8b4hGpxg3xiAyLZBvijR07Fu3bt8emTZvw66+/oqysDFVVVTA1NYWvry/CwsLQp08fqYYnIiIiAyDpEQVdunRBly5doFAocP/+fSgUCjRu3BgmJv8btqioiC/8PgNu+EWkGjfEIzIskrwj4+/vj+XLl/9vECMj2NnZoWnTpjVCzKxZszB48GApSiAiIiIDIEmQyc7ORkFBwRP7ZWZmoqioSIoSiIiIyACo5aOlsLAwZGRk1Lh24sQJ+Pv71/o7xcXFePDgAVxdXdVRAhERERkgtQSZsWPHYvLkycqfBUFAaWkpSktLH/t75ubmmDFjhjpKICIiIgOkliDTv39/HDhwAHK5HKIoYuTIkejbty8iIiJU9hcEARYWFnB0dISlpaU6SiAiIiIDpLZVSx4eHsrvhw8fDl9fX3h7e6vr9kRERESPkGT59dKlS6W4LREREVENku4jk5eXh9u3b6OyshKiKCqvKxQKVFRUID8/H3FxcVi/fr2UZRAREVE9JUmQqaqqwvvvv4+jR49KcXsiIiIiABIFmejoaMTGxgIAHBwcIJPJcP/+fbi5uaGoqAh5eXkAgJYtW+Kdd96RooR6j2ctEanGs5aIDIskG+IdPXoUgiDgyy+/xM8//4z33nsPALB27VqcPn0aBw8ehKurK3Jzc9GlSxcpSiAiIiIDIMmMzJ9//gkPDw8EBAQAADp06ABRFJGYmIiWLVuiffv2iIqKwrBhw7B582YsXLhQijLqNZ4lQ6Qaz1oiMiySzMiUl5fD2dlZ+XPLli0hCAKuXbumvObu7g4fHx9cunRJihKIiIjIAEgSZBo1aoSysjLlz2ZmZnjhhRfwxx9/1OjXvHlz5OTkSFECERERGQBJgky7du1w6dKlGi+ktmrVClevXq2xDDs3N1eK4YmIiMhASBJkXnnlFTx48ADjx4/Hb7/9BgDo2bMnioqK8MUXX6C8vByHDh3CpUuX4OLiIkUJREREZAAkCTIjRoxA165dcfnyZWzZsgUAEBoaCisrK2zatAmdO3fG+++/D0EQMHr0aClKICIiIgMgSZAxMTHB5s2b8dFHH6FPnz4AABsbG3z99ddwdnaGKIowMTHBhAkT8Nprr0lRAhERERkAyY4oMDExwZgxY2pc8/PzQ1xcHAoKCmBtbQ1zc3OphiciIiIDIMmMzMSJEx97cGSTJk0YYoiIiOi5SRJkEhMTkZSUJMWtiYiIajV//nwIgvDUX/Pnz9d26fSMJAkypqamsLKykuLWREREtZo/fz5EUazxlZ6ermxPT09/pF0URQYZPSZJkHnttdcQHx+Pn3/+WYrbExEREQGQ6GVfNzc3ODs7Y9KkSWjZsiU8PT3RuHFjGBmpzk3z5s2TogwiIiKq5yQJMh9++CEEQYAoisjIyEBGRkatfQVBYJAhIiKiZyJJkJk6dSoEQZDi1vR//n78w/MwMzNTy32IdIVCoYBMJtN2GaRDKioqanyvrn9+km6QJMhERERIcVsiIiKiGiTbEI+kZWFhoe0SiHSSkZERnw+q4e/7lpmbm/P/P+oZSVYtEREREWkCgwwRERHpLQYZIiIi0lt8R4aISMuKispw5codbZdRb925k6X8/ty5TGRlKbRYTf3n49McNjaWGhuPQYaISMuuXLmDoUP/q+0y6i25/L7y+3fe2Q1j48Zaq8UQHDoUhl69WmtsPH60RERERHpL0hmZyspKZGdno6ysDApF7VN5Xl5eUpZBRERE9ZRkQebrr7/Gxo0bUVJS8th+giAgOTlZqjKIiIioHpMkyOzbtw8rV64E8NcW+La2tjA2NpZiKCIiIjJgkgSZXbt2QRAEzJkzB2+88QZMTU2lGIaIiIgMnCRB5saNG/Dx8cGbb74pxe2JiIiIAEi0asnMzAwvvPCCFLcmIiIiUpIkyPj4+CA5OfmxK5WIiIiInpckQWbq1KnIycnB6tWrpbg9EREREQCJ3pHJzMyEv78/NmzYgNjYWHTq1Ak2NjYQBEFl/3nz5klRBhEREdVzkgSZOXPmQBAEiKKIrKwsZGVl1dpXEAQGGSIiInomkgSZqVOn1jr7QkREJJWyslMoLz9Ta/uDB+tUXrew6AVLyz5SlUUSkiTIRERESHFbIiKix7K07MNAYmB4+rWeKi8vV8t9zMzM1HIfIl2hUCggk8m0XUad8Rmk+kiTz6GkQSY7Oxvbtm1DQkIC7t69C3NzczRp0gQ9e/bEqFGj4OzsLOXwREREVM9JFmROnjyJ9957D+Xl5RBFUXk9JycHSUlJ2LFjBz777DP4+/tLVUK9ZmFhoe0SiHSSkZERnw8iLdPkcyhJkElPT8fMmTNRXl6O4OBgDB06FM7OzlAoFLh16xYOHz6M2NhYzJo1CzExMZyZISIiomciSZDZuHEjysvL8eGHH2LcuHE12tzc3NCvXz/4+vpi0aJF2Lp1K5dfExER0TORZGffX375Ba1atXokxPzdmDFj0Lp1a5w6dUqKEoiIiMgASBJk8vLy0K5duyf2c3d3R25urhQlEBERkQGQJMhYWVkhPz//if3y8/P5Uh4RERE9M8lOv7548SKSkpJq7ZOcnIwLFy7Ax8dHihKIiIjIAEgSZMaMGQO5XI7w8HAcO3YMcrlc2SaXy3Hs2DGEh4dDFEWMHj1aihKIiIjIAEiyaql///54/fXXsXPnTrz77rswMzNDs2bNAAC5ubmoqKiAKIoIDQ1FQECAFCUQERGRAZBsQ7xPPvkEHh4e2LRpEzIzM3Hz5k1lm4uLC8LCwhAaGirV8ERERGQAJD2iYNSoURg1ahRyc3ORm5sLURTh4OCgnJ0hIiIieh4aOTTSwcEBDg4OmhiKiIiIDIhagszixYsBAFOmTIGdnZ3y57rizr5ERET0LNQSZKKjoyEIAsaMGQM7Ozvlz38/LLI2giAwyBAREdEzUUuQmTp1KgRBgK2tbY2fiYiIiKSkliATERHx2J8fp6ioSB0lEBERkQGSZEM8f39/LF++/In9Zs2ahcGDB0tRAhERERkASYJMdnY2CgoKntgvMzOTMzJERET0zNTy0VJYWBgyMjJqXDtx4gT8/f1r/Z3i4mI8ePAArq6u6iiBiIiIDJBagszYsWMxefJk5c+CIKC0tBSlpaWP/T1zc3PMmDFDHSUQERGRAVJLkOnfvz8OHDgAuVwOURQxcuRI9O3bt9aXfgVBgIWFBRwdHWFpaamOEoiIiMgAqW1nXw8PD+X3w4cPh6+vL7y9vdV1eyIiIqJHSHJEwdKlS6W4LREREVENkp61VFhYiAMHDuDs2bO4e/cujI2N4ejoiL59+2Lw4MGwtraWcngiIiKq5yRZfg0Ax44dw6BBg/D5558jPj4e169fR2pqKn744QfMnz8fwcHBSExMlGp4nSWTyRAcHIyzZ89quxQiIiK9J8mMzOXLl/Hee++hqqoKAwcORGBgIJo3bw5RFJGVlYVjx47hxx9/xNSpU7Fv3z44OztLUYbOqaiowMyZM3H9+nVtl0JERFQvSBJkvv76a8jlcixatAgjR46s0ebn54dhw4Zhx44dWLRoETZu3IiFCxdKUYZOuXHjBmbOnFmngzSJiIiobiT5aOnSpUvw9PR8JMT83ZgxY9CuXTucOnVKihJ0TmJiIl566SXs3r1b26UQERHVG5LMyJSWlqJFixZP7NeqVSv89NNPUpSgc0aPHq3tEoiIiOodSYJMu3btcOnSJchkMpiZmansI4oiUlNT0aZNGylK0KiKigrk5OSobGvSpAlXZxEREUlEko+Wpk+fjry8PHzwwQeQyWQq+6xatQo3b97EpEmTpChBo65cuYKBAweq/IqLi9N2eURERPWWJDMy2dnZ6NevH44ePYrExEQMHDgQrq6uMDY2Rk5ODn766SekpaWhefPmSEhIQEJCQo3fnzdvnhRlSaZLly5IS0vTdhlEREQGR5IgM2/ePAiCAFEUkZubi+3bt0MQBACosWrn9u3biI6OrvG7giDoXZAhIiIi7ZAkyEydOlUZXHRdbm4ugoKCEBERgQkTJjzSXlVVhejoaOzZswdZWVmwt7fHiBEjEB4eDlNTU80XTEREREqSBJnaTr3WNSUlJYiIiEBxcXGtfRYuXIjdu3fDz88PAwYMwIULFxAVFYW0tDRERUVpsFoiIiL6J0nPWtJl2dnZiIiIQFJSUq19Lly4gN27dyMwMBCrV69Wflw2Z84cHDx4ED/++CP69+//1GPzfRoiIiL1kCzI3Lp1C/v370d6ejoqKipq7ScIAr766iupylBpy5YtiIqKQnl5OXr06PHIy8bVduzYAQCYNm2a8qMyQRAwY8YMxMTEYO/evc8UZIiIiEg9JAkyqampeP3111FeXv7ELfm18S7Ntm3b4OTkhAULFiAjI6PWIJOYmAhbW1u4u7vXuO7g4ABXV1ecO3dOE+USERFRLSQJMp999hnKysrQuXNnBAYGolGjRjr18u+CBQvQs2dPGBsbIyMjQ2UfmUyGnJwcdOzYUWW7k5MT0tPTUVhYCDs7OwmrJSIiotpIEmSSkpLg4uKC7du3w8RE917D6d279xP73L9/HwDQsGFDle3V1x8+fMggQ0REpCWS7Owrl8vRvn17nQwxdVVVVQUAtR6xUH39ce//EBERkbQkSRodOnTAtWvXpLi1xlhYWAAAKisrVbZXH71gaWmpsZr+rry8XC33qS2oEekrhUJR69EouojPINVHmnwOJZmRiYyMRFZWFlauXAmFQiHFEJKztraGkZFRrXvMPHz4EEDtHz0RERGR9CSZkenYsSMWLFiADz/8EIcPH4a7uztsbW1V9hUEAUuWLJGijOdiZmYGR0dHZGVlqWzPysqCra0tGjdurNnC/k/1jBER1WRkZMTng0jLNPkcShJk0tLSsGLFCgiCgNu3b+P27du19tXVIAMAfn5+iImJQXp6Olq1aqW8npubi5s3b6Jfv37aK46IiIikCTKff/45ioqK0LJlSwQEBMDOzk6nll/XVUhICGJiYrBy5UqsWrUKRkZGEEURX3zxBURRRGhoqLZLJCIiMmiSBJlLly7B0dERMTExej3F27NnTwQFBeHo0aMIDQ1F9+7dcfHiRSQmJiIwMJAzMkRERFomSZBRKBTw9vbW6xBTbcWKFWjTpg0OHDiArVu3wtHREZGRkZg4caJWZ5m4aolINa5aItI+TT6HkgQZHx8f3LhxQ4pbq92IESMwYsSIWttNTU0xdepUTJ06VYNVERERUV1IEmSmTp2K8ePHY+3atQwAEqkPs11EUuCqJSLt0/tVSzk5Oejbty++/PJL7N+/Hx06dICNjQ1MTU1V9p83b54UZRAREVE9J0mQmTVrFgRBgCiKdVp+zSBDREREz0Kyj5b0cbk1ERER6RdJgkxERIQUtyUiIiKqQX+PpzZwXH5NpBqXXxNpn94vv05KSnqq/l5eXlKUQURERPWcJEHm1Vdffap3ZFJSUqQoo17j8lIi1bj8mkj79H75taOjo8rrCoUCRUVFKCsrAwB069YNTZs2laIEIiIiMgCSBJkffvjhse2pqamYP38+7ty5gzVr1khRAhERERkAI20M2r59e6xbtw75+fkMMkRERPTMtBJkAMDOzg7dunXDiRMntFUCERER6TmtBRkAEEURhYWF2iyBiIiI9JjW9pG5du0aEhIS0KxZM22VoNe4jwyRatxHhkj79H4fmcWLF9faVlVVhby8PMTHx6OyshKDBg2SogQiIiIyAJIEmejoaOWhkY/j6+uLSZMmSVFCvcd9MohU4z4yRNqn9/vITJs2rdY2QRBgZWUFDw8PdO/eXYrhiYiIyEBoPMgQERERqYtWVy0RERERPQ/JVy1VVVXBxOSvYcrLy/Htt9/izp076NixIwICAqQenoiIiOoxyYLM3r17sXr1anz00UcIDAyEXC7HuHHjcPXqVWWfgIAAREVFPdUBk0RERETVJPlo6cyZM/joo4+Qn5+PgoICAMChQ4dw5coV2NjYYNy4cXBxccGJEyewb98+KUogIiIiAyBJkNmxYwcEQcCqVavwxhtvAACOHj0KQRDw8ccfY+7cudi5cycsLS1x8OBBKUogIiIiAyDJR0tXrlxBx44dlZvdyWQy/PbbbzA2Nkb//v0B/HXWkq+vLy5fvixFCfUed/YlUo07+xJpnyafQ0lmZIqKimocPZCYmIiKigp4e3vD0tJSed3S0hKlpaVSlEBEREQGQJIZGXt7e+Tl5Sl/Pn36NARBQI8ePWr0u379Ouzs7KQood7jzqVEqnFnXyLt0+RzKMmMjLu7Oy5evIizZ8/izz//xKFDhwAAAwYMUPbZvHkzMjIy0LlzZylKICIiIgMgyYzMW2+9hTNnziAsLAwAIIoifH190aFDBwBASEgI0tLSYGpqquxDRERE9LQkmZHp3r071q5dC2dnZ5ibm6Nv375YvXp1jT6Ojo7YtGmTMtwQERERPS3JNsTr27cv+vbtq7Jt7dq1cHJykmpoIiIiMhCSHlFQWVmJ7OxslJWVQaFQ1Gi7f/++8nsvLy8pyyAiIqJ6SrIg8/XXX2Pjxo0oKSl5bD9BEJCcnCxVGURERFSPSRJk9u3bh5UrVwL4a7MnW1tbGBsbSzEUERERGTBJgsyuXbsgCALmzJmDN954A6amplIMQ0RERAZOkiBz48YN+Pj44M0335Ti9kREREQAJAoyZmZmeOGFF6S4Nf0fnrVEpBrPWiLSPr0/a8nHxwfJycmPrFQiIiIiUidJZmSmTp2KsWPHYvXq1Xj33XelGMLg8SwZItV41hKR9mnyOZQkyGRmZsLf3x8bNmxAbGwsOnXqBBsbGwiCoLL/vHnzpCiDiIiI6jlJgsycOXMgCAJEUURWVhaysrJq7SsIAoMMERERPRPJPlqqbfaFiIiISF0kCTIRERFS3JaIiIioBklWLRERERFpgqSHRmZnZ2Pbtm1ISEjA3bt3YW5ujiZNmqBnz54YNWoUnJ2dpRyeiIiI6jnJgszJkyfx3nvvoby8HKIoKq/n5OQgKSkJO3bswGeffQZ/f3+pSiAiIqJ6TpIgk56ejpkzZ6K8vBzBwcEYOnQonJ2doVAocOvWLRw+fBixsbGYNWsWYmJiODNDREREz0SSILNx40aUl5fjww8/xLhx42q0ubm5oV+/fvD19cWiRYuwdetWLr8mIiKiZyLJy76//PILWrVq9UiI+bsxY8agdevWOHXqlBQlEBERkQGQJMjk5eWhXbt2T+zn7u6O3NxcKUogIiIiAyBJkLGyskJ+fv4T++Xn5/NMFCIiInpmkrwj4+Pjg19++QVJSUnw8vJS2Sc5ORkXLlxAz549pSih3isvL1fLfczMzNRyHyJdoVAoIJPJtF1GnfEZpPpIk8+hJDMyY8aMgVwuR3h4OI4dOwa5XK5sk8vlOHbsGMLDwyGKIkaPHi1FCURERGQAJJmR6d+/P15//XXs3LkT7777LszMzNCsWTMAQG5uLioqKiCKIkJDQxEQECBFCfUeP5IjUs3IyIjPB5GWafI5lGxDvE8++QQeHh7YtGkTMjMzcfPmTWWbi4sLwsLCEBoaKtXwREREZAAkPaJg1KhRGDVqFHJzc5GbmwtRFOHg4KCcnSEiIiJ6HpIGmWoODg5wcHAAABQXF6OwsBB2dnaaGJqIiIjqMclOvy4sLMTs2bMxZcqUGtfj4+PRu3dvTJ8+vU5LtImIiIhqI8mMTGFhIUaNGoWsrCy4uLjUaKuqqoKxsTG+//57pKamYv/+/bC2tpaiDCIiIqrnJJmR2bRpE7KysjB48GDs2rWrRtvgwYORkJCAIUOG4ObNm1i/fr0UJRAREZEBkCTI/Pzzz3B0dMSKFStUvgvToEEDLFmyBM2aNUNcXJwUJRAREZEBkCTIZGdnw9vbG8bGxrX2MTU1hbe3N+7cuSNFCURERGQAJAky1tbWKCgoeGK/hw8fokGDBlKUQERERAZAkiDj4+ODS5cu4fLly7X2SU1Nxfnz5+Ht7S1FCURERGQAJD1raeLEidi1a1eNZdaFhYXYt28fwsLCIJfLMXbsWClKICIiIgMgyfLrXr16ITw8HBs2bMCCBQuwYMECmJj8NVRVVRUAQBRFvPXWW+jfv78UJRAREZEBkGxn3xkzZqBLly7Yvn07fvvtN1RUVAD46yXfTp06Yfz48Xj55ZelGp6IiIgMgKRHFPTp0wd9+vQBANy/fx9VVVVo3LixcnaGiIiI6HloLFE0btxYU0MRERGRgZDsrCUiIiIiqTHIEBERkd5ikCEiIiK9xSBDREREeovLh/RUeXm5Wu5jZmamlvsQ6QqFQgGZTKbtMuqMzyDVR5p8DjkjQ0RERHpL0hmZhw8fYt++fUhISMDt27fRq1cvvP/++9iwYQM8PT3Rq1cvKYev1ywsLLRdApFOMjIy4vNBpGWafA4lCzLnzp3D9OnTce/ePYiiCEEQ4OHhAQA4cuQIVq5ciSlTpiAiIkKqEoiIiKiek+SjpVu3bmHSpEkoLCxEYGAgli1bBlEUle0DBw6EmZkZ1q1bh19++UWKEoiIiMgASBJk1q9fj5KSEixatAirVq1CSEhIjfZp06Zh9erVEEURW7dulaIEIiIiMgCSBJn4+Hi0bdsWI0eOrLVPv3794OnpiWvXrklRAhERERkASYJMfn4+Wrdu/cR+LVq0QF5enhQlEBERkQGQJMjY2NggKyvrif0yMzNhY2MjRQlERERkACQJMn5+fkhJScH58+dr7ZOQkIDU1FR07txZihKIiIjIAEgSZN5++20AwJQpU7B3794aszMVFRU4evQoZs6cCUEQMH78eClKICIiIgMgyT4ynTp1wty5c7FkyRJ8/PHHAABBEHD06FEcPnwYACCKIiIjI9G1a1cpSiAiIiIDINmGeGPHjkX79u2xadMm/PrrrygrK0NVVRVMTU3h6+uLsLAw9OnTR6rhiYiIyABIekRBly5d0KVLFygUCty/fx8KhQKNGzeGiQnPqiQiIqLnJ3miqKqqgomJCezs7FBeXo49e/bgzp076NSpE/z9/aUenoiIiOoxyYLM3r17sXr1anz00UcIDAyEXC7HuHHjcPXqVeXZSwEBAYiKioIgCFKVQURERPWYJKuWzpw5g48++gj5+fkoKCgAABw6dAhXrlyBjY0Nxo8fDxcXF5w4cQL79u2TogQiIiIyAJIEmR07dkAQBKxatQpvvPEGAODo0aMQBAEff/wx5s6di507d8LS0hIHDx6UogQiIiIyAJIEmStXrqBjx44YNGgQAEAmk+G3336DsbEx+vfvDwCws7ODr68vrl+/LkUJREREZAAkCTJFRUVo1qyZ8ufExERUVFTA29sblpaWyuuWlpYoLS2VogQiIiIyAJIEGXt7+xqHQZ4+fRqCIKBHjx41+l2/fh12dnZSlEBEREQGQJIg4+7ujosXL+Ls2bP4888/cejQIQDAgAEDlH02b96MjIwMnrVEREREz0yS5ddvvfUWzpw5g7CwMAB/HUfg6+uLDh06AABCQkKQlpYGU1NTZR8iIiKipyXJjEz37t2xdu1aODs7w9zcHH379sXq1atr9HF0dMSmTZuU4YaIiIjoaUm2IV7fvn3Rt29flW1r166Fk5OTVEMTERGRgZBkRuZJDCnEZGZmYtKkSejatSv69OmDZcuWoaKiQttlERER1QuSnrWUnZ2Nbdu2ISEhAXfv3oW5uTmaNGmCnj17YtSoUXB2dpZyeK2TyWSYNGkS2rRpg127dqGgoABz584FAMyZM0fL1REREek/yWZkTp48ieDgYGzbtg1paWm4d+8ecnJykJSUhI0bN2LYsGE4efKkVMPrhMuXLyMzMxNLly6Fm5sbunXrhunTp+Pw4cPaLo2IiKhekGRGJj09HTNnzkR5eTmCg4MxdOhQODs7Q6FQ4NatWzh8+DBiY2Mxa9YsxMTE1NuZmdatW2PDhg2wsrJSXhMEATKZTItVERER1R+SBJmNGzeivLwcH374IcaNG1ejzc3NDf369YOvry8WLVqErVu3Yt68eVKUoXV2dnbo2bOn8meFQoHo6Gj4+flpsSoiIqL6Q5Ig88svv6BVq1aPhJi/GzNmDHbs2IFTp05JUYJGVFRUICcnR2VbkyZNYG1tXePa0qVLkZKSwhO/iYiI