{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "710e87b1-3c59-4e3a-b32a-f4f4d1883609",
   "metadata": {},
   "source": [
    "## 1. Visualise the data by plotting the time courses as scatter plots, as follows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efc625e-0ff4-4616-bf05-fda8e2f10f8b",
   "metadata": {},
   "source": [
    "Onthefirst graph, plot all the experiments where a was varied and b kept constantat 24 mM, i.e. a = 0.5,1,2,4 and 8 mM. Plot these on the same set of axes, using different colours to distinguish the datasets. Label the axes and provide a legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0092caa6-a879-4f51-8f60-3bfd6526caab",
   "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",
    "import numdifftools \n",
    "from lmfit import Model\n",
    "from numdifftools import Derivative\n",
    "backupdir = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c44f4150-4590-4032-80da-932c05acc80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a0b0 = pd.read_csv('A0B0.csv', names=['Time', 'NADPH'])\n",
    "a0_5b = pd.read_csv('A0.5B24.csv', names=['Time','NADPH'])\n",
    "a1b = pd.read_csv('A1B24.csv', names=['Time','NADPH'])\n",
    "a2b = pd.read_csv('A2B24.csv', names=['Time','NADPH'])\n",
    "a4b = pd.read_csv('A4B24.csv', names=['Time','NADPH'])\n",
    "a8b = pd.read_csv('A8B24.csv', names=['Time','NADPH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29644a45-2ab2-438e-8ee4-44a5c0aba811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c4187a92b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af7f8796205240c9a826bc42aa7d6ece",
       "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, ax = plt.subplots()\n",
    "ax.scatter(a0_5b.Time, a0_5b.NADPH, label = '[A] = 0.5(mM)')\n",
    "ax.scatter(a1b.Time, a1b.NADPH, label = '[A] = 1(mM)')\n",
    "ax.scatter(a2b.Time, a2b.NADPH, label = '[A] = 2(mM)')\n",
    "ax.scatter(a4b.Time, a4b.NADPH, label = '[A] = 4(mM)')\n",
    "ax.scatter(a8b.Time, a8b.NADPH, label = '[A] = 8(mM)')\n",
    "ax.set_xlabel('Time (minutes)')\n",
    "ax.set_ylabel('[NADPH] (mM)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec127889-8959-4549-97c9-6fce27d17d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08e8b02-9277-401b-bf4d-c633c893adac",
   "metadata": {},
   "source": [
    " Onasecondgraph, do the same for the experiments where b was varied and a kept\n",
    "constant at 8 mM, i.e. b = 1.5,3,6,12 and 24 mM. Format the graph in the same way\n",
    "as the first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38317655-b1a8-4900-93d6-7a5efe2a34b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ab1_5 = pd.read_csv('A8B1.5.csv', names=['Time','NADPH'])\n",
    "ab3 = pd.read_csv('A8B3.csv', names=['Time','NADPH'])\n",
    "ab6 = pd.read_csv('A8B6.csv', names=['Time','NADPH'])\n",
    "ab12 = pd.read_csv('A8B12.csv', names=['Time','NADPH'])\n",
    "ab24 = pd.read_csv('A8B24.csv', names=['Time','NADPH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7418c85-a7ce-4852-8fa8-7dbbfb71cea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c418827380>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8588ac19a73e49ec8158ac18cf1b962e",
       "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, ax = plt.subplots()\n",
    "ax.scatter(ab1_5.Time, ab1_5.NADPH, label = '[B] = 1.5 (mM)')\n",
    "ax.scatter(ab3.Time, ab3.NADPH, label = '[B] = 3 (mM)')\n",
    "ax.scatter(ab6.Time, ab6.NADPH, label = '[B] = 6 (mM)')\n",
    "ax.scatter(ab12.Time, ab12.NADPH, label = '[B] = 12 (mM)')\n",
    "ax.scatter(ab24.Time, ab24.NADPH, label = '[B] = 24 (mM)')\n",
    "ax.set_xlabel('Time (minutes)')\n",
    "ax.set_ylabel('[NADPH] (mM)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "840236a5-07a2-4be4-a654-8531a1c95954",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df169da2-2522-42c6-becc-530e00801a02",
   "metadata": {},
   "source": [
    "Perform linear regressions on each of the datasets to calculate the initial rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bfdb97d8-1398-46c3-94b6-c68bbe37421c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rega0b0 = sp.stats.linregress(a0b0.Time, a0b0.NADPH)\n",
    "rega0_5b = sp.stats.linregress(a0_5b.Time, a0_5b.NADPH)\n",
    "rega1b = sp.stats.linregress(a1b.Time, a1b.NADPH)\n",
    "rega2b = sp.stats.linregress(a2b.Time, a2b.NADPH)\n",
    "rega4b = sp.stats.linregress(a4b.Time, a4b.NADPH)\n",
    "rega8b = sp.stats.linregress(a8b.Time, a8b.NADPH)\n",
    "regab1_5 = sp.stats.linregress(ab1_5.Time, ab1_5.NADPH)\n",
    "regab3 = sp.stats.linregress(ab3.Time, ab3.NADPH)\n",
    "regab6 = sp.stats.linregress(ab6.Time, ab6.NADPH)\n",
    "regab12 = sp.stats.linregress(ab12.Time, ab12.NADPH)\n",
    "regab24 = sp.stats.linregress(ab24.Time, ab24.NADPH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d209730-2d78-4819-b9e8-01f6fe4e159d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004954646341627726\n",
      "0.24118661159479945\n",
      "0.3526304449083254\n",
      "0.5746500696105541\n",
      "0.7322856639928835\n",
      "0.8250788434733143\n",
      "0.25422083243133853\n",
      "0.3679535702854621\n",
      "0.556408829374724\n",
      "0.7012039804663761\n"
     ]
    }
   ],
   "source": [
    "regressions = [rega0b0, rega0_5b, rega1b, rega2b, rega4b, rega8b, regab1_5, regab3, regab6, regab12]\n",
    "rate=[]\n",
    "for reg in regressions:\n",
    "    print(reg.slope)\n",
    "    rate.append(reg.slope)\n",
    "rate = np.array(rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "407aa571-3b36-4811-aae7-5329d4c8e48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00495465, 0.24118661, 0.35263044, 0.57465007, 0.73228566,\n",
