{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "799ae73e-f566-4010-be66-9bc8bba35e7b",
   "metadata": {},
   "source": [
    "## Assessment 1: Tutorial test - 27033910 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "3a86a708-ee0f-422a-9758-5a1b7994afbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "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\n",
    "backupdir = os.getcwd()\n",
    "import pysces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79081f14-3acf-4c2d-8f12-f2290c2099d5",
   "metadata": {},
   "source": [
    "### Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f4e22b62-86be-4e95-8dce-126446e38049",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "a05 = pd.read_csv('A0.5B24.csv',names=['Time', 'NADH'])\n",
    "a1 = pd.read_csv('A1B24.csv',names=['Time', 'NADH'])\n",
    "a2 = pd.read_csv('A2B24.csv',names=['Time', 'NADH'])\n",
    "a4 = pd.read_csv('A4B24.csv',names=['Time', 'NADH'])\n",
    "a8 = pd.read_csv('A8B24.csv',names=['Time', 'NADH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fd16ef1b-4a02-45df-b684-1951d7a146c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'G6P concentrations vs Time')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97c13849dfd04630bf7e1534aab2c645",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(a05.Time,a05.NADH, label='a=0.5')\n",
    "ax.scatter(a1.Time,a1.NADH, label='a=1')\n",
    "ax.scatter(a2.Time,a2.NADH, label='a=2')\n",
    "ax.scatter(a4.Time,a4.NADH, label='a=4')\n",
    "ax.scatter(a8.Time,a8.NADH, label='a=8')\n",
    "ax.legend(loc='best')\n",
    "ax.set_xlabel('Time(min)')\n",
    "ax.set_ylabel('NADH(mM)')\n",
    "ax.set_title('G6P concentrations vs Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a54be9ec-c69d-4616-99ff-f91ef058754d",
   "metadata": {},
   "outputs": [],
   "source": [
    "b15 = pd.read_csv('A8B1.5.csv',names=['Time', 'NADH'])\n",
    "b3 = pd.read_csv('A8B3.csv',names=['Time', 'NADH'])\n",
    "b6 = pd.read_csv('A8B6.csv',names=['Time', 'NADH'])\n",
    "b12 = pd.read_csv('A8B12.csv',names=['Time', 'NADH'])\n",
    "b24 = pd.read_csv('A8B24.csv',names=['Time', 'NADH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "df9790e8-33da-4794-b3c7-6fccf458805f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'NADP concentrations vs Time')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfad9388865844d7a9ca11a8f407d5ff",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(b15.Time,b15.NADH, label='b=1.5')\n",
    "ax.scatter(b3.Time,b3.NADH, label='b=3')\n",
    "ax.scatter(b6.Time,b6.NADH, label='b=6')\n",
    "ax.scatter(b12.Time,b12.NADH, label='b=12')\n",
    "ax.scatter(b24.Time,b24.NADH, label='b=24')\n",
    "ax.legend(loc='best')\n",
    "ax.set_xlabel('Time(min)')\n",
    "ax.set_ylabel('NADH(mM)')\n",
    "ax.set_title('NADP concentrations vs Time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4777a137-9133-4423-ad14-a313126b3182",
   "metadata": {},
   "source": [
    "### Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "7cffcf3b-2c23-46c0-bc4c-36993a9bba26",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "rega05 = sp.stats.linregress(a05.Time, a05.NADH)\n",
    "rega1 = sp.stats.linregress(a1.Time, a1.NADH)\n",
    "rega2 = sp.stats.linregress(a2.Time, a2.NADH)\n",
    "rega4 = sp.stats.linregress(a4.Time, a4.NADH)\n",
    "rega8 = sp.stats.linregress(a8.Time, a8.NADH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "175729a7-6f63-479f-b4fe-6971c65ba74f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24118661159479945\n",
      "0.35263044490832535\n",
      "0.5746500696105541\n",
      "0.7322856639928835\n",
      "0.8250788434733142\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.24118661, 0.35263044, 0.57465007, 0.73228566, 0.82507884])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressions = [rega05, rega1, rega2, rega4, rega8]\n",
    "ratesa = []\n",
    "for reg in regressions:\n",
    "    print(reg.slope)\n",
    "    ratesa.append(reg.slope)\n",
    "ratesa = np.array(ratesa)\n",
    "ratesa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "beb36c8f-8a9d-4317-adc3-b085cf7fc928",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "regb15 = sp.stats.linregress(b15.Time, b15.NADH)\n",
    "regb3 = sp.stats.linregress(b3.Time, b3.NADH)\n",
    "regb6 = sp.stats.linregress(b6.Time, b6.NADH)\n",
    "regb12 = sp.stats.linregress(b12.Time, b12.NADH)\n",
    "regb24 = sp.stats.linregress(b24.Time, b24.NADH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "ed4f477e-c7e7-4ae8-84d3-c7914bedf532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25422083243133853\n",
      "0.3679535702854621\n",
      "0.5564088293747242\n",
      "0.7012039804663761\n",
      "0.8250788434733142\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.25422083, 0.36795357, 0.55640883, 0.70120398, 0.82507884])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressions = [regb15, regb3, regb6, regb12, regb24]\n",
    "ratesb = []\n",
    "for reg in regressions:\n",