1ESSIJOXl4eAgIAn9nN3d8ePP/4oRQkaceXKFYwZM0Zl29KlSzFixAgAf20I+Omnn2Lnzp1YvXo12rZtq8kyiYiI6i1JgoyVlRXy8/Of2C8/Px8WFhZSlKARXbp0QVpa2mP7KBQKfPjhhzh8+DBWrlxZp4BHREREdSPJqiUfHx9cvHgRSUlJtfZJTk7GhQsX4OPjI0UJOmPZsmU4fPgw1qxZg4EDB2q7HCIionpFkiAzZswYyOVyhIeH49ixY5DL5co2uVyOY8eOITw8HKIoYvTo0VKU8ES5ubnw8/PDli1bVLZXVVVhy5YtCAoKQocOHeDv74+1a9eisrKyzmNcunQJW7duRWRkJLy9vZGXl6f8IiIioucnyUdL/fv3x+uvv46dO3fi3XffhZmZGZo1awbgrwBRUVEBURQRGhqqlY9aSkpKEBERgeLi4lr7LFy4ELt374afnx8GDBiACxcuICoqCmlpaYiKiqrTON9//z0A4D//+Q/+85//1GhLSkqCiYmk+xESERHVe5L9m/STTz6Bh4cHNm3ahMzMTNy8eVPZ5uLigrCwMISGhko1fK2ys7MRERHx2I+9Lly4gN27dyMwMBCrV6+GIAgQRRFz5szBwYMH8eOPP6J///5PHOv999/H+++/r87yiYiI6G8knRIYNWoURo0ahdzcXOTm5kIURTg4OChnZzRty5YtiIqKQnl5OXr06IGEhASV/Xbs2AEAmDZtGgRBAPDXRnYzZsxATEwM9u7dW6cgQ0RERNLSyGcbDg4OcHBwAAAUFxejsLAQdnZ2mhi6hm3btsHJyQkLFixARkZGrUEmMTERtra2cHd3r3HdwcEBrq6uOHfunCbKJSIioieQ7KylwsJCzJ49G1OmTKlxPT4+Hr1798b06dPrtERbnRYsWICDBw/C19e31j4ymQw5OTlwcXFR2e7k5IQHDx6gsLBQqjKJiIiojiSZkSksLMSoUaOQlZX1SCCoqqqCsbExvv/+e6SmpmL//v2P7IArld69ez+xz/379wEADRs2VNleff3hw4damVUiIiKi/5FkRmbTpk3IysrC4MGDsWvXrhptgwcPRkJCAoYMGYKbN29i/fr1UpTwzKqqqgAAZmZmKturr1dUVGisJiIiIlJNkhmZn3/+GY6OjlixYgWMjY0faW/QoAGWLFmCc+fOIS4uDu+9954UZTyT6p2Ga9svpvrkaktLS43VpEp5ebla7lNbYCPSVwqFQq9OmOczSPWRJp9DSWZksrOz4e3trTLEVDM1NYW3tzfu3LkjRQnPzNraGkZGRrXuMfPw4UMAtX/0RERERJojyYyMtbU1CgoKntjv4cOHaNCggRQlPDMzMzM4OjoiKytLZXtWVhZsbW3RuHFjzRb2D/p8RhWRlIyMjPh8EGmZJp9Dyc5aunTpEi5fvlxrn9TUVJw/fx7e3t5SlPBc/Pz8kJeXh/T09BrXc3NzcfPmTXTq1Ek7hREREVENkp61NHHiROzatavGMuvCwkLs27cPYWFhkMvlGDt2rBQlPJeQkBAAwMqVK6FQKAAAoijiiy++UB6tQERERNonyUdLvXr1Qnh4ODZs2IAFCxZgwYIFynOFqlcFiaKIt956Syd3yO3ZsyeCgoJw9OhRhIaGonv37rh48SISExMRGBiIfv36abtEIiIigoQ7+86YMQNdunTB9u3b8dtvvymXK5uamqJTp04YP348Xn75ZamGf24rVqxAmzZtcODAAWzduhWOjo6IjIzExIkTlccWEBERkXZJekRBnz590KdPHwB/bTRXVVWFxo0b68SpzyNGjMCIESNqbTc1NcXUqVMxdepUDVZVd1x+TaQal18TaZ8mn0NJEsXjTpbOzc195JqXl5cUZRAREVE9J0mQefXVV5/q45eUlBQpyqjXuLyUSDUuvybSPk0+h5IEGUdHR5XXFQoFioqKUFZWBgDo1q0bmjZtKkUJREREZAAkCTI//PDDY9tTU1Mxf/583LlzB2vWrJGiBCIiIjIAkuwj8yTt27fHunXrkJ+fzyBDREREz0wrQQYA7Ozs0K1bN5w4cUJbJRAREZGe0+o6aFEUUVhYqM0S9BaXXxOpxuXXRNqn96df18W1a9eQkJCAZs2aaasEIiIi0nOSzMgsXry41raqqirk5eUhPj4elZWVGDRokBQl1HtcXkqkGpdfE2mf3i+/jo6OhiAIEEXxsf18fX0xadIkKUogIiIiAyBJkJk2bVqtbYIgwMrKCh4eHujevbsUwxMREZGB0HiQISIiIlIXrb3sS0RERPS8GGSIiIhIb6nlo6XHrVKqi3nz5qmjDIPCfWSIVOM+MkTap8nnUC1Bpq6rlFQRBIFBhoiIiJ6JWoIMX+7VPO6TQaQa95Eh0j6920eGQYaIiIi0Qe3Lr0VRRFlZGRo0aAAAOHXqFP74449H+tnZ2WHYsGHqHp6IiIgMiFqDzKFDh7Bq1SqMHj0a4eHhAIDDhw/jyJEjKvs7ODigR48e6iyBiIiIDIjagsymTZvwn//8B6IoIjMz85H2kJAQ5ffp6em4dOkSli1bhoMHD6qrBCIiIjIwagky2dnZWLlyJRo0aIDly5cjICDgkT5Lly5Vfl9aWoqhQ4ciLS0NCQkJnJUhIiKiZ6KWDfH27NkDhUKBjz76SGWI+acGDRpg+vTpEEURcXFx6iiBiIiIDJBagswvv/yCRo0aPdXLu4GBgbC2tsb58+fVUQIREREZILV8tHTr1i34+PhAEIQ6/46ZmRm8vLyQmpqqjhIMDnf2JVKNO/sSaZ/e7ez78OFDNGrUSGXb4MGD0bZtW5Vttra2KCsrU0cJREREZIDUEmTs7Ozw4MEDlW39+vVDv379VLYVFBSgadOm6ijB4HDnUiLVuLMvkfZp8jlUyzsyDg4OSElJearfkclkSElJgZOTkzpKICIiIgOkliDz4osvorCwED/99FOdfycuLg7FxcXo27evOkogIiIiA6SWIDN06FCIoohPP/0UhYWFT+xfUFCAZcuWwcTEBIMHD1ZHCURERGSA1BJk2rRpg8GDB+PWrVsYNWoUTp8+XWvfs2fPYuTIkSgoKMDEiRPRvHlzdZRAREREBkhtRxQsXLgQWVlZ+P333xEeHo6mTZuie/fucHBwgCiKyM/Px/nz53H79m2IooiXXnqJp2YTERHRc1FbkLGyssL27duxZs0a/L//9/+Ql5eHI0eOKPeWEUURwF+rbd566y1ERETAyEgtE0JERERkoNR6+rWZmRlmzpyJqVOn4sSJE7h06RJycnJQVVWF5s2bw9PTE6+88gqsra3VOSwREREZKLUGmWoWFhYIDg5GcHCwFLcnIiIiAqCml32JiIiItEGSGRmSHs9aIlKNZy0RaZ8mn0POyBAREZHe4oyMnuJZMkSq8awlIu3Tu7OWiIiIiLSBQYaIiIj0FoMMERER6S0GGSIiItJbDDJERESktxhkiIiISG8JYvVpjqQXjIyMIIoibGxs1HZPUa6AorRMbfcj0hajBpYQjPXvv8/kcgVKSvRnEz+ix7GyMoOxGp/DoqIiCIIAhUKhsp37yOgZIyOjWv/HfFaCsRGMG1qp9Z5EVHfGxkZo1Ih73xCpIggCjIxqD0ackSEiIiK9pX9zsERERET/h0GGiIiI9BaDDBEREektBhkiIiLSWwwyREREpLcYZIiIiEhvMcgQERGR3mKQISIiIr3FIENERER6i0GGiIiI9BaDDBEREektHhpJ9VZYWBjOnDmDtWvXIiAgoNZ+crkcvXv3RllZGeLj49GgQYM6jzFgwABkZ2c/tk/79u0RExNT53sS1SfafA6NjIxgZWWFFi1aICAgAO+88w4sLHg4Z33DIEP1VkhICM6cOYPvvvvusf8AjY+PR0FBAYYPH/5U//D8u2nTptXa1rRp02e6J1F9oM3nUC6X4/bt2zh58iTWrFmD33//HRs2bIAgCM90f9JNDDJUb7388suwsrLCDz/8gLKyMlhaWqrsd/jwYQDA8OHDn3msiIiIZ/5dovpMF57D3NxcjBgxAqdOnUJ8fDx69er1zGOQ7uE7MlRvWVhYYNCgQSgtLcVPP/2ksk9ZWRlOnDgBJycndOvWTbMFEhkAXXgOHRwcEBoaCgD45Zdf1H5/0i4GGarXqv/r7ujRoyrbf/jhB5SWliIkJERj0803b97Ee++9h549e8Lb2xuvvPIKvv76a1RWVtboN2DAAIwbNw779+9Hz5490blzZyxbtgxZWVlo164d1q1bh7i4OAwfPhwdOnTAgAEDsHnzZgDA+fPn8cYbb6BTp04YMGAA1qxZg6qqKo38fUT/pAvPYZMmTQAAMpmsxvXi4mJ8/vnnCAgIgLe3N3r37o1PPvkEBQUFj9xj+/btGDFiBDp37gxfX1+88cYb+O6772r0WbNmDdq1a4e0tDQsXrwYPXr0gJ+fHyZMmIDz589L8rcZOgYZqte6dOmCFi1a4Oeff0ZxcfEj7YcPH4YgCM81nf00kpKS8Oqrr+LYsWPo0aMHJkyYABsbG3zxxReYPHky5HJ5jf7Xr1/HwoULERAQgEGDBqFTp07Ktri4OMyYMQNubm4IDQ1FSUkJli1bhsWLF2PChAmwtbXF66+/DlEU8eWXX2LHjh0a+RuJ/kkXnsPTp08D+Ovl+2oPHz7E66+/jo0bN6JFixYYP348OnfujD179mDkyJG4e/eusu+GDRuwePFiAMDo0aMxYsQIZGZm4t///jcOHjz4yHgffPABYmJiEBQUhICAAFy8eBFvvvkmzpw5I9nfaLBEonpu9erVoru7uxgTE1PjemFhoejl5SWOGTPmme/dv39/0d3dXYyKilL5tX//fmVfhUIhBgcHiz4+PuKVK1dq3GfJkiWiu7u7GB0d/ci9t23bVqPvrVu3RHd3d9Hd3V08fvy48vrp06eV1/9+n+r+r7322jP/nUTPSxPP4T9VVFSIGRkZ4tKlS0V3d3fx5ZdfFisqKpTt8+fPf+R5EUVRPHHihOju7i5GRkYqr3Xr1k0MCAgQKysrldfu3Lkjent7iyNGjFBei4qKEt3d3UVfX1/x5s2byuuXLl0SPT09RX9/f7GqquqZ/1Z6FF/2pXovJCQEa9euxdGjRzF06FDl9e+++w6VlZVq+a/AL7/8UuX1bt26YcSIEQCA33//HdeuXcOYMWPg7e1do9/06dOxY8cOfPvttxgzZkyNtsDAQJX3dnJyqrEKxNfXFwDQoEEDjB49Wnm9RYsWaNq06ROXiRNJSRPPYbt27Wpt69atG5YsWQIzMzMAQFVVFQ4ePIi2bds+8sz5+/vD19cXx48fR3FxMaytrSGKIgoLC5Geno62bdsCAJo1a4bvvvsO9vb2j4w3duxYuLi4KH/u2LEjgoKCcOjQIVy6dAl+fn7P/ffSXxhkqN5zcXGBr68vzpw5gwcPHqBRo0YAgCNHjsDS0rLWoPA00tLSntgnKSkJAJCZmYk1a9Y80m5lZYW0tDSIoqh8T8DU1BQvvPCCyvu1bNmyxs/VS1abNWsGY2PjGm3m5uZ48ODBk/8QIolo4jmsXn6tUCiQmpqKn376CY0bN8YXX3yBF198sUbf9PR0lJaWQi6Xq3weKyoqIJfLkZaWBj8/P4SGhmLDhg0YOnQofHx80KdPH/Tt2xc+Pj4qa1H10nKHDh1w6NAhpKamMsioEYMMGYThw4fjwoULiIuLw2uvvYbs7GxcuHABQ4cOhbW1tUZqqA4Sp0+fVn5er0pJSYmypsdt3lXbMtbq/+Ik0jVSP4f/XH594sQJREZG4t1338WOHTvg5uambKt+Hv/8889aZ1QBoKioCAAwY8YMtGzZErt27cLly5fx+++/Y82aNWjVqhU++eSTR4KSg4PDI/eq3lNK1XtC9Oz4si8ZhFdeeQXm5ubKFQaxsbEQRVFjL/kC/5sx+fTTT5GWllbrl6aCFZGmafo5DAgIwOTJk3Hv3j1MmTIFJSUlyjYrKysAwLBhwx77PA4YMAAAIAgCXnvtNezbtw/x8fH4/PPPERgYiIyMDEyePBmFhYU1xi4vL3+knocPHwIAbG1tJfl7DRWDDBmEhg0bwt/fHwkJCXjw4AGOHTsGR0dH9OjRQ2M1VH9+f/Xq1UfaKisrsWzZMmzfvl1j9RBpmjaew8mTJ8PLywsZGRn47LPPlNdbtWoFMzMzJCUlQRTFR35vy5YtWLduHe7du4d79+5hzZo1OHDgAIC/lnIPGTIEUVFRGDFiBMrKypCcnFzj969cufLIPS9evAjgr4+YSH0YZMhghISEoKqqCrt27UJSUhKGDRum0a3Ku3btihYtWmDfvn3Kf6BV27BhAzZv3qx8j4aovtL0c2hiYoJPP/0UxsbG2LVrl/LZMzc3R1BQEG7cuKHcf6nar7/+ihUrVmD//v2wsbGBlZUVtm3bhpUrV+L+/fs1+t6+fRsA4OjoWOP6f//73xrLty9cuIDDhw/Dy8urxhJwen58R4YMRq9evWBvb49169YBeL6t0J+FsbExli9fjokTJ2Ls2LHw9/eHs7Mzrl69ioSEBLRo0QIzZszQaE1EmqaN59DDwwPjx4/H5s2b8fHHH+Pbb7+Fqakp3n//fVy8eBHLly/HyZMn0aFDB+Tm5iIuLg4mJiZYsmQJjIyMYGZmhsjISCxevBjBwcF4+eWXYWFhgXPnzuHKlSsYNmwYWrduXWPMhw8fYvjw4Xj55ZdRXFyM77//HhYWFli0aJHkf6+h4YwMGQxjY2MMGTIEZWVl8PPze2TVjyZ06dIFe/fuxaBBg5CYmIht27bh9u3bGDduHHbv3l3rCiWi+kJbz2FkZCScnJxw7do1bNq0CQBgZ2eHPXv24O2330Zubi62b9+OxMREDBgwAHv27EH37t2Vvz9u3DisXLkSLVq0wNGjR7Fjxw7IZDJ88MEHWLJkySPjffzxxwgICEBsbCxOnz6N/v37Y/fu3fDy8tLI32tIBFHVh4NERET01NasWYMvv/wSa9eufexp36Q+nJEhIiIivcV3ZIj+z5YtW5TLI5/EyclJuWMvEakPn0N6WgwyRP9n27Ztdd7G/+9HDxCR+vA5pKfFd2SIiIhIb/EdGSIiItJbDDJERESktxhkiIiISG8xyBAREZHeYpAhIiIivcUgQ0RERHrr/wMc6dRviwLM2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4h\n",
    "#Glucose consumption specific activities for S. cerevisiae \n",
    "fig = activityPlot(yeast_core, 'glucose')\n",
    "fig.savefig('./Figures/edf4h_yeast_glucose_specific_activity.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3bdcd620",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.200348730640986\n",
      "4.315355483480366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fig. 2b\n",
    "#ATP production specific activities for S. cerevisiae \n",
    "\n",
    "fig = activityPlot(yeast_core, 'ATP')\n",
    "fig.savefig('./Figures/fig2b_scerevisiae_atp_specific_activity_core.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2cc96e3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.218774053414936\n",
      "3.4405669493729447\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 6c\n",
    "#ATP production specific activities for S. cerevisiae with the correlated mitochondrial proteome included\n",
    "\n",
    "fig = activityPlot(yeast_partial_mito, 'ATP')\n",
    "fig.savefig('./Figures/edf6c_scerevisiae_atp_specific_activity_partial_mito.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "faf18298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.210987586875362\n",
      "2.395432898338362\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 6e\n",
    "#ATP production specific activities for S. cerevisiae with the full mitochondrial proteome included\n",
    "\n",
    "fig = activityPlot(yeast_full_mito, 'ATP')\n",
    "fig.savefig('./Figures/edf6e_scerevisiae_atp_specific_activity_full_mito.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1f453c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specific acitivity estimate inputs for mammalian cells\n",
    "\n",
    "#parameter estimates for proteome occupany\n",
    "resp_proteomics_core = pd.read_csv('./Data/2024_02_26_mammalian_specific_actvity_resp_core.csv')\n",
    "resp_proteomics_partial_mito = pd.read_csv('./Data/2024_02_26_mammalian_specific_actvity_resp_partial_mito.csv')\n",
    "resp_proteomics_full_mito = pd.read_csv('./Data/2024_02_26_mammalian_specific_actvity_resp_full_mito.csv')\n",
    "ferm_proteomics = pd.read_csv('./Data/2024_02_26_mammalian_specific_actvity_ferm.csv')\n",
    "\n",
    "#rates\n",
    "rates = pd.read_csv('./Data/mammalian_cellular_rates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a3bebc90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b201f0f5bec541f1b2d88cdc7b51b58a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b430927a9333462d960d3e2d9c82d4d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2986735242d146f4a790be0bbf502e0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculating specific activities\n",
    "mammalian_core = warburgRates(rates, ferm_proteomics, resp_proteomics_core, ferm_proteomics, 2, 24)\n",
    "\n",
    "mammalian_partial_mito = warburgRates(rates, ferm_proteomics, resp_proteomics_partial_mito, ferm_proteomics, 2, 24)\n",