       "       0.82507884, 0.25422083, 0.36795357, 0.55640883, 0.70120398])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d3d227-d89f-4562-933f-3a169d242203",
   "metadata": {},
   "source": [
    "Create a new pandas dataframe to combine the data. (columns: a, b, rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74b5e12d-3b06-4fab-92ab-deb2125dbc04",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([ 0.0, 0.5, 1.0, 2.0, 4.0, 8.0, 8.0, 8.0, 8.0, 8.0])\n",
    "b = np.array([ 0.0, 24, 24, 24, 24, 24, 1.5, 3.0, 6.0, 12])\n",
    "rate = np.array(rate)\n",
    "table = pd.DataFrame({'a': a, 'b': b, 'rate': rate})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8184c9c-d13c-4aaa-b2fc-bd432cc66afb",
   "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>rate</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      rate\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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80994b6-8a4e-49c0-846e-cc69b7d4d731",
   "metadata": {},
   "source": [
    "Fit the data to Equation(1) to obtain estimates for the parameters Vf, Ka and Kb. Perform a global fit on all the data simultaneously. Provide error estimates for each of the parameters\n",
    "and give their units!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2587d837-75ac-48c7-8d43-099ed0ad1f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def v(Vf, a, b, Ka, Kb):\n",
    "    return (Vf*a*b)/((Ka+a)*(Kb+b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa6cbf6f-740f-4e65-80dd-8cc550a0d6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mymod = Model(v, independent_vars=['a','b'])\n",
    "mypar = mymod.make_params(Vf=1, Ka=1, Kb=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5706fe74-a069-49f2-b4c4-c97d9e8efacb",
   "metadata": {},
   "outputs": [],
   "source": [
    "myfit = mymod.fit(rate, mypar, a=a, b=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d18d18b-c944-422f-bf74-ec061a644742",
   "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'>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><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></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'>+0.7926</td></tr><tr><td style='text-align:left'>Vf</td><td style='text-align:left'>Ka</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 0x1c418833cb0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2027005e-ffa8-4b9f-aeda-b9e8ddbb01e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "tableA = table.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e84846a-bf0a-4ce4-9200-0d1554436dba",
   "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>rate</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      rate\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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e66079f0-39b0-4ada-9d4a-6ad350df5661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c419b892b0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "528edacfb5314e90b64a31f823336cbb",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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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": [
    "avals = np.linspace(0,8,10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(tableA.a, tableA.rate, 'o', label='data')\n",
    "ax.plot(avals, myfit.eval(a=avals), label='fit')\n",
    "ax.set_xlabel('[A] (mM)')\n",
    "ax.set_ylabel('rate (mM/min)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b6523f4-0e4d-4b49-9a68-e784f64d011c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004954646341627726\n",
      "0.25422083243133853\n",
      "0.3679535702854621\n",
      "0.556408829374724\n",
      "0.7012039804663761\n",
      "0.8250788434733143\n"
     ]
    }
   ],
   "source": [
    "regressionsb = [rega0b0, regab1_5, regab3, regab6, regab12, regab24]\n",
    "rate2=[]\n",
    "for reg in regressionsb:\n",
    "    print(reg.slope)\n",
    "    rate2.append(reg.slope)\n",
    "rate2 = np.array(rate2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b9383c1-e311-46df-8544-de26f8ebf72d",
   "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>b2</th>\n",
       "      <th>rate2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.254221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.367954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.556409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b2     rate2\n",
       "0   0.0  0.004955\n",
       "1   1.5  0.254221\n",
       "2   3.0  0.367954\n",
       "3   6.0  0.556409\n",
       "4  12.0  0.701204\n",
       "5  24.0  0.825079"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b2 = np.array([0.0, 1.5, 3.0, 6.0, 12, 24])\n",
    "rate2 =np.array(rate2)\n",
    "table2 = pd.DataFrame({'b2': b2, 'rate2': rate2})\n",
    "table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "353d78a9-c109-49b8-b785-aeaf9506c7be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c419bde900>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db2f369b1c654e2786f847c6d66c71ee",
       "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": [
    "bvals = np.linspace(0,24,10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(table2.b2, table2.rate2, 'o', label='data')\n",
    "ax.plot(bvals, myfit.eval(b2=bvals), label='fit')\n",
    "ax.set_xlabel('[B] (mM)')\n",
    "ax.set_ylabel('rate (mM/min)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32b67755-dd35-454e-ae8d-6317277ef723",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec11072a-febf-4cb0-b6e4-e6cf0859b9e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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