    "    print(reg.slope)\n",
    "    ratesb.append(reg.slope)\n",
    "ratesb = np.array(ratesb)\n",
    "ratesb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f670c93-ee35-4c7e-856e-eb8e85a705e0",
   "metadata": {},
   "source": [
    "### Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "535db1a5-2cf6-4f1f-af15-accc357a2c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "concsa = np.array([0.5, 1, 2, 4, 8])\n",
    "s_and_va = pd.DataFrame({'a': concsa, 'rate': ratesa})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "65002948-4b21-42a0-b01a-7f3d0f38019a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.241187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.352630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.574650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.732286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a      rate\n",
       "0  0.5  0.241187\n",
       "1  1.0  0.352630\n",
       "2  2.0  0.574650\n",
       "3  4.0  0.732286\n",
       "4  8.0  0.825079"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_and_va"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "6bd3a61b-caa9-4ca4-9ce5-53c2bc6f9606",
   "metadata": {},
   "outputs": [],
   "source": [
    "concsb = np.array([1.5, 3, 6, 12, 24])\n",
    "s_and_vb = pd.DataFrame({'b': concsb, 'rate': ratesb})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "a65c4d03-abe1-403d-ab14-886ecf832de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.254221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.367954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.556409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      b      rate\n",
       "0   1.5  0.254221\n",
       "1   3.0  0.367954\n",
       "2   6.0  0.556409\n",
       "3  12.0  0.701204\n",
       "4  24.0  0.825079"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_and_vb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c6f10bf6-4140-4a84-b6f4-5f45063e7f5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        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>b</th>\n",
       "      <th>rate</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.254221</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.367954</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.556409</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.701204</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      b      rate  a\n",
       "0   1.5  0.254221  8\n",
       "1   3.0  0.367954  8\n",
       "2   6.0  0.556409  8\n",
       "3  12.0  0.701204  8\n",
       "4  24.0  0.825079  8"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_and_va['b'] = 24\n",
    "s_and_va\n",
    "s_and_vb['a']=8\n",
    "s_and_vb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "9658d744-a506-42f4-9a69-c8a12409f4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_v = pd.concat([s_and_va, s_and_vb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "1e586ac5-cbca-49a8-88fc-3b46cc3e19a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>rate</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.241187</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.352630</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.574650</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.732286</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.825079</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.254221</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.367954</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.556409</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.701204</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.825079</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a      rate     b\n",
       "0  0.5  0.241187  24.0\n",
       "1  1.0  0.352630  24.0\n",
       "2  2.0  0.574650  24.0\n",
       "3  4.0  0.732286  24.0\n",
       "4  8.0  0.825079  24.0\n",
       "0  8.0  0.254221   1.5\n",
       "1  8.0  0.367954   3.0\n",
       "2  8.0  0.556409   6.0\n",
       "3  8.0  0.701204  12.0\n",
       "4  8.0  0.825079  24.0"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ce6286-80d1-48d7-bf2f-f17a4386e791",
   "metadata": {},
   "source": [
    "### Question 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "257a0e18-8026-4690-8598-6f8bd70a18c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def v(a,b,Vf,Ka,Kb):\n",
    "    return (Vf*a*b)/((Ka+a)*(Kb+b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "e405cf40-c600-4830-bce7-1efaef874911",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mymod = Model(v, independent_vars=['a', 'b'])\n",
    "mypar  = mymod.make_params(Vf=1,Ka=1, Kb=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "2d9c3725-3022-4ae3-b561-731be4ae0b1d",
   "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'>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><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></table>"
      ],
      "text/plain": [