    "\n",
    "mammalian_full_mito = warburgRates(rates, ferm_proteomics, resp_proteomics_full_mito, ferm_proteomics, 2, 24)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "be7fa4af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8153459877880278\n",
      "0.021579336238520066\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig. 4i\n",
    "#Glucose consumption specific activities for mammalian cells\n",
    "fig = activityPlot(mammalian_core, 'glucose')\n",
    "fig.savefig('./Figures/edf4i_mammalian_glucose_specific_activity.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c1d0b0f3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6306919755760556\n",
      "0.5179040697244812\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fig. 2c\n",
    "#ATP production specific activities for mammalian cells \n",
    "fig = activityPlot(mammalian_core, 'ATP')\n",
    "fig.savefig('./Figures/fig2c_mammalian_atp_specific_activity_core.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eae617f0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6318622605857913\n",
      "0.27308331519277274\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fig. 6f\n",
    "#ATP production specific activities for mammalian cells \n",
    "fig = activityPlot(mammalian_partial_mito, 'ATP')\n",
    "fig.savefig('./Figures/edf6f_mammalian_atp_specific_activity_partial_mito.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f4661654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6326200205489816\n",
      "0.18797245456242787\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ATP production specific activities for mammalian cells Fig. 2f\n",
    "fig = activityPlot(mammalian_full_mito, 'ATP')\n",
    "fig.savefig('./Figures/extended_data__mammalian_atp_specific_activity_full_mito.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3ba30aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ff/1VCQkJprbChQsrICDA9MfFxSXH/bt27arjx4/r6NGj2r59uzp37mzaVrRoUcXExGTZJywsTJGRkSpWrJiuXr2aZWbq8uXLkpTlsrgM7dq1U6FChbR161Zt3rxZLVu2lLu7u2l7rVq1NG/ePO3bt0+ff/65mjRpok8++URbt27N9nj32t+cCEIAAACAFfTv31/Jycl6/fXXlZKSkmX7jRs3FB0dneP+jz32mMqVK6d33nlHycnJ6tChg2lbnTp1dPr06UwrsMXHx2vQoEHatGmTGjRooJSUlCyBY+PGjXJ0dMxxJsrV1VWtW7fWhg0btG/fPtNlcZK0fPlytWzZUsnJySpUqJAaNWqkt99+W5J04cKFLMe61/7mxmIJAAAAgBVUq1ZNM2bM0IQJE/T000+rW7dueuSRR3Tr1i3t379fa9euVWJiop599tls9zcYDOrcubPmzZunNm3ayM3NzbSta9euWr58uQYPHqxhw4bJzc1NixYtkpOTk7p166aSJUuqfv36eu2113Tx4kU98sgj2rVrl1avXq3BgwerWLFiOdbdpUsX9enTR56engoKCjK1P/bYY5oxY4aGDh2q5557Tvb29lq9erWcnJwUHByc5Tj32t/cCEIAAAAokFz8Kilg0TSrjv+g2rZtq5o1a2rlypX64osvdPHiRUlSpUqV1LNnT/Xq1UulS5fWvn37st2/RYsWmjdvXqaZGen2zM3KlSs1ffp0vfXWWzIajapTp46WLVumUqVKSZI+/fRTzZkzR5999plpBbtJkyapZ8+ed6y5QYMGcnFx0ZNPPpnp+UePPPKIPv30U3344YcaPXq00tLSVLNmTS1evFg+Pj5ZjnOv/c3NYMyLO5GQaxnXWF6/ft2qdVhK7IHDOhw63mzHC1g0TW71A8x2PKCgCQkJ0Y4dO6xdBgAblRfvW06fPi1JefLGuCCaN2+e1qxZox9//DHLwgeWcuDAAT333HPasmWLKlV68DBoKXf73mFGCAAAAChgvvnmG/39999auXKlXn755TwJQXv37tX+/fv19ddfq2XLlvk6BOUGiyUAAAAABUxERIRWr16ttm3b6rnnnsuTMa9du6YlS5aodOnSmjRpUp6MaUnMCAEAAAAFzIQJEzRhwoQ8HbNt27Zq27Ztno5pScwIAQAAALA5BCEAAAAANocgBAAAAMDmEIQAAAAA2ByCEAAAAACbQxACAAAAYHMIQgAAAABsDkEIAAAAgEUYjUZrl5AjHqgKAACAAik2NlGHD1+w2vgBAWXk5lb4vvfv3bu39u/fn6nNYDCoSJEiqlixovr27asnn3zyQcu8J998843Gjx+vXbt2qXTp0g90rD/++EPz58/XggULJElnz55VSEiIZsyYkefnlR2CEAAAAAqkw4cvqFOnRVYbf8OGUAUFVX6gYwQEBGjixImm1+np6bp48aKWLl2qsWPHyt3dXc2bN3/QUnOtRYsW+vLLL+Xh4fHAx1q7dq3++ecf0+tSpUrpyy+/VIUKFR742OZAEAIAAACsxNXVVbVr187S3qxZMzVq1EjffPNNngYhDw8Ps4Sg7BQqVCjbc7UW7hECAAAA8plChQrJ0dFRBoNB0u2Zok8++UStWrVSzZo11aZNG61ZsybTPlFRURo0aJAaNmyoRx99VD169NCuXbtM2z/88EM9/vjj2r59u5544gnVrl1bzz33nP766y9Tn2+++UZ+fn66ePGiJOnVV1/VCy+8oNdff11169ZVz549ZTQaFRMTozfffFPBwcGqWbOmGjRooGHDhuncuXOm/dauXatz587Jz89P33zzjc6ePSs/Pz99++23pvFOnDihIUOGqFGjRgoMDNSLL76oiIgI0/Z9+/bJz89Pe/fuVb9+/fToo4+qSZMmev/995WWlvZAX2OCEAAAAGAlRqNRqamppj+3bt3SiRMnNH78eMXHx5vupZk0aZLmzZunzp0765NPPlFwcLBef/11LV++XNLtoDRw4EAlJiZqxowZ+vjjj+Xu7q7BgwcrKirKNN6VK1f02muvqV+/fnr//feVkJCgPn36mIJPdvbt26fz589r3rx5GjhwoCTpxRdf1N69ezVmzBgtWrRIL730knbv3q1JkyZJkoYMGaKWLVvK09NTX375pVq0aJHluMeOHVPXrl11+fJlTZ48WdOnT9e1a9fUq1evTJfUSdLLL7+sBg0a6NNPP1WHDh20cOFCffPNNw/ypefSOAAAAMBa9u7dqxo1amRqMxgM8vPz09y5cxUcHKxTp07pq6++0tixY/XCCy9IkoKCgpSWlqa5c+eqa9euiouL08mTJzVkyBDTpXS1atXSvHnzdOvWLdOxExISNHXqVLVr106SFBgYqJCQEC1dulTjxo3LtsbU1FRNnjxZ5cuXlyRdvHhRLi4umjhxourUqSNJatiwoaKiorR27VpJUoUKFeTh4ZHpcriEhIRMx/3oo49UuHBhLV26VEWKFJEkNWnSRI8//rg++OADffDBB6a+PXr00JAhQyRJjz32mLZv366ffvpJ3bp1u8ev+P8jCAEAAABWUqtWLb3xxhuSpOjoaM2dO1epqamaPXu2Kle+vRDD3r17ZTQaFRwcrNTUVNO+LVu21NKlS3Xo0CE1aNBAVapU0euvv65ff/1VQUFBatasmcaPH59pPEdHR7Vu3dr0ukSJEqpbt65+//33HGssUqSIKQRJUunSpbV8+XIZjUadPXtWp0+f1smTJ/W///1PKSkpuT73sLAwtWzZ0hSCJMnFxUUtW7bU9u3bM/XNCFz/riExMTHXY2WHIAQAAABYiYuLiwICAiTdXkGudu3a6tSpk0JDQ/X111/Lw8ND169flyS1adMm22NcunRJBoNBixcv1vz587Vt2zatX79ejo6OatWqlSZPniw3NzdJt4OPvb19pv09PDx09uzZHGssWbJklrYNGzZo1qxZunDhgtzd3VW9enU5Ozvf03ODYmNjsz12iRIlFBcXl6nN2dk502s7Ozulp6fneqzsEIQAAACAfKJkyZJ64403NGLECE2dOlUzZ85U0aJFJUkrVqzIEggkmWZrvLy8NGnSJL355puKiIjQli1btHDhQpUoUUKvv/66JJlC1b9dvXpVJUqUyHWNYWFhGjdunPr27avnn39eXl5ekqQZM2bo4MGDuT5OsWLFdOXKlSztly9flru7e66Pc79sarGEjRs3qn379qpVq5batm2r9evX37F/fHy8Jk+erCZNmigwMFD9+/dXZGRklj7vvPOOgoODVadOHfXu3VuHDh2y3EkAAADgodamTRs1bdpUGzdu1P79+1WvXj1Jt2dQAgICTH8uXLigDz74QImJiTp06JAaN26sQ4cOyWAwqHr16ho1apSqVq2qCxf+/6GzSUlJ+u2330yvr1y5ot9//12PPfZYruv7448/lJ6ermHDhplCUFpamvbs2ZNplua/M0//Vb9+ff3444+Z7h1KSEjQjz/+qLp16+a6nvtlMzNCmzdv1pgxY9SnTx81bdpU27dv17hx4+Ts7JzjNOOoUaN0+PBhjR07Vi4uLpo3b5769OmjTZs2mZL5G2+8oZ07d2rMmDHy8fHRkiVL1LdvX23YsEHe3t55eYoAAAA2JSCgjDZsCLXq+JYyYcIEderUSVOmTNG6devUoUMHTZgwQWfOnFH16tX1zz//aNasWapRo4bKli2rkiVLqkiRIho7dqyGDRumkiVLas+ePTp69Kief/75TMceN26cRo8eLVdXV82bN0+urq7q3bt3rmurVauWJOntt9/WU089pdjYWK1cuVIREREyGo1KSkqSs7OzihYtqitXrmjXrl2qXr16luMMHTpU3bt3V79+/dS/f38ZjUZ99tlnSkhI0NChQx/sC5gLNhOEZs2apbZt22rChAmSpKZNmyo2NlZz587NNgiFhYVp165dWrhwoZo1ayZJqlevnkJCQrRq1SoNGDBASUlJ2rx5s4YMGaJnn31W0u2VNxo3bqxvv/1WL730Ut6dIAAAgI1xcyusoKDK1i7DIipXrqzevXtr8eLFWrVqld5991198sknWrFihaKjo1WyZEl17dpVw4cPl3T7uUOLFi3SzJkzNXXqVN24cUMVK1bU22+/bVqCW7o9SzN+/HjNmDFD169fV8OGDfXBBx+oePHiua6tYcOGeuONN7RkyRJt2rRJJUuWVMOGDTVv3jwNHTpUYWFhCgoKUo8ePfTTTz9p6NChGjlyZJb33H5+flq5cqVmzZqlsWPHys7OTvXq1dOXX36pqlWrmucLeQcG473c0VRAnTlzRq1atdKcOXPUtm1bU/vmzZs1cuRIbd++Pcvszdy5c7V06VIdOHAg07TegAEDlJCQoBUrVujmzZuqX7++Xn31VfXr10/S7bXgGzZsqE6dOmnixIn3XGvG9ZDZXb/5MIg9cFiHQ8ffvWMuBSyaJrf6AWY7HlDQhISEaMeOHdYuA4CNyov3LadPn5Yk+fj4WGwMW/Hhhx9q/vz5Cg8Pt3YpeeJu3zs2cY/QyZMnJUmVKlXK1J7xRTl16lS2+/j4+GS5trFChQqm/kWLFlXnzp1NyxbGxsZq5syZio+PN63NDgAAACD/sYkgdPPmTUmSq6trpnYXFxdJyrI8X0bbf/tn7PPv/qNGjZKbm5u6deumBg0a6LPPPtPkyZOzrHWeV0JDrXedLAAAAFBQ2MQ9QhlX/xkMhmzb7eyy5sE7XTGY0f/q1avq3r27ChUqpJkzZ6pEiRL64Ycf9Oabb6pIkSJWmRX676p2AAAAgCQNGzZMw4YNs3YZ+YZNBKGMFd7+O/MTHx+fafu/ubq6Zvtgqfj4eNNM0Zo1a3Tx4kVt27bNdI9Ro0aNdPPmTb399ttq06ZNtiELAO4m9Uac4o9lvWw3O7EHDt+1j4tfJTkUyzrLDQCArbKJIJRxb1BUVJT8/PxM7Rk3UP333qGMtt9++01GozHTTNLp06dN/c+fPy9PT88sCy3Uq1dPGzduVExMTLZPywWAu4k/dip3C4t4Klf9WFgEAIDMbGK6wsfHR+XLl9eWLVsytW/dulUVK1ZU2bJls+wTFBSkGzduaM+ePaa2mJgYhYWFqXHjxpJuh6UrV65kuRzt4MGDcnV1lZubm/lPBgAAwIbY2dkpLS3N2mWgAEpLS7vj1Vk2MSMk3X5g0/jx4+Xm5qYWLVpo586d2rx5s2bPni3pdsiJiopSlSpV5Orqqvr166tBgwYaPXq0xowZI3d3d3344YcqWrSoevXqJUnq2rWrli9frgEDBmjYsGEqUaKEdu7cqfXr12vMmDFydHS05ikDAAAUeM7OzoqLi1NMTIw8PDysXQ4KiJiYGN26dSvbW2Ay2EwQ6tKli5KTk7V48WKtWbNG3t7emj59umlBg59++knjx4/XsmXL1LBhQ0nSvHnz9O6772rGjBlKT09X3bp1NWfOHNNMT9GiRbVq1Sq99957mjJlipKTk1W5cmXNmjVL7du3t9q5AgCAzEJDQ7Vo0SJrl4H7ULJkSd26dUvR0dG6fv16lkebAP+VlpZmCkF3uk3FZoKQJPXs2VM9e/bMdluXLl3UpUuXTG1ubm6aNm2apk2bluMxvby89P7775u1TgAAYF6sqlpwGQwGlStXTleuXFFSUpLS09OtXRLyOUdHR1MI+u+q0f9mU0EIAAAABY/BYJCnp6e1y8BDxiYWSwAAAACAfyMIAQAAALA5BCEAAAAANocgBAAAAMDmsFhCAZF6I07xx07lqm/sgcN33O7iV0kOxVzNURYAAABQIBGECoj4Y6d0OHT83Tt66q79AhZNk1v9ADNVBgAAABQ8XBoHAAAAwOYQhAAAAAqQ0NBQa5cAPBQIQgAAAAVIZGSktUsAHgoWv0coOTlZly9fVmJiohISEpSWlqbChQurSJEiKlasmNzd3S1dAgAAAABkYtYgFBsbq7CwMIWFhemvv/5SVFSULl++LKPRmOM+RYoUUYUKFeTr66s6deqoXr16qlq1qjnLAgAAAIBMHjgIJSYm6ocfftB3332nffv2KS0tLVPwKVy4sDw8POTs7CwnJyfZ29srKSlJycnJSkxMVExMjI4ePaqjR49q48aNMhgMKlWqlNq1a6f27durZs2aD1oiAAAAAGRy30EoNjZWy5cv14oVK3T9+nW5uLioSZMmCggI0COPPKIqVaqodOnScnFxueNxjEajrly5oosXL+rYsWOKiIjQ4cOHtXTpUn3++eeqVauW+vfvr1atWt1vqQAAAACQyT0HofT0dC1fvlwffvihnJyc1KFDB7Vr106PPvqo7O3t77kAg8EgT09PeXp6KiDg/59tExsbq19++UWbNm3SyJEj5evrqzfffFN16tS55zEAAAAA4N/uKQj9888/Gj16tOzt7TVp0iS1bdv2vsJPbri5ualDhw7q0KGDrl69qlWrVmnIkCF64oknNHHiRBUqVMgi4wLIWWhoqBYtWmTtMgAAAB5YrpfPXr9+vYYNG6ZBgwZp3bp16tChg8VC0H+VKFFCL730knbu3CkPDw/16dNHFy9ezJOxAfw/lmwFAAAPi1zNCH3yySe6fPmy1q1bJ2dnZ0vXlKMiRYpo5MiR+ueff/T6669rypQp8vLyslo9AAAA5pJ6I07xx07lqm/sgcN33O7iV0kOxVzNURbw0LprEDpx4oSqVaumQYMG5UU9uVKlShXNnj1bO3bs0JNPPmntcgAAAB5Y/LFTOhw6/u4dPXXXfgGLpsmtfsAd+wC27q5ByNfXV76+vnlRyz1xdXUlBAEAAAC4L7m+RwgAAAAAHhYEoYdMqVSDtUsAAAAA8r08DULVq1fPy+Fs0tBr1lvMAgAAACgo8jQIGY3GvBwOAAAAALKVp0HIYOCyLQAAAADWxz1CAAAABQj3AwPmka+C0LVr17h8DgAA4A64Hxgwj7s+Rygv/Pzzzxo7dqxiY2Pl4OCgKlWqyN/fX/7+/qpRo4aqVasmZ2d+6AEAAACYR74IQlOnTpWnp6deeeUVxcbGKiIiQn/++afWr1+vtLQ02dvb68iRI9YuEwAAAMBDIl8EoYsXL+qDDz5Q8+bNM7UnJSXp6NGjCg8Pt1JlAAAAAB5G+SII+fr6Kjk5OUu7s7OzAgMDFRgYaIWqAAAAADys8sViCS+88IJWr15t7TIAAAAA2Ih8MSO0e/dunT59Wi+99JKGDx+uqlWrWrskwKak3ohT/LFTueobe+DwHbe7+FWSQzFXc5QFAABgMfkiCB06dEgXLlzQ2bNntWPHDpUsWVI1atTI9MfLy8vaZQIPrfhjp3Q4dPzdO3rqrv0CFk2TW/0AM1UGFEyhoaFatGiRtcsAANxBvghCmzZtUnJyso4dO6ajR4+a/ixevFiJiYkyGAw6evSotcsEACBXIiMjrV0CAOAurBaEwsLCVKdOHdnZ3b5NqVChQgoICFBAwP9/kmw0GnXy5EkdO3bMWmUCAIB8zJyX9kpc3gvYkjwNQkaj0fT35557Tl999ZVq1aqlcePGqXr16vL391f16tVVtGhRSZLBYJCvr698fX3zskwAAFBAmPPSXonLewFbkqdByGAwmP7+zTffqEyZMpKko0ePatOmTUpNTZXBYFC5cuVMocjf31/VqlXjHiEAAKyA+50APKysdmmcv7+/6e8bNmxQSkqKjh8/rvDwcNNDVH/99VclJCRwjxAAAFbC/U4AHlb5YrEESXJ0dJS/v3+mgGQ0GnXq1ClFRERYsTIAAAAAD5t8E4SyYzAYVLlyZVWuXNnapQAAAAB4iOSLIJSWlqb169frn3/+UfHixeXn5yd/f395enpauzQAAAAAD6F8EYQmTJigb7/9VsWKFVNSUpKSk5NlMBhUokQJ0wNVhw8fbu0yAQAAADwk7KxdgCRt375dAwYM0P79+/Xnn39q06ZNeu+999SpUyelpKRo9erV1i4RAAAAwEMkX8wIFSlSRI0aNZKU+dlBHTp0sHJlAP6tVKrh7p0AAAAKgHwxI9S+fXvt3r3b2mUAuIuh15ytXQIAAIBZ5IsgNHz4cP3+++9at26dtUsBAAAAYAPyxaVxFy9elL29vSZMmKD58+crKChI/v7+qlmzph555BHZ29tbu0QAAAAAD5F8EYTGjh2ryMhIPfXUU0pMTNTu3bu1atUqSbcftFq1alWtXbvWylUCAAAAeFjkiyB0/PhxTZ06NdPiCHFxcTp69KiOHDmi8PBwK1YHAAAA4GGTL4JQhQoV5OLikqnN1dVV9evXV/369a1UFQAAAICHVb5YLCE0NFRffvmltcsAgAKHJc0BALg/+SII7d69W3///beGDx+uEydOWLscwKxCQ0OtXQIeYixpDgDA/ckXl8YdO3ZMly5d0tatW7Vt2zZ5enqaVo3z9/dXjRo15OXlZe0ygfsSGRlp7RIAAADwH/kiCG3YsEEpKSk6fvy4wsPDdfToUYWHh2vx4sVKSEiQwWDQ0aNHrV0mAAAAgIdEvghC0u1lsv39/eXv729qMxqNOnXqlCIiIqxYGQAAAICHTb4JQtkxGAyqXLmyKleubO1SAAAAADxE8sViCQAAAACQl+4YhH7++WfFxMTkVS0AAAAAkCfueGncgAEDZDAY5OXlZVq9LWMlN09Pz7yqEQAAAADM6o5BaMuWLTpy5Ij++usvHTlyRJ9//rlu3rwpg8GgkiVLqkaNGpn+POgS199++62MRqOeeuqpBzoOAAAAANzJHYNQxYoVVbFiRbVv397Udvr06UzhaPny5bpx44ZZlrieMGGC0tPTCUIAAAAALOqeV43z8fGRj4+P2rVrZ2rLCEcPasGCBUpPT3/g4wAAAADAndzX8tnXrl2Tu7u7DAaDpP8PRw+qSZMmD3wMAAAAALibXAeh5ORkvf/++1qzZo2SkpLk7Oys+vXrq2/fvgQYAAAAAAVKroPQRx99pC+//FLdunVTxYoVdeHCBf3222968cUXNXDgQI0cOdKCZQIAAACA+eQ6CG3cuFGjR49W3759M7V/9913mjBhgqpVq6Y2bdqYvUAAAAAAMLc7PlD1365evarq1atnae/YsaNeeOEFff755+asCwAAAAAsJtdByMfHR+Hh4dlua9y4sf7++2+zFZXhxo0b2rJli3777TfduHHD7McHAAAAYJtyfWlct27dNHfuXNWpU0e1atXKtO38+fNycXExe3EvvviiXF1dlZaWpqNHj8rNzU01a9bU7NmzzT4WAAAAANuR6yD07LPP6vfff9czzzyj1q1bq3nz5vL09NTp06c1b948tWjRwuzFxcTE6KuvvjK9joyM1F9//WX2cQAAyI3UG3GKP3YqV31jDxy+43YXv0pyKOZqjrIAAPch10HIYDBo1qxZWr58uZYsWaJNmzaZtjVt2lRjx441e3G1atUyLdUtSRUrVlTFihXNPg4AALkRf+yUDoeOv3tHT921X8CiaXKrH2CmygAA9+qeHqhqMBjUp08f9enTR5GRkbp69arKlSun0qVLW6Q4Z2dnjRw5UmPHjlXlypUtMgYAAAAA23NPQejf8mJ2pnTp0rp69ar69Omj9PR0BQQEqGbNmho2bJhFxwUAAADwcMt1EEpLS9P69ev1zz//qHjx4vLz85O/v788PT0tVtzw4cNNf4+OjtZff/2lI0eOWGw8AAAAALYh10FowoQJ+vbbb1WsWDElJSUpOTlZBoNBJUqUUI0aNVSjRo1MweVB9O7dW8uXL9e8efNUo0YN1axZU15eXvLy8lJISIhZxgAAAABgu3IdhLZv364BAwZo9OjRMhqNOnnypI4eParw8HBFRERo9erVZgtC8+fPN/39yy+/1BtvvCFJplD00ksvmWUc4EGwehQAAEDBlesgVKRIETVq1EjS7UUTfH195evrqw4dOpi9KFfX228I/x14Ll26pMOHD3NpHPINVo8CAAAouHIdhNq3b6/du3ebwlBeSE9P15YtW2RnZ6c2bdooJCSES+MAAAAAPDC73HYcPny4fv/9d61bt86S9WTy2muvadOmTZo1a5Yk6e+//9aWLVvybHwAAAAAD6dcB6GLFy/K3t5eEyZM0BNPPKG33npLa9euVUREhNLS0ixS3KFDh/TRRx+pSJEikqRKlSrps88+s8hYAAAAAGxHri+NGzt2rCIjI/XUU08pMTFRu3fv1qpVqyRJjo6Oqlq1qtauXWvW4ooVK5bptaOjo8VCFwAAAADbkesgdPz4cU2dOjXT4ghxcXE6evSojhw5ovDwcLMXFxAQoJ07d8pgMEiS4uPjFRcXZ/ZxAAAAANiWXAehChUqyMXFJVObq6ur6tevr/r165u9MEkaOXKkRo8erTNnzujdd9/Vnj171KJFC4uMBQCALeERAABsXa6DUGhoqL788ksFBwdbsp5MPvzwQ33yySfav3+/jhw5okGDBqldu3Z5Nj4AAA8rHgEAwNblerGE3bt36++//9bw4cN14sQJS9Yko9EoSdq7d68kqUGDBnr++efVrl07TZs2zaJjAwAAAHj45XpG6NixY7p06ZK2bt2qbdu2ydPTU/7+/qpZs6b8/f1Vo0YNeXl5maWo+fPna8OGDbpx44aWLFmimjVrqkaNGipSpIh27dql8eNz8QkWAAAAAOQg10Fow4YNSklJ0fHjxxUeHq6jR48qPDxcixcvVkJCggwGg44ePWqWooYMGaLHH39coaGhunDhgrZt26aIiAg5OjoqMDDQLGMAAAAAsF25DkLS7eWr/f395e/vb2ozGo06deqUIiIizFrYI488oiVLlsjX11eSlJ6ersuXL8vT09Os4wAAAACwPbkOQitWrFD16tVVrVq1TKvHGQwGeXl5qVKlSmYvztfXV4cOHdKlS5dUvXp1lStXzuxjAAAAALA9uQ5C06ZNU3p6ugwGg7y9vVWjRg1Vr15d/v7+OnbsmDZt2qSvv/7arMV99NFH+vbbb+Xj46N//vlH9erV09SpU1WoUCGzjgMAAADAtuQ6CP3vf/9TRESE6eGp4eHh2rp1q9LS0iRJRYsWNXtxmzZt0saNG1WoUCGlpqZq9uzZmjFjhiZOnGj2sQBLKZVqsHYJAAAA+I9cByEnJyc9+uijevTRR01tN27c0MqVK7VgwQLNmTPH7MWlpaWZZn8cHBw0ZswYtW/f3uzjAJY09JqztUsAAJvHh1IA/ivXzxHKTrFixTR48GA9/fTTZr8sTpLq1KmjpUuXml5nPF8IAADgXvChFID/eqAglOGxxx7Tjz/+aI5DZTJ+/Hht375dTz/9tN5880317t1bjz32mNnHAQBYX2hoqLVLAADYkFxfGvfhhx+als4uU6ZMpm0xMTGZVpIzB6PRqKSkJC1fvlwHDhzQ0aNHFRQUpFatWpl1HABA/hAZGWntEgAANiTXQWjZsmW6efOmDAaD3N3dTaHI0dFRa9as0dixY81amMFg0PPPP69Nmzapfv36ql+/vlmPDwAAAMB25ToIHThwQFFRUfrrr79MK8d99dVXio2NlSS99dZbWrt2rWrUqKGaNWuqXbt2D1yct7e34uLi5Orq+sDHAgAAAIAMuQ5CklShQgVVqFAhU8g5c+aMwsPDdeTIER05ckTr1q3TkiVLzBKEihYtqsGDB+u1115TtWrVHvh4AAAAACDdYxDKjre3t7y9vdW6dWtT2/nz5x/0sJIkHx8f3bx5U/3791dqaqoCAgIUEBCgYcOGmeX4AAAAAGzTAweh7JQtW9Ysx3nppZdMf7906ZIOHz6sI0eOmOXYAAAAAGyXRYKQuVy+fFkrVqzQtWvXFBAQoCeffFIhISHWLgsAAABAAWeW5whZyrBhw3ThwgVVrFhRu3bt0lNPPaXo6GhrlwUAAACggMvXQSguLk4zZszQCy+8oHnz5umVV17RG2+8cd/H27hxo9q3b69atWqpbdu2Wr9+/R37x8fHa/LkyWrSpIkCAwPVv3//bJ9zsXr1arVt21YBAQFq3bq1li1bdt81AgAAALC8fB2EPDw8FBcXZ3odHBysCxcu3NexNm/erDFjxqhJkyb66KOP1KBBA40bN05btmzJcZ9Ro0Zpy5YtGjNmjKZPn67o6Gj16dNHN2/eNPVZsmSJJk+erNatW2vBggVq3bq1pk6dqlWrVt1XnQAAAAAsL1/fI2Rvb68XX3xREyZMUM2aNXXq1CmVLFnyvo41a9YstW3bVhMmTJAkNW3aVLGxsZo7d67atGmTpX9YWJh27dqlhQsXqlmzZpKkevXqKSQkRKtWrdKAAQMUHx+vDz74QAMHDtTIkSMlSY0aNdK5c+e0e/du9erV6/5OHAAAAIBF5esZoYYNG6pEiRJ66aWXFBAQoA4dOqhy5cr6+eefFRMTk+vjnDlzRlFRUXriiScytbdu3VonT57UmTNnsuyze/duubi4qEmTJqY2Dw8P1a9fXz///LMk6ddff1VCQoKeeeaZTPvOnDlT8+bNu5dTBQAAAJCH8vWM0KBBg0x/v3z5sg4dOqTDhw9r6dKlOnLkiPbu3Zur45w8eVKSVKlSpUztPj4+kqRTp07J29s7yz4+Pj6yt7fP1F6hQgVt3rxZknTs2DG5u7vrwoULGj58uP766y+VKFFCoaGh6tOnz72dLAAAAIA8ky+C0Mcff6waNWqoRo0aOV765unpqZCQkPtaPjvjnh5XV9dM7S4uLpKU6T6kDHFxcVn6Z+yT0T8mJkYpKSkaPHiwXnzxRY0YMULbtm3T1KlT5erqqi5dutxzrQAAAAAsL18EoSVLlpjCRcmSJU2hKOOPl5fXAx3faDRKkgwGQ7btdnZZrxDM2JadjP4pKSmKj4/X6NGj9dxzz0m6fY/Q+fPn9eGHHxKEAMBGlUo13L0TAMCq8kUQ+uabb/TCCy+oVKlSqlixoi5evKgVK1boxo0bkqSKFSuqR48eeu655+TgcO8lFy1aVFLWmZ/4+PhM2//N1dVVZ8+ezdIeHx9vminKmFFq3rx5pj5NmzbVjz/+qJs3b2Z7bADAw23oNWdrlwAAuIt8sVjCxIkT9cQTT2jlypWaOnWqFi1apJ07d2rAgAEqXry4WrRooYULF6pfv35KTk6+5+Nn3BsUFRWVqf306dOZtv93nzNnzmSZGTp9+rSpf8Y9Rv+tKSUlRVLWGSgA+UdoaKi1SwAAAFaUL4LQwYMHFRQUlKnNxcVFo0aNUps2bVSoUCGtXbtWUVFRWrhw4T0f38fHR+XLl8/yzKCtW7eqYsWKKlu2bJZ9goKCdOPGDe3Zs8fUFhMTo7CwMDVu3FjS7ZkfSdq0aVOmfX/88Uf5+flle48RgPwhu4cjAwAA25EvglCJEiV05MiRbLc98cQT+u6771SmTBn169cvS+jIraFDh2rjxo1666239PPPP2vSpEnavHmzRowYIel2yDl48KDp8rn69eurQYMGGj16tNasWaNt27apX79+Klq0qOn5QBUqVFCvXr306aef6uOPP9aePXv02muvae/evabjAgAAAMh/8sU9Qp07d9b8+fMVEBCghg0bZtp28eJFXbt2TZJUvXr1bO/byY0uXbooOTlZixcv1po1a+Tt7a3p06erXbt2kqSffvpJ48eP17Jly0w1zJs3T++++65mzJih9PR01a1bV3PmzJGbm5vpuG+88YbKlCmjr776SvPnz1elSpX04Ycf3tfqdgAAAADyRr4IQoMHD1ZERIT69eunxx9/XE888YQ8PT116tQpzZs3TwEBAZJu34vj6Oh43+P07NlTPXv2zHZbly5dsqzy5ubmpmnTpmnatGk5HtPOzk4DBw7UwIED77suAAAAAHkrXwQhBwcHffTRR/r666+1aNEibd26VQaDQUajUY888ogmT54s6fa9RBUqVLBytQAAAAAKunwRhDI8/fTTevrpp3Xu3DldvHhRHh4eqlixomn1tebNm6tBgwZWrhIAAABAQZevglCGcuXKqVy5clnaa9eunffFAAAAAHjo5IsglJaWpvXr1+uff/5R8eLF5efnJ39/f3l6elq7NAAAAAAPoXwRhCZMmKBvv/1WxYoVU1JSkpKTk2UwGFSiRAnVqFFDNWrU0PDhw61dJgAAAICHRL54jtD27ds1YMAA7d+/X3/++ac2bdqk9957T506dVJKSopWr15t7RIBALBJpVIN1i4BACwiX8wIFSlSRI0aNZIkGQwG+fr6ytfXVx06dLByZQAA2Lah15ytXQIAWES+mBFq3769du/ebe0yAAAAANiIfBGEhg8frt9//13r1q2zdikAAAAAbEC+uDTu4sWLsre314QJEzR//nwFBQXJ399fNWvW1COPPCJ7e3trlwgAAADgIZIvgtDYsWMVGRmpp556SomJidq9e7dWrVolSXJ0dFTVqlW1du1aK1cJAAAA4GGRL4LQ8ePHNXXq1EyLI8TFxeno0aM6cuSIwsPDrVgdAAAAgIdNvghCFSpUkIuLS6Y2V1dX1a9fX/Xr17dSVQAAAAAeVvkiCIWGhurLL79UcHCwtUsBUMCl3ohT/LFTueobe+DwHbe7+FWSQzFXc5QFAADymXwRhHbv3q2///5bw4cP14gRI+Tr62vtkgAUUPHHTulw6Pi7d/TUXfsFLJomt/oBZqoMAADkJ/kiCB07dkyXLl3S1q1btW3bNnl6eppWjfP391eNGjXk5eVl7TIBAAAAPCTyRRDasGGDUlJSdPz4cYWHh+vo0aMKDw/X4sWLlZCQIIPBoKNHj1q7TAAAAAAPiTwNQkajMcdtjo6O8vf3l7+/f6b+p06dUkRERF6UBwAAAMBG5GkQutdAYzAYVLlyZVWuXNlCFQEAAACwRXZ36xAREaHPP/88D0q5N3v27NHy5cutXQYAAACAAuiuQahatWoqVqyYXnnlFd24cSMvarqjW7duafr06dqyZYt69+5t7XIAAAAAFEB3DUKS1KVLF7Vr107dunXTmjVrlJqaaum6svX999+rY8eOKlOmjN566y2r1AAAAACg4Mv1PULBwcGqVKmSJk6cqPnz56tv377q1KmTihcvbsn6lJCQoI0bN2rx4sVydHTU+++/r1q1all0TAAAAAAPt3taLKFixYpasWKF1q1bp7lz5+r9999X8+bNFRwcrKCgILM96+fq1avau3evtm3bpp9++kmFChXSwIED1bdvXzk45IsVvwEAAAAUYPeVKjp37qyOHTtqw4YNWrJkiV577TUZDAZ5e3urWrVq8vPzk7e3t0qVKiUvLy8VLVpUTk5OcnJykp2dnW7duqVbt24pPj5ely5d0uXLlxUZGanjx4/r6NGjOnXqlIxGo0qUKKHBgwfr2Weflaurq7nPHQAAAICNuu/pFQcHB3Xp0kVdunRReHi4Nm3apO3bt2vr1q3aunWrDAbDPR0v4xlDRYsWVbt27dSxY0cFBQUxAwQAAADA7MySMjIehPrKK6/oypUrCgsLU3h4uKKiohQVFaXLly8rMTFRiYmJSktLk7Ozs5ydneXm5qby5curQoUKqlKliurWrSs/P797DlEAAAAAcC/MPt1SsmRJtWnTRm3atDH3oQEAAADALHK1fDYAAAAAPEwIQgAAAABsDkEIAAAAgM2x6JJsN2/e1Nq1a7V3716dP39eQUFBGjdunBYsWCB/f38FBQVZcngAAAAAyJbFgtCBAwc0YsQIXbt2TUajUQaDQdWrV5ckbdy4UbNnz9aQIUM0bNgwS5UAAMgHUm/EKf7YqVz1jT1w+I7bXfwqyaEYz5UDADw4iwShM2fOaNCgQYqPj1ebNm3UokULvfrqq6btTzzxhE6fPq2PP/5Y9erVU6NGjSxRBgAgH4g/dkqHQ8ffvaOn7tovYNE0udUPMFNlAABbZpF7hD755BPFx8fr7bff1pw5c/TUU09l2v7SSy9p7ty5MhqNWrp0qSVKAAAAAIAcWSQI7d69W4888oi6deuWY58WLVrI399ff//9tyVKAAAAAIAcWSQIXblyRZUrV75rv/Lly+vy5cuWKAEAAAAAcmSRIOTm5qazZ8/etV9UVJTc3NwsUQIAAAAA5MgiQahu3bo6evSofv/99xz77N27VxEREQoMDLRECQAAAACQI4sEoRdeeEGSNGTIEK1ZsybT7NCtW7f0/fff6+WXX5bBYFCfPn0sUQIAAAAA5Mgiy2fXrl1bEyZM0DvvvKM33nhDkmQwGPT999/ru+++kyQZjUYNHz5c9evXt0QJAAAAAJAjiz1Q9bnnnlO1atX02Wefad++fUpMTFRqaqocHR1Vp04dhYaGqlmzZpYaHgAAAAByZLEgJEn16tVTvXr1lJ6eruvXrys9PV3u7u5ycLDosAAAAABwR3mSSOzs7OTh4ZHtttjYWFaOAwAAAJCnLLJYQkhIiKZPn37Xfq+88orat29viRIA4I5KpRqsXQIAALAiiwShc+fO6erVq3ftFxUVpdjYWEuUAAB3NPSas7VLAAAAVmSWS+NCQ0MVGRmZqW379u0KCQnJcZ+4uDjduHFDFStWNEcJAAAAAJBrZglCzz33nAYPHmx6bTAYlJCQoISEhDvu5+TkpNGjR5ujBAAAAADINbMEoeDgYK1bt05paWkyGo3q1q2bmjdvrmHDhmXb32AwyNnZWWXLllXhwoXNUQIAAAAA5JrZVo2rXr266e+dO3dWYGCgatasaa7DAwAAAIDZWGT57GnTpkmSLl++rPPnzyslJUVGo9G0PT09Xbdu3dKVK1e0detWffLJJ5YoAwAAAACyZZEglJqaqnHjxun777+3xOEBAAAA4IFYJAitWLFCmzZtkiR5eXkpOTlZ169fl6+vr2JjY3X58mVJko+Pj1588UVLlAAAAAAAObLIc4S+//57GQwGzZs3T7t27dKYMWMkSR999JF++eUXrV+/XhUrVlR0dLTq1atniRIAAAAAIEcWCUInT55U9erV1apVK0lSrVq1ZDQaFRYWJkmqVq2aPvjgA926dUtLliyxRAkAAAAAkCOLBKGkpCR5e3ubXvv4+MhgMOjvv/82tVWtWlUBAQE6ePCgJUoAAAAAgBxZJAgVK1ZMiYmJpteFChVSqVKldOLEiUz9ypQpo4sXL1qiBAAAAADIkUWCkJ+fnw4ePKikpCRTW6VKlfTXX39lWkY7OjraEsMDAAAAwB1ZJAi1bdtWN27cUJ8+fbR//35JUuPGjRUbG6tZs2YpKSlJGzZs0MGDB1WhQgVLlAAAAAAAObJIEOrSpYvq16+vQ4cO6fPPP5ck9ejRQy4uLvrss88UGBiocePGyWAwqGfPnpYoAQAAAAByZJEg5ODgoCVLluj1119Xs2bNJElubm769NNP5e3tLaPRKAcHB/Xr109du3a1RAkAAAAAkCOLPFBVuh2Gnn322UxtdevW1datW3X16lW5urrKycnJUsMDAAAAQI4sFoTupESJEqa/Jycnq1ChQtYoAwAAAICNssilcbn122+/qWPHjtYsAQAAAIANMtuM0K1bt7RixQrt2LFD169fV/Xq1dW/f39Vq1YtS9/Y2Fi9++67Wr9+vbmGBwAAAIBcM0sQSkpKUt++fXXo0CHTc4JOnjypHTt26LPPPlO9evVMfb///ntNnTpVMTExMhqNql69ujlKAAAAAIBcM0sQWrp0qf788085OTnpmWeeUfny5fXjjz/q119/1RtvvKHvv/9eqampeuONN7Ru3ToZjUY5OztryJAhCg0NNUcJAAAAAJBrZglC27Ztk8Fg0Pz589W4cWNJ0rPPPquXX35Z33//vf78808tWLBAO3fulNFoVKNGjfTWW2/J29vbHMMDAAAAwD0xy2IJUVFRqlKliikEZejXr5+MRqPeeecd7dixQ87Oznrrrbe0ZMkSQhAAAAAAqzFLEIqPj5ePj0+W9sqVK0uSDh06JG9vb3399dfq3r27OYYEAMBmcBk5AJifWS6NS0tLU5EiRbK0u7i4SJLs7e312WefZRuWAADAnUVGRlq7BAB46OTJc4QaNmxICAIAAACQb+RJEPLw8MiLYQAAAAAgV/IkCBkMhrwYBgAAAAByJU+CEAAAAADkJ2ZZLEGStm/frpCQkCztBoMhx23/3g4AAAAAecVsQSghIUEJCQn3vI3L5gAAAADkNbMEoWXLlpnjMAAAAACQJ8wShBo0aGCOwwAAAABAnmCxBAAAAAA2hyAEAAAAwOYQhAAAAADYHIIQAAAAAJtDEAIAAABgcwhCAAAAAGwOQQgAAACAzTHLc4RykpKSonPnzikxMVHp6ek59qtRo4YlywAAAACATCwWhD799FMtXLhQ8fHxd+xnMBgUHh5uqTIAAMiXnL3LWLsEALBpFglCa9eu1ezZsyVJhQoVUvHixWVvb2+JoQAAKJDsizgrNjZRhw9fyFX/X389ecftAQFl5OZW2BylAYBNsEgQWr16tQwGg1599VU988wzcnR0tMQwAAAUaIcPX1CnTovu2i8wUHftt2FDqIKCKpurNAB46FkkCP3zzz8KCAhQ3759LXF4AAAAAHggFlk1rlChQipVqpQlDg0AAAAAD8wiQSggIEDh4eF3XCkOAAAAAKzFIkFo6NChunjxoubOnWuJwwMAAADAA7HIPUJRUVEKCQnRggULtGnTJtWuXVtubm4yGAzZ9p84caIlygAAAACAbFkkCL366qsyGAwyGo06e/aszp49m2Nfg8FAEAIAAACQpywShIYOHZrj7A8AAAAAWJtFgtCwYcMscVgAAAAAMAuLLJYAAADMJzGxsLVLAICHjkVmhDKcO3dOy5Yt0969e3Xp0iU5OTmpRIkSaty4sbp37y5vb29LDg8AwEMhIuJRa5cAAA8diwWhHTt2aMyYMUpKSpLRaDS1X7x4UUeOHNHKlSv13nvvKSQkxFIlAAAAAEC2LBKETp06pZdffllJSUnq0KGDOnXqJG9vb6Wnp+vMmTP67rvvtGnTJr3yyiv69ttvmRkCAAAAkKcsEoQWLlyopKQkvfbaa+rdu3embb6+vmrRooXq1Kmjt99+W0uXLmX5bAAAAAB5yiKLJfz222+qVKlSlhD0b88++6wqV66sn3/+2RIlZGvjxo1q3769atWqpbZt22r9+vV37B8fH6/JkyerSZMmCgwMVP/+/RUZGZlj/7i4OAUHB+u1114zb+EAYANKpfLYBQBA3rFIELp8+bL8/Pzu2q9q1aqKjo62RAlZbN68WWPGjFGTJk300UcfqUGDBho3bpy2bNmS4z6jRo3Sli1bNGbMGE2fPl3R0dHq06ePbt68mW3/adOm6fz585Y6BQB4qA295mztEgAANsQil8a5uLjoypUrd+135coVOTvnzT98s2bNUtu2bTVhwgRJUtOmTRUbG6u5c+eqTZs2WfqHhYVp165dWrhwoZo1ayZJqlevnkJCQrRq1SoNGDAgU/9du3Zp8+bNKlq0qOVPBgAAAMADsciMUEBAgP744w8dOXIkxz7h4eH63//+p4CAAEuUkMmZM2cUFRWlJ554IlN769atdfLkSZ05cybLPrt375aLi4uaNGliavPw8FD9+vWzXM4XGxuriRMn6pVXXlGxYsUscxIAAAAAzMYiQejZZ59VWlqaBgwYoC1btigtLc20LS0tTVu2bNGAAQNkNBrVs2dPS5SQycmTJyVJlSpVytTu4+Mj6fYqd9nt4+PjI3t7+0ztFSpUyNL/7bfflq+vb56cCwAAAIAHZ5FL44KDg9WrVy+tWrVKo0aNUqFChVS6dGlJUnR0tG7duiWj0agePXqoVatWlighk4x7elxdXTO1u7i4SLq9yMF/xcXFZemfsc+/+2/btk07duzQd999J4OBG30BAACAgsBiD1R98803Vb16dX322WeKiorS6dOnTdsqVKig0NBQ9ejRw1LDZ5LxQNf/BpWMdju7rBNj/34I7H9l9I+JidGbb76psWPHqnz58uYqFwAAAICFWSwISVL37t3VvXt3RUdHKzo6WkajUV5eXqbZobySsYDBf2d+4uPjM23/N1dXV509ezZLe3x8vGmmaNKkSfL19VXXrl2Vmppq6mM0GpWamioHB4t+eQEAAADcpzx5p+7l5SUvL6+8GCpbGfcGRUVFZVrWO2OW6r/3DmW0/fbbbzIajZlmkk6fPm3q/8MPP0iSatasmWnfr7/+Wl9//bV27NjBTBEAAACQD5klCE2ZMkWSNGTIEHl4eJhe59bEiRPNUUaOfHx8VL58eW3ZskWPP/64qX3r1q2qWLGiypYtm2WfoKAgffLJJ9qzZ49p5biYmBiFhYVp4MCBkqS1a9dm2W/w4MGqVauWBg8erFKlSlnojAAAAAA8CLMEoRUrVshgMOjZZ5+Vh4eH6fWd7rPJYDAYLB6EJGno0KEaP3683Nzc1KJFC+3cuVObN2/W7NmzJd0OOVFRUapSpYpcXV1Vv359NWjQQKNHj9aYMWPk7u6uDz/8UEWLFlWvXr0kKdulvwsVKqTixYvnybLgAAAAAO6PWYLQ0KFDZTAYVLx48Uyv85MuXbooOTlZixcv1po1a+Tt7a3p06erXbt2kqSffvpJ48eP17Jly9SwYUNJ0rx58/Tuu+9qxowZSk9PV926dTVnzhy5ublZ81QAAAAAPCCzBKFhw4bd8fWdxMbGmqOEXOnZs2eOz/rp0qWLunTpkqnNzc1N06ZN07Rp03I9xs6dOx+oRgAAAACWZ5EHqoaEhGj69Ol37ffKK6+offv2ligBAAAAAHJkkSB07tw5Xb169a79oqKi8nRGCAAAAAAkM10aFxoaqsjIyExt27dvV0hISI77xMXF6caNG6pYsaI5SgAAAACAXDNLEHruuec0ePBg02uDwaCEhAQlJCTccT8nJyeNHj3aHCUAAAAAQK6ZJQgFBwdr3bp1SktLk9FoVLdu3dS8efMcF00wGAxydnZW2bJlVbhwYXOUAAAAAAC5ZpYgJEnVq1c3/b1z586qU6eOatasaa7DAwAAAIDZmC0I/dudlpuOi4tTcnKyPDw8LDE0AAAAANyVRVaNk6SYmBiNHTtWQ4YMydS+e/duNW3aVCNGjNCVK1csNTwAAAAA5MgiM0IxMTHq3r27zp49qwoVKmTalpqaKnt7e/3www+KiIjQ119/LVdXV0uUAQAAAADZssiM0GeffaazZ8+qffv2Wr16daZt7du31969e9WxY0edPn1an3zyiSVKAAAAAIAcWSQI7dq1S2XLltWMGTOyvReoSJEieuedd1S6dGlt3brVEiUAAAAAQI4sEoTOnTunmjVryt7ePsc+jo6Oqlmzpi5cuGCJEgAAAAAgRxYJQq6urrp69epd+928eVNFihSxRAkAAAAAkCOLBKGAgAAdPHhQhw4dyrFPRESEfv/9d541BAAAACDPWSQIPfvss0pLS1P//v21evXqTMtkx8TEaO3atQoNDVVaWpqee+45S5QAAAAAADmyyPLZQUFBGjBggBYsWKDJkydr8uTJcnC4PVRqaqokyWg06vnnn1dwcLAlSgAAAACAHFkkCEnS6NGjVa9ePS1fvlz79+/XrVu3JN1eJKF27drq06ePHn/8cUsNDwAAAAA5slgQkqRmzZqpWbNmkqTr168rNTVV7u7uptkhAAAAALCGPEsk7u7ueTUUAAAAANyRRYLQkSNH7ql/jRo1LFEGAAAAAGTLIkHo6aeflsFgyHX/o0ePWqIMAAAAAMiWRYJQ2bJls21PT09XbGysEhMTJUkNGjRQyZIlLVECAAAAAOTIIkFo586dd9weERGhSZMm6cKFC/rwww8tUQIAAAAA5MgiD1S9m2rVqunjjz/WlStXCEIAAAAA8pxVgpAkeXh4qEGDBtq+fbu1SgAAAABgo6wWhCTJaDQqJibGmiUAAAAAsEFWC0J///239u7dq9KlS1urBAAAAAA2yiKLJUyZMiXHbampqbp8+bJ2796tlJQUtWnTxhIlAAAAAECOLBKEVqxYIYPBIKPReMd+derU0aBBgyxRAgAAAADkyCJB6KWXXspxm8FgkIuLi6pXr66GDRtaYngAAAAAuKM8D0IAAAAAYG1WXTUOAAAAAKzBLDNC58+ff6D9y5Yta44yAAAAACBXzBKEQkJC7ntfg8Gg8PBwc5QBAAAAALliliDk7OycpS0tLU3JycmSJFdXV5UrV0729va6dOmSrly5Iun2TFCxYsXMUQIAAAAA5JpZgtAff/yR6fW1a9fUs2dPpaena9KkSWrSpEmm7YcOHdJrr72muLg4ffLJJ+YoAQAAAAByzSKLJcyZM0eXLl3S559/niUESVKtWrW0aNEixcbG6r333rNECQAAAACQI4sEoe3bt6tBgwYqV65cjn1KlSqlhg0b6tdff7VECQAAAACQI4sEoYSEBNnZ3f3QKSkpSktLs0QJAAAAAJAjiwShihUrat++fbp8+XKOfU6ePKl9+/bpkUcesUQJAAAAAJAjiwShbt26KSEhQc8//7z279+faVt6erp27typ0NBQpaamqnfv3pYoAQAAAAByZJZV4/6rV69e2r17t3bs2KG+ffuqcOHCKlWqlCQpOjpaSUlJMhqN6tWrl9q1a2eJEgAAAAAgRxYJQgaDQfPmzdOqVau0YsUKnTx5UpGRkabt1apVU2hoqDp27GiJ4QEAAADgjiwShKTbYeiZZ57RM888o8uXLys6OlqSVKZMGZUoUcJSwwIAAADAXVksCEnSuXPntGzZMu3du1eXLl2Sk5OTSpQoocaNG6t79+7y9va25PAAAAAAkC2LBaEdO3ZozJgxpvuBMly8eFFHjhzRypUr9d577ykkJMRSJQAAAABAtiwShE6dOqWXX35ZSUlJ6tChgzp16iRvb2+lp6frzJkz+u6777Rp0ya98sor+vbbb5kZAgAAAJCnLBKEFi5cqKSkJL322mtZlsf29fVVixYtVKdOHb399ttaunSpJk6caIkyAAAAACBbFnmO0G+//aZKlSrd8RlBzz77rCpXrqyff/7ZEiUAAAAAQI4sEoQuX74sPz+/u/arWrWqaTU5AAAAAMgrFglCLi4uunLlyl37XblyRc7OzpYoAQDyVGhoqLVLAAAA98AiQSggIEB//PGHjhw5kmOf8PBw/e9//1NAQIAlSgCAPPXvh0YDAID8zyJB6Nlnn1VaWpoGDBigLVu2KC0tzbQtLS1NW7Zs0YABA2Q0GtWzZ09LlAAAAAAAObLIqnHBwcHq1auXVq1apVGjRqlQoUIqXbq0JCk6Olq3bt2S0WhUjx491KpVK0uUAAAPzNm7jLVLAAAAFmKxB6q++eabql69uj777DNFRUXp9OnTpm0VKlRQaGioevToYanhAeCB2RdxVmxsog4fvpCr/r/+evKO2wMCysjNrbA5SgMAAA/IYkFIkrp3767u3bsrOjpa0dHRMhqN8vLyMs0OAUB+d/jwBXXqtOiu/QIDddd+GzaEKiiosrlKAwAAD8AiQah///6qXLmyxo8fL0ny8vKSl5eXJYYCAAAAgHtmkSAUFhamxMRESxwaAAAAAB6YRVaNc3R0lIuLiyUODQAAAAAPzCJBqGvXrtq9e7d27dplicMDAAAAwAOxyKVxvr6+8vb21qBBg+Tj4yN/f3+5u7vLzi773DVx4kRLlAEAAAAA2bJIEHrttddkMBhkNBoVGRl5xyeuGwwGghAAAACAPGWRIDR06FAZDAZLHBoA8qXERJ4PBABAQWKRIDRs2DBLHBYA8q2IiEetXQIAALgHZg1Cqamp+v3333X16lWVK1dOtWrVYmYIAAAAQL5jtiD066+/avz48bpy5YqprXLlynr//fdVvXp1cw0DAAAAAA/MLMtnnzhxQkOHDtXly5dlZ2en4sWLy2g06sSJE3r++ed16dIlcwwDAAAAAGZhliC0bNky3bp1S+3bt9eePXu0Z88e/fLLL2ratKliY2P1xRdfmGMYAAAAADALswShAwcOqFSpUpoxY4bc3NwkSZ6enpo9e7acnZ3122+/mWMYAAAAADALswShixcvqmbNmrK3t8/U7urqqlq1aun06dPmGAYAAAAAzMIsQejWrVsqXDj7Z2iULFlScXFx5hgGAAAAAMzCLEEoLS0ty2yQaQA7O6WlpZljGAAAAAAwC7MEIQAAAAAoSAhCAAAAAGwOQQgAAACAzXEw14G2b9+ukJCQLO3Xrl2TpGy3SZLBYND27dvNVQYAAAAA3JXZglBCQoISEhJy3H7u3Lls2w0Gg7lKAAAAAIBcMUsQWrZsmTkOAwAAAAB5wixBqEGDBuY4DAAAAADkCRZLAAAAAGBzCEIAAAAAbA5BCAAAAIDNIQgBAAAAsDkEIQAAAAA2hyAEAAAAwOYQhAAAAADYHLM8RyhDamqqfv/9d129elXlypVTrVq1ZDAYzDkEAAAAADwwswWhX3/9VePHj9eVK1dMbZUrV9b777+v6tWrm2sYAAAAAHhgZrk07sSJExo6dKguX74sOzs7FS9eXEajUSdOnNDzzz+vS5cumWMYAAAAADALswShZcuW6datW2rfvr327NmjPXv26JdfflHTpk0VGxurL774whzDAAAAAIBZmCUIHThwQKVKldKMGTPk5uYmSfL09NTs2bPl7Oys3377zRzDAAAAAIBZmCUIXbx4UTVr1pS9vX2mdldXV9WqVUunT582xzAAAAAAYBZmCUK3bt1S4cKFs91WsmRJxcXFmWMYAAAAADALswShtLS0LLNBpgHs7JSWlmaOYQAAAADALHigKgAAAACbQxACAAAAYHMIQgAAAABsjoO5DrR9+3aFhIRkab927ZokZbtNkgwGg7Zv326uMgAAAADgrswWhBISEpSQkJDj9nPnzmXbbjAYzFUCAAAAAOSKWYLQsmXLzHEYAAAAAMgTZglCDRo0MMdhAAAAACBPmGWxhHnz5nGfDwAAAIACw2xBaOvWreY4FAAAAABYnE0tn71x40a1b99etWrVUtu2bbV+/fo79o+Pj9fkyZPVpEkTBQYGqn///oqMjMzUJy4uTtOnT1erVq1Uu3ZtdezYUV988YWMRqPlTgQAgAIqNDTU2iWYzcN0LoAtMtuqcfnd5s2bNWbMGPXp00dNmzbV9u3bNW7cODk7O6tNmzbZ7jNq1CgdPnxYY8eOlYuLi+bNm6c+ffpo06ZNKlq0qKnPoUOHNHz4cFWuXFl79uzR22+/rZs3b2rgwIF5eYoAAOR7//1AMb9x9i6T6775/VwA3JnNBKFZs2apbdu2mjBhgiSpadOmio2N1dy5c7MNQmFhYdq1a5cWLlyoZs2aSZLq1aunkJAQrVq1SgMGDNDRo0f1888/a86cOWrbtq0kqVGjRrpx44YWLlxIEAIAoICxL+Ks2NhEHT58IVf9f/315B23BwSUkZtbYXOUBsDMzBaEEhMTdf78+fvat2zZsuYqI1tnzpxRVFSURo8enam9devW2rx5s86cOSNvb+9M23bv3i0XFxc1adLE1Obh4aH69evr559/1oABA2Q0GtWjRw81atQo076VK1fWzZs3de3aNRUvXtxyJwYAAMzu8OEL6tRp0V37BQbqrv02bAhVUFBlc5UGwIzMFoS2b99+XyvHGQwGhYeHm6uMbJ08efvTmkqVKmVq9/HxkSSdOnUqSxA6efKkfHx8ZG9vn6m9QoUK2rx5syTJ399fb731Vpbxtm/fLk9PT7m7u5vrFAAAAACYkdkWSzAajff1Jz093Vwl5OjmzZuSJFdX10ztLi4ukm4vePBfcXFxWfpn7JNd/wxLly7V/v371b9/fxkMhgcpGwCAAuFe7qsBgPzCbDNCnTp10owZM8x1OLPKWMHtv8Eko93OLmsevNOqb9n1l6QVK1Zo2rRpatu2rfr06XO/5QIAUKBwXw2AgsgmFkvIWOHtvzM58fHxmbb/m6urq86ePZulPT4+PstMUXp6ut577z0tXrxYHTp00PTp05kNAgDYFFu8ryYxkbAGFGQ28RyhjHuDoqKiMrWfPn060/b/7nPmzJksM0OnT5/O1D8lJUUjR47U4sWL9cILL+j999+Xg4NN5EsAAGxaRMSj1i4BwAOwiSDk4+Oj8uXLa8uWLZnat27dqooVK2a7al1QUJBu3LihPXv2mNpiYmIUFhamxo0bm9omTJigrVu3avz48Ro3bhwzQQAAAEABYDNTF0OHDtX48ePl5uamFi1aaOfOndq8ebNmz54t6XbIiYqKUpUqVeTq6qr69eurQYMGGj16tMaMGSN3d3d9+OGHKlq0qHr16iVJ+umnn7Rhwwa1bNlStWvX1sGDBzON6e/vr0KFCuX1qQIAAAC4C7MEoZdeekl+fn73te+JEyfk6+trjjLuqEuXLkpOTtbixYu1Zs0aeXt7a/r06WrXrp2k26Fm/PjxWrZsmRo2bChJmjdvnt59913NmDFD6enpqlu3rubMmSM3NzdJ0g8//CBJ2rlzp3bu3JllzF27dql06dIWPzcAAAoK7qsBkF+YLQjdC6PRqB07dmjFihXav3+/xZ8jlKFnz57q2bNnttu6dOmiLl26ZGpzc3PTtGnTNG3atGz3udM2ACioQkNDtWjR3W96z+8elvN42HBfDYD8Ik8vjYuJidHatWu1evVqXbhwQUajkXtqACAP3MtzXiIjIy1XSB56WM4DAGAZeRKEDh06pJUrV2rLli1KTk6W0WiUg4ODmjdvrq5du+ZFCQBg03jOCwAAmVksCCUnJ+v777/XypUr9ddff0m6fUmcr6+vunTpoqeeekolSpSw1PAACgAuXcpbtvicFwAAcmL2IHThwgWtWrVKa9eu1bVr12Q0GlW4cGE5Ojrq5s2b2rRpk7mHBFBAcekSAACwFrMFod9++00rVqzQTz/9pPT0dBmNRtWoUUPdu3dXhw4d9NJLL2nv3r3mGg5APnUv96IA94rvLwCAuZglCLVr106nTp2S0WiUm5ubOnTooK5du6p69ermODysiEuXcK+4FwWWZO7vL4nvMQCwVWYJQidPnlThwoU1ZMgQ9evXT46OjuY4LPIBLl3C/eBelIItvz/nxZzfXxLfYwBgq+zMcRAvLy8lJiZq1qxZ6tChg6ZNm6ajR4+a49AAgDzGc14AALbALEHoxx9/1Pz589WiRQudPXtWS5cuVZcuXfT000/ryy+/VFxcnDmGAfCQye8zDwAA4OFllkvj7OzsFBwcrODgYF26dElr167V2rVrdeTIEU2aNEnvvvsul8vlI9xsjPyCmQcAAGAtZl8+u1SpUhoyZIiGDBmiX3/9VV999ZV27typxMREGQwGNWvWTB07dlSnTp3k5+dn7uGRCw/Tzez3EupY+AEAAAAZLPZAVUkKCgpSUFCQYmJi9PXXX2vt2rU6ffq0Fi9erMWLF6tq1ap68skn9cILL1iyDGTjYbmZ/V5CXWRkZL4OdfeCUAcAAPBgLBqEMnh4eKh///7q37+/9u3bp6+++krbtm3TsWPH9N577xGE8EAellB3L1jND7g77kEDANxJngShf2vYsKEaNmyo2NhYrVu3TmvXrs3rEmCjeFME2BbuQQMA3IlZVo27H25uburXr582btxorRJgY/LzmyIWsAAAAMhbeT4jBCCre13AQsrfi1gAAADkdwQhIJ/I7b1O0sN1vxMAAIA1WO3SOAAAAACwFoIQAAAAAJtDEMIdsdJa/sT/FwAAgAdDEMId5eeV1mwZ/18AAAAeDEEIAAAAgM0hCAEAAACwOQQhAAAAADaHIAQAAADA5hCEAAAAANgcghAAAAAAm0MQAgAAAGBzCEIAAAAAbA5BCAAAwIaFhoZauwTAKghCAAAANiwyMtLaJQBWQRACAAB4yDh7l7F2CUC+52DtAgAAAGBe9kWcFRubqMOHL+Sq/6+/nrxrn4CAMnJzK/ygpQH5BkEIAADgIXT48AV16rTorv0CA5Wrfhs2hCooqLI5SgPyBS6NAwAAAGBzCEIAAAA2LDGRy91gmwhCAAAANiwi4lFrlwBYBUEIAAAAgM0hCAEAAACwOQQhAAAAADaHIAQAAADA5hCEAAAAANgcghAAAAAAm0MQAgAAAGBzCEIAAAAAbA5BCAAAAIDNIQgBAAAAsDkEIQAAAAA2hyAEAAAAwOYQhAAAAADYHIIQAAAAAJtDEAIAAABgcwhCAAAAAGwOQQgAAACAzSEIAQAAALA5BCEAAAAANocgBAAAAMDmEIQAAAAA2ByCEAAAAACbQxACAAAAYHMIQgAAAABsDkEIAAAAgM0hCAEAAACwOQQhAAAAADaHIAQAAADA5hCEAAAAANgcghAAAAAAm0MQAgAAAGBzCEIAAAAAbA5BCAAAAIDNIQgBAAAAsDkEIQAAAAA2hyAEAAAAwOYQhAAAAADYHIIQAAAAAJtDEAIAAABgcwhCAAAAAGwOQQgAAACAzSEIAQAAALA5BCEAAAAANocgBAAAAMDmEIQAAAAA2ByCEAAAAACbQxACAAAAYHMIQgAAAABsDkEIAAAAgM0hCAEAAACwOQQhAAAAADaHIAQAAADA5hCEAAAAANgcghAAAAAAm0MQAgAAAGBzCEIAAAAAbA5BCAAAAIDNIQgBAAAAsDkEIQAAAAA2hyAEAAAAwOYQhAAAAADYHIIQAAAAAJtDEAIAAABgcwhCAAAAAGwOQQgAAACAzSEIAQAAALA5NhWENm7cqPbt26tWrVpq27at1q9ff8f+8fHxmjx5spo0aaLAwED1799fkZGRmfqkpqZqzpw5at68uR599FE988wzOnTokOVOAgAAAMADs5kgtHnzZo0ZM0ZNmjTRRx99pAYNGmjcuHHasmVLjvuMGjVKW7Zs0ZgxYzR9+nRFR0erT58+unnzpqnP1KlT9fnnn6t///6aPXu27O3t1a9fP505cyYvTgsAAADAfXCwdgF5ZdasWWrbtq0mTJggSWratKliY2M1d+5ctWnTJkv/sLAw7dq1SwsXLlSzZs0kSfXq1VNISIhWrVqlAQMG6OzZs/ryyy/1+uuvq1evXpKkoKAgtW7dWp999pkmT56cdycIAAAAINdsYkbozJkzioqK0hNPPJGpvXXr1jp58mS2sze7d++Wi4uLmjRpYmrz8PBQ/fr19fPPP0uS9u7dq7S0NLVu3drUp1ChQmrRooWpDwAAAID8x2A0Go3WLsLSdu3apQEDBujbb79VtWrVTO3h4eHq3LlzplmfDCNGjFBUVJTWrVuXqX3KlCnavHmzdu/erRkzZmjt2rXav39/pj6ff/65pk2bpj///FPOzs73VKudnZ2MRqPc3NyybDOmpd/TsXJisLeT0Silp5vneHZ2djIYct/fXOchPTznYu7zkB6ec7nX85AennPhZyUrflZyxs+KeTws55JXPyuxsbEyGAxmHQfIKzZxaVzGPT2urq6Z2l1cXCRJcXFxWfaJi4vL0j9jn4z+d+oj3V5s4X6CUE6/TAz25pvAMxgkezMe757GNvO4D8u5WPM8JM4lx2M9JN9f0sNzLnx/mc/Dci78rORwrDw4D4PBIDs7m7jACA8hmwhCGZNehv98jJHRnt0P8J0myjL659Qnp/FyIzU19Z73AQAAAHBvbCLCFy1aVFLWmZ/4+PhM2//N1dXVtP2/+2TMAt2pT8Z2AAAAAPmPTQShSpUqSZKioqIytZ8+fTrT9v/uc+bMmSyzPqdPnzb1r1y5sq5fv67Y2NgsfcqXL69ChQqZ7RwAAAAAmI9NBCEfHx+VL18+yzODtm7dqooVK6ps2bJZ9gkKCtKNGze0Z88eU1tMTIzCwsLUuHFjSTL994cffjD1SU5O1q5du0zbAAAAAOQ/NnGPkCQNHTpU48ePl5ubm1q0aKGdO3dq8+bNmj17tqTbIScqKkpVqlSRq6ur6tevrwYNGmj06NEaM2aM3N3d9eGHH6po0aKmZwaVK1dOnTt31pQpU5SQkCAfHx8tWbJEsbGxevHFF615ugAAAADuwCaWz86wevVqLV68WBcuXJC3t7cGDBigp556SpL0zTffaPz48Vq2bJkaNmwo6faSkO+++662b9+u9PR01a1bV6+++qoqV65sOmZycrLef/99bdy4UQkJCapRo4bGjh2rRx991BqnCAAAACAXbCoIAQAAAIBkI/cIAQAAAMC/EYQAAAAA2ByCEAAAAACbQxACAAAAYHMIQgAAAABsDkEIAAAAgM0hCCFHrKwOAACAhxVBCCZpaWm6efOmLly4oNTUVBkMBmuXhFzKCK2EV+v599c+PT3dipXcn9TUVMXExGRqK6jfTwW17ocdv6cA5DcO1i4A+UNcXJwmTJigc+fOKTY2VoULF9bgwYPVoEEDlSxZ0trlPZDk5GSFh4fryJEjcnNzU+nSpVWvXj1rl2UWGW8okpOT5eTkJIPBoLS0NNnb21u5MtuTlpYmB4fbv1Lt7ArWZ0xGo1FffPGFUlNT1bdvX9P3T0H6MCQ9PV12dnZKTExU4cKFrV0O/iM+Pl5jxozRxIkTVa5cOWuX80AyvtceRkajsUD93AMPiiAEJSYmqmfPnvLy8lKfPn1UqlQpLV++XC+//LJ69uyp559/XhUqVLB2mfclLi5OI0aM0JkzZxQdHS07Ozv5+vpqypQpqlatmrXLeyDx8fGaPXu2jh8/LicnJzVu3Fj9+vWTvb19vv7HLD/Xdr+OHj2qxYsXKzIyUk5OTurdu7cee+wxubm5Wbu0XDEYDIqNjdVHH32kokWLasuWLapZs6ZGjRpl7dJyJT4+XlOmTNGpU6d08eJFPfXUU2rTpk2B/xnPkNMb74LysxQXF6fevXvr6NGjatasmXr16lVgas+Y3T116pQKFSqk8uXLm/5fFJRzyElaWpoSExN18eJFeXt7y9HR8aENeEBOCELQ+vXrFRcXp2nTpikgIECS1KhRIw0aNEgbNmxQSkqKBg0apPLly1u50nsTHx+vbt26qVSpUho7dqyaN2+uv//+W7GxsaY3SAkJCSpSpIiVK713CQkJ6t69u9zd3eXj46MrV67o3XfflZ2dnfr06ZNv/3H+92zVfz+5L6hvKg4ePKiBAweqXr16qlq1qs6cOWP65LtHjx4F5tPjYcOGKTo6Wm+//bacnZ3Vv39/a5eUKwkJCerZs6fc3d0VEBCgatWq6bPPPtPp06c1fvx4lSpVytolPpDU1FQ5ODgoOTlZx48fV3JyskqUKKEKFSoUiJ+XuLg4derUSeXLl1f58uV18eJFSQVjtjEhIUFz5szRwYMH9ddff8nBwUEVKlRQ+/bt1aFDB3l7exeYn+//io+P19tvv60jR47o+PHjqlKlisaPH68mTZpYuzQgTxGEoHPnzunWrVvy8/OTJN26dUtOTk4qU6aMihcvrp9++knu7u4aOHCgihYtauVqcyclJUXjx49X6dKlNWXKFJUtW1YGg0H+/v6mf4AXLlyoyMhIjRgxosC9WVq+fLmcnJz0zjvvyMfHRzExMRo2bJj++OMP9enTJ1Pf/BIw/h2CPv30U/35558qVaqUOnXqpDp16shgMOSbWnPr+vXreuedd9SqVSuNGzdOxYoVkySFhobq888/V5cuXeTo6GjlKu/u31/35ORkGY1G/fPPP6pZs6ZcXV2tXF3OjEajZs+eLScnJ7377rumn/MKFSpo5syZphnugspoNMrBwUFxcXF6/vnndenSJUVHR6ts2bJq3bq1xo0bZ+qXH39u4uLi9OSTT6pcuXKaNWuW3nzzTR07dkxS/r+8LC4uTj179pSbm5vq1aunYcOGKSoqSjt37tTcuXO1a9cuTZo0SdWqVcu3X/+cxMXFqVu3bipZsqTatm2roUOH6pdffikQv6sAc8u/v4VgcSkpKZIkJycnJSUl6eDBg6bX0u03RKNHj1ZISIiWLVumo0ePSioYN7qePHlSkZGR6tKli8qVK2e6dybjH6uPP/5YM2fO1Ndff605c+bo8uXLVq743mRcguXj4yNJ8vDwUOnSpXXmzBktWLBAH374oSIiIjKdszWlp6ebQtCwYcO0cOFCXblyRd98840mT56snTt3SpIpDBUU169fV3R0tFq0aKFixYopLS1NktSmTRudOnXK9KYvv8q47Cfje6RPnz7asGGD2rZtq+nTp2vdunW6ceOGNUu8o7S0NJ04cULVqlVTuXLlTOfTrl072dvba/fu3Vau8P5lLFiTlpamYcOGycnJSa+//ro+/fRTPfbYY1q2bJnGjBkjKX/+3MTFxalNmzamUFqyZEkFBgYqLCxMly5dytchKDk5WYMGDVLJkiU1depUjR49Wk2bNtWzzz6rRYsWaciQIYqMjNTrr7+uyMjIfPE7NreSk5M1ePBglSpVSlOnTtWQIUPUpk0bTZ06VQ0aNMjSvyAu/ALcC2aEbFBycrIKFSpk+vSnffv2Wrx4sT799FPduHFDPj4+Wrt2rb755hsNHz5c7dq10+HDh/XBBx9o+fLlBeKX/p9//qlz584pKCjI1JbxRnzevHmaP3++Zs6cqdTUVE2cOFFGo1GTJk0yhcD8KuOTRxcXF8XExOjSpUsqVaqUrl+/rl27dsnDw0NfffWVYmJitGHDBk2YMEHBwcHWLtv0pmfKlCkKDw/XwoULFRgYqJ07d2rYsGH69NNPZTAYFBwcXKBmhuzs7JSQkKBLly5J+v/vsRo1aki6/WYwv/r3DN2RI0dkZ2enihUrqnDhwpoxY4bS09M1Y8YMGY1GdenSJdPMUH75NN/BwUFpaWk6d+6cpP//+pcuXVqurq6Kj4+3ZnkPxMHBQQkJCQoPD5e7u7vat2+vVq1aSZICAgLk7++vadOmqVy5cho1alS++3n59NNPVb16dU2ZMsU0K1ejRg2lpaXp0KFDatWqVb5d2OW3337TjRs39Oqrr8rHx8f0OynjA53hw4fL0dFR8+bN09q1azVy5EjZ29vnu/8H2Tl06JASEhI0duzYLItWbNu2TWFhYYqOjlb58uU1ZMgQFSlSJN/8vAOWQBCyMampqXrmmWfk4eGhBQsWSJIqV66sjz/+WC+//LLGjBkjBwcH2dnZaf78+fLy8pIkBQcHa926dbp8+XKBuNQkJSVF6enpSk5OztR+6NAhfffdd3rvvffUrl07SbdnjxYtWqTevXvL39/fGuXmWsY/tHXq1JGnp6fpUsWwsDBVqVJFb775psqWLaukpCQ988wzWrJkSb4IQpJ05coVHT16VIMGDVJgYKDi4uJ0/PhxNW3aVL///rvef/99paenKyQkJN+Gof/WVLRoUVWuXFknT55UbGysaXGEpKQkScpy/1l+Oad/vwF97bXX9Msvv+jSpUt6/PHH1bdvX9WrV0/vv/++DAaDZs6cKTs7OzVq1EhHjhxRp06d8s2bovT0dAUGBurw4cOKiIhQtWrVlJKSYpqZ++8HG/nl659bCxcu1Pz58yVJXbp0kXT7HDw8PEwfUO3cuVN9+vRRiRIlrFmqSVxcnM6fP6+BAwfKaDRmupy6UaNGKlu2rDZs2KBWrVrlyxAkSQcOHNDNmzdVu3Zt0/eLwWCQvb29KRQMHjxY4eHh+v777/XSSy+ZVozM706dOqUTJ06oYsWKsre3V3JysmJiYvT2229rx44dcnR0VJEiRZSYmKj9+/ebLsMGHlYF4ycXZnPr1i1J0s8//6yxY8dqxowZkm7/A7Vu3TpFREQoPT1dtWrVyvQPa+HChWVnZ5dvryFOS0tTWlqaChUqJElyd3dXcnKy/vzzTz3xxBOmN0C1atXSggULTJeUSdLff/+tatWq5dsQlJycrIiICJ07d07FixfXY489ZgpxGZo2baratWubljp3c3PTyJEj9cYbb+iff/6Rr6+v1d8AxsfH688//1THjh0lSb/++qtWrFihGTNmaPz48Xrqqae0ZMkSRUREaMiQIVavNzv/rik1NVXFixfXjBkzdPny5UwrxGW8Ef/3G73o6Gjt3r1bwcHBKl68eN4VnY2MukaMGKE//vhDzzzzjIoVK6Z33nnHdMlsvXr19N5778lgMGjKlCkqV66czp07p7p161pt+eOkpCRt375dsbGxKl++vJo3b67Bgwfrjz/+MC2A4ujoqLS0NCUlJWV6c5qQkKB169YpICBAtWrVskr9d/PfoNazZ08lJSVpyZIlOnjwoJo2bWr6kMDDw0MhISH69ttvde3atXwRhOLi4hQcHGz6Pfvv7/+M8N2pUyetXLlSYWFh+e4xBhlf/6tXr6p06dIqUqRIlv8ndnZ2pnN58skntW3bNv3999/59nvqv4oXLy5XV1dt375d1atX1759+7R27VqdO3dODRs21Pjx4+Xq6qrffvtNH330kf73v/+pUaNG1i4bsBiCkI1xcXFR48aNdf78eW3fvl1DhgzRxx9/LEny8vIyzQDFxMTojz/+UGBgoGJiYnTkyBFVrlxZzs7O1iw/W3FxcRo2bJg6deqktm3bytnZWY8//rjKli2r5cuX6/HHH5fBYDBdEvjvpcAPHTqk6OhotW/fXlL++8Q4Li7OtCT2iRMnVKxYMRUpUkSTJk1SQECAnJyclJ6eLicnpyyf2sXExMjd3V2enp754pzc3Nz05JNPqkKFCrpw4YJeeeUVhYaGqlGjRoqPj1fhwoV19OhR3bx5U127djV9L+YXJ06c0MaNG7Vv3z6lp6erdu3aCg0NlY+PjylYZ7xBSkhIkHT7TbnRaNSFCxc0ZMgQFS1aVE899ZQVz+L/rVmzRhEREfrggw9Uu3ZtJSQkaOvWrfrpp5+UnJysl156SXXq1NGMGTNUtWpVRUdHq2fPnlYLQRlLMN+4cUM3btyQ0WjUihUrVK1aNT322GOS/v+SvdTUVKWkpJhWJYyLi9M777yjbdu26ZtvvrFK/XeTsTpcWlqabty4oeLFi8vLy0uDBg3SjRs3tGDBAvn7+5tmTKXbwbBy5cr5YkGLjNXhatWqpXfeeSfLbE/G6xYtWujTTz/Vzz//rHr16uWL37mJiYmKjo5WxYoVJUkVK1bU9u3bFR4enu0HZBkzohn9Mz48KAiCg4O1cOFCTZkyxRTqAgMDNWjQILVp08Y0g/fkk0/qnXfe0dmzZ61cMWBZBCEb5O7ursKFC6t///6aOXOmhg8frg8++EDS/weBrVu3atGiRUpLS1Pp0qV14sQJLVu2LF8+qHDmzJn67bffdPLkSRUqVEgtW7ZU4cKF1a9fP7399tsaNWqU5syZY5otyvhH99q1a1q1apUSEhLUtm3bTNvyg+TkZA0YMEDOzs6aOHGiSpYsqVOnTmnatGkaM2aMRo8erVatWpkuv7p586YMBoNcXV0VExOjQ4cOqUKFCnl++UnGG7r/cnd316BBg+Tt7a0PP/xQvr6+GjlypKTblyf6+vrqhRdeyDIbmR/8/vvvGjlypCpUqKDSpUsrMTFRUVFRioyMlKenp6lfxtc6454hd3d3RUdHa/To0UpNTdXixYtlZ2eXL978RUREyMfHR7Vr15bRaNTXX38tV1dXjR07VjNnzpTBYNDAgQPVoEEDvfjii0pJSbHajHBqaqpGjBghNzc3TZs2TUWKFFHhwoUzfe0lmb62V69eVUpKikqUKKH09HRNnz5dmzdv1ooVK+Tt7W2Vc7iTjJ+ZjCWN//77b1WsWFE9evQwfUqfmpqqkSNHauTIkapZs6ZSUlK0YsUKlS5d2uofGmSsDuft7a1p06apVKlS2X6PG41G+fn5adCgQfrggw/UqFEjq8823Lp1S+3atdNzzz2n0NBQSZK/v79u3ryp77//Xt7e3llWS804r3379snb29u04mp+lbFojp2dnezt7bVy5UotWrRI0u0PqLp162b63WU0GmU0GnXkyBF5e3s/NM/iAnJCELIRGSsKZVwetnPnTnXq1EkpKSn64IMPNHLkSM2ZM0dr165Vw4YN1bJlSxkMBh0/flylSpXSO++8Y/r0K7/p3r27fvvtN50/f15vvPGGUlJS9NRTT6lt27aKjIzU8uXLFRoaqqFDh6pSpUoqXry4duzYoR9++EG7du3S0qVLVbZsWWufRhYRERGKiorSq6++avrHqGTJknrhhRc0ZswYzZ07V0WKFFFwcLDi4uL0ySefKCIiQm5ubkpMTNTBgwe1bNmyPPm0OCEhQYsXLzZdK5/TTdAZb0Lj4+OVkpKi06dPy83NTWvWrNH169dVu3Ztq1829l9HjhzRkCFD1LFjR/Xt29d0DmfOnDH9/Z9//lGVKlVM+2RcGnf+/Hm99957unnzptavXy9HR8ccg6Il/fv/R8bfDQaDrly5Iun2PRGzZs3SiBEj1K9fP8XGxurTTz/VhQsX1LlzZ/Xv39+ql8XGxcXpwoULev7551W1alXZ2dkpLCxMX3zxha5evap69eqpcePGKlmypAwGg1JTUyXd/nBg2rRp2rBhg1atWpUvL39NT083LZH99NNPS5L8/Pz0yy+/6OTJkxo5cqRatGih119/Xfb29nrvvfckybRS4SeffCKDwWC1G9ozZoIqVqyod955R6VKlco0M3flyhWVLl1a0v8HiJCQEO3evVsff/yxihcvbtU321euXNGFCxcyXS7dtGlT9ezZU0uXLpW3t7c6deqkwoULZwp3MTEx+t///qeAgIB8ea9TUlKSNm7cqLCwMJ07d04ODg564oknVKNGDdWqVUsDBw7M1P/f3z83b97U2rVr5eDgoDJlylijfCDPEIQeYnFxcdq7d69atWqV6ZO58uXLm+456datmxwcHDR79mwFBQXJYDDoq6++UqlSpdSjRw8rVp97Xl5e8vPzU7Vq1VS4cGFNnDhR6enp6tKliwYNGiRPT08tX75czz//vIoWLSoHBwc5OzurTJkyWrlyZaY3sPnJtWvXdOXKFVOQyXgDXbp0aQUHByshIUFvvfWWqlSpoooVK6pChQo6fPiwLl++LD8/P40dO1a+vr55Uuu3336refPmmR7IaW9vf8cVoXx8fLR06VJTcLpw4YI+//zzfBeCbt68qY8++kjBwcEaOnSoqb60tDRTCBo4cKB27dqlBQsWqFmzZpJkmqkYOHCgihcvru+++y5fhKCpU6eqdevWqlevnkJCQlS/fn0lJydrwoQJevLJJ9WvXz9J0tWrV1WuXDn5+/vr8ccfz9N6/ys9PV2xsbE6efKkPDw8ZGdnp61bt2rUqFGqUKGCjEajvvrqK7Vo0UKDBg1S7dq15enpKU9PT82cOVOS9MUXX+TLECTdnsVKSUnRiBEjVKZMGb3zzjsqW7asvvrqK02aNEnz5s2TwWBQ8+bN9eqrr6po0aJaunSpmjVrpq5du97xgwdLS0pKUvfu3eXg4KD3339fHh4eppnD5ORktWzZUiEhIXrzzTczhTRfX1917txZb731llauXKk33njDKkH73wvqZFxanBF2nn/+eV28eFFTp07VlStX9MQTT+iRRx6RJIWHh2v16tX69ddf9cUXX+S7KyUynjuVmpoqZ2dneXp66ty5c5o8ebLKli2rMWPGZLrPdPPmzQoICFCpUqX0+++/a/369dqxY4dWrFhhuu8UeFgRhB5ScXFxatu2rSpWrKimTZuafsmnp6fL3d1dFSpUUGRkpGrVqqW+ffvqu+++0/Hjx1W7dm3TJ0DWvBTmXnh4eKhz58566aWXNGfOHBUpUkRvvvmmDAaDOnfurAEDBqh79+7asGGD6fKxoKAgVahQQe7u7tYuP0dly5aVs7Ozdu/erRYtWpjeQP/www+SpDfeeENDhgzRxIkTtWLFCvXq1UtdunSRk5NTnr/hDgkJ0dWrVzV//nylpaWZ7hH47xu0jNe9evWS0WjUwYMHVbhwYc2cOVOVK1fOs3pzKz4+XkeOHNHw4cMzhaCMcxo4cKCOHDmiwMBAjRo1Su+//76Cg4NNAbRcuXL66quv5ODgkKf/TxITE7Vjxw7T83TS09P1xx//196dB8Sc/38Af06Xo0u5Sa5lSifKfR9RQs5IhRC7vkqicqeorFTrvu/7SqzFWoTkSKhQqdDhiBLNdE3TvH5/9Pt8tly7a3ebab0f/9Bnjl6fZj4z79f7et3j3ycA+ClJz549Q3FxMd8wys7ORl5eHpydnTF+/Hh+SmlV49b0KSkpoVGjRmjfvj0uX76MVq1aYceOHfj+++/h4OAADQ0NREdHw93dHWpqaggODoaGhgbq1KmDN2/e4Oeff1bYzg6uFz4jIwO5ubnw9PREkyZNkJ2djeTkZAwZMgRXr17ltzPv27cv3N3d+UK+qqqqGDZsmNx29Xr16hU/4nPz5k3Y2NhAVVUVxcXFGDNmDPT09DB9+vRKSRCXaNjZ2SE3Nxf9+vWTy/cMEUFJSYlf98olRFynYfPmzeHl5YVt27Zh7dq1OHDgANq2bYu3b99CRUUFEokEu3fvrrLOpj+rqKgIEyZMQJ06dTBv3jwYGxvzt504cQI7duzA3LlzIZPJYGtri/j4eHh5eUFVVRVqamrQ0NCAhoYGv/6OYf7ziPnPEYlE1K9fP5o8eTK9evWKPy6Tyfj/z5kzh3x9fYmIyNPTkzp16kRLly6l9u3b06RJk6o85j+r4jlIpVKSyWT8MR8fH5o3bx49ffqU5syZQ6amphQeHk4lJSXyCvcvkUgklJqaSnFxcfzrtmbNGjIwMKD58+fT2rVrydPTk4yMjOjevXtERLRz506ytLSk+Ph4Iqr896lqOTk5