{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6484bc5d-a1e5-471d-b6a5-a5d3e0eef088",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import scipy as sp\n",
    "import scipy.optimize\n",
    "import scipy.stats\n",
    "import pandas as pd\n",
    "import os\n",
    "from lmfit import Model\n",
    "from numdifftools import Derivative"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f34d82-b0de-447a-a48f-30b5475368a9",
   "metadata": {},
   "source": [
    "Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "57961162-7a32-4d2b-9cec-9fa5f07ea03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a0b0 = pd.read_csv('A0B0.csv', names=['Time', 'NADPH'])\n",
    "a05b24 = pd.read_csv('A0.5B24.csv', names=['Time', 'NADPH'])\n",
    "a1b24 = pd.read_csv('A1B24.csv', names=['Time', 'NADPH'])\n",
    "a2b24 = pd.read_csv('A2B24.csv', names=['Time', 'NADPH'])\n",
    "a4b24 = pd.read_csv('A4B24.csv', names=['Time', 'NADPH'])\n",
    "a8b15 = pd.read_csv('A8B1.5.csv', names=['Time', 'NADPH'])\n",
    "a8b3 = pd.read_csv('A8B3.csv', names=['Time', 'NADPH'])\n",
    "a8b6 = pd.read_csv('A8B6.csv', names=['Time', 'NADPH'])\n",
    "a8b12 = pd.read_csv('A8B12.csv', names=['Time', 'NADPH'])\n",
    "a8b24 = pd.read_csv('A8B24.csv', names=['Time', 'NADPH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3c2d9e62-9046-44fa-af94-d8d1e0d3a164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e0a5b5d310>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M2hNF/QJIAxzNuPgGP64M0MSJE009jhYr6xo+Lu3atTPvO3bskLPPPtvd7gpStI6oUaNGZi2gRx55xH1++vTp5r179+5e30tnnd1yyy0B/dkAACgvh/NM5kBHqZycHBMo6NTthIQEr3Oa9dBgQId0qlSpEuB1gFKLgp8grgNkRwH7nQEAIvb57YsMULhokNOqb9FsLy141pofHfYKcOYHAAAURwAUThrsBHiqOwAAOD0WQgQAALZDAAQAAGyHAAgAANgOARAAALAdAiAAAGA7BEAAAMB2CIAAAIDtEAABAADbIQCyobVr15rd29PS0rzat27dKjfccIPZ20s3LG3durU888wzXn10Z3eHw+F+ab/zzjtPZsyY4dVv0qRJcvHFF5uNUOvVqyf9+vWT7du3l3hNupeYft7UqVMD/NMCAFAcAVAYFRQWyBeZX8jSn5aadz0OhVmzZsl9990na9askd27d7vbN23aJElJSfL666/Lt99+K2PHjpXRo0fLc889V+wzNJjZv3+/bNu2zQQvd999t3z88cfu87qhqu4I//nnn8uKFSskPz9fevfuLbm5ucU+67333pP169dLgwYNgvhTAwDwP2yFESYf7fpIntjwhBw4fsDdllw1WUZdMkp6NekVtO+rAcjChQvliy++kMzMTHn11Vdl3Lhx5txtt93m1bdZs2aybt06eeedd+Tee+/1OqdZncTERPPnoUOHmkyR7ibfs2dP07Zs2bJiu8Dr12iQddlll7nb9+7daz77ww8/lL59+wbt5wYAwBMZoDAFPyNWjvAKflTW8SzTrueDZcGCBdKyZUvzGjRokAlMnE5nif11R93atWuXeF6/VoOdPXv2SKdOnUr9HOX5WYWFhTJ48GB56KGHzDAaAAChQgAUYjrMpZkfpxQPOlxtkzdMDtpw2MyZM03go7QG6NixY15DV540+6PZIh3i8tWwYUOpXr26VK5c2WRuxo8f75XZ8Q2SRowYIV27dpW2bdu62ydPnixxcXEmgwQAQCgxBBZiX2Z9WSzz4xsEZR7PNP0uTrk4oN9b63Y2bNhghrSUBh8DBw40NUG9enkPu2kN0LXXXmuGxy6//PJin5Wenm4KnPPy8sxn6jCWZne0FsiXnvvqq69MzZGLDoW5hs20+BkAYA/OggI5vnGT5B88KHFJSVK1YwdxxMaG/DoIgELs4PGDAe1X3uyPFiOnpqZ6ZWcqVaokhw8fllq1apk2LWz+wx/+IHfccYf8/e9/9/tZTZs2ddcA6fCVFjE//vjjxQIgLbZevHixrF692mSNPAOorKwsady4sbutoKBAHnjgATMTbOfOnQH/+QEA4ZWzfLkcmDhJ8jMz3W1xKSmSPGa0JPTuHdJrYQgsxJKqJgW0X1lp4DNnzhx5+umnZcuWLe6XTn1v0qSJvPHGG+7MT48ePWTIkCEmoCkrnVb/66+/egVWmvnRbNMnn3xiAiZPWvujWSHPa9FZYFoPpAXRAIDoC372DhvuFfyo/AMHTLueDyUyQCHWvl57M9tLC5791QE5xGHOa79AWrJkicny3H777VKzZk2vc9ddd53JDmngoy+drq41OzpLzBXc6PR4T5q9OXHihHsIbO7cueZzXHQK/Lx58+T99983Q2Wuz9LvrWsH1alTx7w8aSYqJSXFFGgDAKJr2OvAxEn6f8d+TjpFHA5zvkbPniEbDiMDFGKxMbFmqrsr2PHkOh55yUjTL5A0wNE6H9/gR/Xv399kYHTNn4MHD5psUP369d0vXdDQlwYpeu6cc86RkSNHmkLpZ5991n1++vTpZuZX9+7dvT5LZ6EBAOzluNb8+GR+vDid5rz2CxUyQGGg6/xM6T7F7zpAGvwEYx2gDz74oMRz7du3L3UqvCcNaMrSt6yf54m6HwCITvkHDwa0XyAQAIWJBjk9GvUws7204FlrfnTYK9CZHwAAwi3Op4ziTPsFAgFQGGmwE+ip7gAAWE3Vjh3MbC8tePZbB+RwSFxysukXKtQAAQCAoNLCZp3qXnTgs/bbqWM9H8r1gAiAAABA0Ok6P6nPTDWZHk96rO2hXgeIITAAABASGuToVHdWggYAALbiiI2Vap0uCfdlMAQGAADshyEwAAAilFU2Fo1EBEAAAEQgK20sGomYBQYAQISx2saikYgAyIbWrl1rNjhNS0srsc/PP/8sDRs2FIfDIdnZ2e72lStXmjbXSzc2Pe+882TGjBleXz9p0iSzh5huhFqvXj3p16+fbN++vcTvp3uJ6edNnTo1QD8lAJRPYaFT9m4/LN9/kWne9TgiNxYVMee1H0rGEJgNx25nzZol9913n7zyyiuye/duady4cbE+umv8BRdcIHv37vX7GRrMJCQkyK+//mr2Gbv77rulefPm0rNnT3N+1apVZkd4DYLy8/Nl7NixZpf5bdu2SbVq1bw+67333pP169dLgwYNgvQTA0DpftycJekLMiQ3O8/dVi0xXroNbCHN29WL2I1FrTDbyqrIAIWJpid/6NlLdg8ZIvsefNC863Gw05a5ubmycOFCE7BcddVV8uqrrxbrozu5a9bnwQcfLPFzNKuTkpIiTZs2laFDh8rZZ58tX375pfv8smXL5JZbbjHZoQsvvFBmz55tgq1Nm7x3+tUA69577zU70FeqVCnAPy0AlC34WfbSN17Bj9JjbdfzVmLFjUUjEQGQzcZuFyxYIC1btjSvQYMGmcDEc+d2zdBMmDBB5syZIzExp//roV+rwc6ePXukU6dOJfY7cuSIea9du7a7rbCwUAYPHiwPPfSQCZQAINR0mEszP6VZszDDUsNhVtxYNBIRANls7HbmzJkm8FFaA3Ts2DH5+OOPzXFeXp7ccMMN8uSTT/odFvOk9UHVq1eXypUrS9++fWX8+PFy2WWXlRgkjRgxQrp27Spt27Z1t0+ePFni4uJMBgkAwmF/RnaxzI+vY4fzTD+rbSxabE8tz41FU1JCurFoJKIGyEZjt1q3s2HDBnnnnXfMsQYfAwcONDVBvXr1ktGjR0vr1q3dAVJp0tPTTYGzBk36mTqMpdkdHVrzpee++uorWbNmjbtNh8KeeeYZM2ymxc8AEA65OXkB7RfKjUV1xMAEQZ7/Qx2mjUUjERkgG43davZHC5JTU1NN8KMvrffRgOjw4cPyySefyFtvveU+5yporlu3rsnweNLan3POOccMXd16661mKOvxxx8v9j212Hrx4sXy6aefmqyRZwCVlZVlMk2u77dr1y554IEHTD0RAIRCtYT4gPaz68aikYgMkE3GbjXw0bqep59+2szG8tS/f39ThLxo0SIzq8vliy++kNtuu80EKzrDqzQ6rd7za3XYS4Ofd99910yd14DJkwZMmnXy1KdPH9OuARUAhEL9Folmtldpw2DVa8WbflZjpY1FIxEBUJjGbrXg2W8dkI7dJicHfOx2yZIlJsuj09tr1qzpde66664z2SEdqvJ06NAh867DYomJ3v/xa/bmxIkT7iGwuXPnms9x0Snw8+bNk/fff98MlWWeGvbT761rB9WpU8e8POksMJ1ZpgXaABAKMTEOM9VdZ3uVpOuAFqafFVllY9FIxBBYmMZuiw58/oMK4titBjiacfENflwZoC1btnhNYz8dDVLq169vhsFGjhxpFjJ89tln3ed1aE1nfnXv3t30c710FhoAWImu85N2Z1uTCfLN/Gi71dYBQmA4nJ5zoGHk5OSYQEEf4LrYnyfNeuzYscMM6VSpUqXCd4w9XEInUL8zANFNp7qbWWE5eabmR4e9rJr5Qfmf374YAgsTxm4BwFo02EltWSvcl4EQIQAKI8ZuAQAID2qAAACA7RAAAQAA2yEAAgAAtkMABAAAbIcACAAA2A4BEAAAsB0CIAAAYDsEQAAAwHYIgGxo7dq1Zvf2tLS0Yud0B/iePXuazU9r1apldo7XfcJcdGd3h8PhfunGpuedd57MmDHD63MmTZokF198sdkItV69etKvXz/Zvn17ideke4np502dOjXAPy0AAMURAIV535m92w/L919kmnc9DoVZs2bJfffdJ2vWrJHdu3e7248ePSp9+vSRxo0by/r168153UtF23777Tevz9BgZv/+/bJt2zYTvNx9993y8ccfu8+vWrXK7Aj/+eefy4oVKyQ/P98EU7m5ucWu57333jPfr0GDBkH+yQEAKMJWGGHy4+YsSV+QIbnZee423Ym428AWQd15WAOQhQsXmkxPZmamvPrqqzJu3Dh3UHP48GGZMGGCNGrUyLSNHz9eLrjgAhMoNW/e3P05mtXRLJEaOnSoPPPMM2Y3ec0eqWXLlnl939mzZ5uv2bRpk1x22WXu9r1798q9994rH374ofTt2zdoPzcAAJbKAL3wwgvuXbo7dOgg6enppfbXzIL20/7NmjWTF198sVif7Oxsk32oX7++6de6dWtZunSpWCn4WfbSN17Bj9JjbdfzwbJgwQJp2bKleQ0aNMgEJk5nUeZJ2+rWrSszZ86UkydPyq+//mr+rENcTZo08ft5+rUa7OzZs0c6depU4vfVnXlV7dq13W2FhYUyePBgeeihh8z3AADAFgGQPoyHDx8uY8eOlc2bN0u3bt3kiiuu8BqW8bRjxw658sorTT/tP2bMGJN9WLRokbuPPrgvv/xy2blzp7z99tsmq/Hyyy9LamqqWIEOc2nmpzRrFmYEbThMAxoNfJTWAB07dsw9dKX1Olrj8/rrr5vanurVq5vMjAaPcXHeycKGDRua85UrVzaZG80UeWZ2fIOkESNGSNeuXaVt27bu9smTJ5vP1d8hAAC2GQKbMmWK3H777fLnP//ZHGsBrD5wp0+fbopofWm2R+tTXIWymtnZuHGjPPXUU9K/f393fcsvv/xiCn0rVapk2krKXoTD/ozsYpkfX8cO55l+qS1rBfR7azC4YcMGeeedd8yxBh8DBw4096xXr14m43PbbbfJpZdeKvPnz5eCggJzbzXo1CEzDYpcNFOnAVNeXp75TB3G0uyO1gL50nNfffWVqSly0aEw17CZFj8DAGCLAEgzNfoQHDVqlFe7Fspq8OLPunXrzHlPWqCrWQ0t0tWAZ/HixdK5c2czBPb+++9LUlKS3HjjjTJy5Egz88kffYjryyUnJ0eCJTcnL6D9ykPvkxYje2bDNDuj901rfzQw0syZ3ueYmKLk4Lx588xsML2X119/vfvrdNjSVQOkw1daxPz4448XC4C02Fp/J6tXrzZZI88AKisrywS0LhpwPfDAAybA1esAACDqAqBDhw6ZB15ycrJXux5rca4/2u6vvz7U9fO05uenn36STz75RG666SYzdJORkWGCIe3jKvb1pdmmRx99VEKhWkJ8QPuVlf78c+bMkaeffrpYEKnZszfeeMMEQxr4eGZkXMdar1MaDS41g+Sin6XBz7vvvmuG1TRg8qS1P5p18g1mtf3WW289w58WAACLzwLzHf7QB2dpQyL++nu264NaZxvpujT6UNaC6X379smTTz5ZYgA0evRoU6PimQFyzYIKtPotEs1sr9KGwarXijf9AmnJkiUmy6NDjjVr1vQ6d91115nskA57aUGyBowavOi9fOKJJ8xQWY8ePby+RrM3J06ccA+BzZ0713yOi36GZo80c6RDZa6gVr+3DqXVqVPHvDxpJiolJcUUYwMAEJVF0DrbSAMU32yPPlh9szwu+nD0118f0K6HqWaBzj33XK/hLq0V0q/TYTd/4uPjzXo3nq9giYlxmKnupek6oIXpF0ga4GjGxTf4cWWAdLHD48ePywcffGDqdXQYUYvNNXjUWV56Xz1pkKJt55xzjhle1LWAnn32Wfd5rePSmV/du3c3/VwvLXwHAMC2GSCdPaTZGV0k7//+7//c7Xp87bXX+v0afSjrA9rT8uXLpWPHju6CZy3g1cyDZi9cdSzff/+9efjq97QCXecn7c62xdYB0syPBj/BWAfI9755at++vTuTpnQWXUk0oPHsW5Ky9PFF3Q8AwBZDYDrspDUfGsBocKPDVjoF/q677nIPTelCeVq7orT9ueeeM193xx13mGJd19CNixbhaiZi2LBhZhhHa4AmTpxouanWGuQ0vTCpaFZYTp6p+dFhr0BnfgAAgMUCIJ2C/fPPP5uVh3VbBV0jRguXXdPWtc1zTSAtpNXz999/vzz//PNm64Rp06a5p8Arrd3RrJD20RWMdcaTBkM6TGM1GuwEeqo7AAA4PYezImMVUU6LoLVWRmtYfOuBtPBXF2R0rV4N6+N3BqAsnAUFcnzjJsk/eFDikpKkascO4ihh+RRE3vPbcrPAAAAIt5zly+XAxEmS7zHRJi4lRZLHjJYEn6VDEB3CvhdYpCJxFjn4XQE4XfCzd9hwr+BH5R84YNr1PKIPAVA5uWab6ZRxRAbX78r1uwMAz2EvzfyIv2qQU216XvshujAEVk66vpBuAaHrD6mqVauyl5WFMz8a/OjvSn9nJW2FAsC+TM1PCbsPGE6nOa/9qnW6JJSXhiAjAKoAXZBRuYIgWJsGP67fGQB40oLnQPZD5CAAqgDddkMXVtQtN3QTVliXDnuR+QFQEp3tFch+iBwEQGdAH6w8XAEgculUd53tpQXPfuuAHA6JS042/RBdKIIGAARUYaFT9m4/LN9/kWne9diqdJ0fnepedOCzEv+pYz3PekDRhwwQACBgftycVWyfw2qJ8WYT6GDscxgIZp2fZ6YWXwcoOZl1gKIYK0Gf4UqSAID/BT/LXvqmxNuhm0BbNQhSrAQd+VgJGgAQUjrMpZmf0qxZmGE2gbbqps86zMVUd/ugBggAcMb2Z2R7DXv5c+xwnukHWAEBEADgjOXm5AW0HxBsBEAAgDNWLSE+oP2AYCMAAgCcsfotEs1sr9JUrxVv+gFWQAAEADjzh0mMw0x1L03XAS0sWwAN+yEAAgAEhE5x16nuvpkgzfxYfQo87IeFEAEAAaNBjk51N7PCcvJMzY8Oe5H5gdUQAAEAAkqDndSWtbirsDQCIACw+AKDZFOAwCMAAgCLisR9tYBIQRE0AFh4Xy3f1ZX1