       "Parameters([('Vf', <Parameter 'Vf', value=1.0, bounds=[-inf:inf]>), ('Ka', <Parameter 'Ka', value=1.0, bounds=[-inf:inf]>), ('Kb', <Parameter 'Kb', value=1.0, bounds=[-inf:inf]>)])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mypar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "15897abf-2f88-46c6-9233-9b688af7b1dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "myfit = mymod.fit(s_v.rate, mypar, a=s_v.a, b=s_v.b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "2dfbac7e-90b0-408c-aa52-df895d8d7b51",
   "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'> 0.00250889</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 3.5841e-04</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'>-76.9049929</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'>-75.9972376</td></tr><tr><td style='text-align:left'>R-squared</td><td style='text-align:right'> 0.99457587</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'>Vf</td><td style='text-align:left'> 1.19714513</td><td style='text-align:left'> 0.03805600</td><td style='text-align:left'>(3.18%)</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'>Ka</td><td style='text-align:left'> 1.59537395</td><td style='text-align:left'> 0.12553842</td><td style='text-align:left'>(7.87%)</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'> 4.84407985</td><td style='text-align:left'> 0.38161484</td><td style='text-align:left'>(7.88%)</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'>Vf</td><td style='text-align:left'>Kb</td><td style='text-align:right'>+0.7587</td></tr><tr><td style='text-align:left'>Vf</td><td style='text-align:left'>Ka</td><td style='text-align:right'>+0.7557</td></tr><tr><td style='text-align:left'>Ka</td><td style='text-align:left'>Kb</td><td style='text-align:right'>+0.2799</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.model.ModelResult at 0x15dce190cd0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499f2d51-2e50-4889-b8cc-aaa05a872e00",
   "metadata": {},
   "source": [
    "Vf (mM/min)\n",
    "Ka (min**-1)\n",
    "Kb (min**-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4204776-2218-47fc-97f8-7b8907bf74c9",
   "metadata": {},
   "source": [
    "### Question 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "a9ac74b7-044b-4707-88c4-ac7be8cab188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>rate</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.241187</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.352630</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.574650</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.732286</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.825079</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a      rate     b\n",
       "0  0.5  0.241187  24.0\n",
       "1  1.0  0.352630  24.0\n",
       "2  2.0  0.574650  24.0\n",
       "3  4.0  0.732286  24.0\n",
       "4  8.0  0.825079  24.0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh=s_v[0:5]\n",
    "sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0ef01aac-5371-4e36-b201-49269dbf83dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.9)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e6c75c011bb47c99ebc7fa5c08cfcc5",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "svals = np.linspace(0,9.0,10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(sh.a, sh.rate, 'o', label='data')\n",
    "ax.plot(svals, myfit.eval(a=svals), label='fit')\n",
    "ax.set_xlabel('[a] (mM)')\n",
    "ax.set_ylabel('rate (mM/min)')\n",
    "ax.legend(loc='best')\n",
    "plt.ylim(0,0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ac57ee1c-f956-4de2-98ac-050601b03b7a",
   "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>rate</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.254221</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.367954</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.556409</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.701204</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.825079</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a      rate     b\n",
       "0  8.0  0.254221   1.5\n",
       "1  8.0  0.367954   3.0\n",
       "2  8.0  0.556409   6.0\n",
       "3  8.0  0.701204  12.0\n",
       "4  8.0  0.825079  24.0"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shl=s_v[5:]\n",
    "shl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "afb4dd6b-3eaf-47f6-a807-243d03feb744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.9)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e49a34aa20b148b9ad88a243149002d4",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svals = np.linspace(0,24.0,10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(shl.b, shl.rate, 'o', label='data')\n",
    "ax.plot(svals, myfit.eval(b=svals), label='fit')\n",
    "ax.set_xlabel('[b] (mM)')\n",
    "ax.set_ylabel('rate (mM/min)')\n",
    "ax.legend(loc='best')\n",
    "plt.ylim(0,0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4952812c-8f5e-4cc6-b658-49eadbaf3248",
   "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
}