tGbNGjI0NCQfHx/+uFQqJaLfY5PJZJSamkrv378nIqKysrKqD/ZPOn/+PLVr1+6Tsbq6ulKfPn0oMTGRUlNTycXFhSwsLCgpKYmIiM6dO0elpaVERPy/VcXb25vMzc1p3759fMw3b94kQ0NDSklJqXTf+/fvk5mZGYWEhNDly5dp0aJF1KNHD3ry5EmVxlyRSCSizp07U3h4OH9s9erV1KVLFzp16hQ5OjrSw4cPKz3ml19+IaFQSJcvXyYiotTUVHr27FkVRv3HpFIplZSUUF5eXqVr9e7duyQUCikyMpKIiMLCwqhnz5706tUrSklJIWNjYxo/fjwFBQUREVFubi4tXryYhEJhpb9RVeLeVw8fPiQrKysaNmwYXbp0iYiIbG1taezYsfTy5ctPPpb7TJCH0tLSSr//zZs3JBQK6fz58/yxD+O7fPkyhYaGkpubG/n4+NCZM2c+e27ytnHjRho5cmSl67zi509UVBSNGDGCunfvTgkJCURElJiYSMHBwRQUFES//PILZWdnV3ncDCMvbEToP4abZ84tWq24iLbi9LiWLVsiPj4e8+fPR1RUFHbu3IlmzZqhXr16OHToEF69esXP61YUhYWFWL9+PfT19WFvb8/3ystkMggEAlhaWmLnzp1QUlLCihUrIJVKsXTpUgDlleYr9myTAixWr0gsFsPDwwMZGRl4/fo11NTU4O/vz/fscVvRamlpYffu3TA3NwcADBgwAEFBQXj58iVMTEzkck7c37Ju3br8aANX+yQwMBDKysqVRhfv37+PlStXQl1dHRs3blToUUdNTU2UlZXhzp07/Lo5AMjPz4empibWr1/P95oOHDgQ169fx/v37wEAgwYNAvD5zSP+TXPmzEFmZiZ27twJqVSKiRMnomHDhtDQ0Pho9MDMzAz9+/fH1q1boaGhAS0tLWzduhUtW7as0pg5YrEYI0aMQNu2bdG1a1f+/TVnzhzExcVh3rx50NLSgpaWFoDf/74dO3aEjo4O0tPTAUDheuqTkpJw+PBhxMbGQiwWo1WrVliyZAn09fXRpEkTjB8/Hi1btsTVq1exceNGbNiwAQ0bNoRYLOZ3HtTV1QVQPgrOjYCZmJjI5Xy4UZ527dohODgYc+fORWhoKPz8/NCwYUOsX7/+s5ueyGtNTUlJCWbNmgUzMzNMnz4dKioqEIlEqF27NtTV1T+Kjxux69OnD/r06SOXmP+qmJgYNGvWrNIoqIqKCn8dde/eHe/evYOPjw+uXbsGY2NjGBgYsNEf5pslIPr/VfRMtUdEmDRpEmJiYnDp0iU0aNAASkpKiI2NRXR0NJ4/fw4DAwPY2NggMzMTEyZMQOPGjRESEoL27dsDKG/glZWVKdxajdLSUvTt2xc5OTlQVVVFp06d4OjoCDMzM75xAAAODg7Q0dHB+vXr8e7dO/j5+eHKlSvw8fHBiBEjFLLoXXFxMcaNGwdtbW3Y29tDTU0Np0+fRmRkJPbv3w9jY2Pk5eXxUxcqJnSxsbHw9PREWFgYnxxVlc+tS3j9+jUOHz6MDRs2wM7ODoGBgQDKGxVxcXFYtWoVEhMTcfDgQYX/8s3KyoKdnR1GjRqF+fPnA6i8zTF3/tyuZoWFhdi2bZtcF0+XlJSgRo0ayM3NhZubG168eIGpU6eiS5cuGDduHMaPH49evXqhWbNm0NHR4d9PsbGxKCsrQ/PmzeW2C1nF3cdWrlz5URwvX77EkiVLcO3aNUybNg0TJ07k1zCkp6dj6tSpcHd3h62trTzC/6y7d+/Czc0NBgYGaNq0KVRVVSESieDg4AAzMzMA5euy6tatCy8vL+Tn52PTpk2QyWS4du0a9uzZw+8WV7Gzo6o3SCgpKUFUVBQSExNRUFAAoVCIAQMGQENDAw8fPoSnpyeysrIwf/58TJgwAcDnPyfkQSQSYfz48SgqKoKTkxMcHBwgFovRrVs3NGnSBL169ULDhg3RsmVLtG3bFhoaGnwB8Yp/a0XrSOO8ffsW9vb2GDNmDFxdXfnppZyKcU+bNg3Z2dk4duyYQncSMsy/TfFahcxXEwgEcHFxQWxsLC5dugQHBwf8+uuv8PT0RLNmzUBEOH36NM6fP4/x48fDxcUFgwcP5gvBERHfy6poVFVV0a9fPxw5cgQGBgZ4+vQpgoKCoKWlBXd3d5iYmEBbWxtubm4IDQ3li/XNnz8fhYWFWLNmDaytrRWi3saHTp06BYFAgMWLF/O9ePr6+oiNjcWePXsQEBAALS0tKCsrIzMzE7dv30aTJk2go6ODU6dOoWbNmlVe26Vi4+a3335DdnY2SktL0adPH7Ro0QKTJ08GAGzYsIEfoUtMTMSPP/6I5OTkapEEyWQy6Onpwc7ODrt374aZmRlsbGwq9a5yHj58iIyMDAwdOlSujT4i4kd8UlJSsH79esycORN79uxBUlISRCIRduzYgd27d0Mmk8HAwAAmJibQ19fHkCFDPtqOuioVFxfD2tqaT4K4BCcrKwslJSXIy8uDubk5vLy8oKSkhO3btyM/Px+jRo1CQUEBfv75ZxQXF6NDhw5yO4dPSUtLw5w5czB48GC4urryDeuK1xAR8aMnhYWFyM/PR1paGpSUlLB161aoq6vzSVDFBnlVJkFisRhTp05FQUEB8vPzIZVK0apVK7Rp0waGhoYwMjJCWFgY3N3dcfToUejo6FRaoyaPHe04RISSkhJoamri0KFDcHNz47eyHzx4MLS0tFC3bl2cP38eRUVFKC4uBlBeSJyrnzdo0CBMmDCB33FREWloaEBFRQXJyckA8NH6PoFAwI/Ot2rVCi9fvoRUKq10P0U9N4b5t7BE6D+EiNCzZ09MmjQJP/30EwQCAU6ePIkZM2Zg9OjR0NHRQXx8PH744QdERUXBz8+vUoFURf0A5L5E/fz8oKamhiNHjuD777+HsrIyUlJSMHXqVPTv3x+DBg2ClZUVlJSUcOnSJVhYWKB+/fr8NDlFTIIA4Pnz5xCLxZWmIrZt2xZt2rRBRkZGpVEsmUyG0NBQFBQUoE6dOlBRUcHatWurrAHLDSBzDTh3d3ckJCSguLgYKioqCAkJwdSpUzFu3Dg4OzsDKE+GcnNzUVxcjOTkZBw4cEBhk6BPNTIHDRqEW7duYfHixSAiDBkyBAKBgC/Sy9V2UldX5+uQyKNXtWLDet68ebh69SpOnDiBtWvX4ocffsD58+ehpaWFn376CTVr1sTdu3dx584dnD17FsXFxejbt2+VxvshqVSKwsJC6Orq8iNB58+fR3BwMN6+fYvCwkK0b98eLi4uWL16NYKCgnDu3DkcPnwYDRo0QO3atbFlyxaF2Qqfew9cvHgRzZs3x8SJEyuNLnw4/YrTtWtX+Pv7Y/LkyRAIBKhXrx527doFgUDAL/CvatwIira2NubOnYu2bdtCVVUVSUlJMDIyAlD+/jMwMEBoaCg8PDywefNm/nqRZ+2swsJCbNy4EY8fP4auri6GDx+OjRs3YsaMGdixYweePHkCsViMefPmoVWrVigqKsKDBw+QmZmJp0+f4tGjR3j16hW6d++uMCNbn0NEMDc3x7Vr1xATEwNLS8uP7sNNRc7JyYGOjg5fh45hvllVtRiJ+XdVXNyZlJRE06ZNIxsbG3Jxcflo4eOFCxdIKBTSlStXqjrMr1bx/BYsWEDGxsYUFhZGubm5FBkZSa6urmRubk4+Pj7k5+dH5ubm/IYCiqikpIQKCwuJiCgkJIQ6d+5MT58+JZlMxi9Cnj9/Pg0cOJCIKp9/VlYW3bx5k6KioqpsUWvF388t8g4JCaGePXtSdHQ0vXjxgoiIPDw8Ki36fvv2La1bt46EQiEZGRlRYmJilcT7NbgFxSUlJZSUlERRUVFUVFRERETR0dE0ZMgQEgqFtGzZMgoPD6d79+7R8uXLyd7enuzt7UkikRCRfBeCE5VvGjBr1iy6efMmFRcXE1H54nonJycSCoW0Z88e/j1WUlJCxcXF9ObNG7nFKxKJaPfu3UREFBkZSUZGRrRv3z5KSkqijh070ooVKygiIoLOnz9PAwYMoH79+vELwR8/fkzR0dGUmJhIubm5cjuHL5kwYQJ5eXl98T6vX78mBwcHOnPmDBERHT58mNasWUObN2+W24YbHJlMRmFhYTR+/PiPNtrgfBhbXFwcWVlZ0YgRI+jEiRNVEeYniUQisrW1pd69e5OtrS0ZGRlRp06dKD4+nt6/f08ODg7UpUsXMjAwoNjY2E8+h1gs5jdLUSSFhYW0c+dO8vPzIz8/P35jkJs3b5JQKCQ3N7dKG55U3DQoMzOTnJycaNOmTUQk3012GEbeWCJUjZWUlFBqauonbwsPDyehUEgzZ87kj3GNnxcvXlCnTp3o2LFjVRLnP0Emk1X6suV2TAoJCaGSkhISi8V08+ZNsrOzI1tbWxIKhRQWFib3RumnFBQUkI2NDV29epWIypOFKVOm8D9zr9PChQtp8ODBJJVK+fOQxw54hYWFNGfOHIqIiKh0zMXFhfz8/PjX5dmzZ9SlSxdaunQpxcbGUnR0NBGVv982b95MT58+rfLY/yyuISASiWjs2LHUuXNnEgqF1KNHDwoJCSGRSERPnjyhoKAg6tatGxkbG5ORkRE5ODhQaGio3BurnOPHj5OpqSmZmZnxDTvuPZObm0vjx4+nvn370u7duxXi2pBKpWRvb8/vVCkSiSgoKIgGDRpEzs7OFBISwncYEJWfQ/fu3WnWrFnyCvkvKSsro2HDhvE7dH6uwSmRSMjU1JTc3Nw+ebu8XytHR0daunTpH95v06ZNlJmZSUREjx49oi5dupCDgwOJRKJ/OcKPcbunOjs78x0wqampZGBgQCtXriQiory8PHJxcSGhUEirV6/mOzO461hREwSRSERDhw6lESNGkK2tLfXr14/Mzc3p2rVrRES0detWMjQ0pHnz5lFcXFylx7569YoWLVpEffr0oYyMDHmEzzAKhU2Nq6ZKSkowevRolJSUwNDQEDNnzkSDBg34ujh2dnZQUlKCoaEhgMrTZt6/fw9NTU1oa2vLK/wvEovF8PT0RP/+/fHdd9+hQ4cOEAgEfO0GNTU1+Pn5QUlJCZs3bwYAODk5oXPnzjh8+DAiIyNx8eJF2NraKuRUhsjISKSlpUEoFAIAdHR0EBgYyE9v46aPvH79Gurq6vw5FBQUIDQ0FKamphg6dGiVTTNJT0/HpUuXkJmZiZo1a8LKygrKysrIyspCkyZNoKKigvT0dIwZMwbdunWDl5cXtm/fjgsXLuDQoUNo3LgxpkyZopCvBfD7tSGTyTB//nz+/dWoUSPs2rULR48eRUZGBhYvXgxvb29MmTKFX0fAFVjknkeexVKB8imVtra2CA8Px/Xr19GhQweoqalBIpFAV1cX69atg7u7O9asWQM1NTWMGzeuSuP9kLKyMgoKCvg1bhoaGhg6dCju3buHW7duwcrKii9WyZ3DxIkTceTIEWRnZ6NBgwYKO6WXiCCTyVCrVi2kpKQAAD+9rWLMUqkUqqqqsLS0xPv37/nNLiqS57WTlZWFx48f8ztCfq6+XGRkJEJDQ6GqqgoXFxcYGhpi586dqF27dpVPSxaLxRg2bBiaNWuGVatW8VMSmzdvju+++46vcVanTh2EhoZi5syZOHv2LHR1deHo6AgVFRW5r2v6nNLSUnh5eaFOnTrw9fVF06ZN8erVK3h7e8Pf3x/Hjx+Hs7MziAirV69GbGwsunbtClNTUzx+/BjPnj1DSkoKNm3axBeFZphvmeJd5cyf8uTJEzx9+hSNGzdGfn4+Ro8eDXd3d5w7dw6vX78GAAwbNgxt2rRBZmYm7t69ixcvXuDp06f8Qml5bbv6R9auXYsrV67gxIkTmDVrFpYsWYKrV68CqLz409fXF/b29ti8eTP27duH7OxsqKmpwcrKCoGBgQq3fS6n4s5DHC4JqthIEovF/OYVYrEYq1atwr59+9C2bdsqa/wREQwMDLBz507k5eVh8+bN+PXXX6GmpgZ9fX08fvwYd+7c4ZOg5cuXo3bt2sjNzUVZWRnfiFXUJAgoj62kpARHjx5FYWEhnJ2dYWVlBVNTU4SEhGDs2LGIiYnBwYMHIZFIUK9evY+2pyUiuZwj9ztDQ0ORkJAAY2NjuLi4wMbGBuvXr8f+/fsBoFIyFBoaCnNzc76gqryUlZUBKP/bVVyr2K5dOzg7O6Nnz578lsVlZWX8tV9cXAyBQABNTU2FTYI4KioqGDt2LO7cuYODBw8C+D0ZqngfoPwar1+/vtyKo1YkkUj4bcgbNGgANTU1xMTEAMBnt7vv1q0bX6gbAL8Zh76+fpXEzCkoKMCIESPw3Xff4ccff0SDBg0gk8kAlBeAzc/Ph6GhIX/tcOvmGjVqhD179mDLli2QSqUKmQQB5efw9OlTDB8+HK1atUKNGjXQvHlzWFlZ4eXLl8jKyoKamhqmTZuG7du3w9TUFNHR0Vi/fj3u3LmDNm3aYPfu3XwnKcN869iIUDVlaGgIJycnnD17FuHh4Th37hzOnTvHb7HavXt3ODg4QE1NDUlJSZg1axZq166NBg0aQCKR8DUqFNHYsWNx/fp1CIVCjBw5EmvXrsVvv/2GFi1aYMqUKTA0NOQXRC9btgwymQxbt25FUVERpk2bhnr16inklxiX5HDJQXFx8Ue9wwKBAGVlZZBKpSgqKkKDBg1QVlaGFStW4JdffkF4eHiVbjTANdrMzc0RGBiIhQsXYuPGjdDU1MSsWbMwbdo0ODo6YsiQIQgMDISamhrev3+P7OxsCIVCvvdYkRusRITNmzfj6NGjyMvLw+zZswGUvz41a9bE7Nmz8fTpU0RERGD69On84z583eQlKysLhw4dwoULFxASEgIDAwN8//33ICL4+/tDIBDwnwVcIrdlyxa5XyPcaJZAIPho4wAbGxv07NkTmpqaEIvFUFFRgbKyMt6+fYuUlBS0atVKYd9T3DXNxWdmZoaOHTti5cqV/IjXh7HHx8fj/fv3sLGxkUfIlRQVFcHZ2RmNGzfG999/DwMDA7Rp0wbR0dFISkr67OePiooK1NTUUFpaCqBqd7TjEBHmzp2LzMxMeHl5oWHDhvz7TCKRwMvLC9nZ2cjKysKPP/6I1q1bo2XLlujQoQP27t0LOzs7fsdVbnaFouCujdLSUrx69YpPSLnzs7S0hEQiwYsXL2BgYACZTIbu3bvzx8ViMerWrQtlZWW5X/sMo0hYIlQNcV+0/fv3x7lz5/DLL79gwoQJGDJkCJ48eYKlS5di8+bNOHv2LMzMzODu7o5FixZBTU0NNWvWhIWFhcLsrPQpurq6aNWqFXJycjBq1Cj06tULZ8+exeXLlzFz5kx+qpWRkRHat28Pf39/yGQyHD16tFJDVVF82DCSSqUAynvpP9WY476oVFRU8PbtWwQEBODMmTM4dOhQlfbiVYxbIpHAwsIC/v7+cHFxQVhYGMaNG4e5c+fip59+glgsRmxsLEQiEc6fP4/Y2FgcPHjwo+1bFUnF87O2tsarV69w4sQJREREwMTEBDVr1uSTIScnJzg6OiI5ORnt2rWTWyOcG0mo+Pv19PSwbds2zJ8/Hx4eHggNDYWBgQF++OEHAOCnkY4bN45/PeQVf0lJCVJTU2FkZFTpvVGx8cwlz5qamnjz5g02bNiAnJwcaGpqIi8vD7Gxsdi3bx/foaBIuBpTRITS0lKoqamhdevWcHV1RXBwMObPn4/c3FyMGjUKmpqakEqlePjwIYKCgqCrq8vX3pGnU6dOISEhAc+ePYOGhgY8PDwwa9YsODk5Ye/evfDw8OC3Nq84TS4xMZEvbA3IZ+dEgUCA//3vf8jMzMT27duhpaWFzp07QyKRYOTIkXjx4gVMTExw/PhxZGVloaysDAKBAHp6eujQoQPGjh2Lnj17KlwSVFhYiMDAQAwfPhwmJiaoV68eP3WUG03lvle4n7lkh6s/p6i7pjKMvLFugWqI+3KxsLBAy5Ytcfz4cQDlQ/z169fni0C2atUKly9fxvDhw3Hy5Eno6upi2LBhCpkEUfnGHQDK18wMGTIEFy9eRFRUFBo1aoTJkydj8eLFUFZWRs2aNREQEIAZM2Zg4cKFuHHjBlasWIELFy5UKq6qCIqKirB8+XLcvn270rHatWt/tiEnk8kgEAjQsGFD3L59G6dOncLBgwfRrl27Kon31q1bAH4fDeKmJRUXFyMgIADNmjXji6bKZDL4+/vjyZMn8PDwQGBgIJ4/f449e/Yo7NREbkoWdx0REdq0aYMZM2bAzs4OFy5cwM6dOwH83qh4/vw5mjZtirp168olieAaOZ9rXJqYmCAwMBACgQAeHh5ISkpC69at8cMPP2D48OHw9fXFsWPH+PvL4xyICJ6enpgzZw7u3LnDH+e27y0qKuLXy3Dq16+PJk2aICkpCUlJSVBXV8eBAwfQtm3bKo//j3BrxAoKCuDj44Pp06dj9uzZePHiBXr37o2FCxeie/fuCAoKwpgxYzB9+nRMmDABixYtgpKSEnbt2sWvXZGnUaNGwdLSEsXFxTh16hSCgoKgpqYGNzc3nDx5EiEhIXj8+DGA36fJvXnzBvv27YNEIkG3bt0AyC/ZNjIyQnBwMN6/f481a9YgOjoaY8aMgbq6Oo4fP44jR44gIiICFy5cwPLly+Hi4gIVFRVcv34dPXr0UMh1M6mpqTh69CjWrFmDzMxMbNiwAT169PjD8heFhYVITk7mOxoYhvmYgCpOVmaqDW44/P79+3B2dsbKlSvRtm1bjB8/Hh07dsSqVaugoaGBe/fu4cKFC4iIiMCuXbsqLe5WZDKZDFOmTEFpaSn27duHpKQkTJw4EZaWlliwYAEyMjKwd+9eXLx4ETo6Ovj555/5goSK5LfffsP//vc/9OnTB66urujQoQOioqIwdepUjB8/Hu3bt0fLli2hrq6OFi1aVJqycPLkSWzfvh0hISFV8roRERYtWoSIiAisWbMG/fr143u4JRIJRowYAQ0NDezduxePHj3C3LlzoaOjg9mzZ8PS0hLp6elQUVFB3bp1FbYwL3c+RUVFOHr0KF68eAFlZWUMHToUBgYGyM7ORmhoKG7fvo1BgwZh9OjRyMzMxPr166GlpYWtW7dW+bQSiUSC0aNHw8bGBjNmzAAArFy5EnXq1PloBDQhIQFeXl5QVlbGqlWrYGhoiJSUFOzZswcTJ06stK5JHh48eID58+dDXV0dbm5u6NatG4YPH47k5GTo6+tDWVkZbdq0gYGBAfT09GBiYgI9PT08f/4cLVq04DdLUTRcglpUVIThw4dDTU0Nurq6SElJgY6ODj9lMScnBw8ePMCJEyfw7t07NGnSBObm5hgzZgyUlZX596e8cN8rR48eRWxsLLS0tLBnzx4MHToUI0aMwKNHjxAWFoaWLVuiT58+6Nu3L+7cuYO4uDjExcVh27ZtClMjLCkpCR4eHnj16hUaN26M48ePo1atWp/sTHj79i2UlZUVcgMhLt4bN27A1dUV5ubmcHd3h4WFBQDw1wQ3Y+LAgQMwNzeHWCyGr68v4uLiEB4ezkaEGOYzWCJUzeXm5mLWrFl8YcfOnTtj6dKlqF+/fqUGW2FhocIVTispKcGFCxcQFxeH/Px89OzZE7169eIb0du3b8fevXsxc+ZMrFq1CpaWlvD19UW9evUgEAggFouRnZ2NGjVqQE9PT85nU1nFBtvJkyfx448/wsjICB4eHpBIJBg3bhy0tbXx/v17AIC6ujqaNGmCunXrQigUom7dumjUqBG6devGT0OpCk+fPoWvry/S09OxYMECWFlZVUqCwsLC0LhxYwDA3bt34eXlBW1tbUydOhXW1tZVFudfVXFKmVgsxtixY/l1AwKBAFlZWXBycsL06dMhFouxbt06nDx5EkpKSujVqxdq1aqFwMBA1KxZs8p3k8rKykJAQAAuX74Mb29vTJo0CcOHD4dYLMbkyZPh6OjI37esrAzR0dFwd3eHkZERvLy8YGJi8tmdvuSBa6BqaWlh0aJF2LRpE8rKytCwYUO8evUKKSkpeP36NT8KBpSPfm/atAnq6uoKtzaISx7Kyspw+vRpnD59GkFBQahduzbS0tLg4+MDIkJISAg/tZUb9a14Lh/uAFiVPnxPp6WlYdq0afD