WNv1PICKIwACgAjdV0v7WXVT0dz1G+TIkn+bdz0GrIYhMACI4H21rFZsnLN8uRyYOEnyMzPdbXEpKZI8ZrQk9O4d1msDPJEBAgCLidR9tTT42TtsuFfwo/IPHDDteh6wCgIgALCYSNxXS4e5NPMjTj/Dcqfa9DzDYbAKAiAAsJhI3Ffr+MZNxTI/XpxOc177AVZAAAQAFhOJ+2rlHzwY0H5AsBEAAYAFRdq+WnFJSQHtBwQbs8AAwKIiaV+tqh07mNleWvDstw7I4ZC45GTTD7ACMkAAEAH7ap17cYp5t2LwoxyxsWaqe9GBzzWeOtbz2g+wAgIgAEBA6Do/qc9MNZkeT3qs7awDBCthCAwAEDAa5NTo2bNoVtjBg6bmR4e9yPzAagiAAAABpcFOtU6XcFdhaQyBAQAA2yEAAgAAtkMABAAAbIcACAAA2A4BEAAAsB0CIAAAYDsEQAAAwHYIgAAAgO0QAAEAANshAAIAALZDAAQAAGyHAAgAANgOARAAALCdMu8Gv3jx4nJ/+OWXXy5nnXVWub8OAADAEgFQv379yvXBDodDMjIypFmzZhW5LgAAAGsMgWVmZkphYWGZXlWrVg3eVQMAAIQiABoyZEi5hrMGDRokCQkJFb0uAACAoHE4nU5n8D4+MuXk5EjNmjXlyJEjBHEAAETh85tZYAAAwHbKXASt/vjHP5ap3zvvvFPR6wEAALBWAKRpJU/z5s2Tq6++WmrUqBHo6wIAALBmDZAGPlu3bo26qe7UAAEAEHmoAQIAACgFRdAAAMB2CIAAAIDtlKsI2nc/MF3x+eOPP5ZvvvnGq/2aa64JzNUBAACEuwg6JiamTHuAFRQUSCSjCBoAgOh+fpcrA6QZHwAAAFvWAOXl5Ulubm7grwYAAMBqAdChQ4ekb9++Ur16dZNa6tKli/z000/BuzoAAIBwB0CjR4+WTZs2yaOPPipPPvmkCYjuvPPOYFwXAABA0JSrBujDDz+UWbNmyZVXXmmO9b1t27by22+/SaVKlYJ1jQAAAOHLAO3bt0/atWvnPm7VqpVUrlzZtAMAAERlAKQz5uPivJNGeszsMAAAELVDYBoA9ezZ0ysIOn78uNkRXjNBLl9++WVgrxIAACBcAdD48eOLtV177bWBvB4ACJrCQqfsz8iW3Jw8qZYQL/VbJEpMjMPSd9xZUCDHN26S/IMHJS4pSap27CCO2NhwXxZgr5Wgg+GFF14wM8r2798v5513nkydOlW6detWYv9Vq1bJiBEj5Ntvv5UGDRrI3/72N7nrrrv89n3zzTflhhtuMEHae++9V+ZrYiVoIPr8uDlL0hdkSG52nrutWmK8dBvYQpq3qydWlLN8uRyYOEnyMzPdbXEpKZI8ZrQk9O4d1msDrKg8z+8Kb4aqU+A3btxopsX//PPPFfqMBQsWyPDhw2Xs2LGyefNmE/hcccUVsnv3br/9d+zYYWaeaT/tP2bMGBk6dKgsWrSoWN9du3bJgw8+WGowBcA+wc+yl77xCn6UHmu7nrdi8LN32HCv4EflHzhg2vU8gBBmgDTzcvfdd8tnn33m1f773/9epk+fLi1btizzZ3Xq1Enat29vvs6ldevW0q9fP5k0aVKx/iNHjjQbsn733XfuNs3+bN26VdatW+du073I9HpuvfVWSU9Pl+zs7FIzQLqytb48I8hGjRqVKYIEYP1hrzlj1hYLfjxVrxUvgx/vYpnhMB32+qFnr2LBj5vDIXHJyXLOxx8xHAaEIgOUmZlpAouDBw/KlClTZOnSpfLvf//bPYSl2ZasrLL9n9TJkydN9qi3TxpXj9euXev3azTI8e3fp08fk4nStYhcJkyYIElJSXL77beX6Vo02NIb5npp8AMgOpian1KCH3XscJ7pZxWm5qek4Ec5nea89gMQgiLof/3rX9KkSROT/alSpYq7PS0tzWSFunbtavr4y974G0LTTE1ycrJXux5roOWPtvvrn5+fbz6vfv365tpmzpwpW7ZsKdcK11pX5JsBAhD5tOA5kP1CQQueA9kPwBlmgFasWGGGoTyDH5ezzjpLHnroIbNadHk4HN4pZx2R8207XX9X+9GjR2XQoEHy8ssvS926dct8DfHx8SZV5vkCEB10tlcg+4WCzvYKZD8AZ5gB0o1PtWanJB07dizz5qgaoMTGxhbL9ugQmm+WxyUlJcVvf12XqE6dOqY+aefOnWZdIhfXIo3aZ/v27dK8efMyXR+A6KBT3XW21+lqgLSfVehUd53tpQXPOtxVUg2Q9gMQggyQZlhKy47UqFFDjh07VqbP0oUTO3ToYLJKnvRYd5n3p3PnzsX6L1++3AReuheZbs3x9ddfm+Ev1+uaa66RHj16mD8zrAXYjxY261T30nQd0MIyBdBK1/nRqe5FBz7XdepYz7MeEBCiDJArCPI3BOaqnSnPpDKtuxk8eLAJYDS4mTFjhpkC71rXR2tz9u7dK3PmzDHH2v7cc8+Zr7vjjjtMUbTW+8yfP9+c1+vSzVk9JSYW/V+dbzsA+9B1ftLubFtsHSDN/GjwY8V1gMw6P89MLb4OUHIy6wAB4dgK49xzzy31fGn1O74GDhxo1hDSWVs6i0yDFJ1ZpoXWSts81wRq2rSpOX///ffL888/bxZCnDZtmvTv3788PwYAG9Igp+mFSRG1ErQGQTV69mQlaCDc6wDpKsxloVPlIxkrQQMAEN3P73JlgMoS2OgaQQAAAFZW4a0wPGkSSYem/vjHP0rDhg0D8ZEAAADWKYL2pFPeZ82aJa+99pqZ/dW3b1+zASkAWBE7qwOocAB04sQJefvtt+WVV16Rzz//XC6//HJTrKzTzJlpBcCq2FkdQIWHwP7617+amVc6A+tPf/qTmaL+wQcfmJlfMTEBGU0DgIBjZ3UAvsoVteg6Pbrnly4+eM8995jVlwHA6sNeupaO3xWVT7Xpee0HwD7KFQDpgoQbNmwwm47qGj5LliwxG5ECgFWxszqiWmGByI50ka/fLnrXY6srtMY1l6sG6MYbbzQv3W9r9uzZJgt0/Phxs9/Wtm3bpE2bNsG7UgCoAHZWR9Tatlhk2UiRnH3/a0toIJI2WaTNNWJJ26xzzRUq3Dn77LPl0UcfNYHQ3LlzzUrMugu7ToEfOnRo4K8SACqIndURlTSQWHizdyChcvYXtet5q9lmrWs+o8plLX5OS0uThQsXyr59++TBBx8s82rRABDKndWLbSrqubN6Sgo7qyNy6JCRZlHE30YOp9qWjbLWcFih9a45YFO3ateuLcOHD5etW7cG6iMB4Iyxszqizq61xbMoXpwiOXuL+lnFLutdc7lqgHTT0rJkhR5++OEzuSYACCh2VkdUOXYgsP1ses3lCoDefffdUgOf7du3m4USCYAAWA07qyNqVE8ObD+bXnO5AqDNmzf7bddVoEeNGiXffPON3HHHHYG6NgAI+HBYtU6XcFcR2Zp0KZo5pcXDfmtqHEXntZ9VNLHeNZ9RDdCOHTvM7K+LL77YbD//7bffyosvvhi4qwMAAN5iYoumjRu+xf2njtOeKOpnFTHWu+YKBUCHDh2S++67T1q1amX2AVu7dq0sWLBAWrRoEfgrBAAA3nTNnAFzRBLqe7drFkXbrbgOUBtrXXO5hsByc3PlqaeekilTpsg555xj9gHr3bt38K4OAAD4pwFDq75FM6e0eFjrZ3QIyUqZHwtfs8Pp9LdBjn8pKSly9OhRk/254YYbTOGzPxdccIFEspycHDOkd+TIEUlISAj35QAAgAA/v8sVAHnu+K7Bj+eXuo71vSDCNxUkAAJOr7DQKfszsiU3J0+qJcRL/RaJEhNTwmKDAGCx53dceYueAeDHzVmSviBDcrPz3DejWmK8dBvYQpq3q8cNAmB5ZQ6AvvrqK2nbtq1XFqg0OiOsZcuWEhdXrhgLQAQEP8te+qZYuwZD2p52Z1uCIACWV+ZZYO3atZOff/65zB/cuXNn2b17d0WvC4BFh70081OaNQszTD8AsLIyp2e0vkdXeK5atWqZ+p88efJMrguABZmaH49hL3+OHc4z/VJb1grZdQFA0AKgyy67zGx1UZ4M0FlnnVXuCwJgXVrwHMh+AGD5AGjlypXBvRIAlqezvQLZDwDC5Yy2wgBgLzrVXWd7laZ6raIp8QBgZQRAAMr+D0aMw0x1L03XAS1YDwiA5REAASgXXedHp7r7ZoI088MUeES0wgKRHekiX79d9K7HiFos0gOgQkFQ0wuTWAka0WPbYpFlI0Vy9nlv0qk7mFtxY1GcMQIgABUeDmOqO6Im+Fl4sy744t2es7+o3aq7q+OMMAQGALAvHebSzI9v8GOcals2iuGwKEQABACwr11rvYe9inGK5Owt6oeoQgAEALCvYwcC2w8RgwAIAGBf1ZMD2w8RgwAIAGBfTboUzfYSRwkdHCIJqUX9EFUIgAAA9hUTWzTV3fANgk4dpz1R1A9RhQAIAGBvOsVdp7on1Pdu18wQU+CjFusAAQACP7VcZ01p4bDWzujwkdUzKBoEteobedeNCiMAAgAETiSvqKzBTtNu4b4KhAhDYACAwK6o7LuujmtFZT0PWAQBEIAKcRYUSO76DXJkyb/Nux7DxlhRGRGGITAA5ZazfLkcmDhJ8jMz//ePSUqKJI8ZLQm9e3NH7ag8KyozzAQLIAMEoNzBz95hw72CH5V/4IBp1/OwIVZURoQhAAJQZjrMpZkfcfrZOPJUm55nOMyGWFEZEYYACECZHd+4qVjmx4vTac5rP9gMKyojwhAAASiz/IMHA9oPUYQVlRFhCIAAlFlcUlJA+yHKsKIyIgizwACUWdWOHcxsLy149lsH5HBIXHKy6QebYkVlRAgyQADKzBEba6a6Fx34bBx56ljPaz/YmGtF5fOvK3pnOwlYEAEQYAGFhU7Zu/2wfP9FpnnXY6vSdX5Sn5lqMj2e9FjbWQcIQCRgCAwIsx83Z0n6ggzJzc5zt1VLjJduA1tI83b1xIo0yKnRs2fRrLCDB03Njw57kfkBECkcTqe/gXx7y8nJkZo1a8qRI0ckISEh3JeDKA9+lr30TYnn0+5sa9kgCAAq4mR+vszbulJ252RK44QUufHC7lI5Li7kz28yQECY6DCXZn5Ks2ZhhjS9MEliYnzqbQAgAj2Z/pbMzZgmzthsd9uUrYkyuMVQeajbn0J6LdQAAWGyPyPba9jLn2OH80w/AIiG4Oe1HydIYYz3v2l6rO16PpQIgIAwyc3JC2g/APYcTnp100cy4dPXzbseW9HJ/HyT+SllAqnM/X5aSK+fITAgTKolxAe0HwB7sdJw0ulozY9eZ0mD+RoEOeOyTb9bOvSSUCAAAsKkfotEM9urtGGw6rXiTT/YWGGByK61Rbut64ajuucW6+rYnms4ScdxHH6Gk5SVgqDdOZkB7RcIDIEBYaKFzTrVvTRdB7SgADrQwcSOdJGv3y5612Mr27ZYZGpbkdeuEll0e9G7Hms7bMuKw0mno7O9AtkvEAiAgDDSKe461V0zQb6ZH6bA2zyY0OtaeLNIzj7v9pz9Re1WvW6EbjjJcfrhJKu48cLu4ihI9LuDjtJ2R36i6RcqDIEBFgiCdKq7mRWWk2dqfnTYi6nvQQgmxOk/mBgwp2gPK6vQzNSykcWv19A2h8iyUSKt+jIcFiHr00T7cNLp6H3U2iQdnjPBjkfw5gqKBp87NKT323q/WcCGNNhJbVkr3JcRnSIxmNCaH9/MjxenSM7eon661xZsVVBsxeGksnDdR9/7HFOQaIKfUN9nAiAA0S0SgwkteA5kP0RVQbFmpjQ40+vzNwymGRUNKkI5nFRWeh+Hdf4/S2TaCIAARLdIDCZ0tlcg++H0BcUx/guKNZjQgmJ9aFtlOMyKw0nlodcVqqnupaEIGkB0i8RgQqe6JzQoGp7zyyGSkFrUD7YrKHZlUoY0Hycxhd7LZGjmR9utlLGyKmuGhwAQ6GBCC5791gFpMNHAWsGE1iKlTT5VuO3wue5TT+q0J6xTsxTBIrGg2IrDSZGIuwQgukVqMKGz0nR2mhZwe9YwabCm12ulWWsRLFILiq02nBSJHE5nSbPy7SsnJ0dq1qwpR44ckYSEhHBfDoBATYUvFkykWj+YYCXooNcAdZzT47QFxRuHfEpmJcqe32SAANiDBjk61T3StpXQ67PK7LQoFOkFxag4fqMA7INgAhGwPg1CgyEwPxgCAwD7iZSVoBGY53fYp8G/8MIL0rRpU6lSpYp06NBB0tPTS+2/atUq00/7N2vWTF588UWv8y+//LJ069ZNatWqZV69evWSDRs2BPmnAABEOldB8bgeg8w7wU90C2sAtGDBAhk+fLiMHTtWNm/ebAKXK664Qnbv3u23/44dO+TKK680/bT/mDFjZOjQobJo0SJ3n5UrV8oNN9wgn376qaxbt04aN24svXv3lr1794bwJwMAAFYW1iGwTp06Sfv27WX69OnuttatW0u/fv1k0qRJxfqPHDlSFi9eLN9995277a677pKtW7eaYMefgoICkwl67rnn5OabdRpscXl5eeblmUJr1KgRs8AAAIggETEEdvLkSdm0aZPJznjS47Vr1/r9Gg1yfPv36dNHNm7cKL/99pvfrzl+/Lg5V7t27RKvRYMtvWGulwY/AAAgeoUtADp06JDJziQney8/r8eZmf5X3NR2f/3z8/PN5/kzatQoSU1NNbVAJRk9erSJFl2vPXv2VOhnAgAAkSHs5e0On5WndETOt+10/f21q3/+858yf/58UxekRdMliY+PNy8A5cACfQAiWNgCoLp160psbGyxbE9WVlaxLI9LSkqK3/5xcXFSp04dr/annnpKJk6cKB999JFccMEFQfgJABvzu6qybtEw2dqrKgNAuIfAKleubKazr1ixwqtdj7t08b8pYefOnYv1X758uXTs2FEqVarkbnvyySflsccek2XLlplzgNU5Cwokd/0GObLk3+Zdjy0d/Oi+Wp7Bj9LNRrVdzwOAxYV1CGzEiBEyePBgE6RocDNjxgwzBV5ndrlqc3T6+pw5c8yxtutsLv26O+64wxRFz5w50wxzeQ57PfzwwzJv3jw5++yz3Rmj6tWrmxdgNTnLl8uBiZMk3yO7GZeSIsljRkuCT9G/JYa9NPPjd1d1bXOILBtVtOWE1beYAGBrYV0HaODAgTJ16lSZMGGCXHTRRbJ69WpZunSpNGnSxJzfv3+/15pAumCinteaHu2vWZ5p06ZJ//79vRZW1Blm1113ndSvX9/90iExwIrBz95hw72CH5V/4IBp1/OWovto+WZ+vDhFcvYW9QMAC2MrDD/YCgOhoMNcP/TsVSz4cXM4JC45Wc75+CNxxFokm/L12yKLbj99v/4zRc6/LhRXBACRtQ4QYHfHN24qOfhRTqc5r/0sQ3dQD2Q/AAgTAiAgTPIPHgxov5Bo0qVotpfW+vjlEElILeoHABZGAASESVxSUkD7hYQWNutUd8M3CDp1nPYEBdAALI8ACAiTqh07mNleWutTYg1QSorpZym6zs+AOSIJ9b3bNTOk7awDBCAChH0laCDQCgudsj8jW3Jz8qRaQrzUb5EoMTElry4eLlrYrFPddbaXCYI89yU+FRTpecsUQHvSIEenuutsr2MHimp+dNiLqe8AIgQBEKLKj5uzJH1BhuRm57nbqiXGS7eBLaR5u3piNWadn2emFl8HKDnZmusAedJgp2m3cF8FAFQI0+D9YBp85AY/y176psTzaXe2tWQQ5JoSb2aFHTxoan502MuSmR8AiJLnNxkgRM2wl2Z+SrNmYYY0vTDJssNh1TpdEu7LAADboAgaUcHU/HgMe/lz7HCe6QcAAAEQooIWPAeyHwAguhEAISpUrV4poP0AANGNAAhRoWb2DxJ/4rD3VHJPTqfEn/jF9AMAgAAIUaHw50PS4oe3ig58g6BTxy1+eNv0AxBcJ/Pz5dVNH8mET18373oMWA2zwBAVdOp4vUNbpe23L0vGOX+SvCq13Ofi8w6b4EfPxyWNEEsqLGBRQUSFJ9PfkrkZ08QZ+78JB1O2JsrgFkPloW5/Cuu1AZ4IgBBV20rUO/CVJB36SrITz5G8ygkSfzJHErN/MAsrW3JbCbVtsciykSI5+7y3ldA9t9hWAhEW/Lz24wRTXOG52ERhTHZRuwhBECyDITBEBde2EubPDpFa2RmSkrXJvLu22rLkthIa/Cy82Tv4UTn7i9r1PBABdJhLMz/Kd3s71/Hc76cxHAbLIABC1NBtI1KfmWq2kfCkx9puuW0ldNhLMz/ir3D7VNuyUUX9AIubt3WlGfYqZW9fccZlm36AFTAEhqiiQU6Nnj0jY1sJ3UjUN/PjxSmSs7eoH3tuweJ252QGtB8QbARAiDoRs62E7qIeyH5AGDVOSAloPyDYGAIDwqV6cmD7AWF044XdxVGQWNpSXOLITzT9ACsgAALCpUmXotleXvNlPDlEElKL+gEWVzkuzkx1L2UpLhl87lDTD7AC/iYC4RITWzTVXWd7mSDI86lxKihKe6KoHwI2U0mLcLUORYdiNBvBAzlwXOv8+K4DFFOQaIIf1gGClTiczpISlvaVk5MjNWvWlCNHjkhCQkK4LwfRzu86QKlFwY+F1wGKtGDC3wJ9OmTDAn2BF2l/N2DP5zcB0BneQMCOK0FHWjDhXqDPZ40a1//+DWk+zpLXDaB8CIDOEAEQED3BhGYjOs7pYVYj9rdGjV63DtFsHPIpWQrARs9viqABRPVqvyzQB8AfAiBE53DSjnSRr98uemclZVsHEyzQB8AfqtIQXdhYNKgiMZiI9AX6KCgGgoMACNHDtbGo795aro1FB8yx9KyqSBCJwYTOQJqyNfG0NUBWXKDPX7G5/ixWLTYHIglDYIgObCwaEpG42m+kLtDnKjbXwM2THmu7ngdQcQRAiA7l2VgUtgsmNFuis9NiChO92jXzY7VZa5FabA5EGmv9KwXLKSx0yv6MbMnNyZNqCfFSv0WixMSUtHVDGLGxaMhE6mq/el3DOv9fRCzQ5y42l9MXm9/SoVeIrw6IDtb7Lx+W8ePmLElfkCG52XnutmqJ8dJtYAtp3q6eWAobi4ZUJAUTnvT6IiFgiMRicyDSWPtfK4Q1+Fn20jfF2jUY0va0O9taKwhybSyqBc++RdDujUUbsLGoDYOJSBSJxeZApKEGCH6HvTTzU5o1CzNMP8ttLGr4DhywsSgiSyQWmwORhgAIxZiaH49hL3+OHc4z/SxFp7jrVPeE+t7tmvlhCjwiSKQWmwORhP96UIwWPAeyX8iDoFZ9I2pjUSCais2BSEEAhGJ0tlcg+4WcBjtNu4X7KgDbFpsDkYD/ilCMTnXX2V6lDYNVr1U0JR5AcFFsDgQHNUAo/pcixmGmupem64AW1lwPCACAMiAAgl86xV2numsmyDfzY7kp8AAAlBNDYCiRBjlNL0yKjJWgAQAoBwIglEqDndSWtSLqLun+SBSNAgBKQwCEqKI7ZPtOG56yNdGsqcK0YQCACwEQoir4ee3HCaayzXOQrjAmu6jdY20VqyFrBQCh5XA6S1ps3b5ycnKkZs2acuTIEUlISAj35aCMAUTHOT1MsKM7ZfvSv+W6gNzGIZ9abg0Vf1kr3QaBrBUABO/5zSwwRAWt+dEAwl/wo7TdGZdt+lkxa6WBm/jJWul5AEDgEQAhKugquYHsF6qslWZ+lG/g5jqe+/000w8AEFgEQCiVPnxf3fSRTPj0dfNu1YexbhEQyH6hEKlZKwCIBtYqhoClRNKMKt0fSa/tdDVA2s8qIjFrBQDRggwQSp9RJYelza5CufTbQvOux1asTdHCZg3MlG9Zv+tYd9C2UgF0JGatACBaWOdpAMvVplySUSi3flQodY/+79yhGiKze8XI3IJpZpdqKwUUrqyUb9ZKMz8a/JC1AgC4WOfpBcvQmpOLf/hFHnhXMz7eah8VefDdQnn6j7+Yfrd06CVWokGOBmaRsBK0K2ulGTXNUnkO3Vk1awUA0YJ/WVHM7ux9csuKouDH4WfMVM/o+U977rPk3dOAwWqBWbRkrQAgWhAAoZjz9uV6DXuJnyBIz2s/2CtrBQDRgn9hUUz3mqmSVcZ+sF/WCgCiAbPAUEyV5JSA9gMAwGoIgFBM1fYXSVw1Eaf43yZO2/W89gMAIBIRAKEYx3/XS/JFv5wqgPYNgpym3Zz/73ruHgAgIhEAobhjBySh0QlJvfSwxJ3lPRU+rmqBadfz2g8AgEhEEXQIFebny/7PPpPcQ0ekWt2aUv/SSyXGijN9qiebNw1yaqSekOMHK0v+iViJq1IgVZNOiiPGux8AAJHGgk/f6PTj4n9L+vITkptfSyMHESmQam+9L916V5Hm1/QVS2nSRSShgUjOfnHEOKVa8kmfDo6i89oPAIAIxBBYiIKfZUurSG5+old7bn5N067nLSUmViRt8qkD36UQTx2nPVHUDwCACEQAFIJhL838SInrKjtlzfJfTT9LaXONyIA5Ign1vds186Pteh4AgAjFEFiQmZofM+xVkhg5ll/b9Ev9/e/FUjTIadVXZNfaooJnrfnRYS8yPwCACEcAFGRa8FxU81OWfhakwU7TbuG+CgAAAoohsCDT2V6B7AcAAM4cAVCQ6VT3anGHT+2h7k+hVI/7xfQDAAChQQAU7BscF2emuhcVQPsGQXrskK69z7LmekAAAESpsAdAL7zwgjRt2lSqVKkiHTp0kPT09FL7r1q1yvTT/s2aNZMXX3yxWJ9FixZJmzZtJD4+3ry/++67Ek66zk/alSekWpx3nU/1uGzTbrl1gAAAiHJhDYAWLFggw4cPl7Fjx8rmzZulW7ducsUVV8ju3bv99t+xY4dceeWVpp/2HzNmjAwdOtQEPC7r1q2TgQMHyuDBg2Xr1q3mfcCAAbJ+fXj3rdIgZ/DTV0mfS/bJpY23mfdBT19N8AMAQBg4nE6n/y2/Q6BTp07Svn17mT59urutdevW0q9fP5k0aVKx/iNHjpTFixfLd99952676667TKCjgY/S4CcnJ0f+85//uPukpaVJrVq1ZP78+WW6Lv36mjVrypEjRyQhIeEMf8pTn7l8uRyYOEnyMzPdbXEpKZI8ZrQk9O4dkO8BAICd5ZTj+R22DNDJkydl06ZN0tvn4a/Ha9eu9fs1GuT49u/Tp49s3LhRfvvtt1L7lPSZKi8vz9w0z1cgafCzd9hwr+BH5R84YNr1PAAACJ2wBUCHDh2SgoICSU723lBTjzN9AgUXbffXPz8/33xeaX1K+kyl2SaNGF2vRo0aSaA4CwpM5kf8JdpOtel57QcAAGxSBO1weG8PoSNyvm2n6+/bXt7PHD16tEmXuV579uyRQDm+cVOxzI/PxZnz2g8AAIRG2OZe161bV2JjY4tlZrKysoplcFxSUlL89o+Li5M6deqU2qekz1Q6W0xfwZB/8GBA+wEAgAjOAFWuXNlMZ1+xYoVXux536dLF79d07ty5WP/ly5dLx44dpVKlSqX2Kekzgy0uKSmg/QAAQIQPgY0YMUJeeeUVmTVrlpnZdf/995sp8DqzyzU0dfPNN7v7a/uuXbvM12l//bqZM2fKgw8+6O4zbNgwE/BMnjxZ/t//+3/m/aOPPjLT7cMh7qIL5VCNmFLWgRZzXvsBAAAbBEA6ZX3q1KkyYcIEueiii2T16tWydOlSadKkiTm/f/9+rzWBdMFEPb9y5UrT/7HHHpNp06ZJ//793X000/Pmm2/K7Nmz5YILLpBXX33VrDekU+7DYf436fLq5aWtAy3mvPYDAAA2WAfIqgK5DtCET1+Xt3ZPlku2F8otKwql7tH/nTtUQ4OfGNnQMkb+1HikjOsx6MwvHgAAm8opx/ObDaiCrHFCinnXIOeLFg5pvccptY6JHK4u8l0jhzhjHF79AABA8BEABdmNF3aXKVsTpTAmWyTGIdua+E7RF4kpSDT9AACATdYBinaV4+JkcIuh5s++g42u48HnDjX9AABAaPDUDYGHuv3JvM/NmCbO2Gx3u2Z+NPhxnQcAAKFBEXSINkNVJ/PzZd7WlbI7J9PU/OiwF5kfAAACgyJoi9Jg55YOvcJ9GQAA2B41QAAAwHYIgAAAgO0QAAEAANshAAIAALZDAAQAAGyHAAgAANgOARAAALAdAiAAAGA7BEAAAMB22AvMD+epXUp1SW0AABAZXM9t13O8NARAfhw9etS8N2rUKNC/GwAAEILnuO7pWRo2Q/WjsLBQ9u3bJzVq1BCHwxHw6FQDqz179gR0o1Vwn8OBv8/c52jD3+nIvs+a+dHgp0GDBhITU3qVDxkgP/SmNWzYUIJJf+EEQMHHfQ4N7jP3Odrwdzpy7/PpMj8uFEEDAADbIQACAAC2QwAUYvHx8TJ+/HjzDu5zpOPvM/c52vB32j73mSJoAABgO2SAAACA7RAAAQAA2yEAAgAAtkMABAAAbIcA6Ay98MIL0rRpU6lSpYp06NBB0tPTS+2/atUq00/7N2vWTF588cVifRYtWiRt2rQx1fH6/u6774rdBfo+v/zyy9KtWzepVauWefXq1Us2bNgQ5J/Cvn+nXd58802zunq/fv3E7oJxn7Ozs+Wee+6R+vXrm36tW7eWpUuXip0F4z5PnTpVWrZsKWeddZZZzfj++++XEydOiJ29UI77vH//frnxxhvNPdSFh4cPH+63X9CfhU5U2JtvvumsVKmS8+WXX3Zu27bNOWzYMGe1atWcu3bt8tv/p59+clatWtX00/76dfr1b7/9trvP2rVrnbGxsc6JEyc6v/vuO/MeFxfn/Pzzz237mwrGfb7xxhudzz//vHPz5s3mPt96663OmjVrOv/73/867SwY99pl586dztTUVGe3bt2c1157rdPOgnGf8/LynB07dnReeeWVzjVr1pj7nZ6e7tyyZYvTroJxn19//XVnfHy884033nDu2LHD+eGHHzrr16/vHD58uNOu3iznfdb7NnToUOdrr73mvOiii0x/X6F4FhIAnYFLLrnEedddd3m1tWrVyjlq1Ci//f/2t7+Z857uvPNO5+9+9zv38YABA5xpaWleffr06eO8/vrrnXYVjPvsKz8/31mjRg3zH6SdBete6/299NJLna+88opzyJAhtg+AgnGfp0+f7mzWrJnz5MmTZfxtR79g3Od77rnH+Yc//MGrz4gRI5xdu3Z12tUl5bzPnn7/+9/7DYBC8SxkCKyCTp48KZs2bZLevXt7tevx2rVr/X7NunXrivXv06ePbNy4UX777bdS+5T0mdEuWPfZ1/Hjx8252rVri10F815PmDBBkpKS5Pbbbxe7C9Z9Xrx4sXTu3NkMgSUnJ0vbtm1l4sSJUlBQIHYUrPvctWtX87muIfOffvrJDDP27dtX7OhkBe5zWYTiWchmqBV06NAh8w+L/kPjSY8zMzP9fo22++ufn59vPk/H7UvqU9JnRrtg3Wdfo0aNktTUVFMLZFfButefffaZzJw5U7Zs2RLU67f7fdYH8SeffCI33XSTeSBnZGSYYEj7jBs3TuwmWPf5+uuvl4MHD5pASEdR9Nzdd99t/g2xo0MVuM9lEYpnIQHQGdKCTk/6H4Rv2+n6+7aX9zPtIBj32eWf//ynzJ8/X1auXGkK+OwukPf66NGjMmjQIFN0Xrdu3SBdcWQK9N/pwsJCqVevnsyYMUNiY2NNIeq+ffvkySeftGUAFKz7rP9OPP7446bot1OnTvLDDz/IsGHDTHD08MMPi105gvDcCvazkACogvQfc/1HxjcazcrKKha1uqSkpPjtHxcXJ3Xq1Cm1T0mfGe2CdZ9dnnrqKTNM8NFHH8kFF1wgdhaMe/3tt9/Kzp075eqrr3af1we10j7bt2+X5s2bi50E6++0PoArVapkPttFZ4Hp1+kwReXKlcVOgnWfNcgZPHiw/PnPfzbH559/vuTm5spf/vIXGTt2rJnVZCd1K3CfyyIUz0J7/aYCSP8x0f/DWrFihVe7Hnfp0sXv1+j4vG//5cuXS8eOHc0/XKX1Kekzo12w7rPS/zN+7LHHZNmyZeac3QXjXrdq1Uq+/vprM/zlel1zzTXSo0cP82edQmw3wfo7femll5pshCvAVN9//70JjOwW/ATzPmu9oG+QowHAqUlFYjeVK3CfyyIkz8KAlVPbeOrfzJkzzdQ/nQapU/90+qnSCvjBgwcXm2J5//33m/76db5TLD/77DMz9e+JJ54wU//0nWnwgb/PkydPdlauXNm07d+/3/06evSo086C8XfaF7PAgnOfd+/e7axevbrz3nvvdW7fvt25ZMkSZ7169Zz/+Mc/nHYVjPs8fvx4M2N0/vz5pv/y5cudzZs3N7OW7OrNct5npUuQ6KtDhw5mWRL987fffhvSZyEB0BnStWSaNGliHqbt27d3rlq1yusfep3i52nlypXOdu3amf5nn322mbrq66233nK2bNnS/IXSqYSLFi1y2l2g77N+lsb/vi/9x83ugvF32hMBUPDus66d0qlTJ7NOjU6Jf/zxx80SBHYW6Pv822+/OR955BET9FSpUsXZqFEj51//+lfn4cOHnXb2fDnvs79/f/XrQ/ksdJy6EAAAANugBggAANgOARAAALAdAiAAAGA7BEAAAMB2CIAAAIDtEAABAADbIQACAAC2QwAEAABshwAIQNR55JFH5KKLLgr3ZQCwMFaCBhBRHA5HqeeHDBkizz33nOTl5bl38AYAXwRAACJKZmam+88LFiyQcePGyfbt291tZ511ltSsWTNMVwcgUjAEBiCipKSkuF8a6GhGyLfNdwjslltukX79+snEiRMlOTlZEhMT5dFHH5X8/Hx56KGHpHbt2tKwYUOZNWuW1/fau3evDBw4UGrVqmWySddee63s3LkzDD81gEAjAAJgC5988ons27dPVq9eLVOmTDFB0lVXXWWCm/Xr18tdd91lXnv27DH9jx8/Lj169JDq1aubr1mzZo35c1pampw8eTLcPw6AM0QABMAWNMszbdo0admypdx2223mXYOcMWPGSIsWLWT06NFSuXJl+eyzz0z/N998U2JiYuSVV16R888/X1q3bi2zZ8+W3bt3y8qVK8P94wA4Q3Fn+gEAEAnOO+88E9C46FBY27Zt3cexsbFmmCsrK8scb9q0SX744QepUaOG1+ecOHFCfvzxxxBeOYBgIAACYAuVKlXyOtbaIX9thYWF5s/63qFDB3njjTeKfVZSUlKQrxZAsBEAAYAf7du3N7PM6tWrJwkJCdwjIMpQAwQAftx0001St25dM/MrPT1dduzYIatWrZJhw4bJf//7X+4ZEOEIgADAj6pVq5rZX40bN5Y//vGPpghai6d//fVXMkJAFGAhRAAAYDtkgAAAgO0QAAEAANshAAIAALZDAAQAAGyHAAgAANgOARAAALAdAiAAAGA7BEAAAMB2CIAAAIDtEAABAADbIQACAABiN/8fd30qtuBZuy0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(a05b24.Time, a05b24.NADPH, label='A0.5B24')\n",
    "ax.scatter(a1b24.Time, a1b24.NADPH, label='A1B24')\n",
    "ax.scatter(a2b24.Time, a05b24.NADPH, label='A2B24')\n",
    "ax.scatter(a4b24.Time, a4b24.NADPH, label='A4B24')\n",
    "ax.scatter(a8b24.Time, a8b24.NADPH, label='A8B24')\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('[NADPH]')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "999812fa-2de1-4e69-b689-c12ce548ae70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e0a5a1a5d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(a8b15.Time, a8b15.NADPH, label='A8B1.5')\n",
    "ax.scatter(a8b3.Time, a8b3.NADPH, label='A8B3')\n",
    "ax.scatter(a8b6.Time, a8b6.NADPH, label='A8B6')\n",
    "ax.scatter(a8b12.Time, a8b12.NADPH, label='A8B12')\n",
    "ax.scatter(a8b24.Time, a8b24.NADPH, label='A8B24')\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('[NADPH]')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7edf813-4cf5-4f26-a9bb-2543d6462f69",
   "metadata": {},
   "source": [
    "Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77b196ed-af76-45d3-994e-ead1eb1cbec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "rega0b0 = sp.stats.linregress(a0b0.Time, a0b0.NADPH)\n",
    "rega05b24 = sp.stats.linregress(a05b24.Time, a05b24.NADPH)\n",
    "rega1b24 = sp.stats.linregress(a1b24.Time, a1b24.NADPH)\n",
    "rega2b24 = sp.stats.linregress(a2b24.Time, a2b24.NADPH)\n",
    "rega4b24 = sp.stats.linregress(a4b24.Time, a4b24.NADPH)\n",
    "rega8b15 = sp.stats.linregress(a8b15.Time, a8b15.NADPH)\n",
    "rega8b3 = sp.stats.linregress(a8b3.Time, a8b3.NADPH)\n",
    "rega8b6 = sp.stats.linregress(a8b6.Time, a8b6.NADPH)\n",
    "rega8b12 = sp.stats.linregress(a8b12.Time, a8b12.NADPH)\n",
    "rega8b24 = sp.stats.linregress(a8b24.Time, a8b24.NADPH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa29e299-30b9-4b6b-be9c-3bbf83f28d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate of A0B0: 0.004954646341627726\n",
      "Rate of A0.5B24: 0.24118661159479945\n",
      "Rate of A1B24: 0.3526304449083254\n",
      "Rate of A2B24: 0.5746500696105541\n",
      "Rate of A4B24: 0.7322856639928835\n",
      "Rate of A8B24: 0.8250788434733143\n",
      "Rate of A8B1.5: 0.25422083243133853\n",
      "Rate of A8B3: 0.3679535702854621\n",
      "Rate of A8B6: 0.556408829374724\n",
      "Rate of A8B12: 0.7012039804663761\n"
     ]
    }
   ],
   "source": [
    "print('Rate of A0B0:', rega0b0.slope)\n",
    "print('Rate of A0.5B24:', rega05b24.slope)\n",
    "print('Rate of A1B24:', rega1b24.slope)\n",
    "print('Rate of A2B24:', rega2b24.slope)\n",
    "print('Rate of A4B24:', rega4b24.slope)\n",
    "print('Rate of A8B24:', rega8b24.slope)\n",
    "print('Rate of A8B1.5:', rega8b15.slope)\n",
    "print('Rate of A8B3:', rega8b3.slope)\n",
    "print('Rate of A8B6:', rega8b6.slope)\n",
    "print('Rate of A8B12:', rega8b12.slope)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74781b9-6971-4b79-ada6-4f67ef6d448f",
   "metadata": {},
   "source": [
    "Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1fae969-7f6b-4dd4-917c-a2cec3eaf03d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>Rates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.241187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.352630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.574650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.732286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.254221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.367954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.556409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B     Rates\n",
       "0  0.0   0.0  0.004955\n",
       "1  0.5  24.0  0.241187\n",
       "2  1.0  24.0  0.352630\n",
       "3  2.0  24.0  0.574650\n",
       "4  4.0  24.0  0.732286\n",
       "5  8.0  24.0  0.825079\n",
       "6  8.0   1.5  0.254221\n",
       "7  8.0   3.0  0.367954\n",
       "8  8.0   6.0  0.556409\n",
       "9  8.0  12.0  0.701204"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conc_a = [0,0.5,1,2,4,8,8,8,8,8]\n",
    "conc_b = [0,24,24,24,24,24,1.5,3,6,12]\n",
    "regressions = [rega0b0, rega05b24, rega1b24, rega2b24, rega4b24,rega8b24, rega8b15, rega8b3, rega8b6, rega8b12 ]\n",
    "rates =[]\n",
    "for reg in regressions:\n",
    "    rates.append(reg.slope)\n",
    "rates = np.array(rates)\n",
    "\n",
    "data = pd.DataFrame({'A':conc_a, 'B':conc_b, 'Rates':rates})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42121fdb-d295-445f-bfba-7f409fa44661",
   "metadata": {},
   "source": [
    "Question 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4f1e625d-e0ec-4d40-8806-c8ebc3d7ecae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def v(A, B, Ka, Kb, Vf):\n",
    "    return (Vf*A*B)/((Ka+A)*(Kb+B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3606d0c3-9e74-4c2a-9bdc-52cfd798cf64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>Ka</td><td style='text-align:left'> 1.00000000</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>Kb</td><td style='text-align:left'> 1.00000000</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>Vf</td><td style='text-align:left'> 1.00000000</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table>"
      ],
      "text/plain": [
       "Parameters([('Ka', <Parameter 'Ka', value=1.0, bounds=[-inf:inf]>), ('Kb', <Parameter 'Kb', value=1.0, bounds=[-inf:inf]>), ('Vf', <Parameter 'Vf', value=1.0, bounds=[-inf:inf]>)])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lmfit import Model\n",
    "mymod = Model(v, independent_vars=['A', 'B'])\n",
    "mypar = mymod.make_params(Ka=1, Kb=1, Vf=1)\n",