29oZAIICbmxuGDRuGSZMmobCwEMHBwUhLS0NhYSEaNGgAExMTuLu7K9wocHJyMtzc3KCuro4FCxbwycOnppgqMu71iY6O5jvUZs+ejQ4dOvD3OXr0KJYtW4aIiAjo6+vD398fP//8M3bt2gVTU1M5Rs8wCu7f25mbqSpnzpwhoVBIU6ZM4QtAEpXXQODqIChaPQSRSET29vY0bNgwGjBgAI0aNYp69epF169f5+8jlUrJ2tqahEIheXh4VCqYqGjnU5FIJKLRo0fT8ePH+WPh4eHUuXNnmjJlCi1fvpy6detG6enpFBMTQ8ePH6fly5eTg4MDWVlZkVAopI4dO/L1OKpaeno6TZgwgfr06UNnz56l4cOH09ixY+n58+f8fbhaR/fv3ycLCwtycnKSS62QP1KxEKJMJiOpVEqzZs0ie3t7Sk1NpYKCAiIicnBw4AstEpUXrfX29qYePXqQn58f/xxcgdKqlpaWRj4+PiQUCun06dP07t07mjx5Ml8TqKL8/Hz+urGzs5NL7ak/kpiYSIMGDaJx48aRqakpBQQE8Le9efOGMjMz6cqVK7R582ZavHjxZwt5Kori4mIKDAyk8ePHk4+PD3+8rKyM4uPjydramqytrenhw4dyjPJjH9a9qljQef/+/dSnTx8SiUR07Ngx/nP47du3JJFIKCcnh2JiYig3N5e/jhRRYmIiDR48mOzt7en27dv8cUX+DuFUjJF7XaKjo8nIyIicnJzo7t27/O0HDx4kY2Njio+PJ39/fzIzM1O49xvDKCKWCP0HvHnzhpydncnR0ZHevn0r73D+UEFBAQ0ZMoQmTZpEMTExfOOSK3pHRHxF7C1btlCXLl34iuuKTiQSUd++fcnJyYlycnIqfZGFh4dT9+7daeDAgdShQwfKz8//6PElJSUUHx9Pr169qsqwP5Kenk6Ojo5kampKAwYM+GRjmju3uLg4vqq5IklKSqJx48bR6dOn+WO5ublka2tLmzdv5o/9/PPPZGhoSPv376cLFy7wyXhaWhp5e3tT586dac2aNVUe/4fS0tLIy8uLhEIhRURE0Lt372jixInUt29f2rVrF3+/jIwMcnNzo9TUVHrx4oUcI/6yR48e0ZAhQ0goFFJQUBB//MPiofIuUvtnpKWl0bBhw8jExITmzJnz0e3x8fFkY2NDtra2HxW4lJeCggKaNGkSeXl50cuXL/lkhvv7Z2Rk0KRJk+jAgQNERLR3714+GVL0xPRDFZOhmJgYeYfzRcXFxfTgwQP+5y8lQ1OmTOG/N8PDw8nIyIjs7OzIxMSk0nMwDPN5LBH6j1izZg2ZmprSvXv3iEj+lcg/RyqV0uLFi8nJyYkyMjL4D3muojcR0bZt26h79+508eJFysvLI0tLS76quSL34nFJ0MSJEyk7O/uT9zl58iR16NCBjIyMKo1+VTx/RZGenk5OTk78a8Gp+Boo8usRFxdHnTp1orFjx9K5c+eIqDwR6tq1K23cuJGIypMgoVBImzZtIiIiHx8fsre35xO/9PR0mj9/PgmFQtqwYYN8TqSCzyVDPXv2pJCQEIqOjiZvb28aOHBgpRFURZWcnEx9+/alQYMG0Z07d/jjFUezFdGnroHU1FSaOHEimZub086dOz96TEJCAnXq1Ik8PT2rKszPkslkNH36dBIKhSQUCsna2poWL15MCQkJle4XFBREvXv35jur9u3bR4aGhjR9+nRKS0uTR+hfLTExkWxtbcna2ppiY2PlHc4nyWQymjlzJllZWVVK2D6VDEVFRVG7du1o2bJlRFTeeSgUCsnU1JSSkpKqNnCGqcZYIlTNcR+QRUVFZGVlRS4uLnKO6Mvev39PI0eOpC1btvA9vdwHOxHRunXrSCgUUp8+fcjW1pYiIyNpx44dZGpqqtA9eSKRiPr160fOzs58EsSdl1QqrTSt7MyZM9SpUyeaMWNGpS9kRWz4cdPkevXqRRcuXOCPK2KsFXHx3b9/n/r3708jR46kX3/9lYiIRo8ezfd0c0mQTCYjiURCrq6u5OrqWum5njx5Qr6+vvwopbxVTIa4aXIzZ84kY2NjsrS0pD59+tCjR4/kHeaflpSURNbW1grfW5+amvpRsvbh/1NSUsjZ2ZkGDhz40ZRF7jkUpZPq4cOHZGtrSyNHjqQxY8aQq6srGRkZ0Y8//kjR0dH8/ezt7WnVqlX8zzt37qSOHTvKfdT6azx48IBGjx4tt2nHf0ZCQgLZ2tp+cSof992ye/duEgqFlJCQQEVFRbRq1SqF+ZximOqCJUL/Adzah7lz51K3bt0Uuif49u3b/Af3h1atWkVGRkZ05coVio6OphEjRpC1tTVt376dhEIhBQYGKkwjoiKxWEz9+vWjKVOm8FORuC+qkpISGjBgAG3durXSqE94eDh16dKFpk+fTrdu3ZJL3H9WxWSo4siQouMaDjExMWRlZUUjRoyg69ev082bN6lLly6VRoKIiJ49e0Z2dnYUHBxc6fFEijdixyVDxsbGdOXKFSopKaH79+/TpUuXqmUDVdF764uKimj48OFkY2Pz2Z567v/Jycnk7OxMAwYMoD179nzy+RTlc+zRo0c0YsQIcnR0pGPHjtHJkyepT58+NGDAAFqwYAE9e/aMQkJCaN68efTu3Tv+cRXX3lU3irhm7kN/tK6J+35JTU2ljh070qVLl4ioepwbwygaVkfoP4CrzO7m5oYDBw4oXC2dDwkEAhQXFwMo3w0HAEQiETIyMrB8+XL07NkTXbt2hYuLC548eQKhUAhHR0eMGjVKbrsqfY5EIsHkyZORl5cHX19fNG7cGBKJBEpKSvy2x5qamhgyZAhUVVX587Wzs8P8+fNx5coV7N+/n/97KCJ9fX0EBASgRYsW8PDwwJUrV+Qd0hdRhY0wuUKwrq6uSExMxE8//YTMzEzMmTMHurq6iImJQXh4OPbs2YM5c+YAANzd3QFU3lFKUXZc486tVatWcHV1RadOnbBjxw5IJBKYmZmhb9++aNiwoZyj/OsMDAwQFBQEdXV1NGjQQN7hfKRmzZpYsWIFlJSUEBwcjJiYGAC/19qq+P+2bdtiyZIlaNq0KQ4ePIgNGzZ89HyK8jlmaGiIFStWIC8vD0eOHIGZmRmOHDkCR0dHXL58GR4eHkhMTMSpU6dw/vx5/nGamppyjPrvUcTt1z9kYGCA0NBQvH//HqtXr670fqu4w19GRga0tbX5a0ZRPqcYpjphidB/SLNmzdC8eXN5h/FFrVu3Rp06dRAREQGgvPo1EUFTUxMrV66EnZ0d3wC9f/8+mjZtik6dOmHRokUKVwOJ+0Jq2rQptLS0sGvXLhQVFUFNTQ1FRUUYOXIkatWqhY0bN/JbTnPnCwDDhg1DUFAQZs+eXakwniLS19eHr68vunTpopDvsaSkJFy+fBnA7w1SmUwGNTU1JCQkYMWKFejduzdycnL4wqKrV69Gbm4uVq9ejRMnTqBFixY4evQoVFRU5F7U8nMqJmetW7dG69atkZOT859oABkZGWH//v0KtxU+x8jICKtWrfpk45S7prl/W7dujYkTJ0JFRQXPnj2rlJwrGkNDQwQHB+Pdu3fw9vbGy5cvMXHiRFy8eBGdO3dGjRo1AADh4eF8h0112Xa6OvswGbp58yYA8EnQ27dvcerUKb7oMMBeF4b5KvIaimK+TYWFhTRz5kzq0qULv4Cd6OM1J2lpaeTo6EjLly8niUSicGtSCgsLafbs2RQZGUkSiYQWL15MPXr0oJUrV5JIJKKhQ4fSmDFj6OXLlx89ViQSfXIxdXWgaFPEiMqng0yePJnMzc35qXvc+rMHDx6QmZkZLVq0iIiI7t69y68Zio6OprKyMnrz5g3l5uby7zFF3qXsxo0bFBUVRUTlGz94enrS1KlTFXr74v+az01b4qa7lZSU0JkzZ8jb25t27tzJH1e0z7APcVua29vb82uEysrKKCcnh/bu3Vvtdor7r0hMTKThw4eTtbU1nTlzhkpKSig2NpYWLFhAnTp1ouTkZHmHyDDVGiuoylS51NRUjB07Fs2aNcOsWbMwYMCASrfn5OQgLCwM169fx+7du6Gvry+nSD8vKSkJ48ePR8uWLTFv3jx07NgR/v7+iIyMRElJCZo3b44dO3ZAQ0OjUi9dQUEBVq5ciSNHjuD8+fMKObpSHT179gxLly7Fs2fPsHDhQlhZWeHBgwdwdHTEsGHD4OPjg1q1akEgEODu3bvw9vaGpqYmXFxcYGtryz8PfaLqvKIoKyvDsWPH4Ovri++++w41a9ZERkYG9uzZA6FQKO/wvilcUVhtbW14enrC0tISQPlUzMuXL8PPzw916tTBqVOn+GKrijId7ksqntecOXPQqVMneYfEAHjy5AmCgoJw9epVaGtrQ1NTExoaGggKCoKBgYG8w2OYao0lQoxc3LhxAz/88AO0tLQwdOhQjBs3DioqKrhy5Qpu3LiBmJgYbN++XSE/5LnG8v379zFv3jyoq6vD29sbFhYW8Pf3x5kzZ2BlZQU/Pz+oqqryjSAuCTp9+jT279+Pdu3ayftU/lMyMjKwYMECvHjxAk5OTli7di2GDBkCHx8fqKurA/h9OuP9+/cxefJkWFtbIyAgQM6R/3lisRjnzp3D9evX0ahRI4wePRqtW7eWd1jfpIpJg4+PD8zNzXHu3DkEBASgXr16OHz4ML8ukJvOVB0kJSVh3rx5KCsrw/Lly9GhQwd5h8Sg/NpPSEjAo0ePYGBggDZt2ijkejqGqW5YIsTITVJSEvz9/fHo0SMUFxdDSUkJenp6MDIywsyZMxW6gcclQ3fu3MHChQtRq1YteHl5wdLSEn5+foiMjMSgQYPg5eUFNTU15OfnIzg4GBERETh48CBLgv4lGRkZWLhwIe7duwcLCwvs2rXro/twr11KSgpatWpVLXrqPySTySAQCBR29OpbUTEZ6tmzJ44fPw4tLS0cO3YMKioqkEqlUFFRkXeYf9nDhw/h6+uL0NBQhV2zxTAM809giRAjVyKRCHl5eXj8+DEEAgHMzc1Rq1Yt1K5dW96hfVLFqVMSiQRqamq4ffs2XFxcIBQKMXfuXFhYWFRKhmbOnIn169fj6NGjLAmqAlwylJ6ejqVLl6J///6VdvYCKr+O1WXaEqOYkpKS4OnpibS0NBgaGuLIkSNQVVWttkkQh/t8YxiG+S9jiRDD/IGioiLEx8ejc+fOAMDvSKasrIzi4mKMGzcOEokERUVF0NLSgo+PD79mKCoqCgKBALm5uThw4ACMjIzkfDbfBm6aXGZmJhYvXsyvQ1PkNUBM9fXw4UNs27YNq1atqtYjQQzDMN8alggxzBcQERYtWoSIiAisWbMG/fr14xs5EokEI0aMgIaGBvbu3YvExER4enpCXV0dPj4+sLCwgI+PD27duoXt27ezBe1VjEuGsrKysGTJEvTr10/eITHfAJYEMQzDVB8sEWKYP/D06VP4+voiPT0dCxYsgJWVVaUkKCwsjK8TdO/ePcybNw/a2tpwd3dHt27dkJeXh/r168v5LL5NGRkZWLRoEWJjY7F7925YWFjIOySGYRiGYRQES4QY5k/gRheeP38Ob29vbNq0CTVq1EBoaChfzI7bHSouLg5Tp05F27ZtsW3bNtSqVUvO0X/bnj59ir1792LhwoVsLRDDMAzDMDyWCDHMn8Qtwo+Pj0eDBg1w5syZjxYTc2tQ4uPjoa2tzeoEKRi2MQLDMAzDMJzqU9yAYeRMX18fK1asgJmZGYqKihAVFcXfVnFXMiKCqakpS4IUEEuCGIZhGIbhsBEhhvmL2I5kDMMwDMMw1R8bEWKYv0hfXx8BAQFo1qwZ/P39cenSJQBgSRDDMAzDMEw1whIhhvkKXDLUokULeHh44MqVK/IOiWEYhmEYhvkLWCLEMF9JX18fvr6+6NKlC1sPxDAMwzAMU82wNUIM8zeVlpZCVVVV3mEwDMMwDMMwfwFLhBiGYRiGYRiG+eawqXEMwzAMwzAMw3xzWCLEMAzDMAzDMMw3hyVCDMMwDMMwDMN8c1gixDAMwzAMwzDMN4clQgzDMP8AmUyGa9euwdPTE9bW1mjfvj3MzMxgbW2NZcuWIS0t7R/7XUKhEEKhEG/fvuWPOTk5QSgU4ty5c3/pObKysv7U/U+cOAGhUIjp06d/VcwMwzAMo2hYIsQwDPM3PX/+HE5OTpg6dSp++eUXqKmpoWvXrrCwsIBIJMKBAwcwbNgw7Ny5U96hMgzDMAzz/1TkHQDDMEx19vr1a4wZMwa5ubno378/vLy80KJFC/720tJSHD58GCtWrEBQUBA0NDQwZswY+QX8lQYOHAgzMzOoq6vLOxSGYRiG+UewESGGYZi/Ye7cucjNzcWgQYOwbt26SkkQAKiqqsLR0RGenp4AgJUrVyI/P18Okf49mpqaaN26NRo1aiTvUBiGYRjmH8ESIYZhmK+UkJCAW7duoUaNGli4cCGUlD7/kerk5ARTU1P07t0bb968qXRbaWkp9u/fj7Fjx6J9+/YwNzeHnZ0dduzYAYlE8m+fxp/yqTVCt27dglAohI+PD16+fAkfHx/06NEDxsbGsLKyQlhYGAoLCz/5fPfv34ebmxu6desGY2Nj9O7dGwsWLEBGRkZVnRLDMAzzjWNT4xiGYb7SqVOnAACWlpZo2LDhF+9bo0YNHD169KPjRUVFcHV1xe3bt6GhoQEzMzPUqlULsbGxWLlyJX799Vds27YNGhoa/8o5/BOysrIwcuRISKVSmJmZgYhw69YtbNy4EXFxcR+tjdq3bx9WrFgBIkK7du1gYWGBtLQ0HD9+HOfOncPGjRvRuXNnOZ0NwzAM861giRDDMMxX4naCa9++/Vc/R0BAAG7fvo0ePXpg1apV0NXVBQCIxWJ4eHjg6tWrCAgIQEBAwD8S878hJiYGPXr0QHBwMHR0dAAA8fHxcHBwQHR0NO7du8f/je7cuYMVK1ZAQ0MDGzZsgKWlJf88hw8fxpIlS+Du7o7z589DW1tbLufDMAzDfBvY1DiGYZivlJ2dDQCoV6/eVz3+zZs3OHHiBNTV1SslQQCgoaGBlStXonbt2oiIiPhoOp2iWbZsGZ8EAYCpqSk6dOgAAEhOTuaPb9u2DTKZDJ6enpWSIACwt7fH4MGDkZeXh2PHjlVN4AzDMMw3iyVCDMMwX0lFpXxQXSqVftXjb9++DalUCkNDw0pJEEdXVxcmJiaQSqWIiYn5W7H+m+rXrw89Pb2Pjjdo0ABA+fQ/ACgrK8OtW7cAAN27d//kc/Xt2xcAcPPmzX8jVIZhGIbhsalxDMMwX6l+/fpISkpCbm7uVz3+xYsXAMqniwmFwi/e9+XLl1/1O6rC56awcYmiTCYDALx7947fPGHAgAFffE5FPl+GYRjmv4ElQgzDMF/JxMQE165dw7179/7U/U+cOIHi4mL06NED+vr6ICIAQIsWLWBiYvLFx+rr6//teP8tAoHgT92PS4iUlZVhY2Pzxft+aoSMYRiGYf5JLBFiGIb5SlZWVtiwYQNiY2Px+vVrfirYp0ilUgQHByM3Nxeenp5wdXVF/fr1AQCGhoYIDg6uqrDlpk6dOlBVVUVpaSn8/f1Rq1YteYfEMAzDfMPYGiGGYZivZGhoiB49ekAikfDbQX/Ohg0bkJubC3V1dYwePRpA+bbbAoEAN27c+GS9HYlEgtGjR2PcuHF48ODBv3YeVUVVVZXfQOHSpUufvM+6deswdOhQbN68uSpDYxiGYb5BLBFiGIb5G3x9faGpqYlz585h5syZyMzMrHS7RCLBxo0bsX79egCAj48PP+1LT08PgwcPxrt37+Du7o6cnJxKj/P19UVCQgJevnz5h2uIqospU6YAAJYvX44bN25Uui0yMhJbtmzB48ePYWhoKI/wGIZhmG8ImxrHMAzzNzRr1gyHDh2Cq6srLl68iMuXL8PQ0BB6enooKipCXFwc3r9/D1VVVXh5eWHs2LGVHr9s2TJkZGTg6tWrGDhwIIyNjaGpqYn4+Hi8efMGmpqaWL9+PVRVVf+V+EePHg1lZeXP3h4YGIhevXr9Y7+vd+/emDVrFtauXYtJkybBwMAA+vr6eP78OR4+fAgA+P777//R38kwDMMwn8ISIYZhmL/pu+++w+nTp3Hs2DFcunQJKSkpSE5OhqqqKvT09DB8+HA4OjqiefPmHz1WW1sbBw8exMGDB3HmzBk8ePAARIQmTZpg8ODBcHFxQZMmTf612PPy8r54u0Qi+cd/5//+9z9YWlpiz549uHfvHtLS0lCvXj307t0bzs7O6NGjxz/+OxmGYRjmQwL60qR2hmEYhmEYhmGY/yC2RohhGIZhGIZhmG8OS4QYhmEYhmEYhvnmsESIYRiGYRiGYZhvDkuEGIZhGIZhGIb55rBEiGEYhmEYhmGYbw5LhBiGYRiGYRiG+eawRIhhGIZhGIZhmG8OS4QYhmEYhmEYhvnmsESIYRiGYRiGYZhvDkuEGIZhGIZhGIb55vwf/bBn+r3UrBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extended Data Fig 5\n",
    "#Cell line specific ATP production rates\n",
    "ferm_proteomics['umol ATP mgprotein-1 min-1'] = ferm_proteomics['value'] * mammalian_core['V_Ferm_ATP'].mean()\n",
    "resp_proteomics_core['umol ATP mgprotein-1 min-1'] = resp_proteomics_core['value'] * mammalian_core['V_Resp_ATP'].mean()\n",
    "resp_proteomics = resp_proteomics_core.copy()\n",
    "\n",
    "#Remove geiger_2012 from variable description\n",
    "ferm_proteomics['group'] = ferm_proteomics['variable'].str.extract(r'geiger_2012_([^_]+)')\n",
    "resp_proteomics['group'] = resp_proteomics['variable'].str.extract(r'geiger_2012_([^_]+)')\n",
    "\n",
    "#Add pathway for hue\n",
    "ferm_proteomics['Pathway'] = 'Glycolysis'\n",
    "resp_proteomics['Pathway'] = 'Respiration'\n",
    "\n",
    "#Combine the dfs\n",
    "combined_proteomics = ferm_proteomics.append(resp_proteomics, ignore_index=True)\n",
    "\n",
    "#plot\n",
    "plt.figure(figsize=(10,10))\n",
    "fig = sns.barplot(x='group', y='umol ATP mgprotein-1 min-1', data=combined_proteomics, hue = 'Pathway', errwidth=1)\n",
    "plt.xlabel('Cell Line', fontsize=23)\n",
    "plt.ylabel(\"ATP Production Rate\" r'$ \\left(\\frac{\\mu mol}{mg_{protein} \\cdot min^{}}\\right)$', fontsize=23)\n",
    "plt.xticks(rotation=45)  # Rotate labels if they are long or overlapping\n",
    "plt.legend(title='Pathway', bbox_to_anchor=(1.03, 1), loc='upper left')\n",
    "\n",
    "plt.savefig('./Figures/edf5_atp_production_rate.svg',format='svg', dpi=1200)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}