    "mypar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "73cf50cb-876a-4ccd-aec8-25e2ecab1b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <p>Model: Model(v)</p> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>25</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>10</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>3</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 0.00248796</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 3.5542e-04</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'>-76.9887719</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'>-76.0810166</td></tr><tr><td style='text-align:left'>R-squared</td><td style='text-align:right'> 0.99588985</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>Ka</td><td style='text-align:left'> 1.61269234</td><td style='text-align:left'> 0.13567971</td><td style='text-align:left'>(8.41%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>Kb</td><td style='text-align:left'> 4.89874506</td><td style='text-align:left'> 0.41256005</td><td style='text-align:left'>(8.42%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>Vf</td><td style='text-align:left'> 1.20594964</td><td style='text-align:left'> 0.04570438</td><td style='text-align:left'>(3.79%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>Kb</td><td style='text-align:left'>Vf</td><td style='text-align:right'>+0.7926</td></tr><tr><td style='text-align:left'>Ka</td><td style='text-align:left'>Vf</td><td style='text-align:right'>+0.7903</td></tr><tr><td style='text-align:left'>Ka</td><td style='text-align:left'>Kb</td><td style='text-align:right'>+0.3778</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.model.ModelResult at 0x1e0a35691d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfit = mymod.fit(data.Rates, mypar, A=data.A, B=data.B)\n",
    "myfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c808a232-5755-457d-9c2a-bb3d3f3bcbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>Rates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.241187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.352630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.574650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.732286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B     Rates\n",
       "0  0.0   0.0  0.004955\n",
       "1  0.5  24.0  0.241187\n",
       "2  1.0  24.0  0.352630\n",
       "3  2.0  24.0  0.574650\n",
       "4  4.0  24.0  0.732286\n",
       "5  8.0  24.0  0.825079"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = data.head(6)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "99b9a345-771c-4e6d-b4b0-f6ba863dc023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e0a5646fd0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avals = np.linspace(0,8,10) # the (0,8,10) should be bigger values like (0,8,101) but it give an error then\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(a.A, a.Rates, 'o', label='Experimental')\n",
    "ax.plot(avals, myfit.eval(a=avals), label='Fitted')\n",
    "ax.set_xlabel('Concentration of A(mM)')\n",
    "ax.set_ylabel('Rates (mM/s)')\n",
    "ax.legend(loc='best')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3d5b6988-d4fa-4d1a-8a20-a59958d5f8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>Rates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.254221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.367954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.556409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A     B     Rates\n",
       "5  8.0  24.0  0.825079\n",
       "6  8.0   1.5  0.254221\n",
       "7  8.0   3.0  0.367954\n",
       "8  8.0   6.0  0.556409\n",
       "9  8.0  12.0  0.701204"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = data.tail(5)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b87d7ce2-adfd-4687-bd07-7715f1020c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e0a597b390>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bvals = np.linspace(0,24,10)\n",
    "fig, ax =plt.subplots()\n",
    "ax.plot(b.B, b.Rates, 'o', label='Experimental')\n",
    "ax.plot(bvals, myfit.eval(b=bvals), label='Fitted')\n",
    "ax.set_xlabel('Concentration of B(mM)')\n",
    "ax.set_ylabel('Rates (mM/s)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60642402-109f-4017-8cb2-9ba7728bc8fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}